CCAMLR

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.

Current circumstances — that the majority of species distribution records exist as presence-only data (e.g. from museums and herbaria), and that there is an established need for predictions of species distributions — mean that scientists and conservation managers seek to develop robust methods for using these data. Such methods must, in particular, accommodate the difficulties caused by lack of reliable information about sites where species are absent. Here we test two approaches for overcoming these difficulties, analysing a range of data sets using the technique of multivariate adaptive regression splines (MARS). MARS is closely related to regression techniques such as generalized additive models (GAMs) that are commonly and successfully used in modelling species distributions, but has particular advantages in its analytical speed and the ease of transfer of analysis results to other computational environments such as a Geographic Information System. MARS also has the advantage that it can model multiple responses, meaning that it can combine information from a set of species to determine the dominant environmental drivers of variation in species composition. We use data from 226 species from six regions of the world, and demonstrate the use of MARS for distribution modelling using presence-only data. We test whether (1) the type of data used to represent absence or background and (2) the signal from multiple species affect predictive performance, by evaluating predictions at completely independent sites where genuine presence–absence data were recorded. Models developed with absences inferred from the total set of presence-only sites for a biological group, and using simultaneous analysis of multiple species to inform the choice of predictor variables, performed better than models in which species were analysed singly, or in which pseudo-absences were drawn randomly from the study area. The methods are fast, relatively simple to understand, and useful for situations where data are limited. A tutorial is included.

Predictionofspecies’distributionsiscentraltodiverseapplicationsinecology,evolutionandconservationscience.Thereisincreasingelectronicaccesstovastsetsofoccurrencerecordsinmuseumsandherbaria,yetlittleeffectiveguidanceonhowbesttousethisinformationinthecontextofnumerousapproachesformodellingdistributions.Tomeetthisneed,wecompared16modellingmethodsover226speciesfrom6regionsoftheworld,creatingthemostcomprehensivesetofmodelcomparisonstodate.Weusedpresence-onlydatatofitmodels,andindependentpresence-absencedatatoevaluatethepredictions.Alongwithwell-establishedmodellingmethodssuchasgeneralisedadditivemodelsandGARPandBIOCLIM,weexploredmethodsthateitherhavebeendevelopedrecentlyorhaverarelybeenappliedtomodellingspecies’distributions.Theseincludemachine-learningmethodsandcommunitymodels,bothofwhichhavefeaturesthatmaymakethemparticularlywellsuitedtonoisyorsparseinformation,asistypicalofspecies’occurrencedata.Presence-onlydatawereeffectiveformodellingspecies’distributionsformanyspeciesandregions.Thenovelmethodsconsistentlyoutperformedmoreestablishedmethods.Theresultsofouranalysisarepromisingfortheuseofdatafrommuseumsandherbaria,especiallyasmethodssuitedtothenoiseinherentinsuchdataimprove.

Australia has developed the “Field identification guide to Heard Island and McDonald Island (HIMI) benthic invertebrates: a guide for scientific observers aboard fishing vessels”. This Fisheries Research and Development Corporation (FRDC), Australian Fisheries Management Authority (AFMA) and industry funded production is first of its kind for the region, and is intended to be used as both a training tool prior to deployment at-sea, as well as for use by trained observers to make accurate identifications of invertebrate by-catch when operating in the HIMI region. Based on available data, this publication will include instructions to observers for collection of benthic organisms at sea, quick-reference pictorial guides to invertebrate phyla, common species and CCAMLR identification codes, and detailed identification notes for each phylum including representative images for more than 400 benthic organisms identified from the HIMI region thus far. This guide will enable observers to gather invertebrate by-catch data at higher taxonomic resolutions.

Following on from earlier analyses presented to WG-EMM, meta-analyses of life-history characteristics and their relationships with physical and chemical variables were undertaken for habitat-forming invertebrates other than cnidarians. Particular focus was placed on bryozoans and sponges which are known to form important biogenic habitat in the Southern Ocean. Data were extracted from a global database of life-history characteristics. Bryozoans showed a strong negative relationship between growth rate and age and a strong positive relationship between growth rate and temperature. A multiple regression model suggests that Southern Ocean bryozoans may take more than 50 years to recover following disturbance. Demosponges showed a strong positive relationship between maximum size and age while there were too few data for glass sponges for robust analysis. Most sponges from the Antarctic showed no or very slow growth over considerable periods and it is hypothesised that they will take many decades or even centuries to recover from disturbance particular in areas where natural disturbance is low.

A global database of life-history characteristics, including growth rate, age, maximum size and reproductive parameters, was established for benthic, habitat-forming taxa and associated physical and chemical variables, including depth, temperature, oxygen concentration, salinity and nutrients. The metadata fields are described. At present more than 850 records representing more than 650 taxa from 29 higher taxonomic groups have been incorporated into the database. The database can be used to investigate relationships between and among life-history characteristics and habitat variables. Members are encouraged to contribute additional data and collaborate with analyses.

Publicly available bathymetry and geophysical data can be used to map geomorphic features of the Antarctic continental margin and adjoining ocean basins at scales of 1:1-5 million. These data can also be used to map likely locations for some Vulnerable Marine Ecosystems. Seamounts over a certain size are readily identified and submarine canyons and mid ocean ridge central valleys which harbour hydrothermal vents can be located. Geomorphic features and their properties can be related to major habitat characteristics such as sea floor type (hard versus soft), ice keel scouring, sediment deposition or erosion and current regimes. Where more detailed data are available, shelf geomorphology can be shown to provide a guide to the distribution in the area of the shelf benthic communities recognised by Gutt (2007). The geomorphic mapping method presented here provides a layer to add to benthic bioregionalistion using readily available data.

Cold seeps and hydrothermal vents can be detected by a number of oceanographic and geophysical techniques as well as the recovery of characteristic organisms. While the definitive identification of a seep or vent and its accompanying fauna is seldom unequivocal without significant effort. We suggest an approach to identifying associated VMEs in the CCAMLR region that uses the results of scientific surveys to identify confirmed features while documenting a series of criteria that can be used by fishing vessels to reduce the accidental disturbance of seep communities.

Implementing measures to avoid significant adverse impacts to vulnerable marine ecosystems (VMEs) requires a specific list of the taxa that are considered vulnerable. New Zealand identified an interim list of taxa to monitor in fishery bycatch in the Ross Sea as part of its 2008 benthic fisheries impact assessment. The rationale for including or excluding each taxonomic group is described relative to the group’s fragility, longevity, organism size, and spatial distribution. Additional considerations such as organism mobility, community diversity, endemism, taxonomic resolution and presence in fishery bycatch are also important considerations. Thirteen coarse-resolution taxonomic groups consistent with FAO guidelines are identified as vulnerable to bottom longline fishing activities in the Ross Sea region, including two indicator-only taxa.

Protecting vulnerable marine ecosystems (VMEs) from significant adverse impacts caused by fishing activities requires knowledge of the distribution of those communities relative to the fishing footprint. If high fish catch rates are associated with habitats where VMEs are found, impacts from fishing are more likely to occur than if VMEs are distributed randomly with respect to fishing locations. This study used the catch of VME indicator organisms reported during the 2008/09 Ross Sea longline fisheries to look for a correlation between fish catch rate and VME indicator organism catch rate on a set-specific basis. Analysis of the data available showed that vessel and longitude were significant factors in predicting the VME indicator organism catch rate. Toothfish catch, when forced into the model, showed a very weak (r2 = 0.02) but statistically significant relationship with VME indicator organism catch. With current data there is no functional relationship between toothfish catch and VME indicator organism catch.