Geology topics

John A. Mack

Publications and source records attributed to John A. Mack.

4 recordsLinked to original sources

USGS2018

Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements

Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground‐based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi‐state mark–recapture methods using a hierarchical N ‐mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state‐space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk ( Cervus elaphus nelsoni ) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N‐mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.

Ecological Applications↗

USGS2001

Ecological studies of bison in the Greater Yellowstone Area: Development and implementation

Bison (Bison bison) of the Greater Yellowstone Area (GYA) are perhaps best known to the scientific community from the classic study of Meagher (1973) that reviewed their ecological status and management from the time of establishment of Yellowstone National Park in 1872 through the last National Park Service (NPS) removals of bison within the park in 1966. Since cessation of herd reductions in the park, bison numbers within Yellowstone increased (Dobson and Meagher 1996), as did range use (Meagher 1989b), including increased frequency and magnitude of movements beyond the park boundaries in winter (Meagher 1989a; Pac and Frey 1991; Cheville et al. 1998).

Wyoming↗

USGS1993

Potential ungulate prey for Gray Wolves

Data were gathered for six ungulate species that reside in or near Yellowstone National Park. If gray wolves ( Canis lupus ) are reintroduced into the Yellowstone area, their avoidance of human activities or their management by human may determine their range. Therefore, the area of wolf occupation cannot be predicted now. We restricted our analysis to Yellowstone National Park and to the adjacent national forest wilderness areas. We included mostly ungulate herds that summer inside or adjacent to the park and that would probably be affected by wolves. Our wolf study area includes Yellowstone National Park and adjacent wilderness areas most likely to be occupied by wolves. We reviewed publications, park records, survey reports, and state fish and game surveys and reports for statistics on ungulate populations. These data [provide an overview of ungulate populations and harvests. Each ungulate herd is described in detail. We restricted our analysis to 1980-89, because population surveys were more complete during that period and because population estimates of most ungulate populations had increased by the 1980's. We feel the higher estimates of the 1980's reflect more up-to-date techniques and are most representative of the situation into which the wolves would be reintroduced.

Idaho, Montana, Wyoming↗

USGS1993

Using Pop-II models to predict effects of wolf predation and hunter harvests on elk, mule deer, and moose on the northern range

The effects of establishing a gray wolf ( Canis lupus ) population in Yellowstone National Park were predicted for three ungulate species—elk ( Cervus elaphus ), mule deer ( Odocoileus hemionus ), and moose ( Alces alces )—using previously developed POP-II population models. We developed models for 78 and 100 wolves. For each wolf population, we ran scenarios using wolf predation rates of 9, 12, and 15 ungulates/wolf/year. With 78 wolves and the antlerless elk harvest reduced 27%, our modeled elk population estimated were 5-18% smaller than the model estimate without wolves. With 100 wolves and the antlerless elk harvest reduced 27%, our elk population estimated were 11-30% smaller than the population estimates without wolves. Wolf predation effects were greater on the modeled mule deer population than on elk. With 78 wolves and no antlerless deer harvest, we predicted the mule deer population could be 13-44% larger than without wolves. With 100 wolves and no antlerless deer harvest, the mule deer population was 0-36% larger than without wolves. After wolf recovery, our POP-II models suggested moose harvests would have to be reduced at least 50% to maintain moose numbers at the levels predicted when wolves were not present. Mule deer and moose population data are limited, and these wolf predation effects may be overestimated if population sizes or male-female ratios were underestimated in our population models. We recommend additional mule deer and moose population data be obtained.

Idaho, Montana, Wyoming↗