FECUNIDITY AND TYPE SPAWNING EEL, Electrophorus electricus (LINNAEUS, 1766) (OSTEICHTHYIES: GYMNOTIFORMES: GYMNOTIDAE) AREA OF ENVIRONMENTAL PROTECTION - APA - RIVER CURIAÚ, MACAPÁ-AP

O presente estudo objetivou estimar a fecundidade e o tipo de desova do peixe-elétrico Electrophorus electricus da Área de Proteção Ambiental - APA do Rio Curiaú, Macapá-Amapá. As análises foram realizadas a partir de 96 exemplares do peixe coletados no período de março de 2006 a fevereiro de 2007. Análises do ciclo anual de estádios de maturação gonadal, exames macro e microscópico das gônadas, verificação dos diâmetros e contagens dos ovócitos foram realizadas. O número de ovócitos relacionados a uma dimensão corpórea (comprimento total) e ao Peso Total (g) foi ajustado a uma regressão linear. Os resultados revelaram que a desova é do tipo total e sincrônico, ocorrendo no início do inverno (janeiro-fevereiro). A fecundidade, estimada variou de 1.730 a 3.063 ovócitos vitelogênicos. A fecundidade mostrou correlação positiva com o comprimento total e peso total do corpo, aumentando proporcionalmente com o tamanho de Electrophorus electricus.Palavras-chave:biologia reprodutiva, Amapá, estádios de maturação gonadal, Brasil.This study investigated the fecundity and spawning type of electric fish Electrophorus electricus of the Environmental Protection Area - APA Curiaú River, Amapá - Macapá. The analyzes were performed from 96 samples of fish collected from March 2006 to February 2007. Analysis of the annual cycle of gonadal maturation stages, macro and microscopic examinations of the gonads, verification and counting of the diameters of oocytes were performed. The number of oocytes is related to a body dimension (length)and the Total Weight (g) was adjusted to a linear regression. The results revealed that spawning is complete and synchronous type, occurring in early winter (January-February). Fecundity, estimated ranged from 1730 to 3063 vitellogenic oocytes. The fecundity was positively correlated with the total length and total body weight, increasing proportionally with the size of Electrophorus electricus.Keywords: reproductive biology; Amapá; maturation stages; Brazil

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