Modeling of Habitat Suitability Using Remote Sensing and Spatio-Temporal Imprecise In Situ Data on the Example of Red Deer

Abstract

This paper presents a streamlined approach to describing potential habitats for red deer (Cervus elaphus) in situations where in situ data collected through observations and monitoring are absent or insufficient. The main objectives of this study were as follows: (a) to minimize the negative effects of limited in situ data; (b) to identify landscape features with a functional relationship between habitat quality and landscape structure; and (c) to use imprecise in situ data for statistical analyses to specify these relationships. The test area was located in the eastern part of Mecklenburg-Western Pomeriania (Germany). For this area, remotely sensed forest maps were used to determine landscape metrics as independent variables. Dichotomous habitat suitability was determined based on hunting distances over a five-year period. Ecological and biological habitat requirements of red deer were derived from suitable landscape measures, which served as model inputs. Correlation analysis identified the most relevant independent landscape metrics. Logistic regression then tested various metric combinations at both class and landscape levels to assess habitat suitability. Within the model variants, the contagion index, edge density, and percentage of forested area showed the largest relative impact on habitat suitability. The approach can also be applied to other mammals, provided there are appropriate structural preferences and empirical data on habitat suitability.