There are a number of key questions asked in the field of wildlife management and ecology:
- How many animals are there?;
- What proportion of animals in the area are detected?;
- What factors influence detection (e.g. rain, glare)?;
- What potential biases are there?;
- Can number of individuals in a specific survey be extrapolated to the population as a whole?; and,
- Why are animals found in particular locations?
Attempts to address these issues spawned a field of statistics called ‘distance sampling’ (Buckland et al., 2001; 2004). In a distance-sampling survey, an observer travels along a line-transect survey or remains at a fixed-point survey location and records distance to all detected individuals of a species of interest. The central premise of distance sampling is the ‘detection function’, which accounts for influence of sighting conditions and distance to survey transect when estimating relative abundance (number of animals) or density (animals per km2). For example, along a straight line through a savannah, it is assumed that every gazelle located within a few metres of the transect line is observed; however, due to their grass-coloured coats, a few individuals located further from the line are likely to be missed, and invisible beyond a certain distance. Sighting conditions also influence detection. For example, in one direction, glare could hinder detection, while in the opposite direction, animals could be nicely illuminated.
When scaling up relative density and abundance estimates (e.g. there are twice as many animals in location A than B) to absolute estimates (there are eight in A and four in B), two types of bias must be taken into account: perception bias and availability bias (Marsh and Sinclair, 1989). Perception bias arises when an animal is present and visible, but not detected – e.g. the observer looks away, blinks or isn’t paying attention. Availability bias occurs when an animal is present but cannot be seen, which is particularly troublesome for marine mammal surveys when animals spend extended periods underwater.
The inclusion of a detection function in analysis, and accounting for bias allows the scaling up of the number of animals observed in a survey to the number actually present in that area.
An essential part of long-term management of cetacean (whale, dolphin and porpoise) populations and their habitat is a robust and cost-effective means of monitoring relative abundance and density before, during, and after any marine activities. Density and abundance estimates can provide useful information on their own; however, more detailed information on the distribution of animals can be obtained by applying additional analysis such as density surfacing modelling (Miller et al., 2013). This technique combines distance sampling with statistical models (e.g. Generalized Additive Models, GAMs), using additional spatially-referenced environmental data (such as depth, temperature, or current speeds) to investigate how density is related to the environment. Recent developments also increase types and flexibility of models that can be used with distance sampling data (e.g. Yuan et al., 2017; Bachl et al., 2019) to predict density variations at fine spatial scales across the whole survey.
The desired precision and unbiased nature of such estimates are dependent on sample size, survey coverage, methodology, and more crucially, accurate measurements of sighting distance. Irrespective of whether surveys are dedicated to monitoring cetaceans, pinnipeds, birds, or terrestrial species, observers should record all incidental-species sightings (as appropriate) to facilitate an ecosystem approach and understanding of inter-population dynamics.
Ocean Science Consulting Limited (OSC) regularly designs these complex methods, relying exclusively on thoroughly-trained observers to perform this specific type of survey. Depending on complexity of a survey, observers can be supervised in the field by technical and statistical experts to ensure that methods are implemented correctly, and all necessary data are recorded accurately. Post survey, the focus of a project shifts to data processing and analysis, the complexity of which is generally the determining factor in the time required for these studies. OSC has in-house PhD-trained staff capable of analysing these data at any level from basic distance sampling to advanced spatial modelling.
Bachl, F.E., Lindgren, F., Borchers, D.L., and Illian, J.B. (2019): inlabru: an R package for Bayesian spatial modelling from ecological survey data. Methods in Ecology and Evolution in press.
Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., Borchers, D.L., and Thomas, L. (2001): Introduction to Distance Sampling: Estimating Abundance of Biological Populations. Oxford University Press, Oxford, UK.
Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., Borchers, D.L., and Thomas, L. (2004): Advanced distance sampling: estimating abundance of biological populations. Oxford University Press, Oxford, UK, pp. 416.
Marsh, H., and Sinclair, D.F. (1989): Correcting for visibility bias in strip transect aerial surveys of marine fauna. Journal of Wildlife Management 53, 1017-1024.
Miller, D.L., Burt, M.L., Rexstad, E.A., and Thomas, L. (2013): Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution 4, 1001-1010.
Yuan, Y., Bachl, F.E., Lindgren, F., Borchers, D.L., Illian, J.B., Buckland, S.T., Rue, H., and Gerrodette, T. (2017): Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales. Ann. Appl. Stat. 11, 2270-2297.