Victor Cazalis
email: victor.cazalis@laposte.net

Megan D Barnes

Alison Johnston

James E M Watson

Cagan H Şekercioğlu

Ana S L Rodrigues

Mismatch between bird species sensitivity and the protection of intact habitats across the Americas

Keywords: Conservation, Wilderness, Protected areas, Human footprint, Threatened species

Protected areas are highly heterogeneous in their effectiveness at buffering human pressure, which may hamper their ability to conserve species highly sensitive to human activities. Here, we use 60 million bird observations from eBird to estimate the sensitivity to human pressure of each bird species breeding in the Americas. Concerningly, we find that ecoregions hosting large proportions of highsensitivity species, concentrated in tropical biomes, do not have more intact protected habitat. Moreover, 266 high-sensitivity species have little or no intact protected habitat within their distributions. Finally, we show that protected area intactness is decreasing faster where highsensitivity species concentrate. Our results highlight a major mismatch between species conservation needs and the coverage of intact protected habitats, which likely hampers the long-term effectiveness of protected areas at retaining species. We highlight ecoregions where protection and management of intact habitats, complemented by restoration, is urgently needed.

Introduction

Protected areas are clearly defined geographical spaces, recognised, dedicated and managed to achieve the long-term conservation of nature (UNEP-WCMC, IUCN and NGS, 2020). Acknowledged as a major biodiversity conservation tools [START_REF] Watson | The performance and potential of protected areas[END_REF][START_REF] Maxwell | Area-based conservation in the twenty-first century[END_REF], there has been a marked expansion in their extent over the past few decades and known coverage currently reaches 15.4% of global land surface (UNEP-WCMC, IUCN and NGS, 2021). Ongoing renegotiation of global policy targets are expected to result in a more ambitious 30% areal target [START_REF] Scbd | Zero draft of the post-2020 global biodiversity framework[END_REF], which is likely to drive further expansion.

However, protected areas vary substantially in both their intended management (i.e., the level of protection as legally defined) and in the practical effectiveness of their implementation [START_REF] Geldmann | A global analysis of management capacity and ecological outcomes in terrestrial protected areas[END_REF][START_REF] Barnes | Wildlife population trends in protected areas predicted by national socio-economic metrics and body size[END_REF]. As a result, many protected areas are currently under intense and increasing human pressure [START_REF] Jones | One-third of global protected land is under intense human pressure[END_REF]Venter et al., 2016a). For this reason, the percentage of area protected is on its own insufficient to evaluate protected area effectiveness [START_REF] Rodrigues | The multifaceted challenge of evaluating protected area effectiveness[END_REF][START_REF] Cazalis | Using a large-scale biodiversity monitoring dataset to test the effectiveness of protected areas at conserving North-American breeding birds[END_REF]. A pure focus on protected area expansion, without guarantee of concomitant quality, has been criticised as encouraging fast expansion into areas of little value to biodiversity, or with little on-the-ground implementation effort [START_REF] Barnes | Prevent perverse outcomes from global protected area policy[END_REF][START_REF] Visconti | Protected area targets post-2020[END_REF]. This high level of degradation of some protected habitats may be a concern in some cases because degradation is known to impact many species [START_REF] Marco | Wilderness areas halve the extinction risk of terrestrial biodiversity[END_REF][START_REF] Watson | The exceptional value of intact forest ecosystems[END_REF][START_REF] Gibson | Primary forests are irreplaceable for sustaining tropical biodiversity[END_REF]. However, it does not necessarily follow that all protected areas need to be in perfectly intact condition. Indeed, while many species are highly sensitive to human pressure [START_REF] Barlow | Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation[END_REF][START_REF] Gibson | Primary forests are irreplaceable for sustaining tropical biodiversity[END_REF], many others can tolerate some levels of, or even benefit from, anthropogenic land use change [START_REF] Guetté | Measuring the synanthropy of species and communities to monitor the effects of urbanization on biodiversity[END_REF][START_REF] Mckinney | Urbanization as a major cause of biotic homogenization[END_REF][START_REF] Şekercioğlu | Long-term declines in bird populations in tropical agricultural countryside[END_REF]. Given the need to reconcile biodiversity conservation and human development, two broad strategies have been proposed: land sparing, focused on setting aside intact habitats while concentrating human pressure elsewhere; and land sharing, integrating conservation and development by spreading human pressure across larger areas, including multiple use protected areas [START_REF] Green | Farming and the Fate of Wild Nature[END_REF][START_REF] Phalan | Reconciling Food Production and Biodiversity Conservation: Land Sharing and Land Sparing Compared[END_REF][START_REF] Williams | Landuse strategies to balance livestock production, biodiversity conservation and carbon storage in Yucatán, Mexico[END_REF]. In practice, the best strategy for each species depends on how it responds to human pressure [START_REF] Green | Farming and the Fate of Wild Nature[END_REF]. Species that respond negatively to even low levels of pressure require strict management of sufficient expanses of intact habitats (with a concomitant concentration of human activities elsewhere; i.e., land sparing), whereas species that can tolerate moderate to high human pressure may be adequately protected in multi-use protected areas, or not need protected areas at all (i.e., land sharing).

The few studies quantifying species responses to increasing human pressure (based on gradients of agriculture yield or urban intensification) in the context of the land sparing/land sharing debate found that many species are strongly impacted by even low levels of human pressure, providing support for the need to set aside intact areas for their conservation [START_REF] Phalan | Reconciling Food Production and Biodiversity Conservation: Land Sharing and Land Sparing Compared[END_REF][START_REF] Williams | Landuse strategies to balance livestock production, biodiversity conservation and carbon storage in Yucatán, Mexico[END_REF][START_REF] Collas | Urban development, land sharing and land sparing: the importance of considering restoration[END_REF][START_REF] Şekercioğlu | Long-term declines in bird populations in tropical agricultural countryside[END_REF]. Understanding how these sensitive species are distributed across the world may in turn shed light on the extent to which different regions are more or less dependent on the protection of intact habitat for the conservation of their biodiversity.

Here, we investigate the large-scale spatial variation in sensitivity to human pressure for 4,362 bird species, contrasting this with the distribution of intact protected habitat to highlight priorities for expanding or reinforcing intact protected habitat coverage. Taking advantage of the millions of field records collated through the eBird citizen science platform, we focus on the Americas as a study region. We first measure the sensitivity of 2,550 breeding species by modelling the relationship between abundance and human pressure as measured by the human footprint index [START_REF] Williams | Change in Terrestrial Human Footprint Drives Continued Loss of Intact Ecosystems[END_REF]. We then impute the sensitivity of the remaining 1,812 breeding species based on their traits, as abundance data were too scarce to measure sensitivity directly. Using ecoregions as spatial unit, we contrast spatial patterns in species sensitivity with the coverage of intact protected habitat of ecoregions, in order to identify the regions with a critical mismatch between the two. In addition, we identify that are highly sensitive to human pressure but whose distributions have minor or no coverage by intact protected habitats. Finally, we analyse these results in light of the recent trends in human footprint to understand if the adequacy of protected areas to the conservation of high-sensitivity species is improving or worsening.

Methods

Study area

We focused on the Americas (i.e., the Nearctic and Neotropical realms) given the large concentration of bird observations and wide ecological variation (from tropical to boreal biomes). We analysed data within the corresponding 325 terrestrial ecoregions [START_REF] Olson | Terrestrial Ecoregions of the World: A New Map of Life on EarthA new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity[END_REF] for which there is human footprint data (Figs. 23).

Species data

Bird distribution and abundance data

We used data from eBird [START_REF] Sullivan | The eBird enterprise: An integrated approach to development and application of citizen science[END_REF], a unique online platform gathering hundreds of millions of bird observations across the globe. Observers report observations in checklists and provide information on sampling effort, allowing eBird data to be transformed into a dataset of relative abundance [START_REF] Sullivan | eBird: A citizen-based bird observation network in the biological sciences[END_REF] while controlling for the most common observation biases [START_REF] Johnston | Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions[END_REF], as described below. We used the eBird dataset released in October 2019 (eBird, 2019).

In addition, we used species distribution data (coarse polygons delimiting the species' ranges) as mapped by BirdLife International and HBW (2019), having aligned the taxonomies between these two datasets by following the latter (Supplementary Methods 1).

Building a standard abundance dataset

We filtered the dataset following guidelines provided by the eBird team [START_REF] Sullivan | eBird: A citizen-based bird observation network in the biological sciences[END_REF][START_REF] Strimas-Mackey | Best Practices for Using eBird Data[END_REF][START_REF] Johnston | Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions[END_REF], similarly to the data filtering process described in [START_REF] Cazalis | Effectiveness of protected areas in conserving tropical forest birds[END_REF]; see details in Supplementary Methods 2.

To enable comparability of checklists, we restricted our dataset to recent observations (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019), for which observers certified having reported all species identified, emerging from stationary counts or transects (with distance travelled <5km and duration between 0.5-10 hours; duration is then controlled for in analyses), and only kept checklists made by experienced observers (Supplementary Methods 3). To avoid pseudo replication, we removed observation duplicates (i.e., checklists made on the same place and date). We removed "not approved" observations, as well as marine and coastal species and we restricted observations to the species breeding range and season (Supplementary Methods 2).

The final dataset in our main analyses consisted of 59,583,879 observations and 404,086,397 inferred absences, structured into 3,449,486 checklists made by 44,013 observers and representing 4,362 species (all species whose breeding distribution intersects with the ecoregions considered in this study).

Accounting for observer differences

Even when the sampling protocol is similar, eBird checklists may greatly differ because of the important heterogeneity in observers' experience, skills, behaviour, and equipment. As mentioned above, observations were filtered to include only those made by observers with a minimum level of experience. In addition, we calculated an individual observer calibration index, which we included as control variable in all subsequent analyses of eBird data. This index, is calculated from a mixed model with random effect of observer, then used to estimate the log-scaled number of species each observer is expected to report in an average sampling event (see details in Supplementary Methods 3 ;[START_REF] Johnston | Estimates of observer expertise improve species distributions from citizen science data[END_REF][START_REF] Kelling | Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves[END_REF].

Bird species traits

For each of the 4,362 species considered, we obtained data on eight species-level variables (that we call "traits" for simplicity; see Supplementary Methods 4). We extracted from BirdBase (Şekercioğlu et al., 2004, 2019) species primary habitat, primary diet, migratory status, specialisation index, body mass and taxonomic Order. We extracted from BirdLife International (2019) Red List status that we used as a quantitative variable, and calculated species' breeding range size.

Landscape data

Protected areas

We used spatial protected area data from the World Database on Protected Areas (UNEP-WCMC & IUCN, 2020), following the standard filtering procedure (UNEP-WCMC and IUCN, 2019) that excludes 'Man and Biosphere' reserves, protected areas with no associated polygons and those that are not yet implemented (i.e., we kept only those 'designated', 'inscribed', or 'established').

Human footprint

We used maps of human footprint in 2000 and in 2013, in raster format at a resolution of ~1km [START_REF] Williams | Change in Terrestrial Human Footprint Drives Continued Loss of Intact Ecosystems[END_REF]. These maps are an updated and more complete version of the 1993-2009 index derived by [START_REF] Venter | Global terrestrial Human Footprint maps for 1993 and 2009[END_REF], generated from the combination of eight human pressure variables (built environments, population density, night-time lights, crop lands, pasture lands, accessibility via roads, railways and navigable waterways) and ranging from 0 (perfect intactness) to 50 (extremely high pressure). Human footprint data have previously been used to analyse species' responses to human pressures (Di [START_REF] Marco | Changes in human footprint drive changes in species extinction risk[END_REF][START_REF] Barnagaud | Functional biogeography of dietary strategies in birds[END_REF][START_REF] Cazalis | Effectiveness of protected areas in conserving tropical forest birds[END_REF].

We assigned to each checklist a value of human footprint in 2013 by calculating the mean value of human footprint in pixels intersecting by at least 1% a buffer around the checklist coordinates. We used a buffer of 2.5 km radius to ensure it covers the large majority of the area sampled by the selected travelling protocols.

Altitude

The altitude of each checklist was calculated as the mean altitude of all pixels from the Global Land One-kilometer Base Elevation raster (National Geophysical Data Center, 1999) intersecting by at least 1% the 2.5 km buffer around the checklist coordinates.

Net Primary Productivity

We calculated a Net Primary Productivity (NPP) value for each checklist, using NASA's Earth Observatory Team (2020) maps. We first created a raster layer by calculating for each cell the mean of NPP values from January 2014 to November 2016 (December 2016 data were not available). We then extracted for each checklist, the mean value of each pixel in this raster that intersected by at least 1% the 2.5 km buffer around the checklist coordinates.

Analyses

We analysed the data to answer four questions: (1) How sensitive are bird species to human footprint? (2) Where across the study area are species the most sensitive? (3) Is current protection of intact habitats matched to species' needs? (4) Are protected areas retaining intact habitats over time?

How sensitive are species to human footprint?

Direct measure of sensitivity (data-rich species)

We quantified directly the sensitivity to human footprint for a subset of 2,550 data-rich species, selected according to three conditions: ≥ 200 records with abundance; ≥80% of all records with abundance (as 'X' often concerns observations with too many individuals to be counted, which could introduce a bias for gregarious species); and with distributions across a wide range of human footprint values. The latter were selected by first calculating the 1% and the 99% quantiles of human footprint from all checklists within each species' breeding distribution, and then keeping only those species for which quantiles differed by ≥ 25 (an arbitrary threshold corresponding to half of the human footprint range, chosen based on data exploration to be wide without excluding too many species). For each of these 2,550 data-rich species, we ran a General Additive Model (GAM) modelling the relationship between species' abundance and human footprint of the checklists' location (assuming a negative binomial distribution of abundance and using the bam function; [START_REF] Wood | Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models: Estimation of Semiparametric Generalized Linear Models[END_REF]). In order to enable a diversity of relationships, from linear to non-monotonous relations, we used a smoothed term on human footprint, but we constrained the degree of the smoothing function to 6 to avoid very complex functions (broadly speaking avoiding overfitting with multimodal relationships, see examples of relations in Fig. 1). In these models, we controlled for some of the most common sampling biases such as differences in sampling effort (logarithm of sampling duration; logarithm of number of observers; observer calibration index, using the maximum if there were multiple observers), differences in ecological conditions (altitude and NPP, both assuming parabolic responses; we could not use smoothed-terms here because of computing limitations), and large-scale patterns of spatial trends (interacting smooth-term with longitude and latitude), with the following structure:

Abundance_species ~ s(human_footprint, k=6) + log(duration) + log(N_observers) + observer_calibration_index + altitude + altitude^2 + productivity + productivity^2 + te(longitude, latitude)

We then predicted the relative abundance of the species across a gradient of human footprint ranging from 0 to the maximum value of human footprint observed within the species' distribution, with a step of 0.05 (fixing all other variables to their median values) and extracted the average human footprint of this distribution weighted by predicted abundance (Fig. 1). Finally, we measured each species' sensitivity as the difference between 50 (i.e., maximum human footprint) and this weighted average (Fig. 1).

Imputed sensitivity (data-poor species)

We used information on species' traits to impute the sensitivity of the remaining (data-poor) 1,812 species, from the 2,550 data-rich species. To do so, we first linked the values of sensitivity measured for data-rich species to their traits using a linear model. We included in the model the species' primary habitat, primary diet, specialisation (log-scaled), body mass (log-scaled), Red List status, range size (log-scaled), migratory status, and taxonomic Order, with the following structure:

Sensitivity ~ Primary_Habitat + Primary_Diet + specialisation + log(Mass) + RedList + log(Range) + migration + Order
We then used this model to impute the sensitivity of the data-poor species (see details in Supplementary Methods 5).

We use species' sensitivity as a relative measure, to compare among species and regions (Supplementary Methods 6). We defined high-sensitivity species as the 25% most sensitive species (Fig. 1).

Where are bird assemblages the most sensitive?

As a measure of the sensitivity of bird assemblages to human footprint, we calculated for each ecoregion the proportion of all breeding species classified as high-sensitivity. The list of breeding species per ecoregion was obtained by overlapping species' breeding distributions with the ecoregion's boundaries.

We analysed whether our results were driven by methodological choices through two additional analyses. In the first one, using all species (N=4,362 species), we mapped two alternative measures of the sensitivity of bird assemblages to human footprint: median sensitivity across all species that breed in the ecoregion; and absolute number of high-sensitivity species per ecoregion (Fig. S5). In the second one, we investigated the effects of extreme imputation errors on the spatial pattern of the proportion of high-sensitivity species per ecoregion (Fig. S6).

Is intact habitat protection matched to species' needs?

We created a 0/1 raster layer of intact protected habitat across the study area as a transformation of the 2013 human footprint raster, by assigning the value 1 only to those pixels with intact habitat (human footprint value <4, [START_REF] Williams | Change in Terrestrial Human Footprint Drives Continued Loss of Intact Ecosystems[END_REF] whose centre was located within a protected area. We then used this layer to investigate if intact habitat protection is matched to species' conservation needs (i.e., their sensitivity), both across space (ecoregions) and across species.

Across ecoregions

For each ecoregion, we measured the level of intact habitat protection as the proportion of its pixels (those included by >50% within the ecoregion) classified as intact protected habitat. We then analysed how this index relates to the proportion of high-sensitivity species per ecoregion, both quantitatively (using the Pearson correlation coefficient) and visually (mapping the correspondence between these variables using a bivariate colour scale).

For comparison, we also analysed two alternative measures of the investment in habitat protection per ecoregion: protected area extent (the fraction of the ecoregion covered by protected areas); and protected area intactness (the mean intactness of protected pixels, where intactness is the opposite of human footprint).

Across species

For each high-sensitivity species, we quantified their coverage by intact protected habitat. Given that a pixel measures ~ 1 km 2 , we calculated the area of intact protected habitat per species by summing the values in the 0/1 raster layer of intact protected habitat for pixels included (by >50%) within the species' breeding distribution.

We then assessed whether high-sensitivity species were adequately covered by intact protected habitat, by comparing their coverage by intact protected habitat against a predefined representation target. For each species, this target was calculated based on a widely-used approach (e.g., [START_REF] Rodrigues | Global gap analysis: priority regions for expanding the global protected-area network[END_REF], [START_REF] Maxwell | Area-based conservation in the twenty-first century[END_REF], [START_REF] Butchart | Shortfalls and Solutions for Meeting National and Global Conservation Area Targets[END_REF]) whereby species with very small ranges (< 1000 km 2 ) have a 100% target, those with very widespread ranges (>250,000 km 2 ) have a 10% target, with the target for species with ranges of intermediate size being interpolated between these two extremes. A high-sensitivity species was considered inadequately covered if its coverage by intact protected habitat falls below this representation target. Among these, we highlight a subset we designate having no or minor coverage (≤ 10% of their target met; i.e., less than 10% of the range for highly-restricted species, less than 1% for species with very large range).

Are protected areas retaining intact habitats over time?

We created a raster layer that maps the trends in human footprint between 2000 and 2013, from the difference between human footprint values in 2013 and in 2000. We then calculated, for each ecoregion, the trend in protected area intactness as the mean decrease in human footprint in all pixels intersecting (by 50%) protected areas (i.e., a positive value means an increase in protected area intactness) and calculated its Pearson correlation coefficient with the proportion of high-sensitivity species.

Results

How sensitive are species to human footprint?

Across the 4,362 bird species that breed in the Americas, values of sensitivity to human footprint range from 2.9 to 50.0, following a Gaussian distribution with a median value of 33 (Fig. 1). Sensitivity is higher in data-poor species than in data-rich ones. The threshold for high-sensitivity species is 37.7. Among the 1,091 high-sensitivity species, 24.2% are threatened with extinction (compared with 7.8% for species not categorised as high-sensitivity).

Fig. 1: Stacked frequency distribution of sensitivity values across all 4,362 bird species that breed in the Americas, including data-rich species for which sensitivity was measured directly (dark grey, N=2,550), and data-poor species for which it was imputed from trait information (light grey, N=1,812). Insets correspond to five examples of data-rich species across a gradient of sensitivity, showing for each: the modelled response of abundance to human footprint; the measure of sensitivity (green horizontal arrow) obtained from the difference between 50 (i.e., the maximum value of human footprint) and the weighted mean value of predicted abundance (pink vertical line). High-sensitivity species were defined as the 25% most sensitive. High-sensitivity species have an abundance strongly biased towards sites with low human footprint (e.g., Leuconotopicus borealis), medium-sensitivity species have an abundance unrelated to human footprint or biased towards medium human footprint (e.g., Icterus gularis), and low-sensitivity species have an abundance biased towards sites with high human footprint (e.g., Turdus rufiventris).

Photo credits: T.r. (Luiz Carlos Rocha, https://www.flickr.com/photos/luizmrocha/), Q.m. (BarbeeAnne, https://pixabay.com/photos/cuba-black-bird-great-tailed-grackle-2555949/), I.g. (Skeeze, https://www.needpix.com/photo/729995/), M.c. (David Rodriguez Arias, https://www.flickr.com/photos/82969027@N04), L.b. (Sam D. Hamilton, Where are bird assemblages the most sensitive?

Assemblages most sensitive to human footprint (i.e., with highest proportion of local species being high-sensitivity) are concentrated in tropical ecoregions, especially along the Andean mountain range and its eastern slopes towards the Amazonian basin, as well as in Central America (Fig. 2A).

We found that this pattern is robust to extreme errors in imputing sensitivity to data-poor species (Fig. S6). Furthermore, a very similar pattern was found when assemblage sensitivity was measured as the number of high-sensitivity species or the median sensitivity of species per ecoregion (Fig. S5). We thus focus henceforth on the proportion of high-sensitivity species measured across all species (Fig. 2A). Is intact habitat protection matched to species sensitivity?

Across ecoregions

Intact habitat protection is highest in the Amazonian basin, Boreal region, western North America, and Patagonia (Fig. 2B, Table S2), where ecoregions combine relatively high protected area extent and high protected area intactness (Fig. S7). Conversely, intact habitat protection is lowest in Eastern North America, and in much of South America outside the Amazonian basin (Fig. 2B). The proportion of high-sensitivity species per ecoregion is not correlated with intact habitat protection (coef = 0.055, P=0.327; Fig. 2D-E), neither with protected area extent, nor with protected area intactness (Fig. S7), and we only found a small correlation for median sensitivity (Fig. S5).

Areas with highly sensitive bird assemblages but low intact habitat protection are concentrated in tropical ecoregions, especially in the tropical Andes and their western slopes towards the Pacific coast, Colombia's Choco region and Magdalena Valley, Venezuela Coastal Range, Central America, and the Cerrado savannahs of Brazil (red in Fig. 2D, Fig. 3). These ecoregions mainly correspond to tropical and subtropical forest biomes, mostly moist broadleaf (21 ecoregions), but also dry broadleaf (15), mangroves ( 7) and coniferous forests (3). They also include grasslands and shrublands, including montane (4), deserts and xeric (2), tropical (1), and flooded (1) (Fig. 3).

Additionally, 23 ecoregions have high proportions of high-sensitivity species, but intermediate levels of intact habitat protection (caramel in Fig. 2D-E; Fig. 3), also mainly concentrated in tropical ecoregions. Finally, 69 ecoregions have intermediate proportions of high-sensitivity species but low intact habitat protection, found not only in tropical eastern South America but also in North American temperate grasslands and West (salmon in Fig. 2D-E; Fig. 3).

Conversely, 30 ecoregions, mainly in the Amazonian basin (including the eastern slope of the Andes) have a high proportion of high-sensitivity species while being relatively well covered by intact protected habitat (purple in Fig. 2D-E), mainly corresponding to tropical and subtropical moist broadleaf forests and mangroves (Fig. 3).

Across species

Among the 519 data-rich high-sensitivity species, 353 (68%) are inadequately covered by intact protected habitat, including 53 (10%; Table S3) with no or only minor coverage. When considering all 1,091 species that we predicted as high-sensitivity (i.e., also including 572 data-poor species), these values increase to 820 (75%) and 266 (24%, including 141 threatened species), respectively (Fig. 2C; Fig. S8). Species with no or minor coverage are mainly concentrated in the East part of North America, Caribbean, Tropical Andes, Venezuela Coastal Range, and Atlantic Forest of Brazil (Fig. 2C, Fig. S8). On average an additional 31% of these species' ranges would need to be covered by intact protected habitats to reach their respective representation targets (Fig. S9). When compared with all breeding species, they are disproportionately sedentary (95% vs. 83%), found in forest habitats (88% vs. 61%), specialised (average of 3.8 vs. 3.3), and have disproportionately small breeding ranges (average of 7.9x10 4 vs. 1.8x10 6 km 2 ).

Our analysis overestimates real intact habitat coverage, because we consider all intact habitat within a species' range as suitable, which may be inaccurate (e.g., intact forest patches in a grassland species' range). Accordingly, the numbers of inadequately covered species we obtained are conservative (i.e., underestimated).

Are protected areas retaining intact habitats over time?

Between 2000 and 2013, protected area intactness: decreased in 161 ecoregions, concentrated in South and Central America; was stable (i.e., |change| < 0.1) in 114 ecoregions, mostly in western North America and the Amazonian basin; and increased in 50 ecoregions, mainly in eastern North America (Fig. 2G). Trends in protected area intactness are negatively correlated with the proportion of high-sensitivity species per ecoregion (coef = -0.23, P=3.9x10 -5 ; Fig. 2F), with most ecoregions with >10% of local species being high-sensitivity (104 of 148; 70%) having experienced a decline in intactness (Fig. 2F).

Discussion

We introduce here a new index of species' sensitivity to anthropogenic land use changes (as measured through the human footprint), derived from field data on the variation of species' abundance over their distributions. We focused on the breeding grounds, but the same methods can be applied to estimate sensitivity during other parts of the species' annual cycle (which may also present important conservation challenges; e.g., [START_REF] Runge | Protected areas and global conservation of migratory birds[END_REF][START_REF] Gaget | Assessing the effectiveness of the Ramsar Convention in preserving wintering waterbirds in the Mediterranean[END_REF] as species sensitivity probably varies with season. Even though adequate field data are not available for the vast majority of species in most regions, citizen science is rendering possible to estimate sensitivity for a growing number of species across the globe. The importance of measuring sensitivity directly from field records is stressed by the relatively poor link between sensitivity and traits that we found (R 2 =0.18), suggesting that traits are a poor proxy for sensitivity (our results are robust to errors in data-poor species imputation; Fig. S6).

Breeding bird species in the Americas vary widely in their sensitivity to human pressure, as evidenced by the diversity of relationships between abundance and human footprint (Fig. 1), as previously shown [START_REF] Rosenberg | Decline of the North American avifauna[END_REF][START_REF] Clavel | Worldwide decline of specialist species: toward a global functional homogenization?[END_REF][START_REF] Guetté | Measuring the synanthropy of species and communities to monitor the effects of urbanization on biodiversity[END_REF][START_REF] Phalan | Reconciling Food Production and Biodiversity Conservation: Land Sharing and Land Sparing Compared[END_REF][START_REF] Williams | Landuse strategies to balance livestock production, biodiversity conservation and carbon storage in Yucatán, Mexico[END_REF]. There is substantial overlap between threatened and high-sensitivity species, but the two measures are not the same. Indeed, whereas sensitivity to human footprint can be seen as an intrinsic ecological trait, threat levels result from the interaction between sensitivity and exposure to human pressure. We found that sensitivity is highly structured in space, being particularly dominant among species within tropical forest ecoregions, especially in the Andes, Central America and Amazonian basin, while being lower in temperate and boreal ecoregions (Fig. 2A). Even though we focused only on birds in the Americas, previous studies reported a high sensitivity of tropical forest assemblages to even low levels of human pressure [START_REF] Gibson | Primary forests are irreplaceable for sustaining tropical biodiversity[END_REF][START_REF] Barlow | Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation[END_REF][START_REF] Newbold | Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change[END_REF], suggesting that our results may reflect a broader pattern applicable to other taxa and regions.

These results confirm our prediction that the need for highly intact protected habitat is not the same everywhere, at least when it comes to the conservation of bird species. Placed in the context of the land sparing/land sharing debate [START_REF] Green | Farming and the Fate of Wild Nature[END_REF][START_REF] Phalan | Reconciling Food Production and Biodiversity Conservation: Land Sharing and Land Sparing Compared[END_REF][START_REF] Williams | Landuse strategies to balance livestock production, biodiversity conservation and carbon storage in Yucatán, Mexico[END_REF], our results suggest that the best way for reconciling conservation and socio-economic targets varies across regions. A land sparing strategy is particularly crucial in the tropics, where bird assemblages include many species whose conservation depends on the maintenance of intact habitats, and that may thus require the establishment of strict protected areas. In contrast, land sharing may prove a more suitable strategy in temperate and boreal ecosystems, where fewer species now depend on intact habitats. This said, we found high-sensitivity species in each one of the 325 ecoregions analysed but two (Table S2), which means that the protection of at least some intact habitat is crucial across all latitudes and biomes.

Intact habitat protection is also widely variable (0% to 81%) and highly structured in space (Fig. 2B), being stronger in the Amazonian basin and some high latitude ecoregions that combine both low human footprint and large coverage by protected area. Worryingly, it does not correlate with the distribution of high-sensitivity species, which need the protection of intact habitats the most. This is consistent with previous work that also found no correlation between the location of protected areas and species' conservation needs, as measured by the presence of threatened species [START_REF] Venter | Bias in protected-area location and its effects on long-term aspirations of biodiversity conventions[END_REF]. Of particular concern are the 54 ecoregions that have very low levels (<5%) of intact habitat protection despite hosting bird assemblages with a large proportion (>14%) of high-sensitivity species (Fig. 2D). These overlap extensively with Biodiversity Hotspots (i.e. biogeographic regions of exceptional plant endemism that have already lost >70% of their natural habitat, [START_REF] Mittermeier | Hotspot revisited[END_REF][START_REF] Myers | Biodiversity hotspots for conservation priorities[END_REF], particularly with the Tropical Andes, Tumbes-Chocó-Magdalena, Mesoamerica, and Cerrado hotspots (47 of the 54 ecoregions overlap one of these hotspots by >90%).

Furthermore, many of these ecoregions (particularly in the Tropical Andes and Central America) cover areas identified as urgent priorities for the expansion of the global network of protected areas [START_REF] Rodrigues | Global gap analysis: priority regions for expanding the global protected-area network[END_REF][START_REF] Butchart | Shortfalls and Solutions for Meeting National and Global Conservation Area Targets[END_REF].

A complementary perspective is obtained by analysing mismatches between sensitivity and protected area coverage at the species level. This is particularly important as some narrow-range species, while living in ecoregions with low intact habitat protection, could be adequately covered if the distribution of intact protected habitats within an ecoregion matches species' distribution. We identified (conservatively) 266 high-sensitivity species whose distributions have no or only minor coverage by intact protected habitat. The latter include species that are not protected at all [START_REF] Maxwell | Area-based conservation in the twenty-first century[END_REF] as well as others whose distributions are apparently well covered by protected areas but these are dominated by transformed habitats. For instance, the Critically Endangered Santa Marta wren, Troglodytes monticola (sensitivity = 41), is protected across 99.7% of its range, and would thus be considered as adequately covered based on protected area coverage alone [START_REF] Butchart | Shortfalls and Solutions for Meeting National and Global Conservation Area Targets[END_REF]), yet we found no intact protected habitat within its range. This corresponds to its Red List assessment, which highlights a long history of severe deforestation and degradation across the species range, which continues apace despite protection (IUCN, 2018). This example illustrates the importance of taking species' sensitivity into account when evaluating the effectiveness of existing networks of protected areas as well as when identifying new priority areas for protection.

The persistence of many high-sensitivity species requires the establishment and effective management of strict protected areas, or other adequate mechanisms that guarantee the long-term maintenance of sufficient extents of intact habitats. This is all the most urgent for the 141 species we highlighted as being of major concern because they are simultaneously high-sensitivity, have no or only minor coverage by intact protected habitats, and are already at risk of extinction (Fig. 2C, Table S3). With nearly all (136; 96%) threatened by habitat loss (i.e., "Residential and commercial development" or "agriculture and aquaculture"; BirdLife International ( 2019)), the mismatch between their high-sensitivity and the poor coverage of their range by intact protected habitats may prove dramatic in the near future in the absence of active measures to protect any remaining intact patches. The distributions of these species highlight ecoregions overlapping the same Biodiversity Hotspots as above, as well as the Atlantic Forest hotspot, all of which have already suffered major loss and transformation of their habitats [START_REF] Williams | Change in Terrestrial Human Footprint Drives Continued Loss of Intact Ecosystems[END_REF]. Restoration of currently degraded habitat is likely to play a key role in the long-term conservation of these species [START_REF] Bull | Net positive outcomes for nature[END_REF][START_REF] Benayas | Enhancement of Biodiversity and Ecosystem Services by Ecological Restoration: A Meta-Analysis[END_REF], and indeed the regions where they occur are recognised global priorities for ecosystem restoration [START_REF] Strassburg | Global priority areas for ecosystem restoration[END_REF].

The importance of effective protected areas in these ecoregions will increase over time as pressures outside mount. Indeed, previous studies have shown that protected areas are becoming the last bastions for some species [START_REF] Pacifici | Protected areas are now the last strongholds for many imperiled mammal species[END_REF][START_REF] Boakes | The extirpation of species outside protected areas[END_REF]. Unfortunately, though, we found that ecoregions with higher proportions of high-sensitivity species have experienced a faster degradation in the intactness of their protected areas, indicative of a growing mismatch between species needs and the availability of intact protected habitat (Fig. 2F-G). Previous studies had already raised stern warnings regarding the mounting human pressure within protected areas [START_REF] Jones | One-third of global protected land is under intense human pressure[END_REF][START_REF] Geldmann | Mapping Change in Human Pressure Globally on Land and within Protected Areas[END_REF][START_REF] Geldmann | A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures[END_REF], through ongoing habitat loss and degradation [START_REF] Spracklen | A Global Analysis of Deforestation in Moist Tropical Forest Protected Areas[END_REF][START_REF] Cuenca | How much deforestation do protected areas avoid in tropical Andean landscapes?[END_REF][START_REF] Bruner | Effectiveness of parks in protecting tropical biodiversity[END_REF][START_REF] Moore | Are ranger patrols effective in reducing poaching-related threats within protected areas?[END_REF]. Here we show that these trends have been faster (between 2000 and 2013) precisely where species need intact habitats the most.

Moreover, it is likely that the situation has worsened for many species since, given ongoing habitat destruction in some parts of the continent, as shown by the 2020 update of the Global Forest Change [START_REF] Hansen | High-Resolution Global Maps of 21st-Century Forest Cover Change[END_REF].

Overall, our results show that the Americas' protected area network is distributed such that it is not strategically located to conserve those bird species that need it the most, undermining its effectiveness in achieving the long-term conservation of nature [START_REF] Rodrigues | The multifaceted challenge of evaluating protected area effectiveness[END_REF][START_REF] Scbd | Aichi Biodiversity Targets[END_REF], and highlight the importance of considering the habitat quality of protected areas [START_REF] Barnes | Prevent perverse outcomes from global protected area policy[END_REF][START_REF] Visconti | Protected area targets post-2020[END_REF]. We highlight ecoregions and species where it is particularly urgent to ensure that remaining intact habitat is preserved, through protected areas and other relevant mechanisms, potentially complemented by the restoration of degraded habitat. With these ecoregions and species mostly concentrated in countries with limited economic resources, international cooperation is key to meeting this goal.

Supplementary methods 1) Alignment with BirdLife International taxonomy

The taxonomy followed by eBird (source of bird observation records) is different from the one followed by BirdLife International (source of species' ranges, and Red List status). We aligned the former with the latter by: (1) replacing the species name used in eBird by the one in BirdLife International in simple cases of different names (but no taxonomic mismatch); (2) merging different eBird species (and summing their local abundance, if applicable) whenever they are lumped into a single species in the BirdLife International taxonomy; and (3) splitting single eBird species that correspond to multiple species in the BirdLife International taxonomy, using species distribution maps to separate the corresponding records (see [START_REF] Cazalis | Effectiveness of protected areas in conserving tropical forest birds[END_REF] for more details and Supplementary Spreadsheet 1 for list of species concerned by these changes).

2) Building a standard abundance dataset

We restricted our dataset to recent (2010-2019) observations, to increase synchronisation between bird records and landscape data. We kept only checklists for which observers certified having reported all species identified, thus obtaining a count dataset that included nondetections. Further, we filtered checklists based on sampling effort and protocol to create a set of checklists with relatively consistent effort. Specifically, we kept only checklists that reported a duration of sampling between 0.5-10 hours and less than 5 km distance travelled arising from: the 'Stationary Points' protocol (i.e., the observer did not move during sampling); the 'Travelling' protocol (i.e., the observer moved during sampling); or 'Historical counts' (if they included information on duration and distance travelled). This protocol selection excludes sampling events targeting particular taxonomic groups (e.g. wader surveys, nocturnal protocols) or using specific methods (e.g. banding). Each checklist is associated with a point, with coordinates provided by observers (often the middle of the route for 'Traveling' checklists).

Pseudo-replication in the database may occur when several observers record birds at the same place at the same time. To eliminate these records, we first used the auk_unique function from the 'auk' package (Strimas-Mackey et al., 2017), specifically designed to process eBird data. This function combines checklists from multiple observers reported to be birding together, in order to obtain a single checklist for each sampling event (the number of observers associated with each checklist alters the detection probability, and will be subsequently controlled for).

Second, because even observers who are not birding together can create pseudo-replication if they overlap in space and time, whenever several checklists were less than 5 km apart on the same day we randomly and sequentially selected one checklist (i.e., iteratively until no two checklists were less than 5 km apart). All the checklists we selected were 'complete' with each observer recording all the species they could detect and identify. However, more experienced observers will be able to detect and identify more species and therefore non-detections will be more likely to equate to real species absences. Therefore, we only considered observations made by observers with a minimum level of experience, i.e. those who submitted ≥ 50 checklists, including ≥ 100 species during the study period (2010-2019; see Supplementary Methods 3).

We excluded from the dataset "not approved" observations (corresponding to exotic, escaped or feral individuals), as well as domestic species, but kept established invasive species. Using the auk_rollup function from the 'auk' package, we removed subspecies, moving all observations to the species level. We also excluded marine species, defined as species with 'sea' or 'coastal' as primary habitat (cf. bird species traits section below).

In many species, individuals are the most territorial and selective in terms of habitat requirements when breeding (Zuckerberg et al., 2016). We thus focused our analyses on potentially breeding individuals, by narrowing each species' observations records to the respective breeding season and breeding grounds. For each 10° latitudinal band, we derived the broad breeding season (all species considered together) based on the temporal distribution of records coded as 'breeding' in the eBird database (e.g., 6 April to 9 August for latitudes 50°N to 60°N; all year round for latitudes 10°S to 10°N; see Supplementary Methods 3). Within these dates, we then focused on the observations made in the breeding grounds of each species (as mapped by BirdLife International and HBW ( 2019)).

We have restricted our dataset of bird records to observations made within each species' broad breeding season and breeding grounds. Bird breeding season is known to vary with latitude (Baker, 1939). Although not all species within a given latitude necessarily breed at the same time (Baker, 1939), we did not have sufficiently detailed information to take these differences into account, and have instead assumed that within a given latitudinal band all species breed within the same broad season. To delimit the breeding season within each 10° latitudinal band, we studied the temporal distribution of the breeding codes that are sometimes associated with eBird observations (available for 1.2% of the observations in our dataset). First, we restricted the breeding codes to those that either correspond to probable breeding or confirmed breeding (i.e., by removing codes: "Flyover", which does not correspond to a breeding behaviour; and "In appropriate habitat" and "Singing male", which correspond to possible breeding only). We then plotted the temporal distribution of these breeding records (all species combined) per 10° latitudinal bands (Fig. S1). Based on these distributions, we considered that breeding occurs all year round between latitudes -10° and 10°. For other latitudinal bands, we defined the limits of the breeding season (Table S1) as the period containing 95% of the observations with breeding codes, bounded by circular quantiles at 2.5 and 97.5% of observations (black lines on Fig. S1) using the circular package (Agostinelli and Lund, 2017). For each species, in each latitudinal band, we included only records inside the boundaries of the breeding season.

In addition, we restricted the observations of each species to their respective breeding grounds, based on the BirdLife International's distribution maps (BirdLife International and HBW, 2019), more specifically the polygons with Season codes 1 (resident) or 2 (breeding season only) and Presence codes 1 (extant), 2 (probably extant), 3 (possibly extant), and 4 (possibly extinct).

Within these temporal and spatial constraints, we considered a species absent if undetected in checklists made during the breeding season and located within the breeding range of the species.

For species recorded as present, we used the count (i.e., number of individuals observed) provided by observers. In some cases (4% of the observations in our dataset) observers did not provide an abundance and instead reported species presence with an "X". We treated these as NA values in our analyses. Vertical lines represent the limits of the breeding season as we defined it (i.e., the 2.5 and 97.5% circular quantiles; see Table S1). Bar colours show whether plotted data fall within (green) or outside (grey) the breeding season.

Table S1: Breeding season per latitudinal band. For two tropical bands (-10° to 0° and 0° to 10°), we considered that breeding occurs all year round. For all other bands, beginning and end dates were derived from the temporal distribution of breeding codes in eBird records (Fig. S1), as the 2.5 and 97.5% circular quantiles of Julian days.

Breeding season (

3) Calculating observer calibration index

We calculated an individual observer calibration index, closely related to the one calculated in Cazalis et al., (2020) (following [START_REF] Kelling | Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves[END_REF] and [START_REF] Johnston | Estimates of observer expertise improve species distributions from citizen science data[END_REF]), for each observer included in the analyses (i.e., with more than a given threshold of experience: ≥ 50 checklists for a total of ≥ 100 species in the Americas during the study period). This index was calculated from a subset of the eBird records, selected through a somewhat different filtering process from the one used in the main analyses. As in the main analyses, we kept only checklists that reported all species detected, observations from 2010-2019, and excluding disapproved observations; in contrast, we did not restrict the dataset based on protocol type or sampling effort.

For any given observer, we calculated the observer calibration index as the log-scaled number of species an observer is expected to report on average during a standard sampling event.

Usually this is done by running a mixed-effects model with the richness of each checklist as the response variable, with sampling effort and ecological drivers of species richness as explanatory variables, and with observer as a random effect [START_REF] Johnston | Estimates of observer expertise improve species distributions from citizen science data[END_REF][START_REF] Kelling | Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves[END_REF][START_REF] Cazalis | Effectiveness of protected areas in conserving tropical forest birds[END_REF]. In this study, because of computing limitations, we split this calculation into two models: first we fitted a GAM assuming a negative binomial distribution (using the function bam from the 'mgcv' package [START_REF] Wood | Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models: Estimation of Semiparametric Generalized Linear Models[END_REF]) with the following structure:

species_richness ~ protocol_type + number_observers + s(duration) + s(starting_time) + te(longitude, latitude, Julian_day), with starting_time the time at which the sampling event started, s() a smoothed term enabling complex relation between variables, and te() an interacting smooth term enabling here richness to vary across space and time. We then extracted residuals of this model and used them in a linear mixed-model (using the function lme from the 'nlme' package (Pinheiro et al., 2020)) with no explanatory variable and a random effect on observer. Finally, we simulated a sampling event under the "Stationary Points" protocol, fixing number of observers, duration, starting time, longitude, latitude and Julian day to their median values.

We predicted the log-scaled species richness that should be detected in this hypothetical sampling event according to the GAM, and summed it with the random effect of each observer in order to get an observer calibration index per individual observer.

4) Bird species traits

BirdBase is a regularly updated global database of the ecology and life history traits of the world's bird species (Şekercioğlu et al., 2004, 2019). We extracted from it the species' primary habitat, structured into 10 classes (after exclusion of 'coastal' and 'sea' species): Artificial, Deserts, Forests, Grasslands, Riparian, Rocky, Savannahs, Shrublands, Wetlands, Woodlands.

Primary diet consisted of eight classes (after combining 'Carnivore', 'Scavenger' and 'Vertebrate' into 'Carnivorous'; combining 'Plant' and 'Herbivore' into 'Herbivorous'; and considering the 42 species with an 'unknown' diet as 'Insectivorous'): Carnivorous, Frugivorous, Granivorous, Herbivorous, Insectivorous, Nectarivorous, Omnivorous, Piscivorous. In addition, we obtained from the same database: migratory status ('strict', 'partial', or 'sedentary'); body mass; and species taxonomic Order (e.g., Accipitriformes, Anseriformes). We calculated a specialisation index for each species based on the number of different habitat (HB) and diet (DB) categories for each species, as in Şekercioğlu (2011):

log [100/(HB x DB)]. We inferred specialisation and body mass values of species for which it was unknown (respectively N=43 and N=358), by using the mean specialisation and mass of the documented species in the same taxonomic Family.

We extracted Red List status from BirdLife International (2019), and (following Butchart et al., 2007) transformed it into a quantitative variable (from Least Concern as 1, to Critically Endangered as 5). We treated the six species for which the Red List status was Data Deficient as LC. We calculated each species' breeding range size (as defined above) from BirdLife

International and HBW (2019), within our study area.

5) Imputing species sensitivity

In order to estimate the sensitivity of the 1,812 data-poor species (i.e. those among the 4,362 that breed in the study area for which we were not able to measure sensitivity directly), we modelled the link between the sensitivity of the 2,550 data-rich species using the following linear model:

Sensitivity ~ Primary_Habitat + Primary_Diet + specialisation + log(Mass) + RedList + log(Range) + Migration +

Order

Even though this model does not have a strong explaining power (R 2 =0.18), results indicate that major habitat significantly affects species sensitivity (P<10 -15 ), with forest, grassland and riparian species being particularly sensitive, while species favouring artificial habitats, deserts, and savannahs being on average less sensitive to human footprint (Fig. S2A). Taxonomic Order also greatly influences species sensitivity (P=3.10 -8 ), with Rheiformes, Phoenicopteriformes, and Cariamiformes at highest sensitivity, while Psittaciformes, Podicipediformes, Falconiformes, Columbiformes, Apodiformes, and Accipitriformes showed lower sensitivity (Fig. S2B). Species diet had a slightly significant effect on sensitivity (P=0.024) with high sensitivity for nectarivorous and low sensitivity for granivorous (Fig. S2C). Migration status greatly influenced sensitivity, with sedentary species showing higher sensitivity than strict migrating species than partial migrating species (P=9.10 -6 ; Fig. S2D). Species sensitivity increased with species specialisation (P<10 -15 , Fig. S2F), decreased with species range size (log-scaled) in the Americas (P=0.042, Fig. S2G), increased with species body mass (P=4.10 -4 , Fig. S2H), and increased with species quantitative Red List status (P=0.049, Fig. S2E). Using the estimates of this model, we then imputed sensitivity for the 1,862 data-poor bird species based on their traits and using the R function predict. One species belongs to an order that was not represented within the 2,550 data-rich species for which we measured sensitivity directly (Pterocliformes), we thus assigned to this species the sensitivity value of the Order for which sensitivity was median (Caprimulgiformes).

This first estimate of imputed sensitivity was biased (as we can see when comparing the measured sensitivity for the 2,550 data-rich species with the estimate that would arise from model predictions; Fig. S3A). We thus corrected it, by scaling the imputed sensitivity and then reversing the scaling using the measured sensitivity parameters (i.e., multiplying by the standard deviation of measured sensitivity and adding its mean value). We then replaced the few imputed values below 0 by 0 and the few values above 50 by 50. This corrected the bias found in the first estimate (Fig. S3B). We then used measured sensitivity for the 2,550 data-rich species and corrected imputed sensitivity for the 1,862 data-poor species (see both distributions in Fig. 1).

6) Sampling bias towards sites with high human footprint

There was across the entire study area a strong bias in sampling towards sites with high human footprint and a near-complete absence of sampling in sites with very low human footprint (Fig. S4). For this reason, values of species' sensitivity cannot be interpreted in absolute terms: a sensitivity of 30 does not (necessarily) mean that 30% of the population is found in areas of human footprint lower than 20 (= 50-30). Instead, we used values of species' sensitivity in relative terms, to compare sensitivity of species and of regions. Fig. S8: Spatial distribution of high-sensitivity species with no or minor coverage by intact protected habitats (as in Fig. 2C), with data-rich species (left) and data-poor species (right).

Fig. S9: Increase needed in the proportion of species range covered by intact protected habitats to reach the target from [START_REF] Rodrigues | Global gap analysis: priority regions for expanding the global protected-area network[END_REF]. For instance, if a species has 20% of its range covered by intact protected habitats and a target of 50%, we consider the "Increase needed" as 30.

Fig. 2 :

 2 Fig. 2: Spatial patterns in bird assemblage sensitivity and intact habitat protection. (A) Assemblage sensitivity measured as the proportion of species per ecoregion that are high-sensitivity species. (B) Intact habitat protection per ecoregion (i.e., proportion of ecoregion area simultaneously protected and with human footprint <4). (C) Spatial distribution of the 266 high-sensitivity species with no or minor coverage by intact protected habitat. (D) Spatial pattern and (E) Scatterplot of the relationship between assemblage sensitivity and intact habitat protection, across ecoregions. The bivariate colour scale in D and E is built by cutting proportion of high-sensitivity species into terciles([0; 0.070[, [0.070; 0.143[, [0.143; 1]) and intact habitat protection into [0; 0.05[, [0.05; 0.17[, [0.17; 1] (dashed lines in E). (G) Trends in protected area intactness per ecoregion (i.e., decrease in human footprint within protected areas between 2000 and 2013); and (F) in relation to the proportion of high-sensitivity species per ecoregion. Green shades show improvement, rose shades degradation.

Fig. 3 :

 3 Fig.3: Distribution of ecoregions per biome (following[START_REF] Olson | Terrestrial Ecoregions of the World: A New Map of Life on EarthA new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity[END_REF], colour-coded according to the relationship between the proportion of high-sensitivity species and intact habitat protection (colours as in Fig.2D-E). The three categories of particular concern are outlined in black.

Fig. S1 :

 S1 Fig. S1: Temporal distributions of eBird observations with associated breeding codes, per 10° latitudinal band.

Fig. S2 :

 S2 Fig. S2: Effects of species traits on sensitivity to human footprint, for 8 traits: (A) primary habitat, (B) taxonomic Order, (C) primary diet, (D) migration status, (E) Red List status, (F) habitat specialisation, (G) range size, and (H) body mass.

Fig. S3 :

 S3 Fig. S3: Imputed sensitivity (left) and corrected imputed sensitivity (right) of the 2,550 species for which sensitivity was directly measured. Correction consisted in scaling the imputed sensitivity, multiplying by the standard deviation of measured sensitivity, adding the mean value of measured sensitivity and finally replacing values below 0 by 0 and values above 50 by 50.

Fig. S4 :

 S4 Fig. S4: Distribution of human footprint raster cells (grey bars) compared with distribution of human footprint of checklists (red lines) per 10° latitudinal bands in the study area.

Fig. S7 :

 S7 Fig.S7: Spatial patterns of the investment in habitat protection across ecoregions, as measured by: (A) protected area extent (fraction of the ecoregion covered by protected areas), and (B) protected area intactness (mean intactness of pixels within the ecoregion that intersect by >50% with protected areas), and the respective (C, D) correlation with the proportion of high-sensitivity species.

Julian days)

	Latitudinal band (degrees)	Beginning	End
	(70,80]	153	214
	(60,70]	97	212
	(50,60]	96	221
	(40,50]	81	226
	(30,40]	57	230
	(20,30]	17	249
	(10,20]	360	292
	(0,10]	0	367
	(-10,0]	0	367
	(-20,-10]	129	94
	(-30,-20]	147	83
	(-40,-30]	192	73
	(-50,-40]	185	77
	(-60,-50]	236	51

Table S2 :

 S2 Results at the ecoregion level for the 325 ecoregions used in the study.

	Ecoregion name Alaska Peninsula montane taiga Alaska/St. Elias Range tundra Alberta Mountain forests Alberta/British Columbia foothills forests Aleutian Islands tundra Allegheny Highlands forests Alta Parano Atlantic forests Alvarado mangroves Amapa mangroves Appalachian mixed mesophytic forests Appalachian/Blue Ridge forests Apure-Villavicencio dry forests Araucaria moist forests Araya and Paria xeric scrub Arctic coastal tundra Arctic foothills tundra Argentine Espinal Argentine Monte Arid Chaco Arizona Mountains forests Aruba-Curacao-Bonaire cactus scrub Atacama desert Atlantic Coast restingas Atlantic coastal pine barrens Atlantic dry forests Baffin coastal tundra Bahamian dry forests Bahamian mangroves Bahia coastal forests Bahia interior forests Bahia mangroves Baja California desert Bajio dry forests Balsas dry forests Belizean Coast mangroves Belizian pine forests Beni savanna Beringia lowland tundra Beringia upland tundra Blue Mountains forests Bocas del Toro-San Bastimentos Island-San Blas mangroves Bolivian montane dry forests Bolivian Yungas British Columbia mainland coastal forests Brooks/British Range tundra Caatinga Caatinga Enclaves moist forests California Central Valley grasslands California coastal sage and chaparral California interior chaparral and woodlands California montane chaparral and woodlands Campos Rupestres montane savanna Canadian Aspen forests and parklands Caqueta moist forests Cascade Mountains leeward forests Catatumbo moist forests Cauca Valley dry forests Cauca Valley montane forests Cayman Islands dry forests Cayos Miskitos-San Andrés & Providencia moist forests Central American Atlantic moist forests Central American dry forests Central American montane forests Central American pine-oak forests Central and Southern Cascades forests Central and Southern mixed grasslands Central Andean dry puna Central Andean puna Central Andean wet puna Central British Columbia Mountain forests Central Canadian Shield forests Central forest/grasslands transition zone Central Mexican matorral Central Mexican wetlands Central Pacific coastal forests Central tall grasslands Central U.S. hardwood forests Cerrado Chaco Chiapas Depression dry forests Chiapas montane forests Chihuahuan desert Chilean matorral Chimalapas montane forests Chiquitano dry forests Choco-Darien moist forests Coastal Venezuelan mangroves Colorado Plateau shrublands Colorado Rockies forests Cook Inlet taiga Copper Plateau taiga Cordillera Central paramo Cordillera de Merida paramo Cordillera La Costa montane forests Cordillera Oriental montane forests Cordoba montane savanna Costa Rican seasonal moist forests Cuban cactus scrub Cuban dry forests Cuban moist forests Cuban pine forests Cuban wetlands Davis Highlands tundra East Central Texas forests Eastern Canadian forests Eastern Canadian Shield taiga Eastern Cascades forests Eastern Cordillera real montane forests Eastern forest/boreal transition Eastern Great Lakes lowland forests Eastern Panamanian montane forests Ecuadorian dry forests Edwards Plateau savanna Enriquillo wetlands Esmeraldes/Choco mangroves Everglades Flint Hills tall grasslands Florida sand pine scrub Fraser Plateau and Basin complex Great Basin montane forests Great Basin shrub steppe Greater Antilles mangroves Guajira-Barranquilla xeric scrub Guayanan Highlands moist forests Guayaquil flooded grasslands Guianan Freshwater swamp forests Guianan mangroves Guianan moist forests Gulf of California xeric scrub Gulf of Fonseca mangroves Gulf of Guayaquil-Tumbes mangroves Gulf of Panama mangroves Gulf of St. Lawrence lowland forests Gurupa varzea Guyanan savanna Hawaii tropical dry forests Hawaii tropical high shrublands Hawaii tropical low shrublands Hawaii tropical moist forests High Arctic tundra Marismas Nacionales-San Northern Pacific coastal forests Rio Negro-Rio San Sun mangroves Southern Dry Pacific Coast Willamette Valley forests Hispaniolan dry forests Hispaniolan moist forests Hispaniolan pine forests Humid Chaco Humid Pampas Ilha Grande mangroves Interior Alaska/Yukon lowland taiga Interior Yukon/Alaska alpine tundra Iquitos varzea Isthmian-Atlantic moist forests Isthmian-Pacific moist forests Jalisco dry forests Jamaican dry forests Jamaican moist forests Japura-Solimoes-Negro moist forests Jurua-Purus moist forests Klamath-Siskiyou forests La Costa xeric shrublands Lara-Falcon dry forests Leeward Islands dry forests Leeward Islands moist forests Leeward Islands xeric scrub Lesser Antilles mangroves Llanos Low Arctic tundra Madeira-Tapajos moist forests Magdalena-Santa Marta mangroves Magdalena-Uraba moist forests Magdalena Valley dry forests Magdalena Valley montane forests Magellanic subpolar forests Maracaibo dry forests Marajo Varzea forests Maranhao Babacu forests Maranhao mangroves Maranon dry forests Blas mangroves Mato Grosso seasonal forests Mayan Corridor mangroves Meseta Central matorral Mexican South Pacific Coast mangroves Mid-Continental Canadian forests Middle Arctic tundra Middle Atlantic coastal forests Midwestern Canadian Shield forests Miskito pine forests Mississippi lowland forests Moist Pacific Coast mangroves Mojave desert Montana Valley and Foothill grasslands Monte Alegre varzea Mosquita-Nicaraguan Caribbean Coast mangroves Motagua Valley thornscrub Muskwa/Slave Lake forests Napo moist forests Nebraska Sand Hills mixed grasslands moist forests New England/Acadian forests Newfoundland Highland forests North Central Rockies forests Northeastern Brazil restingas Northeastern coastal forests Northern Andean paramo Northern California coastal forests Northern Canadian Shield taiga Northern Cordillera forests Northern Dry Pacific Coast mangroves Northern Honduras mangroves Northern mixed grasslands Patagonian steppe forests Pernambuco coastal forests interior forests Peruvian Yungas Peten-Veracruz Petenes mangroves Piney Woods forests Puerto Rican dry forests Puerto Rican moist forests Puget lowland forests Purus-Madeira moist forests Purus varzea Queen Charlotte Islands Rio Lagartos mangroves Rockies forests rocklands conifer forests forests steppe Yungas Mesopotamian savanna grasslands Western short Southern Cone coastal grasslands Western Gulf Southern Andean Lakes forests Western Great Southern Andean moist forests Western Ecuador Southeastern mixed montane forests Wasatch and Uinta Southeastern forests Veracruz montane South Florida forests Veracruz moist South Central Sonoran desert South Avalon-Burin oceanic barrens Veracruz dry forests montane forests Venezuelan Andes moist forests transition forest forests Valdivian temperate subtropical dry mangroves Usumacinta Sonoran-Sinaloan Pernambuco Snake/Columbia moist forest Uruguayan savanna Solimoes-Japura transition zone shrub steppe Forest/Savanna Upper Midwest Patia Valley dry Sinu Valley dry forests forests Ucayali moist Negro-Branco Northern short grasslands Northern tall grasslands Northern transitional alpine forests Northwest Mexican Coast mangroves Northwest Territories taiga Northwestern Andean montane forests Oaxacan montane forests Ogilvie/MacKenzie alpine tundra Rio Negro campinarana Rio Piranhas mangroves Rio Sao Francisco mangroves San Lucan xeric scrub Santa Marta montane forests mangroves Southern Great Lakes forests Windward Islands dry forests Southern Hudson Bay taiga Windward Islands moist forests Southern Pacific dry forests Windward Islands xeric scrub Southwest Amazon moist forests Wyoming Basin shrub steppe Xingu-Tocantins-Santa Marta paramo Talamancan montane forests Araguaia moist forests Sechura desert Semi-arid Pampas Serra do Mar coastal forests Tamaulipan Yucatan dry forests matorral Tamaulipan Yucatan moist forests mezquital Tapajos-Xingu Yukon Interior dry forests moist forests Okanogan dry forests Orinoco Delta swamp forests Orinoco wetlands Ozark Mountain forests Pacific Coastal Mountain icefields and tundra Palouse grasslands Panamanian dry forests Pantanal Pantanos de Centla Para mangroves Parana flooded savanna Patagonian grasslands Sierra Nevada Sinaloan dry forests moist forests Uatuma-Trombetas forests forests Tumbes-Piura dry forests scrub Oriental pine-oak Trinidad mangroves Paraguana xeric Sierra de los Pedro Martir pine-Sierra Madre de Chiapas moist forest Oaxaca pine-oak Sur pine-oak forests Occidental pine-oak Sierra Madre forests forests Tobago moist Trinidad and Sierra Madre Tobago dry forests Trinidad and Sierra Madre del oak forests forests Volcanic Belt pine-Trans-Mexican Sierra Madre de tundra Torngat Mountain moist forests Tocantins/Pindare oak forests prairies Texas blackland Sierra Juarez & San Tepuis Tuxtlas mangroves Sierra de la Laguna pine-oak forests Manchon Tehuantepec-El Sierra de la Laguna dry forests Tehuacan Valley matorral Zacatonal	Eco. code 7 8 10 11 14 15 17 21 22 32 33 35 38 39 40 42 43 44 45 46 48 50 51 54 55 61 62 63 65 66 67 68 69 71 75 77 78 80 81 84 85 88 87 94 95 97 98 100 101 102 103 105 106 112 118 121 122 123 125 126 130 131 132 133 161 162 136 137 138 142 143 163 150 151 152 164 159 165 166 174 175 176 177 178 180 181 184 186 187 189 192 193 196 194 195 209 198 203 204 205 206 207 213 223 235 234 236 238 252 240 250 254 255 260 262 270 277 278 279 288 289 293 295 296 297 298 299 300 304 305 306 308 309 310 311 314 315 316 317 320 444 542 630 718 845 325 326 327 333 334 339 345 346 347 352 353 356 357 358 360 366 387 395 398 399 400 401 402 404 407 422 425 426 423 424 427 437 438 439 440 441 448 450 458 460 461 462 463 465 469 470 473 474 476 478 479 480 484 491 494 495 502 504 513 523 527 530 532 533 535 536 537 549 590 591 595 596 598 599 603 607 608 609 611 610 618 628 686 690 707 708 714 713 716 843 834 832 830 823 818 817 682 685 816 815 600 813 683 812 678 810 675 808 674 806 550 551 552 555 557 558 566 568 629 631 632 643 644 719 846 720 847 726 848 730 850 645 763 851 655 657 660 764 856 765 857 766 858 569 572 573 575 576 579 581 583 584 585 587 589 673 805 668 803 586 664 798 665 667 663 797 796 666 794 790 786 662 776 774 671 670 772 669 771 860	Extent 4.79E+10 1.52E+11 3.99E+10 1.21E+11 5.48E+09 8.41E+10 4.85E+11 4.56E+09 1.57E+09 1.93E+11 1.60E+11 6.87E+10 2.17E+11 5.29E+09 9.84E+10 1.29E+11 1.09E+11 4.10E+11 9.91E+10 1.09E+11 4.60E+08 1.05E+11 7.89E+09 8.98E+09 1.15E+11 9.12E+09 4.81E+09 6.63E+09 1.10E+11 2.30E+11 2.12E+09 7.79E+10 3.75E+10 6.26E+10 2.80E+09 2.84E+09 1.26E+11 1.51E+11 9.76E+10 6.48E+10 5.42E+08 8.05E+10 9.08E+10 1.38E+11 1.60E+11 7.36E+11 4.81E+09 5.52E+10 3.63E+10 6.47E+10 2.05E+10 2.65E+10 3.98E+11 1.85E+11 4.64E+10 2.29E+10 7.36E+09 3.21E+10 1.34E+08 9.54E+07 8.97E+10 6.82E+10 1.33E+10 1.12E+11 4.50E+10 2.83E+11 3.08E+11 1.62E+11 1.18E+11 7.19E+10 4.63E+11 4.08E+11 5.95E+10 2.80E+08 7.38E+10 2.49E+11 2.97E+11 1.92E+12 6.11E+11 1.41E+10 5.79E+09 5.11E+11 1.49E+11 2.09E+09 2.31E+11 7.38E+10 5.86E+09 3.27E+11 1.33E+11 2.79E+10 1.72E+10 1.22E+10 2.82E+09 1.44E+10 6.80E+10 5.83E+10 1.07E+10 3.27E+09 6.59E+10 2.14E+10 6.44E+09 5.68E+09 8.81E+10 5.28E+10 4.88E+11 7.55E+11 5.53E+10 1.03E+11 3.48E+11 1.17E+11 3.05E+09 2.13E+10 6.19E+10 6.32E+08 6.53E+09 2.01E+10 2.97E+10 3.89E+09 1.37E+11 5.80E+09 3.37E+11 1.07E+10 3.17E+10 3.38E+11 2.94E+09 7.74E+09 1.46E+10 5.14E+11 2.36E+10 1.63E+09 3.32E+09 2.43E+09 3.95E+10 9.95E+09 1.05E+11 6.65E+09 1.86E+09 1.53E+09 6.75E+09 4.65E+11 2.04E+09 6.06E+10 4.78E+08 9.08E+08 1.49E+10 1.55E+10 4.61E+10 1.16E+10 3.36E+11 2.41E+11 3.21E+09 4.44E+11 2.33E+11 1.15E+11 5.90E+10 2.94E+10 2.62E+10 2.32E+09 8.31E+09 2.70E+11 2.43E+11 5.04E+10 6.86E+10 1.70E+10 1.49E+08 9.92E+08 1.65E+09 6.53E+08 3.90E+11 7.98E+11 7.21E+11 3.20E+09 7.69E+10 1.97E+10 1.05E+11 1.48E+11 3.03E+10 8.89E+10 1.43E+11 1.13E+10 1.14E+10 4.15E+11 4.10E+09 1.26E+11 1.17E+09 3.69E+11 1.04E+12 1.34E+11 5.47E+11 1.90E+10 1.13E+11 1.60E+09 1.31E+11 8.19E+10 6.70E+10 4.45E+09 2.34E+09 2.63E+11 2.52E+11 6.13E+10 2.13E+11 2.38E+11 1.64E+10 2.46E+11 1.01E+10 8.98E+10 3.00E+10 1.33E+10 6.15E+11 2.63E+11 1.06E+09 1.06E+09 2.19E+11 4.88E+11 2.28E+09 1.76E+10 2.27E+10 1.87E+11 1.98E+09 1.41E+11 1.28E+09 7.55E+09 2.26E+10 1.74E+11 1.78E+11 9.98E+09 3.47E+09 1.60E+11 2.08E+09 2.37E+11 3.49E+11 1.79E+11 6.13E+10 7.78E+10 4.36E+11 8.08E+10 2.75E+11 3.42E+10 4.16E+10 4.97E+09 6.93E+10 2.24E+11 2.03E+09 6.65E+09 2.95E+10 1.49E+11 2.49E+11 5.11E+10 3.14E+09 1.68E+11 3.56E+11 2.19E+11 1.66E+11 2.50E+10 1.15E+11 6.40E+11 7.62E+10 2.57E+10 4.99E+09 3.47E+11 8.14E+10 7.62E+09 2.09E+11 8.10E+10 2.12E+09 2.62E+09 3.89E+09 4.80E+09 2.45E+11 4.93E+08 3.75E+11 2.02E+09 4.25E+10 1.03E+09 7.51E+11 1.33E+11 1.25E+09 1.64E+10 2.67E+11 1.85E+11 3.28E+11 1.05E+11 1.63E+10 4.99E+10 1.42E+11 6.99E+10 3.37E+11 6.25E+10 5.34E+10 2.82E+10 6.03E+09 6.22E+10 1.07E+11 4.70E+10 5.12E+09 1.71E+11 1.72E+10 4.42E+09 3.90E+10 6.33E+10 7.77E+10 4.74E+11 5.30E+10 4.14E+10 1.60E+10 6.58E+10 1.86E+08 1.13E+10 6.13E+10 2.23E+11 4.75E+09 2.72E+08 1.44E+10 9.20E+10 3.24E+10 1.94E+11 4.01E+09 5.04E+10 4.90E+10 3.91E+09 1.07E+09 2.70E+09 3.99E+09 9.91E+09 3.03E+08	PA extent 0.83 0.45 0.68 0.02 0.94 0.04 0.06 0.28 0.8 0.03 0.06 0.11 0.04 0.05 0.13 0.21 0.02 0.08 0.03 0.1 0.1 0.02 0.16 0.26 0.07 0.05 0.01 0.02 0.08 0.04 0.19 0.61 0.07 0.11 0.11 0.03 0.01 0.66 0.43 0.09 0.23 0.02 0.02 0.3 0.63 0.07 0.28 0.04 0.09 0.07 0.24 0.26 0.08 0.27 0.41 0.28 0.02 0.13 0.08 0.38 0.42 0.08 0.31 0.12 0.21 0.01 0.11 0.14 0.12 0.09 0.13 0.02 0.05 0.17 0.12 0.02 0.03 0.07 0.11 0.03 0.04 0.07 0.01 0.14 0.01 0.12 0.27 0.1 0.15 0.31 0.21 0.16 0.72 0.57 0.27 0.06 0.1 0.19 0.06 0.21 0.07 0.62 0.31 0.01 0.17 0.1 0.06 0.19 0.11 0.02 0.71 0.03 0.01 0.62 0.32 0.25 0.02 0.16 0.1 0.5 0.09 0.46 0.04 0.61 0.02 0.08 0.57 0.29 0.49 0.67 0.16 0.05 0.03 0.46 0.22 0.16 0.43 0.08 0.13 0.1 0.91 0.4 0.95 0.16 0.02 0.23 0.11 0.39 0.07 0.05 0.49 0.32 0.17 0.2 0.33 0.1 0.09 0.2 0.14 0.41 0.17 0.15 0.14 0.16 0.01 0.78 0.25 0.31 0.08 0.17 0.27 0.27 0.08 0.02 0.15 0.54 0.15 0.66 0.12 0.88 0.06 0.08 0.82 0.05 0.08 0.18 0.05 0.07 0.14 0.16 0.07 0.26 0.42 0.05 0.16 0.43 0.03 0.14 0.2 0.02 0.29 0.06 0.18 0.24 0.3 0.04 0.42 0.16 0.07 0.19 0.58 0.64 0.08 0.07 0 0.04 0.02 0.15 0.94 0.03 0.08 0.05 0.06 0.51 0.29 0.47 0.47 0.29 0.11 0.05 0.02 0.17 0.06 0.02 0.01 0.12 0.16 0.04 0.1 0.06 0.08 0.16 0.04 0.05 0.5 0.18 0.23 0.05 0.83 0.24 0.03 0.15 0.04 0.03 0.21 0.06 0.08 0.05 0.47 0.08 0.14 0.02 0.11 0.36 0.2 0.14 0.18 0.46 0.01 0.15 0.18 0.39 0.04 0.2 0.19 0.04 0.98 0.57 0.15 0.04 0.01 0.33 0.06 0.1 0.11 0.23 0.48 0.02 0.14 0.51 0.3 0.14 0.42 0.03 0.01 0.03 0.28 0.48 0.24 0.04 0.11 0.45 0.3 0.07 0.12 0.32 0.15 0.17 0.03 0.14 0.3 0.05 0.06 0.18 0.42 0.11 0.17 0.01 0.63 0.39 0.87 0.44 0.23 0.16 0.61	Intact habitat protection 0.8 0.44 0.62 0.01 0.79 0 0.01 0.03 0.81 0 0.01 0.01 0.01 0 0.12 0.22 0 0.06 0.02 0.06 0 0.01 0.03 0 0.05 0.05 0 0 0.01 0.01 0.09 0.46 0.02 0.02 0 0 0 0.62 0.42 0.04 0 0 0.01 0.29 0.63 0.03 0.01 0 0.02 0.01 0.14 0.06 0.01 0.26 0.35 0 0 0 0 0 0.15 0 0.03 0.03 0.11 0 0.06 0.04 0.01 0.09 0.14 0 0.01 0 0.09 0 0 0.04 0.08 0 0.02 0.03 0 0.06 0.01 0.07 0.01 0.02 0.1 0.25 0.21 0.11 0 0 0.06 0.02 0 0 0 0 0 0.21 0.31 0 0.16 0.1 0.01 0.13 0.09 0 0.54 0 0 0.29 0.11 0.16 0 0 0.1 0.29 0.04 0.1 0 0.6 0 0.06 0.32 0.29 0.41 0 0 0 0.02 0.44 0.16 0.07 0.37 0.03 0.09 0.1 0.2 0.34 0.48 0 0 0 0 0.02 0.02 0 0.16 0.32 0.18 0.18 0.05 0.03 0.01 0 0 0.4 0.17 0.09 0 0 0 0 0 0 0.05 0.17 0.27 0.01 0 0 0 0.52 0 0.57 0.04 0.23 0.01 0.06 0.64 0.02 0 0.17 0.05 0.01 0.13 0.04 0.01 0.05 0.18 0.01 0.16 0.03 0 0.14 0.19 0 0.29 0.02 0.17 0.2 0.12 0 0.06 0 0.07 0.19 0 0.15 0 0.04 0 0 0 0.12 0.53 0 0 0 0 0.5 0.3 0.42 0.24 0.23 0.01 0.01 0 0.11 0.02 0 0 0.03 0.07 0 0.06 0 0.02 0.1 0.04 0 0 0.05 0.16 0.02 0.47 0.25 0.01 0.02 0 0 0.2 0.01 0.02 0.05 0.17 0.08 0.03 0.01 0.11 0.37 0 0.01 0.07 0 0 0 0.19 0.01 0.01 0 0.18 0 0 0.27 0.14 0.01 0.01 0.09 0.03 0.03 0.02 0.19 0.44 0.02 0.04 0.44 0.17 0.01 0.42 0.01 0 0.02 0.12 0.11 0.06 0.03 0.04 0.44 0.24 0.02 0 0.15 0 0.08 0.01 0.08 0 0 0.01 0.02 0.43 0.04 0.14 0 0.64 0 0.67 0.1 0.09 0.01 0.28	PA intactness -0.3 -0.3 -0.8 -4.9 -1.2 -9.2 -9.4 -8.2 0 -10.1 -7.3 -9.3 -7.8 -15.8 -0.2 -0.2 -6.7 -3.7 -3.9 -3.5 -22.5 -4.8 -11.8 -15.1 -2.8 -0.2 -11.6 -7.5 -8.3 -7.8 -8.8 -2.3 -10.7 -7.7 -12.6 -6.1 -7.6 -0.5 -0.1 -4.4 -9.5 -19.2 -3.1 -0.7 0 -5.1 -13 -12.8 -8.5 -9.6 -3.4 -8 -11.4 -1 -1.4 -8.6 -21.1 -12.3 -16 -7.7 -5.7 -11.3 -9 -8.5 -3.8 -8.7 -3.4 -6.6 -8.9 -0.4 -0.5 -13.9 -15.8 -25 -2.4 -14.2 -10 -5.6 -2.8 -15.5 -5.2 -5.4 -7.6 -4.4 -2.6 -3.5 -8.9 -6.4 -3.2 -2 -0.9 -3.2 -10.3 -12.2 -6.2 -6.4 -14.4 -14.5 -10.4 -10.9 -11.4 -6 -0.1 -9.8 -0.6 0 -7 -3.1 -3.1 -16.7 -2.2 -9.5 -14.3 -5.6 -4.9 -3.2 -9.4 -12.1 -0.4 -3.9 -4.6 -8.1 -15.9 -0.5 -7.9 -3.6 -3.8 -0.6 -1.9 -8.8 -9.7 -8.7 -4.9 -1.2 -2.5 -4.6 -1.1 -5 -3.5 0 -7 -1.1 -4.2 -16.3 -19.6 -10.2 -11.4 -7.4 -6.4 -11.9 -9.3 -0.3 -0.2 -0.7 -7 -5.8 -8.4 -21.8 -9.9 -0.2 -0.1 -3.5 -13.2 -10 -4 -21.6 -27.3 -25.5 -3.2 0 -0.3 -9 -11.7 -11 -10 -1.3 -12.2 -2.2 -6.3 -7.4 -4.3 -1.9 -1.9 -6.1 -12.5 -0.9 0 -9.1 -0.4 -7.3 -9.7 -6.2 -4.9 -15.1 -0.5 -7.1 -13.1 -0.1 -0.9 -8 -0.2 -5.3 -0.4 -1.8 -6.3 -22.4 -7.3 -9.2 0 -0.2 -16 -7 -12.3 -4.3 -14 -17.1 -16.6 -2.4 -4 -8.7 -15 -18.5 -14.9 -0.2 -0.3 -0.9 -5.2 -1.8 -16.6 -9.9 -12.3 -2.8 -6.1 -6.2 -9 -8.8 -5.2 -7.5 -4.6 -20.3 -6.9 -3.8 -1 -14.4 -11.6 -6.3 -3.1 -6.4 -5.2 -0.4 -7.5 -7.6 -14.4 -8.9 -1.5 -8.6 -8.6 -0.4 -4.8 -0.1 -5.6 -6.1 -0.1 0 -22.2 -20.1 -8.7 -7.4 -15 -27.4 -0.1 -17.4 -9.5 -29.9 -0.8 -8.4 -7.1 -4.7 -0.7 -6.1 -6.1 -11.1 -7.1 -7.8 -14.6 -2.1 -0.7 -0.8 -8.9 -2.2 -4.1 -7.3 -0.2 -7.4 -4 -1.9 -5.5 -7.9 -7.8 -4.8 -6.6 -0.2 -1.9 -5.1 -16.9 -5.1 -25.2 -4.3 -5.6 -3.9 -15.4 -20.1 -11.7 -12.7 0 -5.1 -1.3 -13.7 -0.2 -8.5 -2.9 -7.1 -7 -9.5 -5.2	Trend in PA intactness 0.04 0 0.02 0.48 0.17 0.93 -0.31 -0.1 0 0.92 0.54 -0.57 -0.37 -1.1 -0.01 -0.02 -0.27 -0.15 -0.07 0.05 -0.92 -0.55 -0.74 3.03 -0.06 -0.16 -0.36 -0.01 -0.37 -0.35 -0.68 -0.11 -0.1 0.03 -1 -0.77 -0.07 -0.02 0 0.01 -0.49 -1.2 -0.13 -0.04 0 -0.22 -1.76 0.23 0.24 0.53 0.21 -0.26 0.38 -0.24 0.02 -0.27 0.37 -0.3 0 -1 -0.57 -0.42 -0.64 -0.52 0.06 0.17 -0.06 -0.07 -0.15 0 0 1.41 -0.2 0.36 0.04 1.36 0.89 -0.26 -0.17 0.46 -0.47 -0.06 -0.28 -0.09 -0.1 -0.29 -0.38 0.01 0.03 -0.13 0.03 -0.07 -0.27 -0.84 -0.31 -0.25 -0.55 -0.48 -0.04 -0.09 0.01 -0.04 -0.01 0.09 0.03 0 0.09 -0.39 0.12 1.74 -0.25 -0.17 -0.65 -0.1 -0.46 0.1 0.26 0.28 0 0.03 0 0.01 -0.78 -0.14 0.14 -0.27 -0.69 -0.16 -0.12 -0.36 -0.74 -0.39 0.45 -0.38 -0.49 -0.38 0 0 -0.19 -0.02 -0.22 -0.07 -1.44 -0.38 1.8 -0.71 -0.17 -0.16 -0.18 -0.26 -0.06 0 0 -0.12 -0.48 -0.29 -0.54 -0.37 0.36 -0.04 -0.01 0.01 -1.27 -0.78 0 -0.8 0.33 0.15 -0.38 0 -0.02 -0.16 -0.54 -0.21 -0.24 -0.1 -0.36 -0.27 -0.27 -0.63 -0.35 -0.09 -0.1 -0.11 -0.25 0.01 -0.01 0.45 0.01 -0.98 0.45 -1.26 0.04 0.02 -0.1 -1 0 0 -0.18 0 -0.05 0.26 0 0.04 -0.56 2.89 -0.21 0.42 0 0 0 -0.73 0.05 -0.24 0 -1.47 -1.54 -0.31 -0.1 0.16 3.66 2.52 0.73 -0.01 0 -0.29 0.29 0.02 1.37 0.36 0.56 -0.16 -0.2 -0.06 0.04 0.21 0.21 -0.39 0.28 0.75 -0.08 0 -0.04 -0.42 -0.23 -0.43 -0.3 -0.2 -0.27 -0.11 -0.07 0.03 1.46 -0.52 -0.47 -0.02 0.35 0.01 -0.06 -0.04 -0.49 0.02 0 0 -1.7 -1.28 -0.99 -0.64 2.09 -1.7 0 -0.85 -0.06 -1.58 -0.13 -0.01 -0.53 -0.25 -0.09 -0.35 -0.16 -0.24 -0.73 0.02 -0.07 -0.11 -0.05 0 0.24 -1.27 -0.76 0.05 0 0.41 -0.45 -0.04 -0.16 -0.99 -0.1 -0.26 -0.24 -0.02 0.03 -0.16 -0.9 -0.11 -4.45 -0.22 -0.31 -0.12 -3.23 -2.46 -0.48 -0.16 0 -0.46 -0.01 0 -0.1 -0.19 -0.12 -0.28 -1.02 -0.15 -0.38	Proportion of high-sensitivy species 0.05 0.06 0.05 0.07 0.02 0.03 0.1 0.19 0.17 0.04 0.05 0.22 0.1 0.09 0.05 0.05 0.05 0.07 0.05 0.1 0 0.08 0.1 0.04 0.07 0 0.08 0.1 0.12 0.12 0.1 0.07 0.08 0.15 0.22 0.22 0.17 0.07 0.06 0.07 0.26 0.19 0.2 0.06 0.07 0.11 0.06 0.05 0.07 0.06 0.07 0.1 0.07 0.22 0.08 0.17 0.26 0.29 0.11 0.05 0.25 0.19 0.23 0.24 0.06 0.07 0.18 0.16 0.23 0.05 0.05 0.07 0.09 0.09 0.03 0.06 0.16 0.1 0.2 0.22 0.09 0.07 0.2 0.14 0.3 0.1 0.09 0.08 0.04 0.05 0.26 0.17 0.14 0.24 0.05 0.27 0.12 0.13 0.12 0.12 0.12 0.04 0.04 0.04 0.05 0.06 0.28 0.05 0.05 0.28 0.24 0.06 0.07 0.27 0.02 0.05 0.02 0.07 0.07 0.08 0.1 0.16 0.19 0.2 0.16 0.15 0.16 0.08 0.06 0.17 0.25 0.04 0.19 0.18 0.19 0.18 0.08 0.22 0.03 0.09 0.05 0.28 0.17 0.04 0.1 0.11 0.09 0.07 0.04 0.09 0.07 0.07 0.26 0.28 0.26 0.11 0.02 0.03 0.22 0.22 0.06 0.14 0.09 0.12 0.05 0.07 0.06 0.18 0.04 0.2 0.2 0.22 0.23 0.26 0.08 0.15 0.2 0.13 0.15 0.26 0.18 0.18 0.12 0.09 0.05 0.03 0.05 0.05 0.26 0.06 0.19 0.09 0.09 0.22 0.26 0.22 0.05 0.29 0.07 0.2 0.04 0.04 0.08 0.08 0.04 0.29 0.05 0.04 0.06 0.07 0.27 0.08 0.07 0.29 0.09 0.09 0.26 0.08 0.06 0.06 0.06 0.05 0.22 0.23 0.02 0.14 0.09 0.03 0.04 0.05 0.08 0.1 0.06 0.09 0.04 0.05 0.28 0.07 0.13 0.13 0.09 0.03 0.18 0.18 0.21 0.08 0.12 0.16 0.23 0.07 0.07 0.07 0.16 0.29 0.09 0.06 0.05 0.08 0.05 0.29 0.2 0.06 0.21 0.07 0.09 0.09 0.14 0.04 0.05 0.04 0.09 0.2 0.08 0.24 0.07 0.14 0.26 0.18 0.09 0.05 0.1 0.09 0.14 0.07 0.2 0.2 0.06 0.07 0.12 0.09 0.06 0.05 0.08 0.27 0.1 0.21 0.16 0.04 0.09 0.11 0.22 0.07 0.21 0.13 0.13 0.06 0.15 0.14 0.1 0.06 0.05 0.2 0.14 0.06 0.16 0.06 0.05 0.19 0.19 0.07 0.08 0.09 0.12 0.12	Number of high-sensitivity species 5 9 10 15 1 6 80 62 87 8 9 202 60 38 5 6 17 24 16 24 0 12 69 6 35 0 6 7 79 90 49 11 22 57 71 71 135 10 8 16 109 198 235 14 10 68 20 11 15 15 16 77 20 151 18 107 171 255 5 1 136 132 119 131 13 14 58 122 239 9 9 15 26 21 6 11 10 170 75 85 91 30 13 74 114 261 52 24 19 5 7 204 93 85 285 17 150 14 16 15 14 14 1 7 7 5 15 370 10 10 145 116 9 7 117 3 7 2 15 15 20 24 92 154 72 86 103 129 7 12 54 121 6 129 139 14 10 5 17 1 15 8 87 54 6 11 12 10 42 13 57 11 13 236 203 155 35 2 3 165 144 12 90 39 6 4 6 6 167 5 194 104 139 154 276 11 88 146 67 76 190 142 45 38 18 11 2 8 10 85 9 65 21 21 193 87 86 9 298 11 148 9 5 21 28 9 387 10 7 11 11 82 20 13 141 36 37 360 14 8 6 7 9 162 185 1 31 22 3 6 9 18 61 25 24 9 12 179 15 42 49 25 3 49 128 114 14 31 38 145 36 18 15 92 329 23 13 8 15 9 319 91 9 131 27 39 7 71 9 4 7 8 95 7 348 16 63 172 127 31 13 75 21 32 17 60 149 9 15 73 37 8 9 17 141 63 61 85 15 11 37 185 16 122 64 51 15 54 49 39 16 15 98 64 3 98 8 8 150 52 6 17 7 43 47	Median sensitivity of species 29.63 28.85 28.15 28.42 30.1 28.26 31.18 31.33 31.64 28.29 28.24 33.06 31.04 30.13 29.97 29.93 29.1 29.91 29.28 27.81 26.9 29.15 31.11 28.06 30.31 31.05 27.59 27.6 31.56 31.57 31.1 27.42 29.09 30.35 32.13 31.91 32.7 29.62 29.88 28.31 32.66 32.99 33.1 28.4 30 31.04 29.55 27.44 27.38 27.6 27.33 31.23 28.86 33.17 28.45 31.64 33.51 34.04 27.66 27.66 32.3 31.3 31.98 32.04 27.89 27.68 31.61 31.81 33.67 28.42 28.19 28.35 29.15 28.68 27.38 28.74 28.06 32.29 31.25 31.14 31.75 28.65 30.18 31.23 31.79 34.36 30.54 28.34 28.35 28.43 29.1 33.97 31.8 31.2 33.5 29.54 33.23 29.54 29.6 29.6 29.55 29.55 31.45 27.95 28.38 29.16 28.07 34.03 28.34 28.45 33.62 32.56 27.38 28.31 33.53 26.32 27.62 27.44 28.35 28.05 28.05 28.75 31.27 32.56 31.43 31.6 31.6 32.13 27.33 28.73 30.78 32.56 28.05 32.83 32.48 30.49 27.76 27.67 31.03 32.24 29 28.38 32.93 30.79 27.21 28.84 29.07 28.79 30.77 29.03 30.97 29.58 29.24 34.37 33.31 33.09 29.55 27.79 28.08 33.27 33.56 27.45 31.28 30.34 27.56 26.55 26.65 27.21 32.53 29.63 32.91 31.89 32.53 32.28 33.5 30.41 31.55 32.72 31.37 31.46 33.92 32.7 31.37 29.7 29.5 28.4 31.29 28.09 28.41 32.32 28.26 31.07 27.58 28.45 33.1 32.32 31.76 28.86 34.66 27.64 33.11 28.47 28.29 28.38 29.91 28.34 33.86 26.85 28.75 28.51 28.77 32.81 28.74 30.02 34.34 30.69 30.69 34.21 29.42 26.52 26.53 27.52 33.36 33.49 27.59 30.74 28.42 26.55 27.51 28.4 30.12 30.92 30.12 28.34 28.26 28.52 33.84 28.05 29.88 29.86 28.06 28.05 31.1 28.35 31.99 31.71 30.3 29.15 30.92 30.6 33.59 28.73 28.36 31.3 34.75 28.8 28.4 28.2 28.18 28.86 34.06 31.28 33.31 30.28 30.76 27.24 27.73 28.49 28.31 28.85 27.73 31.24 28.22 33.95 30.89 32.3 33.23 30.88 30.23 29.18 31.3 29.03 30.61 28.73 31.47 32.95 28.36 28.65 30.29 28.06 28.35 28.42 33.12 30.97 31.58 31.6 29.37 30.18 29.55 33.05 27.77 32.18 30.94 29.46 29.18 30.24 29.25 29.36 29.15 30.33 31.26 30 30.29 31.86 26.89 27.63 32.6 31.42 31.11 29.17 27.19 28.31 27.24 29.7 29.73

Table S3 :

 S3 List of the 53 data-rich high-sensitivity species with no or minor coverage by intact protected habitat. Intact protected habitat corresponds to the area within each species' range that is both protected and with a human footprint <4 (see Methods). The proportion of intact protected habitat is obtained by dividing by the species' range size.

	Scelorchilus albicollis	44.6	509	0.49	LC
	Semnornis ramphastinus	40.7	1218	2.71	NT
	Setophaga cerulea	41.4	4804	0.26	NT
	Setophaga chrysoparia	40.4	8	0.02	EN
	Setophaga citrina	38.3	Area of intact 7517	Proportion of 0.38	RL LC
	Name Setophaga kirtlandii Setophaga pityophila	Sensitivity 39.9 39.9	protected habitat (km 2 ) 169 0	intact protected 0.45 habitat (%) 0	status NT LC
	Abeillia abeillei Tangara labradorides	38.6 40.4	2514 1102	1.73 1.05	LC LC
	Agelaius xanthomus Tangara lunigera	43.6 43	0 247	0 0.85	EN LC
	Anisognathus igniventris Trogon aurantius	43 38	789 3005	0.81 0.87	LC LC
	Aphelocoma unicolor Trogon elegans	45.6 38.9	1275 1078	0.85 0.78	LC LC
	Basileuterus trifasciatus Tympanuchus cupido	37.7 48.3	328 110	1.11 0.03	LC VU
	Boissonneaua jardini Urochroa bougueri	42.4 43.1	224 1523	0.72 3.76	LC LC
	Calamospiza melanocorys Zentrygon albifacies	40.5 42	11046 2725	0.66 1.41	LC LC
	Charadrius melodus	42.1	3898	0.79	NT
	Chlorochrysa phoenicotis	44.8	415	1.23	LC
	Coeligena wilsoni	38.8	692	2.01	LC
	Corvus leucognaphalus	41.7	329	3.95	VU
	Corvus nasicus	38.7	1044	3.7	LC
	Cyanocorax sanblasianus	39.8	1186	1.75	LC
	Cyanolyca pumilo	43.6	1320	1.27	LC
	Dendrocygna arborea	47.2	377	0.37	NT
	Eriocnemis derbyi	39.9	843	3.41	NT
	Glaucidium siju	39.5	2459	2.25	LC
	Habia cristata	39.1	155	0.28	LC
	Heliodoxa imperatrix	41.4	795	3.58	LC
	Helmitheros vermivorum	39.8	4880	0.28	LC
	Icterus melanopsis	39	2459	2.24	LC
	Lamprolaima rhami	39.6	1580	1.14	LC
	Leuconotopicus borealis	47.2	3729	0.62	NT
	Limnothlypis swainsonii	40.3	7171	0.63	LC
	Melanerpes superciliaris	38.3	2464	2.17	LC
	Mellisuga helenae	39.1	2459	2.26	NT
	Myioborus chrysops	43.5	295	0.57	LC
	Myiotriccus ornatus	43.4	2572	1.71	LC
	Nesoctites micromegas	37.8	540	0.72	LC
	Odontophorus hyperythrus	39.5	428	0.7	NT
	Passerculus bairdii	42.1	7268	0.88	LC
	Peucaea aestivalis	46.3	3306	0.44	NT
	Piezorina cinerea	40.3	665	1.64	LC
	Poospiza hispaniolensis	42.9	2304	2.03	LC
	Priotelus temnurus	40.2	2459	2.26	LC
	Pseudelaenia leucospodia	40.4	1343	2.67	LC
	Pyrilia pulchra	44.3	2279	2.48	LC
	Pyrrhulagra nigra	41.4	2459	2.24	NT
	Quiscalus nicaraguensis	40.2	0	0	LC

All spatial data analysed are open access and free to use for research. Bird abundance data were taken from eBird, version of October 2019 (accessed on the 30 th of November 2019) and are available on request at http://www.ebird.org. Species distribution maps came from BirdLife International and HBW version 2019.1 and are available on request at http://datazone.birdlife.orghttp://doi.org/10.5281/zenodo.5004010.

Supplementary results

Fig. S5: Spatial patterns in three ecoregion-level metrics of the sensitivity of bird communities to human footprint: (A) proportion of all species breeding in the ecoregion that are high-sensitivity species; (B) median sensitivity across all species that breed in the ecoregion; and (C) number of high-sensitivity species that breed in the ecoregion; and correlation with intact habitat protection (D-F). Intact habitat protection does not correlate with proportion of high-sensitivity species (coef = 0.055, P=0.327; D), or with number of high-sensitivity species (coef = 0.058, P=0.298; F) but slightly correlates with median sensitivity (coef = 0.125 P=0.024; E). Fig. S6: Variation in the proportion of high-sensitivity species based on errors in the imputed sensitivity for datapoor species. A: proportion of high-sensitivity species as used in the paper (e.g., Fig. 1, Fig. 2A), based on measured sensitivity for data-rich species and imputed sensitivity for data-poor species. B: Proportion of high-sensitivity species if we assume that all imputed species are non-sensitive (and E, scatterplot with the proportion used in the paper). C: Proportion of high-sensitivity species if we only consider data-rich species (and F scatterplot with the proportion used in the paper). D: Proportion of high-sensitivity species if we assume that all imputed species are high-sensitivity species (and G, scatterplot with the proportion used in the paper).