Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe.

Aim:Forest understorey microclimates are often buffered against extreme heat or cold, with important implications for the organisms living in these environments. We quantified seasonal effects of understorey microclimate predictors describing canopy structure, canopy composition and topography (i.e., local factors) and the forest patch size and distance to the coast (i.e., landscape factors). Location:Temperate forests in Europe. Time period:2017-2018. Major taxa studied:Woody plants. Methods:We combined data from a microclimate sensor network with weather-station records to calculate the difference, or offset, between temperatures measured inside and outside forests. We used regression analysis to study the effects of local and landscape factors on the seasonal offset of minimum, mean and maximum temperatures. Results:The maximum temperature during the summer was on average cooler by 2.1 °C inside than outside forests, and the minimum temperatures during the winter and spring were 0.4 and 0.9 °C warmer. The local canopy cover was a strong nonlinear driver of the maximum temperature offset during summer, and we found increased cooling beneath tree species that cast the deepest shade. Seasonal offsets of minimum temperature were mainly regulated by landscape and topographic features, such as the distance to the coast and topographic position. Main conclusions:Forest organisms experience less severe temperature extremes than suggested by currently available macroclimate data; therefore, climate-species relationships and the responses of species to anthropogenic global warming cannot be modelled accurately in forests using macroclimate data alone. Changes in canopy cover and composition will strongly modulate the warming of maximum temperatures in forest understories, with important implications for understanding the responses of forest biodiversity and functioning to the combined threats of land-use change and climate change. Our predictive models are generally applicable across lowland temperate deciduous forests, providing ecologically important microclimate data for forest understories

Predicting the spatial and temporal dynamics of species interactions in Fagus sylvatica and Pinus sylvestris forests across Europe

The productivity and functioning of mixed-species forests often differs from that of monocultures. However, the magnitude and direction of these differences are difficult to predict because species interactions can be modified by many potentially interacting climatic and edaphic conditions, stand structure and previous management. Process-based forest growth models could potentially be used to disentangle the effects of these factors and thereby improve our understanding of mixed forest functioning while facilitating their design and silvicultural management. However, to date, the predicted mixing effects of forest growth models have not been compared with measured mixing effects. In this study, 26 sites across Europe, each containing a mixture and monocultures of Fagus sylvatica and Pinus sylvestris, were used to calculate mixing effects on growth and yield and compare them with the mixing effects predicted by the forest growth model 3-PGmix. The climate and edaphic conditions, stand structures and ages varied greatly between sites. The model performed well when predicting the stem mass and total mass (and mixing effects on these components), with model efficiency that was usually >0.7. The model efficiency was lower for growth or smaller components such as foliage mass and root mass. The model was also used to predict how mixing effects would change along gradients in precipitation, temperature, potential available soil water, age, thinning intensity and soil fertility. The predicted patterns were consistent with measurements of mixing effects from published studies. The 3-PG model is a widely used management tool for monospecific stands and this study shows that 3-PGmix can be used to examine the dynamics of mixed-species stands and determine how they may need to be managed.This article is based upon work from COST Action EuMIXFOR, supported by COST (European Cooperation in Science and Technology). Funding for the Czech Republic site was provided by the MŠMT projects COST CZ – LD14063 and LD14074. All contributors thank their national funding institutions and the forest owners for agreeing to establish the plots and to measure and analyse data from the plots. The first author was funded by a Heisenberg Fellowship (FO 791/4-1) from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG). Mário Pereira was supported by European Investment Funds by FEDER/COMPETE/POCI– Operacional Competitiveness and Internacionalization Programme, under Project POCI-01-0145-FEDER-006958 and National Funds by FCT – Portuguese Foundation for Science and Technology, under the project UID/AGR/04033/2013 as well as by project Interact-Integrative Research in Environment, Agro-Chain and Technology, NORTE-01-0145-FEDER-000017, research line BEST, co-financed by FEDER/NORTE 2020

Growth and yield of mixed versus pure stands of Scots pine (Pinus sylvestris L. ) and European beech (Fagus sylvatica L.) analysed along a productivity gradient through Europe

Mixing of complementary tree species may increase stand productivity, mitigate the effects of drought and other risks, and pave the way to forest production systems which may be more resource-use efficient and stable in the face of climate change. However, systematic empirical studies on mixing effects are still missing for many commercially important and widespread species combinations. Here we studied the growth of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) in mixed versus pure stands on 32 triplets located along a productivity gradient through Europe, reaching from Sweden to Bulgaria and from Spain to the Ukraine. Stand inventory and taking increment cores on the mainly 60-80 year-old trees and 0.02-1.55 ha sized, fully stocked plots provided insight how species mixing modifies the structure, dynamics and productivity compared with neighbouring pure stands. In mixture standing volume (+12 %), stand density (+20 %), basal area growth (+12 %), and stand volume growth (+8 %) were higher than the weighted mean of the neighbouring pure stands. Scots pine and European beech contributed rather equally to the overyielding and overdensity. In mixed stands mean diameter (+20 %) and height (+6 %) of Scots pine was ahead, while both diameter and height growth of European beech were behind (−8 %). The overyielding and overdensity were independent of the site index, the stand growth and yield, and climatic variables despite the wide variation in precipitation (520-1175 mm year−1), mean annual temperature (6-10.5 °C), and the drought index by de Martonne (28-61 mm °C−1) on the sites. Therefore, this species combination is potentially useful for increasing productivity across a wide range of site and climatic conditions. Given the significant overyielding of stand basal area growth but the absence of any relationship with site index and climatic variables, we hypothesize that the overyielding and overdensity results from several different types of interactions (light-, water-, and nutrient-related) that are all important in different circumstances. We discuss the relevance of the results for ecological theory and for the ongoing silvicultural transition from pure to mixed stands and their adaptation to climate change.The networking in this study has been sup-ported by COST Action FP1206 EuMIXFOR. All contributors thanktheir national funding institutions to establish, measure, and analysedata from the triplets. The first author also thanks the BayerischenStaatsforsten (BaySF) for supporting the establishment of the plots,the Bavarian State Ministry for Nutrition, Agriculture, and Forestryfor permanent support of the project W 07 ‘‘Long-term experimentalplots for forest growth and yield research’’ (# 7831-22209-2013) andthe German Science Foundation for providing the funds for the pro-jects PR 292/12-1 ‘‘Tree and stand-level growth reactions on droughtin mixed versus pure forests of Norway spruce and European beech’’.Thanks are also due to Ulrich Kern for the graphical artwork, and totwo anonymous reviewers for their constructive criticism

Mycorrhizal feedbacks influence global forest structure and diversity

One mechanism proposed to explain high species diversity in tropical systems is strong negative conspecific density dependence (CDD), which reduces recruitment of juveniles in proximity to conspecific adult plants. Although evidence shows that plant-specific soil pathogens can drive negative CDD, trees also form key mutualisms with mycorrhizal fungi, which may counteract these effects. Across 43 large-scale forest plots worldwide, we tested whether ectomycorrhizal tree species exhibit weaker negative CDD than arbuscular mycorrhizal tree species. We further tested for conmycorrhizal density dependence (CMDD) to test for benefit from shared mutualists. We found that the strength of CDD varies systematically with mycorrhizal type, with ectomycorrhizal tree species exhibiting higher sapling densities with increasing adult densities than arbuscular mycorrhizal tree species. Moreover, we found evidence of positive CMDD for tree species of both mycorrhizal types. Collectively, these findings indicate that mycorrhizal interactions likely play a foundational role in global forest diversity patterns and structure

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Mycorrhizal feedbacks influence global forest structure and diversity

One mechanism proposed to explain high species diversity in tropical systems is strong negative conspecific density dependence (CDD), which reduces recruitment of juveniles in proximity to conspecific adult plants. Although evidence shows that plant-specific soil pathogens can drive negative CDD, trees also form key mutualisms with mycorrhizal fungi, which may counteract these effects. Across 43 large-scale forest plots worldwide, we tested whether ectomycorrhizal tree species exhibit weaker negative CDD than arbuscular mycorrhizal tree species. We further tested for conmycorrhizal density dependence (CMDD) to test for benefit from shared mutualists. We found that the strength of CDD varies systematically with mycorrhizal type, with ectomycorrhizal tree species exhibiting higher sapling densities with increasing adult densities than arbuscular mycorrhizal tree species. Moreover, we found evidence of positive CMDD for tree species of both mycorrhizal types. Collectively, these findings indicate that mycorrhizal interactions likely play a foundational role in global forest diversity patterns and structure

A traffic light enzyme: acetate binding reversibly switches chlorite dismutase from a red- to a green-colored heme protein

Chlorite dismutase is a unique heme enzyme that catalyzes the conversion of chlorite to chloride and molecular oxygen. The enzyme is highly specific for chlorite but has been known to bind several anionic and neutral ligands to the heme iron. In a pH study, the enzyme changed color from red to green in acetate buffer pH 5.0. The cause of this color change was uncovered using UV-visible and EPR spectroscopy. Chlorite dismutase in the presence of acetate showed a change of the UV-visible spectrum: a redshift and hyperchromicity of the Soret band from 391 to 404 nm and a blueshift of the charge transfer band CT1 from 647 to 626 nm. Equilibrium binding titrations with acetate resulted in a dissociation constant of circa 20 mM at pH 5.0 and 5.8. EPR spectroscopy showed that the acetate bound form of the enzyme remained high spin S = 5/2, however with an apparent change of the rhombicity and line broadening of the spectrum. Mutagenesis of the proximal arginine Arg183 to alanine resulted in the loss of the ability to bind acetate. Acetate was discovered as a novel ligand to chlorite dismutase, with evidence of direct binding to the heme iron. The green color is caused by a blueshift of the CT1 band that is characteristic of the high spin ferric state of the enzyme. Any weak field ligand that binds directly to the heme center may show the red to green color change, as was indeed the case for fluoride

Time-based and event-based prospective memory in autism spectrum disorder: The roles of executive function, theory of mind, and time-estimation

Prospective memory (remembering to carry out an action in the future) has been studied relatively little in ASD. We explored time-based (carry out an action at a pre-specified time) and event-based (carry out an action upon the occurrence of a pre-specified event) prospective memory, as well as possible cognitive correlates, among 21 intellectually high-functioning children with ASD, and 21 age- and IQ-matched neurotypical comparison children. We found impaired time-based, but undiminished event-based, prospective memory among children with ASD. In the ASD group, time-based prospective memory performance was associated significantly with diminished theory of mind, but not with diminished cognitive flexibility. There was no evidence that time-estimation ability contributed to time-based prospective memory impairment in ASD

Effect of lysergic acid diethylamide (LSD) on reinforcement learning in humans.

BACKGROUND: The non-selective serotonin 2A (5-HT2A) receptor agonist lysergic acid diethylamide (LSD) holds promise as a treatment for some psychiatric disorders. Psychedelic drugs such as LSD have been suggested to have therapeutic actions through their effects on learning. The behavioural effects of LSD in humans, however, remain incompletely understood. Here we examined how LSD affects probabilistic reversal learning (PRL) in healthy humans. METHODS: Healthy volunteers received intravenous LSD (75 μg in 10 mL saline) or placebo (10 mL saline) in a within-subjects design and completed a PRL task. Participants had to learn through trial and error which of three stimuli was rewarded most of the time, and these contingencies switched in a reversal phase. Computational models of reinforcement learning (RL) were fitted to the behavioural data to assess how LSD affected the updating ('learning rates') and deployment of value representations ('reinforcement sensitivity') during choice, as well as 'stimulus stickiness' (choice repetition irrespective of reinforcement history). RESULTS: Raw data measures assessing sensitivity to immediate feedback ('win-stay' and 'lose-shift' probabilities) were unaffected, whereas LSD increased the impact of the strength of initial learning on perseveration. Computational modelling revealed that the most pronounced effect of LSD was the enhancement of the reward learning rate. The punishment learning rate was also elevated. Stimulus stickiness was decreased by LSD, reflecting heightened exploration. Reinforcement sensitivity differed by phase. CONCLUSIONS: Increased RL rates suggest LSD induced a state of heightened plasticity. These results indicate a potential mechanism through which revision of maladaptive associations could occur in the clinical application of LSD.This study was funded by the Walacea.com crowdfunding campaign and the Beckley Foundation, awarded to R.L.C-H. J.W.K. was supported by a Gates Cambridge Scholarship and an Angharad Dodds John Bursary in Mental Health and Neuropsychiatry, T.W.R. by a Wellcome Trust Senior Investigator Grant 104631/Z/14/Z, and H.E.M.d.O. by the Netherlands Organisation for Scientific Research, NWO. R.N.C.’s research is funded by the UK Medical Research Council (MC_PC_17213, MR/W014386/1). Q.L. was partially sup- ported by grants from the National Key Research and Development Program of China (No. 2019YFA0709502), the National Natural Science Foundation of China (No. 81873909), the Science and Technology Commission of Shanghai Municipality (No.s 20ZR1404900 and 20DZ2260300), the Shanghai Municipal Science and Technology Major Project (No.s 2018SHZDZX01 and 2021SHZDZX0103), and the Fundamental Research Funds for the Central Universities. During the prepar- ation of this manuscript, Q.L. was a Visiting Fellow at Clare Hall, University of Cambridge, Cambridge, UK. This research was supported in part by the UK National Health Service (NHS) National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014); the views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care