university of california santa barbara the impacts of ...€¦ · and gendered access to fishing...

UNIVERSITY OF CALIFORNIA

Santa Barbara

The impacts of heterogeneous behavior on fishing fleet location and performance

A Dissertation submitted in partial satisfaction of the

requirements for the degree Doctor of Philosophy

in Geography

by

Michael Allen Robinson

Committee in charge:

Professor David Siegel, Chair

Professor Christopher Costello

Professor Kostas Goulias

Professor Daniel Montello

Professor Stuart Sweeney

March 2009

ii

The dissertation of Michael Allen Robinson is approved.

____________________________________________ Christopher Costello

____________________________________________ Kostas Goulias

____________________________________________ Daniel Montello ____________________________________________ Stuart Sweeney ____________________________________________ David Siegel, Committee Chair

March 2009

iii


Copyright © 2009

by


iv

ACKNOWLEDGEMENTS

This manuscript is dedicated to Julie, Emily, and Megan for their love, support, and

encouragement. Thanks mom and dad for being you and raising me. Charley, you

help me through the long, dark tea times of my soul. Tim, you inspired me and

proved that anything is possible. Sigur Rós, Radiohead, and Rage Against the

Machine provided the soundtrack for this manuscript. Jeremiah 29:11-12.

Funding for this research was provided by the National Science Foundation, the

Institute for Computational Earth System Science (ICESS) at UCSB, and the

Sustainable Fisheries Group (SFG) at UCSB. Support and direction was provided by

David Siegel, Kostas Goulias, Dan Montello, and Stuart Sweeney (Department of

Geography at UCSB) and Christopher Costello (Donald Bren School of

Environmental Science and Management at UCSB). Special thanks to Kristine

Barsky (California Department of Fish and Game) for graciously facilitating access

to the data used in this research, Barbara Walker (Institute for Social, Behavioral,

and Economic Research at UCSB) for first connecting me with fishermen, and John

Richards and Carrie Culver (Sea Grant Extension) for always encouraging me to

keep moving forward.

v

VITA OF MICHAEL ALLEN ROBINSON March 2009

EDUCATION Bachelor of Science in Environmental Studies. University of California, Santa Barbara, 1996. Master of Arts in Geography, University of California, Santa Barbara, 2004. Doctor of Philosophy in Geography, University of California, Santa Barbara, 2009 (expected). PROFESSIONAL EMPLOYMENT 2002-2007: Teaching Assistant, Department of Geography, University of California, Santa Barbara. Summer 2005: Instructor, Department of Geography, University of California, Santa Barbara. 2004, 2007-2008: Instructor, Department of Geography, Ventura College. 2007-2009: Instructor, Department of Earth and Planetary Sciences, Santa Barbara City College. PUBLICATIONS Walker, BLE, and M. Robinson, “Economic Development, Marine Protected Areas, and Gendered Access to Fishing Resources in a Polynesian Lagoon” Gender, Place, and Culture (accepted, likely published in 2009). Robinson, M., “Santa Barbara Channel Region Selected Commercial Fishing Area Closures” [map] In Culver, C.S., J.B. Richards, and C.M. Pomeroy, Commercial Fisheries of the Santa Barbara Channel and Associated Infrastructure Needs, California Sea Grant College Program: La Jolla, California, 2007, p.100. Robinson, M., and H.A. Loaiciga, “The effects of lake precipitation and evaporation on reservoir modeling” In Operating Reservoirs in Changing Conditions, Proceedings of the 2006 Operations Management Conference, p. 79-92, August 14-

vi

16, 2006, Sacramento, California, ASCE Press, Reston, Virginia. [PROCEEDINGS ARTICLE]. Robinson, M., C. Miller, C. Hoeflinger, B. Walker, “Problems and Recommendations for Using GIS to Improve Decision-Making in California’s Channel Islands Marine Reserves” MPA News, Vol. 7, No. 5 (Nov 2005). PROFESSIONAL PRESENTATIONS “Public Participation GIS and Fishery Management: A Cooperative Investigation.” Ventura College GIS Day, November 2008. [INVITED] Walker, B.L.E. and M. Robinson, “Marine Protected Areas, Economic Development, and Gendered Access to Fishing Resources in Moorea, French Polynesia.” UCSB Environmental Studies Associates community lecture, November 2008. [INVITED] “Location choice and expected catch: Determining causal structures in fisherman travel behaviour.” American Fisheries Society annual meeting, Ottawa, Canada, August 2008. “Using GIS to determine access to marine resources in Moorea, French Polynesia.” Spatial@ucsb: Connecting our region through GIS and geospatial technologies, UCSB, May 2008. “Heterogeneity and consistency in commercial fishing fleets: Effects on fleet modeling and fish stock management.” Association of American Geographers annual meeting, Boston, MA, April 2008. “Location Choice and Expected Catch: Causal Structures in Commercial Fishing Fleet Travel Behavior.” University of California Transportation Center annual meeting, UCSB, January 2008. “Heterogeneous fishing effort and catch: effects on stocks & yields.” Santa Barbara Coastal Long Term Ecological Research annual meeting, UCSB, June 2007. “The Effects of Lake Hydrology on Reservoir Design and Operation.” UCSB Department of Geography colloquium, October 2003. “GIS Environmental Decision Making in California’s Marine Protected Areas: Problems and Prospects.” Association of Pacific Coast Geographers annual meeting, Portland, OR, September 2003.

vii

AWARDS Winter 2007: Jack and Laura Dangermond travel grant Spring 2006: UCSB Geography Department Excellence in Teaching award FIELDS OF STUDY Major Field: Human Environmental Relations (emphasis in marine environments) Studies in Marine Resource Modeling and Statistics with Professor David Siegel Studies in Human Travel Behavior with Professor Kostas Goulias

viii

ABSTRACT


by


Fishery management seeks to balance economic productivity and biological

diversity. Due to imperfect knowledge of the biogeographical environment and

issues of uncertainty and confidentiality associated with fishery dependent data,

fishermen within a fleet are often considered homogeneous and average values are

used to describe fishing effort and catch in stock harvest models.

This research shows that heterogeneity in individual behavior and fishing

performance exists among fishermen within a fishing fleet and addresses the role of

this heterogeneity on fish stock size and fleet catch. Analysis of catch and effort

anomalies in California Department of Fish and Game fish block data reveals fleets

with large, tightly clustered portions of below average effort and catch and smaller

but more widely spread portions of far above average effort and catch. I show that

individuals are consistent in their performance and that fishermen in different

portions of a fleet have some distinctly different characteristics and responses to

environmental and fishery-specific variables. Furthermore, I find significant spatial

ix

and temporal differences between the behavior of the consistently high catch per unit

effort (CPUE) fishermen and the rest of the fishermen in the fleet.

This research employs a variety of techniques to model and predict fishing

fleet effort allocation and harvest. These include analytical and multivariate

regression models, discrete choice random utility (Logit) models, and Generalized

Geoadditive Mixed Models.

As a result of this heterogeneity, the fish biomass removed by the below

average portions of the fishing fleets does not balance the above average portions to

generate predicted “average” fleet catches. The imbalance suggests that harvest

models using average values for fishing effort and harvested biomass could

significantly underestimate fleet impacts on fish stocks. This research ends with a

discussion of management implications under heterogeneous fisheries.

x

Table of Contents

I. Introduction ............................................................................................................1

II. Part 1 - Fishing fleet effort and catch distribution: Effects of heterogeneity and

consistency on stocks and yields............................................................................8

III. Part 2 – Determining causal structures in fishing fleet travel behavior ...............57

IV. Part 3 – Heterogeneous fisherman travel behavior and location choice in

commercial fisheries ..........................................................................................111

V. Conclusion..........................................................................................................151

References ................................................................................................................155

Appendix 1 ...............................................................................................................160

xi

LIST OF FIGURES Figure I-1. Key factors that influence when and where a fisherman decides to go

fishing...............................................................................................................6

Figure I-2. Fisherman behavior flow chart ..................................................................7

Figure II-1. Yearly fishery summary data.................................................................48

Figure II-2. Fishery catch/year and effort/year anomalies, 1998-2005.....................49

Figure II-3. Average standard deviation of CPUE ranks within each segment of the

fleet for increasing years of fishing experience .............................................50

Figure II-4. Fleet CPUE rankings for fishermen with 6 or more years of experience

in the red sea urchin and market squid fleets and 5 or more years of

experience in the spiny lobster flee ................................................................51

Figure II-5. Average catch per day regressed against average CPUE ranks, 1998-

2005................................................................................................................52

Figure II-6. ANOVA results for fishing fleets divided into four categories based on

average CPUE rank ........................................................................................53

Figure II-7. Value of a pound of catch from each fishery over time (a). Value of

fishery relative to all California commercial fishing (b)................................54

Figure II-8. Percentage of fishing fleet with above average catch............................55

Figure II-9. Schaefer model equilibrium stock biomass and fleet catch with

increasing d ....................................................................................................55

xii

Figure II-10. Effects of temporal controls and fleet catch limits on equilibrium fish

stock biomass .................................................................................................56

Figure II-11. Effects of temporal controls and fleet catch limits on equilibrium

fishing fleet catch ...........................................................................................56

Figure III-1. DFG fish blocks and model regions ...................................................108

Figure III-2. Average yearly red sea urchin fleet effort and catch for each fishing

region..................................................................................................................109

Figure III-3. Spiny lobster fleet average yearly effort as average number of events,

average yearly effort as average number of traps, and average yearly catch as

number of legals retained ...................................................................................110

Figure IV-1. Red sea urchin fishing fleet effort and catch data, 1998-2005...........141

Figure IV-2. Fleet CPUE rankings for fishermen with 6 or more years of experience

in the red sea urchin fleet .............................................................................141

Figure IV-3. Santa Barbara Channel Islands with Department of Fish and Game fish

blocks ...........................................................................................................142

Figure IV-4. Total fishing effort at each block .......................................................143

Figure IV-5. Posterior mode and 95% confidence intervals for nonlinear (P-spline)

variables .......................................................................................................144

Figure IV-6. Posterior mode of α for each sea urchin diver ...................................145

Figure IV-7. Red sea urchin commercial fishing fleet spatial model comparison..146

xiii

Figure IV-8. Red sea urchin fishing fleet spatial output comparison .....................147

Figure IV-9. Red sea urchin fishing fleet temporal output comparison..................148

Figure IV-10. Scatterplots of effort and average CPUE at each block for segments of

the commercial red sea urchin fishing fleet .................................................149

Figure IV-11. Red sea urchin habitat suitability model ..........................................150

Figure IV-12. Percent of red sea urchin fishing effort in each season (2001-04) ...150

1

I. Introduction

Managing fish stocks and the fishing fleets that depend on these fish stocks are

worldwide environmental and economic concerns. However, fisheries management

is complex and involves much uncertainty (Botsford et al. 1997, Worm et al. 2006).

Fish move. Recruitment events are often highly variable. Ocean conditions change

daily, seasonally, and over long temporal scales (e.g., El Niño-Southern Oscillation,

Pacific Decadal Oscillation). John Shepherd said, "Managing fisheries is hard: it's

like managing a forest, in which the trees are invisible and keep moving around1".

Certainly without fish there would be no fisheries. However, an essential piece of the

puzzle which also involves an enormous amount of uncertainty is an understanding of

the fishermen and fishing fleets that catch the fish. This research identifies some of

the key factors that influence when and where a fisherman decides to go fishing

(Figure I-1). Temporal restrictions, weather conditions, and economics play

important roles in determining whether or not a fisherman will go fishing. The

biogeographical environment (depth, substrate, kelp, expected fish presence, etc),

spatial restrictions, and human behavior (learning, risk propensity, fleet dynamics and

information exchange) play important roles in determining where a fisherman will go

fishing. Many of these factors are dependent on the time of year (i.e., season) and

may be unpredictable (e.g., daily weather conditions, fish presence for a highly

1 Unpublished lecture at Princeton University, ca 1978 (http://jgshepherd.com/thoughts.asp).

2

pelagic species). Other factors are more regular but still vary over time (e.g.,

temporal and spatial restrictions, kelp density).

While I do not have data for all the variables which influence a fisherman’s

decision regarding when and where to fish, I have panel-type data which follow

fishermen in a fleet and record a number of critical variables such as amount of catch,

location of catch, and fishing trip specific variables such as number of traps, number

of divers, and hours spent diving. In addition to these panel data I collect data on

influential environmental variables such as wind speed, wave height, and water

temperature. I use these data in a variety of models to describe fishermen in a fleet

and understand and predict how a fishing fleet distributes its effort (Figure I-2). This

research does not address all aspects of fishing fleet dynamics, notably learning and

information exchange and individual fisherman characteristics such as marital status

and level of education. Instead I focus on data that are readily available online or

through management agencies.

Oceanographers and marine scientist have contributed much to an

understanding of the physical and biological conditions upon which fisheries depend

(Lynn and Simpson 1987, Hilborn and Walters 1992, Walters and Martell 2004).

Economists have applied economic principles to human behavior and fisheries

management (Smith 1969, Wilson 1990, Smith 2002). Geographers have much to

add to the discussion, particularly in the areas of human geography, travel behavior,

and spatial modeling.

3

This research identifies heterogeneous behavior among fishermen in

commercial fishing fleets. It explores the impacts of this heterogeneity on fishing

fleet performance, spatial and temporal effort, and catch distribution. It discusses

implications of this heterogeneous behavior for fishery management. I show that

individuals exhibit consistent performance over time and that fishermen in different

portions of a fleet have distinctly different characteristics and behaviors. Most

importantly I find noticeable differences in both the spatial and temporal behavior

between those fishermen with consistently high catch per unit of effort (CPUE) and

the rest of the fleet (those with consistently low CPUE and those with highly variable

CPUE). While I do not have data on boat size, engine size, total years of experience,

or similar variables that one would expect to be highly correlated with CPUE, I find

that in many cases a fisherman’s fishing performance is consistent and have thus

developed a method for clustering members of a fishing fleet by their CPUE relative

to the entire fishing fleet. Furthermore, certain variables, both environmental and

fishery-specific, have a strong influence on expected CPUE. Many of these variables

that influence a fisherman’s behavior and performance have a non-linear relationship

with catch rate, requiring flexibility in time trends, seasonal effects, and

environmental and fishery-specific variables and a modeling environment that allows

for a relaxation of fixed parameters. I ultimately choose a setting which addresses a

number of shortcomings of linear models, namely nonlinearity of covariates,

correlation of spatial and temporal observations, and heterogeneity among individuals

and segments of the fishing fleet.

4

Heterogeneity in fishery modeling and fishery management

Millischer & Gascuel (2006) emphasize the patchy nature of fish stocks. Their

research on fishing fleet behavior shows “a strong dependence of the fleet’s

efficiency towards the level of aggregation of the resource.” Wilson (1990),

discussing learning and information sharing in commercial fishing fleet behavior,

concludes that, “in terms of rents, the persistence of differential success among

fishermen suggests that the core of successful fishermen will capture a large part of

the resource rents available from the fishery even at times when many other

fishermen are going bankrupt.” In other words, he finds differential success among

fishermen in a fleet. A few of the fishermen in a fleet produce the majority of the

catch.

In addition to an understanding of fishing fleet heterogeneity, this research

investigates the role of location choice in fisherman and fishing fleet performance.

Mistiaen & Strand (2000) emphasize the importance of location choice: “Fishermen

targeting the same species typically have dramatically different net returns depending

on their location choice.” They discuss the role of uncertainty in regards to risk

preference: “perhaps more important than allowing for risk-loving and risk-averting

behavior, a general model of discrete location choices under uncertainty should also

allow risk preferences to vary among fishermen.” In order to understand how a

fishing fleet allocates effort and catch and predict fleet impacts on fish stocks we

must account for heterogeneous fishermen behavior.

5

Comparing aggregate (pooled) and disaggregate fishing fleet behavior models

I find that differences between consistently high CPUE fishermen and the rest of the

fleet produce “average” conditions which may not, in fact, be indicative of any

members of the fishing fleet. Oftentimes the behavior of the consistently high CPUE

fishermen, a smaller percentage of the fishing fleet, is overwhelmed by the behavior

of the not consistently high CPUE fishermen, the majority of the fleet.

This research makes a number of important contributions to marine resource

management and fishing fleet modeling research. It improves our ability to model

fishing fleets by disaggregating and “segmenting” a fleet based on consistent fishing

performance. It provides a method for using currently available data to predict how a

fishing fleet distributes its effort in space and time. Finally, this research informs

management by helping understand the influences, impacts, and implications of

various spatial and temporal management options.

6

Figure I-1. Key factors that influence when and where a fisherman decides to go fishing.

7

Figure I-2. Fisherman behavior flow chart.

8

II. Part 1 – Fishing fleet effort and catch distribution: Effects of

heterogeneity and consistency on stocks and yields

Abstract

Fishery management seeks to balance economic productivity and biological diversity.

Due to imperfect knowledge of the biogeographical environment and issues of

uncertainty and confidentiality associated with fishery dependent data, fishermen

within a fleet are often considered homogeneous and average values are used to

describe fish and fishing in stock harvest models. In this chapter I show that

heterogeneity exists among fishermen within a fishing fleet and address the role of

this heterogeneity on fish stock size and fleet catch. Analysis of catch and effort in

California Department of Fish and Game fish block data reveals fleets with large,

tightly clustered portions of low effort and catch and smaller but more widely spread

portions of high effort and catch. I show that individuals are consistent over time and

that fishermen in different portions of a fleet have some distinctly different

characteristics and behaviors.

As a result of this heterogeneity, the fish biomass removed by the below

average portions of the fishing fleets does not balance the above average portions to

generate predicted “average” fleet catches. The imbalance suggests that harvest

models using average values for fishing effort and harvested biomass could

significantly underestimate fleet impacts on fish stocks. This chapter ends with a

discussion of management implications under heterogeneous fisheries. While this

9

research does not give hard, fast numbers with which to adjust fish stock models, it

does encourage fishery scientists and managers to analyze individual level data in

more detail to determine the composition of a fleet and to see whether or not a

particular fleet may behave significantly different than models that use average values

for effort and catch would predict.

1. Introduction

1.1. Fishermen heterogeneity

Because ecosystems are dynamic and subject to difficult (or impossible) to predict

natural and anthropogenic change, “average” fish biology and fisherman performance

are often used in fishery modeling. When effort per fisherman is assumed uniform,

the total fleet effort is merely a function of fleet size (Smith 1969). This allows the

exploration and management of long-term, equilibrium conditions. In addition to

computational advantages, fishery-dependent data are often aggregated, averaged, or

both to satisfy confidentiality requirements. While this protects the identity of

individual fishermen and processors, it often masks much of the heterogeneity that

exists in a fishery. To this end, research by Smith and Wilen (2003) addresses, “a

potentially important shortcoming of the vast marine reserves literature, namely the

assumption that fishing effort is fixed and uniformly distributed.”

Walters and Martell (2004) present a case for generally low variance among

commercial fishermen because, “modern industrial fisheries often have relatively

homogeneous technology and highly ‘professional’ fishers with similar knowledge,

10

skills, and information about the stock(s), and fleets are often based in one or a few

ports (where access to vessel and technology servicing is available) so that the fishers

see similar costs to access a given fishery.” While this may hold true for some

fisheries, particularly well-established, mature fisheries, there exists a body of fishing

fleet research which points to significant heterogeneity among fisherman (for

example, see Hilborn 1985 and Branch et al 2006).

1.2. Heterogeneity and fishing fleet modeling

Hilborn and Walters (1992) attribute differential costs, skill, and knowledge to

differences in fisherman location and effort. In a seminal paper Allen and McGlade

(1987) describe a fishing fleet composed of two different types of fishermen;

“stochasts” who are risk-taking fishermen characterized by exploring the unknown

and “Cartesians” who are generally predictable fishermen characterized by exploiting

the known. Risk tolerance heterogeneity is discussed in Smith and Wilen (2005).

Physical and financial risk tolerance affects when and where a fisherman goes

fishing, and this heterogeneity potentially produces diverse effort and catch

distributions. Mistiaen and Strand (2000) determine that location choice is, at least in

part, a function of a fisherman’s willingness to accept economic and/or physical risk

and they model the effects of heterogeneous risk preferences to explain differences in

net returns. Holland and Sutinen (2000) find that, “the amount, quality, and

distribution of information vary greatly among groups of fishers and circumstances.”

Their research indicates significant behavioral differences between average vessels

11

and high-liners2, and accounting for this heterogeneity greatly improves model

predictions. Smith (2002) employs a fleet model based on aggregated monthly data

and one based on individual fisherman decision-making, aggregating the latter for

comparison. Wilson (1990) discusses the differential skill and success of fishermen

in the context of grouping and information sharing, suggesting that a core of

experienced fishermen will harvest a significant amount of the fleet catch. Vignaux

(1996) discusses heterogeneity in location choice and catch per unit effort (CPUE),

producing a model that explores the effects of management scenarios on fleet catch.

1.3. Heterogeneity and fishery management

Addressing the use of marine reserves as a fishery management tool, Smith and Wilen

(2003) maintain that, “the assumption of uniformly distributed and unresponsive

effort used for simplicity in the biological literature biases predictions toward overly

pessimistic status quo harvest and egg production predictions, and toward overly

optimistic predictions of harvest gains and the net economic costs of reserve

formation.” In fact, in a seminal paper addressing the differences in effort and catch

among fishermen, Hilborn (1985) indicates that the most successful fishermen of a

heterogeneous fleet would need to be removed to greatly increase the catch of the less

successful fishermen. This implies that their removal would also be necessary to

significantly reduce total fleet catch, assuming the successful fishermen are not

replaced by other equally successful fishermen.

2 “High-liner” is a term that refers to a fisherman who consistently generates a large catch relative to the “average” catch in a fishing fleet.

12

Here I show three different fisheries with data at three different spatial scales.

Section 2 describes the fishery and environmental data used in this research. Section

3 describes the methodology used in this research. In section 4 I examine the

heterogeneity that exists among fishermen within a fishing fleet and address the role

of this heterogeneity on fish stock size and fleet catch. I show that individuals are

consistent over time and that fishermen in different portions (or “segments”) of a fleet

have distinctly different characteristics and behaviors. This research shows that the

fish biomass removed by the below average portions of the fishing fleets does not

balance the above average portions to generate predicted “average” fleet catches. In

section 5 I describe changes in fleet heterogeneity over time and explore the role of

heterogeneity in fish stock modeling. The imbalance between predicted catch and

actual catch suggests that harvest models which use average values for fishing effort

and harvested biomass could significantly underestimate fleet impacts on fish stocks.

Section 6 concludes the paper with a discussion of management implications under

heterogeneous fisheries.

2. Data

The data used in this research are California Department of Fish and Game (DFG)

fish block data for the red sea urchin (Strongylocentrotus franciscanus), California

spiny lobster (Panulirus interruptus), and market squid (Loligo opalescens) fisheries.

The cumulative 1998-2005 red sea urchin (RSU), spiny lobster (LOB), and market

squid (SQD) catch data record the fishing activity of 218 divers, 238 fishermen, and

13

294 boats, respectively, though not all fishermen were active each year. Table 1

contains descriptive statistics for the three fleets and Figure II-1 shows yearly

summary data for each fishery. Regional average daily wind speed and wave height

data are collected from the National Oceanic and Atmospheric Administration

(NOAA) National Data Buoy Center (NDBC) for additional analysis of the red sea

urchin and spiny lobster fleets.

2.1. Red sea urchin

Red sea urchin, Strongylocentrotus franciscanus, is a long-lived, benthic species

which feeds primarily on leafy algae and favors nearshore rocky habitats. Divers

typically take day trips to urchin grounds where urchin are removed from rocks and

placed in large bags. Urchin is harvested for the gonads, or roe, and the price paid to

fishermen is based on gonad quality (a function of color, texture, size, and firmness).

Though sea urchin are able to survive during periods of food shortage, gonad quality

is highly dependent on food supply and tends to decrease dramatically. This is

particularly noticeable in El Niño years when warm water and lack of nutrients

reduces kelp supply. Demand for urchin roe has traditionally come from international

Asian markets, though there is an increasing domestic demand.

While the fishery is fairly new, having developed rapidly in the last 30 years

or so, it is one of the more commercially valuable in California. In 2001 red sea

urchin catch accounted for approximately 3% of all California catch by volume and

over 11% of all California catch by value (CA DFG 2001a). By 2005 the red sea

14

urchin catch accounted for almost 4% of all California catch by volume and almost

6% of all California catch by value, a drop attributed in part to the loss of the northern

California fishery (CA DFG 2005).

The fishery is considered fully-exploited throughout California and over-

exploited in northern California and parts of southern California (CA DFG 2003a). It

is primarily managed through restricted access (a permit program which began in

1989), limited gear type (hand appliances), minimum size limits, and temporal

restrictions.

The red sea urchin data used in this research were provided by Kristine

Barsky, an invertebrate specialist for the California Department of Fish and Game.

The data include an anonymous but unique identifier for each diver, the date of the

fishing event, the location of the fishing event (from the DFG enumeration blocks3),

the amount of catch (in pounds), the number of divers on the boat, and the total hours

spent diving. The latter variables can be used to further assess unit effort (UE) and

catch per unit effort (CPUE). The raw data contain 28,046 dive events around the

Santa Barbara Channel Islands, though not all records are complete. Each year

contains an average of 3,505 dive events. Records with blank values or with values

far outside “normal” parameters (e.g., greater than five divers per event, less than 1

hour or more than 24 hours of diving per event) were removed from the analysis. On

average less than 1% of the data were removed each year, though two years, 2003 and

3 The DFG imposes a grid of 10 minute x 10 minute cells over the coast. Data are aggregated to this level of resolution to protect confidentiality.

15

2005, lost 1.35% and 1.44% of their data, respectively. In a few cases a particular

diver consistently left data fields blank. Generally this only accounted for a few

records, if any, each year. For three divers the omissions accounted for over 35% of

their effort in one year. One of these divers had average yearly catch but reported the

number of divers on their boat above the cut-off value. The other two divers both had

above average yearly catch but did not report the number of divers on their boats. For

two divers the omissions accounted for over 50% of their effort in one year. One

diver did not report the number of divers on the boat and the other reported the

number of divers on the boat above the cut-off value. Both divers reported below

average catch these years. Given the very large number of dive events these losses

are considered minor and their removal isn’t expected to significantly bias our results.

The red sea urchin fishing effort data contain 27,793 diving events around the

Santa Barbara Channel Islands (Santa Barbara, California). Fleet effort ranged from a

minimum of 1 dive per year to a maximum of 171 dives per year, with an average of

36 (standard deviation = 33) dives per year. The red sea urchin catch accounts for

35,089,790 pounds, ranging from a minimum of 10 pounds per year to a maximum of

392,046 pounds per year, with an average of 45,103 (standard deviation = 53,962)

pounds per year. The red sea urchin CPUE4 ranges from a minimum of 10 pounds

per diver per day (presumably exploratory dives or gear testing) to 4,781 pounds per

4 Catch per unit effort. This research measures CPUE for the red sea urchin as pounds per diver per day, the market squid fisheries as pounds per day, and the spiny lobster fishery as number of legal-sized lobster per trap.

16

diver per day, with an average of 1,156 (standard deviation = 671) pounds per diver

per day.

2.2. California Spiny Lobster

Like the red sea urchin, the California spiny lobster (Panulirus interruptus) is a long-

lived, slow growing benthic species predominantly found in and around nearshore or

shallow rocky outcroppings and reefs. Though the current population size is

unknown, their range is primarily from Point Conception, California (Santa Barbara

County) to Magdalena Bay, Baja California, Mexico. California spiny lobster is a

commercially important species strongly influenced by the temperature, nutrient, and

habitat fluctuations associated with El Niño events. Though domestic markets are

growing, Asian and French markets have traditionally driven the demand for this

species (CA DFG 2003a).

The California spiny lobster fishery has existed since the late 1800s. Lobster

fishermen typically deploy 100 to 500 traps along depth contours in waters less than

100 fathoms deep. In 2001 spiny lobster accounted for less than 1% of the total catch

in California by volume but over 4% of the total catch in California by value (CA

DFG 2001a). In 2005 spiny lobster still accounted for less than 1% of the total catch

in California by volume but had risen to over 5% of the total catch in California by

value (CA DFG 2005)

The fishery is managed through a restricted access lobster operator program

started in 1996. The restricted access program limits the number of permits but does

17

not limit the number of traps, raising concerns over its effectiveness at limiting total

fishing pressure. In addition to this program, the fishery is managed through a closed

season (mid-March to the beginning of October) to protect molting lobsters and egg-

carrying females, a minimum size limit, and a prohibition on the catch of egg-bearing

females (CA DFG 2003a).

The spiny lobster data used in this research were provided by Douglas

Neilson, an associate marine biologist at the California Department of Fish and

Game. The data include an anonymous but unique identifier for each lobster

fisherman, the date of the fishing event, the location of the fishing event (from the

DFG enumeration blocks), the pull of the event5, the number of traps set, the number

of nights the traps soaked before being pulled, the number of legal-sized lobsters

retained, and the number of shorts (sub legal-sized lobsters) released. The latter

variables can be used to further assess unit effort (UE) and catch per unit effort

(CPUE). The raw data contain 94,012 records for fishing events throughout southern

California (roughly from Point Conception to San Diego, including offshore islands),

though many times a few records are different pulls from a single fishermen during a

single fishing event. Each year contains an average of 13,429 fishing records. Seven

records were removed from analysis (two from the 1998-1999 season, three from the

1999-2000 season, and two from the 2000-2001 season) due to incomplete

information on the date of the fishing event. In each year the data removed record a

5 A pull is the removal of any number of traps from a particular location. Lobster fishermen often place traps in a variety of locations and may make a number of pulls each day (typically 1-10, average = 1.7), reporting them as part of one day’s fishing effort.

18

different fisherman’s daily effort. In other words, one day of fishing for three

different fishermen is removed, spread over three different years.

The spiny lobster fishing effort data contain 52,069 fishing events along the

southern California coast (roughly Point Conception to San Diego) and at offshore

islands from the 1998-1999 season through the 2004-2005 season6. Fleet effort

ranged from a minimum of 1 day per year to a maximum of 138 days per year, with

an average of 46 (standard deviation = 28) days per year. The spiny lobster catch

accounts for 3,214,498 legal-sized lobster, with fleet catch ranging from a minimum

of no legal-sized lobster per year to a maximum of 16,690 legal-sized lobster per

year, with an average of 2,830 (standard deviation = 2,769) legal-sized lobster per

year. The spiny lobster CPUE ranges from a minimum of no legal-sized lobster per

trap to 13 legal-sized lobster per trap, with an average of 0.62 (standard deviation =

0.56) legal-sized lobster per trap.

2.3. California market squid

California market squid, Loligo opalescens, is one of California’s most important

fisheries in terms of both volume of catch and revenue from catch, with demand

driven by the international market. Squid is a short-lived, pelagic species which is

highly dependent on water temperature and environmental conditions. However,

typical of a short-lived species, market squid appears to recover quickly from

dramatic declines produced by warm water conditions. Teams of boats typically

6 Spiny lobster season begins in October of one year and runs through March of the following year.

19

work together, with one boat employing strong lights to attract and concentrate

schools of spawning squid in shallow waters where they are caught by another boat

using round haul nets (i.e., purse seines).

While the fishery was historically based in and around Monterey bay, the

Santa Barbara area (particularly the northern Channel Islands) has provided the

majority of catch since 1985. In 2001 market squid accounted for approximately 43%

of the total catch in California by volume and over 16% of the total catch in

California by value (CA DFG 2001a). In 2005 market squid accounted for

approximately 42% of the total catch in California by volume and 29% of the total

catch in California by value (CA DFG 2005).

The size and status of the squid population is uncertain, though research and

catch records indicate a very large biomass. The fishery is primarily managed

through restricted access (a permit program which began in 1998) and weekend

closures designed to limit anthropogenic disturbances during spawning activities (CA

DFG 2001b).

The market squid data used in this research were provided by Brianna Brady,

an associate marine biologist at the California Department of Fish and Game. The

squid data are less detailed than the red sea urchin and spiny lobster data, containing

an anonymous but unique identifier for each boat, yearly summaries of effort (number

of fishing days), and yearly summaries of catch (in pounds) for each boat. Unlike the

red sea urchin and spiny lobster data, the market squid data record fishing effort for

the whole state of California, not a specific region or location. The raw data contain

20

973 records, where each record is the yearly summary of a single boat. Each year

contains an average of 122 fishing summaries. Only one record was removed from

the data as it contained no boat identifier. The boat only reported catch once and it

was far below average.

The market squid fishing effort data contain 24,926 fishing events off the

coast of California. Fleet effort ranged from a minimum of 1 day per year to a

maximum of 130 days per year, with an average of 26 (standard deviation = 28) days

per year. The market squid catch accounts for 1,212,833,456 pounds, ranging from a

minimum of 1 pound per year to a maximum of 10,473,370 pounds per year, with an

average of 1,246,489 (standard deviation = 1,831,099) pounds per year. The market

squid CPUE ranges from a minimum of 1 pound per boat per day (presumably

exploratory trawls or gear testing) to 177,088 pounds per boat per day, with an

average of 30,903 (standard deviation = 28,114) pounds per boat per day.

2.4. Environmental data

Regional wind speed and wave height data were collected from NDBC station

number 46063, located off Point Conception. These two environmental variables are

frequently identified by commercial fishermen as the most important in determining

whether and where to fish7. Hourly data were averaged over each day, with missing

data supplied through regression analysis using the nearby station 46054 buoy (west

Santa Barbara Channel) or more distant station 46053 buoy (east Santa Barbara

7 Personal communication with a variety of fishermen during data collection and mapping projects conducted from 2003-2006.

21

Channel) when station 46054 was not available (for example due to maintenance or

damage). Average daily wind speeds ranged from 0.21 to 14.88 meters per second

(average = 6.96 meters per second). Average daily wave heights ranged from 0.31 to

6.72 meters (average = 2.30 meters). Both variables exhibit strong seasonal patterns

with wind speeds highest in spring and summer and wave heights largest in winter

months.

3. Methodology

This research addresses the role of heterogeneity in fisherman effort and catch on

total fleet catch, individual catch, and fish stock size. It does this by comparing the

catch produced by homogeneous and heterogeneous fleets. To do this I compare

expected fleet catch with actual fleet catch. A difference between the two values is

attributed to heterogeneity within the fishing fleet. Expected annual fleet catch is

defined as

{ } DLNCE fleet = (1)

where N = fleet size (e.g., number of fishermen, number of boats), L = average

fisherman catch per day, and D = average number of fishing days per year (or

season). Actual annual fleet catch is defined as

∑=

=N

iifleet LC

1

(2)

where N = fleet size and Li = actual yearly (or seasonal) catch of fisherman i. This

research will determine if DLNC fleet = . In other words, I ask if actual fleet catch

22

equals the average fisherman catch per day times the average yearly (seasonal)

fisherman effort times the number of fishermen in the fleet. If expected annual fleet

catch and actual annual fleet catch are not equal then fisherman performance

heterogeneity is expected to produce fleet harvest that is different that than predicted

by models which use average values for fleet effort and catch. This heterogeneity

may be attributable to correlations and non-linear relationships between catch and

effort. To identify possible effort-catch correlations let

{ } ( )( )[ ]∑=

′+′+≡=N

iiifleet dDlLDLNCE

1 (3)

where L and D are average fisherman catch per day and average number of fishing days per year as described above, il′ are the anomalies in catch per day, and id ′ are the

anomalies in fishing effort in days per year. By definition ∑=

=′N

iil

10 and ∑

=

=′N

iid

10 .

Then { } ∑=

′′+=N

iiifleet dlDLNCE

1 (4)

Thus the catch-effort relationship for the fleet is described as the average catch and

effort for the entire fleet plus each fisherman’s catch and effort anomalies. In the

absence of correlation the anomalies sum to zero and do not change the expected

catch. However, if li′ and id ′ are correlated the resulting catch-effort relationship

could be different than the average catch and effort would predict. The sign and

magnitude of the difference in expected annual fleet harvest and actual annual fleet

harvest depends on the amount of heterogeneity in the catch-effort relationship. A

positive and strong correlation between fleet effort and fleet catch indicates an actual

23

fleet harvest that is greater than expected fleet harvest. In this case there are

fishermen in the fleet who perform far above what models which use average values

predict, resulting in a fleet harvest greater than expected. A negative and strong

correlation indicates an actual harvest that is smaller than expected fleet harvest. In

this case there are fishermen in the fleet who perform far below what models which

use average values predict, resulting in a fleet harvest smaller than expected.

4. Results

4.1. Are fishing fleets heterogeneous?

Analysis of yearly catch and effort data reveals fleets with a large, tightly clustered

proportion of low effort and catch and a smaller but more widely spread proportion of

increasingly high effort and catch. These data are shown for the red sea urchin

(Figure II-2a), spiny lobster (Figure II-2b), and market squid (Figure II-2c)

fisheries. The correlation coefficients, r, for these data are 0.81 for the red sea urchin

fleet, 0.66 for the spiny lobster fleet, and 0.91 for the squid fleet, indicating

significantly positive correlation for all three fleets, particularly red sea urchin and

market squid. This result is not surprising as one expects people who fish more to

catch more. While effort and catch are strongly correlated there is a noticeable

spread. This spread is due to heterogeneity in the fleet and results in differences in

catch with similar effort. There is also evidence of a non-linear relationship between

catch and effort in each fleet. For the red sea urchin and market squid fleets a non-

linear model better represents the relationship between catch and effort than does a

24

linear model. This is determined by the higher R2 for the non-linear models

compared to the R2 for the linear models. As a result of this heterogeneity the use of

average values does not provide an accurate estimation for fleet effort or catch.

These relationships suggest that in all three fisheries those who fish more on

average tend to catch more on average. In other words, those who fish more tend to

be better, or more efficient, than those who fish less. This relationship is explored

further in the next section.

4.2. Red sea urchin fleet catch and effort

By analyzing the 1998-2005 red sea urchin catch data at a yearly time scale I find that

average fleet effort (days per year) and average fleet catch (pounds per day) tends to

underestimate actual fleet catch. For the remainder of this paper I define the variable,

d, as the percent difference between actual fleet harvest, ∑=

=N

iifleet LC

1

, and expected

fleet harvest (or the fleet harvest predicted by models which use average values),

{ } DLNCE fleet = . For all three fleets d is positive, indicating an underestimation of

actual fleet harvest when using average values for fleet effort and catch.

While there were 1.1% and 2.6% overestimations in 2001 and 2002,

respectively, five years of data underestimate actual fleet catch from 7.4% - 10.8%,

averaging 8.4%. This underestimation is due to the correlation and non-linear

relationship between effort and catch that have been discussed, resulting in far above

average catch by the above-average segment of the fleet. Over this time period the

25

fleet steadily dropped from 129 active divers per year to 79 active divers in 2005,

averaging 97 active divers per year. With the exception of 1999, average effort

generally increased over the time period from a 1998 low of 27 dives per year to a

2005 near-high of 42 dives per year. Average catch nearly doubled from a 2001 low

of 26,204 pounds to a 2005 high of 76,337 pounds. 1999 was a year of relatively

high average effort and average catch (41 dives per year and 44,896 pounds,

respectively), likely attributable to La Niña cold water and upwelling conditions

favorable to increased sea urchin abundance and roe quality due in part to increased

nutrient and kelp habitat availability.

4.3. Spiny lobster fleet catch and effort

The spiny lobster fishery also exhibits an underestimation of predicted fleet effort and

catch when compared with actual fleet catch, though it is much less than the red sea

urchin fishery. While in 2000 there was an overestimation of 4.1%, six years of data

show underestimations between 1.3 - 5.4%, averaging 3.3%. This makes sense as the

spiny lobster fishery has the lowest correlation and most linear relationship between

effort and catch. Also, the above-average fishermen in the spiny lobster fleet are not

as extremely above-average as in the other two fleets. The active fleet steadily

dropped over this time period, from 192 fishermen in the 1998-1999 season to 143

fishermen in the 2004-2005 season, averaging 162 fishermen per season. Average

effort increased slightly over this period, from 41 days per year in 1998-1999 season

to 49 days per year in the 2004-2005 season. Average catch increased over the time

26

period, from 2,046 legal-sized lobster in the 1998-1999 season to 4,211 legal-sized

lobster in 2004-2005.

4.4. Market squid fleet catch and effort

The market squid fishery data are not detailed enough to compare predicted and

actual conditions. However, as with the previous two fleets, the above-average

fishermen in the fleet tend to be far above average. The active fleet steadily dropped

over this time period from 161 boats in 1999 to a 2005 low of 103 boats, averaging

122 boats per year. Effort remained fairly constant, averaging 25 fishing days per

year. Catch also fluctuated around an average of 1,210,646 pounds per year. Notable

exceptions to these effort and catch averages occurred in 1998 and 2002, when both

effort and catch were far below average. These declines can be attributed to warmer

water El Niño conditions, which are unfavorable for squid.

4.5. How do individuals differ?

Not only do I want to see if a heterogeneous fleet produces different fishery impacts

than a homogeneous fleet, I want to see if this heterogeneity can be quantifies. To

this end I test to see whether or not individual fishermen exhibit consistent behavior

over time. Is the performance of the above average fishermen consistently above

average (and vice versa), or is it random from year to year? To address this question

fishermen in the red sea urchin and market squid fleets with six or more years of

fishing experience (based on our data) and fishermen in the spiny lobster fleet with

27

five or more years of fishing experience (based on our data) are ranked by catch per

unit effort (CPUE) relative to the rest of the fleet and their ranks are tracked over

time8. The resulting sample contains 60 fishermen from the red sea urchin fleet

(Figure II-4a), 139 fishermen from the spiny lobster fleet (Figure II-4b), and 71

boats from the market squid fleet (Figure II-4c). Visual inspection of the data

reveals fairly consistent CPUE rankings over time, especially among the very high

and very low CPUE ranks. When each fisherman’s average CPUE rank is regressed

against their average catch per day strong non-linear relationships (Figure II-5) are

apparent, particularly in the red sea urchin (Figure II-5a) and market squid (Figure

II-5c) fleets. The relationship is weaker in the spiny lobster fleet (Figure II-5b),

presumably for some of the reasons discussed above. In all three fisheries it appears

that fishermen who fish more frequently tend to consistently perform “better” (as

described by yearly CPUE ranks) than those who fish less frequently.

Cluster analysis using k-means clustering reveals three distinct “segments”

within each fleet: a consistently above average group of fishermen (consistently high

CPUE rankings), an average group of fishermen (with a large amount of variability in

CPUE rankings), and a consistently below average group of fishermen (consistently

low CPUE rankings). Analysis of variance of the yearly red sea urchin (Table 2),

spiny lobster (Table 3), and market squid (Table 4) CPUE ranks within and between

8 In this research I choose to focus on fishermen with a high degree of participation in our data. However, focusing only on the fishermen who fish every year produces too small of a sample size. To determine an appropriate amount of experience as a lower cut-off value I look at the average standard deviation of CPUE ranks within each segment of the fleet for decreasing years of fishing experience (Figure II-3). The values that provided the lowest average standard deviation yet still allowed for fishermen to “miss” some of the years are six or more years of experience for the red sea urchin and market squid fleets and five or more years of experience for the spiny lobster fleet.

28

these categories produce very large F-statistics and very low p-values, indicating that

the fishermen in each category are likely to come from distinctly different

populations. Figure II-6 shows boxplots9 of yearly CPUE ranks for each fisherman

in each category of the red sea urchin fleet (Figure II-6a), spiny lobster fleet (Figure

II-6b), and market squid fleet (Figure II-6c). All three fleets exhibit distinctly

different groupings, especially between categories 1 (consistently above average

ranks) and 3 (consistently below average ranks) with only a small overlap between

whiskers and a few outliers. In other words, fishermen in the consistently above

average group do not have episodes of very low average yearly CPUE and vice versa,

though there are some noticeable outliers in the more homogeneous spiny lobster

fleet. CPUE ranks are much more variable for fishermen in the average group (i.e.,

inconsistent CPUE ranks) of all three fisheries.

4.6. Heterogeneity among segments of the red sea urchin fleet

Examination of the performance of the red sea urchin fishermen in each of these

ranks shows correlations between a number of key behavioral and environmental

variables (Table 5). Fishermen with higher CPUE ranks (relative to the rest of the

fleet) tend to fish with fewer divers on their boat (r = -0.39). Those in the “above

average” CPUE group average 1.38 divers per trip, those in the “average” group

average 1.80 divers per trip, and those in the “below average” CPUE group average

1.79 divers per trip. CPUE ranks are also negatively correlated with average dive

9 The boxes show median, lower-, and upper-quartiles. The whiskers of each boxplot show the extent

of the data and the plus (‘+’) signs show outlying data.

29

hours (r = -0.49), with those in the “above average” CPUE group averaging 4.15

hours per trip, those in the “average” group averaging 5.49 hours per trip, and those in

the “below average” CPUE group averaging 6.36 hours per trip. Finally, though

CPUE ranks do not appear correlated with average catch per trip, they are correlated

with average catch per hour (r = 0.56). Fishermen in the “above average” CPUE

group average 282 pounds per hour, those in the “average” group average 254 pounds

per hour, and those in the “below average” CPUE group average 180 pounds per

hour. Fishermen with higher CPUE ranks tend to go out in lower average wind

speeds (r = -0.44). Average wind speed for fishermen in the “above average” CPUE

group is 5.80 meters/second, average wind speed for the “average” group is 6.08

meters/second, and average wind speed for those in the “below average” CPUE group

is 6.13 meters/second. There is not a significant relationship between CPUE ranks

and wave height.

To further characterize fishermen in the red sea urchin fishery I evaluate the

statistical significance of the number of divers on effort, catch, and CPUE (Table 6).

In separate regressions I observe a positive relationship between the number of divers

per trip and the average effort (measured as number of diving hours per trip) on a boat

(r = 0.46) and the number of divers per trip and the average catch per trip (r = 0.55).

It follows that more divers would translate into more diving hours as well as more

pounds of sea urchin. Interestingly, there is a negative relationship between the

number of divers per trip and the average CPUE for each diver on a boat (r = -0.41).

It appears that more divers on a boat translates into decreased individual CPUE.

30

Wilson (1990) discusses a declining marginal benefit associated with the addition of

members in a fishing “club”. Two fishermen working together are expected to locate,

or catch, less than twice as many fish as each fisherman working independently. A

similar situation may be occurring here, with reduced average CPUE due to

congestion at dive locations, for example. An alternate, but still plausible,

explanation is that less efficient fishermen could partner with high-liners (e.g., as

apprentices) and thus reduce the average CPUE for that boat.

4.7. Heterogeneity among segments of the spiny lobster fleet

Fishermen in the spiny lobster fleet also exhibit differences in fishing performance

that can be attributed to responses to behavioral and environmental variables (Table

7). Fishermen with higher CPUE ranks (relative to the rest of the fleet) tend to

average higher catches of legal-sized lobster each fishing event (r = 0.60). Those in

the “above average” CPUE group averaged 87 legal-sized lobster per event, those in

the “average” group averaged 72 legal-sized lobster per event, and those in the

“below average” CPUE group averaged 38 legal-sized lobster per event. It is

interesting that there is no significant correlation when it comes to average number of

traps per event, indicating that average trap effort is fairly homogeneous or varies

randomly across the active members of the fleet with five or more years of

experience. Analysis of fishing effort and environmental variables shows a

relationship, though not a particularly strong one, between fishing effort and average

wave height (r = -0.28), where average wave height for fishermen in the “above

31

average” group was 2.36 meters, average wave height for fishermen in the “average”

group was 2.39 meters, and average wave height for fishermen in the “below

average” group was 2.43 meters. There is no significant relationship between CPUE

ranks and wind speed.

To further characterize fishermen in the spiny lobster fishery I evaluate the

statistical significance of the number of traps a fishermen uses on effort, catch, and

CPUE. There is some correlation between the average number of traps a fisherman

uses per event and a fisherman’s total yearly effort (r = 0.24) and between the average

number of traps a fisherman uses and the average number of shorts a fisherman

releases per event (r = 0.21). There is a much stronger correlation between the

average number of traps a fisherman uses per event and the average number of legals

a fisherman catches per event (r = 0.73), indicating that fishermen who fish more

traps on average catch more legal-sized lobster on average, though not necessarily

more sublegal-sized lobster.

These analyses indicate that fishermen, at least those with very high and very

low CPUE, are relatively consistent in their fishing performance and exhibit distinctly

different behavior. It also highlights the need to account for this heterogeneity in

fleet modeling.

32

5. Discussion

As discussed previously, Walters and Martell (2004) present a case for generally low

variance among commercial fishermen, though much of the fishing fleet research

points to significant heterogeneity among fisherman (Branch et al 2006).

I have shown strong correlations and non-linear relationships between effort

and catch in all three fisheries – in other words, individual heterogeneity within

fishing fleets. Moreover, these fisheries, like many other fisheries, exhibit a highly

skewed distribution of effort and catch among fishermen (Hilborn 1985, Kalvass and

Hendrix 1997). Not only does a small portion of the fleet produce a majority of the

catch, those who fish significantly more than average catch significantly more than

average and those who fish less than average usually catch less than average. This

research shows that the fish biomass removed by the members of a fishing fleet

whose catch is at or below average (approximately 60-70% of the fishermen in each

of the fleets), do not balance the high-liners (e.g., those who catch above, and usually

far above, average) to generate the predicted “average” fleet catch, suggesting that

stock harvest models that use average values for fishing effort and harvested biomass

could significantly underestimate fleet impacts. In the following section I explore

variables that affect the heterogeneity within a fleet over time.

5.1. Fleet composition and entry/exit events

During this time period the active red sea urchin fleet decreased by 39% from a 1998

high of 129 divers to a 2005 low of 79 divers, the spiny lobster fleet decreased by

33

26% from a 1998 high of 192 fishermen to a 2005 low of 143 fishermen, and the

market squid fleet decreased by 36% from a 1999 high of 161 boats to a 2005 low of

103 boats. There is a strong inverse correlation between fleet size and the urban west

U.S. Consumer Price Index (CPI) for the red sea urchin fleet (r = -0.94), spiny lobster

fleet (r = -0.94), and market squid fleet (r = -0.65), suggesting that fishermen may

have left a particular fleet (or did not participate in the fleet) as cost of living

increased. There is also a strong negative correlation between unemployment rates

and active fleet size for the red sea urchin fleet (r = -0.81), spiny lobster fleet (r = -

0.66), and market squid fleet (r = -0.73), indicating decreasing active fleet

participation with increasing unemployment rates. However, we do not know if the

fishermen switched to different fisheries or stopped fishing completely. A variety of

factors likely contribute to these declines in active fleet participants. Analysis of CA

DFG commercial landings data allows a glimpse into the economic value of

participating in each of these fisheries over time by tracking the average value of a

pound of catch (Figure II-7a) and shows the value of each of the fisheries relative to

all California commercial fishing (Figure II-7b). In 2000 red sea urchin was a

relatively high value per pound fishery at $0.98 per pound but dropped by almost half

to $0.54 per pound in 2005. Spiny lobster, the fishery with the smallest decrease in

fleet size, is a very high value per pound fishery (averaging $7 per pound) and the

value per pound increased from $6.63 per pound in 2000 to $7.87 per pound in 2005.

Market squid is a very low value per pound fishery (averaging $0.17 per pound) but

the value per pound increased from $0.10 per pound in 2000 to $0.26 per pound in

34

2005. Market squid is the only fishery of the three that increased in value relative to

all California commercial fishing, however the increase to $0.30 of every fishing

dollar occurred in 2005 and averaged $0.20 of every fishing dollar prior to 2005. The

red sea urchin and spiny lobster fisheries, among some of the more valuable fisheries

in California, averaged $0.08 and $0.04 of every fishing dollar, respectively, though

red sea urchin actually dropped from $0.11 of every fishing dollar in 2000 to $0.06 of

every fishing dollar in 2005. In other words, the value of the fishery dropped relative

to all California commercial fisheries. While fishermen are not expected to behave as

purely rational economic agents it appears that increasing cost of living and

decreasing value of catch may have contributed to the declining active fleet

participation we observe.

In all three fleets the percentage of the fleet that is above average stays the

same or increases (Figure II-8), suggesting that the below-average fishermen are

rapidly improving, leaving the fleet, or choosing not to participate (but not necessarily

leaving the fleet). As the fleet sizes drop and the percentage of the fleet that is above

average increases (or even stays the same), I expect to see a decrease in fleet

heterogeneity along with a decrease in the fleet catch underestimation discussed

above. This is due to an increase in average values as the below average fishermen

stop fishing and the above average fishermen remain, resulting in a more

homogeneous fleet.

35

5.2. Fishermen heterogeneity and fish stock modeling

Finally, I compare the fishing impacts of homogeneous and heterogeneous

fleets on a fish stock. The general framework is a Schaefer model:

tt

tt CkBrBB −⎟⎟

⎠

⎞⎜⎜⎝

⎛−=+ 11 (5)

where Bt = total stock biomass at time t, r = intrinsic growth rate of the stock, k =

carrying capacity, and Ct = catch at time t. Fleet catch is modeled as

( ) BEqdC t += 1 (6)

where d is the difference between “average” fleet catch and “actual” fleet catch, as

discussed above. A d of zero is the same as average conditions. As d gets larger in

the positive direction actual fleet harvest is underestimated by using average values

for effort and catch. As d gets larger in the negative direction fleet harvest is

overestimated by using average values for effort and catch.

Standard values from Hilborn and Walters (1992) are used to compare

equilibrium stock biomass and fleet catch as d increases linearly from 0% to

+100%10. For both biomass and catch there are strong negative relationships (Figure

II-9), reducing total fish stock biomass and decreasing total fleet catch as d increases.

10 Though negative d (i.e., expected fleet harvest overestimation) is possible I only consider positive d (i.e., expected fleet harvest underestimation) in this research since these fleets exhibit positive d.

36

The increasing heterogeneity produces a fleet with a small portion of far above-

average fishermen who take much more than a stock assessment model that assumes

a homogeneous, average fleet would predict. Though this initially generates very

high total fleet catch, the fish stock is quickly decimated, resulting in a much lower

equilibrium fish stock biomass and a much smaller equilibrium fleet catch. A fishery

with these particular characteristics crashes at an effort-catch underestimation of

60%. In other words, there is no sustainable equilibrium level for this fishery if the

fish stock models which use average values for effort and catch are underestimating

actual effort and catch impacts by 60% or more. I expect this effect to be most

pronounced in a fishery with long-lived species and/or species with low recruitment

rates, like the spiny lobster fishery, and least pronounced in a fishery with short-lived

species and/or species with high recruitment rates, like the market squid fishery.

6. Conclusions

This research shows that the Santa Barbara Channel Islands red sea urchin fleet,

southern California spiny lobster fleet, and California market squid fleet all exhibit

heterogeneity among their members. For all three fleets the relationship between

effort and catch is correlated and non-linear. Therefore, the fish biomass removed by

the below average portions of the fleets does not tend to balance the above average

portions of the fleets to generate predicted “average” fleet catches. The imbalance

suggests that harvest models which use average values for fishing effort and

harvested biomass could significantly underestimate fleet impacts to fish stocks.

37

The research characterizes some of this heterogeneity in the Santa Barbara red

sea urchin and southern California spiny lobster fleets. I find strong correlations

between average CPUE ranks (which tend to remain consistent over time) and other

variables such as average divers per trip, average hours per trip, and average catch.

6.1. Spatial variability

These strong correlations between effort and catch and the underestimation of

predicted effort and catch when compared with actual catch appear in the fleet data

regardless of spatial extent. The data on the red sea urchin fishery are focused around

the Santa Barbara Channel Islands, the data on the spiny lobster fishery are focused in

southern California (roughly Point Conception to San Diego), and the data on the

market squid fishery comprise effort across the entire state of California. Thus, the

fleet heterogeneity and resulting underestimation of impact to fish stock biomass does

not appear to be an artifact of a particular port or location.

It is interesting to note that, at least in the case of the red sea urchin and spiny

lobster fishing fleets, some of the fishermen in the consistently high CPUE group

exhibit different spatial behavior than those who are not in the consistently high

CPUE group. Visual inspection of the spatial effort allocation of the top five and

bottom five red sea urchin fishermen (ranked by average CPUE relative to the rest of

the fleet) shows the top ranked fishermen focusing their effort around San Miguel and

Santa Rosa Islands while the bottom ranked fishermen tend to fish Anacapa and Santa

Cruz Islands. This may be attributable in part to size of boat or home port (Anacapa

38

and Santa Cruz Islands, the islands nearest the coast, are close to Ventura while San

Miguel and Santa Rosa Islands, the islands farthest from the coast, are more

accessible to Santa Barbara), neither of which we have information on. This may also

indicate a willingness among “better” fishermen to travel farther to what are

considered more productive fishing grounds. This spatial behavior is explored in

subsequent chapters.

The effort of the top five ranked spiny lobster fishermen is focused around

Santa Cruz Island while an aggregation of the bottom five ranked fishermen shows

effort focused around Dana Point. The two groups have a minor overlap around

Santa Catalina Island and the south side of Santa Cruz Island. Unfortunately I do not

have spatial data for the market squid fishery so can not produce a similar analysis.

6.2. Variability among fisheries

These relationships hold across fisheries with very different characteristics. Red sea

urchin is a dive fishery where one or more divers enter the water to remove individual

urchin from rocks and crevices, spiny lobster is a trap fishery where a fisherman and

his tender set and remove traps, and market squid is a fishery that uses nets to catch

large quantities of fish. In addition, spiny lobster is a high value species and

relatively low quantity fishery; red sea urchin is a medium value species, medium

quantity fishery; and market squid is low value species, high quantity fishery. While

these differences may influence the amount of heterogeneity and underestimation

they do not appear to negate them.

39

While it makes intuitive sense that increasing experience correlates with

increasing catch, an underestimation is not expected to occur in a fleet where the

effort and catch of the above-average fishermen balance the effort and catch of the

below-average fishermen. This could be the case if the above-average fishermen in a

fleet were not far above average, for example in a new fishery where everyone is

inexperienced and therefore no one is consistently far above average. It could also

occur in a fishery where the average is relatively high and the above-average

fishermen are not far above average. This situation would not be unexpected in a

very mature fishery where the very experienced and capable fishermen remain and

the inexperienced or unsuccessful fishermen have exited the fishery. Finally, a

fishery that is heavily managed or centrally controlled may not exhibit such a strong

underestimation. In this case homogeneity is imposed upon the fleet as fishermen are

told when to fish and/or how much to catch.

6.3. Management implications

While this research does not give hard, fast numbers with which to adjust fish stock

models, it does encourage fishery scientists and managers to analyze individual level

data in more detail to determine the composition of a fleet and to see whether or not a

particular fleet may behave significantly different than models that use average values

for effort and catch would predict. The composition and behavior of a fishing fleet is

related to the level of development of its fishery (Hilborn and Walters 1992). In this

research the red sea urchin fleet size decreases (active fishermen stop fishing and/or

40

leave the fleet) while the ratio of above average to below average fishermen

decreases. In other words, the remaining fishermen tend to be “better” fishermen.

The spiny lobster fishery, a mature and highly exploited fishery, shows a fleet with a

relatively low ratio of above average to below average fishermen. The fishermen in

this fleet tend to be “better” fishermen and the underestimation from the predicted

effort and catch is relatively small.

This chapter concludes by looking at the possible implications of fishermen

heterogeneity on a variety of management policies. The policies under consideration

include temporal closures, individual catch limits (ex. bag limit, trap limit), fleet catch

limits (ex. total allowable catch [TAC]), and individual transferable quotas (ITQs) –

all common management strategies. For each of these policies I determine the impact

fishermen heterogeneity might have on fish stock biomass, fleet catch, and individual

profits (Table 8). In looking at individual profits, the fleet is separated into “average”

fishermen and “high-liners” (those who fish above, and usually far above, average).

As discussed above, high-liners comprise approximately 30% of the fleet in each of

these fisheries. Additionally, these high-liners produce approximately 85% of the

total fleet catch. Therefore, in each management scenario 85% of the estimated catch

is allocated to 30% of the fleet (the high-liners) while the remaining 15% of the

estimated catch is disbursed among 70% of the fleet (the average fishermen).

41

6.3.1. Temporal restrictions

In modeling temporal restrictions fishermen are allowed to fish 50-100% of the

season. In this case biomass is always higher with temporal controls, and much

higher as more of the season is closed (Figure II-10). Temporal controls greatly

increase stock size, but do not buffer how quickly biomass drops with increasing

heterogeneity. In other words, biomass still drops at about the same rate as without

temporal controls, but overall stock biomass is higher.

Temporal controls improve fleet catch over no controls, but catch still declines

with increasing heterogeneity (though not nearly as fast or as much as open-access

conditions). Fleet catch is higher with increasing temporal controls, particularly at

high levels of heterogeneity (Figure II-11). At very low levels of heterogeneity the

fleet isn’t able to catch as much as they could catch due to the brevity of the fishing

season.

6.3.2. Individual catch limits

This management policy considers the effects of bag limits, trap limits, etc.

Individual catch limits produce a more homogeneous fleet, bring high-liners closer to

average, reduce impacts to biomass, and reduce total fleet catch.

42

6.3.3. Fleet catch limits

In modeling fleet catch limits, the fleet is allowed to take 50-100% of virgin fish

stock. Biomass is (obviously) greatly improved with lower TAC, but only at high

levels of heterogeneity (Figure II-10). Fleet catch limits allow the fleet to remove

fish biomass at the same level as it would under no controls until increased

heterogeneity reaches the TAC threshold and “turns off” fishing effort.

Fleet catch limits never increase equilibrium fleet catch compared to no

controls (Figure II-11). At low levels of heterogeneity the equilibrium fleet catch is

the same as under no controls. Decreasing fleet catch limits respond quickly to

increasing levels of fishing fleet heterogeneity, reducing equilibrium fleet catch to

zero.

6.3.4. Individual transferable quotas

Individual transferable quotas allow below average fishermen to sell their quota to

high-liners. This increases the short-term profits of the below average fishermen,

increases impacts to biomass as the fleet catch shifts to a higher percentage by high-

liners, and increases total fleet catch. If the fleet shifts increasingly towards high-

liners total fleet catch is expected to decrease since biomass is greatly reduced.

6.4. Future research

Future research will address the spatial and temporal aspects of this fleet composition,

addressing issues such as the “type” of fishermen who enter and exit a fleet, how

43

often these entry and exit events occur, and whether or not “average catch”

fishermen, “below average catch” fishermen, and high-liners exhibit different

temporal and spatial behavior.

44

List of Tables and Figures, Part 1

Table 1. California Department of Fish and Game logbook summary data. Table 2. ANOVA for red sea urchin fishermen with 6 or more years of experience

(α=0.01). Table 3. ANOVA for spiny lobster fishermen with 5 or more years of experience

(α=0.01). Table 4. ANOVA for market squid fishermen with 6 or more years of experience

(α=0.01). Table 5. Behavioral variables for red sea urchin fishermen with 6 or more years of

experience based on average CPUE ranks (all significant at α=0.01). Table 6. Red sea urchin fleet analysis of boat power. Table 7. Behavioral variables for spiny lobster fishermen with 5 or more years of

experience based on average CPUE ranks (latter two significant at α=0.01). Table 8. Effects of fishermen heterogeneity on management policies at equilibrium

conditions of the Schaefer model. Figure II-1. Yearly fishery summary data. Figure II-2. Fishery catch/year and effort/year anomalies, 1998-2005. Figure II-3. Average standard deviation of CPUE ranks within each segment of the

fleet for increasing years of fishing experience. Figure II-4. Fleet CPUE rankings for fishermen with 6 or more years of experience in

the red sea urchin and market squid fleets and 5 or more years of experience in the spiny lobster fleet.

Figure II-5. Average catch per day regressed against average CPUE ranks, 1998-2005.

Figure II-6. ANOVA results for fishing fleets divided into four categories based on average CPUE rank.

Figure II-7. Value of a pound of catch from each fishery over time (a). Value of fishery relative to all California commercial fishing (b).

Figure II-8. Percentage of fishing fleet with above average catch. Figure II-9. Schaefer model equilibrium stock biomass and fleet catch with

increasing d. Figure II-10. Effects of temporal controls and fleet catch limits on equilibrium fish

stock biomass. Figure II-11. Effects of temporal controls and fleet catch limits on equilibrium

fishing fleet catch.

45

Tables Table 1. California Department of Fish and Game logbook summary data.

Minimum Maximum Average Standard Deviation Total

RSU yearly effort (days) 1 171 36 33 27 793 RSU yearly catch (pounds) 10 392 046 45 103 53 962 35 089 790 RSU yearly CPUE* 10 4 781 1 156 671 899 363 LOB yearly effort (days) 1 138 46 28 52 069 LOB yearly catch (legals) 0 16 690 2 830 2 769 3 214 498 LOB yearly CPUE† 0 13 0.62 0.56 707 SQD yearly effort (days) 1 130 26 28 24 926 SQD yearly catch (pounds) 1 10 473 370 1 246 489 1 831 099 1 212 833 456 SQD yearly CPUE* 1 177 088 30 903 28 114 30 068 999

* Catch per unit effort, pounds per day † Catch per unit effort, average number of legals retained per trap Table 2. ANOVA of average CPUE ranks for red sea urchin fishermen with 6 or more years of experience (α=0.01).

Groups Count Sum Average Variance Above Average 135 108.69 0.81 0.021 Average 176 93.11 0.53 0.041 Below Average 130 33.05 0.25 0.029 Source of Variation SS df MS F P-value F crit Between Groups 20.10 2 10.05 321.63 0.000 4.654 Within Groups 13.69 438 0.03

Table 3. ANOVA of average CPUE ranks for spiny lobster fishermen with 5 or more years of experience (α=0.01).


46

Table 4. ANOVA of average CPUE ranks for market squid fishermen with 6 or more years of experience (α=0.01).


Table 5. Behavioral variables for red sea urchin fishermen with 6 or more years of experience based on average CPUE ranks (* denotes significance at α=0.01).

Groups

Average divers per event*

Average hours per event*

Average pounds per event

Average pounds per hour*

Average wind speed*

Average wave height

Above average 1.38 4.15 2067.21 281.52 5.80 1.99 Average 1.80 5.49 1182.54 253.78 6.08 2.02 Below average 1.79 6.36 641.15 180.37 6.13 2.03 r -0.39 -0.49 -0.02 0.56 -0.44 -0.18

Table 6. Red sea urchin fleet analysis of boat power.

Groups Count Average hours Average catch Average CPUE 1 diver 11 214 4.1 785.7 199.0 2 divers 13 508 6.1 1 428.4 127.4 3 divers 2 831 7.9 2 190.3 97.4 4 divers 237 9.6 3 284.9 83.2 5 divers* 3 5.0 1 833.3 102.0 p (α=0.01) ----- 0.000 0.000 0.000

* The “5 divers” group contains only three records

47

Table 7. Behavioral variables for spiny lobster fishermen with 5 or more years of experience based on average CPUE ranks (* denotes significance at α=0.01, ** denotes significance at α=0.05).

Groups

Average traps per event

Average shorts per event

Average legals per event*

Average legals per trap*

Average wind speed

Average wave height**

Above average 96.03 116.07 86.81 0.94 6.21 2.36 Average 114.39 149.45 72.25 0.63 6.16 2.39 Below average 102.87 111.41 38.44 0.36 6.27 2.43 r -0.003 0.10 0.60 0.94 -0.14 -0.28

Table 8. Effects of fishermen heterogeneity on management policies at equilibrium conditions of the Schaefer model.

Policy Stock biomass Fleet Catch Individual Profits No effort control

Low d: – High d: – –

Low d: – High d: – –

All: –, – –

Temporal restrictions (ex. # of days per year)

Low d: + + High d: + +

Low d: 0 High d: +

High-liners: –, 0, + Average: –, 0, +

Individual catch limits (ex. bag limit, trap limit)

Low d: 0 High d: +

Low d: 0 High d: –

High-liners: –, – – Average: 0

Fleet catch limits (ex. total allowable catch)

Low d: 0 High d: +

Low d: – – High d: – –

High-liners: –, – – Average: –

Individual transferable quotas (ITQs)

Low d: 0 High d: –, – –

Low d: 0 High d: +, + +

High-liners: + Below average: +, + +

48

Figures

0

50

100

Div

ers

Red Sea UrchinFleet Size

0

10

20

30

40

50

Div

es

Average Effort

1998 2000 2002 20040

2

4

6

8x 104

Year

Pou

nds

Average Catch

0

50

100

150

200

Fish

erm

en

Spiny LobsterFleet Size

0

10

20

30

40

50D

ays

Average Effort

1998 2000 2002 20040

1000

2000

3000

4000

5000

Season

Lega

ls

Average Catch

0

50

100

150

Boa

ts

Market SquidFleet Size

0

10

20

30

Day

s

Average Effort

1998 2000 2002 20040

0.5

1

1.5

2x 106

Year

Pou

nds

Average Catch

Figure II-1. Yearly fishery summary data.

49

Figure II-2. Fishery catch/year and effort/year data, 1998-2005.

b. a.

c.

50

Figure II-3. Average standard deviation of CPUE ranks within each segment of the fleet for increasing years of fishing experience.

a. b.

c.

51

Figure II-4. Fleet CPUE rankings for fishermen with 6 or more years of experience in the red sea urchin and market squid fleets and 5 or more years of experience in the spiny lobster fleet. Dotted lines show cluster analysis cutoffs.

a. b.

c.

52

Figure II-5. Average catch per day regressed against average CPUE ranks, 1998-2005.

a. b.

c.

53

Figure II-6. ANOVA results for fishing fleets divided into three categories (above average, average, and below average) based on cluster analysis of each fisherman’s yearly CPUE ranks. This sample includes fishermen with six or more years of experience in the red sea urchin and market squid fleets and fishermen with five or more years of experience in the spiny lobster fleet. The box plots show the lower quartile, median, and upper quartile values. The whisker plots are the lines extending from each end of the boxes to show the extent of the rest of the data. Outliers (denoted with a '+') are data values beyond the ends of the whiskers (The Mathworks, Inc. 2005).

a. b.

c.

54

Figure II-7. Value of a pound of catch from each fishery over time (a). Value of fishery relative to all California commercial fishing (b).

a.

b.

55

Figure II-8. Percentage of fishing fleet with above average catch.

Figure II-9. Schaefer model equilibrium stock biomass and fleet catch with increasing d.

0 0.1 0.2 0.3 0.4 0.5 0.60

50

100

150

200

250

300

350

d

Sto

ck b

iom

ass,

Fle

et c

atch

Equilibrium conditions, no controls

Stock biomassFleet catch

a. b.

c.

56

0

100

200

300

400

500

600

0 0.1 0.2 0.3 0.4 0.5 0.6

d

Bio

mas

s

no controls70% open90% open70% TAC90% TAC

Figure II-10. Effects of temporal controls and fleet catch limits on equilibrium fish stock biomass.

0

20

40

60

80

100

120

0 0.1 0.2 0.3 0.4 0.5 0.6

d

Flee

t cat

ch

no controls70% open90% open70% TAC90% TAC

Figure II-11. Effects of temporal controls and fleet catch limits on equilibrium fishing fleet catch.

57

III. Part 2 – Determining causal structures in fishing fleet travel behavior

Abstract

This paper investigates causal structures in fisherman behavior modeling. California

Department of Fish and Game (DFG) fishing logbook data and National Oceanic and

Atmospheric Administration (NOAA) National Data Buoy Center (NDBC)

environmental data are used to identify variables responsible for determining when

and where someone goes fishing and produce models that predict fishing location

choice and fisherman catch based on these governing variables. Multivariate linear

regression models determine the expected catch (a continuous variable) for red sea

urchin and spiny lobster fishermen. Multinomial logit (MNL) models determine

expected fishing location (a discrete variable). This research explores two causal

structures for modeling fisherman travel behavior:

• Catch affects location choice

• Location choice affects catch

For both the red sea urchin fleet and the spiny lobster fleet fishery-specific

variables (e.g., number of divers, number of hours, number of traps) can be used

along with environmental variables (e.g., wind speed, wave height) to predict

fisherman effort and catch. For both fisheries models which use catch to determine

location perform better than models which use location to determine catch. In other

words, fishermen appear to choose a fishing location after first determining their

expected catch at each available location.

58

1. Introduction

A principal conclusion of the marine ecosystem-based management literature is our

need to understand and predict the role and response of humans in the systems,

particularly our role in removing fish from a system and our response to different

management regimes (Hilborn 1985, Vignaux 1996, Botsford et al 1997, Smith 2002,

Branch et al 2005, Frid et al 2005). However, understanding and predicting human

behavior is no trivial undertaking. An ongoing debate exists in the fishing fleet travel

behavior literature regarding how a fleet “decides” to distribute its effort (Holland and

Sutinen 2000, Mistiaen and Strand 2000). Are fishermen primarily “creatures of

habit” who, for the most part, choose a fishing location and catch what they can based

on stock size and fishing ability or is location choice a secondary consideration that is

itself a function of expected catch (with the inherent learning and information transfer

which is beyond the scope of this paper) and other variables (e.g., weather)? Though

reality is undoubtedly some combination of the two, it is important to know if one of

these two options presents itself as a dominate paradigm for fishing fleet modeling.

Previous research has addressed the heterogeneous behavior of fishermen in a

fleet and the variability this introduces in fishing fleet modeling by quantifying the

differences in effort and catch between homogeneous and heterogeneous fishing

fleets and addressing potential impacts to fish stocks and fishery management.

Robinson and Siegel (in prep) find that 1) fishing fleet effort and catch are highly

heterogeneous, and 2) fishermen tend to exhibit consistent fishing performance

relative to the rest of the fleet. However, the research does not incorporate the spatial

59

aspects of fishing fleet effort and catch distribution. This research addresses two

causal structures for modeling fisherman travel behavior:

• Expected catch affects location choice

• Location choice affects expected catch

To address these causal structures I must also determine the variables which influence

when and where a fisherman goes fishing and how much they catch. Ultimately I

seek to predict how a fishing fleet will distribute its effort in space and time.

The next section describes two causal structures used in modeling fishing

location choice. The third section follows with a description of the data set. The

fourth section provides a descriptive analysis of the data used in this research. The

fifth section describes the methodology used in the modeling. The six and seventh

sections present results of the causal structures for the red sea urchin and spiny lobster

fishing fleets, respectively. The final section summarizes the research and offers

some concluding remarks.

2. Two causal structures for modeling fishing location choice

This research examines variables which influence fisherman behavior and fishing

fleet modeling. Fleet modeling has traditionally employed analytical bio-economic

models which are primarily aspatial and utilize average effort and catch values at

equilibrium conditions (Gordon 1954, Smith 1969, Hilborn and Walters 1992,

Robinson & Siegel in prep). Discrete choice random utility models, and more

specifically the family of Logit models, have been employed in more recent years for

60

predicting location choice in recreational (Morey et al. 1991, Train 1998) and

commercial (Holland and Sutinen 2000, Smith 2002, Smith and Wilen 2005)

fisheries. While some fishing fleets are better modeled by a particular method based

on fleet-specific and fishery-specific variables (gear type, pelagic vs. sedentary fish,

centrally managed vs. owner operated, etc), it is important to accurately describe, for

a particular fishing fleet, the relationship between location (a discrete variable) and

catch (a continuous variable) as motivators in determining fishing effort. These two

variables are undoubtedly closely intertwined. Employing discrete-continuous

models to determine consumer choice, Hanemann (1984) states, “the optimal discrete

choice… depends partly on the outcome of the continuous choice, and vice versa.

Therefore the two choices should be modeled in a mutually consistent manner.” This

research acknowledges that a fisherman’s location choice will impact his catch while

also recognizing that a fisherman’s location choice is presumably influenced by his

expectation of catch at possible fishing locations. This dilemma is approached

through the development and estimation of a simultaneous equation system for two

different causal structures. The transportation behavior literature addresses similar

types of causal structures by employing discrete-continuous simultaneous equation

models (Hanemann 1984, Comte 1998, Pendyala and Bhat 2004, Srinivasan and Bhat

2006).

If “catch determines location” is the dominant structure then location choice is

a secondary consideration that is itself a function of expected catch and other

variables (e.g., weather) and one may want to consider management controls such as

61

quotas or trip limits rather than spatial management. If “location determines catch” is

the dominant structure then fishermen in a fleet tend to choose a fishing location first

and catch what they can based on stock size and fishing ability and one may want to

consider spatial management controls such as closed areas or dedicated access areas

rather than quotas or trip limits. As describe in Pendyala and Bhat (2006) certain

restrictions must be imposed to maintain logical consistency with these hierarchical

causal structures. In this recursive model system either catch affects location or

location affects catch, but not both. By testing both of these possibilities I will be

able to offer guidance on the correct causal direction for two commercial fishing

fleets.

2.1. Causal Structure C → L

The first scenario considered is “catch determines location.” In this scenario a

fisherman’s expected catch is modeled based on the time of the year, daily

environmental variables, and fishery-specific variables. I then model a fisherman’s

location choice, again taking into account the time of the year, daily environmental

variables, and fishery-specific variables but also including a fisherman’s catch:

(1)

(2)

CiiCFiiCEiiCSiCi FESC εβββα ++++=*

LiiiiLGiiLViiLTiLi CGVTL εγβββα +++++=*

62

where *iC = (continuous) latent variable underlying expected catch for fisherman i;

*iL = (discrete) latent variable underlying location choice for fisherman i; Si and Ti are

vectors of seasonal effects (time of year); Ei and Vi are vectors of the characteristics

of environmental variables (wind speed, wave height, barometric pressure, air

temperature, water temperature, and precipitation); Fi and Gi are vectors of the

characteristics of observed fisherman-specific variables (hours diving, number of

divers, number of traps, number of nights the traps soaked before they were pulled

out of the water, etc) for fisherman i; Ci is the observed counterpart of *iC ; the αs, βs,

and γs are model coefficients to be estimated using regression methods; and the εs are

the random error terms that may be correlated11.

In this first scenario location choice is modeled as a function of expected

catch. A fisherman starts with some expectation of the fish stocks (i.e., how much he

might catch today based on a number of factors) and then he chooses a location where

he goes to fish.

2.2. Causal Structure L → C

In the second scenario each fisherman’s expected location is determined based on the

time of the year, daily environmental variables, and fishery-specific variables. A

11 Correlation may occur if underlying unobserved factors influence both catch and location choice. For example, neither substrate nor distance from home port are included in these models. Either could strongly influence fish stocks (and resulting catch) as well as a fisherman’s desire to choose a particular location.

63

fisherman’s expected catch is then modeled using their location along with the time

of the year, daily environmental variables, and fishery-specific variables:

(3)

(4)

where the symbols are the same as previously described and Li is the observed

counterpart of *iL . In this causal structure expected catch is modeled as a function of

location choice. In other words, people start with some idea of where they will fish

based on a number of factors. A fisherman’s catch is then a function of fish stocks

and environmental conditions at that location as well as a particular fisherman’s

equipment and ability.

These two scenarios point to the importance of determining the causal

structure underlying the relationship between expected catch and location choice. It

is possible that different causal structures apply to different fisheries, gear types, and

fishing types (e.g. commercial or recreational fishermen). However, if the C L

model is the model that more accurately describes fisherman behavior but we use the

L C model to, for example, assess fisherman response to spatial management

controls, one may overestimate the management strategy’s fishing harvest reduction

because fishermen are less tied to a particular location than expected.

LiiLGiiLViiLTiLi GVTL εβββα ++++=*

CiiiiCFiiCEiiCSiCi LFESC εγβββα +++++=*

64

Another set of model variants that takes advantage of the repeated nature of

the data at hand (i.e., the same fishing boat makes multiple fishing trips during the

period of observation) is a set of models that allow their intercept (i.e., coefficients α

in the equations above) to vary across observed fishing boats. When this intercept

takes a fixed value for each fishing boat the model is named a Fixed Effects Model

(FEM) and when the intercept is assumed to be a random variable the model is named

a Random Effects Model (REM). Both types of models are considered and in a

subsequent section I report on which model better represents each fishing fleet.

3. Commercial fishing fleet and environmental data


fish block data for the red sea urchin (Strongylocentrotus franciscanus) and

California spiny lobster (Panulirus interruptus) fisheries at the Santa Barbara,

California Channel Islands. While I recognize some shortcomings inherent in fish

block data including 1) coarse spatial resolution and 2) intentional and unintentional

location errors (Robinson et al 2005), commercial fishery data have been used

successfully in a number of well-cited papers (Hilborn 1985, Vignaux 1996, Smith

2002).

Regional average daily wind speed, wave height, atmospheric pressure, air

temperature, and water temperature data are collected from the National Oceanic and

Atmospheric Administration (NOAA) National Data Buoy Center (NDBC) along

with total daily precipitation at the Santa Barbara Harbor from the Santa Barbara

65

County Flood Control District for additional analysis of red sea urchin and spiny

lobster fishing fleet behavior.

3.1. Red sea urchin







is highly dependent on food supply and tends to decrease dramatically in El Niño

years when warm water and lack of nutrients reduces kelp supply. Demand for

urchin roe has traditionally come from international Asian markets, though there is an

increasing domestic demand.


or so, it is one of the most commercially valuable in California. In 2001 red sea


over 11% of all California catch by value and 3% of all Santa Barbara catch by

volume and over 18% of all Santa Barbara catch by value (CA DFG 2001). By 2005

the red sea urchin catch accounted for almost 4% of all California catch by volume

and almost 6% of all California catch by value (a drop attributed in part to the loss of

66

the northern California fishery) but had increased to almost 12% of all Santa Barbara

catch by volume and over 19% of all Santa B7arbara catch by value (CA DFG 2005).


exploited in northern California and parts of southern California (CA DFG 2003). It



restrictions.

The red sea urchin data used in this research include an anonymous but unique

identifier for each diver, the date of the fishing event, the location of the fishing event

(from the DFG enumeration blocks12, see Figure III-1), the amount of catch (in

pounds), the number of divers on the boat, and the total hours spent diving. The latter


(CPUE). The raw data contain 28,046 dive events, though not all records are

complete. Each year contains an average of 3,505 dive events. Records with blank

values or with values far outside “normal” parameters (e.g. greater than five divers

per event, less than 1 hour or more than 24 hours of diving per event) were removed

from the analysis. On average less than 1% of the data was removed each year,

though two years, 2003 and 2005, lost 1.35% and 1.44% of their data, respectively.

In a few cases a particular diver consistently left data fields blank. Generally this

only accounted for a few records, if any, each year. For three divers the omissions

12 The DFG imposes a grid of 10 minute x 10 minute cells over the coast. Data are aggregated to this level of resolution to protect confidentiality.

67

accounted for over 35% of their effort in one year. One of these divers had average

yearly catch but reported the number of divers on their boat above the cut-off value.

The other two divers both had above average yearly catch but left the number of

divers field blank. For two divers the omissions accounted for over 50% of their

effort in one year. One diver had left the number of divers field blank and the other

reported the number of divers on their boat above the cut-off value. Both divers

reported below average catch these years. Given the very large number of dive

events these losses are considered minor and their removal isn’t expected to bias our

results.

The cumulative 1998-2005 red sea urchin (RSU) catch data record the fishing

activity of 218 boats, though not all boats were active each year. Table 1 provides

descriptive statistics for the red sea urchin logbook data. “Divers” is the number of

divers on a boat for each fishing trip, “Total Hours” is the total hours spent fishing,

“Total Harvest” is the total harvest (in pounds) for the fishing trip, and “CPUE” is the

catch per unit of effort for the fishing trip (in the case of red sea urchin we measure

CPUE as pounds per hour per diver).

The red sea urchin fishing effort data contain 27,793 diving events, ranging

from a minimum of 1 dive per year to a maximum of 171 dives per year, with an

average of 36 (standard deviation = 33) dives per year. The red sea urchin catch

accounts for 35,089,790 pounds, ranging from a minimum of 10 pounds per year to a

maximum of 392,046 pounds per year, with an average of 45,103 (standard deviation

= 53,962) pounds per year. The red sea urchin CPUE ranges from a minimum of 10

68

pounds per diver per day (presumably exploratory dives or gear testing) to 4,781

pounds per diver per day, with an average of 1,156 (standard deviation = 671) pounds

per diver per day.

3.2. California Spiny Lobster

Like red sea urchin, California spiny lobster (Panulirus interruptus) is a long-lived,

slow growing benthic species predominantly found in and around nearshore or

shallow rocky outcroppings and reefs. Though the current population size is

unknown, their range is primarily from Point Conception, California (Santa Barbara

County) to Magdalena Bay, Baja California, Mexico. California spiny lobster is a

commercially important species strongly influenced by the temperature, nutrient, and

habitat fluctuations associated with El Niño events. Though domestic markets are

growing, Asian and French markets have traditionally driven the demand for this

species (CA DFG 2003a).

The California spiny lobster fishery has existed since the late 1800s. Lobster

fishermen typically deploy 100 to 500 traps along depth contours in waters less than

100 fathoms deep. In 2001 spiny lobster accounted for less than 1% of the total catch

in California by volume but over 4% of the total catch in California by value and less

than 1% of the total catch in Santa Barbara by volume but over 9% of the total catch

in Santa Barbara by value (CA DFG 2001). In 2005 spiny lobster still accounted for

less than 1% of the total catch in California by volume but had risen to over 5% of the

69

total catch in California by value and less than 1% of the total catch in Santa Barbara

by volume but over 10% of the total catch in Santa Barbara by value (CA DFG 2005)

The fishery is managed through a restricted access lobster operator program

started in 1996. The restricted access program limits the number of permits but does

not limit the number of traps, raising concerns over its effectiveness at limiting total

fishing pressure. In addition to this program, the fishery is managed through a closed

season (mid-March to the beginning of October) to protect molting lobsters and egg-

carrying females, a minimum size limit, and a prohibition on the catch of egg-bearing

females (CA DFG 2003).

The spiny lobster data used in this research include an anonymous but unique

identifier for each lobster fisherman, the date of the fishing event, the location of the

fishing event (from the DFG enumeration blocks, see Figure III-1), the pull of the

event13, the number of traps set, the number of nights the traps soaked before being

pulled, the number of legal-sized lobsters retained, and the number of shorts (sub

legal-sized lobsters) released. The latter variables can be used to further assess unit

effort (UE) and catch per unit effort (CPUE). The data contain 31,284 fishing events

from seven fishing seasons, with each year containing an average of 4,469 fishing

events. This records the effort of 67 fishermen though on average only 67%

(standard deviation = 8%) of the fleet fished each season.

13 A pull is the removal of any number of traps from a particular location. Lobster fishermen often place traps in a variety of locations and may make a number of pulls each day (range = 1-12, average = 2, standard deviation = 1.2), reporting them as part of one day’s fishing effort.

70

Table 2 provides descriptive statistics for the spiny lobster logbook data.

“Depth” is the depth of the traps, “Traps Pulled” is the number of traps pulled during

a fishing event, “Nights Soaked” is the number of nights the traps soaked before they

were pulled, “Shorts Released” is the number of sublegal-sized lobster that were

released, “Legals Retained” is the number of legal-sized lobster that were retained,

and “CPUE” is the catch per unit of effort for the fishing event (in the case of spiny

lobster we measure CPUE by number of legal-sized lobster per trap), a number that is

indicative of fisherman performance or efficiency.

Fleet effort ranged from a minimum of 1 event per year to a maximum of 443

events per year, with an average of 99 (standard deviation = 18) events per year. The

catch accounts for 1,057,896 legal-sized lobster, ranging from a minimum of 12

legal-sized lobster caught by a single fisherman in one year to a maximum of 15,524

legal-sized lobster caught by a single fisherman in one year, with an average of 3,385

(standard deviation = 986) legal-sized lobster caught by a single fisherman in one

year.

3.3. Environmental data

Regional data on wind speed, wave height, barometric pressure, water temperature,

and atmospheric temperature were collected from NDBC station number 46063,

located off Point Conception. The first two environmental variables are frequently

identified by commercial fishermen as the most important in determining whether and

71

where to fish14. Hourly data were averaged over each day, with missing data supplied

through regression analysis using the nearby station 46054 buoy (west Santa Barbara

Channel) or more distant station 46053 buoy (east Santa Barbara Channel) when

station 46054 was not available (for example due to maintenance or damage).

Average daily wind speeds ranged from 0.21 to 14.88 meters per second (average =

6.96 meters per second). Average daily wave heights ranged from 0.31 to 6.72

meters (average = 2.30 meters). Both variables exhibit strong seasonal patterns with

wind speeds highest in spring and summer and wave heights largest in winter months.

Average daily barometric pressure ranged from 995.92 to 1028.97 millibars (average

= 1015.58 millibars). Average daily water temperature ranged from 9.59 to 19.61

degrees Celsius (average = 13.64 degrees Celsius) and average daily air temperature

ranged from 7.22 to 19.41 degrees Celsius (average = 13.36 degrees Celsius).

Barometric pressure, water temperature, and air temperature all exhibit strong

seasonal patterns with highest pressures in winter and lowest pressures in fall; and

highest temperatures in late summer and early fall and lowest temperatures in late

winter and early spring.

Data on total daily precipitation at the Santa Barbara Harbor were collected

from the Santa Barbara County Flood Control District. Daily precipitation ranged

from 0.1 inches to 4.41 inches (average = 0.64 inches), though precipitation events

only occurred on 12.4% of the days in this time period.

14 Personal communication with a variety of fishermen during data collection and mapping projects conducted from 2003-2006.

72

Table 3 provides descriptive statistics for the environmental data used in this

research. “Wind Speed” is the average daily wind speed in meters per second, “Wave

Height” is the average daily wave height in meters. The use of wind speed and wave

height as important weather variables is a result of five years of interaction with

commercial fishermen. These variables are usually mentioned as the most influential

in a decision whether or not to fish. However, this research looks at the effects and

possible correlations of other environmental variables. “Bar. Pressure” is the

barometric pressure in millibars, “Atmospheric Temp” is the atmospheric temperature

in degrees Celsius, “Water Temp” is the water temperature in degrees Celsius, and

“Precipitation” is the daily rainfall in inches.

Dummy variables are also included in the models to examine possible

seasonal effects. Lynn and Simpson (1987) classify distinct speed and direction

regimes for wind and ocean currents in the Santa Barbara Channel as December

through February, March though May, June through August, and September through

November.

4. Descriptive analysis of location choice and fleet catch

Red sea urchin data are available for the thirteen fish blocks around the Santa Barbara

Channel Islands15, but to improve the reliability and predictive power of our models

these blocks are aggregated into larger regions (Figure III-1). These regions

15 Spiny lobster data are available for much of southern California but I use the same regions as those used for the red sea urchin analysis along with two meta-regions that include any fishing effort north or south of the Channel Islands.

73

correspond to the four northeastern blocks (region 0), three northwestern blocks

(region 1), four southeastern blocks (region 2), and three southwestern blocks (region

3), which roughly separate the study area into two eastern islands (Anacapa Island

and Santa Cruz Island) which are closer to home ports16 and two western islands

(Santa Rosa Island and San Miguel Island) which are farther from home ports.

Figure III-2 shows the average annual effort and catch in each region for the

red sea urchin fleet. As expected there is a strong correlation between effort and

catch, with the western islands noticeably higher in both categories than the eastern

islands. This may be due to better habitat and/or oceanographic conditions, but

increased distance from home ports may also reduce the number of fishermen

(particularly recreational fishermen) to the more distant islands which would in turn

reduce the impact to fish stocks and habitat. Region 3 (in the southwest) presumably

receives less annual effort due to distance from home ports as well as increased

exposure to wind and waves.

Figure III-3 shows the average annual effort and catch in each region for the

spiny lobster fleet. While there is not a correlation between catch and effort as

measured by number of events there is a strong correlation between catch and effort

as measured by number of traps. There is also a very different relationship than that

observed with the red sea urchin fishery, where the fleet tends to concentrate its daily

effort around the islands closest to home ports.

16 Home ports for these fishing fleets are primarily Santa Barbara, Ventura, and Channel Islands (Figure 1).

74

5. Modeling methodology

This research focuses on two different fisheries at the Santa Barbara Channel Islands;

red sea urchin (a dive fishery) and California spiny lobster (a trap fishery).

Multivariate linear regression models are used to first estimate catch with no

location, then catch with observed location, third catch with predicted location, and

finally catch using the probability of location. I compare how well fixed effects and

random effects models capture the unobserved fisherman-specific effects (total

experience, boat size, boat speed, level of education, marital status, etc.).

Multinomial Logit models are used to first estimate location with no catch, then

location with observed catch, and finally location with predicted catch. These models

are useful in that they allow variation in coefficients over fishermen in the fleet,

avoiding the restrictive “independence from irrelevant alternatives” property of

simpler Logit models (Train 1998).

Goodness-of-fit measures assess these hierarchical causal structures and

determine which structure more likely describes each fishing fleet. Models are

compared using the adjusted likelihood ratio index along with an analysis of model

coefficients and log-likelihoods. The adjusted likelihood ratio index at zero (no

model) is found by

( ) ( )( )0

102

LkL −

−=βρ (5)

where k is the number of parameters (see Table 10). The likelihood ratio index for

constants only is found by

75

( ) ( )( )CL

kLC −−=

βρ 12

(6)

As described in Pendyala and Bhat (2004), if the likelihood ratio indices for

estimations with a large number of observations (i.e., more than 250) differ by more

than 0.01, one can infer that the model with the higher index is the better model.

Models and causal structures for the red sea urchin and spiny lobster fleets are

assessed and interpreted in the following sections.

6. Estimation results: Red sea urchin fleet

This research identifies variables responsible for determining when and where

someone goes fishing and produces models that describe the causal structure between

location choice and expected catch based on these underlying variables. This section

provides modeling results for the red sea urchin fleet. Section 6.1 provides a

description and comparison of the red sea urchin fleet models used in this research.

Section 6.2 provides estimation results for the joint model where catch affects

location. Section 6.3 provides estimation results for the joint model where location

affects catch. The first three blocks in Tables 5 and 6 correspond to the MNL

location choice model where Region 0 is considered the base alternative. The fourth

block in Tables 5 and 6 corresponds to the multivariate linear regression model that

estimates a fisherman’s catch. Section 6.4 provides estimation results for the two

causal structures and compares their goodness of fit.

76

6.1. Red sea urchin fleet models

Table 4 provides a comparison of the red sea urchin fleet models. I first estimate a

catch model with no location, then with observed location, then with predicted

location (from the location model without catch), and finally with the probability of

location. The high R2 and log-likelihood of the catch model which uses observed

location suggests that it performs the best, followed by the model which uses

probability of location.

For each model I compare a model with no effects (α is constant for each

fisherman), a Fixed Effects Model (FEM), and a Random Effects Model (REM). In

each case there are large improvements in both R2 (from 0.57 to 0.73) and log-

likelihoods for the Fixed Effects Model and Random Effects Model. The Hausman

specification test (Hausman 1978) is used to assess whether a FEM or a REM is the

more appropriate model, particularly for time series cross-section models. If the

estimators from both models are consistent, the Hausman specification test allows us

to determine which model contains the more efficient estimators. In all four cases the

large Hausman statistics favor the Fixed Effects Models over the Random Effects

Models. This result is in agreement with Robinson and Siegel (in prep), which shows

consistency in fishing performance (for example, catch per unit effort, or CPUE)

among members of a fishing fleet. In other words, a fisherman’s daily performance is

not modeled as a random draw from a distribution of performances. Instead,

fishermen tend to be consistent in their performance relative to the rest of the fleet.

77

Location models are estimated with no catch, then with observed catch, and

finally with predicted catch (from the catch model with no location). Similar to the

catch models, the location model which uses observed catch performs the best. The

location model which uses predicted catch is only a slight improvement over the

model with no catch.

6.2. Red sea urchin fleet: causal structure C → L

Table 5 describes the influential variables for the model where a fisherman’s catch

has an effect on location choice. More divers on a boat and more hours spent fishing

are expected to increase a boat’s total catch. I expect a negative relationship between

inclement weather and sea urchin catch, with smaller catches occurring at higher

wind speeds and larger wave heights. Finally, I expect to see a positive correlation

between a boat’s total catch and fall–winter months when temporal restrictions are

lightest (more days are available for fishing) and consumer demands are highest

(particularly the Asian market). A description of the significant variables and their

relative influence follows.

6.2.1. Catch model

From the catch model in Table 5 we see that one additional diver results in 23.1%

more catch17 while an additional hour diving results in 10.5% more catch. Looking at

the environmental variables we see that a one meter per second increase in wind

17 Compared to the average trip catch of 1,262.5 pounds of red sea urchin per diver.

78

speed results in 0.8% less catch and a one meter increase in wave height results in

7.4% less catch. Regarding temperature we find that a one Celsius degree increase in

air temperature results in 1.6% less catch and a one Celsius degree increase in water

temperature results in 1.9% more catch.

The previous day’s weather appears to have little influence on catch.

However, a one meter per second increase in the previous day’s wind speed results in

0.3% more catch. Relative to Season 0 (December through February, when temporal

restrictions are lightest and consumer demand is highest, particularly the Asian

market) catch decreases by 8.8% in Season 1 (March through May), 13.7% in Season

2 (June through August), and 5.8% in Season 3 (September through November).

Relative to 1998 there is a steady increase in catch in later years, from a 3.6%

increase in 2002 to a 33.6% increase in 2005. This may be attributable to such things

as an increase in demand for red sea urchin, an increase in red sea urchin stock, and

an increase in fisherman efficiency (ex., experience, gear improvements).

6.2.2. Location model

From the location model in Table 5 we see that one additional diver increases the

likelihood that a boat will fish Region 1, Region 2, or Region 3, particularly Region 2

and Region 3 which are outside the Santa Barbara Channel. Pounds of catch have a

large range and I therefore follow common convention and take the logarithm (base

10) of pounds to use in this portion of the model. An increase in the logarithm of

pounds has the strongest association with fishing in Region 1, a positive association

79

with fishing in Region 3, and a negative association with fishing in Region 2. This

reproduces the patterns observed in the data (Figure III-2), where Region 1 is the

most fished area (and has the highest catch) and Region 3 is the next most fished are

(and has the next highest catch).

Regarding environmental variables, increasing wind speed has a small

negative influence on Region 1, a small positive influence on Region 2, and no

significant influence on Region 3 and increasing wave height has a small positive

influence on Region 1 and a positive influence on Region 2 and Region 3. Season 1

has a positive influence on Region 2, Season 2 has a negative influence on Region 1

but a positive influence on Region 2, and Season 3 has a positive influence on Region

1 and Region 3 (again, when restrictions are lightest and demand is highest). Relative

to 1998 later years have a positive influence on Region 2 and a negative influence on

Region 1 and Region 3. In other words, Region 2 is more likely to be a fisherman’s

destination than Region 0 and Region 1 and Region 3 are less likely to be a

fisherman’s destination than Region 0.

6.3. Red sea urchin fleet: causal structure L → C

Table 6 describes the influential variables for the model where a fisherman’s location

choice has an effect on their total catch. More divers on a boat and more hours spent

fishing are expected to correlate positively with a boat traveling farther from home

port. Though it may not necessarily be the case it can be argued that to make a trip

far from home worth the effort (time, fuel) a boat may take more divers and stay for

80

longer compared to a trip that is close to home port. Boats are expected to stay close

to Region 0 and Region 2 in inclement weather as those two regions are more

protected from larger waves and higher wind speeds around the northern Channel

Islands (Lynn and Simpson 1987). Finally, boats are expected to stay closer to

Region 0 and Region 2 during the fall–winter months when weather is more severe.

While these expectations make intuitive sense I recognize that fisherman location

choice may be complicated by the patchy and heterogeneous nature of the red sea

urchin stocks across the islands (Sanchirico and Wilen 1999, Smith 2000). A

description of the significant variables and their relative influence follows.


From the location model in Table 6 we see that Region 1 has a positive and large

constant while Region 2 has a large and negative constant and Region 3 has a small

and negative constant. As observed in the data (Figure III-2), fishermen are most

likely to choose Region 1 and less likely to choose Region 2 than Region 3. An

additional diver means a boat is more likely to choose to Region 1, Region 2, or

Region 3. Again, we see fishermen venturing farther from home port when they have

more divers on board. As in previous models environmental variables are important.

Increasing wind speed has a small negative influence on Region 1, a small positive

influence on Region 2, and no significant influence on Region 3 and increasing wave

height has a positive influence on Region 2 and Region 3. Season 1 has a positive

influence on Region 2, Season 2 has a negative influence on Region 1 but a positive

81

influence on Region 2, and Season 3 has a positive influence on Region 1 and Region

3 (again, when restrictions are lightest and demand is highest). Relative to 1998 later

years have a positive influence on Region 2 and a negative influence on Region 1 and

Region 3. As with the previous model, in recent years Region 2 becomes “more

popular” relative to Region 0 while Region 1 and Region 3 become “less popular”.

6.3.2. Catch model

From the catch model in Table 6 we see that one additional diver results in 22.9%

more catch while one additional hour results in 10.4% more catch. A one meter per

second increase in wind speed results in 0.7% less catch and a one meter increase in

wave height results in 6.7% less catch. Temperatures again play a role in the model.

A one Celsius degree increase in air temperature results in 1.6% less catch and a one

Celsius degree increase in water temperature results in 1.8% more catch.


However, a one meter per second increase in the previous day’s wind speed results in

0.4% more catch. Relative to Region 0 catch increases by 11.7% in Region 1, catch

decreases 2.0% in Region 2, and catch increases 7.4% in Region 3. Relative to

Season 0 (December through February, when temporal restrictions are lightest and

consumer demand is highest, particularly the Asian market) catch decreases by 8.2%

in Season 1 (March through May), 13.8% in Season 2 (June through August), and

7.1% in Season 3 (September through November). Relative to 1998 there is a steady

increase in catch in later years, from a 5.0% increase in 2002 to a 33.1% increase in

82

2005. This may be attributable to such things as an increase in demand for red sea

urchin, an increase in red sea urchin stock, and an increase in fisherman efficiency

(ex. experience, gear improvements).

6.4. Red sea urchin fleet: model comparisons

Model comparisons are shown in Table 10. There is a definite improvement in the

log-likelihood of both model structures (L(C) and L(β)) compared to the log-

likelihood of the base-case model (L(0)). In addition, the adjusted likelihood ratio

indices meet the criteria of a difference larger than 0.01 and can thus be used to help

identify the more appropriate model. While there is not a large difference between

the two structures, based on the smaller log-likelihood and slightly larger adjusted

likelihood ratio index the red sea urchin fleet model structure which uses catch to

determine location appears to perform better than the model structure which uses

location to determine catch. This implies that while fishermen may prefer certain

locations, they are more likely to move to a new location where they perceive better

catch (more fish, better weather, etc) than stay at a habitual location.

7. Estimation results: Spiny lobster fleet

This section provides modeling results for the spiny lobster fleet. Section 7.1

provides a description and comparison of the spiny lobster fleet models used in this

research. Section 7.2 provides estimation results for the joint model where catch

affects location. Section 7.3 provides estimation results for the joint model where

83

location affects catch. As with the red sea urchin models, the first three blocks in

Tables 8 and 9 correspond to the MNL location choice model where Region 0 is

considered the base alternative. The fourth block in Tables 8 and 9 corresponds to the

multivariate linear regression model that estimates a fisherman’s catch. Section 7.4

provides estimation results for the two causal structures and compares their goodness

of fit.

7.1. Spiny lobster fleet models

Table 7 provides a comparison of the spiny lobster fleet models. I first estimate a

catch model with no location, then with observed location, then with predicted

location (from the location model without catch), and finally with the probability of

location. For the spiny lobster fleet the catch model which uses probability of

location performs the best, followed by the model which uses observed location,

though the two models are quite similar.

As with the red sea urchin fleet models we see improvements in both R2 (from

0.43 to 0.53) and log-likelihoods for the fixed effects and random effects models. In

all four cases the large Hausman statistics again favor the Fixed Effects Models over

the Random Effects Models.

I estimate location models with no catch, then with observed catch, and finally

with predicted catch (from the catch model with no location). As before, the location

model which uses observed catch performs the best and the location model which

uses predicted catch is only a slight improvement over the model with no catch. This

84

indicates there are other unobserved effects that are common to the catch equation

and the location equation, making the epsilons in the equations correlated.

7.2. Spiny lobster fleet: causal structure C → L

Table 8 describes the influential variables for the model where a fisherman’s catch

has an effect on their location choice. Fishing with more traps and leaving the traps

to soak for more nights is expected to increase a fisherman’s total catch. As with the

red sea urchin models I expect a negative relationship between inclement weather and

spiny lobster catch, with smaller catches occurring at higher wind speeds and larger

wave heights. Finally, I expected to see a positive relationship between a fisherman’s

total catch and fall through winter months when the fishing season opens and

consumer demands are highest (particularly the Asian market). A description of the

significant variables and their relative influence follows.

7.2.1. Catch model

The catch model in Table 8 highlights important fishery-specific variables related to

the modeling of the spiny lobster fleet. One additional trap results in 1.1% more

catch18. One additional night soaking the traps results in 5.7% more catch. One

additional short-sized lobster released results in 0.7% more catch (more lobsters

total). Not surprisingly, environmental variables also play a significant role for the

spiny lobster fleet. A one meter per second increase in wind speed results in 0.4%

18 Compared to the average trip catch of 33.8 legal-sized lobsters per fisherman.

85

less catch and a one meter increase in wave height results in 3.2% less catch.

Temperature is again important, with a one Celsius degree increase in air temperature

resulting in 4.0% more catch, but there are a number of other significant

environmental variables. A one millibar increase in barometric pressure results in

0.6% less catch and a one inch increase in precipitation results in 4.8% less catch.


However, a one Celsius degree increase in the previous day’s water temperature

results in 5.4% more catch and a one inch increase in the previous day’s precipitation

results in 4.4% more catch. While fishing in Season 1 (March) does not have a

significant influence on catch, fishing in Season 2 (October and November) results in

8.8% less catch than fishing in Season 0. This is a surprising result as Season 2 is

when the fishing seasons begins for spiny lobster and the highest catch is expected

during those months. Perhaps more exploratory fishing occurs at the beginning of the

season, resulting in lower catch per unit effort, and more targeted fishing occurs later

in the season when the spatial distribution of lobster is better understood. Relative to

1998 spiny lobster catch increases in subsequent years, with the exception of 2000

when catch decreases by 4.0%. 2001 is associated with a 9.3% increase, 2002 is

associated with a 30.1% increase, 2003 is associated with a 4.1% increase, 2004 is

associated with an 18.5% increase, and 2005 is associated with a 25.4% increase.

These increases may be attributable to such things as an increase in demand for spiny

lobster, an increase in spiny lobster stock (due to oceanic regime changes, i.e., colder

86

waters), and an increase in fisherman efficiency (e.g., experience, gear

improvements).


From the location model in Table 8 we see that an increase in the number of legal-

sized lobsters has a small but positive association with Region 1 and a small but

negative association with Region 2. This is surprising and a larger and positive

association with Region 2 is expected as the data show the highest catch coming from

that region (Figure III-3). Larger values of “Pull” (later pulls of traps in a series of

pulls) have a positive association with Region 2 and Region 3, suggesting that earlier

pulls are made closer to home port and then fishermen move out to the farther

regions. This is behavior is confirmed by personal communication with lobster

fishermen. Depth has a negative influence on a fisherman’s probability of locating at

Region 1, Region 2, or Region 3. This implies that people fish closer to shore at these

regions relative to Region 0. An increase in the number of traps is associated with an

increased probability of fishing in Region 1, Region 2, or Region 3. An increase in

the number of nights the traps soak is associated with a decreased probability of

fishing in Region 1, Region 2, and Region 3. This is surprising as fishermen are

expected to let traps soak longer if the traps are farther from home port. Instead this

suggests that fishermen do not let their traps soak as long the further they are from

their home port. An increase in the number of shorts in associated with an increased

probability of fishing in Region 1, Region 2, and Region 3.

87

Environmental variables are again important in this model. Increasing wind

speed has a small negative influence on Region 1 and a small positive influence on

Region 2 and Region 3. Increasing wave height has a positive influence on Region 2

and Region 3. Increasing water temperature has a negative influence on Region 1 and

Region 3 and a positive influence on Region 2. Increasing precipitation has a

negative influence on Region 2 but no significant influence on Region 1 or Region 3.

While an increase in the previous day’s average wind speed has a small

positive influence on Region 1 it is associated with a small negative influence on

Region 2 and Region 3. Season 1 (the end of the spiny lobster fishing season) has

positive and significant coefficients for Region 1 (α = 1.239), Region 2 (α = 0.444),

and Region 3 (α = 0.653). This suggests that fishermen are more likely to travel

farther from home at the end of the season, possibly because areas close to home have

been “fished out” already. It may also be that lobster stocks increase farther from

home ports during Season 1. However we also see that Season 2 (the beginning of

the spiny lobster fishing season) has negative and significant coefficients for Region 1

(α = -1.478), Region 2 (α = -0.608), and Region 3 (α = -1.340). This suggests that

fishermen are more likely to stay close to home at the beginning of the season. These

two scenarios considered together paint a picture of spiny lobster fishermen starting

the season close to home ports (Region 0 and Region 2) and moving further afield as

the season progresses (Region 1 and Region 3).

Relative to the 1998 fishing season Region 1 and Region 3 have consistently

positive and significant coefficients and Region 2 oscillates between positive and

88

negative coefficients. This suggests that Region 1 and Region 3 are consistently more

popular locations than Region 0 and Region 2 varies between a more popular and less

popular destination than Region 0.

7.3. Spiny lobster fleet: causal structure L → C

Table 9 describes the influential variables for the model where a fisherman’s location

choice has an effect on their total catch. While a fisherman may not necessarily use

more traps when fishing farther from their home port, they are expected to let their

traps soak for more nights when fishing farther from their home port since those

regions are more exposed during foul weather events and take longer to get to. Both

of these variables may delay a return trip to farther regions. As with the red sea

urchin models, fishermen are expected to stay close to Region 0 and Region 2 in

inclement weather as those two regions are the more protected from larger waves and

higher wind speeds around the northern Channel Islands (Lynn and Simpson 1987).

Finally, fishermen are expected to stay closer to Region 0 and Region 2 during the

fall through winter months when weather is more severe. While these expectations

make intuitive sense I recognize that fisherman location choice may be complicated

by the patchy and heterogeneous nature of the spiny lobster stocks across the islands.

A description of the significant variables and their relative influence follows.

89


From the location model in Table 9 we see that Region 1, Region 2, and Region 3 all

have positive and significant coefficients, suggesting that all are “more popular” than

Region 0. Though Region 2 is expected to be the most likely destination for spiny

lobster fishermen, Region 0 is expected to be a more likely destination that either

Region 1 or Region 3 (Figure III-3). Larger values of “Pull” (later pulls of traps in a

series of pulls) again have a positive association with Region 2 and Region 3,

suggesting that earlier pulls are made closer to home port and then fishermen move

out to the farther regions. Depth has a negative influence on a fisherman’s

probability of locating at Region 1, Region 2, or Region 3 suggesting that lobsters are

caught in shallower waters relative to Region 0. An increase in the number of traps

has a positive association with Region 1, Region 2, and Region 3, suggesting that

fishermen use more traps at the locations farther from home port. Alternately this

could be interpreted to mean that fishermen who use more traps tend to fish farther

from home port. An increase in the number of nights the traps soak is associated with

a decreased probability of fishing in Region 1, Region 2, and Region 3. As with the

previous model this is a surprising result as fishermen are expected to let traps soak

longer if the traps are farther from home port. An increase in the number of shorts is

associated with an increased probability of fishing in Region 1, Region 2, and Region

3.

Increasing wind speed has a small negative influence on Region 1 and a small

positive influence on Region 2. Increasing wave height has a positive influence on

90

Region 2 and Region 3. This suggests that when the waves are larger fishermen are

more likely to choose Region 2 or Region 3 (south side of islands) than Region 0.

Typically the south side of the islands are considered “more exposed” to wind and

waves (particularly Region 3), but the lobster fishing seasons occurs primarily in fall

and winter months when the wind and swell comes primarily from the north and

northwest (Lynn and Simpson 1987). This prevailing storm direction could result in

more “protected” fishing on the south side of the islands. Increasing water

temperature has a negative influence on Region 1 and Region 3 but no significant

influence on Region 2. Increasing precipitation has a negative influence on Region 2

but no significant influence on Region 1 or Region 3.

While an increase in the previous day’s average wind speed has a small

positive influence on Region 1 it is associated with a small negative influence on

Region 2 and Region 3 and an increase in the previous day’s average wave height has

a negative influence on Region 2. As with the previous model, Season 1 (March) is

associated with positive coefficients for Region 1, Region 2, and Region 3 while

Season 2 (October and November) is associated with negative coefficients for these

three regions. This again suggests that fishermen stay closer to home port (Region 0)

at the beginning of the season and move further away later in the season.

Relative to the 1998 fishing season Region 1 and Region 3 have consistently

positive and significant coefficients while Region 2 has positive coefficients in 1999

and 2000, negative coefficients for the 2001-2004 fishing seasons, and a positive

coefficient in 2005. This again suggests that Region 1 and Region 3 are consistently

91

more popular locations than Region 0 and Region 2 varies between a more popular

and less popular destination than Region 0.

7.3.2. Catch model

From the catch model in Table 9 we see that relative to Region 0 catch increases by

30.2% in Region 1, catch decreases 5.6% in Region 2, and catch increases 24.9% in

Region 3. As discussed earlier these are unexpected results as the data show catch

highest in Region 2, next highest in Region 1, third highest in Region 0, and lowest in

Region 3 (Figure III-3). One additional trap results in 1.1% more catch while one

additional night soaking the traps results in 5.5% more catch and one additional short-

sized lobster released results in 0.7% more catch (more lobsters total).

A one meter per second increase in wind speed results in 0.4% less catch and

a one meter increase in wave height results in 3.0% less catch. A one millibar

increase in barometric pressure results in 0.6% less catch. A one Celsius degree

increase in air temperature results in 4.0% more catch and a one inch increase in

precipitation results in 5.2% less catch.


However, a one Celsius degree increase in the previous day’s water temperature

results in 6.1% more catch and a one inch increase in the previous day’s precipitation

results in 4.4% more catch. As we saw in the previous model fishing in Season 1

(March) does not have a significant influence on catch and fishing in Season 2

(October and November) results in 7.7% less catch than fishing in Season 0. Again,

92

this is a surprising result, as Season 2 is when the fishing seasons begins for spiny

lobster and the highest catch is expected during those months. Again this behavior

may be attributable to the exploratory fishing that tends to occur at the beginning of a

season and the more targeted fishing that occurs later in the season (Arentze and

Timmermans 2003).

Relative to 1998 spiny lobster catch increases in subsequent years, with the

exception of 2000 when catch decreases by 4.9% and 2003 which is not significant.

2001 is associated with a 7.5% increase, 2002 is associated with a 27.2% increase,

2004 is associated with a 15.9% increase, and 2005 is associated with a 21.6%

increase. These increases may be attributable to such things as an increase in demand

for spiny lobster, an increase in spiny lobster stock (oceanic regime changes, i.e.,

colder waters), and an increase in fisherman efficiency (experience, gear

improvements).

7.4. Spiny lobster fleet: model comparisons

Spiny lobster fleet model comparisons are shown in Table 10. As seen with the red

sea urchin fleet models, there is a definite improvement in the log-likelihood of both

model structures compared to the log-likelihood with no model. In addition, the

adjusted likelihood ratio indices again meet the criteria of a difference larger than

0.01 and can thus be used to help identify the more appropriate model. The

differences between the two structures are noticeably larger than those we saw in the

red sea urchin fleet models. Based on the greater improvement in the model log-

93

likelihood and the larger adjusted likelihood ratio index, the spiny lobster fleet model

structure which uses catch to determine location appears to perform better than the

model structure which uses location to determine catch. As with the red sea urchin

fleet, we find that spiny lobster fishermen are more likely to move to a new location

where they perceive better catch (more fish, better weather, etc) than stay at a habitual

location.

8. Conclusions

This research explores the relationship between location choice and expected catch

for two commercial fishing fleets. The analysis involves the estimation of joint

models of location choice (a discrete variable) and catch (a continuous variable)

separately for red sea urchin and spiny lobster fishing fleets while allowing for error

correlations in the equations that describe location choice and expected catch.

Department of Fish and Game commercial fishing logbook data, which

include information on fishing location and amount of catch, and environmental

(weather) data are used to describe these relationships. Two causal structures are

explored:

• Catch affects location choice

• Location choice affects catch

These causal structures are analyzed to determine which structure is more likely to be

happening in the Santa Barbara red sea urchin and spiny lobster commercial fishing

fleets.

94

For both the red sea urchin fleet and the spiny lobster fleet fishery-specific

variables (e.g., number of divers, number of hours, number of traps) are identified

which can be used along with environmental variables (e.g., wind speed, wave height)

to predict fisherman effort and catch. These findings agree with previous commercial

fishing fleet behavior research (e.g., Vignaux 1996, Smith 2002). Though intuitive, it

is important to note that more favorable weather (lower wind speeds, smaller wave

heights) result in predictions of more fishing effort and subsequently larger catches.

This result is similar to that of Smith and Wilen (2003).

I find that model structures which use catch to determine location appear to

perform better than the model structures which use location to determine catch. In

other words, fishermen are more likely to choose a fishing location after first

determining their expected catch at each available location. These conclusions are

based on the greater improvements in the model log-likelihoods and the larger

adjusted likelihood ratio indices. In agreement with previous research, Fixed Effects

Models better represent the consistent performance of fishermen within a fishing fleet

when compared with Random Effects Models.

A shortcoming of this research concerns the correlation present in the error

term. Though I am able to explain some of the error and improve the models by

introducing dummy variables for season and year I am not able to explain all of the

model error nor completely address the correlation in the error term. At least one

method exists to examine this subject in more detail (Pendyala and Bhat 2004) but

this research needs to be expanded to true repeated observations (i.e., panel data) to

95

use such a method. Even allowing for this shortcoming the present research remains

a valuable predictive model as supported by Smith (2000)19. Other limitations

involve missing data, measurement errors, and possible model misspecifications.

These limitations can be at least partially addressed through more robust data

collection and analysis and through further testing of these model structures (for

example, by comparing these models with other common fleet models).

This research improves our ability to model fishing fleets. It provides a

method for using currently available data to predict how a fishing fleet distributes its

effort in space and time. It informs management by helping understand the

influences, impacts, and implications of various spatial and temporal management

options.

Future research will address the abovementioned correlation in the error terms

and possible model misspecifications as well as expand the models to include

additional causal relationships. Finally, the application of the models will need to be

tested across a variety of fishing fleets.

19 “If methods used to calculate explanatory variables lead to covariates that are highly correlated with the true covariates, estimated parameters will scale the indirect utilities in a way that still generates reasonable predictions.”

96


Table 1. Descriptive statistics, red sea urchin logbook data. Table 2. Descriptive statistics, spiny lobster logbook data. Table 3. Descriptive statistics, environmental data. Table 4. Red sea urchin fleet model comparison. Table 5. Red sea urchin fleet model: catch affects location. Table 6. Red sea urchin fleet model: location affects catch. Table 7. Spiny lobster fleet model comparison. Table 8. Spiny lobster fleet model: Catch affects location. Table 9. Spiny lobster fleet model: location affects catch. Table 10. Measures of fit for joint catch-location models. Figure III-1. DFG fish blocks and model regions. Figure III-2. Average yearly red sea urchin fleet effort and catch for each fishing

region. Figure III-3. Spiny lobster fleet average yearly effort as average number of events,

average yearly effort as average number of traps, and average yearly catch as number of legals retained.

97

Tables Table 1. Descriptive statistics, red sea urchin logbook data.

Cases Mean St.Dev. Minimum Maximum Divers 27793 1.72 0.68 1 5 Total Hours 27793 5.49 2.77 1 24 Total Harvest (lbs) 27793 1262.54 851.98 5 7380 CPUE* 27793 152.86 91.15 0.83 900

*CPUE = Pounds per hour per diver. Table 2. Descriptive statistics, spiny lobster logbook data.

Cases Mean St.Dev. Minimum Maximum Depth (ft) 31284 47.85 26.50 0.7 301 Traps Pulled 31284 56.18 45.07 1 700 Nights Soaked 31284 3.64 2.03 0 9 Shorts Released 31284 37.41 71.61 0 999 Legals Retained 31284 33.82 42.02 0 748 CPUE† 31284 0.66 0.75 0 41

†CPUE = Legals per trap. Table 3. Descriptive statistics, environmental data.

Cases Mean St.Dev. Minimum Maximum Wind Speed (m/s) 2922 6.96 2.75 0.21 14.88 Wave Height (m) 2922 2.30 0.85 0.31 6.72 Bar. Pressure (mb) 2922 1015.58 3.73 995.92 1028.97 Atmospheric Temp (°C) 2922 13.36 1.48 7.22 19.41 Water Temp (°C) 2922 13.64 1.57 9.59 19.61 Precipitation (in) 2922 0.08 0.33 0.00 4.41

98

Table 4. Red sea urchin fleet model comparison.

Log-likelihood

model

constants only

no model Hausman test

Model

df

R2

L(β)

L(C)

(FEM vs. REM)

CATCH MODEL no location, w/o dummya 27752 0.568 -215244.0 -217138.8 -226897.5 no location, w/ dummyb 27539 0.732 -208620.6 -217138.8 -226897.5 76.69 observed location, w/o dummy 27749 0.583 -214746.2 -217138.8 -226897.5 observed location, w/ dummy 27536 0.735 -208432.6 -217138.8 -226897.5 94.16 predicted location, w/o dummy 27750 0.568 -215222.7 -217138.8 -226897.5 predicted location, w/ dummy 27537 0.732 -208595.6 -217138.8 -226897.5 76.72 probability of location, w/o dummy 27749 0.569 -215211.7 -217138.8 -226897.5 probability of location, w/ dummy 27536 0.732 -208585.3 -217138.8 -226897.5 76.48 LOCATION MODEL no catch 57 0.060 -32288.4 -34348.6 -38516.8 observed catch 60 0.083 -31512.1 -34348.6 -38516.8 predicted catch 60 0.060 -32280.4 -34348.6 -38516.8 a α is constant for each fisherman (no effects) b α varies for each fisherman (FEM or REM)

Table 5. Red sea urchin fleet model: catch affects location.

Variable Coefficient Mean t-Statistic Location model (Region 0 = 0) Region 1

Constant -3.479 ----- -10.970 DIVERS = number of divers on fishing trip 0.289 1.740 6.447 HOURS = number of hours spent diving -0.049 5.431 -4.396 LOG(POUNDS) = catch in logarithm of pounds 1.914 ----- 16.706 WSPD = average daily wind speed (m/s) -0.038 6.111 -3.516 WVHT = average daily wave height (m) 0.091 2.044 2.113

99

WSPDPREV = previous day’s avg daily wind speed -0.038 6.550 -3.695 SEASON2 = 1 if between Jun-Aug ; 0 otherwise -0.179 0.167 -2.464 SEASON3 = 1 if between Sept-Nov ; 0 otherwise 0.147 0.343 2.488 D1999 = 1 if event in 1999 ; 0 otherwise -0.370 0.152 -3.867 D2000 = 1 if event in 2000 ; 0 otherwise -0.318 0.106 -2.867 D2002 = 1 if event in 2002 ; 0 otherwise -0.910 0.128 -9.239 D2003 = 1 if event in 2003 ; 0 otherwise -1.210 0.136 -12.214 D2004 = 1 if event in 2004 ; 0 otherwise -0.794 0.148 -7.937 D2005 = 1 if event in 2005 ; 0 otherwise -0.543 0.123 -5.098

Region 2

Constant 1.490 ----- 4.337 DIVERS = number of divers on fishing trip 0.706 1.740 13.867 LOG(POUNDS) = catch in logarithm of pounds -1.533 ----- -12.182 WSPD = average daily wind speed (m/s) 0.029 6.111 2.389 WVHT = average daily wave height (m) 0.645 2.044 13.646 SEASON1 = 1 if between Mar-May ; 0 otherwise 0.390 0.220 4.282 SEASON2 = 1 if between Jun-Aug ; 0 otherwise 0.386 0.167 4.617 D1999 = 1 if event in 1999 ; 0 otherwise 0.483 0.152 4.124 D2000 = 1 if event in 2000 ; 0 otherwise 1.144 0.106 8.903 D2001 = 1 if event in 2001 ; 0 otherwise 1.524 0.098 11.544 D2002 = 1 if event in 2002 ; 0 otherwise 1.086 0.128 9.372 D2003 = 1 if event in 2003 ; 0 otherwise 0.748 0.136 6.290 D2004 = 1 if event in 2004 ; 0 otherwise 0.339 0.148 2.696 D2005 = 1 if event in 2005 ; 0 otherwise 0.258 0.123 1.860a

Region 3

Constant -4.741 ----- -14.068 DIVERS = number of divers on fishing trip 0.795 1.740 16.982 HOURS = number of hours spent diving -0.117 5.431 -9.849 LOG(POUNDS) = catch in logarithm of pounds 1.665 ----- 13.724 WVHT = average daily wave height (m) 0.259 2.044 5.769 WSPDPREV = previous day’s avg daily wind speed -0.040 6.550 -3.655 SEASON3 = 1 if between Sept-Nov ; 0 otherwise 0.262 0.343 4.203 D2001 = 1 if event in 2001 ; 0 otherwise 0.693 0.098 5.747 D2002 = 1 if event in 2002 ; 0 otherwise 0.225 0.128 2.213 D2003 = 1 if event in 2003 ; 0 otherwise -0.595 0.136 -5.703 D2004 = 1 if event in 2004 ; 0 otherwise -0.683 0.148 -6.373 D2005 = 1 if event in 2005 ; 0 otherwise -0.892 0.123 -7.635

Catch model

DIVERS = number of divers on fishing trip 291.676 1.716 45.703 HOURS = number of hours spent diving 132.610 5.491 91.219 WSPD = average daily wind speed (m/s) -10.338 6.064 -7.978 WVHT = average daily wave height (m) -93.376 2.040 -18.556 ATMP = average daily air temperature (°C) -20.061 13.492 -5.757 WTMP = average daily water temperature (°C) 24.164 13.784 7.066

100

WSPDPREV = previous day’s avg daily wind speed 4.306 6.480 3.391 SEASON1 = 1 if between Mar-May ; 0 otherwise -111.029 0.210 -11.215 SEASON2 = 1 if between Jun-Aug ; 0 otherwise -173.404 0.158 -7.233 SEASON3 = 1 if between Sept-Nov ; 0 otherwise -72.990 0.352 -4.572 D2002 = 1 if event in 2002 ; 0 otherwise 45.007 0.112 3.374 D2003 = 1 if event in 2003 ; 0 otherwise 155.777 0.129 11.938 D2004 = 1 if event in 2004 ; 0 otherwise 394.254 0.138 30.738 D2005 = 1 if event in 2005 ; 0 otherwise 424.590 0.121 24.105

N = 27784 a Significant at the α = 0.1 level.

Table 6. Red sea urchin fleet model: location affects catch.


Constant 1.341 ----- 9.489 DIVERS = number of divers on fishing trip 0.564 1.740 13.526 HOURS = number of hours spent diving 0.034 5.431 3.388 WSPD = average daily wind speed (m/s) -0.044 6.111 -4.131 WSPDPREV = previous day’s avg daily wind speed -0.034 6.550 -3.350 SEASON2 = 1 if between Jun-Aug ; 0 otherwise -0.174 0.167 -2.408 SEASON3 = 1 if between Sept-Nov ; 0 otherwise 0.186 0.343 3.166 D1999 = 1 if event in 1999 ; 0 otherwise -0.306 0.152 -3.212 D2000 = 1 if event in 2000 ; 0 otherwise -0.310 0.106 -2.811 D2002 = 1 if event in 2002 ; 0 otherwise -0.825 0.128 -8.445 D2003 = 1 if event in 2003 ; 0 otherwise -1.007 0.136 -10.310 D2004 = 1 if event in 2004 ; 0 otherwise -0.470 0.148 -4.823 D2005 = 1 if event in 2005 ; 0 otherwise -0.168 0.123 -1.622

Region 2

Constant -2.102 ----- -12.591 DIVERS = number of divers on fishing trip 0.493 1.740 10.379 HOURS = number of hours spent diving -0.071 5.431 -5.922 WSPD = average daily wind speed (m/s) 0.032 6.111 2.652 WVHT = average daily wave height (m) 0.669 2.044 14.250 SEASON1 = 1 if between Mar-May ; 0 otherwise 0.412 0.220 4.553 SEASON2 = 1 if between Jun-Aug ; 0 otherwise 0.375 0.167 4.515 D1999 = 1 if event in 1999 ; 0 otherwise 0.424 0.152 3.646 D2000 = 1 if event in 2000 ; 0 otherwise 1.099 0.106 8.607 D2001 = 1 if event in 2001 ; 0 otherwise 1.500 0.098 11.419 D2002 = 1 if event in 2002 ; 0 otherwise 0.976 0.128 8.493 D2003 = 1 if event in 2003 ; 0 otherwise 0.577 0.136 4.917

101

Region 3

Constant -0.575 ----- -3.825 DIVERS = number of divers on fishing trip 1.034 1.740 23.710 HOURS = number of hours spent diving -0.042 5.431 -3.977 WVHT = average daily wave height (m) 0.214 2.044 4.793 WSPDPREV = previous day’s avg daily wind speed -0.037 6.550 -3.379 SEASON3 = 1 if between Sept-Nov ; 0 otherwise 0.295 0.343 4.756 D2001 = 1 if event in 2001 ; 0 otherwise 0.695 0.098 5.782 D2002 = 1 if event in 2002 ; 0 otherwise 0.304 0.128 3.018 D2003 = 1 if event in 2003 ; 0 otherwise -0.420 0.136 -4.068 D2004 = 1 if event in 2004 ; 0 otherwise -0.402 0.148 -3.830 D2005 = 1 if event in 2005 ; 0 otherwise -0.565 0.123 -4.958

Catch model

DIVERS = number of divers on fishing trip 289.461 1.716 45.625 HOURS = number of hours spent diving 131.278 5.491 90.796 WSPD = average daily wind speed (m/s) -8.680 6.064 -6.726 WVHT = average daily wave height (m) -84.987 2.040 -16.887 ATMP = average daily air temperature (°C) -19.698 13.492 -5.690 WTMP = average daily water temperature (°C) 23.332 13.784 6.868 WSPDPREV = previous day’s avg daily wind speed 4.582 6.480 3.633 REGION1 = 1 if event in region 1 ; 0 otherwise 147.691 0.428 14.363 REGION2 = 1 if event in region 2 ; 0 otherwise -24.972 0.187 -2.103 REGION3 = 1 if event in region 3 ; 0 otherwise 92.987 0.286 8.660 SEASON1 = 1 if between Mar-May ; 0 otherwise -102.951 0.210 -10.458 SEASON2 = 1 if between Jun-Aug ; 0 otherwise -173.738 0.158 -7.294 SEASON3 = 1 if between Sept-Nov ; 0 otherwise -89.501 0.352 -5.633 D2002 = 1 if event in 2002 ; 0 otherwise 62.611 0.112 4.709 D2003 = 1 if event in 2003 ; 0 otherwise 168.564 0.129 12.987 D2004 = 1 if event in 2004 ; 0 otherwise 395.218 0.138 30.977 D2005 = 1 if event in 2005 ; 0 otherwise 417.718 0.121 23.809


102

Table 7. Spiny lobster fleet model comparison.

Log-likelihood

model

constants only

no model

Hausman test

Model

df

R2

L(β)

L(C)

L(0)

(FEM vs. REM)

CATCH MODEL no location, w/o dummya 31252 0.432 -152495.5 -156906.1 -161332.3 no location, w/ dummyb 31187 0.521 -149811.4 -156906.1 -161332.3 48.30 observed location, w/o dummy 31247 0.468 -151463.4 -156906.1 -161332.3 observed location, w/ dummy 31182 0.525 -149684.3 -156906.1 -161332.3 62.56 predicted location, w/o dummy 31247 0.432 -152482.3 -156906.1 -161332.3 predicted location, w/ dummy 31182 0.524 -149727.5 -156906.1 -161332.3 53.09 probability of location, w/o dummy 31247 0.435 -152389.4 -156906.1 -161332.3 probability of location, w/ dummy 31182 0.526 -149640.0 -156906.1 -161332.3 59.78 LOCATION MODEL no catch 100 0.190 -42147.4 -52045.7 -56053.4 observed catch 105 0.234 -39850.9 -52045.7 -56053.4 predicted catch 105 0.191 -42110.4 -52045.7 -56053.4 a α is constant for each fisherman (no effects) b α varies for each fisherman (FEM or REM)

Table 8. Spiny lobster fleet model: Catch affects location.


Constant 3.688 ----- 7.537 LEGALS = number of legal sized lobsters retained 0.002 28.716 1.895a DEPTH = depth of traps in feet -0.020 46.566 -17.482 TRAPS = number of traps pulled from water 0.024 47.995 21.812

103

NIGHTS = number of nights traps soaked in water -0.197 3.649 -14.606 SHORTS = number of undersized lobsters released 0.028 35.682 11.995 WSPD = average daily wind speed (m/s) -0.036 6.156 -2.900 WTMP = average daily water temperature (°C) -0.318 13.900 -9.865 WSPDPREV = previous day’s avg daily wind speed 0.027 6.387 2.200 SEASON1 = 1 if Mar ; 0 otherwise 1.239 0.062 11.831 SEASON2 = 1 if Oct-Nov ; 0 otherwise -1.478 0.466 -17.253 D1999 = 1 if event in 1999 ; 0 otherwise 1.227 0.122 7.766 D2000 = 1 if event in 2000 ; 0 otherwise 1.212 0.137 7.719 D2001 = 1 if event in 2001 ; 0 otherwise 1.346 0.135 8.582 D2002 = 1 if event in 2002 ; 0 otherwise 1.238 0.161 8.049 D2003 = 1 if event in 2003 ; 0 otherwise 1.394 0.162 9.027 D2004 = 1 if event in 2004 ; 0 otherwise 1.503 0.159 9.658 D2005 = 1 if event in 2005 ; 0 otherwise 2.280 0.047 11.548

Region 2

LEGALS = number of legal sized lobsters retained -0.017 28.716 -15.840 PULL = pull number in a sequence of pulls (see 4) 0.169 2.252 9.275 DEPTH = depth of traps in feet -0.039 46.566 -33.314 TRAPS = number of traps pulled from water 0.017 47.995 16.644 SHORTS = number of undersized lobsters released 0.054 35.682 25.339 WSPD = average daily wind speed (m/s) 0.044 6.156 4.169 WVHT = average daily wave height (m) 0.141 2.390 3.523 WTMP = average daily water temperature (°C) 0.056 13.900 2.085 PRECIP = daily precipitation (in) -0.229 0.060 -2.054 WSPDPREV = previous day’s avg daily wind speed -0.028 6.387 -2.711 WVHTPREV = previous day’s avg wave height -0.099 2.481 -2.674 SEASON1 = 1 if Mar ; 0 otherwise 0.444 0.062 3.794 SEASON2 = 1 if Oct-Nov ; 0 otherwise -0.608 0.466 -8.771 D1999 = 1 if event in 1999 ; 0 otherwise 0.464 0.122 4.853 D2000 = 1 if event in 2000 ; 0 otherwise 0.171 0.137 1.788a D2002 = 1 if event in 2002 ; 0 otherwise -0.149 0.161 -1.595a D2003 = 1 if event in 2003 ; 0 otherwise -0.329 0.162 -3.500 D2005 = 1 if event in 2005 ; 0 otherwise 0.546 0.047 3.529

Region 3

Constant 2.679 ----- 4.630 PULL = pull number in a sequence of pulls (see 4) 0.154 2.252 5.719 DEPTH = depth of traps in feet -0.027 46.566 -18.350 TRAPS = number of traps pulled from water 0.027 47.995 22.773 NIGHTS = number of nights traps soaked in water -0.082 3.649 -5.172 SHORTS = number of undersized lobsters released 0.020 35.682 7.444 WSPD = average daily wind speed (m/s) 0.025 6.156 1.694a WVHT = average daily wave height (m) 0.181 2.390 3.315 WTMP = average daily water temperature (°C) -0.327 13.900 -8.555 WSPDPREV = previous day’s avg daily wind speed -0.043 6.387 -2.982 SEASON1 = 1 if Mar ; 0 otherwise 0.653 0.062 5.103

104

SEASON2 = 1 if Oct-Nov ; 0 otherwise -1.340 0.466 -13.258 D1999 = 1 if event in 1999 ; 0 otherwise 1.052 0.122 5.582 D2000 = 1 if event in 2000 ; 0 otherwise 1.364 0.137 7.439 D2001 = 1 if event in 2001 ; 0 otherwise 1.342 0.135 7.255 D2002 = 1 if event in 2002 ; 0 otherwise 1.217 0.161 6.705 D2003 = 1 if event in 2003 ; 0 otherwise 1.076 0.162 5.821 D2004 = 1 if event in 2004 ; 0 otherwise 1.446 0.159 7.876 D2005 = 1 if event in 2005 ; 0 otherwise 1.903 0.047 8.017

Catch model

TRAPS = number of traps pulled from water 0.377 56.178 67.455 NIGHTS = number of nights traps soaked in water 1.941 3.639 19.267 SHORTS = number of undersized lobsters released 0.233 37.409 70.901 WSPD = average daily wind speed (m/s) -0.152 6.179 -1.825a WVHT = average daily wave height (m) -1.094 2.400 -3.724 BAR = average daily barometric pressure (mb) -0.216 1017.52 -3.039 ATMP = average daily air temperature (°C) 1.368 13.513 6.978 PRECIP = daily precipitation (in) -1.625 0.062 -2.475 WSPDPREV = previous day’s avg daily wind speed 0.304 6.414 3.795 WVHTPREV = previous day’s avg wave height 0.933 2.486 3.328 BARPREV = previous day’s barometric pressure -0.326 1017.24 -4.704 WTMPPREV = previous day’s avg water temp 1.835 13.915 7.532 PRECIPPR = previous day’s precipitation 1.472 0.083 2.705 SEASON2 = 1 if Oct-Nov ; 0 otherwise -2.983 0.474 -3.042 D2000 = 1 if event in 2000 ; 0 otherwise -1.346 0.134 -1.539a D2001 = 1 if event in 2001 ; 0 otherwise 3.157 0.127 3.419 D2002 = 1 if event in 2002 ; 0 otherwise 10.178 0.154 12.266 D2003 = 1 if event in 2003 ; 0 otherwise 1.373 0.160 1.555a D2004 = 1 if event in 2004 ; 0 otherwise 6.258 0.151 7.286 D2005 = 1 if event in 2005 ; 0 otherwise 8.575 0.045 8.183


Table 9. Spiny lobster fleet model: location affects catch.


Constant 3.300 ----- 6.875 DEPTH = depth of traps in feet -0.019 46.566 -16.827 TRAPS = number of traps pulled from water 0.022 47.995 23.623 NIGHTS = number of nights traps soaked in water -0.190 3.649 -14.282 SHORTS = number of undersized lobsters released 0.028 35.682 13.468

105

WSPD = average daily wind speed (m/s) -0.041 6.156 -3.347 WTMP = average daily water temperature (°C) -0.291 13.900 -9.153 WSPDPREV = previous day’s avg daily wind speed 0.032 6.387 2.680 SEASON1 = 1 if Mar ; 0 otherwise 1.332 0.062 12.879 SEASON2 = 1 if Oct-Nov ; 0 otherwise -1.361 0.466 -16.161 D1999 = 1 if event in 1999 ; 0 otherwise 1.287 0.122 8.178 D2000 = 1 if event in 2000 ; 0 otherwise 1.235 0.137 7.909 D2001 = 1 if event in 2001 ; 0 otherwise 1.344 0.135 8.614 D2002 = 1 if event in 2002 ; 0 otherwise 1.349 0.161 8.844 D2003 = 1 if event in 2003 ; 0 otherwise 1.470 0.162 9.555 D2004 = 1 if event in 2004 ; 0 otherwise 1.619 0.159 10.474 D2005 = 1 if event in 2005 ; 0 otherwise 2.367 0.047 12.047

Region 2

Constant 1.062 ----- 2.655 PULL = pull number in a sequence of pulls (see 4) 0.183 2.252 10.090 DEPTH = depth of traps in feet -0.039 46.566 -33.802 TRAPS = number of traps pulled from water 0.010 47.995 11.577 NIGHTS = number of nights traps soaked in water -0.030 3.649 -2.592 SHORTS = number of undersized lobsters released 0.042 35.682 22.290 WSPD = average daily wind speed (m/s) 0.044 6.156 4.175 WVHT = average daily wave height (m) 0.146 2.390 3.658 PRECIP = daily precipitation (in) -0.255 0.060 -2.282 WSPDPREV = previous day’s avg daily wind speed -0.030 6.387 -2.892 WVHTPREV = previous day’s avg wave height -0.129 2.481 -3.525 SEASON1 = 1 if Mar ; 0 otherwise 0.404 0.062 3.459 SEASON2 = 1 if Oct-Nov ; 0 otherwise -0.621 0.466 -9.013 D1999 = 1 if event in 1999 ; 0 otherwise 0.483 0.122 5.083 D2000 = 1 if event in 2000 ; 0 otherwise 0.174 0.137 1.837a D2002 = 1 if event in 2002 ; 0 otherwise -0.285 0.161 -3.085 D2003 = 1 if event in 2003 ; 0 otherwise -0.372 0.162 -3.974 D2004 = 1 if event in 2004 ; 0 otherwise -0.198 0.159 -2.050 D2005 = 1 if event in 2005 ; 0 otherwise 0.447 0.047 2.905

Region 3

Constant 2.215 ----- 3.886 PULL = pull number in a sequence of pulls (see 4) 0.139 2.252 5.214 DEPTH = depth of traps in feet -0.026 46.566 -17.690 TRAPS = number of traps pulled from water 0.025 47.995 24.486 NIGHTS = number of nights traps soaked in water -0.069 3.649 -4.430 SHORTS = number of undersized lobsters released 0.019 35.682 7.691 WVHT = average daily wave height (m) 0.168 2.390 3.093 WTMP = average daily water temperature (°C) -0.295 13.900 -7.794 WSPDPREV = previous day’s avg daily wind speed -0.039 6.387 -2.677 SEASON1 = 1 if Mar ; 0 otherwise 0.734 0.062 5.785 SEASON2 = 1 if Oct-Nov ; 0 otherwise -1.223 0.466 -12.264 D1999 = 1 if event in 1999 ; 0 otherwise 1.093 0.122 5.812

106

D2000 = 1 if event in 2000 ; 0 otherwise 1.359 0.137 7.434 D2001 = 1 if event in 2001 ; 0 otherwise 1.325 0.135 7.187 D2002 = 1 if event in 2002 ; 0 otherwise 1.300 0.161 7.204 D2003 = 1 if event in 2003 ; 0 otherwise 1.132 0.162 6.130 D2004 = 1 if event in 2004 ; 0 otherwise 1.523 0.159 8.334 D2005 = 1 if event in 2005 ; 0 otherwise 1.959 0.047 8.278

Catch model

REGION1 = 1 if event in region 1 ; 0 otherwise 10.220 0.099 11.258 REGION2 = 1 if event in region 2 ; 0 otherwise -1.888 0.181 -2.896 REGION3 = 1 if event in region 3 ; 0 otherwise 8.410 0.058 7.911 TRAPS = number of traps pulled from water 0.369 56.178 65.305 NIGHTS = number of nights traps soaked in water 1.868 3.639 18.544 SHORTS = number of undersized lobsters released 0.239 37.409 71.361 WSPD = average daily wind speed (m/s) -0.130 6.179 -1.568a WVHT = average daily wave height (m) -1.021 2.400 -3.487 BAR = average daily barometric pressure (mb) -0.209 1017.52 -2.948 ATMP = average daily air temperature (°C) 1.363 13.513 6.978 PRECIP = daily precipitation (in) -1.743 0.062 -2.665 WSPDPREV = previous day’s avg daily wind speed 0.286 6.414 3.580 WVHTPREV = previous day’s avg wave height 0.880 2.486 3.150 BARPREV = previous day’s barometric pressure -0.322 1017.24 -4.666 WTMPPREV = previous day’s water temperature 2.049 13.915 8.427 PRECIPPR = previous day’s precipitation 1.471 0.083 2.714 SEASON2 = 1 if Oct-Nov ; 0 otherwise -2.603 0.474 -2.659 D2000 = 1 if event in 2000 ; 0 otherwise -1.659 0.134 -1.905 D2001 = 1 if event in 2001 ; 0 otherwise 2.541 0.127 2.760 D2002 = 1 if event in 2002 ; 0 otherwise 9.194 0.154 11.084 D2004 = 1 if event in 2004 ; 0 otherwise 5.364 0.151 6.250 D2005 = 1 if event in 2005 ; 0 otherwise 7.297 0.045 6.963


107

Table 10. Measures of fit for joint catch-location models.

Summary statistic Red sea urchin model Lobster model Catch affects Location Catch affects Location

location affects catch location affects catch

Log-likelihood -265414.3 -265414.3 -217385.7 -217385.7 no model, L(0)

Log-likelihood -251487.4 -251487.4 -208951.8 -208951.8 constants only, L(C)

Log-likelihood -240132.7 -240721.0 -189662.3 -191831.7 model, L(β)

Number of parameters, k 32 35 32 37 Number of observations, N 27784 27784 31284 31284 Adjusted likelihood ratio

index - no model, ρ2(0) 0.095 0.093 0.127 0.117 Adjusted likelihood ratio

index - constants only, ρ2(C) 0.045 0.043 0.092 0.082

108

Figures

Figure III-1. DFG fish blocks and model regions.

109

Figure III-2. Average yearly red sea urchin fleet effort (top) and catch (bottom) for each fishing region.

110

0

100

200

300

400

500

600

700

800

900

0 1 2 3

Region

Ave

rage

Yea

rly F

ishi

ng E

vent

s

0

5000

10000

15000

20000

25000

30000

0 1 2 3

Region

Ave

rage

Yea

rly L

egal

s

0

10000

20000

30000

40000

50000

60000

0 1 2 3

Region

Ave

rage

Yea

rly T

raps

Figure III-3. Spiny lobster fleet average yearly effort as average number of events (top), average yearly catch as number of legals retained (middle), and average yearly effort as average number of traps (bottom).

111

IV. Part 3 – Heterogeneous fisherman travel behavior and location choice in

commercial fisheries

Abstract

Managing fish stocks and the fishing fleets that depend on these fish stocks are

worldwide environmental and economic concerns. Without fish there would

obviously be no fisheries but another essential piece of the fishery management

puzzle is an understanding of the fishermen and fishing fleets that catch the fish. In

order to understand how a fishing fleet allocates effort and catch and predict fleet

impacts on fish stocks one must understand fishermen behavior. In this chapter I

further explore the influence variables which affect fisherman behavior and

characterize spatial and temporal heterogeneity among fishermen in a commercial

fishing fleet.

Longitudinal data from the Santa Barbara Channel Islands red sea urchin

commercial fishing fleet data record each fisherman’s daily fishing activity. Analysis

of these data points to high levels of heterogeneity and consistent catch performance

among fishermen within the fleet (Robinson and Siegel, in prep). The productive

fishermen are consistently far more productive than the average fishermen. This

research addresses the following questions:

• Do highly productive fishermen exhibit different spatial and temporal

behavior than average fishermen?

112

• Do highly productive fishermen exhibit different responses to

environmental and biological variability than average fishermen?

Space-time regression models of this manner require flexibility which can account for

nonlinear dependence and possible spatial and temporal correlations. I therefore

approach this problem through a generalized geoadditive mixed modeling (GGMM)

framework. The models are fit using Bayesian Markov chain Monte Carlo (MCMC)

simulation. The findings point to different responses to seasonal and environmental

variables among segments of the red sea urchin fleet as well as significant spatial and

temporal differences between the behavior of the consistently high catch per unit of

effort fishermen and the rest of the fleet.

1. Introduction

Properly managing fisheries is an important and difficult problem (Botsford et al

1997, Parsons et al 1998, Hanna 1999, Worm et al 2006). A principal conclusion of

the marine ecosystem-based management literature is our need to understand and

predict the role and response of humans in the systems, particularly our role in

removing fish from a system and our response to different management regimes

(Hilborn 1985, Vignaux 1996, Smith 2002, Frid et al 2005). However, understanding

and predicting human behavior is no trivial undertaking. Walters and Martell (2004)

suggest low levels of heterogeneity among commercial fishermen because, “modern

industrial fisheries often have relatively homogeneous technology and highly

113

‘professional’ fishers with similar knowledge, skills, and information about the

stock(s), and fleets are often based in one or a few ports (where access to vessel and

technology servicing is available) so that the fishers see similar costs to access a

given fishery.” However, much of the fishing fleet research points to significant

heterogeneity among fishermen (e.g., Hilborn 1985, Branch et al 2006).

Government oversight requires fishermen in many commercial fisheries to

keep a record of their fishing activities. In addition to catch amounts, fishing

locations are oftentimes a component of these data. Even when fishing locations are

reported as generalized blocks they can still be useful as a record of transportation

behavior and spatial effort allocation (Vignaux 1996, Smith 2002, Robinson and

Goulias in prep). One common family of models used with location data are Logit

models, which essentially assign probabilities of selection to individual location

choices. Structured additive regression (STAR) models are another family of models

which incorporate complex semi-parametric predictors and include generalized

geoadditive models. This research focuses on the latter in an attempt to produce more

spatially explicit models of commercial fishing fleet travel behavior.

Previous research addresses the heterogeneous behavior aspect of fleet

modeling by quantifying the differences in effort and catch between homogeneous

and heterogeneous fishing fleets and addressing potential impacts to fish stocks and

fishery management (Robinson and Siegel in prep). Further research investigates the

questions: What variables influence when and where a fisherman goes fishing? How

114

will a fleet distribute its effort in space and time? It addresses two causal structures

for modeling fisherman travel behavior (Robinson and Goulias in prep):

• Expected catch affects location choice

• Location choice affects expected catch

For each fishery expected catch plays a stronger role than location choice. In other

words, fishermen appear to choose a fishing location after first determining their

expected catch at each available location. This research continues the discussion by

asking the questions: How do we characterize the spatial and temporal heterogeneity

in a fishing fleet? Do we find consistent differences between “segments” of a

commercial fishing fleet? In other words

1. Do highly productive fishermen exhibit different spatial and temporal

behavior than average fishermen?

2. Do highly productive fishermen exhibit different responses to

environmental & biological variability than average fishermen?

I use commercial red sea urchin fishing fleet data that record each person’s daily

fishing activity. Analysis points to high levels of heterogeneity between fishermen

within a fishing fleet (Figure IV-1) and consistent fishing performance (Figure IV-2)

among fishermen within the fleet (Robinson and Siegel in prep). The productive

fishermen are typically far more productive than the average fishermen and the

productive fishermen are consistently productive.

115

The basic problem is the characterization of segments of a commercial fishing

fleet (i.e., fishermen with consistently above average catch performance vs. the rest of

the fleet with inconsistent or consistently low catch performance) and an analysis of

spatial and temporal differences found in each segment. While previous research has

identified variables that influence a fishing fleet’s behavior and performance (Smith

2002, Robinson and Goulias in prep), many of these variables have a non-linear

relationship with catch rate (see also Vignaux 1996). Therefore flexibility in time

trends, seasonal effects, and environmental and fishery-specific variables is required

along with a modeling environment that allows for a relaxation of linear relationships.

A Bayesian setting is chosen in which to do this using the public domain software

package BayesX20. Replacing linear predictors with structured additive predictors

allows one to overcome some of the issues inherent in this problem, namely: 1) the

inappropriateness of strictly linear predictors, 2) spatially and temporally correlated

errors, and 3) heterogeneity among individuals and segments of the fishing fleet

(Brezger et al 2008).

The next section describes the fishing fleet and environmental data used in this

research. The third section discusses the research methods and fleet models. The

fourth section discusses results of the research. The final two sections include a

discussion and conclusions.

20 The software is available at http://www.stat.uni-muenchen.de/~bayesx.

116

2. Data


fish block data for the red sea urchin (Strongylocentrotus franciscanus) fishery at the

Santa Barbara, California Channel Islands. While I recognize some shortcomings

inherent in fish block data including intentional and unintentional location errors

(Robinson et al 2005), commercial fishery data have been used successfully in a

number of well-cited papers (Hilborn 1985, Vignaux 1996, Smith 2002).

Regional average daily wind speed, wave height, water temperature, and air

temperature data are collected from the National Oceanic and Atmospheric

Administration (NOAA) National Data Buoy Center (NDBC) along with total daily

precipitation at the Santa Barbara Harbor from the Santa Barbara County Flood

Control District for additional analysis of red sea urchin fishing fleet behavior.

2.1. Red Sea Urchin fishing fleet







is highly dependent on food supply and tends to decrease dramatically in El Niño

years when warm water and lack of nutrients reduces kelp supply. Demand for

117

urchin roe has traditionally come from international Asian markets, though there is an

increasing domestic demand.


or so, it is one of the most commercially valuable in California. In 2001 red sea


over 11% of all California catch by value and 3% of all Santa Barbara catch by

volume and over 18% of all Santa Barbara catch by value (CA DFG 2001). By 2005

the red sea urchin catch accounted for almost 4% of all California catch by volume

and almost 6% of all California catch by value (a drop attributed in part to the loss of

the northern California fishery) but had increased to almost 12% of all Santa Barbara

catch by volume and over 19% of all Santa Barbara catch by value (CA DFG 2005).


exploited in northern California and parts of southern California (CA DFG 2003). It



restrictions.

The red sea urchin data used in this research include an anonymous but unique

identifier for each diver, the date of the fishing event, the location of the fishing event

(from the DFG enumeration blocks21, see Figure IV-3), the amount of catch (in

pounds), the number of divers on the boat, and the total hours spent diving. The latter

21 The DFG imposes a grid of 10 minute x 10 minute cells over the coast. Data is aggregated to this

level of resolution to protect confidentiality.

118


(CPUE). The raw data contain 28,046 dive events, though not all records are

complete. Each year contains an average of 3,505 dive events. Records with blank

values or with values far outside “normal” parameters (e.g., greater than five divers

per event, less than 1 hour or more than 24 hours of diving per event) were removed

from the analysis. On average less than 1% of the data was removed each year,

though two years, 2003 and 2005, lost 1.35% and 1.44% of their data, respectively.

In a few cases a particular diver consistently left data fields blank. Generally this

only accounted for a few records, if any, each year. For three divers the omissions

accounted for over 35% of their effort in one year. One of these divers had average

yearly catch but reported the number of divers on their boat above the cut-off value.

The other two divers both had above average yearly catch but left the number of

divers field blank. For two divers the omissions accounted for over 50% of their

effort in one year. One diver had left the number of divers field blank and the other

reported the number of divers on their boat above the cut-off value. Both divers

reported below average catch these years. Given the very large number of dive

events these losses are considered minor and their removal isn’t expected to bias our

results.

The cumulative 1998-2005 red sea urchin (RSU) catch data record the fishing

activity of 218 boats, though not all boats were active each year. Table 1 provides

descriptive statistics for the red sea urchin logbook data. “Divers” is the number of

divers on a boat for each fishing trip, “Total Hours” is the total hours spent fishing,

119

“Total Harvest” is the total harvest (in pounds) for the fishing trip, and “CPUE” is the

catch per unit of effort for the fishing trip (in the case of red sea urchin we measure

CPUE as pounds per hour per diver).

The red sea urchin fishing effort data contain 27,793 diving events, ranging

from a minimum of 1 dive per year to a maximum of 171 dives per year, with an

average of 36 (standard deviation = 33) dives per year. The red sea urchin catch

accounts for 35,089,790 pounds, ranging from a minimum of 10 pounds per year to a

maximum of 392,046 pounds per year, with an average of 45,103 (standard deviation

= 53,962) pounds per year. The red sea urchin CPUE ranges from a minimum of 10

pounds per diver per day (presumably exploratory dives or gear testing) to 4,781

pounds per diver per day, with an average of 1,156 (standard deviation = 671) pounds

per diver per day.

This research does not include reported fishing effort at blocks 684 and 713

since these two blocks receive so little effort relative to the other blocks (each with

less than 1% of all diving effort, see Figure IV-4). My intuition is that 684 has little

hard bottom or kelp in that area, both strongly correlated with sea urchin. Since block

713 is far from home ports, exposed to wind and waves, and in deeper waters little

effort is expected there. While block 686 also receives little fishing effort, removing

686 is more problematic because it is a neighbor to two other "valid" cell and might

cause problems in fitting and interpreting the spatial effect. Therefore it is not

removed from analysis.

120

2.2. Environmental

Regional data on wind speed, wave height, water temperature, and atmospheric

temperature are collected from NDBC station number 46063, located off Point

Conception. The first two environmental variables are frequently identified by

commercial fishermen as the most important in determining whether and where to

fish22. Hourly data are averaged over each day, with missing data supplied through

regression analysis using the nearby station 46054 buoy (west Santa Barbara

Channel) or more distant station 46053 buoy (east Santa Barbara Channel) when

station 46054 was not available (for example due to maintenance or damage).

Average daily wind speeds range from 0.21 to 14.88 meters per second (average =

6.96 meters per second). Average daily wave heights range from 0.31 to 6.72 meters

(average = 2.30 meters). Both variables exhibit strong seasonal patterns with wind

speeds highest in spring and summer and wave heights largest in winter months.

Average daily water temperature range from 9.59 to 19.61 degrees Celsius (average =

13.64 degrees Celsius) and average daily air temperature range from 7.22 to 19.41

degrees Celsius (average = 13.36 degrees Celsius). Water temperature and air

temperature both exhibit strong seasonal patterns with highest temperatures in late

summer and early fall and lowest temperatures in late winter and early spring.

Data on total daily precipitation at the Santa Barbara Harbor are collected

from the Santa Barbara County Flood Control District. Daily precipitation ranges

22 Personal communication with a variety of fishermen during data collection and mapping projects

conducted from 2003-2006.

121

from 0.1 inches to 4.41 inches (average = 0.64 inches), though precipitation events

only occur on 12.4% of the days in this time period.

Since annual and seasonal trends are readily apparent in the environmental

data time is modeled with a P-spline (i.e., non-linear) distributional form. Following

Lynn and Simpson (1987) seasonality in the Santa Barbara Channel is divided into

four groupings: 1) December-February, 2) March-May, 3) June-August, and 4)

September-November. To account for annual trends I assign a variable, t, where t1 =

season 1 in 1998, t2 = season 2 in 1998, …, t32 = season 4 in 2005 (Figure IV-5c).

Table 2 provides descriptive statistics for the environmental data used in this

research. “Wind Speed” is the average daily wind speed in meters per second, “Wave

Height” is the average daily wave height in meters. The use of wind speed and wave

height as important weather variables is a result of five years of interaction with

commercial fishermen. These variables are usually mentioned as the most influential

in a decision whether or not to fish. However, this research looks at the effects and

possible correlations of other environmental variables. “Atmospheric Temp” is the

atmospheric temperature in degrees Celsius, “Water Temp” is the water temperature

in degrees Celsius, and “Precipitation” is the daily rainfall in inches.

3. Methods

As noted above, the fleet data is a rich source of information but I suspect it contains

a complex correlation structure including temporal (annual and seasonal), spatial, and

fisherman-specific components. Because of the complex data structure and the

122

exploratory nature of this work, a flexible Bayesian additive mixed modeling strategy

is chosen (Fahrmeir and Lang 2001). In addition to modeling the effects of covariates

on catch rate, one of the primary goals of the analysis is to identify different covariate

effects and to infer different spatial “strategies” for “high-liners” – top performing

fisherman – versus the rest of the fleet.

I explore this by comparing results for a pooled model (the entire fleet) to

models fit to subsets of the data for high-liners and non-high-liners. The high-liners

are identified using the methods developed in Robinson and Siegel (in prep). They

use k-means cluster analysis to separate the red sea urchin fleet into two segments:

fishermen with consistently above average catch per unit of effort (CPUE) and all of

the remaining fishermen in the fleet. The latter group includes fishermen with

consistently below average CPUE, fishermen with inconsistent CPUE, and fishermen

with less than three years of fishing activity in the dataset. Of the 217 fishermen in

the dataset 35 (16%) are included in the consistently high CPUE group and 182

(84%) are included in the not consistently high CPUE group (i.e., everyone not in the

first group) (Figure IV-2).

The basic model structure assumes CPUE is normally distributed and can be

expressed as a function of an additive predictor,

CPUEi | ηi, εi ~ N(ηi, σε). (1)

123

The term, η, is an additive mixed model specification that can include varying and

fixed coefficients in a linear predictor (γui) and non-linear predictors, f(zi), that are

smooth functions over the domain of z. In the specifications below, f( ), is a

parameter vector evaluated at equally or unequally spaced points in z. Covariates X =

{U, Z} can enter as either linear (U) or non-linear (Z) effects with the choice being

guided by specification tests. The non-linear effects are particularly suited to

exploratory work since they impose less structure on the models. The set of

covariates considered for inclusion in the model include the following:

• Fisherman-specific random effects, dj(i): These are included to capture

unobserved heterogeneity among fisherman and to control for correlation due

to repeated observations on the same observational unit, j.

• Temporal effects, t: Catch rates should exhibit seasonal variation (due to

unobserved seasonally-varying environmental conditions) and a long-term

seasonal trend (due to unobserved annual time scale varying environmental

conditions such as the El Niño-Southern Oscillation and Pacific Decadal

Oscillation).

• Environmental effects, E: These are measured at single sites but characterize

the overall environmental conditions that are expected to influence catch rates.

124

They include wind speed (E1), wave height (E2), water temperature (E3), air

temperature (E4), and precipitation (E5).

• Fisherman characteristics, F: number of divers (F1) and hours diving (F2).

• Location, Bs: Location is recorded at the block level as described above. The

location can be specified as an unstructured random effect, or as unstructured

and spatially-structured random effects. The latter enter as Markov Random

Fields as developed by Besag (1974), but generalize in the current framework

using a penalty matrix as another type of smooth additive function f(z).

The starting point is the model,

ηi = dj(i) + γ' Xi + f(Bs(i)) + bs(i), (2)

where Xi = {Ei, Fi, ti} is the linear predictor component, d and b are fisherman-

specific and block-specific unstructured varying coefficients, and the term, f(Bs(i)), is

the spatially structured effect. Assumptions are then relaxed to test a more general

model,

ηi = dj(i) + γ' Xi +

f1(Fi1) + f2(Fi2) + f3(Ei1) + f4(Ei2) + f5(Ei3) +

125

f6(tyear(i)) + f7(tseason(i)) +

f8(Bs(i)) + bs(i). (3)

It includes only two variables, Xi = {Ei4, Ei5}, in the linear predictor with all of the

other covariates entering as additive non-linear effects. In addition to the spatially

structured term the model includes smooth trend and seasonal components. These

two models are compared for the whole fleet, and then the second model is used to

compare the high-liner and non-high-liner segments of the fleet.

The models are fit using full Bayesian inference to recover the posterior

distribution of model parameters conditional on the data. As with any Bayesian

inference, the results can be sensitive to selection of priors and hyperpriors. This

analysis uses standard conservative assumptions and evaluates results for multiple

long MCMC runs and some small variations on hyperpriors. A complete discussion

of model specification and inference is available in Fahrmeir and Lang (2001) or

Brezger and Lang (2004). The smooth functions, f1, ..., f6, use Bayesian p-splines

with second-order random walks as smoothness priors. The seasonal effects, f7, uses

an 11th-order smoothness prior and the spatially structured term, f8, uses a Gaussian

intrinsic autoregressive prior. The models are implemented in the R Language using

an empirical Bayes approach or using full Bayesian inference in the stand alone

package BayesX. All of the results reported here are produced using BayesX.

126

4. Results

Table 3 shows the model fit for the entire red sea urchin fleet as well as for each

segment of the fleet. As a base case I begin with a model of linear effects for the

entire fleet. Next this model is improved by accounting for non-linear effects. The

non-linear model is first used for the entire fleet but then run for each segment of the

fleet: a) just the consistently high CPUE fishermen, b) all the fishermen not in the

consistently high CPUE segment (i.e., the consistently low CPUE fishermen and the

fishermen with inconsistent CPUE), c) just the fishermen with inconsistent CPUE,

and, d) just the consistently low CPUE fishermen. Comparing the fixed effects

(linear) model with each of the GGMM models I find, as expected, that the latter all

perform better based on lower log-likelihood, Akaike information criterion (AIC),

and Bayesian information criterion (BIC) statistics, which are used to measure the

goodness-of-fit of an estimated statistical model. Additionally, the models for each

segment of the fishing fleet perform better than the pooled model, with the exception

of a higher generalized cross validation (GCV) for the consistently high CPUE

fishermen group than for the entire fleet. This is possibly due to greater variability

among fishermen in the consistently high CPUE group than the variability present

among the rest of the fishermen in the fleet. Evidence for this is discussed in the

results section.

Each model predicts similar patterns for the majority of the independent

variables, though there are important differences which will be discussed shortly.

The main differences between segments of the fleet are found in the spatial and

127

temporal components of the models. The remainder of this paper will focus on

differences between the consistently high CPUE fishermen and all the remaining

fishermen in the fleet. I briefly discuss the results on the independent variables

followed by a more detailed discussion of the spatial and temporal components.

4.1. Fishery-specific variables

Low CPUE is generally indicative of inefficient fishing while high CPUE

indicates efficient fishing success. Thus, the negative coefficients on divers (more

divers = lower CPUE) and hours (more hours = lower CPUE) are not unexpected.

The fishermen with consistently high CPUE typically fish with fewer divers on their

boat and typically fish for fewer hours than the rest of the fleet. Figure IV-5a shows

the coefficient dropping to near zero for all the red sea urchin fishermen when the

number of divers on the boat is two. However, the addition of more divers causes the

coefficient to become more negative for the consistently high CPUE fishermen than

for the rest of the fleet and the consistently high CPUE fishermen do not fish with

more than four divers on their boat. It is no surprise to thus see a strongly negative

coefficient in the fixed effects model. Similarly, the coefficient on number of hours

drops rapidly with an increase in the number of hours spent diving for red sea urchin.

For the consistently high CPUE fishermen the coefficient becomes negative at six to

seven hours. For the rest of the fleet the coefficient becomes negative at

approximately ten hours of diving. Again the consistently high CPUE fishermen do

not fish for more than sixteen hours yet members of the rest of the fleet report fishing

128

as much as twenty-four hours. Visual inspection of the data confirms that the

efficient fisherman indeed typically have smaller crews and dive for fewer hours.

Since the data are longitudinal and follow each fisherman’s effort and catch

over time I am able to estimate a unique individual indicator, αi, for each diver. This

variable is a Gaussian (random) effect that influences each fisherman’s predicted

CPUE. A large, positive α indicates an association between a particular fisherman

and consistently high CPUE. A plot of these variables shows a difference between

the consistently high CPUE fishermen as a group and the rest of fleet (Figure IV-6).

Not only do a number of the consistently high CPUE fishermen have an α above any

found for the rest of the fleet they also have few negative α’s and do not have any α’s

as low as those found for the rest of the fleet. The consistently high CPUE fishermen

do appear to have greater variability among themselves than the variability found

among the rest of the fishermen in the fleet. Evidence for this is apparent in the break

between clusters of α’s for the consistently high CPUE fishermen as well as the

greater spread of the 95% confidence intervals on the variables in Figure IV-5 and

seasonal effort in Figure IV-12.

4.2. Environmental variables

The coefficients on wind speed, wave height, and water temperature make

intuitive sense (Figure IV-5). In addition, the expanding bounds at the extreme high

ends of wind speed, wave height, and water temperature show that there is more

129

uncertainty in that part of the relationship because of the infrequency of swells that

large, winds that strong, or water temperatures that high.

Low wind speeds have a positive coefficient but the coefficient decreases as

wind speed increases, becoming negative at approximately six meters per second

(Figure IV-5d). High winds are correlated with larger waves and increased ocean

turbidity (Otero and Siegel 2004). It is no surprise to see a negative coefficient with

increasing wind speeds since conditions become more physically dangerous and

successful diving becomes more difficult due to decreased visibility underwater.

While there is not a strong decline in the coefficient with large increases in wind

speed, the consistently high CPUE fishermen have a large negative coefficient at very

high wind speeds, suggesting that they are less likely to fish when winds are blowing

this strong. Similarly, small wave heights are associated with a positive coefficient

but the coefficient quickly drops with increasing wave height, becoming negative

when wave heights reach two meters (Figure IV-5e). The relationship is more

pronounced for the consistently high CPUE fishermen, starting with a more positive

coefficient and dropping to a more negative coefficient than for the rest of the fleet.

As with the fishery-specific variables there are differences in environmental variables

between the fleet segments. The consistently high CPUE fishermen do not fish at

very large wave heights (above six meters). Again, this is no surprise as high seas are

physically dangerous. Both segments of the fleet exhibit increasing coefficients with

increasing water temperatures (Figure IV-5f). Water temperatures are very seasonal

(Lynn and Simpson 1987) with warmest sea surface temperatures occurring in the fall

130

months. This corresponds with decreasing temporal restrictions and increasing

market demand for red sea urchin.

4.3. Spatial behavior

Since I do not have information on a fisherman’s home port I assume that high-liner

status is independent of home port and that the patterns found here do not simply

reflect the proximity of a fishing location to a fisherman’s home port. Figure IV-7 is

a comparison of the posterior mode of the structured spatial components of the fleet

in aggregate, the consistently high CPUE fishermen, and the not consistently high

CPUE fishermen. I find noticeable spatial differences; mainly that the sign of the

coefficients for the consistently high CPUE fishermen tend to be opposite those of the

rest of the fleet. Maps of these posterior modes (Figure IV-8) reinforce this

conclusion. The fishermen who do not have consistently high CPUE have positive

coefficients at blocks farther from home ports, particularly blocks 689 and 690, and

the consistently high CPUE fishermen have positive coefficients at blocks closer to

home ports and blocks along the south side of the islands. Alternately, the not

consistently high CPUE group has negative coefficients on CPUE at blocks closer to

home ports and the consistently high CPUE group has negative coefficients at blocks

farther from home ports, particularly northern blocks far from home ports (e.g., block

690). A plausible reason for these spatial differences is tied to the extreme fishing

effort that the farthest western fish blocks receive (Figure IV-4). Since these blocks

are the most crowded and receive the most effort (and therefore suffer the most

131

degradation) it stands to reason that the consistently high CPUE fishermen do better

(i.e., have a higher catch per unit effort) at the less crowded and less impacted fish

blocks closer to home ports and at the south sides of the islands.

While the average (i.e., aggregate) fleet model is a fair approximation of the

not consistently high CPUE fishermen, I find the consistently high CPUE fishermen

exhibiting different, oftentimes opposite, behavior. This behavior is further explored

in the discussion section.

4.4. Temporal behavior

From Figure IV-5c we see a negative coefficient on time (measured as sequential

three-month seasons) for the first five years of the data and a steadily increasing

positive coefficient on time for the last three years. This effect is most likely the

result of environmental conditions since the growth and sexual productivity of sea

urchin is depressed during warm water conditions (DFG 2003). During the first few

years of our dataset the urchin stocks were presumably recovering from strong warm

water El Niño conditions in 1997 and 1998.

Figure IV-9 shows a comparison of temporal differences between the

consistently high CPUE fishermen and the rest of the fleet. While the sign of the

temporal component is often, though not always, the same for both segments of the

red sea urchin fleet it appears to have a stronger effect on the consistently high CPUE

fishermen, causing the temporal component to be more strongly positive or negative

than that predicted for the entire fleet.

132

5. Discussion

In this research I characterize Santa Barbara Channel Islands red sea urchin fishermen

by whether or not they exhibit consistently high fishing performance. I examine the

influence of a number of variables on the expected catch per unit of effort for

fishermen within both of these subsets of the fleet. CPUE is influenced by fishery-

specific variables such as number of divers on a boat and number of hours spent

diving. CPUE is also influenced by environmental variables such as wind speed,

wave height, water temperature, atmospheric temperature, and precipitation. I

examine the response of each fisherman in the fleet to season and fishing location and

find that fishery-specific and environmental variables do indeed influence the CPUE

of fishermen in each segment of the fleet to different degrees. I also find differences

in the spatial and temporal behavior between members of the red sea urchin fishing

fleet with consistently high catch performance and those with inconsistent or with

consistently low catch performance. Perhaps the most noticeable differences between

segments of the fleet are realized in spatial and temporal behavior. A discussion of

these differences follows.

5.1. Spatial behavior

The spatial differences between different segments of the red sea urchin fleet

are such that we see an inverse relationship between the expected CPUE at each

fishing location for the consistently high CPUE fishermen and the expected CPUE at

133

each fishing location for the rest of the red sea urchin fleet (Figure IV-8).

Consistently high CPUE fishermen are expected to have a higher CPUE closer to

home ports and at the back (south) sides of the islands closer to home ports. The rest

of the red sea urchin fleet has a higher CPUE at locations farther from home ports,

particularly block 690 on the front (north) side of San Miguel Island. Commercial

sea urchin fishermen report the “common knowledge” that block 690 is “best” for

catching sea urchin. Conversations also suggest that fishermen who want to hide

their actual catch location and fishermen who do not record or recall their exact catch

location often report catch from block 690, a fact which may confound model

predictions23 (Robinson et al 2005).

Further exploration of this spatial behavior reveals different strategies for each

segment of the fleet (Figure IV-10). While blocks 689, 690, and 712 are associated

with relatively high average CPUE for all the fishermen in the fleet, a plot of effort

vs. average CPUE at each fishing block for the different segments of the fleet depicts

a strong relationship (r = 0.64) for the consistently high CPUE fishermen and only a

weak relationship (r = 0.11) for the rest of the fleet. In other words, consistently high

CPUE fishermen are targeting certain blocks and doing better at them while the rest

of fleet is generally not doing as well and is typically more arbitrary in location

choice. This suggests that the consistently high CPUE fishermen target the “better”

fish blocks. What makes a “better” fish block? Airame et al (2003) and Ugoretz

23 This misreporting may in fact overemphasize the spatial differences observed between segments of

the red sea urchin fleet particularly since the fishermen who are not in the consistently high CPUE group have a strong positive association with the blocks in question.

134

(2002) describe three biogeographic regions at these islands24 based on sea surface

temperatures, bathymetry, and a number of physical and biological characteristics: 1)

San Miguel and northern Santa Rosa Islands with physical and biological regimes

characteristic of central and northern California and the Northwestern United States,

2) Anacapa and eastern Santa Cruz Islands with regimes characteristic of southern

California and Baja Mexico, and 3) southern Santa Rosa and Santa Cruz Islands

which are characteristic of a transition between the cooler and warmer regimes.

Figure IV-11 shows a NOAA red sea urchin habitat suitability model for the Channel

Islands. High values (dark colors) indicate a higher likelihood of red sea urchin

presence based primarily on bathymetry (shallower waters) but also on substrate

(hard and rocky bottom). San Miguel Island and the north side of Santa Rosa Island

both show a very high likelihood for red sea urchin presence, supporting the

perception that these are the “best” (and possibly easiest) places to fish for red sea

urchin. These are also the areas that the not consistently high CPUE fishermen

appear to exert much of their effort. However, there are many locations at Santa Cruz

Island, along the south side of Santa Rosa Island, and at Anacapa Island where the

model predicts sea urchin presence. These locations are also where the model

predicts a high posterior mode for the consistently high CPUE fishermen. It is

possible that the sites are “off the beaten path” (and therefore less crowded, less

degraded, and less “picked over”) than those found at San Miguel Island and the

north side of Santa Rosa Island.

24 One must keep in mind that these are generalized results with very dynamic boundaries between regimes.

135

5.2. Temporal behavior

While the sign of the temporal component is often, though not always, the

same for both segments of the red sea urchin fleet it appears to have a stronger effect

on the consistently high CPUE fishermen (Figure IV-9). September through

February, when temporal restrictions are least and market demand is traditionally at

its peak (Robinson and Siegel in prep), exert a much stronger positive effect on the

expected CPUE of the consistently high CPUE fishermen than on the rest of the fleet.

In March though August, when temporal restrictions are greatest and market demand

is traditionally least we see a stronger negative effect on the expected CPUE of the

consistently high CPUE fishermen than on the rest of the fleet, though both segments

are generally fishing less. A comparison of the percent of annual fishing effort in

each season (Figure IV-12) shows that neither group fishes much in the summer

months (season 3), the consistently high CPUE fishermen fish less in the spring

months when market demand is still relatively low and temporal restrictions are

relatively high, and the consistently high CPUE fishermen fish more in the fall and

early winter months when market demand is highest and temporal restrictions are

lightest. Thus, the temporal patterns are likely indicating a rational response among

high-liners to market demand conditions. Since they are most efficient, they do their

most intense fishing, and produce their largest catch, during high market demand

conditions. Other fishermen in the fleet instead choose to fish during weaker market

conditions.

136

6. Conclusions

In this research I analyze California Department of Fish and Game commercial

fishermen logbook data and environmental data to describe and quantify temporal and

spatial behavior for fishermen in the Santa Barbara Channel Islands commercial red

sea urchin fleet. Noticeable differences are found in both the spatial behavior and the

temporal behavior between those fishermen in the fleet with consistently high catch

per unit of effort (CPUE) and the rest of the fleet (those with consistently low CPUE

and those with highly variable CPUE). While I do not have data on boat size, engine

size, total years of experience, or similar variables that one would expect to be highly

correlated with CPUE, I find in general that a fisherman’s fishing performance is

consistent and have thus developed a method for clustering members of a fishing fleet

by their CPUE relative to the entire red sea urchin fleet over an eight-year period.

Furthermore, certain variables are found to have a strong influence on

expected CPUE, most notably the number of divers on a boat, the number of hours

spent diving, and the average daily wave height. Many of these variables that

influence a fisherman’s behavior and performance are non-linear, requiring flexibility

in time trends, seasonal effects, and environmental and fishery-specific variables and

a modeling environment that allows for a relaxation of these fixed parameters. We

therefore choose a modeling setting which allows us to address a number of

shortcomings of linear models, namely nonlinearity of covariates, correlation of

137

spatial and temporal observations, and heterogeneity among individuals and segments

of the fishing fleet.

I learn, in particular, that members of different segments of the fishing fleet

respond differently to weather conditions and fishing season. Consistently high

CPUE fishermen are less likely to fish when wind speeds are high and waves are

large. Season appears to have a more pronounced effect on the behavior of the

consistently high CPUE fishermen. This group is more likely to fish in the fall and

winter when temporal restrictions are lowest and market demand is highest.

Alternately, this group is less likely to fish in spring and summer when temporal

restrictions are highest and market demand is lowest. The rest of the fishermen in the

fleet appear less influenced by season, maintaining fairly consistent fishing effort

year-round rather than focusing their effort on certain seasons. I also find noticeable

differences between the consistently high CPUE fishermen and the rest of the fleet in

regards to spatial location and CPUE. The majority of the red sea urchin fleet targets

the “well known” fishing locations. Though the consistently high CPUE fishermen

also fish at these blocks their highest CPUE is actually associated with blocks where

the majority of the fleet exerts less effort.

Comparison of aggregate (pooled) and disaggregate fishing fleet behavior

models reveals that the behavior and performance of the consistently high CPUE

fishermen tends to be washed out by the behavior and performance of the rest of the

fleet, producing “average” conditions which may not, in fact, be indicative of any

members of a fishing fleet, and certainly not the consistently high CPUE fishermen.

138

While each fleet is different and it may not be possible to generalize findings from

one fleet to those of another (Vignaux 1996), it is important to account for differences

in effort and catch when including the effects of fishermen in fishery management

and fishing fleet modeling.

139


Table 1. Descriptive statistics, red sea urchin logbook data. Table 2. Descriptive statistics, environmental data. Table 3. Red sea urchin commercial fishing fleet spatial model fit. Figure IV-1. Red sea urchin fishing fleet effort and catch data, 1998-2005. Figure IV-2. Fleet CPUE rankings for fishermen with 6 or more years of experience

in the red sea urchin fleet. Figure IV-3. Santa Barbara Channel Islands with Department of Fish and Game fish

blocks. Figure IV-4. Total fishing effort at each block. Figure IV-5. Posterior mode and 95% confidence intervals for nonlinear (P-spline)

variables. Figure IV-6. Posterior mode of α for each sea urchin diver. Figure IV-7. Red sea urchin commercial fishing fleet spatial model comparison. Figure IV-8. Red sea urchin fishing fleet spatial output comparison. Figure IV-9. Red sea urchin fishing fleet temporal output comparison. Figure IV-10. Scatterplots of effort and average CPUE at each block for segments of

the commercial red sea urchin fishing fleet. Figure IV-11. Red sea urchin habitat suitability model. Figure IV-12. Percent of red sea urchin fishing effort in each season (2001-2004).

140

Tables Table 1. Descriptive statistics, red sea urchin logbook data.

Cases Mean St.Dev. Minimum Maximum Divers 27793 1.72 0.68 1 5 Total Hours 27793 5.49 2.77 1 24 Total Harvest (lbs) 27793 1262.54 851.98 5 7380 CPUE* 27793 152.86 91.15 0.83 900

*CPUE = Pounds per hour per diver. Table 2. Descriptive statistics, environmental data.

Cases Mean St.Dev. Minimum Maximum Wind Speed (m/s) 2922 6.96 2.75 0.21 14.88 Wave Height (m) 2922 2.30 0.85 0.31 6.72 Water Temp (°C) 2922 13.64 1.57 9.59 19.61 Atmospheric Temp (°C) 2922 13.36 1.48 7.22 19.41 Precipitation (in) 2922 0.08 0.33 0.00 4.41

Table 3. Red sea urchin commercial fishing fleet spatial model fit.

Linear effects

Non-linear effects

Entire fleet

Entire fleet

HIGH CPUE group

NOHIGH CPUE group

MID CPUE group

LOW CPUE group

-2*log-likelihood

252 888 250 934 86 166 161 712 117 160 43 822.6

Degrees of freedom

218.65 265.93 91.31 230.31 132.17 160.56

(conditional) AIC

253 325 251 465 86 348.6 162 172 117 425 44 143.7

(conditional) BIC

255 125 253 654 86 998 163 976 118 416 45 199

GCV 3 438.23 3 215.05 5 031.35 2 207.99 2 417.83 1 565.97

141

Figures

Figure IV-1. Red sea urchin fishing fleet effort and catch data, 1998-2005.

Figure IV-2. Fleet CPUE rankings for fishermen with 6 or more years of experience in the red sea urchin fleet. Dotted lines show cluster analysis cutoffs.

142

Figure IV-3. Santa Barbara Channel Islands with Department of Fish and Game fish blocks.

143

Figure IV-4. Total red sea urchin fleet fishing effort at each block.

E W

North side of islands

South side of islands

145

Figure IV-5. Posterior mode and 95% confidence intervals for nonlinear (P-spline) variables. Horizontal dashed lines indicate fixed effects model output. ALL = all fishermen in the commercial red sea urchin fishing fleet, HIGH = fishermen with consistently above average CPUE, NOHIGH = fishermen not in the “HIGH” segment of the fleet.

-100 -50 0 50 100 150 2000

5

10

15

20

25

30

35

40

pmode (DiverID)

Freq

uenc

y

NOHIGHHIGH

Figure IV-6. Posterior mode of α for each sea urchin diver.

146

Figure IV-7. Red sea urchin commercial fishing fleet spatial model comparison. ALL = entire fleet, HIGH = fishermen with consistently high CPUE, NOHIGH = the rest of the fleet. FIXED = results of fixed effects (nonlinear) model.

E W

North side of islands

South side of islands

147

Figure IV-8. Red sea urchin fishing fleet spatial output comparison. ALL = entire fleet, HIGH CPUE = fishermen with consistently high CPUE, AVG & LOW CPUE = the rest of the fleet.

Bayesian posterior mode results for red sea urchin fleet. Data: NOAA, CDF&G.

148

-15

-10

-5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

Season

Pos

terio

r Mod

eALLHIGHNOHIGH

Figure IV-9. Red sea urchin fishing fleet temporal output comparison. ALL = entire fleet, HIGH = fishermen with consistently high CPUE, NOHIGH = the rest of the fleet.

149

All fishermen

685

688

689

690

708709

686

687

707

710 711

712

R2 = 0.29

100

120

140

160

180

0 2000 4000 6000 8000

Effort

Avg

CP

UE

Consistently High CPUE fishermen

685

688

689

690

708709686

687

707

710

711

712

R2 = 0.64140

160

180

200

220

240

0 500 1000 1500 2000 2500

Effort

Avg

CP

UE

Consistently notHigh CPUE fishermen

685

688

689

690

708709

686687

707

710 711

712

R2 = 0.11

80

100

120

140

160

0 1000 2000 3000 4000 5000 6000

Effort

Avg

CP

UE

Figure IV-10. Scatterplots of effort and average CPUE at each block for segments of the commercial red sea urchin fishing fleet.

150

Figure IV-11. Red sea urchin habitat suitability model. High values (dark colors) indicate a higher likelihood of red sea urchin presence based on bathymetry and substrate.

15

20

25

30

35

40

45

1 2 3 4

Season

Perc

ent o

f effo

rt (2

001-

2004

)

HIGHNOHIGH

Figure IV-12. Percent of red sea urchin fishing effort in each season (2001-2004). HIGH = fishermen with consistently high CPUE, NOHIGH = the rest of the fleet.

151

V. Conclusion

This research makes a number of important contributions to marine resource

management and fishing fleet modeling research. It improves our ability to model

fishing fleets by disaggregating and “segmenting” a fleet based on consistent fishing

performance. It provides a method for using currently available data to predict how a

fishing fleet distributes its effort in space and time. Finally, this research informs

management by helping understand the influences, impacts, and implications of

various spatial and temporal management options.

A principal conclusion of the marine ecosystem-based management literature

is our need to understand and predict the role of humans in removing fish from the

system and the response of humans to different management regimes. This research

responds to this need in three important ways:

1. By identifying and classifying heterogeneous behavior among fishermen in

commercial fishing fleets. I show that not all fishermen behave or perform the

same and that individuals exhibit consistent performance over time. This

leads to a segmentation of a fishing fleet into high-liners (or consistently

above average CPUE fishermen) and those fishermen in the fleet who

consistently perform below average and those fishermen in the fleet who are

inconsistent in their fishing performance. Additionally, I find different

responses to seasonal and environmental variables among segments of the

fishing fleets. Consistently high CPUE fishermen are less likely to fish when

152

wind speeds are high and waves are large. Season appears to have a more

pronounced effect on the behavior of the consistently high CPUE fishermen.

This group is more likely to fish in the fall and winter when temporal

restrictions are lowest and market demand is highest. Alternately, this group

is less likely to fish in spring and summer when temporal restrictions are

highest and market demand is lowest. The rest of the fishermen in the fleet

appear less influenced by season, maintaining fairly consistent fishing effort

year-round rather than focusing their effort on certain seasons. I also find

significant spatial differences between the behavior of the consistently high

performing fishermen and the rest of the fishermen in a fleet. The majority of

the red sea urchin fleet targets the “well known” fishing locations. Though

the consistently high CPUE fishermen also fish at these blocks their highest

CPUE is actually associated with blocks where the majority of the fleet exerts

less effort. Finally, I find that fisherman responses to these variables typically

have non-linear relationships with fishing performance, requiring increased

flexibility in our modeling environments.

2. By exploring impacts of heterogeneity on spatial and temporal effort, catch

distribution, and fishing fleet performance. For example, while the fishermen

in these fleets may prefer certain locations they are more likely to move to

perceived “better locations” (more fish, more protected from adverse weather)

than stay at habitual locations. In other words, fishermen fish for fish, not

location. This suggests that temporal restrictions, quotas, and trip limits may

153

be more effective than spatial controls when it comes to managing fishing

fleet harvest.

3. By examining the implications of this heterogeneous behavior for fishery

management, focusing on the influences, impacts, and implications of various

spatial and temporal management options. In this research I find that

temporal controls result in higher equilibrium biomass and generally improve

fleet catch, especially at high levels of heterogeneity. I also find that catch

limits result in higher equilibrium biomass at high levels of heterogeneity but

do not improve fleet catch (compared to no controls). In fact catch limits shut

down the fishery at high levels of heterogeneity because the fleet removes so

many fish.

In conclusion, this research encourages fisheries scientists and managers to

disaggregate fleets and analyze individual level data in more detail. A disaggregated

analysis allows us to determine the composition of fleets and thereby determine if a

fleet behaves different than “average” values would predict while accounting for

possible non-linear responses to environmental and fishery-specific variability. This

research also explores the use of next generation fleet models to predict fishing fleet

behavior and help understand the impacts and implications of various spatial and

temporal management options.

154

Next Steps

One of the next steps of this research is to test the responses of fleet segments to

existing management scenarios. This can be addressed through modeling in a similar

fashion to that carried out in this paper. This can also be approached through game

theory and the exploration of hypothetical scenarios through decision-making

experiments with fishermen.

Future research will explain more of the model variability (error) by incorporating

additional explanatory variables such as home port and boat and engine information

as well as a fisherman’s age, total experience, education level, and marital status.

Some of these data are available through management agencies like the California

Department of Fish and Game, but other data may need to be gathered through

focused surveys and interviews.

Finally, future research will examine a variety of fisheries and fishing fleets,

taking advantage of the wealth of knowledge and research in areas such as Alaska,

the Pacific Northwest, and the East Coast along with fisheries in other countries.

155

References

1. Airame, S., J.E. Dugan, K.D. Lafferty, H. Leslie, D.A. McArdle, and R.R. Warner (2003), Applying Ecological Criteria to Marine Reserve Design: A Case Study from the California Channel Islands, Ecological Applications, 13(1), Supplement: The Science of Marine Reserves, pp.S170-S184.

2. Allen, P.M. and J.M. McGlade (1987), Modelling complex human systems: A

fisheries example, European journal of operational research, 30:147-167. 3. Arentze, T. and H. Timmermans (2003), Modeling learning and adaptation

processes in activity-travel choice, Transportation, 30:37-62. 4. Besag, J. (1974), Spatial interaction and statistical analysis of lattice systems,

Journal of the Royal Statistical Society, Series B (Methodological), 36(2):192-236.

5. Botsford L., Castilla J., Peterson C. (1997), The management of fisheries and

marine ecosystems, Science, 277:509–515. 6. Branch, T.A., R. Hilborn, and E. Bogazzi (2005), Escaping the tyranny of the

grid: a more realistic way of defining fishing opportunities, Canadian Journal of Fisheries and Aquatic Sciences, 62:631-642.

7. Branch, T.A., R. Hilborn, A.C. Haynie, G. Fay, L. Flynn, J. Griffiths, K.N.

Marshall, J.K. Randall, J.M. Scheuerell, E.J. Ward, and M. Young (2006), Fleet dynamics and fishermen behavior: lessons for fisheries managers, Canadian Journal of Fisheries and Aquatic Sciences, 63:1647-1668.

8. Brezger, A., T. Kneib, and S. Lang, (2008), BayesX Methodolgy Manual v.1.51,

http://www.stat.uni-muenchen.de/~bayesx. 9. Brezger, A. and S. Lang (2004), Bayesian P-splines. Journal of computational and

graphical statistics, 13(1):183-212. 10. California Department of Fish and Game (2000), California Commercial

Landings, http://www.dfg.ca.gov/marine/landings00.asp, last accessed September 7, 2007.

11. California Department of Fish and Game (2001), California Commercial


156

12. California Department of Fish and Game (2001), California’s Living Marine

Resources: A Status Report, http://www.dfg.ca.gov/mrd/status/index.html, last accessed May 11, 2007.



14. California Department of Fish and Game (2003), Annual Status of the Fisheries

Report Through 2003, http://www.dfg.ca.gov/mrd/status/index.html, last accessed May 11, 2007.







18. Comte, F. (1998), Discrete and continuous time co-integration. Journal of

Econometrics, 88(2):207-226. 19. Fahrmeir, L. and S. Lang (2001), Bayesian inference for generalized additive

mixed models based on Markov random field priors, Journal of the Royal Statistical Society, Series C, Applied statistics, 50:201-220.

20. Frid, C., O. Paramor, and C. Scott (2005), Ecosystem-based fisheries

management: progress in the NE Atlantic, Marine Policy, 29(5):461-469. 21. Gordon, H.S. (1954), The economic theory of a common-property resource: The

fishery, The Journal of Political Economy, 62(2):124-142. 22. Hanemann, W.M. (1984), Discrete/continuous models of consumer demand.

Econometrica, 52:541-561. 23. Hanna, S.S. (1999), Strengthening governance of ocean fishery resources,

Ecological Economics, 31(2):275-286.

157

24. Hausman, J.A. (1978), Specification tests in econometrics, Econometrica, 46(6):1251-1271.

25. Hilborn, R. (1985), Fleet dynamics and individual variations: why some people

catch more fish than others, Canadian Journal of Fisheries and Aquatic Sciences, 42:2-13.

26. Hilborn, R., and C.J. Walters (1992), Quantitative Fisheries Stock Assessment,

Chapman and Hall: New York. 27. Holland, D.S., and J.G. Sutinen (2000), Location Choice in New England Trawl

Fisheries: Old Habits Die Hard, Land Economics, 76(1):133-149. 28. Kalvass, P.E. and J. M. Hendrix (1997), The California red sea urchin,

Strongylocentrotus franciscanus, fishery: Catch, effort, and management trends, Marine Fisheries Review, 59(2):1-17.

29. Lynn, R.J. and J.J. Simpson (1987), The California current system: The seasonal

variability of its physical characteristics, Journal of Geophysical Research, 92(12):12,947-12,996.

30. Millischer, L. and D. Gascuela (2006), Information transfer, behavior of vessels

and fishing efficiency: an individual-based simulation approach, Aquatic Living Resources, 19:1–13.

31. Mistiaen, J.A. and I.E. Strand (2000), Location choice of commercial fishermen

with heterogeneous risk preferences, American Journal of Agriculture Economics, 82(5):1184-1190.

32. Morey, E.R., W.D. Shaw, R.D. Rowe (1991), A discrete-choice model of

recreational participation, site choice, and activity valuation when complete trip data are not available, Journal of Environmental Economics and Management, 20(2):181-201.

33. National Oceanic and Atmospheric Administration, National Data Buoy Center

daily weather statistics, station 46063 (Point Conception buoy), last accessed January 10, 2006.

34. National Research Council (1998), Improving fish stock assessments, National

Academy Press: Washington, D.C. 35. National Research Council (2000), Improving the collection, management, and

use of marine fisheries data, National Academy Press: Washington, D.C.

158

36. Otero, M.P., and D.A. Siegel (2004), Spatial and temporal characteristics of sediment plumes and phytoplankton blooms in the Santa Barbara Channel, Deep-Sea Research II, 51:1129-1149.

37. Parsons, L.S., H. Powles, M.J. Comfort (1998), Science in support of fishery

management: New approaches for sustainable fisheries, Ocean & Coastal Management, 39(1-2):151-166.

38. Pendyala, R.M. and C.R. Bhat (2004), An exploration of the relationship between

timing and duration of maintenance activities, Transportation, 31:429-456. 39. Robinson, M., C. Miller, C. Hoeflinger, and B. Walker (2005), Problems and

Recommendations for Using GIS to Improve Decision-Making in California’s Channel Islands Marine Reserves, MPA News, 7(5):4-5.

40. Robinson, M. and D. Siegel, Fishing fleet effort and catch distribution: Effects of

heterogeneity and consistency on stocks and yields (in prep). 41. Robinson, M. and K. Goulias, Determining causal structures in fishing fleet travel

behavior (in prep). 42. Sanchirico, J.N. and J.E. Wilen (1999), Bioeconomics of spatial exploitation in a

patchy environment, Journal of Environmental Economics and Management, 37:129-150.

43. Smith, V.L. (1969), On models of commercial fishing, Journal of Political

Economy, 77(2):181-198. 44. Smith, M.D. (2000), Spatial search and fishing location choice: methodological

challenges of empirical modeling, American Journal of Agricultural Economics, 82(5):1198-1206.

45. Smith, M.D. (2002), Two econometric approaches for predicting the spatial

behavior of renewable resource harvesters, Land Economics, 78(4):522-538. 46. Smith, M.D. and J.E. Wilen (2003), Economic impacts of marine reserves: the

importance of spatial behavior, Journal of Environmental Economics and Management, 46:183-206.

47. Smith, M.D. and J.E. Wilen (2005), Heterogeneous and correlated risk

preferences in commercial fishermen: The Perfect Storm dilemma, Journal of Risk and Uncertainty, 31(1):53-71.

159

48. Srinivasan, S., and C.R. Bhat (2006), A multiple discrete-continuous model for independent- and joint-discretionary-activity participation decisions, Transportation, 33(5):497-515.

49. Train, K.E. (1998), Recreation demand models with taste differences over people,

Land Economics, 74(2):230-239. 50. Ugoretz, J. (2002), Final 2002 environmental document, marine protected areas in

the National Oceanic and Atmospheric Administration’s Channel Islands National Marine Sanctuary, Santa Barbara: California Department of Fish and Game, Marine Region Field Office.

51. United States Department of Labor, Bureau of Labor Statistics, Consumer Price

Index data, http://www.bls.gov/cpi, last accessed September 7, 2007. 52. United States Department of Labor, Bureau of Labor Statistics, Local Area

Unemployment data, http://www.bls.gov/lau, last accessed September 11, 2007. 53. Vignaux, M. (1996), Analysis of vessel movements and strategies using

commercial catch and effort data from the New Zealand hoki fishery, Canadian Journal of Fisheries and Aquatic Sciences, 53:2126-2136.

54. Walters, C.J. and S.J.D. Martell (2004), Fisheries Ecology and Management,

Princeton University Press: Princeton. 55. Wilson, J.A. (1990), Fishing for knowledge, Land Economics, 66(1):12-29. 56. Worm, B., E.B. Barbier, N. Beaumont, J.E. Duffy, C. Folke, B.S. Halpern, J.B.C.

Jackson, H.K. Lotze, F. Micheli, S.R. Palumbi, E. Sala, K.A. Selkoe, J.J. Stachowicz, R. Watson (2006), Impacts of Biodiversity Loss on Ocean Ecosystem Services, Science, 314(5800):787–790.

160

Appendix 1. Red sea urchin fleet fixed effects model output.

varname pmode std ci95upper ci95lower ci80upper ci80lower pvalue const 255.876 5.835 267.315 244.437 263.355 248.397 0 Divers -55.113 0.837 -53.472 -56.755 -54.040 -56.186 5.51E-23 Hours -8.417 0.191 -8.043 -8.792 -8.173 -8.662 3.37E-20 WSPD -0.658 0.151 -0.361 -0.955 -0.464 -0.852 6.72E-05 WVHT -4.907 0.633 -3.665 -6.148 -4.095 -5.718 2.45E-08 ATMP -2.024 0.434 -1.175 -2.874 -1.469 -2.580 2.80E-05 WTMP 3.092 0.407 3.890 2.294 3.614 2.570 3.28E-08 Precip 0.427 2.582 5.488 -4.635 3.736 -2.883 0.869096 t 2.499 0.125 2.744 2.254 2.659 2.339 9.77E-15

university of california santa barbara the impacts of ...€¦ · and gendered access to fishing...

Documents