A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land-use change.

Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi- or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within-time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site-specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long-term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.

A phenology of fear: Investigating scale and seasonality in predator-prey games between wolves and white-tailed deer.

Predators and prey engage in games where each player must counter the moves of the other, and these games include multiple phases operating at different spatiotemporal scales. Recent work has highlighted potential issues related to scale-sensitive inferences in predator-prey interactions, and there is growing appreciation that these may exhibit pronounced but predictable dynamics. Motivated by previous assertions about effects arising from foraging games between white-tailed deer and canid predators (coyotes and wolves), we used a large and year-round network of trail cameras to characterize deer and predator foraging games, with a particular focus on clarifying its temporal scale and seasonal variation. Linear features were strongly associated with predator detection rates, suggesting these play a central role in canid foraging tactics by expediting movement. Consistent with expectations for prey contending with highly mobile predators, deer responses were more sensitive to proximal risk metrics at finer spatiotemporal scales, suggesting that coarser but more commonly used scales of analysis may miss useful insights into prey risk-response. Time allocation appears to be a key tactic for deer risk management and was more strongly moderated by factors associated with forage or evasion heterogeneity (forest cover, snow and plant phenology) than factors associated with the likelihood of predator encounter (linear features). Trade-offs between food and safety appeared to vary as much seasonally as spatially, with snow and vegetation phenology giving rise to a "phenology of fear." Deer appear free to counter predators during milder times of year, but a combination of poor foraging state, reduced forage availability, greater movements costs, and reproductive state dampen responsiveness during winter. Pronounced intra-annual variation in predator-prey interactions may be common in seasonal environments.

Daily activity timing in the Anthropocene.

Animals are facing novel 'timescapes' in which the stimuli entraining their daily activity patterns no longer match historical conditions due to anthropogenic disturbance. However, the ecological effects (e.g., altered physiology, species interactions) of novel activity timing are virtually unknown. We reviewed 1328 studies and found relatively few focusing on anthropogenic effects on activity timing. We suggest three hypotheses to stimulate future research: (i) activity-timing mismatches determine ecological effects, (ii) duration and timing of timescape modification influence effects, and (iii) consequences of altered activity timing vary biogeographically due to broad-scale variation in factors compressing timescapes. The continued growth of sampling technologies promises to facilitate the study of the consequences of altered activity timing, with emerging applications for biodiversity conservation.

Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management.

Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.

Abundance estimation of unmarked animals based on camera-trap data.

The rapid improvement of camera traps in recent decades has revolutionized biodiversity monitoring. Despite clear applications in conservation science, camera traps have seldom been used to model the abundance of unmarked animal populations. We sought to summarize the challenges facing abundance estimation of unmarked animals, compile an overview of existing analytical frameworks, and provide guidance for practitioners seeking a suitable method. When a camera records multiple detections of an unmarked animal, one cannot determine whether the images represent multiple mobile individuals or a single individual repeatedly entering the camera viewshed. Furthermore, animal movement obfuscates a clear definition of the sampling area and, as a result, the area to which an abundance estimate corresponds. Recognizing these challenges, we identified 6 analytical approaches and reviewed 927 camera-trap studies published from 2014 to 2019 to assess the use and prevalence of each method. Only about 5% of the studies used any of the abundance-estimation methods we identified. Most of these studies estimated local abundance or covariate relationships rather than predicting abundance or density over broader areas. Next, for each analytical approach, we compiled the data requirements, assumptions, advantages, and disadvantages to help practitioners navigate the landscape of abundance estimation methods. When seeking an appropriate method, practitioners should evaluate the life history of the focal taxa, carefully define the area of the sampling frame, and consider what types of data collection are possible. The challenge of estimating abundance of unmarked animal populations persists; although multiple methods exist, no one method is optimal for camera-trap data under all circumstances. As analytical frameworks continue to evolve and abundance estimation of unmarked animals becomes increasingly common, camera traps will become even more important for informing conservation decision-making.

Estimación de la Abundancia de Animales No Marcados con Base en Datos de Cámaras Trampa Resumen La rápida mejoría de las cámaras trampa en las décadas recientes ha revolucionado el monitoreo de la biodiversidad. A pesar de su clara aplicación en las ciencias de la conservación, las cámaras trampa han sido utilizadas pocas veces para modelar la abundancia de las poblaciones de animales no marcados. Buscamos resumir los retos que enfrenta la estimación de la abundancia de animales no marcados, compilar una perspectiva general de los marcos analíticos de trabajo existentes y proporcionar una guía para aquellos practicantes que buscan un método adecuado. Cuando una cámara registra múltiples detecciones de animales no marcados, no se puede determinar si las imágenes representan a diferentes individuos en movimiento o a un solo individuo que entra repetidamente a la zona de visión de la cámara. Sumado a esto, el movimiento animal ofusca una definición clara del área de muestreo y, como resultado, del área a la cual corresponde un estimado de abundancia. Después de reconocer estos retos, identificamos seis estrategias analíticas y revisamos 927 estudios con cámaras trampa publicados entre 2014 y 2019 para evaluar el uso y la prevalencia de cada método. Solamente en el 5% de los estudios se usó cualquiera de los métodos de estimación de abundancia que identificamos. La mayoría de estos estudios estimaron la abundancia local o las relaciones de covarianza en lugar de predecir la abundancia o la densidad a lo largo de áreas más amplias. Después, para cada estrategia analítica, recopilamos los requerimientos de datos, suposiciones, ventajas y desventajas para ayudar a los practicantes a navegar el paisaje de los métodos de estimación de abundancia. Cuando los practicantes busquen un método apropiado deberán evaluar la historia de vida del taxón focal, definir cuidadosamente el área del marco de muestreo y considerar cuáles tipos de recolección de datos son posibles. El reto de estimar la abundancia de poblaciones de animales no marcados persiste; aunque existan muchos métodos, no hay método único óptimo para los datos de las cámaras trampa que cumpla con todas las circunstancias. Mientras los marcos analíticos de trabajo sigan evolucionando y la estimación de la abundancia de animales no marcados sea cada vez más común, las cámaras trampa serán todavía más importantes para informar la toma de decisiones de conservación.

Generalized model-based solutions to false-positive error in species detection/nondetection data.

Detection/nondetection data are widely collected by ecologists interested in estimating species distributions, abundances, and phenology, and are often imperfect. Recent model development has focused on accounting for both false-positive and false-negative errors given evidence that misclassification is common across many sampling protocols. To date, however, model-based solutions to false-positive error have largely addressed occupancy estimation. We describe a generalized model structure that allows investigators to account for false-positive error in detection/nondetection data across a broad range of ecological parameters and model classes, and demonstrate that previously developed model-based solutions are special cases of the generalized model. Simulation results demonstrate that estimators for abundance and migratory arrival time ignoring false-positive error exhibit severe (20-70%) relative bias even when only 5-10% of detections are false positives. Bias increased when false-positive detections were more likely to occur at sites or within occasions in which true positive detections were unlikely to occur. Models accounting for false-positive error following the site-confirmation or observation-confirmation designs generally reduced bias substantially, even when few detections were confirmed as true or false positives or when the process model for false-positive error was misspecified. Results from an empirical example focusing on gray fox (Urocyon cinereoargenteus) abundance in Wisconsin, USA reinforce concerns that biases induced by false-positive error can also distort spatial predictions often used to guide decision making. Model sensitivity to false-positive error extends well beyond occupancy estimation, but encouragingly, model-based solutions developed for occupancy estimators are generalizable and effective across a range of models widely used in ecological research.

Making inference with messy (citizen science) data: when are data accurate enough and how can they be improved?

Measurement or observation error is common in ecological data: as citizen scientists and automated algorithms play larger roles processing growing volumes of data to address problems at large scales, concerns about data quality and strategies for improving it have received greater focus. However, practical guidance pertaining to fundamental data quality questions for data users or managers-how accurate do data need to be and what is the best or most efficient way to improve it?-remains limited. We present a generalizable framework for evaluating data quality and identifying remediation practices, and demonstrate the framework using trail camera images classified using crowdsourcing to determine acceptable rates of misclassification and identify optimal remediation strategies for analysis using occupancy models. We used expert validation to estimate baseline classification accuracy and simulation to determine the sensitivity of two occupancy estimators (standard and false-positive extensions) to different empirical misclassification rates. We used regression techniques to identify important predictors of misclassification and prioritize remediation strategies. More than 93% of images were accurately classified, but simulation results suggested that most species were not identified accurately enough to permit distribution estimation at our predefined threshold for accuracy (<5% absolute bias). A model developed to screen incorrect classifications predicted misclassified images with >97% accuracy: enough to meet our accuracy threshold. Occupancy models that accounted for false-positive error provided even more accurate inference even at high rates of misclassification (30%). As simulation suggested occupancy models were less sensitive to additional false-negative error, screening models or fitting occupancy models accounting for false-positive error emerged as efficient data remediation solutions. Combining simulation-based sensitivity analysis with empirical estimation of baseline error and its variability allows users and managers of potentially error-prone data to identify and fix problematic data more efficiently. It may be particularly helpful for "big data" efforts dependent upon citizen scientists or automated classification algorithms with many downstream users, but given the ubiquity of observation or measurement error, even conventional studies may benefit from focusing more attention upon data quality.

Do the antipredator strategies of shared prey mediate intraguild predation and mesopredator suppression?

Understanding the conditions that facilitate top predator effects upon mesopredators and prey is critical for predicting where these effects will be significant. Intraguild predation (IGP) and the ecology of fear are hypotheses used to describe the effects of top predators upon mesopredators and prey species, but make different assumptions about organismal space use. The IGP hypothesis predicts that mesopredator resource acquisition and risk are positively correlated, creating a fitness deficit. But if shared prey also avoid a top predator, then mesopredators may not have to choose between risk and reward. Prey life history may be a critical predictor of how shared prey respond to predation and may mediate mesopredator suppression. We used hierarchical models of species distribution and abundance to test expectations of IGP using two separate triangular relationships between a large carnivore, smaller intraguild carnivore, and shared mammalian prey with different life histories. Following IGP, we expected that a larger carnivore would suppress a smaller carnivore if the shared prey species did not spatially avoid the large carnivore at broad scales. If prey were fearful over broad scales, we expected less evidence of mesopredator suppression. We tested these theoretical hypotheses using remote camera detections across a large spatial extent. Lagomorphs did not appear to avoid coyotes, and fox detection probability was lower as coyote abundance increased. In contrast, white-tailed deer appeared to avoid areas of increased wolf use, and coyote detection probability was not reduced at sites where wolves occurred. These findings suggest that mesopredator suppression by larger carnivores may depend upon the behavior of shared prey, specifically the spatial scale at which they perceive risk. We further discuss how extrinsic environmental factors may contribute to mesopredator suppression.