Individual-level Variation and Higher-level Interpretations of Space Use in Wide-ranging Species: An Albatross Case Study of Sampling Effects

Abstract

Marine ecologists and managers need to know the spatial extent of at-sea areas most frequented by the groups of wildlife they study or manage. Defining group-specific ranges and distributions (i.e., space use at the level of species, population, age-class, etc.) can help to identify the source or severity of common or distinct threats among different at-risk groups. In biologging studies, this is accomplished by estimating the space use of a group based on a sample of tracked individuals. A major assumption of these studies is consistency in individual movements among members of a group. The implications of scaling up individual-level tracking data to infer higher-level spatial patterns for groups (i.e., size and extent of areas used, overlap or segregation among groups) is not well documented for wide-ranging pelagic species with high potential for individual variation in space use. We present a case study exploring the effects of sampling (i.e., number and identity of individuals contributing to an analysis) on defining group-specific space use with year-round multi-colony tracking data from two highly vagile species, Laysan (Phoebastria immutabilis) and black-footed (P. nigripes) albatrosses. The results clearly demonstrate that caution is warranted when defining space use for a specific species-colony-period group based on datasets of small, intermediate, or relatively large sample sizes (ranging from n = 3-42 tracked individuals) due to a high degree of individual-level variation in movements. Overall, we provide further support to the recommendation that biologging studies aiming to define higher-level patterns in space use exercise restraint in the scope of inference, particularly when pooled Kernel Density Estimation (KDE) techniques are applied to small datasets for wide-ranging species. Transparent reporting in respect to the potential limitations of the data can in turn better inform both biological interpretations and science-based management decisions.

Key winter habitat of the ivory gull Pagophila eburnea in the Canadian Arctic

“…We suggest this as an initial area to be considered, while recommending caution because of the challenges of interpreting ranges from relatively few individual birds (e.g. Soanes et al 2013, Gutowsky et al 2015. However, despite our small sample size of tracked birds, the ancillary information from birds tracked from Norway, the many years of observations of birds at sea, and the aerial survey work of Orr & Parsons (1982) all corroborate interpretations from our small sample.…”

Spatial niche partitioning may promote coexistence of Pygoscelis penguins as climate‐induced sympatry occurs

“…Data were evaluated using the “ks” default grid resolution of n = 151 grid points for 2D estimates and n = 51 grid points for 3D estimates. In order to compare pooled datasets with uneven sample sizes, we employed a data‐based “plug‐in” bandwidth selector (“Hpi”), which was calculated for each pooled dataset separately, similar to Gutowsky, Leonard, Conners, Shaffer, and Jonsen (). For similar reasons, we used a fixed (vs. local) kernel approach, which applied this smoothing factor consistently across each evaluated point within each dataset (Kie, ).…”

The importance of sample size in marine megafauna tagging studies

“…Commonly, tagging studies aim to quantify space use and identify important utilization areas (e.g., 50% kernel densities). Such estimates are highly sensitive to sample size due to variability in movement among individuals, as shown by Gutowsky et al (2015) with albatrosses. That study demonstrated that the sensitivity of grouplevel space-use estimates stabilizes with increasing sample size of albatrosses, in that the areas covered by space use estimates generated from data sets comprising different individuals roughly approached an asymptote in median area estimates around a mean sample size of 17-21 individuals.…”

“…(3) identify specific behaviors (e.g., sex and age differences) defined space use for albatrosses (Gutowsky et al 2015), shags, and kittiwakes (Soanes et al 2013); showed that foraging success of penguins relates to boundary current anomalies in different years (Carroll et al 2016 (1) Quantify habitat use over large spatial scales;…”