Intraspecific variability in the social and genetic mating systems of prairie voles, Microtus ochrogaster

Animal Behaviour 82 (2011) 1387e1398

Contents lists available at SciVerse ScienceDirect

Animal Behaviour journal homepage: www.elsevier.com/locate/anbehav

Intraspecific variability in the social and genetic mating systems of prairie voles, Microtus ochrogaster Craig A. Streatfeild a,1, Karen E. Mabry a, 2, Brian Keane b, 3, Thomas O. Crist a, Nancy G. Solomon a, * a b

Center for Animal Behavior and Department of Zoology, Miami University, Oxford, OH, U.S.A. Center for Animal Behavior and Department of Zoology, Miami University, Hamilton, OH, U.S.A.

a r t i c l e i n f o Article history: Received 27 September 2010 Initial acceptance 28 October 2010 Final acceptance 8 September 2011 Available online 21 October 2011 MS. number: A10-00646R3 Keywords: genetic mating system intraspecific variation mammal mating system Microtus ochrogaster population density social monogamy vegetative structure vole

Intraspecific variability in mating systems has been documented previously, but there are few studies where investigators have used intraspecific comparisons to investigate the influence of ecological or demographic factors on social and genetic mating systems. We studied two populations of prairie voles, Microtus ochrogaster, one near Lawrence, Kansas, U.S.A., and the other in Bloomington, Indiana, U.S.A. We examined differences in spatial structuring of vegetation between sites, which might contribute to intraspecific variation in social and genetic mating patterns. Since space use is often related to the mating system, we calculated home range size of a subset of adult males and females in both populations and used live trapping to examine the number of same- or opposite-sex conspecifics sharing capture locations. We applied social network analysis to live-trapping data to quantify the number and strength of social ties with opposite-sex conspecifics. Genetic parentage analysis provided an estimate of the number of mates per individual. We found that space use and overlap with members of the same and opposite sex were best explained by effects of site and sex. The relative strength of social associations with opposite-sex conspecifics was best predicted by site, density and sex. The genetic mating system was best explained by population density. Although we cannot disentangle population density from ecological variables related to site (e.g. distribution of vegetation) that may have affected social monogamy in our study populations, our study shows that extrinsic variables do affect differences in social and genetic mating systems between populations. Ó 2011 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Many species show spatial and temporal variation in their social and mating systems (Lott 1991; Travis et al. 1995; Moehlman 1998; Brashares & Arcese 2002; Randall et al. 2005; Schradin 2005). Extrapolating from interspecific comparisons, Lott (1991) described a number of ecological factors that may influence intraspecific variation in social mating systems: food resources, spatial distribution of mates and population density. According to classic socioecological theory, food resources can influence mating systems through their distribution, stability, predictability (Powell 1989), or abundance (Moehlman 1989; Lott 1991). For example, unevenly distributed, clumped resources have been proposed to promote social polygyny, because they may result in aggregations of females that can be monopolized by one * Correspondence: N. G. Solomon, Department of Zoology, Miami University, Oxford, OH 45056, U.S.A. E-mail address: [email protected] (N. G. Solomon). 1 C. Streatfeild is now at E3 Consulting Australia Pty Ltd, 21 McLachlan St, Fortitude Valley, Queensland, Australia, 4006. 2 K. Mabry is now at the Biology Department, MSC 3AF, New Mexico State University, Las Cruces, NM 88003, U.S.A. 3 B. Keane is at the Center for Animal Behavior and Department of Zoology, Miami University, Hamilton, OH 45011, U.S.A.

male (Emlen & Oring 1977; Clutton-Brock & Harvey 1978; Slobodchikoff 1984; Clutton-Brock 1989). A uniform distribution of resources may be more conducive to social monogamy. The spatial distribution of females should follow the distribution of resources because successful rearing of offspring requires that females have access to resources (Clutton-Brock & Vincent 1991; Harcourt & Stewart 2007; Verdolin 2007; but see Brotherton & Manser 1997). The distribution of males should be mapped onto that of females because reproductive success (RS) of males is determined by the number of offspring that they successfully sire (Emlen & Oring 1977; Ostfeld 1990; Clutton-Brock & Parker 1992). This classic view is somewhat simplified and in nature the situation is more dynamic. Food can influence the spatial distribution of males, and males can influence the spatial distribution of females (Harcourt & Stewart 2007). Ultimately, the spatial distribution of both females and males can have a strong impact on the mating system. Although some studies did not report differences in social mating systems of birds and mammals with regard to resource distribution (see references in Lott 1991), those that have reported differences found a polygynous social mating system when resources were clumped (e.g. Davies & Lundberg 1984; Cowan & Bell 1986).

0003-3472/$38.00 Ó 2011 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.anbehav.2011.09.023

1388

C. A. Streatfeild et al. / Animal Behaviour 82 (2011) 1387e1398

Demographic variation, particularly fluctuations in population density, may also influence variation in mating systems. Differences in density can affect the social mating system as well as the availability of mates and opportunities for copulating with multiple partners for both sexes (Emlen & Oring 1977; Kokko & Rankin 2006; Iossa et al. 2009; but see Dobson et al. 2010). The differences in reproductive potential should then be reflected in different social and genetic mating systems. When densities are high, some investigators have reported a preponderance of polygynous groups or groups containing multiple males and females (Busher et al. 1983; Kamler et al. 2004; Schradin & Pillay 2005; Schradin et al. 2010), but at least one study found no changes in the social mating system with changes in density (Smith & Ivins 1984). An additional layer of complexity results from more recent observations that the social and genetic mating systems may not be the same within a population (Westneat 1990; Brotherton et al. 1997; Reichard 2003; Lin et al. 2009). For example, individuals may display social monogamy, but they may also engage in extrapair copulations (Westneat & Stewart 2003; Clutton-Brock & Isvaran 2006). A few studies have examined intraspecific variability of genetic mating systems in natural populations (alpine marmots, Marmota marmota: Arnold et al. 1993; Goosens et al. 1998; kit foxes, Vulpes macrotis: Ralls et al. 2007; dusky pipefish, Syngnathus floridae: Mobley & Jones 2007, 2009; guppies, Poecilia reticulata: Neff et al. 2008; mouthbrooding cichlids, Ctenochromis horei: Sefc et al. 2009) but, to our knowledge, none have investigated the relationship between ecological factors and intraspecific variability of both the social and genetic mating systems in more than one natural population of the same species. For example, when population densities are high or resources clumped, many animals coexist in a given area and socially monogamous individuals may have more opportunities to obtain extrapair matings (Getz & Hofmann 1986; Getz et al. 1993). In addition, it is unclear whether the factors that favour the formation and maintenance of sociosexual partnerships are the same as those that enhance an individual’s ability to gain extrapair fertilizations. Therefore, the objective of our study was to determine whether ecological or demographic factors are related to intra- and interpopulational variation in the social and genetic mating patterns of prairie voles, Microtus ochrogaster, by examining two geographically distinct populations with somewhat different climate and habitats. The prairie vole has been classified as socially monogamous on the basis of a long-term field study in Illinois (IL, U.S.A.; Getz & Hofmann 1986; McGuire & Getz 1998), some studies in seminatural populations (Ophir et al. 2007; Solomon et al. 2009), as well as many laboratory studies (Williams et al. 1992; Carter et al. 1995; Cho et al. 1999; Ophir et al. 2007). Despite the classification of socially monogamy, some males display a tactic referred to as wandering (Getz et al. 1993; Solomon & Jacquot 2002; Ophir et al. 2008; McGuire & Getz 2010; also referred to as roaming in some species, e.g. African striped mouse, Rhabdomys pumilio: Schradin & Lindholm 2011) instead of residing at a single nest with one female. The proportion of socially monogamous (maleefemale) pairs can vary with changes in population density. When population density is low, most prairie voles live as maleefemale pairs or as single females, which often remain at a nest after the death of their male social partner. When density becomes high, there are more social groups (Getz et al. 1987; Cochran & Solomon 2000; Lucia et al. 2008). There is also some indication of variation in the social mating system across the species’ range. In particular, several investigators have suggested that fewer prairie voles in Kansas (KS, U.S.A.) display social monogamy than voles in IL (Solomon & Getz 1997; Roberts et al. 1998; reviewed by Cushing & Kramer 2005). In the KS population, the same males and females were not trapped together repeatedly (Danielson & Gaines 1987; but see Fitch 1957

for observations suggesting cohabitation sometimes) as in the IL populations (Getz & Carter 1980). Inferences based on home range size were less clear. Although data from the IL populations showed that males and females had home ranges of approximately equal size and overlapped to a great extent with their presumptive opposite-sex social partner (Hofmann et al. 1984; Gaulin & FitzGerald 1988), voles in KS had sexually dimorphic home range sizes, with those of males being 1.5 larger than those of females (Swihart & Slade 1989) and almost always larger than those of females during the springeautumn breeding season (Gaines & Johnson 1982; but see Martin 1956; Choate & Williams 1978). Additionally, sexual dimorphism in body size was reported in voles from KS (Fitch 1957; Roberts et al. 1998), but not in voles from eastcentral IL (Gaulin & FitzGerald 1988; Roberts et al. 1998; Ophir et al. 2007). Lack of sexual dimorphism suggests social monogamy (Boonstra et al. 1983). Thus, although most of the previous studies suggest that fewer prairie voles from KS display social monogamy compared with voles in east-central IL, a number of these studies were not conducted in natural populations. In addition, although it has been less thoroughly investigated, there is also the possibility of variation in genetic monogamy across the prairie vole range (Solomon et al. 2004; Mabry et al. 2011) since the degree of multiple mating by male and female prairie voles in natural populations in Indiana (IN, U.S.A.), IL and KS is nontrivial (Solomon et al. 2004; Mabry et al. 2011). Researchers have pursued a variety of approaches to understand intraspecific variation in the mating system of prairie voles. Unfortunately, differing methods and conflicting results make it difficult to summarize mating system variation and the ecological factors that may cause this difference. For example, Getz et al. (1992) studied unmanipulated prairie vole populations in two types of habitat (alfalfa and bluegrass) in IL and found no variation in the social mating system of prairie voles between habitats. Roberts et al. (1998) brought voles from two populations (IL and KS) into the laboratory and documented between-population variation in sexual dimorphism and social behaviour. In contrast, Ophir et al. (2007) explored interpopulation variation in the prairie vole mating system in the laboratory and in seminatural enclosures by examining laboratory-born voles descendent from animals originating from IL or Tennessee (TN, U.S.A.). However, to date, no one has examined ecological variables that may be related to intraspecific variation in both the social and genetic mating systems in natural populations of prairie voles from different geographical areas. Therefore, the specific aims of this study were to test two nonmutually exclusive hypotheses: (1) the frequency of social and genetic monogamy is affected by resource distribution; (2) the frequency of social and genetic monogamy is affected by population density. We predicted that social and genetic monogamy should be more common when (1) resources are less aggregated and (2) population density is low. More specifically, we expected that with social monogamy, males and females would have similar home range sizes and share the same trapping locations with fewer opposite- and same-sex conspecifics. Socially monogamous males and females would also be expected to have stronger associations with primarily one opposite-sex conspecific rather than having multiple weaker associations with more than one opposite-sex conspecific. Finally, genetically monogamous individuals should produce offspring with only one opposite-sex conspecific. METHODS Study Sites Our study sites were located at the University of Kansas Nelson Environmental Study Area (12 km northeast of Lawrence, KS,

C. A. Streatfeild et al. / Animal Behaviour 82 (2011) 1387e1398

39
030 0700 N, 95
1102700 W), and the Indiana University Bayles Road Preserve (Bloomington, IN, 39
130 0000 N, 86
320 2700 W). For details on the study areas see Mabry et al. (2011). We conducted fieldwork in KS during MayeJune 2005 and 2006, and in IN during JulyeAugust 2006 and 2007. We began fieldwork earlier in KS because the peak breeding season begins about 1 month earlier in KS than in IN (Corthrum 1967; Rose & Gaines 1978) and because the KS population experiences a breeding lull in midsummer (Gaines & Rose 1976). Live Trapping We characterized the social mating system of prairie voles using live trapping and radiotracking. At each site each year, we conducted live trapping for 4 weeks, which consisted of 1 week of trapping a grid with stations placed at 10 m intervals, then 3 weeks of trapping at known female nests. Live trapping was followed by 1 week of radiotracking. During grid trapping, we placed a single Ugglan multiple capture trap (Grahnab, Hillerstorp, Sweden) in a vole runway within 1 m of each grid marker. We checked grid traps a total of 10 times per week. During the first week, if possible, we identified nests by either radiotracking or fluorescent powder tracking of adult females to nests (see Lucia et al. 2008 for complete methods for locating vole nests). Beginning at least 1 day after a female was radiocollared, we located the female once daily between 1300 and 1500 hours for 2e3 days using a hand-held receiver (Advanced Telemetry Systems, Inc., Isanti, MN, U.S.A.) and a three-element Yagi antenna (AF Antronics, Inc., Urbana, IL, U.S.A.). We conducted radiotracking during the heat of the day to maximize the probability of finding females in their nests. Once we identified nests, we placed four Ugglan multiple capture traps within 30 cm of nest entrances. We used the same trapping schedule that was used for grid trapping during nest trapping, for a total of 30 nest trap checks during 3 weeks. Details of grid and nest trapping are given in Mabry et al. (2011). Upon first capture, we individually marked all animals with a unique toe clip; toes were stored at 20
C for later genetic analyses (see Ethical Note below). At every capture, we recorded the individual ID number, location, sex, age class, reproductive condition (males: scrotal or nonscrotal testes; females: perforate or imperforate vagina and pregnant and/or lactating) and mass (g). We classified animals weighing more than 29 g as adults, animals weighing 21e29 g as subadults, and animals weighing less than 21 g as juveniles (Gaines et al. 1979; Getz et al. 1993). Population Density We estimated prairie vole abundance using the minimum number known alive method (MNKA ¼ number of animals captured at time t plus those individuals not captured at time t but captured both before and after time t), which is highly correlated with other methods of population estimation for prairie voles (Slade & Blair 2000). We considered the effective area sampled to be the size of the trapping grid for each population plus a surrounding boundary strip with a width equal to 5 m, which is half the distance between adjacent grid points. We estimated the mean  SE density of adult voles for each population each year from the average of the MNKA of adults during each of the four trapping weeks divided by the effective grid area. We then categorized density as ‘low’ (<30/ha), ‘medium’ (40e50/ha) or ‘high’ (>80/ha), which was necessary because models that included interactions between MNKA population estimates and additional explanatory variables did not converge. In every case, population density was lower than that considered ‘high’ by Getz et al. (1993): 100/ha. However, our purpose was to examine the relative

1389

influence of density across the range observed in this study, and our categorizations are not meant to quantify population densities for prairie vole populations in general. Distribution of Vegetation and Spatial Locations of Voles Several vegetative factors can influence the social and genetic mating system of prairie voles through their patterns of space use (Batzli & Cole 1979; Lin & Batzli 2001). Therefore, to examine vegetation within and between sites, we collected data on percentage ground cover, maximum vegetation height, ground cover height and density of monocots and dicots. We sampled vegetation in systematically placed 0.36 m2 quadrats within each 10  10 m grid square. We visually estimated percentage of ground cover to the nearest 5%, and measured vegetation height and ground cover height to the nearest centimetre. To analyse female and male locations, we calculated the centre of activity (mean grid coordinates of all captures for each individual) of each adult and assigned each individual to the nearest grid point. Prairie voles show two alternative behavioural tactics with respect to space use: residents (individuals captured 75% of the time at a single nest site; Getz et al. 1992; Cochran & Solomon 2000; Ophir et al. 2007, 2008; McGuire & Getz 2010) and wanderers (individuals that are captured as frequently as residents, but with <75% of captures at one nest site; Solomon & Jacquot 2002; see also Ophir et al. 2008). Since populations may contain a large proportion of wanderers, we also used the location of nest sites as a surrogate for the location of residents in subsequent analysis. Assessment of Space Use To estimate the home range size of adult male and females prairie voles, we radiotracked animals for 1 week at the end of the 4-week trapping session. We fitted males and females (KS F: N ¼ 13, KS M: N ¼ 12; IN F: N ¼ 21, IN M: N ¼ 20; total N ¼ 66) livetrapped during week 4 with PD-2C transmitters (Holohil Systems Ltd, Carp, ON, Canada) under anaesthesia with Isoflurane (VEDCO, St Joseph, MO, U.S.A.) as described in Lucia et al. (2008). After allowing approximately 1 h for recovery from anaesthesia, we released voles at the site of capture. We located animals 10 times/ day for 5 consecutive days, with locations collected approximately 60 min apart for each individual. Five fixes (locations) were obtained for each animal between 0500 and 1000 hours, and five more fixes were obtained per animal between w2000 and 0100 hours. Based on previous studies (Kenwood 1987; Seamon et al. 1999), we estimated that 30e50 fixes were sufficient to calculate the home range for each animal. We located each animal by the radio signal and recorded coordinates using a hand-held global positioning system (GPS) unit (eTrex Legend; Garmin, Olathe, KS, U.S.A.). We calculated the 95% kernel home range (Seamon & Powell 1996; Powell 2000; Kernohan et al. 2001) for each tracked animal in RANGES 8 (Anatrack Ltd, Wareham, U.K.), using the fixed LSCV procedure to select the smoothing parameter h. We also calculated the number of opposite- and same-sex adult conspecifics that shared capture locations as indices of spatial overlap (R. Powell, personal communication). Determining the Relative Strength of Opposite-sex Social Interactions: Social Network Analysis We applied social network analysis to nest-trapping data to determine the strength of social interactions between individuals (Krause et al. 2007; Croft et al. 2008; Wey et al. 2008; Whitehead 2008). For every adult animal caught more than three times during nest trapping, we calculated the pairwise association index

1390

C. A. Streatfeild et al. / Animal Behaviour 82 (2011) 1387e1398

(AI) with every other adult in the population. We considered animals that were trapped at the same nest during the same trap session to be ‘associated’ during that trap session. We used the halfweight association index (Cairns & Schwager 1987) as calculated by SOCPROG (Whitehead 2009) to quantify the strength of social associations. Pairwise AI could range from 0 (2 animals were never associated) to 1 (2 animals were always trapped in association with each other). We also used SOCPROG to permute associations within samples to identify pairwise associations that were stronger than expected based on random interactions among individuals. Some prairie voles interacted with multiple opposite-sex individuals, and in some cases, SOCPROG identified individuals that had stronger-than-expected associations with more than one oppositesex individual. Thus, we also assessed the strength of an animal’s strongest opposite-sex association relative to all others by calculating a ‘relative AI’ for each individual: relative AI ¼ strongest opposite-sex AI/all opposite-sex AIs. ‘All opposite-sex AIs’ equalled the sum of all of an individual’s AIs with individuals of the opposite sex. A relative AI > 0.5 indicated that the interaction of the focal animal with the strongest opposite-sex AI animal was greater than the summed association with all other opposite-sex individuals. We were conservative in our classification of animals as socially monogamous based on these data. To be considered socially monogamous, an individual had to meet two criteria: (1) one and only one association with an opposite-sex individual that was identified as stronger than expected in SOCPROG permutation tests and (2) relative AI with that individual >0.5. That is, a ‘socially monogamous’ animal had to associate with a single individual more often than it associated with all other opposite-sex individuals combined, and that particular pairwise association had to be stronger than expected based on random interactions. Genetic Parentage Analysis To determine the parentage of juveniles, we genotyped all livetrapped voles at six microsatellite loci previously demonstrated to be polymorphic in prairie voles (Solomon et al. 2004; Keane et al. 2007; Ophir et al. 2007; Mabry et al. 2011). See Keane et al. (1994, 2007) and Solomon et al. (2009) for details on determining the genotypes of voles using these microsatellite loci. Before conducting parentage analysis, we calculated observed and expected heterozygosities at each microsatellite locus for all adult voles at each site each year (see Mabry et al. 2011 for more details) and assessed deviations from HardyeWeinberg equilibrium using CERVUS 3.0 (Kalinowski et al. 2007). We then conducted parentage assignments using CERVUS 3.0, which calculates a likelihood ratio score for each candidate parent to identify the male and female that are most likely to be the biological parents of a particular offspring. The statistical confidence of these parentage assignments is calculated using a simulation that takes into account allele frequencies in the population, an estimate of genotyping error, proportion of missing genotypes per locus, total number of candidate parents sampled and the proportion of candidate parents sampled. We conducted separate parentage analyses for animals at each site during each year. We performed all simulations for 10 000 cycles with a genotyping error rate of 0.02. This error rate was based on estimates of two potential sources of error: mutation and misscoring of alleles (see Solomon et al. 2004 for details of estimating the genotyping error rate). To estimate the proportion of candidate parents that were sampled in our free-living populations, we utilized catchemarkerecapture models in Program MARK (White & Burnham 1999) to estimate the recapture probability (i.e.

the probability of an additional capture following the initial capture event) for the entire trapping period at each site each year (KS 2005: 0.83; KS 2006: 0.88; IN 2006: 0.90; IN 2007: 0.79). Because some animals were trapped only once, the probability of recapture should be somewhat lower than the probability that an animal would be trapped at least once. Thus, we have slightly underestimated the proportion of candidate parents that were sampled. Following Winters & Waser (2003), we conducted a multistage genetic parentage analysis, in which we first used the parent-pair option and considered all voles trapped as adults within 20 m of a juvenile’s site of origin (natal nest or first grid site) to be candidate parents. We chose a 20 m criterion because it is approximately equal to the diameter of an average adult home range. If both male and female parents were not assigned with at least 95% confidence using the parent-pair option in the first analysis, we then expanded the set of candidate parents to include all adults captured within 40 m of the juvenile’s site of origin. Finally, if CERVUS 3.0 was able to assign a mother, but not a father, with at least 95% confidence, we used the ‘known mother’ option with all adult males captured within 40 m of the juvenile’s site of origin as candidate fathers. We used live-trapping data, particularly captures of adult females with nursing young, to confirm the assignments of juveniles to a ‘known’ mother. Percentages of genetic monogamy are based on those animals for which offspring could be assigned to both parents at 95% confidence. We used parentage data to estimate the number of different mates with which an animal produced offspring and the total number of offspring each adult produced. Individuals that had two or more offspring with a single opposite-sex partner were considered to be genetically monogamous. Individuals that had two or more offspring with more than one opposite-sex partner were defined as genetically nonmonogamous. We did not treat genetic monogamy as a subset of social monogamy because our previous observations have shown that males may mate with only one female without being socially monogamous (N. G. Solomon & B. Keane, unpublished data). For example, wandering males cannot be socially monogamous by definition but do sire offspring and, in some cases, are genetically monogamous. Data Analysis Autocorrelation of vegetation, individual locations and nest sites To quantify the spatial patterns of vegetative variables, male and female centres of activity and nest site location, we used Moran’s I coefficient, a widely used statistic for analysis of spatial autocorrelation (Legendre & Fortin 1989; Diniz et al. 2003; Fortin & Dale 2005; Rangel et al. 2006). Moran’s I measures the spatial autocorrelation of a variable between pairs of samples separated by different distances, or lag distances, and varies between 1 and 1. If variables are aggregated at fine scales, Moran’s I is positive for pairs of measurements that are closer together (shorter lag distances); larger-scale aggregation is indicated when Moran’s I is positive over a wider range of lag distances. Random spatial patterning occurs when Moran’s I is not significantly different from zero, so lag distances where autocorrelation shifts from significantly positive to zero indicates the spatial scale of clumping in measurement variables. Negative values of Moran’s I may occur at longer lag distances if there are repeating patterns of clumps or if there is a continuous gradient of change in the measurement variable. See Fortin & Dale (2005) for further details on the calculation and interpretation of Moran’s I. In our measures of spatial autocorrelation, we used lag distances of 10 m intervals, equivalent to the distance between pairs of adjacent grid points. We analysed lag distances up to a maximum of 50 m in KS and 80 m in IN, because these distances correspond to

C. A. Streatfeild et al. / Animal Behaviour 82 (2011) 1387e1398

less than half (about 45%) of the total grid size, which is typically recommended to reduce sample size bias at longer lag distances (Fortin & Dale 2005). We constructed two-dimensional spatial correlograms for each vegetative variable, for the mean centre of activity for adult males and adult females and for nest locations. We used the computer program SAM 2.0 (www.ecoevol.ufg.br/sam; Rangel et al. 2006) for autocorrelation analyses, and we assessed the statistical significance of Moran’s I for each lag distance using an overall false discovery rate of a  0.05 corrected for multiple tests (Benjamini & Hochberg 1995). This procedure has greater statistical power than the sequential Bonferroni correction (Rice 1989), especially for large numbers of comparisons (Benjamini & Hochberg 1995). Specifically, for the Kansas study grid, we tested for statistically significant autocorrelation at five lag distances (10e50 m), so the critical P values for the false discovery rate were a  {0.01, 0.02, 0.03, 0.04 and 0.05} for the observed P values of Moran’s I ordered from smallest to largest (Benjamini & Hochberg 1995). Similarly, there were eight lag distances (10e80 m) tested in the autocorrelations for the Indiana study grid, and the corresponding critical P values were a  {0.00625, 0.0125, 0.01875, 0.025, 0.03125, 0.0375, 0.04375 and 0.05} for the observed P values ordered smallest to largest. This procedure therefore provides a way to determine the specific lag distances where Moran’s I is significantly different from zero while controlling the overall false discovery rate. Prior to analysing autocorrelation, we transformed all variables except monocot density to improve normality and homogeneity of variances. We log-transformed vegetation height and ground cover height, square-root transformed dicot density and arcsine-transformed percentage of ground cover. The different transformations reflected differences in measurement scales (log versus square-root transformed) or whether they were counts or proportional data. All results are reported as mean  SE. Model selection procedures We utilized a model selection approach (Burnham & Anderson 2002) to determine the relative effects of site (KS or IN), density (low, medium or high) and sex (M or F) on home range area, overlap with opposite- and same-sex conspecifics, social monogamy and genetic monogamy. Model selection allowed us to determine which of eight alternative candidate models had the greatest support given the observed data. The candidate model set included a model in which neither site, density nor sex were included (‘intercept’), separate models for site, density and sex, models that included effects of site and sex or density and sex but no interactions, and models that included effects of site and sex and the interaction between the two variables, and density and sex and an interaction between these two variables. We ran candidate models as generalized linear models (GLMs) in SPSS 15.0 (SPSS, Chicago, IL, U.S.A.), using a normal distribution for all response variables except social and genetic monogamy, for which we used a binomial distribution with a logit link function. We then ranked candidate models using Akaike’s Information Criterion corrected for small sample sizes (AICc), which incorporates deviance, log likelihood and the number of parameters to determine which model(s) best explain the variation in the data without overfitting. The model with the lowest AICc value can be considered the ‘best approximating model’ to explain the data. In addition to DAICc, which ranks all other models in descending order from the best model, we calculated Akaike weights and evidence ratios for each model, and assessed goodness of fit using chi-square. A model’s Akaike weight can be considered to be the relative probability that the top-ranked model is the best explanation, given the data and the other models within the candidate set. Evidence ratios compare the evidence for a given model to that for the best approximating model. Chi-square values compare the goodness of fit for each model to that for the intercept-

1391

only model, regardless of where the intercept-only model is ranked within the candidate set. Larger chi-square values indicate a better model fit. Recently, some researchers have suggested that more complex models that have less support than less complex models in a candidate set be dropped from consideration in model selection analysis (e.g. Richards 2008). The current debate over this issue has not yet been resolved (Symonds & Moussalli 2011); however, because the retention of all models is necessary for the calculation of parameter weights, we have chosen to follow the current standard practise of maintaining and presenting all candidate models (Tables 2e6). We note that low density only occurred in Kansas during 1 year and high density only occurred in IN during 1 year, while medium density occurred in both populations. Therefore, density and site were somewhat conflated, so we interpret these results with caution. Ethical Note All vole trapping, handling and marking procedures were approved by the Animal Care and Use Committees at Miami University (protocol 689), the University of Kansas (protocol 18501) and Indiana University (protocol 10-020), and were conducted under the relevant state scientific collecting permits. There are multiple methods that may be used for individually marking small mammals (Gannon et al. 2007). Our study required that each individual that was trapped have a unique, permanent mark. We individually marked animals in the field using uniquely coded combinations of toe clips, because no other marking method provided the combination of permanence, a large number of unique marks and the ability to mark juveniles, which was necessary for our study. The protocol is accepted by the American Society of Mammalogists when other marking methods are inappropriate (Gannon et al. 2007). Please consult Mabry et al. (2011) for a detailed rationale. We used a clean, sharp pair of scissors to remove toes. Most often, we clipped 0e1 toes per foot. In rare cases, such as when an animal had already lost a toe naturally, we removed a second toe from a single foot. Only approximately 3% of animals had two toes removed from a single foot. The ASM guidelines generally recommend removing only one toe per foot, but removal of two toes is permitted in unusual circumstances (Gannon et al. 2007). The ASM guidelines do not recommend the use of anaesthetics or analgesics during toe clipping because of the prolonged period of restraint that is necessary to apply them, and because consumption of analgesic substances by licking may cause additional stress to the animal (Gannon et al. 2007). RESULTS Population Density MNKA population densities (adult voles/ha) were highly variable between sites and years. Population densities in KS were 44.25  5.25 individuals/ha (medium density) in 2005 and 27.00  2.71 individuals/ha (low density) in 2006. Densities in IN were 40.00  5.39 individuals/ha (medium density) in 2006 and 84.22  8.99 individuals/ha (high density) in 2007. Spatial Patterns of Vegetation and Vole Locations All vegetative variables varied between sites and years (Table 1). In particular, vegetation height and dicot density were greater in IN than in KS in either year. In KS 2005, percentage of ground cover, vegetation height and ground cover height showed significant

1392

C. A. Streatfeild et al. / Animal Behaviour 82 (2011) 1387e1398

Significant negative autocorrelations in IN also occurred at lags greater than 70 m. Spatial autocorrelation of vegetation in IN was weaker in 2007 than in 2006, but still significantly positive across lag distances of 20e60 m (Fig. 2a, b, d, e). A greater degree of spatial autocorrelation in the vegetation in IN than in KS was associated with significant positive autocorrelations at lags up to 20 m for females and up to 30 m for males in 2006 (Fig. 2c). In addition, the location of nest sites in IN 2006 showed a positive spatial autocorrelation at 10 m lags. Patterns of autocorrelations in male and female presence were similar but slightly weaker in 2007, when significant lag distances were confined to 10 m (Fig. 2f). Spatial autocorrelation in nest sites persisted up to 20 m during 2007 (Fig. 2f).

Table 1 Means  SEs of the variables describing composition of vegetation from Kansas in 2005 and 2006 and from Indiana in 2006 and 2007 (N ¼ number of 10  10 m grid cells sampled within the study area) Variable

Kansas 2005

Kansas 2006

Indiana 2006

Indiana 2007

(N¼101)

(N¼101)

(N¼220)

(N¼150)

Percentage of ground cover Vegetation height (cm) Ground cover height (cm) Monocot density (0.36 m2 quadrat) Dicot density (0.36 m2 quadrat)

57.41.5 28.70.8 4.80.2 50.21.4

96.90.6 27.90.7 3.50.1 58.61.9

97.00.6 43.91.2 2.80.1 41.91.1

86.71.5 34.41.2 5.70.3 56.12.7

10.50.8

16.11.3

26.41.6

43.12.8

(false discovery rate of P < 0.05) levels of positive autocorrelation for pairs of samples 10e20 m apart (Fig. 1a), monocot and dicot plant densities had positive spatial autocorrelation at 10 m lags (Fig. 1b) and dicot density showed positive spatial autocorrelation for pairs of samples 20 m apart. The distributions of vole nest sites, females and males had values of Moran’s I that were not significantly different from zero in KS 2005 (Fig. 1c). In 2006, ground cover height and monocot and dicot densities were again positively spatially dependent at lag distances of 10 m, with similar values to 2005, but percentage of ground cover and vegetation height showed significant positive spatial dependence out to 40 m lags (Fig. 1d, e). A greater range of spatial dependence in vegetation height and percentage cover did not influence the lack of spatial autocorrelation in vole or nest site distributions, however, which remained nonsignificant in 2006 (Fig. 1f). All vegetative variables in IN 2006 were more strongly autocorrelated and over a greater range of spatial scales than in KS, at the comparable maximum lag distances in KS (40e50 m; Fig. 2a, b).

Indices of Social Monogamy Space use The best approximating model to explain variation in home range area included the main effects of site and sex, but support for competing models that included the interaction between site and sex or only the main effect of site was also substantial (Table 2, N ¼ 66 voles, 34 F and 32 M). Thus, the model rankings suggest that both site and sex are important factors explaining variation in home range area, a conclusion borne out by the parameter weights for site and sex, which greatly exceeded the parameter weight for density (see Table 7). Male home ranges were almost twice as large as those of females in IN, but they were not sexually dimorphic in KS (Fig. 3). Variation in the number of opposite-sex conspecifics sharing capture locations was best explained by the variables site and sex (Tables 3, 7, N ¼ 134 KS voles and 483 IN voles). In IN, males shared capture locations with approximately 2.5 times as many females as

Kansas 2005

Kansas 2006

(a)

Percentage ground cover Vegetation height Ground cover height

(d)

(b)

Monocot density Dicot density

(e)

(c)

Nest sites Adult female presence Adult male presence

(f)

0.50 0.25 0 −0.25

Moran's I

0.50 0.25 0 −0.25 0.50 0.25 0 −0.25

10

20

30

40

50

10

20

30

40

50

Lag distance (m) Figure 1. Spatial autocorrelation (Moran’s I) of vegetation (circles) and vole distributions (squares) in Kansas in 2005 and 2006. Filled symbols indicate statistically significant (P < 0.05) positive or negative autocorrelations (adjusted for multiple comparisons); open symbols indicate autocorrelations that did not differ significantly from zero.

C. A. Streatfeild et al. / Animal Behaviour 82 (2011) 1387e1398

Indiana 2007

Indiana 2006 0.50

1393

Percentage ground cover (a) Vegetation height Ground cover height

(d)

Monocot density (b) Dicot density

(e)

(c) Nest sites Adult female Adult male presence

(f)

0.25 0

Moran's I

−0.25 0.50 0.25 0 −0.25 0.50 0.25 0 −0.25

10

20

30

40

50

60

70

10

20

30

40

50

60

70

Lag distance (m) Figure 2. Spatial autocorrelation (Moran’s I) of vegetation (circles) and vole distributions (squares) in Indiana in 2006 and 2007. Filled symbols indicate statistically significant (P < 0.05) positive or negative autocorrelations (adjusted for multiple comparisons); open symbols indicate autocorrelations that did not differ significantly from zero.

Social associations Model selection procedures to explain variation in whether individuals showed patterns of social association consistent with social monogamy yielded somewhat equivocal results (Tables 5, 7). The top-ranked model included only the effect of site (N ¼ 301 voles; Table 5); however, models with either site or density as an explanatory factor were almost identical in AICc, suggesting that these two models provided about equally good explanations for the variation in the data. Furthermore, parameter weights for density and site were very similar (Table 7). In general, a greater percentage of voles were classified as socially monogamous in KS than in IN. At the KS site, approximately 46% (31/68) of males and females were considered socially monogamous. In IN, the frequency of social Table 2 Ranking of candidate models explaining variation in home range area using Akaike’s Information Criterion corrected for small sample sizes (AICc) Model

Parameters

AICc

DAICc Weight Evidence c2

Site, sex Site Site, sex, site*sex Density, sex Sex Density Intercept Density, sex, density*sex

3 2 4 3 2 2 1 4

977.158 977.977 978.448 980.082 980.249 980.843 980.961 983.456

0 0.819 1.290 2.924 3.091 3.685 3.803 6.298

ratio 0.335 0.222 0.176 0.078 0.071 0.053 0.050 0.014

d 1.506 1.906 4.315 4.690 6.312 6.696 23.313

8.269 5.181 9.323 7.689 2.909 4.584 d 9.246

monogamy was lower, about 31% for males and females (73/233). When we examined the effect of population density on association patterns, we found that a smaller percentage of voles were socially monogamous when population density was high (in our study w85 voles/ha). Only 27% (37/137) of voles were categorized as socially monogamous at high density, whereas the percentage of voles that were socially monogamous was greater when population densities were moderate (41.55%; 59/142) or low (36.36%; 8/22). In this data set, ‘moderate’ population densities include 1 year each in KS and IN, while ‘high’ density data represents 1 year in IN, and ‘low’ density data comes from a single year in KS. When considering only the two moderate population densities, 50% (23/46) of the males and females in KS were classified as socially monogamous, while 38% (36/96) of the males and females in IN were classified as socially monogamous.

700 95% kernel home range area (m2)

males in KS. Female voles shared capture locations with about two times as many males in the IN population as females in the KS population (Fig. 4a). Variation in the number of same-sex conspecifics sharing capture locations was best explained by the variables site and sex (N ¼ 134 KS voles, N ¼ 483 IN voles; Tables 4, 7). In IN, males and females shared capture locations with about three times as many same-sex conspecifics as males and females in KS (Fig. 4b).

600

Male Female

N = 20

500 400 300

N = 21 N = 12 N = 13

200 100 0

Kansas

Indiana

Figure 3. Mean  SE home range sizes of adult male and female prairie voles from a population in Kansas and Indiana. Numbers above error bars represent number of voles.

1394

C. A. Streatfeild et al. / Animal Behaviour 82 (2011) 1387e1398

Table 3 Ranking of candidate models explaining variation in the number of opposite-sex conspecifics sharing capture locations using Akaike’s Information Criterion corrected for small sample sizes (AICc) Model

Parameters AICc

Site, sex Site, sex, site*sex Site Density, sex, density*sex Density, sex Density Sex Intercept

3 4 2 4

3171.029 3171.301 3172.129 3173.174

3 2 2 1

3195.081 3195.800 3229.074 3229.922

7

DAICc

Weight Evidence c2 ratio

Model

Parameters AICc

0 0.272 1.100 2.145

0.358 0.313 0.207 0.123

d 1.146 1.733 2.923

62.939 64.699 59.812 66.912

4 3 4

3294.921 0 0.535 3295.204 0.283 0.464 3307.840 12.919 0.001

24.052 24.771 58.045 58.893

0.000 0.000 0.000 0.000

d d d d

40.919 38.167 2.867 d

Site, sex, site*sex Site, sex Density, sex, density*sex Site Density, sex Density Sex Intercept

2 3 2 2 1

3314.175 3335.312 3353.536 3377.039 3393.475

(a)

Number of opposite-sex captures

6 N = 253

5

Males

N = 230

Females 4

3

N = 64 N = 70

2

1

0

Kansas

Indiana

7 (b) N = 253

Number of same-sex captures

6

5 N = 230 4

3

N = 64

1

0

Kansas

DAICc

19.254 40.391 58.615 82.118 98.554

Weight Evidence c2 ratio

0.000 0.000 0.000 0.000 0.000

d 1.152 638.742 d d d d d

104.633 102.317 95.799 81.319 64.241 43.985 18.456 d

Genetic monogamy The microsatellite loci used in genetic parentage analysis had 8e21 unique alleles per site/year combination and polymorphic information content ranged from 0.10 to 0.92 (see Mabry et al. 2011). Observed heterozygosity ranged from 0.08 to 0.97 (for details see Mabry et al. 2011). We were able to assign both parents at 95% confidence to 58.8% (30/51; 2005) and 34.5% (10/29; 2006) of juveniles trapped in KS. In IN, we assigned both parents at 95% confidence to 64.4% (50/78; 2006) and 54.0% (75/139; 2007) of juveniles. A total of 83 adult voles produced two or more offspring and were included in the analysis of frequency of genetic monogamy: 12 KS males, 13 KS females, 26 IN males and 32 IN females. Three models explaining the variation in genetic monogamy were ranked within 2 DAICc (Table 6, N ¼ 83 voles), but a model including the single factor ‘density’ appeared to offer a somewhat better explanation for the data than the other two models (Table 7). Across sites, genetic monogamy was more frequent in KS (72%; 18/ 25) than in IN (39.66%; 23/58). At high density, 37.5% (15/40) of voles were genetically monogamous. Meanwhile, the percentage of voles that displayed genetic monogamy was greater at moderate and low population densities, 54.0% (20/37) and 100% (6/6), respectively. Unfortunately, our sample size of voles that produced more than one genetic offspring when population density was low was small (N ¼ 6), and the very high percentage of genetic monogamy at low population density should be interpreted with caution given the small sample size. As with our results for social monogamy, the data for site and population density were somewhat confounded, with all ‘high density’ data from IN and all ‘low density’ data from KS, as mentioned previously. If we examine only the two populations with moderate densities, genetic monogamy was about 1.5 times more common in KS (63%; 12/19) than in IN (44%; 8/18).

Table 5 Ranking of candidate models explaining variation in social monogamy using Akaike’s Information Criterion corrected for small sample sizes (AICc)

N = 70 2

Table 4 Ranking of candidate models explaining variation in the number of same-sex conspecifics sharing capture locations using Akaike’s Information Criterion corrected for small sample sizes (AICc)

Indiana

Figure 4. Mean  SE number of individual prairie voles that shared capture locations with (a) opposite-sex and (b) same-sex adult conspecifics in Kansas and Indiana. Numbers above error bars represent number of voles.

Model

Parameters

AICc

DAICc

Weight

Evidence ratio

c2

Site Density Density, sex Site, sex Intercept Site, sex, site*sex Sex Density, sex, density*sex

2 2 3 3 1 4 2 4

38.474 38.507 40.408 40.504 41.053 42.558 43.039 44.422

0 0.033 1.934 2.030 2.579 4.084 4.565 5.948

0.304 0.299 0.116 0.110 0.084 0.040 0.031 0.016

d 1.017 2.630 2.759 3.631 7.706 9.801 19.570

4.606 6.613 6.766 4.616 d 4.616 0.040 6.903

C. A. Streatfeild et al. / Animal Behaviour 82 (2011) 1387e1398 Table 6 Ranking of candidate models explaining variation in genetic monogamy using Akaike’s Information Criterion corrected for small sample sizes (AICc) Model

Parameters

AICc

DAICc

Weight

Evidence ratio

c2

Density Site Density, sex Site, sex Site, sex, site*sex Density, sex, density*sex Intercept Sex

2 2 3 3 4 4

25.378 26.801 27.161 28.720 30.922 31.712

0 1.423 1.783 3.342 5.544 6.334

0.446 0.219 0.183 0.084 0.028 0.019

d 2.037 2.439 5.317 15.991 23.736

11.076 7.498 11.502 7.733 7.740 11.543

1 2

32.199 34.006

6.821 8.628

0.015 0.006

30.280 74.739

d 0.293

DISCUSSION Consistent with our initial hypotheses, we found that variation in social and genetic mating systems in the two geographically distinct populations of prairie voles we studied were best explained by models that included site, with their associated differences in vegetative resources or density as variables. Overall, our analyses of space use, social association and genetic parentage data indicate that more prairie voles are socially and genetically monogamous in the KS population than in the IN population. As predicted, social and genetic monogamy were more common when resources were less aggregated and population density was low. However, it is important to note that density and site were somewhat conflated in our study because low population density occurred only in KS and high density occurred only in IN, but medium density occurred in both populations. For this reason, we are somewhat cautious in our interpretation of the results of the study. Nevertheless, when comparing the KS and IN populations when both were at moderate density, more KS voles were socially and genetically monogamous than IN voles. This suggests that the differences in social and genetic monogamy detected between the KS and IN populations could be the result of factors other than density. Some of the differences in the indices of the social mating system of prairie voles in these two geographically distinct populations may be due to differences in ecological variables that would be easy for the voles to assess (Lott 1991). We found differences in the abundance and distribution of vegetation between the two sites. Vegetation height, which provides shelter and predator protection (Stokes 1995), and dicot density, which provides high-quality food for reproduction (Haken & Batzli 1996), were greater in IN than in KS. All else being equal, this suggests that the habitat in IN should be of higher quality for prairie voles. Most vegetative variables also showed greater degrees of spatial structuring in IN than in KS. Because the vegetation in IN was more spatially structured and higher in quality compared to KS, we expected that females would show greater degrees of spatial structuring in IN compared to KS (Slobodchikoff 1984). This prediction

Table 7 Parameter weights for the explanatory variables site, density and sex for all model selection analyses Response variable

Site

Density

Sex

Home range area Shared capture locations: opposite sex Shared capture locations: same sex Social monogamy Genetic monogamy

0.733 0.877 0.999 0.454 0.331

0.145 0.123 0.001 0.431 0.648

0.674 0.793 1.000 0.312 0.320

Parameter weights are the summed Akaike weights for all models within a candidate set that contain that parameter and may be interpreted as the ‘probability’ that a given parameter features in the best model.

1395

was supported by our findings showing significant spatial autocorrelation in female voles from IN, but not in those from KS. For highly aggregated food resources, the costs of resource defence by a female would probably outweigh the benefits to her, resulting in considerable home range overlap among neighbouring females (Wauters & Dondt 1992; Quinn & Whisson 2005) and spatial clumping. This is the pattern found in IN, where more female prairie voles had overlapping home ranges with same-sex conspecifics compared to female voles in KS. In contrast, for less aggregated resources, the benefits of resource defence may outweigh the costs, leading to female home ranges that are primarily exclusive, such as we found in KS. Our results were also partially consistent with the predictions generated by Emlen & Oring’s (1977) hypothesis about the effects of ecological variation on social mating systems. A greater percentage of prairie voles were socially monogamous in the KS population, where resources were less aggregated than in the population in IN. As expected, we found that male prairie voles had larger home ranges than females in IN, but not in KS, consistent with our results showing that a greater proportion of voles in KS were socially monogamous compared with voles in IN. In addition, males in IN shared capture locations with more than twice as many females compared to KS males. Although Emlen & Oring predicted that one male would be able to monopolize a clump of females, we did not find that to be true in IN, where resources and females were aggregated. Instead, we found that males overlapped multiple males and females. Females also mated with multiple males, resulting in polygynandry in the IN population. In contrast, our results from the KS population were consistent with expectations from Emlen & Oring’s hypothesis; we found a greater proportion of voles to be socially monogamous in KS, where resources and females were less aggregated than in IN. In our model selection procedure, models containing density and site and density and sex were the most highly supported in explaining the genetic mating system (Table 6). The model that included only population density appeared to offer the best explanation of the genetic mating system seen in these two populations. Although resources may have been an important influence on the spacing pattern of females and their nests, male and female prairie voles displayed the greatest amount of genetic monogamy when population density was the lowest and they showed the least amount of genetic monogamy when density was highest. High density may result in less social and genetic monogamy because of increased encounter rates between opposite-sex conspecifics (Emlen & Oring 1977; Kokko & Rankin 2006; Iossa et al. 2009). Empirical studies show that adult density influences spacing behaviour of females and males in other mammalian taxa (Watanabe 1981; Busher et al. 1983; Kamler et al. 2004). When density is low, females tend to live further from each other and have nonoverlapping territories. When females’ home ranges are large, males may be unable to monopolize more than one female (Emlen & Oring 1977). Thus, genetic monogamy may be more common in low-density populations. In contrast, when adult density is higher, females are clumped or have overlapping home ranges and extrapair copulations may be more common. However, this result is not universal in previously published studies (Smith & Ivins 1984). We also found that more KS voles were socially and genetically monogamous at moderate density than IN voles, suggesting that the difference between populations in monogamy was not solely due to density. Since we could only obtain tissue samples from offspring when we were able to trap them, we may have been unable to assign parentage to all littermates for most litters. Therefore, we have somewhat overestimated the proportion of genetically monogamous voles in our populations. However, for the purposes of this study we were interested in the relative differences between

1396

C. A. Streatfeild et al. / Animal Behaviour 82 (2011) 1387e1398

populations and not the absolute values of genetic monogamy. Furthermore, the sampling effort was the same at each site. Therefore, we do not think we have mistaken stochastic or sampling effects for a biological process, as has been suggested as a potential problem with brood-based estimates of multiple paternity rates (Eccard & Wolf 2009). The most likely explanation for the differences in genetic monogamy between the populations we examined seems to be that extrapair copulations were more common in IN than in KS. Ophir et al. (2007) detected no differences in the social and mating behaviour of prairie voles that originated from two distinct geographical areas (IL and TN) when they were studied in a laboratory setting and in outdoor enclosures. These findings are consistent with the hypothesis suggested by our data that mating behaviour is socially flexible because it is influenced by ecological and demographic factors. A study design that places animals into identical habitat at identical population densities would not be expected to detect interpopulation variation unless populations had been disruptively selected to behave differently. Phenotypic plasticity in mating behaviour has been detected in several animal species and may be an adaptive response to variation in ecological (food resources) and demographic (e.g. density) conditions (Chapman & Rothman 2009; Karlsson et al. 2010). Although we found a relationship between spatial distribution of some vegetative variables, population density and the social and genetic mating system of prairie voles, there are likely to be multiple factors that may directly or indirectly influence the social and genetic mating system seen in prairie vole populations living in ecologically distinct areas. For instance, climatic variation (e.g. precipitation, temperature) may also affect resources and thus social spacing and the social and genetic mating system seen in a population (Lott 1991; Moehlman 1998; Cushing & Kramer 2005). Natural populations of prairie voles are found in both mesic and more xeric grasslands (Getz 1985). Data on mean annual temperature and precipitation suggest that Lawrence, KS is slightly warmer (13.5
C versus 11.8
C) and drier (109.5 cm versus 99.8 cm of rain) than Bloomington, IN (data from http://countrystudies.us/ united-states/weather/). The average monthly precipitation is also more evenly distributed throughout the year in IN. Furthermore, there is evidence that the amount of precipitation influences reproduction. In a laboratory study, males with restricted access to water had decreased testosterone as well as decreased testicular, seminal vesicle and epididymal masses (Nelson et al. 1989). Additionally, fewer female voles in seminatural enclosures that were not watered were reproductive and these females lost more embryos than females in enclosed populations that were watered (Abdellatif et al. 1982). In the latter study, the investigators hypothesized that these effects were indirect and due to the effects of water on vegetation within the enclosures. We consider our study to be the beginning of a more thorough investigation of ecological and demographic variables that influence behavioural plasticity resulting in differences in the social and genetic mating systems among geographically distinct populations. Many ecological and demographic variables have been investigated with regard to intraspecific variation in social mating systems seen in populations living in environmentally distinct areas within a species range, but only a few investigators have studied the influences of these factors on intraspecific variation in genetic mating patterns (Kelly et al. 1999; Singer et al. 2006; Mobley & Jones 2009). Unfortunately, we cannot disentangle all the ecological variables such as precipitation, distribution of vegetation and population density that may have affected social and genetic monogamy in our study populations because these were related to site in our study. The design of our study also precludes us from ensuring that seasonal changes (i.e. sampling one population in

MayeJune and the other in JulyeAugust) did not influence the results of our study. Since the summer breeding season peaks 1 month earlier in KS than in IN (Corthrum 1967; Rose & Gaines 1978), we think that sampling in KS the month before sampling in IN during midsummer allowed us to examine populations in comparable times during the breeding season. Future studies in which voles from one population are translocated to another population, or a manipulation of ecological or demographic conditions in animals from one population would allow for more definitive conclusions about the effects of ecological factors on indicators of social and genetic mating systems. Acknowledgments We thank J. Beston, K. Keihl, A. Ouellette, T. Prebyl, R. Spradling, A. Wilson and A. Young for providing valuable help in the field. We also thank G. Pittman of the University of Kansas and K. Clay at Indiana University for logistical support. Norm Slade and Roger Powell are thanked for their valuable advice concerning space use, and Lance Waller for discussions on spatial statistics for an earlier version of this manuscript. We thank the people who reviewed this manuscript for their helpful suggestions. Funding for this study was provided by the National Institutes of Health (GM069409) to N.G.S. and by the National Science Foundation (IOS-0614015) to N.S.G. and B.K. References Abdellatif, E. M., Armitage, K. B., Gaines, M. S. & Johnson, M. L. 1982. The effect of watering on a prairie vole population. Acta Theriologica, 27, 243e255. Arnold, W., Klinkicht, M., Rabmann, K. & Tuatz, D. 1993. Molecular analysis of the mating system of alpine marmots (Marmota marmota). Verhandlungen der Deutschen Zoologischen Gesellschaft, 86, 27. Batzli, G. O. & Cole, F. R. 1979. Nutritional ecology of microtine rodents: digestibility of forage. Journal of Mammalogy, 60, 740e750. Benjamini, Y. & Hochberg, Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57, 289e300. Boonstra, R., Gilbert, B. S. & Krebs, C. J. 1983. Mating systems and sexual dimorphism in mass in microtines. Journal of Mammalogy, 74, 224e229. Brashares, J. S. & Arcese, P. 2002. Role of forage, habitat and predation in the behavioural plasticity of a small African antelope. Journal of Animal Ecology, 71, 626e638. Brotherton, P. N. M. & Manser, M. B. 1997. Female dispersion and the evolution of monogamy in the dik-dik. Animal Behaviour, 54, 1413e1424. Brotherton, P. N. M., Pemberton, J. M., Komers, P. E. & Malarky, G. 1997. Genetic and behavioural evidence of monogamy in a mammal, Kirk’s dik-dik (Madoqua kirkii). Proceedings of the Royal Society B, 264, 675e681. Burnham, K. P. & Anderson, D. R. 2002. Model Selection and Multimodel Inference: a Practical Informationetheoretic Approach. 2nd edn. New York: Springer-Verlag. Busher, P. E., Warner, R. J. & Jenkins, S. H. 1983. Population density, colony composition, and local movements in two Sierra Nevadan beaver populations. Journal of Mammalogy, 64, 314e318. Cairns, S. J. & Schwager, S. J. 1987. A comparison of association indexes. Animal Behaviour, 35, 1454e1469. Carter, C. S., DeVries, A. & Getz, L. L. 1995. Physiological substrates of mammalian monogamy: the prairie vole model. Neuroscience and Biobehavioral Reviews, 19, 303e314. Chapman, C. A. & Rothman, J. M. 2009. Within-species differences in primate social structure: evolution of plasticity and phylogenetic constraints. Primates, 50, 12e22. Cho, M. M., DeVries, A. C., Williams, J. R. & Carter, C. S. 1999. The effects of oxytocin and vasopressin on partner preferences in male and female prairie voles (Microtus ochrogaster). Behavioral Neurosciences, 113, 1071e1080. Choate, J. R. & Williams, S. L. 1978. Biogeographic interpretation of variation within and among populations of the prairie voles, Microtus ochrogaster. Occasional Papers the Museum Texas Tech University, 49, 1e25. Clutton-Brock, T. H. 1989. Mammalian mating system. Proceedings of the Royal Society B, 236, 339e372. Clutton-Brock, T. H. & Harvey, P. 1978. Primate ecology and social organization. Journal of Zoology, 183, 1e39. Clutton-Brock, T. H. & Isvaran, K. 2006. Paternity loss in contrasting mammalian societies. Biology Letters, 2, 513e516. Clutton-Brock, T. H. & Parker, G. A. 1992. Potential reproductive rates and the operation of sexual selection. Quarterly Review of Biology, 67, 437e456.

C. A. Streatfeild et al. / Animal Behaviour 82 (2011) 1387e1398 Clutton-Brock, T. H. & Vincent, A. C. J. 1991. Sexual selection and the potential reproductive rates of males and females. Nature, 351, 58e60. Cochran, G. R. & Solomon, N. G. 2000. Effects of food supplementation on the social organization of prairie voles (Microtus ochrogaster). Journal of Mammalogy, 81, 746e757. Corthrum, K. W., Jr. 1967. Reproduction and duration of placental scars in the prairie vole and the eastern vole. Journal of Mammalogy, 48, 287e292. Cowan, D. P. & Bell, D. J. 1986. Leporid social behavior and social organization. Mammal Review, 16, 169e179. Croft, D. P., James, R. & Krause, J. 2008. Exploring Animal Social Networks. Princeton, New Jersey: Princeton University Press. Cushing, B. S. & Kramer, K. M. 2005. Microtines: a model system for studying the evolution and regulation of social monogamy. Acta Zoologica Sinica, 25, 182e199. Danielson, B. J. & Gaines, M. S. 1987. Spatial patterns in two syntopic species of microtines, Microtus ochrogaster and Synaptomys cooperi. Journal of Mammalogy, 68, 313e322. Davies, N. B. & Lundberg, A. 1984. Food distribution and a variable mating system in the dunnock, Prunella modularis. Journal of Animal Ecology, 53, 895e912. Diniz, J. A. F., Bini, L. M. & Hawkins, B. A. 2003. Spatial autocorrelation and red herrings in geographical ecology. Global Ecology and Biogeography, 12, 53e64. Dobson, F. S., Way, B. M. & Baudoin, C. 2010. Spatial dynamics and the evolution of social monogamy in mammals. Behavioral Ecology, 21, 747e752. Eccard, J. A. & Wolf, J. B. W. 2009. Effects of brood size on multiple-paternity rates: a case for ‘paternity share’ as an offspring based estimate. Animal Behaviour, 78, 563e571. Emlen, S. T. & Oring, L. W. 1977. Ecology, sexual selection, and the evolution of mating systems. Science, 197, 215e223. Fitch, H. S. 1957. Aspects of reproduction and development in the prairie vole (Microtus ochrogaster). University of Kansas Publications Museum of Natural History, 10, 129e161. Fortin, M.-J. & Dale, M. R. T. 2005. Spatial Analysis: a Guide for Ecologists. Cambridge: Cambridge University Press. Gaines, M. S. & Johnson, M. L. 1982. Home range size and population dynamics in the prairie vole, Microtus ochrogaster. Oikos, 39, 63e70. Gaines, M. S. & Rose, R. K. 1976. Population dynamics of Microtus ochrogaster in eastern Kansas. Ecology, 57, 1145e1161. Gaines, M. S., Vivas, A. M. & Baker, C. L. 1979. An experimental analysis of dispersal in fluctuating vole populations: demographic parameters. Ecology, 60, 814e828. Gannon, W. L., Sikes, R. S. & The Animal Care and Use Committee of the American Society of Mammalogists 2007. Guidelines of the American Society of Mammalogists for the use of wild mammals in research. Journal of Mammalogy, 88, 809e923. Gaulin, S. J. C. & FitzGerald, R. W. 1988. Home-range as a predictor of mating systems in Microtus. Journal of Mammalogy, 69, 311e319. Getz, L. L. 1985. Habitats. In: Biology of New World Microtus. Special Publication No. 8 (Ed. by R. H. Tamarin), pp. 286e309. Shippensburg, Pennsylvania: American Society of Mammalogists. Getz, L. L. & Carter, C. S. 1980. Social organization in Microtus ochrogaster populations. Biologist, 62, 56e69. Getz, L. L. & Hofmann, J. E. 1986. Social organization in free-living prairie voles, Microtus ochrogaster. Behavioral Ecology and Sociobiology, 18, 275e282. Getz, L. L., Hofmann, J. E. & Carter, C. S. 1987. Mating system and population fluctuations of the prairie vole, Microtus ochrogaster. American Zoologist, 27, 909e920. Getz, L. L., Gudermuth, D. F. & Benson, S. M. 1992. Pattern of nest occupancy of the prairie vole Microtus ochrogaster in different habitats. American Midland Naturalist, 128, 197e202. Getz, L. L., McGuire, B., Pizzuto, T., Hofmann, J. E. & Frase, B. 1993. Social organization of the prairie vole (Microtus ochrogaster). Journal of Mammalogy, 74, 44e58. Goosens, B., Graziani, L., Waits, E. F., Magnolon, S., Coulon, J., Bel, M.-C., Taberlet, P. & Allaine, D. 1998. Extra-pair paternity in the monogamous alpine marmot revealed by nuclear DNA microsatellite analysis. Behavioral Ecology and Sociobiology, 43, 281e288. Haken, A. E. & Batzli, G. O. 1996. Effects of availability of food and interspecific competition on diets of prairie voles (Microtus ochrogaster). Journal of Mammalogy, 77, 315e324. Harcourt, A. H. & Stewart, K. J. 2007. Gorilla Society. Chicago: University of Chicago Press. Hofmann, J. E., Getz, L. L. & Gavish, L. 1984. Home range overlap and nest cohabitation of male and female prairie voles. American Midland Naturalist, 112, 314e319. Iossa, G., Soulsbury, C. D., Baker, P. J., Edwards, K. J. & Harris, S. 2009. Behavioral changes associated with a population density decline in the facultatively social red fox. Behavioral Ecology, 20, 385e395. Kalinowski, S. T., Taper, M. L. & Marshall, T. C. 2007. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Molecular Ecology, 16, 1099e1106. Kamler, J. F., Ballard, W. B., Lemons, P. R. & Mote, K. 2004. Variation in mating system and group structure in two populations of swift foxes, Vulpes velox. Animal Behaviour, 68, 83e88. Karlsson, K., Eroukhmanoff, F. & Svensson, E. I. 2010. Phenotypic plasticity in response to the social environment: effects of density and sex ratio on mating

1397

behaviour following ecotype divergence. PLoS ONE, 5, e12755, doi:10.1371/ journal.pone.0012755. Keane, B., Waser, P. M., Creel, S. R., Creel, N. M., Elliott, L. F. & Minchella, D. J. 1994. Subordinate reproduction in dwarf mongooses. Animal Behaviour, 47, 65e75. Keane, B., Bryant, L., Goyal, U., Williams, S., Kortering, S. L., Lucia, K. E., Richmond, A. R. & Solomon, N. G. 2007. No effect of body condition at weaning on survival and reproduction in prairie voles. Canadian Journal of Zoology, 85, 718e727. Kelly, C. D., Godin, J. G. J. & Wright, J. M. 1999. Geographical variation in multiple paternity within natural populations of the guppy (Poecilia reticulata). Proceedings of the Royal Society B, 361, 2403e2408. Kenwood, R. 1987. Wildlife Radio Tagging: Equipment, Field Techniques and Data Analysis. London: Academic Press. Kernohan, B. J., Gitzen, R. A. & Millspaugh, J. J. 2001. Analysis of animal space use and movements. In: Radio Tracking and Animal Populations (Ed. by J. J. Millspaugh & J. M. Marzluff), pp. 125e166. San Diego: Academic Press. Kokko, H. & Rankin, D. J. 2006. Lonely hearts or sex in the city? Densitydependent effects in mating systems? Philosophical Transactions of the Royal Society of London, Series B, 361, 319e334. Krause, J., Croft, D. P. & James, R. 2007. Social network theory in the behavioural sciences: potential applications. Behavioral Ecology and Sociobiology, 62, 15e27. Legendre, P. & Fortin, M.-J. 1989. Spatial pattern and ecological analysis. Plant Ecology, 80, 107e138. Lin, Y.-T. K. & Batzli, G. O. 2001. The influence of habitat quality on dispersal, demography, and population dynamics of voles. Ecological Monographs, 71, 245e275. Lin, T. T., You, E. M. & Lin, Y. K. 2009. Social and genetic mating systems of the Asian lesser white-toothed shrew, Crocidura shantungensis, in Taiwan. Journal of Mammalogy, 90, 1370e1380. Lott, D. F. 1991. Intraspecific Variation in the Social Systems of Wild Vertebrates. Cambridge: Cambridge University Press. Lucia, K. E., Keane, B., Hayes, L. D., Lin, Y. K., Schaefer, R. L. & Solomon, N. G. 2008. Philopatry in prairie voles: an evaluation of the habitat saturation hypothesis. Behavioral Ecology, 19, 774e783. Mabry, K. E., Streatfeild, C. A., Keane, B. & Solomon, N. G. 2011. Avpr1a length polymorphism is not associated with either social or genetic monogamy in freeliving prairie voles. Animal Behaviour, 81, 11e18. McGuire, B. & Getz, L. L. 1998. The nature and frequency of social interactions among free-living prairie voles (Microtus ochrogaster). Behavioral Ecology and Sociobiology, 43, 271e279. McGuire, B. & Getz, L. L. 2010. Alternative male reproductive tactics in a natural population of prairie voles Microtus ochrogaster. Acta Theriologica, 55, 261e270. Martin, E. P. 1956. A population study of the prairie vole (Microtus ochrogaster) in northeastern Kansas. University of Kansas Publications of the Museum of Natural History, 8, 361e416. Mobley, K. B. & Jones, A. G. 2007. Geographical variation in the mating system of the dusky pipefish (Syngnathus floridae). Molecular Ecology, 6, 2596e2606. Mobley, K. B. & Jones, A. G. 2009. Environmental, demographic, and genetic mating system variation among five geographically distinct dusky pipefish (Syngnathus floridae) populations. Molecular Ecology, 18, 1476e1490. Moehlman, P. 1989. Intraspecific variation in canid social system. In: Carnivore Behavior, Ecology and Evolution (Ed. by J. Gittleman), pp. 143e163. Ithaca, New York: Cornell University Press. Moehlman, P. D. 1998. Feral asses (Equus africanus): intraspecific variation in social organization in arid and mesic habitats. Applied Animal Behaviour Science, 60, 171e195. Neff, B. D., Pitcher, T. E. & Ramnarine, I. W. 2008. Inter-population variation in multiple paternity and reproductive skew in the guppy. Molecular Ecology, 17, 2975e2984. Nelson, R. J., Frank, D., Bennett, S. A. & Carter, C. S. 1989. Simulated drought influences reproduction in male prairie voles. Physiology & Behavior, 46, 849e852. Ophir, A. G., Phelps, S. M., Sorin, A. B. & Wolff, J. O. 2007. Morphological, genetic and behavioral comparisons of two prairie vole populations in the field and laboratory. Journal of Mammalogy, 88, 989e999. Ophir, A. G., Wolff, J. O. & Phelps, S. M. 2008. Variation in neural V1aR predicts sexual fidelity and space use among male prairie voles in semi-natural settings. Proceedings of the National Academy of Sciences, U.S.A., 105, 1249e1254. Ostfeld, R. S. 1990. The ecology of territoriality in small mammals. Trends in Ecology & Evolution, 5, 411e415. Powell, R. A. 1989. Effects of resource productivity, patchiness and predictability on mating and dispersal strategies. In: Comparative Socioecology (Ed. by V. Standen & R. A. Foley), pp. 101e123. Oxford: Blackwell Scientific. Powell, R. A. 2000. Animal home ranges and territories and home range estimators. In: Research Techniques in Animal Ecology Controversies and Consequences (Ed. by L. Boitani & T. K. Fuller), pp. 65e110. New York: Columbia University Press. Quinn, J. H. & Whisson, D. A. 2005. The effects of anthropogenic food on the spatial behaviour of small Indian mongooses (Herpestes javanicus) in a subtropical rainforest. Journal of Zoology, 267, 339e350. Ralls, K., Cypher, B. & Spiegel, L. K. 2007. Social monogamy in kit foxes: formation, association, duration, and dissolution of mated pairs. Journal of Mammalogy, 88, 1439e1446.

1398

C. A. Streatfeild et al. / Animal Behaviour 82 (2011) 1387e1398

Randall, J. A., Rogovin, K., Parker, P. G. & Eimes, J. A. 2005. Flexible social structure of a desert rodent, Rhombomys opimus: philopatry, kinship, and ecological constraints. Behavioral Ecology, 16, 961e973. Rangel, T. F. L. V. B., Diniz-Filho, J. A. F. & Bini, L. M. 2006. Towards an integrated computational tool for spatial analysis in macroecology and biogeography. Global Ecology and Biogeography, 15, 321e327. Reichard, U. H. 2003. Monogamy: past and present. In: Monogamy Mating Strategies and Partnerships in Birds, Humans and Other Mammals (Ed. by U. H. Reichard & C. Boesch), pp. 3e25. Cambridge: Cambridge University Press. Rice, W. R. 1989. Analyzing tables of statistical tests. Evolution, 43, 223e225. Richards, S. A. 2008. Dealing with overdispersed count data in applied ecology. Journal of Animal Ecology, 45, 218e227. Roberts, R. L., Williams, J. R., Wang, A. K. & Carter, C. S. 1998. Cooperative breeding and monogamy in prairie voles: influence of the sire and geographical variation. Animal Behaviour, 55, 1131e1140. Rose, R. K. & Gaines, M. S. 1978. The reproductive cycle of Microtus ochrogaster in eastern Kansas. Ecological Monographs, 48, 21e42. Schradin, C. 2005. When to live alone and when to live in groups: ecological determinants of sociality in the African striped mouse (Rhabdomys pumilio, Sparrman, 1784). Belgian Journal of Zoology, 135, 77e82. Schradin, C. & Lindholm, A. K. 2011. Relative fitness of alternative male reproductive tactics in a mammal varies between years. Journal of Animal Ecology, 80, 908e917. Schradin, C. & Pillay, N. 2005. Intraspecific variation in the spatial and social organization of the African striped mouse. Journal of Mammalogy, 86, 99e107. Schradin, C., König, B. & Pillay, N. 2010. Reproductive competition favours solitary living while ecological constraints impose group-living in African striped mice. Journal of Animal Ecology, 79, 515e521. Seamon, D. E. & Powell, R. A. 1996. An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology, 77, 2075e2085. Seamon, D. E., Millspaugh, J. J., Kernohan, B. J., Brundige, G. C., Raedeke, K. J. & Gitzen, R. A. 1999. Effects of sample size on kernel home range estimates. Journal of Wildlife Management, 63, 739e747. Sefc, K. M., Hermann, C. M. & Koblmuller, S. 2009. Mating system variability in a mouthbrooding fish from a tropical lake. Molecular Ecology, 18, 3508e3517. Singer, A., Varnemo, C., Lindström, K. & Svensson, O. 2006. Genetic mating patterns studied in pools with manipulated nest site availability in two populations of Pomatoschistus minutus. Journal of Evolutionary Biology, 19, 1641e1650. Slade, N. A. & Blair, S. 2000. An empirical test of using counts of individuals captured as indices of population size. Journal of Mammalogy, 81, 1035e1045. Slobodchikoff, C. N. 1984. Resources and the evolution of social behavior. In: A New Ecology: Novel Approaches to Interactive Systems (Ed. by P. W. Price, C. N. Slobodchikoff & W. S. Gaud), pp. 227e251. New York: J. Wiley. Smith, A. T. & Ivins, B. L. 1984. Spatial relationships and social organization in adult pikas: a facultatively monogamous mammal. Zeitschrift für Tierpsycholgie, 66, 289e308. Solomon, N. G. & Getz, L. L. 1997. Examination of alternate hypotheses for cooperative breeding in rodents. In: Cooperative Breeding in Mammals (Ed. by

N. G. Solomon & J. A. French), pp. 199e230. New York: Cambridge University Press. Solomon, N. G. & Jacquot, J. J. 2002. Characteristics of resident and wandering prairie voles, Microtus ochrogaster. Canadian Journal of Zoology, 80, 951e955. Solomon, N. G., Keane, B., Knoch, L. R. & Hogan, P. J. 2004. Multiple paternity in socially monogamous prairie voles (Microtus ochrogaster). Canadian Journal of Zoology, 82, 1667e1671. Solomon, N. G., Richmond, A. R., Harding, P. A., Fries, A., Jacquemin, S., Schaefer, R. L., Lucia, K. E. & Keane, B. 2009. Polymorphism at the avpr1a locus in male prairie voles correlated with genetic but not social monogamy in field populations. Molecular Ecology, 18, 4680e4695. Stokes, M. K. 1995. Selection of refuge sites by sympatric Microtus ochrogaster and Sigmodon hispidus. Journal of Mammalogy, 76, 83e87. Swihart, R. K. & Slade, N. A. 1989. Differences in home-range size between sexes of Microtus ochrogaster. Journal of Mammalogy, 70, 816e820. Symonds, M. R. E. & Moussalli, A. 2011. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behavioral Ecology and Sociobiology, 65, 13e21. Travis, S. E., Slobodchikoff, C. N. & Keim, P. 1995. Ecological and demographic effects on intraspecific variation in the social system of prairie dogs. Ecology, 76, 1794e1803. Verdolin, J. L. 2007. Resources, not male mating strategies, are a determinant of social structure in Gunnison’s prairie dogs (Cynomys gunnisoni). Behaviour, 144, 1361e1382. Watanabe, K. 1981. Variations in group composition and population density of the two sympatric Mentawaian leaf-monkeys. Primates, 22, 145e160. Wauters, L. & Dondt, A. A. 1992. Spacing behaviour of red squirrels, Sciurus vulgaris: variation between habitats and the sexes. Animal Behaviour, 43, 297e311. Westneat, D. F. 1990. Genetic parentage in the indigo bunting: a study using DNA fingerprinting. Behavioral Ecology and Sociobiology, 27, 67e76. Westneat, D. F. & Stewart, I. R. K. 2003. Extra-pair paternity in birds: causes, correlates, and conflict. Annual Reviews in Ecology, Evolution, and Systematics, 34, 365e396. Wey, T., Blumstein, D. T., Shen, W. & Jordan, F. 2008. Social network analysis of animal behaviour: a promising tool for the study of sociality. Animal Behaviour, 75, 333e344. White, G. C. & Burnham, K. P. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study, Supplement, 46, 120e138. Whitehead, H. 2008. Analyzing Animal Societies: Quantitative Methods for Vertebrate Social Analysis. Chicago: University of Chicago Press. Whitehead, H. 2009. SOCPROG programs: analyzing animal social structures. Behavioral Ecology and Sociobiology, 63, 765e778. Williams, J. R., Catania, K. & Carter, C. S. 1992. Development of partner preferences in female prairie voles (Microtus ochrogaster): the role of social and sexual experience. Hormones and Behavior, 26, 339e349. Winters, J. & Waser, P. 2003. Gene dispersal and outbreeding in a philopatric mammal. Molecular Ecology, 12, 2251e2259.