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.     

Conservation of Insect Diversity: a Habitat Approach

Abstract: Neither time nor resources exist to design conservation plans for every species, particularly for little-studied, noncharismatic, but ecologically important taxa that make up most of biodiversity. To explore the feasibility of basing conservation action on community-level biogeography, we sampled a montane insect community. We addressed three issues: (1) the appropriate scale for sampling insect communities; (2) the association of habitat specialization—perhaps a measure of extinction vulnerability—with other ecological or physical traits; and (3) the correlation of diversity across major insect groups. Using malaise traps in Gunnison County, Colorado, we captured 8847 Diptera (identified to family and morphospecies), 1822 Hymenoptera (identified to morphospecies), and 2107 other insects (identified to order). We sampled in three habitat types—meadow, aspen, and conifer—defined on the basis of the dominant vegetation at the scale of hundreds of meters. Dipteran communities were clearly differentiated by habitat type rather than geographic proximity. This result also holds true for hymenopteran communities. Body size and feeding habits were associated with habitat specialization at the family level. In particular, habitat generalists at the family level—taxa perhaps more likely to survive anthropogenic habitat alteration—tended to be trophic generalists. Dipteran species richness was marginally correlated with hymenopteran species richness and was significantly correlated with the total number of insect orders sampled by site. Because these correlations result from differences in richness among habitat types, insect taxa may be reasonable surrogates for one another when sampling is done across habitat types. In sum, community-wide studies appear to offer a practical way to gather information about the diversity and distribution of little-known taxa.     

Use of Multispecies Occupancy Models to Evaluate the Response of Bird Communities to Forest Degradation Associated with Logging

Forest degradation is arguably the greatest threat to biodiversity, ecosystem services, and rural livelihoods. Therefore, increasing understanding of how organisms respond to degradation is essential for management and conservation planning. We were motivated by the need for rapid and practical analytical tools to assess the influence of management and degradation on biodiversity and system state in areas subject to rapid environmental change. We compared bird community composition and size in managed (ejido, i.e., communally owned lands) and unmanaged (national park) forests in the Sierra Tarahumara region, Mexico, using multispecies occupancy models and data from a 2‐year breeding bird survey. Unmanaged sites had on average higher species occupancy and richness than managed sites. Most species were present in low numbers as indicated by lower values of detection and occupancy associated with logging‐induced degradation. Less than 10% of species had occupancy probabilities >0.5, and degradation had no positive effects on occupancy. The estimated metacommunity size of 125 exceeded previous estimates for the region, and sites with mature trees and uneven‐aged forest stand characteristics contained the highest species richness. Higher estimation uncertainty and decreases in richness and occupancy for all species, including habitat generalists, were associated with degraded young, even‐aged stands. Our findings show that multispecies occupancy methods provide tractable measures of biodiversity and system state and valuable decision support for landholders and managers. These techniques can be used to rapidly address gaps in biodiversity information, threats to biodiversity, and vulnerabilities of species of interest on a landscape level, even in degraded or fast‐changing environments. Moreover, such tools may be particularly relevant in the assessment of species richness and distribution in a wide array of habitats. Uso de Modelos de Ocupación para Múltiples Especies para Evaluar la Respuesta de las Comunidades de Aves a la Degradación de Bosques Asociada con la Tala     

Estimating species richness and accumulation by modeling species occurrence and detectability

A statistical model is developed for estimating species richness and accumulation by formulating these community-level attributes as functions of model-based estimators of species occurrence while accounting for imperfect detection of individual species. The model requires a sampling protocol wherein repeated observations are made at a collection of sample locations selected to be representative of the community. This temporal replication provides the data needed to resolve the ambiguity between species absence and nondetection when species are unobserved at sample locations. Estimates of species richness and accumulation are computed for two communities, an avian community and a butterfly community. Our model-based estimates suggest that detection failures in many bird species were attributed to low rates of occurrence, as opposed to simply low rates of detection. We estimate that the avian community contains a substantial number of uncommon species and that species richness greatly exceeds the number of species actually observed in the sample. In fact, predictions of species accumulation suggest that even doubling the number of sample locations would not have revealed all of the species in the community. In contrast, our analysis of the butterfly community suggests that many species are relatively common and that the estimated richness of species in the community is nearly equal to the number of species actually detected in the sample. Our predictions of species accumulation suggest that the number of sample locations actually used in the butterfly survey could have been cut in half and the asymptotic richness of species still would have been attained. Our approach of developing occurrence-based summaries of communities while allowing for imperfect detection of species is broadly applicable and should prove useful in the design and analysis of surveys of biodiversity.     

Extinction risk of the endemic vascular flora of Kauai,Hawaii, based on IUCN assessments

The International Union for Conservation of Nature's Red List of Threatened Species (IUCN Red List) is the world's most comprehensive information source on the global conservation status of species. Governmental agencies and conservation organizations increasingly rely on IUCN Red List assessments to develop conservation policies and priorities. Funding agencies use the assessments as evaluation criteria, and researchers use meta-analysis of red-list data to address fundamental and applied conservation science questions. However, the circa 143,000 IUCN assessments represent a fraction of the world's biodiversity and are biased in regional and organismal coverage. These biases may affect conservation priorities, funding, and uses of these data to understand global patterns. Isolated oceanic islands are characterized by high endemicity, but the unique biodiversity of many islands is experiencing high extinction rates. The archipelago of Hawaii has one of the highest levels of endemism of any floristic region; 90% of its 1367 native vascular plant taxa are classified as endemic. We used the IUCN's assessment of the complete single-island endemic (SIE) vascular plant flora of Kauai, Hawaii, to assess the proportion and drivers of decline of threatened plants in an oceanic island setting. We compared the IUCN assessments with federal, state, and other local assessments of Kauai species or taxa of conservation concern. Finally, we conducted a preliminary assessment for all 1044 native vascular plants of Hawaii based on IUCN criterion B by estimating area of occupancy, extent of occurrence, and number of locations to determine whether the pattern found for the SIE vascular flora of Kauai is comparable to the native vascular flora of the Hawaiian Islands. We compared our results with patterns observed for assessments of other floras. According to IUCN, 256 SIE vascular plant taxa are threatened with extinction and 5% are already extinct. This is the highest extinction risk reported for any flora to date. The preliminary assessment of the native vascular flora of Hawaii showed that 72% (753 taxa) is threatened. The flora of Hawaii may be one of the world's most threatened; thus, increased and novel conservation measures in the state and on other remote oceanic islands are urgently needed.     

Obstruction of biodiversity conservation by minimum patch size criteria

Minimum patch size criteria for habitat protection reflect the conservation principle that a single large (SL) patch of habitat has higher biodiversity than several small (SS) patches of the same total area (SL > SS). Nonetheless, this principle is often incorrect, and biodiversity conservation requires placing more emphasis on protection of large numbers of small patches (SS > SL). We used a global database reporting the abundances of species across hundreds of patches to assess the SL > SS principle in systems where small patches are much smaller than the typical minimum patch size criteria applied for biodiversity conservation (i.e., ∼85% of patches <100 ha). The 76 metacommunities we examined included 4401 species in 1190 patches. From each metacommunity, we resampled species–area accumulation curves to evaluate how biodiversity responded to habitat existing as a few large patches or as many small patches. Counter to the SL > SS principle and consistent with previous syntheses, species richness accumulated more rapidly when adding several small patches (45.2% SS > SL vs. 19.9% SL > SS) to reach the same cumulative area, even for the very small patches in our data set. Responses of taxa to habitat fragmentation differed, which suggests that when a given total area of habitat is to be protected, overall biodiversity conservation will be most effective if that habitat is composed of as many small patches as possible, plus a few large ones. Because minimum patch size criteria often require larger patches than the small patches we examined, our results suggest that such criteria hinder efforts to protect biodiversity.     

Generalized site occupancy models allowing for false positive and false negative errors

Site occupancy models have been developed that allow for imperfect species detection or "false negative" observations. Such models have become widely adopted in surveys of many taxa. The most fundamental assumption underlying these models is that "false positive" errors are not possible. That is, one cannot detect a species where it does not occur. However, such errors are possible in many sampling situations for a number of reasons, and even low false positive error rates can induce extreme bias in estimates of site occupancy when they are not accounted for. In this paper, we develop a model for site occupancy that allows for both false negative and false positive error rates. This model can be represented as a two-component finite mixture model and can be easily fitted using freely available software. We provide an analysis of avian survey data using the proposed model and present results of a brief simulation study evaluating the performance of the maximum-likelihood estimator and the naive estimator in the presence of false positive errors.     

Characterizing species co-occurrence patterns of imperfectly detected stream fishes to inform species reintroduction efforts

Species reintroduction efforts can improve the recovery of imperiled species, but successful implementation of this conservation strategy requires a thorough understanding of the abiotic and biotic factors influencing species viability. Species interactions are especially understudied, in particular by omitting the effect of imperfect detection on negative, neutral, or positive associations within a community. Using repeat surveys from 5 southern Ontario, Canada, Great Lakes tributaries, we quantified species co-occurrence patterns with the eastern sand darter (ESD) (Ammocrypta pellucida), listed as federally threatened, and characterized how imperfect detection during sampling can influence inference regarding these relationships. We used a probabilistic framework that included 3 approaches of increasing complexity: probabilistic co-occurrence analysis ignoring imperfect detection; single-species occupancy models with subsequent co-occurrence analysis; and 2-species occupancy models. We then used our occupancy models to predict suitable sites for potential future reintroduction efforts while considering the influence of negative species interactions. Based on the observed data, ESD showed several positive associations with co-occurring species; however, species associations differed when imperfect detection was considered. Specifically, a negative association between ESD and rosyface shiner (Notropis rubellus) was observed only after accounting for imperfect detection in the Grand River. Alternatively, positive associations in the Grand River between ESD and northern hogsucker (Hypentelium nigricans) and silver shiner (Notropis photogenis) were observed regardless of whether imperfect detection was accounted for. Our models predicted several potential reintroduction sites for ESD in formerly occupied watersheds with high levels of certainty. Overall, our results demonstrate the importance of investigating imperfect detection and species co-occurrence when planning reintroduction efforts.     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.     

Improving inferences about private land conservation by accounting for incomplete reporting

Private lands provide key habitat for imperiled species and are core components of function protectected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social–ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.     

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.     

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.     

Estimating non-native plant richness with a species-accumulation model along roads

Monitoring non-native plant richness is important for biodiversity conservation and scientific research. The species-area model (SA model) has been used frequently to estimate the total species richness within a region. However, the conventional SA model may not provide robust estimations of non-native plant richness because the ecological processes associated with the accumulation of exotic and native plants may differ. Because roads strongly dictate the distributions of exotic plants, we propose a species-accumulation model along roads (SR model), rather than an SA model, to estimate the non-native plant richness within a region. Using 270 simulated data sets, we compared the differences in performance between the SR and SA models. A decision tree based on prediction accuracy was created to guide model application, which was validated using field data from 3 national nature reserves in 3 different provinces in China. The SR model significantly outperformed the SA model when non-native species were restricted to the roadsides and the proportion of uncommon exotic species was small. More importantly, the SR model accurately estimated the non-native plant richness in all field sites with an error of <1 species per site. We believe our new model meets the practical need to efficiently and robustly estimate non-native plant richness, which may facilitate effective biodiversity conservations and promote research on non-native plant invasion and vegetation dynamics.     

Effects of land use,cover, and protection on stream and riparian ecosystem services and biodiversity

Protected areas are an important part of broader landscapes that are often used to preserve biodiversity or natural features. Some argue that protected areas may also help ensure provision of ecosystem services. However, the effect of protection on ecosystem services and whether protection affects the provision of ecosystem services is known only for a few services in a few types of landscapes. We sought to fill this gap by investigating the effect of watershed protection status and land use and land cover on biodiversity and the provision of ecosystem services. We compared the ecosystem services provided in and around streams in 4 watershed types: International Union for Conservation of Nature category II protected forests, unprotected forests, unprotected forests with recent timber harvesting, and unprotected areas with agriculture. We surveyed 28 streams distributed across these watershed types in Quebec, Canada, to quantify provisioning of clean water, carbon storage, recreation, wild foods, habitat quality, and terrestrial and aquatic biodiversity richness and abundance. The quantity and quality of ecosystem services and biodiversity were generally higher in sites with intact forest—whether protected or not—relative to those embedded in production landscapes with forestry or agriculture. Clean-water provision, carbon storage, habitat quality, and tree diversity were significantly higher in and around streams surrounded by forest. Recreation, wild foods, and aquatic biodiversity did not vary among watershed types. Although some services can be provided by both protected and unprotected areas, protection status may help secure the continued supply of services sensitive to changes in land use or land cover. Our findings provide needed information about the ecosystem service and biodiversity trade-offs and synergies that result from developing a watershed or from protecting it.     

Limitations of Biodiversity Databases: Case Study on Seed-Plant Diversity in Tenerife, Canary Islands

Abstract:  Databases on the distribution of species can be used to describe the geographic patterns of biodiversity. Nevertheless, they have limitations. We studied three of these limitations: (1) inadequacy of raw data to describe richness patterns due to sampling bias, (2) lack of survey effort assessment (and lack of exhaustiveness in compiling data about survey effort), and (3) lack of coverage of the geographic and environmental variations that affect the distribution of organisms. We used a biodiversity database (BIOTA-Canarias) to analyze richness data from a well-known group (seed plants) in an intensively surveyed area (Tenerife Island). Observed richness and survey effort were highly correlated. Species accumulation curves could not be used to determine survey effort because data digitalization was not exhaustive, so we identified well-sampled sites based on observed richness to sampling effort ratios. We also developed a predictive model based on the data from well-sampled sites and analyzed the origin of the geographic errors in the obtained extrapolation by means of a geographically constrained cross-validation. The spatial patterns of seed-plant species richness obtained from BIOTA-Canarias data were incomplete and biased. Therefore, some improvements are needed to use this database (and many others) in biodiversity studies. We propose a protocol that includes controls on data quality, improvements on data digitalization and survey design to improve data quality, and some alternative data analysis strategies that will provide a reliable picture of biodiversity patterns.     

Estimating species occurrence, abundance, and detection probability using zero-inflated distributions

Researchers have developed methods to account for imperfect detection of species with either occupancy (presence absence) or count data using replicated sampling. We show how these approaches can be combined to simultaneously estimate occurrence, abundance, and detection probability by specifying a zero-inflated distribution for abundance. This approach may be particularly appropriate when patterns of occurrence and abundance arise from distinct processes operating at differing spatial or temporal scales. We apply the model to two data sets: (1) previously published data for a species of duck, Anas platyrhynchos, and (2) data for a stream fish species, Etheostoma scotti. We show that in these cases, an incomplete-detection zero-inflated modeling approach yields a superior fit to the data than other models. We propose that zero-inflated abundance models accounting for incomplete detection be considered when replicate count data are available.     

Biodiversity Loss in Latin American Coffee Landscapes: Review of the Evidence on Ants,Birds, and Trees

Abstract: Studies have documented biodiversity losses due to intensification of coffee management (reduction in canopy richness and complexity). Nevertheless, questions remain regarding relative sensitivity of different taxa, habitat specialists, and functional groups, and whether implications for biodiversity conservation vary across regions. We quantitatively reviewed data from ant, bird, and tree biodiversity studies in coffee agroecosystems to address the following questions: Does species richness decline with intensification or with individual vegetation characteristics? Are there significant losses of species richness in coffee‐management systems compared with forests? Is species loss greater for forest species or for particular functional groups? and Are ants or birds more strongly affected by intensification? Across studies, ant and bird richness declined with management intensification and with changes in vegetation. Species richness of all ants and birds and of forest ant and bird species was lower in most coffee agroecosystems than in forests, but rustic coffee (grown under native forest canopies) had equal or greater ant and bird richness than nearby forests. Sun coffee (grown without canopy trees) sustained the highest species losses, and species loss of forest ant, bird, and tree species increased with management intensity. Losses of ant and bird species were similar, although losses of forest ants were more drastic in rustic coffee. Richness of migratory birds and of birds that forage across vegetation strata was less affected by intensification than richness of resident, canopy, and understory bird species. Rustic farms protected more species than other coffee systems, and loss of species depended greatly on habitat specialization and functional traits. We recommend that forest be protected, rustic coffee be promoted, and intensive coffee farms be restored by augmenting native tree density and richness and allowing growth of epiphytes. We also recommend that future research focus on potential trade‐offs between biodiversity conservation and farmer livelihoods stemming from coffee production.     

Negative effects of nitrogen deposition on Swiss butterflies

Nitrogen (N) deposition from agriculture and combustion of fossil fuels is a major threat to plant diversity, but its effects on organisms at higher trophic levels are unclear. We investigated how N deposition may affect species richness and abundance (number of individuals per species) in butterflies. We reviewed the peer-reviewed literature on variables used to explain spatial variation in butterfly species richness and found that vegetation variables appeared to be as important as climate and habitat variables in explaining butterfly species richness. It thus seemed likely that increased N deposition could indirectly affect butterfly communities via its influence on plant communities. To test this prediction, we analyzed data from the Swiss biodiversity monitoring program for vascular plants and butterflies in 383 study sites of 1 km² that are evenly distributed throughout Switzerland. The area has a modeled N deposition gradient of 2–44 kg N ha⁻¹ year⁻¹. We used traditional linear models and structural equation models to infer the drivers of the spatial variation in butterfly species richness across Switzerland. High N deposition was consistently linked to low butterfly diversity, suggesting a net loss of butterfly diversity through increased N deposition. We hypothesize that at low elevations, N deposition may contribute to a reduction in butterfly species richness via microclimatic cooling due to increased plant biomass. At higher elevations, negative effects of N deposition on butterfly species richness may also be mediated by reduced plant species richness. In most butterfly species, abundance was negatively related to N deposition, but the strongest negative effects were found for species of conservation concern. We conclude that in addition to factors such as intensified agriculture, habitat fragmentation, and climate change, N deposition is likely to play a key role in negatively affecting butterfly diversity and abundance.     

Identifying the possibilities and pitfalls of conducting IUCN Red List assessments from remotely sensed habitat information based on insights from poorly known Cuban mammals

The International Union for Conservation of Nature's Red List of Threatened Species (RLS) is the key global tool for objective, repeatable assessment of species’ extinction risk status, and plays an essential role in tracking biodiversity loss and guiding conservation action. Satellite remote sensing (SRS) data sets on global ecosystem distributions and functioning show exciting potential for informing range-based RLS assessment, but their incorporation has been restricted by low temporal resolution and coverage of data sets, lack of incorporation of degradation-driven habitat loss, and noninclusion of assumptions related to identification of changing habitat distributions for taxa with varying habitat dependency and ecologies. For poorly known mangrove-associated Cuban hutias (Mesocapromys spp.), we tested the impact of possible assumptions regarding these issues on range-based RLS assessment outcomes. Specifically, we used annual (1985–2018) Landsat data and land-cover classification and habitat degradation analyses across different internal time series slices to simulate range-based RLS assessments for our case study taxa to explore potential assessment uncertainty arising from temporal SRS data set coverage, incorporating proxies of (change in) habitat quality, and assumptions on spatial scaling of habitat extent for RLS parameter generation. We found extensive variation in simulated species-specific range-based RLS assessments, and this variation was mostly associated with the time series over which parameters were estimated. However, results of some species-specific assessments differed by up to 3 categories (near threatened to critically endangered) within the same time series, due to the effects of incorporating habitat quality and the spatial scaling used in RLS parameter estimation. Our results showed that a one-size-fits-all approach to incorporating SRS information in RLS assessment is inappropriate, and we urge caution in conducting range-based assessments with SRS for species for which habitat dependence on specific ecosystem types is incompletely understood. We propose novel revisions to parameter spatial scaling guidelines to improve integration of existing time series data on ecosystem change into the RLS assessment process.