Unlocking the potential of historical abundance datasets to study biomass change in flying insects
Unlocking the potential of historical abundance datasets to study biomass change in flying insects
复制标题
释放历史丰度数据集研究飞行昆虫生物量变化的潜力
DOI：
10.1101/695635
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Kinsella R
中科院分区：
文献类型：
--
作者：
Kinsella R
昆虫丰度的趋势在一些数据集中已经很好地确定，但对丰度测量如何转化为生物量趋势知之甚少。蛾类（鳞翅目）提供了特别好的机会，在大的空间和时间尺度上研究生物量变化的趋势和驱动因素，因为存在长期的丰度数据集。然而，这些分析需要有关蛾类体重的数据，但目前尚不存在此类数据。为了解决这一数据缺口，我们在2018年收集了关于实地采样蛾类前翅长度和干重的经验数据，并使用这些数据训练和测试了一个统计模型，该模型可根据蛾类的前翅长度预测蛾类的体重模型生物量与蛾类生物量的测量值呈正相关，具有较高的解释力（R2= 0.886 ± 0.0006，在10，000次自举重复中）和蛾类混合物种样本的（R2= 0.873 ± 0.0003），表明可以预测生物量达到信息准确度水平，我们的模型允许对历史蛾类丰度数据集进行生物量估计，因此我们的方法将为研究长期和广泛地理区域内昆虫生物量变化的趋势和驱动因素创造机会。
Trends in insect abundance are well established in some datasets, but far less is known about how abundance measures translate into biomass trends. Moths (Lepidoptera) provide particularly good opportunities to study trends and drivers of biomass change at large spatial and temporal scales, given the existence of long‐term abundance datasets. However, data on the body masses of moths are required for these analyses, but such data do not currently exist.To address this data gap, we collected empirical data in 2018 on the forewing length and dry mass of field‐sampled moths, and used these to train and test a statistical model that predicts the body mass of moth species from their forewing lengths (with refined parameters for Crambidae, Erebidae, Geometridae and Noctuidae).Modeled biomass was positively correlated, with high explanatory power, with measured biomass of moth species (R2= 0.886 ± 0.0006, across 10,000 bootstrapped replicates) and of mixed‐species samples of moths (R2= 0.873 ± 0.0003), showing that it is possible to predict biomass to an informative level of accuracy, and prediction error was smaller with larger sample sizes.Our model allows biomass to be estimated for historical moth abundance datasets, and so our approach will create opportunities to investigate trends and drivers of insect biomass change over long timescales and broad geographic regions.
登录
查看更多内容
影响因子：
64.8
作者：
Seibold, Sebastian;Gossner, Martin M.;Weisser, Wolfgang W.
通讯作者：
Weisser, Wolfgang W.
影响因子：
8.8
作者：
Brose, U;Berlow, EL;Martinez, ND
通讯作者：
Martinez, ND
影响因子：
3.5
作者：
C. Shortall;A. Moore;Emma Smith;MIKE J. Hall;I. Woiwod;R. Harrington
通讯作者：
R. Harrington
影响因子：
16.8
作者：
Outhwaite, Charlotte L.;Gregory, Richard D.;Isaac, Nick J. B.
通讯作者：
Isaac, Nick J. B.
影响因子：
1.9
作者：
Groenendijk, D.;Ellis, W. N.
通讯作者：
Ellis, W. N.