ABSTRACT Aim On‐the‐ground conservation efforts require managers to balance various and sometimes conflicting conservation goals. For instance, areas important for conserving threatened and endangered species may have little spatial agreement with high functional redundancy. Using prioritisation tools can further complicate conservation prioritisations if conflicting diversity metrics identify different high‐priority areas. We compared five community‐level diversity metrics for birds across the conterminous US to identify how much agreement existed between each before and after using a prioritisation framework. Location Contiguous US. Methods We examined spatial agreement among metrics before (a priori) and after (a posteriori) prioritisation using integer linear programming. We compared a posteriori outputs for 10% and 30% conservation goals. We also assessed data layer correlation and agreement (i.e., overlap) a priori and a posteriori. Results As expected, the a priori diversity metrics were poorly to moderately correlated (median = 0.31, range = 0.11–0.71), but all a posteriori solutions had areas of agreement. Accordingly, our a posteriori metrics identified different areas as high priority for conservation, none aligning well with the current protected areas (mean = 13%–15% agreement). However, the a posteriori approach allowed us to include a continuity constraint (identify adjacent important pixels) and easily find areas of high‐priority agreement. Main Conclusions Metric agreement depended on a priori or a posteriori evaluation, highlighting managers' challenges when deciding where and how to enact conservation. Given these challenges, a posteriori solutions best support multiple‐objective, complex and large planning conservation problems. Importantly, all of our a posteriori maps agreed in areas, suggesting aggregates of several metrics could instill certainty in decision‐making if prioritisation solutions were obtained at different times. Overall, our results underscore the critical importance of generating maps and metrics useful for on‐the‐ground management, carefully selecting biodiversity metrics that best reflect conservation goals and employing prioritisation software for generating conservation solutions.

The Piedmont Forest in northwest Argentina, like most Neotropical seasonal dry forests, is one of the worlds most threatened ecosystems due to deforestation and climate change. To plan conservation strategies aimed at sustaining this forest type, the response to projected changes in habitat conditions must be anticipated. Our objectives were to determine the potential distribution and identify priority areas for conservation that remain stable for saplings and mature trees of three dominant species (Anadenanthera colubrina, Calycophyllum multiflorum, and Phyllostylon rhamnoides) in land use plan categories, protected areas, and forest types under current and two future climate scenarios in northwest Argentina. Calycophyllum multiflorum has the smallest current potential distribution of the three species, but expands to have the largest potential distribution under future climate scenarios. Deforestation reduced by 11-20 % and protected areas harbor < 10 % of the potential distribution of the three species in both age classes in current and future scenarios. In future scenarios, and compared to the current period, the overlap of the potential distribution will increase in the highest protection category, but also for areas categorized as low conservation value that can be transformed according to the land use plan. Half or more of the co-occurring potential distribution of each species, in each age class in current and future scenarios occurs in the Piedmont Forest. Three priority areas for conservation were identified totaling 5483 km2 of which 9 % are currently within protected areas. Thus, at the end of this century the Piedmont Forest is likely to maintain its structure and function if measures are taken to ensure that natural tree regeneration can occur. In the face of future climate change, management policies can satisfy long-term conservation planning necessary to ensure persistence of Piedmont Forest function by protecting priority areas identified in this study.

Biodiversity science requires effective tools to predict patterns of species diversity at multiple temporal and spatial scales. The Dynamic Habitat Indices (DHIs) are remotely sensed indices that summarize aboveground vegetation productivity in a way that is ecologically relevant for biodiversity assessments. Existing global DHIs, derived from MODIS at 1-km resolution, predict species richness at broad scales well, but that resolution is coarse relative to the grain at which many species perceive their habitat. With the much finer spatial resolution of Sentinel-2 and Landsat data, plus Landsat's longer data record, it is possible to track potential changes of vegetation and its impacts on biodiversity at a finer grain over longer periods. Here, our main goals were to derive the DHIs from 10-m Sentinel-2, 30-m Landsat, and 250-m MODIS data for the conterminous US and compare all DHIs at two spatial extents, and to evaluate the ability of these DHIs to predict bird species richness in 25 National Ecological Observatory Network terrestrial sites. In addition, we derived the Landsat DHIs for 1991-2000 and investigated how they changed by 2011-2020. We found that the Sentinel-2, Landsat, and MODIS DHIs were highly correlated when summarized by ecoregion (Spearman correlation ranging from 0.89 to 0.99), indicating good agreement between them and that we were able to overcome the lower temporal resolution of Sentinel-2 and Landsat. Sentinel-2 and Landsat DHIs outperformed MODIS in modeling species richness for all bird guilds, explaining up to 49% of variance of grassland affiliates in linear regression models. Furthermore medium-resolution DHIs (10-30 m resolution) captured spatial heterogeneity much better than MODIS DHIs. We observed considerable changes in Landsat DHIs from 1991-2000 to 2011-2020, such as increased cumulative DHI along the West Coast, in mountain ranges, and in the South, but lower cumulative DHI in the Midwest. Our newly derived DHIs for the conterminous US have great potential for use in biodiversity science and conservation.

Birds select habitat characteristics, such as variability in habitat structure, across multiple spatial scales (grain and extent). Measuring habitat variability at multiple scales can better capture factors that influence avifauna communities than focusing on one scale only. One valuable tool in assessing habitat heterogeneity is the cumulative dynamic habitat index (DHI), which is derived from satellite data and captures temporal variability in vegetation productivity. Our goals were to develop new habitat measures from the cumulative DHI at multiple scales based on scalograms, and to test their performance in models of bird abundance. We counted birds at 188 plots during three breeding seasons (2007–2009) at Fort McCoy military installation, USA, to assess the abundance of forest (ovenbird), shrubland (indigo bunting), and grassland (grasshopper sparrow ) bird specialists. We then calculated NDVI based on PlanetScope (3 m), Sentinel‐2 (10 m), Landsat‐8 (30 m), and MODIS (250 m) data to quantify cumulative DHI. We summarized the averaged NDVI cumulative DHI within multiple extents around each bird survey and developed 11 new habitat measures to test their predictive power in models of bird abundance. We found positive relationships between cumulative DHI at different extents and the abundances of both ovenbirds and indigo buntings, a forest and a shrubland specialist, respectively; and a negative relationship with grasshopper sparrows, a grassland specialist. In multiple linear regression models that incorporated single‐ and multi‐grain predictors, the scalogram habitat measures explained moderate to high levels of variability in bird abundance, with R 2 = 0.77, 0.37, and 0.75 for our forest, shrubland, and grassland specialists, respectively. Our results show that scalograms are an effective tool for capturing multiscale habitat configuration, because they capture the variability of habitat conditions in forests, shrublands, and grasslands. The scalogram habitat measures that we developed can be computed using our new R package ‘scalogram'.

. Eastern North American woodlands have become rare and degraded by land use conversion and fire suppression, which prevents regeneration of oaks and other shade-intolerant species. The persistence of woodlands now hinges on management using prescribed fire and tree thinning. Simultaneously, most contiguous forests have been lost to deforestation and fragmentation. Converting patches within these forests into woodlands could support a diversity of bird species, but may deter those needing mature forests, creating a potential trade-off. In a three-year field study in the Midwestern USA, we examined the effects of woodland management within interior forests on foliage arthropod biomass, and insectivorous bird species richness and mass-abundance relationships, which describe the energetic limit of a community. Mass-abundance relationships are considered integral to ecological communities and, in ecosystems without anthropogenic threats, typically show a negative relationship between species' body mass and their abundance because of higher energy requirements for larger species; deviations indicate biodiversity responses to habitat disturbances. Working in two managed and two unmanaged woodland sites from 2021 to 2023, we conducted 3809 arthropod branch surveys, captured, banded, and weighed 535 insectivores of 32 avian species, and documented 43 insectivores on point counts. We found that foliage arthropod biomass was higher in managed sites during 2021 and 2022 but not 2023, and that managed woodlands supported higher richness of understory insectivores, all insectivores, and early-successional species, and equal richness of ground foragers and mature forest specialists. The insectivorous bird community in managed sites had significant negative mass-abundance upper bounds limits in all years, while in unmanaged sites this relationship was nonsignificant and appeared to vary widely between years, potentially reflecting differences in arthropod resource availability. Thus, although the anticipated trade-off between woodland management and mature forest bird conservation was not strongly evident, the inconsistent mass-abundance relationships in unmanaged sites suggest lower resilience to resource variability.

Approaches estimating landscape effects on biodiversity frequently focus on a single extent, finding one ‘optimal’ extent, or use narrow extents. However, species perceive the environment in different ways, select habitat hierarchically, and respond to multiple selection pressures at extents that best predict each pressure. We aimed to assess multi-scale relationships between primary productivity and species occurrences and abundances. We used a multi-scale approach, called ‘scalograms’, to assess landscape level effects of primary productivity, in the form of Dynamic Habitat Indices (DHIs) on the occurrences and abundances of 100 Argentinian forest bird species. We used average DHI values within multiple extents (3 × 3 to 101 × 101 pixels; 30 m resolution), and 11 ‘scalogram’ metrics as environmental inputs in occurrence and abundance models. Average cumulative DHI values in extents 81 × 81 to 101 × 101 pixels (5.9 – 9.2 km2) and maximum cumulative DHI across extents were in the top three predictors of species occurrences (included in models for 41

Traditional approaches of forest classifications were based on tree species composition, but recently combine phenology and climate to characterise functional (cyclic and seasonal greenness) rather than structural or compositional components (phenoclusters). The objective was to compare the conservation value (capacity to support more native biodiversity) and provision of ecosystem services (ES) in different phenocluster categories of Nothofagus antarctica forests in Tierra del Fuego (Argentina). We used available models (ES, potential biodiversity) and ground-truth data of 145 stands, comparing phenocluster values using uni- and multivariate analyses. Conservation value and capacity to supply ES significantly varied among phenocluster categories: (i) cultural, regulating, and provisioning ES and potential biodiversity at landscape level, (ii) soil carbon and nitrogen, (iii) dominant height, crown cover, basal area, total volume, and domestic animal stock, and (iv) understory plant richness and cover at stand level. These differences are linked to the forest capacity to support more native biodiversity and ES. Besides, multivariate analyses supporting the split of this forest type into four phenocluster subtypes (coast, highland, ecotone with other types, and degraded or secondary forests). Our findings suggest the needs of specific management and conservation proposals, based on phenoclusters rather than forest types defined by tree canopy-cover composition.

Oak woodlands are threatened across North America due to land use change, fire exclusion, and the spread of invasive species following European settlement. Effective conservation of woodlands-and associated biodiversity-is dependent on management (prescribed fire and tree thinning) emulating natural disturbance and historic cultural burning. We examined the effects of woodland management during the avian breeding season in the upper Midwest (WI, USA), collecting data at three trophic levels: vegetation, arthropods, and insectivorous birds. Compared to unmanaged sites, managed sites had lower basal area, understory density and snag abundance, and higher tree diameter, herbaceous plant cover, and soil moisture. Mean caterpillar biomass was higher in managed sites, as was mean aerial insect biomass. Avian species richness was higher in managed sites, and was negatively associated with canopy cover and positively associated with herbaceous plant cover. Detectioncorrected abundance estimates of the 21 most common insectivorous bird species indicated that ten species were more abundant in managed sites, four were more abundant in unmanaged sites, and seven were distributed equally. Six of 12 foliage-gleaning species, two of three aerial insectivores, and two of five ground foragers were more abundant in managed sites. For all but two species (American Redstart, Setophaga ruticilla; Eastern WoodPewee, Contopus virens), density of breeding territories was better explained by habitat characteristics than by arthropod resources. Our results indicate that managed woodlands support higher arthropod biomass and have the potential to benefit a wide range of bird species.

Summary Medium- and large-sized mammals play important roles in maintaining forest ecosystem functions, and these functions often diminish when mammal species are depleted by human activities. Understanding the sensitivity or tolerance of mammal species to human pressure and detecting species changes through monitoring programmes can inform appropriate management decisions. The objective of our study was to identify medium- and large-sized mammal species that can be included in a monitoring programme in the Southern Yungas of Argentina. We used occupancy modelling to estimate the probability of habitat use (ψ) of 13 of 25 mammal species detected by 165 camera traps placed in forests across a range of human footprint index (HFI) values. As defined by the HFI, 54% of the study area is wilderness. The probabilities of habitat use of two mammal species were significantly associated with the HFI: the lowland tapir ( Tapirus terrestris ; ψ = 0.33, range = 0.22–0.50) was inversely associated with HFI values, whereas the grey brocket deer ( Mazama gouazoubira ; ψ = 0.79, range = 0.67–0.87) was positively associated with the HFI. Monitoring the probability of habitat use of the sensitive species (lowland tapir) could help us to detect changes in areas experiencing anthropogenic impacts before they cause extirpation, whereas the high probability of the habitat use values of the tolerant species (grey brocket deer) might indicate that anthropogenic impacts are strongly influencing habitat, signalling that mitigation strategies might be warranted. The Southern Yungas retains an intact mammal fauna, and we showed that the HFI is useful for monitoring anthropogenic impacts on these mammals. There are still opportunities to develop conservation strategies to minimize threats to mammal species in the region by implementing a monitoring programme with the proposed species.

Heterogeneous vegetation supports higher species richness than homogenous vegetation, which is why efficiently identifying heterogenous vegetation can be useful for biodiversity conservation. Satellite remote-sensing data provide an opportunity to generate vegetation heterogeneity metrics and to explore the phenology of vegetation patterns. Phenoclusters are vegetation types with similar phenological characteristics, and valuable for capturing vegetation habitat heterogeneity patterns. Our goal was to map phenoclusters for Wisconsin, USA, at 10-m spatial resolution based on land surface phenology metrics from EVI (Enhanced Vegetation Index) Sentinel-2 data. We characterized each phenocluster based on landcover composition and structure, phenology timing, and environmental factors, and compared them to bird species richness. We also calculated the diversity of phenoclusters at multiple spatial extents. We identified 14 phenoclusters in Wisconsin, each with distinct landcover composition and structure, and unique phenological characteristics. Our remotely-sensed phenoclusters effectively captured environmental gradients, with elevation and temperature emerging as the most important driving variables. Furthermore, the phenoclusters successfully captured bird biodiversity patterns, especially richness of forest and grassland specialist. Our results identified phenological patterns among Wisconsin’s forests, shrublands, and grasslands, capturing phenological timing both among and within the same tree species. Phenoclusters are a valuable tool for capturing vegetation habitat heterogeneity, phenology diversity and biodiversity patterns, as well as climate change effects.

Temperate woodlands are biodiverse natural communities threatened by land use change and fire suppression. Excluding historic disturbance regimes of periodic groundfires from woodlands causes degradation, resulting from changes in the plant community and subsequent biodiversity loss. Restoration, through prescribed fire and tree thinning, can reverse biodiversity losses, however, because the diversity of woodland species spans many taxa, efficiently quantifying biodiversity can be challenging. We assessed whether soundscapes in an eastern North American woodland reflect biodiversity changes during restoration measured in a concurrent multitrophic field study. In five restored and five degraded woodland sites in Wisconsin, USA, we sampled vegetation, measured arthropod biomass, conducted bird surveys, and recorded soundscapes for five days of every 15-day period from May to August 2022. We calculated two complementary acoustic indices: Soundscape Saturation, which focuses on all acoustically active species, and Acoustic Complexity Index (ACI), which was developed to study vocalizing birds. We used generalized additive models to predict both indices based on Julian date, time of day, and level of habitat degradation. We found that restored woodlands had higher arthropod biomass, and higher richness and abundance of breeding birds. Additionally, soundscapes in restored sites had higher mean Soundscape Saturation and higher mean ACI. Restored woodland acoustic indices exhibited greater magnitudes of daily and seasonal peaks. We conclude that woodland restoration results in higher soundscape saturation and complexity, due to greater richness and abundance of vocalizing animals. This bioacoustic signature of restoration offers a promising monitoring tool for efficiently documenting differences in woodland biodiversity.

There is ongoing debate among conservationists regarding the value of small habitat patches to sustain wild populations in farmlands. Our goal was to assess bird abundance in riparian forests differing in terms of size, configuration, landscape conditions and degradation level, to both inform the debate and to identify conservation strategies to maintain diverse agricultural landscapes. We conducted bird point-counts in 91 sites in 2016 across an agricultural valley in Chile. Using models that accounted for imperfect detection, we assessed variation in bird densities in riparian forests with different sizes and configuration, landscapes, and habitat characteristics. We found support in univariates models for our prediction that bird densities varied across riparian forest of various sizes and configuration for 10 of 16 bird species. However, when we added landscape and habitat characteristics to the model, we found that the densities of many of the birds were best explained by forest cover around their local (1 ha) and broader (50 ha) landscape combined with forests characteristics (e.g., invasive tree abundance). For example, Black-throated huet-huet and Chucao Tapaculo were positively associated with forest cover at the broader landscape (50 ha), but showed no response to number of patches, patch-size and Euclidean distance. Our results showed no evidence of negative fragmentation effect per se (i.e., after controlling for habitat area). While agricultural landscapes provide habitat for some species that use small forest patches, conservation strategies focusing on maintaining high level of forest cover and native vegetation are required to secure populations of forest affiliated species.

Animal communication plays a crucial role in biology, yet the wide variability in vocalizations is not fully understood. Previous studies in birds have been limited in taxonomic and analytical breadth. Here, we analyse an extensive dataset of >140 000 recordings of vocalizations from 8450 bird species, representing nearly every avian order and family, under a structural causal model framework, to explore the influence of eco-evolutionary traits on acoustic frequency characteristics. We find that body mass, beak size, habitat associations and geography influence acoustic frequency characteristics, with varying degrees of interaction with song acquisition type. We find no evidence for the influence of vegetation density, sexual dimorphism, range size and competition on our measures of acoustic frequency characteristics. Our results, built on decades of researchers’ empirical observations collected across the globe, provide a new breadth of evidence about how eco-evolutionary processes shape bird communication.

Human influence on ecosystems is rapidly expanding, and one consequence is the increased availability of human food subsidies to wildlife. Human food subsidies like refuse and food scraps are widely hypothesized to be ‘junk food’ that is nutritionally incomplete; however, the impacts of ‘junk foods’ on the health and fitness of individual organisms remain unclear. In this study, we aimed to understand how human food consumption affects the body condition and fecundity of a generalist predator, the Steller’s jay (Cyanocitta stelleri). We used stable isotope analysis to quantify individual human food consumption (using δ13C as a proxy), estimated individual body condition based on body mass and feather growth bar width and assessed jay fecundity. Adults consumed more human food than juveniles on average, and we observed sex-specific responses to human food use where male body condition tended to increase, whereas female body condition tended to decline with human food consumption. However, fecundity was not strongly related. Thus, we found some evidence for the ‘junk food’ hypothesis in this system, which suggests that human foods may not be an equal replacement for natural foods from a nutritional perspective, especially for females. Human foods tend to be carbohydrate rich, but protein poor, which may benefit males because they are larger and limited overall by calorie intake. Females, particularly reproducing females, are more nutritionally limited and thus may experience fewer benefits from ‘junk food’. Our study advances knowledge of human–wildlife interactions by increasing the resolution of our understanding of the fitness benefits, or detriments, experienced by individuals that consume human foods.

Animals select habitat at multiple spatial scales, suggesting that biodiversity modeling, for example of species richness, should be based on environmental data gathered at multiple spatial scales, and especially multiple grain sizes. Different satellite sensors collect data at different spatial resolutions and therefore provide opportunities for multi-grain habitat measures. The dynamic habitat indices (DHIs), which are derived from satellite data, capture patterns of vegetative productivity and predict bird species richness well. However, the DHIs have only been analyzed at single resolutions (e.g., 1-km), and have not yet been derived from high-resolution satellite data (< 10-m). Our goal was to predict bird species richness based on measures of vegetation productivity (DHIs, NDVI median and NDVI percentile 90th) across a range of spatial resolutions both from different sensors, and from resampled high-resolution imagery. We analyzed bird species richness within 215 forest, grassland and shrubland plots (56.25 ha) located at 26 terrestrial field sites of the National Ecology Observatory Network (NEON), in the continental US. To obtain our multi-resolution measures of vegetation productivity, we acquired data from Planetscope (3-m), RapidEye (5-m), Sentinel-2 (10-m), Landsat-8 (30-m) and MODIS (250-m) from 2017 to 2020, generated time series of NDVI, calculated the three DHIs (cumulative, minimum and variation), NDVI median and the 90th percentile NDVI and calculated 1st and 2nd order texture measures. We evaluated the performance of the derived measures to predict bird species richness of habitat specialist guilds based on (i) univariate models (ii) multivariate models with single-resolution measures and (iii) multivariate models with multi-resolution measures. Single-spatial resolution measures predicted bird species richness moderately well (R2 up to 0.51) and the best performing spatial resolution and measure differed among bird species guilds. High -spatial resolution (3-5 m) measures outperformed medium-resolution measures (10-250 m). Models for all guilds performed best when incorporating multiple resolutions, including for all species richness (R2 = 0.63) and for forest (R2 = 0.72), grassland (R2 = 0.53) and shrubland specialists (R2 = 0.46). In addition, models based on multi-resolution data from different sensors performed better than models based on resampled high-resolution data for any of the guilds. Our results highlight, first, the value of the DHIs derived from high-resolution sat-ellite data to predict bird species richness and, second, that remotely-sensed vegetation productivity measures from multiple spatial resolutions offer great promise for quantifying biodiversity.

Species distribution models are vital to management decisions that require understanding habitat use patterns, particularly for species of conservation concern. However, the production of distribution maps for individual species is often hampered by data scarcity, and existing species maps are rarely spatially validated due to limited occurrence data. Furthermore, community-level maps based on stacked species distribution models lack important community assemblage information (e.g., competitive exclusion) relevant to conservation. Thus, multispecies, guild, or community models are often used in conservation practice instead. To address these limitations, we aimed to generate fine-scale, spatially continuous, nationwide maps for species represented in the North American Breeding Bird Survey (BBS) between 1992 and 2019. We developed ensemble models for each species at three spatial resolutions-0.5, 2.5, and 5 km-across the conterminous United States. We also compared species richness patterns from stacked single-species models with those of 19 functional guilds developed using the same data to assess the similarity between predictions. We successfully modeled 192 bird species at 5-km resolution, 160 species at 2.5-km resolution, and 80 species at 0.5-km resolution. However, the species we could model represent only 28%-56% of species found in the conterminous US BBSs across resolutions owing to data limitations. We found that stacked maps and guild maps generally had high correlations across resolutions (median = 84%), but spatial agreement varied regionally by resolution and was most pronounced between the East and West at the 5-km resolution. The spatial differences between our stacked maps and guild maps illustrate the importance of spatial validation in conservation planning. Overall, our species maps are useful for single-species conservation and can support fine-scale decision-making across the United States and support community-level conservation when used in tandem with guild maps. However, there remain data scarcity issues for many species of conservation concern when using the BBS for single-species models.

Detailed maps of forest structure attributes are crucial for sustainable forest management, conservation, and forest ecosystem science at the landscape level. Mapping the structure of broad heterogeneous forests is chal-lenging, but the integration of extensive field inventory plots with wall-to-wall metrics derived from synthetic aperture radar (SAR) and optical remote sensing offers a potential solution. Our goal was to map forest structure attributes (diameter at breast height, basal area, mean height, dominant height, wood volume and canopy cover) at 30-m resolution across the diverse 463,000 km2 of native forests of Argentina based on SAR Sentinel-1, vegetation metrics from Sentinel-2 and geographic coordinates. We modelled the forest structure attributes based on the latest national forest inventory, generated uncertainty maps, quantified the contribution of the predictors, and compared our height predictions with those from GEDI (Global Ecosystem Dynamics Investiga-tion) and GFCH (Global Forest Canopy Height). We analyzed 3788 forest inventory plots (1000 m2 each) from Argentina's Second Native Forest Inventory (2015-2020) to develop predictive random forest regression models. From Sentinel-1, we included both VV (vertical transmitted and received) and VH (vertical transmitted and horizontal received) polarizations and calculated 1st and 2nd order textures within 3 x 3 pixels to match the size of the inventory plots. For Sentinel-2, we derived EVI (enhanced vegetation index), calculated DHIs (dynamic habitat indices (annual cumulative, minimum and variation) and the EVI median, then generated 1st and 2nd order textures within 3 x 3 pixels of these variables. Our models including metrics from Sentinel-1 and 2, plus latitude and longitude predicted forest structure attributes well with root mean square errors (RMSE) ranging from 23.8% to 70.3%. Mean and dominant height models had notably good performance presenting relatively low RMSE (24.5% and 23.8%, respectively). Metrics from VH polarization and longitude were overall the most important predictors, but optimal predictors differed among the different forest structure attributes. Height predictions (r = 0.89 and 0.85) outperformed those from GEDI (r = 0.81) and the GFCH (r = 0.66), suggesting that SAR Sentinel-1, DHIs from Sentinel-2 plus geographic coordinates provide great opportunities to map multiple forest structure attributes for large areas. Based on our models, we generated spatially-explicit maps of multiple forest structure attributes as well as uncertainty maps at 30-m spatial resolution for all Argentina's native forest areas in support of forest management and conservation planning across the country.

Closing the research-implementation gap is key for advancing biodiversity conservation. One approach is to generate ecologically relevant spatial datasets that integrate easily with existing management plans. Our goal was to identify priority forest conservation areas in Argentina by combining species distributions, human footprint data, and existing forest zoning. We: (i) mapped potential habitat distributions of 70 plant and animal species associated with forests, and of recognized social and ecological importance, (ii) combined the species distributions with human footprint data to identify priority conservation areas, and (iii) evaluated the juxtaposition of our priority conservation areas with current forest management zones. We found that priority conservation areas (i.e., high number of species and low human footprint) are poorly protected by the current zoning scheme. While the Andean-Patagonian region had a substantial portion (57 %) of priority conservation areas in high protection zones, in four other forest regions we evaluated, only 16-37 % of priority areas had high protection levels. Of great concern are the Chaco and Espinal regions, where 36 % and 39 %, respectively, of priority conservation areas are in low protection zones, where conversion to other uses (row crops, livestock) is allowed. Our results provide new spatial information to managers and conservationists highlighting where current forest zoning performs well, and where it may warrant re-evaluation. Overall, our study highlights the value of integrating species distributions and human footprint maps into existing land use plans to guide conservation efforts in data-poor countries, and is an example of a strategy for closing the research-implementation gap.

Wildfire risks to homes are increasing, especially in the wildland-urban interface (WUI), where wildland vegetation and houses are in close proximity. Notably, we found that more houses are exposed to and destroyed by grassland and shrubland fires than by forest fires in the United States. Destruction was more likely in forest fires, but they burned less WUI. The number of houses within wildfire perimeters has doubled since the 1990s because of both housing growth (47% of additionally exposed houses) and more burned area (53%). Most exposed houses were in the WUI, which grew substantially during the 2010s (2.6 million new WUI houses), albeit not as rapidly as before. Any WUI growth increases wildfire risk to houses though, and more fires increase the risk to existing WUI houses.