Floristic composition and structure of tree community on the transition Lowland - Lowermontane Ombrophilous Dense Forest in Núcleo Picinguaba/Serra do Mar State Park, Ubatuba, southeastern Brazil

We conducted floristic and structural surveys on arboreous component (circumference at breast high &gt; 15 cm) in 1 ha plot (100 sub-plots of 10 × 10 m) located in a stretch of Atlantic Ombrophilous Dense Forest Lowland-Lower montane transition, in Núcleo Picinguaba/PESM, Ubatuba, São Paulo State. The regenerating layer (H &gt; 1.5 m and circumference at breast height < 15 cm) was sampled in 0.4 ha (40 sub-plots of 10 × 10 m) within the plot. The richness and diversity were 156 species and H' = 4.00 for the arboreous component in 1 ha, 173 and H' = 4.25 for arboreous (113 species) and regenerating (134 species) layers together in 0.4 ha, and 192 species considering the whole sample. The most abundant species in the tree layer were Euterpe edulis, with 191 individuals (14.8%), Mollinedia schottiana (5.1%), Rustia formosa (4.8%), Chrysoplhyllum flexuosum (4.7%), Coussarea meridionalis var. porophylla (4.7%) and Guapira opposita (4.4%). These species were also among the most abundant in the regenerating layer. The richest families were Myrtaceae (32 spp.), Rubiaceae (15), Fabaceae (13), Sapotaceae (10), Moraceae (eight), Euphorbiaceae (seven) and Lauraceae (six). Tree species richness varied positively with density along the vertical structure of vegetation, being higher in lower high classes, where the density is much larger. On the other hand, the evenness in these classes was lower, increasing toward the upper classes, where trees are not concentrated in few species.Realizamos o levantamento florístico e estrutural do componente arbóreo (PAP &gt; 15 cm) em uma parcela de 1 ha (100 sub-parcelas de 10 × 10 m) localizada em um trecho da transição Floresta Ombrófila Densa Atlântica das Terras Baixas-Submontana, no Núcleo Picinguaba/PESM, Ubatuba, Estado de São Paulo. O estrato regenerante (H &gt; 1,5 m e PAP < 15 cm) foi amostrado em 0,4 ha (40 sub-parcelas 10 × 10 m) dentro da parcela. A riqueza florística e a diversidade foram de 156 espécies e H' = 4,00 para o componente arbóreo em 1 ha, 173 e H' = 4,25 para os estratos arbóreo (113 espécies) e regenerante (134) em 0,4 ha e 192 espécies considerando toda a amostragem. As espécies mais abundantes no estrato arbóreo foram Euterpe edulis, com 191 indivíduos (14,8%), Mollinedia schottiana (5,1%), Rustia formosa (4,8%), Chrysoplhyllum flexuosum (4,7%), Coussarea meridionalis var. porophylla (4,7%) e Guapira opposita (4,4%). Estas espécies estiveram entre as mais abundantes também no estrato regenerante. As famílias com maior riqueza foram Myrtaceae (32 spp.), Rubiaceae (15), Fabaceae (13), Sapotaceae (10), Moraceae (oito), Euphorbiaceae (sete) e Lauraceae (seis). A riqueza de espécies arbóreas variou positivamente com a densidade ao longo do gradiente vertical estrutural da vegetação, sendo maior nas classes mais baixas de altura, onde a densidade é expressivamente maior. Por outro lado, nestas classes a equabilidade é mais baixa, aumentando em direção às classes superiores, onde as árvores não estão concentradas em poucas espécies.285299Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

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