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Mechanistic explanations in ecology

The increasing availability of global-scale data on plant traits, species distribution (e.g. GBIF), climate variables, sophisticated numerical methods (e.g. machine learning tools, R packages) and computing power (e.g. cloud computing) has enabled researchers to understand our biosphere in an unprecedented manner. However, these techno-scientific advances come with a cost. Researchers with sufficient technical skills in data management can now study global patterns and produce numerically sophisticated and apparently robust papers, without a clear hypothesis to test nor attempt to interpret any patterns from a mechanistic perspective. In addition, these broad-scale analyses tend to use the most readily available data rather than necessarily the most relevant data. This is further fuelled by the growing culture that values ‘fast’ science over research that may take years to complete (the publish-or-perish culture). As a consequence, there is an increase in research based on correlating ‘everything’ to see if any patterns emerge, instead of a hypothesis-driven approach. An outcome for plant ecology is that key factors in determining plant fitness, such as fire regime, light availability, herbivory, pollinator availability and other biotic interactions, are underconsidered in broad-scale studies, as they are less available than climate information, in particular. This is exacerbated by the long-standing belief that climate is the major factor shaping ecological patterns [1]. Studying global-scale patterns also tends to hide biological mechanisms, as these act at local scales and may vary across environments; thus, broad-brush approaches may mask key local processes.

In this letter [2], we highlight the potential for broad-scale correlative studies that ignore mechanisms to hinder progress in ecology. We first present examples related to seed dormancy [3], and then a few other recent examples to illustrate that this is currently a general problem in ecological studies. We end by emphasizing the importance of mechanistic understanding in ecology. Global analyses are an ambitious endeavour to find universal rules, but it needs to be appreciated that such rules may fail at identifying mechanisms that create broad-scale patterns if likely causal variables are not included in the first place. Such a broad-scale approach may even hide key local ecological processes; more integration between broad-scale description and hypothesis-based studies is needed. Furthermore, hypothesis-driven science cannot be replaced by computer mining of immense databases; the scientific method can be enriched by the use of large databases but not replaced by it. If ecology aims to be a predictive science, we should focus more on a mechanistic understanding than on describing correlations with vast amounts of data [2].

I would rather discover one cause than gain the kingdom of Persia.

Democritus (460–370 BC)


[1] Pausas J.G. & Bond W.J. 2019. Humboldt and the reinvention of nature. J. Ecol. 107: 1031-1037.  [doi | jecol blog | jgp blog | pdf]

[2] Pausas JG, Lamont BB, Keeley JE, Bond WJ. 2024. The need for mechanistic explanations in (seed) ecology. New Phytol. 242: 2394-2398.  [doi | wiley | pdf]

[3] Pausas JG, Lamont BB, Keeley JE., Bond, WJ. 2022. Bet-hedging and best-bet strategies shape seed dormancy. New Phytol. 236: 1232-1236. [ doi | wiley | pdf]

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