Mechanistic models to meet the challenge of climate change in plant-pathogen systems

Abstract

Evidence that climate change will impact the ecology and evolution of individual plant species is growing. However, little, as yet, is known about how climate change will affect interactions between plants and their pathogens. Climate drivers could affect the physiology, and thus demography, and ultimately evolutionary processes affecting both plant hosts and their pathogens. Because the impacts of climate drivers may operate in different directions at different scales of infection, and, furthermore, may be nonlinear, abstracting across these processes may mis-specify outcomes. Here, we use mechanistic models of plant-pathogen interactions to illustrate how counterintuitive outcomes are possible, and we introduce how such framing may contribute to understanding climate effects on plant-pathogen systems. We discuss the evidence-base derived from wild and agricultural plant-pathogen systems that could inform such models, specifically in the direction of estimates of physiological, demographic and evolutionary responses to climate change. We conclude by providing an overview of knowledge gaps and directions for future research in this important area. This article is part of the theme issue 'Infectious disease ecology and evolution in a changing world'.

Keywords: climate change; mechanistic model; plant pathogen.

Figure 1.

(a) Life cycle diagram of the plant where individuals from seeds recruit (black arrows produced by all plants) and then may become infected (red arrow indicates transmission, plots below show the initial distribution of the pathogen burden, z(t), on host plants), after which the burden of infection grows (red dots on the leaves, plot below shows how the pathogen burden grows from z(t) to z(t + 1) in one time step (arbitrary units, e.g. weeks) showing a baseline scenario (solid line), a scenario where temperature increases (dot-dash line) or decreases (dashed line) pathogen burden growth). Throughout, host plants may die (plot to the right shows host survival in one time step, showing a baseline scenario (solid line) and a situation where temperature might decrease survival (dotted line), or decrease survival even more at higher pathogen burdens (dash-dotted line)). (b) Simulated trajectory of a monotonically increasing climate variable such as temperature (arbitrary units) and (c) resulting dynamics of numbers of total, susceptible and infected host plant individuals. (d) Corresponding distribution of burden (z) across infected host plant individuals is shown in the bottom panels for each of the five scenarios, with darker red indicating more individuals. Each panel has a concentration of individuals at the lowest end of the burden scale, indicating newly infected individuals (i.e. following the distribution in (a)) and one around the asymptote of burden growth (intersection of the x = y line and the burden growth curve in (a)). The location of this latter concentration then shifts according to the effects of temperature on the growth of burden, first declining and then increasing (panels 2 and 3), and total number is shaped by survival, e.g. falling off rapidly on the last panel. Parameter values are shown in table 1, and model extensions are delineated in table 2. Time is in arbitrary units. (Figure 1 was created with BioRender.com.)

Figure 2.

Climate drivers and wild plant–pathogen systems. Arrows indicate multiple scales of influence and feedbacks between climate change, physiology, demography and evolution. Climate influences processes at all three scales of plant pathogen epidemiology.