Abstract

ABSTRACTEstimating 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. © 2021 The Authors. Wildlife Society Bulletin published by Wiley Periodicals LLC on behalf of The Wildlife Society.

Highlights

  • The introduction page allows the user to configure certain app settings, and provides guidance on how to use ctmmweb

  • We completed all steps of the workflow from import through conditional analyses including Autocorrelated kernel density estimation (AKDE) home range estimation and home range overlap via the Bhattacharyya coefficient

  • Autocorrelation has become a critical issue in animal tracking data that can no longer be ignored

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Summary

Introduction

The introduction page allows the user to configure certain app settings (e.g., whether or not to use multiple processor cores for parallelization), and provides guidance on how to use ctmmweb. An archive of the user’s ctmmweb session can be downloaded at any time (and from any page) by pressing the “Save Progress” button on the bottom of the sidebar. The guide is off by default and requires the user to select a goal (or set of goals) from the listed checkboxes. The required steps to achieve the chosen goal are show with boldface type and a green icon in the sidebar. The user only needs to follow the highlighted steps sequentially from the top to the bottom of the sidebar to achieve their selected goal. A series of vignettes on ctmmweb and the analyses it supports are provide to give more detailed guidance

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