Estimating abundance is critical for effective management and conservation [1
]. Many studies rely on live-trapping and radio telemetry (e.g., [2
]). However, both methods are labor intensive, expensive, and impractical in wilderness areas with harsh climate [4
]. Further, capture and handling is invasive, can elicit a behavioral response (e.g., trap shyness) that may bias population estimates [5
], and potentially dangerous to the animal. Remotely-triggered cameras are increasingly used to identify individuals for species with unique pelage patterns [7
], morphological characteristics [9
], and artificial markings [11
]. Similar to non-invasive genetic sampling [13
], this approach can increase sample size and may reduce effort and cost [15
]. However, failure to consistently identify individuals is common for camera-trap studies and can result in sampling bias if not adequately addressed [15
American martens (Martes americana
) are a prime candidate for camera-trap abundance estimation. They generally occupy remote wilderness areas throughout the northern United States and Canada [16
], making it difficult to monitor populations [17
]. Moreover, martens are sexually dimorphic [18
], with unique ventral patterns that can potentially be used for individual identification [8
], and respond positively to baited camera-traps [20
] making them ideal candidates for photo identification. Importantly, density estimates are critical to provide input to conservation of this species which is sensitive to habitat loss and fragmentation [21
] and predicted to decline along southern range boundaries due to climate change [24
The use of camera-trapping is increasingly common [4
], yet methods for analyzing its reliability are still evolving (e.g., [26
]). Numerous sources of variability can influence photo identification of individuals, including prominence of unique markings within a population [15
], camera angle and lighting [29
], and camera brands [30
]. Trap configuration can also have a significant effect [9
]. Magoun et al.
] used a baited camera-trap that required a wolverine (Gulo gulo
) to reveal its ventral marks for a prolonged period while feeding. Although this design may work for martens [8
], they are considerably smaller than wolverines, requiring cameras to be closer which can complicate identification [26
The temporal distribution of photographic captures can have an impact on individual identification as well. Studies often require >1 picture to identify individuals with varying criteria for independent visits [29
]. For example, Mendoza et al.
] utilized temporally clustered bobcat (Lynx rufus
) photographs at unbaited camera stations to determine independent visits and increase consensus among observers. However, if temporal activity is not evaluated or arbitrarily assigned, visits by multiple individuals within a photographic cluster may go unnoticed and violate assumptions of capture–mark–recapture analyses [15
]. Although some misidentification errors can be explicitly modeled [34
] further development is required [35
]; therefore, it is prudent to screen data prior to the modeling process [31
]. To our knowledge, no studies have used artificially marked individuals to evaluate temporal activity at camera-traps and assess the use of photographic clusters to improve individual identification and define independent observations.
Inconsistency among observers can also pose a problem [29
]. To reduce observer bias, camera-trap studies have utilized ≥2 independent observers to assign the identity of individuals and construct capture histories [29
]. Although this approach may reduce the proportion of individuals identified and result in a lower sample size [29
], culling erroneous identity assignments can increase the accuracy of density estimates [15
]. Further, while automated computer software provides accurate assignments and saves time, it does not always perform well with the poor quality pictures that occur in camera-trap studies [38
]. Variation caused by shadows, camera angles, scale, time of day, and different camera brands make automated computer processing difficult [40
Recent spatial capture–recapture (SCR) models incorporate the movement of individuals between trap locations, resolving the influence of space on both the ecological and observational processes that generate capture–recapture data [41
]. Compared with SCR models, buffer methods (e.g., 0.5× mean maximum distance moved; [43
]) may provide biased estimates of density [2
], except when home ranges are elongated or irregular [46
]. The SCR approach provides flexible model selection for researchers and can be useful for studies with small sample size and imbalanced designs [41
]. Because SCR models are used increasingly for minimally invasive surveys, it is important to evaluate performance with empirical data [15
] and compare with traditional methods (e.g., live-trapping) to validate the method [26
We documented camera-trap visits by radio-collared martens with unique artificial markings and un-collared martens over two winters in a remote forest in northern New Hampshire. Our objectives were to: (1) identify the optimal camera-trap configuration to effectively photograph marten ventral patches of both sexes; (2) evaluate the use of temporally clustered photographs of radio-collared martens to improve identification of individuals and determine independence; (3) utilize observers to record the total number of un-collared individuals, identify the proportion of unique captures that were identifiable, and develop a capture history; (4) determine factors that influenced identification probability of photo captured martens; and (5) compare camera- and live-trapping datasets using a SCR modeling framework to evaluate the hypothesis that camera-trapping would provide more accurate and precise density estimates due to increased recaptures and longer effort.
Our results show that optimal spatial configuration and temporal clustering of photographs can have a substantial effect on the identification of individual animals. Ventral scores were greatly improved when the front feet of martens were either both down on the platform or positioned in a manner that allowed the ventral patches to be photographed (e.g., one up). The foot positioning was influenced by trap position, with higher scores when traps were 15–20 cm above the platform due to optimized capture of ventral patches. Although sample size of female martens was small, those visiting traps within this range had higher ventral scores than those visiting traps spaced higher above the platform. The use of footholds provided equivocal results; while wider spacing provided leverage to access bait, the front feet would sometimes block ventral patches. Although camera distance was not an influential factor, ventral marks were most visible when cameras were at an intermediate distance (48–68 cm) to the trap. We suggest incorporating the camera and trap within a partially or fully closed design and use a single brand incandescent flash camera, as martens did not appear to be trap-shy and this camera type provided the best images. Importantly, reducing variability of equipment and light conditions increases the ability of automated computer identification systems [40
Temporal clustering of photographs had a positive effect on probability of identification as well. We quantified camera-trap visits of radio-collared martens and documented long visits, providing ample opportunity for capturing ventral patches for photo identification. Importantly, simultaneous visits (i.e.
, more than one marten visiting the trap at the same time) were never documented and visits by different martens at the same camera-trap were typically spaced >15 min which was higher than the threshold delineated for clustered photographs of the same individual (1.25 min). Similarly, although the identity of a marten could not always be confirmed, observers never assigned >1 marten within a cluster, corroborating visitation patterns of telemetered individuals at camera-traps. Previous studies have either used field data to assign independent observations (e.g., >3 min between consecutive bobcat (Lynx rufus
) pictures; [31
]) or by using recommendations from the literature (e.g., [9
]). However, what constitutes an independent visit for one species may not be applicable to others. We recommend future studies quantify camera-trap visits to determine species-specific cut-off points. This is especially relevant for baited designs that result in large photographic databases and require considerable pre-processing prior to the identification process (independent observers or automated software).
The proportion of un-collared martens we identified is considered high for camera-trap studies [29
]. We had similar agreement of the number of martens identified (O1 = 8, O2 = 8, O3 = 9) and the proportion of martens that were identifiable and not identifiable within clusters. The two independent observers also reported that viewing clustered photographs increased their ability to identify martens in other clusters due to multiple views of marten ventral patches. Using clustered photographs of marten was a significant improvement compared to using one picture/video to identify marten (36%), and it increased the efficiency of classifiers to identify individuals and create a capture history (~4 h) compared to using single files alone (~40 h; A. Siren, unpublished data). We believe that this process contributed to the high agreement of capture history among observers and suggest using this approach for other species with distinct pelage characteristics or morphological traits.
Similarly, we determined several factors that influenced the ability of observers to identify martens. As predicted, the identification probability was directly proportional to the length of time martens visited camera-traps, inversely proportional to the number of days since traps were baited, and martens visited traps less often as the number of days increased since baiting, indicating a behavioral response to an empty bait can. These results indicate that identification probability can be controlled by ensuring that camera-traps are rebaited at short intervals. In 2012 camera-traps were rebaited approximately once every 4 days, whereas they were only checked and rebaited once weekly in 2011. Although not significant, identification probability remained higher with a 4-day tend compared to every 7 days; the tending schedule may need to be modified if marten density is higher. We included the number of martens that visited each camera-trap as a proxy for density and although there was weak support for this model, it is plausible that when more martens are present bait will be consumed quicker. However, the territorial behavior of martens may prevent subordinate individuals from accessing bait, except potentially when resources are particularly abundant [73
While the identification of individuals is achievable, the ability to correctly classify the sex and distinguish between juvenile and adults increases the complexity. Differentiating sex is easier to accomplish with dimorphic species such as martens and was attempted in this study. Our method might be improved by widening the capture zone to include the genital area as was done with wolverines (Gulo gulo
) in Alaska [8
]; however, it should not compromise the identification of ventral patches. Using landmarks that are readily captured and in close proximity to the ventral patches may be more practical for distinguishing sex (e.g., head and front feet). For example, male martens have wider and longer skulls than females [19
], and combined with front foot morphology may increase classification. Providing a ruler to make comparisons is important, and may be integrated with photo analysis software to provide quantitative comparisons of morphology. Thompson [74
] used the ratio of head morphology and camera-trap landmarks (ear-width/treadle width) to classify the gender of fisher and correctly classified 82.5% of known males and 94.7% of known females. While identification of sex is possible, the greatest challenge is to distinguish juvenile and adult marten because they reach adult size their first winter.
This study featured a rare comparison between camera- and live-trapping using a sample of radio-collared and un-collared individuals. Model selection, and parameter estimates for both years of camera-trapping were alike; the top models for both years included a behavioral × site learned response. This is unsurprising as marten are a territorial species and respond positively to baited camera-traps after the initial detection [55
], similar to wolverines [77
]. Although, density was higher during winter 2012, capture probability and σ were similar in both winters, providing evidence that camera-trapping provided a relatively unbiased estimate of marten spacing and movement patterns. The latter statistic (i.e.
, σ) was similar to the average home range radius of telemetered marten with males having larger home ranges (Figure 5
). Interestingly, precision was greater for all parameter estimates for winter 2011, likely attributed to increased effort and sample size. This is expected as non-invasive methods such as camera-trapping [15
] and genetic tagging [13
] provide robust sampling which can improve parameter estimation and precision.
Comparatively, live-trapping density for winter 2012 was lower and less precise compared to camera SCR and the σ value was considerably higher compared to both years of camera-trapping (Figure 5
). This was likely due to reduced effort and a lower recapture rate. Further, AICc model selection of live-trapping data indicated that sample size was likely too small, as the null model performed as well as those which included behavioral and trap specific responses (Table 2
). Although SCR models can deal with sparse datasets it is advisable to have ≥20 recaptures [41
], 33% more than in this study (n
= 15). However, if ancillary data is available (as in this study), combining multiple data sources can improve parameter estimation and the precision of density estimates [78
]. Although we consider our live-trapping density estimates to be biased, both methods (i.e.
, camera- and live-trapping) were within the range of previous studies that used genetic data [76
], live-trapping [80
], and radio-telemetry [23
While trap shyness is common for live-trapping, it has also been documented in camera-trap studies (e.g., [81
]) due to camera flash. Martens are very tolerant of camera flash as our data suggests; however, trap shyness is common for martens in live-trapping studies [84
]. For traditional capture mark-recapture modeling, trap shyness creates negative bias toward capture probability and positive bias toward abundance when using a null model which assumes no behavioral response [33
]. When there are sufficient data, this behavioral response can be accounted for and appropriately modeled; however, when effort and sampling are reduced, behavioral responses might remain undetected resulting in biased estimates of detection probability and density [15
]. For SCR models, small sample size and/or biased recaptures might produce the opposite trend compared to traditional CMR models [45
]. For example, when recaptures are sparse and biased towards individuals that exhibit different space use, the mean movement parameter will be skewed [45
]. This pattern was observed for black bears where male movements were overestimated leading to underestimated density [45
]. Similarly, the movement parameter (i.e.
, σ) was high for live-trapping compared to both years of camera-trapping in our study (Figure 5
) and attributed to two males that were caught most often during live-trapping (Appendix
) and had the longest movements. Further, several marten (especially females) became trap shy during live-trapping and this likely contributed towards fewer and biased recapture (Appendix
). We posit that this dynamic contributed to our negatively biased density estimate. Further, because live-traps are single catch devices that limit capture opportunities compared to camera-traps, this greatly reduced the sampling opportunity.
Understanding the degree to which study animals exhibit a behavioral response can be difficult to determine, especially when sample size is low and cannot be explicitly incorporated in model evaluation. Considering that minimally invasive survey techniques often have low sample size and lack demographic data, the potential for biased density estimates may occur at a greater likelihood. Also, because SCR is increasingly used to reanalyze existing live- trapping datasets, it is important to identify potential behavioral responses. Although effort was minimal for live-trapping in this study, it was comparable to previously published studies that used live-trapping to estimate marten density (e.g., [22
]). Further, the spatial ecology of a species may vary by sex and age influencing capture heterogeneity as well. These potential problems provide further support to develop sampling techniques that provide demographic information on captured individuals. While the identity of sex is plausible using camera-trapping (see [8
]) and was estimated in this study, it may be difficult to reliably determine the age of an individual. However, recent genetic methods to determine age (e.g., [87
]) might work well in tandem with camera-trapping and provide important covariates to be included in SCR density estimation.