Estimating effective survey duration in camera trap distance sampling surveys

Hjalmar S Kühl; Stephen T Buckland; Maik Henrich; Eric Howe; Marco Heurich

doi:10.1002/ece3.10599

Estimating effective survey duration in camera trap distance sampling surveys

Ecol Evol. 2023 Oct 13;13(10):e10599. doi: 10.1002/ece3.10599. eCollection 2023 Oct.

Authors

Hjalmar S Kühl^{1

2

3}, Stephen T Buckland⁴, Maik Henrich^{5

6}, Eric Howe⁷, Marco Heurich^{5

6

8}

Affiliations

¹ Senckenberg Museum for Natural History Görlitz Senckenberg - Member of the Leibniz Association Görlitz Germany.
² International Institute Zittau, Technische Universität Dresden Zittau Germany.
³ German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Leipzig Germany.
⁴ Centre for Research into Ecological and Environmental Modelling University of St Andrews, The Observatory St Andrews UK.
⁵ Department of National Park Monitoring and Animal Management Bavarian Forest National Park Grafenau Germany.
⁶ Faculty of Environment and Natural Resources Albert Ludwigs University of Freiburg Freiburg Germany.
⁷ Wildlife Research and Monitoring Section Ontario Ministry of Natural Resources and Forestry Peterborough Ontario Canada.
⁸ Institute for Forest and Wildlife Management Inland Norway University of Applied Science Koppang Norway.

Abstract

Among other approaches, camera trap distance sampling (CTDS) is used to estimate animal abundance from unmarked populations. It was formulated for videos and observation distances are measured at predetermined 'snapshot moments'. Surveys recording still images with passive infrared motion sensors suffer from frequent periods where animals are not photographed, either because of technical delays before the camera can be triggered again (i.e. 'camera recovery time') or because they remain stationary and do not immediately retrigger the camera following camera recovery time (i.e. 'retrigger delays'). These effects need to be considered when calculating temporal survey effort to avoid downwardly biased abundance estimates. Here, we extend the CTDS model for passive infrared motion sensor recording of single images or short photo series. We propose estimating 'mean time intervals between triggers' as combined mean camera recovery time and mean retrigger delays from the time interval distribution of pairs of consecutive pictures, using a Gamma and Exponential function, respectively. We apply the approach to survey data on red deer, roe deer and wild boar. Mean time intervals between triggers were very similar when estimated empirically and when derived from the model-based approach. Depending on truncation times (i.e. the time interval between consecutive pictures beyond which data are discarded) and species, we estimated mean time intervals between retriggers between 8.28 and 15.05 s. Using a predefined snapshot interval, not accounting for these intervals, would lead to underestimated density by up to 96% due to overestimated temporal survey effort. The proposed approach is applicable to any taxa surveyed with camera traps. As programming of cameras to record still images is often preferred over video recording due to reduced consumption of energy and memory, we expect this approach to find broad application, also for other camera trap methods than CTDS.

Keywords: animal abundance; camera recovery time; camera trap distance sampling; retrigger delay; still images; video.