A Framework for Predicting Plant-Fungus Symbiosis under Climate Change

Thesis Abstract

Soil-plant-mycorrhizal symbioses are increasingly disrupted by climate change, leading to altered nutrient exchange, plant resilience, and ecosystem productivity in natural and agricultural systems. This study develops a predictive framework that integrates ecological theory, functional traits, and climate covariates to forecast plant-fungus symbiotic outcomes under future environmental conditions. The aim is to construct a mechanistic yet scalable model that informs management decisions for sustaining plant performance in variable climates. Specific objectives are (i) to synthesize a typology of plant-mycorrhizal interactions across major fungal guilds (arbuscular, ectomycorrhizal, and orchid mycorrhizas) and plant functional groups; (ii) to identify trait- and environment-driven pathways governing symbiotic benefits (nutrient uptake, drought tolerance, pathogen shielding); (iii) to quantify climate sensitivity of symbiotic exchange using hierarchical Bayesian networks and structural equation modeling to capture direct and indirect effects; (iv) to validate the predictive framework with multi-site field data and controlled-environment experiments; and (v) to produce decision-ready risk indicators for ecosystem management and crop production under climate projections. The methodology adopts an integrative, multi-scale research design combining observational field studies, controlled greenhouse experiments, and meta-analytic synthesis. The population encompasses temperate and tropical biomes with representative plant species spanning annuals, perennials, and woody taxa, and their associated fungal symbionts. A stratified sampling approach will select 40 sites per biome, yielding approximately 1600 plant-fungus pairs for field data. In the greenhouse, a factorial experiment will manipulate temperature (+2°C, +4°C), precipitation regimes (ambient, moderate drought, severe drought), and soil phosphorus availability (low, medium, high) across 12 species-fungus combinations, each replicated five times. Data collection instruments include root- and rhizosphere sampling for sequencing-based taxonomic and functional profiling (ITS and LSU/SSU markers, metatranscriptomics), nearby soil physicochemical measurements, plant physiological metrics (photosynthetic rate, stomatal conductance, leaf N and P content), and symbiotic effectiveness indicators (mycorrhizal colonization percentage, nutrient transfer assays using isotopic tracers). The instrument suite is complemented by climate covariates drawn from downscaled CMIP6 projections for scenario planning. Validity and reliability are ensured through standard calibration procedures, pilot testing of survey instruments, and cross-validation of sequencing data with mock communities. Analytical approaches include (i) generalized linear mixed models to quantify the effects of climate variables, plant traits, and fungal identity on symbiotic outcomes; (ii) structural equation modeling to disentangle direct and indirect causal pathways among environmental drivers, fungal functional gene expression, and plant performance; (iii) hierarchical Bayesian networks to integrate multi-omics data and handle uncertainty in species interactions; (iv) regression-based meta-analysis to synthesize prior studies and update model priors; and (v) scenario analysis using climate projections to generate probabilistic risk indicators. A conceptual model will be iteratively refined to incorporate emergent patterns from data and existing theories in mycorrhizal ecology and plant resilience. Expected findings include robust, climate-informed quantification of how mycorrhizal benefits shift with temperature and moisture regimes, identification of key trait-fungus combinations that maximize plant performance under drought or heat stress, and recognition of context-dependent symbiotic switches (mutualistic to parasitic under extreme conditions). The study will contribute to knowledge by integrating trait-based frameworks with climate-sensitive causal models to produce a transferable framework for predicting symbiotic dynamics across ecosystems and crop systems. The main conclusion is that a unified predictive framework, grounded in ecological theory and validated with diverse data, can reliably forecast plant-fungus symbiosis under climate change and support targeted management to sustain productivity. Recommendations include prioritizing conservation of core mycorrhizal networks, deploying inoculation strategies aligned with climate projections, and incorporating symbiotic indicators into routine monitoring programs.