Explore the vast range of titles available.

Background The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global health crisis, directly and indirectly impacting all spheres of human life. Some pharmacological measures have been proposed to prevent COVID-19 or reduce its severity, such as vaccinations. Previous reports indicate that influenza vaccination appears to be negatively correlated with COVID-19-associated mortality, perhaps as a result of heterologous immunity or changes in innate immunity. The understanding of such trends in correlations could prevent deaths from COVID-19 in the future. The aim of this study was therefore to analyze the association between COVID-19 related deaths and influenza vaccination rate (IVR) in elderly people worldwide. Methods To determine the association between COVID-19 deaths and influenza vaccination, available data sets from countries with more than 0.5 million inhabitants were analyzed (in total 39 countries). To accurately estimate the influence of IVR on COVID-19 deaths and mitigate effects of confounding variables, a sophisticated ranking of the importance of different variables was performed, including as predictor variables IVR and some potentially important geographical and socioeconomic variables as well as variables related to non-pharmaceutical intervention. The associations were measured by non-parametric Spearman rank correlation coefficients and random forest functions. Results The results showed a positive association between COVID-19 deaths and IVR of people ≥65 years-old. There is a significant increase in COVID-19 deaths from eastern to western regions in the world. Further exploration is needed to explain these findings, and additional work on this line of research may lead to prevention of deaths associated with COVID-19.

Biological diversity can be defined as variability among living organisms from all sources, including terrestrial organisms, marine and other aquatic ecosystems, and the ecological complexes which they are part of. This includes diversity within species, between species, and of ecosystems. Numerous diversity indices combine richness and evenness in a single expression, and several climate-based explanations have been proposed to explain broad-scale diversity patterns. However, climate-based water-energy dynamics appears to be an essential factor that determines patterns of diversity. The Mexican Sierra Madre Occidental occupies an area of about 29 million hectares and is located between the Neotropical and Holarctic ecozones. It shelters a high diversity of flora, including 24 different species of Pinus (ca. 22% on the whole), 54 species of Quercus (ca. 9-14%), 7 species of Arbutus (ca. 50%) and many other trees species. The objectives of this study were to model how tree species diversity is related to climatic and geographic factors and stand density and to test the Metabolic Theory, Productivity-Diversity Hypothesis, Physiological Tolerance Hypothesis, Mid-Domain Effect, and the Water-Energy Dynamic Theory on the Sierra Madre Occidental, Durango. The results supported the Productivity-Diversity Hypothesis, Physiological Tolerance Hypothesis and Water-Energy Dynamic Theory, but not the Mid-Domain Effect or Metabolic Theory. The annual aridity index was the variable most closely related to the diversity indices analyzed. Contemporary climate was found to have moderate to strong effects on the minimum, median and maximum tree species diversity. Because water-energy dynamics provided a satisfactory explanation for the patterns of minimum, median and maximum diversity, an understanding of this factor is critical to future biodiversity research. Quantile regression of the data showed that the three diversity parameters of tree species are generally higher in cold, humid temperate climates than in dry, hot climates.

Centrolobium ochroxylum Rose ex Rudd, known as Amarillo Guayaquil (AG), is a tropical tree species found in secondary vegetation or the wild in the western lowland region of Ecuador (WLRE). AG has heavy (0.78 g/cm 3 ) and durable wood, with whitish sapwood and orange-yellow heartwood, making it ideal for carpentry and construction. The International Union for Conservation of Nature in 2021 classified AG as a threatened and critically endangered tree species. However, information on the forest's growth and yield is limited. The primary objective of this study was to evaluate the first provisional models of growth, yield, site index (SI), volume, and diameter at breast height ( DBH ) - total height ( H ) relationships developed for AG planted in live fences in WLRE. A total of 415 sample plots, each measuring one ha in area, were surveyed. AG trees were arranged in live fences, and UTM coordinates and planting dates were recorded. H and DBH were measured in 160 trees per plot in 2004, 2009, 2012, 2016, and 2018. To model volume, diameters were measured at different heights on randomly selected trees in 195 study sites. Cross-validation revealed that the CR-GADA model, with its three parameters, achieved a better balance between fitness and generalisability than the CR-H model. The Spurr function was found to be the best model for determining the total volume. The linear model was selected to describe the H-DBH relationship in the study region because of its stability and statistical significance. However, the model of Larson showed better overall indicators of fit. Variation of the H-DBH relationship was observed according to the SI. The maximum MAI was 14.8 m 3 ha −1 yr −1 at age 26 years on the best sites, whereas, on less favorable sites, the maximum MAI was 4.4 m 3 ha −1 yr −1 at age 30 years. These models are preliminary and require validation with independent samples. Future studies should include data from mature plots and conduct economic analyses on silvopastoral systems, as well as study the carbon sequestration of AG to encourage reforestation.

The flow of water in temperate forests depends on the amount of precipitation, type of soil, topographic features, and forest cover, among other factors. Unlike the first three, forest cover can be modified by silvicultural treatments, the effects of which manifest in the quality and quantity of water, as well as in the transport of sediments and soil nutrients. The objective of this study was to analyze the effect of some stand variables on surface runoff and stemflow in pine-oak forests of northern Mexico. The stand variables included tree diameter at breast height, basal area, canopy cover, and volume. They were collected in eight 0.1-ha circular plots, measured in 2016 and re-measured in 2018. Nonlinear quantile regression was used to determine the best-fit relationships between the variables. Results indicated that surface runoff was most closely and inversely related to basal area. Stemflow was related to diameter at breast height, while showing no statistical significance. A stemflow funneling ratio did show an inverse, statistically-significant relationship with diameter at breast height. These results can help determine best forest management regimes compatible with the quantity and quality of water fluxes in this type of ecosystem.

This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics-stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)-for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees. The novelty of this study lies in applying five machine learning algorithms-Random Forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)-to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. For comparison, we also applied univariate allometric models, adjusted with nonlinear least squares and quantile regression. The results indicate that Random Forest, k-NN, and SVM outperformed the other algorithms, demonstrating superior predictive accuracy for both biomass and volume. A key innovation of this study is its demonstration of how machine learning, with its ability to model complex, nonlinear relationships, can serve as a powerful tool for forest management. Quantile regression, combined with nonlinear least squares, proves most effective when the relationships are well-defined, allowing for tailored parameter adjustments that enhance predictions, particularly in the presence of heteroscedasticity. Innovative use of machine learning in forest management practices Enhanced predictive accuracy for plant biomass and volume metrics Assessment of total biomass and its components (leaves, branches, stems, and roots) Leveraging machine learning to predict essential forest metrics

Water availability is a major constraint on socioeconomic development in northeastern Mexico, highlighting the need for effective water resource planning that accounts for the variability and extremes of precipitation. In this study, seasonal precipitation reconstructions were developed using tree-ring chronologies from spruce species (Picea spp.). A representative chronology for Picea mexicana Martínez was developed from two populations and spans the period 1786–2020, while a chronology for Picea martinezii T.F. Patterson was established from three populations covering 1746–2020. Both species exhibited significant positive correlations with January–May precipitation (r = 0.65 and 0.71, respectively; p < 0.01) and negative correlations with maximum temperature over the same period (r = −0.52 and −0.59, respectively). Two January–May precipitation reconstructions were produced for periods with adequate sample depth (EPS > 0.85): 1851–2020 for P. mexicana and 1821–2020 for P. martinezii. Both reconstructions revealed pronounced interannual variability, with recurrent droughts and persistently dry conditions, particularly evident in the P. mexicana series. Spatial correlation analyses indicated a historical link between reconstructed precipitation and the El Niño–Southern Oscillation (ENSO). These results highlight the value of spruce species for dendroclimatic reconstruction and their sensitivity to precipitation variability, especially as rising maximum temperatures may compromise their persistence in the Sierra Madre Oriental.

Biomass usage for energy purposes has emerged in response to global energy demands and environmental problems. The large amounts of by-products generated during logging are rarely utilized. In addition, some species (e.g., spp.) are considered less valuable and are left in the cutting areas. Production of pellets from this alternative source of biomass may be possible for power generation. Although the pellets may be of lower quality than other types of wood pellets, because of their physical and technological properties, the addition of different raw materials may improve the characteristics of the oak pellets. Sawdust from the oak species and was mixed with sawdust from the pine in different ratios of oak to pine (100:0, 80:20, 60:40, 40:60 and 20:80). Physical and mechanical properties of the pellets were determined, and calorific value tests were carried out. For each variable, Kolmogorov-Smirnov normality and Kruskal-Wallis tests were performed and Pearson's correlation coefficients were determined (considering a significance level of < 0.05). The moisture content and fixed carbon content differed significantly ( < 0.05) between the groups of pellets (i.e., pellets made with different sawdust mixtures). The moisture content of all pellets was less than 10%. However, volatile matter and ash content did not differ significantly between groups ( ≥ 0.05). The ash content was less than 0.7% in all mixtures. The addition of sawdust to the mixtures improved the bulk density of the pellets by 18%. Significant differences ( < 0.05) in particle density were observed between species, mixtures and for the species × mixture interaction. The particle density was highest in the 80:20 and 60:40 mixtures, with values ranging from 1,245 to 1,349 kg m . Bulk density and particle density of the pellets were positively correlated with the amount of sawdust included. The mechanical hardness and impact resistance index (IRI) differed significantly ( < 0.05) between groups. The addition of pine sawdust decreased the mechanical hardness of the pellets, up to 24%. The IRI was highest (138) in the pellets (100:0). The mechanical hardness and IRI of the pellets were negatively correlated with the amount of sawdust added. The bulk density of the pellets was negatively correlated with mechanical hardness and IRI. The calorific value of mixtures and the species × mixture interaction differed significantly between groups. Finally, the mean calorific value was highest (19.8 MJ kg ) in the 20:80 mixture. The calorific value was positively related to the addition of sawdust.

The phenotype of trees is determined by the relationships and interactions among genetic and environmental influences. Understanding the patterns and processes that are responsible for phenotypic variation is facilitated by studying the relationships between phenotype and the environment among many individuals across broad ecological and climatic gradients. We used Pinus strobiformis , which has a wide latitudinal distribution, as a model species to: (a) estimate the relative importance of different environmental factors in predicting these morphological traits and (b) characterize the spatial patterns of standing phenotypic variation of cone and seed traits across the species’ range. A large portion of the total variation in morphological characteristics was explained by ecological, climatic and geographical variables (54.7% collectively). The three climate, vegetation and geographical variable groups, each had similar total ability to explain morphological variation (43.4%, 43.8%, 51.5%, respectively), while the topographical variable group had somewhat lower total explanatory power (36.9%). The largest component of explained variance (33.6%) was the four-way interaction of all variable sets, suggesting that there is strong covariation in environmental, climate and geographical variables in their relationship to morphological traits of southwest white pine across its range. The regression results showed that populations in more humid and warmer climates expressed greater cone length and seed size. This may in part facilitate populations of P. strobiformis in warmer and wetter portions of its range growing in dense, shady forest stands, because larger seeds provide greater resources to germinants at the time of germination. Our models provide accurate predictions of morphological traits and important insights regarding the factors that contribute to their expression. Our results indicate that managers should be conservative during reforestation efforts to ensure match between ecotypic variation in seed source populations. However, we also note that given projected large range shift due to climate change, managers will have to balance the match between current ecotypic variation and expected range shift and changes in local adaptive optima under future climate conditions.

Seedling growth and survival depend on seedling quality. However, there is no experimental evidence showing that the seedling dimensions of the abundant, economically important and widely distributed tree species , , , , and and their hybrids effectively improve survival and growth in reforestations and plantations in Mexico. Therefore, the aim was to evaluate the influence of initial morphological parameters of 2,007 nursery seedlings of these species and their hybrids on their growth and survival 44 months after planting in the Sierra Madre Occidental, Mexico. Spearman's coefficient ( ) and the unbiased conditional pseudo coefficient of determination ( ) between each specific predictor and each response variable and their 95% confidence interval ( ) were determined using Random Forest, generalized linear model, and bootstrapping. By bootstrapping, the potential environmental heterogeneity inside the trial fields and its impact on the results were also quantified. Among the studied species and their hybrids moderate correlations were observed between the nursery seedling dimensions and the plant dimensions 44 months after planting. However, only weak significant correlations were found between survival rate ( ) and height ( ) ( = 0.10) and between and robustness index ( ) both before planting ( = 0.06). Also, weak significant values of the seedlings , and were detected with respect to the corresponding and 44 months after planting, respectively. Furthermore, the predictor variable \"seed provenance\" (with 23 provenances) significantly explained the variation in the post-planting and of the seedlings, with values ranging from 0.10 to 0.15. The low width of the shows that the environmental conditions in the trial fields were quite homogeneous. The results also show that the inclusion of \"confounding\" variables in the statistical analysis of the study was crucial. Important factors to explain this low association could be the strong damage observed caused by pocket gopher, the typically low winter-spring precipitation in both field trials and adaptation factors. The study findings provide preliminary insights and information aimed at helping to design more appropriate standards for nurseries.

A lack of optimal gene combinations, as well as low levels of genetic diversity, is often associated with the formation of species range margins. Conservation efforts rely on predictive modelling using abiotic variables and assessments of genetic diversity to determine target species and populations for controlled breeding, germplasm conservation and assisted migration. Biotic factors such as interspecific competition and hybridization, however, are largely ignored, despite their prevalence across diverse taxa and their role as key evolutionary forces. Hybridization between species with well‐developed barriers to reproductive isolation often results in the production of offspring with lower fitness. Generation of novel allelic combinations through hybridization, however, can also generate positive fitness consequences. Despite this possibility, hybridization‐mediated introgression is often considered a threat to biodiversity as it can blur species boundaries. The contribution of hybridization towards increasing genetic diversity of populations at range margins has only recently gathered attention in conservation studies. We assessed the extent to which hybridization contributes towards range dynamics by tracking spatio‐temporal changes in the central location of a hybrid zone between two recently diverged species of pines: Pinus strobiformis and P. flexilis. By comparing geographic cline centre estimates for global admixture coefficient with morphological traits associated with reproductive output, we demonstrate a northward shift in the hybrid zone. Using a combination of spatially explicit, individual‐based simulations and linkage disequilibrium variance partitioning, we note a significant contribution of adaptive introgression towards this northward movement, despite the potential for differences in regional population size to aid hybrid zone movement. Overall, our study demonstrates that hybridization between recently diverged species can increase genetic diversity and generate novel allelic combinations. These novel combinations may allow range margin populations to track favourable climatic conditions or facilitate adaptive evolution to ongoing and future climate change.