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USGS2021

Linking modern pollen accumulation rates to biomass: Quantitative vegetation reconstruction in the western Klamath Mountains, NW California, USA

Quantitative reconstructions of vegetation abundance from sediment-derived pollen systems provide unique insights into past ecological conditions. Recently, the use of pollen accumulation rates (PAR, grains cm −2 year −1 ) has shown promise as a bioproxy for plant abundance. However, successfully reconstructing region-specific vegetation dynamics using PAR requires that accurate assessments of pollen deposition processes be quantitatively linked to spatially-explicit measures of plant abundance. Our study addressed these methodological challenges. Modern PAR and vegetation data were obtained from seven lakes in the western Klamath Mountains, California. To determine how to best calibrate our PAR-biomass model, we first calculated the spatial area of vegetation where vegetation composition and patterning is recorded by changes in the pollen signal using two metrics. These metrics were an assemblage-level relevant source area of pollen (aRSAP) derived from extended R-value analysis ( sensu Sugita, 1993) and a taxon-specific relevant source area of pollen (tRSAP) derived from PAR regression ( sensu Jackson, 1990). To the best of our knowledge, aRSAP and tRSAP have not been directly compared. We found that the tRSAP estimated a smaller area for some taxa (e.g. a circular area with a 225 m radius for Pinus ) than the aRSAP (a circular area with a 625 m radius). We fit linear models to relate PAR values from modern lake sediments with empirical, distance-weighted estimates of aboveground live biomass (AGL dw ) for both the aRSAP and tRSAP distances. In both cases, we found that the PARs of major tree taxa – Pseudotsuga, Pinus, Notholithocarpus , and TCT (Taxodiaceae, Cupressaceae, and Taxaceae families) – were statistically significant and reasonably precise estimators of contemporary AGL dw . However, predictions weighted by the distance defined by aRSAP tended to be more precise. The relative root-mean squared error for the aRSAP biomass estimates was 9% compared to 12% for tRSAP. Our results demonstrate that calibrated PAR-biomass relationships provide a robust method to infer changes in past plant biomass.

California, Oregon↗

USGS2010

The bioenergetic consequences of invasive-induced food web disruption to Lake Ontario alewives

Alewives Alosa pseudoharengus are the dominant prey fish in Lake Ontario, and their response to ecological change can alter the structure and function of the Lake Ontario food web. Using stochastic population-based bioenergetic models of Lake Ontario alewives for 1987–1991 and 2001–2005, we evaluated changes to alewife production, consumption, and associated bioenergetic ratios after invasive-induced food web disruption. After the disruption, mean biomass of alewives declined from 28.0 to 14.6 g/m 2 , production declined from 40.8 to 13.6 g·m −2 ·year −1 , and consumption declined from 342.1 to 137.2 g·m −2 ·year −1 , but bootstrapping of error sources suggested that the changes were not statistically significant. Population-based bioenergetic ratios of production to biomass ( P/B ratio), total consumption to biomass ( Q/B ratio), and production efficiency did not change. Pathways of energy flow measured as prey-group-specific Q/B ratios changed significantly between the two time periods for invasive predatory cladocerans (from 0.6 to 1.3), Mysis diluviana (from 0.4 to 2.5), and other prey (from 0.8 to 0.1), but the observed decline in the zooplankton Q/B ratio (from 10.6 to 5.5) was not significant. Gross production efficiency did not change; values ranged from 8% to 15%. Age-group mean gross conversion efficiency (GCE) declined with age; GCE ranged from 7.5% to 11.0% for yearlings, was approximately 5% for age-2 alewives, and was less than 2% for age-3 and older alewives. The GCE increased significantly between the time periods for yearling alewives. Our analyses support the hypothesis that after 2003, alewives could not sustain their growth while feeding on zooplankton closer to shore. Modeling of observed spatial variation in diet and alternative occupied temperatures demonstrates the potential for reducing consumption by alewives. Our results suggest that Lake Ontario alewives can exploit spatial heterogeneity in resource patches and thermal habitat to partially mitigate the effects of food web disruption. Fish management implications are discussed.

North American Journal of Fisheries Management↗

USGS2022

Characterization of and temporal changes in groundwater quality of the Upper Black Squirrel Creek Basin, El Paso County, Colorado, 2018–20

In 2018–20, the U.S. Geological Survey, in cooperation with Upper Black Squirrel Creek Ground Water Management District, sampled 48 wells for Phase III of a multiphase plan investigating groundwater quality in the alluvial aquifer of the Upper Black Squirrel Creek Basin (UBSB), El Paso County, Colorado. Results for samples collected from October to December each year were used to assess spatial and temporal changes in groundwater quality and to differentiate sources of nitrate. Groundwater was predominantly classified as mixed-cation and mixed-anion water type in the aquifer, with variable chemistry along the periphery. Concentrations of constituents in groundwater were generally less than regulatory standards, except for nitrate in four wells. Isotopes of nitrogen and oxygen in nitrate identified four different potential sources or processes affecting nitrate in the alluvial aquifer: naturally occurring nitrate from soils, nitrate from animal and (or) human waste, and an unknown source, along with evidence of denitrification. Pharmaceutical compounds and personal-care products were detected in seven wells, with three wells having multiple detections. Stable isotopes of water indicated variability in seasonality of recharge throughout the UBSB alluvial aquifer. Nitrate concentrations from the 1984 study and the 1996 study were compared to the more recent concentrations in the 2013 study and the 2018–20 study. The northern one-third of the UBSB alluvial aquifer had a statistically significant increase in nitrate concentration from the 2013 study to the 2018–20 study, but no change was shown from the 1984 study to the 1996 study. The opposite was found true for the southern two-thirds of the UBSB alluvial aquifer with no statistically significant difference in nitrate concentration from the 2013 study to the 2018–20 study. Analysis of temporal changes indicated an increase in median and maximum nitrate concentrations from the 2013 study to the 2018–20 study throughout the UBSB alluvial aquifer. Continued sampling of wells in the UBSB would be beneficial to better determine temporal changes in groundwater quality, characterize human effects on water quality, and understand characteristics of the alluvial aquifer pertaining to sustainability of the resource.

Colorado↗

USGS2001

A spatially referenced regression model (SPARROW) for suspended sediment in streams of the Conterminous U.S.

Suspended sediment has long been recognized as an important contaminant affecting water resources. Besides its direct role in determining water clarity, bridge scour and reservoir storage, sediment serves as a vehicle for the transport of many binding contaminants, including nutrients, trace metals, semi-volatile organic compounds, a nd numerous pesticides (U.S. Environmental Protection Agency, 2000a). Recent efforts to addr ess water-quality concerns through the Total Maximum Daily Load (TMDL) process have iden tified sediment as the single most prevalent cause of impairment in the Nation’s streams a nd rivers (U.S. Environmental Protection Agency, 2000b). Moreover, sediment has been identified as a medium for the tran sport and sequestration of organic carbon, playing a potentia lly important role in understa nding sources and sinks in the global carbon budget (Stallard, 1998). A comprehensive understanding of sediment fate a nd transport is considered essential to the design and implementation of effective plans for sediment management (Osterkamp and others, 1998, U.S. General Accounting Office, 1990). An exte nsive literature addr essing the problem of quantifying sediment transport has produced a nu mber of methods for estimating its flux (see Cohn, 1995, and Robertson and Roerish, 1999, for us eful surveys). The accuracy of these methods is compromised by uncertainty in the concentration measurements and by the highly episodic nature of sediment movement, particul arly when the methods are applied to smaller basins. However, for annual or decadal flux es timates, the methods are generally reliable if calibrated with extended periods of data (Robertson and Roerish, 1999). A substantial literature also supports the Universal Soil Loss Equation (U SLE) (Soil Conservation Service, 1983), an engineering method for estimating sheet and rill erosion, although the empirical credentials of the USLE have recently been questioned (Tri mble and Crosson, 2000). Conversely, relatively little direct evidence is available concerning the fate of sediment. The common practice of quantifying sediment fate with a sediment deliv ery ratio, estimated from a simple empirical relation with upstream basin area, does not artic ulate the relative importance of individual storage sites within a basin (Wolman, 1977). Rates of sediment deposition in reservoirs and flood plains can be determined from empirical measurement s , but only a limited number of sites have been monitored, and net rates of deposition or loss from other potential sinks and sources is largely unknown (Stallard, 1998). In particular, little is known about how much sediment loss from fields ultimately makes its way to stream channels, and how much sediment is subsequently stored in or lost from th e streambed (Meade and Parker, 1985, Trimble and Crosson, 2000). This paper reports on recent progress made to a ddress empirically the question of sediment fate and transport on a national scale. The model pres ented here is based on the SPAtially Referenced Regression On Watershed attr ibutes (SPARROW) methodology, fi rst used to estimate the distribution of nutrients in str eams and rivers of the United Stat es, and subsequently shown to describe land and stream processes affecting the delivery of nutrients (Smith and others, 1997, Alexander and others, 2000, Preston and Brakeb ill, 1999). The model makes use of numerous spatial datasets, available at the national level, to explain long-term sediment water-quality conditions in major streams and rivers throughou t the United States. Sediment sources are identified using sediment erosion rates from the National Resources I nventory (NRI) (Natural Resources Conservation Service, 2000) and apportioned over the landscape according to 30- meter resolution land-use information from th e National Land Cover Data set (NLCD) (U.S. Geological Survey, 2000a). More than 76,000 reservoirs from the National Inventory of Dams (NID) (U.S. Army Corps of Engin eers, 1996) are identified as pot ential sediment sinks. Other, non-anthropogenic sources and sinks are identified using soil in formation from the State Soil Survey Geographic (STATSGO) data base (Schwarz and Alexander, 1995) and spatial coverages representing surficial rock t ype and vegetative cover. The SPA RROW model empirically relates these diverse spatial datasets to estimates of long-term, mean annual sediment flux computed from concentration and flow measurements co llected over the period 1985 -95 from more than 400 monitoring stations maintained by the Na tional Stream Quality Accounting Network (Alexander and others, 1998), the National Wa ter Quality Assessment Program, and U.S. Geological Survey District offices (Turcios and Gray, in press). Th e calibrated model is used to estimate sediment flux for over 60,000 stream segments included in the River Reach File 1 (RF1) stream network (Alexander and others, 1999). SPARROW uses statis tical methods to calibrate a simple, structural model of riverine water quality, one that imposes mass ba lance in accounting for changes in contaminant flux. As applied here, the mass-balance approach facilitates the interpretation of model results in terms of physical processes affecting sediment transport, and makes possible the estimation of various rates of sediment generation and loss associated with stream channels and features of the landscape. The statistical approach provides a basi s for assessing the error of these inferred rates and of the error in extrapolated estimates of sediment flux made for streams in the RF1 network. An important implication of the holistic modeling approach adopted in this analysis is that estimates of sediment production and loss ar e based on, and therefore consistent with, measurements of in-stream flux. Other ancillary information, such as direct measurements of long-term sediment storage and release from rese rvoirs (Steffen, 1996), is incorporated into the analysis by specifying additional equations expl aining these ancillary variables. The imposition of cross-equation constraints affords this info rmation a statistically consistent weight in explaining in-stream sediment flux. Thus, the me thodology described here represents a general framework for synthesizing a wide spectrum of available information relevant to the understanding of sediment fate and transport.

Conterminous United States↗

USGSDate not supplied

Development of stochastic modeling systems using deterministic models and GIS: Principles and a case study in the Atlantic Zone of Costa Rica

The most important requirements for large-area environmental modeling are a tight integration between models and data, and a close match of the spatial scale at which the model is developed with the scale at which the model is to be applied. To better match the scale of data with that of the model, we propose a set of principles for the development of stochastic modeling systems based on linkage of deterministic models with GIS data. For modeling purposes, a region is usually rasterized into cells and the environmental conditions of those cells are specified by ranges or classes using GIS data layers. It is not necessary to simulate each and every GIS cell in the study area because many cells may have similar environmental conditions and can be grouped together to form cohorts. We define a cohort as the assembly of the cells sharing a unique combination of environmental conditions within the study region. Multiple model simulations can be performed for any given cohort. For each simulation, some of the parameter values can be randomly generated within the specified environmental conditions of the cohort according to a certain statistical distribution which, in turn, can be specified by GIS data layers. By this method the variance and covariance of environmental variables in space and time are integrated into the simulation processes with these modeling systems to make full use of the available data and to assess the uncertainties of the simulated results. An integrated simulation system between CENTURY model and GIS was developed to demonstrate the value of the concepts imbedded in stochastic simulation systems for large area studies.

Conference Paper↗

USGS2013

Quantifying landscape change in an arctic coastal lowland using repeat airborne LiDAR

Increases in air, permafrost, and sea surface temperature, loss of sea ice, the potential for increased wave energy, and higher river discharge may all be interacting to escalate erosion of arctic coastal lowland landscapes. Here we use airborne light detection and ranging (LiDAR) data acquired in 2006 and 2010 to detect landscape change in a 100 km 2 study area on the Beaufort Sea coastal plain of northern Alaska. We detected statistically significant change (99% confidence interval), defined as contiguous areas (>10 m 2 ) that had changed in height by at least 0.55 m, in 0.3% of the study region. Erosional features indicative of ice-rich permafrost degradation were associated with ice-bonded coastal, river, and lake bluffs, frost mounds, ice wedges, and thermo-erosional gullies. These features accounted for about half of the area where vertical change was detected. Inferred thermo-denudation and thermo-abrasion of coastal and river bluffs likely accounted for the dominant permafrost-related degradational processes with respect to area (42%) and volume (51%). More than 300 thermokarst pits significantly subsided during the study period, likely as a result of storm surge flooding of low-lying tundra (<1.4 m asl) as well as the lasting impact of warm summers in the late-1980s and mid-1990s. Our results indicate that repeat airborne LiDAR can be used to detect landscape change in arctic coastal lowland regions at large spatial scales over sub-decadal time periods.

Alaska↗

USGS2004

Water-resources-related information for the Milwaukee Metropolitan Sewerage District planning area, Wisconsin, 1970-2002

The Milwaukee Metropolitan Sewerage District (MMSD) Corridor Study is a three-phase project designed to improve the understanding of water resources in the stream corridors of the MMSD planning area by initially compiling existing data and using the compiled information to develop 3-year baseline and long-term monitoring plans. This report is one of the products of Phase I of the Corridor Study. A literature review of surface-water-quality, surface water- quantity, and ecology studies conducted from 1970 through 2001 was completed and is summarized in this report. An inventory of Geographic Information System spatial coverages available for the MMSD planning area has been assembled. A database of water, sediment, and tissue (fish, shellfish, and others) chemistry, macroinvertebrates, fish, algae, habitat, geomorphic, and other physical and ecological data was compiled from data sets from MMSD, U.S. Geological Survey, Wisconsin Department of Natural Resources, and the U.S. Environmental Protection Agency. More than 2.7 million results are available in the MMSD Corridor Study database and the compilation of multiple datasets allows for retrieving data from a central database rather than from each of the source datasets. Data for 1970 through 2002 were collected for the 420-square-mile planning area by various agencies using different field data-collection and laboratory analysis methods. Chemical constituents and ecological components that are important to an urban setting and well represented in the database were selected for further investigation. Each constituent or component is described in this report with some or all of the following: a text summary, map of sampling locations, and in some cases median concentrations, statistical distributions of concentrations by subwatershed, table of summary statistics by subwatershed, and graphs of temporal and (or) seasonal trends. Physical data presented in the report include streamflow, stream stage, and precipitation data. Chemical indicators of water quality presented in the report include field measurements and miscellaneous constituents (pH, alkalinity, specific conductance, hardness, dissolved oxygen, biochemical oxygen demand, and chloride), sediment (total suspended solids and suspended sediment), nutrients (total nitrogen, nitrate, Kjeldahl nitrogen, total phosphorus, and dissolved phosphorus), trace elements (cadmium, mercury, copper, lead, arsenic, chromium, nickel, and zinc), pesticides (historically used pesticides and pesticides still in use), and polychlorinated biphenyls. Ecological indicators of water quality discussed in the report include community surveys of macroinvertebrates and fish, chlorophyll a concentrations, habitat assessments and channel-measurement data, and fecal coliform and E. coli bacterial counts. In addition to the compilation of the database, a major purpose of this investigation was to identify additional sampling that should be conducted under the baseline monitoring phase, which will be the second phase of the Corridor Study. Additional sampling may include: • Some subwatersheds, such as those in the headwaters. • Emerging contaminants such as pharmaceuticals and personal care products (PPCPs), human hormones, organic wastewater contaminants, and other constituents that result from human activity. • E. coli, which can serve as an indicator of health risk to swimmers and other recreational water users. • Pesticides in all media. • PCBs. • Trace elements in water, bed sediment, and tissues (fish, shellfish, and others). • Samples during winter months or during early snowmelt episodes to address constituents such as chloride and some nutrients that have seasonal variability and that may be affected by factors such as road deicing during the winter. • Samples for macroinvertebrate and fish-community data and habitat assessments. • Physical data such as stream-channel cross-section profiles, bridge-scour assessments, flood-plain maps, structures, and shoreline conditions.

Wisconsin↗

USGS1994

Analysis of data on nutrients and organic compounds in ground water in the upper Snake River basin, Idaho and western Wyoming, 1980-91

Nutrient and organic compound data from the U.S. Geological Survey and the U.S. Environmental Protection Agency STORET data bases provided information for development of a preliminary conceptual model of spatial and temporal ground-water quality in the upper Snake River Basin. Nitrite plus nitrate (as nitrogen; hereafter referred to as nitrate) concentrations exceeded the Federal drinking-water regulation of 10 milligrams per liter in three areas in Idaho" the Idaho National Engineering Laboratory, the area north of Pocatello (Fort Hall area), and the area surrounding Burley. Water from many wells in the Twin Falls area also contained elevated (greater than two milligrams per liter) nitrate concentrations. Water from domestic wells contained the highest median nitrate concentrations; water from industrial and public supply wells contained the lowest. Nitrate concentrations decreased with increasing well depth, increasing depth to water (unsaturated thickness), and increasing depth below water table (saturated thickness). Kjeldahl nitrogen concentrations decreased with increasing well depth and depth below water table. The relation between kjeldahl nitrogen concentrations and depth to water was poor. Nitrate and total phosphorus concentrations in water from wells were correlated among three hydrogeomorphic regions in the upper Snake River Basin, Concentrations of nitrate were statistically higher in the eastern Snake River Plain and local aquifers than in the tributary valleys. There was no statistical difference in total phosphorus concentrations among the three hydrogeomorphic regions. Nitrate and total phosphorus concentrations were correlated with land-use classifications developed using the Geographic Information Retrieval and Analysis System. Concentrations of nitrate were statistically higher in area of agricultural land than in areas of rangeland. There was no statistical difference in concentrations between rangeland and urban land and between urban land and agricultural land. There was no statistical difference in total phosphorus concentrations among any of the land-use classifications. Nitrate and total phosphorus concentrations also were correlated with land-use classifications developed by the Idaho Department of Water Resources for the Idaho part of the upper Snake River Basin. Nitrate concentrations were statistically higher in areas of irrigated agriculture than in areas of dryland agriculture and rangeland. There was no statistical difference in total phosphorus concentrations among any of the Idaho Department of Water Resources land-use classifications. Data were sufficient to assess long-term trends of nitrate concentrations in water from only eight wells: four wells north of Burley and four wells northwest of Pocatello. The trend in nitrate concentrations in water from all wells in upward. The following organic compounds were detected in ground water in the upper Snake River Basin: cyanazine, 2,4-D DDT, dacthal, diazinon, dichloropropane, dieldrin, malathion, and metribuzin. Of 211 wells sampled for organic compounds, water from 17 contained detectable concentrations.

Idaho;Montana;Nevada;Utah;Wyoming↗

USGS1997

Daily air temperature interpolated at high spatial resolution over a large mountainous region

Two methods are investigated for interpolating daily minimum and maximum air temperatures ( T min and T max ) at a 1 km spatial resolution over a large mountainous region (830000 km 2 ) in the U.S. Pacific Northwest. The methods were selected because of their ability to (1) account for the effect of elevation on temperature and (2) efficiently handle large volumes of data. The first method, the neutral stability algorithm (NSA), used the hydrostatic and potential temperature equations to convert measured temperatures and elevations to sea-level potential temperatures. The potential temperatures were spatially interpolated using an inverse-squared-distance algorithm and then mapped to the elevation surface of a digital elevation model (DEM). The second method, linear lapse rate adjustment (LLRA), involved the same basic procedure as the NSA, but used a constant linear lapse rate instead of the potential temperature equation. Cross-validation analyses were performed using the NSA and LLRA methods to interpolate T min and T max each day for the 1990 water year, and the methods were evaluated based on mean annual interpolation error (IE). The NSA method showed considerable bias for sites associated with vertical extrapolation. A correction based on climate station/grid cell elevation differences was developed and found to successfully remove the bias. The LLRA method was tested using 3 lapse rates, none of which produced a serious extrapolation bias. The bias-adjusted NSA and the 3 LLRA methods produced almost identical levels of accuracy (mean absolute errors between 1.2 and 1.3°C), and produced very similar temperature surfaces based on image difference statistics. In terms of accuracy, speed, and ease of implementation, LLRA was chosen as the best of the methods tested.

Columbia River basin↗

USGS2023

A comprehensive plan for in-water sea turtle data collection in the US Gulf of Mexico

The Deepwater Horizon Open Ocean Trustee Implementation Group (OO TIG) released a Final Open Ocean Restoration Plan 2 in 2019, which included a project titled Developing a Gulf-wide Comprehensive Plan for In-water Sea Turtle Data Collection. This document, A Comprehensive Plan for In-water Sea Turtle Data Collection in the US Gulf of Mexico (Plan), is the culmination of that OO TIG project. This Plan serves as the OO TIG project’s technical report as well as a framework for a biologically and statistically-sound plan to support coordinated in-water sea turtle data collection in the United States (US) Gulf of Mexico (GoM) to determine sea turtle abundance and population trends. The purpose of this Plan is to act as a guide for collecting biologically and statistically robust, in-water sea turtle data in a comprehensive, coordinated, and standardized fashion in the US GoM. Several sea turtle in-water monitoring efforts are underway in the GoM; however, additional coordination and standardization of these efforts will benefit current restoration and recovery objectives. These efforts will aid in restoration project design, assess long-term effectiveness of restoration activities, and create abundance and distribution baselines across the GoM. This Plan provides guidance for researchers investigating sea turtle abundance and demographic questions, as well as for management agencies and restoration planners. A Steering Committee (SC) was assembled to develop this Plan and to recommend a coordinated approach to the formulation of an improved understanding of sea turtle population baselines in the GoM, from which determination of large-scale population changes, effects of specific threats (e.g., oil spills, anthropogenic hazards), and effects of changes in ocean conditions (e.g., climate change) can later be evaluated. In crafting this guidance, the SC considered species distribution and life history characteristics, spatial and logistical considerations, level of effort required to detect trends, methods available and the pros and cons of each, associated assumptions and biases with suggested monitoring methods, and standardization of data collection. Given the current level of data available, the SC has recommended species monitoring in two main phases in neritic and oceanic waters, with additional recommended sampling for surface pelagic drift communities. The two phases in this Plan focus on 1) monitoring a limited number of sites in the first 5 to 8 years, followed by 2) a refined monitoring design. To support implementation of this Plan, the SC also considered broader programmatic needs, including supplemental data collection, program and data management, potential international partnerships, program expansion, and applications including future technology.

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2025

Mapping ecological states in the upper Colorado River basin: Implications for fire management

Spatially explicit information on ecosystem dynamics that offers a mechanistic understanding of ecological processes can benefit environmental management. Broad-scale maps based on state-and-transition models provide valuable insight into transitions among ecological states resulting from specific drivers within areas sharing similar climatic and edaphic characteristics ecological sites (ES). We aimed to quantify ecological dynamics of two ES groups in the Upper Colorado River Basin from 1986 to 2022 through annual maps of ecological states and assess potential drivers of observed state change. This region comprises important sagebrush shrublands and pinyon-juniper woodlands affected by non-native annual grass invasion, wildfires, and drought-induced tree mortality. Using field-based and remote sensing data, we modeled vegetation states using random forest models and mapped the states annually from 1986 to 2022. To demonstrate the utility of the state maps for monitoring and management, we used this time series of maps to investigate the influences of fire and drought on state occurrence. Our findings revealed a statistically significant increase in states invaded by non-native annual species (Invaded state), which replaced Grassland and Shrubland states, while Shrubland states decreased significantly, transitioning to invaded and Woodland states. Invaded states had the highest likelihood of burning, followed by Woodlands. Drought was associated with increased area of Grassland and Bare states, but with decreased area of invaded and Shrubland states. These results indicate an accelerating fire cycle is potentially leading to ongoing regional environmental degradation. Despite increasing drought conditions during the study period, the invaded states continued to increase in area, indicating additional underlying mechanisms. Our reproducible, broad-scale, ecologically-driven state mapping process enhances understanding of how drought, fire, and invasion by non-native plants can transform semiarid landscapes of the western USA.

Arizona, Colorado, New Mexico, Utah, Wyoming↗

USGS2011

Elk Monitoring Protocol for Lewis and Clark National Historical Park, Version 1.0

Maintaining elk (Cervus elaphus roosevelti) herds that frequent Lewis and Clark National Historical Park (NHP) is central to the park’s purpose of preserving the historic, cultural, scenic, and natural resources. Elk were critical to sustaining the members of the Lewis and Clark expedition by providing food and clothing over the winter of 1805-1806. Today, elk viewing opportunities in the park and surrounding region generate broad appeal with the visiting public, which number over 250,000 per year at the Fort Clatsop visitor center. This protocol describes procedures for monitoring trends in the use of the Fort Clatsop area by Roosevelt elk. Specific objectives of elk monitoring in Lewis and Clark NHP are to measure the relative use and proportion of area used by elk during winter in the Fort Clatsop Unit of the park, and the rate at which elk are sighted from roads in and around the park. Relative use and the proportion of area used by elk are determined from elk fecal pellet surveys conducted every other year in the Fort Clatsop park unit. Pairs of observers visit a systematic array of permanent plots in the fall to clear them of elk fecal pellets, and return to the plots in late winter to count elk fecal pellets that have accumulated during winter. Half of the subplots are counted by two independent observers, which allows for the estimation of relative use and proportion of area occupied by elk with analyses of detection biases that account for unseen elk pellet groups. Standardized road surveys are conducted in and near the Fort Clatsop park unit three or four times monthly during alternate months. Data from road surveys are used to quantify the rate that park visitors would be expected to see elk, when driving the selected set of routes. The monitoring protocol is based on three field seasons of development and testing. The protocol narrative describes the background, rationale, sampling design, field methods, analytical methods, data management, reporting, personnel requirements, and operational requirements for elk monitoring in Lewis and Clark NHP. The sampling design reflects tradeoffs between statistical and ecological considerations, safety, and current budget considerations. The protocol provides adequate power to detect a doubling or halving of elk use in the Fort Clatsop unit and surrounding areas within 15 years. Step-by-step guidance for planning and completing the monitoring tasks are in the attached standard operating procedures (SOPs). Information on the status and trends of elk use in Lewis and Clark NHP will allow park managers to assess the effects on elk of restoration programs within the park, build community partnerships, and identify potential linkages between regional land use changes and elk use of the Park. Lewis and Clark NHP has an active ecological restoration program that aims to recreate, where possible, ecological conditions that Lewis and Clark encountered. The restoration program includes an extensive exotic plant removal program, wetland restoration, and silvicultural treatments that will hasten development of late-seral conditions in recently acquired forest lands of the Fort Clatsop park unit. In the future, monitoring results can be used to test for spatial associations between ecological restoration treatments and relative use by elk. The park also plans to feature results from elk monitoring prominently in its educational outreach activities to help interpret the historical and current ecological context of the Lewis and Clark story, and engender public support for the park mission and management activities. Although NPS does not manage non-park lands, information about trends in the distribution of elk use will be valuable in public outreach and discussions with other partnering agencies and regional private landowners.

Natural Resource Report↗

USGS2011

Unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance

Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientific questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mechanisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unified modeling interface. The R package unmarked provides such a unified modeling framework, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.

Journal of Statistical Software↗

USGS2009

Effects of experimental protocol on global vegetation model accuracy: a comparison of simulated and observed vegetation patterns for Asia

Prognostic vegetation models have been widely used to study the interactions between environmental change and biological systems. This study examines the sensitivity of vegetation model simulations to: (i) the selection of input climatologies representing different time periods and their associated atmospheric CO 2 concentrations, (ii) the choice of observed vegetation data for evaluating the model results, and (iii) the methods used to compare simulated and observed vegetation. We use vegetation simulated for Asia by the equilibrium vegetation model BIOME4 as a typical example of vegetation model output. BIOME4 was run using 19 different climatologies and their associated atmospheric CO 2 concentrations. The Kappa statistic, Fuzzy Kappa statistic and a newly developed map-comparison method, the Nomad index, were used to quantify the agreement between the biomes simulated under each scenario and the observed vegetation from three different global land- and tree-cover data sets: the global Potential Natural Vegetation data set (PNV), the Global Land Cover Characteristics data set (GLCC), and the Global Land Cover Facility data set (GLCF). The results indicate that the 30-year mean climatology (and its associated atmospheric CO 2 concentration) for the time period immediately preceding the collection date of the observed vegetation data produce the most accurate vegetation simulations when compared with all three observed vegetation data sets. The study also indicates that the BIOME4-simulated vegetation for Asia more closely matches the PNV data than the other two observed vegetation data sets. Given the same observed data, the accuracy assessments of the BIOME4 simulations made using the Kappa, Fuzzy Kappa and Nomad index map-comparison methods agree well when the compared vegetation types consist of a large number of spatially continuous grid cells. The results of this analysis can assist model users in designing experimental protocols for simulating vegetation.

Ecological Modelling↗

USGS2024

Assessing trade-offs in developing a landscape-scale nest monitoring programme for a threatened shorebird

Effective monitoring of wildlife species requires thorough planning and development of survey programmes that can address management and conservation objectives. Decisions about monitoring programmes include where to survey, survey design and how much effort to allocate at survey sites are typically predicated on limited budgets and available resources. When the scope of inference requires monitoring on a broad spatial scale, predictions of habitat distribution or suitability may be useful for identifying potential survey sites. We focused on a threatened but widely distributed shorebird, the piping plover ( Charadrius melodus ), which is actively monitored across some, but not all of its range. Our objective was to use piping plover habitat distribution maps, which vary annually, to assess the effectiveness of multiple monitoring programme scenarios and their associated costs. In the breeding range, efforts to improve productivity for species of conservation concern often focus on improving probabilities of nest survival. Consequently, collecting adequate nesting data is crucial for obtaining accurate and precise estimates of nest survival and for evaluating the effectiveness of management actions. By simulating the nest monitoring process, we evaluated how much area, where and how often to survey each site when estimating nest survival and detecting effects of potential management actions. As expected, precision increased and bias decreased around nest survival estimates with greater survey coverage and nest visit frequency. We also identified monitoring programmes with negative net values where survey costs outweighed statistical benefits. Although we applied our simulation framework to evaluate nest monitoring designs for piping plovers, it could be extended to assess whether different monitoring programmes can detect changes in the distribution of other species or occupancy of habitats over time.

Ecological Solutions and Evidence↗

USGS2018

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R 2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282 .

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2015

Predictions of future ephemeral springtime waterbird stopover habitat availability under global change

In the present period of rapid, worldwide change in climate and landuse (i.e., global change), successful biodiversity conservation warrants proactive management responses, especially for long-distance migratory species. However, the development and implementation of management strategies can be impeded by high levels of uncertainty and low levels of control over potentially impactful future events and their effects. Scenario planning and modeling are useful tools for expanding perspectives and informing decisions under these conditions. We coupled scenario planning and statistical modeling to explain and predict playa wetland inundation (i.e., presence/absence of water) and ponded area (i.e., extent of water) in the Rainwater Basin, an anthropogenically altered landscape that provides critical stopover habitat for migratory waterbirds. Inundation and ponded area models for total wetlands, those embedded in rowcrop fields, and those not embedded in rowcrop fields were trained and tested with wetland ponding data from 2004 and 2006–2009, and then used to make additional predictions under two alternative climate change scenarios for the year 2050, yielding a total of six predictive models and 18 prediction sets. Model performance ranged from moderate to good, with inundation models outperforming ponded area models, and models for non-rowcrop-embedded wetlands outperforming models for total wetlands and rowcrop-embedded wetlands. Model predictions indicate that if the temperature and precipitation changes assumed under our climate change scenarios occur, wetland stopover habitat availability in the Rainwater Basin could decrease in the future. The results of this and similar studies could be aggregated to increase knowledge about the potential spatial and temporal distributions of future stopover habitat along migration corridors, and to develop and prioritize multi-scale management actions aimed at mitigating the detrimental effects of global change on migratory waterbird populations.

Nebraska↗

USGS2007

Geographic Information System Software to Remodel Population Data Using Dasymetric Mapping Methods

The U.S. Census Bureau provides decadal demographic data collected at the household level and aggregated to larger enumeration units for anonymity purposes. Although this system is appropriate for the dissemination of large amounts of national demographic data, often the boundaries of the enumeration units do not reflect the distribution of the underlying statistical phenomena. Conventional mapping methods such as choropleth mapping, are primarily employed due to their ease of use. However, the analytical drawbacks of choropleth methods are well known ranging from (1) the artificial transition of population at the boundaries of mapping units to (2) the assumption that the phenomena is evenly distributed across the enumeration unit (when in actuality there can be significant variation). Many methods to map population distribution have been practiced in geographic information systems (GIS) and remote sensing fields. Many cartographers prefer dasymetric mapping to map population because of its ability to more accurately distribute data over geographic space. Similar to ?choropleth maps?, a dasymetric map utilizes standardized data (for example, census data). However, rather than using arbitrary enumeration zones to symbolize population distribution, a dasymetric approach introduces ancillary information to redistribute the standardized data into zones relative to land use and land cover (LULC), taking into consideration actual changing densities within the boundaries of the enumeration unit. Thus, new zones are created that correlate to the function of the map, capturing spatial variations in population density. The transfer of data from census enumeration units to ancillary-driven homogenous zones is performed by a process called areal interpolation.

Techniques and Methods↗