John Parslow

INFLUENCE OF IRRADIANCE ON CELL VOLUME AND CARBON QUOTA FOR TEN SPECIES OF MARINE PHYTOPLANKTON1

Ten species of marine phytoplankton were grown under a range of photosynthetic photon flux densities (PFDs) and examined for variation in cell volume and carbon quota. Results suggest that in response to low PFDs phytoplankton generally reduce their cell volume and frequently reduce their carbon quota. A significant linear relationship between the log of PFD (I) and cell volume (in nine of ten species) and log I and carbon quota (four of ten species) was demonstrated.

The use of mechanistic descriptions of algal growth and zooplankton grazing in an estuarine eutrophication model

A simple model of estuarine eutrophication is built on biomechanical (or mechanistic) descriptions of a number of the key ecological processes in estuaries. Mechanistically described processes include the nutrient uptake and light capture of planktonic and benthic autotrophs, and the encounter rates of planktonic predators and prey. Other more complex processes, such as sediment biogeochemistry, detrital processes and phosphate dynamics, are modelled using empirical descriptions from the Port Phillip Bay Environmental Study (PPBES) ecological model. A comparison is made between the mechanistically determined rates of ecological processes and the analogous empirically determined rates in the PPBES ecological model. The rates generally agree, with a few significant exceptions. Model simulations were run at a range of estuarine depths and nutrient loads, with outputs presented as the annually averaged biomass of autotrophs. The simulations followed a simple conceptual model of eutrophication, suggesting a simple biomechanical understanding of estuarine processes can provide a predictive tool for ecological processes in a wide range of estuarine ecosystems.

The analysis of alternative formulations in a simple model of a coastal ecosystem

Abstract Ecosystems are complex and often require complex models if their detailed behaviour is to be replicated. However, such complex models are difficult to analyse due to their nonlinearities and the large number of parameters that most such models have. One approach that allows greater understanding of basic process is the development of simplified models. We have developed a series of simple models describing alternative formulations of a coastal ecosystem, as a tool to aid development and analysis of more sophisticated models. Sediment biogeochemistry plays a critical role in many coastal ecosystems, and much of the nitrogen input load is lost through denitrification, provided eutrophication has not set in. We have dealt with the sediment and water column response separately in simple models by exploiting the different time scales of sediment and water column response. In simple water column models, we have considered a variety of common formulations of phytoplankton–zooplankton interactions, and their implications for the steady-state response of phytoplankton and nutrients to increased nutrient load. For most formulations, we have derived explicit formulae linking model parameters to predicted mean, steady-state concentration and biomass. The simple model results provide considerable insight into the response of the bay to changes in nutrient load. In particular, the sediment model identifies a maximum denitrification capacity for the bay. Once loads exceed this capacity, denitrification declines, and nutrients are instead lost through export. This decline in denitrification results in a switch from mesotrophic to eutrophic conditions. The water column model analysis confirms the importance of the zooplankton mortality formulation in N–P–Z models in determining the dependence of steady-state phytoplankton biomass on nutrient load, and the stability of steady-state solutions.

Optical properties of waters in the Australasian sector of the Southern Ocean

During March 1998 we studied in situ bio-optical parameters along a north-south transect (142?°E) between 42° and 55?°S. Surface chorophyll a (chl a) reflected mixed layer chl a concentrations and showed a general decrease with increasing latitude. Changes in chl a concentration often coincided with physical boundaries, and differences in fluorescence yield and photoadaption by the phytoplankton were observed north and south of the Subantarctic Front. In this region chromophoric dissolved organic matter (CDOM) absorption, generally exceeded phytoplankton pigment absorption at 443 nm. Satellite-derived chl a, using the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) OC4 algorithm, generally underestimated the in situ chl a concentration except in areas of low chl a (<0.15 mg m−3) where the SeaWiFS algorithm was found to overestimate in situ chl a.

Adaptive management for water quality planning - from theory to practice

Adaptive management has been promoted as a structured approach to learning in response to the uncertainty associated with managing complex systems. We developed and tested a protocol to guide an adaptive approach to water quality management in north-eastern Australia. The protocol articulates a framework for documenting uncertainties and performance expectations, negotiating feedback and anticipating iterative and transformative responses to future scenarios. A Water Quality Improvement Plan developed for the Tully-Murray catchment in the Great Barrier Reef region was used to test the protocol and three benefits of its use were identified. First, developing rigorous and timely monitoring and evaluation ensures that opportunities for iterative planning are realised. Second, anticipating future endogenous or exogenous changes to the plan enables the early initiation of actions to inform transformative planning responses. Finally, the protocol exposed the need to coordinate multi-scalar responses to tackle environmental knowledge and management uncertainties and assumptions. The protocol seeks to provide a practical translation of adaptive planning theory that will enable the benefits of adaptive management to be realised on the ground.

Use of remote sensing and mathematical modelling to predict the flux of dimethylsulfide to the atmosphere in the Southern Ocean

Abstract An existing ecological model of DMS production has been extended and applied to the spring-summer period in the Subantarctic Southern Ocean. The model predicts that production of phytoplankton and dissolved DMS will increase during spring to reach a maximum in summer consistent with the atmospheric data collected at the Cape Grim baseline station. Archival Coastal Zone Color Scanner satellite imagery has been used to define the seasonal range in phytoplankton concentration in the study region and validate model predictions. Local measured wind and sea temperature data have been used to calculate the DMS transfer velocity which is used to compute the sea-to-air flux of DMS. The seasonal trend in predicted DMS flux is in good agreement with the flux estimates made from observations.

Bayesian learning and predictability in a stochastic nonlinear dynamical model

Bayesian inference methods are applied within a Bayesian hierarchical modeling framework to the problems of joint state and parameter estimation, and of state forecasting. We explore and demonstrate the ideas in the context of a simple nonlinear marine biogeochemical model. A novel approach is proposed to the formulation of the stochastic process model, in which ecophysiological properties of plankton communities are represented by autoregressive stochastic processes. This approach captures the effects of changes in plankton communities over time, and it allows the incorporation of literature metadata on individual species into prior distributions for process model parameters. The approach is applied to a case study at Ocean Station Papa, using particle Markov chain Monte Carlo computational techniques. The results suggest that, by drawing on objective prior information, it is possible to extract useful information about model state and a subset of parameters, and even to make useful long-term forecasts, based on sparse and noisy observations.

Linking ocean models to coastal management on Australia's north west shelf

The need for integrated environmental studies to support the management of marine systems is now widely accepted. A significant number of such studies have been undertaken in the past two decades, particularly in coastal bays and estuaries; see, for example, Harris and Crossland [1999]. These studies have generally led to improved scientific understanding of various components of the natural ecosystem and direct impacts of human activities. However, the integration of this information into a single coherent framework has usually only been attempted in the final stages of a project or not at all [Knuttle, 2000]. Managers are then left with the daunting task of interpreting a disparate set of scientific results and incorporating them into a decision-making process.

Development of operational data-assimilating water quality modelling system for South-East Tasmania

With the rapid advances in on-line observing system applications, the paradigm in environmental modelling is shifting from one-off models for specific purposes, to operational models, sequentially assimilating data streams from in situ and remote sensors. Such models can provide products and services to support a wide range of applications, from short-term forecasting to long-term scenarios, and are expected to deliver superior performance much more cost-effectively. In the marine field, this is most advanced for circulation models at large ocean scales. The potential benefit from these advances is even greater in the coastal zone, where human uses, impacts and ecosystem services are concentrated. However, there are substantial challenges to be overcome. Coastal applications typically require biogeochemical, ecological, and ultimately socioeconomic models. These additional models are more complex, with higher uncertainty, and require different approaches to data assimilation and uncertainty analysis. The uncertainties arise from a number of sources including poorly known parameters, structural errors and stochastic forcing. When model realisations are sufficiently fast, Monte Carlo techniques can be used to improve the model performance and assess its quality, otherwise alternative estimation techniques are required. This paper describes the development of an operational, data-assimilating coastal model for SE Tasmania, integrating across hydrodynamics, sediment dynamics and biogeochemistry. Inputs and outputs from the model are expected to be integrated into the regional information system (INFORMD), and to be used directly in multiple management applications, and as input into ecosystem models. A hydrodynamic model, nested inside an operational global model, will be assimilating data from the coastal sensor network and other sources, including remote sensing. The model is based on an operational modelling platform developed by CSIRO through the BlueLink project (ROAM), and will be used to implement and test data-assimilation techniques for coastal models under development in BlueLink. Operational sediment dynamic and biogeochemical models, will be coupled to the hydrodynamic model, either directly or through intermediate transport models. Data-assimilating techniques for these models currently are under development in Computational and Simulation Sciences theme, CSIRO. This paper outlines preliminary results from these developments. A number of candidate techniques including Kalman Filter, Particle Filter and MCMC are discussed. The utility of fast and cheap statistical surrogates of complex models (emulators) for sequential data assimilation is illustrated through the trial application of emulators to one-dimensional sediment/pollutant and 3-d sediment transport models.

Interpretation of a modified linear model of catch-per-unit-effort data in a spatially-dynamic fishery

Abstract The spatial and temporal dynamics of populations of migratory fish and associated fishing patterns give rise to data from which it is difficult to obtain clear signals about population size and distribution. Use of modified General Linear Models (GLMs) for obtaining information about the population is explained and demonstrated by way of example. Two modified GLMs for obtaining relative indices of abundance for migratory pelagic fish are presented.