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Quantifying and dealing with uncertainty are key aspects of ecological studies. Population parameter estimation from mark- recapture analyses of photo-identification data hinges on correctly matching individuals from photographs and assumes that identifications are detected with certainty, marks are not lost over time, and that individuals are recognised when they are resighted. Matching photographs is an inherently subjective process. Traditionally, two photographs are not considered a “match” unless the photo reviewer is 100% certain. This decision may carry implications with respect to sample size and the bias and precision of the resultant parameter estimates. Here, we present results from a photo-identification experiment on Pacific white-sided dolphins to assign one of three levels of certainty that a pair of photographs represented a match. We then illustrate how estimates of abundance and survival varied as a function of the matching certainty threshold used. As expected, requiring 100% certainty of a match resulted in fewer matches, which in turn led to higher estimates of abun- dance and lower estimates of survival than if a lower threshold were used to determine a match. The tradition to score two photographs as a match only when the photo reviewer is 100% certain stems from a desire to be conservative, but potential over-estimation of abundance means that there may be applications (e.g., assessing sustainability of bycatch) in which it is not precautionary. We recommend exploring the consequences of matching uncertainty and incorporating that uncertainty into the resulting estimates of abundance and survival. Keywords Abundance · Capture-recapture · Photo-identification · Precautionary principle · Survival Introduction modelling, and mark-recapture approaches (Hammond et al. 2021). The individual recognition data obtained by identify- A variety of methods are available for estimating the abun- ing and following individual animals used in mark-recapture dance of marine mammals (and other species) including cor- approaches to estimate abundance can also be used to esti- recting and extrapolating counts, transect sampling, spatial mate survival rates (e.g., Lebreton et al. 1992; Ramp et al. 2014; Arso Civil et al. 2019) and reproductive rates (e.g., Barlow and Clapham 1997; Arso Civil et al. 2017; Coxon Handling editors: Stephen C.Y. Chan and Leszek Karczmarski. et al. 2022), which are essential parameters when modelling the dynamics and assessing the conservation status of animal This article is a contribution to the special issue on “Individual populations. Identification and Photographic Techniques in Mammalian Photo-identification has become widely used to follow Ecological and Behavioural Research – Part 1: Methods and marine mammals since researchers first noticed that some Concepts” — Editors: Leszek Karczmarski, Stephen C.Y. Chan, Daniel I. Rubenstein, Scott Y.S. Chui and Elissa Z. Cameron. individuals possessed naturally occurring, identifiable, and persistent features. The unique markings of bottlenose dol- * Erin Ashe phins (Tursiops truncatus) were recorded and tracked as ea84@st-andrews.ac.uk early as the 1950s (Caldwell 1955). Photo-identification of Sea Mammal Research Unit, Scottish Oceans Institute, killer whales in the northeastern Pacific Ocean began in the University of St Andrews, St Andrews, Fife KY16 8LB, 1970s (Bigg 1982), and the resulting demographic records Scotland, UK now span decades. Other examples of photo-identification Present Address: Oceans Initiative, 117 E Louisa St #135, studies that have generated long-term datasets include Seattle, WA 98102, USA Vol.:(0123456789) 1 3 782 E. Ashe, P. S. Hammond bottlenose dolphins in Sarasota Bay, Florida (Wells and to defining a match (Urian et al. 2015). Historically, ceta- Scott 1990), North Atlantic right whales (Eubalaena gla- cean studies use “conservative” protocols and, after seek- cialis; Pace et al. 2017), North Atlantic humpback whales ing advice from experienced colleagues in the case of any (Megaptera novaeangliae; Stevick et al. 2003), blue (Balae- ambiguity, only score two photographs as a match if there is noptera musculus) and fin (B. physalus) whales in the Gulf consensus among observers (Friday et al. 2000; Stevick et al. of St Lawrence (Ramp et al. 2006, 2014); and southern right 2001; Urian et al. 2015). Most protocols reviewed by Urian whales off Argentina (Eubalaena australis; Agrelo et al. et al. (2015) are inherently averse to false positives; the cor- 2021). ollary to this is that false negatives will arise as a result Data used in mark-recapture analysis must meet a num- (Stevick et al. 2001). Not all researchers use protocols that ber of assumptions if reliable parameter estimates are to be are averse to false positives. Urian and colleagues reported made: (1) marks are unique; (2) marks cannot be lost or “an unsettling degree of variation among researchers in the missed; (3) all marks are correctly recorded and reported evaluation of image quality, distinctiveness, images selected (Hammond 2018). The purpose of this study is to explore the and matches. Participants from the same institution gener- variability inherent in correctly matching individuals among ally had similar results, suggesting that most variation was sampling occasions. due to the different methods used by each laboratory.” Many Errors in individual identification are known to occur researchers may be trained to quantify their level of cer- (Payne et al. 1983; Langtimm et al. 2004), and the few stud- tainty that two photographs do or do not represent a match, ies that have explored effects of misidentification have found but there is little guidance from statisticians about how to that, even at small rates, errors in identification can bias incorporate that uncertainty into the binary framework of parameter estimates (Stevick et al. 2001; Lukacs and Burn- conventional mark-recapture models. ham 2005; Yoshizaki et al. 2009). Misidentification involves Erring on the side of false negatives is not always a pre- many factors, but a recurring theme involves the importance cautionary approach. Deciding always to call ambiguous of choosing the right features to use as a natural mark in matches a non-match will cause recapture rates to be biased order to satisfy assumption 2, above. Anatomical features low, which will cause estimates of abundance to be posi- should be chosen so that the natural markings used in a tively biased and estimates of survival rates to be negatively mark-recapture experiment will last longer than the experi- biased (Hammond 1986; Hammond et al. 1990; Friday et al. ment and should not change in such a way that might affect 2008). For management procedures that set allowable harm the ability to recognize it in future (Wilson et al. 1999). limits based on abundance (e.g., Wade 1998; Winship et al. For killer whales, the shape of the dorsal fin and patterns 2006; Genu et al. 2021) a positively biased abundance esti- in the saddle patch are most often used as natural marks mate could lead to overexploitation. The extent to which this (Bigg 1982; Kuningas et al. 2014). For humpback whales, is a problem for real-world conservation and management pigmentation patterns on the underside of the flukes, as well decisions is case-specific, but few studies have estimated as the edge of the flukes themselves, are used to identify the magnitude of bias in abundance and survival estimates individuals (Stevick et al. 2001, 2003). In this study, natu- depending on matching uncertainty. rally occurring nicks and notches in a Pacific white-sided There are two primary reasons for misidentification dolphin’s (Lagenorhynchus obliquidens; Fig. A1) dorsal fin errors: (1) errors in identification due to changes in the natu- were used as natural marks, such that it could be recognized ral markings; and (2) misidentification as a result of varia- from both left- and right-side photographs. tion at the level of the matching process. The first can occur Observers tend to conflate photo-quality with animal dis- if individuals acquire new marks such as scars or damage tinctiveness because a well-marked individual is more easily due to predation or intra-specific interactions (Gordon 1987; recognized than a subtly marked individual in a poor-quality Steiger et al. 2008), or if marks such as scratches or pig- photograph (Urian et al. 2015). As a result, previous ceta- mentation patterns heal and subsequently disappear (Dufault cean studies have relied on strict protocols when gauging and Whitehead 1995). Dufault and Whitehead (1995) found whether two photographs are a match (Wilson et al. 1999; that mark acquisition occurred at a higher rate than mark Read et al. 2003). However, the final dataset used to esti- loss. Mark acquisition is presumed less likely to cause misi- mate population parameters is still subject to human error, dentification, especially in small populations (Urian et al. because it is dependent on a somewhat subjective decision 2015), but it is easy to imagine a scenario in which mark about whether a human observer is convinced that a pair of acquisition may lead to changes that are substantial enough photographs represent two encounters of the same individual for false negative errors to occur. For larger, wide-ranging or two different individuals. Little attention has been paid to populations, it is recommended that mark acquisition rates the process by which researchers reach a final decision about are estimated and that strict animal distinctiveness criteria whether two photographs constitute a match, but a survey that rely on markings that are unlikely to change over time has shown that researchers vary widely in their approach are used (Urian et al. 2015). 1 3 Effect of matching uncertainty on population parameter estimation in mark‑recapture analysis… 783 The second misidentification process, errors that occur followed those of a previous study as closely as possible at the level of the matching process, has received compara- (Morton 2000). Photo-identification surveys for Pacific tively little attention. Previous analyses have shown that white-sided dolphins in the Broughton Archipelago were conflating photo-quality with individual distinctiveness conducted from 2008 to 2013. Photo-ID effort was dis- biases the matching process and, subsequently, the param- tributed throughout the year but was restricted by weather eter estimates from mark-recapture analyses (Arnbom 1987; conditions. Groups of dolphins were found using a com- Friday et al. 2000, 2008). False rejections of true matches, bination of boat-based searches and from radio reports and field protocols that photograph individuals using non- and communication from local mariners. Reports from a symmetrical markings on left and right sides, can result in a stationary hydrophone network (OrcaLab) monitored 24 h/ dataset containing multiple encounter histories for an indi- day (Morton and Symonds 2002; Deecke et al. 2010), were vidual (Hiby et al. 2012). In addition, many animals may used to direct dolphin searches. Searches and photographic simply have similar markings. As the number of individu- encounters were limited to sea conditions of a maximum als in a population increases, so too does the difficulty in Beaufort scale = 2 for reasons of safety and sightability. distinguishing individuals with similar natural markings. For each encounter, a GPS position and an estimate of The extent to which matching uncertainty biases resulting group size was made in the field and recorded. Total group estimates of population parameters requires investigation size was estimated by tallying the number of individuals in for each study. Differences in protocols among individu - smaller subgroups (typically 2–8 individuals) at intervals als and laboratories will likely result in different biases in throughout the encounter (Morton 2000). A group was parameter estimates, as long as protocols require investiga- defined as all of the dolphins encountered in a discrete tors to force an inherently subjective matching process into location in a day. Finer scale information (e.g., groups a binary (match/not-a-match) outcome (Urian et al. 2015). defined using a 15 m ‘chain rule’; Smolker et al. 1992) This issue has become increasingly important as long-term on group composition was collected from 2011 onwards cetacean studies have switched from film to digital photog- to inform studies of sociality. Encounters with dolphins raphy, which may introduce heterogeneity in matches (Urian lasted a minimum of 20 min during which the following et al. 2015). Ideally, the level of uncertainty associated with data were recorded: an estimate of group size (minimum, any given match should be quantified and incorporated into maximum, and best estimate); location; predominant resulting population parameter estimates (Urian et al. 2015). group activity state (although scan-sample data were col- Acknowledging explicitly the uncertainty in the photo lected at 5-min intervals during longer encounters), and matching process, the aim of this study was to use 6 years number of calves in the group (minimum, maximum, and of photographic data on Pacific white-sided dolphins to best estimate). Photographs were collected with digital quantify the extent to which matching uncertainty affects SLR cameras. the bias and precision of abundance and survival estimates. Groups of dolphins were approached slowly in an effort to The study also explores the challenges inherent in datasets reduce the probability of bow-riding behaviour, which brings with relatively low rates of recapture and the effect of match- some individuals, especially juveniles, very close to the boat ing uncertainty in these cases. and makes other individuals less available for photographic capture. Large groups were generally traversed in two passes to try to obtain photographs from both sides. In the first pass, individuals were photographed in sub-groups as each sub- Methods group came into photographing range until the far edge of the group was reached. In the second pass, the group was tra- Study area versed in the opposite direction and at the same angle as the first pass and individuals were photographed in sub-groups The study took place in the waters between northeastern in the same manner as the first pass. Dolphins were photo- Vancouver Island, British Columbia (BC), Canada and the graphed almost exclusively while engaged in slow, milling Broughton Archipelago and Knight Inlet on BC’s mainland (non-directional) behaviour in tight groups (behaviour typi- coast. The study area is characterized by a complex geogra- cally observed following medium to high speed travel). The phy of numerous islands, narrow inlets, and fjords (Fig. 1). non-directional/milling behaviour facilitated photographing both right and left sides of the dorsal fin. Data collection Photo-ID efforts ended when dolphins engaged in activity states (e.g., high-speed travel) that resulted in water splash- To ensure consistency in data collection with an exist- ing around the dorsal fin, which results in poor quality photo- ing, long-term catalogue and to maximise the number of graphs. An encounter ended when all of the members of the long-term resightings, field protocols in the current study group had been approached, if weather conditions changed 1 3 784 E. Ashe, P. S. Hammond Fig. 1 Map of the study area. The blue polygon represents the study area of the Broughton Archipelago, British Columbia, Canada and adjacent waters or when the time limit of the research permit (30 min per to grade the distinctiveness of each individual. The distinc- sub-group) was reached. tiveness score ranged from D1 (Highly distinctive) to D4 (Unmarked). A distinctiveness score of D2 (Moderately Photo‑processing methods distinctive) included fins with intermediate features such as a small nick, or many small nicks that are detectable from All photographs of a dorsal fin were graded for quality of both sides and D3 (Somewhat distinctive) included fins with the image and distinctiveness of the markings in two inde- subtle features such as such as black scratches or other long- pendent stages (Urian et al. 2015). Information on quality, lasting distinguishing marks that are only identifiable from distinctiveness and other attributes were entered into Photo one side. The D3 score category does not include nicks and Mechanic 5 (Camera Bits) photo-processing software. First, notches on the trailing edge of the dorsal fin. A separate set photographs were graded for photographic quality using of photo reviewers conducted the matching step (see below). a standardized set of photographic quality criteria rang- ing from 1 (poor quality) to 3 (high quality) following the Photo‑matching image quality scoring criteria used in studies of bottlenose dolphins in Scotland (Wilson et al. 1999). Dorsal fins of A team of six photo reviewers (including EA) conducted Pacific white-sided dolphins varied among individuals from the photographic matching of the current study’s cata- extremely well marked with nicks and scars, to completely logue in Photo Mechanic 5. The pattern of nicks on the clean, unmarked fins. Thus, not all dolphins were distinc- trailing edge of the dorsal fin was the primary mode of tive enough to be included in mark-recapture analyses. A identification. Fin shape provided a secondary indicator. separate photo reviewer scored each quality 3 photograph Dorsal notches had to match in size, angle of tear and other details. The definition of a match allowed for acqui- sition of marks over time, but no loss of nicks; that is, if there was an additional notch on the more recent photo, https:// store. camer abits. com/ produ cts/ photo- mecha nic- versi on-5. 1 3 Effect of matching uncertainty on population parameter estimation in mark‑recapture analysis… 785 but the original nick or notch was present in both photo- graphs, then this was scored as a potential match. Nicks, notches and tears in the fin, along with the shape of the fin itself, are detectible from both sides, so the decision to include these features in the distinctiveness scoring and thresholds meant that both left- and/or right-side photo- graphs could be used to identify individuals. Only photographs of quality 3 and with a distinctive- ness score of D1 or D2 (i.e., symmetrical markings that would be recognized from both sides) were included in the analysis. This protocol was chosen to reduce misiden- tification errors, while allowing both left- and right-side photographs to be included in the analysis. Photographs of individuals believed to be calves (i.e., small, ruffled dorsal fin, orange colouration, foetal fold marks on the body, photographed alongside mother) were excluded from the analysis. The high-quality subset of photographs Fig. 2 Number of times an individual was seen, tallied for each cer- was then matched within each photographic encounter, tainty level and each individual was assigned a preliminary identifi- cation code. Identified individuals within an encounter were matched and a certainty score of “Certain” (100% confident), “Likely” (< 100% but ≥ 90% confident), or two-sample estimator to account for small sample bias was “Possible” (< 90% but ≥ 50% confident) was assigned to used to estimate abundance (Hammond 1986; Seber 2002). putative matches between pairs of photographs based on the degree of confidence in each match. (n + 1)(n + 1) 1 2 N = − 1, An encounter history of 1 s and 0 s, corresponding to (m + 1) whether a putative individual was or was not detected where N is the abundance estimate; estimate of population (i.e., captured) during each sampling encounter, respec- size, n is the number of individuals captured during the first tively, was created for each individual for each matching sampling occasion, n is the number of individuals captured certainty level for analysis. during the second sampling occasion, m is the number of individuals recaptured. That is, the number of animals cap- tured during the first sampling occasion that were also cap- Available data tured during the second sampling occasion. For this analysis, each year was treated as a sampling The number of sampling occasions and the months in occasion, and recaptures were restricted to individuals which sampling took place each year varied widely seen in adjacent pairs of years. Given the low number of throughout this study. Between 2008 and 2013, a total of recaptures in adjacent pairs of years and the compara- 34 photographic encounters with dolphins occurred. Of tively large number of photographs taken in 2010, a sep- these, 32 encounters contained photographs of sufficient arate within-year analysis was conducted for 2010 (see quality and distinctiveness to enter the analysis for the “Results”). “Certain” and “Likely” certainty levels, whereas all 34 Variance was estimated as: encounters contained photographs that were of sufficient quality to create encounter histories at the “Possible” cer- n + 1 n + 1 n − m n − m 1 2 1 2 2 2 tainty level. The frequency of capture of individuals for var N = . the three certainty levels is shown in Fig. 2. The single m + 1 m + 2 2 2 encounter from 2008 was not included in the analysis due to low sample size (only 2 individuals were identified). Log-normal 95% confidence intervals were calculated (Borchers et al. 2002) as N /d t o N *d, where Estimation of abundance z var ln N d = e , The encounter histories for each of the three certainty levels were analysed to produce three estimates of abun- dance. Chapman’s modification to the Lincoln–Petersen 1 3 786 E. Ashe, P. S. Hammond estimating abundance, the objective of this exercise was to z = 1.96 for a 95% CI. 0.025 assess the relative importance of matching uncertainty on and the resulting estimates of survival, rather than generating the best estimates for wider use, and any assumption violations var(N) are unlikely to compromise these results. var ln N = ln 1 + Results The simple two-sample estimator used makes a number of The number of individual dolphins photographed and recap- assumptions regarding population closure and capture prob- tured in each sampling period and at each matching certainty abilities (Hammond 2018), violation of which can introduce level is shown in Table 1. Including less certain matches bias in abundance estimates. However, the objective of this resulted in fewer individual dolphin identifications overall, exercise was to assess the relative importance of matching because a more permissive matching threshold will decrease uncertainty on the resulting abundance estimates, not to the number of putative individuals in n and n , and increase 1 2 generate a robust abundance estimate for use in decision- the number of matches in m . Sampling effort was great- making. Any such violations should affect estimates in a est during 2010 and high in 2011, resulting in the great- comparable way and therefore are unlikely to compromise est number of recaptures between these years. The lack of these results. recaptures in pairs of years not including 2010 preclude esti- mation of abundance. Consequently, data from 2012 to 2013 were pooled to boost the number of recaptures with 2011 Estimating adult annual survival rate (Table 1). Compared to other years, 2010 had a substantially higher number of within-year recaptures, so data were also Annual encounter histories from 2008 to 2013 were cre- analysed using two sampling occasions within 2010 (2010a: ated for each certainty level to estimate annual apparent April–June; 2010b: July–November; Table 1). survival rate using a Cormack-Jolly-Seber model (Cormack Two-sample estimates at each matching certainty level 1964; Jolly 1965; Seber 1965; Amstrup et al. 2005). Models produced abundance estimates of individually identifiable were explored that allowed survival and recapture probabil- dolphins in the population for 2009–2010, 2010–2011, ity to vary over time or to be constant, and the model with 2011–2012 + 2013 (data pooled for 2012 and 2013), and for the lowest value of Akaike’s Information Criterion (AIC) time periods within 2010 (Table 2). Abundance of marked was selected as that which had the most support from the dolphins at the “Certain” matching level, ranged from a low data. Analysis was carried out in software MARK (White of 985 (CV = 0.55) in the “2011–2012 + 2013” sample, to and Burnham 1999) version 6.1. No goodness of fit tests a high of 2005 (CV = 0.31) in 2010 (Table 2). As expected, were conducted to test for lack of fit. However, similar to the “Certain” matching level produced abundance estimates Table 1 Number of individual dolphins photographed in the two sampling periods (n , n2), and the number of matches between these (m ), for 1 2 each of three matching certainty levels Matching cer- 2008–2009 2009–2010 2010–2011 2011–2012 2012–2013 2011– 2010a–2010b tainty level 2012 + 2013 Certain n 1 37 247 33 2 33 106 n 37 247 33 2 55 57 149 m 0 4 5 0 0 1 7 Certain + Likely n 1 37 245 33 2 33 140 n 37 245 33 2 55 51 244 m 0 4 5 0 0 2 18 Certain + Likely + Possible n 1 37 239 33 2 33 137 n 37 239 33 2 55 57 241 m 0 5 6 0 0 2 24 Data are summarised for consecutive years in the period 2008–2013, and additionally for 2011 and 2012–2013, and for two time periods within 2010 (2010a: April–June; 2010b: July–November) 1 3 Effect of matching uncertainty on population parameter estimation in mark‑recapture analysis… 787 the apparent survival estimates increased slightly and the Table 2 Two-sample estimates of abundance ( N ) for pairs of years from 2009 to 2011, within 2010 (2010a: April–June; 2010b: July– precision of the estimates also increased slightly. The very November), and for 2011 with pooled data for 2012 and 2013 low estimates of apparent survival are likely a result of emi- gration out of the study area over the period of the study. Matching certainty level 95% CI CV 2009–2010 Certain 1884 (922, 3848) 0.38 Discussion Certain + Likely 1869 (915, 3816) 0.38 Certain + Likely + Possible 1513 (787, 2907) 0.34 The study demonstrates that matching uncertainty has the 2010–2011 potential to introduce substantial degrees of both bias and Certain 1404 (736, 2680) 0.34 uncertainty in a real-world photo-identification study of a Certain + Likely 1393 (730, 2659) 0.34 dolphin species with a low capture probability. The extent to Certain + Likely + Possible 1159 (640, 2103) 0.31 which this translates into conservation or management risk 2010a–2010b hinges on the extent to which the less-than-certain matches Certain 2005 (1103, 3645) 0.31 are actually true matches rather than false positives. If all Certain + Likely 1817 (1233, 2678) 0.20 the less-than-certain matches are false positives, then using Certain + Likely + Possible 1335 (962, 1852) 0.17 the highest certainty level as a threshold to define a match 2011–2012 + 2013 will give the least biased abundance estimate. But if all Certain 985 (359, 2701) 0.55 or some of the less-than-certain matches are actually true Certain + Likely 656 (276, 1564) 0.47 matches, then using the highest certainty level as the thresh- Certain + Likely + Possible 656 (275, 1564) 0.47 old to define a match means that the abundance estimates are positively biased. This pattern shows up in our abundance estimates Table 3 Apparent survival rate estimates derived from three different (Table 2) and, with a smaller effect, in our survival esti- levels of certainty in photographic matching from 2008 to 2013 mates (Table 3). Abundance estimates were found to vary among years and matching certainty levels. Because of Matching certainty level Survival SE 95% CI inter-annual variation in effort and the low rate of recapture Certain 0.458 0.288 (0.183, 1.000) in 2009 and 2011–2013, it is most informative to focus on Certain + Likely 0.460 0.287 (0.184, 1.000) a comparison of within-year estimates from 2010. Within Certain + Likely + Possible 0.468 0.276 (0.191, 1.000) 2010, the abundance estimates ranged from 2005 (95% CI 1103–3645) for “Certain” matches to 1335 (95% CI 962–1852) for “Certain + Likely + Possible” matches. The that were greater than the estimates at the “Certain + Likely” “Possible” and “Likely” categories may contain false posi- or “Certain + Likely + Possible” levels in the same pairs of tive matches, which will cause negative bias in abundance samples (Table 2). In 2010–2011, for example, abundance estimates (Yoshizaki et al. 2009). However, false positive was estimated as 1404 (CV = 0.34) at the highest level of errors typically arise from inclusion of poor-quality pho- certainty (“Certain”) and at 1159 (CV = 0.31) at the “Cer- tographs (Stevick et al. 2001; Friday et al. 2008; Barlow tain + Likely + Possible” matching level. The greater the et al. 2011) and only the highest quality photographs were matching certainty level, the lower the precision of the abun- included in this analysis. dance estimates (Table 2). Notably, there is still much variation in abundance estimates depending on matching certainty level, despite restricting our analyses to photographs of the highest quality. Estimates of annual survival rate While restricting analysis to the best quality photographs of marked individuals, there may still be substantial false posi- Annual apparent survival rate for well-marked adult dol- tive errors in species in which individuals may share simi- phins was estimated using a CJS model at each matching lar marks. As the number of individuals in the population certainty level for the period 2008–2013. The model for con- increases, so too will the probability of seeing two dolphins stant survival and time-varying recapture probability had with very similar markings on their dorsal fins. As more ten- the best support from the data at all certainty levels. Annual uous matches were categorised as recaptures in the analyses, survival for the “Certain” matching level was estimated as the abundance estimates decreased (i.e., assumed to become 0.458 (SE = 0.288) rising slightly to 0.468 (SE = 0.276) for negatively biased if the lower certainty level matches were the “Certain + Likely + Possible” matching level (Table 3). not true matches) and the apparent precision increased. But Thus, as less certain matches were included in the analyses, if some of these less than certain matches actually were 1 3 788 E. Ashe, P. S. Hammond matches, the estimates from the “Certain + Likely” and lead to overestimates of allowable harm limit for assessing “Certain + Likely + Possible” scenarios are less biased than sustainability of bycatch (Wade 1998; Punt et al. 2020). the “Certain” scenarios, and the abundance estimate from When the trade-off makes the difference between hav - the “Certain” scenarios were biased high. Although the ing a biased estimate versus no estimate at all, then it is number of recaptures in this study is low, this effect was better to report an estimate, as long as the sources of bias most obvious in the within-2010 analysis, when effort was are acknowledged. One could potentially quantify that bias highest and there were a substantial number of matches. In using an approach such as the one presented here. Selecting terms of survival, estimates of survival rate and their preci- an acceptable trade-off between bias and precision may be sion increased only slightly as less certain matches were more challenging to consider with small sample sizes. categorized as a match (Table 3). It is a well-known problem that traditional mark-recapture Although the direction of these trends can be predicted methods are sensitive to misidentification of animals that from first principles, the magnitude of the effect found in are recognised from natural markings (Link et al. 2010). our study was unexpectedly high, at least for abundance. Previous studies have considered effects of photo-quality and The “Certain” abundance estimates were ~ 50% higher animal distinctiveness on bias and precision in abundance than estimates derived from “Cer tain + Likely + Possible” and survivorship estimates (Friday et al. 2000; Stevick et al. matches in the “Within 2010” and the “2011–2012 + 2013” 2001). Although matching certainty is clearly confounded scenarios (Table 2). The “Certain” abundance estimates with photographic quality and animal distinctiveness, the were 25 and 21% higher than estimates derived from “Cer- current study examined matching uncertainty with a data- tain + Likely + Possible” matches in the “2009–2010” and set of high-quality photographs of well-marked individual “2010–2011” scenarios, respectively (Table 2). While reli- dolphins. The aim was to evaluate the effect on estimates ance on absolute certainty is often described as a “conserva- of population parameters caused by uncertainty in individ- tive” and “recommended” feature of any photo matching ual identification. Results showed that while this issue had protocol (e.g., Friday et al. 2008), our results suggest that use relatively modest impact on estimates of apparent survival, of an overly conservative threshold to define a match could abundance estimates could vary by 20–50% as a result of result in substantial bias (20–50%) in abundance estimates. this source of uncertainty (Table 2). It is difficult to evaluate The trade-off illustrated in this study exemplifies the the effect of matching uncertainty on precision of estimates, need for researchers to decide whether it is better to include however, due to low number of recaptures in our study. Nev- uncertain matches to increase sample size to get a more pre- ertheless, it is worth noting that in the within-2010 abun- cise (but potentially biased) estimate, or to prioritise accu- dance estimates, which had the largest number of recaptures, racy over precision. This decision is inherently case-specific. there is a big difference in CV among certainty levels. The If monitoring for overall trends in abundance (Wilson et al. next problem, of course, is how to resolve this. 1999; Gerrodette and Forcada 2005), precision may be para- This study has looked at misidentification by examining mount and bias less of a concern, as long as the bias remains the impact on abundance and survival estimates that arises consistent over the period of interest (Taylor and Gerrodette at the processing level. Conventionally, photo reviewers 1993; Taylor et al. 2007; Williams et al. 2016a). processing ID photographs are instructed to assert that two Pacific white-sided dolphins are known to be caught in photographs are or are not a match. Depending on the pro- salmon gillnet fisheries in British Columbia, and our ability tocols used by a given research team, most researchers will to assess the sustainability of that bycatch hinges on improv- be averse to false positives and will default to a non-match in ing the accuracy and precision of both the estimates of dol- the case of < 100% certainty, whereas others may be equally phin abundance and bycatch rate (Williams et al. 2008). A averse to false negatives (Urian et al. 2015). Nonetheless, new trade rule requires countries wishing to export sea- the conventional mark-recapture models require a binary food to US markets to demonstrate that their management decision to be made. The current study shows that there is schemes are comparable in effectiveness to those under the value to having a number of photo reviewers, matchers, and US Marine Mammal Protection Act (Williams et al. 2016b). experienced researchers record their level of certainty that We anticipate investments in filling data gaps in many under - two photographs represent a match, because there is useful studied species and regions to facilitate compliance with this information contained in that matching certainty level (Urian new rule (Ashe et al. 2021a,b; Hammond et al. 2021; Punt et al. 2015). The size of effect due to matching uncertainty et al. 2021). Given the potential positive bias in abundance found here, as high as a third on abundance, may not be estimates using overly strict matching criteria, assuming that typical, but the approach could easily be incorporated in some likely or possible matches were actually true matches other photo-ID studies (Urian et al. 2015). In many cases, (Table 2), it will be useful to investigate the potential for the effect on estimates of abundance or survival of match - matching uncertainty to bias abundance estimates in new ing uncertainty may be negligible, but it will be impossible studies, because positively biased abundance estimates could to know this unless matching protocols instruct matchers 1 3 Effect of matching uncertainty on population parameter estimation in mark‑recapture analysis… 789 to record their level of confidence in a match so that this most important question of all—does any of this uncertainty information can be used at the analysis stage. affect the ultimate category of risk to which we assign a spe- Our study confirms much what has been said in other cies (Brooks et al. 2008)? studies of misidentification in mark-recapture arising from issues of data quality, and the advice for coping with the source of misidentification. Mark misidentification can arise Appendix when the sampling (e.g., photo-ID) introduces heterogeneity in the observer’s ability to recognise marks. One study that identified a minimum quality level when including photo- graphic data in a mark-recapture analysis using two kinds of tags (i.e., photo-ID and genetics) placed bounds on the uncertainty and incorporated that uncertainty in a bootstrap estimate of the variance around the abundance estimate (Stevick et al. 2001). Conceptually, the recommendation of Stevick et al. (2001) could apply here as well: encounter histories corresponding to the three matching certainty lev- els could be resampled via a bootstrap, to incorporate this source of uncertainty in estimates of abundance, survival and their variances. This will be more pragmatic in the short term than the suggestion to use genetic double-tagging to minimise or avoid misidentification in the first place (Lukacs and Burnham 2005; Link et al. 2010). For some cetacean Fig. A1 Two Pacific white-sided dolphins (Lagenorhynchus species, it may be possible to use natural markings on two obliquidens) leap during a bout of foraging activity near the morphological features as another way to investigate misi- Broughton Archipelago, British Columbia, Canada. (Photo: E. Ashe, dentification, e.g., northern bottlenose whales (Hyperoodon under research permit) ampullatus, Gowans and Whitehead 2001) and bottlenose dolphins (Tursiops truncatus, Genov et al. 2018). Misidentification can also arise when marks change Supplementary Information The online version contains supplemen- through time. With a sufficiently large number of individuals tary material available at https://doi. or g/10. 1007/ s42991- 022- 00236-4 . followed through time, it may be possible to build mecha- nistic models to understand how marks evolve. Quantifying Acknowledgements The authors wish to thank Alexandra Morton and Rob Williams for advice and logistical support that facilitated Erin’s this effect could then allow to account for misidentification Ph.D. thesis, in which an earlier version of this paper formed a chap- in the resulting demographic parameters. Simulation studies ter. We thank Paul Spong and Helena Symonds (OrcaLab) for shar- suggest that these mechanistic models will only work when ing sightings, and Don Willson for field support. This study involved capture probability is higher than that observed during this processing large numbers of photographs for image quality, animal distinctiveness, and potential matches. We thank Melissa Boogaards, study (i.e., > 0.2) and when the absolute number of resight- Nicole Koshure, Leila Fouda, Marie Fournier, Emily Hague, Christie ings is sufficiently large to have enough data to estimate McMillan, and Alyssa Rice for help with that effort. Thanks also to demographic parameters and changes in marks simultane- the SeaDoc Society, Molly and John Bailey, the Richardson family, ously (Yoshizaki et al. 2009). Sarah Haney (Canadian Whale Institute), National Geographic, and Beto Bedolfe at the Marisla Foundation for financial support over Statistically, the process of misidentification that this the years, and to The Willow Grove Foundation for supporting the study discusses is quite challenging to address using tra- Knight Inlet expedition that made 2010 so productive. Erin thanks ditional likelihood methods, but could be handled in a Air Canada’s Aeroplan Beyond Miles program for travel support, and straightforward way using Bayesian methods (Link et al. Prof. Sascha Hooker and Dr. Jaume Forcada for examining her PhD. Their feedback, along with advice from the editors, Kimberly Nielsen, 2010). Bayesian mark-recapture methods have been an and two anonymous reviewers, improved this manuscript. Erin was a active research area for some time (Schofield et al. 2009). beneficiary of a writing retreat for women in science supported by Lyda As Bayesian survival estimators become more commonly Hill Philanthropies and the National Geographic Explorers Program. used and associated code made accessible to population This paper represents the third thesis chapter to be published with that support for travel, accommodation, and childcare. ecologists (Gimenez et al. 2009), addressing misidentifica- tion in a Bayesian framework may be the next logical step. Author contributions EA: conceptualization, data collection, data By integrating all known sources of uncertainty into a single analysis and writing-original draft preparation. PH: supervision, writ- analytical framework, Bayesian methods offer the potential ing–reviewing and editing. to assess whether misidentification (or any other source of uncertainty) has the potential to affect what is perhaps the 1 3 790 E. Ashe, P. S. Hammond Borchers DL, Buckland ST, Zucchini W (2002) Estimating animal Declarations abundance: closed populations. Springer Verlag, London Brooks SP, Freeman SN, Greenwood JJ, King R, Mazzetta C (2008) Conflict of interest On behalf of all authors, the corresponding author Quantifying conservation concern-Bayesian statistics, birds and state that there is no conflict of interest. the red lists. Biol Conserv 141:1436–1441. https:// doi. org/ 10. 1016/j. biocon. 2008. 03. 009 Open Access This article is licensed under a Creative Commons Attri- Caldwell DK (1955) Evidence of home range of an Atlantic bottlenose bution 4.0 International License, which permits use, sharing, adapta- dolphin. J Mammal 36:304–305. https://doi. or g/10. 2307/ 13759 13 tion, distribution and reproduction in any medium or format, as long Cormack RM (1964) Estimates of survival from the sighting of marked as you give appropriate credit to the original author(s) and the source, animals. Biometrika 51:429–438. https:// doi. org/ 10. 2307/ 23341 provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are Coxon J, Arso Civil M, Claridge D, Dunn C, Hammond PS (2022) included in the article's Creative Commons licence, unless indicated Investigating local population dynamics of bottlenose dolphins in otherwise in a credit line to the material. If material is not included in the northern Bahamas and the impact of hurricanes on survival. the article's Creative Commons licence and your intended use is not In: Karczmarski L, Chan SCY, Rubenstein DI, Chui SYS, Cam- permitted by statutory regulation or exceeds the permitted use, you will eron EZ (eds) Individual identification and photographic tech- need to obtain permission directly from the copyright holder. 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Mammalian Biology – Springer Journals
Published: Jun 1, 2022
Keywords: Abundance; Capture-recapture; Photo-identification; Precautionary principle; Survival
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