Geology topics

Byron K. Williams

Publications and source records attributed to Byron K. Williams.

At least 37 records · Page 2Linked to original sources

USGS2015

Value of information in natural resource management: technical developments and application to pink-footed geese

The “value of information” (VOI) is a generic term for the increase in value resulting from better information to guide management, or alternatively, the value foregone under uncertainty about the impacts of management (Yokota and Thompson, Medical Decision Making 2004; 24 : 287). The value of information can be characterized in terms of several metrics, including the expected value of perfect information and the expected value of partial information. We extend the technical framework for the value of information by further developing the relationship between value metrics for partial and perfect information and describing patterns of their performance. We use two different expressions for the expected value of partial information to highlight its relationship to the expected value of perfect information. We also develop the expected value of partial information for hierarchical uncertainties. We highlight patterns in the value of information for the Svalbard population of the pink-footed goose ( Anser brachyrhynchus ), a population that is subject to uncertainty in both reproduction and survival functions. The framework for valuing information is seen as having widespread potential in resource decision making, and serves as a motivation for resource monitoring, assessment, and collaboration.

Svalbard↗

USGS2015

Markov decision processes in natural resources management: observability and uncertainty

The breadth and complexity of stochastic decision processes in natural resources presents a challenge to analysts who need to understand and use these approaches. The objective of this paper is to describe a class of decision processes that are germane to natural resources conservation and management, namely Markov decision processes, and to discuss applications and computing algorithms under different conditions of observability and uncertainty. A number of important similarities are developed in the framing and evaluation of different decision processes, which can be useful in their applications in natural resources management. The challenges attendant to partial observability are highlighted, and possible approaches for dealing with it are discussed.

Ecological Modelling↗

USGS2015

An assessment of two methods for identifying undocumented levees using remotely sensed data

Many undocumented and commonly unmaintained levees exist in the landscape complicating flood forecasting, risk management, and emergency response. This report describes a pilot study completed by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers to assess two methods to identify undocumented levees by using remotely sensed, high-resolution topographic data. For the first method, the U.S. Army Corps of Engineers examined hillshades computed from a digital elevation model that was derived from light detection and ranging (lidar) to visually identify potential levees and then used detailed site visits to assess the validity of the identifications. For the second method, the U.S. Geological Survey applied a wavelet transform to a lidar-derived digital elevation model to identify potential levees. The hillshade method was applied to Delano, Minnesota, and the wavelet-transform method was applied to Delano and Springfield, Minnesota. Both methods were successful in identifying levees but also identified other features that required interpretation to differentiate from levees such as constructed barriers, high banks, and bluffs. Both methods are complementary to each other, and a potential conjunctive method for testing in the future includes (1) use of the wavelet-transform method to rapidly identify slope-break features in high-resolution topographic data, (2) further examination of topographic data using hillshades and aerial photographs to classify features and map potential levees, and (3) a verification check of each identified potential levee with local officials and field visits.

Minnesota↗

USGS2014

Uncertainty, robustness, and the value of information in managing an expanding Arctic goose population

We explored the application of dynamic-optimization methods to the problem of pink-footed goose (Anser brachyrhynchus) management in western Europe. We were especially concerned with the extent to which uncertainty in population dynamics influenced an optimal management strategy, the gain in management performance that could be expected if uncertainty could be eliminated or reduced, and whether an adaptive or robust management strategy might be most appropriate in the face of uncertainty. We combined three alternative survival models with three alternative reproductive models to form a set of nine annual-cycle models for pink-footed geese. These models represent a wide range of possibilities concerning the extent to which demographic rates are density dependent or independent, and the extent to which they are influenced by spring temperatures. We calculated state-dependent harvest strategies for these models using stochastic dynamic programming and an objective function that maximized sustainable harvest, subject to a constraint on desired population size. As expected, attaining the largest mean objective value (i.e., the relative measure of management performance) depended on the ability to match a model-dependent optimal strategy with its generating model of population dynamics. The nine models suggested widely varying objective values regardless of the harvest strategy, with the density-independent models generally producing higher objective values than models with density-dependent survival. In the face of uncertainty as to which of the nine models is most appropriate, the optimal strategy assuming that both survival and reproduction were a function of goose abundance and spring temperatures maximized the expected minimum objective value (i.e., maxi–min). In contrast, the optimal strategy assuming equal model weights minimized the expected maximum loss in objective value. The expected value of eliminating model uncertainty was an increase in objective value of only 3.0%. This value represents the difference between the best that could be expected if the most appropriate model were known and the best that could be expected in the face of model uncertainty. The value of eliminating uncertainty about the survival process was substantially higher than that associated with the reproductive process, which is consistent with evidence that variation in survival is more important than variation in reproduction in relatively long-lived avian species. Comparing the expected objective value if the most appropriate model were known with that of the maxi–min robust strategy, we found the value of eliminating uncertainty to be an expected increase of 6.2% in objective value. This result underscores the conservatism of the maxi–min rule and suggests that risk-neutral managers would prefer the optimal strategy that maximizes expected value, which is also the strategy that is expected to minimize the maximum loss (i.e., a strategy based on equal model weights). The low value of information calculated for pink-footed geese suggests that a robust strategy (i.e., one in which no learning is anticipated) could be as nearly effective as an adaptive one (i.e., a strategy in which the relative credibility of models is assessed through time). Of course, an alternative explanation for the low value of information is that the set of population models we considered was too narrow to represent key uncertainties in population dynamics. Yet we know that questions about the presence of density dependence must be central to the development of a sustainable harvest strategy. And while there are potentially many environmental covariates that could help explain variation in survival or reproduction, our admission of models in which vital rates are drawn randomly from reasonable distributions represents a worst-case scenario for management. We suspect that much of the value of the various harvest strategies we calculated is derived from the fact that they are state dependent, such that appropriate harvest rates depend on population abundance and weather conditions, as well as our focus on an infinite time horizon for sustainability.

Ecological Modelling↗

USGS2014

USGS ecosystem research for the next decade: advancing discovery and application in parks and protected areas through collaboration

Ecosystems within parks and protected areas in the United States and throughout the world are being transformed at an unprecedented rate. Changes associated with natural hazards, greenhouse gas emissions, and increasing demands for water, food, land, energy and mineral resources are placing urgency on sound decision making that will help sustain our Nation’s economic and environmental well-being (Millennium Ecosystem Assessment, 2005). In recognition of the importance of science in making these decisions, the U.S. Geological Survey (USGS) in 2007 identified ecosystem science as one of six science directions included in a comprehensive decadal strategy (USGS 2007). The Ecosystems Mission Area was identified as essential for integrating activity within the USGS and as a key to enhanced integration with other Federal and private sector research and management organizations (Myers at al., 2007). This paper focuses on benefits to parks and protected areas from the USGS Ecosystems Mission Area plan that expanded the scope of the original 2007 science strategy, to identify the Bureau’s work in ecosystem science over the next decade (Williams et al., 2013). The plan describes a framework that encompasses both basic and applied science and allows the USGS to continue to contribute meaningfully to conservation and management issues related to the Nation’s parks and ecological resources. This framework relies on maintaining long-standing, collaborative relationships with partners in both conducting science and applying scientific results. Here we summarize the major components of the USGS Ecosystems Science Strategy, articulating the vision, goals and strategic approaches, then outlining some of the proposed actions that will ultimately prove useful to those managing parks and protected areas. We end with a discussion on the future of ecosystem science for the USGS and how it can be used to evaluate ecosystem change and the associated consequences to management of our Nation’s natural resources.

The George Wright Forum↗

USGS2013

U.S. Geological Survey ecosystems science strategy—Advancing discovery and application through collaboration

Executive Summary Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policymakers are faced with countless decisions each year at local, regional, and national levels on issues as diverse as renewable and nonrenewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decisionmaking is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and wellbeing of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decisionmakers as they develop policies to adapt to these changes. This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that U.S. Geological Survey (USGS) scientists and partners can play in building scientific understanding and providing timely information to decisionmakers. The strategy underscores the connection between scientific discoveries and the application of new knowledge, and it integrates ecosystem science and decisionmaking, producing new scientific outcomes to assist resource managers and providing public benefits. We envision the USGS as a leader in integrating scientific information into decisionmaking processes that affect the Nation’s natural resources and human well-being. The USGS is uniquely positioned to play a pivotal role in ecosystem science. With its wide range of expertise, the Bureau can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the Department of the Interior (DOI) and other partners in the natural sciences, in both conducting science and applying the results. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct. The heart of this strategy is a framework for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected components that reinforce our vision of the USGS providing science that is at the forefront of decisionmaking: Improve understanding of ecosystem structure, function, and processes. The focus for this goal is an understanding of how ecosystems work, including the dynamics of species, their populations, interactions, and genetics, and how they change across spatial and temporal scales. Advance understanding of how drivers influence ecosystem change. The challenges here are explaining the drivers of ecosystem change, their spatio-temporal patterns, their uncertainties and interactions, and their influence on ecosystem processes and dynamics. Improve understanding of the services that ecosystems provide to society. Here the emphasis is on the measurement of environmental capital and ecosystem services, and the identification of sources and patterns of change in space and time. Develop tools, technologies, and capacities to inform decisionmaking about ecosystems. This includes developing new technologies and approaches for conducting applications-oriented ecosystem science. A principal challenge will be how to quantify uncertainty and incorporate it in decision analysis. Apply science to enhance strategies for management, conservation, and restoration of ecosystems. These challenges include development of novel approaches to monitoring, assessment, and restoration of ecosystems; new methods to address species of concern and communities at risk; and innovations in decision analysis and support to address imminent ecosystem changes or those that are underway. Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy: Assess information needs for ecosystem science through enhanced partnerships. Work with the DOI and other agencies and institutions to identify, design, and implement priority decision-driven ecological research. Promote the use of interdisciplinary ecosystem science. Design and conduct interdisciplinary process-oriented research in ecosystem science. Enhance modeling and forecasting. Build models to forecast ecosystem change, assess future management scenarios, and reduce uncertainties through an adaptive learning process. Support decisionmaking. Use quantitative approaches to assess the vulnerabilities of ecosystems, habitats, and species, and evaluate strategies for adaptation, restoration, and sustainable management. Following the four strategic approaches are a set of proposed actions that represent a sampling of specific USGS activities that align with this strategy and that address the Nation’s most pressing environmental needs. The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio. Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role in guiding sound decisionmaking during the next decade by advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.

Circular↗

USGS2013

Confronting dynamics and uncertainty in optimal decision making for conservation

The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making--a careful consideration of values, actions, and outcomes.

Environmental Research Letters↗

USGS2013

Resilience thinking and a decision-analytic approach to conservation: strange bedfellows or essential partners?

There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is held to reduce spatial, temporal, or organizational heterogeneity that would otherwise limit efficiency, leading to homogenization of a system and making it less able to cope with unexpected changes or disturbances. For their part, decision analysts have been critical of resilience proponents for not providing much practical advice to decision makers. We believe a key source of tension between resilience thinking and application of decision science is the pursuit of efficiency in the latter (i.e., choosing the “best” management action or strategy option to maximize productivity of one or few resource components), vs. a desire in the former to keep options open (i.e., maintaining and enhancing diversity). It seems obvious, however, that with managed natural systems, there must be a principle by which to guide decision making, which at a minimumallows for a comparison of projected outcomes associated with decision alternatives. This is true even if the primary concern of decision making is the preservation of system resilience. We describe how a careful framing of conservation problems, especially in terms of management objectives and predictive models, can help reduce the purported tension between resiliencethinking and decision analysis. In particular, objective setting in conservation problems needs to be more attuned to the dynamics of ecological systems and to the possibility of deep uncertainties that underlie the risk of unintended, if not irreversible, outcomes. Resilience thinking also leads to the suggestion that model development should focus more on process rather than pattern, on multiple scales of influence, and on phenomena that can create alternative stability regimes. Although we acknowledge the inherent difficulties in modeling ecological processes, we stress that formulation of useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner.

Ecology and Society↗

USGS2012

The U.S. Geological Survey Ecosystem Science Strategy, 2012-2022 - Advancing discovery and application through collaboration

Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policy-makers are faced with countless decisions each year at local, state, tribal, territorial, and national levels on issues as diverse as renewable and non-renewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decision-making is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and well-being of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decision-makers as they develop policies to adapt to these changes. This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that USGS scientists and partners can play in building scientific understanding and providing timely information to decision-makers. The strategy underscores the connection between scientific discoveries and the application of new knowledge. The strategy integrates ecosystem science and decision-making, producing new scientific outcomes to assist resource managers and providing public benefits. The USGS is uniquely positioned to play an important role in ecosystem science. With its wide range of expertise, the agency can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the DOI and other partners in the natural sciences, in both conducting science and its application. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct. The heart of this strategy is a framework and vision for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected and reinforcing components: • Improve understanding of ecosystem structure, function, and processes. The focus for this goal is an understanding of how ecosystems work, including the dynamics of species, their populations, interactions, and genetics, and how they change across spatial and temporal scales. • Advance understanding of how drivers influence ecosystem change. The challenges here are explaining the drivers of ecosystem change, their spatio-temporal patterns, their uncertainties and interactions, and their influence on ecosystem processes and dynamics. • Improve understanding of the services that ecosystems provide to society. Here the emphasis is on the measurement of environmental capital and ecosystem services, and the identification of sources and patterns of change in space and time. • Develop tools, technologies, and capacities to inform decision-making about ecosystems. This includes developing new technologies and approaches for conducting applications-oriented ecosystem science. A principal challenge will be how to quantify uncertainty and incorporate it in decision analysis. • Apply science to enhance strategies for management, conservation, and restoration of ecosystems. These challenges include development of novel approaches to monitoring, assessment, and restoration of ecosystems; new methods to address species of concern and communities at risk; and innovations in decision analysis and support to address imminent ecosystem changes or those that are underway. Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy: • Assess information needs for ecosystem science through enhanced partnerships. Work with the DOI and other agencies and institutions to identify, design, and implement priority decision-driven ecological research. • Promote the use of interdisciplinary ecosystem science. Design and conduct interdisciplinary process-oriented research in ecosystem science. • Enhance modeling and forecasting. Build models to forecast ecosystem change, assess future management scenarios, and reduce uncertainties through an adaptive learning process. • Support decision-making. Use quantitative approaches to assess the vulnerabilities of ecosystems, habitats, and species, and evaluate strategies for adaptation, restoration, and sustainable management. Following the strategic approaches are a set of proposed actions that represent a sampling of specific activities that align with this strategy and that address the Nation’s most pressing environmental needs. The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct both discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio. Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role over the next decade in advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.

Open-File Report↗

USGS2011

Adaptive resource management and the value of information

The value of information is a general and broadly applicable concept that has been used for several decades to aid in making decisions in the face of uncertainty. Yet there are relatively few examples of its use in ecology and natural resources management, and almost none that are framed in terms of the future impacts of management decisions. In this paper we discuss the value of information in a context of adaptive management, in which actions are taken sequentially over a timeframe and both future resource conditions and residual uncertainties about resource responses are taken into account. Our objective is to derive the value of reducing or eliminating uncertainty in adaptive decision making. We describe several measures of the value of information, with each based on management objectives that are appropriate for adaptive management. We highlight some mathematical properties of these measures, discuss their geometries, and illustrate them with an example in natural resources management. Accounting for the value of information can help to inform decisions about whether and how much to monitor resource conditions through time.

Ecological Modelling↗

USGS2009

Adaptive management of watersheds and related resources

The concept of learning about natural resources through the practice of management has been around for several decades and by now is associated with the term adaptive management. The objectives of this paper are to offer a framework for adaptive management that includes an operational definition, a description of conditions in which it can be usefully applied, and a systematic approach to its application. Adaptive decisionmaking is described as iterative, learning-based management in two phases, each with its own mechanisms for feedback and adaptation. The linkages between traditional experimental science and adaptive management are discussed.

Conference Paper↗

USGS2009

Adaptive management of watersheds and related resources

Conference Paper↗

USGS1995

Managing North American waterfowl in the face of uncertainty

Informed management of waterfowl (or any animal population) requires management goals and objectives, the ability to implement management actions, periodic information about population and goal-related varlables, and knowledge of effects of management actions on population and goal-related variables. In North America, international treaties mandate a primary objective of protecting migratory bird populations, with a secondary objective of providing hunting opportunity in a manner compatible with such protection. Through the years, annual establishment of hunting regulations and acquisition and man-agement of habitat have been the primary management actions taken by federal agencies. Various information-gathering programs were established and, by the 1960s, had developed into arguably the best monitoring system in the world for continentally distributed animal populations. Retrospective analyses using estimates from this monitoring system have been used to investigate effects of management actions on waterfowl population and harvest dynamics, but key relationships arc still characterized by uncertainty. We recommend actively adaptive management as an approach that can meet short-term harvest objectives, while reducing uncertainty and ensuring sustainable populations over the long-term.

Annual Review of Ecology and Systematics↗

USGS1990

Stability and bias of classification rates in biological applications of discriminant analysis

We assessed the sampling stability of classification rates in discriminant analysis by using a factorial design with factors for multivariate dimensionality, dispersion structure, configuration of group means, and sample size. A total of 32,400 discriminant analyses were conducted, based on data from simulated populations with appropriate underlying statistical distributions. Simulation results indicated strong bias in correct classification rates when group sample sizes were small and when overlap among groups was high. We also found that stability of the correct classification rates was influenced by these factors, indicating that the number of samples required for a given level of precision increases with the amount of overlap among groups. In a review of 60 published studies, we found that 57% of the articles presented results on classification rates, though few of them mentioned potential biases in their results. Wildlife researchers should choose the total number of samples per group to be at least 2 times the number of variables to be measured when overlap among groups is low. Substantially more samples are required as the overlap among groups increases

Journal of Wildlife Management↗

USGS1989

Generalized procedures for testing hypotheses about survival or recovery rates

Comparisons of survival or recovery rates from different time periods or geographic regions may be difficult to accomplish using the Z-tests suggested by Brownie et al. (1985). We propose a general Chi-square statistic that addresses an unambiguous null hypothesis of homogeneity among several survival or recovery rates. With this statistic, specific hypotheses of differences in rates can be simultaneously tested using contrasts. If necessary, a posteriori multiple comparisons can also be conducted that incorporate an adjustment for Type I error.

Journal of Wildlife Management↗

USGS1988

Assessment of sampling stability in ecological applications of discriminant analysis

A simulation study was undertaken to assess the sampling stability of the variable loadings in linear discriminant function analysis. A factorial design was used for the factors of multivariate dimensionality, dispersion structure, configuration of group means, and sample size. A total of 32 400 discriminant analyses were conducted, based on data from simulated populations with appropriate underlying statistical distributions. Results from the simulations suggest that minimum sample sizes must exceed multivariate dimensionality by at least a factor of three to achieve reasonable levels of stability in discriminant function loadings. However, the requisite sample size would vary with respect to each of the design factors and, especially, with the overall amount of system variation. A review of 60 published studies and 142 individual analyses indicated that sample sizes in ecological studies often have met that requirement. However, individual group sample sizes frequently were very unequal, and checks of assumptions usually were not reported. We recommend that ecologists obtain group sample sizes that are at least three times as large as the number of variables measured.

Ecology↗

USGS1986

Influence of nutrients on feed intake and condition of captive canvasbacks in winter

Dramatic changes in the food habits and distribution of canvasbacks ( Aythya valisineria ) in Chesapeake Bay during the 1970's generated a need to evaluate the nutritional value of food items and the requirements of this species on its wintering grounds. Groups of captive canvasbacks were maintained ad libitum on 5 diets during the winters of 1978-79 and 1979-80 to evaluate the effects of varying protein and energy levels on feed intake and condition. Feed intake during the 1979-80 winter was 42% greater for those ducks fed the low energy (1,543 kcal/kg) diet than for those fed the high energy (3,638 kcal/kg) diet. Canvasbacks fed the high energy diet, however, consumed 317 kcal/bird day, whereas those fed the low energy diet consumed only 191 kcal/bird day. Body weight of males and females did not differ among groups fed different diets, but there were seasonal differences (P < 0.05) for both sexes aggregated across diets. Data from this study indicate that canvasbacks may be unable to adjust intake rates to compensate for low energy foods and subsequently may store less fat or modify behavior. However, decreased weight, feed intake, and activity of ducks fed ad libitum rations occurred in mid-winter irrespective of diet quality and appeared to be an endogenous component of their annual cycle that persists in captivity. These changes apparently have a selective advantage of increasing the probability of survival in ducks by decreasing energy expenditure during periods of winter stress.

Maryland, Virginia↗

USGS1986

Blood chemistry and hematocrit of captive and wild canvasbacks

Blood chemistry and packed cell volume (PCV) did not vary among groups of captive canvasbacks ( Aythya valisineria ) maintained ad libitum on 5 diets varying in metabolizable energy (ME) and protein. Ducks fed low quality diets increased their consumption so that all ducks were obtaining similar amounts of energy and protein. Some variables, including cholesterol, were found to differ between the sexes and ages of captive ducks. Seasonal differences were detected in the blood chemistry of captive canvasbacks. Four of the 5 enzyme values increased from October to January and then declined to April. PCV increased from 45 in October to 51 in April. Overall, blood chemistry values and PCV's were highly variable and did not appear to be good discriminators for age, sex, and diet of captive canvasbacks. Differences detected between captive and wild canvasbacks were attributed to increased stress of wild ducks during handling.

Journal of Wildlife Management↗