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

Substitution of Byproducts for Corn Grain in Confined Lactating Cows Diets

Com o intuito de avaliar a substituição do milho em grãos por alimentos alternativos em rações de vacas em lactação, foram conduzidos três experimentos iguais, em que a única diferença foi o alimento testado: No experimento 1 avaliou-se a inclusão do farelo de glúten de milho 21 (FGM-21) em três níveis (0, 10 e 20% da MS) em substituição ao milho moído das rações. A ingestão diária de MS (21,19 kg/an), a produção de leite (24,88 kg/an), a produção de leite corrigido para 3,5% de gordura (25,34 kg/an), o teor de gordura (3,62%), e o teor de sólidos totais (11,86%) não foram afetados pelos tratamentos (P>0,05). A inclusão do FGM-21 afetou os teores de proteína e lactose do leite e a concentração de nitrogênio uréico do leite (P>0,05). No experimento 2 avaliou-se a inclusão da casca de soja (CS) em três níveis (0, 10 e 20% da MS) em substituição ao milho moído das rações. A inclusão da CS não afetou o consumo de matéria seca (22,84 kg/d), nem a produção de leite (28,33 kg/d) e produção de leite corrigido para gordura (28,48 kd/d) (P>0,05). No entanto a inclusão do subproduto aumentou linearmente a produção total de gordura (P0.10) daily dry matter intake (DMI) (21.19 kg/cow), milk yield (24.88 kg/cow), 3,5% fat corrected milk yield (FCM) (25.34 kg/cow), milk fat content (3.62%), and milk total solids (11.86%). Inclusion of corn gluten feed affected milk protein, lactose and urea concentrations (P0.05). However, inclusion of CS linearly increased total milk fat yield (P<0,05) and linearly decreased MUN (P<0,01). In trial 3 the inclusion of three doses of wheat middlings (FT) (0, 10 and 20% DM) in substitution for ground corn was evaluated. Inclusion of FT reduced (P<0,05) dry matter intake (22.20 kg/d average) and milk yield (P<0,01) (31.65 kg/d average), FCM yield (27.44 kg/d average), total milk fat, protein and lactose, and milk total solids (P<0,05). Milk components concentration was not affected by treatments. Inclusion of the byproduct increased MUN concentration (P<0,01)

Evaluation of sugarcane laboratory ensiling and analysis techniques

The objective was to evaluate the effects of laboratory-silo type and method of silage extract production, respectively, on sugarcane silage fermentation and recovery of fermentation products. Sugarcane was mechanically harvested and ensiled in three different types of laboratory silos (five replicates): 9.7 × 30 cm PVC tubes with tight lids, equipped or unequipped with Bunsen valves, and 20 L plastic buckets with tight lids and Bunsen valves. Three methods were used to produce silage extracts for pH, ethanol, acetic and lactic acids determination: extraction of silage juice by a hydraulic press and production of water extracts using a stomacher or a blender. Total dry matter loss (231 g/kg DM) was not affected by silo type. No interactions between silo type and method of silage extract production were observed for ethanol and organic acids contents in the silages. Interaction between silo type and method of silage extract preparation was detected for pH. Silo type affected ethanol content but did not affect lactic and acetic acids concentration in the silages. Dry matter, crude protein, neutral detergent fiber and ash were not affected by silo type. The method used to produce silage extracts affected the recovery of all fermentation products analyzed in the silages. Recovery of ethanol and acetic acid was higher when silage extracts were produced using a blender. For lactic acid recovery, the hydraulic press method was superior to the other two methods. Silage fermentation pattern is not affected by silo type, but the method used to produce silage extracts and some characteristics of silos affect the recovery of volatile fermentation products