The importance of incorporating functional habitats into conservation planning for highly mobile species in dynamic systems

The distribution of mobile species in dynamic systems can vary greatly over time and space. Estimating their population size and geographic range can be problematic and affect the accuracy of conservation assessments. Scarce data on mobile species and the resources they need can also limit the type of analytical approaches available to derive such estimates. We quantified change in availability and use of key ecological resources required for breeding for a critically endangered nomadic habitat specialist, the Swift Parrot (Lathamus discolor). We compared estimates of occupied habitat derived from dynamic presence‐background (i.e., presence‐only data) climatic models with estimates derived from dynamic occupancy models that included a direct measure of food availability. We then compared estimates that incorporate fine‐resolution spatial data on the availability of key ecological resources (i.e., functional habitats) with more common approaches that focus on broader climatic suitability or vegetation cover (due to the absence of fine‐resolution data). The occupancy models produced significantly (P < 0.001) smaller (up to an order of magnitude) and more spatially discrete estimates of the total occupied area than climate‐based models. The spatial location and extent of the total area occupied with the occupancy models was highly variable between years (131 and 1498 km²). Estimates accounting for the area of functional habitats were significantly smaller (2–58% [SD 16]) than estimates based only on the total area occupied. An increase or decrease in the area of one functional habitat (foraging or nesting) did not necessarily correspond to an increase or decrease in the other. Thus, an increase in the extent of occupied area may not equate to improved habitat quality or function. We argue these patterns are typical for mobile resource specialists but often go unnoticed because of limited data over relevant spatial and temporal scales and lack of spatial data on the availability of key resources. Understanding changes in the relative availability of functional habitats is crucial to informing conservation planning and accurately assessing extinction risk for mobile resource specialists.     

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.     

Effects of Detectability on Estimates of Geographic Range Size in Bignonieae

Abstract: Extinction risk has not been evaluated for 96% of all described plant species. Given that the Global Strategy for Plant Conservation proposes preliminary conservation assessments of all described plant species by 2010, herbarium specimens (i.e., primary occurrence data) are increasingly being used to infer threat components from estimates of geographic range size. Nevertheless, estimates of range size based on herbarium data may be inaccurate due to collection bias associated with interspecific variation in detectability. We used data on 377 species of Bignonieae to test the hypothesis that there is a positive relationship between detectability and estimates of geographic range size derived from herbarium specimens. This relationship is expected if the proportion of the true geographic range size of a species that is documented by herbarium specimens is given by the product of the true geographic range size and the detectability of the species, assuming no relationship between true geographic range size and detectability. We developed 4 measures of detectability that can be estimated from herbarium data and examined the relationship between detectability and 2 types of estimates of geographic range size: area of occupancy and extent of occurrence. Our results from regressing estimates of extent of occurrence and area of occupancy on detectability across genera provided no support for this hypothesis. The same was true for regressions of estimated extent of occurrence on detectability across species within genera. Nevertheless, regressions of estimated area of occupancy on detectability across species within genera provided partial support for our hypothesis. We considered 3 possible explanations for this mixed outcome: violation of the assumption of no relationship between true geographic range size and detectability; the relationships between estimated geographic range size and detectability may be an artifact of a negative relationship between estimated area of occupancy and the sampling variance of detectability; detectability may have had 2 opposite effects on estimated species range sizes: one determines the proportion of the true range of a species documented by herbarium specimens and the other determines the distribution of true range size for the species actually observed with herbarium data. Our findings should help improve understanding of the potential biases incurred with the use of herbarium data.     

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.     

Improving effectiveness of systematic conservation planning with density data

Systematic conservation planning aims to design networks of protected areas that meet conservation goals across large landscapes. The optimal design of these conservation networks is most frequently based on the modeled habitat suitability or probability of occurrence of species, despite evidence that model predictions may not be highly correlated with species density. We hypothesized that conservation networks designed using species density distributions more efficiently conserve populations of all species considered than networks designed using probability of occurrence models. To test this hypothesis, we used the Zonation conservation prioritization algorithm to evaluate conservation network designs based on probability of occurrence versus density models for 26 land bird species in the U.S. Pacific Northwest. We assessed the efficacy of each conservation network based on predicted species densities and predicted species diversity. High‐density model Zonation rankings protected more individuals per species when networks protected the highest priority 10‐40% of the landscape. Compared with density‐based models, the occurrence‐based models protected more individuals in the lowest 50% priority areas of the landscape. The 2 approaches conserved species diversity in similar ways: predicted diversity was higher in higher priority locations in both conservation networks. We conclude that both density and probability of occurrence models can be useful for setting conservation priorities but that density‐based models are best suited for identifying the highest priority areas. Developing methods to aggregate species count data from unrelated monitoring efforts and making these data widely available through ecoinformatics portals such as the Avian Knowledge Network will enable species count data to be more widely incorporated into systematic conservation planning efforts.     

A Mechanistic Approach to Evaluation of Umbrella Species as Conservation Surrogates

Abstract:  Although species with large area requirements are sometimes used as umbrella species, their general utility as conservation tools is uncertain. We surveyed the species diversity of birds, butterflies, carabids, and forest-floor plants in forest sites across an area (1600 km²) in which we delineated large breeding home ranges of Northern Goshawk ( Accipiter gentilis ). We tested whether protection of the home ranges could serve as an effective umbrella to protect sympatric species of the four taxa. We also used an empirical habitat model of occupancy of home range to examine mechanisms by which the Northern Goshawk acts as an umbrella species. Among species richness, abundance, and species composition of the four taxa, only abundance and species composition of birds differed between sites located inside and outside home ranges, which was due to greater abundance of bird species that were prey of Northern Goshawks inside the home ranges. Thus, although home range indicated areas with high abundance of certain bird prey species, it was not effective as an indicator of the species diversity of all four taxa. We also did not find any difference in species richness, abundance, and species composition between sites predicted as occupied and unoccupied using the habitat model. In contrast, when we selected sites on the basis of each habitat variable in the model, habitat variables that selected sites either in agricultural or forested landscapes encompassed sites with high species richness or particular species composition. This result suggests that the low performance of the Northern Goshawk as an umbrella species is due to this species' preference for habitat in both agricultural and forested landscapes. Species that can adjust to changes in habitat conditions may not act as effective umbrella species despite having large home ranges.     

Occupancy estimation and modeling with multiple states and state uncertainty

The distribution of a species over space is of central interest in ecology, but species occurrence does not provide all of the information needed to characterize either the well-being of a population or the suitability of occupied habitat. Recent methodological development has focused on drawing inferences about species occurrence in the face of imperfect detection. Here we extend those methods by characterizing occupied locations by some additional state variable (e.g., as producing young or not). Our modeling approach deals with both detection probabilities <1 and uncertainty in state classification. We then use the approach with occupancy and reproductive rate data from California Spotted Owls (Strix occidentalis occidentalis) collected in the central Sierra Nevada during the breeding season of 2004 to illustrate the utility of the modeling approach. Estimates of owl reproductive rate were larger than na?ve estimates, indicating the importance of appropriately accounting for uncertainty in detection and state classification.     

Avoiding Pitfalls of Using Species Distribution Models in Conservation Planning

Abstract:   Museum records have great potential to provide valuable insights into the vulnerability, historic distribution, and conservation of species, especially when coupled with species-distribution models used to predict species' ranges. Yet, the increasing dependence on species-distribution models in identifying conservation priorities calls for a more critical evaluation of model robustness. We used 11 bird species of conservation concern in Brazil's highly fragmented Atlantic Forest and data on environmental conditions in the region to predict species distributions. These predictions were repeated for five different model types for each of the 11 bird species. We then combined these species distributions for each model separately and applied a reserve-selection algorithm to identify priority sites. We compared the potential outcomes from the reserve selection among the models. Although similarity in identification of conservation reserve networks occurred among models, models differed markedly in geographic scope and flexibility of reserve networks. It is essential for planners to evaluate the conservation implications of false-positive and false-negative errors for their specific management scenario before beginning the modeling process. Reserve networks selected by models that minimized false-positive errors provided a better match with priority areas identified by specialists. Thus, we urge caution in the use of models that overestimate species' occurrences because they may misdirect conservation action. Our approach further demonstrates the great potential value of museum records to biodiversity studies and the utility of species-distribution models to conservation decision-making. Our results also demonstrate, however, that these models must be applied critically and cautiously.     

Geographic range size and extinction risk assessment in nomadic species

Geographic range size is often conceptualized as a fixed attribute of a species and treated as such for the purposes of quantification of extinction risk; species occupying smaller geographic ranges are assumed to have a higher risk of extinction, all else being equal. However many species are mobile, and their movements range from relatively predictable to‐and‐fro migrations to complex irregular movements shown by nomadic species. These movements can lead to substantial temporary expansion and contraction of geographic ranges, potentially to levels which may pose an extinction risk. By linking occurrence data with environmental conditions at the time of observations of nomadic species, we modeled the dynamic distributions of 43 arid‐zone nomadic bird species across the Australian continent for each month over 11 years and calculated minimum range size and extent of fluctuation in geographic range size from these models. There was enormous variability in predicted spatial distribution over time; 10 species varied in estimated geographic range size by more than an order of magnitude, and 2 species varied by >2 orders of magnitude. During times of poor environmental conditions, several species not currently classified as globally threatened contracted their ranges to very small areas, despite their normally large geographic range size. This finding raises questions about the adequacy of conventional assessments of extinction risk based on static geographic range size (e.g., IUCN Red Listing). Climate change is predicted to affect the pattern of resource fluctuations across much of the southern hemisphere, where nomadism is the dominant form of animal movement, so it is critical we begin to understand the consequences of this for accurate threat assessment of nomadic species. Our approach provides a tool for discovering spatial dynamics in highly mobile species and can be used to unlock valuable information for improved extinction risk assessment and conservation planning.     

Ants as a Measure of Effectiveness of Habitat Conservation Planning in Southern California

Abstract: In the United States multispecies habitat conservation plans were meant to be the solution to conflicts between economic development and protection of biological diversity. Although now widely applied, questions exist concerning the scientific credibility of the conservation planning process and effectiveness of the plans. We used ants to assess performance of one of the first regional conservation plans developed in the United States, the Orange County Central‐Coastal Natural Community Conservation Plan (NCCP), in meeting its broader conservation objectives of biodiversity and ecosystem‐level protection. We collected pitfall data on ants for over 3 years on 172 sites established across a network of conservation lands in coastal southern California. Although recovered native ant diversity for the study area was high, site‐occupancy models indicated the invasive and ecologically disruptive Argentine ant (Linepithema humile) was present at 29% of sites, and sites located within 200 m of urban and agricultural areas were more likely to have been invaded. Within invaded sites, native ants were largely displaced, and their median species richness declined by more than 60% compared with uninvaded sites. At the time of planning, 24% of the 15,133‐ha reserve system established by Orange County NCCP fell within 200 m of an urban or agricultural edge. With complete build out of lands surrounding the reserve, the proportion of the reserve system vulnerable to invasion will grow to 44%. Our data indicate that simply protecting designated areas from development is not enough. If habitat conservation plans are to fulfill their conservation promise of ecosystem‐level protection, a more‐integrated and systematic approach to the process of habitat conservation planning is needed.     

Conserving tigers in working landscapes

Tiger (Panthera tigris) conservation efforts in Asia are focused on protected areas embedded in human‐dominated landscapes. A system of protected areas is an effective conservation strategy for many endangered species if the network is large enough to support stable metapopulations. The long‐term conservation of tigers requires that the species be able to meet some of its life‐history needs beyond the boundaries of small protected areas and within the working landscape, including multiple‐use forests with logging and high human use. However, understanding of factors that promote or limit the occurrence of tigers in working landscapes is incomplete. We assessed the relative influence of protection status, prey occurrence, extent of grasslands, intensity of human use, and patch connectivity on tiger occurrence in the 5400 km² Central Terai Landscape of India, adjacent to Nepal. Two observer teams independently surveyed 1009 km of forest trails and water courses distributed across 60 166‐km² cells. In each cell, the teams recorded detection of tiger signs along evenly spaced trail segments. We used occupancy models that permitted multiscale analysis of spatially correlated data to estimate cell‐scale occupancy and segment‐scale habitat use by tigers as a function of management and environmental covariates. Prey availability and habitat quality, rather than protected‐area designation, influenced tiger occupancy. Tiger occupancy was low in some protected areas in India that were connected to extensive areas of tiger habitat in Nepal, which brings into question the efficacy of current protection and management strategies in both India and Nepal. At a finer spatial scale, tiger habitat use was high in trail segments associated with abundant prey and large grasslands, but it declined as human and livestock use increased. We speculate that riparian grasslands may provide tigers with critical refugia from human activity in the daytime and thereby promote tiger occurrence in some multiple‐use forests. Restrictions on human‐use in high‐quality tiger habitat in multiple‐use forests may complement existing protected areas and collectively promote the persistence of tiger populations in working landscapes.     

Ecological niche modeling of customary medicinal plant species used by Australian Aborigines to identify species-rich and culturally valuable areas for conservation

Customary medicinal plant species used by Australian Aborigines are disappearing rapidly with its associated knowledge, due to the loss of habitats. Conservation and protection of these species is important as they represent sources of novel therapeutic phytochemical compounds and are culturally valuable. Information on the spatial distribution and use of customary medicinal plants is often inadequate and fragmented, posing limitations on the identification and conservation of species-rich areas and culturally valuable habitats.In this study, the habitat suitability modeling program, MaxEnt, was used to predict the potential ecological niches of 431 customary medicinal plant species, based on bioclimatic variables. Specimen locality records were obtained from the Global Biodiversity Information Facility (GBIF) data portal and from Australia's Virtual Herbarium (AVH).Ecological niche models of 414 predicted species, which had 30 or more occurrence points, were used to produce maps indicating areas that were ecologically suitable for multiple species (concordance of high predicted ecological suitability) and having cultural values. For the concordance map, individual species niche models were thresholded and summed. To derive a map of culturally valuable areas, customary medicinal uses from Customary Medicinal Knowledgebase (CMKb) (www.biolinfo.org/cmkb) were used to weight individual species models, resulting in a value within each grid cell reflecting its cultural worth.Even though the available information is scarce and fragmented, our approach provides an opportunity to infer areas predicted to be suitable for multiple species (i.e. concordance hotspots) and to estimate the cultural value of a particular geographical area. Our results also indicate that to conserve bio-cultural diversity, comprehensive information and active participation of Aboriginal communities is indispensable.     

A GIS-Based Comparison of the Mexican National and IUCN Methods for Determining Extinction Risk

Abstract:  The national systems used in the evaluation of extinction risk are often touted as more readily applied and somehow more regionally appropriate than the system of the International Union for Conservation of Nature (IUCN). We compared risk assessments of the Mexican national system (method for evaluation of risk of extinction of wild species [MER]) with the IUCN system for the 16 Polianthes taxa (Agavaceae), a genus of plants with marked variation in distribution sizes. We used a novel combination of herbarium data, geographic information systems (GIS), and species distribution models to provide rapid, repeatable estimates of extinction risk. Our GIS method showed that the MER and the IUCN system use similar data. Our comparison illustrates how the IUCN method can be applied even when all desirable data are not available, and that the MER offers no special regional advantage with respect to the IUCN regional system. Instead, our results coincided, with both systems identifying 14 taxa of conservation concern and the remaining two taxa of low risk, largely because both systems use similar information. An obstacle for the application of the MER is that there are no standards for quantifying the criteria of habitat condition and intrinsic biological vulnerability. If these impossible-to-quantify criteria are left out, what are left are geographical distribution and the impact of human activity, essentially the considerations we were able to assess for the IUCN method. Our method has the advantage of making the IUCN criteria easy to apply, and because each step can be standardized between studies, it ensures greater comparability of extinction risk estimates among taxa.     

Contrasting ecological information content in whaling archives with modern cetacean surveys for conservation planning and identification of historical distribution changes

Many species are restricted to a marginal or suboptimal fraction of their historical range due to anthropogenic impacts, making it hard to interpret their ecological preferences from modern-day data alone. However, inferring past ecological states is limited by the availability of robust data and biases in historical archives, posing a challenge for policy makers . To highlight how historical records can be used to understand the ecological requirements of threatened species and inform conservation, we investigated sperm whale (Physeter macrocephalus) distribution in the Western Indian Ocean. We assessed differences in information content and habitat suitability predictions based on whale occurrence data from Yankee whaling logs (1792–1912) and from modern cetacean surveys (1995–2020). We built maximum entropy habitat suitability models containing static (bathymetry-derived) variables to compare models comprising historical-only and modern-only data. Using both historical and modern habitat suitability predictions  we assessed marine protected area (MPA) placement by contrasting suitability in- and outside MPAs. The historical model predicted high habitat suitability in shelf and coastal regions near continents and islands, whereas the modern model predicted a less coastal distribution with high habitat suitability more restricted to areas of steep topography. The proportion of high habitat suitability inside versus outside MPAs was higher when applying the historical predictions than the modern predictions, suggesting that different marine spatial planning optimums can be reached from either data sources. Moreover, differences in relative habitat suitability predictions between eras were consistent with the historical depletion of sperm whales from coastal regions, which were easily accessed and targeted by whalers, resulting in a modern distribution limited more to steep continental margins and remote oceanic ridges. The use of historical data can provide important new insights and, through cautious interpretation, inform conservation planning and policy, for example, by identifying refugee species and regions of anticipated population recovery.     

Incorporating species relocation in reserve design models: An example from Ft. Benning GA

Given the increasing expansion of human dominated landscapes it often becomes necessary to relocate endangered and at-risk species from existing habitat areas. Further, there is growing research stating that climatic and atmospheric changes attributed to climate change are already affecting species distributions and geographic ranges, requiring endangered species to be relocated. Existing reserve design models mostly focus on choosing the optimal land area given existing species distributions and have not incorporated species relocation as a criterion. This paper introduces linear integer programming formulations for the relocation of multiple populations of a species at risk to clustered conservation areas. We present a basic clustered relocation model and extend the model to minimize the distances of relocation. We apply the models to a dataset related to Gopher Tortoise (GT), a keystone species currently considered ‘at risk’, at Ft. Benning Georgia where expanding military training needs require the relocation of GTs. We present the results and discuss the trade-off between compactness and relocation considerations using an efficiency frontier.     

Species Review of Amphibian Extinction Risks in Madagascar: Conclusions from the Global Amphibian Assessment

Abstract:  We assessed the extinction risks of Malagasy amphibians by evaluating their distribution, occurrence in protected areas, population trends, habitat quality, and prevalence in commercial trade. We estimated and mapped the distribution of each of the 220 described Malagasy species and applied, for the first time, the IUCN Red List categories and criteria to all species described at the time of the assessment. Nine species were categorized as critically endangered, 21 as endangered, and 25 as vulnerable. The most threatened species occur on the High Plateau and/or have been subjected to overcollection for the pet trade, but restricted extent of occurrence and ongoing habitat destruction were identified as the most important factors influencing extinction threats. The two areas with the majority of threatened species were the northern Tsaratanana-Marojejy-Masoala highlands and the southeastern Anosy Mountains. The current system of protected areas includes 82% of the threatened amphibian species. Of the critically endangered species, 6 did not occur in any protected area. For conservation of these species we recommend the creation of a reserve for the species of the Mantella aurantiaca group, the inclusion of two Scaphiophryne species in the Convention on the International Trade in Endangered Species Appendix II, and the suspension of commercial collecting for Mantella cowani . Field surveys during the last 15 years reveal no pervasive extinction of Malagasy amphibians resulting from disease or other agents, as has been reported in some other areas of the world.     

Habitat Loss and Changes in the Species-Area Relationship

Abstract: The species-area relationship (SAR) has been used successfully to predict extinction from extent of habitat reduction. These extinction estimates assume that species have uniformly distributed range requirements and a minimum abundance level required for persistence; how many species are lost depends solely on how much habitat is removed, not on where it is removed. We consider another limiting case in which range requirements, rather than abundances, determine extinctions. We used a new method for constructing SARs based on assumptions about geographic ranges of species. Our results show that habitat destruction can change the SAR and consequently the number of species predicted to be lost due to habitat destruction. Our method generates SARs that vary in shape according to the specific distributions of geographic range and occupancy but that have the common feature of being described by a power law with an exponent of <1. When the geographic range of species was included in the SAR, the way habitat was lost became important. Although the SAR before habitat destruction is often used to predict species loss after habitat destruction, assumptions must be clearly stated. To predict the damage caused by habitat loss with our model, it is necessary to know the fraction of aggregated species, the distribution of geographic ranges, the form of habitat destruction, and the sampling protocol. The remaining theoretical challenge is to develop a full theory that links abundance and range.     

Quantifying Habitat Requirements of Tree-Living Species in Fragmented Boreal Forests with Bayesian Methods

Abstract:  Quantitative conservation objectives require detailed consideration of the habitat requirements of target species. Tree-living bryophytes, lichens, and fungi are a critical and declining biodiversity component of boreal forests. To understand their requirements, Bayesian methods were used to analyze the relationships between the occurrence of individual species and habitat factors at the tree and the stand scale in a naturally fragmented boreal forest landscape. The importance of unexplained between-stand variation in occurrence of species was estimated, and the ability of derived models to predict species' occurrence was tested. The occurrence of species was affected by quality of individual trees. Furthermore, the relationships between occurrence of species at the tree level and size and shape of stands indicated edge effects, implying that some species were restricted to interior habitats of large, regular stands. Yet for the habitat factors studied, requirements of many species appeared similar. Species occurrence also varied between stands; most of the seemingly suitable trees in some stands were unoccupied. The models captured most variation in species occurrence at tree level. They also successfully accounted for between-stand variation in species occurrence, thus providing realistic simulations of stand-level occupancy of species. Important unexplained between-stand variation in species occurrence warns against a simplified view that only local habitat factors influence species' occurrence. Apparently, similar stands will host populations of different sizes due to historical, spatial, and stochastic factors. Thus, habitat suitability cannot be assessed simply by population sizes, and stands lacking a species may still provide suitable habitat and merit protection.     

Systematic Conservation Planning and the Cost of Tackling Conservation Conflicts with Large Carnivores in Italy

Abstract:  Conservation in Europe (including the establishment of protected areas) is undertaken mainly through legislation and on densely populated private land. Consequently, conflicts of interest arise between human economic activities and biodiversity conservation. We used a systematic approach to conservation planning to explore different conservation scenarios for the Apennine populations of wolves (Canis lupus) and bears (Ursus arctos marsicanus) in Italy. The conservation measures we considered were electrified fences and guard dogs to prevent wolves and bears from preying on sheep. We used habitat suitability models of the two species as an estimate of their distributions. Across the study area, we estimated the potential intensity of conflict caused by predation on sheep and the cost of the antipredator measures. We examined scenarios for the conservation of wolves and bears that identified systems of sites where antipredator measures should be applied to either minimize the economic cost of the plan or tackle a predetermined amount of conflict. The overall cost of the conservation plans ranged between €1,486,000 and €16,876,000, depending on the scenario and on the size of the conservation target. Because potential conflict intensity (i.e., potential predation) and cost of conflict resolution were correlated, the scenarios that minimized cost also minimized the amount of conflict that was addressed. Conserving these two species by addressing their predation on sheep was up to 4.36 times more expensive than conserving them by providing suitable habitat in areas of low conflict. Yet avoiding conflicts is not always desirable because it can drastically reduce the options for conservation. Choosing a conservation plan requires consideration of the level of threat to the target species and their sensitivity to conflicts.     

Automated conservation assessment of the orchid family with deep learning

International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only for a fraction of global species richness. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. We conducted automated conservation assessments for 13,910 species (47.3% of the known species in the family) of the diverse and globally distributed orchid family (Orchidaceae), for which most species (13,049) were previously unassessed by IUCN. We used a novel method based on a deep neural network (IUC-NN). We identified 4,342 orchid species (31.2% of the evaluated species) as possibly threatened with extinction (equivalent to IUCN categories critically endangered [CR], endangered [EN], or vulnerable [VU]) and Madagascar, East Africa, Southeast Asia, and several oceanic islands as priority areas for orchid conservation. Orchidaceae provided a model with which to test the sensitivity of automated assessment methods to problems with data availability, data quality, and geographic sampling bias. The IUC-NN identified possibly threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias relative to the IUCN Red List and was robust even when data availability was low and there were geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in identifying species at the greatest risk of extinction.