Bioclimatic Characterisation of Specific Native Californian Pinales and Their Future Suitability under Climate Change.

Rising temperatures and changes in precipitation patterns under climate change scenarios are accelerating the depletion of soil moisture and increasing the risk of drought, disrupting the conditions that many plant species need to survive. This study aims to establish the bioclimatic characterisation, both qualitative and quantitative, of ten native Californian Pinales for the period 1980-2019, and to determine their habitat suitability by 2050. To achieve this, an exhaustive search of the Gbif database for records of ten conifer taxa was carried out. To conduct the bioclimatic characterisation of the studied taxa, we worked with the monthly values of average temperature and precipitation for the period 1980-2019 from 177 meteorological stations. Linear regressions was performed in order to compile the future evolution of California's climate. Suitable areas and optimal areas were defined at the present time (1980-2019) and its future projection (2050). We applied Boolean logic and, in this investigation, the Conditional Logic Operator (CON) was used to determine the possible species presence (one) or absence (zero) for each of the 15 variables analysed. In general, most of the conifers studied here will experience a reduction in their habitat range in California by the year 2050 due to climate change, as well as the displacement of species towards optimal areas. Furthermore, the results have highlighted the applicability of bioclimatology to future conditions under climate change. This will aid conservation managers in implementing strategic measures to ameliorate the detrimental impacts of climate change, thereby ensuring the ecological integrity and sustainability of the affected conifer species.

Contribution to the Knowledge of Rocky Plant Communities of the Southwest Iberian Peninsula.

The rocky habitats of southern Portugal are ecosystems with extreme xericity conditions, associated with special abiotic strains. In these unstable ecological conditions, a considerable diversity of plant communities occurs. The objective of this study, carried out in the Algarve and Monchique, and the Mariánica Range biogeographical sectors, is to compare chasmo-chomophytic communities of the southwestern Iberian Peninsula, using a phytosociological approach (Braun-Blanquet methodology) and numerical analysis (hierarchical cluster analysis). From these results, two new communities were identified, Sanguisorbo rupicolae-Dianthetum crassipedis and Antirrhinetum onubensis, as a result of floristic and biogeographical differences from other associations already described within the alliances Rumici indurati-Dianthion lusitani and Calendulo lusitanicae-Antirrhinion linkiani, both included in the Phagnalo saxatilis-Rumicetea indurate class.

Concentric Ring Method for generating pollen maps. Quercus as case study.

Mapping pollen concentrations is of great interest to study the health impact and ecological implications or for forestry or agronomical purposes. A deep knowledge about factors affecting airborne pollen is essential for predicting and understanding its dynamics. The present work sought to predict annual Quercus pollen over the Castilla and León region (Central and Northern Spain). Also to understand the relationship between airborne pollen and landscape. Records of Quercus and Quercus pyrenaica pollen types were collected at 13 monitoring sites over a period of 8years. They were analyzed together with land use data applying the Concentric Ring Method (CRM), a technique that we developed to study the relationship between airborne particle concentrations and emission sources in the region. The maximum correlation between the Quercus pollen and forms of vegetation was determined by shrubland and "dehesa" areas. For the specific Qi pyrenaica model (Q. pyrenaica pollen and Q. pyrenaica forest distribution), the maximum influence of emission sources on airborne pollen was observed at 14km from the pollen trap location with some positive correlations up to a distance of 43km. Apart from meteorological behavior, the local features of the region can explain pollen dispersion patterns. The method that we develop here proved to be a powerful tool for multi-source pollen mapping based on land use.

Interlaboratory evaluation of dry-cured hams (from France and Spain) by assessors from two different nationalities.

Two panels of assessors from France and Spain assessed 41 dry-cured hams from diverse geographical origins, pig breedings and feedings. Univariate (Kolmogorov-Smirnov test and t-test) and multivariate (canonical correlation and principal component analysis) statistical procedures have been used to explain the agreement and disagreement between panels evaluating similar and dissimilar sensory attributes quantified with a 9-points structure scale. The results pointed out that there were basic agreements between panels, although some disagreements were detected in mould, acorn and crust attributes. The classification of Iberian and white dry-cured hams and the sensory attributes that characterise them are also displayed.

An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species.

Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles) are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification.