American Society of Naturalists

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“Metapopulation structure predicts population dynamics in the Cakile maritima–Alternaria brassicicola host–pathogen interaction”

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Julien Papaïx, Jeremy J. Burdon, Emily Walker, Luke G. Barrett, and Peter H. Thrall (Feb 2021)

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Black spots on <i>Cakile maritima</i> stems due to infection by <i>Alternaria brassicicola</i>.<br />(Credit: Peter H. Thrall)
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Black spots on Cakile maritima stems due to infection by Alternaria brassicicola.
(Credit: Peter H. Thrall)

Predicting disease dynamics in the wild is challenging because interdependent demographic dynamics between the host and its pathogen as well as heterogeneities in environmental conditions and genetic structure can result in extreme variability in population dynamics at local geographic scales. In such situations, the relative importance of biotic and abiotic factors in determining ecological processes is complicated to decipher. Researchers from CSIRO–Canberra and INRAE–Avignon developed a spatially explicit epidemiological model to better understand how environmental conditions (e.g. habitat quality, climatic conditions), the spatial structure of a population network and dispersal of interacting species jointly determine metapopulation dynamics in plant-pathogen interactions.

Using a detailed multi-year survey of three metapopulations of the succulent plant Cakile maritima and the necrotrophic fungus Alternaria brassicicola located along the southeastern Australian coast, their research showed that climatic conditions are important drivers, likely explaining the high synchrony among populations. Host availability, landscape features facilitating dispersal, and local habitat conditions also impact the occurrence and spread of disease. Overall, this study demonstrates that the collection of longitudinal data on host and pathogen population dynamics, in combination with spatially explicit epidemiological modeling, makes it possible to accurately predict disease dynamics in wild systems.

Typical landscape of the coast of New South Wales, Australia, between Durras and Central Tilba, consisting of extensive series of sandy to pebbly beaches separated by rocky, often forested, headlands or points. Clumps of <i>Cakile maritima</i> are visible, some of them being diseased.<br />(Credit: Peter H. Thrall)
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Typical landscape of the coast of New South Wales, Australia, between Durras and Central Tilba, consisting of extensive series of sandy to pebbly beaches separated by rocky, often forested, headlands or points. Clumps of Cakile maritima are visible, some of them being diseased.
(Credit: Peter H. Thrall)

Abstract

In symbiotic interactions, spatio-temporal variation in the distribution or population dynamics of one species represents spatial and temporal heterogeneity of the landscape for the other. Such interdependent demographic dynamics result in situations where the relative importance of biotic and abiotic factors in determining ecological processes is complicated to decipher. Using a detailed survey of three metapopulations of the succulent plant Cakile maritima and the necrotrophic fungus Alternaria brassicicola located along the southeastern Australian coast, we developed a series of statistical analyses, namely synchrony analysis, patch occupancy dynamics, and spatially explicit metapopulation model, to understand how habitat quality, weather conditions, dispersal, and spatial structure determine metapopulation dynamics. Climatic conditions are important drivers, likely explaining the high synchrony among populations. Host availability, landscape features facilitating dispersal, and habitat conditions also impact the occurrence and spread of disease. Overall, we show that the collection of extensive data on host and pathogen population dynamics, in combination with spatially explicit epidemiological modelling, makes it possible to accurately predict disease dynamics – even when there is extreme variability in host population dynamics. Finally, we discuss the importance of genetic information for predicting demographic dynamics in this pathosystem.