Doctoral School of Environmental Sciences, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary.
Research Institute of Multidisciplinary Ecotheology, John Wesley Theological College, Budapest, Hungary.
Environ Monit Assess. 2021 Sep 29;193(10):676. doi: 10.1007/s10661-021-09463-7.
We studied the patterns of pre-collapse communities, the small-scale and the large-scale signals of collapses, and the environmental events before the collapses using four paleoecological and one modern data series. We applied and evaluated eight indicators in our analysis: the relative abundance of species, hierarchical cluster analysis, principal component analysis, total abundance, species richness, standard deviation (without a rolling window), first-order autoregression, and the relative abundance of the dominant species. We investigated the signals at the probable collapse triggering unusual environmental events and at the collapse zone boundaries, respectively. We also distinguished between pulse and step environmental events to see what signals the indicators give at these two different types of events. Our results show that first-order autoregression is not a good environmental event indicator, but it can forecast or indicate the collapse zones in climate change. The rest of the indicators are more sensitive to the pulse events than to the step events. Step events during climate change might have an essential role in initiating collapses. These events probably push the communities with low resilience beyond a critical threshold, so it is crucial to detect them. Before collapses, the total abundance and the species richness increase, the relative abundance of the species decreases. The hierarchical cluster analysis and the relative abundance of species together designate the collapse zone boundaries. We suggest that small-scale signals should be involved in analyses because they are often earlier than large-scale signals.
我们使用四个古生态学和一个现代数据系列研究了崩塌前群落的模式、崩塌的小规模和大规模信号以及崩塌前的环境事件。我们在分析中应用和评估了八个指标：物种相对丰度、层次聚类分析、主成分分析、总丰度、物种丰富度、无滚动窗口的标准差、一阶自回归和优势种的相对丰度。我们分别研究了可能引发异常环境事件的崩塌触发点和崩塌区边界处的信号。我们还区分了脉冲和阶跃环境事件，以了解这些两种不同类型的事件中指标给出的信号。我们的研究结果表明，一阶自回归不是一个很好的环境事件指标，但它可以预测或指示气候变化中的崩塌区。其余的指标对脉冲事件比阶跃事件更敏感。气候变化期间的阶跃事件可能在引发崩塌方面起着至关重要的作用。这些事件可能会将弹性较低的群落推过一个临界点，因此检测到这些事件至关重要。在崩塌之前，总丰度和物种丰富度增加，物种的相对丰度减少。层次聚类分析和物种的相对丰度共同指定了崩塌区边界。我们建议在分析中涉及小规模信号，因为它们通常比大规模信号更早。
