Falk Huettmann

RESEARCH AND MANAGEMENT VIEWPOINT: DATABASES AND SCIENCE-BASED MANAGEMENT IN THE CONTEXT OF WILDLIFE AND HABITAT: TOWARD A CERTIFIED ISO STANDARD FOR OBJECTIVE DECISION-MAKING FOR THE GLOBAL COMMUNITY BY USING THE INTERNET

Abstract Adaptive and science-based management is widely accepted as necessary to safeguard wildlife and their habitats into the future. However, many of the decisions in this field are still based on unsupported ideas that lack validation with real data and which do not make their analysis available for a public review. Decisions based on soft foundations can be harmful to wildlife, habitat and the survival of both. I suggest a new wildlife management approach founded on scientific databases that has become possible, if not imperative, with improved technology and increasing access to freely available data over the Word Wide Web (WWW). This approach is partly a consequence of the effective implementation of the U.S. Freedom of Information Act and the National Spatial Data Infrastructure. In order to justify management decisions relating to wildlife, habitat, and conservation, the listing, use, and full investigation of all available and relevant databases needs to be implemented, voluntarily or legally, as a prerequisite. Second, similar to International Organization for Standardization (ISO) Standards, each major wildlife management decision needs to add a standard management documentation system as a backup that clearly records what data and research were or were not available at that time and what the recommended research still needs to address in order to complete the data situation and to decrease uncertainty.

Assessment of different link functions for modeling binary data to derive sound inferences and predictions

Binary data are widely used for spatial modeling and when inferences and predictions are to be derived. If a Generalized Linear Model (GLM) is applied, logit functions are often used. Here we show alternatives to the traditional logit approach using probit and the complementary log log link functions. We present a software-based approach and two methods of assessing which link function performs best for inferences and for predictions. The first decision criterion is centered around the model deviance, e.g. relevant for inferences. The second criterion is based on predicting the findings back to the training data and then using the differences between expected and predicted values for known presences and absences as an indication of the fit. As an example we use Marbled Murrelet (Brachyramphus marmoratus) nesting habitat data derived from aerial telemetry and overlaid with GIS habitat layers (DEM and Forest Cover). This data set is large and carries inherent noise due to field data and a complex landscape; therefore it well covers the extremes of the fitted link functions. It is a representative example for a situation where the selection of a link function could affect the results. Findings indicate that for our data all three link functions behave similar, but logit link functions perform better than the cloclog and probit link functions when inferences as well as predictions are the study goals.