SEARCH · Search USGS

Results for “Environmental and Ecological Statistics”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources

USGS2015

Influences of supplemental feeding on winter elk calf:cow ratios in the southern Greater Yellowstone Ecosystem

Several elk herds in the Greater Yellowstone Ecosystem are fed during winter to alleviate interactions with livestock, reduce damage to stored crops, and to manage for high elk numbers. The effects of supplemental feeding on ungulate population dynamics has rarely been examined, despite the fact that supplemental feeding is partially justified as necessary for maintaining or enhancing population growth rates. We used linear regression to assess how the presence of feedgrounds, snowpack, summer rainfall, indices of grizzly bear density and wolves per elk, elk population trend counts, brucellosis seroprevalence, and survey date were correlated with midwinter calf:cow ratios, a metric correlated with population growth, from 1983–2010 from 12 ecologically similar elk herd units (7 fed and 5 unfed) in Wyoming, USA. Our statistical approach allowed for rigorous tests of the hypotheses that supplemental feeding had positive effects on calf:cow ratios and reduced sensitivity of calf:cow ratios to bottom-up limitation relative to top-down limitation from native predators. Calf:cow ratios generally declined across all herd units over the study period and varied widely among units with feedgrounds. We found no evidence that the presence of feedgrounds had positive effects on midwinter calf:cow ratios in Wyoming. Further, fed elk showed stronger correlations with environmental factors, whereas calf:cow ratios for unfed elk showed stronger correlations with predator indices. Although we found no consistent association between winter feeding and higher calf:cow ratios, we did not assess late winter mortality and differences in human offtake between fed and unfed regions, which remain a priority for future research.

Wyoming↗

USGS2003

Sexual selection affects local extinction and turnover in bird communities

Predicting extinction risks has become a central goal for conservation and evolutionary biologists interested in population and community dynamics. Several factors have been put forward to explain risks of extinction, including ecological and life history characteristics of individuals. For instance, factors that affect the balance between natality and mortality can have profound effects on population persistence. Sexual selection has been identified as one such factor. Populations under strong sexual selection experience a number of costs ranging from increased predation and parasitism to enhanced sensitivity to environmental and demographic stochasticity. These findings have led to the prediction that local extinction rates should be higher for species/populations with intense sexual selection. We tested this prediction by analyzing the dynamics of natural bird communities at a continental scale over a period of 21 years (1975-1996), using relevant statistical tools. In agreement with the theoretical prediction, we found that sexual selection increased risks of local extinction (dichromatic birds had on average a 23% higher local extinction rate than monochromatic species). However, despite higher local extinction probabilities, the number of dichromatic species did not decrease over the period considered in this study. This pattern was caused by higher local turnover rates of dichromatic species, resulting in relatively stable communities for both groups of species. Our results suggest that these communities function as metacommunities, with frequent local extinctions followed by colonization. Anthropogenic factors impeding dispersal might therefore have a significant impact on the global persistence of sexually selected species.

Proceedings of the National Academy of Sciences↗

USGS2019

Nonlinear reaction–diffusion process models improve inference for population dynamics

Partial differential equations (PDEs) are a useful tool for modeling spatiotemporal dynamics of ecological processes. However, as an ecological process evolves, we need statistical models that can adapt to changing dynamics as new data are collected. We developed a model that combines an ecological diffusion equation and logistic growth to characterize colonization processes of a population that establishes long-term equilibrium over a heterogeneous environment. We also developed a homogenization strategy to statistically upscale the PDE for faster computation and adopted a hierarchical framework to accommodate multiple data sources collected at different spatial scales. We highlighted the advantages of using a logistic reaction component instead of a Malthusian component when population growth demonstrates asymptotic behavior. As a case study, we demonstrated that our model improves spatiotemporal abundance forecasts of sea otters in Glacier Bay, Alaska. Furthermore, we predicted spatially varying local equilibrium abundances as a result of environmentally driven diffusion and density-regulated growth. Integrating equilibrium abundances over the study area in our application enabled us to infer the overall carrying capacity of sea otters in Glacier Bay, Alaska.

Alaska↗

USGS2024

First records distribution models to guide biosurveillance for non-native species

Quickly locating new populations of non-native species can reduce the ecological and economic costs of species invasions. However, the difficulty of predicting which new non-native species will establish, and where, has limited active post-border biosurveillance efforts. Because pathways of introduction underlie spatial patterns of establishment risk, an intuitive approach is to search for new non-native species in areas where many non-native species have first been detected in the past. We formalize this intuition via first records distribution models (FRDMs), which apply species distribution modeling methods to the collection of first occurrence records across species (i.e. one record per species). We define FRDMs as statistical models that quantify environmental conditions associated with species' first naturalized records to predict spatial patterns of establishment risk. We model the first records of non-native plants in the conterminous USA as a proof-of-concept. The novelty of FRDMs is that their inferences apply not just to the species that contributed data; they provide a rigorous framework for predicting hotspots of invasion for new non-native taxa that share a pathway of introduction with the modeled species. FRDMs can guide survey efforts for new non-native taxa at multiple scales and across ecosystems.

conterminous United States↗

USGS2021

Regional target loads of atmospheric nitrogen and sulfur deposition for the protection of stream and watershed soil resources of the Adirondack Mountains, USA

Acidic deposition contributes to a range of environmental impacts across forested landscapes, including acidification of soil and drainage water, toxic aluminum mobilization, depletion of available soil nutrient cations, and impacts to forest and aquatic species health and biodiversity. In response to decreasing levels of acidic deposition, soils and drainage waters in some regions of North America have become gradually less acidic. Thresholds of atmospheric deposition at which adverse ecological effects are manifested are called critical loads (CLs) and/or target loads (TLs). Target loads are developed based on approaches that account for spatial and temporal aspects of acidification and recovery. Exceedance represents the extent to which current or projected future levels of acidic deposition exceed the level expected to cause ecological harm. We report TLs of sulfur (S) and nitrogen (N) deposition and the potential for ecosystem recovery of watershed soils and streams in the Adirondack region of New York State, resources that have been less thoroughly investigated than lakes. Regional TLs were calculated by statistical extrapolation of hindcast and forecast simulations of 25 watersheds using the process-based model PnET-BGC coupled with empirical observations of stream hydrology and established sensitivity of sugar maple ( Acer saccharum ) to soil base saturation and brook trout ( Salvelinus fontinalis ) to stream acid neutralizing capacity (ANC). Historical impacts and the expected recovery timeline of regional soil and stream chemistry and fish community condition within the Adirondack Park were evaluated. Analysis suggests that many low-order Adirondack streams and associated watershed soils have low TLs (<40 meq/m 2 /yr of N+S deposition) to achieve specified benchmarks for recovery of soil base saturation or stream ANC. Acid-sensitive headwater and low-order streams and watershed soils in the region are expected to experience continued adverse effects from N and S deposition well into the future even under aggressive emissions reductions. Watershed soils and streams in the western Adirondack Park are particularly vulnerable to acidic deposition and currently in exceedance of TLs. The methods used for linking statistical and process-based models to consider chemical and biological response under varying flow conditions at the regional scale in this study can be applied to other areas of concern.

New York↗

USGS2021

Improved fire severity mapping in the North American boreal forest using a hybrid composite method

Fire severity is a key driver shaping the ecological structure and function of North American boreal ecosystems, a biome dominated by large, high-intensity wildfires. Satellite-derived burn severity maps have been an important tool in these remote landscapes for both fire and resource management. The conventional methodology to produce satellite-inferred fire severity maps generally involves comparing imagery from 1 year before and 1 year after a fire, yet environmental conditions unique to the boreal have limited the accuracy of resulting products. We introduce an alternative method – the ‘hybrid composite’ – based on deriving mean severity over time on a per-pixel basis within the cloud-computing environment of Google Earth Engine. It constructs the post-fire image from satellite data composited from all valid images (i.e., clear-sky and snow-free) acquired in the time period immediately after fire through the early growing season of the following year. We compare this approach to paired-scene and composite approaches where the post-fire time period is from the growing season 1 year after fire. Validation statistics based on field-derived data for 52 fires across Alaska and Canada indicate that the hybrid composite method outperforms the other approaches. This approach presents an efficient and cost-effective means to monitor and explore trends and patterns across broad spatial domains, and could be applied to fires in other regions, especially those with frequent cloud cover or rapid vegetation recovery.

Alaska↗

USGS2009

Assessing rates of forest change and fragmentation in Alabama, USA, using the vegetation change tracker model

Forest change is of great concern for land use decision makers and conservation communities. Quantitative and spatial forest change information is critical for addressing many pressing issues, including global climate change, carbon budgets, and sustainability. In this study, our analysis focuses on the differences in geospatial patterns and their changes between federal forests and nonfederal forests in Alabama over the time period 1987–2005, by interpreting 163 Landsat Thematic Mapper (TM) scenes using a vegetation change tracker (VCT) model. Our analysis revealed that for the most part of 1990 s and between 2000 and 2005, Alabama lost about 2% of its forest on an annual basis due to disturbances, but much of the losses were balanced by forest regeneration from previous disturbances. The disturbance maps revealed that federal forests were reasonably well protected, with the fragmentation remaining relatively stable over time. In contrast, nonfederal forests, which are predominant in area share (about 95%), were heavily disturbed, clearly demonstrating decreasing levels of fragmentation during the time period 1987–1993 giving way to a subsequent accelerating fragmentation during the time period 1994–2005. Additionally, the identification of the statistical relationships between forest fragmentation status and forest loss rate and forest net change rate in relation to land ownership implied the distinct differences in forest cutting rate and cutting patterns between federal forests and nonfederal forests. The forest spatial change information derived from the model has provided valuable insights regarding regional forest management practices and disturbance regimes, which are closely associated with regional economics and environmental concerns.

Forest Ecology and Management↗

USGS2023

Cryptic tolerant fish species and their potential effect on index of biotic integrity (IBI) scores

Indices such as the Index of Biotic Integrity (IBI) are often used by management agencies to estimate the abstract property of stream health. These indices are usually predicated on the belief that certain fish species are tolerant to environmental perturbation while others are sensitive. Species are usually designated as either tolerant or sensitive in these analyses based on inherent ecological or taxonomic characteristics. However, previous literature has shown that certain species from ecological or taxonomic “sensitive” groups experience increased abundance in degraded streams. We term such species “cryptic tolerants”. Using a stream fish assemblage dataset of 433 unique sample locations across the state of Alabama and the National Landcover Dataset, our objectives were to 1) identify the most common cryptic tolerant species, 2) investigate how cryptic tolerant species might inflate indices of stream health, and 3) compare an alternative measure of stream health in which species are statistically defined rather than defined using the traditional trait-based approach. We identified cryptic tolerants using Nonmetric Multidimensional Scaling in six ecoregions. A series of regressions revealed that the proportion of cryptic tolerant species decreased in response to an increasing proportion of forested land in catchments while the proportion of true sensitives increased in all ecoregions except for the Cumberland Plateau. An index that simply used the percentage of statistically defined, non-tolerant species generally had lower p-values and higher r 2 values than IBI scores when both were regressed against percentage of forest in catchment. However, both indices had low degrees of correlation with expected disturbance, indicating a univariate index may be inadequate to characterize stream health. Our results highlight a potential issue with applying the IBI to diverse southeastern systems in the United States, which may be alleviated by designating species sensitivity based on empirical response to disturbance rather than taxonomic or ecological characteristics.

Alabama↗

USGS2022

Coupling validation effort with in situ bioacoustic data improves estimating relative activity and occupancy for multiple species with cross-species misclassifications

The increasing complexity and pace of ecological change requires natural resource managers to consider entire species assemblages. Acoustic recording units (ARUs) require minimal cost and effort to deploy and inform relative activity, or encounter rates, for multiple species simultaneously. ARU-based surveys require post-processing of the recordings via software algorithms that assign a species label to each recording. The automated classification process can result in cross-species misidentifications that should be accounted for when employing statistical modelling for conservation decision-making. Using simulation and ARU-based detection counts from 17 bat species in British Columbia, Canada, we investigate three strategies for adjusting statistical inference for species misclassification: (a) ‘coupling’ ambiguous and unambiguous detections by validating a subset of survey events post-hoc, (b) using a calibration dataset on the software algorithm's (in)accuracy for species identification or (c) specifying informative Bayesian priors on classification probabilities. We explore the impact of different Bayesian prior specifications for the classification probabilities on posterior estimation. We then consider how the quantity of data validated post-hoc impacts model convergence and resulting inferences for bat species relative activity as related to nightly conditions and yearly site occupancy after accounting for site-level environmental variables. Coupled methods resulted in less bias and uncertainty when estimating relative activity and species classification probabilities relative to calibration approaches. We found that species that were difficult-to-detect and those that were often inaccurately identified by the software required more validation effort than more easily detected and/or identified species. Our results suggest that, when possible, acoustic surveys should rely on coupled validated detection information to account for false-positive detections, rather than uncoupled calibration datasets. However, if the assemblage of interest contains a large number of rarely detected or less prevalent species, an intractable amount of effort may be required, suggesting there are benefits to curating a calibration dataset that is representative of the observation process. Our findings provide insights into the practical challenges associated with statistical analyses of ARU data and possible analytical solutions to support reliable and cost-effective decision-making for wildlife conservation/management in the face of known sources of observation errors.

British Columbia↗

USGS2023

A novel approach to assessing natural resource injury with Bayesian networks

Quantifying the effects of environmental stressors on natural resources is problematic because of complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aim to restore natural resources injured by oil spills and hazardous substances released into the environment; exploration of alternative statistical methods to evaluate effects could help address NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used to estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream-dwelling bird species. We compared the BN-generated probability estimates for injury with a more traditional approach using toxicity thresholds for water and sediment chemistry. Bayesian networks offered several distinct advantages over traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field-based application are needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many US federal, state, and tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants

Integrated Environmental Assessment and Management↗

USGS2010

Determination of biologically significant hydrologic condition metrics in urbanizing watersheds: an empirical analysis over a range of environmental settings

We investigated the relations among 83 hydrologic condition metrics (HCMs) and changes in algal, invertebrate, and fish communities in five metropolitan areas across the continental United States. We used a statistical approach that employed Spearman correlation and regression tree analysis to identify five HCMs that are strongly associated with observed biological variation along a gradient of urbanization. The HCMs related to average flow magnitude, high-flow magnitude, high-flow event frequency, high-flow duration, and rate of change of stream cross-sectional area were most consistently associated with changes in aquatic communities. Although our investigation used an urban gradient design with short hydrologic periods of record (≤1 year) of hourly cross-sectional area time series, these five HCMs were consistent with previous investigations using long-term daily-flow records. The ecological sampling day often was included in the hydrologic period. Regression tree models explained up to 73, 92, and 79% of variance for specific algal, invertebrate, and fish community metrics, respectively. National models generally were not as statistically significant as models for individual metropolitan areas. High-flow event frequency, a hydrologic metric found to be transferable across stream type and useful for classifying habitat by previous research, was found to be the most ecologically relevant HCM; transformation by precipitation increased national-scale applicability. We also investigated the relation between measures of stream flashiness and land-cover indicators of urbanization and found that land-cover characteristic and pattern variables, such as road density, percent wetland, and proximity of developed land, were strongly related to HCMs at both a metropolitan and national scale and, therefore, may be effective land-use management options in addition to wholesale impervious-area reduction.

Milwaukee-Green Bay; Boston; Raleigh; Atlanta; Bir↗

USGS2003

Development and testing of method for assessing and mapping agricultural areas susceptible to atrazine leaching in the state of Washington

In a joint effort by the Washington State Department of Agriculture, the Washington Department of Ecology, and the U.S. Geological Survey, the Environmental Protection Agency's Pesticide Root Zone Model and a Geographic Information System were used to develop and test a method for screening and mapping the susceptibility of ground water in agricultural areas to pesticide contamination. The objective was to produce a map that would be used by the Washington State Department of Agriculture to allocate resources for monitoring pesticide levels in ground water. The method was tested by producing a map showing susceptibility to leaching of the pesticide atrazine for the Columbia Basin Irrigation Project, which encompasses an area of intensive agriculture in eastern Washington. The reliability of the atrazine map was assessed by using statistical procedures to determine whether the median of the percentage of atrazine simulated to leach below the root zone in wells where atrazine was detected was statistically greater than the median percentage at wells where atrazine was not detected (at or above 0.001 microgram per liter) in 134 wells sampled by the U.S. Geological Survey. A statistical difference in medians was not found when all 134 wells were compared. However, a statistical difference was found in medians for two subsets of the 134 wells that were used in land-use studies (studies examining the quality of ground water beneath specific crops). The statistical results from wells from the land-use studies indicate that the model potentially can be used to map the relative susceptibility of agricultural areas to atrazine leaching. However, the distinction between areas of high and low susceptibility may not yet be sufficient to use the method for allocating resources to monitor water quality. Several options are offered for improving the reliability of future simulations.

Water-Resources Investigations Report↗

USGS2025

The continued decline of the Palila (Loxioides bailleui) on Mauna Kea, Island of Hawaiʻi

Palila ( Loxioides bailleui ) are critically endangered Hawaiian honeycreepers specializing on māmane ( Sophora chrysophylla ) seeds and restricted to Mauna Kea volcano on the Island of Hawaiʻi. Recently, the population was estimated to decline by 89% between 1998 and 2021, despite decades of ungulate removal, fence construction, māmane regeneration, fire suppression, and predator control. To inform managers with the most recent update on the status and trends of the Palila population, we analyzed annual bird survey data collected using point-transect distance sampling since 1998, including new annual survey data from 2022, 2023, and 2024. Prior to analysis, we predicted the population trajectory would change between 2021 and 2024 because of continued management actions promoting habitat recovery. We used distance sampling, log-linear regression, and state-space modeling to produce the new estimates and analyze trends across the time series. The 2022 population estimate was 367 to 742 birds (95% confidence interval; point estimate: 545), the lowest in recorded history. The 2023 and 2024 estimates of 374 to 842 birds (point estimate: 596) and 412 to 970 birds (point estimate: 666) were the second and third lowest in our time series, respectively. Our estimates for years before 2022 show population fluctuations between 4000 to 6800 birds from 1998 to 2005, then a steep decline through 2010. For the next decade, abundance fluctuated around 1000 birds, before declining again in 2021 to less than 700 birds. From 1998 to 2024, the population declined by more than 90%, or 205 birds per year, with 100% statistical support for an overall downward trend, despite significant management efforts and research. The greatest threats facing the Palila, if familiar, are not being eliminated swiftly enough to promote their recovery. The currently small and range-limited population is vulnerable to future climate-related events such as drought and fire. Continued monitoring can help to assess the response of Palila to adaptive management actions and changing environmental conditions.

Hawaii↗

USGS2025

Development of genomic markers for monitoring and research on plethodontid salamanders

Despite the importance of plethodontid salamanders and their vulnerability to ongoing environmental change, they are inherently difficult to monitor due to their cryptic nature. Recent advances in genomics have created new opportunities for monitoring of populations and their responses to environmental perturbations. In this study, we developed a new target capture-based genomic panel for the purposes of genetic monitoring in plethodontid salamanders. We demonstrate its utility in several distantly related species and present an example application in two representative species with co-occurring distributions but different ecological attributes and expected patterns of population structure: Plethodon jordani and Desmognathus wrighti . Although the number of successfully assembled loci declined with phylogenetic distance from the original reference species ( Desmognathus spp), we obtained high-quality data from thousands of loci from species in all four genera tested ( Desmognathus , Plethodon , Eurycea , and Gyrinophilus ), which span the deepest split in Plethodontidae. Landscape genetic analyses detected weak but statistically significant geographic structure in P. jordani , and much stronger geographic structure in D. wrighti , as expected based on the lower population density and likely lower dispersal ability of D. wrighti . Our target capture panel is broadly applicable across salamanders in Plethodontidae and has the potential to provide data for a wide range of phylogenetic, biogeographic, and population genetics research questions.

North Carolina, Tennessee↗

USGS2022

Greater than the sum of its parts: Computationally flexible Bayesian hierarchical modeling

We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computations to take place in parallel form using software that is most appropriate for each data partition. The full hierarchical model is then approximated by the product of independent normal distributions for the data component of the model. In the second stage, the Bayesian maximum a posteriori (MAP) estimator is found by maximizing the approximated posterior density with respect to the parameters. If the parameters of the model can be represented as normally distributed random effects, then the second-stage optimization is equivalent to fitting a multivariate normal linear mixed model. We consider a third stage that updates the estimates of distinct parameters for each data partition based on the results of the second stage. The method is demonstrated with two ecological data sets and models, a generalized linear mixed effects model (GLMM) and an integrated population model (IPM). The multistage results were compared to estimates from models fit in single stages to the entire data set. In both cases, multistage results were very similar to a full MCMC analysis. Supplementary materials accompanying this paper appear online.

Journal of Agricultural, Biological and Environmen↗

USGS2004

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

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

Wisconsin↗

USGS2024

An integrated analysis for estimation of survival, growth, and movement of unmarked juvenile anadromous fish

Managers invest substantial resources to promote recovery of declining anadromous fish stocks. Recovery strategies are manifold and often include management actions intended to stimulate somatic growth, increase in-river survival, and motivate juvenile outmigration during favorable environmental conditions. Evaluating the efficacy of these management actions is difficult, however, because monitoring data that explicitly track individuals from egg deposition to juvenile outmigration are typically lacking. We developed an integrated population model that links two different and often collected types of anadromous fish monitoring data: spawning ground surveys and rotary screw trap juvenile catch data. The integrated model accounts for incomplete detection and uses the two sources of data to estimate juvenile demographic parameters in a multistate framework. We evaluated the model's performance using simulated data under a range of conditions typically encountered in similar surveys. Simulation results indicated that the model estimated juvenile survival, growth, and movement with no-to-minimal bias (i.e., ≥ 50 % of simulations ± 0–0.05). As an example case study, we fit the model to empirical fall-run Chinook Salmon ( Oncorhynchus tshawytscha ) monitoring data collected in California's Central Valley, U.S.A. In doing so, we evaluated the influence of environmental conditions (e.g., discharge, water temperature) and habitat availability on juvenile demographic rates. We demonstrated that through our integrated approach we could estimate state transition probabilities that are typically inestimable for naturally produced, unmarked juvenile fish when using traditional statistical approaches to analyze these types of monitoring data. Furthermore, the structure of our model can serve as a useful foundation for decision-support models within adaptive management programs by directly linking management actions, decision-support-model predictions, and monitoring.

California↗

USGS1997

Stochastic seasonality and nonlinear density-dependent factors regulate population size in an African rodent

Ecology has long been troubled by the controversy over how populations are regulated. Some ecologists focus on the role of environmental effects, whereas others argue that density-dependent feedback mechanisms are central. The relative importance of both processes is still hotly debated, but clear examples of both processes acting in the same population are rare. Keyfactor analysis (regression of population changes on possible causal factors) and time-series analysis are often used to investigate the presence of density dependence, but such approaches may be biased and provide no information on actual demographic rates. Here we report on both density-dependent and density-independent effects in a murid rodent pest species, the multimammate rat Mastomys natalensis (Smith, 1834), using statistical capture-recapture models. Both effects occur simultaneously, but we also demonstrate that they do not affect all demographic rates in the same way. We have incorporated the obtained estimates of demographic rates in a population dynamics model and show that the observed dynamics are affected by stabilizing nonlinear density-dependent components coupled with strong deterministic and stochastic seasonal components.

Nature↗