a landscape approach to biodiversity conservation of sacred groves in the western ghats of india

A Landscape Approach to Biodiversity Conservationof Sacred Groves in the Western Ghats of IndiaSHONIL A. BHAGWAT,∗§ CHEPPUDIRA G. KUSHALAPPA,† PAUL H. WILLIAMS,‡AND NICK D. BROWN∗∗Oxford Forestry Institute, Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, United Kingdom†University of Agricultural Sciences College of Forestry, Kunda Road, Ponnampet 571216, South Kodagu, Karnataka, India‡Biogeography and Conservation Laboratory, Natural History Museum, Cromwell Road, London SW7 5BD, United Kingdom

Abstract: Although sacred groves are important for conservation in India, the landscape that surroundsthem has a vital influence on biodiversity within them. Research has focused on tree diversity inside theseforest patches. In a coffee-growing region of the Western Ghats, however, landscape outside sacred groves isalso tree covered because planters have retained native trees to provide shade for coffee plants. We examinedthe diversity of trees, birds, and macrofungi at 58 sites—10 forest-reserve sites, 25 sacred groves, and 23 coffeeplantations— in Kodagu district. We measured landscape composition and configuration around each site witha geographic information system. To identify factors associated with diversity we constructed multivariatemodels by using a decision-tree technique. The conventional measures of landscape fragmentation such aspatch size did not influence species richness. Distance of sacred groves from the forest reserve had a weakinfluence. The measures of landscape structure (e.g., tree cover in the surroundings) and stand structure(e.g., variability in canopy height) contributed to the variation in species richness explained by multivariatemodels. We suggest that biodiversity present within sacred groves has been influenced by native tree cover inthe surrounding landscape. To conserve this biodiversity the integrity of the tree-covered landscape matrix willneed to be conserved.

Key Words: landscape ecology, multivariate analyses

Un Enfoque de Paisaje la Conservacion de Bosques Sagrados en los Ghats Occidentales de India

Resumen: Aunque los bosques sagrados son importantes para la conservacion en India, el paisaje que losrodea tiene una influencia vital sobre su biodiversidad. La investigacion se ha centrado en la diversidad dearboles dentro de estos parches de bosque. Sin embargo, en una region cafetalera de los Ghats Occidentales, elpaisaje tambien es arbolado porque los duenos han retenido arboles nativos para que proporcionen sombraa las plantas de cafe. Examinamos la diversidad de arboles, aves y macro hongos en 58 sitios, incluyendo 10sitios en reservas forestales, 25 bosques sagrados y 23 plantaciones de cafe en el distrito Kodagu. Medimos lacomposicion y configuracion del paisaje alrededor de cada sitio con un sistema de informacion geografica.Construimos modelos multivariados con la tecnica de arbol de decisiones para identificar factores asociadoscon la diversidad. Las medidas convencionales de la fragmentacion del paisaje, como tamano del parche, noinfluyeron sobre la riqueza de especies. La distancia entre bosques sagrados y las reservas forestales tuvo unainfluencia debil. Las medidas de la estructura del paisaje (e. g., cobertura de arboles en los alrededores) yestructura del bosque (e. g., variabilidad en la altura del dosel) contribuyeron a la variacion en la riquezade especies explicada por los modelos multivariados. Sugerimos que la biodiversidad presente en los bosquessagrados ha sido influenciada por la cobertura de arboles nativos en el paisaje circundante. Para conservaresta biodiversidad, sera necesario conservar la integridad de la matriz del paisaje arbolado.

Palabras Clave: analisis multivariado, ecologıa de paisaje

§email [email protected] submitted March 24, 2004; revised manuscript accepted February 1, 2005.

1853

Conservation Biology 1853–1862C©2005 Society for Conservation BiologyDOI: 10.1111/j.1523-1739.2005.00248.x

1854 Sacred Groves in Western Ghats Bhagwat et al.

Introduction

Sacred groves are protected in the belief that “to keepthem in a relatively undisturbed state is expressive ofan important relationship of human beings with nature”(Hughes & Chandran 1998). In India this community-based tradition has received considerable attention froma sociological as well as a biological perspective (e.g.,Ramakrishnan 1996; Chandrashekara & Sankar 1998; Ra-manujam & Kadamban 2001). Although they may covera miniscule proportion of the total area of the coun-try, the number of such groves is estimated to be be-tween 100,000 and 150,000 (Malhotra 1998). With about600,000 villages in the Indian countryside (Census of In-dia 2001), sacred groves form an integral part of the rurallandscape.

This informal network of nature reserves plays an im-portant role in maintaining tree diversity. Results of acomparison between sacred groves and formal reserveshave shown that sacred groves shelter a high diversity ofmedicinal plants and have more vigorous regeneration oftrees than do formal reserves (Boraiah et al. 2003). Sacredgroves also have higher diversity of tree species diver-sity than do formal reserves (Bhandary & Chandrashekar2003; Jamir & Pandey 2003; Ramanujam & Cyril 2003).With ever-increasing pressure on the Indian countryside,sacred groves have become patches of seminatural forestin an otherwise cultivated landscape. Biological researchhas been confined to studies of biodiversity within thescared-grove patches themselves. The influence of thehuman-modified landscape surrounding sacred groves onspecies diversity, however, is rarely studied.

We used a landscape approach to study sacred grovesin the Kodagu region of India. This is a coffee-growingregion where a high percentage of native tree cover is stillintact to provide shade for coffee plants. The presence ofnative trees in the landscape matrix surrounding sacred

Figure 1. The study area in theKodagu district of Karnataka stateof India. Sampling sites were in thesouthwestern part of the district,where the continuous forest reserveadjoins tree-covered, cultivatedlandscape consisting of coffeecultivation and sacred groves.

groves is likely to influence biodiversity within the forestpatches. Although our emphasis was on patterns of treediversity, we also compared diversity patterns of birds andmacrofungi. We ask, What influence do the compositionand configuration of the surrounding landscape have onbiodiversity within sacred groves?

Methods

Study Area

The Kodagu district of Karnataka state in the WesternGhats of India extends between 11

◦56′–12

◦52′N and

75◦22′–76

◦11′E (Pascal & Meher-Homji 1986) (Fig. 1). The

formal network of protected areas (forest reserves) in theregion consists of three wildlife sanctuaries and one na-tional park, which stretch continuously along the westernand the southwestern boundaries of the district, occupy-ing approximately 30% of the area. Shade-grown coffeeplantations occupy much of the remaining landscape (ap-proximately 60%). and trees other than coffee providemost of the shade for the plantations. About 8% of thetotal area is treeless, and land uses such as paddy culti-vation and sacred groves occupy only about 2% of thestudy area. The region, however, has a high density ofsacred groves—one grove in every 300 ha (Kushalappa& Bhagwat 2001). These groves range in size from a frac-tion of a hectare to a few tens of hectares (S.A.B. & C.G.K.,personal observation) and are often surrounded by shade-grown coffee cultivation.

Sampling

We selected 58 sites in three land-use types—sacredgroves, coffee plantations, and the forest reserve—in a

Conservation BiologyVolume 19, No. 6, December 2005

Bhagwat et al. Sacred Groves in Western Ghats 1855

600-km2 landscape in southwestern Kodagu. We sampledtrees, birds, and macrofungi in 25 sacred groves, 23 cof-fee plantations, and 10 forest reserve sites in 1999 and2000. We selected sacred groves so that they were welldistributed across the study area and across the range ofdifferent patch sizes (min. 0.2 ha, max. 48.1 ha, mean13.2 ha, median 7.4 ha) and different distances from theforest reserve (min. 1.0 km, max. 8.6 km, mean 4.4 km,median 4.6 km). We ensured that sampling sites in cof-fee plantations and forest reserve sites were also well dis-tributed across the study area. Our strategy was to sample,at random, a predetermined number of individuals (ob-servations in the case of birds and macrofungi) at eachsite rather than sampling equal areas (Condit et al. 1996;Bibby et al. 1998) to overcome the problem of variablesizes of sampling sites and differences in the biologicaland ecological characteristics of organisms in question.We identified trees and birds to species and macrofungi torecognizable taxonomic units according to their morpho-logical features (i.e., morphotypes referred to as specieshereafter).

At each site, we selected a base line (between st and fnin Fig. 2), often a natural or human-made linear landscapefeature (e.g., cart track, path, fence, boundary, stream),that ran across the extent of the area. In most cases thelandscape feature was <1 m wide and canopy covered,thus minimally disturbed by human activity. Although thestarting point of transect was on the base line, the rest ofthe transect was perpendicular to the base line, away fromit. Our objective was to obtain a sample of biodiversitythat represented all habitats within the site rather thanthe “best” one. Therefore, we assumed that the proxim-

Figure 2. Schematic diagram of a representativesampling site in Kodagu. The base line runs across thepatch and the framework of transects is placed atrandom points along the base line on a randomlychosen side.

ity of base line to human-made landscape features in oursampling design was acceptable.

Before visiting a sampling site (for tree sampling) wegenerated random numbers in multiples of five. The start-ing points of individual transects were in the same se-quence as the random numbers (Fig. 2). For example,if the first random number was 100, we placed transectnumber one at 100 m from the starting point along thebase line on a randomly chosen side—left or right. Aftercompleting sampling along the first transect, we placedthe second transect at a distance equal to the second ran-dom number (e.g., 225 m, Fig. 2) from the starting point.We continued laying transects until we had counted atleast 1000 trees ≥1 cm dbh (diameter breast height) insacred groves and forest reserve sites and 100 trees ≥10cm dbh in coffee plantations. We repeated the processat each site. The lengths of transects varied between 20and 100 m in accordance with patch sizes. We demar-cated the base line by painting blue arrows on adjacenttrees. The direction of the base line was usually along acardinal direction. Therefore, we established vegetationtransects exactly along a north–south line if the base linewas roughly east–west and vice versa. Seventy-five per-cent of our sampling sites were <5 ha. As a result, theframework of base line and transects was spread acrossthe entire area of the sampling site, allowing us to ob-tain a sample that characterized biodiversity of the wholesite.

We used the same framework of base line and transectsto sample birds and macrofungi. We used the fixed-radiuspoint count method (Hutto et al. 1986) for bird sampling.We carried out between 5 and 15 12-minute point countsat each site until we made at least 50 individual bird sight-ings (e.g., Thiollay 1994). We sampled macrofungal sporo-carps in at least 500 m2 along 5-m-wide transects at eachsite (e.g., Senn-Irlet & Bieri 1999) on three different oc-casions during the monsoon season ( June–September).

Measurement of Landscape Structure

In India many topographic maps (1:50,000 or 1:25,000)are restricted to military use and are difficult to ob-tain. Village land survey maps (scale—1:7920) are locallyavailable from land revenue departments. These maps,however, are simple line drawings—often very old—containing numbered polygons corresponding to theboundaries of landholdings of each village family. Themaps also show community land such as sacred groves.

We carried out global positioning system (GPS) surveysto verify areas of sacred groves on 42 village maps. Wedigitized village boundaries and those of treeless polygons(paddy fields) within village boundaries. We then fittedtogether this jigsaw of village maps to create a landscapemap of the study area (Fig. 3).



Figure 3. Landscape map of Kodagushowing sampling sites in the forestreserve and sacred groves (R, forestreserve sites; S, sacred forests). Thedigit following S represents thedistance band where the samplingsite belongs (1, <1 km; 2, 1.5–5 km;3, >5 km from the forest reserve).The digit following R and thatfollowing the decimal point in a titlewith prefix S indicates ordering ofthe site within a particular distanceband (e.g., S 2.2 indicates a sacredforest site that is 1.5–5 km from thereserve and is the second nearest siteto the reserve in that band).

The geographic information system (GIS) software(MapInfo Corporation 2001) enabled us to georeferencethe landscape map and verify areas of sacred groves andtheir distances from the forest reserve. We used three mea-sures of landscape structure to quantify integrity, hetero-geneity, and complexity of the landscape matrix withinthe zones of a given distance (250-, 500-, 750-, and 1000-mbuffers) around each sampling site. We used five vari-ables to quantify the configuration of forest stands fol-lowing Zenner and Hibbs (2000) (Table 1). Our choiceof landscape variables was based on a literature review.

Table 1. Measures of landscape and stand structure quantified at each sampling site in Kodagu, Western Ghats of India.

Variable Description of landscape or stand structure Measure

Size area of sacred groves (m2) patch sizeDRF distance from the forest reserve (m) distanceAT area of tree-covered land (%) landscape integrityNPT total number of patches within a zone of a given landscape heterogeneity

distance surrounding a patch (buffer)LET total length (m) of edges within a buffer landscape complexitySD number of stems (≥10 cm dbh) per ha forest structural complexityBAR basal area (m2) range (5–95 percentiles) forest structural heterogeneityHTR canopy height (m) range (5–95 percentiles) extent of disturbance to the ecosystemCCR canopy-scope∗ measurement range (5–95 percentiles) microhabitat heterogeneity in the canopyLIA number of lianas per ha microhabitat complexity in the understory

∗We redesigned the Moosehorn (Garrison 1949) as a transparent Perspex screen with a 20-cm cord attached to one corner. The cord was usedto ensure that the screen was always held at the same distance from the eye. The screen was engraved with 25 dots, approximately 1 mm indiameter spaced 3 cm apart (center to center), in a 5 × 5 square array. We renamed this instrument canopy scope.

Previous landscape studies (e.g., Opdam et al. [1985] forbirds, Luiselli & Capizzi [1997] for reptiles, Bowman et al.[2001] for small mammals) determined that characteris-tics of the landscape matrix up to 1 km away can influencediversity. In the absence of such information for trees andmacrofungi, which are immobile and therefore possiblypoorer dispersers than reptiles, birds, or small mammals,we assumed that measuring variables within 1 km wassufficient. We did not intend to test specific hypothesesabout the effects of landscape variables on biodiversity.Our objective was to explore the association of these



variables with landscape-scale distribution of biodiversityin a region where little information on this is available.

Statistical Analyses

We used Hurlbert’s (1971) rarefaction method to calcu-late the expected number of species from a sample of nindividuals (or observations) selected at random (withoutreplacement) from a collection containing N individuals,S species, and Ni individuals in the ith species:

E (Sn) =∑

i

(1 −

[( N − Ni

n

) / ( Nn

)]).

Rarefaction of the frequency distribution of individu-als (trees) or observations (birds and macrofungi) overspecies allows reliable comparisons of diversity (Heck etal. 1975; Gotelli & Colwell 2001). The maximum valuethat permitted calculation of rarefied species richness was50 individuals for trees ≥10 cm dbh, 650 individuals fortrees ≥1 cm dbh, 15 observations for birds, and 12 obser-vations for macrofungi. These numbers allowed the inclu-sion of all sampling sites in the analysis. In more than 90%of the sites, rarefied species richness accumulated consis-tently over the number of individuals sampled (for trees)or observations made (for birds and macrofungi). There-fore, although we rarefied species to a smaller numberof individuals (trees) or observations (birds and macro-fungi) than sampled, our estimate can be used as a reliableindex of diversity.

Our sampling sites in coffee plantations were in thevicinity of sacred groves. To explore whether this sam-pling bias affected our results significantly, we comparedpair-wise similarities in species composition of 35 pairseach of coffee-plantation sites nearest to groves andcoffee-plantation sites farthest from sacred groves. If twosacred groves were at the same distance from a coffeeplantation, we paired both of them separately with thecoffee plantation. As a result, we had more pairs for com-parison than the actual number of coffee plantations sam-pled. We used the Bray–Curtis similarity measure in Esti-mateS to conduct the comparisons (Colwell 1994–2004).

To identify the factors associated with diversity, weused a nonparametric technique called chi-squared au-tomatic interaction detection (CHAID) for constructingdecision trees (e.g., Breiman et al. 1984; Death & Fabri-cius 2000; Manne & Williams 2003). The CHAID analysisproduced a tree diagram (not illustrated). The trunk wascomposed of all the samples in the pool. We assessed a se-ries of independent variables with SPSS Answer Tree (SPSS1989–1999) to determine at each step whether splittingthe sample pool based on the independent variables led toa statistically significant discrimination of the dependentvariable. For the ease of data management and interpreta-tion we set the options so that a branch with fewer than

10 observations could not be split further and terminalbranches had at least five observations. We set the split-ting probability to 0.05 and used Bonferroni adjustment tocalculate the p value of each predictor. This adjustmenttakes into account a large number of variables enteredinto the analysis simultaneously and adjusts p values ac-cordingly, thus correcting the problem of multicollinear-ity in independent variables (Bland & Altman 1995). Ateach step we chose the variable with highest F value orlowest adjusted p value, or both, to define splits. For eachnew group formed we identified the next most signifi-cant variable (which may include the independent vari-able used earlier) to split the branch further. We prunedthe terminal branches with nonsignificant splits to obtaina tree with all statistically significant ( p < 0.05) splits andgroups that were maximally different from one another(e.g., Huba 2000).

To examine the effect of the distance from the forestreserve on tree diversity, we considered only 24 sacredgroves and eight forest reserve sites (Fig. 3) because stemdensities in coffee plantations were artificial—small stemsare regularly cut back and the shade trees are thinned forplanting coffee. We excluded one sacred grove and twoforest reserve sites because they did not have sufficientdata for stems ≥1 cm dbh. We divided the 32 samplingsites into four distance bands. The first band includedeight forest reserve sites. The second, third, and the fourthbands included seven, nine, and eight sacred groves thatwere <1.5, 1.5–5, and >5 km from the edge of the forestreserve, respectively. We did not examine ecological char-acteristics of individual species. Therefore, we rankedtree species according to their abundances and assumedthat the identity of a species at the given rank was notimportant (e.g., Tokeshi 1993). We plotted the mean ofthe relative species abundance in each rank (irrespectiveof the identity of the species) against the correspondingrank on a semilog plot. We compared average species-abundance distributions of sacred groves in each of thethree distance bands and the average distribution of thereserve with a two-sample Kolmogorov–Smirnov test.

To examine the similarity between the rank-abundancedistributions of trees ≥1 cm dbh in the forest reserve andsacred groves, we used nonmetric multidimensional scal-ing (NMDS). We excluded coffee plantations from thisanalysis because they do not contain trees ≥1 and <10 cmdbh. An NMDS assigns each site (represented by a point)to a specific location in a conceptual low-dimensionalspace such that the distances between points in the spacerepresent the given similarities between sites as closelyas possible. The result is a least-squared representationof the communities, which helps in understanding thedata structure (Kenkel & Orloci 1986). We carried outthe analyses with SPSS 10 (SPSS 1989–1999). We used Eu-clidean distance measure for comparing similarities andevaluated the results based on the standardized residual



sum of squares (STRESS), which is a measure of closenessto the original distances between sites.

Results

Species Diversity

We recorded 215 tree species, 86 bird species, and 163macrofungus species. Forty-five percent of all tree spe-cies, 40% of bird species, and 39% of macrofungus specieswere found in all three land-use types—forest reserve,sacred groves, and coffee plantations. Coffee plantationsshared 26% of tree species, 35% of bird species, and 21% ofmacrofungus species with the other two land-use types.

There were no significant differences in the mean rar-efied species richness of trees (one-way analysis of vari-ance [ANOVA] F = 1.271, df = 2, p = 0.289), birds (F =2.037, df = 2, p = 0.140), or macrofungi (F = 2.805, df= 2, p = 0.069) across the three land-use types.

The possible bias due to nearness of the coffee planta-tion and sacred grove sampling sites did not affect our re-sults. There were no significant differences in Bray–Curtispair-wise similarities in species composition between thepairs of coffee–plantation sites nearest to sacred grovesand coffee–plantation sites farthest from sacred groves(ANOVA) (trees, F = 0.395, df = 1, p = 0.532; birds, F =1.459, df = 1, p = 0.231; macrofungi, F = 0.347, df = 1,p = 0.558).

Exploration of Variables with CHAID

Landscape and stand structure variables (Table 1) ex-plained only a small proportion of variation (8–10%) in

Figure 4. Tree speciesrank-abundance curves for theforest reserve (0 km) and sacredgroves (<1.5, 1.5–5, >5 km) in fourdistance bands, increasing distancefrom the forest reserve. Species ranksreflect species abundances (totalnumber of individuals [trees ≥1 cmdbh], N = 39,271; total number ofsites, n = 32; N and n are 9827,8641, 10814, and 9989 and 8, 9, 7,and 8, respectively, for four distancebands along the increasing distancegradient).

tree, bird, and macrofungal diversity. Distance from theforest reserve, variability in canopy height, and stem den-sity contributed to the variation in diversity of trees (≥10cm dbh). The integrity of the landscape in the surround-ings, variability in canopy height, canopy closure, andbasal area (BAR) contributed to the variation in diversityof birds. The landscape complexity, variability in canopyclosure, density of lianas, and variability in basal area con-tributed to the variation in macrofungal diversity. We alsoexamined the subsets of sampling sites created by CHAID.The decision trees did not show distinctions among sa-cred groves, coffee plantations, and forest reserve sites.

Patch Size and Distance from Forest Reserve

There was no significant correlation (trees ≥10 cm dbh,Spearman’s r = 0.022, p = 0.958; trees ≥1 cm dbh, Spear-man’s r = 0.061, p = 0.225) between patch size and rar-efied species richness. Birds (Spearman’s r = 0.019, p =0.720) and macrofungi (Spearman’s r = 0.020, p = 0.383)showed similar patterns.

The diversity of trees ≥1 cm dbh was negatively corre-lated (Spearman’s r = 0.27, p < 0.001) with the distanceof a patch from the forest reserve. There appeared to bea decline in diversity in more distant sacred groves.

The curve for the rank-abundance distributions of trees≥1 cm dbh in the forest reserve (0 km) had the low-est gradient, and that for sites more than 5 km awaywas the steepest (Fig. 4). According to the two-sampleKolmogorov–Smirnov test, however, the differences be-tween the distributions were not significant (Z = 0.707,0.950, and 1.166 with respective p values of 0.700, 0.328,and 0.132 and n = 9, 7, and 8, with increasing distance



from the reserve). The distance of a grove from the forestreserve appeared to have had only a weak influence ontree diversity.

The results from the NMDS also suggested that dis-tances of sacred groves had little influence on their simi-larity with forest reserve sites. Although four out of eightforest reserve sites clustered together, there was no or-dering in sacred groves (S-STRESS = 0.00602). Results forbirds (S-STRESS = 0.02332) and macrofungi (S-STRESS =0.00482) were similar.

Discussion

Importance of Landscape Surrounding Sacred Groves

Sacred groves in Kodagu are patches of forest in a land-scape that probably once had continuous tree cover. Thebiogeographic processes related to species loss from for-est remnants (e.g., Turner 1996; Turner et al. 1996) havehad an obvious effect on sacred groves (S.A.B., unpub-lished data), but it appears that the tree-covered natureof the surrounding landscape may have reduced the in-tensity of the species loss and maintained the similarityin species composition of sacred groves and the forestreserve despite the distance. We also found no significantdifferences in the distribution of biodiversity in the for-est reserve, sacred groves, and coffee plantations, and nosignificant decline in biodiversity of sacred groves withdecrease in patch size or with increase in distance fromthe forest reserve.

Although sacred groves alone cannot represent all bio-diversity in the region, the loss of these groves would re-sult in the decline of landscape-scale heterogeneity thatthese patches provide (Quinn & Harrison 1988; Lapin& Barnes 1995). The landscape outside forest reserve inKodagu shelters species that are not protected by theformal reserve network. Threatened tree species such asActinodaphne lawsonii Gamble, Hopea ponga (Dennst.)Mabberley, Madhuca neriifolia (Thw.) H.J. Lam, andSyzygium zeylanicum (L.) DC. (e.g., FRLHT 1999; IUCN2003) were found exclusively in sacred groves. We foundother threatened species such as Michelia champaca L.and endemic species such as Pittosporum dasycaulonMiq. in sacred groves and coffee plantations but not inthe forest reserve. Between 17 and 90% of stems of thethreatened and endemic species were between 1 and 10cm dbh, suggesting that these species are able to regen-erate in sacred groves. Because these species cannot re-generate in coffee plantations where all small individualsare regularly cut back, their future survival will requirepropagation as shade trees to maintain tree cover in thelandscape.

Bird species such as Loten’s Sunbird (Nectarinia lote-nia L.), an endemic species, and the Nilgiri Flycatcher(Eumyias albicaudata Jerdon), an endemic and threat-

ened species, were restricted to sacred groves and coffeeplantations. Forty-nine out of 163 species of macrofungiwere unique to sacred groves. Their survival will requireconservation of land outside the forest reserve. The com-plexity of land management types in Kodagu (e.g., Elouard2000) means future management will have to consider theexisting land-management practices. However, the prin-ciple of maintaining native tree cover holds if the goalis to achieve conservation of biodiversity in the Kodagulandscape.

Biodiversity-Friendly Coffee Production

Native tree cover remains intact in many parts of Kodagubecause of the production of shade-grown coffee. Ourresults suggest that the tree-covered nature of these cof-fee plantations may have made an important contributionto maintaining biodiversity within sacred groves. The im-proved irrigation, however, has allowed planters to fell na-tive trees (which retain moisture in the plantations due totheir dense foliage) and replace them with exotics (whichoften have sparse canopies) (e.g., Perfecto et al. 1996;Moguel & Toledo 1999). Introduced trees such as Grevil-lea robusta Cunn. have straight boles, which can also beused to train black pepper ( Piper nigrum L.) vines, an im-portant source of additional income for coffee planters.Conservationists in Latin America are promoting cultiva-tion of shade coffee because of the importance of nativeshade trees for biodiversity conservation in coffee pro-duction areas (but see Philpott & Dietsch 2003; Rappoleet al. 2003a, b). In our opinion, a similar promotion wouldbe beneficial in Kodagu.

In Latin America when local landowners reach some ba-sic level of economic security they are likely to becomeinterested in ecological sustainability, long-term environ-mental planning, and biodiversity conservation (South-gate & Clark 1993). Gobbi (2000) suggests that in El Sal-vador, biodiversity conservation in shade coffee planta-tions can be viable but incentives to small farmers fromthe government are necessary. These could be in form oftax reductions, loan facilities, subsidies, and a secure mar-ket for biodiversity-friendly coffee. For such cultivationpractices to be successful in Kodagu, it would be neces-sary for the government to support small-scale planters sothey will in turn support biodiversity conservation. Cer-tification of coffee plantations by setting standards forsustainable management can encourage planters to growcoffee in a biodiversity-friendly manner (Bray et al. 2002;Philpott & Dietsch 2003). The recent efforts in Kodagutoward organic coffee cultivation are steps in the rightdirection. In addition to this, the market access of certi-fied products should be facilitated by specific governmentpolicies and appropriate legislation. We believe that theeffective marketing of biodiversity-friendly coffee in theinternational market can complement the policies and



legislation. Such steps will promote shade-grown coffeecultivation in Kodagu.

Scope and Limitations

Our choice of sampling sites was often constrained byour ability to make logistic arrangements for field sam-pling in inaccessible areas of the forest reserve. Althoughwe were unable to select sacred grove sites randomly,we ensured that they were distributed across the studyarea (Fig. 3). We hoped this would allow us to capturethe variation in site quality, patch sizes, and distances ofgroves from the reserve so as to get a representative sam-ple of sacred groves in the Kodagu region. Our samplingsites in coffee plantations were in the vicinity of sacredgroves. This may have resulted in a biased sample of bio-diversity in the landscape surrounding sacred groves. Thebias did not, however, affect our results significantly. Theland-survey maps we digitized had limited informationon them, which was reflected in our classification of landinto three tree-covered land-use types and treeless land.Although this classification was representative of broadland cover in Kodagu, the tree-covered land use is muchmore complex because of historical land management(e.g., Elouard 2000). A more focused investigation may berequired to make site-specific management recommenda-tions.

We chose decision trees for multivariate analysis be-cause they are ideally suited for analyzing complex eco-logical data, which require a flexible and robust methodthat can address nonlinear relationships, high order in-teractions, and missing values (e.g., Death & Fabricius2000). The CHAID is an exploratory data analysis methodused for studying the relationships between a dependentmeasure and a large series of possible predictor variables,which themselves may interact. This method is usefulbecause of its flexibility such that (1) the level of mea-surement of the dependent and predictor variables canbe nominal, ordinal, or continuous; (2) not all predictorsneed to be measured at the same scale; and (3) the analy-sis is not affected by missing values and partial data can beused if necessary (Huba 2000). The CHAID is, however,a stepwise model-fitting method. The sequential model-fitting algorithm means that the later effects are depen-dent on the earlier ones because all effects are not fittedsimultaneously. Nonetheless, in areas such as landscapeecology, where there is a lack of strong theory to indicateclearly which variables are or are not predictors of par-ticular independent variables, CHAID is useful to identifymajor data trends.

There was no clear indication that any one of the vari-ables we measured explained a large proportion of varia-tion in diversity, and the decision trees did not distinguishbetween sacred groves, coffee plantations, and forest re-serve sites, possibly as a result of a high tree cover in

the landscape. Patch size and distance between patchesare often used to explain diversity within patches in frag-mented landscapes (Lynch & Whigham 1984; Turner et al.1996; Miller & Cale 2000), but these failed to explain di-versity patterns in Kodagu. The tree-covered landscape ofKodagu may mean that patches do not have well-definedecological boundaries. Consequently, the area of forestthat can host forest-dependent species is not limited bypatch edges. As a result of the high tree cover, patch sizeexplained little of the variation in species diversity, pre-sumably because many species behave as though the ma-trix were forest.

Distances of patches from the forest reserve had a weakinfluence on the similarity in tree diversity but did not af-fect bird diversity. Birds are more mobile than trees, andthe tree-covered landscape in Kodagu provides continu-ous habitat for birds. The variables identified by multivari-ate models for explaining tree diversity did not explaindiversity of birds or macrofungi, possibly because of dif-ferences in biological and ecological characteristics of thegroups of organisms in question. Because our choice ofvariables was based on the review of existing literaturerather than specific hypotheses, it is possible that wemay have overlooked the variables that are strongly as-sociated with landscape-scale distribution of trees, birds,and macrofungi. Future research should explore specifichypotheses to understand better the landscape-scale ef-fects on these and other groups of organisms.

Conclusion

In many parts of the world the fate of biodiversity is be-lieved to depend on the forest remnants in human-madelandscapes (e.g., Brussard et al. 1992; Luck & Daily 2003;Rosenzweig 2003). It is recommended that conservationmeasures in agricultural landscapes should include step-ping stones of native woodlands for maintaining diversity(e.g., Schwartz 1997; Pirnat 2000; Duelli & Obrist 2003)and gene flow (Bawa & Ashton 1991; Hannah et al. 1998;Rouget et al. 2003). In recent years, the conservation com-munity has come to realize that the long-term survivalof biodiversity depends on the effectiveness with whichlandscape between the forest remnants can be managed(Gould 2000; Faith & Walker 2002; du Toit et al. 2004).Our results suggest that maintaining the integrity of coffeeplantations is necessary for conserving biodiversity of sa-cred groves in Kodagu. A patch-scale study alone is insuffi-cient to understand the role of sacred groves; a landscapeapproach is essential. Involving local people in manage-ment is also key to successful biodiversity conservationbecause of the anthropogenic nature of this landscape.The government must ensure that their policies favor lo-cal planters so as to promote biodiversity-friendly coffeecultivation.



Acknowledgments

This project was funded by a research grant to Ox-ford Forestry Institute from the Conservation, Food andHealth Foundation, Boston, Massachusetts. S.A.B.’s doc-toral study was supported by the Rhodes Trust, the Rad-hakrishnan Memorial Bequest, Linacre College, and theUniversity of Oxford Graduate Studies Committee. Wethank Md. Ashfaq, K. T. Boraiah, H. R. Kamal Kumar, K.M. Nanaya, C. Shivanad, and B. S. Tambat for their as-sistance during the fieldwork in Kodagu. The cosupervi-sion from S. Jennings and P. Savill during S.A.B.’s doctoralstudy is gratefully acknowledged. The discussion with R.Whittaker and M. Swaine was very useful. The commentsfrom B. McComb, G. Meffe, K. Vance-Borland, R. Vane-Wright, and an anonymous referee were very valuable inimproving the manuscript. S.A.B. is grateful to the Biodi-versityWorld project (www.bdworld.org) for supportinghis current postdoctoral position at the Natural HistoryMuseum, London.

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a landscape approach to biodiversity conservation of sacred groves in the western ghats of india

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