ترجمه مقاله نقش ضروری ارتباطات 6G با چشم انداز صنعت 4.0
- مبلغ: ۸۶,۰۰۰ تومان
ترجمه مقاله پایداری توسعه شهری، تعدیل ساختار صنعتی و کارایی کاربری زمین
- مبلغ: ۹۱,۰۰۰ تومان
Abstract
The extent, thickness and age of Arctic sea ice has dramatically declined since the late 1990’s, and these trends are predicted to continue. Exploring the habitat use of sea-icedependent species can help us understand which resources they use and how their distribution depends on sea ice conditions in a changing environment. The goal of this study was to develop predictive models of the habitat use of an arctic apex predator. Polar bear (Ursus maritimus) habitat use of the Barents Sea subpopulation was modelled with seasonal resource selection functions (RSFs) using satellite-linked telemetry data from 294 collars deployed on female polar bears between 1991 and 2015. Polar bears selected habitat in the Marginal Ice Zone, with a preference for intermediate sea ice concentrations [40-80%]. They spent most time in areas with relatively short travel distances to 15% or 75% ice concentration, and during spring and autumn they exhibited a preference for sea ice areas over the continental shelf (i.e. over shallow water). Predictions of the distribution of polar bears in the Barents Sea area can be made for specific sea ice scenarios using these models. Two such predictive distribution maps based on the autumn seasonal model were made and validated against two independent polar bear survey datasets collected in August 2004 and August 2015. The distribution of optimal polar bear habitat has shifted strongly northwards in all seasons of the year during the 25 year study period.
Discussion
Habitat Selection Patterns
Habitat selection patterns were successfully identified for polar bears in the Barents Sea subpopulation using three seasonal RSFs. Polar bears in this area preferentially used intermediate rather than high or low ice concentrations, confirming patterns seen in studies from many other areas (Arthur et al. 1996, Durner et al. 2009, Wilson et al. 2014). Prior studies on this population indicate that this is a robust preference, which holds across high and low availability levels for this ice type (Mauritzen et al. 2003). Some of the other environmental variables explored in this study had notable seasonal differences in selection. During spring and autumn, the polar bears had a higher probability of using sea ice over shallow water, and selected for short distances to the ice edge, defined by the 15% ice concentration. Taken together, the selection of these variables indicates a general selection for the MIZ, which is a highly productive region during the period with daylight in the Arctic. Not surprisingly, polar bears’ sea ice preferences in the MIZ corresponds closely with those of their main prey, ringed seals (Pusa hispida). Part of the ringed seal population in Svalbard, mainly sub-adult individuals (Hamilton et al. 2015), travel from coastal areas where they overwinter, into the MIZ where they select habitat with 40-80% ice cover, during the summer feeding period that extends from post-moulting in July through until near the time when fjords typically froze in Svalbard, in November (Freitas et al. 2008a).
Most subpopulations of polar bears occur over shelf seas, and Durner et al.’s (2009) circumpolar model identified ocean depth to be a strong determinant of polar bear habitat selection on a year-round basis. Increasing probability of use with decreasing depth was also seen in models for the Beaufort Sea (Durner et al. 2004). Only in one region that is relatively shallow in its entirety, has the opposite trend been found, i.e. a selection for deeper depths (Western Hudson Bay – see McCall et al. 2016). The preference for shallow shelf areas where sea ice can be found, has been linked to higher productivity and consequently higher abundance of prey for polar bears compared to areas with deep water (Hamilton et al. 2015). Despite a wide range of ocean depths in the study area, ocean depth was only retained as being important in the spring and autumn models in this study.