ESTIMATING ANIMAL UTILIZATION DISTRIBUTIONS FROM MULTIPLE DATA TYPES: A JOINT SPATIOTEMPORAL POINT PROCESS FRAMEWORK

• 成果类型: Article
• 署名作者: Watson, Joe; Joy, Ruth; Tollit, Dominic; Thornton, Sheila J.; Auger-Methe, Marie
• 署名单位: University of British Columbia; Simon Fraser University; Fisheries & Oceans Canada; University of British Columbia
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1472
• 发表日期: 2021
• 页码: 1872-1896
• 关键词: resident killer whales; presence-only data; PROCESS MODELS; home-range; inference; EQUIVALENCE; occupancy; selection; patterns; BIAS

摘要

Models of the spatial distribution of animals provide useful tools to help ecologists quantify species-environment relationships, and they are increasingly being used to help determine the impacts of climate and habitat changes on species. While high-quality survey-style data with known effort are sometimes available, often researchers have multiple datasets of varying quality and type. In particular, collections of sightings made by citizen scientists are becoming increasingly common, with no information typically provided on their observer effort. Many standard modelling approaches ignore observer effort completely which can severely bias estimates of an animal's distribution. Combining sightings data from observers who followed different protocols is challenging. Any differences in observer skill, spatial effort and the detectability of the animals across space all need to be accounted for. To achieve this, we build upon the recent advancements made in integrative species distribution models and present a novel marked spatiotemporal point process framework for estimating the utilization distribution (UD) of the individuals of a highly mobile species. We show that, in certain settings, we can also use the framework to combine the UDs from the sampled individuals to estimate the species' distribution. We combine the empirical results from a simulation study with the implications outlined in a causal directed acyclic graph to identify the necessary assumptions required for our framework to control for observer effort when it is unknown. We then apply our framework to combine multiple datasets collected on the endangered Southern Resident Killer Whales to estimate their monthly effort-corrected space-use.