Yan Zhang

Dr. Yan Zhang


Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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4 August 2025 in Agronomy
Verified
Journal Article
Open access
PDF file
SWShi min Wei
Shi min Wei
JHJian Hou
Jian Hou
YZYan Zhang
Yan Zhang
YTYang Tai
Yang Tai
XHXiaohui Huang
Xiaohui Huang
XGXiaochen Guo
Xiaochen Guo
An in-depth understanding of the spatiotemporal heterogeneity of ecosystem service (ES) trade-offs and synergies, along with their driving factors, is crucial for formulating key ecological restoration strategies and effectively allocating ecological environmental resources in the Hulunbuir region. This study employed an integrated analytical approach combining the InVEST model, ArcGIS geospatial processing, R software environment, and Optimal Parameter Geographical Detector (OPGD). The spatiotemporal patterns and driving factors of the interaction of four major ES functions in Hulunbuir area from 2000 to 2020 were studied. The research findings are as follows: (1) carbon storage (CS) and soil conservation (SC) services in the Hulunbuir region mainly show a distribution pattern of high values in the central and northeast areas, with low values in the west and southeast. Water yield (WY) exhibits a distribution pattern characterized by high values in the central–western transition zone and southeast and low values in the west. For forage supply (FS), the overall pattern is higher in the west and lower in the east. (2) The trade-off relationships between CS and WY, CS and SC, and SC and WY are primarily concentrated in the western part of Hulunbuir, while the synergistic relationships are mainly observed in the central and eastern regions. In contrast, the trade-off relationships between CS and FS, as well as FS and WY, are predominantly located in the central and eastern parts of Hulunbuir, with the intensity of these trade-offs steadily increasing. The trade-off relationship between SC and FS is almost widespread throughout HulunBuir. (3) Fractional vegetation cover, mean annual precipitation, and land use type were the primary drivers affecting ESs. Among these factors, fractional vegetation cover demonstrates the highest explanatory power, with a q-value between 0.6 and 0.9. The slope and population density exhibit relatively weak explanatory power, with q-values ranging from 0.001 to 0.2. (4) The interactions between factors have a greater impact on the inter-relationships of ESs in the Hulunbuir region than individual factors alone. The research findings have facilitated the optimization and sustainable development of regional ES, providing a foundation for ecological conservation and restoration in Hulunbuir.

27 November 2025 in Agronomy
Verified
Journal Article
Open access
PDF file
YDYvonne Doyne
Yvonne Doyne
YZYan Zhang
Yan Zhang
JHJian Hou
Jian Hou
YTYang Tai
Yang Tai
XHXiaohui Huang
Xiaohui Huang
CXChen Xing
Chen Xing
Ecological restoration in degraded landscapes requires understanding the factors driving ecosystem function. We ask the central question: Do microtopography and plant functional traits control water-carbon coupling efficiency (WCCI) in mining-affected grasslands? We developed a novel, decoupled WCCI metric balancing water-use efficiency (1/SLA) and carbon-stock potential (Height + Foliage Cover). We hypothesized that (1) microhabitats with severe environmental filters (e.g., drought, erosion) would exhibit the lowest WCCI, and (2) this function could be optimized by assembling species that balance these two distinct trait strategies. Our objectives were to: (i) quantify the new WCCI across five microhabitat zones (A–E); (ii) assess how soil filters shape existing community functions; and (iii) identify optimized, zone-specific species assemblages using a dual-filter CATS model that maximizes WCCI. Results show significant variability in WCCI. The most degraded zones, A (arid) and B (high erosion), exhibited the lowest functional performance (mean WCCI = 0.029 and 0.078), supporting our first hypothesis. The dual-filter CATS model, constrained by abiotic targets (Tolerance = 10%) and a diversity cap (Max Abundance = 30%), successfully generated distinct, functionally tailored species assemblages for each zone. For instance, the optimized community for arid Zone A included the drought-adapted grass Stipa capillata (15.9%), while the resource-rich Zone D was recommended Medicago lupulina (12.7%). Conclusion: These findings confirm that a “one-size-fits-all” approach is insufficient. We demonstrate the necessity of a trait-based, microhabitat-specific framework to move beyond taxonomic mimicry and truly optimize biogeochemical functions in restoration.