Habitat assignation. We considered a species marine when a large proportion of the total population foraged in the marine environment for at least part of the year49,50. The rest of the species were classified as either aquatic freshwater or terrestrial based on ref51 for mammals and refs49,50 for birds, revised with more recent information from IUCN52. In birds, the above criteria yielded to classify 339 avian species as marine, 785 as aquatic and 8865 as terrestrial. In mammals, we excluded Chiropters from the analyses because the clade does not contain marine species and its life history has largely been shaped by flight. This led to classify 125 species as marine, 133 as aquatic and 4150 as terrestrial. For some analyses, we further subdivided marine species into primarily ‘Pelagic’ or ‘coastal’. ‘Pelagic’ species were those that primarily use marine pelagic deep water and/or marine neritic pelagic continental shelf water. ‘Coastal’ species were those that primarily use coastal inshore water (sea along coasts, typically 8 km from the shoreline) throughout the year, excluding species that may occasionally use this habitat, but do not do so typically.
Life history characterization. We characterized the life history of mammals and birds based on 7 life history traits: maximum longevity, age at first breeding, gestation/incubation time, weaning/fledging time, litters/broods per year, litter/clutch size and fecundity (i.e. the product of the last two traits)(table S1). Data were extracted from53, updated with information from51 for mammals, and from54–57 and HBW Alive (https://birdsoftheworld.org/bow/home) for birds. For birds, information on maximum longevity was complemented with data from long-term capture-recapture schemes (e.g. USGS, EURING, ABBBS or SAFRING). For species with information on three or more life history traits, we imputed the other life history traits by combining the available data with phylogenetic information, using the Rphylopars package58. To explore the co-variation between life history traits, we described the main axes of variation using a principal component analysis. Following Pigot et al.59, we centred and rescaled each life history trait to unit variance before performing PCAs. All life history traits were log10-transformed, except broods/litters per year. In both birds and mammals, the first axis captured most variation in life history (54% and 75%, respectively; Supplementary Table S2), and described well the fast-slow continuum (SupplementaryFig. S3)9,60. Because of the potential for imputed data and PCA to introduce undesirable statistical artefacts, we also ran analyses on raw, non-imputed traits61. Analyses with both raw and imputed data were highly consistent. In the main text, we present the results of the PCA that includes imputed data, along with analyses of individual life history traits based solely on available data.
Confounding variables. Comparative analyses seeking to identify the mechanisms behind the adaptive significance of a phenotypic trait need to consider the possible effects of confounding variables. Potential confounds at the macroecological scale of our analyses may include the trophic level, migratory strategy and whether the species was tropical, temperate or polar. For example, if species at higher trophic levels have slower life histories and are overrepresented among marine endotherms, this can create a spurious relationship between slow life histories and marine environments. A spurious relationship can also arise in traits that are overrepresented in non-marine species. For example, migratory species are typically characterized by fast-lived life histories. If migration is more common in non-marine species, this may lead to find differences in life history between marine and non-marine species. Finally, trade-offs in life-history can be masked by trophic level due to the fact that different species may have different amounts of resources to allocate between survival and reproduction. We used published information on trophic level (carnivore, omnivore, herbivore and scavenger), migratory strategy (migratory or resident) and whether the species was tropical, temperate, polar or widespread from the PanTHERIA62 and AVONET63. We classified species as tropical, temperate, polar, or widespread based on their breeding latitude, with the tropics defined as -23.4° to 23.4° and polar regions as below -60° or above 60°.
Phylogenetic hypotheses. We used the most comprehensive, updated phylogenies currently available, Lum et al.24 for birds and Upham et al.23 for mammals. Since our models require highly sampled clades, we included DNA-missing species randomly assigned to topological positions within taxonomic constraints (genus or family) across the credible set of trees. To deal with this and other sources of phylogenetic uncertainty, we repeated the analyses across a sample of trees (see details below).
Testing for life history differences between marine and non-marine species. Because our working hypothesis is that marine environments select for slower life histories, we first confirmed that these species differ in life history from non-terrestrial species. We used the function ‘phylolm’ in the R-package phylolm64 to test for differences in the fast-slow continuum and the function ‘mvgls’ in mvMORPH36 to test for multivariate differences in the underlying life history traits (incubation, fecundity and maximum longevity, all log-transformed). The error term was defined as Brownian motion. To deal with measurement error, we assumed that the variance of measurement errors was the same for all species, and estimated it from the data.
Evolutionary transitions between environments. We used the phylogenies to reconstruct evolutionary transitions between marine, aquatic and terrestrial environments. We used a stochastic character mapping approach that applies a Monte Carlo algorithm to sample the posterior probability distribution of ancestral states and timings of transitions on phylogenetic branches under a Markov process of evolution65,66. In our reconstructions, we considered phylogenetic uncertainty by integrating results from the 100 randomly sampled trees of the posterior distribution of phylogenies, running 5 reconstructions for each phylogenetic tree. Thus, we obtained 500 reconstructed ancestral character stages. We allowed the transitions to be asymmetrical between character stages. To do so, we used the ‘make.simmap’ function in R package phytools67 to build the stochastic character-mapped reconstructions with model ‘ARD’, and then applied the ‘describe.simmap’ function to summarize the results.
Reconstruction of ancestral characters. We used the function ‘fastAnc’ from phytools67 to estimate the ancestral values of the fast-slow for each node of the avian and mammalian phylogenies. We then extracted the values for the ancestors of the marine clades. To deal with phylogenetic uncertainty in ancestral estimations, we estimated the values for 100 phylogenies.
Testing the adaptive significance of the fast-slow: univariate approach. We examined the impact of marine environment invasion on the pace-of-life within specific clades, namely Aequorlitornithes and Anseriformes in birds, and Cetartiodactyla and Carnivora in mammals. We excluded Sirenia from the analysis due to the reduced number of species. To assess whether life history changed adaptively after the invasion of marine environments, we first used the R package OUwie68 to fit several univariate models of phenotypic evolution: 1) single-rate Brownian motion (BM1) model, indicating shared evolutionary history and random change as the best explanation for species similarity; 2) multiple-rate Brownian motion (BMS) model, indicating shared evolutionary history as the best explanation for species similarity, but allowing evolutionary rate to vary across habitats; 3) single-optimum Ornstein-Uhlenbeck (OU1) process, suggesting adaptation to a single selective regime characterized by a specific optimum trait for the entire clade; 4) multiple-optima OU (OUM), with different selective regimes and optima for marine, aquatic and terrestrial species; and 5) a Multi-rate multi-optima OU (OUMV), which also allows to the evolutionary rate to vary across habitats. More complex OU models (e.g., OUMA, OUMVA), led to frequent convergence issues and hence they were not included in the main analyses. All models were run on a random sample of 100 phylogenies, with those used in models with multi-optima sampled from stochastic character mapping trees. Model comparison was based on the second-order Akaike information criterion (AICc).
Testing the adaptive significance of the fast-slow: a multivariate approach. Because we found support for models suggesting that life history has evolved in marine environments toward a different optimum in relation to non-marine environments, we used a variety of multivariate multiple-optima OU models (mvOU) to test whether there is a significant interaction in the selective patterns for life history traits. We used the models to investigate if life history traits evolved 1) independently (setting the co-variation between alpha and sigma to zero), 2) in a correlated fashion as a response to similar selective pressures (setting the co-variation between alpha to zero), 3) in a correlated fashion because there is a statistically significant interaction between traits toward the optimum (setting the co-variation between sigma to zero; by constraining the alpha matrix but not the sigma matrix this tests for a significant interaction in the "selection" strength); and 4) a correlated fashion because with both sigma and alpha allowed to co-variate. Using the R-package mvMORPH36, we fitted these models with non-imputed data for three traits, maximum longevity, fecundity and incubation/ gestation time, which are major components of the fast-slow continuum and are available for many species (Table S1). We also used similar models to investigate the co-evolution of the fast-slow axis with body size.
Path analyses for adaptive responses in marine Cetartiodactyla and Aequorlitornithes. We used phylogenetic path analyses69,70 to investigate the links between life history and buffer adaptations to thrive in marine environments. We focused on three general buffer adaptations: a large body size30, an encephalized brain71 and a morphology for efficient locomotion35,57. Body size was extracted from PanTHERIA62 and AVONET63. Encephalization, which reflects a higher accumulation of pallial neurons and is correlated with enhanced cognition41, was estimated as the residuals of a log-log a phylogenetic generalized least square model72,73 of brain mass against body mass, with brain data extracted from ref73 for birds and ref 74 for Cetaceans. To describe morphology, we used published data on the hand-wing index57 for birds and streamlining for cetaceans35. The hand-wing index is a morphological metric linked to wing aspect ratio, and associated with avian flight efficiency and dispersal ability57. The streamlining index describes whether a whale species is more or less streamlined based on a log-linear regression of body mass versus body length, with positive residuals indicating ‘less-streamlined’ and negative residuals ‘more-streamlined’35. We estimated the residuals based on a phylogenetic generalized least square model, with body mass and body length data from ref35. We decided to exclude ‘Balaena mysticetus’ from our analysis due to its outlier status and the inability to verify its data, as it originated from a single individual. In mammals, lifespan has also been linked to social organization75, yet current information on social cohesion is insufficient to test the relevance of this factor. To further delve into foraging links, we used data on the main foraging strategy from ref 76 for marine Cetartiodactyla (capturing single prey, either primarily squids or other vertebrates and filtering zooplankton) and from the HBW Alive (https://birdsoftheworld.org/bow/home) for marine Aequorlitornithes (dipping, generalist, lunge diving, pursuit diving and surface seizing). For Cetartiodactyla, we also used data on fasting strategy (fasting vs non-fasting) and social complexity (mostly social, mostly solitary, and both social and solitary) from ref76 (see main text for justification).
- Croxall, J. P. et al. Seabird conservation status, threats and priority actions: A global assessment. Bird Conservation International22, 1–34 (2012).
- Oppel, S. et al. Spatial scales of marine conservation management for breeding seabirds. Marine Policy98, 37–46 (2018).
- Soria, C. D., Pacifici, M., Marco, M. D., Stephen, S. M. & Rondinini, C. COMBINE: a coalesced mammal database of intrinsic and extrinsic traits. Ecology102, (2021).
- IUCN. 2023. The IUCN Red List of Threatened Species. Version 2023-1. https://www.iucnredlist.org.
- Myhrvold, N. P. et al. An amniote life‐history database to perform comparative analyses with birds, mammals, and reptiles: Ecological Archives E096‐269. Ecology96, 3109–3109 (2015).
- Sol, D., Sayol, F., Ducatez, S. & Lefebvre, L. The life-history basis of behavioural innovations. Phil. Trans. R. Soc. B371, 20150187 (2016).
- Gonzalez‐Voyer, A. et al. Sex roles in birds: Phylogenetic analyses of the influence of climate, life histories and social environment. Ecology Letters25, 647–660 (2022).
- Bird, J. P. et al. Generation lengths of the world’s birds and their implications for extinction risk. Conservation Biology34, 1252–1261 (2020).
- Sheard, C. et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nature Communications11, (2020).
- Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods in Ecology and Evolution8, 22–27 (2017).
- Pigot, A. L. et al. Macroevolutionary convergence connects morphological form to ecological function in birds. Nature Ecology & Evolution4, 230–239 (2020).
- Hernández-Yáñez, H., Kim, S. Y. & Che-Castaldo, J. P. Demographic and life history traits explain patterns in species vulnerability to extinction. PLoS ONE17, e0263504 (2022).
- Davis, A. M. & Betancur-R, R. Widespread ecomorphological convergence in multiple fish families spanning the marine–freshwater interface. Proc. R. Soc. B.284, 20170565 (2017).
- Jones, K. E. et al. PanTHERIA: a species‐level database of life history, ecology, and geography of extant and recently extinct mammals: Ecological Archives E090‐184. Ecology90, 2648–2648 (2009).
- Tobias, J. A. AVONET: morphological, ecological and geographical data for all birds. Ecology Letters (2021).
- Lam, A. et al. Package ‘ phylolm ’. (2016).
- Bollback, J. P. SIMMAP: Stochastic character mapping of discrete traits on phylogenies. BMC Bioinformatics7, 88 (2006).
- Revell, L. J. A comment on the use of stochastic character maps to estimate evolutionary rate variation in a continuously valued trait. Systematic Biology62, 339–345 (2013).
- Revell, M. L. J. Package ‘ phytools ’. (2012).
- Beaulieu, A. J. M., Meara, B. O. & Beaulieu, M. J. M. Package ‘ OUwie ’. (2012).
- Hardenberg, A. von & Gonzalez-Voyer, A. Disentangling evolutionary cause-effect relationships with phylogenetic confirmatory path analysis. Evolution67, 378–387 (2013).
- Bijl, W. van der. phylopath: Easy phylogenetic path analysis in R. PeerJ2018, (2018).
- Sol, D. Revisiting the cognitive buffer hypothesis for the evolution of large brains. Biol. Lett.5, 130–133 (2009).
- Orme, D. The caper package: comparative analysis of phylogenetics and evolution in R. R package version 0.5, 2 1–36 (2013) doi:1.
- Sayol, F., Downing, P. A., Iwaniuk, A. N., Maspons, J. & Sol, D. Predictable evolution towards larger brains in birds colonizing oceanic islands. Nat Commun9, 2820 (2018).
- Ridgway, S. H., Carlin, K. P. & Alstyne, K. R. V. Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls. Marine Mammal Science34, 420–439 (2018).
- Zhu, P. et al. Correlated evolution of social organization and lifespan in mammals. Nature Communications14, (2023).
- Albouy, C. et al. Global vulnerability of marine mammals to global warming. Sci Rep10, 548 (2020)-