Warren, D.L., Wright, A.N., Seifert, S.N. and Shaffer, H.B. (2014) Incorporating Model Complexity and Spatial Sampling Bias into Ecological Niche Models of Climate Change Risks Faced by 90 California Vertebrate Species of Concern. Diversity and Distributions, 20, 334-343.
https://doi.org/10.1111/ddi.12160

has been cited by the following article:

• TITLE: Spatial Transferability of Vegetation Types in Distribution Models Based on Sample Surveys from an Alpine Region

  AUTHORS: Linda Aune-Lundberg, Anders Bryn

  KEYWORDS: Area Frame Survey, Ecological Distance, GIS, Independent Evaluation Data, Maximum Entropy Modelling, Vegetation Mapping

  JOURNAL NAME: Journal of Geographic Information System, Vol.10 No.1, February 9, 2018

  ABSTRACT: Vegetation mapping using field surveys is expensive. Distribution modelling, based on sample surveys, might overcome this challenge. We tested if models trained from sample surveys could be used to predict the distribution of vegetation types in neighbourhood areas, and how reliable the spatial transferability was. We also tested whether we should use ecological dissimilarity or spatial distance to foresee modelling performance. Maximum entropy models were run for three vegetation types based on a vegetation map within a mountain range. Environmental variables were selected backwards, model complexity was kept low. The models are based on points from a small part of each study site, transferred into the entire sites, and then tested for performance. Environmental distance was tested using principle component analysis. All models had high uncorrected AUC values. The ability to predict presences correctly was low. The ability to predict absences correctly was high. The ability to transfer the distribution model depended on environmental distance, not spatial distance.