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Remote sensing can provide continuous spatiotemporal information about vegetation to inform wildlife habitat estimates, but these methods are often limited in availability or lack adequate resolution to capture the three‐dimensional vegetative details critical for understanding habitat. The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne light detection and ranging system (LiDAR) that has revolutionized the availability of high‐quality three‐dimensional vegetation measurements of the Earth's temperate and tropical forests. To date, wildlife‐related applications of GEDI data or GEDI‐fusion products have been limited to estimate species habitat use, distribution, and diversity. Here, our goal was to expand the use of GEDI‐based applications to wildlife demography by evaluating if GEDI data fusions could aid in characterizing demographic parameters of wildlife. We leveraged a recently published dataset of GEDI‐fusion forest structures and capture–mark–recapture data to estimate the density and survival of two small mammal species, Humboldt's flying squirrel (Glaucomys oregonensis) and Townsend's chipmunk (Neotamias townsendii), from three studies in western Oregon spanning 2014–2021. We used capture histories in Huggins robust design models to estimate apparent annual survival and density as a derived parameter. We found strong support that both flying squirrel and chipmunk density were associated with GEDI‐fusion forest structures of foliage height diversity and plant area volume density in the 5–10 m strata for flying squirrels and proportionately higher plant area volume density in the 0–20 m strata for chipmunks, as well as other spatiotemporal factors such as elevation. We found weak support that apparent annual survival was associated with GEDI‐fusion forest structures for flying squirrels but not for chipmunks. We demonstrate further utility of these methods by creating spatially explicit density maps of both species that could aid management and conservation policies. Our work represents a novel application of GEDI data to evaluate wildlife demography and produce continuous spatially explicit density predictions for these species. We conclude that aspects of small mammal demography can be explained by forest structure as characterized via GEDI data fusions. 

Arthropods contribute importantly to ecosystem functioning but remain understudied. This undermines the validity of conservation decisions. Modern methods are now making arthropods easier to study, since arthropods can be mass-trapped, mass-identified, and semi-mass-quantified into ‘many-row (observation), many-column (species)‘ datasets, with homogeneous error, high resolution, and copious environmental-covariate information. These ‘novel community datasets’ let us efficiently generate information on arthropod species distributions, conservation values, uncertainty, and the magnitude and direction of human impacts. We use a DNA-based method (barcode mapping) to produce an arthropod-community dataset from 121 Malaise-trap samples, and combine it with 29 remote-imagery layers using a deep neural net in a joint species distribution model. With this approach, we generate distribution maps for 76 arthropod species across a 225 km²temperate-zone forested landscape. We combine the maps to visualize the fine-scale spatial distributions of species richness, community composition, and site irreplaceability. Old-growth forests show distinct community composition and higher species richness, and stream courses have the highest site-irreplaceability values. With this ‘sideways biodiversity modelling’ method, we demonstrate the feasibility of biodiversity mapping at sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres. This article is part of the theme issue ‘Towards a toolkit for global insect biodiversity monitoring’. 

Abstract The biodiversity crisis necessitates spatially extensive methods to monitor multiple taxonomic groups for evidence of change in response to evolving environmental conditions. Programs that combine passive acoustic monitoring and machine learning are increasingly used to meet this need. These methods require large, annotated datasets, which are time‐consuming and expensive to produce, creating potential barriers to adoption in data‐ and funding‐poor regions. Recently released pre‐trained avian acoustic classification models provide opportunities to reduce the need for manual labelling and accelerate the development of new acoustic classification algorithms through transfer learning. Transfer learning is a strategy for developing algorithms under data scarcity that uses pre‐trained models from related tasks to adapt to new tasks.Our primary objective was to develop a transfer learning strategy using the feature embeddings of a pre‐trained avian classification model to train custom acoustic classification models in data‐scarce contexts. We used three annotated avian acoustic datasets to test whether transfer learning and soundscape simulation‐based data augmentation could substantially reduce the annotated training data necessary to develop performant custom acoustic classifiers. We also conducted a sensitivity analysis for hyperparameter choice and model architecture. We then assessed the generalizability of our strategy to increasingly novel non‐avian classification tasks.With as few as two training examples per class, our soundscape simulation data augmentation approach consistently yielded new classifiers with improved performance relative to the pre‐trained classification model and transfer learning classifiers trained with other augmentation approaches. Performance increases were evident for three avian test datasets, including single‐class and multi‐label contexts. We observed that the relative performance among our data augmentation approaches varied for the avian datasets and nearly converged for one dataset when we included more training examples.We demonstrate an efficient approach to developing new acoustic classifiers leveraging open‐source sound repositories and pre‐trained networks to reduce manual labelling. With very few examples, our soundscape simulation approach to data augmentation yielded classifiers with performance equivalent to those trained with many more examples, showing it is possible to reduce manual labelling while still achieving high‐performance classifiers and, in turn, expanding the potential for passive acoustic monitoring to address rising biodiversity monitoring needs. 

Abstract There are increasing concerns about the declining population trends of small mammalian carnivores around the world. Their conservation and management are often challenging due to limited knowledge about their ecology and natural history. To address one of these deficiencies for western spotted skunks (Spilogale gracilis), we investigated their diet in the Oregon Cascades of the Pacific Northwest during 2017–2019. We collected 130 spotted skunk scats opportunistically and with detection dog teams and identified prey items using DNA metabarcoding and mechanical sorting. Western spotted skunk diet consisted of invertebrates, such as wasps, millipedes, and gastropods; vertebrates, such as small mammals, amphibians, and birds; and plants, such asGaultheria,Rubus, andVaccinium. Diet also consisted of items, such as black‐tailed deer, that were likely scavenged. Comparison in diet by season revealed that spotted skunks consumed more insects during the dry season (June–August), particularly wasps (75% of scats in the dry season), and marginally more mammals during the wet season (September–May). We observed a similar diet in areas with no record of human disturbance and areas with a history of logging at most spatial scales, but scats collected in areas with older forest within a skunk's home range (1‐km buffer) were more likely to contain insects. Western spotted skunks provide food web linkages between aquatic, terrestrial, and arboreal systems and serve functional roles of seed dispersal and scavenging. Due to their diverse diet and prey switching, western spotted skunks may dampen the effects of irruptions of prey, such as wasps, during dry springs and summers. By studying the natural history of western spotted skunks in the Pacific Northwest forests, while they are still abundant, we provide key information necessary to achieve the conservation goal of keeping this common species common. 

Many ecologists have lamented the demise of natural history and have attributed this decline to a misguided view that natural history is outdated and unscientific. Although there is a perception that the focus in ecology and conservation have shifted away from descriptive natural history research and training toward hypothetico-deductive research, we argue that natural history has entered a new phase that we call “next-generation natural history.” This renaissance of natural history is characterized by technological and statistical advances that aid in collecting detailed observations systematically over broad spatial and temporal extents. The technological advances that have increased exponentially in the last decade include electronic sensors such as camera-traps and acoustic recorders, aircraft- and satellite-based remote sensing, animal-borne biologgers, genetics and genomics methods, and community science programs. Advances in statistics and computation have aided in analyzing a growing quantity of observations to reveal patterns in nature. These robust next-generation natural history datasets have transformed the anecdotal perception of natural history observations into systematically collected observations that collectively constitute the foundation for hypothetico-deductive research and can be leveraged and applied to conservation and management. These advances are encouraging scientists to conduct and embrace detailed descriptions of nature that remain a critically important component of the scientific endeavor. Finally, these next-generation natural history observations are engaging scientists and non-scientists alike with new documentations of the wonders of nature. Thus, we celebrate next-generation natural history for encouraging people to experience nature directly. 

Abstract AimThe assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. LocationConterminous United States. Time Period2010–2021. Major Taxa StudiedTwenty‐five species of mammals. MethodsWe analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. ResultsClimate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard‐mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine‐scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main ConclusionsOur results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa. 

ABSTRACT MotivationSNAPSHOT USA is an annual, multicontributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables ContainedSNAPSHOT USA 2019–2023 contains 987,979 records of camera trap image sequence data and 9694 records of camera trap deployment metadata. Spatial Location and GrainData were collected across the United States of America in all 50 states, 12 ecoregions and many ecosystems. Time Period and GrainData were collected between 1st August and 29th December each year from 2019 to 2023. Major Taxa and Level of MeasurementThe dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software FormatSNAPSHOT USA 2019–2023 comprises two .csv files. The original data can be found within the SNAPSHOT USA Initiative in the Wildlife Insights platform. 

Abstract Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.