Mapping community change in modified landscapes

Biological Conservation 142 (2009) 2872–2880

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Biological Conservation journal homepage: www.elsevier.com/locate/biocon

Mapping community change in modified landscapes Robert M. Ewers a,*, Valerie Kapos b,c, David A. Coomes d, Raffaele Lafortezza d,e, Raphael K. Didham f a

Imperial College London, Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK UNEP World Conservation Monitoring Centre, 219 Huntingdon Rd., Cambridge CB3 0DL, UK c Conservation Science Group, Department of Zoology, University of Cambridge, Downing St., Cambridge CB2 3EJ, UK d Department of Plant Sciences, University of Cambridge, Downing St., Cambridge CB2 3EA, UK e Department of Plant Production Science, University of Bari, Via Amendola 165/A, 70126 Bari, Italy f School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch, New Zealand b

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Article history: Received 29 January 2009 Received in revised form 18 June 2009 Accepted 20 June 2009 Available online 21 July 2009 Keywords: Biodiversity index Community composition Deforestation Habitat fragmentation Habitat loss Landscape pattern

a b s t r a c t Habitat loss is not randomly distributed across modified landscapes, yet spatial patterns of habitat cover are not routinely combined with biodiversity data when assessing or predicting the biodiversity impacts of land use change. Here, we convert point observations of more than 28,000 beetles from 851 species into a continuous biodiversity surface representing the similarity of ecological communities relative to that of pristine forest, effectively integrating on-the-ground biodiversity data with remotely sensed land cover data to predict the magnitude of community change in a modified landscape. We generated biodiversity surfaces for both present-day and pre-human landscapes to map spatial patterns of change in a diverse ecological community to calculate the combined biodiversity impacts of habitat loss and fragmentation that accounts for the exact spatial pattern of deforestation. Our spatially-explicit, landscape-scale index of community change shows how the fine-scale configuration of habitat loss sums across a landscape to determine changes in biodiversity at a larger spatial scale. After accounting for naturally occurring within-forest heterogeneity, we estimate that the conversion of 43% of forest to grassland in a 1300 km2 landscape in New Zealand resulted in a 47% change to the beetle community. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Land use change is arguably the greatest threat facing global biodiversity in the immediate future (Sala et al., 2000), with habitat loss being directly implicated in the extinction of a broad suite of species from around the world (Pimm and Askins, 1995; Brook et al., 2003; Hanski et al., 2007). Quantifying the deterioration of biodiversity that is attributable to habitat loss and land use change has more than mere academic interest: in assessing progress toward the global political commitment to reduce the rate of biodiversity loss before 2010 (Balmford et al., 2005), one of the biggest challenges facing conservation scientists has been to rigorously quantify the state of the environment (Mace and Baillie, 2007). Moreover, burgeoning awareness of the economic value of natural ecosystems (Costanza et al., 1997; Ricketts et al., 2004) raises the need to accurately determine the rate of degradation of ecological communities that provide humans with vital ecosystem services. Consequently, it is now more important than ever to understand how biodiversity had changed, is changing and might change in the future in response to management and development decisions, across space and through time. * Corresponding author. Tel.: +44 (0)20 759 42231; fax: +44 (0)1344 874957. E-mail address: [email protected] (R.M. Ewers). 0006-3207/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2009.06.022

Deforestation is a key driver of biodiversity change that has been directly linked to species extinctions (Brook et al., 2003; Hanski et al., 2007). As a result, historic change in forest cover forms the basis for several high-profile methods of estimating biodiversity loss (Pimm and Askins, 1995; Scholes and Biggs, 2005). The widely reported species–area relationship (SAR; Rosenzweig, 1995) has been used to predict species loss from absolute habitat loss (Pimm and Askins, 1995; Brook et al., 2003), and new indices such as the Biodiversity Intactness Index (BII) (Scholes and Biggs, 2005) have been developed to quantify absolute levels of population change for multiple taxa across modified landscapes. The former of these approaches counts the number of species and the latter counts the number of individuals (Faith et al., 2008); both variables are crucial components of ecological communities and both can be simultaneously incorporated into a single index that combines the SAR and BII (Faith et al., 2008). A key feature of habitat-based biodiversity indices is that they focus on habitat quantity to the exclusion of habitat quality and spatial patterns of habitat heterogeneity within a landscape. This is despite ample evidence showing that habitat fragmentation consistently alters the quality of habitat remnants relative to continuous habitats (Ewers and Didham, 2006), such that 50 forest fragments of 1-ha do not support the same biodiversity as one fragment of 50 hectares (Watling and Donnelly, 2006). Moreover,

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the prevalence of nested patterns of community structure in habitat fragments (Ewers and Didham, 2006; Watling and Donnelly, 2006) strongly indicates that the biodiversity value of remnant patches declines consistently with patch area, a concept that is strongly backed by metapopulation theory which shows that not all habitat patches contribute equally to maintaining a population within a landscape (Hanski and Ovaskainen, 2000; Ovaskainen and Hanski, 2003). The implication is that within-habitat heterogeneity and the spatial patterns of habitat loss – which vary from landscape to landscape – will influence patterns of biodiversity change above and beyond that expected from the amount of habitat conversion alone (Fahrig, 2003; Ewers and Didham, 2006). In this study, we take the novel step of directly incorporating the spatial pattern of historic deforestation in a forest–grassland landscape into an estimate of biodiversity change, showing how the finescale configuration of habitat loss sums across a landscape to determine changes in biodiversity at a larger spatial scale.

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(Ewers et al., 2007). One-way ANOVAs testing differences in the mean values of these variables between the forest control sites (N = 5) and the forest fragment sites (N = 103) were non-significant for all five variables (P > 0.27 for all tests). We sampled beetles using flight interception traps in which the collecting funnel was buried up to the rim in the ground, and thus acted as both a pitfall trap and a low-level flight intercept trap. Traps were operated continuously for 10 weeks during the austral summer (30 November 2000 to 10 February 2001) and were cleared every two weeks. We selected all samples from the first, third and fifth collection periods for sorting, although some samples were lost due to stock interference and high winds. All sites were sampled for a minimum of 30 trap-days and the mean sampling effort per site was 48 trap-days. All beetle specimens were sorted to recognizable taxonomic units, hereafter referred to as species. Where possible, species names were assigned according to Leschen et al. (2003). 2.2. Modelling changes to community composition

2. Materials and methods 2.1. Study site and invertebrate sampling To estimate the impact of landscape-specific patterns of land use change on biodiversity, we modelled spatial variation in community composition of a very diverse group of terrestrial organisms, beetles, in a 1300 km2 forest–grassland landscape in the Southern Alps of New Zealand (Fig. 1). Prior to human colonisation, the region was almost completely forested below the treeline with Nothofagus spp. (Nothofagaceae; Fig. 2a). In the ensuing 800 years of human occupation, 43% of the original forest cover within this landscape has been destroyed (Fig. 2b). Deforestation in the study area ceased around 50 years previous to this study, and forest structure and composition across the study area is highly uniform, with just three species of canopy tree all belonging to the genus Nothofagus (mean ± SD tree species diversity in forest sites = 1.41 ± 1.00). The tree community is dominated by Nothofagus solandri, accounting for around 80% of the canopy trees across the study area. Within this landscape, we selected 15 forest fragments that ranged in size from 0.01 ha to more than 1 million ha, embedded in a matrix of modified grassland (Ewers et al., 2007; Ewers and Didham, 2008). This gradient of fragment sizes encompasses the full range of sizes in New Zealand, with the smallest fragment being a single tree and the largest being part of the single largest continuous forest area in the country. Sampling points were located at up to 11 distances from forest edges into forest interiors (0, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024 m) and at the same series of distances out from these forest edges into the adjacent grassland matrix (+2, +4, +8, +16, +32, +64, +128, +256, +512, +1024 m). It was not possible to extend the full edge gradient in fragments with a minimum diameter of less than 2 km, so larger edge distances were sequentially omitted from smaller fragments. In addition, we selected five forest control sites in deep forest located in a fully forested valley, and a further five sites in deep matrix. These 10 sites were 2 km from the nearest forest–grassland edge, and were separated from each other by at least 500 m and as much as 2 km. Edge gradients were oriented so that all sampling distances were at approximately the same altitude in a given fragment (i.e. distance from edge was not confounded with increasing altitude). Forest structure was measured in previous studies and did not differ significantly between the control sites and sites inside forest fragments. Within a 10  10 m quadrat centred on each sample site, we recorded tree basal area, litter biomass, the volume of standing dead wood and coarse woody debris, and canopy height

There are several approaches that could be taken to map changes in community composition across a landscape, and each of these has relative strengths and weaknesses (Ferrier and Guisan, 2006). First, variation in the abundance of each species comprising the community could be modelled separately, with the resulting species distributions stacked on top of each other to generate a predicted community at unmeasured locations. Second, variation in a composite measure of community composition could be modelled directly, with the resulting community–environment relationship used to generate a predicted community at unmeasured locations. The former of these approaches has advantages in that it allows more flexibility in modelling the responses of individual species, but it also has the drawback that rare species cannot be modelled effectively and must be omitted from any analysis. Moreover, compiling a community from the overlapping distributions of species from individual models fails to account for the degree to which species interactions can alter the ‘emergent properties’ of community assembly and composition. By contrast, modelling community measures directly has the advantage that the resulting model outputs are more likely to be congruent with the composition of known communities and retain information on rare species. This comes, however, with the drawback that there is little flexibility in the model to allow individualistic species responses to the landscape. We chose to model a measure of community composition directly, on the conceptual grounds that summing individual species distributions will never capture the emergent properties of community composition, and on the practical grounds that many of the species in our samples were too infrequently encountered to be modelled individually. In a previously published power analysis we showed that only species with >40 individuals were abundant enough to detect strong fragmentation effect sizes of 0.35 (as defined by Cohen (1988)), using standard values of a = 0.05 and power = 0.8 (Ewers and Didham, 2008). Based on this criterion, we would have had to exclude 86% (735 of 851) of species from the analysis, representing a massive loss of species-level information that community modelling retains. For each sampling site, we calculated the average similarity of community composition relative to the five control sites situated deep inside continuous forest using the Bray–Curtis (BC) index, a widely used abundance-based measure of similarity between ecological communities (Magurran, 1988). Values are bounded at zero and one, with one in this case representing a ‘pristine’ community and a value of zero indicating a community that has no species in common with the control sites. Beta-diversity among the control sites means that values are always less than one, so we re-scaled the values to standardise for natural levels of spatial turnover in

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Fig. 1. Map of the Hope River Forest Fragmentation Project in the South Island of New Zealand, showing the location of sampling sites. For clarity, small fragments of less than 3 ha in area are highlighted with open circles. The continuous forest area (>1,000,000 ha) extends north and west of the study area to the coastline.

the abundance of species by setting the average BC values observed among the control sites to one. BC values were calculated from logtransformed beetle abundance using the ‘vegan’ package in R v.2.7.0 software (R Development Core Team, 2004). It is expected that communities located far apart in space should have a less similar composition than communities located close together, and this effect could confound our spatial predictions of community similarity. Consequently, we used a Mantel test to test for a relationship between BC values and geographic distance between sample sites. Moreover, because we were working in a topographically complex landscape, we also used a Mantel test to detect a potential confounding effect of altitude on BC values. To ensure that the detection of these confounding distance and altitude effects were not themselves confounded by habitat

type or edge effects (known to penetrate deep inside the forest fragments in this study area (Ewers and Didham, 2008)), the Mantel tests used only data from sites located more than 250 m inside continuous forest or forest fragments. These sites were spatially separated by between 0.2 and 20.6 km, adequately representing the range of spatial separation observed between sites in the full dataset (<0.1–20.8 km). Significant confounding effects were removed from the data by predicting the expected BC value for each sampling site based on the confounding variable(s) alone. We then used the difference between the observed and expected BC values as the response for a regression analysis. This measure reflects deviation in community similarity from the ‘pristine’ state that can not be explained by the potentially confounding variables of geographic distance or altitude. For ease of interpretation, we

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Fig. 2. Pre-human and present-day patterns of deforestation, beetle community composition, and change in community composition resulting from deforestation and forest fragmentation in the Hope River area, New Zealand. (a) Pre-human forest cover. (b) Present-day forest cover (green) and deforested areas (pink). (c, d) Similarity of beetle communities to the community observed in the forest interior for the pre-human (c) and present-day (d) landscapes, with estimates of similarity re-scaled to account for naturally occurring within-habitat heterogeneity and for geographic distance between pixels and the control sites. (e) Predicted proportional change in community composition due to deforestation and forest fragmentation. Values of zero represent a community that is unchanged, while values of 1 represent a community that retains none of the original forest species. Grey areas on all panels are naturally occurring alpine grasslands which were excluded from this analysis.

re-scaled the response variable so that the control values (intact forest biodiversity) were equal to one. This re-scaled BC variable represents the similarity of communities to the control community after accounting for within-habitat heterogeneity and distance from the control. We used multiple linear regression on the re-scaled similarity values (arcsin-square root-transformed) to explain patterns of spatial variation in the beetle community. Previous analyses have

shown that spatial community patterns in this landscape are predominately affected by anthropogenic edge and area effects and their interaction (Ewers et al., 2007; Ewers and Didham, 2008), so we used log2-transformed distance to forest edge (reflecting the log2-sampling design of the edge gradients) and log10-transformed fragment area as predictors. Non-forest habitat was coded as having zero area. We did not incorporate spatial autocorrelation into the analysis for three reasons: (1) the individual traps used to

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collect samples were separated far enough apart to ensure there was no trap interference, meaning it was valid to treat each sample as an independent point in the regression analysis; (2) the BC values had already been re-scaled to remove the potentially confounding distance effects from the data; and (3) examination of a correlogram of the residuals from the resulting regression equation, which included variables with a spatial component, showed no remaining spatial signature, suggesting that the regression had adequately explained the spatial structure of the data. Partialling out spatial autocorrelation would have removed much of the ecological signal in which we were interested. 2.3. Mapping community change in modified landscapes To map re-scaled similarity values across the 1300 km2 study landscape, we generated maps of the landscape in which each 25  25 m pixel was classified according to: (1) the size of the forest fragment in which it was located (with non-forest pixels assigned an area of zero) and (2) the distance of that pixel from the forest edge (with negative values assigned to pixels inside forest and positive values to pixels outside forest). To generate the rescaled similarity map, we applied the regression equation determined above to predict the value for each 25  25 m pixel across the landscape. This approach is conceptually similar to that pioneered by Ferrier and colleagues (2007), although we employed landscape rather than climatic and topographic features to predict community composition, and we explicitly transformed composition into a measure of community difference from a control site. Confidence limits from the regression allowed us to estimate uncertainty in the predicted value at each pixel, which in turn generates a 95% confidence interval around the final estimate of total community change across the landscape. Maps of re-scaled similarity values were generated using digital landcover maps for the present-day landscape and for modelled predictions of pre-human vegetation patterns (Leathwick et al., 2004). In the ideal situation, we would use data from the same spatial locations before and after habitat modification to estimate biodiversity loss. However, these data are seldom available, and are impossible to obtain when pre-human biodiversity is being studied. We took the most practical solution to this problem by: (a) assuming that patterns of spatial variation in community composition in relation to landscape variables that are observed in the present-day would also have been observed in the pre-human landscape; and (b) applying statistical models of the present-day patterns to maps of pre-human vegetation composition. The use of BC values to quantify ecological change, which has been previously applied to aquatic systems (Faith et al., 1995; Bailey et al., 1998; Collier, 2008), has one significant assumption: that the ecological community of the control sites, located deep inside continuous primary forest, reflects the community that would have occurred in the forests prior to the arrival of humans. However, in most instances long-term degradation of the control sites is likely to go unnoticed and result in a ‘shifting baseline’ for our comparison (Baum and Myers, 2004; Saenz-Arroyo et al., 2005). A shifting baseline would make our final estimate of ecological change conservative, as it would not account for any change in the continuous forest community. Values of re-scaled similarity were predicted only for pixels occurring below the natural treeline of the region (86% of the total landscape), as no invertebrate samples were collected beyond this ecological zone. To estimate total community change across the landscape that resulted from deforestation and forest fragmentation, we adopted a simple community change index (CCI), calculated as the geometric mean of the proportional change in prehuman to present-day re-scaled similarity scores for all pixels in the landscape that were forested prior to human occupation. The

CCI is bounded at zero and one, with values of zero indicating there has been complete turnover in the composition of the community across the entire landscape, and values of one indicating a landscape in which the community has not been impacted by habitat modification. CCI is, therefore, a spatially-explicit landscape-scale index of community change. 2.4. Comparing the CCI to alternative indices of biodiversity change We calculated alternative estimates of biodiversity change in the landscape using two previously published and widely used indices. First, the Biodiversity Intactness Index (BII) assesses the average change in relative population size of all taxa across all land use types in a given region, by comparing the total population size of species group i in land use k in ecosystem j relative to the population size in a reference landscape (Scholes and Biggs, 2005). That proportion is termed the ‘population impact’ and is represented as Iijk, which we were able to estimate directly from our data. Our analysis was restricted to a single species group (Coleoptera) in a single ecosystem (the Hope River area), and we had just two land use types (forest and grassland). Consequently, the BII was calculated as:

P j Ri Ak I ik BII ¼ P j R i Ak where Ri is the number of species in species group i (equivalent here to the total number of beetle species collected) and Ak is the area of land use k. The second alternative estimate of biodiversity change was derived from the widely reported species–area relationship (SAR), S = cAz, which has been used to estimate species extinctions from habitat loss (Pimm and Askins, 1995; Brooks and Balmford, 1996; Didham et al., 2005). SAR predictions are sensitive to the value of the slope z, so we estimated species loss using z-values varying from 0.25 to 0.35. This range of z-values is typical of many island studies (Rosenzweig, 1995) and is widely used in estimates of bird extinctions (Pimm and Askins, 1995; Brooks and Balmford, 1996; Didham et al., 2005), but is higher than typically observed in subdivided (or fragmented) habitats (Rosenzweig, 1995). It is important to note that the assumptions of the SAR approach to predicting biodiversity loss are not met when working at local or regional scales such as this study. The SAR predicts species extinctions due to habitat loss, but habitat loss should more correctly be the random loss of habitat at biogeographic scales. Non-random habitat loss within local- or regional-scale landscapes may be mitigated if a lower proportion of habitat has been lost from the surrounding landscapes. Moreover, SAR predictions assume that fragments are surrounded by a matrix habitat that is completely inhospitable to all taxa, when in reality many species are able to tolerate the habitat conditions present in the matrix (Ewers and Didham, 2006). In fact, a species’ ability to tolerate matrix conditions is one of the best predictors of its response to forest fragmentation (Laurance, 1991). However, we present the SAR prediction because it is the most commonly used method for predicting the amount of biodiversity loss that occurs in response to habitat modification. 2.5. Ability of the CCI to generate predicted landscape-scale patterns To test the efficacy of our approach of scaling point data to assess landscape effects, we investigated the emergent patterns of the CCI against a theoretically predicted relationship between habitat loss and biodiversity at the landscape scale. A widely cited meta-analysis suggested that, at the landscape level, the proportion of habitat cover is a more important correlate of biodiversity

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change than the spatial arrangement of habitat (Fahrig, 2003). Consequently, for the CCI to be a valid method for scaling data collected at individual sites to a landscape-level estimate of biodiversity change, it is important to assess the degree to which the CCI generates this expected pattern at the landscape scale. We randomly selected 1000 pixels (<0.1% of all pixels) that were forested prior to human colonisation within the 1300 km2 study area. For each pixel, we obtained the estimate of community change and determined the amount of forest lost from nested, square landscapes of 1  1, 2  2, 4  4 and 8  8 km, centred on the pixel. We regressed the CCI value for each pixel against the proportion of forest loss in the surrounding landscape (for each of the four scales separately) to describe the relationship between landscape-level forest loss and site-specific estimates of community change. 3. Results

4. Discussion 4.1. Mapping community change in modified landscapes

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Significant alterations to the composition and trophic structure of invertebrate communities across space can potentially have large effects on rates of biological processes, such as herbivory (Valladares et al., 2006; Urbas et al., 2007), predator–prey interactions (Thies et al., 2003), or nutrient cycling (Larsen et al., 2005), that are fundamental to ecosystem health. Virtually all taxa, including beetles, are affected by habitat fragmentation (Henle et al., 2004; Watling and Donnelly, 2006), as well as other components of spatial variation in habitat degradation. Although we focus primarily on area and edge effects in a comparatively simple landscape, the same spatial modelling approach could be applied to many existing ecological datasets and extended to investigate the effects of other anthropogenic impacts that vary across space. For other taxa in other landscapes, it may be more appropriate to use different combinations of: (a) spatial features of the landscape such as fragment connectivity or the proportion of forest in the landscape surrounding sampling sites, (b) variables describing underlying environmental gradients across the landscape such as climatic and topographic variables (Ferrier et al., 2007), or (c) spa-

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We collected a total of 28,756 beetles identified to 851 species from the 201 sampling sites. A species accumulation curve suggested that we had collected most species from the study area (Fig. 3), and the species-abundance distribution approximated a log-normal distribution of beetle abundance among sample sites (median = 111, 1st and 3rd quantiles = 71 and 168, respectively). Species richness was significantly lower in matrix samples (median = 26) than in forest samples (median = 50) (ANOVA on logtransformed species richness, F1, 199 = 162.0, P < 0.0001). The average BC value among the five deep forest control sites was 0.588, indicating that natural forest beetle communities sampled in different locations are expected to differ by ca. 41%. Consequently, the BC value that represented a ‘pristine’ community was also taken to be 0.588, as it was unrealistic to expect samples from different sites within a spatially heterogeneous forest to have perfect similarity values (BC = 1.0). Mantel tests showed that there was a significant change in composition with respect to distance from the control sites (r = 0.33; P < 0.01), but did not detect a confounding effect of altitude (r = 0.18; P > 0.05). Using multiple regression, and after controlling for the potentially confounding effect of distance between sample sites and the forest controls, we found that the two landscape variables, distance to forest edge and fragment area, explained 82% of the variation in re-scaled similarity values at the 201 sampling sites (F2, 198 = 458.0, P < 0.0001). An ANOVA on hierarchical models showed that a more complex model containing an edge  area interaction term did not add significant explanatory power to a simpler model containing main effects only (F = 0.90, P > 0.05). Residuals from the final model had a weak correlation with geo-

graphic distance (r = 0.161), and examination of the resulting correlogram suggested that the spatial structure of the data had been explained by the regression (Fig. 4). Extrapolation of the resulting regression formula allowed us to predict the relative community composition at each point location across the pre-human and present-day landscapes (Fig. 2c and d). A pixel-by-pixel comparison of pre-human and current community structure allowed us to derive a map of ecological change at an unprecedented level of detail (Fig. 2e). The resulting overall CCI estimate was a compositional change of 47% (confidence interval 45–49%) across the landscape. This was more than twice the change in biodiversity that was predicted using the BII (22% decline in biodiversity) or the SAR (13–18% reduction in species richness). We found that the CCI generated landscape-scale patterns consistent with those expected from analyses using landscapes rather than sites as the unit of replication. At all four landscape scales investigated, there was a strong (P < 0.0001), non-linear relationship between landscape-scale forest loss and our estimate of community change (Fig. 5). The relationship was strongest for the 1-km landscape (R2 = 0.80) and weakened as the size of the landscape was increased to 2, 4 and 8 km (R2 = 0.77, 0.67 and 0.59, respectively).

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Fig. 4. Correlogram of residuals from a regression equation predicting community similarity as a function of fragment size and distance to forest edge.

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Forest loss Fig. 5. Impacts of forest loss at the landscape scale on the community change index (CCI). Points represent the CCI values for 1000 randomly-selected pixels from within the 1300 km2 study area and are plotted against the proportion of forest cover that had been lost in the landscape surrounding those pixels. Landscapes varied in size from 1  1, 2  2, 4  4 and 8  8 km, and are represented in panels (a–d), respectively. CCI values of zero represent a community that is unchanged, while values of 1 represent a community that retains none of the original forest species.

tial variation in a range of other threatening processes, such as land-use intensification, hunting, livestock grazing, pollution, or the prevalence of invasive species. For the beetle communities sampled in this study, the overall community change index (CCI) indicated a net compositional change of 47% over the landscape. This was more than twice the impact estimated from the BII (22% decline in biodiversity) or the SAR (13–18%). Although all three estimates of biodiversity change are presented as percentages, it is important to stress that they measure different attributes of biodiversity that are not directly comparable. The BII and SAR estimate changes in species abundance and richness, respectively, whereas we have quantified net community change using a spatially-explicit CCI that incorporates both abundance and richness. To illustrate how important this distinction can be, in both the BII and SAR, the invasion, or increase in abundance, of a disturbance-tolerant species would cancel out the extinction or decrease in abundance of a disturbance-sensitive species, resulting in no net change in index values, yet the two scenarios reflect very different realities. Changes of this nature can only be detected by using a community-based measure of ecological integrity, such as CCI, that incorporates both changes in abundance and changes in the identities of species. The final output of the spatial modelling approach we have developed here is a map where each pixel represents a predicted difference in community composition relative to a control community. In the future, further attempts to map community change could also validate and refine predictions that emerge from the regression models. For example, additional samples collected from within the study area could be compared to the map-based prediction, independently validating

the landscape-scale estimate of community change and allowing predictions to be refined as more data become available. 4.2. Time lags to community changes Of course, it is important to remember that the response of species and communities to landscape change is not an instantaneous phenomenon (Ewers and Didham, 2006), so there is a danger that the use of snap-shot data will bias any estimate of community change. Time lags in species responses to forest fragmentation have been well documented for birds (Brooks et al., 1999; Ferraz et al., 2003) and there is also evidence from small-scale grassland manipulations that the detection of invertebrate responses to fragmentation are also partially dependent on the time since fragmentation (Gonzalez, 2000; Gonzalez and Chaneton, 2002; Braschler and Baur, 2003). Across taxa and systems, time lags take longer to fully manifest in large relative to small fragments, suggesting that estimates of community composition derived from snap-shot samples in a single landscape that has not yet fully equilibriated may generate an artificially strong gradient of compositional changes with respect to fragment area. The effect of time lags on our dataset was likely minimal, as deforestation in the study region ceased around 50 years prior to sampling and because our study taxa were beetles with rapid generation times. The problem is, however, likely to be severe should the CCI be applied to taxa with long generation times or to more dynamic landscapes. In these cases, we suggest that authors attempt to quantify the rate at which time lags manifest themselves and include this variable into their estimates of community change.

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A similar problem to time lags is that shifting baselines may bias our estimates of community change. Our analysis is susceptible to shifting baselines as, like the vast majority of land use change studies, we have no pre-deforestation data from which to quantify community composition. Rather, we relied on the commonly-used approach of substituting space for time by considering the beetle community sampled from the largest, most intact tract of forest in New Zealand as representative of the intact beetle community at all sites. Although the tree species composition of the forest in this area is not likely to have changed significantly since the arrival of humans (Hall and McGlone, 2006), the structure of the understorey almost certainly has changed due to the invasion of browsing mammals such as deer and pigs (Husheer et al., 2003). Any flow-on effects from understorey changes to the composition of the ground-dwelling beetle fauna, as indicated by Wardle et al. (2001), would be undetected by the use of a space-fortime substitution, generating a ‘shifting baseline’ (Baum and Myers, 2004; Saenz-Arroyo et al., 2005) against which community differences were assessed. However, any changes to forest structure have not been biased in a systematic way to have either a greater or lesser impact on forest fragments than the forest control sites, as evidenced by the lack of significant differences between five measures of habitat structure. This suggests that the patterns of beetle community composition we observed across the study area are unlikely to be an artefact of changes to forest structure. 4.3. Accounting for habitat heterogeneity and b-diversity The statistical approach to calculating biodiversity change that we employed accounts for the fact that communities within habitats are not homogenous. Beetle community samples from the forest control in this study differed by around 40%, showing a moderate degree of within-forest heterogeneity. High species turnover among sample sites is to be expected when sampling from a highly diverse community such as this one, and this presents a challenge for estimating community change. By re-scaling similarity values to control for within-habitat heterogeneity in community composition, we ensured that only biotic changes that exceeded levels of naturally expected community turnover were included in the final index of community change. Consequently, it will always be more difficult to detect a significant impact of habitat modification for communities and habitats that have naturally high levels of turnover and heterogeneity, and it is very likely that our final estimates of community change have underestimated the actual level of change. 4.4. Scaling from sites to landscapes Scaling biodiversity patterns from site-level data to generate landscape-level patterns is potentially contentious. A recent meta-analysis by Fahrig (2003) strongly suggested that when landscapes are the unit of analysis, the biological impacts of habitat loss greatly outweigh the impacts of habitat configuration. Our analysis in this study was based entirely on patch-level measures of area and distance from habitat edges, yet generated patterns at the landscape scale that were consistent with Fahrig’s hypothesis (Fig. 5). In fact, landscape-scale habitat loss appeared to explain more than 50% of the variation in site-specific CCI scores. The fact that the extrapolation of site-level patterns of community change generated landscape-level patterns almost certainly reflects the fact that patch and landscape variables are invariably correlated with each other (Fahrig, 2003; Cushman and McGarigal, 2004). The confounded nature of patch and landscape variables ensures that analysing species responses to forest patterns at either scale will give similar results (Koper et al., 2007). However, in the context of this study, the ability of patch-based regressions to generate

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predicted landscape-level patterns may also suggest that landscape-level patterns are legitimately generated by the accumulation of patch-level effects across a large number of patches. The index of community change we have presented explicitly combines biological data with the exact spatial configuration of a given landscape. The only other existing method that takes this approach is the measurement of the metapopulation capacity of a landscape, which was designed to compare the likelihood of species persistence in landscapes with varying habitat configuration (Hanski and Ovaskainen, 2000). There is one important difference between metapopulation capacity and the CCI, in that the former is based on information from a single species whereas the latter is community-based. It is not clear how metapopulation capacity could be scaled up to estimate the community-level value of a landscape, although the regression approach used in CCI could easily be adapted to estimate a landscape value for a single species. Both metapopulation capacity and the CCI can be used to directly compare the value of existing landscapes for a specific taxon and, by recalculating the indices on landscapes in which specific patch(es) are selectively ‘removed’ in a GIS, both methods can be used to identify the relative contribution of specific habitat patches to the landscape as a whole. Moreover, by comparing expected patterns of biodiversity change under competing scenarios of land use change, it becomes feasible to quantitatively compare the likely biodiversity outcomes from alternative development or restoration schemes in which habitats are destroyed, created, or undergo changes in shape, isolation and area. 5. Conclusions Threats to biodiversity have distinct spatial signatures, so quantitative estimates of biodiversity change should explicitly incorporate landscape-specific spatial variation in threat intensity. Although it is easy to overlook, fine-scale patterns of habitat distribution within and among landscapes play a crucial role in the longterm persistence of endangered species (Hanski and Ovaskainen, 2000), the central remit of conservation biology. Rapid advances in the ability to map land cover from space, combined with newly developed techniques to associate land cover patterns with on-theground measures of biodiversity, provides an unparalleled opportunity to assess and combat human impacts on the natural world. Acknowledgements We thank Jos Barlow, Toby Gardner, Simon Ferrier, Ilkka Hanski, Bill Sutherland and Teja Tscharntke for helpful discussions about this topic, and two reviewers for their constructive comments on the manuscript. The Hope River Forest Fragmentation Project is supported by the University of Canterbury, the Brian Mason Scientific and Technical Trust, and the Todd Foundation. The data used in this study can be obtained by contacting RME directly. References Bailey, R.C., Kennedy, M.G., Dervish, M.Z., Taylor, A.R.M., 1998. Biological assessment of freshwater ecosystems using a reference condition approach: comparing predicted and actual benthic invertebrate communities in Yukon streams. Freshwater Biology 39, 765–774. Balmford, A., Bennun, L., ten Brink, B., Cooper, D., Côté, I.M., Crane, P., Dobson, A., Dudley, N., Dutton, I., Green, R.E., Gregory, R.D., Harrison, J., Kennedy, E.T., Kremen, C., Leader-Williams, N., Lovejoy, T.E., Mace, G., May, R.M., Mayaux, P., Morling, P., Phillips, J., Redford, K., Ricketts, T.H., Rodríguez, J.P., Sanjayan, M., Schei, P.J., van Jaarsveld, A.S., Walther, B.A., 2005. The convention on biological diversity’s 2010 target. Science 307, 212–213. Baum, J.K., Myers, R.A., 2004. Shifting baselines and the decline of pelagic sharks in the Gulf of Mexico. Ecology Letters 7, 135–145. Braschler, B., Baur, B., 2003. Effects of experimental small-scale grassland fragmentation on spatial distribution, density, and persistence of ant nests. Ecological Entomology 28, 651–658.

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