Comparing sea ice habitat fragmentation metrics using integrated step selection analysis

• Published in: Ecology and evolution Vol. 10; no. 11; pp. 4791 - 4800
• Main Authors: Biddlecombe, Brooke A., Bayne, Erin M., Lunn, Nicholas J., McGeachy, David, Derocher, Andrew E.
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01-06-2020; John Wiley and Sons Inc; Wiley
• Subjects: Animal populations; Autocorrelation; Climate change; Ecosystems; Environmental changes; Fragmentation; Habitat fragmentation; Habitat selection; Habitats; Ice; Ice breakup; Ice cover; integrated step selection analysis; Original Research; polar bear; Polar bears; Polar environments; Sea ice; spatial autocorrelation; Telemetry; Terrestrial ecosystems; Terrestrial environments; Canada; Hudson Bay; Arctic region; spatial autocorrelation; polar bear; integrated step selection analysis; sea ice; habitat fragmentation
• ISSN: 2045-7758; 2045-7758
• DOI: 10.1002/ece3.6233

Habitat fragmentation occurs when continuous habitat gets broken up as a result of ecosystem change. While commonly studied in terrestrial ecosystems, Arctic sea ice ecosystems also experience fragmentation, but are rarely studied in this context. Most fragmentation analyses are conducted using patch‐based metrics, which are potentially less suitable for sea ice that has gradual changes between sea ice cover, than distinct “long‐term” patches. Using an integrated step selection analysis, we compared the descriptive power of a patch‐based metric to a more novel metric, the variation in local spatial autocorrelation over time. We used satellite telemetry data from 39 adult female polar bears (Ursus maritimus) in Hudson Bay to examine their sea ice habitat using Advanced Microwave Scanning Radiometer 2 data during sea ice breakup in May through July from 2013–2018. Spatial autocorrelation resulted in better model fits across 64% of individuals, although both metrics were more effective in describing movement patterns than habitat selection. Variation in local spatial autocorrelation allows for the visualization of sea ice habitat at complex spatial and temporal scales, condensing a targeted time period of habitat that would otherwise have to be analyzed daily.
Sea ice fragmentation does not create distinct patches in the same way terrestrial fragmentation does. We compared traditional patch‐based metrics to our novel spatial autocorrelation metric using integrated step selection analyses to determine which metric was better for predicting polar bear movement and habitat selection. Spatial autocorrelation provided a better model fit for 64% of individuals.