Estimating encounter location distributions from animal tracking data

Abstract

1. Ecologists have long been interested in linking individual behavior with higher-level processes. For motile species, this 'upscaling' is governed by how well any given movement strategy maximizes encounters with positive factors, and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modeling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounter rates while no theoretical framework exists for directly relating individual movement with the spatial locations of encounter events in the environment. 2. Here, we bridge this gap by introducing a new theoretical concept describing the long-term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open source software, and demonstrate the broad ecological relevance of this novel concept. 3. We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation-based scenarios that occur routinely in biological systems: i) a population of individuals with home ranges that overlap with neighbors; ii) a pair of individuals with a hard territorial border between their home ranges; and iii) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white-faced capuchins (Cebus capucinus) tracked on Barro Colorado Island, Panama, and sleepy lizards (Tiliqua rugosa) tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the location-specific potential for competition, and/or identify any changes in behaviour that directly result from location-specific encounter probability. 4. This novel target distribution enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialised data collection protocols. This method is now openly available via the ctmm R package.

Unifying spatial and social network analysis in disease ecology

“…the similarity or proportional overlap of two individuals' spatial environments; Albery, Morris, et al., 2020; Noonan et al., 2020; Pullan et al., 2012). The two may correlate—for example, individuals living closer together will share more of their home ranges—but these different types of spatial behaviour can operate differently, potentially offering different insights, and may have additive benefits for inference when considered simultaneously (Albery, Morris, et al., 2020; Noonan et al., 2020). Although many network analyses consider interactions as taking place in a conceptual space, a recent analytical approach was developed to identify the locations of the interactions themselves using telemetry data (Noonan et al., 2020).…”

“…The two may correlate—for example, individuals living closer together will share more of their home ranges—but these different types of spatial behaviour can operate differently, potentially offering different insights, and may have additive benefits for inference when considered simultaneously (Albery, Morris, et al., 2020; Noonan et al., 2020). Although many network analyses consider interactions as taking place in a conceptual space, a recent analytical approach was developed to identify the locations of the interactions themselves using telemetry data (Noonan et al., 2020). The relative advantages of the spatial measures used may depend on the system itself; for example, home range overlap will be uninformative for parasitism when species are territorial or at such low density that their home ranges rarely overlap.…”

“…where an individual is on a variable landscape) or space sharing effects (i.e. the similarity or proportional overlap of two individuals' spatial environments; Albery, Morris, et al., 2020; Noonan et al., 2020; Pullan et al., 2012). The two may correlate—for example, individuals living closer together will share more of their home ranges—but these different types of spatial behaviour can operate differently, potentially offering different insights, and may have additive benefits for inference when considered simultaneously (Albery, Morris, et al., 2020; Noonan et al., 2020).…”

Multi-species mechanistic model of functional response provides new insights into predator-mediated interactions in a vertebrate community

“…With recent advances in biologging technology, high-frequency GPS, acoustic and accelerometer data are increasingly used to study free-ranging organisms (Williams et al, 2014; Pagano et al, 2018; Studd et al, 2019). The parametrization of mechanistic models with such data is a promising method to quantify interaction strength in natural systems (Merrill et al, 2010; Noonan et al, 2021; Studd et al, 2021).…”

Estimating encounter location distributions from animal tracking data

“…The white‐faced capuchin Cebus capucinus and sleepy lizard Tiliqua rugosa data are openly available through the Dryad Data Repository https://doi.org/10.5061/dryad.sf7m0cg5d (Noonan et al., 2021). The ctmm code is openly available on CRAN via https://cran.r-project.org/package=ctmm.…”