Collaborative Research: IIBR Informatics: Data integration to improve population distribution estimation with animal tracking data

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

Identifying how environmental factors affect where particular species occur is important for the preservation and maintenance of biodiversity. Specifically, this knowledge can be used to delineate species' ecological niches, provide benchmarks for measuring change, and help prioritize areas for conservation. Given this importance, ecologists have developed many statistical tools for identifying linkages between environmental factors and species occurrence patterns. Most of these tools can be sorted into two categories, based on whether they use traditional survey data, or animal tracking data. In either category, the amount and quality of available data is frequently limiting. This project aims to unify these two approaches under a single methodology that can simultaneously use both types of data. This is important because it can help overcome limitations in each data source, and because these different data types have complementary strengths, and are thus more informative in combination. Project work will focus on at-risk species including jaguars and lowland tapirs, where both data types are available, to demonstrate how these techniques can inform conservation efforts. By combining the strengths of multiple data sources, these new methods will be able to better resolve priority habitats and areas for these vulnerable species. Senior project personnel will participate in the AniMove.org animal movement analysis courses to teach students to apply these methods to conservation problems and will also host a data-integration workshop at the North Carolina Museum of Natural Science (NCMNS). Leveraging the >1 million yearly visitors that NCMNS receives, this project?s outreach efforts will focus on creating and displaying immersive videos that bring to life the entire scientific process, ranging from study design and field work, through analysis and forecasting, and on to informed conservation decision making. Tools that identify linkages between environmental drivers and species' occurrence patterns are routinely used in ecology, with species distribution models (SDMs) and resource selection functions (RSFs) being especially prominent examples. Though these approaches are closely related, SDMs tend to be employed on large scales with survey data, while RSFs are typically used for local populations and applied to animal tracking data. The ubiquitous auto-correlation within, and frequent cross-correlation among, individual tracking datasets violates the key independence assumption of standard distribution models. To unify these approaches, a novel weighted log-likelihood function will be developed to account for non-independence both within and among tracking datasets, as well as for differing sampling schedules and study duration. This weighted log -likelihood will be integrated with both presence-only and presence-absence survey data in the very general in homogeneous Poisson point process framework for distribution modeling. This approach has two primary advantages. First, it would allow accumulating stockpiles of tracking data to validly inform a broad range of distribution analyses, from RSFs at the local scale, to SDMs at the geographic range scale. Second, it will counteract the often -pronounced spatial biases in survey data by leveraging the fact that tracked animals frequently go where surveyors don? t. Compared to conventional distribution models, this novel methodology will scale seamlessly from local populations to geographic ranges, increase overall sample size, and exploit the contrasting properties of the different data types to reduce spatial bias and more accurately estimate uncertainty. To facilitate broad use of this methodology, a freely available software tool, the Distribution Data Integration Module (DDIM), will be developed to both construct the necessary multi-source datasets, and annotate these data with relevant environmental covariates. Project results will be available at http://biology.umd.edu/movement.html. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.