Selection of den sites and chronology of denning by black bears in the eastern Sierra Nevada and western Great Basin.

Use of dens during winter is an important strategy for American black bears (Ursus americanus) for both energy conservation and reproduction; and occupancy of suitable den sites has implications for reproductive fitness. Denning strategies may change as a result of changing climatic conditions and habitat loss. Black bears occupy arid environments in the eastern Sierra Nevada and the western ranges of the Great Basin Ecosystem. Our objectives were to identify: (1) which physical characteristics of habitat influenced selection of den sites at multiple spatial scales and (2) which environmental factors influenced timing of entrance and exit of dens by females and males. We evaluated selection of den sites by black bears at three spatial scales (300, 1000, and 4000 m) from 2011 to 2022. Terrain ruggedness was important for selection of den sites at all spatial scales. Within a 300-m buffer from the den, bears selected den sites with rugged terrain, lower horizontal visibility, and greater canopy cover, resulting in more concealment and protection than that of the surrounding environment. Within 1000- and 4000-m buffers around each den, bears selected den sites with rugged terrain, northern aspects, and steep slopes. At the 4000-m scale, we observed interactions between sex with slope and distance to roads; females selected den sites on steeper slopes and closer to roads than did males. Females remained in the dens longer than males by entering earlier in the autumn and exiting later in the spring. Male bears exited their dens earlier with increasing consecutive days above freezing temperatures, but that relationship was weak for females. Knowing what characteristics are important for selection of den sites, and influence timing of denning, will be important for understanding how shifting climatic patterns will affect bears, particularly in arid environments that may be prone to wider fluctuations in climatic drivers of denning in the future.

Effects of large-scale gold mining on habitat use and selection by American pronghorn.

Anthropogenic disturbances, including extraction of natural resources and development of alternative energy, are reducing and fragmenting habitat for wildlife across the globe. Effects of those disturbances have been explored by studying populations that migrate through oil and gas fields or alternative energy facilities. Extraction of minerals, including precious metals and lithium, is increasing rapidly in remote areas, which results in dramatically altered landscapes in areas of resident populations of wildlife. Our goal was to examine how a resident population of American pronghorn (Antilocapra americana) in the Great Basin ecosystem selected resources near a large-scale disturbance year around. We investigated how individuals selected resources around a large, open-pit gold mine. We classified levels of disturbance associated with the mine, and used a random forest model to select ecological covariates associated with habitat selection by pronghorn. We used resource selection functions to examine how disturbances affected habitat selection by pronghorn both annually and seasonally. Pronghorn strongly avoided areas of high disturbance, which included open pits, heap leach fields, rock disposal areas, and a tram. Pronghorn selected areas near roads, although selection was strongest about 2 km away. We observed relatively broad variation among individuals in selection of resources, and how they responded to the mine. The Great Basin is a mineral-rich area that continues to be exploited for natural resources, especially minerals. Sagebrush-dependent species, including pronghorn, that rely on this critical habitat were directly affected by that transformation of the landscape, which is likely to increase with expansion of the mine. As extraction of minerals from remote landscapes around the world continues to fragment habitats for wildlife, increasing our understanding of impacts of those changes on behaviors of wildlife before populations decline, may assist in the mitigation and minimization of negative impacts on mineral-rich landscapes and on wildlife populations.

Testosterone identifies hatchling sex for Mojave desert tortoises (Gopherus agassizii).

The threatened Mojave desert tortoise (Gopherus agassizii) exhibits temperature-dependent sex determination, and individuals appear externally sexually monomorphic until sexual maturity. A non-surgical sex identification method that is suitable for a single in situ encounter with hatchlings is essential for minimizing handling of wild animals. We tested (1) whether plasma testosterone quantified by enzyme-linked immunosorbent assay differentiated males from females in 0-3 month old captive hatchlings, and (2) whether an injection of follicle-stimulating hormone (FSH) differentially elevates testosterone in male hatchlings to aid in identifying sex. We validated sex by ceolioscopic (laparoscopic) surgery. We then fit the testosterone concentrations to lognormal distributions and identified the concentration below which individuals are more likely female, and above which individuals are more likely male. Using a parametric bootstrapping procedure, we estimated a 0.01-0.04% misidentification rate for naïve testosterone samples, and a 1.26-1.39% misidentification rate for challenged (post-FSH injection) testosterone samples. Quantification of plasma testosterone concentration from small volume (0.1 mL) blood samples appears to be a viable, highly accurate method to identify sex of 0-3 month old hatchlings and could be a valuable tool for conservation measures and investigation of trends and variation in sex ratios for in situ wild nests.

Integrating multiple sign types to improve occupancy estimation for inconspicuous species.

Standard occupancy models enable unbiased estimation of occupancy by accounting for observation errors such as missed detections (false negatives) and, less commonly, incorrect detections (false positives). Occupancy models are fitted to data from repeated site visits in which surveyors record evidence of species presence. Use of indirect sign (e.g., scat, tracks) as evidence of presence can vastly improve survey efficiency for inconspicuous species but can also introduce additional sources of error. We developed a "multi-sign" occupancy approach to model the detection process separately for unique sign types and used this method to improve estimates of occupancy dynamics for an inconspicuous species, the American pika (Ochotona princeps). We investigated how estimates of pika occupancy and environmental drivers differed under four increasingly realistic representations of the observation process: (1) perfect detection (commonly assumed for modeling pika occupancy), (2) standard occupancy model (single observation process without possibility of false detection), (3) multi-sign with no false detections (non-false positive model), and (4) multi-sign with false detections (full model). For the multi-sign occupancy models, we modeled the detection of each sign type (fresh scat, fresh haypiles, pika calls, and pika sightings) separately as a function of climatic and environmental covariates. Estimates of occupancy processes and inferences about environmental drivers were sensitive to different detection models. Simplified representations of the detection processes generally resulted in higher occupancy estimates and higher turnover rates than the full multi-sign model. Environmental drivers also varied in their influence on occupancy models, where (e.g.) forb cover was estimated to more strongly influence occupancy in the full multi-sign model than the simpler models. As has been reported previously in other contexts, unmodeled heterogeneity in the observation process can lead to biases in occupancy processes and uncertainty in the relationships between occupancy and environmental covariates. Overall, our multi-sign approach to dynamic occupancy modeling, which accounts for spatio-temporal variation in reliability among sign types, has strong potential to generate more realistic estimates of occupancy dynamics for inconspicuous species.

Changes in selection of resources with reproductive state in a montane ungulate.

Animals select habitats based on food, water, space, and cover. Each of those components are essential to the ability of an individual to survive and reproduce in a particular habitat. Selection of resources is linked to reproductive fitness and individuals likely vary in how they select resources relative to their reproductive state: during pregnancy, while provisioning young when nutritional needs of the mother are high, but offspring are vulnerable to predation, or if they lose young to mortality. We investigated the effects of reproductive state on selection of resources by maternal female desert bighorn sheep (Ovis canadensis nelsoni) by comparing selection during the last trimester of gestation, following parturition when females were provisioning dependent young, and if the female lost an offspring. We captured, and recaptured each year, 32 female bighorn sheep at Lone Mountain, Nevada, during 2016-2018. Captured females were fit with GPS collars and those that were pregnant received vaginal implant transmitters. We used a Bayesian approach to estimate differences in selection between females provisioning and not provisioning offspring, as well as the length of time it took for females with offspring to return levels of selection similar to that observed prior to parturition. Females that were not provisioning offspring selected areas with higher risk of predation, but greater nutritional resources than those that were provisioning dependent young. When females were provisioning young immediately following parturition, females selected areas that were safe from predators, but had lower nutritional resources. Females displayed varying rates of return to selection strategies associated with access to nutritional resources as young grew and became more agile and less dependent on mothers. We observed clear and substantial shifts in selection of resources associated with reproductive state, and females exhibited tradeoffs in favor of areas that were safer from predators when provisioning dependent young despite loss of nutritional resources to support lactation. As young grew and became less vulnerable to predators, females returned to levels of selection that provided access to nutritional resources to restore somatic reserves lost during lactation.

A big data-model integration approach for predicting epizootics and population recovery in a keystone species.

Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data-model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.

COMPUTED TOMOGRAPHY FOR MEASURING BODY FAT RESERVES IN THE THREATENED MOJAVE DESERT TORTOISE (GOPHERUS AGASSIZII).

Noninvasive methods for measuring fat reserves in both captive and free-ranging animals are important for monitoring individual and population health, but chelonian anatomy and physiology present challenges to accurate measurements. Standard field-based methods for assessing body condition in Mojave desert tortoises (Gopherus agassizii) involve the qualitative body condition score, which relies on the apparent height of the temporalis muscle relative to the sagittal crest (in addition to other characteristics) and quantitative body condition indices that measure relative mass at size. However, it is unclear how these metrics relate to body fat reserves in this species. The aims of this study were to (1) describe the use of noninvasive computed tomography in measuring body fat volume of Mojave desert tortoises, (2) describe the location of fat reserves, (3) investigate relationships between fat reserves and body condition score and body condition index, and (4) explore whether relative temporalis muscle depth, measured via computed tomography, correlates with body condition score. Body condition scores were assessed for eight captive Mojave desert tortoises prior to euthanasia, and computed tomography was performed postmortem to quantify fat volume and measure temporalis muscle depth. At necropsy, the distribution of fat was documented. Fat volume calculated by computed tomography ranged from 2.83 to 145.38 cm3 (0.07-2.5% body volume). Neither qualitative body condition score nor quantitative body condition index was correlated with fat volume. Bladder content did not compromise body condition index. Body condition score was not correlated with relative temporalis muscle depth. Computed tomography is a noninvasive method for successfully identifying fat reserves and estimating total fat volume in Mojave desert tortoises. The lack of a relationship between computed tomography-determined metrics and commonly used body condition metrics indicates that computed tomography fills a critical gap in the health assessment tool kit for captive and free-ranging Mojave desert tortoises.

A comparison of non-surgical methods for sexing young gopher tortoises (Gopherus polyphemus).

Many turtle species have temperature-dependent sex determination (TSD), raising the prospect that climate change could impact population dynamics by altering sex ratios. Understanding how climate change will affect populations of animals with TSD requires a reliable and minimally invasive method of identifying the sexes of young individuals. This determination is challenging in many turtles, which often lack conspicuous external sexual dimorphism until years after hatching. Here, we explore four alternatives for sexing three age classes of captive-reared young gopher tortoises (Gopherus polyphemus), a terrestrial turtle of conservation concern native to the southeastern United States: (1) naive testosterone levels, (2) testosterone levels following a follicle stimulating hormone (FSH) challenge, (3) linear morphological measurements, and (4) geometric morphometrics. Unlike some other turtle species, male and female neonatal gopher tortoises have overlapping naive testosterone concentration distributions, justifying more complicated methods. We found that sex of neonates (<7 days old) is best predicted by a "random forest" machine learning model with naive testosterone levels and morphological measurements (8% out-of-bag error). Sex of hatchlings (4-8 months old) was predicted with 11% error using a simple threshold on naive testosterone levels, or with 4% error using a simple threshold on post-FSH testosterone levels. Sex of juveniles (approximately 3.5 years old) was perfectly predicted using a simple threshold on naive testosterone levels. Sexing hatchlings at >4 months of age is the easiest and most reliable non-surgical method for sex identification. Given access to a rearing facility and equipment to perform hormone assays, these methods have the potential to supplant laparoscopic surgery as the method of choice for sexing young gopher tortoises.

Process-explicit models reveal pathway to extinction for woolly mammoth using pattern-oriented validation.

Pathways to extinction start long before the death of the last individual. However, causes of early stage population declines and the susceptibility of small residual populations to extirpation are typically studied in isolation. Using validated process-explicit models, we disentangle the ecological mechanisms and threats that were integral in the initial decline and later extinction of the woolly mammoth. We show that reconciling ancient DNA data on woolly mammoth population decline with fossil evidence of location and timing of extinction requires process-explicit models with specific demographic and niche constraints, and a constrained synergy of climatic change and human impacts. Validated models needed humans to hasten climate-driven population declines by many millennia, and to allow woolly mammoths to persist in mainland Arctic refugia until the mid-Holocene. Our results show that the role of humans in the extinction dynamics of woolly mammoth began well before the Holocene, exerting lasting effects on the spatial pattern and timing of its range-wide extinction.

Adaptive divergence despite low effective population size in a peripherally isolated population of the pygmy rabbit, Brachylagus idahoensis.

Local adaptation can occur when spatially separated populations are subjected to contrasting environmental conditions. Historically, understanding the genetic basis of adaptation has been difficult, but increased availability of genome-wide markers facilitates studies of local adaptation in non-model organisms of conservation concern. The pygmy rabbit (Brachylagus idahoensis) is an imperiled lagomorph that relies on sagebrush for forage and cover. This reliance has led to widespread population declines following reductions in the distribution of sagebrush, leading to geographic separation between populations. In this study, we used >20,000 single nucleotide polymorphisms, genotype-environment association methods, and demographic modeling to examine neutral genetic variation and local adaptation in the pygmy rabbit in Nevada and California. We identified 308 loci as outliers, many of which had functional annotations related to metabolism of plant secondary compounds. Likewise, patterns of spatial variation in outlier loci were correlated with landscape and climatic variables including proximity to streams, sagebrush cover, and precipitation. We found that populations in the Mono Basin of California probably diverged from other Great Basin populations during late Pleistocene climate oscillations, and that this region is adaptively differentiated from other regions in the southern Great Basin despite limited gene flow and low effective population size. Our results demonstrate that peripherally isolated populations can maintain adaptive divergence.

Biogeochemical recuperation of lowland tropical forest during succession.

High rates of land conversion and land use change have vastly increased the proportion of secondary forest in the lowland tropics relative to mature forest. As secondary forests recover following abandonment, nitrogen (N) and phosphorus (P) must be present in sufficient quantities to sustain high rates of net primary production and to replenish the nutrients lost during land use prior to secondary forest establishment. Biogeochemical theory and results from individual studies suggest that N can recuperate during secondary forest recovery, especially relative to P. Here, we synthesized 23 metrics of N and P in soil and plants from 45 secondary forest chronosequences located in the wet tropics to empirically explore (1) whether there is a consistent change in nutrients and nutrient cycling processes during secondary succession in the biome; (2) which metrics of N and P in soil and plants recuperate most consistently; (3) if the recuperation of nutrients during succession approaches similar nutrient concentrations and fluxes as those in mature forest in ~100 yr following the initiation of succession; and (4) whether site characteristics, including disturbance history, climate, and soil order are significantly related to nutrient recuperation. During secondary forest succession, nine metrics of N and/or P cycling changed consistently and substantially. In most sites, N concentrations and fluxes in both plants and soil increased during secondary succession, and total P concentrations increased in surface soil. Changes in nutrient concentrations and nutrient cycling processes during secondary succession were similar whether mature forest was included or excluded from the analysis, indicating that nutrient recuperation in secondary forest leads to biogeochemical conditions that are similar to those of mature forest. Further, of the N and P metrics that recuperated, only soil total P and foliar δ15 N were strongly influenced by site characteristics like climate, soils, or disturbance history. Predictable nutrient recuperation across a diverse and productive ecosystem may support future forest growth and could provide a means to quantify successful restoration of ecosystem function in secondary tropical forest beyond biomass or species composition.

A machine-learning approach for extending classical wildlife resource selection analyses.

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known-use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine-learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine-learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model-based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine-learning tools like RF in addition to "classical" tools (e.g., mixed-effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.

Differential Effects of Climate on Survival Rates Drive Hybrid Zone Movement.

Climate change has been implicated as driving shifts of hybridizing species' range limits [1, 2]. Whether and how much hybrid zones move depends on the relative fitness of hybridzing species under changing conditions [3, 4]. However, fitness is rarely linked to both climatic conditions and movement of hybrid zones, such that the relationship between climate change and hybrid zone dynamics remains tenuous [5]. Here we report how interactions between climate (seasonal precipitation) and competitor densities result in steep differentials in survival, which in turn drive hybrid zone movement for two woodrat species (Neotoma fuscipes and N. macrotis) in central California, USA. Using 6 years of capture-mark-recapture data, we found that the smaller-bodied species, N. macrotis, and hybrids had survival advantages over the larger-bodied N. fuscipes in the contact region during dry winters and wet springs. This pattern of differential survival, with N. macrotis having a consistent advantage over N. fuscipes during our study period, matched the spatial dynamics of the hybrid zone, which moved steadily north into N. fuscipes territory, with its estimated center moving ∼150 m north in 6 years. Our findings provide a unique demonstration of range movements emerging from a complex interplay between climate and competition. Although all study site areas experienced the same climatic conditions, competitive effects created a complex spatial pattern of survival differentials, which in turn influenced hybrid zone movement. Characterization of fitness differentials derived from replicated demographic studies of contact regions between competitors should greatly improve our ability to understand and forecast climate-driven range dynamics.

Geography of current and future global mammal extinction risk.

Identifying which species are at greatest risk, what makes them vulnerable, and where they are distributed are central goals for conservation science. While knowledge of which factors influence extinction risk is increasingly available for some taxonomic groups, a deeper understanding of extinction correlates and the geography of risk remains lacking. Here, we develop a predictive random forest model using both geospatial and mammalian species' trait data to uncover the statistical and geographic distributions of extinction correlates. We also explore how this geography of risk may change under a rapidly warming climate. We found distinctive macroecological relationships between species-level risk and extinction correlates, including the intrinsic biological traits of geographic range size, body size and taxonomy, and extrinsic geographic settings such as seasonality, habitat type, land use and human population density. Each extinction correlate exhibited ranges of values that were especially associated with risk, and the importance of different risk factors was not geographically uniform across the globe. We also found that about 10% of mammals not currently recognized as at-risk have biological traits and occur in environments that predispose them towards extinction. Southeast Asia had the most actually and potentially threatened species, underscoring the urgent need for conservation in this region. Additionally, nearly 40% of currently threatened species were predicted to experience rapid climate change at 0.5 km/year or more. Biological and environmental correlates of mammalian extinction risk exhibit distinct statistical and geographic distributions. These results provide insight into species-level patterns and processes underlying geographic variation in extinction risk. They also offer guidance for future conservation research focused on specific geographic regions, or evaluating the degree to which species-level patterns mirror spatial variation in the pressures faced by populations within the ranges of individual species. The added impacts from climate change may increase the susceptibility of at-risk species to extinction and expand the regions where mammals are most vulnerable globally.

Pan-Antarctic analysis aggregating spatial estimates of Adélie penguin abundance reveals robust dynamics despite stochastic noise.

Colonially-breeding seabirds have long served as indicator species for the health of the oceans on which they depend. Abundance and breeding data are repeatedly collected at fixed study sites in the hopes that changes in abundance and productivity may be useful for adaptive management of marine resources, but their suitability for this purpose is often unknown. To address this, we fit a Bayesian population dynamics model that includes process and observation error to all known Adélie penguin abundance data (1982-2015) in the Antarctic, covering >95% of their population globally. We find that process error exceeds observation error in this system, and that continent-wide "year effects" strongly influence population growth rates. Our findings have important implications for the use of Adélie penguins in Southern Ocean feedback management, and suggest that aggregating abundance across space provides the fastest reliable signal of true population change for species whose dynamics are driven by stochastic processes.Adélie penguins are a key Antarctic indicator species, but data patchiness has challenged efforts to link population dynamics to key drivers. Che-Castaldo et al. resolve this issue using a pan-Antarctic Bayesian model to infer missing data, and show that spatial aggregation leads to more robust inference regarding dynamics.

Climatic and geographic predictors of life history variation in Eastern Massasauga (Sistrurus catenatus): A range-wide synthesis.

Elucidating how life history traits vary geographically is important to understanding variation in population dynamics. Because many aspects of ectotherm life history are climate-dependent, geographic variation in climate is expected to have a large impact on population dynamics through effects on annual survival, body size, growth rate, age at first reproduction, size-fecundity relationship, and reproductive frequency. The Eastern Massasauga (Sistrurus catenatus) is a small, imperiled North American rattlesnake with a distribution centered on the Great Lakes region, where lake effects strongly influence local conditions. To address Eastern Massasauga life history data gaps, we compiled data from 47 study sites representing 38 counties across the range. We used multimodel inference and general linear models with geographic coordinates and annual climate normals as explanatory variables to clarify patterns of variation in life history traits. We found strong evidence for geographic variation in six of nine life history variables. Adult female snout-vent length and neonate mass increased with increasing mean annual precipitation. Litter size decreased with increasing mean temperature, and the size-fecundity relationship and growth prior to first hibernation both increased with increasing latitude. The proportion of gravid females also increased with increasing latitude, but this relationship may be the result of geographically varying detection bias. Our results provide insights into ectotherm life history variation and fill critical data gaps, which will inform Eastern Massasauga conservation efforts by improving biological realism for models of population viability and climate change.

Inferring the nature of anthropogenic threats from long-term abundance records.

Diagnosing the processes that threaten species persistence is critical for recovery planning and risk forecasting. Dominant threats are typically inferred by experts on the basis of a patchwork of informal methods. Transparent, quantitative diagnostic tools would contribute much-needed consistency, objectivity, and rigor to the process of diagnosing anthropogenic threats. Long-term census records, available for an increasingly large and diverse set of taxa, may exhibit characteristic signatures of specific threatening processes and thereby provide information for threat diagnosis. We developed a flexible Bayesian framework for diagnosing threats on the basis of long-term census records and diverse ancillary sources of information. We tested this framework with simulated data from artificial populations subjected to varying degrees of exploitation and habitat loss and several real-world abundance time series for which threatening processes are relatively well understood: bluefin tuna (Thunnus maccoyii) and Atlantic cod (Gadus morhua) (exploitation) and Red Grouse (Lagopus lagopus scotica) and Eurasian Skylark (Alauda arvensis) (habitat loss). Our method correctly identified the process driving population decline for over 90% of time series simulated under moderate to severe threat scenarios. Successful identification of threats approached 100% for severe exploitation and habitat loss scenarios. Our method identified threats less successfully when threatening processes were weak and when populations were simultaneously affected by multiple threats. Our method selected the presumed true threat model for all real-world case studies, although results were somewhat ambiguous in the case of the Eurasian Skylark. In the latter case, incorporation of an ancillary source of information (records of land-use change) increased the weight assigned to the presumed true model from 70% to 92%, illustrating the value of the proposed framework in bringing diverse sources of information into a common rigorous framework. Ultimately, our framework may greatly assist conservation organizations in documenting threatening processes and planning species recovery.

Warning times for species extinctions due to climate change.

Climate change is likely to become an increasingly major obstacle to slowing the rate of species extinctions. Several new assessment approaches have been proposed for identifying climate-vulnerable species, based on the assumption that established systems such as the IUCN Red List need revising or replacing because they were not developed to explicitly consider climate change. However, no assessment approach has been tested to determine its ability to provide advanced warning time for conservation action for species that might go extinct due to climate change. To test the performance of the Red List system in this capacity, we used linked niche-demographic models with habitat dynamics driven by a 'business-as-usual' climate change scenario. We generated replicate 100-year trajectories for range-restricted reptiles and amphibians endemic to the United States. For each replicate, we categorized the simulated species according to IUCN Red List criteria at annual, 5-year, and 10-year intervals (the latter representing current practice). For replicates that went extinct, we calculated warning time as the number of years the simulated species was continuously listed in a threatened category prior to extinction. To simulate data limitations, we repeated the analysis using a single criterion at a time (disregarding other listing criteria). Results show that when all criteria can be used, the Red List system would provide several decades of warning time (median = 62 years; >20 years for 99% of replicates), but suggest that conservation actions should begin as soon as a species is listed as Vulnerable, because 50% of replicates went extinct within 20 years of becoming uplisted to Critically Endangered. When only one criterion was used, warning times were substantially shorter, but more frequent assessments increased the warning time by about a decade. Overall, we found that the Red List criteria reliably provide a sensitive and precautionary way to assess extinction risk under climate change.

Demographic outcomes and ecosystem implications of giant tortoise reintroduction to Española Island, Galapagos.

Restoration of extirpated species via captive breeding has typically relied on population viability as the primary criterion for evaluating success. This criterion is inadequate when species reintroduction is undertaken to restore ecological functions and interactions. Herein we report on the demographic and ecological outcomes of a five-decade-long population restoration program for a critically endangered species of "ecosystem engineer": the endemic Española giant Galapagos tortoise (Chelonoidis hoodensis). Our analysis of complementary datasets on tortoise demography and movement, tortoise-plant interactions and Española Island's vegetation history indicated that the repatriated tortoise population is secure from a strictly demographic perspective: about half of tortoises released on the island since 1975 were still alive in 2007, in situ reproduction is now significant, and future extinction risk is low with or without continued repatriation. Declining survival rates, somatic growth rates, and body condition of repatriates suggests, however, that resources for continued population growth are increasingly limited. Soil stable carbon isotope analyses indicated a pronounced shift toward woody plants in the recent history of the island's plant community, likely a legacy of changes in competitive relations between woody and herbaceous plants induced by now-eradicated feral goats and prolonged absence of tortoises. Woody plants are of concern because they block tortoise movement and hinder recruitment of cactus--a critical resource for tortoises. Tortoises restrict themselves to remnant cactus patches and areas of low woody plant density in the center of the island despite an apparent capacity to colonize a far greater range, likely because of a lack of cactus elsewhere on the island. We conclude that ecosystem-level criteria for success of species reintroduction efforts take much longer to achieve than population-level criteria; moreover, reinstatement of endangered species as fully functioning ecosystem engineers may often require large-scale habitat restoration efforts in concert with population restoration.