Search USGSSearch

Geology topics

Daniel J. Hocking

Publications and source records attributed to Daniel J. Hocking.

3 recordsLinked to original sources

Range-wide salamander densities reveal a key component of terrestrial vertebrate biomass in eastern North American forests

Characterizing the population density of species is a central interest in ecology. Eastern North America is the global hotspot for biodiversity of plethodontid salamanders, an inconspicuous component of terrestrial vertebrate communities, and among the most widespread is the eastern red-backed salamander, Plethodon cinereus . Previous work suggests population densities are high with significant geographic variation, but comparisons among locations are challenged by lack of standardization of methods and failure to accommodate imperfect detection. We present results from a large-scale research network that accounts for detection uncertainty using systematic survey protocols and robust statistical models. We analysed mark–recapture data from 18 study areas across much of the species range. Estimated salamander densities ranged from 1950 to 34 300 salamanders ha −1 , with a median of 9965 salamanders ha −1 . We compared these results to previous estimates for P. cinereus and other abundant terrestrial vertebrates. We demonstrate that overall the biomass of P. cinereus , a secondary consumer, is of similar or greater magnitude to widespread primary consumers such as white-tailed deer ( Odocoileus virginianus ) and Peromyscus mice, and two to three orders of magnitude greater than common secondary consumer species. Our results add empirical evidence that P. cinereus , and amphibians in general, are an outsized component of terrestrial vertebrate communities in temperate ecosystems.

Biology Letters

State-space analysis of power to detect regional brook trout population trends over time

Threats to aquatic biodiversity are expressed at broad spatial scales, but identifying regional trends in abundance is challenging owing to variable sampling designs, and temporal and spatial variation in abundance. We compiled a regional dataset of brook trout Salvelinus fontinalis counts across their southern range representing 326 sites from eight states between 1982-2014, and conducted a statistical power analysis using Bayesian state-space models to evaluate the ability to detect temporal trends by characterizing posterior distributions with three approaches. A combination of monitoring periods, number of sites and electrofishing passes, decline magnitude and different revisit patterns were tested. Power increased with monitoring periods and decline magnitude. Trends in adults were better detected than young-of-the-year fish, which showed greater inter-annual variation in abundance. The addition of weather covariates to account for the temporal variation increased power only slightly. Single- and three-pass electrofishing methods were similar in power. Finally, power was higher for sampling designs with more frequent revisits over the duration of the monitoring program. Our results provide guidance for broad-scale monitoring designs for temporal trend detection.

Canadian Journal of Fisheries and Aquatic Sciences

A geostatistical state‐space model of animal densities for stream networks

Population dynamics are often correlated in space and time due to correlations in environmental drivers as well as synchrony induced by individual dispersal. Many statistical analyses of populations ignore potential autocorrelations and assume that survey methods (distance and time between samples) eliminate these correlations, allowing samples to be treated independently. If these assumptions are incorrect, results and therefore inference may be biased and uncertainty underestimated. We developed a novel statistical method to account for spatiotemporal correlations within dendritic stream networks, while accounting for imperfect detection in the surveys. Through simulations, we found this model decreased predictive error relative to standard statistical methods when data were spatially correlated based on stream distance and performed similarly when data were not correlated. We found that increasing the number of years surveyed substantially improved the model accuracy when estimating spatial and temporal correlation coefficients, especially from 10 to 15 yr. Increasing the number of survey sites within the network improved the performance of the nonspatial model but only marginally improved the density estimates in the spatiotemporal model. We applied this model to brook trout data from the West Susquehanna Watershed in Pennsylvania collected over 34 yr from 1981 to 2014. We found the model including temporal and spatiotemporal autocorrelation best described young of the year (YOY) and adult density patterns. YOY densities were positively related to forest cover and negatively related to spring temperatures with low temporal autocorrelation and moderately high spatiotemporal correlation. Adult densities were less strongly affected by climatic conditions and less temporally variable than YOY but with similar spatiotemporal correlation and higher temporal autocorrelation.

Ecological Applications