Morgane Tidière
email: morgane.tidiere@univ-lyon1.fr

Jean-Michel Gaillard
email: jean-michel.gaillard@univ-lyon1.fr

Dennis W H Müller

Laurie Bingaman Lackey

Olivier Gimenez
email: olivier.gimenez@cefe.cnrs.fr

Marcus Clauss
email: mclauss@vetclinics.uzh.ch

Jean-François Lemaître
email: jean-francois.lemaitre@univ-lyon1.fr

Does sexual selection shape sex differences in longevity and senescence patterns across vertebrates? A review and new insights from captive ruminants

Keywords: ageing, ungulates, mating system, reproductive costs

In vertebrates, both sexes mostly exhibit a different pattern of senescence and lifespan, often male-biased in mammals and female-biased in birds. Since polygynous males are under intense sexual competition to secure mating opportunities, they allocate into costly conspicuous sexual traits that might explain sex-differences in longevity and senescence patterns. However, comparative studies performed to date have led to conflicting results. We aimed to solve this problem by first reviewing and discussing published case studies of the relationship between the strength of sexual selection and age-specific survival metrics. Then, we performed a comparative analysis to test whether such relationships occur among species of captive large herbivores. We found that the strength of sexual selection negatively influenced the onset of actuarial senescence in males, with males both senescing earlier in polygynous than in monogamous species and showing greater between-sex differences in onset of senescence in favor of females. Moreover, males of territorial species had earlier onset of senescence than males of species displaying harem or tending mating tactics. We did not detect any influence of the strength of sexual selection on the rate of actuarial senescence.

Our findings demonstrate that the onset of actuarial senescence, rather than its rate, is the target of sexual selection.

During the last two decades, demographic analyzes based on long-term studies have demonstrated that actuarial senescence, a progressive decrease of survival with age [START_REF] Monaghan | The evolutionary ecology of senescence[END_REF], is almost an ubiquitous process in vertebrates [START_REF] Brunet-Rossini | Senescence in wild populations of mammals and birds[END_REF]Gaillard and Bonenfant 2008;[START_REF] Nussey | Measuring senescence in wild animal populations: towards a longitudinal approach[END_REF][START_REF] Nussey | Senescence in natural populations of animals: widespread evidence and its implications for biogerontology[END_REF]. Nevertheless, actuarial senescence is not expressed with the same intensity across the tree of life [START_REF] Nussey | Senescence in natural populations of animals: widespread evidence and its implications for biogerontology[END_REF][START_REF] Jones | Diversity of ageing across the tree of life[END_REF]. These differences in the intensity of senescence among species are generally driven by the pace of life [START_REF] Jones | Senescence rates are determined by ranking on the fast-slow life-history continuum[END_REF] that is strongly associated with body mass [START_REF] Calder | Size, function, and life history[END_REF], phylogeny [START_REF] Gaillard | An analysis of demographic tactics in birds and mammals[END_REF], and habitat [START_REF] Wiersma | Tropical birds have a slow pace of life[END_REF].

Within species, the intensity of actuarial senescence also varies, especially according to sex [START_REF] Clutton-Brock | Sex differences in ageing in natural populations of vertebrates[END_REF][START_REF] Maklakov | Evolution of sex differences in lifespan and aging: Causes and constraints[END_REF][START_REF] Regan | Gender and longevity: Why do men die earlier than women? Comparative and experimental evidence[END_REF][START_REF] Adler | Sexual conflict, life span, and aging[END_REF]. For example, mammalian males generally live shorter and senesce faster than females in a given species or population (Lemaître and Gaillard, 2013a;[START_REF] Ramp | Sex-specific survival in the humpback whale Megaptera novaeangliae in the Gulf of St. Lawrence, Canada[END_REF]; but see [START_REF] Gamelon | Do age-specific survival patterns of wild boar fit current evolutionary theories of senescence? Evolution n/an/a[END_REF]. The magnitude of such between-sex differences in actuarial senescence is generally assumed to be associated with the strength of sexual selection [START_REF] Clutton-Brock | Sex differences in ageing in natural populations of vertebrates[END_REF]Lemaître and Gaillard, 2013a). Sexual selection causes the evolution of traits and/or behaviors that are associated with mating success [START_REF] Darwin | On the origin of species[END_REF] and individuals from species subjected to a strong sexual selection will generally suffer from high cost of production and maintenance of these sexually selected traits (e.g. an increased reproductive senescence for males controlling the largest harems in the polygynous red deer, Cervus elaphus, Lemaître et al., 2014a). In most species, the intensity of sexual selection is stronger in males than in females because fathers generally allocate much less than mothers to the production and rearing of offspring [START_REF] Bateman | Intra-sexual selection in drosophila[END_REF][START_REF] Trivers | Mother-offspring conflict[END_REF][START_REF] Andersson | Sexual Selection[END_REF]. Then, males can allocate much more than females to intraspecific sexual competition to obtain additional mating and thereby to increase their individual fitness. Males of species subjected to a low intra-sexual competition will both undergo a lower sexual selection pressure and allocate less energy to mate. They can then allocate more to somatic maintenance and thereby enjoy higher survival and weaker senescence than males of species deeply involved in intra-sexual competition [START_REF] Bonduriansky | Sexual selection, sexual conflict and the evolution of ageing and life span[END_REF]. Therefore, sexual selection leads to the production and maintenance of costly sexual traits in males and associated risky behaviors, which are likely to cause higher mortality and accelerated senescence, and thus increased between-sex differences in actuarial senescence (Williams, 1957;[START_REF] Orme | The caper package: comparative analysis of phylogenetics and evolution in R[END_REF][START_REF] Bonduriansky | Sexual selection, sexual conflict and the evolution of ageing and life span[END_REF]. We thus expect a greater sex-bias in senescence pattern in favor of females to occur in species in which males are subjected to high levels of sexual competition.

The influence of the strength of sexual selection on senescence or survival patterns is a key topic of evolutionary biology that is currently highly debated. From a literature survey, we have identified 10 studies of the link between the strength of sexual selection and survival patterns at the inter-specific level (Table 1) and only six of these studies provided support for a cost of sexual selection in terms of survival. However, only three studies involved actuarial senescence sensu stricto. The other studies focused on age-independent metrics such as maximum longevity, lifespan, or average adult mortality, which do not provide any reliable information about actuarial senescence and are highly dependent of sample size [START_REF] Krementz | Model-based estimates of annual survival rate are preferable to observed maximum lifespan statistics for use in comparative life-history studies[END_REF][START_REF] Moorad | A comparative assessment of univariate longevity measures using zoological animal records[END_REF]. Moreover, for a given metric, the measure retained strongly differed from one study to another. For example, sexual size dimorphism (SSD), a commonly used proxy of the strength of sexual selection, was measured either as the ratio between male and female body mass [START_REF] Promislow | Costs of Sexual Selection in Natural Populations of Mammals[END_REF][START_REF] Orme | The caper package: comparative analysis of phylogenetics and evolution in R[END_REF], as the residuals from the linear regression of male body mass against female body mass (Toïgo and Gaillard, 2003), by including both male and female body mass as additive covariates in the model (Lemaître and Gaillard, 2013a), or as the [START_REF] Lovich | A review of techniques for quantifying sexual size dimorphism[END_REF]'s ratio of the mean size between males and females when males are larger [START_REF] Bro-Jørgensen | Longevity in bovids is promoted by sociality, but reduced by sexual selection[END_REF]. Such a diversity of SSD measures often makes difficult to compare results among studies.

Conclusions from studies based on actuarial senescence metrics are also largely inconsistent. So far, only one provided evidence of a stronger male-biased senescence in species with strong inter-male competition for mating [START_REF] Clutton-Brock | Sex differences in ageing in natural populations of vertebrates[END_REF]. The other two studies did not report statistically significant relationship between different rates of actuarial senescence and the strength of sexual selection in either males (Lemaître and Gaillard, 2013a) or females (Lemaître and Gaillard, 2013b). Moreover, all these studies were based on the rate of senescence, which only depicts the intensity of actuarial senescence. The onset of senescence provides another potentially powerful metric that is most often overlooked in senescence studies. However, the few studies available for this trait have demonstrated its biological relevance by showing that observed variation in the onset of senescence across species is mostly shaped by variation in the pace of life (e.g. [START_REF] Jones | Senescence rates are determined by ranking on the fast-slow life-history continuum[END_REF][START_REF] Péron | Age at the onset of senescence in birds and mammals is predicted by early-life performance[END_REF]. Moreover, a recent study has shown that the highly fecund wild boar (Sus scrofa) females suffered from earlier, not stronger, actuarial senescence than similar-sized female mammals with much lower fecundity [START_REF] Gamelon | Do age-specific survival patterns of wild boar fit current evolutionary theories of senescence? Evolution n/an/a[END_REF]. These recent findings thus emphasize that the onset of senescence could potentially better assess the potential costs of sexual selection in terms of actuarial senescence.

In the present study, we strive to make a fairly comprehensive investigation of the relationship between various metrics of actuarial senescence and survival and the strength of sexual selection both across species and between sexes. For this purpose, we analyzed a high quality dataset on age-specific survival of large herbivores living in captivity. In large herbivores, males are often subjected to intensive sexual competition [START_REF] Clutton-Brock | Review lecture: mammalian mating systems[END_REF][START_REF] Bro-Jørgensen | Intra-and intersexual conflicts and cooperation in the evolution of mating strategies: lessons from ungulates[END_REF] and there is a large variation in the intensity of sexual selection among species. To gain mating opportunities males subjected to strong sexual selection often display phenotypic responses [START_REF] Preston | Overt and covert competition in a promiscuous mammal: the importance of weaponry and testes size to male reproductive success[END_REF], which can be costly and lead to increased mortality. In particular, these males allocate a lot in body mass, which confers advantages in contest with competing males [START_REF] Jarman | Mating system and sexual dimorphism in large, terrestrial, mammalian herbivores[END_REF][START_REF] Andersson | Sexual Selection[END_REF]. Thus, a positive correlation between the magnitude of SSD and male-biased mortality is expected because of the high energy requirements associated with large body size [START_REF] Jarman | Mating system and sexual dimorphism in large, terrestrial, mammalian herbivores[END_REF][START_REF] Weckerly | Sexual-size dimorphism: influence of mass and mating systems in the most dimorphic mammals[END_REF].

Moreover, large herbivores exhibit different mating systems [START_REF] Clutton-Brock | Review lecture: mammalian mating systems[END_REF][START_REF] Bro-Jørgensen | Longevity in bovids is promoted by sociality, but reduced by sexual selection[END_REF] that are likely to mediate the intensity of male-male competition, and thus traits such as SSD (Loison et al., 1999a). In large herbivores, three types of mating systems are predominant: monogamy when males mate with the same female in several reproductive attempts, promiscuity when males and females mate with several partners and there is no continuing bond between individual males and females after mating, and polygyny when males mate with the same group of females in successive mating attempts [START_REF] Clutton-Brock | Review lecture: mammalian mating systems[END_REF]. Inter-male competition for mating is expected to be higher in polygynous species, a little less in promiscuous species, and much lower in monogamous species. This explains why monogamous species are generally less dimorphic in size than promiscuous and polygynous species [START_REF] Jarman | Mating system and sexual dimorphism in large, terrestrial, mammalian herbivores[END_REF]. Furthermore, in both promiscuous and polygynous species, the mating tactic can differ markedly [START_REF] Clutton-Brock | Review lecture: mammalian mating systems[END_REF], encompassing territoriality (when female groups are stable and home-range defensible, or when females are constrained to use a given area, males defend part or all the home range of a female group), harem (a male defends access to a group of females rather than to a territory), and tending (when female groups are stable with a home range too large to be defensible or when females are solitary or live in small groups and are unpredictably distributed, males range widely searching for oestrus females, pairing with them and defending them against other males during oestrus).

Defending a territory should be more energetically costly than defending a harem, and even more than doing tending because the time and energy devoted to harem or territory defense is no longer available to repair injuries, DNA damages or to fight against diseases. In the same way, allocating in sexually selected traits like horns or antlers should increase the intensity of senescence because of inherent trade-offs in energy allocation between reproduction and maintenance [START_REF] Williams | Adaptation and natural selection: a critique of some current evolutionary thought[END_REF]. In support, the relationship between antler or horn size and strength of sexual selection in large herbivores is now well established [START_REF] Plard | Revisiting the allometry of antlers among deer species: male-male sexual competition as a driver[END_REF][START_REF] Bro-Jørgensen | The intensity of sexual selection predicts weapon size in male bovids[END_REF]Lemaître et al., 2014b).

We used data from 64 species of large herbivores living in captivity, which were obtained from the International Species Information System (ISIS) database that combines individual data for each animal living in captivity in 850 member institutions from more than 80 countries. Such protected conditions allow eliminating mortality due to environmental causes such as predation, climatic harshness or disease transmission, and thereby better identifying the underlying causes of senescence. To assess the relationship between the magnitude of the male-bias of actuarial senescence and the strength of sexual selection, we used different measures of the intensity of sexual competition simultaneously: SSD, mating system, mating tactic (in polygynous and promiscuous species only) and horn size dimorphism (HSD) (in bovids only). We had too few cervids (N = 14) in our dataset to include male antler size in a specific analysis. In addition, we measured the intensity of male survival costs and of sex differences in actuarial senescence and survival using different measures previously selected in other comparative studies: maximum longevity (Bro-Jørgensen, 2012), Gompertz rate of senescence [START_REF] Ricklefs | Life-history connections to rates of aging in terrestrial vertebrates[END_REF], age at the onset of senescence [START_REF] Péron | Age at the onset of senescence in birds and mammals is predicted by early-life performance[END_REF] and a linear rate of senescence between well-defined cut-off ages [START_REF] Jones | Senescence rates are determined by ranking on the fast-slow life-history continuum[END_REF]Lemaître and Gaillard, 2013a), for each sex. We investigated whether increasing allocation by males in sexual selection leads to increasing mortality and actuarial senescence in males on both an absolute scale and relative to females. More specifically, we tested the following predictions. First, we expected that (i) males of polygynous species and/or with large SSD and HSD should have earlier and stronger actuarial senescence and should live shorter than males of monogamous species and/or with a weak SSD and HSD.

Within polygynous and promiscuous species, we expected males of territorial species to have earlier and stronger actuarial senescence than males of non-territorial species. (ii) between-sex differences in actuarial senescence and longevity should be greater in species in which males face strong sexual selection.

Materials and methods

METRICS OF SURVIVAL AND ACTUARIAL SENESCENCE

We first estimated survival at each age for each sex of each species of large herbivores present in the International Species Information System (ISIS) [START_REF] Müller | Mating system, feeding type and ex situ conservation effort determine life expectancy in captive ruminants[END_REF]Lemaître et al., 2013), which combines individual data for a given species since 1973, from 850 member institutions (Zoos, national parks) in over 80 countries. Thus, for each individual, information such as sex and dates of birth and death are available. Considering each species of captive population of each zoo as a "meta-population", it is possible to compute the parameters that characterize the average population living in captivity. In sum, this "metapopulation" describes the average performance for each species in captivity and not a specific population. We only used cohorts of animals for which both dates of birth and death were known, which implies that animals were born in captivity. The complete dataset included data for 95 species of large herbivores. However, we kept only species for which 25 individuals at least were alive at 1 year-old (N = 64 species) to get accurate estimates of age-specific survival.

To measure species-and sex-specific pattern of survival and senescence, we used four different and complementary metrics: the maximum longevity, the Gompertz rate of senescence, the onset of senescence, and the linear rate of senescence between two well-defined cut-off ages (between 6 and 12 years of age) (See Table S1). Such a diversity of metrics is important for two reasons. First it allows comparing our finding with previous results published in the literature and second it helps to disentangle possible confounding effects between the rate (i.e. intensity) and the onset (i.e. timing) of senescence.

We extracted the maximum longevity recorded for each sex from species-specific life tables. This trait (called 'longevity' hereafter) is not a direct measure of senescence because it does not include any information about age-dependent survival. However, longevity allows measuring the overall difference in survival between the sexes and can therefore be linked with the energy allocation in maintenance during the lifespan.

The second metric we used was the Gompertz rate of senescence (after called 'Gompertz rate') that measures the sexand species-specific average exponential rate of mortality with age. To measure actuarial senescence, many mathematical functions have been proposed [START_REF] Ricklefs | Biological implications of the Weibull and Gompertz models of aging[END_REF]. The Gompertz function is the most commonly used to study human survival, survival in laboratory or in captivity [START_REF] Ricklefs | Biological implications of the Weibull and Gompertz models of aging[END_REF] and has been shown to provide a reliable description of age-specific survival in large herbivores [START_REF] Gaillard | Assessing senescence patterns in populations of large mammals[END_REF]. We first built the capture history of each individual of each sex and each of the 64 species of large herbivores included in our dataset (coded as "1" when the individual was observed alive and as "0" when it was dead at a given age from birth). These capture histories were then loaded into a CMR (capture-mark-recapture) software (E-SURGE, [START_REF] Choquet | Program E-SURGE: a software application for fitting multievent models[END_REF] to get reliable age-specific estimates [START_REF] Lebreton | Modeling Survival and Testing Biological Hypotheses Using Marked Animals -a Unified Approach with Case-Studies[END_REF], knowing that detection probabilities were equal to 1 in captivity. We then fitted different agedependent models including a model with a constant survival (i.e. no age-dependence), a model with two age-classes (first year of life vs. older ages) to account for the lower survival in juveniles than in older individuals that characterizes large herbivores [START_REF] Gaillard | Temporal variation in fitness components and population dynamics of large herbivores[END_REF], a model with full age-dependence (i.e. one survival rate per age), and a series of Gompertz models with onsets of senescence varying between 1 year of age and the age corresponding to the first two thirds of lifespan for each sex in each species. In Gompertz models, age variation in survival was constrained from the age at the onset of senescence to be linear on a log(-log) scale [START_REF] Gaillard | Assessing senescence patterns in populations of large mammals[END_REF]. We selected a final model using AIC (Akaïke's Information Criterion, [START_REF] Burnham | Model selection and multimodel inference: a practical information-theoretic approach[END_REF]. Then, we retained the estimate of the Gompertz rate in species for which a Gompertz model provided a better fit than either the constant or the two-age class model (N = 54 species, see Table S2).

The Gompertz rates we estimated were thus dependent on the onset of senescence. An early onset of senescence is indeed associated with a low actuarial senescence rate (0.27 ± 0.13; t = 2.07; P = 0.04; λ < 0.001). In other words, delaying the onset of senescence should lead to increase the rate of senescence. To correct for that constraint, we estimated the onset of senescence and the rate of senescence independently from the Gompertz models. The age at the onset of senescence was then estimated as the age at which the mortality rate was the lowest [START_REF] Jones | Senescence rates are determined by ranking on the fast-slow life-history continuum[END_REF]. However, to avoid extreme mortality values caused by a small number of old individuals, we restricted the range of ages to the first two thirds of the maximum lifespan recorded for a given sex in a given species (See Figure S1 for an example).

We also measured the rate of senescence as the proportional decrease of survival between 6 and 12 years of age. We fixed these age thresholds because most of large herbivores species initiated their senescence between these age threshold as demonstrated on detailed analyzes of age-specific variation in intensively monitored populations of large herbivores (e.g. [START_REF] Loison | Age-specific survival in five populations of ungulates: evidence of senescence[END_REF][START_REF] Festa-Bianchet | Variable age structure and apparent density dependence in survival of adult ungulates[END_REF][START_REF] Gaillard | Assessing senescence patterns in populations of large mammals[END_REF][START_REF] Toïgo | Sex-and age-specific survival of the highly dimorphic Alpine ibex: evidence for a conservative life-history tactic[END_REF], which allowed us to make estimates directly comparable among species. Saïga (Saiga tartarica), Speke's gazelle (Gazella spekii), Goitered gazelle (Gazella subgutturosa) and Greater kudu (Tragelaphus strepsiceros) were excluded for this analysis because all males died before 12 years of age in these species. Therefore, the rate of senescence of males between 6 and 12 years of age was analyzed using data from 60 species. In addition, the longest-lived female of Water deer (Hydropotes inermis) died before reaching 12 years of age. We therefore restricted the analysis of between-sex differences in senescence rate of females between 6 and 12 years of age to 59 species.

Dependent variables (longevity, Gompertz rate, onset of senescence and rate of senescence between 6 and 12 years of age) were all log-transformed to account for the allometric nature of the relationships (see [START_REF] Houle | Measurement and meaning in biology[END_REF]. For longevity and onset of senescence, the between-sex difference was calculated as the female value minus the male value. For both rates of senescence, the between-sex difference was calculated as the male rate minus the female rate. All data are provided in Table S1.

METRICS OF SEXUAL SELECTION

To assess thoroughly the strength of sexual selection in a given species, we used 4 metrics: sexual size dimorphism (SSD), mating system, mating tactic (only for polygynous and promiscuous species) and horn size dimorphism (HSD) for bovids only (See Table S2).

The first and most commonly used metric in such analyzes is the SSD (Toïgo and Gaillard, 2003; Table 1), which allows taking into account the difference in mass between sexes, assuming that males will invest more strongly than females to increase their probability of success in male-male competition [START_REF] Bateman | Intra-sexual selection in drosophila[END_REF][START_REF] Bonduriansky | Sexual selection, sexual conflict and the evolution of ageing and life span[END_REF]. Sex-specific body masses (in kg) were collected for both sexes in the same free-ranging populations as used by Bro-Jørgensen (2012) for bovids and [START_REF] Plard | Revisiting the allometry of antlers among deer species: male-male sexual competition as a driver[END_REF] for cervids, and completed using more specific sources when required. As several measures of SSD have been previously used (see Table 1), we replicated the analyzes using other SSD measures but results remained qualitatively unchanged. We thus report results from analyzes including both male and female body masses as covariates in the model (both log-transformed) as advocated by [START_REF] Freckleton | The seven deadly sins of comparative analysis[END_REF] to avoid potentially biased estimates in phylogenetically controlled analyzes.

Moreover, doing that allows to correct analyzes for among-species variation in the pace of life, because of the strong allometric relationship linking body mass and generation time, a reliable metric for assessing the pace of life [START_REF] Gaillard | Generation Time: A Reliable Metric to Measure Life-History Variation among Mammalian Populations[END_REF].

We assessed the mating system (monogamous, promiscuous or polygynous) of each species according to [START_REF] Zerbe | Reproductive seasonality in captive wild ruminants: implications for biogeographical adaptation, photoperiodic control, and life history[END_REF]'s classification. This metric was entered as a discrete factor in models, using monogamous species as a reference. In the same way, we identified the mating tactic (territoriality, harem or tending) of each species thanks to further studies (listed in Table S3) using the tending tactic as a reference.

Finally, we used the horn size dimorphism (HSD) of bovids as a last metric of the strength of sexual selection. Female bovids bear horns like males, but generally of smaller size [START_REF] Bro-Jørgensen | The intensity of sexual selection predicts weapon size in male bovids[END_REF]. We thus assumed that increasing sexual selection leads to increase horn size in males relatively to females. For this study, we included only 37 species

ACCOUNTING FOR POSSIBLE CONFOUNDING VARIABLES

We controlled for possible confounding factors previously reported to influence survival and actuarial senescence in captive large herbivores. First, body mass was included to control for the strong allometric relationship linking body mass and adult survival or actuarial senescence rates in large herbivores [START_REF] Sacher | Causes of sex-biased adult survival in ungulates: sexual size dimorphism, mating tactic or environment harshness?[END_REF][START_REF] Gaillard | Ecological correlates of life span in populations of large herbivorous mammals[END_REF]Lemaître and Gaillard, 2013b). Moreover, accounting for body mass allows taking into account most variation generated by the pace of life because of the strong allometric component of the pace of life in large herbivores (Gaillard et al., 2008). The average species-specific body mass for each sex was recovered from the literature (see above and Table S3).

Additionally, in captivity, food supplied to large herbivores is sometimes not optimal [START_REF] Müller | Mating system, feeding type and ex situ conservation effort determine life expectancy in captive ruminants[END_REF] and grazers (whose diet consists mainly of grass) generally meet their food needs in captivity better than browsers (whose diet consists mainly of leaves or twigs).

This leads grazers to have a longer longevity and a reduced actuarial senescence in captivity compared to their wild counterparts while the opposite is observed for browsers [START_REF] Müller | Mating system, feeding type and ex situ conservation effort determine life expectancy in captive ruminants[END_REF]Lemaître et al., 2013). Therefore, diet is a structuring factor for survival and senescence in large herbivores and in the present study, the effect of diet on different metrics for males has been included to avoid confounding effects of a poorly adapted diet for browsers in captivity. To do that, the percentage of grass in a species' natural diet has been obtained from [START_REF] Zerbe | Reproductive seasonality in captive wild ruminants: implications for biogeographical adaptation, photoperiodic control, and life history[END_REF]'s supplement data (see Table S3) and included as a covariate in our models.

STATISTICAL ANALYZES

To avoid biased assessment of the relationships between sexual selection and survival patterns due to phylogenetic inertia, we controlled all the analyzes for the non-independence between species due to sharing ancestry using "Phylogenetic Generalized Least-Squares" (PGLS) models [START_REF] Freckleton | Phylogenetic Analysis and Comparative Data: A Test and Review of Evidence[END_REF]. The PGLS method provides an estimate of the phylogenetic signal λ, which allows controlling for the phylogenetic dependence (between 0 and 1). If λ is not statistically different from 0, phylogeny does not have a detectable effect and a linear regression without correcting for phylogeny can be safely used. A phylogeny of the 64 species of large herbivores used in this study (see Figure 1) was built using the phylogenetic super-tree of mammals published by [START_REF] Bininda-Emonds | The delayed rise of present-day mammals[END_REF], 2008). This near completed, at the species-level, super-tree is building using a 66-gene alignment in conjunction with 30 cladistically robust fossil calibration points. Branch lengths are proportional to time. To confirm our results, analyzes were repeated with Agnarsson and May-Collado (2008)'s near species-completed phylogeny of 266 species of Cetartiodactyla based on cytochrome b sequence data and using recent [START_REF] Figuet | Mitochondrial DNA as a tool for reconstructing past life-history traits in mammals[END_REF]'s phylogenetic tree reconstructed from complete mitochondrial genome of 201 species of Cetartiodactyla. In all cases, results were qualitatively similar and we only present in this paper those obtained with the phylogenetic super-tree of mammalian species [START_REF] Bininda-Emonds | The delayed rise of present-day mammals[END_REF], 2008).

Phylogenetically controlled analyzes were performed using the package caper [START_REF] Orme | The caper package: comparative analysis of phylogenetics and evolution in R[END_REF].

In our analyzes dependent variables included longevity, Gompertz rate, onset of senescence and rate of senescence between 6 and 12 years of age. For each dependent variable entered in the full model we included the percentage of grass in a species' natural diet (only when studying male traits but not between-sex differences because diet is the same for both sexes, which leads their survival patterns to be biased in the same way), the SSD (by entering both male and female body mass, as repeatedly advocated, [START_REF] Freckleton | On the misuse of residuals in ecology: regression of residuals vs. multiple regression[END_REF][START_REF] Freckleton | The seven deadly sins of comparative analysis[END_REF], mating system, and the two-way interaction between mating system and male body mass. We added this interaction because the largest herbivores tend to be polygynous and the lightest ones tend to be monogamous, leading a cost of SSD to be possibly detected only in polygynous species. Moreover, as the estimate of the Gompertz rate depends on the age at the onset of senescence used to model senescence, the onset of senescence included in the selected model was entered as a covariate in the analysis of Gompertz rates.

To analyze the effects of mating tactics on survival patterns, the full model included diet, SSD, mating tactic and the two-way interaction between mating tactic and male body mass (because males of harem and territorial species tend to be larger than males of tending species and senescence could potentially be only detected in harem and territorial species) (See Table S3).

When we analyzed bovids separately, the full model included diet, SSD, HSD (with male and female horn size added as covariate) and the two-way interaction between body mass and horn size (See Table S3). Dependent variables were the same as for the analysis of the entire dataset.

Finally, to select the best model of variation in longevity or senescence metrics, we used a model selection procedure based on the Akaïke Information Criterion (AIC, [START_REF] Burnham | Model selection and multimodel inference: a practical information-theoretic approach[END_REF]. For each senescence or survival metric, we retained the model with the lowest AIC. When the difference of AIC between competing models was less than 2, we retained the simplest model to satisfy parsimony rules [START_REF] Burnham | Model selection and multimodel inference: a practical information-theoretic approach[END_REF]. In addition, the AIC weight (w i ) was calculated to measure the relative likelihood of each model to be the best among the set of fitted models. When lambda was not statistically different from 0 we performed linear models without correcting for phylogeny to avoid any possible bias induced by the inclusion of a non-required phylogenetic index and provided estimates from linear models without correcting for phylogeny [START_REF] Revell | Phylogenetic signal and linear regression on species data[END_REF]. However, results were qualitatively the same with and without this correction. When lambda was statistically different from 0, parameter estimates were obtained using the PGLS method (Tables 2 and3).

Analyzes were performed with R version 2.14.0 (R Development Core Team, 2011) and parameter estimates are given ± SE.

Results

INFLUENCE OF SEXUAL SELECTION ON MALE LONGEVITY AND SENESCENCE PATTERNS

Following parsimony rules, the model selected for male longevity included diet and male body mass (w i = 0.22; λ < 0.001; Table S4). Longevity of males increased with the percentage of grass in the species natural diet (Table 2, Figure S2A) and longevity increased with body mass in males (Table 2, Figure S3A). Although the model with the lowest AIC included an additional effect of mating system (w i = 0.30; λ < 0.001; Table S4), the effect size was very small, with males tending to live slightly longer in monogamous and in promiscuous than in polygynous species (difference of longevity compared to monogamous species: -1.02 year for promiscuous species, 95%CI [-1.27 -1.22]; -1.15 year for polygynous species, 95%CI [-1.43 -1.09]).

The selected model for Gompertz rates included the onset of senescence (w i = 0.27; λ < 0.001; Table S4). As expected, males with earlier onset of senescence had lower Gompertz rates (Table 2). The models including additive effects of diet (ΔAIC = 0.56; w i = 0.21; λ < 0.001; Table S4), or of body mass (ΔAIC = 1.00; w i = 0.16; λ < 0.001; Table S4) also provided good fits, although the effect sizes were very small in both cases. As expected, male senescence rate tended to decrease with both the proportion of grass in species natural diet (β = -0.0030 ± 0.0026; Figure S2B) and with males body mass (β = -0.057 ± 0.058; Figure S3B).

The selected model of onset of senescence in males (w i = 0.51; λ < 0.001; Table S4) included diet, body mass and mating system. Males of grazer species senesced later than males of browser species (Table 2, Figure S2C), males of large species senesced later than males of small species (Table 2, Figure S3C), and compared to males of monogamous species, males of promiscuous and polygynous species tended to senesce 1.3 (95%CI [-1.8 -1.1]) and 1.6 (95%CI [-2.3 --1.1]) years earlier (Table 2, Figure 2). Lastly, the model selected for the rate of senescence between 6 and 12 years of age only included body mass (w i = 0.28; λ < 0.001; Table S4). Males of small species senesced faster than males of large species (Table 2, Figure S2D). Just as for male longevity, the model with the lowest AIC included additional effects of diet (w i = 0.30; λ < 0.001; Table S4), and as predicted males of grazers senesced at a slower rate than males of browsers (β = -0.002 ± 0.001; Figure S3D).

Analyzes restricted to polygynous and promiscuous species failed to detect any effect of mating tactic on either longevity or Gompertz rate (Table S5). However, the onset of senescence was influenced by the mating tactic with males of territorial species doing harem or tending senescing 1.4 (95%CI [-1.9 --1.1]) years earlier than males of species doing harem or tending (Tables S5 andS6, Figure 3). In addition, the model selected for the senescence rate between 6 and 12 years of age included diet, body mass, mating tactic and the two-way interaction between mating tactic and male body mass (w i = 0.88; λ = 0.984; Table S5).

Territorial males senesced slower between 6 and 12 years of age than males of species with harem or tending mating tactics (difference of rate compared to males of tending species: -0.49 ± 1.20 for species with harem; -2.61 ± 0.84 for territorial species; Table S6).

Complementary analyzes performed in bovids have revealed no effect of horn size dimorphism on longevity, onset of senescence, Gompertz rate and senescence rate between 6 and 12 years of age (Appendix S1, Tables S7 andS8).

INFLUENCE OF SEXUAL SELECTION ON THE SEX-DIFFERENCES IN LONGEVITY AND SENESCENCE PATTERNS

Males had, on average, a shorter longevity than females (male: 16.3 years, 95%CI and their rate of senescence between 6 and 12 years of age was stronger than that of females (male: 0.69, 95%CI [0.65 -0.74]; female: 0.56, 95%CI [0.09 -0.17]) with a difference of 0.13 (95%CI [2.1 -3.5]). However, both sexes had similar Gompertz rates (male: 0.17, 95%CI [0.14 -0.19]; female: 0.16, 95%CI [0.14 -0.18]) with a non-significant difference of 0.008 (95%CI [-0.013 -0.028]).

When testing the effect of sexual selection on the sex-differences in longevity and senescence patterns, the model selected for longevity (w i = 0.24; λ < 0.001; Table S9),

Gompertz rate (w i = 0.53; λ < 0.001; Table S9) and senescence rate between 6 and 12 years of age (w i = 0.45; λ < 0.001; Table S9) was the constant model.

The model selected for the onset of senescence only included the mating system (w i = 0.45; λ < 0.001; Table S9) and the between-sex difference in the onset of senescence was greater in polygynous than in monogamous species (monogamy: -1.0 year, 95%CI [-1.4 -1.3]; promiscuity: 1.1 years, 95%CI [-1.2 -1.6]; polygyny: 1.4 years, 95%CI [1.0 -2.0];

Table 3).

When focusing on the effects of mating tactic, in polygynous and promiscuous species only the selected model was the constant model, for longevity (w i = 0.47; λ < 0.001; Table S10), Gompertz rate of senescence (w i = 0.44; λ < 0.001; Table S10) and onset of senescence (w i = 0.28; λ < 0.001; Table S10). However, the model with the lowest AIC for between-sex difference in the onset of senescence included mating tactic (w i = 0.37; λ < 0.001; Table S10) with territorial species that tended to display a larger between-sex difference in the onset of senescence than harem-holding species and tending species (tending: 1.0 year, 95%CI [-1.3 -1.3]; harem: 1.3 years, 95%CI [-1.2 -1.9]; territorial: 1.4 years, 95%CI [1.0 -1.9]; Table S11). The model selected for between-sex differences in senescence rate between 6 and 12 years of age included mating tactic, body mass and the two-way interaction between male body mass and mating tactic (w i = 1.00; λ < 0.001; Table S10). The sex-bias in rate of senescence between 6 and 12 years of age increased with male body mass in territorial species, whereas it decreased with body mass in harem species and even more in tending species (Table S11).

In bovids, the sex-bias in longevity and senescence patterns was not affected by the HSD because no selected model included both male and female horn size (Tables S12 andS13).

Discussion

Our results suggest that in large herbivores the intensity of sexual competition affects male senescence patterns and consequently the sex-differences in senescence patterns. This finding principally relies on an effect of mating on the age of onset of senescence with males from polygynous species starting to senesce earlier (but not faster) than males from promiscuous and monogamous species, both on an absolute scale and relative to females.

Findings presented here, thus support the hypothesis that, at least in the range of species considered in our study, high level of sexual competition leads to severe survival costs. However, the impact was limited to the age at the onset of senescence (i.e. the timing of senescence) and did not affect the rate of senescence (i.e. the intensity of senescence). The absence of any relationship between our proxies of sexual competition level and actuarial senescence rates is in line with a previous report from Lemaître and Gaillard (2013a) based on a much smaller dataset of wild herbivores. In fact, the only reported effect of sexual selection on senescence rates comes from the finding that in birds and mammals, the sex-differences in senescence rates is bigger in polygynous than in monogamous species (Clutton-Brock and Isvaran, 2007, Table 1). Therefore, until now no inter-specific study have clearly demonstrated that high level of sexual competition fasten actuarial senescence rates in males.

Males of polygynous herbivores start to senesce earlier than males of promiscuous and monogamous species. For instance, if we compare two similar size ungulate species, we observe than monogamous Bay duiker males (Cephalophus dorsalis) begin to senesce at 7 years of age whereas polygynous Speke's gazelle males (Gazella spekei) start at 3 year-olds, which shows that costs associated with polygynous mating system lead to an earlier onset of senescence. Survival costs linked to species-specific mating systems have already been reported in a few studies (Table 1). For example, [START_REF] Promislow | Costs of Sexual Selection in Natural Populations of Mammals[END_REF] found that the mean adult mortality rate is female-biased in monogamous mammals and [START_REF] Müller | Mating system, feeding type and ex situ conservation effort determine life expectancy in captive ruminants[END_REF] found a longer relative life expectancy in males of monogamous species compared to males of polygynous species. However, to our knowledge, we provide a first evidence of a cost in terms of earlier senescence. This notably comes from the fact that comparative studies interested in such questions have focused mostly on longevity and in a few instances on actuarial senescence rates, without considering a possible impact in terms of timing of senescence (Table 1). Recently, a wide diversity of senescence patterns have been described

in the living world [START_REF] Nussey | Senescence in natural populations of animals: widespread evidence and its implications for biogerontology[END_REF][START_REF] Jones | Diversity of ageing across the tree of life[END_REF] and the age at the onset of senescence is now increasingly recognized as a key factor characterizing senescence patterns [START_REF] Jones | Senescence rates are determined by ranking on the fast-slow life-history continuum[END_REF][START_REF] Péron | Age at the onset of senescence in birds and mammals is predicted by early-life performance[END_REF][START_REF] Jones | Diversity of ageing across the tree of life[END_REF][START_REF] Gamelon | Do age-specific survival patterns of wild boar fit current evolutionary theories of senescence? Evolution n/an/a[END_REF]. While it has already been shown that the onset of senescence co-varies with other time variable to shape the pace of life in mammals and birds [START_REF] Jones | Senescence rates are determined by ranking on the fast-slow life-history continuum[END_REF], our study demonstrates that other factors such as the intensity of sexual selection contributes to the diversity of age at the onset of senescence observed in the wild.

In addition, we found that among polygynous and promiscuous species, males from territorial species also senesce earlier than males from species with harem-holding or tending mating tactic. Again, if we look at two similar size species, we can see that males Bighorn sheep males (Ovis canadensis) which display a tending mating tactic, start to senesce at 9 years of age, whereas territorial Lechwe males (Kobus leche) senesce from 3 years of age.

Inter-specific differences in senescence patterns between territorial and non-territorial males are likely to arise from the important energetically and physical demands to maintain territories. For instance, in impala (Aepyceros melampus), [START_REF] Van Rooyen | Variation in body condition of impala and nyala in relation to social status and reproduction[END_REF] found that territorial males lose more body condition during the rut than males not involved in the defense of a territory, a decline that seems to result from a decrease in food intake in parallel to an increased activity both linked to territorial tenure [START_REF] Murray | The rut of impala -Aspects of seasonal mating under tropical conditions[END_REF]. Moreover, such costs might be associated with an increased probability to face aggressive contests and injuries, as observed in males of territorial pudu (Pudu puda),where fights between males during rutting season includes cause injuries [START_REF] Macnamara | Behavior and reproduction in captive pudu (Pudu puda) and red brocket (Mazama americana), a descriptive and comparative analysis[END_REF] but also in other vertebrates such as lizards species, where male-male interactions in territorial species are more likely to lead to fighting than in non-territorial species [START_REF] Vanhooydonck | Does dewlap size predict male bite performance in Jamaican Anolis lizards?[END_REF][START_REF] Lailvaux | The Evolution of Performance-Based Male Fighting Ability in Caribbean Anolis Lizards[END_REF].

Although we provided clear evidence that both mating systems and tactics influence the age at the onset of senescence, we did not find any relationship between sexual size dimorphism and any of our metrics of survival and senescence. This is surprising because the large amount of energy required for male growth compared to female growth in highly sexually dimorphic species is expected to cause survival or ageing costs (e.g. Toïgo and Gaillard, 2003;[START_REF] Bro-Jørgensen | Longevity in bovids is promoted by sociality, but reduced by sexual selection[END_REF]. Interestingly, our review of the literature reveals that the relationship between sexual size dimorphism and senescence per se has almost never been investigated (but see Lemaître and Gaillard 2013a). Most studies so far have documented survival costs of sexual size dimorphism through a negative relationship between SSD and adult mortality [START_REF] Promislow | Costs of Sexual Selection in Natural Populations of Mammals[END_REF][START_REF] Promislow | Mortality costs of sexual dimorphism in birds[END_REF]but see Owens and[START_REF] Orme | The caper package: comparative analysis of phylogenetics and evolution in R[END_REF][START_REF] Toïgo | Sex-and age-specific survival of the highly dimorphic Alpine ibex: evidence for a conservative life-history tactic[END_REF]Gaillard, 2003) or longevity (Bro-Jørgensen 2012). However, these studies have used different metrics to assess the level of sexual size dimorphism. For instance, [START_REF] Promislow | Costs of Sexual Selection in Natural Populations of Mammals[END_REF] measured SSD as the ratio of male body mass on female body mass while Bro-Jørgensen (2012) used the Lovich and Gibbons (1992)'s ratio. In the present study, we measured SSD by adding both male and female body mass as covariates in our analyzes, a method that has been repeatedly advocated in comparative studies to avoid bias resulting when predictors are collinear in multi-factorial analyzes [START_REF] Freckleton | Phylogenetic Analysis and Comparative Data: A Test and Review of Evidence[END_REF][START_REF] Freckleton | The seven deadly sins of comparative analysis[END_REF]. However, when we replicated our analyzes using other metrics previously used of sexual size dimorphism, all results remained qualitatively unchanged (Tables S14 andS15).

Therefore, the observed discrepancy between the results we report here and previously published studies are likely to involve either the absence of corrections for confounding factors (e.g. pace of life, see below) or the type and quality of data analyzed. For instance, for a trait like longevity the use of different sources can rapidly lead to divergent outcomes (see [START_REF] Lemaître | A test of the metabolic theory of ecology with two longevity data sets reveals no common cause of scaling in biological times: Longevity scaling in mammals[END_REF] for an example). In the present study, we have used data from the ISIS database rather than published records of longevity. ISIS data allows working on long-term population with the huge advantage of not having to consider capture probabilities as individuals living in captivity are known without doubt to be alive or dead in a given year.

This allows getting highly reliable estimates of survival and senescence. Furthermore, life in captivity eliminates environmentally-driven sources of mortality (e.g. predation or climate harshness) and allows to better assessing how costs associated with sexual selection and thus intrinsic changes in animals conditions influence survival.

Since sexual selection acts primarily on males, female longevity and senescence patterns are not expected to be directly influenced by the intensity of sexual competition.

Therefore, if sexual selection generates sex-differences in survival and senescence patterns, the effect should be mainly driven by changes in males. Our results are in full agreement with this assumption. Indeed, similarly to our analyzes strictly focused in males, we found an effect of mating system on the sex-differences in the age at the onset of senescence, with larger differences in onset of senescence in polygynous species than in promiscuous and monogamous species. Although not focused on the onset of senescence, results from comparative analyzes that have investigated this topic have revealed a link between the mating system and the sex-differences in longevity or senescence patterns. These studies looked at the between-sex differences of longevity in mammals [START_REF] Clutton-Brock | Sex differences in ageing in natural populations of vertebrates[END_REF][START_REF] Müller | Mating system, feeding type and ex situ conservation effort determine life expectancy in captive ruminants[END_REF] and birds [START_REF] Clutton-Brock | Sex differences in ageing in natural populations of vertebrates[END_REF], or the between-sex differences in adult mortality in birds [START_REF] Liker | Mortality costs of sexual selection and parental care in natural populations of birds[END_REF], have systematically been found to be higher in polygynous than in monogamous species. In the present study, we reported strong between-sex differences as strong as 1.5 years in the onset of senescence in polygynous species, whereas this difference was less than 1 year in monogamous species.

These results show that the sex-bias in senescence patterns is driven by costs linked to mating.

In addition, our study documents a strong allometric relationship between body mass and senescence metrics. In large herbivores, body mass is tightly linked to the pace of life [START_REF] Gaillard | Ecological correlates of life span in populations of large herbivorous mammals[END_REF], with small species having a relatively fast pace of life, characterized by an early and fast senescence compared to larger species. In addition, and in accordance with previous reports [START_REF] Müller | Mating system, feeding type and ex situ conservation effort determine life expectancy in captive ruminants[END_REF]Lemaître et al., 2013), we found that diet is associated with senescence in captive ungulates. Typically, grazer species (e.g. African buffalo, Syncerus caffer) have a delayed onset of senescence, a slower rate of senescence, and live longer compared to browser species such as roe deer (Capreolus capreolus). Overall, these analyzes emphasize the importance of correcting for the pace of life and for diet when working on captive populations to avoid biased results. However, most studies listed in Table 1 failed to control for such confounding variables, which might explain part of the discrepancy observed between these studies. Using the three different methods employed to assess SSD in the context of sexual selection (see Table 1), we thus tested in complementary analyses how an absence of correction for body mass, diet or both variables influences our results.

Interestingly, when body mass or both body mass and diet are removed from the models, mating system was found in best models for longevity, onset of senescence and rate of senescence between two ages, and SSD was associated with onset of senescence in large herbivore in the expected direction (i.e. a positive association between the intensity of sexual selection and the intensity of senescence, see Tables S16 andS17). This demonstrates the importance to control analyzes for confounding effects of diet and pace of life to assess reliably the relationship between sexual selection and senescence pattern at inter-specific level and stress the importance to consider sparingly results from analyzes non-corrected for these factors.

Finally, our analyzes and review of the literature show that the relationship between the intensity of sexual selection and the strength of male senescence is complex. Then, the lack of SSD effect along with the clear evidence of an effect of the mating system on the onset of senescence in males indicate that the costs associated with sexual selection via intrasexual competition are generated by risky and energy-demanding behaviours rather than by large size in itself. These results demonstrate that only some traits like the timing of actuarial senescence and/or behaviors like territoriality involved in sexual selection are costly in terms of survival.

Table 3: Parameter estimates from the models selected to account for between-sex variation observed among species of large herbivores living in captivity in longevity and metrics of actuarial senescence. In all analyzes performed, phylogeny had no statistically significant effects (λ < 0.001) and parameters were thus estimated from linear models (LM) without any phylogenetic correction. Effect size statistically different from 0 occur in bold.

  because we were not able to find horn size data for at least one sex in 11 species (American bison Bison bison, European bison Bison bonasus, Gaur Bos gaurus, Wild goat Capra aegargus, Blesbok Damaliscus pygargus, Mountain gazelle Gazella gazella, Dama gazelle Nanger dama, Scimitar oryx Oryx dammah, Bighorn sheep Ovis Canadensis, Mouflon Ovis orientalis and Common eland Tragelaphus oryx). Data of horn size were also obtained from free-ranging populations.

[

  15.5 -17.2]; female: 19.1 years, 95%.0]), with a difference of 2.81 years (95% CI [2.1 -3.5]). Male senesced earlier than females (male: 8.7 years, 95% CI [78.0 -9.5]; female: 10.3 years,), with a difference of 1.6 years (95%CI [0.9 -2.3]),

Figure 1 :

 1 FIGURE LEGENDS:

Figure 2 :

 2 Figure 2: Relationships between the age at the onset of actuarial senescence in males

Figure 3 :

 3 Figure 3: Relationships between the age at the onset of actuarial senescence in males

  Figure 2

ACKNOWLEDGMENTS

M. Tidière is funded by the French Ministry of Education and Research. We are grateful to I.

Agnarsson from providing us access to an unpublished phylogenetic tree of ruminants.

Table 1: Survey of comparative analyzes investigating the relationship between the strength of sexual selection and mortality or actuarial senescence metrics across species. Gray-shaded results correspond to studies reporting support for a statistically significant association between the strength of sexual selection and the level of mortality or actuarial senescence. Results in bold correspond to studies based on metrics of actuarial senescence per se. To identify these comparatives studies, we entered the keywords 'senescence', 'ag(e)ing', and 'sexual selection' into the search engine database of Web of Science (SCI-Expanded).


A A Karamanlidis

M De Gabriel

L Krambokoukis

& O Gimenez

Evidence of a large carnivore population recovery: Counting bears in Greece

Keywords: brown bear, Greece, Jolly-Seber capture-mark-recapture modelling framework, nonivasive genetic sampling, population monitoring, power pole hair traps, Ursus arctos

Introduction

The brown bear (Ursus arctos) is a charismatic key species of the biodiversity of the Holarctic region (Servheen, Herrero & Peyton, 1999), capable of attracting attention and resources to conservation efforts through its function as an iconic flagship species [START_REF] Simberloff | Biodiversity And Bears -A Conservation Paradigm Shift[END_REF]. Once, extirpated and threatened by extinction throughout large parts of Europe [START_REF] Zedrosser | Status and management of the brown bear in Europe[END_REF], bears have made a remarkable recovery, with populations increasing and expanding in several countries [START_REF] Deinet | Wildlife comeback in Europe: The recovery of selected mammal and bird species[END_REF]. The benefits for nature conservation notwithstanding, such recoveries create the potential of increased wildlife mortality and humanwildlife conflicts [START_REF] Gardner | Estimating black bear density using DNA data from hair snares[END_REF]. In this context, informed, science-based conservation and management decisions are urgently required to safeguard the recovery and survival of small brown bear populations, which has been identified as an important European conservation priority (Habitat Directive 92/43/CEE). Since conservation priorities often arise in relation to bear numbers, reliable population assessments are fundamental in conservation and management planning [START_REF] Mowat | Estimating population size of grizzly bears using hair capture, DNA profiling, and mark-recapture analysis[END_REF][START_REF] Lorenzini | Non-invasive genotyping of the endangered Apennine brown bear: a case study not to let one's hair down[END_REF], Kendall et al., 2008).

Brown bears in Greece reach their southern-most distribution in Europe and are therefore an important aspect of European biodiversity. They belong to the Dinaric-Pindos (DP) bear population, which has been identified as one of the largest and most important on the European continent [START_REF] Zedrosser | Status and management of the brown bear in Europe[END_REF]; the DP bear population appears to be stable, numbering more than 3,000 individuals [START_REF] Deinet | Wildlife comeback in Europe: The recovery of selected mammal and bird species[END_REF][START_REF] Kaczensky | Status, management and distribution of large carnivores -bear, lynx, wolf & wolverine -in Europe -Part 1[END_REF]. In Greece, brown bears are considered to be endangered [START_REF] Mertzanis | Ursus arctos (Linnaeus, 1758)[END_REF].

In recent years the increase of extra-limital appearances of bears throughout the country [START_REF] Karamanlidis | Challenges and problems arising from the range expansion of brown bears in Greece[END_REF], and the increase in negative humanbear interactions (i.e., mainly damages by bears to human property) [START_REF] Karamanlidis | Structural and economic aspects of human-bear conflicts in Greece[END_REF] and bearvehicle collisions [START_REF] Karamanlidis | Bear -vehicle collisions: a holistic approach to evaluating and mitigating the effects of this new conservation threat to brown bears in Greece[END_REF] suggest that the brown bear population in Greece might be recovering. The arising conservation issues of this population expansion and growth make the development and implementation of an effective Action Plan for the species an immediate conservation priority [START_REF] Mertzanis | Ursus arctos (Linnaeus, 1758)[END_REF].

Despite public interest and costly management programs, no rigorous and reliable estimates of abundance and density of brown bears in Greece exist.

Because of their crepuscular and nocturnal activity patterns, large home range size, shyness, low density and the rugged and forested terrain they inhabit, counting brown bears by traditional field methods (e.g., direct observations, extrapolations of counts of mother with cubs of the year) is expensive or sometimes even unfeasible [START_REF] Mace | Estimating grizzly bear population size using camera sightings[END_REF]. Traditional field methods are viewed as less rigorous than more efficient current capture-mark-recapture (CMR) and DNA-based methods [START_REF] Garshelis | On the allure of noninvasive genetic sampling -putting a face to the name[END_REF]. The size given therefore for the brown bear population in Greece [i.e., 190 -260 individuals (Mertzanis et al., 2009)], which is presumably a combination of expert knowledge and observation counts, should be considered an educated guess that might not reflect actual population processes [START_REF] Nichols | Monitoring for conservation[END_REF]. The current lack of reliable data for the brown bear population in Greece sharply contradicts with the need to develop a sound, effective and outreaching management and conservation strategy in the country [START_REF] Ciucci | The Apennine brown bear: A critical review of its status and conservation problems[END_REF].

Genetic methods play a pivotal role in bear conservation and cannot be neglected when developing effective conservation strategies and planning the long-term survival of threatened bear populations [START_REF] Swenson | Genetics and conservation of European brown bears Ursus arctos[END_REF]. One of the most attractive applications of molecular methods, commonly used now by management agencies [START_REF] Mccall | Combined use of Mark-Recapture and genetic analyses reveals response of a black bear population to changes in food productivity[END_REF], is the possibility of estimating the (minimum) number of free-ranging individuals (Schwartz, Tallmon & Luikart, 1998;Kohn et al., 1999). Coupled with noninvasive genetic sampling (NGS), which is ideal for studying small, endangered populations as it precludes the unnecessary capture of individuals, and CMR methods, genetic study methods have been successfully deployed to obtain bear abundance and density estimates for large [START_REF] Mowat | Estimating population size of grizzly bears using hair capture, DNA profiling, and mark-recapture analysis[END_REF][START_REF] Boersen | Estimating black bear population density and genetic diversity at Tensas River, Louisiana using microsatellite DNA markers[END_REF][START_REF] Mccall | Combined use of Mark-Recapture and genetic analyses reveals response of a black bear population to changes in food productivity[END_REF] and small study areas with irregular sampling designs and small population sizes [<100 individuals [START_REF] Gardner | Hierarchical models for estimating density from DNA mark-recapture studies[END_REF][START_REF] Obbard | Empirical comparison of density estimators for large carnivores[END_REF][START_REF] Gervasi | A multiple data source approach to improve abundance estimates of small populations: The brown bear in the Apennines, Italy[END_REF][START_REF] Latham | Non-invasive genetic sampling of brown bears and Asiatic black bears in the Russian Far East: a pilot study[END_REF]].

The goal of our study was to apply noninvasive genetic sampling as part of a capture-mark-recapture (CMR) study design for estimating bear abundance and density in Greece.

Methods

Study area

Brown bears in Greece occur in two disjunct populations in the western and eastern part of the country (Figure 1A); the population in the western part of the country is stable or locally increasing and is estimated to number approximately 190 -230 individuals [START_REF] Mertzanis | Ursus arctos (Linnaeus, 1758)[END_REF]. The study was carried out in the western part of the brown bear range in Greece, at the approximately 250 kmlong Greek part of the Pindos mountain range, extending from the Albanian and the Former Yugoslav Republic (FYR) of Macedonian -Greek border in the northwest of the country to the region of Central Greece (Figure 1B). The study area forms a mosaic of elevations and habitats, from deep canyons and low altitudes to steep and high mountains. Elevations range from a couple of hundred meters above sea up to the peak of Mount Smolikas at 2,637 m above sea level. This wide range of elevations results in two major forest zones: A higher elevation conifer zone, with tree species such as the endemic Greek fir (Abies cephalonica), the European black pine (Pinus nigra) and the Bosnian pine (Pinus heldreichii) and a lower elevation mixed broadleaf forest, which is dominated by oak (Quercus sp.), beech (Fagus sp.) and fir (Abies sp.). The local fauna includes other large and medium-sized carnivores, such as the grey wolf (Canis lupus) and the golden jackal (Canis aureus), and intensity of human activity ranges from areas with low human presence to major cities with populations > 50,000.

Sampling methods

Genetic sampling was based on the natural marking and rubbing behavior of bears [START_REF] Green | Tree rubbing by Yellowstone grizzly bears Ursus arctos[END_REF] on wooden poles of the telephone and electricity network (hereafter power poles) in Greece [START_REF] Karamanlidis | Using sign at power poles to document presence of bears in Greece[END_REF] and therefore we did not use any attractant to lure bears to the sampling stations. In the preparatory phase of the study (2002)(2003)(2004)(2005)(2006) we inspected and evaluated the suitability of power poles as non-invasive genetic sampling stations according to predefined criteria that considered among others marking and rubbing frequency and intensity, type of habitat, vegetation density and intensity of human activity [START_REF] Karamanlidis | Development of an innovative method for studying genetic, demographic and behavioural aspects of the brown bear (Ursus arctos)[END_REF]; then we conducted a pilot study that confirmed the suitability of power poles for the non-invasive genetic monitoring of brown bears [START_REF] Karamanlidis | Noninvasive genetic studies of brown bears using power poles[END_REF].

For the purposes of this study and following the inspection and evaluation of 4147 power poles in the study area we selected 171 and placed barbed-wire hair traps [START_REF] Kendall | Hair collection[END_REF] on them, thus creating four different sampling areas (i.e., Vitsi -Varnoundas, and Northern, Central and Southern Pindos) (Figure 1B, Table 1). We selected location and size of the sampling areas so as to cover the maximum of the core range of the species in the Pindos mountain range; sampling areas were separated either by distance or/and geomorphological features (e.g. extensive plains and valleys) and human infrastructure (e.g. major highways). We calculated the size of the sampling areas by drawing a buffer zone of 5.86 km around the Minimum Convex Polygon (MCP) defined by the outermost sampling stations in each study area; 5.86 km was the mean distance between all the individual bear recaptures recorded during the study. We carried out systematic sampling efforts in all four sampling areas from October 2007 to October 2010, when poles were inspected and hair samples were collected monthly.

Genetic Methods 5

Each tuft of hairs on a set of barbs was considered a sample; hair samples were 6 collected without contact to human skin, were placed in uniquely numbered 7 paper envelopes and then stored at room temperature in zip-lock bags with silica 8 gel [START_REF] Roon | A quantitative evaluation of two methods for preserving hair samples[END_REF] until being analyzed by Wildlife Genetics 9

International (Nelson, British Columbia, Canada), a laboratory that specializes in non-invasively obtained samples that often have low quantities of DNA. DNA was extracted using the DNeasy Blood & Tissue kits (QIAGEN, Hilden, Germany), following the manufacturer's instructions. All extractions took place in a facility in which amplified DNA had never been handled before.

To determine individual identity each sample was genotyped at the microsatellite loci G1D, G10J, G10L [START_REF] Paetkau | Microsatellite analysis of genetic variation in black bear populations[END_REF][START_REF] Paetkau | Gene flow between insular, coastal and interior populations of brown bears in Alaska[END_REF], G10C, G10P [START_REF] Paetkau | Microsatellite analysis of population structure in Canadian polar bears[END_REF], MU51 and MU59 [START_REF] Taberlet | Noninvasive genetic tracking of the endangered Pyrenean brown bear population[END_REF]. Gender identification was established through the analysis of the amelogenin gene [START_REF] Ennis | A PCR based sex determination assay in cattle based on bovine Amelogenin locus[END_REF]. Up to 10 additional loci were analyzed for ≥1 sample from each individual to enable more detailed population genetic analyses. These extended genotypes were used to confirm differences between individuals with similar 7-locus genotypes.

Thermal cycling was performed using a MJ Research PTC100 thermocycler with 96 well 'Gold' blocks (MJ Research Inc., St. Bruno, Quebec, Canada).

Polymerase chain reaction (PCR) buffers and conditions were used according to [START_REF] Paetkau | Gene flow between insular, coastal and interior populations of brown bears in Alaska[END_REF], except that markers were not co-amplified, because coamplification may reduce the success rates for hair samples (D. Paetkau, Wildlife Genetics International, personal communication). Two mM MgCl2 was used for all markers except G10J (1.8 mM). An automated sequencer (ABI 310) was used, and genotypes were determined using ABI Genescan and Genotyper version 2.1 software (Perkin Elmer-Applied Biosystems, Foster City, California, USA). The sizing of the PCR products was performed using capillary electrophoresis.

Mixed samples (samples with hair from >1 bear) were reliably identified by evidence of ≥3 alleles at ≥1 locus [START_REF] Roon | Evaluating mixed samples as a source of error in non-invasive genetic studies using microsatellites[END_REF]. To minimize genotyping errors in the final data set, low-quality and putatively mixed samples were excluded from further analyses (Paetkau 2003). Genotypes were replicated for all 1) individuals identified in one sample, 2) pairs of individuals that differed at only 1 or 2 loci (1-and 2-mismatch pairs) and 3) pairs of individuals that differed at 3 loci when ≥1 locus was consistent with allelic dropout. Test for allelic dropout, presence of null alleles, and scoring errors caused by stutter peaks were performed with Micro-Checker version 2.2.3 (van Oosterhout, Hutchinson & Wills, 2004). We used the observed number of alleles (A) and expected heterozygosity (H e ) to express genetic variation in our population. We calculated the probability of identity (P ID ) and of siblings (P SIB ) to describe the power of our markers to identify individuals [START_REF] Waits | Estimating the probability of identity among genotypes in natural populations: cautions and guidelines[END_REF] using the software GIMLET version 1.3.2 (Valière 2002). To allow for the possibility of mismatches caused by genotyping error, we also looked for the pairs of genotypes that were matched at all but 1, 2, and 3 loci (1-MM, 2-MM, and 3-MM pairs) using the program GenAlEx 6 (Peakall and Smouse 2006).

Estimating Abundance and Density

To estimate abundance, we used the Jolly-Seber (JS) CMR modeling framework that allows accounting for detectability less than one and dealing with open populations, i.e., systems in which births and deaths occur [START_REF] Jolly | Explicit estimates from capture-recapture data with both death and immigration -Stochastic model[END_REF][START_REF] Seber | A note on the multiple recapture census[END_REF]. JS models provide estimates of abundance, probabilities of survival, detection and recruitment [sensu [START_REF] Schwarz | A general methodology for the analysis of open-model capture recapture experiments[END_REF] i.e., the probability that an animal from the hypothetical super-population would enter the population on a particular time interval]. We analyzed each of the four regions separately.

We considered time and sex effects as possibly explaining environmental and individual variation in these demographic rates, entering in an additive or interactive way in the model, which led to a total of 125 candidate models. To select among these models, we used the Akaike's Information Criterion corrected for small sample size (AICc) and considered the model with the lowest AICc value as being the model that best balanced bias and precision [START_REF] Burnham | Model selection and inference: a practical information theoretic approach[END_REF]. In addition, we used changes in AICc values (ΔAICc) to compare model support with reference to the model best supported by the data.

Annual abundance estimates were obtained by model averaging in which each model contributed to the final estimate according to its AICc weight [START_REF] Burnham | Model selection and inference: a practical information theoretic approach[END_REF]. The analyses were performed using POPAN [START_REF] Arnason | POPAN-4. Enhancements to a system for the analysis of mark-recapture data from open populations[END_REF] available in MARK [START_REF] White | Program MARK: survival estimation from populations of marked animals[END_REF]) that was called from R (R Development Core Team, 2011) with package RMark [START_REF] Laake | RMarkan alternative approach to building linear models in MARK[END_REF].

We tested the validity of important assumptions underlying the safe use of the JS model like the presence of transient individuals or an effect of trapping using standard goodness-of-fit tests (Pradel, Gimenez & Lebreton, 2005) as implemented in program U-CARE [START_REF] Choquet | U-CARE: Utilities for performing goodness of fit tests and manipulating Capture-Recapture data[END_REF].

Although we considered a potential effect of sex on the detection probability, other sources of individual heterogeneity might go undetected in the present analysis, which could lead to severe bias in abundance estimates (e.g., [START_REF] Cubaynes | Importance of accounting for detection heterogeneity when estimating abundance: the case of French wolves[END_REF]. This might be for example due to individuals being more active than others at the proximity of power poles, hence making them more capturable. To assess whether some individual heterogeneity was present besides gender effects, we compared for each of the four study areas the model best supported by the data to its counterpart, incorporating heterogeneity in the detection probability. To do so, we considered finite-mixture JS models [START_REF] Pledger | Open Capture-Recapture Models with Heterogeneity: II. Jolly-Seber Model[END_REF] that extend standard JS models by assuming that the animals come from different classes of detection, although we do not know which class each individual is from. Heterogeneous JS models were fitted using the R package 'hetage' that is available from http://homepages.ecs.vuw.ac.nz/~shirley/.

To compare homogeneous and heterogeneous models, AIC could not be used because of the violation of regularity conditions (e.g. (Gimenez & Choquet, 2010) in a CMR context), and we resorted to a likelihood ratio test that was distributed under the null hypothesis of homogeneous detection probabilities as a 50:50 mixture of chi-square distributions with 0 and 1 degrees of freedom [START_REF] Self | Asymptotic properties of maximum likelihood estimates and likelihood ratio tests under non-standard conditions[END_REF]. Note that, to get the P-value of this test, using the mixture corresponds to halving the P-value from using the standard chi-square distribution with 1 degree of freedom.

We calculated bear density using the sampled area size of each sampling network respectively (i.e., Vitsi -Varnounds: 1270.39 km 2 ; Northern Pindos: 1066.22 km 2 ; Central Pindos: 999.72 km 2 ; Southern Pindos: 954.33 km 2 , Table 1). Less than 3% of the study areas were not suitable habitat for bears (e.g., lakes, rivers and human settlements); therefore we retained these areas in the sampled area and density calculations.

Results

Sampling Effort

From 2007-2010, we conducted 4422 inspections to the 171 sampling sites and collected 860 hair samples (Table 2). We collected bear hair at 92.25% of all hair traps (i.e., Vitsi -Varnoundas: 95%; Northern Pindos: 100%; Central Pindos: 95%; Southern Pindos: 79%) during 25.75% of all power pole visits (i.e., Vitsi -Varnoundas: 30%; Northern Pindos: 34%; Central Pindos: 26%; Southern Pindos: 13%).

Genotypic Success, Marker Power, and Quality Control

Of the 860 samples sent to the laboratory, we excluded 250 (29%) from the 1 analysis due to insufficient genetic material (i.e., hair without follicles). Of the 610 2 samples that we attempted to analyze, 11 (1.8%) appeared to contain DNA from 3 >1 individual, and 154 samples (25%) failed the DNA extraction process. From 4 the remaining 445 samples (64% of total collected) 211 (61F, 151M) individuals 5 were identified and 102 individuals were recaptured (Table 2). From the 211 6 individuals identified, 206 individuals (98%) produced complete 7-locus 7 genotypes plus gender assignment. 8 Mean observed heterozygocity across the 7 markers used to identify 11 individuals was 0.70 (Table 3). The probability that 2 randomly drawn, unrelated 12 individuals would share the same genotype (P ID ) was 0.0000005, and the 13 probability that full siblings would have identical genotypes (P SIB ) was 0.002 14 (Table 3). Based on the observed distribution of genotype similarity for the 7 loci 15 used for individual identifications, we predicted that no pair of matching 16 genotypes could exist within our dataset. Of the 211 individuals present in our 14 analysis, 91% had ≥ 10-locus genotypes and, when all available loci were considered, all individual bears differed at ≥ 3 loci.

Discussion

We present the first rigorous estimate of brown bear abundance and density in the western range of brown bears in Greece. By using genetic data from hair samples collected from poles of the electricity and telephone network, an approach that has not been implemented in this scale before, we obtained precise population estimates despite fairly low capture and recapture rates. The remarkable consistency of our estimates between years (especially the years 2008 and 2009) lends credibility to our results.

There are several assumptions that need to be checked to use the Jolly-Seber model and obtain reliable abundance estimates. First, we assumed that there was no immigration or emigration (i.e., closure assumption) in the four regions we studied. This was not entirely true as several individuals moved from region XX to region XX during the study period. A modelling option would have been to use multisite JS models (e.g., [START_REF] Lebreton | Modeling individual animal histories with multistate Capture-Recapture models[END_REF] instead of considering the region as a factor, hence allowing the estimation of movement between regions.

However, with so few individuals actually making the transitions between regions, fitting multisite JS models would be numerically unstable with movement parameters estimated on the boundary [START_REF] Gimenez | Efficient profile-likelihood confidence intervals for Capture-Recapture models[END_REF] and identifiability issues [START_REF] Gimenez | Parameter redundancy in multistate capture-recapture models[END_REF]. Add here a sentence on how we should interpret our abundance estimates consequently. A second assumption underlying the application of JS models is that all animals, marked or unmarked, should have the same capturability (i.e., homogeneous capturability assumption).

Here, there was no reason to believe that the use of poles to "capture" individuals would distinguish between marked and unmarked individuals. Third, the duration of the sampling period should be short in comparison to the time between sampling occasions. In our study, most of the observations were made over eight months, which was considered a short enough with regard to the yearly time interval we used between sampling periods as bear survival was high [START_REF] Hargrove | Pooled population parameter estimates from Mark-Recapture data[END_REF]. Fourth, and probably the most crucial assumption, detection probabilities were assumed homogeneous between individuals, heterogeneity if present and ignored being source of strong bias in the abundance estimates (Cubaynes et al., 2010a). We found a sex effect for the Vitsi -Varnoundas population, males being more detectable than females (0.18 [SE=0.12] for females and 0.38 [SE=0.08] for males), and this difference was accounted for when calculating abundance estimates. We also tested for extra individual heterogeneity not explained by differences between males and females, but did not find any, which suggest that the risk of bias in abundance estimates was avoided. Last but not least, it is usually assumed that individuals retain their mark throughout the study and that these marks are read correctly. When interpreted in the context of DNA marking, these two assumptions refer to data quality and the possibility of genotyping error. Last but not least, issue of the coverage of the poles.

Management implications

Importance of estimating abundance for conservation and management.

Originality of the poles as non-invasive monitoring method. Very efficient when combined with capture-recapture methods that account for detection < 1 and demography (mortality / birth).

Conclusions

Figure 1 Figure 1

 11 Figure 1

a

  H E = expected heterozygosity; H O = observed heterozygosity; A = number of alleles; P ID = probability of identity; P SIB = probability of identity among siblings Abundance and density estimation The standard assumptions of the JS model were valid as showed by the goodness-of-fit tests that were non-significant for all regions ( = 2.45, p-value = 0.49 for Vitsi -Varnoundas; = 3.58, p-value = 0.47 for Northern Pindos; = 0.47, p-value = 0.99 for Central Pindos; = 0.71, p-value = 0.70 for Southern Pindos). Model selection showed contrasted effects between regions (Table 4). Survival was constant in Vitsi -Varnoundas and Southern Pindos and sexspecific in Central and Northern Pindos with the addition of time in the latter. Recruitment was constant in all regions but Northern Pindos in which it was sex and time dependent on an additive scale. Detection was constant in all regions but Vitsi -Varnoundas in which it was sex-specific. There was substantial uncertainty regarding the best model to select for all regions, with several models within the range ΔAICc < 2, which motivated the use of model-averaged abundance estimates (see below). We did not detect extra heterogeneity in the detection probability ( = 2.5, p-value = 0.06 for Vitsi -Varrnoundas; = 2.6, p-value = 0.31 for Northern Pindos; = 8.5, p-value = 0.15 for Central Pindos; = 0.6, p-value = 0.22 for Southern Pindos). Table 4. Model selection results from CMR analysis of the brown bear populations in Greece using the Jolly-Seber modelling framework. For each of the four regions (i.e., Vitsi -Varnoundas, Northern, Central and Southern Pindos), the Akaike's Information Criterion value corrected for small sample sizes (AICc), the difference in AICc value between the ith model and the model with the lowest AICc value (ΔAICc). Constant (.), sex (s) and time (t) effects were considered on survival (φ), detection (p) and entry (b) probabilities either as main effect, in an additive (+) or in interactive (×) fashion. Note that, for each of the four regions, only the 10 top ranked models are displayed although 125 models were originally fitted to the data.

2 Table 1

 21 Descriptive statistics of four sampling areas for the non-invasive 3 collection of genetic samples from brown bears inGreece (2007Greece ( -2010)).

	4
	Sampling area	Location	Sampling area size (km 2 ) Sampling stations (N)
	Vitsi -Varnoundas N: 40.6935,	1270.39	40
		E: 21.3192
	Northern Pindos	N: 40.3707,	1066.22	45
		E: 21.0907
	Central Pindos	N: 39.9197,	999.72	47
		E: 21.4054
	Southern Pindos	N: 39.6305,	954.33	39
		E: 21.3711
	Total		4290.66	171

Table 2 .

 2 Descriptive statistics of efforts at four sampling areas in Greece (2007 -9 2010) for the non-invasive collection of brown bear hair samples 10

	Sampling area		Visits	Bear	Hair	Females	Males	Total N	N of
				presence	samples	identified	identified	identified	animals
					collected				recaptured
	Vitsi	-	1115	358	215	13	39	52	28
	Varnoundas
	Northern Pindos		1146	421	295	26	51	77	29
	Central Pindos		1220	351	249	19	42	61	33
	Southern Pindos	941	132	101	3	18	21	12
	Total		4422	1262	860	61	150	211	102

Table 3 .

 3 Variability of microsatellite markers used to determine individual identity of brown bears inGreece, 2007Greece, -2010. .

	Marker	H E	a	H O a	A a	P ID a	P SIB a
	G1D	0.78	0.80	6	0.08	0.37
	MU59	0.78	0.76	8	0.07	0.38
	G10C	0.76	0.76	6	0.09	0.39
	G10P	0.75	0.67	9	0.10	0.40
	G10J	0.70	0.77	6	0.14	0.43
	G10L	0.57	0.55	5	0.23	0.52
	MU51	0.55	0.57	5	0.24	0.53
	x	0.70	0.70	6.42
	Overall probability of identity					5.299e-07 0.002

Table 5 .

 5 Abundance estimates from CMR analysis of the brown bear populations in Greece using the Jolly-Seber modeling framework. For each of the four regions (i.e., Vitsi -Varnoundas and Northern, Central and Southern Pindos), the time and sex-specific population size estimate, as well as the lower (LCL) and upper (UCL) confidence limits of the 95% confidence interval obtained from model averaging are given.

	Region	Model	k	AICc	ΔAICc
		φ(.) p(s) b(.)	5 95.17	0.00
		φ(s + t) p(.) b(s)	7 96.49	1.34
	Vitsi-Varnoundas	φ(s) p(.) b(s) φ(t) p(s) b(.) φ(.) p(s) b(s)	6 97.51 6 97.51 6 97.54	2.36 2.37 2.39
		φ(.) p(s) b(t)	6 97.57	2.42
		φ(s) p(s) b(.)	6 97.58	2.43

Acknowledgements