Accurate spatio-temporal predictions of land-cover are fundamentally important for assessing geomorphological and ecological patterns and processes. This study quantifies the epistemic uncertainty in the species distribution modeling, which is generated by spatio-temporal gaps between the biogeographical data, model selection and model complexity. Epistemic uncertainty is generally given by the sum of subjective and objective uncertainty. The subjective uncertainty generated by the modeler-choice in the manipulation of the environmental variables was analyzed. The Snowy Plover in Florida (Charadrius alexandrinus nivosus, SP), a residential shorebird whose geographic range is extended along the Panhandle-Big Bend-Peninsula Gulf coast was considered as case-study. The first fundamental step for studying the species distribution and how it will be affected by climate change is to obtain an accurate description of the shorebird coastal habitat. The land-cover was translated into ecosystem classes using a land-cover model that predicts the evolution of coastal ecosystems affected by sea-level rise scenarios. The best land-cover map decreased the objective uncertainty (intrinsically present in data or models) in representing the spatial structure of the coastal ecosystem, reduced the temporal gaps with the occurrence data, and diminished the subjective uncertainty due to the conversion from land-cover to model-classes. Multimodeling was performed to reduce the uncertainty in the prediction of the species distribution related to model uncertainty. The best representation of the species distribution was performed by MaxEnt. The area under the receiver operating characteristic curve (AUC), the omission/commission test, the similarity index of the response curves, and the jackknife test were used simultaneously as indicators of the predictability of each species distribution model. The availability of updated high-resolution biogeoclimatological data was proven to be necessary in order to properly predict the species ranges for conservation purposes.
Bibliographical noteFunding Information:
The authors acknowledge the funds from SERDP-DOD (project SI-1699 ). Two anonymous reviewers are gratefully acknowledged for the comments about the first version of the manuscript. MC acknowledges Jason Roberts and Ben Best (Duke University) for the support with the GAM and GLM codes, Renato De Giovanni (CRIA) for the assistance with openModeller, and Jason K. Blackburn (Emerging Pathogens Institute & Department of Geography, University of Florida) for the stimulating discussion about GARPbs and MaxEnt . MC also is grateful for the service made by Google Earth Pro (Google Earth Pro # 621778859 ). R.A. Pruner (MSc University of Florida) is gratefully acknowledged for the discussions about the biology of the SP and the data collected on the field. K. Baker and M. Barber (MIT) are kindly acknowledged for reviewing the latest version of the manuscript. The research was finalized when Paul Welle performed his research internship at the Risk and Decision Science Team advised by IL and MC. MLC-A was at the University of Florida at the time of this research. Permission was granted by the USACE Chief of Engineers to publish this material. The views and opinions expressed in this paper are those of the individual authors and not those of the US Army, or other sponsor organizations.
Copyright 2012 Elsevier B.V., All rights reserved.
- Epistemic uncertainty
- Snowy Plover
- Species distribution models