Assessing large-scale wildlife responses to humaninfrastructure developmentAurora Torresa,1, Jochen A. G. Jaegerb, and Juan Carlos Alonsoa

aDepartamento de Ecología Evolutiva, Museo Nacional de Ciencias Naturales, Consejo Superior de Investigaciones Científicas (CSIC), E-28006 Madrid, Spain;and bDepartment of Geography, Planning, and Environment, Concordia University, Montreal, QC, Canada H3G 1M8

Edited by Rodolfo Dirzo, Stanford University, Stanford, CA, and approved May 25, 2016 (received for review November 13, 2015)

Habitat loss and deterioration represent the main threats to wildlifespecies, and are closely linked to the expansion of roads and humansettlements. Unfortunately, large-scale effects of these structuresremain generally overlooked. Here, we analyzed the European trans-portation infrastructure network and found that 50% of the conti-nent is within 1.5 km of transportation infrastructure. We presenta method for assessing the impacts from infrastructure on wildlife,based on functional response curves describing density reductions inbirds and mammals (e.g., road-effect zones), and apply it to Spain as acase study. The imprint of infrastructure extends over most of thecountry (55.5% in the case of birds and 97.9% for mammals), withmoderate declines predicted for birds (22.6% of individuals) andsevere declines predicted for mammals (46.6%). Despite certain limi-tations, we suggest the approach proposed is widely applicable to theevaluation of effects of planned infrastructure developments undermultiple scenarios, and propose an internationally coordinated strat-egy to update and improve it in the future.

anthropogenic development | birds | Europe | mammals | road-effect zone

Habitat loss and degradation are the primary drivers of thedecline and extinction of wildlife populations in terrestrial

ecosystems (1), with the main precursors of these impacts beingroads and human settlements (2). If current trends continue, by2030, urban areas will increase by 1.2 million km2 globally and,by 2050, our planet will accommodate more paved-lane kilo-meters than required to reach Mars (3, 4). The largest expectedinfrastructural undertakings will occur in developing nations (3,4), including many regions that sustain exceptional levels ofbiodiversity and vital ecosystem services. These structures willalter ecological conditions, cut through highly suitable habitat,and further reduce the populations of many wildlife species (5–7). However, large-scale consequences of these trends remainunknown (8). Global and continental schemes for prioritizingroad building have recently been proposed to limit the envi-ronmental costs of infrastructure expansion while maximizing itsbenefits for human development (9, 10). The refinement of thesezoning plans would greatly benefit from more detailed estimatesof the imprint of infrastructure on wildlife populations. Humanfootprint models combine spatial data regarding human activitieswith assessments of their effects to estimate their overall impact(11–13). The burgeoning availability of detailed geospatial layersof infrastructure contrasts with the lack of quantification of theireffects, which still relies on expert knowledge and is mostly basedon single species or local studies (14). As a result, mapping of thearea of influence of infrastructure ranges from a few hundredmeters (15) up to 50 km (10, 11, 16, 17).The main difficulty in quantifying the area of influence of

infrastructure on wildlife, that is, the area over which the eco-logical effects extend into the adjacent landscape [e.g., “road-effect zone” (2)] has been the lack of reliable distance thresholdsfor these effects (18). Most effects on local species abundancesoccur within a specific distance from the infrastructure and leveloff as distance increases (19, 20). For instance, this decrease inpopulation density varies by taxonomic class, with mammalsbeing affected over larger distances than birds (21).

The objective of our work is to assess the spatial extent of theimpacts from infrastructure on wildlife populations at a large scale,based on taxa-specific functional distance-decay curves (Fig. 1). Wefirst examine the pervasiveness of transportation infrastructure inEurope, a continent with extensive data and broad variability inboth infrastructure development and wildlife distribution, andthen, using Spain as an example, we explore how the pervasivenessof infrastructure translates into the distribution of six emblematicspecies of the Iberian fauna, pointing out large-scale effects andstrengthening the evidentiary basis of impact assessments onwildlife at regional or national scales. Finally, we present a methodto model the area of influence of infrastructure and apply it forbirds and mammals in Spain. Worldwide, the Mediterranean Basinis the biodiversity hotspot most affected by urban expansion (4);thus, our results for Spain may help predict the level of threat forother biodiversity hotspots undergoing rapid development.Our results reveal both the pervasiveness of human infrastructure

and its negative influence on wildlife populations, particularlyamong wide-ranging mammals. Despite its limitations, our approachmay represent a useful tool for conservation and land management,enabling (i) assessments of the human footprint of infrastructureor wilderness mapping, (ii) the definition of roadless areas, and(iii) projections of future human influence under alternative sce-narios, as well as supporting strategic infrastructure planning.

ResultsHow Far to the Nearest Infrastructure? Almost a quarter of all landarea in Europe (22.4%) is located within 500 m of the nearesttransport infrastructure, and 50% is within 1.5 km (Table S1).For the EU-28 (the 28 member states currently forming theEuropean Union), these numbers are almost identical (22.8%and 1.5 km, respectively). Ninety-five percent of all Europe is

Significance

Nature is increasingly threatened by rapid infrastructure ex-pansion. For the first time, to our knowledge, we quantify thehigh pervasiveness of transportation infrastructure in all Eu-ropean countries. Unfortunately, spatial definition of the areasecologically affected by infrastructure at large scales is com-plicated. Thus, we present a method for assessing the spatialextent of the impacts on birds and mammals at regional andnational scales. As an illustration, its application to Spainshows that most of the country is affected, predicting moder-ate and severe declines for birds and mammals, respectively.The lack of areas that could be used as controls implies thatscientists may no longer be able to measure the magnitude ofroad effects on wide-ranging mammals in most of Europe.

Author contributions: A.T. designed research; A.T. performed research; A.T. analyzeddata; and A.T., J.A.G.J., and J.C.A. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.1To whom correspondence should be addressed. Email: aurora.torres@mncn.csic.es.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1522488113/-/DCSupplemental.

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located within 9.2 km of a transport infrastructure (within 8 km inthe case of EU-28), with the farthest distances in Iceland (83.5 km).The densest transport network is located in Central Europe,particularly in the three Benelux countries (Belgium, The Neth-erlands, and Luxembourg; Fig. 2), whereas landscapes with lowroad density are located in northern latitudes and in areas withlarge mountain ranges (Alps and Carpathians). Spain stands out asthe country with the highest median and average distances totransport infrastructure (1.9 and 2.7 km, respectively), with theexception of most of the northern countries, namely, Iceland,Norway, Estonia, Finland, Latvia, and Lithuania, as well as thePrincipality of Andorra. This median distance is almost halvedwhen using the more precise Base Cartográfica Nacional (BCN100;centrodedescargas.cnig.es/CentroDescargas/index.jsp) (869 m) in-stead of EuroGlobalMap (EGM; www.eurogeographics.org/content/euroglobalmap), revealing the underrepresentation of transport in-frastructure in the EGM. Aside from transportation infrastructure,50% of all land area in Spain is located within 1.6 km of the nearestbuilt-up area and within 718 m from the nearest impervious in-frastructure (Fig. 3). Most land is located near infrastructure, and theproportion of land added to the accumulation curve rapidly becomessmaller as the distance increases, so 99% of Spanish land is within7.6, 6.4, and 5.2 km from a built-up area, transport corridor, andimpervious infrastructure, respectively, whereas the farthest locationsare at 15.4, 16.6, and 13.4 km, respectively.Regarding the effects of proximity to infrastructure on em-

blematic species, the distribution maps of all six species show thehighest number of cells with positive presence data within thesecond band (at 500–1,000 m from the infrastructure) (Fig. 4).However, prevalence shows differences between taxa; highervalues at increasing distances to transport infrastructure in theSpanish imperial eagle, Iberian lynx, and Brown bear; and noclear pattern in the Tawny owl, Great bustard, and Gray wolf.

What Is the Area of Influence of Infrastructure on Birds and Mammalsin Spain? The area of influence of infrastructure, as reflected bya mean species abundance (MSA) < 0.95 compared with non-disturbed distances, covers 55.5% [confidence interval (CI) =48.3–64.4%] of the country in the case of birds, and extends overalmost all of Spain for mammals (97.9%, CI = 95.1–99.2%). Theresults for transportation infrastructure alone are very similar(birds: 49.4%, CI = 42.6–58.0%; mammals: 95.8%, CI = 91.8–

98.2%). For birds, spatial clusters of low MSA values are clearlyobserved, but many large unaffected areas remain available (Fig.5A), whereas for mammals, low MSA values prevail across Spain(Fig. 5B; MSA values for transport infrastructure alone are shownin Fig. S1). TheseMSA values predict an average decline of 22.6%(CI = 16.7–29.7%; for transport infrastructure alone: 19.0%, CI =9.6–25.6%) in bird numbers and 46.6% (CI = 33.0–60.7%; fortransport infrastructure alone: 42.9%, CI = 29.6–56.9%) in mam-mal numbers compared with the undisturbed situation.

Are All Habitats Similarly Affected? Although all habitat typesshowed similar patterns of proximity to human infrastructure,some differences were observed (Fig. 6A). Farmland is most af-fected by transport infrastructure and built-up areas, and thelowest MSA values are found here (mean ± SD = 0.729 ± 0.277and 0.496 ± 0.168 for birds and mammals, respectively; Fig. 6B).The second most affected habitat is wetlands (birds: mean ± SD =0.790 ± 0.254; mammals: 0.539 ± 0.176,), due mostly to the in-fluence of maritime wetlands (Table S2). Forests and scrublandsshare similar effect values, whereas bare lands are the least af-fected. In the remotest locations (beyond 10 km to imperviousareas), the differences among habitats are more evident. Thoselocations mainly correspond to bare rocks (32.8%), natural grass-lands (23.6%), and sclerophyllous vegetation (22.9%).

DiscussionIn Europe, half of the continent’s surface is located within 1.5 km,and almost all land within 10 km, from a paved road or a railwayline. Riitters and Wickham (22) reported shorter distances to thenearest road in the United States, where 50% of the land waswithin 382 m of a road (compared with 869 m in Spain). However,the US road map at that time included unpaved and private roads.

Fig. 1. Relationships between MSA of birds and mammals and distance toinfrastructure obtained by Benítez-López et al. (21) through metaregressionsand used in the present study to model the area of influence of in-frastructure in Spain. Solid lines represent the MSA curve estimated for birds(gray) and mammals (black) as a function of distance to infrastructure.Dashed lines represent the 95% confidence bands for the predictions.

0 300 600 km

0510

≥ 50

Distance to the nearest transport corridor (km)

Fig. 2. Mapped distances to the nearest transport infrastructure (paved roadsand railways; details are provided in Table S3) in Europe (36 countries; Table S1)based on the small-scale pan-European topographic dataset EGM v7.0 (2014),using a Lambert azimuthal equal area projection. Distances were quantified ata resolution of 50 m for inland Europe and islands larger than 3,000 km2 andranged from 0 to 83.5 km.

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Another reason for the difference is that over much of the lessdeveloped United States, the road system results from the originalsubdivision of land into rectangular ownership parcels with roadsregularly spaced along owner boundaries (23). Thus, the US sys-tem was really designed to minimize the distance to the nearestroad. Given that the more accurate input map of paved roads andrailway lines in Spain halved the estimated distance obtained fromthe EGM, we consider the European estimates to be very con-servative. However, the observed patterns are consistent withprevious measurements of landscape fragmentation, urban sprawl,and wilderness areas (24–26).Spain is one of the European countries less affected by road-

mediated effects and where many roadless areas can still be rec-ognized; however, on the other hand, this country is under a highhuman footprint from a global perspective (27). All of our examplespecies were more frequently distributed at relatively close dis-tances to transportation infrastructure, because most of the land islocated at such short distances (Fig. 3), so wildlife does not havemany options to occupy remote areas. Even so, the first 500-mband is systematically being “avoided” by four species with differ-ent ecological requirements and functional traits (Great bustard,Spanish imperial eagle, Iberian lynx, and Brown bear), even thougha high percentage of land is available within that band (Fig. S2).Given that these analyses are based on occurrences, and that thepresence cells in the first 500-m band probably hold lower numbersof individuals than presence cells in subsequent bands, these fourspecies would not only be found farther from infrastructure if landat such distances were available but could also be less abundant incells that are closer to infrastructure. Also, the increasing preva-lence of some species with higher distances to transport infra-structure (Spanish imperial eagle, Iberian lynx, and Brown bear)suggests that they prefer remote sites or that they were better ableto persist there in past times of strong direct persecution. Thesedetrimental effects at large scale illustrate the high level of exposurefor wide-ranging carnivores, like the critically endangered Iberianlynx, for which road casualties are a major mortality cause (20road-kill mortalities in 2014 in a total population of ca. 320 in-dividuals; www.iberlince.eu/index.php/port/). In contrast, the Tawnyowl and the Gray wolf are known to use areas next to roads (28, 29),whereas the Great bustard is characteristic of cereal farmland, ahabitat strongly pervaded by infrastructure (Fig. 6).

Area of Influence of Human Infrastructure for Birds and Mammals.Proximity to infrastructure contributes to average decreases by

25% and 50% compared with the undisturbed situation in birdsand mammals, respectively, based on data from Benítez-Lópezet al. (21). Moreover, in the case of mammals, there is almost noarea left unaffected from transport infrastructure. For roadecology, this result implies that researchers may no longer beable to measure the whole extent of road effects on wide-rangingmammals as well as birds with large effect-distances, becausecore areas of significant size that could be used as controls arenow almost inexistent, and this implication extends to most ofEurope and a sizeable part of the United States (30) (Fig. 2).Farmland plays an important role in the conservation of bio-

diversity throughout Europe, with more than half of all speciesdepending on this habitat type (31). We found the effects fromimpervious infrastructure to be more evident in farmlands, sothis threat may also be contributing to the biodiversity decline

Fig. 3. Accumulation curves for the proportion of total land area in Spainlocated within a certain distance from the nearest built-up area, transportinfrastructure (paved roads and railways), and impervious infrastructure (in-cluding built-up areas, transport infrastructure, and other sealed surfaces).

Fig. 4. Level of exposure to human infrastructure varies throughout aspecies’ distribution, which we illustrate by considering the distributions ofsix emblematic species of the Mediterranean fauna. The bars (Left, y axis)indicate the proportions of each species’ distribution found within each500-m distance band to transport infrastructure (x axis), whereas the bluedots (Right, y axis) indicate the prevalence for each band (i.e., the ratiobetween the number of cells in which the species was present divided by thetotal number of cells available at such distances in peninsular Spain).

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that has mostly been associated with the agricultural intensificationprocess (32, 33). Moreover, in farmlands and other open-habitattypes like bare lands, the infrastructure imprint is potentially widerthan what our results indicate because of the higher visibility ofinfrastructure (14, 21). A future meta-analysis should determinethe specific distance decay functions for different types of habitatonce enough data are available.Areas characterized by a low imprint of infrastructure may

clearly be priority sites for protecting roadless areas (17, 34).However, some places still hosting important biodiversity are nolonger in remote areas, suggesting that extinction debts are likely.In this regard, the reductions predicted for birds and mammals areinherently based on how we have managed wildlife over the pastdecades in the affected areas. Hence, areas with a high imprint ofinfrastructure have become challenges for conservation planning,where potential extinctions (which are most likely debts at pre-sent) should be prevented by reinforcing remnant populations andrestoring vital ecological processes.

Applicability of the Approach and Next Steps.The approach explainedhere for Spain provides the most detailed picture obtainable now-adays of the magnitude and spatial distribution of infrastructure-induced effects on birds and mammals, is readily transferableto other places, and can contribute to future regional and national

infrastructure planning. However, it has certain limitations:(i) geographic bias, (ii) undistinguished effects of differentinfrastructure types, and (iii) low inferential strength of the studiesconsidered in the meta-analysis. There is a major geographicbias in the research conducted about the impacts of roads onwildlife, with vast areas of the globe being largely ignored (35).This aspect is not a major problem for the present study be-cause species from Europe are well represented in Benítez-López et al.’s meta-analysis (21), but the applicability of thisapproach beyond Europe and North America may be limited.As for the second limitation cited above, previous studies havefound different effect distances for different road types ortraffic levels (36), which would affect the accuracy of estimates.However, there is still a substantial debate around this topic;thus, we decided to ignore differences between infrastructuretypes to retain consistency with Benítez-López et al. (21), whodid not find a significant difference. Finally, most studies used inthe meta-analysis followed a control-impact study design, bycomparing bird and mammal numbers in the impacted area with areference state. Although this design is widely used to quantifyimpacts from a variety of pressures (e.g., 37), it has lower in-ferential strength than a before-after-control-impact (BACI) de-sign (38). Unfortunately, due to time and logistical constraints, theproportion of BACI-designed studies is still very small (39).Most of the urban development and more than one-third of the

transportation infrastructure expected to exist by 2050 are not yetbuilt (3, 4). Nine-tenths of all road construction in the coming 40 yis expected to occur in developing nations (3, 4) and to be aimed atimproving the conditions of large human populations with lowaverage incomes. Infrastructure-mediated impacts are expected tobe most damaging in species-rich ecosystems, such as tropicalforests, where few roads currently exist (9, 40). Our approach canbe used in those areas for regulating the expansion of new in-frastructure, supporting regional planning and road developmentschemes, and increasing the efforts to mitigate their detrimentaleffects. As infrastructure building progresses, it will be increasinglydifficult to quantify its effects, because the core areas that can beused as control sites will be rare and more isolated. Therefore,there is a trade-off between the uncertainty of using effect mea-sures from studies with low inferential strength and the urgent needto respond to rapid development using the evidence available to-day, in consideration of the precautionary principle. We propose toovercome, at least partially, the weaknesses of our approachthrough regular updates of the wildlife-response meta-analysis(21). The addition of new species’ datasets would allow fine-tuningof the parameters of the response functions, as well as revealing thedifferences among habitat types. Moreover, the investigation ofgroups of species with similar functional traits that may providenew response functions would be a useful means of developing theapplicability of this study further, when conservation needs to befocused on particular taxa or wildlife communities or where thereare fewer data available. In general, large-sized mammals withlower reproductive rates and larger home ranges are more sus-ceptible to negative road effects (41), but for tropical areas, wewould expect larger effect distances on apex predators, large-sizedmammals and birds, and forest specialists because of their markedavoidance behavior (40). As a first step, we have conducted a re-view of five major traits, namely, body mass, home range size, re-productive rate, longevity, and trophic level, of the 232 speciesincluded in the study of Benítez-López et al. (21). By creating thisdatabase (available in Dataset S1) we intend to ease the way forbroader application of the insights derived from this study and giveimpetus to further applied research in developing regions, whichare in great need of solutions and increased representation (7, 42).In moving forward, we are making a call to scientists and practi-tioners to coordinate a database and network of studies aboutinfrastructure-mediated impacts on wildlife populations across

A

B

Fig. 5. Predicted MSA of birds (A) and mammals (B) across Spain (Left; twolarge maps) according to proximity to human infrastructure, based on theeffect distance-decay curves fitted for empirical data by Benítez-López et al.(21). (Right) Adjacent smaller maps represent the upper (Top) and lower(Bottom) CIs. MSA layers were reclassified into six effect intensity zones forrepresentation. (Upper Right) Small map showing the location of five majorcities is included for reference.

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ecosystems and geographical areas (43) and to make use of thisapproach as a powerful conservation planning tool.

Materials and MethodsDistance Analysis. We measured proximity to transportation infrastructure ininland Europe (and islands larger than 3,000 km2, as well as Malta) based onthe EGM v7.0 (1:1,000,000 scale; EuroGeographics, 2014), a pan-Europeanopen dataset containing seamless and harmonized geographic information.We exclusively considered paved roads and railway lines, excluding aban-doned and underground sections (Table S3). We then calculated Euclideandistances to the nearest transport infrastructure for 36 countries, at a res-olution of 50 m.

The consistency of the EGM database was assessed against the most recentand precise geographical information system (GIS) database of transportationinfrastructure for Spain (BCN100, 1:100,000 scale; National Geographic Instituteof Spain, 2014; Table S4). In addition, we measured the pervasiveness of built-up areas and all infrastructure combined. We used the Spanish Land Cover andUse Information System (1:25,000 scale; National Geographic Institute of Spain,2005; www.siose.es) to create the map of built-up areas (Table S5) and otherimpervious infrastructure (e.g., parking lots, irrigation ponds; Table S5). Allmaps were converted to raster format (15 m). For each cell, we calculated theEuclidean distance to the nearest transport infrastructure, built-up area, and allimpervious infrastructure combined. We were not able to calculate distancesfor Europe and Spain for even higher resolution because of computationallimitations for smaller pixel sizes.

Effects of Proximity to Transportation Infrastructure on Species Distribution.We overlaid distance maps to transportation infrastructure with distributionmaps (10 × 10-km cells) (44) of six emblematic species of the Iberian faunaknown to be negatively affected by roads at local scales: Strix aluco (Tawnyowl), Otis tarda (Great bustard), Aquila adalberti (Spanish imperial eagle),Canis lupus (Gray wolf), Lynx pardinus (Iberian lynx), and Ursus arctos (Brownbear) (28, 29, 38, 45–47). For each species, we quantified the median distanceto transport infrastructure in presence cells and classified resulting dis-tances by bands of 500 m from the nearest infrastructure for graphicalrepresentation as a normalized histogram. Most wildlife species affected

by human development have escape distances on this order of magnitude orhigher and home ranges of many hectares to several square kilometers, so thisbandwidth seemed appropriate. A more detailed, continuous distribution ofeach species in relation to the nearest transport infrastructure and considering allpixels in each distribution cell is shown in Fig. S2. Counting how many presencecells fell into each 500-m band, we calculated both the relative proportion ofthe species distribution that each band represented and their prevalence (i.e.,the presence cells divided by the total number of cells available in each band).

Modeling the Area of Influence of Infrastructure. We estimated the overalleffect of the Spanish transportation, and other impervious infrastructure onmean species abundances for birds (MSAb) and mammals (MSAm) and de-termined the spatial distribution of the predicted effect zones. The MSA in-dicator expresses the difference between the averaged mean abundance forvarious species in the proximity of an infrastructure relative to their abun-dance in a control location free of infrastructure (48). MSA values range fromno individuals remaining (0) to no effect on species abundance (1). Using ameta-analytical approach, Benítez-López et al. (21), within the framework ofthe Global Biodiversity model GLOBIO assessments, tested the relationshipbetweenMSA and distance to infrastructure through generalized linear mixedmodels (GLMM), and provided functional distance-decay response curves forbirds and mammals (Fig. 1). This study was undertaken using 49 studies and 90datasets, which included 201 bird species (52% present in Spain) and 33 mammalspecies (12% present in Spain), but it shows a substantial geographic bias be-cause 88% of the studies came from Europe and North America. In addition, themammal datasets were biased toward ungulates (representing 58.1% of thedatasets considered, whereas carnivores, rodents, proboscideans, and lagomorphsrepresented, respectively, 16.3%. 18.1%, 4.7%, and 2.3% of the datasets). How-ever, because ungulates are species with usually very large home ranges andmany large carnivores worldwide have also been shown to be severely affectedby the presence of roads, the findings are likely to be applicable to many otherplaces worldwide. These functions have been previously applied only once, toassess the impacts on roads in areas of high diversity value in Sweden (49).

Based on the statistics from themetaregressions, we generated two spatialdatasets about the predicted infrastructure effects on birds and mammalsand four spatial datasets showing the associated upper and lower 95% CIs ata resolution of 15 m by applying a logit transformation

A B

Fig. 6. Variations through habitat types in the exposure to human infrastructure and in predicted detrimental effects on birds and mammals in Spain. (A) Boxplots of the distances to the nearest built-up area, transport infrastructure, and all impervious infrastructure combined for the five habitat types considered.(B) Proportion of land inside each intensity zone (Fig. 2) for birds and mammals per habitat type, based on proximity to impervious infrastructure (outside circle) ortransport infrastructure alone (inside circle) (colors correspond to MSA legend in Fig. 2). Habitat illustrations courtesy of Marina Pinilla (Valencia, Spain).

8476 | www.pnas.org/cgi/doi/10.1073/pnas.1522488113 Torres et al.

MSAðestimatedÞ =  eu

1+   eu  ,

where MSA(estimated) is the predicted MSA at the observed distance from theinfrastructure and u is the linear equation describing the log-transformedprobability of the presence of a species at a certain distance x from theinfrastructure

u= ln �

Pi1−   Pi

�= β0 + β1x,

where β0 is the intercept (β0-birds = −0.863; β0-mammals = −0.607) and β1is the regression coefficient for the distance (β1-birds = 0.00447 m−1;β1-mammals = 0.00083 m−1). The coefficients were obtained from the authorsof the meta-analysis. The distance variable x could take the value of each cellin the raster containing the Euclidean distance from an infrastructure. Giventhat 61.1% of the datasets considered by Benítez-López et al. (21) correspondedto road effects and the rest to other infrastructure, we used both a raster ofdistances to transportation infrastructure alone (as a conservative measure) and

another with all impervious infrastructure combined to explore the sensitivityof our estimates.

Finally, we analyzed the overall effect of the infrastructure by habitattypes on a national scale, by overlaying distance and MSA layers on a landcover map [European Commission Program to Coordinate Information onthe Environment (Corine) land cover 2006; www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-version-3] and calculating statistics for eachhabitat. We report the results for five major classes in Results, namely,wetland, bare land (open space with little or no vegetation), farmland,scrubland, and forest, but the results for land cover classes at finer thematicresolution are available in Table S2.

ACKNOWLEDGMENTS. We thank A. Benítez, R. Alkemade, and P. Verweijfor sharing the statistics from their meta-analysis; R. Early and F. Ferri-Yañez for comments on an earlier version of this paper; and E. T. Game,M. D. Madhusudan, and two anonymous reviewers for useful commentsthat greatly improved the manuscript. The Spanish Ministry for Scienceand Innovation provided funding for this study (Project CGL2008-02567).A.T.’s work was funded through a FPU (Formación de Profesorado Univer-sitario) PhD grant from the Spanish Ministry of Education.

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