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The need for alternative plant species interaction models
Journal of Plant Ecology ( IF 2.7 ) Pub Date : 2021-03-26 , DOI: 10.1093/jpe/rtab030
Christian Damgaard 1 , Jacob Weiner 2
Affiliation  

Abstract
Aims
The limitations of classical Lotka–Volterra models for analyzing and interpreting competitive interactions among plant species have become increasingly clear in recent years. Three of the problems that have been identified are (i) the absence of frequency-dependence, which is important for long-term coexistence of species, (ii) the need to take unmeasured (often unmeasurable) variables influencing individual performance into account (e.g. spatial variation in soil nutrients or pathogens) and (iii) the need to separate measurement error from biological variation.
Methods
We modified the classical Lotka–Volterra competition models to address these limitations. We fitted eight alternative models to pin-point cover data on Festuca ovina and Agrostis capillaris over 3 years in an herbaceous plant community in Denmark. A Bayesian modeling framework was used to ascertain whether the model amendments improve the performance of the models and increase their ability to predict community dynamics and to test hypotheses.
Important Findings
Inclusion of frequency-dependence and measurement error, but not unmeasured variables, improved model performance greatly. Our results emphasize the importance of comparing alternative models in quantitative studies of plant community dynamics. Only by considering possible alternative models can we identify the forces driving community assembly and change, and improve our ability to predict the behavior of plant communities.


中文翻译:

对替代植物物种相互作用模型的需求

摘要
目的
近年来,经典的Lotka–Volterra模型在分析和解释植物物种之间的竞争性相互作用方面的局限性越来越明显。已发现的三个问题是:(i)缺乏频率依赖性,这对于物种的长期共存很重要;(ii)需要考虑影响个人绩效的不可衡量的变量(通常是无法衡量的变量)(例如土壤养分或病原体的空间变化),以及(iii)必须将测量误差与生物学变化区分开来。
方法
我们修改了经典的Lotka–Volterra竞争模型以解决这些限制。我们在丹麦的一个草本植物群落中安装了八种替代模型,以准确定位3年内的Festuca卵Agrostis毛细血管的覆盖数据。使用贝叶斯建模框架来确定模型修正是否可以改善模型的性能,并提高其预测社区动态和检验假设的能力。
重要发现
包括频率依赖性和测量误差,但不包括未测量的变量,极大地改善了模型性能。我们的结果强调了在植物群落动态定量研究中比较替代模型的重要性。只有考虑可能的替代模型,我们才能确定推动社区组装和变化的因素,并提高我们预测植物社区行为的能力。
更新日期:2021-05-25
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