Chilean blue whales as a case study to illustrate methods to estimate abundance and evaluate conservation status of rare species

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Chilean blue whales as a case study to illustrate methods to estimate abundance and evaluate conservation status of rare species
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  University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Publications, Agencies and Sta of the U.S.Department of CommerceUS Department of Commerce1-1-2011 Chilean Blue Whales as a Case Study to IllustrateMethods to Estimate Abundance and EvaluateConservation Status of Rare Species Rob Williams University of St. Andrews  , rmcw@st-andrews.ac.uk  Sharon L. Hedley  University of St. Andrews Trevor A. Branch University of Washington - Seale Campus Mark B. Bravington Castray Esplanade  Alexandre N. Zerbini University of Washington - Seale Campus See next page for additional authors Follow this and additional works at:hp://digitalcommons.unl.edu/usdeptcommercepubPart of theEnvironmental Sciences Commons is Article is brought to you for free and open access by the US Department of Commerce at DigitalCommons@University of Nebraska - Lincoln. Ithas been accepted for inclusion in Publications, Agencies and Sta of the U.S. Department of Commerce by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln.  Williams, Rob; Hedley, Sharon L.; Branch, Trevor A.; Bravington, Mark B.; Zerbini, Alexandre N.; and Findlay, Ken P., "Chilean Blue Whales as a Case Study to Illustrate Methods to Estimate Abundance and Evaluate Conservation Status of Rare Species" (2011).  Publications, Agencies and Sta of the U.S. Department of Commerce. Paper 329.hp://digitalcommons.unl.edu/usdeptcommercepub/329   Authors Rob Williams, Sharon L. Hedley, Trevor A. Branch, Mark B. Bravington, Alexandre N. Zerbini, and Ken P.Findlay  is article is available at DigitalCommons@University of Nebraska - Lincoln:hp://digitalcommons.unl.edu/usdeptcommercepub/329  Contributed Paper Chilean Blue Whales as a Case Study to IllustrateMethods to Estimate Abundance and EvaluateConservation Status of Rare Species ROB WILLIAMS, ∗ § SHARON L. HEDLEY,† TREVOR A. BRANCH,‡ MARK V. BRAVINGTON, §§  ALEXANDRE N. ZERBINI, ∗∗ ,†† AND KEN P. FINDLAY‡‡ ∗ Canada-US Fulbright Visiting Research Chair, Jackson School, University of Washington, Seattle, WA 98195, U.S.A.†Centre for Research into Ecological and Environmental Modelling, The Observatory, Buchanan Gardens, University of St. Andrews,St. Andrews, Fife KY16 9LZ, United Kingdom‡School of Aquatic and Fishery Sciences, Box 355020, University of Washington, Seattle, WA 98195, U.S.A. §§ CSIRO Mathematical and Information Sciences, Marine Laboratories, Castray Esplanade, GPO Box 1538 Hobart, Tasmania 7001, Australia ∗∗ National Marine Mammal Laboratory, Alaska Fisheries Science Center/NOAA, 7600 Sand Point Way NE, Seattle, WA 98115-6349,U.S.A.††Cascadia Research Collective, 218 1/2 W 4th Avenue, Olympia, WA 98501, U.S.A.‡‡Oceanography Department, University of Cape Town, Private Bag, Rondebosch 7701, South Africa  Abstract: Often abundance of rare species cannot be estimated with conventional design-based methods, so we illustrate with a population of blue whales (  Balaenoptera musculus  ) a spatial model-based method toestimate abundance. We analyzed data from line-transect surveys of blue whales off the coast of Chile, wherethe population was hunted to low levels. Field protocols allowed deviation from planned track lines to collect identification photographs and tissue samples for genetic analyses, which resulted in an ad hoc sampling design with increased effort in areas of higher densities. Thus, we used spatial modeling methods to estimateabundance. Spatial models are increasingly being used to analyze data from surveys of marine, aquatic, and terrestrial species, but estimation of uncertainty from such models is often problematic. We developed a new,broadlyapplicablevarianceestimatorthatshowedtherewerelikely303whales(95%CI176–625)inthestudyarea. The survey did not span the whales’ entire range, so this is a minimum estimate. We estimated current minimum abundance relative to pre-exploitation abundance (i.e., status) with a population dynamics model that incorporated our minimum abundance estimate, likely population growth rates from a meta-analysis of rates of increase in large baleen whales, and two alternative assumptions about historic catches. From thismodel, we estimated that the population was at a minimum of 9.5% (95% CI 4.9–18.0%) of pre-exploitationlevels in 1998 under one catch assumption and 7.2% (CI 3.7–13.7%) of pre-exploitation levels under the other.Thus, although Chilean blue whales are probably still at a small fraction of pre-exploitation abundance, eventheseminimumabundanceestimatesdemonstratethattheirstatusisbetterthanthatofAntarcticbluewhales,which are still  < 1% of pre-exploitation population size. We anticipate our methods will be broadly applicablein aquatic and terrestrial surveys for rarely encountered species, especially when the surveys are intended tomaximize encounter rates and estimate abundance. Keywords: abundance, Balaenoptera musculus , distance sampling, line transect, rare, spatial model, varianceBallenas Azules Chilenas como Caso de Estudio para Ilustrar M´etodos para Estimas la Abundancia y Evaluar elEstatus de Conservaci´on de Especies Raras § Current address: Sea Mammal Research Unit, Gatty Marine Laboratory, University of St. Andrews, St. Andrews Fife KY16 8LB Scotland, United  Kingdom, email rmcw@st-andrews.ac.uk Paper submitted June 2, 2010; revised manuscript accepted October 6, 2010. 526 Conservation Biology , Volume 25, No. 3, 526–535 C  2011 Society for Conservation Biology DOI: 10.1111/j.1523-1739.2011.01656.x This article is a U.S. government work, and is not subject to copyright in the United States.  Williams et al  . 527 Resumen: La abundancia de especies raras a menudo no puede ser estimada mediante m´ etodos conven- cionalesbasadosendise˜ no,as´ ıqueilustramos—conunapoblaci ´ ondeballenaazul(  Balaenopteramusculus  )—un m´ etodo basado en modelo espacial para estimar la abundancia. Analizamos datos muestreo en transectoslineales cerca de la costa de Chile, donde la poblaci ´ on fue llevada a niveles bajos por la cacer ´ ıa. Los protocolosde campo permitieron el desv´ ıo de las l ´ ıneas trazadas para la colecci ´ on de fotograf ´ ıas de identificaci ´ on ymuestras de tejidos para an´ alisis gen´ eticos, lo que result ´ o un dise˜ no de muestreo ad hoc con incrementode esfuerzo en´ areas con densidades mayores. Por lo tanto, utilizamos m´ etodos de modelaje espacial paraestimar la abundancia. Los modelos espaciales son usados cada vez m´ as para analizar datos de muestreosde especies marinas, dulceacu´ ıcolas y terrestres, pero la estimaci ´ on de la incertidumbre de tales modelosa menudo es problem´ atica. Desarrollamos un estimador de varianza nuevo y de aplicaci ´ on general quemostr ´ o que por lo menos hab´ ıa 303 ballenas (95% IC 176–625) en la poblaci ´ on. Estimamos la abundanciarelativa m´ ınima en relaci ´ on con la abundancia anterior a la explotaci ´ on (i.e., estatus) con un modelo de din´ amica poblacional que incorpor ´ o nuestra abundancia m´ ınima estimada, las tasas de crecimiento pobla- cional probables derivadas del meta-an´ alisis de las tasas de incremento de ballenas barbadas, y dos supuestosalternativos sobre capturas hist ´ oricas. De este modelo, estimamos que, en 1998, la poblaci ´ on estaba en unm´ ınimo de 9.5% (95% CI 4.9–18.0%) respecto a niveles previos a la explotaci ´ on bajo un supuesto de captura y 7.2% (IC 3.7–13.7%) respecto al otro. Por lo tanto, las ballenas azules chilenas probablemente est ´ an enuna peque˜ na fracci ´ on de la abundancia previa a la explotaci ´ on, estas estimaciones de abundancia m´ ınimademuestran que su estatus es mejor que el de ballenas azules en la Ant ´ artica, que aun est ´ an < 1% del tama˜ node la poblaci ´ on previa a la explotaci ´ on. Anticipamos que nuestros m´ etodos ser ´ an ampliamente aplicablesen muestreos acu´ aticos y terrestres de especies raras, especialmente cuando los objetivos del muestreo est ´ andise˜ nados para maximizar las tasas de encuentro y estimar la abundancia. Palabras Clave: abundancia, Balaenoptera musculus , modelo espacial, muestreo a distancia, transecto lineal,raro, varianza Introduction Estimating the abundance of rare species (i.e., small pop-ulation size), elusive animals, and animals that are abun-dant overall but occur over vast areas at low densities isa perennially difficult problem (Thompson 2004). Many endangered taxa fall into this category. For example, onecriterion used by the International Union for Conserva-tion of Nature (IUCN) to define endangered taxa is apopulation estimated to number “fewer than 250 ma-ture individuals” (IUCN 2001). When few animals areavailable to be sampled, it becomes difficult to collectenough data to reliably estimate population parameters.This difficulty can occur with both mark-recapture es-timates and line-transect surveys, which are part of thedistance-sampling family of methods commonly used toestimate abundance of marine and terrestrial animals(Buckland et al. 2001). If there are too few sightingsto reliably estimate the width of the rectangular areasearched along a transect (hereafter, the effective strip width), then abundance cannot be estimated. Low over-all encounter rates can be ameliorated if information onanimal distribution is available from telemetry studies,catch records, data from preliminary surveys, or localknowledge. Such information can be incorporated intothe design of line-transect surveys (Thomas et al. 2007)to ensure sufficient sighting effort is allocated to high-density areas to increase the number of sightings suffi-ciently to allow estimation of the effective strip width (Williams & Thomas 2009). It may also be necessary tomaximize the number of encounters for other reasons,such as to collect tissue samples or identify individu-als for long-term mark-recapture studies. Nevertheless,for species with poorly known or unpredictable distribu-tions, it may be impossible to devise in advance a survey design within a given budget that will ensure enough en-counters.Instead,itmaybenecessarytoadjustthedesignduring the survey to increase encounters. The problem with this ad hoc approach is that such adjustments inval-idate some of the assumptions of standard line-transectanalyses and require the development of more sophis-ticated methods of abundance estimation. Nevertheless,thehighcostofshipboardsurveysmeansitisdesirabletogenerate even rough abundance estimates from surveysthatwould,moreideally,bethoughtofasreconnaissanceor pilot surveys. We examined data from a sightings survey of a popu-lation of blue whales (   Balaenoptera musculus  ) off thecoast of Chile to illustrate one solution to the problemof estimating abundance and conservation status of rarespecies. Here status means “abundance relative to pre-exploitation levels,” which is a standard way of measur-ing depletion in natural resources and the definition usedby the International Whaling Commission (IWC) to as-sess whale populations. Many blue whale populations were hunted to near extinction in the twentieth century,and although the species was protected internationally in the 1960s, illegal whaling continued into the 1970s(Branch et al. 2004). In the southeast Pacific blue whales were caught primarily off Chile, but some were alsotaken off Peru and Ecuador (Clarke et al. 1978; Ram´ırez1983;VanWaerebeeketal.1997).Hundredswerecaught Conservation Biology  Volume 25, No. 3, 2011  528 Estimating Abundance of Rare Species annually in many years from the 1910s–1960s in Chilean waters (Clarke et al. 1978; Van Waerebeek et al. 1997). Whaling off Chile, Ecuador, and Peru probably led tosubstantial decreases in abundance of blue whales in thesoutheast Pacific. This decrease was not thought to havebeen as severe as for other populations of blue whales,but the extent of decreases and of any subsequent recov-eryremainedunknown(Donovan1984).Tobetterunder-stand the current status of Chilean blue whales and their relation to other populations of blue whales, a research cruise was undertaken off Chile in 1997. The cruise waspart of the Southern Ocean Whale and Ecosystem Re-search (SOWER) program of the IWC (for full details seeFindlay et al. [1998]).The primary aims of the SOWER surveys were to iden-tify which subspecies was found in Chilean waters, max-imize encounters with blue whales, collect genetic andacoustic data, photograph individuals for identification,and videotape activity for subsequent behavioral analy-ses. Two recognized subspecies of blue whales occur intheSouthernHemisphere:Antarctic(ortrue)bluewhales(   B. musculus intermedia  ) and pygmy blue whales (   B.m. brevicauda  ). During the austral summer, nearly all Antarctic blue whales are in the Southern Ocean south of 55 ◦ S, whereas pygmy blue whales are in more northerly  waters, primarily in the Indian Ocean and around Australia and New Zealand (e.g., Ichihara 1966;Branch et al. 2007 a ; Branch & Mikhalev 2008). Blue whales also occur off Chile, Peru, and Ecuador, but it wasnotclearatthetimeofthesurveywhethertheseblue whales were Antarctic blue whales, pygmy blue whales(Aguayo 1974; Van Waerebeek et al. 1997), or an unde-scribed subspecies (Branch et al. 2007 a , 2007 b , 2009).Duringthesurvey,37groups(45animals)wererecordedas pygmy blue whales and 2 groups were recorded to thelevel of subspecies (Findlay et al. 1998). Visual identi-fication to subspecies level is unreliable, however, andgenetic data indicate blue whales in the southeastern Pa-cific (Chilean blue whales), Indian Ocean (pygmy blue whales),andtheSouthernOcean(Antarcticbluewhales)form three distinct groups (Leduc et al. 2007). Therefore we considered all these sightings to be of undeterminedsubspecies. Although the survey was designed primarily for dis-crimination of subspecies, researchers on the survey didcollect line-transect data that could be used to estimateabundanceprovidedthenonrandomplacementofsearch effort could be addressed statistically. The survey pro-ceeded as follows. Two vessels departed from Iquique,Chile(20 ◦ 12  S70 ◦ 09   W),inDecember1997.Oneheadedto 18 ◦ 30  S and began surveying southward and the other headed to 38 ◦ S and began surveying northward. The in-ner boundary of the survey region was defined as the12-nautical-mile (22.2 km) territorial boundary of Chile.The outer boundary was delineated by historical catch distribution limits, the 200 nautical mile (370.4 km) ex-  Figure 1. Sightings of blue whales (filled circles) and  survey track lines (lines within the polygon) made byvessels surveying in Chilean waters (inset shows South America south of 15 o S) for blue whales from December 1997 through January 1998. Polygonoutline marks the boundary of the survey region. clusive economic zone and the time limits of the survey (Fig. 1).Priortothesurvey,littlewasknownaboutwhaledistri-butioninthearea,soaseriesofsystematictracklineswasplanned throughout the survey area. To maximize whaleencounters, track-line design was ad hoc and track lines were not followed rigidly if aggregations of whales werefound. Thus, these data werenot collected systematically and do not lend themselves to conventional line-transect Conservation Biology  Volume 25, No. 3, 2011
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