Co-Occurrence Patterns Do Not Predict Mutualistic Interactions Between Plant and Butterfly Species

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Esteban Menares, Hugo Saíz, Noëlle Schenk, Enrique G. de la Riva, Jochen Krauss, Klaus Birkhofer
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Abstract

Biotic interactions are crucial for determining the structure and dynamics of communities; however, direct measurement of these interactions can be challenging in terms of time and resources, especially when numerous species are involved. Inferring species interactions from species co-occurrence patterns is increasingly being used; however, recent studies have highlighted some limitations. To our knowledge, no attempt has been made to test the accuracy of the existing methods for detecting mutualistic interactions in terrestrial ecosystems. In this study, we compiled two literature-based, long-term datasets of interactions between butterflies and herbaceous plant species in two regions of Germany and compared them with observational abundance and presence/absence data collected within a year in the same regions. We tested how well the species associations generated by three different co-occurrence analysis methods matched those of empirically measured mutualistic associations using sensitivity and specificity analyses and compared the strength of associations. We also checked whether flower abundance data (instead of plant abundance data) increased the accuracy of the co-occurrence models and validated our results using empirical flower visitation data. The results revealed that, although all methods exhibited low sensitivity, our implementation of the Relative Interaction Intensity index with pairwise null models performed the best, followed by the probabilistic method and Spearman's rank correlation method. However, empirical data showed a significant number of interactions that were not detected using co-occurrence methods. Incorporating flower abundance data did not improve sensitivity but enhanced specificity in one region. Further analysis demonstrated incongruence between the predicted co-occurrence associations and actual interaction strengths, with many pairs exhibiting high interaction strength but low co-occurrence or vice versa. These findings underscore the complexity of ecological dynamics and highlight the limitations of current co-occurrence methods for accurately capturing species interactions.

Abstract Image

共生模式无法预测植物与蝴蝶物种之间的互惠相互作用
生物间的相互作用对确定群落的结构和动态至关重要;然而,直接测量这些相互作用在时间和资源方面都具有挑战性,尤其是在涉及众多物种的情况下。从物种共存模式推断物种间的相互作用正被越来越多地使用;然而,最近的研究也凸显了一些局限性。据我们所知,目前还没有人尝试测试现有方法在陆地生态系统中检测互惠相互作用的准确性。在这项研究中,我们汇编了德国两个地区蝴蝶与草本植物物种之间相互作用的两个基于文献的长期数据集,并将其与在同一地区一年内收集的观测丰度和存在/缺失数据进行了比较。我们使用灵敏度和特异性分析方法测试了三种不同的共生分析方法得出的物种关联与经验测量的互生关联的匹配程度,并比较了关联的强度。我们还检验了花卉丰度数据(而非植物丰度数据)是否提高了共生模型的准确性,并利用经验花卉访问数据验证了我们的结果。结果表明,虽然所有方法都表现出较低的灵敏度,但我们采用的成对无效模型的相对交互作用强度指数表现最好,其次是概率法和斯皮尔曼秩相关法。然而,经验数据显示,有大量的交互作用是共现方法无法检测到的。纳入花卉丰度数据并没有提高灵敏度,但却提高了一个区域的特异性。进一步的分析表明,预测的共生关联与实际的交互作用强度不一致,许多配对表现出高交互作用强度,但共生率却很低,反之亦然。这些发现强调了生态动态的复杂性,并突出了目前的共现方法在准确捕捉物种相互作用方面的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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