DESENVOLVIMENTO INICIAL E QUALIDADE DE MUDAS DE Copaifera langsdorffii Desf. SOB DIFERENTES NÍVEIS DE SOMBREAMENTO

http://dx.doi.org/10.5902/1980509821061The gallery forests are being fragmented, leading to loss of its high diversity, becoming indispensable studies assessing the environmental performance of their tree species. The objective of this study was to test the hypothesis that the production of seedlings Copaifera langsdorffii is influenced by ambient light, higher quality and initial development at intermediate light levels. Plants were tested in full sun, 30%, 50%, 70% and 90% shading in order to evaluate the number of leaves, height and diameter at 60, 90, 120 and 191 days after emergence (DAE) and dry root and shoot biomass and Dickson quality index (DQI) at the end of the experiment (191 DAE). The effect of shading levels was analyzed by means of regression analysis. The plants showed good growth plasticity at different levels of lightness, but with better development and quality (IQD) at 50% shade, corroborating the hypothesis tested. The excessive lightness or shading should be avoided to ensure the production of more vigorous seedlings of C. langsdorffii. Thus, production of seedlings of this species under 50% shade is recommended to promote their quality and possibly ensure better survival in the field.http://dx.doi.org/10.5902/1980509821061As florestas de galeria vêm sendo fragmentadas, levando à perda de sua elevada diversidade, tornando-se imprescindíveis estudos que avaliem o comportamento ecológico de suas espécies arbóreas. O presente estudo teve como objetivo testar a hipótese de que a produção de mudas de Copaifera langsdorffii é influenciada pela luminosidade do ambiente, apresentando maior qualidade e desenvolvimento inicial em níveis intermediários de luz. As plantas foram testadas em pleno sol, 30%, 50%, 70% e 90% de sombreamento, avaliando-se número de folhas, altura e diâmetro aos 60, 90, 120 e 191 dias após a emergência (DAE) e massa seca aérea e radicular e, índice de qualidade de Dickson ao final do experimento (191 DAE). O efeito dos níveis de sombreamento foi analisado por meio de análise de regressão. As plantas apresentaram boa plasticidade de crescimento nos diferentes níveis de luminosidade, mas com melhor desenvolvimento e qualidade (IQD) em 50% de sombreamento, corroborando a hipótese testada. A luminosidade ou sombreamento excessivo devem ser evitados para garantir a produção de mudas mais vigorosas de C. langsdorffii. Assim, recomenda-se a produção de mudas desta espécie sob 50% de sombreamento para favorecer a sua qualidade e possivelmente garantir melhor sobrevivência em campo

Soil pyrogenic carbon in southern Amazonia: Interaction between soil, climate, and above-ground biomass

The Amazon forest represents one of the world’s largest terrestrial carbon reservoirs. Here, we evaluated the role of soil texture, climate, vegetation, and distance to savanna on the distribution and stocks of soil pyrogenic carbon (PyC) in intact forests with no history of recent fire spanning the southern Amazonia forest-Cerrado Zone of Transition (ZOT). In 19 one hectare forest plots, including three Amazonian Dark Earth (ADE, terra preta) sites with high soil PyC, we measured all trees and lianas with diameter ≥ 10 cm and analyzed soil physicochemical properties, including texture and PyC stocks. We quantified PyC stocks as a proportion of total organic carbon using hydrogen pyrolysis. We used multiple linear regression and variance partitioning to determine which variables best explain soil PyC variation. For all forests combined, soil PyC stocks ranged between 0.9 and 6.8 Mg/ha to 30 cm depth (mean 2.3 ± 1.5 Mg/ha) and PyC, on average, represented 4.3% of the total soil organic carbon (SOC). The most parsimonious model (based on AICc) included soil clay content and above-ground biomass (AGB) as the main predictors, explaining 71% of soil PyC variation. After removal of the ADE plots, PyC stocks ranged between 0.9 and 3.8 Mg/ha (mean 1.9 ± 0.8 Mg/ha–1) and PyC continued to represent ∼4% of the total SOC. The most parsimonious models without ADE included AGB and sand as the best predictors, with sand and PyC having an inverse relationship, and sand explaining 65% of the soil PyC variation. Partial regression analysis did not identify any of the components (climatic, environmental, and edaphic), pure or shared, as important in explaining soil PyC variation with or without ADE plots. We observed a substantial amount of soil PyC, even excluding ADE forests; however, contrary to expectations, soil PyC stocks were not higher nearer to the fire-dependent Cerrado than more humid regions of Amazonia. Our findings that soil texture and AGB explain the distribution and amount of soil PyC in ZOT forests will help to improve model estimates of SOC change with further climatic warming

Regeneração de espécies lenhosas sob a influência do bambu Actinocladum verticillatum (Nees) McClure ex Soderstr. (Poaceae) em cerradão e cerrado típico na transição Cerrado-Amazônia

In this study, we analyzed and compared the floristic composition, species richness and diversity, and structure of the natural regeneration of two phytophysiognomies (savanna forest and typical cerrado) under the effect of natural clumps of the bamboo Actinocladum verticillatum (Nees) McClure ex Soderstr. at the Bacaba City Park, Nova Xavantina municipality (Mato Grosso state, Brazil). In each phytophysiognomy, we established 15 contiguous plots in a bamboo-free control site (NB) and 15 in a site densely occupied by bamboos (WB). The natural regeneration and the woody vegetation (WV, diameter ≥ 3 cm at 30 cm above ground) were sampled in plots of 1 x 1 m, 2 x 2 m, 5 x 5 m (natural regeneration) and 10 x 10 m (WV). In the natural regeneration, we found 55 species in the savanna forest (NB = 49, WB = 34) and 76 in the typical cerrado (NB = 68, WB = 51). Overall, the NB sites from both phytophysiognomies had the highest values of species diversity, evenness and abundance of individuals, which indicates a possible interference of the clumps with the structure of the natural regeneration. The occurrence of Brosimum gaudichaudii Trécul and Myrcia splendens (Sw.) DC at most stages of regeneration highlights their potential for the recovery of lands occupied by these clumps.No presente estudo foram analisadas e comparadas a composição florística, riqueza, diversidade de espécies e a estrutura da regeneração natural em duas fitofisionomias (cerradão e cerrado típico) sob o efeito de adensamentos naturais do bambu Actinocladum verticillatum (Nees) McClure ex Soderstr. no Parque Municipal do Bacaba, Nova Xavantina-MT. Em cada fitofisionomia foram alocadas 15 parcelas contíguas em um sítio controle, com ausência completa de bambu (SB) e 15 em um sítio com bambu (CB). A regeneração natural e a vegetação lenhosa (VL, ≥ 3 cm de diâmetro a 30 cm do solo) das duas fitofisionomias foram amostradas em parcelas de 1 x 1 m, 2 x 2 m, 5 x 5 m (regeneração natural) e 10 x 10 m (VL). Foram encontradas, na regeneração natural, 55 espécies no cerradão (SB = 49 e CB = 34) e 76 no cerrado típico (SB = 68 e CB = 51). De forma geral, os sítios SB nas duas fitofisionomias detiveram os maiores valores de diversidade de espécies, equabilidade e abundância de indivíduos, o que indica possível interferência dos adensamentos na estrutura da regeneração natural. A ocorrência de Brosimum gaudichaudii Trécul e Myrcia splendens (Sw.) DC. na maioria dos estádios da regeneração, reforça o potencial dessas espécies na recuperação de áreas ocupadas por estes adensamentos.

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