Stream diatom assemblages as environmental indicators – A cross-regional assessment
Stream diatom assemblages as environmental indicators – A cross-regional assessment
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DOI：
10.1016/j.ecolind.2020.106183
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发表时间：
2020-06
影响因子：
6.9
通讯作者：
V. Pajunen;M. Kahlert;J. Soininen
中科院分区：
文献类型：
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作者：
V. Pajunen;M. Kahlert;J. Soininen
持续的气候变化和人为压力凸显了可靠评估淡水生态状况的重要性。生物指示剂广泛用于生态评估，因为生物组合反映了当前生态系统的环境条件。然而，生物指示剂的稳健性依赖于其来源区域之外的指数和模型的可转移性。我们利用瑞典的硅藻组合数据开发了一个预测模型，并用它来模拟芬兰的溪流条件，从而测试了溪流硅藻组合作为跨区域评估中水化学和气候因素指标的可靠性。推理模型和预测是使用提升回归树（BRT）方法分别在物种和属级别进行的。瑞典校准模型对水化学和气候变量的预测性能在物种和属水平上都为良好或中等。最重要的气候（生长度日，r2= 0.57）和水化学变量（pH，r2= 0.65；总磷（TP），r2= 0.52）可以从硅藻组合中相对较好地推断出来。然而，跨区域模型的预测性能较低（r2≤0.13）。尽管如此，水化学变量、电导率 (r2= 0.13) 和 TP (r2= 0.12) 被预测为最佳。瑞典和芬兰之间最重要的气候和环境变量硅藻指标有所不同。研究表明，硅藻组合可以作为推断模型校准区域内水化学和气候变量的可靠指标。然而，它们的指标能力在区域之间可能较弱。硅藻推断模型可移植性低的原因可能源于物种实现的生态位、物种库和/或生态系统、当地适应或物种识别的区域间差异。因此，模型只能在其开发地以外的地理环境中谨慎使用。校准数据应覆盖尽可能大的地理区域，以便在应用于较小区域时提供可靠的预测。
Ongoing climatic change and anthropogenic pressure highlight the importance of reliable assessment of the ecological status of freshwaters. Bioindicators are used widely in ecological assessments as biotic assemblages reflect the environmental conditions in current ecosystems. However, the robustness of bioindicators relies on the transferability of indices and models outside the regions they were derived from. We tested the reliability of stream diatom assemblages as indicators of water chemistry and climatic factors in a cross-regional assessment by developing a predictive model with diatom assemblage data from Sweden and using it to model stream conditions in Finland. The inference models and predictions were performed using the Boosted Regression Trees (BRT) method, separately in species and genus levels. The predictive performance of the calibration models in Sweden were good or moderate for both water chemistry and climatic variables, both at species and genus levels. The most important climatic (growing degree days, r2= 0.57) and water chemistry variables (pH, r2= 0.65; and total phosphorus (TP), r2= 0.52) could be inferred from diatom assemblages relatively well. However, predictive performances of the cross-regional models were low (r2≤ 0.13). Nevertheless, water chemistry variables, conductivity (r2= 0.13) and TP (r2= 0.12), were predicted the best. The most important diatom indicators for climatic and environmental variables varied between Sweden and Finland. The study showed that diatom assemblages can be robust indicators of water chemistry and climatic variables within the region where the inference models are calibrated. However, their indicator ability may be weak between regions. The reason for the low transferability of the diatom inference models may stem from between-region differences in species realized niches, species pools and/or ecosystems, local adaptation or species identification. Hence, models should only be used with caution in geographical contexts other than the one where they were developed. The calibration data should cover as large geographical area as possible to give reliable predictions when applied in smaller regions.