CCAMLR

This paper is presented for the Commission’s consideration and sets out the projected outcome of the budget for 2008, a draft of the 2009 budget and an indicative forecast for the 2010 budget. The presentation is in the format determined by the Commission at its 2002 Meeting.

The long-term benthic disturbance experiment (BENDEX) was started on the eastern Weddell Sea shelf off Austasen (Antarctica) during ‘Polarstern’ cruise ANT XXI/2 in December 2003 to simulate the impact of grounding icebergs on the seabed and follow the steps and timescales of recovery of disturbed benthos and demersal fish communities. Here, we report the basic approach and first results for this experimental field study. By means of 11 densely-placed hauls with a modified bottom trawl, a seabed area of approximately 100 x 1000 m was artificially scoured to inflict a similar damage to the benthic habitats as a grounding iceberg. Before the disturbance event and 11 days after it, the seafloor communities were sampled (invertebrate assemblages by multibox corers, the fish fauna by trawl hauls) and comparatively analyzed. Sediment texture and chemistry was not significantly altered by the heavy disturbance inflicted by repeated trawling, whereas the fauna was negatively affected. Invertebrate benthic biomass was drastically reduced by a factor of 10, while mean abundances were only slightly reduced. Demersal fish biomass and abundance were slightly but not significantly smaller after the disturbance. Effects of disturbance became more evident in the composition of the fish fauna, with Trematomus pennelli and T. hansoni being dominant at disturbed sites, whereas Chionodraco myersi was the dominant species in trawl catches from undisturbed stations.

We analysed relationships between demersal fish species richness, environment and trawl characteristics using an extensive collection of trawl data from the oceans around New Zealand. Analyses were carried out using both generalised additive models and boosted regression trees (sometimes referred to as ‘stochastic gradient boosting’). Depth was the single most important environmental predictor of variation in species richness, with highest richness occurring at depths of 900 to 1000 m, and with a broad plateau of moderately high richness between 400 and 1100 m. Richness was higher both in waters with high surface concentrations of chlorophyll a and in zones of mixing of water bodies of contrasting origins. Local variation in temperature was also important, with lower richness occurring in waters that were cooler than expected given their depth. Variables describing trawl length, trawl speed, and cod-end mesh size made a substantial contribution to analysis outcomes, even though functions fitted for trawl distance and cod-end mesh size were constrained to reflect the known performance of trawl gear. Species richness declined with increasing cod-end mesh size and increasing trawl speed, but increased with increasing trawl distance, reaching a plateau once trawl distances exceed about 3 nautical miles. Boosted regression trees provided a powerful analysis tool, giving substantially superior predictive performance to generalized additive models, despite the fitting of interaction terms in the latter.

Generalized dissimilarity modelling (GDM) is a statistical technique for analysing and predicting spatial patterns of turnover in community composition (beta diversity) across large regions. The approach is an extension of matrix regression, designed specifically to accommodate two types of nonlinearity commonly encountered in large-scaled ecological data sets: (1) the curvilinear relationship between increasing ecological distance, and observed compositional dissimilarity, between sites; and (2) the variation in the rate of compositional turnover at different positions along environmental gradients. GDM can be further adapted to accommodate special types of biological and environmental data including, for example, information on phylogenetic relationships between species and information on barriers to dispersal between geographical locations. The approach can be applied to a wide range of assessment activities including visualization of spatial patterns in community composition, constrained environmental classification, distributional modelling of species or community types, survey gap analysis, conservation assessment, and climate-change impact assessment.