Search for: All records

ABSTRACT Crowd‐sourced biodiversity data, such as those housed in the iNaturalist platform, are increasingly used to monitor species distributions. Such data represent unstructured biodiversity surveys that are generally comprised of incidental observations and do not report variation in sampling effort. These discrepancies may yield data that is incongruent with data from structured surveys. To assess whether mammalian iNaturalist data are reflective of data from traditional structured surveys, we calculated and compared measures of mammalian species richness and species pool similarity using data from unstructured surveys (i.e., iNaturalist) and data from structured camera trap surveys and bat acoustic surveys. We found that data from structured and unstructured surveys generally document similar mammalian species richness, but the two survey types document different species pools. Human population density and proxies for species pool breadth were most strongly associated with discrepancies in datasets, with data being most similar in areas of high human population density and lower species richness. Our analyses revealed that dataset similarity varied across geography and community metric for most taxa, but that structured and unstructured surveys produced consistently unreconcilable datasets for bats. These findings suggest that unstructured datasets like iNaturalist may offer reliable data for some taxa and geographies, but that these data are not universally applicable to all research scenarios. 

Abstract Site occupancy models (SOMs) are a common tool for studying the spatial ecology of wildlife. When observational data are collected using passive monitoring field methods, including camera traps or autonomous recorders, detections of animals may be temporally autocorrelated, leading to biased estimates and incorrectly quantified uncertainty. We presently lack clear guidance for understanding and mitigating the consequences of temporal autocorrelation when estimating occupancy models with camera trap data.We use simulations to explore when and how autocorrelation gives rise to biased or overconfident estimates of occupancy. We explore the impact of sampling design and biological conditions on model performance in the presence of autocorrelation, investigate the usefulness of several techniques for identifying and mitigating bias and compare performance of the SOM to a model that explicitly estimates autocorrelation. We also conduct a case study using detections of 22 North American mammals.We show that a join count goodness‐of‐fit test previously proposed for identifying clustered detections is effective for detecting autocorrelation across a range of conditions. We find that strong bias occurs in the estimated occupancy intercept when survey durations are short and detection rates are low. We provide a reference table for assessing the degree of bias to be expected under all conditions. We further find that discretizing data with larger windows decreases the magnitude of bias introduced by autocorrelation. In our case study, we find that detections of most species are autocorrelated and demonstrate how larger detection windows might mitigate the resulting bias.Our findings suggest that autocorrelation is likely widespread in camera trap data and that many previous studies of occupancy based on camera trap data may have systematically underestimated occupancy probabilities. Moving forward, we recommend that ecologists estimating occupancy from camera trap data use the join count goodness‐of‐fit test to determine whether autocorrelation is present in their data. If it is, SOMs should use large detection windows to mitigate bias and more accurately quantify uncertainty in occupancy model parameters. Ecologists should not use gaps between detection periods, which are ineffective at mitigating temporal structure in data and discard useful data. 

Abstract Tree diversity can promote both predator abundance and diversity. However, whether this translates into increased predation and top‐down control of herbivores across predator taxonomic groups and contrasting environmental conditions remains unresolved. We used a global network of tree diversity experiments (TreeDivNet) spread across three continents and three biomes to test the effects of tree species richness on predation across varying climatic conditions of temperature and precipitation. We recorded bird and arthropod predation attempts on plasticine caterpillars in monocultures and tree species mixtures. Both tree species richness and temperature increased predation by birds but not by arthropods. Furthermore, the effects of tree species richness on predation were consistent across the studied climatic gradient. Our findings provide evidence that tree diversity strengthens top‐down control of insect herbivores by birds, underscoring the need to implement conservation strategies that safeguard tree diversity to sustain ecosystem services provided by natural enemies in forests. 

Summary As climate change drives increased drought in many forested regions, mechanistic understanding of the factors conferring drought tolerance in trees is increasingly important. The dendrochronological record provides a window through which we can understand how tree size and traits shape growth responses to droughts.We analyzed tree‐ring records for 12 species in a broadleaf deciduous forest in Virginia (USA) to test hypotheses for how tree height, microenvironment characteristics, and species’ traits shaped drought responses across the three strongest regional droughts over a 60‐yr period.Drought tolerance (resistance, recovery, and resilience) decreased with tree height, which was strongly correlated with exposure to higher solar radiation and evaporative demand. The potentially greater rooting volume of larger trees did not confer a resistance advantage, but marginally increased recovery and resilience, in sites with low topographic wetness index. Drought tolerance was greater among species whose leaves lost turgor (wilted) at more negative water potentials and experienced less shrinkage upon desiccation.The tree‐ring record reveals that tree height and leaf drought tolerance traits influenced growth responses during and after significant droughts in the meteorological record. As climate change‐induced droughts intensify, tall trees with drought‐sensitive leaves will be most vulnerable to immediate and longer‐term growth reductions. 

Abstract SNAPSHOT USA is a multicontributor, long‐term camera trap survey designed to survey mammals across the United States. Participants are recruited through community networks and directly through a website application (https://www.snapshot-usa.org/). The growing Snapshot dataset is useful, for example, for tracking wildlife population responses to land use, land cover, and climate changes across spatial and temporal scales. Here we present the SNAPSHOT USA 2021 dataset, the third national camera trap survey across the US. Data were collected across 109 camera trap arrays and included 1711 camera sites. The total effort equaled 71,519 camera trap nights and resulted in 172,507 sequences of animal observations. Sampling effort varied among camera trap arrays, with a minimum of 126 camera trap nights, a maximum of 3355 nights, a median 546 nights, and a mean 656 ± 431 nights. This third dataset comprises 51 camera trap arrays that were surveyed during 2019, 2020, and 2021, along with 71 camera trap arrays that were surveyed in 2020 and 2021. All raw data and accompanying metadata are stored on Wildlife Insights (https://www.wildlifeinsights.org/), and are publicly available upon acceptance of the data papers. SNAPSHOT USA aims to sample multiple ecoregions in the United States with adequate representation of each ecoregion according to its relative size. Currently, the relative density of camera trap arrays varies by an order of magnitude for the various ecoregions (0.22–5.9 arrays per 100,000 km²), emphasizing the need to increase sampling effort by further recruiting and retaining contributors. There are no copyright restrictions on these data. We request that authors cite this paper when using these data, or a subset of these data, for publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.