5 out of the 25 global terrestrial biodiversity hotspots,
identified by Myers et al (2000), are located on the South American continent.
Humid tropical rainforests include some 60%
of all known plant and animal biodiversity – and the Amazon is the largest of
such biomes on Earth.
The land use changes in South America (not only in the
Amazon!) have induced the conversion of a high percentage of its land cover from
forest (rainforest and savannah biomes) to agricultural pasture or monoculture
plantations. A reduction in biodiversity is obvious – but there are problems with evaluating this change.
As most impact-studies are on a too short time-scale, biodiversity
change post deforestation is often estimated by comparing modified land cover
with "forest baseline". A point of discussion in these studies is whether the 'primary forests' indeed capture the realistic biodiversity change, as even categorized 'untouched' forest areas are likely to have been impacted by human uses, edge effects or selective harvesting (reference here is made to the impact of the impact of the more subtle process of degradation - see previous post for more info please!).
While further investigating the methods used to evaluate land cover changes, I became aware of the oversimplification of some land transformations (in particular through reading Lambi et al. (2003)). A certain land cover category consists of specified biophysical variables and other attributes of the earth's surface (e.g. soil, biota, topography, water resources etc.). Modelling studies use data sets to represent the land cover by a set of spatial units associated with the attributes included in the specified land cover 'category'. This way of grouping attributes into categories leads easily to a discrete representation of land cover. Using this approach applied to the real transformations of the South American continent, it oversimplifies the subtleties and lags of land cover conversions (e.g. deforestation to other specific agricultural use) and completely neglects land cover modifications (smaller changes that affect parts of the specified attributes of the land cover category without changing its actual classification completely) (Lambin and Geist, 2006)
I hope this raised your attention to the possible difficulties in examining the biodiversity changes of large spatial areas that have not been assessed in detail on the ground.
My final point integrates all these insecurities in assessing biodiversity changes: I want to alert to the fact that the importance of preserving the 'natural' forest cover stems from its unknown realistic value and extent of inherent biodiversity and rare species.
While further investigating the methods used to evaluate land cover changes, I became aware of the oversimplification of some land transformations (in particular through reading Lambi et al. (2003)). A certain land cover category consists of specified biophysical variables and other attributes of the earth's surface (e.g. soil, biota, topography, water resources etc.). Modelling studies use data sets to represent the land cover by a set of spatial units associated with the attributes included in the specified land cover 'category'. This way of grouping attributes into categories leads easily to a discrete representation of land cover. Using this approach applied to the real transformations of the South American continent, it oversimplifies the subtleties and lags of land cover conversions (e.g. deforestation to other specific agricultural use) and completely neglects land cover modifications (smaller changes that affect parts of the specified attributes of the land cover category without changing its actual classification completely) (Lambin and Geist, 2006)
I hope this raised your attention to the possible difficulties in examining the biodiversity changes of large spatial areas that have not been assessed in detail on the ground.
My final point integrates all these insecurities in assessing biodiversity changes: I want to alert to the fact that the importance of preserving the 'natural' forest cover stems from its unknown realistic value and extent of inherent biodiversity and rare species.
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