modeling the effects of dnr forest management … the effects of dnr forest management alternatives...

Modeling the Effects of DNR Forest Management Alternatives on Marbled Murrelets in Washington:

A Population Viability Analysis Approach

Zach Peery and Gavin JonesDepartment of Forest and Wildlife Ecology

University of Wisconsin-Madisoncc: Glenn Bartley

cc: Nicholas Hatch

There is no standard definition, most scientists favor a definition that explicitly requires a mathematical model.

We define PVA as an analysis that uses data in an analytical or simulation model to calculate the risk

of extinction or a closely related measure of population viability (Ralls et al. 2002)

What is a PVA?

Population Viability Analysis (PVA)

Why use a PVA? To help make management decisions about species of conservation concern.

Assessing extinction risk for a population or species Determine MVP needed to achieve the desired level of protection Informing population recovery - is the species still imperiled? Identifying key life stages or threats that should be managed (sensitivity

analyses) Evaluating risks or benefits associated with different management options

Common PVA Applications

cc: Bill and Dot Bell cc: Sheila Whitmore

Challenges to Strong Inference from PVAs Insufficient data to parameterize model Little information on species-environment relationships Little ability to test model-based predictions of risk

PVAs: Panacea or Wishful Thinking?

Panacea?Population Viability Analyses represent the flagship technology of the field of Conservation Biology.

Wishful Thinking?

Reality

Michael Soule (2002)

All models are wrong, but some are usefulBox and Draper (1987)

For example, Akakaya and Atwood (1997. Cons. Bio) ranked management options for California Gnatcatchers based on relative risk of extinction

Absolute projections of extinction risk are generally considered unreliable Assessment of risk should instead be made on a relative basis (e.g. among

management alternatives)

Management Option

Extin

ctio

n Ri

sk

Absolute vs Relative Risk

cc: PJ Rambler

Biology of Small Populations

Small populations are more likely to go extinct than large populations

Image Credit: http://www.randykinnick.com/wp-content/uploads/2011/09/Lost-Springs-Population.jpg

Richard Primack (2010) Essentials of Conservation Biology, 5th edition

Perc

enta

ge o

f Po

pula

tions

Per

sistin

g

Small Populations affected by both Deterministic and Stochastic Factors

Deterministic factors: factors that change population size in a relatively predictable manner such as habitat loss.

Stochastic factors: factors that result in less predictable changes in population size (e.g., genetics, random demography, weather, food supplies)

cc: Tom MacKenziehttp://www.fws.gov/caribbean/ES/Parrot-Gallery.html

Beissinger et al. 2009. Ecological Monographs.

Num

ber o

f Par

rots

Year

Deterministic and Stochastic Factors can Interact

0

2000

4000

6000

8000

10000

12000

0 10 20 30 40 50 60

Area of HabitatPopulation Abundance

PVAs can measure risk during the bottleneck (small population) phase

Area

or A

bund

ance

Years into Future

Expert Opinion Conservation Principles Rules of Thumb

Degree of ThreatLow High

High

Low

Unc

erta

inty

Uncertainty, Threat, and Assessment Method

Habitat Mapping

PVAMarbled Murrelets

cc: Glenn Bartley

Simplest PVAs are based only on numbers of individuals in a population

Need estimates of historic population growth rates, current population size, and effects of stochasticity

Use mathematical model (equations) to project simulated populations in forward in time

0

10

20

30

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1

Nt

Year, t

N observed in pastPredicted future N

Time

Present

Population Viability AnalysisPV-what?Po

pula

tion

Size

(N)

Extinction probability:proportion of simulated populations where N becomes zero

Expected change in population size: average difference in beginning and ending N

Risk Metrics

Chart2

2022

18.4625.4865.486

152.4262.426

172.7332.733

1433

1044

1533

1155

1622

1355

Year, t

Nt

data

Survey TypeLand TypeYearCountSE

RadarMMCAs-1020.0002.000




-614.0003.000

-510.0004.000

-415.0003.000

-311.0005.000

-216.0002.000

-113.0005.000

0113

110.452

29.92753

39.4311253

48.959568752

58.51159031251

68.08601079692

77.6817102573

87.29762474422

96.9327435073

106.58610633162

data

22

5.4865.486

2.4262.426

2.7332.733

33

44

33

55

22

55

Year, t

Nt

MMCAs

Reserves

AV Count (SE)

1

1

1

1

1

1

1

Incorporating Environmental Effects with Model Parameters

How Do We Estimate Model Parameters?

Preferably with data!Change in fox abundance = x + x

Effect of rain on foxes

Coyote abundance

Effect of coyotes on foxes

Amount of rainfall

0100200300400500600700800

50 75 100 125 150 175 200

Coyote Abundance

Fox

Abun

danc

e

Cc: Peterson Moose

Modified from: Dennis et al. (2000) Journal of Wildlife Management

Wikipedia Commons

Applying PVA methods to Marbled Murreletsand Forest Management in Washington

Developed a demographic, metapopulation PVA model that projects murreletpopulations forward in time

Estimates risk under various forest management alternatives.

Basic model structure developed previously for a corvid-based murrelet PVA in California

A Meta-population Model

A metapopulation consists of a group of spatially separated subpopulations linked by the dispersal of individuals.

Dispersal of individuals influences local population dynamics

The murrelet PVA model assumes two simplified populations (DNR and non-DNR)

DNRsubpopulation

Individual dispersal

non-DNRsubpopulation

A Demographic PVA Model The model projects the WA murrelet population forward in time based on

demographic rates - reproductive and survival rates - within subpopulations

100Juveniles

100Adults

Adult survival rate = 0.90(90 adults survive and remain adults)

Year 2

Year 1

140Adults

Reproductive rate = 0.5 (50 juvs produced by

100 adults)

50Juveniles

140Adults

Year 1 Population = 200

Year 2 Population = 210

Quiz: how do we incorporate stochasticity?

Estimating Survival Rates

Non-juvenile survival rates estimated based on a mark-recapture study of murreletsin California (Peery et al. 2006)

Estimates ranged from ca. 0.87 to 0.90

Juvenile survival rates assumed to be 70% of non-juvenile survival rates (Beissinger 2005)

Half Moon Bay

Santa CruzAno

Nuevo Baycc: Zach Peery

cc: Zach Peery

cc: Zach Peery

Reproductive Rule Sets:Linking the Analytical and PVA Models

Habitat quality (6 Pstages) affects max nesting density - Platform density, canopy layers, stand origin, forest type

Increasing Max Nesting Density

Pstage: 0 0.25 0.36 0.47 0.62 and 0.89 1

For example, max nesting density in pstage 1 is 4x greater than in pstage 0.25

Max Nest Density

Soft: 0.60 x DHard: 0.25 x D

Edge conditions affect max nesting density


OE

IE

IF

Soft: 0.80 x DHard: 0.625 x D

D

Interior Forest

Area of Nesting Habitat

Nes

ting

Carr

ying

Cap

acity

Slope = 1

Nesting carrying capacity = nesting habitat area x max nesting density (Pstage and edge)

Area = AArea = A


Pstage = 0.47

Area = A

Carrying capacity = K

Nesting habitat area affects nesting carrying capacity in a 1 to 1 manner

Carrying capacity = 2 x K

Pstage = 0.47

Area = 2 x A

A

K

2A

2K

Relationship between MurreletsNumbers and Nesting Habitat

Raphael (2006). Conservation Biology.

Mea

n Co

unt

Amount of Habitat

Raphael et al. (2002). Condor.

Nesting Habitat (km2)

At se

a Po

pula

tion

Size

Nest Success

0.380

Edge conditions also affect nest success


OE

IE

IF

0.465

0.550

Interior Forest

Habitat Conditions and the Landscape Scale Current habitat conditions were aggregated across each

landownership to determine:- Nesting carrying capacity - Nest success

Habitat conditions on DNR lands projected forward in time (50 years) using the Forest Vegetation Simulator

Assumed no change in habitat on non-DNR lands

cc: M. Hobson

ModelInputs Outputs

Initial Abundance(by stage class)

Carrying Capacity (number of nests)

DNR Lands

Non-DNR Lands

dispersal

Demography(survival, breeding,

dispersal)

RelativeExtinction Risk

RelativeChange in

Population Size

Nest Success

Habitat Quantity and Quality

(Pstage)

Edge Conditions

Habitat Population

Environmental Variability

A Conceptual Representation of the PVA Model

Habitat Quantity (acreage)

Demographic Submodel

New breeders do not preferentially select high-quality habitat

Breeders stay in the same landownership unless they are displaced by habitat loss

Displaced breeders become nonbreeders for at least one year

Displaced breeders become breeders again if nesting habitat becomes available

Some Additional Model Rules and Assumptions

cc: Tom Johnson

Matching the Model to RealityThe Reality

At-sea monitoring indicates ~5% annual declines in WA from 2001 to 2014

0

3000

6000

9000

12000

15000

2000 2005 2010 2015 2020 2025 2030

Mur

rele

tPop

ulat

ion

Size

in W

ashi

ngto

n

The Problem

Using values for survival and reproductive rates that yielded 5% declines resulted in little ability for recruits to fill into potential new nesting habitat

Future HabitatModeled Future Population

The Solution

Conduct parallel Population Risk and Enhancement analyses with different capacities for murrelets to fill into new nesting habitat

Falxa et al. (2014), Lance and Pearson (2015)

Risk vs Enhancement AnalysesEnhancement analysis: How do the alternatives differ in ability to

enhance regional (WA) and local (DNR) murrelet populations?

Assumes nesting habitat loss is the primary factor cause of murrelet population declines

Uses a relatively optimistic values for adult survival (0.90)

Greater capacity for new recruits to fill into new nesting habitat

Assumes number of breeders > nesting carrying capacity

Risk analysis:

How do the alternatives differ in their effects on risk to regional (WA) and local (DNR) murrelet populations?

Assumes both nesting habitat loss andchronic environmental stressors caused murrelet population declines

Uses a relatively pessimistic values for adult survival (0.87)

Less capacity for new recruits to fill into new nesting habitat

Assumes number of breeders > nesting carrying capacity

Annu

al p

opul

atio

n gr

owth

rate

0.9

0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

1.02

0 10 20 30 40 50

Risk Analysis

Stable

Enhancement Analysis

0.9

0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

1.02

0 10 20 30 40 50

Years into future

Stable

Deterministic Expectations under the Risk and Enhancement Analyses

Years into futureAn

nual

pop

ulat

ion

grow

th ra

te

Incorporating Stochasticity

0

2000

4000

6000

8000

10000

12000

14000

16000

2000 2002 2004 2006 2008 2010 2012 2014

Estimated amount of annual variation in population size from at-sea monitoring

Used this variation to determine how much survival and reproductive rates should vary from year to year

Initial population size = avg from

2010-2014

Mur

rele

tPop

ulat

ion

Size

in W

ashi

ngto

n

Falxa et al. (2014), Lance and Pearson (2015)

or = 0(Regional) (DNR)

Parameter Risk EnhancementAnnual non-juvenile survival rate 0.87 0.90Annual juvenile survival 0.70 x non-juvenileAnnual dispersal rate

DNR non-DNR = 0.91non-DNR DNR = 0.09

WA:DNR non-DNR = 0.91non-DNR DNR = 0.09

DNR: 0Initial female population size DNR: 311

non-DNR: 3,129

Initial nesting carrying capacity 40% > Initial number of females of breeding age

Variance in reproductive rates 0.012Variance in survival rates 0.003

Parameters Used (non-reproductive)

0.500

0.505

0.510

0.515

0.520

0.525

0.530

0 5 10 15 20 25 30 35 40 45 50 -

50

100

150

200

250

300

350

400

0 5 10 15 20 25 30 35 40 45 50

Management Alternatives Considered(DNR-managed Lands)

Num

ber o

f Nes

ts

Nesting Carrying Capacity

Nes

t Suc

cess

Rat

e

Nest Success

Years into the Future

Risk Analysis State of Washington

Risk Analysis DNR Lands Only

Enhancement Analysis State of Washington

Enhancement Analysis DNR Lands Only (no dispersal)

Proposed Sensitivity Analyses

What are the most important habitat conditions?

- Habitat amount- Habitat quality- Edge-interior configuration

How sensitive are result to model uncertainties?

What are the consequences of less nesting habitat development than predicted?

(for illustration purpose only, analyses in progress)

Incr

ease

in R

isk

Preliminary Thoughts on PVA Results

Differences in state-level risk were small among the four alternatives considered

While differences were small, Alternative E reduced state-level risk the most

The ability to contribute to state-level enhancement was very similar among alternatives

Alternative E led to somewhat more murreletson DNR lands than other alternatives under the enhancement analyses

Greater risk was estimated for the no change scenario than for other alternatives under some assumptions and at some scales

Next Steps

Model Alternatives C and D Formalize and conduct

sensitivity analyses Write report and

manuscript for publication Peer review both

documents

Modeling the Effects of DNR Forest Management Alternatives on Marbled Murrelets in Washington: A Population Viability Analysis ApproachSlide Number 2Slide Number 3Slide Number 4Slide Number 5Biology of Small PopulationsSlide Number 7Deterministic and Stochastic Factors can InteractSlide Number 9Population Viability AnalysisIncorporating Environmental Effects with Model ParametersApplying PVA methods to Marbled Murrelets and Forest Management in WashingtonA Meta-population ModelA Demographic PVA ModelEstimating Survival RatesReproductive Rule Sets:Linking the Analytical and PVA ModelsReproductive Rule Sets:Linking the Analytical and PVA ModelsReproductive Rule Sets:Linking the Analytical and PVA ModelsSlide Number 19Reproductive Rule Sets:Linking the Analytical and PVA ModelsHabitat Conditions and the Landscape ScaleSlide Number 22Slide Number 23Some Additional Model Rules and AssumptionsMatching the Model to RealityRisk vs Enhancement AnalysesSlide Number 27Incorporating StochasticityParameters Used (non-reproductive)Management Alternatives Considered(DNR-managed Lands)Slide Number 31Slide Number 32Slide Number 33Slide Number 34Proposed Sensitivity AnalysesPreliminary Thoughts on PVA ResultsNext Steps

modeling the effects of dnr forest management … the effects of dnr forest management alternatives...

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