# Diverse aging rates in ectothermic tetrapods provide insights for the evolution of aging and longevity

Beth Reinke, Hugo Cayuela, Fredric J. Janzen, Jean-François Lemaître, Jean-Michel Gaillard, A. Michelle Lawing, John B. Iverson, Ditte Marie Christiansen, Iñigo Martínez-Solano, Gregorio Sánchez-Montes, et al.

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## To cite this version:

Beth Reinke, Hugo Cayuela, Fredric J. Janzen, Jean-François Lemaître, Jean-Michel Gaillard, et al. Diverse aging rates in ectothermic tetrapods provide insights for the evolution of aging and longevity. Science, 2022, 376 (6600), pp.1459-1466. 10.1126/science.abm0151. hal-03703901

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**Title:** Diverse aging rates in ectothermic tetrapods provide insights for the evolution of aging and longevity

**Authors:** Beth A. Reinke<sup>1,2*</sup>, Hugo Cayuela<sup>3</sup>, Fredric J. Janzen<sup>4,5</sup>, Jean-François Lemaître<sup>6</sup>, Jean-Michel Gaillard<sup>6</sup>, A. Michelle Lawing<sup>7</sup>, John B. Iverson<sup>8</sup>, Ditte G. Christiansen<sup>9</sup>, Iñigo Martínez-Solano<sup>10</sup>, Gregorio Sánchez-Montes<sup>10</sup>, Jorge Gutiérrez-Rodríguez<sup>10,11</sup>, Francis L. Rose<sup>12</sup>, Nicola Nelson<sup>13</sup>, Susan Keall<sup>13</sup>, Alain J. Crivelli<sup>14</sup>, Theodoros Nazirides<sup>15</sup>, Annegret Grimm-Seyfarth<sup>16</sup>, Klaus Henle<sup>16</sup>, Emiliano Mori<sup>17</sup>, Gaëtan Guiller<sup>18</sup>, Rebecca Homan<sup>19</sup>, Anthony Olivier<sup>14</sup>, Erin Muths<sup>20</sup>, Blake R. Hossack<sup>21</sup>, Xavier Bonnet<sup>22</sup>, David S. Pilliod<sup>23</sup>, Marieke Lettink<sup>24</sup>, Tony Whitaker<sup>25,†</sup>, Benedikt R. Schmidt<sup>9,26</sup>, Michael G. Gardner<sup>27-28</sup>, Marc Cheylan<sup>29</sup>, Françoise Poitevin<sup>29</sup>, Ana Golubović<sup>30</sup>, Ljiljana Tomović<sup>30</sup>, Dragan Arsovski<sup>31</sup>, Richard A. Griffiths<sup>32</sup>, Jan W. Arntzen<sup>33</sup>, Jean-Pierre Baron<sup>34</sup>, Jean-François Le Galliard<sup>34,35</sup>, Thomas Tully<sup>35</sup>, Luca Luiselli<sup>36-38</sup>, Massimo Capula<sup>39</sup>, Lorenzo Rugiero<sup>36</sup>, Rebecca McCaffery<sup>40</sup>, Lisa A. Eby<sup>41</sup>, Venetia Briggs-Gonzalez<sup>42</sup>, Frank Mazzotti<sup>42</sup>, David Pearson<sup>43</sup>, Brad A. Lambert<sup>44</sup>, David M. Green<sup>45</sup>, Nathalie Jreidini<sup>45</sup>, Claudio Angelini<sup>46</sup>, Graham Pyke<sup>47-48</sup>, Jean-Marc Thirion<sup>49</sup>, Pierre Joly<sup>50</sup>, Jean-Paul Léna<sup>50</sup>, Tony Tucker<sup>51</sup>, Col Limpus<sup>52</sup>, Pauline Priol<sup>53</sup>, Aurélien Besnard<sup>54</sup>, Pauline Bernard<sup>55</sup>, Kristin Stanford<sup>56</sup>, Richard King<sup>57</sup>, Justin Garwood<sup>58</sup>, Jaime Bosch<sup>10,59-60</sup>, Franco Souza<sup>61</sup>, Jaime Bertoluci<sup>62</sup>, Shirley Famelli<sup>63-64</sup>, Kurt Grossenbacher<sup>65</sup>, Omar Lenzi<sup>9</sup>, Kathleen Matthews<sup>66</sup>, Sylvain Boitaud<sup>67</sup>, Deanna H. Olson<sup>68</sup>, Tim S. Jessop<sup>69</sup>, Graeme Gillespie<sup>70</sup>, Jean Clobert<sup>71</sup>, Murielle Richard<sup>71</sup>, Andrés Valenzuela-Sánchez<sup>72-73</sup>, Gary M. Fellers<sup>74†</sup>, Patrick M. Kleeman<sup>74</sup>, Brian J. Halstead<sup>75</sup>, Evan H. Campbell Grant<sup>76</sup>, Phillip G. Byrne<sup>77</sup>, Thierry Frétey<sup>78</sup>, Bernard Le Garff<sup>79</sup>, Pauline Levionnois<sup>80</sup>, John C. Maerz<sup>81</sup>, Julian Pichenot<sup>82</sup>, Kurtuluş Olgun<sup>83</sup>, Nazan Üzüm<sup>83</sup>, Aziz Avcı<sup>83</sup>, Claude Miaud<sup>29</sup>, Johan Elmberg<sup>84</sup>, Gregory P. Brown<sup>48</sup>, Richard Shine<sup>48</sup>, Nathan F. Bendik<sup>85</sup>, Lisa O'Donnell<sup>86</sup>, Courtney L.
Davis<sup>87</sup>, Michael J. Lannoo<sup>88</sup>, Rochelle M. Stiles<sup>89</sup>, Robert M. Cox<sup>90</sup>, Aaron M. Reedy<sup>90-91</sup>, Daniel A. Warner<sup>91</sup>, Eric Bonnaire<sup>92</sup>, Kristine Grayson<sup>93</sup>, Roberto Ramos-Targarona<sup>94</sup>, Eyup Baskale<sup>95</sup>, David Muñoz<sup>2</sup>, John Measey<sup>96</sup>, F. Andre de Villiers<sup>96</sup>, Will Selman<sup>97</sup>, Victor Ronget<sup>98</sup>, Anne M. Bronikowski<sup>4‡</sup>, David A. W. Miller<sup>2‡</sup>

**Affiliations:**

<sup>1</sup> Department of Biology, Northeastern Illinois University; Chicago, IL, USA.

<sup>2</sup> Department of Ecosystem Science and Management, Pennsylvania State University; State College, PA, USA.

<sup>3</sup> Department of Ecology and Evolution, University of Lausanne; Lausanne, Switzerland.

<sup>4</sup> Department of Ecology, Evolution, and Organismal Biology, Iowa State University; Ames, IA, USA.

<sup>5</sup> W.K. Kellogg Biological Station, Michigan State University; Hickory Corners, MI, USA.

<sup>6</sup> Université Lyon 1, Laboratoire de Biométrie et Biologie Evolutive; Villeurbanne, France.

<sup>7</sup> Department of Ecology and Conservation Biology, Texas A&M University; College Station, TX, USA.

<sup>8</sup> Department of Biology, Earlham College; Richmond, IN, USA.

<sup>9</sup> Department of Evolutionary Biology and Environmental Studies, University of Zurich; Zurich, Switzerland.

<sup>10</sup> Departmento de Biodiversidad y Biología Evolutiva, Museo Nacional de Ciencias Naturales, CSIC; Madrid, Spain.

<sup>11</sup> Department of Integrative Ecology, Estación Biológica de Doñana (EBD-CSIC); Seville, Spain.

<sup>12</sup> Department of Biological Sciences, Texas Tech University; Lubbock, TX, USA.

<sup>13</sup> School of Biological Sciences, Victoria University of Wellington; Wellington, New Zealand.
¹⁴ Research Institute for the Conservation of Mediterranean Wetlands, Tour du Valat; Arles, France.

¹⁵ Vironia, Greece.

¹⁶ Department Conservation Biology, Helmholtz Centre for Environmental Research – UFZ; Leipzig, Germany.

¹⁷ Consiglio Nazionale delle Ricerche, Istituto di Ricerca sugli Ecosistemi Terrestri; Sesto Fiorentino, Italy.

¹⁸ Le Grand Momesson, Bouvron, France.

¹⁹ Biology Department, Denison University; Granville, OH, USA.

²⁰ U.S. Geological Survey, Fort Collins Science Center; Fort Collins, CO, USA.

²¹ U.S. Geological Survey, Northern Rocky Mountain Science Center, Wildlife Biology Program, University of Montana; Missoula, MT, USA.

²² Centre d'Etudes Biologiques de Chizé, CNRS UMR 7372 - Université de La Rochelle; Villiers-en-Bois, France.

²³ U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center; Boise, ID, USA.

²⁴ Fauna Finders; Lyttelton, Christchurch 8082, New Zealand.

²⁵ Orinoco, RD1; Motueka 7196, New Zealand.

²⁶ Info Fauna Karch; Neuchâtel, Switzerland.

²⁷ College of Science and Engineering, Flinders University; Adelaide, South Australia, Australia.

²⁸ Evolutionary Biology Unit, South Australian Museum; South Australia, Australia.

²⁹ PSL Research University, Université de Montpellier, Université Paul-Valéry; Montpellier, France.

³⁰ Institute of Zoology, Faculty of Biology, University of Belgrade; Belgrade, Serbia.
³¹ Macedonian Ecological Society; Skopje, North Macedonia

³² Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, University of Kent; Canterbury, Kent, UK.

³³ Naturalis Biodiversity Center; Leiden, The Netherlands.

³⁴ Ecole normale supérieure, PSL University, Département de biologie, CNRS, UMS 3194, Centre de recherche en écologie expérimentale et prédictive (CEREEP-Ecotron IleDeFrance); Saint-Pierre-lès-Nemours, France.

³⁵ Sorbonne Université, CNRS, INRA, UPEC, IRD, Institute of Ecology and Environmental Sciences, iEES-Paris; Paris, France.

³⁶ Institute for Development, Ecology, Conservation and Cooperation; Rome, Italy.

³⁷ Department of Animal and Applied Biology, Rivers State University of Science and Technology; Port Harcourt, Nigeria.

³⁸ Department of Zoology, University of Lomé; Lomé, Togo.

³⁹ Museo Civico di Zoologia; Rome, Italy.

⁴⁰ U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center; Port Angeles, WA, USA.

⁴¹ Wildlife Biology Program, University of Montana; Missoula, MT, USA.

⁴² Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of Florida; Fort Lauderdale, FL, USA.

⁴³ Department of Biodiversity, Conservation and Attractions; Wanneroo, WA, Australia.

⁴⁴ Colorado Natural Heritage Program, Colorado State University; Fort Collins, CO, USA.

⁴⁵ Redpath Museum, McGill University; Montreal, Canada.

⁴⁶ Salamandrina Sezzese Search Society; Sezze, Italy.
47 Key Laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences; CN, Kunming, PR China.

48 Department of Biological Sciences, Macquarie University; NSW, Australia.

49 Objectifs Biodiversité; Pont-l'Abbé-d'Arnoult, France.

50 Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE; UMR5023 LEHNA, Villeurbanne, France.

51 Department of Biodiversity, Conservation and Attractions, Parks and Wildlife Service-Marine Science Program; Kensington, WA, Australia.

52 Threatened Species Operations, Queensland Department of Environment and Science, Ecosciences Precinct; Dutton Park, Qld 4102, Australia

53 Statipop, Scientific Consulting; Ganges, France.

54 CNRS, EPHE, UM, SupAgro, IRD, INRA, UMR 5175 CEFE, PSL Research University; Montpellier, France.

55 Conservatoire d'espaces naturels d'Occitanie; Montpellier, France.

56 Ohio Sea Grant and Stone Laboratory, The Ohio State University; Put-In-Bay, OH, USA.

57 Department of Biological Sciences, Northern Illinois University; DeKalb, IL, USA.

58 California Department of Fish and Wildlife; Arcata, CA, USA.

59 IMIB-Biodiversity Research Unit (University of Oviedo-Principality of Asturias; Mieres, Spain.

60 Centro de Investigación, Seguimiento y Evaluación, Sierra de Guadarrama National Park; Rascafría, Spain.

61 Instituto de Biociências, Universidade Federal de Mato Grosso do Sul, Campo Grande; Mato Grosso do Sul, Brazil.
⁶² Departamento de Ciências Biológicas, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo; São Paulo, Brazil.

⁶³ School of Science, RMIT University; Melbourne, Australia

⁶⁴Environmental Research Institute, North Highland College, University of the Highlands and Islands; Scotland, UK

⁶⁵ Abteilung Wirbeltiere, Naturhistorisches Museum; Bern, Switzerland.

⁶⁶ USDA Forest Service, Pacific Southwest Research Station; Albany, CA, USA.

⁶⁷ Laboratoire d'Ecologie des Hydrosystèmes Naturels et Anthropisés; Villeurbanne, France.

⁶⁸ USDA Forest Service, Pacific Northwest Research Station; Corvallis, OR, USA.

⁶⁹ Centre for Integrative Ecology, Deakin University; Waurn Ponds, Geelong, Victoria, Australia.

⁷⁰ Department of Environment and Natural Resources; Palmerston, NT, Australia.

⁷¹ Station d'Ecologie Théorique et Expérimentale de Moulis; CNRS-UMR532, Saint Girons, France.

⁷² Instituto de Conservación, Biodiversidad y Territorio, Facultad de Ciencias Forestales y Recursos Naturales, Universidad Austral de Chile; Valdivia, Chile.

⁷³ ONG Ranita de Darwin; Valdivia, Chile.

⁷⁴ US Geological Survey, Western Ecological Research Center; Point Reyes National Seashore, Point Reyes, CA, USA.

⁷⁵ US Geological Survey, Western Ecological Research Center, Dixon Field Station; Dixon, CA, USA

⁷⁶ U.S. Geological Survey Eastern Ecological Research Center (form. Patuxent Wildlife Research Center), S.O. Conte Anadromous Fish Research Center; Turners Falls, MA, USA.
⁷⁷ School of Earth, Atmospheric and Life Sciences, University of Wollongong; Wollongong, NSW, Australia.

⁷⁸ Association Racine; Saint Maugan, France.

⁷⁹ Musée de Beaulieu, Université de Rennes; Rennes CEDEX, France.

⁸⁰ Office National des Forêts; Direction territoriale Grand Est, France.

⁸¹ Warnell School of Forestry and Natural Resources; University of Georgia, Athens, GA, USA.

⁸² Université de Reims Champagne-Ardenne, Centre de Recherche et de Formation en Écothologie; URCA-CERFE, Boult-aux-Bois, France.

⁸³ Department of Biology, Faculty of Science and Arts, Aydın Adnan Menderes University; Aydın, Turkey.

⁸⁴ Department of Environmental Science and Bioscience, Kristianstad University; Kristianstad, Sweden.

⁸⁵ Watershed Protection Department, City of Austin; Austin, TX, USA.

⁸⁶ Balcones Canyonlands Preserve, City of Austin; Austin, TX, USA.

⁸⁷ Cornell Lab of Ornithology, Cornell University; Ithaca, NY, USA.

⁸⁸ Indiana University School of Medicine–TH; Terre Haute, IN, USA.

⁸⁹ San Francisco Zoological Society; San Francisco, CA, USA.

⁹⁰ Department of Biology, University of Virginia; Charlottesville, VA, USA.

⁹¹ Department of Biological Sciences, Auburn University; Auburn, AL, USA.

⁹² Office National des Forêts, Agence de Meurthe-et-Moselle; Nancy, France.

⁹³ Department of Biology, University of Richmond; Richmond, VA, USA.

⁹⁴ Ministerio de Ciencias, Tecnología y Medio Ambiente; Cienaga de Zapata, Cuba.

⁹⁵ Department of Biology, Faculty of Science and Arts, Pamukkale University; Denizli, Turkey.
†Deceased

‡These authors contributed equally as co-senior authors

*Corresponding authors. Emails: e-reinke@neiu.edu, abroniko@iastate.edu

<footnote n="96">⁹⁶ Centre for Invasion Biology, Department of Botany & Zoology, Stellenbosch University;
Stellenbosch, South Africa.</footnote>

<footnote n="97">⁹⁷ Department of Biology, Millsaps College; Jackson, MS, USA.</footnote>

<footnote n="98">⁹⁸ Unité Eco-anthropologie (EA), Muséum National d’Histoire Naturelle, CNRS, Université Paris Diderot; Paris, France.</footnote>
1 Abstract:

Comparative studies of mortality in the wild are necessary to understand the evolution of aging, yet ectothermic tetrapods are under-represented in this comparative landscape despite their suitability for testing evolutionary hypotheses. We present a study of aging rates and longevity across wild tetrapod ectotherms, utilizing data from 107 populations (77 species) of reptiles and amphibians. We test hypotheses of how thermoregulatory mode, temperature, protective phenotypes, and pace of life-history contribute to aging. Controlling for phylogeny and body size, ectotherms display a higher diversity of aging rates than endotherms, and include phylogenetically-widespread evidence of negligible aging. Protective phenotypes and life-history strategies further explain macroevolutionary patterns of aging. By adding ectothermic tetrapods, our comparative analyses enhance our understanding of the evolution of aging.

**One-Sentence Summary:** Aging rates are highly variable among ectothermic tetrapods in the wild, ranging from negligible aging to fast aging rates, and aging rates decrease in species with protective phenotypes (e.g., armor, venom) and slow-paced life histories.

**Main Text:** Comparative studies of animal aging rates in the wild are critical for assessing the potential limits of longevity and understanding ecological and evolutionary factors shaping variation in aging strategies (1–3). Demographic indicators of aging include adult longevity and whether, and at what rate, age-specific mortality accelerates with advancing adult age. Previous comparative studies have provided important insights regarding the evolution of demographic aging in the endothermic tetrapods (birds and mammals; e.g., 2–6). However, ectotherms hold most of the records for animal longevity, and comprise 26 of the 30 known records for vertebrate species with maximum longevity estimated to be over 100 years (8–11) (tetrapod examples include Galapagos tortoises, eastern box turtles, European pond turtles, and *Proteus*)
24 salamanders). Additionally, some ectothermic tetrapods may exhibit low or even negligible 25 (sensu 12) mortality and reproductive aging (1, 13–18). Understanding whether, and how, natural 26 selection has shaped mortality trajectories and longevity requires phylogenetically-controlled 27 tests to determine whether these species-specific results are anomalies that evolved in specific 28 lineages of ectotherms, or if they are common and repeated evolutionary outcomes. Recent 29 advances in contrasting endotherm and ectotherm longevity have contributed to a phylogenetic 30 perspective on lifespan (10, 11), but often use maximum longevity as their metric, which is not 31 based on the age-specific mortality trajectory and is strongly influenced by sample size, so can 32 lead to inaccurate conclusions (19, 20). Additionally, the lack of comparative analyses of aging 33 rates in ectothermic tetrapods is a major gap in our knowledge(21). A comprehensive analysis of 34 aging across ectothermic tetrapods requires decades of field-based population-level research, 35 international collaborations, and powerful quantitative tools. Integrating these efforts across 36 studies and taxa allows for using data from ectotherms to test evolutionary hypotheses of aging 37 (21), and for a phylogenetic understanding of the evolution of aging across tetrapods.

38 The evolutionary genetics of aging result from age-specific mutation-selection balance 39 trajectories where mutations have age-specific effects that may be strictly deleterious in later 40 adult stages or ages, and/or beneficial earlier (i.e., antagonistically pleiotropic, 22). Hypotheses 41 for how natural selection and the environment interact to shape this balance were first formulated 42 by Medawar (23), and further developed by Hamilton (24) and others (25–27). In ectotherms, 43 body temperature varies with the environment and, because metabolism responds to temperature, 44 ectothermic metabolism and cellular processes downregulate in cold temperatures, allowing for 45 extended periods of brumation and aestivation. In addition, after controlling for body size, 46 ectotherms have lower resting metabolic rates than endotherms (28). Accordingly, the
47 Thermoregulatory Mode Hypothesis predicts that ectothermic lineages have evolved lower aging rates and greater longevities than their similarly-sized endothermic counterparts (29, 30).

49 Layered on top of metabolic mode, environmental temperature itself is expected to be a strong driver of mortality in ectotherms, impacting both the evolution and the plasticity of aging through metabolic mechanisms (10, 31, 32, but see 33). Within many endothermic species, individuals with lower body temperatures have been found to live longer and age slower than those with higher body temperatures (29, 34), but across species, this pattern is less clear (35).

54 Similarly, ectotherms in cooler climates may also exhibit longer lifespans compared to those in warmer climates (10, 11; Temperature Hypothesis, hereafter).

56 Phenotypes that alter age-specific mutation-selection trajectories would be expected to result in the evolution of altered rates of aging (24), provided genetic variation exists (27, 36).
57 For example, species with phenotypes that reduce mortality risk are expected to have lower rates of aging than those without (21) (the so-called Protective Phenotypes Hypothesis). Previous work shows that ectothermic tetrapods, such as amphibians, with chemical protection mechanisms can live longer than those without, though how this trait (and any associated behavior) affects the rate of aging remains unknown (11, 37, 38). Tetrapod ectotherms are well-suited for enabling direct comparisons of the rates of aging among species with and without phenotypes that have such physical or chemical protections. Within reptiles, diverse traits may confer protection from predation and/or environmental stressors, including turtle shells, crocodilian armor, and snake venom (even if such traits are exaptations sensu 39). Similarly, in amphibians, many species produce toxic or unpalatable secretions (40). Despite these characteristics, the Protective Phenotypes Hypothesis has not been tested across ectothermic tetrapods using robust aging metrics (but see 10, 11, 37 for analyses using maximum longevities)
Aging and longevity may co-evolve through direct or indirect selection on life-history traits that are genetically correlated (3). Under antagonistic pleiotropy, genes that confer higher fitness in early life relative to late life will increase in frequency in populations that are skewed toward younger age classes (24). Because many ectothermic tetrapods have indeterminate growth and fecundity (41, 42), life-history theory predicts that such species should have stronger selection against deleterious late-age mutations relative to species with determinate growth and fecundity (21). Indeed, any species in which individuals from older age classes contribute more to population growth (e.g., fecundity, behavior) relative to other species should have concomitant slower aging. Thus, the aging rate may evolve from genetic covariation among life-history traits, such as annual fecundity, age of first reproduction, and annual survival. This results in a slow-fast continuum of life histories (43–46) that should match slow vs. fast aging rates (the Slow-Fast Continuum Hypothesis). For example, fast aging, expected to be correlated with a short reproductive lifespan, should evolve in a correlated manner with fast pace of life, and vice versa (43, 47). Therefore, the existence of a strong positive covariation among life-history traits (e.g., 48) predicts that the aging rate should covary with age of first reproduction (negatively) and with annual fecundity (positively) such that species that mature relatively early or that allocate relatively more energy to reproduction early in life display faster aging and shorter longevities (45, 49, 50).

We applied comparative phylogenetic methods to tetrapod mark-recapture data to analyze variation within ectotherm demographic aging and longevity in the wild, to compare aging and longevity to endotherms, and to address the following four distinct but not mutually-exclusive hypotheses: i) Thermoregulatory Mode, ii) Temperature, iii) Protective Phenotypes, and iv) Slow-Fast Continuum. We analyzed long-term capture-recapture data collected in the
wild from 107 populations of 77 species, with study length averaging 17 years (ranging from 4 to 60 years), to assess macroevolutionary patterns of aging rate and longevity in free-living amphibians and non-avian reptiles. We present the first comprehensive comparative analysis of patterns of aging across these ectotherms and estimate both the rate of aging (computed as the slope of the relative rate of increasing age-specific mortality derived from the Gompertz model—$β_1$) and longevity (computed as the number of years after the age of first reproduction until 95% of adults in a given cohort have died, as opposed to the age of the longest-lived individual).

Specifically, we test: i) whether ectotherms consistently age more slowly and live longer than endotherms, ii) whether annual mean, minimum, or maximum environmental temperature experienced by a population covaries with rate of aging and longevity, iii) whether species with protective phenotypes (either physical or chemical) age slower and live longer than those without physical or chemical protections, and iv) whether the rate of aging and longevity strongly covary with other biological traits such as age at first reproduction and annual fecundity.

## 106 Aging in ectothermic tetrapods

All orders represented by the 77 reptile and amphibian species for which age-specific estimates of mortality could be estimated had at least one species with negligible aging ($β_1$ ~ 0; Fig 1, Data S1). Notably, turtles had slow rates of aging (mean $β_1$ ± SE = 0.04 ± 0.01), with a narrow range relative to the number of species represented (-0.013 – 0.225 for 14 species; Fig 2, Table S1). When corrected for body size and phylogeny (Table S2), crocodilians, tuatara, and salamanders were similarly slow in aging (crocodilians: mean $β_1$ = 0.14 ± 0.06, tuatara: 0.005, and salamanders: 0.18 ± 0.05) in comparison to squamates (mean $β_1$ = 0.55 ± 0.14) and frogs (mean $β_1$ ± SE = 0.41 ± 0.06; Fig 2, Data S1). Turtles and tuatara exhibited greater longevity (95% of adult lifespan) than most other ectothermic tetrapods, with mean longevities of 39 (± SE
116 6) and 137 years, respectively, compared to crocodilians (21 years ± 5), squamates (12 years ±
117 2), frogs (8 years ± 0.6), and salamanders (10 years ± 1; Table S1-S2, Data S1), again when
118 corrected for the potential confounding effects of body size and phylogeny.

119 **Thermoregulatory Mode Hypothesis**

120 Controlling for phylogeny and body size, across tetrapods, aging rate and longevity did
121 not differ between ectotherms and endotherms (Table 1, Fig 3, see Fig S1 for raw values by
122 class). Ectotherms ranged well above and below the known aging rates for endotherms (C<sub>v</sub>= 1.40
123 for ectotherms and 1.15 for endotherms), and had the greatest longevities (C<sub>v</sub>=0.37 for
124 ectotherms and 0.32 for endotherms; Fig S1). The aging patterns of ectotherms were thus more
125 diverse, rather than slower than those reported in endotherms. Indeed, the ectotherm variance in
126 aging rate was significantly greater than the endotherm variance (F<sub>106/118</sub> = 5.49, p=<0.001),
127 although the variance in longevities was not statistically different (F<sub>106/118</sub> =1.31, p=0.16). As
128 expected, there was a negative relationship between aging rate and longevity in both groups, with
129 faster aging rates corresponding to shorter longevity, but the slope of the relationship was more
130 steeply negative in ectotherms than in endotherms (Table 1, Fig 3C). The negative association
131 between rate of aging and longevity varied considerably among mammals, birds, reptiles, and
132 amphibians, when considered by taxonomic class (Fig S2; Table S3).

133 **Temperature Hypothesis**

134 Within ectotherms, the rate of aging increased with mean temperature in reptiles, but
135 decreased with mean temperature in amphibians (Table 2, Fig S3). Models using minimum and
136 maximum temperatures instead of mean showed the same patterns (Table S4).

137 **Protective Phenotypes Hypothesis**
We considered three categories of protection: physical (armor and shells), chemical (venom and skin toxins), and neither physical nor chemical (Fig S4). Within ectothermic tetrapods, species with physical or chemical protection aged slower than species with neither physical nor chemical protection (mean $β_1$ ± SE; physical: 0.05 ± 0.01, chemical: 0.29 ± 0.06, neither: 0.47 ± 0.07). Species with physical protection lived longer than those with no protection and those with chemical protection (mean years ± SD; physical: 36 ± 5, neither: 10 ± 3, chemical: 11 ± 1, Table S5, Data S1).

## Slow-fast Continuum Hypothesis

We examined relationships between both the age of first reproduction and annual fecundity and rate of aging and longevity. As expected under the slow-fast continuum hypothesis, the rate of aging was negatively associated with the log age of first reproduction, and positively associated with the log annual fecundity (Table 2). However, because class (i.e., reptile or amphibian) influenced these relationships, we further investigated these associations within class. This analysis revealed a class-dependent structure of the slow-fast continuum results. A decreasing rate of aging with increasing age of first reproduction structured the life history variation in reptiles, whereas an increasing rate of aging with increasing annual fecundity structured the life history variation in amphibians (Table S6, Fig 4A-4B). In both amphibians and reptiles, longevity was positively associated with the age of first reproduction, as expected under the slow-fast continuum hypothesis (Table 2, Fig. 4C). However, longevity was not related to annual fecundity in either class (Table S6, Fig 4D). Importantly, all of these results were still true when we restricted the analyses to the best quality datasets (i.e., >25% of known-age individuals monitored and study length equal to or longer than the median longevity), demonstrating that variable data quality among populations has no detectable influence (Table S8).
161 **Discussion**

162 We found greater variation in aging rates and longevities across wild ectothermic
163 tetrapods than in birds and mammals. Our study thus demonstrates that the parameter space for
164 aging rates and longevities is much larger in ectotherms than previously thought. Turtles,
165 crocodilians, and salamanders have remarkably low aging rates and extended longevities for
166 their size. Most turtles have physical protection (bony shells), as well as a relatively slow pace of
167 life, which both contribute to the negligible aging and exceptional longevity. Future work that
168 focuses on turtles with soft shells (versus rigid, as in this study) may help disentangle causes of
169 slow turtle aging. While turtle aging rates are low overall, they are surprisingly variable. For
170 example, in *Chrysemys picta*, age at maturity, longevity, and aging rates vary significantly
171 among populations (8, 11, 12, this study). Our analyses thus provide clear evidence that
172 ectotherms have a remarkable diversity of aging rates and longevities and add to the growing
173 literature on ectotherm longevity (e.g., *10, 11*). Within ectotherms, rates of aging ranged from -
174 0.013 to 2.0, corresponding to a continuum from negligible aging to fast aging. Ectotherm
175 longevity (measured here as the age when 95% of adults have died) ranged from 1 year to 137
176 years. For comparison, primate aging rates are between 0.04 – 0.50 (longevity: 4 – 84 years),
177 with a human aging rate of about 0.1(longevity:100 years; 2). The overall mammalian rates of
178 aging ranged from 0.03 to 1.2, with a single high value observed in eastern moles (*Scalopus
179 aquaticus*) representing an outlier (Fig S1). Although negligible aging was not observed in the
180 mammals included in our sample, it has been identified in naked mole-rats (51). One notable
181 group of vertebrates missing from our comparisons is fishes, which themselves have highly
182 variable aging rates and longevities and contain species of great interest to aging biology (e.g.,
183 rock fish, bigmouth buffalo, and short-lived poeciliids) (52–55).
In addition to expanding the domain for aging research and gaining insights into ectotherm aging, we used novel data to test four hypotheses on the evolution of aging in a comparative framework. Our test of the Thermoregulatory Mode Hypothesis revealed that, contrary to the expectation, ectotherms did not have slower rates of aging (Adjusted R²=0.05, p=0.26) or longer lifespans (Adjusted R²=0.20, p=0.64) than similar-sized endotherms.

However, thermoregulatory mode does appear to modulate the relationship between aging rate and longevity (when phylogenetically and body-mass controlled: Adjusted R²=0.38, Fig 3C).

We found mixed support for the Temperature Hypothesis as it relates to rate of aging (in agreement with Stark and colleagues' work on maximum longevity; 10, 11); mean environmental temperature interacted with class such that the rate of aging increased with temperature in reptiles, but decreased with temperature in amphibians (Fig S3, Table 2).

Moreover, this interaction corresponded to the same directionalities when we tested for a relationship with minimum or maximum environmental temperature (Table S4). We found no association between longevity and mean, minimum, or maximum environmental temperature.

Because temperature is a proximate mediator of cellular and biochemical processes, it is also likely a driver of local adaptation among populations – and plasticity within individuals – for phenotypes related to aging and longevity (31, reviewed in 56). Temperature increases ectotherm metabolic rate (or at least the length of the active season) and putatively hastens accumulation of molecular damage via multiple processes such as free radical production, telomere attrition, secretion of cytokines from senescent cells, and DNA damage (56). For example, in garter snakes and at least one frog species, thermal differences among populations have been hypothesized to be an agent of selection for life-history divergence, including aging (33, 57).

Laboratory experiments that raise ectotherms under different thermal regimes can directly test
207 for the proximate effect of temperature on aging (e.g., 58), and are necessary to tease apart how
208 temperature might influence the evolution of aging. Whether and how global warming will
209 impact the evolution of aging rates remains unknown, but will become especially important to
210 understand for making management and conservation decisions to avoid species extinctions.

211 Our analyses also support the Protective Phenotypes Hypothesis within ectothermic
212 tetrapods. Species with physically protective phenotypes, such as armor, spines, or shells, aged
213 more slowly and lived much longer for their size than those without protective phenotypes
214 (Table S5). Though species with chemical protection have greater maximum longevities than
215 those without (38, 59), we provide here the first evidence that metrics describing the adult
216 mortality trajectory are linked to these protective phenotypes. This result may explain uniquely
217 slow rates of aging in turtles, coupled with extended longevities. Salamanders also aged slowly
218 relative to other tetrapod ectotherms. We were unable to include behaviors such as fossorial
219 lifestyles or seasonal activity, that may function as behavioral protections by reducing predation
220 risk, with a consequence of low mortality rates (3, though see 11 which found that microhabitat
221 preference, including fossorial behavior, did not influence maximum longevity). Moreover,
222 many salamanders have regenerative capabilities that could contribute to slowing aging through
223 greater damage repair efficiency (15, 60, 61).

224 Lastly, we document that the slow-fast continuum of life histories is strongly involved in
225 shaping aging. Both rates of aging and longevities were associated with other biological traits
226 (e.g., age at first reproduction and annual fecundity) in reptiles and amphibians. Earlier age at
227 first reproduction in reptiles was correlated with faster aging rates (Table 2, Fig 4). A similar
228 pattern has been documented in birds and mammals, where an earlier age at first reproduction
229 corresponded to an earlier age at the onset of senescence (62, 63). Amphibian species with
higher annual fecundities, and therefore greater annual reproductive allocation, had faster rates of aging, which has also been found in birds and mammals, and supports Hamilton's original prediction (24). Earlier age of first reproduction was also associated with shorter longevity in both amphibians and reptiles (Fig 4). Heralded as a key component of the life-history portfolio (64, 65), this positive relationship between age at first reproduction and adult longevity is thus robust across tetrapod ectotherms as well. These results are congruent with patterns detected in endothermic vertebrates (4) and fit into an existing evolutionary framework of genetic correlations underlying relationships among life-history traits, including aging and longevity.

Further work on the quantitative genetic and genomic bases of aging and longevity are necessary to test broadly whether the phenotypic associations are underlain by genetic correlations.

The evolution of aging rates and longevity has seemingly multiple determinants from the genetic architecture of life-history traits to morphological adaptations, yielding complex aging patterns across free-ranging tetrapods (1). Long-term studies of species from wild populations are necessary for understanding such complexity in the natural context in which aging evolved (66), and enable the use of more accurate aging metrics. Our compilation of long-term field studies clarifies mechanisms underlying the evolution of aging rate in tetrapod vertebrates, highlighting links among protective phenotypes, life-history tactics, and aging variation in the wild.

248

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437 Acknowledgments: Please see supplemental information for study-specific acknowledgements,

438 contact information, and funding. This manuscript is contribution #805 of the USGS

439 Amphibian Research and Monitoring Initiative. Any use of trade, firm, and product names

440 is for descriptive purposes only and does not imply endorsement by the U.S. Government.

441 Funding: National Institutes of Health Grant R01AG049416 (AMB, FJJ, DAWM)

442 Author Contributions:

443 Conceptualization: BAR, HC, AMB, FJJ, DAWM,

444 Data Collection: All authors

445 Analysis: BAR, AML, DAWM

446 Visualization: BAR, HC

447 Funding Acquisition: AMB, FJJ, DAWM

448 Project Administration: BAR, DAWM

449 Writing—original draft: BAR, HC, and DAWM

450 Writing—review & editing: AMB, FJJ, JFL, JMG, AML

451 Writing—final draft: All authors

452 Competing interests: Authors declare that they have no competing interests.

453 Data and materials availability: Data and code used for these analyses are available in Dryad

454 (#TBD). Individual mark-recapture datasets can be obtained by contacting specific dataset

455 owners (see Data S1).

456 Supplementary Materials

457 Materials and Methods

458 Supplementary Text

459 Figs. S1 to S7
460 Tables S1 to S8

461 References (67-89)

462 Data S1
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Tetrapod ectotherms and their measures of aging. The rate of aging is the Gompertz slope parameter indicating how mortality risk increases with age (in number of years since first reproduction). Longevity is the number of years from the age of first reproduction at which 95% of the individuals in a population have died. Error bars show +/-1 SD for species for which multiple populations were analyzed. Shading represents taxonomic orders. The number next to the bar represents the number of populations included in this study. Figure was made with iTOL (89) and silhouettes are available on phylopic.org.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Measures of rates of aging and longevity across ectotherms. Letters denote statistical significance (*p* < 0.05) across orders after correcting for body mass and phylogeny (Table S2).</figcaption></figure>

Bars show +/- 1 SE. Points are uncorrected values for visualization. The rate of aging here is the mortality slope derived from a Gompertz model. Longevity is the number of years from the age of first reproduction at which 95% of the individuals in a population have died. Green denotes reptiles and purple denotes amphibians.
<figure><img src="image_5.png" /><figcaption>Fig. 3. Comparison between ectothermic and endothermic tetrapods for (A) rates of aging, (B) longevity, and (C) the relationship between aging rate and longevity. Trend lines indicate the estimated slopes of each relationship, representing the terms of interest for each model (predicted values not shown). Orange denotes endotherms and blue denotes ectotherms. Black lines in A and B show the conditional effect where the interaction term equals zero (no difference between endotherms and ectotherms). See Table 1 for P-values of these interactions.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig 4. Slow-fast Continuum Hypothesis.** Solid lines show the estimated statistically significant (p < 0.05) relationships between variables and are derived from PGLSs from Table 2. Dashed lines are included for visualizing the contrasting class. Predicted values are not shown. Green denotes reptiles and purple denotes amphibians. The black line in C denotes the overall effect (no difference between reptiles and amphibians). Age at first reproduction and annual fecundity themselves did not differ by class (when controlling for phylogeny and body mass; Table S7).</figcaption></figure>
Table 1. Statistical output for PGLSs and phylogenetic ANCOVAs comparing ectotherms and endotherms for the Thermoregulatory Mode Hypothesis. Group is a factor with two levels: ectotherms vs. endotherms. Interaction (bold) terms denote group differences after correcting for body mass plotted in Fig 3. These were not statistically significant for rate of aging or longevity (see Fig 3).

<table><thead><tr><th>Model</th><th>Df</th><th>Sum Sq</th><th>Mean Sq</th><th>F value</th><th>Est</th><th>P-value</th></tr></thead><tbody><tr><td>Ectotherms vs Endotherms</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Rate of aging (Adj R<sup>2</sup> =0.06)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Group</td><td>1</td><td>0.01</td><td>0.01</td><td>0.001</td><td>-0.39</td><td>0.76</td></tr><tr><td>Log mass</td><td>1</td><td>129.00</td><td>129.00</td><td>14.46</td><td>-0.08</td><td>&lt;0.001</td></tr><tr><td>Log mass × group</td><td>1</td><td>16.44</td><td>16.44</td><td>1.84</td><td>0.04</td><td><strong>0.18</strong></td></tr><tr><td>Residuals</td><td>222</td><td>1979.85</td><td>8.92</td><td></td><td></td><td></td></tr><tr><td>Log longevity (Adj R<sup>2</sup> =0.20)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Group</td><td>1</td><td>1.96</td><td>1.96</td><td>0.08</td><td>-0.30</td><td>0.89</td></tr><tr><td>Log mass</td><td>1</td><td>1479.83</td><td>1479.83</td><td>59.16</td><td>0.22</td><td>&lt;0.001</td></tr><tr><td>Log mass × group</td><td>1</td><td>6.83</td><td>6.83</td><td>0.27</td><td>-0.03</td><td><strong>0.60</strong></td></tr><tr><td>Residuals</td><td>222</td><td>5620.40</td><td>25.32</td><td></td><td></td><td></td></tr><tr><td>Log longevity (Adj R<sup>2</sup> =0.37)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Rate of aging</td><td>1</td><td>1787.63</td><td>1787.63</td><td>90.24</td><td>-0.87</td><td>&lt;0.001</td></tr><tr><td>Group</td><td>1</td><td>2.23</td><td>2.23</td><td>0.11</td><td>-0.63</td><td>0.74</td></tr><tr><td>Log mass</td><td>1</td><td>851.72</td><td>851.72</td><td>42.99</td><td>0.17</td><td>&lt;0.001</td></tr><tr><td>Rate of aging × group</td><td>1</td><td>107.47</td><td>107.47</td><td>5.43</td><td>0.56</td><td><strong>0.02</strong></td></tr><tr><td>Residuals</td><td>221</td><td>4378.00</td><td>19.81</td><td></td><td></td><td></td></tr></tbody></table>

513

514

515

516

517

518

519

520

521
<caption>Table 2. Statistical output for ectotherm PGLSs showing output of all predictor variables for the Temperature, Protective Phenotypes, and Slow-fast Continuum Hypotheses.</caption>

Protection is a factor with three levels: none, chemical, and physical. Class is a factor with two levels: reptile and amphibian. Bold P-values correspond to tests of the specific hypothesis in question.

<table><thead><tr><th>PGLS Model</th><th>Df</th><th>Sum Sq</th><th>Mean Sq</th><th>F value</th><th>Est</th><th>P-value</th></tr></thead><tbody><tr><td colspan="7"><strong>Temperature Hypothesis</strong></td></tr><tr><td>Rate of aging (Adj R<sup>2</sup> = 0.06, λ = 0)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Class</td><td>1</td><td>0.01</td><td>0.01</td><td>0.05</td><td>-0.28</td><td>0.18</td></tr><tr><td><strong>Mean temp</strong></td><td>1</td><td>0.003</td><td>0.003</td><td>0.002</td><td>-0.002</td><td><strong>0.08</strong></td></tr><tr><td>Class × mean temp</td><td>1</td><td>1.05</td><td>1.05</td><td>5.56</td><td>0.004</td><td>0.02</td></tr><tr><td>Log mass</td><td>1</td><td>1.01</td><td>1.01</td><td>5.39</td><td>-0.07</td><td>0.003</td></tr><tr><td>Residuals</td><td>102</td><td>18.54</td><td>0.20</td><td></td><td></td><td></td></tr><tr><td>Log longevity (Adj R<sup>2</sup> = 0.15, λ = 0.67)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Class</td><td>1</td><td>0.72</td><td>0.72</td><td>0.66</td><td>0.42</td><td>0.71</td></tr><tr><td><strong>Mean temp</strong></td><td>1</td><td>1.23</td><td>1.23</td><td>1.12</td><td>-0.001</td><td><strong>0.51</strong></td></tr><tr><td>Class × mean temp</td><td>1</td><td>0.18</td><td>0.18</td><td>0.17</td><td>-0.001</td><td>0.66</td></tr><tr><td>Log mass</td><td>1</td><td>22.34</td><td>22.34</td><td>20.41</td><td>0.18</td><td>&lt;0.001</td></tr><tr><td>Residuals</td><td>102</td><td>109.44</td><td>1.07</td><td></td><td></td><td></td></tr><tr><td colspan="7"><strong>Protective Phenotypes Hypothesis</strong></td></tr><tr><td>Rate of aging (Adj R<sup>2</sup> = 0.11, λ = 0)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Protection</strong></td><td>2</td><td>2.79</td><td>1.40</td><td>7.85</td><td>None: 0.20<br>Physical: -0.32<br>Chemical: 0.22</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Log mass</td><td>1</td><td>0.13</td><td>0.13</td><td>0.75</td><td>0.02</td><td>0.39</td></tr><tr><td>Residuals</td><td>103</td><td>18.34</td><td>0.18</td><td></td><td></td><td></td></tr><tr><td>Log longevity (Adj R<sup>2</sup> = 0.44, λ = 0)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Protection</strong></td><td>2</td><td>35.25</td><td>17.62</td><td>42.15</td><td>None: -0.32<br>Physical: 0.89<br>Chemical: 2.08</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Log mass</td><td>1</td><td>1.25</td><td>1.25</td><td>3.00</td><td>0.06</td><td>0.09</td></tr><tr><td>Residuals</td><td>103</td><td>43.06</td><td>0.42</td><td></td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th colspan="7">Slow-fast Continuum Hypothesis</th></tr></thead><tbody><tr><td>Rate of aging (Adj R<sup>2</sup> = 0.18, &lambda; = 0)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Log age at repro</td><td>1</td><td>1.08</td><td>1.08</td><td>6.48</td><td>-0.26</td><td><strong>0.01</strong></td></tr><tr><td>Log annual fecundity</td><td>1</td><td>0.39</td><td>0.39</td><td>2.34</td><td>0.07</td><td><strong>0.04</strong></td></tr><tr><td>Class</td><td>1</td><td>0.28</td><td>0.28</td><td>1.71</td><td>0.46</td><td>0.02</td></tr><tr><td>Log mass</td><td>1</td><td>2.78</td><td>2.78</td><td>16.73</td><td>-0.03</td><td>0.41</td></tr><tr><td>Residuals</td><td>99</td><td>16.44</td><td>0.17</td><td></td><td></td><td></td></tr><tr><td>Log longevity (Adj R<sup>2</sup> = 0.52, &lambda; = 0)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Log age at repro</td><td>1</td><td>9.75</td><td>9.75</td><td>26.34</td><td>0.77</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Log annual fecundity</td><td>1</td><td>8.89</td><td>8.89</td><td>24.00</td><td>-0.06</td><td><strong>0.22</strong></td></tr><tr><td>Class</td><td>1</td><td>4.20</td><td>4.20</td><td>11.34</td><td>-0.06</td><td>0.85</td></tr><tr><td>Log mass</td><td>1</td><td>19.38</td><td>19.38</td><td>52.32</td><td>0.05</td><td>0.32</td></tr><tr><td>Residuals</td><td>99</td><td>36.66</td><td>0.37</td><td></td><td></td><td></td></tr></tbody></table>