Assessing analytical methods for detecting spatiotemporal interactions between species from camera trapping data

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.

A sparse observation model to quantify species distributions and their overlap in space and time

“…Records from camera traps or other continuous recording devices may also be used to study the interaction of species in space and time. To infer temporal interactions, two classes of methods exist (Niedballa et al 2019): In a first class, temporal avoidance is quantified as the degree to which a first species influences subsequent visits of a second species (Harmsen et al 2009, Karanth et al 2017). These methods generally compare time intervals between observations of the first and the second species to determine statistical dependence.…”

“…This is particularly apparent for Δ T that is biased away from 1.0 and may hence result in wrongly inferred differences between species. Niedballa et al (2019) previously reported this issue and proposed a bootstrap approach to test if an estimate of Δ T is significantly different from 1.0.…”

Anthropogenic influences on primate antipredator behavior and implications for research and conservation

“…Motion‐sensitive camera traps are widely used for remotely collecting data on animal abundance and density and can provide estimates of cooccurrence between predators and prey. Though much of the camera‐trap work focuses on estimating population sizes (Burton et al, ), they can be used for measuring behaviors such as temporal or spatial avoidance of competitors or of predators by prey (Farris et al, ; Niedballa, Wilting, Sollmann, Hofer, & Courtiol, ). Although camera traps have typically been used on terrestrial wildlife, recent studies have validated their use at ground level for semiterrestrial species (Cappelle, Després‐Einspenner, Howe, Boesch, & Kühl, ) and, by placing cameras strategically along natural crossing points in forest strata, for arboreal primates (Gregory, Carrasco Rueda, Deichmann, Kolowski, & Alonso, ).…”

Exploring and interpreting spatiotemporal interactions between native and invasive carnivores across a gradient of rainforest degradation

“…Investigations of carnivore communities have revealed that carnivores alter their spatial distribution (Durant 1998;Hersteinsson, Macdonald 1992;Linnell, Strand 2000;Mills, Gorman 1997;Mitchell, Banks 2005;Rich et al 2017;Vanak et al 2013b) or their daily activity patterns Kitchen et al 1999;Major, Sherburne 1987;Palomares, Caro 1999;Wang et al 2015;Wilson et al 2010) due to interspecific interactions. Investigations of interspecific interactions that combine spatial and temporal analyses simultaneously, however, are exceedingly rare for elusive carnivores (Karanth et al 2017;Li et al 2019;Moll et al 2018;Niedballa et al 2019;Smith et al 2019). Yet, such studies may provide heightened insight on the ultimate causes driving co-occurrence of species' populations within communities, since investigations utilizing one dimension alone (spatial or temporal) may fail to elucidate how species alter both spatial and temporal use simultaneously to promote or discourage potential interactions.…”

“…While there are numerous spatiotemporal modelling approaches (see Cressie, Wikle 2015), few can accommodate typically sparse datasets that are common in carnivore studies. Attempts to model and evaluate spatiotemporal interactions between co-occurring carnivores and carnivore-prey pairings includes investigation via linear models and frequentist statistics (Niedballa et al 2019), analyses based on radio-tagged animals and step selection functions (Vanak et al 2013b), as well as analyses combing temporal activity patterns and occupancy modelling (Karanth et al 2017;Smith et al 2019). While many of these approaches were designed for non-invasive sampling of carnivore populations, few (if any) provide a combined spatio-temporal interaction estimate that is also allowed to vary across changing landscape and/or habitat variables.…”