Eur J Wildl Res (2012) 58:933–943 DOI 10.1007/s10344-012-0636-0
ORIGINAL PAPER
Population structure, habitat features and genetic structure of managed red deer populations J. Pérez-González & A. C. Frantz & J. Torres-Porras & L. Castillo & J. Carranza
Received: 11 July 2011 / Revised: 18 April 2012 / Accepted: 22 April 2012 / Published online: 12 May 2012 # Springer-Verlag 2012
Abstract Management of game ungulates alters population structure and habitat features, with potential effects on genetic structure. Here, we study 26 red deer (Cervus elaphus) populations in Spain. We used census data and habitat features as well as genetic information at 11 microsatellite markers from 717 individuals. We found that metapopulations presented a distribution associated with forest interruptions. Within metapopulations, fences did not have a significant effect on red deer genetic structure. The metapopulations we studied presented similar population structure, but they differed in habitat features and genetic structure. The metapopulation with higher resource availability showed a genetic structure pattern in which genetic
relatedness between geographically close individuals was high while relatedness between geographically distant individuals was low. Contrarily, the metapopulation with lower resource availability presented a genetic structure pattern in which the genetic relatedness between individuals of different populations was independent of the geographic distance. We discuss the possible connection between resource availability and genetic structure. Finally, we did not find any population or environmental variable related to genetic differentiation within metapopulations. Keywords Cervus elaphus . Fences effect . Fragmentation . Gene flow . Landscape genetics.
Communicated by C. Gortzar Electronic supplementary material The online version of this article (doi:10.1007/s10344-012-0636-0) contains supplementary material, which is available to authorized users. J. Pérez-González (*) : L. Castillo : J. Carranza Biology and Ethology Unit, Veterinary Faculty, University of Extremadura, Avda. Universidad s/n, 10071 Cáceres, Spain e-mail:
[email protected] J. Pérez-González : A. C. Frantz Department of Animal and Plant Sciences, University of Sheffield, S10 2TN Sheffield, UK A. C. Frantz Zoologisches Institut, Ernst-Moritz-Arndt Universität Greifswald, 17487 Greifswald, Germany J. Torres-Porras : J. Carranza Ungulate Research Unit, CRCP, University of Córdoba, 14071 Córdoba, Spain
Introduction Sport hunting has a controversial relationship with animal conservation. In spite of some people arguing that wellregulated hunting can benefit wildlife, game activity implies the action of complex threats that might put harvested populations at risk (Loveridge et al. 2006; Mysterud 2010). Hunting can decrease population sizes, even to extinction through overexploitation (Burney and Flannery 2005). Furthermore, hunting frequently focuses on one of the sexes or in some age classes producing biased populations structures that might hinder species conservation (Milner et al. 2007). Harvesting can act directly to population structure by changing census size, sex ratios or age structure (Mysterud 2010). But additionally, human activity also affects habitat features, either within the framework of game management programs, for example to favour pasturelands, or as accordingly to global tendencies towards habitat loss, habitat fragmentation and pollution (Frankham et al. 2009).
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One common consequence of changes in population structure and habitat features due to harvesting and management is the loss of genetic diversity (Allendorf et al. 2008; DiBattista 2008). The existence of low censuses due to overexploitation or habitat loss, as well as altered population structures due to biased harvesting, tend to decrease effective population size, and hence, increase the intensity of genetic drift (Hauser et al. 2002). On the other hand, habitat fragmentation decreases gene flow (Frankham et al. 2009). Both high intensity of genetic drift and low gene flow tend to decrease genetic diversity and increase isolation of populations (Wright 1978). Genetic diversity and isolation have important implications in animal conservation (Frankham et al. 2009). For instance, heterozygosity has been associated with resistance to diseases (Acevedo-Whitehouse et al. 2005; Paterson et al. 1998). Furthermore, genetic isolation increases vulnerability of populations against environmental changes due to the lack of new variation by gene flow (Frankham et al. 2009). Thus, patterns of genetic variation, or genetic structure, might be used to infer the conservation status of exploited species (DiBattista 2008). Red deer (Cervus elaphus) is one of the most abundant, widespread and both ecologically and economically influential ungulate in Iberian Peninsula (Carranza 2010; Vingada et al. 2010). In Spain, more than 70,000 stags are hunted per year, and the total estimated population is over 800,000 individuals (Carranza 2010). However, Iberian red deer populations suffer an intense management focused on the animals and their environment, which may affect genetic conservation. In Spain, most of red deer populations are located in private hunting estates (typical size between 750 and 3,000 ha). In some estates, managers have introduced individuals from other countries in an attempt to increase trophy size (Carranza et al. 2003). Currently, this practice is being controlled, and during the last years, the introductions of non-Iberian red deer may have been reduced. However, other threats such as alterations of population structure and habitat modifications might put Iberian red deer at risk. Regarding management actions, managers try to maintain stable populations. Each manager has particular criteria to effect stability. However, animals move between estates and managers have adopted two possible strategies. Firstly, some managers have fenced the estates with high mesh fences to prevent migration (fenced estates). These fences are normally 2 m high and are continuously revised by managers. Fences placement began around 30 years ago (around ten red deer generations). In such fenced estates, hunting is targeted on mature males and population structure tends to be balanced, although gene flow could be strongly affected. In fact, fences could be expected to cause population isolation. Fence placement facilitates population management and the number of fenced estates has increased in the last years. Secondly, some other managers refrain from
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fencing (open estates) but adopt a biased hunting policy targeting on males as young as 2 years old, intending to harvest them earlier than the neighbouring hunting estates (see Pérez-González and Carranza 2009). Under this circumstance, environmental organizations defend fence removal on the basis of genetic and welfare arguments. Fence placement has generated a controversial situation between estate owners and environmental organizations. This controversy has not been completely solved due to the lack of scientific support for the relative effect of fences on red deer genetic structure (see Martinez et al. 2002). Additionally, altered population structures in open estates might have important consequences in genetic structure due to its influence on effective population size (e.g. Nunney 1993) or dispersal (Pérez-González and Carranza 2009). On the other hand, land managers conduct important changes on habitats. Firstly, managers clear areas of forest in order to create pasturelands in hunting estates (Carranza 1999). The aim of this change is to increase the surface of herbage layer for herbivores such as livestock or game ungulates. This management activity in Spain has been responsible of forest destruction and fragmentation during centuries. Secondly, during the 1950s and 1960s, land managers replaced Mediterranean forests by Pinus spp. or Eucaliptus spp. reforestations for timber and paper industry. Habitat quality and resource availability in these reforested areas are lower than in non-modified Mediterranean forests (Caballero 1985). These changes in habitat features might affect genetic structure of populations (Bennet 1998; Lesica and Allendorf 1995). Here, we describe the population genetic structure of Iberian red deer populations and infer its dependency on population and environmental variables. Firstly, we determined the number of metapopulations we sampled in our study area and investigated the habitat factors that lead to fragmentation of red deer distribution (see definition of metapopulation below). Secondly, we assessed the effect of fences on genetic structure. Moreover, we compared population structure, habitat features and genetic structure of the obtained metapopulations. Finally, we looked for population structure and habitat variables that might be related to genetic differentiation (Fst) of populations within metapopulations.
Materials and methods Study area and populations The study was conducted in Mediterranean ecosystems in Southwestern Spain. Areas typically include a part of a mountain range covered by Mediterranean scrub and forest, and a lower and flatter land, covered by open, oak (Quercus
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spp.) woodland or dehesa. Within this study area, we worked on 26 estates (10 fenced and 16 open estates) located in two game districts: San Pedro in Extremadura region (13 estates) and Los Santos-Hornachuelos in Andalucía region (13 estates). Both game districts are separated by a mean distance of 205 km and between them there are large areas in which red deer does not occur. We considered that each estate represented a deer population because each estate has different management criteria and habitat characteristics (see Pérez-González and Carranza 2009; PérezGonzález et al. 2009). On average, the two study districts shared similar ecosystem composition and landscape structure, but Los Santos-Hornachuelos experienced greater surface of Pinus spp. reforestation (see Table S1 in the Electronic supplementary material (ESM)). In addition, forests are more fragmented in San Pedro compared to Los Santos-Hornachuelos (Fig. 1). Owners and land managers ensured us that they did not conduct any translocations of red deer specimens into the estates we studied. In our study areas, the main economic activity is hunting, red deer and wild boar (Sus scrofa) being the main game species. Other wild ungulates such as fallow deer (Dama dama) or mouflon (Ovis aries musimon) were also present in some estates, although in lower density. On the other hand, livestock is an economic activity with little importance in the study area, so domestic species have only a marginal representation. Population structure variables During the rutting periods of September 2004 in San Pedro, and September 2005 in Los Santos-Hornachuelos, we conducted field censuses by making journeys with the land manager’s vehicle at 10–20 km/h. Managers continually drive their cars within the estates, so the animals ignored Fig. 1 Location of the study areas, estates and the main environmental factors that might act as barriers to deer gene flow (forest gaps, roads and rivers). Black polygons fenced estates; grey polygons open estates; grey areas forest distribution; continuous grey lines rivers; scaled lines roads; dashed circles location of genetic clusters inferred with STRUCTURE (see Fig. 2)
SP2
us, which minimized the risk of recording the same individual more than once. To ensure counting the maximum number of deer (Clutton-Brock et al. 1982), surveys were always performed by at least two observers (the manager and a researcher), at sunset (between 1700 and 1900 solar time) and during the peak of the rut (following managers’ advice). We recorded the presence of all individuals, indicating their sex and estimated age class (yearling, 1 year old; young, 2 years old; and adults, more than 2 years old) (see Pérez-González et al. 2010a for a more detailed description of the counting method). With these counts, we determined the deer density (number of individuals per hectare we ranged), sex ratio (number of females divided by the number of individuals) and the proportion of adult males in each population (number of adult males divided by the number of males). Calves and yearling males were excluded from the quantification of sex ratio and proportion of adult males. We excluded calves because of the difficulty for sex determination. Yearling males were excluded because lowly developed yearling males are very similar to females (see Clutton-Brock et al. 1997; Pérez-González and Carranza 2011). Habitat variables With the help of aerial ortophotographs, we mapped the following environmental variables that could act as barriers to the gene flow of red deer: forest gaps, roads and rivers (Bennet 1998; Pérez-Espona et al. 2008). We used the GIS software ARC VIEW 3.2 (ESRI Inc., New York, USA) for map processing. We described habitat features by using two variables: habitat quality and resource availability. We determined habitat quality in each estate by following a method broadly used by deer managers in Southwestern Spain. This method applies Hobbs et al. (1982) model to the data contributed by
San Pedro
Los SantosHornachuelos SH SP1
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Rodríguez-Berrocal (1979) for Mediterranean ecosystems (Caballero 1985). With the help of black and white aerial photographs we mapped in ARC VIEW the areas of dehesa, crops, Pinus reforestation and forest within each estate. We characterized the areas of forest in three categories (Mediterranean forest, noble shrubland and serial jaral) by in situ observations carried out in February–August 2004 for San Pedro, and February–August 2005 for Los SantosHornachuelos. For each estate, we determined the proportion of the area that was occupied by each habitat type. Since each habitat type had an associated average biomass per hectare, we quantified the mean biomass per hectare of each estate. We used the biomass per hectare of an estate to estimate the habitat quality in this estate (see details of forest information in Pérez-González and Carranza (2009) and in the ESM). As resource availability variable, we used the browsing impact of red deer on vegetation. We used the amount of browsing on woody vegetation because of its central ecological role at landscape level (Pulido et al. 2000). We assumed that browsing impact on woody vegetation was conducted mainly by red deer since wild boar rarely presents browsing behaviour (e.g. Herrera et al. 2006) and the densities of other ungulates were null or very low (see above). Vegetation with high browsing impact indicates low resource availability relative to the density of red deer. In each estate, we made 10–30 (depending on forest sizes in the estates) 50×2 m transects to see the impact of deer browsing on adult plants. We recorded a plant as browsed when browsing affected >50 % of its exposed surface. The switch point between browsed and non-browsed plants was established by visual inspection of always the same trained observers (JPG and JTP). In each transect, we determined the percentage of browsed plants. We used mean percentage of browsed plants for the transects in an estate as a measure of browsing impact in the estate. We divided plant species in highly palatable and lowly palatable species (see Gómez et al. (1978) and the ESM). Palatability integrates smell, taste, texture and processes following the intake of nutrients and toxics (Provenza 1995). Thus, we obtained two measures of browsing impact in each estate, on highly palatable species and on lowly palatable species, simply by considering only either type of species in transect data. We made transects during the summers of 2004 in San Pedro, and 2005 in Los Santos-Hornachuelos because the impact of browsing is exacerbated by environmental harshness during the summer in Mediterranean forests, when conditions are unfavourable for recovery after damage (Saether 1997). In Iberian Peninsula, food supplementation during summer and autumn is an important element of habitat. Food supplementation strongly affects deer movement and induces high spatial aggregations (Pérez-González et al. 2010a). We used female aggregation as an estimator of the importance of food supplementation in a population. We quantified female aggregation by
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using Ripley’s K function (see details in Pérez-González et al. 2010a). Individuals, sample collection and microsatellite genotyping From all 26 populations, we collected muscle samples from carcases of legally culled individuals (802 individuals) (see details in Pérez-González et al. 2009). Tissue samples were frozen at −20°C until processing. Genomic DNA was purified by proteinase K digestion and salting out procedure (Millar et al. 1988). We typed individuals at 11 microsatellite loci: OarFCB193, OarFCB304, CelJP38, CelJP15, TGLA94, TGLA53, BM1818, CSSM22, CSSM16, ILSTS06 and CSPS115 (e.g. Kuehn et al. 2003; Marshall et al. 1998). Following polymerase chain reaction, we used an ABI3130 DNA sequencer and the GENEMAPPER software (Applied Biosystems, New Jersey, USA) to determine allele sizes. We combined the markers in four multiplex or simplex PCRs: 1×6, 1×3 and 2×1 (number of PCRs× number of markers; see details in Pérez-González and Carranza 2009). We eliminated from genetic analyses those individuals with less than nine genotyped markers, so our data set reduced to 717 individuals. Genetic and statistical analyses 1. Genetic description We assessed departures from Hardy–Weinberg equilibrium (HWE) by exact tests using Markov chain as implemented by GENEPOP version 3.4 (Raymond and Rousset 1995). Departures from equilibrium for each area were examined with sequential Bonferroni correction for multiple comparisons. Additionally, we used Fisher’s exact tests, implemented in GENEPOP, to assess the presence of linkage disequilibrium between loci. Observed heterozygosity (Ho), expected heterozygosity (He) and Fis values were quantified with GENETIX version 4.05 (Belkhir et al. 2004). Significant departures of Fis values from zero were assessed by 10,000 permutations implemented in GENETIX. 2. Location of metapopulations in the study area We applied the Bayesian methodology implemented by STRUCTURE version 2.0 (Pritchard et al. 2000) to determine the level of genetic substructure in our data independently of the known origin (i.e. sampling location) of individuals. To determine the number of groups with genetic cohesion (K), five independent runs from K01 to K012 were carried out with 500,000 iterations, following a burnin period of 100,000 iterations. In those cases in which posterior probabilities did not reach clear maxima, we used the ad hoc statistic ΔK described in Evanno et al. (2005) to identify the true number of genetic clusters. After a first run
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that identified the mayor genetic discontinuities, we made second runs separately with each genetic group obtained in the first run. Thus, we looked for minor genetic discontinuities within mayor genetic areas. If the obtained genetic clusters were geographically separated, we regarded them as metapopulations composed of a set of populations. 3. Barriers In those cases in which a geographic area was divided into several metapopulations, we used partial Mantel tests implemented in FSTAT (Goudet 2001) to assess environmental features that acted as barriers (see debate of partial Mantel test for instance in Raufaste and Rousset 2001; Castellano and Balletto 2002). For the partial Mantel tests, we constructed six matrices in which we paired the populations: genetic distance (paired (Fst)), geographic distance (Euclidean distance), fence matrix ((1) populations not separated by fences and (2) populations separated by fences), forest matrix ((1) populations not separated by forest gaps and (2) populations separated by forest gaps), road matrix ((1) populations not separated by roads and (2) populations separated by roads) and river matrix ((1) populations not separated by rivers and (2) populations separated by rivers). In a first step, we used a simple Mantel test in which we compared genetic and geographic distances. After that, we conducted partial Mantel tests with three matrices. In these partial Mantel tests we assessed the relationship between the genetic distance matrix and each environmental matrix after controlling for geographic distance. Mantel and partial Mantel tests were assessed by 10,000 permutations. Additionally, we assessed the effect of fences on genetic structure. Within each obtained metapopulation we compared genetic differentiation (Fst) and genetic diversity (Ho) after grouping open and fenced populations. Comparison was conducted by using a permutation test (10,000 permutations) implemented with FSTAT. 4. Differences among metapopulations We compared population structure (sex ratio, proportion of adult males and density) and habitat characteristics (habitat quality, browsing impact and female aggregation) of the obtained metapopulations. We conducted these comparisons by linear models in which these variables were used as dependent variables and the metapopulation the populations belonged to as factor. Before conducting these analyses, we assessed normality of dependent variables. Female aggregation was log-transformed to reach normality. We also compared genetic structure of metapopulations. Genetic differentiation (Fst) and genetic diversity (Ho) of the metapopulations were compared by using a permutation test (10,000 permutations) implemented with FSTAT. Finally, for each metapopulation we assessed the relationship between
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geographical distance and genetic relatedness between pairs of individuals with SPAGEDI software (Hardy and Vekemans 2002). We considered as the geographic location of individuals the geographic center of the estates in which they were sampled. We used the relatedness coefficient described in Queller and Goodnight (1989). Average relatedness coefficient estimates were taken for pairs of individuals separated by distance intervals of 5 km. Permutations of spatial locations (10,000 permutations) were used to test for spatial genetic structure. 5. Variables and factors affecting genetic structure of populations We used GESTE (Foll and Gaggiotti 2006) to test for the relationships between population-habitat variables and genetic differentiation within each metapopulation. GESTE implements a hierarchical Bayesian approach to estimate population-specific Fst values and to evaluate which local factor contributed to the observed genetic structure. The approach is based on a generalized linear model and uses MCMC methods to obtain posterior estimates. As local factors, we used connectivity, sample sex ratio, sex ratio in the population, proportion of adult males in the population, deer density, habitat quality, resource availability and female aggregation. We measured the connectivity of a population as the percentage of the perimeter that is not closed by fences. A fenced population would have a connectivity of zero, an open population surrounded by open populations would have a connectivity of 100, an open population surrounded by open and fenced populations would have connectivity higher than 0 and lower than 100. We used sex ratio in sample and in population because of the possible effect of sex-biased dispersal on genetic structure (Chesser 1991). Proportion of adult males might affect genetic differentiation since dispersal is mainly carried out by young individuals (Clutton-Brock et al. 1982). Deer density might influence genetic structure due to its effect on effective population size or dispersal (e.g. Travis et al. 1999). Habitat variables such as habitat quality, resource availability and food supplementation (estimated by female aggregation) might also affect genetic structure (e.g. Lesica and Allendorf 1995).
Results None of the microsatellite markers presented consistent departures from HWE across populations (Table 1). No pairs of loci were in significant linkage disequilibrium. Table 1 also shows genetic diversity and some population/ environmental features for each population. Five populations showed significant deficit of heterozygotes and one population from Los Santos-Hornachuelos showed a significant excess of heterozygotes.
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Table 1 Genetic variability estimates of studied populations Pop
Study area
Fence
N
No HWE
Metapop
Ho
He
Fis
AC AZ BB CG CL CN MEL MER
San San San San San San San San
Pedro Pedro Pedro Pedro Pedro Pedro Pedro Pedro
Open Fenced Fenced Open Open Open Open Open
31 37 38 26 34 25 26 26
1 (TGLA53) 0 0 0 0 0 0 0
SP1 SP1 SP1 SP1 SP1 SP1 SP1 SP1
0.610 0.647 0.674 0.722 0.656 0.652 0.632 0.660
0.672 0.673 0.664 0.696 0.690 0.674 0.661 0.673
MO VA PA SC TO ADE AL AM BA CH CHI CV ES FV JA PED RO TA Mean
San Pedro San Pedro San Pedro San Pedro San Pedro Sant-Hornach Sant-Hornach Sant-Hornach Sant-Hornach Sant-Hornach Sant-Hornach Sant-Hornach Sant-Hornach Sant-Hornach Sant-Hornach Sant-Hornach Sant-Hornach Sant-Hornach
Open Open Open Fenced Open Open Fenced Open Fenced Fenced Open Open Fenced Open Fenced Open Fenced Fenced
35 25 24 30 28 13 36 26 21 35 18 21 35 37 27 17 27 19 25.413
1 (FCB193) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 (JP38) 0
SP1 SP1 SP2 SP2 SP2 SH SH SH SH SH SH SH SH SH SH SH SH SH
0.653 0.677 0.659 0.553 0.690 0.771 0.768 0.717 0.789 0.761 0.740 0.666 0.730 0.739 0.750 0.814 0.748 0.807 0.710 0.064
SD
6.912
Sex ratio
Hab qual
0.094a 0.040 −0.015 −0.037 0.051 0.032 0.044 0.018
0.751 0.468 0.566 0.744 0.810 0.675 0.688 0.744
273.165 275.723 257.581 258.149 264.413 258.357 285.377 289.091
0.667 0.671 0.682 0.617 0.660 0.805 0.759 0.769 0.758 0.761 0.758 0.758 0.769 0.769 0.780 0.801 0.751 0.733 0.722
0.021 −0.009 0.034 0.106a −0.047 0.044 −0.012 0.069a −0.041 0.000 0.023 0.124a 0.052a 0.039 0.040 −0.017 0.004 −0.115a 0.021
0.709 0.782 0.746 0.582 0.746 0.617 0.466 0.857 0.533 0.446 0.918 0.730 0.683 0.666 0.551 0.815 0.602 0.526 0.680
254.367 272.356 287.844 282.641 290.291 153.152 237.001 280.282 267.533 224.429 179.535 235.816 284.693 244.447 249.502 179.154 203.688 216.867 245.166
0.053
0.051
0.126
37.474
Table shows sample sizes and variables related to population structure and environmental features. Mean and standard deviation (SD) are also showed Fence whether populations are included in open or fenced estates, N sample size, No HWE loci with significant departures from Hardy–Weinberg equilibrium, Metapop metapopulation after STRUCTURE analysis, Ho observed heterozygosity, He expected heterozygosity, Sex rat sex ratio, Hab qual habitat quality a
Fis values (significant departures from zero)
Location of metapopulations in the study area The Bayesian structure analysis shows that K02 was the most probable number of genetic clusters (Fig. 2a). These clusters corresponded to both game districts (San Pedro and Los Santos-Hornachuelos populations (Fig 1; Fig S1a in the ESM). In the second run using populations from San Pedro, ΔK was maximized in K02 (Fig. 2b). These clusters divided San Pedro into two population sets: SP1 in the centre-east and SP2 in the west (Fig 1; Fig S1b in the ESM). In the second run using populations from Los Santos-Hornachuelos, K01 was the most probable result (Fig. 2c). We coded the population set of this cluster as SH (Fig. 1; Fig. S1c in the ESM). Populations in each
genetic cluster (SP1, SP2 and SH) conformed a separated geographic area, hence we considered these clusters as metapopulations. Barriers In San Pedro game district, Mantel test shows a positive relationship between geographic and genetic distances (Table 2). But also, partial Mantel tests show a significant effect of forest gaps on genetic distance among populations (Table 2). The presence of roads, rivers and fences were not related to pairwise Fst values (Table 2). Thus, the presence of two metapopulations in San Pedro is probably due to the presence of forest gaps.
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-23000
7000
-24000
6000
Matrix
4000
-26000
3000 -27000 2000 -28000
1000
-29000 -30000
0 0
2
4
6
8
10
-1000
12
350
-11200
300
-11400
250
-11600
150
-12000 100
-12200 -12400
50
-12600
0 0
1
2
3
4
5
6
7
8
9
-50
K
p
0.790
0.002
0.061
