Conservation planning in a changing world
215
Fern á ndez - Palacios, 2007 ). Intuitively, however, ordering by species richness would appear the most appropriate approach.
Apart from sequential extinctions, a variety of different mechanisms can also produce nestedness patterns
(see Table 8.3 ), some of which are deterministic and
some of which are stochastic, requiring different
metrics for quantifying nestedness (Wright et al. , 1998 ;
Ulrich et al. , 2009 ). All of the explanations for
nested subsets can be seen as variations of ordered
colonizations or extinctions along environmental or
biological gradients (area, isolation, habitat) of the
target areas. Frequently, these mechanisms cannot be
distinguished by just establishing the statistical pattern
of nestedness. Inferences of causation ideally require
independent lines of verifi cation beyond manipulations and analyses of the original presence/absence
matrix (Ulrich et al. , 2009 ).
Although nestedness can be driven by a number of
processes, it appears that differential extinction plays a
major role in producing nested structure in many
habitat island data sets (Wright et al. , 1998 ). Knowledge
of nested subset structure might therefore provide a
basis for predicting the ultimate community composition of a fragmented landscape, particularly if it is possible to attribute patterns to particular causes (Fischer
& Lindenmayer, 2005 ; Fleishman et al. , 2007 ).
Feeley ’ s (2003) study of bird communities inhabiting recently isolated land bridge islands in Lago Guri,
Venezuela, showed how nestedness calculations can
provide useful insights. Lago Guri is a large hydroelectric reservoir created in 1986 in east - central Venezuela.
The inundation of an area of hilly terrain expanding
over 4,000 km
2 resulted in the fragmentation of once -
continuous forest into hundreds of land bridge islands
(e.g. Terborgh et al. , 2001 ). Feeley found that the
resident forest - interior bird communities displayed a
signifi cantly nested distributional pattern that was
hypothesized to be the result of species ’ differential
extinction rates.
In an earlier study of forest birds, Blake (1991) also
found a signifi cant degree of nestedness, particularly
among birds breeding in the forest interior and among
species wintering in the tropics. By contrast, species
breeding in forest - edge habitat showed more variable
distribution patterns.
These fi ndings concur with those of Patterson
(1990) from S ã o Paulo, Brazil (original data from
Willis, 1979 ). Patterson reported signifi cant nestedness amongst sedentary bird species but, when
transient species were also included, the system as a
whole was found to be non - nested.
These results are indicative of a large number of
studies of nestedness, which show the outcome of
nestedness analyses to be variable across different
systems and for different ecological groups of species,
but which show that signifi cant nestedness is a
common pattern. Such analyses often indicate that
species that are restrictive habitat specialists, including
many of high conservation value, do require larger,
more species - rich patches (Fleishman et al. , 2007 ;
Whittaker & Fern á ndez - Palacios, 2007 ).
In theory, a nestedness analysis can contribute a
simple answer to the SLOSS question, as a strong
degree of nestedness implies that most species could be
represented by conserving the richest (largest) patch.
According to Atmar & Patterson (1993) , the widespread occurrence of nested subsets speaks for the
value of larger protected areas. However, Boecklen
(1997) and Fischer & Lindenmayer (2005) convincingly showed that this argument is only valid for perfectly nested subsets, which are very rare in nature.
Even for highly signifi cantly (but not perfectly) nested
subsets, the total species numbers from subsets of
many smaller sites are often higher than the respective
number of species from a single larger site of the equivalent total area (Ulrich et al. , 2009 ). On the other hand,
a low degree of nestedness may be considered as indicative that specifi c habitat patches are sampling distinct
species sets, and thus an array of reserves of differing
size and internal richness may be required to maximize
regional diversity in such circumstances (e.g. Kellman,
1996 ).
Broadly speaking, a nestedness index can provide
one compositional descriptor and can perhaps aid
identifi cation of risk - prone species. However, it should
not be given primacy in conservation planning.
Identifying a community as nested at a certain point in
time, has limited predictive ability as to the probability
of the community maintaining the same sets of species
(or even a single species) over time (Simberloff &
Martin, 1991 ). The isolates may be subject to turnover
and/or species attrition in new ways dictated by the
changing biogeographical circumstances of the landscape in which the fragments occur. As Worthen
( 1996 , p. 419) put it, nestedness is not a ‘ magic bullet ’ ,
‘ … no single index should be expected to distil the
informational content of an entire community, let
alone predict how it will react to habitat reduction or
fragmentation ’ .
215
Fern á ndez - Palacios, 2007 ). Intuitively, however, ordering by species richness would appear the most appropriate approach.
Apart from sequential extinctions, a variety of different mechanisms can also produce nestedness patterns
(see Table 8.3 ), some of which are deterministic and
some of which are stochastic, requiring different
metrics for quantifying nestedness (Wright et al. , 1998 ;
Ulrich et al. , 2009 ). All of the explanations for
nested subsets can be seen as variations of ordered
colonizations or extinctions along environmental or
biological gradients (area, isolation, habitat) of the
target areas. Frequently, these mechanisms cannot be
distinguished by just establishing the statistical pattern
of nestedness. Inferences of causation ideally require
independent lines of verifi cation beyond manipulations and analyses of the original presence/absence
matrix (Ulrich et al. , 2009 ).
Although nestedness can be driven by a number of
processes, it appears that differential extinction plays a
major role in producing nested structure in many
habitat island data sets (Wright et al. , 1998 ). Knowledge
of nested subset structure might therefore provide a
basis for predicting the ultimate community composition of a fragmented landscape, particularly if it is possible to attribute patterns to particular causes (Fischer
& Lindenmayer, 2005 ; Fleishman et al. , 2007 ).
Feeley ’ s (2003) study of bird communities inhabiting recently isolated land bridge islands in Lago Guri,
Venezuela, showed how nestedness calculations can
provide useful insights. Lago Guri is a large hydroelectric reservoir created in 1986 in east - central Venezuela.
The inundation of an area of hilly terrain expanding
over 4,000 km
2 resulted in the fragmentation of once -
continuous forest into hundreds of land bridge islands
(e.g. Terborgh et al. , 2001 ). Feeley found that the
resident forest - interior bird communities displayed a
signifi cantly nested distributional pattern that was
hypothesized to be the result of species ’ differential
extinction rates.
In an earlier study of forest birds, Blake (1991) also
found a signifi cant degree of nestedness, particularly
among birds breeding in the forest interior and among
species wintering in the tropics. By contrast, species
breeding in forest - edge habitat showed more variable
distribution patterns.
These fi ndings concur with those of Patterson
(1990) from S ã o Paulo, Brazil (original data from
Willis, 1979 ). Patterson reported signifi cant nestedness amongst sedentary bird species but, when
transient species were also included, the system as a
whole was found to be non - nested.
These results are indicative of a large number of
studies of nestedness, which show the outcome of
nestedness analyses to be variable across different
systems and for different ecological groups of species,
but which show that signifi cant nestedness is a
common pattern. Such analyses often indicate that
species that are restrictive habitat specialists, including
many of high conservation value, do require larger,
more species - rich patches (Fleishman et al. , 2007 ;
Whittaker & Fern á ndez - Palacios, 2007 ).
In theory, a nestedness analysis can contribute a
simple answer to the SLOSS question, as a strong
degree of nestedness implies that most species could be
represented by conserving the richest (largest) patch.
According to Atmar & Patterson (1993) , the widespread occurrence of nested subsets speaks for the
value of larger protected areas. However, Boecklen
(1997) and Fischer & Lindenmayer (2005) convincingly showed that this argument is only valid for perfectly nested subsets, which are very rare in nature.
Even for highly signifi cantly (but not perfectly) nested
subsets, the total species numbers from subsets of
many smaller sites are often higher than the respective
number of species from a single larger site of the equivalent total area (Ulrich et al. , 2009 ). On the other hand,
a low degree of nestedness may be considered as indicative that specifi c habitat patches are sampling distinct
species sets, and thus an array of reserves of differing
size and internal richness may be required to maximize
regional diversity in such circumstances (e.g. Kellman,
1996 ).
Broadly speaking, a nestedness index can provide
one compositional descriptor and can perhaps aid
identifi cation of risk - prone species. However, it should
not be given primacy in conservation planning.
Identifying a community as nested at a certain point in
time, has limited predictive ability as to the probability
of the community maintaining the same sets of species
(or even a single species) over time (Simberloff &
Martin, 1991 ). The isolates may be subject to turnover
and/or species attrition in new ways dictated by the
changing biogeographical circumstances of the landscape in which the fragments occur. As Worthen
( 1996 , p. 419) put it, nestedness is not a ‘ magic bullet ’ ,
‘ … no single index should be expected to distil the
informational content of an entire community, let
alone predict how it will react to habitat reduction or
fragmentation ’ .