RESULTS: ASSESSMENT OF UNGULATE-HABITAT RELATIONS

Transcription

1 CHAPTER 3 RESULTS: ASSESSMENT OF UNGULATE-HABITAT RELATIONS 84

2 3. RESULTS: ASSESSMENT OF UNGULATE-HABITAT RELATIONS SUMMARY 1. Understanding the influence of natural factors and anthropogenic impacts on populations of wild ungulates is of fundamental importance to their management. However, the spatial processes and observation processes involved in field studies often obscure our understanding of the ecological process underlying the distribution and abundance of ungulates, which is the process of interest to us. Modern hierarchical modeling approach deals with these methodological challenges. In this Chapter, I investigate ungulate-habitat relations in a tropical deciduous forested landscape in the central Western Ghats, India by confronting a rigorous hierarchical spatial model with carefully gathered field survey data. 2. I examined the patterns of spatial distribution in abundance among five threatened ungulate species, which differed in terms of their body sizes and diets. I also assessed a set of hypothesized ecological determinants of the association between abundance of each ungulate species with its habitat. 3. Transect line count data on ungulates were obtained from 77 samplers placed systematically across the 1400 km 2 study landscape with a 85

3 random start. These data were used to investigate the influence of forest vegetation type, availability of palatable forage, topographic features, proximity to water, intensity of human impacts and effectiveness of protection, on ungulate abundance patterns. I applied a Poissonregression model under the class of Generalized Linear Mixed Models within an overarching Bayesian analytical framework. The model was confronted with survey data to assess the relative influence of hypothesized determinants in actually shaping abundance patterns of ungulate species. 4. Different predictor variables impacted each ungulate species broadly in the expected direction based on their ecological traits. However, the strength of these discerned relationships varied considerably. The patterns of ungulate abundance distributions reflected the extent to which local and landscape level factors satisfied habitat requirements of each species. 5. For sambar, chital and wild pig, forest vegetation type mattered most, while terrain feature was the most influential determinant of gaur abundance. Muntjac was most vulnerable to human impacts. However, none of the covariates, on its own could adequately explain the abundance patterns of any species. Different combinations of predictor variables, however, could satisfactorily describe observed patterns. Within the deciduous forest landscape I studied, effectiveness of 86

4 6. Synthesis and applications: Extensive deciduous forests remaining in India still provide an opportunity for conservation and recovery of several threatened wild ungulates. Management actions focused at reducing human impacts on ungulate habitats can substantially increase the carrying capacity for ungulates in these forests. 87

5 3.1 Introduction Large ungulate species (>20 kg body mass) are known to play an important role in maintaining the ecological integrity, structure and functioning of forest ecosystems (Augustine and McNaughton 2006; Duncan et al. 2006; Pringle et al. 2007). The role of ungulates in influencing ecosystem processes (Augustine and McNaughton 2006), including increasing primary productivity, nutrient-recycling and soil fertility (McNaughton 1985; Archer 1995; Anderson and Briske 1995; McNaughton et al. 1997; Pastor and Cohen 1997; Ritchie et al. 1998; Frank et al. 1998, 2002; Bardgett and Wardle 2003), is well documented. Recent studies have also demonstrated the importance of ungulates as initiators of strong interactive effects on other taxonomic groups at both trophic and nontrophic levels (Pringle et al. 2007). Furthermore, ungulates determine carrying capacities of the habitat for endangered large carnivores (Sunquist et al. 1999; Karanth et al. 2004). For all the reasons above, maintaining high densities of large sized ungulate species becomes an integral component of retaining ecologically functional natural landscapes (Karanth and Sunquist 1995; Ross 2000; Parrish et al. 2003; Karanth et al. 2004). Increasing human demands for space and natural resources from forested landscapes have led to severe loss, degradation and fragmentation of ungulate habitat (Macdonald 2001; Baillie et al. 2004). Consequently, ungulate populations are dwindling, both in terms of their spatial 88

6 distribution and abundance globally (Cebellos et al. 2005, Schipper et al. 2008) and specifically in India (Karanth et al. 2009, 2010). Many wild ungulate species in the tropics are now confined to isolated habitat fragments amidst human-dominated landscapes. While the effects of fragmentation on forests themselves are fairly well studied, ecological studies on ungulate-habitat relations in the context of these changes have been very few. Such assessments are essential for conservation planning and management particularly in South and Southeast Asia, regions which are experiencing rapid human population growth and economic development and yet harbor several threatened ungulate species. The decline in ungulate distribution and abundance in India has been linked to extensive changes in land use and human impact patterns that are driving changes in landscape and habitat structures. Some studies have explored impact of human pressures like illegal hunting and grazing on ungulate abundance in Asia s tropical forests at a local scale (Madhusudan and Karanth 2002; Madhusudan 2004). At a broader scale, bio-physical factors (e.g. water, terrain feature) and eco-climatic features (e.g. deciduousness of the habitat) are also likely to influence forest vegetation (Krishnaswamy et al. 2009; Vaidyanathan et al. 2010), which in turn will influence ungulate abundance and distribution. Furthermore, ecological traits such as body size and diet also impose limits to species abundance levels (Eisenberg 1980; Hudson 1984). Recent studies (Karanth 89

7 et al. 2008) and empirical observations at a few sites (Khan et al. 1996; Karanth et al. 1999) have shown that management interventions such as relocation of human settlements and effective patrolling could potentially enhance ungulate habitat quality, enabling their increased abundance. Thus, an array of local as well as broad-scale factors linked physical environment, habitat ecology and management will drive spatial variation in ungulate abundance. Such mechanisms of ungulate-habitat relations need deeper investigation urgently, in view of the paucity of studies in this area. I explore these issues in this chapter in a specific conservation context in the tropical deciduous forests of India. Details of 1400 km 2 study area in Nagarahole-Bandipur region and general ecological descriptions of five ungulate study species (red muntjac Muntiacus muntjak, wild pig Sus scrofa, chital Axis axis, sambar Rusa unicolor and gaur Bos frontalis) have already been provided in chapter 1. In this chapter, I examine spatial abundance patterns of an ungulate community in response to a set of likely ecological factors identified a priori, in a tropical deciduous forest landscape of high conservation value using a specifically developed hierarchical spatial model within a Bayesian inferential framework as described in chapter 2. In this chapter I specifically try to answer the following questions: (i) What are the key drivers of patterns of spatial distribution of abundance of an ungulate species, and, how do these patterns vary among different species? (ii) Do 90

8 the predictor variables selected a priori have unique, measurable effects on the observed abundance patterns of ungulates? (iii) What ecological factors correlate most markedly with the abundance of ungulates? (iv) What is the relative importance of these different drivers on patterns of ungulate abundance? These questions are explored in detail in the remaining sections of this chapter. 3.2 Field survey data on ungulate species Ungulates were surveyed on foot along each of the 77 spatially replicated transects systematically placed at 3 km apart covering the entire study region (see Fig. 3.1), using standard protocols developed earlier (Thomas et al. 2002; Karanth et al. 2002). I define each of these line transects as a site and the study region as the landscape for the purposes of investigating influence of covariates on ungulate abundances at these two distinct spatial scales. In this section, I report ungulate survey data. In the subsequent section, descriptions of site-specific (transect level) and landscape level (covering the entire study region) covariates selected for study are provided. 91

9 Figure 3.1: Map showing the system of line transects in Nagarahole- Bandipur study region. Inset map shows the location of study area in India. The line transect survey design was generated for Nagarahole and Bandipur reserves separately using the automated survey design features implemented in program DISTANCE 4.1 (Thomas et al. 2010). Under this protocol, initially a 0.8 km long transect segment (chosen for logistical reasons, see Karanth et al for details) was placed with a random orientation and random start. These transect segments were later transformed to form a 3.2 km long square sampler. Subsequently 77 samplers were placed systematically at an uniform spacing of 3 km, using survey design tools in program DISTANCE (Strindberg et al. 2004; Thomas et al. 2010). The field surveys were conducted by two survey 92

10 personnel following detailed protocols (see Karanth et al. 2002) to record species, cluster size, as well as sighting distances and angles for each encounter with a cluster of ungulate species. The surveys were conducted by well-trained personnel within a span of days each in Nagarahole (during April 2005) and Bandipur (April 2006) in the dry season, to minimize variations in detectability. All sites (transect lines) were visited at least six times during the entire survey period (three times in the morning and three times in the evenings). These transect line survey field protocols are described more fully in Karanth et al. (2002). A total of 464 transect walks thus cumulatively covered a distance of 1404 km along transects and yielded 1216 counts of animal-clusters (chital 559, sambar 337, gaur 154, wild pig 73 and muntjac 93) and their associated distance data for five ungulate species. 3.3 Predictors of ungulate abundance Identification of environmental covariates of ungulate abundance Based on the biology of the study species, I identified three groups of explanatory variables that were likely to strongly influence ungulate abundance. These variables were related to habitat type, physical factors and management factors. Furthermore, I recognized that these variables can potentially affect ungulate abundance at two spatial levels: site (or local scale) and landscape (or broad scale). Therefore, I collected these covariate data at both spatial scales. Throughout this chapter, I interchangeably use 93

11 the terms site-level with local scale, and, landscape-level with broad scale, respectively, depending on the context. The first group of ecological habitat variables identified by me included: (a) eco-climatic distance, which is a quantitative, remotely-sensed surrogate for the forest vegetation type. It defines forest habitat structure and composition across a moisture gradient at a landscape-level; (b) forage index that describes potential forage availability at local scale. The second group of physical habitat variables included: (c) availability of water and (d) topographic variance in slope that reflected terrain flatness. The third group of variables included measures of management effectiveness: (e) a protection effectiveness index that reflected deterrence against illegal hunting, and (f) a quantitative human impact index reflecting intensity of habitat disturbance on ungulate habitats. I stress that the last two variables mentioned are not habitat suitability indices, but are indices of anthropogenic disturbance, the former directly depressing ungulate numbers and the latter depriving habitat resources for ungulates. I also note that that all these forms of anthropogenic disturbance are consequences of management ineffectiveness in preventing their intrusions. I hypothesized that ungulate abundance will vary because of heterogeneity in landscape characteristics defined by three groups of explanatory variables mentioned above. I specifically expected that the six 94

12 variables chosen a priori influence ungulate abundance at different levels and that five study species will respond differently to these local and landscape level factors. I also expected that the direction and strength of these relations will vary depending on the specific ecological traits of individual species. These a priori hypotheses about species-specific habitat relations are in Table 3.1. In the following section, I explain the rationale behind the choice of these specific predictive covariates of ungulate abundance in greater detail. Table 3.1: Predicted response of five ungulate species to each of the explanatory variables in Nagarahole-Bandipur region in India based on a priori hypotheses. Legend: (+) influences positively; (-) influences negatively; (0) no influence. COVARIATES Muntjac Wild Chital Sambar Gaur pig Forage (+) (+) (+) (+) (+) Human impact (-) (0) (-) (-) (-) Variance in slope (0) (0) (-) (+) (+) Distance from water (-) (-) (-) (-) (-) Eco-climatic Distance (-) (0) (-) (-) (-) Protection ineffectiveness (-) (-) (-) (-) (-) 95

13 3.3.2 Covariates influencing ungulate abundance Of the six variables chosen, four (distance to water, terrain complexity, ecoclimatic distance, and protection effectiveness index) are likely to operate at landscape (broad scale) level, while two others (forage availability and human impact measured along the transect line) are expected to operate at local (site-level) level. Site-level covariate data were collected from each transect line, and the landscape-level (broad scale) covariate data were collected for each of the 1-km grid square laid across the study area. Values of all these covariates were centered and scaled to have mean 0 and standard deviation 1, to improve model convergence. Centering and scaling is a standard data treatment method where variables are standardized by subtracting their averages and dividing by their standard deviations to give each variable equal importance (Bolker 2008) Site-level covariates An index of ungulate forage available per unit area was computed for each transect from nested vegetation survey plots following the protocols developed by Reddy et al. (Unpublished data). Under this protocol, vegetation plots measuring 50*4 m were laid perpendicularly on each transect line at 200 m intervals. All trees with > 30 cm GBH (Girth at Breast Height) were counted. Within this primary vegetation plot, a 4*4 m plot was placed to record saplings with > 10 and < 30 cm GGH (Girth at Ground Height). All plants of < 10 cm GGH were counted within a 1*1 m 96

14 plot placed at the diagonal ends of each primary plot. Using a combination of published information, local ethno-botanical knowledge and empirical observations, I assessed palatability of each plant species for ungulates to compute mean number of forage plants / unit area available at each transect (site) for different ungulate species. Because surveys were conducted at the peak of dry season, when grasses are dry, I expected the measured forage availability to positively influence abundance of all study species at site level. To compute the human impact index, I first recorded all signs of human activities such as cut plant stems, cut bamboo stems, logged trees, lopped trees, tree-notches, fire, etc., that were encountered along each 100 m segment of all transects. Based on these data I computed a composite human disturbance index for each transect as the product of intensity (summation of all human impact signs encountered) and frequency (the proportion of segments in which such signs were found) per unit km of walk effort. I predicted this index would negatively influence local ungulate abundance. Both vegetation and human impact surveys for collecting the site-level covariate data were conducted during the dry season when ungulate surveys were conducted. 97

15 Landscape-level covariates As described earlier, a grid with cells of 1-km 2 area was draped on the landscape and measured covariate values of the four broad scale variables were compiled for each cell. I used eco-climatic distance as a good surrogate of forest vegetation type since other discrete categories of forest vegetation types (Pascal 1986; Meher-Homji 1990) fail to adequately capture factors like plant-phenology, plant-physiology and bio-climate on a continual scale, which ultimately determine availability of forage for ungulates (Krishnaswamy et al. 2009). Eco-climatic distance is a quantitative, remotely-sensed metric derived from the multi-date Normalized Difference Vegetation Index (NDVI) that describes variability in forest type at an ecologically relevant continual scale (Krishnaswamy et al. 2009). More importantly, since NDVI is an index of plant productivity, which in turn is strongly related to plant available moisture, the ecoclimatic distance reflects the gradient in green biomass that matters most for ungulates. Thus, very low values of eco-climatic distance correspond to wet and moist forest patches (with higher plant productivity and lower seasonal variation) with higher values representing dry deciduous forests (with lower plant productivity and high seasonal variation). I extracted eco-climatic distance values for each of the 1-km 2 grid-cell of the study region from the data layers developed separately for another conservation study (Bawa et al. 2007). 98

16 Here, it is useful to point out that tropical evergreen forests have zero or low values of eco-climatic distance, and also tend to support low densities of ungulates, in contrast to the tropical deciduous forests of the study area. Although the plant productivity is highest in these evergreen forests, plant-available nutrients essential for ungulates are in short supply (Olff et al. 2002). Therefore, a quadratic relationship between ungulate abundance and eco-climatic distance can be expected, with ungulate abundance initially increasing with eco-climatic distance and then declining after reaching a threshold level. Within this overall pattern, I expected an inverse linear relationship between degree of deciduousness and ungulate abundance in the study area. Distance from water and variance in topographic slope were the other two landscape-level variables of interest. All perennial water sources, both natural and man-made, in the study area were mapped using a GARMIN 12 XL GPS. The digital overlays of perennial streams, rivers and reservoirs were used to compute mean distance from each 1-km 2 grid-cell to nearest water source. I expected cell specific abundance of all ungulate species to have an inverse relation with distance to water. I also predicted terrain steepness to influence abundances of ungulate species differently based on species biology. Such steepness of terrain is best described by the variance of the slope, with low values representing relatively flatter terrain 99

17 and higher variances indicating relatively undulating terrain. These values of variance in slope were extracted for each cell from remotely sensed Shuttle Radar Topography Mission (SRTM) elevation data at a 200 m pixel resolution (Jarvis et al. 2008). Although the study area has been reasonably well protected for more than three decades (Karanth et al. 1999; Karanth et al. 2001), the effectiveness of such protection varies across grid cells in the landscape due to a number of factors: history of protection, available infrastructure, proximity to human settlements, quality of personnel deployed and patrol frequency. These factors can only be assessed empirically and subjectively. The overall index of protection effectiveness was based on an assessment of such factors (Karanth et al. 2001). Based on this assessment, the effectiveness of protection level in each cell was categorized as low, medium and high. A management ineffectiveness covariate (which is inversely related to this effectiveness of protection) was used in the models. I expected high abundance of ungulates in cells with low scores of the management ineffectiveness. 3.4 Data Analyses Hierarchical modeling The detailed formulation of the hierarchical spatial model to assess the effects of predictor variables and their relative importance in driving 100

18 ungulate abundances has been explained in the previous chapter. The model specification code was run for each ungulate species with at least 30,000 iterations and appropriate initial burn-in and thinning. I used three parallel markovian chains with random starting values to better assess model convergence (Gelman et al. 2004). MCMC convergence was assessed using standard diagnostic procedures (Gelman and Rubin 1996). The hierarchical spatial model for each species generated abundance estimates for each 1- km 2 cell. These abundance values were imported into GIS software (ArcView 3.1) to map spatial patterns of variations in ungulate abundance across the study site. 3.5 Results Ungulate abundance The detection probability was positively influenced by body size and cluster size and negatively by perpendicular distance from transect for all study species (see Fig. 3.2 a-e). These data show that estimating detection probabilities to address imperfect detections (detection probabilities being <1 in the sampled area) was critically important. 101

19 Figure 3.2: Plots of detection function of five ungulate species in Nagarahole-Bandipur region in India. Figure 3.2 a: Chital Plot of detection function of 12 cluster-size groups (each depicted in different colors). Each cluster-size group consists of 5 individuals. The X- axis is the perpendicular distance in 19 categories and the Y-axis is the estimated detection probability that ranges between 0 and 1. Each distance category depicts 20 m and the maximum observed distance is 366 m. 102

20 Figure 3.2 b: Sambar Plot of detection function of 9 cluster-size groups (each depicted in different colors). Each cluster-size group consists of 1 individual. The X- axis is the perpendicular distance in 11 categories and the Y-axis is the estimated detection probability that ranges between 0 and 1. Each distance category depicts 20 m and the maximum observed distance is 212 m. 103

21 Figure 3.2 c: Gaur Plot of detection function of 13 cluster-size groups (each depicted in different colors). Each cluster-size group consists of 2 individuals. The X- axis is the perpendicular distance in 13 categories and the Y-axis is the estimated detection probability that ranges between 0 and 1. Each distance category depicts 20 m and the maximum observed distance is 250 m. 104

22 Figure 3.2 d: Wild pig Plot of detection function of 10 cluster-size groups (each depicted in different colors). Each cluster-size group consists of 1 individual. The X- axis is the perpendicular distance in 14 categories and the Y-axis is the estimated detection probability that ranges between 0 and 1. Each distance category depicts 10 m and the maximum observed distance is 137 m. 105

23 Figure 3.2 e: Muntjac Plot of detection function of 2 cluster-size groups (each depicted in different colors). Each cluster-size group consists of 1 individual. The X- axis is the perpendicular distance in 17 categories and the Y-axis is the estimated detection probability that ranges between 0 and 1. Each distance category depicts 10 m and the maximum observed distance is 163 m. 106

24 The posterior distributions of cluster size variable in different cluster-size categories for each of the ungulate species are provided in Table 3.2. The product of mid-value of the cluster-size category and its expected mean value of the probability distribution was summed across all categories to compute the expected cluster size. A comparison of the expected cluster size with the observed mean cluster size confirms the definite presence of a cluster size bias (effect on detection probability) for all the species except for muntjac, which is a small solitary ungulate. Any failure to account for this positive relationship between detectability and cluster size would have induced a positive bias in these ungulate abundance estimates. 107

25 Table 3.2: Posterior distributions of cluster-size variable (gs[ ]) in each cluster-size category for five ungulate species in Nagarahole-Bandipur study region. The expected cluster size is the product of mid-value of each cluster-size category and its mean expectation of the cluster size distribution summed over all categories. Table 3.2 a. Chital Observed mean cluster size = 5.2; Expected cluster size = 4.7 (Number of cluster-size categories: 12, Number of individuals in each category: 5) Node mean sd 2.5% median 97.5% gs[1] gs[2] gs[3] gs[4] gs[5] E gs[6] 6.07E E E E-4 9.4E-4 gs[7] 8.2E E E E-5 1.4E-4 gs[8] 9.723E E E E-6 1.8E-5 gs[9] 1.028E E E E-7 2.1E-6 gs[10] 9.809E E E E-8 2.1E-7 gs[11] 8.535E E E E-9 2.0E-8 gs[12] 6.83E E E E E-9 108

26 Table 3.2 b. Sambar Observed mean cluster size = 1.9; Expected cluster size = 1.8 (Number of cluster-size categories: 9, Number of individuals in each category: 1) Node mean sd 2.5% median 97.5% gs[1] gs[2] gs[3] gs[4] gs[5] gs[6] E gs[7] 5.96E E E E gs[8] 1.028E E E E-5 1.9E-4 gs[9] 1.58E E E E-5 3.4E-5 109

27 Table 3.2 c. Gaur Observed mean cluster size = 4.2; Expected cluster size = 3.6 (Number of cluster-size categories: 13, Number of individuals in each category: 2) Node mean sd 2.5% median 97.5% gs[1] gs[2] gs[3] gs[4] gs[5] gs[6] gs[7] gs[8] E gs[9] 6.204E E E E gs[10] 1.474E E E E-4 3.2E-4 gs[11] 3.198E E E E-5 7.7E-5 gs[12] 6.383E E E E-6 1.7E-5 gs[13] 1.181E E E E-7 3.4E-6 110

28 Table 3.2 d. Wild pig Observed mean cluster size = 2.2; Expected cluster size = 2.0 (Number of cluster-size categories: 10, Number of individuals in each category: 1) Node mean sd 2.5% median 97.5% gs[1] gs[2] gs[3] gs[4] gs[5] gs[6] gs[7] E E gs[8] 2.843E E E E-4 7.9E-4 gs[9] 5.32E E E E-5 1.7E-4 gs[10] 9.065E E E E-6 3.3E-5 Table 3.2 e. Muntjac Observed mean cluster size = 1.08; Expected cluster size = 1.07 (Number of cluster-size categories: 2, Number of individuals in each category: 1) Node mean sd 2.5% median 97.5% gs[1] gs[2]

29 Posterior summaries of the distributions of the predicted abundance of ungulate clusters in each sample unit (1-km 2 grid-cell) for all species are in Table 3.3. The expected abundance of three species showed a skewed distribution and hence I used median values to map the spatial variations of abundance of chital, sambar and gaur over study landscape. For wild pig and muntjak, I used mean values. These maps (Fig. 3.3 a-e) show hot spots of local abundance of individual ungulate species within the Nagarahole-Bandipur study region. Table 3.3: Posterior summaries of predicted number of animal clusters in each 1-km 2 grid-cell for five ungulate species in Nagarahole-Bandipur study region. Species mean sd 2.5% median 97.5% Chital Sambar Gaur Wild pig Muntjac

30 Figure 3.3 a: Spatial variation of chital abundance and its hot-spots of local abundance in Nagarahole-Bandipur region in India. Figure 3.3 b: Spatial variation of sambar abundance and its hot-spots of local abundance in Nagarahole-Bandipur region in India. 113

31 Figure 3.3 c: Spatial variation of gaur abundance and its hot-spots of local abundance in Nagarahole-Bandipur region in India. Figure 3.3 d: Spatial variation of wild pig abundance and its hot-spots of local abundance in Nagarahole-Bandipur region in India. 114

32 Figure 3.3 e: Spatial variation of muntjac abundance and its hot-spots of local abundance in Nagarahole-Bandipur region in India Predictors of ungulate abundance Posterior summaries of the distributions of all the explanatory variables (see Table 3.4 a-e) showed the covariate effects on ungulate abundance in the expected direction (except for one variable human impact index for chital), while the strength of these relationships varied considerably among species. Abundance of all ungulates was positively influenced by the forage available locally, although this influence was weak in the case of wild pig (mean SD 0.2). This covariate effect was strongest for muntjac and chital. The other site-level variable examined, human impact index, significantly depressed abundances of muntjac, sambar and gaur, whereas it 115

33 had a weak negative influence on wild pig abundance (mean 0.08, SD 0.23) and a weak positive effect on chital abundance (mean +0.1, SD 0.13). Table 3.4: Posterior distributions of explanatory variables (alpha1=forage index, alpha2=human impact index, beta0=intercept, beta1=variance in slope, beta2=distance to water, beta3=eco-climatic distance, beta4=protection ineffectiveness index) for five ungulate species in Nagarahole-Bandipur study region. Table 3.4 a. Chital Node mean sd 2.5% median 97.5% alpha alpha beta beta beta beta beta

34 Table 3.4 b. Sambar Node mean sd 2.5% median 97.5% alpha alpha beta beta beta beta beta4-9.41e Table 3.4 c. Gaur Node mean sd 2.5% median 97.5% alpha alpha beta beta beta beta beta

35 Table 3.4 d. Wild pig Node mean sd 2.5% median 97.5% alpha alpha beta beta beta beta E-4 beta Table 3.4 e. Muntjac Node mean sd 2.5% median 97.5% alpha alpha beta beta beta beta beta

36 Eco-climatic distance, which I used as a surrogate for degree of deciduousness and openness of forest, had a strong negative impact on all ungulates except gaur. Abundances of chital, sambar, wild pig and muntjac were highest in moist deciduous forest patches with lower degree of deciduousness (and the eco-climatic distance values). Gaur and chital densities responded positively to higher levels of protection, but evidence of this effect was weak for the three species sambar, wild pig and muntjac (see Discussion). Of the two other landscape-level variables examined, distance to water had a negative influence on the abundances of all species, the relationship being most pronounced for wild pig and chital. Physical terrain had a strong effect on three ungulate species. While abundances of gaur and sambar were higher in steeper terrain, chital favored flatter plains. These terrain features had least effect on wild pig and muntjac densities Relative importance of predictors of abundance The posterior summaries of the distributions of indicator variables for each of the covariate effects on all species are in Table 3.5 (a-e). These posterior model weights clearly indicate that, although most of the covariates had strong individual effects on ungulate abundance, only a few of them were relatively important in determining the abundance patterns in each individual species. 119

37 Table 3.5: Posterior summaries of indicator variables for each covariate effect (wa1=forage index, wa2=human impact index, w1=variance in slope, w2=distance to water, w3=eco-climatic distance, w4=protection ineffectiveness index) included in the abundance models of five ungulate species in Nagarahole-Bandipur study region. Table 3.5 a. Chital Node mean sd w w w w wa wa Table 3.5 b. Sambar Node mean sd w w w w wa wa

38 Table 3.5 c. Gaur Node mean sd w w w w wa wa Table 3.5 d. Wild pig Node mean sd w w w w wa wa

39 Table 3.5 e. Muntjac Node mean sd w w w w wa wa Relative ranking of the abundance covariates based on posterior probabilities of the indicator variables (Table 3.6) suggests that no single covariate can adequately explain the observed abundance patterns of all ungulate species. In fact, different combinations of habitat, bio-physical and anthropogenic factors were important for different ungulate species (Table 3.7). 122

40 Table 3.6: Ranking of explanatory variables (PAF=Forage index, HAD=Human impact index, VAS=Variance in slope, DTW=Distance to water, ECD=Eco-climatic distance, PIE=Protection ineffectiveness index) based on posterior weights of indicator variables for five ungulate species in Nagarahole-Bandipur study region. Rank Chital Sambar Gaur Wild pig Muntjac 1 ECD ECD VAS ECD HAD 2 PAF VAS PIE DTW ECD 3 DTW HAD HAD VAS PIE 4 PIE PIE ECD PIE PAF 5 VAS DTW PAF HAD VAS 6 HAD PAF DTW PAF DTW 123

41 Table 3.7: Bayesian model assessment results for the ungulate data from Nagarahole-Bandipur study region. Each row describes a model in a binary sequence that indicates if effect of each variable (a1=forage index, a2=human impact index, b1=variance in slope, b2=distance to water, b3=eco-climatic distance, b4=protection ineffectiveness index) is included (1) or not (0). Posterior model weights of the candidate set models are given for each ungulate species columns (CHT=Chital, SBR=Sambar, GAR=Gaur, PIG=Wild pig, MJK=Muntjac). The most credible model for each species is highlighted in yellow. Model Posterior model probabilities a1 a2 b1 b2 b3 b4 CHT SBR GAR PIG MJK

42 Except the index of human impact variable, combined effect of all the five predictor variables lead to high levels of chital abundance. Sambar and wild pig abundance levels were best explained by the habitat deciduousness factor alone, although a second influential factor was steep terrain for sambar and water availability for wild pig. For gaur, the most appropriate model had protection effectiveness and undulating terrain as important variables. Muntjac abundance was influenced most by habitat impact level, while protection effectiveness and deciduousness were also important variables. Thus, the eco-climatic distance that represented the gradient in plant productivity, wetness and canopy cover was the most important abundance predictor for three ungulate species (sambar, chital and wild pig), while local forage availability was important for chital. Of the two variables that characterized terrain complexity, terrain feature mattered most for gaur and sambar. Both these species preferred steep terrain. Distance to water, the other bio-physical variable, was the second most important factor influencing wild pig abundance. Three species (gaur, muntjac and chital) were most vulnerable to levels of protection effectiveness. Muntjac was most sensitive to human use of the habitats, which also ranked as the third most influencing factor on abundances of sambar and gaur. All species 125

43 positively responded to protection efficiency, although this effect was most pronounced for gaur, muntjac and chital. 3.6 Discussion The utility of the hierarchical spatial model developed in this study to rigorously evaluate species-habitat associations has been discussed in detail in the previous chapter. Here below, I discuss the ecological implications of my results Ungulate abundance: Implications for Management Unlike the conventional line transect estimator of ungulate density that generates a single estimate of abundance for the study area (Buckland et al. 2001), the spatially explicit hierarchical models can estimate abundance for each 1-km 2 grid-cell. This enables to derive ungulate abundance at any subset area of interest to managers or ecologists. Such assessment of spatial distribution of animal densities helps in identification of habitat requirements of individual species, as well as identifying least preferred habitats. These detailed assessments can enable comparison of ungulate densities across different management units and estimation of carrying capacities as noted earlier. The spatial distribution maps (Fig. 3.3 a-e) can also help in fixing potential management targets for increasing ungulate densities, which is of a key goal in managing reserves for large predators such as tigers, leopards and dholes. 126

44 3.6.2 Predictors of Ungulate Abundance The six covariates together captured the spatial variation in ungulate species abundances very well and their influences on abundances were in the expected direction. However, the strength of their relationship to abundance levels varied among species. Two habitat variables, a) habitat quality as reflected by the eco-climatic distance and b) quantity as measured by local forage availability, influenced ungulate abundance in the expected direction for all the species. However, gaur abundance did not show a strong association with forest deciduousness although effects of other covariates were marked. Gaurs are large-bodied bulk feeders (Hofmann 1989). Their diets are composed of coarse and dry grasses including bamboo, as well as browse such as leaves and, twigs of shrubs, forbs and trees (Schaller 1967). Thus, gaur abundance is more likely to depend on quantity, rather than quality of forage available compared to more selective feeders. In contrast, results show that for all other ungulate species higher degree of deciduousness (higher levels of eco-climatic distance) had a strong negative influence on the abundances. Moist forest patches (lower eco-climatic distances) supported higher abundances of chital, sambar, muntjac and wild pig. However, it is important to note that these moist-deciduous forests do not occur in a continuous formation in the study area and are in fact a mosaic of heterogeneous micro-habitat types with a lot of edge, eco-tones and higher levels of productivity (Karanth and Sunquist 1992). I speculate 127

45 that this higher productivity in microhabitats accentuates the difference in ungulate densities between moist-deciduous forests and dry-deciduous forests, which lack these micro-habitats. Local forage availability had a weak effect on wild pig abundance, in contrast to other species. Wild pigs are the most generalist non-ruminant ungulate, feeding on a variety of food types including roots, tubers, bulbs, fruits, seeds, soil invertebrates and often carrion (Karanth and Sunquist 2000) and thus least influenced by plant forage availability that I used as a metric. In contrast, muntjac is a specialist, concentrate feeder (Hofmann 1989) depending on shoots and leaves, seasonal fruits and berries (Barrette 1977; Karanth and Sunquist 1992), explaining the strong dependence of muntjac abundance on local forage availability. Forage availability also strongly influenced chital abundance, a preferential grazer (Schaller 1967) and an opportunistic browser (Hofmann 1989). Densities of sambar, an intermediate selective feeder (Hofmann 1989), and gaur, a grass roughage eater (Schaller 1967), were moderately influenced by the availability of forage. It is important to note that in the dry season when surveys were conducted, most grasses dry up and large ungulates are compelled to shift to browse opportunistically (Hofmann 1989). The two bio-physical variables that I measured (access to water and terrain characteristics) did influence the abundance of ungulates in 128

46 predicted directions. Variance in slope, which characterized steep terrain, had different effects on different species. It induced a negative influence on chital abundance, but had a strong positive effect on sambar and gaur abundance. This covariate also had a weak association with the abundance of both wild pig and muntjac. These results of the modeling effort are consistent with natural history observations (Prater 1985) and scientific studies (Mishra and Wemmer 1987; Khan et al. 1996; Bagchi et al. 2003; Ahrestani 2009) carried out in Indian subcontinent on some of these species. The weak inverse relationship between landscape level abundance and distance to water found for all species (except wild pig and chital) was possibly due to an overarching presence of three major water reservoirs, several streams and more than 250 natural and man-made perennial water sources in the study area (Pers. Obs.). Despite widespread water availability, distance to water had a strong influence on chital and wild pig, two species with most pronounced water needs (Schaller 1967, Prater 1985). The two management variables assessed by the spatial hierarchical model showed mixed effects on the abundance of study species. These were the site-level (local scale) human impact index which reflected the intensity of human-use, and, the landscape-level anti-poaching index (protection 129

47 effectiveness index). Gaur and chital densities were strongly depressed where illegal hunting pressure was higher, whereas this relationship was, however, weak in the case of muntjac, wild pig and sambar. Madhusudan and Karanth (2000) found similar patterns of densities in an earlier comparative study in Nagarahole, actually showing that hunting pressures on muntjac and sambar were lower compared to chital. Long term observations for more than two decades by local conservation monitoring teams (K. U. Karanth and K. M. Chinnappa, Pers. Comm.) also suggest that poaching of chital is more prevalent compared to other species because of their diurnal habits and preference for open terrain. The human impact index used in this study is a quantitative measure of anthropogenic disturbance at the local scale, which is a direct consequence of management ineffectiveness. Except for chital and wild pig, all the study species responded strongly to the intensity of human-use of the ungulate habitat. Muntjac, sambar and gaur avoided areas where human impacts were highest. This association was rather weak in the case of wild pig and showed a weak positive effect, on chital abundance. The human impact index is a composite measure of various forest-resource extractive activities, and, some of these activities create open habitats, which are preferred by chital. However, there is weak support (mean +0.1 SD 0.13) for this hypothesis. 130

48 3.6.3 Relative importance of predictors of ungulate spatial abundance patterns The independent contribution of each abundance predictor was ranked either first or second for different study species (see Table 3.6). In spite of strong individual relationships that emerged, relative importance of some of these variables diminished when considered jointly with other predictors. For example, the most credible model for chital abundance included five of the six proposed variables suggesting that all these factors were indeed important. In contrast, protection efficiency and terrain features were the only two variables in the best fit model for gaur. Similarly, in spite of strong associations observed with several individual covariates, only ecoclimatic factor was sufficient to explain sambar and wild pig abundance, when considered jointly. Muntjac showed significant preference for least human impacted habitats. These results indicate the relative importance of habitat related covariates on ungulate abundance is often conditional upon jointly examining the effects of other important variables. Thus, it is important to avoid placing strong emphasis on just one single explanatory habitat-related factor and more useful to investigate cumulative and joint effects of several factors that determine ungulate abundance, keeping in view considerations of model parsimony and rigor. Many species have different niche requirements, even when they occur sympatrically (Hutchinson 1957). Bayesian inference, hierarchical 131

49 modeling and variable selection methods used in this study permitted clear identification of subset of ecological factors that contribute to animal abundances in a given landscape (Brown et al. 1995). The relative ranking of the variables and their combined effects on abundance, permitted by such modeling are thus a powerful descriptor of species-specific niches in a multi dimensional habitat. In summary, different ungulate species responded differently to different covariates of abundance at both local and landscape levels. The set of six explanatory variables I chose to model did adequately characterize the bio-physical, habitat and management attributes within a relatively large landscape of high conservation priority. These ecological and management factors supported relatively high abundances of all the five ungulate species in Nagarahole-Bandipur area. Individual ungulate species attained different abundance levels in different parts of the landscape. The spatial patterns of distribution of their densities largely reflected the extent to which their multi-dimensional niche requirements were met (Hutchinson 1957). These results are consistent with findings from previous studies (Karanth and Sunquist 1992; Karanth and Kumar 2001; Karanth et al. 2008) in this landscape and provide further evidence to the role of management in positively influencing spatial abundance patterns of ungulate species. 132

50 APPENDIX 3.1 WinBUGS model specification for a model assessment exercise to evaluate the relative importance of explanatory variables through their indicator variables. ### observed distance from transect, cluster size are detection covariates ### alpha1, alpha2 are site-level abundance covariates ### beta1, beta2, beta3, beta4 are 1-km 2 grid-cell level abundance covariates ### wa1, wa2 are indicator variables for site-level abundance covariates ### w1, w2, w3, w4 are indicator variables for 1-km 2 grid-cell level abundance covariates model { beta0~dnorm(0,.001) sigma0~dunif(0,10) p~dunif(0,10) # modeling sigma parameter for cluster size effects for(k in 1:ngs) { log(sigma[k])<-sigma0 + p*(k-1) sigma2[k]<-sigma[k]*sigma[k] } for(k in 1:ngs){ totmass[k]<-sum(pdf[,k]) 133

51 totmasscomp[k]<-sum(comppdf[,k]) } # modeling detection function using half-normal function for(j in 1:ndistcat){ for(k in 1:ngs) { log(pdf[j,k])<- -1*( (j*j)/)/sigma2[k] comppdf[j,k]<- 1-pdf[j,k] mncell[j,k]<-pdf[j,k]/(totmass[k] + totmasscomp[k]) } } # modeling cluster-size variable using zero-truncated Poisson distribution for (k in 1:ngs) { gs[k]<- exp(-lams)*pow(lams,k)/(fack[k]*(1-exp(-lams))) } lams~dunif(0,50) # modeling site-level covariate effects, variable sampling efforts, spatial mis-alignment and indicator variables for site-level covariate effects for(i in 1:ntrans){ lam[i]<- ndistwalk[i]*(bigm[i,7]*z[bigm[i,2]] + bigm[i,8]*z[bigm[i,3]] + bigm[i,9]*z[bigm[i,4]]+bigm[i,10]*z[bigm[i,5]] + bigm[i,11]*z[bigm[i,6]]) * exp(wa1*alpha1*covsites[i,2] + wa2*alpha2*covsites[i,3]) 134