On Mushrooms, Monitoring, and Prediction

Mushrooms probably run the world but we do not know this yet. My old friend Jim Trappe from Oregon State told me this long ago, and partly as a result of this interaction we began counting mushrooms at our boreal forest sites near Kluane, Yukon in 1993, long ago and even before the iPhone was invented. Being zoologists, we never perhaps appreciated mushrooms in the forest, but we began counting and measuring mushrooms appearing above ground on circular plots of 28m2. With the help of many students, we have counted about 12,000 plots over 24 years, even after being told by one Parks Canada staff member that they could not assist us because “real men do not count mushrooms”. At least we know our position in life.

At any rate the simple question we wanted to ask is whether we can predict mushroom crops one year ahead. We know that many species eat these mushrooms, from red squirrels (who dry mushrooms on spruce tree branches so they can be stored for later consumption), to moose (Alice Kenney has photographed them kneeling down to munch mushrooms), to caribou (Art Rodgers has videoed) to small rodents and insects, not to mention Yukon residents. We know from natural history observations that mushroom crops in the boreal forest are highly variable from year to year, ranging from 0.1 to 110 g/10m2 wet weight, for a CV of 138% (Krebs et al. 2008). The question is how best to predict what the crop will be next year.  Why do we want to know next year’s crop? Two reasons are that large crops provide food for many animals and thus affect overall ecosystem dynamics, and secondly that the essence of understanding in science is the ability to understand why changes occur and if possible to be able to predict them.

We assume it has to be driven by climate, so we can gather together climate data and it is here that the questions arise as to how to proceed. At one extreme we can gather annual temperatures and annual rainfall, and at the other extreme we can gather daily rainfall. We first make the assumption that it is only the weather during the summer from May to August that is relevant for our statistical model, so annual data are not useful. But then we are faced with a nearly infinite number of possible weather variables. We have chosen months as the relevant weather grouping and so we tally May temperature averages, May rainfall totals, growing degree days above 5°C, etc. for all the years involved. This leads us into a statistical nightmare of having far more independent variables than measurements of mushroom crops. If we have, for example, 15 possible measures of temperature and rainfall we can generate 32,768 models ignoring all the interactive models. There are several standard ways of dealing with this statistical dilemma, with stepwise regression being the old fashioned approach. But new methods and advice continue to appear (e.g. Elith et al. 2008, Ives 2015). The ability to compare different regression models with the AIC approach helps (Anderson 2008) as long as there is some biological basis to the models.

We adopted a natural history approach, given that many people believe that large mushroom crops are associated with above average rainfall. We are blessed in the Yukon with only one possible crop of mushrooms per year (at least for the present), so that also simplifies the kinds of models one might use. At any rate (as of 2016) the simplest regression model to predict mushroom biomass in a particular year turned out to involve only rainfall from May (early spring) of the previous year, with R2 = 0.55. But this success has just led us into more questions of why we cannot find a model that will explain the remaining 45% of the variance in annual crops. Should one just give up at this point and be happy that we can explain a large part of the annual variation, or should one press on doing more modelling and looking for other variables? Data dredging is more and more becoming an issue in the ecological literature, and in particular in ecological events likely to be at least partly associated with climate (Norman 2014).

Another ecological problem has been that we do not identify the species of mushrooms involved and deal only in biomass. It may be that species identification would help us to improve predictability. But there are perhaps 40 or more species of mushrooms in our part of the boreal forest, and so we now have to become mycologists. And then as Jim Trappe would tell me, all of this ignores the important questions of what is going on with these fungi underground, so we have only scratched the surface.

The next question is how long a predictive model based on weather will continue to hold in an area subject to rapid climate change. Climate change in the southern Yukon is relatively rapid but highly variable from year to year, and only continuing monitoring will keep us informed about how the physical measurements of temperature and rainfall translate into events in the biological world.

All of this is to say that counting and measuring mushrooms is enjoyable and keeps one connected to the real world. It is also a free type of good exercise, and part of citizen science. Continued monitoring is necessary to see how the boreal ecosystem responds to changing climate and to see if good years for mushroom crops become more frequent. And in good years, many kinds of mushrooms are good to eat if you can beat the squirrels to them.

Anderson, D.R. (2008) Model Based Inference in the Life Sciences: A Primer on Evidence. Springer, New York. ISBN: 978-0-387-74073-7

Elith, J., Leathwick, J.R. & Hastie, T. (2008) A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802-813. doi: 10.1111/j.1365-2656.2008.01390.x

Ives, A.R. (2015) For testing the significance of regression coefficients, go ahead and log-transform count data. Methods in Ecology and Evolution, 6, 828-835. doi: 10.1111/2041-210X.12386

Krebs, C.J., Carrier, P., Boutin, S., Boonstra, R. & Hofer, E.J. (2008) Mushroom crops in relation to weather in the southwestern Yukon. Botany, 86, 1497-1502. doi: 10.1139/B08-094

Norman, G.G. (2014) Data dredging, salami-slicing, and other successful strategies to ensure rejection: twelve tips on how to not get your paper published. Advances in Health Sciences Education, 19, 1-5. doi: 10.1007/s10459-014-9494-8

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