Using predictive models to identify kelp refuges in marine protected areas for management prioritisation

Kelp forests serve as the foundation for shallow marine ecosystems in many
temperate areas of the world but are under threat from various stressors,
including climate change. To better manage these ecosystems now and into
the future, understanding the impacts of climate change and identifying
potential refuges will help to prioritise management actions. In this
study, we use a long-term dataset of observations of kelp percentage cover
for two dominant canopy-forming species off the coast of Victoria,
Australia: Ecklonia radiata and Phyllospora comosa. These observations
were collected across three scuba sampling programs that extend from 1998
to 2019. We then associated those observations with habitat and
environmental variables including depth, seafloor structure, wave climate,
currents, temperature, and population connectivity in generalised additive
mixed effects models and used these models to develop predictive maps of
kelp cover across the Victorian MPAs. These models were also used to
project kelp coverage into the future by replacing wave climate and
temperature with future projections (2090, RCP4.5 and RCP8.5). Once all
the past, current, and future predictions were compiled, we calculated
percent cover change from 1998-2019, stability over the same period, and
future predicted change in percent cover (2019-2090) to understand the
dynamics for each species across the MPAs. We also used the current
percentage cover, stability, and future percentage cover to develop a
ranking system for classifying the maps into high climate vulnerability,
climate vulnerability, neutral, potential refugia, and likely refugia. A
management framework was then developed to use those refugia ranking
values to inform management actions and we applied this framework across
three case studies: one at the scale of the MPA network and two at the
scale of individual MPAs, one where management decisions were the same for
both species and one where the actions were species-specific. This study
shows how species distribution models, both contemporary and with future
projections, can help to identify potential refugia areas that can be used
prioritise management decisions and future-proof restoration actions.