If you listen to the media in any form, you will find that you are bombarded with facts provided with no evidence. Unfortunately, this tendency has been moving into science in a way that is potentially dangerous. At worst such a move could call scientific information into disrepute. The current worst case is all the information we have been given on Covid vaccines, and the dispute whether we need any vaccines now for anything. Most scientists would classify these disputes as lunacy, but we are too polite to say this openly. Climate change is another current problem that has subdivided the public into four camps – (1) the climate has always changed back and forth in the past so we should not worry about it. (2) Human caused climate change is happening but there is nothing we as a small city or nation can do anything about, so carry on. (3) It is an emergency but fear not, science will find a technical solution like carbon capture that will take care of the problem. So again, we do not have to do anything. (4) It is a critical threat and demands immediate action to reduce greenhouse gas emissions.
Compounding the failure to recognize evidence, we mix the climate emergency issue with economics and GDP growth so that we can take no serious actions on the problem because economic growth will be affected. There is a hint of evidence coming in economics now that some economists recognize that the ‘evidence’ put out by economic models for future change and policies are largely from failed models of how the economic system works (Chatziantoniou et al. 2019).
These kinds of observations should alert us to the models we use to understand population changes and to predict the success of a particular manipulation that will solve conservation and management problems. Hone and Krebs (2023) have just published a paper on cause and effect, what does it mean, and if we posit that a particular cause or set of causes is producing an effect, what is the strength of evidence for this particular hypothesis? I suspect that if we took a poll of conservation, wildlife, and fisheries ecologists, our recent paper would be low on the reading list. Yet the question of cause and effect is central to all of science and deserves scrutiny. There are a series of criteria that can help ecologists determine a measure of strength of evidence so that we can avoid the twin problems of current management – “I have a model that predicts XYZ so that is the way to go”, or alternatively “I know what is going on in the ecosystem so we must do ABC” (Dennis et al. 2019). Opinion vs evidence. No one likes to be told that a particular statement they announce is just an opinion. If you think this is not a central issue of today, read the news and the controversies that continue about how to avoid getting Covid, or how to slow climate change, or how much land and water do we need to protect in parks and reserves. If we have no evidence about what changes to make to solve a particular problem in conservation ecology or management, we must act but we should do so in a way that provides data via adaptive management (Taper et al. 2021, Johnson et al 2015, Westgate et al. 2013).
Perhaps one of the critical communication problems of our time involves evidence of the rapid loss of global biodiversity which is based on incomplete studies. Anyone who is involved in a serious local study of biodiversity change will attest to the problems explored by Cardinale et al. (2018) on the need for high quality datasets that are long-term and provide the evidence for conservation programs that inform global change (Watson et al. 2022). Evidence and more evidence is badly needed.
Cardinale, B.J., Gonzalez, A., Allington, G.R.H. & Loreau, M. (2018) Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biological Conservation, 219, 175-183.doi: 10.1016/j.biocon.2017.12.021.
Chatziantoniou, I., Degiannakis, S., Filis, G. & Lloyd, T. (2021) Oil price volatility is effective in predicting food price volatility. Or is it? The Energy Journal 42, 25-48. doi: 10.5547/01956574.42.6.icha
Dennis, B., Ponciano, J.M., Taper, M.L. & Lele, S.R. (2019) Errors in statistical inference under model misspecification: Evidence, hypothesis testing, and AIC. Frontiers in Ecology and Evolution, 7, 372. doi: 10.3389/fevo.2019.00372.
Hone, J. & Krebs, C.J. (2023) Causality and wildlife management. Journal of Wildlife Management, 2023, e22412. doi: 10.1002/jwmg.22412.
Johnson, F.A., Boomer, G.S., Williams, B.K., Nichols, J.D. & Case, D.J. (2015) Multilevel Learning in the Adaptive Management of Waterfowl Harvests: 20 Years and Counting. Wildlife Society Bulletin, 39, 9-19.doi: 10.1002/wsb.518.
Serrouya, R., Seip, D.R., Hervieux, D., McLellan, B.N., McNay, R.S., Steenweg, R., Heard, D.C., Hebblewhite, M., Gillingham, M. & Boutin, S. (2019) Saving endangered species using adaptive management. Proceedings of the National Academy of Sciences, 116, 6181-6186.doi: 10.1073/pnas.1816923116.
Taper, M., Lele, S., Ponciano, J., Dennis, B. & Jerde, C. (2021) Assessing the global and local uncertainty of scientific evidence in the presence of model misspecification. Frontiers in Ecology and Evolution, 9, 679155. doi: 10.3389/fevo.2021.679155.
Watson, R., Kundzewicz, Z.W. & Borrell-Damián, L. (2022) Covid-19, and the climate change and biodiversity emergencies. Science of The Total Environment, 844, 157188.doi: 10.1016/j.scitotenv.2022.157188.
Westgate, M.J., Likens, G.E. & Lindenmayer, D.B. (2013) Adaptive management of biological systems: A review. Biological Conservation, 158, 128-139.doi: 10.1016/j.biocon.2012.08.016.