Towards an increase of flash flood geomorphic effects due to gravel mining and ground subsidence in Nogalte stream (Murcia, SE Spain)

Transition from endorheic alluvial fan environments to well-channelized fluvial systems in natural conditions may occur in response to base-level fluctuations. However, human-induced changes in semi-arid regions can also be responsible for similar unforeseen modifications. Our results confirm that in-channel gravel mining and aquifer overexploitation over the last 50 years in the case study area have changed the natural stability of the Nogalte stream and, as a result, its geomorphic parameters including channel depth and longitudinal profile have begun to adapt to the new situation. Using interferometric synthetic aperture radar (InSAR) data we obtain maximum values for ground subsidence in the Upper Guadalentín Basin of  ∼ 10 cm yr−1 for the period 2003–2010. In this context of a lowered base level, the river is changing its natural flood model to a more powerful one. A comparison of the 1973 flood event, the most dramatic flood event ever recorded in the area, with the 2012 event, where there was a similar discharge but a sediment load deficit, reveals greater changes and a new flooding pattern and extension. In-channel gravel mining may be responsible for significant local changes in channel incision and profile. This, together with the collateral effects of aquifer overexploitation, can favour increased river velocity and stream power, which intensify the consequences of the flooding. The results obtained here clearly demonstrate an existing transition from the former alluvial pattern to a confined fluvial trend, which may become more pronounced in the future due to the time lag between the drop in aquifer level and ground subsidence, and introduce a new scenario to be taken into consideration in future natural hazard planning in this area.s. This research was partially funded by projects CGL 2011-23857, ESP2013-47780-C2-2-R and CGL2013-47412-C2-1-P (Spanish Ministry of Economy and Competitiveness).Peer reviewe

Nonparametric analysis of casein complex genes' epistasis and their effects on phenotypic expression of milk yield and composition in Murciano-Granadina goats

Improving knowledge on the causative polymorphisms or genes regulating the expression of milk quantitative and qualitative traits and their interconnections plays a major role in dairy goat breeding programs and genomic research. This information enables optimization of predictive and selective tools, to obtain better-performing animals to help satisfy market demands more efficiently. Goat milk casein proteins (αS1, αS2, β, and κ) are encoded by 4 loci (CSN1S1, CSN1S2, CSN2, and CSN3) clustered within 250 kb on chromosome 6. Among the statistical methods used to identify epistatic interactions in genome-wide qualitative association studies (GWAS), gene-based methods have recently grown in popularity due to their better statistical power and biological interpretability. However, most of these methods make strong assumptions about the magnitude of the relationships between SNP and phenotype, limiting statistical power. Thus, the aims of this study were to quantify the epistatic relationships among 48 SNP in the casein complex on the expression of milk yield and components (fat, protein, dry matter, lactose, and somatic cells) in MurcianoGranadina goats, to explain the qualitative nature of the SNP used to quantify the genotypes produced as a result. Categorical principal component analysis (CATPCA) was used to delimit and group the number of SNP studied depending on their implications in the explanation of milk yield and components variability. Afterward, nonlinear canonical correlation analysis was used to identify relationships among and within the SNP groups detected by CATPCA. Our results suggest that 79.65% of variability in the traits evaluated may be ascribed to the epistatic relationships across and within 7 SNP groups. Two partially overlapping groups of epistatically interrelated SNP were detected: one group of 21 SNP, explaining 57.56% of variability, and another group of 20 SNP, explaining 42.43% (multiple fit ≥ 0.1). Additionally, SNP18, 32, and 36 (CSN1S2, CSN1S1, and CSN2 loci, respectively) were the most significant SNP to explain intragroup epistatic variability (component loading > |0.5|). Conclusively, milk yield and quality may not only depend on the specific casein gene pool of individuals, but may also be relevantly conditioned by the relationships set across and within such genes. Hence, studying epistasis in isolation may be crucial to optimize selective practices for economically important dairy traits

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Considering casein haplotype variants rather than SNPs may maximize the understanding of heritable mechanisms and their implication on the expression of functional traits related to milk production. Effects of casein complex haplotypes on milk yield, milk composition, and curve shape parameters were used using a Bayesian inference for ANOVA. We identified 48 single nucleotide polymorphisms (SNPs) present in the casein complex of 159 unrelated individuals of diverse ancestry, which were organized into 86 haplotypes. The Ali and Schaeffer model was chosen as the best fitting model for milk yield (Kg), protein, fat, dry matter, and lactose (%), while parabolic yield-density was chosen as the best fitting model for somatic cells count (SCC × 103 sc/mL). Peak and persistence for all traits were computed respectively. Statistically significant differences (p < 0.05) were found for milk yield and components. However, no significant difference was found for any curve shape parameter except for protein percentage peak. Those haplotypes for which higher milk yields were reported were the ones that had higher percentages for protein, fat, dry matter, and lactose, while the opposite trend was described by somatic cells counts. Conclusively, casein complex haplotypes can be considered in selection strategies for economically important traits in dairy goats

Software-Automatized Individual Lactation Model Fitting, Peak and Persistence and Bayesian Criteria Comparison for Milk Yield Genetic Studies in Murciano-Granadina Goats

SPSS model syntax was defined and used to evaluate the individual performance of 49 linear and non-linear models to fit the lactation curve of 159 Murciano-Granadina goats selected for genotyping analyses. Lactation curve shape, peak and persistence were evaluated for each model using 3107 milk yield controls with an average of 3.78 ± 2.05 lactations per goat. Best fit (Adjusted R2) values (0.47) were reached by the five-parameter logarithmic model of Ali and Schaeffer. Three main possibilities were detected: non-fitting (did not converge), standard (Adjusted R2 over 75%) and atypical curves (Adjusted R2 below 75%). All the goats fitted for 38 models. The ability to fit different possible functional forms for each goat, which progressively increased with the number of parameters comprised in each model, translated into a higher sensitivity to explaining curve shape individual variability. However, for models for which all goats fitted, only moderate increases in explanatory and predictive potential (AIC, AICc or BIC) were found. The Ali and Schaeffer model reported the best fitting results to study the genetic variability behind goat milk yield and perhaps enhance the evaluation of curve parameters as trustable future selection criteria to face the future challenges offered by the goat dairy industry

Goat Milk Nutritional Quality Software-Automatized Individual Curve Model Fitting, Shape Parameters Calculation and Bayesian Flexibility Criteria Comparison

SPSS syntax was described to evaluate the individual performance of 49 linear and non-linear models to fit the milk component evolution curve of 159 Murciano-Granadina does selected for genotyping analyses. Peak and persistence for protein, fat, dry matter, lactose, and somatic cell counts were evaluated using 3107 controls (3.91 ± 2.01 average lactations/goat). Best-fit (adjusted R2) values (0.548, 0.374, 0.429, and 0.624 for protein, fat, dry matter, and lactose content, respectively) were reached by the five-parameter logarithmic model of Ali and Schaeffer (ALISCH), and for the three-parameter model of parabolic yield-density (PARYLDENS) for somatic cell counts (0.481). Cross-validation was performed using the Minimum Mean-Square Error (MMSE). Model comparison was performed using Residual Sum of Squares (RSS), Mean-Squared Prediction Error (MSPE), adjusted R2 and its standard deviation (SD), Akaike (AIC), corrected Akaike (AICc), and Bayesian information criteria (BIC). The adjusted R2 SD across individuals was around 0.2 for all models. Thirty-nine models successfully fitted the individual lactation curve for all components. Parametric and computational complexity promote variability-capturing properties, while model flexibility does not significantly (p > 0.05) improve the predictive and explanatory potential. Conclusively, ALISCH and PARYLDENS can be used to study goat milk composition genetic variability as trustable evaluation models to face future challenges of the goat dairy industry

Does the Acknowledgement of αS1-Casein Genotype Affect the Estimation of Genetic Parameters and Prediction of Breeding Values for Milk Yield and Composition Quality-Related Traits in Murciano-Granadina?

A total of 2090 lactation records for 710 Murciano-Granadina goats were collected during the years 2005–2016 and analyzed to investigate the influence of the αS1-CN genotype on milk yield and components (protein, fat, and dry matter). Goats were genetically evaluated, including and excluding the αS1-CN genotype, in order to assess its repercussion on the efficiency of breeding models. Despite no significant differences being found for milk yield, fat and dry matter heritabilities, protein production heritability considerably increased after aS1-CN genotype was included in the breeding model (+0.23). Standard errors suggest that the consideration of genotype may improve the model’s efficiency, translating into more accurate genetic parameters and breeding values (PBV). Genetic correlations ranged from −0.15 to −0.01 between protein/dry matter and milk yield/protein and fat content, while phenotypic correlations were −0.02 for milk/protein and −0.01 for milk/fat or protein content. For males, the broadest range for reliability (RAP) (0.45–0.71) was similar to that of females (0.37–0.86) when the genotype was included. PBV ranges broadened while the maximum remained similar (0.61–0.77) for males and females (0.62–0.81) when the genotype was excluded, respectively. Including the αS1-CN genotype can increase production efficiency, milk profitability, milk yield, fat, protein and dry matter contents in Murciano-Granadina dairy breeding programs

Speleoseismology and palaeoseismicity of Benis Cave (Murcia, SE Spain): coseismic effects of the 1999 Mula earthquake (mb 4.8)

This work describes the coseismic ceiling block collapse within Benis Cave (−213 m; Murcia, SE Spain), associated with the 1999 Mula earthquake (mb=4.8, MSK VII). The collapse occurred at −156 m into the Earthquake Hall, and as a consequence one small gallery became blind. We studied the geology, topography and active tectonic structures relevant to the cave. In addition, we carried out a seismotectonic analysis of the focal mechanism solutions, and also a fault population analysis on slickensides measured in fault planes in the cave. The stress and strain regime is interpreted as being congruent with the palaeoseismic evidence, and agrees with the fault kinematics established for cave galleries developed within fault planes and growth anomalies of coral flowstone. Our analysis suggests that one active segment (NNE–SSW) determined the morphology and topography of the Benis Cave, where strong to moderate palaeoearthquakes (6≤M≤7) took place. As a consequence of this intense seismic activity a small gallery collapsed. A new palaeoseismic structure, or seismothem, has been recognized, namely the effect of palaeoearthquakes affecting the pattern of development of the spatial coral flowstone distribution located at the bottom of the cave

Advances in geobotany and new tools in biogeographic and bioclimatic maps: Sierra de Guadarrama National Park

20 páginas, 14 tablas, 3 mapas

Psychological Impact of COVID-19 Lockdown on Well-being: Comparisons between People with Obesity, with Diabetes and without Diseases

Introduction: Obesity and type 2 diabetes mellitus are two chronic diseases most associated with hospitalizationsand deaths from COVID-19.Background: This study compared psychological impact of COVID-19 lockdown in people with obesity, people with type 2 diabetes (T2D) and people without diseases, and determined the factors associated with well-being.Materials and methods: An online survey on negative affect, attitudes, social support and sharing, coping,well-being, and eating behavior was conducted in 157 people with obesity, 92 with type 2 diabetes and 288without diseases.Results: People with obesity were the most worried of getting infected (70%) or dying (64%) and had the highest levels of emotional eating. People with T2D showed better coping strategies and higher well-being. Negative affect, worries about COVID-19 consequences and uncontrolled eating had negative impact, but social support, social sharing, and coping contributed positively (p &lt; 0.001) to well-being. A 48.7% of people with obesity experienced more difficulties to adhere to treatment compared to only 11.1% of people with T2D.Conclusions: People with obesity had less well-being and more COVID-19 worries and emotional eating than people with T2D and without diseases. Well-being depends on negative affect, worries and eating behavior. Future research about the impact in long-term on weight and health status in patients with chronic diseases is needed

InSAR-Based Mapping to Support Decision-Making after an Earthquake

It has long been recognized that earthquakes change the stress in the upper crust around the fault rupture and can influence the behaviour of neighbouring faults and volcanoes. Rapid estimates of these stress changes can provide the authorities managing the post-disaster situation with valuable data to identify and monitor potential threads and to update the estimates of seismic and volcanic hazard in a region. Here we propose a methodology to evaluate the potential influence of an earthquake on nearby faults and volcanoes and create easy-to-understand maps for decision-making support after large earthquakes. We apply this methodology to the Mw 7.8, 2016 Ecuador earthquake. Using Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and continuous GPS data, we measure the coseismic ground deformation and estimate the distribution of slip over the fault rupture. We also build an alternative source model using the Global Centroid Moment Tensor (CMT) solution. Then we use these models to evaluate changes of static stress on the surrounding faults and volcanoes and produce maps of potentially activated faults and volcanoes. We found, in general, good agreement between our maps and the seismic and volcanic events that occurred after the Pedernales earthquake. We discuss the potential and limitations of the methodology.This work is supported by the European Commission, Directorate-General Humanitarian Aid and Civil Protection (ECHO) under the SAFETY (Sentinel for Geohazards regional monitoring and forecasting) project (ECHO/SUB/2015/718679/Prev02) and by the Spanish Ministry of Economy and Competitiveness under INTERGEOSIMA (CGL2013-47412) and ACTIVESTEP (CGL2017-83931-C3), QUAKESTEP (1-P) + 3GEO(2-P) + GEOACTIVA (3-P) projects