Taking account of dependent species in management of the Southern Ocean krill fishery: estimating crabeater seal abundance off east Antarctica

1. The crabeater seal Lobodon carcinophaga is considered to be a key species in the krill-based food web of the Southern Ocean. Reliable estimates of the abundance of this species are necessary to allow the development of multispecies, predator–prey models as a basis for management of the krill fishery in the Southern Ocean. 2. A survey of crabeater seal abundance was undertaken in 1500 000 km2 of pack-ice off east Antarctica between longitudes 64–150° E during the austral summer of 1999/2000. Sighting surveys, using double observer line transect methods, were conducted from an icebreaker and two helicopters to estimate the density of seals hauled out on the ice in survey strips. Satellite-linked dive recorders were deployed on a sample of seals to estimate the probability of seals being hauled out on the ice at the times of day when sighting surveys were conducted. Model-based inference, involving fitting a density surface, was used to infer densities in the entire survey region from estimates in the surveyed areas. 3. Crabeater seal abundance was estimated to be between 0.7 and 1.4 million animals (with 95% confidence), with the most likely estimate slightly less than 1 million. 4. Synthesis and applications. The estimation of crabeater seal abundance in Convention for the Conservation of Antarctic Marine Living Resources (CCAMLR) management areas off east Antarctic where krill biomass has also been estimated recently provides the data necessary to begin extending from single-species to multispecies management of the krill fishery. Incorporation of all major sources of uncertainty allows a precautionary interpretation of crabeater abundance and demand for krill in keeping with CCAMLR’s precautionary approach to management. While this study focuses on the crabeater seal and management of living resources in the Southern Ocean, it has also led to technical and theoretical developments in survey methodology that have widespread potential application in ecological and resource management studies, and will contribute to a more fundamental understanding of the structure and function of the Southern Ocean ecosystem.

Distance software: design and analysis of distance sampling surveys for estimating population size

1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance. 2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use. 3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated. 4. A first step in analysis of distance sampling data is modeling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance. 5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap. 6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modeling analysis engine for spatial and habitat-modeling, and information about accessing the analysis engines directly from other software. 7. Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of- the-art software that implements these methods is described that makes the methods accessible to practicing ecologists.