Models of plant-plant interactions underpin our understanding of species coexistence, invasive plant impacts and plant community responses to climate change. In recent studies, models of competitive interactions failed predictive tests, thereby casting doubt on results of many past studies. We believe these model failures owe at least partly to heterogeneity in unmodelled factors (e.g. nutrients, soil pathogens) that affect both target plants and neighboring competitors. Such heterogeneity is ubiquitous, and models that do not account for it will suffer omitted variable bias. We used instrumental variables analysis to test for and correct omitted variable bias in studies that followed common protocols for measuring plant competition. In an observational study, omitted variables caused competition to seem like mutualism. In a quasi-experiment that partially controlled competitor abundances with seeding, omitted variables caused competition to seem about 35% weaker than it really was, even though the experiment occurred in an abandoned agricultural field where environmental heterogeneity was expected to be relatively low. Despite decades of research, consistently accurate estimates of competitive interactions remain elusive. The most foolproof way around this problem is true experiments that avoid omitted variable bias by completely controlling competitor abundances, but such experiments are rare.

中文翻译：

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忽略植物相互作用研究中的变量偏差

植物-植物相互作用模型巩固了我们对物种共存、入侵植物影响和植物群落对气候变化的反应的理解。在最近的研究中，竞争互动模型未能通过预测测试，从而对过去许多研究的结果产生怀疑。我们认为这些模型失败至少部分是由于影响目标植物和邻近竞争者的未建模因素（例如养分、土壤病原体）的异质性。这种异质性无处不在，不考虑它的模型将遭受遗漏变量偏差。在遵循测量植物竞争的通用协议的研究中，我们使用工具变量分析来测试和纠正遗漏变量偏差。在一项观察性研究中，遗漏的变量导致竞争看起来像是互惠互利。在通过播种部分控制竞争者丰度的准实验中，遗漏的变量导致竞争似乎比实际弱 35%，即使该实验发生在一个废弃的农田，那里的环境异质性预计相对较低。尽管进行了数十年的研究，对竞争相互作用的一致准确估计仍然难以捉摸。解决这个问题最简单的方法是通过完全控制竞争对手的丰度来避免遗漏变量偏差的真实实验，但这样的实验很少见。即使实验发生在一个废弃的农田里，那里的环境异质性预计相对较低。尽管进行了数十年的研究，但对竞争相互作用的一致准确估计仍然难以捉摸。解决这个问题最简单的方法是通过完全控制竞争对手的丰度来避免遗漏变量偏差的真实实验，但这样的实验很少见。即使实验发生在一个废弃的农田里，那里的环境异质性预计相对较低。尽管进行了数十年的研究，对竞争相互作用的一致准确估计仍然难以捉摸。解决这个问题最简单的方法是通过完全控制竞争对手的丰度来避免遗漏变量偏差的真实实验，但这样的实验很少见。