Central Santa Catarina coastal dunefields chronology and their relation to relative sea level and climatic changes

During the past decades, there have been contrarian explanations for the formation and stabilization of coastal dunefields: while many authors believe the dunes formation would be enhanced by falling sea level, others argue that a rising or stable sea level context would be favorable. For Brazilian coastal dunefields, the second hypothesis seems to be more consistent with the luminescence ages found so far; however, most of these data were obtained without using the SAR protocol. Another point of concern is the role of climate change in the aeolian system, which is still not very clear. The aim of this paper is to try to clarify these two questions. To this end, five coastal dunefields were selected in central Santa Catarina coast. The remote sensing and dating results allowed the discrimination and mapping of at least four aeolian generations. Their age distribution in relation to the global curve of relative sea level variation during the Late Pleistocene allows us to suggest that the formation of Aeolian dunefields in the coastal context is supported by stable relative sea level. However, relative sea level is not the only determinant for the formation and preservation of the aeolian coastal dunes. Evidences of climatic control indicate that the initiation of dunefields would be favored by periods of less humidity while their stabilization would occur preferably during the periods of rain intensification, connected to monsoon activity

Global relationships in tree functional traits

Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.Environmental Biolog

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Prediction of landslides risk with fuzzy logic

The stability of slopes is a topic of great interest to the geotechnical engineer, given the significant economic losses, and even human, resulting from the slopes collapse . It's estimated that the landslides outbreak has already caused thousands of deaths and tens of billions of dollars in annual losses worldwide. The phenomena of instability of slopes are conditioned by many factors, such as climate, the lithology and structures of rock, the morphology, the anthropic and others. The anlaysis of geological and geotechnical conditions of landslides provides and appraisal of each of the factors involved in the processes of instability of slopes, allowing the achievement of results of interest with regard to the mode of acton of fators. The current work aims at the use of Fuzzy Logic to create a model that, in qualitative form, provide an estimate of the risk of landslides on the slope of residual soil. To the development of the model was examined and extensive database of landslides in Rio de Janeiro, provided by the GeoRio Foundation. Among the main findings includes the capability of fuzzy logic predicting risk of landslides on the slope of residual soil, appearing as a tool capable of assisting in the detection of areas of risk.Peer Reviewe