The Effects of Trap Response on Tag Recapture Estimates
TL;DR: In this article, the authors examine the effect of certain departures on the so-called Petersen index and discuss briefly a regression method of Marten [1969] for detecting these departures, and examine the robustness of their statistical methods with respect to departures from the assumptions and where possible to test for these departures.
Abstract: In recent years capture-tag-recapture experiments have been widely used for estimating the size of animal populations (cf. Cormack [1968] for an excellent review). However, such methods are only applicable when a number of restrictive assumptions are known to be true, or at least approximately true. For example, it is generally assumed that all animals are equicatchable, and that trapping and tagging do not affect future catchability. However, in practice catchability may vary from individual to individual, e.g. variations between sexes, age groups, species (Kikkawa [1964]); gear selectivity in fish populations (International Commission [1963]); the effect of bait, trap, and habitat preferences upon catchability (Chitty and Shorten [1946], Corbet [1952]); and the effect of trap spacing on catchability (Kikkawa [1964]). Also trapping itself can affect the future catchability of animals as they may become 'trap-shy' or 'trap-addicted,' e.g. Evans [1951], Geis [1955], Morris [1955], Flyger [1959], Huber [1962], to mention just a few. Trap respongse can often be minimised by using prebaited traps (Chitty and Kempson [1949]), though this does not always work (Young et al. [1952], Croweroft and Jeff ers [1961]). Therefore in spite of careful experimentation the experimenter can never be sure that the underlying assumptions necessary for statistical analysis are satisfied. It is then imperative that the experimenter know something about the robustness of his statistical methods with respect to departures from the assumptions and where possible to test for these departures. As a step in this direction we shall examine the effect of certain departures on the so called Petersen estimate (commonly called the Lincoln index, cf. Le Cren [1965]) and discuss briefly a regression method of Marten [1969] for detecting these departures.
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15 Dec 1999
TL;DR: Lohr's SAMPLING: DESIGN and ANALYSIS, 2ND EDITION as mentioned in this paper provides guidance on how to tell when a sample is valid or not, and how to design and analyze many different forms of sample surveys.
Abstract: For a current, practical introduction to the field of sampling that you'll want to keep close at hand, Sharon L. Lohr's SAMPLING: DESIGN AND ANALYSIS, 2ND EDITION, answers the call. Practical and authoritative, the book is listed as a standard reference for training on real-world survey problems by a number of prominent surveying organizations. Lohr concentrates on the statistical aspects of taking and analyzing a sample, incorporating a multitude of applications from a variety of disciplines. The text gives guidance on how to tell when a sample is valid or not, and how to design and analyze many different forms of sample surveys. Recent research on theoretical and applied aspects of sampling is included, as well as technology instructions for using statistical software with survey data.
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TL;DR: This chapter summarizes the current information on population cycles in small rodents, and first looks at the general questions about cycles, and then discusses the demographic machinery which drives the changes in numbers.
Abstract: Publisher Summary This chapter summarizes the current information on population cycles in small rodents It first looks at the general questions about cycles, and then discusses the demographic machinery which drives the changes in numbers And finally, analyzes the current theories which explain population cycles in rodents Population cycles in voles and lemmings are accompanied by a series of changes A few of them include fluctuations occurring in a variety of genera and species from arctic to temperate areas, from Mediterranean to continental climates, from snowy areas to snow-free areas Populations living in a wide variety of plant communities in a small geographic area all fluctuate in the same way, often in phase Survival of adult males fluctuates independently of that of adult females, when viewed on a weekly time scale Males may suffer heavy losses in the decline for a few weeks when females are surviving very well, and vice versa
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TL;DR: In this article, the authors derived the "asymptotic" biases of the Jolly-Seber estimates arising from a failure of the hypothesis of equal catchability, and discussed the dependence of these biases on the parameters of the model, and of the distribution of catchability.
Abstract: If the number of immigrants per inter-sample period, and the probabilities of survival, capture and death on capture are all assumed constant in time, the "asymptotic" biases of the Jolly-Seber estimates arising from a failure of the hypothesis of equal catchability can be derived analytically. The dependence of these biases on the parameters of the model, and of the distribution of catchability, is discussed for stable populations with no deaths on capture, For smaller populations, simulation leads to conclusions consistent with these results and provides information on the suitability of Jolly's formulae for the estimated variances.
222 citations
Journal Article•
TL;DR: In this article, the authors used a maximum-likelihood estimator modified to accommodate temporary movements of marked animals into and from search areas to estimate population size of bears in Alaska during 1985 through 1992 using 2-9 replicates of capture-mark-resight (CMR) techniques in 17 different areas.
Abstract: Accurate density and population estimates are needed to manage bear populations but are difficult to obtain. Most such estimates reported for bears are largely subjective and lack estimates of precision. Fifteen brown bear (Ursus arctos) and 3 black bear (U. americanus) density estimates were obtained in Alaska during 1985 through 1992 using 2-9 replicates of capture-mark-resight (CMR) techniques in 17 different areas. Our studies used radiotelemetry to document movements of marked animals into and from search areas. This procedure essentially eliminated the need to correct density estimates for edge or periphery effects caused by absence of geographic closure. To estimate population size, we used a maximum-likelihood estimator modified to accommodate temporary movements of marked animals into and from our search areas. Our approach permitted direct calculations of density from our population estimates. Our procedures provided density estimates that were repeatable, were comparable among areas, included estimates of precision, and were more objective than methods historically used to estimate bear abundance. Our density estimation procedures have widespread applicability for other wildlife studies using radiotelemetry. Our estimates were obtained within a wide spectrum of habitats and provided a range of Alaskan densities from 10.1 to 551 brown bears (all ages)/1,000 km 2 and from 89 to 289 black bears (all ages)/1,000 km 2 . Our highest brown bear density is probably near the maximum for this species, but areas with lower densities (3.9/1,000 km 2 ) have been reported in Alaska. Areas with black bear densities higher than in our study areas probably occur in Alaska. Brown bear densities were 6-80 times greater in coastal areas where abundant runs of multiple species of salmon (Oncorhynchus spp.) were available to bears than in interior areas. Our CMR technique provided useful data for bear population management and impact assessments and has potential for application to other species and areas.
203 citations
TL;DR: The Petersen model is compared with theorectical sighting distributions to examine the effects of model and sampling biases, and the technique successfully estimated populations of badgers, bison, and crested porcupines.
Abstract: The use of capture–resight data for population estimation has seldom been exploited. It offers potential flexibility and advantages to the design of biological investigations in which a population estimate is required. Presently, the Petersen model is the only method for estimating closed populations using capture—resight data. A simple Monte Carlo simulation method can lead to a full probability distribution for the population. From this probability distribution, one can compute maximum likelihood estimates and a likelihood interval on the population. The shape and asymmetry of the distribution and width of likelihood intervals are determined by sampling heterogeneity and sample size. The method is simple and can be used by anyone with access to a microcomputer. Since it is data—intensive, estimates based on small data sets (including capture—recapture) with few animals can be quickly calculated. The method is especially applicable to species and habitats in which capture–resight, radiotelemetry, or other tracking data can be obtained and to situations in which nonrandom catchability or sightability is likely after the initial capture. The technique successfully estimated populations of badgers, bison, and crested porcupines. We compare observed with theorectical sighting distributions to examine the effects of model and sampling biases.
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