Gary C. White

Program MARK: survival estimation from populations of marked animals

MARK provides parameter estimates from marked animals when they are re-encountered at a later time as dead recoveries, or live recaptures or re-sightings. The time intervals between re-encounters do not have to be equal. More than one attribute group of animals can be modelled. The basic input to MARK is the encounter history for each animal. MARK can also estimate the size of closed populations. Parameters can be constrained to be the same across re-encounter occasions, or by age, or group, using the parameter index matrix. A set of common models for initial screening of data are provided. Time effects, group effects, time x group effects and a null model of none of the above, are provided for each parameter. Besides the logit function to link the design matrix to the parameters of the model, other link functions include the log—log, complimentary log—log, sine, log, and identity. The estimates of model parameters are computed via numerical maximum likelihood techniques. The number of parameters that are...

Analysis of wildlife radio-tracking data

Preliminaries. Design of Radio-Tracking Studies. Effects of Tagging on the Animal. Estimating Animal Locations. Designing and Testing Triangulation Systems. Simple Movements. Home Range Estimation. Habitat Analysis. Survival Rate Estimation. Population Estimation. Data Analysis System. Appendices. Each chapter includes references. Index.

Design and analysis methods for fish survival experiments based on release-recapture

Design and analysis methods for fish survival experiments based on release-recapture / , Design and analysis methods for fish survival experiments based on release-recapture / , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Capture-recapture and removal methods for sampling closed populations

The problem of estimating animal abundance is common in wildlife management and environmental impact asessment. Capture-recapture and removal methods are often used to estimate population size. Statistical Inference From Capture Data On Closed Animal Populations, a monograph by Otis et al. (1978), provides a comprehensive synthesis of much of the wildlife and statistical literature on the methods, as well as some extensions of the general theory. In our primer, we focus on capture-recapture and removal methods for trapping studies in which a population is assumed to be closed and do not treat open-population models, such as the Jolly-Seber model, or catch-effort methods in any detail. The primer, written for students interested in population estimation, is intended for use with the more theoretical monograph.

ADVANCED TECHNIQUES FOR MODELING AVIAN NEST SURVIVAL

Estimation of avian nest survival has traditionally involved simple measures of apparent nest survival or Mayfield constant-nest-survival models. However, these methods do not allow researchers to build models that rigorously assess the importance of a wide range of biological factors that affect nest survival. Models that incorporate greater detail, such as temporal variation in nest survival and covariates representative of individual nests represent a substantial improvement over traditional estimation methods. In an attempt to improve nest survival estimation procedures, we introduce the nest survival model now available in the program MARK and demonstrate its use on a nesting study of Mountain Plovers (Charadrius montanus Townsend) in Montana, USA. We modeled the daily survival of Mountain Plover nests as a function of the sex of the incubating adult, nest age, year, linear and quadratic time trends, and two weather covariates (maximum daily temperature and daily precipitation) during a six-year stud...

AIC Model Selection in Overdispersed Capture‐Recapture Data

Selection of a proper model as a basis for statistical inference from capture- recapture data is critical. This is especially so when using open models in the analysis of multiple, interrelated data sets (e.g., males and females, with 2-3 age classes, over 3-5 areas and 10-15 yr). The most general model considered for such data sets might contain 1000 survival and recapture parameters. This paper presents numerical results on three information-theoretic methods for model selection when the data are overdispersed (i.e., a lack of independence so that extra-binomial variation occurs). Akaikes information criterion (AIC), a second-order adjustment to AIC for bias (AICc), and a dimension- consistent criterion (CAIC) were modified using an empirical estimate of the average overdispersion, based on quasi-likelihood theory. Quality of model selection was evaluated based on the Euclidian distance between standardized a and 0 (parameter 0 is vector valued); this quantity (a type of residual sum of squares, hence denoted as RSS) is a combination of squared bias and variance. Five results seem to be of general interest for these product- multinomial models. First, when there was overdispersion the most direct estimator of the variance inflation factor was positively biased and the relative bias increased with the amount of overdispersion. Second, AIC and AICc, unadjusted for overdispersion using quasi-likelihood theory, performed poorly in selecting a model with a small RSS value when the data were overdispersed (i.e., overfitted models were selected when compared to the model with the minimum RRS value). Third, the information-theoretic criteria, ad- justed for overdispersion, performed well, selected parismonious models, and had a good balance between under- and overfitting the data. Fourth, generally, the dimension-consis- tent criterion selected models with fewer parameters than the other criteria, had smaller RSS values, but clearly was in error by underfitting when compared with the model with the minimum RSS value. Fifth, even if the true model structure (but not the actual pa- rameter values in the model) is known, that true model, when fitted to the data (by parameter estimation) is a relatively poor basis for statistical inference when that true model includes several, let alone many, estimated parameters that are not significantly different from 0.

Monitoring vertebrate populations

Preface. Basic Concepts. Sampling Designs and Related Topics. Enumeration Methods. Community Surveys. Detection of a Trend in Population Estimates. Guidelines for Planning Surveys. Fish. Amphibians and Reptiles. Birds. Mammals. Glossary of Terms. Glossary of Notation. Sampling Estimators. Common and Scientific Names of Cited Vertebrates. Subject Index.

ANALYSIS OF FREQUENCY COUNT DATA USING THE NEGATIVE BINOMIAL DISTRIBUTION

The statistical distributions of the counts of organisms are generally skewed, and hence not normally distributed, nor are variances constant across treatments. We present a likelihood—ratio testing framework based on the negative binomial distribution that tests for the goodness of fit of this distribution to the observed counts, and then tests for differences in the mean and/or aggregation of the counts among treatments. Inferences about differences in means among treatments as well as the dispersion of the counts are possible. Simulations demonstrated that the statistical power of ANOVA is about the same as the likelihood—ratio testing procedure for testing equality of means, but our proposed testing procedure also provides information on dispersion. Type I error rates of Poisson regression exceeded the expected 5%, even when corrected for overdispersion. Count data on Orange—crowned Warblers (Vermivora celata) are used to demonstrate the procedure.

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 animals movement during this time frame, should not be affected by length of the sampling interval and autocorrelation. Intensive sampling of the individuals 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.

Numerical estimation of survival rates from band-recovery and biotelemetry data

The estimation of survival rates from tagging or banding data has been well developed by Brownie et al. However, problems occur when sparse data sets result in undefined estimates, when survival estimates exceed unity, when a hypothesis about the data cannot be tested by any of the available models, andwhen constraints on model estimators are desired. This paper presents a general analysis method whereby of the models Brownie et al. and many other methods described in the literature are merely special cases. Models are specified algebraically as cell probabilities consisting of functions of the survival rates and other parameters to be estimated. These algebraic expressions and the observed cell values are input to the computer program SURVIV to provide maximum-likelihood estimates of the unknown parameters and perform hypothesis tests on the data. The generality of the model specification also allows estimation of survival rates form biotelemetry data. 3 tables, 1 figure.