Journal of Plant Ecology ›› 2022, Vol. 15 ›› Issue (2): 253-265.DOI: 10.1093/jpe/rtab088

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  • 收稿日期:2021-03-03 修回日期:2021-04-17 接受日期:2021-07-03 出版日期:2022-04-01 发布日期:2022-04-25

Spatio-temporal modelling of the effect of selected environmental and land-use factors on acid grassland vegetation

Christian Damgaard*   

  1. Department of Bioscience, Aarhus University, Vejlsøvej 25, 8600 Silkeborg, Denmark

    *Corresponding author. E-mail: cfd@bios.au.dk
  • Received:2021-03-03 Revised:2021-04-17 Accepted:2021-07-03 Online:2022-04-01 Published:2022-04-25

摘要: 特定环境和土地利用因素对酸性草原植被影响的时空建模

酸性草原受到了农业集约化作业(伴随着养分添加)、牲畜密度增加以及土地撂荒等多种因素的威胁。为了认识和量化所选环境和土地利用因子对酸性草地植被观测变化的影响,本研究采用结构方程模型拟合了大尺度时空精度植被覆盖监测数据。通过分层模型结构将测量和采样不确定性的重要来源纳入其中。此外,本研究也将测量和采样的不确定性与过程的不确定性分离,这在生成可能反馈给当地保护管理决策的生态预测时有着重要的意义。研究结果表明,一般而言,大气氮沉降的增加会导致非禾本草本植物的盖度,取而代之的会是更多的以禾草植物为主的酸性草原生境。沙质土壤的酸性相对较强,而土壤类型既会对植被构成直接的影响,也会通过影响土壤pH值的方式对植被产生间接影响。土壤的类型和土壤的pH值都会对酸性草原上的植被造成影响。对于植被覆盖情况在时间上的变化,尽管该模型仅解释了其中相对较小的一部分,但在使用该模型对局部生态状况进行预测并制定具有自适应性的管理计划时,对不确定性的量化仍然是有价值的。

关键词: 植物丰度联合分布, 盖度时空变化, 层次贝叶斯模型, 精度盖度数据, 结构方程模型, 酸性草地植被

Abstract:

Acid grasslands are threatened both by agricultural intensification with nutrient addition and increased livestock densities as well as by land abandonment. In order to understand and quantify the effect of selected environmental and land-use factors on the observed variation and changes in the vegetation of acid grasslands, large-scale spatial and temporal pin-point plant cover monitoring data are fitted in a structural equation model. The important sources of measurement and sampling uncertainties have been included using a hierarchical model structure. Furthermore, uncertainties associated with the measurement and sampling are separated from the process uncertainty, which is important when generating ecological predictions that may feed into local conservation management decisions. Generally, increasing atmospheric nitrogen deposition led to more grass-dominated acid grassland habitats at the expense of the cover of forbs. Sandy soils were relatively more acidic, and the effects of soil type on the vegetation include both direct effects of soil type and indirect effects mediated by the effect of soil type on soil pH. Both soil type and soil pH affected the vegetation of acid grasslands. Even though only a relatively small proportion of the temporal variation in cover was explained by the model, it would still be useful to quantify the uncertainties when using the model for generating local ecological predictions and adaptive management plans.

Key words: joint distribution of plant abundance, spatial and temporal variation of cover, hierarchical Bayesian models, pin-point cover data, structural equation modelling, acid grassland vegetation