scholarly journals A Guide to Pre-Processing High-Throughput Animal Tracking Data

2020 ◽  
Author(s):  
Pratik Rajan Gupte ◽  
Christine E Beardsworth ◽  
Orr Spiegel ◽  
Emmanuel Lourie ◽  
Sivan Toledo ◽  
...  
Keyword(s):  
High Throughput ◽  
Animal Movement ◽  
Calibration Data ◽  
Tracking Data ◽  
Location Error ◽  
Step Size ◽  
Animal Tracking ◽  
Location Errors ◽  
Processing Pipeline ◽  
Movement Data

Modern, high-throughput animal tracking studies collect increasingly large volumes of data at very fine temporal scales. At these scales, location error can exceed the animal step size, confounding inferences from tracking data. Cleaning the data to exclude positions with large location errors prior to analyses is one of the main ways movement ecologists deal with location errors. Cleaning data to reduce location error before making biological inferences is widely recommended, and ecologists routinely consider cleaned data to be the ground-truth. Nonetheless, uniform guidance on this crucial step is scarce. Cleaning high-throughput data must strike a balance between rejecting location errors without discarding valid animal movements. Additionally, users of high-throughput systems face challenges resulting from the high volume of data itself, since processing large data volumes is computationally intensive and difficult without a common set of efficient tools. Furthermore, many methods that cluster movement tracks for ecological inference are based on statistical phenomena, and may not be intuitive to understand in terms of the tracked animal biology. In this article we introduce a pipeline to pre-process high-throughput animal tracking data in order to prepare it for subsequent analysis. We demonstrate this pipeline on simulated movement data to which we have randomly added location errors. We further suggest how large volumes of cleaned data may be synthesized into biologically meaningful residence patches. We then use calibration data to show how the pipeline improves its quality, and to verify that the residence patch synthesis accurately captures animal space-use. Finally, turning to real tracking data from Egyptian fruit bats (Rousettus aegyptiacus), we demonstrate the pre-processing pipeline and residence patch method in a fully worked out example. To help with fast implementations of our pipeline, and to help standardise methods, we developed the R package atlastools, which we introduce here. Our pre-processing pipeline and atlastools can be used with any high-throughput animal movement data in which the high data volume combined with knowledge of the tracked individuals biology can be used to reduce location errors. The use of common pre-processing steps that are simple yet robust promotes standardised methods in the field of movement ecology and better inferences from data.

scholarly journals A comprehensive framework for handling location error in animal tracking data*

2020 ◽  
Author(s):  
C. H. Fleming ◽  
J. Drescher-Lehman ◽  
M. J. Noonan ◽  
T. S. B. Akre ◽  
D. J. Brown ◽  
...  
Keyword(s):  
Home Range ◽  
Large Scale ◽  
Animal Movement ◽  
Tracking Data ◽  
Fine Scale ◽  
Sampled Data ◽  
Location Error ◽  
Animal Tracking ◽  
Wide Range ◽  
Scale Data

AbstractAnimal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While location error can often be ignored in coarsely sampled data, fine-scale data require much more care, and tools to do this have been lacking. Current approaches to dealing with location error largely fall into two categories—either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. Unfortunately, both of these approaches have serious flaws. Here, we provide a general framework to account for location error in the analysis of animal tracking data, so that their potential can be unlocked. We apply our error-model-selection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices are used to track a wide range of animal species comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. Then, using empirical data on tracked individuals from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. We furthermore demonstrate how error-informed analyses on calibrated tracking data can be necessary to ensure that estimates are accurate and insensitive to location error, and allow researchers to use all of their data. Because error-induced biases depend on so many factors—sampling schedule, movement characteristics, tracking device, habitat, etc.—differential bias can easily confound biological inference and lead researchers to draw false conclusions.

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 Moderating Argos location errors in animal tracking data

2012 ◽  
Vol 3 (6) ◽  
pp. 999-1007 ◽  
Author(s):  
David C. Douglas ◽  
Rolf Weinzierl ◽  
Sarah C. Davidson ◽  
Roland Kays ◽  
Martin Wikelski ◽  
...  
Keyword(s):  
Tracking Data ◽  
Animal Tracking ◽  
Location Errors

scholarly journals Integrating animal movement with habitat suitability for estimating dynamic landscape connectivity

2017 ◽  
Author(s):  
Mariëlle L. van Toor ◽  
Bart Kranstauber ◽  
Scott H. Newman ◽  
Diann J. Prosser ◽  
John Y. Takekawa ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Disease Ecology ◽  
Animal Movement ◽  
Landscape Connectivity ◽  
Population Level ◽  
Tracking Data ◽  
Migratory Connectivity ◽  
Ecological Processes ◽  
Movement Model ◽  
Movement Data

AbstractContextHigh-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species’ potential to link patches or populations are of importance.ObjectivesWe introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories’ plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range.MethodsWe used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species’ range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature.ResultsSimulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corresponding to previous findings for this species.ConclusionsWe show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.

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 A Quantitative Framework for Identifying Patterns of Route-Use in Animal Movement Data

2022 ◽  
Vol 9 ◽  
Author(s):  
Shauhin E. Alavi ◽  
Alexander Q. Vining ◽  
Damien Caillaud ◽  
Ben T. Hirsch ◽  
Rasmus Worsøe Havmøller ◽  
...  
Keyword(s):  
Animal Movement ◽  
Sympatric Species ◽  
High Fidelity ◽  
Cognitive Mechanisms ◽  
Fundamental Interest ◽  
Animal Tracking ◽  
Environmental Features ◽  
Movement Data ◽  
High Conservation ◽  
Further Development

Animal movement along repeatedly used, “habitual” routes could emerge from a variety of cognitive mechanisms, as well as in response to a diverse set of environmental features. Because of the high conservation value of identifying wildlife movement corridors, there has been extensive work focusing on environmental factors that contribute to the emergence of habitual routes between protected habitats. In parallel, significant work has focused on disentangling the cognitive mechanisms underlying animal route use, as such movement patterns are of fundamental interest to the study of decision making and navigation. We reviewed the types of processes that can generate routine patterns of animal movement, suggested a new methodological workflow for classifying one of these patterns—high fidelity path reuse—in animal tracking data, and compared the prevalence of this pattern across four sympatric species of frugivorous mammals in Panama. We found the highest prevalence of route-use in kinkajous, the only nocturnal species in our study, and propose that further development of this method could help to distinguish the processes underlying the presence of specific routes in animal movement data.

scholarly journals Mobilizing Animal Movement Data: API use and the Movebank platform

2021 ◽  
Vol 5 ◽  
Author(s):  
Sarah Davidson ◽  
Gil Bohrer ◽  
Andrea Kölzsch ◽  
Candace Vinciguerra ◽  
Roland Kays
Keyword(s):  
Data Acquisition ◽  
Large Scale ◽  
Animal Movement ◽  
Mobile Apps ◽  
Environmental Parameters ◽  
Data Access ◽  
Environmental Data ◽  
Sensor Data ◽  
Animal Tracking ◽  
Movement Data

Movebank, a global platform for animal tracking and other animal-borne sensor data, is used by over 3,000 researchers globally to harmonize, archive and share nearly 3 billion animal occurrence records and more than 3 billion other animal-borne sensor measurements that document the movements and behavior of over 1,000 species. Movebank’s publicly described data model (Kranstauber et al. 2011), vocabulary and application programming interfaces (APIs) provide services for users to automate data import and retrieval. Near-live data feeds are maintained in cooperation with over 20 manufacturers of animal-borne sensors, who provide data in agreed-upon formats for accurate data import. Data acquisition by API complies with public or controlled-access sharing settings, defined within the database by data owners. The Environmental Data Automated Track Annotation System (EnvDATA, Dodge et al. 2013) allows users to link animal tracking data with hundreds of environmental parameters from remote sensing and weather reanalysis products through the Movebank website, and offers an API for advanced users to automate the submission of annotation requests. Movebank's mobile apps, the Animal Tracker and Animal Tagger, use APIs to support reporting and monitoring while in the field, as well as communication with citizen scientists. The recently-launched MoveApps platform connects with Movebank data using an API to allow users to build, execute and share repeatable workflows for data exploration and analysis through a user-friendly interface. A new API, currently under development, will allow calls to retrieve data from Movebank reduced according to criteria defined by "reduction profiles", which can greatly reduce the volume of data transferred for many use cases. In addition to making this core set of Movebank services possible, Movebank's APIs enable the development of external applications, including the widely used R programming packages 'move' (Kranstauber et al. 2012) and 'ctmm' (Calabrese et al. 2016), and user-specific workflows to efficiently execute collaborative analyses and automate tasks such as syncing with local organizational and governmental websites and archives. The APIs also support large-scale data acquisition, including for projects under development to visualize, map and analyze bird migrations led by the British Trust for Ornithology, the coordinating organisation for European bird ringing (banding) schemes (EURING), Georgetown University, National Audubon Society, Smithsonian Institution and United Nations Convention on Migratory Species. Our API development is constrained by a lack of standardization in data reporting across animal-borne sensors and a need to ensure adequate communication with data users (e.g., how to properly interpret data; expectations for use and attribution) and data owners (e.g., who is using publicly-available data and how) when allowing automated data access. As interest in data linking, harvesting, mirroring and integration grows, we recognize needs to coordinate API development across animal tracking and biodiversity databases, and to develop a shared system for unique organism identifiers. Such a system would allow linking of information about individual animals within and across repositories and publications in order to recognize data for the same individuals across platforms, retain provenance and attribution information, and ensure beneficial and biologically meaningful data use.

scholarly journals A Guide to Pre‐Processing High‐Throughput Animal Tracking Data

2021 ◽  
Author(s):  
Pratik Rajan Gupte ◽  
Christine E. Beardsworth ◽  
Orr Spiegel ◽  
Emmanuel Lourie ◽  
Sivan Toledo ◽  
...  
Keyword(s):  
High Throughput ◽  
Tracking Data ◽  
Animal Tracking

scholarly journals Movement syndromes of a Neotropical frugivorous bat inhabiting heterogeneous landscapes in Brazil

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Patricia Kerches-Rogeri ◽  
Danielle Leal Ramos ◽  
Jukka Siren ◽  
Beatriz de Oliveira Teles ◽  
Rafael Souza Cruz Alves ◽  
...  
Keyword(s):  
Site Fidelity ◽  
Animal Movement ◽  
Radio Telemetry ◽  
Food Resource ◽  
Movement Behavior ◽  
Movement Activity ◽  
Landscape Features ◽  
Heterogeneous Landscapes ◽  
Movement Data ◽  
High Site

Abstract Background There is growing evidence that individuals within populations can vary in both habitat use and movement behavior, but it is still not clear how these two relate to each other. The aim of this study was to test if and how individual bats in a Stunira lilium population differ in their movement activity and preferences for landscape features in a correlated manner. Methods We collected data on movements of 27 individuals using radio telemetry. We fitted a heterogeneous-space diffusion model to the movement data in order to evaluate signals of movement variation among individuals. Results S. lilium individuals generally preferred open habitat with Solanum fruits, regularly switched between forest and open areas, and showed high site fidelity. Movement variation among individuals could be summarized in four movement syndromes: (1) average individuals, (2) forest specialists, (3) explorers which prefer Piper, and (4) open area specialists which prefer Solanum and Cecropia. Conclusions Individual preferences for landscape features plus food resource and movement activity were correlated, resulting in different movement syndromes. Individual variation in preferences for landscape elements and food resources highlight the importance of incorporating explicitly the interaction between landscape structure and individual heterogeneity in descriptions of animal movement.

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.

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