Geology topics

Richard J. Barker

Publications and source records attributed to Richard J. Barker.

11 recordsLinked to original sources

USGS2024

Leveraging natural capital accounting to support businesses with nature-related risk assessments and disclosures

Nature loss threatens businesses, the global economy and financial stability. Understanding and addressing these risks for business will require credible measurement approaches and data. This paper explores how natural capital accounting (NCA) can support business data and information needs related to nature, including disclosures aligned with the Taskforce on Nature-related Financial Disclosures recommendations. As businesses seek to measure, manage and disclose their nature-related risks and opportunities, they will need well-organized, consistent and high-quality information regarding their dependencies and impacts on nature, which few businesses currently collect or track in-house. NCA may be useful for these purposes but has not been widely used or applied by businesses. National NCA guided by the U.N. System of Environmental-Economic Accounting may provide: (i) a useful framework for businesses in conceptualizing, organizing and managing nature-related data and statistics; and (ii) data and information that can directly support business disclosures, corporate NCA and other business applications. This paper explores these opportunities as well as synergies between national and corporate natural capital accounts. In addition, the paper discusses key barriers to advancing the wider use and benefits of NCA for business, including: awareness of NCA, data access, business capabilities related to NCA, spatial and temporal scales of data, audit and assurance considerations, potential risks, and costs and incentives.

Philosophical Transactions of the Royal Society B:↗

USGS2018

On the robustness of N‐mixture models

N ‐mixture models provide an appealing alternative to mark–recapture models, in that they allow for estimation of detection probability and population size from count data, without requiring that individual animals be identified. There is, however, a cost to using the N ‐mixture models: inference is very sensitive to the model's assumptions. We consider the effects of three violations of assumptions that might reasonably be expected in practice: double counting, unmodeled variation in population size over time, and unmodeled variation in detection probability over time. These three examples show that small violations of assumptions can lead to large biases in estimation. The violations of assumptions we consider are not only small qualitatively, but are also small in the sense that they are unlikely to be detected using goodness‐of‐fit tests. In cases where reliable estimates of population size are needed, we encourage investigators to allocate resources to acquiring additional data, such as recaptures of marked individuals, for estimation of detection probabilities.

Ecology↗

USGS2017

On the reliability of N‐mixture models for count data

N‐mixture models describe count data replicated in time and across sites in terms of abundance N and detectability p . They are popular because they allow inference about N while controlling for factors that influence p without the need for marking animals. Using a capture–recapture perspective, we show that the loss of information that results from not marking animals is critical, making reliable statistical modeling of N and p problematic using just count data. One cannot reliably fit a model in which the detection probabilities are distinct among repeat visits as this model is overspecified. This makes uncontrolled variation in p problematic. By counter example, we show that even if p is constant after adjusting for covariate effects (the “constant p ” assumption) scientifically plausible alternative models in which N (or its expectation) is non‐identifiable or does not even exist as a parameter, lead to data that are practically indistinguishable from data generated under an N‐mixture model. This is particularly the case for sparse data as is commonly seen in applications. We conclude that under the constant p assumption reliable inference is only possible for relative abundance in the absence of questionable and/or untestable assumptions or with better quality data than seen in typical applications. Relative abundance models for counts can be readily fitted using Poisson regression in standard software such as R and are sufficiently flexible to allow controlling for p through the use covariates while simultaneously modeling variation in relative abundance. If users require estimates of absolute abundance, they should collect auxiliary data that help with estimation of p .

Biometrics↗

USGS2015

Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective

Statistical inference begins with viewing data as realizations of stochastic processes. Mathematical models provide partial descriptions of these processes; inference is the process of using the data to obtain a more complete description of the stochastic processes. Wildlife and ecological scientists have become increasingly concerned with the conditional nature of model-based inference: what if the model is wrong? Over the last 2 decades, Akaike's Information Criterion (AIC) has been widely and increasingly used in wildlife statistics for 2 related purposes, first for model choice and second to quantify model uncertainty. We argue that for the second of these purposes, the Bayesian paradigm provides the natural framework for describing uncertainty associated with model choice and provides the most easily communicated basis for model weighting. Moreover, Bayesian arguments provide the sole justification for interpreting model weights (including AIC weights) as coherent (mathematically self consistent) model probabilities. This interpretation requires treating the model as an exact description of the data-generating mechanism. We discuss the implications of this assumption, and conclude that more emphasis is needed on model checking to provide confidence in the quality of inference.

Journal of Wildlife Management↗

USGS2010

Bayesian Inference: with ecological applications

This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.

Book↗

USGS2009

Modeling individual animal histories with multistate capture–recapture models

Many fields of science begin with a phase of exploration and description, followed by investigations of the processes that account for observed patterns. The science of ecology is no exception, and recent decades have seen a focus on understanding key processes underlying the dynamics of ecological systems. In population ecology, emphasis has shifted from the state variable of population size to the demographic processes responsible for changes in this state variable: birth, death, immigration, and emigration. In evolutionary ecology, some of these same demographic processes, rates of birth and death, are also the determinants of fitness. In animal population ecology, the estimation of state variables and their associated vital rates is especially problematic because of the difficulties in sampling such populations and detecting individual animals. Indeed, early capture–recapture models were developed for the purpose of estimating population size, given the reality that all animals are not caught or detected at any sampling occasion. More recently, capture–recapture models for open populations were developed to draw inferences about survival in the face of these same sampling problems. The focus of this paper is on multi‐state mark–recapture models (MSMR), which first appeared in the 1970s but have undergone substantial development in the last 15 years. These models were developed to deal explicitly with biological variation, in that animals in different “states” (classes defined by location, physiology, behavior, reproductive status, etc.) may have different probabilities of survival and detection. Animal transitions between states are also stochastic and themselves of interest. These general models have proven to be extremely useful and provide a way of thinking about a remarkably wide range of important ecological processes. These methods are now at a stage of refinement and sophistication where they can readily be used by biologists to tackle a wide range of important issues in ecology. In this paper, we draw together information on the state of the art in multistate mark–recapture methods, explaining the models and illustrating their use. We provide a modeling philosophy and a series of general principles on how to conduct analyses. We cover key issues and features, and we anticipate the ways in which we expect the models to develop in the years ahead.

Advances in Ecological Research↗

USGS2005

Modeling association among demographic parameters in analysis of open population capture-recapture data

We present a hierarchical extension of the Cormack–Jolly–Seber (CJS) model for open population capture–recapture data. In addition to recaptures of marked animals, we model first captures of animals and losses on capture. The parameter set includes capture probabilities, survival rates, and birth rates. The survival rates and birth rates are treated as a random sample from a bivariate distribution, thus the model explicitly incorporates correlation in these demographic rates. A key feature of the model is that the likelihood function, which includes a CJS model factor, is expressed entirely in terms of identifiable parameters; losses on capture can be factored out of the model. Since the computational complexity of classical likelihood methods is prohibitive, we use Markov chain Monte Carlo in a Bayesian analysis. We describe an efficient candidate-generation scheme for Metropolis–Hastings sampling of CJS models and extensions. The procedure is illustrated using mark-recapture data for the moth Gonodontis bidentata .

Biometrics↗

USGS1994

Within-site variability in surveys of wildlife populations

Most large-scale surveys of animal populations are based on counts of individuals observed during a sampling period, which are used as indexes to the population. The variability in these indexes not only reflects variability in population sizes among sites but also variability due to the inexactness of the counts. Repeated counts at survey sites can be used to document this additional source of variability and, in some applications, to mitigate its effects. We present models for evaluating the proportion of total variability in counts that is attributable to this within-site variability and apply them in the analysis of data from repeated counts on routes from the North American Breeding Bird Survey. We analyzed data on 98 species, obtaining estimates of these percentages, which ranged from 3.5 to 100% with a mean of 36.25%. For at least 14 of the species, more than half of the variation in counts was attributable to within-site sources. Counts for species with lower average counts had a higher percentage of within-site variability. We discuss the relative cost efficiency of replicating sites or initiating new sites for several objectives, concluding that it is frequently better to initiate new sites than to attempt to replicate existing sites.

Ecology↗

USGS1993

Optimal allocation of point-count sampling effort

Both unlimited and fixed-radius point counts only provide indices to population size. Because longer count durations lead to counting a higher proportion of individuals at the point, proper design of these surveys must incorporate both count duration and sampling characteristics of population size. Using information about the relationship between proportion of individuals detected at a point and count duration, we present a method of optimizing a point-count survey given a fixed total time for surveying and travelling between count points. The optimization can be based on several quantities that measure precision, accuracy, or power of tests based on counts, including (1) mean-square error of estimated population change; (2) mean-square error of average count; (3) maximum expected total count; or (4) power of a test for differences in average counts. Optimal solutions depend on a function that relates count duration at a point to the proportion of animals detected. We model this function using exponential and Weibull distributions, and use numerical techniques to conduct the optimization. We provide an example of the procedure in which the function is estimated from data of cumulative number of individual birds seen for different count durations for three species of Hawaiian forest birds. In the example, optimal count duration at a point can differ greatly depending on the quantities that are optimized. Optimization of the mean-square error or of tests based on average counts generally requires longer count durations than does estimation of population change. A clear formulation of the goals of the study is a critical step in the optimization process.

The Auk↗

USGS1992

Sources of nonresponse to the Federal Waterfowl Hunter Questionnaire Survey

Response rates to the Federal Waterfowl Hunter Questionnaire Survey (WHQS) have declined since the 1950's, suggesting that harvest estimates may be biased. Consequently, we investigated reasons for WHQS nonresponse using surveys of waterfowl hunters in Arkansas, California, Louisiana, Minnesota, New Jersey, and Texas. Sampling frames were constructed using lists of buyers of state hunting licenses or state duck stamps. We mailed questionnaires to 16,452 randomly selected hunters, with 2 follow-up mailings at 3-week intervals. Questionnaires were completed by 8,812 respondents, and a further 587 interviews were conducted by telephone. Post offices accounted for between 53.7% (Minn.) and 92.8% (N.J.) of federal waterfowl duck stamp sales, and stores accounted for most other sales. Of hunters who bought a federal waterfowl stamp from sample post offices, between 16.7% (Minn.) and 40.0% (Ark.) reported receiving a WHQS contact card. Of those receiving contact cards, between 30.0% (N.J.) and 64.3% (La. and Tex.) reported returning them. Because survey coverage of the target population is poor, we recommend that a new sampling frame be developed for the WHQS.

Arkansas, California, Louisiana, Minnesota, New Je↗

USGS1991

Effect of hunting on annual survival of grey ducks in New Zealand

We used band recovery data from grey ducks ( Anas superciliosa ) banded in New Zealand between 1957 and 1974 to test 2 null hypotheses: (1) hunting mortality is completely additive to natural sources of mortality, and (2) hunting mortality is completely compensated by changes in natural mortality. We modeled annual survival as a function of survival in the absence of hunting and the probability of death from hunting. The complete compensation hypothesis was rejected, but we were unable to reject the completely additive hypothesis. There was no evidence of sex- or age-specificity for the relationship between kill rate and annual survival rate. We used simulated data to evaluate model performance. Parameter estimates were unbiased despite the inclusion of estimates that lay outside the bounds of the parameter space, although model-based variance estimates were consistently less than empirical variances. Our results imply that harvest-restrictions may be useful in effecting change in annual survival rates of grey ducks.

Journal of Wildlife Management↗