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At least 1,063 records · Page 59Linked to original sources
USGS2014

Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States

BACKGROUND Complex environmental models are frequently extrapolated to overcome data limitations in space and time, but quantifying data worth to such models is rarely attempted. The authors determined which field observations most informed the parameters of agricultural system models applied to field sites in Nebraska (NE) and Maryland (MD), and identified parameters and observations that most influenced prediction uncertainty. RESULTS The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration. CONCLUSIONS Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty. RESULTS: The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration. CONCLUSIONS: Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty.

Maryl;Nebraska↗
USGS2017

Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.

International Journal of Applied Earth Observation↗
USGS2021

National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project

The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program is an effort to classify, describe, and map existing vegetation communities in national park units throughout the United States. The NPS VMI Program is managed by the NPS Natural Resource Stewardship and Science Inventory and Monitoring Program and provides baseline vegetation information to natural resource managers, researchers, and ecologists. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, and NPS Great Smoky Mountains National Park (GRSM, also referred to as the “Park”) have completed vegetation classification and mapping of GRSM, including the Foothills Parkway, for the NPS VMI Program. Mappers, ecologists, and botanists collaborated to affirm vegetation types of GRSM and to determine how best to map the vegetation types by using aerial imagery. A vegetation classification developed in 2003 by NatureServe and the NPS served as a foundation to further classify and map the vegetation types of the Park. Data from an additional 10 vegetation plots supported vegetation types either rare or not documented in the 2003 classification. Data from 203 verification sites were collected to test the field key to vegetation types and the application of vegetation types to a sample set of map polygons. Furthermore, data from 972 accuracy assessment (AA) sites were collected (of which 966 were used to test accuracy of the vegetation map layer). This GRSM vegetation mapping project identified 112 vegetation types consisting of 105 association types in the U.S. National Vegetation Classification (USNVC), 2 “park-special” types, 1 “map-special” type, and 4 cultural types in the USNVC. To map the vegetation and land cover of GRSM, 52 map classes were developed. Of these 52 map classes, 46 represent natural (including ruderal) vegetation types, most of which types are recognized in the USNVC. For the remaining 6 of the 52 map classes, 4 represent USNVC cultural types for agricultural and developed areas, and 2 represent non-USNVC types for nonvegetated open water and nonvegetated rock. Features were interpreted from viewing four-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems; digital aerial imagery was collected during September 23–October 30, 2015. The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in a geographic information system. Polygon units were mapped to either a 0.5- or 0.25- hectare (ha) minimum mapping unit, depending on vegetation type. A geodatabase containing several feature-class layers and tables provides the locations and data of USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, AA sites, project boundary extent, and aerial image centers and flight lines. Covering 210,875 ha, the feature-class layer and related tables for the vegetation map layer provide 34,084 polygons of detailed attribute data when special modifiers are not considered (average polygon size of 6.2 ha) and 36,589 polygons of detailed attribute data when special modifiers are considered (average polygon size of 5.8 ha). Each map polygon is assigned a map-class code and name and, when applicable, are linked to USNVC classification tables within the geodatabase. The vegetation map extent includes the administrative boundary for GRSM and the Foothills Parkway. A summary report, generated from the vegetation map layer, concludes that the 46 map classes representing natural (including ruderal) vegetation types apply to 99.2% of polygons (33,797 polygons; average size of 6.2 ha) and cover 98.6% of the Park (207,971.4 ha). Further broken down, map classes representing natural vegetation types indicate that the Park is 97.7% forest and woodland (205,882.5 ha), 0.6% shrubland (1,174.6 ha), and 0.4% herbaceous (914.3 ha). Map classes representing cultural vegetation types apply to 0.8% of polygons (259 polygons; average size of 4.9 ha) and cover 0.6% of the Park (1,277.4 ha). Map classes representing nonvegetation open and flowing water and unvegetated rock apply to 0.08% of polygons (28 polygons; average size of 58.1 ha) and cover 0.8% of the Park (1,625.9 ha). A thematic AA study was completed of map classes representing the natural (including ruderal) vegetation types of the Park. Initial AA results were discussed with NPS staff from the Park. Following input from NPS staff on how to handle map classes that fell below accuracy standards, adjustments were made to the vegetation map layer. Final results indicate an overall accuracy of 80.64% (kappa index of 79.96% for chance agreements) based on data from 966 of the 972 AA sites. Most individual map-class themes exceed the NPS VMI Program standard of 80% with a 90% confidence interval. The GRSM vegetation mapping project delivers many geospatial and vegetation data products, including an in-depth project report discussing methods and results, which includes map classification and map-class descriptions. This suite of products also includes descriptions and a field key to vegetation types; a database of vegetation plots, verification sites, and AA sites; digital images of field sites; field data sheets; digital aerial imagery; hardcopy and digital maps; a geodatabase of vegetation and land cover (map layer), field sites (vegetation plots, verification sites, and AA sites), aerial imagery index, project boundary, and metadata; and a contingency table listing AA results. Geospatial products are projected in the Universal Transverse Mercator, Zone 17 North, by using the North American Datum of 1983. Information on the NPS VMI Program and completed mapping projects are on the internet at https://www.nps.gov/im/vegetation-inventory.htm.

North Carolina, Tennessee↗
USGS2011

Characterization of salinity and selenium loading and land-use change in Montrose Arroyo, western Colorado, from 1992 to 2010

Salinity and selenium are naturally occurring and perva-sive in the lower Gunnison River Basin of Colorado, includ-ing the watershed of Montrose Arroyo. Although some of the salinity and selenium loading in the Montrose Arroyo study area is from natural sources, additional loading has resulted from the introduction of intensive irrigation in the water-shed. With increasing land-use change and the conversion from irrigated agricultural to urban land, land managers and stakeholders need information about the long-term effects of land-use change on salinity and selenium loading. In response to the need to advance salinity and selenium science, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, Colorado River Basin Salinity Control Forum, and Colorado River Water Conservation District, developed a study to characterize salinity and selenium loading and how salinity and selenium sources may relate to land-use change in Montrose Arroyo. This report characterizes changes in salinity and selenium loading to Montrose Arroyo from March 1992 to February 2010 and the magnitude of land-use change between unirrigated desert, irrigated agricultural, and urban land-use/land-cover types, and discusses how the respective loads may relate to land-use change. Montrose Arroyo is an approximately 8-square-mile watershed in Montrose County in western Colorado. Salinity and selenium were studied in Montrose Arroyo in a 2001 study as part of a salinity- and selenium-control lateral project. The robust nature of the historical dataset indicated that Montrose Arroyo was a prime watershed for a follow-up study. Two sites from the 2001 study were used to monitor salinity and selenium loads in Montrose Arroyo in the follow-up study. Over the period of 2 water years and respective irrigation seasons (2008-2010), 27 water-quality samples were collected and streamflow measurements were made at the historical sites MA2 and MA4. Salinity and selenium concen-trations, loads, and streamflow were compared between the pre-lateral-project and post-growth periods and between the post-lateral-project and post-growth periods. No significant differences in streamflow, salinity (concen-tration and load), or selenium (concentration and load) were found at MA4 between the pre-lateral project and post-growth periods or between the post-lateral-project and post-growth periods. The statistical analysis indicated no significant dif-ferences in streamflow or salinity (both concentration and load) between the pre-lateral-project and post-growth periods or between the post-lateral-project and post-growth periods at MA2; however, selenium concentrations and loads were significantly greater between the pre-lateral-project and post-growth periods and between the post-lateral-project and post-growth periods at MA2. Land-use change between MA4 and MA2 may have contributed to the determined differences in selenium values, but the specific mechanisms causing the increases between periods are unknown. The size of the urbanized area in Montrose Arroyo was quantified for 1993, 2002, and 2009 by using a geographic information system (GIS) with imagery from the specified years. The greatest change in land use from 1993 to 2009 was the increase of urban land due to conversion from irrigated agricultural land. The conversion of previously unirrigated desert to urban land or irrigated agriculture could become more common if urbanization and development expands into the eastern part of the watershed because a majority of the un-urbanized land in eastern Montrose Arroyo is unirrigated desert. By applying GIS to the City of Montrose 2008 com-prehensive growth plan, it was estimated that approximately 786 acres of previously irrigated agricultural land will be converted to urban land and 689 acres of unirrigated desert will be converted to urban land under the plan scenario. New development on previously unirrigated land in shale areas would likely increase the potential for mobilization of sele-nium and salinity from new sources to Montrose Arroyo and the Lower Gunnison River Basin.

Scientific Investigations Report↗
USGS2015

Testing the use of bulk organic δ 13 C, δ 15 N, and C org :N tot ratios to estimate subsidence during the 1964 great Alaska earthquake

During the M w 9.2 1964 great Alaska earthquake, Turnagain Arm near Girdwood, Alaska subsided 1.7 ± 0.1 m based on pre- and postearthquake leveling. The coseismic subsidence in 1964 caused equivalent sudden relative sea-level (RSL) rise that is stratigraphically preserved as mud-over-peat contacts where intertidal silt buried peaty marsh surfaces. Changes in intertidal microfossil assemblages across these contacts have been used to estimate subsidence in 1964 by applying quantitative microfossil transfer functions to reconstruct corresponding RSL rise. Here, we review the use of organic stable C and N isotope values and C org :N tot ratios as alternative proxies for reconstructing coseismic RSL changes, and report independent estimates of subsidence in 1964 by using δ 13 C values from intertidal sediment to assess RSL change caused by the earthquake. We observe that surface sediment δ 13 C values systematically decrease by ∼4‰ over the ∼2.5 m increase in elevation along three 60- to 100-m-long transects extending from intertidal mud flat to upland environments. We use a straightforward linear regression to quantify the relationship between modern sediment δ 13 C values and elevation ( n = 84, R 2 = 0.56). The linear regression provides a slope–intercept equation used to reconstruct the paleoelevation of the site before and after the earthquake based on δ 13 C values in sandy silt above and herbaceous peat below the 1964 contact. The regression standard error (average = ±0.59‰) reflects the modern isotopic variability at sites of similar surface elevation, and is equivalent to an uncertainty of ±0.4 m elevation with respect to Mean Higher High Water. To reduce potential errors in paleoelevation and subsidence estimates, we analyzed multiple sediment δ 13 C values in nine cores on a shore-perpendicular transect at Bird Point. Our method estimates 1.3 ± 0.4 m of coseismic RSL rise across the 1964 contact by taking the arithmetic mean of the differences ( n = 9) between reconstructed elevations for sediment above and below the 1964 earthquake subsidence contact. This estimate compares well with independent subsidence estimates derived from post-earthquake leveling in Turnagain Arm, and from microfossil transfer functions at Girdwood (1.50 ± 0.32 m). While our results support the use of bulk organic δ 13 C for reconstructing RSL change in southern Alaska, the variability of stable isotope values in modern and buried intertidal sediment required the analysis of multiple samples to reduce error.

Alaska↗
USGS2016

Temperature is better than precipitation as a predictor of plant community assembly across a dryland region

Question How closely do plant communities track climate? Research suggests that plant species converge toward similar environmental tolerances relative to the environments that they experience. Whether these patterns apply to severe environments or scale up to plant community-level patterns of relative climatic tolerances is poorly understood. Using estimates of species' climatic tolerances acquired from occurrence records, we determined the contributions of individual species' climatic niche breadths and environmental filtering to the relationships between community-average climatic tolerances and the local climates experienced by those communities. Location Southwestern United States drylands. Methods Interspecific variation in niche breadth was assessed as a function of species' climatic optima (median climatic niche value). The relationships between climatic optima and tolerances were used as null expectations for the relationship between abundance-weighted mean climatic tolerances of communities and the local climate of that community. Deviations from this null expectation indicate that species with greater or lesser climatic tolerances are favoured relative to co-occurring species. The intensity of environmental filtering was estimated by comparing the range of climatic tolerances within each community to a null distribution generated from a random assembly algorithm. Results The temperature niches of species were consistently symmetrical and of similar breadths, regardless of their temperature optima. In contrast, precipitation niches were skewed toward wetter conditions, and niche breadth increased with increasing precipitation optima. At the community level, relationships with climate were much stronger for temperature than for precipitation. Furthermore, cold and heat were stronger assembly filters than drought or precipitation, with the intensity of environmental filtering increasing at both ends of climatic gradients. Community-average climatic tolerances did deviate significantly from null expectations, indicating that species with higher or lower relative climatic tolerances were favoured under certain conditions. Conclusions Despite strong water limitation of plant performance in dryland ecosystems, communities tracked variation in temperature much more closely, intimating strong responses to anticipated temperature increases. Furthermore, abundance distributions were biased toward species with higher or lower relative climatic tolerances under different climatic conditions, but predictably so, indicating the need for assembly models that include processes other than simple environmental filtering.

Journal of Vegetation Science↗
USGS2018

Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure

Elevation data are critical for assessments of sea-level rise (SLR) and coastal flooding exposure. Previous research has demonstrated that the quality of data used in elevation-based assessments must be well understood and applied to properly model potential impacts. The cumulative vertical uncertainty of the input elevation data substantially controls the minimum increments of SLR and the minimum planning horizons that can be effectively used in assessments. For regional, continental, or global assessments, several digital elevation models (DEMs) are available for the required topographic information to project potential impacts of increased coastal water levels, whether a simple inundation model is used or a more complex process-based or probabilistic model is employed. When properly characterized, the vertical accuracy of the DEM can be used to report assessment results with the uncertainty stated in terms of a specific confidence level or likelihood category. An accuracy evaluation has been conducted of global DEMs to quantify their inherent vertical uncertainty to demonstrate how accuracy information should be considered when planning and implementing a SLR or coastal flooding assessment. The evaluation approach includes comparison of the DEMs with high-accuracy geodetic control points as the independent reference data over a variety of coastal relief settings. The global DEMs evaluated include SRTM, ASTER GDEM, ALOS World 3D, TanDEM-X, NASADEM, and MERIT. High-resolution, high-accuracy DEM sources, such as airborne lidar and stereo imagery, are also included to give context to the results from the global DEMs. The accuracy characterization results show that current global DEMs are not adequate for high confidence mapping of exposure to fine increments (<1 m) of SLR or with shorter planning horizons (<100 years) and thus they should not be used for such mapping, but they are suitable for general delineation of low elevation coastal zones. In addition to the best practice of rigorous accounting for vertical uncertainty, other recommended procedures are presented for delineation of different types of impact areas (marine and groundwater inundation) and use of regional relative SLR scenarios. The requirement remains for a freely available, high-accuracy, high-resolution global elevation model that supports quantitative SLR and coastal inundation assessments at high confidence levels.

Frontiers in Earth Science↗
USGS2025

Constraining landslide frequency across the United States to inform county-level risk reduction

Informative landslide hazard estimates are needed to support landslide mitigation strategies to reduce landslide risk across the United States. Whereas existing national-scale landslide susceptibility products assess where landslides are likely to occur, they do not address how often , which is a critical element of landslide hazard and risk assessments. In particular, the U.S. Federal Emergency Management Agency's National Risk Index (NRI) requires landslide frequency estimates to inform expected annual loss estimates. We present county-level landslide frequency (landslides per area per year) estimates for the 50 US states. We applied Bayesian negative binomial regression to estimate both the expected (average) reported landslide frequency and full distribution of annual landslide counts for each county. We compared a suite of models that used combinations of landslide-susceptible area, probability of potentially triggering earthquakes, frequency of potentially triggering precipitation, and ecological region as predictors. We trained our models with landslide inventory data from counties with the most comprehensive records available nationwide and used zero-inflated negative binomial distributions as an incompleteness model to correct for temporal reporting gaps. We selected a preferred frequency model to inform the NRI based on information criteria and physically plausible parameter estimates. The model showed that average annual reported landslide frequencies vary by 5 orders of magnitude across US counties, ranging from 0.002 (0.00015–0.05) landslides 1000 km −2 yr −1 in Kusilvak Census Area, Alaska, to 29 (19–46) landslides 1000 km −2 yr −1 in Lake County, California, reflecting the country's strong variations in landslide susceptibility, earthquake probability, and other factors for which ecological region serves as a proxy. Counties with estimated frequencies in the top 20 % of all counties are predominately along the West Coast of the continental United States, in mountainous regions of the Pacific Northwest and Intermountain West, in locally steep or earthquake-prone regions of the Midwest and Southeast, along the Appalachians, in southern and southeastern Alaska, and on some Hawaiian islands. By examining the number of landslides predicted in 99th percentile years for each county, we identified that 26 % of US counties likely have potential for widespread landsliding with more than 10 landslides 1000 km −2 yr −1 , even when such large events have not been reported in the training data for that county. Overall, our results better represent the range of possible landslide frequencies and spatial variations than previous national-scale estimates reported in the NRI, and our approach can inform other risk-reduction and loss-mitigation efforts across the United States and globally.

Natural Hazards and Earth System Sciences↗
USGS2021

Toward improved decision-support tools for Delta Smelt management actions

The Collaborative Science and Adaptive Management Program (CSAMP) has endorsed a goal of reversing the recent downward trajectory of the Delta Smelt population within 5-10 generations, with the long-term aim of establishing a self-sustaining population. An ambitious agenda of management actions is planned, and more management actions are being considered. This White Paper furthers one of the recommendations in the 2019 Delta Smelt Science Plan – the need to predict the potential ecological effects of taking a management action. Existing statistical models can be highly informative in assessing the response of Delta Smelt to changing system conditions and management actions. However, management actions can shift or alter conditions in ways that models based on analysis of historical data may not be able to represent, and short-term or localized effects may be missed with models designed to assess effects at the population level. Decision support tools (DSTs) are computer-based tools developed to assist decision-making, often combining computationally intensive analysis and spatial mapping of environmental relationships. DSTs can be used in planning processes that evaluate an array of actions, such as in Structured Decision Making (SDM), where DSTs are needed to compare among alternatives. DSTs can also be used to explore the potential effects of different approaches to implementing management actions. The goal of this White Paper is to identify plausible options for DSTs that could be developed for future use to evaluate management actions that seek to either reverse the decline of Delta Smelt or minimize or mitigate the effects of other water management actions. Different types of management actions lead to different needs for DSTs. This White Paper was developed using three types of actions currently being considered to enhance the Delta Smelt population: Supplementation with Hatchery Fish, Summer-Fall Habitat, and Food Enhancement actions. These three management actions target different parts of the estuary and different processes, with a variety of possible metrics to gauge performance. Three DSTs are proposed that collectively address management questions related to the management actions considered, with each requiring a slightly different set of processes to be included and producing an array of outputs at varying spatial and temporal scales: DST 1. Modeling Fish Movement, Survival, and Reproduction Across Their Range. This DST can address management questions that require information about Delta Smelt spatial distribution and movement. • DST 1 could be used to compare conditions with and without management actions in place, how the management action performs among different types of water years (with varied flow and associated abiotic conditions), and to assess relative change with different variations and strategies of the management actions. • DST 2. Changes in Habitat Conditions and Delta Smelt Response. This DST is intended to evaluate combinations of conditions that are considered to provide suitable habitat for Delta Smelt, and Delta Smelt response. Delta Smelt habitat is generally described as open water with low salinity (0 to 6), turbidity of at least 12 NTU, suitable temperature conditions, and sufficient food availability to support growth. • DST 3. Regional Effects of Food Subsidy. This DSTs seeks to evaluate effectiveness of food enhancement actions by providing information on responses of the immediate targets of the action (i.e., phytoplankton or zooplankton) and tracing those to projected growth responses of Delta Smelt. There is not a single DST that adequately addresses management questions relevant to all management actions, although there is some overlap in the management questions each of the three DSTs can address. For each of the DSTs a substantial foundation of models and approaches already exists and modeling has already been applied to several of the management actions described. However, a number of outstanding issues remain for further development of the proposed DSTs. These are summarized in this White Paper together with potential approaches that could be applied or tested. Some components for the DSTs are already available and thus development could be relatively easy. However, for several of the topics identified there are gaps in knowledge that currently limit formulation of model structure and process representations. This presents challenges to readily incorporate some needed mechanisms into the models. Eleven next steps, aligned with relevant DSTs, are outlined. The next steps vary in their complexity or technical ‘lift’ required. Many build on existing work, or methods and approaches that have already been developed or are underway, while others require additional thinking to establish a viable approach. Some interim utility for decisions could be gained during initial development of the DSTs with further features added over time. Development of a DST requires engagement of both managers and scientists. Identifying the outputs and resolution needed for management purposes early in development of any DST is essential for effective pursuit of next steps and suitable approaches to address challenges. Dialog between managers and technical experts also informs what process-based simulation can do, and what tradeoffs are acceptable to meet a given purpose. To further develop the DSTs outlined here for application in the estuary requires: - Engagement of a committed group of technical experts with appropriate expertise. - The development of a coordinated workplan including appropriate project management and tracking. - Dialog between potential users (i.e., managers and policy makers) and technical experts. - Resources to pursue DST development including personnel and computational resources. This White Paper demonstrates the potential for moving toward DSTs for a variety of management actions in support of Delta Smelt that include mechanistic representations of physical and biological processes. Through focused effort from technical experts, managers and policy makers, DSTs can be developed to provide quantitative predictions of management effects on the ecosystem, targeting the changes the management actions seek to achieve, how these effects compare to ambient conditions, and how the effects vary among water year types or with timing and location of actions. Importantly, solid foundations exist which can be leveraged, refined, and built upon to specifically inform current and future management decisions.

White Paper↗
USGS2025

Quality assessment of past spawning mark estimations from a long-term survey in the Connecticut River watershed

The calcified structures of fishes provide insight into their periodic growth rates and can be combined with other biological variables to identify metrics such as size or age at maturity and mortality rates. Collecting this information on growth and life history can help evaluate the success of conservation efforts and inform future management decisions for a species in need. However, before these life history data can be applied to larger stock assessments that direct management decisions, confidence in the validity of the data needs to be reported through metrics of accuracy and precision. For this report, we assessed the bias and precision of paired reader estimations of spawning marks on scales of Blueback herring (Alosa aestivalis) collected in the U.S. Fish & Wildlife (USFWS) Annual Adult River Herring Stock Assessment for the lower Connecticut River basin. The paired reads on scales from a total of 8,698 fish over the ten years of the long term monitoring program were evaluated for the annual presence of systematic bias and precision using a combination of qualitative (i.e., frequency tables) and quantitative (i.e., Evan’s and Hoenig’s Test of Symmetry, and Coefficient of Variation (CV) calculations) analyses. While seven out of the ten survey years had systematic bias detected by the tests of symmetry, only three years (2013, 2016, 2018) had imprecision values >10% CV threshold. Data were further categorized into specific age classes within survey years to increase our resolution on where bias and imprecision was most prevalent. While the ability for accurate bias detection was limited by sufficient sample sizes (>25 fish), average imprecision values increased with age, and median age classes (4 through 6) commonly had bias detected. However, the removal of insufficient age classes prior to calculating average annual CV did not significantly change the initial average. Lastly, 2023, which was the first year to implement a standardized training procedure prior to production estimating, had the highest precision for both the annual average and specific age classes compared to all prior survey years. This standardized training procedure will continue to be used by USFWS for the lower Connecticut River tributaries, and can be modified for other river systems. Overall, this report’s results highlight the importance of assessing precision and encourage the standardization of spawning mark identification quality control and assurance for future studies. With more quality assessments and baseline information on precision and bias, there can be more beneficial discussion on defining thresholds and how to implement spawning history variability into catch curve analyses.

Connecticut, Massachusetts↗
USGS2023

Application of a catch multiple survey analysis for Atlantic horseshoe crab Limulus polyphemus in the Delaware Bay

Objective This paper applies a catch multiple survey analysis (CMSA) to Atlantic horseshoe crabs Limulus polyphemus in the Delaware Bay to generate robust population estimates for harvest management. Currently, horseshoe crabs along the U.S. Atlantic coast are harvested as bait for other fisheries and collected for their blood, which is used in a biomedical industry. The Delaware Bay is home to the largest population of horseshoe crabs and is a significant stopover for shorebirds to rebuild energy by consuming horseshoe crab eggs prior to completing their northward migration. To address this interrelationship, the Adaptive Resource Management (ARM) Framework has been used since 2013 to ensure that horseshoe crab harvest within the region takes into account the forage needs of migratory birds. Since its inception, the ARM Framework has used a single trawl survey's swept area-based population estimates of horseshoe crab relative abundance and a theoretical population model developed primarily from literature-derived values. With more data collected in the region in recent years and other sources of mortality that can now be quantified, a catch survey model can provide horseshoe crab population estimates going forward. Methods A CMSA was used to estimate male and female horseshoe crab population size for 2003–2021 using all quantifiable sources of mortality and three fishery-independent indices of abundance. Result The CMSA results indicated that adult abundance of male and female horseshoe crabs was stable from 2003 to 2013 and then began to increase through 2017, a result that is consistent with stock rebuilding following a period of harvest restrictions as recommended by the ARM Framework. Population estimates were lower in recent years but remained above the levels estimated before implementation of the ARM Framework. In 2021, the CMSA estimated that there were over 6 million mature females and nearly 16 million mature male horseshoe crabs in the region. Conclusion The CMSA provides the best and most comprehensive population estimates of horseshoe crabs in Delaware Bay and will improve modeling efforts within the ARM Framework going forward.

Delaware, Maryland, New Jersey↗
USGS1986

This week?s Citation Classic: 'Stickel, L. F. A comparison of certain methods of measuring ranges of small mammals. J. Mammalogy 35: 1-15, 1954.'

During my first employment as a junior biologist conducting food habits analyses at the Patuxent Research Refuge (now Patuxent Wildlife Research Center) of the US Fish and Wildlife Service, the first station director, Arnold L. Nelson, gave each staff biologist the opportunity to spend a little time conducting field studies on the 2,600-acre research area. These studies were to help in ecological evaluation of the area, but also, no doubt, functioned as a morale builder for biologists daily engaged in difficult, demanding and confining laboratory work. I undertook to measure the population density of small mammals in different habitats. The state-of-the-art methods recommended to me and prevalent in the literature worried me, especially after preliminary field work. Reading the classic, paper by W.H. Burt [Terrioriality and home range concepts as applied to mammals. J. Mammalogy 24:346-352, 1943] was a turning point for me in seeing that knowledge of home-range size was the key to measuring population density. My first papers employed these concepts in field evaluations. However, they could give-only limited attention to the question of how home ranges could be measured. Furthermore, the importance of knowledge of the home range to habitat evaluation and to understanding animal behavior was becoming increasingly apparent. Advocacy of many different methods of measuring home ranges appeared in the literature. It did not seem possible to evaluate them objectively. It occurred to me that artificial populations could be used to help in understanding the mechanisms of measuring home ranges. The effects of random trap-visiting, bias toward central traps, trap-spacing, range shape, and other factors could be considered. So grids of traps were inked onto oversize graph paper, and simulated ranges were cut from plastic to be tossed as randomly as possible on the trap-grids. It was a slow, laborious job. Analysis showed that random captures in artificial populations produced patterns that were similar in many ways to those observed in field studies; the necessity of appropriate trap-spacing in relation to range size became obvious. Some commonly used methods of expressing range size proved to be clearly superior to others. However, the artificial populations could not fully simulate actual populations, where use patterns changed as conditions changed and the ?boundaries? of ranges tended to shift continuously or with time. Progress was made, but problems remained. I suspect that the reasons the paper has been cited frequently rest on the continuing interest in home-range behavior and the continuing difficulty in field evaluations, despite the many advances in observational methodology. The sophisticated techniques that became possible with the use of computers [R.I. Jennrich and F.B. Turner. Measurement of non-circular home range. J. Theor. Biol 23:227-237, 1969] also presented new possibilities for analysis and understanding of home-range characteristics and created a resurgence of interest in home-range behavior and its biological significance.

Current Contents / Agriculture, Biology & Environm↗
USGS2013

Native Prairie Adaptive Management: a multi region adaptive approach to invasive plant management on Fish and Wildlife Service owned native prairies

Much of the native prairie managed by the U.S. Fish and Wildlife Service (FWS) in the Prairie Pothole Region (PPR) of the northern Great Plains is extensively invaded by the introduced cool-season grasses, smooth brome ( Bromus inermis ) and Kentucky bluegrass ( Poa pratensis ). Management to suppress these invasive plants has had poor to inconsistent success. The central challenge to managers is selecting appropriate management actions in the face of biological and environmental uncertainties. In partnership with the FWS, the U.S. Geological Survey (USGS) developed an adaptive decision support framework to assist managers in selecting management actions under uncertainty and maximizing learning from management outcomes. This joint partnership is known as the Native Prairie Adaptive Management (NPAM) initiative. The NPAM decision framework is built around practical constraints faced by FWS refuge managers and includes identification of the management objective and strategies, analysis of uncertainty and construction of competing decision models, monitoring, and mechanisms for model feedback and decision selection. Nineteen FWS field stations, spanning four states of the PPR, have participated in the initiative. These FWS cooperators share a common management objective, available management strategies, and biological uncertainties. Though the scope is broad, the initiative interfaces with individual land managers who provide site-specific information and receive updated decision guidance that incorporates understanding gained from the collective experience of all cooperators. We describe the technical components of this approach, how the components integrate and inform each other, how data feedback from individual cooperators serves to reduce uncertainty across the whole region, and how a successful adaptive management project is coordinated and maintained on a large scale. During an initial scoping workshop, FWS cooperators developed a consensus management objective: increase the composition of native grasses and forbs on native sod while minimizing cost. Cooperators agreed that decision guidance should be provided annually and should account for local, real-time vegetation conditions observed on the ground. Over the course of development, two prototypes of the decision framework were considered. The final framework recognized four alternative actions that managers could take in any given year: (1) Graze—targeted use of grazing ungulates to achieve defoliation, (2) Burn—application of prescribed fire as the single form of defoliation, (3) Burn/Graze—a combination treatment, and (4) Rest—no action. The study area included northern mixed-grass and tallgrass prairie. Native vegetation in mixed–grass prairie has a strong cool-season component and thus the dominant native species have a phenology similar to that of smooth brome and Kentucky bluegrass, making management of those species challenging. In contrast, tallgrass prairie has a strong warm-season native component, leading to an existence of cool-season windows, periods of time in the fall and spring when cool‐season invasive grass species are actively growing and vulnerable to damage via select management actions, but warm‐season grass species are not active and are thus less susceptible to damage via the same actions. This dichotomy between prairie types necessitated the development of separate but parallel decision support systems for mixed-grass and tallgrass biomes. Management units are parcels of native prairie that receive a single management treatment at any one time over their entire extent. At any particular time, the vegetation state of each management unit is characterized by the amount of cover of native grasses and forbs and the type of invasive grass that is dominant. In addition, each unit has a defoliation state which reflects the number of years since the last defoliation event and an index to how intensively the unit was managed during the previous 7 years. State-transition models are used to predict the state of a management unit in year t+1 from its state in year t and a prescribed management action that was applied between the two monitoring events. Alternative models are built around key uncertainties that make choice of a management action difficult. Three uncertainties revolve around whether the effect of management actions depends on (1) type of dominant invader, (2) past defoliation history, and (3) level of invasion. Two additional uncertainties are considered when choosing a management action for tallgrass units: (4) the effectiveness of grazing within the cool-season window as a surrogate for burning when smooth brome is the dominant invader, and (5) the differential effect of active management outside the window as compared to rest. Because data on the probability of transitioning from one state to another under the various models were lacking, expert opinion and elicitation were used to parameterize the models. In addition, cooperators participated in elicitation exercises to extract their beliefs regarding the value of having native prairie compared to the cost of achieving it. Quantifying the subjective expression of utility in this way allowed for mathematical representation of the management objective into an objective function. By maximizing the objective function, cumulative utility is maximized, leading to the identification of a sequence of decisions that will achieve the management objective. The NPAM system adopted a vegetation monitoring protocol that was rapid, inexpensive, and familiar to many of the cooperators. The monitoring protocol served three purposes: (1) determining current vegetation and defoliation states of each unit, (2) evaluating progress toward the management objective, and (3) assessing predictive performance of the alternative models. The management year runs from September 1 to August 31. Management can be applied anytime during that period and monitoring takes places from late June to mid-August. Cooperators enter vegetation data and management information into a centralized database by August 25 of each year. Given the current state of the system (vegetation and defoliation states) and the current understanding of the system (or the belief state), identifying the current best management decision is a matter of looking up the combination (that is, system state and belief state) in the appropriate (mixed-grass or tallgrass) optimal decision table. Given complete uncertainty at the outset of decision-making, initial assignment of equal belief weights to each model was believed reasonable. The decisions in the optimal decision table that correspond to the current belief state constitute the current optimal decision policy. By August 31 of each year, individual cooperators are provided with a recommended management action for each of their management units for the upcoming management year. Upon receiving the management recommendations for their units, managers consider the recommendation, along with other relevant information, and at some point during the year one of the management alternatives is carried out. This iterative cycle of making and implementing a management decision, predicting the response, monitoring the outcome, comparing predicted and observed outcomes, updating model weights, and recommending a management action for the next cycle is expected to result in an accumulation of weight on a representative model of system dynamics, thereby increasing understanding needed to effectively manage native prairies. The NPAM system is now entering its second full year of complete operation, and represents one of only a few fully implemented applications of adaptive management within the U.S. Fish and Wildlife Service. NPAM is truly unique in that it originated from the ground up as a result of the leadership and steadfastness of several refuge biologists and managers confronted with a common problem. These biologists recognized that working together across a large landscape presented perhaps the best opportunity for halting and reversing the invasion of native grasslands by non-native cool-season grasses. Importantly, the NPAM system encapsulates the collective thinking and experience of tens if not hundreds of individuals who have battled this vexing problem for much of their careers. The NPAM initiative is rooted in principles of adaptive management, thereby affording the opportunity for grassland managers to pursue management objectives while acquiring information to reduce uncertainty and improve future management. The project introduced a number of technical innovations that will serve as templates for conservation efforts throughout and beyond the U.S. Fish and Wildlife Service. First, NPAM is an on-the-ground implementation of active adaptive management—possibly the first of its kind in conservation management—in which recommended management actions result from a prospective analysis of future learning (Williams, 1996). Second, by the use of dynamic optimization, NPAM demonstrates how decisions can be made that take into account possible future transitions of the system. Third, NPAM demonstrates how models of partial controllability are an effective means of accommodating unpredictable circumstances that cause a manager to follow a different course than was intended. Finally, the database developed for NPAM is an unparalleled system that enables the rapid integration of data from the field for the generation of ‘just-in-time’ management recommendations. In all, NPAM provides an example of how a science-management partnership can be forged to achieve large-scale conservation objectives.

Minnesota;Montana;North Dakota;South Dakota↗
USGS2006

Application of artificial neural networks to assess pesticide contamination in shallow groundwater

In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient (R = 0.87) and pesticide detection efficiency (E = 89%), as well as good match between the observed and predicted "class" groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R, E, mean error (ME), root mean square error (RMSE), and pesticide occurrence "class" groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R, E, ME, RMSE, and pesticide occurrence "class" groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination. ?? 2006 Elsevier B.V. All rights reserved.

Science of the Total Environment↗
USGS2023

Vulnerability to climate change of managed stocks in the California Current large marine ecosystem

Introduction: Understanding how abundance, productivity and distribution of individual species may respond to climate change is a critical first step towards anticipating alterations in marine ecosystem structure and function, as well as developing strategies to adapt to the full range of potential changes. Methods: This study applies the NOAA (National Oceanic and Atmospheric Administration) Fisheries Climate Vulnerability Assessment method to 64 federally-managed species in the California Current Large Marine Ecosystem to assess their vulnerability to climate change, where vulnerability is a function of a species’ exposure to environmental change and its biological sensitivity to a set of environmental conditions, which includes components of its resiliency and adaptive capacity to respond to these new conditions. Results: Overall, two-thirds of the species were judged to have Moderate or greater vulnerability to climate change, and only one species was anticipated to have a positive response. Species classified as Highly or Very Highly vulnerable share one or more characteristics including: 1) having complex life histories that utilize a wide range of freshwater and marine habitats; 2) having habitat specialization, particularly for areas that are likely to experience increased hypoxia; 3) having long lifespans and low population growth rates; and/or 4) being of high commercial value combined with impacts from non-climate stressors such as anthropogenic habitat degradation. Species with Low or Moderate vulnerability are either habitat generalists, occupy deep-water habitats or are highly mobile and likely to shift their ranges. Discussion: As climate-related changes intensify, this work provides key information for both scientists and managers as they address the long-term sustainability of fisheries in the region. This information can inform near-term advice for prioritizing species-level data collection and research on climate impacts, help managers to determine when and where a precautionary approach might be warranted, in harvest or other management decisions, and help identify habitats or life history stages that might be especially effective to protect or restore.

California, Oregon, Washington↗
USGS2019

Early-warning signals of individual tree mortality based on annual radial growth

Tree mortality is a key driver of forest dynamics and its occurrence is projected to increase in the future due to climate change. Despite recent advances in our understanding of the physiological mechanisms leading to death, we still lack robust indicators of mortality risk that could be applied at the individual tree scale. Here, we build on a previous contribution exploring the differences in growth level between trees that died and survived a given mortality event to assess whether changes in temporal autocorrelation, variance, and synchrony in time-series of annual radial growth data can be used as early warning signals of mortality risk. Taking advantage of a unique global ring-width database of 3065 dead trees and 4389 living trees growing together at 198 sites (belonging to 36 gymnosperm and angiosperm species), we analyzed temporal changes in autocorrelation, variance, and synchrony before tree death (diachronic analysis), and also compared these metrics between trees that died and trees that survived a given mortality event (synchronic analysis). Changes in autocorrelation were a poor indicator of mortality risk. However, we found a gradual increase in inter-annual growth variability and a decrease in growth synchrony in the last ∼20 years before mortality of gymnosperms, irrespective of the cause of mortality. These changes could be associated with drought-induced alterations in carbon economy and allocation patterns. In angiosperms, we did not find any consistent changes in any metric. Such lack of any signal might be explained by the relatively high capacity of angiosperms to recover after a stress-induced growth decline. Our analysis provides a robust method for estimating early-warning signals of tree mortality based on annual growth data. In addition to the frequently reported decrease in growth rates, an increase in inter-annual growth variability and a decrease in growth synchrony may be powerful predictors of gymnosperm mortality risk, but not necessarily so for angiosperms.

Frontiers in Plant Science↗
USGS2019

Case studies in groundwater contaminant fate and transport

A case study of groundwater contamination is a detailed study of a single site contaminated with a chemical or mixture that is known to be a problem at many sites. The goal of case studies is to provide insights into the physical, chemical, and biological processes controlling migration, natural attenuation, or remediation of common groundwater contaminants. Ideally, processes occurring at a case study site are representative of other sites so that knowledge gained from these intensive studies can be applied at thousands of sites where fewer data are available. Several characteristics of case studies contribute to their value. First, they may have tens to hundreds of monitoring wells, compared to fewer than ten wells at some contaminated sites. Second, some case studies continue for many years or even decades, providing insights into temporal progression of slow processes. Third, analytical methods prohibitively expensive for routine use or under development may be tested at case study sites. Finally, the ongoing characterization typical of case study sites builds a foundation of knowledge that facilitates sophisticated experimental design and testing of new methods. This article is divided into sections based on the contaminant type because the chemical and biological processes required for remediation vary for each contaminant. Most importantly, some contaminants can be biodegraded whereas metals and radionuclides cannot be destroyed but can be immobilized or rendered less toxic. The emphasis is on case studies of natural processes that control the fate and transport of contaminants in groundwater rather than on active remediation methods. The principles learned from these studies may form the basis for design of remedial strategies. The organic contaminants are divided into: petroleum hydrocarbons, fuel oxygenates, coal tar and wastes from manufactured gas plants, and chlorinated solvents. The inorganic contaminants covered are metals and radionuclides, arsenic, and nitrate. Case studies of mixed waste plumes from landfills are also described. Experimental sites where contaminants have been introduced into an aquifer as an emplaced source or a controlled release may not meet the above definition of case studies, but some are included because the overall goal is to impart lessons learned from detailed field studies. It is impossible to cover all case studies in this short format. Conversely, focusing on one or two does not convey the breadth of research results in entire range of case studies. Instead, the strategy is to describe the evolution of knowledge for each contaminant class while providing citations of relevant case studies. Much of the progress in understanding of the fate of contaminants in groundwater is based on laboratory studies; thus whenever possible, papers that included both field and laboratory results have been included among the citations. Two topics of growing importance have not been covered. These are the fate of pharmaceuticals in groundwater and discharge of contaminant plumes to surface water. These topics merit coverage in the future as knowledge grows and case studies increase in number.

Environmental Science↗
USGS2013

Tracing groundwater with low-level detections of halogenated VOCs in a fractured carbonate-rock aquifer, Leetown Science Center, West Virginia, USA

Measurements of low-level concentrations of halogenated volatile organic compounds (VOCs) and estimates of groundwater age interpreted from 3 H/ 3 He and SF 6 data have led to an improved understanding of groundwater flow, water sources, and transit times in a karstic, fractured, carbonate-rock aquifer at the Leetown Science Center (LSC), West Virginia. The sum of the concentrations of a set of 16 predominant halogenated VOCs (TDVOC) determined by gas chromatography with electron-capture detector (GC–ECD) exceeded that possible for air–water equilibrium in 34 of the 47 samples (median TDVOC of 24,800 pg kg −1 ), indicating that nearly all the water sampled in the vicinity of the LSC has been affected by addition of halogenated VOCs from non-atmospheric source(s). Leakage from a landfill that was closed and sealed nearly 20 a prior to sampling was recognized and traced to areas east of the LSC using low-level detection of tetrachloroethene (PCE), methyl chloride (MeCl), methyl chloroform (MC), dichlorodifluoromethane (CFC-12), and cis-1,2-dichloroethene (cis-1,2-DCE). Chloroform (CHLF) was the predominant VOC in water from domestic wells surrounding the LSC, and was elevated in groundwater in and near the Fish Health Laboratory at the LSC, where a leak of chlorinated water occurred prior to 2006. The low-level concentrations of halogenated VOCs did not exceed human or aquatic-life health criteria, and were useful in providing an awareness of the intrinsic susceptibility of the fractured karstic groundwater system at the LSC to non-atmospheric anthropogenic inputs. The 3 H/ 3 He groundwater ages of spring discharge from the carbonate rocks showed transient behavior, with ages averaging about 2 a in 2004 following a wet climatic period (2003–2004), and ages in the range of 4–7 a in periods of more average precipitation (2008–2009). The SF 6 and CFC-12 data indicate older water (model ages of 10s of years or more) in the low-permeability shale of the Martinsburg Formation located to the west of the LSC. A two-a record of specific conductance, water temperature, and discharge recorded at 30-min intervals demonstrated an approximately 3-month lag in discharge at Gray Spring. The low groundwater ages of waters from the carbonate rocks support rapid advective transport of contaminants from the LSC vicinity, yet the nearly ubiquitous occurrence of low-level concentrations of halogenated VOCs at the LSC suggests the presence of long-term persistent sources, such as seepage from the closed and sealed landfill, infiltration of VOCs that may persist locally in the epikarst, exchange with low-permeability zones in fractured rock, and upward leakage of older water that may contain elevated concentrations of halogenated VOCs from earlier land use activities.

West Virginia↗