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Plants allocate nutrients to specific leaf cell types, with commelinoid monocots preferentially allocating phosphorus (P) to the mesophyll and calcium (Ca) to the epidermis, whereas the opposite is thought to occur in eudicots. However, Proteaceae from severely P-impoverished habitats present the same P-allocation pattern as monocots. This raises the question of whether preferential P allocation to mesophyll cells is a phylogenetically conserved trait, exclusive to commelinoid monocots and a few Proteaceae, or a trait that has evolved multiple times to allow plants to cope with very low soil P availability. We analysed the P-allocation patterns of 16 species from 10 genera, eight families and six orders within three major clades of eudicots across different P-impoverished environments in Australia and Brazil, using elemental X-ray mapping to quantitatively determine leaf cell-specific nutrient concentrations. Many of the analysed species showed P-allocation patterns that differed substantially from that expected for eudicots. Instead, P-allocation patterns were strongly associated with the P availability in the natural habitat of the species, suggesting a convergent evolution of P-allocation patterns at the cellular level, with P limitation as selective pressure and without a consistent P-allocation pattern within eudicots. Here, we show that most eudicots from severely P-impoverished environments preferentially allocated P to their mesophyll. We surmise that this preferential P allocation to photosynthetically active cells might contribute to the very high photosynthetic P-use efficiency of species adapted to P-impoverished habitats.

Phosphorus deficiency is one of the major constraints for crop growth in Karst rocky desertification regions. Different phosphorus treatments have become important strategies for enhancing agricultural productivity; however, the effects of phosphorus fertilization on nutrient allocation and root endophytic microbiome at different growth stages under field conditions remain inadequately explored. Therefore, this study implemented four phosphorus application treatments in a field-based maize cultivation system: P0 (0 kg ha⁻¹), P1 (75 kg ha⁻¹), P2 (150 kg ha⁻¹), and P3 (225 kg ha⁻¹). Nutrient distribution and root endophytic microbial community dynamics were analyzed at the jointing and milk-ripening stages. The results demonstrated that: (1) With increasing phosphorus application, the total nitrogen (TN) and total phosphorus (TP) in roots and leaves at the jointing, silking, and milk-ripening stages exhibited a pattern of “low phosphorus treatment enhancing TP content, while high phosphorus treatment suppressing TP uptake.” Total potassium (TK) content showed a decreasing trend, with the highest nutrient uptake observed at the phosphorus application rate of 150 kg ha⁻¹. (2) Analysis of the root bacterial community revealed a decline in bacterial diversity with increasing phosphorus levels, but the abundance of Proteobacteria and Actinobacteria were significantly enhanced. (3) Correlation analysis indicated that low phosphorus treatment (P0) induced microbial community restructuring, high phosphorus treatment (P3) promoted the proliferation of functional taxa such as Pseudomonadaceae, while medium phosphorus treatment (P2) showed the most significant correlation between microbial community structure and phosphorus availability. This study provides valuable scientific insights for optimizing phosphorus fertilization in maize production in karst regions.

Seagrass meadows are declining globally. The decrease of seagrass area is influenced by the simultaneous occurrence of many factors at the local and global scale, including nutrient enrichment and climate change. This study aims to find out how increasing temperature and nutrient enrichment affect the morphological, biochemical and physiological responses of three coexisting tropical species, Thalassia hemprichii , Cymodocea serrulata and Halophila stipulacea . To achieve these aims, a 1-month experiment under laboratory conditions combining two temperature (maximum ambient temperature and current average temperature) and two nutrient (high and low N and P concentrations) treatments was conducted. The results showed that the seagrasses were differentially affected by all treatments depending on their life-history strategies. Under higher temperature treatments, C. serrulata showed photo-acclimation strategies, while T. hemprichii showed decreased photo-physiological performance. In contrast, T. hemprichii was resistant to nutrient over-enrichment, showing enhanced nutrient content and physiological changes, but C. serrulata suffered BG nutrient loss. The limited response of H. stipulacea to nutrient enrichment or high temperature suggests that this seagrass is a tolerant species that may have a dormancy state with lower photosynthetic performance and smaller-size individuals. Interaction between both factors was limited and generally showed antagonistic effects only on morphological and biochemical traits, but not on physiological traits. These results highlight the different effects and strategies co-inhabiting seagrasses have in response to environmental changes, showing winners and losers of a climate change scenario that may eventually cause biodiversity loss. Trait responses to these stressors could potentially make the seagrasses weaker to cope with following events, due to BG biomass or nutrient loss. This is of importance as biodiversity loss in tropical seagrass ecosystems could change the overall effectiveness of ecosystem functions and services provided by the seagrass meadows.

Accurate estimations of carbon (C), nitrogen (N), and phosphorus (P) densities in shrublands are pivotal for assessing terrestrial ecosystem carbon sequestration. Combining in‐situ investigations and machine learning facilitates large‐scale patterns mapping, however, which often overlooks underlying ecological regulations. Here we utilize data from 1,122 survey plots across China's shrublands and develop a novel knowledge‐based deep learning framework that integrates a structural equation model (SEM) to elucidate mechanisms and construct an artificial neural network (ANN) based on these causal relationships. Results show that biomass allocation to different organs follows allometric regulations and that N and P concentrations maintain a degree of stoichiometric homeostasis following biological stoichiometry theory. This insight guides the construction of our ANN, which outperforms both SEM and other prevalent machine learning methods. By leveraging ecological theories to inform model construction, our framework not only enhances prediction accuracy and explainability but also provides a methodological blueprint for ecological research. Plain Language Summary China has set a goal to achieve carbon neutrality by 2060, and one way to achieve this is by utilizing terrestrial ecosystems, which can absorb CO2 from the atmosphere. The effectiveness of natural carbon sinks is often limited by the availability of essential nutrients such as nitrogen (N) and phosphorus (P). Shrublands are unique and contribute the most uncertainty in estimating China's carbon storage. Thus, accurately mapping shrubland vegetation C, N, and P densities is critical. Previous studies usually apply data‐driven methods to scale up site information to larger scales, often failing to consider underlying ecological regulations. Here, we advance this approach by integrating deep learning (DL) with causal understanding. We found that C, N, and P allocation to different organs is relatively consistent, and their ratios maintain generally stable. These relationships are then applied to the DL algorithm. The knowledge‐based DL model outperforms popular machine learning methods. Our framework not only improves the ability to predict nutrient distributions in shrublands but also serves as a blueprint for further ecological research, enhancing both the accuracy and the explainability of ecological models. Key Points Biomass and nutrient allocation follow allometry and biological stoichiometry theory Structural equation model (SEM) and artificial neural network (ANN) are combined to achieve casual interference and accurate prediction Prior knowledge‐based deep learning can advance ecological modeling

Background Ecological stoichiometry serves as a foundational framework for understanding plant nutrient acquisition, allocation, and conservation strategies in extreme environments. The Wudalianchi volcanic lava platform represents a naturally oligotrophic habitat where Populus koreana functions as a keystone pioneer species during early ecological succession. However, the physiological and stoichiometric responses of P. koreana to nitrogen (N) enrichment remain poorly understood. This knowledge gap constrains both theoretical development of ecological stoichiometry in geologically extreme systems and the formulation of evidence-based ecological restoration strategies for volcanic substrates. To address this, we conducted a controlled short-term N addition experiment across 12 randomized plots (10 m × 10 m each), applying four N treatments: 0 (control), 4, 8, and 16 g N·m⁻²·yr⁻¹. Results N addition increased soil total organic carbon (TOC) (23.04–142.68%) and total N (TN) (2.94–95.59%), reduced total phosphorus (TP) (7.55–15.09%) and available N: P ratio. N and P concentrations in mature leaves increased significantly (by 28.76–49.87% and 11.13–24.75%, respectively), whereas P concentrations decreased in fine branches and fine roots. Senescent leaf N and P concentrations rose by 6.57–17.60% and 14.10–38.06%, respectively, with a greater increase in P leading to a lower senescent leaf N: P ratio. N resorption efficiency (NRE) increased significantly (by 23.34–33.26%), while P resorption efficiency (PRE) decreased (by 5.67–11.43%). Mature leaf N and P concentrations were positively correlated with soil TN and available P (AP), respectively. In contrast, P concentrations and N: P ratios in fine branches and fine roots correlated negatively with soil AP and the available N: AP ratio, but positively with soil TP and the TN: TP ratio. NRE correlated positively with leaf N concentration, while PRE correlated negatively with both mature leaves P concentration and soil AP. Conclusion Short-term N addition reshapes soil nutrient availability, driving P. koreana to adopt a “photosynthetic organ-prioritized” nutrient allocation strategy and an N–P resorption trade-off. While N addition partially alleviates N limitation, it concurrently exacerbates P demand, thereby accelerating the transition from N limitation toward N–P co-limitation. These findings refine ecological stoichiometric theory in nutrient-impoverished volcanic ecosystems and offer mechanistically grounded, quantitative guidance for targeted vegetation restoration on lava platforms.

Background and aims Bark contains a substantial fraction of the nutrients stored in woody biomass, however the degree of functional coordination of bark, wood, and foliar nutrient pools, and its relationship to soil nutrient availability remains poorly understood. Methods Bark thickness and nitrogen, phosphorus, potassium, calcium, and magnesium concentrations were measured in 23 tree species present in two premontane wet tropical forests in western Panama differing in soil nutrient availability. Bark data were combined with existing wood and leaf data from the same species. Results Bark nutrients were positively correlated with leaf and wood nutrients for all elements. The low fertility site had both lower bark nutrient concentrations and thicker bark, driven primarily by species compositional differences between sites, and secondarily by intraspecific variation. Across species, bark nutrient concentration varied 4 to 25 fold, with the highest variation for calcium. Overall, bark accounted for the largest percent of Ca in above-ground biomass nutrient pools (22–82%) and a large fraction of the other nutrients studied (N: 6–53%, P: 5–50%, K: 4–40%, and Mg: 2–35%). Conclusions Bark represents a substantial, and highly variable, pool of biomass nutrients. The functional role of bark nutrients, the causes and consequences of this variation, and its relation to other bark traits, including bark thickness, deserve further study.

The allocation of limiting elements among plant organs is an important aspect of the adaptation of plants to their ambient environment. Although eutrophication can extremely alter light and nutrient availability, little is known about nutrient partitioning among organs of submerged macrophytes in response to eutrophication. Here, we analyzed the stoichiometric scaling of carbon (C), nitrogen (N), and phosphorus (P) concentrations among organs (leaf, stem, and root) of 327 individuals of seven common submerged macrophytes (three growth forms), sampled from 26 Yangtze plain lakes whose nutrient levels differed. Scaling exponents of stem nutrients to leaf (or root) nutrients varied among the growth forms. With increasing water total N (WTN) concentration, the scaling exponents of stem C to leaf (or root) C increased from <1 to >1, however, those of stem P to root P showed the opposite trend. These results indicated that, as plant nutrient content increased, plants growing in low WTN concentration accumulated leaf C (or stem P) at a faster rate, whereas those in high WTN concentration showed a faster increase in their stem C (or root P). Additionally, the scaling exponents of stem N to leaf (or root) N and stem P to leaf P were consistently large than 1, but decreased with a greater WTN concentration. This suggested that plants invested more N and P into stem than leaf tissues, with a higher investment of N in stem than root tissues, but eutrophication would decrease the allocation of N and P to stem. Such shifts in plant nutrient allocation strategies from low to high WTN concentration may be attributed to changed light and nutrient availability. In summary, eutrophication would alter nutrient allocation strategies of submerged macrophytes, which may influence their community structures by enhancing the competitive ability of some species in the process of eutrophication.

Efficient allocation of mineral nutrients and photoassimilates is essential for grain development in rice. However, the transcriptional programs governing nutrient transport at key reproductive stages remain largely unresolved. Here, we performed a comprehensive transcriptome analysis of rice (Oryza sativa L.) across spatial (nodes, roots, and five other tissues) and temporal (seven reproductive stages) dimensions to elucidate the molecular basis of nutrient transport and allocation. RNA-seq profiling of node I identified stage-specific gene expression patterns, with the grain filling stage marked by strong induction of transporters involved in mineral allocation (e.g., OsYSL2, OsZIP3, OsSULTR3;3, SPDT) and carbohydrate distribution (e.g., OsSWEET13, OsSWEET14, OsMST6). Comparative analysis with the neck-panicle node (NPN) and root revealed tissue-specific regulatory networks, including nitrate (OsNRT1.1A, OsNRT2.3) and phosphate (OsPHT1;4, OsPHO1;3) transporters enriched at the grain filling stage. Root expression of Cd/As-related transporters (OsNRAMP5, OsCd1, OsLsi1, OsLsi2, OsLsi3) during grain filling highlights the contribution of belowground uptake to grain metal accumulation. Together, our study establishes a spatiotemporal atlas of nutrient transporter gene activity during rice reproductive development and identifies candidate genes regulating upward and lateral nutrient allocation. These findings provide insights into improving nutrient use efficiency and reducing toxic metal accumulation in rice grains through targeted manipulation of nodal and root transport systems.

Carbon, nitrogen and phosphorus, as the basic components of plants, determine plant growth and adaptation strategies, while there are certain differences in nutrient allocation among different plant organs. However, little is known about the manner in which resource allocation mediates the plant life history strategy. Here, we collected three census field survey datasets from the Heishiding 50-ha dynamic plot showing functional traits and nutrient allocation among leaves and roots (⍺ ) from 92 woody species to determine the relationship between nutrient allocation and the plant life history strategy. Carbon allocation ⍺ was mainly determined by intraspecific variation while nitrogen allocation ⍺ and phosphorus allocation ⍺ was determined by interspecific variation. Species allocating more nitrogen to leaves showed greater resource acquisition traits, while species allocating more nitrogen to roots showed greater resource conservation traits. We found a trade-off between the plant relative growth rate and conspecific density dependence; fast-growing species showed higher mortality with conspecific neighbors but tended to allocate more nitrogen to leaves rather than roots. Our study revealed interspecific variation in nutrient allocation among leaves and roots as well as their relationship with functional traits and the plant life history strategy.

The Hailuogou Glacier has been continuously retreating since the end of the Little Ice Age, resulting in a 125-year soil chronosequence and a complete primary forest succession sequence. Nutrient cycling and utilization are the foundation to forest succession processes and dynamic changes, directly influencing the structure and stability of ecosystems. However, our understandings on the characteristics of ecosystem nutrient accumulation and recycling during succession, especially in the context of primary succession within glacier retreat areas, remain limited. To address this, we investigated nutrient characteristics across six forest primary succession sites in the Hailuogou Glacier retreat area. Six sites representing three forest stages: the pioneer plant stage (S1), the broad-leaved forest stage (S2-S4), and the coniferous forest stage (S5-S6). Three quadrats were established at each site, and measurements of biomass as well as soil characteristics were documented within each quadrat. Subsequently, we collected samples of vegetation, soil and litter. By measuring the concentrations of N, P, K, Ca, and Mg in vegetation and soil and combining with the data of the quadrat survey, the pools and nutrient characteristics of N, P, K, Ca, and Mg in various components of the ecosystem were calculated at each site. Our findings indicated that: (1) Nutrient pools, excluding the soil C layer, increased with forest primary succession, reaching 5,995.71 kg hm N, 461.83 kg hm P, 3,798.09 kg hm K, 7,559.81 kg hm Ca and 1,948.13 kg hm Mg at site S6; however, the pools of P, K, and Mg in the Oa layer, and Ca and Mg in the tree layer, attained their peak levels at sites S3 to S4. (2) The pools of N, Ca, and Mg in the organic soil were significantly greater than vegetation. Although over 60% of the P and K were stored in the organic soil at site S1, these proportions shifted, with vegetation holding 60.71% of P and 56.86% of K at site S5. (3) Broad-leaved forests exhibited higher nutrient return, cycling, and absorption, thereby accelerating nutrient circulation and depleting soil nutrients to maintain growth. In contrast, coniferous forests were more efficient at nutrient utilization and storage, retaining nutrients and maintaining high biomass and productivity in nutrient-poor environments. Overall, these findings highlighted that the nutrients in each component of the ecosystem continue to accumulate with forest primary succession. Coniferous forests' nutrient cycling mechanisms offer a competitive edge in nutrient-poor environments, enhancing ecosystem stability.