Transferable, predictive models of benthic communities informs marine spatial planning in a remote and data‐poor region

Abstract

Systematic conservation planning requires spatial information on biodiversity. Such information is often unavailable, forcing spatial planning to rely on assumed relationships between species and environmental features. This problem is particularly acute in large, remote marine protected areas that are proliferating rapidly. Here, we use models to predict whether (a) macrobenthic biodiversity across four taxa (gorgonians, soft corals, hard corals, and sponges) with different life histories are congruent within seascape features through regional space; and (b) models generated in an intensively‐sampled area in one region can predict the occurrence of habitat‐forming macrobenthos in neighboring ones. All four taxa studied showed similar habitat preferences, but high variability in distributions among and within features suggesting factors other than simple geomorphology influence these regional biodiversity patterns. Nonetheless, models derived from one region accurately predicted the presence and absence of the same taxa hundreds of kilometers away. This transferability of models of species occurrences has the potential to deliver improved reserve design in data‐deficient regions.

Geomorphological drivers of deeper reef habitats around Seychelles

“…This is particularly acute for the Western Indian Ocean (WIO) region where there is very little information on deeper reefs (Pyle and Copus 2019;Osuka et al 2021). Improving our understanding of the environmental factors that drive deeper reef ecosystems is important for ecosystem-based management approaches aiming to identify seascape features important for ecosystem health and functioning (Hinderstein et al 2010;Bridge et al 2020).…”

“…The quantification of seabed structural patterns from bathymetric surveys combined with data from visual surveys of deeper reefs has enabled the application of predictive distribution modelling as a tool to address biogeographical and ecological knowledge gaps (Guisan et al 2013;Costa et al 2015). Predictive distribution models that result in maps of expected habitat suitability have become critical tools to inform the design of effective conservation measures (Bridge et al 2020). The majority of deeper reef distribution modelling studies have focused on a dominant mesophotic species or taxon (Costa et al 2015;Veazey et al 2016;Silva and MacDonald 2017).…”

Stakeholder‐derived recommendations and actions to support deep‐reef conservation in the Western Indian Ocean

“…More applied research avenues, such as focusing on deep‐reef ecosystems functioning and their links to shallow reefs (Stefanoudis et al., 2020), or defining ecosystem services provided (Holstein et al., 2019) and their monetary value (e.g., Armstrong et al., 2012), will help reach a broader audience outside academia. Research outputs should be tailored to the needs of sustainable management and conservation priorities (e.g., modelling deepreef distribution to prioritize Australia's marine reserve design; Bridge et al., 2020), which will require effective communication between scientists, marine practitioners, and policy makers. Regional symposia and meetings between these stakeholders should be conducted regularly to break through silos.…”

Guidelines for model adaptation: A study of the transferability of a general seagrass ecosystem Dynamic Bayesian Networks model

“…In ecology, there are many examples of spatial and temporal transfer of species distribution models (Bridge et al, 2020 ; Sequeira et al, 2016 , 2018 ). Models have also been used in different locations (Barbosa et al, 2009 ; Lauria et al, 2015 ; Randin et al, 2006 ) and times (Barbosa et al, 2009 ; Moreno‐Amat et al, 2015 ; Rapacciuolo et al, 2012 ; Tuanmu et al, 2011 ) to which they were originally developed.…”