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Road networks predict human influence on Amazonian bird communities Sadia E. Ahmed, Alexander C. Lees, Nárgila G. Moura, Toby A. Gardner, Jos Barlow, Joice Ferreira and Robert M. Ewers Proc. R. Soc. B 2014 281, 20141742, published 1 October 2014

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Road networks predict human influence on Amazonian bird communities rspb.royalsocietypublishing.org

Sadia E. Ahmed1,2, Alexander C. Lees3, Na´rgila G. Moura4, Toby A. Gardner5,6, Jos Barlow3,7, Joice Ferreira8 and Robert M. Ewers2 1

Computational Science Laboratory, Microsoft Research, 21 Station Road, Cambridge CB1 2FB, UK Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK 3 Coordenac¸a˜o de Zoologia Museu Paraense Emı´lio Goeldi, CP 399, Avenida Perimetral, 1901, Terra Firme, Bele´m, Para´ 66077– 530, Brazil 4 Curso de Po´s-Graduac¸a˜o de Zoologia, Universidade Federal do Para´/Museu Paraense Emı´lio Goeldi, Caixa Postal 399, Bele´m, Para´ 66040–170, Brazil 5 Stockholm Environment Institute, 87D Linegatan, Stockholm, Sweden 6 International Institute for Sustainability, Estrada Dona Castorina 124, Rio de Janeiro 22460–320, Brazil 7 Lancaster Environment Centre, Lancaster University, Lancaster LA1 3HE, UK 8 Embrapa Amazoˆnia Oriental, Travessa Dr. Ene´as Pinheiro s/n, Caixa Postal 48, Bele´m, Para´ 66095–100, Brazil 2

Research Cite this article: Ahmed SE, Lees AC, Moura NG, Gardner TA, Barlow J, Ferreira J, Ewers RM. 2014 Road networks predict human influence on Amazonian bird communities. Proc. R. Soc. B 281: 20141742. http://dx.doi.org/10.1098/rspb.2014.1742

Received: 14 July 2014 Accepted: 29 August 2014

Subject Areas: ecology Keywords: road density, road effects, infrastructure development, biodiversity, forest birds, Amazon

Author for correspondence: Sadia E. Ahmed e-mail [email protected]

Electronic supplementary material is available at http://dx.doi.org/10.1098/rspb.2014.1742 or via http://rspb.royalsocietypublishing.org.

Road building can lead to significant deleterious impacts on biodiversity, varying from direct road-kill mortality and direct habitat loss associated with road construction, to more subtle indirect impacts from edge effects and fragmentation. However, little work has been done to evaluate the specific effects of road networks and biodiversity loss beyond the more generalized effects of habitat loss. Here, we compared forest bird species richness and composition in the municipalities of Santare´m and Belterra in Para´ state, eastern Brazilian Amazon, with a road network metric called ‘roadless volume (RV)’ at the scale of small hydrological catchments (averaging 3721 ha). We found a significant positive relationship between RV and both forest bird richness and the average number of unique species (species represented by a single record) recorded at each site. Forest bird community composition was also significantly affected by RV. Moreover, there was no significant correlation between RV and forest cover, suggesting that road networks may impact biodiversity independently of changes in forest cover. However, variance partitioning analysis indicated that RV has partially independent and therefore additive effects, suggesting that RV and forest cover are best used in a complementary manner to investigate changes in biodiversity. Road impacts on avian species richness and composition independent of habitat loss may result from road-dependent habitat disturbance and fragmentation effects that are not captured by total percentage habitat cover, such as selective logging, fire, hunting, traffic disturbance, edge effects and road-induced fragmentation.

1. Introduction Infrastructure developments, particularly roads, are a ubiquitous feature of humanmodified landscapes. While roads and roadsides already account for 1–2% of the land surface in some developed countries [1], rates of road expansion are fastest in developing countries where they are afforded a high priority by governments wanting to encourage growth and reduce poverty by increasing spatial connectivity between population centres and access to natural resources [2–5]. Despite the irrefutable socio-economic benefits that roads bring to humans, their proliferation typically leads to negative impacts on the environment and native biota [5–9]. Plant, insect, herptile, bird and mammal richness and community composition have all been shown to be affected by roads [10–14]. Biodiversity loss may occur directly via road-kill events, disturbance or pollution, or indirectly by stimulating and facilitating loss of habitat, and forming barriers to dispersal and gene flow. Roads also affect biodiversity through reduction in habitat quality, facilitating human access to frontier landscapes, increasing opportunities for selective logging and bushmeat hunting,

& 2014 The Author(s) Published by the Royal Society. All rights reserved.

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(a) Study region Our fieldwork was carried out in the municipalities of Santare´m and Belterra, Para´ state, Brazil (figure 1) under the auspices of the Sustainable Amazon Network [38]. The study region was divided into 286 natural water catchments (mean area 3721 ha, s.d. ¼ 2747), delineated using a digital elevation model (DEM) and Soil and Water Assessment Tool (SWAT) in ARCGIS v. 9.3

A subset of 18 catchments (figure 1) was chosen to represent a gradient of forest cover from 10 to 100%. A stratified-random sampling design was employed to help ensure a representative assessment of bird species richness in each land cover (along a gradient of environmental impact ranging from undisturbed primary forest to soya bean fields) within the catchments, resulting in between 6 and 12 transects per catchment. A sample of 300 m transects were distributed randomly across the catchments to increase the likelihood that important internal heterogeneities in land cover were captured. A minimum separation distance rule of 1500 m between transects was employed to maximize independence between sampling points (see the electronic supplementary material and [36] for more details). Bird surveys were conducted by A.C.L., N.G.M., Christian B. Andretti, Bradley J. W. Davis & Edson V. Lopes, between 16 October 2010 and 8 February 2011. Two repetitions of three fixed radius (75 m) 15-minute point counts per transect were conducted, located at 0, 150 and 300 m along the transect (see figure 1 for location of study catchments, for further details on the project design, bird survey methodology and links to digital vouchers see Lees et al. [36]). All bird species recorded by sight and sound were recorded, but this study only considers forest-associated bird species (any species occurring within extensive areas of intact forest) assigned based on the classifications of Henriques et al. [39] and Lees et al. [37]. In order to evaluate sample representativeness, estimates of bird species richness (chao1) were calculated using ESTIMATES software [40] individually for each catchment and for total samples at catchment scale. Moran’s I [41] was used to test the richness data for spatial autocorrelation.

(c) Calculating roadless volume Two satellite images from Landsat location 227/062, covering the period between 2000 and 2008, were manually digitized to generate road network maps [34] following the methods described by Brandao & Souza [42] and were used to calculate RV in ARCGIS v. 9.3. RV represents the amount of space existing between roads, with the ecological ‘value’ of that space weighted by distance to the nearest road, such that areas that are less disturbed by roads have a higher RV value. RV is simple to calculate making it an attractive option for estimating the extent to which roads pervade landscapes at multiple spatial scales. All visible roads on the satellite images were included in the maps and treated equally. We first generated a ‘distance to nearest road’ raster grid, using the ARCGIS Euclidean Distance tool at 30 m resolution (figure 1, inset (c)), then using the Hawth’s tools Zonal Statistics tool [43], we calculated the sum of raster cells for each catchment, i.e. the sum distances to nearest road. These values were divided by catchment area to give a standardized metric of RV for each catchment. RV was log(e) transformed for subsequent analyses. A strong relationship between roads and habitat loss (deforestation) may confound any relationship between roads and species richness, thus we investigated the degree to which RV and percentage forest cover were correlated. Forest cover was derived from Prodes satellite data, from 2001 and 2008 (we used 2001 data as a proxy for 2000, because the 2000 data are not based on as many satellite images as subsequent years and thus are not as robust). Percentage forest cover was log(e) transformed for the Pearson’s correlation. The relative importance of RV and habitat cover in relation to species richness was determined using variance partitioning on general linear models with RV and per cent forest cover as explanatory variables.

(d) Comparing roadless volume with bird richness and composition Linear regression was used to determine the relationship between RV, forest cover and forest species richness. Three models were

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increased risk of forest fires and the creation of edge effects at road-habitat boundaries [6,15,16]. The impacts of roads on birds have been well studied and roads have been found to negatively affect avian site occupancy [17,18], mortality [19], reproductive success [20], movement patterns [21–23] and even vocal activity [24,25]. Avian dispersal capacity, and hence gap-crossing propensity, is highly species/ guild specific; while some species routinely undertake non-stop migrations between hemispheres [26], other species of similar body size are physiologically incapable of flying more than 100 m [27]. Quantifying the link between roads and bird diversity is especially important in tropical deforestation frontiers, where forest-dependent birds are particularly vulnerable to human impacts [28–31]. Here, we focus on Amazonia, the world’s largest remaining expanse of tropical forest [32], host to the world’s most speciose and increasingly endangered avifauna [33]. Road networks in the Brazilian Amazon grew by almost 17 000 km annually between 2004 and 2007 [34]. This growth is underpinned by major government-led development plans in infrastructure, including the construction of major highways such as the BR-10, BR-163, BR-219 and BR-319 that have largely defined regional development patterns. Individual Brazilian states have their own transport development plans, such as PELT-Para´ for the state of Para´. Based on predictions of economic growth and transport logistics, it is estimated that over 27 000 km of roads will be constructed, improved (paved), expanded (more lanes), extended and maintained in Para´ by the year 2031 (Pelt Para´. http://www.setran.pa.gov. br/PELT/tranporte/arquivos/evol_futura_transp.pdf ). We are unaware of any environmental assessment associated with the PELT-Para´ plan, raising concern that biodiversity impacts associated with these infrastructural changes will not be appropriately avoided and mitigated against. Given anticipated Amazonian road network expansion, it is of the utmost importance that links between roads and biodiversity be quantified to facilitate accurate assessment of potential impacts. Roadless volume (RV) [35] is a relatively new metric of road network density that accounts for the exact spatial pattern of roads within an area of interest. However, we are unaware of any validation of the RV metric demonstrating a correlation with changes in biodiversity, hindering its potential use in assessing the impacts of road network changes. Here, we use data on forest bird species richness collected in a deforestation frontier region in southern Amazonia to (i) determine whether there is a relationship between RV and biodiversity, (ii) to quantify the relative importance of roads versus habitat amount (measured as forest cover) in determining biodiversity patterns, and (iii) to estimate the impacts of historical road network expansion on biodiversity in a region where road networks are expanding rapidly.

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Figure 1. Study region (a) location within the Brazilian Amazon Legal, (b) municipalities of Santare´m and Belterra, with 18 study catchments highlighted in black, (c) distance to nearest road calculated on a 30 m grid, with river location and roads in 2008 shown in black. The 18 study catchments are highlighted in white (numbered as in [36,37]). (d ) RV calculated over an equal area 60 m grid. (e) RV and forest cover for the 18 study catchments.

used: (i) only RV as a predictor of species richness, (ii) only per cent forest cover as a predictor of species richness, and (iii) both RV and per cent forest cover as predictors of species richness. Variance partitioning and ANOVAs were used to compare the models. To examine how RV influences community composition at the catchment scale, the number of log(e) transformed unique species (i.e. species represented by a single record in the sampled data) for each catchment was regressed against RV. We also quantified community composition using detrended correspondence analysis (DCA) scores to represent the composition pattern of bird communities within catchments. We then performed linear regressions of the DCA axis 1 scores to investigate whether the forest bird community changed with changing RV. We extrapolated the relationship between RV, percentage forest cover and forest species to predict the species richness of forest birds in each of the 286 catchments (including 268

unsampled catchments) in the Santare´m region. Catchments were delineated as before, using DEM and SWAT. We used the same ‘distance to nearest road’ surface calculated over a 30 m grid for initial analyses to calculate the RV for all 286 catchments in 2008. A second ‘distance to nearest road’ surface was generated for the earliest digitized road map available (2000) and the RV for each catchment in 2000 was calculated. Based on the relationship established between RV, per cent forest cover and species richness, we estimated species richness in each catchment for both 2000 and 2008, and compared the two to estimate the number of local extinctions over the 8 year period following the road network expansion across the study site. We also estimated the number of local extinctions for the RV and per cent forest cover only models. To identify catchments that experienced species loss because of the independent effects of RV change, we used a subset of

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Figure 2. Regression models of RV (first column) and percentage forest cover (second column) against (a) species richness, (b) the number of unique species present at any given catchment and (c) DCA axis 1 scores representing bird community composition at the catchment scale across 18 catchments.

catchments, which experienced low (%) change in forest cover but high (%) change in RVs. Cut-offs of the second quartile (for Forest cover) and third quartile for RV change were used to identify high versus low changes. All detailed analyses were carried out in the statistical platform R v. 2.10.1 [44] unless otherwise stated.

3. Results A total of 11 028 detections of 384 bird species were recorded during the timed point counts. Of these, 8743 detections of 298 forest-associated bird species were selected for analysis. Chao1 estimates of forest bird species richness were generally higher than observed forest bird richness, with an estimated mean species richness of 152 (s.d. ¼ 39) and an average observed richness of 119 (s.d. ¼ 28) at the catchment scale. Richness estimates suggested sampling had captured an

average of 79% of species within each catchment (s.d. ¼ 5.7%). Little spatial correlation was found, with only catchments up to 22 km apart being significantly correlated (Moran’s I ¼ 0.35, n ¼ 55, p ¼ 0.013). RV was positively, but not significantly, correlated with percentage forest cover within the sampled catchments (figure 1, r ¼ 0.45, 95% CI ¼ 20.02–0.76, d.f. ¼ 16, p ¼ 0.063). Linear regression models showed a significant positive relationship between species richness and RV (figure 2, r 2 ¼ 0.73, slope ¼ 324, d.f. ¼ 16, p , 0.001), and between species richness and percentage forest cover (r 2 ¼ 0.57, slope ¼ 19, d.f. ¼ 16, p , 0.001). These two models were not significantly different from each other, although per cent forest cover had a lesser effect on species richness, and both models performed significantly worse than a model including both variables (table 1). The regression model incorporating both variables (RV and percentage forest cover) performed well, explaining 91%

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Table 1. Variance partitioning of the effects of RV and percentage forest cover (%FC) on bird species richness. All models shown are significant with p , 0.001. Shown in brackets is the individual contribution (IC) to variance explained of RV and %FC.

r 2 (IC)

d.f.

sig. diff. compared to RV 1 %FC?

sig. diff compared to RV?

AIC

average species loss (s.d.)

RV þ %FC

0.91

15

n.a.

yes

133.37

83 (34)

RV %FC

0.72 (0.34) 0.57 (0.19)

16 16

yes yes

n.a. no

152.81 160.46

68 (29) 10 (14)

These reductions in species richness represent species loss predominantly determined by change in RV. Interestingly, these catchments form two ‘fronts’ (indicated by arrows, figure 4c) bordering extensive areas of forest.

4. Discussion We found a clear positive relationship between RV and forestassociated bird species richness and composition, demonstrating that road networks can impact biodiversity partly independently, and hence additively to any effects of habitat loss. We estimate that catchments along the deforestation frontier in Para´ state have lost an average of 83 species of forest-associated birds between 2000 and 2008. We found that the number of unique species present in a given catchment increased with increasing RV, possibly because species with very low abundances are highly sensitive to road presence and so are unlikely to be detected in catchments with high road densities. Roads are one of the key spatial predictors of deforestation patterns within tropical regions [45 –49] facilitating access to frontier areas. While we recognize that our comparison between road networks and forest cover is limited by our small sample size of 18 micro-catchments, it is also possible that the partial independence we observed between RV and forest cover may reflect a time lag between road network development and deforestation. Reductions in RV may therefore act as an ‘early warning metric’ of species loss in forest landscapes, as road construction commonly represents the first sign of anthropogenic disturbance in forest systems detected by means of remote sensing [50]. A great deal of attention has been paid to the effects of deforestation on species richness, which is described well by the metric per cent forest cover [51]. However, habitat degradation is often overlooked, despite being more extensive [52,53]. This is likely related to the fact that deforestation can be monitored remotely, while habitat degradation is much more difficult to monitor [52,54]. By assessing road and forest loss impacts, we have demonstrated that road networks can impact biodiversity both independently and additively to any effects of habitat loss. Road networks may affect bird species richness and community composition in three main ways that are additional to any impacts from associated habitat loss. All three of these processes are linked to habitat degradation, suggesting that RV may be used as a proxy for habitat degradation impacts on biodiversity. Firstly, roads fragment habitat and create barriers to movement between patches. Even very narrow roads can disrupt the movement of many forest-associated bird species, particularly terrestrial insectivorous passerines [21,22,55,56]. The conclusions of these observational studies of bird movements are further supported by experimental and

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of the variation in bird species richness (r 2 ¼ 0.91, d.f. ¼ 15, p , 0.01). There was, however, no significant interaction between RV and percentage forest cover (F ¼ 53.49, p ¼ 0.84, d.f. ¼ 14). RV alone explained 15% more variance in species richness compared with per cent forest cover alone (figure 1; electronic supplementary material). RV also exerted a significant effect on species composition, reflected in analyses of the overall community composition (DCA score) and the number of unique species per catchment. There was a significant positive relationship between RV and DCA scores (r 2 ¼ 0.29, slope ¼ 28.4, d.f. ¼ 16, p , 0.05, figure 2). We also found that the number of unique species present in any given catchment increased with increasing RV (r 2 ¼ 0.32, slope ¼ 5.8, d.f. ¼ 16, p , 0.05, figure 2). Species that do not occur in catchments with log(e) RVs less than approximately 2 ranged from specialized understorey insectivores such as the ant-following bare-eyed antbird Rhegmatorhina gymnops, and the obligate mixed flock-following tawny-crowned greenlet Hylophilus ochraceiceps to large huntingsensitive species such as razor-billed currasow Pauxi tuberosum. Similar relationships were found between percentage forest cover and DCA (r 2 ¼ 0.70, slope ¼ 20.8, d.f. ¼ 16, p , 0.001) and between percentage forest cover and unique species (r 2 ¼ 0.28, slope ¼ 0.4, d.f. ¼ 16, p , 0.05, figure 2). Between 2000 and 2008, there were 2773 km of new roads constructed in the study area, primarily offshoots of the central BR163 highway, concentrated in the less-developed south of the study region (figures 1 and 3a). However, the majority of forest loss occurred in the north of the study area (figure 3b). Areas of predicted high bird species richness and high RV corresponded to the remaining areas of largely undisturbed primary forest—the Floresta Nacional do Tapajo´s forest reserve along the western border and areas of unprotected primary forest in the southeast (figure 3c). These areas were predicted to have retained high avian species richness throughout the 8 year period. The model with both RV and forest cover predicted an average forest-associated bird species richness of 177 (s.d. ¼ 31) per catchment in 2000, reduced by an average of 83 (s.d. ¼ 34) species per catchment by the year 2008. Spatial patterns of local extinction matched those of road expansion. The model considering forest cover alone produced significantly lower estimates of species loss compared with RV alone or models containing both predictors (ANOVA F ¼ 592, d.f. ¼ 2,855, p , 0.001). Both percentage change in forest (0 –80%) and percentage change in RV (0–25%) varied across all 286 catchments (figure 4). Eighty-four catchments experienced forest cover change within the first quartile of values (less than 5.6%) and RV changes higher than the third quartile (more than 10.7%), with an average forest change of 2% (s.d. ¼ 1.5), average RV change of 13% (s.d. ¼ 2.4) and average predicted species loss of 74 (s.d. ¼ 16.5) within this subset of regions.

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Figure 3. (a) Road network, (b) forest cover and (c) predicted species richness in 2000 and 2008 based on RV and forest cover from corresponding years. genetic studies of understory bird species which indicate that landscape connectivity may be more important for bird dispersal than distance alone [27,57,58]. By disrupting patterns of movement, roads influence richness and composition by creating unstable meta-populations, isolating sub-populations of species which (if small) may render them vulnerable to stochastically mediated local extinctions [59]. Secondly, road construction precipitates environmental changes [6,7,60], such as the proliferation of habitat edges resulting in altered microclimate, light and foliage levels [61 –63]. This may result in a turnover in species composition favouring edge and/or gap species, despite there being only very modest changes in the total amount of forest. Road construction is thus likely to be a significant driver of the

loss of sensitive forest-interior species and areas with high RV may even impact populations of more generalist forest species, resulting in local increase in edge and gap species. Laurance [55] and Laurance et al. [21] found that edge and gap-specialist bird species were more prevalent close to road edges than deeper inside the forest, whereas the abundance of specialist insectivorous, terrestrial and solitary species declined closer to roads. Finally, the presence of roads may be a good proxy for more cryptic forms of disturbance and habitat degradation that depend on, or are exacerbated by, improved human access to forest areas, such as selective timber extraction, fire and hunting [50] which are all known to have significant effects on forest bird communities [31,64 –69]. Timber

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change in species 1–10 11–20 21–40 41–60 61–80

change RV 0%

change FC 0%

81–100 101–120 121–140

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Figure 4. (a) Per cent RV change, (b) per cent forest cover change and (c) predicted number of species lost between 2000 and 2008, identifying in bold those catchments where there has been low forest cover change but high RV change. As roads are the initial stage in deforestation opening up new areas of land, the presence of road development (a) in areas of low deforestation (b) are areas that are potentially ‘newly’ accessible and likely to undergo deforestation in the future. Such areas may be considered frontiers of development. The 84 catchments that fit these criteria (high RV change and low forest cover change) are clustered around nominally undisturbed forest. Based on where the road network is and where the undisturbed and unprotected forest is, we can assume that deforestation and any future road development will move into the forested areas indicated by arrows, which indicate potential development fronts. extraction results both in a loss of tree biomass—important for species such as long-tailed woodcreeper Deconychura longicauda, and the associated canopy perforation leads to changes in understory microclimatic conditions and a surge in understorey vegetative growth that renders habitat unsuitable for species such as musician wren Cyphorhinus arada. Both these species were restricted in our landscape to areas with low RV. Hunting almost invariably accompanies timber extraction [70], which may not only be directed at species valued for their bushmeat, e.g. the aforementioned razor-billed currasow, but may also afflict raptorial species which may become targets because of human–wildlife conflicts [71]. We found that raptors such as ornate hawk-eagle Spizaetus ornatus and great black hawk Urubitinga urubitinga were associated with areas with low RV in our study region. Understorey wildfires also open up the canopy and reduce forest biomass [67], but are generally more severe than logging [72] and alter bird community composition for many years after the fire event [73]. Finally, roads cause direct mortality which has been known to affect bird community demography [19]. As a result of the partly independent and additive effects that roads have on birds, RV appears to be an important metric to include when estimating forest bird species richness in human-modified forest landscapes, because it is a more accurate surrogate of habitat degradation events than forest cover alone. Furthermore, models using forest cover alone predicted significantly fewer species losses than the combined model or model with RV alone suggesting that ignoring roads underestimates species loss. However, this is based on total forest cover; it is possible that more stringent metrics of forest condition, such as per cent of primary forest with no detectable disturbance from logging or fire, would yield different results. While RV proved to be a better predictor of species richness, per cent forest cover was a much better predictor of species composition, reiterating the point that assessing changes in species richness and composition is best achieved including both variables. We found that areas subject to pronounced change in RV but little change in forest cover were concentrated at the edge of development ‘fronts’, adjoining undisturbed areas of forest. As such our results indicate that RV may act as an

‘early warning metric’ of species loss, picking up regions that have not yet been deforested, but that are subject to other disturbance events. Further, the presence of roads may indicate that these areas have been subject to habitat degradation despite retaining high levels of forest cover and are likely to have experienced species losses that would not have been detected by assessing forest cover alone. This is very important, as it is easier to establish the presence of a road than it is to determine the level of habitat degradation of the forest from a satellite image, which requires a complex series of analysis to be undertaken successfully [74]. We treat all roads the same regardless of paving or traffic activity, both of which are known to influence the ecological effects of roads [75,76] and future research should explore any differences in road ‘taxonomy’ (e.g. paved versus unpaved, official versus unofficial) on species richness and composition. Road construction is likely to be a significant driver of the loss of sensitive forest-interior species and areas with high RV may even impact populations of more generalist forest species, resulting in local increase in edge and gap species. However, there may also be some survey bias leading to this pattern, because catchments with low RV had a weak, albeit non-significant, tendency to have more forest cover. The Brazilian road network is forecast to continue expanding in the foreseeable future [77], requiring as little as 60 years for local road networks to reach maximum observed densities from the first road being laid [34,78]. Considering increasing investment in development plans from regional (e.g. PELT Para´) to continental scales (e.g. Initiative for the Integration of the Regional Infrastructure of South America [77]), it is important that we can forecast potential biodiversity changes resulting from current and future road network improvements. Our observed relationships provide a good basis for large-scale scenario modelling. Such forecasting ability would help road planners to investigate scenarios of ‘ideal’ future road networks that minimize biodiversity impacts. We here demonstrate that forest-associated bird species responses to RV are useful to forecast such changes and may be used as a disturbance proxy to examine the potential impacts of competing road layouts on local biodiversity and improve the design of future road networks.

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we are grateful to the Rede Amazonia Sustentavel project and the following for financial support; Instituto Nacional de Cieˆncia e Tecnologia—Biodiversidade e Uso da Terra na Amazoˆnia (CNPq 574008/2008– 0), EmpresaBrasileira de PesquisaAgropecua´ria— Embrapa (SEG: 02.08.06.005.00), the UK government Darwin Initiative (17 –023), The Nature Conservancy and Natural Environment Research Council (NERC) (NE/F01614X/1 and NE/G000816/1). T.A.G. thanks the Swedish Research Council Formas (grant no. 2013– 1571). We also thank the farmers and workers unions of Santare´m and Belterra, all collaborating private landowners and the Large-scale Biosphere-Atmosphere Experiment in Amazonia (LBA) for their support.

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Forman RTT. 1998 Road ecology, a solution to the giant embracing us. Landscape Ecol. 13, 3 –5. (doi:10.1023/A:1008036602639) Munnell AH. 1992 Infrastructure investment and economic growth. J. Econ. Perspect. 6, 189 –198. (doi:10.1257/jep.6.4.189) Calderon C, Serven L. 2004 The effects of infrastructure development on growth and income distribution, Central Bank of Chile Working papers, number 270. See http://www.bcentral.cl/estudios/documentos-trabajo/ pdf/dtbc270.pdf (accessed 12 December 2012). Straub S. 2008 Infrastructure and growth in developing countries: recent advances and research challenges, World bank policy research working paper, number 4460. See http://papers.ssrn.com/ sol3/papers.cfm?abstract_id=1080475 (accessed 11 December 2012). Perz SG et al. 2012 Regional integration and local change: road paving, community connectivity and social-ecological resilience in a tri-national frontier, south-western Amazonia. Reg. Environ. Change 12, 35 –53. (doi:10.1007/s10113-011-0233-x) Forman RTT, Alexander LE. 1998 Roads and their major ecological effects. Annu. Rev. Ecol. Syst. 29, 207–231. (doi:10.1146/annurev.ecolsys.29.1.207) Spellberg IF. 1998 Ecological effects of roads and traffic: a literature review. Glob. Ecol. Biogeogr. Lett. 7, 317–333. Fahrig L, Rytwinski T. 2009 Effects on animal abundance: an empirical review and synthesis. Ecol. Soc. 14, 21. Laurance WF, Goosem M, Laurance GW. 2009 Impacts of roads and linear clearings on tropical forests. Trends Ecol. Evol. 24, 659 –669. (doi:10. 1016/j.tree.2009.06.009) Seibert HC, Conover JH. 1991 Mortality of vertebrates and invertebrates on an Athens County, Ohio, highway. Ohio J. Sci. 91, 163– 166. Findlay S, Bourdages J. 2000 Response time of wetland biodiversity to road construction on adjacent lands. Conserv. Biol. 14, 86 – 94. (doi:10. 1046/j.1523-1739.2000.99086.x) Forman RTT et al. 2003 Road ecology science and solutions. Washington, DC: Island Press. Chen X, Roberts K. 2008 Roadless areas and biodiversity: a case study in Alabama, USA. Biodivers. Conserv. 17, 2013– 2022. (doi:10.1007/ s10531-008-9351-2)

14. Spooner GP, Smallbone L. 2009 Effects of road age on the structure of roadside vegetation in southeastern Australia. Agric. Ecosyst. Environ. 129, 57 –64. (doi:10.1016/j.agee.2008.07.008) 15. Keller I, Largiader CR. 2003 Recent habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. Proc. R. Soc. Lond. B 270, 417 –423. (doi:10.1098/rspb.2002.2247) 16. Jaeger JAG et al. 2005 Predicting when animal populations are at risk from roads: an interactive model of road avoidance behavior. Ecol. Model. 185, 329 –348. (doi:10.1016/j.ecolmodel.2004.12.015) 17. Ortega YK, Capen DE. 1999 Effects of forest roads on habitat quality for ovenbirds in a forested landscape. Auk 116, 937 –946. (doi:10.2307/ 4089673) 18. Forman RTT, Deblinger RD. 2000 The ecological road-effect zone of a Massachusetts (USA) suburban highway. Conserv. Biol. 14, 36 –46. (doi:10.1046/j. 1523-1739.2000.99088.x) 19. Mumme RL, Schoech SJ, Woolfenden GE, Filzpatrick JW. 2000 Life and death in the fast lane: demographic consequences of road mortality in the Florida scrub-jay. Conserv. Biol. 14, 501 –512. (doi:10.1046/j.1523-1739.2000.98370.x) 20. King DI, DeGraaf RM. 2002 The effect of forest roads on the reproductive success of forest-dwelling passerine birds. Forest Sci. 48, 391 –396. 21. Laurance SG, Stouffer PC, Laurance WF. 2004 Effects of road clearings on movement patterns of understory rainforest birds in central Amazonia. Conserv. Biol. 18, 1099 –1109. (doi:10.1111/j.15231739.2004.00268.x) 22. Lees AC, Peres CA. 2009 Gap-crossing movements predict species occupancy in Amazonian forest fragments. Oikos 118, 280 –290. (doi:10.1111/j. 1600-0706.2008.16842.x) 23. Tremblay MA, St. Clair CC. 2009 Factors affecting the permeability or transportation and riparian corridors to the movements of songbirds in an urban landscape. J. Appl. Ecol. 46, 1314–1322. 24. Brumm H. 2004 The impact of environmental noise on song amplitude in a territorial bird. J. Anim. Ecol. 73, 434–440. (doi:10.1111/j.0021-8790. 2004.00814.x) 25. Slabbekoon H, Ripmeester EAP. 2008 Birdsong and anthropogenic noise: implications and applications

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

for conservation. Mol. Ecol. 17, 72 –83. (doi:10. 1111/j.1365-294X.2007.03487.x) Bairlein F, Norris DR, Nagel R, Bulte M, Voigt CC, Fox JW, Hussell DJT. 2012 Cross-hemisphere migration of a 25g songbird. Biol. Lett. 8, 505 –507. (doi:10.1098/rsbl.2011.1223) Moore RP, Robinson WD, Lovette IJ, Robinson TR. 2008 Experimental evidence for extreme dispersal limitation in tropical forest birds. Ecol. Lett. 11, 960–968. (doi:10.1111/j.1461-0248.2008.01196.x) O’Neill RV. 1976 Ecosystem persistence and heterotrophic regulation. Ecology 57, 1244–1253. (doi:10.2307/1935048) Wright SJ, Muller-Landau HC. 2006 The future of tropical forest species. Biotropica 38, 287–301. (doi:10.1111/j.1744-7429.2006.00154.x) Gardner TA, Barlow J, Cazdon R, Ewers RM, Harvey CA, Peres CA, Sodhi NJ. 2009 Prospects for tropical forest biodiversity in a human-modified world. Ecol. Lett. 12, 561–582. (doi:10.1111/j.1461-0248.2009. 01294.x) Moura NG, Lees AC, Andretti CB, Davis BJW, Aleixo A, Barlow J, Ferreira J, Gardner TA. 2013 Avian biodiversity in multiple-use landscapes of the Brazilian Amazon. Biol. Conserv. 167, 339–348. (doi:10.1016/j.biocon.2013.08.023) Foley JA et al. 2007 Amazonia revealed: forest degradation and the loss of ecosystem services in the Amazon basin. Front. Ecol. Environ. 5, 25–32. (doi:10. 1890/1540-9295(2007)5[25:ARFDAL]2.0.CO;2) Bird JP, Buchanan GM, Lees AC, Clay RP, Develey PF, Ye´pez I, Butchart SH. 2012 Integrating spatially explicit habitat projections into extinction risk assessments: a reassessment of Amazonian avifauna incorporating projected deforestation. Divers. Distrib. 18, 273 –281. (doi:10.1111/j.1472-4642.2011. 00843.x) Ahmed SE, Souza CM, Ribeiro J, Ewers RM. 2013 Temporal patterns of road network development in the Brazilian Amazon. Reg. Environ. Change 13, 927–937. (doi:10.1007/s10113-012-0397-z) Watts RD, Compton RW, McCammaon JH, Rich CL, Wright SM, Owens T, Ouren DS. 2007 Roadless space of the conterminous United States. Science 316, 736–738. (doi:10.1126/science.1138141) Lees AC et al. 2013 One hundred and thirty-five years of avifaunal surveys around Santare´m, central Brazilian Amazon. Rev. Bras. Ornitol. 21, 16 –57.

Proc. R. Soc. B 281: 20141742

References

8

rspb.royalsocietypublishing.org

Acknowledgements. We would like to thank the two anonymous reviewers whose comments helped greatly to improve this manuscript. We thank our field team including Renilson M. Freitas, Eucielde P. Oliveira, Gilson J. Oliveira, Jony M. Oliveira, and the late Manoel A. do Nascimento and bird fieldworkers Christian B. Andretti, Bradley J.W. Davis and Edson V. Lopes. This is publication number 27 of the Sustainable Amazon Network series (www.redeamazoniasustentavel.org) and a contribution to Imperial College’s Grand Challenges in Ecosystems and the Environment initiative. Funding statement. We would also like to thank Microsoft Research, the Grantham Institute for Climate Change and European Research Council ( project number 281986) for funding this work. Additionally,

Downloaded from rspb.royalsocietypublishing.org on October 2, 2014

52.

53.

55.

56.

57.

58.

59.

60.

61. 62.

63.

64.

65.

66.

67.

68.

69.

70.

71.

72.

73.

74.

75.

76.

77.

78.

Amazonian forests. Biol. Conserv. 105, 157–169. (doi:10.1016/S0006-3207(01)00177-X) Barlow J, Peres CA. 2004 Ecological responses to El Nin˜o-induced surface fires in central Brazilian Amazonia: management implications for flammable tropical forests. Phil. Trans. R. Soc. Lond. B 359, 367–380. (doi:10.1098/rstb.2003.1423) Borges SH. 2007 Bird assemblages in secondary forests developing after slash and burn agriculture in the Brazilian Amazon. J. Trop. Ecol. 23, 469 –477. (doi:10.1017/S0266467407004105) Aubad J, Aragon P, Rodriguez MA. 2010 Human access and landscape structure effects on Andean forest bird richness. Acta Oecol. 36, 369 –402. (doi:10.1016/j.actao.2010.03.009) Poulsen JR, Clark CJ, Bolker BM. 2011 Decoupling the edge effects of logging and hunting on an Afrotropical animal community. Ecol. Appl. 21, 1819– 1836. (doi:10.1890/10-1083.1) Trinca CT, Ferrari SF, Lees AC. 2008 Curiosity killed the bird: arbitrary hunting of harpy eagles Harpia harpyja on an agricultural frontier in southern Brazilian Amazonia. Cotinga 30, 12 –15. Barlow J, Peres CA, Henriques LMP, Stouffer PC, Wunderle JM. 2006 The responses of understory birds to forest fragmentation, logging and wildfires: an amazonian synthesis. Biol. Conserv. 128, 182–192. (doi:10.1016/j.biocon.2005.09.028) Mestre LA, Cochrane MA, Barlow J. 2013 Long term changes in bird communities after wildfires in the central Brazilian Amazon. Biotropica 45, 480 –488. (doi:10.1111/btp.12026) Souza CM. 2013 Ten year Landsat classification of deforestation and forest degradation in the Brazilian Amazon. Remote Sens. 5, 5493 –5513. (doi:10. 3390/rs5115493) Nepstad D et al. 2001 Road paving, fire regimes, and the future of Amazon forests. Forest Ecol. Manage. 154, 395 –407. (doi:10.1016/S03781127(01)00511-4) Soares-Filho B. 2006 Modelling conservation in the Amazon. Nature 440, 520–523. (doi:10.1038/ nature04389) Killeen TJ. 2005 A perfect storm in the Amazon wilderness: development and conservation in the context of the initiative for the integration of the regional infrastructure of South America (IIRSA). Advances in applied biodiversity series, number 7, Centre for Applied BiodiversityScience (CABS), Conservation International, USA. See http://www. conservation.org/publications/Documents/AABS. 7_Perfect_storm_English.low.res.pdf (accessed 8 January 2013). Ahmed SE, Ewers RM, Smith MJ. 2013 Large scale spatiotemporal patterns of road development in the Amazon rainforest. Environ. Conserv. 41, 253 –264. (doi:10.1017/S0376892913000520)

9

Proc. R. Soc. B 281: 20141742

54.

Conserv. 133, 189 –211. (doi:10.1016/j.biocon.2006. 06.005) Putz FE, Redford KH. 2010 The importance of defining ‘forest’: tropical forest degredation, deforestation, long-term phase shifts and further transitions. Biotropica 42, 10– 20. (doi:10.1111/j. 1744-7429.2009.00567.x) Berenguer E et al. 2014 A large scale field assessment of carbon stocks in human modified tropical landscapes. Glob. Change Biol. (doi:10.1111/ gcb.12627) Wilkie DS, Bennett EL, Peres CA, Cunningham AA. 2011 The empty forest revisited. Ann. NY Accad. Sci. 1223, 120 –128. (doi:10.1111/j.1749-6632.2010. 05908.x) Laurance SG. 2004 Responses of understory rain forest birds to road edges in central Amazonia. Ecol. Appl. 14, 1344 –1357. (doi:10.1890/03-5194) Laurance SG, Gomez MS. 2005 Clearing width and movements of understory rainforest birds. Biotropica 37, 149–152. (doi:10.1111/j.1744-7429.2005.04099.x) Woltman S, Kreiser BR, Sherry TW. 2012 Fine-scale genetic population structure of an understory rainforest bird in Costa Rica. Conserv. Genet. 13, 925 –935. (doi:10.1007/s10592-012-0341-2) Woltman S, Kreiser BR, Sherry TW. 2012 A genetic approach to estimating natal dispersal distances and self-recruitment in resident rainforest birds. J. Avian Biol. 43, 33 –42. (doi:10.1111/j.1600-048X.2011. 05572.x) Taylor BD, Goldingay RL. 2010 Roads and wildlife: impacts, mitigation and implications for wildlife management in Australia. Wildlife Res. 37, 1–12. (doi:10.1071/WR09171) Ritters KH, Wickham JD. 2003 How far to the nearest road? Front. Ecol. Environ. 1, 125–129. (doi:10.1890/1540-9295(2003)001[0125:HFTTNR]2. 0.CO;2) Malcom JR. 1994 Edge effects in central. Amazonian Forest Fragments 75, 2438–2445. Camargo JLC, Kapos V. 1995 Complex edge effects on soil moisture and microclimate in central Amazonian forest. J. Trop. Ecol. 11, 205–221. (doi:10.1017/S026646740000866X) Murcia C. 1995 Edge effects in fragmented forests: implications for conservation. Trends Ecol. Evol. 10, 58 –62. (doi:10.1016/S0169-5347(00)88977-6) Johns AD. 1988 Effects of ‘selective’ timber extraction on rain forest structure and composition and some consequences for frugivores and folivores. Biotropica 20, 31 –37. (doi:10.2307/2388423) Thiollay J. 1997 Disturbance, selective logging and bird diversity: a neotropical forest study. Biodivers. Conserv. 6, 1155– 1173. (doi:10.1023/A:101838 8202698) Barlow J, Haugassen T, Peres CA. 2002 Effects of ground fires on understory bird assemblages in

rspb.royalsocietypublishing.org

37. Lees AC, Zimmer KJ, Marantz CM, Whittaker A, Davis BJW, Whitney BM. 2013 Alta Floresta revisited: an updated review of the avifauna of the most intensively surveyed site in south-central Amazonia. Bull. Br. Ornithol. Club 133, 178–239. 38. Gardner TA et al. 2013 A social and ecological assessment of tropical land uses at multiple scales: the Sustainable Amazon Network. Phil. Trans. R. Soc. B 368, 20130307. (doi:10.1098/rstb.2013.0307) 39. Henriques LMP, Wunderle JM, Willing M. 2003 Birds of the Tapajos national forest, Brazilian Amazon: a preliminary assessment. Ornitol. Neotrop. 14, 307–338. 40. Colwell RK. 2009 EstimateS: statistical estimation of species richness and shared species from samples, v. 8.2. User’s guide and application published. See http://purl.oclc.org/estimates. 41. Legendre P, Legendre L. 1998 Numerical ecology, second edition. Developments in environmental modelling 20. Amsterdam, The Netherlands: Elsevier. 42. Brandao AO, Souza CM. 2006 Mapping unofficial roads with Landsat images, a new tool to improve the monitoring of the Brazilian Amazon forest. Int. J. Remote Sens. 27, 177 –189. (doi:10.1080/ 01431160500353841) 43. Beyer HL. 2004 Hawth’s analysis tools for ArcGIS. See http://www.spatialecology.com/htools. 44. Development Core Team. 2009 R, a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. 45. Geist HJ, Lambin EF. 2002 Proximate causes and underlying driving forces of tropical deforestation. BioScience 52, 143– 150. (doi:10.1641/0006-3568 (2002)052[0143:PCAUDF]2.0.CO;2) 46. Walker R, Drzyzga SA, Li Y, Qi J, Caldas M, Arima E, Vergara D. 2004 A behavioral model of landscape change in the Amazon, basin, the colonist case. Ecol. Appl. 14, 299 –312. (doi:10.1890/01-6004) 47. Perz SG, Caldas MM, Arima E, Walker RJ. 2007 Unofficial road building in the Amazon: socioeconomic and biophysical explanations. Dev. Change 38, 529– 551. (doi:10.1111/j.1467-7660. 2007.00422.x) 48. Fearnside PM. 2008 The roles and movements of actors in the deforestation of Brazilian Amazonia. Ecol. Soc. 13, 23 –45. 49. Finer M, Jenkins CN, Pimm SL, Keane B, Ross C. 2008 Oil and gas projects in the Western Amazon, threats to wilderness, biodiversity, and indigenous people. PLoS ONE 3, e2932. (doi:10.1371/journal. pone.0002932) 50. Peres CA, Barlow J, Laurance WF. 2006 Detecting anthropogenic disturbance in tropical forests. TREE 21, 227–229. (doi:10.1016/j.tree.2006.03.007) 51. Lees AC, Peres CA. 2006 Rapid avifaunal collapse along the Amazonian deforestation frontier. Biol.