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

Caracterização de isolados de Xanthomonas campestris pv campestris de sistemas de produção orgânico e reação de brássicas à podridão-negra Characterization of strains of Xanthomonas campestris pv campestris from organic farming systems and reaction of brassicas to black rot

Noventa isolados de Xanthomonas campestris pv. campestris (Xcc) de brássicas oriundas de sistemas de produção orgânico das Zonas da Mata e Agreste de Pernambuco foram caracterizados com base na sensibilidade a antibióticos e sulfato de cobre e atividade de esterase. A maioria apresentou alta sensibilidade à tetraciclina (76,6%), eritromicina (63,3%) e estreptomicina (63,3%), resistência à amoxicilina (70%), gentamicina (40,0%) e norfloxacin (45,5%) e média sensibilidade (44,4%) ou resistência (44,4%) à neomicina. Cinqüenta e cinco isolados de Xcc foram resistentes ao sulfato de cobre na concentração de 50 mg/mL e todos foram sensíveis ao produto na concentração de 200 mg/mL. Atividade de esterase foi apresentada por 92,22% dos isolados. A análise Euclidiana por ligação simples evidenciou variabilidade entre os isolados separando-os em sete grupos de similaridade. Foi estudada também a reação de 14 cultivares de brássicas à podridão-negra, utilizando o isolado "B21" de Xcc. As cultivares diferiram significativamente entre si em relação ao período de incubação, incidência e severidade final da doença. Os maiores valores de severidade final da doença foram verificados em brócolos "Ramoso", couve-flor "Bola de Neve" e "Piracicaba de Verão", e repolho "Chato de Quintal". Os híbridos de couve-chinesa "AF 70", "AF 72", "AF 69" e "AF 66" mostraram-se altamente resistentes à doença, enquanto que brócolos "Ramoso" e "Precoce Piracicaba", couve-flor "Piracicaba de Verão" e "Híbrido Cindy" e repolho "60 Dias" foram medianamente resistentes.<br>Ninety strains of Xanthomonas campestris pv. campestris (Xcc) from brassicas grown under organic farming systems in the "Mata" and "Agreste" regions of Pernambuco, Brazil, were characterized based upon sensitivity to antibiotics and copper sulfate, and esterase activity. Most of the strains showed high sensitivity to tetracycline (76.6%), erythromycin (63.3%) and streptomycin (63.3%), resistance to amoxicilin (70%), gentamicin (40.0%) and norfloxacin (45.5%) and medium sensitivity (44.4%) or resistance (44.4%) to neomycin. Fifty-five strains of Xcc were resistant to copper sulfate at 50 mg mL-1 and all of them to 200 mg mL-1; 92.22% of the strains showed esterase activity. Strains were grouped in seven similarity groups by the Euclidean analysis-single linkage. The reaction of 14 genotypes of brassicas to strain "B21" of Xcc was also studied. The genotypes significantly differed among them in relation to incubation period, incidence and disease severity. The highest disease severity was recorded on broccoli "Ramoso", cauliflower "Bola de Neve" and "Piracicaba de Verão", and cabbage "Chato de Quintal", classified as highly susceptible to black rot. The Chinese cabbage hybrids "AF 70", "AF 72", "AF 69" and "AF 66" were highly resistant to black-rot, while broccolis "Ramoso" and "Piracicaba Precoce", cauliflower "Piracicaba de Verão" and "Híbrido Cindy" and cabbage "60 Dias" showed intermediate resistance