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

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

Production components of Jatropha under irrigation and nitrogen fertilization in the semiarid region of Ceará

ABSTRACTJatropha curcas L. proves to be a promising species, considering its inclusion in the National Program of Biodiesel Production and Use. However, since it has not been genetically improved, agronomic information is still scarce in the literature, especially under conditions of water and nutritional stress. Thus, this field study aimed to evaluate the effects of irrigation depths (735; 963; 1,191; 1,418 and 1,646 mm) and nitrogen fertilization (0; 25; 50 and 75 kg ha-1) on the production of Jatropha plants. Plants under the highest irrigation depth showed the highest values of number of fruits and productivity of fruits, seeds and albumen. Plants under the irrigation depth of 1,191 mm showed the highest values of mean mass of albumen and the ratios between mass of albumen and mass of seeds and between mass of albumen and mass of fruits. Nitrogen fertilization did not influence the production components of Jatropha

Chuvas intensas no Estado da Bahia High intensity rains in the Bahia State - Brazil

Séries históricas de precipitação pluvial de 19 estações pluviográficas localizadas no Estado da Bahia e operadas pela Agência Nacional de Energia Elétrica (ANEEL), foram analisadas, objetivando-se ajustar modelos teóricos de distribuição de probabilidade aos dados de chuvas intensas e estabelecer a relação entre intensidade, duração e freqüência da precipitação pluvial. Para cada estação pluviográfica determinaram-se as séries de intensidade máxima anual das precipitações com durações de 10, 20, 30, 40, 50, 60, 120, 180, 240, 360, 720 e 1.440 min. Os modelos probabilísticos testados foram os de Gumbel, Log-Normal a dois e três parâmetros, Pearson e Log-Pearson III. As equações de intensidade-duração-freqüência da precipitação pluvial foram ajustadas utilizando-se o método de regressão não-linear de Gauss-Newton. O teste de aderência de Kolmogorov-Smirnov, utilizado para a verificação do ajuste dos modelos aos dados de chuvas intensas, evidenciou que o modelo de Gumbel foi o que melhor se ajustou para a maior parte das combinações entre estações pluviográficas e durações estudadas. Foram evidenciadas, para uma mesma duração, grandes variações nas intensidades de precipitação entre as estações estudadas.<br>This study was conducted for fitting probabilistic models to data of rain storms. The intensity-duration-frequency relationships were established for 19 locations in Bahia State. Series with the annual maximum rainfall intensities for the durations of 10, 20, 30, 40, 50, 60, 120, 180, 240, 360, 720 and 1440 min were used. Significant differences were observed in the maximum rainfall intensity values among the studied stations. The models utilized were Gumbel, Log-Normal (with two and three parameters), Pearson and Log-Pearson III. The Kolmogorov-Smirnov test was used to analyze the correlation between the model results and the rainfall data. The Gumbel model presented the best results for each duration. The intensity-duration-frequency equations were obtained using the Gauss-Newton method for non linear regression