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Updated: Aug 23, 2025

Methods for Characterizing the Co-development of Biofilm and Habitat Heterogeneity
Published on: March 11, 2015
Spatial correlation reverses the compound effect of multiple stressors on rocky shore biofilm
Luca Rindi1, Jianyu He1, Lisandro Benedetti-Cecchi1
1Department of Biology University of Pisa, CoNISMa Pisa Italy.
Abstract:
Understanding how multifactorial fluctuating environments affect species and communities remains one of the major challenges in ecology. The spatial configuration of the environment is known to generate complex patterns of correlation among multiple stressors. However, to what extent the spatial correlation between simultaneously fluctuating variables affects ecological assemblages in real-world conditions remains poorly understood. Here, we use field experiments and simulations to assess the influence of spatial correlation of two relevant climate variables - warming and sediment deposition following heavy precipitation - on the biomass and photosynthetic activity of rocky intertidal biofilm. First, we used a response-surface design experiment to establish the relation between biofilm, warming, and sediment deposition in the field. Second, we used the response surface to generate predictions of biofilm performance under different scenarios of warming and sediment correlation. Finally, we tested the predicted outcomes by manipulating the degree of correlation between the two climate variables in a second field experiment. Simulations stemming from the experimentally derived response surface showed how the degree and direction (positive or negative) of spatial correlation between warming and sediment deposition ultimately determined the nonlinear response of biofilm biomass (but not photosynthetic activity) to fluctuating levels of the two climate variables. Experimental results corroborated these predictions, probing the buffering effect of negative spatial correlation against extreme levels of warming and sediment deposition. Together, these results indicate that consideration of nonlinear response functions and local-scale patterns of correlation between climate drivers can improve our understanding and ability to predict ecological responses to multiple processes in heterogeneous environments.
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