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Plant mixture balances terrestrial ecosystem C:N:P stoichiometry
Plant and soil C:N:P ratios are of critical importance to productivity, food-web dynamics, and nutrient cycling in terrestrial ecosystems worldwide. Plant diversity continues to decline globally; however, its influence on terrestrial C:N:P ratios remains uncertain. By conducting a global meta-analysis of 2049 paired observations in plant species mixtures and monocultures from 169 sites, we show that, on average across all observations, the C:N:P ratios of plants, soils, soil microbial biomass and enzymes did not respond to species mixture nor to the species richness in mixtures. However, the mixture effect on soil microbial biomass C:N changed from positive to negative, and those on soil enzyme C:N and C:P shifted from negative to positive with increasing functional diversity in mixtures. Importantly, species mixture increased the C:N, C:P, N:P ratios of plants and soils when background soil C:N, C:P, and N:P were low, but decreased them when the respective background ratios were high. Our results demonstrate that plant mixtures can balance terrestrial plant and soil C:N:P ratios dependent on background soil C:N:P. Our findings highlight that plant diversity conservation does not only increase plant productivity, but also optimizes ecosystem stoichiometry for the diversity and productivity of today’s and future vegetation.
Plant and soil C:N:P ratios are critical to ecosystem functioning, but it remains uncertain how plant diversity affects terrestrial C:N:P. In this meta-analysis of 169 studies, the authors find that plant mixtures can balance plant and soil C:N:P ratios according to background soil C:N:P.
Journal Article
Negative effects of fertilization on plant nutrient resorption
Plants in infertile habitats are thought to have a high rate of nutrient resorption to enable them reuse nutrients more efficiently than those in fertile habitats. However, there is still much debate on how plant nutrient resorption responds to nutrient availability. Here we used a meta-analysis from a global data set of 9703 observations at 306 sites from 508 published articles to examine the effects of nitrogen (N) and phosphorus (P) fertilization on plant foliar N and P concentrations and resorption efficiency. We found that N fertilization enhanced N concentration in green leaves by 27% and P fertilization enhanced green-leaf P by 73% on average. The N and P concentrations in senesced leaves also increased with respective nutrient fertilization. Resorption efficiencies (percentage of nutrient recovered from senescing leaves) of both N and P declined in response to respective nutrient fertilization. Combined N and P fertilization also had negative effects on both N and P resorption efficiencies. Whether nutrient resorption efficiency differs among plant growth types and among ecosystems, however, remains uncertain due to the limited sample sizes when analyzed by plant growth types or ecosystem types. Our analysis indicates that fertilization decreases plant nutrient resorption and the view that nutrient resorption is a critical nutrient conservation strategy for plants in nutrient-poor environments cannot be abandoned. The response values to fertilization presented in our analysis can help improve biogeochemical models.
Journal Article
Meta-analysis shows positive effects of plant diversity on microbial biomass and respiration
Soil microorganisms are key to biological diversity and many ecosystem processes in terrestrial ecosystems. Despite the current alarming loss of plant diversity, it is unclear how plant species diversity affects soil microorganisms. By conducting a global meta-analysis with paired observations of plant mixtures and monocultures from 106 studies, we show that microbial biomass, bacterial biomass, fungal biomass, fungi:bacteria ratio, and microbial respiration increase, while Gram-positive to Gram-negative bacteria ratio decrease in response to plant mixtures. The increases in microbial biomass and respiration are more pronounced in older and more diverse mixtures. The effects of plant mixtures on all microbial attributes are consistent across ecosystem types including natural forests, planted forests, planted grasslands, croplands, and planted containers. Our study underlines strong relationships between plant diversity and soil microorganisms across global terrestrial ecosystems and suggests the importance of plant diversity in maintaining belowground ecosystem functioning.
The effect of plant biodiversity on microbial function has been tested in limited studies and is likely to be context-dependent. In this meta-analysis of 106 prior studies comparing plant monocultures to mixtures, the authors find that plant diversity increases microbial biomass and respiration rates, an effect moderated by stand age.
Journal Article
Adverse Effects of Ozone Pollution on Net Primary Productivity in the North China Plain
Tropospheric ozone significantly damages vegetation and reduces net primary productivity (NPP). We developed a stable linear NPP response model based on accumulated ozone exposure over a threshold of 40 parts per billion (ppb) (AOT40). We then estimated the effects of regional ozone damage on NPP for different vegetation types. The study suggests an average decrease in NPP of 24.7% due to ozone pollution in the North China Plain, similar to previous estimates ranging from 10.1% to 24.7%, with a maximum reduction exceeding 200 [g C m‐2 yr‐1] and more than 50%. Vegetation types such as broadleaf forests, needleleaf forest, crops, and grasses showed significant NPP decreases of 47.1%, 37.8%, 36.7%, and 44.6%, respectively. Declining NPP also had negative impacts on several Chinese crop species. Our work highlights the need for urgent and effective action to mitigate ozone pollution's substantial detrimental effects on ecosystem health and productivity. Plain Language Summary Ozone pollution led to an average 25% decrease in net primary productivity in the North China Plain. This decline is consistent with previous estimates that ranged from 10% to 25%. Productivity of forest trees, crops, and grasses declined significantly, ranging from 36.7% to 47.1%. These results have implications for Chinese crops, as lower crop productivity can negatively affect crop yields. Our study highlights the urgent need for action to mitigate the detrimental effects of ozone pollution on ecosystem health and productivity. Key Points We developed a stable model to estimate ozone impact on the productivity of plants' Ozone pollution resulted in an average decline in productivity of 25% per year in all ecosystem types in the North China Plain region Ozone pollution led to net primary productivity declines in forest, crops, and grasses, with reductions ranging from 36.7% to 47.1%
Journal Article
Individual size inequality links forest diversity and above‐ground biomass
Despite the mounting evidence for positive diversity–productivity relationships found in controlled experiments, diversity effects on productivity in natural systems remain hotly debated. Understanding the multivariate links between diversity and productivity in natural systems, in particular natural forests that host the majority of terrestrial biodiversity and provide essential services for humanity, remains a critical challenge for ecologists. We analysed data from 448 plots of varying tree species diversity, stand ages and local nutrient availability in Canada's boreal forest (52°30′–55°24′ N latitude and 102°36′–108° W longitude). We used structural equation models to link multivariate relationships between above‐ground biomass, tree species diversity, stand age and soil nutrient availability. Above‐ground biomass increased with diversity indirectly via increasing tree size inequality, increased with stand age and was higher on sites of medium soil nutrient regime directly as well as indirectly via increased tree size inequality. Synthesis. Our results demonstrate positive diversity effects on above‐ground biomass in natural forests of diverse forest ages and soil resource availability. Furthermore, we show that tree size inequality acts as a mechanism for the positive diversity effects on above‐ground biomass and as a mechanism in regulating above‐ground biomass and species diversity simultaneously via interactions among individuals in natural forests.
Journal Article
Global negative effects of nitrogen deposition on soil microbes
Soil microbes comprise a large portion of the genetic diversity on Earth and influence a large number of important ecosystem processes. Increasing atmospheric nitrogen (N) deposition represents a major global change driver; however, it is still debated whether the impacts of N deposition on soil microbial biomass and respiration are ecosystem-type dependent. Moreover, the extent of N deposition impacts on microbial composition remains unclear. Here we conduct a global meta-analysis using 1408 paired observations from 151 studies to evaluate the responses of soil microbial biomass, composition, and function to N addition. We show that nitrogen addition reduced total microbial biomass, bacterial biomass, fungal biomass, biomass carbon, and microbial respiration. Importantly, these negative effects increased with N application rate and experimental duration. Nitrogen addition reduced the fungi to bacteria ratio and the relative abundances of arbuscular mycorrhizal fungi and gram-negative bacteria and increased gram-positive bacteria. Our structural equation modeling showed that the negative effects of N application on soil microbial abundance and composition led to reduced microbial respiration. The effects of N addition were consistent across global terrestrial ecosystems. Our results suggest that atmospheric N deposition negatively affects soil microbial growth, composition, and function across all terrestrial ecosystems, with more pronounced effects with increasing N deposition rate and duration.
Journal Article
Prevalence of neural collapse during the terminal phase of deep learning training
Modern practice for training classification deepnets involves a terminal phase of training (TPT), which begins at the epoch where training error first vanishes. During TPT, the training error stays effectively zero, while training loss is pushed toward zero. Direct measurements of TPT, for three prototypical deepnet architectures and across seven canonical classification datasets, expose a pervasive inductive bias we call neural collapse (NC), involving four deeply interconnected phenomena. (NC1) Cross-example within-class variability of last-layer training activations collapses to zero, as the individual activations themselves collapse to their class means. (NC2) The class means collapse to the vertices of a simplex equiangular tight frame (ETF). (NC3) Up to rescaling, the last-layer classifiers collapse to the class means or in other words, to the simplex ETF (i.e., to a self-dual configuration). (NC4) For a given activation, the classifier’s decision collapses to simply choosing whichever class has the closest train class mean (i.e., the nearest class center [NCC] decision rule). The symmetric and very simple geometry induced by the TPT confers important benefits, including better generalization performance, better robustness, and better interpretability.
Journal Article
Forest productivity increases with evenness, species richness and trait variation: a global meta‐analysis
1. Although there is ample support for positive species richness–productivity relationships in planted grassland experiments, a recent 48‐site study found no diversity–productivity relationship (DPR) in herbaceous communities. Thus, debate persists about diversity effects in natural versus planted systems. Additionally, current knowledge is weak regarding the influence of evenness on the DPRs, how DPRs are affected by the variation in life‐history traits among constituent species in polycultures and how DPRs differ among biomes. The impacts of these factors on DPRs in forest ecosystems are even more poorly understood. 2. We performed a meta‐analysis of 54 studies to reconcile DPRs in forest ecosystems. We quantified the net diversity effect as log effect size [ln(ES)], the log ratio of the productivity in polycultures to the average of those in monocultures within the same type of mixture, site condition and stand age of each study. The first use of a boosted regression tree model in meta‐analysis, a useful method to partition the effects of multiple predictors rather than relying on vote‐counting of individual studies, unveiled the relative influences of individual predictors. 3. Global average ln(ES) was 0.2128, indicating 23.7% higher productivity in polycultures than monocultures. The final model explained 21% of the variation in ln(ES). The predictors that substantially accounted for the explained variation included evenness (34%), heterogeneity of shade tolerance (29%), richness (13%) and stand age (15%). In contrast, heterogeneity of nitrogen fixation and growth habits, biome and stand origin (naturally established versus planted) contributed negligibly (each ≤ 4%). Log effect size strongly increased with evenness from 0.6 to 1 and with richness from 2 to 6. Furthermore, it was higher with heterogeneity of shade tolerance and generally increased with stand age. 4. Synthesis. Our analysis is, to our knowledge, the first to demonstrate the critical role of species evenness, richness and the importance of contrasting traits in defining net diversity effects in forest polycultures. While testing the specific mechanisms is beyond the scope of our analysis, our results should motivate future studies to link richness, evenness, contrasting traits and life‐history stage to the mechanisms that are expected to produce positive net biodiversity effects such as niche differentiation, facilitation and reduced Janzen–Connell effects.
Journal Article
A Study of Fall Detection in Assisted Living: Identifying and Improving the Optimal Machine Learning Method
This paper makes four scientific contributions to the field of fall detection in the elderly to contribute to their assisted living in the future of Internet of Things (IoT)-based pervasive living environments, such as smart homes. First, it presents and discusses a comprehensive comparative study, where 19 different machine learning methods were used to develop fall detection systems, to deduce the optimal machine learning method for the development of such systems. This study was conducted on two different datasets, and the results show that out of all the machine learning methods, the k-NN classifier is best suited for the development of fall detection systems in terms of performance accuracy. Second, it presents a framework that overcomes the limitations of binary classifier-based fall detection systems by being able to detect falls and fall-like motions. Third, to increase the trust and reliance on fall detection systems, it introduces a novel methodology based on the usage of k-folds cross-validation and the AdaBoost algorithm that improves the performance accuracy of the k-NN classifier-based fall detection system to the extent that it outperforms all similar works in this field. This approach achieved performance accuracies of 99.87% and 99.66%, respectively, when evaluated on the two datasets. Finally, the proposed approach is also highly accurate in detecting the activity of standing up from a lying position to infer whether a fall was followed by a long lie, which can cause minor to major health-related concerns. The above contributions address multiple research challenges in the field of fall detection, that we identified after conducting a comprehensive review of related works, which is also presented in this paper.
Journal Article