Unraveling the Role of Natural Attributes in Driving Lake Ecosystem Response Patterns to Nutrient Variations

TY - JOUR

T1 - Unraveling the Role of Natural Attributes in Driving Lake Ecosystem Response Patterns to Nutrient Variations

AU - Xue, Yufei

AU - Kong, Xiangzhen

AU - Xue, Bin

AU - Yi, Yujun

AU - van Wijk, D.

N1 - Data archiving: no NIOO data

PY - 2025/1/1

Y1 - 2025/1/1

N2 - The worldwide compound effects of human activities and climate change increase the risk of catastrophic regime shifts in lake ecosystems—sudden switches to a contrasting condition that can drastically alter ecological states and are often irreversible. Lake natural attributes (LNAs) are critical determinants of ecosystem responses to global changes. Yet, the role of LNAs in dictating regime shifts in lakes is largely underestimated compared to that of anthropogenic factors such as eutrophication and fishery. Here, we collected LNA parameter data for 344 lakes in northern China (surface area >5 km2) covering a large gradient of morphology, climate, and sediment types. Using the PCLake model, we conducted a bifurcation analysis to simulate the dynamic response of chlorophyll-a (chl-a) to nutrient loads and then analyzed the critical thresholds, maximum chl-a levels, and their relationships with LNAs. The results indicate that water depth is the primary factor determining lake response patterns. Hysteresis responses often occur in shallow lakes (average depth of 2.17 m), while deep lakes typically show linear responses. Among the lakes exhibiting hysteresis response, LNAs determine response characteristics with a predominant explanatory variance (71%). Stepwise regression models further reveal that water depth and temperature are key factors influencing the critical thresholds and chl-a levels in shallow lakes. As water depth decreases, critical thresholds rise, potentially leading to higher chl-a levels after transition from clear to turbid states. Our study offers insights to help maintain the long-term stability and sustainability of lake ecosystems by qualifying ecological thresholds and regime shifts through advanced ecosystem modeling.

AB - The worldwide compound effects of human activities and climate change increase the risk of catastrophic regime shifts in lake ecosystems—sudden switches to a contrasting condition that can drastically alter ecological states and are often irreversible. Lake natural attributes (LNAs) are critical determinants of ecosystem responses to global changes. Yet, the role of LNAs in dictating regime shifts in lakes is largely underestimated compared to that of anthropogenic factors such as eutrophication and fishery. Here, we collected LNA parameter data for 344 lakes in northern China (surface area >5 km2) covering a large gradient of morphology, climate, and sediment types. Using the PCLake model, we conducted a bifurcation analysis to simulate the dynamic response of chlorophyll-a (chl-a) to nutrient loads and then analyzed the critical thresholds, maximum chl-a levels, and their relationships with LNAs. The results indicate that water depth is the primary factor determining lake response patterns. Hysteresis responses often occur in shallow lakes (average depth of 2.17 m), while deep lakes typically show linear responses. Among the lakes exhibiting hysteresis response, LNAs determine response characteristics with a predominant explanatory variance (71%). Stepwise regression models further reveal that water depth and temperature are key factors influencing the critical thresholds and chl-a levels in shallow lakes. As water depth decreases, critical thresholds rise, potentially leading to higher chl-a levels after transition from clear to turbid states. Our study offers insights to help maintain the long-term stability and sustainability of lake ecosystems by qualifying ecological thresholds and regime shifts through advanced ecosystem modeling.

U2 - 10.34133/ehs.0376

DO - 10.34133/ehs.0376

M3 - Article

SN - 2096-4129

VL - 11

JO - Ecosystem Health and Sustainability

JF - Ecosystem Health and Sustainability

M1 - 0376

ER -