Philippa Kaur

Protein domains show a Gompertzian growthThe protein domain RSA distributions of 3368 bacterial genomes were obtained as detailed in the “Materials and methods” section. Briefly, for each bacterial genome we retrieved all the identifiable protein domains. Then, we computed the RSA by counting the number of protein domains belonging to each protein domain family.Three evolutionary hypotheses were tested by fitting the empirical RSAs with the Log-Series [Eq. (7)], the Negative Binomial (Eq. (6)) and the Poisson Log-Normal (Eq. (4)) distribution (Fig. 1a). According to the Akaike Information Criterion (AIC)30, in (99.97%) of bacteria the selected model was the Poisson Log-Normal (Fig. 1b). This model performed better than both the Log-Series and the Negative Binomial and described the data well, with an average (R^2) of 0.97 (minimum (R^2)=0.86). The selection of the Poisson Log-Normal model instead of the Negative Binomial or the Log-Series, implies that the protein domains evolution process is characterized by a Gompertzian density regulation function ((g(x)=gamma ln (x+epsilon ))) rather than a linear one ((g(x)=eta x)). This suggests an asymmetric process in which the proliferation rate for low abundant protein domains is faster than for the high abundant ones.Figure 1Fit of protein domains RSA. (a) Example of protein domains Preston plot fitted with three different distributions: the Poisson Log-Normal, the Negative Binomial and the Log-Series. Results refer to the bacterial genome (text {GCA}_000717515). The Negative Binomial and the Log-Series fit overlap. This implies that the dispersion parameter (alpha) of the Negative Binomial distribution (see Eq. (6)) is close to zero. The mean and the median of the dispersion parameter obtained for the 3368 bacterial genomes are ({2.67times 10^{-4}}) and ({2.62times 10^{-7}}), in agreement with the observed overlap. (b) Distribution of the difference between the AIC obtained with the Poisson Log-Normal model (PL) and the Log-Series (LS) or the Negative Binomial (NB) model, considering all the 3368 bacterial genomes.Full size imageProtein domains deactivation is faster than duplicationThe examination of the Poisson Log-Normal scale ((mu)) and location ((sigma ^2)) parameters (see Eq. (4) and Supplementary Material) estimated by the fitting procedure for each bacterial genome, allows us to reveal further features of the evolutionary process of protein domains.First of all, Fig. 2 shows that (mu) has negative values in all bacterial genomes. Recalling that (mu =r/gamma), where r is the growth rate and (gamma) is the multiplicative constant of the Gompertzian function, which must be positive, this implies that the growth rate of protein domains, r, is also negative. Notice that the growth rate can be expressed as the difference between the birth and the death rate, (r=b-d). Hence, a negative r means that the death rate is greater than the birth rate ((d > b)). In the evolutionary model of protein domains, the birth rate b has the meaning of duplication rate, while the death rate d is the rate at which protein domains are deactivated. A negative r hence implies that protein domain deactivation, which is related to the accumulation of events which disrupt the coding sequence of protein domains, happens at a faster rate than the duplication of the whole protein domain sequence within the genome.Figure 2Distribution of species according to the model parameters. Scatter plot of Poisson Log-Normal parameters (mu) versus (sigma ^2) obtained fitting the protein domains RSAs. Only species represented by at least 10 different strains were included in the plot, for a total of 1173 bacterial genomes which belong to 48 different species. Different colors represent different species as indicated in the legend.Full size imageFurthermore, the plot of (mu) as a function of (sigma ^2) (Fig. 2) highlights the negative linear relationship between (mu) and (sigma ^2). Such relationship can also be deduced mathematically.Starting from the expressions (mu =r/gamma) and (sigma ^2=sigma _e^2 / 2gamma), and after simple algebraic manipulation, we can in fact obtain that (mu = 2rsigma ^2 / sigma _e^2), which explains the negative linear relationship between the two parameters.Besides the negative relationship, the plot of (mu) versus (sigma ^2) also highlights the presence of clusters of bacterial genomes with similar ecological features, which are pictured in the plot as roughly parallel stripes (Fig. 2). When we depict bacterial strains belonging to the same species using the same color, it emerges that the stripes are related to the bacterial taxonomy. This result motivates us to introduce a new approach to bacterial phylogeny based on the ecological modeling of protein domains and the consequent estimation of the parameters (mu) and (sigma ^2).Protein domain RSA and evolutionary distanceWe propose to calculate the pairwise evolutionary distances between bacteria based on three parameters: the Poisson Log-Normal scale and location parameters discussed above ((mu) and (sigma)), and the density of protein domains in the bacterial genome. Such density describes to which extend the whole bacterial genome is populated with protein domains and it hence constitutes an additional feature of the protein domain ecological dynamics. As detailed in the Materials and Methods, the distance between bacteria is specifically computed as the 3D euclidean distance in the scaled space of (mu), (sigma), and protein domain density. In the following, we refer to such distance as ‘RSA distance’.To evaluate the bacterial interrelationships derived from the RSA distances, we compared our results with both the bacterial taxonomic classification and the 16S rRNA gene-based phylogeny. Specifically, starting from the RSA distance matrix we computed a hierarchical clustering of bacteria and we compared the resulting clusters with those obtained from the 16S rRNA gene-based distance matrix. Both clustering results were then compared with the bacterial taxonomic classification.Notice that the usage of both 16S rRNA phylogeny and bacterial taxonomic classification allows us to exploit the complementary information that these two approaches provide, despite their intrinsic connection. Namely, modern microbial taxonomy is mostly based on 16S rRNA gene6 and, on the other hand, the cutoffs commonly used in 16S rRNA phylogeny originated from phenotype-based taxonomy31. However, while taxonomy allows us to assign human interpretable names to bacteria, to associate such names with phenotypic properties, and to classify bacteria into a predefined hierarchy, 16S rRNA phylogeny provides a quantitative measurement of the evolutionary distance between bacteria that can be compared with the RSA distance without setting any pre-defined threshold. Moreover, the usage of 16S rRNA phylogeny allows us to investigate the bacterial relationships at the intraspecies level, for which the taxonomic classification is not available.As detailed in the Materials and Methods, 16S rRNA distances were calculated based on the bacterial 16S rRNA gene reference sequences, following the standard procedure32. Taxonomic classification, instead, was retrieved from NCBI and included the following levels: phylum, class, order, family, genus and species. In order to obtain a comparable number of clusters from all three methods, we considered separately each taxonomic level and we cut the 16S rRNA and the RSA -based hierarchical trees so as to get a number of clusters equivalent to the number of taxa available at the selected taxonomic level.At each taxonomic level, the Normalized Mutual Information (NMI) was used as a measurement of agreement between different clustering solutions33. Notice that, while the theoretical range of the NMI score is the interval (left[ 0,1right]), NMI is biased towards clustering solutions with more clusters and fewer data points34. Consequently, the baseline of NMI score in practise is not zero and relatively high NMI scores can be an artifact caused by the low ratio between number of bacteria and number of taxonomic groups. To make the comparison fair, we used simulations to calculate the baseline NMI for each taxonomic level (box plots of Fig. 3).As expected by their intrinsic relationship, taxonomy and 16S rRNA phylogeny show high agreement (red dots in Fig. 3). RSA-based clusters, instead, show a certain deviation from both taxonomy (blue dots in Fig. 3) and phylogeny (green dots in Fig. 3). For both comparisons, however, the NMI scores are still evidently higher than the baseline, signifying that the RSA model captures phylogenetic signals to a certain degree. Comparing the obtained NMI scores with the baseline, we notice that the agreement between RSA-based clusters and both taxonomy and phylogeny increases at lower taxonomic levels, reaching the maximum at species level. Taking as ground truth the taxonomic classification, the total purity of the RSA-based clusters at species level is 0.65, signifying that 65% of bacteria are correctly classified.Figure 3Comparison between the three clustering results at different taxonomic levels. NMI scores (y-axis) are calculated as a measurement of agreement between clusters based on: RSA method and taxonomy (blue), 16S rRNA gene and taxonomy (red), RSA method and 16S rRNA gene (green). Different taxonomic levels are considered for the comparison: phylum, class, order, family, genus and species (x-axis). The box plots represent the baselines of NMI score and are based on simulations.Full size imageTo assess the robustness and stability of the RSA-based phylogeny, with regard to the choice of protein domains, we randomly selected subsamples of protein domains in different proportions (from (10%) to 90% of all protein domains). The reconstructed phylogenetic trees were then compared with the phylogenetic tree obtained using all protein domains (see Materials and Methods for details), and the correlation between the trees was calculated (see Supplementary Fig. S6). As expected, with larger proportions of protein domains taken into account, the correlation between subsample-based phylogeny and base phylogeny increases. For larger subsampling proportions, the compared phylogenetic trees are in good agreement: for a subsample with 90% of protein domains, the mean cophenetic correlation is equal to 0.74, and the mean common-nodes-correlation is equal to 0.68. We notice that the common-nodes-correlation is more stable compared to the cophenetic correlation, as expected since cophenetic correlation is affected by the height of the phylogenetic trees. The results suggest that the overall structure of the phylogenies is stable even for smaller subsampling proportions, while subsampling height of the branches correlates with the full-data height only at larger subsampling proportions.To evaluate the intraspecies composition obtained from the RSA-based clustering, we selected the subset of species for which at least 10 different strains were present in our data (48 species). Among them, we selected the species where hierarchical clustering showed a clear separation of clusters (including outliers) and for which published literature characterizing at least some of the strains was available (6 out of 48 species). For these 6 species, we again assessed the robustness and stability of RSA phylogenies, as detailed in the “Materials and methods” section. Our results suggest (see Supplementary Fig. S7) that the subsample-based phylogenies are in good agreement with the full-data phylogenies, especially for larger subsampling proportions. We notice the correlations is larger than in the case of phylogenetic trees for randomly selected 100 bacteria (Supplementary Fig. S6), especially for certain species (i.e., Xanthomonas citri). This could be attributed to the smaller size of the phylogenetic tree. However, the species with similar phylogenetic tree size still show differences in correlation (i.e., Xanthomonas citri and Francisella tularensis), suggesting that the RSA-based distance matrix between the strains of Xanthomonas citri carries stronger phylogenetic signal. Comparing 6 observed species with the randomly sampled subsets of 100 bacteria, we can analogously conclude that the RSA-captured phylogenetic signal is stronger within the species. In the following, we discuss the results obtained for the 6 selected bacterial species in more details.Figure 4(Previous page.) Hierarchical clustering of bacteria at the intraspecies level, comparing solutions obtained by RSA and 16S rRNA method. Each subplot shows a tanglegram with RSA-based dendrogram on the left and 16S rRNA-based dendrogram on the right. Lines connect the same bacteria from two dendrograms. The color/type of the line represents the feature of the bacterium it connects. (a) 22 strains of Xanthomonas citri belong to two different pathovars: A (orange) and (hbox {A}^{mathrm{W}}) (purple). (b) 10 strains of Chlamydia pneumoniae are isolated from different tissues: conjuctival (yellow), respiratory (magenta) and vascular (violet). 9 strains represented with solid line are human (Homo sapiens) pathogens while the one strain represented by dashed line is koala (Phascolarctos cinereus) pathogen. (c) 14 strains of Vibrio cholerae are colored based on their karyotype. 11 strains have two circular chromosomes Chr1 ((sim 3) Mb) and Chr2 ((sim 1) Mb) (magenta). 2 strains have one (sim)4 Mb long circular chromosome (yellow). One strain has three chromosomes Chr1 ((sim)3 Mb), Chr2 ((sim)1 Mb) and Chr3 ((sim)1 Mb) (violet).Full size imageRSA-based method distinguishes subspecies infecting different hostsXanthomonas citri subsp. citri (XCC) and Chlamydia pneumoniae (Cpn) are two species whose subspecies can infect different hosts. Here we show that the RSA-based method correctly discriminates such subspecies even when their divergence is not detected comparing the 16S rRNA gene sequences.Xanthomonas citri subsp. citri (XCC) is a causal agent of citrus canker type A, a bacterial disease affecting different plants from the genus Citrus. While citrus canker A infects most citrus species, two of its variants, A* and (hbox {A}^{mathrm{W}}), have a much more limited host range with XCC pathotype (hbox {A}^{mathrm{W}}) infecting only Key lime (C. aurantifolia) and alemow (C. macrophylla)2. Our data set includes 17 strains of XCC pathotype A and 5 strains of XCC pathotype (hbox {A}^{mathrm{W}})2. RSA-based clustering of the 22 XCC strains identifies two separated clusters (Fig. 4a, left) which coincide with the two XCC pathotypes. Concurrently, clustering based on 16S rRNA gene fails to identify the two pathotypes of XCC (Fig. 4a, right). This suggests that even though pathotypes A and (hbox {A}^{mathrm{W}}) have different hosts, their diversification is not reflected in the variability of the 16S rRNA gene. On the other hand, modeling the protein domain RSA of the two pathotypes succesfully captures the different functions of their proteomes.Another important aspect of the citrus canker is the geographical spread of the disease. The 22 strains of XCC included in our data set have diverse geographical origin. While all (hbox {A}^{mathrm{W}}) strains were sampled from USA, strains of pathotype A originate from USA, Brazil and China. RSA clustering of 17 A-type strains colored by their sampling location shows a geographical pattern (Supplementary Fig. S2) similar to the one obtained by Patané et al.2 using a maximum likelihood tree based on 1785 concatenated unicopy genes, with the only exception of strain jx-6 ((text {GCA}_001028285)) coming from China.For what concerns Chlamydia pneumoniae (Cpn), this is an obligate intercellular parasite which is widespread in human population and causes acute respiratory disease. Besides humans, different animal species can be infected with Chlamydia pneumoniae. Our data set includes 9 strains which infect humans (Homo sapiens) and 1 strain isolated from koala (Phascolarctos cinereus). RSA-based clustering clearly separates such isolate from the group of highly similar human isolates (Fig. 4b, left). This result is confirmed by 16S rRNA-based clustering (Fig. 4b, right) and is in agreeement with previous results in which the comparison of four human-derived isolates and the koala strain LPCoLN ((text {GCA}_000024145)) through whole-genome sequencing showed a much higher variation between human and koala-derived strains than within the human-derived strains35.Another peculiarity of Chlamydia pneumoniae is tissue tropism. The human-derived strains of Chlamydia pneumoniae can in fact be divided into conjuctival, raspiratory and vascular based on their tissue of origin. Cpn tissue tropism was the focus of the study conducted by Weinmaier et al., where whole-genome sequences of multiple Cpn strains isolated from different human anatomical sites were compared and animal isolates were used as outgroup3. Weinmaier et al. found a good agreement between the anatomical origin of strains and the maximum likelihood phylogenetic tree based on all SNPs. However, they could not obtain a clear separation between anatomical subgroups of Cpn. Our results show that the RSA-based method partially succeeds in separating subspecies related to different tissues (Fig. 4b, left). The RSA-based dendrogram, in fact, shows a cluster of four respiratory bacteria. However, it does not separate the other subspecies by infection site, suggesting that tissue tropism is not entirely captured by our method.RSA-based method discriminates subspecies with different genome compositionIn some cases, subspecies of the same species are characterized by global differences in the genome composition. This is, for example, the case of Vibrio cholerae and Buchnera aphidicola. Here, we show that the RSA-based model is able to capture such differences and to discriminate subspecies with known different genomic peculiarities.Vibrio cholerae is the causative agent of cholera disease. Its genome is normally composed of two chromosomes: Chr1 ((sim 3) Mb) and Chr2 ((sim 1) Mb). However, some strains show a different karyotype. The two strains (1154text {-}74) ((text {GCA}_000969235)) and (10432text {-}62) ((text {GCA}_000969265)), for instance, underwent the process of chromosomal fusion and possess only one (sim 4) Mb long circular chromosome, which shows a high degree of synteny with the two chromosomes of the more common strains36. The strain (text {TSY}216) ((text {GCA}001045415)), on the other hand, besides having the original two chromosomes, also contains an additional (sim 1) Mb long replicon, which does not share any conserved region with Chr1 and Chr237. For these reasons, we expect the single- and two-chromosome strains to be phylogenetically closer to each other than to the three-chromosome strain, which contains the extra replicon. The 16S rRNA gene-based clustering, however, does not identify any clear separation between the three types of strains (Fig. 4c, right). As a matter of fact, all the 16S rRNA gene copies of all the Vibrio cholerae strains included in our data set are located on (sim 3) Mb long chromosome, which shows high synteny across all strains. It is therefore not surprising that the comparison of the 16S rRNA genes does not capture the global genomic differences that exist between the considered strains. On the other hand, the results obtained with the RSA-based clustering show a clear distinction of the strains with different genomic structure (Fig. 4c, left). The reason for the success of the RSA-based method lies in the theoretical definition of RSA-based distance. In fact, the RSA-based distance depends on the Poisson Log-Normal location parameter (sigma ^2), which increases with the genome length (Supplementary Fig. S1): by definition, (sigma ^2 = sigma _e^2 / 2gamma), and, while the environmental noise (sigma _e^2) can be reasonably considered independent of the genome length, the density regulation (gamma) is expected to be stronger for smaller genomes, which repesent a scarcer environment with less resources.Buchnera aphidicola is a bacterial species in mutualistic endosymbiotic relationship with different aphids (members of superfamily Aphidoidea). As many endosymbionts, Buchnera aphidicola underwent the process of genome reduction as an adaptation to the host-associated lifestyle and has a genome with length ( More

Bauer, T., Bäte, D. A., Kempfer, F. & Schirmel, J. Differing impacts of two major plant invaders on urban plant-dwelling spiders (Araneae) during flowering season. Biol. Invasions 23(5), 1473–1485. https://doi.org/10.1007/s10530-020-02452-w (2021).Article 

Google Scholar 
Ustinova, E. N., Schepetov, D. M., Lysenkov, S. N. & Tiunov, A. V. Soil arthropod communities are not affected by invasive Solidago gigantea Aiton (Asteraceae), based on morphology and metabarcoding analyses. Soil Biol. Biochem. 159, 108288. https://doi.org/10.1016/j.soilbio.2021.108288 (2021).CAS 
Article 

Google Scholar 
Tanner, R. A. et al. Impacts of an Invasive Non-Native Annual Weed, Impatiens glandulifera, on Above- and Below-Ground Invertebrate Communities in the United Kingdom. PLoS ONE 8(6), e67271. https://doi.org/10.1371/journal.pone.0067271 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wei, Q. et al. The diversity of soil mesofauna decline after bamboo invasion in subtropical China. Sci. Total Environ. 789, 147982. https://doi.org/10.1016/j.scitotenv.2021.147982 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Szymura, M. & Szymura, T. H. Growth, phenology, and biomass allocation of alien Solidago species in central Europe. Plant Species Biol. 30(4), 245–256. https://doi.org/10.1111/1442-1984.12059 (2015).Article 

Google Scholar 
Bobuľská, L., Demková, L., Čerevková, A. & Renčo, M. Invasive goldenrod (Solidago gigantea) influences soil microbial activities in forest and grassland ecosystems in central Europe. Diversity 11(8), 134. https://doi.org/10.3390/d11080134 (2019).CAS 
Article 

Google Scholar 
Sterzyńska, M., Shrubovych, J. & Nicia, P. Impact of plant invasion (Solidago gigantea L.) on soil mesofauna in a riparian wet meadows. Pedobiologia 64, 1–7. https://doi.org/10.1016/j.pedobi.2017.07.004 (2017).Article 

Google Scholar 
Zubek, S. et al. Solidago canadensis invasion in abandoned arable fields induces minor changes in soil properties and does not affect the performance of subsequent crops. Land Degrad. Dev. 31(3), 1–12. https://doi.org/10.1002/ldr.3452 (2019).Article 

Google Scholar 
Čerevková, A., Miklisová, D., Bobul’ská, L. & Renčo, M. Impact of the invasive plant Solidago gigantea on soil nematodes in a semi-natural grassland and a temperate broadleaved mixed forest. J. Helminthol. 94, 1–14. https://doi.org/10.1017/S0022149X19000324 (2020).Article 

Google Scholar 
de Groot, M., Kleijn, D. & Jogan, N. Species groups occupying different trophic levels respond differently to the invasion of semi-natural vegetation by Solidago canadensis. Biol. Conserv. 136(4), 612–617. https://doi.org/10.1016/j.biocon.2007.01.005 (2007).Article 

Google Scholar 
Baranová, B., Manko, P. & Jászay, T. Differences in surface-dwelling beetles of grasslands invaded and non-invaded by goldenrods (Solidago canadensis, S. gigantea) with special reference to Carabidae. J. Insect. Conserv. 18(4), 623–635. https://doi.org/10.1007/s10841-014-9666-0 (2014).Article 

Google Scholar 
Lenda, M., Witek, M., Skórka, P., Moroń, D. & Woyciechowski, M. Invasive alien plants affect grassland ant communities, colony size and foraging behaviour. Biol. Invasions 15(11), 2403–2414. https://doi.org/10.1007/s10530-013-0461-8 (2013).Article 

Google Scholar 
Kajzer-Bonk, J., Szpiłyk, D. & Woyciechowski, M. Invasive goldenrods affect abundance and diversity of grassland ant communities (Hymenoptera: Formicidae). J. Insect Conserv. 20(1), 99–105. https://doi.org/10.1007/s10841-016-9843-4 (2016).Article 

Google Scholar 
Trigos-Peral, G. et al. Ant communities and Solidago plant invasion: Environmental properties and food sources. Entomol. Sci. 21(3), 270–278. https://doi.org/10.1111/ens.12304 (2018).Article 

Google Scholar 
Fenesi, A. et al. Solidago canadensis impacts on native plant and pollinator communities in different-aged old fields. Basic Appl. Ecol. 16(4), 335–346. https://doi.org/10.1016/j.baae.2015.03.003 (2015).Article 

Google Scholar 
Sheley, R. L., Mangold, J. M. & Anderson, J. L. Potential for successional theory to guide restoration of invasive-plant-dominated rangeland. Ecol. Monogr. 76(3), 365–379. https://doi.org/10.1890/0012-9615(2006)076[0365:PFSTTG]2.0.CO;2 (2006).Article 

Google Scholar 
Byun, C., de Blois, S. & Brisson, J. Management of invasive plants through ecological resistance. Biol. Invasions 20(1), 13–27. https://doi.org/10.1007/s10530-017-1529-7 (2018).Article 

Google Scholar 
Weidlich, E. W. A., Flórido, F. G., Sorrini, T. B. & Brancalion, P. H. S. Controlling invasive plant species in ecological restoration: A global review. J. Appl. Ecol. 57(9), 1806–1817. https://doi.org/10.1111/1365-2664.13656 (2020).Article 

Google Scholar 
Zaller, J. G. et al. Effects of glyphosate-based herbicides and their active ingredients on earthworms, water infiltration and glyphosate leaching are influenced by soil properties. Environ. Sci. Eur. 33(1), 1–16. https://doi.org/10.1186/s12302-021-00492-0 (2021).CAS 
Article 

Google Scholar 
Szymura, M., Świerszcz, S. & Szymura, T. H. Restoration of ecologically valuable grassland on sites degraded by invasive Solidago: Lessons from a six year experiment. Land Degrad. Dev. https://doi.org/10.1002/ldr.4278 (2022).Article 

Google Scholar 
Świerszcz, S., Szymura, M., Wolski, K. & Szymura, T. H. Comparison of methods for restoring meadows invaded by Solidago species. Pol. J. Environ. Stud. 26(3), 1251–1258. https://doi.org/10.15244/pjoes/67338 (2017).Article 

Google Scholar 
Nagy, D. U. et al. The more we do, the less we gain? Balancing effort and efficacy in managing the Solidago gigantea invasion. Weed Res. 60(3), 232–240. https://doi.org/10.1111/wre.12417 (2020).Article 

Google Scholar 
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511. https://doi.org/10.1038/nature13855 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Bardgett, R. D. & Wardle, D. A. Aboveground-Belowground Linkages: Biotic Interactions, Ecosystem Processes, and Global Change (Oxford University Press, Oxford, 2010).
Google Scholar 
Gruss, I. et al. Microarthropods and vegetation as biological indicators of soil quality studied in poor sandy sites at former military facilities. Land Degrad. Dev. 33(2), 358–367. https://doi.org/10.1002/ldr.4157 (2022).Article 

Google Scholar 
Sabais, A. C. W., Scheu, S. & Eisenhauer, N. Plant species richness drives the density and diversity of Collembola in temperate grassland. Acta Oecol. 37(3), 195–202. https://doi.org/10.1016/j.actao.2011.02.002 (2011).ADS 
Article 

Google Scholar 
Kardol, P. & Wardle, D. A. How understanding aboveground-belowground linkages can assist restoration ecology. Trends Ecol. Evol. 25(11), 670–679. https://doi.org/10.1016/j.tree.2010.09.001 (2010).Article 
PubMed 

Google Scholar 
Eviner, V. T. & Hawkes, C. V. Embracing variability in the application of plant-soil interactions to the restoration of communities and ecosystems. Restor. Ecol. 16(4), 713–729. https://doi.org/10.1111/j.1526-100X.2008.00482.x (2008).Article 

Google Scholar 
Zhao, J., Chen, J., Wu, H., Li, L. & Pan, F. Effects of mowing frequency on soil nematode diversity and community structure in a chinese meadow steppe. Sustainability 13, 5555. https://doi.org/10.3390/su13105555 (2021).Article 

Google Scholar 
Hyvönen, T. et al. Aboveground and belowground biodiversity responses to seed mixtures and mowing in a long-term set-aside experiment. Agric. Ecosyst. Environ. https://doi.org/10.1016/j.agee.2021.107656 (2021).Article 

Google Scholar 
Gilmullina, A., Rumpel, C., Blagodatskaya, E. & Chabbi, A. Management of grasslands by mowing versus grazing – impacts on soil organic matter quality and microbial functioning. Appl. Soil Ecol. https://doi.org/10.1016/j.apsoil.2020.103701 (2020).Article 

Google Scholar 
Kladivko, E. J. Tillage systems and soil ecology. Soil Tillage Res. 61(1–2), 61–76. https://doi.org/10.1016/S0167-1987(01)00179-9 (2001).Article 

Google Scholar 
Bispo, A. et al. Indicators for monitoring soil biodiversity. Integr. Environ. Assess. Manag. 5(4), 717–719 (2009).CAS 
Article 

Google Scholar 
Santorufo, L., van Gestel, C. A. M., Rocco, A. & Maisto, G. Soil invertebrates as bioindicators of urban soil quality. Environ. Pollut. 161, 57–63. https://doi.org/10.1016/j.envpol.2011.09.042 (2012).CAS 
Article 
PubMed 

Google Scholar 
Boyce R. L. Life Under Your Feet: Measuring soil invertebrate diversity. Teaching Issues and Experiments in Ecology, Ecological Society of America, 3: Experiment #1. https://tiee.esa.org/vol/v3/experiments/soil/downloads.html (2005).Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–656 (1948).MathSciNet 
Article 

Google Scholar 
Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144. https://doi.org/10.1016/0022-5193(66)90013-0 (1966).ADS 
Article 

Google Scholar 
Margalef, R. Information theory in ecology. Gen. Syst. 3, 36–71 (1958).
Google Scholar 
Jones, H. P. Impact of ecological restoration on ecosystem services. In Encyclopedia of Biodiversity (ed. Levin, S. A.) 199–208 (Academic Press, New York, 2013).Chapter 

Google Scholar 
Menta, C. Soil fauna diversity – function, soil degradation, biological indices, soil restoration. In Biodiversity Conservation and Utilization in a Diverse World (ed. Lameed, G. A.) (IntechOpen, London, 2012).
Google Scholar 
Hoffland, E., Kuyper, T. W., Comans, R. N. & Creamer, R. E. Eco-functionality of organic matter in soils. Plant Soil 455(1), 1–22. https://doi.org/10.1007/s11104-020-04651-9 (2020).CAS 
Article 

Google Scholar 
Huera-Lucero, T., Labrador-Moreno, J., Blanco-Salas, J. & Ruiz-Téllez, T. A framework to incorporate biological soil quality indicators into assessing the sustainability of territories in the Ecuadorian Amazon. Sustainability 12(7), 3007. https://doi.org/10.3390/su12073007 (2020).Article 

Google Scholar 
van Eekeren, N. et al. Microarthropod communities and their ecosystem services restore when permanent grassland with mowing or low-intensity grazing is installed. Agric. Ecosyst. Environ. 323, 107682. https://doi.org/10.1016/j.agee.2021.107682 (2022).Article 

Google Scholar 
Humbert, J. Y., Ghazoul, J., Sauter, G. J. & Walter, T. Impact of different meadow mowing techniques on field invertebrates. J. Appl. Entomol. 134(7), 592–599. https://doi.org/10.1111/j.1439-0418.2009.01503.x (2010).Article 

Google Scholar 
Steidle, J. L. M., Kimmich, T., Csader, M. & Betz, O. Negative impact of roadside mowing on arthropod fauna and its reduction with ‘arthropod-friendly’ mowing technique. J. Appl. Entomol. https://doi.org/10.1111/jen.12976 (2022).Article 

Google Scholar 
Briones, M. J. Soil fauna and soil functions: a jigsaw puzzle. Front. Environ. Sci. 2, 7. https://doi.org/10.3389/fenvs.2014.00007 (2014).Article 

Google Scholar 
Shao, C., Chen, J., Li, L. & Zhang, L. Ecosystem responses to mowing manipulations in an arid Inner Mongolia steppe: An energy perspective. J. Arid Environ. 82, 1–10. https://doi.org/10.1016/j.jaridenv.2012.02.019 (2012).ADS 
Article 

Google Scholar 
de Almeida, T., Forey, E. & Chauvat, M. Alien invasive plant effect on soil fauna is habitat dependent. Diversity 14(2), 61. https://doi.org/10.3390/d14020061 (2022).CAS 
Article 

Google Scholar 
Wissuwa, J., Salamon, J. A. & Frank, T. Effects of habitat age and plant species on predatory mites (Acari, Mesostigmata) in grassy arable fallows in Eastern Austria. Soil Biol. Biochem. 50, 96–107. https://doi.org/10.1016/j.soilbio.2012.02.025 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Petersen, H. Collembolan communities in shrublands along climatic gradients in Europe and the effects of experimental warming and drought on population density, biomass and diversity. Soil Org. 83(3), 463–488 (2011).
Google Scholar 
Eisenhauer, N. et al. Plant community impacts on the structure of earthworm communities depend on season and change with time. Soil Biol. Biochem. 41(12), 2430–2443. https://doi.org/10.1016/j.soilbio.2009.09.001 (2009).CAS 
Article 

Google Scholar 
Eisenhauer, N. et al. Plant diversity surpasses plant functional groups and plant productivity as driver of soil biota in the long term. PLoS ONE 6(1), 15–18. https://doi.org/10.1371/journal.pone.0016055 (2011).CAS 
Article 

Google Scholar 
Gao, D., Wang, X., Fu, S. & Zhao, J. Legume plants enhance the resistance of soil to ecosystem disturbance. Front. Plant Sci. 8, 1295. https://doi.org/10.3389/fpls.2017.01295 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Yang, G., Roy, J., Veresoglou, S. D. & Rillig, M. C. Soil biodiversity enhances the persistence of legumes under climate change. New Phytol. 229(5), 2945–2956. https://doi.org/10.1111/nph.17065 (2021).CAS 
Article 
PubMed 

Google Scholar 
Zhao, J., Zeng, Z., He, X., Chen, H. & Wang, K. Effects of monoculture and mixed culture of grass and legume forage species on soil microbial community structure under different levels of nitrogen fertilization. Eur. J. Soil Biol. 68, 61–68. https://doi.org/10.1016/j.ejsobi.2015.03.008 (2015).CAS 
Article 

Google Scholar 
Zhao, J., Wang, X., Wang, X. & Fu, S. Legume-soil interactions: legume addition enhances the complexity of the soil food web. Plant Soil 385(1), 273–286. https://doi.org/10.1007/s11104-014-2234-2 (2014).CAS 
Article 

Google Scholar 
Bonkowski, M., Villenave, C. & Griffiths, B. Rhizosphere fauna: the functional and structural diversity of intimate interactions of soil fauna with plant roots. Plant Soil 321, 213–233. https://doi.org/10.1007/s11104-009-0013-2 (2009).CAS 
Article 

Google Scholar 
Hector, A., Dobson, K., Minns, A., Bazeley-White, E. & Hartley Lawton, J. Community diversity and invasion resistance: an experimental test in a grassland ecosystem and a review of comparable studies. Ecol. Res. 16(5), 819–83. https://doi.org/10.1046/j.1440-1703.2001.00443.x (2001).Article 

Google Scholar 
Gastine, A., Scherer-Lorenzen, M. & Leadley, P. W. No consistent effects of plant diversity on root biomass, soil biota and soil abiotic conditions in temperate grassland communities. Appl. Ecol. 24, 101–111. https://doi.org/10.1016/S0929-1393(02)00137-3 (2003).Article 

Google Scholar 
Scherber, C. et al. Effects of plant diversity on invertebrate herbivory in experimental grassland. Oecologia 147(3), 489–500. https://doi.org/10.1007/s00442-005-0281-3 (2006).ADS 
Article 
PubMed 

Google Scholar 
Viketoft, M., Palmborg, C., Sohlenius, B., Huss-Danell, K. & Bengtsson, J. Plant species effects on soil nematode communities in experimental grasslands. Appl. Soil Ecol. 30(2), 90–103. https://doi.org/10.1016/j.apsoil.2005.02.007 (2005).Article 

Google Scholar 
Viketoft, M. et al. Long-term effects of plant diversity and composition on soil nematode communities in model grasslands. Ecology 90(1), 90–99. https://doi.org/10.1890/08-0382.1 (2009).Article 
PubMed 

Google Scholar  More

As the Italian probe LICIACube whizzed past asteroids Didymos (bottom) and Dimorphos (top), it captured a debris plume spraying out from the DART spacecraft as it smashed into Dimorphos.Credit: ASI/NASA

Astronomers see fireworks as spacecraft ploughs into asteroidTelescopes in space and across Earth captured the spectacular aftermath of NASA’s Double Asteroid Redirection Test (DART) spacecraft crashing into the asteroid Dimorphos on 26 September.The goal was to knock the harmless space rock into a slightly different orbit to test whether humanity could do such a thing if a dangerous asteroid were ever detected heading for Earth. The smash-up was “the first human experiment to deflect a celestial body”, says Thomas Zurbuchen, NASA’s associate administrator for science, and “an enormous success”.A ringside view came from LICIACube, a tiny Italian spacecraft that flew along with DART and photographed the impact, which took place 11 million kilometres from Earth. LICIACube’s first images, released by the Italian Space Agency on 27 September, show a large fireworks-like plume of rocks and other debris coming off Dimorphos (pictured, top) after DART had ploughed into it.It will take days to weeks before mission scientists can confirm whether the test worked, and did in fact cut the time it takes Dimorphos to orbit its partner asteroid, Didymos (pictured, bottom), by 10–15 minutes.

The shell of the endangered hawksbill sea turtle (pictured) is prized for trinkets and jewellery.Credit: Reinhard Dirscherl/SPL

Sea turtles swim more freely as poaching declinesPoaching is less of a threat to the survival of sea turtles than it once was, an analysis suggests (J. F. Senko et al. Glob. Change Biol. https://doi.org/gqrzzn; 2022). Illegal sea-turtle catch has dropped sharply since 2000, and most current exploitation occurs in areas with relatively healthy turtle populations.The analysis is the first worldwide estimate of the number of adult sea turtles that are moved on the black market. The authors surveyed sea-turtle specialists and sifted through documents to derive an estimate that around 1.1 million sea turtles were illegally harvested between 1990 and 2020. Nearly 90% of them were funnelled into China and Japan. Of the species that could be identified, the critically endangered hawksbill turtle (Eretmochelys imbricata; pictured), prized for its beautiful shell, was among the most frequently exploited.But the team also found that the illegal catch from 2010 to 2020 was nearly 30% lower than in the previous decade. “The silver lining is that, despite the seemingly large illegal take, exploitation is not having a negative impact on sea-turtle populations on a global scale,” says co-author Jesse Senko, a marine-conservation scientist at Arizona State University in Tempe. More

Wilson, E. O. Sociobiology: The New Synthesis (Harvard University Press, 1975).
Google Scholar 
Székely, T., Remeš, V., Freckleton, R. P. & Liker, A. Why care? Inferring the evolution of complex social behaviour. J. Evol. Biol. 26, 1381–1391 (2013).PubMed 
Article 

Google Scholar 
Royle, N. J., Smiseth, P. T. & Kölliker, M. The Evolution of Parental Care (Oxford University Press, 2012).Book 

Google Scholar 
Clutton-Brock, T. H. The Evolution of Parental Care (Princeton University Press, 1991).Book 

Google Scholar 
Székely, T., Webb, J. N., Houston, A. I. & McNamara, J. M. An evolutionary approach to offspring desertion in birds. In Current Ornithology Vol. 13 (eds Nolan, V. & Ketterson, E. D.) (Springer, 1996).
Google Scholar 
McGraw, L., Székely, T. & Young, L. J. Pair bonds and parental behaviour. In Social Behaviour: Genes, Ecology and Evolution (eds Székely, T. et al.) (Cambridge University Press, 2010).
Google Scholar 
Smiseth, P. T., Kölliker, M. & Royle, N. J. What is parental care? In The Evolution of Parental Care (eds Royle, N. J. et al.) 1–17 (Oxford Univ. Press, 2012).
Google Scholar 
Mank, J. E., Promislow, D. E. L. & Avise, J. C. Phylogenetic perspectives in the evolution of parental care in ray-finned fishes. Evolution 59, 1570–1578 (2005).PubMed 
Article 

Google Scholar 
Benun Sutton, F. & Wilson, A. B. Where are all the moms? External fertilization predicts the rise of male parental care in bony fishes. Evolution 73, 2451–2460 (2019).PubMed 
Article 

Google Scholar 
Furness, A. I. & Capellini, I. The evolution of parental care diversity in amphibians. Nat. Commun. 10, 4709 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vági, B., Végvári, Z., Liker, A., Freckleton, R. P. & Székely, T. Terrestriality and the evolution of parental care in frogs. Proc. R. Soc. Lond. B. 286, 20182737 (2019).
Google Scholar 
Vági, B., Végvári, Z., Liker, A., Freckleton, R. P. & Székely, T. Climate and mating systems as drivers of global diversity of parental care in frogs. Glob. Ecol. Biogeogr. 29, 1373–1386 (2020).Article 

Google Scholar 
Gilbert, J. D. J. & Manica, A. Parental care trade-offs and life-history relationships in insects. Am. Nat. 176, 212–226 (2010).PubMed 
Article 

Google Scholar 
Gilbert, J. D. & Manica, A. The evolution of parental care in insects: A test of current hypotheses. Evolution 69, 1255–1270 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Reynolds, J. D., Goodwin, N. B. & Freckleton, R. P. Evolutionary transitions in parental care and live bearing in vertebrates. Philos. Trans. R. Soc. Lond. B. 357, 269–281 (2002).Article 

Google Scholar 
AlRashidi, M., Kosztolányi, A., Shobrak, M., Küpper, C. & Székely, T. Parental cooperation in an extreme hot environment: Natural behaviour and experimental evidence. Anim. Behav. 82, 235–243 (2011).Article 

Google Scholar 
Vincze, O. et al. Parental cooperation in a changing climate: Fluctuating environments predict shifts in care division. Glob. Ecol. Biogeogr. 26, 347–385 (2017).Article 

Google Scholar 
Martin, K. L. & Carter, A. L. Brave new propagules: Terrestrial embryos in anamniotic eggs. Integr. Comp. Biol. 53, 233–247 (2013).CAS 
PubMed 
Article 

Google Scholar 
Ishimatsu, A., Mai, H. V. & Martin, K. L. Patterns of fish reproduction at the interface between air and water. Integr. Comp. Biol. 58, 1064–1085 (2018).CAS 
PubMed 

Google Scholar 
Bickford, D. P. Differential parental care behaviors of arboreal and terrestrial microhylid frogs from Papua New Guinea. Behav. Ecol. Sociobiol. 55, 402–409 (2004).Article 

Google Scholar 
Poo, S. & Bickford, D. P. The adaptive significance of egg attendance in a South-East Asian tree frog. Ethology 119, 1–9 (2013).Article 

Google Scholar 
Gomez-Mestre, I., Pyron, R. A. & Wiens, J. J. Phylogenetic analyses reveal unexpected patterns in the evolution of reproductive modes in frogs. Evolution 66, 3687–3700 (2012).PubMed 
Article 

Google Scholar 
Wells, K. D. The Ecology and Behaviour of Amphibians (University of Chicago Press, 2007).Salthe, S. N. Reproductive modes and the number and sizes of ova in Urodeles. Am. Midl. Nat. 81, 467–490 (1969).Article 

Google Scholar 
Nussbaum, R. A. The Evolution of Parental Care in Salamanders. (University of Michigan Press, 1985).Nussbaum, R. A. Parental care and egg size in salamanders: An examination of the safe harbor hypothesis. Res. Popul. Ecol. 29, 27–44 (1987).Article 

Google Scholar 
Furness, A. I., Venditti, C. & Capellini, I. Terrestrial reproduction and parental care drive rapid evolution in the trade-off between offspring size and numbers across amphibians. PLoS Biol. 20, e3001495 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Beck, C. W. Mode of fertilization and parental care in anurans. Anim. Behav. 55, 439–449 (1998).CAS 
PubMed 
Article 

Google Scholar 
Kahn, A. T., Schwanz, L. E. & Kokko, H. Paternity protection can provide a kick-start for the evolution of male-only parental care. Evolution 67, 2207–2217 (2013).PubMed 
Article 

Google Scholar 
Summers, K., McKeon, C. S. & Heying, H. The evolution of parental care and egg size: A comparative analysis in frogs. Proc. R. Soc. B 273, 687–692 (2006).PubMed 
Article 

Google Scholar 
Lack, D. L. Ecological Adaptations for Breeding in Birds (Methuen, 1968).Suski, C. D. & Ridgway, M. S. Climate and body size influence nest survival in a fish with parental care. J. Anim. Ecol. 76, 730–739 (2007).CAS 
PubMed 
Article 

Google Scholar 
Oneto, F., Ottonello, D., Pastorino, M. V. & Salvidio, S. Posthatching parental care in salamanders revealed by infrared video surveillance. J. Herpetol. 44, 649–653 (2010).Article 

Google Scholar 
Reinhard, S., Voitel, S. & Kupfer, A. External fertilisation and paternal care in the paedomorphic salamander Siren intermedia Barnes, 1826. Zool. Anz. 253, 1–5 (2013).Article 

Google Scholar 
Amphibiaweb. University of California. https://amphibiaweb.org (2021).Vial, J. L. The ecology of the tropical salamander, Bolitoglossa pesrubra Costa Rica. Rev. Biol. Trop. 15, 13–115 (1968).
Google Scholar 
Han, X. & Fu, J. Does life history shape sexual size dimorphism in anurans? A comparative analysis. BMC Evol. Biol. 13, 27 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Prado, C. P. A. & Haddad, C. F. B. Size-fecundity relationships and reproductive investment in female frogs in the Pantanal, South-Western Brasil. Herpetol. J. 15, 181–189 (2005).
Google Scholar 
Kupfer, A., Maxwell, E., Reinhard, S. & Kuehnel, S. The evolution of parental investment in caecilian amphibians: A comparative approach. Biol. J. Linn. Soc. 119, 4–14 (2016).Article 

Google Scholar 
Smith, R. J. Statistics of sexual size dimorphism. J. Hum. Evol. 36, 423–458 (1999).CAS 
PubMed 
Article 

Google Scholar 
Fairbairn, D. J. Introduction: The enigma of sexual size dimorphism. In Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (eds Fairbairn, D. J. et al.) (Oxford University Press, 2007).Chapter 

Google Scholar 
Lhotka, O., Kyselý, J. & Farda, A. Climate change scenarios of heat waves in Central Europe and their uncertainties. Theor. Appl. Climatol. 131, 1043–1054 (2018).ADS 
Article 

Google Scholar 
Lion, M. B. et al. Global patterns of terrestriality in amphibian reproduction. Glob. Ecol. Biogeogr. 28, 744–756 (2019).Article 

Google Scholar 
Bivand, R. & Lewin-Koh, N. maptools: Tools for Handling Spatial Objects. R Package Version 0.9-9. https://CRAN.R-project.org/package=maptools (2019).Hijmans, R. J. raster: Geographic Data Analysis and Modelling. R Package Version 3.0-7. R package. https://CRAN.R-project.org/package=raster (2015).Bivand, R. et al. Package ‘rgdal’. Bindings for the Geospatial Data Abstraction Library. https://cran.r-project.org/web/packages/rgdal/index.html (2017).IUCN. The IUCN Red List of threatened species. https://www.iucnredlist.org (2021).WorldClim. Maps, Graphs, Tables and Data of the Global Climate. https://www.worldclim.org (2021).Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).PubMed 
Article 

Google Scholar 
Boyko, J. D. & Beaulieu, J. M. Generalized hidden Markov models for phylogenetic comparative datasets. Methods Ecol. Evol. 12, 468–478 (2021).Article 

Google Scholar 
Ho, L. S. T. et al. Package ‘Phylolm’. https://cran.r-project.org/web/packages/phylolm (2018).Jetz, W. et al. VertLife. https://vertlife.org (2021).R-Core-Team R. Version 4.0.4. A Language and Environment for Statistical Computing. http://www.r-project.org/ (2021).Gross, M. R. & Shine, R. Parental care and mode of fertilization in ectothermic vertebrates. Evolution 35, 775–793 (1981).PubMed 
Article 

Google Scholar 
Ridley, M. & Rechten, C. Female sticklebacks prefer to spawn with males whose nests contain eggs. Behaviour 76, 152–161 (1981).Article 

Google Scholar 
Jackson, M. E., Scott, D. E. & Estes, R. A. Determinants of nest success in the marbled salamander (Ambystoma opacum). Can. J. Zool. 67, 2277–2281 (1989).Article 

Google Scholar 
Petranka, J. W. Observations on nest site selection, nest desertion and embryonic survival in marbled salamanders. J. Herpetol. 24, 229–234 (1990).Article 

Google Scholar 
Croshaw, A. & Scott, D. E. Experimental evidence that nest attendance benefits female marbled salamanders (Ambystoma opacum) by reducing egg mortality. Am. Midl. Nat. 154, 398–411 (2005).Article 

Google Scholar 
Knapp, R. A. & Sargent, R. C. Egg mimicry as a mating strategy in the fantail darter, Ethiostoma flabellare: Females prefer males with eggs. Behav. Ecol. Sociobiol. 25, 321–326 (1989).Article 

Google Scholar 
Okada, S., Fukuda, Y. & Takahashi, M. K. Paternal care behaviors of Japanese giant salamander Andrias japonicus in natural populations. J. Ethol. 33, 1–7 (2015).Article 

Google Scholar 
Browne, R. K. et al. The giant salamanders (Cryptobranchidae): Part B. Biogeography, ecology and reproduction. Amphib. Reptile Conserv. 5, 30–50 (2014).
Google Scholar 
Taborsky, M. Sperm competition in fish: ‘Bourgeois’ males and parasitic spawning. Trends Ecol. Evol. 13, 222–227 (1998).CAS 
PubMed 
Article 

Google Scholar 
Vieites, D. R. et al. Post-mating clutch-piracy in an amphibian. Nature 431, 305–308 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Balshine, S. & Abate, M. E. Parental care in cichlid fishes. In The Behavior, Ecology and Evolution of Cichlid Fishes (eds Abate, M. E. & Noakes, D. L. G.) (Springer, 2021).
Google Scholar 
Ota, K., Kohda, M. & Sato, T. Unusual allometry of sexual size dimorphism in a cichlid where males are extremely larger than females. J. Biosci. 35, 257–265 (2010).PubMed 
Article 

Google Scholar 
Mokos, J., Scheuring, I., Liker, A., Freckleton, R. P. & Székely, T. Degree of anisogamy is unrelated to the intensity of sexual selection. Sci. Rep. 11, 19424 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bourne, G. R. Amphisexual parental behaviour of a terrestrial breeding frog Eleutherodactylus johnstonei in Guyana. Behav. Ecol. 9, 1–7 (1998).Article 

Google Scholar 
Beal, C. A. & Tallamy, D. W. A new record of amphisexual care in an insect with extensive parental care: Rhynocoris tristis (Heteroptera: Reduviidae). J. Ethol. 24, 305–307 (2006).Article 

Google Scholar 
Ringler, E. et al. Flexible compensation of uniparental care: Female poison frogs take over when males disappear. Behav. Ecol. 26, 1219–1225 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Tumulty, J., Morales, V. & Summers, K. The biparental care hypothesis for the evolution of monogamy: Experimental evidence in an amphibian. Behav. Ecol. 25, 262–270 (2014).Article 

Google Scholar 
Remeš, V., Freckleton, R. P., Tökölyi, J., Liker, A. & Székely, T. The evolution of parental cooperation in birds. Proc. Natl. Acad. Sci. USA 112, 12603–13608 (2015).Article 

Google Scholar 
Guex, G.-D. & Chen, P. S. Epitheliophagy: Intrauterine cell nourishment in the viviparous alpine salamander, Salamandra atra (Laur.). Experientia 42, 1205–1218 (1986).CAS 
PubMed 
Article 

Google Scholar 
Goycoechea, O., Garrido, O. & Jorquera, B. Evidence for a trophic paternal-larval relationship in the frog Rhinoderma darwinii. J. Herpetol. 20, 168–178 (1986).Article 

Google Scholar 
Hansen, R. W. About our cover: Ecnomyohyla rabborum. Herpetol. Rev. 42, 3 (2012).
Google Scholar 
Brown, J. L., Morales, V. & Summers, K. A key ecological trait drove the evolution of biparental care and monogamy in an amphibian. Am. Nat. 175, 436–446 (2010).PubMed 
Article 

Google Scholar 
Dugas, M. B., Moore, M. P., Martin, R. A., Richards-Zawacki, C. L. & Sprehn, Z. G. The pay-offs of maternal care increase as offspring develop, favouring extended provisioning in an egg-feeding frog. J. Evol. Biol. 29, 1977–1985 (2016).CAS 
PubMed 
Article 

Google Scholar 
Kupfer, A. et al. Parental investment by skin feeding in a caecilian amphibian. Nature 440, 926–929 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Shine, R. Propagule size and parental care: The “safe harbour” hypothesis. J. Theor. Biol. 75, 417–424 (1978).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Székely, T., Webb, J. N. & Cuthill, I. C. Mating patterns, sexual selection and parental care: An integrative approach. In Vertebrate Mating Systems (eds Apollonio, M. et al.) (World Scientific Press, 2000).
Google Scholar 
Ah-King, M., Kvarnemo, C. & Tullberg, B. S. The influence of territoriality and mating system on the evolution of parental care: A phylogenetic study on fish. J. Evol. Biol. 18, 371–382 (2005).CAS 
PubMed 
Article 

Google Scholar 
Zamudio, K. R., Bell, R. C., Nali, R. C., Haddad, C. F. B. & Prado, C. P. A. Polyandry, predation and the evolution of frog reproductive modes. Am. Nat. 188, S41–S61 (2016).PubMed 
Article 

Google Scholar 
Graham, S. P., Kline, R., Steen, D. A. & Kelehear, C. Description of an extant salamander from the Gulf Coastal Plain of North America: The Reticulated Siren, Siren reticulata. PLoS ONE 13, e0207460 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yan, F. et al. The Chinese giant salamander exemplifies the hidden extinction of cryptic species. Curr. Biol. 28, R590–R592 (2018).CAS 
PubMed 
Article 

Google Scholar 
Jaramillo, A. F. et al. Vastly underestimated species richness of Amazonian salamanders (Plethodontidae: Bolitoglossa) and implications about plethodontid diversification. Mol. Phylogenet. Evol. 149, 106841 (2020).PubMed 
Article 

Google Scholar 
Parra-Olea, G. et al. Biology of tiny animals: Three new species of minute salamanders (Plethodontidae: Thorius) from Oaxaca, Mexico. PeerJ 4, e2694 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Balázs, G., Lewarne, B. & Herczeg, G. Extreme site fidelity of the olm (Proteus anguinus) revealed by a long-term capture-mark-recapture study. J. Zool. 311, 99–105 (2020).Article 

Google Scholar  More

Calculation of flight-step headings and movementTerms defining flight-step movement, precision and geophysical orientation cues are listed in Table 1. Since seasonal migration nearly ubiquitously proceeds from higher to lower latitudes, it is convenient to define headings clockwise from geographic South (counter-clockwise from geographic North for migration commencing in the Southern Hemisphere). Assuming a spherical Earth, a sequence of N migratory flight-steps with corresponding headings, αi, i = 0,…, N−1, the latitudes, ∅i+1, and longitudes, λi+1, on completion of each flight-step can be calculated using the Haversine Equation76, which we approximated by stepwise planar movement using Eqs. (1) and (2). For improved computational accuracy and to accommodate within flight-step effects, we updated simulated headings and corresponding locations hourly. A migrant’s flight-step distance ({R}_{{{mathrm {step}}}}=3.6{V}_{{mathrm {a}}}{cdot n}_{{mathrm {H}}}/{R}_{{{mathrm {Earth}}}}) (in radians), depends on its flight speed, Va (m/s) relative to the mean Earth radius REarth (km), and flight-step hours, nH. With a geomagnetic in-flight compass, expected hourly geographic headings are modulated by changes in magnetic declination, i.e., the clockwise difference between geographic and geomagnetic South10,32.Formulation of compass coursesFor simplicity, we consider the case of a single inherited or imprinted heading. This can be extended to include sequences of preferred headings. Expected geographic loxodrome headings remain unchanged en route, i.e.,$${bar{{{{{{rm{alpha }}}}}}}}_{i}={bar{{{{{{rm{alpha }}}}}}}}_{0}$$
(5)
Relative to geographic axes, expected geomagnetic loxodrome headings remain unchanged relative to proximate geomagnetic South, i.e., are offset by geomagnetic declination on departure (updated hourly in simulations)$${bar{{{{{{rm{alpha }}}}}}}}_{i}={bar{{{{{{rm{alpha }}}}}}}}_{0}+{delta }_{{mathrm {m}},i}$$
(6)
As described and illustrated in detail by Kiepenheuer13, the magnetoclinic compass was hypothesized to explain the prevalence of “curved” migratory bird routes, i.e., for which local geographic headings shift gradually but substantially en route. A migrant with a magnetoclinic compass adjusts its heading at each flight-step to maintain a constant transverse component, γ′, of the experienced inclination angle, γ, so that error-free headings are (see Fig. S5 in ref. 34)$${{bar{{{{{{rm{alpha }}}}}}}}_{i}={{sin }}}^{-1}left(frac{{{tan }}{gamma }_{i}}{{{tan }}{gamma }^{{prime} }}right){={{sin }}}^{-1}left(frac{{{tan }}{gamma }_{i}{{sin }}{bar{{{{{{rm{alpha }}}}}}}}_{0}}{{{tan }}{gamma }_{0}}right).$$
(7)
In a geomagnetic dipole field, the horizontal (Bh) and vertical (Bz) field, and therefore also inclination, each depends solely on geomagnetic latitude, ∅m:(gamma ={{{tan }}}^{-1}left({B}_{{mathrm {z}}}/{B}_{{mathrm {h}}}right)={{{tan }}}^{-1}left(2{{sin }}{phi }_{{mathrm {m}}}/{{cos }}{phi }_{{mathrm {m}}}right)={{{tan }}}^{-1}left(2{{tan }}{phi }_{{mathrm {m}}}right).) The projected transverse component, therefore, becomes$${gamma }^{{prime} }={{{tan }}}^{-1}left(frac{{{tan }}{gamma }_{0}}{{{sin }}{bar{{{alpha }}}}_{0}}right)={{{tan }}}^{-1}left(frac{2{{tan }}{{{phi }}}_{{mathrm {m}},0}}{{{sin }}{bar{{{{{{rm{alpha }}}}}}}}_{0}}right),$$which can be substituted into Eq. (7) to produce a closed formula for magnetoclinic headings in a dipole as a function of geomagnetic latitude$${bar{{{{{{rm{alpha }}}}}}}}_{i}left({{{phi }}}_{{mathrm {m}},i}right)={{{sin }}}^{-1}left(frac{{{sin }}{bar{{{{{{rm{alpha }}}}}}}}_{0}}{{{tan }}{{{phi }}}_{{mathrm {m}},0}}cdot {{tan }}{{{phi }}}_{{mathrm {m}},i}right),$$
(8)
with the expected initial heading, ({bar{{{{{{rm{alpha }}}}}}}}_{0}), and initial geomagnetic latitude, ∅m,0, being constants. Equations (7) and (8) have no solution when inclination increases en route, which could occur following substantial orientation error or in strongly non-dipolar fields. We followed previous studies in allowing magnetoclinic migrants to head towards magnetic East or West until inclination decreased sufficiently33,34,46, but also included orientation error based on the modelled compass precision.To assess sun-compass sensitivity algebraically, and also to improve computational efficiency, we used a closed-form equation for sunset azimuth, θs (derived in Supplementary Note 3 and see ref. 23),$${theta }_{{mathrm {s}}}={{{cos }}}^{-1}left(frac{-{{sin }}{delta }_{{mathrm {s}}}}{{{cos }}{{phi }}}right),$$
(9)
where δs is the solar declination, which varies between −23.4° and 23.4° with season and latitude23. Sunset azimuth is the positive and sunrise azimuth is the negative solution to Eq. (9) (relative to geographic N–S).Fixed sun-compass headings represent a uniform (clockwise) offset, ({bar{{{{{{rm{alpha }}}}}}}}_{{mathrm {s}}}) to sunrise or sunset azimuth, θs,i (calculated using Eq. (9))$${{bar{{{{{{rm{alpha }}}}}}}}_{i}={bar{{{{{{rm{alpha }}}}}}}}_{{mathrm {s}}}+theta }_{{mathrm {s}},i}$$
(10)
where the preferred heading on commencement of migration, ({bar{{{{{{rm{alpha }}}}}}}}_{{mathrm {s}}}={bar{{{{{{rm{alpha }}}}}}}}_{0}-{theta }_{{mathrm {s}},0}), is presumed to be imprinted using an inherited geographic or geomagnetic heading2,10,30.With a TCSC, preferred headings relative to sun azimuth are adjusted according to the time of day. In the context of sun-compass use during migration, Alerstam and Pettersson22 related the hourly “clock-shift” induced by crossing bands of longitude (∆h = 12 ∆λ/π), to a migrant’s time-compensated adjustment given the rate of change (i.e., angular speed) of sun azimuth close to sunset$$frac{partial {theta }_{{mathrm {s}}}}{partial h}cong frac{2pi {{sin }}{{phi }}}{24},$$
(11)
resulting in a “time-compensated” offset in heading on departure ((varDelta bar{{{{{{rm{alpha }}}}}}}cong varDelta {{{{{rm{lambda }}}}}},sin phi), which Eq.(4)). Equation (4) results in near-great-circle trajectories for small ranges in latitude, ∅, until inner clocks are reset. The feasibility of TCSC courses over longer distances (latitude ranges) relies on two critical but little-explored assumptions: (1) time-compensated orientation adjustments are presumed to follow the angular speed of sun azimuth (Eq. (11)) retained from the most recent clock-reset site, and (2) to negotiate unpredictable migratory schedules, migrants are presumed to retain their preferred geographic heading on arrival at extended stopovers22.Regarding the first assumption, time-compensated adjustments could also be influenced by proximate speeds of sun azimuth even when inner clocks are not fully reset. We, therefore, use distinct indices to keep track of “reference” flight-steps for clock-resets (cref,i) and time-compensated adjustments (sref,i). TCSC flight-step headings can then be written as$${bar{{{{{{rm{alpha }}}}}}}}_{i}=left{begin{array}{cc}{bar{{{{{{rm{alpha }}}}}}}}_{{c}_{{{mathrm {ref}}},i}}+left({theta }_{{mathrm {s}},i}-{theta }_{{mathrm {s}},{c}_{{{mathrm {ref}}},i}}right)+left({{{{{{rm{lambda }}}}}}}_{i}-{{{{{{rm{lambda }}}}}}}_{{c}_{{{mathrm {ref}}},i}}right){{sin }}{phi }_{{s}_{{{mathrm {ref}}},i}}, & {i,ne, c}_{{{mathrm {ref}}},i} ; (12a)\ {{{{{{rm{alpha }}}}}}}_{i-1}, & {i=c}_{{{mathrm {ref}}},i} ; (12b)end{array}right.,$$where θs,i represents the sunset azimuth on departures, cref,i specifies the most recent clock-reset site (during which geographic headings are also retained, i.e., ({bar{{{{{{rm{alpha }}}}}}}}_{i}={{{{{{rm{alpha }}}}}}}_{i-1})), and sref,i specifies the site defining the migrant’s temporal (hourly) rate of “time-compensated” adjustments (Eq. (11)). For TCSC courses as conceived by Alerstam and Pettersson22, reference rates of adjustment to sun azimuth are reset in tandem during stopovers, i.e., ({s}_{{{mathrm {ref}}},i}={c}_{{{mathrm {ref}}},i}), but we also considered a proximately gauged TCSC, where migrants gauge their adjustments to currently experienced speed of sun azimuth, i.e., ({s}_{{{mathrm {ref}}},i}=i).Regarding the second assumption, retaining geographic headings on arrival at stopovers is not consistent with ignoring geographic headings between consecutive nightly flight-steps, and may be difficult to achieve while landing. We, therefore, examined a more parsimonious alternative (Fig. 7d, Supplementary Fig. 3) where migrants retain their (usual) TCSC heading from the first night of stopovers, i.e., as if they would have departed on the first night. This alternative also simplifies Eq. (12) to$${bar{{{{{{rm{alpha }}}}}}}}_{i}={bar{{{{{{rm{alpha }}}}}}}}_{{c}_{{{mathrm {ref}}},i}}+left({theta }_{{mathrm {s}},({t}_{i-1}+1)}-{theta }_{{mathrm {s}},{t}_{i-1}}right)+left({{{{{{rm{lambda }}}}}}}_{i}-{{{{{{rm{lambda }}}}}}}_{{c}_{{{mathrm {ref}}},i}}right){{sin }}{{{phi }}}_{{s}_{{{mathrm {ref}}},i}}$$
(12c)
where the index ti−1 here represents the departure date from the previous flight.Sensitivity of compass-course headingsSensitivity was assessed by the marginal change in expected heading from previous (imprecise) headings, (partial {bar{alpha }}_{i}/partial {alpha }_{i-1}). When this is positive, small errors in headings will perpetuate, and therefore expected errors in migratory trajectories will grow iteratively. Conversely, negative sensitivity implies self-correction between successive flight-steps. Geographic and geomagnetic loxodromes are per definition constant relative to their respective axes so have “zero” sensitivity, as long as cue-detection errors are stochastically independent.For magnetoclinic compass courses in a dipole field, sensitivity can be calculated by differentiating Eq. (8) with respect to previous headings:$$frac{{mathrm {d}}{bar{{{{{{rm{alpha }}}}}}}}_{i}}{{mathrm {d}}{{{{{{rm{alpha }}}}}}}_{i-1}}=frac{{sin bar{{{{{{rm{alpha }}}}}}}}_{0}}{tan {phi }_{{mathrm {m}},0}}cdot frac{1}{cos {bar{alpha }}_{i}{cos }^{2}{phi }_{{mathrm {m}},i}}frac{partial {phi }_{{mathrm {m}},i}}{partial {alpha }_{i-1}}=frac{{R}_{{mathrm {step}}},sin {alpha }_{i-1}{sin bar{{{{{{rm{alpha }}}}}}}}_{0}}{cos {bar{alpha }}_{i}{cos }^{2}{phi }_{{mathrm {m}},i},tan {phi }_{{mathrm {m}},0}}$$
(13)
All three terms in the denominator indicate, as illustrated in Fig. 3b, that magnetoclinic courses become unstably sensitive at both high and low latitudes, and any heading with a significantly East–West component.Sensitivity of fixed sun compass headings is non-zero due to sun azimuth dependence on location (Eq. (9)):$$frac{{mathrm {d}}{bar{{{{{{rm{alpha }}}}}}}}_{i}}{{mathrm {d}}{{{{{{rm{alpha }}}}}}}_{i-1}} = , frac{sin {delta }_{{mathrm {s}},i}}{sin {theta }_{{mathrm {s}},i}}cdot frac{sin {phi }_{i}}{{cos }^{2}{phi }_{i}}frac{partial {phi }_{i}}{partial {alpha }_{i-1}}=frac{sin {delta }_{{mathrm {s}},i}}{sin {theta }_{{mathrm {s}},i}}cdot frac{{R}_{{mathrm {step}}},sin {phi }_{i},sin {alpha }_{i-1}}{{cos }^{2}{phi }_{i}}\ = , {R}_{{mathrm {step}}}cdot ,sin {alpha }_{i-1}frac{tan {phi }_{i}}{tan {theta }_{{mathrm {s}},i}}$$
(14)
The sine factor on the right-hand side in Eq. (14) causes the sign of (partial {bar{alpha }}_{i}/partial {alpha }_{i-1}) to be opposite for East to West or West to East headings, and tan θs also change sign at the fall equinox (due to solar declination changing sign). The azimuth term in the denominator indicates heightened sensitivity closer to the summer or winter equinox and at high latitudes, and, conversely, heightened robustness to errors closer to the spring or autumnal equinox (since ({{tan }}{theta }_{{mathrm {s}},0}to pm infty)). This seasonal and directional asymmetry is illustrated in Fig. 3c, e.TCSC courses (Eq. (12)) involve up to three sensitivity terms, due to dependencies on sun azimuth, longitude and latitude:$$ frac{{mathrm {d}}{bar{{{{{{rm{alpha }}}}}}}}_{i}}{{mathrm {d}}{{{{{{rm{alpha }}}}}}}_{i-1}} = , {R}_{{{mathrm {step}}}}cdot {{sin }}{alpha }_{i-1}frac{{{tan }}{phi }_{i}}{{{tan }}{theta }_{{mathrm {s}},i}}+frac{{mathrm {d}}{lambda }_{i}}{{mathrm {d}}{{{{{{rm{alpha }}}}}}}_{i-1}}{{sin }}{{{phi }}}_{{c}_{{{mathrm {ref}}}},i}+left({{{{{{rm{lambda }}}}}}}_{i}-{{{{{{rm{lambda }}}}}}}_{{c}_{{{mathrm {ref}}}},i}right)frac{{mathrm {d}}{{sin }}{phi }_{{s}_{{{mathrm {ref}}}},i}}{{mathrm {d}}{{{{{{rm{alpha }}}}}}}_{i-1}}\ =, left{begin{array}{cc}{R}_{{{mathrm {step}}}}cdot left[{{sin }}{alpha }_{i-1}frac{{{tan }}{phi }_{i}}{{{tan }}{theta }_{{mathrm {s}},i}}-frac{{{cos }}{{{{{{rm{alpha }}}}}}}_{i-1}{{sin }}{phi }_{{s}_{{{mathrm {ref}}}},i}}{{{cos }}{phi }_{i-1}}right],hfill & {{{{{rm{classic}}}}}} ; (15{{{{{rm{a}}}}}})\ {R}_{{{mathrm {step}}}}left[{{sin }}{alpha }_{i-1}frac{{{tan }}{phi }_{i}}{{{tan }}{theta }_{{mathrm {s}},i}}-frac{{{cos }}{{{{{{rm{alpha }}}}}}}_{i-1}{{sin }}{phi }_{{s}_{{{mathrm {ref}}}},i}}{{{cos }}{phi }_{i-1}}+left({{{{{{rm{lambda }}}}}}}_{i}-{{{{{{rm{lambda }}}}}}}_{{c}_{{{mathrm {ref}}}},i}right){{sin }}{alpha }_{i-1}{{cos }}{phi }_{i}right], & {{{{{rm{proximate}}}}}} ; left(15{{{{{rm{b}}}}}}right).end{array}right.$$The first square-bracketed terms in Eqs. (15a, b) are identical to the fixed sun compass (Eq. (14)), reflecting seasonal and latitudinal dependence in sun-azimuth. For headings with a Southward component (α0  1) and nonexistent for North–South headings (G = 1, reflecting no longitude bands being crossed). We expected this factor to affect compass courses differentially according to their error-accumulating or self-correcting nature.We further modified the effective goal-area breadth to account for a (geographically) circular goal area on the sphere, i.e., effectively modulating the longitudinal component of the goal-area breadth at the arrival latitude, ∅A:$${beta }_{{mathrm {A}}}=beta sqrt{{{{{sin }}}^{2}bar{alpha }+left({{cos }}bar{alpha }/{{cos }}{{{phi }}}_{{mathrm {A}}}right)}^{2}}.$$
(19)
To account for differential sensitivity among compass-courses, we generalized the normal many-wrongs relation between performance and number of steps, (1/{hat{N}}^{eta }), from η = 0.5 (Eqs. (3) and (16)) to$$eta left({sigma }_{{step}}|s,bright)=left(0.5+bright){e}^{-s{{sigma }_{{step}}}^{2}},$$
(20)
where b  0 self-correction, and s represents a modulating exponential damping factor, consistent with the limiting circular-uniform case (as κ → 0, i.e., ({sigma }_{{{mathrm {step}}}}to infty)), where no (timely) convergence of heading is expected with an increasing number of steps.In assessing performance, we also accounted for seasonal migration constraints via a population-specific maximum number of steps, Nmax (Table 2; this became significant for the longest-distance simulations with large expected errors, i.e., small ({{{{{{rm{kappa }}}}}}}_{{{mathrm {step}}}}=1/{sigma }_{{{mathrm {step}}}}^{2})). The probability of having arrived at the goal latitude can be estimated using the Central Limit Theorem:$${p}_{{{phi }},{N}_{{max }}}cong frac{1}{2}left[1-{erf}left(left(frac{{N}_{0}}{{N}_{{max }}}-frac{{I}_{1}left({{{{{{rm{kappa }}}}}}}_{{{mathrm {step}}}}right)}{{I}_{0}left({{{{{{rm{kappa }}}}}}}_{{{mathrm {step}}}}right)}right)cdot frac{{{cos }}bar{alpha }}{{sigma }_{{mathrm {C}}}sqrt{2}}right)right],$$
(21)
where Ij is the modified Bessel function of the first kind and order j53, and σC (the standard deviation in the latitudinal component of flight-step distance) can be calculated using Bessel functions together with known properties of sums of cosines53,77 (Supplementary Note 2).Regression-estimated performanceWe fit the parameters in the spherical-geometry factor (Eq. (18)) and many-wrongs effect (Eq. (20)) according to expected performance, estimated as the product of sufficiently timely migration (Eq. (21)) and sufficiently precise migration, now generalized from Eq. (16), i.e.$${p}_{beta ,hat{N}}cong {erf}left(frac{{beta }_{{mathrm {A}}}}{{G}^{{g}}sqrt{2left({{sigma }_{{{mathrm {ind}}}}}^{2}+{sigma }_{{{mathrm {step}}}}/{hat{N}}^{n}right)}}right),$$
(22)
This resulted in up to four fitted parameters for each compass course

i.

an exponent, g, to the spherical-geometry factor (Eq. (19)), i.e., Gg, reflecting how growth or self-correction in errors between steps further augments or reduces this factor,

ii.

a baseline offset, b0, to the “normal” exponent η = 0.5, which mediates the relation between the number of steps and performance (Eq. (20)),

iii.

an exponent s reflecting how decreasing precision among flight-steps dampens the many-wrongs convergence (Eq. (20)),

iv.

for TCSC courses, a modulation, ρ, to the offset, b0, quantifying the extent to which self-correction increases with increased flight-step distance Rstep, i.e., ({{b={b}_{0}R}_{{{mathrm {step}}}}^{{prime} }}^{rho }) in Eq. (20), where ({R}_{{{mathrm {step}}}}^{{prime} })is the flight-step distance scaled by its median value among species.

Parameters were fit using MATLAB routine fitnlm based on compass course performance among species and seven error scenarios (5°, 10°, 20°, 30°, 40°, 50°, and 60° directional precision among flight-steps), for all combinations (including or excluding the four parameters). The most parsimonious combination of parameters was selected using MATLAB routine aicbic, based on the AICc, the Akaike information criterion corrected for small sample size57. Null values for the spherical-geometry parameter were set to g = 1, and for the parameters governing convergence of route-mean headings b0 = 0, s = 0, and, for TCSC courses, ρ = 0 (for loxodrome courses, ρ = 0 by default, i.e., was not fitted).Statistics and reproducibilityOur simulation results, regression fitting and AICc-model selection are reproducible using the MATLAB scripts (see the section “Code availability”).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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doi: https://doi.org/10.1038/d41586-022-03116-6

References

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Suttle, C. A. Marine viruses — major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).CAS 
PubMed 
Article 

Google Scholar 
Angly, F. E. et al. The marine viromes of four oceanic regions. PLOS Biol. 4, 2121–2131 (2006).CAS 
Article 

Google Scholar 
Labonté, J. M. & Suttle, C. A. Previously unknown and highly divergent ssDNA viruses populate the oceans. ISME J. 7, 2169–2177 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Brum, J. R. et al. Patterns and ecological drivers of ocean viral communities. Science 348, 1261498 (2015).PubMed 
Article 

Google Scholar 
Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016).CAS 
PubMed 
Article 

Google Scholar 
Allen, L. Z. et al. The Baltic sea virome: diversity and transcriptional activity of DNA and RNA viruses. mSystems 2, e00125–00116 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gregory, A. C. et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell 177, 1109–1123 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hurwitz, B. L., Hallam, S. J. & Sullivan, M. B. Metabolic reprogramming by viruses in the sunlit and dark ocean. Genome Biol. 14, R123 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Howard-Varona, C. et al. Phage-specific metabolic reprogramming of virocells. ISME J. 14, 881–895 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kim, J.-G. et al. Spindle-shaped viruses infect marine ammonia-oxidizing thaumarchaea. Proc. Natl Acad. Sci. USA 116, 15645–15650 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Danovaro, R. et al. Major viral impact on the functioning of benthic deep-sea ecosystems. Nature 454, 1084–1087 (2008).CAS 
PubMed 
Article 

Google Scholar 
Danovaro, R. et al. Viriobenthos in freshwater and marine sediments: a review. Freshw. Biol. 53, 1186–1213 (2008).Article 

Google Scholar 
Engelhardt, T., Kallmeyer, J., Cypionka, H. & Engelen, B. High virus-to-cell ratios indicate ongoing production of viruses in deep subsurface sediments. ISME J. 8, 1503–1509 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Middelboe, M., Glud, R. N. & Filippini, M. Viral abundance and activity in the deep sub-seafloor biosphere. Aquat. Micro. Ecol. 63, 1–8 (2011).Article 

Google Scholar 
Li, Z. et al. Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity. ISME J. 15, 2366–2378 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zheng, X. et al. Extraordinary diversity of viruses in deep-sea sediments as revealed by metagenomics without prior virion separation. Environ. Microbiol 23, 728–743 (2021).CAS 
PubMed 
Article 

Google Scholar 
Helton, R. R., Liu, L. & Wommack, K. E. Assessment of factors influencing direct enumeration of viruses within estuarine sediments. Appl Environ. Microbiol 72, 4767–4774 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pan, D., Morono, Y., Inagaki, F. & Takai, K. An improved method for extracting viruses from sediment: detection of far more viruses in the subseafloor than previously reported. Front Microbiol 10, 878 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Armanious, A. et al. Viruses at solid–water interfaces: a systematic assessment of interactions driving adsorption. Environ. Sci. Technol. 50, 732–743 (2016).CAS 
PubMed 
Article 

Google Scholar 
Trubl, G. et al. Optimization of viral resuspension methods for carbon-rich soils along a permafrost thaw gradient. PeerJ 4, e1999 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Maat, D. S., Prins, M. A. & Brussaard, C. P. D. Sediments from arctic tide-water glaciers remove coastal marine viruses and delay host infection. Viruses 11, 123 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
Loveland, J. P., Ryan, J. N., Amy, G. L. & Harvey, R. W. The reversibility of virus attachment to mineral surfaces. Colloids Surf. A Physicochem. Eng. Asp. 107, 205–221 (1996).CAS 
Article 

Google Scholar 
Fuhs, G. W., Chen, M., Sturman, L. S. & Moore, R. S. Virus adsorption to mineral surfaces is reduced by microbial overgrowth and organic coatings. Microb. Ecol. 11, 25–39 (1985).CAS 
PubMed 
Article 

Google Scholar 
Whitman, W. B., Coleman, D. C. & Wiebe, W. J. Prokaryotes: the unseen majority. Proc. Natl Acad. Sci. USA 95, 6578–6583 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cai, L. et al. Active and diverse viruses persist in the deep sub-seafloor sediments over thousands of years. ISME J. 13, 1857–1864 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Manea, E. et al. Viral infections boost prokaryotic biomass production and organic C cycling in hadal trench sediments. Front Microbiol 10, 1952 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Danovaro, R. et al. Virus-mediated archaeal hecatomb in the deep seafloor. Sci. Adv. 2, e1600492 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Yoshida, M., Takaki, Y., Eitoku, M., Nunoura, T. & Takai, K. Metagenomic analysis of viral communities in (hado)pelagic sediments. PLoS One 8, e57271 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bäckström, D. et al. Virus genomes from deep sea sediments expand the ocean megavirome and support independent origins of viral gigantism. mBio 10, e02497–02418 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Jian, H. et al. Diversity and distribution of viruses inhabiting the deepest ocean on Earth. ISME J. 15, 3094–3110 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Anantharaman, K. et al. Sulfur oxidation genes in diverse deep-sea viruses. Science 344, 757–760 (2014).CAS 
PubMed 
Article 

Google Scholar 
Zhou, H. et al. Revealing the viral community in the hadal sediment of the New Britain Trench. Genes 12, 990 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dell’Anno, A., Corinaldesi, C. & Danovaro, R. Virus decomposition provides an important contribution to benthic deep-sea ecosystem functioning. Proc. Natl Acad. Sci. USA 112, E2014–E2019 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Jamieson, A. J., Fujii, T., Mayor, D. J., Solan, M. & Priede, I. G. Hadal trenches: the ecology of the deepest places on Earth. Trends Ecol. Evol. 25, 190–197 (2010).PubMed 
Article 

Google Scholar 
Glud, R. N. et al. High rates of microbial carbon turnover in sediments in the deepest oceanic trench on Earth. Nat. Geosci. 6, 284–288 (2013).CAS 
Article 

Google Scholar 
Luo, M. et al. Benthic carbon mineralization in hadal trenches: insights from in situ determination of benthic oxygen consumption. Geophys. Res. Lett. 45, 2752–2760 (2018).CAS 
Article 

Google Scholar 
Hiraoka, S. et al. Microbial community and geochemical analyses of trans-trench sediments for understanding the roles of hadal environments. ISME J. 14, 740–756 (2020).CAS 
PubMed 
Article 

Google Scholar 
Cui, G., Li, J., Gao, Z. & Wang, Y. Spatial variations of microbial communities in abyssal and hadal sediments across the Challenger Deep. PeerJ 7, e6961 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou, Y.-L., Mara, P., Cui, G.-J., Edgcomb, V. P. & Wang, Y. Microbiomes in the Challenger Deep slope and bottom-axis sediments. Nat. Commun. 13, 1515 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marquet M., et al. What the Phage: a scalable workflow for the identification and analysis of phage sequences. bioRxiv https://www.biorxiv.org/content/10.1101/2020.07.24.219899v1 (2020).Roux, S. et al. Minimum Information about an Uncultivated Virus Genome (MIUViG). Nat. Biotechnol. 37, 29–37 (2019).CAS 
PubMed 
Article 

Google Scholar 
Gregory, A. C. et al. Genomic differentiation among wild cyanophages despite widespread horizontal gene transfer. BMC Genomics 17, 930 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Bobay, L.-M. & Ochman, H. Biological species are universal across life’s domains. Genome Biol. Evol. 9, 491–501 (2017).PubMed Central 
Article 

Google Scholar 
Nayfach S., et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2020).Bin Jang, H. et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat. Biotechnol. 37, 632–639 (2019).Article 

Google Scholar 
Dalcin Martins, P. et al. Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems. Microbiome 6, 138 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Emerson, J. B. et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat. Microbiol. 3, 870–880 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhao, J. et al. Novel viral communities potentially assisting in carbon, nitrogen, and sulfur metabolism in the upper slope sediments of Mariana Trench. mSystems 7, e01358–01321 (2022).CAS 
PubMed Central 
Article 

Google Scholar 
Walker, P. J. et al. Changes to virus taxonomy and to the International Code of Virus Classification and Nomenclature ratified by the International Committee on Taxonomy of Viruses (2021). Arch. Virol. 166, 2633–2648 (2021).CAS 
PubMed 
Article 

Google Scholar 
Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8, 90 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gregory, A. C. et al. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host Microbe 28, 724–740 e728 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nishimura, Y. et al. Environmental viral genomes shed new light on virus-host interactions in the ocean. mSphere 2, e00359–00316 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vik, D. R. et al. Putative archaeal viruses from the mesopelagic ocean. PeerJ 5, e3428 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Pachiadaki, M. G. et al. Major role of nitrite-oxidizing bacteria in dark ocean carbon fixation. Science 358, 1046–1051 (2017).CAS 
PubMed 
Article 

Google Scholar 
Daly, R. A. et al. Viruses control dominant bacteria colonizing the terrestrial deep biosphere after hydraulic fracturing. Nat. Microbiol. 4, 352–361 (2019).CAS 
PubMed 
Article 

Google Scholar 
Wilhelm, S. W. & Suttle, C. A. Viruses and nutrient cycles in the sea: viruses play critical roles in the structure and function of aquatic food webs. BioScience 49, 781–788 (1999).Article 

Google Scholar 
Peoples, L. M. et al. Microbial community diversity within sediments from two geographically separated hadal trenches. Front. Microbiol. 10, 347 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Mara, P. et al. Viral elements and their potential influence on microbial processes along the permanently stratified Cariaco Basin redoxcline. ISME J. 14, 3079–3092 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Breitbart, M., Thompson, L. R., Suttle, C. A. & Sullivan, M. B. Exploring the vast diversity of marine viruses. Oceanography 20, 135–139 (2007).Article 

Google Scholar 
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rubino, F. M. Toxicity of glutathione-binding metals: a review of targets and mechanisms. Toxics 3, 20–62 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jozefczak, M., Remans, T., Vangronsveld, J. & Cuypers, A. Glutathione is a key player in metal-induced oxidative stress defenses. Int J. Mol. Sci. 13, 3145–3175 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liu, M. et al. Methylmercury bioaccumulation in deepest ocean fauna: implications for ocean mercury biotransport through food webs. Environ. Sci. Technol. Lett. 7, 469–476 (2020).CAS 
Article 

Google Scholar 
Welty, C. J., Sousa, M. L., Dunnivant, F. M. & Yancey, P. H. High-density element concentrations in fish from subtidal to hadal zones of the Pacific Ocean. Heliyon 4, e00840 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, P. et al. Revealing the full biosphere structure and versatile metabolic functions in the deepest ocean sediment of the Challenger Deep. Genome Biol. 22, 207 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Williams, A. H. & Raetz, C. R. H. Structural basis for the acyl chain selectivity and mechanism of UDP-N-acetylglucosamine acyltransferase. Proc. Natl Acad. Sci. USA 104, 13543–13550 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ahmad, S., Raza, S., Abro, A., Liedl, K. R. & Azam, S. S. Toward novel inhibitors against KdsB: a highly specific and selective broad-spectrum bacterial enzyme. J. Biomol. Struct. Dyn. 37, 1326–1345 (2019).CAS 
PubMed 
Article 

Google Scholar 
Gronow, S., Brabetz, W. & Brade, H. Comparative functional characterization in vitro of heptosyltransferase I (WaaC) and II (WaaF) from Escherichia coli. Eur. J. Biochem. 267, 6602–6611 (2000).CAS 
PubMed 
Article 

Google Scholar 
Abeyrathne, P. D., Daniels, C., Poon, K. K. H., Matewish, M. J. & Lam, J. S. Functional characterization of WaaL, a ligase associated with linking O-antigen polysaccharide to the core of Pseudomonas aeruginosa Lipopolysaccharide. J. Bacteriol. 187, 3002–3012 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kneidinger, B. et al. Biosynthesis pathway of ADP-l-glycero-β-manno-heptose in Escherichia coli. J. Bacteriol. 184, 363–369 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sullivan, M. B., Coleman, M. L., Weigele, P., Rohwer, F. & Chisholm, S. W. Three Prochlorococcus cyanophage genomes: Signature features and ecological interpretations. Plos Biol. 3, 790–806 (2005).CAS 
Article 

Google Scholar 
Sullivan, M. B. et al. Genomic analysis of oceanic cyanobacterial myoviruses compared with T4-like myoviruses from diverse hosts and environments. Environ. Microbiol. 12, 3035–3056 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Auer, G. K. & Weibel, D. B. Bacterial cell mechanics. Biochemistry 56, 3710–3724 (2017).CAS 
PubMed 
Article 

Google Scholar 
Castelán-Sánchez, H. G. et al. Extremophile deep-sea viral communities from hydrothermal vents: structural and functional analysis. Mar. Genomics 46, 16–28 (2019).PubMed 
Article 

Google Scholar 
Chothi, M. P. et al. Identification of an L-rhamnose synthetic pathway in two nucleocytoplasmic large DNA viruses. J. Virol. 84, 8829–8838 (2010).CAS 
Article 

Google Scholar 
Bachy C., et al. Viruses infecting a warm water picoeukaryote shed light on spatial co-occurrence dynamics of marine viruses and their hosts. ISME J. 15, 3129–3147 (2021).Zhang, W. et al. Four novel algal virus genomes discovered from Yellowstone Lake metagenomes. Sci. Rep. 5, 15131 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clerissi, C. et al. Prasinovirus distribution in the Northwest Mediterranean Sea is affected by the environment and particularly by phosphate availability. Virology 466–467, 146–157 (2014).PubMed 
Article 

Google Scholar 
Mistou, M. Y., Sutcliffe, I. C. & van Sorge, N. M. Bacterial glycobiology: rhamnose-containing cell wall polysaccharides in Gram-positive bacteria. FEMS Microbiol. Rev. 40, 464–479 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wendlinger, G., Loessner, M. J. & Scherer, S. Bacteriophage receptors on Listeria monocytogenes cells are the N-acetylglucosamine and rhamnose substituents of teichoic acids or the peptidoglycan itself. Microbiology 142, 985–992 (1996).CAS 
PubMed 
Article 

Google Scholar 
Michael, V. et al. Biofilm plasmids with a rhamnose operon are widely distributed determinants of the ‘swim-or-stick’ lifestyle in roseobacters. ISME J. 10, 2498–2513 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, X., Weinbauer, M. G., Jiao, N. & Zhang, R. Revisiting marine lytic and lysogenic virus-host interactions: Kill-the-Winner and Piggyback-the-Winner. Sci. Bull. 66, 871–874 (2021).CAS 
Article 

Google Scholar 
Breitbart, M., Bonnain, C., Malki, K. & Sawaya, N. A. Phage puppet masters of the marine microbial realm. Nat. Microbiol. 3, 754–766 (2018).CAS 
PubMed 
Article 

Google Scholar 
Howard-Varona, C., Hargreaves, K. R., Abedon, S. T. & Sullivan, M. B. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 11, 1511–1520 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).CAS 
PubMed 
Article 

Google Scholar 
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gao, Z.-M. et al. In situ meta-omic insights into the community compositions and ecological roles of hadal microbes in the Mariana Trench. Environ. Microbiol. 21, 4092–4108 (2019).CAS 
PubMed 
Article 

Google Scholar 
Pratama A. A., et al. Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene. PeerJ 9, e11447 (2021).Amgarten, D., Braga, L. P. P., da Silva, A. M. & Setubal, J. C. MARVEL, a tool for prediction of bacteriophage sequences in metagenomic bins. Front Genet. 9, 304 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Ren, J., Ahlgren, N. A., Lu, Y. Y., Fuhrman, J. A. & Sun, F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome 5, 69 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Fang Z., et al. PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning. GigaScience 8, giz066 (2019).Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. VirSorter: mining viral signal from microbial genomic data. PeerJ 3, e985 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Jurtz, V. I., Villarroel, J., Lund, O., Voldby Larsen, M. & Nielsen, M. MetaPhinder—identifying bacteriophage sequences in metagenomic data sets. PLoS One 11, e0163111 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Ren, J. et al. Identifying viruses from metagenomic data using deep learning. Quant. Biol. 8, 64–77 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Abdelkareem A. O., Khalil M. I., Elaraby M., Abbas H. & Elbehery A. H. A. VirNet: deep attention model for viral reads identification. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES) (2018).Starikova, E. V. et al. Phigaro: high-throughput prophage sequence annotation. Bioinformatics 36, 3882–3884 (2020).CAS 
PubMed 
Article 

Google Scholar 
Guo, J. et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 9, 37 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Auslander, N., Gussow, A. B., Benler, S., Wolf, Y. I. & Koonin, E. V. Seeker: alignment-free identification of bacteriophage genomes by deep learning. Nucleic Acids Res. 48, e121–e121 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2018).PubMed Central 
Article 

Google Scholar 
Paez-Espino, D., Pavlopoulos, G. A., Ivanova, N. N. & Kyrpides, N. C. Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data. Nat. Protoc. 12, 1673–1682 (2017).CAS 
PubMed 
Article 

Google Scholar 
Gao, S.-M. et al. Depth-related variability in viral communities in highly stratified sulfidic mine tailings. Microbiome 8, 89 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Dudek, N. K., Sun, C., Burstein, D., Kantor, R. & Relman, D. Novel microbial diversity and functional potential in the marine mammal oral microbiome. Curr. Biol. 27, 3752–3762.e3756 (2017).CAS 
PubMed 
Article 

Google Scholar 
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shkoporov, A. N. et al. The human gut virome is highly diverse, stable, and individual specific. Cell Host Microbe 26, 527–541.e525 (2019).CAS 
PubMed 
Article 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).Article 

Google Scholar 
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 
Article 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Letunic, I., Khedkar, S. & Bork, P. SMART: recent updates, new developments and status in 2020. Nucleic Acids Res. 49, D458–D460 (2021).CAS 
PubMed 
Article 

Google Scholar 
Skennerton, C. T., Imelfort, M. & Tyson, G. W. Crass: identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res. 41, e105 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bland, C. et al. CRISPR recognition tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinform. 8, 209 (2007).Article 

Google Scholar 
Lowe, T. M. & Eddy, S. R. tRNAscan-SE: A program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Galiez, C., Siebert, M., Enault, F., Vincent, J. & Söding, J. WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics 33, 3113–3114 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://ui.adsabs.harvard.edu/abs/2013arXiv1303.3997L (2013).Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Wessel, P. et al. The Generic Mapping Tools version 6. Geochem. Geophys. Geosystems 20, 5556–5564 (2019).Article 

Google Scholar 
Tozer, B. et al. Global bathymetry and topography at 15 Arc Sec: SRTM15+. Earth Space Sci. 6, 1847–1864 (2019).Article 

Google Scholar  More

Arias, P. A. et al. Technical summary. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2021).Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Chang. 2, 686–690 (2012).ADS 
Article 

Google Scholar 
Fry, F. Effects of the environment on animal activity. Publ. Ontario Fish. Res. Lab. 55, 1–62 (1947).
Google Scholar 
Lutterschmidt, W. I. & Hutchison, V. H. The critical thermal maximum: history and critique. Can. J. Zool. 75, 1561–1574, https://doi.org/10.1139/z97-783 (2011).Article 

Google Scholar 
Rezende, E. L., Castañeda, L. E. & Santos, M. Tolerance landscapes in thermal ecology. Funct. Ecol. 28, 799–809 (2014).Article 

Google Scholar 
Bozinovic, F., Calosi, P. & Spicer, J. I. Physiological correlates of geographic range in animals. Annu. Rev. Ecol. Evol. S. 42, 155–179 (2011).Article 

Google Scholar 
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl. Acad. Sci. USA 111, 5610–5615 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl. Acad. Sci. USA 105, 6668–6672 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Comte, L. & Olden, J. D. Climatic vulnerability of the world’s freshwater and marine fishes. Nat. Clim. Chang. 7, 718–722 (2017).ADS 
Article 

Google Scholar 
Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pottier, P., Burke, S., Drobniak, S. M. & Nakagawa, S. Methodological inconsistencies define thermal bottlenecks in fish life cycle: a comment on Dahlke et al. 2020. Evol. Ecol. 36, 287–292 (2022).Article 

Google Scholar 
Dahlke, F., Butzin, M., Wohlrab, S. & Pörtner, H.-O. Reply to: methodological inconsistencies define thermal bottlenecks in fish life cycle. Evol. Ecol. 36, 293–298 (2022).Article 

Google Scholar 
Pottier, P. et al. Developmental plasticity in thermal tolerance: Ontogenetic variation, persistence, and future directions. Ecol. Lett. (2022).Morley, S. A., Peck, L. S., Sunday, J. M., Heiser, S. & Bates, A. E. Physiological acclimation and persistence of ectothermic species under extreme heat events. Glob. Ecol. Biogeogr. 28, 1018–1037 (2019).Article 

Google Scholar 
Rohr, J. R. et al. The complex drivers of thermal acclimation and breadth in ectotherms. Ecol. Lett. 21, 1425–1439 (2018).PubMed 
Article 

Google Scholar 
Gunderson, A. R. & Stillman, J. H. Plasticity in thermal tolerance has limited potential to buffer ectotherms from global warming. Proc. R. Soc. B-Biol. Sci. 282, 20150401 (2015).Article 

Google Scholar 
Weaving, H., Terblanche, J. S., Pottier, P. & English, S. Meta-analysis reveals weak but pervasive plasticity in insect thermal limits. Nat. Commun. 13, 1–11 (2022).Article 

Google Scholar 
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).PubMed 
Article 

Google Scholar 
Bennett, J. M. et al. The evolution of critical thermal limits of life on Earth. Nat. Commun. 12, 1198 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hoffmann, A. A., Chown, S. L. & Clusella-Trullas, S. Upper thermal limits in terrestrial ectotherms: how constrained are they? Funct. Ecol. 27, 934–949 (2013).Article 

Google Scholar 
Kellermann, V. et al. Upper thermal limits of Drosophila are linked to species distributions and strongly constrained phylogenetically. Proc. Natl. Acad. Sci. USA 109, 16228–16233 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morgan, R., Finnøen, M. H., Jensen, H., Pélabon, C. & Jutfelt, F. Low potential for evolutionary rescue from climate change in a tropical fish. Proc. Natl. Acad. Sci. USA 117, 33365–33372 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bennett, J. M. et al. GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms. Sci. Data 5, 180022 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Leiva, F. P., Calosi, P. & Verberk, W. C. E. P. Scaling of thermal tolerance with body mass and genome size in ectotherms: a comparison between water- and air-breathers. Philos. Trans. R. Soc. B-Biol. Sci. 374, 20190035 (2019).Article 

Google Scholar 
Clusella-Trullas, S., Blackburn, T. M. & Chown, S. L. Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. Am. Nat. 177, 738–751 (2011).PubMed 
Article 

Google Scholar 
Nakagawa, S. & Freckleton, R. P. Missing inaction: the dangers of ignoring missing data. Trends Ecol. Evol. 23, 592–596 (2008).PubMed 
Article 

Google Scholar 
Johnson, T. F., Isaac, N. J. B., Paviolo, A. & González-Suárez, M. Handling missing values in trait data. Glob. Ecol. Biogeogr. 30, 51–62 (2021).Article 

Google Scholar 
Foo, Y. Z., O’Dea, R. E., Koricheva, J., Nakagawa, S. & Lagisz, M. A practical guide to question formation, systematic searching and study screening for literature reviews in ecology and evolution. Methods Ecol. Evol. 12, 1705–1720 (2021).Article 

Google Scholar 
Reboredo Segovia, A. L., Romano, D. & Armsworth, P. R. Who studies where? Boosting tropical conservation research where it is most needed. Front. Ecol. Environ. 18, 159–166 (2020).Article 

Google Scholar 
White, C. R. et al. Geographical bias in physiological data limits predictions of global change impacts. Funct. Ecol. 35, 1572–1578 (2021).Article 

Google Scholar 
Amano, T., González-Varo, J. P. & Sutherland, W. J. Languages are still a major barrier to global Science. PLoS Biol. 14, e2000933 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Bennett, S., Duarte, C. M., Marbà, N. & Wernberg, T. Integrating within-species variation in thermal physiology into climate change ecology. Philos. Trans. R. Soc. B-Biol. Sci. 374, 20180550 (2019).Article 

Google Scholar 
Noble, D. W. A. et al. Meta-analytic approaches and effect sizes to account for ‘nuisance heterogeneity’ in comparative physiology. J. Exp. Biol. 225, jeb243225 (2022).PubMed 
Article 

Google Scholar 
Peralta-Maraver, I. & Rezende, E. L. Heat tolerance in ectotherms scales predictably with body size. Nat. Clim. Chang. 11, 58–63 (2021).ADS 
Article 

Google Scholar 
McKenzie, D. J. et al. Intraspecific variation in tolerance of warming in fishes. J. Fish Biol. 98, 1536–1555 (2021).PubMed 
Article 

Google Scholar 
Morrissey, M. B. Meta-analysis of magnitudes, differences and variation in evolutionary parameters. J. Evol. Biol. 29, 1882–1904 (2016).CAS 
PubMed 
Article 

Google Scholar 
Duffy, G. A., Kuyucu, A. C., Hoskins, J. L., Hay, E. M. & Chown, S. L. Adequate sample sizes for improved accuracy of thermal trait estimates. Funct. Ecol. 35, 2647–2662 (2021).CAS 
Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. https://www.iucnredlist.org (2021).Harfoot, M. B. J. et al. Using the IUCN Red List to map threats to terrestrial vertebrates at global scale. Nat. Ecol. Evol. 5, 1510–1519 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Sodhi, N. K. et al. Measuring the meltdown: Drivers of global amphibian extinction and decline. PLoS One 3 (2008).Nowakowski, A. J. et al. Tropical amphibians in shifting thermal landscapes under land-use and climate change. Conserv. Physiol. 31, 96–105 (2017).
Google Scholar 
Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl. Acad. Sci. USA 110, E2602–E2610 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ouzzani, M., Hammady, H., Fedorowicz, Z. & Elmagarmid, A. Rayyan—a web and mobile app for systematic reviews. Syst. Rev. 5, 210 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Vera-Baceta, M.-A., Thelwall, M. & Kousha, K. Web of Science and Scopus language coverage. Scientometrics 121, 1803–1813 (2019).Article 

Google Scholar 
Giustini, D. & Boulos, M. N. K. Google Scholar is not enough to be used alone for systematic reviews. Online J. Public Health Inform. 5, 214 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Haddaway, N. R., Collins, A. M., Coughlin, D. & Kirk, S. The role of Google Scholar in evidence reviews and its applicability to grey literature searching. PLoS One 10, e0138237 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Gusenbauer, M. & Haddaway, N. R. Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Res. Synth. Methods 11, 181–217 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Harzing, A. Publish or perish. Res. Int. Manag. Softw. Release 27 (2007).Cheng, C.-B. A study of warming tolerance and thermal acclimation capacity of tadpoles in Taiwan. (Tunghai University, 2017).Wu, Q.-H. & Hsieh, C.-H. Thermal tolerance and population genetics of Hynobius fuca. (Chinese Culture University, 2016).Jørgensen, L. B., Malte, H., Ørsted, M., Klahn, N. A. & Overgaard, J. A unifying model to estimate thermal tolerance limits in ectotherms across static, dynamic and fluctuating exposures to thermal stress. Sci. Rep. 11, 1–14 (2021).Article 

Google Scholar 
Agudelo-Cantero, G. A. & Navas, C. A. Interactive effects of experimental heating rates, ontogeny and body mass on the upper thermal limits of anuran larvae. J. Therm. Biol. 82, 43–51 (2019).PubMed 
Article 

Google Scholar 
Alveal Riquelme, N. Relaciones entre la fisiología térmica y las características bioclimáticas de Rhinella spinulosa (Anura: Bufonidae) en Chile a través del enlace mecanicista de nicho térmico. (Universidad de Concepción, 2015).Alves, M. Tolerância térmica em espécies de anuros neotropicais do gênero Dendropsophus Fitzinger, 1843 e efeito da temperatura na resposta à predação. (Universidade Estadual de Santa Cruz, 2016).Anderson, R. C. O. & Andrade, D. V. Trading heat and hops for water: Dehydration effects on locomotor performance, thermal limits, and thermoregulatory behavior of a terrestrial toad. Ecol. Evol. 7, 9066–9075 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Aponte Gutiérrez, A. Endurecimiento térmico en Pristimantis medemi (Anura: Craugastoridae), en coberturas boscosas del Municipio de Villavicencio (Meta). (Universidad Nacional de Colombia, 2020).Arrigada García, K. Conductas térmica en dos poblaciones de Batrachyla taeniata provenientes de la localidad de Ucúquer en la región de O’Higgins y de la localidad de Hualpén en la región del Bío-Bío (Universidad de Concepción, 2019).Azambuja, G., Martins, I. K., Franco, J. L. & Santos, T. Gdos Effects of mancozeb on heat shock protein 70 (HSP70) and its relationship with the thermal physiology of Physalaemus henselii (Peters, 1872) tadpoles (Anura: Leptodactylidae). J. Therm. Biol. 98, 102911 (2021).CAS 
PubMed 
Article 

Google Scholar 
Bacigalupe, L. D. et al. Natural selection on plasticity of thermal traits in a highly seasonal environment. Evol. Appl. 11, 2004–2013 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Barria, A. M. & Bacigalupe, L. D. Intraspecific geographic variation in thermal limits and acclimatory capacity in a wide distributed endemic frog. J. Therm. Biol. 69, 254–260 (2017).PubMed 
Article 

Google Scholar 
Beltrán, I., Ramírez-Castañeda, V., Rodríguez-López, C., Lasso, E. & Amézquita, A. Dealing with hot rocky environments: critical thermal maxima and locomotor performance in Leptodactylus lithonaetes (anura: Leptodactylidae). Herpetol. J. 29, 155–161 (2019).Article 

Google Scholar 
Berkhouse, C. & Fries, J. Critical thermal maxima of juvenile and adult San Marcos salamanders (Eurycea nana). Southwest. Nat. 40, 430–434 (1995).
Google Scholar 
Blem, C. R., Ragan, C. A. & Scott, L. S. The thermal physiology of two sympatric treefrogs Hyla cinerea and Hyla chrysoscelis (Anura; Hylidae). Comp. Biochem. Physiol. 85, 563–570 (1986).CAS 
Article 

Google Scholar 
Bonino, M. F., Cruz, F. B. & Perotti, M. G. Does temperature at local scale explain thermal biology patterns of temperate tadpoles? J. Therm. Biol. 94 (2020).Bovo, R. P. Fisiologia térmica e balanço hídrico em anfíbios anuros. (Universidad Estadual Paulista, 2015).Brattstrom, B. H. Thermal acclimation in Australian amphibians. Comp. Biochem. Physiol. 35, 69–103 (1970).Article 

Google Scholar 
Brattstrom, B. H. & Regal, P. Rate of thermal acclimation in the Mexican salamander. Chiropterotriton. Copeia 1965, 514–515 (1965).Article 

Google Scholar 
Brattstrom, B. H. A preliminary review of the thermal requirements of amphibians. Ecology 44, 238–255 (1963).Article 

Google Scholar 
Brattstrom, B. H. Thermal acclimation in anuran amphibians as a function of latitude and altitude. Comp. Biochem. Physiol. 24, 93–111 (1968).CAS 
PubMed 
Article 

Google Scholar 
Brattstrom, B. H. & Lawrence, P. The rate of thermal acclimation in anuran amphibians. Physiol. Zool. 35, 148–156 (1962).Article 

Google Scholar 
Brown, H. A. The heat resistance of some anuran tadpoles (Hylidae and Pelobatidae). Copeia 1969, 138 (1969).Article 

Google Scholar 
Burke, E. M. & Pough, F. H. The role of fatigue in temperature resistance of salamanders. J. Therm. Biol. 1, 163–167 (1976).Article 

Google Scholar 
Burrowes, P. A., Navas, C. A., Jiménez-Robles, O., Delgado, P. & De La Riva, I. Climatic heterogeneity in the Bolivian andes: Are frogs trapped? S. Am. J. Herpetol. 18, 1–12 (2020).Article 

Google Scholar 
Bury, R. B. Low thermal tolerances of stream amphibians in the Pacific Northwest: Implications for riparian and forest management. Appl. Herpetol. 5, 63–74 (2008).Article 

Google Scholar 
Castellanos García, L. A. Days of futures past: Integrating physiology, microenvironments, and biogeographic history to predict response of frogs in neotropical dry-forest to global warming. (Universidad de los Andes, 2017).Castro, B. Influence of environment on thermal ecology of direct-developing frogs (Anura: Craugastoridae: Pristimantis) in the eastern Andes of Colombia. (Universidad de los Andes, 2019).Catenazzi, A., Lehr, E. & Vredenburg, V. T. Thermal physiology, disease, and amphibian declines on the eastern slopes of the Andes. Conserv. Biol. 28, 509–517 (2014).PubMed 
Article 

Google Scholar 
Chang, L.-W. Heat tolerance and its plasticity in larval Bufo bankorensis from different altitudes. (National Cheng Kung University, 2002).Chavez Landi, P. A. Fisiología térmica de un depredador Dasythemis sp. (Odonata: Libellulidae) y su presa Hypsiboas pellucens (Anura: Hylidae) y sus posibles implicaciones frente al cambio climático. (Pontificia Universidad Católica Del Ecuador, 2017).Chen, T.-C., Kam, Y.-C. & Lin, Y.-S. Thermal physiology and reproductive phenology of Buergeria japonica (Rhacophoridae) breeding in a stream and a geothermal hotspring in Taiwan. Zool. Sci. 18, 591–596 (2001).Article 

Google Scholar 
Cheng, Y.-J. Effect of salinity on the critical thermal maximum of tadpoles living in brackish water. (Tunghai University, 2017).Christian, K. A., Nunez, F., Clos, L. & Diaz, L. Thermal relations of some tropical frogs along an altitudinal gradient. Biotropica 20, 236–239 (1988).Article 

Google Scholar 
Claussen, D. L. The thermal relations of the tailed frog, Ascaphus truei, and the pacific treefrog, Hyla regilla. Comp. Biochem. Physiol. 44, 137–153 (1973).Article 

Google Scholar 
Claussen, D. L. Thermal acclimation in ambystomatid salamanders. Comp. Biochem. Physiol. 58, 333–340 (1977).Article 

Google Scholar 
Contreras Cisneros, J. Temperatura crítica máxima, tolerancia al frío y termopreferendum del tritón del Montseny (Calotriton arnoldii). (Universitat de Barcelona, 2019).Contreras Oñate, S. Posible efecto de las temperaturas de aclimatación sobre las respuestas térmicas en temperaturas críticas máximas (TCmás) y mínimas (TCmín) de una población de Batrachyla taeniata (Universidad de Concepción, 2016).Cooper, R. D. & Shaffer, H. B. Allele-specific expression and gene regulation help explain transgressive thermal tolerance in non-native hybrids of the endangered California tiger salamander (Ambystoma californiense). Mol. Ecol. 30, 987–1004 (2021).CAS 
PubMed 
Article 

Google Scholar 
Crow, J. C., Forstner, M. R. J., Ostr, K. G. & Tomasso, J. R. The role of temperature on survival and growth of the barton springs salamander (Eurycea sosorum). Herpetol. Conserv. Biol. 11, 328–334 (2016).
Google Scholar 
Cupp, P. V. Thermal tolerance of five salientian amphibians during development and metamorphosis. Herpetologica 36, 234–244 (1980).
Google Scholar 
Dabruzzi, T. F., Wygoda, M. L. & Bennett, W. A. Some like it hot: Heat tolerance of the crab-eating frog, Fejervarya cancrivora. Micronesica 43, 101–106 (2012).
Google Scholar 
Dainton, B. H. Heat tolerance and thyroid activity in developing tadpoles and juvenile adults of Xenopus laevis (Daudin). J. Therm. Biol. 16, 273–276 (1991).Article 

Google Scholar 
Daniel, N. J. J. Impact of climate change on Singapore amphibians. (National University of Singapore, 2013).Davies, S. J., McGeoch, M. A. & Clusella-Trullas, S. Plasticity of thermal tolerance and metabolism but not water loss in an invasive reed frog. Comp. Biochem. Physiol. 189, 11–20 (2015).CAS 
Article 

Google Scholar 
de Oliviera Anderson, R. C., Bovo, R. P. & Andrade, D. V. Seasonal variation in the thermal biology of a terrestrial toad, Rhinella icterica (Bufonidae), from the Brazilian Atlantic Forest. J. Therm. Biol. 74, 77–83 (2018).Article 

Google Scholar 
de Vlaming, V. L. & Bury, R. B. Thermal selection in tadpoles of the tailed-frog. Ascaphus truei. J. Herpetol. 4, 179–189 (1970).Article 

Google Scholar 
Delson, J. & Whitford, W. G. Critical thermal maxima in several life history stages in desert and montane populations of Ambystoma tigrinum. Herpetologica 29, 352–355 (1973).
Google Scholar 
Duarte, H. et al. Can amphibians take the heat? Vulnerability to climate warming in subtropical and temperate larval amphibian communities. Glob. Chang. Biol. 18, 412–421 (2012).ADS 
Article 

Google Scholar 
Duarte, H. S. A comparative study of the thermal tolerance of tadpoles of Iberian anurans. (Universidade de Lisboa, 2011).Dunlap, D. Evidence for a daily rhythm of heat resistance in cricket frogs, Acris crepitans. Copeia. 4, 852–854 (1969).Article 

Google Scholar 
Dunlap, D. G. Critical thermal maximum as a function of temperature of acclimation in two species of hylid frogs. Physiol. Zool. 41, 432–439 (1968).Article 

Google Scholar 
Elwood, J. R. L. Variation in hsp70 levels and thermotolerance among terrestrial salamanders of the Plethodon glutinosus complex. (Drexel University, 2003).Enriquez-Urzelai, U. et al. Ontogenetic reduction in thermal tolerance is not alleviated by earlier developmental acclimation in Rana temporaria. Oecologia 189, 385–394 (2019).ADS 
PubMed 
Article 

Google Scholar 
Enriquez-Urzelai, U. et al. The roles of acclimation and behaviour in buffering climate change impacts along elevational gradients. J. Anim. Ecol. 89, 1722–1734 (2020).PubMed 
Article 

Google Scholar 
Erskine, D. J. & Hutchison, V. H. Reduced thermal tolerance in an amphibian treated with melatonin. J. Therm. Biol. 7, 121–123 (1982).CAS 
Article 

Google Scholar 
Escobar Serrano, D. Acclimation scope of the critical thermal limits in Agalychnis spurrelli (Hylidae) and Gastrotheca pseustes (Hemiphractidae) and their implications under climate change scenarios. (Pontificia Universidad Católica Del Ecuador, 2016).Fan, X., Lei, H. & Lin, Z. Ontogenetic shifts in selected body temperature and thermal tolerance of the tiger frog. Hoplobatrachus chinensis. Acta Ecol. Sin. 32, 5574–5580 (2012).
Google Scholar 
Fan, X. L., Lin, Z. H. & Scheffers, B. R. Physiological, developmental, and behavioral plasticity in response to thermal acclimation. J. Therm. Biol. 97 (2021).Fernández-Loras, A. et al. Infection with Batrachochytrium dendrobatidis lowers heat tolerance of tadpole hosts and cannot be cleared by brief exposure to CTmax. PLoS ONE 14 (2019).Floyd, R. B. Ontogenetic change in the temperature tolerance of larval Bufo marinus (Anura: bufonidae). Comp. Biochem. Physiol. 75, 267–271 (1983).Article 

Google Scholar 
Floyd, R. B. Effects of photoperiod and starvation on the temperature tolerance of larvae of the giant toad, Bufo marinus. Copeia 1985, 625–631 (1985).MathSciNet 
Article 

Google Scholar 
Fong, S.-T. Thermal tolerance of adult Asiatic painted frog Kaloula pulchra from different populations. (National University of Tainan, 2014).Frishkoff, L. O., Hadly, E. A. & Daily, G. C. Thermal niche predicts tolerance to habitat conversion in tropical amphibians and reptiles. Glob. Chang. Biol. 21, 3901–3916 (2015).ADS 
PubMed 
Article 

Google Scholar 
Frost, J. S. & Martin, E. W. A comparison of distribution and high temperature tolerance in Bufo americanus and Bufo woodhousii fowleri. Copeia 1971, 750 (1971).Article 

Google Scholar 
Gatz, A. J. Critical thermal maxima of Ambystoma maculatum (Shaw) and Ambystoma jeffersonianum (Green) in relation to time of breeding. Herpetologica 27, 157–160 (1971).
Google Scholar 
Gatz, A. J. Intraspecific variations in critical thermal maxima of Ambystoma maculatum. Herpetologica 29, 264–268 (1973).
Google Scholar 
Geise, W. & Linsenmair, K. E. Adaptations of the reed frog Hyperolius viridiflavus (Amphibia, Anura, Hyperoliidae) to its arid environment – IV. Ecological significance of water economy with comments on thermoregulation and energy allocation. Oecologia 77, 327–338 (1988).ADS 
CAS 
PubMed 
Article 

Google Scholar 
González-del-Pliego, P. et al. Thermal tolerance and the importance of microhabitats for Andean frogs in the context of land use and climate change. J. Anim. Ecol. 89, 2451–2460 (2020).PubMed 
Article 

Google Scholar 
Gouveia, S. F. et al. Climatic niche at physiological and macroecological scales: The thermal tolerance-geographical range interface and niche dimensionality. Glob. Ecol. Biogeogr. 23, 446–456 (2014).Article 

Google Scholar 
Gray, R. Lack of physiological differentiation in three color morphs of the cricket frog (Acris crepitans) in Illinois. Trans. Ill. State Acad. Sci. 70, 73–79 (1977).ADS 

Google Scholar 
Greenspan, S. E. et al. Infection increases vulnerability to climate change via effects on host thermal tolerance. Sci. Rep. 7 (2017).Guevara-Molina, E. C., Gomes, F. R. & Camacho, A. Effects of dehydration on thermoregulatory behavior and thermal tolerance limits of Rana catesbeiana (Shaw, 1802). J. Therm. Biol. 93 (2020).Gutiérrez Pesquera, L. Una valoración macrofisiológica de la vulnerabilidad al calentamiento global. Análisis de los límites de tolerancia térmica en comunidades de anfibios en gradients latitudinales y altitudinales. (Pontificia Universidad Católica Del Ecuador, 2015).Gutiérrez Pesquera, M. Thermal tolerance across latitudinal and altitudinal gradients in tadpoles. (Universidad de Sevilla, 2016).Gutiérrez-Pesquera, L. M. et al. Testing the climate variability hypothesis in thermal tolerance limits of tropical and temperate tadpoles. J. Biogeogr. 43, 1166–1178 (2016).Article 

Google Scholar 
Gvoždík, L., Puky, M. & Šugerková, M. Acclimation is beneficial at extreme test temperatures in the Danube crested newt, Triturus dobrogicus (Caudata, Salamandridae). Bio. J. Linn. Soc. 90, 627–636 (2007).Article 

Google Scholar 
Heatwole, H., De Austin, S. B. & Herrero, R. Heat tolerances of tadpoles of two species of tropical anurans. Comp. Biochem. Physiol. 27, 807–815 (1968).Article 

Google Scholar 
Heatwole, H., Mercado, N. & Ortiz, E. Comparison of critical thermal maxima of two species of Puerto Rican frogs of the genus. Eleutherodactylus. Physiol. Zool. 38, 1–8 (1965).Article 

Google Scholar 
Holzman, N. & McManus, J. J. Effects of acclimation on metabolic rate and thermal tolerance in the carpenter frog. Rana vergatipes. Comp. Biochem. Physiol. 45, 833–842 (1973).CAS 
Article 

Google Scholar 
Hoppe, D. M. Thermal tolerance in tadpoles of the chorus frog Pseudacris triseriata. Herpetologica 34, 318–321 (1978).
Google Scholar 
Hou, P.-C. Thermal tolerance and preference in the adult amphibians from different altitudinal LTER sites. (National Cheng Kung University, 2003).Howard, J. H., Wallace, R. L. & Stauffer, J. R. Jr Critical thermal maxima in populations of Ambystoma macrodactylum from different elevations. J. Herpetol. 17, 400–402 (1983).Article 

Google Scholar 
Hutchison, V. H. & Ritchart, J. P. Annual cycle of thermal tolerance in the salamander. Necturus maculosus. J. Herpetol. 23, 73–76 (1989).Article 

Google Scholar 
Hutchison, V. H. The distribution and ecology of the cave salamander, Eurycea lucifuga. Ecol. Monogr. 28, 2–20 (1958).Article 

Google Scholar 
Hutchison, V. H. Critical thermal maxima in salamanders. Physiol. Zool. 34, 92–125 (1961).Article 

Google Scholar 
Hutchison, V. H., Engbretson, G. & Turney, D. Thermal acclimation and tolerance in the hellbender, Cryptobranchus alleganiensis. Copeia 1973, 805–807 (1973).Article 

Google Scholar 
Hutchison, V. H. & Rowlan, S. D. Thermal acclimation and tolerance in the mudpuppy. Necturus maculosus. J. Herpetol. 9, 367–368 (1975).Article 

Google Scholar 
Jiang, S., Yu, P. & Hu, Q. A study on the critical thermal maxima of five species of salamanders of China. Acta Herpetol. Sin. 6, 56–62 (1987).
Google Scholar 
John-Alder, H. B., Morin, P. J. & Lawler, S. Thermal physiology, phenology, and distribution of tree frogs. Am. Nat. 132, 506–520 (1988).Article 

Google Scholar 
Johnson, C. R. Daily variation in the thermal tolerance of Litoria caerulea (Anura: Hylidae). Comp. Biochem. Physiol. 40, 1109–1111 (1971).Article 

Google Scholar 
Johnson, C. R. Thermal relations and water balance in the day frog, Taudactylus diurnus, from an Australian rain forest. Aust. J. Zool. 19, 35–39 (1971).Article 

Google Scholar 
Johnson, C. R. Diel variation in the thermal tolerance of Litoria gracilenta (Anura: Hylidae). Comp. Biochem. Physiol. 41, 727–730 (1972).CAS 
Article 

Google Scholar 
Johnson, C. R. & Prine, J. E. The effects of sublethal concentrations of organophosphorus insecticides and an insect growth regulator on temperature tolerance in hydrated and dehydrated juvenile western toads. Bufo boreas. Comp. Biochem. Physiol. 53, 147–149 (1976).CAS 
Article 

Google Scholar 
Johnson, C. R. Observations on body temperatures, critical thermal maxima and tolerance to water loss in the Australian hylid, Hyla caerulea (White). Proc. R. Soc. Qld. 82, 47–50 (1970).
Google Scholar 
Johnson, C. R. Thermal relations and daily variation in the thermal tolerance in. Bufo marinus. J. Herpetol. 6, 35 (1972).Article 

Google Scholar 
Johnson, C. Thermal relations in some southern and eastern Australian anurans. Proc. R. Soc. Qld. 82, 87–94 (1971).
Google Scholar 
Johnson, C. The effects of five organophosphorus insecticides on thermal stress in tadpoles of the Pacific tree frog. Hyla regilla. Zool. J. Linn. Soc. 69, 143–147 (1980).ADS 
Article 

Google Scholar 
Katzenberger, M., Duarte, H., Relyea, R., Beltrán, J. F. & Tejedo, M. Variation in upper thermal tolerance among 19 species from temperate wetlands. J. Therm. Biol. 96 (2021).Katzenberger, M. et al. Swimming with predators and pesticides: How environmental stressors affect the thermal physiology of tadpoles. PLoS ONE 9 (2014).Katzenberger, M., Hammond, J., Tejedo, M. & Relyea, R. Source of environmental data and warming tolerance estimation in six species of North American larval anurans. J. Therm. Biol. 76, 171–178 (2018).PubMed 
Article 

Google Scholar 
Katzenberger, M. Thermal tolerance and sensitivity of amphibian larvae from Palearctic and Neotropical communities. (Universidade de Lisboa, 2013).Katzenberger, M. Impact of global warming in holarctic and neotropical communities of amphibians. (Universidad de Sevilla, 2014).Kern, P., Cramp, R. L. & Franklin, C. E. Temperature and UV-B-insensitive performance in tadpoles of the ornate burrowing frog: An ephemeral pond specialist. J. Exp. Biol. 217, 1246–1252 (2014).PubMed 

Google Scholar 
Kern, P., Cramp, R. L., Seebacher, F., Ghanizadeh Kazerouni, E. & Franklin, C. E. Plasticity of protective mechanisms only partially explains interactive effects of temperature and UVR on upper thermal limits. Comp. Biochem. Physiol. 190, 75–82 (2015).CAS 
Article 

Google Scholar 
Kern, P., Cramp, R. L. & Franklin, C. E. Physiological responses of ectotherms to daily temperature variation. J. Exp. Biol. 218, 3068–3076 (2015).PubMed 

Google Scholar 
Komaki, S., Igawa, T., Lin, S.-M. & Sumida, M. Salinity and thermal tolerance of Japanese stream tree frog (Buergeria japonica) tadpoles from island populations. Herpetol. J. 26, 207–211 (2016).
Google Scholar 
Komaki, S., Lau, Q. & Igawa, T. Living in a Japanese onsen: Field observations and physiological measurements of hot spring amphibian tadpoles. Buergeria japonica. Amphib. Reptil. 37, 311–314 (2016).Article 

Google Scholar 
Krakauer, T. Tolerance limits of the toad, Bufo marinus, in South Florida. Comp. Biochem. Physiol. 33, 15–26 (1970).CAS 
PubMed 
Article 

Google Scholar 
Kurabayashi, A. et al. Improved transport of the model amphibian, Xenopus tropicalis, and its viable temperature for transport. Curr. Herpetol. 33, 75–87 (2014).Article 

Google Scholar 
Lau, E. T. C., Leung, K. M. Y. & Karraker, N. E. Native amphibian larvae exhibit higher upper thermal limits but lower performance than their introduced predator. Gambusia affinis. J. Therm. Biol. 81, 154–161 (2019).PubMed 
Article 

Google Scholar 
Layne, J. R. & Claussen, D. L. Seasonal variation in the thermal acclimation of critical thermal maxima (CTMax) and minima (CTMin) in the salamander. Eurycea bislineata. J. Therm. Biol. 7, 29–33 (1982).Article 

Google Scholar 
Layne, J. R. & Claussen, D. L. The time courses of CTMax and CTMin acclimation in the salamander. Desmognathus fuscus. J. Therm. Biol. 7, 139–141 (1982).Article 

Google Scholar 
Lee, P.-T. Acidic effect on tadpoles living in container habitats. (Tunghai University, 2019).Longhini, L. S., De Almeida Prado, C. P., Bícego, K. C., Zena, L. A. & Gargaglioni, L. H. Measuring cardiorespiratory variables on small tadpoles using a non-invasive methodology. Rev. Cuba. Investig. Biomed. 38 (2019).López Rosero, A. C. Ontogenetic variation of thermal tolerance in two anuran species of Ecuador: Gastrotheca pseustes (Hemiphractidae) and Smilisca phaeota (Hylidae) and their relative vulnerability to environmental temperature change. (Pontificia Universidad Católica Del Ecuador, 2015).Lotshaw, D. P. Temperature adaptation and effects of thermal acclimation in Rana sylvatica and Rana catesbeiana. Comp. Biochem. Physiol. 56, 287–294 (1977).Article 

Google Scholar 
Lu, H.-L., Wu, Q., Geng, J. & Dang, W. Swimming performance and thermal resistance of juvenile and adult newts acclimated to different temperatures. Acta Herpetol. 11, 189–195 (2016).
Google Scholar 
Lu, H. L., Geng, J., Xu, W., Ping, J. & Zhang, Y. P. Physiological response and changes in swimming performance after thermal acclimation in juvenile chinese fire-belly newts, Cynops orientalis. Acta Ecol. Sin. 37, 1603–1610 (2017).
Google Scholar 
Lutterschmidt, W. I. & Hutchison, V. H. The critical thermal maximum: Data to support the onset of spasms as the definitive end point. Can. J. Zool. 75, 1553–1560 (1997).Article 

Google Scholar 
Madalozzo, B. Variação latitudinal nos limites de tolerância e plasticidade térmica em anfíbios em um cenário de mudanças climáticas: efeito dos micro-habitats, sazonalidade e filogenia. (Universidade Federal de Santa Maria, 2018).Mahoney, J. J. & Hutchison, V. H. Photoperiod acclimation and 24-hour variations in the critical thermal maxima of a tropical and a temperate frog. Oecologia 2, 143–161 (1969).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Maness, J. D. & Hutchison, V. H. Acute adjustment of thermal tolerance in vertebrate ectotherms following exposure to critical thermal maxima. J. Therm. Biol. 5, 225–233 (1980).Article 

Google Scholar 
Manis, M. L. & Claussen, D. L. Environmental and genetic influences on the thermal physiology of Rana sylvatica. J. Therm. Biol. 11, 31–36 (1986).Article 

Google Scholar 
Markle, T. M. & Kozak, K. H. Low acclimation capacity of narrow-ranging thermal specialists exposes susceptibility to global climate change. Ecol. Evol. 8, 4644–4656 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Marshall, E. & Grigg, G. C. Acclimation of CTM, LD50, and rapid loss of acclimation of thermal preferendum in tadpoles of Limnodynastes peronii (Anura, Myobatrachidae). Aust. Zool. 20, 447–456 (1980).
Google Scholar 
Mathias, J. H. The Comparative ecologies of two species of Amphibia (B. bufo and B. calamita) on the Ainsdale Sand Dunes National Nature Reserve. (The University of Manchester, 1971).McManus, J. J. & Nellis, D. W. The critical thermal maximum of the marine toad, Bufo marinus. Caribb. J. Sci. 15, 67–70 (1975).
Google Scholar 
Menke, M. E. & Claussen, D. L. Thermal acclimation and hardening in tadpoles of the bullfrog, Rana catesbeiana. J. Therm. Biol. 7, 215–219 (1982).Article 

Google Scholar 
Merino-Viteri, A. R. The vulnerability of microhylid frogs, Cophixalus spp., to climate change in the Australian Wet Tropics. (James Cook University, 2018).Messerman, A. F. Tales of an ‘invisible’ life stage: Survival and physiology among terrestrial juvenile ambystomatid salamanders. (University of Missouri, 2019).Meza-Parral, Y., García-Robledo, C., Pineda, E., Escobar, F. & Donnelly, M. A. Standardized ethograms and a device for assessing amphibian thermal responses in a warming world. J. Therm. Biol. 89 (2020).Miller, K. & Packard, G. C. Critical thermal maximum: Ecotypic variation between montane and piedmont chorus frogs (Pseudacris triseriata, Hylidae). Experientia 30, 355–356 (1974).CAS 
PubMed 
Article 

Google Scholar 
Miller, K. & Packard, G. C. An altitudinal cline in critical thermal maxima of chorus frogs (Pseudacris triseriata). Am. Nat. 111, 267–277 (1977).Article 

Google Scholar 
Mueller, C. A., Bucsky, J., Korito, L. & Manzanares, S. Immediate and persistent effects of temperature on oxygen consumption and thermal tolerance in embryos and larvae of the baja California chorus frog, Pseudacris hypochondriaca. Front. Physiol. 10 (2019).Navas, C. A., Antoniazzi, M. M., Carvalho, J. E., Suzuki, H. & Jared, C. Physiological basis for diurnal activity in dispersing juvenile Bufo granulosus in the Caatinga, a Brazilian semi-arid environment. Comp. Biochem. Physiol. 147, 647–657 (2007).Article 

Google Scholar 
Navas, C. A., Úbeda, C. A., Logares, R. & Jara, F. G. Thermal tolerances in tadpoles of three species of Patagonian anurans. S. Am. J. Herpetol. 5, 89–96 (2010).Article 

Google Scholar 
Nietfeldt, J. W., Jones, S. M., Droge, D. L. & Ballinger, R. E. Rate of thermal acclimation of larval Ambystoma tigrinum. J. Herpetol. 14, 209–211 (1980).Article 

Google Scholar 
Nol, R. & Ultsch, G. R. The roles of temperature and dissolved oxygen in microhabitat selection by the tadpoles of a frog (Rana pipiens) and a toad (Bufo terrestris). Copeia 1981, 645–652 (1981).Article 

Google Scholar 
Novarro, A. J. Thermal physiology in a widespread lungless salamander. (University of Maryland, 2018).Nowakowski, A. J. et al. Thermal biology mediates responses of amphibians and reptiles to habitat modification. Ecol. Lett. 21, 345–355 (2018).PubMed 
Article 

Google Scholar 
Orille, A. C., McWhinnie, R. B., Brady, S. P. & Raffel, T. R. Positive effects of acclimation temperature on the critical thermal maxima of Ambystoma mexicanum and Xenopus laevis. J. Herpetol. 54, 289–292 (2020).Article 

Google Scholar 
Oyamaguchi, H. M. et al. Thermal sensitivity of a neotropical amphibian (Engystomops pustulosus) and its vulnerability to climate change. Biotropica 50, 326–337 (2018).Article 

Google Scholar 
Paez Vacas, M. I. Mechanisms of population divergence along elevational gradients in the tropics. (Colorado State University, 2016).Paulson, B. K. & Hutchison, V. H. Blood changes in Bufo cognatus following acute heat stress. Comp. Biochem. Physiol. 87, 461–466 (1987).CAS 
Article 

Google Scholar 
Paulson, B. & Hutchison, V. Origin of the stimulus for muscular spasms at the critical thermal maximum in anurans. Copeia 810–813 (1987).Percino-Daniel, R. et al. Environmental heterogeneity shapes physiological traits in tropical direct-developing frogs. Ecol. Evol. (2021).Perotti, M. G., Bonino, M. F., Ferraro, D. & Cruz, F. B. How sensitive are temperate tadpoles to climate change? The use of thermal physiology and niche model tools to assess vulnerability. Zoology 127, 95–105 (2018).PubMed 
Article 

Google Scholar 
Pintanel, P., Tejedo, M., Almeida-Reinoso, F., Merino-Viteri, A. & Gutiérrez-Pesquera, L. M. Critical thermal limits do not vary between wild-caught and captive-bred tadpoles of Agalychnis spurrelli (Anura: Hylidae). Diversity 12, 43 (2020).Article 

Google Scholar 
Pintanel, P., Tejedo, M., Ron, S. R., Llorente, G. A. & Merino-Viteri, A. Elevational and microclimatic drivers of thermal tolerance in Andean Pristimantis frogs. J. Biogeogr. 46, 1664–1675 (2019).Article 

Google Scholar 
Pintanel, P. Thermal adaptation of amphibians in tropical mountains. Consequences of global warming. (Universitat de Barcelona, 2018).Pintanel, P., Tejedo, M., Salinas-Ivanenko, S., Jervis, P. & Merino-Viteri, A. Predators like it hot: Thermal mismatch in a predator-prey system across an elevational tropical gradient. J. Anim. Ecol. 90, 1985–1995 (2021).PubMed 
Article 

Google Scholar 
Pough, F. H. Natural daily temperature acclimation of eastern red efts, Notophthalmus v. viridescens (Rafinesque) (Amphibia: Caudata). Comp. Biochem. Physiol. 47, 71–78 (1974).CAS 
Article 

Google Scholar 
Pough, F. H., Stewart, M. M. & Thomas, R. G. Physiological basis of habitat partitioning in Jamaican. Eleutherodactylus. Oecologia 27, 285–293 (1977).ADS 
PubMed 
Article 

Google Scholar 
Quiroga, L. B., Sanabria, E. A., Fornés, M. W., Bustos, D. A. & Tejedo, M. Sublethal concentrations of chlorpyrifos induce changes in the thermal sensitivity and tolerance of anuran tadpoles in the toad Rhinella arenarum? Chemosphere 219, 671–677 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rausch, C. The thermal ecology of the red-spotted toad, Bufo punctatus, across life history. (University of Nevada, 2007).Reichenbach, N. & Brophy, T. R. Natural history of the peaks of otter salamander (Plethodon hubrichti) along an elevational gradient. Herpetol. Bull. 141, 7–15 (2017).
Google Scholar 
Reider, K. E., Larson, D. J., Barnes, B. M. & Donnelly, M. A. Thermal adaptations to extreme freeze–thaw cycles in the high tropical Andes. Biotropica 53, 296–306 (2021).Article 

Google Scholar 
Richter-Boix, A. et al. Local divergence of thermal reaction norms among amphibian populations is affected by pond temperature variation. Evolution 69, 2210–2226 (2015).PubMed 
Article 

Google Scholar 
Riquelme, N. A., Díaz-Páez, H. & Ortiz, J. C. Thermal tolerance in the Andean toad Rhinella spinulosa (Anura: Bufonidae) at three sites located along a latitudinal gradient in Chile. J. Therm. Biol. 60, 237–245 (2016).PubMed 
Article 

Google Scholar 
Ritchart, J. P. & Hutchison, V. H. The effects of ATP and cAMP on the thermal tolerance of the mudpuppy. Necturus maculosus. J. Therm. Biol. 11, 47–51 (1986).CAS 
Article 

Google Scholar 
Rivera-Burgos, A. C. Habitat suitability for Eleutherodactylus frogs in Puerto Rico: Indexing occupancy, abundance and reproduction to climatic and habitat characteristics. (North Carolina State University, 2019).Rivera-Ordonez, J. M., Nowakowski, A. J., Manansala, A., Thompson, M. E. & Todd, B. D. Thermal niche variation among individuals of the poison frog, Oophaga pumilio, in forest and converted habitats. Biotropica 51, 747–756 (2019).Article 

Google Scholar 
Romero Barreto, P. Requerimientos fisiológicos y microambientales de dos especies de anfibios (Scinax ruber e Hyloxalus yasuni) del bosque tropical de Yasuní y sus implicaciones ante el cambio climático. (Pontificia Universidad Católica Del Ecuador, 2013).Ruiz-Aravena, M. et al. Impact of global warming at the range margins: Phenotypic plasticity and behavioral thermoregulation will buffer an endemic amphibian. Ecol. Evol. 4, 4467–4475 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Ruthsatz, K. et al. Thyroid hormone levels and temperature during development alter thermal tolerance and energetics of Xenopus laevis larvae. Conserv. Physiol. 6 (2018).Ruthsatz, K. et al. Post-metamorphic carry-over effects of altered thyroid hormone level and developmental temperature: physiological plasticity and body condition at two life stages in Rana temporaria. J. Comp. Physiol. B: Biochem. Syst. Environ. Physiol. 190, 297–315 (2020).CAS 
Article 

Google Scholar 
Rutledge, P. S., Spotila, J. R. & Easton, D. P. Heat hardening in response to two types of heat shock in the lungless salamanders Eurycea bislineata and Desmognathus ochrophaeus. J. Therm. Biol. 12, 235–241 (1987).Article 

Google Scholar 
Sanabria, E. et al. Effect of salinity on locomotor performance and thermal extremes of metamorphic Andean Toads (Rhinella spinulosa) from Monte Desert, Argentina. J. Therm. Biol. 74, 195–200 (2018).CAS 
PubMed 
Article 

Google Scholar 
Sanabria, E. A., González, E., Quiroga, L. B. & Tejedo, M. Vulnerability to warming in a desert amphibian tadpole community: the role of interpopulational variation. J. Zool. 313, 283–296 (2021).Article 

Google Scholar 
Sanabria, E. A. & Quiroga, L. B. Change in the thermal biology of tadpoles of Odontophrynus occidentalis from the Monte desert, Argentina: Responses to photoperiod. J. Therm. Biol. 36, 288–291 (2011).Article 

Google Scholar 
Sanabria, E. A., Quiroga, L. B., González, E., Moreno, D. & Cataldo, A. Thermal parameters and locomotor performance in juvenile of Pleurodema nebulosum (Anura: Leptodactylidae) from the Monte Desert. J. Therm. Biol. 38, 390–395 (2013).Article 

Google Scholar 
Sanabria, E. A., Quiroga, L. B. & Martino, A. L. Seasonal changes in the thermal tolerances of the toad Rhinella arenarum (Bufonidae) in the Monte Desert of Argentina. J. Therm. Biol. 37, 409–412 (2012).Article 

Google Scholar 
Sanabria, E. A., Quiroga, L. B. & Martino, A. L. Seasonal Changes in the thermal tolerances of Odontophrynus occidentalis (Berg, 1896) (Anura: Cycloramphidae). Belg. J. Zool. 143, 23–29 (2013).
Google Scholar 
Sanabria, E. A. et al. Thermal ecology of the post-metamorphic Andean toad (Rhinella spinulosa) at elevation in the monte desert, Argentina. J. Therm. Biol. 52, 52–57 (2015).PubMed 
Article 

Google Scholar 
Sanabria, E. A., Vaira, M., Quiroga, L. B., Akmentins, M. S. & Pereyra, L. C. Variation of thermal parameters in two different color morphs of a diurnal poison toad, Melanophryniscus rubriventris (Anura: Bufonidae). J. Therm. Biol. 41, 1–5 (2014).PubMed 
Article 

Google Scholar 
Sanabria, E. A. & Quiroga, L. B. Thermal parameters changes in males of Rhinella arenarum (Anura: Bufonidae) related to reproductive periods. Rev. Biol. Trop. 59, 347–353 (2011).PubMed 

Google Scholar 
Scheffers, B. R. et al. Thermal buffering of microhabitats is a critical factor mediating warming vulnerability of frogs in the Philippine biodiversity hotspot. Biotropica 45, 628–635 (2013).Article 

Google Scholar 
Scheffers, B. R., Edwards, D. P., Diesmos, A., Williams, S. E. & Evans, T. A. Microhabitats reduce animal’s exposure to climate extremes. Glob. Chang. Biol. 20, 495–503 (2014).ADS 
PubMed 
Article 

Google Scholar 
Schmid, W. D. High temperature tolerances of Bufo Hemiophrys and Bufo Cognatus. Ecology 46, 559–560 (1965).Article 

Google Scholar 
Sealander, J. A. & West, B. W. Critical thermal maxima of some Arkansas salamanders in relation to thermal acclimation. Herpetologica 25, 122–124 (1969).
Google Scholar 
Seibel, R. V. Variables affecting the critical thermal maximum of the leopard frog, Rana pipiens Schreber. Herpetologica 26, 208–213 (1970).
Google Scholar 
Sherman, E. Ontogenetic change in thermal tolerance of the toad Bufo woodhousii fowleri. Comp. Biochem. Physiol. 65, 227–230 (1980).ADS 
Article 

Google Scholar 
Sherman, E. Thermal biology of newts (Notophthalmus viridescens) chronically infected with a naturally occurring pathogen. J. Therm. Biol. 33, 27–31 (2008).Article 

Google Scholar 
Sherman, E., Baldwin, L., Fernandez, G. & Deurell, E. Fever and thermal tolerance in the toad Bufo marinus. J. Therm. Biol. 16, 297–301 (1991).Article 

Google Scholar 
Sherman, E. & Levitis, D. Heat hardening as a function of developmental stage in larval and juvenile Bufo americanus and Xenopus laevis. J. Therm. Biol. 28, 373–380 (2003).Article 

Google Scholar 
Shi, L., Zhao, L., Ma, X. & Ma, X. Selected body temperature and thermal tolerance of tadpoles of two frog species (Fejervarya limnocharis and Microhyla ornata) acclimated under different thermal conditions. Acta Ecol. Sin. 32, 0465–0471 (2012).Article 

Google Scholar 
Simon, M. N., Ribeiro, P. L. & Navas, C. A. Upper thermal tolerance plasticity in tropical amphibian species from contrasting habitats: Implications for warming impact prediction. J. Therm. Biol. 48, 36–44 (2015).PubMed 
Article 

Google Scholar 
Simon, M. Plasticidade fenotípica em relação à temperatura de larvas de Rhinella (Anura: Bufonidae) da caatinga e da floresta Atlântica. (Universidade de Sao Paulo, 2010).Skelly, D. K. & Freidenburg, L. K. Effects of beaver on the thermal biology of an amphibian. Ecol. Lett. 3, 483–486 (2000).Article 

Google Scholar 
Sos, T. Thermoconformity even in hot small temporary water bodies: a case study in yellow-bellied toad (Bombina v. variegata). Herpetol. Rom. 1, 1–11 (2007).
Google Scholar 
Spotila, J. R. Role of temperature and water in the ecology of lungless salamanders. Ecol. Monogr. 42, 95–125 (1972).Article 

Google Scholar 
Tracy, C. R., Christian, K. A., Betts, G. & Tracy, C. R. Body temperature and resistance to evaporative water loss in tropical Australian frogs. Comp. Biochem. Physiol. 150, 102–108 (2008).Article 

Google Scholar 
Turriago, J. L., Parra, C. A. & Bernal, M. H. Upper thermal tolerance in anuran embryos and tadpoles at constant and variable peak temperatures. Can. J. Zool. 93, 267–272 (2015).Article 

Google Scholar 
Vidal, M. A., Novoa-Muñoz, F., Werner, E., Torres, C. & Nova, R. Modeling warming predicts a physiological threshold for the extinction of the living fossil frog Calyptocephalella gayi. J. Therm. Biol. 69, 110–117 (2017).PubMed 
Article 

Google Scholar 
von May, R. et al. Divergence of thermal physiological traits in terrestrial breeding frogs along a tropical elevational gradient. Ecol. Evol. 7, 3257–3267 (2017).Article 

Google Scholar 
von May, R. et al. Thermal physiological traits in tropical lowland amphibians: Vulnerability to climate warming and cooling. PLoS ONE 14 (2019).Wagener, C., Kruger, N. & Measey, J. Progeny of Xenopus laevis from altitudinal extremes display adaptive physiological performance. J. Exp. Biol. 224 (2021).Wang, H. & Wang, L. Thermal adaptation of the common giant toad (Bufo gargarizans) at different earlier developmental stages. J. Agric. Univ. Hebei 31, 79–83 (2008).
Google Scholar 
Wang, L. The effects of constant and variable thermal acclimation on thermal tolerance of the common giant toad tadpoles (Bufo gargarizans). Acta Ecol. Sin. 34, 1030–1034 (2014).
Google Scholar 
Wang, L.-Z. & Li, X.-C. Effect of temperature on incubation and thermal tolerance of the Chinese forest frog. Chin. J. Zool. (2007).Wang, L. & Li, X.-C. Effects of constant thermal acclimation on thermal tolerance of the Chinese forest frog (Rana chensineniss). Acta Hydrobiol. Sin. 31, 748–750 (2007).CAS 

Google Scholar 
Wang, L.-Z., Li, X.-C. & Sun, T. Preferred temperature, avoidance temperature and lethal temperature of tadpoles of the common giant toad (Bufo gargarizans) and the Chinese forest frog (Rana chensinensis). Chin. J. Zool. 40, 23–27 (2005).
Google Scholar 
Warburg, M. R. On the water economy of Israel amphibians: The anurans. Comp. Biochem. Physiol. 40, 911–924 (1971).CAS 
Article 

Google Scholar 
Warburg, M. R. The water economy of Israel amphibians: The urodeles Triturus vittatus (Jenyns) and Salamandra salamandra (L.). Comp. Biochem. Physiol. 40, 1055–1056, IN11,1057–1063 (1971).Willhite, C. & Cupp, P. V. Daily rhythms of thermal tolerance in Rana clamitans (Anura: Ranidae) tadpoles. Comp. Biochem. Physiol. 72, 255–257 (1982).Article 

Google Scholar 
Wu, C.-S. & Kam, Y.-C. Thermal tolerance and thermoregulation by Taiwanese rhacophorid tadpoles (Buergeria japonica) living in geothermal hot springs and streams. Herpetologica 61, 35–46 (2005).Article 

Google Scholar 
Xu, X. The effect of temperature on body temperature and thermoregulation in different geographic populations of Rana dybowskii. (Harbin Normal University, 2017).Yandún Vela, M. C. Capacidad de aclimatación en renacuajos de dos especies de anuros: Rhinella marina (Bufonidae) y Gastrotheca riobambae (Hemiphractidae) y su vulnerabilidad al cambio climático. (Pontificia Universidad Católica Del Ecuador, 2017).Young, V. K. H. & Gifford, M. E. Limited capacity for acclimation of thermal physiology in a salamander. Desmognathus brimleyorum. J. Comp. Physiol. B: Biochem. Syst. Environ. Physiol. 183, 409–418 (2013).CAS 
Article 

Google Scholar 
Yu, Z., Dickstein, R., Magee, W. E. & Spotila, J. R. Heat shock response in the salamanders Plethodon jordani and Plethodon cinereus. J. Therm. Biol. 23, 259–265 (1998).CAS 
Article 

Google Scholar 
Zheng, R.-Q. & Liu, C.-T. Giant spiny-frog (Paa spinosa) from different populations differ in thermal preference but not in thermal tolerance. Aquat. Ecol. 44, 723–729 (2010).Article 

Google Scholar 
Zweifel, R. G. Studies on the critical thermal maxima of salamanders. Ecology 38, 64–69 (1957).Article 

Google Scholar 
Pick, J. L., Nakagawa, S. & Noble, D. W. A. Reproducible, flexible and high-throughput data extraction from primary literature: The metaDigitise r package. Methods Ecol. Evol. 10, 426–431 (2019).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing.Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).PubMed 
Article 

Google Scholar 
AmphibiaWeb. https://amphibiaweb.org. University of California, Berkeley, California, USA (2022).Schwanz, L. E. et al. Best practices for building and curating databases for comparative analyses. J. Exp. Biol. 225, jeb243295 (2022).PubMed 
Article 

Google Scholar 
Pottier, P. et al. A comprehensive database of amphibian heat tolerance, Zenodo, https://doi.org/10.5281/zenodo.6565454 (2022).Lajeunesse, M. J. Recovering Missing or Partial Data from Studies: A Survey of Conversions and Imputations for Meta-analysis. in Hanbook of Meta-analysis in Ecology and Evolution 195–206 (Princeton University Press, 2013).Nakagawa, S., et al. A robust and readily implementable method for the meta-analysis of response ratios with and without missing standard deviations. EcoEvoRxiv, https://doi.org/10.32942/osf.io/7thx9 (2022)Pottier, P., Burke, S., Drobniak, S. M., Lagisz, M. & Nakagawa, S. Sexual (in)equality? A meta-analysis of sex differences in thermal acclimation capacity across ectotherms. Funct. Ecol. 35, 2663–2678 (2021).Article 

Google Scholar 
Sunday, J. et al. Thermal tolerance patterns across latitude and elevation. Philos. Trans. R. Soc. B-Biol. Sci. 374, 20190036 (2019).Article 

Google Scholar 
Truebano, M., Fenner, P., Tills, O., Rundle, S. D. & Rezende, E. L. Thermal strategies vary with life history stage. J. Exp. Biol. 221, jeb171629 (2018).PubMed 
Article 

Google Scholar 
Rezende, E. L., Tejedo, M. & Santos, M. Estimating the adaptive potential of critical thermal limits: methodological problems and evolutionary implications. Funct. Ecol. 25, 111–121 (2011).Article 

Google Scholar 
Terblanche, J. S., Deere, J. A., Clusella-Trullas, S., Janion, C. & Chown, S. L. Critical thermal limits depend on methodological context. Proc. R. Soc. B-Biol. Sci. 274, 2935–2943 (2007).Article 

Google Scholar 
Hangartner, S., Sgrò, C. M., Connallon, T. & Booksmythe, I. Sexual dimorphism in phenotypic plasticity and persistence under environmental change: An extension of theory and meta-analysis of current data. Ecol. Lett. (2022).Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 
Article 

Google Scholar 
Dunnington, D. & Thorne, B. ggspatial: Spatial Data Framework for ggplot2. R package (2020).Brownrigg, M. R. Package ‘maps’. R package (2013).Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T.-Y. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).Article 

Google Scholar 
Xu, S. et al. ggtreeExtra: Compact visualization of richly annotated phylogenetic data. Mol. Biol. Evol. 38, 4039–4042 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Campitelli, E. ggnewscale: Multiple fill and colour scales in “ggplot2”. R package (2020).Pedersen, T. L. patchwork: The Composer of Plots. R package (2020).Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 
Article 

Google Scholar  More