Methods to detect spatial biases in tracking studies caused by differential representativeness of individuals, populations and time

Abstract

Aim: Over the last decades, the study of movement through tracking data has grown exceeding the expectations of movement ecologists. This has posed new challenges, specifically when using individual tracking data to infer higher-level distributions (e.g. population and species). Sources of variability such as individual site fidelity (ISF), environmental stochasticity over time, and space-use variability across species ranges must be considered, and their effects identified and corrected, to produce accurate estimates of spatial distribution using tracking data. Innovation:We developed R functions to detect the effect of these sources of variability in the distribution of animal groups when inferred from individual tracking data.These procedures can be adapted for their use in most tracking datasets and tracking techniques. We demonstrated our procedures with simulated datasets and showed their applicability on a real-world dataset containing 1346 year-round migratory trips from 805 individuals of three closely related seabird species breeding in 34 colonies in the Mediterranean Sea and the Atlantic Ocean, spanning 10 years. We detected an effect of ISF in one of the colonies, but no effect of the environmental stochasticity on the distribution of birds for any of the species. We also identified among-colony variability in nonbreeding space use for one species, with significant effects of population size and longitude. Main conclusions:This work provides a useful, much-needed tool for researchers using animal tracking data to model species distributions or establish conservation measures. This methodology may be applied in studies using individual tracking data to accurately infer the distribution of a population or species and support the delineation of important areas for conservation based on tracking data. This step, designed to precede any analysis, has become increasingly relevant with the proliferation of studies using large tracking datasets that has accompanied the globalization process in science driving collaborations and tracking data sharing initiatives.

Nest Change and Individual Fitness in a Scopoli’s Shearwater Population: A Capture-Recapture Multistate Analysis

“…Based on genetic differences, the species was recently separated from the other members of the genus: Cory's shearwater and Cape Verde shearwater (C. ed wardsii) [38]. It winters mainly in the Canary Current in the eastern part of the Atlantic Ocean and off of the western African coast from Senegal to Angola [39][40][41][42]. It breeds in all Mediterranean (including African) coasts except Libya, Egypt, and the Levantine Sea.…”

Aspects of Breeding Performance of Scopoli’s Shearwater (Calonectris diomedea): The Case of the Largest Colony in Greece

“…The burrowing seabird targeted in this study is a long-lived migrant procellariid species, well-known for nest site tenacity, mate fidelity, pelagic distribution even during breeding, and nocturnal behavior as an adaptative strategy to avoid terrestrial predators [14][15][16][17]. The breeding sites of the Scopoli's Shearwater (Calonectris diomedea), which lay only one egg per pair, are widespread across the Mediterranean Basin, whereas its wintering grounds are mainly located in the North Atlantic Ocean where the Canary Current occurs and, secondly, in the pelagic and coastal equatorial areas of the Eastern Atlantic, as well as in the South Atlantic Ocean along the west coast of Southern Africa [18]. Scopoli's Shearwaters have strong interactions with fishery operations in the Ionian Sea.…”

ExMove: An open‐source toolkit for processing and exploring animal‐tracking data in R

“…Numerous software tools have been developed to facilitate animal movement research (Beal et al., 2021; Joo et al., 2020). However, these tools largely focus on evaluating data set suitability (Bjørneraas et al., 2010; Morera‐Pujol et al., 2023) and conducting movement analyses of cleaned and/or collated data (Jonsen et al., 2023; Kölzsch et al., 2022). The upstream pre‐processing steps required to generate an appropriate cleaned data set have been given scant attention (however see Gupte et al., 2022).…”