Abstract

The long-term viability of small, isolated populations has been questioned in light of stochasticity and bottlenecks. Following a drought in northwestern Montana in 2003, the pronghorn (Antilocapra americana) of the National Bison Range experienced a demographic bottleneck. We used information theoretic approaches to examine the influence of sex, age, genetic variation, summer lactation, mate-search effort by females, and mating effort by males on subsequent winter survival and spring fecundity. Survival of males was influenced by age, whereas survival of females was influenced by prior energy expenditure and genetic variation, implicating inbreeding depression as the mechanism. Fecundity of females also was influenced by prior energy expenditure and genetic variation, with heterosis as the apparent mechanism. Our results agree with those of other studies that have emphasized the need to maintain genetic variation and limit inbreeding in small, isolated populations, and to account for stochasticity in population viability assessments and long-term management planning.

A major concern in conservation biology is the long-term viability of small, isolated populations (Keller et al. 1994; Westermeier et al. 1998). Of particular interest is the impact of stochasticity on these populations (Frankham 1995; Saether 1997), because stochastic variation in environmental conditions can lead to population bottlenecks and extinction (Foley 1994; Keller et al. 1994). Environmental variation affects early-winter body condition (Garroway and Broders 2005) and subsequent fecundity (Lawrence et al. 2004) and survival (Lawrence et al. 2004; Milner et al. 1999). These effects may covary with effects such as age (Gaillard et al. 2000), sex (Toigo and Gaillard 2003), genetic variation (Keller et al. 1994), and prior energy expenditure (Gaillard et al. 2000).

Under normal environmental conditions, juveniles and senescing individuals show year-to-year variation in survival and fecundity (Gaillard et al. 2000), whereas middle-aged adults exhibit the lowest mean and variance in these parameters (Festa-Bianchet et al. 2003; Gaillard et al. 2000). Males of polygynous species often show higher mortality rates than females (Promislow 1992; Toigo and Gaillard 2003). Attempts to identify correlations between genetic variation and measures of fitness have had mixed results (Amos et al. 2001; Coltman et al. 1998; Coltman and Slate 2003; Coulson et al. 1998, 1999). Activities such as mate-guarding and male–male combat can be energetically costly for males (Bear 1971; Byers et al. 1994; Lawrence et al. 2004; Yoccoz et al. 2002), whereas a sampling strategy that involves movement between harems is more costly to females than remaining in 1 location and mating with a single male (Byers et al. 2005, 2006). Lactation also is costly, and females that wean no offspring expend much less energy during the summer months than females that wean offspring (Bear 1971; Lawrence et al. 2004). These differences in energy expenditure translate directly into early-winter body condition if late-summer forage is inadequate to replenish fat reserves (Gaillard et al. 2000). Although much is known about mechanisms contributing to survival and fecundity under normal environmental conditions, little is known about their effects during a bottleneck, because there is a lack of long-term data sets that encompass the times before and after a bottleneck (Keller et al. 1994; Westermeier et al. 1998).

The pronghorn (Antilocapra americana) of the National Bison Range (NBR) have been studied extensively since 1981. Data on age, sex, survival, fecundity, genetics, and reproductive effort have been collected on an annual basis for nearly every individual within the population. A severe drought in the summer of 2003 caused vegetation to senesce early and food availability to be limited (Byers et al. 2006). In late summer of 2003, many individual pronghorn appeared emaciated with rib and pelvis outlines visible. The drought resulted in high overwinter mortality and widespread reproductive failure in the population. This bottleneck allowed us to test mechanisms leading to reduced survival and fecundity during a stochastic environmental event.

We investigated how various factors associated with reproduction and mating strategies contributed to winter survival and spring fecundity for the pronghorn population at the NBR during 2003–2004. Our objectives were to ascertain whether survival during winter 2003–2004 was influenced by sex, age, genetic variation, summer lactation (females only), mate-choice strategy (females only), mating success (males only), or a combination of these; and to ascertain whether fecundity of females in spring 2004 was influenced by age, genetic variation, summer lactation, mate-choice strategy, or a combination of these.

Materials and Methods

Study site.—The NBR is a 7,504-ha National Wildlife Refuge in western Montana (see description by Byers [1997a]). A 2.5-m fence creates an outer boundary that is impermeable to pronghorn, whereas internal fences with 0.5-m clearance divide the area into 8 permeable sections. The NBR consists largely of Palouse prairie grassland, with patches of Douglas-fir (Pseudotsuga douglasii) and ponderosa pine (Pinus ponderosa). The NBR pronghorn population is descended from transplants brought from Yellowstone National Park in 1951 and 1952 (Dow and Wright 1962). Subsequently, the United States Fish and Wildlife Service placed small numbers of pronghorn (mostly from Montana) on the NBR on 5 separate occasions (Byers 1997a). Other ungulate species that inhabit the NBR include bison (Bison bison), white-tailed deer (Odocoileus virginianus), mule deer (Odocoileus hemionus), bighorn sheep (Ovis canadensis), and elk (Cervus elaphusByers 1997a).

Field data collection.—All pronghorn on the NBR are marked or are individually identifiable (Byers 1997a). Every year since 1982, newborn fawns have been captured and ear-tagged according to the procedures established by Byers (1997b), which have been shown not to expose fawns to added risk. Since 1998, fawns also have been tissue-sampled. Untagged individuals are recognized by size and morphology of horns, neck-band pattern, tail pattern, and other distinctive features.

In September 2003, we made behavioral observations of the pronghorn rut. All harems were located daily and observed for 1–6 h. Length of observation was determined by the activity seen within the 1st hour; if no courtship was observed, the researcher relocated to another harem. We noted the identity of the harem male and identities of all females present. Mating effort of males was determined by their harem-days, the sum of their daily harem sizes across all days of the rut (Byers 1997a:223). Mating effort of females was based on the number of switches (movement from one harem to another) that the female made during the mate-sampling period (the 15 days before and including estrus—Byers et al. 1994, 2006).

Total censuses of individuals were conducted in November 2003 (prebottleneck), May 2004 (postbottleneck), and August 2004 (postweaning). We are certain that our censuses were complete because the population is enclosed on the NBR by a fence and we are familiar with all possible hiding areas on the refuge. We used the census data, along with data on age and sex, to construct a postbottleneck age–sex distribution of the NBR pronghorn population.

Genetic analysis.—A 0.5-cm disc of tissue was removed from the ear of each fawn at the time of capture. Each sample was stored in 100% ethanol while in the field, and then at −80°C in the laboratory. Samples were analyzed according to the procedures described by Carling et al. (2003a). The genotypes of most individuals were identified at 10 polymorphic microsatellite loci: Aam1–Aam8 (GenBank accession numbers AF525012–AF525019—Carling et al. 2003a, 2003b), ADCYC (Lou 1998), and PRM6506 (Stephen et al. 2005).

We used the program Cervus (Marshall et al. 1998) to test for Hardy–Weinberg equilibrium and to assign paternity at the 95% confidence level. We compared population-level hetero-zygosity and allelic diversity in 2003 versus 2004 using a Mann–Whitney U-test. We also calculated the following measures of individual genetic variation: multilocus heterozy-gosity (MLH), standardized multilocus heterozygosity (MLHst), mean d2 (d2), and standardized mean d2 (d2st). MLH was defined as the proportion of an individual's loci that were heterozygous. MLHst was defined as the proportion of an individual's loci that were heterozygous, accounting for the number of loci at which the individual was typed (Coltman et al. 1999). Mean d2 was defined as the distance in tandem repeats between the 2 alleles at each locus, averaged over all loci (Coulson et al. 1999; Pemberton et al. 1999). Standardized mean d2 was defined as the distance in tandem repeats between the 2 alleles at each locus, averaged over all loci within an individual, and accounting for differences in the variance of d2 at each locus (Pemberton et al. 1999).

Statistical analysis.—We used SAS 9.1 (SAS Institute, Inc., Cary, North Carolina) for our statistical analyses. To determine what factors influenced winter survival and spring fecundity, we ran 4 analyses using information theoretic criteria (Burnham and Anderson 2002): winter survival of males and females (pooled), winter survival of males, winter survival of females, and spring fecundity of females. The response variables were binary: winter survival was scored as survived or died, and fecundity was scored as female gave birth or did not give birth. Explanatory variables included sex (categorical; winter survival only), age (categorical), MLH (quantitative), MLHst (quantitative), d2 (quantitative), d2st (quantitative), fawns weaned in 2003 (categorical; females only), number of harem switches during the 2003 rut (quantitative; females only), and harem-days during the 2003 rut (quantitative; males only). We developed a set of candidate models (∼12 models per analysis) using the aforementioned variables and ran each as a mixed model with fixed effects using maximum-likelihood estimation. For each of the 4 analyses, we recorded Akaike information criterion corrected for small sample sizes (AICc) scores for each model (Burnham and Anderson 2002) and kept only those models with ΔAICc scores within approximately 2 points of AICc-min. We then calculated Akaike weights (wi) and likelihoods (L) for each of the remaining models.

Research met guidelines approved by the American Society of Mammalogists (Gannon et al. 2007), and was approved by the University of Idaho Institutional Animal Care and Use Committee.

Results

Of the 34 males, 66 females, and 10 fawns that were alive in the November 2003 census, only 7, 41, and 2, respectively, survived until May 2004. There was 100% mortality of males in most age categories, except ages 1–3 years (Fig. 1). Mortality of females occurred across all age categories, but none reached 100% (Fig. 1). Thirty-four (83%) of the surviving females also did not give birth. The age–sex distribution of pronghorn on the NBR in August 2004 consisted of 7 males between the ages of 2 and 4 years and 41 females between the ages of 1 and 9 years (Fig. 2).

Fig. 1

Percentage and total number of pronghorn (Antilocapra americana) on the National Bison Range, Montana, that died in the 2003–2004 winter by age and sex.

Fig. 1

Percentage and total number of pronghorn (Antilocapra americana) on the National Bison Range, Montana, that died in the 2003–2004 winter by age and sex.

Fig. 2

Number of pronghorn (Antilocapra americana) alive on the National Bison Range, Montana, during August 2004 census by age and sex.

Fig. 2

Number of pronghorn (Antilocapra americana) alive on the National Bison Range, Montana, during August 2004 census by age and sex.

Two of the 10 microsatellite loci were out of Hardy–Weinberg equilibrium (ADCYC: expected heterozygosity = 0.808, observed heterozygosity = 0.749, P < 0.01; and Aam3: expected heterozygosity = 0.741, observed heterozygosity = 0.673, P < 0.01). However, we chose to use these loci in our analysis because they were not far out of equilibrium. On average, we were able to assign paternities to 80% of fawns with 95% confidence. Neither population-level heterozygosity nor allelic diversity changed during the bottleneck (Table 1). However, 1 allele was lost during the bottleneck.

Table 1

Results of Mann–Whitney U-test comparing population-level heterozygosity and allelic diversity measures for the pronghorn (Antilocapra americana) of the National Bison Range, Montana, in 2003 and 2004.

 2003 2004 U P 
Average heterozygosity 0.5939 0.6029 0.159 
Allelic diversity 4.9 4.8 0.159 
 2003 2004 U P 
Average heterozygosity 0.5939 0.6029 0.159 
Allelic diversity 4.9 4.8 0.159 

In the analysis of winter survival for combined males and females, 3 competing models were selected as most parsimonious. Sex appeared in all 3 models, whereas age appeared in 2 models and d2 appeared in 1 model (Table 2). In the analysis of winter survival of males only, a single most-parsimonious model was selected. The only variable in the top model was age (Table 2). No other model was within 2 ΔAICc points of this model. In the analysis of winter survival of females only, 8 competing models were selected. Fawns weaned appeared 3 times and d2 appeared twice in the top models, whereas d2st appeared in 1 of the top models (Table 2). Switches appeared twice, and MLH and MLHst each appeared in 1 model (Table 2). In the analysis of fecundity, d2st appeared in all of the 4 competing models, whereas fawns weaned and switches in rut each appeared in 2 of the top models (Table 2).

Table 2

Results of information theoretic analysis of factors affecting 2003–2004 overwinter survival and 2004 fecundity in pronghorn (Antilocapra americana) of the National Bison Range, Montana.a

Analysis Variables in model Sample size AICc ΔAICc L wi 
Overwinter survival of males and females Age, sex 95 132 0.464 
 Age, sex, d2 95 133 1.1 0.577 0.268 
 Sex 95 133 1.1 0.577 0.268 
Overwinter survival of females Fawns weaned 52 74.7 0.267 
 d2, fawns weaned 52 76.2 1.5 0.472 0.126 
 d2 52 76.2 1.5 0.472 0.126 
 d2st 52 76.5 1.8 0.407 0.108 
 Fawns weaned, switches 52 76.8 2.1 0.35 0.093 
 MLH 52 76.8 2.1 0.35 0.093 
 MLHst 52 76.8 2.1 0.35 0.093 
 Switches 52 76.8 2.1 0.35 0.093 
Overwinter survival of males Age 31 34 
Fecundity of females d2st 34 28.6 0.347 
 d2st, fawns weaned 34 28.9 0.3 0.861 0.299 
 d2st, switches 34 29.9 1.3 0.522 0.181 
 d2st, fawns weaned, switches 34 30 1.4 0.497 0.173 
Analysis Variables in model Sample size AICc ΔAICc L wi 
Overwinter survival of males and females Age, sex 95 132 0.464 
 Age, sex, d2 95 133 1.1 0.577 0.268 
 Sex 95 133 1.1 0.577 0.268 
Overwinter survival of females Fawns weaned 52 74.7 0.267 
 d2, fawns weaned 52 76.2 1.5 0.472 0.126 
 d2 52 76.2 1.5 0.472 0.126 
 d2st 52 76.5 1.8 0.407 0.108 
 Fawns weaned, switches 52 76.8 2.1 0.35 0.093 
 MLH 52 76.8 2.1 0.35 0.093 
 MLHst 52 76.8 2.1 0.35 0.093 
 Switches 52 76.8 2.1 0.35 0.093 
Overwinter survival of males Age 31 34 
Fecundity of females d2st 34 28.6 0.347 
 d2st, fawns weaned 34 28.9 0.3 0.861 0.299 
 d2st, switches 34 29.9 1.3 0.522 0.181 
 d2st, fawns weaned, switches 34 30 1.4 0.497 0.173 
a

Variables and statistics are defined in the “Materials and Methods.”

Discussion

Because of the poor range condition of the NBR in fall 2003 (Byers et al. 2006), the pronghorn population experienced a bottleneck. One stochastic environmental event, the drought, led to low overwinter survival and spring fecundity. The effects of the drought on survival and fecundity seemed only to affect the pronghorn of the NBR—to our knowledge, annual counts of ungulates did not show any other species on the NBR to have exceptionally low survival or fecundity rates. Under normal environmental conditions, nearly 100% of female pronghorn ≥18 months of age produce twins, with no apparent decline in reproductive rate attributable to age (Byers 1997a; Byers and Hogg 1995; Byers and Moodie 1990). However, our study demonstrated that female pronghorn can exhibit climate-induced reproductive failure. Because of the additional stress placed on pregnant pronghorn during the drought, it was likely that females were unable to maintain a level of maternal energy expenditure sufficient to carry twin fawns to term. It is possible that the drought affected fecundity only of pronghorn because other ungulate species at the NBR fall lower on the maternal energy-expenditure spectrum than pronghorn (Byers 1997a; Byers and Hogg 1995; Byers and Moodie 1990; Oftedal 1985).

Models that included sex as a factor were selected as more parsimonious than competing models predicting survival during the winter of 2003–2004. Many mammalian species exhibit male-biased mortality (Gaillard et al. 2000), and pronghorn are no exception (Byers 1997a). Explanations for this phenomenon in mammals include unequal buildup of winter fat reserves (Garroway and Broders 2005) and differential energy expenditure during fall ruts (Bear 1971; Lawrence et al. 2004; Yoccoz et al. 2002). Male pronghorn always have less fat reserves than females (Bear 1971). However, when examined specifically in the survival analysis, prior energy expenditure in males (harem-days) was not a factor in the top model.

Mortality was distributed across all age classes of females, but in males occurred primarily in the youngest and oldest age classes. The effect of age on winter survival for the combination of male and female pronghorn was due to the distribution of mortality among males, rather than females. Indeed, age of males appeared in the analysis of their survival, whereas age of females did not. Fawns of both sexes typically have high rates of winter mortality because they do not have sufficient amounts of fat when winter begins (Byers 1997a). Because male fawns grow more in size but not mass than female fawns, they have effectively less fat reserved for the winter (Byers 1997a). Also, most males do not mate until they are 3 years old (Byers 1997a), so middle-aged males may die during winter at higher rates than juveniles (with the exception of fawns) because they expend more energy during the fall rut (Byers 1997a).

The appearance of d2 in 1 of the top overwinter survival models for combined males and females is probably due to its influence on survival of females. Mean d2 and d2st were included in 3 of the 4 highest-ranked models in the analysis of overwinter survival of females and d2st was included in all 4 top-ranked models in the analysis of fecundity. MLH and MLHst also were included in models selected in the analysis of overwinter survival of females. Mean d2 and d2st assume a stepwise mutation model of microsatellite evolution (Valdes et al. 1993) and are measures of an individual's inbreeding coefficient (Coulson et al. 1998); the higher the level of d2, the less inbred an individual is. Theory suggests that d2 and d2st may not always correlate with fitness in isolated populations (Coulson et al. 1999); however, some studies have demonstrated a correlation between d2 and birth weight and subsequent neonatal survival in red deer (Cervus elaphusCoulson et al. 1998) and birth weight and survival to weaning in pups of harbor seals (Phoca vitulinaColtman et al. 1998). Correlations with fitness imply either heterosis or inbreeding depression (Coulson et al. 1998). MLH–fitness correlations imply more recent inbreeding events (Coulson et al. 1998), and there is support for their existence in the literature (Amos et al. 2001; Coltman et al. 1998). Given that all measures of genetic variation appeared in top models of survival of female pronghorn, the underlying cause of the association appears to be inbreeding depression (Coulson et al. 1999). On the other hand, d2st was the only measure of genetic variation that appeared in models of fecundity, and thus heterosis appears to be the underlying mechanism (Coulson et al. 1999).

Mate sampling by female pronghorn is energetically costly (Byers et al. 2005, 2006). We found negative correlations between harem switches (an index of sampling energy expenditure) and survival and fecundity. Our findings support the conclusions of Byers et al. (2005, 2006) that the energy cost of mate sampling by female pronghorn is, in some circumstances, large enough to create a fitness cost.

As mentioned previously, pronghorn fall on the high end of the maternal energy-expenditure spectrum (Byers 1997a; Byers and Hogg 1995; Byers and Moodie 1990; Oftedal 1985). Female pronghorn that lactated throughout summer 2003 and weaned 1 fawn (no females weaned 2 fawns in 2003) certainly expended more energy than females that lost both fawns early in the summer. This cost was evident in both the analysis of overwinter survival of females and the analysis of fecundity. Confirming this importance, none of the females that weaned fawns in 2003 gave birth in 2004.

Our study has led to some considerations that need to be made when managing this and other small populations in the future. First and foremost, it is important to consider the implications of fences and other barriers that prohibit the natural migration of wild populations of pronghorn (O'Gara and Yoakum 2004). Second, given the number of recent studies that have demonstrated correlations between genetic variation, inbreeding, and fitness, it is important that managers strive to maximize genetic variation and minimize inbreeding in populations, particularly those that are small, isolated, or both. Finally, recent studies have demonstrated that factors such as age, sex, genetic variation, and prior energy expenditure may further influence survival and fecundity when environmental conditions are extreme (Keller et al. 1994). When assessing population viability and planning long-term management, managers should consider the possibility that stochastic environmental events such as droughts and severe winters could heavily affect or even eliminate a population that may otherwise seem stable.

Acknowledgments

We thank all of our volunteer field assistants; K. Dunn for assisting with field data collection and manuscript comments; L. Waits for assistance with genetic analysis; and K. K. Barnowe-Meyer, E. Clancey, W. Godsoe, and M. Oswald for manuscript comments. We thank the United States Fish and Wildlife Service and the NBR staff. This work was supported by National Science Foundation grants IBN 0097115 to JAB and IOS 0738012 to JAB and L. Waits.

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Author notes

Associate Editor was Rick A. Sweitzer.