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Gillison, A. N., N. Liswanti, S. Budidarsono, M. van Noordwijk, and T. P. Tomich. 2004. Impact of cropping methods on biodiversity in coffee agroecosystems in Sumatra, Indonesia. Ecology and Society 9(2): 7. [online] URL: http://www.ecologyandsociety.org/vol9/iss2/art7/
Report Impact of Cropping Methods on Biodiversity in Coffee Agroecosystems in Sumatra, Indonesia Andrew N. Gillison1, Nining Liswanti2, Suseno Budidarsono, Meine van Noordwijk, and Thomas P. Tomich3
1Center for Biodiversity Management; 2Center for International Forestry Research; 3Alternatives to Slash and Burn (ASB), World Agroforestry Centre
- Abstract
- Introduction
- Methods
- Results
- Discussion
- Conclusions
- Responses to this Article
- Acknowledgments
- Literature Cited
The sustainable management of biodiversity and productivity in forested lands requires an understanding of key interactions between socioeconomic and biophysical factors and their response to environmental change. Appropriate baseline data are rarely available. As part of a broader study on biodiversity and profitability, we examined the impact of different cropping methods on biodiversity (plant species richness) along a subjectively determined land-use intensity gradient in southern Sumatra, ranging from primary and secondary forest to coffee-farming systems (simple, complex, with and without shade crops) and smallholder coffee plantings, at increasing levels of intensity. We used 24 (40 x 5 m) plots to record site physical data, including soil nutrients and soil texture together with vegetation structure, all vascular plant species, and plant functional types (PFTs—readily observable, adaptive, morphological features). Biodiversity was lowest under simple, intensive, non-shaded farming systems and increased progressively through shaded and more complex agroforests to late secondary and closed-canopy forests. The most efficient single indicators of biodiversity and soil nutrient status were PFT richness and a derived measure of plant functional complexity. Vegetation structure, tree dry weight, and duration of the land-use type, to a lesser degree, were also highly correlated with biodiversity. Together with a vegetation, or V index, the close correspondence between these variables and soil nutrients suggests they are potentially useful indicators of coffee production and profitability across different farming systems. These findings provide a unique quantitative basis for a subsequent study of the nexus between biodiversity and profitability.
KEY WORDS: biodiversity indicators, coffee-farming systems, plant functional types, v-index.
Published: September 27, 2004
Sustainable forest management and biodiversity conservation are rarely viable financial propositions. Uncontrolled forest exploitation or deforestation can contribute to major biodiversity loss yet continue to be highly profitable in the short term. The underlying problem of weak incentives for forest management and conservation is of increasing concern and contributes significantly to biodiversity loss. For biodiversity to be managed sustainably, the links between the natural resource base, management systems, and income must be understood in order to develop acceptable tradeoffs. Because biodiversity is consistently undervalued, it is important to know how to allocate a biodiversity value to forested lands, especially those with high “externality” values associated with environmental functions and biodiversity conservation (Richards 1999). Short-term profit incentives in primary production are frequently supported by permanent, intensive cropping systems as an alternative to slash and burn. Where capital is available, this is usually accompanied by the addition of artificial fertilizers, pesticides, and herbicides. These short-term gains can be seriously eroded by a gradual decline in crop yields engendered by increasing soil acidity, pesticide resistance, and herbicide-resistant, invasive exotic weeds. In many tropical countries, the widespread and often uncontrolled removal of land cover continues to deplete soil reserves leading to a significant reduction in environmental services and water quality often with a dramatic loss in biological diversity. Current methodologies severely restrict the kinds of multidisciplinary baseline studies needed to provide a meaningful economic value to biodiversity for policy development.
As one element of a broader effort to redress this problem, we report the results of a baseline study using recently developed rapid survey techniques to identify key links and indicators of interactions between biodiversity and cropping systems in a typical, upland, tropical coffee-based agroecosystem. The biophysical baseline data reported here will form the basis for a second report to deal more specifically with dynamic links between biodiversity and profitabilty. Coffee is one of the principal agricultural products in international trade volume, having a market value of almost US$19.5 billion per year; most of it is grown and exported by more than 50 developing countries. Baseline information that can lead to policy changes in the management of coffee agroecosystems over the long term, therefore, has the potential to significantly influence both international trade and natural resource capital, as well as long-term local livelihoods and sustainable biodiversity management.
Biodiversity Indicators in AgroecosystemsIn its broadest sense, biodiversity is defined as the “variety of life on earth,” otherwise described in terms of gene, species, and ecosystem ( cf. Heywood and Baste 1995). However, a specific application by Sala et al. (2000) excludes “exotic organisms that have been introduced and communities such as agricultural fields that are maintained by regular intervention.” Although conceptually useful, such definitions are inappropriate for management where quantification is vital and where any species, either exotic or indigenous, is an integral part of the bio-ecological landscape. In this paper, we examine the potential value of certain suites of indicators of change in biodiversity under different forms of land use in coffee-based agroecosystems involving both exotic and indigenous plant species. In so doing, we focus on causal rather than correlative links within the context of management scale and purpose.
For logistic, scientific, and management purposes there is a need for cost-efficient methods to identify and locate simple, readily observable indicators of complex sets of biota and related agricultural productivity. In order to be able to monitor and forecast the impacts of land use on biodiversity and, conversely, any potentially negative feedback of biodiversity loss on productivity, it is desirable that such indicators should possess some demonstrable, functional as well as correlative relationship with the physical environment. So far the “ideal” is far from reality and much debate surrounds the definition, measurement, and reliability of biodiversity indicators (cf. Noss 1990, Lawton et al.1998, Watt et al.1998, Lindenmayer et al. 2000). Although there is no “best surrogate” (Margules and Pressey 2000), certain biophysical indicators may be used to estimate both the type and distribution of organisms in time and space. Conversely, it is possible to use taxonomic subsets of species assemblages or functional characteristics as indicators of specific abiotic features of the environment, such as soil quality, potential productivity, profitability, or the state of ecosystem health (e.g., level of pollution or degradation) (Gillison 2001). The use of environmental indices, composite environmental indices, and socio-economic indicators is reviewed by Bakkes et al. (1994) and the role of biodiversity indicators in forested landscapes by Gillison (2001). Apart from managers, policy makers also require indicators for biodiversity management (McNeely 1990, Reid et al.1993).
Despite a continuing search for alternatives, until now the species remains the common currency of biodiversity. Multivariate approaches to the use of composite species sets as indicators commonly include some form of species indicator analysis (Schwartz and Wein 1997, Hobson and Schieck 1999). In Ugandan forests, Howard et al. (1997) applied a range of biological indicator taxa in a wide-ranging study of biodiversity conservation procedures and concluded that practical factors compel their use. In our study, rather than persist solely with taxa as sole indicators in complex, tropical environments where species identification is often problematic, we examine the potential complementary use of plant functional types (PFTs) for which there is at least some evidence for a role in ecosystem function. The relatively recent and novel use of PFTs in biodiversity assessment requires some explication within the context of more traditional species-based, biodiversity indicators.
Diaz (1998) describes functional groups or functional types (FTs) as “sets of organisms showing similar responses to environmental conditions and having similar effects on the dominant ecosystem processes” (see also Cramer 1996, Cramer et al.1999, Tilman, 2001). A more practical definition (Shugart 1996) refers to FTs as “species or groups of species that exhibit similar responses to a suite of environmental conditions.” FTs are most commonly described as “guilds” or groups of individuals that exploit an existing resource in a similar way, such as raptors, folivores, plant parasites, saprophytes, etc. (Gillison 1981, Huston 1994, Smith et al. 1996, Gillison and Carpenter 1997, Gitay et al. 1999, Duckworth et al. 2000). Functional types can help reduce complex species groups to more manageable entities and facilitate comparison of responses of individuals, for example, between geographically remote locations where environments and adaptive morphologies are simlar but where species differ. In this way, FTs achieve a practical and logistic advantage over species as more generic indicators. Nonetheless, field studies suggest measurement of biodiversity impact can benefit from including records of both species and FTs (Gillison 1981, 2002, Cowling et al. 1994, Huston 1994, Martinez 1996, Diaz et al. 1999, Duckworth et al. 2000).
At both local and global scales, there is a generally negative relation between the diversity of plant species and potential agricultural productivity (Huston 1993) and, although agricultural expansion benefits from biodiversity via integrated pest management among other things, it remains one of the greatest contributors to its loss (Miller et al. 1995). A consensus on a functional role for species diversity (richness) remains elusive; however, certain functional groups can significantly influence ecosystem processes (Folke et al. 1996, Tilman et al. 1997, Knops et al. 1999, Tilman 1999). And, although PFT richness itself may be a useful indicator of biodiversity condition, differences in PFT composition can help explain more variation in ecosystem processes, such as production and nitrogen dynamics, than the number of functional groups present (Hooper and Vitousek 1998). Plant functional types based on adaptive morphologies are known to influence soil organic carbon (SOC) distribution in the soil profile (cf. Jobbágy and Jackson 2000). An intensive, multitaxa baseline study in lowland Sumatra showed PFTs and plant species were closely correlated with SOC, soil nutrient availability, above-ground carbon, and land-use intensity (Gillison 2000, Hairiah and van Noordwijk 2000). The same study provided strong statistical support for the use of PFTs in combination with vascular plant species as indicators of certain groups of insects, especially termites, and birds along a lowland, tropical, forested land-use intensity gradient (Bignell et al. 2000, Jepson and Djarwadi 2000, Gillison 2000, Jones et al. 2000, 2002, Gillison et al. 2003).
Traditional farming practices that maintain landscape mosaics rather than monoculture cropping systems are more likely to support natural pest control through maintenance of different types of natural enemies (Abate et al. 2000). The maintenance of landscape complexity can, therefore, help sustain many useful functional aspects of environmental services (Giller et al. 1997, Vandermeer et al. 1998, van Noordwijk and Swift 1999). For this reason, traditional land-use mosaics containing woodlands, forests, and agroforests and other complex agroecosystems, are likely to be more beneficial in the long term for adaptive management and sustainable biodiversity than broadscale agricultural mono-cropping systems. The environmental impacts of traditional, rotational systems involving forest succession, such as shifting cultivation, may be much less on biodiversity and soil erosion, than those from other forest land uses (Angelsen 1995, Tomich et al. 2001). Profitable agroforestry systems are potentially sustainable, controlling erosion, enhancing biodiversity, and conserving carbon, provided nutrient offtake is balanced by nutrient returns via litter and the strategic use of fertilizers, particularly phosphorus (Sanchez 1995). The solution to maintaining biodiversity through an intermediate level of disturbance of the kind found in more complex agroforests is relatively well established, where, through the maintenance of disturbance, an equilibrium is never reached and higher biodiversity is maintained. Forest management can dictate the disturbance regime (frequency, size, and intensity) which must be fitted to the relevant attributes, or life histories, of the organisms to be managed (Attiwill 1994).
The above findings are generally consistent with what is known about coffee agroecosystems, although some field studies provide conflicting evidence for the impact of specific cropping methods on biodiversity. Most studies show biodiversity is enhanced through increasing shade treatments, although short-term profitability may be reduced (Perfecto et al.1996). In Costa Rica, Perfecto and Snelling (1995) found vegetational diversity varies proportionally with the diversity of foraging ants. This is supported by Roberts et al. (2000) who also showed that in western Panama, mid-elevational, traditional shade-coffee plantations provide additional habitat for diverse avifauna that attend army ant swarms—a trend that is reversed when coffee is grown without shade at increasing levels of intensity. On the other hand, in Guatemala, Greenberg et al. (2000) found no evidence for shade-related levels of insectivory.
In a Colombian coffee agroecosystem, Armbrecht et al. (2004) showed plant species diversity of ant nesting resources varied directly with ant species diversity, thus providing experimental evidence that, at least in this case, biodiversity begets biodiversity.
A Typology of Coffee-farming Systems
In order to erect a suitable sampling framework, the research team (comprising ecologists and economists) developed a typology of coffee-farming systems and associated tenure systems (see Methods) through a series of visits to the study region and discussions with local farmers. Related studies elsewhere in Sumatra suggest cropping systems and tenure systems evolve jointly and hence are largely determined simultaneously (Otsuka et al. 2001, Suyanto et al. 2001a,b). Thus, pioneer coffee systems are associated with insecure tenure, complex coffee systems always occur with (at least informal) tenure security, and simple coffee monocultures occur across shades of insecurity in land and tree tenure associated with varying intensities of purchased inputs and other productivity-sustaining investments (Budidarsono et al. 2000). “Pioneer” systems are established by clearing natural forest on State forest land (often gazetted as parks and conservation areas) and hence involve a high degree of tenure insecurity for the smallholders.
Because these “low intensity” systems aim to maximize coffee yields in the short run (over 5–7 years), no shade trees are planted. Absence of shade trees boosts coffee yields in years 4 and 5 but, combined with lack of fertilizer applications, coffee yields drop dramatically after the peak harvest in year 7. Hence, instead of pruning to establish a steady-state coffee system, the plots are fallowed from year 8 in the pioneer system. The pioneer coffee system also represents a shifting cultivation technique of coffee farming that most indigenous Semendonese practiced in the early stage of coffee cultivation in Sumberjaya when land was still abundant. Although it is unusual, this system still occurs in the frontier area, where it is practiced by others as well as Semendonese more or less as a “hit and run” strategy.
Under the simple monocropping system, coffee trees are fertilized and pruned at about year 8 in order to sustain coffee production and avoid reversion to the bush fallow that characterizes the pioneer system. As with pioneer coffee, simple coffee systems with insecure title are derived from conversion of State forest land, but often from watershed protection areas instead of parks and conservation areas. Simple coffee systems with insecure tenure are derived from conversion of State forest land but not necessarily from forested land. In many cases, simple coffee with insecure tenure is derived from an old abandoned coffee garden that belonged to the Semendonese. In this case, the old coffee garden was purchased by an immigrant (mostly Javanese), to establish a new coffee garden (slash and burn), or to just rejuvenate them (by pruning the main stem) to be managed as simple, permanent coffee system. Alternatively, the immigrant, a sharecropper, could be given the old abandoned land to manage. Simple coffee systems with secure tenure are located outside State forest land. Not surprisingly, the greater security of tenure over land and, hence, over trees is associated with increased investment in maintaining tree productivity through application of fertilizer and, occasionally, grafting.
Our target area was a coffee-based production system in a relatively remote, upland tropical forested landscape with many similarities to production systems in other areas of the tropics where biodiversity is under increasing threat and where management uncertainties are high. Most studies on coffee agroecosystems focus on impacts within coffee plantations under varying levels of management and rarely consider biodiversity response within a broader land-use context, for example, along regional land-use intensity gradients. Previous studies (Gillison 2000) show that expanding the sample base to include gradient extremes of land use and the biophysical environment greatly improves the value of a baseline survey and the robustness of biodiversity indicators. Gradient-oriented transects or gradsects (Gillison and Brewer 1985, Wessels et al. 1998) also indicate considerable improvements over standard, random sampling methods in detecting biodiversity pattern. The methods used for vegetation assessment in this survey were those applied in other ASB ecoregional baseline studies (Gillison 2000) using gradsect sampling, to which the reader is referred for detail. (The Alternatives to Slash and Burn project operates as an institutional consortium hosted by the International Centre for Research in Agroforestry (ICRAF).)
We selected a series of representative locations within Sumberjaya, a coffee-farming area within Lampung Province, Sumatra, Indonesia where approximately 115 000 ha of a total 410 000 ha have been converted to coffee plantings. The area lies within one the world’s top five biodiversity “hotspots” (Myers et al. 2000). At the beginning of the 20
Geocordinates for each site were obtained using a Garmin GPS with an estimated accuracy of ± 80m. In each LUT and cropping system, a 40 x 5 m plot (strip transect) was laid out along a contour and according to site biophysical data (Table 5). A field botanist recorded all vascular plant species, for which voucher specimens were collected and later identified at the Herbarium Bogoriense. These were cross-referenced with PFTs (or functional modi), recorded using the classification method of Gillison and Carpenter (1997). In the field, these biophysical data were collated and stored in a laptop computer using the public domain CIFOR “VegClass” ® (beta v. 1.5) Windows®-based software package (Gillison 2002). VegClass uses a standard protocol to collate, store, and tabulate proforma data and can be used to generate graphic output of meta-data and to export summary data to a wide range of industry-standard spreadsheet and relational database programs. Estimates of above-ground tree dry weight were obtained from two sources (Brown 1997, Ketterings et al. 2001) and used to quantify above-ground carbon (van Noordwijk et al. 1997).
Soil samples were collected (composited from eight sample points per 200 m2 sampling area) for the 0–5, 5–15, and 15–30 cm depth zone below the litter layer. The samples were passed through a 2-mm sieve and air dried for texture analysis (sand, silt, clay; pipette method), pH (in a 1: 2.5 soil: solution extract
with water or 1 N KCl), PBrayI, Corg (Walkley and Black), Ntot (Kjeldahl), exchangeable K, Ca, Mg, Na, (exchanged with a 1 N NH4-Acetate solution of pH7) and exchangeable Al and H (exchanged with a 1 N KCl solution). Effective cation exchange capacity was obtained by summation of these cations. These measurements were undertaken in the laboratory of the Centre for Soil and Agroclimate Research (CSAR) in Bogor, Indonesia. All mineral contents were expressed per unit soil dried at 105°C. On the basis of texture and pH, the reference organic C (Cref) content was calculated using a regression equation derived from a large data set for Sumatra (van Noordwijk et al. 1997): Cref = EXP(1.333 + 0.00994 * clay% + 0.00699 * silt% - 0.156 * pH(KCl)). Because plant performance along land-use intensity gradients appears to be more closely associated with the physico-chemical nature of the upper rather than lower soil horizons, only data from the top 0–5cm layer are used in this analysis.
The high biophysical complexity inherent in the plot samples, together with difficulties in locating adequate plot replicates, prohibited standard sampling and statistical treatments as a means of detecting meaningful pattern
between biodiversity and cropping methods. Instead, we applied a multivariate exploratory data analysis package (PATN©, Belbin 1992) to all plant-based data using a Gower similarity measure (Gower 1967) with an unweighted pair-group averaging fusion strategy to produce classifications and multi-dimensional scaling (MDS) ordinations. Multi-dimensional scaling was also used to extract the single best eigenvector scores for all plant-based variables across all plots. These eigenvector values were then incorporated as an additional predictive variable in subsequent standard regression analyses. Linear (Pearson) and polynomial regressions were used to seek out the most highly correlated variables within the entire data set using the MINITAB© ver.13.2 statistical package. Although diversity indices are traditionally derived from species abundance data, for logistic reasons they are rarely used in species-rich, tropical forests. Instead, we used a more logistically acceptable estimate of the better known indices (Shannon-Wiener, Simpson’s, and Fisher’s alpha) based on numbers of species per PFT
rather than the number of individuals per species (Gillison and Carpenter (unpubl.), Gillison 2000). We also applied a measure of plant functional complexity (PFC) which is the total minimum spanning tree distance in a computed matrix
of functional “distance” between all PFTs within a site (Gillison 2000).
Certain key vegetation features (mean height, basal area, species and PFT richness, and species:PFT ratio) can vary predictably with specific measures of biodiversity and LUT (Gillison 2000). In the present study, these features were compared across all plots together with a relative vegetation index (“V”-index, Gillison 2002) that integrates these values for each plot measured as the single best eigenvector score from an MDS. These scores are standardized and used to rank LUTs—in this case, the 24 plots of the present study. The V index is a potentially useful measure of biodiversity relative to LUT and has been shown to be closely correlated with the presence of certain animal groups and their habitats in lowland Sumatra (Gillison 2000, 2002).
Site physical features, site history, and vegetation structural data are summarized in Tables 1 and 2. Typology of coffee-farming systems and associated teunure systems and their transitional sequences are cross-referenced in Table 3 and the sample design based on LUT and tenure class is outlined in Table 4. Richness values for vascular plant species and PFTs and diversity indices for PFTs are listed in Table 6 with richness at plant Family, Genus and Species summarized in Table 7. A complete listing of all plant taxa and PFTs and species coding per quadrat for each site is available from the contact author. The data are generally consistent with those from other ASB ecoregional sites, showing typically high richness values for both species and PFTs in the older, forested plots. Soil analytical data are summarized in Tables 8a and 8b.
Exploratory data analysis shows that species-weighted PFTs (number of species per PFT) alone account for a clear separation of closed forest types (LAM2, 12, 13, 20) but include an 8-year-old site (4) in an agroforestry plantation
(Figs 2, 3, 4, 5). Apart from this, the PFTs also clearly identify 30-year-old coffee sites (LAM5, 7, 14, 18) as a tight cluster in the dendrogram (Fig. 2) but in an otherwise loose grouping in the classification of 8-year-old plots with a diverse land-use history. The MDS (Fig. 3) on the other hand reveals close links between the 8-year sites based on PFTs. When species alone are used, apart from older growth sites (LAM2, 12, 13, 20) the pattern tends to be confused, with little if any interpretative value (Figs. 4, 5).
Cluster analysis of mean canopy height, basal area, total species, total PFTs, and species:PFT ratios produced readily observable patterns in transitional vegetational sequences. Tight clusters in both classification and ordination
(Figs. 6, 7) can be seen in early 1- and 2-year fallows (LAM1, 6, 8, 15, 23), mid-stage, 8-year plots (LAM4, 9, 10, 16, 22), and older growth (LAM2, 12, 13, 20). The group with
the “noisiest” configuration included the 30-year-old coffee plantations with varying mixtures of species and shade treatments. A simple plot of species against PFTs shows a highly significant linear correlation that tends to correspond with LUT with highest plant-based biodiversity toward the forested plots and plantations with shade treatments (Fig.8).
When ranked against LUT, the V-index values reveal a clear response to shade treatment over otherwise mixed assemblages of different age groups (Fig. 9). The shaded groups include 8-, 20-, and 30-year-old plots. The least biodiverse plots are not the 1- and 2-year fallows but the unshaded, pruned, and fertilized
coffee gardens under simple, secure title.
The soil analytical data (
In the coffee gardens, soil pH tends to decrease slightly with depth but, due to the strong relationship between soil pH and Al saturation, this implies an increase of Al saturation from 0.19 to 0.31 to 0.48, respectively, for the coffee monoculture in the 0–5, 5–15 and 15–30 cm layers, and from 0.14 to 0.22 to 0.29 in the mixed coffee systems, respectively. Any (further) loss of topsoil is thus likely to increase Al toxicity of crops and increase drought sensitivity caused by shallow root development. When soil texture and soil pH are taken into account in the Cref value, soil organic matter contents for the forest sites are in the range expected for Sumatra (van Noordwijk et al. 1997). As expected, the relative soil C content (C/Cref) varies with depth (1.3 for 0–5 cm, 0.75 for 5–15, and 0.45 for the 15–30 cm layer in the forest sites, except for LAM12), but is higher than than for the mixed coffee gardens (on average 0.93, 0.62, and 0.33, respectively) and monoculture coffee (0.69, 0.46, and 0.26, respectively). When plotted against the time since forest conversion, there is an indication of decline of the C/Cref value with time in the coffee monocultures, and a possible recovery toward forest values in the mixed coffee gardens (van Noordwijk et al. 2002).
The differences between forests and coffee gardens in total N content are smaller than those in Corg because the C/N ratio is higher for the forest soils than for the coffee gardens. Available P, as measured by the Bray I method, varies over a wide range in this data set: from <1 to 182 mg kg-1 for the topsoil of LAM12 in the peat layer. High values in the some of the coffee gardens (125 in LAM04 and 75 in LAM10) indicate heavy P fertilizer use, at rates that are probably not justified by the yield response. Other coffee gardens, however, have a PBray index of less than 10 for the 0–5 cm depth layer and probably operate in the P deficiency range. Across all land-use types, the top 5 cm of soil has a PBray index that is 3.5 times higher than that of the 5–15 cm depth and 9 times higher than that of the 15–30 cm depth layer. Loss of topsoil may lead to P shortage in the crops, unless compensated for by high P fertilizer rates. The exchangeable cation concentrations are less variable than the available P values and, as such, are not limiting to growth.
Tree dry weight shows highly significant linear correlations with all of the attributes used in the calculation of the V-Index, with the exception of species:PFT ratio (Table 9). Of all of the plant-based variables, tree weight is also most closely correlated with soil (Table 10). Closer inspection shows this may be artificially high given a skewed distribution of the data, with the highest values associated with a relatively few older forest sites. From a biodiversity standpoint, PFT richness is marginally more closely related to C%, N%, C/N% Na and H+ than species richness. Not surprisingly, diversity indices based on PFT data are highly correlated with species, PFTs, and species:PFT ratios (Table 11). Nonetheless, they are also strongly correlated with vegetation structure (mean canopy height and basal area), age of LUT, and tree dry weight, as well as soil KCL,C%, N%, C/N%, Na and H+ (Table 12).
Results overall indicate that, whereas plant biodiversity is highest in closed canopy rain forest and late secondary forest, among the more intensive cropping systems for coffee, highest biodiversity is to be found under the more complex agro-forest systems and coffee grown with shade. This is consistent with response patterns in other crops, such as rubber and cocoa, where “jungle” rubber and “jungle” cocoa rank highest in biodiversity in tree crop systems across all LUTs (cf. Kotto-Same et al. 2000). Co-located site tree dry weight data were highly correlated with age and with mean canopy height. Correlations between PFTs and tree dry weight were marginally higher than with vascular plant species. Soil data were more closely correlated with tree dry weight than with any other plant-based variable, including basal area estimates of all woody plants. The “V” index is also highly correlated with tree dry weight.
Despite sampling limitations caused by highly dissected, lowland, tropical terrain with difficult access, experience by the same team members in other areas of Central and Southern Sumatra suggests that, although certain LUTs could not be included (e.g., some of the more intact but more remote forests), key land-use and tenurial combinations were covered in this study. The team was fortunate in having the services of a qualified field botanist with rapid follow-up
identification through a herbarium. Such facilities are rarely available in many tropical developing countries and their absence can influence data quality as a result. Where formal species identification is not feasible, the use of PFTs facilitates rapid and uniform data collection that is largely independent of the need for identification of a scientific name, provided morphospecies are recognizable. Recent studies (Gillison 2002) suggest composition rather than richness of PFTs or species determines ecosystem behavior and this is consistent with other views (cf. Folke et al. 1996). Only PFT and species richness were analyzed in this study and found useful for predicting site and soil condition. By association, these are testable predictors of coffee production and profitability. Both PFTs and plant functional compexity (PFC) emerge as the most potentially useful indicators overall of both biodiversity (expressed in terms of both species and PFTs) and soil nutrient status.
The Shannon-Wiener, Simpson’s and Fisher’s alpha diversity indices performed less efficiently as indicators than the PFC measure, which is not, strictly speaking, a true diversity index. The closer correlations between PFTs and soil variables may be interpreted in part as a plant morphological adaptive response to available soil nutrients. The species:PFT richness ratio, although found useful as a predictor of faunal diversity in other surveys (Gillison 2000), provided no useful correlation with LUT, land tenure class or soil condition in the present study. In this study, we investigated only linear and polynomial regressions with single variables. Additional analysis (not reported here) suggests that, although there would be some improvement in predictors using multiple regression, complex predictors of this kind are likely to be counter-productive where simple indicators are needed in the field. Our study shows that age of the particular land-use sequence is a potentially useful and simple indicator of biodiversity and soil nutrients within the context of the sites chosen. Tree dry weight is also a strong predictor of site condition and biodiversity, but this may be due in part to a sampling artifact that produced highly polarized data distributions. Although mean canopy height and basal area are also potentially useful indicators within a sub-region, there can be no guarantee that they will carry the same predictive capacity between regions where vegetation structure can be similar but where the ecological conditions differ. Under such circumstances, e.g., in inter-regional and inter-country comparisons, PFTs are of greater generic indicator value.
The V-index data produced a readily interpretable relationship between biodiversity and LUT (Fig. 9). As with vegetation structure, the V-index values are locally useful but may be regionally limiting in under-sampled situations. Studies in other tropical countries (Gillison 2000) indicate a high level of congruence between similar LUTs and V-index along similar land-use intensity gradients. If such congruence can be shown to hold across a variety of tropical agroforests, this would enhance its relative value as a simple integral measure of vegetation components and as an indicator of biodiversity and corresponding land-use impact. As such, it could be useful in a matrix analysis of tradeoffs, as already described by Tomich et al. (1998, 2001). Using GIS-based spatial analytical tools, it should be possible to test the indicators derived here by field survey. In this way, a model could be developed to help refine indicators and pave the way for supervised remote sensing applications for regional planning purposes.
Within the range of LUTs examined, plant biodiversity was lowest under simple, non-shaded, intensive farming systems and increased progressively through shaded and more complex agroforests to late secondary and closed-canopy forests. The most efficient single indicators of biodiversity and soil nutrient availability were PFT richness and a derived measure of plant functional complexity. Vegetation structure, tree dry weight, and age of the land-use type were also highly correlated with biodiversity. The close correspondence between these variables together with a vegetation or V index and soil nutrient values suggests they are potentially useful indicators of coffee production and profitability across different farming systems within the region. The present baseline study now makes possible a study of links between biodiversity, profitability, and tenure; this will be the subject of a forthcoming paper. Baseline information of this kind is an essential component in the construction of a tradeoffs matrix for assessing policy options and for aiding in decision support for urgently needed adaptive management in some of the world’s biodiversity hotspots.
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The botanical assistance provided by Afriastini and staff of the Herbarium
Bogoriense is much appreciated, as is the assistance from Praktikno and
Hendrik of ICRAF-Lampung. Two anonymous referees provided valuable comments
on the manuscript.
The cost of publishing this article was offset by a grant from the
Open Society institute
and by the Resilience
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Address of Correspondent:
Andrew N. Gillison
Center for Biodiversity Management,
P. O. Box 120,
Yungaburra 4884
Queensland, Australia
(formerly affiliated with Center for International Forestry Research (CIFOR), Bogor, Indonesia)
Phone: 61-740-953224
andy.gillison@austarnet.com.au
