Post-Modification of the Electronic Properties by Addition of π-Stacking Additives in N-Heterocyclic Carbene Complexes with Extended Polyaromatic Systems

A series of iridium complexes containing phenanthro[4,5-abc]phenazino[11,12-d]imidazol-2-ylidene and acetonaphtho[1,2-b]quinoxaline[11,12-d]imidazol-2-ylidene ligands have been obtained and fully characterized. These complexes display highly extended polyaromatic systems attached to the backbone of the N-heterocyclic carbene. The presence of this extended polyaromatic system makes the electron-donating character of these ligands sensitive to the presence of π-stacking additives, such as pyrene and hexafluorobenzene. The computational studies predict that the addition of pyrene affords an increase of the electron-donating character of the polyaromatic ligand (TEP decreases), while the addition of hexafluorobenzene has the opposite effect (TEP increases). This prediction is experimentally corroborated by IR spectroscopy, by measuring the shift of the CO stretching bands of a series of IrCl(NHC)(CO)2 complexes, where NHC is the N-heterocyclic carbene ligand with the polyaromatic system. Finally, the energy of the π-stacking interaction of one of the key Ir(I) complexes with pyrene and hexafluorobenzene has been estimated by using the Benesi-Hildebrand treat-ment, based on the δ-shifts observed by 1H NMR spectroscopy.MEC of Spain (CTQ2011-24055/BQU

Planktotrons: A novel indoor mesocosm facility for aquatic biodiversity and food web research

We established a new indoor mesocosm facility, 12 fully controlled “Planktotrons”, designed to conduct marine and freshwater experiments for biodiversity and food web approaches using natural or artificial, benthic or planktonic communities. The Planktotrons are a unique and custom-tailored facility allowing long-term experiments. Wall growth can be inhibited by a rotating gate paddle with silicone lips. Additionally, temperature and light intensity are individually controllable for each Planktotron and the large volume (600 L) enables high-frequency or volume-intense measurements. In a pilot freshwater experiment various trophic levels of a pelagic food web were maintained for up to 90 d. First, an artificially assembled phytoplankton community of 11 species was inoculated in all Planktotrons. After 22 d, two ciliates were added to all, and three Daphnia species were added to six Planktotrons. After 72 d, dissolved organic matter (DOM, an alkaline soil extract) was added as an external disturbance to six of the 12 Planktotrons, involving three Planktotrons stocked with Daphnia and three without, respectively. We demonstrate the suitability of the Planktotrons for food web and biodiversity research. Variation among replicated Planktotrons (n = 3 minimum) did not differ from other laboratory systems and field experiments. We investigated population dynamics and interactions among the different trophic levels, and found them affected by the sequence of ciliate and Daphnia addition and the disturbance caused by addition of DOM

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

Global lake responses to climate change

Climate change is one of the most severe threats to global lake ecosystems. Lake surface conditions, such as ice cover, surface temperature, evaporation and water level, respond dramatically to this threat, as observed in recent decades. In this Review, we discuss physical lake variables and their responses to climate change. Decreases in winter ice cover and increases in lake surface temperature modify lake mixing regimes and accelerate lake evaporation. Where not balanced by increased mean precipitation or inflow, higher evaporation rates will favour a decrease in lake level and surface water extent. Together with increases in extreme-precipitation events, these lake responses will impact lake ecosystems, changing water quantity and quality, food provisioning, recreational opportunities and transportation. Future research opportunities, including enhanced observation of lake variables from space (particularly for small water bodies), improved in situ lake monitoring and the development of advanced modelling techniques to predict lake processes, will improve our global understanding of lake responses to a changing climate

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