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MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION
Citation:
Cross, C L. AND C. E. Petersen. MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION. JOURNAL OF HERPETOLOGY 35(4):590-597, (2001).
Impact/Purpose:
The primary objectives of this research are to:
Develop methodologies so that landscape indicator values generated from different sensors on different dates (but in the same areas) are comparable; differences in metric values result from landscape changes and not differences in the sensors;
Quantify relationships between landscape metrics generated from wall-to-wall spatial data and (1) specific parameters related to water resource conditions in different environmental settings across the US, including but not limited to nutrients, sediment, and benthic communities, and (2) multi-species habitat suitability;
Develop and validate multivariate models based on quantification studies;
Develop GIS/model assessment protocols and tools to characterize risk of nutrient and sediment TMDL exceedence;
Complete an initial draft (potentially web based) of a national landscape condition assessment.
This research directly supports long-term goals established in ORDs multiyear plans related to GPRA Goal 2 (Water) and GPRA Goal 4 (Healthy Communities and Ecosystems), although funding for this task comes from Goal 4. Relative to the GRPA Goal 2 multiyear plan, this research is intended to "provide tools to assess and diagnose impairment in aquatic systems and the sources of associated stressors." Relative to the Goal 4 Multiyear Plan this research is intended to (1) provide states and tribes with an ability to assess the condition of waterbodies in a scientifically defensible and representative way, while allowing for aggregation and assessment of trends at multiple scales, (2) assist Federal, State and Local managers in diagnosing the probable cause and forecasting future conditions in a scientifically defensible manner to protect and restore ecosystems, and (3) provide Federal, State and Local managers with a scientifically defensible way to assess current and future ecological conditions, and probable causes of impairments, and a way to evaluate alternative future management scenarios.
Description:
Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require such assumptions, was developed to model habitat use patterns from snake radiotelemetry data. Two case study examples were used: the copperhead ( Agkistrodon contortrix) in a forested habitat in Chesapeake, Virginia, USA, and the cottonmouth (Agkistrodon p. piscivorus in a wetland habitat in Virginia Beach, Virginia, USA. The model was developed based on grid-cell counts of the habitat used by adult snakes that were radiotracked for a minimum of one activity season (early spring through late fall). In addition to PLR modeling, a nonparametric MANOVA procedure based on ranked data from presence versus random sites was developed for comparative purposes. Although the results were similar in terms of the variables chosen by the models, nonparametric procedures lack predictive power, and the conclusions drawn from them are sometimes questionable or difficult to interpret Polytomous logistic regression provides a useful alternative to traditional modeling approaches that require ancillary data from random sites; PLR requires data only from sites used by the snakes, and was developed based on use-intensity categorization. Suggestions for model implementation (e.g., in a GIS) are discussed,