A modeling analysis of spatial statistical indicators of thresholds for algal blooms
A modeling analysis of spatial statistical indicators of thresholds for algal blooms
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DOI：
10.1002/lol2.10091
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发表时间：
2018-10-01
影响因子：
7.8
通讯作者：
Pace, M. L.
中科院分区：
文献类型：
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作者：
Buelo, C. D.;Carpenter, S. R.;Pace, M. L.
预测水生生态系统内部和之间的藻华很重要，但也很困难，因为有多种因素促进和抑制藻华。基于时间序列的统计指标（如方差和自相关）可以为各种复杂系统的转变提供警告，包括从清澈的水到藻华的转变。类似的空间指标已经用陆地植被生态系统的模型和经验数据证明。在这里，我们使用营养物-浮游植物空间模型来测试空间指标对藻华的适用性。我们发现标准差和自相关可以很好地区分花开状态和是否接近过渡，而偏度和峰度则比较模糊。我们的研究结果表明，尽管动态的物理-生物相互作用可能会减少可探测信号，但某些空间指标适用于水生生态系统。收集藻类生物量空间数据的能力不断增强，为将空间指标应用于藻华的研究和管理提供了一个令人兴奋的机会。
Predicting algal blooms both within and among aquatic ecosystems is important yet difficult because multiple factors promote and suppress blooms. Statistical indicators (e.g., variance and autocorrelation) based on time series can provide warning of transitions in diverse complex systems, including shifts from clear water to algal blooms. Analogous spatial indicators have been demonstrated with models and empirical data from vegetated terrestrial ecosystems. Here, we test the applicability of spatial indicators to algal blooms using a nutrient-phytoplankton spatial model. We found that standard deviation and autocorrelation successfully distinguished bloom state and proximity to transitions, while skewness and kurtosis were more ambiguous. Our findings suggest certain spatial indicators are applicable to aquatic ecosystems despite dynamic physical-biological interactions that could reduce detectable signals. The growing capacity to collect spatial data on algal biomass presents an exciting opportunity for application and testing of spatial indicators to the study and management of blooms.