Modelling the effects of climate change on the distribution and production of marine fishes: accounting for trophic interactions in a dynamic bioclimate envelope model

Climate change has already altered the distribution of marine fishes. Future predictions of fish distributions and catches based on bioclimate envelope models are available, but to date they have not considered interspecific interactions. We address this by combining the species-based Dynamic Bioclimate Envelope Model (DBEM) with a size-based trophic model. The new approach provides spatially and temporally resolved predictions of changes in species' size, abundance and catch potential that account for the effects of ecological interactions. Predicted latitudinal shifts are, on average, reduced by 20% when species interactions are incorporated, compared to DBEM predictions, with pelagic species showing the greatest reductions. Goodness-of-fit of biomass data from fish stock assessments in the North Atlantic between 1991 and 2003 is improved slightly by including species interactions. The differences between predictions from the two models may be relatively modest because, at the North Atlantic basin scale, (i) predators and competitors may respond to climate change together; (ii) existing parameterization of the DBEM might implicitly incorporate trophic interactions; and/or (iii) trophic interactions might not be the main driver of responses to climate. Future analyses using ecologically explicit models and data will improve understanding of the effects of inter-specific interactions on responses to climate change, and better inform managers about plausible ecological and fishery consequences of a changing environment.

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.

Effects Of Low Metal Levels On A Clonal Hydroid

We frequently require sensitive bioassay techniques with which to study the effects of marine contaminants at environmentally realistic concentrations. Unfortunately, it is difficult to achieve sensitivity and precision in an organism amenable to indefinite periods of laboratory culture. Results from different laboratories are often extremely variable: LC50 values for the same substance, using the same organism, may differ by two or even three orders of magnitude (Wilson, Cowell & Beynon, 1975). Moreover, some of the most sensitive bioassay organisms require nutrient media, which may alter the availability and toxicity of metals by complexing them (Jones, 1964; Kamp-Nielsen, 1971; Hannan & Patouillet, 1972) and often contain metal impurities at significant levels (Albert, 1968; Steeman Nielsen & Wium Anderson, 1970). The object of the work reported here has been to develop a technique by which these problems might be minimized or avoided. Hydroids were chosen as bioassay organisms for a variety of reasons. They are tolerant but sensitive to small variations in their chemical environment. Techniques for growing hydroids are simple and they can be cultured under conditions of near optimal temperature, salinity and food supply, thus minimizing the errors frequent in bioassay work arising from variations in the history of the test organisms, their size, sex or physiological state. An important source of variability in all work with organisms is that inherent in the genetic material, but with hydroids this can be avoided by the use of a single clone.

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.

The impact of ocean acidification and warming on the skeletal mechanical properties of the sea urchin Paracentrotus lividus from laboratory and field observations

Increased atmospheric CO2 concentration is leading to changes in the carbonate chemistry and the temperature of the ocean. The impact of these processes on marine organisms will depend on their ability to cope with those changes, particularly the maintenance of calcium carbonate structures. Both a laboratory experiment (long-term exposure to decreased pH and increased temperature) and collections of individuals from natural environments characterized by low pH levels (individuals from intertidal pools and around a CO2 seep) were here coupled to comprehensively study the impact of near-future conditions of pH and temperature on the mechanical properties of the skeleton of the euechinoid sea urchin Paracentrotus lividus. To assess skeletal mechanical properties, we characterized the fracture force, Young's modulus, second moment of area, material nanohardness, and specific Young's modulus of sea urchin test plates. None of these parameters were significantly affected by low pH and/or increased temperature in the laboratory experiment and by low pH only in the individuals chronically exposed to lowered pH from the CO2 seeps. In tidal pools, the fracture force was higher and the Young's modulus lower in ambital plates of individuals from the rock pool characterized by the largest pH variations but also a dominance of calcifying algae, which might explain some of the variation. Thus, decreases of pH to levels expected for 2100 did not directly alter the mechanical properties of the test of P. lividus. Since the maintenance of test integrity is a question of survival for sea urchins and since weakened tests would increase the sea urchins' risk of predation, our findings indicate that the decreasing seawater pH and increasing seawater temperature expected for the end of the century should not represent an immediate threat to sea urchins vulnerability.