
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
When and how can we predict adaptive responses to climate change?

AbstractPredicting if, when, and how populations can adapt to climate change constitutes one of the greatest challenges in science today. Here, we build from contributions to the special issue on evolutionary adaptation to climate change, a survey of its authors, and recent literature to explore the limits and opportunities for predicting adaptive responses to climate change. We outline what might be predictable now, in the future, and perhaps never even with our best efforts. More accurate predictions are expected for traits characterized by a well-understood mapping between genotypes and phenotypes and traits experiencing strong, direct selection due to climate change. A meta-analysis revealed an overall moderate trait heritability and evolvability in studies performed under future climate conditions but indicated no significant change between current and future climate conditions, suggesting neither more nor less genetic variation for adapting to future climates. Predicting population persistence and evolutionary rescue remains uncertain, especially for the many species without sufficient ecological data. Still, when polled, authors contributing to this special issue were relatively optimistic about our ability to predict future evolutionary responses to climate change. Predictions will improve as we expand efforts to understand diverse organisms, their ecology, and their adaptive potential. Advancements in functional genomic resources, especially their extension to non-model species and the union of evolutionary experiments and “omics,” should also enhance predictions. Although predicting evolutionary responses to climate change remains challenging, even small advances will reduce the substantial uncertainties surrounding future evolutionary responses to climate change.
- Station d'Ecologie Expérimentale de Moulis France
- University of Helsinki Finland
- Uppsala University Sweden
- University of Montpellier France
- University of Helsinki Finland
[SDE] Environmental Sciences, 570, global change; climate change; evolvability; prediction; adaptation; evolutionary rescue, 577, adaptation, evolvability, Evolutionsbiologi, Conclusion, Climate change, Adaptation, Global change, global change, Ekologi, Evolutionary Biology, Ecology, Evolutionary rescue, prediction, Climate Science, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, Environmental sciences, climate change, evolutionary rescue, [SDE]Environmental Sciences, Evolvability, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Prediction, Klimatvetenskap
[SDE] Environmental Sciences, 570, global change; climate change; evolvability; prediction; adaptation; evolutionary rescue, 577, adaptation, evolvability, Evolutionsbiologi, Conclusion, Climate change, Adaptation, Global change, global change, Ekologi, Evolutionary Biology, Ecology, Evolutionary rescue, prediction, Climate Science, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, Environmental sciences, climate change, evolutionary rescue, [SDE]Environmental Sciences, Evolvability, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Prediction, Klimatvetenskap
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).18 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
