How range residency and long-range perception change encounter rates

Martínez-García, Ricardo; Fleming, Christen H.; Seppelt, Ralf; Fagan, William F.; Calabrese, Justin M.

doi:10.1101/712182

Cited by 21 publications

(37 citation statements)

References 81 publications

(96 reference statements)

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“…Despite this, the relationship between individual movement strategies and encounter processes has remained conspicuously understudied. Furthermore, previous work has focused almost exclusively on relating animal movement to encounter rates (e.g., Gerritsen & Strickler, 1977;Visser & Kiørboe, 2006;Hutchinson & Waser, 2007;Bartumeus et al, 2008;Gurarie & Ovaskainen, 2013;Martinez-Garcia et al, 2020). This has left researchers interested in understanding the spatial dynamics of encounter processes with only empirically based null models (Spiegel et al, 2016(Spiegel et al, , 2018 or indirect ad hoc measures of the spatial distribution of encounters.…”

Section: Discussionmentioning

confidence: 99%

“…If we assume that individual movement is isotropic and described by a pair of Ornstein-Uhlenbeck (OU) processes, then we can use the results obtained in recent work by Martinez-Garcia et al (2020). Specifically, Eq.…”

Section: Discussionmentioning

confidence: 99%

“…A key limitation of these models, however, is that they result in uniformly distributed patterns of space use, meaning encounters between individuals also occur uniformly in space. In stark contrast, most real animals exhibit non-uniform space use within spatially restricted home ranges (Burt, 1943;Bowen, 1982;Powell, 2000;Moorcroft et al, 2006b;Kie et al, 2010;Fleming et al, 2014a;Noonan et al, 2019b;Martinez-Garcia et al, 2020), and encounters between individuals do not occur uniformly in space, but are instead concentrated at territorial boundaries (Nievergelt et al, 1998;Bermejo, 2004;Wilson et al, 2012;Ellwood et al, 2017), in/around heavily used habitats and/or habitat-features (Weckel et al, 2006;Whittington et al, 2011), or at key resources (De Boer et al, 2010;Price-Rees et al, 2013). We therefore base our estimation framework on recent analytical work by Martinez-Garcia et al (2020) incorporating non-uniform movement within home ranges into encounter theory.…”

Section: Motionmentioning

confidence: 99%

“…In our third scenario, we simulated tracking data for a predator with a large home range that encompassed a population of 30 prey individuals. Notably, this scenario would also be appropriate for species where large male home ranges overlap numerous smaller female ranges (Clutton-Brock, 1989;Calabrese et al, 2020). For each individual we sampled 500 locations from an isotropic, IID process.…”

Section: Ecologically Guided Case Studiesmentioning

confidence: 99%

“…Despite the keystone importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with the developments in animal tracking (Wikelski et al, 2007;Kays et al, 2015;Noonan et al, 2015) or movement modelling (Johnson et al, 2008;Gurarie et al, 2009;Benhamou, 2014;Fleming et al, 2014a;Michelot & Blackwell, 2019;Hooten et al, 2019). Furthermore, existing work has focused primarily on the relationship between animal movement and encounter rates (e.g., Gerritsen & Strickler, 1977;Visser & Kiørboe, 2006;Hutchinson & Waser, 2007;Bartumeus et al, 2008;Gurarie & Ovaskainen, 2013;Martinez-Garcia et al, 2020), while no formal framework directly relates individual movement with the spatial locations of encounter events in the environment (but see Spiegel et al, 2016). This is a notable limitation as the probability of encountering another individual has well documented effects on animal behaviour.…”

Section: Introductionmentioning

confidence: 99%

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Estimating encounter location distributions from animal tracking data

Noonan

Martínez-García²,

Davis

et al. 2020

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Motionmentioning

confidence: 99%

Section: Ecologically Guided Case Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimating encounter location distributions from animal tracking data

Noonan

Martínez-García²,

Davis

et al. 2020

Preprint

Self Cite

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Estimation of the maximum utilization area including home range and peripheral sites

Terayama

Ebihara

Seino

et al. 2022

Ecology and Evolution

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There is increasing evidence that occasional utilization area (peripheral sites), in addition to typical utilization area (home range), is important for wildlife conservation and management. Here we estimated the maximum utilization area (MUA), including both typical and occasional utilization areas, based on asymptotic curves of utilization area plotted against sample size. In previous studies, these curves have conventionally been plots of cumulative utilization area versus sample size, but this cumulative method is sensitive to stochastic effects. We propose a new method based on simulation studies where outcomes of replicated simulations are averaged to reduce stochastic effects. In this averaged method, possible combinations of sample size with the same number of location data replicated from a dataset were averaged and applied to the curves of utilization area. The cumulative method resulted in a large variation of MUA estimates, depending on the start date as well as total sample size of the dataset. In the averaged method, MUA estimates were robust against changes in the start date and total sample size. The large variation of MUA estimates arose because location data on any day including the start date are affected by unpredictable effects associated with animal activity and environmental conditions. In the averaged method, replicates of sample size resulted in a reduction of temporal stochasticity, suggesting that the method stably provides reliable estimates for MUA.

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ctmmweb: A Graphical User Interface for Autocorrelation‐Informed Home Range Estimation

Calabrese

Fleming

Noonan

et al. 2021

Wildlife Society Bulletin

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Estimating animal home ranges is a primary purpose of collecting tracking data. Many widely used home range estimators, including conventional kernel density estimators, assume independently-sampled data. In stark contrast, modern animal tracking datasets are almost always strongly autocorrelated. The incongruence between estimator assumptions and empirical reality often leads to systematically underestimated home ranges. Autocorrelated kernel density estimation (AKDE) directly models the observed autocorrelation structure of tracking data during home range estimation, and has been shown to perform accurately across a broad range of tracking datasets. However, compared to conventional estimators, AKDE requires additional modeling steps and has heretofore only been accessible via the command-line ctmm R package. Here, we introduce ctmmweb, which provides a point-and-click graphical interface to ctmm and streamlines AKDE, its prerequisite autocorrelation modeling steps, and a number of additional movement analyses. We demonstrate ctmmweb's capabilities, including AKDE home range estimation and subsequent home range overlap analysis, on a dataset of four jaguars from the Brazilian Pantanal tracked between 2013 and 2015. We intend ctmmweb to open AKDE and related autocorrelation-explicit analyses to a wider audience of wildlife and conservation professionals.

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How range residency and long-range perception change encounter rates

Cited by 21 publications

References 81 publications

Estimating encounter location distributions from animal tracking data

Estimating encounter location distributions from animal tracking data

Estimation of the maximum utilization area including home range and peripheral sites

ctmmweb: A Graphical User Interface for Autocorrelation‐Informed Home Range Estimation

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