, Volume 140, Issue 3, pp 398-406
Date: 04 Jun 2004

Weather variation and trophic interaction strength: sorting the signal from the noise

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Abstract

Weather can have important consequences for the structure and function of ecological communities by substantially altering the nature and strength of species interactions. We examined the role of intra- and inter-annual weather variability on species interactions in a seasonal old-field community consisting of spider predators, grasshopper herbivores, and grass and herb plants. We experimentally varied the number of trophic levels for 2 consecutive years and tested for inter-annual variation in trophic abundances. Grasshopper emergence varied between years to the extent that the second growing season was 20% shorter than the first one. However, the damage grasshoppers inflicted on plants was greater in the second, shorter growing season. This inter-annual variation in plant abundance could be explained using the foraging-predation risk trade-off displayed by grasshoppers combined with their survival trajectory. Decreased grasshopper survival not only reduced the damage inflicted on plants, it weakened the strength of indirect effects of spiders on grass and herb plants. The most influential abiotic factor affecting grasshopper survival was precipitation. We found a negative association between grasshopper survival and the total yearly precipitation. A finer scale analysis, however, showed that different precipitation modalities, namely, number of rainy days and average precipitation per day, had opposing effects on grasshopper survival, which were inconsistent between years. Furthermore, our results suggest that small changes in these factors should result in changes of up to several orders of magnitude in the mortality rate of grasshoppers. We thus conclude that in this system the foraging-predation risk trade-off displayed by grasshoppers combined with their survival trajectory and relevant weather variability should be incorporated in analytical theory, whose goal is to predict community dynamics.