Limits to thermal adaptation in ectotherms

Climate change is expected to affect biological systems across multiple scales through its direct effects on the physiology of individual organisms. Therefore, to predict how communities and ecosystems will be impacted by changes in climate, it is key to understand the extent to which ectotherm physiology can change through thermal adaptation. In this thesis, we examine the influence of possible constraints on thermal adaptation, as predicted by the Metabolic Theory of Ecology. In Chapter 2 we describe the consequences of violating a key assumption of a model used for quantifying the thermal performance curve, i.e., the relationship of biological rates with temperature. We then proceed in Chapter 3 to evaluate the impact of thermodynamic constraints on the evolution of the thermal performance curves of phytoplankton. We show that thermodynamic constraints have a very weak effect on thermal adaptation, with phylogenetically structured variation being present across the entire thermal performance curve. Further support for such a conclusion is obtained in Chapter 4 through a phylogenetic comparative analysis of the evolution of thermal sensitivity across prokaryotes, phytoplankton, and plants. This reveals that thermal sensitivity is much more variable than expected, as it can change drastically within short amounts of evolutionary time. In Chapter 5, we finally investigate thermal adaptation at the molecular level, examining if changes in temperature can alter the effects of nonsynonymous mutations. We show that across prokaryotes, mutations become increasingly detrimental to the stability of proteins with temperature. In response, thermophile species evolve enzymes that are more robust to mutations and exhibit low substitution rates. Overall, these results further our understanding of how thermal physiology evolves and indicate areas where the theory – as it currently stands – may need to be modified.Open Acces

Data from: Numerous independent gains of torpor and hibernation across endotherms, linked with adaptation to diverse environments

CONTENTS OF THIS DATASET1) time_calibrated_phylogeny.nwk: The phylogeny of 1,338 endotherm species that were included in our study.2) dataset.csv: All raw data per species that we analysed in this study. This file is composed of the following columns:Species: the scientific name of the species.Common_name: the common name of the species.Class: the class to which the species belongs.Order: the order to which the species belongs.Family: the family to which the species belongs.Dormancy: NO / Torpor / Hibernation, standing for lack of dormancy, daily torpor, or prolonged torpor / hibernation, respectively.Max_longevity_years: maximum longevity in units of years.Migratory: no / yes.Body_mass_g: body mass in g units.BMR_Watt: basal metabolic rate in W units.Brain_size_g: brain mass in g units.Diet: carnivore / herbivore / omnivore.Fossoriality: nonfossorial / semifossorial / fossorial.Daily_activity: cathemeral / crepuscular / diurnal / nocturnal.Aquatic_affinity: very low / low / moderate / high.Range_size_km2: the range size in km2 units.Mid_range_lat_dd: the latitude at the centre of the range in decimal degrees.Mid_range_lon_dd: the longitude at the centre of the range in decimal degrees.Mean_temp: the mean temperature at the centre of the range in °C units.SD_temp: the temperature seasonality at the centre of the range in °C units.Annual_precip: the annual precipitation at the centre of the range in kg ⋅ m-2 ⋅ yr-1 units.CV_precip: the precipitation seasonality at the centre of the range in kg ⋅ m-2 units.NPP: the net primary productivity at the centre of the range in g C ⋅ m-2 ⋅ yr-1 units.Seasonal_dormancy: NO / YES (whether dormancy occurs in only a single season).Predictable_dormancy: NO / YES (whether conspecifics tend to enter dormancy in a similar manner).Hibernation_with_preparation: NO / YES.Data sources and further information about these variables are available in our study. </p

Adaptive evolution shapes the present-day distribution of the thermal sensitivity of population growth rate.

Developing a thorough understanding of how ectotherm physiology adapts to different thermal environments is of crucial importance, especially in the face of global climate change. A key aspect of an organism's thermal performance curve (TPC)-the relationship between fitness-related trait performance and temperature-is its thermal sensitivity, i.e., the rate at which trait values increase with temperature within its typically experienced thermal range. For a given trait, the distribution of thermal sensitivities across species, often quantified as "activation energy" values, is typically right-skewed. Currently, the mechanisms that generate this distribution are unclear, with considerable debate about the role of thermodynamic constraints versus adaptive evolution. Here, using a phylogenetic comparative approach, we study the evolution of the thermal sensitivity of population growth rate across phytoplankton (Cyanobacteria and eukaryotic microalgae) and prokaryotes (bacteria and archaea), 2 microbial groups that play a major role in the global carbon cycle. We find that thermal sensitivity across these groups is moderately phylogenetically heritable, and that its distribution is shaped by repeated evolutionary convergence throughout its parameter space. More precisely, we detect bursts of adaptive evolution in thermal sensitivity, increasing the amount of overlap among its distributions in different clades. We obtain qualitatively similar results from evolutionary analyses of the thermal sensitivities of 2 physiological rates underlying growth rate: net photosynthesis and respiration of plants. Furthermore, we find that these episodes of evolutionary convergence are consistent with 2 opposing forces: decrease in thermal sensitivity due to environmental fluctuations and increase due to adaptation to stable environments. Overall, our results indicate that adaptation can lead to large and relatively rapid shifts in thermal sensitivity, especially in microbes for which rapid evolution can occur at short timescales. Thus, more attention needs to be paid to elucidating the implications of rapid evolution in organismal thermal sensitivity for ecosystem functioning

Metabolic plasticity can amplify ecosystem responses to global warming

Metabolic rate data for freshwater invertebrates sampled in the Hengill geothermal valley Iceland in the summers of 2015-2018. Includes body mass, acute temperature exposures, and chronic temperature exposures

Clinico-laboratory values of an adult patient with Kawasaki disease in Europe

<p>Kawasaki disease is an acute febrile syndrome that mainly hurts the skin, mucosa and lymph nodes, occasionally causing coronary artery aneurysms if left untreated. It occurs most often in Japan and Korea, affecting infants and small children, whereas few adult cases have been reported. Its pathogenesis remains mostly unknown.</p> <p>This dataset consists of clinico-laboratory values of a case of adult Kawasaki disease in Europe.<br><br>When citing this dataset, please also cite the following paper:<br>Kontopoulou T, Kontopoulos DG, Vaidakis E, Mousoulis GP: <strong>Adult Kawasaki disease in a European patient: a case report and review of the literature.</strong> <em>Journal of Medical Case Reports</em> 2015, 9:75, http://dx.doi.org/10.1186/s13256-015-0516-9.</p

Global Biotic Interactions food web map

<p>Using GloBI data archive (http://globalbioticinteractions.org accessed at June 9, 2015) we have generated a graph of predator-prey relations on species level, using R (http://r-project.org) 'igraph' library. Then another graph with the same species was generated, with weighted edges, representing similarity of position in the original graph (modified Jaccard similarity index (with min(A,B) instead of (A ∪ B) in the denominator) was used for that). The latter graph was preliminary clustered with 'infomap' algorithm, then individual cliques were extracted from clusters and several passes of moving species from clique to clique were performed in order to increase average weight of within-clique edges. After that, these cliques were used to merge nodes and merge edges in the original graph. After labeling nodes (with data acquired using 'Reol' package) and ascribing them values corresponding to geographical metadata (using 'sp' package and ecoregion maps published at http://wwf.panda.org/about_our_earth/ecoregions/maps/), graph went to 'Cytoscape' to be laid out with 'yFiles Organic' layout. Resulting image was post-processed in 'Adobe Illustrator'. <br>Visualization was made during IVMOOC 2014 (http://cns.iu.edu/all_news/event/ivmooc2014open.html)</p

Integrating gene annotation with orthology inference at scale

Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, and handles even highly fragmented assemblies. TOGA scales to hundreds of genomes, which we demonstrate by applying it to 488 placental mammal and 501 bird assemblies, creating the largest comparative gene resources so far. Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality. TOGA is a powerful and scalable method to annotate and compare genes in the genomic era

Integrating gene annotation with orthology inference at scale

Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, and handles even highly fragmented assemblies. TOGA scales to hundreds of genomes, which we demonstrate by applying it to 488 placental mammal and 501 bird assemblies, creating the largest comparative gene resources so far. Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality. TOGA is a powerful and scalable method to annotate and compare genes in the genomic era