Kernel density estimators are widely applied to area-related problems in ecology, from estimating the home range of an individual to estimating the geographic range of a species. Currently, area estimates are obtained indirectly, by first estimating the location distribution from tracking (home range) or survey (geographic range) data, and then estimating areas from that distribution. This indirect approach leads to biased area estimates and difficulty in deriving reasonable confidence intervals.
We introduce a new kernel density estimator and associated confidence intervals focused specifically on area estimation that apply to both independently sampled survey data and autocorrelated tracking data. We test our methods against simulated movement data and demonstrate its use with African buffalo data.
The area-corrected kernel density estimator produces much more accurate area estimates, particularly at small sample sizes, and the newly derived confidence intervals are more reliable than existing alternatives.
This new method is the most efficient non-parametric home-range estimator for animal tracking data, and should also be considered when calculating non-parametric range estimates from survey data. This estimator is now the default method in the ctmm R package. 


This article is protected by copyright. All rights reserved.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12673

A new kernel density estimator for accurate home-range and species-range area estimation

Increased availability of high-resolution movement data has led to the development of numerous methods for studying changes in animal movement behavior. Path segmentation methods provide basics for detecting movement changes and the behavioral mechanisms driving them. However, available path segmentation methods differ vastly with respect to underlying statistical assumptions and output produced. Consequently, it is currently difficult for researchers new to path segmentation to gain an overview of the different methods, and choose one that is appropriate for their data and research questions. Here, we provide an overview of different methods for segmenting movement paths according to potential changes in underlying behavior. To structure our overview, we outline three broad types of research questions that are commonly addressed through path segmentation: 1) the quantitative description of movement patterns, 2) the detection of significant change-points, and 3) the identification of underlying processes or ‘hidden states’. We discuss advantages and limitations of different approaches for addressing these research questions using path-level movement data, and present general guidelines for choosing methods based on data characteristics and questions. Our overview illustrates the large diversity of available path segmentation approaches, highlights the need for studies that compare the utility of different methods, and identifies opportunities for future developments in path-level data analysis.

/pdf/path-segmentation-for-beginners-an-overview-of-current-1l5hie18fy.pdf

Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns

Animal telemetry is a powerful tool for observing marine animals and the physical environments that they inhabit, from coastal and continental shelf ecosystems to polar seas and open oceans. Satellite-linked biologgers and networks of acoustic receivers allow animals to be reliably monitored over scales of tens of meters to thousands of kilometers, giving insight into their habitat use, home range size, the phenology of migratory patterns and the biotic and abiotic factors that drive their distributions. Furthermore, physical environmental variables can be collected using animals as autonomous sampling platforms, increasing spatial and temporal coverage of global oceanographic observation systems. The use of animal telemetry, therefore, has the capacity to provide measures from a suite of essential ocean variables (EOVs) for improved monitoring of Earth's oceans. Here we outline the design features of animal telemetry systems, describe current applications and their benefits and challenges, and discuss future directions. We describe new analytical techniques that improve our ability to not only quantify animal movements but to also provide a powerful framework for comparative studies across taxa. We discuss the application of animal telemetry and its capacity to collect biotic and abiotic data, how the data collected can be incorporated into ocean observing systems, and the role these data can play in improved ocean management.

/pdf/animal-borne-telemetry-an-integral-component-of-the-ocean-2woti3rpb0.pdf

Animal-borne telemetry: An integral component of the ocean observing toolkit

The spatial scale of animal space use, e.g. measured as individual home range size, is a key trait with important implications for ecological and evolutionary processes as well as management and conservation of populations and ecosystems. Explaining variation in home range size has therefore received great attention in ecological research. However, few studies have examined multiple hypotheses simultaneously, which is important provided the complex interactions between life history, social system and behaviour. Here, we review previous studies on home range size in ungulates, supplementing with a meta-analysis, to assess how differences in habitat use and species characteristics affect the relationship between body mass and home range size. Habitat type was the main factor explaining interspecific differences in home range size after accounting for species body mass and group size. Species using open habitats had larger home ranges for a given body mass than species using closed habitats, whereas species in open habitats showed a much weaker allometric relationship compared with species living in closed habitats. We found no support for relationships between home range size and species diet or mating system, or any sexual differences. These patterns suggest that the spatial scale of animal movement mainly is a combined effect of body mass, group size and the landscape structure. Accordingly, landscape management must acknowledge the influence of spatial distribution of habitat types on animal behaviour to ensure natural processes affecting demography and viability of ungulate populations.

https://royalsocietypublishing.org/doi/pdf/10.1098/rspb.2016.1234

Home ranges, habitat and body mass: simple correlates of home range size in ungulates.

Telemetry is a key, widely used tool to understand marine megafauna distribution, habitat use, behavior, and physiology; however, a critical question remains: "How many animals should be tracked to acquire meaningful data sets?" This question has wide-ranging implications including considerations of statistical power, animal ethics, logistics, and cost. While power analyses can inform sample sizes needed for statistical significance, they require some initial data inputs that are often unavailable. To inform the planning of telemetry and biologging studies of marine megafauna where few or no data are available or where resources are limited, we reviewed the types of information that have been obtained in previously published studies using different sample sizes. We considered sample sizes from one to >100 individuals and synthesized empirical findings, detailing the information that can be gathered with increasing sample sizes. We complement this review with simulations, using real data, to show the impact of sample size when trying to address various research questions in movement ecology of marine megafauna. We also highlight the value of collaborative, synthetic studies to enhance sample sizes and broaden the range, scale, and scope of questions that can be answered.

/pdf/the-importance-of-sample-size-in-marine-megafauna-tagging-30c32dv0m2.pdf

The importance of sample size in marine megafauna tagging studies

Improvements in telemetry technology are allowing us to monitor animal movements with increasing accuracy, precision and frequency. The increased complexity of the data collections, however, demands additional software and programming skills to process, store and disseminate the datasets. Recent focus on data availability has also heightened the need for sustainable data management solutions to ensure data integrity and provide longer term access. In the last ten years, a number of online facilities have been developed for the archiving, processing and sharing of telemetry data. These facilities offer secure storage, multi-user support and analysis tools and are a step along the way to improving data access, long-term data preservation and science communication. While these software platforms promote data sharing, access to the majority of the data and to the software behind these systems remains restricted. In this paper, we present a comprehensive, highly accessible and fully transparent software facility for animal movement data. The online system we developed (
 http://oztrack.org
 ) offers a set of robust, up-to-date and accessible tools for managing, processing, visualising and analysing animal location data and linking these outputs with environmental datasets. As OzTrack uses exclusively free and open-source software, and the source code is available online, the system promotes open access not only to data but also to the tools and software underpinning the system. We outline the capabilities and limitations of the infrastructure design and discuss the uptake of this platform by the Australasian biotelemetry community. We discuss whether an open approach to analysis tools and software encourages a more open approach to sharing data, information and knowledge. Finally, we discuss why a free and open approach enhances longer term sustainability and enables data storage facilities to evolve in parallel with the telemetry devices themselves.

/pdf/an-open-web-based-system-for-the-analysis-and-sharing-of-2km1nlm1al.pdf

An open Web-based system for the analysis and sharing of animal tracking data

The aim of the OzTrack project is to provide common e-Science infrastructure to support the management, pre-processing, analysis and visualization of animal tracking data generated by researchers who are using telemetry devices to study animal behavior and ecology in Australia. This paper describes the technical challenges and design decisions associated with the development of the OzTrack system. It also describes the pre-processing, analysis and visualization services that we have developed to help researchers understand how their study species move across space and time. Finally this paper outlines the systems' current limitations and preliminary results and feedback from its evaluation to date.

OzTrack -- E-Infrastructure to Support the Management, Analysis and Sharing of Animal Tracking Data

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Wilfred Brimblecombe

Author Tools

Chat about Author

Papers

An open Web-based system for the analysis and sharing of animal tracking data

OzTrack -- E-Infrastructure to Support the Management, Analysis and Sharing of Animal Tracking Data