Free-ranging domestic cats (Felis catus) are globally distributed invasive carnivores. While recognition of their impacts has focused on consumption of vertebrates, increasing evidence suggests that they also consume large numbers of invertebrate species. Given the ongoing concern over invertebrate population declines across the planet, we compiled and analyzed a global database of invertebrate species reported in studies of cat diet. Despite making up >90% of all terrestrial animal species, invertebrates constituted only 7% of the >2000 species we identified as eaten by cats. However, when invertebrates were recorded in cat dietary studies, few were identified to species-level. Four of the 148 invertebrate species we reported to be eaten by cats are considered threatened by the IUCN, but cat predation is not recognized as a threat in their IUCN accounts. IUCN accounts do report cat predation as a threat for 48 invertebrate species of conservation concern, however none of these appear in our database. Insects (especially beetles) constituted similar to 80% of the 148 invertebrate species reported in cat dietary studies, with crustacean, arachnid, centipede, snail and slug, and millipede species occurring less frequently. Our results add to the growing consensus that cats consume a wide variety of invertebrate species and that they depredate more invertebrate species than is currently recognized. We recommend more cat dietary studies using eDNA (complemented by a more comprehensive eDNA library of invertebrate species), more autecological studies of threatened invertebrate species, and studies of responses of invertebrates to eradication or exclusion of feral cats.

Targeted monitoring by trained professionals has been the standard to inform evidence-based regulatory and conservation planning decisions for migratory waterfowl and other wildlife in North America. As inferential expectations derived from professional surveys grow and funding wanes, decision-makers are turning toward citizen science as supplemental or alternative data sources. However, external validation of citizen science data, in cooperation with experts, is recommended before their use in management decisions. We compiled data from professional surveys across 7 U.S. states within the Upper Mississippi / Great Lakes Joint Venture (JV) for 16 species of waterfowl (ducks, geese, and swans) to compare with and potentially validate temporal trends in weekly relative abundance predictions (i.e., migration curves) by eBird citizen science data. We demonstrated that eBird weekly relative abundance produced similar migration curves to those derived from professional surveys for most species. Concordance between migration curves for uncommon or vagrant species was less reliable, especially at the county level. However, at spatial scales more relevant for regulatory or conservation planning decisions (e.g., state or JV scale), correlation between eBird and professionally derived migration curves increased to adequate levels of concordance for most species (p ≥ 0.70). We concluded that eBird weekly relative abundance is a suitable supplement to professional surveys to generate migration curves for waterfowl conservation planning during the nonbreeding season. Additionally, states and JVs that lack monitoring at appropriate temporal frequency may consider using eBird relative abundance to derive migration chronologies for priority species that are well-distributed throughout their region. Broadly, we encourage continued external validation of citizen science data—which requires close partnership between researchers and decision-makers—for its wise use in management decisions.

AimHalting widespread biodiversity loss will require detailed information on species' trends and the habitat conditions correlated with population declines. However, constraints on conventional monitoring programs and commonplace approaches for trend estimation can make it difficult to obtain such information across species' ranges. Here, we demonstrate how recent developments in machine learning and model interpretation, combined with data sources derived from participatory science, enable landscape-scale inferences on the habitat correlates of population trends across broad spatial extents.LocationWorldwide, with a case study in the western United States.MethodsWe used interpretable machine learning to understand the relationships between land cover and spatially explicit bird population trends. Using a case study with three passerine birds in the western U.S. and spatially explicit trends derived from eBird data, we explore the potential impacts of simulated land cover modification while evaluating potential co-benefits among species.ResultsOur analysis revealed complex, non-linear relationships between land cover variables and species' population trends as well as substantial interspecific variation in those relationships. Areas with the most positive impacts from a simulated land cover modification overlapped for two species, but these changes had little effect on the third species.Main ConclusionsThis framework can help conservation practitioners identify important relationships between species trends and habitat while also highlighting areas where potential modifications to the landscape could bring the biggest benefits. The analysis is transferable to hundreds of species worldwide with spatially explicit trend estimates, allowing inference across multiple species at scales that are tractable for management to combat species declines.

Conducting assessments to understand the effects of changing environmental conditions on polar bear (Ursus maritimus) demography has become increasingly important to inform management and conservation. Here, we combined physical (2005-2007) and genetic (2017-2018) mark-recapture with harvest recovery data (2005-2018) to estimate demographic rates of the Davis Strait polar bear subpopulation and examine the possible effects of climate, dynamic ice habitat, and prey resources on survival. Large sample sizes (e.g., 2,513 marked animals) allowed us to estimate temporal variation in annual survival rates using multistate mark-recapture-recovery models. We did not detect statistically significant effects of climate, ice habitat, and prey during the 13-year study. Estimated total abundance in 2006 was 2,190, credible interval (CRI) [1,954, 2,454] and 1,944, CRI [1,593, 2,366] in 2018. Geometric mean population growth rate (0.99, 95% CRI [0.97, 1.01]) indicated the subpopulation may have declined slightly between 2006 and 2018. However, we did not detect a declining trend in survival or substantial change in reproductive metrics over this period. Given forecasts of major environmental change we emphasize the need to review monitoring programs for this subpopulation.

Classifying species into risk categories is a ubiquitous process in conservation decision-making affecting regulatory procedures, conservation actions, and guiding resource allocation at global, national, and regional scales. However, monitoring programs often do not provide data required for accurate species classification decisions. Misclassification can lead to otherwise preventable species extinctions, undue regulatory burden, poor allocation of limited conservation resources, and can undermine species conservation legislation. We developed a framework that evaluates monitoring designs based on the ability to correctly inform a species classification decision, where minimizing the risk of misclassification is the central objective. We further evaluated monitoring designs by calculating the expected value of information and explored the relationship between statistical power to detect trends and misclassification. Our measure of misclassification risk, which can be tailored to the decision context, clarified the costs of over- and under-protection. High power to detect trends often corresponded to accurate species classification decisions. However, in several scenarios power to detect trends was low but the ability to correctly inform the classification decision was high. The value of information generally increased with monitoring intensity and quantified the tradeoffs between spatial and temporal replication. Our framework allows managers to assess monitoring program performance with direct implications for conservation decision-making. Our framework affords practitioners an opportunity to evaluate the effectiveness of monitoring programs a priori focusing on improving conservation decisions. We demonstrate that prioritizing monitoring to minimize misclassification errors can improve monitoring efficiency and conservation decision-making with considerable practical applications and benefits for species conservation.

Free-ranging cats ( Felis catus ) are globally distributed invasive carnivores that markedly impact biodiversity. Here, to evaluate the potential threat of cats, we develop a comprehensive global assessment of species consumed by cats. We identify 2,084 species eaten by cats, of which 347 (16.65%) are of conservation concern. Islands contain threefold more species of conservation concern eaten by cats than continents do. Birds, reptiles, and mammals constitute ~90% of species consumed, with insects and amphibians being less frequent. Approximately 9% of known birds, 6% of known mammals, and 4% of known reptile species are identified in cat diets. 97% of species consumed are <5 kg in adult body mass, though much larger species are also eaten. The species accumulation curves are not asymptotic, indicating that our estimates are conservative. Our results demonstrate that cats are extreme generalist predators, which is critical for understanding their impact on ecological systems and developing management solutions.

AbstractPopulation models often require detailed information on sex‐, age‐, or size‐specific abundances, but population monitoring programs cannot always acquire data at the desired resolution. Thus, state uncertainty in monitoring data can potentially limit the demographic resolution of management decisions, which may be particularly problematic for stage‐ or size‐structured species subject to consumptive use. American alligators (Alligator mississippiensis; hereafter alligator) have a complex life history characterized by delayed maturity and slow somatic growth, which makes the species particularly sensitive to overharvest. Though alligator populations are subject to recreational harvest throughout their range, the most widely used monitoring method (nightlight surveys) is often unable to obtain size class‐specific counts, which limits the ability of managers to evaluate the effects of harvest policies. We constructed a Bayesian integrated population model (IPM) for alligators in Georgetown County, SC, USA, using records of mark–recapture–recovery, clutch size, harvest, and nightlight survey counts collected locally, and auxiliary information on fecundity, sex ratio, and somatic growth from other studies. We created a multistate mark–recapture–recovery model with six size classes to estimate survival probability, and we linked it to a state‐space count model to derive estimates of size class‐specific detection probability and abundance. Because we worked from a count dataset in which 60% of the original observations were of unknown size, we treated size class as a latent property of detections and developed a novel observation model to make use of information where size could be partly observed. Detection probability was positively associated with alligator size and water temperature, and negatively influenced by water level. Survival probability was lowest in the smallest size class but was relatively similar among the other five size classes (>0.90 for each). While the two nightlight survey count sites exhibited relatively stable population trends, we detected substantially different patterns in size class‐specific abundance and trends between each site, including 30%–50% declines in the largest size classes at the site with greater harvest pressure. Here, we illustrate the use of IPMs to produce high‐resolution output of latent population structure that is partially observed during the monitoring process.

Assessing species status and making classification decisions under the Endangered Species Act is a critical step towards effective species conservation. However, classification decisions are liable to two errors: i) failing to classify a species as threatened or endangered that should be classified (underprotection), or ii) classifying a species as threatened or endangered when it is not warranted (overprotection). Recent surveys indicate threatened spectacled eider populations are increasing in western Alaska, prompting the U.S. Fish and Wildlife Service to reconsider the federal listing status. There are multiple criteria set for assessing spectacled eider status, and here we focus on the abundance and decision analysis criteria. We estimated population metrics using state-space models for Alaskan breeding populations of spectacled eiders. We projected abundance over 50 years using posterior estimates of abundance and process variation to estimate the probability of quasi-extinction. The decision analysis maps the risk of quasi-extinction to the loss associated with making a misclassification error (i.e., underprotection) through a loss function. Our results indicate that the Yukon Kuskokwim Delta breeding population in western Alaska has met the recovery criteria but the Arctic Coastal Plain population in northern Alaska has not. The methods employed here provide an example of accounting for uncertainty and incorporating value judgements in such a way that the decision-makers may understand the risk of committing a misclassification error. Incorporating the abundance threshold and decision analysis in the reclassification criteria greatly increases the transparency and defensibility of the classification decision, a critical aspect for making effective decisions about species management and conservation.

Abstract The Arctic is undergoing rapid and accelerating change in response to global warming, altering biodiversity patterns, and ecosystem function across the region. For Arctic endemic species, our understanding of the consequences of such change remains limited. Spectacled eiders (Somateria fischeri), a large Arctic sea duck, use remote regions in the Bering Sea, Arctic Russia, and Alaska throughout the annual cycle making it difficult to conduct comprehensive surveys or demographic studies. Listed as Threatened under the U.S. Endangered Species Act, understanding the species response to climate change is critical for effective conservation policy and planning. Here, we developed an integrated population model to describe spectacled eider population dynamics using capture–mark–recapture, breeding population survey, nest survey, and environmental data collected between 1992 and 2014. Our intent was to estimate abundance, population growth, and demographic rates, and quantify how changes in the environment influenced population dynamics. Abundance of spectacled eiders breeding in western Alaska has increased since listing in 1993 and responded more strongly to annual variation in first‐year survival than adult survival or productivity. We found both adult survival and nest success were highest in years following intermediate sea ice conditions during the wintering period, and both demographic rates declined when sea ice conditions were above or below average. In recent years, sea ice extent has reached new record lows and has remained below average throughout the winter for multiple years in a row. Sea ice persistence is expected to further decline in the Bering Sea. Our results indicate spectacled eiders may be vulnerable to climate change and the increasingly variable sea ice conditions throughout their wintering range with potentially deleterious effects on population dynamics. Importantly, we identified that different demographic rates responded similarly to changes in sea ice conditions, emphasizing the need for integrated analyses to understand population dynamics.

The Alaskan breeding population of Steller's eiders (Polysticta stelleri) was listed as threatened under the Endangered Species Act in 1997 in response to declines in abundance and a contraction in their breeding and nesting range. Aerial surveys suggest the breeding population is small and breeds in highly variable numbers, with zero birds counted in five of the last 25 years. The primary objective of this research was to evaluate competing population process models of Alaskan breeding Steller's eiders through comparison of model projections to aerial survey data. To evaluate model efficacy and estimate demographic parameters, we used a Bayesian state-space modeling framework and fit each model to counts from the annual aerial surveys using sequential importance sampling/resampling. The results strongly support that the Alaskan breeding population experiences population-level non-breeding events, and is open to exchange with the larger Russian-Pacific breeding population. Current recovery criteria for the Alaskan breeding population rely heavily on the ability to estimate population viability. Our results provide an informative model of population process that can be used to examine future population states and assess the population in terms of the current recovery and reclassification criteria.

First posted October 11, 2016 For additional information, contact: Chief, Cooperative Research Units U.S. Geological Survey 12201 Sunrise Valley Drive Reston, VA 20192-0002 https://www.usgs.gov/science/mission-areas/ecosystems The U.S. Fish and Wildlife Service is tasked with setting objective and measurable criteria for delisting species or populations listed under the Endangered Species Act. Determining the acceptable threshold for extinction risk for any species or population is a challenging task, particularly when facing marked uncertainty. The Alaskan breeding population of Steller’s eiders (Polysticta stelleri) was listed as threatened under the Endangered Species Act in 1997 because of a perceived decline in abundance throughout their nesting range and geographic isolation from the Russian breeding population. Previous genetic studies and modeling efforts, however, suggest that there may be dispersal from the Russian breeding population. Additionally, evidence exists of population level nonbreeding events. Research was conducted to estimate population viability of the Alaskan breeding population of Steller’s eiders, using both an open and closed model of population process for this threatened population. Projections under a closed population model suggest this population has a 100 percent probability of extinction within 42 years. Projections under an open population model suggest that with immigration there is no probability of permanent extinction. Because of random immigration process and nonbreeding behavior, however, it is likely that this population will continue to be present in low and highly variable numbers on the breeding grounds in Alaska. Monitoring the winter population, which includes both Russian and Alaskan breeding birds, may offer a more comprehensive indication of population viability.

First posted October 11, 2016 For additional information, contact: Chief, Cooperative Research Units U.S. Geological Survey 12201 Sunrise Valley Drive Reston, VA 20192-0002 https://www.usgs.gov/science/mission-areas/ecosystems The Alaskan breeding population of Steller’s eiders (Polysticta stelleri) was listed as threatened under the Endangered Species Act in 1997 in response to perceived declines in abundance throughout their breeding and nesting range. Aerial surveys suggest the breeding population is small and highly variable in number, with zero birds counted in 5 of the last 25 years. Research was conducted to evaluate competing population process models of Alaskan-breeding Steller’s eiders through comparison of model projections to aerial survey data. To evaluate model efficacy and estimate demographic parameters, a Bayesian state-space modeling framework was used and each model was fit to counts from the annual aerial surveys, using sequential importance sampling and resampling. The results strongly support that the Alaskan breeding population experiences population level nonbreeding events and is open to exchange with the larger Russian-Pacific breeding population. Current recovery criteria for the Alaskan breeding population rely heavily on the ability to estimate population viability. The results of this investigation provide an informative model of the population process that can be used to examine future population states and assess the population in terms of the current recovery and reclassification criteria.

We examined the effects of complexity and priors on the accuracy of models used to estimate ecological and observational processes, and to make predictions regarding population size and structure. State-space models are useful for estimating complex, unobservable population processes and making predictions about future populations based on limited data. To better understand the utility of state space models in evaluating population dynamics, we used them in a Bayesian framework and compared the accuracy of models with differing complexity, with and without informative priors using sequential importance sampling/resampling (SISR). Count data were simulated for 25 years using known parameters and observation process for each model. We used kernel smoothing to reduce the effect of particle depletion, which is common when estimating both states and parameters with SISR. Models using informative priors estimated parameter values and population size with greater accuracy than their non-informative counterparts. While the estimates of population size and trend did not suffer greatly in models using non-informative priors, the algorithm was unable to accurately estimate demographic parameters. This model framework provides reasonable estimates of population size when little to no information is available; however, when information on some vital rates is available, SISR can be used to obtain more precise estimates of population size and process. Incorporating model complexity such as that required by structured populations with stage-specific vital rates affects precision and accuracy when estimating latent population variables and predicting population dynamics. These results are important to consider when designing monitoring programs and conservation efforts requiring management of specific population segments. (C) 2016 Elsevier B.V. All rights reserved.

Very little is known about the population dynamics of American alligators in northern latitudes. To better define the characteristics of the northern population, we combined published life-history and vital rate data for studies conducted in North Carolina and South Carolina; for comparison, we gleaned the same information from the literature for the southern (Florida and Louisiana) population. We constructed a 5-stage Lefkovitch matrix model for each population. The models showed that the southern population was stable and slightly increasing (lambda = 1.02), whereas the northern population was in decline (lambda = 0.870). We integrated potential impacts of climate change into the northern population model to determine how the population might respond to increased temperature and decreased precipitation. An increase in temperature would benefit the northern population; however, a decrease in precipitation or the combined effects of temperature increase and precipitation decrease would negatively affect the viability of the northern population. Two priorities result from modeling these scenarios: 1) a long-term monitoring program is needed to acquire the life-history and vital rate data on the northern population, and 2) current alligator habitat must be conserved or improved to insulate the species from potential drought associated with climate change. (C) 2014 The Wildlife Society.