@Article{Malusare2023, author = {Sarthak P. Malusare and Giacomo Zilio and Emanuel A. Fronhofer}, journal = {J. Evol. Biol.}, title = {Evolution of thermal performance curves: a meta-analysis of selection experiments}, year = {2023}, number = {1}, pages = {15--28}, volume = {36}, abstract = {Temperatures are increasing due to global changes, putting biodiversity at risk. Organisms are faced with a limited set of options to cope with this situation: adapt, disperse or die. We here focus on the first possibility, more specifically, on evolutionary adaptations to temperature. Ectotherms are usually characterized by a hump-shaped relationship between fitness and temperature, a non-linear reaction norm that is referred to as thermal performance curve (TPC). To understand and predict impacts of global change, we need to know whether and how such TPCs evolve. Therefore, we performed a systematic literature search and a statistical meta-analysis focusing on experimental evolution and artificial selection studies. This focus allows us to directly quantify relative fitness responses to temperature selection by calculating fitness differences between TPCs from ancestral and derived populations after thermal selection. Out of 7561 publications screened, we found 47 studies corresponding to our search criteria representing taxa across the tree of life, from bacteria, to plants and vertebrates. We show that, independently of species identity, the studies we found report a positive response to temperature selection. Considering entire TPC shapes, adaptation to higher temperatures traded off with fitness at lower temperatures, leading to niche shifts. Effects were generally stronger in unicellular organisms. By contrast, we do not find statistical support for the often discussed “Hotter is better” hypothesis. While our meta-analysis provides evidence for adaptive potential of TPCs across organisms, it also highlights that more experimental work is needed, especially for under-represented taxa, such as plants and non-model systems.}, data_doi = {https://doi.org/10.5281/zenodo.7038539}, doi = {10.1111/jeb.14087}, funding = {ANR JCJC E-SIASH ANR-19-CE02-529-0015}, hal_id = {hal-03849301}, isem_pub_no = {ISEM-2022-173}, preprint_doi = {https://doi.org/10.1101/2022.05.09.491229}, }