Coral reefs and other coastal ecosystems such as seagrasses and mangroves are widely recognized to provide protection against
the devastating effects of strong waves associated with tsunamis and storms. The predicted warming climate brings to fore
the role of these ecosystems in providing protection against stronger typhoons that can result in more devastating waves of
greater amplitude. We performed a model simulation of storm generated waves on a Philippine reef, which is located along the
path of tropical storms, i.e., at least 10 typhoons on the average pass through the study site yearly. A model to simulate
wave propagation was developed using Simulating Waves Nearshore (SWAN) and DELFT3D-WAVE computer simulation software. Scenarios
involving local monsoonal wind forcing and storm conditions were simulated. In addition, as climate change may also result
to increased relative sea level, a 0.3 m and 1 m rise in sea level scenarios were also used in the wave model simulations.
Results showed that the extensive reef system in the site helped dissipate wave energy that in turn reduced wave run-up on
land. A significant reduction in wave energy was observed in both climate change, i.e., stronger wind and higher sea level,
and non-climate change scenarios. This present study was conducted in a reef whose coral cover is in excellent condition (i.e.,
50 to 80% coral cover). Estimates of coral reef growth are in the same order of magnitude as estimates of relative sea level
rise based on tide gauge and satellite altimeter data, thus it is possible that the role of reefs in attenuating wave energy
may be maintained if coral reef growth can keep up with the change in sea level. Nonetheless, to maintain reef growth, it
is imperative to manage coral reef ecosystems sustainably and to eliminate the stressors that are within human control. Minimizing
activities such as illegal and destructive blast and poison fishing methods, pollution and siltation, is crucial to minimize
the impacts of high-energy waves that may increase with climate change. 相似文献
Many forest pest species strongly depend on temperature in their population dynamics, so that rising temperatures worldwide as a consequence of climatic change are leading to increased frequencies and intensities of insect-pest outbreaks. In the Mediterranean area, the climatic conditions are strongly linked to the effects of the North Atlantic Oscillation (NAO). The aim of this work is to analyze the dynamics of the pine processionary moth (Thaumetopoea pityocampa), a severe pest of Pinus species in the Circunmediterranean, throughout a region of southern Spain, in relation to NAO indices. We related the percentage of forest plots with high defoliation by pine processionary moth each year with NAO values for the present and the three previous winters, using generalized linear models with a binomial error distribution. The time series is 16-year long, and we performed analyses for the whole database and for the five main pine species separately. We found a consistent relationship between the response variable and the NAO index. The relationship is stronger with pine species living at medium-high altitudes, such as Aleppo (P. halepensis), black (P. nigra), and Scots (Pinus sylvestris) pine, which show the higher defoliation intensities up to 3?years after a negative NAO phase. The results highlight, for the first time, the usefulness of using global drivers in order to understand the dynamics of pest outbreaks at a regional scale, and they open the window to the development of NAO-based predictive models as an early-warning signal of severe pest outbreaks. 相似文献
Dairy farmers face increasing pressure to decrease environmental impact while remaining economically viable. Adaptation of farm management practices in response to seasonal climate forecasts may be one means of achieving these objectives. This paper describes the interactive and iterative process by which farmers, researchers, extension agents, regulatory agencies, and other stakeholders collaborated to create, calibrate, and validate the Dynamic North Florida Dairy Farm model (DyNoFlo), a whole-farm decision support system to decrease nitrogen leaching while maintaining profitability under variable climate conditions. Participatory modeling may enhance the creation of adoptable and adaptable user-friendly models that include environmental, economic and biophysical components. By providing farmers, policy makers, and other stakeholders with a more holistic view of current practices, common ground among them was more easily identified and collaboration was fostered. Farmer values included willingness to be good environmental stewards when they are profitable. The participatory research and development process enhanced understanding of and potential adaptation to seasonal climate variability conditioned to the El Niño Southern Oscillation (ENSO) phases in light of increasing environmental regulations and economic challenges. Adoption of the collaboratively-developed DyNoFlo is expected to be higher than usual because stakeholders feel greater ownership of the final product. 相似文献
With an increasing demand for raw materials, predictive models that support successful mineral exploration targeting are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in an ArcGIS toolbox. Performance was tested on an exploration dataset from the Iberian Pyrite Belt (IPB) with respect to accuracy, performance, stability, and robustness. Boosting algorithms are ensemble methods used in supervised learning for regression and classification. They combine weak classifiers, i.e., classifiers that perform slightly better than random guessing to obtain robust classifiers. Each time a weak learner is added; the learning set is reweighted to give more importance to misclassified samples. Our test area, the IPB, is one of the oldest mining districts in the world and hosts giant volcanic-hosted massive sulfide (VMS) deposits. The spatial density of ore deposits, as well as the size and tonnage, makes the area unique, and due to the high data availability and number of known deposits, well-suited for testing machine learning algorithms. We combined several geophysical datasets, as well as layers derived from geological maps as predictors of the presence or absence of VMS deposits. Boosting algorithms such as BrownBoost and Adaboost were tested and compared to Logistic Regression (LR), Random Forests (RF) and Support Vector machines (SVM) in several experiments. We found performance results relatively similar, especially to BrownBoost, which slightly outperformed LR and SVM with respective accuracies of 0.96 compared to 0.89 and 0.93. Data augmentation by perturbing deposit location led to a 7% improvement in results. Variations in the split ratio of training and test data led to a reduction in the accuracy of the prediction result with relative stability occurring at a critical point at around 26 training samples out of 130 total samples. When lower numbers of training data were introduced accuracy dropped significantly. In comparison with other machine learning methods, Adaboost is user-friendly due to relatively short training and prediction times, the low likelihood of overfitting and the reduced number of hyperparameters for optimization. Boosting algorithms gave high predictive accuracies, making them a potential data-driven alternative for regional scale and/or brownfields mineral exploration.
The Upper Miocene Cerro Morado Andesites constitutes a mafic volcanic field (100 km2) composed of andesite to basaltic andesite rocks that crop out 75 km to the east from the current arc, in the northern Puna of Argentina. The volcanic field comprises lavas and scoria cones resulting from three different eruptive phases developed without long interruptions between each other. Lavas and pyroclastic rocks are thought to be sourced from the same vents, located where orogen-parallel north-south faults crosscut transverse structures.The first eruptive phase involved the effusion of extensive andesitic flows, and minor Hawaiian-style fountaining which formed subordinate clastogenic lavas. The second phase represents the eruption of slightly less evolved andesite lavas and pyroclastic deposits, only distributed to the north and central sectors of the volcanic field. The third phase represents the discharge of basaltic andesite magmas which occurred as both pyroclastic eruptions and lava effusion from scattered vents distributed throughout the entire volcanic field. The interpreted facies model for scoria cones fits well with products of typical Strombolian-type activity, with minor fountaining episodes to the final stages of eruptions.Petrographic and chemical features suggest that the andesitic units (SiO2 > 57%) evolved by crystal fractionation. In contrast, characteristics of basaltic andesite rocks are inconsistent with residence in upper-crustal chambers, suggesting that batches of magmas with different origins or evolutive histories arrived at the surface and erupted coevally.Based on the eruptive styles and lack of volcanic quiescence gaps between eruptions, the Cerro Morado Andesites can be classified as a mafic volcanic field constructed from the concurrent activity of several small, probably short-lived, monogenetic centers. 相似文献
The regulation of minimum legal size (MLS) of catches is a tool widely applied in the management of fisheries resources, although the MLS does not always coincide with the length at first maturity (LFM). The optimization of this management tool requires a series of quality control in fish markets and transportation. A software application has been developed to make the control of the landings of several target species easier and faster. In order to test and make this tool operational, six species of commercial interest were selected: four species of fish and two species of bivalves. It is proposed to estimate the proportion of illegal specimens in the studied lot from the proportion of illegal individuals found in the samples taken from this lot. The input data for the application are the minimum legal size (MLS) of the species and the total length (TL) of each specimen sampled. The output data is a statistical summary of the percentage of specimens of size less than the legal minimum (TL≤MLS) within different confidence intervals (90%, 95% and 99%). The software developed will serve as a fast, efficient and easy to manage tool that allows inspectors to determine the degree of compliance on MLS control and to make a decision supported by statistical proof on fishing goods. 相似文献
