Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Wildlifer”

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

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

At least 505 records · Page 28Linked to original sources
USGS2017

No evidence of infection or exposure to Highly Pathogenic Avian Influenzas in peridomestic wildlife on an affected poultry facility

We evaluated the potential transmission of avian influenza viruses (AIV) in wildlife species in three settings in association with an outbreak at a poultry facility: 1) small birds and small mammals on a poultry facility that was affected with highly pathogenic AIV (HPAIV) in April 2015; 2) small birds and small mammals on a nearby poultry facility that was unaffected by HPAIV; and 3) small birds, small mammals, and waterfowl in a nearby natural area. We live-captured small birds and small mammals and collected samples from hunter-harvested waterfowl to test for active viral shedding and evidence of exposure (serum antibody) to AIV and the H5N2 HPAIV that affected the poultry facility. We detected no evidence of shedding or specific antibody to AIV in small mammals and small birds 5 mo after depopulation of the poultry. We detected viral shedding and exposure to AIV in waterfowl and estimated approximately 15% viral shedding and 60% antibody prevalence. In waterfowl, we did not detect shedding or exposure to the HPAIV that affected the poultry facility. We also conducted camera trapping around poultry carcass depopulation composting barns and found regular visitation by four species of medium-sized mammals. We provide preliminary data suggesting that peridomestic wildlife were not an important factor in the transmission of AIV during the poultry outbreak, nor did small birds and mammals in natural wetland settings show wide evidence of AIV shedding or exposure, despite the opportunity for exposure.

Journal of Wildlife Diseases↗
USGS2022

A serological survey of Francisella tularensis exposure in wildlife on the Arctic Coastal Plain of Alaska

Tularemia is an infectious zoonotic disease caused by one of several subspecies of Francisella tularensis bacteria. Infections by F. tularensis are common throughout the northern hemisphere and have been detected in more than 250 wildlife species. In Alaska, US, where the pathogen was first identified in 1938, studies have identified F. tularensis antibodies in a diverse suite of taxa, including insects, birds, and mammals. However, few such investigations have been conducted recently and knowledge about the current distribution and disease ecology of F. tularensis is limited, particularly in Arctic Alaska, an area undergoing rapid environmental changes from climate warming. To help address these information gaps and provide insights about patterns of exposure among wildlife, we assessed the seroprevalence of F. tularensis antibodies in mammals and tundra-nesting geese from the Arctic Coastal Plain of Alaska, 2014–17. With a commercially available slide agglutination test, we detected antibodies in 14.7% of all individuals sampled ( n =722), with titers ranging from 1:20 to 1:320. We detected significant differences in seroprevalence between family groups, with Canidae (foxes, Vulpes spp.) and Sciuridae (Arctic ground squirrel, Spermophilus parryii ) having the highest seroprevalence at 21.5% and 33.3%, respectively. Mean seroprevalence for Ursidae (polar bears, Ursus maritimus ) was 13.3%, whereas Cervidae (caribou, Rangifer tarandus ) had comparatively low seroprevalence at 6.5%. Antibodies were detected in all Anatidae species sampled, with Black Brant ( Branta bernicla nigricans ) having the highest seroprevalence at 13.6%. The detection of F. tularensis antibodies across multiple taxa from the Arctic Coastal Plain and its nearshore marine region provides evidence of exposure to this pathogen throughout the region and highlights the need for renewed surveillance in Alaska.

Alaska↗
USGS1991

Mitochondrial DNA in wildlife forensic science: Species identification of tissues

A common problem in wildlife law enforcement is identifying the species of origin of carcasses, meat, or blood when morphological characters such as hair or bones are not available. Immunological and protein electrophoretic (allozyme or general protein) procedures have been used in species identification with considerable success (Bunch et al. 1976, McClymont et al. 1982, Wolfe 1983, Mardini 1984, Pex and Wolfe 1985, Dratch 1986), However, immunological tests often are not sensitive enough to distinguish closely related species. Furthermore, electrophoretically detectable protein polymorphisms may be lacking in certain populations or species and may not be species-specific. Analysis of DNA in human and wildlife forensics has been shown to be a potentially powerful tool for identification of individuals (Jeffreys et al. 1985, Vassartet al. 1987, Thommasen et al. 1989). Differences in copy number and nucleotide sequence of repetitive sequences in the nuclear (chromosomal) DNA result in hypervariability and individual-specific patterns which have been termed DNA "fingerprints." However, these patterns may be too variable for species identification necessitating analyses of more conservative parts of the genome. Mitochondrial DNA (mtDNA) is haploid, maternally inherited, similar in nucleotide sequence among conspecifics from the same geographic region, and more suitable for species identification, in contrast to hypervariable DNA fingerprints. MtDNA has several characteristics which make it useful as a species-specific marker. In mammals, individuals have a single mtDNA genotype shared by all tissues. Because mtDNA is haploid and reflects only maternal ancestry, the mtDNA gene number in a population is 4 times less than the nuclear gene number (Birky et al. 1983). This can result in relatively rapid loss or fixation of mtDNA genotypes so that all individuals in a population may be descended from a single ancestral female in as few as 4N ( N = population size) generations (Avise et al., 1984). This suggests there is great potential for identification of species-specific or population-specific mtDNA genotypes, Within species, mtDNA genotypes are thought to be selectively neutral, i.e., there is little evidence that genotypes differ in fitness (Nigro and Prout 1990).

Wildlife Society Bulletin↗
USGS2015

A hidden view of wildlife conservation: How camera traps aid science, research and management

Florida panthers are among the world’s most endangered — and elusive — animals. For approximately four decades, scientists have been researching this small population of panthers that inhabit the dense forests and swamps of south Florida. Because of their wide habitat range along with an absence of clear visual features, these animals are difficult to detect and identify. In 2013, however, researchers released a study that used camera trap images collected between 2005 and 2007 to generate the first statistically reliable density estimates for the remaining population of this subspecies. Camera traps — remotely activated cameras with infrared sensors — first gained measurable popularity in wildlife conservation in the early 1990s. Today, they’re used for a variety of activities, from species-specific research to broad-scale inventory or monitoring programs that, in some cases, attempt to detect biodiversity across vast landscapes. As this modern tool continues to evolve, it’s worth examining its uses and benefits for wildlife management and conservation.

The Wildlife Professional↗
USGS2015

Silvio O. Conte National Fish and Wildlife Refuge: Draft comprehensive conservation plan and environmental impact statement

The Connecticut River is treasured by all for its majesty and significance in supporting life along its winding 410-mile passage through urban and rural communities in New Hampshire, Vermont, Massachusetts, and Connecticut. Working with our partners, we are inspired to protect and enhance the natural and cultural richness throughout the watershed, especially on lands and waters entrusted to our agency as the Silvio O. Conte National Fish and Wildlife Refuge. Together with our partners, we design, support, and implement strategic conservation actions across the watershed, and communicate conservation needs and successes through extensive outreach and education programs. On refuge lands, we offer visitor programs and activities that promote an appreciation of the Connecticut River watershed as an intact, interconnected, and healthy ecosystem. Visitors respond to this greater awareness by becoming active stewards of the watershed’s natural and cultural resources. Our actions exemplify the Service’s vital role in conserving the Connecticut River watershed and the refuge’s important contribution to the mission of the National Wildlife Refuge System.

Vermont↗
USGS2025

Using community-reported data to understand how boat speed affects marine wildlife: An example with the Florida manatee

Boat collisions are a known and increasing threat to many marine wildlife populations. The Florida manatee Trichechus manatus latirostris is a key example of a species with high boat-related mortality, whose long-term viability and population are limited by human activities in shared habitats. The goal of this work was to quantify the probability of lethal injury to Florida manatees using community-reported data on collisions with boats. We test the hypothesis that higher boat speeds increase the probability of lethal injury to manatees. Empirical data to test this hypothesis are collected opportunistically, with low sample sizes and uncertainty in reported boat speed. We fit a logistic regression model using Bayesian inference with Markov Chain Monte Carlo to community-reported collision data. We also present results for two errors-in-variables modelling approaches that account for uncertainty in boat speeds reported as qualitative values. The first uses a multiple imputation approach, whereas the second uses Bayesian estimation with informed priors. We evaluated issues related to quasi-separation, sample size, and measurement errors using simulated data. The models predicted that the probability of lethal injury increased at greater strike speed. However, the small number of records with low boat speed or where the injury was considered non-lethal contributed to uncertainty around this functional relationship. Although the relationships were consistent among models, the uncertainty was greater for the errors-in-variables models. Practical implication . When combined with information on manatee and boat abundance and behaviour, the results of this analysis can be used to predict the number of deadly collisions, test alternative management scenarios and inform speed zone regulations. We also identify ways to improve data reporting to reduce uncertainty in the effect of boat speed on lethal injury to marine wildlife. This type of analysis can be applied to any marine animal where records of collisions with boats are kept.

Florida↗
USGS2021

Assessing the robustness of time-to-event models for estimating unmarked wildlife abundance using remote cameras

Recently developed methods, including time-to-event and space-to-event models, estimate the abundance of unmarked populations from encounter rates with camera trap arrays, addressing a gap in noninvasive wildlife monitoring. However, estimating abundance from encounter rates relies on assumptions that can be difficult to meet in the field, including random movement, population closure, and an accurate estimate of movement speed. Understanding how these models respond to violation of these assumptions will assist in making them more applicable in real-world settings. We used simulated walk models to test the effects of violating the assumptions of the time-to-event model under four scenarios: (1) incorrectly estimating movement speed, (2) violating closure, (3) individuals moving within simplified territories (i.e., movement restricted to partially overlapping circles), (4) and individuals clustering in preferred habitat. The time-to-event model was robust to closure violations, territoriality, and clustering when cameras were placed randomly. However, the model failed to estimate abundance accurately when movement speed was incorrectly estimated or cameras were placed nonrandomly with respect to habitat. We show that the time-to-event model can provide unbiased estimates of abundance when some assumptions that are commonly violated in wildlife studies are not met. Having a robust method for estimating the abundance of unmarked populations with remote cameras will allow practitioners to monitor a more diverse array of populations noninvasively. With the time-to-event model, placing cameras randomly with respect to animal movement and accurately estimating movement speed allows unbiased estimation of abundance. The model is robust to violating the other assumptions we tested.

Ecological Applications↗
USGS2022

A comparison of monitoring designs to assess wildlife community parameters across spatial scales

Dedicated long-term monitoring at appropriate spatial and temporal scales is necessary to understand biodiversity losses and develop effective conservation plans. Wildlife monitoring is often achieved by obtaining data at a combination of spatial scales, ranging from local to broad, to understand the status, trends, and drivers of individual species or whole communities and their dynamics. However, limited resources for monitoring necessitates tradeoffs in the scope and scale of data collection. Careful consideration of the spatial and temporal allocation of finite sampling effort is crucial for monitoring programs that span multiple spatial scales. Here we evaluate the ability of five monitoring designs - stratified random, weighted effort, indicator unit, rotating panel, and split panel - to recover parameter values that describe the status (occupancy), trends (change in occupancy), and drivers (spatially-varying covariate and an autologistic term) of wildlife communities at two spatial scales. Using an amphibian monitoring program that spans a network of U.S. National Parks as a motivating example, we conducted a simulation study for a regional community occupancy sampling program to compare the monitoring designs across varying levels of sampling effort (ranging from 10 to 50%). We found that the stratified random design outperformed the other designs for most parameters of interest at both scales, and was thus generally preferable in balancing the estimation of status, trends, and drivers across scales. However, we found that other designs had improved performance in specific situations. For example, the rotating panel design performed best at estimating spatial drivers at a regional level. Thus, our results highlight the nuanced scenarios in which various design strategies may be preferred, and offer guidance as to how managers can balance common tradeoffs in large-scale and long-term monitoring programs in terms of the specific knowledge gained. Monitoring designs that improve accuracy in parameter estimates are needed to guide conservation policy and management decisions in the face of broad-scaled environmental challenges, but the preferred design is sensitive to the specific objectives of a monitoring program.

Ecological Applications↗
USGS2025

The effectiveness of harvest for limiting wildlife disease: Insights from 20 years of chronic wasting disease in Wyoming

Effective, practical options for managing disease in wildlife populations are limited, especially after diseases become established. Removal strategies (e.g., hunting or culling) are used to control wildlife diseases across a wide range of systems, despite conflicting evidence of their effectiveness. This is especially true for chronic wasting disease (CWD), an untreatable, fatal prion disease threatening cervid populations across multiple countries, for which recreational harvest has been suggested as an important disease control strategy. Using observational data to evaluate whether harvest effectively limits CWD prevalence has been difficult because statistical relationships between harvest and disease prevalence can arise from a causal effect of harvest (i.e., harvest's impacts on prevalence via changes in transmission or demographic structure) or from a number of alternative mechanisms. For instance, correlations between harvest and disease prevalence can also be driven by disease's impacts on population size and harvest (i.e., reverse causality) or from confounding variables (e.g., habitat or geographic location) that impact both harvest rates and disease prevalence. We analyzed two decades of surveillance data (2000–2021) from 10 mule deer herds in Wyoming, using statistical approaches informed by causal inference theory, to test for the effects of harvest on CWD prevalence. Herds with consistently high harvest pressure across 20 years had significantly lower prevalence. Our models predicted that harvesting 40% of adult males per year across 20 years would maintain prevalence below 5% on average, whereas if only 20% of males were harvested in each year, prevalence would increase to >30% by year 20. Moreover, shifting the relative harvest pressure within a herd over a shorter period (3 years) reduced subsequent prevalence, albeit to a smaller degree. Although high harvest is unlikely to completely eradicate CWD, our analysis suggests that maintaining hunting pressure on adult males is an important tactic for slowing CWD epidemics within mule deer herds. Our study also provides guidance for future analyses of longitudinal surveillance data, including the importance of demographic data and appropriate time lags.

Wyoming↗
USGS2025

Expanding national-scale wildlife disease surveillance systems with research networks

Efficient learning about disease dynamics in free-ranging wildlife systems can benefit from active surveillance that is standardized across different ecological contexts. For example, active surveillance that targets specific individuals and populations with standardized sampling across ecological contexts (landscape-scale targeted surveillance) is important for developing a mechanistic understanding of disease emergence, which is the foundation for improving risk assessment of zoonotic or wildlife-livestock disease outbreaks and predicting hotspots of disease emergence. However, landscape-scale targeted surveillance systems are rare and challenging to implement. Increasing experience and infrastructure for landscape-scale targeted surveillance will improve readiness for rapid deployment of this type of surveillance in response to new disease emergence events. Here, we describe our experience developing and rapidly deploying a landscape-scale targeted surveillance system for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in two free-ranging deer species across their ranges in the United States. Our surveillance system was designed to collect data across individual, population, and landscape scales for future analyses aimed at understanding mechanisms and risk factors of SARS-CoV-2 transmission, evolution, and persistence. Our approach leveraged partnerships between state and federal public service sectors and academic researchers in a landscape-scale targeted surveillance research network. Methods describe our approach to developing the surveillance network and sampling design. Results report challenges with implementing our intended sampling design, specifically how the design was adapted as different challenges arose and summarize the sampling design that has been implemented thus far. In the discussion, we describe strategies that were important for the successful deployment of landscape-scale targeted surveillance, development and operation of the research network, construction of similar networks in the future, and analytical approaches for the data based on the sampling design.

Ecology and Evolution↗
USGS2022

Optimizing trilateration estimates for tracking fine-scale movement of wildlife using automated radio telemetry networks

A major advancement in the use of radio telemetry has been the development of automated radio tracking systems (ARTS), which allow animal movements to be tracked continuously. A new ARTS approach is the use of a network of simple radio receivers (nodes) that collect radio signal strength (RSS) values from animal-borne radio transmitters. However, the use of RSS-based localization methods in wildlife tracking research is new, and analytical approaches critical for determining high-quality location data have lagged behind technological developments. We present an analytical approach to optimize RSS-based localization estimates for a node network designed to track fine-scale animal movements in a localized area. Specifically, we test the application of analytical filters (signal strength, distance among nodes) to data from real and simulated node networks that differ in the density and configuration of nodes. We evaluate how different filters and network configurations (density and regularity of node spacing) may influence the accuracy of RSS-based localization estimates. Overall, the use of signal strength and distance-based filters resulted in a 3- to 9-fold increase in median accuracy of location estimates over unfiltered estimates, with the most stringent filters providing location estimates with a median accuracy ranging from 28 to 73 m depending on the configuration and spacing of the node network. We found that distance filters performed significantly better than RSS filters for networks with evenly spaced nodes, but the advantage diminished when nodes were less uniformly spaced within a network. Our results not only provide analytical approaches to greatly increase the accuracy of RSS-based localization estimates, as well as the computer code to do so, but also provide guidance on how to best configure node networks to maximize the accuracy and capabilities of such systems for wildlife tracking studies.

Ecology and Evolution↗
USGS2016

Structured decision making as a framework for large-scale wildlife harvest management decisions

Fish and wildlife harvest management at large spatial scales often involves making complex decisions with multiple objectives and difficult tradeoffs, population demographics that vary spatially, competing stakeholder values, and uncertainties that might affect management decisions. Structured decision making (SDM) provides a formal decision analytic framework for evaluating difficult decisions by breaking decisions into component parts and separating the values of stakeholders from the scientific evaluation of management actions and uncertainty. The result is a rigorous, transparent, and values-driven process. This decision-aiding process provides the decision maker with a more complete understanding of the problem and the effects of potential management actions on stakeholder values, as well as how key uncertainties can affect the decision. We use a case study to illustrate how SDM can be used as a decision-aiding tool for management decision making at large scales. We evaluated alternative white-tailed deer ( Odocoileus virginianus ) buck-harvest regulations in New York designed to reduce harvest of yearling bucks, taking into consideration the values of the state wildlife agency responsible for managing deer, as well as deer hunters. We incorporated tradeoffs about social, ecological, and economic management concerns throughout the state. Based on the outcomes of predictive models, expert elicitation, and hunter surveys, the SDM process identified management alternatives that optimized competing objectives. The SDM process provided biologists and managers insight about aspects of the buck-harvest decision that helped them adopt a management strategy most compatible with diverse hunter values and management concerns.

Ecosphere↗
USGS2020

Assessing population-level consequences of anthropogenic stressors for terrestrial wildlife

Human activity influences wildlife. However, the ecological and conservation significances of these influences are difficult to predict and depend on their population‐level consequences. This difficulty arises partly because of information gaps, and partly because the data on stressors are usually collected in a count‐based manner (e.g., number of dead animals) that is difficult to translate into rate‐based estimates important to infer population‐level consequences (e.g., changes in mortality or population growth rates). However, ongoing methodological developments can provide information to make this transition. Here, we synthesize tools from multiple fields of study to propose an overarching, spatially explicit framework to assess population‐level consequences of anthropogenic stressors on terrestrial wildlife. A key component of this process is using ecological information from affected animals to upscale from count‐based field data on individuals to rate‐based demographic inference. The five steps to this framework are (1) framing the problem to identify species, populations, and assessment parameters; (2) field‐based measurement of the effect of the stressor on individuals; (3) characterizing the location and size of the populations of interest; (4) demographic modeling for those populations; and (5) assessing the significance of stressor‐induced changes in demographic rates. The tools required for each of these steps are well developed, and some have been used in conjunction with each other, but the entire group has not previously been unified together as we do in this framework. We detail these steps and then illustrate their application for two species affected by different anthropogenic stressors. In our examples, we use stable hydrogen isotope data to infer a catchment area describing the geographic origins of affected individuals, as the basis to estimate population size for that area. These examples reveal unexpectedly greater potential risks from stressors for the more common and widely distributed species. This work illustrates key strengths of the framework but also important areas for subsequent theoretical and technical development to make it still more broadly applicable.

Ecosphere↗
USGS2002

Large scale wildlife monitoring studies: Statistical methods for design and analysis

Techniques for estimation of absolute abundance of wildlife populations have received a lot of attention in recent years. The statistical research has been focused on intensive small-scale studies. Recently, however, wildlife biologists have desired to study populations of animals at very large scales for monitoring purposes. Population indices are widely used in these extensive monitoring programs because they are inexpensive compared to estimates of absolute abundance. A crucial underlying assumption is that the population index ( C ) is directly proportional to the population density ( D ). The proportionality constant, β , is simply the probability of 'detection' for animals in the survey. As spatial and temporal comparisons of indices are crucial, it is necessary to also assume that the probability of detection is constant over space and time. Biologists intuitively recognize this when they design rigid protocols for the studies where the indices are collected. Unfortunately, however in many field studios the assumption is clearly invalid. We believe that the estimation of detection probability should be built into the monitoring design through a double sampling approach. A large sample of points provides an abundance index, and a smaller sub-sample of the same points is used to estimate detection probability. There is an important need for statistical research on the design and analysis of these complex studies. Some basic concepts based on actual avian, amphibian, and fish monitoring studies are presented in this article.

Environmetrics↗
USGS2016

Unmanned aircraft systems in wildlife research: Current and future applications of a transformative technology

Unmanned aircraft systems (UAS) – also called unmanned aerial vehicles (UAVs) or drones – are an emerging tool that may provide a safer, more cost-effective, and quieter alternative to traditional research methods. We review examples where UAS have been used to document wildlife abundance, behavior, and habitat, and illustrate the strengths and weaknesses of this technology with two case studies. We summarize research on behavioral responses of wildlife to UAS, and discuss the need to understand how recreational and commercial applications of this technology could disturb certain species. Currently, the widespread implementation of UAS by scientists is limited by flight range, regulatory frameworks, and a lack of validation. UAS are most effective when used to examine smaller areas close to their launch sites, whereas manned aircraft are recommended for surveying greater distances. The growing demand for UAS in research and industry is driving rapid regulatory and technological progress, which in turn will make them more accessible and effective as analytical tools.

Frontiers in Ecology and the Environment↗
USGS2017

Using decision analysis to support proactive management of emerging infectious wildlife diseases

Despite calls for improved responses to emerging infectious diseases in wildlife, management is seldom considered until a disease has been detected in affected populations. Reactive approaches may limit the potential for control and increase total response costs. An alternative, proactive management framework can identify immediate actions that reduce future impacts even before a disease is detected, and plan subsequent actions that are conditional on disease emergence. We identify four main obstacles to developing proactive management strategies for the newly discovered salamander pathogen Batrachochytrium salamandrivorans ( Bsal ). Given that uncertainty is a hallmark of wildlife disease management and that associated decisions are often complicated by multiple competing objectives, we advocate using decision analysis to create and evaluate trade-offs between proactive (pre-emergence) and reactive (post-emergence) management options. Policy makers and natural resource agency personnel can apply principles from decision analysis to improve strategies for countering emerging infectious diseases.

Frontiers in Ecology and the Environment↗
USGS2023

Toxicological effects assessment for wildlife in the 21st Century: Review of current methods and recommendations for a path forward

Model species (e.g., granivorous gamebirds, waterfowl, passerines, domesticated rodents) have been used for decades in guideline laboratory tests to generate survival, growth and reproductive data for prospective Ecological Risk Assessments (ERAs) for birds and mammals, while officially adopted risk assessment schemes for amphibians and reptiles do not exist. There are recognized shortcomings of current in vivo methods as well as uncertainty around the extent to which species with different life histories (e.g., terrestrial amphibians, reptiles, bats) than these commonly used models are protected by existing ERA frameworks. Approaches other than validating additional animal models for testing are being developed, but incorporation of such new approach methodologies (NAMs) into risk assessment frameworks will require robust validations against in vivo responses. This takes time, and the ability to extrapolate findings from non-animal studies to organism- and population-level effects in terrestrial wildlife remains weak. Failure to adequately anticipate and predict hazards could have economic and potentially even legal consequences for regulators and product registrants. In order to be able to use fewer animals or replace them altogether in the long-term, vertebrate use and whole organism data will be needed to provide data for NAMs validation in the short term. Therefore, it is worth investing resources for potential updates to existing standard test guidelines used in the laboratory as well as addressing the need for clear guidance on conduct of field studies. Herein we review the potential for improving standard in vivo test methods and for advancing the use of field studies in wildlife risk assessment, as these tools will be needed into the foreseeable future.

Integrated Environmental Assessment and Management↗
USGS2006

Development of water level regulation strategy for fish and wildlife, upper Mississippi River system

Water level regulation has been proposed as a tool for maintaining or enhancing fish and wildlife resources in navigation pools and associated flood plains of the Upper Mississippi River System. Research related to the development of water level management plans is being conducted under the Long Term Resource Monitoring Program. Research strategies include investigations of cause and effect relationships, spatial and temporal patterns of resource components, and alternative problem solutions. The principal hypothesis being tested states that water level fluctuations resulting from navigation dam operation create less than optimal conditions for the reproduction and growth of target aquatic macrophyte and fish species. Representative navigation pools have been selected to describe hydrologic, engineering, and legal constraints within which fish and wildlife objectives can be established. Spatial analyses are underway to predict the magnitude and location of habitat changes that will result from controlled changes in water elevation.

Mississippi River↗