Efficiency of the multilocus analysis for the construction of genetic maps

The use of genetic maps is a useful tool in genetic research. The association between map distance andrecombination frequency is expressed by a genetic mapping function. However, several of these functions do not presupposethe joint recombination percentage. In other words, they are not multilocus probabilities. This work aimed to compare,through simulations, the efficiency in the use of different mapping functions with and without multilocus analysis as a tool inthe construction of genetic maps. A genome constituted of three linkage groups (50, 100 and 200 cM) was simulated for acomparative study. Four mapping populations were simulated, F2, with 50, 100, 200 and 400 individuals, with 10 replicaseach. It was verified, after the analyses, that the multilocus analysis was not efficient to rescue the size of the connectiongroups, concluding that the non use of the multilocus analysis would be viable

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

Ganhos genéticos preditos por diferentes métodos de seleção em progênies de Eucalyptus urophylla Predicted genetic gains by various selection methods in Eucalyptus urophylla progenies

O objetivo deste trabalho foi avaliar parâmetros genéticos e comparar os ganhos preditos por meio de diferentes métodos de seleção em famílias de meios-irmãos de Eucalyptus urophylla. Foi utilizada seleção entre e dentro, seleção combinada e seleção com base em modelos mistos (REML/BLUP) para os caracteres diâmetro à altura do peito, altura total e volume total com casca. Foi utilizado o teste de progênie constituído de 100 famílias de meios-irmãos com 55 meses de idade, em espaçamento de 3x2 m, em delineamento de blocos ao acaso, com cinco repetições. As progênies apresentaram variabilidade genética significativa e elevada magnitude de herdabilidade para os caracteres estudados, o que evidencia alto controle genético e condições favoráveis para seleção. Todos os métodos avaliados foram eficientes para aplicação no melhoramento de eucalipto. No entanto, a seleção combinada e a seleção por modelos mistos (BLUP) proporcionam estimativas de ganhos significativamente maiores às obtidas com a seleção entre e dentro, e maior eficiência na escolha dos melhores indivíduos dentro da população.<br>The objective of this work was to evaluate genetic parameters and to compare predicted gains using different selection methods in half-sib families of Eucalyptus urophylla. Within and between selection, combined selection and selection based on mixed model equations (REML/BLUP) were used for the traits diameter at breast height, total height and total volume with bark. The progeny test used consisted of 100 55-month-old half-sib families distributed in a 3x2-m spacing, in randomized complete block design with five replicates. The progenies showed significant genetic variability and high heritability for the studied traits, which indicates high genetic control and favorable conditions for selection. All the methods tested were efficient in eucalyptus breeding. However, the combined selection and the selection based on mixed models (BLUP) provided gains significantly larger than those obtained with within and between selections, and were more efficient in the selection of the best individuals in the population

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data