The distribution of diversity: challenges and applications
151
network. For example, two proposed new areas, both
with high species richness, may have different numbers
of surrogates that can be captured in the reserve
network. The effi cient choice would be selecting the
area that adds the most complementarity. Complementarity is, therefore, related to the concept of beta
diversity (Whittaker, 1972 ), but whereas beta diversity
is the difference between two areas, complementarity
is a measure of the dissimilarity between the species
complements of sets of selected areas.
It is important to note that the principle of effi ciency
is not simply about achieving complementarity. As we
discussed earlier, achieving an effi cient network is also
a matter of achieving objectives for the least possible
cost, where cost may refl ect the fi nancial cost of implementing and managing protected areas or the costs of
lost opportunities for economic development (Naidoo
et al ., 2006 ).
There is an increasing number of examples of where
cost data have been implemented into systematic conservation analyses. For example, in the Californian
marine case study outline in Box 6.3 , the authors conducted and interviewed 109 commercial fi shermen to
fi nd spatial data on fi shing effort and to map their
fi shing grounds. From this, an index of relative fi shing
effort was used to calculate the impact of fi sheries in
the reserve design (i.e. those marine waters that would
be closed to fi shing). Using these stakeholder data,
Klein et al . (2008a,b) were able to produce a systematic
conservation plan that was effi cient in that it maximized biodiversity conservation and minimized cost to
livelihoods for fi sherman.
As outlined in Section 6.3.3 , it is also possible to
factor in the returns from ecosystem service protection
into conservation planning analyses. There may,
however, be trade - offs between the achievement of
objectives (Mertz et al ., 2007 ; K.A. Wilson et al ., 2009 ),
depending on the spatial congruence between ecosystem services and between ecosystem services and biodiversity features. Some analyses have found high
levels of congruence (Turner et al ., 2007 ; Venter et al .,
2009 ), but in other areas overlap has been more
limited (Chan et al ., 2006 ; Naidoo et al ., 2008 ).
There are several ways to integrate ecosystem services into conservation planning analyses (Egoh et al .,
2007 ). Ecosystem services can be included as a feature
for which a target can be set (Chan et al ., 2006 ) and
the set of planning units that meet these and other
targets for the lowest cost can be identifi ed. Alternatively,
it is possible to modify the relative weighting for
(Rouget et al ., 2003 ; Possingham et al ., 2005 ; Pressey
et al ., 2007 ). However, in a national scale analysis in
Australia, Klein et al . (2009) accommodated ecological
and evolutionary processes in four ways:
1 using sub - catchments as planning units rather than
arbitrarily delineated grids;
2 targeting refugia from drought;
3 targeting evolutionary refugia;
4 preferentially selecting planning units along connected waterways.
The researchers identifi ed drought refugia as areas
with relatively high and regular herbage production,
while evolutionary refugia were identifi ed as areas
thought to be important for maintaining and generating biota during long - term climatic changes. They
identifi ed priority areas for conservation in Australia
that met biodiversity and ecological process targets
while minimizing acquisition cost.
Other examples of incorporating ecological processes in conservation planning include the comprehensive analyses undertaken in South Africa, where
spatial surrogates for processes, such as edaphic interfaces, animal movement corridors, and macroclimatic
and environmental gradients were targeted (Cowling
et al ., 1999, 2003 ; Rouget et al ., 2003, 2006 ).
Clearly, the dynamic nature of ecological processes
makes them diffi cult to quantify (Possingham et al .,
2005 ), but they are now recognized as an important
consideration when persistence objectives are being
defi ned. See further discussion in Chapter 7 .
6.4.3 Achieving e ffi ciency
As discussed in section 6.2 , a key concept in identifying
areas to achieve representation effi ciently is complementarity. The basic idea behind complementarity is
that conservation areas should complement one
another in terms of the ‘ features ’ they contain, the
species, communities, habitats, ecological processes,
etc. Each conservation area should be as different from
the others as possible until all the ‘ differences ’ (e.g.
different species, communities, etc.) are adequately
represented.
Complementarity can be defi ned in a number of
ways. The most commonly used implementation is that
a proposed new conservation area is assigned a higher
complementarity value than another if it has more
surrogates that have not already met their assigned
target of representation in a conservation area
151
network. For example, two proposed new areas, both
with high species richness, may have different numbers
of surrogates that can be captured in the reserve
network. The effi cient choice would be selecting the
area that adds the most complementarity. Complementarity is, therefore, related to the concept of beta
diversity (Whittaker, 1972 ), but whereas beta diversity
is the difference between two areas, complementarity
is a measure of the dissimilarity between the species
complements of sets of selected areas.
It is important to note that the principle of effi ciency
is not simply about achieving complementarity. As we
discussed earlier, achieving an effi cient network is also
a matter of achieving objectives for the least possible
cost, where cost may refl ect the fi nancial cost of implementing and managing protected areas or the costs of
lost opportunities for economic development (Naidoo
et al ., 2006 ).
There is an increasing number of examples of where
cost data have been implemented into systematic conservation analyses. For example, in the Californian
marine case study outline in Box 6.3 , the authors conducted and interviewed 109 commercial fi shermen to
fi nd spatial data on fi shing effort and to map their
fi shing grounds. From this, an index of relative fi shing
effort was used to calculate the impact of fi sheries in
the reserve design (i.e. those marine waters that would
be closed to fi shing). Using these stakeholder data,
Klein et al . (2008a,b) were able to produce a systematic
conservation plan that was effi cient in that it maximized biodiversity conservation and minimized cost to
livelihoods for fi sherman.
As outlined in Section 6.3.3 , it is also possible to
factor in the returns from ecosystem service protection
into conservation planning analyses. There may,
however, be trade - offs between the achievement of
objectives (Mertz et al ., 2007 ; K.A. Wilson et al ., 2009 ),
depending on the spatial congruence between ecosystem services and between ecosystem services and biodiversity features. Some analyses have found high
levels of congruence (Turner et al ., 2007 ; Venter et al .,
2009 ), but in other areas overlap has been more
limited (Chan et al ., 2006 ; Naidoo et al ., 2008 ).
There are several ways to integrate ecosystem services into conservation planning analyses (Egoh et al .,
2007 ). Ecosystem services can be included as a feature
for which a target can be set (Chan et al ., 2006 ) and
the set of planning units that meet these and other
targets for the lowest cost can be identifi ed. Alternatively,
it is possible to modify the relative weighting for
(Rouget et al ., 2003 ; Possingham et al ., 2005 ; Pressey
et al ., 2007 ). However, in a national scale analysis in
Australia, Klein et al . (2009) accommodated ecological
and evolutionary processes in four ways:
1 using sub - catchments as planning units rather than
arbitrarily delineated grids;
2 targeting refugia from drought;
3 targeting evolutionary refugia;
4 preferentially selecting planning units along connected waterways.
The researchers identifi ed drought refugia as areas
with relatively high and regular herbage production,
while evolutionary refugia were identifi ed as areas
thought to be important for maintaining and generating biota during long - term climatic changes. They
identifi ed priority areas for conservation in Australia
that met biodiversity and ecological process targets
while minimizing acquisition cost.
Other examples of incorporating ecological processes in conservation planning include the comprehensive analyses undertaken in South Africa, where
spatial surrogates for processes, such as edaphic interfaces, animal movement corridors, and macroclimatic
and environmental gradients were targeted (Cowling
et al ., 1999, 2003 ; Rouget et al ., 2003, 2006 ).
Clearly, the dynamic nature of ecological processes
makes them diffi cult to quantify (Possingham et al .,
2005 ), but they are now recognized as an important
consideration when persistence objectives are being
defi ned. See further discussion in Chapter 7 .
6.4.3 Achieving e ffi ciency
As discussed in section 6.2 , a key concept in identifying
areas to achieve representation effi ciently is complementarity. The basic idea behind complementarity is
that conservation areas should complement one
another in terms of the ‘ features ’ they contain, the
species, communities, habitats, ecological processes,
etc. Each conservation area should be as different from
the others as possible until all the ‘ differences ’ (e.g.
different species, communities, etc.) are adequately
represented.
Complementarity can be defi ned in a number of
ways. The most commonly used implementation is that
a proposed new conservation area is assigned a higher
complementarity value than another if it has more
surrogates that have not already met their assigned
target of representation in a conservation area