全球气候变化下黄脊竹蝗Ceracris kiangsu Tsai发生的物候学模型

The bamboo locust Ceracris kiangsu Tsai is one of major forest pest insects that feeds on bamboo leaves in southern China. It is important to master its occurrence rules and characteristics for well preventing and controlling its damage and for further strengthening the supervision and management on bamboo forests. Previous studies have demonstrated that climate change could significantly affect the population dynamics of grassland locusts, but it is unknown whether climate change can also have great influence on that of the bamboo locust. Different from grassland locusts, the bamboo locust has a relatively small activity range because of preference for bamboo leaves, and its wintering eggs are usually laid near its host plants. Traditional phenological models only consider the effects of meteorological variables on the occurrence of forest pest insects, and potentially neglect the effect of spatial variable. The present study aims to build a phenological model with meterological and spatial variables based on the following studies: (i) the spatial distributional pattern for wintering eggs of bamboo locusts; (ii) the effect of annual extreme lowest temperature on the survival rate of wintering eggs; and (iii) the effect of accumulated degree days on the developmental times for different stages, in order to more accurately predicting the occurrence processes and population dynamics of bamboo locusts on the background of global climate change.

黄脊竹蝗Ceracris kiangsu Tsai是一种对竹林危害严重的食叶害虫，了解其发生的规律和特点对于预防其危害、促进竹林的健康经营具有重要的现实意义。国内外研究已经证实气候变化对于草原蝗虫大发生具有显著的影响，而气候变化是否会对黄脊竹蝗种群动态产生重要的影响目前的研究尚不清晰。黄脊竹蝗不同于草原蝗虫，其对寄主植物存在一定的偏好，喜吃竹叶，活动范围受寄主植物影响较大，其越冬卵产于距离寄主植物较近的位置。传统的物候学模型往往只考虑单纯的气象变量对森林害虫发生的影响，而潜在地忽略了空间变量的影响。此研究准备在综合考虑黄脊竹蝗越冬卵的空间分布模式、年极端最低温对越冬卵存活率的影响以及累积度日对黄脊竹蝗各虫态发育历期的影响基础上，构建一个既含有气象变量又含有空间变量的物候学模型，用于更为准确地预测在全球气候变化背景下黄脊竹蝗发生的进度和种群动态。

随着世界人口总量急剧增长、人类活动日趋频繁、化石燃料的广泛使用，人类导致的气候变暖已经是不争的事实。气候变化对重要农林害虫的越冬死亡率、分布界限、个体行为、种群动态都产生了重要影响。温度能够显著影响昆虫的生长和发育，一些数学模型被构建用以描述温度对昆虫发育速率的影响。本项目系统对比了当前流行的温依赖发育速率的数学模型包括Arrhenius，Logan，Briére，Lactin，Peroformance，Beta，LRF，SSI和ROR模型，通过比较拟合优度、模型的复杂性程度、近似线性的效果以及模型构建的理论根基，发现LRF模型为标准曲线环境下（即不同常温下发育速率呈现为右偏的似钟型曲线）可以获得最好的近似线性效果，同时拟合优度也较高。而运用累计发育进度法来融于这些模型预测黄脊竹蝗卵的孵化时间和武汉大学樱花始花期时，发现Arrhenius方程非常适合用于预测春季物候学事件的发生时间，这是因为春季昆虫和植物更多经历了低温和适温，而不存在高温胁迫。春季物候除了受到春季温度的发育速率累加效果影响外，还受到冬季低温的影响。暖冬条件下冬季低温不低从而降低了冷刺激的积累进程，延缓了春季物候事件，所以气候变暖未必导致物候学事件的提前，物候学事件究竟呈现出提前或滞后主要取决于气候变化对冬季冷刺激积累进程和春季有效温度积累进程之间的均衡。本研究可以用于预测重要农林害虫特定物候学事件的发生时间，对于害虫综合治理具有重要意义。