6 research outputs found
Mitogoniella mucuri Azara et al. 2013
No full text51. <i>Mitogoniella mucuri</i> Ázara <i>et al.</i>, 2013 <p>(Figs 15E, F, 37C, D)</p> <p> <b>Records in caves.</b> MINAS GERAIS. Caraí: <i>Caverna do Sumidouro</i>, (ISLA 3971), (ISLA 3968); Matutina: <i>Lapa</i></p> <p> <i>do Campo de Futebol</i>; Novo Oriente de Minas: <i>Caverna do Roxo</i>; <i>Cabeceira da Americaninha</i>; <i>Caverna do Ribeirão Anastácio I</i>. Padre Paraíso: <i>Lapa do Córrego Vieira</i> (Ázara <i>et al.</i> 2013).</p> <p> <b>Epigean records.</b> BRAZIL. MINAS GERAIS. Alagoa; Caraí (Ázara <i>et al.</i> 2013).</p> <p> <b>Remark.</b> This species was recorded associated with artificial mines in the municipalities of Alagoa and Caraí (Ázara <i>et al.</i> 2013).</p>Published as part of <i>Ázara, Ludson Neves De & Ferreira, Rodrigo Lopes, 2018, Annotated checklist of Gonyleptoidea (Opiliones: Laniatores) associated with Brazilian caves, pp. 1-107 in Zootaxa 4439 (1)</i> on pages 54-55, DOI: 10.11646/zootaxa.4439.1.1, <a href="http://zenodo.org/record/1298055">http://zenodo.org/record/1298055</a>
Eusarcus elinae KURy 2008
No full text107. Eusarcus elinae Kury, 2008 Records in caves. BAHIA. Iraquara: Caverna Pedra Furada (Kury 2008); Ourolândia: Toca dos Ossos, id. by L.N. Ázara, (ISLA 12101), (ISLA 12100). Remark. This species was originally described as troglobitic. However, the occurrence of a population in Toca dos Ossos cave (Ourolândia municipality) raises a question on the real “status” of the species. The distance between the type-locality and the new occurrence of the species is around 160 km in straight line. In between, there are several rock types, some of them with low probability of ocurrance of caves. Hence, the possibility of occurrence of subterranean continuities between both caves is unlike, especially considering the species size, that would not allow dispersion through small voids. Furthermore, Kury (2008) presented, as troglomorphic traits for the species, the weak reduction in body pigmentation, the elongation of the dorsal scutum and pedipalpus, the elongate and relatively unarmed legs (although, according to him, E. elinae present the rows of spines in femur III of male typical of a group of genera in Pachylinae) and the high tarsal counts. However, Kury (2008) mentioned that most traits listed above are more evident in other troglobitic species described in the same paper (Discocyrtus pedrosoi). Finally, the author attested that the ocular apparatus seems to be normal in comparison to the epigean Eusarcus. Given the weak troglomorphic traits observed in the species, allied to the lack of inventories in many epigean environmets in between both caves and the considerable distance between the caves, it may be that E. elinae is in fact a troglophile, rather than a troglobitic species. However, there is also the possibility that the species recorded in Ourolândia correspond to a cryptic species, morphologically identical to E. elinae. Cryptic species in cave habitats are revealing to be more common that previously though. According to Niemiller et al. (2013), many troglobitic species anteriorly considered to be a single species were divided in several cryptic species, with lower geographic ranges. Since there are no molecular data from those species currently available, it is not possible to define whether hypothesis is more likely. Accordingly, it is reccomendable that future studies perform systematic samplings in external environments of central Bahia state, to actually determine the status of many species of harvestman found in the caves of the region.Published as part of Ázara, Ludson Neves De & Ferreira, Rodrigo Lopes, 2018, Annotated checklist of Gonyleptoidea (Opiliones: Laniatores) associated with Brazilian caves, pp. 1-107 in Zootaxa 4439 (1) on page 72, DOI: 10.11646/zootaxa.4439.1.1, http://zenodo.org/record/129805
Pervasive gaps in Amazonian ecological research
No full textBiodiversity 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
No full textBiodiversity 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
No full textBiodiversity 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
