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

This complex 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 conception and is the follow-up spouse to advent to Distance Sampling (OUP, 2001). during this textual content, a common theoretical foundation is tested for ways of estimating animal abundance from sighting surveys, and a variety of methods to the layout and research of distance sampling surveys is explored. those ways comprise: modelling animal detectability as a functionality of covariates, the place the consequences of habitat, observer, climate, and so forth. on detectability will be assessed; estimating animal density as a functionality of position, taking into consideration instance animal density to be concerning habitat and different locational covariates; estimating switch through the years in inhabitants abundance, an important element of any tracking programme; estimation whilst detection of animals at the line or on the aspect is doubtful, as usually happens for marine populations, or while the survey sector has dense conceal; automatic new release of survey designs, utilizing geographic info structures; 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 effortlessly detected in sightings surveys, yet could be trapped; and trying out of tools by means of simulation, so the functionality of the strategy in various situations will be assessed. Authored through a number one group, this article is geared toward pros in executive and atmosphere firms, 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

Example text

The binomial is a poor model for these cases. 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).

Alternatively, it can be estimated from the full likelihood. e. uniformly in space in the vicinity of the observer) and/or to estimate their distribution when it is unknown. 8). 7 Estimating animal density surfaces It is often the case that animals are not uniformly distributed through the survey region. Survey regions typically span regions of suitable habitat in which animal density tends to be high, and less suitable habitat when animal density tends to be lower. It is also often the case that there is interest in the relationships between habitat variables and density, and in identifying regions of high and low density.

The maximum likelihood estimate is that value of N that maximizes this function. 21) where the square brackets mean ‘the integer part of’. We usually omit these brackets, and allow non-integer animals. 20) is appropriate for quadrat, strip, or circular plot surveys, in which all animals in the covered region are detected. In practice, however, inference from this sort of survey is seldom conducted by maximum likelihood. One reason for this is that the likelihood contains stronger assumptions about the independence of animal locations than are reasonable in most cases.

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