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蚊子的温度依赖性:比较机理和机器学习方法。

Temperature dependence of mosquitoes: Comparing mechanistic and machine learning approaches.

机构信息

Harvard Medical School, Boston, Massachusetts, United States of America.

Department of Biology, Stanford University, Stanford, California, United States of America.

出版信息

PLoS Negl Trop Dis. 2024 Sep 16;18(9):e0012488. doi: 10.1371/journal.pntd.0012488. eCollection 2024 Sep.

Abstract

Mosquito vectors of pathogens (e.g., Aedes, Anopheles, and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change.

摘要

病媒蚊(例如,传播登革热、寨卡、基孔肯雅热、西尼罗河、疟疾等疾病的伊蚊、疟蚊和库蚊属)是全球公共卫生日益关注的问题。这些病媒蚊在气候和其他人为因素变化的影响下,在地理上发生了转移。作为小体型的变温动物,蚊子受温度的影响很大,这导致蚊子的生活史特征(如叮咬率、成虫死亡率、蚊子发育率和卵到成虫的存活率)呈单峰反应,在实验室研究中表现出上下热限和中间热最优值。然而,目前尚不清楚在实验室实验中测量的蚊子热反应与蚊子在野外的实际热反应之间的关系。为了解决这一差距,我们利用数千个全球蚊子出现和高空间分辨率的地理空间卫星数据来构建基于机器学习的物种分布模型,从这些模型中估计蚊子的热反应。我们应用方法将模型限制在相关的蚊子活动季节,并在生态上合理的以生态区为中心的空间背景进行采样,以便与蚊子出现记录进行比较。我们发现,从实验室研究中估计的热下限与从物种分布中估计的热下限高度相关(r = 0.87)。热最优值的相关性较弱(r = 0.69)。对于大多数物种,我们没有从它们的观察分布中检测到热上限,因此无法与基于实验室的估计进行比较。结果表明,实验室研究有可能高度可用于预测野外蚊子的较低热限和热最优值。同时,基于实验室的模型可能捕捉到了蚊子在高温下持续存在的生理限制,这在基于野外的观测研究中并不明显,但可能对蚊子对气候变暖的反应具有重要影响。我们的结果表明,基于实验室和基于野外的研究高度互补;协同进行分析可以帮助更全面地了解蚊子对气候变化的反应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b11/11460681/d4d05accf4cb/pntd.0012488.g001.jpg

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