Science is suffering a reproducibility crisis that is associated with the impacts of many biases on the outcomes of scientific research. Among these biases, those caused by the properties of the human mind are seldom appreciated by scientists working in the fields of ecology and environmental science. In the present paper, we asked to what extent studies of spatial patterns in insect herbivory suffer from various biases. We answered this question by comparing the outcomes of studies that have used methods of data collection which were potentially prone or not prone to particular biases. This comparison was conducted by meta-analysis of 125 scientific paper containing data on plant losses to insects in different environments. We found that biases emerged when scientists non-randomly selected one or a few plant species when assessing community-wide herbivory or when scientists did not apply randomisation procedures when selecting leaves for measurements of herbivory. The reported effects were greater when the persons that collected samples and measured leaf damage were aware of the research hypothesis or sample origin than when the collectors/measurers were blinded to these factors. We conclude that our present-day knowledge regarding differences in insect herbivory between environments is considerably biased due to an unconscious tendency of researchers to find support for their hypotheses and expectations, as this generally leads to overestimation of the effects under study. These cognitive biases can be avoided by (i) applying randomisation procedures in sampling and (ii) blinding of the research hypotheses and sample origins. These measures should be seen as obligatory; otherwise, accumulation of biased results in primary studies can ultimately lead to false general conclusions in subsequent research syntheses.
Reference: Zvereva, E., Kozlov, M.V. (2019) Biases in studies of spatial patterns in insect herbivory. Ecological Monographs.
https://doi.org/10.1002/ecm.1361
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Picture: A tropical herbivore (bush cricket) mimicking leaf with insect damage (photo: V. Zverev, 2019)