Mae Chaem, Northern Thailand
“Best bet” Land-use Systems
Thematic reports
Impact of different land uses on biodiversity
Biodiversity and Productivity Assessment for Sustainable Agroforest Ecosystems
Unique id: IDA1AINC
Source file: D:\Projects\ASB\ASB Country and Thematic reports\Above ground biodiversity assessmet WG\PART D.xml
Authors: A.N. Gillison, N.Liswanti
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Period: 4 May to 5 June 1999
Funding agency: ACIAR
(
CIFOR code: R-BIO-16-1-ICR02
Summary
An ecoregional survey was conducted in the Mae Chaem
watershed of
Results indicate that the socioeconomic and biophysical complexity of the watershed, in particular the overlay of differing historical land use patterns, is such that it was under-represented by the selected sites. Although broadly representative of the biophysical aspects of the watershed, these sites did not account for sufficient variation in land management due to ethnic differences and recent immigration patterns. Sufficient data to establish linkages between biodiversity and profitability could not be obtained in the time available. Despite these limitations, for biodiversity we found significant correlations between PFTs, soils and birds. As with the Jambi survey, these indicate that PFTs, rather than species, can be used to characterise soil nutrient availability. This is important in assessing agricultural productivity and related profitability. Plant-bird correlations indicate that PFTs are potentially more efficient in predicting bird distribution than plant species, but that vegetation structure is also useful. The most productive dryland agriculture appears to be associated with intensive, permanent cropping systems with the lowest recorded biodiversity. In such systems, intensive use of pesticides, herbicides and fertilizers, together with the loss of key biota and environmental services, suggests that high profitability may be relatively transient. For this reason, aspects of integrated pest management, the cultural and material values attached to non-timber forest products, and environmental services provided by forested lands must be considered when seeking tradeoffs between biodiversity and profitability for planned land management. By itself, species richness is an inappropriate indicator of potential profitability. Upland, dry, deciduous, Dipterocarp/Oak, open forests and woodlands were found to be unusually rich in plant species, functional types and birds. When converted to forestry (Pinus kesiya) plantations, with moderate tending by fire and slashing, such areas retained most of the plant species and PFTs. Nonetheless, significant changes in vegetation structure and, thus, animal habitat result in a 50% reduction in bird species in such plantations. Conversion of such areas to intensive agricultural cropping systems results in dramatic losses in biodiversity.
While surveys of this kind can play an important role in
providing baseline information on biodiversity and productivity, the challenge
is to convey this information to planners and managers so that an effective
value can be placed on biodiversity. This is necessary in order to arrive at
acceptable tradeoffs between sustaining biodiversity and to ensure an
acceptable economic return. The greatest challenge will be to communicate this
to recent immigrants in the Mae Chaem region who are unfamiliar with the
existing natural resource base and who are as a result, inclined to value it
least. Significant outputs of the survey are: a refined and readily
transferable survey method and software package for data entry, storage and
analysis; key data for a policy analysis matrix; new and significant additions
to baseline biodiversity data for Thailand; preliminary statistical models for
forecasting impacts of land use on biodiversity; linkages between plant functional
types, soil nutrient availability and bird species richness; effective
‘training of trainers’ and a means of establishing indicators of biodiversity
and agricultural productivity. The methods applied in this survey and the
information acquired are relevant to biodiversity and productivity assessment
within similar montane landscapes in mainland
Part D: Mae
Chaem List of Tables, Figures and Annexes
Tables
Table 1 Site locations and descriptions surveyed within the Mae Chaem
Watershed
Table 2a Site physical environmental features with symbols used in analyses
Table 2b Summary data, diversity indices and complexity measures for plant
species and PFTs
Table 2c Site vegetation structural data
Table 3 Presence – absence data for birds (26 plots)
Table 4 Soil physico-chemical data for all plots
Table 5 Soil correlates with first two MDS vector scores from each data set
Table 6 Correlations between key plant and soil attributes for all sites
Table 7 Correlation table for MDS eigenvector scores for all data sets
Table 8 Plant-based correlates with bird and soil vectors
Table 9 Correlation of plant diversity indices with birds and soil
Figures
Fig. 1 Mae Chaem Watershed showing topography and plot locations
Fig. 2 Classification of all sites according to presence-absence of all vascular plant species (ref: Table 2a for interpretation of symbols)
Fig. 3 Multi-Dimensional Scaling (MDS) of all sites according to presence-absence of all vascular plant species
Fig. 4 Classification of all sites according to species-weighted Plant Functional
Attributes (PFAs)
Fig. 5 MDS of all sites according to species-weighted Plant Functional Attributes
(PFAs)
Fig. 6 Classification of all sites according to species-weighted Plant Functional Types (PFTs)
Fig. 7 MDS of all sites according to species-weighted Plant Functional Types
(PFTs)
Fig. 8 Classification of 26 sites according to presence-absence of bird species
Fig. 9 MDS of 26 sites according to presence-absence of bird species
Fig. 10 Classification of all sites according to soil physico-chemical data
Fig. 11 Classification of all sites according to soil physico-chemical data
Fig. 12 Correlation between soil pH (H20) and the first MDS vector for PFTs
(ref: Table 5)
Fig. 13 Correlation between soil Nitrogen and the first MDS vector for PFTs
(ref: Table 5)
Fig. 14 Relationship between species richness and PFT richness across all Land
Use Types.
Annexes
Annex I List of plant families, genera and species together with PFTs and plant
uses, arranged according to sites. Mae Chaem watershed
Annex II List of proposed sites following initial reconnaissance of Mae Chaem
watershed
Annex III List of participants at meeting with staff from Thailand Royal Forestry
Department
Annex IV List of participants in survey of Mae Chaem watershed
Annex V Itinerary of A.N. Gillison and N. Liswanti
Annex VI Profiles of species, PFT and spp/PFT richness /area curves for Mae Chaem
Plots showing sample representativeness and response characteristics for
each LUT
Annex VII Land use in the Mae Chaem wateshed
Cool, moist, upland, evergreen myrtaceous forest (2330m) Doi Inthanon
(site 21)
(b) Dry, deciduous, Dipterocarp/ Oak open forest (site 4).
Coppicing Dipterocarpus tuberculatus; leaves commonly used for roofing
thatch
Deciduous geophytic liane , edible yam (Dioscorea sp.) in upland, dry,
deciduous, Dipterocarp/Oak open forest (site 23)
Intensive, upland, permanent cropping systems, Ban Mae Tho (near site
23)
(f) Mushrooms are an important non-timber forest product in Mae Chaem
1. Purpose
To undertake a baseline study of biodiversity and
associated profitability under different land use conditions within the Mae
Chaem watershed in
To present preliminary results at a methodology workshop on ‘Environmental Services and Land Use Change: Bridging the Gap Between Policy and Research ‘ (at Chiang Mai).
2. Background
The survey was originally proposed and accepted by the ASB
Global Steering Group and ACIAR as being parallel and complementary to the
previously completed, intensive biodiversity baseline study in a lowland,
tropical forested landscape, in Jambi,
In the Jambi survey, a calibrational study for biodiversity indicators included multiple plant and animal taxa, as well as soils and other site physical variables. That survey revealed a significant correlation between plant attributes (vegetation structure, vascular species and Plant Functional Types or PFTs), several insect groups, and birds. Although the insect taxa constitute a very important element of biodiversity, their collection was logistically demanding and subsequent identification costly. Because birds tend to be conspicuous, usually well known and relatively easy to document, it was decided that for the Mae Chaem survey only plants and birds would be surveyed, together with soils and other key physical environmental variables. It was assumed the combined information from both plants and birds would adequately represent the broad biodiversity pattern and provide a basis for examining linkages with profitability. Site reconnaissance of the Mae Chaem watershed was completed with a team from ICRAF in August 1998. From this, 22 potential sites were identified (Annex II) across a broad range of land use types (LUTs) and natural environments. These formed the basis for a subsequent survey in May 1999, in which 28 sites were selected and documented (Table 1).
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3. Objectives The survey goal was to provide data to help:
Develop a
generic method of rapid biodiversity assessment that can be used to
characterise and quantify impacts on biodiversity and productivity along
definable land use intensification gradients at the landscape level.
Identify
key biophysical determinants of above-ground biodiversity and productivity
for human needs.
Provide a
means of identifying readily measureable components of biodiversity that can
be used to build acceptable management models by maximising profitability
while sustaining biodiversity, i.e. best bet alternatives for slash and burn.
Define
indicators of thresholds of sustainablility that can be used by managers to
detect conditions that may lead to irreversible decline in resource capacity.
Provide a
means of ready communication and transfer of methods to prospective clients
and beneficiaries (e.g. training, computer and other media). 4. Hypothesis to be tested
Above-ground
biodiversity, as measured by the PFA vegetation assessment proforma, and
avifauna diversity vary with profitability, as measured in dollars per
hectare. 5. Outputs
Field
manual and CD-ROM package for rapid survey proforma using PFAs, for use in
English and
Transfer of
technology by ‘training the trainers’ in
Spatial
models of representative sub-sets of biophysical, ecoregional gradients in
the Mae Chaem watershed in northern
Correlative
models of key biophysical features for extraction of biodiversity and total
factor productivity (profitability) indicators.
Means for
coupling biodiversity and productivity with other ASB-related co-located
studies of GHGs and carbon stocks.
Standardised
database for the above.
Capacity
for generating and testing thematic maps of key groups of biota and related
productivity potential under different land use conditions.
Output to
systems and linkages models for ASB. |
6. Methods
A reconnaissance (Annex II) of the Mae Chaem watershed during a visit in September, 1998 provided a useful basis for the subsequent selection of sites in May, 1999 (Table 1, Annexes IV,VII). Meetings with representatives of the Royal Forestry Department (Annex III) and personnel from Chiang Mai University (CMU) (socio-economists and soil specialists) provided useful background for site selection. Criteria for site selection therefore involved a tradeoff between sampling a representative range of land use types (LUTs) and capturing the different socioeconomic conditions in the time available. Using the gradsect site selection procedure (Gillison and Brewer,1985; Wessels et al.,1998), a primary climate gradient was selected according to elevation (thermal) and known rainfall pattern. The ICRAF office at Chiang Mai provided a comprehensive Digital Elevation Model (DEM) (Fig.1b) that was used to help locate elevational gradients, mapped parent rock types and land cover. Sites were selected to cover a wide range of LUTs, fallow systems and forest successional types. Against this biophysical platform, 28 sites were selected to represent, as far as possible, different ethnic group management approaches to land use (Hmong, Karen, and lowland Thai). Their location is shown in Fig 1b. While these provided a useful coverage of land use conditions in the watershed they constituted only a very minimal stratification within a highly complex biophysical and socioeconomic matrix.
Within each site, a 40 x 5m transect was marked out and
vegetation sampled according to the method described in Part C, Section 2 for
the Jambi survey, using the PFA rapid survey proforma. As in earlier surveys,
voucher specimens were taken for every species in every plot and later
identified at the herbarium at the Royal Forestry Department in
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Activities
Outputs
Potential application 1. Site location Baseline digital database; bio- Biodiversity and co-located physical environmental data. socioeconomic studies 2. Baseline survey Geo-referenced data Basis for potential mapping for plant and animal of key taxa and functional distribution; link with groups; spatial models of GHGs, soil, carbon stocks, biodiversity and productivity productivity data per land for human needs. use type. 3. Predictive modelling Indicators of biodiversity Thematic maps of and productivity. biodiversity and related productivity. Identification of thresholds Use in monitoring resource of sustainability of conditions and in forecasting biodiversity and productivity. impacts of land use. Use bio-indicators to forecast productivity potential; Means of identifying ‘best-bet’ alternatives to Slash and Burn. 4. Field testing of models Refined models. Same as 3 above. 5. Comparisons of Generic method with Cost-efficient basis for ecoregional data and broad ecoregional RBA and for broad-scale spatial models. application. geographic and physical environmental comparisons |
These data are expected to complement ongoing profitability studies by ICRAF staff in Chiang Mai. Soil samples were collected at 0-15 cm depth, at three equally spaced locations in each transect, and bulked. These were stored in double thickness nylon mesh bags and transported to CMU as vehicles became available. Soil physico-chemical elements were analysed as indicated in Table 4.
Unlike the Jambi survey, the long distances between sites created logistic difficulties for sampling avifauna, which meant the ornithologist worked solo on many occasions. This also limited the time available at each site, although a dawn and an evening census were conducted for 26 plots (Table 3), using a similar species-asymptote approach to that described in Part C, Section 4 of the Jambi report (Jepson and Djarwadi, 1999). Some uncertainties in identification limited subsequent data analysis. Data were analysed using the same methods described for the Jambi survey. This involved simple linear and polynomial regression combined with standard techniques of exploratory data analysis. In addition to these, new diversity indices developed by Gillison et al., (1999) were used to explore correlations between avifauna and soil features. These included Shannon-Wiener and Simpson diversity indices for both plant species and PFTs (Table 2b), as well as Fisher’s alpha index, which is less sensitive to plot size. A ‘complexity’ measure (also described by Gillison et al.,1999 and Gillison, Carpenter and Thomas, unpubl.) is derived from computing a minimum spanning tree distance between unique PFTs in each plot. The present study provided an opportunity to evaluate the utility of each of these measures for developing biodiversity indicators albeit within a very complex environment.
7. Results
Records of a total of 1590 plant species (665 unique species) were obtained from the 28 sites, together with 418 Plant Functional Types (Annex I). For each record, local Karen or Thai names were recorded wherever possible, together with uses by the Karen people. These ‘utility’ data are one component of profitability data that are being recorded as part of an ongoing project by staff from ICRAF and CMU. At the completion of the present survey there were insufficient socioeconomic data available to identify useful connections between biodiversity and profitability. Nevertheless, variation in soil nutrients correspond closely with agricultural productivity, the highest nutrient levels occurring in permanent cropping systems with high fertilizer input. Exploratory data analysis of the plant, bird, and soil data sets revealed the following patterns:
Plant species
Classification (Fig. 2) of presence-absence data using a Bray-Curtis (default Czechanowski) coefficient (Belbin, 1992) produced three identifiable groups. Two contained both high elevation closed and open forests, each containing Pinus kesiya and dipterocarps, but with otherwise marked species differences. These are represented by a cluster from Doi Inthanon (sites 1,2,3,4,5,6) and another from Ban Mae Tho and Ban Mae Aep in the West of the watershed (13,23,24,25). The third group represents a mixed group broadly divided into upland and lowland fallows (refer symbols in Table 2a). This pattern is broadly reflected in the ordination (Fig. 3) produced from Multi-dimensional Scaling (MDS).
Plant functional
attributes (PFAs)
The classification of species-weighted, individual PFAs (Fig. 4), reveals a primary pattern of four groups, represented by mixed upland forests and open forests combining both Doi Inthanon and Ban Mae Tho and Ban Mae Aep (1,2,9,13,23,24,25,26) with the next division, containing a mixed group with Pinus kesiya and upland fallows (3,6,7,11,12,14,27). The next division contains low elevation, dry, deciduous Oak/ Dipterocarp woodland (4,5) and fallows derived from this vegetation type (8,16,18), a montane evergreen forest (21), an agroforestry Coffee plantation (15), and a mixed group of early to mid-stage upland fallows (10,17,28). The final group contains three permanent cropping systems (19,20,22). This relatively confused pattern is brought into clearer perspective in the ordination (Fig. 5), where the permanent cropping systems (D) are clearly isolated from the rest of the 28, sites as are the fallows from the Dipterocarp/Oak woodlands, next to which are one-year, upland fallows. Later stage, upland fallows containing Pinus kesiya are shown further along a gradient towards closed forests and their late-stage fallows. The bottom left-hand corner of the graph shows two isolated upland open forests, represented by a species and PFT-rich Dipterocarp/Oak site (23) and a plantation, both with Pinus kesiya (24). This ordination reflects a gradient of increasing functional complexity along a series of fallows of increasing age and variable origin, towards complex forests and woodlands. These are readily distinguished along a seasonality/precipitation gradient separating evergreen from deciduous vegetation types.
Plant functional types
(PFTs)
A classification of 485 unique PFTs (each PFT is a unique combination of PFAs) in Fig. 6 shows a similar trend to that in Fig. 4. While interpretation is aided by comparison with the ordination (Fig. 7) the intense clustering makes detection of sub-clusters difficult. Outliers (including a mixed, deciduous, community forest represented by an asterisk), climax evergreen forests and permanent cropping systems are clearly differentiated from the central cluster.
Birds
Presence-absence data are listed in Table 3. As only 26 of the 28 sites were recorded for birds, no direct comparison can be made with the plant-based analyses. Nonetheless, the classification (Fig. 8) shows two clear divisions; one dominated by closed forests and late-stage fallows and the other by open woodlands and early fallows. Other than this, no other interpretable pattern can be detected with respect to vegetation or land use type. The ordination (Fig. 9) shows more interpretable outliers with respect to closed forests, upland dry, deciduous forests with Pinus kesiya and a mixed deciduous community forest. Somewhat surprisingly, two closed-secondary forests (late fallows aged 10, and 20 years respectively) are at opposite ends of the gradient from primary closed forests (bottom of graph).
Soils
A classification based on all physico-chemical data (Table 4) shows a primary division that accords generally with parent rock type (Fig 10). The first of these (sites 1,2,3,5,16,17,18,21,25,26,27) represent quartzites, gneisses and amphibolites. The second division indicates soils with higher fertility and higher pH, derived from quartzites, phyllites and granites with limestone inliers. Certain permanent cropping or agroforestry systems with high fertilizer input (15,22) are also included in this second group. Within these two divisions but with a few exceptions, lower-level clustering reflects differences in LUT. The ordination (Fig. 11) shows similar patterning, with a strong fertility gradient expressed along vector 2 from low nutrient upland forests on gneiss(1,21) to heavily fertilized, base-rich soils in permanent cropping systems (22) and late fallows with high organic matter and closed canopy of pioneer trees (9,13).
In seeking predictive connections between soil variables with other data sets, it is useful to explore correlations between the ‘raw’ soil data and the eigenvector scores from plant and bird data sets. This is appropriate, partly because of the sparse nature of some data (e.g. presence-absence rather than abundance data), but also because most of the variance in each data set can be accounted for in the first two MDS vectors. For these reasons, it is logical to examine the potential value of correlations between the ‘raw’ soil data and each of the vectors obtained from the plant and bird data sets. The results are shown in Table 5, where each soil variable is compared with the vectors from PFAs, PFTs (modi), plant species and bird species. An inspection of the table reveals closest correlation between soil pH, CEC, organic matter, N and the first vector from the PFA and PFT analyses. Soil P is highly correlated with the first PFT vector. There are no significant correlations between plant species and soils or between soils and birds (bird data are missing for sites 20,22). Figure 12 indicates how pH (H20) varies directly with PFT vector scores along what can be generally assumed to be an increasing gradient of nutrient availability. Soil N, on the other hand, shows an inverse relationship with PFT vector scores (Fig.13). While this may run counter to a perceived gradient, the highest N values arise from deep litter accumulation in forests and late fallows that may be supported on otherwise low nutrient soils (e.g. high elevation forests (1,21) in the Doi Inthanon region). Both relationships can be related to adaptive responses of plants to nutrient availability as expressed in PFAs and PFTs.
Linear regression between soil attribute values and certain
key plant attributes such as species, PFT richness, and vegetation structural
variables (mean canopy height and basal area) reveals a significant correlation
only between P, PFT and species richness (Table 6). This correlation is heavily
influenced by outlying high P values in site 22 – an intensive, fertilised,
cropping system. On the other hand, very
high correlations can be observed between pH, CEC, organic carbon (
Plant attributes and land use type
Richness in vascular plant species is highly correlated (R-Sq 86.6%) with richness in PFTs (Fig. 14). This, in turn, corresponds with gradients of land use intensity. It is reflected along a gradient of richness extremes from a depauperate, permanent cropping systems, through systems in which herbicides and organic pesticides are used, to late fallows, to closed upland forests, to increasingly species-rich and PFT-rich upland dry, deciduous, open, Dipterocarp/ Oak forests and plantations with Pinus kesiya .
When cumulative species/ area, PFT/area and spp/PFT /area curves are constructed for each 40x5m plot in 5x5m increments, variation in the slope of the curves tends to correspond with LUT. This is generally visible across all cumulative values, but is particularly reflected in the spp/PFT ratios. Annex VI provides graphic profiles for all LUTs and illustrates a number of cases where, for example, a species and PFT-poor, high elevation (2330m) evergreen montane rain forest can be distinguished from a lower elevation (1590m) rain forest and secondary forest (20 year fallow, 1214m) by the cumulative species/PFT richness ratio values. In closed canopy, mature forests where ecological niches are apparently reduced due to the heavily buffered light regime, more species are contained in fewer PFTs than in more disturbed or open, patchier habitats, such as dry deciduous woodlands and open forests (e.g. mixed, deciduous forest (6) and dry, deciduous, Oak woodland (5) which is both burned and grazed). In species-rich conditions, where habitat is modified by plantation tending, the high ratio values tend to plateau early (2,13,15,24). In one year, species-poor fallows in permanent cropping systems, the ratio values are typically low and the curve tends to asymptote early or else continues to drop (19, 22). Dry, intermediate fallows in flooded paddy rice terraces, on the other hand, provide a unique environment that supports a moderately high number of species and PFTs, with consistently high ratios (20).
8. Discussion
The highly complex socioeconomic and biophysical environment
in the Mae Chaem watershed generated sampling problems that were not
encountered in surveys in other ecoregional studies (e.g.
Develop a
generic method of rapid biodiversity assessment that can be used to
characterise and quantify impacts on biodiversity and productivity along
definable land use intensification gradients at the landscape level.
The gradient-based survey and rapid vegetation recording method proved efficient in previous multi-taxa surveys, especially in the multi-taxa survey in Jambi. Subsequent evaluation during the Mae Chaem survey was limited, due to restricted numbers of samples and limited avifaunal data. For these reasons, the present survey does not represent an adequate test of the method. Despite sampling limitations, certain aspects of the survey method are encouraging. Exploratory data analysis has shown potentially useful correspondences between plant species, PFAs, PFTs and LUTs. In particular, the PFT and PFA data sets are more predictive of LUT soil nutrient availability than are plant species (Tables 5,6), and only the PFAs and PFTs are significantly correlated with birds (Table 6). Vegetation structure (mean canopy height and basal area) corresponds more closely with certain soil variables than do species, PFAs or PFTs (Table 6). While this may be useful within a region (or watershed), indicators based solely on structure must be used with caution as structural equivalence between different regions may be otherwise ecologically distinct. Generic plant functional attributes and types, on the other hand, can provide a more sensitive means of ecological comparison when used both within and among regions (Gillison and Thomas, 1999).
Identify the
key biophysical determinants of above-ground biodiversity and productivity for
human needs.
Analyses to date indicate plant
functional characteristics are most closely associated with avifauna
distribution and exhibit the most significant correlations with certain soil
attributes (pH, CEC,
Provide a
means of identifying readily measureable components of biodiversity that can be
used to help build acceptable management models by maximising profitability
while sustaining biodiversity, i.e. for ‘best bet’ alternatives to slash and
burn.
There seems little doubt the most
agriculturally productive systems (fertilised, permanent cropping systems) are
the poorest in biodiversity. While this suggests productivity/profitability
potential may be contra-indicated by increasing species and PFT richness it
does not take into account inceasing soil acidity and overall loss of
environmental services that are difficult to quantify but can be inferred from
empirical observations. Lessons learnt in other tropical countries in similar
environments suggest intensive systems of this kind inevitably lead to a
decline in crop production. Outside these intensive systems, simple measures of
richness in species-weighted PFTs, PFAs, mean canopy height and basal area can
be used in a policy analysis matrix framework (in progress by ICRAF, Chiang
Mai) to help indicate acceptable tradeoffs between biodiversity and
profitability. As a first pass in
resource asessment, vegetation structure alone may give a reasonable indication
of both biodiversity and soil nutrient availability in the Mae Chaem watershed.
This can be refined where needed, by adding species-weighted PFAs and PFTs. The
high value placed on non-timber forest products by most ethnic groups suggests
this should be carefully considered when resolving tradeoffs with other
economic benefits gained by land-clearing and fertilising. The only
‘agroforest’ examined in the survey was a coffee plantation containing other
mixed crops (site 15). The owner of this site preferred the economic returns
from this management system to those obtained from more labour-intensive
rice-cropping, citing as part of his reason, the improved ecosystem services
arising from agroforestry.
Define
indicators of thresholds of sustainablility that can be used by managers to
detect conditions that may lead to irreversible regression in resource
capacity.
The survey found no indicators of thresholds of sustainability. This is
partly because complex lag effects in landscape dynamics prevent early
detection of such thresholds. By the time indicators are apparent, it will
almost certainly be too late for remedial action. Nonetheless, the framework of
land-use types within which the study was conducted provides guidelines that can
be used to maintain production while sustaining biodiversity. The most serious
threat to biodiversity loss is wholesale land clearing, with its concomitant
erosion, in-filling of streams,
intensive use of pesticides and herbicides, uncontrolled burning and
hunting. At an absolute minimum, reservoirs of living propagules are needed to
ensure that viable populations of biota are maintained and available to
reoccupy rehabilitated landscapes. Such reservoirs are either severely depleted
or are totally absent in widespread, intensive cropping systems. Provided
sufficient forested patches and representative water catchments can be
maintained within an agricultural mosaic, this will help to maintain
biodiversity and ensure continued access to culturally and economically
important non-timber forest products. There is no simple answer to the question
of what a ‘minimal area’ for conservation management should be. The key to
sustainable management will be a function of the value that landowners place on
a piece of land in terms of its potential for economic return and for
sustaining forest-based livelihood. While indigenous landowners are generally
well aware of the value of forested lands, many immigrants are not. It is the
latter group of land managers who require access to information that will
allow them to more effectively value the
natural resource in order to achieve sustainable biodiversity and
profitability. The present study has provided only a very limited baseline
against which land-use planning can take place and only then for certain areas.
Provide a
means of ready communication and transfer of methods to prospective clients and
beneficiaries (training, computer and other media).
While more sampling is clearly needed, the results suggest the survey method can be readily applied by persons with limited technical experience to acquire a basic knowledge of the natural resource. The training course that preceded the survey was highly successful, with several participants subsequently using the method for research associated with land management. The use of a rapid survey proforma has clear potential, and when coupled with the CIFOR-developed, user-friendly, ‘Vegclass’ software (previously PFAPro), data entry, data management and limited analysis of metadata can be undertaken by people with limited training. A training manual on rapid vegetation classification and survey is in preparation. Its completion in early 2000 will enable this package to be transferred to collaborating agencies. It should be made clear the methodology described in this report is aimed not at the average land owner. It is directed at mid and upper level management, for whom the objective is to rapidly assess the natural resource and produce information that can serve as a decision support to facilitate management adaptation to changing physcial environments and economic climates. Key data from surveys can be used to assist in evaluating the living resource base so that appropriate policy interventions can be developed.
Hypotheses tested
Above-ground
biodiversity, as measured by the PFA vegetation assessment proforma, and
avifauna diversity vary with profitability, as measured in dollars per hectare.
In the present study it economic data were insufficient to evaluate profitability although this study is ongoing. In terms of resource off-take, it seems that highest profitability in dollar terms will come from the most intensive, permanent cropping systems where biodiversity is lowest. In testing this hypothesis, one must consider both long- and short-term biophysical and economic outcomes. Empirical evidence suggests that in very intensive and widespread cropping systems, long-term buildup of pesticide and herbicide residues, soil acidification and/or salinisation, together with breakdown in soil structure, will ultimately lead to crop decline and an increase in crop pathogens and pests. This will almost certainly be associated with a loss in environmental services and contribute to a decline in health and economic status in the local human populace. It is, therefore, possible to speculate that, whereas short-term economic gains may offset biodiversity loss (i.e. an inverse relationship between profitability and biodiversity), in the long term, there must be a better balance in land management planning at the outset to ensure the subsequent maintenance of both biodiversity and environmental services, as well as an acceptable economic return. Under such conditions, biodiversity may tend to vary directly, rather than inversely, with profitability.
9.Outcomes
As a result of this survey, a field manual for rapid
vegetation assessment has been modifed and software re-designed to match the
field proforma format. Field trials suggest both the ‘VegClass’ software
(formerly PFApro) and the proforma can be used with relatively little training
by persons with limited background in botany, ecology or computing
applications. A training manual that includes this package is planned for
completion in early 2000, and it is intended this will be made available to all
collaborating agencies in CD-ROM and hard-copy, together with a translation
into
Because all surveys are spatially referenced, it is now possible to generate spatial distribution models of key biota for certain areas of the Mae Chaem watershed. The recent availability of a very comprehensive DEM with roading infrastructure and other data overlays from ICRAF Chiang Mai now makes exploratory modeling possible. This database can be used in conjunction with the DOMAIN spatial modelling software to identify key gaps in the sampling framework for further survey, especially in the north of the watershed. It can also be used to help model the impact of specific land management practices in a limited number of areas.
Correlative models suggest that plant functional
characteristics are potentially useful as indicators of both agricultural and
agroforestry productivity potential (cf. Vanclay et al.,1997) as
such as species richness in birds (cf.
As at the other ASB, ecoregional sites, the survey has provided a framework for the co-location of sites for studies of GHGs and carbon stocks. The regression models derived from the Jambi survey (see also Hairiah and van Noordwijk, 1999), using mean canopy height and basal area, can be used to generate a first estimate of above-ground carbon for the sites investigated in the Mae Chaem survey.
The program ‘VegClass’ has been used to generate a standardised database for actual and derived meta-data for all 28 sites. The software can be used to export data to industry-standard Excel and MS-Access formats, thereby making it readily accessible for statistical analysis. These and other data sets included in the present report will be made available to all collaborating agencies. These procedures will facilitate data and information transfer to ASB systems and linkages models.
Data acquired from the Mae Chaem survey have been added to those from earlier ecoregional surveys. They will form an integral part of a system-wide database now being developed and examined for congruent patterns of biodiversity response to land use impact. This ‘global’ analysis will help evaluate the generic capacity of the survey method as well as providing information relevant to other research areas within ASB and elsewhere.
10. Conclusions
The survey identified key biodiversity elements across a
wide range of biophysical and socioeconomic conditions. One such element
consists of the upland dry, deciduous woodlands and open forests. These are
extraordinarily rich in plant species and functional types, and are also
important indigenous reservoirs of non-timber forest products. Conversion of
these to Pinus kesiya
forestry plantations with a moderate tending and fire-hazard reduction program
and limited grazing appears to have only a limited impact on many
naturally-occurring biota. This is partly because of the numerous cryptic
(below-ground storage organs) and other strategies employed by many plants for
fire and drought avoidance and tolerance. The conversion of such resources to
widespread, intensive, permanent agricultural cropping systems increases
productivity but dramatically reduces biological diversity. Empirical evidence suggests this relationship
cannot be sustained in the long term, due to potential crop decline under
increasing soil acidity and pesticide resistance as well as increasing loss in
environmental services and key biota.
Such biota are potentially important players in integrated pest
management, as well as significant contributors to forest-based resources that
are important to the long-term survival of local people. While there were
insufficient data to indicate predictive relationships between profitability
and biodiversity, significant correlations were found between plant functional
types, avifauna and soil nutrient availability. These suggest that plant-based
features may be useful in identifying and monitoring biodiversity and in
deriving indicators of biodiversity and related productivity for human needs.
The extent to which these reflect
profitability needs further exploration. Regionally specific conditions must be
carefully evaluated before any generic conclusions can be reached; models of
profitability developed in Jambi Province of Central Sumatra may not apply in
Mae Chaem, due to socioeconomic and cultural differences. The survey provided a
useful, preliminary framework for identifying important trends in biodiversity
and related impact from varying land management regimes. The complexity of the Mae Chaem watershed,
with its elevational, geological and biological extremes, coupled with
historical overlays of variable land-use practices by different ethnic groups,
requires more comprehensive sampling before any realistic models of land-use
impact on biodiversity can be constructed and tested. The Mae Chaem watershed
has many features in common with the extensive, montane, forested landscapes of
the South-Asia mainland, where some ethnic groups range from the Burmese border
to