Geology topics

Ryan M. Nielson

Publications and source records attributed to Ryan M. Nielson.

5 recordsLinked to original sources

USGS2013

Response of cackling geese ( Branta hutchinsii taverneri to spatial and temporal variation in production of crowberries on the Alaska Peninsula

Arctic geese often feed on berries during premigratory fattening. We hypothesized that during autumn staging on the Alaska Peninsula, the distribution of Taverne's cackling geese ( Branta hutchinsii taverneri ) would be correlated with spatial variation in crowberry ( Empetrum nigrum ) abundance. We also predicted that daily rates of fat increase among cackling geese would be higher in years when crowberries were abundant, compared to years when the crowberry crop was poor. Apparent distribution of geese based on fecal densities mirrored patterns of berry abundance, with areas that had highest densities of crowberries being used most heavily by geese. In areas where apparent use was greatest, geese consumed approximately 30 % of the berry crop between early September and mid-October. From 1999 to 2002, annual mean crowberry density in early September ranged from 205 berries m -2 (1999) to 12 berries m -2 (2002). Daily rates of lipid increase averaged 7.6 g day -1 for juvenile and 11.4 g -1 day for adult cackling geese and did not differ among years despite a >90 % difference in annual berry abundance. Although cackling geese used areas with higher densities of berries and apparently consumed a relatively large percentage of the crowberry crop, we could not detect an effect of annual variation in berry abundance on rates of fattening. Berries may have provided relatively little metabolizable biomass due to their high (90 %) water content. However, consumption of crowberries may provide geese with other physiological benefits such as water for osmoregulation or antioxidants and fatty acids that contribute to metabolic performance during migration.

Alaska↗

USGS2013

Golden eagle population trends in the western United States: 1968-2010

In 2009, the United States Fish and Wildlife Service promulgated permit regulations for the unintentional lethal take (anthropogenic mortality) and disturbance of golden eagles (Aquila chrysaetos). Accurate population trend and size information for golden eagles are needed so agency biologists can make informed decisions when eagle take permits are requested. To address this need with available data, we used a log-linear hierarchical model to average data from a late-summer aerial-line-transect distance-sampling survey (WGES) of golden eagles in the United States portions of Bird Conservation Region (BCR) 9 (Great Basin), BCR 10 (Northern Rockies), BCR 16 (Southern Rockies/Colorado Plateau), and BCR 17 (Badlands and Prairies) from 2006 to 2010 with late-spring, early summer Breeding Bird Survey (BBS) data for the same BCRs and years to estimate summer golden eagle population size and trends in these BCRs. We used the ratio of the density estimates from the WGES to the BBS index to calculate a BCR-specific adjustment factor that scaled the BBS index (i.e., birds per route) to a density estimate. Our results indicated golden eagle populations were generally stable from 2006 to 2010 in the 4 BCRs, with an estimated average rate of population change of −0.41% (95% credible interval [CI]: −4.17% to 3.40%) per year. For the 4 BCRs and years, we estimated annual golden eagle population size to range from 28,220 (95% CI: 23,250–35,110) in 2007 to 26,490 (95% CI: 21,760–32,680) in 2008. We found a general correspondence in trends between WGES and BBS data for these 4 BCRs, which suggested BBS data were providing useful trend information. We used the overall adjustment factor calculated from the 4 BCRs and years to scale BBS golden eagle counts from 1968 to 2005 for the 4 BCRs and for 1968 to 2010 for the 8 other BCRs (without WGES data) to estimate golden eagle population size and trends across the western United States for the period 1968 to 2010. In general, we noted slightly declining trends in southern BCRs and slightly increasing trends in northern BCRs. However, we estimated the average rate of golden eagle population change across all 12 BCRs for the period 1968–2010 as +0.40% per year (95% CI = −0.27% to 1.00%), suggesting a stable population. We also estimated the average rate of population change for the period 1990–2010 was +0.5% per year (95% CI = −0.33% to 1.3%). Our annual estimates of population size for the most recent decade range from 31,370 (95% CI: 25,450–39,310) in 2004 to 33,460 (95% CI: 27,380–41,710) in 2007. Our results clarify that golden eagles are not declining widely in the western United States. © 2013 The Wildlife Society.

Arizona;California;Colorado;Idaho;Iowa;Kansas;Minn↗

USGS2012

A framework for understanding semi-permeable barrier effects on migratory ungulates

1. Impermeable barriers to migration can greatly constrain the set of possible routes and ranges used by migrating animals. For ungulates, however, many forms of development are semi-permeable, and making informed management decisions about their potential impacts to the persistence of migration routes is difficult because our knowledge of how semi-permeable barriers affect migratory behaviour and function is limited. 2. Here, we propose a general framework to advance the understanding of barrier effects on ungulate migration by emphasizing the need to (i) quantify potential barriers in terms that allow behavioural thresholds to be considered, (ii) identify and measure behavioural responses to semi-permeable barriers and (iii) consider the functional attributes of the migratory landscape (e.g. stopovers) and how the benefits of migration might be reduced by behavioural changes. 3. We used global position system (GPS) data collected from two subpopulations of mule deer Odocoileus hemionus to evaluate how different levels of gas development influenced migratory behaviour, including movement rates and stopover use at the individual level, and intensity of use and width of migration route at the population level. We then characterized the functional landscape of migration routes as either stopover habitat or movement corridors and examined how the observed behavioural changes affected the functionality of the migration route in terms of stopover use. 4. We found migratory behaviour to vary with development intensity. Our results suggest that mule deer can migrate through moderate levels of development without any noticeable effects on migratory behaviour. However, in areas with more intensive development, animals often detoured from established routes, increased their rate of movement and reduced stopover use, while the overall use and width of migration routes decreased. 5. Synthesis and applications . In contrast to impermeable barriers that impede animal movement, semi-permeable barriers allow animals to maintain connectivity between their seasonal ranges. Our results identify the mechanisms (e.g. detouring, increased movement rates, reduced stopover use) by which semi-permeable barriers affect the functionality of ungulate migration routes and emphasize that the management of semi-permeable barriers may play a key role in the conservation of migratory ungulate populations.

Journal of Applied Ecology↗

USGS2004

Using discrete choice modeling to generate resource selection functions for female polar bears in the Beaufort Sea

Polar bears ( Ursus maritimus ) depend on ice-covered seas to satisfy life history requirements. Modern threats to polar bears include oil spills in the marine environment and changes in ice composition resulting from climate change. Managers need practical models that explain the distribution of bears in order to assess the impacts of these threats. We explored the use of discrete choice models to describe habitat selection by female polar bears in the Beaufort Sea. Using stepwise procedures we generated resource selection models of habitat use. Sea ice characteristics and ocean depths at known polar bear locations were compared to the same features at randomly selected locations. Models generated for each of four seasons confirmed complexities of habitat use by polar bears and their response to numerous factors. Bears preferred shallow water areas where different ice types intersected. Variation among seasons was reflected mainly in differential selection of total ice concentration, ice stages, floe sizes, and their interactions. Distance to the nearest ice interface was a significant term in models for three seasons. Water depth was selected as a significant term in all seasons, possibly reflecting higher productivity in shallow water areas. Preliminary tests indicate seasonal models can predict polar bear distribution based on prior sea ice data.

Alaska↗

USGS2003

The use of sea ice habitat by female polar bears in the Beaufort Sea

Polar bears ( Ursus maritimus ) depend on ice-covered seas to satisfy life history requirements. Modern threats to polar bears include oil spills in the marine environment and changes in ice composition resulting from climate change. Managers need practical models that explain the distribution of bears in order to assess the impacts of these threats. We used stepwise procedures to create resource selection models of habitat use for radio-collared female polar bears in the Beaufort Sea. Sea ice characteristics and ocean depths at known polar bear locations were compared to the same features at randomly selected locations. Models generated for each of four seasons confirmed complexities of habitat use by polar bears and their response to numerous factors. Bears preferred shallow water areas where ice concentrations were > 80 % and different ice types intersected. Variation among seasons was reflected mainly in differential selection of ice stages, floe sizes, and their interactions. Water depth, total ice concentration and distance to the nearest interface between different ice types were significant terms in models for most seasons. Variation in ice stage and form also appeared in three models, and several interaction effects were identified. Habitat selection by polar bears is likely related to prey abundance and availability. Use of habitats in shallow water possibly reflects higher productivity in those areas. Habitat use in close proximity to ice edges is probably related to greater access of prey in those habitats.

Conference Paper↗