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.

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.

Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.