186
Planning for persistence in a changing world
Box 7.5 Integrating s ocio - e conomic s cenarios into r eserve n etwork d esign
It is an interesting aspect of much contemporary conservation modelling that the dynamic nature of
social change is rarely incorporated (Ladle & Jepson, 2008 ). For example, the greatest threat to
Amazonian lowland forests has, historically, been conversion to agriculture. This was initially due to
the actions of small - scale farming but, more recently, has been predominantly attributable to the
actions of well - capitalized organizations producing agricultural and forestry products for distant
markets (Margulis, 2004 ; Rudel et al. , 2009 ).
This shift in the direct drivers of deforestation is associated with decreasing deforestation rates
and may even be responsible for reforestation in some tropical uplands (Rudel et al. , 2009 ). The key
point is that human development trajectories matter to conservation planning, and there is an
increasing awareness that conservation planning models need to fi nd novel ways to capture the
‘ biocultural ’ aspect of conservation.
An example is provided by a recent study by Ara ú jo et al . (2008) , who set out to investigate whether
21st century environmental change will infl uence the commonly observed tendency for areas with
high biodiversity also to have a high concentration of human activities. To assess this proposition,
they used the most extensive available data set on species ’ distributions in Europe, providing data
for 3,143 species. They then generated four complementarity sets to prioritize ten per cent of the
grid cells (173 cells) using a maximum coverage algorithm for four taxa in turn: birds, plants,
mammals and herptiles. As indicators of contemporary anthropogenic pressure, they used observed
land use in terms of urbanization, cropland and grassland use intensities.
Adopting an approach employed in assessing future economic development trajectories by the
Intergovernmental Panel on Climate Change, the authors used four ‘ storylines ’ or narratives describing alternative development pathways developed by the IPCC for Europe for 2021 – 2050. They
measured exposure of biodiversity to human activities as changes in the three land - use pressure
indices, and they analysed how these patterns of change correlated with the hypothetical reserve
networks for each taxon (see Chapter 6 for details of designing reserve networks to maximize
representation).
The outcome of this modelling exercise was that, under each of the four alternative scenarios,
there was a tendency for the areas selected in the complementarity sets to experience increasing
urbanization (Figure B7.5a ) and decreasing cropland intensities. Hence, irrespective of whether a
future scenario based on fossil fuel - intensive industrial growth was explored, or one based on local
solutions and environmental sustainability was considered, some trends appeared to be a consistent
feature of the models. However, other details varied between the scenarios in quite complex ways.
None of the four scenarios emerged as clearly ‘ better ’ for biodiversity in the analysis.
Once again, it should be borne in mind that the uncertainties involved in such sophisticated modelling approaches are enormous. At the present time, such efforts should be regarded as of solely
heuristic value, allowing the exploration of hypothetical ‘ what - if ’ scenarios for the future, rather than
as a source for fi rm guidance for policy makers.
Planning for persistence in a changing world
Box 7.5 Integrating s ocio - e conomic s cenarios into r eserve n etwork d esign
It is an interesting aspect of much contemporary conservation modelling that the dynamic nature of
social change is rarely incorporated (Ladle & Jepson, 2008 ). For example, the greatest threat to
Amazonian lowland forests has, historically, been conversion to agriculture. This was initially due to
the actions of small - scale farming but, more recently, has been predominantly attributable to the
actions of well - capitalized organizations producing agricultural and forestry products for distant
markets (Margulis, 2004 ; Rudel et al. , 2009 ).
This shift in the direct drivers of deforestation is associated with decreasing deforestation rates
and may even be responsible for reforestation in some tropical uplands (Rudel et al. , 2009 ). The key
point is that human development trajectories matter to conservation planning, and there is an
increasing awareness that conservation planning models need to fi nd novel ways to capture the
‘ biocultural ’ aspect of conservation.
An example is provided by a recent study by Ara ú jo et al . (2008) , who set out to investigate whether
21st century environmental change will infl uence the commonly observed tendency for areas with
high biodiversity also to have a high concentration of human activities. To assess this proposition,
they used the most extensive available data set on species ’ distributions in Europe, providing data
for 3,143 species. They then generated four complementarity sets to prioritize ten per cent of the
grid cells (173 cells) using a maximum coverage algorithm for four taxa in turn: birds, plants,
mammals and herptiles. As indicators of contemporary anthropogenic pressure, they used observed
land use in terms of urbanization, cropland and grassland use intensities.
Adopting an approach employed in assessing future economic development trajectories by the
Intergovernmental Panel on Climate Change, the authors used four ‘ storylines ’ or narratives describing alternative development pathways developed by the IPCC for Europe for 2021 – 2050. They
measured exposure of biodiversity to human activities as changes in the three land - use pressure
indices, and they analysed how these patterns of change correlated with the hypothetical reserve
networks for each taxon (see Chapter 6 for details of designing reserve networks to maximize
representation).
The outcome of this modelling exercise was that, under each of the four alternative scenarios,
there was a tendency for the areas selected in the complementarity sets to experience increasing
urbanization (Figure B7.5a ) and decreasing cropland intensities. Hence, irrespective of whether a
future scenario based on fossil fuel - intensive industrial growth was explored, or one based on local
solutions and environmental sustainability was considered, some trends appeared to be a consistent
feature of the models. However, other details varied between the scenarios in quite complex ways.
None of the four scenarios emerged as clearly ‘ better ’ for biodiversity in the analysis.
Once again, it should be borne in mind that the uncertainties involved in such sophisticated modelling approaches are enormous. At the present time, such efforts should be regarded as of solely
heuristic value, allowing the exploration of hypothetical ‘ what - if ’ scenarios for the future, rather than
as a source for fi rm guidance for policy makers.