Movement ecology is a young sub-discipline in ecology in which researchers apply high resolution location and activity data to analyze animal behavior across multiple scales: from individual foraging decisions to population-level space-use patterns. These analyses con-tribute to various other subfields within ecology—inter alia behavioral, disease, landscape, resource, and wildlife—and may also facilitate novel exploration in fields ranging from conservation planning to public health.
Using a decade of GPS relocation data from zebra (Equus quagga), black rhino (Diceros bicornis), and African elephant (Loxodonta africana) captured and collared in Etosha National Park from 2008–2018, this dissertation reviews developing methods within movement ecology, extends and applies these methods to a threatened and understudied species, and presents a new software package distilling a growing movement ecology tool set for researchers and managers unfamiliar with the domain specific analyses and/or the command line interface of modern statistical analysis (e.g. R).
Despite the growing availability of animal movement data and the potential for broad application in geographic analysis beyond animal ecology, the analytical methods of movement ecology have yet to be fully incorporated in a broader understanding of geographic analysis. Chapter 2, a review written for the Geographical Information Sciences (GIS) community, provides an overview of the most common movement metrics and methods of analysis em-ployed by animal ecologists and emphasizes the potential for movement analyses to promote transdisciplinary research: comparing advances in the young field of movement ecology to parallel developments within the broader field of geographic information sciences.
Two limitations remain common within the growing field of movement analysis. First, within movement ecology, many, even most, analyses require clean, complete, and regular time series of relocations, limiting the available research on species that are hard to track and/or often return gappy, irregular data; including some of the world’s most endangered animals, e.g. black rhinos. In chapter 3, extending and applying recursion analyses to irregular spatio-temporal data from this understudied and critically endangered species, I investigated daily, biweekly and annual recursion behaviors of rhinos, to aid conservation applications and increase our fundamental knowledge about these important ecosystem engineers. Results indicate that rhinos may frequently stay within the same area of their home ranges for days at a time, and possibly return to the same general area days in a row especially during morning foraging bouts. Initial results indicate that recursion at the daily and biweekly scales maybe driven by hydration and productivity cycles respectively. Recursion across larger timescales is also evident and likely a contributing mechanism for maintaining open landscapes and browsing lawns of the savanna.
A second, and equally challenging, limitation to the growing movement ecology tool kit is accessibility. The growth in analysis techniques, and the concomitant growth of open-source software for analysis, pose a stumbling block to general acceptance in interdisciplinary and management settings, where researchers may be unfamiliar with the expansive set of tools or the command line interface of modern analysis packages. In chapter 4, to reduce this friction and enhance the accessibility of exploratory data analysis tools for animal movement data, I built stmove, an R package designed to make report building and exploratory data analysis simple for users who may not be familiar with the extent of available analytical tools. Furthermore, stmove sets forth a framework of best practice analyses, which offers a common starting point for the interpretation of terrestrial movement data, promoting comparability of results across movement ecology studies.
The datasets, analyses, and tools presented in this dissertation seek to enhance communication, application, and accessibility of a growing movement ecology toolkit while providing a special glimpse into a diverse ecological community and the individual and population movement behavior through within Etosha National Park over the last decade. We demonstrate new tools built for exploratory data analysis in movement ecology using this data and explore how insights from movement ecology can help inform successful conservation efforts in the region and beyond.
|Commitee:||Boots, Michael, Brashares, Justin|
|School:||University of California, Berkeley|
|Department:||Environmental Science, Policy, & Management|
|School Location:||United States -- California|
|Source:||DAI-A 81/5(E), Dissertation Abstracts International|
|Subjects:||Ecology, African Studies, Conservation biology|
|Keywords:||Animal movement, Etosha National Park, GPS tracking, Movement ecology|
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