SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.

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.

Tick Densities and Infection Prevalence on Coastal Islands in Massachusetts, USA: Establishing a Baseline.

Tick-borne diseases and a tick-induced red meat allergy have become increasingly common in the northeastern USA and elsewhere. At the scale of local communities, few studies have documented tick densities or infection levels to characterize current conditions and provide a baseline for further monitoring. Using the town of Nantucket, MA, as a case study, we recorded tick densities by drag sampling along hiking trails in nature preserves on two islands. Nymphal blacklegged ticks (Ixodes scapularis Say) were most abundant at shadier sites and least common in grasslands and scrub oak thickets (Quercus ilicifolia). Lone star ticks (Amblyomma americanum L.) were common on Tuckernuck Island and rare on Nantucket Island, while both tick species were more numerous in 2021 compared to 2020 and 2022. We tested for pathogens in blacklegged nymphs at five sites over two years. In 2020 and 2021, infection levels among the four Nantucket Island sites averaged 10% vs. 19% for Borrelia burgdorferi, 11% vs. 15% for Babesia microti, and 17% (both years) for Anaplasma phagocytophilum, while corresponding levels were significantly greater on Tuckernuck in 2021. Our site-specific, quantitative approach represents a practical example of how potential exposure to tick-borne diseases can be monitored on a local scale.

The forestecology R package for fitting and assessing neighborhood models of the effect of interspecific competition on the growth of trees.

Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking.We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross-validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model.We demonstrate the package's functionality using data from the Smithsonian Conservation Biology Institute's large forest dynamics plot, part of the ForestGEO global network of research sites. Given ForestGEO's data collection protocols and data formatting standards, the package was designed with cross-site compatibility in mind. We highlight the importance of spatial cross-validation when interpreting model results.The package features (a) tidyverse-like structure whereby verb-named functions can be modularly "piped" in sequence, (b) functions with standardized inputs/outputs of simple features sf package class, and (c) an S3 object-oriented implementation of the Bayesian linear regression model. These three facts allow for clear articulation of all the steps in the sequence of analysis and easy wrangling and visualization of the geospatial data. Furthermore, while the package only has Bayesian linear regression implemented, the package was designed with extensibility to other methods in mind.

The influence of metamorphic grade on arsenic in metasedimentary bedrock aquifers: a case study from Western New England, USA.

Elevated As occurs in many meta-sedimentary bedrock aquifers where elevated bulk-rock As content is one of the primary controls on the concentration of As in groundwater. This study was designed to determine As concentrations in a black shale, black slate and black phyllite sequence that comprises the bedrock aquifer system of the Taconic Mountain region of southwestern Vermont and adjacent New York State. Variability in groundwater As concentrations provides the impetus for this study: 25% of wells in weakly metamorphosed shales and slates (