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Testing unidimensional species distribution models to forecast and hindcast changes in marsh vegetation over 40 years
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics. Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, 4888 Shengbeida Rd, Changchun 130102, Jilin, Peoples R China.
Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, 266 Fangzheng Ave, Chongqing 400714, Peoples R China.
Northeast Normal Univ, Sch Environm, 2555 Jingyue St, Changchun 130117, Jilin, Peoples R China.
Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, 4888 Shengbeida Rd, Changchun 130102, Jilin, Peoples R China.
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2019 (English)In: Ecological Indicators, ISSN 1470-160X, E-ISSN 1872-7034, Vol. 104, p. 341-346Article in journal (Refereed) Published
Abstract [en]

Species distribution models (SDM) predicting changes in species occurrences and abundance are increasingly being used as a tool in biogeography and conservation biology. However, we have little information on their predictive performance. Here we used archive-recorded predictor and field-observational verifier data associated with water level to evaluate the performance of response curves over 40 years for marsh plant species in Northeast China. A consensus approach (AUC: area-under-curve) was used as the test measure for internal evaluation and external evaluation (forecast and hindcast). Our results demonstrated that there is no significant differences between internal and external evaluation, and they both showed reasonable accuracy (AUC=0.73, respectively). There was considerable variation across species and projection direction in model accuracy, and accuracy of model fitting in internal evaluation and restricting the environmental range of data in different time periods may impact the performance of model projection over time. The performance of generalized additive models (GAM) is similar with that of extended Huisman-Olff-Fresco models (eHOF). Cover model is a little better than presence/absence models in prediction over time. Our findings provide some guidelines for the use of SDM for predictions under environmental change.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2019. Vol. 104, p. 341-346
Keywords [en]
Environmental change, Extended Huisman-Olff-Fresco models (eHOF), Generalized additive models (GAM), Herbaceous marsh, Model evaluation, Prediction, Water depth, Wetlands
National Category
Ecology
Identifiers
URN: urn:nbn:se:uu:diva-387711DOI: 10.1016/j.ecolind.2019.05.024ISI: 000470966000035OAI: oai:DiVA.org:uu-387711DiVA, id: diva2:1331107
Available from: 2019-06-26 Created: 2019-06-26 Last updated: 2019-06-26Bibliographically approved

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Rydin, Håkan

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