对农业有害生物种群动态的多尺度环境影响分级:以塞内加尔果园桔小实蝇种群年增长为例

Cecile Caumette, Paterne Diatta, Sylvain Piry, Marie-Pierre Chapuis, Emile Faye, Fabio Sigrist, Olivier Martin, Julien Papaix, Thierry Brevault, Karine Berthier
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引用次数: 0

摘要

实施综合病虫害管理计划以限制农业病虫害损害需要了解环境变化和人口统计过程之间的相互作用。然而,确定有害生物种群时空动态的关键环境驱动因素仍然具有挑战性,因为许多候选因素可以在一系列尺度上起作用,从田间(如农业实践)到区域尺度(如天气变化)。在这种情况下,应用于预先存在数据的数据驱动方法可能允许识别模式、相关性和趋势,这些模式、相关性和趋势可能通过更有限的假设驱动研究而不明显。由此产生的见解可以产生新的假设,并为未来的实验工作提供信息,重点关注有限且相关的一组环境预测因子。在这项研究中,我们开发了一种生态信息学方法来揭示导致塞内加尔芒果果园早期再次遭受主要害虫——东方果蝇背小实蝇(BD)侵袭的多尺度环境条件。我们从对69个芒果果园进行的为期三年的监测中收集了大量数据,以及一系列空间尺度上的环境数据(即果园管理、景观结构和天气变化)。然后,我们开发了一个灵活的分析管道,以最近的机器学习算法(GPBoost)为中心,该算法允许梯度增强和混合效应模型或高斯过程的结合,对多尺度环境变量对果园年度BD种群增长时间的影响进行分层。研究发现,物理因素(温度、湿度)以及一定程度上的景观特征是果园种群生长发生时空变异的主要驱动因素。这些结果表明,有利的小气候条件可以为小BD种群提供避难所,这些种群可以在芒果淡季期间存活,很少或没有繁殖,然后在下一个芒果季节开始时重新定居邻近的果园。确认这一假设有助于在避难地区确定监测和预防控制行动的优先次序。
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Hierarchizing multi-scale environmental effects on agricultural pest population dynamics: a case study on the annual onset of Bactrocera dorsalis population growth in Senegalese orchards
Implementing integrated pest management programs to limit agricultural pest damage requires an understanding of the interactions between the environmental variability and population demographic processes. However, identifying key environmental drivers of spatiotemporal pest population dynamics remains challenging as numerous candidate factors can operate at a range of scales, from the field (e.g. agricultural practices) to the regional scale (e.g. weather variability). In such a context, data-driven approaches applied to pre-existing data may allow identifying patterns, correlations, and trends that may not be apparent through more restricted hypothesis-driven studies. The resulting insights can lead to the generation of novel hypotheses and inform future experimental work focusing on a limited and relevant set of environmental predictors. In this study, we developed an ecoinformatics approach to unravel the multi-scale environmental conditions that lead to the early re-infestation of mango orchards by a major pest in Senegal, the oriental fruit fly Bactrocera dorsalis (BD). We gathered abundance data from a three-year monitoring conducted in 69 mango orchards as well as environmental data (i.e. orchard management, landscape structure and weather variability) across a range of spatial scales. We then developed a flexible analysis pipeline centred on a recent machine learning algorithm (GPBoost), which allows the combination of gradient boosting and mixed-effects models or Gaussian processes, to hierarchize the effects of multi-scale environmental variables on the timing of annual BD population growth in orchards. We found that physical factors (temperature, humidity), and to some extent landscape features, were the main drivers of the spatio-temporal variability of the onset of population growth in orchards. These results suggest that favourable microclimate conditions could provide refuges for small BD populations that could survive, with little or no reproduction, during the mango off-season and, then, recolonize neighbouring orchards at the beginning of the next mango season. Confirmation of such a hypothesis could help to prioritize surveillance and preventive control actions in refuge areas.
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