Geology topics

B.S. Cade

Publications and source records attributed to B.S. Cade.

At least 19 recordsLinked to original sources

USGS2011

Genetic and environmental influences on leaf phenology and cold hardiness of native and introduced riparian trees

To explore the roles of plasticity and genetic variation in the response to spatial and temporal climate variation, we established a common garden consisting of paired collections of native and introduced riparian trees sampled along a latitudinal gradient. The garden in Fort Collins, Colorado (latitude 40.6°N), included 681 native plains cottonwood ( Populus deltoides subsp. monilifera ) and introduced saltcedar ( Tamarix ramosissima , T. chinensis and hybrids) collected from 15 sites at 29.2–47.6°N in the central United States. In the common garden both species showed latitudinal variation in fall, but not spring, leaf phenology, suggesting that the latitudinal gradient in fall phenology observed in the field results at least in part from inherited variation in the critical photoperiod, while the latitudinal gradient in spring phenology observed in the field is largely a plastic response to the temperature gradient. Populations from higher latitudes exhibited earlier bud set and leaf senescence. Cold hardiness varied latitudinally in both fall and spring for both species. For cottonwood, cold hardiness began earlier and ended later in northern than in southern populations. For saltcedar northern populations were hardier throughout the cold season than southern populations. Although cottonwood was hardier than saltcedar in midwinter, the reverse was true in late fall and early spring. The latitudinal variation in fall phenology and cold hardiness of saltcedar appears to have developed as a result of multiple introductions of genetically distinct populations, hybridization and natural selection in the 150 years since introduction.

International Journal of Biometeorology↗

USGS2011

Trophic magnification of PCBs and its relationship to the octanol-water partition coefficient

We investigated polychlorinated biphenyl (PCB) bioaccumulation relative to octanol-water partition coefficient (KOW) and organism trophic position (TP) at the Lake Hartwell Superfund site (South Carolina). We measured PCBs (127 congeners) and stable isotopes (??15N) in sediment, organic matter, phytoplankton, zooplankton, macroinvertebrates, and fish. TP, as calculated from ??15N, was significantly, positively related to PCB concentrations, and food web trophic magnification factors (TMFs) ranged from 1.5-6.6 among congeners. TMFs of individual congeners increased strongly with log KOW, as did the predictive power (r2) of individual TP-PCB regression models used to calculate TMFs. We developed log KOW-TMF models for eight food webs with vastly different environments (freshwater, marine, arctic, temperate) and species composition (cold- vs warmblooded consumers). The effect of KOW on congener TMFs varied strongly across food webs (model slopes 0.0-15.0) because the range of TMFs among studies was also highly variable. We standardized TMFs within studies to mean = 0, standard deviation (SD) = 1 to normalize for scale differences and found a remarkably consistent KOW effect on TMFs (no difference in model slopes among food webs). Our findings underscore the importance of hydrophobicity (as characterized by KOW) in regulating bioaccumulation of recalcitrant compounds in aquatic systems, and demonstrate that relationships between chemical KOW and bioaccumulation from field studies are more generalized than previously recognized. ?? This article not subject to U.S. Copyright. Published 2011 by the American Chemical Society.

Environmental Science & Technology↗

USGS2011

Estimating equivalence with quantile regression

Equivalence testing and corresponding confidence interval estimates are used to provide more enlightened statistical statements about parameter estimates by relating them to intervals of effect sizes deemed to be of scientific or practical importance rather than just to an effect size of zero. Equivalence tests and confidence interval estimates are based on a null hypothesis that a parameter estimate is either outside (inequivalence hypothesis) or inside (equivalence hypothesis) an equivalence region, depending on the question of interest and assignment of risk. The former approach, often referred to as bioequivalence testing, is often used in regulatory settings because it reverses the burden of proof compared to a standard test of significance, following a precautionary principle for environmental protection. Unfortunately, many applications of equivalence testing focus on establishing average equivalence by estimating differences in means of distributions that do not have homogeneous variances. I discuss how to compare equivalence across quantiles of distributions using confidence intervals on quantile regression estimates that detect differences in heterogeneous distributions missed by focusing on means. I used one-tailed confidence intervals based on inequivalence hypotheses in a two-group treatment-control design for estimating bioequivalence of arsenic concentrations in soils at an old ammunition testing site and bioequivalence of vegetation biomass at a reclaimed mining site. Two-tailed confidence intervals based both on inequivalence and equivalence hypotheses were used to examine quantile equivalence for negligible trends over time for a continuous exponential model of amphibian abundance. ?? 2011 by the Ecological Society of America.

Ecological Applications↗

USGS2009

Spectral distance decay

Remotely sensed data represents key information for character-izing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance may allow us to quantitatively estimate how beta-diversity in species changes with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological datasets are characterized by a high number of zeroes that can add noise to the regression model. Quantile regression can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this paper, we used ordinary least square (OLS) and quantile regression to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.05) considering both OLS and quantile regression. Nonetheless, OLS regression estimate of mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when spectral distance approaches zero, was very low compared with the intercepts of upper quantiles, which detected high species similarity when habitats are more similar. In this paper we demonstrated the power of using quantile regressions applied to spectral distance decay in order to reveal species diversity patterns otherwise lost or underestimated by ordinary least square regression.

Photogrammetric Engineering and Remote Sensing↗

USGS2008

Movement patterns and the conservation of amphibians breeding in small, temporary wetlands

Many amphibians breed in water but live most of their lives in terrestrial habitats. Little is known, however, about the spatial distribution of these habitats or of the distances and directions amphibians move to reach breeding sites. The amphibian community at a small, temporary pond in northcentral Florida was monitored for 5 years. Based on captures and recaptures of more than 2500 striped newts (Notophthalmus perstriatus) and 5700 eastern narrow-mouthed toads (Gastrophryne carolinensis), we tabulated the angles of orientation that these amphibians entered and exited the pond basin. Our results showed that movements of these species between the pond and terrestrial habitats were nonrandom in orientation, but that narrow corridors did not appear to be used. Differences between the species likely reflect differences in habitat preferences, whereas intraspecific differences among years and between the sexes likely reflect variation among individuals. For terrestrial buffer zones to be effective at conserving pond-breeding amphibian communities, they need both a distance and a directional component. The determination of a directional component may be obscured if studies are carried out over a short time span. Conservation efforts for wetland-breeding amphibians that concentrate solely on the wetland likely will fail without consideration of the adjacent terrestrial habitat.

Florida↗

USGS2008

Estimating fish body condition with quantile regression

We used quantile regression to compare the body condition of walleye Sander vitreus and white bass Morone chrysops before (1980-1988) and after (1989-2004) the establishment of alewives Alosa pseudoharengus in Lake McConaughy, Nebraska. Higher quantiles (percentiles = 100% x quantiles [0, 1]) of weight (W) at the same total length (TL) were indicative of better body condition in an allometric growth model that included separate slopes and intercepts for the before and after groups. All quantiles of walleye weights by TL increased in the years after alewife introduction, ranging from 1.01 to 1.12 times weights in the years before alewife introduction, with greatest increases for the lower (<0.50) quantiles and greater TLs. Quantiles up to 0.25 (the lowest 25th percentiles) of white bass weights were reduced in years after alewife introduction for TLs less than 300 mm, ranging from 0.78 to 0.98 times weights in the years before alewife introduction. However, quantiles greater than or equal to 0.50 (the upper 50th percentiles) of white bass weights increased for all TLs, ranging from 1.01 to 1.06 times the pre-1988 weights. A three-group analysis, which improved the model fit for longer white bass, indicated a reduction (0.80-1.0) in white bass body condition across all TLs in the first 2 years (1989-1990) after alewife introduction, whereas body condition actually improved (1.02-1.12) across all TLs in later years (1991-2004). Thus, after the establishment of alewives in 1988, walleye body condition improved for all fish at all lengths (the greatest improvement occurring among fish in poorer condition), whereas white bass body condition was initially reduced for all fish at all lengths for 2 years and improved in subsequent years. The approach that we developed for comparing fish body condition before and after a management action in Lake McConaughy could be applied to other weight-length data sets typically evaluated with relative weight indices. ?? Copyright by the American Fisheries Society 2008.

North American Journal of Fisheries Management↗

USGS2008

Quantile regression applied to spectral distance decay

Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.01), considering both OLS and quantile regressions. Nonetheless, the OLS regression estimate of the mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when the spectral distance approaches zero, was very low compared with the intercepts of the upper quantiles, which detected high species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.

IEEE Geoscience and Remote Sensing Letters↗

USGS2008

A quantile count model of water depth constraints on Cape Sable seaside sparrows

1. A quantile regression model for counts of breeding Cape Sable seaside sparrows Ammodramus maritimus mirabilis (L.) as a function of water depth and previous year abundance was developed based on extensive surveys, 1992-2005, in the Florida Everglades. The quantile count model extends linear quantile regression methods to discrete response variables, providing a flexible alternative to discrete parametric distributional models, e.g. Poisson, negative binomial and their zero-inflated counterparts. 2. Estimates from our multiplicative model demonstrated that negative effects of increasing water depth in breeding habitat on sparrow numbers were dependent on recent occupation history. Upper 10th percentiles of counts (one to three sparrows) decreased with increasing water depth from 0 to 30 cm when sites were not occupied in previous years. However, upper 40th percentiles of counts (one to six sparrows) decreased with increasing water depth for sites occupied in previous years. 3. Greatest decreases (-50% to -83%) in upper quantiles of sparrow counts occurred as water depths increased from 0 to 15 cm when previous year counts were 1, but a small proportion of sites (5-10%) held at least one sparrow even as water depths increased to 20 or 30 cm. 4. A zero-inflated Poisson regression model provided estimates of conditional means that also decreased with increasing water depth but rates of change were lower and decreased with increasing previous year counts compared to the quantile count model. Quantiles computed for the zero-inflated Poisson model enhanced interpretation of this model but had greater lack-of-fit for water depths > 0 cm and previous year counts 1, conditions where the negative effect of water depths were readily apparent and fitted better with the quantile count model.

Journal of Animal Ecology↗

USGS2006

Evaluating redband trout habitat in sagebrush desert basins in southwestern Idaho

We estimated abundance quantiles of redband trout Oncorhynchus mykiss gairdneri relative to five site-specific habitat variables (stream shading, bank cover, bank stability, fine sediment in the stream substrate, and cover for adults) and one landscape variable (distance from stream headwaters) on 30 streams in southwestern Idaho during 1993–1998. In addition, the five site-specific habitat variables were used to calculate a habitat suitability rating (HSR) used by the U.S. Bureau of Land Management to determine habitat quality of sagebrush desert streams for redband trout. Variation in abundance increased significantly with increasing HSR; the highest abundances were only found with high HSRs, indicating that the HSR model correctly predicted habitat quality for redband trout. However, a model that consisted of stream shade, distance from stream headwaters, and their interaction best predicted redband trout density, explaining 36% of the variation in adult density in sagebrush desert basin streams; stream shade explained most of the variation in redband trout density. When habitat quality was modeled on shade alone, the precision in predicting adult redband trout density was similar to that of the HSR model, as evaluated with tolerance intervals that contained 80% of future observations of redband trout density with 95% confidence. Increasing stream shade in the uppermost 50 km of a stream would result in the greatest increase in redband trout density. We recommend that land managers primarily evaluate the habitat quality of sagebrush desert streams by quantifying the amount of stream shade provided by riparian shrubs and trees. Use of a multivariable habitat model should be retained for desert streams where shade from riparian plant communities is limited.

North American Journal of Fisheries Management↗

USGS2006

The effect of multiple stressors on salt marsh end-of-season biomass

It is becoming more apparent that commonly used statistical methods (e.g. analysis of variance and regression) are not the best methods for estimating limiting relationships or stressor effects. A major challenge of estimating the effects associated with a measured subset of limiting factors is to account for the effects of unmeasured factors in an ecologically realistic matter. We used quantile regression to elucidate multiple stressor effects on end-of-season biomass data from two salt marsh sites in coastal Louisiana collected for 18 yr. Stressor effects evaluated based on available data were flooding, salinity air temperature, cloud cover, precipitation deficit, grazing by muskrat, and surface water nitrogen and phosphorus. Precipitation deficit combined with surface water nitrogen provided the best two-parameter model to explain variation in the peak biomass with different slopes and intercepts for the two study sites. Precipitation deficit, cloud cover, and temperature were significantly correlated with each other. Surface water nitrogen was significantly correlated with surface water phosphorus and muskrat density. The site with the larger duration of flooding showed reduced peak biomass, when cloud cover and surface water nitrogen were optimal. Variation in the relatively low salinity occurring in our study area did not explain any of the variation in Spartina alterniflora biomass.

Estuaries and Coasts↗

USGS2006

The effect of multiple stressors on salt marsh end-of-season biomass

It is becoming more apparent that commonly used statistical methods (e.g., analysis of variance and regression) are not the best methods for estimating limiting relationships or stressor effects. A major challenge of estimating the effects associated with a measured subset of limiting factors is to account for the effects of unmeasured factors in an ecologically realistic matter. We used quantile regression to elucidate multiple stressor effects on end-of-season biomass data from two salt marsh sites in coastal Louisiana collected for 18 yr. Stressor effects evaluated based on available data were flooding, salinity, air temperature, cloud cover, precipitation deficit, grazing by muskrat, and surface water nitrogen and phosphorus. Precipitation deficit combined with surface water nitrogen provided the best two-parameter model to explain variation in the peak biomass with different slopes and intercepts for the two study sites. Precipitation deficit, cloud cover, and temperature were significantly correlated with each other. Surface water nitrogen was significantly correlated with surface water phosphorus and muskrat density. The site with the larger duration of flooding showed reduced peak biomass, when cloud cover and surface water nitrogen were optimal. Variation in the relatively low salinity occurring in our study area did not explain any of the variation in Spartina alterniflora biomass. ?? 2006 Estuarine Research Federation.

Estuaries and Coasts↗

USGS2005

Linear models: permutation methods

Permutation tests (see Permutation Based Inference) for the linear model have applications in behavioral studies when traditional parametric assumptions about the error term in a linear model are not tenable. Improved validity of Type I error rates can be achieved with properly constructed permutation tests. Perhaps more importantly, increased statistical power, improved robustness to effects of outliers, and detection of alternative distributional differences can be achieved by coupling permutation inference with alternative linear model estimators. For example, it is well-known that estimates of the mean in linear model are extremely sensitive to even a single outlying value of the dependent variable compared to estimates of the median [7, 19]. Traditionally, linear modeling focused on estimating changes in the center of distributions (means or medians). However, quantile regression allows distributional changes to be estimated in all or any selected part of a distribution or responses, providing a more complete statistical picture that has relevance to many biological questions [6]...

Book chapter↗

USGS2005

Determinants of woody cover in African savannas

Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties 1-3. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover1,2,4,5, but perceptions differ on which of these are the primary drivers of savanna structure. Here we show, using data from 854 sites across Africa, that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than ???650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered 'stable' systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of ???650 mm, savannas are 'unstable' systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation 6 may considerably affect their distribution and dynamics. ?? 2005 Nature Publishing Group.

Nature↗

USGS2005

Quantile regression reveals hidden bias and uncertainty in habitat models

We simulated the effects of missing information on statistical distributions of animal response that covaried with measured predictors of habitat to evaluate the utility and performance of quantile regression for providing more useful intervals of uncertainty in habitat relationships. These procedures were evaulated for conditions in which heterogeneity and hidden bias were induced by confounding with missing variables associated with other improtant processes, a problem common in statistical modeling of ecological phenomena. Simulations for a large ( N = 10 000) finite population representing grid locations on a landscape demonstrated various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable. Quantile (0 ≤ τ ≤ 1) regression parameters for linear models that excluded the important, unmeasured variable revealed bias relative to parameters from the generating model. Depending on whether interactions of the measured and unmeasured variables were negative (interference interactions) or positive (facilitation interactions) in simulations without spatial structuring, either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Heterogeneous, nonlinear response patterns occurred with correlations between the measured and unmeasured variables. When the unmeasured variable was spatially structured, variation in parameters across quantiles associated with heterogeneous effects of the habitat variable was reduced by modeling the spatial trend surface as a cubic polynomial of location coordinates, but substantial hidden bias remained. Sampling ( n = 20–300) simulations demonstrated that regression quantile estimates and confidence intervals constructed by inverting weighted rank score tests provided valid coverage of these parameters. Local forms of quantile weighting were required for obtaining correct Type I error rates and confidence interval coverage. Quantile regression was used to estimate effects of physical habitat resources on a bivalve ( Macomona liliana ) in the spatially structured landscape on a sandflat in a New Zealand harbor. Confidence intervals around predicted 0.10 and 0.90 quantiles were used to estimate sampling intervals containing 80% of the variation in densities in relation to bed elevation. Spatially structured variation in bivalve counts estimated by a cubic polynomial trend surface remained after accounting for the nonlinear effects of bed elevation, indicating the existence of important spatially structured processes that were not adequately represented by the measured habitat variables.

Ecology↗

USGS2004

Isolation of Snake River islands and mammalian predation of waterfowl nests

In 1990–1992, we studied predation of waterfowl nests by mammalian predators on 30 islands in a 64-km reach of the Snake River in southwestern Idaho, USA, to identify river flows necessary to protect and enhance migratory bird use of Deer Flat National Wildlife Refuge. We monitored 235–314 Canada goose ( Branta canadensis ) nests each year and 122 duck nests, primarily mallard ( Anas platyrhynchos ), in 1991. We calculated island isolation as the flow energy (width × average water velocity [m 2 /sec]) a predator encountered in crossing to an island. Density of goose nests increased as island isolation increased ( P < 0.001). In contrast, visits of terrestrial predators to islands, mammalian predation of goose nests, and variance in predation rates decreased as island isolation increased ( P < 0.01). Nests were most frequently depredated by raccoons ( Procyon lotor ), coyotes ( Canis latrans ), badgers ( Taxidea taxus ), and mink ( Mustela vison ). Multiple regression models explained 67% of the variance in nest density and 48% of variance in predation rates ( P < 0.01). Variables of secondary importance relative to island isolation were island size and percentage of farmland on the adjacent mainland. Fewer geese nested ( P < 0.01) and predation rates were greater ( P = 0.06) on large islands, and more geese nested on islands adjacent to farmland ( P = 0.04). We also analyzed rates of nest predation relative to river flows for 21 islands from 1953 to 1992. Mammalian predation of goose nests decreased as island isolation increased with greater river flows ( P < 0.01). To identify levels of isolation needed to protect use of islands by nesting waterfowl, we estimated rates of change in nest densities and predation rates with island isolation for a range of regression quantiles. We then calculated levels of island isolation at river flows ranging from 110 to 340 m 3 /sec. Below flows of 250 m 3 /sec, nest predation rates increased rapidly. Flows ≥340 m 3 /sec adequately isolated Snake River islands for nesting waterfowl. Predator visitation and nest predation rates were at a low level, and variability in the rates was at a minimum for these flows.

Journal of Wildlife Management↗

USGS2003

Estimating effects of constraints on plant performance with regression quantiles

Rates of change in final summer densities of two desert annuals, Eriogonum abertianum and Haplopappus gracilis , as constrained by their initial winter germination densities were estimated with regression quantiles and compared with mechanistic fits based on a self-thinning rule proposed by Guo et al. (1998); Oikos 83: 237–245). The allometric relation used was equivalent to S = N f ( N i )−1= c f ( N i )−1, where S is the ratio of final to initial densities (survivorship), c f is a constant that is a final density specific to the species and environment, N i is the initial plant density, and N f is final plant density. We used regression quantiles to estimate c f assuming the exponent of −1 was fixed (model 1, N f ( N i )−1= c f ( N i )−1) and also obtained estimates by treating the exponent as a parameter to estimate (model 2, N f ( N i )−1= c f ( N i )λ). Regression quantiles allow rates of change to be estimated through any part of a data distribution conditional on some linear function of covariates. We focused on estimates for upper (90–99th) quantiles near the boundary of the summer density distributions where we expected effects of self-thinning to operate as the primary constraint on plant performance. Allometric functions estimated with regression quantiles were similar to functions fit by Guo et al. (1998) when the exponent was constrained to −1. However, the data were more consistent with estimates for model (2), where exponents were closer to −0.4 than −1, although model fit was not as good at higher initial plant densities as when the exponent was fixed at −1. An exponential form (model 3, N f ( N i )−1= c f ( N i )λ eγNi) that is a generalization of the discrete logistic growth function, where estimates of λ were −0.23 to −0.28 and estimates of γ were −0.003 to −0.006, provided better fit from low to high initial germination densities. Model 3 predictions were consistent with an interpretation that final summer densities were constrained by initial germination densities when these were low (<40 per 0.25 m2 for Eriogonum and <100 per 0.25 m2 for Haplopappus ) and were constrained by the self-thinning process at higher germination densities. Our exponential model (3) estimated with regression quantiles had similar form to the mechanistic relation of Guo et al. (1998) when plotted as a survivorship function, but avoided the unrealistic assumption that all populations attained a similar final density, and was based on a statistical model that has formal rules for estimation and inference.

Oikos↗

USGS2002

Influences of spatial and temporal variation on fish-habitat relationships defined by regression quantiles

We used regression quantiles to model potentially limiting relationships between the standing crop of cutthroat trout Oncorhynchus clarki and measures of stream channel morphology. Regression quantile models indicated that variation in fish density was inversely related to the width:depth ratio of streams but not to stream width or depth alone. The spatial and temporal stability of model predictions were examined across years and streams, respectively. Variation in fish density with width:depth ratio (10th-90th regression quantiles) modeled for streams sampled in 1993-1997 predicted the variation observed in 1998-1999, indicating similar habitat relationships across years. Both linear and nonlinear models described the limiting relationships well, the latter performing slightly better. Although estimated relationships were transferable in time, results were strongly dependent on the influence of spatial variation in fish density among streams. Density changes with width:depth ratio in a single stream were responsible for the significant ( P < 0.10) negative slopes estimated for the higher quantiles (>80th). This suggests that stream-scale factors other than width:depth ratio play a more direct role in determining population density. Much of the variation in densities of cutthroat trout among streams was attributed to the occurrence of nonnative brook trout Salvelinus fontinalis (a possible competitor) or connectivity to migratory habitats. Regression quantiles can be useful for estimating the effects of limiting factors when ecological responses are highly variable, but our results indicate that spatiotemporal variability in the data should be explicitly considered. In this study, data from individual streams and stream-specific characteristics (e.g., the occurrence of nonnative species and habitat connectivity) strongly affected our interpretation of the relationship between width:depth ratio and fish density.

Transactions of the American Fisheries Society↗

USGS2002

Evaluation of a habitat suitability index model

We assisted with development of a model for maternity habitat of the Indiana bat (Myotis soda/is), for use in conducting assessments of projects potentially impacting this endangered species. We started with an existing model, modified that model in a workshop, and evaluated the revised model, using data previously collected by others. Our analyses showed that higher indices of habitat suitability were associated with sites where Indiana bats were present and, thus, the model may be useful for identifying suitable habitat. Utility of the model, however, was based on a single component-density of suitable roost trees. Percentage of landscape in forest did not allow differentiation between sites occupied and not occupied by Indiana bats. Moreover, in spite of a general opinion by participants in the workshop that bodies of water were highly productive feeding areas and that a diversity of feeding habitats was optimal, we found no evidence to support either hypothesis.

Conference Paper↗