Ecologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages in which certain features of a model are compared while other features are held constant. Defining these multi‐stage strategies requires making a series of decisions, which may potentially impact inferences, but have not been critically evaluated. We begin by identifying key features of strategies, introducing descriptive terms when they did not already exist in the literature. Strategies differ in how they define and order model building stages. Sequential‐by‐sub‐model strategies focus on one sub‐model (parameter) at a time with modeling of subsequent sub‐models dependent on the selected sub‐model structures from the previous stages. Secondary candidate set strategies model sub‐models independently and combine the top set of models from each sub‐model for selection in a final stage. Build‐up approaches define stages across sub‐models and increase in complexity at each stage. Strategies also differ in how the top set of models is selected in each stage and whether they use null or more complex sub‐model structures for non‐target sub‐models. We tested the performance of different model selection strategies using four data sets and three model types. For each data set, we determined the "true" distribution of AIC weights by fitting all plausible models. Then, we calculated the number of models that would have been fitted and the portion of "true" AIC weight we recovered under different model selection strategies. Sequential‐by‐sub‐model strategies often performed poorly. Based on our results, we recommend using a build‐up or secondary candidate sets, which were more reliable and carrying all models within 5–10 AIC of the top model forward to subsequent stages. The structure of non‐target sub‐models was less important. Multi‐stage approaches cannot compensate for a lack of critical thought in selecting covariates and building models to represent competing a priori hypotheses. However, even when competing hypotheses for different sub‐models are limited, thousands or more models may be possible so strategies to explore candidate model space reliably and efficiently will be necessary.

Passive acoustic monitoring is an emerging approach to wildlife monitoring that leverages recent improvements in automated recording units and other technologies. A central challenge of this approach is the task of locating and identifying target species vocalizations in large volumes of audio data. To address this issue, we developed an efficient data processing pipeline using a deep convolutional neural network (CNN) to automate the detection of owl vocalizations in spectrograms generated from unprocessed field recordings. While the project was initially focused on spotted and barred owls, we also trained the network to recognize northern saw‐whet owl, great horned owl, northern pygmy‐owl, and western screech‐owl. Although classification performance varies across species, initial results are promising. Recall, or the proportion of calls in the dataset that are detected and correctly identified, ranged from 63.1% for barred owl to 91.5% for spotted owl based on raw network output. Precision, the rate of true positives among apparent detections, ranged from 0.4% for spotted owl to 77.1% for northern saw‐whet owl based on raw output. In limited tests, the CNN performed as well as or better than human technicians at detecting owl calls. Our model output is suitable for developing species encounter histories for occupancy models and other analyses. We believe our approach is sufficiently general to support long‐term, large‐scale monitoring of a broad range of species beyond our target species list, including birds, mammals, and others.

Differential habitat use and intraguild competition are both thought to be important drivers of animal population sizes and distributions. Habitat associations for individual species are well-established, and interactions between particular pairs of species have been highlighted in many focal studies. However, community-wide assessments of the relative strengths of these two factors have not been conducted. We built multi-scale habitat occupancy models for five carnivore taxa of New York’s Adirondack landscape and assessed the relative performance of these models against ones in which co-occurrences of potentially competing carnivore species were also incorporated. Distribution models based on habitat performed well for all species. Black bear (Ursus americanus) and fisher (Martes pennanti) distribution was similar in that occupancy of both species was negatively associated with paved roads. However, black bears were also associated with larger forest fragments and fishers with smaller forest fragments. No models with habitat features were more supported than the null habitat model for raccoons (Procyon lotor). Martens (Martes americana) were most associated with increased terrain ruggedness and elevation. Weasel (Mustela spp.) occupancy increased with the cover of deciduous forest. For most species dyads habitat-only models were more supported than those models with potential competitors incorporated. The exception to this finding was for the smallest carnivore taxa (marten and weasel) where habitat plus coyote abundance models typically performed better than habitat-only models. Assessing this carnivore community as whole, we conclude that differential habitat use is more important than species interactions in maintaining the distribution and structure of this carnivore guild.

With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14‐week period (17 August–24 November of 2019). We sampled wildlife at 1,509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian’s eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the United States. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban–wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot‐usa, as will future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species‐specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.