Rapid microgeographic evolution in response to climate change
2021; Oxford University Press; Volume: 75; Issue: 11 Linguagem: Inglês
10.1111/evo.14350
ISSN1558-5646
AutoresA. Z. Andis Arietta, David K. Skelly,
Tópico(s)Ecology and Vegetation Dynamics Studies
ResumoEvolutionVolume 75, Issue 11 p. 2930-2943 ORIGINAL ARTICLE Rapid microgeographic evolution in response to climate change A. Z. Andis Arietta, Corresponding Author A. Z. Andis Arietta a.andis@yale.edu orcid.org/0000-0002-3368-1346 School of the Environment, Yale University, New Haven, Connecticut, 06520 Email: a.andis@yale.eduSearch for more papers by this authorDavid K. Skelly, David K. Skelly orcid.org/0000-0002-5067-4535 School of the Environment, Yale University, New Haven, Connecticut, 06520Search for more papers by this author A. Z. Andis Arietta, Corresponding Author A. Z. Andis Arietta a.andis@yale.edu orcid.org/0000-0002-3368-1346 School of the Environment, Yale University, New Haven, Connecticut, 06520 Email: a.andis@yale.eduSearch for more papers by this authorDavid K. Skelly, David K. Skelly orcid.org/0000-0002-5067-4535 School of the Environment, Yale University, New Haven, Connecticut, 06520Search for more papers by this author First published: 14 September 2021 https://doi.org/10.1111/evo.14350Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Environmental change is predicted to accelerate into the future and will exert strong selection pressure on biota. Although many species may be fated to extinction, others may survive through their capacity to evolve rapidly at highly localized (i.e., microgeographic) scales. Yet, even as new examples have been discovered, the limits to such evolutionary responses have not often been evaluated. One of the first examples of microgeographic variation involved pond populations of wood frogs (Rana sylvatica). Although separated by just tens to hundreds of meters, these populations exhibited countergradient variation in intrinsic embryonic development rates when reared in a common garden. We repeated this experiment 17 years (approximately six to nine generations) later and found that microgeographic variation persists in contemporary populations. Furthermore, we found that contemporary embryos have evolved to develop 14–19% faster than those in 2001. Structural equation models indicate that the predominant cause for this response is likely due to changes in climate over the intervening 17 years. Despite potential for rapid and fine-scale evolution, demographic declines in populations experiencing the greatest changes in climate and habitat imply a limit to the species' ability to mitigate extreme environmental change. Citing Literature Supporting Information Filename Description evo14350-sup-0001-SuppMat.pdf768 KB Figure S1. Comparison of spring (A, leaf-off) and spring + summer (B, weighted mean leaf-on and leaf-off over wood frog aquatic development period) canopy closure estimates (GSF = Global Site Factor) for 14 wood frog breeding ponds averaged over 2–172 photos captured at the intersection of 5 m cartesian grids (Cartesian Grid) or a subsample of five photos captured at the four cardinal points and center (Cardinal Points) of each pond during 1999 and 2000 seasons. Colors indicate ponds that are represented in both experimental timepoints (black), only the 2001 experiment (red), and only the 2018 experiment (gray).Figure S2. The number of daily observations across all 34 ponds and 18 years (2001–2019) included in the random forest training dataset.Figure S3. Decrease in error, estimated by out-of-bag cross-validation, for increasing numbers of trees included in the random forest model predicting daily pond water temperature.Figure S4. Importance of variable in predictive accuracy of the random forest model prediction daily pond water temperatures measured by the percentage decrease in mean square error of the out-of-bag cross-validation estimates when each variable is included (A) and the total decrease in node impurity (i.e., number of correctly estimated leaves) by splitting on each variable (B).Table S1. Change in environmental variables between the 2001 and 2018 experiments.Figure S5. Change in pond temperature in relation to canopy for 14 wood frogs ponds between 2001 and 2018 for spring (A) and spring and summer (B) seasonal windows.Figure S6. Change in pond temperature in relation to canopy for 16 wood frogs ponds between 2001 (point) and 2018 (label) for spring (A) and spring and summer (B) seasonal windows.Figure S7. Embryonic development rates of wood frog embryos collected within 24 h of oviposition in 2001 (A) and 2018 (B) and reared in incubators representing high (red) or low (blue) temperatures experienced across natal ponds until hatching (approximately Gosner state 20).Figure S8. Embryonic period duration for each embryo was estimated from developmental growth rates corrected for differences in realized incubator temperatures.Table S2. Mixed effect model results testing for difference in initial embryo volume between 2001 and 2018 experiment with random intercepts for clutchmates nested within pond.Table S3. Mixed effect model results testing for difference in initial embryonic period between 2001 and 2018 experiment with random intercepts for ponds.Table S4. Model selection table predicting embryonic period by leaf-off canopy (Can) and spring water temperatures (Temp) for the 2001 and 2018 experimental cohorts and vary with temperature treatment (Treat).Figure S9. Partial effect plots for embryonic period duration estimated with linear mixed effect models for 2018 (top row, A and B) and 2001 (bottom row, C and D) cohorts when reared in a common garden at high (red) and low (blue) temperature treatments.Table S5. Mixed effect regression results for estimating the effect of larval period pond temperatures (PondTemp) and spring and summer canopy cover (Canopy) on embryonic period for the 2018 and 2001 experimental cohorts.Table S6. Exposition of causal assumption tested by the structural equation models. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. 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