Estimating Cattle Density Using Wildlife Cameras

Quantifying the abundance and distribution of animal populations is critical for effective wildlife research and management. Due to their cost-effectiveness, wildlife cameras have become an increasingly popular tool for estimating population densities. Previously, this technique relied on ‘capture-recapture’ models that utilized re-sightings of individually marked animals, but in recent years methods have been developed to estimate the population densities of unmarked animals. One such method is the random encounter and staying time (REST) technique, which does this by assuming that the cumulative time animals stay within the view of the camera scales linearly with the number of individuals. This allows for a density estimate without the need to determine individual identity. To evaluate the accuracy and precision of the REST method, I compared cattle (Bos taurus) density estimates based on trail-camera photos to the actual number of cattle stocked on a U.S. Forest Service (USFS) grazing allotment. Photos were collected across 96 motion-activated cameras distributed across a single grazing allotment in Spanish Fork, Utah. Based on the USFS grazing plan, the allotment operated under a rest-rotation grazing system, and therefore was divided into three pastures, only one of which held cattle at any given time in the year. Based on this plan cattle numbers also varied throughout the year according to a set schedule. For each stocking period and pasture, we generated REST-based abundance estimates, including empirical confidence bounds derived using either spatial or temporal averaging. Our results indicate very poor agreement between REST-based estimates and USFS stocking rates, where, at the allotment level, the former are typically 50-350% higher than the latter. Whether this indicates REST-based estimates are biased or inaccurate is hard to say; there is no doubt our cameras had detected cows (sometimes a lot of cows) in places and times that no cows should have been in based on USFS records. We thus have little confidence in the reliability of these records. As for precision, coefficient of variation values for our estimates ranged between 0.1 and 0.5 (depending on the number of active camera days used to calculate the estimate, and on whether densities were averaged across space or across time). This indicates that REST-based estimates are at least precise enough to be reasonably consistent across time (and to a lesser degree, space), and may hence be a valuable tool at the hand of wildlife managers

BioTIME:a database of biodiversity time series for the Anthropocene

Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of two, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology andcontextual information about each record.Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1 000 000 000 000 cm2).Time period and grain: BioTIME records span from 1874 to 2016. The minimum temporal grain across all datasets in BioTIME is year.Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton, and terrestrial invertebrates to small and large vertebrates.Software format: .csv and .SQ

Are rare species useful species? Obstacles to the conservation of tree diversity in the dry forest zone agro-ecosystems of Mesoamerica

Aim To test the potential to conserve rare dry forest tree and shrub species circa situm.Location Oaxaca, Mexico and Southern Honduras.Methods Local uses (timber, posts and firewood) of species were determined principally through semistructured interviews with 20 rural householders in each of four communities in Honduras and four in Oaxaca. Tree and shrub diversity inventories were carried out in a total of 227 forest patches and parcels of farmland in those eight communities. Species’ conservation priorities were determined using the star system of Hawthorne (1996) and IUCN listings.Results Despite a large number of useful species, remarkably few were also conservation priorities. Useful species were found to be substitutable as is illustrated by Bombacopsis quinata, Cordia alliodora, Guaiacum sanctum and G. coulteri.Conclusions In these areas, circa situm conservation is inhibited by the lack of species that are both rare and useful. Usefulness must be interpreted as a function of substitutability. Natural regeneration provides an abundance of diversity, farmers are unlikely to invest in the management of a species when suitable substitutes are freely available. The key to conserving rare species may be in maintaining or enhancing the value of the landscape elements in which they are found

BioTIME:a database of biodiversity time series for the Anthropocene

Abstract Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km² (158 cm²) to 100 km² (1,000,000,000,000 cm²). Time period and grain: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. Software format: .csv and .SQL