Modeling what we sample and sampling what we model: challenges for zooplankton model assessment

Zooplankton are the intermediate trophic level between phytoplankton and fish, and are an important component of carbon and nutrient cycles, accounting for a large proportion of the energy transfer to pelagic fishes and the deep ocean. Given zooplankton's importance, models need to adequately represent zooplankton dynamics. A major obstacle, though, is the lack of model assessment. Here we try and stimulate the assessment of zooplankton in models by filling three gaps. The first is that many zooplankton observationalists are unfamiliar with the biogeochemical, ecosystem, size-based and individual-based models that have zooplankton functional groups, so we describe their primary uses and how each typically represents zooplankton. The second gap is that many modelers are unaware of the zooplankton data that are available, and are unaccustomed to the different zooplankton sampling systems, so we describe the main sampling platforms and discuss their strengths and weaknesses for model assessment. Filling these gaps in our understanding of models and observations provides the necessary context to address the last gap—a blueprint for model assessment of zooplankton. We detail two ways that zooplankton biomass/abundance observations can be used to assess models: data wrangling that transforms observations to be more similar to model output; and observation models that transform model outputs to be more like observations. We hope that this review will encourage greater assessment of zooplankton in models and ultimately improve the representation of their dynamics

A database of marine phytoplankton abundance, biomass and species composition in Australian waters

There have been many individual phytoplankton datasets collected across Australia since the mid 1900s, but most are unavailable to the research community. We have searched archives, contacted researchers, and scanned the primary and grey literature to collate 3,621,847 records of marine phytoplankton species from Australian waters from 1844 to the present. Many of these are small datasets collected for local questions, but combined they provide over 170 years of data on phytoplankton communities in Australian waters. Units and taxonomy have been standardised, obviously erroneous data removed, and all metadata included. We have lodged this dataset with the Australian Ocean Data Network (http://portal.aodn.org.au/) allowing public access. The Australian Phytoplankton Database will be invaluable for global change studies, as it allows analysis of ecological indicators of climate change and eutrophication (e.g., changes in distribution; diatom:dinoflagellate ratios). In addition, the standardised conversion of abundance records to biomass provides modellers with quantifiable data to initialise and validate ecosystem models of lower marine trophic levels

Characterising and Predicting Benthic Biodiversity for Conservation Planning in Deepwater Environments

Understanding patterns of biodiversity in deep sea systems is increasingly important because human activities are extending further into these areas. However, obtaining data is difficult, limiting the ability of science to inform management decisions. We have used three different methods of quantifying biodiversity to describe patterns of biodiversity in an area that includes two marine reserves in deep water off southern Australia. We used biological data collected during a recent survey, combined with extensive physical data to model, predict and map three different attributes of biodiversity: distributions of common species, beta diversity and rank abundance distributions (RAD). The distribution of each of eight common species was unique, although all the species respond to a depth-correlated physical gradient. Changes in composition (beta diversity) were large, even between sites with very similar environmental conditions. Composition at any one site was highly uncertain, and the suite of species changed dramatically both across and down slope. In contrast, the distributions of the RAD components of biodiversity (community abundance, richness, and evenness) were relatively smooth across the study area, suggesting that assemblage structure (i.e. the distribution of abundances of species) is limited, irrespective of species composition. Seamounts had similar biodiversity based on metrics of species presence, beta diversity, total abundance, richness and evenness to the adjacent continental slope in the same depth ranges. These analyses suggest that conservation objectives need to clearly identify which aspects of biodiversity are valued, and employ an appropriate suite of methods to address these aspects, to ensure that conservation goals are met

Corrigendum: A database of marine phytoplankton abundance, biomass and species composition in Australian waters (Scientific Data (2016) 3 (160043) DOI: 10.1038/sdata201643))

© The Author(s) 2016. A series of errors in our database were brought to our attention by readers, and have been corrected in an updated version of this database, which is accessible via the AODN at the following link: https://portal.aodn.org.au/search?uuid =75f4f1fc-bee3-4498-ab71-aa1ab29ab2c0 The custodian details of several datasets were incorrect. These fields in the metadata table have been updated to correctly assign P744, P746, P748, and P778 to the Australian Antarctic Division, and P752 to the Royal Belgian Institute of Natural Sciences. Species names and functional group assignments have been changed for a small number of records to fix identified errors. Tripos brevis and Tripos arietinus were spelt incorrectly, and have been duly corrected. Pedinellaceae was wrongly assigned to dinoflagellate as a functional group, and has now been re-assigned to flagellate. The 'Naked flagellate' group has been renamed 'Flagellate' as there is some inconsistency in the use of the term 'Naked flagellate' and what precisely would be included. The functional group 'Other', has also been excluded as this contained data that was not necessarily phytoplankton but had been found in phytoplankton counts. The macroalgae Murrayella australica, Cladophora spp., Chlorohormidium sp., Eudorina spp., Tribonema spp., Chlorohormidium spp. were also removed. In addition to these corrections, three datasets have been extended to include more recently acquired data: P 597 IMOS Australian Continuous Plankton Recorder survey (ongoing dataset, 59089 new records as of 2016-08-31); P599 IMOS National Reference Stations (ongoing dataset, 14669 new records as of 2016-08-31); and P1068 Great Barrier Reef Expedition 1928-29 (new dataset, 1340 new records). Table 1 provides a summary of the overall change in database contents. (Table Presented). This dataset will continue to grow and will be regularly updated with new data and any further corrections to the data. Users can email imos-planktonatcsiro.au with any comments, which will be reviewed and included in future updates if applicable. The AODN portal will always direct the user to the most recent version, the original version will remain available at http://dx.doi.org/10.4225/69/ 56454b2ba2f79, and interim versions will be available on request

Introduced species survey, Port of Fremantle and Cockburn Sound, Western Australia

A prerequisite for any attempt to control the spread by shipping of introduced marine pest species in Australian waters is a knowledge of the current distribution and abundance of exotic species in Australian ports. This information base is lacking for nearly all Australian ports. The current port survey program is a joint initiative of the Australian Association of Port and Marine Authorities (AAPMA) and the CSIRO Centre for Research on Introduced Marine Pests (CRIMP) and is supported by the Australian Ballast Water Management Advisory Council (ABWMAC). The program seeks to redress the lack of knowledge about the occurrence of exotic species in Australian ports and provide a consistent basis on which the introduced species status of individual ports can be assessed. Port surveys designed to identify all exotic species will inevitably be subject to scientific, logistic and cost constraints that will limit both their taxonomic and spatial scope. Recognition of these constraints has lead AAPMA and CRIMP to adopt a targeted approach which concentrates on a known group of species and provides a cost effective approach to the collection of baseline data for all ports. The targeted surveys are designed to determine the distribution and abundance of a limited number of species in each port. These species are listed in Appendix 1 and are made up of: • those species listed on the Australia Ballast Water Management Advisory Council's (ABWMAC) schedule of introduced pest species; • a group of species which are major pests in overseas ports and which, on the basis of their invasive history and projected shipping movements, might be expected to colonise Australian ports; and • those known exotic species in Australian waters that currently are not assigned pest status. The targeted surveys will also identify species of uncertain status (endemic or introduced) that are abundant in a port and/or are likely to become major pest species. A major component of each port survey is a local public awareness program designed to collect information that might indicate the presence of introduced species in the port and adjacent areas, the approximate date of introduction, and potential impacts on native marine communities. This report details the targeted ABWMAC pest species results of an introduced species survey of the Port of Fremantle, Western Australia, carried out between 18th April to 14th May, 1999. The survey was funded by a consortium of interests led by the Fremantle Port Authority and was conducted by CRIMP in conjunction with the Marine and Freshwater Research Laboratory, Murdoch University

Port of Hastings National Demonstration Project - Verification of the Type II error rate of the Ballast Water Decision Support System (DSS).

In July 2001, Australia introduced a risk-based Decision Support System (DSS) to manage ballast water on international shipping. Up until now, however, the accuracy of the risk assessments made by the DSS have never been evaluated or tested. It was not until the DSS was considered as a mechanism to assist the management of domestically sourced ballast water, coupled with advances in genetic technologies, that the opportunity arose to evaluate the accuracy of the predictions of the DSS. The results of that evaluation are reported here..