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USGS2024

Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin

As part of the 2014 Chesapeake Bay Watershed Agreement, all Bay States and the District of Columbia have committed to improving the condition of the Bay, which includes a goal to achieve sustainable fisheries. One outcome under that broad goal is improved effectiveness of fish habitat conservation and preservation efforts. In support of that outcome, the U.S. Geological Survey Eastern Ecological Science Center (USGS-EESC) and the National Oceanic and Atmospheric Association’s National Centers for Coastal Ocean Science (NOAA-NCCOS) are actively developing datasets, methods, and analyses to conduct fish habitat assessments in the Chesapeake Bay watershed, guided by recommendations from a regional stakeholder workshop held by the Chesapeake Bay Program’s (CBP) Fish Habitat Action Team (FHAT) in 2018. The joint USGS and NOAA team has been collaborating on methods for conducting inland and estuarine assessments and exploring whether a seamless headwater to estuary assessment could be developed. The goals of this assessment are to benefit both State and Federal fisheries managers, help advance fisheries science, and provide beneficial information for the public. While past national and regional assessments (e.g. the National Fish Habitat Partnership National Assessment) treated inland and estuarine fish habitat conditions separately due to differences in environments, GIS data representation, and data availability, a seamless habitat assessment could be of value for a broad range of stakeholders as many fish species, several of which are invasive or under federal jurisdiction, use habitats across both inland and estuarine waters. This project developed a pilot framework, explored and tested methods necessary for a finer scale, seamless assessment across both inland and estuarine waters, and demonstrated its use. Although there was interest by the CBP FHAT for the generation of a Baywide fish habitat assessment that spanned tidal salt, tidal fresh, warm non-tidal and cold non-tidal waters, there are a myriad of implementation details and considerations around conducting a Baywide assessment across all four of these general habitat areas. Therefore, the practical need to conduct a tributary-specific pilot assessment arose. At the beginning of this pilot process, members of the FHAT were presented with a decision matrix to choose a study basin using factors such as data availability and tributary size. FHAT members chose the Patuxent River basin, which has been relatively well sampled and studied. Several spatial frameworks were considered before selection of an inclusive gridded framework for summary and analysis that represented inland drainage networks and landscape influences as well as estuarine bathymetry. A suite of landscape and in-water stressor variables were summarized into the framework and were largely generalized over time. In order to assess the viability of the framework, we chose to use species distribution modeling for each of the species to test the framework’s ability to predict habitat use of non-tidal resident, estuarine resident, and migratory species. Tessellated darter (Etheostoma olmstedi), American eel (Anguilla rostrata), and white perch (Morone americana) were chosen as illustrative fish species based on data availability, and differences in life history and habitat use. A nested modeling approach, which involved successive model runs at multiple scales (1000m, 100m, and 10m raster grids) was developed to examine differences in variable importance at different spatial scales and to enhance modeling efficiency. For white perch, a complementary modeling analysis was performed for variables available only in estuarine waters. For all testing, an ensemble modeling approach was conducted, using a suite of potential statistical techniques driven by model strength and variable predictive power. The statistical testing that we conducted was intended only to test the framework and modeling approach, and not to definitively predict all habitats where specific fish species might be present. The modeling we conducted to test the framework did have some limitations. For example, the spatial distribution of favorable habitat areas for white perch was likely influenced by the predominance of fish survey locations near the center channel of the river and the use of generalized in-water conditions. For all species, the use of juvenile and adult fish survey data limits the estimation of habitat use to those life stages. Despite such limitations of the data inputs and modeling approach, we found the framework could seamlessly predict fish habitat distribution across freshwater and tidal environments and integrate the influence of landscape stressors with local in-water factors. The developed framework presented to the Sustainable Fisheries Goal Implementation Team (GIT) and FHAT is informative and could potentially be used for other modeling applications in the Chesapeake Bay watershed and elsewhere. In particular the framework and modeling approach lend themselves to evaluating living resource distributions and underlying habitat conditions in shallow tidal waters and beyond, as recommended by the recent Comprehensive Evaluation of System Response (CESR) report from the Chesapeake Bay Program.

Maryland↗

USGS2011

Tumor prevalence and biomarkers of genotoxicity in brown bullhead (Ameiurus nebulosus) in Chesapeake Bay tributaries

We surveyed four Chesapeake Bay tributaries for skin and liver tumors in brown bullhead ( Ameiurus nebulosus ). We focused on the South River, where the highest skin tumor prevalence (53%) in the Bay watershed had been reported. The objectives were to 1) compare tumor prevalence with nearby rivers (Severn and Rhode) and a more remote river (Choptank); 2) investigate associations between tumor prevalence and polynuclear aromatic hydrocarbons (PAHs) and alkylating agents; and 3) statistically analyze Chesapeake Bay bullhead tumor data from 1992 through 2008. All four South River collections exhibited high skin tumor prevalence (19% to 58%), whereas skin tumor prevalence was 2%, 10%, and 52% in the three Severn collections; 0% and 2% in the Choptank collections; and 5.6% in the Rhode collection. Liver tumor prevalence was 0% to 6% in all but one South River collection (20%) and 0% to 6% in the three other rivers. In a subset of samples, PAH-like biliary metabolites and 32 P-DNA adducts were used as biomarkers of exposure and response to polycyclic aromatic compounds (PACs). Adducts from alkylating agents were detected as O6-methyl-2′-deoxyguanosine (O6Me-dG) and O6-ethyl-2′-deoxyguanosine (O6Et-dG) modified DNA. Bullheads from the contaminated Anacostia River were used as a positive control for DNA adducts. 32 P-DNA adduct concentrations were significantly higher in Anacostia bullhead livers compared with the other rivers. We identified alkyl DNA adducts in bullhead livers from the South and Anacostia, but not the Choptank. Neither the PAH-like bile metabolite data, sediment PAH data, nor the DNA adduct data suggest an association between liver or skin tumor prevalence and exposure to PACs or alkylating agents in the South, Choptank, Severn, or Rhode rivers. Logistic regression analysis of the Chesapeake Bay database revealed that sex and length were significant covariates for liver tumors and length was a significant covariate for skin tumors.

Chesapeake Bay watershed, South River↗

USGS2007

Factors associated with sources, transport, and fate of volatile organic compounds and their mixtures in aquifers of the United States

Factors associated with sources, transport, and fate of volatile organic compounds (VOCs) in groundwater from aquifers throughout the United States were evaluated using statistical methods. Samples were collected from 1631 wells throughout the conterminous United States between 1996 and 2002 as part of the National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey. Water samples from wells completed in aquifers used to supply drinking water were analyzed for more than 50 VOCs. Wells were primarily rural domestic water supplies (1184), followed by public water supplies (216); the remaining wells (231) supplied a variety of uses. The median well depth was 50 meters. Age-date information shows that about 60% of the samples had a fraction of water recharged after 1953. Chloroform, toluene, 1,2,4-trimethylbenzene, and perchloroethene were some of the frequently detected VOCs. Concentrations generally were less than 1 ??g/L. Source factors include, in order of importance, general land-use activity, septic/sewer density, and sites where large concentrations of VOCs are potentially released, such as leaking underground storage tanks. About 10% of all samples had VOC mixtures that were associated with concentrated sources; 20% were associated with dispersed sources. Important transport factors included well/screen depth, precipitation/groundwater recharge, air temperature, and various soil characteristics. Dissolved oxygen was strongly associated with VOCs and represents the fate of many VOCs in groundwater. Well type (domestic or public water supply) was also an important explanatory factor. Results of multiple analyses show the importance of (1) accounting for both dispersed and concentrated sources of VOCs, (2) measuring dissolved oxygen when sampling wells to help explain the fate of VOCs, and (3) limiting the type of wells sampled in monitoring networks to avoid unnecessary variance in the data, or controlling for this variance during data analysis.

Environmental Science & Technology↗

USGS2018

Surrounding land cover types as predictors of palustrine wetland vegetation quality in conterminous USA

The loss of wetland habitats and their often-unique biological communities is a major environmental concern. We examined vegetation data obtained from 380 wetlands sampled in a statistical survey of wetlands in the USA. Our goal was to identify which surrounding land cover types best predict two indices of vegetation quality in wetlands at the regional scale. We considered palustrine wetlands in four regions (Coastal Plains, North Central East, Interior Plains, and West) in which the dominant vegetation was emergent, forested, or scrub-shrub. For each wetland, we calculated weighted proportions of eight land cover types surrounding the area in which vegetation was assessed, in four zones radiating from the edge of the assessment area to 2 km. Using Akaike's Information Criterion, we determined the best 1-, 2- and 3-predictor models of the two indices, using the weighted proportions of the land cover types as potential predictors. Mean values of the two indices were generally higher in the North Central East and Coastal Plains than the other regions for forested and emergent wetlands. In nearly all cases, the best predictors of the indices were not the dominant surrounding land cover types. Overall, proportions of forest (positive effect) and agriculture (negative effect) surrounding the assessment area were the best predictors of the two indices. One or both of these variables were included as predictors in 65 of the 72 models supported by the data. Wetlands surrounding the assessment area had a positive effect on the indices, and ranked third (33%) among the predictors included in supported models. Development had a negative effect on the indices and was included in only 28% of supported models. These results can be used to develop regional management plans for wetlands, such as creating forest buffers around wetlands, or to conserve zones between wetlands to increase habitat connectivity.

Science of the Total Environment↗

USGS2002

Comparisons of likelihood and machine learning methods of individual classification

Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k -nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin (“assignment tests”). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high F ST ), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout ( Salvelinus namaycush ) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k -nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0–2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to “learn” and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks. In recent years, characterization of highly polymorphic molecular markers such as mini- and microsatellites and development of novel methods of analysis have enabled researchers to extend investigations of ecological and evolutionary processes below the population level to the level of individuals (e.g., Bowcock et al. 1994 ; Estoup and Angers 1998 ; Jarne and Lagoda 1996 ). Analyses of individual-based genotypic information could substantially improve our understanding of evolutionary phenomena and contribute to effective management of natural populations (review in Bernatchez and Duchesne 2000 ). The use of individual-based methods remained largely unexplored in animal populations until recently due to a lack of highly polymorphic markers ( Bernatchez and Duchesne 2000 ; Smouse and Chevillon 1998 ). Traditional analytical methods in population genetics rely almost exclusively on descriptors of genetic characterizations of populations ( Bernatchez and Duchesne 2000 ) and not on individual genotypes. “Assignment tests” are designed to determine population membership for individuals. One particular application based on a likelihood estimate (LE) was introduced by Paetkau et al. (1995 ; see also Vásquez-Domínguez et al. 2001) to assign an individual to the population of origin on the basis of multilocus genotype and expectations of observing this genotype in each potential source population. The LE approach can be implemented statistically in a Bayesian framework as a convenient way to evaluate hypotheses of plausible genealogical relationships (e.g., that an individual possesses an ancestor in another population) ( Dawson and Belkhir 2001 ; Pritchard et al. 2000 ; Rannala and Mountain 1997 ). Other studies have evaluated the confidence of the assignment ( Almudevar 2000 ) and characteristics of genotypic data (e.g., degree of population divergence, number of loci, number of individuals, number of alleles) that lead to greater population assignment ( Bernatchez and Duchesne 2000 ; Cornuet et al. 1999 ; Haig et al. 1997 ; Shriver et al. 1997; Smouse and Chevillon 1998 ). Main statistical and conceptual differences between methods leading to the use of an assignment test are given in, for example, Cornuet et al. (1999) and Rosenberg et al. (2001) . However, the relative power of those tests has certainly not been fully appreciated and empirical comparisons are scarce ( Eldridge et al. 2001 ). Assignment tests can also be considered as surrogates at the individual level (sensu Hansen et al. 2001a ) for other statistical tools developed earlier, such as mixed-stock analysis (e.g., Pella and Masuda 2001 ; Pella and Milner 1987 ). Detailed theoretical comparison of the interests and limitations of both methods are still lacking, but empirical studies have revealed correlations between outputs of methods ( Knutsen et al. 2001 ; Potvin and Bernatchez 2001 ). Assignment tests have been widely used in different applications, including determination of degree of population differentiation or to establish the relationship among individuals within and among various taxonomic groupings (e.g., Bogdanowicz et al. 1997 ; Koskinen et al. 2001 ; Marshall et al. 2000 ; Müller 2000 ; Neraas and Spruell 2001 ; Nielsen et al. 2001b ; Polzhien et al. 2000 ; Primmer et al. 1999 ; Roeder et al. 2001 ; Roques et al. 1999 ; Schulte-Hostedde et al. 2001 ; Sefc et al. 2000 ; Spidle et al. 2001 ; Vásquez-Domínguez et al. 2001 ), including hybrids (e.g., Beaumont et al. 2001 ; Congiu et al. 2001 ; Randi et al. 2001 ), introgressed individuals (e.g., Martinez et al. 2001 ; Randi and Lucchini 2002 ), and ecotypes (e.g., Taylor et al. 2000 ). Applications of assignment tests also include [human] forensics (e.g., Evett and Weir 1998 ; Primmer et al. 2000 ), identification and/or source of dispersers (e.g., Davies et al. 1999 ; Eldridge et al. 2001 ; Galbusera et al. 2000 ; Petersson et al. 2001 ; Tsutsui et al. 2001 ; Vasemägi et al. 2001), phylogeographical analyses (e.g., King et al. 2001 ; Zeisset and Beebee 2001 ), and the evaluation of the contribution of stocked individuals to natural populations (e.g., Fritzner et al. 2001 ; Hansen et al. 2000 , 2001b ) and of supportive breeding programs ( Nielsen et al. 2001a ; Olsen et al. 2000 ). Fish are among the organisms that have received considerable attention using such tools (see Hansen et al. [2001a] for a review). Moreover, these techniques are now used for profiles of traits outside the limited scope of population genetics ( Thorrold et al. 2001 ). Methods of classification vary widely based on several criteria (e.g., Jain et al. 2000 ) ( Figure 1 ). Two basic classification processes are traditionally recognized in machine learning: supervised classifiers and unsupervised classifiers ( Figure 1 ; e.g., Duda et al. 2000 ; Jain et al. 2000 ). Supervised classifiers represent a group of methods whereby individual assignment is made to predefined classes (i.e., populations of origin). Unsupervised classification classes are unknown and are defined a posteriori on the basis of the degree of difference or similarity in attributes characterized from sampled individuals. Clustering methods (e.g., multidimensional scaling, principal component analysis) are examples of unsupervised classification. Applications of assignment testing in population genetics first used supervised parametric likelihood-based approaches ( Figure 1 ). Other machine learning classification methods are widely used in the physical and social sciences and in other biological disciplines (e.g. Boddy et al. 2000 ; Leung and Tran 2000 ; Manel et al. 1999 ; Raymer et al. 1997 ). Artificial neural networks (ANNs) are a popular technique used in machine learning (e.g., Boddy and Morris 1999 ; Duda et al. 2000 ; Lek and Guégan 2000 ; Ripley 1996 ). However, while recognized ( Hansen et al. 2001a ), ANN methods rarely have been employed for population genetics applications ( Aurelle 1999 ; Aurelle et al. 1999 ; Cornuet et al. 1996 ; Curtis et al. 2001 ; Giraudel et al. 2000 ; Grigull et al. 2001 ; Taylor et al. 1994 ; Whitler et al. 1994 ). Other popular classification methods in machine learning, such as decision trees (e.g., Bell 1996 , 1999 ; Duda et al. 2000 ; Mitchell 1997 ) and k -nearest neighbor analysis ( k -NN; e.g., Dasarathy 1991 ; Duda et al. 2000 ) have yet to be applied in population genetics ( Figure 1 ). Moreover, there has not been a directed effort to compare machine learning methodologies with the likelihood-based procedures widely used in population genetics. Cornuet et al. (1996) compared the relative merits of ANNs to discriminant analysis in an empirical study involving different populations and subspecies of honeybee ( Apis mellifera ). However, they did not compare LE and ANN supervised classifiers. Aurelle (1999) used the approach of Rannala and Mountain (1997) ( Figure 1 ) and ANN analysis using brown trout ( Salmo trutta ) microsatellite data; however, he did not provide a direct comparison of classification results or accuracies. Hansen et al. (2001a) briefly presented ANNs, but rejected their use without really testing their ability to classify individuals. The objective of this article is to describe several of the more widely used machine learning classifiers that may have utility when used with empirical population genetics data. We compare likelihood-based “assignment tests” ( Paetkau et al. 1995 ) with supervised machine learning classifiers including ANN, decision tree, and a k -NN clustering. Simulations were conducted which estimated and compared the assignment accuracy associated with different classifiers using ranges of parameter values (number of loci, allelic diversity, and interpopulation variance in allele frequency) typically encountered in natural populations. Comparative analyses were extended to empirical examples using lake trout ( Salvelinus namaycush ; Salmonidae).

Journal of Heredity↗

USGS2004

Application of health-based screening levels to ground-water quality data in a state-scale pilot effort

A state-scale pilot effort was conducted to evaluate a Health-Based Screening Level (HBSL) approach developed for communicating findings from the U.S. Geological Survey (USGS) National Water-Quality Assessment Program in a human-health context. Many aquifers sampled by USGS are used as drinking-water sources, and water-quality conditions historically have been assessed by comparing measured contaminant concentrations to established drinking-water standards and guidelines. Because drinking-water standards and guidelines do not exist for many analyzed contaminants, HBSL values were developed collaboratively by the USGS, U.S. Environmental Protection Agency (USEPA), New Jersey Department of Environmental Protection, and Oregon Health & Science University, using USEPA toxicity values and USEPA Office of Water methodologies. The main objective of this report is to demonstrate the use of HBSL approach as a tool for communicating water-quality data in a human-health context by conducting a retrospective analysis of ground-water quality data from New Jersey. Another important objective is to provide guidance on the use and interpretation of HBSL values and other human-health benchmarks in the analyses of water-quality data in a human-health context. Ground-water samples collected during 1996-98 from 30 public-supply, 82 domestic, and 108 monitoring wells were analyzed for 97 pesticides and 85 volatile organic compounds (VOCs). The occurrence of individual pesticides and VOCs was evaluated in a human-health context by calculating Benchmark Quotients (BQs), defined as ratios of measured concentrations of regulated compounds (that is, compounds with Federal or state drinking-water standards) to Maximum Contaminant Level (MCL) values and ratios of measured concentrations of unregulated compounds to HBSL values. Contaminants were identified as being of potential human-health concern if maximum detected concentrations were within a factor of 10 of the associated MCL or HBSL (that is, maximum BQ value (BQmax) greater than or equal to 0.1) in any well type (public supply, domestic, monitoring). Most (57 of 77) pesticides and VOCs with human-health benchmarks were detected at concentrations well below these levels (BQmax less than 0.1) for all three well types; however, BQmax values ranged from 0.1 to 3,000 for 6 pesticides and 14 VOCs. Of these 20 contaminants, one pesticide (dieldrin) and three VOCs (1,2-dibromoethane, tetrachloroethylene, and trichloroethylene) both (1) were measured at concentrations that met or exceeded MCL or HBSL values, and (2) were detected in more than 10 percent of samples collected from raw ground water used as sources of drinking water (public-supply and (or) domestic wells) and, therefore, are particularly relevant to human health. The occurrence of multiple pesticides and VOCs in individual wells also was evaluated in a human-health context because at least 53 different contaminants were detected in each of the three well types. To assess the relative human-health importance of the occurrence of multiple contaminants in different wells, the BQ values for all contaminants in a given well were summed. The median ratio of the maximum BQ to the sum of all BQ values for each well ranged from 0.83 to 0.93 for all well types, indicating that the maximum BQ makes up the majority of the sum for most wells. Maximum and summed BQ values were statistically greater for individual public-supply wells than for individual domestic and monitoring wells. The HBSL approach is an effective tool for placing water-quality data in a human-health context. For 79 of the 182 compounds analyzed in this study, no USEPA drinking-water standards or guidelines exist, but new HBSL values were calculated for 39 of these 79 compounds. The new HBSL values increased the number of detected pesticides and VOCs with human-health benchmarks from 65 to 77 (of 97 detected compounds), thereby expanding the basis for interpreting contaminant-occu

Scientific Investigations Report↗

USGS2009

Archive of digital boomer and CHIRP seismic reflection data collected during USGS field activity 08LCA03 in Lake Panasoffkee, Florida, May 2008

In May of 2008, the U.S. Geological Survey (USGS) conducted geophysical surveys in Lake Panasoffkee, located in central Florida, as part of the USGS Lakes and Coastal Aquifers (LCA) study. This report serves as an archive of unprocessed digital boomer and Compressed High Intensity Radar Pulse (CHIRP)* seismic reflection data, trackline maps, navigation files, Field Activity Collection System (FACS) logs, Geographic Information System (GIS) files, and formal Federal Geographic Data Committee (FGDC) metadata. Filtered and gained (a relative increase in signal amplitude) digital images of the seismic profiles and geospatially corrected interactive profiles are also provided. Refer to the Acronyms page for expansions of acronyms and abbreviations used in this report. *Due to poor data acquisition conditions associated with the lake bottom sediments, only two CHIRP tracklines were collected during this field activity. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are provided. The USGS Florida Integrated Science Center (FISC) - St. Petersburg assigns a unique identifier to each cruise or field activity. For example, 08LCA03 tells us the data were collected in 2008 for the Lakes and Coastal Aquifers (LCA) study and the data were collected during the third field activity for that study in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the field activity ID. The naming convention used for each seismic line is as follows: yye##a, where 'yy' are the last two digits of the year in which the data were collected, 'e' is a 1-letter abbreviation for the equipment type (for example, b for boomer and c for CHIRP), '##' is a 2-digit number representing a specific track, and 'a' is a letter representing the section of a line if recording was prematurely terminated or rerun for quality or acquisition problems. The boomer plate is an acoustic energy source that consists of capacitors charged to a high voltage and discharged through a transducer in the water. The transducer is towed on a sled floating on the water surface and, when discharged, emits a short acoustic pulse, or shot, which propagates through the water, sediment column, or rock beneath. The acoustic energy is reflected at density boundaries (such as the seafloor, sediment, or rock layers beneath the seafloor), detected by the receiver, and recorded by a PC-based seismic acquisition system. This process is repeated at timed intervals (for example, 0.5 s) and recorded for specific intervals of time (for example, 100 ms). In this way, a two-dimensional (2-D) vertical profile of the shallow geologic structure beneath the ship track is produced. Figure 1 displays the boomer acquisition geometry. The EdgeTech SB-424 CHIRP system used for this survey has a vertical resolution of 4 - 8 cm, a penetration depth that is usually less than 2 m beneath the seafloor, and uses a signal of continuously varying frequency. The towfish is a sound source and receiver, which is typically towed 2 - 5 m above the seafloor. The acoustic energy is reflected at density boundaries (such as the seafloor or sediment layers beneath the seafloor), detected by a receiver, and recorded by a PC-based seismic acquisition system. This process is repeated at timed intervals (for example, 0.125 s) and recorded for specific intervals of time (for example, 50 ms); the resulting profile is a two-dimensional vertical image of the shallow geologic structure beneath the ship track. Figure 2 displays the acquisition geometry for the CHIRP system. Refer to table 1 for a summary of acquisition parameters and table 2 for trackline statistics.

Florida↗

USGS2016

Spatiotemporal patterns of mercury accumulation in lake sediments of western North America

For the Western North America Mercury Synthesis, we compiled mercury records from 165 dated sediment cores from 138 natural lakes across western North America. Lake sediments are accepted as faithful recorders of historical mercury accumulation rates, and regional and sub-regional temporal and spatial trends were analyzed with descriptive and inferential statistics. Mercury accumulation rates in sediments have increased, on average, four times (4×) from 1850 to 2000 and continue to increase by approximately 0.2 μg/m 2 per year. Lakes with the greatest increases were influenced by the Flin Flon smelter, followed by lakes directly affected by mining and wastewater discharges. Of lakes not directly affected by point sources, there is a clear separation in mercury accumulation rates between lakes with no/little watershed development and lakes with extensive watershed development for agricultural and/or residential purposes. Lakes in the latter group exhibited a sharp increase in mercury accumulation rates with human settlement, stabilizing after 1950 at five times (5×) 1850 rates. Mercury accumulation rates in lakes with no/little watershed development were controlled primarily by relative watershed size prior to 1850, and since have exhibited modest increases (in absolute terms and compared to that described above) associated with (regional and global) industrialization. A sub-regional analysis highlighted that in the ecoregion Northwestern Forest Mountains, <1% of mercury deposited to watersheds is delivered to lakes. Research is warranted to understand whether mountainous watersheds act as permanent sinks for mercury or if export of “legacy” mercury (deposited in years past) will delay recovery when/if emissions reductions are achieved.

Alaska, Alberta, Arizona, British Columbia, Califo↗

USGS2017

A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA

Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50 ppb and probability of dissolved oxygen concentration to be below 0.5 ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971–2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative impact on nitrate predictions. Three-dimensional visualization indicates that nitrate predictions depend on the probability of anoxic conditions and other factors, and that nitrate predictions generally decreased with increasing groundwater age.

California↗

USGS2023

Diving deeper into seep distribution along the Cascadia Convergent Margin, USA

Previous margin-wide studies of methane seep distribution along the Cascadia Subduction Zone indicate peaks in seep density within the landward limit of the of gas hydrate stability zone (GHSZ; ≤500 m depth), suggesting a link between current ocean warming, acceleration of hydrate dissociated, and methane emissions. This inferred connection, however, may not account for regional geologic and/or structural complexities driving methane seepage. Expanding upon an existing seep database by adding new seeps data, we conducted statistical and spatial analyses to determine margin-wide distribution trends and offer a tectonic framework for understanding the tendency toward non-normality and spatial clustering. We then highlight the role of local-scale drivers of seep formation in addition to the first-order tectonic framework, using systematic geologic/geomorphic characterization of seep emission sites in southern Cascadia and case studies using meta-attribute analysis of seismic reflection data.. Seep distribution along the margin is non-random, but instead of clustering along the 500-m isobath, regions of high seep density occur in canyons and topographic highs. New findings from this study conclude that co-location of the outer arc high (OAH) and the landward limit of the GHSZ may explain high concentrations of seeps where deformation is the greatest and hydrates are unstable. Detailed analysis of the spatial relationships between seep sites and geologic-geomorphic features in southern Cascadia reveal a link between seeps and anticlines, with 52% of the seeps found in association with anticlines, 36% found at faults, 16% associated with canyons, and 11% at seafloor failure scarps. Given that a majority of anticlines are located along or seaward of the OAH in the actively deforming outer wedge, we suggest that the location of the OAH is a primary structural control on seep distribution. This scenario is supported by neural network analysis of multichannel seismic data revealing zones of probable fluid migration along vertical pipes, faults, and chimneys in the vicinity of active seep sites on anticlines. Determining linkages between seeps and submarine tectonic geomorphology is a crucial first step for understanding and forecasting the distribution of methane seepage, but also a necessity for evaluating causal relationships between ocean warming and gas-hydrate stability.

California, Oregon, Washington↗

USGS1999

Vegetation responses to natural regulation of elk in Rocky Mountain National Park

Little experimental information is available on the relationship between herbivory by native ungulates and vegetation in relatively undisturbed environments. A quasi-experimental situation exists in Rocky Mountain National Park, where elk (Cervus elaphus) populations have increased about 3-fold since 1968, following their release from artificial controls within the park boundaries. We reviewed data collected on vegetation transects established and monitored over the 25-year period from 1968 through 1992. Data were subjected to rigorous statistical analysis to detect trends following the release of elk from artificial controls. Increases in elk habitat use and decreases in deer habitat use were observed on all transects over the 25-year period. Significant increases in moss and lichen cover occurred in three offour vegetation types. Percent cover of bare ground, forbs (particularly Selaginella densa), and Carex spp. increased on grassland transects. Increases in timothy (Phleum pratense) were observed on meadow transects. Graminoid and litter cover increased on sagebrush transects, and shrub and litter cover increased on bitterbrush transects. We concluded the lack of control (fenced) plots in this sampling design, the types ofmeasures, the small number of replicates, and nonrandom placement of plots limit the inferences and sensitivity from the work. Unique strengths ofthe work included the long time period (25 years), good distribution of samples, consistency ofthe observer (D. Stevens), and placement of the plots in the most heavily grazed sites. Some grazing-induced responses were detected. Grazing-resistant species such as sedges (native), timothy (exotic), and club mosses increased and the amount of bare ground increased on some grazed sites. However, the changes within this sampling program alone were not alarming. The amount of bare ground increase was minor (4%), and grass and shrub cover increased in the shrub plots. The inferential power ofthis sample design was limited to the study plots only. Other factors (climate change, succession) were not controlled for using fenced plots and the sensitivity ofthe methods and plots to detect change were limited. For example, the low number oftransects in willow was not adequate to monitor conditions on the entire winter range. Lacking controls, observed changes may have been due to other factors (climate trends, beaver dam abandonment, stream channel changes), not elk herbivory alone. We recommend using a new sampling design that would include controls, pretreatment data, random site selection, and much more replication.

Colorado↗

USGS2021

Concentrations, loads, and associated trends of nutrients entering the Sacramento-San Joaquin Delta, California

Statistical modeling of water-quality data collected at the Sacramento River at Freeport and San Joaquin River near Vernalis, California, USA, was used to examine trends in concentrations and loads of various forms of dissolved and particulate nitrogen and phosphorus that entered the Sacramento–San Joaquin River Delta (Delta) from upstream sources between 1970 and 2019. Ammonium concentrations and loads decreased at the Sacramento River site from the mid-1970s through 1990 because of the consolidation of wastewater treatment and continuously reduced from the mid-1970s to 2019 at the San Joaquin River site. Current ammonium concentrations are mostly below 4 µM (0.056 mg N L –1 ) at both sites, a concentration above which reductions in phytoplankton productivity or changes in algal species composition may occur. The Sacramento River at Freeport site is located upstream of the Sacramento Regional County Sanitation District’s treatment facility’s discharge point; nutrient water quality there is representative of upstream sources. Inorganic nitrogen (nitrate plus ammonium) concentrations and loading differed at both sites. At the Sacramento River location, concentrations decrease in the summer agricultural season, reducing the molar ratios of nitrogen to phosphorus. In contrast, inorganic nitrogen concentrations increase in the San Joaquin River during the agricultural season as a result of irrigation runoff, increasing the molar ratio of nitrogen to phosphorus. This increase suggests a possible nitrogen limitation in the northern Delta and a phosphorus limitation in the southern Delta, as indicated by the molar ratios of bioavailable nitrogen to bioavailable phosphorus. Planned upgrades to the Sacramento Regional Wastewater Treatment Plant (SRWTP) will reduce inorganic nitrogen inputs to the northern Delta. Consequently, the supply of bioavailable nitrogen throughout the upper estuary should diminish. Source modeling of nitrogen and phosphorus identifies agriculture, atmospheric deposition, and wastewater effluent as sources of total nitrogen in the Central Valley. In contrast, geologic sources, agriculture, and wastewater discharge are the primary sources of phosphorus.

California↗

USGS2024

Analysis and review of fishery-dependent data for Hawaiian nearshore noncommercial fisheries

Noncommercial, shore-based fisheries provide economic, social, and cultural services to communities throughout the Hawaiian Islands. The State of Hawai‘i Department of Land and Natural Resources (DLNR), Division of Aquatic Resources (DAR) routinely conducts surveys to monitor noncommercial fisheries such that estimates of fishing effort and catch by gear type can be generated and used to implement more sustainable management practices. DAR executes both the Hawai‘i Marine Recreational Fishery Survey (HMRFS), a nationally standardized survey that focuses on intercepting fishers at access points (i.e., boat ramps) across the main Hawaiian Islands, and a set of roving creel surveys on O‘ahu, Maui Nui, and Kaua‘i that observe and intercept fishers at locations along the shoreline outside of those targeted by HMRFS. The latter set of creel surveys were designed to complement HMRFS by expanding its geographic coverage and thus providing a more representative picture of noncommercial fishing in Hawai‘i. Sustainable management priorities set by DAR rely on the availability of statewide, fishery- dependent data. Thus, we collate information from island-based roving creel surveys into a cohesive Statewide Creel Survey Database. Further, we provide preliminary analyses and describe ways that surveys could be streamlined to improve future data collection, analysis, and utility. In so doing, we synthesize the most detailed information to-date about noncommercial shore-based fisheries of Hawai‘i. The unprecedented spatial and temporal coverage of DAR’s dataset reveals the value of their survey efforts over the last decade to address fishery management needs. Our primary objectives, results, and conclusions are summarized below: 1) Integrate DAR roving creel survey data from different islands into a single Statewide Creel Survey Dataset (Chapter II). We describe the collation of creel survey data from O‘ahu, Maui Nui, and Kaua‘i into a statewide dataset. We also offer ways in which these surveys could be streamlined to meet the needs of managers and decision makers. Briefly, these are to create a statewide strategic plan, standardize the execution of standard operating procedures, centralize the creel survey database and associated metadata, and consider using technology that improves the data pipeline, including transitioning from paper-based to electronic systems for data entry and processing. 2) Assess whether the new Statewide Creel Survey Dataset can provide inputs for length-based stock assessments (Chapter III). Only on Maui were interviews conducted with associated catch data. There was reasonably high taxonomic coverage (42 species from 186 interviews with 310 fishers), but low sample sizes for nearly all species precluded the development of length-based stock assessments. We provide summary statistics from the existing data and briefly discuss how technologies could be used to automate analysis of images of noncommercial catch. 3) Analyze the Statewide Creel Survey Dataset for spatial and temporal patterns in fishing effort (Chapter IV): a. Visualizing noncommercial fishing pressure . We found that fishing effort (mean number of fishers observed per survey event at a site) on O'ahu was over three times greater than that recorded during similar surveys conducted on Maui or Kaua'i. We create maps that display the distribution of angling and spearfishing effort around each of the three islands. b. Factors that predict fishing “hotspots” around Maui . Fishing effort on Maui was associated with areas with more wave power and less parking availability. There were half as many fishers in areas with parking lots than in areas with parking on the road shoulder only. c. Changes in fishing effort during the COVID-19 pandemic . There was no change in fishing effort on O‘ahu during the first year of the pandemic, but there was a 20% decline in year 2 and a 33% decline in year 3, both in comparison to pre-pandemic levels. Pre-pandemic creel survey data were unavailable for Maui and Kaua‘i, but fishing effort on these islands also declined as the pandemic progressed at similar or greater rates than those observed on O‘ahu. 4) Quantify potential bias in survey methods by experimentally deriving fisher detection probabilities of shore-based and drone-based surveys (Chapter V): a. Shore-based surveys . We conducted roving creel surveys for four months at three locations around Hilo Bay, designed to emulate and estimate the efficacy of DAR standard operating procedures. There was high agreement between paired observers in counting fishers, leading to near-perfect detection probabilities of both anglers (94%) and spearfishers (97%), but relatively low agreement and detection probabilities of other fishers (throw net, ‘opihi picking, etc.) (52%). b. Drone-based surveys . We used an unmanned aerial vehicle (UAV; operated by DAR staff) to collect imagery of fishers along the Hilo Bay shoreline. We used still images and video clips (with known fishing activity) to build an online survey that was distributed to DAR and HCFRU personnel, asking them to count and categorize resource users as a snorkeler, spearfisher, angler, or other fisher. Only 40.0% of the responses correctly counted and categorized resource users in the image. Anglers were correctly identified and enumerated in 90.0% of the responses, but the correct response rates of the other three user categories ranged from 67.8% – 79.4%. Snorkelers and anglers tended to be undercounted while spearfishers and other fishers were overcounted. 5) Review the potential for incorporating emerging technologies that will improve, augment, and evolve creel survey data collection, especially for spearfishing (Chapter VI). Within the context of monitoring shore-based noncommercial fishing, we review the use of electronic data entry/processing systems with geospatial and image capabilities, field cameras, drones, smart buoys, citizen science apps, data mining social media, artificial intelligence and machine learning. We highlight several of the challenges and considerations when implementing these technologies into creel surveys and provide a synthesis of options that could be used to better estimate spearfishing. The general conclusion of this assessment is that the DAR roving creel survey program is collecting valuable data that supplement the existing HMRFS efforts. However, there are a number of areas that could be improved to make these efforts a more effective tool for decision-making processes in resource management and conservation: 1) Establishment of clear statewide and island objectives for the Statewide Creel Survey Dataset. Currently, data collection efforts are focused towards addressing a very broad purpose – supplementing the HMRFS data collection efforts. However, the results of the preliminary analyses conducted as part of this project suggest that the data could be used to address other areas of need if these objectives were clearly defined. Further, the design of the creel survey would benefit from greater standardization of survey protocols between islands and an effort to define a) the acceptable margins of error associated with the estimates generated by these data and b) the minimum level of change that the surveys would need to detect to be useful to managers. 2) Centralization of data entry, data quality assessment, and data accessibility. Currently, each DAR office manages data entry, checks the data for errors, and is responsible for managing and storing the data. Instituting a centralized data entry system, particularly an online database that can receive survey data from tablets or smartphones running a standardized data collection application would improve efficiency, reduce data entry errors, and accelerate the availability of data to managers. A substantial amount of time and effort from the project described in this report was devoted to checking the dataset for errors. The development and application of data quality assurance protocols would ensure that the data are reliable and available in a timely fashion to support management decisions. 3) Address lingering questions regarding the efficacy of current survey protocols to capture and characterize the spearfishing component of the noncommercial fishery. The results presented in the report suggest that the current creel survey protocols do a good job detecting spearfishers when present but are not capturing sufficient data about their catch or total effort. There are also questions remaining as to whether the survey times and sites are sufficiently capturing the behavior of spearfishers in Hawai‘i. A more thorough assessment – whether through additional research, alteration of survey design, or review of data by representatives of the spearfishing community – would provide insight on how to use the Statewide Creel Survey Database to inform management of spearfishing. 4) Investigate the integration of technological advancements into the creel survey methods. As priorities and needs are developed and formalized, it would be valuable to consider how various technological advancements might enhance and streamline data collection or open new avenues of inquiry.

Hawaii↗

USGS2021

An approach for decomposing river water-quality trends into different flow classes

A number of statistical approaches have been developed to quantify the overall trend in river water quality, but most approaches are not intended for reporting separate trends for different flow conditions. We propose an approach called FN 2Q , which is an extension of the flow-normalization (FN) procedure of the well-established WRTDS (“Weighted Regressions on Time, Discharge, and Season”) method. The FN 2Q approach provides a daily time series of low-flow and high-flow FN flux estimates that represent the lower and upper half of daily riverflow observations that occurred on each calendar day across the period of record. These daily estimates can be summarized into any time period of interest (e.g., monthly, seasonal, or annual) for quantifying trends. The proposed approach is illustrated with an application to a record of total nitrogen concentration (632 samples) collected between 1985 and 2018 from the South Fork Shenandoah River at Front Royal, Virginia (USA). Results show that the overall FN flux of total nitrogen has declined in the period of 1985–2018, which is mainly attributable to FN flux decline in the low-flow class. Furthermore, the decline in the low-flow class was highly correlated with wastewater effluent loads, indicating that the upgrades of treatment technology at wastewater treatment facilities have likely led to water-quality improvement under low-flow conditions. The high-flow FN flux showed a spike around 2007, which was likely caused by increased delivery of particulate nitrogen associated with sediment transport. The case study demonstrates the utility of the FN 2Q approach toward not only characterizing the changes in river water quality but also guiding the direction of additional analysis for capturing the underlying drivers. The FN 2Q approach (and the published code) can easily be applied to widely available river monitoring records to quantify water-quality trends under different flow conditions to enhance understanding of river water-quality dynamics.

Virginia↗

USGS2016

Estimating black bear density in New Mexico using noninvasive genetic sampling coupled with spatially explicit capture-recapture methods

During the 2004–2005 to 2015–2016 hunting seasons, the New Mexico Department of Game and Fish (NMDGF) estimated black bear abundance (Ursus americanus) across the state by coupling density estimates with the distribution of primary habitat generated by Costello et al. (2001). These estimates have been used to set harvest limits. For example, a density of 17 bears/100 km2 for the Sangre de Cristo and Sacramento Mountains and 13.2 bears/100 km2 for the Sandia Mountains were used to set harvest levels. The advancement and widespread acceptance of non-invasive sampling and mark-recapture methods, prompted the NMDGF to collaborate with the New Mexico Cooperative Fish and Wildlife Research Unit and New Mexico State University to update their density estimates for black bear populations in select mountain ranges across the state. We established 5 study areas in 3 mountain ranges: the northern (NSC; sampled in 2012) and southern Sangre de Cristo Mountains (SSC; sampled in 2013), the Sandia Mountains (Sandias; sampled in 2014), and the northern (NSacs) and southern Sacramento Mountains (SSacs; both sampled in 2014). We collected hair samples from black bears using two concurrent non-invasive sampling methods, hair traps and bear rubs. We used a gender marker and a suite of microsatellite loci to determine the individual identification of hair samples that were suitable for genetic analysis. We used these data to generate mark-recapture encounter histories for each bear and estimated density in a spatially explicit capture-recapture framework (SECR). We constructed a suite of SECR candidate models using sex, elevation, land cover type, and time to model heterogeneity in detection probability and the spatial scale over which detection probability declines. We used Akaike’s Information Criterion corrected for small sample size (AICc) to rank and select the most supported model from which we estimated density. We set 554 hair traps, 117 bear rubs and collected 4,083 hair samples. We identified 725 (367 M, 358 F) individuals; the sex ratio for each study area was approximately equal. Our density estimates varied within and among mountain ranges with an estimated density of 21.86 bears/100 km2 (95% CI: 17.83 – 26.80) for the NSC, 19.74 bears/100 km2 (95% CI: 13.77 – 28.30) in the SSC, 25.75 bears/100 km2 (95% CI: 13.22 – 50.14) in the Sandias, 21.86 bears/100 km2 (95% CI: 17.83 – 26.80) in the NSacs, and 16.55 bears/100 km2 (95% CI: 11.64 – 23.53) in the SSacs. Overall detection probability for hair traps and bear rubs, combined, was low across all study areas and ranged from 0.00001 to 0.02. We speculate that detection probabilities were affected by failure of some hair samples to produce a complete genotype due to UV degradation of DNA, and our inability to set and check some sampling devices due to wildfires in the SSC. Ultraviolet radiation levels are particularly high in New Mexico compared to other states where NGS methods have been used because New Mexico receives substantial amounts of sunshine, is relatively high in elevation (1,200 m – 4,000 m), and is at a lower latitude. Despite these sampling difficulties, we were able to produce density estimates for New Mexico black bear populations with levels of precision comparable to estimated black bear densities made elsewhere in the U.S. Our ability to generate reliable black bear density estimates for 3 New Mexico mountain ranges is attributable to our use of a statistically robust study design and analytical method. There are multiple factors that need to be considered when developing future SECR-based density estimation projects. First, the spatial extent of the population of interest and the smallest average home range size must be determined; these will dictate size of the trapping array and spacing necessary between hair traps. The number of technicians needed and access to the study areas will also influence configuration of the trapping array. We believe shorter sampling occasions could be implemented to reduce degradation of DNA due to UV radiation; this might help increase amplification rates and thereby increase both the number of unique individuals identified and the number of recaptures, improving the precision of the density estimates. A pilot study may be useful to determine the length of time hair samples can remain in the field prior to collection. In addition, researchers may consider setting hair traps and bear rubs in more shaded areas (e.g., north facing slopes) to help reduce exposure to UV radiation. To reduce the sampling interval it will be necessary to either hire more field personnel or decrease the number of hair traps per sampling session. Both of these will enhance detection of long-range movement events by individual bears, increase initial capture and recapture rates, and improve precision of the parameter estimates. We recognize that all studies are constrained by limited resources, however, increasing field personnel would also allow a larger study area to be sampled or enable higher trap density. In conclusion, we estimated the density of black bears in 5 study areas within 3 mountains ranges of New Mexico. Our estimates will aid the NMDGF in setting sustainable harvest limits. Along with estimates of density, information on additional demographic rates (e.g., survival rates and reproduction) and the potential effects that climate change and future land use may have on the demography of black bears may also help inform management of black bears in New Mexico, and may be considered as future areas for research.

New Mexico↗

USGS2003

A Framework for Long-term Ecological Monitoring in Olympic National Park: Prototype for the Coniferous Forest Biome

This report is the result of a five-year collaboration between scientists of the U.S. Geological Survey Forest and Rangeland Ecosystem Science Center, Olympic Field Station, and the natural resources staff of Olympic National Park to develop a comprehensive strategy for monitoring natural resources of Olympic National Park. Olympic National Park is the National Park Serviceʼs prototype monitoring park, representing parks in the coniferous forest biome. Under the umbrella of the National Park Serviceʼs prototype parks program, U.S. Geological Survey and Olympic National Park staffs are obligated to: develop strategies and designs for monitoring the long-term health and integrity of national park ecosystems with a significant coniferous forest component. design exportable monitoring protocols that can be used by other parks within the coniferous forest biome (i.e., parks having similar environments), and create a demonstration area and ʻcenter of excellenceʼ for assisting other parks in developing ecological monitoring programs. Olympic National Park is part of the North Coast and Cascades Network, a network of seven Pacific Northwestern park units created recently by the National Park Serviceʼs Inventory and Monitoring Program to extend the monitoring of ʻvital signsʼ of park health to all National Park Service units. It is our intent and hope that the monitoring strategies and conceptual models described here will meet the overall purpose of the prototype parks monitoring program in proving useful not only to Olympic National Park, but also to parks within the North Coast and Cascades Network and elsewhere. Part I contains the conceptual design and sampling framework for the prototype long-term monitoring program in Olympic National Park. In this section, we explore key elements of monitoring design that help to ensure the spatial, ecological, and temporal integration of monitoring program elements and discuss approaches used to design an ecosystem-based monitoring program. Basic monitoring components include ecosystem drivers, (e.g., climate, atmospheric inputs, human pressures), indicators of ecosystem integrity (e.g., biogeochemical indicators), known threats (e.g., impacts of introduced mountain goats), and focal or ʻkeyʼ species (e.g., rare or listed species, Roosevelt elk). Monitoring system drivers and key indicators of ecosystem integrity provide the long-term baseline needed to judge what constitutes ʻunnaturalʼ variation in park resources and provide the earliest possible warning of unacceptable change. Monitoring effects of known threats and the status of focal species will provide information useful to park managers for dealing with current park issues. In Part I we describe the process of identifying potential indicators of ecological condition and present conceptual models of park ecosystems. In addition we report results from several workshops held in conjunction with Olympic National Park aimed at identifying potential indicators of change in the parkʼs ecosystem. First, we describe the responses of Olympic National Park staff to the generic question, “What is the most important resource to monitor in Olympic National Park and why?” followed by the responses from resource and land managers from areas adjoining the park. We also catalogue the responses of various expert groups that we asked to help identify the most appropriate system drivers and indicators of change in the Olympic National Park ecosystems. Results of the workshops provided the justification for selecting basic indicators of ecosystem integrity, effects of current threats to park resources, and focal resources of parks to detect both the currently evident and unforeseeable changes in park resources. We conclude Part I by exploring several generic statistical issues relevant to monitoring natural resources in Olympic National Park. Specifically we discuss trade-offs associated with sampling extensively versus sampling intensively in smaller geographic regions and describe a conceptual framework to guide development of a generic sampling frame for monitoring. We recommend partitioning Olympic National Park into three zones of decreasing accessibility to maximize monitoring efficiency. We present examples of how the generic sampling frame could be used to help ensure spatial integration of individual monitoring projects. Part II of the report is a record of the potential monitoring questions and indicators identified to date in our workshops. The presentation is organized according to the major system drivers, components, and processes identified in the intermediate-level working model of the Olympic National Park ecosystem. For each component of the park system, we develop the need and justification for monitoring, articulate park management issues, and describe key resources and ecosystem functions. We also present a pictorial conceptual model of each ecological subsystem, identify monitoring questions, and list potential indicators for each monitoring question. We conclude each section by identifying linkages of indicators to other ecological subsystems in our general ecosystem model, spatial and temporal contexts for monitoring (where and how often to monitor), and research and development needs. Part II represents the most current detailed listing of potential indicators—the material for subsequent discussions of monitoring priorities and selection of indicators for protocol development. Collectively, the sections of this report contain a comprehensive list of the important monitoring questions and potential indicators as well as recommendations for designing an integrated monitoring program. In Part I, Chapter 6 we provide recommendations on how to proceed with the important next steps in the design process: establishing priorities among the many possible monitoring questions and indicators, and beginning to research and design effective long-term monitoring protocols.

Information and Technology Report↗

USGS2016

Users and uses of Landsat 8 satellite imagery—2014 survey results

Executive Summary In 2013, Landsat 8 began adding high quality, global, moderate-resolution imagery to the more than 40-year archive of Landsat imagery. To assess the potential effects of the availability of Landsat 8 imagery on users and their work, the U.S. Geological Survey (USGS) Land Remote Sensing Program (LRS) initiated a survey of Landsat users. The objectives of the survey were to 1. Characterize various Landsat user groups, such as United States (U.S.) and international users and Landsat 8 and non-Landsat 8 users; 2. Identify any differences among user groups in uses and preferences; 3. Measure the importance of and satisfaction with Landsat 8 attributes; 4. Assess the importance to users of the frequency of usable imagery; and 5. Determine any challenges in using Landsat 8. The online survey was sent to 51,617 Landsat users registered with USGS in May 2014. Almost 13,000 people responded to the survey for a response rate of 25 percent (n = 12,966). Current Landsat users (users who had used Landsat in their work in the year prior to the survey) composed 89 percent of the sample (n = 11,549) and past Landsat users composed 11 percent (n = 1,417). The results reported here apply to current Landsat users registered with the USGS Earth Resources Observation and Science (EROS) Center. Users from 161 countries responded to the survey. Of those, 19 percent were citizens or permanent residents of the United States and 81 percent resided in other countries. More than 70 percent of current users had used Landsat 8 in the year prior to the survey. The majority of Landsat 8 users (65 percent) were established users who used Landsat imagery regularly both before and after Landsat 8 imagery became available. The average current Landsat user was male, 36 years old, and highly educated, with 9 years of experience using satellite imagery or geographic information system (GIS) software. Landsat 8 users had, on average, two more years of experience than non-Landsat 8 users. Users were employed predominantly by academic institutions (65 percent), followed by private businesses (13 percent), Federal governments (10 percent), State and local governments (6 percent), and nonprofit organizations (6 percent). Of the Landsat imagery obtained in the past year by current users, on average 31 percent came from a Landsat 8 sensor. An equivalent amount came from the Landsat 7 ETM+ sensor (33 percent); slightly less came from Landsats 4 and 5 TM sensors (27 percent). Much less came from Landsats 1 through 5 MSS sensors (5 percent). Overall, more than a third of users’ work used Landsat imagery (38 percent). Of this work, on average, 37 percent of the work was operational. Landsat 8 users considered a greater proportion of their work operational than non-Landsat 8 users (39 percent compared with 29 percent). Environmental sciences and management were the most commonly selected primary applications (selected by 42 percent of users). Land use/land cover (23 percent) was the second most commonly selected primary application, followed by education (12 percent), agriculture (9 percent), and planning and development (6 percent). Landsat 8 users were asked to rank the importance of certain attributes in determining whether to use Landsat 8 imagery in their work. The archive was ranked most important, followed by cost, spatial resolution, extent of coverage, data quality, and frequency of revisit. Users were asked how satisfied they were with these same attributes as they currently apply to Landsat 8 imagery. On average, users were most satisfied with lack of cost, extent of coverage, data quality, and the archive, but they were satisfied with all attributes. Users were asked how often they needed Landsat imagery to meet various requirements for their primary application. The survey question specifically asked how often users needed usable imagery, which differs from how often they would like the Landsat satellites to acquire an image. Users were asked to identify their needed frequency of usable imagery for the following levels: 1. Threshold level—the minimum frequency of usable imagery needed to be of any value to their primary application. 2. Breakthrough level—the frequency of usable imagery that would result in a significant improvement for their primary application of the imagery. 3. Target level—the frequency of usable imagery that would only provide a limited additional increase in the expected performance for their primary application. To meet the threshold level, three-quarters of users needed usable imagery every 17 days or less frequently. At the breakthrough level, two-thirds of users (64 percent) needed a usable image every 5–16 days. The current constellation of two satellites (Landsat 7 and 8) is capable of meeting the threshold and breakthrough needs of most users at least some of the time, but a single satellite would be highly unlikely to do so. Two-fifths of users (40 percent) felt that usable imagery provided every 4 days or more frequently would meet their target level which the current Landsat constellation cannot provide. Landsat 8 users were significantly more likely than non-Landsat 8 users to need usable imagery more frequently to meet their target levels. Additionally, U.S. Landsat 8 users were significantly more likely than other Landsat users to need usable imagery more frequently in order meet both their breakthrough and target levels. To explore the effect of the availability of Landsat 8 imagery on Landsat imagery use in general, established users (those who had consistently used Landsat imagery both before and after Landsat 8 imagery became available) using Landsat 8 imagery were asked about changes in the amount of Landsat imagery they used. The majority of established users using Landsat 8 imagery (60 percent) reported an average increase of 51 percent in the number of scenes obtained after Landsat 8 imagery became available. Landsat 8 users were asked if they had encountered challenges in using Landsat 8 whereas non-Landsat 8 users were asked if such challenges had played a role in why they were not using Landsat 8 imagery. Although many users did not encounter challenges when using or trying to use Landsat 8 data, slightly less than 30 percent did encounter issues with processing the data to a usable point. The most common issue reported was not being able to create or have access to a surface reflectance corrected product. Other challenges were related to the file sizes of images being too large to download, store, or analyze. There were no statistically significant differences between Landsat 8 and non-Landsat 8 users in terms of challenges encountered when using or trying to use the imagery, which indicates that users were not unduly discouraged by the challenges they may have encountered. When asked about potential consequences of not using Landsat 8, more than half of the non-Landsat 8 users did not report detrimental effects on their work from not using the imagery. Of those who did report detrimental effects, decreased quality of work, decreased scope of work, and increased time spent on work were the most common.

Open-File Report↗

USGS1980

Handbook of acute toxicity of chemicals to fish and aquatic invertebrates : summaries of toxicity tests conducted at Columbia National Fisheries Research Laboratory, 1965-78

Acute toxicity is a major subject of research at Columbia National Fisheries Research Laboratory for evaluating the impact of toxic chemicals on fishery resources. The Laboratory has played a leading role in developing research technology for toxicity testing and data interpretation. In 1965-78, more than 400 chemicals were tested against a variety of invertebrates and fish species representative of both cold- and warm-water climates. The use of acute toxicity tests for assessing the potential hazard of chemical contaminants to aquatic organisms is well documented (Boyd 1957; Henderson et al. 1960; Sanders and Cope 1966; Macek and McAllister 1970). Static acute toxicity tests provide rapid and (within limits) reproducible concentration-response curves for estimating toxic effects of chemicals on aquatic organisms. These tests provide a database for determining relative toxicity of a large number of chemicals to a variety of species and for estimating acute effects of chemical spills on natural aquatic systems; they also assist in determining priority and design of additional toxicity studies. Acute toxicity tests usually provide estimates of the exposure concentration causing 50% mortality (LC50) to test organisms during a specified period of time. For certain invertebrates, the effective concentration is based on immobilization, or some other identifiable endpoint, rather than on lethality. The application of the LC50 has gained acceptance among toxicologists and is generally the most highly rated test for assessing potential adverse effects of chemical contaminants to aquatic life (Brungs and Mount 1978; American Institute for Biological Sciences 1978 a ). The literature contains numerous papers dealing with the acute toxicity of chemicals to freshwater organisms. However, there is a tremendous need for a concise compendium of toxicity data covering a large variety of chemicals and test species. This Handbook is a compilation of a large volume of acute toxicity data from the Columbia Laboratory and its field laboratories. It presents definitive acute toxicity data on 271 chemicals tested against a variety of freshwater invertebrates and fishes. The chemicals represent all major groups of pesticides, as well as numerous industrial chemicals. This compilation should serve as a useful database for the many agencies and organizations dealing with research and management programs concerned with the impact of chemicals on aquatic resources. The Columbia Laboratory has played a major role in developing currently used standard methodology for static acute toxicity testing. The use of standardized methodology greatly reduces variation in results. The data presented here have been carefully scrutinized to eliminate tests that failed to follow acceptable procedures. Handling of test organisms and procedures for static toxicity tests followed those described by Lennon and Walker (1964) and Macek and McAllister (1970), and conform well with those recommended by Brauhn and Schoettger (1975) and the Committee on Methods for Toxicity Tests with Aquatic Organisms (1975). The species of fish and invertebrates that were tested are listed in phylogenetic order in Tables 1 and 2. Fish were obtained from Federal and State hatcheries as either eggs or fry. Original stocks of invertebrates were collected and cultured from wild populations with no known source of contamination; these populations were replenished regularly. The invertebrates were cultured in the Laboratory by methods similar to those described by Sanders and Cope (1966). Test chemicals usually consisted of technical or analytical grade samples of known purity. Formulations of the chemicals were also tested when available. When purity of test chemicals was known, all calculated concentrations were based on percent active ingredients. Stock solutions were prepared immediately before each test, with commercial grade acetone as the carrier solvent. Occasionally, ethanol or dimethyl-formamide was substituted. Solvent concentrations did not exceed 0.5 mL/L in final dilution water. Test water (dilution water) was reconstituted from deionized water of at least 10 6 ohms resistivity by the addition of appropriate reagent grade chemicals (Marking 1969). Water was buffered to maintain a pH of 7.2 to 7.5, an alkalinity of 30 to 35 mg/L, and a hardness of 40 to 50 mg/L as CaCO 3 . Test water was mixed thoroughly and aerated before transfer into test chambers. Fish were acclimated to dilution water by gradually changing the water in acclimated tanks from 100% well water to 100% reconstituted water over a 1- to 3-day period at the desired testing temperature. Invertebrates were acclimated from well water to dilution water over a 4- to 6-h period. Toxicity tests were conducted under static conditions without aeration, and the organisms were not fed during acclimation or testing. Temperature of test solutions was maintained within ± 1°C of that required for a given test. Toxicity tests with fish were conducted in 18.9-liter (5-gal) wide-mouthed jars containing 15 liters of test solution. Fingerling fish weighing 0.2 to 1.5 g were tested at each concentration. Caution was taken not to exceed 0.8 g of test organisms per liter of solution. Duplicate test chambers were used to accommodate larger fish. Test chambers varied in size for invertebrates, depending on the species used; volume of test solution ranged from 0.25 to 4 liters. At least 10 organisms were exposed to each concentration for all definitive tests. At least six concentrations were used per toxicity test. The tests began upon initial exposure to the toxicant and continued for 96 h. Immobilization tests with invertebrates were conducted for only 48 h. The number of dead or affected organisms in each test chamber were recorded and the dead organisms were removed every 24 h; general observations on the condition of test organisms were also recorded at these times. Toxicity data were analyzed by a statistical method described by Litchfield and Wilcoxon (1949) to determine LC50 (theoretical estimate of the concentration lethal to 50% of the test animals) and 95% confidence intervals. This method is recommended by the American Public Health Association (1971) and by Sprague (1969) for determining median lethal concentrations. The procedure is easily modified for computing a single LC50 when replicate tests are performed.

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