Sarah Ishak, Université du Québec à Montréal, discusses their article: Modelling the distribution of plant-associated microbes with species distribution models
The array of bacteria, fungi, protists, viruses, and archaea that live inside or outside of plant tissue, AKA plant-associated microbes, perform functions that vary from beneficial to parasitic to simply neutrally existing with the plant. With climate change posing unpredictable and potentially existential threats to local ecosystems, understanding how plant-associated microbes respond to these changes is necessary to maintain the health and resilience of not only plants, but the organisms and systems that require plants to live (including people).
Spatial distribution models (SDMs) have often been used to model the distribution of a single or small number of plant or animal species under environmental gradients. Could we extend this framework to plant-associated microbes to predict their responses to changing climatic variables? Unfortunately, microbes are challenging for this task. Unlike many other commonly-studied organisms, they are broadly asexual, difficult to observe, and phylogenetically flexible, with the ability to share genes, mutate, or adapt very rapidly.
Despite the advances of microbial sequencing, we are usually only left with a snapshot in time, a small glimpse into the microbial world from which we can attempt to make predictions. Furthermore, plant-associated microbes exist in a unique context in which they live in an environment within an environment. The first environment, the host plant, will have biotic factors that directly shape microbial communities. The second environment is the external environment – where the host plant is growing. If we want to predict how climatic variables will drive the distribution of plant-associated microbes, we must also account for the biotic context in which they live. In our paper, we discuss three major ways that this can be done:
1. Incorporating host plants as a “static layer” in an SDM by constraining predictions of plant-associated microbe distribution by host plant species. This means that any predictions made about plant-associated microbe distribution assumes that microbes are more similar within the same host plant and passively accounts for the current distribution of the host plant species across an environmental/climatic gradient.
2. A nested SDM in which we model the effects of climate on plant distribution first, before incorporating these predictions into the microbial distribution model. This incorporates host plants as a “dynamic layer” and can provide better predictions on how plant-associated microbes will respond to climate change, because it accounts for how the distribution of host plants themselves will respond.
3. Using joint species distribution models (JSDMs) accounts for the relationship between plants and their microbes, i.e. host plants affect microbe distribution and microbes affect plant distribution. This model has been developed to look at species-species interactions, therefore including both plant and microbe species in the same community matrix can show plant-plant, microbe-microbe, and plant-microbe interactions.
We ran a case study using all three of these methods to predict how the range of leaf-associated bacteria would change under different climatic variables and found that:
- Incorporating host-plant data was very important in predicting the distribution of our sampled bacteria.
- Adding host as a “dynamic” layer (Model 2), provided more biologically relevant predictions.
- JSDMs were able to specifically predict the range changes of specific bacterial taxa.
The frameworks which we have proposed are a promising step in implementing SDMs for plant-associated microbial communities. However, we encourage more testing, more data collection, and development of more statistical tools before we are able to accurately make predictions on how climate change will affect these plant-associated microbes.
