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Hybrid zones: windows on climate change - PubMed

Review

Hybrid zones: windows on climate change

Scott A Taylor et al. Trends Ecol Evol. 2015 Jul.

Abstract

Defining the impacts of anthropogenic climate change on biodiversity and species distributions is currently a high priority. Niche models focus primarily on predicted changes in abiotic factors; however, species interactions and adaptive evolution will impact the ability of species to persist in the face of changing climate. Our review focuses on the use of hybrid zones to monitor responses of species to contemporary climate change. Monitoring hybrid zones provides insight into how range boundaries shift in response to climate change by illuminating the combined effects of species interactions and physiological sensitivity. At the same time, the semipermeable nature of species boundaries allows us to document adaptive introgression of alleles associated with response to climate change.

Keywords: adaptive introgression; distribution; gene flow; hybridization; range limits.

Copyright © 2015 Elsevier Ltd. All rights reserved.

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Figures

Figure 1
Figure 1. Hybrid zone case studies

(A) Spruce hybrid zones (Picea glauca × P. engelmannii and P. glauca × P. sitchensis) [90]. The map to the right shows species distributions (indicated by dark gray (P. sitchensis), medium gray (P. engelmannii) and light gray (P. glauca)) and locations of the two hybrid zones. The triangle on the left summarizes estimated gene flow between the three species. Line width roughly corresponds to the percentage of shared alleles. Dashed lines indicate potential gene flow between two parental species and admixed individuals from the other hybrid zone. Climatic variables that are associated with each hybrid zone are indicated on the outside of the triangle. (B) Swallowtail butterfly hybrid zone (Papilio glaucus and P. canadensis) [13]. The map indicates the hybrid zone (dashed line) running east to west across Wisconsin. Lines represent the extent and direction (north or south) of species-specific trait introgression from 1998 to 2011. (C) Chickadee hybrid zone (Poecile atricapillus and P. carolinensis) [7]. Locus specific allele frequencies are plotted against distance along a linear transect (geographic clines) from historical (gray) and contemporary (black) sampling. Average shift north of 11.5 km in 10 years.

BOX 1, Figure I
BOX 1, Figure I. Temporal and geographic sampling of hybrid zones in a changing climate

The top panels represent northward shifts of a southern species (gray) and a northern species (white) for (A) clinal and (B) mosaic hybrid zones. The gray shading represents the northern range edge of the southern species' historical (light gray, dotted line) and contemporary (dark gray, solid line) distributions. Arrows highlight the shift in the species distribution. Lines 1 and 2 represent north-south transects across the hybrid zone. The change in the northern species' allele frequency along each transect for historical (dotted) and contemporary (solid) samples are plotted for the clinal hybrid zone (C) and the mosaic hybrid zone (D). The clinal hybrid zone forms an extensive and narrow zone of contact that extends east and west. In the mosaic hybrid zone, parental forms occupy distinct habitat patches in a heterogeneous landscape and hybridization occurs across patch boundaries. The patterns of variation in allele frequency differ depending on the transect. As the range of the southern species shifts north, habitat patches alter; patches disappear, new patches form and the area of each patch changes.

BOX 3, Figure I
BOX 3, Figure I

Clinal analyses for estimating introgression across a hybrid zone. Each panel depicts expected clines for a hybrid zone that is maintained by local adaptation, premating barriers that prevent the formation of hybrids, or selection against hybrids. The black lines depict a locus under selection; one that contributes to local adaptation or reproductive barriers. The gray represents the expected range of cline shapes for unlinked neutral markers. A) Classic geographic clines. B) Genomic clines modeled using multinomial regression [80, 96]. C) Genomic clines modeled using either Barton's concordance method [97], Bayesian genomic clines [98] or the log-logistic method[74].

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