Assessing large-scale wildlife responses to human infrastructure development

Significance

Nature is increasingly threatened by rapid infrastructure expansion. For the first time, to our knowledge, we quantify the high pervasiveness of transportation infrastructure in all European countries. Unfortunately, spatial definition of the areas ecologically affected by infrastructure at large scales is complicated. Thus, we present a method for assessing the spatial extent of the impacts on birds and mammals at regional and national scales. As an illustration, its application to Spain shows that most of the country is affected, predicting moderate and severe declines for birds and mammals, respectively. The lack of areas that could be used as controls implies that scientists may no longer be able to measure the magnitude of road effects on wide-ranging mammals in most of Europe.

Abstract

Habitat loss and deterioration represent the main threats to wildlife species, and are closely linked to the expansion of roads and human settlements. Unfortunately, large-scale effects of these structures remain generally overlooked. Here, we analyzed the European transportation infrastructure network and found that 50% of the continent is within 1.5 km of transportation infrastructure. We present a method for assessing the impacts from infrastructure on wildlife, based on functional response curves describing density reductions in birds and mammals (e.g., road-effect zones), and apply it to Spain as a case study. The imprint of infrastructure extends over most of the country (55.5% in the case of birds and 97.9% for mammals), with moderate declines predicted for birds (22.6% of individuals) and severe declines predicted for mammals (46.6%). Despite certain limitations, we suggest the approach proposed is widely applicable to the evaluation of effects of planned infrastructure developments under multiple scenarios, and propose an internationally coordinated strategy to update and improve it in the future.

Habitat loss and degradation are the primary drivers of the decline and extinction of wildlife populations in terrestrial ecosystems (1), with the main precursors of these impacts being roads and human settlements (2). If current trends continue, by 2030, urban areas will increase by 1.2 million km² globally and, by 2050, our planet will accommodate more paved-lane kilometers than required to reach Mars (3, 4). The largest expected infrastructural undertakings will occur in developing nations (3, 4), including many regions that sustain exceptional levels of biodiversity and vital ecosystem services. These structures will alter 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 remain unknown (8). Global and continental schemes for prioritizing road building have recently been proposed to limit the environmental costs of infrastructure expansion while maximizing its benefits for human development (9, 10). The refinement of these zoning plans would greatly benefit from more detailed estimates of the imprint of infrastructure on wildlife populations. Human footprint models combine spatial data regarding human activities with assessments of their effects to estimate their overall impact (11–13). The burgeoning availability of detailed geospatial layers of infrastructure contrasts with the lack of quantification of their effects, which still relies on expert knowledge and is mostly based on single species or local studies (14). As a result, mapping of the area of influence of infrastructure ranges from a few hundred meters (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 ecological effects extend into the adjacent landscape [e.g., “road-effect zone” (2)] has been the lack of reliable distance thresholds for these effects (18). Most effects on local species abundances occur within a specific distance from the infrastructure and level off as distance increases (19, 20). For instance, this decrease in population density varies by taxonomic class, with mammals being affected over larger distances than birds (21).

The objective of our work is to assess the spatial extent of the impacts from infrastructure on wildlife populations at a large scale, based on taxa-specific functional distance-decay curves (Fig. 1). We first examine the pervasiveness of transportation infrastructure in Europe, a continent with extensive data and broad variability in both infrastructure development and wildlife distribution, and then, using Spain as an example, we explore how the pervasiveness of infrastructure translates into the distribution of six emblematic species of the Iberian fauna, pointing out large-scale effects and strengthening the evidentiary basis of impact assessments on wildlife at regional or national scales. Finally, we present a method to model the area of influence of infrastructure and apply it for birds and mammals in Spain. Worldwide, the Mediterranean Basin is the biodiversity hotspot most affected by urban expansion (4); thus, our results for Spain may help predict the level of threat for other biodiversity hotspots undergoing rapid development.

Fig. 1.

Relationships between MSA of birds and mammals and distance to infrastructure obtained by Benítez-López et al. (21) through metaregressions and used in the present study to model the area of influence of infrastructure 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.

Our results reveal both the pervasiveness of human infrastructure and its negative influence on wildlife populations, particularly among wide-ranging mammals. Despite its limitations, our approach may represent a useful tool for conservation and land management, enabling (i) assessments of the human footprint of infrastructure or wilderness mapping, (ii) the definition of roadless areas, and (iii) projections of future human influence under alternative scenarios, as well as supporting strategic infrastructure planning.

Results

How Far to the Nearest Infrastructure?

Almost a quarter of all land area in Europe (22.4%) is located within 500 m of the nearest transport infrastructure, and 50% is within 1.5 km (Table S1). For the EU-28 (the 28 member states currently forming the European Union), these numbers are almost identical (22.8% and 1.5 km, respectively). Ninety-five percent of all Europe is located within 9.2 km of a transport infrastructure (within 8 km in the 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 Netherlands, and Luxembourg; Fig. 2), whereas landscapes with low road density are located in northern latitudes and in areas with large mountain ranges (Alps and Carpathians). Spain stands out as the country with the highest median and average distances to transport infrastructure (1.9 and 2.7 km, respectively), with the exception of most of the northern countries, namely, Iceland, Norway, Estonia, Finland, Latvia, and Lithuania, as well as the Principality of Andorra. This median distance is almost halved when using the more precise Base Cartográfica Nacional (BCN100; centrodedescargas.cnig.es/CentroDescargas/index.jsp) (869 m) instead of EuroGlobalMap (EGM; www.eurogeographics.org/content/euroglobalmap), revealing the underrepresentation of transport infrastructure in the EGM. Aside from transportation infrastructure, 50% of all land area in Spain is located within 1.6 km of the nearest built-up area and within 718 m from the nearest impervious infrastructure (Fig. 3). Most land is located near infrastructure, and the proportion of land added to the accumulation curve rapidly becomes smaller as the distance increases, so 99% of Spanish land is within 7.6, 6.4, and 5.2 km from a built-up area, transport corridor, and impervious infrastructure, respectively, whereas the farthest locations are at 15.4, 16.6, and 13.4 km, respectively.

Table S1.

Summary statistics of the distances to the nearest transport infrastructure (meters) per country sorted by increasing median distance

Ranking	Country	ICC*	Median	Maximum	Mean	SD
	Europe		1,543	83,479	2,849.96	4,741.27
	EU-28†		1,513	54,294	2,539.32	3,508.17
1	Belgium	BE	565	6,436	785.59	757.64
2	Luxembourg	LU	583	4,560	794.69	735.68
3	Netherlands	NL	873	13,024	1,300.04	1,502.07
4	Czech Republic	CZ	901	10,169	1,228.56	1,166.19
5	United Kingdom	GB-ND	939	20,334	1,694.28	2,224.41
6	Germany	DE	992	11,950	1,358.64	1,304.25
7	Croatia	HR	1,011	10,890	1,448.82	1,438.23
8	Denmark	DK	1,025	12,264	1,422.30	1,374.46
9	Slovenia	SI	1,025	10,223	1,380.91	1,288.84
10	Liechtenstein	LI	1,060	5,408	1,451.03	1,281.61
11	Ukraine	UA	1,096	22,500	1,523.20	1,592.70
12	Hungary	HU	1,159	10,630	1,540.88	1,401.27
13	Moldova	MD	1,192	9,190	1,484.90	1,247.54
14	Ireland	IE	1,234	13,144	1,677.31	1,579.85
15	Cyprus	CY	1,237	15,140	1,766.36	1,768.91
16	Italy	IT	1,358	21,955	2,037.33	2,132.04
17	France	FR	1,394	22,040	2,121.17	2,287.91
18	Greece	GR	1,443	36,176	2,103.20	2,296.25
19	Switzerland	CH	1,503	17,952	2,315.98	2,376.10
20	Romania	RO	1,595	38,181	2,345.24	2,665.37
21	Poland	PL	1,686	16,174	2,242.81	2,024.56
22	Portugal	PT	1,708	20,539	2,414.21	2,347.30
23	Sweden	SE	1,735	54,294	4,156.82	6,681.55
24	Bulgaria	BG	1,750	17,862	2,364.78	2,229.73
25	Austria	AT	1,812	18,752	2,654.19	2,657.55
26	Serbia	RS	1,833	16,813	2,457.38	2,249.60
27	Slovakia	SK	1,860	14,502	2,454.31	2,208.29
28	Republic of Macedonia	MK	1,882	15,093	2,453.27	2,142.14
29	Spain	ES	1,900	31,727	2,660.43	2,648.00
30	Andorra	AD	1,930	9,253	2,290.22	1,754.44
31	Lithuania	LT	2,360	19,227	3,033.88	2,609.37
32	Latvia	LV	2,901	19,872	3,528.01	2,863.85
33	Finland	FI	2,944	49,202	4,841.46	5,830.66
34	Estonia	EE	2,951	31,433	4,078.30	3,954.39
35	Norway	NO	3,326	56,455	5,653.55	6,751.04
36	Iceland	IS	9,722	83,479	16,497.40	17,510.10

Details of the infrastructure included and their buffer areas are available in Table S3. It should be noted that distances were quantified for inland Europe and islands larger than 3,000 km² (except Malta). Thus, results for countries with islands of small size would slightly change.

^*International country codes (ICC) used in the EGM dataset.

^†EU-28 refers to the current 28 member states of the European Union (since 2013).

Fig. 2.

Mapped distances to the nearest transport infrastructure (paved roads and 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 at a resolution of 50 m for inland Europe and islands larger than 3,000 km² and ranged from 0 to 83.5 km.

Fig. 3.

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

Regarding the effects of proximity to infrastructure on emblematic species, the distribution maps of all six species show the highest number of cells with positive presence data within the second band (at 500–1,000 m from the infrastructure) (Fig. 4). However, prevalence shows differences between taxa; higher values at increasing distances to transport infrastructure in the Spanish imperial eagle, Iberian lynx, and Brown bear; and no clear pattern in the Tawny owl, Great bustard, and Gray wolf.

Fig. 4.

Level of exposure to human infrastructure varies throughout a species’ distribution, which we illustrate by considering the distributions of six emblematic species of the Mediterranean fauna. The bars (Left, y axis) indicate the proportions of each species’ distribution found within each 500-m distance band to transport infrastructure (x axis), whereas the blue dots (Right, y axis) indicate the prevalence for each band (i.e., the ratio between the number of cells in which the species was present divided by the total number of cells available at such distances in peninsular Spain).

What Is the Area of Influence of Infrastructure on Birds and Mammals in Spain?

The area of influence of infrastructure, as reflected by a mean species abundance (MSA) < 0.95 compared with nondisturbed distances, covers 55.5% [confidence interval (CI) = 48.3–64.4%] of the country in the case of birds, and extends over almost all of Spain for mammals (97.9%, CI = 95.1–99.2%). The results 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 clearly observed, 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 shown in Fig. S1). These MSA 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%; for transport infrastructure alone: 42.9%, CI = 29.6–56.9%) in mammal numbers compared with the undisturbed situation.

Fig. 5.

Predicted MSA of birds (A) and mammals (B) across Spain (Left; two large maps) according to proximity to human infrastructure, based on the effect 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 for representation. (Upper Right) Small map showing the location of five major cities is included for reference.

Fig. S1.

Predicted MSA of birds (A) and mammals (B) across Spain (Left, two large maps) according to proximity to transportation infrastructure. (Right) Adjacent smaller maps represent the upper (Top) and lower (Bottom) CIs. MSA layers were reclassified into six effect intensity zones for representation.

Are All Habitats Similarly Affected?

Although all habitat types showed similar patterns of proximity to human infrastructure, some differences were observed (Fig. 6A). Farmland is most affected by transport infrastructure and built-up areas, and the lowest MSA values are found here (mean ± SD = 0.729 ± 0.277 and 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 influence of maritime wetlands (Table S2). Forests and scrublands share similar effect values, whereas bare lands are the least affected. In the remotest locations (beyond 10 km to impervious areas), the differences among habitats are more evident. Those locations mainly correspond to bare rocks (32.8%), natural grasslands (23.6%), and sclerophyllous vegetation (22.9%).

Fig. 6.

Variations through habitat types in the exposure to human infrastructure and in predicted detrimental effects on birds and mammals in Spain. (A) Box plots 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) or transport infrastructure alone (inside circle) (colors correspond to MSA legend in Fig. 2). Habitat illustrations courtesy of Marina Pinilla (Valencia, Spain).

Table S2.

Summary of the distances to the nearest built-up area, transport infrastructure, and all impervious infrastructure combined, based on Corine land cover (2006) classes

Level*	Label code	Label name	Built-up areas		Transport infrastructure		All impervious infrastructure
			Range, km	Median, km	Range, km	Median, km	Range, km	Median, km
L1	2	Agricultural areas
L2	21	Arable land	0–13.050	1.323	0–16.195	0.724	0–9.972	0.560
L3	211	Nonirrigated arable land	0–13.050	1.463	0–16.195	0.783	0–9.972	0.646
L3	212	Permanently irrigated land	0–10.775	0.813	0–11.761	0.500	0–7.397	0.320
L3	213	Rice fields	0–6.601	1.161	0–10.866	1.124	0–3.408	0.255
L2	22	Permanent crops	0–10.954	1.260	0–11.248	0.630	0–6.945	0.496
L3	221	Vineyards	0–10.038	1.612	0–8.166	0.697	0–6.945	0.594
L3	222	Fruit trees and berry plantations	0–10.954	0.684	0–8.772	0.411	0–6.869	0.279
L3	223	Olive groves	0–10.771	1.442	0–11.248	0.735	0–6.486	0.599
L2	23	Pasture	0–9.838	0.653	0–9.827	0.255	0–8.637	0.212
L2	24	Heterogeneous agricultural areas	0–15.344	1.288	0–12.447	0.658	0–10.358	0.530
L3	241	Annual crops associated with permanent crops	0–6.146	1.077	0–5.790	0.495	0–4.044	0.422
L3	242	Complex cultivation patterns	0–11.622	0.845	0–10.402	0.429	0–7.597	0.331
L3	243	Land principally occupied by agriculture, with significant areas of natural vegetation	0–12.466	1.179	0–10.352	0.636	0–10.358	0.536
L3	244	Agro-forestry areas	0–15.344	2.193	0–12.447	1.277	0–8.981	0.966
L1	3	Forest and seminatural areas
L2	31	Forests	0–15.436	1.935	0–12.056	1.124	0–10.993	1.004
L3	311	Broad-leaved forest	0–15.436	1.794	0–12.056	1.081	0–10.478	0.959
L3	312	Coniferous forest	0–14.287	2.280	0–11.97	1.320	0–10.993	1.188
L3	313	Mixed forest	0–11.561	1.339	0–8.98	0.753	0–8.927	0.660
L2	32	Scrub and/or herbaceous vegetation associations	0–15.444	1.991	0–13.329	1.190	0–13.324	1.033
L3	321	Natural grasslands	0–14.469	1.794	0–13.329	1.023	0–13.324	0.851
L3	322	Moors and heathland	0–11.744	1.576	0–10.406	0.945	0–10.405	0.868
L3	323	Sclerophyllous vegetation	0–15.306	2.036	0–12.751	1.250	0–11.172	1.094
L3	324	Transitional woodland-shrub	0–15.444	2.153	0–13.011	1.277	0–11.464	1.110
L2	33	Open spaces with little or no vegetation	0–14.522	2.100	0–13.400	1.541	0–13.395	1.271
L3	331	Beaches, dunes, sands	0–9.493	0.933	0–12.441	0.566	0–7.398	0.423
L3	332	Bare rocks	0–14.522	3.124	0–13.400	2.590	0–13.395	2.364
L3	333	Sparsely vegetated areas	0–13.318	1.958	0–12.35	1.442	0–12.35	1.161
L3	334	Burnt areas	0–11.552	2.312	0–7.680	1.214	0–7.122	1.108
L3	335	Glaciers and perpetual snow	0–10.645	4.727	0–9.246	4.854	0–9.241	4.717
L1	4	Wetlands
L2	41	Inland wetlands	0–9.936	3.679	0–16.566	6.318	0–7.176	1.100
L3	411	Inland marshes	0–9.936	3.694	0–16.566	6.392	0–7.176	1.095
L3	412	Peat bogs	0.045–7.447	2.430	0–6.826	2.128	0–5.444	1.745
L2	42	Maritime wetlands	0–9.871	0.865	0–10.698	0.771	0–5.312	0.450
L3	421	Salt marshes	0–9.709	0.777	0–10.698	0.720	0–5.312	0.414
L3	422	Salines	0–9.871	1.125	0–9.449	1.005	0–3.603	0.564
L3	423	Intertidal flats	0–2.565	0.375	0–2.375	0.294	0–2.013	0.212

Results for artificial cover types and water bodies are not presented in this table.

^*Corine land cover uses a hierarchical scheme of three levels to describe land cover.

Discussion

In 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 railway line. Riitters and Wickham (22) reported shorter distances to the nearest road in the United States, where 50% of the land was within 382 m of a road (compared with 869 m in Spain). However, the US road map at that time included unpaved and private roads. Another reason for the difference is that over much of the less developed United States, the road system results from the original subdivision of land into rectangular ownership parcels with roads regularly spaced along owner boundaries (23). Thus, the US system was really designed to minimize the distance to the nearest road. Given that the more accurate input map of paved roads and railway lines in Spain halved the estimated distance obtained from the EGM, we consider the European estimates to be very conservative. However, the observed patterns are consistent with previous 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 recognized; however, on the other hand, this country is under a high human footprint from a global perspective (27). All of our example species were more frequently distributed at relatively close distances to transportation infrastructure, because most of the land is located at such short distances (Fig. 3), so wildlife does not have many options to occupy remote areas. Even so, the first 500-m band is systematically being “avoided” by four species with different ecological requirements and functional traits (Great bustard, Spanish imperial eagle, Iberian lynx, and Brown bear), even though a high percentage of land is available within that band (Fig. S2). Given that these analyses are based on occurrences, and that the presence cells in the first 500-m band probably hold lower numbers of individuals than presence cells in subsequent bands, these four species would not only be found farther from infrastructure if land at such distances were available but could also be less abundant in cells that are closer to infrastructure. Also, the increasing prevalence of some species with higher distances to transport infrastructure (Spanish imperial eagle, Iberian lynx, and Brown bear) suggests that they prefer remote sites or that they were better able to persist there in past times of strong direct persecution. These detrimental effects at large scale illustrate the high level of exposure for wide-ranging carnivores, like the critically endangered Iberian lynx, for which road casualties are a major mortality cause (20 road-kill mortalities in 2014 in a total population of ca. 320 individuals; www.iberlince.eu/index.php/port/). In contrast, the Tawny owl and the Gray wolf are known to use areas next to roads (28, 29), whereas the Great bustard is characteristic of cereal farmland, a habitat strongly pervaded by infrastructure (Fig. 6).

Fig. S2.

(A) Accumulation curves for the proportion of total land area in the distribution map of each species and in peninsular Spain, in relation to the distance from the nearest transport infrastructure (paved roads and railways). (B) Detailed view of these curves within the first 500 m.

Fig. S3.

Proportion of the distribution of each species inside each intensity zone, based on the MSA predicted for birds and mammals as a function of proximity to transportation infrastructure (colors correspond to the colors in Figs. 5 and 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 birds and mammals, respectively, based on data from Benítez-López et al. (21). Moreover, in the case of mammals, there is almost no area left unaffected from transport infrastructure. For road ecology, this result implies that researchers may no longer be able to measure the whole extent of road effects on wide-ranging mammals as well as birds with large effect-distances, because core areas of significant size that could be used as controls are now almost inexistent, and this implication extends to most of Europe and a sizeable part of the United States (30) (Fig. 2).

Farmland plays an important role in the conservation of biodiversity throughout Europe, with more than half of all species depending on this habitat type (31). We found the effects from impervious infrastructure to be more evident in farmlands, so this threat may also be contributing to the biodiversity decline that has mostly been associated with the agricultural intensification process (32, 33). Moreover, in farmlands and other open-habitat types like bare lands, the infrastructure imprint is potentially wider than what our results indicate because of the higher visibility of infrastructure (14, 21). A future meta-analysis should determine the specific distance decay functions for different types of habitat once 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 no longer in remote areas, suggesting that extinction debts are likely. In this regard, the reductions predicted for birds and mammals are inherently based on how we have managed wildlife over the past decades in the affected areas. Hence, areas with a high imprint of infrastructure have become challenges for conservation planning, where potential extinctions (which are most likely debts at present) should be prevented by reinforcing remnant populations and restoring vital ecological processes.

Applicability of the Approach and Next Steps.

The approach explained here for Spain provides the most detailed picture obtainable nowadays of the magnitude and spatial distribution of infrastructure-induced effects on birds and mammals, is readily transferable to other places, and can contribute to future regional and national infrastructure planning. However, it has certain limitations: (i) geographic bias, (ii) undistinguished effects of different infrastructure types, and (iii) low inferential strength of the studies considered in the meta-analysis. There is a major geographic bias in the research conducted about the impacts of roads on wildlife, with vast areas of the globe being largely ignored (35). This aspect is not a major problem for the present study because species from Europe are well represented in Benítez-López et al.’s meta-analysis (21), but the applicability of this approach beyond Europe and North America may be limited. As for the second limitation cited above, previous studies have found different effect distances for different road types or traffic 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 infrastructure types to retain consistency with Benítez-López et al. (21), who did not find a significant difference. Finally, most studies used in the meta-analysis followed a control-impact study design, by comparing bird and mammal numbers in the impacted area with a reference state. Although this design is widely used to quantify impacts from a variety of pressures (e.g., 37), it has lower inferential strength than a before-after-control-impact (BACI) design (38). Unfortunately, due to time and logistical constraints, the proportion 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 yet built (3, 4). Nine-tenths of all road construction in the coming 40 y is expected to occur in developing nations (3, 4) and to be aimed at improving the conditions of large human populations with low average incomes. Infrastructure-mediated impacts are expected to be most damaging in species-rich ecosystems, such as tropical forests, where few roads currently exist (9, 40). Our approach can be used in those areas for regulating the expansion of new infrastructure, supporting regional planning and road development schemes, and increasing the efforts to mitigate their detrimental effects. As infrastructure building progresses, it will be increasingly difficult to quantify its effects, because the core areas that can be used as control sites will be rare and more isolated. Therefore, there is a trade-off between the uncertainty of using effect measures from studies with low inferential strength and the urgent need to respond to rapid development using the evidence available today, in consideration of the precautionary principle. We propose to overcome, at least partially, the weaknesses of our approach through regular updates of the wildlife-response meta-analysis (21). The addition of new species’ datasets would allow fine-tuning of the parameters of the response functions, as well as revealing the differences among habitat types. Moreover, the investigation of groups of species with similar functional traits that may provide new response functions would be a useful means of developing the applicability of this study further, when conservation needs to be focused on particular taxa or wildlife communities or where there are fewer data available. In general, large-sized mammals with lower reproductive rates and larger home ranges are more susceptible to negative road effects (41), but for tropical areas, we would expect larger effect distances on apex predators, large-sized mammals and birds, and forest specialists because of their marked avoidance behavior (40). As a first step, we have conducted a review of five major traits, namely, body mass, home range size, reproductive rate, longevity, and trophic level, of the 232 species included in the study of Benítez-López et al. (21). By creating this database (available in Dataset S1) we intend to ease the way for broader application of the insights derived from this study and give impetus to further applied research in developing regions, which are in great need of solutions and increased representation (7, 42). In moving forward, we are making a call to scientists and practitioners to coordinate a database and network of studies about infrastructure-mediated impacts on wildlife populations across ecosystems and geographical areas (43) and to make use of this approach as a powerful conservation planning tool.

Materials and Methods

Distance Analysis.

We measured proximity to transportation infrastructure in inland Europe (and islands larger than 3,000 km², as well as Malta) based on the EGM v7.0 (1:1,000,000 scale; EuroGeographics, 2014), a pan-European open dataset containing seamless and harmonized geographic information. We exclusively considered paved roads and railway lines, excluding abandoned and underground sections (Table S3). We then calculated Euclidean distances to the nearest transport infrastructure for 36 countries, at a resolution of 50 m.

Table S3.

Linear transport infrastructure and its corresponding buffer distance (applied on either side) considered in this study for Europe

Element	Buffer, m
Motorway	15
Primary route	10
Secondary route	6
Local route	5
High-speed railway	4
Conventional railway	2

Data are from the small-scale pan-European topographic dataset EGM v7.0 (1:1,000,000 scale; 2013). Underground or abandoned sections were excluded, as well as roads whose surface type was loose/unpaved. Settlements (population ≥ 50,000 inhabitants and size ≥ 0.5 km²) were merged to this layer because transport infrastructure inside urban areas is commonly underrepresented.

The consistency of the EGM database was assessed against the most recent and precise geographical information system (GIS) database of transportation infrastructure for Spain (BCN100, 1:100,000 scale; National Geographic Institute of Spain, 2014; Table S4). In addition, we measured the pervasiveness of built-up areas and all infrastructure combined. We used the Spanish Land Cover and Use 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 other impervious infrastructure (e.g., parking lots, irrigation ponds; Table S5). All maps were converted to raster format (15 m). For each cell, we calculated the Euclidean distance to the nearest transport infrastructure, built-up area, and all impervious infrastructure combined. We were not able to calculate distances for Europe and Spain for even higher resolution because of computational limitations for smaller pixel sizes.

Table S4.

Linear transport infrastructure and its corresponding buffer distance (applied on either side) considered in this study for Spain

Element	Buffer, m
Motorway (autopista)	15
Motorway (autovía)	15
National road	10
Autonomous road	6
Connecting road	5
Street	5
High-speed railway	4
Conventional railway	2

Data are from the BCN100 database (1:100,000 scale; National Geographic Institute of Spain, 2014). Underground or abandoned sections were excluded, as well as unpaved roads.

Table S5.

Attribution of land covers from the Spanish Land Cover and Use Information System (SIOSE) project to built-up areas and impervious infrastructure

Artificial cover types	Class (English)	Class (Spanish)*	Label	Included as built-up area	Included as impervious infrastructure
Simple cover types
	Building	Edificación	EDF	Yes	Yes
	Green space	Zona verde artificial y arbolado urbano	ZAU	Yes, if size ≤ 4 ha†	Yes, if size ≤ 4 ha
	Artificial water body	Lámina de agua artificial	LAA	No	Yes
	Street boundaries without vegetation, parking place	Vial, aparcamiento o zona peatonal sin vegetación	VAP	No	Yes
	Other constructions	Otras construcciones	OCT	Yes	Yes
	Land without current use	Suelo no edificado	SNE	No	No
	Earthwork and landfill sites	Zonas de extracción o vertido	ZEV	No	No
Predefined compound cover types (compound by simple cover types)‡
	Farmhouse	Asentamiento agrícola-residencial	AAR	Yes	Yes
	Continuous urban area–urban center	Urbano mixto–casco	UCS	Yes	Yes
	Continuous urban area–expansion area	Urbano mixto–ensanche	UEN	Yes	Yes
	Discontinuous urban area	Urbano mixto–discontinuo	UDS	Yes	Yes
	Organized industrial estate	Polígono industrial ordenado	IPO	Yes	Yes
	Disorganized industrial estate	Polígono industrial sin ordenar	IPS	Yes	Yes
	Isolated industry	Industria aislada	IAS	Yes	Yes
	Agricultural and livestock development	Agrícola/Ganadero	PAG	Yes	Yes
	Forestry	Forestal	PFT	Yes, if EDF ≥ 50%§	Yes, if EDF ≥ 50%
	Mining, gravel pits, etc.	Minero extractive	PMX	Yes, if EDF ≥ 50%	Yes, if EDF ≥ 50%
	Fish farm	Piscifactoría	PPS	Yes, if EDF ≥ 50%	Yes, if EDF ≥ 50%
	Commercial unit	Comercial y oficinas	TCO	Yes	Yes
	Hotel development	Complejo hotelero	TCH	Yes	Yes
	Leisure facilities	Parque recreativo	TPR	Yes	Yes
	Campsite	Camping	TCG	Yes, if EDF ≥ 50%	Yes, if EDF ≥ 50%
	Administrative or institutional facility	Equipamiento/Dotacional–administrativo/institucional	EAI	Yes	Yes
	Hospital or health center	Equipamiento/Dotacional–sanitario	ESN	Yes	Yes
	Cemetery	Equipamiento/Dotacional–cementerio	ECM	Yes	Yes
	Education center	Equipamiento/Dotacional–educación	EDU	Yes	Yes
	Penitentiary	Equipamiento/Dotacional–penitenciario	EPN	Yes	Yes
	Religious building	Equipamiento/Dotacional–religioso	ERG	Yes	Yes
	Cultural center	Equipamiento/Dotacional–cultural	ECL	Yes	Yes
	Sport facilities	Equipamiento/Dotacional–deportivo	EDP	Yes, if ZAU ≤ 4 ha¶	Yes, if ZAU ≤ 4 ha
	Golf club	Equipamiento/Dotacional–campo de golf	ECG	No	No
	Urban park	Equipamiento/Dotacional–parque urbano	EPU	Yes, if ZAU ≤ 4 ha	Yes, if ZAU ≤ 4 ha
	Road network and facilities	Infraestructuras–red viaria	NRV	Yes, if EDF ≥ 50%	Yes
	Railway network and facilities	Infraestructuras–red ferroviaria	NRF	Yes, if EDF ≥ 50%	Yes
	Port	Infraestructuras–portuario	NPO	Yes	Yes
	Airport	Infraestructuras–aeroportuario	NAP	Yes	Yes
	Wind power plant	Infraestructuras–energía eólica	NEO	Yes, if EDF ≥ 50%	Yes, if EDF ≥ 50%
	Solar power plant	Infraestructuras–energía solar	NSL	Yes, if EDF ≥ 50%	Yes, if EDF ≥ 50%
	Nuclear power plant	Infraestructuras–energía nuclear	NCL	Yes	Yes
	Power plant	Infraestructuras–energía electrica	NEL	Yes	Yes
	Thermal power station	Infraestructuras–energía thermica	NTM	Yes	Yes
	Water power station	Infraestructuras–energía hidroeléctrica	NHD	Yes	Yes
	Pipeline	Infraestructuras–gaseoducto/oleoducto	NGO	Yes, if EDF ≥ 50%	Yes
	Transmitter station, RADAR station, etc.	Infraestructuras–telecomunicaciones	NTC	Yes, if EDF ≥ 50%	Yes, if EDF ≥ 50%
	Sewage treatment plant	Infraestructuras–depuradoras y potabilizadoras	NDP	Yes	Yes
	Desalinization plant	Infraestructuras–desalinizadoras	NDS	Yes	Yes
	Channel	Infraestructuras–conducciones y canales	NCC	Yes, if EDF ≥ 50%	Yes
	Dump	Infraestructuras–vertederos y escombreras	NVE	Yes	Yes
	Waste treatment plant	Infraestructuras–plantas de tratamiento	NPT	Yes	Yes
No predefined compound cover types (compound by simple and compound cover types)ǁ
	Association	Asociación	A	Yes	Yes
	Irregular mosaic	Mosaico irregular	I	Yes	Yes
	Regular mosaic	Mosaico regular	R	Yes	Yes

^*Spanish name of the class is provided because the source database is in Spanish.

^†In accordance with other sources of built-up areas, such as VECTOR25 (www.swisstopo.admin.ch/internet/swisstopo/en/home/products/landscape/vector25.html).

^‡Polygons of predefined compound cover types were incorporated into the built-up and impervious infrastructure if at least 50% of their surface was occupied by simple cover types that met the corresponding inclusion criteria.

^§Polygons of this class were selected only when 50% of their surface was occupied specifically by buildings (simple class with the label EDF). Given that we are not considering polygons with less than 50% of their surface being built up, this measure of the extent of built-up areas is conservative. However, the estimated extent of built-up areas in Spain is much higher (2.87%) than the one estimated from the European Commission Program to Coordinate Information on the Environment (Corine) land cover (2%).

^¶Polygons of this class were selected only when the surface of green space (simple class with label ZAU) was lower than 4 ha.

^ǁPolygons of no predefined compound covers were incorporated into the built-up and impervious infrastructure maps if at least 50% of their surface was occupied by simple or predefined compound covers that met the corresponding inclusion criteria.

Effects of Proximity to Transportation Infrastructure on Species Distribution.

We overlaid distance maps to transportation infrastructure with distribution maps (10 × 10-km cells) (44) of six emblematic species of the Iberian fauna known to be negatively affected by roads at local scales: Strix aluco (Tawny owl), Otis tarda (Great bustard), Aquila adalberti (Spanish imperial eagle), Canis lupus (Gray wolf), Lynx pardinus (Iberian lynx), and Ursus arctos (Brown bear) (28, 29, 38, 45–47). For each species, we quantified the median distance to transport infrastructure in presence cells and classified resulting distances by bands of 500 m from the nearest infrastructure for graphical representation as a normalized histogram. Most wildlife species affected by human development have escape distances on this order of magnitude or higher and home ranges of many hectares to several square kilometers, so this bandwidth seemed appropriate. A more detailed, continuous distribution of each species in relation to the nearest transport infrastructure and considering all pixels in each distribution cell is shown in Fig. S2. Counting how many presence cells fell into each 500-m band, we calculated both the relative proportion of the 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 overall effect of the Spanish transportation, and other impervious infrastructure on mean species abundances for birds (MSA_b) and mammals (MSA_m) and determined the spatial distribution of the predicted effect zones. The MSA indicator expresses the difference between the averaged mean abundance for various species in the proximity of an infrastructure relative to their abundance in a control location free of infrastructure (48). MSA values range from no individuals remaining (0) to no effect on species abundance (1). Using a meta-analytical approach, Benítez-López et al. (21), within the framework of the Global Biodiversity model GLOBIO assessments, tested the relationship between MSA and distance to infrastructure through generalized linear mixed models (GLMM), and provided functional distance-decay response curves for birds and mammals (Fig. 1). This study was undertaken using 49 studies and 90 datasets, which included 201 bird species (52% present in Spain) and 33 mammal species (12% present in Spain), but it shows a substantial geographic bias because 88% of the studies came from Europe and North America. In addition, the mammal datasets were biased toward ungulates (representing 58.1% of the datasets considered, whereas carnivores, rodents, proboscideans, and lagomorphs represented, respectively, 16.3%. 18.1%, 4.7%, and 2.3% of the datasets). However, because ungulates are species with usually very large home ranges and many large carnivores worldwide have also been shown to be severely affected by the presence of roads, the findings are likely to be applicable to many other places worldwide. These functions have been previously applied only once, to assess the impacts on roads in areas of high diversity value in Sweden (49).

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

$$MS{A}_{\left(\text{estimated}\right)}=\hspace{0.17em}\frac{e^u}{1+\hspace{0.17em}{e}^u}\hspace{0.17em},$$

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

$$u=\text{ln}\hspace{0.17em}\left(\frac{P_i}{1-\hspace{0.17em}{P}_i}\right)={\beta }_0+{\beta }_{1}x,$$

where β₀ is the intercept (β₀-birds = −0.863; β₀-mammals = −0.607) and β₁ is the regression coefficient for the distance (β₁-birds = 0.00447 m⁻¹; β₁-mammals = 0.00083 m⁻¹). The coefficients were obtained from the authors of the meta-analysis. The distance variable x could take the value of each cell in the raster containing the Euclidean distance from an infrastructure. Given that 61.1% of the datasets considered by Benítez-López et al. (21) corresponded to road effects and the rest to other infrastructure, we used both a raster of distances to transportation infrastructure alone (as a conservative measure) and another with all impervious infrastructure combined to explore the sensitivity of our estimates.

Finally, we analyzed the overall effect of the infrastructure by habitat types on a national scale, by overlaying distance and MSA layers on a land cover map [European Commission Program to Coordinate Information on the Environment (Corine) land cover 2006; www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-version-3] and calculating statistics for each habitat. 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 thematic resolution are available in Table S2.