Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts.

Autor: Bothwell HM; Environmental Genetics & Genomics Facility, Department of Biological Sciences, Northern Arizona University, 617 South Beaver Street, PO Box 5640, Flagstaff, Arizona, 86011, USA., Evans LM; Environmental Genetics & Genomics Facility, Department of Biological Sciences, Northern Arizona University, 617 South Beaver Street, PO Box 5640, Flagstaff, Arizona, 86011, USA., Hersch-Green EI; Environmental Genetics & Genomics Facility, Department of Biological Sciences, Northern Arizona University, 617 South Beaver Street, PO Box 5640, Flagstaff, Arizona, 86011, USA., Woolbright SA; Environmental Genetics & Genomics Facility, Department of Biological Sciences, Northern Arizona University, 617 South Beaver Street, PO Box 5640, Flagstaff, Arizona, 86011, USA., Allan GJ; Environmental Genetics & Genomics Facility, Department of Biological Sciences, Northern Arizona University, 617 South Beaver Street, PO Box 5640, Flagstaff, Arizona, 86011, USA.; Merriam-Powell Center for Environmental Research, Northern Arizona University, 800 South Beaver Street, PO Box 6077, Flagstaff, Arizona, 86011, USA., Whitham TG; Environmental Genetics & Genomics Facility, Department of Biological Sciences, Northern Arizona University, 617 South Beaver Street, PO Box 5640, Flagstaff, Arizona, 86011, USA.; Merriam-Powell Center for Environmental Research, Northern Arizona University, 800 South Beaver Street, PO Box 6077, Flagstaff, Arizona, 86011, USA.
Jazyk: angličtina
Zdroj: Ecological applications : a publication of the Ecological Society of America [Ecol Appl] 2021 Apr; Vol. 31 (3), pp. e02254. Date of Electronic Publication: 2021 Jan 20.
DOI: 10.1002/eap.2254
Abstrakt: Ecological niche models (ENMs) have classically operated under the simplifying assumptions that there are no barriers to gene flow, species are genetically homogeneous (i.e., no population-specific local adaptation), and all individuals share the same niche. Yet, these assumptions are violated for most broadly distributed species. Here, we incorporate genetic data from the widespread riparian tree species narrowleaf cottonwood (Populus angustifolia) to examine whether including intraspecific genetic variation can alter model performance and predictions of climate change impacts. We found that (1) P. angustifolia is differentiated into six genetic groups across its range from México to Canada and (2) different populations occupy distinct climate niches representing unique ecotypes. Comparing model discriminatory power, (3) all genetically informed ecological niche models (gENMs) outperformed the standard species-level ENM (3-14% increase in AUC; 1-23% increase in pROC). Furthermore, (4) gENMs predicted large differences among ecotypes in both the direction and magnitude of responses to climate change and (5) revealed evidence of niche divergence, particularly for the Eastern Rocky Mountain ecotype. (6) Models also predicted progressively increasing fragmentation and decreasing overlap between ecotypes. Contact zones are often hotspots of diversity that are critical for supporting species' capacity to respond to present and future climate change, thus predicted reductions in connectivity among ecotypes is of conservation concern. We further examined the generality of our findings by comparing our model developed for a higher elevation Rocky Mountain species with a related desert riparian cottonwood, P. fremontii. Together our results suggest that incorporating intraspecific genetic information can improve model performance by addressing this important source of variance. gENMs bring an evolutionary perspective to niche modeling and provide a truly "adaptive management" approach to support conservation genetic management of species facing global change.
(© 2020 by the Ecological Society of America.)
Databáze: MEDLINE
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