Contemporary genetic adaptation in zoos and conservation breeding programs

Contemporary genetic adaptation in zoos and conservation breeding programs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Contemporary genetic adaptation in zoos and conservation breeding programs Drew Sauve, Amy Chabot, Denis Réale This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5588650/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Sep, 2025 Read the published version in Biological Conservation → Version 1 posted You are reading this latest preprint version Abstract Conservation breeding programs can be vital for combating biodiversity loss, but optimization of these programs is crucial for success. Genetic adaptation to captivity is suspected to hinder reintroductions yet remains largely untested. Using an extensive dataset of breeding programs, we assessed adaptive genetic change in 31 vertebrate populations, marking the broadest estimation of additive genetic variance of fitness to date. Our findings indicate widespread, adaptive genetic change in ex situ populations, with estimates comparable to wild populations. While this adaptation may enhance fitness in captivity, concerns arise regarding how genetic divergence could impact reintroduction success. Our results confirm the importance of anthropogenic effects on microevolution and highlight the need for a better understanding of genetic adaptation in captivity to ensure breeding populations can fulfill their potential. Biological sciences/Evolution/Evolutionary genetics Earth and environmental sciences/Ecology/Conservation biology Figures Figure 1 Figure 2 Significance Statement Genetic adaptation to the captive environment can reduce the effectiveness of conservation breeding programs. This study presents the largest analysis to date of adaptive genetic change in animal populations, revealing widespread genetic adaptation to the zoo environment occurring at rates comparable to those seen in the wild. We argue that these findings have major implications for conservation breeding programs, as adaptation to captivity may reduce the success of reintroductions into wild habitats. Our work the emphasizes need to integrate evolutionary considerations into conservation breeding strategies to ensure the long-term viability of endangered species. Main text In response to the current biodiversity crisis, the global biodiversity framework includes the need for in situ and ex situ conservation programmes for endangered populations ( 1 ). Conservation breeding is an action that is increasingly mandated, yet if we are to invest time and money into breeding programs, we need to optimize their success. Broadly, reintroduced individuals have reduced fitness compared to their wild counterparts and genetic adaptation to captivity is an often-suspected cause ( 2 – 5 ). Conservation breeding programs are managed to preserve genetic diversity and reduce the risk of inbreeding ( 6 – 8 ). Generally, these plans focus on determining mating pairs to minimize the average kinship in a population. While this approach reduces loss of genetic variation and slows rates of adaptation ( 6 , 9 ), examples of selection, phenotypic change and genetic adaptation for specific traits in captivity exist, suggesting that genetic adaptation occurs in conservation breeding programs ( 4 , 10 – 14 ). While evolution is often perceived as a slow process, particularly when considering processes like speciation, evidence from experiments and long-term studies of wild populations suggests that microevolution operates on contemporary timescales and could, in theory, impact demographic processes ( 15 – 19 ). In a captive breeding environment selective pressures differ from their wild context ( 20 – 22 ). Indeed, domestication is a well-known process of intentional and unintentional adaptation for populations managed by humans ( 23 – 26 ). If adaptive genetic change is occurring faster than expected in the wild, it may also be occurring more rapidly than expected in captivity, which may affect ex situ populations ability to fulfil roles related to preventing biodiversity loss ( 3 , 21 ). An assessment of adaptive evolution is needed to empirically test whether adaptation to a captive breeding environment is likely to be common for conservation breeding programs. In this study, we address the question: to what degree is adaptive genetic change occurring in populations maintained in zoological facilities? Recent advances in statistical methods have made it possible to measure more accurately fundamental measures of adaptive evolutionary change ( 27 , 28 ). A special case of Price’s fundamental theorem of evolution – The fundamental theorem of natural selection – allows us to estimate adaptive genetic change i.e. change that leads to increases in the average fitness of a population in a specific environment ( 29 – 31 ). The fundamental theorem of natural selection states that the change in mean absolute fitness caused by natural selection is equal to the additive genetic variance in relative fitness, . On average, values greater than zero indicate that traits are evolving such that they increase the average fitness in a population. A caveat is that factors that cause imperfect transmission between generations are not accounted for, thus observed changes in fitness may differ from the theorem’s predictions because of mutation, gene flow, environmental change, and genotype by environment interactions. Regardless, across contexts, a non-zero value of indicates natural selection is causing an on average increase in mean fitness. In the context of small discrete populations (like those in zoo breeding programs) it is also possible that some of the variance in fitness is caused by genetic drift ( 32 – 34 ). Previously, statistical challenges and a lack of adequate datasets made the estimation of difficult ( 27 , 28 ). However, recent statistical advancements now allow more accurate distributions of individual fitness and the data required to estimate are available for many species held in zoological facilities ( 35 , 36 ). Therefore, we now have the opportunity to measure the rate of adaptive genetic change in zoo populations, which is fundamental to ex situ conservation programs because it allows us to identify how fast populations managed for conservation purposes are likely diverging from their wild counterparts through genetic adaptation. We apply newly developed quantitative genetic methods to 31 populations of captive-bred vertebrate species (19 mammals, 11 birds, and 1 amphibian) with long-term pedigree and life-reproductive success data maintained in the Species360 Zims database (see table S1, S2; https://species360.org/). Our dataset encompasses more populations and a higher diversity of species than any previous meta-analysis of . The duration of recorded data for a given species varied from 31 to 129 years (mean = 72), with the total number of years equaling 2531. The total number of individuals included in our study was 125,799 (mean per species = 2255). For each species, absolute fitness was calculated as the number of offspring an individual produced over its lifetime, which follows individual fitness defined in quantitative genetic theory and recent work conducted on wild populations ( 15 ). We modelled absolute fitness using a generalized linear mixed effects model with a zero-inflated over dispersed Poisson distribution. We analyzed absolute fitness but obtained estimates of relative fitness (see methods for details). We incorporated fixed effects—including sex, inbreeding, genetic group, and cohort—and random effects, including additive genetic effects, effects of breeding facility at hatch/birth, cohort (year of birth), and maternal effects (see tables S3, S4 and S5, supplementary text S1 for model details). We followed ( 15 ), using parameter estimates from the link-function scales and back transformed them to get estimates of all variance components on the scale of the data (supplementary text S2 and figs. S1, S2, and S3 for evaluation of model goodness of fit). We ran one model per population then combined results to obtain a meta-analytic estimate of To evaluate and control for the possibility that genetic drift at different facilities might contribute to some of the variation in fitness we estimated the amount of variance in fitness that is attributed to birth facility and estimated gene flow among facilities for each species. We found evidence that animal populations in zoological facilities had rates of adaptive genetic change similar to estimates from wild populations ( 15 ). Our median estimate of was 0.11, with the entire among population ranging from 0.008 to 0.50 (Fig. 1). In 16 out of 31 populations (~52%), 95% credible intervals (95% CI) excluded values below 0.001 indicating evidence of genetic adaptation (see supplementary text S3 for our link-scale and data-scale estimates). Our meta-analysis of indicated mean of 0.17 (95 % CI = 0.09, 0.29) with standard deviation of of 0.14 (95 % CI = 0.08, 0.34). In comparison, estimate of in wild populations ( 15 ) was 0.19 (95 % CI = 0.09, 0.30) with standard deviation of of 0.11 (95 % CI = 0.01, 0.26). We expect that genetic drift has a limited on our estimate of as our analysis of gene flow found widespread gene flow among breeding facilities (see supplementary text S4 and figs. S4, S5, and S6 and table S6 for our estimates of gene flow). Captive environments likely present distinct selection pressures compared to wild environments, due to the absence of predators, a novel stress environment, and altered population densities ( 3 , 4 , 35 , 36 ). For example, behavioural, morphological, and reproductive traits have all been observed to be under selection and changing in the ex situ environment ( 10 , 12 , 14 ). Our results provide evidence that adaptive genetic change is widespread in populations of animals held in zoological facilities, supporting the hypothesis that rapid genetic adaptation is occurring in ex situ populations. Assuming a limited impact of genetic drift on our estimates of , our results indicate that populations of animals in captivity are under selective pressure, with evolution acting on a scale equivalent to that of their wild counterparts. Selection in ex situ environments raises concerns because selective pressures likely differ from those faced in the wild, leading to divergence in traits among captive and wild counterparts. Phenotypic divergence could impact success of efforts for captive populations that are being maintained as part of species conservation efforts, for example, when ex situ management is used for demographic or genetic augmentation ( 1 , 21 ). Our work thus suggests a potential paradox for conservation managers: increased average fitness resulting from adaptive genetic change could lead to increased numbers of animals (see supplementary material S5 and see figs. S7, S8, and S9 expected increases in mean fitness), which would be a benefit from an ex situ perspective (e.g. increased numbers of animals for release, or more sustainable insurance populations). But the selection pressures may differ from that experienced by wild counterparts, potentially reducing the fitness of animals which would be reintroduced in situ ( 2 ). While lower fitness upon release could be a result of several factors, one explanation is maladaptation to the release environment caused by adaptation to the captive environment. Comparative fitness landscapeswould enable managers to identify when selection is shaping traits differently in situ versus ex situ and the impacts genetic adaptation to the ex situ environment might have upon release( 37 , 38 ). Generally, this approach aligns well with recent calls to improve the coordination between in situand ex situ conservation efforts and the understanding breeding programs as part of the large meta-population ( 35 , 39 ). Our results provide some insight into the environmental factors influencing fitness in ex situ populations. Variation among cohorts (year of birth), facilities, and individual dams significantly contribute to the observed variation in fitness (Table 1; Fig. 2), The impact of facility, is perhaps not dissimilar to the patterns that have been observed for lifetime reproductive success among habitats in wild populations ( 40 ). Despite efforts to standardize management, differences among zoological facilities are to be expected – but the impact on reproductive success due to cohort is noteworthy. Widespread management changes or disease outbreaks are both possible explanations that warrant further investigation. Where ex situ populations are being used to support in situ conservation efforts, understanding these environmental factors is crucial to identify management actions to avoid or practices to adopt. Variation in our estimate of indicates that some species managed in zoos likely had higher rates of adaptive genetic change than others. Why do some populations exhibit a higher rate of adaptive genetic change than others? The management of species held in zoological facilities has evolved over time ( 41 , 42 ). Originally, the management strategies for some of these species did not consider species release, preserving genetic diversity, or avoiding adaptation to the captive environment ( 43 , 44 ). Managers ability to adhere strictly to breeding recommendations (i.e. manage breeding to minimizing mean kinship) will depend on a variety of factors (e.g. transportation, life-history, individual animal behaviour). The degree it is possible to adhere to a breeding plan will likely impact the observed rate of adaptive genetic change ( 45 , 46 ). Finally, the fitness landscape in captivity might be more or less similar to the wild environment for some species introducing variable strengths of selection acting on a population in the captive environment. Our results provide a starting point for researchers and managers to identify populations with high or low rates of adaptive genetic change and work to identify causes. Adaptive genetic changes are occurring on ecological timescales and contributing to ecological processes, both in the wild, and within human managed systems. Our estimates of adaptive genetic change indicate that the degree of change in populations in human care is substantial and occurring on a similar scale to that observed in wild populations. Such high rates of adaptation warrant monitoring, especially when conservation depends on ex situ support ( 1 ). Target 4 of the Kunming-Montreal convention on biological diversity specifically calls for the use of ex situ to prevent biodiversity loss, successfully achieving this target with ex situ population will depend on understanding the repercussions of genetic adaptation in captive populations. As both ex situ and in situ systems are dynamic, but not necessarily on the same trajectory regarding trait change, changes in either could impact our ability to address biodiversity loss. Declarations Acknowledgements: We acknowledge the many zookeepers, conservation staff, organizations, and institutions that worked to care for animals and record detailed breeding program records. In particular, we would like to thank the Species360 and Zoological Information Management System team for curating, collecting, and sharing the extensive records on zoo and conservation breeding programs. We thank A. J. Wilson for comments on an earlier version of this manuscript. Funding: MITACs Elevate Postdoctoral Fellowship Competing interests: The authors declare that they have no competing interests. Data and materials availability: All code and data will be made available upon publication on FigShare. 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Individual fitness was calculated as the number of offspring an individual produced over its lifetime. We include all offspring recorded in the database whether or not they survived to a reproductive age. We included populations representing both species maintained primarily for conservation purposes as well as those maintained in zoo populations for educational purposes. We prioritized species with extensive pedigree records and representative of various IUCN conservation statuses. We acknowledge the inherent bias in our sampling, which favors mammals and birds, due to the richness of available data within these taxonomic groups. We restricted our analyses to long-term data and large sample sizes (Table S1). Pedigrees were constructed using records maintained by studbook keepers at zoological and conservation breeding facilities across the globe. We used these pedigrees to count the total number of offspring produced by an individual over its lifetime and to calculate pairwise genetic relatedness among all individuals. We restricted our analysis to individuals that were already dead to ensure we are not including individuals that had yet to reproduce. Lifetime breeding success may not be measured perfectly for all individuals in our analysis as records may be missing or offspring not recorded in some circumstances. Therefore, our analysis assumes that individuals are missing at random with respect to their genetic value for lifetime breeding success. Further, our analysis and results are specific to the human-managed environment, and we were inevitably unable to measure the lifetime breeding success of released individuals. Statistical Analysis Zero-inflated Poisson animal model We followed the statistical methods of ( 15 ), that were further developed and described in ( 27 , 28 , 47 ). We followed these methods as closely as possible to allow our estimates to be directly comparable to a wild context. Briefly, we fit an animal model of individual lifetime breeding success using a zero-inflated Poisson model. In the zero-inflated Poisson model, absolute fitness of an individual is modelled as the product of a Bernoulli and a Poisson process. The Bernoulli process corresponds to the excess of zeros expected for lifetime breeding success beyond that expected from a Poisson process. We then back-transformed link-scale estimates to obtain data-scale estimates, integrating over fixed and other random effects as described in ( 15 , 27 , 28 , 47 , 48 ). Random effects in a zero-inflated Poisson model have a joint-normal distributions with covariation between the zero-inflated and conditional-Poisson component. So, the two components of each random effect were transformed from the link-scale estimate (i.e. in Table S3) to a single estimate of variance (i.e. Table 1) in expected absolute fitness on the data scale. Next the absolute variance was transformed to relative fitness following methods in ( 15 , 47 – 49 ). Estimated model effects Our basic model included fixed effects of pedigree inbreeding coefficient (computed with the R-package nadiv ( 50 ), cohort (a continuous variable of birth or hatch year), sex, and genetic group to account for genetic structure within the studied populations ( 50 , 51 ). We included random effects of associated with additive genetic effects, maternal effects, cohort effects, and facility effects. As inbreeding can bias estimated additive genetic (co)variance parameters individual inbreeding coefficient was included to reduce this bias. Cohort was included as a continuous fixed effect of year to correct for potential biases in the estimation of breeding values through time caused by a directional change in mean fitness driven by the environment. However, to be as comparable as possible to ( 15 ) we only included this for the Poisson part of the model for all populations. We included a genetic group as a fixed effect to control for a potential effect of gene flow from the wild to the captive stock on lifetime breeding success. Genetic group was calculated as the proportion of an individual’s genome that was derived from two types of ancestors: “new founders” and those with ancestors belonging to the “focal population”. A high genetic group value corresponds to individuals with a high proportion of new founders among its ancestors. The package R package nadiv was used to calculate this proportion ( 50 ). The initial 10% of individuals in the population with unknown parents were categorized as the “focal population”. Any subsequent individuals with unknown parents were categorized as new founders or immigrants to the population. Accounting for the genetic structure in this way accounts for some of the changes caused by gene flow instead of local genetic changes. In the datasets used, individuals counted as “new founders” may be true immigrants brought in from wild populations or in some circumstances individuals where parentage was not recorded, so some caution should be used in interpreting this effect as a true effect of gene flow from the wild. Examining the effects of genetic group on fitness goes beyond the scope of this paper, and we here consider genetic group as a confounding effect (see below: impact of genetic group structure). We included a fixed effect of sex to account for possible differences in the mean estimates of lifetime breeding success between sexes in the dataset. Accounting for the effect of sex could improve the fit of the models and avoid a potential bias caused by variation in records and data collected for each sex. We used random effects to estimate additive genetic variance, maternal variance, variation among cohorts (years), and variance among rearing facilities of an animal. Bayesian Implementation We ran all models in R version 4.4.1 using the Bayesian package MCMCglmm version 2.35 ( 52 , 53 ). We used Gaussian priors with means of 0 and variances of 10 for fixed effects and we used parameter expanded priors with degree-of-belief parameters equal to 3, working mean equal to 0, and working variance equal to 1000 for random effects. We ran models for 130,000 iterations with a burnin of 30,000 and a thinning interval of 100 to have a posterior sample size of 1000. Our posterior effective sample sizes for all parameters were always at least 10% of the sample size (i.e. >100). For each model, convergence was assessed by visual examination of the MCMC chains traces and posterior density distributions. We examined chains for a lack of trends and a single mode. Meta-analytic estimates We followed the same meta-analytic approach as ( 15 ), using the posterior distributions for each of our variance components in relative fitness on the scale of the data for each of the 31 populations. We used calculated estimates of each variance following a multiple imputation framework in the R package brms ( 54 ). We fitted a linear mixed model for each parameter with the species identity set as a random on 100 tables (each with one row per species identity) generated by drawing from the posterior samples from the focal parameter (i.e. one of our variance parameters). Similarly to ( 15 ), we tried three different priors for the intercept and each gave the same result for the posterior distribution: a normal distribution with a mean of 0 and standard deviation of 20, a normal distribution with a mean of 0 and standard deviation with a lower limit of 0, and a uniform distribution from 0 to 20. For each parameter we used the global intercept from the model as the meta-analytic estimate for that parameter and we reported the standard deviation in the random effect of species identity as the measure of variation in estimates among populations. We report the posterior mode and 95% highest posterior density credible interval for each estimate. Table Table 1: Meta estimates of back-transformed variance parameters Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5588650","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":389436385,"identity":"023efa55-7e10-418c-90a1-9e88ecef5cbd","order_by":0,"name":"Drew Sauve","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDCCA2DSRoYNQjMwsBGpJY0HojKNeC2HeRhgWggCvvOHn0nzVJzn4WPvPfzhR4JdPh8D88NHN/BokbyRZibNc+Y2DxvPuTTJnoRkyzYGNmPjHDxaDG4wmEnObANqkcgxY+D9wWzAxsDDJo1Xy/nj3yRn/jsH0mL88U9CPRFaDuSYSXxsOADSYiDNk3CYsBbJGznFFh+OJQP9csZMWibhuAEbMwG/8J0/vvFGQo2dnHx7j/HHNwnVBvLtzQ8f49OCBTCTpnwUjIJRMApGARYAAOXDQba3iWlmAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6318-3719","institution":"African Lion Safari \u0026 Universite du Quebec a Montreal","correspondingAuthor":true,"prefix":"","firstName":"Drew","middleName":"","lastName":"Sauve","suffix":""},{"id":389436386,"identity":"925800ee-0042-4568-9c81-5274c9c0c21b","order_by":1,"name":"Amy Chabot","email":"","orcid":"","institution":"African Lion Safari","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"","lastName":"Chabot","suffix":""},{"id":389436387,"identity":"93a638bf-cf12-424a-a9f3-15dca6f49165","order_by":2,"name":"Denis Réale","email":"","orcid":"https://orcid.org/0000-0002-0419-7125","institution":"Université du Québec a Montréal","correspondingAuthor":false,"prefix":"","firstName":"Denis","middleName":"","lastName":"Réale","suffix":""}],"badges":[],"createdAt":"2024-12-05 17:00:47","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5588650/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5588650/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.biocon.2025.111392","type":"published","date":"2025-10-01T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71763452,"identity":"cdfbf91a-c7ca-400f-8585-4fa14cb5aedd","added_by":"auto","created_at":"2024-12-18 11:21:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1235974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePosterior distribution of additive genetic variance estimates for fitness (lifetime reproductive success), V\u003c/strong\u003e\u003csub\u003eA\u003c/sub\u003e(ω)\u003cstrong\u003e, in 31 populations of captive-bred vertebrate species.\u003c/strong\u003e Distributions are coloured based on whether the lower credible interval (95%) overlaps with 0.001 (light blue), does not overlap with 0.001 (light purple), or does not overlap with 0.01 (dark purple). The approximate mode of the prior distribution for \u003cstrong\u003eV\u003c/strong\u003e\u003csub\u003eA\u003c/sub\u003e (ω) was 0.001 and 0.01 was the threshold between small and moderate adaptive genetic change defined in (\u003cem\u003e15\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-5588650/v1/03f20236d88df50e9cda7375.png"},{"id":71763451,"identity":"a36d2977-0961-4cc0-bf87-e67024328082","added_by":"auto","created_at":"2024-12-18 11:21:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2895596,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePosterior distribution of the proportion of total phenotypic variance in fitness (lifetime reproductive success), V\u003c/strong\u003eA(ω) \u003cstrong\u003eexplained by additive genetic (light blue), maternal environmental (purple; dam), year of birth (yellow; cohort), and facility effects (red), in 31 populations of captive-bred vertebrate species.\u003c/strong\u003e A single asterisk indicates that the lower credible interval does not overlap with 0.001 and a double asterisk indicates that the credible interval does not overlap with 0.01. The approximate mode of the prior distribution for \u003cstrong\u003eV\u003c/strong\u003eA(ω) was 0.001 and 0.01 was the threshold between small and moderate adaptive genetic change defined in (\u003cem\u003e15\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-5588650/v1/a067e9c26738f079f93d47b6.png"},{"id":87876646,"identity":"3b3b5fce-6a77-4cf7-b715-9bee5e33f250","added_by":"auto","created_at":"2025-07-30 02:39:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4932656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5588650/v1/fedebca5-0d97-4e71-9935-53a02c0868b6.pdf"},{"id":71763454,"identity":"db4279df-43c8-479f-a581-cac6d9e93b22","added_by":"auto","created_at":"2024-12-18 11:21:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6241151,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytext.docx","url":"https://assets-eu.researchsquare.com/files/rs-5588650/v1/ab03ce475c58db53314133c0.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Contemporary genetic adaptation in zoos and conservation breeding programs","fulltext":[{"header":"Significance Statement","content":"\u003cp\u003eGenetic adaptation to the captive environment can reduce the effectiveness of conservation breeding programs. This study presents the largest analysis to date of adaptive genetic change in animal populations, revealing widespread genetic adaptation to the zoo environment occurring at rates comparable to those seen in the wild. We argue that these findings have major implications for conservation breeding programs, as adaptation to captivity may reduce the success of reintroductions into wild habitats. Our work the emphasizes need to integrate evolutionary considerations into conservation breeding strategies to ensure the long-term viability of endangered species.\u003c/p\u003e"},{"header":"Main text","content":"\u003cp\u003eIn response to the current biodiversity crisis, the global biodiversity framework includes the need for in situ and ex situ conservation programmes for endangered populations (\u003cem\u003e1\u003c/em\u003e). Conservation breeding is an action that is increasingly mandated, yet if we are to invest time and money into breeding programs, we need to optimize their success. Broadly, reintroduced individuals have reduced fitness compared to their wild counterparts and genetic adaptation to captivity is an often-suspected cause (\u003cem\u003e2\u003c/em\u003e\u0026ndash;\u003cem\u003e5\u003c/em\u003e). Conservation breeding programs are managed to preserve genetic diversity and reduce the risk of inbreeding (\u003cem\u003e6\u003c/em\u003e\u0026ndash;\u003cem\u003e8\u003c/em\u003e). Generally, these plans focus on determining mating pairs to minimize the average kinship in a population. While this approach reduces loss of genetic variation and slows rates of adaptation (\u003cem\u003e6\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e), examples of selection, phenotypic change and genetic adaptation for specific traits in captivity exist, suggesting that genetic adaptation occurs in conservation breeding programs (\u003cem\u003e4\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e\u0026ndash;\u003cem\u003e14\u003c/em\u003e). While evolution is often perceived as a slow process, particularly when considering processes like speciation, evidence from experiments and long-term studies of wild populations suggests that microevolution operates on contemporary timescales and could, in theory, impact demographic processes (\u003cem\u003e15\u003c/em\u003e\u0026ndash;\u003cem\u003e19\u003c/em\u003e). In a captive breeding environment selective pressures differ from their wild context (\u003cem\u003e20\u003c/em\u003e\u0026ndash;\u003cem\u003e22\u003c/em\u003e). Indeed, domestication is a well-known process of intentional and unintentional adaptation for populations managed by humans (\u003cem\u003e23\u003c/em\u003e\u0026ndash;\u003cem\u003e26\u003c/em\u003e). If adaptive genetic change is occurring faster than expected in the wild, it may also be occurring more rapidly than expected in captivity, which may affect ex situ populations ability to fulfil roles related to preventing biodiversity loss (\u003cem\u003e3\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e). An assessment of adaptive evolution is needed to empirically test whether adaptation to a captive breeding environment is likely to be common for conservation breeding programs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; In this study, we address the question: to what degree is adaptive genetic change occurring in populations maintained in zoological facilities? Recent advances in statistical methods have made it possible to measure more accurately fundamental measures of adaptive evolutionary change (\u003cem\u003e27\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e). \u0026nbsp;A special case of Price\u0026rsquo;s fundamental theorem of evolution \u0026ndash; The fundamental theorem of natural selection \u0026ndash; allows us to estimate adaptive genetic change i.e. change that leads to increases in the average fitness of a population in a specific environment (\u003cem\u003e29\u003c/em\u003e\u0026ndash;\u003cem\u003e31\u003c/em\u003e). The fundamental theorem of natural selection states that the change in mean absolute fitness caused by natural selection is equal to the additive genetic variance in relative fitness, \u0026nbsp;. On average, \u0026nbsp; values greater than zero indicate that traits are evolving such that they increase the average fitness in a population. A caveat is that factors that cause imperfect transmission between generations are not accounted for, thus observed changes in fitness may differ from the theorem\u0026rsquo;s predictions because of mutation, gene flow, environmental change, and genotype by environment interactions. Regardless, across contexts, a non-zero value of \u0026nbsp; indicates natural selection is causing an on average increase in mean fitness. In the context of small discrete populations (like those in zoo breeding programs) it is also possible that some of the variance in fitness is caused by genetic drift (\u003cem\u003e32\u003c/em\u003e\u0026ndash;\u003cem\u003e34\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePreviously, statistical challenges and a lack of adequate datasets made the estimation of \u0026nbsp; difficult (\u003cem\u003e27\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e). However, recent statistical advancements now allow more accurate distributions of individual fitness and the data required to estimate \u0026nbsp; are available for many species held in zoological facilities (\u003cem\u003e35\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e). Therefore, we now have the opportunity to measure the rate of adaptive genetic change in zoo populations, which is fundamental to ex situ conservation programs because it allows us to identify how fast populations managed for conservation purposes are likely diverging from their wild counterparts through genetic adaptation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe apply newly developed quantitative genetic methods to 31 populations of captive-bred vertebrate species (19 mammals, 11 birds, and 1 amphibian) with long-term pedigree and life-reproductive success data maintained in the Species360 Zims database (see table S1, S2; https://species360.org/). Our dataset encompasses more populations and a higher diversity of species than any previous meta-analysis of \u0026nbsp;. The duration of recorded data for a given species varied from 31 to 129 years (mean = 72), with the total number of years equaling 2531. The total number of individuals included in our study was 125,799 (mean per species = 2255). For each species, absolute fitness was calculated as the number of offspring an individual produced over its lifetime, which follows individual fitness defined in quantitative genetic theory and recent work conducted on wild populations (\u003cem\u003e15\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eWe modelled absolute fitness using a generalized linear mixed effects model with a zero-inflated over dispersed Poisson distribution. We analyzed absolute fitness but obtained estimates of relative fitness (see methods for details). We incorporated fixed effects\u0026mdash;including sex, inbreeding, genetic group, and cohort\u0026mdash;and random effects, including additive genetic effects, effects of breeding facility at hatch/birth, cohort (year of birth), and maternal effects (see tables S3, S4 and S5, supplementary text S1 for model details). We followed (\u003cem\u003e15\u003c/em\u003e), using parameter estimates from the link-function scales and back transformed them to get estimates of all variance components on the scale of the data (supplementary text S2 and figs. S1, S2, and S3 for evaluation of model goodness of fit). We ran one model per population then combined results to obtain a meta-analytic estimate of \u0026nbsp; \u0026nbsp;To evaluate and control for the possibility that genetic drift at different facilities might contribute to some of the variation in fitness we estimated the amount of variance in fitness that is attributed to birth facility and estimated gene flow among facilities for each species.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found evidence that animal populations in zoological facilities had rates of adaptive genetic change similar to estimates from wild populations (\u003cem\u003e15\u003c/em\u003e). Our median estimate of \u0026nbsp; was 0.11, with the entire among population \u0026nbsp; ranging from 0.008 to 0.50 (Fig. 1). In 16 out of 31 populations (~52%), 95% credible intervals (95% CI) excluded values below 0.001 indicating evidence of genetic adaptation (see supplementary text S3 for our link-scale and data-scale estimates). Our meta-analysis of indicated mean \u0026nbsp; of 0.17 (95 % CI = 0.09, 0.29) with standard deviation of \u0026nbsp; of 0.14 (95 % CI = 0.08, 0.34). In comparison, estimate of \u0026nbsp; in wild populations (\u003cem\u003e15\u003c/em\u003e) was 0.19 (95 % CI = 0.09, 0.30) with standard deviation of \u0026nbsp; of 0.11 (95 % CI = 0.01, 0.26). We expect that genetic drift has a limited on our estimate of \u0026nbsp; as our analysis of gene flow found widespread gene flow among breeding facilities (see supplementary text S4 and figs. S4, S5, and S6 and table S6 for our estimates of gene flow). Captive environments likely present distinct selection pressures compared to wild environments, due to the absence of predators, a novel stress environment, and altered population densities (\u003cem\u003e3\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e). For example, behavioural, morphological, and reproductive traits have all been observed to be under selection and changing in the ex situ environment (\u003cem\u003e10\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e). Our results provide evidence that adaptive genetic change is widespread in populations of animals held in zoological facilities, supporting the hypothesis that rapid genetic adaptation is occurring in ex situ populations.\u003c/p\u003e\n\u003cp\u003eAssuming a limited impact of genetic drift on our estimates of \u0026nbsp;, our results indicate that populations of animals in captivity are under selective pressure, with evolution acting on a scale equivalent to that of their wild counterparts. Selection in ex situ environments raises concerns because selective pressures likely differ from those faced in the wild, leading to divergence in traits among captive and wild counterparts. Phenotypic divergence could impact success of efforts for captive populations that are being maintained as part of species conservation efforts, for example, when ex situ management is used for demographic or genetic augmentation (\u003cem\u003e1\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e). Our work thus suggests a potential paradox for conservation managers: increased average fitness resulting from adaptive genetic change could lead to increased numbers of animals (see supplementary material S5 and see figs. S7, S8, and S9 expected increases in mean fitness), which would be a benefit from an ex situ perspective (e.g. increased numbers of animals for release, or more sustainable insurance populations). But the selection pressures may differ from that experienced by wild counterparts, potentially reducing the fitness of animals which would be reintroduced in situ (\u003cem\u003e2\u003c/em\u003e). While lower fitness upon release could be a result of several factors, one explanation is maladaptation to the release environment caused by adaptation to the captive environment. Comparative fitness landscapeswould enable managers to identify when selection is shaping traits differently in situ versus ex situ and the impacts genetic adaptation to the ex situ environment might have upon release(\u003cem\u003e37\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e). Generally, this approach aligns well with recent calls to improve the coordination between in situand ex situ conservation efforts and the understanding breeding programs as part of the large meta-population (\u003cem\u003e35\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our results provide some insight into the environmental factors influencing fitness in ex situ populations. Variation among cohorts (year of birth), facilities, and individual dams significantly contribute to the observed variation in fitness (Table 1; Fig. 2), The impact of facility, is perhaps not dissimilar to the patterns that have been observed for lifetime reproductive success among habitats in wild populations (\u003cem\u003e40\u003c/em\u003e). Despite efforts to standardize management, differences among zoological facilities are to be expected \u0026ndash; but the impact on reproductive success due to cohort is noteworthy. Widespread management changes or disease outbreaks are both possible explanations that warrant further investigation. Where ex situ populations are being used to support in situ conservation efforts, understanding these environmental factors is crucial to identify management actions to avoid or practices to adopt.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVariation in our estimate of \u0026nbsp; \u0026nbsp;indicates that some species managed in zoos likely had higher rates of adaptive genetic change than others. Why do some populations exhibit a higher rate of adaptive genetic change than others? The management of species held in zoological facilities has evolved over time (\u003cem\u003e41\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e). Originally, the management strategies for some of these species did not consider species release, preserving genetic diversity, or avoiding adaptation to the captive environment\u0026nbsp;(\u003cem\u003e43\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e).\u0026nbsp;Managers ability to adhere strictly to breeding recommendations (i.e. manage breeding to minimizing mean kinship) will depend on a variety of factors (e.g. transportation, life-history, individual animal behaviour). The degree it is possible to adhere to a breeding plan will likely impact the observed rate of adaptive genetic change (\u003cem\u003e45\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e). Finally, the fitness landscape in captivity might be more or less similar to the wild environment for some species introducing variable strengths of selection acting on a population in the captive environment. Our results provide a starting point for researchers and managers to identify populations with high or low rates of adaptive genetic change and work to identify causes.\u003c/p\u003e\n\u003cp\u003eAdaptive genetic changes are occurring on ecological timescales and contributing to ecological processes, both in the wild, and within human managed systems. Our estimates of adaptive genetic change indicate that the degree of change in populations in human care is substantial and occurring on a similar scale to that observed in wild populations. Such high rates of adaptation warrant monitoring, especially when conservation depends on ex situ support (\u003cem\u003e1\u003c/em\u003e). Target 4 of the Kunming-Montreal convention on biological diversity specifically calls for the use of ex situ to prevent biodiversity loss, successfully achieving this target with ex situ population will depend on understanding the repercussions of genetic adaptation in captive populations. As both ex situ and in situ systems are dynamic, but not necessarily on the same trajectory regarding trait change, changes in either could impact our ability to address biodiversity loss.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe acknowledge the many zookeepers, conservation staff, organizations, and institutions that worked to care for animals and record detailed breeding program records. In particular, we would like to thank the Species360 and Zoological Information Management System team for curating, collecting, and sharing the extensive records on zoo and conservation breeding programs. We thank A. J. Wilson for comments on an earlier version of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMITACs Elevate Postdoctoral Fellowship\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u003c/strong\u003e All code and data will be made available upon publication on FigShare.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eConvention on Biological Diversity, \u0026ldquo;15/4.Kunming-Montreal Global Biodiversity Framework\u0026rdquo; (UN Environment Programme, Montreal, Canada, 2022).\u003c/li\u003e\n\u003cli\u003eI. P. Gross, A. E. Wilson, M. E. 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We acknowledge the inherent bias in our sampling, which favors mammals and birds, due to the richness of available data within these taxonomic groups. We restricted our analyses to long-term data and large sample sizes (Table S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePedigrees were constructed using records maintained by studbook keepers at zoological and conservation breeding facilities across the globe. We used these pedigrees to count the total number of offspring produced by an individual over its lifetime and to calculate pairwise genetic relatedness among all individuals. We restricted our analysis to individuals that were already dead to ensure we are not including individuals that had yet to reproduce. Lifetime breeding success may not be measured perfectly for all individuals in our analysis as records may be missing or offspring not recorded in some circumstances. Therefore, our analysis assumes that individuals are missing at random with respect to their genetic value for lifetime breeding success. Further, our analysis and results are specific to the human-managed environment, and we were inevitably unable to measure the lifetime breeding success of released individuals.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eZero-inflated Poisson animal model\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe followed the statistical methods of (\u003cem\u003e15\u003c/em\u003e), that were further developed and described in (\u003cem\u003e27\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e). We followed these methods as closely as possible to allow our estimates to be directly comparable to a wild context. Briefly, we fit an animal model of individual lifetime breeding success using a zero-inflated Poisson model. In the zero-inflated Poisson model, absolute fitness of an individual is modelled as the product of a Bernoulli and a Poisson process. The Bernoulli process corresponds to the excess of zeros expected for lifetime breeding success beyond that expected from a Poisson process. We then back-transformed link-scale estimates to obtain data-scale estimates, integrating over fixed and other random effects as described in (\u003cem\u003e15\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e). Random effects in a zero-inflated Poisson model have a joint-normal distributions with covariation between the zero-inflated and conditional-Poisson component. So, the two components of each random effect were transformed from the link-scale estimate (i.e. in Table S3) to a single estimate of variance (i.e. Table 1) in expected absolute fitness on the data scale. Next the absolute variance was transformed to relative fitness following methods in (\u003cem\u003e15\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e\u0026ndash;\u003cem\u003e49\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eEstimated model effects\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eOur basic model included fixed effects of pedigree inbreeding coefficient (computed with the R-package nadiv (\u003cem\u003e50\u003c/em\u003e), cohort (a continuous variable of birth or hatch year), sex, and genetic group to account for genetic structure within the studied populations (\u003cem\u003e50\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e). We included random effects of associated with additive genetic effects, maternal effects, cohort effects, and facility effects. As inbreeding can bias estimated additive genetic (co)variance parameters individual inbreeding coefficient was included to reduce this bias. Cohort was included as a continuous fixed effect of year to correct for potential biases in the estimation of breeding values through time caused by a directional change in mean fitness driven by the environment. However, to be as comparable as possible to (\u003cem\u003e15\u003c/em\u003e) we only included this for the Poisson part of the model for all populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe included a genetic group as a fixed effect to control for a potential effect of gene flow from the wild to the captive stock on lifetime breeding success. Genetic group was calculated as the proportion of an individual\u0026rsquo;s genome that was derived from two types of ancestors: \u0026ldquo;new founders\u0026rdquo; and those with ancestors belonging to the \u0026ldquo;focal population\u0026rdquo;. A high genetic group value corresponds to individuals with a high proportion of new founders among its ancestors. The package R package nadiv was used to calculate this proportion (\u003cem\u003e50\u003c/em\u003e). The initial 10% of individuals in the population with unknown parents were categorized as the \u0026ldquo;focal population\u0026rdquo;. Any subsequent individuals with unknown parents were categorized as new founders or immigrants to the population. Accounting for the genetic structure in this way accounts for some of the changes caused by gene flow instead of local genetic changes. In the datasets used, individuals counted as \u0026ldquo;new founders\u0026rdquo; may be true immigrants brought in from wild populations or in some circumstances individuals where parentage was not recorded, so some caution should be used in interpreting this effect as a true effect of gene flow from the wild. Examining the effects of genetic group on fitness goes beyond the scope of this paper, and we here consider genetic group as a confounding effect (see below: impact of genetic group structure).\u003c/p\u003e\n\u003cp\u003eWe included a fixed effect of sex to account for possible differences in the mean estimates of lifetime breeding success between sexes in the dataset. Accounting for the effect of sex could improve the fit of the models and avoid a potential bias caused by variation in records and data collected for each sex. We used random effects to estimate additive genetic variance, maternal variance, variation among cohorts (years), and variance among rearing facilities of an animal.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eBayesian Implementation\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe ran all models in R version 4.4.1 using the Bayesian package MCMCglmm version 2.35 (\u003cem\u003e52\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e). We used Gaussian priors with means of 0 and variances of 10 for fixed effects and we used parameter expanded priors with degree-of-belief parameters equal to 3, working mean equal to 0, and working variance equal to 1000 for random effects. We ran models for 130,000 iterations with a burnin of 30,000 and a thinning interval of 100 to have a posterior sample size of 1000. Our posterior effective sample sizes for all parameters were always at least 10% of the sample size (i.e. \u0026gt;100). For each model, convergence was assessed by visual examination of the MCMC chains traces and posterior density distributions. We examined chains for a lack of trends and a single mode.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/h3\u003e\n\u003ch2\u003e\u003cstrong\u003eMeta-analytic estimates\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe followed the same meta-analytic approach as (\u003cem\u003e15\u003c/em\u003e), using the posterior distributions for each of our variance components in relative fitness on the scale of the data for each of the 31 populations. We used calculated estimates of each variance following a multiple imputation framework in the R package brms (\u003cem\u003e54\u003c/em\u003e). We fitted a linear mixed model for each parameter with the species identity set as a random on 100 tables (each with one row per species identity) generated by drawing from the posterior samples from the focal parameter (i.e. one of our variance parameters). Similarly to (\u003cem\u003e15\u003c/em\u003e), we tried three different priors for the intercept and each gave the same result for the posterior distribution: a normal distribution with a mean of 0 and standard deviation of 20, a normal distribution with a mean of 0 and standard deviation with a lower limit of 0, and a uniform distribution from 0 to 20. For each parameter we used the global intercept from the model as the meta-analytic estimate for that parameter and we reported the standard deviation in the random effect of species identity as the measure of variation in estimates among populations. We report the posterior mode and 95% highest posterior density credible interval for each estimate.\u0026nbsp;\u003c/p\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1: Meta estimates of back-transformed variance parameters\u003c/p\u003e\n\u003cp\u003e\u003cimg 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Genetic adaptation to captivity is suspected to hinder reintroductions yet remains largely untested. Using an extensive dataset of breeding programs, we assessed adaptive genetic change in 31 vertebrate populations, marking the broadest estimation of additive genetic variance of fitness to date. Our findings indicate widespread, adaptive genetic change in ex situ populations, with estimates comparable to wild populations. While this adaptation may enhance fitness in captivity, concerns arise regarding how genetic divergence could impact reintroduction success. Our results confirm the importance of anthropogenic effects on microevolution and highlight the need for a better understanding of genetic adaptation in captivity to ensure breeding populations can fulfill their potential.\u003c/p\u003e","manuscriptTitle":"Contemporary genetic adaptation in zoos and conservation breeding programs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 11:21:09","doi":"10.21203/rs.3.rs-5588650/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e855de6-e0ef-4311-9ec6-458eb84170f6","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41503673,"name":"Biological sciences/Evolution/Evolutionary genetics"},{"id":41503674,"name":"Earth and environmental sciences/Ecology/Conservation biology"}],"tags":[],"updatedAt":"2025-07-30T02:39:18+00:00","versionOfRecord":{"articleIdentity":"rs-5588650","link":"https://doi.org/10.1016/j.biocon.2025.111392","journal":{"identity":"biological-conservation","isVorOnly":true,"title":"Biological Conservation"},"publishedOn":"2025-10-01 00:00:00","publishedOnDateReadable":"October 1st, 2025"},"versionCreatedAt":"2024-12-18 11:21:09","video":"","vorDoi":"10.1016/j.biocon.2025.111392","vorDoiUrl":"https://doi.org/10.1016/j.biocon.2025.111392","workflowStages":[]},"version":"v1","identity":"rs-5588650","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5588650","identity":"rs-5588650","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}