If we want to model where soil microbes are living, and why, traditional distribution models will not do. In a new paper in FEMS Microbiology Ecology, we suggest that accuracy can be achieved, if we just change our mindset and start thinking from the soil microbes’ perspective. Our three key points in that regard: measure hierarchically, interpolate local variability, and don’t forget biotic interactions!
Ecologists are getting increasingly better at describing and predicting where species live, especially thanks to a now widely famous class of models called ‘habitat suitability and distribution models’ (HSDMs). Indeed, we see global databases of species distributions becoming more and more established, and remote sensing data, for example from better and better satellites, adding to our knowledge of the enviroment. This, combined with a wider and better range of modelling tools, has caused an explosion of studies looking at where species live, and why.
Despite this rapid surge in knowledge on species distributions, there is one group of species that has remained particularly underexplored: soil microbes. The reasons for this are perhaps rather obvious: it is just so much more difficult to know what’s happening belowground, let alone model which microbes are living where.
In a new paper in the journal FEMS Microbiology Ecology, we argue however that there is no need anymore to leave soil microbe distributions understudied anymore. We can do this, we just have to approach the question with a local-scale, ‘microbe-specific’ mindset. In our conceptual paper, we provide the necessary details on how that mindset should look.
First of all, and perhaps most importantly: soil microbes operate on a much smaller scale than aboveground organisms. That means that the environmental data we link their distributions to needs to be much more local as well. The coarse global climate data at 1 km resolution that is traditionally used for HSDMs is even less useful than it is for aboveground organisms. And even worse, climate measured in standardized weather stations is completely meaningless for soil microbes, where temperatures are often several degrees different from what is measured in that white box above the lawn. And then we are not even including all these other important abiotic variables yet, like soil moisture or acidity.
Importantly, however, there is no way that one can increase the resolution of the environmental data to be exactly at the level of what these microbes ‘see’: we can’t plug loggers in every centimeter of soil over a vast area. We argue however that such a dramatic hunt for a higher resolution is not necessary. Using a hierarchical sampling approach, where high resolution data is collected in a selection of plots, which can then be linked to a broad range of plots across the landscape, one can improve the resolution of environmental data, and model and predict the local variation in environmental conditions, without the need to measure it everywhere.
Finally, for soil microbes even more than for any other organism group, their distributions are not only affected by the environment, but at least as much by other species living in their neighbourhood. These interactions are a lot harder to incorporate in HSDMs, but are nevertheless critical to really understand where and why these species can be found. Luckily, the necessary modelling tools are now available to incorporate these interactions when modelling distributions, for example using Joint Distribution Models, which analyze the distribution of several species at once, and take their co-occurrence into account.
We hope that our conceptual paper encourages scientists to tackle the spatial distribution of soil microbes, as this knowledge is from critical importance to predict how these important components of our living world are dealing with the challenges of our times.
Lembrechts JJ, Broeders L, De Gruyter J, Radujković D, Ramirez-Rojas I, Lenoir J, Verbruggen E (2020), A framework to bridge scales in distribution modelling of soil microbiota, FEMS Microbiology Ecology, , fiaa051, https://doi.org/10.1093/femsec/fiaa051