Tag Archives: Predictions in ecology

How Much Can Ecologists Lie?

An ethical dilemma arises in ecology for scientists who have strong beliefs about climate change and the protection of biodiversity. Should you tell lies in your scientific papers or to the media regarding ecological issues? In essence the simple answer is never. Science is the search for the truth and the truth should be obtained from empirical evidence. This does not mean that a scientist cannot have any opinions about which you can shout very loudly, if for example you think that poached eggs are better than scrambled eggs. But there should be a general taboo about lying and we should keep a sharp distinction that opinions ≠ evidence.

But in the real world these distinctions are not always clear. How much should you as a scientist bend or gloss over the evidence? If you find that a particular pollutant will have a 75% chance of killing fish in a river system, should you stand aside as an industry argues that there is a 25% chance that nothing will happen, and profits must come before caution. In these kinds of discussions environmentalists always lose because of the precautionary principle and information is never 100% precise. The temptation is to avoid losing by lying, stretching the evidence, or shouting.

In this era of the climate disaster, it is difficult to restrain from talking about what will happen in the future. While opinions fly back and forth on what is happening, whether it will all reverse, and how soon changes will occur, ecologists must remain trustworthy by countering misinterpretations of ecological trends without rancour. When our research is incomplete, we should say so and indicate what needs to be done next to fill in the gaps in knowledge. If we are wrong in our predictions, we should admit it and discuss why. We need to point out the problems and the potential consequences from what we know today. This is not as difficult as it sounds, and it requires only to draw a line between the existing evidence and likely extrapolations from current knowledge.

A major part of current misinformation on social media about scientific issues is that existing evidence is blown out of proportion in an attempt to get some kind of specific action by governments or corporations. Lies or disinformation are more interesting to the media than the details of what is actually reliable knowledge. Uncertain predictions about future changes by scientists are often translated in social media as certain predictions. Perhaps the most important but most difficult aspect of predictions is the need to go back one or more years and list the predictions that were made and evaluate how accurate they were. Model systems for the sciences are perhaps earthquake predictions and weather predictions. While we know a great deal about the geological causes of earthquakes and have mapped major faults along which they occur, so all would agree that we “understand earthquakes scientifically”, we are not able to predict exactly where and when the next major quake will occur. Similarly we are all familiar with weather predictions which are limited to short time intervals even though we have detailed knowledge of the physical laws that govern air mass movements.

Some samples of the very large literature on forecasting earthquakes (Fallou et al. 2022, Wikelski et al. 2020), and on biotic extinctions (Cowie et al. 2022, Kehoe et al. 2021, Lambdon and Cronk 2020, Nikolaou and Katsanevakis 2023, Williams et al. 2021) provide an introduction to finding out how scientists deal with the uncertainties of prediction in these two example areas of science. Knowledge is power but it is not infinite power, and all scientists should qualify their predictions or projections as possibly in error. Lying about complex questions is not part of science.  

Cowie, R.H., Bouchet, P. & Fontaine, B. (2022) The Sixth Mass Extinction: fact, fiction or speculation? Biological Reviews, 97, 640-663.doi: 10.1111/brv.128161.

Fallou, L., Corradini, M. & Cheny, J.M. (2022) Preventing and debunking earthquake misinformation: Insights into EMSC’s practices. Frontiers in Communication, 7, 993510.doi. 10.3389/fcomm.2022.993510

Kehoe, R., Frago, E. & Sanders, D. (2021) Cascading extinctions as a hidden driver of insect decline. Ecological Entomology, 46, 743-756.doi: 10.1111/een.129851.

Lambdon, P. & Cronk, Q. (2020) Extinction dynamics under extreme conservation threat: The Flora of St Helena. Frontiers in Ecology and Evolution, 8, 41.doi: 10.3389/fevo.2020.00041.

Nikolaou, A. & Katsanevakis, S. (2023) Marine extinctions and their drivers. Regional Environmental Change, 23, 88.doi: 10.1007/s10113-023-02081-8.

Wikelski, M., Mueller, U., Scocco, P., Catorci, A., Desinov, L.V., Belyaev, M.Y., Keim, D., Pohlmeier, W., Fechteler, G. & Martin Mai, P. (2020) Potential short-term earthquake forecasting by farm animal monitoring. Ethology, 126, 931-941.doi: 10.1111/eth.13078.

Williams, N.F., McRae, L. & Clements, C.F. (2021) Scaling the extinction vortex: Body size as a predictor of population dynamics close to extinction events. Ecology and Evolution, 11, 7069-7079.doi: 10.1002/ece3.7555.