摘要
农田杂草是阻碍农业生产的主要因素之一.明确农田杂草丰富度分布格局对农业生产管理具有重要意义.以青藏高原农田杂草为研究对象,利用物种分布模型探讨基于县域尺度的农田杂草物种丰富度分布格局及其未来(2050s)的变化,利用逐步回归筛选影响物种丰富度的环境因子,基于传统最小二乘法(OLS)和地理加权回归模型(GWR)分析环境因子对农田杂草物种丰富度的影响,并对两种分析方法进行比较.结果显示:(1)分布在青藏高原的农田主要杂草有51科284种,其中59种单子叶杂草、222种双子叶杂草、135种一年生杂草和149种多年生杂草.青藏高原农田杂草物种丰富度呈由西向东递增的变化规律,物种丰富度中心(丰富度值为167-194)主要集中在一江两河、河湟谷地和川西北等地区;(2)全球气候变化背景下,未来(2050s)青藏高原农田杂草物种丰富度整体呈由东南向西北方向增加的趋势,其中SSP1-2.6情境下最多增加43种,SSP5-8.5情境下最多增加49种;(3)GWR模型优于OLS,其结果表明青藏高原农田杂草物种丰富度的主要驱动因子是最冷季平均温、太阳辐射和最干月降水量,上述变量对杂草丰富度的影响存在明显的空间差异性,其中最冷季平均温由南向北逐渐从负向影响转变为正向影响.太阳辐射整体在青藏高原东部边缘等地区对农田杂草丰富度起正向的影响,在藏东南、青藏高原北部边缘等地区起负向的影响.最干月降水量对整个研究区域起负向影响,并表现出影响力由南向北逐步递增的趋势.上述结果表明青藏高原农田杂草物种丰富度调查不足,实际观测到的丰富度值明显低于当前气候下潜在的丰富度值,存在低估现象.当前气候背景下的农田杂草物种丰富度中心分布地区在未来仍是重点监管对象,且未来青藏高原部分地区作物可能面临新的杂草入侵风险.建议未来研究应注重于青藏高原
Weeds in agricultural lands are one of the main factors hindering agricultural production. Clarifying the distribution pattern of weed species in agricultural lands is of great importance for agricultural production management. The study used a species distribution model to construct species richness and used stepwise regression to select ecological factors to explain the distribution pattern of species richness. We explored the effects of these factors on species richness based on ordinary least squares(OLS) and geographically weighted regression(GWR) models. The results showed that:(1) there were 284 species of farmland weeds from 51families on the Qinghai-Tibet Plateau, including 59 monocotyledonous weed species, 222 dicotyledonous weed species, 135 annual weed species, and 149 perennial weed species. The species richness of weeds in agriculture on the Qinghai-Tibet Plateau showed a pattern of increasing from west to east, and the species richness hotspots(richness values of 167-194) were mainly concentrated in the Three-Rivers Region in Tibet,Hehuang Valley, and northwest Sichuan.(2) Under several future climate change simulations, the future weed species richness of agricultural lands on the Qinghai-Tibet Plateau showed an overall increasing trend from southeast to northwest. The maximum increase was 43 species in the SSP1-2.6 scenario and 49 species in the SSP5-8.5 scenario.(3) The prediction accuracy of the GWR model is better than the OLS model. Simulation results of the GWR model showed that the mean temperature of coldest quarter, soil acidity, and precipitation of driest month were the main factors affecting the species richness of weeds. Moreover, the effects on species richness of aforementioned factors indicate significant spatial differences. Overall, these results indicate that current weed richness surveys in agricultural fields on the Qinghai-Tibet Plateau are insufficient. The observed weed richness was substantially lower than the potential richness under the current climate, and there was an
作者
申源
邱鹏
廖梓延
伍小刚
孙晓铭
张林
潘志芬
潘开文
SHEN Yuan;QIU Peng;LIAO Ziyan;WU Xiaogang;SUN Xiaoming;ZHANG Lin;PAN Zhifen;PAN Kaiwen(CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization&Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province,Chengdu Institute of Biology,Chinese Academy of Sciences,Chengdu 610041,China;University of Chinese Academy of Sciences,Beijing 100049,China;Sichuan Academy of Forestry,Chengdu,610081,China)
出处
《应用与环境生物学报》
CAS
CSCD
北大核心
2022年第4期897-908,共12页
Chinese Journal of Applied and Environmental Biology
基金
第二次青藏高原综合科学考察研究项目(2019QZKK0303)资助。
关键词
农田杂草
地理加权回归模型
物种丰富度
驱动因子
青藏高原
agricultural weeds
geographically weighted regression
species richness
driving factor
Qinghai-Tibet Plateau
