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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 Although this section is likely to become out-of-date quickly, we feel that it is important to illustrate how technology is not only changing our everyday lives, but also having a tremendous impact on the way we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.

We conducted annual ship-board surveys to determine the density and distribution of seabirds off central California in relation to marine climate, from 1985 to 1994. Summarized here are results for the sooty shearwater Puffinus griseus, the common murre Uria aalge, and Cassin's auklet Ptychoramphus aleuticus, the 3 most abundant seabirds in the central part of the California Current (91% of seabird abundance and biomass). During the study, sea-surface temperature, wind speed and thermocline depth all increased, salinity decreased and thermocline intensity (slope) showed no consistent trend. Periods of cool water and warm water, as well as offshore and inshore excursions of the shelfbreak front, alternated and were mediated by the Southern Oscillation and upwelling intensity. The responses to climate variation by the 3 seabird species were in accord with their respective morphologies and natural history patterns. All moved closer to shore and away from the shelf-break front (which also weakened). Abundance of the shallow-diving shearwater and auklet decreased dramatically, but those of the deeper-diving murre did not. The shearwater, which nests in the southern hemisphere and is the most mobile of the 3 seabirds, likely changed its migration routes and reduced its association with the California Current. The auklet, which breeds within the study area and lacks mobility, declined in number, most likely due to reduced breeding success and subsequent reduced population size. Remaining auklets moved away from the shelfbreak, but not as far inshore as the shearwater. The murre, which also breeds locally, is the most adaptable owing to its deeperdiving capabilities. It shifted distribution within the study area to feed on alternative prey found throughout the water column.

Modelling spatio‐temporal changes in species abundance and attributing those changes to potential drivers such as climate, is an important but difficult problem. The standard approach for incorporating climatic variables into such models is to include each weather variable as a single covariate, whose effect is expressed through a low‐order polynomial or smoother in an additive model. This, however, confounds the spatial and temporal effects of the covariates. We developed a novel approach to distinguish between three types of change in any particular weather covariate. We decomposed the weather covariate into three new covariates by separating out temporal variation in weather (averaging over space), spatial variation in weather (averaging over years) and a space–time anomaly term (residual variation). These three covariates were each fitted separately in the models. We illustrate the approach using generalized additive models applied to count data for a selection of species from the UK's Breeding Bird Survey, 1994–2013. The weather covariates considered were the mean temperatures during the preceding winter and temperatures and rainfall during the preceding breeding season. We compare models that include these covariates directly with models including decomposed components of the same covariates, considering both linear and smooth relationships. The lowest QAIC values were always associated with a decomposed weather covariate model. Different relationships between counts and the three new covariates provided strong evidence that the effects of changes in covariate values depended on whether changes took place in space, in time, or in the space–time anomaly. These results promote caution in predicting species distribution and abundance in future climate, based on relationships that are largely determined by environmental variation over space. Our methods estimate the effect of temporal changes in weather, while accounting for spatial effects of long‐term climate, improving inference on overall and/or localized effects of climate change. With increasing availability of large‐scale datasets, need is growing for appropriate analytical tools. The proposed decomposition of the weather variables represents an important advance by eliminating the confounding issue often inherent in analyses of large‐scale datasets.