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Mechanisms promoting coexistence between closely related species are fundamental for maintaining species diversity. Mechanisms of niche differentiation include allochrony which offsets the peak timing of resource utilisation between species. Many studies focus on spatial and temporal niche partitioning during the breeding season, few have investigated the role allochrony plays in influencing interspecific segregation of foraging distribution and ecology between congeneric species during the non-breeding season. We investigated the non-breeding migrations of Snares (Eudyptes robustus) and Fiordland penguins (Eudyptes pachyrhynchus), closely related species breeding between 100–350 km apart whose migration phenology differs by two months. Using light geolocation tracking, we examined the degree of overlap given the observed allochrony and a hypothetical scenario where the species commence migration simultaneously. We found that Fiordland penguins migrated to the Sub-Antarctic Frontal Zone and Polar Frontal Zone in the austral autumn whereas Snares penguins disperse westwards staying north of the Sub-Tropical Front in the austral winter. Our results suggest that allochrony is likely to be at the root of segregation because the relative profitability of the different water masses that the penguins forage in changes seasonally which results in the two species utilising different areas over their core non-breeding periods. Furthermore, allochrony reduces relatively higher levels of spatiotemporal overlap during the departure and arrival periods, when the close proximity of the two species’ colonies would cause the birds to congregate in similar areas, resulting in high interspecific competition just before the breeding season. Available evidence from other studies suggests that the shift in phenology between these species has arisen from adaptive radiation and phenological matching to the seasonality of local resource availability during the breeding season and reduced competitive overlap over the non-breeding season is likely to be an incidental outcome.

Mechanisms promoting coexistence between closely related species are fundamental for maintaining species diversity. Mechanisms of niche differentiation include allochrony which offsets the peak timing of resource utilisation between species. Many studies focus on spatial and temporal niche partitioning during the breeding season, few have investigated the role allochrony plays in influencing interspecific segregation of foraging distribution and ecology between congeneric species during the non-breeding season. We investigated the non-breeding migrations of Snares (Eudyptes robustus) and Fiordland penguins (Eudyptes pachyrhynchus), closely related species breeding between 100-350 km apart whose migration phenology differs by two months. Using light geolocation tracking, we examined the degree of overlap given the observed allochrony and a hypothetical scenario where the species commence migration simultaneously. We found that Fiordland penguins migrated to the Sub-Antarctic Frontal Zone and Polar Frontal Zone in the austral autumn whereas Snares penguins disperse westwards staying north of the Sub-Tropical Front in the austral winter. Our results suggest that allochrony is likely to be at the root of segregation because the relative profitability of the different water masses that the penguins forage in changes seasonally which results in the two species utilising different areas over their core non-breeding periods. Furthermore, allochrony reduces relatively higher levels of spatiotemporal overlap during the departure and arrival periods, when the close proximity of the two species' colonies would cause the birds to congregate in similar areas, resulting in high interspecific competition just before the breeding season. Available evidence from other studies suggests that the shift in phenology between these species has arisen from adaptive radiation and phenological matching to the seasonality of local resource availability during the breeding season and reduced competitive overlap over the non-breeding season is likely to be an incidental outcome.

Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST's ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST's ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST's response to less resolved data. Finally, we evaluate RST's performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.

Species distribution models (SDMs) are increasingly applied in conservation management to predict suitable habitat for poorly known populations. High predictive performance of SDMs is evident in validations performed within the model calibration area (interpolation), but few studies have assessed SDM transferability to novel areas (extrapolation), particularly across large spatial scales or pelagic ecosystems. We performed rigorous SDM validation tests on distribution data from three populations of a long-ranging marine predator, the grey petrel Procellaria cinerea, to assess model transferability across the Southern Hemisphere (25-65°S). Oceanographic data were combined with tracks of grey petrels from two remote sub-Antarctic islands (Antipodes and Kerguelen) using boosted regression trees to generate three SDMs: one for each island population, and a combined model. The predictive performance of these models was assessed using withheld tracking data from within the model calibration areas (interpolation), and from a third population, Marion Island (extrapolation). Predictive performance was assessed using k-fold cross validation and point biserial correlation. The two population-specific SDMs included the same predictor variables and suggested birds responded to the same broad-scale oceanographic influences. However, all model validation tests, including of the combined model, determined strong interpolation but weak extrapolation capabilities. These results indicate that habitat use reflects both its availability and bird preferences, such that the realized distribution patterns differ for each population. The spatial predictions by the three SDMs were compared with tracking data and fishing effort to demonstrate the conservation pitfalls of extrapolating SDMs outside calibration regions. This exercise revealed that SDM predictions would have led to an underestimate of overlap with fishing effort and potentially misinformed bycatch mitigation efforts. Although SDMs can elucidate potential distribution patterns relative to large-scale climatic and oceanographic conditions, knowledge of local habitat availability and preferences is necessary to understand and successfully predict region-specific realized distribution patterns.