Search

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 theoryStructural equation model (SEM) and artificial neural network (ANN) are combined to achieve casual interference and accurate predictionPrior knowledge‐based deep learning can advance ecological modeling [ABSTRACT FROM AUTHOR]

Summary: Darwin's two opposing hypotheses, proposing that non‐native species closely or distantly related to native species are more likely to succeed, are known as 'Darwin's Naturalization Conundrum'. Recently, invasion ecologists have sought to unravel these hypotheses. Studies that incorporate rich observational data in disturbed ecosystems that integrate phylogenetic and functional perspectives have potential to shed light on the conundrum.Using 313 invaded plant communities including 46 invasive plant species and 531 native plant species across the Three Gorges Reservoir Area in China, we aim to evaluate the coexistence mechanisms of invasive and native plants by integrating phylogenetic and functional dimensions at spatial and temporal scales.Our findings revealed that invasive plants tended to co‐occur more frequently with native plant species that were phylogenetically distant but functionally similar in the reservoir riparian zone. Furthermore, our study demonstrated that the filtering of flood‐dry‐flood cycles played a significant role in deepening functional similarities of native communities and invasive‐native species over time.Our study highlights the contrasting effects of phylogenetic relatedness and functional similarity between invasive and native species in highly flood‐disturbed habitats, providing new sights into Darwin's Naturalization Conundrum. [ABSTRACT FROM AUTHOR]

Quercus myrsinifolia is one of the dominant species in the evergreen broad-leaf forest on the southern slope of Shennongjia. The study of spatial distribution pattern and spatial correlation of Q. myrsinifolia population will help to understand population development and potential ecological processes; as well as the structure and biodiversity maintenance mechanism of evergreen broad-leaf forests at the northern edge of the subtropics. Based on forest dynamic monitoring data from one 1 hm2 permanent plot on the southern slope of Shennongjia' we employed pair correlation functions g( r) and marked correlation functions to analyze the diameter structure of the Q. myrsinifolia population; spatial distribution patterns at different diameter classes; and intraspecific and interspecific spatial associations. The results showed that diameter structure of Q. myrsinifolia population exhibited an inverted ' J ' - shaped distribution; suggesting a healthy regeneration status and belonging to a growing population type. The spatial distribution showed a decreasing trend in aggregation with increasing diameter. Positive correlations among individuals strengthened with closer diameter classes; while weakening with larger diameter differences. Interspecific spatial associations showed an increasing correlation of Q. myrsinifolia with understory dominant species with increasing spatial scales; but no correlation was observed with canopy-dominant species. Our results suggested that the spatial pattern of Q. myrsinifolia populations on the southern slope of Shennongjia was mainly influenced by habitat filtering ' seed dispersal limitation; and intraspecific and interspecific competition. Furthermore; the adaptive strategies of Q. myrsinifolia varied when they coexisted with different species. [ABSTRACT FROM AUTHOR]

Copyright of Ecological Applications is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aims Carbon (C), nitrogen (N) and phosphorus (P) stoichiometry strongly affect functions and nutrient cycling within ecosystems. However, the related researches in shrubs were very limited. In this study, we aimed to investigate leaf stoichiometry and its driving factors in shrubs, and whether stoichiometry significantly differs among closely related species. Methods We analyzed leaf C, N and P concentrations and their ratios in 32 species of Ericaceae from 161 sites across southern China. We examined the relationships of leaf stoichiometry with environmental variables using linear regressions, and quantified the interactive and independent effects of climate, soil and species on foliar stoichiometry using general linear models (GLM). Important Findings The foliar C, N and P contents of Ericaceae were 484.66, 14.44 and 1.06 mg g−1, respectively. Leaf C, N and P concentrations and their ratios in Ericaceae were significantly related with latitude and altitude, except the N:P insignificantly correlated with latitude. Climate (mean annual temperature and precipitation) and soil properties (soil C, N and P and bulk density) were significantly influenced element stoichiometry. The GLM analysis showed that soil exerted a greater direct effect on leaf stoichiometry than climate did, and climate affected leaf traits mainly via indirect ways. Further, soil properties had stronger influences on leaf P than on leaf C and N. Among all independent factors examined, we found species accounted for the largest proportion of the variation in foliar stoichiometry. These results suggest that species can largely influence foliar stoichiometry, even at a lower taxonomic level. [ABSTRACT FROM AUTHOR]

The unique hydrological regime of Three Gorges Dam (TGD) has brought enormous challenges to the riparian zone (RRZ), which plays an important role in regulating sediment and nutrient transport into the Yangtze River. The soil in a RRZ is one of the key factors that affect the water quality. After 4 years of winter‐flooding, the concentration of soil nutrients and potentially toxic metals between the flooded reservoir RRZ and adjacent non‐flooded upland (Upland) are examined. There are two main results: First, soil organic carbon and soil available potassium concentration were significantly lower in RRZ than in Upland. Soil total nitrogen (TN) concentration showed the same trend, but with no significance. Soil available phosphorus concentration is significantly higher in RRZ. Second, the concentrations of all the tested potentially toxic metals (Cd, Cr, Cu, Pb, Zn), their geoaccumulation index (Igeo) values and potential ecological risk index (Ei) values are significantly higher in RRZ than in Upland. Cd had the highest Igeo and Ei values in RRZ. The present results indicated that the flood–dry–flood cycle caused by TGD has changed the soil nutrient concentrations and increased the potentially toxic metal concentrations. There is a cross‐contamination risk of the soil in RRZ, perhaps related to fertilization. As the last protective barrier for the Yangtze River, soil potentially toxic metals pollution control should deserve a considerable attention in the RRZ formed by TGD. Compared to adjacent non‐flooded upland (Upland), the flood‐dry‐flood cycle caused by Three Gorges Dam has changed soil nutrient concentrations and increased potentially toxic metal concentrations in the riparian zone (RRZ), indicating that there is a cross‐contamination risk of the soil in RRZ. Cd is worthy of special attention since it had a considerable ecological risk in RRZ. [ABSTRACT FROM AUTHOR]

Biological invasion is a worldwide ecological phenomenon, but its mechanism is still not very clear. Invasive species give impacts on native species and ecosystems through competitions, predations, changing habitats, and dispersing diseases. They pose an increasing threat to the composition and structure of natural communities across the globe. Biological invasion has been greatly damaging the ecological and evolutionary integrity of natural ecosystems, which will weaken the functions of the ecosystems and frequently cause natural disasters. A better understanding of the causes, patterns, predictability, consequences, and management options associated with this threat to biodiversity is necessary to guide managers, policy makers, researchers, and general publics. Biological invasion also causes huge economic losses, and 137 billion dollar losses per year from biological invasion were estimated in USA. Invasive diseases impair human health and kill thousands and thousands of people, and invasive bacteria lead to so serious social panic and turbulence that people could feel uneasy even when eating and sleeping. Biological invasion largely decreases global biodiversity, which will threaten the survival and development of our descendants. Three steps are used in prevention and control of biological invasions. Comprehensive quarantine is the most effective way to prevent exotic invasion by accident. Ecological evaluation and monitoring is helpful to avoid disasters from species introduction. Physical methods, chemical approaches and biological controls are used to eradicate and control the spread of invaded species. Before biological controls are chosen, risk analysis of controlling organism is needed. Ideally, there should be both pre-eradication assessment to tailor removal to avoid unwanted ecological effects and post-removal assessment of eradication effects on both the target organism and the invaded ecosystem.