The Arctic region Norwegian Bay saw a summer sea ice loss of some 26.5 percent per decade between 1979 and 2019 – the largest loss recorded amongst all Arctic regions. At the same time, this region also featured the second-lowest polar bear population as of 2021, with about 200 bears.

Denning is a critical behavioral adaptation for brown bears (Ursus arctos) to cope with winter, a period of extended resource scarcity. Bears reduce their body temperature, heart rate, and metabolism during this time to minimize energy expenditures. The Arctic has among the most pronounced and longest period of resource scarcity. Thus, we predicted bears in the region would respond by having among the longest recorded denning periods. We used GPS data from brown bears to determine the den entry, den exit, and denning duration for a population living primarily above the Arctic Circle. On average, brown bears in the region denned for 206 days, the longest duration reported using GPS data of which we are aware. The longest denning duration for any individual bear was a remarkable 233 days (64% of the year), which is near the theoretical maximum of 241 days. We found that food availability in fall delayed den entrance, with bears that appeared to consume more salmon entering their dens later. Bears showed greater synchrony in den exiting than den entrance, and female bears with cubs exited their dens more than a week after other bears. Later snow melt out in spring was also associated with later den exits. Climate change has the potential to affect the denning ecology of arctic brown bears by altering the availability of food, ambient temperature, and precipitation, all of which can alter the costs and benefits of hibernation for brown bears. Methods Our objectives were to describe the denning chronology of brown bears near the northern extent of their North American range and identify links among intrinsic characteristics (e.g., reproductive status, age, and body mass), environmental conditions, and the timing of denning behavior as a necessary first step to identifying the potential consequences of climate change and proposed industrial development on this behavior. We hypothesized that (1) sex, reproductive status, and stored energy resources, or (2) a combination of intrinsic factors and environmental covariates associated with diet and weather would explain the timing of den entry and den exit. We predicted that (1a) gestating females would enter dens earlier and exit dens later and (1b) body fat would explain date of den exit because stored energy would allow longer denning duration. We also predicted that (2a) low ambient temperatures and late snowmelt in spring would be associated with late den exits, and (2b) bears that used salmon in autumn would den later due to increased availability of energy-rich food resources.

Bear Capture and Tracking

We captured and collared 51 brown bears (32 females and 19 males) between 2014 and 2016 using aerial darting techniques. Capture and handling techniques were in accordance with guidelines of the American Society of Mammalogists (Sikes and Gannon 2011) and were approved by USGS and National Park Service Institutional Animal Care and Use Committees (IACUCs; 2014-1 and 2014A2, respectively). We fitted bears with Telonics (Mesa, AZ) Gen IV Global Positioning System collars. The collars were programmed to obtain a location every 90 minutes between March 15 and November 15 and once a day between November 16 and March 14. A tri-axial accelerometer housed in the collar was programmed to record activity during the five-minute period immediately after a GPS location was recorded.

Den Locations and Chronologies

We identified den sites following the methods of Sorum et al. (2019a), which involved locating clusters of GPS locations prior to den entry and then visiting a subset of dens for validation (Pigeon et al. 2014). Across 33 validated dens, Sorum et al. (2019a) found that the average den location determined from GPS collars was within 25m (SE = 9m) of the actual den location.

The timing of den entry and exit is often inferred from GPS collar data. However, bear behaviors such as lingering near the den site before and after the denning period can decrease the reliability of location data as an indicator of the timing of hibernation. Similarly, some collared bears in dens can continue to record GPS locations in shallow dens or with favorable arrangements of overhead GPS satellites. To address such shortcomings, we used both GPS fix rate and accelerometer activity sensor readings to infer the beginning and end of the denning period. Because the patterns of these variables can be complex due to situations like a bear briefly entering a den, appearing to enter hibernation, then exiting and re-entering a second time, we used a manual graphical technique to select the den entry and exit periods. Abrupt decreases in both GPS fix rates and activity rates were used to infer the beginning of denning, while abrupt increases in both fix rates and activity rates were used to infer den exit timing (see Supporting Information).

We estimated denning duration as the number of days between den entrance and exit for each bear. Because two bears moved between multiple dens in a single winter, we identified the interval from the date of entrance at the first den to date of emergence at the last den as the denning period for these individuals (Waller et al. 2012). To understand bear behavior following den emergence, we also calculated the post-emergence period, which we defined as the number of days it took each bear to first move 1km from their dens (Anderson et al. 2024).

Intrinsic Covariates

We tested the effect of six intrinsic covariates on the date of den entry and exit: reproductive status and sex (combined), bear age, percent body fat, body mass, den elevation, and a measure of salmon use in September (Table 1). We determined the reproductive status of females via observations during telemetry flights or capture events. We used activity sensors in collars to detect birth events during denning by identifying short spikes of activity, which have been shown to reliably indicate birth (similar to Lemeire et al. 2022 but as in Roberts et al. in prep). Females with no cubs observed during at least 2 repeated aerial observations before denning and who did not give birth to cubs in the den were labelled as lone females. Females observed with at least 1 older cub (1- or 2-year-olds) after den exit were classified as females with cub at the time of den entry. To simplify analyses, we generated a variable combining sex and the reproductive status of females (RS), which had 4 classes during den entry (male, solo female, pregnant, female with cub) and 3 classes during den exit (male, solo female, and female with cub). RS information was missing for one individual which was excluded from analyses including RS.

Age was estimated by highly experienced biologists from tooth wear at capture (Hilderbrand et al. 2018) and projected forward throughout the study. This method has similar accuracy and is much less invasive than aging based on teeth annuli (Hilderbrand et al. 2018). However, inaccurate age estimates may have persisted and prevented us from detecting a true effect of age on den entrance or exit, but we did not have a way to address this issue. Body mass (to nearest 0.5 kg) was determined by using an electronic load cell (MSI-7200, Measurement Systems International, Seattle, WA, USA; Hilderbrand et al. 2018). Percent body fat was estimated at capture by bioelectric impedance analyses (RJL Systems, Clinton Township, MI, USA; Farley and Robbins 1994, Hilderbrand et al. 1998, Hilderbrand et al. 2018). Den elevation was extracted from a Digital Elevation Model with 20m resolution.

We estimated each bear’s use of salmon as a food resource before den entry based on their proximity to salmon-bearing streams. Previous studies in Alaska have found that time spent near streams is a reliable predictor of salmon consumption because bears respond to seasonal changes in salmon availability: 70% of the variation in the percent of assimilated bear diets consisting of salmon was explained by the amount of time GPS collared bears spent near streams (Deacy et al. 2016, 2018). Bears are mostly absent from salmon streams when salmon are not spawning but become ephemerally super abundant during salmon runs (Schindler et al. 2013, Deacy et al. 2016). We used the Alaska Anadromous Streams Catalog (AASC) and field observations to identify the water bodies in north-central Alaska where bears have access to salmon (Figure 1). We included all portions of streams where salmon are considered present. Although Deacy et al. (2016, 2018) used a 50m buffer to identify GPS locations associated with salmon fishing, that research occurred on small high-gradient streams with well-defined channels. Upon viewing satellite imagery with the AASC line features, we identified many clusters of GPS points on off-channel habitat that was up to 1000m from the line feature which defined the main channel. In other areas, the main channel was distant from its associated line feature, possibly because channels tend to migrate in dynamic flood plain rivers. To account for these issues, we considered individuals to be foraging for salmon when their GPS locations were <1000m from a salmon stream. Although some of these locations are likely associated with activities other than catching and eating salmon (e.g. travel, eating other foods), we found that the percent of individual brown bear GPS locations within 1000m of streams in September was positively correlated (r>0.64) with percent of salmon and terrestrial meat in the diet of individual bears documented by Mangipane et al. (2020), which lends support for our approach. Following these procedures, we defined the variable ‘September salmon use’ as the percentage of a bear’s September GPS locations within 1000m of salmon streams.

Environmental Covariates

We tested the effect of four environmental covariates on the date of den exit (snow depth, mean air temperature, maximum air temperature, and the end of the continuous snow season; Table 1). We also included

In this study, we sampled 54 Scots pine populations from the Norwegian coast over the Arctic Circle to western Russia covering 47.3 longitudes or more than 1/8th of the earth’s circumference, which represents the most comprehensive coverage of Northern Europe to date. We inferred variation in autumn phenology and dormancy progression from freeze hardiness tests conducted on >5000 seedlings, of which >900 seedlings from 24 populations were genotyped using genotyping-by-sequencing (GBS). Our main goal was to evaluate adaptive responses in Scots pine at phenotype and genotype levels. Evaluation of cold hardiness along environmental and geographical gradients would contribute to an understanding of the performance of these gradients for predicting freeze damage levels. The genotype data allow evaluation of genetic variance across landscapes and thus shed light on the degree of genetic-environmental association and the recolonization history of Scots pine in Scandinavia.

Text file as gzipped vcf (.vcf.gz). See "pheno_pop.txt" for more information on each individual.

In this study, we sampled 54 Scots pine populations from the Norwegian coast over the Arctic Circle to western Russia covering 47.3 longitudes or more than 1/8th of the earth’s circumference, which represents the most comprehensive coverage of Northern Europe to date. We inferred variation in autumn phenology and dormancy progression from freeze hardiness tests conducted on >5000 seedlings, of which >900 seedlings from 24 populations were genotyped using genotyping-by-sequencing (GBS). Our main goal was to evaluate adaptive responses in Scots pine at phenotype and genotype levels. Evaluation of cold hardiness along environmental and geographical gradients would contribute to an understanding of the performance of these gradients for predicting freeze damage levels. The genotype data allow evaluation of genetic variance across landscapes and thus shed light on the degree of genetic-environmental association and the recolonization history of Scots pine in Scandinavia. Text file as gzipped vcf (.vcf.gz). See "pheno_pop.txt" for more information on each individual. I den här studien undersökte vi den lokala anpassningen av tidspunkten för invintring och den genetiska variationen hos naturligt tallbestånd i norra Europa. Det geografiska området av studien sträcker sig från Norge i väster till och Komi-regionen i västra Ryssland fram till Uralbergen. Drygt 5000 frön från 56 populationer såddes upp och utsattes för frystemperaturer för att skatta deras frosttolerans och variationen i egenskapen över det geografiska området av denna studie. Från 24 av dessa 56 populationer samlades barr för att extrahera DNA till genotypning för att också kvantifiera den genetiska variationen hos tall i norra Europa och för att undersöka om det finns en tydlig populationsstruktur. Dessutom skulle det studeras om det finns spår av lokal anpassning i den del av genomet som sekvenserades och om dessa spår går att koppla till variationen i frosttolerans. Text fil enligt vcf format som är komprimerad med GNU zip (.vcf.gz). Se "pheno_pop.txt" för information om varje individ.

Introduction

Climate Central’s Surging Seas: Risk Zone map shows areas vulnerable to near-term flooding from different combinations of sea level rise, storm surge, tides, and tsunamis, or to permanent submersion by long-term sea level rise. Within the U.S., it incorporates the latest, high-resolution, high-accuracy lidar elevation data supplied by NOAA (exceptions: see Sources), displays points of interest, and contains layers displaying social vulnerability, population density, and property value. Outside the U.S., it utilizes satellite-based elevation data from NASA in some locations, and Climate Central’s more accurate CoastalDEM in others (see Methods and Qualifiers). It provides the ability to search by location name or postal code.

The accompanying Risk Finder is an interactive data toolkit available for some countries that provides local projections and assessments of exposure to sea level rise and coastal flooding tabulated for many sub-national districts, down to cities and postal codes in the U.S. Exposure assessments always include land and population, and in the U.S. extend to over 100 demographic, economic, infrastructure and environmental variables using data drawn mainly from federal sources, including NOAA, USGS, FEMA, DOT, DOE, DOI, EPA, FCC and the Census.

This web tool was highlighted at the launch of The White House's Climate Data Initiative in March 2014. Climate Central's original Surging Seas was featured on NBC, CBS, and PBS U.S. national news, the cover of The New York Times, in hundreds of other stories, and in testimony for the U.S. Senate. The Atlantic Cities named it the most important map of 2012. Both the Risk Zone map and the Risk Finder are grounded in peer-reviewed science.

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This map is based on analysis of digital elevation models mosaicked together for near-total coverage of the global coast. Details and sources for U.S. and international data are below. Elevations are transformed so they are expressed relative to local high tide lines (Mean Higher High Water, or MHHW). A simple elevation threshold-based “bathtub method” is then applied to determine areas below different water levels, relative to MHHW. Within the U.S., areas below the selected water level but apparently not connected to the ocean at that level are shown in a stippled green (as opposed to solid blue) on the map. Outside the U.S., due to data quality issues and data limitations, all areas below the selected level are shown as solid blue, unless separated from the ocean by a ridge at least 20 meters (66 feet) above MHHW, in which case they are shown as not affected (no blue).

Areas using lidar-based elevation data: U.S. coastal states except Alaska
Elevation data used for parts of this map within the U.S. come almost entirely from ~5-meter horizontal resolution digital elevation models curated and distributed by NOAA in its Coastal Lidar collection, derived from high-accuracy laser-rangefinding measurements. The same data are used in NOAA’s Sea Level Rise Viewer. (High-resolution elevation data for Louisiana, southeast Virginia, and limited other areas comes from the U.S. Geological Survey (USGS)).

Areas using CoastalDEM™ elevation data: Antigua and Barbuda, Barbados, Corn Island (Nicaragua), Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, San Blas (Panama), Suriname, The Bahamas, Trinidad and Tobago.

CoastalDEM™ is a proprietary high-accuracy bare earth elevation dataset developed especially for low-lying coastal areas by Climate Central. Use our contact form to request more information.

In this study, we sampled 54 Scots pine populations from the Norwegian coast over the Arctic Circle to western Russia covering 47.3 longitudes or more than 1/8th of the earth’s circumference, which represents the most comprehensive coverage of Northern Europe to date. We inferred variation in autumn phenology and dormancy progression from freeze hardiness tests conducted on >5000 seedlings, of which >900 seedlings from 24 populations were genotyped using genotyping-by-sequencing (GBS). Our main goal was to evaluate adaptive responses in Scots pine at phenotype and genotype levels. Evaluation of cold hardiness along environmental and geographical gradients would contribute to an understanding of the performance of these gradients for predicting freeze damage levels. The genotype data allow evaluation of genetic variance across landscapes and thus shed light on the degree of genetic-environmental association and the recolonization history of Scots pine in Scandinavia. The phenotypes of all genotyped individuals. Includes each individuals population number and seedlot I den här studien undersökte vi den lokala anpassningen av tidspunkten för invintring och den genetiska variationen hos naturligt tallbestånd i norra Europa. Det geografiska området av studien sträcker sig från Norge i väster till och Komi-regionen i västra Ryssland fram till Uralbergen. Drygt 5000 frön från 56 populationer såddes upp och utsattes för frystemperaturer för att skatta deras frosttolerans och variationen i egenskapen över det geografiska området av denna studie. Från 24 av dessa 56 populationer samlades barr för att extrahera DNA till genotypning för att också kvantifiera den genetiska variationen hos tall i norra Europa och för att undersöka om det finns en tydlig populationsstruktur. Dessutom skulle det studeras om det finns spår av lokal anpassning i den del av genomet som sekvenserades och om dessa spår går att koppla till variationen i frosttolerans. Den uppmätta normaliserade skadegraden hos varje fröplanta efter exponering för köld. Innehåller även populationsnummer och såddnummer.