Response of sulfate concentration to eutrophication on spatio-temporal scale in freshwater lakes.

The dramatical increase of sulfur concentration in eutrophic lakes, especially sulfate (SO42-), has brought attention to the impact on the lake ecosystem; however, the mechanisms driving the intensification of eutrophication and the role of SO42- concentrations remain poorly understood. To assess the impact of eutrophication on SO42- dynamics in lakes, this study monitored SO42- concentrations in water and sediments across seven lakes with varying trophic statuses on a spatial scale, and in the eutrophic Lake Taihu over one year on a temporal scale, as well as a series of microcosms with different initial SO42- concentrations. Exogenous sulfur input is the primary driver of increased SO42- concentrations in lakes, the highest SO42- concentration in overlying water was 100 mg/L, as well as which reached 310.9 mg/L in sediment. The concurrent input of nutrients such as nitrogen and phosphorus exacerbated eutrophication, resulting in the destabilization of the sulfur cycle. Eutrophication promoted the SO42- concentration on the spatio-temporal scale, especially in sediment, and trophic lake index (TLI) showed a positive correlation with the SO42- in sediments (R2 = 0.99; 0.88). The SO42- concentration in water and TLI showed a nonlinear correlation on the temporal scale (R2 = 0.44), and showed a positive correlation on the spatial scale (R2 = 0.49). Microscopic experiments demonstrate that the anaerobic environment created by cyanobacteria decomposition induced sulfate reduction and significantly reduces SO42- concentrations. Concurrently, the anaerobic environment facilitates the coupling of iron reduction with sulfate reduction, leading to a substantial increase in Acid Volatile Sulfides (AVS) in the sediment. These findings reveal that eutrophication has a dual effect on the dynamic change of SO42- concentrations in overlying water, which is helpful to accurately evaluate and predict the change of SO42- concentrations in lakes.

Domain knowledge-enhanced multi-spatial multi-temporal PM2.5 forecasting with integrated monitoring and reanalysis data.

Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize multi-scale information, both spatially and temporally. There is a lack of integration between individual monitoring stations and city-wide scales. Temporally, the periodic nature of air quality variations is often overlooked or inadequately considered. To overcome these limitations, we conduct a thorough analysis of the data and tasks, integrating spatio-temporal multi-scale domain knowledge. We present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales. The proposed framework consists of two modules: Multi-scale Spatial GCN (MS-GCN) for spatial information fusion and Multi-scale Temporal GRU (MT-GRU) for temporal information integration. In the spatial dimension, the MS-GCN module employs a bidirectional learnable structure and a residual structure, enabling comprehensive information exchange between individual monitoring stations and the city-scale graph. Regarding the temporal dimension, the MT-GRU module adaptively combines information from different temporal scales through parallel hidden states. Leveraging meteorological indicators and four air quality indicators, we present comprehensive comparative analyses and ablation experiments, showcasing the higher accuracy of M2G2 in comparison to nine currently available advanced approaches across all aspects. The improvements of M2G2 over the second-best method on RMSE of 72-h future predictions are as follows: PM2.5: 6%∼10%; PM10: 5%∼7%; NO2: 5%∼16%; O3: 6%∼9%. Furthermore, we demonstrate the effectiveness of each module of M2G2 by ablation study. We conduct a sensitivity analysis of air quality and meteorological data, finding that the introduction of O3 adversely impacts the prediction accuracy of PM2.5.

Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling.

A deep learning architecture, denoted as CNNsLSTM, is proposed for hourly rainfall-runoff modeling in this study. The architecture involves a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory (LSTM) network. In the proposed framework, multiple layers of the CNN component process long-term hourly meteorological time series data, while the LSTM component handles short-term meteorological time series data and utilizes the extracted features from the 1D-CNN. In order to demonstrate the effectiveness of the proposed approach, it was implemented for hourly rainfall-runoff modeling in the Ishikari River watershed, Japan. A meteorological dataset, including precipitation, air temperature, evapotranspiration, longwave radiation, and shortwave radiation, was utilized as input. The results of the proposed approach (CNNsLSTM) were compared with those of previously proposed deep learning approaches used in hydrologic modeling, such as 1D-CNN, LSTM with only hourly inputs (LSTMwHour), a parallel architecture of 1D-CNN and LSTM (CNNpLSTM), and the LSTM architecture, which uses both daily and hourly input data (LSTMwDpH). Meteorological and runoff datasets were separated into training, validation, and test periods to train the deep learning model without overfitting, and evaluate the model with an independent dataset. The proposed approach clearly improved estimation accuracy compared to previously utilized deep learning approaches in rainfall = runoff modeling. In comparison with the observed flows, the median values of the Nash-Sutcliffe efficiency for the test period were 0.455-0.469 for 1D-CNN, 0.639-0.656 for CNNpLSTM, 0.745 for LSTMwHour, 0.831 for LSTMwDpH, and 0.865-0.873 for the proposed CNNsLSTM. Furthermore, the proposed CNNsLSTM reduced the median root mean square error (RMSE) of 1D-CNN by 50.2%-51.4%, CNNpLSTM by 37.4%-40.8%, LSTMwHour by 27.3%-29.5%, and LSTMwDpH by 10.6%-13.4%. Particularly, the proposed CNNsLSTM improved the estimations for high flows (≧75th percentile) and peak flows (≧95th percentile). The computational speed of LSTMwDpH is the fastest among the five architectures. Although the computation speed of CNNsLSTM is slower than LSTMwDpH's, it is still 6.9-7.9 times faster than that of LSTMwHour. Therefore, the proposed CNNsLSTM would be an effective approach for flood management and hydraulic structure design, mainly under climate change conditions that require estimating hourly river flows using meteorological datasets.

Short-versus long-term effects of nitrogen addition and warming on soil nitrogen mineralization and leaching in a grass-dominated old field.

Increased atmospheric nitrogen (N) deposition and climate warming are both anticipated to influence the N dynamics of northern temperate ecosystems substantially over the next century. In field experiments with N addition and warming treatments, cumulative treatment effects can be important for explaining variation in treatment effects on N dynamics over time; however, comparisons between data collected in the early vs. later years potentially can be confounded with interactions between treatment effects and inter-annual variation in environmental conditions or other factors. We compared the short-term versus long-term effects of N addition and warming on net N mineralization and N leaching in a grass-dominated old field using in situ soil cores. We added new N addition and warming plots (3 years old) to an existing field experiment (16 years old), which enabled comparison of the treatment effects at both time scales while controlling for potential inter-annual variation in other factors. For net N mineralization, there was a significant interaction between plot age and N addition over the growing season, and for extractable inorganic N there was a significant interaction between plot age and warming over winter. In both cases, the directions of the treatment effects differed among old and new plots. Moreover, the responses in the new plots differed from the responses observed previously when the 16-year-old plots had been new. These results demonstrate how inter-annual variation in responses, independent from cumulative treatment effects, can play an important role in interpreting long-term effects on soil N cycling in global change field experiments.

Synchronous Changes of GPP and Solar-Induced Chlorophyll Fluorescence in a Subtropical Evergreen Coniferous Forest.

Using in situ near-surface observations of solar-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) of a subtropical evergreen coniferous forest in southern China, this study analyzed the dynamics of SIF, GPP and their environmental responses, and explored the potential of SIF in characterizing the variation of GPP. The results showed that SIF and GPP have similar diurnal and seasonal variation and both reach the highest value in summer, indicating that the SIF can be applied to indicate the seasonal variation of GPP for the subtropical evergreen co-niferous. With the increase in temporal scale, the correlation between SIF and GPP becomes more linear. The diurnal variations of both SIF and GPP were characterized by photosynthetically active radiation (PAR), the seasonal variations of SIF and GPP were influenced by air temperature (Ta) and PAR. Probably due to the absent of drought stress during the study period, no significant correlation was detected between soil water content (SWC) and either SIF or GPP. With the in-crease in Ta, PAR or SWC, the linear correlation between the SIF and GPP gradually decreased, and when Ta or PAR was relatively higher, the correlation between SIF and GPP become weakly. Further research is still needed to illustrate the relationship between SIF and GPP under drought condition which occurred frequently in this region based on longer observation.

Gradient adaptive sampling and multiple temporal scale 3D CNNs for tactile object recognition.

Tactile object recognition (TOR) is very important for the accurate perception of robots. Most of the TOR methods usually adopt uniform sampling strategy to randomly select tactile frames from a sequence of frames, which will lead to a dilemma problem, i.e., acquiring the tactile frames with high sampling rate will get lots of redundant data, while the low sampling rate will miss important information. In addition, the existing methods usually adopt single time scale to construct TOR model, which will induce that the generalization capability is not enough for processing the tactile data generated under different grasping speeds. To address the first problem, a novel gradient adaptive sampling (GAS) strategy is proposed, which can adaptively determine the sampling interval according to the importance of tactile data, therefore, the key information can be acquired as much as possible when the number of tactile frames is limited. To handle the second problem, a multiple temporal scale 3D convolutional neural networks (MTS-3DCNNs) model is proposed, which downsamples the input tactile frames with multiple temporal scales (MTSs) and extracts the MTS deep features, and the fused features have better generalization capability for recognizing the object grasped with different speed. Furthermore, the existing lightweight network ResNet3D-18 is modified to obtain a MR3D-18 network which can match the tactile data with smaller size and prevent the overfitting problem. The ablation studies show the effectiveness of GAS strategy, MTS-3DCNNs, and MR3D-18 networks. The comprehensive comparisons with advanced methods demonstrate that our method is SOTA on two benchmarks.

Scale-invariant sensitivity for multi-purpose water reservoirs management with temporal scale-dependent modeling.

High-resolution temporal data (e.g., daily) is valuable for the decision-making of water resources management because it more accurately captures fine-scale processes and extremes than the coarse temporal data (e.g., weekly or monthly). However, many studies rarely consider this superior suitability for water resource modeling and management; instead, they often use whichever data is more readily available. So far, no comparative investigations have been conducted to determine if access to different time-scale data would change decision-maker perceptions or the rationality of decision making. This study proposes a framework for assessing the impact of different temporal scales on water resource management and the performance objective's sensitivity to uncertainties. We built the multi-objective operation models and operating rules of a water reservoir system based on daily, weekly, and monthly scales, respectively, using an evolution multi-objective direct policy search. The temporal scales of the input variables (i.e., streamflow) affect both the model structures and the output variables. In exploring these effects, we reevaluated the temporal scale-dependent operating rules under uncertain streamflow sets generated from synthetic hydrology. Finally, we obtained the output variable's sensitivities to the uncertain factors at different temporal scales using the distribution-based sensitivity analysis method. Our results show that water management based on too coarse resolution might give decision makers the wrong perception because the effect of actual extreme streamflow process on the performance objectives is ignored. The streamflow uncertainty is more influential than the uncertainty associated with operating rules. However, the sensitivities are characterized by temporal scale invariance, as the differences of the sensitivity between different temporal scales are not obvious over the uncertainties in streamflow and thresholds. These results show that water management should consider the resolution-dependent effect of temporal scales for balancing modeling complexity and computational cost.

Seasonal variation in the soil fungal community structure of Larix gmelinii forests in Northeast China.

Soil fungi play an indispensable role in forest ecosystems by participating in energy flow, material circulation, and assisting plant growth and development. Larix gmelinii is the dominant tree species in the greater Khingan Mountains, which is the only cold temperate coniferous forest in China. Understanding the variations in underground fungi will help us master the situation of L. gmelinii above ground. We collected soil samples from three seasons and analyzed the differences in soil fungal community structure using high-throughput sequencing technology to study the seasonal changes in soil fungal community structure in L. gmelinii forests. We found that the Shannon and Chao1 diversity in autumn was significantly lower than in spring and summer. The community composition and functional guild varied significantly between seasons. Furthermore, we showed that ectomycorrhizal fungi dominated the functional guilds. The relative abundance of ectomycorrhizal fungi increased dramatically from summer to autumn and was significantly negatively correlated with temperature and precipitation. Temperature and precipitation positively affect the alpha diversity of fungi significantly. In addition, pH was negatively correlated with the Chao1 diversity. Temperature and precipitation significantly affected several dominant genera and functional guilds. Among the soil physicochemical properties, several dominant genera were affected by pH, and the remaining individual genera and functional guilds were significantly correlated with total nitrogen, available phosphorus, soil organic carbon, or cation exchange capacity. For the composition of total fungal community, temperature and precipitation, as well as soil physicochemical properties except AP, significantly drove the variation in community composition.

Seasonal and elevational variation in thermal ecology of the crevice-dwelling knob-scaled lizard Xenosaurus fractus from central-eastern Mexico.

There is strong covariation between the thermal physiology of ectothermic animals and their thermal environment. Spatial and temporal differences in the thermal environment across a species' range may result in changes in thermal preferences between populations of that species. Alternatively, thermoregulatory-based microhabitat selection can allow individuals to maintain similar body temperatures across a broad thermal gradient. Which strategy a species adopts is often dependent on taxon-specific levels of physiological conservatism or ecological context. Identifying which strategies species use in response to spatial and temporal variation in environmental temperatures requires empirical evidence, which then can support predictions as to how a species might respond to a changing climate. Here we present findings of our analyses of the thermal quality, thermoregulatory accuracy and efficiency for the lizard, Xenosaurus fractus, across an elevation-thermal gradient and over the temporal thermal variation associated with seasonal changes. Xenosaurus fractus is a strict crevice-dweller, a habitat that can buffer this lizard from extreme temperatures and is a thermal conformer (body temperatures reflect air and substrate temperatures). We found populations of this species differed in their thermal preferences along an elevation gradient and between seasons. Specifically, we found that habitat thermal quality, thermoregulatory accuracy and efficiency (all measures of how well the lizards' body temperatures compared to their preferred body temperatures) varied along thermal gradients and with season. Our findings indicate that this species has adapted to local conditions and shows seasonal flexibility in those spatial adaptations. Along with their strict crevice-dwelling habitat, these adaptations may provide some protection against a warming climate.

Elephant rewilding affects landscape openness and fauna habitat across a 92-year period.

Trophic rewilding aims to promote biodiverse self-sustaining ecosystems through the restoration of ecologically important taxa and the trophic interactions and cascades they propagate. How rewilding effects manifest across broad temporal scales will determine ecosystem states; however, our understanding of post-rewilding dynamics across longer time periods is limited. Here we show that the restoration of a megaherbivore, the African savannah elephant (Loxodonta africana), promotes landscape openness (i.e., various measures of vegetation composition/complexity) and modifies fauna habitat and that these effects continue to manifest up to 92 years after reintroduction. We conducted a space-for-time floristic survey and assessment of 17 habitat attributes (e.g., floristic diversity and cover, ground wood, tree hollows) across five comparable nature reserves in South African savannah, where elephants were reintroduced between 1927 and 2003, finding that elephant reintroduction time was positively correlated with landscape openness and some habitat attributes (e.g., large-sized tree hollows) but negatively associated with others (e.g., large-sized coarse woody debris). We then indexed elephant site occurrence between 2006 and 2018 using telemetry data and found positive associations between site occurrence and woody plant densities. Taken alongside the longer-term space-for-time survey, this suggests that elephants are attracted to dense vegetation in the short term and that this behavior increases landscape openness in the long term. Our results suggest that trophic rewilding with elephants helps promote a semi-open ecosystem structure of high importance for African biodiversity. More generally, our results suggest that megafauna restoration represents a promising tool to curb Earth's recent ecological losses and highlights the importance of considering long-term ecological responses when designing and managing rewilding projects.

Seasonality, density dependence, and spatial population synchrony.

Studies of spatial population synchrony constitute a central approach for understanding the drivers of ecological dynamics. Recently, identifying the ecological impacts of climate change has emerged as a new important focus in population synchrony studies. However, while it is well known that climatic seasonality and sequential density dependence influences local population dynamics, the role of season-specific density dependence in shaping large-scale population synchrony has not received attention. Here, we present a widely applicable analytical protocol that allows us to account for both season and geographic context-specific density dependence to better elucidate the relative roles of deterministic and stochastic sources of population synchrony, including the renowned Moran effect. We exemplify our protocol by analyzing time series of seasonal (spring and fall) abundance estimates of cyclic rodent populations, revealing that season-specific density dependence is a major component of population synchrony. By accounting for deterministic sources of synchrony (in particular season-specific density dependence), we are able to identify stochastic components. These stochastic components include mild winter weather events, which are expected to increase in frequency under climate warming in boreal and Arctic ecosystems. Interestingly, these weather effects act both directly and delayed on the vole populations, thus enhancing the Moran effect. Our study demonstrates how different drivers of population synchrony, presently altered by climate warming, can be disentangled based on seasonally sampled population time-series data and adequate population models.

Assessment of groundwater salinity using principal component analysis (PCA): a case study from Mewat (Nuh), Haryana, India.

In the present study, principal component analysis (PCA) is used to investigate the processes controlling groundwater salinity in the Mewat (Nuh) district, Haryana, India. Twenty groundwater samples were collected from salinity-affected areas in the March-April months of years 2018 and 2019 and were analyzed for chemical variables pH, EC, Ca2+, Mg2+, Na+, K+, [Formula: see text], Cl-, SO42-, [Formula: see text], TDS, and total hardness. Three principal components were selected based on the eigen value, which explains 79.58% and 85.08% of the total variation in the years 2018 and 2019, respectively. The first principal component (PC-1) is identified with salinity, the second principal component (PC-2) with alkalinity, and the third principal component (PC-3) described the pollution. When the yearly comparison was made, the samples collected in 2019 were found to have an increased salinity compared to 2018, which shows an increased vulnerability to the aquifer of Mewat on account of the decline in rainfall recharge. It was also evident that declining recharge also triggered the recharge from other sources; thus, the impact of pollution is more pronounced in 2019 compared to 2018.

Acute, diel, and annual temperature variability and the thermal biology of ectotherms.

Global warming is increasing mean temperatures and altering temperature variability at multiple temporal scales. To better understand the consequences of changes in thermal variability for ectotherms it is necessary to consider thermal variation at different time scales (i.e., acute, diel, and annual) and the responses of organisms within and across generations. Thermodynamics constrain acute responses to temperature, but within these constraints and over longer time periods, organisms have the scope to adaptively acclimate or evolve. Yet, hypotheses and predictions about responses to future warming tend not to explicitly consider the temporal scale at which temperature varies. Here, focusing on multicellular ectothermic animals, we argue that consideration of multiple processes and constraints associated with various timescales is necessary to better understand how altered thermal variability because of climate change will affect ectotherms.

Integrating geographic ranges across temporal scales.

Geographic ranges are a fundamental unit of biogeography and macroecology. Increasingly, paleontologists and ecologists alike are reconstructing geographic ranges of species from fossils, in order to understand the long-term processes governing biogeographic and macroevolutionary patterns. As these reconstructions have become increasingly common, uncertainty has arisen over the equivalency of paleo-ranges and modern ranges. Here, we argue geographic ranges are time-averaged at all temporal scales, and reflect the biotic and abiotic processes operating across the equivalent range of time and space scales. This conceptual framework integrates the study of geographic ranges reconstructed using modern and ancient data, and highlights the potential for ranges to illuminate processes responsible for diversity patterns over intervals spanning days to tens of millions of years of Earth history.

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Plant species diversity is one of the critical factors for maintaining multi-function and stability of terrestrial ecosystem. We reviewed the traditional methods for measuring plant species diversity of grassland (PSDG), and then introduced the new ideas and methods used for PSDG monitoring. Traditionally, PSDG monitoring depended heavily on ground-based investigation, which usually required large amounts of time, labor, and cost, and therefore was only suitable for small scale investigation. Grassland plant species were typically small in size and highly mixed. It was difficult to identify and measure by remote sensing due to the limitation of resolution. Consequently, most studies on PSDG were based on remote-sensing retrieval or habitat simulation. Characterized with high spatial-temporal resolution, flexible and low cost, the unmanned aerial vehicle (UAV) technology was regarded as the bridge between ground-based investigation and satellite remote sensing. It could be the breakthrough for monitoring PSDG accurately at large scales. In the future, we should establish PSDG monitoring network by combining the fixed monitoring sites and dynamic monitoring sites of UAV and satellite remote sensing, and integrating UAV and automatic target recognition organically.

Using the WEI+ index to evaluate water scarcity at highly regulated river basins with conjunctive uses of surface and groundwater resources.

This paper discusses the role and limitations of using WEI+ as a water resource management tool for highly regulated river basins, with a conjunctive use of surface and groundwater resources. By considering flow regulation by reservoirs and aquifer systems, seasonality of water availability and demand, returns from water uses and environmental flow requirements, WEI+ constitutes an improvement to existing quantitative water scarcity indexes. However, the computation of WEI+ in complex river basins systems requires detailed data on water availability and water allocation to various uses, which are hard to obtain from monitoring records. The paper describes how the combined use of hydrological and water allocation models can help to overcome data gaps in water accounting and contribute to an improved analysis of water scarcity in heterogeneous and intricate river basins. It also examines the information provided by WEI+ and by other widely used water scarcity indexes, such as the Water Stress Index and the Criticality Ratio, as well as discusses the ability of WEI+ to measure the performance of hydraulic systems, usually evaluated by parameters such as reliability, vulnerability, and resilience. The Tagus River transboundary basin was selected as case study due to massive flow regulation by multi-purpose reservoirs and significant seasonality of water availability and demand. Results show the benefits of using WEI+ to define levels of water scarcity, over other indexes. Within the Tagus River systems, high values of WEI+ are reached during the summer months in regions with intensive agriculture, denoting severe water stress conditions in most sub-basins. The analysis also reveals the strong dependence of Portugal, the downstream country, on flows from Spain, the upstream country.

Nitrogen enrichment alters climate sensitivity of biodiversity and productivity differentially and reverses the relationship between them in an alpine meadow.

Biodiversity and productivity that highly determine ecosystem services are varying largely under global change. However, the climate sensitivity of them and their relationship are not well understood, especially in the context of increasing nitrogen (N) deposition. Here, based on a six-year N manipulation experiment in an alpine meadow, we quantified interannual climate sensitivity of species richness (SR) and above-ground net primary productivity (ANPP) as well as SR-ANPP relationship as affected by six N addition rate (Nrate) gradients. We found that interannual variations in ANPP and SR were mainly driven by temperature instead of precipitation. In the plots without N addition, higher temperature substantially increased ANPP but reduced SR across years, thus resulting in a negative SR-ANPP relationship. However, the negative and positive responses of SR and ANPP to temperature increased and declined significantly with increasing Nrate, respectively, leading to a shift of the negative relationship between SR and ANPP into a positive one under high Nrate. Moreover, the adverse influence of drought on SR and ANPP would be aggravated by N fertilization, as indicated by the increased positive effect of precipitation on them under N enrichment. Our findings indicate that climate sensitivity of productivity and biodiversity may be misestimated if the impact of N deposition is not considered, and the importance of biodiversity to maintain productivity would enhance as N deposition increases. This study provides a new insight to explain variation of biodiversity-productivity relationship along with environmental changes.

Species pool size and rainfall account for the relationship between biodiversity and biomass production in natural forests of China.

The strength of biodiversity-biomass production relationships increases with increasing environmental stress and time. However, we know little about the effects of abiotic (e.g., climate) and biotic (e.g., species pool and community composition) factors on this trend. Whether variation in biomass production is best explained by phylogenetic diversity metrics or traditional measures of species richness also remains elusive. We compiled estimates of community composition and biomass production for tree species in 111 permanent quadrats spanning three natural forests (tropical, subtropical, and temperate) in China. Based on ~10 years of data, we compared temperature, rainfall, species pool size, and community composition in each forest each year. We estimated species richness and phylogenetic diversity in each quadrat each year; the latter metric was based on the sum of branch lengths of a phylogeny that connects species in each quadrat each year. Using generalized linear mixed-effect models, we found that top-ranked models included the interaction between forest and biodiversity and the interaction between forest and year for both biodiversity metrics. Variation in biomass production was best explained by phylogenetic diversity; biomass production generally increased with phylogenetic diversity, and the relationship was stronger in subtropical and temperate forests. Increasing species pool size, temperature, and rainfall and decreasing inter-quadrat dissimilarity range shifted the relationship between biomass production and phylogenetic diversity from positive to neutral. When considered alone, species pool size had the strongest influence on biomass production, while species pool size, rainfall, and their interaction with phylogenetic diversity constituted the top-ranked model. Our study highlights the importance of species pool size and rainfall on the relationship between phylogenetic diversity and biomass production in natural forest ecosystems.

Neurophysiological dynamics for psychological resilience: A view from the temporal axis.

When an individual is faced with adversity, the brain and body work cooperatively to adapt to it. This adaptive process is termed psychological resilience, and recent studies have identified several neurophysiological factors ("neurophysiological resilience"), such as monoamines, oscillatory brain activity, hemodynamics, autonomic activity, stress hormones, and immune systems. Each factor is activated in an interactive manner during specific time windows after exposure to stress. Thus, the differences in psychological resilience levels among individuals can be characterized by differences in the temporal dynamics of neurophysiological resilience. In this review, after briefly introducing the frequently used approaches in this research field and the well-known factors of neurophysiological resilience, we summarize the temporal dynamics of neurophysiological resilience. This viewpoint clarifies an important time window, the more-than-one-hour scale, but the neurophysiological dynamics during this window remain elusive. To address this issue, we propose exploring brain-wide oscillatory activities using concurrent functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) techniques.