大豆農業生態系中土壤環境因子與蚜蟲豐度關聯性

Abstract

在農業生態系統中，植食性害蟲是限制農業生產力的關鍵因子。深入探究田間植物與植食性昆蟲之間的交互作用，對於開發永續之綜合害蟲管理策略至關重要。植物昆蟲交互作用會受到多種環境因子影響，既有研究顯示土壤因子為重要因子之一。過去研究多單獨評估土壤水分或離子濃度對植食性昆蟲的影響，然而不同土壤指標（如溼度與導電度）如何共同或相對地預測蚜蟲動態，仍缺乏系統性比較。本研究旨在解決上述知識空缺，我們利用田間微氣候感測設備，在2024年間兩個生長季（春作與秋作）連續監測大豆根區附近的土壤環境因子（土壤濕度與土壤導電度），並定期調查蚜蟲豐度。結果顯示，土壤指標與蚜蟲豐度的關聯性受生長季節、生長階段的干擾，在控制生長季節與生長階段（種植後月數）後，土壤濕度殘差與對數轉換後蚜蟲豐度殘差呈現正相關，而土壤導電度殘差與對數轉換後蚜蟲豐度殘差呈現出相對不穩定的關聯性。以土壤相關指標建構的蚜蟲豐度預測模型，發現土壤濕度為相對土壤導電度穩定之預測因子，以土壤濕度變數建構的蚜蟲豐度預測模型成功解釋了約三分之二的蚜蟲豐度變異，證實了高解析度地下環境數據的應用潛力。本研究藉由分析高時間解析度的土壤指標以及蚜蟲豐富度數據，發現了土壤水分相關因子（濕度與導電度）與蚜蟲族群動態的具有不同程度的關聯性。這些發現為水分－植物－昆蟲交互作用的理論提供了新的觀察結果，也提供未來害蟲預測系統開發的重要科學依據。

In agricultural ecosystems, herbivorous insect pests are a major threat to crop productivity. A mechanistic understanding of field-level interactions between plants and herbivorous insects is therefore essential for the development of sustainable integrated pest management strategies. Plant–insect interactions are shaped by multiple environmental factors, among which soil conditions have been identified as particularly important. Previous studies have largely examined the effects of soil moisture or soil ionic conditions in isolation; however, how different soil indicators, such as soil moisture and electrical conductivity, jointly or comparatively predict aphid population dynamics remains poorly understood. To address this knowledge gap, we deployed field-based microclimate sensors to continuously monitor soil conditions in the soybean root zone during two growing seasons (spring and autumn crop in 2024). Soil moisture and soil electrical conductivity were recorded at high temporal resolution, and aphid abundance was surveyed at regular intervals. Our results indicate that the relationships between soil variables and aphid abundance were strongly modulated by growing season and plant developmental stage. After accounting for seasonal effects and plant age (months after planting), residuals of soil moisture was positively associated with residuals of log-transformed aphid abundance, whereas residuals of soil electrical conductivity exhibited a comparatively less predictable relationship with aphid abundance. We further constructed aphid abundance prediction models based on soil-related variables and found that soil moisture was a more robust and consistent predictor than soil electrical conductivity. Models incorporating soil moisture alone explained approximately two-thirds of the variation in aphid abundance, demonstrating the predictive value of high-resolution belowground environmental data. By integrating temporally resolved soil varaible with aphid population data, this study reveals that soil water–related variables (moisture and electrical conductivity) differ in both the strength and consistency of their associations with aphid dynamics. Together, these findings provide new empirical insights into water–plant–insect interactions and establish an important scientific basis for the development of future pest forecasting and decision-support systems.