A dynamic occupancy model for interacting species with two spatial scales

Abstract

Occupancy models have been developed independently to account for multiple spatial scales and species interactions in a dynamic setting. However, as interacting species (e.g., predators and prey) often operate at different spatial scales, including nested spatial structure might be especially relevant in models of interacting species. Here we bridge these two model frameworks by developing a multi-scale two-species occupancy model. The model is dynamic, i.e. it estimates initial occupancy, colonization and extinction probabilities - including probabilities conditional to the other species' presence. With a simulation study, we demonstrate that the model is able to estimate parameters without bias under low, medium and high average occupancy probabilities, as well as low, medium and high detection probabilities. We further show the model's ability to deal with sparse field data by applying it to a multi-scale camera trapping dataset on a mustelid-rodent predator-prey system. The field study illustrates that the model allows estimation of species interaction effects on colonization and extinction probabilities at two spatial scales. This creates opportunities to explicitly account for the spatial structure found in many spatially nested study designs, and to study interacting species that have contrasted movement ranges with camera traps.

Using camera traps to monitor cyclic vole populations

“…For instance, elucidating the role of predation opts for a design that encompass the relatively small scale of both prey (rodents) and larger scale of predator (e.g mustelids). This may be achieved by a two‐level, spatial hierarchical design with replication at both the scale of the prey and the predator (Kleiven et al., 2021). Moreover, much current key interest is devoted to the influence of climate on rodent population dynamics, which may opt for spatial design encompassing climate gradients (Ims et al., 2011).…”

Non‐invasive methods for monitoring weasels: emerging technologies and priorities for future research

“…5; Salvador et al 2022) and metal boxes for long-tailed weasel and Colombian weasel Neogale felipei in Colombia (Cepeda-Duque et al 2023). Another boxed camera trap, developed to monitor small mammals under snow (Soininen et al 2015), has shown success in detecting Mustela nivalis nivalis and Mustela erminea in arctic systems (Kleiven et al 2023).…”

“…Data from enclosed camera traps have been used to estimate occurrence, relative activity, and daily and seasonal activity patterns (Mos & Hofmeester 2020, Amber et al 2021b, and predator-prey dynamics (Kleiven et al 2023). Hofmeester et al (in press) compared relative abundance estimated from Mostela data using Royle-Nichols models (Royle & Nichols 2003) with the minimum number of weasels known to occur in the area based on live-trapping.…”

“…Soininen et al 2015). Simultaneous sampling of weasels and their potential prey can reveal interactions and provide data to analyse prey-related demographic fluctuations (Hamed MK, Holloway AW, Watts C, Webster A, Tanner K, and Moore T, unpublished data, Kleiven et al 2023). Because drift fences associated with AHDriFT systems increase the overall area sampled by the device, they might make these systems more effective at recording weasels and their prey.…”

Using passive acoustic monitoring to estimate northern spotted owl landscape use and pair occupancy

“…We recognize the potential for unequal availability for detection among ARU stations, further highlighting the strength of the multistate framework (landscape use and pair occupancy) and the need to have multiple ARUs in each hexagon with placement not determined by the suitability of forest for nesting and roosting. Future efforts may explicitly model the relationship between station-level detection and hexagon-level occupancy processes with a multistate, multiscale approach, but to our knowledge these models have only been explored preliminarily (Kleiven et al, 2021).…”