scholarly journals A fresh look at an old concept: home-range estimation in a tidy world

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11031
Author(s):  
Johannes Signer ◽  
John R. Fieberg
Keyword(s):  
Home Range ◽  
General Framework ◽  
Point Of View ◽  
Statistical Techniques ◽  
Home Ranges ◽  
Telemetry Data ◽  
Range Estimation ◽  
Computational Point ◽  
Qualitative And Quantitative ◽  
New Methods

A rich set of statistical techniques has been developed over the last several decades to estimate the spatial extent of animal home ranges from telemetry data, and new methods to estimate home ranges continue to be developed. Here we investigate home-range estimation from a computational point of view and aim to provide a general framework for computing home ranges, independent of specific estimators. We show how such a workflow can help to make home-range estimation easier and more intuitive, and we provide a series of examples illustrating how different estimators can be compared easily. This allows one to perform a sensitivity analysis to determine the degree to which the choice of estimator influences qualitative and quantitative conclusions. By providing a standardized implementation of home-range estimators, we hope to equip researchers with the tools needed to explore how estimator choice influences answers to biologically meaningful questions.

scholarly journals A fresh look at an old concept: Home-range estimation in a tidy world

2020 ◽  
Author(s):  
Johannes Signer ◽  
John Fieberg
Keyword(s):  
Home Range ◽  
General Framework ◽  
Point Of View ◽  
Home Ranges ◽  
List Type ◽  
Telemetry Data ◽  
Range Estimation ◽  
Computational Point ◽  
Qualitative And Quantitative ◽  
New Methods

AbstractA rich set of statistical techniques have been developed over the last several decades to estimate the spatial extent of animal home ranges from telemetry data, and new methods to estimate home ranges continue to be developed.Here we investigate home-range estimation from a computational point of view and aim to provide a general framework for computing home ranges, independent of specific estimators.We show how such a workflow can help make home-range estimation easier and more intuitive, and we provide a series of examples illustrating how different estimators can be compared easily, so that one can perform a sensitivity analysis to determine the degree to which the choice of estimator influences qualitative and quantitative conclusions.By providing a standardized, tidy implementation of home-range estimators, we hope to equip analysts with the tools needed to explore how estimator choice influences answers to biologically meaningful questions.

scholarly journals Revisiting reptile home ranges: moving beyond traditional estimators with dynamic Brownian Bridge Movement Models

2020 ◽  
Author(s):  
Inês Silva ◽  
Matt Crane ◽  
Benjamin Michael Marshall ◽  
Colin Thomas Strine
Keyword(s):  
Home Range ◽  
Animal Movement ◽  
Brownian Bridge ◽  
Movement Patterns ◽  
Estimation Methods ◽  
Home Ranges ◽  
Telemetry Data ◽  
Range Estimation ◽  
Ambush Predator ◽  
Movement Models

AbstractAnimal movement, expressed through home ranges, can offer insights into spatial and habitat requirements. However, home range estimation methods vary, directly impacting conclusions. Recent technological advances in animal tracking (GPS and satellite tags), have enabled new methods for home range estimation, but so far have primarily targeted mammal and avian movement patterns. Most reptile home range studies only make use of two older estimation methods: Minimum Convex Polygons (MCP) and Kernel Density Estimators (KDE), particularly with the Least Squares Cross Validation (LSCV) and reference (href) bandwidth selection algorithms. The unique characteristics of reptile movement patterns (e.g. low movement frequency, long stop-over periods), prompt an investigation into whether newer movement-based methods –such as dynamic Brownian Bridge Movement Models (dBBMMs)– are applicable to Very High Frequency (VHF) radio-telemetry tracking data. To assess home range estimation methods for reptile telemetry data, we simulated animal movement data for three archetypical reptile species: a highly mobile active hunter, an ambush predator with long-distance moves and long-term sheltering periods, and an ambush predator with short-distance moves and short-term sheltering periods. We compared traditionally used home range estimators, MCP and KDE, with dBBMMs, across eight feasible VHF field sampling regimes for reptiles, varying from one data point every four daylight hours, to once per month. Although originally designed for GPS tracking studies, we found that dBBMMs outperformed MCPs and KDE href across all tracking regimes, with only KDE LSCV performing comparably at some higher-frequency sampling regimes. The performance of the LSCV algorithm significantly declined with lower-tracking-frequency regimes, whereas dBBMMs error rates remained more stable. We recommend dBBMMs as a viable alternative to MCP and KDE methods for reptile VHF telemetry data: it works under contemporary tracking protocols and provides more stable estimates, improving comparisons across regimes, individuals and species.

scholarly journals Home-range estimation within complex restricted environments: importance of method selection in detecting seasonal change

2009 ◽  
Vol 36 (3) ◽  
pp. 213 ◽  
Author(s):  
Carolyn M. Knight ◽  
Robert E. Kenward ◽  
Rodolphe E. Gozlan ◽  
Kathryn H. Hodder ◽  
Sean S. Walls ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Seasonal Variation ◽  
Home Range ◽  
Reserve Design ◽  
Linear Estimator ◽  
Home Ranges ◽  
Telemetry Data ◽  
Range Estimation ◽  
River Bank ◽  
Wide Range

Estimating the home ranges of animals from telemetry data can provide vital information on their spatial behaviour, which can be applied by managers to a wide range of situations including reserve design, habitat management and interactions between native and non-native species. Methods used to estimate home ranges of animals in spatially restricted environments (e.g. rivers) are liable to overestimate areas and underestimate travel distances by including unusable habitat (e.g. river bank). Currently, few studies that collect telemetry data from species in restricted environments maximise the information that can be gathered by using the most appropriate home-range estimation techniques. Simulated location datasets as well as radio-fix data from 23 northern pike (Esox lucius) were used to examine the efficiency of home-range and travel estimators, with and without correction for unusable habitat, for detecting seasonal changes in movements. Cluster analysis most clearly demonstrated changes in range area between seasons for empirical data, also showing changes in patchiness, and was least affected by unusable-environment error. Kernel analysis showed seasonal variation in range area more clearly than peripheral polygons or ellipses. Range span, a linear estimator of home range, had no significant seasonal variation. Results from all range area estimators were smallest in autumn, when cores were least fragmented and interlocation movements smallest. Cluster analysis showed that core ranges were largest and most fragmented in summer, when interlocation distances were most variable, whereas excursion-sensitive methods (e.g. kernels) recorded the largest outlines in spring, when interlocation distances were largest. Our results provide a rationale for a priori selection of home-range estimators in restricted environments. Contours containing 95% of the location density defined by kernel analyses better reflected excursive activity than ellipses or peripheral polygons, whereas cluster analyses better defined range cores in usable habitat and indicate range fragmentation.

scholarly journals ctmmweb: A graphical user interface for autocorrelation-informed home range estimation

2020 ◽  
Author(s):  
Justin M. Calabrese ◽  
Christen H. Fleming ◽  
Michael J. Noonan ◽  
Xianghui Dong
Keyword(s):  
Home Range ◽  
Kernel Density ◽  
R Package ◽  
Home Ranges ◽  
Tracking Data ◽  
Sampled Data ◽  
Convex Polygons ◽  
Range Estimation ◽  
Empirical Reality ◽  
Range Overlap

ABSTRACTEstimating animal home ranges is a primary purpose of collecting tracking data. All conventional home range estimators in widespread usage, including minimum convex polygons and kernel density estimators, assume independently sampled data. In stark contrast, modern GPS animal tracking datasets are almost always strongly autocorrelated. This incongruence between estimator assumptions and empirical reality leads to systematically underestimated home ranges. Autocorrelated kernel density estimation (AKDE) resolves this conflict by modeling 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. We intend ctmmweb to open AKDE and related autocorrelation-explicit analyses to a wider audience of wildlife and conservation professionals.

scholarly journals Modelling of framework materials at multiple scales: current practices and open questions

2018 ◽  
Author(s):  
Guillaume Fraux ◽  
Siwar Chibani ◽  
François-Xavier Coudert
Keyword(s):  
Atomistic Simulation ◽  
Multiple Scales ◽  
Point Of View ◽  
Computational Point ◽  
Framework Materials ◽  
New Methods ◽  
Modelling Framework ◽  
Current State ◽  
Open Questions ◽  
The Family

The last decade has seen an explosion of the family of framework materials and their study, both from the experimental and computational point of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of materials databases, and the growing use of machine learning for properties prediction.

The role of the bandwidth matrix in influencing kernel home range estimates for snakes using VHF telemetry data

2015 ◽  
Vol 42 (5) ◽  
pp. 437 ◽  
Author(s):  
Javan M. Bauder ◽  
David R. Breininger ◽  
M. Rebecca Bolt ◽  
Michael L. Legare ◽  
Christopher L. Jenkins ◽  
...  
Keyword(s):  
Home Range ◽  
Cross Validation ◽  
Radio Telemetry ◽  
High Sensitivity ◽  
Home Range Size ◽  
Range Size ◽  
Home Ranges ◽  
Telemetry Data ◽  
History Of ◽  
Sampling Duration

Context Despite the diversity of available home range estimators, no single method performs equally well in all circumstances. It is therefore important to understand how different estimators perform for data collected under diverse conditions. Kernel density estimation is a popular approach for home range estimation. While many studies have evaluated different kernel bandwidth selectors, few studies have compared different formulations of the bandwidth matrix using wildlife telemetry data. Additionally, few studies have compared the performance of kernel bandwidth selectors using VHF radio-telemetry data from small-bodied taxa. Aims In this study, we used eight different combinations of bandwidth selectors and matrices to evaluate their ability to meet several criteria that could be potentially used to select a home range estimator. Methods We used handheld VHF telemetry data from two species of snake displaying non-migratory and migratory movement patterns. We used subsampling to estimate each estimator’s sensitivity to sampling duration and fix rate and compared home range size, the number of disjunct volume contours and the proportion of telemetry fixes not included in those contours among estimators. Key Results We found marked differences among bandwidth selectors with regards to our criteria but comparatively little difference among bandwidth matrices for a given bandwidth selector. Least-squares cross-validation bandwidths exhibited near-universal convergence failure whereas likelihood cross-validation bandwidths showed high sensitivity to sampling duration and fix rate. The reference, plug-in and smoothed cross-validation bandwidths were more robust to variation in sampling intensity, with the former consistently producing the largest estimates of home range size. Conclusions Our study illustrates the performance of multiple kernel bandwidth estimators for estimating home ranges with datasets typical of many small-bodied taxa. The reference and plug-in bandwidths with an unconstrained bandwidth matrix generally had the best performance. However, our study concurs with earlier studies indicating that no single home range estimator performs equally well in all circumstances. Implications Although we did not find strong differences between bandwidth matrices, we encourage the use of unconstrained matrices because of their greater flexibility in smoothing data not parallel to the coordinate axes. We also encourage researchers to select an estimator suited to their study objectives and the life history of their study organism.

scholarly journals Modelling of framework materials at multiple scales: current practices and open questions

2019 ◽  
Author(s):  
Guillaume Fraux ◽  
Siwar Chibani ◽  
François-Xavier Coudert
Keyword(s):  
Atomistic Simulation ◽  
Multiple Scales ◽  
Point Of View ◽  
Computational Point ◽  
Framework Materials ◽  
New Methods ◽  
Modelling Framework ◽  
Current State ◽  
Open Questions ◽  
The Family

The last decade has seen an explosion of the family of framework materials and their study, both from the experimental and computational point of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of materials databases, and the growing use of machine learning for properties prediction.

Home Range Responses of Feral Goats.

1996 ◽  
Vol 18 (1) ◽  
pp. 144 ◽  
Author(s):  
C Holt ◽  
G Pickles
Keyword(s):  
Home Range ◽  
Control Strategies ◽  
Radio Telemetry ◽  
Effective Control ◽  
Home Ranges ◽  
Telemetry Data ◽  
Cooperative Approach ◽  
Feral Goat ◽  
Control Tool

The variability in size and the extent of the overlap of feral goat home ranges are important considerations when formulating control strategies. Radio telemetry data revealed home range sizes were similar to what was found in other studies performed in pastoral areas. This study confirms the need for a wide ranging cooperative approach, by neighbouring pastoral properties, to feral goat control if all the feral goats using an area are to be targeted. Aerial control activities had little effect on the home ranges of resident feral goats and so can continue to be an effective control tool without causing the reinfestation of previously cleared areas.

scholarly journals Modelling of framework materials at multiple scales: current practices and open questions

2019 ◽  
Author(s):  
Guillaume Fraux ◽  
Siwar Chibani ◽  
François-Xavier Coudert
Keyword(s):  
Atomistic Simulation ◽  
Multiple Scales ◽  
Point Of View ◽  
Computational Point ◽  
Framework Materials ◽  
New Methods ◽  
Modelling Framework ◽  
Current State ◽  
Open Questions ◽  
The Family

The last decade has seen an explosion of the family of framework materials and their study, both from the experimental and computational point of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of materials databases, and the growing use of machine learning for properties prediction.

scholarly journals Modelling of framework materials at multiple scales: current practices and open questions

2018 ◽  
Author(s):  
Guillaume Fraux ◽  
Siwar Chibani ◽  
François-Xavier Coudert
Keyword(s):  
Atomistic Simulation ◽  
Multiple Scales ◽  
Point Of View ◽  
Computational Point ◽  
Framework Materials ◽  
New Methods ◽  
Modelling Framework ◽  
Current State ◽  
Open Questions ◽  
The Family

The last decade has seen an explosion of the family of framework materials and their study, both from the experimental and computational point of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of materials databases, and the growing use of machine learning for properties prediction.

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