Improving the random encounter model method to estimate carnivore densities using data generated by conventional camera-trap design

Garrote, Germán; Ayala, Ramón Pérez de; Álvarez, Antón; Martín, Jose Manuel; Ruíz, Manuel; Lillo, Santiago de; Simón, Miguel Ángel

doi:10.1017/s0030605318001618

Cited by 12 publications

(10 citation statements)

References 35 publications

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“…Looking into the bibliography (Appendix ), we observed that most of the deficient procedures to estimate day range are those in which tagged animals with GPS collars were used to estimate day range without accounting for tortuosity (e.g. Caravaggi et al., 2016; Garrote et al., 2021; Massei et al., 2018; Rovero & Marshall, 2009; Zero et al., 2013). It is well described that estimate day range assuming straight‐line distances between consecutive fixes notably underestimate day range, and some studies concluded that more than 5 fixes·min 1 would be required to get tolerably accurate estimates (Marcus Rowcliffe et al., 2012; Sennhenn‐Reulen et al., 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Looking into literature (Appendix ), we observed that habitual practice is to determine the dimensions of the detection zone by a series of trials in which the camera was approached by a person from varying directions (e.g. Cusack et al., 2015; Garrote et al., 2021; Loonam et al., 2021; Massei et al., 2018; Rowcliffe et al., 2008). In this respect, some studies have evidenced that detection zone is determined by different factors such as environmental conditions and camera trap settings (Palencia, Vicente, et al., 2021; Rowcliffe et al., 2011).…”

Section: Discussionmentioning

confidence: 99%

“…whether or not they accounted for variance in all parameters) or reported explicitly that they had not considered precision in some of the measured variables (e.g. Balestrieri et al., 2016; Garrote et al., 2021; Pfeffer et al., 2018).…”

Section: Methodsmentioning

confidence: 99%

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Random encounter model is a reliable method for estimating population density of multiple species using camera traps

Palencia

Barroso

Vicente

et al. 2022

Remote Sens Ecol Conserv

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Population density estimates are important for wildlife conservation and management. Several camera trapping-based methods for estimating densities have been developed, one of which, the random encounter model (REM), has been widely applied due to its practical advantages such as no need for speciesspecific study design. Nevertheless, most of the studies in which REM has been assessed against referenced methods have sampled one population, precluding evaluation of the circumstances under which REM does or does not perform well. At this point, a review of all REM assessments could be useful to provide an overview of method reliability and highlight the main factors determining REM performance. Here we used a combination of literature review and empirical study to compare the performance of REM with independent methods. We reviewed 34 studies where REM was applied to 45 species, reporting 77 REMreference density comparisons; and we also sampled 13 populations (ungulates and lagomorphs) in which we assessed REM performance against independent densities. The results suggested that appropriate procedures to estimate REM parameters (namely day range, detection zone and encounter rate) are mandatory to obtain unbiased densities. Deficient estimates of day range and encounter rate lead to an overestimation of density, while deficient estimates of detection zone conducted to underestimations. Finally, the precision achieved by REM was lower than reference methods, mainly because of the high levels of spatial aggregation observed in natural populations. In this situation, simulation-based results suggest that c. 60 camera placements should be sampled to achieve acceptable precision (i.e. coefficient of variation below 0.20). The wide range of situations and scenarios included in this study allow us to conclude that REM is a reliable method for estimating wildlife population density when using appropriate estimates of REM parameters and sampling designs. Overall, these results pave the way to wider application of REM for monitoring terrestrial mammals.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Random encounter model is a reliable method for estimating population density of multiple species using camera traps

Palencia

Barroso

Vicente

et al. 2022

Remote Sens Ecol Conserv

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“…This study, using camera traps in a transportation management setting, represents a valuable addition to the growing literature on camera trap applications for wildlife conservation and management ( e.g . Caravaggi et al., 2018; Garrote et al., 2019; Hofmeester et al., 2020; Jachowski et al., 2015; Schwartz et al., 2018). More importantly, it demonstrates the suitability of this survey method for quantifying ecological phenomena of management concern in busy, dynamic and heavily regulated environments.…”

Section: Discussionmentioning

confidence: 99%

Hareport hazard: Identifying hare activity patterns and increased mammal–aircraft strike risk at an International Airport

Ball

Caravaggi

Butler

2022

Remote Sens Ecol Conserv

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Reported strike events between wildlife and aircraft are hazardous to aircraft and airfield operations and are increasing globally. To develop effective mitigation strategies, the relative hazard a species poses to aircraft, as well as information relating to its life history, are key to the development of effective mitigation strategies in Wildlife Hazard Management Plans. However, given the complex nature of airfield environments with access restrictions and the presence of sensitive equipment, the collection of high‐quality ecological data can be difficult. Here we use motion‐activated camera traps to collect activity data on a population of Irish hares (Lepus timidus hibernicus) inhabiting the airfield at Dublin International Airport, to investigate the link between hare activity and aircraft activity in relation to hare strikes. Camera traps revealed that the hare population at the airfield largely displayed a bimodal crepuscular activity pattern, with activity peaking at sunrise and at sunset. Recorded hare strike times at the airfield were closely associated with hare activity times with a high temporal overlap between these datasets. In comparison, hare activity and aircraft movement activity had a moderate overlap across all seasons, with strikes peaking at times with low aircraft movements. We demonstrate the importance of understanding the circadian and seasonal activity patterns of hazardous species at airfields for targeted strike mitigation.

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“…Therefore, REM method could not be applied to study design, commonly used in carnivores, as Iberian lynx, where cameras are placed on tracks to maximize capture probability. Nevertheless, Garrote et al (2019) developed a correction factor (CF) for expected deviations from REM density estimates using data generated by conventional camera-trap design. The correction factor corrects for the differential use-rate between tracks and the rest of the area made by lynx:…”

Section: Random Encounter Model Approachmentioning

confidence: 99%

Guidance on estimation of abundance and density of wild carnivore population:methods, challenges, possibilities

Podgórski

Acevedo

Apollonio

et al. 2020

EFS3

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This guidance reviews the methods for estimating relative abundance and density in nine large European wild carnivore species, somerepresenting relevant health concerns andprovides insights on how to obtain reliable estimations by using those methods. On a local scale, the appropriate method should take into accountthe characteristics of the study area, the estimated survey efforts, the expected results (i.e. a measure of true density or just an index of abundance to monitor the trend in space and time) the level of accuracy and precision, and a proper design so to obtain a correct interpretation of the data. Among all methods, the camera trapping (CT) methods, especially those recently developed, are the most promising for the collection of robust data and can be conducted in a wide range of species, habitats, seasons and densities with minimal adjustments. Some recently developed CT methods do not require individual recognition of the animals and are a good compromise of cost, effort and accuracy. Linear transects,particularly Kilometric Abundance Index (KAI) is applicable for monitoring large regions.A large challenge is compiling and validating abundance data at different spatial scales. Based on ENETWILD initiative, we recommend developing a permanent network and a data platform to collect and share local density estimates, so as abundance in the EU, which would enable to validate predictions for larger areas by modelling. It would allow to identify gaps in the data on wild carnivores (including the species not assessed in the present report) and to focus on these areas for improving predictions. This platform must facilitate the reporting by wildlife policy makers and relevant stakeholders, but also citizen science initiatives.Also, there is need to improve the reliability of local density estimations by developing practical research on methods able to derive densities in untested species and situations, making the application of methods easier for local teams.

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Improving the random encounter model method to estimate carnivore densities using data generated by conventional camera-trap design

Cited by 12 publications

References 35 publications

Random encounter model is a reliable method for estimating population density of multiple species using camera traps

Random encounter model is a reliable method for estimating population density of multiple species using camera traps

Hareport hazard: Identifying hare activity patterns and increased mammal–aircraft strike risk at an International Airport

Guidance on estimation of abundance and density of wild carnivore population:methods, challenges, possibilities

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