Sex Ratios in Extant Ungulates: Products of Contemporary Predation or Past Life Histories?

American Society of Mammalogists Sex Ratios in Extant Ungulates: Products of Contemporary Predation or Past Life Histories?

Author(s): Joel Berger and Matthew E. Gompper Source: Journal of Mammalogy, Vol. 80, No. 4 (Nov., 1999), pp. 1084-1113 Published by: American Society of Mammalogists Stable URL: http://www.jstor.org/stable/1383162 Accessed: 07-11-2015 15:59 UTC REFERENCES Linked references are available on JSTOR for this article: http://www.jstor.org/stable/1383162?seq=1&cid=pdf-reference#references_tab_contents You may need to log in to JSTOR to access the linked references.

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SEX RATIOS IN EXTANT UNGULATES: PRODUCTS OF CONTEMPORARY PREDATION OR PAST LIFE HISTORIES? ANDMATTHEW E. GOMPPER JOELBERGER

Program in Ecology, Evolution,and ConservationBiology, Universityof Nevada (MS#186), 1000 ValleyRoad, Reno, NV 89512 and WildlifeConservationSociety, PO Box 340, Moose, WY83012 (JB) Centerfor EnvironmentalResearch and Conservation,ColumbiaUniversity, 1200 AmsterdamAvenue, Mail Code 5556, New York,NY 10027 (MEG) Variation in mammalian adult sex ratios (ASR) is striking both within and among species.

Darwin (1871) originally suggested that causes of variation included competition among males for females and predation. He also recognized that intensity of competition might be greater in species adorned with secondary sexual traits. Assuming that sexual dimorphism is a reasonable indicator of intrasexual competition, we predicted that ASR would become increasingly skewed among dimorphic species and this pattern would be exacerbated by the intensity of predation. With effects of common ancestry removed by computing phylogenetically independent contrasts, we failed to detect strong relationships between ASR and either sexual dimorphism or body mass of males or females. Presence of predators also had no consistent effect on these broader patterns, and even within Cervidae and Bovidae, there was not a solid relationship between dimorphism and ASR. The only generalized pattern that emerged was that males were killed disproportional to their abundance (74% of 31 species). However, differences between live and killed sex ratios in a population were not correlated with female or male body mass or sexual dimorphism. Our results suggest that relationships between sexual dimorphism and mortality are not as straightforward as presumed. Nevertheless, at a proximate level, predation directly affects patterns of sex ratio variation among adult ungulates, but differences in survival of sexes may arise as a direct consequence of greater age-specific mortality among males, for which the ultimate cause is likely to be selection operating differently on males and females. A challenge for the future lies not so much in the separation of proximate from ultimate factors but in evaluating what, if any, life-history traits predispose sexes to differential mortality and the extent to which predation may shape these characteristics.

Key words: predation, carnivores, ungulates, sex ratios, sexual selection, sexual dimorphism, large mammals cupines (Erethizon dorsatum), a bias occurs in the opposite direction because predators kill females at a higher rate than males, and surviving females emigrate (Sweitzer et al., 1997). Large differences can typify other taxa. In ungulates, adult sex ratios (ASR) may be as great as 1.42 males per female in agrimi (Capra aegargrus) or as few as 0.22 males per female in African buffalo and Davis, (Syncerus caffer-Husband 1984; Prins and Iason, 1989). Sex ratios based on predator kills also show marked

Despite sex ratios at birth that approach parity, abundance of males and females in wild mammals at adulthood displays striking variation. In the dasyurid Antechinus swainsonii, the skew in sex ratio is at times so great that few or no adult males remain alive and the entire population is made up of females, a pattern best explained by abrupt post-mating mortality of males -51 year of age and before the synchronized birthing season, and female survival for -3 years (Cockburn et al., 1985). Among porJournalof Mammalogy,80(4):1084-1113, 1999

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discordance. For Thomson gazelles (Gazella thomsonii) a six-fold disparity exists between sexes, with wild dogs (Lycaon pictus) killing males (Fitzgibbon and Lazurus, 1995) and cheetahs (Acinoyx jubatus) killing females (Caro and Fitzgibbon, 1992).

The same species of carnivore also may promote interpopulational variation; lions (Panthera leo) killed about six times as many wildebeest (Connochaetes taurinus) males as females in the Serengeti compared with the Kalahari Desert (Mills, 1990; Schaller, 1972). Perhaps the most profound gender-specific skew due to predation is in African buffalo where 7.3 males were preyed on for every female or in reedbuck (Redunca redunca) where only females were killed (Schaller, 1972).

Why sexes differ in abundance at sites not affected by humans is a continuing source of evolutionary, ecological, and economic interest. Darwin (1871:232) commented that "the practice of polygamy leads to the same results as would follow from an actual inequality in the number of the sexes; for if each male secures two or more females, many males cannot pair."

Darwin, of course, was aware of relationships among reproductive competition, sexual dimorphism, and weaponry but indicated that predation may have an overriding effect on ASR; "when the males are provided with weapons which in females are absent, there can be no doubt that they serve for fighting with other males. [...] they (the males) must also be often exposed to various dangers, while wandering about in eager search for females." Since then, much attention has focused on evolutionary causes of sex ratio variation, particularly the primary rather than secondary sex ratio (Charnov, 1982; Fisher, 1930; Hamilton, 1967; Trivers and Willard, 1973). For practical reasons, such as the study of aging and differential mortality in humans (Millar, 1983; Smith, 1989) and the economics of trophy hunting and the harvest of whales (Ginsberg and Milner-Gulland, 1994; Oshumi, 1979), considerable effort also has tar-

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geted causes and consequences of sex ratio variation at non-biological, demographic, and evolutionary levels (Clutton-Brock and Iason, 1986; Festa-Bianchet, 1996; Williams, 1979).

Gender-specific survival is influenced by both proximate and ultimate factors.

Among mammals variation in adult sex ratio (ASR) has been attributed to at least five major, but not necessarily exclusive, causes.

From birth until adulthood, these are: 1) sex-specific resource allocation by mothers with attendant disparities in the primary sex ratio (Byers and Hogg, 1995; Byers and Moodie, 1990; Clutton-Brock, 1991); 2) enhanced growth rates in young males which, in conjunction with other factors, predispose males to greater mortality than females (Widdowson, 1976); 3) patterns of emigration, in which the dispersing sex experiences heightened mortality (Dobson, 1982); 4) increased fat reserves in females resulting in decreased starvation-related mortality compared with males (Frisch, 1984); and 5) intensity of intrasexual competition, usually among males, resulting in greater male mortality (Clutton-Brock et al., 1982; Ralls et al., 1980). Additional sources of sex-ratio variation include catabolic effects of corticosteroids on males (Lee et al., 1976) and parasites for either sex (Festa-Bianchet, 1989). Nevertheless, to discern which, if any, of the above factors has a greater effect on asymmetries in ASR requires that proximate and ultimate factors be disentangled as major mortality agents.

In an ideal sense, such a distinction may appear straightforward, but it is not. If, for example, a male dies of starvation or a healthy female falls prey to a carnivore, proximate factors may be discerned because causes of mortality can be identified. However, the proximate agent of death may be more associated with one sex than the other simply as an inevitable consequence of evolutionary inertia. Consider sexual segregation in which males and females of many sexually dimorphic species occupy different habitats (Bowyer et al., 1996, 1997). Such

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areas not only vary in food quality but also density of predators (Bleich et al., 1997).

So, if one sex is more likely to be preyed upon, then observed variation in ASR may result because of current predation per se, but it also could be a likely consequence of past selection pressures that operated on each of the sexes differently. Hence, proximate causes of mortality are identifiable but ultimate ones only with much less precision.

Somewhat similarly, in dimorphic species, a relationship between polygyny and the adult sex ratio is expected (Weckerly, 1998). Because reproductive competition tends to increase with the degree of sexual dimorphism (Alexander et al., 1979; Clutton-Brock et al., 1980, 1982), a link between body form and mating systems is likely to occur, which may contribute directly to observed sex differential mortality in extant populations (Andersson, 1994; Owens and Bennett, 1994). Additionally, mortality among cervids is greater in males during periods of resource scarcity (Clutton-Brock et al., 1997) although this pattern does not necessarily apply to Perissodactyls (Berger, 1986; Berger and Cunningham, 1995) and relationships to other taxa are still unclear. So, while proximate factors mediate disparities in ASR, relative roles and interactions between proximate and ultimate factors as determinants of sex differential mortality are not easily evaluated.

Because selection has frequently shaped life-history traits and behavior of males and females separately (Stearns, 1992), assessment of how they contribute to variation in ASR is not often amenable to experimental manipulation and, therefore, must rely on inference (Byers and Bekoff, 1990).

Nevertheless, it is possible to explore the extent to which sex ratios are skewed, especially by predation because: 1) abundant literature describes species and population sex ratios in the presence and absence of predators; 2) sex-specific data on kill rate also are available; and 3) new techniques

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for comparative analyses enable statistical control of phylogenetically associated traits.

Here, we examined general patterns of sex differential mortality in adult ungulates.

Specifically, we asked to what extent does predation contribute to differences in adult sex ratios? We addressed that question using two approaches: 1) intra- and interpopulational contrasts at sites with and without predation and 2) interspecific comparisons so that possible effects of mating systems and other evolved traits on sex differential mortality were accounted for.

We began with the initial assumption that sexual dimorphism may be used as an indicator of intrasexual competition and predict that ASR becomes increasingly skewed among dimorphic species and that pattern is exacerbated by predation.

METHODS

We use the term adult sex ratio (ASR) to distinguish it from the more commonly used phrase, operationalsex ratio (OSR-Emlen and Oring, 1977). The OSR is frequently used in evolutionary and behavioral ecology literature when the ratioof fertilizablefemales to sexually active males is known. Our intent is to move beyond the traditionalpopulation approachin which a great deal is known about reproductive status of individuals so that sex ratio patterns may be contrastedamongpopulationsor species for which data on breeding status are less complete. The ASR is measuredas the number of adult males to the number of adult females in the population.The ASR and OSR are often correlated (Berger and Cunningham,1994).

Our sample was based on review of studiesin which data on ASR in populations not influenced in majorways by humanswere presented.

Thus, we attemptedto exclude all studieswhere huntingwas known to affect sex ratios.For species in which sexes were nearlyidenticalin body size or difficultto identifyby genderbut hunted, we assumedthat the 'kill' sample was collected randomly,and we then used those data to estimate ASR (e.g., peccaries).Severalinherentdifficulties exist in ASR data including a lack of known-aged adults, imprecise literature estimates, probable inclusion in an undetermined numberof cases of non-breedingmales and fe-

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males, and, where sexual segregation occurs, a possible underestimation of the true number of males. When possible, we attempted to balance those problems by using estimates derived during the mating season when a higher proportion of males was present.

Other variables used in analyses were body mass (kg) and the ratio of female:male body mass. The latter measurement approximated sexual dimorphism (Clutton-Brock et al., 1985), although linear measurements may offer better assessments of size because they are less affected by seasonal conditions (Ralls, 1976). We also evaluated if potential predators were present or absent for each population and assumed that where major predators had been eliminated, effects of smaller ones were negligible. For instance, if elk (Cervus elaphus) were sympatric with wolves (Canis lupus) and foxes (Vulpes) at some sites but only with foxes at others, the latter would be considered predation-free but not the former.

Data on asymmetry between sexes in kills of predators also were contrasted with sex ratios in the "live" population to investigate if predation resulted in disproportionate removal of one sex.

When frequency of kills was known but not the live ASR, an estimate of the latter was derived based on the mean from other sites (e.g., waterbuck, Kobus ellipsiprymnus, or wildebeest).

When data on gender-specific mortality that involved multiple carnivores killing the same species of prey at the same study area (e.g., lions, hyenas, Crocuta crocuta, and wild dogs preying on Thomson's gazelles in the Serengeti) were available, the mean was taken to generate the male:female kill ratio for that site. In circumstances (e.g., reedbuck; caribou, Rangifer tarandus) when kills were of one sex only, we added a single kill to the sex in which none had been recorded. That enabled calculation of a kill ratio with a denominator of one. For instance, if 15 males and zero females were preyed upon, the kill ratio would be 15:1. We then explored if relationships existed between "live" and "kill" ASR. We compiled data on ASR for 275 populations representing 76 species (Appendix I).

These included as few as a single datum per species or as many as 26 populations as with caribou. Because sex ratios vary greatly among populations of the same species and intraspecific trends cannot be assumed to parallel cross-spe-

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cies patterns (Gompper and Gittleman, 1991), we attempted to incorporate data from multiple populations for each species. In cases where data existed for different years or different studies of the same population, mean values were calculated. Data on male and female body mass were generally not available for each study or each population. Therefore, a single estimate of mass for each species was obtained from the literature (Appendix I); use of a single estimate for mass reduced the detectable variation inherent in our sample simply because any resultant variation in a population's ASR cannot be evaluated at a local or geographically specific site.

Statistical methods that treat species or population values as statistically independent are not valid because closely related species share many characters due to common ancestry rather than independent evolution (Harvey and Pagel, 1991). To circumvent this problem, the independent-contrasts method (Felsenstein, 1985) was used. That method assumed a specific model of trait evolution by calculating expected contrast values between pairs of taxa at each bifurcation in a phylogeny. The independent-contrasts method was especially appropriate for analyzing data for which number of species was >50 and for which phylogenetic tree information, rather than solely taxonomic rank information, existed (Gittleman and Luh, 1994). The tree used to calculate contrasts resulted from our combining results of several recent studies of ungulate phylogenetics (Fig. 1). Those studies were partial; no single published phylogeny or combination of phylogenies included all species of interest.

Therefore, we inferred additional phylogenetic information from taxonomic classifications. To calculate standardized independent contrasts, we used the computer program CAIC (Purvis and Rambaut, 1995), setting all branch lengths equal and using the "crunch" option. Relationships of body mass, sexual dimorphism, and sex ratio were examined by linear regression of the logtransformed calculated contrast for each variable. It was possible that any putative relationship between body mass and sexual dimorphism resulted because data were highly correlated, which may have occurred when a component of the same variables appeared on both axes. To evaluate that possibility, we also analyzed data using a principal component analysis (PCA) that was reduced to two variables, or a major-axis regression, which enabled an estimate of the

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A

C

Cervidae Bovidae

Antilocapraamericana Giraffacamelopardalis Phacochoerusaethiopicus Suas scrofaaKobus scrofa SUS TCayassuaacKobus Catagonuswagneri Lamaguanicoe Equusasinus Equushemonius Equusburchellii Equuscaballus Rhinocerosuniconis Ceratotherium simum Dicerosbicornis

B

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Elaphurus davidianus

Cervusduvaucelfi Axisaxis Axisporcinus Damadama Cervusunicolor Cervuselaphus(3 subspp.)

Cervusnippon Muntiacusreevesi Muntiacusmuntjak Rangifertarandus Alcesalces Mazamaamericana Blastocerusdichotomus Blastoceruscampestris Odocoileusvirginianus Odocoileushemionus Capreoluscapreolus Moschuschrysogaster

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Raphiceros campestris

Madoqua kirkii

Ourebiaourebi Procaprapicticaudata Aepycerosmelampus Reduncaredunca Reduncafulvorufula vardoni ellipsiprymmnus Alcelaphusbuselaphus Connochaetes taurinus Damaliscusdorcas Damaliscuslunatus Pseudoisnayaur Pantholopshodgsonii Hemitragusjemlahicus Caprafalconeri Capraibex Capraaegagrus Ovisorientalis Ovisammon Ovisnivicola Ovisaries

Ovisda grimsmia ensis SylOviscaprna Sylvicapragrimmia Oreamnosamericanus Capricornis crispus Ovibosmoscatus Boselaphustragocamelus oryx Taurotragus Tragelaphus eurycerus Tragelaphus scriptus Tragelaphus strepsiceros Tragelaphus angasii Antilopecervicapra Antidorcasmarsupialis Gazellabennetti Gazellathomsonii Oryxgazella Oryxbeisa Synceruscaffer Bubalusbubalis Bos taurus Bisonbison Bosgaurus

FIG.1.-Hypothesized phylogeny for the Ungulata.The following references were used to constructthe phylogeny:Blake et al. (1997), Cronin(1991), Croninet al. (1996), Douzery and Catzeflis (1995), Douzery and Randi (1997), Gatesy et al. (1992), George and Ryder (1986), Georgiadiset al.

(1990), Groves and Grubb (1987), Groves and Shields (1996), Janecek et al. (1996), Kraus and Miyamoto (1991), Modi et al. (1996), Montegelardet al. (1997), Moralesand Melnick (1994), Pitra et al. (1997), Springerand Kirsch (1993), Stanley et al. (1994), Wall et al. (1992), and Xu et al.

(1996). Branch lengths are hypotheticaland were not used in the analyses: A) all ungulates, B) Cervidae, and C) Bovidae. proportionof variationthatwas accountedfor in the correlations(Sokal and Rolhf, 1995).

RESULTS Phylogeny and sex ratios without prefactors may affect dation. -Numerous ASR, including phylogeny, predation, and possibly some interaction that might involve sexual dimorphism. To evaluate po-

tential relationships, we first explored a subset of all data using only ASR from "live" individuals (i.e., excluding data based on kills made by predators). No relationship between sexual dimorphism and adult sex ratio was detected; standardized contrasts of dimorphism explained virtually none of the variance in the standardized contrasts of adult sex ratio (n = 76 species,

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Standardized contrasts of log(adult sex ratio) FIG.2.-The relationshipbetween standardized contrasts of sexual dimorphismand standardized contrasts of adult sex ratio based on data from living individuals;sex-ratio estimates exclude measures derived by assessing diets of predators:A) all contrastsincluded, and B) excluding contrastsof sexual dimorphismequal to zero.

275 populations, 102 contrasts; P = 0.120; Fig. 2a). A great deal of variation existed in contrasts of adult sex ratio where contrasts of sexual dimorphism equaled zero.

This is because we used only a single estimate of body mass for all populations of a single species, and thus at interpopulational nodes in the phylogeny, variation in sex ratio could have been extensive but variation in body mass and dimorphism was zero. It was unlikely, however, that lack of variation in our estimates of body mass explained the lack of significance in the relationship between dimorphism and sex ratio, because as variation in mass, and thus variation in dimorphism, were likely small relative to observed variation in sex ratio.

Nonetheless, excluding data points where

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contrasts in dimorphism equaled zero, the relationship was significant although little of the variation in sex ratio was explained (n = 46 contrasts, r2 = 0.09; P = 0.037; Fig. 2b). Because of how contrasts were calculated, our analysis was not the same as eliminating population-level variation from the analysis, but it suggested a weak association that was overwhelmed by population-level variation.

Two additional subsets of data were analyzed to assess potential relationships between sexual dimorphism and ASR: "live" estimates excluding monomorphic species (sexual dimorphism = 1) and "live" estimates from populations in predation-free environments. For both subsets, the relationship between dimorphism and sex ratio was not significant (excluding monomorphic, P = 0.52; predation-free environment, P = 0.137). It is possible, however, that the relationship between dimorphism and sex ratio was specific to one clade or another of organisms. Therefore, we reanalyzed the "live" data for only Cervidae (using the phylogeny illustrated in Fig. ib) and for only Bovidae (using the phylogeny in Fig. ic). The relationship was not significant in either family (P = 0.414 and 0.282, respectively).

We also explored if variation in body mass within a sex was related to variation in sexual dimorphism (n = 48 species, 107 contrasts), but the relationship between standardized contrasts of female body mass and standardized contrasts of sexual dimorphism was not significant, explaining <1% of the variation (P = 0.381; Fig. 3a). For males, however, body mass explained 7% of the variance in sexual dimorphism (P = 0.004; Fig. 3b) and arose due to the contribution of bovids. Nonetheless, there was no significant relationship between standardized contrasts of sex ratio and standardized contrasts of either male or female body mass (P = 0.173 and 0.393, respectively).

Among Cervidae, no relationship was detected between either standardized contrasts of male or female body mass and sexual

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.08

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Standardizedcontrasts of log(male mass)

FIG.3.-Relationships between standardized contrasts of body mass and standardizedcontrastsof sexual dimorphism:A) females, and B) males.

FIG. 4.-Relationships between standardized contrasts of body mass and standardized contrasts of sexual dimorphism among the Bovidae: A) females, and B) males.

dimorphism (n = 30 contrasts; P = 0.598 and 0.483, respectively). Female Bovidae also showed no relationship (P = 0.388), but there was a relationship among male Bovidae (n = 57 contrasts, r2 = 0.08, P = 0.038; Fig. 4).

Results of the above analyses were supported when data were examined with PCA. Percent variance explained by the first principal component and eigenvalues for the major (MA) and minor (MI) axes, respectively, are indicated for a mass variable and its relationship with sexual dimorphism: female body mass (variance = 56.7%; MA = 1.085; MI = 0.914); male body mass (variance = 63.6%; MA = 1.273; MI = 0.727); female body mass of cervids (variance = 56.7%; MA = 1.134; MI = 0.866); male body mass of cervids (variance = 55.1%; MA = 1.101; MI = 0.989); female body mass of bovids (variance = 55.9; MA = 1.117; MI = 0.883), and male body mass

of bovids (variance = 63.8%; MA = 1.276; MI = 0.724). Hence, results were consistent with prior analyses because little variance in the major axis was explained by the first principal component. Thus, a large proportion of the variation was not explained for any of the regressions phism on body mass.

of sexual

dimor-

Differential mortality of sexes due to predation.--Although we found no robust relationship between ASR and dimorphism in areas without predation, we assessed if pre-

dation might have an effect by comparing sites with and without predation, taking into account sites where sex ratios of actual kills were known. Live females were more abundant than males in 74% of 31 species. On a per species basis, adult males were preyed upon more frequently than adult females relative to their abundance in the population. Among Cervidae, those included (number of studies supporting the skew for

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that species) caribou (4), chital (Axis axis, 3), elk (2), moose (Alces alces, 2), mule deer (Odocoileus hemionus, 2), white-tailed deer (0. virginianus, 2), and sambar (C. unicolor, 2). Among Bovidae, those included bison (Bison bison, 2), buffalo (2), Dall sheep (Ovis dalli, 2), gaur (Bos gaurus, 2), gemsbok (Oryx gazella, 2), hartebeest (Alcelaphus buselaphus, 2), impala (Aepyceros melampus, 2), kudu (Tragelaphus strepsiceros, 2) markhor (Capra falconeri, 2), springbok (Antidorcas marsupialis, 2), Thomson's gazelle (2), waterbuck (2), and wildebeest (4). Other ungulates were warthog (Phacochoerus aethiopicus, 2), and common zebra (Equus burchelli, 3). Those cases in which predation on adult females exceeded that of males were barasingha (C. duvauceli, 2), chital (2), and hog deer (A. porcinus, 2), moose (1), guanaco (Lama guanicoe, 1), impala (1), reedbuck (1), wildebeest (1), and common zebra (1). Those differences between sexes were significant regardless if populations were treated independently of species or pooled, and despite evidence indicating that females may be preyed upon more than males in some populations (Table 1). In essence, the evidence was strong (P < 0.01) that when predation existed, ASR was likely to be skewed in favor of live females because males were killed disproportional to their abundance.

To assess if the pattern of predation-induced male mortality may have arisen as a consequence of life-history phenomena, we examined relationships between the mean difference in ASR between 'live' individuals and those killed by predators with female mass, male mass, and sexual dimorphism. Female and male mass were not related to the difference in ASR between live and killed animals. Regardless if data were transformed, the greatest amount of variance explained in ASR was low (female mass, r2 = 0.01, P = 0.638; male mass, r2 = 0.09, P = 0.675; n = 24 species). In contrast, there was an inverse relationship between the log of sexual dimorphism and

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differences in ASR between live and kill samples (r2 = 0.192, P = 0.028). However, that was influenced by an outlier (markhor, Capra facloneri) in which 11 males and no females were killed by wolves (Schaller, 1977). With markhor removed, the relationship vanished (r2 = 0.003, P = 0.81).

Hence, we were unable to find correlates between several life-history variables and ASR with or without predation.

DIscusSION

Sex ratios, predation, and life history.Sexual selection theory assumes that traits such as conspicuous coloration, horns, and other forms of ornamentation associated with mate acquisition are costly because they compromise an individual's ability to survive (Andersson, 1994; Darwin, 1871; Trivers, 1972). Some support exists for the idea that sexual size dimorphism in birds and mammals is associated with skewed secondary sex ratios (Promislow, 1992, Promislow et al., 1992; Searcy and Yasukawa, 1981). Overall, it appears that ASR is biased in favor of females in a high proportion of dimorphic mammals (Geist, 1971; Ralls et al., 1980). Among ungulates, relationships among life-history traits and mating competition have been contradictory. For instance, sexual dimorphism and polygyny are related in cervids but not African bovids (Clutton-Brock et al., 1980; Owen-Smith, 1993). Nevertheless, because comparative approaches that incompletely account for the historical relationships of taxa may be unable to separate effects of phylogeny from ecology (Gittleman and Luh, 1994; Harvey and Pagel, 1991), it is important to reduce possible sources of variation. We attempted to do this by accounting for common ancestry and by considering the extent to which the ASR may be modified by predation.

Beginning with the assumption that costs of intrasexual competition in polygynous mammals fall primarily on males (CluttonBrock et al., 1997), we expected a skew in ASR to be related positively to the degree

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TABLE1.-Comparison of numbers of cases in the literature of differential predation of sexes on ungulate species. Unidirectional are cases in which all studies indicate that one sex is preyed upon more than the other; bidirectional are cases in which the evidence from multiple sites demonstrates that either males or females may be taken more than the other sex.

Direction of Sex Differential Predation Category

Males > Females

Males < Females

P<

40 23

9 8

0.0001 0.010

28 18

3 3

0.001 0.001

Unidirectional All samplesa Single species onlyb Bidirectional All samplesa Single Species onlyb a

Includesmultiplestudiesof same species but in differentpopulations. bMultiplesites discountedso thatonly a single datumper species is used. P is the binomialprobabilityof deviationfroma 50: 50 expectationin all calculationsexcept for the 'Unidirectional'sample,which is based on the normaldistributionbecause the total samplewas > 35 (Siegel and Castellan,1988).

of sexual dimorphism. We then hypothesized that this putative relationship would be exacerbated by predation because males may be increasingly susceptible due to mate searching (Darwin, 1871), loss in condition and mass during reproductive competition (Berger and Cunningham, 1994), or other factors associated with dominance (Byers, 1997). Our working assumption was based on the idea that sexual dimorphism in body size reflected past reproduction competition. That idea has received strong empirical support (Alexander et al., 1979; Andersson, 1994; Clutton-Brock et al., 1982) and generally is accepted as standard dogma (cf. Owens and Bennett, 1994; Promislow, 1992). However, if this relationship does not hold across extant species and taxonomically diverse groups, our predictions about skew in ASR and subsequent effects of predation may not have a logical basis.

In fact, if the assumption is ill-founded, then the null hypothesis of no effect (of dimorphism on ASR) may be a more appropriate starting point with a postulated effect serving as the alternate hypothesis. The distinction, while somewhat subtle, is important because it underscores whether it is more appropriate to begin with putative standard dogma as the working hypothesis or the null, and what logically follows as

an alternative hypothesis. Regardless, our empirical interspecific evidence does not offer strong support to either of our predictions.

Where predation was absent, the greatest amount of variation in ASR explained under the most liberal scenarios was only 9% (Fig. 2b). When the most diverse and species-rich families, Bovidae and Cervidae, were examined independently, none of the predicted relationships approached statistical significance (P = 0.282 and P = 0.414, respectively). Body mass itself was a poor predictor of sexual dimorphism and ASR. The only associations that we consistently detected were: 1) when predation was greater on one sex than the other, it fell disproportionately upon males (74% of 31 species, P < 0.01), and 2) when differential predation of sexes was equivocal (because some studies reported higher predation on females than on males), males had higher average mortality in 86% of the reports (Table 1). Additionally, neither female or male mass nor indices of dimorphism were related to differences in which males and females were killed relative to their proportion in the living population. Our inability to detect patterns other than the general finding that male ungulate are killed more than females by the total predator commu-

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nity raises the question of why other researchers have found or suspected an across-species pattern of sex differential mortality. Three, not necessarily exclusive, factors appear to be involved.

First, our analyses focused on variation in sex ratios at adulthood and not on differences that accrue at various stages of the life cycle when selection may operate differently (Stearns, 1992). If juveniles experience differential mortality of sexes that increases with sexual dimorphism, the case can be made that, in the absence of predation, the primary mortality cost of dimorphism is not associated directly with reproductive competition among adults, although patterns of juvenile growth will be influenced by selection at different stages of the life cycle (Stearns, 1992). That a relationship between differential juvenile mortality of sexes and size dimorphism across a small cohort of mammals was reported by Clutton-Brock et al. (1985) suggests that sexual selection (and hence, ultimate factors) may drive the difference in ASR. However, our failure to find a relationship between ASR and dimorphism within the Ungulata enables several other non-exclusive interpretations. It may be that 1) when a greater number of taxa are included, such as in our study, robustness of the generality of the pattern vanishes, 2) the previously reported association might arise as a consequence of common ancestry, but because of the small sample, it is not possible to discern, or 3) relationships between differential juvenile mortality and dimorphism are simply more detectable during pre-adulthood.

Second, environmental noise rather than life-history variables per se, may obscure identification of true correlates of variation in ASR other than predation. Sources might include different aging criteria, density-related effects on survival, errors in census methodology, and regional variation in diets of carnivores, food availability, or density and diversity of communities of carnivores.

A more liberal or restrictive analysis might produce different results. For instance, in-

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clusion or exclusion of different studies might have been more or less acceptable to others. Additionally, if data on sexual dimorphism had been analyzed categorically, it is possible that a relationship between dimorphism and ASR might have involved only the largest members of a clade (Weckerly, 1998).

Finally, variation in ASR is tremendous, ranging from more than six times as many live adult females than males to situations where females represent the primary kills.

Variation also can be extreme among populations of individual species. Clearly, small samples of known "kill" sex ratios, which are typically available, may introduce considerable sources of error, but the amount of variation in ASR is often much more than that found among body masses.

Hence, it is not surprising that, other than sex differential predation itself, the detection of life history correlates of ASR are not especially clear.

Proximate and ultimate factors.-Despite the general pattern of greater predation on males than females, exceptions are numerous. Among Plain's zebras, males or females may be preyed upon more than the other sex, with determining factors being species of predator and gender-specific behavioral differences (Berger, 1986), which occur in Thomson gazelles and other social ungulates (Caro and Fitzgibbons, 1992; Fanshaw and Fitzgibbon, 1993; Fitzgibbon and Lazurus, 1995). Similarly, rutting or lekking males in dimorphic ungulates may be preyed upon more than females, either because they lose body condition or become less wary while attempting to attract mates (Deutsch and Weeks, 1992; OwenSmith, 1993).

Black rhinoceros (Diceros bicornis), on the other hand, are monomorphic, polygynous, and, unlike zebras, virtually immune to predation due to their large size (OwenSmith, 1989). If polygyny per se is a reasonable marker of intrasexual competition (Alexander et al., 1979; Clutton-Brock et al., 1982), live ASR in black rhinoceros

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should be female-biased. Alternatively, if sexual dimorphism is associated with asymmetrical ASR, sexes of black rhinos should approach parity because males and females are similar in body size. In populations of black rhinoceros protected from poaching (by humans), adult males outnumbered females (Berger, 1994; Berger and Cunningham, 1995)-the converse of what is expected for polygynous species but what would be predicted for monomorphic ones if polygyny is a poor surrogate for gauging reproductive competition. In either case, with the advent of poaching, ASR of black rhinos shifted to favor females primarily because females were more wary and males more stolid (Berger and Cunningham, 1995). Thus, differences in behavior rendered one sex more vulnerable to predation.

This may be the norm for most ungulate species. In fact, the reverse situation would be counterintuitive; that is, if sexes failed to behave differently irrespective of body size, habitat use would not differ intersexually when species are dimorphic, or predation pressure on males and females would be identical.

In these cases and others, distinction between proximate and ultimate factors that are responsible for observed variation in ASR is not obvious. Whereas predation, starvation, and disease can all be verified as an immediate mortality agent, the argument to favor life-history or sexually selected traits often relies on evolutionary insight. In pronghorn (Antilocapra americana), for example, the primary sex ratio is equal, but males are the less numerous sex as adults due to injury and starvation, even when the population is below ecological carrying capacity and in the absence of predation (Byers, 1997). While fewer adult males may arise as an inevitable consequence of greater age-specific mortality even when sexual dimorphism is not involved, the ultimate cause is likely to be sexual selection operating on traits and behaviors associated with enhancing reproductive capabilities.

Thus, an elevated-hazard factor for early se-

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nescence may be created. A challenge for the future lies not so much in the separation of proximate from ultimate factors but in evaluating which, if any, life-history traits predispose sexes to differential mortality and the extent to which predation may shape these traits.

ACKNOWLEDGMENTS

We are gratefulto T. Bowyer,C. Cunningham, L. Bellis, J. Byers, J. Gittleman,R. Powell, and E Weckerly, for insights, comments, and suggestions, and to NSF for support.G. Kirkland, Jr. was instrumentalin the making of this symposium and undauntingin his encouragement.

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APPENDIXI

Summary information on adult sex ratios and body masses of female and male ungulates derived from indica and Peacock (1988), Caro and Fitzgibbon (1992), Crichton (1998), Dinerstein (1980), Johnson (1983), K (1972, 1998), Silva and Downing (1995), and Skinner and Smithers (1990).

Species

Latin binomial

Argali Ass (feral)

Ovis ammon Equus asinus

Barasingha

Cervus duvaucell

Bison

Bison bison

Blackbuck

Antilope cervicapra

Blesbok Boar (wild) Bongo Buffalo

Damaliscus dorcas Sus scrofa Tragelaphus eurycerus Syncerus caffer

Bushbuck

Tragelaphus scriptus

Caribou

Rangifer tarandus

Population

Live/ killed

Ratio M:F

Ladakh, India Western Arizona, USA Ossabou, Georgia, USA Kahna, India Kaziranga, India Western Kheri, India Badlands, South Dakota, USA Mackenzie, NWT, Canada Wood Buffalo, AB, Canada Wood Buffalo, AB, Canada Kahna, India Sikandra, India Queen Elizabeth Park, Uganda Ussuri, Russia Bangangai, Sudan Kafue, Zambia Kruger, South Africa Lake Manyara, Tanzania Lake Manyara, Tanzania

Live Live Live Live Live Kill Live Live Live Kill Live Live Live Kill Live Kill Kill Live Kill

0.80 0.75 0.89 0.90 0.79 0.70 0.81 0.84 0.98 0.77 0.71 0.84 0.80 1.00 0.39 1.60 0.50 0.22 4.02

?

No No Yes Yes Yes No Yes Yes Yes Yes No Yes Yes ?

Yes Yes Yes Yes

Serengeti, Tanzania Serengeti, Tanzania Chobe, Botswana Queen Elizabeth Park, Uganda Arctic Herd, Alaska, USA Avalon Peninsula, Quebec, Canada Barff Herd, South Georgia Burwash Uplans, Yukon, Canada Buser Herd, South Georgia

Kill Live Live Live Live Live

1.95 0.63 1.01 0.40 0.42 0.35

Yes Yes Yes Yes Yes ?

Live Live

0.30 1.50

No ?

Live

0.54

No

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Predators

Female mass 68 260 162

406

35 61 32 253 637

32 94

APPENDIX I-Continued.

Species

Latin binomial

Cattle (feral)

Bos taurus

Chiru

Panthoiops hodgsoni

Chital

Axis axis

Population Central and Western Siberia, Russia Central Chukotka, Russia Denali, Alaska, USA Forty Mile, Alaska, USA Hokkaido, Japan Interior Newfoundland, Canada Kuhmo, Finland Lapland, Russia Lapland, Russia Mealy Mountains, Labrador, Canada Mount Albert, Quebec, Canada Northwestern Alaska, USA Novaya Zemlya, Russia Royal Bay, South Georgia Southern Chukotka, Russia Spitzbergen, Svalbard, Norway St. Mathews Isle, Alaska, USA Sundrun, Russia Taimyr, Russia Trans-Baikal, Russia Yakutia, Russia Yakutia, Russia Old Crow, Yukon, Canada Amsterdam Isle, Indian Ocean Totohe, China Wudaoliang, China Chitwan, Nepal Corbett, India Corbett, India Gir, India Guinda, India Hawaii, USA

Live/ killed

Ratio M:F

Live

0.55

Yes

Live Kill Live Live Live Live Kill Live Live

0.33 1.20 0.40 0.14 0.32 0.26 9.15 0.51 0.40

?

Yes Yes ? ? ?

Yes Yes ?

Live Kill Live Live Live Live Live Live Live Live Live Kill Live Live

0.36 1.60 0.80 0.57 0.51 0.34 0.41 0.44 0.25 0.83 0.49 2.00 0.73 0.76

?

Yes ?

No ?

No No Yes Yes ?

Yes Yes ?

No

Live Live Live Live Kill Live Live Live

0.74 0.51 0.54 0.70 2.00 0.49 0.38 0.77

Yes Yes Yes Yes Yes Yes ? ?

This content downloaded from 129.82.28.144 on Sat, 07 Nov 2015 15:59:40 UTC All use subject to JSTOR Terms and Conditions

Predators

Female mass

293 25 57

M m

APPENDIX I-Continued.

Species

Deer, Chinese Water Fallow Hog

Marsh Black-Tailed

Latin binomial

Hydropotes inermis Cervud dama Axis porcinus

Blastoceras dichotomus Odocoileus hemionus

Live/ killed

Ratio M:F

Kahna, India Kahna, India Keoladeo Ghana, India Nagarhol, India Nagarhol, India Royal Kamali-Bardia, Nepal Varibihar, India Western Bastar, India Western Kheri Forest, India

Live Kill Live Live Kill Live Live Live Live

0.72 0.50 0.71 0.69 2.48 0.64 0.73 0.69 0.79

Yes Yes Yes Yes Yes Yes

Jiangik, China Donana, Spain Northeast of Toulouse, France Chitwan, Nepal

Live Live Live Live

0.90 0.48 0.81 0.48

? ? ?

Yes

Chitwan, Nepal Corbett, India Kaziranga, India Western Kheri, India Parana River, Brazil Big Flat, California, USA Hopland, California, USA Elephant Mountain, Texas, USA Idaho Primitive Area, Idaho, USA Idaho Primitive Area, Idaho, USA Southern Arizona, USA Southeastern Idaho, USA Sequoia, California, USA Three Bar, Arizona, USA Western Oregon, USA Yellowstone, Wyoming, USA Picacho Mountains, Arizona, USA

Kill Live Live Live Live Live Live Live

0.40 0.90 1.03 0.75 0.36 0.27 0.91 1.83

Yes Yes Yes ? ?

Yes ?

Live

0.19

Yes

Kill

1.20

Yes

Live Live Live Live Live Live Live

0.50 0.91 0.74 0.83 0.45 0.38 1.25

? ? ? ? ?

Yes Yes

Population

This content downloaded from 129.82.28.144 on Sat, 07 Nov 2015 15:59:40 UTC All use subject to JSTOR Terms and Conditions

Predators

Female mass

Yes Yes 15 43 32

30 45

APPENDIX I-Continued.

Species

Latin binomial

Musk

Moschus chrysogaster

Pampas Pere David's

Blastoceras campestris Elaphurus davidianus

Red

Cervus elaphus

Red-brocket Roe

Mazama americana Capreolus capreolus

Sika White-tailed

Cervis nippon Odocoileus virginianus

Population East Sayan (B), Russia Kedramath, India East Sayan (A), Russia Emas Park, Brazil World's (non-Woburn) population Rhum, Scotland, United Kingdom Rhum, Scotland, United Kingdom Coastal Suriname Suffolk, Great Britain Switzerland Nara Park, Japan Key Deer Refuge, Florida, USA Adirondack Mountains, New York, USA Algonquin Park, Ontario, Canada Big Bend, Texas, USA Big Bend, Texas, USA Columbia, Washington, USA Columbia, Washington, USA Eastcentral Ontario, Canada George Reserve, Michigan, USA Glacier, Montana, USA Northcentral Minnesota, USA Northeastern Minnesota, USA Oklahoma and Arkansas, USA Riding Mountain, Manitoba, Canada Superior Forest, Minnesota, USA

Live/ killed

Ratio M:F

Live Live Live Live Live

1.35 1.20 1.10 0.78 0.72

Yes Yes Yes ?

No

25 159

Live

0.30

No

128

Live

1.00

No

Kill Live Live Live Live

0.96 0.50 0.67 0.38 0.52

Humans ? ? ? ?

Live

0.38

?

Kill

1.30

Yes

Live Kill Live Kill Kill Live

0.60 2.20 0.42 0.50 2.50 0.70

Yes Yes Yes Yes Yes ?

Kill Kill Kill Live Kill

0.85 0.88 2.50 0.49 1.63

Yes Yes Yes ?

Yes

Live

0.54

Yes

This content downloaded from 129.82.28.144 on Sat, 07 Nov 2015 15:59:40 UTC All use subject to JSTOR Terms and Conditions

Predators

Female mass 12

50 21 20 60

APPENDIX I-Continued.

Species

Latin binomial

Dik dik

Madoqua kirki

Duiker

Sylvicapra grammia

Eland

Tragelaphus oryx

Elk

Cervus elaphus

Gazelle Indian Thomson's

Gazella bennetti Gazella thomsonii

Tibetan Gemsbok

Procapra picticaudata Oryx gazella

Giraffe

Giraffa camelopardis

Live/ killed

Ratio M:F

Predators

Serengeti, Tanzania Communcal lands, Kenya Etosha, Namibia Simien Mountains, Ethiopia Umfolozi, South Africa Nairobi Park, Kenya

Live Live Live Live Kill Live

1.00 0.95 0.90 1.00 0.92 0.63

Yes ?

Yes ?

Humans ?

Banff, Alberta, Canada Banff, Alberta, Canada Elk Island, Alberta, Canada Glacier, Montana, USA Idaho Primitive Area, Idaho, USA Idaho Primitive Area, Idaho, USA Riding Mountain, Manitoba, Canada Yellowstone, Wyoming, USA Sikote-Alin Mountains, Russia Nagarhol, India Nagarhol, India Nagarhol, India

Kill Live Live Kill Live

1.42 0.28 0.85 1.08 0.17

Yes Yes ?

Yes Yes

Kill

0.56

Yes

Kill

2.20

Yes

Live Kill Live Live Kill

0.74 0.45 0.27 0.27 0.65

Yes Yes Yes Yes Yes

Rajasthan, India Serengeti, Tanzania

Live Live

0.74 0.74

?

Yes

Serengeti, Tanzania

Kill

2.45

Yes

Aru Basin, Tibet, China Namib Desert, Namibia Kalahari Desert, South Africa Kalahari Desert, South Africa Kruger, South Africa

Live Live Live Kill Kill

0.93 0.75 0.84 1.60 0.50

Yes ?

Yes Yes Yes

Population

This content downloaded from 129.82.28.144 on Sat, 07 Nov 2015 15:59:40 UTC All use subject to JSTOR Terms and Conditions

Female mass 5

21 450 128

18 18

18 213

828

1

APPENDIX I-Continued.

Species Goat Mountain

Wild

Latin binomial Oreamos americanus

Capra aegagrus

Guanaaco

Lama guanicoe

Hartebeest

Alcelaphus buselaphus

Horse (feral)

Equus caballus

Population Bitteroot Mountains, Montana, USA Chopaka, Washington, USA Collegiate Range, Colorado, USA Glacier Park, Montana, USA Lake Chelan, Washington, USA Olympic Mountains, Washington, USA Pasayten, Washington, USA Selway, Idaho, USA Swan Mountains, Montana, USA Wallowa Mountains, Oregon, USA Crete Karchat, Pakistan Kirthar, Pakistan Patagonia, Chile Torres del Paine, Chile Torres del Paine, Chile Nairobi Park, Kenya Nairobi Park, Kenya Kalahari Desert, South Africa Kalahari Desert, South Africa Assateague, Maryland, USA Grand Canyon, Arizona, USA Granite Range, Nevada, USA Jicarilla, New Mexico, USA Pryor Mountains, Montana, USA Sable Island Newfoundland, Canada

Live/ killed

Ratio M:F

Live

0.87

?

Live Live

0.98 1.00

? ?

Live Live

0.87 0.83

?

Live

0.70

?

Live Live Live

0.77 0.72 0.56

? ? ?

Live

0.80

?

Live Live Live Live Live Kill Live Kill Live Kill Live Live Live Live Live

1.10 1.20 0.56 0.71 0.93 0.34 0.74 2.40 0.49 3.30 0.37 0.79 0.76 0.45 0.54

No Yes ?

Yes Yes Yes Yes Yes Yes Yes No No No No No

Live

1.30

No

This content downloaded from 129.82.28.144 on Sat, 07 Nov 2015 15:59:40 UTC All use subject to JSTOR Terms and Conditions

Predators

Female mass 72

21

80

120

413

M m

APPENDIX I-Continued.

Species

Latin binomial

Ibex

Capra ibex

Impala

Aepyceros melampus

Kudu, Greater

Tragelaphus strepsiceros

Kulan Markhor

Equus hemionus Capra falconeri

Moose

Alces alces

Population Stone Cabin, Nevada, USA Sundre, Alberta, Canada Pryor Mountains, Montana, USA Llanos, Venezuela Alps, France En Gedi, Israel Saudi Arabia Kruger, South Africa Kruger, South Africa Serengeti, Tanzania Kaokoveld, Namibia Mkuzi, South Africa Kruger, South Africa Kruger, South Africa Kyle, Zimbabwe, Africa Pretorius Kop, South Africa Southeast lowveld, Zimbabwe, Africa Tahokwane, South Africa Tahokwane, South Africa Vosloo, South Africa Zambia, South Africa Gobi Desert, Mongolia Pamir Mountains, Pakistan Chitral Gol, Pakistan Turkmenistan Denali, Alaska, USA Eastcentral Alaska, USA Eastern and southcentral Alaska, USA Grimso, Sweden Isle Royale, Michigan, USA Isle Royale, Michigan, USA

Live/ killed

Ratio M:F

Live Live Live

0.89 0.84 0.51

No No No

Live Live Live Live Kill Live Live Live Live Kill

0.26 0.96 0.61 1.00 0.98 0.74 0.50 0.37 0.41 1.05

No Yes ? ?

Yes Yes Yes ? ?

Yes

Live Live Live Live

0.48 0.47 0.49 0.54

Yes Yes Yes Yes

Live Kill Live Live Live Kill Live Live Kill Live Kill

0.59 2.09 0.45 0.42 0.59 11.00 0.53 0.33 0.56 0.82 0.73

Yes Yes Yes Yes Yes Yes No ?

Yes Yes Yes

Live Live Kill

0.53 0.96 0.68

?

Yes Yes

This content downloaded from 129.82.28.144 on Sat, 07 Nov 2015 15:59:40 UTC All use subject to JSTOR Terms and Conditions

Predators

Female mass

53

44

157

204 37

376

APPENDIX I-Continued.

Species

Muntjac Reeve's

Indian

Latin binomial

Muntiacus reevsi

Muntiacus muntiak

Musk Ox

Ovibus moscatus

Nilgai

Boselaphus tragocamelus

Nyala

Tragelaphus angasi

Oribi

Ourebi ourebi

Orxy

Oryx beisa

Live/ killed

Ratio M:F

Northcentral Colorado, USA Northwestern Alaska, USA Susitna River Valley, Alaska, USA Yellowstone, Wyoming, USA

Live Kill Live

0.54 0.40 0.49

No Yes Yes

Live

0.68

No

Chitwan, Nepal Suffolk, Great Britain Nagarhol, India Java, Indonesia Wilpattu, Sri Lanka Banks Island, Canada Devon Island, Canada Jameson Land, Greenland Melville Island, Canada Nunivak Island, Alaska, USA Prince of Wales Island, Canada Tasiujaq, Quebec, Canada Kedadeo, India Royal Kamali-Bardia, Nepal Vanbihar, India False Bay, South Africa Hluluwe, South Africa Ndumu, South Africa Akagera, Rwanda Amsterdam, South Africa Kidepo, Uganda Murchison Falls, Uganda Natal, South Africa Nazinga, West Africa Piet Retief, South Africa Sereneti, Tanzania Serengeti, Tanzania Tarangire, Tanzania

Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live Live

0.78 0.50 0.71 0.86 0.91 0.78 1.08 0.65 0.31 0.70 0.90 0.71 0.59 1.41 0.37 1.00 0.98 2.94 0.91 0.81 0.61 0.51 0.97 1.00 0.76 0.77 0.65 0.79

Yes ?

Yes ? ? ?

Yes ? ? ? ? ?

Yes Yes Yes ?

Yes ? ? ? ? ? ? ? ?

Yes Yes ?

Population

This content downloaded from 129.82.28.144 on Sat, 07 Nov 2015 15:59:40 UTC All use subject to JSTOR Terms and Conditions

Predators

Female mass

12

15 114

181

62

14

213

M m

APPENDIX I-Continued.

Species

Latin binomial

Peccary Chacoan Collared

Catagonus wagneri Tayassu tacju

Pig

Sus scrofa

Pronghorn

Antilocapra americana

Puku Reedbuck

Kobus vardoni Redunca fulvorfula Redunca redunca

Rhinoceros Black

Diceros bicornis

Population Uruguay Southern Arizona, USA Big Bend, Texas, USA San Patricio, Texas, USA Nagarhol, India Nagarhol, India Chitwan, Nepal Eastcentral Colorado, USA Montana, USA Mountain View, Wyoming, USA Bison Range, Montana, USA Northwestern Colorado, USA Sheldon, Nevada, USA Yellowstone, Wyoming, USA Luangwa, Zambia Rolfontein, South Africa Doornkloof, South Africa Kafue, Zambia Serengeti, Tanzania

Unifolozi Corridor, South Africa Etosha, Namibia Hluhuwe, South Africa Kaokoveld, Namibia Kariba Basin, Zimbabwe Luangwa., Zambia Masai Mara, Kenya Mkuzi, South Africa Ndumu, South Africa Ngorongoro Crater, Tanzania Olduvai, Kenya

Live/ killed

Ratio M:F

Kill Kill Live Live Live Kill Live Live Live Live

1.14 1.10 0.88 0.93 0.48 1.00 0.93 0.71 1.00 0.35

Human Human ? ?

Yes Yes Yes Yes

Live Live Live Live Live Live Live Kill Live

0.88 0.55 0.50 0.54 1.42 0.56 0.44 0.86 0.66

? ? ? ? ? ? ?

Yes Yes

Live

1.10

No

Live Live Live Live Live Live Live Live Live Live

1.20 1.10 1.10 1.10 1.30 1.20 1.30 0.83 1.30 1.20

No No No No No No No No No No

This content downloaded from 129.82.28.144 on Sat, 07 Nov 2015 15:59:40 UTC All use subject to JSTOR Terms and Conditions

Predators

Female mass 35 22

55

50

?

61 30

1,081

1

APPENDIX I-Continued.

Species

Latin binomial

One-Homed White Sambar

Rhinoceros unicornis Ceratotherium simum Cervus unicolor

Serow

Capricornis crispus

Sheep Bighorn

Ovis canadensis

Blue

Pseudosis nayaur

Live/ killed

Ratio M:F

Serengeti, Tanzania Tsavo, Kenya Umfolozi, South Africa Chitwan, Nepal Umfolozi, South Africa Chitwan, Nepal Kahna, India Kahna, India Nagarhol, India Nagarhol, India Akita Prefecture, Japan

Live Live Live Live Live Live Live Kill Live Kill Live

0.83 0.92 1.40 0.77 1.00 0.55 0.30 1.20 0.37 1.30 1.05

No No No No No Yes Yes Yes Yes Yes ?

Absaroka Mountains, Wyoming, USA Aravaipa, Arizona, USA Banff, Alberta, Canada Big Hatchet, New Mexico, USA Gros Ventre, Wyoming, USA Idaho Primitive Area, Idaho, USA Southwestern Arizona, USA San Bernadino, California, USA San Gabriel, California, USA Sheep River, Alberta, Canada Sierra Nevada, California, USA Southwestern Alberta, Canada White Mountains, California, USA Yellowstone, Wyoming, USA Multiple sites, western USA Annapurna, Nepal Lapoche, Pakistan

Live

0.56

Yes

Live Live Live

0.60 0.91 0.88

? ? ?

Live Live

0.56 0.48

?

Yes

Live Live

0.56 0.81

?

Yes

Live Kill Live Live Live

0.72 0.67 0.73 0.77 0.63

Yes Yes Yes Yes Yes

Live Kill Live Live

0.61 0.56 1.02 1.30

Yes Yes Yes Yes

Population

This content downloaded from 129.82.28.144 on Sat, 07 Nov 2015 15:59:40 UTC All use subject to JSTOR Terms and Conditions

Predators

Female mass

1,600 1,600 164

91

60

39

2 2

APPENDIX I-Continued.

Species

Dall's

Snow Soay Springbok

Latin binomial

Ovis dalli

Ovis nivicola Ovis aires Antidorcas marsupialis

Tahr

Hemitragus jemlahicus

Topi Urial

Damalascus lunatus Ovis orientalis

Warthog

Phacochoerus aethiopicus

Water Buffalo Waterbuck

Bubalus bubalus Kobus ellpsiprymus

Population Dogadi, Nepal Mustang, Nepal Phosumdo, Pakistan Shey, Pakistan Sun Dah, Nepal Denali, Alaska, USA Gladys, British Columbia, Canada Kluane, Yukon, Canada Koryak, Siberia, Russia Hirta, Great Britain Bontebok Park, South Africa Mountain Zebra Park, South Africa Kalahari, South Africa Kalahari, South Africa Kruger, South Africa Bhota Kosi, Nepal Macauley, New Zealand Rancho Piedra, California, USA Serengeti, Tanzania Baltoro, Pakistan Kalabagh, Pakistan Salt range, Pakistan Kafue, Zambia Kruger, South Africa Nairobi Park, Kenya Queen Elizabeth Park, Uganda Selous Reserve, Tanzania Sengwa, Zimbabwe Southern Zimbabwe Zaire Kosi Tappu, Nepal Kafue, Zambia Kafue, Zambia

Live/ killed

Ratio M:F

Live Live Live Live Live Kill Live

1.00 0.86 0.70 1.30 1.23 1.20 0.61

Yes Yes Yes Yes Yes Yes Yes

Live Live Live Live Live

0.90 0.65 1.20 0.85 0.71

Yes ?

No ? ?

Live Kill Live Live Live Live

0.85 1.44 1.15 0.48 1.10 0.77

Yes Yes Yes Yes No No

Kill Live Live Live Kill Kill Live Live Live Live Live Live Live Kill Live

1.30 1.00 0.96 1.00 3.40 1.40 0.46 0.97 0.92 0.51 0.73 0.52 1.01 1.54 1.0

Yes ? ?

No Yes Yes Yes ? ?

Yes Yes Yes ?

Yes Yes

This content downloaded from 129.82.28.144 on Sat, 07 Nov 2015 15:59:40 UTC All use subject to JSTOR Terms and Conditions

Predators

Female mass

50

50 24 37

59

130 27

57

576 188

APPENDIX I-Continued.

Species

Wildebeest

Zebra, (Plain's)

Latin binomial

Connochaetes taurinus

Equus burchelli

Population Serengeti, Tanzania Kruger, South Africa Lake Nakuru, Kenya Luangwa, Zambia Queen Elizabeth Park, Uganda Umfolozi, South Africa Kruger, South Africa Ngorongoro Crater, Tanzania Ngorongoro Crater, Tanzania Serengeti, Tanzania Serengeti, Tanzania Kalahari, South Africa Kalahari, South Africa Timbavati, South Africa Kruger, South Africa Central Kruger, South Africa Northern Kruger, South Africa Pretoriskop, Kruger, South Africa Croc Bridge, Kruger, South Africa Kafue, Zambia Ngorongoro Crater, Tanzania Serengeti, Tanzania

Live/ killed

Ratio M:F

Live Kill Live Live Live Live Kill Live Kill Kill Live Live Kill Kill Live Live Live Live

0.4 1.68 0.63 0.80 0.63 0.38 0.99 1.12 0.94 3.00 1.10 0.50 0.91 1.20 0.71 0.72 0.66 0.74

Yes Yes ? ?

Yes ?

Yes Yes Yes yes yes yes yes yes yes ? ? ?

Live

0.84

?

Kill Kill Kill

0.17 0.41 0.85

yes Yes yes

This content downloaded from 129.82.28.144 on Sat, 07 Nov 2015 15:59:40 UTC All use subject to JSTOR Terms and Conditions

Predators

Female mass

164 164

302

M m