Fusariose do abacaxizeiro: incidência no fruto em função da época de produção

Observations on the development of "fusariose" in commercial plantations of pineapple were carried out for two years. The heaviest losses, due to Fusarium moniliforme var. subglutinans, occurred in the months of August and September, while the mildest ones, from May to July.Foram feitas avaliações periódicas, por dois anos consecutivos, para quantificar a incidência de Fusarium moniliforme var. subglutinans em frutos de abacaxi, durante os meses de produção. Foi constatado que as maiores perdas, atribuídas ao patógeno, ocorreram nos meses de agosto e setembro, enquanto nas colheitas de maio a julho, a incidência do patógeno foi a mais reduzida do ano agrícola

Long-term perturbations due to a disturbing body in elliptic inclined orbit

In the current study, a double-averaged analytical model including the action of the perturbing body's inclination is developed to study third-body perturbations. The disturbing function is expanded in the form of Legendre polynomials truncated up to the second-order term, and then is averaged over the periods of the spacecraft and the perturbing body. The efficiency of the double-averaged algorithm is verified with the full elliptic restricted three-body model. Comparisons with the previous study for a lunar satellite perturbed by Earth are presented to measure the effect of the perturbing body's inclination, and illustrate that the lunar obliquity with the value 6.68\degree is important for the mean motion of a lunar satellite. The application to the Mars-Sun system is shown to prove the validity of the double-averaged model. It can be seen that the algorithm is effective to predict the long-term behavior of a high-altitude Martian spacecraft perturbed by Sun. The double-averaged model presented in this paper is also applicable to other celestial systems.Comment: 28 pages, 6 figure

Local hydrological conditions influence tree diversity and composition across the Amazon basin

Tree diversity and composition in Amazonia are known to be strongly determined by the water supplied by precipitation. Nevertheless, within the same climatic regime, water availability is modulated by local topography and soil characteristics (hereafter referred to as local hydrological conditions), varying from saturated and poorly drained to well-drained and potentially dry areas. While these conditions may be expected to influence species distribution, the impacts of local hydrological conditions on tree diversity and composition remain poorly understood at the whole Amazon basin scale. Using a dataset of 443 1-ha non-flooded forest plots distributed across the basin, we investigate how local hydrological conditions influence 1) tree alpha diversity, 2) the community-weighted wood density mean (CWM-wd) – a proxy for hydraulic resistance and 3) tree species composition. We find that the effect of local hydrological conditions on tree diversity depends on climate, being more evident in wetter forests, where diversity increases towards locations with well-drained soils. CWM-wd increased towards better drained soils in Southern and Western Amazonia. Tree species composition changed along local soil hydrological gradients in Central-Eastern, Western and Southern Amazonia, and those changes were correlated with changes in the mean wood density of plots. Our results suggest that local hydrological gradients filter species, influencing the diversity and composition of Amazonian forests. Overall, this study shows that the effect of local hydrological conditions is pervasive, extending over wide Amazonian regions, and reinforces the importance of accounting for local topography and hydrology to better understand the likely response and resilience of forests to increased frequency of extreme climate events and rising temperatures

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution