Authors: Guiming Zhang*, University of Denver, A-Xing Zhu, University of Wisconsin-Madison, Steve K. Windels, Voyageurs National Park, National Park Service, Cheng-Zhi Qin, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences
Topics: Geographic Information Science and Systems, Environmental Science, Quantitative Methods
Keywords: Habitat suitability modelling and mapping, presence-only data, resource availability, kernel density estimation
Session Type: Paper
Start / End Time: 9:55 AM / 11:35 AM
Room: Taylor, Marriott, Mezzanine Level
Presentation File: No File Uploaded
We present a novel approach for modelling and mapping habitat suitability from species presence-only data. The approach models the relationship between species habitat suitability and environment conditions using probability distributions of species presence over environmental factors. Resource availability is an important issue for modelling habitat suitability from presence-only data, but it is in lack of consideration in many existing methods. Our approach accounts for resource availability by computing habitat suitability based on the ratio of species presence probability over environmental factors to background probability of environmental factors in the study area. A case study of modelling and mapping habitat suitability of the white-tailed deer (Odocoileus virginianus) using presence locations recorded in aerial surveys at Voyageurs National Park, Minnesota, USA was conducted to demonstrate the approach. Performance of the approach was evaluated through randomly splitting the presence locations into training data to build the model and test data to evaluate prediction accuracy of the model. Results show that the approach achieved prediction accuracy that is comparable to the state-of-the-art MAXENT method. In addition, the suitability-environment responses modelled using our approach are more amenable to ecological interpretation compared to MAXENT. Compared to modelling habitat suitability purely based on species presence probability distribution, incorporating background distribution to account for resource availability effectively improved model performance. The proposed approach offers a flexible framework for modelling and mapping species habitat suitability from species presence-only data.