Comparing sea ice habitat fragmentation metrics using integrated step selection analysis

Abstract

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.

Ontogeny of movement patterns and habitat selection in juvenile albatrosses

“…by comparing juveniles and adults, rather than the mechanisms underpinning individual movement decisions (Andersen et al 2013, Ketchum et al 2013, Gutowsky et al 2014). However, the latter may be explored by new analytical frameworks which model animal movement as a series of discrete steps, characterised by specific velocity and autocorrelation distributions, and provide tools for identifying the key extrinsic (environmental) drivers (Breed et al 2018, Biddlecombe et al 2020, Carter et al 2020). In particular, integrated step‐selection functions seem well‐suited for investigating how strategies develop in naïve individuals, as they can be used to examine the processes influencing foraging‐habitat selection (e.g.…”

Spatiotemporal Evolution of Habitat Quality in Typical Resource- Depleted Cities in China Based on Land Use Changes

“…Global autocorrelation of habitat quality can measure the distribution characteristics between spatial elements across the study area and is assessed using the Moran index with a threshold value of [-1,1]. The nature and degree of autocorrelation are reflected by positive or negative values and the magnitude of the value [9,34], where [-1, 0) indicates a negative correlation, i.e., the habitat quality of the area is spatially different from the neighboring areas, (0, 1] indicates a positive correlation, i.e., areas with higher or lower habitat quality are spatially clustered, and 0 indicates a random distribution [35]. In this study, the spatial autocorrelation tool in ArcGIS 10.5 was used to analyze the spatial clustering Habitat quality hot (cold) spot analysis reflects whether there is a statistical clustering of high-value areas (hot spots) and low-value areas (cold spots) in the spatial distribution of habitat quality [36], which to some extent can be considered a reflection of relatively better or worse habitat quality in the region.…”

Black‐tailed deer seasonal habitat selection: accounting for missing global positioning system fixes

“…Their model is an integrated step selection function (iSSF) combined with a model for the probability of detection given use. Thus, hereafter we refer to the model as a probability of detection integrated step selection function (PDiSSF) for clarity and drawing parallels to the current literature (Avgar et al 2016, Prokopenko et al 2017, Biddlecombe et al 2020). The PDiSSF does not require stationary collar trials and relies on the entire sequence of fix attempts, not just successful fixes, for modeling.…”