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

Cross-Talk Between AT 1 and AT 2 Angiotensin Receptors in Rat Anococcygeus Smooth Muscle

ABSTRACT Schild regressions for the selective AT 1 and AT 2 receptor antagonists, losartan and PD123319 (S-[ϩ]-1-[(4-dimethylamino]-3-methylphenyl)methyl]-5- [diphenylacetyl]-4,5,6,7-tetrahydro-1H-imidazol[4,5-c]pyridine-6-carboxilic acid), respectively, were calculated to analyze the heterogeneity of receptor populations in the rat anococcygeus muscle. For a one-receptor system, the Schild regression has a slope of unity and an intercept of K B for competitive antagonists. However, in a two-receptor system, a deviation from the single-receptor plot will occur. This is predicated on the assumption that the secondary receptor is less sensitive to the antagonist than the primary receptor. Results showed that the Schild regression for losartan did not produce a slope of unity, and PD123319 did not produce any effect. However, tissue incubation with losartan plus PD123319 resulted in a Schild regression that has a slope of unity and a pK B of 9.32. In the presence of prazosin, an ␣ 1 -adrenoceptor antagonist, losartan did not produce any effect. Conversely, PD123319 enhanced the angiotensin II (Ang II)-induced contraction in a concentration-dependent fashion, suggesting an inhibitory AT 2 -mediated effect. This effect was confirmed with assays that showed a relaxant response induced by Ang II on precontracted tissues incubated with prazosin. PD123319 and N G -nitro-L-arginine methyl ester [nitric-oxide (NO) synthase inhibitor)] markedly inhibited the relaxant response of Ang II. In contrast, losartan did not produce any significant effect. Consequently, results show that the mechanism underlying the AT 2 -mediated effect is highly dependent on NO generation. Results indicate the presence of a heterogeneous angiotensin receptor population in the rat anococcygeus muscle following a negative cross-talk relationship between the AT 1 and AT 2 subtypes

Arachidonic acid metabolites follow the preferential course of cyclooxygenase pathway for the basal tone in the internal anal sphincter

Present studies determined the roles of the cyclooxygenase (COX) versus the lipoxygenase (LO) pathways in the metabolic pathway of arachidonic acid (AA) in the internal anal sphincter (IAS) tone. Studies were performed in the rat IAS versus the nontonic rectal smooth muscle (RSM). Indomethacin, the dual COX inhibitor, but not nordihydroguaiaretic acid (NDGA), the LO inhibitor, produced a precipitous decrease in the IAS tone. However, when added in the background of indomethacin, NDGA caused significant reversal of the IAS tone. These inhibitors had no significant effect on the RSM. To follow the significance of COX versus LO pathways, we examined the effects of AA and its metabolites. In the IAS, AA caused an increase in the IAS tone (Emax = 38.8 ± 3.0% and pEC50 = 3.4 ± 0.1). In the RSM, AA was significantly less efficacious and potent (Emax = 11.3 ± 3.5% and pEC50 = 2.2 ± 0.3). The AA metabolites (via COXs) PGF2α and U-46619 (a stable analog of thromboxane A2) produced increases in the IAS tone and force in the RSM. Conversely, AA metabolites (via 5-LO) lipoxin A4, 5-HETE, and leukotriene C4 decreased the IAS tone. Finally, the contractile effects of AA in the IAS were selectively attenuated by the COX-1 but not the COX-2 inhibitor. Collectively, the specific effects of AA and the COX inhibitor, the Western blot and RT-PCR analyses showing specifically higher levels of COX-1, suggest a preferential role of the COX (specifically COX-1) pathway versus the LO in the regulation of the IAS tone