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Geology topics

Brian S. Cade

Publications and source records attributed to Brian S. Cade.

At least 37 records · Page 2Linked to original sources
USGS2011

Genetic diversity and species diversity of stream fishes covary across a land-use gradient

Genetic diversity and species diversity are expected to covary according to area and isolation, but may not always covary with environmental heterogeneity. In this study, we examined how patterns of genetic and species diversity in stream fishes correspond to local and regional environmental conditions. To do so, we compared population size, genetic diversity and divergence in central stonerollers ( Campostoma anomalum ) to measures of species diversity and turnover in stream fish assemblages among similarly sized watersheds across an agriculture–forest land-use gradient in the Little Miami River basin (Ohio, USA). Significant correlations were found in many, but not all, pair-wise comparisons. Allelic richness and species richness were strongly correlated, for example, but diversity measures based on allele frequencies and assemblage structure were not. In-stream conditions related to agricultural land use were identified as significant predictors of genetic diversity and species diversity. Comparisons to population size indicate, however, that genetic diversity and species diversity are not necessarily independent and that variation also corresponds to watershed location and glaciation history in the drainage basin. Our findings demonstrate that genetic diversity and species diversity can covary in stream fish assemblages, and illustrate the potential importance of scaling observations to capture responses to hierarchical environmental variation. More comparisons according to life history variation could further improve understanding of conditions that give rise to parallel variation in genetic diversity and species diversity, which in turn could improve diagnosis of anthropogenic influences on aquatic ecosystems.

Ohio↗
USGS2011

Quantile equivalence to evaluate compliance with habitat management objectives

Equivalence estimated with linear quantile regression was used to evaluate compliance with habitat management objectives at Arapaho National Wildlife Refuge based on monitoring data collected in upland (5,781 ha; n = 511 transects) and riparian and meadow (2,856 ha, n = 389 transects) habitats from 2005 to 2008. Quantiles were used because the management objectives specified proportions of the habitat area that needed to comply with vegetation criteria. The linear model was used to obtain estimates that were averaged across 4 y. The equivalence testing framework allowed us to interpret confidence intervals for estimated proportions with respect to intervals of vegetative criteria (equivalence regions) in either a liberal, benefit-of-doubt or conservative, fail-safe approach associated with minimizing alternative risks. Simple Boolean conditional arguments were used to combine the quantile equivalence results for individual vegetation components into a joint statement for the multivariable management objectives. For example, management objective 2A required at least 809 ha of upland habitat with a shrub composition ≥0.70 sagebrush ( Artemisia spp.), 20–30% canopy cover of sagebrush ≥25 cm in height, ≥20% canopy cover of grasses, and ≥10% canopy cover of forbs on average over 4 y. Shrub composition and canopy cover of grass each were readily met on >3,000 ha under either conservative or liberal interpretations of sampling variability. However, there were only 809–1,214 ha (conservative to liberal) with ≥10% forb canopy cover and 405–1,098 ha with 20–30% canopy cover of sagebrush ≥25 cm in height. Only 91–180 ha of uplands simultaneously met criteria for all four components, primarily because canopy cover of sagebrush and forbs was inversely related when considered at the spatial scale (30 m) of a sample transect. We demonstrate how the quantile equivalence analyses also can help refine the numerical specification of habitat objectives and explore specification of spatial scales for objectives with respect to sampling scales used to evaluate those objectives.

Colorado↗
USGS2011

Chapter 4: A sampling and analytical approach to develop spatial distribution models for sagebrush-associated species

Understanding multi-scale floral and faunal responses to human land use is crucial for informing natural resource management and conservation planning. However, our knowledge on how land use influences sagebrush (Artemisia spp.) ecosystems is limited primarily to site-specific studies. To fill this void, studies across large regions are needed that address how species are distributed relative to type, extent, and intensity of land use. We present a study design for the Wyoming Basin Ecoregional Assessment (WBEA) to sample sagebrush-associated flora and fauna along a land cover-human land use gradient. To minimize field costs, we sampled various taxonomic groups simultaneously on transects (ungulates and lagomorphs), point counts (song birds), and area-searches of 7.29-ha survey blocks (pellet counts, burrow counts, reptile surveys, medium-sized mammals, ant mounds, rodent trapping, and vegetation sampling of native and exotic plants). We then present an exploratory approach to develop species occurrence and abundance models when a priori model building is not an option. Our study design has broad applications for large-scale evaluations of arid ecosystems.

Wyoming↗
USGS2011

Chapter 6: Detectability adjusted count models of songbird abundance

Sagebrush ( Artemisia spp.) steppe ecosystems have experienced recent changes resulting not only in the loss of habitat but also fragmentation and degradation of remaining habitats. As a result, sagebrush-obligate and sagebrush associated songbird populations have experienced population declines over the past several decades. We examined landscape-scale responses in occupancy and abundance for six focal songbird species at 318 survey sites across the Wyoming Basins Ecoregional Assessment (WBEA) area. Occupancy and abundance models were fit for each species using datasets developed at multiple moving window extents to assess landscape-scale relationships between abiotic, habitat, and anthropogenic factors. Anthropogenic factors had less influence on species occupancy or abundance than abiotic and habitat factors. Sagebrush measures were strong predictors of occurrence for sagebrush-obligate species, such as Brewer’s sparrows ( Spizella breweri ), sage sparrows ( Amphispiza belli ) and sage thrashers ( Oreoscoptes montanus ), as well as green-tailed towhees ( Pipilo chlorurus ), a species associated with mountain shrub communities. Occurrence for lark sparrows ( Chondestes grammacus ) and vesper sparrows ( Pooecetes gramineus ), considered shrub steppe-associated species, was also related to big sagebrush communities, but at large spatial extents. Although relationships between anthropogenic variables and occurrence were weak for most species, the consistent relationship with sagebrush habitat variables suggests direct habitat loss and not edge or additional fragmentation effects are causing declines in the avifauna examined in the WBEA area. Thus, natural and anthropogenic disturbances that result in loss of critical habitats are the biggest threats to these species. We applied our models spatially across the WBEA area to identify and prioritize key areas for conservation.

Wyoming↗
USGS2010

Assessing conservation relevance of organism-environment relations using predicted changes in response variables

1. Organism–environment models are used widely in conservation. The degree to which they are useful for informing conservation decisions – the conservation relevance of these relations – is important because lack of relevance may lead to misapplication of scarce conservation resources or failure to resolve important conservation dilemmas. Even when models perform well based on model fit and predictive ability, conservation relevance of associations may not be clear without also knowing the magnitude and variability of predicted changes in response variables. 2. We introduce a method for evaluating the conservation relevance of organism–environment relations that employs confidence intervals for predicted changes in response variables. The confidence intervals are compared to a preselected magnitude of change that marks a threshold (trigger) for conservation action. To demonstrate the approach, we used a case study from the Chihuahuan Desert involving relations between avian richness and broad-scale patterns of shrubland. We considered relations for three winters and two spatial extents (1- and 2-km-radius areas) and compared predicted changes in richness to three thresholds (10%, 20% and 30% change). For each threshold, we examined 48 relations. 3. The method identified seven, four and zero conservation-relevant changes in mean richness for the 10%, 20% and 30% thresholds respectively. These changes were associated with major (20%) changes in shrubland cover, mean patch size, the coefficient of variation for patch size, or edge density but not with major changes in shrubland patch density. The relative rarity of conservation-relevant changes indicated that, overall, the relations had little practical value for informing conservation decisions about avian richness. 4. The approach we illustrate is appropriate for various response and predictor variables measured at any temporal or spatial scale. The method is broadly applicable across ecological environments, conservation objectives, types of statistical predictive models and levels of biological organization. By focusing on magnitudes of change that have practical significance, and by using the span of confidence intervals to incorporate uncertainty of predicted changes, the method can be used to help improve the effectiveness of conservation efforts.

Methods in Ecology and Evolution↗
USGS2010

Measuring bulrush culm relationships to estimate plant biomass within a southern California treatment wetland

Assessment of emergent vegetation biomass can be time consuming and labor intensive. To establish a less onerous, yet accurate method, for determining emergent plant biomass than by direct measurements we collected vegetation data over a six-year period and modeled biomass using easily obtained variables: culm (stem) diameter, culm height and culm density. From 1998 through 2005, we collected emergent vegetation samples ( Schoenoplectus californicus and Schoenoplectus acutus ) at a constructed treatment wetland in San Jacinto, California during spring and fall. Various statistical models were run on the data to determine the strongest relationships. We found that the nonlinear relationship: C B = β 0 D H β 1 10 ε , where CB was dry culm biomass (g m −2 ), DH was density of culms × average height of culms in a plot, and β 0 and β 1 were parameters to estimate, proved to be the best fit for predicting dried-live above-ground biomass of the two Schoenoplectus species. The random error distribution, ε, was either assumed to be normally distributed for mean regression estimates or assumed to be an unspecified continuous distribution for quantile regression estimates.

Wetlands↗
USGS2009

Application of the Hydroecological Integrity Assessment Process for Missouri Streams

Natural flow regime concepts and theories have established the justification for maintaining or restoring the range of natural hydrologic variability so that physiochemical processes, native biodiversity, and the evolutionary potential of aquatic and riparian assemblages can be sustained. A synthesis of recent research advances in hydroecology, coupled with stream classification using hydroecologically relevant indices, has produced the Hydroecological Integrity Assessment Process (HIP). HIP consists of (1) a regional classification of streams into hydrologic stream types based on flow data from long-term gaging-station records for relatively unmodified streams, (2) an identification of stream-type specific indices that address 11 subcomponents of the flow regime, (3) an ability to establish environmental flow standards, (4) an evaluation of hydrologic alteration, and (5) a capacity to conduct alternative analyses. The process starts with the identification of a hydrologic baseline (reference condition) for selected locations, uses flow data from a stream-gage network, and proceeds to classify streams into hydrologic stream types. Concurrently, the analysis identifies a set of non-redundant and ecologically relevant hydrologic indices for 11 subcomponents of flow for each stream type. Furthermore, regional hydrologic models for synthesizing flow conditions across a region and the development of flow-ecology response relations for each stream type can be added to further enhance the process. The application of HIP to Missouri streams identified five stream types ((1) intermittent, (2) perennial runoff-flashy, (3) perennial runoff-moderate baseflow, (4) perennial groundwater-stable, and (5) perennial groundwater-super stable). Two Missouri-specific computer software programs were developed: (1) a Missouri Hydrologic Assessment Tool (MOHAT) which is used to establish a hydrologic baseline, provide options for setting environmental flow standards, and compare past and proposed hydrologic alterations; and (2) a Missouri Stream Classification Tool (MOSCT) designed for placing previously unclassified streams into one of the five pre-defined stream types.

Open-File Report↗
USGS2009

Quantifying Equid Behavior - A Research Ethogram for Free-Roaming Feral Horses

Feral horses (Equus caballus) are globally distributed in free-roaming populations on all continents except Antarctica and occupy a wide range of habitats including forest, grassland, desert, and montane environments. The largest populations occur in Australia and North America and have been the subject of scientific study for decades, yet guidelines and ethograms for feral horse behavioral research are largely absent in the scientific literature. The U.S. Geological Survey (USGS) Fort Collins Science Center conducted research on the influences of the immunocontraceptive porcine zona pellucida (PZP) on feral horse behavior from 2003-2006 in three discrete populations in the American west. These populations were the Little Book Cliffs Wild Horse Range in Colorado, McCullough Peaks Herd Management Area in Wyoming, and Pryor Mountain Wild Horse Range in Montana; the research effort included over 1,800 hours of behavioral observations of 317 adult free-roaming feral horses. An ethogram was developed during the course of this study to facilitate accurate scientific data collection on feral horse behavior, which is often challenging to quantify. By developing this set of discrete behavioral definitions and a set of strict research protocols, scientists were better able to address both applied questions, such as behavioral changes related to fertility control, and theoretical questions, such as understanding networks and dominance hierarchies within social groups of equids.

Techniques and Methods↗
USGS2006

A permutation test for quantile regression

A drop in dispersion, F -ratio like, permutation test ( D ) for linear quantile regression estimates (0≤τ≤1) had relative power ≥1 compared to quantile rank score tests ( T ) for hypotheses on parameters other than the intercept. Power was compared for combinations of sample sizes ( n =20−300) and quantiles (τ=0.50−0.99) where both tests maintained valid Type I error rates in simulations with p =2 and 6 parameters in homogeneous and heterogeneous error models. The D test required two modifications of permuting residuals from null, reduced parameter models to maintain correct Type I error rates when null models were constrained through the origin or included multiple parameters. A double permutation scheme was used when null models were constrained through the origin and all but 1 of the zero residuals were deleted for null models with multiple parameters. Although there was considerable overlap in sample size, quantiles, and hypotheses where both the D and rank score tests maintained correct Type I error rates, we identified regions at smaller n and more extreme quantiles where one or the other maintained better error rates. Confidence intervals on parameters for an ecological application relating Lahontan cutthroat trout densities to stream channel width:depth were estimated by test inversion, demonstrating a smoother pattern of slightly narrower intervals across quantiles than those provided by the rank score test.

Journal of Agricultural, Biological, and Environme↗
USGS2005

User manual for Blossom statistical package for R

Blossom is an R package with functions for making statistical comparisons with distance-function based permutation tests developed by P.W. Mielke, Jr. and colleagues at Colorado State University (Mielke and Berry, 2001) and for testing parameters estimated in linear models with permutation procedures developed by B. S. Cade and colleagues at the Fort Collins Science Center, U.S. Geological Survey. This manual is intended to provide identical documentation of the statistical methods and interpretations as the manual by Cade and Richards (2005) does for the original Fortran program, but with changes made with respect to command inputs and outputs to reflect the new implementation as a package for R (R Development Core Team, 2012). This implementation in R has allowed for numerous improvements not supported by the Cade and Richards (2005) Fortran implementation, including use of categorical predictor variables in most routines.

Open-File Report↗
USGS2005

Evaluation of models and data for assessing whooping crane habitat in the central Platte River, Nebraska

The primary objectives of this evaluation were to improve the performance of the Whooping Crane Habitat Suitability model (C4R) used by the U.S. Fish and Wildlife Service (Service) for defining the relationship between river discharge and habitat availability, and to assist the Service in implementing improved model(s) with existing hydraulic files. The C4R habitat model is applied at the scale of individual river cross-sections, but the model outputs are scaledup to larger reaches of the river using a decision support “model” comprised of other data and procedures. Hence, the validity of the habitat model depends at least partially on how its outputs are incorporated into this larger context. For that reason, we also evaluated other procedures including the PHABSIM data files, the FORTRAN computer programs used to implement the model, and other parameters used to simulate the relationship between river flows and the availability of Whooping Crane roosting habitat along more than 100 miles of heterogeneous river channels. An equally important objective of this report was to fully document these related procedures as well as the model and evaluation results so that interested parties could readily understand the technical basis for the Service’s recommendations.

Nebraska↗
USGS2005

Vegetation changes over 12 years in ungrazed and grazed Conservation Reserve Program Grasslands in the central and southern plains

The Conservation Reserve Program (CRP) established under the 1985 Food Security Act has the fundamental objectives of jointly providing economic support to segments of the agricultural community and conservation of natural resources (Osborn, 1997; Heard and others, 2000). Although soil loss on highly erodable lands was the principal natural resource conservation issue addressed in the 1985 CRP, improving water quality and wildlife habitat both became important considerations as the program evolved (Farmer and others, 1988). For example, Best and others (1997) found that production of young birds on CRP fields in the Midwest was ≥15 times the production on row-crop fields because of improved habitat. The increasing importance of wildlife habitat is reflected in continuing refinement of the Environmental Benefits Index (EBI) used by the U.S. Department of Agriculture (USDA) to quantify the potential benefits of enrolling lands in CRP (Osborn, 1997; Ribaudo and others, 2001). The refinements reflect input furnished by federal, state, and non-government organizations seeking greater wildlife habitat quality on CRP lands (Roseberry and David, 1994; Hughes and others, 1995; Millenbah and others, 1996; Patterson and Best, 1996; Rodgers, 1999; Allen and others, 2001). Refinement in the EBI has changed the types of grasses planted on newly enrolled land. In early CRP signups (1 through 11), 71% of new grassland acres were planted to introduced grasses and legumes [Conservation Practice (CP) 1] while 29% of the acres were planted to native grasses (CP2) (Osborn and others, 1992). By the 27th signup in July 2004, over 34.8 million acres (14 million ha) were enrolled in the CRP. More than 73% of these lands were planted to various mixtures of introduced (CP1) or native (CP2) grasses for a minimal contract period of 10 years (USDA, 2004). Continuation of grass plantings under the 2002 Farm Bill may result in CRP lands furnishing grass dominated cover for 20 or more consecutive years. The species of grass established in seeded grasslands can have a major influence on the potential quality of wildlife habitat where vegetation is maintained over a multi-year period. Different species of grass may have comparable abilities to alleviate soil erosion but furnish dissimilar qualities of wildlife habitat (fig. 1). For example, smooth brome, an introduced cool-season grass (grass species and scientific names are presented in table 1), is highly valued for its erosion control and forage attributes (Casler and Carlson, 1995). Switchgrass, a native warm-season grass, also is valued for its soil and water conservation qualities (Moser and Vogel, 1995) but provides greater benefits for some species of wildlife (Clubine, 1995). The quality of nesting and winter cover for ring-necked pheasants (Phasianus colchicus) furnished by smooth brome on northeastern Colorado CRP lands is inferior to that provided by the taller, more robust switchgrass (Allen, 1994). Characteristics of the agricultural landscape surrounding individual CRP fields also play a role in the wildlife habitat potential of CRP plantings (Weber and others, 2002; Nusser and others, 2004). of native and seeded grasslands change in response to the presence (and absence) of physical disturbances such as fire, grazing, tillage, and haying (Hobbs and Huenneke, 1992; Millenbah and others, 1996; Allen and others, 2001; Renfrew and Ribic, 2001; Swengel and Swengel, 2001). The perpetuation of diversity in species composition and vegetation structure following disturbance sustains desirable habitat for a variety of grassland-dependent wildlife (Hall and Willig, 1994; Barnes and others, 1995; King and Savidge, 1995; Granfors and others, 1996; Herkert and others, 1996; Kurzejeski, 1996; Patterson and Best, 1996; Klute and others, 1997). Undisturbed grasslands have lower grass and forb species diversity, greater amounts of dead plant material, decreased as well as seasonally delayed productivity, and diminished structural diversity of vegetation (Peet and others, 1975; Rice and Parenti, 1978; Butler and Briske, 1988; Campa and Winterstein, 1992). Recommendations for the timing of disturbance to increase grass and forb species diversity range from 3 to 8 years following establishment of seeded grasslands in the northern Great Plains and Midwest (Duebbert and others, 1981; Higgens, 1987; Millenbah and others, 1996). The management interval, however, is affected by climatic conditions, soils, grass species, and management history of the individual stand. We quantified changes in vegetation structure and species composition across the typical 10-year contract period in undisturbed southern and central Great Plains CRP fields (fig. 2) planted to introduced and native grasses. In addition, we compared changes in vegetation in fields grazed during the emergency release of 1996 by comparing conditions prior to grazing and two and four years post grazing relative to changes in similar fields that were not grazed. Documentation of long-term changes in vegetation structure and composition for fields planted to common grass seed mixtures across a wide range of environmental conditions provides information to improve long-term wildlife habitat potential, guide program administration, and define management practices that yield economic benefits to operators while still meeting wildlife and conservation objectives. Emergency grazing provisions of the CRP are controversial. Although grazing can alter vegetative characteristics and reduce habitat quality in the short-term (Temple and others, 1999), periodic disturbance may be necessary to maintain habitat quality, and more information is needed assessing long-term effects of emergency grazing on vegetative structure and species composition.

Conference Paper↗
USGS2002

Selection of nesting habitat by sharp-tailed grouse in the Nebraska sandhills

We evaluated nesting habitat selection (disproportionate use compared to availability) by plains sharp-tailed grouse ( Tympanuchus phasianellus jamesi ) on rangelands grazed by cattle ( Bos taurus ) relative to height, density, and heterogeneity of residual herbaceous vegetation remaining from previous growing seasons. Residual cover is critical for nesting sharp-tailed grouse and can be lacking on grazed rangelands. Aerial photography and a geographic information system were used to analyze residual cover height classes and several measures of residual cover heterogeneity in nest (n = 38) and random (n = 38) plots. Height classes corresponded to visual obstruction readings (VORs), the height to which total visual obstruction by vegetation occurs. Analyses were conducted for five spatial scales ranging from 1 to 16 ha to test for scale effects on nesting habitat selection. Sharp-tailed grouse selected nesting habitat with more area in tall (greater than or equal to 4 cm VOR) residual cover than at random sites at all scales, less area in short residual cover (less than 2 cm VOR) at the I-ha scale, and less area in short and medium (2 to 3.9 cm YOR) residual cover at the 2- through 16-ha scales. Selection of shrub habitat containing patches of shrubs was evident only at the 16-ha scale. Patches of tall residual cover were larger in nest plots than in random plots at the 8- and 16-ha scales, and patches of short cover were smaller in nest plots at the I-ha scale. Two scales of pattern defined by mean patch size were detected for overall residual cover, but did not relate to nesting habitat selection.

Nebraska↗
USGS1999

Estimating effects of limiting factors with regression quantiles

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e.g., 90-95th) were greater than if effects were estimated by changes in the means from standard linear model procedures. Estimating a range of regression quantiles (e.g., 5-95th) provides a comprehensive description of biological response patterns for exploratory and inferential analyses in observational studies of limiting factors, especially when sampling large spatial and temporal scales.

Ecology↗