From correlation to causation: Causal mechanisms, scale dependency, and structure–function mismatch in landscape-water quality responses in plateau lake basins

• Causal Forest reveals context-dependent effects of landscape on water quality. • Ecological space concentration worsens water quality under high pollution loads. • Structure–function mismatch challenges conventional landscape management paradigms. • The same landscape pattern exhibits opposite effects across pollution contexts. • Management must shift from structural optimization to functional design. Deciphering the causal effects of landscape patterns on water quality is critical for watershed management, yet most studies are confined to correlation analyses that fail to provide reliable causal evidence, particularly in sensitive plateau lake basins under compounded anthropogenic pressures. Taking the globally representative Fuxian Lake basin as a case study, this study developed an integrated analytical framework of production-living-ecological spaces, landscape structure, and water quality response (PLES-LS-WQ). Machine learning (XGBoost-SHAP) and causal inference model (Causal Forest) were combined with land-use scenario simulation (PLUS model) to quantify the true causal effects of key landscape pattern factors on water quality (TN, TP, COD) across multiple scales and scenarios. The results challenged several conventional assumptions: (1) Significant context dependency was revealed. The causal effects of landscape patterns were not fixed but determined by the dominant pollution source (mining, urban, or agricultural). For instance, the aggregation of production space improves water quality in mining-dominated areas but degrades it in agricultural zones. (2) The study provided the first causal evidence for a structure–function mismatch. Contrary to the traditional belief that ecological connectivity is beneficial, clustered ecological spaces under high pollution loads (mining and urban) exerted significant negative causal effects on water quality, indicating that their physical structure failed to translate into ecological purification functions and even facilitated pollutant transport. (3) Scale analysis and scenario simulation further indicated that ignoring context dependency and functional mismatch may lead to management failure or even adverse outcomes. By providing robust causal evidence, the study argued for a paradigm shift in watershed management from the pursuit of universal structural optimization towards a context-specific, function-oriented approach.

Institutions

Publication Details

Journal
Ecological Indicators
Published
2025-09-24
DOI
https://doi.org/10.1016/j.ecolind.2025.114240
Citations
1
Primary Topic
Service-Learning and Community Engagement
Type
article
Field-Weighted Citation Impact
8.93
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From correlation to causation: Causal mechanisms, scale dependency, and structure–function mismatch in landscape-water quality responses in plateau lake basins

1 citations
Ecological Indicators
Service-Learning and Community Engagement
8.93
article

From correlation to causation: Causal mechanisms, scale dependency, and structure–function mismatch in landscape-water quality responses in plateau lake basins

article en
1 citations

Abstract

• Causal Forest reveals context-dependent effects of landscape on water quality. • Ecological space concentration worsens water quality under high pollution loads. • Structure–function mismatch challenges conventional landscape management paradigms. • The same landscape pattern exhibits opposite effects across pollution contexts. • Management must shift from structural optimization to functional design. Deciphering the causal effects of landscape patterns on water quality is critical for watershed management, yet most studies are confined to correlation analyses that fail to provide reliable causal evidence, particularly in sensitive plateau lake basins under compounded anthropogenic pressures. Taking the globally representative Fuxian Lake basin as a case study, this study developed an integrated analytical framework of production-living-ecological spaces, landscape structure, and water quality response (PLES-LS-WQ). Machine learning (XGBoost-SHAP) and causal inference model (Causal Forest) were combined with land-use scenario simulation (PLUS model) to quantify the true causal effects of key landscape pattern factors on water quality (TN, TP, COD) across multiple scales and scenarios. The results challenged several conventional assumptions: (1) Significant context dependency was revealed. The causal effects of landscape patterns were not fixed but determined by the dominant pollution source (mining, urban, or agricultural). For instance, the aggregation of production space improves water quality in mining-dominated areas but degrades it in agricultural zones. (2) The study provided the first causal evidence for a structure–function mismatch. Contrary to the traditional belief that ecological connectivity is beneficial, clustered ecological spaces under high pollution loads (mining and urban) exerted significant negative causal effects on water quality, indicating that their physical structure failed to translate into ecological purification functions and even facilitated pollutant transport. (3) Scale analysis and scenario simulation further indicated that ignoring context dependency and functional mismatch may lead to management failure or even adverse outcomes. By providing robust causal evidence, the study argued for a paradigm shift in watershed management from the pursuit of universal structural optimization towards a context-specific, function-oriented approach.

Ecological IndicatorsVol. 179
Yunnan Normal University (CN), Yunnan Environmental Protection Bureau (CN)
Clean water and sanitation
Openalex Percentile: Top 2%
Service-Learning and Community Engagement
8.93
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.