Behav Ecol Sociobiol (2004) 56:393–403 DOI 10.1007/s00265-004-0790-9
ORIGINAL ARTICLE
T. Fredsted · C. Pertoldi · J. M. Olesen · M. Eberle · P. M. Kappeler
Microgeographic heterogeneity in spatial distribution and mtDNA variability of gray mouse lemurs (Microcebus murinus, Primates: Cheirogaleidae) Received: 5 November 2003 / Revised: 12 March 2004 / Accepted: 16 March 2004 / Published online: 22 April 2004 Springer-Verlag 2004
Abstract The objective of our study was to investigate the spatial distribution and genetic structure of a solitary primate at the microgeographical scale of adjacent local populations. We obtained spatial data and tissue samples for mtDNA analysis from 205 gray mouse lemurs (Microcebus murinus) captured along transects and within 3 grid systems within a 12.3 km2 area in Kirindy Forest, western Madagascar. Our capture data revealed that, even though the forest was continuous, gray mouse lemurs were not evenly distributed, and that daily and maximum dispersal distances were significantly greater in males. Communicated by C. Nunn T. Fredsted ()) · J. M. Olesen Department of Ecology and Genetics, Institute of Biological Sciences, University of Aarhus, Ny Munkegade, Building 540, 8000 rhus C, Denmark e-mail:
[email protected] Tel.: +45-89423127 Fax: +45-86127191 C. Pertoldi Department of Wildlife Ecology and Biodiversity, National Environmental Research Institute, Kalø Grenvej 14, 8410 Rønde, Denmark C. Pertoldi Department of Applied Biology, Estacin Biolgica Don´ana, CSIC, Pabelln del Perffl, Avda. Maria Luisa, s/n 41013 Seville, Spain M. Eberle · P. M. Kappeler Department of Sociobiology, German Primate Centre, Kellnerweg 4, 37077 Gttingen, Germany M. Eberle Zoological Institute & Museum, Section Ecology & Conservation, University of Hamburg, Martin-Luther-King-Platz 3, 20146 Hamburg, Germany P. M. Kappeler Institute of Zoology und Anthropology, University of Gttingen, 37077 Gttingen, Germany
The frequency distribution of 22 mtDNA D-loop haplotypes was highly skewed. Nine haplotypes were unique to males, indicating male-mediated gene flow from surrounding areas. The geographic distribution of haplotypes revealed that males were also more dispersed than females. Females with the same haplotype showed a tendency towards spatial aggregation, and the correlation between genetic and geographic distances was higher in females. In several areas of the forest, however, spatially clustered females were not of the same haplotype, and females were not always found in clusters. Hence, in contrast to suggestions from previous studies, matrilineal clustering is not the only way females are socially organized. In addition, our study revealed heterogeneity and patterns in population structure that were not evident at smaller spatial scales, some of which may be relevant for designing conservation strategies. Keywords Genetic structure · Social organization · mtDNA · Dispersal · Microcebus
Introduction Studying the genetic structure of a population is essential to understanding its evolutionary properties (Whitlock and McCauley 1999). Variation in genetic structure can have far-reaching evolutionary and behavioral consequences for a given population or deme (Bossart and Prowell 1998). Processes, such as speciation along environmental gradients (Doebeli and Dieckmann 2003), gene flow (Bohonak 1999; Richardson et al. 2002) or local extinction (Hedrick 2001), as well as cooperative behavior (Gompper et al. 1997; Burland et al. 2002), affect and reflect patterns of genetic sub-structuring. Genetic structure is also intimately intertwined with social organization, i.e., the age-sex composition and spatio-temporal cohesion of a social unit (Sugg et al. 1996; Dobson 1998; Storz 1999; Ross 2001). However, it is not possible to predict one from knowledge of the other because different mating systems are possible under a particular social or-
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ganization (Perrin and Mazalov 2000; Kappeler and van Schaik 2002). For comprehensive analyses of the evolution of animal societies, it is therefore necessary to collect and link behavioral, demographic and genetic data (Sugg et al. 1996). In addition, it is possible to reconstruct evolutionary transitions among different social systems with information on genetic population structure (Foley and Lee 1989; Kappeler et al. 2002). Genetic data, in particular, data on individual variation in mitochondrial DNA, can contribute importantly to the identification of structure in populations (Avise et al. 1987). For the analysis of processes that occur on larger time scales, such as gene flow and population turnover, genetic methods provide information that is not available with direct methods. Hence, it is important to acknowledge the complementary utility of genetic and demographic data to analyze population structure and dynamics (Neigel 1997). Previous socio-genetic studies revealed that the social systems of most mammals from voles to elephants are characterized by a polygynous mating system, female philopatry and concomitant formation of clusters of closely related females (matrilines), as well as male-biased dispersal (Kleiman 1977; Chepko-Sade and Sade 1979; Greenwood 1980; Clutton-Brock 1989; Packer et al. 1991; Lehman et al. 1992; Girman et al. 1997; Ishibashi et al. 1997; Surridge et al. 1999; Fernando and Lande 2000; Kappeler et al. 2002). Furthermore, populations of many mammalian species are subdivided into behaviorally segregated breeding groups maintained by sex-biased philopatry and territorial exclusion of immigrants (Clutton-Brock 1989). Finally, the vast majority of mammalian species has a solitary social organization, i.e., adults only rarely and briefly interact with conspecifics and spend most of their activity period alone, even though some individuals may spend the period of inactivity together (e.g. Ims 1987; Sandell 1989; Mller and Thalmann 2000; Kappeler and van Schaik 2002; Wolff 2003). To emphasize this difference between periods of activity and inactivity, some primatologists have classified these animals as solitary foragers (Bearder 1987), which is also not a contrast to “social” (see Kappeler and van Schaik 2002). Many solitary species are characterized by a heterogeneous spatial distribution of individuals, where the home range of each individual borders on and/or overlaps with those of several other conspecifics of both sexes (Leyhausen 1965; Waser and Jones 1983). It is therefore difficult to identify borders of social units because each individual can be seen as the center of a social unit surrounded by concentric rings of individuals with which it interacts with decreasing frequencies. Under such conditions, genetic data can contribute unique information to identify the structure of higher-order social units. About a third of all primate species, including the orangutan and the majority of prosimians, are characterized by a solitary social organization (Bearder 1999; Mller and Thalmann 2000; Kappeler and van Schaik 2002). Phylogenetic reconstructions confirmed earlier assertions that a solitary lifestyle is ancestral for primates (Charles-
Dominique and Martin 1970; Wrangham 1980; Kappeler 1999; Martin 2000; Mller and Thalmann 2000), so that solitary prosimians provide the baseline from which to reconstruct primate social evolution (Kappeler et al. 2002). Furthermore, some solitary primates are among the most endangered primates (Bearder 1999; Kappeler 2000) and socio-genetic data on their population structure are needed for the development of conservation plans (Kohn et al. 1995; Jabbour et al. 1997; Tomiuk et al. 1998). However, the social organization of solitary primates has only begun to be illuminated in the past decade, and information on genetic structure is currently available for only three species: Lepilemur ruficaudatus, Mirza coquereli, and Microcebus murinus (Tomiuk et al. 1997; Bachmann et al. 2000; Radespiel et al. 2001; Kappeler et al. 2002; Wimmer et al. 2002). These data were collected either with small local populations or across large parts of a species’ range; however, no information exists about population and genetic structure at the intermediate scale, i.e. at the level of adjacent local populations over several kilometers. The gray mouse lemur (Microcebus murinus) is a small (60 g), nocturnal primate endemic to Madagascar. The social organization of single local populations has been studied at several sites in western and southern Madagascar (Martin 1973; Pags-Feuillade 1988; Fietz 1999; Radespiel 2000; Eberle and Kappeler 2002). Behavioral observations revealed that gray mouse lemurs range solitarily for most of the night, foraging for fruit, gum and invertebrate prey, and they range in 1- to 2-ha home ranges that overlap with those of several other males and females. During the day, the sexes are mostly segregated, with males sleeping alone or in duos, whereas females form stable sleeping groups of two to four individuals (Martin 1973; Radespiel et al. 1998; Schmid 1998), which appear to represent stable social networks (Radespiel et al. 1998; Eberle and Kappeler 2002). Previous genetic studies revealed a hierarchical structure within local study populations. Closely related females form stable sleeping groups (Radespiel et al. 2001; Wimmer et al. 2002) and members of several adjacent sleeping groups share a mitochondrial haplotype and are spatially distinct from neighboring matrilineal clusters (Wimmer et al. 2002). Most adult males within the range of a matriline possessed a divergent mitochondrial haplotype, indicating that they immigrated into the area (Wimmer et al. 2002; see also Radespiel et al. 2003). These previous genetic studies illuminated aspects of the social system that were not detectable with behavioral data alone, thereby contributing new information for the interpretation of the mating system and social behavior of these primates. These studies also raised several new questions, however. First, what is the distribution and abundance of gray mouse lemurs at the next spatial scale, i.e. at the level of adjacent local populations? In one intensely studied population, for example, mouse lemurs were distributed heterogeneously at the smallest spatial scale, with virtually all of the approximately 50 study individuals
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being restricted to about half of the 9-ha study area in continuous forest (see Fig. 3 in Wimmer et al. 2002), but it is not known how representative such a pattern is. The existence of isolated population nuclei has been postulated for this and other solitary primates, based on pioneering field studies (Charles-Dominique and Martin 1970; Martin 1973), but sampling at a spatial scale necessary to test this prediction has not yet been performed. Second, what is the genetic structure of a solitary primate at the level of adjacent local populations? There is evidence for male-biased natal dispersal in gray mouse lemurs (Wimmer et al. 2002; Radespiel et al. 2003), but we do not know how far males disperse from their natal area. There is also suggestive evidence for occasional female dispersal (Wimmer et al. 2002), but we lack information from studies at larger spatial scales to assess its prevalence. In this paper, we address these questions for a solitary primate, using spatial and mitochondrial DNA data obtained at the geographic scale of several square kilometers in a species where individual home ranges measure only 1–2 ha. These data provide information on the social and genetic structure at a new spatial scale, and may also have practical implications for planning conservation strategies.
Methods Field work and sample collection This study was conducted in Kirindy Forest, a dry deciduous forest located about 60 km northeast of Morondava in western Madagascar (E40 400 , S20 40 ) (Sorg and Rohner 1996). Between October and December 2001, which included the annual mating season, 205 adult individuals of gray mouse lemur were captured, using Sherman live traps. The traps were placed every 25 m along transect lines and at the intersections of three study grid systems with footpaths at 25 m intervals. The coordinates of transect endpoints and grid system corners were determined with the help of a GPS, so that the x,y coordinates of all other points with the resulting map could be calculated. Most transects followed small dirt roads (about 4 m wide). Trapping along these transects occurred simultaneously in four parallel lines, creating a corridor of about 50 m width; one line of traps was placed near each side of the road and one about 25 m inside the forest along both sides of the road. A total of 160 traps were used simultaneously for a trapping unit of three consecutive nights. These traps were distributed over 1 km of transect or about 8 ha of a grid system. The total length of all transects was 10 km (total transect area, 50 ha) and the total area covered within the grid systems was about 46 ha, resulting in a total study area of 96 ha. These sampling areas were situated within a study area of 5.6 2.2 km (12.3 km2=1,231 ha, Fig. 1). Traps were placed in the vegetation at a height of 40–200 cm. They were baited with pieces of banana in the late afternoon and checked the following morning. Newly captured animals were sexed, subjected to standard field measurements and individually marked with a subdermally injected transponder (Trovan Small Animal Marking System; Telinject, Rmerberg, Germany). In addition, small (2–3 mm2) ear biopsies were taken for later DNA extraction. Each tissue sample was immediately transferred to 70% ethanol. The animals were released at their capture site in the following late afternoon.
Genetic analyses Genomic DNA was extracted by using the Qiagen Dneasy Tissue Kit (Chatsworth, Calif., cat. no. 69504). A 529-bp fragment of the mtDNA D-loop (control region) was amplified via PCR, using the mammalian control region primers L15997 50 -CACCATTAGCACCCAAAGCT-30 located in the tRNA gene, and H16498 50 CCTGAAGTAGGAACCAGATG-30 (see Gerloff et al. 1999). In a 10 ml reaction, 1 ml buffer (1.5 mm MgCl2), 1.6 ml dNTP (1.25 mm of A, C, G, T, respectively), 0.5 ml of each primer (10 pmol/ml), 1 ml (20–60 ng/ml) template and 0.1 ml Taq polymerase (Amersham Pharmacia Biotech) were used. Cycling conditions were as follows: an initial denaturation step of 3 min at 94 C, followed by 35 cycles of 30 s at 94 C, 40 s at 50 C, 1 min at 72 C and a final extension step of 7 min at 72 C. The samples were cycle sequenced by using DYEnamic ET Dye Terminator Cycle Sequencing Kit (Amersham Pharmacia Biotech). In a 10 ml sequence reaction, PCR 0.5 ml primer (3 pmol/ml), 5 ml water, 4 ml sequence mix and 0.5 ml template was used. Cycle conditions were as follows: 30 cycles of 20 s at 95 C, 15 s at 50 C, 4 min at 60 C. After PCR, 10 ml of water was added to obtain a final volume of 20 ml. Afterwards, both strands were analyzed by gel electrophoresis with an automated capillary DNA sequencer model ABI3100. Data analyses The raw sequence data were analyzed with Chromas 2 Version 2.21 (Technelysium) and sequences were aligned using ClustalX 1.81 (Thompson et al. 1997) and edited using Genedoc Version 2.6.002 (Nicholas and Nicholas 1997). Identical haplotypes among the 205 sequences were found using Collapse Version 1.1 (Posada 1999). Variation in the D-loop was estimated as haplotype diversity, polymorphic sites, theta (q=2 Nem for mitochondrial DNA) and nucleotide diversity (p) (Nei 1978). This was done using DnaSP Version 3.51 (Rozas and Rozas 1999). Spatial distribution of individuals with different haplotypes was described using ArcView GIS Version 3.1. A minimum spanning network with 95% confidence was constructed using TCS Version 1.13, which produces a statistical parsimony network (Clement et al. 2000). Unresolved reticulations between haplotypes were chosen according to the optimal tree obtained from a maximum-likelihood analysis in PAUP 4.02b (1,000 replicates) (Swofford 2001). The network shows the frequency of each haplotype and the number of changes between each haplotype. Spatial analyses For recaptured animals, dispersal distance was conservatively estimated as the maximum distance dispersed between successive trapping nights. Mean distance to the nearest individual of the same sex and haplotype, as well as the mean distance dispersed per day, were compared between males and females with a Mann-Whitney U-test (Zar 1984). Since genetic drift and gene flow affect genetic variation over time (Neigel 1997), a correlation between geographic and genetic distance has often been used as evidence for isolation-by-distance (Slatkin 1993). Hence, we performed a Mantel’s test on males and females separately in order to examine the correlation between geographic and genetic distances (Smouse et al. 1986). Genetic distances were calculated as the pairwise number of nucleotide differences between two haplotypes. In order to compare gene flow between males and females, we compared their z-transformed correlation coefficients (Zar 1984) between geographic and genetic distance with a t-test (Sokal and Rohlf 1981). Using Spatial Genetic Software (SGS, Degen et al. 2000), we calculated the distance between individuals of the same sex with the same haplotype and we tested for clumpiness by calculating an aggregation index. The value of R in the aggregation index determines whether one finds a random ([R]=1) or a clumped distribution ([R]q). However, a recent population expansion or a selective sweep are possible explanations when Tajima’s D is negative (p0.10). Hence, there was no evidence for either population fusion or any selective pressures, indicating that sequences evolved neutrally. Spatial analyses
Results Genetic diversity We identified 22 mtDNA haplotypes (GenBank, accession numbers AY234386–AY234407) among the 205 captured mouse lemurs (102 females, 102 males, 1 not sexed; sex ratio1). The D-loop fragment had 69 polymorphic sites and a total of 72 mutations. The range of the sequence divergence among haplotypes was 0.19–8.20% (mean 4.3%) and the mean total nucleotide diversity (p) was 0.04236€0.00217. The parameter theta, q, was 5.85. The frequency distribution of the mtDNA haplotypes was highly skewed with two very common haplotypes and the remaining ones at low frequencies (Fig. 2). The sexes differed in haplotype frequency and composition. Females exhibited a total of 13 haplotypes, and males a total of 20 haplotypes (Fig. 2). There were two unique female haplotypes, whereas nine haplotypes (41%) were exclusively
Fig. 2 Frequency distribution of the haplotypes in males and females. Haplotype 6 is mainly represented by individuals from an intensively studied population in the southeastern grid (see Wimmer et al. 2002)
Table 1 Spatial distribution of mouse lemurs. Mean (€SE) and median (€IQR) of the daily travel distance and the distance to the nearest individual with the same haplotype. We tested for differences between the medians of the distributions of males and females using a Mann-Whitney U-test. The aggregation index ([R])
Males Females
During subsequent nights, males traveled significantly longer distances than females (Table 1). From recapture data obtained during different trapping units, we also obtained an estimate of the maximum sex-specific dispersal distance during the mating season (Table 1). Maximum dispersal distance was also greater for males by a factor of 2.2. The aggregation index was smaller for females than for males (Table 1) indicating stronger aggregation, and females sharing a haplotype were more aggregated in space than males with the same haplotype. Furthermore, the frequency distribution of geographic distance to the nearest individual with the same haplotype revealed that about 75% of the females were very close to females with the same haplotype (distance interval 0–100 m) whereas only about 35% of the males were near other males with the same haplotype (Fig. 3). Furthermore, the range of the geographic distance was much wider in males than in females (Fig. 3). The spatial autocorrelation analysis using SGS showed that, within a biologically meaningful distance class (250 m) for their home ranges (~1–2 ha), females had a positive spatial structure, indicating that proximal females were more genetically similar, i.e. related, than expected for a random distribution (Fig. 4a). Under restricted gene flow, populations are characterized by positive spatial genetic autocorrelation at short distance classes, subsequently declining through zero and becoming negative (Smouse and Peakall 1999). However, breeding patterns associated with social organization can also generate nonrandom genetic patterns (Scribner and Chesser 1993). In males, this pattern was not observed. Instead, males were shown not to deviate significantly from a random spatial distribution of genotypes within the first two distance classes (up to ~1,100 m) because within these distance classes the mean remained within the 95% confidence limits of a random distribution (Fig. 4b). In the was calculated using SGS ([R]=1 random distribution, [R]
