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Abstract Decades of theory and empirical studies have demonstrated links between biodiversity and ecosystem functioning, yet the putative processes that underlie these patterns remain elusive. This is especially true for forest ecosystems, where the functional traits of plant species are challenging to quantify. We analyzed 74,563 forest inventory plots that span 35 ecoregions in the contiguous USA and found that in ~77% of the ecoregions mixed mycorrhizal plots were more productive than plots where either arbuscular or ectomycorrhizal fungal-associated tree species were dominant. Moreover, the positive effects of mixing mycorrhizal strategies on forest productivity were more pronounced at low than high tree species richness. We conclude that at low richness different mycorrhizal strategies may allow tree species to partition nutrient uptake and thus can increase community productivity, whereas at high richness other dimensions of functional diversity can enhance resource partitioning and community productivity. Our findings highlight the importance of mixed mycorrhizal strategies, in addition to that of taxonomic diversity in general, for maintaining ecosystem functioning in forests. 

Background Pituitary adenylate cyclase-activating polypeptide (PACAP) is a multipotent neuropeptide widely distributed in the trigeminovascular system (TVS) and higher brain regions. At present, the underlying mechanism of PACAP/PACAP type1 (PAC1) receptor in migraine generation remains unclear. Methods The rat model of chronic migraine (CM) was established by repeated intraperitoneal injection of nitroglycerin (NTG). Von Frey filaments and hot plate tests were used to measure the mechanical and thermal thresholds. The expression levels of c-Fos, calcitonin gene-related peptide (CGRP), PACAP, PAC1, protein kinase A (PKA) and phosphorylated extracellular signal-regulated kinase (ERK) were assessed by western blotting or immunofluorescence staining. The internalization of PAC1 receptor was visualized by fluorescence microscope and laser scanning confocal microscope. Results The results showed that c-Fos and CGRP expression significantly increased after repeated administrations of NTG or PACAP. Pitstop2 notably improved hyperalgesia in CM rats, while PACAP6–38 offered no benefit. In addition, PACAP-induced PAC1 receptor internalization, PKA and ERK pathways activation were blocked by Pitstop2 instead of PACAP6–38. Conclusions Our results demonstrate that inhibition of PAC1 receptor internalization could effectively improve allodynia in CM rats by restraining ERK signaling pathway activation in a chronic migraine rat model. Modulation of receptor internalization may be a novel perspective to explore specific mechanisms of PACAP signaling activation in the trigeminal vascular system. 

Basal area is a key measure of forest stocking and an important proxy of forest productivity in the face of climate change. Black walnut ( Juglans nigra ) is one of the most valuable timber species in North America. However, little is known about how the stocking of black walnut would change with differed bioclimatic conditions under climate change. In this study, we projected the current and future basal area of black walnut. We trained different machine learning models using more than 1.4 million tree records from 10,162 Forest Inventory and Analysis (FIA) sample plots and 42 spatially explicit bioclimate and other environmental attributes. We selected random forests (RF) as the final model to estimate the basal area of black walnut under climate change because RF had a higher coefficient of determination ( R 2 ), lower root mean square error (RMSE), and lower mean absolute error (MAE) than the other two models (XGBoost and linear regression). The most important variables to predict basal area were the mean annual temperature and precipitation, potential evapotranspiration, topology, and human footprint. Under two emission scenarios (Representative Concentration Pathway 4.5 and 8.5), the RF model projected that black walnut stocking would increase in the northern part of the current range in the USA by 2080, with a potential shift of species distribution range although uncertainty still exists due to unpredictable events, including extreme abiotic (heat, drought) and biotic (pests, disease) occurrences. Our models can be adapted to other hardwood tree species to predict tree changes in basal area based on future climate scenarios. 

Enhancing tree diversity may be important to fostering resilience to drought‐related climate extremes. So far, little attention has been given to whether tree diversity can increase the survival of trees and reduce its variability in young forest plantations.

We conducted an analysis of seedling and sapling survival from 34 globally distributed tree diversity experiments (363,167 trees, 168 species, 3744 plots, 7 biomes) to answer two questions: (1) Do drought and tree diversity alter the mean and variability in plot‐level tree survival, with higher and less variable survival as diversity increases? and (2) Do species that survive poorly in monocultures survive better in mixtures and do specific functional traits explain monoculture survival?

Tree species richness reduced variability in plot‐level survival, while functional diversity (Rao's Q entropy) increased survival and also reduced its variability. Importantly, the reduction in survival variability became stronger as drought severity increased. We found that species with low survival in monocultures survived comparatively better in mixtures when under drought. Species survival in monoculture was positively associated with drought resistance (indicated by hydraulic traits such as turgor loss point), plant height and conservative resource‐acquisition traits (e.g. low leaf nitrogen concentration and small leaf size).

Synthesis.The findings highlight: (1) The effectiveness of tree diversity for decreasing the variability in seedling and sapling survival under drought; and (2) the importance of drought resistance and associated traits to explain altered tree species survival in response to tree diversity and drought. From an ecological perspective, we recommend mixing be considered to stabilize tree survival, particularly when functionally diverse forests with drought‐resistant species also promote high survival of drought‐sensitive species.

 

One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness. 

Abstract The OSIRIS detector is a subsystem of the liquid scintillator filling chain of the JUNO reactor neutrino experiment. Its purpose is to validate the radiopurity of the scintillator to assure that all components of the JUNO scintillator system work to specifications and only neutrino-grade scintillator is filled into the JUNO Central Detector. The aspired sensitivity level of $$10^{-16}\hbox { g/g}$$ 10 - 16 g/g of $$^{238}\hbox {U}$$ 238 U and $$^{232}\hbox {Th}$$ 232 Th requires a large ( $$\sim 20\,\hbox {m}^3$$ ∼ 20 m 3 ) detection volume and ultralow background levels. The present paper reports on the design and major components of the OSIRIS detector, the detector simulation as well as the measuring strategies foreseen and the sensitivity levels to U/Th that can be reached in this setup. 

Abstract Atmospheric neutrinos are one of the most relevant natural neutrino sources that can be exploited to infer properties about cosmic rays and neutrino oscillations. The Jiangmen Underground Neutrino Observatory (JUNO) experiment, a 20 kton liquid scintillator detector with excellent energy resolution is currently under construction in China. JUNO will be able to detect several atmospheric neutrinos per day given the large volume. A study on the JUNO detection and reconstruction capabilities of atmospheric $$\nu _e$$ ν e  and $$\nu _\mu $$ ν μ  fluxes is presented in this paper. In this study, a sample of atmospheric neutrino Monte Carlo events has been generated, starting from theoretical models, and then processed by the detector simulation. The excellent timing resolution of the 3” PMT light detection system of JUNO detector and the much higher light yield for scintillation over Cherenkov allow to measure the time structure of the scintillation light with very high precision. Since $$\nu _e$$ ν e  and $$\nu _\mu $$ ν μ  interactions produce a slightly different light pattern, the different time evolution of light allows to discriminate the flavor of primary neutrinos. A probabilistic unfolding method has been used, in order to infer the primary neutrino energy spectrum from the detector experimental observables. The simulated spectrum has been reconstructed between 100 MeV and 10 GeV, showing a great potential of the detector in the atmospheric low energy region.