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Gary C. White

Publications and source records attributed to Gary C. White.

At least 37 records · Page 2Linked to original sources
USGS2009

A generalized mixed effects model of abundance for mark-resight data when sampling is without replacement

In recent years, the mark-resight method for estimating abundance when the number of marked individuals is known has become increasingly popular. By using field-readable bands that may be resighted from a distance, these techniques can be applied to many species, and are particularly useful for relatively small, closed populations. However, due to the different assumptions and general rigidity of the available estimators, researchers must often commit to a particular model without rigorous quantitative justification for model selection based on the data. Here we introduce a nonlinear logit-normal mixed effects model addressing this need for a more generalized framework. Similar to models available for mark-recapture studies, the estimator allows a wide variety of sampling conditions to be parameterized efficiently under a robust sampling design. Resighting rates may be modeled simply or with more complexity by including fixed temporal and random individual heterogeneity effects. Using information theory, the model(s) best supported by the data may be selected from the candidate models proposed. Under this generalized framework, we hope the uncertainty associated with mark-resight model selection will be reduced substantially. We compare our model to other mark-resight abundance estimators when applied to mainland New Zealand robin (Petroica australis) data recently collected in Eglinton Valley, Fiordland National Park and summarize its performance in simulation experiments.

Book chapter↗
USGS2009

A cautionary note on substituting spatial subunits for repeated temporal sampling in studies of site occupancy

1. Assessing the probability that a given site is occupied by a species of interest is important to resource managers, as well as metapopulation or landscape ecologists. Managers require accurate estimates of the state of the system, in order to make informed decisions. Models that yield estimates of occupancy, while accounting for imperfect detection, have proven useful by removing a potentially important source of bias. To account for detection probability, multiple independent searches per site for the species are required, under the assumption that the species is available for detection during each search of an occupied site. 2. We demonstrate that when multiple samples per site are defined by searching different locations within a site, absence of the species from a subset of these spatial subunits induces estimation bias when locations are exhaustively assessed or sampled without replacement. 3. We further demonstrate that this bias can be removed by choosing sampling locations with replacement, or if the species is highly mobile over a short period of time. 4. Resampling an existing data set does not mitigate bias due to exhaustive assessment of locations or sampling without replacement. 5. Synthesis and applications. Selecting sampling locations for presence/absence surveys with replacement is practical in most cases. Such an adjustment to field methods will prevent one source of bias, and therefore produce more robust statistical inferences about species occupancy. This will in turn permit managers to make resource decisions based on better knowledge of the state of the system.

Journal of Applied Ecology↗
USGS2006

Temporal, spatial, and environmental influences on the demographics of grizzly bears in the Greater Yellowstone Ecosystem

During the past 2 decades, the grizzly bear ( Ursus arctos ) population in the Greater Yellowstone Ecosystem (GYE) has increased in numbers and expanded in range. Understanding temporal, environmental, and spatial variables responsible for this change is useful in evaluating what likely influenced grizzly bear demographics in the GYE and where future management efforts might benefit conservation and management. We used recent data from radio-marked bears to estimate reproduction (1983–2002) and survival (1983–2001); these we combined into models to evaluate demographic vigor (lambda [λ]). We explored the influence of an array of individual, temporal, and spatial covariates on demographic vigor.

Idaho, Montana, Wyoming↗
USGS2006

Comment on 'Are survival rates for northern spotted owls biased?'

Loehle et al. recently estimated survival rates from radio-telemetered northern spotted owls (Strix occidentalis caurina (Merriam, 1898)) and suggested that survival rates estimated for this species from capture-recapture studies were negatively biased, which subsequently resulted in the negatively biased estimates of rates of population change (lambda) reported by Anthony et al. (Wildl. Monogr. No. 163, pp. 1-47 (2006)). We argue that their survival estimates were inappropriate for comparison with capture-recapture estimates because (i) the manner in which they censored radio-telemetered individuals had the potential to positively bias their survival estimates, (ii) their estimates of survival were not valid for evaluating bias, and (iii) the size and distribution of their radiotelemetry study areas were sufficiently different from capture-recapture study areas to preclude comparisons. In addition, their inferences of negative bias in rates of population change estimated by Anthony et al. were incorrect and reflected a misunderstanding about those estimators.

Canadian Journal of Zoology↗
USGS2006

Multistate survival models and their extensions in Program MARK

Program MARK provides .100 models for the estimation of population parameters from mark?encounter data. The multistate model of Brownie et al. (1993) and Hestbeck et al. (1991) allows animals to move between states with a probability of transition. The simplest multistate model is an extension of the Cormack?Jolly?Seber (CJS) live recapture model. arameters estimated are state-specific survival rates and encounter probabilities and transition probabilities between states. The multistate model provides a valuable framework to evaluate important ecological questions. For example, estimation of state-specific survival and transition probabilities between the biological states of breeders and nonbreeders allows estimation of the cost of reproduction. Transitions between physical states, such as spatial areas, provide estimates needed for meta-population models. The basic multistate model uses only live recaptures, but 3 extensions are included in MARK. A multistate model with live and dead encounters is available, although the dead encounters are not state specific. Robust-design multistate models are also included in MARK, with both open and closed robust designs. These models assume that animals move between states only between primary sessions of the robust design. For the closed robust design, we can specify 12 different data types for the modeling of encounter probabilities during the primary session, including 6 versions of the closed model likelihood incorporating population size (N) directly in the likelihood, and 6 versions of the Huggins model in which N is estimated as a derived parameter outside the likelihood. One assumption that is generally necessary to estimate state-specific survival rates in the multistate model is that transitions take place immediately before encounter occasions. Otherwise, survival rates over the interval between encounter occasions are a mix of survival rates over multiple states. Advantages of using MARK to estimate the parameters of the various multistate models include flexibility of model specification to include group, time, and individual covariates, estimation of variance components, model averaging of parameter estimates, and Bayesian parameter estimation using Markov chain Monte Carlo procedures on the logit scale.

Journal of Wildlife Management↗
USGS2005

Mountain plover population responses to black-tailed prairie dogs in Montana

We studied a local population of mountain plovers (Charadrius montanus) in southern Phillips County, Montana, USA, from 1995 to 2000 to estimate annual rates of recruitment rate (f) and population change (??). We used Pradel models, and we modeled ?? as a constant across years, as a linear time trend, as year-specific, and with an additive effect of area occupied by prairie dogs (Cynomys ludovicianus). We modeled recruitment rate (f) as a function of area occupied by prairie dogs with the remaining model structure identical to the best model used to estimate ??. Our results indicated a strong negative effect of area occupied by prairie dogs on both ?? (slope coefficient on a log scale was -0.11; 95% CI was -0.17, -0.05) and f (slope coefficient on a logit scale was -0.23; 95% CI was -0.36, -0.10). We also found good evidence for a negative time trend on ??; this model had substantial weight (wi = 0.31), and the slope coefficient on the linear trend on a log scale was -0.10 (95% CI was -0.15, -0.05). Yearly estimates of ?? were >1 in all years except 1999, indicating that the population initially increased and then stabilized in the last year of the study. We found weak evidence for year-specific estimates of ??; the best model with year-specific estimates had a low weight (wi = 0.02), although the pattern of yearly estimates of ?? closely matched those estimated with a linear time trend. In southern Phillips County, the population trend of mountain plovers closely matched the trend in the area occupied by black-tailed prairie dogs. Black-tailed prairie dogs declined sharply in the mid-1990s in response to an outbreak of sylvatic plague, but their numbers have steadily increased since 1996 in concert with increases in plovers. The results of this study (1) increase our understanding of the dynamics of this population and how they relate to the area occupied by prairie dogs, and (2) will be useful for planning plover conservation in a prairie dog ecosystem.

Journal of Wildlife Management↗
USGS2004

Population dynamics of the California Spotted Owl (Strix occidentalis occidentalis): a meta-analysis

We conducted a meta-analysis to provide a current assessment of the population characteristics of California Spotted Owls (Strix occidentalis occidentalis) resident on four study areas in the Sierra Nevada and one study area in southern California. Our meta-analysis followed rigorous a priori analysis protocols, which we derived through extensive discussion during a week-long analysis workshop. Because there is great interest in the owl?s population status, we used state-of-the-art analytical methods to obtain results as precise as possible. Our meta-analysis included data from five California study areas located on the Lassen National Forest (1990-2000), Eldorado National Forest (1986-2000), Sierra National Forest (1990-2000), Sequoia and Kings Canyon national parks (1990-2000), and San Bernardino National Forest (1987-1998). Four of the five study areas spanned the length of the Sierra Nevada, whereas the fifth study area encompassed the San Bernardino Mountains in southern California. Study areas ranged in size from 343 km2 (Sequoia and Kings Canyon) to 2,200 km (Lassen). All studies were designed to use capture-recapture methods and analysis. We used survival in a meta-analysis because field methods were very similar among studies. However, we did not use reproduction in a meta-analysis because it was not clear if variation among individual study-area protocols used to assess reproductive output of owls would confound results. Thus, we analyzed fecundity only by individual study area. We examined population trend using the reparameterized Jolly-Seber capture-recapture estimator (8t) We did not estimate juvenile survival rates because of estimation problems and potential bias because of juvenile emigration from study areas. We used mark-recapture estimators under an information theoretic framework to assess apparent survival rates of adult owls. The pooled estimate for adult apparent survival for the five study areas was 0.833, which was lower than pooled adult survival rates (0.850) from 15 Northern Spotted Owl (S. o. caurina) studies. Estimates of survival from the best model on the Lassen (N = 0.829, 95% confidence intervals [CI = 0.798 to 0.857), Eldorado (N = 0.815, 95% CI = 0.772 to 0.851), Sierra (N = 0.818, 95% CI = 0.781 to 0.850), and San Bernardino (N = 0.813, 95% CI = 0.782 to 0.841) were not different. However, the Sequoia and Kings Canyon population had a higher survival rate (N = 0.877, 95% CI = 0.842 to 0.905) than the other study areas. Management history and forest structure (e.g. presence of giant sequoia [Sequoiadendron giganteum]) on the Sequoia and Kings Canyon study area differed from all other study areas. There appears to be little or no evidence for temporal variation in adult apparent survival on any of the study areas. Although we did not directly compare fecundity estimates were highly variable among years within all study areas (CV of temporal process variation = 0.672-0.817). Estimates for fecundity among the study populations were Lassen (b = 0.336, SE = 0.083), Eldorado (b = 0.409, SE = 0.087), Sierra (b = 0.284, SE = 0.073), Sequoia and Kings Canyon (b = 0.289, SE = 0.074), and San Bernardino (b = 0.362, SE = 0.038). During most years, the Sierra Nevada populations showed either moderate or poor fecundity. However, 1992 appeared to be an exceptional reproductive year for owls in the Sierra Nevada. In contrast, the San Bernardino population had less variable reproduction (CV of temporal process variation = 0.217), but experienced neither the exceptional reproduction of 1992 nor the extremely poor years that characterized all of the Sierra Nevada study areas. Because fecundity may be influenced by weather patterns, it was possible that the different weather patterns between southern California and the Sierra Nevada accounted for that difference. Except for Eldorado, all estimates for 8t, were <1.0, but none was different from 8 = 1.0 given the 95% confidence i

Ornithological Monographs↗
USGS2004

Computing and software

The reality is that the statistical methods used for analysis of data depend upon the availability of software. Analysis of marked animal data is no different than the rest of the statistical field. The methods used for analysis are those that are available in reliable software packages. Thus, the critical importance of having reliable, up–to–date software available to biologists is obvious. Statisticians have continued to develop more robust models, ever expanding the suite of potential analysis methods available. But without software to implement these newer methods, they will languish in the abstract, and not be applied to the problems deserving them. In the Computers and Software Session, two new software packages are described, a comparison of implementation of methods for the estimation of nest survival is provided, and a more speculative paper about how the next generation of software might be structured is presented. Rotella et al. (2004) compare nest survival estimation with different software packages: SAS logistic regression, SAS non–linear mixed models, and Program MARK. Nests are assumed to be visited at various, possibly infrequent, intervals. All of the approaches described compute nest survival with the same likelihood, and require that the age of the nest is known to account for nests that eventually hatch. However, each approach offers advantages and disadvantages, explored by Rotella et al. (2004). Efford et al. (2004) present a new software package called DENSITY. The package computes population abundance and density from trapping arrays and other detection methods with a new and unique approach. DENSITY represents the first major addition to the analysis of trapping arrays in 20 years. Barker & White (2004) discuss how existing software such as Program MARK require that each new model’s likelihood must be programmed specifically for that model. They wishfully think that future software might allow the user to combine pieces of likelihood functions together to generate estimates. The idea is interesting, and maybe some bright young statistician can work out the specifics to implement the procedure. Choquet et al. (2004) describe MSURGE, a software package that implements the multistate capture–recapture models. The unique feature of MSURGE is that the design matrix is constructed with an interpreted language called GEMACO. Because MSURGE is limited to just multistate models, the special requirements of these likelihoods can be provided. The software and methods presented in these papers gives biologists and wildlife managers an expanding range of possibilities for data analysis. Although ease–of–use is generally getting better, it does not replace the need for understanding of the requirements and structure of the models being computed. The internet provides access to many free software packages as well as user–discussion groups to share knowledge and ideas. (A starting point for wildlife–related applications is (http://www.phidot.org).

Animal Biodiversity and Conservation↗
USGS2004

Evaluation of ultrastructure and random effects band recovery models for estimating relationships between survival and harvest rates in exploited populations

Increased population survival rate after an episode of seasonal exploitation is considered a type of compensatory population response. Lack of an increase is interpreted as evidence that exploitation results in added annual mortality in the population. Despite its importance to management of exploited species, there are limited statistical techniques for comparing relative support for these two alternative models. For exploited bird species, the most common technique is to use a fixed effect, deterministic ultrastructure model incorporated into band recovery models to estimate the relationship between harvest and survival rate. We present a new likelihood-based technique within a framework that assumes that survival and harvest are random effects that covary through time. We conducted a Monte Carlo simulation study under this framework to evaluate the performance of these two techniques. The ultrastructure models performed poorly in all simulated scenarios, due mainly to pathological distributional properties. The random effects estimators and their associated estimators of precision had relatively small negative bias under most scenarios, and profile likelihood intervals achieved nominal coverage. We suggest that the random effects estimation method approach has many advantages compared to the ultrastructure models, and that evaluation of robustness and generalization to more complex population structures are topics for additional research. ?? 2004 Museu de Cie??ncies Naturals.

Animal Biodiversity and Conservation↗
USGS2004

Encounter history modeling of joint mark-recapture, tag-resighting and tag-recovery data under temporary emigration

We describe a joint analysis of mark-recapture, tag-resight and tag-recovery data that directly models the encounter history of an animal. The probability of the encounter history for each animal is partitioned into survival, recapture, resighting, and recovery components, and a component for the probability that the animal is never encountered again. Temporary migration enters into the likelihood through the recapture component, and movement of marked animals in and out of the area where they are subject to capture is modeled using a Markov chain. Random temporary emigration and permanent emigration are special cases. An important feature of directly modeling the encounter histories is that covariates that are specific to individuals can be included in the analysis. The model is applied to a brown trout tagging data set and provides strong evidence of Markovian temporary emigration. The new model is needed to provide correct estimates of trout survival probabilities which are shown to depend on the length of the fish at first capture.

Statistica Sinica↗
USGS2003

Small-mammal density estimation: A field comparison of grid-based vs. web-based density estimators

Statistical models for estimating absolute densities of field populations of animals have been widely used over the last century in both scientific studies and wildlife management programs. To date, two general classes of density estimation models have been developed: models that use data sets from capture–recapture or removal sampling techniques (often derived from trapping grids) from which separate estimates of population size ( NÌ‚ ) and effective sampling area ( AÌ‚ ) are used to calculate density ( DÌ‚ = NÌ‚ / AÌ‚ ); and models applicable to sampling regimes using distance-sampling theory (typically transect lines or trapping webs) to estimate detection functions and densities directly from the distance data. However, few studies have evaluated these respective models for accuracy, precision, and bias on known field populations, and no studies have been conducted that compare the two approaches under controlled field conditions. In this study, we evaluated both classes of density estimators on known densities of enclosed rodent populations. Test data sets ( n = 11) were developed using nine rodent species from capture–recapture live-trapping on both trapping grids and trapping webs in four replicate 4.2-ha enclosures on the Sevilleta National Wildlife Refuge in central New Mexico, USA. Additional “saturation” trapping efforts resulted in an enumeration of the rodent populations in each enclosure, allowing the computation of true densities. Density estimates ( DÌ‚ ) were calculated using program CAPTURE for the grid data sets and program DISTANCE for the web data sets, and these results were compared to the known true densities ( D ) to evaluate each model's relative mean square error, accuracy, precision, and bias. In addition, we evaluated a variety of approaches to each data set's analysis by having a group of independent expert analysts calculate their best density estimates without a priori knowledge of the true densities; this “blind” test allowed us to evaluate the influence of expertise and experience in calculating density estimates in comparison to simply using default values in programs CAPTURE and DISTANCE. While the rodent sample sizes were considerably smaller than the recommended minimum for good model results, we found that several models performed well empirically, including the web-based uniform and half-normal models in program DISTANCE, and the grid-based models M b and M bh in program CAPTURE (with AÌ‚ adjusted by species-specific full mean maximum distance moved (MMDM) values). These models produced accurate DÌ‚ values (with 95% confidence intervals that included the true D values) and exhibited acceptable bias but poor precision. However, in linear regression analyses comparing each model's DÌ‚ values to the true D values over the range of observed test densities, only the web-based uniform model exhibited a regression slope near 1.0; all other models showed substantial slope deviations, indicating biased estimates at higher or lower density values. In addition, the grid-based DÌ‚ analyses using full MMDM values for WÌ‚ area adjustments required a number of theoretical assumptions of uncertain validity, and we therefore viewed their empirical successes with caution. Finally, density estimates from the independent analysts were highly variable, but estimates from web-based approaches had smaller mean square errors and better achieved confidence-interval coverage of D than did grid-based approaches. Our results support the contention that web-based approaches for density estimation of small-mammal populations are both theoretically and empirically superior to grid-based approaches, even when sample size is far less than often recommended. In view of the increasing need for standardized environmental measures for comparisons among ecosystems and through time, analytical models based on distance sampling appear to offer accurate density estimation approaches for research studies involving small-mammal abundances.

Ecological Monographs↗
USGS2001

Kullback-Leibler information in resolving natural resource conflicts when definitive data exist

Conflicts often arise in the management of natural resources. Often they result from differing perceptions, varying interpretations of the law, and self-interests among stakeholder groups (for example, the values and perceptions about spotted owls and forest management differ markedly among environmental groups, government regulatory agencies, and timber industries). We extend the conceptual approach to conflict resolution of Anderson et al. (1999) by using information-theoretic methods to provide quantitative evidence for differing stakeholder positions. Importantly, we assume that relevant empirical data exist that are central to the potential resolution of the conflict. We present a hypothetical example involving an experiment to assess potential effects of a chemical on monthly survival probabilities of the hen clam (Spisula solidissima). The conflict centers on 3 stakeholder positions: 1) no effect, 2) an acute effect, and 3) an acute and chronic effect of the chemical treatment. Such data were given to 18 analytical teams to make independent analyses and provide the relative evidence for each of 3 stakeholder positions in the conflict. The empirical evidence strongly supports only one of the 3 positions in the conflict: the application of the chemical causes acute and chronic effects on monthly survival, following treatment. Formal inference from all the stakeholder positions is provided for the 2 key parameters underlying the hen clam controversy. The estimates of these parameters were essentially unbiased (the relative bias for the control and treatment group's survival probability was -0.857% and 1.400%, respectively) and precise (coefficients of variation were 0.576% and 2.761%, respectively). The advantages of making formal inference from all the models, rather than drawing conclusions from only the estimated best model, is illustrated. Finally, we contrast information-theoretic and Bayesian approaches in terms of how positions in the controversy enter the formal analysis.

Wildlife Society Bulletin↗
USGS1999

Autocorrelation of location estimates and the analysis of radiotracking data

The wildlife literature has been contradictory about the importance of autocorrelation in radiotracking data used for home range estimation and hypothesis tests of habitat selection. By definition, the concept of a home range involves autocorrelated movements, but estimates or hypothesis tests based on sampling designs that predefine a time frame of interest, and that generate representative samples of an animal's movement during this time frame, should not be affected by length of the sampling interval and autocorrelation. Intensive sampling of the individual's home range and habitat use during the time frame of the study leads to improved estimates for the individual, but use of location estimates as the sample unit to compare across animals is pseudoreplication. We therefore recommend against use of habitat selection analysis techniques that use locations instead of individuals as the sample unit. We offer a general outline for sampling designs for radiotracking studies.

Journal of Wildlife Management↗
USGS1998

Sampling-variance effects on detecting density dependence from temporal trends in natural populations

Monte Carlo simulations were conducted to evaluate robustness of four tests to detect density dependence, from series of population abundances, to the addition of sampling variance. Population abundances were generated from random walk, stochastic exponential growth, and density-dependent population models. Population abundance estimates were generated with sampling variances distributed as lognormal and constant coefficients of variation ( cv ) from 0.00 to 1.00. In general, when data were generated under a random walk, Type I error rates increased rapidly for Bulmer's R, Pollard et al.'s, and Dennis and Taper's tests with increasing magnitude of sampling variance for n > 5 yr and all values of process variation. Bulmer's R * test maintained a constant 5% Type I error rate for n > 5 yr and all magnitudes of sampling variance in the population abundance estimates. When abundances were generated from two stochastic exponential growth models ( R = 0.05 and R = 0.10), Type I errors again increased with increasing sampling variance; magnitude of Type I error rates were higher for the slower growing population. Therefore, sampling error inflated Type I error rates, invalidating the tests, for all except Bulmer's R * test. Comparable simulations for abundance estimates generated from a density-dependent growth rate model were conducted to estimate power of the tests. Type II error rates were influenced by the relationship of initial population size to carrying capacity ( K ), length of time series, as well as sampling error. Given the inflated Type I error rates for all but Bulmer's R *, power was overestimated for the remaining tests, resulting in density dependence being detected more often than it existed. Population abundances of natural populations are almost exclusively estimated rather than censused, assuring sampling error. Therefore, because these tests have been shown to be either invalid when only sampling variance occurs in the population abundances (Bulmer's R, Pollard et al.'s, and Dennis and Taper's tests) or lack power (Bulmer's R * test), little justification exists for use of such tests to support or refute the hypothesis of density dependence.

Ecological Monographs↗
USGS1990

Error in telemetry studies: Effects of animal movement on triangulation

We used Monte Carlo simulations to investigate the effects of animal movement on error of estimated animal locations derived from radio-telemetry triangulation of sequentially obtained bearings. Simulated movements of 0-534 m resulted in up to 10-fold increases in average location error but <10% decreases in location precision when observer-to-animal distances were <1,000 m. Location error and precision were minimally affected by censorship of poor locations with Chi-square goodness-of-fit tests. Location error caused by animal movement can only be eliminated by taking simultaneous bearings.

Journal of Wildlife Management↗