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AGING
Diverse aging rates in ectothermic tetrapods provide
insights for the evolution of aging and longevity
Beth A. Reinke
1,2
*, Hugo Cayuela
3
, Fredric J. Janzen
4,5
, Jean-François Lemaître
6
,
Jean-Michel Gaillard
6
, A. Michelle Lawing
7
, John B. Iverson
8
, Ditte G. Christiansen
9
,
Iñigo Martínez-Solano
10
, Gregorio Sánchez-Montes
10
, Jorge Gutiérrez-Rodríguez
10,11
,
Francis L. Rose
12
, Nicola Nelson
13
, Susan Keall
13
, Alain J. Crivelli
14
, Theodoros Nazirides
15
,
Annegret Grimm-Seyfarth
16
, Klaus Henle
16
, Emiliano Mori
17
, Gaëtan Guiller
18
, Rebecca Homan
19
,
Anthony Olivier
14
, Erin Muths
20
, Blake R. Hossack
21
, Xavier Bonnet
22
, David S. Pilliod
23
,
Marieke Lettink
24
, Tony Whitaker
25
†, Benedikt R. Schmidt
9,26
, Michael G. Gardner
27,28
,
Marc Cheylan
29
, Françoise Poitevin
29
, Ana Golubović
30
, Ljiljana Tomović
30
, Dragan Arsovski
31
,
Richard A. Griffiths
32
, Jan W. Arntzen
33
, Jean-Pierre Baron
34
, Jean-François Le Galliard
34,35
,
Thomas Tully
35
, Luca Luiselli
36,37,38
, Massimo Capula
39
, Lorenzo Rugiero
36
, Rebecca McCaffery
40
,
Lisa A. Eby
41
, Venetia Briggs-Gonzalez
42
, Frank Mazzotti
42
, David Pearson
43
, Brad A. Lambert
44
,
David M. Green
45
, Nathalie Jreidini
45
, Claudio Angelini
46
, Graham Pyke
47,48
, Jean-Marc Thirion
49
,
Pierre Joly
50
, Jean-Paul Léna
50
, Anton D. Tucker
51
, Col Limpus
52
, Pauline Priol
53
, Aurélien Besnard
54
,
Pauline Bernard
55
, Kristin Stanford
56
, Richard King
57
, Justin Garwood
58
, Jaime Bosch
10,59,60
,
Franco L. Souza
61
, Jaime Bertoluci
62
, Shirley Famelli
63,64
, Kurt Grossenbacher
65
, Omar Lenzi
9
,
Kathleen Matthews
66
, Sylvain Boitaud
67
, Deanna H. Olson
68
, Tim S. Jessop
69
, Graeme R. Gillespie
70
,
Jean Clobert
71
, Murielle Richard
71
, Andrés Valenzuela-Sánchez
72,73
, Gary M. Fellers
74
†,
Patrick M. Kleeman
74
, Brian J. Halstead
75
, Evan H. Campbell Grant
76
, Phillip G. Byrne
77
,
Thierry Frétey
78
, Bernard Le Garff
79
, Pauline Levionnois
80
, John C. Maerz
81
, Julian Pichenot
82
,
KurtuluşOlgun
83
, Nazan Üzüm
83
, Aziz Avcı
83
, Claude Miaud
29
, Johan Elmberg
84
, Gregory P. Brown
48
,
Richard Shine
48
, Nathan F. Bendik
85
, Lisa O’Donnell
86
, Courtney L. Davis
87
, Michael J. Lannoo
88
,
Rochelle M. Stiles
89
, Robert M. Cox
90
, Aaron M. Reedy
90,91
, Daniel A. Warner
91
, Eric Bonnaire
92
,
Kristine Grayson
93
, Roberto Ramos-Targarona
94
, Eyup Baskale
95
, David Muñoz
2
, John Measey
96
,
F. Andre de Villiers
96
,WillSelman
97
, Victor Ronget
98
,AnneM.Bronikowski
4,5
*‡, David A. W. Miller
2
*‡
Comparative studies of mortality in the wild are necessary to understand the evolution of aging; yet,
ectothermic tetrapods are underrepresented in this comparative landscape, despite their suitability
for testing evolutionary hypotheses. We present a study of aging rates and longevity across wild
tetrapod ectotherms, using data from 107 populations (77 species) of nonavian reptiles and amphibians.
We test hypotheses of how thermoregulatory mode, environmental temperature, protective phenotypes,
and pace of life history contribute to demographic aging. Controlling for phylogeny and body size,
ectotherms display a higher diversity of aging rates compared with endotherms and include
phylogenetically widespread evidence of negligible aging. Protective phenotypes and life-history
strategies further explain macroevolutionary patterns of aging. Analyzing ectothermic tetrapods in a
comparative context enhances our understanding of the evolution of aging.
Comparative studies of animal aging rates
in the wild are critical for assessing the
potential limits of longevity and for un-
derstanding ecological and evolutionary
factors shaping variation in aging strat-
egies (1–3). Demographic indicators of aging
include adult longevity and measures that cap-
ture 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 endothermic tetrapods
[birds and mammals; e.g., (2–7)]. However,
ectotherms hold most of the records for animal
longevity and make up 26 of the 30 known
records for vertebrate species with maximum
longevity estimated to be >100 years (8–11)
(tetrapod examples include Galápagos tor-
toises, eastern box turtles, European pond
turtles, and Proteus salamanders). Addition-
ally, some ectothermic tetrapods may exhibit
low or even negligible [sensu (12)] mortality
and reproductive aging (1,13–18). Understand-
ing whether and how natural selection has
shaped mortality trajectories and longevity
requires phylogenetically controlled tests to
determine whether these species-specific re-
sults are anomalies that evolved in specific
lineages of ectotherms or if they are com-
mon and recurrent evolutionary outcomes.
Recent advances in contrasting endotherm
and ectotherm longevity have contributed to
a phylogenetic perspective on lifespan (10,11)
but often use maximum longevity as their
metric. This metric is not based on the age-
specific mortality trajectory and is influenced
by sample size, so it can lead to inaccurate
conclusions (19,20). Additionally, the lack of
comparative analyses of mortality trajectories
in ectothermic tetrapods—and the aging met-
rics that derive from them—is a major knowl-
edge gap (21). A comprehensive analysis of
demographic aging across ectothermic tetra-
pods requires decades of field-based population-
level research, international collaborations, and
powerful quantitative tools. Integrating these
efforts across studies and taxa allows for testing
evolutionary hypotheses of aging (21)andfora
phylogenetic understanding of the evolution of
aging across tetrapods.
The evolutionary genetics of aging result
from age-specific mutation-selection balance
trajectories, where mutations have age-specific
effects that may be strictly deleterious in later
adult stages or ages and/or beneficial earlier
(i.e., antagonistically pleiotropic) (22). Hypotheses
for how natural selection and the environment
interact to shape this balance were first formu-
lated by Medawar (23) and further developed by
Hamilton (24)andothers(25–27). In ectotherms,
body temperature varies with the ambient en-
vironment and, because metabolism responds
to temperature, ectothermic metabolism and
cellular processes down-regulate in cold tem-
peratures, which allows for extended periods
of brumation. Additionally, after controlling
for body size, ectotherms have lower resting
metabolic rates than endotherms (28). Accord-
ingly, the thermoregulatory mode hypothesis
predicts that ectothermic lineages have evolved
lower aging rates and greater longevities than
their similarly sized endothermic counterparts
(29,30). Layered on top of metabolic mode,
environmental temperature itself is expected
to be a strong driver of mortality in ecto-
therms, affecting both the evolution and the
plasticity of aging through metabolic mech-
anisms [(10,31,32), but see (33)]. Within many
endothermic species, individuals with lower body
temperatures live longer and age slower than
those with higher body temperatures (29,34),
but across species, this pattern is less clear (35).
Similarly, ectotherms in cooler climates may
also exhibit longer lifespans compared with
those in warmer climates [(10,11); referred to
as the temperature hypothesis hereafter].
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).
For example, species with phenotypes that
reducemortalityriskareexpectedtohave
lower rates of aging than those without [(21);
the protective phenotypes hypothesis]. Previous
work has shown that ectothermic tetrapods,
such as amphibians, with chemical protection
mechanisms can live longer than those with-
out; however, how this trait (and any associ-
ated 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, di-
verse traits may confer protection from preda-
tion and/or environmental stressors, including
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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 ectother-
mic tetrapods using robust aging metrics [but
see (10,11,37) for analyses using maximum
longevities].
Aging and longevity may coevolve through
direct or indirect selection on life-history traits
that are genetically correlated (3). Under antag-
onistic 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). Be-
cause many ectothermic tetrapods have in-
determinate growth and fecundity (41,42),
life-history theory predicts that such spe-
cies should have stronger selection against
mutations with deleterious late-age effects
(be they antagonistically pleiotropic or not)
relative to species with determinate growth
and fecundity (21). Any species in which indi-
viduals from older age classes contribute more
to population growth (e.g., through fecundity
or 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
at first reproduction, and annual survival. This
results in a slow-fast continuum of life histories
(43–46) that should match slow-to-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 (48) predicts that the aging rate should
covary with age at first reproduction (negatively)
and with annual fecundity (positively) such that
species that mature relatively early or those that
allocate relatively more energy to reproduction
early in life display faster aging and shorter
longevities (45,49,50).
We applied comparative phylogenetic meth-
ods to mark-recapture data from tetrapods to
analyze variation in ectotherm demographic
aging and longevity in the wild, to compare
aging and longevity (using a mortality trajectory–
derived metric) with 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 amphibians and nonavian
reptiles (hereafter referred to as reptiles in con-
trast with the endothermic avian reptiles, here-
after referred to as birds). 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 age-specific mortality derived
from the Gompertz model, b
1
) and longevity
(computed as the number of years after the
age at 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 the following: (i) whether ectotherms
consistently age more slowly and live longer
1460 24 JUNE 2022 •VOL 376 ISSUE 6600 science.org SCIENCE
1
Department of Biology, Northeastern Illinois University, Chicago, IL, USA.
2
Department of Ecosystem Science and Management, Pennsylvania State University, State College, PA, USA.
3
Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.
4
Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA.
5
W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI, USA.
6
Université Lyon 1, Laboratoire de Biométrie et Biologie Evolutive, Villeurbanne, France.
7
Department of
Ecology and Conservation Biology, Texas A&M University, College Station, TX, USA.
8
Department of Biology, Earlham College, Richmond, IN, USA.
9
Department of Evolutionary Biology and
Environmental Studies, University of Zürich, Zürich, Switzerland.
10
Departamento de Biodiversidad y Biología Evolutiva, Museo Nacional de Ciencias Naturales, CSIC, Madrid, Spain.
11
Department
of Integrative Ecology, Estación Biológica de Doñana (EBD-CSIC), Seville, Spain.
12
Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA.
13
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
Independent researcher, Vironia,
Greece.
16
Department Conservation Biology and Social-Ecological Systems, 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
US Geological Survey, Fort
Collins Science Center, Fort Collins, CO, USA.
21
US 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
US Geological Survey, Forest and Rangeland Ecosystem Science Center, Boise, ID, USA.
24
Fauna Finders, Lyttelton, Christchurch, New Zealand.
25
Orinoco, RD1, Motueka, New Zealand.
26
Info Fauna Karch, Neuchâtel, Switzerland.
27
College of Science and Engineering, Flinders
University, Adelaide, SA, Australia.
28
Evolutionary Biology Unit, South Australian Museum, Adelaide, SA, 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, 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-Nemo urs, 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
US 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, QC, 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, Sydney, NSW, Australia.
49
Objectifs Biodiversité, Pont-l’Abbé-d’Arnoult, France.
50
Université de
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, Australia.
53
Statipop, Scientific Consulting, Ganges, France.
54
CNRS, EPHE, UM, SupAgro, IRD, INRA, UMR 5175 CEFE, PSL Research University, Montpelier, 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,
VIC, Australia.
64
Environmental Research Institute, North Highland College, University of the Highlands and Islands, Thurso, Scotland, UK.
65
Abteilung Wirbeltiere, Naturhistorisches Museum,
Bern, Switzerland.
66
USDA Forest Service (Retired), 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, VIC, 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
US Geological Survey Eastern Ecological Research Center (formerly 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 Eco-éthologie, 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 Univ ersity, 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,
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.
96
Centre for
Invasion Biology, Department of Botany & Zoology, Stellenbosch University, Stellenbosch, South Africa.
97
Department of Biology, Millsaps College, Jackson, MS, USA.
98
Unité Eco-anthropologie
(EA), Muséum National d’Histoire Naturelle, CNRS, Université Paris Diderot, Paris, France.
*Corresponding author. Email: e-reinke@neiu.edu (B.A.R.); abroniko@msu.edu (A.M.B.); dxm84@psu.edu (D.A.W.M.)
†Deceased. ‡These authors contributed equally to this work.
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than endotherms; (ii) whether annual mean,
minimum, or maximum environmental tem-
perature experienced by a population covaries
with rate of aging and longevity; (iii) whether
species with protective phenotypes (either phys-
ical or chemical) age slower and live longer than
those without physical or chemical protection;
and (iv) whether rate of aging and longevity
strongly covary with other biological traits, such
as age at first reproduction and annual fecundity.
Aging in ectothermic tetrapods
All orders represented by the 77 reptile and
amphibian species for which age-specific esti-
mates of mortality were estimated had at least
one species with negligible aging (b
1
~0;Fig.1
and data S1). Notably, turtles had slow rates
of aging (mean b
1
± SE = 0.04 ± 0.01), with a
narrow range relative to the number of spe-
cies represented (−0.01 to 0.23 for 14 species;
Fig. 2 and table S1). When corrected for body
SCIENCE science.org 24 JUNE 2022 •VOL 376 ISSUE 6600 1461
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 estimated number
of years from the age at 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. The number next to the bar represents the number
of populations included in this study. Shading represents taxonomic orders.
Figure was made with iTOL (67), and silhouettes are available on phylopic.org.
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size and phylogeny (table S2), crocodilians,
tuatara, and salamanders were similarly slow
in aging (crocodilians: mean b
1
=0.14±0.06;
tuatara: 0.005; and salamanders: 0.18 ± 0.05) in
comparison with squamates (mean b
1
=0.55±
0.14) and frogs (mean b
1
=0.41±0.06)(Fig.2
and data S1). Turtles and tuatara exhibited
greater longevity than most other ectothermic
tetrapods, with mean longevities of 39 (SE,
±6 years) and 137 years, respectively, com-
pared with crocodilians (21 ± 5 years), squamates
(12 ± 2 years), frogs (8 ± 0.6 years), and sala-
manders (10 ± 1 years) (tables S1 and S2 and data
S1), again when corrected for the potential con-
founding effects of body size and phylogeny.
Thermoregulatory mode hypothesis
Controlling for phylogeny and body size across
tetrapods, aging rate and longevity did not
differ between ectotherms and endotherms
(Table 1 and Fig. 3; see fig. S1 for raw values
by taxonomic class). Ectotherms ranged well
above and below the known aging rates for
endotherms (C
v
= 1.40 for ectotherms and 1.15
for endotherms, where C
v
is the coefficient of
variation) and had the greatest longevities (C
v
=
0.37 for ectotherms and 0.32 for endotherms)
(fig. S1). The aging patterns of ectotherms
were thus more diverse rather than slower
than those reported in endotherms. The
ectotherm variance in aging rate was signif-
icantly greater than the endotherm variance
(F
106/118
=5.49,whereF
106/118
is the Fsta-
tistic on 106 and 118 degrees of freedom; P<
0.001), although the variance in longevities
was not statistically different (F
106/118
= 1.31;
P= 0.16). As expected, there was a negative
relationship between aging rate and longevity
in both groups, with faster aging rates corre-
sponding to shorter longevity, but the slope of
the relationship was more negative in ecto-
therms than in endotherms (Table 1 and Fig. 3C).
The negative association between rate of aging
and longevity varied considerably among mam-
mals, birds, reptiles, and amphibians when con-
sidered by taxonomic class (fig. S2 and table S3).
Temperature hypothesis
Within ectotherms, the rate of aging increased
with mean environmental temperature in rep-
tiles but decreased with mean temperature in
amphibians (Table 2 and fig. S3). Models using
minimum and maximum temperatures instead
of mean temperature showed the same patterns
(table S4).
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 phys-
ical nor chemical protection (mean b
1
±SE:
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Table 1. Statistical output for PGLSs and phylogenetic analyses of covariance (ANCOVAs)
comparing ectotherms and endotherms for the thermoregulatory mode hypothesis. Group is a
factor with two levels: ectotherms versus endotherms. Dashes indicate not applicable. Df, degrees of
freedom; Sum sq, sum of squares; Mean sq, mean of the sum of squares; Est, estimate; Adj R
2
,
adjusted coefficient of determination.
Model Df Sum sq Mean sq F value Est Pvalue
Ectotherms versus endotherms
.................................... ....................................... ........................................ .......................................... ....................................... .................
Rate of aging (Adj R
2
= 0.05)
.................................... ....................................... ........................................ .......................................... ....................................... .................
Group 1 0.01 0.01 0.001 −0.38 0.77
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log mass 1 126.84 126.84 14.20 −0.08 <0.001
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log mass × group 1 15.64 15.64 1.75 0.04 0.19
.................................... ....................................... ........................................ .......................................... ....................................... .................
Residuals 222 1982.80 8.93 –––
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log longevity (Adj R
2
= 0.20)
.................................... ....................................... ........................................ .......................................... ....................................... .................
Group 1 2.00 1.96 0.08 −0.31 0.89
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log mass 1 1496.7 1496.7 59.10 0.22 <0.001
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log mass × group 1 6.50 6.50 0.26 −0.03 0.61
.................................... ....................................... ........................................ .......................................... ....................................... .................
Residuals 222 5621.90 25.32 –––
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log longevity (Adj R
2
= 0.38)
.................................... ....................................... ........................................ .......................................... ....................................... .................
Rate of aging 1 1787.63 1787.63 90.30 −0.87 <0.001
.................................... ....................................... ........................................ .......................................... ....................................... .................
Group 1 2.23 2.23 0.11 −0.63 0.74
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log mass 1 855.15 855.15 43.14 0.17 <0.001
.................................... ....................................... ........................................ .......................................... ....................................... .................
Rate of aging × group 1 108.01 108.01 5.46 0.56 0.02
.................................... ....................................... ........................................ .......................................... ....................................... .................
Residuals 221 4374.00 19.79 –– –
.................................... ....................................... ........................................ .......................................... ....................................... .................
Fig. 2. Measures of
rates of aging and
longevity across
ectotherms. Groups
that share letters are
not significantly differ-
ent (P> 0.05) after
correcting for body
mass and phylogeny
(table S2). Bars show
±1 SE. Points are
uncorrected values for
visualization. The
rate of aging is the
mortality slope derived
from a Gompertz
model. Longevity is
the number of years
from the age at first
reproduction at which
95% of the individuals
in a population have
died. Green denotes
reptiles, and purple
denotes amphibians.
Squamates
Squamates
Frogs
Frogs
Salamanders
Salamanders
Crocs
Crocs
Turtles
Turtles
Tuatara
Tuatara
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EMBARGOED UNTIL 2PM U.S. EASTERN TIME ON THE THURSDAY BEFORE THIS DATE:
physical, 0.05 ± 0.01; chemical, 0.28 ± 0.06;
neither, 0.47 ± 0.07). Species with physical
protection lived longer than those with no
protection and those with chemical pro-
tec tion (mean years ± SE: physical, 36 ± 5;
neither, 11 ± 3; chemical, 11 ± 1) (table S5 and
data S1).
Slow-fast continuum hypothesis
We examined relationships between both the
age at 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-transformed age at first reproduction and
positively associated with the log-transformed
annual fecundity (Table 2). However, because
reptiles and amphibians differed in these rela-
tionships, we further investigated these associ-
ations within each class. This analysis revealed
a class-dependent structure of the slow-fast
continuum results. Across reptiles, slower rates
of aging corresponded to later ages at first
reproduction (table S6 and Fig. 4, A and B).
Across amphibians, faster rates of aging were
associated with larger annual fecundities (table
S6 and Fig. 4, A and B). In both amphibians
and reptiles, longer 95% longevity was positively
associated with later ages at first reproduction,
as expected under the slow-fast continuum hy-
pothesis (Table 2 and Fig. 4C). However, lon-
gevity was not related to annual fecundity in
either class (table S6 and Fig. 4D).
Robustness of findings
Notably, none of these results changed 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). This demonstrates
that variable data quality among populations
has no detectable influence (table S8).
Discussion
We found greater variation in aging rates and
longevities across wild ectothermic tetrapods
than in birds and mammals. Turtles, crocodi-
lians, and salamanders have notably low aging
rates and extended longevities for their size.
Most turtles have physical protection (bony
shells) as well as a relatively slow pace of life,
both of which contribute to their negligible
aging and exceptional longevity. Future work
that focuses on turtles with soft shells (versus
rigid, as in this study) may help disentangle
causes of slow turtle aging. Although turtle
aging rates are low overall, they are surprisingly
variable. For example, within Chrysemys picta,
age at maturity, longevity, and aging rates vary
greatly even among populations (13,16,17).
Moreover, in this issue, da Silva et al.(51)show
that turtles in captivity demonstrate slow-to-
negligible aging rates, similar to our findings in
wild species. Our analyses thus provide clear
evidence that ectotherms have a great diver-
sity of aging rates and longevities and add to
the growing literature on ectotherm aging
(10,11). Within ectotherms, rates of aging
ranged from −0.013 to 2.1, corresponding to a
continuum from negligible aging to very fast
aging. Ectotherm longevity (estimated as the
number of years after first reproduction when
95% of adults have died) ranged from 1 to
137 years. For comparison, primate aging
rates are between 0.04 and 0.50 (longevity:
4 to 84 years), with a human aging rate of ~0.1
[longevity:100 years (2)]. The overall mamma-
lian rates of aging ranged from 0.03 to 0.63,
with a single high value of 1.6 observed in
eastern moles (Scalopus aquaticus), repre-
senting an outlier (fig. S1). Although negli-
gible aging was not observed in any mammals
included in our analyses, it has been identifie d
in naked mole rats (52). One notable group of
vertebrates missing from our comparisons is
fishes, which have highly variable aging rates
SCIENCE science.org 24 JUNE 2022 •VOL 376 ISSUE 6600 1463
Fig. 3. Comparison
between ectothermic
and endothermic
tetrapods for rates
of aging, longevity,
and the relationship
between aging rate
and longevity.
(A) Comparison for
rates of aging.
(B) Comparison for
longevity. (C) Compar-
ison for relationship
between aging rate
and longevity. Trend
lines indicate the esti-
mated slopes of each
relationship, represent-
ing the terms of inter-
est 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 (i.e.,
no difference between
endotherms and
ectotherms). See
Table 1 for Pvalues of
these interactions.
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and longevities and contain species of great
interest to aging biology (e.g., rock fish, big-
mouth buffalo, and short-lived poeciliids)
(53–56).
In addition to expanding the domain for
agingresearchandgaininginsightsinto
ectotherm aging, we used newly collected
data to test four hypotheses on the evolution
of aging in a phylogenetic comparative frame-
work. Our test of the thermoregulatory mode
hypothesis revealed that, contrary to expecta-
tions, ectotherms did not have slower rates
of aging or longer lifespans compared with
similar-sized endotherms. However, thermo-
regulatory mode does appear to modulate the
relationship between aging rate and longevity
(when phylogenetically and body-mass con-
trolled: Fig. 3C).
We found mixed support for the temper-
ature hypothesis as it relates to rate of aging
[in agreement with Stark and colleagues’work
on maximum longevity (10,11)]; mean envi-
ronmental temperature interacted with class
such that the rate of aging increased with tem-
perature in reptiles but decreased with temper-
ature in amphibians (fig. S3 and Table 2).
Moreover, this interaction corresponded to
the same directionalities when we tested for a
relationship with minimum or maximum en-
vironmental temperature (table S4). We found
no association between longevity and mean,
minimum, or maximum environmental tem-
perature. Because temperature is a proximate
mediator of cellular and biochemical processes,
it is also likely an agent of selection for local
adaptation among populations—and plasticity
within individuals—for phenotypes related to
aging and longevity [(31), reviewed in (57)]. By
definition, increasing environmental temper-
ature increases ectotherm metabolic rate (barring
offsetting thermoregulatory behavior) and puta-
tively hastens accumulation of molecular damage
through multiple processes, such as free radical
production, telomere attrition, secretion of cyto-
kines from senescent cells, and DNA damage
(57). For example, in garter snakes and the
Columbia spotted frog, thermal differences
among populations have been hypothesized
to be an agent of selection for life-history
divergence, including aging (33,58). Labora-
tory experiments that raise ectotherms under
different thermal regimes can directly test for
the proximate effect of temperature on aging
(59) and are necessary to tease apart how
temperature might influence the evolution of
aging. Whether and how global warming will
affect the evolution of aging rates remains
unknown but will become especially important
to understand for making management and
conservation decisions that prevent species
extinctions.
Our analyses also support the protective
phenotypes hypothesis within ectothermic
tetrapods. Species with physically protective
phenotypes, such as armor, spines, or shells,
aged more slowly and lived much longer for
their size than those without protective pheno-
types (table S5). Although species with chemical
protection have greater maximum longevities
than those without (37,38), we provide evi-
dence that metrics describing the adult mor-
tality trajectory are linked to these protective
phenotypes. This result may explain uniquely
slow rates of aging in turtles coupled with
extended longevities. Salamanders also aged
slowly relative to other tetrapod ectotherms.
We were unable to include behaviors, such as
fossorial lifestyles, aquatic versus terrestrial
behavior, or seasonal activity, that may func-
tion as behavioral protections by reducing pre-
dation risk and lowering mortality rates [(3)
though see (11), which found that microhabitat
preference, including fossorial behavior, did
not influence maximum longevity]. More-
over, many salamanders have regenerative
capabilities that could contribute to slowing
1464 24 JUNE 2022 •VOL 376 ISSUE 6600 science.org SCIENCE
Table 2. Statistical output for ectotherm PGLSs showing output of all predictor variables for
the temperature, protective phenotypes, and slow-fast continuum hypotheses. Protection is a
factor with three levels: none, chemical, and physical. Class is a factor with two levels: reptile and
amphibian. Dashes indicate not applicable. l, Pagel’sl.
PGLS model Df Sum sq Mean sq F value Est Pvalue
Temperature hypothesis
.................................... ....................................... ........................................ .......................................... ....................................... .................
Rate of aging (Adj R
2
= 0.06, l=0)
.................................... ....................................... ........................................ .......................................... ....................................... .................
Class 1 0.01 0.01 0.05 −0.28 0.17
.................................... ....................................... ........................................ .......................................... ....................................... .................
Mean temp 1 0.0003 0.0003 0.002 −0.002 0.09*
.................................... ....................................... ........................................ .......................................... ....................................... .................
Class × mean temp 1 1.02 1.02 5.41 0.004 0.02
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log mass 1 0.96 0.96 5.09 −0.06 0.004
.................................... ....................................... ........................................ .......................................... ....................................... .................
Residuals 102 19.27 0.19 –– –
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log longevity (Adj R
2
= 0.14, l= 0.68)
.................................... ....................................... ........................................ .......................................... ....................................... .................
Class 1 0.71 0.71 0.63 0.42 0.71
.................................... ....................................... ........................................ .......................................... ....................................... .................
Mean temp 1 1.26 1.26 1.12 −0.001 0.50*
.................................... ....................................... ........................................ .......................................... ....................................... .................
Class × mean temp 1 0.15 0.15 0.13 −0.001 0.72
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log mass 1 22.34 22.34 19.83 0.18 <0.001
.................................... ....................................... ........................................ .......................................... ....................................... .................
Residuals 102 114.90 1.13 –––
.................................... ....................................... ........................................ .......................................... ....................................... .................
Protective phenotypes hypothesis
.................................... ....................................... ........................................ .......................................... ....................................... .................
Rate of aging (Adj R
2
= 0.12, l=0)
.................................... ....................................... ........................................ .......................................... ....................................... .................
Protection 2 2.96 1.48 8.41 None: 0.22
Physical: −0.31
Chemical: 0.21
<0.001*
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log mass 1 0.15 0.15 0.88 0.02 0.35
.................................... ....................................... ........................................ .......................................... ....................................... .................
Residuals 103 18.14 0.18 –– –
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log longevity (Adj R
2
= 0.44, l=0)
.................................... ....................................... ........................................ .......................................... ....................................... .................
Protection 2 35.42 17.71 42.38 None: −0.33
Physical: 0.91
Chemical: 2.09
<0.001*
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log mass 1 1.10 1.10 2.64 0.06 0.11
.................................... ....................................... ........................................ .......................................... ....................................... .................
Residuals 103 43.04 0.42 –– –
.................................... ....................................... ........................................ .......................................... ....................................... .................
Slow-fast continuum hypothesis
.................................... ....................................... ........................................ .......................................... ....................................... .................
Rate of aging (Adj R
2
= 0.17, l=0)
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log age at reproduction 1 1.09 1.09 6.44 −0.26 0.01*
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log annual fecundity 1 0.36 0.36 2.11 0.07 0.04*
.................................... ....................................... ........................................ .......................................... ....................................... .................
Class 1 0.25 0.25 1.49 0.45 0.03
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log mass 1 2.63 2.63 15.74 −0.03 0.41
.................................... ....................................... ........................................ .......................................... ....................................... .................
Residuals 99 16.64 0.17 –– –
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log longevity (Adj R
2
= 0.50, l=0)
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log age at reproduction 1 9.08 9.08 23.50 0.75 <0.001*
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log annual fecundity 1 8.70 8.70 22.54 −0.06 0.19*
.................................... ....................................... ........................................ .......................................... ....................................... .................
Class 1 4.36 4.36 11.28 −0.08 0.79
.................................... ....................................... ........................................ .......................................... ....................................... .................
Log mass 1 18.52 18.52 47.96 0.06 0.25
.................................... ....................................... ........................................ .......................................... ....................................... .................
Residuals 99 38.22 0.39 –– –
.................................... ....................................... ........................................ .......................................... ....................................... .................
*Pvalues correspond to tests of the specific hypothesis in question.
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EMBARGOED UNTIL 2PM U.S. EASTERN TIME ON THE THURSDAY BEFORE THIS DATE:
aging through greater damage repair efficiency
(15,60,61).
Lastly, we document that the slow-fast con-
tinuum of life histories is correlated with aging
patterns. Both rates of aging and longevities
were associated with other biological traits
(e.g., age at first reproduction and annual
fecundity) in reptiles and amphibians. Earlier
age at first reproduction in reptiles was cor-
related with faster aging rates (Table 2 and
Fig. 4). A similar pattern has been documented
in birds and mammals, where an earlier age at
first reproduction corresponded to an earlier
age of onset of senescence (62,63). Amphibian
species with higher annual fecundities, and
therefore greater annual reproductive allo-
cation, had faster rates of aging, which has
also been found in birds and mammals and
supports Hamilton’soriginalprediction(24).
Earlier age at first reproduction was also as-
sociated with shorter longevity in both amphib-
ians 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 re-
sults 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 is nec-
essary to broadly test whether genetic correla-
tions underlie these phenotypic associations.
The evolution of aging rates and longevity
has seemingly multiple determinants, from life-
history traits to morphological adaptations,
yielding complex aging patterns across free-
ranging tetrapods (1). Long-term studies of
species from wild populations were necessary
for understanding such complexity in the
natural context in which aging evolved (66)
and enabled the use of more-accurate aging
metrics. Our compilation of long-term field
studies clarifies patterns underlying the evo-
lution of aging rate in tetrapod vertebrates,
highlighting links among protective pheno-
types, life-history tactics, and aging variation
in the wild.
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ACKNOW LEDGM ENTS
Please see supplementary materials for study-specific
acknowledgments, contact information, and funding. This
manuscript is contribution no. 805 of the USGS Amphibian
Research and Monitoring Initiative. Any use of trade, firm, and
product names is for descriptive purposes only and does not imply
endorsement by the US government. Funding: This study was
supported by National Institutes of Health grant R01AG049416 (to
A.M.B., F.J.J., and D.A.W.M.). H.C. was supported as a postdoctoral
researcher by the Swiss National Science Foundation (grant no.
31003A_182265). Author contributions: Conceptualization:
B.A.R., H.C., A.M.B., F.J.J., and D.A.W.M. Data collection: All
authors. Analysis: B.A.R., A.M.L., and D.A.W.M. Visualization:
B.A.R. and H.C. Funding acquisition: A.M.B., F.J.J., and D.A.W.M.
Project administration: B.A.R. and D.A.W.M. Writing –original draft:
B.A.R., H.C., and D.A.W.M. Writing –review & editing: A.M.B.,
F.J.J., J.-F.L., J.-M.G., and A.M.L. Writing –final draft: All authors.
Competing interests: The authors declare that they have no
competing interests. Data and materials availability: Data and
code used for these analyses are available in Dryad (68). Individual
mark-recapture datasets can be obtained by contacting specific
dataset owners (see data S1 for details). License information:
Copyright © 2022 the authors, some rights reserved; exclusive
licensee American Association for the Advancement of Science. No
claim to original US government works. https://www.science.org/
about/science-licenses-journal-article-reuse
SUPPLEMENTARY MATERIALS
science.org/doi/10.1126/science.abm0151
Materials and Methods
Supplementary Text
Figs. S1 to S7
Tables S1 to S8
References (69–90)
MDAR Reproducibility Checklist
Data S1
Submitted 20 August 2021; accepted 29 April 2022
10.1126/science.abm0151
REPORTS
◥
AGING
Slow and negligible senescence among testudines
challenges evolutionary theories of senescence
Rita da Silva
1,2,3
*†, Dalia A. Conde
1,2,3
, Annette Baudisch
3,4
, Fernando Colchero
2,3,5
*
Is senescence inevitable and universal for all living organisms, as evolutionary theories predict?
Although evidence generally supports this hypothesis, it has been proposed that certain species,
such as turtles and tortoises, may exhibit slow or even negligible senescence—i.e., avoiding the
increasing risk of death from gradual deteriorationwithage.Inanextensivecomparativestudyof
turtles and tortoises living in zoos and aquariums, we show that ~75% of 52 species exhibit slow
or negligible senescence. For ~80% of species, aging rates are lower than those in modern humans.
We find that body weight positively relates to adult life expectancy in both sexes, and sexual
size dimorphism explains sex differences in longevity. Unlike humans and other species, we
show that turtles and tortoises may reduce senescence in response to improvements in
environmental conditions.
How much can aging be altered, slowed,
or brought to a halt altogether? In the
past century, we have witnessed unprec-
edented increases in human longevity
(1). Yet, research on humans and non-
human primates shows that these improvements
have resulted from averting early deaths and
age-independent sources of mortality, not from
reducing the rate of aging (2,3). The rate of
aging is a measure of the speed at which the
risk of mortality increases with age. It is the
direct result of senescence, a gradual deteri-
oration of bodily functions that manifests
as an increase in mortality risk with age af-
ter sexual maturity (4). Current evolution-
arytheoriesofsenescencestatethat,among
all organisms with a clear separation be-
tween somatic and germline cell lineages,
senescence is inevitable (4,5). Paradoxically,
empirical evidence (6,7) and evolutionary
demographic models (8,9) have proposed
that evolution may permit some species to
reduce or even avoid the effects of senes-
cence (i.e., negligible senescence).
Species that continue growing after repro-
ductive maturity (e.g., turtles and tortoises) (8)
are the prime candidates for escaping senes-
cence. These indeterminately growing species
may gain survival advantages and larger repro-
ductive potential with age, which allows them
to invest more in somatic maintenance and
potentially slowing senescence. To date, only
a handful of studies have investigated senes-
cence in animal species with indeterminate
growth, such as turtles and tortoises (10–13),
where different populations of the same spe-
cies can show evidence of both senescence
and negligible senescence (12–15). Thus, the
question remains: Can some species slow or
even avoid growing old? And if so, under what
circumstances?
In this work, we carried out an extensive
study of age- and sex-specific mortality and
growth patterns in turtles and tortoises (order
Testudines). Using the Species360 Zoological
Information Management System (ZIMS) (16),
we obtained husbandry records for 52 species
spanning a diversity of life-history strategies,
body weights, and longevities (table S1). Using
Bayesian survival trajectory analysis (17,18),
we estimated for females (47 species) and males
(39 species) adult age-specific mortality, re-
maining adult life expectancy, and aging
rates. From the best-fitting models, we cal-
culated 95% credible intervals (CIs) of aging
rates at the age when the survival function
reached 0.2 (i.e., when 80% of adults are ex-
pected to have died) (19). We considered this
age to be sufficiently advanced to occur after
the onset of senescence but not so late as to
greatly increase the uncertainty in the esti-
mated aging rates.
CIs of aging rates included zero for 74.5%
of species (35 species) for females and 79.5%
(31 species) for males (Fig. 1). CIs of some
species were either negative [i.e., Testudo graeca
and Siebenrockiella crassicollis,4.2%(2species)
for females and 2.6% (1 species) for males] or
spanned narrowly around zero (e.g., females of
Aldabrachelys gigantea and males of Gopherus
berlandieri), which may suggest the existence of
negligible senescence among these species. CIs
1466 24 JUNE 2022 •VOL 376 ISSUE 6600 science.org SCIENCE
1
Department of Biology, University of Southern Denmark,
5230 Odense M, Denmark.
2
Species360 Conservation
Science Alliance, Bloomington, MN 55425, USA.
3
Interdisciplinary Centre on Population Dynamics, University
of Southern Denmark, 5230 Odense M, Denmark.
4
Danish
Centre for Population Research, University of Southern
Denmark, 5230 Odense M, Denmark.
5
Department of
Mathematics and Computer Science, University of Southern
Denmark, 5230 Odense M, Denmark.
*Corresponding author. Email: colchero@imada.sdu.dk (F.C.);
a.ritasilva.15@gmail.com (R.d.S.)
†Present address: CIBIO, Centro de Investigação em Biodiversidade
e Recursos Genéticos, InBIO Laboratório Associado and BIOPOLIS
Program in Genomics, Biodiversity and Land Planning, Campus de
Vairão, Universidade do Porto, 4485-661 Vairão, Portugal.
RESEARCH
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