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At least 649 records · Page 36Linked to original sources
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↗
USGS2019

Spatial integration of biological and social objectives to identify priority landscapes for waterfowl habitat conservation

Waterfowl population management and habitat conservation compose one of the oldest and most successful adaptive management frameworks in the world. Since its inception, the North American Waterfowl Management Plan (NAWMP) has emphasized strategically targeted conservation investments in regions that most affect waterfowl population dynamics. By 2012, regional conservation had progressively become more science-based and strategic: many migratory bird partnerships had initiated or completed projects on mapping and modeling waterfowl distribution and abundances using geospatial techniques. However, when developing a map depicting and titled “Areas of Greatest Continental Significance to North American Ducks, Geese, and Swans” for the 2012 NAWMP Revision, waterfowl professionals articulated the need for improved decision frameworks and use of consistent datasets for refining large-scale spatial products depicting priority areas for waterfowl and people. This report describes a framework for developing a spatial value model to support the identification of North American geographies of importance to waterfowl during the breeding and non-breeding periods and to resource users who could potentially support (financially and (or) politically) waterfowl habitat conservation. Objectives used to identify priority geographies were determined through a collaborative process of the NAWMP Science Support Team, Priority Landscapes Committee (PLC), and other experts in the fields of waterfowl biology and ecology, environmental science, and human dimensions. ArcGIS Desktop was used as the platform for managing, analyzing, combining and displaying the spatial data as well as producing new data through spatial analysis functions. Thirty-eight spatial layers were developed, and several composite spatially explicit products (maps of North America) were produced based on PLC recommendations. The composite products have extensive similarities to the 2012 NAWMP map depicting areas of greatest continental significance to North American waterfowl. There are also some differences, especially in regions of the high Arctic and in Mexico. These differences between spatial value model maps and the 2012 NAWMP output likely arose from inclusion of social objectives, reduced dependence on expert opinion to generate abundance estimates, lack of population surveys in some regions and availability of expanded survey data in other regions, and use of model-based waterfowl population estimates for some unsurveyed areas. The structured decision-making framework application in this study is discussed, and the appropriate use of the products and their limitations are outlined. Additionally, options for future improvements are presented by identifying gaps in data collection, waterfowl-habitat association assumptions, and uncertainties related to social objectives. These spatial products are intended for use by national, regional, and province/state level wildlife professionals to aid their decisions in targeting waterfowl habitat conservation.

Open-File Report↗
USGS2022

Hydrology and water quality in 15 watersheds in DeKalb County, Georgia, 2012–16

The U.S. Geological Survey, in cooperation with DeKalb County Department of Watershed Management, established a long-term water-quantity and water-quality monitoring program in 2012 to monitor and analyze the hydrologic and water-quality conditions of 15 watersheds in DeKalb County, Georgia—an urban and suburban area located in north-central Georgia that includes the easternmost part of the City of Atlanta. This report synthesizes the watershed characteristics and monitoring data collected for the first 5 years of the program, 2012 through 2016. The study area was predominantly medium-density residential (43.9 percent), commercial/industrial/institutional (21.4 percent), forest/park/agriculture (13.6 percent), and high-density residential (11.5 percent) land uses. Land-surface slope averaged 8.7 percent, imperviousness averaged 25.3 percent, and population density averaged 2,936 people per square mile. Watershed imperviousness ranged from 8.7 to 36.6 percent. In the study area for 2014 to 2016 (when streamflow data were available for all watersheds), runoff represented 40.9 percent of precipitation. Hydrograph separations indicated that 43 percent of runoff occurred as base flow, whereas the remainder occurred as stormflow. Higher watershed imperviousness was significantly related to higher amounts of runoff (Pearson product-moment correlation coefficient [ r ] = 0.517), higher runoff ratios ( r = 0.646), and lower amounts ( r = −0.637) and proportions ( r = −0.898) of base-flow runoff. Stormwater best management practices have been implemented in the study watersheds; however, these practices do not appear to fully mitigate the effects of urban development and land use on stream hydrology. Total copper, lead, and zinc concentrations in base-flow and stormflow samples exceeded the national recommended aquatic life criteria for chronic and acute conditions, respectively, to varying degrees. Escherichia coli density predictive regression models indicated that the U.S. Environmental Protection Agency’s Beach Action Value was exceeded at individual watersheds between 44.6 and 100 percent of the time. Exceedance of the Beach Action Value indicates possible unsafe conditions for primary contact recreation and could be used for timely notification of the potential health risks. Annual loads and yields were estimated for 15 constituents. Loads were typically higher for years with higher runoff while variations among watershed yields appear associated with watershed and land use characteristics. The lowest yields for almost all constituents occurred in the Stone Mountain Creek watershed—likely the result of the retention of sediment and reduction of nutrients in Stone Mountain Lake and two smaller downstream reservoirs within the watershed. The Little Stone Mountain Creek watershed also had some of the lowest yields for most constituents, likely due to the lack of many pollutant sources associated with its predominantly medium-density residential land use (95.5 percent), but had the highest total nitrate plus nitrite yields. The Intrenchment Creek watershed consistently had some of the highest yields across all constituents except for total nitrate plus nitrite. The high yields may be related to its high percentage of impervious area (36.0 percent) and high amount of heavily developed land use (high-density residential, 29.9 percent and commercial/industrial/institutional, 26.0 percent). Mean watershed constituent yields in this study were significantly higher than those from a similar analysis of 13 suburban to urban watersheds in adjacent Gwinnett County for 6 of the 10 constituents compared. This study provides a thorough assessment of watershed characteristics, hydrology, and water-quality conditions of the 15 study watersheds and can be used to identify possible factors that affect runoff and water quality. Watershed managers can use these data and analyses to inform management decisions regarding the designated uses of streams, minimization of flooding, protection of aquatic habitats, and optimization of the effectiveness of best management practices.

Georgia↗
USGS2007

Effective groundwater model calibration: With analysis of data, sensitivities, predictions, and uncertainty

Methods and guidelines for developing and using mathematical models Turn to Effective Groundwater Model Calibration for a set of methods and guidelines that can help produce more accurate and transparent mathematical models. The models can represent groundwater flow and transport and other natural and engineered systems. Use this book and its extensive exercises to learn methods to fully exploit the data on hand, maximize the model's potential, and troubleshoot any problems that arise. Use the methods to perform: Sensitivity analysis to evaluate the information content of data Data assessment to identify (a) existing measurements that dominate model development and predictions and (b) potential measurements likely to improve the reliability of predictions Calibration to develop models that are consistent with the data in an optimal manner Uncertainty evaluation to quantify and communicate errors in simulated results that are often used to make important societal decisions Most of the methods are based on linear and nonlinear regression theory. Fourteen guidelines show the reader how to use the methods advantageously in practical situations. Exercises focus on a groundwater flow system and management problem, enabling readers to apply all the methods presented in the text. The exercises can be completed using the material provided in the book, or as hands-on computer exercises using instructions and files available on the text's accompanying Web site. Throughout the book, the authors stress the need for valid statistical concepts and easily understood presentation methods required to achieve well-tested, transparent models. Most of the examples and all of the exercises focus on simulating groundwater systems; other examples come from surface-water hydrology and geophysics. The methods and guidelines in the text are broadly applicable and can be used by students, researchers, and engineers to simulate many kinds systems.

Book↗
USGS2024

Counting the chorus: A bioacoustic indicator of population density

Passive acoustic monitoring has grown in utility for tracking wildlife populations, although challenges remain when using acoustic detections to monitor population size and density. Distance sampling is considered the ‘gold standard’ for estimating animal densities but has several important limitations, especially for rare, cryptic, and high-density species. Here, we test the performance of a simple, quickly derived bioacoustic indicator for monitoring population density: call density—the proportion of recording samples containing vocalizations. Over three years, we collected synchronized bioacoustic and point-transect distance sampling data for eight forest bird species native to the Island of Hawai‘i, including four endangered species, across diverse ecosystems ranging from subalpine dry woodland to montane rainforest. The species studied exhibit varied population structures, from gregarious flocks to small, territorial family groups. Our results revealed significant, strong correlations between call density and distance sampling-based animal density estimates for all species, demonstrating that call density is a reliable indicator of animal density that can be used independently or in combination with traditional monitoring methods. Our analysis uses a fixed amount of manual validation of machine learning classifier output examples, without requiring prohibitively high classifier performance, and is robust to variation in vocal activity rates across time and space, making it both adaptable and scalable. This approach could enhance passive acoustic monitoring by providing a more sensitive population health indicator than commonly used detection/nondetection methods, facilitating prompt conservation and management decisions, particularly for species that are difficult to monitor with distance sampling.

Hawaii↗
USGS1999

The insignificance of statistical significance testing

Despite their use in scientific journals such as The Journal of Wildlife Management , statistical hypothesis tests add very little value to the products of research. Indeed, they frequently confuse the interpretation of data. This paper describes how statistical hypothesis tests are often viewed, and then contrasts that interpretation with the correct one. I discuss the arbitrariness of P-values, conclusions that the null hypothesis is true, power analysis, and distinctions between statistical and biological significance. Statistical hypothesis testing, in which the null hypothesis about the properties of a population is almost always known a priori to be false, is contrasted with scientific hypothesis testing, which examines a credible null hypothesis about phenomena in nature. More meaningful alternatives are briefly outlined, including estimation and confidence intervals for determining the importance of factors, decision theory for guiding actions in the face of uncertainty, and Bayesian approaches to hypothesis testing and other statistical practices.

Journal of Wildlife Management↗
USGS1978

Understanding natural systems; a perspective for land-use planning in Appalachian Kentucky

An eight-county area at the headwaters of the Kentucky River has been designated the Kentucky River Area Development District (KRADD) by the Appalachian Regional Commission. The objective of the project described in this report has been to provide materials to KRADD planners in a format and containing terminology usable by local people untrained in earth science. Experimental maps (not included in this report) have been prepared largely from preexisting data. Time and cost limitations required a regional analysis as well as somewhat more detailed examples of selected localities. Most of the maps produced to meet these needs show the abundance and distribution of naturally occurring materials and the areas affected by various geomorphic processes. Three types of maps, showing current land use, slope, and flood-prone areas, present both basic and derived data directly applicable to specific land-use decisions. Basic map information on quality and quantity of surface and ground water, bedrock and surficial geology, and mineral fuels can be interpreted for a wide variety of current and potential uses. Texts accompanying the maps explain bedrock control of geomorphic processes, distribution and significance of surficial deposits, and hydrologic characteristics of the intricately dissected eastern Kentucky terrain. Within this conceptual framework, geomorphic processes and the landscape may be evaluated in humanly significant terms of low to high potential risk, thereby indicating both opportunities and limitations for land use.

Kentucky↗
USGS2014

Using a Bayesian Network to predict shore-line change vulnerability to sea-level rise for the coasts of the United States

Sea-level rise is an ongoing phenomenon that is expected to continue and is projected to have a wide range of effects on coastal environments and infrastructure during the 21st century and beyond. Consequently, there is a need to assemble relevant datasets and to develop modeling or other analytical approaches to evaluate the likelihood of particular sea-level rise impacts, such as coastal erosion, and to inform coastal management decisions with this information. This report builds on previous work that compiled oceanographic and geomorphic data as part of the U.S. Geological Survey’s Coastal Vulnerability Index (CVI) for the U.S. Atlantic coast, and developed a Bayesian Network to predict shoreline-change rates based on sea-level rise plus variables that describe the hydrodynamic and geologic setting. This report extends the previous analysis to include the Gulf and Pacific coasts of the continental United States and Alaska and Hawaii, which required using methods applied to the USGS CVI dataset to extract data for these regions. The Bayesian Network converts inputs that include observations of local rates of relative sea-level change, mean wave height, mean tide range, a geomorphic classification, coastal slope, and observed shoreline-change rates to calculate the probability of the shoreline-erosion rate exceeding a threshold level of 1 meter per year for the coasts of the United States. The calculated probabilities were compared to the historical observations of shoreline change to evaluate the hindcast success rate of the most likely probability of shoreline change. Highest accuracy was determined for the coast of Hawaii (98 percent success rate) and lowest accuracy was determined for the Gulf of Mexico (34 percent success rate). The minimum success rate rose to nearly 80 percent (Atlantic and Gulf coasts) when success included shoreline-change outcomes that were adjacent to the most likely outcome. Additionally, the probabilistic approach determines the confidence in calculated outcomes as the probability of the most likely outcome. The confidence was highest along the Pacific coast and it was lowest along the Alaskan coast.

Open-File Report↗
USGS2014

Basin-scale simulation of current and potential climate changed hydrologic conditions in the Lake Michigan Basin, United States

The Great Lakes Restoration Initiative (GLRI) is the largest public investment in the Great Lakes in two decades. A task force of 11 Federal agencies developed an action plan to implement the initiative. The U.S. Department of the Interior was one of the 11 agencies that entered into an interagency agreement with the U.S. Environmental Protection Agency as part of the GLRI to complete scientific projects throughout the Great Lakes basin. The U.S. Geological Survey, a bureau within the Department of the Interior, is involved in the GLRI to provide scientific support to management decisions as well as measure progress of the Great Lakes basin restoration efforts. This report presents basin-scale simulated current and forecast climatic and hydrologic conditions in the Lake Michigan Basin. The forecasts were obtained by constructing and calibrating a Precipitation-Runoff Modeling System (PRMS) model of the Lake Michigan Basin; the PRMS model was calibrated using the parameter estimation and uncertainty analysis (PEST) software suite. The calibrated model was used to evaluate potential responses to climate change by using four simulated carbon emission scenarios from eight general circulation models released by the World Climate Research Programme’s Coupled Model Intercomparison Project phase 3. Statistically downscaled datasets of these scenarios were used to project hydrologic response for the Lake Michigan Basin. In general, most of the observation sites in the Lake Michigan Basin indicated slight increases in annual streamflow in response to future climate change scenarios. Monthly streamflows indicated a general shift from the current (2014) winter-storage/snowmelt-pulse system to a system with a more equally distributed hydrograph throughout the year. Simulated soil moisture within the basin illustrates that conditions within the basin are also expected to change on a monthly timescale. One effect of increasing air temperature as a result of the changing climate was the appreciable increase in the length of the growing season in the Lake Michigan Basin. The increase in growing season will cause an increase in evapotranspiration across the Lake Michigan Basin, which will directly affect soil moisture and late growing season streamflows. Output from the Lake Michigan Basin PRMS model is available through an online dynamic web mapping service available at (http://pubs.usgs.gov/sir/2014/5175/). The map service includes layers for the each of the 8 global climate models and 4 carbon emission scenarios combinations for 12 hydrologic model state variables. The layers are pre-rendered maps of annual hydrologic response from 1977 through 2099 that provide an easily accessible online method to examine climate change effects across the Lake Michigan Basin.

Indiana, Illinois, Michigan, Wisconsin↗
USGS2018

Nutrient and metal loads estimated by using discrete, automated, and continuous water-quality monitoring techniques for the Blackstone River at the Massachusetts-Rhode Island State line, water years 2013–14

Flow-proportional composite water samples were collected in water years 2013 and 2014 by the U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, from the Blackstone River at Millville, Massachusetts (U.S. Geological Survey station 01111230), about 0.5 mile from the border with Rhode Island. Samples were collected in order to better understand the dynamics of selected nutrient and metal constituents, assist with planning, guide activities to meet water-quality goals, and provide real-time water-quality information to the public. An automated system collected the samples at 14-day intervals to determine total and dissolved nitrogen and phosphorus concentrations, to provide accurate monthly nutrient concentration data, and to calculate monthly load estimates. Concentrations of dissolved trace metals and total aluminum were determined from 4-day composite water samples that were collected twice monthly by the automated system. Results from 4-day composites provide stakeholders with information to evaluate trace metals on the basis of chronic 4-day exposure criteria for aquatic life, and the potential to use the biotic ligand model to evaluate copper concentrations. Nutrient, trace metal, suspended sediment, dissolved organic carbon, and chlorophyll a concentrations were determined from discrete samples collected at the Millville station and from across the stream transect at the upstream railroad bridge, and these concentrations served as a means to evaluate the representativeness of the Millville point location. Analytical results from samples collected with the automated flow-proportional sampling system provided the means to calculate monthly and annual loading data. Total nitrogen and total phosphorus loads in water year (WY) 2013 were about 447,000 and 36,000 kilograms (kg), respectively. In WY 2014, annual loads of total nitrogen and total phosphorus were about 342,000 and 21,000 kg, respectively. Total nitrogen and total phosphorus loads from WYs 2013 and 2014 were about 56 and 65 percent lower than those reported for WYs 2008 and 2009. The higher loads in 2008 and 2009 may be explained by the higher than average flows in WY 2009 and by facility upgrades made by wastewater treatment facilities in the basin. Median loads were determined from composite samples collected with the automated system between October 2012 and October 2014. Median dissolved cadmium and chromium 4-day loads were 0.55 and 0.84 kg, respectively. Dissolved copper and total lead median 4-day loads were 8.02 and 1.42 kg, respectively. The dissolved nickel median 4-day load was 5.45 kg, and the dissolved zinc median 4-day load was 36 kg. Median total aluminum 4-day loads were about 197 kg. Spearman’s rank correlation analyses were used with discrete sample concentrations and continuous records of temperature, specific conductance, turbidity, and chlorophyll a to identify correlations between variables that could be used to develop regression equations for estimating real-time concentrations of constituents. Correlation coefficients were generated for flow, precipitation, antecedent precipitation, physical parameters, and chemical constituents. A 95-percent confidence limit for each value of Spearman’s rho was calculated, and multiple linear regression analysis using ordinary least squares regression techniques was used to develop regression equations for concentrations of total phosphorus, total nitrogen, suspended sediment concentration, total copper, and total aluminum. Although the correlations are based on the limited amount of data collected as part of this study, the potential to monitor water-quality changes in real time may be of value to resource managers and decision makers.

Massachusetts, Rhode Island↗
USGS2019

Rigorously valuing the role of U.S. coral reefs in coastal hazard risk reduction

The degradation of coastal habitats, particularly coral reefs, raises risks by increasing the exposure of coastal communities to flooding hazards. The protective services of these natural defenses are not assessed in the same rigorous economic terms as artificial defenses, such as seawalls, and therefore often are not considered in decision making. Here we combine engineering, ecologic, geospatial, social, and economic tools to provide a rigorous valuation of the coastal protection benefits of all U.S. coral reefs in the States of Hawaiʻi and Florida, the territories of Guam, American Samoa, Puerto Rico, and Virgin Islands, and the Commonwealth of the Northern Mariana Islands. We follow risk-based valuation approaches to map flood zones at 10-square-meter resolution along all 3,100+ kilometers of U.S. reef-lined shorelines for different storm probabilities to account for the effect of coral reefs in reducing coastal flooding. We quantify the coastal flood risk reduction benefits provided by coral reefs across storm return intervals using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis to identify their annual expected benefits, a measure of the annual protection provided by coral reefs. Based on these results, the annual protection provided by U.S. coral reefs is estimated in: avoided flooding to more than 18,180 people; avoided direct flood damages of more than \$825 million to more than 5,694 buildings; avoided flooding to more than 33 critical infrastructure facilities, including essential facilities, utility systems, and transportation systems; and avoided indirect damages of more than \$699 million in economic activity of individuals and more than \$272 million in avoided business interruption annually. Thus, the annual value of flood risk reduction provided by U.S. coral reefs is more than 18,000 lives and \$1.805 billion in 2010 U.S. dollars. These data provide stakeholders and decision makers with spatially explicit, rigorous valuation of how, where, and when U.S. coral reefs provide critical coastal storm flood reduction benefits. The overall goal is to ultimately reduce the risk to, and increase the resiliency of, U.S. coastal communities.

American Samoa, Commonwealth of the Northern Maria↗
USGS2024

Fish invasion of prairie pothole wetlands reduces amphipod abundance, a key vertebrate forage

Fishes have spread into previously fishless wetlands, likely affecting other species. In the Prairie Pothole Region of North America, the invasion of fish into wetlands is facilitated by interactions of altered land use, climate, and hydrology. We aimed to understand the effects of fishes on amphipods, which are macroinvertebrates that vertebrates rely on as forage. We hypothesized the presence and abundance of fish, particularly benthivores, would have detrimental effects on amphipod abundance. Our study design targeted a large gradient of amphipod abundances among wetlands, including very high abundances of two amphipod species: Gammarus lacustris and Hyalella azteca . We found that fishless basins had twice as many amphipods as those with fish, on average. Gammarus lacustris were not detected in the presence of Black Bullhead Ameiurus melas . The abundance of both amphipod species had negative associations with the most common fishes, Fathead Minnow Pimephales promelas and Brook Stickleback Culaea inconstans . A multivariate community analysis showed the benthivore-fish functional feeding guild was negatively associated with the amphipod community, as hypothesized. However, our study design captured several wetlands with anomalies of high abundances of both fish and amphipods, obscuring their relationships. Our results aid resource managers by confirming several fish guilds and species are associated with lower abundances of amphipods. These findings can inform resource managers who make decisions about managing for fish and wildlife; for example, they may choose to manage existing fish populations or protect existing wetlands with high amphipod densities from new fish invasions.

Wetlands↗
USGS2019

Socioecological determinants of drought impacts and coping strategies for ranching operations in the Great Plains

In Great Plains rangelands, drought is a recurring disturbance. Ranchers in this region expect to encounter drought but may not be adequately prepared for it. Efforts to encourage drought preparedness would benefit from a better understanding of the conditions under which managers make decisions to minimize the impacts of drought. We tested the direct and moderating roles of the drought hazard and the social-ecological context on drought impacts and response. This study was conducted with ranchers in western and central South Dakota and Nebraska following the drought that began in 2012. We surveyed ranchers regarding the effects of the drought and their responses and used multimodel analysis to explore the relationships among measures of drought preparedness, drought response, and drought impacts. Drought severity was the primary predictor of all impacts, but specific types of impacts were varied depending on the operation’s enterprise mix, resources, and management. The socioecological characteristics of the ranch system predicted drought response actions taken, by either providing the necessary resources and capacity to take action or creating sensitivity in the system that required action to be taken. We conclude with recommendations for learning from current drought experiences in order to better adapt to future drought events.

Rangeland Ecology and Management↗
USGS2009

Predictive Models of the Hydrological Regime of Unregulated Streams in Arizona

Three statistical models were developed by the U.S. Geological Survey in cooperation with the Arizona Department of Environmental Quality to improve the predictability of flow occurrence in unregulated streams throughout Arizona. The models can be used to predict the probabilities of the hydrological regime being one of four categories developed by this investigation: perennial, which has streamflow year-round; nearly perennial, which has streamflow 90 to 99.9 percent of the year; weakly perennial, which has streamflow 80 to 90 percent of the year; or nonperennial, which has streamflow less than 80 percent of the year. The models were developed to assist the Arizona Department of Environmental Quality in selecting sites for participation in the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program. One model was developed for each of the three hydrologic provinces in Arizona - the Plateau Uplands, the Central Highlands, and the Basin and Range Lowlands. The models for predicting the hydrological regime were calibrated using statistical methods and explanatory variables of discharge, drainage-area, altitude, and location data for selected U.S. Geological Survey streamflow-gaging stations and a climate index derived from annual precipitation data. Models were calibrated on the basis of streamflow data from 46 stations for the Plateau Uplands province, 82 stations for the Central Highlands province, and 90 stations for the Basin and Range Lowlands province. The models were developed using classification trees that facilitated the analysis of mixed numeric and factor variables. In all three models, a threshold stream discharge was the initial variable to be considered within the classification tree and was the single most important explanatory variable. If a stream discharge value at a station was below the threshold, then the station record was determined as being nonperennial. If, however, the stream discharge was above the threshold, subsequent decisions were made according to the classification tree and explanatory variables to determine the hydrological regime of the reach as being perennial, nearly perennial, weakly perennial, or nonperennial. Using model calibration data, misclassification rates for each model were 17 percent for the Plateau Uplands, 15 percent for the Central Highlands, and 14 percent for the Basin and Range Lowlands models. The actual misclassification rate may be higher; however, the model has not been field verified for a full error assessment. The calibrated models were used to classify stream reaches for which the Arizona Department of Environmental Quality had collected miscellaneous discharge measurements. A total of 5,080 measurements at 696 sites were routed through the appropriate classification tree to predict the hydrological regime of the reaches in which the measurements were made. The predictions resulted in classification of all stream reaches as perennial or nonperennial; no reaches were predicted as nearly perennial or weakly perennial. The percentages of sites predicted as being perennial and nonperennial, respectively, were 77 and 23 for the Plateau Uplands, 87 and 13 for the Central Highlands, and 76 and 24 for the Basin and Range Lowlands.

Open-File Report↗
USGS2010

Developing an ecosystem services online decision support tool to assess the impacts of climate change and urban growth in the Santa Cruz watershed: Where we live, work, and play

Using respective strengths of the biological, physical, and social sciences, we are developing an online decision support tool, the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM), to help promote the use of information relevant to water allocation and land management in a binational watershed along the U.S.-Mexico border. The SCWEPM will include an ES valuation system within a suite of linked regional driver-response models and will use a multicriteria scenario-evaluation framework that builds on GIS analysis and spatially-explicit models that characterize important ecological, economic, and societal endpoints and consequences that are sensitive to climate patterns, regional water budgets, and regional LULC change in the SCW.

Arizona↗
USGS2024

Predicted potential for aquatic exposure effects of per- and polyfluorinated alkyl substances (PFAS) in Pennsylvania’s statewide network of streams

Per- and polyfluoroalkyl substances (PFAS) are contaminants that can lead to adverse health effects in aquatic organisms, including reproductive toxicity and developmental abnormalities. To assess the ecological health risk of PFAS in Pennsylvania stream surface water, we conducted a comprehensive analysis that included both measured and predicted estimates. The potential combined exposure effects of 14 individual PFAS to aquatic biota were estimated using the sum of exposure-activity ratios ( Σ EARs) in 280 streams. Additionally, machine learning techniques were utilized to predict potential PFAS exposure effects in unmonitored stream reaches, considering factors such as land use, climate, and geology. Leveraging a tailored convolutional neural network (CNN), a validation accuracy of 78% was achieved, directly outperforming traditional methods that were also used, such as logistic regression and gradient boosting (accuracies of ~65%). Feature importance analysis highlighted key variables that contributed to the CNN’s predictive power. The most influential features highlighted the complex interplay of anthropogenic and environmental factors contributing to PFAS contamination in surface waters. Industrial and urban land cover, rainfall intensity, underlying geology, agricultural factors, and their interactions emerged as key determinants. These findings may help to inform biotic sampling strategies, water quality monitoring efforts, and policy decisions aimed to mitigate the ecological impacts of PFAS in surface waters.

Pennsylvania↗
USGS2018

Landsat time series analysis of fractional plant cover changes on abandoned energy development sites

Oil and natural gas development in the western United States has increased substantially in recent decades as technological advances like horizontal drilling and hydraulic fracturing have made extraction more commercially viable. Oil and gas pads are often developed for production, and then capped, reclaimed, and left to recover when no longer productive. Understanding the rates, controls, and degree of recovery of these reclaimed well sites to a state similar to pre-development conditions is critical for energy development and land management decision processes. Here we use a multi-decadal time series of satellite imagery (Landsat 5, 1984–2011) to assess vegetation regrowth on 365 abandoned well pads located across the Colorado Plateau in Utah, Colorado, and New Mexico. We developed high-frequency time series of the Soil-Adjusted Total Vegetation Index (SATVI) for each well pad using the Google Earth Engine cloud computing platform. BFAST time-series models were used to fit temporal trends, identifying when vegetation was cleared from the site and the magnitudes and rates of vegetation change after abandonment. The time series metrics are used to calculate the relative fractional vegetation cover (RFVC) of each pad, a measure of post-abandonment vegetation cover relative to pre-drilling condition. Mean and median RFVC were 36% (s.d. 33%) and 26%, respectively, five years after abandonment, with one third of well pads having RFVC greater than 50%. Statistical analyses suggest that much of the high vegetation cover is associated with weedy invasive annual species such as cheatgrass (Bromus tectorum) and Russian thistle (Salsola spp.). Climate conditions and the year of abandonment also play a role, with increased cover in later years associated with a wetter period. Non-linear change at many pads suggests longer recovery times than would be estimated by linear extrapolation. New techniques implemented here address a complex response of cover change to soils, management, and climate over time, and can be extended to the operational monitoring of energy development across large areas.

International Journal of Applied Earth Observation↗
USGS2014

Causal networks clarify productivity-richness interrelations, bivariate plots do not

Perhaps no other pair of variables in ecology has generated as much discussion as species richness and ecosystem productivity, as illustrated by the reactions by Pierce (2013) and others to Adler et al.'s (2011) report that empirical patterns are weak and inconsistent. Adler et al. (2011) argued we need to move beyond a focus on simplistic bivariate relationships and test mechanistic, multivariate causal hypotheses. We feel the continuing debate over productivity–richness relationships (PRRs) provides a focused context for illustrating the fundamental difficulties of using bivariate relationships to gain scientific understanding. Pierce (2013) disputes Adler et al.'s (2011) conclusion that bivariate productivity–richness relationships (PRRs) are ‘weak and variable’. He argues, instead, that relationships in the Adler et al. data are actually strong and, further, that failure to adhere to the humped-back model (HBM; sensu Grime 1979) threatens scientists' ability to advise conservationists. Here, we show that Pierce's reanalyses are invalid, that statistically significant boundary relations in the Adler et al. data are difficult to detect when proper methods are used and that his advice neither advances scientific understanding nor provides the quantitative forecasts actually needed by decision makers. We begin by examining Grimes' HBM through the lens of causal networks. We first translate the ideas contained in the HBM into a causal diagram, which shows explicitly how multiple processes are hypothesized to control biomass production and richness and their interrelationship. We then evaluate the causal diagram using structural equation modelling and example data from a published study of meadows in Finland. Formal analysis rejects the literal translation of the HBM and reveals additional processes at work. This exercise shows how the practice of abstracting systems as causal networks (i) clarifies possible hypotheses, (ii) permits explicit testing and (iii) provides more powerful and useful predictions. Building on the Finnish meadow example, we contrast the utility of bivariate plots compared with structural equation models for investigating underlying processes. Simulations illustrate the fallibility of bivariate analysis as a means of supporting one theory over another, while models based on causal networks can quantify the sensitivity of diversity patterns to both management and natural constraints. A key piece of Pierce's critique of Adler et al.'s conclusions relies on upper boundary regression which he claims to reveal strong relationships between production and richness in Adler et al.'s original data. We demonstrate that this technique shows strong associations in purely random data and is invalid for Adler et al.'s data because it depends on a uniform data distribution. We instead perform quantile regression on both the site-level summaries of the data and the plot-level data (using mixed-model quantile regression). Using a variety of nonlinear curve-fitting approaches, we were unable to detect a significant humped-shape boundary in the Adler et al. data. We reiterate that the bivariate productivity–richness relationships in Adler et al.'s data are weak and variable. We urge ecologists to consider productivity–richness relationships through the lens of causal networks to advance our understanding beyond bivariate analysis. Further, we emphasize that models based on a causal network conceptualization can also provide more meaningful guidance for conservation management than can a bivariate perspective. Measuring only two variables does not permit the evaluation of complex ideas nor resolve debates about underlying mechanisms.

Functional Ecology↗