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

A 50 l CYGNO prototype overground characterization

The nature of dark matter is still unknown and an experimental program to look for dark matter particles in our Galaxy should extend its sensitivity to light particles in the GeV mass range and exploit the directional information of the DM particle motion (Vahsen et al. in CYGNUS: feasibility of a nuclear recoil observatory with directional sensitivity to dark matter and neutrinos, arXiv:2008.12587, 2020). The CYGNO project is studying a gaseous time projection chamber operated at atmospheric pressure with a Gas Electron Multiplier (Sauli in Nucl Instrum Meth A 386:531, https://doi.org/10.1016/S0168-9002(96)01172-2, 1997) amplification and with an optical readout as a promising technology for light dark matter and directional searches. In this paper we describe the operation of a 50 l prototype named LIME (Long Imaging ModulE) in an overground location at Laboratori Nazionali di Frascati (LNF) of INFN. This prototype employs the technology under study for the 1 cubic meter CYGNO demonstrator to be installed at the Laboratori Nazionali del Gran Sasso (LNGS) (Amaro et al. in Instruments 2022, 6(1), https://www.mdpi.com/2410-390X/6/1/6, 2022). We report the characterization of LIME with photon sources in the energy range from few keV to several tens of keV to understand the performance of the energy reconstruction of the emitted electron. We achieved a low energy threshold of few keV and an energy resolution over the whole energy range of 10–20%, while operating the detector for several weeks continuously with very high operational efficiency. The energy spectrum of the reconstructed electrons is then reported and will be the basis to identify radio-contaminants of the LIME materials to be removed for future CYGNO detectors

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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