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USGS2020

Do empirical observations support commonly-held climate change range shift hypotheses? A systematic review protocol

Background Among the most widely anticipated climate-related impacts to biodiversity are geographic range shifts, whereby species shift their spatial distribution in response to changing climate conditions. In particular, a series of commonly articulated hypotheses have emerged: species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to climate change, reflecting an underlying hypothesis that species will move to cooler locations to track spatial changes in the temperature of their current range. Yet, many species are not demonstrating range shifts consistent with these hypotheses. Resolving this discrepancy and providing effective explanations for the observed variability in species’ range shifts is urgently needed to help support a range of natural resource management decisions. Here, we propose a protocol to review the body of evidence for commonly-held climate change range shift hypotheses at the species level focusing on observed latitudinal, longitudinal, elevational, and depth shifts in response to temperature and precipitation changes. We aim to answer the question: what is the impact of anthropogenic climate change (specifically changes in temperature and precipitation) on species ranges? Methods In this review protocol, we propose to conduct a systematic search of literature from internet databases and search engines in English. Articles will be screened in a two-stage process (title/abstract and full text) to evaluate whether they meet a list of eligibility criteria (e.g., presents species-level data, compares >1 time period). Initial data coding and extraction will be completed by four reviewers and checked by a secondary reviewer from among our co-authors. We will perform a formal meta-analysis to document estimated effect size using the subset of available range-shift data expressed in distance per time (e.g., km/decade). We will also use multinomial logistic regression models to assess the probability that species are shifting in a direction that supports our hypotheses (i.e. towards higher latitudes, greater elevations, and deeper depths). We will account for study methodology as a potential source of variation.

Environmental Evidence↗

USGS2013

Refinement of regression models to estimate real-time concentrations of contaminants in the Menomonee River drainage basin, southeast Wisconsin, 2008-11

In 2008, the U.S. Geological Survey and the Milwaukee Metropolitan Sewerage District initiated a study to develop regression models to estimate real-time concentrations and loads of chloride, suspended solids, phosphorus, and bacteria in streams near Milwaukee, Wisconsin. To collect monitoring data for calibration of models, water-quality sensors and automated samplers were installed at six sites in the Menomonee River drainage basin. The sensors continuously measured four potential explanatory variables: water temperature, specific conductance, dissolved oxygen, and turbidity. Discrete water-quality samples were collected and analyzed for five response variables: chloride, total suspended solids, total phosphorus, Escherichia coli bacteria, and fecal coliform bacteria. Using the first year of data, regression models were developed to continuously estimate the response variables on the basis of the continuously measured explanatory variables. Those models were published in a previous report. In this report, those models are refined using 2 years of additional data, and the relative improvement in model predictability is discussed. In addition, a set of regression models is presented for a new site in the Menomonee River Basin, Underwood Creek at Wauwatosa. The refined models use the same explanatory variables as the original models. The chloride models all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity. Total suspended solids and total phosphorus models used turbidity as the only explanatory variable, and bacteria models used water temperature and turbidity as explanatory variables. An analysis of covariance (ANCOVA), used to compare the coefficients in the original models to those in the refined models calibrated using all of the data, showed that only 3 of the 25 original models changed significantly. Root-mean-squared errors (RMSEs) calculated for both the original and refined models using the entire dataset showed a median improvement in RMSE of 2.1 percent, with a range of 0.0–13.9 percent. Therefore most of the original models did almost as well at estimating concentrations during the validation period (October 2009–September 2011) as the refined models, which were calibrated using those data. Application of these refined models can produce continuously estimated concentrations of chloride, total suspended solids, total phosphorus, E. coli bacteria, and fecal coliform bacteria that may assist managers in quantifying the effects of land-use changes and improvement projects, establish total maximum daily loads, and enable better informed decision making in the future.

Wisconsin↗

USGS2012

Habitat associations of fish species of greatest conservation need in wadeable Iowa streams

Fish and habitat data were collected from 84 wadeable stream reaches in the Mississippi River drainage of Iowa to predict the occurrences of seven fish species of greatest conservation need and to identify the relative importance of habitat variables measured at small (e.g., depth, velocity, and substrate) and large (e.g., stream order, elevation, and gradient) scales in terms of their influence on species occurrences. Multiple logistic regression analysis was used to predict fish species occurrences, starting with all possible combinations of variables (5 large-scale variables, 13 small-scale variables, and all 18 variables) but limiting the final models to a maximum of five variables. Akaike's information criterion was used to rank candidate models, weight model parameters, and calculate model-averaged predictions. On average, the correct classification rate (CCR = 80%) and Cohen's kappa (κ = 0.59) were greatest for multiple-scale models (i.e., those including both large-scale and small-scale variables), intermediate for small-scale models (CCR = 75%; κ = 0.49), and lowest for large-scale models (CCR = 73%; κ = 0.44). The occurrence of each species was associated with a unique combination of large-scale and small-scale variables. Our results support the necessity of understanding factors that constrain the distribution of fishes across spatial scales to ensure that management decisions and actions occur at the appropriate scale.

Iowa↗

USGS2024

U.S. Geological Survey data strategy 2023–33

The U.S. Geological Survey (USGS) has long recognized the strategic importance and value of well-managed data assets as an integral component of scientific integrity and foundational to the advancement of scientific research, decision making, and public safety. The USGS investment in the science lifecycle, including collection of unbiased data assets, interpretation, peer review, interpretive publications, and data release, ultimately contributes to the transparency and availability of science. Foundational Government directives and laws, such as the Foundations for Evidence-Based Policymaking Act of 2018 (Public Law 115–435, 132 Stat. 5529) as well as Executive Order 13642, “Making Open and Machine Readable the New Default for Government Information,” provide a framework for addressing strategic data management. The USGS Data Strategy builds on that framework by outlining high-level goals and objectives that serve as a long-term, decadal guide toward achieving a broad, data-focused vision. Benefits of the USGS Data Strategy are many. The USGS will contribute to open science by increasing efficiencies in the consistent management of valuable data assets; driving innovation that results in modernized capabilities to ensure data are analysis ready; increasing data skills across the Bureau to enhance workforce data literacy; broadening capacity to understand and address needs of stakeholders; and measuring progress in producing findable, accessible, interoperable, and reusable (FAIR) data products. The major goals and objectives of the USGS Data Strategy promote maximizing the utility of USGS data based on stakeholder needs, promoting data innovation, coordinating common data practices, modernizing our USGS enterprise data architecture, and enhancing our data-centric culture. The goals and objectives in the strategy align with other Bureau strategic plans, guidance, and directives from the Department of the Interior and the Federal Government. This strategy is a key component to strengthen the Bureau’s data ecosystem to ensure a relevant, long-term capacity that supports internal needs and achieves its scientific mission in the most efficient and effective manner.

Circular↗

USGS2023

The relative stability of planktic foraminifer thermal preferences over the past 3 million years

Stationarity of species’ ecological tolerances is a first-order assumption of paleoenvironmental reconstruction based upon analog methods. To test this and other assumptions used in quantitative analysis of foraminiferal faunas for paleoceanographic reconstruction, we analyzed paired alkenone unsaturation ratio ( U 37 K ′ )   "> U K ′ 37 ) 37′) sea surface temperature (SST) estimates and relative abundances of planktic foraminifera within Late Pliocene assemblages. We established Pliocene temperature preferences for nine species in the North Atlantic: Dentoglobigerina altispira, Globorotalia menardii, Globoconella puncticulata, Neogloboquadrina atlantica, Neogloboquadrina incompta, Neogloboquadrina pachyderma, Trilobatus sacculifer, Globigerinita glutinata, and Globigerina bulloides. We compared these to the temperature preferences of the same extant species, and in the three cases where the species are now extinct ( Dentoglobigerina altispira, Neogloboquadrina atlantica, and Globoconella puncticulata ), comparisons were made to either the descendant species or other modern species commonly used as analogs. In general, the taxa tested show similar temperature responses in both Late Pliocene and present-day (core-top) distributions. The data from these comparisons are mostly encouraging, supporting past paleoceanographic conclusions, and are otherwise valuable for testing previous taxonomic grouping decisions that are often necessary for interpreting the paleoenvironment based upon Pliocene foraminiferal assemblages.

Geosciences↗

USGS2020

Resource partitioning across a trophic gradient between a freshwater fish and an intraguild exotic

The introduction of exotic species has the potential to cause resource competition with native species and may lead to competitive exclusion when resources are limiting. On the other hand, information is lacking to predict under what alternate trophic conditions coexistence may occur. Comparing diets of native yellow perch Perca flavescens and nonindigenous white perch Morone americana , we examined variation in resource partitioning and body condition across a prominent longitudinal nutrient gradient in Lake Erie (north‐eastern United States, Canada). As measured with Analysis of Similarity and Schoener's index, diet similarity declined monotonically from west to east tracking declines in nutrients, productivity and relative abundance of both species. Additionally, diet similarity increased from spring through fall, following seasonal development of stratification and hypolimnetic hypoxia—phenomena which tend to increase spatial overlap between these species. Finally, relative weights of both species peaked in the Central Basin (relative weights > 0.85), which, on average, had intermediate values of prey diversity, ecosystem trophic status and water clarity. Our results highlight that native yellow perch coexist with invasive white perch under a wide range of trophic conditions. Of importance to fishery managers, mesotrophy in the Central Basin correlated with the highest body conditions and intermediate prey resource partitioning, although the effect size was small and variable. While competitive exclusion appears unlikely, the goal of reducing nutrient inputs in Lake Erie could affect not only the distributions of both species but also stakeholder decisions about where to fish.

Lake Erie↗

USGS2011

Development of a high-resolution binational vegetation map of the Santa Cruz River riparian corridor and surrounding watershed, southern Arizona and northern Sonora, Mexico

This report summarizes the development of a binational vegetation map developed for the Santa Cruz Watershed, which straddles the southern border of Arizona and the northern border of Sonora, Mexico. The map was created as an environmental input to the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM) that is being created by the U.S. Geological Survey for the watershed. The SCWEPM is a map-based multicriteria evaluation tool that allows stakeholders to explore tradeoffs between valued ecosystem services at multiple scales within a participatory decision-making process. Maps related to vegetation type and are needed for use in modeling wildlife habitat and other ecosystem services. Although detailed vegetation maps existed for the U.S. side of the border, there was a lack of consistent data for the Santa Cruz Watershed in Mexico. We produced a binational vegetation classification of the Santa Cruz River riparian habitat and watershed vegetation based on NatureServe Terrestrial Ecological Systems (TES) units using Classification And Regression Tree (CART) modeling. Environmental layers used as predictor data were derived from a seasonal set of Landsat Thematic Mapper (TM) images (spring, summer, and fall) and from a 30-meter digital-elevation-model (DEM) grid. Because both sources of environmental data are seamless across the international border, they are particularly suited to this binational modeling effort. Training data were compiled from existing field data for the riparian corridor and data collected by the NM-GAP (New Mexico Gap Analysis Project) team for the original Southwest Regional Gap Analysis Project (SWReGAP) modeling effort. Additional training data were collected from core areas of the SWReGAP classification itself, allowing the extrapolation of the SWReGAP mapping into the Mexican portion of the watershed without collecting additional training data.

Arizona↗

USGS2008

Trends and spatial distribution of annual and seasonal rainfall in Ethiopia

As a country whose economy is heavily dependent on low-productivity rainfed agriculture, rainfall trends are often cited as one of the more important factors in explaining various socio-economic problems such as food insecurity. Therefore, in order to help policymakers and developers make more informed decisions, this study investigated the temporal dynamics of rainfall and its spatial distribution within Ethiopia. Changes in rainfall were examined using data from 134 stations in 13 watersheds between 1960 and 2002. The variability and trends in seasonal and annual rainfall were analysed at the watershed scale with data (1) from all available years, and (2) excluding years that lacked observations from at least 25% of the gauges. Similar analyses were also performed at the gauge, regional, and national levels. By regressing annual watershed rainfall on time, results from the one-sample t -test show no significant changes in rainfall for any of the watersheds examined. However, in our regressions of seasonal rainfall averages against time, we found a significant decline in June to September rainfall (i.e. Kiremt) for the Baro-Akobo, Omo-Ghibe, Rift Valley, and Southern Blue Nile watersheds located in the southwestern and central parts of Ethiopia. While the gauge level analysis showed that certain gauge stations experienced recent changes in rainfall, these trends are not necessarily reflected at the watershed or regional levels.

International Journal of Climatology↗

USGS2011

Particle-size dependence on metal(loid) distributions in mine wastes: Implications for water contamination and human exposure

The mining and processing of metal-bearing ores has resulted in contamination issues where waste materials from abandoned mines remain in piles of untreated and unconsolidated material, posing the potential for waterborne and airborne transport of toxic elements. This study presents a systematic method of particle size separation, mass distribution, and bulk chemical analysis for mine tailings and adjacent background soil samples from the Rand historic mining district, California, in order to assess particle size distribution and related trends in metal(loid) concentration as a function of particle size. Mine tailings produced through stamp milling and leaching processes were found to have both a narrower and finer particle size distribution than background samples, with significant fractions of particles available in a size range (???250 ??m) that could be incidentally ingested. In both tailings and background samples, the majority of trace metal(loid)s display an inverse relationship between concentration and particle size, resulting in higher proportions of As, Cr, Cu, Pb and Zn in finer-sized fractions which are more susceptible to both water- and wind-borne transport as well as ingestion and/or inhalation. Established regulatory screening levels for such elements may, therefore, significantly underestimate potential exposure risk if relying solely on bulk sample concentrations to guide remediation decisions. Correlations in elemental concentration trends (such as between As and Fe) indicate relationships between elements that may be relevant to their chemical speciation. ?? 2011 Elsevier Ltd.

Applied Geochemistry↗

USGS2026

Seasonal trophic dynamics drive growth potential and predation risk for reintroduced Chinook salmon in Shasta Reservoir

Reservoir ecosystems can significantly affect anadromous salmon populations reintroduced upstream of impassable high-head dams. In this study, we examine how a novel reservoir food web created by an impoundment can limit juvenile growth or survival through the seasonal production or access to food, promote competition for available resources, and affect risk of the food supply or predation mortality, all of which can be strongly influenced by the thermal regime. Shasta Reservoir, the largest impoundment in California’s Central Valley Project, presents opportunities and risks for winter-run Chinook salmon ( Oncorhynchus tshawytscha ), a federally endangered population targeted for reintroduction into the McCloud River, which flows into the reservoir. We integrated field sampling, stable isotope analysis, hydroacoustic surveys, and bioenergetics modeling to characterize Shasta Reservoir’s food web and evaluate seasonal growth potential and predation risk for juvenile salmon. Zooplankton, dominated by Daphnia , provided favorable foraging conditions in spring but declined sharply by late summer, coinciding with high consumption demand from abundant threadfin shad ( Dorosoma petenense ). Bioenergetics simulations indicated that fry that would ordinarily enter the reservoir in autumn would face poor growth opportunities. They would also be exposed to elevated predation risk driven by warm temperatures and high metabolic demand, particularly from piscivorous salmonids and Sacramento pikeminnow ( Ptychocheilus grandis ). Limited information on predator abundances precludes the ability to quantify total predation demand. Alternatively, juveniles entering the reservoir the following spring would encounter greater prey availability and reduced predation pressure. These findings highlight the strong influence of seasonal thermal structure and food web dynamics on reservoir constraints and underscore the need to incorporate the dynamics of key habitats into reintroduction management and decisions. Our framework provides a quantitative, mechanistically-based approach for evaluating the role of reservoirs in salmon reintroductions above high-head dams.

California↗

USGS2016

Model analysis of check dam impacts on long-term sediment and water budgets in southeast Arizona, USA

The objective of this study was to evaluate the effect of check dam infrastructure on soil and water conservation at the catchment scale using the Soil and Water Assessment Tool (SWAT). This paired watershed study includes a watershed treated with over 2000 check dams and a Control watershed which has none, in the West Turkey Creek watershed, Southeast Arizona, USA. SWAT was calibrated for streamflow using discharge documented during the summer of 2013 at the Control site. Model results depict the necessity to eliminate lateral flow from SWAT models of aridland environments, the urgency to standardize geospatial soils data, and the care for which modelers must document altering parameters when presenting findings. Performance was assessed using the percent bias (PBIAS), with values of ±2.34%. The calibrated model was then used to examine the impacts of check dams at the Treated watershed. Approximately 630 tons of sediment is estimated to be stored behind check dams in the Treated watershed over the 3-year simulation, increasing water quality for fish habitat. A minimum precipitation event of 15 mm was necessary to instigate the detachment of soil, sediments, or rock from the study area, which occurred 2% of the time. The resulting watershed model is useful as a predictive framework and decision-support tool to consider long-term impacts of restoration and potential for future restoration.

Arizona↗

USGS2009

Stakeholder Evaluation for Canaan Valley National Wildlife Refuge: Completion Report

The National Wildlife Refuge System, managed by the U.S. Fish and Wildlife Service (FWS), is the largest system of public lands in the world dedicated to wildlife conservation. There are over 545 national wildlife refuges nationwide, encompassing 95 million acres. As part of the National Wildlife Refuge System Improvement Act of 1997, each refuge is developing 15-year comprehensive conservation plans (CCPs). Each CCP describes a vision and desired future condition for the refuge and outlines goals, objectives, and management strategies for each refuge's habitat and visitor service programs. The CCP process for Canaan Valley National Wildlife Refuge (Refuge) in Davis, West Virginia was initiated in 2006. This planning process provides a unique opportunity for public input and involvement. Public involvement is an important part of the CCP process. Participation by parties with a stake in the resource (stakeholders) has the potential to increase understanding and support and reduce conflicts. Additionally, meaningful public participation in a decision process may increase trust and provide satisfaction in terms of both process and outcome for management and the public. Public meetings are a common way to obtain input from community members, visitors, and potential visitors. An 'Issues Workbook' is another tool the FWS uses to obtain public input and participation early in the planning process. Sometimes, however, these traditional methods do not capture the full range of perspectives that exist. A stakeholder evaluation is a way to more fully understand community preferences and opinions related to key topics in refuge planning. It can also help refuge staff understand how changes in management affect individuals in terms of their preference for services and experiences. Secondarily, a process such as this can address 'social goals' such as fostering trust in regulating agencies and reducing conflict among stakeholders. As part of the CCP planning effort at Canaan Valley National Wildlife Refuge, the FWS sponsored a stakeholder evaluation conducted by the Policy Analysis and Science Assistance Branch of U.S. Geological Survey, Fort Collins Science Center the winter of 2006-2007. The stakeholder evaluation was intended to answer the following questions: 1)Which Refuge management issues are most important, and to whom?, 2)How do opinions about what is most important to stakeholders overlap or conflict?, 3)Why do stakeholders emphasize specific issues, and what values are driving this?, and 4)What potential solutions do stakeholders have for addressing important issues? This information will be used by the Refuge to help guide development of their CCP as they strive to balance stakeholder desires with their charge to manage the unique wetlands and uplands of the Canaan Valley for wildlife conservation.

Open-File Report↗

USGS2019

Atlantic Salmon (Salmo salar) climate scenario planning pilot report

Scenario planning is a structured process that embraces uncertainty and explores plausible alternative future conditions under different assumptions to help manage risk and prioritize actions ( Schwartz 1996, Peterson et al . 2003). It has been used by a variety of organizations to explore and help prepare for the future, lends itself well to exploring the uncertainty surrounding changing environmental conditions, and is widely applicable to natural resource management issues. The conservation and management of protected resources for example, can be particularly challenging when the rate and magnitude of climate-related changes, and the response of species to those changes, are uncertain (NMFS 2016). The structured process of scenario planning can help resource managers navigate through potentially paralyzing uncertainties, manage risk, and evaluate/prioritize management actions associated with adapting to, and managing for, climate change (Moore et al . 2013). Atlantic salmon ( Salmo salar ) is a species highly vulnerable to climate change in the Northeast Atlantic (Hare et al . 2016a). Based on this and the above reasons, a scenario planning initiative was piloted by NOAA Fisheries to explore what the agency can do to improve U.S.Atlantic salmon population resilience to changing climate conditions in riverine, estuarine(transition), and marine environments across its current range (U.S. headwaters to Greenland). Project objectives were: 1) to better understand the challenges of managing Atlantic salmon in a changing climate; 2) to identify and discuss potential management actions and research activities that can be undertaken to increase our understanding of the drivers of Atlantic salmon productivity and resilience; 3) to increase collaborations and coordination related to the speciesrecovery; and 4) to explore how scenario planning can be used to support decisions. Outcomes from this initiative included, but were not limited to, the identification of high priority research and management actions to further collaborations and efforts to recover this species. The identified high priority actions were those that could be undertaken in the near-term(1-5 years) using current resources and in consideration of potential future conditions. Examples of identified actions by habitat (not in order of priority) included: 1) synthesize and refine range-wide life stage specific quantitative environmental thresholds for temperature, flow, etc.; 2) assess watershed habitat productivity; 3) assess forage fish and survival connection and options for marine migration monitoring; and 4) reduce dam-associated indirect estuarine mortality rate. In addition, a number of high priority climate-related actions were included in the revised Atlantic Salmon Recovery Plan (USFWS and NMFS 2019, Appendix 16) and at least two newly NOAA Fisheries funded projects are now underway (1. conduct range-wide habitat analysis and synthesize life stage specific quantitative thresholds and 2. identify locations of cold water refugia under a changing climate). This is the first use of the scenario planning process (NPS 2013) by NOAA Fisheries. This report documents an important example of applying scenario planning to marine species/environments and may serve as a useful reference for other case studies.

Atlantic Ocean↗

USGS2017

Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results

Drought is a natural climatic phenomenon that occurs throughout the world and impacts many sectors of society. To help decision-makers reduce the impacts of drought, it is important to improve monitoring tools that provide relevant and timely information in support of drought mitigation decisions. Given that drought is a complex natural hazard that manifests in different forms, monitoring can be improved by integrating various types of information (e.g., remote sensing and climate) that is timely and region specific to identify where and when droughts are occurring. The Vegetation Drought Response Index for Canada (VegDRI-Canada) is a recently developed drought monitoring tool for Canada. VegDRI-Canada extends the initial VegDRI concept developed for the conterminous United States to a broader transnational coverage across North America. VegDRI-Canada models are similar to those developed for the United States, integrating satellite observations of vegetation status, climate data, and biophysical information on land use and land cover, soil characteristics, and other environmental factors. Collectively, these different types of data are integrated into the hybrid VegDRI-Canada to isolate the effects of drought on vegetation. Twenty-three weekly VegDRI-Canada models were built for the growing season (April–September) through the weekly analysis of these data using a regression tree-based data mining approach. A 15-year time series of VegDRI-Canada results (s to 2014) was produced using these models and the output was validated by randomly selecting 20% of the historical data, as well as holdout year (15% unseen data) across the growing season that the Pearson’s correlation ranged from 0.6 to 0.77. A case study was also conducted to evaluate the VegDRI-Canada results over the prairie region of Canada for two drought years and one non-drought year for three weekly periods of the growing season (i.e., early-, mid-, and late season). The comparison of the VegDRI-Canada map with the Canadian Drought Monitor (CDM), an independent drought indicator, showed that the VegDRI-Canada maps depicted key spatial drought severity patterns during the two targeted drought years consistent with the CDM. In addition, VegDRI-Canada was compared with canola yields in the Prairie Provinces at the regional scale for a period from 2000 to 2014 to evaluate the indices’ applicability for monitoring drought impacts on crop production. The result showed that VegDRI-Canada values had a relatively higher correlation (i.e., r > 0.5) with canola yield for nonirrigated croplands in the Canadian Prairies region in areas where drought is typically a limiting factor on crop growth, but showed a negative relationship in the southeastern Prairie region, where water availability is less of a limiting factor and in some cases a hindrance to crop growth when waterlogging occurs. These initial results demonstrate VegDRI-Canada’s utility for monitoring drought-related vegetation conditions, particularly in drought prone areas. In general, the results indicated that the VegDRI-Canada models showed sensitivity to known agricultural drought events in Canada over the 15-year period mainly for nonirrigated areas.

GIScience and Remote Sensing↗

USGS2014

Integrated hydrologic model of Pajaro Valley, Santa Cruz and Monterey Counties, California

Increasing population, agricultural development (including shifts to more water-intensive crops), and climate variability are placing increasingly larger demands on available groundwater resources in the Pajaro Valley, one of the most productive agricultural regions in the world. This study provided a refined conceptual model, geohydrologic framework, and integrated hydrologic model of the Pajaro Valley. The goal of this study was to produce a model capable of being accurate at scales relevant to water management decisions that are being considered in the revision and updates to the Basin Management Plan (BMP). The Pajaro Valley Hydrologic Model (PVHM) was designed to reproduce the most important natural and human components of the hydrologic system and related climatic factors, permitting an accurate assessment of groundwater conditions and processes that can inform the new BMP and help to improve planning for long-term sustainability of water resources. Model development included a revision of the conceptual model of the flow system, reevaluation of the previous model transformed into MODFLOW, implementation of the new geohydrologic model and conceptual model, and calibration of the transient hydrologic model. The PVHM model, using MODFLOW with the Farm Process (MF-FMP2), is capable of being accurate at seasonal to interannual time frames and subregional to valley-wide spatial scales for the assessment of the groundwater hydrologic budget for water years 1964–2009, as well as potential assessment of the BMP components and sustainability analysis of conjunctive use. The model provides a good representation of the regional flow system and the use and movement of water throughout the valley. Simulated changes in storage over time show that, prior to the 1984–92 dry period, significant withdrawals from storage occurred only during drought years. Since about 1993, growers in the Pajaro Valley have shifted to more water intensive crops, such as strawberries, bushberries, and vegetable row crops, as well as making additional rotational plantings, which have increased demand on limited groundwater resources. Simulated groundwater flow indicates that vertical hydraulic gradients between horizontal layers fluctuate and even reverse in several parts of the basin as recharge and pumpage rates change seasonally and annually. The majority of recharge predominantly enters the Alluvial aquifer system, and along with pumpage and the largest fractions of storage depletion, occurs in the inland regions. Coastal inflow as seawater intrusion replaces much of the potential storage depletion in the coastal regions. The simulated long-term imbalance between inflows and outflows indicates overdraft of the groundwater basin averaging about 12,950 acre-feet per year (acre-ft/yr) over the 46-year period of water years (1964–2009). Annual overdraft varies considerably from year to year, depending on land use, pumpage, and climate conditions. Climatically driven factors can affect inflows, outflows, and water use by as much as a factor of two between wet and dry years. Coastal inflows and outflows vary by year and by aquifer; the net coastal inflow, or seawater intrusion, ranges from about 1,000 to more than 6,000 acre-ft/yr. Maps of simulated and measured water-level elevations indicate regions with water levels below sea level in the alluvium and Aromas layers. Ongoing expansion of local hydrologic monitoring networks indicates the importance of these networks to the understanding of changes in groundwater flow, streamflow, and streamflow infiltration. In particular, the monitoring of streamflow, groundwater pumpage, and groundwater levels throughout the valley not only indicates the state of the resources, but also provides valuable information for model calibration and for model-based evaluation of management actions. The HS-ASR was simulated for the years 2002–09, and replaced about about 1,290 acre-ft of coastal pumpage. This was combined with the simulation of additional 6,200 acre-ft of deliveries from supplemental wells, recycled water, and city connection deliveries through the CDS that also supplanted some coastal pumpage. Total simulated deliveries were 7,350 acre-ft of the 7,500 acre-ft of reported deliveries for the period 2002-09. The completed CDS should be capable of delivering about 8.8 million cubic meters (7,150 acre-ft) of water per year to coastal farms within the Pajaro Valley, if all the local supply components were fully available for this purpose. This would represent about 15 percent of the 48,300 acre-ft (59.6 million cubic meters) average agricultural pumpage for the period 2005 to 2009. Combined with the potential capture and reuse of some of the return flows and tile-drain flows, this could represent an almost 70 percent reduction of average overdraft for the entire valley and a large part of the coastal pumpage that induces seawater intrusion.

California↗

USGS2013

LANDFIRE 2010 - updated data to support wildfire and ecological management

Wildfire is a global phenomenon that affects human populations and ecosystems. Wildfire effects occur at local to global scales impacting many people in different ways (Figure 1). Ecological concerns due to land use, fragmentation, and climate change impact natural resource use, allocation, and conservation. Access to consistent and current environmental data is a constant challenge, yet necessary for understanding the complexities of wildfire and ecological management. Data products and tools from the LANDFIRE Program help decision-makers to clarify problems and identify possible solutions when managing fires and natural resources. LANDFIRE supports the reduction of risk from wildfire to human lives and property, monitoring of fire danger, prediction of fire behavior on active incidents, and assessment of fire severity and impacts on natural systems [1] [2] [3]. LANDFIRE products are unique in that they are nationally consistent and provide the only complete geospatial dataset describing vegetation and wildland fuel information for the entire U.S. As such, LANDFIRE data are useful for many ecological applications [3]. For example, LANDFIRE data were recently integrated into a decision-support system for resource management and conservation decision-making along the Appalachian Trail. LANDFIRE is a joint effort between the U.S. Department of the Interior Office of Wildland Fire, U.S. Department of Agriculture Forest Service Fire & Aviation Management, and The Nature Conservancy. To date, seven versions of LANDFIRE data have been released, with each successive version improving the quality of the data, adding additional features, and/or updating the time period represented by the data. The latest version, LANDFIRE 2010 (LF 2010), released mid-2013, represents circa 2010 landscape conditions and succeeds LANDFIRE 2008 (LF 2008), which represented circa 2008 landscape conditions. LF 2010 used many of the same processes developed for the LF 2008 effort [3]. Ongoing refinement of the LANDFIRE vegetation and fuel data is necessary to improve the quality and usability of the data and to capture landscape disturbance. LANDFIRE relies on Landsat multi-spectral imagery to produce and update vegetation and fuel data. The deep Landsat archive provides data needed for vegetation classification, change analysis, and historical disturbance characterization, for which LANDFIRE has used more than 24,000 image scenes since the program’s inception. In addition, LF 2010 used airborne and spaceborne lidar, and spaceborne synthetic aperture radar (SAR) to map vegetation structure in areas where ground-based field information was lacking, including Alaska and U.S.-affiliated islands in the Caribbean and the Pacific. The mapping of insular areas is new for the 2010 data release; previous versions of LANDFIRE were limited to the conterminous U.S., Alaska, and Hawaii.

Earthzine↗

USGS2022

Analysis of per capita contributions from a spatial model provides strategies for controlling spread of invasive carp

Metapopulation models may be applied to inform natural resource management to guide actions targeted at location-specific subpopulations. Model insights frequently help to understand which subpopulations to target and highlight the importance of connections among subpopulations. For example, managers often treat aquatic invasive species populations as discrete populations due to hydrological (e.g., lakes, pools formed by dams) or jurisdictional boundaries (e.g., river segments by country or jurisdictional units such as states or provinces). However, aquatic invasive species often have high rates of dispersion and migration among heterogenous locations, which complicates traditional metapopulation models and may not conform to management boundaries. Controlling invasive species requires consideration of spatial dynamics because local management activities (e.g., harvest, movement deterrents) may have important impacts on connected subpopulations. We expand upon previous work to create a spatial linear matrix model for an aquatic invasive species, Bighead Carp, in the Illinois River, USA, to examine the per capita contributions of specific subpopulations and impacts of different management scenarios on these subpopulations. Managers currently seek to prevent Bighead Carp from invading the Great Lakes via a connection between the Illinois Waterway and Lake Michigan by allocating management actions across a series of river pools. We applied the model to highlight how spatial variation in movement rates and recruitment can affect decisions about where management activities might occur. We found that where the model suggested management actions should occur depend crucially on the specific management goal (i.e., limiting the growth rate of the metapopulation vs. limiting the growth rate of the invasion front) and the per capita recruitment rate in downstream pools. Our findings illustrate the importance of linking metapopulation dynamics to management goals for invasive species control.

Illinois↗

USGS2006

MARKOV: A methodology for the solution of infinite time horizon MARKOV decision processes

Algorithms are described for determining optimal policies for finite state, finite action, infinite discrete time horizon Markov decision processes. Both value-improvement and policy-improvement techniques are used in the algorithms. Computing procedures are also described. The algorithms are appropriate for processes that are either finite or infinite, deterministic or stochastic, discounted or undiscounted, in any meaningful combination of these features. Computing procedures are described in terms of initial data processing, bound improvements, process reduction, and testing and solution. Application of the methodology is illustrated with an example involving natural resource management. Management implications of certain hypothesized relationships between mallard survival and harvest rates are addressed by applying the optimality procedures to mallard population models.

Applied Stochastic Models and Data Analysis↗