Long-term individual identification and site fidelity of white sharks, <Emphasis Type=Italic>Carcharodon carcharias</Emphasis>, off California using dorsal fins

Mark-recapture techniques can be used to estimate white shark (Carcharodon carcharias) population abundance. These frameworks are based on assumptions that marks are conserved and animals are present at the sampling location over the entire duration of the study. Though these assumptions have been validated across short-time scales for white sharks, long-term studies of population trends are dependent on these assumptions being valid across longer periods. We use 22 years of photographic data from aggregation sites in central California to support the use of dorsal fin morphology as long-term individual identifiers. We identified five individuals over 16–22 years, which support the use of dorsal fins as long-time individual identifiers, illustrate strong yearly site fidelity to coastal aggregation sites across extended time periods (decades), and provide the first empirical validation of white shark longevity >22 years. These findings support the use of fin morphology in mark-recapture frameworks for white sharks.     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

Phosphorus transformations in plant-based and bio-waste materials induced by pyrolysis

Strategies are needed to increase the sustainability of phosphorus (P) fertiliser management in agriculture. This paper reports on the potential of pyrolysis treatment to recycle P from renewable materials previously regarded as wastes. The study used K-edge X-ray absorption near-edge structure (XANES) spectroscopy to examine chemical forms of P in the waste feedstock materials and corresponding biochars (pyrolysis at 480–500 °C) of four ligno-cellulosic, plant-based residues and five relatively P-rich livestock and water-treatment by-products, to acquire information on changes in potential P fertiliser value. Pyrolysis enriched P in the biochars by factors of 1.3–4.3, thus offering wide-ranging P fertiliser potential. XANES spectroscopy revealed hydroxyapatite (HAP) as one of the dominant chemical P compounds in the feedstocks, ranging from 14% (rice husks) to 98% (animal bone) of total P. For most materials, pyrolysis increased the proportion of HAP, and pyrophosphates were generated in several cases. These alterations possibly lead to diversity in the P solubility characteristics of the biochars if used as soil amendments; this is an important property of environmentally sound P fertilisers.     

Females go where the food is: does the socio-ecological model explain variation in social organisation of solitary foragers?

The socio-ecological model (SEM) links ecological factors with characteristics of social systems and allows predictions about the relationships between resource distribution, type of competition and social organisation. It has been mainly applied to group-living species but ought to explain variation in social organisation of solitary species as well. The aim of this study was to test basic predictions of the SEM in two solitary primates, which differ in two characteristics of female association patterns: (1) spatial ranging and (2) sleeping associations. Beginning in August 2002, we regularly (re-)captured and marked individuals of sympatric populations of Madame Berthe's and grey mouse lemurs (Microcebus berthae, Microcebus murinus) in Kirindy Forest (Madagascar). We recorded data on spatial patterns, feeding and social behaviour by means of direct observation of radio-collared females. The major food sources of M. berthae occurred in small dispersed patches leading to strong within-group scramble competition and over-dispersed females with a low potential for female associations. In contrast, M. murinus additionally used patchily distributed, high-quality (large) resources facilitating within-group contest competition. The combined influence of less strong within-group scramble and contest as well as between-group contest over non-food resources allowed females of this species to cluster in space. Additionally, we experimentally manipulated the spatial distribution of food sources and found that females adjusted their spatial patterns to food resource distribution. Thus, our results support basic predictions of the SEM and demonstrated that it can also explain variation in social organisation of solitary foragers.     

Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach

Macroalgae are a major benthic component of coral reefs and their dynamics influence the resilience of coral reefs to disturbance. However, the relative importance of physical and ecological processes in driving macroalgal dynamics is poorly understood. Here we develop a Bayesian belief network (BBN) model to integrate many of these processes and predict the growth of coral reef macroalgae. Bayesian belief networks use probabilistic relationships rather than deterministic rules to quantify the cause and effect assumptions. The model was developed using both new empirical data and quantified relationships elicited from previous studies. We demonstrate the efficacy of the BBN to predict the dynamics of a common Caribbean macroalgal genus Dictyota. Predictions of the model have an average accuracy of 55% (implying that 55% of the predicted categories of Dictyota cover were assigned to the correct class). Sensitivity analysis suggested that macroalgal dynamics were primarily driven by top–down processes of grazing rather than bottom–up nutrification. BBNs provide a useful framework for modelling complex systems, identifying gaps in our scientific understanding and communicating the complexities of the associated uncertainties in an explicit manner to stakeholders. We anticipate that accuracies will improve as new data are added to the model.     

Pollution and potential mobility of Cd, Ni and Pb in the sediments of a wastewater-receiving river in Hanoi, Vietnam

Large quantities of untreated industrial and domestic wastewater are discharged from the city of Hanoi into urban rivers. Sediment samples from three sites in the To Lich River in Hanoi were assessed with respect to the concentrations and potential mobility of cadmium (Cd), nickel (Ni) and lead (Pb). Due to very high Cd concentrations up to 700 mg kg^?1 at one site, the sediment was considered highly unsuitable for any types of land use if dredged and disposed of on land. Chemical sequential extractions of wet and anoxic sediment samples showed that Cd and Pb were largely associated with the redox-sensitive fractions and could thus be mobilised following measures such as resuspension or dredging. To assess the potential mobilisation of heavy metals from the anoxic sediment due to oxidation, the samples were exposed to different oxidants (i.e. atmospheric air and hydrogen peroxide) and afterwards submitted to a leaching test. These experiments showed that although oxidation may increase the equilibrium pore water concentrations of heavy metals in the sediments, other sediment mineral fractions seem to effectively immobilise heavy metals potentially released from the oxidisable fraction.