Geology topics

Paul Doherty

Publications and source records attributed to Paul Doherty.

3 recordsLinked to original sources

USGS2018

Testing infrared camera surveys and distance analyses to estimate feral horse abundance in a known population

We tested the use of high‐resolution infrared (IR) camera technology and distance sampling analyses to estimate abundance of feral horses ( Equus caballus ) during 2015–2016 in the McCullough Peaks Herd Management Area, Wyoming, USA. Infrared technology is becoming more common in ungulate population monitoring. The quality of IR cameras now allows ungulate species to be differentiated. Imperfect detection is a common problem in aerial surveys, so we tested the use of distance sampling analyses to account for imperfect detection probability. We conducted 2 aerial surveys in a sagebrush ecosystem with a demographically closed horse population. True abundance was known to within ±4 animals as a result of intensive, ground‐based monitoring of each animal, all of which are uniquely identifiable. After truncation of our data, the most supported detection function was a uniform function with a detection probability equal to 1.0 out to 255 m. Our analyses yielded results that were within 10% of true abundance, but the coefficient of variation (CV) was large (36–58%) assuming a small sampling fraction. However, our truncated surveys covered approximately 95% of the herd management area. By including a finite population correction factor in our calculations of variance estimates, CVs (8–13%) were dramatically reduced. We found the combination of IR surveys and distance sampling analysis to be a useful method to estimate feral horse abundance in sagebrush vegetation type, which had limited cover to obscure horses. Repeated testing in sagebrush ecosystems as well as further testing in other habitat types and under differing conditions will inform how general our approach can be.

Wyoming↗

USGS2013

Modeling trends from North American Breeding Bird Survey data: a spatially explicit approach

Population trends, defined as interval-specific proportional changes in population size, are often used to help identify species of conservation interest. Efficient modeling of such trends depends on the consideration of the correlation of population changes with key spatial and environmental covariates. This can provide insights into causal mechanisms and allow spatially explicit summaries at scales that are of interest to management agencies. We expand the hierarchical modeling framework used in the North American Breeding Bird Survey (BBS) by developing a spatially explicit model of temporal trend using a conditional autoregressive (CAR) model. By adopting a formal spatial model for abundance, we produce spatially explicit abundance and trend estimates. Analyses based on large-scale geographic strata such as Bird Conservation Regions (BCR) can suffer from basic imbalances in spatial sampling. Our approach addresses this issue by providing an explicit weighting based on the fundamental sample allocation unit of the BBS. We applied the spatial model to three species from the BBS. Species have been chosen based upon their well-known population change patterns, which allows us to evaluate the quality of our model and the biological meaning of our estimates. We also compare our results with the ones obtained for BCRs using a nonspatial hierarchical model (Sauer and Link 2011). Globally, estimates for mean trends are consistent between the two approaches but spatial estimates provide much more precise trend estimates in regions on the edges of species ranges that were poorly estimated in non-spatial analyses. Incorporating a spatial component in the analysis not only allows us to obtain relevant and biologically meaningful estimates for population trends, but also enables us to provide a flexible framework in order to obtain trend estimates for any area.

PLoS ONE↗

USGS2013

Comparison of elevation and remote sensing derived products as auxiliary data for climate surface interpolation

Climate models may be limited in their inferential use if they cannot be locally validated or do not account for spatial uncertainty. Much of the focus has gone into determining which interpolation method is best suited for creating gridded climate surfaces, which often a covariate such as elevation (Digital Elevation Model, DEM) is used to improve the interpolation accuracy. One key area where little research has addressed is in determining which covariate best improves the accuracy in the interpolation. In this study, a comprehensive evaluation was carried out in determining which covariates were most suitable for interpolating climatic variables (e.g. precipitation, mean temperature, minimum temperature, and maximum temperature). We compiled data for each climate variable from 1950 to 1999 from approximately 500 weather stations across the Western United States (32° to 49° latitude and −124.7° to −112.9° longitude). In addition, we examined the uncertainty of the interpolated climate surface. Specifically, Thin Plate Spline (TPS) was used as the interpolation method since it is one of the most popular interpolation techniques to generate climate surfaces. We considered several covariates, including DEM, slope, distance to coast (Euclidean distance), aspect, solar potential, radar, and two Normalized Difference Vegetation Index (NDVI) products derived from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). A tenfold cross-validation was applied to determine the uncertainty of the interpolation based on each covariate. In general, the leading covariate for precipitation was radar, while DEM was the leading covariate for maximum, mean, and minimum temperatures. A comparison to other products such as PRISM and WorldClim showed strong agreement across large geographic areas but climate surfaces generated in this study (ClimSurf) had greater variability at high elevation regions, such as in the Sierra Nevada Mountains.

International Journal of Climatology↗