Summary

Abstract

SummaryDistance sampling methods have been successfully applied to estimate density or abundance of populations of a large variety of taxa ranging across many habitats. These are typically very cost-effective methods, especially for species which occur at low densities over large areas. Methods are nonetheless not a panacea, and in many circumstances, assumption violation, and hence bias, might be severe.Assumption violations are often ignored by practitioners, who might simply report density estimates without considering possible failures of assumptions. We consider that a good distance sampling paper should include in the discussion an assessment of which assumptions hold to a good approximation, and which do not. For those that do not, an assessment of the likely magnitude and direction of the bias should be made.When planning a survey, the key assumptions should be considered. Are they likely to hold, given what you know about your population and study area? If not, how might you modify your design, or change your field methods, to reduce assumption violations? Do you need to test your methods out in a pilot survey? If at least one assumption remains a serious concern, do you need to resort to more sophisticated methods, as discussed in Chap. 11?It is far better to address problems when planning the survey than to attempt to rectify problems at the analysis stage. Careful planning can anticipate and negate most problems likely to be encountered, although some populations are inherently difficult to survey in a way that yields reliable density estimates.In Sect. 12.1, we provide advice on points to consider when planning a distance sampling survey. Although we are not keen on a cookbook approach, we believe that a check-list can be useful. We conclude this book with Sect. 12.2, which deals with technological advances. Although this section is likely to become out-of-date quickly, we feel that it is important to illustrate how technology is not only changing our everyday lives, but also having a tremendous impact on the way we monitor animal populations. We therefore explore how animal survey methods might change in the near future.

Whale, Whale, Everywhere: Increasing Abundance of Western South Atlantic Humpback Whales (Megaptera novaeangliae) in Their Wintering Grounds

“…Other sources of positive bias include the potential for double counting, which could occur in areas of high density where distinguishing new groups from those that had already been recorded could be difficult [ 54 ]. In the present study, measures to minimize double counting were taken by placing additional observers in the flying bridge to help primary observers to track sightings that had already been called in.…”

Quantifying the ecological success of a community-based wildlife conservation area in Tanzania

“…We estimated annual ungulate densities for each species in both sites using program DISTANCE 6.0 ( Thomas et al 2010 ). We first tested for between-sites similarity in annual densities during the “before” period of 2012 and 2013 using a 2-sample z -test for comparing 2 means ( Buckland et al 2001 , 2015 ). We then tested for significant between-site differences in density in 2015, “after” management began in the Randilen WMA.…”

“…Wildlife distances were recorded to the nearest meter, livestock data were binned into distances of 0–50, 51–100, 101–200, 201–300, 301–400, and 401–500 m. Our study design implemented identical replicate surveys to conduct an impact assessment by obtaining annual estimates of density for each site and calculating before-after similarity and difference between sites. Buckland et al (2001 , 2015 ) recommend systematic random designs, ≥ 10 replicate transect lines, and ≥ 60 observations for estimation. Our design did not conform to these recommendations, but our design of 6 surveys per year, where each survey was a single long transect in each site, was an appropriate design for our purposes.…”

Patients with genetically heterogeneous synchronous colorectal cancer carry rare damaging germline mutations in immune-related genes

“…Second, the expected number of alterations ( E ) at clonality ( I ) was calculated as the probability of each alteration P ( x ) multiplied by the total number of alterations ( n ) 62 :…”