Assessing analytical methods for detecting spatiotemporal interactions between species from camera trapping data. Issue 3 (1st February 2019) - Niedballa, Jürgen; Wilting, Andreas; Sollmann, Rahel; Hofer, Heribert; Courtiol, Alexandre
Abstract: Assessing spatiotemporal interactions between species is of fundamental interest to behavioural and community ecology. Observer‐independent methods such as camera trapping facilitate the study of interactions, but analyses are hampered by the lack of comparative assessment of available approaches. We present a flexible and expandable framework to simulate and explore spatiotemporal interactions between species from camera trapping data with well‐defined properties, and compare methods to detect such interactions in a two‐species system with two types of (spatio)temporal interactions: spatiotemporal avoidance (of a site by a species after the presence of another species) and temporal segregation (shifts in daily activity patterns between species), across a range of daily activity patterns and interaction strengths. For spatiotemporal avoidance, we analysed time intervals between species records using linear models, the Mann–Whitney U ‐test, a permutation test and a test based on randomly generated records. For temporal segregation, we applied a permutation test. Statistical power (the ability to detect an existing effect) for detecting spatiotemporal avoidance between species was strongly affected by interaction strength, highest for linear models and reliable above 50 records per species. Reliably detecting strong temporal segregation required fewer records (10–20 records) but depended heavily on the underlying activity pattern. All tests were valid (uniform distribution of P ‐values under the null hypothesis) even at low sample sizes above a minimum of 10 records per species. Linear models were the most suitable approach to analyse spatiotemporal avoidance and can easily correct for other sources of variation in interactions. The framework presented here can help to improve survey design in camera trapping and be extended to more complex settings (e.g. with imperfect detection). In addition, it allows researchers to validate the methods used for inference of spatiotemporal interactions from camera trapping data in their specific circumstances. Abstract : We assess and compare statistical methods for detecting (spatio)temporal interactions between a primary and a secondary species in camera trapping data simulated with a flexible R function. While various methods detected spatiotemporal avoidance, we found linear models comparing time intervals between interacting species to be the most powerful method. Our simulation approach can help in study design and allocating survey effort, and provides a tool for testing and developing new methods for detecting species interactions in camera trapping data.