Inferring Species Extinction: the Use of Sighting Records

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Provided byUCLDiscovery Accepted Article CORE extinction Keywords: Estimating extinction, time series, record, sightings Poisson process, local extinction Inferring head: Running 1139 202 0776 [email protected], Boakes, Elizabeth author: Corresponding Gowe CollegeEnvironment, London, University This articleThis protectedis by Allcopyright. rights reserved. cite this article as doi: 10.1111/2041-210X.12365 which may lead to differences between thisversion and the Version of Record. Please been not throughthe copyediting,typesetting, pagination and proofreading process, This article hasbeen accepted for publicationand undergone fullpeer review but has 3 2 1 Boakes Elizabeth records sighting of the use Inferringextinction: species Editor : David Hodgson Article type : Review Date: 24-Feb-2015 Accepted Revised :12-Feb-2015 Date Received: Date 12-Jun-2014 School Biologicalof Sciences, University of Queensland, Lucia, St 4072, Australia Australia 3010, Parkville, Melbourne, of University BioSciences, of School & Evolution Genetics, of Department Research, Environment & forBiodiversity Centre 1 , Tracy Rout , Tracy 2,3

and Ben Collen Ben and 1 r Street, London WC1E 6BT, United Kingdom Kingdom United 6BT, WC1E Street, London r

Metadata, citationandsimilarpapersatcore.ac.uk Accepted Article This articleThis protectedis by Allcopyright. rights reserved. public credibilityconservation in hard,not isscience. It impossible, if often know how to repo incorrect funds, of misallocation to lead can it since undesirable is extinct actually is it when existence) in (still extant as species a Listing 2009). (Turvey period during this underest considerable 2014) ,certainly almost a sixty species have been documented becoming as extinct wild the in since AD (IUCN 1500 and hundred Eight time. over trends extinction of understanding detailed more a provide international conservationtargets (e.g. Conven of light in particularly loss, biodiversity monitor to rate ofextinction measure global exti of recent inventory accurate an ensuring difficultdetect, to and therefore usually must inferred be (Diamond However, 1987). very is observed, rarely is species, of a individual thelast of disappearance the Extinction, Introduction inference. extinction and decision-making conservation informing research, conservation across applied usefully been have methods the how show to literature the from examples give We 4. for aparticular sighting record. method appropriate most the selecting on guidance We provide known. if considerations, assumptions, the data required, the scenarios it wasdeveloped for and power its explaining model each for methods, cited frequently more the review We 3. choice. daunting present a array can methods increasing of The extinction. species’ about inferences a make to information this use which developed been have tests statistical Several data. sighting information historical only is available the often For species, little-studied 2. since a small population may go undetected formany years. difficult is extinct became species a which at time exact the but ascertaining management conservation and monitoring biodiversity in needed are extinction of measures Accurate 1. Abstract: nctions is important, nctions to least is estimate a not tion on Biological Diversity and to 2010) imation of the true extent of extinction extinction of extent true imationof the rting of current extinction rates and loss and of rates ofcurrent loss rting extinction Accepted Article recorded as present.Although as recorded been has species a which at thetimes as records sighting of series atime define We recordsSighting into conservation decision-making. incorporated be could they how suggest and literature, recent in used have been they how describe beapplied, could each which to thescenarios explaining methods these summarise contempor thatuse methods the through user the guide to is review this of aim The 2009). Roy & Hunt Rivadeneira, (e.g. data sighting of the 1995; Duffy Ferson & Grimson Burgman, (e.g. record sightings toa methods multiple toapply literature This articleThis protectedis by Allcopyright. rights reserved. Solow McInerny 2003; been developed purpose for this 1993a; (Solow Burgman,Grimson &Ferson 1995;Roberts & sighti to restricted often is However, data sightings, measures of sighting effort, the likelihood of observing speciesa extant). ifit is non- (for example, extinction infer to used be would information available all Ideally traits). history life (e.g. information taxon intrinsic and species, the threatening processes of extent and severity the abundance, to relationship its and habitat remaining potential trajectories), population (i.e. over time abundance in change species, the for searching species), the likelihood of detection given that the record to failed have surveys which at dates (i.e. absences of series atime sightings, historical of series time a beused: can information of variety A species. a dateof extinction the infer to beused can observations observed, not directly is extinction species When early (Collar 1998). too off written were birds endemic its of 8 Cebu and that suggest remained none thought been had it where forest of primary evidence historical with along flowerpecker Cebu the of therediscovery – species of extirpation the to case, one least at in and effects undesirable mistak happy as viewed be can incidences 2004; Butchart, Stattersfield &Brooks &Blomberg 2006; Fisher 2011). Whilst these been declared extinct, tobe only rediscovered tobe (e.g. 2001;KeithFuller &Burgman have which species converse, ofthe examples numerous are there but occurs mistake a such etal.

2009) although2009) thepowerof methods the differs dependingon the nature etal. 2006) (see Table2006) for1 full list). has There been atendency inthe commonly referred to as a ‘sighting’ in the literature, the the literature, in the ‘sighting’ a as to referred commonly ngs data and quantitative techniques have thus thus have techniques quantitative and data ngs es, they too can totheaforementioned lead theytoo es, the species is present, the effort expended in in expended effort present, the the species is ary sightings toinferextinction.ary data We Accepted Article as a Poisson process (Box 1). 1). (Box process a Poisson as occur sightings that methods assume the of All calculated. be may probability an extinction thus true, of being probability a with variable arandom as treated is hypothesis a however, species either not is extinct;‘extinctionis or no there is probability’.statistics, Bayesian In Chong Grimson 1998; &Ferson 1995;McCarthy McPh Burgman, (e.g. literature existing the in confused been have outputs two these which in the probability species a that is extant, sighting given the data. are There instancesseveral estimate of extincti an to generate rearranged stillchance a this that rejectionincorrect). is probability The statement can also be th ‘reject’ can therefore and is extant species observed even enough, 0.05, an have we say < If this is extant. species the that hypothesis null the given hypothesis), null (or sighting data obtaining of the probability a generate models (NHST) testing significance hypothesis null method, frequentist of type One outputs. their in differences important they but have 2) Table (see records sighting analyse to beendeveloped both have methods Bayesian and Frequentist Methods 2014; Thompson Elphick, Roberts Reed Roberts,2010; & Elphick &Reed 2010; Lee in sighting data discussed are indetail elsewhere (McKelvey, &Schwartz Aubry 2008; Thompson 2014; Beet This articleThis protectedis by Allcopyright. rights reserved. (Solow sightings uncertain incorporate can extinction inferring for developments Recent use. before verified be should data occurrence anecdotal particular, In difficulties. present can data sighting of veracity Uncertain independent. as viewed be not would day same the on and location multip 1): Box (see another one of independent be must sightings here, described methods the of purposes the For presence. species’ a of record reliable any mean to ‘sighting’ use theterm also we literature, Following earlier of indicator diagnostic other orsome observation visual a record, acoustic an specimen, museum a by berepresented may presence et al. et 2012). It is important to remember that in classical, frequentist statistics, the classical, statistics, the frequentist thatin to It remember important 2012). is et al. et et al. et 2014). (seeTable2014) 3), complications the arising fromuncertainty etal. more extreme sighting data further from the from further sighting extreme data more e null hypothesis (understanding that there is thereis that (understanding hypothesis null e on time. In contrast, Bayesian methods give give methods Bayesian contrast, In on time. t that is highly improbable given that the erson & Myers 2009; Jaric & Ebenhard 2010; 2010; & Myers2009; erson Jaric &Ebenhard le sightings of a taxon made at the same same atthe made ataxon of le sightings 2012; Lee 2014; Lee 2014; Lee 2012; presence such as a hair or faecal sample. sample. faecal or hair a as such presence etal. et al. et 2014;Solow &Beet p- value, which is the which isthe value, 2014; Solow & p -value is small small is -value Accepted Article The smallerThe the probability statement an upper (1- anupper statement probability the crop up in the literature. However, they have limited conservation applications being mainly mainly being applications conservation limited have they However, literature. the in up crop generalisesthis allow to start and the end the speciesof sighting rangeunknown. tobe Marshall (1997)further and Strauss (1989) &Sadler modify basicthe methodfrom Solow to(1993a) allow both the ( period observation The abovemethods assume that the species is Information. Supplementary of examples worked For year. each expended effort collection of survey indicesor the equation’, that equation used adiscrete-time as an estimate of the overall sighting rate. (i.e. occurred extinctionalready has thenullhypothesis not tests that method zero.falls to The of extinction et al et McInerny while interval, time asingle within sightings independent multiple accommodates example,Grimson(eq Burgman,(1995) Ferson, & The The makes that the moreprobable populatio it at some unknown time time unknown some at assumes the sighting Poisson a record is proces earlier, the earlier, the This articleThis protectedis by Allcopyright. rights reserved. at time occurring recent most the present time (Figure 1). There are timese This occurred. extinction atwhich time the or estimating extant is a taxon thehypothesis that testing Using a time seriesofhistorical sightings, methods Frequentist n a fixed significance level sightings would occur before time p T . (2006) aim to reduce the influence of the length of the observation period by using by period aim using of the observation of the length the influence . to (2006) reduce E -value corresponding to the null hypothesis that extinction has not occurred is ( occurred not has extinction that hypothesis null to the corresponding -value > T ) using the likelihood ratio statistic; an unusually large ‘gap’ between between ‘gap’ large unusually an statistic; ratio likelihood the ) using p t -value should not beconfused with the pr n T is relativeto E .

Solow’s original equation (1993a) has been modified in various ways: for for ways: various in modified been has (1993a) equation original Solow’s t = 0).In contexts some = ( ries occurs over an over ries occurs T λ α E , ( is assumed, and extinction occurs at occurs extinction and assumed, is

t until which the sighting rate rate thesighting until which T, ) to vary. We mention thesetw Wemention ) tovary. the smaller this the smaller t n n . Like all of. Like all methods, the Solow’s 1993a method recorded sightings of the with within thistime,taxon the of sightings recorded t n given that speciesthe (As mentioned is given that extant. α ) confidence of limit Solow (1993a) proposed a simple method for observation period [0, period observation McCarthy (1998)introduced ‘Partial a Solow n has gone extinct during that interval. interval. that during extinct gone n has e.g. p the methods, Table2,Box2and the the see already in existence at the start of the of start at the existence in already -value, i.e. the smaller the probability that all all that probability the thesmaller i.e. -value, s (Box 1). The 1). The extinctionsof taxon (Box the occurs fossil records) this may not be appropriate, beappropriate, not may this records) fossil uationoutline 2) discrete-time a form that obability that the extant.)species is If λ ( t ) is constant, and after which which after and ) is constant, o methods since they regularly they regularly since methods o α -1/ T T n E t < T ], where n is obtained for the time obtained is thetime for , then by rearranging rearranging ,by then T is usually the the usually is t n and t T n

/ λ T ( n t ) ) / n . t n

Accepted Article (here, thelatest sighting,(here, variables random distributed identically and of independent maximum ofthe properties the nonparam estimation, also termed linear Optimal 2005). (Solow extinction for testing for p-value approximate an produce Solow outputs 2003) the year of extinction although the model can rearranged be to An alternative frequentist approach, optimal linear estimation,Li (Cooke 1996; & Roberts & intervals ( in overtime, changing been has rate sighting pair of successive sightings. equationThe modified isfurther forcase the of aspecies where two sightings the time elapsedbetween last the replacing (2010), Ebenhard and Jaric by been modified has method Roberts’ Solow and increasing (Rivadeneira, Hunt 2009;Clements &Roy methodThe tendstooverestimate the the number on relies it Again data. of sample independent an using distribution a of endpoint the of estimation the considered who (1964) Whitlock and Robson of work the on based is method The (2003). Roberts & Solow by discussed An alternative simple approach that does not requirespecifying aparametric form for rate function of the form the function of rate value. example, For Solow calculates(1993b) significancethe givenlevel log-linear sightinga assumption about the form of of theform assumption about approximated by a particular 3-parameter distribution known as the generalised extreme extreme generalised the as known distribution 3-parameter particular bya approximated well- is maximum the of distribution the sightings, of the thedistribution of regardless that This articleThis protectedis by Allcopyright. rights reserved. [ is occurred not has extinction the for equation general A 1998). constant (McCarthy The Solow method 1993a extends naturally case when tothe sighting the rate further. them not discuss do we and be extinct extinction commonof known to a taxa group used tofor timein test a c ). n of sightings, the sighting times are independently and identically distributed. distributed. identically and independently are times sighting the sightings, of λ t n ( ,) conditionally upon there being being there upon conditionally ,) t ) =exp( the sighting function the sighting rate Λ ( t n etric, uses extreme value theory. This theory applies to Thistheory applies value usesextreme theory. etric, )/ a + Λ (T)] bt extinction date, particularly when when particularly date, extinction ). ). n

where with the average time elapsed between each between time elapsed average the with troducing a coefficient of trend in sighting trend of coefficient troducing a the property described in Box 1 that, given given that, 1 Box in described theproperty

Λ ( et al. et t ) = p -value of the null hypothesis that hypothesis null the of -value λ ( 2014) (Table 1). (Table 2014) t ), it ), it is possiblecalculate to this n observations in [0, in observations . By making an making By . λ ( t ) isconstant or λ ( t T ) is not not ) is ]. It shows λ ( t ) is p - Accepted Article Burgman ho from deviation of patterns to different therefore and effort search and abundance in change of patterns different to sensitive being in change and records of number in to variation responses microcosm data (Clements microcosm data Turvey 2009;Duffy conservation status (e.g. Burgman,Grimson Ferson 1995;Solow & &Roberts Collen2003; & Rivadeneira, Hunt &Roy2009; Collen,Purvis &Mace 2010)or speciesknown of performance using either simulated data (e.g. Burgman (e.g. data simulated either using performance covering thefreque of literature Much the Model selection 1).2014) (Table probability of observing thespecies low is (Rivadeneira, Hunt Roy 2009; Clements & the when and decline abundance gradual undergoing is aspecies when methods other po real and experimental forboth investigated methods of mean error thelowest having and or underestimation over towards no bias methods showingingeneral, of optimal the mostfound robust tobethe linearestimation over time,Clements and of sighting number the with increased precision from considering microcosms extinctionalone, date and Clements data experimental Using estimates. the of bounds theupper increased sightings of numbers Rivadeneira future. the into oftenmillennia were assigning extinction times species for with fewer 5or sightings since confidence upper limits for uninformative was method The sightings. between gaps increased with increased limits confidence upper although sightings thelast of 5 than to more applied when predictions hold (Solow 2005). Collen suffer from the small sample size but if the This articleThis protectedis by Allcopyright. rights reserved. Poissonto isapplied theory of a the process. The extreme value sightings is specification parametric of a for form approximationwill good not one be a unless the and one, asymptotic an is thejustification that however, Note, distribution. value n sightings, so that the appropriate choice of of choice appropriate the that so sightings, et al. et 2000) (but (Vogel see 2000) et al. et 2009), with a recent addition testing models on experimental testing experimental on recentmodels addition with 2009), a et al. et al. et al. (2014)strongly recommend (2010) found that the technique generally technique gave accurate generally the that found (2010) 2013; Clements et al. λ ( k t asymptoticis too large the assumption may not ) but does still assume that the underlying process process underlying the that assume still does but ) mogeneity (Burgman, Grimson & Ferson 1995; 1995; Ferson & Grimson (Burgman, mogeneity 2009). In studies optimalto date, linear ntist models is devoted is their totesting models ntist n is large. This approach does not require the the require not does approach This is large. pulations. It appeared to perform better than than better perform to appeared It pulations. s used except when search effort decreased decreased effort search when except s used etal. k et al. et is an issue; if if issue; an is 2014).Each model has different (2009) found that including large large including that found (2009) et al. et λ k ( t > 10. Clements > 2000;Solow Roberts & 2003; ) (summarised in Table 2), 2), Table in ) (summarised k is toosmall estimationwill et al. (2013) found, (2013) k et al most recent of of recent most . (2014) etal.

Accepted Article regime) also affects accuracy, models accuracy, alsoregime) respo affects observed immediately the period precedin in probability of observing aspecies decreases over time, the species less is tobelikely (Clements trajectory thepopulation mirror not does rate sighting in change the meaning gathered, be can populations wild of sightings sporadic temporally (Clements decline rapid on accuracy greater showed populations wild well-studied on tests data, microcosm and simulations on based expectations to Contrary must care However, 2). (Tablee selected be can al. of sightings with number (Clements This articleThis protectedis by Allcopyright. rights reserved. Hunt &RoyClements 2009; accuracy across models populationwhen dec higher for atendency shown have data andmicrocosm onsimulated performed Tests average (Rivadeneira, Hunt &Roy2009). exti true the covers interval confidence 95% a example, for that, so interval confidence the of level nominal the equal should coverage within bounds the confidenceof the interval (Rivadeneira, Hunt &Roy2009). Ideally, estimated ofdate extinction, is, that probability the the that parametertrue occurs value (Clements dates 2010). Simulations can bealso used compareto estimated dates of extinctionwith actual expected levels given significance (usually ofthe the test 0.05)(Collen,level Purvis &Mace calc be can errors.Theseerror rates rejected) and butrejected) true is hypothesis (thenull I of Type rates by examining assessed be can methods testing hypothesis of performance The rule. general a into findings these extrapolating against caution would we small, so is certainty 2014). However,given that the number actualof cases in which extinction isknownwith all,circumstances Roy majority the Clements (Rivadeneira, 2009; Huntnot but of, & estimation (Roberts &Solow seems to 2003) give most the accurate extinction in estimates 2014) (see Table2014) 1). Ifinformation regarding etal. 2014), and to assess the coverage of confidence intervals around the the around intervals confidence of coverage the assess to and 2014), et al. et et al. 2014). This isprobably due limited to the time from which 2013;Clements et al. et 2014). search sighting The 2014). (affected rate by nding differently todi differently nding nction time of 95% of simulation runs on on runs simulation of of95% time nction g extinction, overstating early for evidence g line israpid (except Solow 1993b)(Rivadeneira, ulated through simulation, and compared with with compared and simulation, through ulated populations experiencing gradual rather than rather gradual experiencing populations Type II (the null hypothesis is false but not not but false is hypothesis null (the II Type be taken in interpreting sighting records. sighting records. ininterpreting betaken λ etal. ( t ) is known, the most appropriate model model appropriate themost known, ) is 2014) and accuracy tends to increase increase to tends and accuracy 2014) et al. et fferent rates(Clements fferent 2014). When the 2014). etal. et

Accepted Article This articleThis protectedis by Allcopyright. rights reserved. the that results, remembered interpreting be itmust (When with2) (Box explored is rates sighting increasing and decreasing constant, of effect The ago. years a typical (Collen, PurvisMace 2010) & records7 spanning years, 126 with the sighting last 60 containing records sighting hypothetical three the by 2 in Box illustrated as results, different areunder processes underlying themodels’ that Table 2), dueallow violate theirassumptions (see that data appliedto not are course models of providing 2000). sensible isbroadly This (Grimson, Aldrich &Wanzer Drane 1992;Burgman, Grimson Ferson & 1995; Burgman dataset particular a on poorly faring test one to due missed not are changes significant that order in models multiple using beanalysed should data sightings that been suggested has It 2009). Roy & Hunt (Rivadeneira, narrow too are that intervals confidence producing and extinction lead to a high high a to lead species’ threat status. A very recent sighting of last the remaining individualofspecies a will sighting rate is increasing and higher higher and increasing is rate sighting lower giving record, sighting the within gaps more or one of distribution the by affected are thus and rate sighting constant a assume donot (2010)) Ebenhard & Jaric Roberts (1993b), (Solow othermethods The incorrectly inferred/dismissed. same the give thus records, of number and sightings and last thefirst only use methods The plausible. is extinction that thus and be rejected values ranging from to 0.044 0.065, these methods indicate that nullthe hypothesis might McInerny and Grimson Burgman, (1995) &Ferson (Solow (1993a), methods ofThree frequentist the record (Solow Beet2014). & themode of processes underlying the understanding 2.) Table in analysis sensitivity the see would violate their assumption of a constant sighting rate meaning extinction meaning rate sighting constant a of assumption their violate would rate sighting decreasing/increasing a with torecords methods these However, applying p -values ranging from 0.032 to 0.271, to 0.356 0.032 from ranging -values et al et p . (2006)) can only be used for records with a constant sighting rate. With With rate. sighting constant a with records for be used can only (2006)) . -value despite the species’ rapidly approaching and unavoidable extinction, extinction, unavoidable and approaching rapidly species’ the despite -value p

-values when the sighting the when isdecreasing. Solow -values rate These examples illustrate the importance of & Solow (2003), Solow & Roberts (2003) and (2003) Roberts & Solow (2003), Solow & stood. The different models can give quite quite give can models different The stood. ance ismade for multiple model testing and and0.032to 0.920 0.123 to respectively. p ls and the ‘natural history’ of a sighting sighting a of history’ ‘natural the and ls value regardless of sighting rate. rate. ofsighting regardless value p -value does not relate to the does relate not -value p -values when the

might be be might etal. p -

Accepted Article This articleThis protectedis by Allcopyright. rights reserved. Scheme Monitoring Bird Common Pan-European (e.g. surveys systematic from arise sightings If 2). (Table period theobservation across effort survey continual of level some assume methods of majority the since models these using when imperative is generated were data sightings which by process the of An understanding rate. sighting or the sightings, of number bythe constrained be may model of choice toreader quicklywhich select models may (not the allowing of theaim with assumptions, and data inputs models’ each shows 2 Table below). extinction’ declare factors such as available the financial resources andof role the‘Decidingthe species (see to effort/conserv survey further whether decide to used be can they information, additional of absence the in However, extinct. is species beexpected rarely can models history these sightings their of understanding of lack and species many for data of paucity the Given the decreasing sighting over reachrate, 2000years into the future. linear estimation increase optimal by given limits confidence upper The extant. is thespecies that doubt to reason 1995) to 0.989 (Solow 1993b decreasing sightingalthough rate) ofnone the methods gives Ferson & Grimson (Burgman, 0.642 from ranging p-values of variation wide a is there ago years 10 of sighting last a with Even widen. to tends year p-values/extinction between gap years to 50,40,…10 years ago th (Table 2).As of10 steps in forward moved is sighting last the if year extinction p-values/estimated on the effect the shows Box 2 in given records sighting hypothetical the using analysis A sensitivity 2). (Table or increasing extinction at or 1988, 1956depending 2089 whether the sightingconstant, rate is decreasing about assumptions no whichmakes estimation, linear Optimal records. two sighting last the only uses which (2003) Roberts & over Solow available) data (if preferred be thus might and rate sighting in change the reflecting data, record sighting of the use most makes method (2010) Ebenhard’s & Jaric decline). slow record on berun can although is increasing rate sighting the if beused cannot and population declining an exponentially assumes (1993b) as the last sighting year becomes more recent and, in the case of of the case in and, morerecent becomes year sighting last the as to provide a definitive answer to whether a whether to answer adefinitive provide to ation actionmight be justified alongside e last sighting year becomes more recent, the the recent, more becomes year sighting e last ) be appropriate for their data. For example, example, For data. their for beappropriate ) s with a constant sighting rate (an infinitely infinitely rate(an sighting a constant with s the sighting rate, estimates the year of the sighting year the rate, estimates (European Bird Census Council 2006)) or Council Bird Census (European Accepted Article generate a high ahigh generate populati than rather extinction for test methods mentioned above, itisincorrect tousethe their to according taxa ranking priority-setting; conservation in is methods the probabilistic of application cited widely One Applications years. than rather effort equal of periods across standardised be could record thesighting understood, well is effort search If time. of extinction 2013). This relationship differ may across species and could lead premature estimationa to (McCarthy abundance with change density population and size group as such factors how to according linear be not may abundance and rate sightings between relationship This articleThis protectedis by Allcopyright. rights reserved. sturgeon sturgeon theAtlantic of extinctions local of probability the infer to used been have they example, For spatial-scales. smaller at informatively severa are There problems. conservation other to application their advocate and we extinction global of topic tothe restricted tobe need qualitative conclusions about extinction (Solow 2005).However, these methods do not more on check aquantitative provide to be may application useful most their respect this in Indeed, extinction. global taxon’s a of declaration aformal support to used been noinstances in To knowledgeare there our sighting data (McCarthy 1998;Solow 1999; McPherson & Myers 2009). ca that methods statistical alternative are There Maxted HawkinsKjellberg 2005; Robbirt, & Roberts 2006; (Burgman groups plant several in vulnerability taxon species for (e.g. Boakes threatened t arisk is there abundance to as well as effort the observed since Additionally, justifiable. be not may assumption perhaps halted been has effort survey orif effort 2003). However, sightings if occur sporadic from expeditions separated by periods of no a thenthis observations incidental from A. nudiventris A.

p -value regardless of trends in population abundance or sighting frequency. frequency. sighting or abundance population in of trends regardless -value (Dulvy etal. p -value is a strategy that has been used to assess relative relative assess to beenused has that strategy is a -value et al. et 2004; Jaric 2004; ssumption may be reasonable (Solow & Roberts Roberts & (Solow reasonable maybe ssumption 2010) could 2010) mask an abundance decline. The p which thehypothesis-t which -value as a a extinction of measure riskthe since as -value hat the recent trend in increasing survey effort effort survey increasing trend in recent hat the et al. l instances in which they have been applied applied been which they have in l instances n be used to infer population declines from from declines population toinfer beused n on decline, and a very recent sighting will will sighting recent very a and decline, on due to a period of political instability, this this instability, political of period to a due et al. 2009) and of the smalltooth sawfish sawfish smalltooth the and of 2009) Acipenser sturio sturio Acipenser et al. 2000;Ungricht, Rasplus & 2008). However,as esting methods have have esting methods λ ( t ) is proportional to to proportional ) is and the ship ship the and et al. et

Accepted Article of an invasive Amurspecies, the sleeper Jaric make etal innovative (2012) of use Solow’s 1993a model toinvestigateexpansion the dynamics. population into newinsights provide to used be can data prov study andtheir realise managers many than flawed with regard to theiroriginal collection purpose, actually contain far more information Cassey, Lockwood &Fennl(2007) concluded that Ammodramus maritimus mirabilis sparrow seaside Sable Cape theendangered in abandonment survey site estimate b has data survey scale, morelocal an even On states. England New in taxa plant of persistence inferred (2006) Ogurcak and Farnsworth Likewise, reptiles. and frogs for theexercise repeated (2010) McDonnell and Hamer groups. species between varied persistence how investigating and areas government local and outer Me thegreater of in mammals persistence inferthe to used themethods (2005) ReeandMcCarthy vanvein, similar der In a This articleThis protectedis by Allcopyright. rights reserved. to calculate a current not allthe 2, in seen Table As can be methods Bayesian distributions. species invasive riverine of assessment preliminary a as managers conservation to value potential of therefore and apply to is quick it assessment distribution species simplistic very a method gives the probability species a that is locality presentcertain at a the Although river. in the down most fromthe records sighting ordering distribution, temporal for length river of distribution spatial dimensional one the substituting extinction Bennett (e.g. to vulnerable particularly are species thatsome theories to support lending and Neotropics lo thefirsttobe been also had park from the beenlost have to likely were which species that finding to1970, prior Mexico, park, national Palenque the birds in non-migrant for history asightings establish to able were (2010) Patten Smith- & deSilva Gomez Patten, management; area protected in used be can methods The pectinata Pristis p -value for the null hypothesis that the taxon is extant. Bayesian methods can can methods Bayesian is extant. taxon the that hypothesis null the for -value (del Monte-Luna (del et al. et 2005). 2005). lbourne area, Australia, comparing persistence between inner inner between persistence comparing Australia, area, lbourne et al. et inthe Everglades (Cassey,Lockwood & Fenn 2007). 2007; del Monte-Luna, Castro-Aguirre &Brook 2009). Perccottus glenii een used in conjunction with the methodsconjunctionwith to the een usedin st from forestst from fragmentsother in of areas the methods in the literature use sighting records records sighting use literature the in methods stream to the most upstream, they estimate moststream to they upstream, the ides an excellent example of how sightings sightings how of example excellent an ides long-term surveylong-term data, even if statistically , in the Danube River. By River. theDanube , in Accepted Article is: probability2). theorem, From the Bayes that extent of threats affecting the species. Within the above equation, the prior could be could prior the equation, above the Within species. the affecting threats of extent independently be generated (McCarthy Masters 2005). An & informative priorprobability for the extant taxon could a is which sighting rate the is the prior probability that the taxon is extant, independent of the observed sighting record. record. sighting theobserved of independent is extant, thetaxon that probability prior is the time Priorscan beelicited experts (Martin from the precision of estimates without systematically reducing accuracy (Morris increase can priors informative appropriately, specified When 0.5. of probability prior to the close remain will extant is species the that theprobability information, little contain and thus scarce are data Ifsighting available. data sighting of amount the on depends then prior the the sighting data to dominate the calculation of the posterior probability. The influence of allows prior non-informative a Using extinct. or extant tobe likely equally as judged is taxon withou that means which 0.5, of probabilities prior usednon-informative to have appear 2010) 2007;Hamer Gerlach McDonnell & Studies that have applied methodthis tospecies data(e.g. (van Ree&McCarthyder 2005; where where This articleThis protectedis by Allcopyright. rights reserved. time unknown atsome occurs extinction that and process, aPoisson represents test, this hypothesis with Ba theanalogous Solow included(1993a) aBayesian adaptationof his aforementioned hypothesis test. As section). next decisions (see management to inform appl be thatcan measure a obtains it because of interpretation over confusion potential any it avoids because the probability the that taxon isextant, Thisobserved given advantagefirstly isan the data. the contextsighting recordmethods, of they be used maketo probabilistic statements about state the ofworld the (McCarthy 2007). In p ( T E T , > p p ( T ( t t | | | t T T ) E E = > ≤ T

T p ) is the is likelihood) sightingof record the ) is the likelihood) is of sighting the species if record the extinct by is ( t | T E λ ( > t ) is constant but unknown and after which which after and unknown but ) isconstant T p from sighting history by considering, for example, the type and and type the example, for considering, by history sighting from ) ( π t | + T E p ( > t | T T ) E π ≤ et al. yesian model assumes the sighting record T can directly calculate the variable of interest: interest: of thevariable calculate directly can t the information from the sighting data, the the data, sighting the from t theinformation the taxon is extant given the sighting record record thesighting given extant is taxon the ied unambiguously in a structured framework framework structured a in unambiguously ied )( 1 2005) orpredicted through meta-analysis − π ) , t given that the species is extant at extant is species the that given p -values and secondly secondly and -values λ ( t ) falls to zero (Table (Table zero ) falls to et al. T 2015). E until until T , and t π

Accepted Article sightings vary in uncertainty, e.g. photographs, photographs, e.g. uncertainty, in vary sightings Thompson to sightings certain rate (Thompson throug time atany occur can sightings uncertain general more for aimed papers Subsequent sightings. certain as rate the same at occurring thelatter with sightings, uncertain valid and uncertain false both comprised sightings Uncertain sightings. stationary Poisson processes, withcertain sightings occurring first, followed by uncertain si uncertain and Certain uncertain. as others principalis Campephilus woodpecker ivory-billed of the record sighting controversial the an example as using (2012) extended the 1993a Bayesianmethod incorporateto sightings of uncertain validity, This article isprotected bycopyright. All rights reserved. calculations (Roberts, Elphick &Reed 2010; Rout, Heinze&McCarthy 2010). Solow extinction of theresults on effect substantial havea often and make to difficult be can valid themas treat or sightings uncertain exclude to whether on Decisions valid. to opposed as aural observationcan be termed‘uncertain’ sinc Thompson (Solow sightings of validity the Solow’s Bayesian 1993a method has also been extended to accommodate uncertainty as to record). sightings historical the of outside data additional it uses because 2 Table in this not included have we – rate sighting in of decline magnitude the to estimate data trapping used (2010) McCarthy & Heinze Rout, (Similarly, 2009). McCarthy & Salomon (Rout, magnitudes possible all for solution a providing than rather record sighting the from decline rate sighting of magnitude the estimating by obtained was extinction of probability difficulties in obtaining analytical solutions formore complex this Bayesian model, a in apopulation declining toextinction (Rout, Salomon& McCarthy 2009). Because of occur would as rate, sighting decreasing a for account to extended been has It development. this method, frequentist analogous the with As accessible. less calculation expressed probability as a distribution instead of value,fixed a although wouldthis make the et al. et al . 2013and. Lee 2014) as summarised 2014) inTableexample, 3.For sightings basedon visual or . Sightings based on physical evidence are classed as certain, the certain, as classed are evidence physical on based Sightings . etal. et al . 2014 account for the fact that different types of of types different that fact the for account 2014 . 2012; Lee 2014;2012; Lee et al . 2014, Lee characterisations of this problem where where problem this of characterisations ghtings were assumed to follow independent independent tofollow assumed were ghtings simple model provided a basis for further for basis provided a model simple expert visual records, audio records, local local records, audio records, visual expert hout the sightings record, and at a different different a at and record, sightings the hout e they could potentially be false (mistaken) (mistaken) befalse potentially could they e et al. etal. 2014, Solow Beet2014). and 2014; Solow 2014; Beet2014; & et al . Accepted Article allows the user to combine and utilise different types of information in a transparent way atransparent in of information types different utilise and combine to user the allows be can information prior of incorporation The methods. Bayesian the of performance the regarding known is less Consequently, statistics. be to tending biologists conservation perhaps and 2014), (Alroy empiricists and theorists between communication of alack comparable, particul a for being developed methods Bayesian relative recentness of manyof the Bayesian met frequentist. the than 1) (Table here described ha data real and/or onsimulated tests Fewer Applications increasing). or decreasing be could (it trajectory population the about made are no assumptions but toextinction, prior constant not is size population where species to beapplied can model this input, as record thesighting only Using size. model whichdetection in and persistence probabilities function aspopulation vary a can of general more a for abasis as this used (2014) Barry and Caley record). sightings historical the Table this in not included have (we extinction of point the until up size population constant a assumes essentially this and persistence, year. year to from persist will it probability the and record, sighting fromits calculated of detection, probability annual its given extant Regan Regan (Lee2014). persists uncertainty include canalso that spreadsheet This article isprotected bycopyright. All rights reserved. Thompson of One known. rarely is effort search practice, in data), unpublished McCarthy, M.A. which search Bayesianeffort in models other reduces uncertai the survey. feature Whilst this surveys, of using expert-derived estimates the oc sightings where cases accommodating effort, Thompson time. than continuous rather time intervals uniform discrete into record thesighting partition methods Both reports. et al et . (2006) developed a simple model for calculating the probability a species is aspecies is calculating theprobability for (2006)developedmodel asimple . et al .’s (2014) (2014) .’s solution methods provided is within freely a available Excel et al In assuming constant probabilities of detection detection of probabilities constant assuming In . 2014 also takes into account historical search search historical account into also takes . 2014 ve been conducted on the Bayesian methods methods theBayesian on conducted ve been is not incorporated (C.F. Clements, T.E. Lee & (C.F.Clements, incorporated Lee& is not T.E. 2 because it uses additional data outside of of outside data additional uses it because 2 in the priorin probability speciescurrentlythe an advantage of Bayesian methods because it it because methods ofBayesian an advantage more familiar with frequentist than Bayesian Bayesian than with frequentist more familiar This is likely due to a number of factors: the factors: of number a to due likely is This probability of sighting the species during a nty in extinction when compared to three cur both withinoutside and of structured hods compared to frequentist, several of the ofthe several frequentist, to compared hods ar set of circumstancesar setof beingand thus less Accepted Article to damaging impacts caused by a remnant po remnant a by caused impacts to damaging when species stillthe present. is For invasivefalsely species, declaring eradicationcan lead eradication declaring of thecost against balanced surveys, more performing by certainty declared be should aspecies which at certainty the context of declaring eradication of an invasi Regan Regan co and therisks and management, conservation is important toconsider the implications of declaringextinction, such as thecessation of it threshold, this setting In 2001). (IUCN doubt”? of level “reasonable a is what i.e., extinct, of leveltothe determine decision makersto While thesestatistical methods measure a provide extinction declare to Deciding decisions. management in appropriate) them(as utilise to as well as methods Bayesian of accuracy and thepower test to confident more feel will practitioners conservation hope that we methods published with provided This article isprotected bycopyright. All rights reserved. Thompson of development Ba recent hasbeen much There to theprior require principles Bayesian since use its in caution urge we but extinction, predicting in robust tobe appears 2007) (Arecent‘Bayesian’ method whichuses the sighting data tosetthe prior (Alroy 2014) constructed be must they so posterior, the have can probabilities prior used, information scopeof threats to which hasbeen exposedit (Lee Regardless 2014). of the type of Starling Mountain Pohnpei the 0.48 that 0.24and between of prior probability a estimated experts outcomesspecies’ the and lifehistory traits), the and processes threatening between interaction the (including processes threatening of historicala sighting record includes information about the timing and severity of (McCarthy 2007). Additional information relevant toassessing extinction independentbut et al et Aplonis pelzelni . (2006)provide. the first formal analysisofthis decision taxon, for a although in et al. 2014, Lee was was extant in based2014, the severity, on duration geographic and et al be set independent of the data.) data.) the of independent be set . 2014, Lee 2014) and with code generally now being now being generallycode with and . 2014, Lee 2014) in a logical and repeatable fashion (McCarthy (McCarthy fashion and repeatable logical a in certainty at which a taxon should be declared declared be should ataxon at which certainty of recent targeted surveys. For example, surveys.example, For targeted recent of pulation allowed to grow to allowed pulation an important influence on the calculation of of calculation the on influence important an eradicated depends on the cost of increasing increasing of cost the on depends eradicated ve species. They argue that the level of level ve species. argue the They that nsequences of making of nsequences yesian methods for inferring extinction (e.g. (e.g. extinction inferring for methods yesian ofthe certaintyof extinction, itstill falls the wrong decision. decision. wrong the undetected. Regan Regan undetected. Accepted Article models are either extremely hard to judge, or rarely achievable in the haphazard manner manner haphazard the in achievable rarely or judge, to hard extremely areeither models thevarious for required are that theassumptions of many First, records. sighting using make to required are developments related Two relevant information of forms other systematic the for sighting with of integration framework records development the a of recommend strongly we and record sighting in a contained information limited the not just information, available all incorporate to is important it population ofa status the assessing when However, decision-making. conservation inform help and status population’s a of understanding our increase can application correct Their use. to knowledge limited this put to way practical a are methods these sightings, opportunistic to is limited population a of knowledge which in instances the In taxon. their for method appropriate most the choose to users enable to hope we 3), and 2 1, Tables and text (main method of each time series sighting records. of In this reviewwe summarise the range of statis Conclusions 2010). McCarthy& Rout, Salomon Heinze McCarthy 2009; & (Rout, records sighting historical of analysis Bayesian the including 2009), Morrison & Parkes (Regan is extant taxon undetected an probability of variety a analyses, decision these Within the taxon is extant. cost-effectivewhen iscost-effective to that given it theprobability survey, tomanage and possibly of management tothe economic approach This article isprotected bycopyright. All rights reserved. cost. thetotal Chadès tominimise expected extinct be declared taxashould when calculate and loss, monetary expected an into this Theytranslate ceased. as the increased expectation that species extinctthe will management if go intervention is extinction declaring wrongly of consequence the describe They extinction. declaring about decisions cost-effective to making approach this adapt (2010) McCarthy & Heinze Rout, detection, without surveys of anumber as al et . (2006) calculate. (2006) when aspecies should be declared successfully eradicated, expressed By providing information on the assumptions and limitations to the assessment of extinction. totheassessment of extinction. to minimise the total expected cost. tominimisecost. expected thetotal methods have been used to calculate the the calculate to have beenused methods tical methods used to infer extinction froma tical usedtoinfer methods further progress in inferring extinction extinction progress in inferring further etal. extinct taxa, finding scenarios when it is when itis scenarios finding taxa, extinct 2006;Chadés et al et al. .(2008) also take.(2008) an 2008; Ramsey, 2008; Accepted Article based particularly on this last property. property. last this on particularly based are extinction testing for methods Thestandard period. time the over distributed uniformly λ are there that Then, given interval. time that dt over independently rate integrated bythe given is thedistribution of mean the where distribution, [0, period time afixed in sightings number of the process, aPoisson For all for constant is a This article isprotected bycopyright. All rights reserved. record sightings theoverallsighting rate then but were decreasing abundance if for example, is assumption that natural is interpretation etc. The that i.e. taxon, the of conspicuousness the as well it as observe to made is that of effort amount the and taxon, of the abundance the on both depends sighting given any of chance The time. aparticular at region study the in anywhere detected) otherwise (or observed is it if sighted is taxon A thetaxon. of of sightings by process Poisson a of function therate denote We time. through taxon particular aresightingsevents ofa of interest In another. one of independent statistically are time of intervals non-overlapping in events of numbers and time continuous in Poisso bya represented be can sightings species is that extinction inferring for methods most statistical underlying assumption basic The process Poisson The 1: Box and decisions on thresholds can be effort survey quality, where settings, in appropriate most be will here, covered those as such models, extinction the but longer the areno intensity. threat and effort survey incorporate and quality, record for account ofrecords, time-series of analyses into uncertainty withwhich many sightingsrecords are accumula ( t )/ Λ ( T ). In the special case case when special Inthe ).

and identically distributed t , then Poisson the process ishomogeneous (stationary). λ ( t ) is directly proportional to bo to proportional directly is ) λ ( t ) is the instantaneous rate of ) is instantaneous time sightings rateof the at λ made dependent on the desired use. desired on the dependent made ( t ) is a constant, constant, a ) is over the time periodwithover probability time the density the statistical methods discussed here, the here, discussed methods statistical the data required to produce them. Techniques Techniques them. produce to required data could remain constant or even increase. If constantor increase. even remain could whether it is cryptic, camouflaged, nocturnal nocturnal camouflaged, cryptic, is it whether Second, many of the limitations of inferring of inferring limitations the of many Second, sufficient effort were made to obtain and and obtain to weremade effort sufficient n process, one in which events occur singly ted. Better ways are needed to incorporate incorporate to Better areneeded ways ted. f λ n ( , which in this case represents the rate rate the represents case this in which , sightings in[0, t ) =1/ th abundance and effort so that, that, so effort and abundance th T so that the sightings are are thesightings sothat T ], thesesightings are T ] has a Poisson Poisson a has ] Λ t ( and a a and T ) = ∫

f λ ( ( t λ t ) = ( ) t ) Accepted Article Method Constant Method This article isprotected bycopyright. All rights reserved. Grimson &Ferson 1995) Discrete time form (Burgman, Solow 1993b Solowfrequentist 1993a rate sighting Increasing rate sighting Decreasing rate sighting Constant records: Sighting calculate the discrete in period one time within we form time Grimson (Burgman, the 1995) &Ferson sightings multiple of thecase illustrate To described. methods frequentist of the 7 using rates sighting and increasing decreasing constant, with records sighting hypothetical three Here wecalculate Box Example2: Calculations can temporal be if resolved resolution a frequent sightings, Poisson then the assumptionbecomewill appropriate; less this situation in resulting regions spatial larger into areaggregated data if However, important. to be unlikely is distinction thenthis region, study the rarein relatively are thesightings as long As time. continuous in occur sightings the that assume and distinction this ignore to convenient it is below,Grimson Inthedescriptions resolved &Ferson adequately (Burgman, 1995)). data are the provided used be may unit any although years (usually intervals time of set discrete a in sightings of absence or thepresence of records of theyconsist location, aparticular at of sightings times exact of list a being than rather is, That discretised. often In aPoisson process, events occurcontinuous in time, while in practice, sightings data are 1 p 8819 9414 9815 1954 1952 1954 1948 1890 1940 1858 18281924 1892 1842 18281834 1830 1828 1849 -value based on an extra sighting in one of the time periods. periods. thetime of one in extra sighting an on based -value p -values (as calculated for for calculated (as -values 1 1 1849 8019 9213 1954 1933 1912 1891 1870 Sighting record Sighting sighting rate .7 0.920 0.044 0.271 0.065 llows the time intervals to be shortened. shortened. intervals tollows be thetime T = 2014)or estimated= year of extinction using

sighting rate Decreasing sighting rate Increasing Accepted Article brackets in given This article isprotected bycopyright. All rights reserved. 2 1 & Solow 2003) Optimal linear estimation (Roberts 6 eqn (2010) Ebenhard & Jaric McInerny (2003) Solow Roberts & Estimated year of extinction with lower and upper confidence limits for calculated Estimated upper extinctionyear and of with lower p-value for the null hypothesis that the species is extant extant thespecies is that hypothesis null the p-value for et al et . (2006) 2

1 1 1 .5 .5 0.123 0.032 0.356 0.516 0.258 0.032 0.259 (1956, 2178) 1988 (2020, 2089) 2089 (1954, 2006) 1956 α =0.05 Tends to Tends to Accuracy underestimat overestimat increases Model e extinction e extinction with Notes time time number of

Article sightings

Accuracy decreases with decreasing search effort1 and gradually decreasing Solow 1993a 1 1 population2.

Low power if extinction time is close to the end of the observation period3 , if n

3 1 Solow 1993b 1 1 < 15 or if search effort is increasing .

Upper confidence interval can be infinitely high3.

Discrete-time form Tends to overestimate extinction time, particularly when search effort is (Burgman, Grimson & 1 constant or increasing1 . Ferson 1995).

Partial Solow Equation Accuracy decreases with gradually decreasing population2 . (McCarthy 1998).

Optimal Linear 1,4  No bias towards under/over estimation but greater mean error in Estimation (Roberts &

1,4

Accuracy increases with number of sightings with a recommended k >5 and preferably >10.

Type I (extant species declared extinct) and Type II (extinct species declared extant) errors high under exponential, accelerating and linear abundance declines. Upper confidence interval can be millennia into the future for species with few or widely spaced sightings4 .

Accuracy declines with irregular and/or decreasing search effort5 .

Robust to gradual population decline and low sighting probability2 .

Tends to overestimate extinction time, particularly when search effort is Solow and Roberts 1,2 1,2 constant or increasing . 2003 Confidence interval can be very wide (Solow 2005).

Unaffected by n and by the length of the observation period thus suitable for

McInerny et al. 2006 taxa that have been discovered relatively recently.

Accuracy decreases with gradually declining population2 . Accepted

Article Table 1. Summary of the frequentist models’ performance on simulated and microcosm data. For a detailed study of the models’ error rates see 1Clements et al. (2014), 2Rivadeneira, Hunt & Roy (2009), 3Solow (1993b), 4Collen, Purvis & Mace (2010), 5Clements et al. (2013) and 6Jaric & Ebenhard (2010).

This article is protected by copyright. All rights reserved. Accepted Accepted Article uncertainty sightings. in Table 3. Summaryof data the requirements of the Bayesian models that allow for example, consider that that consider example, t period observation the of start the define to be used may sighting Thefirst (1995)). Ferson sight of a 1: representation Figure A schematic This article isprotected bycopyright. All rights reserved. 4 3 2 1 2 Table

= (55/100) Using Solow’s 1993a method, the corresponding in an inincrease sighting -blue,rate decreasing sighting rate with the sightinglast data increasing incrementsin 10 years of from 1954to 2004, constant usin sighting to records additional information availability. Sensitivity analyses havebeen conducted for models whichdo not require Caley and Barry (2014) Barry and Caley Lee (2014) Lee sExtinct package (2013), ALanguage in R: and Environment for Statistical Computing (2012) 0 , and this in case would omittedbe from number the of sightingstotal toleave et al et . Summarymodels’ of the outputs, assumptions, data requirements codeand . (2014) . 5 = 0.0503 (Burgman,Grimson &Ferson 1995). T = 100, = and the most recent observation p -value, extinction year or p -value of the null hypothesis that the species is extant is is extant is species the that hypothesis null the of -value - red, increasing sighting rate - yellow), yellow), - rate sighting red, increasing -

g the hypothetical records given inBox 2and ing record (with credit to Burgman, Grimson Burgman, & to credit (with ing record P(extant) t 5 madewas at .

n t = 55. =55. = 5. For For 5. = p

Accepted Article This article isprotected bycopyright. All rights reserved. ex Quantifying (2014) S.C. &Barry, Caley, P. (1995) S. Ferson, & Grimson, R.C. Burgman,M.A., Mace, & K. O'Connor, N.E., Clark, C.-Q., Ding, R.A., Fuller, P.J.K., McGowan, E.H., Boakes, Alroy, AJ. (2014) smiple Bayesia Literature cited data. include not does manuscript The Data Accessibility manuscript. comments the on for their referees two anonymous Bu Mark Butchart, Stuart thank We Decisions. Excellence for Biosecurity Risk Analysis and at developed was manuscript The Foundation. Rufford bythe supported partly was BC Program, Research Environmental National (DP110101499) and the EnvironmentalDecisions Council, TMR was supported byan Australian Research Council APD Fellowship Research Environment Natural The and Trust Leverhulme The by supported was EHB Acknowledgements Butchart, S.H.M., Stattersfield, &Brooks, A.J. T.M. Going(2006) or gone: defining 'Possibly D. Burgman, M.A., Andrewartha, Maslin, B.R., Bennett, P.M., Owens, I.P.F., Nussey,D., Garnett, & Crowley, G. S.T. Mechanisms (2005) of inferences and uncertainties. uncertainties. and inferences Conservation Biology, data. occurrence G.M. Distorted (2010) views of biodiversity: spatial and temporal bias inspecies Cambridge. GittlemanA. 317-336. pp. Brooks), J.L. &T. Purvis, Ornithologists' Club, Ornithologists' extinctions. recent of picture truer a give to species Extinct' (eds FersonS. &M.A. Burgman), pp. 7-26. Western Australian (2000) Inferring threat from scientific collections: power tests and an application to threats. and ecology phylogeny, birds: in extinction

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9, 923-928. n method of inferring extinction. extinction. inferring of n method specise. 7-24. PLoS One, PLoS

8, e1000385. Quantitative Methods for Conservation Biology The Centreof ExcellenceThe Environmentalfor tinction probabilities from sighting records: , Keatley, M.R., Boek, C. & McCarthy, M.A. rgman, Chris rgman, Clements, Greg McInerny and a workshop funded by The Centre of of Centre The by funded workshop a

9, Hubof the AustralianGovernment’s Inferring threat fom scientific collections. collections. scientific fom Inferring threat

e95857. Springer-Verlag, York. New

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40, 584-607. (eds Accepted Article This article isprotected bycopyright. All rights reserved. to information occupancy long-term Using (2007) K.H. & Fenn, J.L. Lockwood, P., Cassey, Chong, K.Y., Lee, S.M.L.,Gwee, A.T., Leong,P.K.F., Ahmad, S., Ang, W.F.,Lok, A.F.S.L., Yeo, &Possingham, M. Linkie, B.A., Wintle, M.A., McCarthy, E., McDonald-Madden, I., Chadés, Cooke,P. &Li, S.(1996) Convention on Biological Diversity (2010)COP Decision 10 - X/2 Strategic Plan for ProbabiliCollen, (2009) Turvey, S.T. B. & Collen,Purvis, B., &Mace, G.M.A. When is(2010) a species really extinct? Testing extinction Collar, N.J. Extinction(1998) by assumption: the or Romeo on Error Cebu. Clements, C.F., Worsfold, N.T., Warren,P.H., Coll Clements, C.F., Collen, Blackburn, B., &Petchey, T.M. O.L. Recent environmental(2014) Clements, C.F.sExtinct: (2013) Calculates historicthe date of extinction of series given a del Monte-Luna, P., Lluch-Belda, D., Serviere-Zaragoza, E., Carmona, R., Reyes-Bonilla, H., H., Reyes-Bonilla, R., Carmona, E., D., Serviere-Zaragoza, Lluch-Belda, P., Monte-Luna, del del Monte-Luna,P., Castro-Aguirre, Brook,(2009) J.L. & Putative B.W. extinctionof two Conservation, Sable Cape of management the inform of presumed nationally extinct species. species. extinct nationally presumed of C.K., Corlett, R.T.&Tan, (2012)Herbarium H.T.W. records do not predict rediscovery 105, America, of States United the of Sciences of Academy National the of Proceedings H.P. When (2008) to stop managing or crypticsurveying threatened species. Biodiversity 2011-2020. Montreal, Canada. Oxford. Press, chronologies. Distributions, inference from asighting recordconservation to inform assessment. optimal linear estimationmodel. O.L. Experimentally (2013) testing the accuracy of an extinction estimator: Solow's 981. extinction. of inference accurate affect may change sighting events. 2599. Brook, MarineB.W. (2007) extinctions revisited. Aurioles-Gamboa,D., Castro-Aguirre, J.L., GuzmanProo, del S.A., Trujillo-Millan,O. & 512. sawfish species in Mexico and the United States. Statistics Deparment, University of New South Wales, Sydney. 13936-13940.

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7, 28, 239-244. Biological Biological 509- 971-

. Accepted Article Jaric, I. & Ebenhard, T. (2010) A method for for A method (2010) T. Ebenhard, I. & Jaric, Jaric, I., Cvijanovic,G.,Hegedis, Lenhardt,& A. Assessing(2012) M. the range newlyof IUCN (2014) IUCN Red List of Threatened Species. (2001) IUCN urbanization: to herpetofauna of response The (2010) M.J. McDonnell, & Hamer, A.J. Gerlach, J.(2007) Short-termclimatechange extinction the and snail of the Fuller, E.(2001) Grimson, R.C., Aldrich, & Wanzer Drane, T.E. J. Clustering(1992) in sparse and data an Fisher, D.O. Blomberg, & S.P. Correlates(2011) of rediscovery anddetectability the of New in rareplants of decline and Biogeography (2006) D.E. Orgucak, & E.J. Farnsworth, European Bird Census Council (2006) European common bird index: population trends of of trends population index: bird common European (2006) Council Census Bird European This article isprotected bycopyright. All rights reserved. Elphick, C.S., Roberts,D.L.& Reed, (2010)Estimated J.M. dates of recent extinctions for Diamond, Extant (1987) unless J. provenextinct extinct? Or, unless proven extant? Dulvy, N.K., Ellis,Goodwin, J.R., Grant, N.B., A., Reynolds, J.D. Jennings,& Methods S. (2004) Duffy, K.J., Kingston, N.E.,Sayers, B.A., Roberts, Commission. IUCN,Gland, Switzerland and Cambridge, UK. 580. Inferring patterns persistenceof from wildlife databases. that change in frequency over time. time. over frequency in change that 680, established invasiveusingprobabilisticin rivers speciesmethods. analysis of aldabrae 1090-1097. extinction in mammals. 16, monitoring. contemporary and evidence Historical England: European common common European North American and Hawaiian birds. birds. Hawaiian and American North Conservation Biology, of assessinginfishes. extinction marine risk Biology, and regional declines of rare orchid species with probabilistic models. 1327-1337. 171-178. IUCN Red List Categories and Criteria: Version 3.1 Version Criteria: and Categories List Red IUCN

Extinct Birds 23, (Gastropoda: Pulmonata). Pulmonata). (Gastropoda: Rhabdomyosarcoma 184-195. birds 2005 update. 2005update. birds . Cornell. UniversityPress, Ithaca, New York.

1, Proceedings of the RoyalSociety B-Biological Sciences, 77-79. incidence. incidence. inferring extinction based on sighting records records sighting on based extinction inferring Biology Letters, Wildlife Biology, Biological Conservation, Biological Pan-European Common Bird Monitoring Bird Common Pan-European D.L. Stout,& J.C. Inferring (2009) national Statistics in Medicine, in Statistics Fish and Fisheries, and Fish

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Biological Conservation, Biological 20, Diversity and Distributions, and Diversity 717-726. 562-657. Journal of Applied and and Diversity and Methods in Methods in Acipenser

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Accepted Article Solow, (2005)Inferring A.R. extinction from asightingrecord. Solow, (1999)Evidence A.R. ofdeclining abunda Solow, (1993b) A.R. Inferring extinction inadeclining population. Solow, (1993a) A.R. Inferring extinction from sighting data. Rout, T.M., Salomon,McCarthy, & Y. Using M.A. (2009) sighting records todeclare Rout, T.M., Heinze, D.& McCarthy, M.A.(2010) Optimal allocation of conservation resources Robson,D.S. &Whitlock, J.H. (1964) Estimation a truncationof point. become extinct? dodo did the When (2003) A.R. Solow, & D.L. Roberts, This article isprotected bycopyright. All rights reserved. Roberts, D.L., Elphick,& Reed, C.S. (2010)Identifying J.M. anomalous putatively reports of Robbirt, K.M., Roberts, D.L. &Hawkins, Comparing J.A. (2006) IUCN and probabilistic Rivadeneira, M.M., Hunt, G. &Roy, use The K.(2009) of sighting records toinfer species Regan, T.J., McCarthy, M.A., Baxter, P.W.J., Panetta, F.D. Possingham, & H.P. (2006) Optimal Quantifying Morrison, (2009) Parkes,Ramsey, & D.S.,S.A. L.J. eradication success: the R:LanguageR Core (2012) A Team and Environment Statisticalfor Computing. bird the in changes Long-term (2010) B.D. &Smith-Patten, H. Silva, de Gomez M.A., Patten, Morris, P.J. W.K.,Vesk, M.A., The P.A., (2015) Bunyavejchewin, McCarthy, Baker, & S. 195, Zoology, of Journal Israel Biology, eradicationof an invasive species. to species that may be extinct. 245. extinct speciesmatters. why and it Biodiversity and Conservation, assessments of threat: reddo listIUCN criteriaconflate rarity and threat? extinctions: an evaluation of different methods. eradication: stoplookingplant.an invasivewhenfor to 449-459. California. CruzIsland, Santa from pigs of feral removal Conservation, community of Palenque, Chiapas, inresponse torainforest loss. accuracy. model on effect priors' informative testing study case a shed: ecologist's Bayesian the in tool neglected 47-55.

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Accepted Article Willis, (1999) of roleJ.H. The of genes large effect on inbreeding depression in Vogel, R.M., Hosking, J.R.M., Elphick, C.S., Roberts, D.L. &Reed, (2009)Goodness J.M. of fit van Ree, der & McCarthy, R. M.A.(2005) Inferring persistence of indigenous mammals in Ungricht, Rasplus,S., &Kjellberg, J. Extinction F. (2005) threat evaluation of endemic fig (2009) Turvey, S.T. Thompson, C.J., Lee, T.E., Stone,L., McCarthy, M.A. & Burgman, M.A. (2014) Inferring This article isprotected bycopyright. All rights reserved. Strauss, D. Sadler, & P.M.Classical (1989) confidence intervals and Bayesian probability Solow, W., M.A., Burgman, Rout, Smith, A.R., sighting a on based forextinction test Anonparametric (2003) D.L. &Roberts, A.R. Solow, Solow, OnA.R. &Beet, uncertain A.R. (2014) sightings and inference about extinction. guttatus Biology, Mathematical of probability distributions for sightings as species approach extinction. urbanisation. to response herbarium collections. trees of New Caledonia: priority assessment for taxonomy and conservation with extinction risks from sighting records. estimates ends of taxonlocal ranges. Biology, Uncertain sightings and extinctionthe ivory-billed of the woodpecker. record. Conservation Biology, Ecology,

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