unintended biological invasions: does risk vary by trading partner? c. costello, m. springborn, c....

Unintended biological invasions: does risk vary by trading partner?

C. Costello, M. Springborn, C. McAusland, & A. Solow

JEEM 2007

The non-indigenous species problemPecuniary damage• ~50,000 non-indigenous species

introduced to the U.S. (Pimentel, 2005)

• Estimation of yearly U.S. monetary losses:– $4.7 – 6.5 billion (Office of

Technology Assessment, 1993)– $120 billion (Pimentel, 2005)

Ecological damage• ~400 of the 958 species on the

Endangered Species list are there primarily because of NIS (Wilcove et al. 1998)

• SF Bay: no shallow water habitat remains uninvaded

Asian Tiger Mosquito – introduced via tire shipments

The Atlantic Green Crab, first seen in S.F. Bay in 1989, preys on juvenile Dungeness Crab.

Invasive Species - Rules of Thumb

• Disturbed land is more susceptible to invasion (e.g. agricultural use versus primeval forest)

• Trade in goods and services provides platform for unintentional introductions of non-native species (esp. agricultural imports, shipping and packing materials, ballast water, tourism)

• Successful introductions are facilitated by bio-geographic similarities between host and source region

Invasive Species - Rules of Thumb continued.

• Likelihood that an “arrival” will become established is increasing in the number of times the species is “exposed” to host region

• “tens rule”: 10% of introduced species become casual , 10% of these become established (10% of these become a pest)

• A Source’s potential pool of exotics is finite---sampling without replacement

• Newly arrived exotics aren’t usually discovered for quite some time (chance, damages high, systematic species survey)

Unintentional introductions of NIS

• Market failure stemming from foreign trade– Ballast water, Packing materials, Ship hulls,

Hitchhiking with traded goods

• Policy response depends on whether:– Most NIS already here (reactive policies)– Lots of new NIS likely to arrive (proactive

policies)

Blunt Policy Response

• Jenkins (1996)– “broad tools such as bans or restrictions on

imports may be necessary to protect biodiversity”

Advisable?

Costello, Christopher and Carol McAusland. 2003. "

Protectionism, Trade, and Measures of Damage from Exotic Species Introductions

" American Journal of Agricultural Economics, 85(4) 2003: 964-975.

• Build a dynamic general equilibrium model with stochastic introductions and damages– Arrivals increasing in volume of imports– Exogenous probability that a new arrival will

become established– Damage from a newly established exotic is

also a random variable

• Show that import tariffs have two effects on damages for invasives– Tariffs reduce volume of imports

• Shrinks the platform for new arrivals

– Tariffs cause protected sector to expand• If country is a net importer of agricultural goods, then

tariffs cause agriculture to expand– More agricultural activity

» -> more disturbed land on which invasive species can get a foothold

» -> more crops that can be damaged

• Possibility: tariffs on agricultural goods can lead to higher estimates of agricultural damage

Shortcomings

• Ignores attenuation

• Treats all trade partners as identical

• No data– Referee: “prove this has happened even

once”

Example - Sugar

• US support for sugar generates US price = 2 x ROW price – is similar to a 100% tariff except no beneficial

tariff rent

• Since 1934 harvested acreage for all crops in US fell by .1%/annum

• over same period, land under sugarcane production grew at average annual rate of 1.6%

Mexican rice borer

• currently infests 20% of Texan sugarcane

• believed to have come in on imported goods – detected on sugarcane, lemon

grass, sorghum and broomcorn imports

• Texas damages estimated at $10 -$20 million (/yr?) while harvest valued only at $64 million

Source: Texas A&M Uhttp://insects.tamu.edu/images/insects/color/sorghum/sor069.jpg

• Use data from San Francisco Bay to address role of attenuation and biogeographic similarity of partners

• Asks: How risky is future trade?

Costello, Sprigborn, McAusland and Solow (2007)

Complications

1. Introduction rates may differ by region based on biogeographic differences

– Region-specific information could be used to tailor policy

2. Attenuation: how might the introduction rate change over time and by partner?

• Each region has a finite number of species to “contribute” to host region

• Once an NIS is established, future introductions pose no risk

• The more trade we’ve had with a particular partner, the lower the marginal invasion risk.

Complications cont.

3. Discovery lag– discoveries reflect both an introduction process and

a discovery process.– Delay may be influenced by observational effort,

species population growth rate, level of damage, etc.

– Solow and Costello (2004) show that even if introduction rate is constant and detection effort is constant, it will appear as if species are being introduced at an increasing rate if don’t account for discovery lag

Model features

• We would like to estimate the region-specific invasion risk by trade partner

• Must account for:– “Baseline invasion risk” (differs by region)– “Attenuation rate” as function of trade volume (differs

by region)– “Discovery lag” (same for all regions)

• We will require data on:– Trade volume over time (by region of origin)– Species discovery dates and region of origin

Literature treatment of dynamics

Previous literature focused on estimating invasion risk

(A) (B) (C) (D)Link NIS to

tradeAttenuation Discovery

lagRegion-specific

Dalmazzone (2000)

X

Ruiz et al. (2000) XLevine & D’Antonio (2003)

X X

Drake & Lodge (2004)

X

Solow & Costello (2004)

X X

Literature treatment of dynamics

Previous literature focused on estimating invasion risk

(A) (B) (C) (D)Link NIS to

tradeAttenuation Discovery

lagRegion-specific

Dalmazzone (2000)

X

Ruiz et al. (2000) XLevine & D’Antonio (2003)

X X

Drake & Lodge (2004)

X

Solow & Costello (2004)

X X

Model: introductions

Cum. shipping, St

λt

λt attenuates at rate γ

Let Nit measure (unobserved) introductions from region j in year t. Assume Njt has Poisson distribution with mean (rate per unit of imports)

(1)

βj is region specific “intrinsic infectiousness” of imports to importersjt=import volume (measured in short tons) from region j in year tSjt=cumulative import volume from region j through year tγj measures rate at which introductions attenuate (if γ<0) with cumulative import volumeω reflects time trend (e.g. if shipping speeds improve over time, may see more hitchhikers surviving passage)

Discoveries

• Yjt defined as number of NIS from source j discovered in year t.

• Define put as probability that an NIS introduced in year u is discovered in year t

• Yit will have Poisson distribution with mean

djt=∑tu=0γjuput

(2)

Problem: we don’t have data on the discovery process

1)1( utu tp

Assume post-introduction waiting time to discovery is geometrically distributed:

where π = probability that a species is observed (not necessarily for the first time) in any given year

Assumptions

• Intrinsic infectiousness (β) and attenuation (γ) are region specific

• Time trend (ω) and observation rate (π) are universal

Combining introductions & discoveries

• Expected number of NIS discovered (for the first time) in year t:

• This is “thinning” of a Poisson process• Therefore, number of discoveries in year t is a non-

homogeneous Poisson random variable with mean (rate) dt.

t ttttt pppd 2211

Arrived at time 1, notdiscovered until time t

Arrived at time 2, notdiscovered until time t

Arrived at time tDiscovered immediately

Estimator

• Our data (by trade region) are– NIS discoveries from that region over time– Shipping volume over time

• Given discoveries distributed Poisson with rate dt, the likelihood of observed discovery record is

• To estimate, choose β, γ, ω to maximize integrated likelihood

• Then estimate “nuisance parameter” π by maximizing

Empirical application to SF BayNIS discovery data (1853-1994): Cohen and Carlton (1995). “Nonindigenous aquatic species in a United States estuary: A case study of the biological invasions of the San Francisco bay and delta”

Import data (1856-2000):• 1856-1945: Foreign

Commerce and Navigation of the United States

• 1946-1967: Foreign Trade through the San Francisco Customs District

• 1968-1989: Annual Import Data Bank Files (tape)

• 1990-2000: U.S. Imports of Merchandise (CD-ROM)

U.S. Department of Commerce, Bureau of the Census records

NIS and trade into San Francisco

Region NIS Discoveries to 1994

Imports (million tons) to 1994

Atlantic/Mediterranean (ATM) 74 62

West Pacific (WPC) 43 202

Indian Ocean (ION) 3 74

SE Pacific (SEP) 1 10

SE Atlantic (SEA) 1 2

NE Pacific (NEP) 0 77

SW Atlantic (SWA) 0 5

Unknown 32 2

NIS and trade into San Francisco

Region NIS Discoveries to 1994

Imports (million tons) to 1994

Atlantic/Mediterranean (ATM) 74 62

West Pacific (WPC) 43 202

Indian Ocean (ION) 3 74

SE Pacific (SEP) 1 10

SE Atlantic (SEA) 1 2

NE Pacific (NEP) 0 77

SW Atlantic (SWA) 0 5

Unknown 32 2

Restrict attention to regions with more than one NIS introduction between 1856 and 1994

Results and Predictions

Using the likelihood ratio test, we reject the hypotheses that

• βATM = βWPC

•γATM = γWPC = γION

Fail to reject that βION is equal to either βATM or βWPC

Subsequently we restrict attenuation in WPC infection rate (γWPC=0).

Maximum likelihood estimates for three jointly estimated regions (90% Confidence Intervals via Parametric Bootstrap)

Trade Region ω

ATM 2.3

(1.3, 4.0)

-0.08

(-0.15, -0.04)

0.015

(0.001,0.03)

WPC 0.07

(0.02, 0.21)

-0.002

(-0.01, 0.006)

ION 1.3

(0.1, 7.5)

-1.06

(-3.45, -0.18)

Observations

• ω=0.15 implies introductions increase 1.5% per year (other things equal)

• Implied π=0.048 <-> discovery lag of 13 years.

• Attenuation– Even though ATM has higher “intrinsic”

infectiousness, attenuation is quick (about 8% per million short tons of trade)

– WPC has lower β but reject attenuation

– ION attenuation almost instantaneous (106% per million short tons)

Calculating Marginal Invasion Risk (MIR)

• MIRjt=βjexp(γjSjt+ωt)

Calculated MIR as of 1994

Alternative hypotheses

• Attenuation is a result of global, not regional, trade

• Test: re-estimate model using

where St=∑jSjt is global trade.

Results: fitted betas, gammas and omegas nearly identical, while fitted n is small (0.0011) and statistically insignificant (p= 0.62)

(6)

Model fit (ATM region)

Discovery data

Fitted introductions

Marginal Invasion Risk = 0.11Estimated Number of Undiscovered Species

Fitted discoveries

Cumulative Import Volume (x106 mt)

“Undiscovered” species

• Our model includes a lag between species introduction and discovery

• At any point in time, we can estimate the number of undiscovered species– Equals # introduced species - # discovered

Trade Region # Discovered

(data)

Estimated # Introductions

Estimated # Undiscovered

ATM 74 80.6 6.6

WPC 43 60.0 17.0

ION 3 3.1 .1

Forecasting trade

• Forecasts of future imports into San Francisco Customs District are taken from Haveman and Hummels (2004).

• Their forecasted values are drawn from GTAP, the Global Trade Analysis Project using the Walmsley et al. (2000) extension of the basic GTAP model

cumulativetrade to 2000

AT

MW

PC

ION

cum. disc.

fitted disc.

fitted intro.

Predictions of future introductions and discoveries using forecasted trade volumes

Estimated new introductions

Predicted number of new NIS

So should we restrict trade?

Thought experiment

• Suppose the US used trade restrictions to reduce by one the expected number of NIS in 2020 originating from each region.– By how much would the US have to reduce

imports?– What are the costs of these trade restrictions?– How do they measure against the benefits of

avoiding one NIS?

Aside

• Do Costello, Springborn, Solow and I really want to imply that trade volumes should be curtailed?

• No. But the thought experiment puts the damages from NIS into an economic context — lets the reader judge how big a problem trade-facilitated introductions are.

• Standard formula for DWL from trade restriction Feenstra (2004, p.217)

• DWLt ≈ -½ Mt2PtMt/ε where

– DWLt is deadweight loss from year t trade restrictions,

– M is percentage reduction in imports, – ε is elasticity of import demand

• ε = -1.23 (Hooper and Marquez 1995)

– PM is value of imports.• Use actual M for 1995-2000 and forecasted M for 2001-2020

• Calculate Pi so PM2002,forecasted exactly equals PM2002,actual

– PATM=$1769

– PWPC=$3399

^

^

• In order to reduce by one number of expected NIS from ATM by 2020 need to reduce imports from ATM by 90%!

• …from WPC…by 2%

• Using 5% discount rate, total discounted DWL from restrictions on imports from– ATM = $9,520 million– WPC = $44 million

Benefits (B) from reducing trade

Bt=D∑ts=1995[λu

s- λrs] where

– D=annual damage from an average NIS

– λus=mean introductions in year s when trade is

unrestricted

– λrs=…restricted

In order for costs and benefits of trade restrictions to balance…

• would need annual damages from (prevented) NIS to be about

• $1,063 million/year (ATM)

• $8 million/year (WPC)

Damages from an “average” NIS

• Pimental et al (2005)– 50,000 NIS present in US– Annual damage from NIS = $120 billion

→ $2.4 million = Crude estimate of average annual damage

What if we knew we were avoiding one of the worst NIS?

Teredo Navalis (Atlantic Shipworm)

• $205 million/year (structural damage)

Zebra Mussel

• $700 million/year (clogs intake valves, alters filtration)

Asian Clams

• $1billion/year– “These small freshwater mussels can be

drawn into power plants along with coolant water and clog tubes and pipes, resulting in economic costs (Fuller & Benson, 2003).

– “The asian clam will also compete with native clams and mussels for habitat and food, and change benthic substrates (USGS, 2001).”

• http://www.biology.duke.edu/bio217/2005/cjc6/Asianclam.html

Conclusions

• Key policy variable is marginal invasion risk– Possible difference across trade partners– Theoretical reasons to expect attenuation– We find both (to some extent)

• Riskiest partners likely to be new partners – not those who have delivered most species in past– Expect: 1.4 (ATM), 52.4 (WPC), and 0 (ION) by 2020

• Crudely restricting trade with either WTC or ATM not advisable

Caveats

• “unknown region”

• discovery effort

• Stepping stones– How do ATM species get to California?– trade between California and ATM? – Or trade between ATM and US-Atlantic region

paired with trade along US coasts?