Abstract
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.
Highlights
The idea that environmental management should consider the ecosystem or landscape as a whole is decades old and has spawned entire branches of ecology (Slocombe, 1993; Grumbine, 1994)
To avoid effects of multicollinearity, we examined the environmental covariates using Spearman’s correlations and Variance Inflation Factors (VIF)
Based on the resulting Bayesian Information Criterion (BIC) values, we chose to proceed with the 10-archetype model, beyond which additional archetypes offer minimal improvements in BIC (Figure S1; Dunstan et al, 2011)
Summary
The idea that environmental management should consider the ecosystem or landscape as a whole is decades old and has spawned entire branches of ecology (Slocombe, 1993; Grumbine, 1994). The challenges to implementing ecosystem-based management are problematic in marine systems, where this management approach may offer a way to reverse widespread declines in coastal and oceanic ecosystems and in the functions and services they provide (Leslie and McLeod, 2007). Despite this potential, marine ecosystem-based management lags behind its terrestrial counterpart in implementation (Townsend et al, 2018). Marine management instead relies heavily on singlespecies population dynamics or habitat suitability models, despite the wealth of ecological research showing the importance of entire communities and biodiversity on ecosystem structure and function (Thrush et al, 2016). More sophisticated approaches to marine ecosystem-based management, such as adaptive management of sites over time, have been limited by the difficulty of predicting and detecting ecological change in diverse ecosystems that are inherently difficult to access and observe (Barbier et al, 2008; Ellingsen et al, 2017)
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