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 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 Understanding the Importance of Dynamic Landscape Connectivity

Land ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 303
Author(s):  
Katherine Zeller ◽  
Rebecca Lewsion ◽  
Robert Fletcher ◽  
Mirela Tulbure ◽  
Megan Jennings
Keyword(s):  
Landscape Connectivity ◽  
Conservation Strategy ◽  
Dynamic Processes ◽  
Negative Effects ◽  
Ecological Processes ◽  
Movement Data ◽  
Dynamic Landscape ◽  
Spatiotemporal Scales ◽  
Intrinsic Component ◽  
Dynamic Connectivity

Landscape connectivity is increasingly promoted as a conservation tool to combat the negative effects of habitat loss, fragmentation, and climate change. Given its importance as a key conservation strategy, connectivity science is a rapidly growing discipline. However, most landscape connectivity models consider connectivity for only a single snapshot in time, despite the widespread recognition that landscapes and ecological processes are dynamic. In this paper, we discuss the emergence of dynamic connectivity and the importance of including dynamism in connectivity models and assessments. We outline dynamic processes for both structural and functional connectivity at multiple spatiotemporal scales and provide examples of modeling approaches at each of these scales. We highlight the unique challenges that accompany the adoption of dynamic connectivity for conservation management and planning in the context of traditional conservation prioritization approaches. With the increased availability of time series and species movement data, computational capacity, and an expanding number of empirical examples in the literature, incorporating dynamic processes into connectivity models is more feasible than ever. Here, we articulate how dynamism is an intrinsic component of connectivity and integral to the future of connectivity science.

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

2018 ◽  
Vol 33 (6) ◽  
pp. 879-893 ◽  
Author(s):  
Mariëlle L. van Toor ◽  
Bart Kranstauber ◽  
Scott H. Newman ◽  
Diann J. Prosser ◽  
John Y. Takekawa ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Animal Movement ◽  
Migratory Connectivity

scholarly journals Simulation and Analysis of Animal Movement Paths using Numerus Model Builder

2019 ◽  
Author(s):  
Wayne M. Getz ◽  
Ludovica Luisa Vissat ◽  
Richard Salter
Keyword(s):  
Animal Movement ◽  
Internal State ◽  
Step Length ◽  
Movement Model ◽  
Model Builder ◽  
Movement Data ◽  
Test Models ◽  
Detailed Simulation ◽  
Biased Correlated Random Walk ◽  
Open Question

ABSTRACTAnimal movement paths are represented by point-location time series called relocation data. How well such paths can be simulated, when the rules governing movement depend on the internal state of individuals and environmental factors (both local and, when memory is involved, global) remains an open question. To answer this, we formulate and test models able to capture the essential statistics of multiphase versions of such paths (viz., movement-phase-specific step-length and turning-angle means, variances, auto-correlation, and cross correlation values), as well as broad scale movement patterns. The latter may include patchy coverage of the landscape, as well as the existence of home-range boundaries and gravitational centers-of-movement (e.g., centered around nests). Here we present a Numerus Model Builder implementation of two kinds of models: a high-frequency, multi-mode, biased, correlated random walk designed to simulate real movement data at a scale that permits simulation and identification of path segments that range from minutes to days; and a model that uses statistics extracted from relocation data—either empirical or simulated—to construct movement modes and phases at subhourly to daily scales. We evaluate how well our derived statistical movement model captures patterns produced by our more detailed simulation model as a way to evaluate how well derived statistical movement models may capture patterns occurring in empirical data.

scholarly journals From single steps to mass migration: the problem of scale in the movement ecology of the Serengeti wildebeest

2018 ◽  
Vol 373 (1746) ◽  
pp. 20170012 ◽  
Author(s):  
Colin J. Torney ◽  
J. Grant C. Hopcraft ◽  
Thomas A. Morrison ◽  
Iain D. Couzin ◽  
Simon A. Levin
Keyword(s):  
Community Dynamics ◽  
Animal Movement ◽  
Multiple Scales ◽  
Movement Ecology ◽  
Population Fluctuations ◽  
Ecological Processes ◽  
Spatial And Temporal Scales ◽  
Collective Movement ◽  
Movement Data ◽  
Mass Migration

A central question in ecology is how to link processes that occur over different scales. The daily interactions of individual organisms ultimately determine community dynamics, population fluctuations and the functioning of entire ecosystems. Observations of these multiscale ecological processes are constrained by various technological, biological or logistical issues, and there are often vast discrepancies between the scale at which observation is possible and the scale of the question of interest. Animal movement is characterized by processes that act over multiple spatial and temporal scales. Second-by-second decisions accumulate to produce annual movement patterns. Individuals influence, and are influenced by, collective movement decisions, which then govern the spatial distribution of populations and the connectivity of meta-populations. While the field of movement ecology is experiencing unprecedented growth in the availability of movement data, there remain challenges in integrating observations with questions of ecological interest. In this article, we present the major challenges of addressing these issues within the context of the Serengeti wildebeest migration, a keystone ecological phenomena that crosses multiple scales of space, time and biological complexity. This article is part of the theme issue 'Collective movement ecology'.

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 Performance of a novel system for high-resolution tracking of marine fish societies

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Eneko Aspillaga ◽  
Robert Arlinghaus ◽  
Martina Martorell-Barceló ◽  
Guillermo Follana-Berná ◽  
Arancha Lana ◽  
...  
Keyword(s):  
High Resolution ◽  
Social Interactions ◽  
Acoustic Telemetry ◽  
Animal Movement ◽  
Population Level ◽  
Fish Population ◽  
Telemetry System ◽  
Ecological Processes ◽  
Detection Range ◽  
Coastal Species

AbstractRecent advances in tracking systems have revolutionized our ability to study animal movement in the wild. In aquatic environments, high-resolution acoustic telemetry systems make it technically possible to simultaneously monitor large amounts of individuals at unprecedented spatial and temporal resolutions, providing a unique opportunity to study the behaviour and social interactions using a reality mining approach. Despite the potential, high-resolution telemetry systems have had very limited use in coastal marine areas due to the limitations that these environments pose to the transmission of acoustic signals. This study aims at designing and testing a high-resolution acoustic telemetry system to monitor, for the first time, a natural fish population in an open marine area. First, we conducted preliminary range tests and a computer simulation study to identify the optimal design of the telemetry system. Then, we performed a series of stationary and moving tests to characterize the performance of the system in terms of positioning efficiency and precision. Finally, we obtained a dataset corresponding to the movements of 170 concurrently tagged individuals to demonstrate the overall functioning of the system with a real study case of the behaviour of a small-bodied coastal species. Our results show that high-resolution acoustic telemetry systems efficiently generate positional data in marine systems, providing a precision of few meters, a temporal resolution of few seconds, and the possibility of tracking hundreds of individuals simultaneously. Data post-processing using a trajectory filter and movement models proved to be key to achieve a sub-meter positioning precision. The main limitation detected for our system was the restricted detection range, which was negatively affected by the stratification of the water column. Our work demonstrates that high-resolution acoustic telemetry systems are an effective method to monitor the movements of free-ranging individuals at the population level in coastal sites. By providing highly precise positioning estimates of large amounts of individuals, these systems represent a powerful tool to study key ecological processes regarding the social interactions of individuals, including social dynamics, collective movements, or responses to environmental perturbations, and to extend the studies to poorly studied small-sized species or life-stages.

scholarly journals A framework for leveraging animal movement to understand spatio-temporal disease dynamics

2021 ◽  
Author(s):  
Mark Wilber ◽  
Anni Yang ◽  
Raoul Boughton ◽  
Kezia Manlove ◽  
Ryan Miller ◽  
...  
Keyword(s):  
Disease Ecology ◽  
Animal Movement ◽  
Disease Outbreaks ◽  
Transmission Risk ◽  
Indirect Contact ◽  
Pathogen Invasion ◽  
Movement Data ◽  
Spatio Temporal ◽  
Diverse Data ◽  
Fine Resolution

The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal, and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable framework that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible framework “Movement-driven modeling of spatio-temporal infection risk” (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.

scholarly journals A multilayer perspective for inferring spatial and social functioning in animal movement networks

2019 ◽  
Author(s):  
Johann Mourier ◽  
Elodie J. I. Lédée ◽  
David M. P. Jacoby
Keyword(s):  
Animal Movement ◽  
Single Layer ◽  
Social Contact ◽  
Movement Patterns ◽  
Population Level ◽  
Individual Variability ◽  
List Type ◽  
Multilayer Network ◽  
Network Analyses ◽  
Movement Data

ABSTRACTAnimal movement patterns are increasingly analysed as spatial networks. Currently, structures of complex movements are typically represented as a single-layer (or monoplex) network. However, aggregating individual movements, to generate population-level inferences, considerably reduces information on how individual or species variability influences spatial connectivity and thus identifying the mechanisms driving network structure remains difficult.Here, we propose incorporating the recent conceptual advances in multilayer network analyses with the existing movement network approach to improve our understanding of the complex interaction between spatial and/or social drivers of animal movement patterns.Specifically, we explore the application and interpretation of this framework using an empirical example of shark movement data gathered using passive remote sensors in a coral reef ecosystem. We first show how aggregating individual movement networks can lead to the loss of information, potentially misleading our interpretation of movement patterns. We then apply multilayer network analyses linking individual movement networks (i.e. layers) to the probabilities of social contact between individuals (i.e. interlayer edges) in order to explore the functional significance of different locations to an animal’s ecology.This approach provides a novel and holistic framework incorporating individual variability in behaviour and inter-individual interactions. We discuss how this approach can be used in applied ecology and conservation to better assess the ecological significance of variable space use by mobile animals within a population. Further, we argue that the uptake of multilayer networks will significantly broaden our understanding of long-term ecological and evolutionary processes, particularly in the context of information or disease transfer between individuals.

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