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USGS2021

Consequences of migratory coupling of predators and prey when mediated by human actions

Aim Animal migrations influence ecosystem structure, dynamics and persistence of predator and prey populations. The theory of migratory coupling postulates that aggregations of migrant prey can induce large-scale synchronized movements in predators, and this coupling is consequential for the dynamics of ecological communities. The degree to which humans influence these interactions remains largely unknown. We tested whether creation of large resource pulses by humans such as seasonal herding of reindeer Rangifer tarandus and hunting of moose, Alces alces , can induce migratory coupling with Golden Eagles, Aquila chrysaetos, and whether these lead to demographic consequences for the eagles. Location Fennoscandia. Methods We used movement data from 32 tracked Golden Eagles spanning 125 annual migratory cycles over 8 years. We obtained reindeer distribution data through collaboration with reindeer herders based on satellite tracking of reindeer, and moose harvest data from the national hunting statistics for Sweden. We assessed demographic consequences for eagles from ingesting lead from ammunition fragments in moose carcasses through survival estimates and their links with lead concentrations in eagles' blood. Results In spring, eagles migrated hundreds of kilometres to be spatially and temporally coupled with calving reindeer, whereas in autumn, eagles matched their distribution with the location and timing of moose hunt. Juveniles were more likely to couple with reindeer calving, whereas adults were particularly drawn to areas of higher moose harvest. Due to this coupling, eagles ingested lead from spent ammunition in moose offal and carcasses and the resulting lead toxicity increased the risk of mortality by 3.4 times. Main conclusions We show how migratory coupling connects landscape processes and that human actions can influence migratory coupling over large spatial scales and increase demographic risks for predators. We provide vital knowledge towards resolving human–wildlife conflicts and the conservation of protected species over a large spatial and temporal scale.

Diversity and Distributions↗

USGS2022

Scale-dependent influence of the sagebrush community on genetic connectivity of the sagebrush obligate Gunnison sage-grouse

Habitat fragmentation and degradation impacts an organism's ability to navigate the landscape, ultimately resulting in decreased gene flow and increased extinction risk. Understanding how landscape composition impacts gene flow (i.e., connectivity) and interacts with scale is essential to conservation decision-making. We used a landscape genetics approach implementing a recently developed statistical model based on the generalized Wishart probability distribution to identify the primary landscape features affecting gene flow and estimate the degree to which each component influences connectivity for Gunnison sage-grouse ( Centrocercus minimus ). We were interested in two spatial scales: among distinct populations rangewide and among leks (i.e., breeding grounds) within the largest population, Gunnison Basin. Populations and leks are nested within a landscape fragmented by rough terrain and anthropogenic features, although requisite sagebrush habitat is more contiguous within populations. Our best fit models for each scale confirm the importance of sagebrush habitat in connectivity, although the important sagebrush characteristics differ. For Gunnison Basin, taller shrubs and higher quality nesting habitat were the primary drivers of connectivity, while more sagebrush cover and less conifer cover facilitated connectivity rangewide. Our findings support previous assumptions that Gunnison sage-grouse range contraction is largely the result of habitat loss and degradation. Importantly, we report direct estimates of resistance for landscape components that can be used to create resistance surfaces for prioritization of specific locations for conservation or management (i.e., habitat preservation, restoration, or development) or as we demonstrated, can be combined with simulation techniques to predict impacts to connectivity from potential management actions.

Colorado, Utah↗

USGS2005

Topography and vegetation as predictors of snow water equivalent across the alpine treeline ecotone at Lee Ridge, Glacier National Park, Montana, U.S.A.

We derived and implemented two spatial models of May snow water equivalent (SWE) at Lee Ridge in Glacier National Park, Montana. We used the models to test the hypothesis that vegetation structure is a control on snow redistribution at the alpine treeline ecotone (ATE). The statistical models were derived using stepwise and “best” subsets regression techniques. The first model was derived from field measurements of SWE, topography, and vegetation taken at 27 sample points. The second model was derived using GIS-based measures of topography and vegetation. Both the field- ( R 2 = 0.93) and GIS-based models ( R 2 = 0.69) of May SWE included the following variables: site type (based on vegetation), elevation, maximum slope, and general slope aspect. Site type was identified as the most important predictor of SWE in both models, accounting for 74.0% and 29.5% of the variation, respectively. The GIS-based model was applied to create a predictive map of SWE across Lee Ridge, predicting little snow accumulation on the top of the ridge where vegetation is scarce. The GIS model failed in large depressions, including ephemeral stream channels. The models supported the hypothesis that upright vegetation has a positive effect on accumulation of SWE above and beyond the effects of topography. Vegetation, therefore, creates a positive feedback in which it modifies its environment and could affect the ability of additional vegetation to become established.

Montana↗

USGS2017

Forecasting stream habitat and Brook Trout responses to climate change in Catoctin Mountain Park

Anticipating and mitigating the effects of climate change is a fundamental challenge for natural resource conservation. In this report, we respond to research needs identified by Catoctin Mountain Park (CATO) for native Brook Trout (Salvelinus fontinalis) conservation and management as part of the US Geological Survey (USGS) Natural Resources Preservation Program in FY15-16. We addressed three overarching research questions: (1) How will anticipated changes in air temperature affect stream habitats? (2) How will changes to stream habitat affect the distribution of Brook Trout? (3) Which stream segments are most and least vulnerable to the effects of climate change? First, we surveyed Brook Trout abundance and fish community composition using electrofishing techniques within three watersheds: Owens Creek, upper Big Hunting Creek, and Blue Blazes Creek (a tributary to Big Hunting Creek). Second, we deployed a network of stream temperature gages to assess spatial variation in stream temperature and groundwater (GW) influence. Third, we used modeling techniques to forecast future stream temperatures that account for GW influences and air temperature scenarios. Fish sampling detected 13 species and 15,345 individual fish, the majority of which were Blacknose Dace (60%), Blue Ridge Sculpin (26%), and Brook Trout (6%). Brook Trout were not observed in Blue Blazes Creek and exhibited higher densities in Owens Creek than upper Big Hunting Creek (average densities = 19 fish/100 m and 4 fish/100 m, respectively). In contrast, Brown Trout were present in Blue Blazes Creek and exhibited greater density in Blue Blazes Creek than either Owens Creek or upper Big Hunting Creek (average densities = 3.0 fish/100 m, 0.3 fish/100 m, and 1.7 fish/100 m, respectively). Brown Trout occurred in sympatry with Brook Trout in Owens Creek and upper Big Hunting Creek, but appeared to have replaced Brook Trout in Blue Blazes Creek. Our fish surveys also revealed important locations for Brook Trout reproduction and young-of-year (YOY) dispersal within the Owens Creek watershed. Our study also revealed surprising differences in the distribution of Blue Ridge Sculpin among CATO streams. This species was abundant in Owens Creek (average density = 83 fish/100 m) but was less common in Blue Blazes Creek (average density = 12 fish/100 m) and was not detected in upper Big Hunting Creek. Histological examination of several specimens from Blue Blazes Creek by V. Blazer at the USGS Leetown Science Center revealed the presence of a novel parasite (Dermosystidium sp.) which has been linked to fish population declines elsewhere (Blazer et al. 2016). The parasite was not detected in Blue Ridge Sculpin samples from Owens Creek, and all trout appeared to be uninfected. Our survey results suggest that Blue Ridge Sculpin have been extirpated from upper Big Hunting Creek and have not recolonized from downstream source populations due to the fish passage barrier of Cunningham Falls. We recommend additional research to (1) evaluate the feasibility of reintroducing Blue Ridge Sculpin into upper Big Hunting Creek and (2) continue monitoring the distribution and potential spread of Dermocystidium in downstream waters. Stream temperatures ranged from 9.6 – 27.6 ºC during baseflow conditions in 2015 and 2016. Sites within upper Big Hunting Creek were consistently warmer than in Owens Creek or Blue Blazes Creek, suggesting an effect of headwater ponds outside CATO on upper Big Hunting Creek temperatures. For instance, in 2016 the maximum observed temperature in upper Big Hunting Creek was 27.6 ºC whereas Owens Creek reached a maximum of 23.7 ºC that year. Stream temperature data also revealed that 2016 was warmer than 2015 throughout the study area but did not exceed thermal tolerance limits for Brook Trout in either year. We estimated the influence of GW on stream temperatures using a statistical modeling approach based on the relationship between daily mean air temperature and stream temperature over time. Results indicated that effects of GW were generally stronger in the Owens Creek watershed than in Blue Blazes or upper Big Hunting Creek. However, we detected substantial spatial variation in GW influence among Owens Creek sites, with stream temperatures at some locations showing relatively little GW influence and others showing very strong influences (and correspondingly small influence of daily mean air temperatures). Although incoming lateral seeps were detected in upper Big Hunting Creek (D. Ferrier, Hood College, personal communication), the strongest effects of GW in the study area were due to GW upwelling within portions of the Owens Creek watershed (i.e., Tributary C in Figure 4) where we also observed high numbers of Brook Trout juveniles. Our results therefore identified potential high-priority areas for Brook Trout conservation in CATO. Finally, we modeled future stream temperatures based on scenarios characterizing GW sensitivity to air temperature and future air temperature increases. Stream temperature forecasts revealed important differences in habitat suitability for Brook Trout within and among watersheds. Big Hunting Creek sites were generally more sensitive to air temperature increases than sites in Owens Creek or Blue Blazes Creek. For instance, an increase in mean annual air temperature of 1.5 ºC (lowest level evaluated) exceeded thermal thresholds for Brook Trout in the majority of sites within that watershed, regardless of GW influence levels. In contrast, an air temperature increase of 1.5 ºC did not exceed thermal thresholds for Brook Trout in Owens Creek. However, modeled air temperature increases of 5 ºC resulted in a loss of Brook Trout thermal suitability throughout the study area. Model results revealed spatially patchy responses to air temperature increases that could provide an early-warning system for trout monitoring designs in CATO.

Maryland↗

USGS2018

Spatial and temporal patterns in population trends and burrow usage of burrowing owls in North America

Many researchers have suggested that abundance of Burrowing Owls ( Athene cunicularia ) has declined in many portions of their breeding range, but a thorough review of their population trends over time is lacking. Published population trends from the North American Breeding Bird Survey program suggested that Burrowing Owl populations in the US have declined over the past 60 yr, but the declines were not considered significant until 2014. However, accurate trend estimates and the statistical significance of those estimates were hampered by low relative abundance of owls. Moreover, many authors have suggested that eradication of burrowing animals is a major cause of Burrowing Owl declines, because burrows dug by burrowing animals are a critical resource for Western Burrowing Owls ( A. cunicularia hypugaea ). Despite this, we currently lack a range-wide summary of the burrowing animals on which Western Burrowing Owls depend. To help fill these two information gaps, my objectives were to: (1) use Breeding Bird Survey (BBS) data to examine geographic patterns in population trends of Burrowing Owls throughout their breeding range in the USA, and (2) use past studies to provide the first summary of the spatial extent to which Western Burrowing Owls rely on the suite of burrowing animals throughout their breeding range. Significantly more BBS routes in the US show declining counts of owls than show increasing or stable counts, and the declines were most apparent prior to 1995. Counts of Burrowing Owls declined most precipitously on the northern edge and southern edge of the owl's US breeding range. Western Burrowing Owls primarily use black-tailed prairie dog ( Cynomys ludovicianus ) burrows in the eastern portion of their breeding range, whereas the diversity of burrowing species on which the owls depend is much greater in the western and central portions of their breeding range. Burrowing owl declines have been most apparent in portions of their range where they rely primarily on Richardson's ground squirrels ( Urocitellus richardsonii ), California ground squirrels ( Otospermophilus beecheyi ), black-tailed prairie dogs, and American badgers ( Taxidea taxus ).

Journal of Raptor Research↗

USGS2010

Identifying sites for elk restoration in Arkansas

We used spatial data to identify potential areas for elk ( Cervus elaphus ) restoration in Arkansas. To assess habitat, we used locations of 239 elk groups collected from helicopter surveys in the Buffalo National River area of northwestern Arkansas, USA, from 1992 to 2002. We calculated the Mahalanobis distance ( D 2 ) statistic based on the relationship between those elk-group locations and a suite of 9 landscape variables to evaluate winter habitat in Arkansas. We tested model performance in the Buffalo National River area by comparing the D 2 values of pixels representing areas with and without elk pellets along 19 fixed-width transects surveyed in March 2002. Pixels with elk scat had lower D 2 values than pixels in which we found no pellets (logistic regression: Wald χ 2 = 24.37, P < 0.001), indicating that habitat characteristics were similar to those selected by the aerially surveyed elk. Our D 2 model indicated that the best elk habitat primarily occurred in northern and western Arkansas and was associated with areas of high landscape heterogeneity, heavy forest cover, gently sloping ridge tops and valleys, low human population density, and low road densities. To assess the potential for elk–human conflicts in Arkansas, we used the analytical hierarchy process to rank the importance of 8 criteria based on expert opinion from biologists involved in elk management. The biologists ranked availability of forage on public lands as having the strongest influence on the potential for elk–human conflict (33%), followed by human population growth rate (22%) and the amount of private land in row crops (18%). We then applied those rankings in a weighted linear summation to map the relative potential for elk–human conflict. Finally, we used white-tailed deer ( Odocoileus virginianus ) densities to identify areas where success of elk restoration may be hampered due to meningeal worm ( Parelaphostrongylus tenuis ) transmission. By combining results of the 3 spatial data layers (i.e., habitat model, elk–human conflict model, deer density), our model indicated that restoration sites located in west-central and north-central Arkansas were most favorable for reintroduction.

Arkansas↗

USGS2022

Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA

Building continental-scale hydrologic models in data-sparse regions requires an understanding of spatial variation in hydrologic processes. Extending these models to ungaged locations requires techniques to group ungaged locations with gaged ones to make process importance and model parameter transfer decisions to ungaged locations. This analysis (1) tested the utility of fundamental streamflow statistics (FDSS) in defining hydrologic regions across Alaska, USA; (2) evaluated if the hydrologic regions represented different hydrologic processes; and (3) tested the ability of random forest and direct assignment techniques, informed by statistically estimated FDSS (FDSSest) and basin characteristics (BCs), to correctly assign ungaged locations to hydrologic regions. Six hydrologic regions were identified across the domain using FDSS. Differences in mean flow, phase shift of the seasonal cycle, and skewness were the primary characteristics defining each region. Two regions represented arctic and continental climates, generally in the northern portion of the domain; four regions represented the southern, maritime portion of the domain. Random forest modeling with BCs (67% success rate) outperformed FDSSest (58% success rate) suggesting that no statistically estimated streamflow was needed to assign ungaged locations to a region. For regions with many sites, most region assignment techniques performed similarly. Random forest modeling performance declined when BCs and FDSSest were both used to predict region membership, suggesting FDSSest had little information in addition to BCs. This analysis demonstrated that FDSS-based hydrologic regions discern process differences across a data-sparse and hydrologically diverse landscape. Process importance rankings from random forest-derived BCs provided model-independent information for making modeling decisions.

Alaska↗

USGS2023

Turbulence near a sandbar island in the lower Missouri River

River turbulence is spatially variable due to interactions between morphology of rivers and physical mechanics of flowing water. Understanding the variation of turbulence in rivers is important for characterizing transport processes of soluble and particulate materials in these systems. We present an exploratory effort to understand ecologically relevant flow patterns using measurements of mean flow and turbulence in a highly engineered river channel around an island in the lower Missouri River. Specifically, the profiles of mean river velocities were investigated to examine the logarithmic relation and associated parameters, including shear velocity and bed roughness. Turbulence intensity and Reynolds shear stress were compared with classic open-channel profiles and previously reported river data in the hydraulics literature. With the capability of pulse-to-pulse coherent Doppler velocity profiling in high spatial resolution, we estimated the profiles of turbulence dissipation rate using resolved one-dimensional velocity spectra. These measurement data allow us to examine the validity of turbulence production-dissipation balance and the classic open-channel profiles of turbulence statistics, including turbulence intensity, Reynolds shear stress, dissipation rate, and eddy viscosity. The field data show a strong variation of turbulence profiles in close vicinity of the river island. In shallow water depths close to the island, turbulence is substantially enhanced in comparison with classic open-channel profiles. Such turbulence enhancement is likely attributed to non-uniformity of the flow structures.

Missouri↗

USGS1985

Streamflow variability in the United States: 1931-1978.

Systematic modes of spatial and temporal variation in a 48-year record of streamflow are defined using principal components. The components were calculated from a matrix of annual streamflow departures for 106 grid cells covering the United States in the years 1931-78. Five statistically significant components are found to account for more than 56% of the total variance. A varimax orthogonal rotation of the original components describes regional anomaly cores located in the middle Mississippi Valley, Pacific Northwest, Far West, Northeast, and northern Great Plains. -from Author

Journal of Climate and Applied Meteorology↗

USGS2013

Multiscale sagebrush rangeland habitat modeling in the Gunnison Basin of Colorado

North American sagebrush-steppe ecosystems have decreased by about 50 percent since European settlement. As a result, sagebrush-steppe dependent species, such as the Gunnison sage-grouse, have experienced drastic range contractions and population declines. Coordinated ecosystem-wide research, integrated with monitoring and management activities, is needed to help maintain existing sagebrush habitats; however, products that accurately model and map sagebrush habitats in detail over the Gunnison Basin in Colorado are still unavailable. The goal of this project is to provide a rigorous large-area sagebrush habitat classification and inventory with statistically validated products and estimates of precision across the Gunnison Basin. This research employs a combination of methods, including (1) modeling sagebrush rangeland as a series of independent objective components that can be combined and customized by any user at multiple spatial scales; (2) collecting ground measured plot data on 2.4-meter QuickBird satellite imagery in the same season the imagery is acquired; (3) modeling of ground measured data on 2.4-meter imagery to maximize subsequent extrapolation; (4) acquiring multiple seasons (spring, summer, and fall) of Landsat Thematic Mapper imagery (30-meter) for optimal modeling; (5) using regression tree classification technology that optimizes data mining of multiple image dates, ratios, and bands with ancillary data to extrapolate ground training data to coarser resolution Landsat Thematic Mapper; and 6) employing accuracy assessment of model predictions to enable users to understand their dependencies. Results include the prediction of four primary components including percent bare ground, percent herbaceous, percent shrub, and percent litter, and four secondary components including percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata wyomingensis), and shrub height (centimeters). Results were validated with an independent accuracy assessment, with root mean square error values ranging from 3.5 (percent big sagebrush) to 10.8 (percent bare ground) at the QuickBird scale, and from 4.5 (percent Wyoming sagebrush) to 12.4 (percent herbaceous) at the full Landsat scale. These results offer significant improvement in sagebrush ecosystem quantification across the Gunnison Basin, and also provide maximum flexibility to users to employ for a wide variety of applications. Further refinement of these remote sensing component predictions in the future will be most likely achieved by focusing on more extensive ground plot sampling, employing new high and moderate-resolution satellite sensors that offer additional spectral bands for vegetation discrimination, and capturing more dates of satellite imagery to better represent phenological variation.

Colorado↗

USGS2019

Using the precipitation-runoff modeling system to predict seasonal water availability in the upper Klamath River basin, Oregon and California

Accurate forecasts of the streamflow expected during late spring and summer in the Upper Klamath River Basin in southern-central Oregon and northern California are used by water management agencies to balance water allocations for agriculture, aquatic habitat, and hydropower-production needs. Streamflow forecasts are also used by irrigation farmers for planning. The forecasts are typically made twice a month starting as early in the water year as December. Multiple regression equations relating real-time snowpack and precipitation conditions to seasonal streamflow volumes have been used for many years in forecasting. However, with warming temperature trends and lower snowpack, such forecasts based on historical data could become less reliable in the future. If the timing and relation of snowpack and precipitation are outside of the range of the historical data used to create the equations, the forecasts become extrapolations. Statistical forecast equations are also limited in their ability to forecast streamflow in groundwater-dominated basins having inter-annual lag. As an additional method for seasonal streamflow forecasting, a physical-process-based hydrologic model employing the Precipitation-Runoff Modeling System (PRMS) was developed in cooperation with the U.S. Bureau of Reclamation for the Upper Klamath Basin in this study. The model was calibrated for the portion of the basin draining into Upper Klamath Lake. PRMS is a deterministic, distributed-parameter, physical-process-based modeling system developed by the U.S. Geological Survey. It simulates daily streamflow, snow, solar radiation, evapotranspiration, surface-water, and groundwater processes within the basin. A model calibration and validation period for water years 2000–15 and water years 1984–99, respectively, was used. The model was calibrated and validated using measured streamflow, snowpack, evapotranspiration, and solar radiation data sets. Interpolated daily precipitation and air temperature data from 32 meteorological stations within and surrounding the Upper Klamath Basin were used as model input. Performance statistics, used to evaluate how well simulated daily streamflow matched with measured streamflow included percent bias, percent relative error, and root-mean-square error. The statistics were computed annually, monthly, for October–March, and for April–September. With the exception of the October–March period, percent bias statistics were all within plus or minus 5-percent for both the calibration and validation periods. Limitations to using the model are error in the precipitation and air temperature input time series data, which include measurement error and error in the spatial interpolation method. Other errors include measured daily streamflow data, which were adjusted for consumptive use losses to make them more closely resemble natural streamflow for calibration. The model developed for the Upper Klamath Basin can be used to forecast streamflow from the Sprague and Williamson River Basins and inflow to Upper Klamath Lake. Reliable forecasts at these locations are needed for managing water for irrigation, ecosystem health, and power production. Using the models in a forecast application requires assembling model input data sets of anticipated daily precipitation and minimum and maximum air temperature for the period after the date the forecast is made and the end of the forecasted period. These climate data sets can be based on historical or synthetic records, at the discretion of the forecaster. With the Ensemble Streamflow Prediction method, a suite of streamflow scenarios is simulated using multiple years of climate data as model input. The forecasted streamflow is determined from knowing the exceedance probabilities of the simulated streamflows. In this study, the model and the Ensemble Streamflow Prediction method were used to forecast the volume of inflow to Upper Klamath Lake for a 6-month period from April 1, 2015, to September 30, 2015, using a range of climate data sets based on El Niño Southern Oscillation (ENSO) criteria. Because 2015 was a warm phase ENSO period, climate data for 10 warm phase ENSO years from 1980 to 2010 were used as input to the model. The simulated April–September 2015 UKL inflow volume based on measured 2015 climate data was 482,000 acre-feet, which was very close to the 50th percent exceedance probability computed from 10 simulated scenarios that used warm phase ENSO climate input data from 1980–2010.

California, Oregon↗

USGS2006

Modeling approaches in avian conservation and the role of field biologists

This review grew out of our realization that models play an increasingly important role in conservation but are rarely used in the research of most avian biologists. Modelers are creating models that are more complex and mechanistic and that can incorporate more of the knowledge acquired by field biologists. Such models require field biologists to provide more specific information, larger sample sizes, and sometimes new kinds of data, such as habitat-specific demography and dispersal information. Field biologists need to support model development by testing key model assumptions and validating models. The best conservation decisions will occur where cooperative interaction enables field biologists, modelers, statisticians, and managers to contribute effectively. We begin by discussing the general form of ecological models—heuristic or mechanistic, "scientific" or statistical—and then highlight the structure, strengths, weaknesses, and applications of six types of models commonly used in avian conservation: (1) deterministic single-population matrix models, (2) stochastic population viability analysis (PVA) models for single populations, (3) metapopulation models, (4) spatially explicit models, (5) genetic models, and (6) species distribution models. We end by considering their unique attributes, determining whether the assumptions that underlie the structure are valid, and testing the ability of the model to predict the future correctly.

Ornithological Monographs↗

USGS1994

Spatial variability in biodegradation rates as evidenced by methane production from an aquifer

Accurate predictions of carbon and energy cycling rates in the environment depend on sampling frequencies and on the spatial variability associated with biological activities. We examined the variability associated with anaerobic biodegradation rates at two sites in an alluvial sand aquifer polluted by municipal landfill leachate. In situ rates of methane production were measured for almost a year, using anaerobic wells installed at two sites. Methane production ranged from 0 to 560 μmol · m -2 · day -1 at one site (A), while a range of 0 to 120,000 μmol · m -2 · day -1 was measured at site B. The mean and standard deviations associated with methane production at site A were 17 and 57 μmol · m -2 · day -1 , respectively. The comparable summary statistics for site B were 2,000 and 9,900 μmol · m -2 · day -1 . The coefficients of variation at sites A and B were 340 and 490%, respectively. Despite these differences, the two sites had similar seasonal trends, with the maximal rate of methane production occurring in summer. However, the relative variability associated with the seasonal rates changed very little. Our results suggest that (i) two spatially distinct sites exist in the aquifer, (ii) methanogenesis is a highly variable process, (iii) the coefficient of variation varied little with the rate of methane production, and (iv) in situ anaerobic biodegradation rates are lognormally distributed.

Applied and Environmental Microbiology↗

USGS2024

Spatial and seasonal variability in trophic relationships and carbon sources of two key invertebrate species in Lake Ontario

Mysids ( Mysis diluviana) and dreissenids ( Dreissena polymorpha and mostly D. bugensis ) are important invertebrate taxa in the food webs of the Laurentian Great Lakes but there are uncertainties about the seasonal and spatial variability in their stable isotope signatures. We quantified δ 13 C and δ 15 N in 304 mysid and 366 dreissenid samples across five spatial ecoregions, varying site depth, and three seasons (spring, summer, and fall) in Lake Ontario in 2012 and 2013. Particulate organic matter (POM) was also collected across site depth and season from the Deep Hole ecoregion for use as an isotopic baseline. Lipid normalization models for δ 13 C were generated for both taxa to reduce lipid bias in our statistical analysis. Season was a significant predictor of POM stable isotopes, with δ 13 C lower in the summer and δ 15 N decreasing from spring to summer before increasing into fall. Mysid lipid normalized δ 13 C varied by site depth and ecoregion while δ 15 N decreased across season and did not vary by site depth or ecoregion. Dreissenid stable isotopes varied significantly across season, depth, and ecoregion, with site depth having positive relationship with δ 15 N. Mysids and dreissenids were two trophic positions higher than POM based on δ 15 N; this comparison was restricted to the one region where POM was collected. Isotopic variability suggested selective feeding within POM and differing trophic pathways between mysids and dreissenids. Collecting an appropriate taxon across all observed variables to serve as an isotopic baseline, particularly in spatial and temporal studies, is critical to the correct interpretation of trophic relationships.

Lake Ontario↗

USGS2009

Ground and surface temperature variability for remote sensing of soil moisture in a heterogeneous landscape

At the Little River Watershed (LRW) heterogeneous landscape near Tifton Georgia US an in situ network of stations operated by the US Department of Agriculture-Agriculture Research Service-Southeast Watershed Research Lab (USDA-ARS-SEWRL) was established in 2003 for the long term study of climatic and soil biophysical processes. To develop an accurate interpolation of the in situ readings that can be used to produce distributed representations of soil moisture (SM) and energy balances at the landscape scale for remote sensing studies, we studied (1) the temporal and spatial variations of ground temperature (GT) and infra red temperature (IRT) within 30 by 30 m plots around selected network stations; (2) the relationship between the readings from the eight 30 by 30 m plots and the point reading of the network stations for the variables SM, GT and IRT; and (3) the spatial and temporal variation of GT and IRT within agriculture landuses: grass, orchard, peanuts, cotton and bare soil in the surrounding landscape. The results showed high correlations between the station readings and the adjacent 30 by 30 m plot average value for SM; high seasonal independent variation in the GT and IRT behavior among the eight 30 by 30 m plots; and site specific, in-field homogeneity in each 30 by 30 m plot. We found statistical differences in the GT and IRT between the different landuses as well as high correlations between GT and IRT regardless of the landuse. Greater standard deviations for IRT than for GT (in the range of 2-4) were found within the 30 by 30 m, suggesting that when a single point reading for this variable is selected for the validation of either remote sensing data or water-energy models, errors may occur. The results confirmed that in this landscape homogeneous 30 by 30 m plots can be used as landscape spatial units for soil moisture and ground temperature studies. Under this landscape conditions small plots can account for local expressions of environmental processes, decreasing the errors and uncertainties in remote sensing estimates caused by landscape heterogeneity.

Journal of Hydrology↗

USGS2002

Spatial and temporal distribution of horseshoe crab (Limulus polyphemus) Spawning Delaware Bay: Implications for monitoring

Concern for the status of horseshoe crab ( Limulus polyphemus ) has increased as harvest for conch and eel bait has increased and spawning habitat has decreased. In early 1999 a workshop was held at the behest of the Atlantic States Marine Fisheries Commission to design a statistically valid survey of horseshoe crab spawning in Delaware Bay. The survey that resulted was a redesign of a volunteer-based spawning survey that began in 1990, and its network of volunteers was relied on to implement the three-stage sampling design in 1999. During May and June of 1999, 163 participants surveyed during the highest of the daily high tides on 16 beaches (8 on each site of Delaware Bay). During the first half of the spawning season, spawning was associated with lunar phases, but moderated by wave height. Disproportionately more spawning occurred within 3 d of the first new and full moons, and spawning activity (measured by an index of female density) was correlated inversely to the percent of beaches with waves ≥0.3 m. Spawning was heaviest on the Delaware shore around the full moon in May in spite of low waves in New Jersey during the new and full moons in May. Number of beaches sampled was the most important factor in determining the precision of the spawning index and power to detect a decline. Explicit consideration of statistical power has been absent from the current debate on horseshoe crab status and harvest. Those who argue against harvest restrictions because of a lack of statistically significant declines take on a burden to show that the surveys they cite have high statistical power. We show the Delaware Bay spawning survey will achieve high statistical power with sufficient sampling intensity and duration. We recommend that future Delaware Bay spawning surveys sample on 3 d around each new and full moon in May and June and increase the number of beaches to ensure high statistical power to detect trends in baywide spawning activity.

Delaware, New Jersey↗

USGS2025

The joint effect of changes in urbanization and climate on trends in floods: A comparison of panel and single-station quantile regression approaches

Estimates of annual maximum (peak) flow quantiles are needed for basins undergoing changes in both urbanization and climate. Most previous work on the effect of urbanization on peak flows has considered urbanization alone and only the spatial variation in flood quantiles or its mean temporal effect, and most work on the effect of nonstationarity in climate has focused on single-station analyses, which give uncertain results for extreme quantiles. To address these gaps, three approaches to the statistical estimation of the joint effects of changes in impervious cover and climate on the estimation of peak-flow quantiles were compared: single-station quantile regression; a fixed effect panel-quantile regression (pQR) method using a location (mean) shift to homogenize the panel; and a location-scale panel regression model (pQRmom), which accounts for both scale (variance) and location effects. The different approaches were applied to a dataset consisting of instantaneous annual peak flows from 127 minimally nested basins in the midwestern United States with at least 4 % change in imperviousness. The annual maximum daily discharge from a water-balance model was selected as the primary climate predictor; in addition, to provide a comparison of climate predictors, precipitation was also considered. The coefficients from single-station regressions were usually sufficiently certain to determine the effects of climate variation but usually too uncertain to estimate the effects of urbanization. The panel-quantile regression approaches give much more certain results, but their estimates of quantile dependence differ: although both indicate urbanization effects decreasing with decreasing annual exceedance probability (AEP), the pQRmom urbanization coefficients are insignificantly different from zero for AEPs less than 0.10, whereas the pQR coefficients remain positive and are significant except for AEP = 0.01, the smallest AEP value considered. Although the location-scale structure of the pQRmom approach has less flexible quantile dependence than the pQR approach, the pQRmom approach has somewhat lower overall error, and it is found that by subsetting the dataset to homogenize the scale effects, the pQR and pQRmom results become similar, indicating the insignificant urbanization coefficients for small AEPs of the pQRmom results are likely correct for the study dataset.

Arkansas, Illinois, Indiana, Iowa, Michigan, Minne↗

USGS1998

An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains

Time-integrated normalized difference vegetation index (TI NDVI) derived from the multitemporal satellite imagery (1989–1993) was used as a surrogate for primary production to investigate climate impacts on grassland performance for central and northern Great Plains grasslands. Results suggest that spatial and temporal variability in growing season precipitation, potential evapotranspiration, and growing degree days are the most important controls on grassland performance and productivity. When TI NDVI and climate data of all grassland land cover classes were examined as a whole, a statistical model showed significant positive correlation between the TI NDVI and accumulated spring and summer precipitation, and a negative correlation between TI NDVI and spring potential evapotranspiration. The coefficient of determination (R 2 ) of the general model was 0.45. When the TI NDVI-climate relationship was examined by individual land cover type, the relationship was generally better defined in terms of the variance accounted for by class-specific models . The photosynthetic pathway is an important determinant of grassland performance with northern mixed prairie (mixture of C 3 and C 4 grassland) TI NDVI affected by both thermal and moisture conditions during the growing season while southern plains grasslands (primarily C 4 grassland) were predominantly influenced by spring and summer precipitation. Grassland land cover classes associated with sandy soils also demonstrated a strong relationship between TI NDVI and growing season rainfall. Significant impact of interannual climate variability on the TI NDVI–climate relationship was also observed. The study suggests an integrated approach involving numerical models, satellite remote sensing, and field observations to monitor grassland ecosystem dynamics on a regional scale.

Remote Sensing of Environment↗