scholarly journals Animal movement tools (amt ): R package for managing tracking data and conducting habitat selection analyses

2019 ◽  
Vol 9 (2) ◽  
pp. 880-890 ◽  
Author(s):  
Johannes Signer ◽  
John Fieberg ◽  
Tal Avgar
Keyword(s):  
Habitat Selection ◽  
Animal Movement ◽  
R Package ◽  
Tracking Data

scholarly journals Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

2020 ◽  
Author(s):  
Moritz Mercker ◽  
Philipp Schwemmer ◽  
Verena Peschko ◽  
Leonie Enners ◽  
Stefan Garthe
Keyword(s):  
Habitat Selection ◽  
Resource Selection ◽  
Large Scale ◽  
Animal Movement ◽  
Computation Time ◽  
Process Models ◽  
Tracking Data ◽  
Predictive Capacity ◽  
Logistic Regression Models ◽  
Animal Tracking

Abstract Background: New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods. Methods: We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared the performances of spatial logistic regression models (SLRMs), point process models (PPMs), and integrated step selection models ((i)SSMs) and their interplays with habitat, tracking-device, and animal movement properties. Results: We demonstrated that SLRMs were inappropriate for large-scale attraction studies and prone to bias when inferring habitat selection. In contrast, PPMs and (i)SSMs showed comparable (unbiased) performances for both habitat selection and large-scale effect studies. However, (i)SSMs had several advantages over PPMs with respect to robustness, user-friendly implementation, and computation time. Conclusions: We recommend the use of (i)SSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. This method has several practical advantages over PPMs and additionally extends SSMs, thus increasing its predictive capacity and allowing the derivation of mechanistic movement models.

scholarly journals Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Moritz Mercker ◽  
Philipp Schwemmer ◽  
Verena Peschko ◽  
Leonie Enners ◽  
Stefan Garthe
Keyword(s):  
Habitat Selection ◽  
Large Scale ◽  
Error Rates ◽  
Process Models ◽  
Statistical Hypothesis ◽  
Type I ◽  
Tracking Data ◽  
Selection Models ◽  
Animal Tracking ◽  
Step Selection

Abstract Background New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods. Methods We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing. Results We demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs. Conclusions Based on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.

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 Behavioural valuation of landscapes using movement data

2019 ◽  
Vol 374 (1781) ◽  
pp. 20180046 ◽  
Author(s):  
George Wittemyer ◽  
Joseph M. Northrup ◽  
Guillaume Bastille-Rousseau
Keyword(s):  
Animal Movement ◽  
Temporal Dynamics ◽  
Economic Valuation ◽  
Movement Ecology ◽  
Optimization Theory ◽  
Population Level ◽  
Animal Behaviour ◽  
Tracking Data ◽  
Movement Data ◽  
Spatio Temporal

Wildlife tracking is one of the most frequently employed approaches to monitor and study wildlife populations. To date, the application of tracking data to applied objectives has focused largely on the intensity of use by an animal in a location or the type of habitat. While this has provided valuable insights and advanced spatial wildlife management, such interpretation of tracking data does not capture the complexity of spatio-temporal processes inherent to animal behaviour and represented in the movement path. Here, we discuss current and emerging approaches to estimate the behavioural value of spatial locations using movement data, focusing on the nexus of conservation behaviour and movement ecology that can amplify the application of animal tracking research to contemporary conservation challenges. We highlight the importance of applying behavioural ecological approaches to the analysis of tracking data and discuss the utility of comparative approaches, optimization theory and economic valuation to gain understanding of movement strategies and gauge population-level processes. First, we discuss innovations in the most fundamental movement-based valuation of landscapes, the intensity of use of a location, namely dissecting temporal dynamics in and means by which to weight the intensity of use. We then expand our discussion to three less common currencies for behavioural valuation of landscapes, namely the assessment of the functional (i.e. what an individual is doing at a location), structural (i.e. how a location relates to use of the broader landscape) and fitness (i.e. the return from using a location) value of a location. Strengthening the behavioural theoretical underpinnings of movement ecology research promises to provide a deeper, mechanistic understanding of animal movement that can lead to unprecedented insights into the interaction between landscapes and animal behaviour and advance the application of movement research to conservation challenges. This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.

scholarly journals Correlation and studies of habitat selection: problem, red herring or opportunity?

2010 ◽  
Vol 365 (1550) ◽  
pp. 2233-2244 ◽  
Author(s):  
John Fieberg ◽  
Jason Matthiopoulos ◽  
Mark Hebblewhite ◽  
Mark S. Boyce ◽  
Jacqueline L. Frair
Keyword(s):  
Habitat Selection ◽  
Resource Selection ◽  
New Technologies ◽  
Animal Movement ◽  
Mixed Effects ◽  
Correlated Data ◽  
Two Stage ◽  
Wide Range ◽  
Spatio Temporal ◽  
Post Hoc

With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, ‘two-stage’ approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use–availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses that explicitly model correlation rather than consider it a nuisance, like mixed effects and state-space models, offer potentially novel insights into the process of resource selection, but additional work is needed to make them more generally applicable to large datasets based on the use–availability designs. Until then, variance inflation techniques and two-stage approaches should offer pragmatic and flexible approaches to modelling correlated data.

scholarly journals An animal movement model incorporating home range and habitat selection

2007 ◽  
Vol 15 (1) ◽  
pp. 27-38 ◽  
Author(s):  
Aaron Christ ◽  
Jay Ver Hoef ◽  
Dale L. Zimmerman
Keyword(s):  
Habitat Selection ◽  
Home Range ◽  
Animal Movement ◽  
Movement Model

scholarly journals Accounting for animal movement improves vaccination strategies against wildlife disease in heterogeneous landscapes

2021 ◽  
Author(s):  
Katherine M McClure ◽  
Guillaume Bastille-Rousseau ◽  
Amy J Davis ◽  
Carolyn A Stengel ◽  
Kathleen Nelson ◽  
...  
Keyword(s):  
Habitat Selection ◽  
Animal Movement ◽  
Landscape Heterogeneity ◽  
Habitat Preferences ◽  
Movement Ecology ◽  
Suburban Areas ◽  
Preferred Habitat ◽  
Animal Populations ◽  
Oral Rabies Vaccine ◽  
Better Than

Oral baiting is used to deliver vaccines to wildlife to prevent, control, and eliminate infectious diseases. A central challenge is how to spatially distribute baits to maximize encounters by target animal populations, particularly in urban and suburban areas where wildlife like raccoons (Procyon lotor) are abundant and baits are delivered along roads. Methods from movement ecology that quantify movement and habitat selection could help to optimize baiting strategies by more effectively targeting wildlife populations across space. We developed a spatially explicit, individual-based model of raccoon movement and oral rabies vaccine seroconversion to examine whether and when baiting strategies that match raccoon movement patterns perform better than currently employed baiting strategies in an oral rabies vaccination zone in greater Burlington, Vermont, USA. Habitat selection patterns estimated from locally radio-collared raccoons were used to parameterize movement simulations. We then used our simulations to estimate raccoon population rabies seroprevalence under currently used baiting strategies (actual baiting) relative to habitat selection-based baiting strategies (habitat baiting). We conducted simulations on the Burlington landscape and artificial landscapes that varied in heterogeneity relative to Burlington in the proportion and patch size of preferred habitats. We found that the benefits of habitat baiting strongly depended on the magnitude and variability of raccoon habitat selection and the degree of landscape heterogeneity within the baiting area. Habitat baiting improved seroprevalence over actual baiting for raccoons characterized as habitat specialists but not for raccoons that displayed weak habitat selection similar to radio-collared individuals - except when baits were delivered off roads where preferred habitat coverage and complexity was more pronounced. In contrast, in artificial landscapes with either more strongly juxtaposed favored habitats and/or higher proportions of favored habitats, habitat baiting performed better than actual baiting, even when raccoons displayed weak habitat preferences and where baiting was constrained to roads. Our results suggest that habitat selection-based baiting could increase raccoon population seroprevalence in urban-suburban areas, where practical, given the heterogeneity and availability of preferred habitat types in those areas. Our novel simulation approach provides a flexible framework to test alternative baiting strategies in multiclass landscapes to optimize bait distribution strategies.

scholarly journals Multi-source data fusion of optical satellite imagery to characterize habitat selection from wildlife tracking data

2020 ◽  
Vol 60 ◽  
pp. 101149
Author(s):  
Vanessa Brum-Bastos ◽  
Jed Long ◽  
Katharyn Church ◽  
Greg Robson ◽  
Rogério de Paula ◽  
...  
Keyword(s):  
Habitat Selection ◽  
Data Fusion ◽  
Satellite Imagery ◽  
Tracking Data ◽  
Wildlife Tracking ◽  
Source Data
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