Geology topics

Robin E. Russell

Publications and source records attributed to Robin E. Russell.

57 records · Page 4Linked to original sources

USGS2012

U.S. Geological Survey, National Wildlife Health Center, 2011 report of selected wildlife diseases

The National Wildlife Health Center (NWHC) was founded in 1975 to provide technical assistance in identifying, controlling, and preventing wildlife losses from diseases, conduct research to understand the impact of diseases on wildlife populations, and devise methods to more effectively manage these disease threats. The impetus behind the creation of the NWHC was, in part, the catastrophic loss of tens of thousands of waterfowl as a result of an outbreak of duck plague at the Lake Andes National Wildlife Refuge in South Dakota during January 1973. In 1996, the NWHC, along with other Department of Interior research functions, was transferred from the U.S. Fish and Wildlife Service to the U.S. Geological Survey (USGS), where we remain one of many entities that provide the independent science that forms the bases of the sound management of the Nation’s natural resources. Our mission is to provide national leadership to safeguard wildlife and ecosystem health through dynamic partnerships and exceptional science. The main campus of the NWHC is located in Madison, Wis., where we maintain biological safety level 3 (BSL–3) diagnostic and research facilities purposefully designed for work with wildlife species. The NWHC provides research and technical assistance on wildlife health issues to State, Federal, and international agencies. In addition, since 1992 we have maintained a field station in Hawaii, the Honolulu Field Station, which focuses on marine and terrestrial natural resources throughout the Pacific region. The NWHC conducts diagnostic investigations of unusual wildlife morbidity and mortality events nationwide to detect the presence of wildlife pathogens and determine the cause of death. This is also an important activity for detecting new, emerging and resurging diseases. The NWHC provides this crucial information on the presence of wildlife diseases to wildlife managers to support sound management decisions. The data and information generated also allows for further indepth analyses for determining the biological and ecological significance of disease events, detecting disease trends over time and space, as well as detecting any significant changes to how diseases manifest in the field. Moreover, this information allows us to gain insight into the significance of future wildlife disease events. The purpose of this report is to provide a sample of NWHC data that are available from our Laboratory Information Management System (LIMS). These data are presented in summary format with minimal statistical analysis and interpretation. The goal is to share these data with wildlife managers and other stakeholders, promote the use of NHWC data, and encourage the sharing of wildlife disease data to improve temporal and geographic surveillance coverage. Continued national surveillance for wildlife diseases is essential for providing early detection and warning of events that have the potential to result in harm to human health, economic losses, declines in wildlife populations, and subsequent ecological disturbances. Increased collaboration, coordination, and sharing of surveillance data will enhance this Nation’s ability to detect and respond to wildlife disease threats.

Scientific Investigations Report↗

USGS2012

Estimating abundance of mountain lions from unstructured spatial sampling

Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark–recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture–recapture data have produced methods estimating abundance and density of animals from spatially explicit capture–recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture–recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km 2 ) in west-central Montana using 2 methods: snow back-tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km 2 (95% Cl 2.3–5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% Cl 3.1–11.0) under the full model (including effects of distance, sex, survey effort, and distance x sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% Cl 182–451) to 529 (95% Cl 245–870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km 2 for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture–recapture analysis can be an effective method for estimating large carnivore densities.

Journal of Wildlife Management↗

USGS2011

Wolf population dynamics in the U.S. Northern Rocky Mountains are affected by recruitment and human-caused mortality

Reliable analyses can help wildlife managers make good decisions, which are particularly critical for controversial decisions such as wolf (Canis lupus) harvest. Creel and Rotella (2010) recently predicted substantial population declines in Montana wolf populations due to harvest, in contrast to predictions made by Montana Fish, Wildlife and Parks (MFWP). We replicated their analyses considering only those years in which field monitoring was consistent, and we considered the effect of annual variation in recruitment on wolf population growth. Rather than assuming constant rates, we used model selection methods to evaluate and incorporate models of factors driving recruitment and human-caused mortality rates in wolf populations in the Northern Rocky Mountains. Using data from 27 area-years of intensive wolf monitoring, we show that variation in both recruitment and human-caused mortality affect annual wolf population growth rates and that human-caused mortality rates have increased with the sizes of wolf populations. We document that recruitment rates have decreased over time, and we speculate that rates have decreased with increasing population sizes and/or that the ability of current field resources to document recruitment rates has recently become less successful as the number of wolves in the region has increased. Estimates of positive wolf population growth in Montana from our top models are consistent with field observations and estimates previously made by MFWP for 2008–2010, whereas the predictions for declining wolf populations of Creel and Rotella (2010) are not. Familiarity with limitations of raw data, obtained first-hand or through consultation with scientists who collected the data, helps generate more reliable inferences and conclusions in analyses of publicly available datasets. Additionally, development of efficient monitoring methods for wolves is a pressing need, so that analyses such as ours will be possible in future years when fewer resources will be available for monitoring.

Idaho, Montana, Wyoming↗