Integrating habitat-masked range maps with quantifications of prevalence to estimate area of occupancy in IUCN assessments

Estimates of species geographic ranges constitute critical input for biodiversity assessments, including those for the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species. Area of occupancy (AOO) is one metric that IUCN uses to quantify a species’ range, but data limitations typically lead to either under- or overestimates (and unnecessarily wide bounds of uncertainty). Fortunately, existing methods in which range maps and land-cover data are used to estimate the area currently holding habitat for a species can be extended to yield an unbiased range of plausible estimates for AOO. Doing so requires estimating the proportion of sites (currently containing habitat) that a species occupies within its range (i.e., prevalence). Multiplying a quantification of habitat area by prevalence yields an estimate of what the species inhabits (i.e., AOO). For species with intense sampling at many sites, presence–absence data sets or occupancy modeling allow calculation of prevalence. For other species, primary biodiversity data (records of a species’ presence at a point in space and time) from citizen-science initiatives and research collections of natural history museums and herbaria could be used. In such cases, estimates of sample prevalence should be corrected by dividing by the species’ detectability. To estimate detectability from these data sources, extensions of inventory-completeness analyses merit development. With investments to increase the quality and availability of online biodiversity data, consideration of prevalence should lead to tighter and more realistic bounds of AOO for many taxonomic groups and geographic regions. By leading to more realistic and representative characterizations of biodiversity, integrating maps of current habitat with estimates of prevalence should empower conservation practitioners and decision makers and thus guide actions and policy worldwide.     

Applying habitat and population-density models to land-cover time series to inform IUCN Red List assessments

The IUCN (International Union for Conservation of Nature) Red List categories and criteria are the most widely used framework for assessing the relative extinction risk of species. The criteria are based on quantitative thresholds relating to the size, trends, and structure of species’ distributions and populations. However, data on these parameters are sparse and uncertain for many species and unavailable for others, potentially leading to their misclassification or classification as data deficient. We devised an approach that combines data on land-cover change, species-specific habitat preferences, population abundance, and dispersal distance to estimate key parameters (extent of occurrence, maximum area of occupancy, population size and trend, and degree of fragmentation) and hence predict IUCN Red List categories for species. We applied our approach to nonpelagic birds and terrestrial mammals globally (∼15,000 species). The predicted categories were fairly consistent with published IUCN Red List assessments, but more optimistic overall. We predicted 4.2% of species (467 birds and 143 mammals) to be more threatened than currently assessed and 20.2% of data deficient species (10 birds and 114 mammals) to be at risk of extinction. Incorporating the habitat fragmentation subcriterion reduced these predictions 1.5–2.3% and 6.4–14.9% (depending on the quantitative definition of fragmentation) for threatened and data deficient species, respectively, highlighting the need for improved guidance for IUCN Red List assessors on the application of this aspect of the IUCN Red List criteria. Our approach complements traditional methods of estimating parameters for IUCN Red List assessments. Furthermore, it readily provides an early-warning system to identify species potentially warranting changes in their extinction-risk category based on periodic updates of land-cover information. Given our method relies on optimistic assumptions about species distribution and abundance, all species predicted to be more at risk than currently evaluated should be prioritized for reassessment.     

Understanding the drivers of expert opinion when classifying species as extinct

The criteria as laid out by the International Union for the Conservation of Nature (IUCN) Red List are the gold standard by which the extinction risk of a species is assessed and where appropriate biological extinctions are declared. However, unlike all other categories, the category of extinct lacks a quantitative framework for assigning this category. Given its subjective nature, we surveyed expert assessors working on a diversity of taxa to explore the attributes they used to declare a species extinct. Using a choice experiment approach, we surveyed 674 experts from the IUCN Species Survival Commission specialist groups and taskforces. Data availability, time from the last sighting, detectability, habitat availability, and population decline were all important attributes favored by assessors when inferring extinction. Respondents with red-listing experience assigned more importance to the attributes data availability, time from the last sighting, and detectability when considering a species extinction, whereas those respondents working with well-known taxa gave more importance to the time from the last sighting. Respondents with no red-listing experience and those working with more well-known taxa (i.e., mammals and birds) were overall less likely to consider species extinct. Our findings on the importance assessors place on attributes used to declare a species extinct provide a basis for informing the development of specific criteria for more accurately assessing species extinctions.     

Building a better baseline to estimate 160 years of avian population change and create historically informed conservation targets

Globally, anthropogenic land-cover change has been dramatic over the last few centuries and is frequently invoked as a major cause of wildlife population declines. Baseline data currently used to assess population trends, however, began well after major changes to the landscape. In the United States and Canada, breeding bird population trends are assessed by the North American Breeding Bird Survey, which began in the 1960s. Estimates of distribution and abundance prior to major habitat alteration would add historical perspective to contemporary trends and allow for historically based conservation targets. We used a hindcasting framework to estimate change in distribution and abundance of 7 bird species in the Willamette Valley, Oregon (United States). After reconciling classification schemes of current and 1850s reconstructed land cover, we used multiscale species distribution models and hierarchical distance sampling models to predict spatially explicit densities in the modern and historical landscapes. We estimated that since the 1850s, White-breasted Nuthatch (Sitta carolinensis) and Western Meadowlark (Sturnella neglecta) populations, 2 species sensitive to fragmentation of oak woodlands and grasslands, declined by 93% and 97%, respectively. Five other species we estimated nearly stable or increasing populations, despite steep regional declines since the 1960s. Based on these estimates, we developed historically based conservation targets for amount of habitat, population, and density for each species. Hindcasted reconstructions provide historical perspective for assessing contemporary trends and allow for historically based conservation targets that can inform current management.     

The importance of Indigenous Peoples’ lands for the conservation of terrestrial mammals

Indigenous Peoples’ lands cover over one-quarter of Earth's surface, a significant proportion of which is still free from industrial-level human impacts. As a result, Indigenous Peoples and their lands are crucial for the long-term persistence of Earth's biodiversity and ecosystem services. Yet, information on species composition on these lands globally remains largely unknown. We conducted the first comprehensive analysis of terrestrial mammal composition across mapped Indigenous lands based on data on area of habitat (AOH) for 4460 mammal species assessed by the International Union for Conservation of Nature. We overlaid each species’ AOH on a current map of Indigenous lands and found that 2695 species (60% of assessed mammals) had ≥10% of their ranges on Indigenous Peoples’ lands and 1009 species (23%) had >50% of their ranges on these lands. For threatened species, 473 (47%) occurred on Indigenous lands with 26% having >50% of their habitat on these lands. We also found that 935 mammal species (131 categorized as threatened) had ≥ 10% of their range on Indigenous Peoples’ lands that had low human pressure. Our results show how important Indigenous Peoples’ lands are to the successful implementation of conservation and sustainable development agendas worldwide.     

Evidence-based guidelines for automated conservation assessments of plant species

Assessing species’ extinction risk is vital to setting conservation priorities. However, assessment endeavors, such as those used to produce the IUCN Red List of Threatened Species, have significant gaps in taxonomic coverage. Automated assessment (AA) methods are gaining popularity to fill these gaps. Choices made in developing, using, and reporting results of AA methods could hinder their successful adoption or lead to poor allocation of conservation resources. We explored how choice of data cleaning type and level, taxonomic group, training sample, and automation method affect performance of threat status predictions for plant species. We used occurrences from the Global Biodiversity Information Facility (GBIF) to generate assessments for species in 3 taxonomic groups based on 6 different occurrence-based AA methods. We measured each method's performance and coverage following increasingly stringent occurrence cleaning. Automatically cleaned data from GBIF performed comparably to occurrence records cleaned manually by experts. However, all types of data cleaning limited the coverage of AAs. Overall, machine-learning-based methods performed well across taxa, even with minimal data cleaning. Results suggest a machine-learning-based method applied to minimally cleaned data offers the best compromise between performance and species coverage. However, optimal data cleaning, training sample, and automation methods depend on the study group, intended applications, and expertise.     

Comparing wildlife habitat suitability models based on expert opinion with camera trap detections

Expert knowledge is used in the development of wildlife habitat suitability models (HSMs) for management and conservation decisions. However, the consistency of such models has been questioned. Focusing on 1 method for elicitation, the analytic hierarchy process, we generated expert-based HSMs for 4 felid species: 2 forest specialists (ocelot [Leopardus pardalis] and margay [Leopardus wiedii]) and 2 habitat generalist species (Pampas cat [Leopardus colocola] and puma [Puma concolor]). Using these HSMs, species detections from camera-trap surveys, and generalized linear models, we assessed the effect of study species and expert attributes on the correspondence between expert models and camera-trap detections. We also examined whether aggregation of participant responses and iterative feedback improved model performance. We ran 160 HSMs and found that models for specialist species showed higher correspondence with camera-trap detections (AUC [area under the receiver operating characteristic curve] >0.7) than those for generalists (AUC < 0.7). Model correspondence increased as participant years of experience in the study area increased, but only for the understudied generalist species, Pampas cat (β = 0.024 [SE 0.007]). No other participant attribute was associated with model correspondence. Feedback and revision of models improved model correspondence, and aggregating judgments across multiple participants improved correspondence only for specialist species. The average correspondence of aggregated judgments increased as group size increased but leveled off after 5 experts for all species. Our results suggest that correspondence between expert models and empirical surveys increases as habitat specialization increases. We encourage inclusion of participants knowledgeable of the study area and model validation for expert-based modeling of understudied and generalist species.     

Using expert knowledge to support Endangered Species Act decision-making for data-deficient species

Many questions relevant to conservation decision-making are characterized by extreme uncertainty due to lack of empirical data and complexity of the underlying ecologic processes, leading to a rapid increase in the use of structured protocols to elicit expert knowledge. Published ecologic applications often employ a modified Delphi method, where experts provide judgments anonymously and mathematical aggregation techniques are used to combine judgments. The Sheffield elicitation framework (SHELF) differs in its behavioral approach to synthesizing individual judgments into a fully specified probability distribution for an unknown quantity. We used the SHELF protocol remotely to assess extinction risk of three subterranean aquatic species that are being considered for listing under the U.S. Endangered Species Act. We provided experts an empirical threat assessment for each known locality over a video conference and recorded judgments on the probability of population persistence over four generations with online submission forms and R-shiny apps available through the SHELF package. Despite large uncertainty for all populations, there were key differences between species’ risk of extirpation based on spatial variation in dominant threats, local land use and management practices, and species’ microhabitat. The resulting probability distributions provided decision makers with a full picture of uncertainty that was consistent with the probabilistic nature of risk assessments. Discussion among experts during SHELF's behavioral aggregation stage clearly documented dominant threats (e.g., development, timber harvest, animal agriculture, and cave visitation) and their interactions with local cave geology and species’ habitat. Our virtual implementation of the SHELF protocol demonstrated the flexibility of the approach for conservation applications operating on budgets and time lines that can limit in-person meetings of geographically dispersed experts.     

A strategy for the next decade to address data deficiency in neglected biodiversity

Measuring progress toward international biodiversity targets requires robust information on the conservation status of species, which the International Union for Conservation of Nature (IUCN) Red List of Threatened Species provides. However, data and capacity are lacking for most hyperdiverse groups, such as invertebrates, plants, and fungi, particularly in megadiverse or high-endemism regions. Conservation policies and biodiversity strategies aimed at halting biodiversity loss by 2020 need to be adapted to tackle these information shortfalls after 2020. We devised an 8-point strategy to close existing data gaps by reviving explorative field research on the distribution, abundance, and ecology of species; linking taxonomic research more closely with conservation; improving global biodiversity databases by making the submission of spatially explicit data mandatory for scientific publications; developing a global spatial database on threats to biodiversity to facilitate IUCN Red List assessments; automating preassessments by integrating distribution data and spatial threat data; building capacity in taxonomy, ecology, and biodiversity monitoring in countries with high species richness or endemism; creating species monitoring programs for lesser-known taxa; and developing sufficient funding mechanisms to reduce reliance on voluntary efforts. Implementing these strategies in the post-2020 biodiversity framework will help to overcome the lack of capacity and data regarding the conservation status of biodiversity. This will require a collaborative effort among scientists, policy makers, and conservation practitioners.     

Selection of both habitat and genes in specialized and endangered caribou

Genetic mechanisms determining habitat selection and specialization of individuals within species have been hypothesized, but not tested at the appropriate individual level in nature. In this work, we analyzed habitat selection for 139 GPS-collared caribou belonging to 3 declining ecotypes sampled throughout Northwestern Canada. We used Resource Selection Functions comparing resources at used and available locations. We found that the 3 caribou ecotypes differed in their use of habitat suggesting specialization. On expected grounds, we also found differences in habitat selection between summer and winter, but also, originally, among the individuals within an ecotype. We next obtained Single Nucleotide Polymorphisms (SNPs) for the same caribou individuals, we detected those associated to habitat selection, and then identified genes linked to these SNPs. These genes had functions related in other organisms to habitat and dietary specializations, and climatic adaptations. We therefore suggest that individual variation in habitat selection was based on genotypic variation in the SNPs of individual caribou, indicating that genetic forces underlie habitat and diet selection in the species. We also suggest that the associations between habitat and genes that we detected may lead to lack of resilience in the species, thus contributing to caribou endangerment. Our work emphasizes that similar mechanisms may exist for other specialized, endangered species.     

Quantification of Extinction Risk: IUCN's System for Classifying Threatened Species

Abstract: The International Union for Conservation of Nature (IUCN) Red List of Threatened Species was increasingly used during the 1980s to assess the conservation status of species for policy and planning purposes. This use stimulated the development of a new set of quantitative criteria for listing species in the categories of threat: critically endangered, endangered, and vulnerable. These criteria, which were intended to be applicable to all species except microorganisms, were part of a broader system for classifying threatened species and were fully implemented by IUCN in 2000. The system and the criteria have been widely used by conservation practitioners and scientists and now underpin one indicator being used to assess the Convention on Biological Diversity 2010 biodiversity target. We describe the process and the technical background to the IUCN Red List system. The criteria refer to fundamental biological processes underlying population decline and extinction. But given major differences between species, the threatening processes affecting them, and the paucity of knowledge relating to most species, the IUCN system had to be both broad and flexible to be applicable to the majority of described species. The system was designed to measure the symptoms of extinction risk, and uses 5 independent criteria relating to aspects of population loss and decline of range size. A species is assigned to a threat category if it meets the quantitative threshold for at least one criterion. The criteria and the accompanying rules and guidelines used by IUCN are intended to increase the consistency, transparency, and validity of its categorization system, but it necessitates some compromises that affect the applicability of the system and the species lists that result. In particular, choices were made over the assessment of uncertainty, poorly known species, depleted species, population decline, restricted ranges, and rarity; all of these affect the way red lists should be viewed and used. Processes related to priority setting and the development of national red lists need to take account of some assumptions in the formulation of the criteria.     

A composite measure of habitat loss for entire assemblages of species

Habitat destruction is among the greatest threats facing biodiversity, and it affects common and threatened species alike. However, metrics for communicating its impacts typically overlook the nonthreatened component of assemblages. This risks the loss of habitat going unreported for species that comprise the majority of assemblages. We adapted a widely used measure for summarizing researcher output (the h index) to provide a metric that describes natural habitat loss for entire assemblages, inclusive of threatened and nonthreatened species. For each of 447 Australian native terrestrial bird species, we combined information on their association with broad vegetation groups with distributional range maps to identify the difference between the estimated pre-European and current extents of potential habitat, defined as vegetation groups most closely associated with each species. From this, we calculated the loss index (LI), which revealed that 30% of native birds have each lost at least 30% of their potential natural habitat (LI = 30). At the subcontinental scale, LIs ranged from 15 in arid Australia to 61 in the highly transformed southeastern part of the country. Different subcomponents of the assemblage had different LI values. For example, Australia's parrots (n = 52 species) had an LI of 38, whereas raptors (n = 32 species) had an LI of 25. The LI is simple to calculate and can be determined using readily available spatial information on species distributions, native vegetation associations, and human impacts on natural land cover. This metric, including the curves used to deduce it, could complement other biodiversity indices if it is used for regional and global biodiversity assessments that compare the status of natural habitat extent for assemblages within and among nations, monitor changes through time, and forecast future changes to guide strategic land-use planning. The LI is an intuitive tool that can be used to summarize and communicate how human actions affect whole assemblages, not just threatened species.     

Using experimental reintroductions to resolve the roles of habitat quality and metapopulation dynamics on patch occupancy in fragmented landscapes

Declines of species in fragmented landscapes can potentially be reversed either by restoring connectivity or restoring local habitat quality. Models fitted to snapshot occupancy data can be used to predict the effectiveness of these actions. However, such inferences can be misleading if the reliability of the habitat and landscape metrics used is unknown. The only way to unambiguously resolve the roles of habitat quality and metapopulation dynamics is to conduct experimental reintroductions to unoccupied patches so that habitat quality can be measured directly from data on vital rates. We, therefore, conducted a 15-year study that involved reintroducing a threatened New Zealand bird to unoccupied forest fragments to obtain reliable data on their habitat quality and reassess initial inferences made by modeling occupancy against habitat and landscape metrics. Although reproductive rates were similar among fragments, subtle differences in adult survival rates resulted in λ (finite rate of increase) estimations of <0.9 for 9 of the 12 fragments that were previously unoccupied. This was the case for only 1 of 14 naturally occupied fragments. This variation in λ largely explained the original occupancy pattern, reversing our original conclusion from occupancy modeling that this occupancy pattern was isolation driven and suggesting that it would be detrimental to increase connectivity without improving local habitat quality. These results illustrate that inferences from snapshot occupancy should be treated with caution and subjected to testing through experimental reintroductions in selected model systems.     

The difference conservation makes to extinction risk of the world's ungulates

Previous studies show that conservation actions have prevented extinctions, recovered populations, and reduced declining trends in global biodiversity. However, all studies to date have substantially underestimated the difference conservation action makes because they failed to account fully for what would have happened in the absence thereof. We undertook a scenario‐based thought experiment to better quantify the effect conservation actions have had on the extinction risk of the world's 235 recognized ungulate species. We did so by comparing species’ observed conservation status in 2008 with their estimated status under counterfactual scenarios in which conservation efforts ceased in 1996. We estimated that without conservation at least 148 species would have deteriorated by one International Union for Conservation of Nature (IUCN) Red List category, including 6 species that now would be listed as extinct or extinct in the wild. The overall decline in the conservation status of ungulates would have been nearly 8 times worse than observed. This trend would have been greater still if not for conservation on private lands. While some species have benefited from highly targeted interventions, such as reintroduction, most benefited collaterally from conservation such as habitat protection. We found that the difference conservation action makes to the conservation status of the world's ungulate species is likely to be higher than previously estimated. Increased, and sustained, investment could help achieve further improvements.     

Use of opportunistic sightings and expert knowledge to predict and compare Whooping Crane stopover habitat

Predicting a species’ distribution can be helpful for evaluating management actions such as critical habitat designations under the U.S. Endangered Species Act or habitat acquisition and rehabilitation. Whooping Cranes (Grus americana) are one of the rarest birds in the world, and conservation and management of habitat is required to ensure their survival. We developed a species distribution model (SDM) that could be used to inform habitat management actions for Whooping Cranes within the state of Nebraska (U.S.A.). We collated 407 opportunistic Whooping Crane group records reported from 1988 to 2012. Most records of Whooping Cranes were contributed by the public; therefore, developing an SDM that accounted for sampling bias was essential because observations at some migration stopover locations may be under represented. An auxiliary data set, required to explore the influence of sampling bias, was derived with expert elicitation. Using our SDM, we compared an intensively managed area in the Central Platte River Valley with the Niobrara National Scenic River in northern Nebraska. Our results suggest, during the peak of migration, Whooping Crane abundance was 262.2 (90% CI 40.2?3144.2) times higher per unit area in the Central Platte River Valley relative to the Niobrara National Scenic River. Although we compared only 2 areas, our model could be used to evaluate any region within the state of Nebraska. Furthermore, our expert‐informed modeling approach could be applied to opportunistic presence‐only data when sampling bias is a concern and expert knowledge is available.     

Impact of alternative metrics on estimates of extent of occurrence for extinction risk assessment

In International Union for Conservation of Nature (IUCN) Red List assessments, extent of occurrence (EOO) is a key measure of extinction risk. However, the way assessors estimate EOO from maps of species’ distributions is inconsistent among assessments of different species and among major taxonomic groups. Assessors often estimate EOO from the area of mapped distribution, but these maps often exclude areas that are not habitat in idiosyncratic ways and are not created at the same spatial resolutions. We assessed the impact on extinction risk categories of applying different methods (minimum convex polygon, alpha hull) for estimating EOO for 21,763 species of mammals, birds, and amphibians. Overall, the percentage of threatened species requiring down listing to a lower category of threat (taking into account other Red List criteria under which they qualified) spanned 11–13% for all species combined (14–15% for mammals, 7–8% for birds, and 12–15% for amphibians). These down listings resulted from larger estimates of EOO and depended on the EOO calculation method. Using birds as an example, we found that 14% of threatened and near threatened species could require down listing based on the minimum convex polygon (MCP) approach, an approach that is now recommended by IUCN. Other metrics (such as alpha hull) had marginally smaller impacts. Our results suggest that uniformly applying the MCP approach may lead to a one‐time down listing of hundreds of species but ultimately ensure consistency across assessments and realign the calculation of EOO with the theoretical basis on which the metric was founded.     

Effects of climate and land-cover change on the conservation status of gibbons

Species shift their distribution in response to climate and land-cover change, which may result in a spatial mismatch between currently protected areas (PAs) and priority conservation areas (PCAs). We examined the effects of climate and land-cover change on potential range of gibbons and sought to identify PCAs that would conserve them effectively. We collected global gibbon occurrence points and modeled (ecological niche model) their current and potential 2050s ranges under climate-change and different land-cover-change scenarios. We examined change in range and PA coverage between the current and future ranges of each gibbon species. We applied spatial conservation prioritization to identify the top 30% PCAs for each species. We then determined how much of the PCAs are conserved in each country within the global range of gibbons. On average, 31% (SD 22) of each species’ current range was covered in PAs. PA coverage of the current range of 9 species was <30%. Nine species lost on average 46% (SD 29) of their potential range due to climate change. Under climate-change with an optimistic land-cover-change scenario (B1), 12 species lost 39% (SD 28) of their range. In a pessimistic land-cover-change scenario (A2), 15 species lost 36% (SD 28) of their range. Five species lost significantly more range under the A2 scenario than the B1 scenario (p = 0.01, SD 0.01), suggesting that gibbons will benefit from effective management of land cover. PA coverage of future range was <30% for 11 species. On average, 32% (SD 25) of PCAs were covered by PAs. Indonesia contained more species and PCAs and thus has the greatest responsibility for gibbon conservation. Indonesia, India, and Myanmar need to expand their PAs to fulfill their responsibility to gibbon conservation. Our results provide a baseline for global gibbon conservation, particularly for countries lacking gibbon research capacity.     

Determinants of bird conservation‐action implementation and associated population trends of threatened species

Conservation actions, such as habitat protection, attempt to halt the loss of threatened species and help their populations recover. The efficiency and the effectiveness of actions have been examined individually. However, conservation actions generally occur simultaneously, so the full suite of implemented conservation actions should be assessed. We used the conservation actions underway for all threatened and near‐threatened birds of the world (International Union for Conservation of Nature Red List of Threatened Species) to assess which biological (related to taxonomy and ecology) and anthropogenic (related to geoeconomics) factors were associated with the implementation of different classes of conservation actions. We also assessed which conservation actions were associated with population increases in the species targeted. Extinction‐risk category was the strongest single predictor of the type of conservation actions implemented, followed by landmass type (continent, oceanic island, etc.) and generation length. Species targeted by invasive nonnative species control or eradication programs, ex situ conservation, international legislation, reintroduction, or education, and awareness‐raising activities were more likely to have increasing populations. These results illustrate the importance of developing a predictive science of conservation actions and the relative benefits of each class of implemented conservation action for threatened and near‐threatened birds worldwide.     

Value of protected areas to avian persistence across 20 years of climate and land-use change

Establishing protected areas, where human activities and land cover changes are restricted, is among the most widely used strategies for biodiversity conservation. This practice is based on the assumption that protected areas buffer species from processes that drive extinction. However, protected areas can maintain biodiversity in the face of climate change and subsequent shifts in distributions have been questioned. We evaluated the degree to which protected areas influenced colonization and extinction patterns of 97 avian species over 20 years in the northeastern United States. We fitted single-visit dynamic occupancy models to data from Breeding Bird Atlases to quantify the magnitude of the effect of drivers of local colonization and extinction (e.g., climate, land cover, and amount of protected area) in heterogeneous landscapes that varied in the amount of area under protection. Colonization and extinction probabilities improved as the amount of protected area increased, but these effects were conditional on landscape context and species characteristics. In this forest-dominated region, benefits of additional land protection were greatest when both forest cover in a grid square and amount of protected area in neighboring grid squares were low. Effects did not vary with species’ migratory habit or conservation status. Increasing the amounts of land protection benefitted the range margins species but not the core range species. The greatest improvements in colonization and extinction rates accrued for forest birds relative to open-habitat or generalist species. Overall, protected areas stemmed extinction more than they promoted colonization. Our results indicate that land protection remains a viable conservation strategy despite changing habitat and climate, as protected areas both reduce the risk of local extinction and facilitate movement into new areas. Our findings suggest conservation in the face of climate change favors creation of new protected areas over enlarging existing ones as the optimal strategy to reduce extinction and provide stepping stones for the greatest number of species.     

Validating graph-based connectivity models with independent presence–absence and genetic data sets

Habitat connectivity is a key objective of current conservation policies and is commonly modeled by landscape graphs (i.e., sets of habitat patches [nodes] connected by potential dispersal paths [links]). These graphs are often built based on expert opinion or species distribution models (SDMs) and therefore lack empirical validation from data more closely reflecting functional connectivity. Accordingly, we tested whether landscape graphs reflect how habitat connectivity influences gene flow, which is one of the main ecoevolutionary processes. To that purpose, we modeled the habitat network of a forest bird (plumbeous warbler [Setophaga plumbea]) on Guadeloupe with graphs based on expert opinion, Jacobs’ specialization indices, and an SDM. We used genetic data (712 birds from 27 populations) to compute local genetic indices and pairwise genetic distances. Finally, we assessed the relationships between genetic distances or indices and cost distances or connectivity metrics with maximum-likelihood population-effects distance models and Spearman correlations between metrics. Overall, the landscape graphs reliably reflected the influence of connectivity on population genetic structure; validation R² was up to 0.30 and correlation coefficients were up to 0.71. Yet, the relationship among graph ecological relevance, data requirements, and construction and analysis methods was not straightforward because the graph based on the most complex construction method (species distribution modeling) sometimes had less ecological relevance than the others. Cross-validation methods and sensitivity analyzes allowed us to make the advantages and limitations of each construction method spatially explicit. We confirmed the relevance of landscape graphs for conservation modeling but recommend a case-specific consideration of the cost-effectiveness of their construction methods. We hope the replication of independent validation approaches across species and landscapes will strengthen the ecological relevance of connectivity models.