Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2010) 19, 741–754
M ACRO E C O LO G IC A L
M E T H OD S
Assessing the impacts of fragmentation
on plant communities in New Zealand:
scaling from survey plots to landscapes
geb_542
741..754
Raffaele Lafortezza,1,2* David A. Coomes,1 Valerie Kapos3,4 and
Robert M. Ewers5
1
Department of Plant Sciences, University of
Cambridge, Downing Street, Cambridge CB2
3EA, UK, 2Department of Scienze delle
Produzioni Vegetali, University of Bari, Via
Amendola 165, Bari 70126, Italy, 3Department
of Zoology, University of Cambridge, Downing
Street, Cambridge CB2 3EA, UK, 4UNEP
World Conservation Monitoring Centre,
Cambridge CB3 0DL, UK, 5Division of
Biology, Imperial College London, Silwood
Park Campus, Ascot SL5 7PY, UK
A B S T R AC T
Aim Few studies have attempted to assess the overall impact of fragmentation at the
landscape scale. We quantify the impacts of fragmentation on plant diversity by
assessing patterns of community composition in relation to a range of fragmentation
measures.
Location The investigation was undertaken in two regions of New Zealand – a
relatively unfragmented area of lowland rain forest in south Westland and a highly
fragmented montane forest on the eastern slopes of the Southern Alps.
Methods We calculated an index of community similarity (Bray–Curtis) between
forest plots we regarded as potentially affected by fragmentation and control forest
plots located deep inside continuous forest areas. Using a multiple nonlinear regression technique that incorporates spatial autocorrelation effects, we analysed plant
community composition in relation to measures of fragmentation at the patch and
landscape levels. From the resulting regression equation, we predicted community
composition for every forest pixel on land-cover maps of the study areas and used
these maps to calculate a landscape-level estimate of compositional change, which we
term ‘BioFrag’. BioFrag has a value of one if fragmentation has no detectable effect on
communities within a landscape, and tends towards zero if fragmentation has a strong
effect.
Results We detected a weak, but significant, impact of fragmentation metrics operating at both the patch and landscape levels. Observed values of BioFrag ranged from
0.68 to 0.90, suggesting that patterns of fragmentation have medium to weak impacts
on forest plant communities in New Zealand. BioFrag values varied in meaningful ways
among landscapes and between the ground-cover and tree and shrub communities.
*Correspondence: Raffaele Lafortezza,
Department of Scienze delle Produzioni
Vegetali, University of Bari, Via Amendola 165,
Bari 70126, Italy.
E-mail: [email protected]
Main conclusions BioFrag advances methods that describe spatial patterns of
forest cover by incorporating the exact spatial patterns of observed species responses
to fragmentation operating at multiple spatial scales. BioFrag can be applied to any
landscape and ecological community across the globe and represents a significant
step towards developing a biologically relevant, landscape-scale index of habitat
fragmentation.
Keywords
Biodiversity indicators, fragmentation, New Zealand, plant communities,
similarity index, spatial modelling.
INTR O D U C TI O N
Forest fragmentation poses a substantial threat to global biodiversity and may cause cascading impacts on a wide range of
ecosystem functions and services (Wu et al., 2003; Millennium
© 2010 Blackwell Publishing Ltd
Ecosystem Assessment, 2005). Fragmentation occurs when
tracts of continuous forest are broken up into smaller pieces as
a result of land-use change (Chalfoun et al., 2002; Franklin et al.,
2002; Fahrig, 2003; Watson et al., 2004; Sekercioglu & Sodhi,
2007), creating new edges between forest and other vegetation
DOI: 10.1111/j.1466-8238.2010.00542.x
www.blackwellpublishing.com/geb
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R. Lafortezza et al.
types, disconnecting patches from adjacent, continuous habitat
and reducing patch sizes (Collinge, 1996; Fahrig, 2003; Saura &
Carballal, 2004), thereby disrupting the movement patterns of
many organisms and isolating populations (Redpath, 1995). Isolated populations in fragments are more sensitive to stochastic
events, which can lead to population decline or extinction
(Driscoll & Weir, 2005; Arroyo-Rodríguez et al., 2007), and fragmentation is also likely to constrain the ability of many species
to move in response to changing climatic conditions (Collingham & Huntley, 2000; Ewers & Didham, 2006). Mounting levels
of forest fragmentation and habitat loss are altering habitats
around the world, with well-documented effects on the distribution and abundance of individual species and on the composition of communities (Laurance et al., 2002; Kupfer et al.,
2006; Watling & Donnelly, 2006; Ewers et al., 2007; Fischer &
Lindenmayer, 2007).
There are key disconnects between field ecologists and
remote-sensing researchers in the ways that they assess and
quantify the degree, or intensity, of fragmentation effects.
Remote-sensing researchers and landscape ecologists have
developed tools, such as the well-respected fragstats package
(McGarigal & Marks, 1995), which enable them to calculate
numerous metrics of fragmentation from maps indicating the
position, size and shape of fragments within a landscape
(O’Neill et al., 1988; Riitters et al., 1995). Yet these summary
spatial statistics provide little information on the biological
effect of the fragmentation pattern that is observed (Davidson,
1998). By contrast, field ecologists routinely measure the abundance of species or the structure of biological communities at
point locations within fragmented landscapes and then relate
these measures to metrics of habitat fragmentation (Lindenmayer & Fischer, 2007). Typically, field ecologists will focus on
biological responses to one or a few attributes of the fragments
or landscape such as area (Watling & Donnelly, 2006), edge
(Laurance et al., 2002), shape (Saura & Carballal, 2004), isolation (Schmiegelow et al., 1997), landscape forest cover (Trzcinski et al., 1999) or matrix quality (Baum et al., 2004; Watling &
Donnelly, 2006). Biological studies rarely attempt to explicitly
quantify the landscape-level significance of the effects observed
in their sampling plots. Typically, authors stop once they have
explained the ecological patterns of species responses to habitat
loss and fragmentation, rather than trying to scale up those
point-based observations to estimate the overall effect of fragmentation on the landscape as a whole. This leaves the field in a
position where it is possible to determine whether fragmentation does or does not have an impact on biodiversity in a given
landscape, but unable to quantify the magnitude of that impact
for that landscape. For example, the use of point data can be
used to assess species responses to habitat edges and to show that
edges do have an impact on species abundances (Ewers &
Didham, 2008), but it is only possible to quantify the net impact
on the populations of those species when species responses are
combined with spatially explicit data on the distribution of
habitat edges (Ewers & Didham, 2007; Ewers et al., 2009). It
follows that more research is needed to develop robust methods
for extrapolating biological data, collected at the plot scale, to
742
the wider patterns of fragmentation observed with remote
sensing and geographical information system (GIS) techniques
routinely applied at the landscape level. Such methods would
build on the current ability of researchers to relate observed
biological changes to remotely sensed spatial patterns of fragmentation, and take them a step further to quantify the net
impact of habitat fragmentation on landscapes. The end result
would be a biologically relevant index of habitat fragmentation
that directly quantifies the net impact of fragmentation on a
measure of biodiversity across an entire landscape.
Biologically relevant indices of habitat fragmentation would
find immediate uses in conservation biology, and are also
needed to help support policy at global and other scales. The
Convention on Biological Diversity has identified forest fragmentation as one of a suite of indicators for tracking progress
towards its ‘2010 target’ on reducing biodiversity loss (UNEP/
CBD, 2002, 2004) and policy-makers in the UK identified the
development of such indicators as being of key importance
(CBD Secretariat, 2001; Sutherland et al., 2006).
In this paper, we examine the impacts of fragmentation on
plant communities in New Zealand forests. We develop and
describe a new method for converting point-based observations
into a continuous biodiversity surface representing the similarity of fragmented biological communities to those in unfragmented forest. We apply the method to two forest landscapes in
New Zealand, one mountainous and naturally highly fragmented and the other lowland and fragmented to a much lesser
extent. Using a multiple nonlinear regression technique that
accounts for spatial autocorrelation effects, we analyse plant
community composition in relation to various metrics of fragmentation. By extrapolating the best-supported regression
model in a GIS, we predict a Bray–Curtis similarity value for
each forest pixel within the two landscapes and use these
mapped outputs to calculate a landscape-scale metric of the
effects of fragmentation on biological diversity. Our method
scales up data on biodiversity observed in tree plots into a
landscape-level statistic reflecting the net biological impact of
fragmentation.
We use this statistic to test three hypotheses. First, we hypothesized that the landscape-level impact of fragmentation on plant
communities in the highly fragmented mountainous region
would be greater than on the plant communities in the relatively
intact lowland region. This expectation arises because the more
compact shape and larger size of patches in relatively unfragmented landscapes should ensure that populations in those
landscapes suffer relatively little impact from forest fragmentation (Ewers & Didham, 2007). Our second hypothesis was that
ground-layer vegetation in these forests would be more influenced by fragmentation than the tree and shrub layer. This
expectation arises from the difference in generation time of
ground-layer plants and trees that will result in different time
lags before the impacts of forest fragmentation on communities
become discernible (Ewers & Didham, 2006). Deforestation and
fragmentation in the study landscapes occurred less than one
generation ago for many of the tree species in the region (Ewers
et al., 2006), so relatively little time has elapsed in which those
Global Ecology and Biogeography, 19, 741–754, © 2010 Blackwell Publishing Ltd
Forest fragmentation at the landscape scale
species and communities may have changed their spatial patterns. By contrast, ground layer plants with relatively fast generation times are expected to have more rapidly altered their
patterns of community composition with respect to the changed
environmental conditions in fragmented landscapes. Finally, we
hypothesized that exotic plants are important components of
communities growing near forest edges and would have a strong
influence on the (dis)similarity between communities at forest
margins and interior forest. We based this prediction on literature documenting the invasion of native forests in New Zealand
by exotic species that has been facilitated by the creation of
forest edges (e.g. Wiser et al. 1998; Standish, 2002).
DATA A N D M ET H O D S
Study landscapes and forest survey data
The impact of fragmentation on plant diversity was modelled by
analysing changes in species composition in two contrasting
regions of the South Island of New Zealand (see Fig. 1 and
Table 1). The first landscape, located in south Westland, represents one of the least fragmented areas in New Zealand (Ewers
et al., 2006). This landscape stretches from sea level to an altitude of 1132 m (mean 240 m) (Table 1) with forests dominated
by the conifers Dacrycarpus dacrydioides (A. Rich.) Laub.
growing on alluvial flats close to rivers, merging into Dacrydium
cupressinum Sol. ex Lamb. on older terraces. Other species
include Prumnopitys ferruginea (D. Don.) deLaub. (Podocar-
paceae) and the angiosperm Weinmannia racemosa L. f.
(Cunoniaceae) in the lowlands, which merge into Nothofagus
forests on the slopes (Wardle, 1991; Duncan, 1993). Variation in
the structure and composition of these forests is mainly determined by natural disturbances, probably including catastrophic
earthquake events such as those observed further north on the
west coast (Wells et al., 2001), rather than human-induced
impacts. However, humans have altered the distribution of
forest cover in the lowlands through agricultural expansion
leading to the creation of non-natural forest fragments and
edges embedded within a pasture matrix.
By contrast, the second landscape comprises highly fragmented forests on the drier side of the Southern Alps (Ewers
et al., 2006). The dominant tree species in this region is Nothofagus solandri, consisting of subspecies solandri (Hook. f.) Oerst.
(black beech) below 600 m altitude and subspecies cliffortioides
(Hook. f.) Poole. (mountain beech) at higher altitude (Wardle,
1984). The lower boundaries of the forest extent are mostly
human-made, usually as a result of pre-European (800–200
years bp) and post-European (0–200 years bp) burning. In some
areas the hill-slope forests have also been cleared by fire and now
provide rough grazing for sheep (Wardle, 1984). However, these
forests are also heavily fragmented by natural processes because
the steep mountain slopes are divided by frequent scree slopes
and give way to alpine grasslands at an altitude of around
1300 m.
In the two regions, forest survey data were drawn from
the New Zealand National Vegetation Survey Databank. In
Figure 1 Location of the two analysed regions of the South Island of New Zealand. (a) Lowland rain forest in the Westland district.
(b) Nothofagus-dominated landscape on the eastern slopes of the Southern Alps. Dark shading represents the forest cover for the two
landscapes, and white lines delineate the boundaries of political districts.
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R. Lafortezza et al.
Table 1 Summary characteristics of the two study landscapes at plot and landscape level.
Variable
Lowland rain forest
Nothofagus forest
Area of forest (% of landscape)
Number of forest patches per 100 ha
Average patch size (95% CI)
Average nearest neighbouring distance (95% CI)
Average altitude (range)
Species richness*
Ground-layer species richness
Tree and shrub species richness
Species richness per plot (95% CI)
Number of exotic species
Mean similarity (BC) of ground-layer data (95% CI)
Mean (95% CI) similarity (BC) of tree and shrub layer data
Number of exotic species per plot (95% CI)
210,000 ha (67.2%)
0.2
6.6 ha (0.4–198)
150 m (78.8–195.9)
240 m (0–1 1332)
224
199†
131
84 (66–136)
28
0.6 (0.2–0.9)
0.6 (0.3–0.9)
2.1 (0.1–4.1)
545,000 ha (12.4%)
0.9
3.6 ha (0.2–109)
227 m (100.6–303.5)
801 m (60–1418)
201
173‡
83
24 (7–42)
34
0.3 (0.1–0.6)
0.6 (0.2–0.9)
1.9 (0.8–3.1)
*Species present > 1% of plots.
†106 species in common with tree and shrub species.
‡55 species in common with tree and shrub species.
CI, confidence interval; BC, Bray-Curtis index.
particular, we used data collected in the period 1979–85, using a
standard reconnaissance method (Allen & McLennan, 1983;
Allen, 1992) in which a visual estimate was made of the percentage of foliage cover of all vascular plant species within each of six
height tiers. The tiers represented ground-cover species and any
seedlings of shrubs or trees (T6, < 0.30 m), shrub (T5, 0.3–2 m),
lower subcanopy (T4, 2–5 m), upper subcanopy (T3, 5–12 m),
canopy (T2, 12–25 m) and emergent above-canopy vegetation
(T1, > 25 m) (Miller, 2004). Cover was estimated by cover classes
of: < 1, 1–5, 6–25, 26–50, 51–75 and 76–100% (Allen, 1992). All
forest survey data were collected in 20 ¥ 20 m plots. A total of
7799 forest plots were used in this study: 4637 plots in south
Westland and 3162 plots in the montane Nothofagus-dominated
forest.
Calculation of fragmentation metrics
To characterize the fragmentation patterns within these two
landscapes, we used indigenous forest cover data stored in
the New Zealand Topographic Database (NZTopo, http://
www.linz.govt.nz/topography/topographic-data); this database
provides information on a wide range of land features, including
forest vegetation boundaries in vector format, at a scale of
1:50,000. The data were derived from aerial photographs taken
mostly in the 1970s and 1980s (black and white, scale 1:25,000),
which is roughly consistent with the period during which plots
were surveyed in the two regions. Vector polygons were converted into 25 ¥ 25 m grid cells. We defined the landscape for
analysis as being the land surface located within a 5000-m buffer
around the survey plots.
Many metrics of landscape fragmentation are closely correlated with one another (Riitters et al., 1995; McGarigal &
Cushman, 2002; Neel et al., 2004) because they are based on
related properties of fragmented landscapes such as fragment
744
area, edges, shape, isolation and the characteristics of the surrounding land cover (Hargis et al., 1998; Griffith et al., 2000). We
selected nine fragmentation measures that previous studies have
reported to be relatively uncorrelated with each other (Table 2)
(Cain et al., 1997; Trani & Giles, 1999; Honnay et al., 2003; De
Clercq et al., 2006), although we found that in our study areas
some of these measures were still significantly inter-correlated
(see Tables S1 & S2 in Supporting Information). The nine measures were grouped into component-based metrics, which are
based on patch-level calculations, and landscape-based metrics,
which examine the attributes of neighbouring cells around a
focal cell (see Table 2 for more details). Metrics were calculated
from the rasterized forest-cover data (cell size = 25 m) using
fragstats v.3.3 (McGarigal & Marks, 1995). Landscape-level
metrics (termed ‘class-level metrics’ in fragstats) were calculated using neighbourhood radii of 500 m and 1000 m.
Calculation of similarity indices
Our analyses are based on the premise that community composition within control plots, located deep inside continuous
indigenous forest, is representative of the community that
would have occurred in the absence of forest fragmentation, and
that community composition will be more similar between
control sites than between a control site and a location that is
affected by forest fragmentation (Ewers et al., 2009). To assess
the similarity of communities at different point locations, we
used the Bray–Curtis index (BC), one of the most common
measures of community similarity in the ecological literature
(Magurran, 2004):
BC = 1 −
∑Y
∑ (Y
ij
− Yik
ij
+ Yik )
,
(1)
Global Ecology and Biogeography, 19, 741–754, © 2010 Blackwell Publishing Ltd
Forest fragmentation at the landscape scale
Table 2 Fragmentation measures used in this study.
Type of measure
Variable
Description
Unit
Range
Component-based metrics
DIST.EDGE
P.SIZE
P.SHAPE
P.ENN
PD.500†
ED.500†
PNF.500†
MSI.500†
ENN.500†
Distance to the nearest forest edge
Size of the forest patch
Shape index of the forest patch‡
Distance to the nearest neighbouring forest patch
Number of forest patches per unit of area
Total length of forest edges per unit of area
Percent of non-forest areas
Mean shape index of forest patches‡
Average distance to the nearest neighbouring forest patches
km
ha
–
km
#.km-2
km
%
km
m
DIST.EDGE ⱖ 0
P.SIZE > 0
P.SHAPE ⱖ 1
P.ENN ⱖ 0
PD.500 ⱖ 0
ED.500 > 0
PNF.500 ⱖ 0
MSI.500 ⱖ 1
ENN.500 ⱖ 0
Landscape-based metrics*
*Landscape-based metrics were computed using a moving window of radius of 500 m and 1000 m.
†The same description applies to each measure computed within a radius of 1000 m (e.g. PD.1000).
‡The shape index: describes the deviation of each forest patch from circular: a circle has a value of 1 whereas forest fragments with irregular shapes will
have higher values.
Metrics have been grouped into component-based metrics, which are based on patch-level calculations, and landscape-based metrics, which examine the
attributes of neighbouring cells around a focal cell. A detailed description of each metric can be found in McGarigal & Marks (1995).
A
B
Figure 2 Relationship between the arcsin-square-root transformed Bray–Curtis (BC) values observed for each pair of ‘PFA-to-control
plots’ and the distance from edge in the two landscapes (PFA = plots that were potentially affected by fragmentation). As the distance from
the forest edge increases, the BC shows an increase with an exponential asymptotic trend. Thus plots near forest edges are, on average, less
similar to the control plots than those deep inside the forest. Data refer to ground-layer composition of (a) lowland rain forest landscape
and (b) Nothofagus-forest landscape. A three-parameter exponential model was fitted to data (lowland landscape, r2 = 0.11; Nothofagus
landscape, r2 = 0.17).
where Yij is the abundance of species i in site j, Yik is the abundance of species i in site k, and the summation is over all
species found at the two sites. BC values range from 0 (no
species in common) to 1 (identical abundance of all species).
Using vegetation cover scores as a proxy for abundance
(Duncan et al., 1997; Wiser & Buxton, 2008), we calculated the
average BC between each plot that we regarded as being potentially affected by fragmentation (henceforth PFA plots) and the
nearest control plot that differed in elevation by less than
100 m from the PFA. To avoid rare species greatly skewing BC
estimates, we used only species that were present in more than
1% of plots (Tables 1 & S3). In the case of the lowland rain
forest (south Westland), plots less than 1500 m from the
nearest forest edge were initially selected as PFA plots on the
basis of previous studies investigating the spatial scale of edge
effects (Ewers & Didham, 2008). Using this approach, 4564 out
of 4637 plots were designated as PFA plots and the remaining
73 plots were denoted as control plots (0.95 quantile =
1170 m). To validate our arbitrary choice of 1500 m, we
plotted PFA–control plot similarity against the distance to the
forest edge and examined the shape of the curve (Fig. 2). For
the lowland rain forest, we found that the value of edge distance at which BC reached 0.99 of the asymptote value was
1482 m. Consequently, we considered our choice of control
plots as appropriate for the scope of the study. For the
Nothofagus-dominated landscape, BC reached 0.99 of its
asymptotic value at an edge distance of 1027 m, so we therefore decided that 1000 m was an appropriate threshold distance from the edge for distinguishing control plots (n = 465)
from PFA plots (n = 2697).
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R. Lafortezza et al.
In both landscapes, we calculated similarity values for two
layers of plants: the ground layer (T6), consisting of angiosperm
herbs, fern species and seedlings of woody species, and an amalgamation of all other canopy layers (T1–T5) consisting mostly
of woody angiosperm and conifer species but also tree ferns and
other ferns such as Blechnum discolor. If a woody species
appeared in more than one of the T1–T5 layers then we used the
maximum cover score observed. The classification of control
and PFA plots was based on species composition data from the
ground-cover layer (T6). In addition we ran a set of analyses in
which exotic species were excluded, to test the hypothesis that
exotic species invading from forest edges were contributing substantially to the patterns of plot similarities observed (Wiser
et al. 1998).
Multiple non-linear regression on similarity indices
Because BC is a proportional value, we arcsin-square-root transformed it before using it in regression analyses as recommended
by Sokal & Rohlf (1995). When back-transformed, all BC values
that we predicted from the final regression relationships
were bounded between 0 and 1. Preliminary inspections of
asin BC plotted against the selected fragmentation metrics
revealed nonlinear relationships that could be effectively modelled using a three-parameter asymptotic exponential function
(e.g. Fig. 2):
a sin BC = a − be − cx ,
(2)
where x is a fragmentation metric, and a, b and c are parameters
estimated by least-squares regression. Parameter a is the asymptotic value that asin BC tends toward when comparing two
plots deep in the interior of the forest (i.e. two control plots),
while a - b is the similarity when x = 0 (for example, on the edge
of a patch when x is distance from the forest edge). Parameter c
describes the steepness of the response curve.
We extended this approach to nonlinear multiple regression,
allowing us to model the effects of several fragmentation metrics
by expanding on the single-variable function:
a sin BC = a − b1e − c1x1 − b2 e − c2 x2 − b3e − c3DIST .
(3)
Here x1 and x2 are two fragmentation metrics (more could be
included) and a, b1, c1, b2, c2, b3 and c3 are the parameters to be
estimated by regression. The final term in the model, DIST, is the
distance between the PFA and control plots and is included to
explicitly account for the effects of spatial autocorrelation, based
on the recognition that communities located far apart in space
are likely to be less similar in composition than communities
located close together, and that this effect could potentially
confound our estimates of the biological impacts of forest
fragmentation.
Nonlinear multiple regression models were fitted using the
nls function in R v.2.6.2 (R Development Core Team, 2004). We
started by entering each fragmentation metric into the model as
a single term (alongside an autocorrelation term) and noted
746
which of the alternative models had the lowest Akaike information criterion (AIC) (Burnham & Anderson, 2002). We then
constructed a series of models containing two fragmentation
measures, one of which was the term from the best-supported
single-term model, the other being another fragmentation
metric. We took the best-supported two-term model and
attempted to construct more complex models, containing three
or more fragmentation indices. We followed this approach
because models with many terms often failed to converge, so
building complex models from simpler ones in a forward stepwise manner was the most pragmatic approach. For each combination of landscape ¥ tier ¥ full or native-only community
subset, we selected the best set of three candidate models, as
defined by AIC model selection criteria, and calculated Akaike
weights (wi) to determine the probability that any given model
was the ‘best’ of the candidate set (Burnham & Anderson, 2002).
The degree of support for the final models was established by
comparing their AIC values with those of ‘null’ models containing only an intercept term and a spatial autocorrelation term.
Generating similarity maps and calculating
landscape-level fragmentation impacts
We used the best-supported regression model to generate continuous GIS surfaces for each landscape, representing the predicted similarity of each grid cell to control sites deep within the
forest. For example, if the modelling identified that distance
from edge (DIST.EDGE) was the only significant explanatory
variable (over and above the effects of spatial autocorrelation),
then for a grid cell with coordinates [x,y] the predicted value
would be
pred ( BC xy ) = sin ( a − b1ec1DIST . EDGE xy − bs e − cs DIST ) ,
2
(4)
where DIST.EDGExy is the distance from the forest edge to grid
cell [x,y]. These predictions were used to generate a map in
which all grid cells designated as control sites (based on their
distance from forest edges) have the same predicted BC value,
2
equal to BC max = sin ( a − bs e − cs DIST ) , representing the average
similarity between interior plots that are DIST metres apart.
When making our predictions, we assigned DIST the value of
the mean inter-plot distance for all PFA–control sites in each
dataset. In the regression analysis (equation 3), this variable was
computed as the distance between any given pair of PFA-tocontrol plots, and so has a specific value only for pixels that can
be paired with a control pixel. When predicting BC values for
new pixels, the location of an appropriate ‘control’ pixel for
those new pixels is not clearly defined, so an exact value of DIST
cannot be estimated directly from a GIS. Consequently, we used
the mean DIST value taken from all PFA-to-control plot pairs in
each dataset: 30.3 km (lowland rain forest landscape; range 0.1–
143.9 km) and 39.7 km (Nothofagus forest landscape; range 0.1–
217.5 km). This approach effectively treats each pixel in the
landscape as being the same distance from a control plot,
thereby standardizing for the potentially confounding effect
of spatial autocorrelation on our predictions of BC values.
Global Ecology and Biogeography, 19, 741–754, © 2010 Blackwell Publishing Ltd
Forest fragmentation at the landscape scale
Sensitivity analysis showed that our final estimates of fragmentation impacts were insensitive to our choice of DIST, which
influenced our estimates of the impact by less than 1% and had
no discernible influence when DIST was greater than approximately 10 km (Fig. S1). The resulting maps of predicted BC
values represent the extent to which the community in each grid
cell is expected to differ from that of the control sites.
Finally, we computed a landscape-scale indicator to quantify
the overall effects of fragmentation on species communities:
BioFrag =
pred ( BC xy )
(5)
BC max
where the mean value of pred(BCxy) is computed over all forested grid cells. If there is no biological effect of fragmentation,
then the best supported regression model would predict that all
grid cells in the PFA sites and control sites would have BC values
equal to BCmax and BioFrag would have a value of 1. By contrast,
BioFrag approaches 0 when fragmentation generates very strong
effects that have resulted in a complete turnover in the composition of biological communities. In reality, BioFrag can never
actually be 0, because there must be at least one control site
within the landscape from which to estimate the similarity of
PFA, and the presence of these control sites ensures the value of
the numerator will always be greater than 0.
tively correlated with other fragmentation measures such as the
percentage of non-forest (PNF), edge density (ED) and patch
density (PD).
In the lowland rain forests of the Westland district, the most
plausible model for ground-cover species (T6) included distance
from edge (DIST.EDGE), percentage of non-forest (PNF.1000)
and edge density (ED.1000) as explanatory variables (Tables 3 &
4). For trees and shrub species (T1–T5), the best-supported
model included distance from edge (DIST.EDGE) and percentage of non-forest (PNF.500) (Tables 3 & 4). In the Nothofagusdominated landscape, the ground-cover species were best
modelled with patch size (P.SIZE) and edge density (ED.500),
whereas for species in T1–T5 the best-supported model
included distance from edge (DIST.EDGE) and nearest neighbour distance (ENN.500; Tables 3 & 4).
All models showed significant effects of forest fragmentation
on plant communities, with models explaining up to 35% of the
variation in PFA–control community similarity (Table 4). In the
Westland district, models explained 15–16% of all variation in
community similarity index between PFA and control plots,
whereas in the Nothofagus-dominated landscape, the bestsupported models explained up to 35% of the variation in community similarity. This particular model, explaining patterns of
ground cover (T6), found that community similarity was positively correlated with patch size and negatively correlated with
edge density (Table 4).
Landscape-level variation in a-diversity
In addition to calculating the BioFrag index, which analyses
changes in community composition among forest locations (i.e.
b-diversity), we assessed the effects of fragmentation on species
richness within communities (i.e. a-diversity). We avoid the
inclusion of rare species by considering only those species that
were present in more than 1% of plots. In both regions, we
computed the number of species surveyed in each plot. We fitted
a linear model using generalized least squares (gls) using the
nlme package in R v.2.6.2 and determined the extent to which
a-diversity was explained by fitting a full model containing a
linear combination of all nine fragmentation measures, then
using model simplification procedures (AIC) to produce a
minimal model containing only significant terms. The gls function allowed us to model the effects of spatial autocorrelation on
a-diversity using a spherical correlation structure (corSpher
class) (R Development Core Team, 2004).
RESU LTS
Regression analysis of similarity indices
A preliminary screening of similarity indices for all analyses in
response to the nine fragmentation measures indicated a positive correlation with distance from edge (DIST.EDGE) and
patch size (P.SIZE), meaning the similarity between plant communities in PFA and control plots increased with increasing
values of the distance to the nearest forest edge (Fig. 2) and/or
with fragment size. By contrast, similarity indices were nega-
Effects of excluding exotic species
Exotic species represented approximately 10% of total species
richness in the two landscapes (Table 1), and we found that
excluding them from the total list of species did not significantly
alter the model selection statistics. Best models were the same in
each tier group regardless of whether exotic species were
included or excluded; in no cases did removing the exotic species
change the sign of a parameter estimate, although model parameters were slightly different in most cases. Overall, and contrary
to our expectations, exotic plants did not drive the patterns of
(dis)similarity observed between PFA and control sites (Tables 3
& 4).
Analysis of a-diversity
On the whole, fragmentation metrics explained little of the
spatial variation in species richness (i.e. a-diversity). In the
lowland rain forest landscape, percentage of non-forest
(PNF.1000) (slope = -0.32; P = 0.004) was the only variable with
a significant effect on ground-cover (T6) species richness. No
fragmentation metrics explained the a-diversity of tree and
shrub species. In the Nothofagus-dominated landscapes, edge
density (ED.500) (slope = 0.07; P = 0.041) was the only significant factor explaining variation in a-diversity for ground-cover
species, and none of the fragmentation metrics had significant
effects on species richness in tiers T1–T5. Similar results were
obtained for both study areas when exotic species were excluded
from the analysis.
Global Ecology and Biogeography, 19, 741–754, © 2010 Blackwell Publishing Ltd
747
R. Lafortezza et al.
Table 3 Model selection statistics for candidate models predicting changes in community composition in two New Zealand landscapes
that vary in terms of fragmentation history and intensity.
Landscape
(A) Lowland rain forest
Native + exotic
Ground layer
(Tier 6)
Tree + shrub species (tiers 1–5)
Native only
Ground layer
(Tier 6)
Tree + shrub species (tiers 1–5)
(B) Nothofagus forest
Native + exotic
Ground layer
(Tier 6)
Tree + shrub species (tiers 1–5)
Native only
Ground layer
(Tier 6)
Tree + shrub species (tiers 1–5)
DAIC
Model
AIC
wi
DIST.EDGE + PNF.1000
DIST.EDGE + ED.1000
DIST.EDGE + PNF.1000 + ED.1000
DIST.EDGE + MSI.1000
DIST.EDGE + PNF.1000
DIST.EDGE + PNF.500
-3247.5
-3254.5
-3256.5
-3057.1
-3061.3
-3064.1
9.1
2.1
0.0
7.0
2.8
0.0
0.01
0.26
0.73
0.03
0.19
0.78
DIST.EDGE + PNF.1000
DIST.EDGE + ED.1000
DIST.EDGE + PNF.1000 + ED.1000
DIST.EDGE + MSI.1000
DIST.EDGE + PNF.1000
DIST.EDGE + PNF.500
-3257.1
-3262.9
-3265.2
-3064.4
-3068.6
-3071.5
8.2
2.2
0.0
6.9
2.8
0.0
0.02
0.24
0.74
0.03
0.19
0.78
P.SIZE + PD.500
P.SIZE + ED.1000
P.SIZE + ED.500
DIST.EDGE + ENN.1000
DIST.EDGE + ENN.1000
DIST.EDGE + ENN.500
-724.5
-735.5
-739.1
252.6
245.5
171.1
14.6
3.66
0.0
81.5
74.4
0.0
0.00
0.14
0.86
0.00
0.00
1.00
P.SIZE + PD.500
P.SIZE + ED.1000
P.SIZE + ED.500
DIST.EDGE + ENN.1000
DIST.EDGE + ENN.1000
DIST.EDGE + ENN.500
-699.9
-708.2
-711.3
255.6
248.7
174.7
11.4
3.1
0.0
80.9
73.9
0.0
0.00
0.18
0.82
0.00
0.00
1.00
Models were fitted by nonlinear least square regression and selected using the Akaike information criterion (AIC). Models are ranked from worst to best
fitting, with the best-fitting model highlighted in bold; wi are the Akaike weights computed considering the difference in AIC between each model and
the best-fitting model in the candidate set. Abbreviations of variable names are described in Table 2 and full equations for the best-fitting models are
presented in Table 4.
Landscape-level assessment of forest
fragmentation impacts
In the two landscapes, we used the best supported of our multiple regression models (Table 4) to generate continuous grid
maps representing the predicted similarity of each pixel to
control sites located deep within the forest (Fig. 3). These similarity maps provide a visual indication of the likely spatial variation of community composition across the landscapes as a result
of forest fragmentation.
Similarity maps were used to derive the value of the BioFrag
index for each landscape (equation 5, Table 4): BioFrag is a
landscape-level estimate of the impact of forest fragmentation
on plant communities that explicitly incorporates the spatial
pattern of forest cover. BioFrag values ranged between 0.86 (T6)
and 0.90 (T1–T5) in the Westland district, suggesting that plant
communities in the lowland rain forest landscape are relatively
little affected by fragmentation. In the Nothofagus-dominated
748
landscape, BioFrag ranged between 0.68 (T1 and T5) and 0.86
(T6) indicating a higher fragmentation effect on tree and shrub
communities than on ground-cover composition.
DISC USSIO N
A biologically meaningful index of
habitat fragmentation
We devised a method for exploiting spatial patterns of community similarity to generate an index of fragmentation impacts,
BioFrag, that accounts for the exact spatial patterns of habitat
cover in a landscape. BioFrag represents two important advances
over alternative measures of fragmentation effects. First, BioFrag
is derived from observed patterns of biological responses to
fragmentation metrics, rather than relying solely on descriptors
of spatial patterns of habitat cover. Simple pattern descriptions
Global Ecology and Biogeography, 19, 741–754, © 2010 Blackwell Publishing Ltd
Forest fragmentation at the landscape scale
Table 4 Regression models explaining variation in plant community composition for two fragmented landscapes in New Zealand.
(A) Lowland
rain forest
Native +
exotic
Native
only
(B) Nothofagus
forest
Native +
exotic
Native
only
Ground cover species (tier 6)
Tree + shrub species (tiers 1–5)
Ground cover species (tier 6)
Tree + shrub species (tiers 1–5)
Ground cover species (tier 6)
Tree + shrub species (tiers 1–5)
Ground cover species (tier 6)
Tree + shrub species (tiers 1–5)
Regression model
r2
DAIC*
BioFrag
0.59 - 0.13e-7.87 DIST.EDGE + 0.032e-0.19 PNF.1000 + 0.15e-0.012 ED.1000
+ 0.14e-0.33 DIST
0.67 - 0.11e-8.39 DIST.EDGE + 0.052e-0.18 PNF.500 + 0.18e-0.30 DIST
0.59 - 0.13e-7.89 DIST.EDGE + 0.033e-0.19 PNF.1000 + 0.14e-0.011 ED.1000
+ 0.14e-0.33 DIST
0.67 - 0.11e-8.41 DIST.EDGE + 0.051e-0.18 PNF.500 + 0.18e-0.30 DIST
0.39 - 0.38e-0.01 P.SIZE + 0.082e-0.043 ED.500 + 0.28e-0.012 DIST
0.40 - 0.091e-14.42 DIST.EDGE + 0.41e-0.012 ENN.500 + 0.15e-0.021 DIST
0.39 - 0.39e-0.011 P.SIZE + 0.073e-0.04 ED.500 + 0.29e-0.012 DIST
0.40 - 0.091e-14.48 DIST.EDGE + 0.42e-0.012 ENN.500 + 0.14e-0.022 DIST
0.16
-350.7
0.87
0.15
0.16
-261.2
-347.1
0.90
0.86
0.15
0.35
0.14
0.34
0.14
-259.1
-231.6
-114.4
-225.6
-113.5
0.90
0.86
0.68
0.87
0.68
*DAIC = difference between the Akaike information criterion (AIC) value for the current model and value for a null model incorporating spatial
autocorrelation effects only: y = a - b2 exp(-c2 DIST).
Analyses were run separately for ground cover and tree + shrub communities, and for the full community and for the community with exotic species
excluded. The response variable for all models is arcsin-square-root transformed Bray–Curtis values (equation 3). The last column reports the value of
the BioFrag index for each of the landscape ¥ community combinations that were analysed. Codes for all variables in the regression models are explained
in Table 2.
assign a ‘value’ to a landscape, but ignore the fact that different
taxa will respond in different manners to that pattern. For
example, multi-taxa studies in human-modified and fragmented landscapes have empirically demonstrated that the biological value of a landscape is taxon dependent (Laurance et al.,
2002; Barlow et al., 2007). By relying on observed patterns of
community changes in response to habitat fragmentation,
BioFrag values are implicitly taxon-specific and will, for
example, be different for bird and tree communities in the same
landscape.
The second advantage of BioFrag is the spatially explicit
nature by which it is calculated. This enforces the principle that
not all forest is equal. It has long been established that small
areas of isolated forest do not support the same biological value
as equal areas of forest that are part of a large, continuous
forest tract (Pardini et al., 2005; Giraudo et al., 2008). BioFrag
encompasses this principle by weighting the index value of a
forested pixel according to its position in a landscape using
variables such as the size of the forest patch it is embedded
within, the amount of forest surrounding the pixel, and the
distance of that pixel to the forest edge. Consequently, the
BioFrag index accounts for the exact pattern of fragmentation
within a landscape, with the result that landscapes of equal area
and with the same proportion of habitat cover will have different index values that depend on the spatial pattern of habitat
cover.
We calculated BioFrag index values for two contrasting landscapes in New Zealand. Overall, BioFrag values were relatively
high (typically >0.8), indicating that the influences of fragmentation on these forest plant communities were only moderate.
This suggests that in these regions a large proportion of the
between-plot variation in community composition is due to
underlying variation in factors such as climatic conditions, soil
composition and fertility that do not spatially covary with the
fragmentation metrics. The high BioFrag values also reflect the
fact that the two landscapes we studied contained extensive
areas of continuous forest. The impacts of fragmentation are
likely to be much greater in landscapes that are more heavily
fragmented. Despite the relatively low impact of fragmentation,
we observed important differences within and between the
two landscapes. Index values were approximately equal for
ground-cover communities in both landscapes, despite the
massive difference in the degree of fragmentation among landscapes. This result shows that BioFrag generates values that
have a very intuitive property: biological communities that are
minimally impacted by fragmentation, as indicated by the
limited ability of fragmentation metrics to explain patterns of
community composition, return similar BioFrag values when
assessed at the landscape scale, regardless of whether they are
assessed in a landscape with low or high degrees of habitat
fragmentation.
By contrast, BioFrag values in the heavily fragmented
montane forests were considerably lower (0.68) for the tree and
shrub community than for the ground-cover community (0.86).
This pattern occurred despite the ground-cover community
having much stronger regression models than the tree and shrub
community, giving a superficial impression that it is the groundcover community that is more strongly impacted by habitat
fragmentation. The reason for this apparent incongruity in the
results is that while the ground-cover community responds
strongly to features of the landscape, the effect size of that
response is small relative to the effect size of the response
observed in the tree and shrub community. Similar statistical
issues have been raised elsewhere (Northcott, 2008), showing
how variables that appear to have a greater statistical significance can have lower impacts on a response than other seemingly less significant variables. By extrapolating regression
equations defining community responses to fragmentation
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749
R. Lafortezza et al.
Figure 3 Grid maps representing the predicted similarity (Bray–Curtis index) of each pixel to control sites located deep within the forest:
(a) lowland rain forest (ground-cover species); (b) lowland rain forest (tree and shrub); (c) Nothofagus forest (ground-cover species); (d)
Nothofagus forest (tree and shrub). The higher the value of the index (blue colour in the legend), the higher the (predicted) community
similarity between a control site and a location that is potentially affected by fragmentation.
metrics across the full landscape, BioFrag effectively reports the
net magnitude of the biological impact of fragmentation. By
contrast, measures of goodness-of-fit for the regression equation that feeds into BioFrag provide an estimate of the strength
of the statistical relationship between communities and fragmentation metrics. Consequently, BioFrag provides a measure
of fragmentation impacts that is based more on effect sizes than
on statistical significance, providing additional support for our
statement above that BioFrag values provide a biologically relevant measure of the impact of habitat fragmentation.
Beyond landscapes: scaling BioFrag estimates to
nations and biomes
Our spatially explicit landscape-scale index of community
change, BioFrag, shows how the fine-scale configuration of
750
habitat loss sums across a landscape to determine changes in
biodiversity at a larger spatial scale. Within a specific habitat,
BioFrag estimates can be calculated at almost any spatial scale
that is smaller than the spatial extent of that particular habitat.
For example, using our parameter estimates we could generate a
BioFrag estimate for every 100 ¥ 100 km, 10 ¥ 10 km or even 1
¥ 1 km grid square within the landscapes we studied. However,
scaling BioFrag estimates to quantify fragmentation impacts
across larger spatial scales such as nations and/or biomes represents an important challenge. Developing rigorous methods for
scaling BioFrag from individual landscapes to much larger
spatial extents is of crucial importance for assessing progress
towards the Convention on Biological Diversity (CBD) 2010
target to reduce rates of biodiversity loss (UNEP/CBD, 2002,
2004). The 2010 target is a global target, although the actions,
monitoring and reporting of progress will be done by individual
Global Ecology and Biogeography, 19, 741–754, © 2010 Blackwell Publishing Ltd
Forest fragmentation at the landscape scale
nations which vary greatly in terms of their land area and the
diversity of habitats they contain.
Our analyses showed that the floral communities inhabiting
two different habitat types within a single island of New Zealand
responded differently to different components of the fragmented landscape. Therefore, a direct extrapolation of parameter estimates from one type of habitat would not generate a
reliable estimate of fragmentation impacts in a second type of
habitat, and it is not yet possible to generate a single BioFrag
estimate for a region encompassing multiple habitat types. We
propose two potential routes for generating BioFrag estimates at
very large spatial scales. First and most obviously, one could
generate an individual BioFrag estimate for each distinct habitat
type within a nation or biome, and combine those estimates
using a weighted average to reflect the relative extent of each
habitat within the larger region. Second, one could estimate
community responses to the individual fragmentation parameters in a number of landscapes across the nation or biome.
Parameter values for communities inhabiting landscapes that
were not sampled could then be predicted from the sampled
landscapes using spatial interpolation techniques. This
approach would allow a researcher to predict BioFrag values in
unsampled areas and, consequently, across much larger spatial
scales than can be directly sampled, paving the way to generating
national-scale estimates of the impacts of fragmentation and
thereby contribute to assessing the rate of loss of biodiversity
that is required by the CBD (UNEP/CBD, 2004).
Community responses to habitat fragmentation
The process of calculating the BioFrag index requires the user to
link remotely sensed patterns of habitat cover with field-based
biological observations, generating useful information about the
spatial determinants of community changes in fragmented
landscapes. For example, we found that landscape edge density
was an important determinant of ground-cover plant communities in both landscapes, as well as explaining variation in the
a-diversity in the montane ground-cover community. This
finding should be interpreted cautiously, as edge density was
significantly correlated with several other landscape metrics that
we used in this study, yet it is consistent with other literature
suggesting that many herb species have a general preference for
edge-disturbed locations (Ross et al., 2002; Hobbs & Yates,
2003). In the lowland rain forest, we also detected an effect of
distance from edge, and landscape forest cover on ground-cover
communities, whereas in the montane forests, our results suggested that the size of forest fragments was more relevant. By
contrast to the ground-cover community, spatial variation in the
tree and shrub plant communities of both landscapes was partially explained by distance from edge, with landscape forest
cover and nearest neighbour distance playing important roles in
the lowland and montane landscapes, respectively. Across the
eight landscape ¥ community combinations that we analysed,
the direction, or polarity, of the relationship between the biological data and landscape metrics was remarkably consistent.
For example, plots in landscapes with high edge density that
were close to forest edges or were in small forest patches tended
to have reduced similarity between the PFA and control plots
than other plots. Similarly, community similarity was reduced in
landscapes with low relative to high landscape forest cover. We
found that communities differed in the strength of their relationship with landscape metrics, such that patterns detected in
one landscape were weaker or absent in another. However, in no
instance did we detect a reversal of polarity, in which the relationship between community similarity and a given landscape
metric had the opposite sign for different communities or landscapes. This suggests that the mechanisms underlying the
responses of the communities we analysed are remarkably consistent across landscapes.
Taken together, our analyses of the spatial determinants of
plant community patterns highlight two important points. First,
they show that different communities respond to different fragmentation metrics. We expect that this difference would be even
more noticeable when comparing fragmentation patterns of
floral with faunal communities, which differ greatly in terms of
their habitat requirements, modes of reproduction and dispersal
mechanisms, and all of which influence the way in which a given
species will respond to the spatial structure of a landscape.
Second, we found that in different landscapes the same communities were influenced by different characteristics of the fragmented landscapes. Although edge effects appear to be a
significant driver of the impacts of fragmentation for both plant
communities in both landscapes, the relative importance of
other variables such as fragment size and isolation varied among
landscapes. This result may reflect the fact that the communities
in the two landscapes are different and have spatial patterns that
are governed by different ecological requirements of those
species or by the different histories of the two landscapes. Moreover, it is possible that the correlations between community
composition and variables such as fragment size and isolation
may vary among landscapes with differing amounts of habitat, a
process that could also result in apparently variable effects of
fragment size and isolation on tree communities.
We used BioFrag index values to test three specific hypotheses
regarding the responses of plant communities to forest fragmentation. First, we had predicted that plant communities in the
heavily fragmented landscape would be more negatively affected
than those in the less fragmented landscape. Our results provided equivocal support for this hypothesis, which was supported for the tree and shrub community. By contrast, and
contrary to our second hypothesis, the impacts of fragmentation
on the herb layer community were similar in both landscapes,
and considerably smaller than the influence on the tree and
shrub community in the heavily fragmented landscape.
However, in line with our hypothesis, we found that fragmentation metrics had significant impacts on the a-diversity of the
ground-cover community in both landscapes, but did not detect
a similar impact on the tree and shrub community. Our final
hypothesis, that the impacts of fragmentation would be driven
by exotic species, was not supported. Excluding exotic species
from the analyses did not result in significant changes to the
observed relationships between community composition and
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751
R. Lafortezza et al.
fragmentation metrics, or to the final estimates of the impacts of
fragmentation as indicated by BioFrag values. This does not
suggest that exotic species do not respond to patterns of fragmentation, as we know that forest edges in New Zealand act as a
focal point for plant invasions (Wiser et al. 1998). What this
result does indicate is that the responses of native species to
patterns of fragmentation are at least as strong as, or perhaps
even override, the responses of exotic species to those same
spatial patterns of forest cover.
the many researchers involved in collecting and storing this
information. We are grateful to Dr Robert Allen (Landcare
Research, New Zealand) for permission to include his Harper/
Avoca dataset in our study, and to two referees for their constructive comments on the manuscript. This study was supported by a
grant from the Isaac Newton Trust (Cambridge, UK), Conservation Biology Initiative 2007–08 and by resources from the Global
Environment Facility’s 2010 Biodiversity Indicators Partnership
project (via UNEP-WCMC). It forms a contribution to the work
of the 2010 Biodiversity Indicator Partnership.
LI M I TATI O N S A N D FU T U R E D I R E C T I O N S
Fragmentation affects the structure of landscapes, causing shifts
in the diversity and distribution of plant species that could lead
to extinctions of populations in forest fragments (ArroyoRodríguez et al., 2007; Barbaro et al., 2007). Quantifying the
biological effects of fragmentation at multiple spatial scales is a
prerequisite to establishing strategies and policies for counteracting loss of biodiversity at the global, regional and national
levels (Millennium Ecosystem Assessment, 2005) and for quantifying progress towards global targets to reduce the rate of loss
of biodiversity (CBD Secretariat, 2001).
Recent advances in remote sensing and geospatial technologies
allow the routine assessment of fragment properties at the landscape scale, such as the distribution of patch sizes and shapes, and
the spatial configuration of forest patches (Riitters et al., 1995;
McGarigal & Cushman, 2002). Nevertheless, new methods are
still needed for these structural properties to be interpreted in
terms of biological and functional impacts on ecosystems and
landscapes. We developed the BioFrag index as an important step
towards this goal, but it is not the final step. BioFrag estimates the
impact of fragmentation on communities within a single habitat
type, but ignores the fact that many species and many important
ecological processes and services are not bounded by the edges of
that habitat (Fischer & Lindenmayer, 2006). Rather, forest
patches are just one habitat embedded within landscape mosaics
that incorporate multiple ecosystems. Moreover, the individual
habitats comprising landscape mosaics change over the large
spatial scales that are relevant to assessments of the impacts of
fragmentation at national and global scales. Biologically meaningful measures of regional-scale landscape mosaics still need to
be developed before we can translate complex spatial patterns of
land cover and land use in terms of biological or ecological
impacts. This next generation of landscape metrics will need to
integrate patterns of biodiversity across the full range of habitats
and land-cover types that occur within a given landscape. These
metrics are required to quantify current trends in the impacts of
land-use change on biodiversity and ecosystems. Ultimately,
indices that extend beyond BioFrag will provide essential support
for quantifying progress towards globally agreed policy targets on
biodiversity and habitat conservation.
A C K N O W L ED G E M E N T S
The authors of this paper acknowledge the use of data drawn
from the National Vegetation Survey (NVS) Database, and thank
752
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SUPPO RTING INF O R MATIO N
Additional Supporting Information may be found in the online
version of this article:
Figure S1 BioFrag index in response to variation in the Euclidean distance between the forest plots that are potentially affected
by fragmentation and control plots.
Table S1 Lowland rain forest landscape – correlation matrix of
fragmentation measures.
Table S2 Nothofagus forest landscape – correlation matrix of
fragmentation measures.
Table S3 Most common species in the two regions.
As a service to our authors and readers, this journal provides
supporting information supplied by the authors. Such materials
are peer-reviewed and may be re-organized for online delivery,
but are not copy-edited or typeset. Technical support issues
arising from supporting information (other than missing files)
should be addressed to the authors.
B IO SK ETC H
Raffaele Lafortezza is a Research Associate at the
University of Cambridge, UK and Lecturer at the
University of Bari, Italy. He is a landscape ecologist and
GIS analyst with an interest in geospatial analysis
applied to forest conservation and biodiversity
assessment at multiple scales.
Editor: Jeremy T. Kerr
Global Ecology and Biogeography, 19, 741–754, © 2010 Blackwell Publishing Ltd