Constructing a hybrid species distribution model from standard large-scale distribution data

•Process-based projections of species geographic range shifts suffer from data gaps.•A hybrid-model framework using commonly available biogeographic data is developed.•Dynamic biotic processes can act at large resolution.•Calibration and spatial projection reveal biotic impact and sources of uncerta...

Full description

Saved in:
Bibliographic Details
Published inEcological modelling Vol. 373; pp. 39 - 52
Main Authors Singer, Alexander, Schweiger, Oliver, Kühn, Ingolf, Johst, Karin
Format Journal Article
LanguageEnglish
Published Elsevier B.V 10.04.2018
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:•Process-based projections of species geographic range shifts suffer from data gaps.•A hybrid-model framework using commonly available biogeographic data is developed.•Dynamic biotic processes can act at large resolution.•Calibration and spatial projection reveal biotic impact and sources of uncertainty.•This fosters mechanistic insight in species ranges and tailored data acquisition. Species range shifts under climate change have predominantly been projected by models correlating species observations with climatic conditions. However, geographic range shifting may depend on biotic factors such as demography, dispersal and species interactions. Recently suggested hybrid models include these factors. However, parameterization of hybrid models suffers from lack of detailed ecological data across many taxa. Further, it is methodologically unclear how to upscale ecological information from scales relevant to ecological processes to the coarser resolution of species distribution data (often 100 km2 or even 2500 km2). We tackle these problems by developing a novel modelling and calibration framework, which allows hybrid model calibration from (static) presence-absence data that is available for many species. The framework improves understanding of the influence of biotic processes on range projections and reveals critical sources of uncertainty that limit projection reliability. We demonstrate its performance for the case of the butterfly Titania’s Fritillary (Boloria titania).
ISSN:0304-3800
1872-7026
DOI:10.1016/j.ecolmodel.2018.02.002