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Under globally accelerating rates of ecosystem degradation, maintaining ecosystem function is a priority to avoid loss of valuable ecosystem services. Two factors are important: changes to the disturbance regime (stresses imposed) and resilience of biodiversity and ecosystem functions (the ecosystem's capacity to respond to change). Various attributes at different scales of ecological organisation confer resilience (from individual species to communities at landscape scales), but it is critical to understand how these attributes interact to inform how ecosystem function changes with disturbances that vary in intensity, spatial extent, and frequency. Individual species attributes influence their resistance, while attributes at the landscape-scale influence recovery of communities and function. Understanding resilience to disturbances requires defining the characteristics of a resilient community at multiple scales., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The effective application of biodiversity-ecosystem function (BEF) research to societal needs amid the Anthropocene represents the next grand challenge for ecology. Biodiversity knowledge that is most meaningful to society must reconcile insights derived from theory with detailed experiments and broad-scale trends. This perspective requires science that addresses high species richness, redundancy, and natural variability, which simplified 'model systems' cannot mimic. Here, we illustrate solutions of biodiversity knowledge to management and societal problems that combine BEF with scaling experiments, analysis of BEF along environmental gradients, and mapping technologies. We primarily draw examples from biophysical interactions in seafloor environments, which cover 70% of the Earth and add significantly to global ecosystem functions and services., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The spatial patterns exhibited by soft-bottom macrobenthic organismshave become recognized for their potential to play an importantrole in determining both the ecology of these species and our ability to study it. Recent studies have shown that spatial scales of field sampling or experimentation are important influences on data interpretation. The presence of patches, density gradients and spatially autocorrelated variables may confound designs and affect the validity of inferential statistics. Future studies must integrate the intensity and form of patterns from various spatial and temporal scales if we are to understand the process responsible for generating pattern., (Copyright © 1991. Published by Elsevier Ltd.)