6 research outputs found
Como o protocolo de amostragem influĂŞncia a riqueza e a abundância de pequenos mamĂferos? Um exemplo na Mata Atlântica, Brasil
Small mammals are important elements of tropical forests but there is not a protocol for sampling these animals. In this study, we aimed at evaluating which sampling design maximizes the richness and/or the abundance of small mammals in a given area. We used data available in studies carried out in the Brazilian Atlantic forest. The variables analyzed for each study were number of marsupial species, number of rodent species, number of marsupial individuals and number of rodent individuals (dependent variables), sampling effort (trap-nights), design (grid or transect), number of strata sampled, number of nights and number of trap types used (independent variables). We did an analysis of covariance using the type of forest (evergreen or semideciduous) as the co-factor and factoring out the sampling effort to verify if the patterns of richness and abundance of species changed between these types of forests. The same analysis was done using the design as the cofactor in different forest types. Therefore, we performed analyses of variance in each forest type using the number of strata sampled, number of traps types and number of nights as factors to verify the effects of these factors on richness and abundance of the species. The capture effort was the most important variable to explain the richness and abundance of small mammals. The forest type influenced the abundance of species. Marsupials seemed to be more abundant in the semideciduous forest and rodents in the evergreen forest. Key words: live trap, inventories, sampling design, sampling effort.Os pequenos mamĂferos sĂŁo importantes elementos das florestas tropicais, entretanto nĂŁo existe um protocolo de como amostrar esses animais. O principal objetivo desse estudo Ă© avaliar qual desenho amostral maximiza a riqueza e a abundância de pequenos mamĂferos em uma dada área. Para isso, utilizamos dados disponĂveis na literatura de estudos realizados na Mata Atlântica. As variáveis analisadas de cada estudo foram nĂşmero de espĂ©cies de marsupiais, nĂşmero de espĂ©cies de roedores, nĂşmero de indivĂduos marsupiais e nĂşmero de indivĂduos roedores (variáveis dependentes), esforço de amostragem (armadilhas/ noite), desenho amostral (transecto ou grades), nĂşmero de estratos amostrados, nĂşmero de noites, tipos de armadilhas utilizadas e tamanho da área amostrada (variáveis independentes). Para verificar se os padrões de riqueza e abundância das espĂ©cies mudam de acordo com o tipo de floresta, foi realizada uma análise de co-variância usando o tipo de floresta como co-fator. A mesma análise foi feita usando o desenho amostral como co-fator nos diferentes tipos de florestas. Depois foram realizadas análises de variância em cada tipo de floresta, usando o nĂşmero de estratos amostrados, o nĂşmero de noites e os tipos de armadilhas como fatores para verificar os efeitos desses fatores na riqueza e na abundância das espĂ©cies. O esforço de captura foi a variável que mais influenciou a riqueza e a abundância de pequenos mamĂferos. O tipo de floresta tambĂ©m influenciou a riqueza e a abundância das espĂ©cies. Os marsupiais parecem ser mais abundantes na floresta semidecidual, e os roedores, na floresta ombrĂłfila densa. Palavras-chave: armadilhas de captura viva, desenho amostral, esforço de captura, inventários
Como o protocolo de amostragem influĂŞncia a riqueza e a abundância de pequenos mamĂferos? Um exemplo na Mata Atlântica, Brasil
Small mammals are important elements of tropical forests but there is not a protocol for sampling these animals. In this study, we aimed at evaluating which sampling design maximizes the richness and/or the abundance of small mammals in a given area. We used data available in studies carried out in the Brazilian Atlantic forest. The variables analyzed for each study were number of marsupial species, number of rodent species, number of marsupial individuals and number of rodent individuals (dependent variables), sampling effort (trap-nights), design (grid or transect), number of strata sampled, number of nights and number of trap types used (independent variables). We did an analysis of covariance using the type of forest (evergreen or semideciduous) as the co-factor and factoring out the sampling effort to verify if the patterns of richness and abundance of species changed between these types of forests. The same analysis was done using the design as the cofactor in different forest types. Therefore, we performed analyses of variance in each forest type using the number of strata sampled, number of traps types and number of nights as factors to verify the effects of these factors on richness and abundance of the species. The capture effort was the most important variable to explain the richness and abundance of small mammals. The forest type influenced the abundance of species. Marsupials seemed to be more abundant in the semideciduous forest and rodents in the evergreen forest. Key words: live trap, inventories, sampling design, sampling effort.Os pequenos mamĂferos sĂŁo importantes elementos das florestas tropicais, entretanto nĂŁo existe um protocolo de como amostrar esses animais. O principal objetivo desse estudo Ă© avaliar qual desenho amostral maximiza a riqueza e a abundância de pequenos mamĂferos em uma dada área. Para isso, utilizamos dados disponĂveis na literatura de estudos realizados na Mata Atlântica. As variáveis analisadas de cada estudo foram nĂşmero de espĂ©cies de marsupiais, nĂşmero de espĂ©cies de roedores, nĂşmero de indivĂduos marsupiais e nĂşmero de indivĂduos roedores (variáveis dependentes), esforço de amostragem (armadilhas/ noite), desenho amostral (transecto ou grades), nĂşmero de estratos amostrados, nĂşmero de noites, tipos de armadilhas utilizadas e tamanho da área amostrada (variáveis independentes). Para verificar se os padrões de riqueza e abundância das espĂ©cies mudam de acordo com o tipo de floresta, foi realizada uma análise de co-variância usando o tipo de floresta como co-fator. A mesma análise foi feita usando o desenho amostral como co-fator nos diferentes tipos de florestas. Depois foram realizadas análises de variância em cada tipo de floresta, usando o nĂşmero de estratos amostrados, o nĂşmero de noites e os tipos de armadilhas como fatores para verificar os efeitos desses fatores na riqueza e na abundância das espĂ©cies. O esforço de captura foi a variável que mais influenciou a riqueza e a abundância de pequenos mamĂferos. O tipo de floresta tambĂ©m influenciou a riqueza e a abundância das espĂ©cies. Os marsupiais parecem ser mais abundantes na floresta semidecidual, e os roedores, na floresta ombrĂłfila densa. Palavras-chave: armadilhas de captura viva, desenho amostral, esforço de captura, inventários
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