A multispecies hierarchical model to integrate count and distance-sampling data.

Integrated community models-an emerging framework in which multiple data sources for multiple species are analyzed simultaneously-offer opportunities to expand inferences beyond the single-species and single-data-source approaches common in ecology. We developed a novel integrated community model that combines distance sampling and single-visit count data; within the model, information is shared among data sources (via a joint likelihood) and species (via a random-effects structure) to estimate abundance patterns across a community. Parameters relating to abundance are shared between data sources, and the model can specify either shared or separate observation processes for each data source. Simulations demonstrated that the model provided unbiased estimates of abundance and detection parameters even when detection probabilities varied between the data types. The integrated community model also provided more accurate and more precise parameter estimates than alternative single-species and single-data-source models in many instances. We applied the model to a community of 11 herbivore species in the Masai Mara National Reserve, Kenya, and found considerable interspecific variation in response to local wildlife management practices: Five species showed higher abundances in a region with passive conservation enforcement (median across species: 4.5× higher), three species showed higher abundances in a region with active conservation enforcement (median: 3.9× higher), and the remaining three species showed no abundance differences between the two regions. Furthermore, the community average of abundance was slightly higher in the region with active conservation enforcement but not definitively so (posterior mean: higher by 0.20 animals; 95% credible interval: 1.43 fewer animals, 1.86 more animals). Our integrated community modeling framework has the potential to expand the scope of inference over space, time, and levels of biological organization, but practitioners should carefully evaluate whether model assumptions are met in their systems and whether data integration is valuable for their applications.

A century of statistical Ecology.

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.

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.

Human disturbance compresses the spatiotemporal niche.

Human disturbance may fundamentally alter the way that species interact, a prospect that remains poorly understood. We investigated whether anthropogenic landscape modification increases or decreases co-occurrence-a prerequisite for species interactions-within wildlife communities. Using 4 y of data from >2,000 camera traps across a human disturbance gradient in Wisconsin, USA, we considered 74 species pairs (classifying pairs as low, medium, or high antagonism to account for different interaction types) and used the time between successive detections of pairs as a measure of their co-occurrence probability and to define co-occurrence networks. Pairs averaged 6.1 [95% CI: 5.3, 6.8] d between detections in low-disturbance landscapes (e.g., national forests) but 4.1 [3.5, 4.7] d between detections in high-disturbance landscapes, such as those dominated by urbanization or intensive agriculture. Co-occurrence networks showed higher connectance (i.e., a larger proportion of the possible co-occurrences) and greater proportions of low-antagonism pairs in disturbed landscapes. Human-mediated increases in species abundance (possibly via resource subsidies) appeared more important than behavioral mechanisms (e.g., changes in daily activity timing) in driving these patterns of compressed co-occurrence in disturbed landscapes. The spatiotemporal compression of species co-occurrences in disturbed landscapes likely strengthens interactions like competition, predation, and infection unless species can avoid each other at fine spatiotemporal scales. Regardless, human-mediated increases in co-occurrence with-and hence increased exposure to-predators or competitors might elevate stress levels in individual animals, with possible cascading effects across populations, communities, and ecosystems.

Antipredator behaviors in urban settings: Ecological experimentation powered by citizen science.

Animal behaviors are often modified in urban settings due to changes in species assemblages and interactions. The ability of prey to respond to a predator is a critical behavior, but urban populations may experience altered predation pressure, food supplementation, and other human-mediated disturbances that modify their responsiveness to predation risk and promote habituation.Citizen-science programs generally focus on the collection and analysis of observational data (e.g., bird checklists), but there has been increasing interest in the engagement of citizen scientists for ecological experimentation.Our goal was to implement a behavioral experiment in which citizen scientists recorded antipredator behaviors in wild birds occupying urban areas. In North America, increasing populations of Accipiter hawks have colonized suburban and urban areas and regularly prey upon birds that frequent backyard bird feeders. This scenario, of an increasingly common avian predator hunting birds near human dwellings, offers a unique opportunity to characterize antipredator behaviors within urban passerines.For two winters, we engaged citizen scientists in Chicago, IL, USA to deploy a playback experiment and record antipredator behaviors in backyard birds. If backyard birds maintained their antipredator behaviors, we hypothesized that birds would decrease foraging behaviors and increase vigilance in response to a predator cue (hawk playback) but that these responses would be mediated by flock size, presence of sentinel species, body size, tree cover, and amount of surrounding urban area.Using a randomized control-treatment design, citizen scientists at 15 sites recorded behaviors from 3891 individual birds representing 22 species. Birds were more vigilant and foraged less during the playback of a hawk call, and these responses were strongest for individuals within larger flocks and weakest in larger-bodied birds. We did not find effects of sentinel species, tree cover, or urbanization.By deploying a behavioral experiment, we found that backyard birds inhabiting urban landscapes largely maintained antipredator behaviors of increased vigilance and decreased foraging in response to predator cues. Experimentation in citizen science poses challenges (e.g., observation bias, sample size limitations, and reduced complexity in protocol design), but unlike programs focused solely on observational data, experimentation allows researchers to disentangle the complex factors underlying animal behavior and species interactions.

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.