Analysis of the expression of S alleles in "Rocha" pear

Analysis of the expression of S alleles in ‘Rocha’ pear. In this work, the expression of incompatibility genes (S-alleles) in ‘Rocha’ pear was investigated. The comparison of these alleles with the alleles of other cultivars may constitute a tool to help the technicians and farmers to choose pollinators when planning a new ‘Rocha’ orchard. We have used molecular techniques that include pistil RNA extraction from three clones of cultivar ‘Rocha’ (R1, R2 and R4C), fragment amplification by RT-PCR, cloning and sequencing. The flowers utilized to extract RNA were isolated before anthesis to guarantee the absence of pollen from other cultivars in their pistils, which could interfere with the rejection process of the auto-pollen. Analysing the genetic sequences obtained from the three clones, no differences between clones were detectable and the sequences corresponded to Sj and Sa alleles. ‘Rocha’ pear is semicompatible with cultivars which do not present Sj or Sa, totally incompatible with cultivars that carry Sj and Sa, and totally compatible with cultivars that do not carry Sj and Sa

Identification of S alleles of different pear cultivars of the "Oeste" region and evaluation of their gametophytic compatibility with 'Rocha' pear

In order to select pear cultivar which are genetically compatible with ‘Rocha’ (Sa, Sj), the identification of S alleles sequences of commercial cultivars with bloom periods overlapping (partially or totally) the bloom period of ‘Rocha’ was performed. Partial sequences of the S alleles from the different cultivars were amplified with specific primers, cloned and sequenced. ‘Pêra De Água’ (S22, Sb) ‘Général Leclerc’ (Sl, Sq) and ‘Alexandrine Douillard’ (Sb, Sk) should be totally compatible. It was identified a Sj allele in the cultivars ‘Amêndoa’, ‘Beurré Precoce Morettini’, ‘Clapp’s Rouge’ e ‘Clapp’s Favourite’ and an allele Sa in ‘Beurré Clairgeau’, indicating that these cultivars are semi-compatible with ‘Rocha’. ‘Carapinheira’ (Sb) and ‘Pérola’ (Sk) carry a different allele from the ones of ‘Rocha’ and an amplification pattern completely dissimilar suggesting full compatibility. In ‘Passe Crassane’ (Sr) and ‘Beurré D’Avril’ (S4), a different allele from the S alleles of ‘Rocha’ was identified indicating compatibility, but total or partial compatibility remained unclear, since the amplification pattern does not exclude that the unknown allele might be Sa from ‘Rocha’

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