Evaluating species-specific responses to camera-trap survey designs

Iannarilli, Fabiola; Erb, John D.; Arnold, Todd W.; Fieberg, John

doi:10.2981/wlb.00726

Cited by 30 publications

(25 citation statements)

References 59 publications

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“…The relatively high detection rate via camera traps (0.027; SD: 0.007) is encouraging, as it suggests that obtaining suitable occupancy dataset for the integrated model should be relatively easy. The use of lures is widely used to improve the detection probability of carnivores in camera‐trapping studies (e.g., Avrin et al, 2021 ; Holinda et al, 2020 ; Iannarilli et al, 2021 ), and lure selection can significantly affect the ability to detect rare and elusive species either by making them appearing sooner, moving closer or stay longer in the cameras field of view (Ferreras et al, 2018 ; Tourani et al, 2020 ). Therefore, an appropriate selection of the most effective lures could contribute to optimizing camera‐trapping sampling protocols (Figure 4 ) and lead to better precision in SCR‐IM estimates (Gerber et al, 2011 ; Macaulay et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Occupancy data improves parameter precision in spatial capture–recapture models

Jímenez

Díaz‐Ruiz

Monterroso

et al. 2022

Ecology and Evolution

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Population size is one of the basic demographic parameters for species management and conservation. Among different estimation methods, spatially explicit capturerecapture (SCR) models allow the estimation of population density in a framework that has been greatly developed in recent years. The use of automated detection devices, such as camera traps, has impressively extended SCR studies for individually identifiable species. However, its application to unmarked/partially marked species remains challenging, and no specific method has been widely used. We fitted an SCRintegrated model (SCR-IM) to stone marten Martes foina data, a species for which only some individuals are individually recognizable by natural marks, and estimate population size based on integration of three submodels: (1) individual capture histories from live capture and transponder tagging; (2) detection/nondetection or "occupancy" data using camera traps in a bigger area to extend the geographic scope of capture-recapture data; and (3) telemetry data from a set of tagged individuals. We estimated a stone marten density of 0.352 (SD: 0.081) individuals/km 2 . We simulated four dilution scenarios of occupancy data to study the variation in the coefficient of variation in population size estimates. We also used simulations with similar characteristics as the stone marten case study, comparing the accuracy and precision obtained from SCR-IM and SCR, to understand how submodels' integration affects the posterior distributions of estimated parameters. Based on our simulations, we found that population size estimates using SCR-IM are more accurate and precise. In our stone marten case study, the SCR-IM density estimation increased the precision by 37% when compared to the standard SCR model as regards to the coefficient of variation. This model has high potential to be used for species in which individual recognition by natural markings is not possible, therefore limiting the need to rely on invasive sampling procedures.

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Section: Discussionmentioning

confidence: 99%

Occupancy data improves parameter precision in spatial capture–recapture models

Jímenez

Díaz‐Ruiz

Monterroso

et al. 2022

Ecology and Evolution

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“…Sparse arrays of acoustic or camera monitors may be useful for confirming a species’ occupancy, or for estimating animal activity patterns, abundance, or species diversity in an area, especially when robust methods for confirming species presence have been developed [ 100 , 101 ], but much larger and denser arrays would be needed to infer population movement through or within an area [ 46 ]. Differences in camera trap survey designs, including baited versus unbaited stations, have been found to have significant consequences for occurrence frequency and detection rates [ 102 , 103 ]. However, there are several examples of such arrays that have been used to infer population movement—acoustic recordings for bat occupancy trends across space and through time [ 104 ], camera traps for raptor prevalence and migration [ 46 ] and migration timing and speed of caribou and ptarmigan [ 105 ].…”

Section: Occurrence Datamentioning

confidence: 99%

“…Other effort-based measures may be used, such as time spent searching, distance traveled while searching, number of observers in the search party, or time of day (e.g., eBird [ 36 ]). For camera traps or acoustic sensors, effort-based measures could include fields for the number of days a camera/acoustic sensor was active, whether it was baited or not, or if sensors were placed randomly or chosen opportunistically to increase detection, for example, by focusing on known travel routes or previous occurrence locations [ 103 ]. Such measures can help to control for crowdsourced effort differences across time and space, rather than using the raw occurrence data.…”

Section: Analytical Approaches To Estimate Population-level Movementmentioning

confidence: 99%

Estimating the movements of terrestrial animal populations using broad-scale occurrence data

et al. 2021

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As human and automated sensor networks collect increasingly massive volumes of animal observations, new opportunities have arisen to use these data to infer or track species movements. Sources of broad scale occurrence datasets include crowdsourced databases, such as eBird and iNaturalist, weather surveillance radars, and passive automated sensors including acoustic monitoring units and camera trap networks. Such data resources represent static observations, typically at the species level, at a given location. Nonetheless, by combining multiple observations across many locations and times it is possible to infer spatially continuous population-level movements. Population-level movement characterizes the aggregated movement of individuals comprising a population, such as range contractions, expansions, climate tracking, or migration, that can result from physical, behavioral, or demographic processes. A desire to model population movements from such forms of occurrence data has led to an evolving field that has created new analytical and statistical approaches that can account for spatial and temporal sampling bias in the observations. The insights generated from the growth of population-level movement research can complement the insights from focal tracking studies, and elucidate mechanisms driving changes in population distributions at potentially larger spatial and temporal scales. This review will summarize current broad-scale occurrence datasets, discuss the latest approaches for utilizing them in population-level movement analyses, and highlight studies where such analyses have provided ecological insights. We outline the conceptual approaches and common methodological steps to infer movements from spatially distributed occurrence data that currently exist for terrestrial animals, though similar approaches may be applicable to plants, freshwater, or marine organisms.

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“…Direct observations provide unambiguous proof of predation, but they require spending long hours in the field (Harvey & Gittleman, 1992). The use of automated camera traps partially solved this issue, but although prices are dropping, this technology remains expensive and appropriate mostly only for large predators and prey (O'brien & Kinnaird, 2008; Muiruri et al, 2016; Akcali et al, 2019; Iannarilli et al, 2021). An alternative is to focus on the outcome of predation, rather than on predation itself.…”

Section: Introductionmentioning

confidence: 99%

Following the track: accuracy and reproducibility of predation assessment on artificial caterpillars

Valdés‐Correcher

Mäntylä

Barbaro

et al. 2022

Entomologia Exp Applicata

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By initiating top-down trophic cascades, predators can indirectly control the amount of plant biomass consumed by insect herbivores in both natural and agricultural landscapes (Vidal & Murphy, 2018;Abdala-Roberts et al., 2019). As a key biotic interaction, predation has been an ecological process scrutinized by ecologists for decades (Holmes et al., 1979;Fowler & Knight, 1991;Mäntylä et al., 2011).Yet, predation is a fleeting phenomenon that is difficult to track in real time, especially because it has delayed effects on prey demography and plant response. Direct observations provide unambiguous proof of predation, but they require spending long hours in the field (Harvey & Gittleman, 1992). The use of automated camera traps partially solved this issue, but although prices are dropping, this technology remains expensive and appropriate mostly only for large predators and prey (O'brien & Kinnaird, 2008;

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Evaluating species-specific responses to camera-trap survey designs

Abstract: BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Cited by 30 publications

References 59 publications

Occupancy data improves parameter precision in spatial capture–recapture models

Occupancy data improves parameter precision in spatial capture–recapture models

Estimating the movements of terrestrial animal populations using broad-scale occurrence data

Following the track: accuracy and reproducibility of predation assessment on artificial caterpillars

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