An experimental test of a Bayesian method for inferring extinction with varying search efforts

21 Determining whether a species is extinct or extant is notoriously difficult, but is 22 fundamental to both our understanding of biodiversity loss, and our ability to 23 implement effective conservation measures. Many methods have been proposed in an 24 attempt to infer quantitatively whether a species has gone extinct, with many seeking 25 to do so by using sets of historic sighting events. Until recently, however, no methods 26 have been proposed that explicitly take into account search effort (the proportion of a 27 habitat searched when looking for a species), a key determinant of if/when historic 28 sighting events have occurred. Here we present the first test of a recently proposed 29 Bayesian approach for inferring the extinction status of a species from a set of historic 30 sighting events where the search effort that has produced the sightings can be 31 explicitly included in the calculation. We utilize data from a highly tractable 32 experimental system, as well as simulated data, to test whether the method is robust to 33 changing search efforts, and different levels of detectability of a species. We find that, 34 whilst in general the method performs well, it is susceptible to both changes in search 35 effort through time, as well as how detectable a species is. In addition, we show that 36 the value of the prior expectation that the species is extant has a large impact on the 37 accuracy of the methods, and that selecting correct priors is critical for accurate 38 inference of extinction status. 39 40 Introduction 41 Accurately determining whether a species is extant or extinct is notoriously 42 difficult (Keith & Burgman 2004), with many quantitative methods developed to infer 43 extinction status (e.g. Solow 1993a, 2005; McCarthy 1998; Gotelli et al. 2011). Such 44 methods have typically concentrated on inferring the extinction status of a species 45 from sets of historic sighting events (i.e. a species has been observed as being extant 46 PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.466v1 | CC-BY 4.0 Open Access | received: 15 Aug 2014, published: 15 Aug P re P rin ts at a given point in time, e.g. Roberts & Solow (2003)), as these are often the only 47 records of a species prior to a possible extinction event. Such sighting events are a 48 product of the detectability of a species (how easy it is to observe), the amount of the 49 species’ habitat you search, and the abundance of that species. For example, if a 50 species has a very high population size, and is very detectable, only a small fraction of 51 its habitat needs be searched for the species to be observed, whereas low population 52 sizes and low detectabilities mean that even searching a large proportion of the habitat 53 may not guarantee a sighting. Consequently, information on the search efforts that 54 produced a set of historic sighting events could provide important additional 55 information in determining whether species is extant or extinct. Whilst in the majority 56 of instances these data are unlikely to be available, in some circumstances active and 57 systematic searches for particular species are conducted to determine population size, 58 or to definitively classify a species as extant or extinct (e.g. Turvey et al. 2007). Such 59 scenarios can provide information in addition to simple presence/absence data. This 60 potentially important additional information is disregarded by many estimators of 61 extinction (Solow 1993b, 2005; Roberts & Solow 2003). 62 Recently, in an attempt to overcome this issue, a Bayesian approach has been 63 proposed that explicitly accounts for the effort invested in producing sighting events, 64 as well as implicitly taking into account the detectability of a species (Thompson et al. 65 2013). Such a method might significantly improve attempts to classify a species as 66 extant or extinct, if data are available to parameterize it. However, as with many 67 traditional parametric and non-parametric based methods, testing the performance of 68 these techniques for inferring extinction remains limited (Clements et al. 2013). 69 Here we tackle this problem by using data generated in highly controlled, small70 scale experimental systems (shown to provide realistic sets of historic sighting events 71 PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.466v1 | CC-BY 4.0 Open Access | received: 15 Aug 2014, published: 15 Aug P re P rin ts (Clements et al. 2014)), and simulated population declines, to test the reliability of a 72 proposed Bayesian method for estimating extinction status (Thompson et al. 2013). 73 We simulate a range of search efforts, and species detectabilities, to produce sets of 74 historic sighting events (as proposed in Clements et al. 2013). We then assess whether 75 the method of Thompson et al. (2013) performs better than a Bayesian method for 76 inferring extinction that does not incorporate search effort (Solow 1993a), and how 77 robust the method is to varying search efforts, and levels of detectability, and values 78