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PEER-REVIEWED

Introduction

For many species our knowledge of their persistence is based on sightings that vary in quality and therefore the level of reliability (Roberts, Elphick & Reed, 2010). For species that are approaching extinction or that may already be extinct acknowledging this uncertainty can have profound effects on conservation decision-making, as erroneous evidence based on uncertain sightings can result in wasted resources (McKelvey et al., 2008). For example in 2005, based on a brief sighting and a pixelated image, the ivory-billed woodpecker was declared to have been rediscovered (Fitzpatrick et al., 2005), resulting in the mobilisation of resources for management strategies and recovery plans (Gotelli et al., 2012). However, based on the evidence its rediscovery was brought into question (Sibley et al., 2006), and subsequent extensive searches have failed to result in further sightings (Gotelli et al., 2012)

Several methods have been developed for the inference of extinction based on sighting data (see Solow, 2005 for a review), however until recently, these methods treated all sightings as certain. It has therefore been the responsibility of those using the methods to decide what data should be used and what should be discarded. Recently a number of methods have been developed that incorporate uncertainty (e.g., Solow et al., 2012; Jarić & Roberts, 2014; Lee et al., 2014).

Elphick, Roberts & Reed (2010) estimated the time of extinction for 38 of 52 North American and Hawaiian bird taxa and populations that are thought to be potentially extinct, along with the likelihood of extinction by 2009. In the study they based their analysis on sightings that are assumed to have the highest level of reliability (e.g., museum specimens), and then repeated the analysis by including additional sightings for which sufficient documentation exists to satisfy experts. In this way Elphick, Roberts & Reed (2010) attempted to acknowledge the issue of sighting uncertainty and incorporate it into their analysis on an ad hoc based criteria. Their analysis, however, excluded a number of controversial sightings that experts disagreed as to whether they should be accepted. In this paper we revisit this study, using a method that explicitly incorporates sighting uncertainty (Jarić & Roberts, 2014), to investigate the impact of accounting for sighting uncertainty when inferring extinction.

Methods

We apply here the approach of Jarić & Roberts (2014) that represents a modification of the existing methods for inferring extinction based on sighting records, which allows for inclusion of specific sighting reliabilities of individual observations. In line with the original approach, we apply it to the standard Solow method (Solow, 1993), which was also used to infer extinction by Elphick, Roberts & Reed (2010). For details on Solow method modification, see Jarić & Roberts (2014) as well as Supplemental Information 1.

We revisited the 52 North American bird taxa and populations assessed by Elphick, Roberts & Reed (2010) that are presumed to be extinct, or whose persistence is a point of discussion. In their study and used here, Elphick, Roberts & Reed (2010—supplementary material) compiled sighting records for all taxa but divided the sightings into three categories that form a nest hierarchy:

  1. Physical Evidence (PE)—e.g., museum specimens, but also uncontroversial photographs, video, and sound recordings.

  2. Independent Expert Opinion (IEO)—evidence that experts deemed sufficiently documented to confirm the record.

  3. Controversial sightings (CS)—sightings judged to lack firm evidence including any sighting for which there is published disagreement between experts.

Elphick, Roberts & Reed (2010) used the method of Solow (1993) for the inference of extinction (but also see Solow, 2005) and based their analysis on PE and PE + IEO, but excluded CS. Following Jarić & Roberts (2014), who applied the sighting reliability scoring system used by BirdLife International (Table 1 of Lee et al., 2014), we assign PE sightings (i.e., Lee et al.’s “Record described as being based on collected individual”) with a lower limit of reliability of 0.8, and upper limit of 0.9 and a mean of 0.85. This was repeated for IEO (i.e., Lee et al.’s “Record based on observation described in the literature as ‘confirmed’ or considered fairly convincing”) and CS (i.e., Lee et al.’s “Record described in the literature as (or judged to be) unconfirmed or questionable”), 0.6, 0.8, 0.7 and 0.1, 0.4, 0.25 respectively.

First sightings in each sighting record dataset were used to establish the beginning of the sighting period, and excluded from the analysis (Solow, 2005). Minimum number of sightings in a sighting record (n ≥ 5, i.e., 4 following the exclusion of the first sighting) was defined in line with Solow (2005) and Elphick, Roberts & Reed (2010). Consequently, analyses were conducted only for sighting records and reliability score setups with the most likely number of observations (r value, see Jarić & Roberts, 2014) of at least 3.5 (i.e., excluding the reliability score for the first sighting). The approach was used to estimate the p value for each species (with T = 2009 in line with Elphick, Roberts & Reed, 2010), probable extinction time (TE) and the upper bound (TCI) of a 1-α confidence interval (α = 0.05).

Results

Of the 52 taxa and populations, there were sufficient sightings to conduct analyses for 41, compared with 38 taxa and populations analyzed by Elphick, Roberts & Reed (2010). Estimated extinction dates (TE) ranged from 1855 to 2008, with the upper 95% bounds (TCI) on these estimates ranging from 1863 to 2113 (Table 1). Based on these analyses, there is no indication that any taxa and populations are likely to persist, including the ‘Alalā (Hawaiian crow, Corvus hawaiiensis) which was the only taxa in Elphick, Roberts & Reed’s (2010) study for which there was any indication of likely persistence. Taxa and populations for which the 95% confidence interval around the predicted extinction date includes dates after 2008 were Eskimo Curlew (Numenius borealis), Ivory-billed woodpecker (Campephilus principalis), ‘Alalā (Hawaiian crow), Kaua‘i ‘ō‘ō (Moho braccatus), O‘ahu ‘ō‘ō (M. apicalis), Kama‘o (Myadestes myadestinus), Oloma‘o (Moloka‘i) (M. lanaiensis rutha), ‘Ō‘ū (Kaua‘i) (Psittirostra psittacea), Nukupu‘u (Kaua‘i) (Hemignathus lucidus hanapepe), Nukupu‘u (Maui) (H. l. affinis), O‘ahu ‘alauahio (Paroreomyza maculata), Maui ‘akepa (Loxops coccineus ochraceus), Oahu ‘akepa (L. c. rufus) and the Po‘o-uli (Melamprosops phaeosoma) (indicated in bold in Table 1). In comparison, Elphick, Roberts & Reed’s (2010) analysis only observed such confidence intervals for the ‘Alalā (Hawaiian crow), as well as partly for Kama‘o, O‘ahu ‘alauahio and the Po‘o-uli (i.e., they had TCI > 2009 only when using PE, while for PE + IEO combination it was TCI < 2009). Elphick, Roberts & Reed (2010) only provided sighting data to 2009, and therefore other, most likely controversial, sightings may have occurred during the following years, assuming no further sightings have actually occurred since 2009. Taxa and populations for which the 95% confidence intervals around the predicted extinction dates include dates after 2016 were ‘Alalā (Hawaiian crow), Oloma‘o (Moloka‘i), Nukupu‘u (Kaua‘i), Nukupu‘u (Maui), O‘ahu ‘alauahio, Maui ‘akepa and the Oahu ‘akepa (Table 1).

Table 1:
Evaluated North American and Hawaiian bird taxa potentially considered extinct.
IUCN Red List category (http://www.birdlife.org/datazone/species accessed July 2016; CR(PE), Critically Endangered (Possibly Extinct); EW, Extinct in the Wild; EX, Extinct), year of last reported sighting including controversial sightings reported up to 2009 (Elphick, Roberts & Reed, 2010—supplementary material), number of years with confirmed records (n). Sighting reliability estimates give the upper, mean and lower sighting reliabilities as described in the methods. p is the probability of a sighting record in 2009, TE estimated year of extinction, and TCI the upper 95% bound on that estimate of TE. Years highlighted in bold represent results that do not support extinction.
Species IUCN Red List Last sighting n Sighting reliability p TE TCI
Labrador duck (Camptorhynchus labradorius) EX 1878 13 Upper 3E−7 1880 1889
Mean 7E−7 1879 1889
Lower 2E−6 1879 1890
Heath hen (Tympanuchus c. cupido) EX 1932 39 Upper 4E−15 1933 1936
Mean 7E−14 1933 1936
Lower 1E−12 1933 1936
Laysan rail (Zapornia palmeri) EX 1945 29 Upper 7E−9 1946 1951
Mean 3E−8 1946 1952
Lower 1E−7 1946 1952
Hawaiian rail (Zapornia sandwichensis) EX 1893 9 Upper 0.010 1905 1956
Mean 0.014 1903 1961
Lower 0.018 1900 1965
Eskimo curlew (Numenius borealis) CR(PE) 2006 49 Upper 0.062 2003 2010
Mean 0.028 1999 2007
Lower 0.004 1989 1997
Great auk (Pinguinus impennis) EX 1888 24 Upper 8E−10 1872 1879
Mean 1E−9 1865 1872
Lower 1E−9 1855 1863
Passenger pigeon (Ectopistes migratorius) EX 1907 26 Upper 3E−15 1906 1909
Mean 2E−14 1905 1908
Lower 8E−14 1904 1907
Carolina parakeet (Conuropsis carolinensis) EX 1950 50 Upper 1E−10 1946 1950
Mean 4E−10 1942 1947
Lower 3E−10 1933 1938
Ivory-billed woodpecker (Campephilus principalis) CR 2006 68 Upper 0.065 2005 2010
Mean 0.019 2000 2006
Lower 5E−4 1987 1993
‘Alalā (Hawaiian crow) (Corvus hawaiiensis) EW 2003 68 Upper 0.220 2007 2015
Mean 0.251 2007 2017
Lower 0.286 2008 2018
Kaua’i ‘ō‘ō (Moho braccatus) EX 2001 43 Upper 0.103 2002 2013
Mean 0.080 2000 2012
Lower 0.055 1996 2010
O‘ahu ‘ō‘ō (Moho apicalis) EX 1976 10 Upper 0.292 1994 2113
Mean
Lower
Bishop’s ‘ō‘ō(Moloka‘i) (Moho bishopi) EX 1904 5 Upper 3E−4 1907 1919
Mean
Lower
Hawai‘i ‘ō‘ō (Moho nobilis) EX 1976 24 Upper 0.008 1974 1991
Mean 0.006 1967 1985
Lower 0.001 1944 1963
San Clemente [Bewick’s] wren (Thryomanes bewickii leucophrys) 1941 20 Upper 7E−7 1944 1951
Mean 1E−6 1944 1951
Lower 3E−6 1944 1952
Laysan millerbird (Acrocephalus f. familiaris) EX 1916 12 Upper 2E−6 1919 1927
Mean 4E−6 1919 1928
Lower 9E−6 1919 1929
Kama‘o (Myadestes myadestinus) EX 1999 50 Upper 0.069 2001 2011
Mean 0.067 2000 2011
Lower 0.050 1997 2009
Oloma‘o (Moloka‘i) (Myadestes lanaiensis rutha) CR(PE) 2005 16 Upper 0.188 2001 2025
Mean 0.154 1998 2024
Lower 0.129 1993 2024
Oloma‘o (Lāna‘i) (Myadestes l. lanaiensis) CR(PE) 1934 9 Upper 0.001 1941 1960
Mean 0.003 1941 1963
Lower 0.005 1942 1967
Bachman’s warbler (Vermivora bachmanii) CR(PE) 2001 61 Upper 0.004 1997 2002
Mean 0.001 1993 1998
Lower 4E−5 1981 1987
Dusky seaside sparrow (Ammodramus maritimus nigrescens) EX 1980 48 Upper 6E−5 1983 1988
Mean 1E−4 1983 1989
Lower 2E−4 1983 1989
‘Ō‘ū (Kaua‘i) (Psittirostra psittacea) CR(PE) 1997 33 Upper 0.057 2000 2010
Mean 0.060 1999 2010
Lower 0.055 1996 2010
‘Ō‘ū (Hawai‘i) (Psittirostra psittacea) CR(PE) 1987 42 Upper 0.004 1990 1998
Mean 0.007 1991 2000
Lower 0.013 1991 2001
‘Ō‘ū (Moloka‘i) (Psittirostra psittacea) CR(PE) 1965 6 Upper 0.015 1940 1978
Mean 0.010 1929 1964
Lower
‘Ō‘ū (Lāna‘i) (Psittirostra psittacea) CR(PE) 1927 8 Upper 9E−4 1933 1951
Mean 0.001 1933 1953
Lower 0.002 1933 1955
‘Ō‘ū (Maui) (Psittirostra psittacea) CR(PE) 1945 7 Upper 0.004 1927 1954
Mean 0.004 1919 1947
Lower 0.003 1911 1938
Greater koa-finch (Rhodacanthis palmeri) EX 1967 8 Upper 0.007 1943 1970
Mean 0.003 1928 1952
Lower 7E−4 1911 1927
Greater ‘amakihi (Hemignathus sagittirostris) EX 1901 5 Upper 9E−5 1903 1912
Mean
Lower
Lesser ‘akialoa (Hemignathus obscurus) EX 1940 19 Upper 5E−6 1923 1934
Mean 4E−6 1917 1928
Lower 3E−6 1911 1923
Greater ‘akialoa (Kaua‘i) (Hemignathus ellisianus stejnegeri) EX 1995 21 Upper 0.027 1991 2004
Mean 0.016 1985 2000
Lower 0.009 1978 1994
Nukupu‘u (Kaua‘i) (Hemignathus lucidus hanapepe) CR(PE) 1996 24 Upper 0.179 2002 2022
Mean 0.198 2001 2028
Lower 0.083 1983 2019
Nukupu‘u (Maui) (Hemignathus lucidus affinis) CR(PE) 1996 24 Upper 0.256 2004 2029
Mean 0.346 2007 2047
Lower 0.322 2001 2086
O‘ahu ‘alauahio (Paroreomyza maculata) CR(PE) 2002 46 Upper 0.218 2006 2019
Mean 0.191 2004 2020
Lower 0.099 1995 2016
Maui ‘alauahio (Lāna‘i) (Paroreomyza montana) EX 1937 10 Upper 7E−4 1942 1958
Mean 0.001 1942 1960
Lower 0.002 1942 1961
Kākāwahie (Paroreomyza flammea) EX 1963 16 Upper 0.006 1970 1987
Mean 0.008 1969 1988
Lower 0.009 1968 1989
Maui ‘akepa (Loxops coccineus ochraceus) EX 1995 21 Upper 0.147 2001 2019
Mean 0.144 1999 2021
Lower 0.122 1995 2021
Oahu ‘akepa (Loxops coccineus rufus) EX 1976 7 Upper 0.125 1965 2053
Mean 0.097 1950 2044
Lower
Hawai‘i mamo (Drepanis pacifica) EX 1960 12 Upper 0.033 1943 1996
Mean 0.035 1935 1996
Lower 0.041 1926 2000
Black mamo (Drepanis funerea) EX 1955 6 Upper 0.024 1944 1987
Mean
Lower
Laysan honeycreeper [‘apapane] (Himatione sanguinea freethii) EX 1923 14 Upper 6E−4 1930 1950
Mean 9E−4 1930 1952
Lower 0.001 1929 1954
Po‘o-uli (Melamprosops phaeosoma) CR(PE) 2004 27 Upper 0.037 2005 2009
Mean 0.050 2005 2009
Lower 0.068 2005 2010
DOI: 10.7717/peerj.2426/table-1

Discussion

Incorporating uncertainty in the inference of extinction of a species has two effects that run counter to each other, one potentially pushing forward the date of extinction and the other drawing it to an earlier year. Firstly, by reducing the reliability from 1.0 it increases uncertainty in the date of extinction and therefore results in the inferred persistence of the taxa being potentially pushed beyond those inferred through methods that do not incorporate uncertainty. Secondly, however, by allowing for the incorporation of uncertainty it is possible to incorporate controversial sightings (i.e., Elphick, Roberts & Reed, 2010 only incorporate PE and IEO). This results in more sightings within a record and therefore fewer gaps between years in the sighting record, thus potentially drawing the extinction date closer to the time of the last sighting, although the date of the last sighting is by definition uncertain (see Jarić & Roberts, 2014).

In this study, by incorporating sighting uncertainty into the inference of extinction it allowed us to assess an additional 3 taxa and populations beyond Elphick, Roberts & Reed’s (2010) 38, due to the additional data this brings from the controversial sightings. Furthermore, the number of taxa and populations for which the 95% confidence interval around the predicted extinction date includes dates after 2008 increased from 6 to 14. This has potentially important implications in terms of conservation management and the distribution of resources for the additional 8 taxa and populations. Further, improper classification of these taxa could have resulted in Romeo’s Error (Collar, 1998), where the taxon is assumed to be extinct, which results in a lack of appropriate and timely conservation efforts, and consequently precipitates its true extinction.

Sighting observations of species or individuals are likely to have some level of uncertainty as to whether a correct identification has been made. Few have, however, attempted to quantify the level of uncertainty (e.g., Lee et al., 2015), test for the level of accuracy experimentally (e.g., Gibbon, Bindemann & Roberts, 2015) or incorporated this into their analyses (e.g., Jarić & Roberts, 2014; Lee et al., 2014). As we have shown here, acknowledging such uncertainties can have a profound impact on decision-making; in the case of a critically endangered species, it may influence whether it is considered extinct or extant and therefore whether conservation efforts and resources should be allocated. For some species, extinction may occur within years of being described as a new taxon to science. As an example, a cryptically coloured treehunter from Brazil, Cichlocolaptes mazarbarnetii, described in 2014, was last seen in 2007, but had lain misidentified in the National Museum of Brazil for over 20 years having been collected in 1986 (Lees & Pimm, 2015).

Finally, while we incorporated sighting uncertainty into a time-based extinction model, such sightings with spatial data are frequently used in occupancy modelling with apparently little consideration to the underlying uncertainty of the identification (but see Romero et al., 2014). This is likely to be particularly an issue when using historic sightings, whose location data may also be imprecise. Much of this data is becoming increasingly available online and can be accessed rapidly. However, consideration should be given to the quality of the data, including spatial and temporal inaccuracies (Yesson et al., 2007), particularly identification uncertainties.

Supplemental Information

Supplemental Information 1

DOI: 10.7717/peerj.2426/supp-1