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USGS2018

Effects of contemporary land-use and land-cover change on the carbon balance of terrestrial ecosystems in the United States

Changes in land use and land cover (LULC) can have profound effects on terrestrial carbon dynamics, yet their effects on the global carbon budget remain uncertain. While land change impacts on ecosystem carbon dynamics have been the focus of numerous studies, few efforts have been based on observational data incorporating multiple ecosystem types spanning large geographic areas over long time horizons. In this study we use a variety of synoptic-scale remote sensing data to estimate the effect of LULC changes associated with urbanization, agricultural expansion and contraction, forest harvest, and wildfire on the carbon balance of terrestrial ecosystems (forest, grasslands, shrublands, and agriculture) in the conterminous United States (i.e. excluding Alaska and Hawaii) between 1973 and 2010. We estimate large net declines in the area of agriculture and forest, along with relatively small increases in grasslands and shrublands. The largest net change in any class was an estimated gain of 114 865 km 2 of developed lands, an average rate of 3282 km 2 yr −1 . On average, US ecosystems sequestered carbon at an annual rate of 254 Tg C yr −1 . In forest lands, the net sink declined by 35% over the study period, largely a result of land-use legacy, increasing disturbances, and reductions in forest area due to land use conversion. Uncertainty in LULC change data contributed to a ~16% margin of error in the annual carbon sink estimate prior to 1985 (approximately ±40 Tg C yr −1 ). Improvements in LULC and disturbance mapping starting in the mid-1980s reduced this uncertainty by ~50% after 1985. We conclude that changes in LULC are a critical component to understanding ecosystem carbon dynamics, and continued improvements in detection, quantification, and attribution of change have the potential to significantly reduce current uncertainties.

Environmental Research Letters↗

USGS2023

Elevation-based probabilistic mapping of irregularly flooded wetlands along the northern Gulf of Mexico coast

Irregularly flooded wetlands are found above the mean high water tidal datum and are exposed to tides and saltwater less frequently than daily. These wetlands provide important ecosystem services, such as providing habitat for fish and wildlife, enhancing water quality, ameliorating flooding impacts, supporting coastal food webs, and protecting upslope areas from erosion. Mapping irregularly flooded wetlands is challenging given their expansive coverage and dynamic nature. Furthermore, coastal wetlands are expected to change over the coming century due to sea-level rise and changes in the frequency and intensity of extreme storms. Consequently, coastal managers need baseline information on the spatial distribution of wetlands along with efficient and repeatable methods for observing changes. In this study, we used coastal wetlands from existing land use land cover data, best available lidar-derived digital elevation models, and Monte Carlo simulations to incorporate elevation uncertainty to create a probabilistic map of irregularly flooded wetlands along the northern Gulf of Mexico coast (USA). Our approach integrated findings from a review of coastal wetland elevation error in lidar datasets and an analysis of spatial autocorrelations of wetland elevation. We found a positive correlation ( r = 0.563, p < 0.0001) when comparing the probability estimated from a digital elevation model and in situ elevation observations. The differences in probability had a mean bias error of −0.04 (i.e., digital elevation model-based probability tends to be slightly lower), a mean absolute error of 0.20, and a root mean square error of 0.26. Beyond this overall validation, we explored error metrics for land cover classes and lidar collection details. To quantify areal coverage of the probabilistic output, we classified the probability values into equal bins using an interval of 0.33. The areal coverage of the lowest probability bin (“unlikely”; probability ≤0.33) was separated into the upper and lower portions of the irregularly flooded wetland zone. Of the coastal wetlands along the northern Gulf of Mexico coast about 38% were classified as unlikely and low with the greatest coverage in south Louisiana and the Everglades and around 33% were classified as unlikely and high with the greatest coverage in the Everglades and Texas. The relative coverage within the highest probability bin (“likely”; probability >0.66) covered around 13%, with the greatest coverage in south Florida, south Louisiana, and Texas. The framework developed in this study can be transferred to other coastal wetland areas and updated to observe changes with sea-level rise.

Gulf of Mexico coastline↗

USGS2024

Assessing the vertical accuracy of digital elevation models by quality level and land cover

The vertical accuracy of elevation data in coastal environments is critical because small variations in elevation can affect an area’s exposure to waves, tides, and storm-related flooding. Elevation data contractors typically quantify the vertical accuracy of lidar-derived digital elevation models (DEMs) on a per-project basis to gauge whether the datasets meet quality and accuracy standards. Here, we collated over 5200 contractor elevation checkpoints along the Atlantic and Gulf of Mexico coasts of the United States that were collected for project-level analyses produced for assessing DEMs acquired for the U.S. Geological Survey’s Three-Dimensional Elevation Program. We used land cover data to quantify non-vegetated vertical accuracy and vegetated vertical accuracy statistics (overall and by point spacing bins) and assessed elevation error by land cover class. We found the non-vegetated vertical accuracy had an overall root mean square error of 6.9 cm and vegetated areas had a 95th percentile vertical error of 22.3 cm. Point spacing was generally positively correlated to elevation accuracy, but sample size limited the ability to interpret results from accuracy by land cover, particularly in wetlands. Based on the specific questions a researcher may be asking, use of literature or fieldwork could assist with enhancing error statistics in underrepresented classes.

Remote Sensing Letters↗

USGS2012

Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States

The Great Plains of the United States has undergone extensive land-use and land-cover change in the past 150 years, with much of the once vast native grasslands and wetlands converted to agricultural crops, and much of the unbroken prairie now heavily grazed. Future land-use change in the region could have dramatic impacts on ecological resources and processes. A scenario-based modeling framework is needed to support the analysis of potential land-use change in an uncertain future, and to mitigate potentially negative future impacts on ecosystem processes. We developed a scenario-based modeling framework to analyze potential future land-use change in the Great Plains. A unique scenario construction process, using an integrated modeling framework, historical data, workshops, and expert knowledge, was used to develop quantitative demand for future land-use change for four IPCC scenarios at the ecoregion level. The FORE-SCE model ingested the scenario information and produced spatially explicit land-use maps for the region at relatively fine spatial and thematic resolutions. Spatial modeling of the four scenarios provided spatial patterns of land-use change consistent with underlying assumptions and processes associated with each scenario. Economically oriented scenarios were characterized by significant loss of natural land covers and expansion of agricultural and urban land uses. Environmentally oriented scenarios experienced modest declines in natural land covers to slight increases. Model results were assessed for quantity and allocation disagreement between each scenario pair. In conjunction with the U.S. Geological Survey's Biological Carbon Sequestration project, the scenario-based modeling framework used for the Great Plains is now being applied to the entire United States.

Great Plains↗

USGS2016

Using maximum entropy to predict suitable habitat for the endangered dwarf wedgemussel in the Maryland Coastal Plain

Species distribution modelling can be useful for the conservation of rare and endangered species. Freshwater mussel declines have thinned species ranges producing spatially fragmented distributions across large areas. Spatial fragmentation in combination with a complex life history and heterogeneous environment makes predictive modelling difficult. A machine learning approach (maximum entropy) was used to model occurrences and suitable habitat for the federally endangered dwarf wedgemussel, Alasmidonta heterodon , in Maryland's Coastal Plain catchments. Landscape-scale predictors (e.g. land cover, land use, soil characteristics, geology, flow characteristics, and climate) were used to predict the suitability of individual stream segments for A. heterodon . The best model contained variables at three scales: minimum elevation (segment scale), percentage Tertiary deposits, low intensity development, and woody wetlands (sub-catchment), and percentage low intensity development, pasture/hay agriculture, and average depth to the water table (catchment). Despite a very small sample size owing to the rarity of A. heterodon , cross-validated prediction accuracy was 91%. Most predicted suitable segments occur in catchments not known to contain A. heterodon , which provides opportunities for new discoveries or population restoration. These model predictions can guide surveys toward the streams with the best chance of containing the species or, alternatively, away from those streams with little chance of containing A. heterodon . Developed reaches had low predicted suitability for A. heterodon in the Coastal Plain. Urban and exurban sprawl continues to modify stream ecosystems in the region, underscoring the need to preserve existing populations and to discover and protect new populations.

Maryland↗

USGS2022

Incorporating interpreter variability into estimation of the total variance of land cover area estimates under simple random sampling

Area estimates of land cover and land cover change are often based on reference class labels determined by analysts interpreting satellite imagery and aerial photography. Different interpreters may assign different reference class labels to the same sample unit. This interpreter variability is typically not accounted for in variance estimators applied to area estimates of land cover. A simple measurement model provides the basis for an estimator of the total variance ( V Total ) that takes into account both sampling variance and interpreter variance. This method requires two or more reference class interpretations (i.e., repeated measurements) obtained by analysts, working independently of each other, for the full sample or a random subsample of the full sample. Estimators of the total variance ( V ̂ Total "> V̂Total ) and the variance component attributable to interpreters ( V ̂ 1 "> V̂1 ) were obtained for the case of two reference class interpretations per repeated sample unit. To evaluate the effect of interpreter variability on variance estimation, we used land cover reference data interpreted by seven analysts who each interpreted the same 300 sample pixels from a region of the Pacific Northwest of the United States. From these data, we estimated the contribution of interpreter variance to the total variance (i.e., V ̂ 1 / V ̂ Total "> V̂1/V̂Total ) and the relative bias of the standard simple random sampling variance estimator ( V ̂ stand "> V̂stand ) as an estimator of V Total , defined as 100%*( V ̂ stand − V ̂ Total "> V̂stand−V̂Total )/ V ̂ Total "> V̂Total . For each of five land cover classes, we computed V ̂ 1 "> V̂1 , V ̂ Total "> V̂Total , and V ̂ stand "> V̂stand using the sample data from each of the 21 possible pairwise combinations of the seven interpreters, and then calculated the mean of V ̂ 1 / V ̂ Total "> V̂1/V̂Total and the mean of the estimated relative bias of V ̂ stand "> V̂stand over these 21 pairs. Based on the mean of V ̂ 1 / V ̂ Total "> V̂1/V̂Total per class, interpreter variance contributed from 25% (cropland) to 76% (grass/shrub) of the total variance, indicating that interpreter variance was a non-negligible component of the total variance. Typically, the standard variance estimator, V ̂ stand "> V̂stand , underestimated the total variance with the mean estimated relative bias ranging from −3% (cropland) to −33% (grass/shrub). Classes with greater inconsistency between pairs of interpreters had larger contributions of interpreter variance to the total variance ( V ̂ 1 / V ̂ Total "> V̂1/V̂Total ) and larger negative estimated relative bias of V ̂ stand "> V̂stand . Given that interpreter variance can contribute substantially to the total variance, the repeated measurements approach offers a practical way to incorporate this variability into an estimator of the total variance.

Washington↗

USGS2015

Examining the utility of satellite-based wind sheltering estimates for lake hydrodynamic modeling

Satellite-based measurements of vegetation canopy structure have been in common use for the last decade but have never been used to estimate canopy's impact on wind sheltering of individual lakes. Wind sheltering is caused by slower winds in the wake of topography and shoreline obstacles (e.g. forest canopy) and influences heat loss and the flux of wind-driven mixing energy into lakes, which control lake temperatures and indirectly structure lake ecosystem processes, including carbon cycling and thermal habitat partitioning. Lakeshore wind sheltering has often been parameterized by lake surface area but such empirical relationships are only based on forested lakeshores and overlook the contributions of local land cover and terrain to wind sheltering. This study is the first to examine the utility of satellite imagery-derived broad-scale estimates of wind sheltering across a diversity of land covers. Using 30 m spatial resolution ASTER GDEM2 elevation data, the mean sheltering height, h s , being the combination of local topographic rise and canopy height above the lake surface, is calculated within 100 m-wide buffers surrounding 76,000 lakes in the U.S. state of Wisconsin. Uncertainty of GDEM2-derived h s was compared to SRTM-, high-resolution G-LiHT lidar-, and ICESat-derived estimates of h s , respective influences of land cover type and buffer width on h s are examined; and the effect of including satellite-based h s on the accuracy of a statewide lake hydrodynamic model was discussed. Though GDEM2 h s uncertainty was comparable to or better than other satellite-based measures of h s , its higher spatial resolution and broader spatial coverage allowed more lakes to be included in modeling efforts. GDEM2 was shown to offer superior utility for estimating h s compared to other satellite-derived data, but was limited by its consistent underestimation of h s , inability to detect within-buffer h s variability, and differing accuracy across land cover types. Nonetheless, considering a GDEM2 h s -derived wind sheltering potential improved the modeled lake temperature root mean square error for non-forested lakes by 0.72 °C compared to a commonly used wind sheltering model based on lake area alone. While results from this study show promise, the limitations of near-global GDEM2 data in timeliness, temporal and spatial resolution, and vertical accuracy were apparent. As hydrodynamic modeling and high-resolution topographic mapping efforts both expand, future remote sensing-derived vegetation structure data must be improved to meet wind sheltering accuracy requirements to expand our understanding of lake processes.

Remote Sensing of Environment↗

USGS2014

Human infrastructure and invasive plant occurrence across rangelands of southwestern Wyoming, U.S.A.

Although human influence across rural landscapes is often discussed, interactions between the native, natural systems and human activities are challenging to measure explicitly. We assessed the distribution of introduced, invasive species as related to anthropogenic infrastructure and environmental conditions across southwestern Wyoming. to discern direct correlations as well as covariate influences between land use, land cover, and abundance of invasive plants, and assess the supposition that these features affect surrounding rangeland conditions. Our sample units were 1 000 m long and extended outward from target features, which included roads, oil and gas well pads, pipelines, power lines, and featureless background sites. Sample sites were distributed across the region using a stratified, random design with a frame that represented features and land-use intensity. In addition to land-use gradients, we captured a representative, but limited, range of variability in climate, soils, geology, topography, and dominant vegetation. Several of these variables proved significant, in conjunction with distance from anthropogenic features, in regression models of invasive plant abundance. We used general linear models to demonstrate and compare associations between invasive plant frequency and Euclidian distance from features, natural logarithm transformed distances (log-linear), and environmental variables which were presented as potential covariates. We expected a steep curvilinear (log or exponential) decline trending towards an asymptote along the axis representing high abundance near features with rapid decrease beyond approximately 50–100 m. Some of the associations we document exhibit this pattern, but we also found some invasive plant distributions that extended beyond our expectations, suggesting a broader distribution than anticipated. Our results provide details that can inform local efforts for management and control of invasive species, and they provide evidence of the different associations between natural patterns and human land use exhibited by nonnative species in this rural setting, such as the indirect effects of humans beyond impact areas.

Wyoming↗

USGS2019

Geographic context affects the landscape change and fragmentation caused by wind energy facilities

Wind energy generation causes transformation of landscapes as new roads, pads, and transmission lines are constructed. Limiting the landscape change and fragmentation caused by these facilities likely minimizes impacts to biodiversity and sensitive wildlife species. We examined the effects of wind energy facilities’ geographic context on changes in landscape patterns. We digitized the footprints of 39 wind facilities and the surrounding land cover using high-resolution imagery. We then measured landscape pattern before and after facility construction using 3 metrics associated with species responses to habitat loss and fragmentation (amount, core area, and connectivity of undeveloped land) within 1km around newly constructed turbines and roads. New facilities decreased the amount of undeveloped land, on average, by just 1.8% while changes in metrics of landscape pattern were relatively much larger (50 to 140%). Statistical models indicated levels of pre-construction development were a key factor explaining the impact of new wind facilities on landscape metrics, with pre-construction road networks, turbine spacing, and topography having smaller influences. As the proportion of developed land around facilities increased, a higher proportion of the facility utilized pre-construction developed land and a lower density of new roads were built, resulting in smaller impacts to undeveloped landscapes. Building of new road networks was also a predictor of landscape fragmentation. Density of new roads was higher in places with little pre-existing development and may be influenced by facility design. Utilizing existing development and carefully placing turbines may provide opportunities to minimize the impacts of new wind energy facilities.

PeerJ↗

USGS2005

A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multi-temporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forests near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r 2 values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI "cancellation effect" where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence with normalized data. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared to some previous relative radiometric normalization methods, this new method does not require high level programming and statistical skills, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare. While this normalization method allowed detection of a range of land use, land cover, and phenological/biophysical changes in the Siberian boreal forest region studied here, it is necessary to further examine images representing a wide variety of ecoregions to thoroughly evaluate the TIC method against other normalization schemes. ?? 2005 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗

USGS2012

Future scenarios of land-use and land-cover change in the United States--the Marine West Coast Forests Ecoregion

Detecting, quantifying, and projecting historical and future changes in land use and land cover (LULC) has emerged as a core research area for the U.S. Geological Survey (USGS). Changes in LULC are important drivers of changes to biogeochemical cycles, the exchange of energy between the Earth’s surface and atmosphere, biodiversity, water quality, and climate change. To quantify the rates of recent historical LULC change, the USGS Land Cover Trends project recently completed a unique ecoregion-based assessment of late 20th century LULC change for the western United States. To characterize present LULC, the USGS and partners have created the National Land Cover Database (NLCD) for the years 1992, 2001, and 2006. Both Land Cover Trends and NLCD projects continue to evolve in an effort to better characterize historical and present LULC conditions and are the foundation of the data presented in this report. Projecting future changes in LULC requires an understanding of the rates and patterns of change, the major driving forces, and the socioeconomic and biophysical determinants and capacities of regions. The data presented in this report is the result of an effort by USGS scientists to downscale the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) to ecoregions of the conterminous United States as part of the USGS Biological Carbon Sequestration Assessment. The USGS biological carbon assessment was mandated by Section 712 of the Energy Independence and Security Act of 2007. As part of the legislative mandate, the USGS is required to publish a methodology describing, in detail, the approach to be used for the assessment. The development of future LULC scenarios is described in chapter 3.2 and appendix A. Spatial modeling is described in chapter 3.3.2 and appendix B and in Sohl and others (2011). In this report, we briefly summarize the major components and methods used to downscale IPCC-SRES scenarios to ecoregions of the conterminous United States, followed by a description of the Marine West Coast Forests Ecoregion, and lastly a description of the data being published as part of this report.

California;Oregon;Washington↗

USGS2023

Seasonal abundance and habitat associations of American Kestrels on the Southern High Plains of Texas

American Kestrel ( Falco sparverius ) populations are generally declining across the species' North American distribution but the population in the Southern High Plains region currently appears to be stable. Historical evidence suggests the region formerly had a low abundance of kestrels, and that their current numbers are due to landscape changes associated with European settlement. We conducted monthly surveys for American Kestrels across 2 yr to estimate seasonal densities and identify land cover associations in the Southern High Plains of Texas. We found an overall estimated density of 0.99 birds/km 2 (95% CI = 0.406, 1.582) across the 2-yr period, with seasonal estimated densities highest in autumn and winter (0.92–2.53/ km 2 ), and lowest in spring (0.49–0.67/km 2 ). Whereas other studies have found that temperature influenced detection of wintering kestrels, we found an interaction of drought conditions and snow most strongly influenced the number of kestrels in our study area. Kestrels largely used land cover types in proportion to availability but there was some evidence of seasonal shifts. Generally, they tended to avoid cotton fields and sometimes selected for areas with woodlots, abandoned or occupied houses, and barns, all of which likely provided nesting and roosting opportunities. Our study provides the first contemporary assessment of seasonal abundance and habitat associations of American Kestrels in the Southern High Plains, where their presence and abundance has been unintentionally facilitated by landscape changes following settlement. We provide a baseline for population monitoring and studies assessing response to additional landscape changes (e.g., development of wind energy facilities) and a changing climate.

Journal of Raptor Research↗

USGS2001

Factors affecting reservoir and stream-water quality in the Cambridge, Massachusetts, drinking-water source area and implications for source-water protection

This report presents the results of a study conducted by the U.S. Geological Survey, in cooperation with the city of Cambridge, Massachusetts, Water Department, to assess reservoir and tributary-stream quality in the Cambridge drinking-water source area, and to use the information gained to help guide the design of a comprehensive water-quality monitoring program for the source area. Assessments of the quality and trophic state of the three primary storage reservoirs, Hobbs Brook Reservoir, Stony Brook Reservoir, and Fresh Pond, were conducted (September 1997-November 1998) to provide baseline information on the state of these resources and to determine the vulnerability of the reservoirs to increased loads of nutrients and other contaminants. The effects of land use, land cover, and other drainage-basin characteristics on sources, transport, and fate of fecal-indicator bacteria, highway deicing chemicals, nutrients, selected metals, and naturally occurring organic compounds in 11 subbasins that contribute water to the reservoirs also was investigated, and the data used to select sampling stations for incorporation into a water-quality monitoring network for the source area. All three reservoirs exhibited thermal and chemical stratification, despite artificial mixing by air hoses in Stony Brook Reservoir and Fresh Pond. The stratification produced anoxic or hypoxic conditions in the deepest parts of the reservoirs and these conditions resulted in the release of ammonia nitrogen orthophosphate phosphorus, and dissolved iron and manganese from the reservoir bed sediments. Concentrations of sodium and chloride in the reservoirs usually were higher than the amounts recommended by the U.S. Environmental Protection agency for drinking-water sources (20 milligrams per liter for sodium and 250 milligrams per liter for chloride). Maximum measured sodium concentrations were highest in Hobbs Brook Reservoir (113 milligrams per liter), intermediate in Stony Brook Reservoir (62 milligrams per liter), and lowest in Fresh Pond (54 milligrams per liter). Bed sediments in Hobbs Brook and Stony Brook Reservoirs were enriched in iron, manganese, and arsenic relative to those in the impounded lower Charles River in Boston, Massachusetts. Trophic state indices, calculated for each reservoir based on nutrient concentrations, water-column transparency, and phytoplankton abundances, indicated that the upper and middle basins of Hobbs Brook Reservoir were moderately to highly productive and likely to produce algal blooms; the lower basin of Hobbs Brook Reservoir and Stony Brook Reservoir were similar and intermediate in productivity, and Fresh Pond was relatively unproductive and unlikely to produce algal blooms. This pattern is likely due to sedimentation of organic and inorganic particles in the three basins of Hobbs Brook Reservoir and in Stony Brook Reservoir. Molar ratios of nitrogen to phosphorus ranged from 55 in Stony Brook Reservoir to 120 in Hobbs Brook Reservoir, indicating that phytoplankton algae in these water bodies may be phosphorus limited and therefore sensitive to small increases in phosphorus loading from the drainage basin. Nitrogen loads were found to be less important than phosphorus to the trophic condition of the reservoirs. Hobbs Brook and Stony Brook, the two principle streams draining the Cambridge drinking-water source area, differed in their relative contributions to many of the estimated constituent loads. The estimated load of fecal coliform bacteria was more than seven times larger for the mainly residential Stony Brook subbasin upstream from Kendal Green, Mass., than it was for the more commercial and industrial Hobbs Brook subbasin, though the drainage areas of the two subbasins differ only by about 20 percent. The State standard for fecal coliform bacteria in streams in the Cambridge drinking-water source area (20 colony forming units per 100 milliliters) was exceeded at all sampling stations. Estimated s

Massachusetts↗

USGS2010

Floral and nesting resources, habitat structure, and fire influence bee distribution across an open-forest gradient

Given bees' central effect on vegetation communities, it is important to understand how and why bee distributions vary across ecological gradients. We examined how plant community composition, plant diversity, nesting suitability, canopy cover, land use, and fire history affected bee distribution across an open-forest gradient in northwest Indiana, USA, a gradient similar to the historic Midwest United States landscape mosaic. When considered with the other predictors, plant community composition was not a significant predictor of bee community composition. Bee abundance was negatively related to canopy cover and positively to recent fire frequency, bee richness was positively related to plant richness and abundance of potential nesting resources, and bee community composition was significantly related to plant richness, soil characteristics potentially related to nesting suitability, and canopy cover. Thus, bee abundance was predicted by a different set of environmental characteristics than was bee species richness, and bee community composition was predicted, in large part, by a combination of the significant predictors of bee abundance and richness. Differences in bee community composition along the woody vegetation gradient were correlated with relative abundance of oligolectic, or diet specialist, bees. Because oligoleges were rarer than diet generalists and were associated with open habitats, their populations may be especially affected by degradation of open habitats. More habitat-specialist bees were documented for open and forest/scrub habitats than for savanna/woodland habitats, consistent with bees responding to habitats of intermediate woody vegetation density, such as savannas, as ecotones rather than as distinct habitat types. Similarity of bee community composition, similarity of bee abundance, and similarity of bee richness between sites were not significantly related to proximity of sites to each other. Nestedness analysis indicated that species composition in species-poor sites was not merely a subset of species composition at richer sites. The lack of significant proximity or nestedness effects suggests that factors at a small spatial scale strongly influence bees' use of sites. The findings indicate that patterns of plant diversity, nesting resource availability, recent fire, and habitat shading, present at the scale of a few hundred meters, are key determinants of bee community patterns in the mosaic open-savanna-forest landscape. ?? 2010 by the Ecological Society of America.

Ecological Applications↗

USGS2015

Current land bird distribution and trends in population abundance between 1982 and 2012 on Rota, Mariana Islands

The western Pacific island of Rota is the fourth largest human-inhabited island in the Mariana archipelago and designated an Endemic Bird Area. Between 1982 and 2012, 12 point-transect distance-sampling surveys were conducted to assess bird population status. Surveys did not consistently sample the entire island; thus, we used a ratio estimator to estimate bird abundances in strata not sampled during every survey. Trends in population size were reliably estimated for 11 of 13 bird species, and 7 species declined over the 30-y time series, including the island collared-dove Streptopelia bitorquata , white-throated ground-dove Gallicolumba xanthonura , Mariana fruit-dove Ptilinopus roseicapilla , collared kingfisher Todiramphus chloris orii , Micronesian myzomela Myzomela rubratra , black drongo Dicrurus macrocercus , and Mariana crow Corvus kubaryi . The endangered Mariana crow (x̄ = 81 birds, 95% CI 30–202) declined sharply to fewer than 200 individuals in 2012, down from 1,491 birds in 1982 (95% CI = 815–3,115). Trends increased for white tern Gygis alba , rufous fantail Rhipidura rufifrons mariae , and Micronesian starling Aplonis opaca . Numbers of the endangered Rota white-eye Zosterops rotensis declined from 1982 to the late 1990s but returned to 1980s levels by 2012, resulting in an overall stable trend. Trends for the yellow bittern Ixobrychus sinensis were inconclusive. Eurasian tree sparrow Passer montanus trends were not assessed; however, their numbers in 1982 and 2012 were similar. Occupancy models of the 2012 survey data revealed general patterns of land cover use and detectability among 12 species that could be reliably modeled. Occupancy was not assessed for the Eurasian tree sparrow because of insufficient detections. Based on the 2012 survey, bird distribution and abundance across Rota revealed three general patterns: 1) range restriction, including Mariana crow, Rota white-eye, and Eurasian tree sparrow; 2) widespread distribution, low abundance, including collared kingfisher, island collared-dove, white-throated ground-dove, Mariana fruit-dove, white tern, yellow bittern, black drongo, and Micronesian myzomela; and 3) widespread distribution, high abundance, including rufous fantail and Micronesian starling. The Mariana crow was dispersed around the periphery of the island in steep forested land-cover types. In contrast, the Rota white-eye was restricted to the high-elevation mesa. Only for the white-throated ground-dove was there a significant difference among cover types, with lower occupancy in open field than in forested areas. Vegetation was included in the best-fit occupancy models for yellow bittern, black drongo, Micronesian myzomela, and Micronesian starling, but vegetation type was not a significant variable nor included in the top models for the remaining five species: white tern, island collared-dove, Mariana fruit-dove, collared kingfisher, and rufous fantail. Given declining population trends, the Rota bird-monitoring program could benefit from establishing threshold and alert limits and identifying alternative research and management actions. Continued monitoring and demographic sampling, in conjunction with ecological studies, are needed to understand why most bird species on Rota are declining, identify the causative agents, and assess effectiveness of conservation actions, especially for the Mariana crow.

Rota↗

USGS2011

Landscape drivers of regional variation in the relationship between total phosphorus and chlorophyll in lakes

1. For north temperate lakes, the well-studied empirical relationship between phosphorus (as measured by total phosphorus, TP), the most commonly limiting nutrient and algal biomass (as measured by chlorophyll a , CHL) has been found to vary across a wide range of landscape settings. Variation in the parameters of these TP–CHL regressions has been attributed to such lake variables as nitrogen/phosphorus ratios, organic carbon and alkalinity, all of which are strongly related to catchment characteristics (e.g. natural land cover and human land use). Although this suggests that landscape setting can help to explain much of the variation in ecoregional TP–CHL regression parameters, few studies have attempted to quantify relationships at an ecoregional spatial scale. 2. We tested the hypothesis that lake algal biomass and its predicted response to changes in phosphorus are related to both local-scale features (e.g. lake and catchment) and ecoregional-scale features, all of which affect the availability and transport of covarying solutes such as nitrogen, organic carbon and alkalinity. Specifically, we expected that land use and cover, acting at both local and ecoregional scales, would partially explain the spatial pattern in parameters of the TP–CHL regression. 3. We used a multilevel modelling framework and data from 2105 inland lakes spanning 35 ecoregions in six US states to test our hypothesis and identify specific local and ecoregional features that explain spatial heterogeneity in TP–CHL relationships. We include variables such as lake depth, natural land cover (for instance, wetland cover in the catchment of lakes and in the ecoregions) and human land use (for instance, agricultural land use in the catchment of lakes and in the ecoregions). 4. There was substantial heterogeneity in TP–CHL relationships across the 35 ecoregions. At the local scale, CHL was negatively and positively related to lake mean depth and percentage of wooded wetlands in the catchment, respectively. At the ecoregional scale, the slope parameter was positively related to the percentage of pasture in an ecoregion, indicating that CHL tends to respond more rapidly to changes in TP where there are high levels of agricultural pasture than where there is little. The intercept (i.e. the ecoregion-average CHL) was negatively related to the percentage of wooded wetlands in the ecoregion. 5. By explicitly accounting for the hierarchical nature of lake–landscape interactions, we quantified the effects of landscape characteristics on the response of CHL to TP at two spatial scales. We provide new insight into ecoregional drivers of the rate at which algal biomass responds to changes in nutrient concentrations. Our results also indicate that the direction and magnitude of the effects of certain land use and cover characteristics on lake nutrient dynamics may be scale dependent and thus likely to represent different underlying mechanisms regulating lake productivity.

Freshwater Biology↗

USGS2006

Effects of urbanization on stream ecosystems in the South Platte River basin, Colorado and Wyoming

This report describes the effects of urbanization on physical, chemical, and biological characteristics of stream ecosystems in 28 basins along an urban land-use gradient in the South Platte River Basin, Colorado and Wyoming, from 2002 through 2003. Study basins were chosen to minimize natural variability among basins due to factors such as geology, elevation, and climate and to maximize coverage of different stages of urban development among basins. Because land use or population density alone often are not a complete measure of urbanization, land use, land cover, infrastructure, and socioeconomic variables were integrated in a multimetric urban intensity index to represent the degree of urban development in each study basin. Physical characteristics studied included stream hydrology, stream temperature, and habitat; chemical characteristics studied included nutrients, pesticides, suspended sediment, sulfate, chloride, and fecal bacteria concentrations; and biological characteristics studied included algae, fish, and invertebrate communities. Semipermeable membrane devices (SPMDs), passive samplers that concentrate trace levels of hydrophobic organic contaminants like polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs), also were used. The objectives of the study were to (1) examine physical, chemical, and biological responses along the gradient of urbanization; (2) determine the major physical, chemical, and landscape variables affecting the structure of aquatic communities; and (3) evaluate the relevance of the results to the management of water resources in the South Platte River Basin. Commonly observed effects of urbanization on instream physical, chemical, and biological characteristics, such as increased flashiness, higher magnitude and more frequent peak flows, increased concentrations of chemicals, and changes in aquatic community structure, generally were not observed in this study. None of the hydrologic, temperature, habitat, or chemical variables were correlated strongly (Spearman's rho greater than or equal to 0.7) with urban intensity, with the exception of some of the SPMD-based toxicity and chemical variables. SPMD-based measures of potential toxicity and PAH concentrations were positively correlated with urban intensity. The PAH concentrations also were positively correlated with measures of road density and negatively correlated with distance to the nearest road, indicating that automobile exhaust is a major source of these compounds in the study area. This source may be localized enough that the transport of PAHs would be minimally affected by water-management practices such as diversion or storage upstream. In contrast, the predominant sources of nutrients, bacteria, suspended sediment, sulfate, chloride, and pesticides may be more dispersed throughout the drainage area and, therefore, their transport to downstream sites may be subject to greater disruption by water regulation. Although no direct link was found between most water-chemistry characteristics and urbanization, invertebrate, algae, and fish-community characteristics were strongly associated with nutrients, pesticides, sulfate, chloride, and suspended sediment. None of the biological community variables were strongly correlated with the urban intensity index. Algal biomass predominantly was associated with total nitrogen concentrations, nitrite-plus-nitrate concentrations, and the duration of high flows. Fish communities predominantly were associated with housing age, the percentage of suspended sediment finer than 0.063 millimeters and chloride concentrations. Invertebrate communities predominantly were associated with the frequency of rising and falling flow events, the duration of high flows, total nitrogen concentrations, nitrite-plus-nitrate concentrations, and total herbicide concentrations. Historical records indicate that aquatic communities in the region may have been altered prior to any substantial urban development by early agricultural and water-management practices. Present-day aquatic communities are composed primarily of tolerant species even in areas of minimal urban development; when development does occur, the communities already may be resistant to disturbance. In addition to the effects of historical stressors on aquatic community structure, it is possible that current water-management practices in the study basins are having an effect. In the absence of natural, unaltered hydrologic conditions, more sensitive taxa may be unable to recolonize urban streams. The movement and storage of water also may lead to a disconnect between the land surface and streams, resulting in instream physical, chemical, and biological characteristics that, to some degree, are independent of land-cover characteristics.

Colorado, Wyoming↗

USGS2016

San Pedro River Aquifer Binational Report

The United States and Mexico share waters in a number of hydrological basins and aquifers that cross the international boundary. Both countries recognize that, in a region of scarce water resources and expanding populations, a greater scientific understanding of these aquifer systems would be beneficial. In light of this, the Mexican and U.S. Principal Engineers of the International Boundary and Water Commission (IBWC) signed the “Joint Report of the Principal Engineers Regarding the Joint Cooperative Process United States-Mexico for the Transboundary Aquifer Assessment Program" on August 19, 2009 (IBWC-CILA, 2009). This IBWC “Joint Report” serves as the framework for U.S.-Mexico coordination and dialogue to implement transboundary aquifer studies. The document clarifies several details about the program such as background, roles, responsibilities, funding, relevance of the international water treaties, and the use of information collected or compiled as part of the program. In the document, it was agreed by the parties involved, which included the IBWC, the Mexican National Water Commission (CONAGUA), the U.S. Geological Survey (USGS), and the Universities of Arizona and Sonora, to study two priority binational aquifers, one in the San Pedro River basin and the other in the Santa Cruz River basin. This report focuses on the Binational San Pedro Basin (BSPB). Reasons for the focus on and interest in this aquifer include the fact that it is shared by the two countries, that the San Pedro River has an elevated ecological value because of the riparian ecosystem that it sustains, and that water resources are needed to sustain the river, existing communities, and continued development. This study describes the aquifer’s characteristics in its binational context; however, most of the scientific work has been undertaken for many years by each country without full knowledge of the conditions on the other side of the border. The general objective of this study is to use new and existing research to define the general hydrologic framework of the Binational San Pedro Aquifer (BSPA), to gather hydrogeological and other relevant data in preparation for future work such as an updated groundwater conceptual model and budget and to establish the basis for a binational numerical model. The specific objectives are as follows: Understand the current state of knowledge with respect to climate, geology, soils, land cover, land use, and hydrology of the aquifer in its binational context; Compile and create a database of scientific information from both countries; Identify data gaps and identify what data would be necessary to update, in a subsequent phase, the hydrologic model of the aquifer system, including surface- and groundwater interactions on a binational level. The BSPB is one of the most studied basins in the region, and a database of publications has been compiled as part of this project. Previous studies include topics that range from geophysics and hydrogeology to biology and ecosystem services. The economic drivers on each side of the border are quite different. In the Arizona 4 portion of the basin military and tourism dominate while in the Sonoran portion, mining is the most important industry. Water management is also different in the two countries. In Mexico, primary authority for management of water resources devolves from the federal government. In the United States, primary authority rests with the states except in cases of interstate surface waters. Binational waters are not currently jointly managed by the two countries except in cases where treaties have been negotiated such as for the Rio Grande and Colorado Rivers. Thus, there is currently no binational coordination or treaty governing the management of groundwater.

Report↗