Identifying vulnerable marine ecosystems: an image-based vulnerability index for the Southern Ocean seafloor

Abstract

A significant proportion of Southern Ocean seafloor biodiversity is thought to be associated with fragile, slow growing, long-lived, and habitat-forming taxa. Minimizing adverse impact to these so-called vulnerable marine ecosystems (VMEs) is a conservation priority that is often managed by relying on fisheries bycatch data, combined with threshold-based conservation rules in which all “indicator” taxa are considered equal. However, VME indicator taxa have different vulnerabilities to fishing disturbance and more consideration needs to be given to how these taxa may combine to form components of ecosystems with high conservation value. Here, we propose a multi-criteria approach to VME identification that explicitly considers multiple taxa identified from imagery as VME indicator morpho-taxa. Each VME indicator morpho-taxon is weighted differently, based on its vulnerability to fishing. Using the “Antarctic Seafloor Annotated Imagery Database”, where 53 VME indicator morpho-taxa were manually annotated generating >40000 annotations, we computed an index of cumulative abundance and overall richness and assigned it to spatial grid cells. Our analysis quantifies the assemblage-level vulnerability to fishing, and allows assemblages to be characterized, e.g. as highly diverse or highly abundant. The implementation of this quantitative method is intended to enhance VME identification and contextualize the bycatch events.

Using joint species distribution modelling to predict distributions of seafloor taxa and identify vulnerable marine ecosystems in New Zealand waters

“…Adapting the approach developed by Gros et al (2023), the spatial predictions of abundance from the JSDMs were used to map areas most likely to contain VMEs. This approach consisted of: 1) assessing the vulnerability to bottom shing of all taxa within our dataset that were identi ed as VME indicator taxa; 2) calculating and mapping an "abundance-based VME index", using the cumulative abundance of VME indicator taxa weighted by a VME vulnerability score; 3) calculating and mapping a "richness-based VME index", using the richness of VME indicator taxa weighted by the VME vulnerability score; 4) mapping a "con dence index" which estimates the con dence associated with the spatial predictions of the VME indicator taxa; and 5) identifying the most likely areas in which VMEs might occur, based on the overlap of the highest abundance-based and richness-based VME index scores in areas with the highest model prediction certainty.…”

“…For the abundance-based VME index, spatial predictions of abundance for each VME indicator taxon were rst multiplied by their vulnerability scores, so that areas with high predicted densities would have higher vulnerability scores than those with low densities. Spatial estimates of taxon-speci c vulnerability scores were then summed across all VME indicator taxa to produce an overall abundance-based VME index (Gros et al, 2023). For the richnessbased VME index, spatial estimates of each taxon's probability of occurrence were multiplied by its vulnerability to bottom shing score and then summed, to produce a richness-based index of vulnerability (Gros et al, 2023).…”

“…Spatial estimates of taxon-speci c vulnerability scores were then summed across all VME indicator taxa to produce an overall abundance-based VME index (Gros et al, 2023). For the richnessbased VME index, spatial estimates of each taxon's probability of occurrence were multiplied by its vulnerability to bottom shing score and then summed, to produce a richness-based index of vulnerability (Gros et al, 2023). Despite the similarity in these two VME indices, abundance and richness represent distinct and important measures by which VMEs can be identi ed (Lockhart and Hocevar, 2021).…”

“…Management measures can include the designation of marine protected areas and implementing regulations to reduce the impacts of human activities on VMEs (e.g., Brodie and Clark, 2003;Australia and New Zealand, 2020). One approach to VME management involves identifying VME indicator taxa (e.g., Parker and Bowden, 2010), and then calculating vulnerability indices to assess the susceptibility of VMEs to various stressors, including shing activities (Clark et al, 2016;Morato et al, 2018;Gros et al, 2023). If benthic taxa data exist, the vulnerability of areas based on the occurrence or abundance of VME indicator taxa can be used to highlight particular areas which may represent a VME (Gros et al, 2023).…”

“…One approach to VME management involves identifying VME indicator taxa (e.g., Parker and Bowden, 2010), and then calculating vulnerability indices to assess the susceptibility of VMEs to various stressors, including shing activities (Clark et al, 2016;Morato et al, 2018;Gros et al, 2023). If benthic taxa data exist, the vulnerability of areas based on the occurrence or abundance of VME indicator taxa can be used to highlight particular areas which may represent a VME (Gros et al, 2023). This approach can help policymakers and stakeholders prioritize areas for protection and management and make more informed decisions about the sustainable use of marine resources.…”