Above-ground biodiversity
“Best bet” Land-use Systems
Country reports
Alternatives to Slash-and-Burn in Brazil
Global Environmental Concerns
Unique id: IDA1TG3B
Source file: D:\Projects\ASB\ASB Country and Thematic
reports\Brazil country report\ASB Brazil Summary Report.xml
Authors: S. Vosti, C. L.
Carpentier, J. Witcover, . Carvalho dos Santos, E. Muñoz Braz, J.
Ferreira Valentim, S. J. de Magalhães de Oliveira, C. Palm, F. de Souza
Moreira, A. Cattaneo, A. Gillison, A. Mansur Mendes, V. Rodrigues, T. C. de
Araújo Gomes, M. V. Neves d’Oliveira, E. do Amaral, S. Fujisaka, C. Castilla,
T. Tomich, D. Bignell, D. Gonçalves Cordeiro, A. Hermes Vieira, R.S. Correira da
Costa, M. Faminow, M. Locatelli, M. Swift, S. Weise, M. van Noordwijk, N.
Sampaio, I. L. Franke, H. J. Borges de Araujo, L. M. Rossi, E. Barros, B.
Feigl, S.P. Huang, J. Cares, C. Pinho de Sá, . Carneiro, P. Woomer
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Background
Humid tropical forests are home to the greatest terrestrial
abundance and diversity of species on Earth. Deforestation poses a significant
threat to biodiversity and to the ecosystem services biodiversity provides,
both in the forests themselves and in other habitats throughout the humid
tropics.Biodiversity continues to be threatened globally because it is
undervalued and because there are insufficient market and other mechanisms
available to provide private financial incentives for its maintenance.
Improvements in agricultural productivity usually come at the expense of the
indigenous biodiversity of a given area. The reasons for this undervaluing and
lack of maintenance are inherent in biological complexity and the consequent
difficulty of developing and implementing efficient methods for assessing and
valuing biodiversity. There are few published data that demonstrate links
between biodiversity and profitability.
ASB sought to develop generic biodiversity assessment
methods that could be used to compare vegetation patterns in different LUS and
across different regions, where environment and plant adaptation may be similar
but species composition may differ. This required careful sampling design and
measurement of features other than species richness. The scientists in the
biodiversity working group developed a series of ecoregional biophysical
baselines for use in identifying and evaluating some of the key predictive
relationships among plant and animal species, functional types and the physical
environment. By extrapolating these relationships over space and time, it
should be possible to forecast the impact of land use change on biodiversity
and thus to provide a basis for deciding how the management of a LUS might be
adapted to improve biodiversity or at least limit its loss. Digital Elevation
Models (DEMs) were developed for each benchmark site, the ‘representativeness’
of which in relation to the humid tropics was mapped using DOMAIN software (see
Figure 1 in Section 1). These data will serve as the basis for future
extrapolation and prediction of the impacts of land use change on above-ground
plant biodiversity.
In Brazil,
21 plots (each 40 m x 5 m) were sampled along a gradsect (Table 5).[1] Using the rapid survey proforma employed at
all sites (Gillison, 1988; Gillison and Carpenter, 1997), a minimum set of key
biophysical parameters was measured. Recorded data included:
• Site physical
features: latitude, longitude, percentage slope, aspect, elevation, parent
rock type, soil type, soil depth, terrain unit and litter depth.
• Vegetation structure:
mean canopy height, crown cover percentage, cover-abundance estimates of woody
plants (up to 1.5 m tall) and of broyphytes, furcation index (Gillison, 1988)
to describe tree architecture, and basal area.
• Plant species:
all vascular plants, which are higher plants excluding mosses and liverworts.
These plants remain the major currency unit by which biodiversity is assessed,
despite a growing number of challenges to this position.
• Plant functional
types (PFTs) or modi (Gillison and Carpenter, 1997). These are combinations
of adaptive morphological or functional attributes (e.g. leaf size class, leaf
inclination class, leaf form and type, and distribution of chlorophyll tissue)
coupled with a modified Raunkiaerean life form (a classification of plants
according to their ability to survive the most unfavourable season) and the
type of above-ground rooting system. PFTs are derived according to specific
rules from a minimum set of 35 functional attributes. For example, an
individual plant with microphyll-sized,
vertically inclined, dorsiventral leaves supported by a phanerophyte life form would be of a
PFT expressed as MI-VE-DO-PH.
Although PFTs tend to be indicative of species, they are in fact independent,
in that more than one species can occur in a PFT and more than one PFT in a
species. PFTs allow the recording of genetically determined, adaptive responses
of individual plants that can reveal intra-specific as well as inter-specific
responses to the environment in a way not usually indicated by the name or
description of a species. Because they are generic, they have a singular
advantage for the purposes of ASB’s research in that they can be used to record
and compare data sets derived from geographically remote regions where adaptive
responses and environments may be similar but where species may differ
(Gillison, 2000).
Results
The species richness recorded at each site can be found in
column 5 of Table 5, which lists land uses (rows) in order of most to least
rich in terms of plant biodiversity. Multi-strata agroforests are the highest
in species richness after forests, followed by the improved fallows with tree
species.
However, given that the goal of ASB is to link biodiversity
with other environmental and social factors, the biodiversity working group
explored the use of other variables to find better correlations and develop a
predictive indicator of the impact of land use and environmental change. Mere
taxonomic data mask wide variations in the range and ecological behaviour of
plants. Using data from an intensive baseline study in Sumatra,[2] the working group
identified five key indicators for all ASB sites, including Brazil:
the mean canopy height of a plant, its basal area, total vascular plant
species, total PFTs or functional modi and a ratio of plant species richness to
PFT richness. Using a multi-dimensional scaling analysis, the single ‘best bet’
of values (or eigenvector scores) can be extracted for a specific set of sites
characterized according to these variables. When standardized, these values can
be used as a relative index of vegetation that, for the ASB data, corresponds
closely with the observed impacts of land use on biodiversity and crop
production and reflects the ‘time since opening’ or, in other words, since
forest clearing. This set of values is
termed a ‘V index’(see column 7 of Table 5, and Figure 8).
While there are close correspondences with plant and animal
biodiversity, the V index is more a habitat or site characterization indicator
than an actual index of biodiversity. However, it does allow the cross-site and
cross-region comparison of data on above-ground biodiversity with those on
carbon storage, below-ground biodiversity and socio-economic factors, as will
be demonstrated later in this report (Gillison, 2000).
Figure 8. LUS at the benchmark sites ranked for plant
diversity (V-index)
