Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1016/j.ecolmodel.2018.02.002
Volltext Akzeptiertes Manuskript
Titel (primär) Constructing a hybrid species distribution model from standard large-scale distribution data
Autor Singer, A.; Schweiger, O.; Kühn, I. ORCID logo ; Johst, K.
Quelle Ecological Modelling
Erscheinungsjahr 2018
Department BZF; OESA; iDiv
Band/Volume 373
Seite von 39
Seite bis 52
Sprache englisch
Supplements https://ars.els-cdn.com/content/image/1-s2.0-S0304380018300437-mmc1.pdf
Keywords Biotic interaction; Colonization; Extinction; Range projection; Process-based; Dispersal
UFZ Querschnittsthemen RU1;
Abstract 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).
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=19971
Singer, A., Schweiger, O., Kühn, I., Johst, K. (2018):
Constructing a hybrid species distribution model from standard large-scale distribution data
Ecol. Model. 373 , 39 - 52 10.1016/j.ecolmodel.2018.02.002