USGS ScienceSearch

Geology topics

Michael P. Strager

Publications and source records attributed to Michael P. Strager.

4 recordsLinked to original sources

A multiscale investigation of habitat use and within-river distribution of sympatric sand darter species

The western sand darter Ammocrypta clara, and eastern sand darter Ammocrypta pellucida, are sand-dwelling fishes of conservation concern. Past research has emphasized the importance of studying individual populations of conservation concern, while recent research has revealed the importance of incorporating landscape scale processes that structure habitat mosaics and local populations. We examined habitat use and distributions of western and eastern sand darters in the lower Elk River of West Virginia. At the sandbar habitat use scale, western sand darters were detected in sandbars with greater area, higher proportions of coarse grain sand and faster bottom current velocity, while the eastern sand darter used a wider range of sandbar habitats. The landscape scale analysis revealed that contributing drainage area was an important predictor for both species, while sinuosity, which presumably represents valley type, also contributed to the western sand darter’s habitat suitability. Sandbar quality (area, grain size, and velocity) and fluvial geomorphic variables (drainage area and valley type) are likely key driving factors structuring sand darter distributions in the Elk River. This multiscale study of within-river species distribution and habitat use is unique, given that only a few sympatric populations are known of western and eastern sand darters.

West Virginia

Impacts of mountaintop mining on terrestrial ecosystem integrity: Identifying landscape thresholds for avian species in the central Appalachians, United States

Context Mountaintop removal/valley fill (MTR/VF) mining in the central Appalachians is a major driver of landscape change within terrestrial ecosystems. Objective We quantified avian community and individual taxa thresholds in response to changing landscapes from MTR/VF using a Threshold Indicator Taxa Analysis approach. Methods We conducted 50-m fixed radius avian surveys (n = 707) within forest adjacent to mine lands in 2012–2013 and obtained data for additional surveys (n = 905) sampled using comparable methods during 2008–2013. We quantified positive and negative community, habitat guild, and species thresholds in abundance and occurrence for each of five landscape metrics within a 1-km radius of each survey point. Results Reclaimed mine-dominated landscapes (less forest and more grassland/shrubland cover) elicited more negative (57 %) than positive (39 %) species responses. Negative thresholds for each landscape metric generally occurred at lower values than positive thresholds, thus negatively responding species were detrimentally affected before positively responding species benefitted. Forest interior birds generally responded negatively to landscape metric thresholds, interior edge species responses were mixed, and early successional birds responded positively. The forest interior guild declined most at 4 % forest loss, while the shrubland guild increased greatest after 52 % loss. Based on random forest importance ranks, total amount of landscape grassland/shrubland had the most influence, although this varied by guild. Conclusions Because of little overlap in habitat requirements, managing landscapes simultaneously to maximally benefit both guilds may not be possible. Our avian thresholds identify single community management targets accounting for scarce species. Guild or individual species thresholds allow for species-specific management.

Kentucky, Tennessee, Virginia, West Virginia

The overlooked terrestrial impacts of mountaintop mining

Ecological research on mountaintop mining has been focused on aquatic impacts because the overburden (i.e., the mountaintop) is disposed of in nearby valleys, which leads to a wide range of water-quality impacts on streams. There are also numerous impacts on the terrestrial environment from mountaintop mining that have been largely overlooked, even though they are no less wide ranging, severe, and multifaceted. We review the impacts of mountaintop mining on the terrestrial environment by exploring six broad themes: (1) the loss of topographic complexity, (2) forest loss and fragmentation, (3) forest succession and soil loss, (4) forest loss and carbon sequestration, (5) biodiversity, and (6) human health and well-being.

BioScience

Advantages of geographically weighted regression for modeling benthic substrate in two Greater Yellowstone Ecosystem streams

Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m 2 cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r 2 . For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r 2 values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.

Wyoming