Nitrogen is the nutrient that most often limits plant growth, and human activities—from fossil fuel combustion to intensive agriculture—have more than doubled the amount of reactive nitrogen cycling through the global environment. When this nitrogen rains down on forests, it can fertilize trees, acidify soils, and shift the delicate balance of elements such as carbon, nitrogen, and phosphorus within living tissue. Ecologists have long predicted that forests receiving chronically high nitrogen inputs should eventually reach

The concept of nitrogen saturation, formalized in influential syntheses of temperate forest research in the late 1990s, describes a sequence of stages through which a forest ecosystem passes as chronic nitrogen inputs accumulate. In the earliest stages, added nitrogen is captured efficiently by plants and soil microbes, stimulating growth and enhancing nutrient uptake. As deposition continues, however, the system’s capacity to retain nitrogen becomes exhausted: excess nitrate leaches into streams, soils acidify, base cations are depleted, and the availability of other nutrients, particularly phosphorus, becomes the principal constraint on plant productivity. Under this framework, forests in regions with decades of elevated deposition were expected to exhibit symptoms of saturation, including diminished growth responses to further nitrogen inputs and declining foliar nitrogen relative to phosphorus. The new study from western China complicates this tidy progression, finding that a dominant tree in a high-deposition subtropical forest continues to respond nutritionally to added nitrogen rather than showing the plateau or decline that saturation would predict.

The setting matters enormously for interpreting this result. Subtropical China receives some of the highest rates of atmospheric nitrogen deposition anywhere on Earth, driven by dense industrial activity, intensive fertilizer use, and rapid urbanization in the region. Yet the soils and vegetation of these humid, warm forests differ in fundamental ways from the temperate and boreal systems where the saturation model was originally developed. Subtropical forests tend to be phosphorus-limited rather than nitrogen-limited, with highly weathered, acidic soils that hold relatively little labile phosphorus. In such systems, nitrogen deposition can act as a partial fertilizer even at high background rates, because the trees have evolved under conditions where nitrogen availability fluctuates and phosphorus scarcity, not nitrogen scarcity, sets the ceiling on productivity. The evergreen secondary forest where Michelia wilsonii grows represents exactly this kind of environment, where the interplay between abundant nitrogen and constrained phosphorus shapes every aspect of plant nutrient strategy.

Stoichiometry, the study of the ratios of elements such as carbon, nitrogen, and phosphorus in living tissue, provides a powerful lens for reading these nutrient dynamics. Leaf carbon concentrations are typically quite stable across environmental gradients, reflecting the structural and metabolic constancy of the photosynthetic apparatus. Nitrogen and phosphorus, by contrast, vary considerably with supply, because both are essential to proteins, nucleic acids, and the energy-transfer machinery of cells. The ratio of nitrogen to phosphorus in leaves is widely used as an indicator of which nutrient limits plant growth at a given moment, while carbon-to-nutrient ratios reflect how efficiently plants convert assimilated carbon into nutrient-rich tissue. When nitrogen deposition alters these ratios, it signals a shift in the internal economy of the plant, with cascading consequences for herbivores, decomposers, and the recycling of nutrients through the ecosystem.

The experimental design employed in the study followed a now-standard approach in deposition research: plots received supplemental nitrogen at rates of zero, twenty, and forty kilograms of nitrogen per hectare per year, spanning the range of additional inputs that forests in the region might plausibly experience. By measuring leaf carbon, nitrogen, and phosphorus concentrations alongside soil and microbial biomass pools, the researchers could trace how nitrogen moved through the ecosystem and where its effects originated. This multi-tiered sampling is critical because leaf chemistry does not respond to deposition in isolation; it reflects the integrated outcome of soil nutrient availability, microbial competition for nutrients, and the tree’s own physiological regulation of uptake and internal recycling.

One of the most intriguing patterns in the results is the non-linear response of leaf nitrogen and phosphorus, which rose at moderate nitrogen addition and then declined at the highest rate. This initial-increase-then-decline trajectory suggests that moderate nitrogen inputs relieve a nutrient constraint and allow the tree to enrich its foliage, but that heavier inputs trigger compensatory mechanisms or stress responses that pull nutrient concentrations back down. Possible explanations include increased leaching of nutrients from soils under heavier loading, soil acidification that reduces phosphorus availability, or physiological downregulation of uptake when the tree has accumulated sufficient nitrogen. The corresponding seasonal shifts in carbon-to-nitrogen and carbon-to-phosphorus ratios in summer samples reinforce the picture of a tree actively recalibrating its tissue chemistry as inputs change, rather than passively accumulating nitrogen.

Nutrient resorption, the process by which trees withdraw nitrogen and phosphorus from leaves before they are shed, is another key thread in the study. Resorption efficiency is a central component of nutrient conservation in evergreen species, which must sustain their foliage for multiple growing seasons in nutrient-poor environments. When soil nitrogen is abundant, trees typically reduce their reliance on resorption and instead draw more nitrogen directly from the soil, a shift that can loosen the tight internal cycling characteristic of infertile sites. The finding that resorption efficiencies followed the same rise-and-fall pattern as leaf nutrient concentrations indicates that deposition is reshaping not just what the leaves contain but how the tree manages its nutrient capital over time. Changes in resorption feed back into litter quality, which in turn alters decomposition rates and the release of nutrients back into the soil, closing a loop that connects deposition to the entire biogeochemical cycle of the forest floor.

The soil and microbial measurements add an important belowground dimension to the story. Soil organic carbon and microbial biomass carbon emerged as the primary determinants of variation in leaf stoichiometry, implicating the microbial community as a gatekeeper controlling nutrient flows to tree roots. Microbes and plant roots compete directly for nitrogen and phosphorus in the soil, and the elemental composition of microbial biomass determines whether immobilized nutrients are locked up in microbial tissue or released for plant uptake. Nitrogen deposition is known to shift microbial communities, favoring some groups over others and altering the balance of fungal and bacterial dominance, with consequences for carbon storage and nutrient turnover. The strong correlations observed between microbial biomass composition and leaf chemistry in this forest suggest that belowground responses are not merely a side effect of deposition but an active mediator of how trees experience added nitrogen.

The absence of nitrogen saturation in this system deserves particular attention. Several factors could explain why the forest has not crossed the saturation threshold despite high background deposition. The humid subtropical climate supports rapid plant growth and high nutrient demand, allowing trees and microbes to absorb substantial nitrogen inputs. Deep soils and abundant organic matter may provide large exchange and retention capacities. Moreover, if phosphorus availability, while low, is sufficient to support continued growth, then added nitrogen can still be converted into biomass rather than accumulating as excess. The authors’ conclusion that high deposition continues to enhance the nutrition and growth of dominant species implies that these forests remain in the fertilization stage of the saturation sequence, a finding that extends the applicability of the saturation framework by revealing how differently it can unfold in subtropical versus temperate settings.

The ecological implications of this continued fertilization are far-reaching. If dominant species such as Michelia wilsonii gain a nutritional advantage under sustained deposition, they may outcompete subordinate species that are less able to exploit the extra nitrogen, simplifying forest composition and altering canopy structure. Shifts in leaf chemistry also propagate upward and downward through the food web, affecting insect herbivores whose foliar diets become more nitrogen-rich, and decomposer communities whose litter inputs change in quality. Over longer timescales, the combination of enhanced growth, altered litter chemistry, and modified microbial activity could change how much carbon these forests store, a question of global relevance given the role of subtropical forests in the terrestrial carbon sink. At the same time, the non-linear responses observed here caution against assuming that fertilization benefits will persist indefinitely; the decline in leaf nutrients at the highest addition rate hints that thresholds may exist beyond which negative effects emerge.

More broadly, the study underscores the value of examining plant responses to pollution through the integrated framework of ecological stoichiometry, which links leaf chemistry, soil processes, and microbial ecology into a single analytical picture. Rather than treating nitrogen deposition as a simple dose of fertilizer or toxin, this approach reveals it as a force that reorganizes the flow of multiple elements through an ecosystem, with effects that depend on season, soil properties, and the identity of the organisms involved. For forests across subtropical Asia, where deposition rates remain high and may continue to rise, understanding these element-coupled responses will be essential for predicting which species thrive, which decline, and how the structure and function of some of the world’s most biodiverse ecosystems will be reshaped in the decades ahead.

Subject of Research: Effects of nitrogen addition on leaf carbon, nitrogen, and phosphorus stoichiometry of the dominant tree Michelia wilsonii in a high-nitrogen-deposition subtropical forest in western China.

Article Title: No N saturation, N addition still alters leaf stoichiometry of Michelia wilsonii in a high-N input forest

Article References: Liu, S., Zheng, X., Xiao, Y., Wang, L., Li, H., You, C., Xu, L., Xu, H., Xu, Z., Tan, B., Yuan, Y., & Zhang, L. (2026). No N saturation, N addition still alters leaf stoichiometry of Michelia wilsonii in a high-N input forest. Plant and Soil. https://doi.org/10.1007/s11104-026-09111-4

Image Credits: AI Generated

DOI: 10.1007/s11104-026-09111-4

Keywords: nitrogen deposition, nitrogen saturation, leaf stoichiometry, Michelia wilsonii, ecological stoichiometry, soil organic carbon, microbial biomass, nutrient resorption, subtropical forest, phosphorus limitation, forest ecology, Plant and Soil

The research, published in Environmental Earth Sciences, was led by Yong Li and Zhongyao Liang of Xiamen University together with colleagues at the Tianjin Municipal Engineering Design and Research Institute and the Nanjing Institute of Geography and Limnology. The team built a multi-scale interpretable machine-learning framework and applied it to nearly four years of daily water-quality monitoring at Shahu Lake, a shallow, 45-square-kilometre water body lying at 1,093 to 1,102 metres above sea level where the foothills of the Helan Mountains meet the alluvial plain of the Yellow River. Like many lakes in arid and semi-arid China, Shahu Lake experiences pronounced swings in its eutrophication status, driven by artificial water replenishment and a warming, variable climate.

At the heart of the study is a model built on the XGBoost algorithm, a gradient-boosted tree method prized for its efficiency with noisy, multivariate environmental data. The researchers fed the model daily measurements of water temperature, chemical oxygen demand measured by the manganese method, ammonia nitrogen, total phosphorus, total nitrogen, turbidity, and the nitrogen-to-phosphorus ratio, all drawn from the China National Environmental Monitoring Centre between November 2021 and August 2025. After screening out eighteen anomalous readings, just over one percent of the record, the team retained a complete set of 1,370 daily observations, splitting them eighty-twenty into training and independent testing sets. Hyperparameters were tuned with Bayesian optimization combined with five-fold cross-validation.

The resulting model performed remarkably well. It achieved a coefficient of determination of 0.889 on the training data and 0.810 on the unseen testing data, with root-mean-square errors of 3.78 and 5.13 micrograms per litre respectively and Kling-Gupta efficiencies above 0.81 in both cases. Those figures indicate strong predictive accuracy and, crucially, good generalisation, meaning the model had not simply memorised the training record but had learned relationships that hold for data it had never seen. Yet prediction alone was never the goal. The real innovation lies in how the researchers interrogated the model, using the TreeSHAP recursive algorithm, an exact method for attributing each prediction to individual input variables, to peel apart the drivers of chlorophyll-a, the standard proxy for phytoplankton biomass.

What makes the study distinctive is that the interpretation is deliberately multi-scale. Most previous applications of SHAP in aquatic science have averaged feature importance across an entire dataset, a global view that can mask drivers that matter only during particular seasons or events. Li and colleagues computed global importance across all 1,370 days, regional importance within the summer bloom window of July to September, and local importance for the ten most extreme chlorophyll-a events on record. At the regional scale they normalised SHAP values within each feature to remove scale differences, a methodological refinement designed to make season-specific comparisons meaningful.

The global picture largely confirms classical eutrophication theory. Water temperature emerged as the single most influential driver, with the model showing chlorophyll-a responses turning sharply positive as waters warm from about 15 to 24 degrees Celsius before plateauing at higher temperatures. Chemical oxygen demand, a proxy for organic matter and its mineralisation, ranked second, pointing to the role of internal nutrient recycling in shallow lakes, where decaying organic matter on the lakebed releases nitrogen and phosphorus back into the water column. Among nutrients, phosphorus consistently outweighed nitrogen in long-term importance, echoing the foundational whole-lake experiments of David Schindler and supporting the long-standing paradigm that phosphorus control is the backbone of lasting eutrophication management.

But the regional analysis revealed something the global view had concealed. During the July-to-September bloom period, the hierarchy of drivers shifted markedly. Nitrogen-related variables, particularly ammonia nitrogen and the nitrogen-to-phosphorus ratio, rose in importance, while the system effectively transitioned from chronic phosphorus limitation to transient nitrogen limitation under elevated thermal conditions. The response to water temperature peaked narrowly between 23 and 24 degrees Celsius, matching the optimal thermal niche for rapid cyanobacterial proliferation. The authors interpret this seasonal nitrogen stress not as an independent cause of blooms but as a consequence of them: as phosphorus fuels rapid growth, phytoplankton draw down available nitrogen until it becomes the immediate bottleneck, constraining bloom persistence and even influencing how much Microcystis biomass sinks out of the water column.

The local-scale analysis added a further layer of nuance. When the researchers examined the ten highest chlorophyll-a events individually, they found that each followed a distinct decision pathway through the model, even though the samples shared broadly similar environmental backgrounds. Water temperature contributed a strong, consistent 15.6 to 17.5 micrograms per litre in every case, functioning as the essential prerequisite for extreme blooms, with chemical oxygen demand adding a stable 3.65 to 5.08 micrograms per litre. Nutrient contributions, however, diverged sharply from event to event. Under persistently high phosphorus, the influence of total nitrogen versus the nitrogen-to-phosphorus ratio shifted depending on immediate nitrogen availability, and in a few instances nitrogen variables actually registered negative contributions, which the authors attribute to Liebig-style co-limitation by light, temperature, or phosphorus rather than any evidence that nitrogen depletion suppresses blooms under natural conditions.

These findings carry direct management implications. The framework argues for a dual-tiered strategy: long-term recovery still demands sustained reduction of both external and internal phosphorus loads, but during the summer bloom window managers may need adaptive controls on short-term nitrogen fluxes as well. Ignoring the transient nitrogen limitation, the authors warn, could foster nitrogen-depleted conditions that favour cyanobacterial dominance, a concern consistent with recent dual-nutrient reduction studies on Lake Erie and other systems. More broadly, the study positions interpretable machine learning as a state-dependent decision-support tool, capable of telling lake managers not just what matters on average but what matters now, in this season, under these conditions.

The authors are careful to note the limits of their approach. Because the analysis rests on observational data from a single monitoring station, the identified relationships are statistical associations rather than strict causal mechanisms, and the specific feature attributions may not extrapolate to lakes with different data distributions. Nonetheless, the framework’s central lesson is likely to resonate far beyond Shahu Lake: eutrophication is not governed by a single rule but by rules that change with time and state, and only analytical tools that respect those shifts, combining high-frequency monitoring, gradient-boosted models, and exact multi-scale attribution, can hope to keep pace with blooms that are becoming more frequent and more intense in a warming world.

Subject of Research: Multi-scale interpretable machine learning analysis of chlorophyll-a dynamics and eutrophication drivers in Shahu Lake

Article Title: Unraveling multi-scale drivers of Chl-a dynamics via interpretable machine learning: a case study of Shahu Lake

Article References: Li, Y., Huang, Y., Lin, L., Kong, X., & Liang, Z. (2026). Unraveling multi-scale drivers of Chl-a dynamics via interpretable machine learning: a case study of Shahu Lake. Environmental Earth Sciences, 85(15), Article 398. https://doi.org/10.1007/s12665-026-13133-7

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13133-7

Keywords: chlorophyll-a, algal blooms, eutrophication, Shahu Lake, interpretable machine learning, XGBoost, SHAP, phosphorus limitation, nitrogen limitation, water quality monitoring, cyanobacteria, lake management