Changes in soils and vegetation in a Mediterranean coastal salt marsh impacted by human activities

This paper reports changes in vegetation distribution and species cover in relation to soil factors and hydrology in a semiarid Mediterranean salt marsh adjacent to the Mar Menor saline lagoon. Species cover, soil salinity, and the groundwater level were monitored between 1991 and 1993 and between 2002 and 2004, and total organic carbon, total nitrogen, total phosphorus, nitrates, ammonium and exchangeable phosphorus were measured in the soils in both study periods. In addition, three soil profiles were described in August 1992 and August 2004. The results indicate an elevation of the water table throughout the 13-year period, which was attributable to water flowing from areas with intensive agriculture. Flooding increased and soil salinity dropped in the most saline sites and increased in the least saline ones. The morphology of the soil profiles reflected the increase in flooding periods, due to the appearance of a greyer matrix in the deeper horizons and a more diffuse pattern of Fe mottles. Following these environmental changes, Sarcocornia fruticosa, Phragmites australis and Juncus maritimus strongly expanded at the wettest sites, which led to the disappearance of the original zonation pattern. The cover of Limonium delicatulum, in turn, decreased with the increase in moisture but increased following the increase in salinity. Changes in soil nutrients were only very evident in the sandy soils of the beach, probably due to the influence of organic debris deposited on the shoreline by the storms and due to the strong increase in the colonisation of this habitat by perennial species. According to the results obtained, control measures are needed in order to preserve habitat diversity in this and other salt marshes of this area. Monitoring of the vegetation distribution could be a useful tool to identify environmental impacts, in order to implement remedial actions.     

Indicator Simulation Accounting for Multiple-Point Statistics

Geostatistical simulation aims at reproducing the variability of the real underlying phenomena. When nonlinear features or large-range connectivity is present, the traditional variogram-based simulation approaches do not provide good reproduction of those features. Connectivity of high and low values is often critical for grades in a mineral deposit. Multiple-point statistics can help to characterize these features. The use of multiple-point statistics in geostatistical simulation was proposed more than 10 years ago, on the basis of the use of training images to extract the statistics. This paper proposes the use of multiple-point statistics extracted from actual data. A method is developed to simulate continuous variables. The indicator kriging probabilities used in sequential indicator simulation are modified by probabilities extracted from multiple-point configurations. The correction is done under the assumption of conditional independence. The practical implementation of the method is illustrated with data from a porphyry copper mine.     

Wind and Disk Diagnostics in PMS Stars. A Critical Analysis

The relationships among traditional wind and disk diagnostics - Hα and [OI]λ6300 lines and IR luminosity excesses, respectively - and star parameters are critically analysed. The total sample includes 109 PMS stars - 20 Weak-line T Tauri (WTTS), 45 Classical T Tauri (CTTS) and 44 HAeBe stars-. Our results suggest that Hα is neither a wind nor an accretion tracer. Hα and [OI] emissions seem to correlate very well with the photospheric luminosity and not with ΔL_IR/L_ph, a parameter related to the origin of the IR excesses. This revised version was published online in July 2006 with corrections to the Cover Date.     

Diel and seasonal variability in the fish community structure of a mud‐bottom estuarine habitat in the Gulf of Mexico

We hypothesized that temporal variation in fish species composition and community structure in a low complexity habitat in the Pueblo Viejo Lagoon, Mexico, is influenced by diel light/dark cycles and tidal stage, and by seasonal changes in salinity and temperature. We collected a total of 17,661 individuals during 2‐h interval sampling over six bi‐monthly 24‐h sampling cycles representing 53 species, of which 11 (～20%) were previously unknown in the system. Diel variation indicated that significantly higher numbers of individuals and species were caught at night, whereas diversity and evenness were higher during the day. Species richness was significantly higher in July and January, whereas diversity and evenness peaked around May; both were correlated with temperature. Diel variation in species composition was influenced primarily by the light/dark cycle. Cluster analyses of each diel cycle separated fish assemblages from midday samples from those of nocturnal samples, separated by an extended wide transition period as fish moved at dawn and during the late afternoon/dusk. Significant shifts (as determined by MANOVA) in assemblage structure occurred between months. Canonical correspondence analysis showed that temperature and day/night effects were the most important environmental variables structuring the fish community. This constrained ordination also defined species with specific habitat preferences as follows: (i) diurnal, warm temperature species (mainly planktivores) (Brevoortia gunteri, Cetengraulis edentulus, Diapterus auratus, and Membras martinica); (ii) nocturnal, warm temperature species (mainly predators) (Citharichthys spilopterus, Cathorops melanopus, and Bairdiella spp.); and (iii) low temperature, diurnal species (Brevoortia patronus and Mugil curema) or those with twilight and nocturnal distributions (Anchoa mitchilli, the most numerically abundant species). Our results indicate that diel and seasonal changes in fish community structure were mainly related to day/night cycles and temperature regimes.     

TNOs are Cool: A Survey of the Transneptunian Region

Over one thousand objects have so far been discovered orbiting beyond Neptune. These trans-Neptunian objects (TNOs) represent the primitive remnants of the planetesimal disk from which the planets formed and are perhaps analogous to the unseen dust parent-bodies in debris disks observed around other main-sequence stars. The dynamical and physical properties of these bodies provide unique and important constraints on formation and evolution models of the Solar System. While the dynamical architecture in this region (also known as the Kuiper Belt) is becoming relatively clear, the physical properties of the objects are still largely unexplored. In particular, fundamental parameters such as size, albedo, density and thermal properties are difficult to measure. Measurements of thermal emission, which peaks at far-IR wavelengths, offer the best means available to determine the physical properties. While Spitzer has provided some results, notably revealing a large albedo diversity in this population, the increased sensitivity of Herschel and its superior wavelength coverage should permit profound advances in the field. Within our accepted project we propose to perform radiometric measurements of 139 objects, including 25 known multiple systems. When combined with measurements of the dust population beyond Neptune (e.g. from the New Horizons mission to Pluto), our results will provide a benchmark for understanding the Solar debris disk, and extra-solar ones as well.     

Baja California 边缘PC14 岩芯海水表层温度及生产力变化的百年尺度记录

加利福尼亚流是北太平洋环流的重要组成部分,它的变化对全球气候及碳循环有重要影响.对位于东北太平洋低纬区BajaCalifornia外海的PC14柱状样进行了有机地球化学分析,重建了过去14ka来这一地区百年尺度的海水表面温度及生产力变化,发现其存在明显的B/A(Bling-Aller)和YD(Younger Drays)事件,但B/A和YD事件温度变化幅度小于中纬度地区.与中纬度加利福尼亚流系早中全新世温度降低不同,PC14全新世温度无明显变化趋势.这些结果表明了东太平洋低纬地区对高纬地区的气候响应以及沿岸上升流和ENSO现象对加利福尼亚流系中纬和低纬地区的影响不同.从冰消期到全新世,PC14生产力呈现阶段上升趋势,指示了由E1 Nino气候向La Nifia气候的转化以及沿岸上升流加强的总体趋势.在全新世中后期(6.5-3.8ka)生产力的降低,则可能是受ENSO加强的影响.在B/A暖期,PC14生产力增加不多,但氧最小层强度增加,这表明生产力变化不是东太平洋边缘海的氧最小层强化的控制因素.     

Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox

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.

     

Climate change and the incidence of a forest pest in Mediterranean ecosystems: can the North Atlantic Oscillation be used as a predictor?

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.