Optimizing mating strategies to maximize genetic diversity in the mhorr gazelle (Nanger dama mhorr) ex situ breeding program

Optimizing mating strategies to maximize genetic diversity in the mhorr gazelle (Nanger dama mhorr) ex situ breeding program | 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 Research Article Optimizing mating strategies to maximize genetic diversity in the mhorr gazelle (Nanger dama mhorr) ex situ breeding program Sonia Domínguez, Juan Pablo Gutiérrez, Eulalia Moreno, Isabel Cervantes This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7687577/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in BMC Zoology → Version 1 posted 13 You are reading this latest preprint version Abstract To minimize coancestry among the offspring by making the contributions of parents more equal is currently one of the most widely used methods to maximize the genetic diversity in conservation programs of threatened species. The objective of this study was to compare the performance of 22 different mating strategies to limit the loss of genetic diversity in a real captive population of mhorr gazelle ( Nanger dama mhorr ) via computer simulations. This objective was achieved by monitoring the evolution of the effective population size ( Ne ) throughout 15 generations. The studied population consisted of 87 breeding animals (25 males and 62 females), from a total pedigree of 3059 records. The scenarios were designed according to different assumptions: the use of parents coancestry or offspring coancestry, the use of the coefficients themselves in calculations or their increases, and the number of males and females involved in the mating. The results for this captive population showed that strategies to minimize the parent’s coancestry were the best in the short term, but mixed strategies to minimize coancestry of both parents and offspring were better in the medium and long term when a weight to coancestry in the parents' generation between 5% and 50% was applied. Furthermore, it was observed that these mixed strategies improved their performance when all females in the population participated in breeding, but not all males. These results suggest that each managed population may need different mating strategies, considering its generation interval, the species' breeding system, the expected time horizon of the conservation program, or other species-specific considerations of the population. critically endangered captive breeding dama gazelle mating strategies genetic diversity effective population size Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Threatened species suffer a greater loss of genetic diversity due to their small size population and increased relatedness between individuals (Blomqvist et al. 2010 ). Ex situ breeding programs aim to maintain self-sustaining populations that prevent the complete extinction of the species and release new individuals into the wild, when suitable natural habitat exists and the threat to the species in the wild is controlled (Wakchaure & Ganguly 2016 ). From a genetic point of view, ex situ breeding programs should retain maximum genetic variability of the population, as it will determine its future adaptive potential (Falconer 1981 ) and limit the accumulation of inbreeding, which can lead to a reduction in fitness (Lacy et al. 1993 ). To this end, careful choice of breeding animals and effective mating strategies are essential to maintain genetic integrity of target populations, as well as monitoring the genetic consequences of these captive breeding programs and the genetic diversity obtained in the offspring (Wacker et al. 2019 ). The effective population size ( Ne ) is a widely used parameter to monitor the status of genetic diversity in breeding populations (Johnson et al. 2024 ), as it provides information on the rate of inbreeding (Rahimmadar et al. 2021 ) and genetic changes due to genetic drift (Kvalnes et al. 2024 ). The effective population size of a population refers to the hypothetical number of breeding animals in an idealized population that would lead to the actual increase in inbreeding if they contribute equally to the next generation (Wright 1931 ). The dama gazelle ( Nanger dama ) is a critically endangered antelope native to North Africa (IUCN SSC Antelope Specialist Group 2016 ). Indiscriminate hunting and habitat destruction have been the main causes of its decline, leading even to the extinction in the wild of the western subspecies, the mhorr gazelle ( Nanger dama mhorr ) (Durant et al. 2014 ). The complete disappearance of the mhorr gazelle was avoided with the creation in 1971 of an ex situ breeding program from some of the last living specimens at “La Hoya” Experimental Field Station (EEZA-CSIC), in Almeria (Spain). The captive population has gradually increased to over 300 individuals at the present, distributed across different centres in Europe, North America and the Arabian Peninsula (Domínguez 2022 ). The ultimate goal of this ex situ breeding program is the restoration of the mhorr gazelle in its natural habitat. The dama gazelle is a social species usually forming herds made up of one adult male, several females and their youngsters. It follows a polygynous mating system, where dominant males becoming territorial during the mating season (Beudels-Jamar et al. 2005 ). Females reach sexual maturity at 9–12 months of age, and males between 18 and 24 months. The gestation has a duration of 6.5 months and only one calf is born per birth (Barbosa & Espeso 2005 ). In the present study, the performance of different mating strategies to limit the losses of genetic diversity was compared in a real captive population of mhorr gazelle via computer simulations. The effect of different aspects on the effectiveness of the mating schemes was evaluated, such as the use of parents coancestry or offspring coancestry, the number of males and females involved in the mating, and the use of the coefficients themselves in calculations or their increases. In all cases, the effective population size was used as a measure of the evolution of the genetic diversity in the population, both in the short and long term. MATERIALS AND METHODS Study population The mating group used for this work included all living animals present at the Almeria breeding centre at the time of the study (December 31, 2024), to recreate a situation as realistic as possible. This reference population consisted of 87 animals (25 males and 62 females) born between 2010 and 2024 (see details in Table 1 ), from a total pedigree of 3059 records. The population of Almeria was chosen because it is the largest in the world and maintains the reproduction of several breeding groups simultaneously, while other zoological institutions only hold one breeding group, of usually no more than 5–10 animals. In Almeria, breeding herds are generally formed with 1 male and 4–8 females. Breeding males are selected following a mating strategy of minimum coancestry of offspring and they are changed annually in each group of females to maximize genetic diversity. Table 1 Age structure of the reference population of Almeria according to the year of birth. Year of birth Males Females Total animals 2010 0 2 2 2011 0 1 1 2012 0 2 2 2013 0 4 4 2015 1 3 4 2016 1 8 9 2018 0 6 6 2019 1 12 13 2020 1 3 4 2021 3 1 4 2022 3 4 7 2023 5 3 8 2024 10 13 23 Total reference population 25 62 87 The mhorr gazelle living population of Almeria has an average generation interval (James 1977 ) of 5.87 years, and a number of equivalent complete generations (Maignel et al. 1996 ) of 8.72, which indicates a high pedigree completeness level. In this living population the average value of individual inbreeding coefficient (Meuwissen & Luo 1992 ) is 0.28. The effective population size based on individual increase in inbreeding ( \(\:\stackrel{-}{{N}_{ei}}\:\) ) (Gutierrez et al. 2009 ) and based on individual increase in coancestry ( \(\:\stackrel{-}{{N}_{ec}}\:\) ) (Cervantes et al. 2011 ) are, respectively, 13.2 and 12.1. The ratio \(\:\stackrel{-}{{N}_{ec}}/\stackrel{-}{{N}_{ei}}\) close to one evidences that Almeria does not have a structured population (Domínguez et al. 2024 ). Mating strategies Different mating strategies were tested by minimizing a function Fx (Ojeda-Marín et al. 2021 ). The strategies were classified into three groups according to their purpose: Strategies to minimize the parent’s coancestry (or inbreeding): Strategy of minimum parent’s coancestry (F): each animal mates with the least related individual of the opposite sex, so that inbreeding of the offspring is minimal. In this strategy \(\:Fx=\:\sum\:{C}_{jk}\) , where \(\:{C}_{jk}\) is the coancestry between male j and female k . Two alternatives were tested: Strategy F0: all females contribute offspring to the next generation, but not all males. Strategy F1: all females and all males contribute offspring to the next generation. Strategy of minimum increase in parent’s coancestry (ΔF): since coancestry accumulates per generation, under this strategy differences in pedigree depth of individuals were considered. Thus, \(\:Fx=\:\sum\:{\varDelta\:C}_{jk}\) , where \(\:{\varDelta\:C}_{jk}=1-\:\sqrt[\frac{{g}_{j}+{g}_{k}}{2}]{1-\:{C}_{jk}}\) (Ivy & Lacy 2012 ), where g j and g k are the equivalent discrete generations known for individuals j and k , and C jk is the inbreeding of a descendent of these two individuals (Maignel et al. 1996 ). Again, two alternatives were tested: Strategy ΔF0: all females contribute offspring to the next generation, but not all males. Strategy ΔF1: all females and all males contribute offspring to the next generation. Strategy of minimum parent’s coancestry weighted by contributions (Fw): this strategy takes into account the current representation of each individual in the mating group and penalizes those who are already highly represented. In this case, \(\:Fx=\:\sum\:{{m}_{j}C}_{jk}{m}_{k}\) ,where m j (and m k ) is the mean coancestry between the individual j (and k ) and all the other animals in the mating group. Two alternatives were checked: Strategy Fw0: all females contribute offspring to the next generation, but not all males. Strategy Fw1: all females and all males contribute offspring to the next generation. Strategy of minimum increase in parent’s coancestry weighted by contributions (ΔFw): this strategy simultaneously accounts possible differences in pedigree depth and representation within the mating group. Function \(\:Fx=\:\sum\:{{\varDelta\:m}_{j}\varDelta\:C}_{jk}\varDelta\:{m}_{k}\) , where Δ m j (and Δ m k ) is the mean of the increase in coancestry between the individual j (and k ) and all the other animals involved in the mating plan. Likewise, two alternatives were studied: Strategy ΔFw0: all females contribute offspring to the next generation, but not all males. Strategy ΔFw1: all females and all males contribute offspring to the next generation. Strategies to minimize the offspring’s coancestry: Strategy of minimum offspring’s coancestry (C): unlike previous methods that minimize the inbreeding of the descendants, this strategy seeks that the average kinship between all the resulting descendants is minimal. Here \(\:Fx=\:\sum\:{C}_{lm}\) , where \(\:{C}_{lm}\) is the coancestry between two descendants l and m of the mating design. In this case, three alternatives were analyzed: Strategy C0: all females contribute offspring to the next generation, but not all males. Strategy C1: all females and all males contribute offspring to the next generation. Strategy C2: not all females and not all males participated with offspring for the next generation, while others had a higher number of descendants. Strategy of minimum increase in offspring’s coancestry (ΔC): pedigree depth information is taken into account to calculate kinship. In this strategy \(\:Fx=\:\sum\:{\varDelta\:C}_{lm}\) , where \(\:{\varDelta\:C}_{lm}\) is the increase in the coancestry between two descendants l and m of the mating design. Three alternatives were tested: Strategy ΔC0: all females contribute offspring to the next generation, but not all males. Strategy ΔC1: all females and all males contribute offspring to the next generation. Strategy ΔC2: not all females and not all males participated with offspring for the next generation. Strategies to minimize a combination of the parent’s coancestry and the offspring’s coancestry: Mixed strategy (M): combines information from two generations, parents and offspring. Thus, function \(\:Fx=\:p\sum\:{C}_{jk}+(1-\:{p}_{1})\sum\:{C}_{lm}\) was minimized, where again \(\:{C}_{jk}\) is the coancestry between male j and female k , and therefore the inbreeding of their offspring, and \(\:{C}_{lm}\) is the coancestry between two descendants l and m of the mating design. In this equation p is a value between 0 and 1 indicating the weight that will be given to coancestry in the parents' generation. The following values were tested for p : 0.01 (M 1–99), 0.05 (M 5–95), 0.50 (M 50–50), and 0.95 (M 95 − 5). Thus, the first value indicated in the strategy refers to the weight given to minimize parent’s coancestry, and the second value, to the weight to minimize the offspring’s coancestry. Two different alternatives were studied: Strategy M0: all females contribute offspring to the next generation, but not all males. Strategy M2: not all females and not all males participated with offspring for the next generation. Computations performed A total of 22 mating strategies were tested. Fifty replicates of each strategy were conducted during 15 discrete generations. According to a previous study of Ojeda-Marín et al. ( 2021 ) in a species with a similar generation interval and taxonomically close to the mhorr gazelle, this number of discrete generations was sufficient to compare the proposed strategies, since no differences were observed between the results in the fifteenth generation and the twentieth generation, while the simulation process is extremely time consuming. Each generation was formed by a random number of animals simulated from a Poisson distribution of the reference population to be mated, where the number of males was assumed to be equal to or lower than the number of females, and a similar sex ratio was maintained throughout the generations by randomly being born as male or female according to their current representation in the population. The performance of the mating strategies was evaluated by the evolution of the effective population size over the 15 generations. The value of the effective population size used to monitor the strategies was the one based on individual increase in inbreeding \(\:\left(\stackrel{-}{{N}_{ei}}=\:\:\:\frac{1}{2\stackrel{-}{{\Delta\:}F}}\right)\) (Gutiérrez et al. 2008 ; Gutierrez et al. 2009 ) instead of the effective population size based on individual increase in coancestry \(\:\left(\stackrel{-}{{N}_{ec}}=\:\:\:\frac{1}{2\stackrel{-}{{\Delta\:}C}}\right)\) (Cervantes et al. 2011 ), because problems of inbreeding depression were recently described in this ex situ population (Domínguez et al., submitted) and \(\:\stackrel{-}{{N}_{ei}}\) minimizes the rate of inbreeding (ΔF) due to its inverse relationship (Caballero & Toro 2000 ). In addition, high \(\:\stackrel{-}{{N}_{ec}}\) values ​​could mask difficulties in the viability of populations with low relatedness between individuals, but with high inbreeding (Ojeda-Marín 2019 ). All the analyses were computed using the ENDOG program v4.9 (Gutiérrez & Goyache 2005 ). RESULTS Figure 1 shows the effective population size in generations 1, 5 and 15 of the different simulated strategies. In the short term (generation 1), the best results were from strategies ΔF0 and ΔFw0. In contrast, the worst strategies were C0, C1, C2, ΔC1 and M2 1–99. In the medium term (generation 5), strategy M0 50–50 was significantly the best, followed by strategy M0 5–95. The worst performance was obtain using strategy M2 95 − 5. In the long term (generation 15), strategies M0 5–95 and M0 50–50 showed the best effective population size values. The strategies that gave higher losses of genetic variability in the long term were Fw0, ΔFw0 and M2 95 − 5. It can be seen the evolution of effective population size throughout generations according to the mating strategies studied in Fig. 2 . Overall, mixed strategies had the best performance in the long term, followed by the strategies that only minimize the offspring’s coancestry, and the strategies that minimize the parent’s coancestry obtain the lower effective population size values. Comparing the number of males and females involved in the mating, differences were also observed between the strategies in generation 15. Methods that minimize the parent’s coancestry improved when all females and all males participated in breeding (F1, ΔF1, Fw1 and ΔFw1), compared to those where all females contribute offspring to the next generation, but not all males (F0, ΔF0, Fw0 and ΔFw0). Among the strategies that minimize the offspring’s coancestry, the effective population size values ​​were slightly better when not all females and not all males contribute offspring to the next generation (C2 and ΔC2), but these differences were only significant for strategy ΔC2. In the case of the mixed strategies, the results were always better when all females were expected to contribute to the next generation, but not all males (M0), than when not all females nor all males participate in breeding (M2). Regarding the strategies that minimize the parent’s coancestry, methods that minimized increases (ΔF0, ΔF1, ΔFw0 and ΔFw1) instead of the coefficients themselves (F0, F1, Fw0 and Fw1) had better results in the short term, but no significant differences were obtain in the long term. Within the methods that minimize the offspring’s coancestry, strategies ΔC0 and ΔC2 achieved significantly higher effective population size than strategies C0 and C2 in the short term. Similarly, in no case significant differences were found in the long term. Finally, Fig. 3 shows the evolution of inbreeding coefficient by generation across methodologies. It can be noticed that there is a direct inverse relationship between the strategies that have the highest values ​​of effective population size and the lowest inbreeding coefficients as expected. All the numeric results of the effective population size and the inbreeding coefficient, along with their standard errors, obtained from each strategy throughout the generations are compiled in Table S1 and Table S2 , respectively. DISCUSSION It is widely recognized that the optimal method for maximizing the genetic diversity in the conservation programs managed using pedigree information is to minimize coancestry among the offspring by making the contributions of parents more equal (Sonesson & Meuwissen 2000 ; Fernández et al. 2011 ; Ivy & Lacy 2012 ). However, it has been suggested that the best mating strategy can vary depending on the managed population and each case needs to be studied individually (Ojeda-Marín et al. 2021 ). With this approach, the present work has intended to analyze how the genetic diversity of a real captive population of a critically endangered species varies according to the different mating strategies raised. Firstly, although the strategies that minimize the parent’s coancestry were the best in the short term, their results were the worst in the long term, apart from those of the M2 95 − 5 strategy. These strategies that focus, exclusively or to a high percentage, on minimizing the inbreeding of offspring, mate each animal with the least related individual of the opposite sex and obtain a high value of the effective population size in the first generation. However, as consequence, the second generation is made up of individuals that, although they have a low average inbreeding, are in a large proportion siblings or half-siblings, which is to say there is a high average kinship between all of them. This initial and significant loss of genetic diversity then becomes unrecoverable in subsequent generations, preventing the effective population size from reaching better values ​​as with other strategies. It is also interesting to note how, despite the poor results of these strategies in the long-term, when all females and all males contribute to the next generation, the effective population size is clearly better than when all females contribute, but not all males. Therefore, a greater participation of breeding animals, both males and females, helps to reduce the levels of inbreeding and slow down the loss of genetic diversity (Welsh & Jackson 2014 ; Pavlova et al. 2024 ). Methods that minimized increases instead of the coefficients themselves were beneficial in the short term, but not in the long term. In the first generations, the use of increase in parent’s coancestry allows standardizing the inbreeding coefficients based on the pedigree completeness (Cervantes et al. 2011 ). Nevertheless, this is no longer an advantage in the latest generations, when the amount of genealogical information for all animals has become equal. In second place, the strategies that minimize the offspring’s coancestry has the lowest effective population size values in the short term, besides the mixed strategies M0 1–99 and M2 1–99, that also focus preferably on minimizing this parameter. However, in the medium term these strategies were similar to those that minimize the parent’s coancestry and in the long term they surpassed them, which coincide with the most widely accepted theory at present (Fernández et al. 2003 ). In this case, not forcing the participation of either all males or all females improved the effective population size values, since preferential matings between relatives are favoured (Sánchez et al., 2003 ). For these strategies, methods that minimize the increase in coancestry also showed an advantage in the first generations, but when the simulation of generations progresses, they are equated with the methods that minimize the coefficient of coancestry. Finally, it can be seen how giving only minimal weight to coancestry in the parents' generation of the mixed strategies (M0 1–99 and M2 1–99), the value of the effective population size increases significantly from the fifth generation onwards compared to the strategies that only minimize the offspring’s coancestry. Performance continues to improve further with a weight to coancestry in the parents' generation of 5% and 50%, but then drops off at a weight of 95%. It should be noted that the simulations in this work did not include the potential negative impact on population viability of high inbreeding in early generations. Nevertheless, adding some weight to the parent’s coancestry, these mixed strategies avoid a high individual increase in inbreeding in animals at birth, and thus prevent the deleterious effects of inbreeding depression and a higher initial risk of population disappearance, while maximizing the effective population size in the long term. Regarding the number of individuals involved in the mating, it can be observed that forcing the participation of all females, but not all males, always had better results than not forcing the contribution of either sex to the next generation in these mixed strategies; arising an intermediate situation between the strategies that minimize the parent’s coancestry, where the contribution of all males and all females was the best option, and the strategies that minimize the offspring’s coancestry, where, on the contrary, the best performances were obtained when not all females nor all males participated with offspring for the next generation. In the wild, due to the territorial behaviour of males and the social organization of this species in harems, all females have the opportunity to contribute to the next generation, but only dominant and successful males in fights will be able to access the females and mate (Mungall 2018 ). Therefore, applying in captive matings this methodology that resembles the polygynous nature of the species seems reasonable, although in this case the selection criteria for breeding males are defined by the population managers and not by the physical superiority of the animals. Nevertheless, it cannot be forgotten that in polygynous mating systems, where very few males obtain almost all of the matings, the high variance in male reproductive success may result in a low effective population size (Nunney 1993 ; Nunney & Elam 1994 ). Under extreme forms of polygyny, such as harem polygyny and dominance polygyny, this reduction can be even more marked. Therefore, the mhorr gazelle's mating system makes this species naturally more prone to lower effective population sizes than other monogamous or random mating species. The international community of zoos and wildlife breeding centers have pedigree analysis software available to carry out the demographic and genetic management of the small populations they maintain (Lacy et al. 2012 ). Although traditionally most of these tools were designed for diploid species with sexual reproduction, they have subsequently incorporated new technological advances that better align with different species management (e.g. group living species) and breeding systems (e.g. hermaphroditic selfing, cloning or autozygous production of haploid offspring) (Lacy 2012 ; Jiménez-Mena et al. 2016 ). In the same way, a further step should be taken in the genetic management of wild species to adapt mating strategies to the particular needs of conservation programs, individually evaluating the best option for each population, and thus they can retain its maximum genetic diversity and minimize the potential deleterious effects of inbreeding. Another factor to take into account is the expected time horizon. In this study population, due to the serious conservation status of the species and the need for several decades to restore natural populations, it can be foreseen that it will be essential to maintain captive populations to ensure the long-term viability of the conservation program, so it was decided to compare mating strategies up to a high number of generations. However, in populations of species with different degrees of threat, generation intervals or conservation objectives, comparing the performance of strategies in the short or medium term could be sufficient. To conclude we can say that in this mhorr gazelle captive population the strategies M0 5–95 and M0 50–50 were the best overall in the medium and long term, and also had some of the highest effective population size values in the short term. Both strategies showed similar long-term performance, but M0 50–50 was significantly superior in the first and fifth generations than M0 5–95, so this would be the mating strategy of choice for this mhorr gazelle population. Although with some differences, these two strategies also had the best performance for the Cuvier's gazelle population from the same ex situ breeding center (Ojeda-Marín et al. 2021 ), so applying a weight to coancestry in the parents' generation between 5% and 50%, seems to have advantages in these particular cases. So far, the mating strategy applied in this mhorr gazelle population has been based exclusively on minimizing the offspring’s coancestry, but given the results obtained in this study, it would be highly recommended to replace it with the mixed strategy M0 50–50, whose performance has been shown to be clearly better than the method currently used by providing higher effective population size values. However, it should not be forgotten that coancestry coefficients can also be obtained from molecular information (Caballero & Toro 2002 ; Eding et al. 2002 ) and used for monitoring genetic diversity in conservation programs (Gómez-Romano et al. 2013 ). Although molecular analyses have sometimes not differed from the results obtained from pedigree data (Ivy et al. 2016 ; Barrett et al. 2022 ), or have even shown limited effectiveness if used alone (Fernández et al. 2005 ), it would be advisable to expand this work with molecular techniques to determine the definitive mating strategy for this population. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval and consent to participate Not applicable. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution All authors contributed to the study conception and design. Material preparation and data collection were performed by S.D. Analyses were carried out by S.D. and J.P.G., supported by I.C. and E.M.. The first draft of the manuscript was written by S.D. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement The authors want to thank all those who have collected data for the mhorr gazelle studbook, to the Estación Experimental de Zonas Áridas for providing free access to the records, and to the staff of “La Hoya” Field Station for their expert handling and care of the animals in Almería. Data Availability The pedigree dataset analysed during the current study is available online:http://www.eeza.csic.es/documentos/Studbook\_2021\_Nanger\_dama\_mhorr.pdf References Barbosa A, Espeso G. International studbook gazella dama mhorr. Consejo Superior de Investigaciones Científicas; 2005. Barrett KG, Amaral G, Elphinstone M, McAdie ML, Davis CS, Janes JK, Carnio J, Moehrenschlager A, Gorrell JC. Genetic management on the brink of extinction: Sequencing microsatellites does not improve estimates of inbreeding in wild and captive Vancouver Island marmots ( Marmota vancouverensis ). Conserv Genet. 2022;23(2):417–28. https://doi.org/10.1007/s10592-022-01429-7 . Beudels-Jamar RC, Devillers P, Lafontaine RM, Devillers-Terschuren J, Beudels MO. Sahelo-Saharian antelopes. Status and perspectives. CMS Technical Series Publication; 2005. Blomqvist D, Pauliny A, Larsson M, Flodin L-Å. Trapped in the extinction vortex? Strong genetic effects in a declining vertebrate population. BMC Evol Biol. 2010;10(1):33. https://doi.org/10.1186/1471-2148-10-33 . Caballero A, Toro MA. Interrelations between effective population size and other pedigree tools for the management of conserved populations. Genet Res. 2000;75(3):331–43. https://doi.org/10.1017/S0016672399004449 . Caballero A, Toro MA. Analysis of genetic diversity for the management of conserved subdivided populations. Conserv Genet. 2002;3:289–99. https://doi.org/10.1023/A:1019956205473 . Cervantes I, Goyache F, Molina A, Valera M, Gutiérrez JP. Estimation of effective population size from the rate of coancestry in pedigreed populations. J Anim Breed Genet. 2011;128(1):56–63. https://doi.org/10.1111/j.1439-0388.2010.00881.x . Domínguez S. (2022) International studbook for the mhorr gazelle. http://www.eeza.csic.es/documentos/Studbook_2021_Nanger_dama_mhorr.pdf . Accessed 10 October 2024. Domínguez S, Cervantes I, Gutiérrez JP, Moreno E. Pedigree analysis in the mhorr gazelle ( Nanger dama mhorr ): Genetic variability evolution of the captive population. Ecol Evol. 2024;14(2):e10876. https://doi.org/10.1002/ece3.10876 . Domínguez S, Gutiérrez JP, Moreno E, Cervantes I. (2025) Genetic analysis of juvenile survival in the captive population of mhorr gazelle ( Nanger dama mhorr ) and the effect of inbreeding. Conserv Genet (Submitted number: 745ff122-0907-4996-a901-d98b52e3e9fe). Durant SM, Wacher T, Bashir S, Woodroffe R, De Ornellas P, Ransom C, Newby J, Abáigar T, Abdelgadir M, El Alqamy H, Baillie J, Beddiaf M, Belbachir F, Belbachir-Bazi A, Berbash AA, Bemadjim NE, Beudels-Jamar R, Boitani L, Breitenmoser C, Cano M, Chardonnet P, Collen B, Cornforth WA, Cuzin F, Gerngross P, Haddane B, Hadjeloum M, Jacobson A, Jebali A, Lamarque F, Mallon D, Minkowski K, Monfort S, Ndoassal B, Niagate B, Purchase G, Samaïla S, Samna AK, Sillero-Zubiri C, Soultan AE, Stanley Price MR, Pettorelli N. Fiddling in biodiversity hotspots while deserts burn? Collapse of the Sahara’s megafauna. Divers Distrib. 2014;20(1):114–22. https://doi.org/10.1111/ddi.12157 . Eding H, Crooijmans RPMA, Groenen MAM, Meuwissen THE. Assessing the contribution of breeds to genetic diversity in conservation schemes. Genet Sel Evol. 2002;34(5):613. https://doi.org/10.1186/1297-9686-34-5-613 . Falconer DS. Introduction to Quantitative Genetics. 2nd ed. London: Longman Group Ltd.; 1981. pp. 1–133. Fernández J, Toro MA, Caballero A. Fixed contributions designs vs . minimization of global coancestry to control inbreeding in small populations. Genet. 2003;165(2):885–94. https://doi.org/10.1093/genetics/165.2.885 . Fernández J, Villanueva B, Pong-Wong R, Toro MÁ. Efficiency of the use of pedigree and molecular marker information in conservation programs. Genet. 2005;170(3):1313–21. https://doi.org/10.1534/genetics.104.037325 . Fernández J, Meuwissen THE, Toro MA, Mäki-Tanila A. Management of genetic diversity in small farm animal populations. Anim. 2011;5(11):1684–98. https://doi.org/10.1017/S1751731111000930 . Gómez-Romano F, Villanueva B, Rodríguez De Cara MÁ, Fernández J. Maintaining genetic diversity using molecular coancestry: The effect of marker density and effective population size. Genet Sel Evol. 2013;45(1):38. https://doi.org/10.1186/1297-9686-45-38 . Gutiérrez JP, Goyache F. A note on ENDOG: A computer program for analysing pedigree information. J Anim Breed Genet. 2005;122(3):172–6. https://doi.org/10.1111/j.1439-0388.2005.00512.x . Gutiérrez JP, Cervantes I, Molina A, Valera M, Goyache F. Individual increase in inbreeding allows estimating effective sizes from pedigrees. Genet Sel Evol. 2008;40(4):359–78. https://doi.org/10.1186/1297-9686-40-4-359 . Gutierrez JP, Cervantes I, Goyache F. Improving the estimation of realized effective population sizes in farm animals. J Anim Breed Genet. 2009;126(4):327–32. https://doi.org/10.1111/j.1439-0388.2009.00810.x . IUCN SSC Antelope Specialist Group. (2016) Nanger dama [Dataset]. International Union for Conservation of Nature. https://doi.org/10.2305/IUCN.UK.2016-2.RLTS.T8968A50186128.en Ivy JA, Lacy RC. A comparison of strategies for selecting breeding pairs to maximize genetic diversity retention in managed populations. J Hered. 2012;103(2):186–96. https://doi.org/10.1093/jhered/esr129 . Ivy JA, Putnam AS, Navarro AY, Gurr J, Ryder OA. Applying SNP-derived molecular coancestry estimates to captive breeding programs. J Hered. 2016;107(5):403–12. https://doi.org/10.1093/jhered/esw029 . James JW. A note on selection differential and generation length when generations overlap. Anim Sci. 1977;24(1):109–12. https://doi.org/10.1017/S0003356100039271 . Jiménez-Mena B, Schad K, Hanna N, Lacy RC. Pedigree analysis for the genetic management of group‐living species. Ecol Evol. 2016;6(10):3067–78. https://doi.org/10.1002/ece3.1831 . Johnson JP, Piche L, Worral H, Atanda SA, Coyne CJ, McGee RJ, McPhee K, Bandillo N. Effective population size in field pea. BMC Genomics. 2024;25(1):695. https://doi.org/10.1186/s12864-024-10587-6 . Kvalnes T, Flagstad Ø, Våge J, Strand O, Viljugrein H, Sæther B. Harvest and decimation affect genetic drift and the effective population size in wild reindeer. Evol Appl. 2024;17(4):e13684. https://doi.org/10.1111/eva.13684 . Lacy RC, Petric A, Warneke M. Inbreeding and outbreeding depression in captive populations of wild animals. In: Thornhill MW, editor. The natural history of inbreeding and outbreeding: Theoretical and empirical perspectives. 1st ed. University of Chicago Press; 1993. pp. 352–74. Lacy RC. Extending pedigree analysis for uncertain parentage and diverse breeding systems. J Hered. 2012;103(2):197–205. https://doi.org/10.1093/jhered/esr135 . Lacy RC, Ballou JD, Pollak JP. PMx: Software package for demographic and genetic analysis and management of pedigreed populations. Methods Ecol Evol. 2012;3(2):433–7. https://doi.org/10.1111/j.2041-210X.2011.00148.x . Maignel L, Boichard D, Verrier É. (1996) Genetic variability of French dairy breeds estimated from pedigree information. Interbull Bull 14, Article 14. Meuwissen T, Luo Z. Computing inbreeding coefficients in large populations. Genet Sel Evol. 1992;24(4):305–13. https://doi.org/10.1186/1297-9686-24-4-305 . Mungall EC. Territoriality. In: Mungall EC, editor. The dama gazelles: Last members of a critically endangered species. 1st ed. Texas A&M University; 2018. pp. 55–61. Nunney L. The influence of mating system and overlapping generations on effective population size. Evol. 1993;47(5):1329–41. https://doi.org/10.1111/j.1558-5646.1993.tb02158.x . Nunney L, Elam DR. Estimating the effective population size of conserved populations. Conserv Biol. 1994;8(1):175–84. https://doi.org/10.1046/j.1523-1739.1994.08010175.x . Ojeda-Marín C. Optimización del diseño de apareamientos en programas de conservación: Aplicación en el conejo ibicenco [ Final Degree Project in Veterinary Medicine . Complutense University of Madrid]; 2019. Ojeda-Marín C, Cervantes I, Moreno E, Goyache F, Gutiérrez JP. Breeding strategies to optimize effective population size in low census captive populations: The case of Gazella cuvieri . Anim. 2021;11(6):1559. https://doi.org/10.3390/ani11061559 . Pavlova A, Schneller NM, Lintermans M, Beitzel M, Robledo-Ruiz DA, Sunnucks P. Planning and implementing genetic rescue of an endangered freshwater fish population in a regulated river, where low flow reduces breeding opportunities and may trigger inbreeding depression. Evol Appl. 2024;17(4):e13679. https://doi.org/10.1111/eva.13679 . Rahimmadar S, Ghaffari M, Mokhber M, Williams JL. Linkage disequilibrium and effective population size of buffalo populations of Iran, Turkey, Pakistan, and Egypt using a medium density SNP array. Front Genet. 2021;12:608186. https://doi.org/10.3389/fgene.2021.608186 . Sánchez L, Bijma P, Woolliams JA. Minimizing inbreeding by managing genetic contributions across generations. Genet. 2003;164(4):1589–95. https://doi.org/10.1093/genetics/164.4.1589 . Sonesson AK, Meuwissen TH. Mating schemes for optimum contribution selection with constrained rates of inbreeding. Genet Sel Evol. 2000;32(3):231. https://doi.org/10.1186/1297-9686-32-3-231 . Wacker S, Larsen BM, Jakobsen P, Karlsson S. Multiple paternity promotes genetic diversity in captive breeding of a freshwater mussel. Glob Ecol Conserv. 2019;17:e00564. https://doi.org/10.1016/j.gecco.2019.e00564 . Wakchaure R, Ganguly S. Captive breeding in endangered wildlife: A review. J Biol Sci Opin. 2016;4(5):186–7. https://doi.org/10.7897/2321-6328.04544 . Welsh AB, Jackson JR. The effect of multi-year vs single-year stocking on lake sturgeon ( Acipenser fulvescens Rafinesque, 1817) genetic diversity. J Appl Ichthyol. 2014;30(6):1524–30. https://doi.org/10.1111/jai.12544 . Wright S. Evolution in mendelian populations. Genet. 1931;16(2):97–159. Additional Declarations No competing interests reported. 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21:00:09","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123065,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7687577/v1/ca7b928918451559b83781fc.html"},{"id":93268014,"identity":"6224f553-bf8c-4fde-b2a7-6d25d354d905","added_by":"auto","created_at":"2025-10-10 21:00:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43547,"visible":true,"origin":"","legend":"\u003cp\u003eEffective population size obtained by the different strategies in generations 1, 5, and 15: based on minimum parent’s coancestry (F0 and F1), on minimum increase in parent’s coancestry (ΔF0 and ΔF1), on weighted parent’s coancestry (Fw0 and Fw1), on weighted increase in parent’s coancestry (ΔFw0 and ΔFw1), on minimum offspring’s coancestry (C0, C1 and C2), on minimum increase in offspring’s coancestry (ΔC0, ΔC1 and ΔC2), and on mixed information from parent’s coancestry and offspring’s coancestry (M0 1-99, M0 5-95, M0 50-50, M0 95-5, M2 1-99, M2 5-95, M2 50-50 and M2 95-5).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7687577/v1/b6a0a10593cb74bdc171f647.png"},{"id":93268024,"identity":"abbe790c-05e5-4f55-9078-7d3652f5fd17","added_by":"auto","created_at":"2025-10-10 21:00:09","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":704230,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of effective population size obtained by the different strategies throughout 15 generations: based on minimum parent’s coancestry (F0 and F1), on minimum increase in parent’s coancestry (ΔF0 and ΔF1), on weighted parent’s coancestry (Fw0 and Fw1), on weighted increase in parent’s coancestry (ΔFw0 and ΔFw1), on minimum offspring’s coancestry (C0, C1 and C2), on minimum increase in offspring’s coancestry (ΔC0, ΔC1 and ΔC2), and on mixed information from parent’s coancestry and offspring’s coancestry (M0 1-99, M0 5-95, M0 50-50, M0 95-5, M2 1-99, M2 5-95, M2 50-50 and M2 95-5).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7687577/v1/6793ce83757a473f3486c142.jpeg"},{"id":93268026,"identity":"f1dca3a9-42d6-4171-a252-a50431e9dcd6","added_by":"auto","created_at":"2025-10-10 21:00:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165422,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of inbreeding coefficient obtained by the different strategies throughout 15 generations: based on minimum parent’s coancestry (F0 and F1), on minimum increase in parent’s coancestry (ΔF0 and ΔF1), on weighted parent’s coancestry (Fw0 and Fw1), on weighted increase in parent’s coancestry (ΔFw0 and ΔFw1), on minimum offspring’s coancestry (C0, C1 and C2), on minimum increase in offspring’s coancestry (ΔC0, ΔC1 and ΔC2), and on mixed information from parent’s coancestry and offspring’s coancestry (M0 1-99, M0 5-95, M0 50-50, M0 95-5, M2 1-99, M2 5-95, M2 50-50 and M2 95-5).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7687577/v1/44d7a139d988d5a4dfa6ef98.png"},{"id":108437558,"identity":"b7442476-52ae-479c-8302-5cccbcfba34f","added_by":"auto","created_at":"2026-05-04 15:59:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1186267,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7687577/v1/045d9e3e-9111-428a-a179-cdb163e94628.pdf"},{"id":93268016,"identity":"a4dc8713-ec09-43ec-9b04-706685ad3780","added_by":"auto","created_at":"2025-10-10 21:00:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":34988,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7687577/v1/2fa1b8fa91a2ae46bf72f843.docx"},{"id":93268018,"identity":"40c24ff9-c8d1-446c-ad54-ae950f37cc63","added_by":"auto","created_at":"2025-10-10 21:00:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32289,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7687577/v1/ae71e05a3ccd4caf44a6487c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing mating strategies to maximize genetic diversity in the mhorr gazelle (Nanger dama mhorr) ex situ breeding program","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThreatened species suffer a greater loss of genetic diversity due to their small size population and increased relatedness between individuals (Blomqvist et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Ex situ breeding programs aim to maintain self-sustaining populations that prevent the complete extinction of the species and release new individuals into the wild, when suitable natural habitat exists and the threat to the species in the wild is controlled (Wakchaure \u0026amp; Ganguly \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). From a genetic point of view, ex situ breeding programs should retain maximum genetic variability of the population, as it will determine its future adaptive potential (Falconer \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) and limit the accumulation of inbreeding, which can lead to a reduction in fitness (Lacy et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). To this end, careful choice of breeding animals and effective mating strategies are essential to maintain genetic integrity of target populations, as well as monitoring the genetic consequences of these captive breeding programs and the genetic diversity obtained in the offspring (Wacker et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The effective population size (\u003cem\u003eNe\u003c/em\u003e) is a widely used parameter to monitor the status of genetic diversity in breeding populations (Johnson et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), as it provides information on the rate of inbreeding (Rahimmadar et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and genetic changes due to genetic drift (Kvalnes et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The effective population size of a population refers to the hypothetical number of breeding animals in an idealized population that would lead to the actual increase in inbreeding if they contribute equally to the next generation (Wright \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1931\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe dama gazelle (\u003cem\u003eNanger dama\u003c/em\u003e) is a critically endangered antelope native to North Africa (IUCN SSC Antelope Specialist Group \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Indiscriminate hunting and habitat destruction have been the main causes of its decline, leading even to the extinction in the wild of the western subspecies, the mhorr gazelle (\u003cem\u003eNanger dama mhorr\u003c/em\u003e) (Durant et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The complete disappearance of the mhorr gazelle was avoided with the creation in 1971 of an ex situ breeding program from some of the last living specimens at \u0026ldquo;La Hoya\u0026rdquo; Experimental Field Station (EEZA-CSIC), in Almeria (Spain). The captive population has gradually increased to over 300 individuals at the present, distributed across different centres in Europe, North America and the Arabian Peninsula (Dom\u0026iacute;nguez \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The ultimate goal of this ex situ breeding program is the restoration of the mhorr gazelle in its natural habitat. The dama gazelle is a social species usually forming herds made up of one adult male, several females and their youngsters. It follows a polygynous mating system, where dominant males becoming territorial during the mating season (Beudels-Jamar et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Females reach sexual maturity at 9\u0026ndash;12 months of age, and males between 18 and 24 months. The gestation has a duration of 6.5 months and only one calf is born per birth (Barbosa \u0026amp; Espeso \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the present study, the performance of different mating strategies to limit the losses of genetic diversity was compared in a real captive population of mhorr gazelle via computer simulations. The effect of different aspects on the effectiveness of the mating schemes was evaluated, such as the use of parents coancestry or offspring coancestry, the number of males and females involved in the mating, and the use of the coefficients themselves in calculations or their increases. In all cases, the effective population size was used as a measure of the evolution of the genetic diversity in the population, both in the short and long term.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThe mating group used for this work included all living animals present at the Almeria breeding centre at the time of the study (December 31, 2024), to recreate a situation as realistic as possible. This reference population consisted of 87 animals (25 males and 62 females) born between 2010 and 2024 (see details in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), from a total pedigree of 3059 records. The population of Almeria was chosen because it is the largest in the world and maintains the reproduction of several breeding groups simultaneously, while other zoological institutions only hold one breeding group, of usually no more than 5\u0026ndash;10 animals. In Almeria, breeding herds are generally formed with 1 male and 4\u0026ndash;8 females. Breeding males are selected following a mating strategy of minimum coancestry of offspring and they are changed annually in each group of females to maximize genetic diversity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAge structure of the reference population of Almeria according to the year of birth.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear of birth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal animals\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal reference population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe mhorr gazelle living population of Almeria has an average generation interval (James \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) of 5.87 years, and a number of equivalent complete generations (Maignel et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) of 8.72, which indicates a high pedigree completeness level. In this living population the average value of individual inbreeding coefficient (Meuwissen \u0026amp; Luo \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) is 0.28. The effective population size based on individual increase in inbreeding (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{N}_{ei}}\\:\\)\u003c/span\u003e\u003c/span\u003e) (Gutierrez et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and based on individual increase in coancestry (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{N}_{ec}}\\:\\)\u003c/span\u003e\u003c/span\u003e) (Cervantes et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) are, respectively, 13.2 and 12.1. The ratio \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{N}_{ec}}/\\stackrel{-}{{N}_{ei}}\\)\u003c/span\u003e\u003c/span\u003e close to one evidences that Almeria does not have a structured population (Dom\u0026iacute;nguez et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMating strategies\u003c/h3\u003e\n\u003cp\u003eDifferent mating strategies were tested by minimizing a function \u003cem\u003eFx\u003c/em\u003e (Ojeda-Mar\u0026iacute;n et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The strategies were classified into three groups according to their purpose:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eStrategies to minimize the parent\u0026rsquo;s coancestry (or inbreeding):\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eStrategy of minimum parent\u0026rsquo;s coancestry (F): each animal mates with the least related individual of the opposite sex, so that inbreeding of the offspring is minimal. In this strategy \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Fx=\\:\\sum\\:{C}_{jk}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{jk}\\)\u003c/span\u003e\u003c/span\u003e is the coancestry between male \u003cem\u003ej\u003c/em\u003e and female \u003cem\u003ek\u003c/em\u003e. Two alternatives were tested:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy F0: all females contribute offspring to the next generation, but not all males.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy F1: all females and all males contribute offspring to the next generation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy of minimum increase in parent\u0026rsquo;s coancestry (ΔF): since coancestry accumulates per generation, under this strategy differences in pedigree depth of individuals were considered. Thus, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Fx=\\:\\sum\\:{\\varDelta\\:C}_{jk}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:C}_{jk}=1-\\:\\sqrt[\\frac{{g}_{j}+{g}_{k}}{2}]{1-\\:{C}_{jk}}\\)\u003c/span\u003e\u003c/span\u003e (Ivy \u0026amp; Lacy \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), where g\u003csub\u003ej\u003c/sub\u003e and g\u003csub\u003ek\u003c/sub\u003e are the equivalent discrete generations known for individuals \u003cem\u003ej\u003c/em\u003e and \u003cem\u003ek\u003c/em\u003e, and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ejk\u003c/em\u003e\u003c/sub\u003e is the inbreeding of a descendent of these two individuals (Maignel et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Again, two alternatives were tested:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy ΔF0: all females contribute offspring to the next generation, but not all males.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy ΔF1: all females and all males contribute offspring to the next generation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy of minimum parent\u0026rsquo;s coancestry weighted by contributions (Fw): this strategy takes into account the current representation of each individual in the mating group and penalizes those who are already highly represented. In this case, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Fx=\\:\\sum\\:{{m}_{j}C}_{jk}{m}_{k}\\)\u003c/span\u003e\u003c/span\u003e ,where \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e (and \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e) is the mean coancestry between the individual \u003cem\u003ej\u003c/em\u003e (and \u003cem\u003ek\u003c/em\u003e) and all the other animals in the mating group. Two alternatives were checked:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy Fw0: all females contribute offspring to the next generation, but not all males.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy Fw1: all females and all males contribute offspring to the next generation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy of minimum increase in parent\u0026rsquo;s coancestry weighted by contributions (ΔFw): this strategy simultaneously accounts possible differences in pedigree depth and representation within the mating group. Function \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Fx=\\:\\sum\\:{{\\varDelta\\:m}_{j}\\varDelta\\:C}_{jk}\\varDelta\\:{m}_{k}\\)\u003c/span\u003e\u003c/span\u003e, where Δ\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e (and Δ\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e) is the mean of the increase in coancestry between the individual \u003cem\u003ej\u003c/em\u003e (and \u003cem\u003ek\u003c/em\u003e) and all the other animals involved in the mating plan. Likewise, two alternatives were studied:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy ΔFw0: all females contribute offspring to the next generation, but not all males.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy ΔFw1: all females and all males contribute offspring to the next generation.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eStrategies to minimize the offspring\u0026rsquo;s coancestry:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eStrategy of minimum offspring\u0026rsquo;s coancestry (C): unlike previous methods that minimize the inbreeding of the descendants, this strategy seeks that the average kinship between all the resulting descendants is minimal. Here \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Fx=\\:\\sum\\:{C}_{lm}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{lm}\\)\u003c/span\u003e\u003c/span\u003e is the coancestry between two descendants \u003cem\u003el\u003c/em\u003e and \u003cem\u003em\u003c/em\u003e of the mating design. In this case, three alternatives were analyzed:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy C0: all females contribute offspring to the next generation, but not all males.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy C1: all females and all males contribute offspring to the next generation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy C2: not all females and not all males participated with offspring for the next generation, while others had a higher number of descendants.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy of minimum increase in offspring\u0026rsquo;s coancestry (ΔC): pedigree depth information is taken into account to calculate kinship. In this strategy \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Fx=\\:\\sum\\:{\\varDelta\\:C}_{lm}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:C}_{lm}\\)\u003c/span\u003e\u003c/span\u003e is the increase in the coancestry between two descendants l and \u003cem\u003em\u003c/em\u003e of the mating design. Three alternatives were tested:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy ΔC0: all females contribute offspring to the next generation, but not all males.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy ΔC1: all females and all males contribute offspring to the next generation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy ΔC2: not all females and not all males participated with offspring for the next generation.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eStrategies to minimize a combination of the parent\u0026rsquo;s coancestry and the offspring\u0026rsquo;s coancestry:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMixed strategy (M): combines information from two generations, parents and offspring. Thus, function \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Fx=\\:p\\sum\\:{C}_{jk}+(1-\\:{p}_{1})\\sum\\:{C}_{lm}\\)\u003c/span\u003e\u003c/span\u003e was minimized, where again \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{jk}\\)\u003c/span\u003e\u003c/span\u003e is the coancestry between male \u003cem\u003ej\u003c/em\u003e and female \u003cem\u003ek\u003c/em\u003e, and therefore the inbreeding of their offspring, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{lm}\\)\u003c/span\u003e\u003c/span\u003e is the coancestry between two descendants \u003cem\u003el\u003c/em\u003e and \u003cem\u003em\u003c/em\u003e of the mating design. In this equation \u003cem\u003ep\u003c/em\u003e is a value between 0 and 1 indicating the weight that will be given to coancestry in the parents' generation. The following values were tested for \u003cem\u003ep\u003c/em\u003e: 0.01 (M 1\u0026ndash;99), 0.05 (M 5\u0026ndash;95), 0.50 (M 50\u0026ndash;50), and 0.95 (M 95\u0026thinsp;\u0026minus;\u0026thinsp;5). Thus, the first value indicated in the strategy refers to the weight given to minimize parent\u0026rsquo;s coancestry, and the second value, to the weight to minimize the offspring\u0026rsquo;s coancestry. Two different alternatives were studied:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy M0: all females contribute offspring to the next generation, but not all males.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrategy M2: not all females and not all males participated with offspring for the next generation.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\n\u003ch3\u003eComputations performed\u003c/h3\u003e\n\u003cp\u003eA total of 22 mating strategies were tested. Fifty replicates of each strategy were conducted during 15 discrete generations. According to a previous study of Ojeda-Mar\u0026iacute;n et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in a species with a similar generation interval and taxonomically close to the mhorr gazelle, this number of discrete generations was sufficient to compare the proposed strategies, since no differences were observed between the results in the fifteenth generation and the twentieth generation, while the simulation process is extremely time consuming. Each generation was formed by a random number of animals simulated from a Poisson distribution of the reference population to be mated, where the number of males was assumed to be equal to or lower than the number of females, and a similar sex ratio was maintained throughout the generations by randomly being born as male or female according to their current representation in the population.\u003c/p\u003e\u003cp\u003eThe performance of the mating strategies was evaluated by the evolution of the effective population size over the 15 generations. The value of the effective population size used to monitor the strategies was the one based on individual increase in inbreeding \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\stackrel{-}{{N}_{ei}}=\\:\\:\\:\\frac{1}{2\\stackrel{-}{{\\Delta\\:}F}}\\right)\\)\u003c/span\u003e\u003c/span\u003e (Guti\u0026eacute;rrez et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gutierrez et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) instead of the effective population size based on individual increase in coancestry \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\stackrel{-}{{N}_{ec}}=\\:\\:\\:\\frac{1}{2\\stackrel{-}{{\\Delta\\:}C}}\\right)\\)\u003c/span\u003e\u003c/span\u003e (Cervantes et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), because problems of inbreeding depression were recently described in this ex situ population (Dom\u0026iacute;nguez et al., submitted) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{N}_{ei}}\\)\u003c/span\u003e\u003c/span\u003e minimizes the rate of inbreeding (ΔF) due to its inverse relationship (Caballero \u0026amp; Toro \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In addition, high \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{N}_{ec}}\\)\u003c/span\u003e\u003c/span\u003e values ​​could mask difficulties in the viability of populations with low relatedness between individuals, but with high inbreeding (Ojeda-Mar\u0026iacute;n \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll the analyses were computed using the ENDOG program v4.9 (Guti\u0026eacute;rrez \u0026amp; Goyache \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the effective population size in generations 1, 5 and 15 of the different simulated strategies. In the short term (generation 1), the best results were from strategies ΔF0 and ΔFw0. In contrast, the worst strategies were C0, C1, C2, ΔC1 and M2 1\u0026ndash;99. In the medium term (generation 5), strategy M0 50\u0026ndash;50 was significantly the best, followed by strategy M0 5\u0026ndash;95. The worst performance was obtain using strategy M2 95\u0026thinsp;\u0026minus;\u0026thinsp;5. In the long term (generation 15), strategies M0 5\u0026ndash;95 and M0 50\u0026ndash;50 showed the best effective population size values. The strategies that gave higher losses of genetic variability in the long term were Fw0, ΔFw0 and M2 95\u0026thinsp;\u0026minus;\u0026thinsp;5.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIt can be seen the evolution of effective population size throughout generations according to the mating strategies studied in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, mixed strategies had the best performance in the long term, followed by the strategies that only minimize the offspring\u0026rsquo;s coancestry, and the strategies that minimize the parent\u0026rsquo;s coancestry obtain the lower effective population size values.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eComparing the number of males and females involved in the mating, differences were also observed between the strategies in generation 15. Methods that minimize the parent\u0026rsquo;s coancestry improved when all females and all males participated in breeding (F1, ΔF1, Fw1 and ΔFw1), compared to those where all females contribute offspring to the next generation, but not all males (F0, ΔF0, Fw0 and ΔFw0). Among the strategies that minimize the offspring\u0026rsquo;s coancestry, the effective population size values ​​were slightly better when not all females and not all males contribute offspring to the next generation (C2 and ΔC2), but these differences were only significant for strategy ΔC2. In the case of the mixed strategies, the results were always better when all females were expected to contribute to the next generation, but not all males (M0), than when not all females nor all males participate in breeding (M2).\u003c/p\u003e\u003cp\u003eRegarding the strategies that minimize the parent\u0026rsquo;s coancestry, methods that minimized increases (ΔF0, ΔF1, ΔFw0 and ΔFw1) instead of the coefficients themselves (F0, F1, Fw0 and Fw1) had better results in the short term, but no significant differences were obtain in the long term. Within the methods that minimize the offspring\u0026rsquo;s coancestry, strategies ΔC0 and ΔC2 achieved significantly higher effective population size than strategies C0 and C2 in the short term. Similarly, in no case significant differences were found in the long term.\u003c/p\u003e\u003cp\u003eFinally, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the evolution of inbreeding coefficient by generation across methodologies. It can be noticed that there is a direct inverse relationship between the strategies that have the highest values ​​of effective population size and the lowest inbreeding coefficients as expected.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll the numeric results of the effective population size and the inbreeding coefficient, along with their standard errors, obtained from each strategy throughout the generations are compiled in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, respectively.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIt is widely recognized that the optimal method for maximizing the genetic diversity in the conservation programs managed using pedigree information is to minimize coancestry among the offspring by making the contributions of parents more equal (Sonesson \u0026amp; Meuwissen \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ivy \u0026amp; Lacy \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, it has been suggested that the best mating strategy can vary depending on the managed population and each case needs to be studied individually (Ojeda-Mar\u0026iacute;n et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With this approach, the present work has intended to analyze how the genetic diversity of a real captive population of a critically endangered species varies according to the different mating strategies raised.\u003c/p\u003e\u003cp\u003eFirstly, although the strategies that minimize the parent\u0026rsquo;s coancestry were the best in the short term, their results were the worst in the long term, apart from those of the M2 95\u0026thinsp;\u0026minus;\u0026thinsp;5 strategy. These strategies that focus, exclusively or to a high percentage, on minimizing the inbreeding of offspring, mate each animal with the least related individual of the opposite sex and obtain a high value of the effective population size in the first generation. However, as consequence, the second generation is made up of individuals that, although they have a low average inbreeding, are in a large proportion siblings or half-siblings, which is to say there is a high average kinship between all of them. This initial and significant loss of genetic diversity then becomes unrecoverable in subsequent generations, preventing the effective population size from reaching better values ​​as with other strategies. It is also interesting to note how, despite the poor results of these strategies in the long-term, when all females and all males contribute to the next generation, the effective population size is clearly better than when all females contribute, but not all males. Therefore, a greater participation of breeding animals, both males and females, helps to reduce the levels of inbreeding and slow down the loss of genetic diversity (Welsh \u0026amp; Jackson \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pavlova et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Methods that minimized increases instead of the coefficients themselves were beneficial in the short term, but not in the long term. In the first generations, the use of increase in parent\u0026rsquo;s coancestry allows standardizing the inbreeding coefficients based on the pedigree completeness (Cervantes et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Nevertheless, this is no longer an advantage in the latest generations, when the amount of genealogical information for all animals has become equal.\u003c/p\u003e\u003cp\u003eIn second place, the strategies that minimize the offspring\u0026rsquo;s coancestry has the lowest effective population size values in the short term, besides the mixed strategies M0 1\u0026ndash;99 and M2 1\u0026ndash;99, that also focus preferably on minimizing this parameter. However, in the medium term these strategies were similar to those that minimize the parent\u0026rsquo;s coancestry and in the long term they surpassed them, which coincide with the most widely accepted theory at present (Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In this case, not forcing the participation of either all males or all females improved the effective population size values, since preferential matings between relatives are favoured (S\u0026aacute;nchez et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). For these strategies, methods that minimize the increase in coancestry also showed an advantage in the first generations, but when the simulation of generations progresses, they are equated with the methods that minimize the coefficient of coancestry.\u003c/p\u003e\u003cp\u003eFinally, it can be seen how giving only minimal weight to coancestry in the parents' generation of the mixed strategies (M0 1\u0026ndash;99 and M2 1\u0026ndash;99), the value of the effective population size increases significantly from the fifth generation onwards compared to the strategies that only minimize the offspring\u0026rsquo;s coancestry. Performance continues to improve further with a weight to coancestry in the parents' generation of 5% and 50%, but then drops off at a weight of 95%. It should be noted that the simulations in this work did not include the potential negative impact on population viability of high inbreeding in early generations. Nevertheless, adding some weight to the parent\u0026rsquo;s coancestry, these mixed strategies avoid a high individual increase in inbreeding in animals at birth, and thus prevent the deleterious effects of inbreeding depression and a higher initial risk of population disappearance, while maximizing the effective population size in the long term. Regarding the number of individuals involved in the mating, it can be observed that forcing the participation of all females, but not all males, always had better results than not forcing the contribution of either sex to the next generation in these mixed strategies; arising an intermediate situation between the strategies that minimize the parent\u0026rsquo;s coancestry, where the contribution of all males and all females was the best option, and the strategies that minimize the offspring\u0026rsquo;s coancestry, where, on the contrary, the best performances were obtained when not all females nor all males participated with offspring for the next generation. In the wild, due to the territorial behaviour of males and the social organization of this species in harems, all females have the opportunity to contribute to the next generation, but only dominant and successful males in fights will be able to access the females and mate (Mungall \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, applying in captive matings this methodology that resembles the polygynous nature of the species seems reasonable, although in this case the selection criteria for breeding males are defined by the population managers and not by the physical superiority of the animals. Nevertheless, it cannot be forgotten that in polygynous mating systems, where very few males obtain almost all of the matings, the high variance in male reproductive success may result in a low effective population size (Nunney \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Nunney \u0026amp; Elam \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Under extreme forms of polygyny, such as harem polygyny and dominance polygyny, this reduction can be even more marked. Therefore, the mhorr gazelle's mating system makes this species naturally more prone to lower effective population sizes than other monogamous or random mating species.\u003c/p\u003e\u003cp\u003eThe international community of zoos and wildlife breeding centers have pedigree analysis software available to carry out the demographic and genetic management of the small populations they maintain (Lacy et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although traditionally most of these tools were designed for diploid species with sexual reproduction, they have subsequently incorporated new technological advances that better align with different species management (e.g. group living species) and breeding systems (e.g. hermaphroditic selfing, cloning or autozygous production of haploid offspring) (Lacy \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Jim\u0026eacute;nez-Mena et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the same way, a further step should be taken in the genetic management of wild species to adapt mating strategies to the particular needs of conservation programs, individually evaluating the best option for each population, and thus they can retain its maximum genetic diversity and minimize the potential deleterious effects of inbreeding. Another factor to take into account is the expected time horizon. In this study population, due to the serious conservation status of the species and the need for several decades to restore natural populations, it can be foreseen that it will be essential to maintain captive populations to ensure the long-term viability of the conservation program, so it was decided to compare mating strategies up to a high number of generations. However, in populations of species with different degrees of threat, generation intervals or conservation objectives, comparing the performance of strategies in the short or medium term could be sufficient.\u003c/p\u003e\u003cp\u003eTo conclude we can say that in this mhorr gazelle captive population the strategies M0 5\u0026ndash;95 and M0 50\u0026ndash;50 were the best overall in the medium and long term, and also had some of the highest effective population size values in the short term. Both strategies showed similar long-term performance, but M0 50\u0026ndash;50 was significantly superior in the first and fifth generations than M0 5\u0026ndash;95, so this would be the mating strategy of choice for this mhorr gazelle population. Although with some differences, these two strategies also had the best performance for the Cuvier's gazelle population from the same ex situ breeding center (Ojeda-Mar\u0026iacute;n et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), so applying a weight to coancestry in the parents' generation between 5% and 50%, seems to have advantages in these particular cases. So far, the mating strategy applied in this mhorr gazelle population has been based exclusively on minimizing the offspring\u0026rsquo;s coancestry, but given the results obtained in this study, it would be highly recommended to replace it with the mixed strategy M0 50\u0026ndash;50, whose performance has been shown to be clearly better than the method currently used by providing higher effective population size values. However, it should not be forgotten that coancestry coefficients can also be obtained from molecular information (Caballero \u0026amp; Toro \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Eding et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and used for monitoring genetic diversity in conservation programs (G\u0026oacute;mez-Romano et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Although molecular analyses have sometimes not differed from the results obtained from pedigree data (Ivy et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Barrett et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), or have even shown limited effectiveness if used alone (Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), it would be advisable to expand this work with molecular techniques to determine the definitive mating strategy for this population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation and data collection were performed by S.D. Analyses were carried out by S.D. and J.P.G., supported by I.C. and E.M.. The first draft of the manuscript was written by S.D. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors want to thank all those who have collected data for the mhorr gazelle studbook, to the Estaci\u0026oacute;n Experimental de Zonas \u0026Aacute;ridas for providing free access to the records, and to the staff of \u0026ldquo;La Hoya\u0026rdquo; Field Station for their expert handling and care of the animals in Almer\u0026iacute;a.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe pedigree dataset analysed during the current study is available online:http://www.eeza.csic.es/documentos/Studbook\\_2021\\_Nanger\\_dama\\_mhorr.pdf\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarbosa A, Espeso G. International studbook gazella dama mhorr. Consejo Superior de Investigaciones Cient\u0026iacute;ficas; 2005.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarrett KG, Amaral G, Elphinstone M, McAdie ML, Davis CS, Janes JK, Carnio J, Moehrenschlager A, Gorrell JC. Genetic management on the brink of extinction: Sequencing microsatellites does not improve estimates of inbreeding in wild and captive Vancouver Island marmots (\u003cem\u003eMarmota vancouverensis\u003c/em\u003e). Conserv Genet. 2022;23(2):417\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10592-022-01429-7\u003c/span\u003e\u003cspan address=\"10.1007/s10592-022-01429-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeudels-Jamar RC, Devillers P, Lafontaine RM, Devillers-Terschuren J, Beudels MO. Sahelo-Saharian antelopes. Status and perspectives. CMS Technical Series Publication; 2005.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlomqvist D, Pauliny A, Larsson M, Flodin L-\u0026Aring;. Trapped in the extinction vortex? Strong genetic effects in a declining vertebrate population. BMC Evol Biol. 2010;10(1):33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2148-10-33\u003c/span\u003e\u003cspan address=\"10.1186/1471-2148-10-33\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaballero A, Toro MA. Interrelations between effective population size and other pedigree tools for the management of conserved populations. Genet Res. 2000;75(3):331\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0016672399004449\u003c/span\u003e\u003cspan address=\"10.1017/S0016672399004449\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaballero A, Toro MA. Analysis of genetic diversity for the management of conserved subdivided populations. Conserv Genet. 2002;3:289\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1019956205473\u003c/span\u003e\u003cspan address=\"10.1023/A:1019956205473\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCervantes I, Goyache F, Molina A, Valera M, Guti\u0026eacute;rrez JP. Estimation of effective population size from the rate of coancestry in pedigreed populations. J Anim Breed Genet. 2011;128(1):56\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1439-0388.2010.00881.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1439-0388.2010.00881.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDom\u0026iacute;nguez S. (2022) International studbook for the mhorr gazelle. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.eeza.csic.es/documentos/Studbook_2021_Nanger_dama_mhorr.pdf\u003c/span\u003e\u003cspan address=\"http://www.eeza.csic.es/documentos/Studbook_2021_Nanger_dama_mhorr.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 10 October 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDom\u0026iacute;nguez S, Cervantes I, Guti\u0026eacute;rrez JP, Moreno E. Pedigree analysis in the mhorr gazelle (\u003cem\u003eNanger dama mhorr\u003c/em\u003e): Genetic variability evolution of the captive population. Ecol Evol. 2024;14(2):e10876. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.10876\u003c/span\u003e\u003cspan address=\"10.1002/ece3.10876\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDom\u0026iacute;nguez S, Guti\u0026eacute;rrez JP, Moreno E, Cervantes I. (2025) Genetic analysis of juvenile survival in the captive population of mhorr gazelle (\u003cem\u003eNanger dama mhorr\u003c/em\u003e) and the effect of inbreeding. Conserv Genet (Submitted number: 745ff122-0907-4996-a901-d98b52e3e9fe).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDurant SM, Wacher T, Bashir S, Woodroffe R, De Ornellas P, Ransom C, Newby J, Ab\u0026aacute;igar T, Abdelgadir M, El Alqamy H, Baillie J, Beddiaf M, Belbachir F, Belbachir-Bazi A, Berbash AA, Bemadjim NE, Beudels-Jamar R, Boitani L, Breitenmoser C, Cano M, Chardonnet P, Collen B, Cornforth WA, Cuzin F, Gerngross P, Haddane B, Hadjeloum M, Jacobson A, Jebali A, Lamarque F, Mallon D, Minkowski K, Monfort S, Ndoassal B, Niagate B, Purchase G, Sama\u0026iuml;la S, Samna AK, Sillero-Zubiri C, Soultan AE, Stanley Price MR, Pettorelli N. Fiddling in biodiversity hotspots while deserts burn? Collapse of the Sahara\u0026rsquo;s megafauna. Divers Distrib. 2014;20(1):114\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ddi.12157\u003c/span\u003e\u003cspan address=\"10.1111/ddi.12157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEding H, Crooijmans RPMA, Groenen MAM, Meuwissen THE. Assessing the contribution of breeds to genetic diversity in conservation schemes. Genet Sel Evol. 2002;34(5):613. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1297-9686-34-5-613\u003c/span\u003e\u003cspan address=\"10.1186/1297-9686-34-5-613\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFalconer DS. Introduction to Quantitative Genetics. 2nd ed. London: Longman Group Ltd.; 1981. pp. 1\u0026ndash;133.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez J, Toro MA, Caballero A. Fixed contributions designs \u003cem\u003evs\u003c/em\u003e. minimization of global coancestry to control inbreeding in small populations. Genet. 2003;165(2):885\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/genetics/165.2.885\u003c/span\u003e\u003cspan address=\"10.1093/genetics/165.2.885\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez J, Villanueva B, Pong-Wong R, Toro M\u0026Aacute;. Efficiency of the use of pedigree and molecular marker information in conservation programs. Genet. 2005;170(3):1313\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1534/genetics.104.037325\u003c/span\u003e\u003cspan address=\"10.1534/genetics.104.037325\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez J, Meuwissen THE, Toro MA, M\u0026auml;ki-Tanila A. Management of genetic diversity in small farm animal populations. Anim. 2011;5(11):1684\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1751731111000930\u003c/span\u003e\u003cspan address=\"10.1017/S1751731111000930\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026oacute;mez-Romano F, Villanueva B, Rodr\u0026iacute;guez De Cara M\u0026Aacute;, Fern\u0026aacute;ndez J. Maintaining genetic diversity using molecular coancestry: The effect of marker density and effective population size. Genet Sel Evol. 2013;45(1):38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1297-9686-45-38\u003c/span\u003e\u003cspan address=\"10.1186/1297-9686-45-38\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuti\u0026eacute;rrez JP, Goyache F. A note on ENDOG: A computer program for analysing pedigree information. J Anim Breed Genet. 2005;122(3):172\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1439-0388.2005.00512.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1439-0388.2005.00512.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuti\u0026eacute;rrez JP, Cervantes I, Molina A, Valera M, Goyache F. Individual increase in inbreeding allows estimating effective sizes from pedigrees. Genet Sel Evol. 2008;40(4):359\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1297-9686-40-4-359\u003c/span\u003e\u003cspan address=\"10.1186/1297-9686-40-4-359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGutierrez JP, Cervantes I, Goyache F. Improving the estimation of realized effective population sizes in farm animals. J Anim Breed Genet. 2009;126(4):327\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1439-0388.2009.00810.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1439-0388.2009.00810.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIUCN SSC Antelope Specialist Group. (2016) \u003cem\u003eNanger dama\u003c/em\u003e [Dataset]. International Union for Conservation of Nature. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2305/IUCN.UK.2016-2.RLTS.T8968A50186128.en\u003c/span\u003e\u003cspan address=\"10.2305/IUCN.UK.2016-2.RLTS.T8968A50186128.en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIvy JA, Lacy RC. A comparison of strategies for selecting breeding pairs to maximize genetic diversity retention in managed populations. J Hered. 2012;103(2):186\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jhered/esr129\u003c/span\u003e\u003cspan address=\"10.1093/jhered/esr129\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIvy JA, Putnam AS, Navarro AY, Gurr J, Ryder OA. Applying SNP-derived molecular coancestry estimates to captive breeding programs. J Hered. 2016;107(5):403\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jhered/esw029\u003c/span\u003e\u003cspan address=\"10.1093/jhered/esw029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJames JW. A note on selection differential and generation length when generations overlap. Anim Sci. 1977;24(1):109\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0003356100039271\u003c/span\u003e\u003cspan address=\"10.1017/S0003356100039271\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJim\u0026eacute;nez-Mena B, Schad K, Hanna N, Lacy RC. Pedigree analysis for the genetic management of group‐living species. Ecol Evol. 2016;6(10):3067\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.1831\u003c/span\u003e\u003cspan address=\"10.1002/ece3.1831\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson JP, Piche L, Worral H, Atanda SA, Coyne CJ, McGee RJ, McPhee K, Bandillo N. Effective population size in field pea. BMC Genomics. 2024;25(1):695. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12864-024-10587-6\u003c/span\u003e\u003cspan address=\"10.1186/s12864-024-10587-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKvalnes T, Flagstad \u0026Oslash;, V\u0026aring;ge J, Strand O, Viljugrein H, S\u0026aelig;ther B. Harvest and decimation affect genetic drift and the effective population size in wild reindeer. Evol Appl. 2024;17(4):e13684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/eva.13684\u003c/span\u003e\u003cspan address=\"10.1111/eva.13684\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLacy RC, Petric A, Warneke M. Inbreeding and outbreeding depression in captive populations of wild animals. In: Thornhill MW, editor. The natural history of inbreeding and outbreeding: Theoretical and empirical perspectives. 1st ed. University of Chicago Press; 1993. pp. 352\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLacy RC. Extending pedigree analysis for uncertain parentage and diverse breeding systems. J Hered. 2012;103(2):197\u0026ndash;205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jhered/esr135\u003c/span\u003e\u003cspan address=\"10.1093/jhered/esr135\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLacy RC, Ballou JD, Pollak JP. PMx: Software package for demographic and genetic analysis and management of pedigreed populations. Methods Ecol Evol. 2012;3(2):433\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2041-210X.2011.00148.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2041-210X.2011.00148.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaignel L, Boichard D, Verrier \u0026Eacute;. (1996) Genetic variability of French dairy breeds estimated from pedigree information. Interbull Bull 14, Article 14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeuwissen T, Luo Z. Computing inbreeding coefficients in large populations. Genet Sel Evol. 1992;24(4):305\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1297-9686-24-4-305\u003c/span\u003e\u003cspan address=\"10.1186/1297-9686-24-4-305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMungall EC. Territoriality. In: Mungall EC, editor. The dama gazelles: Last members of a critically endangered species. 1st ed. Texas A\u0026amp;M University; 2018. pp. 55\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNunney L. The influence of mating system and overlapping generations on effective population size. Evol. 1993;47(5):1329\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1558-5646.1993.tb02158.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1558-5646.1993.tb02158.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNunney L, Elam DR. Estimating the effective population size of conserved populations. Conserv Biol. 1994;8(1):175\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1523-1739.1994.08010175.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1523-1739.1994.08010175.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOjeda-Mar\u0026iacute;n C. Optimizaci\u0026oacute;n del dise\u0026ntilde;o de apareamientos en programas de conservaci\u0026oacute;n: Aplicaci\u0026oacute;n en el conejo ibicenco [\u003cem\u003eFinal Degree Project in Veterinary Medicine\u003c/em\u003e. Complutense University of Madrid]; 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOjeda-Mar\u0026iacute;n C, Cervantes I, Moreno E, Goyache F, Guti\u0026eacute;rrez JP. Breeding strategies to optimize effective population size in low census captive populations: The case of \u003cem\u003eGazella cuvieri\u003c/em\u003e. Anim. 2021;11(6):1559. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ani11061559\u003c/span\u003e\u003cspan address=\"10.3390/ani11061559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePavlova A, Schneller NM, Lintermans M, Beitzel M, Robledo-Ruiz DA, Sunnucks P. Planning and implementing genetic rescue of an endangered freshwater fish population in a regulated river, where low flow reduces breeding opportunities and may trigger inbreeding depression. Evol Appl. 2024;17(4):e13679. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/eva.13679\u003c/span\u003e\u003cspan address=\"10.1111/eva.13679\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahimmadar S, Ghaffari M, Mokhber M, Williams JL. Linkage disequilibrium and effective population size of buffalo populations of Iran, Turkey, Pakistan, and Egypt using a medium density SNP array. Front Genet. 2021;12:608186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2021.608186\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2021.608186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eS\u0026aacute;nchez L, Bijma P, Woolliams JA. Minimizing inbreeding by managing genetic contributions across generations. Genet. 2003;164(4):1589\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/genetics/164.4.1589\u003c/span\u003e\u003cspan address=\"10.1093/genetics/164.4.1589\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSonesson AK, Meuwissen TH. Mating schemes for optimum contribution selection with constrained rates of inbreeding. Genet Sel Evol. 2000;32(3):231. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1297-9686-32-3-231\u003c/span\u003e\u003cspan address=\"10.1186/1297-9686-32-3-231\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWacker S, Larsen BM, Jakobsen P, Karlsson S. Multiple paternity promotes genetic diversity in captive breeding of a freshwater mussel. Glob Ecol Conserv. 2019;17:e00564. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gecco.2019.e00564\u003c/span\u003e\u003cspan address=\"10.1016/j.gecco.2019.e00564\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWakchaure R, Ganguly S. Captive breeding in endangered wildlife: A review. J Biol Sci Opin. 2016;4(5):186\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7897/2321-6328.04544\u003c/span\u003e\u003cspan address=\"10.7897/2321-6328.04544\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWelsh AB, Jackson JR. The effect of multi-year vs single-year stocking on lake sturgeon (\u003cem\u003eAcipenser fulvescens\u003c/em\u003e Rafinesque, 1817) genetic diversity. J Appl Ichthyol. 2014;30(6):1524\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jai.12544\u003c/span\u003e\u003cspan address=\"10.1111/jai.12544\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright S. Evolution in mendelian populations. Genet. 1931;16(2):97\u0026ndash;159.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-zoology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bzoo","sideBox":"Learn more about [BMC Zoology](http://bmczool.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bzoo/default.aspx","title":"BMC Zoology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"critically endangered, captive breeding, dama gazelle, mating strategies, genetic diversity, effective population size","lastPublishedDoi":"10.21203/rs.3.rs-7687577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7687577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo minimize coancestry among the offspring by making the contributions of parents more equal is currently one of the most widely used methods to maximize the genetic diversity in conservation programs of threatened species. The objective of this study was to compare the performance of 22 different mating strategies to limit the loss of genetic diversity in a real captive population of mhorr gazelle (\u003cem\u003eNanger dama mhorr\u003c/em\u003e) via computer simulations. This objective was achieved by monitoring the evolution of the effective population size (\u003cem\u003eNe\u003c/em\u003e) throughout 15 generations. The studied population consisted of 87 breeding animals (25 males and 62 females), from a total pedigree of 3059 records. The scenarios were designed according to different assumptions: the use of parents coancestry or offspring coancestry, the use of the coefficients themselves in calculations or their increases, and the number of males and females involved in the mating. The results for this captive population showed that strategies to minimize the parent\u0026rsquo;s coancestry were the best in the short term, but mixed strategies to minimize coancestry of both parents and offspring were better in the medium and long term when a weight to coancestry in the parents' generation between 5% and 50% was applied. Furthermore, it was observed that these mixed strategies improved their performance when all females in the population participated in breeding, but not all males. These results suggest that each managed population may need different mating strategies, considering its generation interval, the species' breeding system, the expected time horizon of the conservation program, or other species-specific considerations of the population.\u003c/p\u003e","manuscriptTitle":"Optimizing mating strategies to maximize genetic diversity in the mhorr gazelle (Nanger dama mhorr) ex situ breeding program","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 21:00:04","doi":"10.21203/rs.3.rs-7687577/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-02T09:39:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T20:03:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T10:05:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220464361159519762235552865718484364338","date":"2025-11-12T04:41:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285095574991849612571632220338218077586","date":"2025-11-10T08:35:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-09T12:09:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247575877951759579093690978189633218411","date":"2025-10-04T08:27:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73042101466980632395260804139154803055","date":"2025-10-01T08:20:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-29T07:44:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-25T17:11:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T03:37:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-25T03:37:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Zoology","date":"2025-09-22T18:00:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-zoology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bzoo","sideBox":"Learn more about [BMC Zoology](http://bmczool.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bzoo/default.aspx","title":"BMC Zoology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6fbe3ff0-f56f-4092-b608-ef9d18f6e1dd","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T15:59:02+00:00","versionOfRecord":{"articleIdentity":"rs-7687577","link":"https://doi.org/10.1186/s40850-026-00264-4","journal":{"identity":"bmc-zoology","isVorOnly":false,"title":"BMC Zoology"},"publishedOn":"2026-04-27 15:57:11","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2025-10-10 21:00:04","video":"","vorDoi":"10.1186/s40850-026-00264-4","vorDoiUrl":"https://doi.org/10.1186/s40850-026-00264-4","workflowStages":[]},"version":"v1","identity":"rs-7687577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7687577","identity":"rs-7687577","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}