The properties of the human mind are responsible for a number of biases that affect the quality of scientific research. However, scientists working in the fields of ecology and environmental science rarely take these biases into account. We conducted a meta‐analysis of data extracted from 125 publications comparing woody plant damage by defoliating insects in different environments in order to understand the extent to which our knowledge on spatial patterns in herbivory is affected by various biases. We asked which research methods are most prone to biases and whether these biases lead to overestimation of the effects under study. The effect sizes (ESs) decreased with increases in the numbers of plant species involved in the study, with 61% lower ESs for herbivory estimated on all plants growing in study plots compared to herbivory on selected species. ESs also depended on the leaf sampling procedure: when all leaves from a tree or branch were sampled for measurements of herbivory or when random or systematic selection protocols were applied, ESs were 74% smaller than in cases of more subjective haphazard selection. In addition, ESs were 97% and 135% greater when the person conducting sampling and measuring was aware of the research hypothesis or sample origin, when compared with situations when the observer was blinded to these factors. The impacts of cognitive biases on the study outcomes significantly decreased with the increase in publication year; however, this pattern emerged mostly due to high‐ranked journals and was non‐significant for other journals. Using the studies of spatial patterns in herbivory as an example, we showed that our ecological and environmental knowledge is considerably biased due to an unconscious tendency of researchers to find support for their hypotheses and expectations, which generally leads to overestimation of the effects under study. Cognitive biases can be avoided by using different methods, such as applying randomization procedures in sampling and blinding of research hypotheses and sample origins. These measures should be seen as obligatory; otherwise, accumulation of the biased results in primary studies may ultimately lead to false general conclusions in subsequent research synthesis.

中文翻译：

---

昆虫食草动物空间格局研究中的偏见

人类思维的特性是造成影响科研质量的许多偏见的原因。但是，从事生态学和环境科学领域的科学家很少考虑这些偏见。我们对从125种出版物中提取的数据进行了荟萃分析，比较了通过在不同环境中对昆虫进行脱叶而对木本植物造成的损害，以便了解我们对食草动物空间格局的了解受到各种偏见的影响程度。我们询问了哪种研究方法最容易产生偏差，以及这些偏差是否导致对研究结果的高估。随着研究中所涉及植物物种数量的增加，效应大小（ESs）减小，与所选物种的食草相比，研究小区中所有植物的食草ES估计低61％。ESs还取决于叶子采样程序：当对一棵树或树枝上的所有叶子进行采样以测量草食性时，或者当应用随机或系统选择方案时，ESs比主观随意选择更为小74％。此外，与观察者不知道这些因素的情况相比，当进行采样和测量的人知道研究假设或样品来源时，ES分别增加97％和135％。认知偏见对研究结果的影响随着出版年份的增加而显着降低。但是，这种模式的出现主要是由于期刊排名靠前，对其他期刊而言并不重要。以草食动物的空间格局研究为例，我们发现我们的生态和环境知识因研究人员无意识地倾向于寻找支持其假设和期望的趋势而有很大的偏见，这通常导致对研究结果的高估。可以通过使用不同的方法来避免认知偏差，例如在研究假设和样本来源的抽样和盲化中应用随机化程序。这些措施应视为强制性的；否则，在初步研究中积累有偏见的结果可能最终导致随后的研究综合中出现错误的一般性结论。我们表明，由于研究人员无意识地倾向于为他们的假设和期望寻求支持，因此我们的生态和环境知识有很大的偏见，这通常会导致对研究结果的高估。可以通过使用不同的方法来避免认知偏差，例如在研究假设和样本来源的抽样和盲化中应用随机化程序。这些措施应视为强制性的；否则，在初步研究中积累有偏见的结果可能最终导致随后的研究综合中出现错误的一般性结论。我们表明，由于研究人员无意识地倾向于为他们的假设和期望寻求支持，因此我们的生态和环境知识有很大的偏见，这通常会导致对研究结果的高估。可以通过使用不同的方法来避免认知偏差，例如在研究假设和样本来源的抽样和盲化中应用随机化程序。这些措施应视为强制性的；否则，在初步研究中积累有偏见的结果可能最终导致随后的研究综合中出现错误的一般性结论。例如将随机程序应用于研究假设和样本来源的抽样和盲目化。这些措施应视为强制性的；否则，在初步研究中积累有偏见的结果可能最终导致随后的研究综合中出现错误的一般性结论。例如将随机程序应用于研究假设和样本来源的抽样和盲目化。这些措施应视为强制性的；否则，在初步研究中积累有偏见的结果可能最终导致随后的研究综合中出现错误的一般性结论。