Modelling Species Habitat Suitability from Presence-only Data using Kernel Density Estimation

Authors

Guiming Zhang, University of Denver, University of Wisconsin-MadisonFollow
A-Xing Zhu, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, State Key Laboratory Cultivation Base of Geographical Environment Evolution, State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, University of Wisconsin-Madison
Steve K. Windels, Voyageurs National Park, National Park Service
Cheng-Zhi Qin, State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

Publication Date

5-21-2018

Document Type

Article

Organizational Units

Geography and the Environment

Keywords

Habitat suitability modelling and mapping, Presence-only data, Resource availability, Kernel density estimation, Ecological monitoring

Abstract

We present a novel approach for modelling and mapping habitat suitability from species presence-only data that is useful for ecosystem and species monitoring. 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 (repeated 100 times). Results show that the approach fit training data well (average training area under the curve AUC = 0.792, standard deviation SD = 0.029) and achieved better-than-random prediction accuracy (average test AUC = 0.664, SD = 0.025) that is comparable to the state-of-the-art MAXENT method (average training AUC = 0.784, SD = 0.021; average test AUC = 0.673, SD = 0.027). 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 (average training AUC = 0.743, SD = 0.030; average test AUC = 0.645, SD = 0.023), 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. The modelled species-environment responses and mapped species habitat suitability can be very useful for ecological monitoring at ecosystem or species level.

Publication Statement

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Recommended Citation

Zhang, Guiming, et al. “Modelling Species Habitat Suitability from Presence-Only Data Using Kernel Density Estimation.” Ecological Indicators, vol. 93, 2018, pp. 387–396. doi: 10.1016/j.ecolind.2018.04.002.