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Assessing ecosystem integrity by monitoring populations and communities is an important management tool, but is often limited by the immense variety of species and the rarity of many of them. Grouping species by their responses to variation in the environment is one approach to choosing species to serve as effective indicators of community change. Moreover, identifying species that are characterized by similar archetypical responses to the environment increases the power to predict their occurrence and simplifies management of diverse species assemblages by focusing on a much smaller number of archetypes. To this end, we used the species archetype model (SAM) to fit generalized linear models of environmental covariates to species distribution data in order to identify environmentally correlated groups of kelp forest species in the Santa Barbara Channel region. Eighty-two species of macroalgae, invertebrates, and fish monitored in kelp forests across the channel were grouped into one of 10 archetypes based on their similar responses to environmental parameters, with water temperature emerging as one of the strongest drivers of archetype differences. Predictive maps of the distribution of species archetypes identified sites where multiple archetypes are common, indicating high diversity, as well as sites where rare species are more likely to occur. Potential indicator species were identified for each archetype. New monitoring efforts across the growing Marine Biodiversity Observation Network could use modeling approaches like SAM to guide their designs, optimizing the cost-to-benefit ratio of monitoring whole communities.

Species Archetype Models of Kelp Forest Communities Reveal Diverse Responses to Environmental Gradients