USGS Science⌕ Search

Geology topics

E.O. Garton

Publications and source records attributed to E.O. Garton.

3 recordsLinked to original sources
USGS2000

Sensitivity of species habitat-relationship model performance to factors of scale

Researchers have come to different conclusions about the usefulness of habitat-relationship models for predicting species presence or absence. This difference frequently stems from a failure to recognize the effects of spatial scales at which the models are applied. We examined the effects of model complexity, spatial data resolution, and scale of application on the performance of bird habitat relationship (BHR) models on the Craig Mountain Wildlife Management Area and on the Idaho portion of the U.S. Forest Service's Northern Region. We constructed and tested BHR models for 60 bird species detected on the study areas. The models varied by three levels of complexity (amount of habitat information) and three spatial data resolutions (0.09 ha, 4 ha, 10 ha). We tested these models at two levels of analysis: the site level (a homogeneous area <0.5 ha) and cover-type level (an aggregation of many similar sites of a similar land-cover type), using correspondence between model predictions and species detections to calculate kappa coefficients of agreement. Model performance initially increased as models became more complex until a point was reached where omission errors increased at a rate greater than the rate at which commission errors were decreasing. Heterogeneity of the study areas appeared to influence the effect of model complexity. Changes in model complexity resulted in a greater decrease in commission error than increase in omission error. The effect of Spatial data resolution on the performance of BHR models was influenced by the variability of the study area. BHR models performed better at cover-type levels of analysis than at the site level for both study areas. Correct-presence estimates (1 - minus percentage omission error) decreased slightly as number of species detections increased on each study area. Correct-absence estimates (1 - percentage commission error) increased as number of species detections increased on each study area. This suggests that a large number of detections may be necessary to achieve reliable estimates of model accuracy.

Idaho↗
USGS1994

Horvitz-Thompson survey sample methods for estimating large-scale animal abundance

Large-scale surveys to estimate animal abundance can be useful for monitoring population status and trends, for measuring responses to management or environmental alterations, and for testing ecological hypotheses about abundance. However, large-scale surveys may be expensive and logistically complex. To ensure resources are not wasted on unattainable targets, the goals and uses of each survey should be specified carefully and alternative methods for addressing these objectives always should be considered. During survey design, the impoflance of each survey error component (spatial design, propofiion of detected animals, precision in detection) should be considered carefully to produce a complete statistically based survey. Failure to address these three survey components may produce population estimates that are inaccurate (biased low), have unrealistic precision (too precise) and do not satisfactorily meet the survey objectives. Optimum survey design requires trade-offs in these sources of error relative to the costs of sampling plots and detecting animals on plots, considerations that are specific to the spatial logistics and survey methods. The Horvitz-Thompson estimators provide a comprehensive framework for considering all three survey components during the design and analysis of large-scale wildlife surveys. Problems of spatial and temporal (especially survey to survey) heterogeneity in detection probabilities have received little consideration, but failure to account for heterogeneity produces biased population estimates. The goal of producing unbiased population estimates is in conflict with the increased variation from heterogeneous detection in the population estimate. One solution to this conflict is to use an MSE-based approach to achieve a balance between bias reduction and increased variation. Further research is needed to develop methods that address spatial heterogeneity in detection, evaluate the effects of temporal heterogeneity on survey objectives and optimize decisions related to survey bias and variance. Finally, managers and researchers involved in the survey design process must realize that obtaining the best survey results requires an interactive and recursive process of survey design, execution, analysis and redesign. Survey refinements will be possible as further knowledge is gained on the actual abundance and distribution of the population and on the most efficient techniques for detection animals.

Transactions of the North American Wildlife and Na↗