By S.T. Buckland, D.R Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, L. Thomas

This complicated textual content makes a speciality of the makes use of of distance sampling to estimate the density and abundance of organic populations. It addresses new methodologies, new applied sciences and up to date advancements in statistical thought and is the follow-up significant other to creation to Distance Sampling (OUP, 2001). during this textual content, a basic theoretical foundation is proven for ways of estimating animal abundance from sighting surveys, and quite a lot of techniques to the layout and research of distance sampling surveys is explored. those methods comprise: modelling animal detectability as a functionality of covariates, the place the results of habitat, observer, climate, and so on. on detectability should be assessed; estimating animal density as a functionality of situation, taking into account instance animal density to be regarding habitat and different locational covariates; estimating swap over the years in inhabitants abundance, an important point of any tracking programme; estimation while detection of animals at the line or on the aspect is doubtful, as usually happens for marine populations, or while the survey quarter has dense hide; automatic new release of survey designs, utilizing geographic details platforms; adaptive distance sampling equipment, which focus survey attempt in components of excessive animal density; passive distance sampling tools, which expand the appliance of distance sampling to species that can not be quite simply detected in sightings surveys, yet might be trapped; and checking out of equipment via simulation, so the functionality of the process in various conditions could be assessed. Authored by means of a number one workforce, this article is aimed toward pros in executive and atmosphere enterprises, statisticians, biologists, flora and fauna managers, conservation biologists and ecologists, in addition to graduate scholars, learning the density and abundance of organic populations.

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Extra resources for Advanced Distance Sampling: Estimating Abundance of Biological Populations

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One way of dealing with populations that cluster or are territorial is to use a probability density function (pdf) that is more able to deal with this than is the binomial. The negative binomial is a flexible candidate (see below). In practice, however, design-based methods (Chapter 10) are usually used in preference to maximum likelihood methods for these surveys. Design-based methods do not rely on any assumptions about animal distribution. The Horvitz–Thompson estimator proper was developed for design-based sampling theory and it is directly applicable here because the inclusion probability (Pc ) is known (from the design).

19) Maximum likelihood estimation The description above begs the question of how to estimate the unknown inclusion probabilities. They can be factorized into a probability of the animal (cluster) being in the covered region (Pc ) and a probability that it is detected, given that it is in the covered region (Pa ). The coverage probability Pc is conventionally treated as known (it is determined by the survey design), but Pa must be estimated from the observed distances. In this book we focus on estimation by maximum likelihood.

The negative binomial is a flexible candidate (see below). In practice, however, design-based methods (Chapter 10) are usually used in preference to maximum likelihood methods for these surveys. Design-based methods do not rely on any assumptions about animal distribution. The Horvitz–Thompson estimator proper was developed for design-based sampling theory and it is directly applicable here because the inclusion probability (Pc ) is known (from the design). 21) in this case; the difference between the two methods lies in the estimation of variance.

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