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

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

PHYSIOLOGICAL AND PHENOLOGICAL VEGETATIVE RESPONSES OF Campomanesia adamantium (Cambess) O. Berg (Myrtaceae) TO THE HYDRIC SEASONALITY OF RUPESTRIAN FIELDS

ABSTRACT The rupestrian fields have two well-defined seasons throughout the year, with rainfall rates that reflect the rainy and dry seasons. This distinction in water availability affects the morphology, physiology and chemistry of plants, among other characteristics. Thus, it is aimed at evaluating the leaf water status, vegetative phenology and photosynthetic behavior of Campomanesia adamantium from a rupestrian field during the dry and rainy season. The study was conducted in Serra do Cipó, Minas Gerais, Brazil. From November 2011 to November 2012 it was examined vegetative phenophases and development of six individuals. Water potential, stomatal conductance, quantum yield and concentration of pigments were evaluated from four leaves of 3rd node per individual (n = 4-5) in the dry and rainy seasons. C. adamantium is an evergreen type and presents mature leaves and sprouting throughout the year. This species showed strategies that reduce water loss during the dry season in rupestrian field, such as decrease in stomatal conductance throughout the day, also associated with a reduction in leaf water potential. However, low water availability did not affect the photosynthetic performance, which enables the construction of new leaves and renovation of the crown even in dry periods. Finally, little reduction in the values of Fv/Fm throughout the day and increase the values of ΔF/Fm' in warmer times, both in the dry season, reiterates the ability of C. adamantium to adjust their physiology to seasonal water deficit of the rupestrian field

The influence of light intensity on anatomical structure and pigment contents of Tradescantia pallida (Rose) Hunt. cv. purpurea Boom (Commelinaceae) leaves

The aim of this work was to study the effects of five different light intensities on the anatomical structure and on the pigment contents in leaves of Tradescantia pallida cv. purpurea. Once light intensity became lower, the thickness of leaf lamina and mesophyll were reduced. Adjustments in light-harvesting antenna size were observed: an increase in chlorophyll a + b/carotenoids ratio at low-light growth conditions. There was a strong positive linear correlation between the light intensity values and anthocyanin contents. Hence, T. pallida cv. purpurea acclimation to distinct environmental conditions might be related to its capacity of altering structurally and physiologically its phenotype