Conservation planning in a changing world
203
‘ endangered ’ or ‘ critically endangered ’ , largely based
on reductions in range or population estimates (Ribon
et al. , 2003 ). Despite the criticism, especially of methodology and taxonomic bias (e.g. R é gnier et al. , 2009 ),
the IUCN Red List has become an essential source of
information for conservation action and is widely recognized as the most comprehensive compilation of
extinct and threatened species (Mace & Lande, 1991 ;
Rodrigues et al. , 2006 ).
Brooks and Balmford (1996) compared losses of
birds in the region, projected using a species – area
model, with those listed by the IUCN as ‘ threatened ’ ,
and they found congruence. They concluded that the
forecasts of looming extinction are basically correct,
but that there is a substantial lag between the habitat
loss/fragmentation process and global extinction of the
species. There is, moreover, good evidence of local
extirpation within the existing range of many bird
species that live in devastated habitats such as the Mata
Atlantica.
Thus, within the Vi ç osa region (a 120 km
2 area in
south - eastern Brazil) over the last 70 years, it appears
that at least 28 bird species have become locally extinct,
with 43 being classifi ed as ‘ critically endangered ’ and
25 ‘ vulnerable ’ . In total, 61 per cent of the original
avifauna has been signifi cantly reduced in incidence
(Ribon et al. , 2003 ). Nectarivorous species appear to
have been affected least, followed by omnivores and
carnivores, with frugivores and insectivores hit the
hardest. Assuming relaxation to be well under way in
these fragmented systems, the questions of estimating
the time lag between habitat loss and eventual species
losses, and of predicting the identities and numbers of
these losses, remain unanswered.
In this context, as noted by Raheem et al . (2009) , it
is surprising that fragment age (i.e. the time of
isolation/creation of a fragment) has received little
attention from ecologists and conservationists. In most
landscapes, it is the productive and/or most accessible
areas that are deforested fi rst, thus producing a non -
random spatial and temporal distribution of habitat
fragments (Laurance et al. , 2002 ; Ewers et al. , 2006 ).
A topographically diverse landscape, such as on most
oceanic islands, will therefore typically contain an
assortment of older, smaller and more degraded fragments at lower elevations and younger, larger and less
degraded fragments at higher elevations (see for
example Box 8.2 ).
The above example illustrates the diffi culties of
untangling causal processes underlying species
relaxation within real landscapes. Part of this complexity is generated by the operation of two overlapping temporal scales that are critical in determining
net rates of species loss across fragments: the rate at
which habitat is being lost from a region (considering
also the possible recovery of habitat; see Wright &
Muller - Landau, 2006 ); and the age of the habitat fragments created within that region. Untangling the contribution of these two interlinked age - dependent
factors may be critical to a better understanding of the
relaxation process and thus for more accurate predictions of species losses and relaxation lag time.
Recently, Raheem et al . (2009) , studying the land
snails assemblages in fragments of natural rain forest
in Sri Lanka ’ s wet zone, concluded that fragment age,
along with fragment shape complexity, were the only
two signifi cant determinants of fragmentation - related
changes in community composition. Attributes of
fragments such as area, distance - to - edge and matrix
quality, which have been traditionally linked to species
losses, exhibited no obvious effect (see also below). In
practice, review of the literature on such effects reveals
many such idiosyncrasies between studies. At least
some part of the differences in fi ndings from one case
study to the next refl ects differences in the ‘ experimental design ’ of the fragmented systems analysed and, in
particular, the range in values of properties such as
area, age, distance from source, habitat complexity, etc.
that each study encompasses.
8.2.3 Ecosystem c ollapse and t hreshold
r esponses in h abitat i slands
Reduction of habitat area can causes super - saturation
as immigration rate declines and extinction rate rises
(above). In the most extreme scenario, where the loss
of habitat is so extreme that immigration into a patch
virtually ceases, species richness may, in theory, collapse catastrophically (see Whittaker & Fern á ndez -
Palacios, 2007 , their fi gure 10.5; and also Vandermeer
& Lin, 2008 ). Although the process of species richness
collapse and associated loss of ecosystem function is
not presently well - defi ned or understood, it is thought
to be linked to extreme impoverishment of the available
resources that are required for a system to sustain its
functionality (e.g. Dobson et al. , 2006 ).
One of the most emblematic examples of an ecosystem collapsing comes from the island literature. Easter
Island (Rapa Nui) was once one of the most isolated
203
‘ endangered ’ or ‘ critically endangered ’ , largely based
on reductions in range or population estimates (Ribon
et al. , 2003 ). Despite the criticism, especially of methodology and taxonomic bias (e.g. R é gnier et al. , 2009 ),
the IUCN Red List has become an essential source of
information for conservation action and is widely recognized as the most comprehensive compilation of
extinct and threatened species (Mace & Lande, 1991 ;
Rodrigues et al. , 2006 ).
Brooks and Balmford (1996) compared losses of
birds in the region, projected using a species – area
model, with those listed by the IUCN as ‘ threatened ’ ,
and they found congruence. They concluded that the
forecasts of looming extinction are basically correct,
but that there is a substantial lag between the habitat
loss/fragmentation process and global extinction of the
species. There is, moreover, good evidence of local
extirpation within the existing range of many bird
species that live in devastated habitats such as the Mata
Atlantica.
Thus, within the Vi ç osa region (a 120 km
2 area in
south - eastern Brazil) over the last 70 years, it appears
that at least 28 bird species have become locally extinct,
with 43 being classifi ed as ‘ critically endangered ’ and
25 ‘ vulnerable ’ . In total, 61 per cent of the original
avifauna has been signifi cantly reduced in incidence
(Ribon et al. , 2003 ). Nectarivorous species appear to
have been affected least, followed by omnivores and
carnivores, with frugivores and insectivores hit the
hardest. Assuming relaxation to be well under way in
these fragmented systems, the questions of estimating
the time lag between habitat loss and eventual species
losses, and of predicting the identities and numbers of
these losses, remain unanswered.
In this context, as noted by Raheem et al . (2009) , it
is surprising that fragment age (i.e. the time of
isolation/creation of a fragment) has received little
attention from ecologists and conservationists. In most
landscapes, it is the productive and/or most accessible
areas that are deforested fi rst, thus producing a non -
random spatial and temporal distribution of habitat
fragments (Laurance et al. , 2002 ; Ewers et al. , 2006 ).
A topographically diverse landscape, such as on most
oceanic islands, will therefore typically contain an
assortment of older, smaller and more degraded fragments at lower elevations and younger, larger and less
degraded fragments at higher elevations (see for
example Box 8.2 ).
The above example illustrates the diffi culties of
untangling causal processes underlying species
relaxation within real landscapes. Part of this complexity is generated by the operation of two overlapping temporal scales that are critical in determining
net rates of species loss across fragments: the rate at
which habitat is being lost from a region (considering
also the possible recovery of habitat; see Wright &
Muller - Landau, 2006 ); and the age of the habitat fragments created within that region. Untangling the contribution of these two interlinked age - dependent
factors may be critical to a better understanding of the
relaxation process and thus for more accurate predictions of species losses and relaxation lag time.
Recently, Raheem et al . (2009) , studying the land
snails assemblages in fragments of natural rain forest
in Sri Lanka ’ s wet zone, concluded that fragment age,
along with fragment shape complexity, were the only
two signifi cant determinants of fragmentation - related
changes in community composition. Attributes of
fragments such as area, distance - to - edge and matrix
quality, which have been traditionally linked to species
losses, exhibited no obvious effect (see also below). In
practice, review of the literature on such effects reveals
many such idiosyncrasies between studies. At least
some part of the differences in fi ndings from one case
study to the next refl ects differences in the ‘ experimental design ’ of the fragmented systems analysed and, in
particular, the range in values of properties such as
area, age, distance from source, habitat complexity, etc.
that each study encompasses.
8.2.3 Ecosystem c ollapse and t hreshold
r esponses in h abitat i slands
Reduction of habitat area can causes super - saturation
as immigration rate declines and extinction rate rises
(above). In the most extreme scenario, where the loss
of habitat is so extreme that immigration into a patch
virtually ceases, species richness may, in theory, collapse catastrophically (see Whittaker & Fern á ndez -
Palacios, 2007 , their fi gure 10.5; and also Vandermeer
& Lin, 2008 ). Although the process of species richness
collapse and associated loss of ecosystem function is
not presently well - defi ned or understood, it is thought
to be linked to extreme impoverishment of the available
resources that are required for a system to sustain its
functionality (e.g. Dobson et al. , 2006 ).
One of the most emblematic examples of an ecosystem collapsing comes from the island literature. Easter
Island (Rapa Nui) was once one of the most isolated