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

Calabrese, Justin M.; Fleming, Christen H.; Noonan, Michael; Dong, Xianghui

doi:10.1002/wsb.1154

Cited by 28 publications

(21 citation statements)

References 35 publications

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“…Initial exploratory analyses were carried out in ctmmweb (version 0.2.11, [ 10 ]. All formal statistical analysis and plotting were performed using R (version 4.0.5, R Core Team 2021 [ 56 ]), with the packages ctmm (version 0.6.1, [ 9 ], mgcv (version 1.8-36, [ 80 ], ggplot2 (version 3.3.4, [ 79 ], ggmap (version 3.0.0, [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

Movement ecology of vulnerable lowland tapirs between areas of varying human disturbance

et al. 2022

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Background Animal movement is a key ecological process that is tightly coupled to local environmental conditions. While agriculture, urbanisation, and transportation infrastructure are critical to human socio-economic improvement, these have spurred substantial changes in animal movement across the globe with potential impacts on fitness and survival. Notably, however, human disturbance can have differential effects across species, and responses to human activities are thus largely taxa and context specific. As human disturbance is only expected to worsen over the next decade it is critical to better understand how species respond to human disturbance in order to develop effective, case-specific conservation strategies. Methods Here, we use an extensive telemetry dataset collected over 22 years to fill a critical knowledge gap in the movement ecology of lowland tapirs (Tapirus terrestris) across areas of varying human disturbance within three biomes in southern Brazil: the Pantanal, Cerrado, and Atlantic Forest. Results From these data we found that the mean home range size across all monitored tapirs was 8.31 km2 (95% CI 6.53–10.42), with no evidence that home range sizes differed between sexes nor age groups. Interestingly, although the Atlantic Forest, Cerrado, and Pantanal vary substantially in habitat composition, levels of human disturbance, and tapir population densities, we found that lowland tapir movement behaviour and space use were consistent across all three biomes. Human disturbance also had no detectable effect on lowland tapir movement. Lowland tapirs living in the most altered habitats we monitored exhibited movement behaviour that was comparable to that of tapirs living in a near pristine environment. Conclusions Contrary to our expectations, although we observed individual variability in lowland tapir space use and movement, human impacts on the landscape also had no measurable effect on their movement. Lowland tapir movement behaviour thus appears to exhibit very little phenotypic plasticity in response to human disturbance. Crucially, the lack of any detectable response to anthropogenic disturbance suggests that human modified habitats risk being ecological traps for tapirs and this information should be factored into conservation actions and species management aimed towards protecting lowland tapir populations.

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Section: Methodsmentioning

confidence: 99%

Movement ecology of vulnerable lowland tapirs between areas of varying human disturbance

et al. 2022

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“…These methods were explicitly designed to work synergistically, eliminating discrepancies between empirical reality and estimator assumptions that drive home range under-or overestimation with conventional techniques. Furthermore, these techniques can be implemented with open-source software and code (Calabrese et al, 2016(Calabrese et al, , 2021, and new movement processes can be easily added into the AKDE workflow as they are developed. This flexibility 'future proofs' the AKDE family of analyses by allowing it to be tailored to new datasets, movement behaviours and species as necessary.…”

Section: Negative Values Denote Underperformancementioning

confidence: 99%

“…The ctmm workflow also allows researchers to partially account for the location errors associated with their tracking datasets (Fleming et al., 2021). These methods can be run using the programming language R (http://www.r-project.org) and the ctmm or amt packages (Calabrese et al., 2016; Signer & Fieberg, 2021), or the ctmmweb graphical user interface (https://ctmm.shinyapps.io/ctmmweb; Calabrese et al., 2021). In addition to offering flexible and open‐source tools for home range estimation, these software programs allow easy documentation and implementation of new methods by sharing code and workflows.…”

Section: Introductionmentioning

confidence: 99%

“…The ctmm workflow also allows researchers to partially account for the location errors associated with their tracking datasets . These methods can be run using the programming language R (www.r-proje ct.org) and the ctmm or amt packages (Calabrese et al, 2016;Signer & Fieberg, 2021), or the ctmmweb graphical user interface (https:// ctmm.shiny apps.io/ctmmweb; Calabrese et al, 2021). In addition to…”

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Autocorrelation‐informed home range estimation: A review and practical guide

et al. 2021

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1. Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over VHF radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under-or overestimate home range areas.2. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes, and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously.3. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs.4. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

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“…AKDE and associated corrections have been shown to outperform traditional home range estimators across species, degrees of autocorrelation, and sample size (Noonan et al, 2019). These methods can be run using the programming language R (www.r-project.org) and the ctmm or amt packages Signer & Fieberg, 2021), or the ctmmweb graphical user interface (https://ctmm.shinyapps.io/ctmmweb; Calabrese et al, 2021). In addition to offering flexible and open-source tools for home range estimation, these software programs allow easy documentation and implementation of new methods by sharing code and workflows.…”

Section: Introductionmentioning

confidence: 99%

Autocorrelation-informed home range estimation: a review and practical guide

Silva¹,

Fleming²,

Noonan³

et al. 2021

Preprint

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1. Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over VHF radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. 2. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes, and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. 3. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs.4. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

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

Cited by 28 publications

References 35 publications

Movement ecology of vulnerable lowland tapirs between areas of varying human disturbance

Movement ecology of vulnerable lowland tapirs between areas of varying human disturbance

Autocorrelation‐informed home range estimation: A review and practical guide

Autocorrelation-informed home range estimation: a review and practical guide

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