Using camera traps to monitor cyclic vole populations

• Published in: Remote Sensing in Ecology and Conservation
• Main Authors: Kleiven, Eivind Flittie, Nicolau, Pedro Guilherme, Sørbye, Sigrunn Holbek, Aars, Jon, Yoccoz, Nigel Gilles, Ims, Rolf Anker
• Other Authors: Rowcliffe, Marcus, Rovero, Francesco, Norges Forskningsråd, Tromsø Forskningsstiftelse
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: Nature and Landscape Conservation; Computers in Earth Sciences; Ecology; Ecology, Evolution, Behavior and Systematics; Arctic; Tundra
• Online Access: http://dx.doi.org/10.1002/rse2.317; https://onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.317; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/rse2.317; https://zslpublications.onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.317

Abstract Camera traps have become popular labor‐efficient and non‐invasive tools to study animal populations. The use of camera trap methods has largely focused on large animals and/or animals with identifiable features, with less attention being paid to small mammals, including rodents. Here we investigate the suitability of camera‐trap‐based abundance indices to monitor population dynamics in two species of voles with key functions in boreal and Arctic ecosystems, known for their high‐amplitude population cycles. The targeted species—gray‐sided vole ( Myodes rufocanus ) and tundra vole ( Microtus oeconomus )—differ with respect to habitat use and spatial‐social organization, which allow us to assess whether such species traits influence the accuracy of the abundance indices. For both species, multiple live‐trapping grids yielding capture‐mark‐recapture (CMR) abundance estimates were matched with single tunnel‐based camera traps (CT) continuously recording passing animals. The sampling encompassed 3 years with contrasting abundances and phases of the population cycles. We used linear regressions to calibrate CT indices, based on species‐specific photo counts over different time windows, as a function of CMR‐abundance estimates. We then performed inverse regression to predict CMR abundances from CT indices and assess prediction accuracy. We found that CT indices (for windows maximizing goodness‐of‐fit of the calibration models) predicted adequately the CMR‐based estimates for the gray‐sided vole, but performed poorly for the tundra vole. However, spatially aggregating CT indices over nearby camera traps enabled reliable abundance indices also for the tundra vole. Such species differences imply that the design of camera trap studies of rodent population dynamics should be adapted to the species in focus, and adequate spatial replication must be considered. Overall, tunnel‐based camera traps yield much more temporally resolved abundance metrics than alternative methods, with a large potential for revealing new ...