Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.ecolmodel.2018.02.002 |
| Document | accepted manuscript |
| Title (Primary) | Constructing a hybrid species distribution model from standard large-scale distribution data |
| Author | Singer, A.; Schweiger, O.; Kühn, I.
; Johst, K. |
| Source Titel | Ecological Modelling |
| Year | 2018 |
| Department | BZF; OESA; iDiv |
| Volume | 373 |
| Page From | 39 |
| Page To | 52 |
| Language | 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 wide themes | 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). |
| Persistent UFZ Identifier | 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 |
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