Deciphering the evolutionary origin of the exceptional slow pace-of-life of marine endotherms

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Given that reproducing earlier can also ensure that offspring start breeding sooner, there is strong pressure on individuals to reproduce as early as possible. Why, then, some organisms mature late, defer reproduction and age slowly? Major evolutionary transitions in life history are believed to result from the invasion of niches altering extrinsic mortality and imposing new adaptive challenges. However, limited research on these transitions has hindered understanding their role in the evolution of extremely slow-lived strategies. We reveal here that the invasion of marine environments by birds and mammals triggered profound adaptive shifts towards extremely slower life histories, particularly in pelagic lineages. Such life history convergences were facilitated by the slow-paced nature of their non-marine ancestors, and were associated with adaptations for enhanced energy acquisition and storage, facilitating a long reproductive lifespan at the expense of extended development. Ancestral traits and lifestyle changes might thus have been essential in shaping the evolution of exceptionally slow life histories. Biological sciences/Ecology/Evolutionary ecology Biological sciences/Evolution/Evolutionary theory Figures Figure 1 Figure 2 Figure 3 Figure 4 Main Life history theory has achieved considerable success in explaining why some organisms live fast and die young 5 . Instead, the evolutionary reasons why others mature late, defer reproduction and age slowly remain less well-understood 6 . A wide-held hypothesis is that these organisms live in environments where extrinsic mortality is low 2–4 . When the level of extrinsic mortality decreases for adults, selection is predicted to extend lifespan by removing deleterious genes and directing greater investment in building and maintaining a durable soma 1,7 . However, relaxing extrinsic mortality seems insufficient to explain the evolution of extremely slow life histories. For such a strategy to evolve, the fitness contribution of individuals to reproductive success should shift from juveniles to adults 8,9 , and this necessarily involves the acquisition of adaptations that buffer adults against extrinsic mortality. If developing these adaptations requires diverting important resources and time, increasing the fitness value of adults should also result in reduced reproductive effort and delayed onset of first reproduction 1 . While life history evolution is marked by major niche changes that have altered the selective pressures on traits affecting age-specific mortality 10,11 , limited exploration of these scenarios has hindered a better understanding of the evolution of slow-lived strategies 6 . For terrestrial vertebrates, a particularly relevant scenario is the invasion of the marine environment. Some of the most fascinating and enigmatic examples of extremely long longevities are found in terrestrial endotherms that have returned to the sea 12–14 . Whales boast longer lifespans than most contemporary mammals, with the potential to reach up to 200 years. Among birds, albatrosses hold the record for maximum lifespan, living for up to 70 years. The remarkable longevity of these marine endotherms is associated with delayed reproduction, longer development periods and reduced fecundity, raising the intriguing hypothesis that the invasion of marine environments has favored the evolution of a slow pace-of-life 13,14 . This theory is grounded in the unique challenges posed by marine habitats. While in the sea the risk of predation is relatively low for endotherms, a major challenge is the need to exploit widely dispersed, commonly clumped prey that have low spatial-temporal predictability 12,14–16 . Exploiting marine prey also requires foraging strategies that differ from those used on the mainland, and that must often be adapted to harsh climatic conditions —as marine birds and mammals exhibit their greatest diversity in cold, temperate waters 17 . In environments where resources are scarce or challenging to obtain but the threats for adults are not as severe, life history theory predicts that it may pay to delay reproduction and invest in adaptations that extend lifespan and iterated reproduction 1,2 . The questions of whether and how the invasion of marine environments have favored the evolution of slow life histories remains unresolved. Alternative, non-exclusive explanations include historical legacies linked to phylogenetic inertia and idiosyncratic responses to specific marine lifestyles within clades. Although the existence of repeated, independent transitions between land and sea provides good opportunities for macroevolutionary analyses 18 , contrasting the above hypotheses is challenging because life history traits do not leave traces in the fossil record. Yet, new advances in phylogenetic reconstructions and retrospective comparative analyses 19–21 provide now opportunities to infer past evolutionary changes based on information about extant species. We use here these advances to explore the evolution of extremely slow life histories in marine endotherms (birds and mammals). Recolonization of the sea Despite oceans comprise 70% of the planet, only a few terrestrial animal clades have successfully colonized the sea 22 ( Fig. 1A ). Using species-level information from 9,991 birds and 4,408 mammals ( table S1 ), we reconstructed transitions from terrestrial, aquatic freshwater and marine environments in calibrated phylogenies 23,24 by means of stochastic character mappings 25 ( Supplementary Figs. S1 and S2 ). In birds, the average number of independent transitions to marine lifestyles was 10.7 (CI 95 = 7-15), all within two major radiations (Aequorlitornithes and Anseriformes). In mammals, the number of transitions was 8.6 (CI 95 = 5.0-15.5) in three major radiations (Cetartiodactyla, Carnivora and Sirenia). Interestingly, transitions to a marine life generally occurred from aquatic ancestors, but rarely directly from terrestrial ancestors ( Supplementary Fig. S3 ). Likewise, transitions from marine to terrestrial environments have also been extremely rare ( Supplementary Fig. S3 ). While these figures are probably underestimations, as suggested by analyses of the fossil record 22 , both the scarcity of independent invasions from non-marine ancestors and the gradual nature of the transitions highlight the challenges that marine environments pose for colonization. Marine endotherms in the fast-slow continuum To investigate whether the invasion of marine environments has led to changes in life history, we described the fast-slow continuum based on seven relevant life history traits: maximum longevity, age at first breeding, gestation/incubation time, weaning/fledging time, litters/broods per year, litter/clutch size and fecundity ( Supplementary T able S2 ). The position of species along the fast-slow continuum was assigned using a Principal Component Analysis. The fast-slow continuum was the first axis ( Supplementary T able S3 ), characterized by positive values for longevity and developmental traits, and negative values for fecundity traits. The species' position on this continuum reflected variation in life expectancy post-maturity, generation time and elasticity in fecundity derived from demographic matrices ( Supplementary Fig. S4 ), underscoring the effectiveness of the axis in capturing the trade-off between survival and fecundity underpinning the fast-slow continuum 1,26 . Along the fast-slow continuum, marine species tend to consistently occupy the slower extreme ( Fig. 1B-I ), with the only exception of marine ducks ( Fig. 1F ). Phylogenetic multivariate analyses on traits describing longevity, fecundity and development time statistically confirmed the pattern ( Supplementary Fig. S5 ). Marine species show lower fecundity yet longer lifespan and development periods compared with species that are primarily aquatic or terrestrial ( Supplementary Fig. S6 ), clear signatures of a slow pace-of-life. Although life history is known to vary with trophic niche, migratory tendency and climatic region, the above pattern held when these alternative explanations were considered in the analyses ( Supplementary Fig. S7 ). Thus, our results confirm and generalize that marine birds and mammals are at the slow extreme of the fast-slow continuum. The adaptive significance of slow-lived strategies Phylogenetic reconstructions suggest that the ancestors of lineages transitioning to marine lifestyles generally had a slow pace of life ( Fig. 1; Supplementary Figs. S8-S9 ). This is true for all clades except Anseriformes. While phylogenetic reconstructions are prone to uncertainties, the consistent results across the three major marine radiations underscore the importance of a slow life history for successful colonization of marine environments; it suggests that the successful colonization of marine environments likely demanded the life history to be co-opted to thrive in these environments. Having a slow ancestor is relevant because whether a lineage evolves toward a fast or slow life history can be constrained by the initial position of the ancestor in the fast-slow continuum 27 . This opens the possibility that the marine environment may have selected for even slower life histories ( Fig. 1 ). An appropriate framework to address this hypothesis is to base the analyses on an Ornstein-Uhlenbeck (OU) process 19–21 . Unlike the commonly used Brownian motion process, which assumes that traits evolve along a lineage through stochastic changes, the OU process also considers the possibility that the trait changes around one or several adaptive optima dictated by different selective regimes. To test whether transitions from aquatic to marine environments have selected for slower life histories, we fitted OU models in a sample of 100 stochastic character mapping reconstructions. The analysis revealed that in all main marine radiations except Anseriformes, an OU model with multiple optima (OUMV) provides a superior fit to the data compared to models featuring a single optimum (OU1) or Brownian motion models (BM) with either a single or multiple evolutionary rates ( Table 1 ). These models estimate a slower life history optimum (parameter θ in the OU model) for marine species in comparison to terrestrial and aquatic species ( Fig. 2 ; figs. S10-S13 ), indicating a convergent evolutionary trend toward slower life histories. Table 1. Model selection for the evolution of the fast-slow continuum in birds and mammals. The models were estimated with the package OUwie. Values are the average AICc over a sample of 100 stochastic character mappings. Within parentheses, it is shown the percentage of times each model has the lowest AICc. The AICc of the most frequently selected models is shown in bold. Abbreviations: Aequol = Aequolitornithes, Anser = Anseriformes, Cetar = Cetartiodactyla and Carn = Carnivora. For details on the models, we refer the reader to the Supplementary material. Model Type Optima (θ) Alpha (𝛼) Sigma (σ) AICc Aequol AICc Anser AICc Cetar AICc Carn 1 BM1 - - single 1035.9 (0%) 358.8 (0%) 403.1 (0%) 467.8 (0%) 2 BMS - - multiple 1014.8 (0%) 351.9 (9%) 386.9 (15%) 449.3 (0%) 3 OU1 single single single 1007.7 (0%) 349.2 (44%) 400.8 (0%) 455.1 (0%) 4 OUM multiple single single 983.2 (7%) 349.4 (14%) 395.3 (0%) 443.7 (0%) 5 OUMV multiple single multiple 973.1 (93%) 348.1 (33%) 382.8 (85%) 430.8 (100%) The marine environment exhibits significant heterogeneity 28 . Coastal areas, in particular, boast a higher abundance and diversity of resources in comparison to the vast open ocean 29 . If the challenging and unpredictable nature of the marine environment is the driving force behind selection for slower life histories, one could reasonably expect distinct optima for coastal and oceanic species. Indeed, marine Aequolitornithes tend to exhibit slower strategies than Anseriformes while Cetaceans exhibit slower strategies than Carnivores, in line with their predominantly pelagic habits. Within Aequolitornithes, the only clade with a good representation of both pelagic and coastal species ( table S1 ), an OU model with distinct optima for pelagic and coastal species outperforms a model where both environments are pooled together (average AICc = 897.2 vs 973.1; fig. S14 ), with lower AICc in 78% of the models ran with the 100 stochastic character mappings. All the above conclusions hold when jointly modelling longevity, fecundity and development time with multivariate multi-regime OU models ( Supplementary Fig. S15 ), underscoring the robustness of the results. Body size and the slow-lived strategy Several adaptive theories might explain how birds and mammals living in the sea have evolved slower life histories, but one that is expected to be particularly relevant is selection for larger body size 30–32 . Body size is a major correlate of the fast-slow continuum 1,33 , and there is evidence that clades that have diversified in open water have experienced selection for larger body size 31 . A large body may provide advantages when foraging requires the exploration of large areas, as it allows for reduced metabolic rates and the storage of significant energy reserves, thereby reducing the need for frequent foraging to meet energetic needs. A large body also provides insulation against cold water temperatures, and access to prey that smaller animals cannot handle ¾such as deep-sea organisms that require long dives or small prey that must be amassed in large amount with a single foraging bout 34 . Thus, even though a large body demands extended development and a greater food supply for maintenance, these costs may be counterbalanced by a substantial improvement in survival prospects during sea foraging, ultimately enabling longer reproductive spans 35 . In a multivariate OU framework, the co-evolution of life history and body size can be analyzed by estimating the covariance in the strength of selection (parameter alpha, 𝛼), which describes the extent to which the evolution of life history is influenced by body size or vice-versa 36 . Jointly modelling the fast-slow and body size by means of a bi-variate mvOUM, we found that models where 𝛼 is allowed to co-vary were generally favored over other models ( table S4 ). The best mvOUM also allowed covariance in the stochastic element sigma ( σ ). This might reflect a situation where life history and body size also respond to similar factors (like secondary adaptive optima or developmental/functional constrains), in addition to being constrained by each other. A phylomorphospace suggests that while the slower strategy in Anseriformes and Carnivores can mainly be explained by allometric effects, in Aequalithornites and Cetartiodactyla there is a notable grade shift in the allometric curve accompanied by a more relaxed association between life history and body size ( Fig. 3 ). Importantly, marine Aequalithornites and Cetartiodactyla stand out as the clades containing the species with more pelagic habits and the slowest life histories among all endotherms ( Supplementary Figs. S16, S17 ). Consequently, these two clades present valuable opportunities for delving into adaptations other than body size that may shed further light on the underlying factors contributing to their distinctive life histories. Adaptive basis of the slow-lived strategy The challenges of capitalizing on the spatial and temporal aggregation of marine food resources are contingent on the nature of the prey consumed and the adaptations for efficient resource acquisition 12 . In cetaceans, for example, species that mainly feed on plankton (Mysticetes) face abundant but sporadic food availability. This not only requires large bodies to amass prey in large amounts and convert them into fat reserves, but also high mobility to efficiently track such temporarily and spatially variable food resources 37 . Indeed, large whales can make long seasonal migrations between the tropics and polar waters, which can involve several months without feeding. Odontocetes, instead, are smaller and primarily prey on fish and squids, resources that are more predictable but more challenging to capture. Exploiting these prey may require accumulating knowledge and socially learning a variety of complex foraging skills, including cooperative hunting and tool use, which may have been facilitated by an encephalized brain and enhanced body maneuverability 38,39 . A phylogenetic path analysis 40 supports the importance of encephalization and high mobility in cetaceans’ life history ( Fig. 4 ). The best supported model highlights the central importance of body size, but also reveals that slow-lived species are characterized by highly encephalized brains and streamlined body types that offer less resistance to move throughout water ( Fig. 4; Supplementary Figs. S18-S19 ). These correlates may signify the advantages of such adaptations in providing "buffer adaptations" against environmental unpredictability, enhancing survival and enabling longer lives. At the same time, the positive associations of body size and encephalization with development time suggests secondary consequences for life history in terms of extended growth and maturation. Restricting the analysis to odontocetes generally aligns with these discoveries but, in line with their dependence on single-prey and their tendency for shorter-distance movements, suggests a diminished significance of body size and shape ( Fig. 4; Supplementary Figs. S19 -S22 ). Instead, encephalization gains greater importance, especially in social cetaceans. In Aequalithornites, flight limits body size and the capacity to accumulate reserves. However, being large enough to accumulate fat can still be important in some species, particularly those that have either lost the ability to fly or utilize dynamic soaring to reduce travel costs. Additionally, it may be essential for nestlings to endure extended periods without food. Another factor potentially influencing their life history is the need to travel long distances to meet daily energy requirements and feed the offspring. Albatrosses, shearwaters and petrels can travel vast distances to forage, sometimes covering up to 1,000 km in a single day 14 . This remarkable ability, which is facilitated by their skill in extracting energy from wind currents to soar, not only enables them to efficiently track changes in food abundance, but it also provides the freedom to choose remote, secure breeding locations that may be far from optimal foraging areas. Besides body size and mobility, cognition can also be relevant for marine birds that rely on resources that vary in time and space, notably to create visual maps of suitable foraging areas in the vast areas of the sea or to develop complex foraging techniques to capture prey in the water. In these birds, acquiring information on resource availability and improving foraging skills may take several years. Similar to cetaceans, a phylogenetic path analysis supports the significance of body size, flying efficiency and encephalization for the life history of seabirds. Their remarkable slow life histories are associated with larger bodies, but also with high levels of encephalization and wing morphologies better suited for long-distance movements ( Fig. 4C,D; fig. S23 ). As in cetaceans, the correlation between these adaptations and life history is likely indicative of both benefits and developmental costs (see also 14,41 ). The way life history co-varies with body size, encephalization and mobility also changes depending on the foraging strategy, being more pronounced in pelagic species that use surface seizing to access vastly distributed resources that vary in time and space ( Fig. 4D; Supplementary Figs. S24-S25 ). Thus, our results support the notion that the slow life history of marine endotherms is not merely a consequence of their large body but also reflects other adaptations to efficiently exploit marine resources. While our analyses focus on general adaptations to understand evolutionary convergences across birds and mammals, this does not preclude the relevance of more specific adaptations for particular taxa 14,16 . Longevity as a central component of the slow-pace-of-life Our analyses suggest that the evolution towards extremely slow life histories partly reflects selection for adaptations that enable the efficient exploitation of challenging food resources. Although these adaptations often come at the cost of longer development, the major expected fitness benefit that compensates for such a cost is improved adult survival, thereby allowing an extended reproductive lifespan. The central role of longevity for fitness is supported by growing evidence from long-term population studies 42 ( Supplementary Fig. S26 ). In the Kittiwake ( Rissa tridactyla ), which can live up to 28 years, between 80-83% of variation in lifetime reproductive success can be attributed to longevity 43 . When the contribution of older age classes to reproductive success increases, longevity can be extended because selection becomes more efficient against the accumulation of deleterious mutations or antagonistic pleiotropic, slowing the aging process and decreasing intrinsic mortality. The recent analysis of the genome and transcriptome of the bowhead whale has revealed selection for genes related to cell cycle, DNA repair, cancer, and aging 44 , substantiating such a possibility. When fitness largely depends on a long reproductive life, a reduction in fecundity is expected to mitigate the costs of reproduction 1,26 . This cost has been well-documented in marine endotherms, and is exemplified by individuals skipping reproduction when conditions are unfavorable 45 . The severe energy limitations imposed by pelagic ecosystems might also directly select for reduced fecundity by limiting the number of offspring parents can successfully raise 14 . In seabirds that need to travel long distances to find food, offspring number may be constrained by the amount of food that parents can bring in each trip. Similarly, in migratory whales where breeding females spend several weeks without feeding while nursing their calves, commitment to lactation may restrict their capacity to care for a larger number of offspring. When energy limitations are high and development takes a long time, a more successful strategy than producing many offspring is to invest in a few larger offspring that are better prepared to withstand adverse weather conditions and extended periods without food 11,15 . Such a strategy ensures that at least some offspring will survive to pass the parents' genes to the next generation, particularly significant considering that a low fecundity implies producing fewer offspring during their lifetime. Compared to fecundity, the reasons why marine endotherms delay the onset of first reproduction appear less obvious because postponing reproduction should reduce the duration of the reproductive life. However, selection may favor postponing reproduction if breeding earlier imposes mortality costs and is unlikely to produce viable offspring 46 . In elephant seals ( Mirounga angustirostris ), females that bred early have decreased lifespan, low weaning success, and lower lifetime reproductive success than females that postpone first breeding 47 . Postponing reproduction is often interpreted as reflecting insufficient maturation. Bowhead whales only reach their final body size when 40 or 50 years old, implying that foraging proficiency can be impaired for long periods. The time required to acquire sufficient foraging proficiency is often cited as an explanation for why some marine endotherms exhibit some of the longest periods to independence. Towards a lifestyle perspective of life history evolution Our study highlights that ancestral legacies and adaptations to exploit scarce or challenging food resources have favored convergent evolution towards slow life histories in endotherms that secondarily returned to the marine environment. Although the exact evolutionary pathways remain idiosyncratic, due to differences in ancestors and the new niches occupied 48 , our analyses suggest that such evolutionary convergences partially arise because the adaptations necessary to thrive in marine environments are costly to produce; yet, once developed, they offer protection against extrinsic mortality. As a result, the age-specific contribution to fitness has further shifted from juveniles to adults. Thus, our findings support claims to broaden life history theory 6 , highlighting the need to move beyond classic “mouse-to-elephant curves” and “relaxed extrinsic mortality” paradigms to consider the central role of the ecological lifestyle of organisms in favoring slow-lived strategies. Declarations Acknowledgments: We thank Jose Luís Copete for helping us in gathering data, and Jeremy Bellieur and Julien Clavel for advice in the use of their respective R -packages OUwie and mvMorph. Funding: This research was funded by MINECO (PID2020-119514GB-I00 to DS) Author contributions: Original idea: DS, AHM Conceptualization: DS Conceptualization – refinements: MG, DO, AHM Methodology: DS Data collection: AP, LO, DS Analysis: DS, AP, LO Funding acquisition: DS Writing – original draft: DS Writing – review & editing: DS, AP, LO, MG, DO, AHM Competing interests: Authors declare that they have no competing interests. Data and materials availability: All data and code used in the analysis will be available in Dryad. References Stearns, S. C. The Evolution of Life Histories . (Oxford University Press, 1992). Reznick, D., Bryant, M. J. & Bashey, F. r- and K- selection revisited: The role of population regulation in life-history evolution. Ecology 83 , 1509–1520 (2002). Shattuck, M. R. & Williams, S. A. Arboreality has allowed for the evolution of increased longevity in mammals. Proceedings of the National Academy of Sciences 107 , 4635–4639 (2010). Healy, K. et al. 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Evolution 70 , 296–313 (2016). 47. Boeuf, B. L., Condit, R. & Reiter, J. Lifetime reproductive success of northern elephant seals (Mirounga angustirostris). Canadian Journal of Zoology 97 , 1 Methods 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 year 49,50 . The rest of the species were classified as either aquatic freshwater or terrestrial based on ref 51 for mammals and refs 49,50 for birds, revised with more recent information from IUCN 52 . 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 from 53 , updated with information from 51 for mammals, and from 54–57 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 package 58 . 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 log 10 -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 T able S2 ), and described well the fast-slow continuum ( Supplementary F ig. 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 traits 61 . 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 PanTHERIA 62 and AVONET 63 . 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 phylolm 64 to test for differences in the fast-slow continuum and the function ‘mvgls’ in mvMORPH 36 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 evolution 65,66 . 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 phytools 67 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 phytools 67 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 OUwie 68 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 mvMORPH 36 , 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 analyses 69,70 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 size 30 , an encephalized brain 71 and a morphology for efficient locomotion 35,57 . Body size was extracted from PanTHERIA 62 and AVONET 63 . Encephalization, which reflects a higher accumulation of pallial neurons and is correlated with enhanced cognition 41 , was estimated as the residuals of a log-log a phylogenetic generalized least square model 72,73 of brain mass against body mass, with brain data extracted from ref 73 for birds and ref 74 for Cetaceans. To describe morphology, we used published data on the hand-wing index 57 for birds and streamlining for cetaceans 35 . The hand-wing index is a morphological metric linked to wing aspect ratio, and associated with avian flight efficiency and dispersal ability 57 . 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 ref 35 . 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 organization 75 , 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 ref 76 (see main text for justification). Croxall, J. P. et al. Seabird conservation status, threats and priority actions: A global assessment. Bird Conservation International 22 , 1–34 (2012). Oppel, S. et al. Spatial scales of marine conservation management for breeding seabirds. Marine Policy 98 , 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. Ecology 102 , (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. Ecology 96 , 3109–3109 (2015). Sol, D., Sayol, F., Ducatez, S. & Lefebvre, L. The life-history basis of behavioural innovations. Phil. Trans. R. Soc. B 371 , 20150187 (2016). Gonzalez‐Voyer, A. et al. Sex roles in birds: Phylogenetic analyses of the influence of climate, life histories and social environment. Ecology Letters 25 , 647–660 (2022). Bird, J. P. et al. Generation lengths of the world’s birds and their implications for extinction risk. Conservation Biology 34 , 1252–1261 (2020). Sheard, C. et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nature Communications 11 , (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 Evolution 8 , 22–27 (2017). Pigot, A. L. et al. Macroevolutionary convergence connects morphological form to ecological function in birds. Nature Ecology & Evolution 4 , 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 ONE 17 , 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. Ecology 90 , 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 Bioinformatics 7 , 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 Biology 62 , 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. Evolution 67 , 378–387 (2013). Bijl, W. van der. phylopath: Easy phylogenetic path analysis in R. PeerJ 2018 , (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 Commun 9 , 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 Science 34 , 420–439 (2018). Zhu, P. et al. Correlated evolution of social organization and lifespan in mammals. Nature Communications 14 , (2023). Albouy, C. et al. Global vulnerability of marine mammals to global warming. Sci Rep 10 , 548 (2020)- Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4863546","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":340587118,"identity":"2aef2b0e-7695-49f4-b2cb-6c6aec5f8ca0","order_by":0,"name":"Daniel Sol","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDCCA0D8oALEYnwA5RKjJeEMiMVsAOUyE6ElsY0ULXwH2B8+SJxnl8/PfpiBmafmDgN/+3n8rpM8wGNskLgt2XJmTzJQy7FnDBJnkvHbYnCAh00icdsBA4MD+QeYedgOMxgwENTC/kwicc4BA/vzj4G2/ANq4X9MSAuDmURiA9AWCaDDeNuAWiQI2CJ5GOiXhGPJBhI3HjMcnNv3jAfIMMCrhe94+8MHH2rsDPj7kxkfvPl2R46/P/EBfmuQI+EAEPPgVz4KRsEoGAWjgCgAAFjERK64IHprAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6346-6949","institution":"CREAF (Center for Ecological Research and Applied Forestries)","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Sol","suffix":""},{"id":340587119,"identity":"77fb1e42-74c8-4974-bc2a-64cfb4dbb7b6","order_by":1,"name":"Anton Prego","email":"","orcid":"","institution":"Universitat de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Anton","middleName":"","lastName":"Prego","suffix":""},{"id":340587120,"identity":"9f9a458e-abe8-43a9-aed4-ec79fbe6fdaa","order_by":2,"name":"Laura Olive","email":"","orcid":"","institution":"CREAF (Center for Ecological Research and Applied Forestries)","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Olive","suffix":""},{"id":340587121,"identity":"75dc8fa7-7b7c-4714-af9c-fb40e23dfa6c","order_by":3,"name":"Meritxell Genovart","email":"","orcid":"https://orcid.org/0000-0003-2919-1288","institution":"CEAB (CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Meritxell","middleName":"","lastName":"Genovart","suffix":""},{"id":340587122,"identity":"bccc7210-3c2c-475e-a955-07f34c7d5268","order_by":4,"name":"Daniel Oro","email":"","orcid":"https://orcid.org/0000-0003-4782-3007","institution":"CEAB (CSIC)","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Oro","suffix":""},{"id":340587123,"identity":"0a25f86f-7f2a-42cd-95ad-f9fb80e5c565","order_by":5,"name":"Antonio Hernandez-Matías","email":"","orcid":"","institution":"Universitat de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Hernandez-Matías","suffix":""}],"badges":[],"createdAt":"2024-08-05 16:55:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4863546/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4863546/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-59273-5","type":"published","date":"2025-05-08T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64474936,"identity":"9fa1dd02-0d41-4933-bb2d-becbafffb235","added_by":"auto","created_at":"2024-09-13 15:15:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMajor marine radiations in birds and mammals and position of species in the fast-slow continuum. A.\u003c/strong\u003e Taxonomic Orders containing marine species. \u003cstrong\u003eB-I.\u003c/strong\u003e Density plots describing variation in the fast-slow continuum among marine, aquatic and terrestrial species. Smaller values along the fast-slow axis indicate faster life histories, while higher values correspond to slower life histories. The red line represents the estimated value of the ancestor of the marine species, estimated for 100 phylogenies with the function fastAnc from Phytools. The position of marine species in the fast-slow continuum is shown for all birds (\u003cstrong\u003eB\u003c/strong\u003e) and mammals (\u003cstrong\u003eC\u003c/strong\u003e), and within major marine radiations: Aequolitornithes, split in non-Charadriformes (\u003cstrong\u003eD\u003c/strong\u003e) and Charadriiformes (\u003cstrong\u003eE\u003c/strong\u003e), Anseriformes (\u003cstrong\u003eF\u003c/strong\u003e), Cetartiodactyla (\u003cstrong\u003eG\u003c/strong\u003e), Carnivora (\u003cstrong\u003eH\u003c/strong\u003e) and Sirenia (\u003cstrong\u003eI\u003c/strong\u003e)). Sample sizes (species number, birds/mammals): Marine = 339/125, Aquatic = 786/133, Terrestrial = 8,866/4,150.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4863546/v1/b87255e149aa1196dcbae143.png"},{"id":64475761,"identity":"175479b7-d2c7-4af7-a9cc-194be545b577","added_by":"auto","created_at":"2024-09-13 15:23:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53664,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenograms for the four main radiations of marine endotherms. \u003c/strong\u003eThe phenograms are represented for Aequolitornithes (\u003cstrong\u003eA\u003c/strong\u003e), Anseriformes (\u003cstrong\u003eB\u003c/strong\u003e), Cetartiodactyla (\u003cstrong\u003eC\u003c/strong\u003e) and Carnivora (\u003cstrong\u003eD\u003c/strong\u003e). Inside panels show the kernel probability density of the optima (\u003cem\u003eθ\u003c/em\u003e) estimated from OUMVs based on a sample of 100 stochastic character mappings (see \u003cstrong\u003efigs. S11-S15\u003c/strong\u003e for estimations of θ for particular phylogenies). Color codes like in \u003cstrong\u003eFig. 1\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4863546/v1/320c584aa299e8bea8312dcd.png"},{"id":64474935,"identity":"3acb7445-b270-4f24-bced-983053c77dc2","added_by":"auto","created_at":"2024-09-13 15:15:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46684,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylomorphospace representing the fast-slow continuum as a function of body size for Aequolitornithes (A), Anseriformes (B), Cetartiodactyla (C) and Carnivora (D). \u003c/strong\u003eColor codes like in Fig. 1.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4863546/v1/069ba2a499ff693c28e5d22b.png"},{"id":64474933,"identity":"bbbf9bf2-590b-4bca-baec-90b9b070588a","added_by":"auto","created_at":"2024-09-13 15:15:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneral adaptations proposed to have slowed down the pace of life to track temporal and spatial variation in food resources. \u003c/strong\u003eCausal associations tested (\u003cstrong\u003eA\u003c/strong\u003e) and best causal models based on phylogenetic path analyses for marine Cetartiodactyla (\u003cstrong\u003eB\u003c/strong\u003e), marine Cetartiodactyla excluding Mysticetes (\u003cstrong\u003eC\u003c/strong\u003e), marine Aequalithornites (\u003cstrong\u003eD\u003c/strong\u003e), and marine Aequalithornites excluding pinguins (\u003cstrong\u003eE\u003c/strong\u003e). The scenarios address the possibility that variation in the slow pace-of-life is driven by 1) direct causal links between the studied adaptations and longevity, 2) common causes (development and development-mediated by body size), and 3) indirect causes (e.g. locomotory morphology affects longevity through its relationship with body size or body size explains variation in body size because its relationship with locomotor morphology). Purple arrows denote positive effects, while brown arrows represent negative effects. Although fecundity is a major component of the fast-slow continuum, it is not included here because, in slow-lived animals, fecundity variation has less significance for fitness and most marine species exhibit similarly low fecundities. Abbreviations: LO = Maximum longevity; BO = Body size; RB = Relative brain size; ST = Streamlined body type; GE = Generation time; IN = Incubation time; WI = Hand-wing index.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4863546/v1/a389665f476c82ff989bc032.png"},{"id":82235551,"identity":"759291a4-3f90-40b0-bf53-9a96dec8f337","added_by":"auto","created_at":"2025-05-08 07:05:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1666642,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4863546/v1/0b010c15-6779-4936-93f6-7de6d34dc28a.pdf"},{"id":64474938,"identity":"d0ed5fb4-c596-45a7-83b0-e23c033a6930","added_by":"auto","created_at":"2024-09-13 15:15:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32666591,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"SoletalNatureVersionSupplementaryInformation050824Formated.docx","url":"https://assets-eu.researchsquare.com/files/rs-4863546/v1/ed0c6e69be5e198a5490c424.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Deciphering the evolutionary origin of the exceptional slow pace-of-life of marine endotherms","fulltext":[{"header":"Main","content":"\u003cp\u003eLife history theory has achieved considerable success in explaining why some organisms live fast and die young\u003csup\u003e5\u003c/sup\u003e. Instead, the evolutionary reasons why others mature late, defer reproduction and age slowly remain less well-understood\u003csup\u003e6\u003c/sup\u003e. A wide-held hypothesis is that these organisms live in environments where extrinsic mortality is low\u003csup\u003e2\u0026ndash;4\u003c/sup\u003e. When the level of extrinsic mortality decreases for adults, selection is predicted to extend lifespan by removing deleterious genes and directing greater investment in building and maintaining a durable soma\u003csup\u003e1,7\u003c/sup\u003e. However, relaxing extrinsic mortality seems insufficient to explain the evolution of extremely slow life histories. For such a strategy to evolve, the fitness contribution of individuals to reproductive success should shift from juveniles to adults\u003csup\u003e8,9\u003c/sup\u003e, and this necessarily involves the acquisition of adaptations that buffer adults against extrinsic mortality. If developing these adaptations requires diverting important resources and time, increasing the fitness value of adults should also result in reduced reproductive effort and delayed onset of first reproduction\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile life history evolution is marked by major niche changes that have altered the selective pressures on traits affecting age-specific mortality\u003csup\u003e10,11\u003c/sup\u003e, limited exploration of these scenarios has hindered a better understanding of the evolution of slow-lived strategies\u003csup\u003e6\u003c/sup\u003e. For terrestrial vertebrates, a particularly relevant scenario is the invasion of the marine environment. Some of the most fascinating and enigmatic examples of extremely long longevities are found in terrestrial endotherms that have returned to the sea\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e. Whales boast longer lifespans than most contemporary mammals, with the potential to reach up to 200 years. Among birds, albatrosses hold the record for maximum lifespan, living for up to 70 years. The remarkable longevity of these marine endotherms is associated with delayed reproduction, longer development periods and reduced fecundity, raising the intriguing hypothesis that the invasion of marine environments has favored the evolution of a slow pace-of-life\u003csup\u003e13,14\u003c/sup\u003e.\u0026nbsp;This theory is grounded in the unique challenges posed by marine habitats. While in the sea the risk of predation is relatively low for endotherms,\u0026nbsp;a major challenge is the need to exploit widely dispersed, commonly clumped prey that have low spatial-temporal predictability\u003csup\u003e12,14\u0026ndash;16\u003c/sup\u003e. Exploiting marine prey also requires foraging strategies that differ from those used on the mainland, and that must often be adapted to harsh climatic conditions \u0026mdash;as marine birds and mammals exhibit their greatest diversity in cold, temperate waters\u003csup\u003e17\u003c/sup\u003e.\u0026nbsp;In environments where resources are scarce or challenging to obtain but the threats for adults are not as severe, life history theory predicts that it may pay to delay reproduction and invest in adaptations that extend lifespan\u0026nbsp;and iterated reproduction\u003csup\u003e1,2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe questions of whether and how the invasion of marine environments have favored the evolution of slow life histories remains unresolved. Alternative, non-exclusive explanations include historical legacies linked to phylogenetic inertia and idiosyncratic responses to specific marine lifestyles within clades. Although the existence of repeated, independent transitions between land and sea provides good opportunities for macroevolutionary analyses\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e, contrasting the above hypotheses is challenging because life history traits do not leave traces in the fossil record. Yet, new advances in phylogenetic reconstructions and retrospective comparative analyses\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e provide now opportunities to infer past evolutionary changes based on information about extant species. We use here these advances to explore the evolution of extremely slow life histories in marine endotherms (birds and mammals).\u003c/p\u003e"},{"header":"Recolonization of the sea","content":"\u003cp\u003eDespite oceans comprise 70% of the planet, only a few terrestrial animal clades have successfully colonized the sea\u003csup\u003e22\u003c/sup\u003e (\u003cstrong\u003eFig. 1A\u003c/strong\u003e). Using species-level information from 9,991 birds and 4,408 mammals (\u003cstrong\u003etable S1\u003c/strong\u003e), we reconstructed transitions from terrestrial, aquatic freshwater and marine environments in calibrated phylogenies\u003csup\u003e23,24\u003c/sup\u003e by means of stochastic character mappings\u003csup\u003e25\u003c/sup\u003e (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eFigs. S1\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;S2\u003c/strong\u003e). In birds, the average number of independent transitions to marine lifestyles was 10.7 (CI\u003csub\u003e95\u003c/sub\u003e = 7-15), all within two major radiations (Aequorlitornithes and Anseriformes). In mammals, the number of transitions was 8.6 (CI\u003csub\u003e95\u003c/sub\u003e = 5.0-15.5) in three major radiations (Cetartiodactyla, Carnivora and Sirenia). Interestingly, transitions to a marine life generally occurred from aquatic ancestors, but rarely directly from terrestrial ancestors (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eFig. S3\u003c/strong\u003e). Likewise, transitions from marine to terrestrial environments have also been extremely rare (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eFig. S3\u003c/strong\u003e). While these figures are probably underestimations, as suggested by analyses of the fossil record\u003csup\u003e22\u003c/sup\u003e, both the scarcity of independent invasions from non-marine ancestors and the gradual nature of the transitions highlight the challenges that marine environments pose for colonization.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Marine endotherms in the fast-slow continuum","content":"\u003cp\u003eTo investigate whether the invasion of marine environments has led to changes in life history, we described the fast-slow continuum based on seven relevant life history traits: maximum longevity, age at first breeding, gestation/incubation time, weaning/fledging time, litters/broods per year, litter/clutch size and fecundity (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eable S2\u003c/strong\u003e). The position of species along the fast-slow continuum was assigned using a Principal Component Analysis. The fast-slow continuum was the first axis (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eable S3\u003c/strong\u003e), characterized by positive values for longevity and developmental traits, and negative values for fecundity traits. The species\u0026apos; position on this continuum reflected variation in life expectancy post-maturity, generation time and elasticity in fecundity derived from demographic matrices (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eFig. S4\u003c/strong\u003e), underscoring the effectiveness of the axis in capturing the trade-off between survival and fecundity underpinning the fast-slow continuum\u003csup\u003e1,26\u003c/sup\u003e. Along the fast-slow continuum, marine species tend to consistently occupy the slower extreme (\u003cstrong\u003eFig. 1B-I\u003c/strong\u003e), with the only exception of marine ducks (\u003cstrong\u003eFig. 1F\u003c/strong\u003e). Phylogenetic multivariate analyses on traits describing longevity, fecundity and development time statistically confirmed the pattern (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eFig. S5\u003c/strong\u003e). Marine species show lower fecundity yet longer lifespan and development periods compared with species that are primarily aquatic or terrestrial (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eFig. S6\u003c/strong\u003e), clear signatures of a slow pace-of-life. Although life history is known to vary with trophic niche, migratory tendency and climatic region, the above pattern held when these alternative explanations were considered in the analyses (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eFig. S7\u003c/strong\u003e). Thus, our results confirm and generalize that marine birds and mammals are at the slow extreme of the fast-slow continuum.\u003c/p\u003e"},{"header":"The adaptive significance of slow-lived strategies","content":"\u003cp\u003ePhylogenetic reconstructions suggest that the ancestors of lineages transitioning to marine lifestyles generally had a slow pace of life (\u003cstrong\u003eFig. 1; Supplementary\u003c/strong\u003e\u003cstrong\u003eFigs. S8-S9\u003c/strong\u003e). This is true for all clades except Anseriformes. While phylogenetic reconstructions are prone to uncertainties, the consistent results across the three major marine radiations underscore the importance of a slow life history for successful colonization of marine environments; it suggests that the successful colonization of marine environments likely demanded the life history to be co-opted to thrive in these environments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHaving a slow ancestor is relevant because whether a lineage evolves toward a fast or slow life history can be constrained by the initial position of the ancestor in the fast-slow continuum\u003csup\u003e27\u003c/sup\u003e. This opens the possibility that the marine environment may have selected for even slower life histories (\u003cstrong\u003eFig. 1\u003c/strong\u003e). An appropriate framework to address this hypothesis is to base the analyses on an Ornstein-Uhlenbeck (OU) process\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e. Unlike the commonly used Brownian motion process, which assumes that traits evolve along a lineage through stochastic changes, the OU process also considers the possibility that the trait changes around one or several adaptive optima dictated by different selective regimes. To test whether transitions from aquatic to marine environments have selected for slower life histories, we fitted OU models in a sample of 100 stochastic character mapping reconstructions. The analysis revealed that in all main marine radiations except Anseriformes, an OU model with multiple optima (OUMV) provides a superior fit to the data compared to models featuring a single optimum (OU1) or Brownian motion models (BM) with either a single or multiple evolutionary rates (\u003cstrong\u003eTable 1\u003c/strong\u003e). These models estimate a slower life history optimum (parameter \u003cem\u003e\u0026theta;\u003c/em\u003e in the OU model) for marine species in comparison to terrestrial and aquatic species (\u003cstrong\u003eFig. 2\u003c/strong\u003e; \u003cstrong\u003efigs. S10-S13\u003c/strong\u003e), indicating a convergent evolutionary trend toward slower life histories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Model selection for the evolution of the fast-slow continuum in birds and mammals.\u0026nbsp;\u003c/strong\u003eThe models were estimated with the package OUwie. Values are the average AICc over a sample of 100 stochastic character mappings. Within parentheses, it is shown the percentage of times each model has the lowest AICc. The AICc of the most frequently selected models is shown in bold. Abbreviations: Aequol = Aequolitornithes, Anser = Anseriformes, Cetar = Cetartiodactyla and Carn = Carnivora. For details on the models, we refer the reader to the Supplementary material.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"478\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.297071129707113%\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.506276150627615%\"\u003e\n \u003cp\u003eOptima\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; (\u0026theta;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003eAlpha\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; (𝛼)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003eSigma\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; (\u0026sigma;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.853556485355648%\"\u003e\n \u003cp\u003eAICc\u0026nbsp;Aequol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003eAICc Anser\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003eAICc Cetar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.832635983263598%\"\u003e\n \u003cp\u003eAICc Carn\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.297071129707113%\"\u003e\n \u003cp\u003eBM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.506276150627615%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.853556485355648%\"\u003e\n \u003cp\u003e1035.9\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e358.8\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e403.1\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.832635983263598%\"\u003e\n \u003cp\u003e467.8\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.297071129707113%\"\u003e\n \u003cp\u003eBMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.506276150627615%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003emultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.853556485355648%\"\u003e\n \u003cp\u003e1014.8\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e351.9\u003c/p\u003e\n \u003cp\u003e(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e386.9\u003c/p\u003e\n \u003cp\u003e(15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.832635983263598%\"\u003e\n \u003cp\u003e449.3\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.297071129707113%\"\u003e\n \u003cp\u003eOU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.506276150627615%\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.853556485355648%\"\u003e\n \u003cp\u003e1007.7 \u0026nbsp; (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e\u003cstrong\u003e349.2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e400.8\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.832635983263598%\"\u003e\n \u003cp\u003e455.1\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.297071129707113%\"\u003e\n \u003cp\u003eOUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.506276150627615%\"\u003e\n \u003cp\u003emultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.853556485355648%\"\u003e\n \u003cp\u003e983.2 \u0026nbsp; \u0026nbsp; (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e\u003cstrong\u003e349.4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e395.3\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.832635983263598%\"\u003e\n \u003cp\u003e443.7\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.297071129707113%\"\u003e\n \u003cp\u003eOUMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.506276150627615%\"\u003e\n \u003cp\u003emultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.878661087866108%\"\u003e\n \u003cp\u003emultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.853556485355648%\"\u003e\n \u003cp\u003e\u003cstrong\u003e973.1 \u0026nbsp;\u0026nbsp;\u003c/strong\u003e(93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e\u003cstrong\u003e348.1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.251046025104603%\"\u003e\n \u003cp\u003e\u003cstrong\u003e382.8\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.832635983263598%\"\u003e\n \u003cp\u003e\u003cstrong\u003e430.8\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe marine environment exhibits significant heterogeneity\u003csup\u003e28\u003c/sup\u003e. Coastal areas, in particular, boast a higher abundance and diversity of resources in comparison to the vast open ocean\u003csup\u003e29\u003c/sup\u003e. If the challenging and unpredictable nature of the marine environment is the driving force behind selection for slower life histories, one could reasonably expect distinct optima for coastal and oceanic species. Indeed, marine\u0026nbsp;Aequolitornithes\u0026nbsp;tend to exhibit slower strategies than Anseriformes while Cetaceans exhibit slower strategies than Carnivores, in line with their predominantly pelagic habits. Within\u0026nbsp;Aequolitornithes, the only clade with a good representation of both pelagic and coastal species (\u003cstrong\u003etable S1\u003c/strong\u003e), an OU model with distinct optima for pelagic and coastal species outperforms a model where both environments are pooled together (average AICc = 897.2 vs 973.1;\u003cstrong\u003e\u0026nbsp;fig. S14\u003c/strong\u003e), with lower AICc in 78% of the models ran with the 100 stochastic character mappings.\u0026nbsp;All the above conclusions hold when jointly modelling longevity, fecundity and development time with multivariate multi-regime\u0026nbsp;OU models\u0026nbsp;(\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eFig. S15\u003c/strong\u003e), underscoring the robustness of the results.\u003c/p\u003e"},{"header":"Body size and the slow-lived strategy","content":"\u003cp\u003eSeveral adaptive theories might explain how birds and mammals living in the sea have evolved slower life histories, but one that is expected to be particularly relevant is selection for larger body size\u003csup\u003e30\u0026ndash;32\u003c/sup\u003e. Body size is a major correlate of the fast-slow continuum\u003csup\u003e1,33\u003c/sup\u003e, and there is evidence that clades that have diversified in open water have experienced selection for larger body size\u003csup\u003e31\u003c/sup\u003e. A large body may provide advantages when foraging requires the exploration of large areas, as it allows for reduced metabolic rates and the storage of significant energy reserves, thereby reducing the need for frequent foraging to meet energetic needs. A large body also provides insulation against cold water temperatures, and access to prey that smaller animals cannot handle\u0026nbsp;\u0026frac34;such as deep-sea organisms that require long dives or small prey that must be amassed in large amount with a single foraging bout\u003csup\u003e34\u003c/sup\u003e. Thus, even though a large body demands extended development and a greater food supply for maintenance, these costs may be counterbalanced by a substantial improvement in survival prospects during sea foraging, ultimately enabling longer reproductive spans\u003csup\u003e35\u003c/sup\u003e. In a multivariate OU framework, the co-evolution of life history and body size can be analyzed by estimating the covariance in the strength of selection (parameter alpha,\u0026nbsp;𝛼), which describes the extent to which the evolution of life history is influenced by body size or vice-versa\u003csup\u003e36\u003c/sup\u003e. Jointly modelling the fast-slow and body size by means of a bi-variate mvOUM, we found that models where\u0026nbsp;𝛼\u0026nbsp;is allowed to co-vary were generally favored over other models (\u003cstrong\u003etable S4\u003c/strong\u003e). The best mvOUM also allowed covariance in the stochastic element sigma (\u003cem\u003e\u0026sigma;\u003c/em\u003e). This might reflect a situation where life history and body size also respond to similar factors (like secondary adaptive optima or developmental/functional constrains), in addition to being constrained by each other. A phylomorphospace suggests that while the slower strategy in Anseriformes and Carnivores can mainly be explained by allometric effects, in Aequalithornites and Cetartiodactyla there is a notable grade shift in the allometric curve accompanied by a more relaxed association between life history and body size (\u003cstrong\u003eFig. 3\u003c/strong\u003e). Importantly, marine Aequalithornites and Cetartiodactyla stand out as the clades containing the species with more pelagic habits and the slowest life histories among all endotherms (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eFigs. S16, S17\u003c/strong\u003e). Consequently, these two clades present valuable opportunities for delving into adaptations other than body size that may shed further light on the underlying factors contributing to their distinctive life histories.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Adaptive basis of the slow-lived strategy","content":"\u003cp\u003eThe challenges of capitalizing on the spatial and temporal aggregation of marine food resources are contingent on the nature of the prey consumed and the adaptations for efficient resource acquisition\u003csup\u003e12\u003c/sup\u003e. In cetaceans, for example, species that mainly feed on plankton (Mysticetes) face abundant but sporadic food availability. This not only requires large bodies to amass prey in large amounts and convert them into fat reserves, but also high mobility to efficiently track such temporarily and spatially variable food resources\u003csup\u003e37\u003c/sup\u003e. Indeed, large whales can make long seasonal migrations between the tropics and polar waters, which can involve several months without feeding. Odontocetes, instead, are smaller and primarily prey on fish and squids, resources that are more predictable but more challenging to capture. Exploiting these prey may require accumulating knowledge and socially learning a variety of complex foraging skills, including cooperative hunting and tool use, which may have been facilitated by an encephalized brain and enhanced body maneuverability\u003csup\u003e38,39\u003c/sup\u003e. A\u0026nbsp;phylogenetic path analysis\u003csup\u003e40\u003c/sup\u003e supports the importance of encephalization and high mobility in cetaceans\u0026rsquo; life history (\u003cstrong\u003eFig. 4\u003c/strong\u003e). The best supported model highlights the central importance of body size, but also reveals that slow-lived species are characterized by highly encephalized brains and streamlined body types that offer less resistance to move throughout water (\u003cstrong\u003eFig. 4; Supplementary\u003c/strong\u003e\u003cstrong\u003eFigs. S18-S19\u003c/strong\u003e). These correlates may signify the advantages of such adaptations in providing \u0026quot;buffer adaptations\u0026quot; against environmental unpredictability, enhancing survival and enabling longer lives. At the same time, the positive associations of body size and encephalization with development time suggests secondary consequences for life history in terms of extended growth and maturation. Restricting the analysis to odontocetes generally aligns with these discoveries but, in line with their dependence on single-prey and their tendency for shorter-distance movements, suggests a diminished significance of body size and shape (\u003cstrong\u003eFig. 4; Supplementary\u003c/strong\u003e\u003cstrong\u003eFigs. S19\u003c/strong\u003e\u003cstrong\u003e-S22\u003c/strong\u003e). Instead, encephalization gains greater importance, especially\u0026nbsp;in social cetaceans.\u003c/p\u003e\n\u003cp\u003eIn Aequalithornites, flight limits body size and the capacity to accumulate reserves. However, being large enough to accumulate fat can still be important in some species, particularly those that have either lost the ability to fly or utilize dynamic soaring to reduce travel costs. Additionally, it may be essential for nestlings to endure extended periods without food. Another factor potentially influencing their life history is the need to travel long distances to meet daily energy requirements and feed the offspring. Albatrosses, shearwaters and petrels can travel vast distances to forage, sometimes covering up to 1,000 km in a single day\u003csup\u003e14\u003c/sup\u003e. This remarkable ability, which is facilitated by their skill in extracting energy from wind currents to soar, not only enables them to efficiently track changes in food abundance, but it also provides the freedom to choose remote, secure breeding locations that may be far from optimal foraging areas. Besides body size and mobility, cognition can also be relevant for marine birds that rely on resources that vary in time and space, notably to create visual maps of suitable foraging areas in the vast areas of the sea or to develop complex foraging techniques to capture prey in the water. In these birds, acquiring information on resource availability and improving foraging skills may take several years. Similar to cetaceans, a phylogenetic path analysis supports the significance of body size, flying efficiency and encephalization for the life history of seabirds. Their remarkable slow life histories are associated with larger bodies, but also with high levels of encephalization and wing morphologies better suited for long-distance movements (\u003cstrong\u003eFig. 4C,D; fig. S23\u003c/strong\u003e). As in cetaceans, the correlation between these adaptations and life history is likely indicative of both benefits and developmental costs (see also\u003csup\u003e14,41\u003c/sup\u003e). The way life history co-varies with body size, encephalization and mobility also changes depending on the foraging strategy, being more pronounced in pelagic species that use surface seizing to access vastly distributed resources that vary in time and space (\u003cstrong\u003eFig. 4D; Supplementary\u003c/strong\u003e\u003cstrong\u003eFigs. S24-S25\u003c/strong\u003e). Thus, our results support the notion that the slow life history of marine endotherms is not merely a consequence of their large body but also reflects other adaptations to efficiently exploit marine resources. While our analyses focus on general adaptations to understand evolutionary convergences across birds and mammals, this does not preclude the relevance of more specific adaptations for particular taxa\u003csup\u003e14,16\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Longevity as a central component of the slow-pace-of-life","content":"\u003cp\u003eOur analyses suggest that the evolution towards extremely slow life histories partly reflects selection for adaptations that enable the efficient exploitation of challenging food resources. Although these adaptations often come at the cost of longer development, the major expected fitness benefit that compensates for such a cost is improved adult survival, thereby allowing an extended reproductive lifespan. The central role of longevity for fitness is supported by growing evidence from long-term population studies\u003csup\u003e42\u003c/sup\u003e (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eFig. S26\u003c/strong\u003e). In the Kittiwake (\u003cem\u003eRissa tridactyla\u003c/em\u003e), which can live up to 28 years, between 80-83% of variation in lifetime reproductive success can be attributed to longevity\u003csup\u003e43\u003c/sup\u003e. When the contribution of older age classes to reproductive success increases, longevity can be extended because selection becomes more efficient against the accumulation of deleterious mutations or antagonistic pleiotropic, slowing the aging process and decreasing intrinsic mortality.\u0026nbsp;The recent analysis of the genome and transcriptome of the bowhead whale has revealed selection for genes related to cell cycle, DNA repair, cancer, and aging\u003csup\u003e44\u003c/sup\u003e, substantiating such a possibility.\u003c/p\u003e\n\u003cp\u003eWhen fitness largely depends on a long reproductive life, a reduction in fecundity is expected to mitigate the costs of reproduction\u003csup\u003e1,26\u003c/sup\u003e. This cost has been well-documented in marine endotherms, and is exemplified by individuals skipping reproduction when conditions are unfavorable\u003csup\u003e45\u003c/sup\u003e. The severe energy limitations imposed by pelagic ecosystems might also directly select for reduced fecundity by limiting the number of offspring parents can successfully raise\u003csup\u003e14\u003c/sup\u003e. In seabirds that need to travel long distances to find food, offspring number may be constrained by the amount of food that parents can bring in each trip. Similarly, in migratory whales where breeding females spend several weeks without feeding while nursing their calves, commitment to lactation may restrict their capacity to care for a larger number of offspring. When energy limitations are high and development takes a long time, a more successful strategy than producing many offspring is to invest in a few larger offspring that are better prepared to withstand adverse weather conditions and extended periods without food\u003csup\u003e11,15\u003c/sup\u003e. Such a strategy ensures that at least some offspring will survive to pass the parents\u0026apos; genes to the next generation, particularly significant considering that a low fecundity implies producing fewer offspring during their lifetime.\u003c/p\u003e\n\u003cp\u003eCompared to fecundity, the reasons why marine endotherms delay the onset of first reproduction appear less obvious because postponing reproduction should reduce the duration of the reproductive life. However, selection may favor postponing reproduction if breeding earlier imposes mortality costs and is unlikely to produce viable offspring\u003csup\u003e46\u003c/sup\u003e. In elephant seals (\u003cem\u003eMirounga angustirostris\u003c/em\u003e), females that bred early have decreased lifespan, low weaning success, and lower lifetime reproductive success than females that postpone first breeding\u003csup\u003e47\u003c/sup\u003e. Postponing reproduction is often interpreted as reflecting insufficient maturation. Bowhead whales only reach their final body size when 40 or 50 years old, implying that foraging proficiency can be impaired for long periods. The time required to acquire sufficient foraging proficiency is often cited as an explanation for why some marine endotherms exhibit some of the longest periods to independence.\u003c/p\u003e"},{"header":"Towards a lifestyle perspective of life history evolution","content":"\u003cp\u003eOur study highlights that ancestral legacies and adaptations to exploit scarce or challenging food resources have favored convergent evolution towards slow life histories in endotherms that secondarily returned to the marine environment. Although the exact evolutionary pathways remain idiosyncratic, due to differences in ancestors and the new niches occupied\u003csup\u003e48\u003c/sup\u003e, our analyses suggest that such evolutionary convergences partially arise because the adaptations necessary to thrive in marine environments are costly to produce; yet, once developed, they offer protection against extrinsic mortality. As a result, the age-specific contribution to fitness has further shifted from juveniles to adults. Thus, our findings support claims to broaden life history theory\u003csup\u003e6\u003c/sup\u003e, highlighting the need to move beyond classic \u0026ldquo;mouse-to-elephant curves\u0026rdquo; and \u0026ldquo;relaxed extrinsic mortality\u0026rdquo; paradigms to consider the central role of the ecological lifestyle of organisms in favoring slow-lived strategies.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We thank Jose Lu\u0026iacute;s Copete for helping us in gathering data, and Jeremy Bellieur and Julien Clavel for advice in the use of their respective \u003cem\u003eR\u003c/em\u003e-packages OUwie and mvMorph.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by MINECO (PID2020-119514GB-I00 to DS)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOriginal idea: DS, AHM\u003c/p\u003e\n\u003cp\u003eConceptualization: DS\u003c/p\u003e\n\u003cp\u003eConceptualization \u0026ndash; refinements: MG, DO, AHM\u003c/p\u003e\n\u003cp\u003eMethodology: DS\u003c/p\u003e\n\u003cp\u003eData collection: AP, LO, DS\u003c/p\u003e\n\u003cp\u003eAnalysis: DS, AP, LO\u003c/p\u003e\n\u003cp\u003eFunding acquisition: DS\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: DS\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing: DS, AP, LO, MG, DO, AHM\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e Authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u003c/strong\u003e All data and code used in the analysis will be available in Dryad.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStearns, S. 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Lifetime reproductive success of northern elephant seals (Mirounga angustirostris). \u003cem\u003eCanadian Journal of Zoology\u003c/em\u003e \u003cstrong\u003e97\u003c/strong\u003e, 1\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eHabitat assignation. \u003c/strong\u003eWe considered a species marine when a large proportion of the total population foraged in the marine environment for at least part of the year\u003csup\u003e49,50\u003c/sup\u003e. The rest of the species were classified as either aquatic freshwater or terrestrial based on ref\u003csup\u003e51\u003c/sup\u003e for mammals and refs\u003csup\u003e49,50\u003c/sup\u003e for birds, revised with more recent information from IUCN\u003csup\u003e52\u003c/sup\u003e. 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 \u0026lsquo;Pelagic\u0026rsquo; or \u0026lsquo;coastal\u0026rsquo;. \u0026lsquo;Pelagic\u0026rsquo; species were those that primarily use marine pelagic deep water and/or marine neritic pelagic continental shelf water. \u0026lsquo;Coastal\u0026rsquo; 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLife history characterization. \u003c/strong\u003eWe 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)(\u003cstrong\u003etable S1\u003c/strong\u003e). Data were extracted from\u003csup\u003e53\u003c/sup\u003e, updated with information from\u003csup\u003e51\u003c/sup\u003e for mammals, and from\u003csup\u003e54\u0026ndash;57\u003c/sup\u003e 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 package\u003csup\u003e58\u003c/sup\u003e. 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.\u003csup\u003e59\u003c/sup\u003e, we centred and rescaled each life history trait to unit variance before performing PCAs. All life history traits were log\u003csub\u003e10\u003c/sub\u003e-transformed, except broods/litters per year. In both birds and mammals, the first axis captured most variation in life history (54% and 75%, respectively; \u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e T\u003c/strong\u003e\u003cstrong\u003eable S2\u003c/strong\u003e), and described well the fast-slow continuum (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003cstrong\u003eig. S3\u003c/strong\u003e)\u003csup\u003e9,60\u003c/sup\u003e. Because of the potential for imputed data and PCA to introduce undesirable statistical artefacts, we also ran analyses on raw, non-imputed traits\u003csup\u003e61\u003c/sup\u003e. 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfounding variables\u003c/strong\u003e. 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 PanTHERIA\u003csup\u003e62\u003c/sup\u003e and AVONET\u003csup\u003e63\u003c/sup\u003e. We classified species as tropical, temperate, polar, or widespread based on their breeding latitude, with the tropics defined as -23.4\u0026deg; to 23.4\u0026deg; and polar regions as below -60\u0026deg; or above 60\u0026deg;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic hypotheses. \u003c/strong\u003eWe used the most comprehensive, updated phylogenies currently available, Lum et al.\u003csup\u003e24\u003c/sup\u003e for birds and Upham et al.\u003csup\u003e23\u003c/sup\u003e 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTesting for life history differences between marine and non-marine species. \u003c/strong\u003eBecause 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 \u0026lsquo;phylolm\u0026rsquo; in the R-package phylolm\u003csup\u003e64\u003c/sup\u003e to test for differences in the fast-slow continuum and the function \u0026lsquo;mvgls\u0026rsquo; in mvMORPH\u003csup\u003e36\u003c/sup\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvolutionary transitions between environments. \u003c/strong\u003eWe 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 evolution\u003csup\u003e65,66\u003c/sup\u003e. 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 \u0026lsquo;make.simmap\u0026rsquo; function in R package phytools\u003csup\u003e67\u003c/sup\u003e to build the stochastic character-mapped reconstructions with model \u0026lsquo;ARD\u0026rsquo;, and then applied the \u0026lsquo;describe.simmap\u0026rsquo; function to summarize the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReconstruction of ancestral characters. \u003c/strong\u003eWe used the function \u0026lsquo;fastAnc\u0026rsquo; from phytools\u003csup\u003e67\u003c/sup\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTesting the adaptive significance of the fast-slow: univariate approach. \u003c/strong\u003eWe 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 OUwie\u003csup\u003e68\u003c/sup\u003e 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTesting the adaptive significance of the fast-slow: a multivariate approach. \u003c/strong\u003eBecause 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 mvMORPH\u003csup\u003e36\u003c/sup\u003e, 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 (\u003cstrong\u003eTable S1\u003c/strong\u003e). We also used similar models to investigate the co-evolution of the fast-slow axis with body size.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePath analyses for adaptive responses in marine Cetartiodactyla and Aequorlitornithes.\u003c/strong\u003e We used phylogenetic path analyses\u003csup\u003e69,70\u003c/sup\u003e 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 size\u003csup\u003e30\u003c/sup\u003e, an encephalized brain\u003csup\u003e71\u003c/sup\u003e and a morphology for efficient locomotion\u003csup\u003e35,57\u003c/sup\u003e. Body size was extracted from PanTHERIA\u003csup\u003e62\u003c/sup\u003e and AVONET\u003csup\u003e63\u003c/sup\u003e. Encephalization, which reflects a higher accumulation of pallial neurons and is correlated with enhanced cognition\u003csup\u003e41\u003c/sup\u003e, was estimated as the residuals of a log-log a phylogenetic generalized least square model\u003csup\u003e72,73\u003c/sup\u003e of brain mass against body mass, with brain data extracted from ref\u003csup\u003e73\u003c/sup\u003e for birds and ref\u0026nbsp;\u003csup\u003e74\u003c/sup\u003e for Cetaceans. To describe morphology, we used published data on the hand-wing index\u003csup\u003e57\u003c/sup\u003e for birds and streamlining for cetaceans\u003csup\u003e35\u003c/sup\u003e. The hand-wing index is a morphological metric linked to wing aspect ratio, and associated with avian flight efficiency and dispersal ability\u003csup\u003e57\u003c/sup\u003e. 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 \u0026lsquo;less-streamlined\u0026rsquo; and negative residuals \u0026lsquo;more-streamlined\u0026rsquo;\u003csup\u003e35\u003c/sup\u003e. We estimated the residuals based on a phylogenetic generalized least square model, with body mass and body length data from ref\u003csup\u003e35\u003c/sup\u003e. We decided to exclude \u0026lsquo;Balaena mysticetus\u0026rsquo; 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 organization\u003csup\u003e75\u003c/sup\u003e, yet current information on social cohesion is insufficient to test the relevance of this factor. 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Given that reproducing earlier can also ensure that offspring start breeding sooner, there is strong pressure on individuals to reproduce as early as possible. Why, then, some organisms mature late, defer reproduction and age slowly? Major evolutionary transitions in life history are believed to result from the invasion of niches altering extrinsic mortality and imposing new adaptive challenges. However, limited research on these transitions has hindered understanding their role in the evolution of extremely slow-lived strategies. We reveal here that the invasion of marine environments by birds and mammals triggered profound adaptive shifts towards extremely slower life histories, particularly in pelagic lineages. Such life history convergences were facilitated by the slow-paced nature of their non-marine ancestors, and were associated with adaptations for enhanced energy acquisition and storage, facilitating a long reproductive lifespan at the expense of extended development. Ancestral traits and lifestyle changes might thus have been essential in shaping the evolution of exceptionally slow life histories.","manuscriptTitle":"Deciphering the evolutionary origin of the exceptional slow pace-of-life of marine endotherms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-13 15:15:01","doi":"10.21203/rs.3.rs-4863546/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d8f79d8b-6c8a-4fd2-9bbd-441ef3fb714b","owner":[],"postedDate":"September 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36084178,"name":"Biological sciences/Ecology/Evolutionary ecology"},{"id":36084179,"name":"Biological sciences/Evolution/Evolutionary theory"}],"tags":[],"updatedAt":"2025-05-08T07:05:54+00:00","versionOfRecord":{"articleIdentity":"rs-4863546","link":"https://doi.org/10.1038/s41467-025-59273-5","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-05-08 04:00:00","publishedOnDateReadable":"May 8th, 2025"},"versionCreatedAt":"2024-09-13 15:15:01","video":"","vorDoi":"10.1038/s41467-025-59273-5","vorDoiUrl":"https://doi.org/10.1038/s41467-025-59273-5","workflowStages":[]},"version":"v1","identity":"rs-4863546","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4863546","identity":"rs-4863546","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}