Influência de sistemas de cultivo sobre a comunidade da fauna edáfica no nordeste do Brasil

Soil biotic and abiotic characteristics are influenced by crop system in semiarid environments. Crop systems which promote soil organic carbon content at high levels could positively promote soil fauna diversity and soil quality. Our aim with this study was to evaluate the effects of crop systems (No-tillage vs. Integrated crop-livestock-forestry system) on soil chemical properties, soil fauna composition, and soil quality in a Planossolo at semiarid environment from Brazilian Northeast. Combining protocols for soil chemical characterization and soil fauna extraction and identification, we observed that both crop systems studies increased soil organic carbon and available P when compared to control treatment. We found 11 orders plus 3 larvae groups (Larvae of Coleoptera, Diptera, and Lepidoptera) from soil fauna organisms, however, only Araneae, Coleoptera, Diptera, and Hymenoptera were significantly affected (p < 0.01) by crop systems. We also observed that soil quality index in all crop systems studies was higher than in the control treatment, and it corroborates with the follow hypothesis that crop system which consider habitat and energy maintenance for soil fauna contributes to a diverse soil food web establishment that favours soil nutrient cycling. Our results highlight the importance to consider crop systems which promotes soil organic carbon and ideal condition into soil ecosystem to sustain a diverse and function soil fauna community in semiarid conditions.As características bióticas e abióticas dos solos variam substancialmente em função dos sistemas de cultivo empregados em ambientes semiáridos. Sistemas de cultivo que favoreçam a manutenção dos teores de carbono orgânico no solo (e.g., provisão de habitat) podem influenciar positivamente a diversidade da comunidade da fauna edáfica e a qualidade do solo. Objetivou-se neste estudo avaliar o efeito de diferentes sistemas de cultivo (e.g., plantio direto vs. integração lavoura-pecuária-floresta) sobre as propriedades químicas do solo, a composição da fauna edáfica e a qualidade de um Planossolo Nátrico em condições semiáridas do Nordeste do Brasil. Usando métodos combinados de coleta de amostras de solo para caracterização química com a extração de indivíduos da fauna edáfica e sua identificação taxonômica foi observado que ambos sistemas de plantio avaliados neste estudo promoveram incrementos nos teores de carbono orgânico no solo e fósforo disponível em relação ao controle. Foram observadas 11 ordens e mais 3 grupos de larvas (Larvas de Coleoptera, Diptera e Lepidoptera) da fauna edáfica, contudo, apenas Araneae, Coleoptera, Diptera e Hymenoptera foram influenciadas de forma significativa (p < 0,01) pelos sistemas de cultivo. Além disso, os índices de qualidade do solo em todos os sistemas de cultivo foram superiores em comparação ao controle, o que reforça a hipótese de que sistemas de plantio que consideram a manutenção de habitat e energia para fauna do solo contribuem para o estabelecimento de uma teia trófica diversificada e provisão de serviços ecossistêmicos favorecendo a ciclagem de nutrientes. Por fim, o estudo demonstra a importância de considerar sistemas de cultivo que promovam a manutenção dos teores de carbono orgânico no solo e a provisão de condições ideais no ecossistema solo para sustentar uma comunidade da fauna edáfica e funcional em condições semiáridas

Atributos físicos e químicos do solo cultivado com gravioleira, sob adubação orgânica e mineral

A gravioleira é uma espécie frutífera que vem despertando interesse dentre os fruticultores do Brasil, no entanto, existem poucas informações relativas ao manejo da adubação. Sendo assim, o objetivo deste experimento foi avaliar os atributos físicos e químicos do solo de um pomar de gravioleira em função da adubação orgânica e mineral. Foi instalado um experimento em um pomar de gravioleira com 42 meses de idade entre os meses de janeiro a setembro de 2014, na cidade Areia-PB. O delineamento experimental foi em blocos casualizados, com sete tratamentos e quatro repetições. Os tratamentos utilizados foram: T1= testemunha (sem adubação); T2= adubação mineral (NPK); T3= esterco de bovino; T4= esterco de aves; T5= adubação mineral + esterco de caprino; T6= esterco de aves + 3 kg de pó de rocha; T7= esterco de caprino. As variáveis de fertilidade e de física do solo avaliadas foram: pH, fósforo, potássio, sódio, cálcio, magnésio, alumínio, hidrogênio + alumínio, carbono, matéria orgânica, densidade e porosidade do solo. Os dados foram submetidos à análise de variância e ao o teste de Tukey.  A adubação com esterco bovino influenciou positivamente os teores de potássio, sódio, carbono e matéria orgânica do solo; O esterco de aves isolado e adicionado ao pó de rocha, manteve o pH do solo numa faixa que favorece a disponibilidade de nutrientes, além de fornecer maior teor de fósforo no solo; Nenhum dos tratamentos afetou os teores de cálcio, magnésio, alumínio, hidrogênio + alumínio, além da densidade e porosidade total do solo

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