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Risk Factors in Extinction and Overexploitation '

Risk Factors in Extinction and Overexploitation '

1 ; lCES CM 19971P:1O Diadr:imous : 1fueats on LOCal and Global Scales

. .~. ~ , , , FACTORS IN EXTINCTION AND

RUSSELL LANDE

(l) ofpopulation extinctiori from a variet)r ofdetenninistic and stochastie ~ ...... factors are eategonzed. Anthropogenie factors generany eonstitute the primary eauses ofendangennent and extinction..The prim:iry anthropogenie factors have :rn.muying • ecological änd genetie effects. All factors 3.ffecting extinction risk are expressed. änd ean be evaluated. through their operation on dynamies. (2) Results ofa quantitative population,viability analysis ofspring ehinook saltnori in the South Umpqua , Oiegon, indicate that linder ewrent conditions tlle wild population has a high probability ofperSistence for 200 years. However, ifhabitat ' degradation contimies at historical rates, tlle population will altnost'certainly become extinet withm 100 years. (3) Threshold harvesting strategies for sustamable eXploitation offluctuating are diseussed in comparisori with commonly . used flshenes practices. Based on recent analytieaI results itis coricluded thllt the thooretieal justifleation for constant rate strategies is extremely we3.k in eomparison to thai for optinial threshold strategies.

Ke)rwords: extinetion, overexploitation, population viability analysis; stochasticity, thn~shold harvesting, uneertainty

RussezlLtiluie:Dept. ofBiology. University ofOregon,'Eugene OR 97403-1210 USA L- • [tel: +1'. 54i 346 2697.fax: +1 541'.3462364, email: [email protected]öregon.edul.

• , • j',. • ,~. "- ANTHROPOGENIe, ECOLOGlCAL AND GENETIC FACfORS IN EXTINCfION RISK

A varieiy ofdetenninistie and stochasrle factorS contlibute to extinction risk. The folloWing list orders risk faetors by descending general iniponance aInong and within categories, although the fankirig offactors differs 'among speeies arid should be evaluated on a ease-by-ease basis. AnthropÜgeme factors generally eonstitute theijrirriary eauses ofendangerment and extinction. The Priiriäi:Y anthrOpogenie factors have raInifying ecolpgieal and genetic effects. All factors , affecting extirierlon risk are expressid. and ean be evaJuated. through their operation on population dynamies. With sufflcient data on aparticular popUlation or . a quantitative PopuIätion Viability Analysis (PVA) ean be peiforined. inc1uding factors affecting the dynamics of small populations. A PVA estimates the probablliiy ofpopuhition extiriction orcollapse.WithiIl ä specified ume (Sh3.ffer 1981, Gilpin an~ Soul6 1986). For species with insufflcient daci to ..------

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perfonn a quantitative PVA, objective population-baSed criteiia can be employed to qualltatively categorize extinetion risk (Maee and Lande 1991, IUCN 1994). Foradditional details and referenees see Lande (1998). . ANTIIROPOGENIC FACfORS development: habitat destruetion & fragmentation Overexploitation Unregulated Eeonomie diseounting Species translocations & introduetions . . ',"' ECOLOGlCAL FACfORS Environmental fluetuations & eatastrophes dynamies Oocal extinction & eolonization) Small Demographie stochasticity GENETIC FACfORS J Hybridization with nonadapted pools Seleetive breeding and harvesting : " depression . Loss ofgenetie variability Fixation ofriew

~ 1. >, POPULATION yrIABILITY ANALYSIS OF CHINOOK IN THE SOUTH UMPQUA RIVER, OREGON A comprehensive qualitative assessment ofPaeific sahIlon by Nehlsen et al. (i991)"showed. • that about one-quarter oftlie histOIical salmon runs are now extinet, an equal number are at high risk ofextinetion, and most ofthe rest are in serious decline. Totest the validitY ofthe qualitative assessment methods used by Nehlsen et al (1991) and proposed by Allendorfet al. (1997), we . performoo a quantitative PVA on a particular run for whieh relatively extensive popUlation data were available. Run sizes ofspring chinook salrrion in the S01ith Umpqua Riverin Oregon have deeliried dramatieally sinee the early part ofthis eeritury. Habitat degradation is though to be an important factor in eontributing tothe deeline ofthis stock, arid qualit3.tive assessment suggests thai the stock is at moderate risk of extinction~ Data from this and similar stocks were used to develop an age-struetured, density-dependerii mOdel ofthe population dymimies thiu incoi-porates both demographie and envirOnrriental stochasneity. Under the assumption ofrio fuIther habitat degrad3tion, the population is prewctoo 10 have a greater than 95% probabilityofpersistence far 200 years. However, sensitiVity analysis for the derisHy-dependenee estimated from. historieal run-retUrn data shows that substantially lower predicted viabilities are also statistieally consistent with the data..A model that simulates eontinued habitat degradation results in almost certain 3 J exiiricrlon witlun 100 years. This firiding supports the conclusions oftheeariier qualitative, , assessment (Nehlson et al. 1991) thai this stock is at niOderilte risk ofextinction. For additional details see Rattler et al. (1997).

" , 1HRESHOLD HARVESTING FOR OF FLUcruATING

Although temPoral varlabÜitY is a ubiqwtous feature ofrenewable dynam1cs, it has Teceived serious attention in harvesting theory onlydUrlng the past two decades. Coefficients of variation in änrimil abimdance for unexploited populations typiCaily are in the range of 20% to 80% ormore (Pimm 1991). Exploited.populations also are highly variable (Myers et al. ' 1995), not onlydue to natural environmeriiaI stochasticity, but because exploicition usually reduces popUlation stability (Beddington and May i977). Ifharirested populations beCome small, they aTe • vulrierable to demographie stochasticity arising from chanee eventS ofindividual. niortality and reproduetion (Lande 1993). Environmental and demographie stOchastieity together guarantee that extirietion is the eventUal fate ofall species (reg3.idless of whether they are harvested), and evidenee from indieates thai the vast majority ofspecies that ever existed are now extinet. Unfortunateiy, exploitation cari aceelerate this seemingly ineVitable march to extinction. in pärticular, modem arid rapidly iricreasing reoource demandS from the growing hunian population, coupled with madequaie management and regulation, have causect rnany eonmiercially importlnt species to be overexploited to the Pointofdepletion; and occasionally 10 extinction (Ludwig et al. 1993, Rosenberg et al. 1993, Groom1Jridge 1992). AbOut half ofthe ,commercial in the Uniied States and were receritly dassified,as overexploited (Rosenberg et al. 1993)., Teiresirial habitat alteratioris, primarily by deaiing and agrlculture, have ., , threaiened a large proportion ofveriebraie species in develojx;d eountiies; approxim3.tely one-third ofthe endangered and one-half of the endangered m3.mmals ofthe wofld are threatened with extinetion by hwiiing and international trade (Grocimbridge 1992, Redford 1992).. . ' Despiie these observations, imtil receritly, little effort has beeil made tri understand how , • stoehastie Population dynamies affeets the risk:. ofresource collapse or extinction (Getz and Haight 1989). Laride et al' (1997) review recent developments toward a unified 'theory ofoptiriial härvesnng that explicitly iricorpcirates the risk ofdepletion or extinction offluctuating resources. Tbey focus ori analytical results pertaining io biologiCal criteria oflong-term sustainability, eombming approaches from conservation arid ti-aditiorial harvesiing theory. Theyshow .that for alarge dass ofpopulation dynamics arid arange ofbiological optirillzation ciiteria the optimal strategies aIways irivolve a threshold population size above whieh an excess individuals are haivesied and below whieh there is no harvesting. Insofar as possible, threshold harvesting strategies simultaneously increase expeeted yields from harvestirig arid reduee risk ofresource depletion or extinction. Tbe position of the threshold in this management approach detennines the level ofrisk. ,Tbe major drawback of threshold strategies is that they eniail ahigh vananee in . annual yield beeause, offrequerit years ofno harvest when the population is below the t:hieshold. Lande et aI. (1997) elucidate generaIized thTeshold strategieS that mitigate this drawback by , reducing the variance in anilUa! yield th.rOugh harVesting stnitegies that aCcOurit for limited haiVesting 'capacity ärid ~nceItaintY in populatiori estiniates. 4

Biological resources often are exploited using a constant effort or constant harvest rate strategy combined With a t.h.feshold or "escapement" level beIow which harvesting ceases. However. thresholds frequently are set far too low to sustain the resources. with harvesiing stopped omy arter severe depletion, as evidenced bythe large fuicition oföverexploited resoirrces on which harvesting coritinues, and numerous others that havecollapsed (Ludwig et al. 1993, Rosenberg et al. 1993, Hutchirigs arid Myers 1994). Lande et al. (1997) argue that the thecireticaljusiificarlon for conscini harvest raie strategies is extremely weaIc in comparison to thai for optiIrial threshold strategies. Opilirial threshold strategies have the importarii propenythat. insofar as possible. they , simultaneouslyreduce the nskofdepletion or extiriction äIld increase the rriean anmialannual yield. Using'a general diffusion model ofstochastic population dynarDics, Lande et al. (1995) established tWo optimal propemes oftbreshold strategies. (1) At any sustainable harvesting level, the optimal threshold strategy produces a ofdepletion orextiriction than any other strategy. This is becausethreshold harvestiilg iends to mamtairi a resource at rela.tively high levels by allowing itto recover at the maximum mitural rate without harvesiing when its population is belm,y the thfeshold. (2) At any level ofrisk ofdepletion or extinctiori, the optiIIial threshold strategy produces a higher mean annual yield than any other strategy. The appropriate threshold iri a parncular situation depends on the optiinization criterion, which may range [rom aggressive exploitation to extreme conseivation. . For the conservative crlterion cf maximizirig the expected cuniulative yield before extinciion, regardless ofthe fonn of pOpulation dynamies ordemogTaphic arid environmental stochasticity, the optimal threshold is at the stable equilibrium ofthe expected d)rnarillcs. orcaiTying capacity, K (Lande et al. 1994, 1995, Whittle and Horwooo 1995). This strategy is remarkable foi"its generality arid siinplicitY, and is appealing because harvesting only above carryirig capacity proouces ooth a small risk of extinction and a relatively low impa.ct on the and of a population and its ., " The triiditional criterion ofmaximizing the meäIl arinual yield has an opiliIlaJ. threshold below . K, at a value dependirig on the form ofdensity-dependence arid the magnitude ofstochastic variability. For species with density-dependence ai iriiennediate population sizes, as iri the iogistic model; increasing envirOninental stochasticitY inereases the optimal threshold. For species that experience density-deperidence only very cIose io , the optiIrial threshold starts elose to Kbut decreases with increasing environmental stochasticity (Srether ei al. 1996). The major drawbäck ofoptimal th!eshold harvesting stcitegies is thai they eriiail a high vanance in arinual yield because offrequent years ofrio harvest when the population is below the threshold; this could be detriinental to industries specializect on harvesting a single resource: Threshold strategies also restrict the range ofpopulation fluctuations in comparison to other strategies, . rilaking it more difficult to obtain accurate statistical estimates ofpopulation dyniunic parameters necessary for the developmerit ofoptimal harvestrng strategies (Getz and Haight 1989). Hiloom and Walters (1992 pp. 291-292,454-458,469-470) therefore criticize optimal threshold strategies and prefer constani hiuvest rate strategies. . . . Two features ofthe generalized threshold strategies largely answer the a1x>ve criticisms of threshold strategies (Laride et al. 1997). FirS~ urider ariy optiinizatiori criterion, limited haivesting capacity deex-eases the optimal threshold. Secorid. with high uncertiürit)r in population estimates and low stOchasticity a striltegy superior to pure thi"eshold harvesting is proponional thresnold . Juirvesting, which involves a decreasect thieshold and harvesting only a fracti()r1 of the excess of ------~~---~-~--_._-----~-~-

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tlle estimatoo population aoove the threshold; even wl1en itis not the suPerior strategy, proportional threshold harvesting entails only a sffiall reduction in expected yields (Engen et al. 1997). Both limited harvesting capacity and proportional threshold hmvesting allow the'l>opUlatlon to flucttiate over a wider range, inc1uding abOve the threshold, prömotlng more accurate estimates of population dynarillc parameters. These generalized t1ITeshold stnltegies also stabilize anriual )'felds by reducing the. frequency ofyears without harvest The principal objections to optinial thfeshold sti'aiegies raised by Hilbom and Walters (1992) therefore loose much ofth6ir force. Hilbom arid Walters (1992) give for tWo reasons for preferring constant haNest rate strategies, in which a fcied fnicnon ofthe populauon is harvested each year. First is the "nsk averse" property ofthe conscirit harvest rate stnliegy, derived from a pai1icular model by neriso (1985) in which the optimiZation goal is to iriaximize the expected logantluil ofaniiual riither than the mean anmial ha1Vest Harvesting contliiues even at very low resource beCause the logarithmic utility places an inflnite penalty on years ofzero hmvest. The "risk averse" propertY ofa consiani harvest rate refers to economic risk to industrY rather than biological nsk to • the resoUrce (Fredeiick and Petennan 1995) änd is therefore short-sighted.The cönstaiii hmvest rate strategy rieglects th6 relatively high risk ofreSoUrce depletlon or extmction cäused by the destabÜizing effect ofharvesiliig ai low resource aburidarice (Beddington and May 1977), which is magmfled by unceitainty in population estimates. This could devastate a specialized industIy as weil as having other repercussions to the ecosystem. Lande et al. (1995) showed that a constirit haivest rate stnitegy entails a much gi-eater rlsk ofextlnctlon ordepletion. thliri ihreshold harvestirig. It is imi>ortant note thai Deiiso (1985) used aiumrealistic in which there is rio possibiliiy of extinction and an irifmitely Iarge cariyirig capacity. Mendelssohn (1982) previously showed that With a Ricker spaWner-recruit relationship arid a 10g3.rlthmic utilit}r function the optimal harvestirig stnltegy is clC;ser to thfeshold hmvestirig than to a constiint harvest rate. The secondreason that Hilbom and Walters (1992) prefer constaßt hiuvest rate strategies concems nrlXed stock flsheries, such as Paciflc salmon, where harvest quotas are baSed solelyon the total popUlation ofmultiple stockS. Hilboin (1985) simUlated 10 stocks with Ricker spawner­ recrirlt dyilärillcs and envirOmrieriial variation in recruittrient rates. For uriequal stock pfoductivities arid completely correlated vanation in reciuiUrieiii rates, iriaxirirlzing themean value ofthe . . logarithm ofthe anriuaI catch plus one was accomplished by a constant I1mvest rate strategy. Iri an cases where the optimiZation criierion was the mean anriuaI catch, the optimal strategy was either pure threshold harvesting (for-different prcx:luctlVities and cOmpletely correlatoo variation) or an . "iritermewate" strategy eqUivalerit to proportional tbreshold harvestirig (for equal productiVities and uricorrelated variation)..With kriowledge ofollly the total population size of al1 stOCks, the . sitUation With equal prcxluctivities and uncorrelated (or pä.itially corielated) variation in reciuitinent rates is forinally equivalent to a mOdel with Uricertiinty iJi the pOpUlation siie ofan iridividual . stock; this explains why in this case proportional threshold hmvestirig niaxiinizes the me annual harvest The aoove arguments indicate that the theoretical justification fOf constant rate harvestan strategies is extremely weak (Lande et al. 1997). . ThresholdS are a necessary feature ofany harvestirig strategy witb the objective ofminiInizing riskS ofresource depietion or extinction while öpnmizing yields in vririable envirönmentS. .. Fisherles bioiogists are iricreasingly interested in quantifying bioiogical risks to resources as well as econorilic risks to indusiry (Kruse et al. 1993, Smith et al. 1993, Rosenberg änd Restrepo : I 1994) and have recently perforrned stochastic simulations for severiU typCs ofthreshold harvesting strategies with uncertairitY, althOligh with linIe analytical theory to guide theni (Quirin et al. 1990, 6

Eggers 1993, Thompson 1993, Zheng et a1. 1993, Mace 1994, Megmy et al. 1994, FrOOerick arid Petennan 1995). These simulations usOO either tinlimited or constmt harvesting rate aoove the thfeshold, rather than a haiVest rate that increases lliieariy fr()m zero at the threshold as in the propOrtional thfeshold strätegy. This may explam why in such simulations thfeshold sttategies often do not produce mean annual yields as large as with a constiU'it harvest rate (Getz and Haight 1989, Frederick arid Peterman 1995; but see also Eggers 1993). management should not be driven by short-term economie and politieal . considerations at the cost ofcontintied overexploitation, resource eollapse, and extlnetion. A eombination ofgeneral arialysis and detailed sUnulations ofpartieular eases ean ilrovide a rigorous . scientifie basis for plaeing increaSect emphasis on biological risks to susiairiabilit}' offluetuating . resourees. Acknow[edgements.--This work was supponed by NSF grant DEB-9225127. • LITERATURE CITED AllendoIf, F.W. et aI. 1997. Pri6ritizing Pacifie salrrion strickS for coriselVation. ConselVati6n Biology 11:140-152.'. .. , BOOdington, J. R arid R M. May. 1977. Harvesnng populations in a rand6Inly fluematirig erivironment. Scienee 197:463-465.. · . .' Deriso,.R B. 1985. Risk averse harvestirig siraiegies. Pages 65-73 in M. Mangel, editor. Resouree Managemerit, Proceedirigs ofthe SecondRalfYorciue Workshop. Leettire Notes in Biomathematics No. 61. Spririger-:-Verlag, Berliri. , , '. .. Eggers, D. M. 1993. Robust harvest policies for Pacifie salmori fisheries. Pages 85-106 in Proceernngs ofthe international symposium on management strategies for exploitoo fish populations. Edited by G. Kruse, D. M. Eggers, R J. Maraseo, c. Pautzke, arid T. J. Quinn U. Alaska Grant College Program Report No. 93-02. University ofAlaska, Fairbanks, Alaska.',. . .. . Engen, S., R. Lande arid B.-E. Srether. 1997., Harvesting strategies for fluctuatiiig populations based on uneertain populatiori estimätes. JoUinal ofTheoretieal Biology 186:20f·212. Frederiek, S. W. and R. M. Peterman. 1995. Choosirigfisheries haiVest policies: when does uneertainty ? Canadiari Journal ofFisheries and Aquatic Sciences 52:291~306. Getz,W. M~ and R G. Haight.: 1989. Population harvesting: demographie models offish, fOTest, and resourees. Pririeeton University Press, Princeton, NJ~ , Gilpin, M. E. and M. E. Soule..1986. Minimum viable populations: processes ofspecies extinetion. Pages 19-34 in M. E. Soule (00.), ConselVation biology, the seierice of searcit}r arid diversity. Sinauer, Sunderlarid, MA.. '" Groombridge, B. Ed. 1992. Global : status ofthe eanh's livirig resourees. , .Chapnian arid Hall, New York. . . Hiloorn, R 1985. A comparisori cfharvest policies for mixed stock fishenes. Pages75-87 in M. Mangel, editor. Resource management, Proceedings ofthe seeond RalfYorque . .,Workshop. Lecture Notes in Biomathematics No. 61. Springer-Verlag, Berlin~ HilbOrri, R. and. C. J. Walters. 1992.. Quantit~tive fisheries stock assessment: choice, dynamies. , and uncertamty. Chapman and Hall, NewYork. .' .' ". ' ., Hutchings,1. A. and R. A. MYers. 1994. What can be leamcil from the eolhipse ofa renewable resouree? Atlantic eOd, Gadu.s morhUa. ofNeWfoUndlarid arid Labnidor•. Canadian Journal ofFisheries and Aquatie Sciences51:2126-2146. '. .... " mCN. 1994.. IDCN Red List Categories. IUCN Species SiirVival CorriInission, Glarid, SWitiefland. 7

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