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.

Automated detection of frog calls and choruses by pulse repetition rate.

Anurans (frogs and toads) are among the most globally threatened taxonomic groups. Successful conservation of anurans will rely on improved data on the status and changes in local populations, particularly for rare and threatened species. Automated sensors, such as acoustic recorders, have the potential to provide such data by massively increasing the spatial and temporal scale of population sampling efforts. Analyzing such data sets will require robust and efficient tools that can automatically identify the presence of a species in audio recordings. Like bats and birds, many anuran species produce distinct vocalizations that can be captured by autonomous acoustic recorders and represent excellent candidates for automated recognition. However, in contrast to birds and bats, effective automated acoustic recognition tools for anurans are not yet widely available. An effective automated call-recognition method for anurans must be robust to the challenges of real-world field data and should not require extensive labeled data sets. We devised a vocalization identification tool that classifies anuran vocalizations in audio recordings based on their periodic structure: the repeat interval-based bioacoustic identification tool (RIBBIT). We applied RIBBIT to field recordings to study the boreal chorus frog (Pseudacris maculata) of temperate North American grasslands and the critically endangered variable harlequin frog (Atelopus varius) of tropical Central American rainforests. The tool accurately identified boreal chorus frogs, even when they vocalized in heavily overlapping choruses and identified variable harlequin frog vocalizations at a field site where it had been very rarely encountered in visual surveys. Using a few simple parameters, RIBBIT can detect any vocalization with a periodic structure, including those of many anurans, insects, birds, and mammals. We provide open-source implementations of RIBBIT in Python and R to support its use for other taxa and communities.

Los anuros (ranas y sapos) se encuentran dentro de los grupos taxonómicos más amenazados a nivel mundial. La conservación exitosa de los anuros dependerá de información mejorada sobre el estado y los cambios en las poblaciones locales, particularmente para las especies raras y amenazadas. Los sensores automatizados, como las grabadoras acústicas, tienen el potencial para proporcionar dicha información al incrementar masivamente la escala espacial y temporal de los esfuerzos de muestreo poblacional. El análisis de dicha información requerirá herramientas robustas y eficientes que puedan identificar automáticamente la presencia de una especie en las grabaciones de audio. Como las aves y los murciélagos, muchas especies de anuros producen vocalizaciones distintivas que pueden ser capturadas por las grabadoras acústicas autónomas y también son excelentes candidatas para el reconocimiento automatizado. Sin embargo, a diferencia de las aves y los murciélagos, todavía no se cuenta con una disponibilidad extensa de herramientas para el reconocimiento acústico automatizado de los anuros. Un método efectivo para el reconocimiento automatizado del canto de los anuros debe ser firme ante los retos de los datos reales de campo y no debería requerir conjuntos extensos de datos etiquetados. Diseñamos una herramienta de identificación de las vocalizaciones: la herramienta de identificación bioacústica basada en el intervalo de repetición (RIBBIT), el cual clasifica las vocalizaciones de los anuros en las grabaciones de audio con base en su estructura periódica. Aplicamos la RIBBIT a las grabaciones de campo para estudiar a dos especies: la rana coral boreal (Pseudacris maculata) de los pastizales templados de América del Norte y la rana arlequín variable (Atelopus varius), críticamente en peligro de extinción, de las selvas tropicales de América Central. Mostramos que RIBBIT puede identificar correctamente a las ranas corales boreales, incluso cuando vocalizan en coros con mucha superposición, y puede identificar las vocalizaciones de la rana arlequín variable en un sitio de campo en donde rara vez se le ha visto durante censos visuales. Mediante relativamente unos cuantos parámetros simples, RIBBIT puede detectar cualquier vocalización con una estructura periódica, incluyendo aquellas de muchos anuros, insectos, aves y mamíferos. Proporcionamos implementaciones de fuente abierta de RIBBIT en Python y en R para fomentar su uso para otros taxones y comunidades.

Long-term reevaluation of spatially explicit models as a means for adaptive wildlife management.

We evaluated a 20-yr-old spatially explicit model (SEM) that predicted the spatial expansion of reintroduced Persian fallow deer in northern Israel. Using the current distribution of the deer and based on multi-model inference we assessed the accuracy of the SEM's prediction and what other factors affected the population's current distribution. If the SEM's projection was still valid, the leading model in the multi-model inference would include only the SEM's projection as an explanatory variable with a good fit. Different leading models would reveal key variables overlooked when the SEM was constructed or changes in the landscape unforeseen at the time, thus assisting adaptive management and decision-making. We assessed deer presence from camera trap encounter counts analyzed using N-mixture models. Models included various combinations of seven predictors: the 20-yr predictions of an SEM developed during the initial phases of the reintroduction, three key landscape characteristics on which the SEM was originally based but updated to reflect current conditions, distance from the release site, elevation, and the distribution of gray wolves (a predator that was absent from the area when the SEM was developed). Competing models were ranked by Akaike information criterion (AIC). Wolf distribution was the key predictor explaining the current deer distribution, appearing in all three leading models (∆AIC < 2.0) and carrying 71% of the AIC weight (coefficient = -14.86 ± 5.6 [mean ± SE]). Of these three models, the SEM 20-yr prediction appeared in two, but explained only a fraction of the variance (coefficient = 0.001 ± 0.08). The contribution of all other predictors was negligible. While the SEM failed to accurately predict the 20-yr deer distribution, the divergence between its projection and reality pointed to the probable cause (wolves) of this discrepancy. The inclusion of the SEM prediction in the leading models indicates that had the wolves not spread to the study area, the predictions would still have merit suggesting that long-term SEMs can potentially be robust. Long-term reevaluation of SEMs can be beneficial even if model projections fail, as the process can uncover the specific factors driving this failure, supporting adaptive management procedures.

Increased songbird nest depredation due to Aleppo pine (Pinus halepensis) encroachment in Mediterranean shrubland.

BACKGROUND: In recent decades, a decrease of passerine densities was documented in Mediterranean shrublands. At the same time, a widespread encroachment of Aleppo pines (Pinus halepensis) to Mediterranean shrubland occurred. Such changes in vegetation structure may affect passerine predator assemblage and densities, and in turn impact passerine densities. Depredation during the nesting season is an important factor to influence passerine population size. Understanding the effects of changes in vegetation structure (pine encroachment) on passerine nesting success is the main objective of this study. We do so by assessing the effects of Aleppo pine encroachment on Sardinian warbler (Sylvia melanocephala) nest depredation in Mediterranean shrublands. We examined direct and indirect predation pressures through a gradients of pine density, using four methods: (1) placing dummy nests; (2) acoustic monitoring of mobbing events; (3) direct observations on nest predation using cameras; and (4) observation of Eurasian jay (Garrulus glandarius) behaviour as indirect evidence of predation risk. RESULTS: We found that Aleppo pine encroachment to Mediterranean shrublands increased nest predation by Eurasian jays. Nest predation was highest in mixed shrubland and pines. These areas are suitable for warblers but had high occurrence rate of Eurasian jays. CONCLUSIONS: Encroaching pines directly increase activity of Eurasian jays in shrubland habitats, which reduced the nesting success of Sardinian warblers. These findings are supported by multiple methodologies, illustrating different predation pressures along a gradient of pine densities in natural shrublands. Management of Aleppo pine seedlings and removal of unwanted trees in natural shrubland might mitigate arrival and expansion of predators and decrease the predation pressure on passerine nests.