Seabed morphology and bed shear stress predict temperate reef habitats in a high energy marine region

Jackson-Bué, Tim; Williams, Gareth J.; Whitton, Timothy A.; Roberts, Michael J.; Goward‐Brown, Alice; Amir, Hana; King, J. W.; Powell, Ben; Rowlands, Steven J.; Jones, Gerallt Llewelyn; Davies, Andrew J.

doi:10.1016/j.ecss.2022.107934

Cited by 8 publications

(7 citation statements)

References 74 publications

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“…Furthermore, as the observational species abundance data used here were exclusively acquired through grab and core samples, the modeled layers assume that the whole study region consists of sediments that can be sampled using these devices. It is well understood that there are seabed regions within our spatial extent which comprise coarser areas of the seabed (e.g., gravel, cobble and rock; Irving, 2009;Jackson-Bué et al, 2022) which the current sediment predictor layer models do not identify (see Mitchell, Aldridge, & Deising, 2019). To address this issue, coarse habitat areas should be clipped from the spatial raster layers during subsequent iterations of our numerical modeling.…”

Section: Discussionmentioning

confidence: 99%

Mapping marine benthic biological traits to facilitate future sustainable development

Bolam

Cooper

Downie

2023

Ecological Applications

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Escalating societal demands placed on the seabed mean there has never been such a pressing need to align our understanding of the relationship between the physical impact of anthropogenic activities (e.g., installation of wind turbines, demersal fishing) and the structure and function of the seabed assemblages. However, spatial differences in benthic assemblages based on empirical data are currently not adequately incorporated into decision‐making processes regarding future licensable activities or wider marine spatial planning frameworks. This study demonstrates that, through harnessing a Big Data approach, large‐scale, continuous coverage maps revealing differences in biological traits expressions of benthic assemblages can be produced. We present independent maps based on a suite of response traits (depicting differences in responses to natural or anthropogenically induced change) and effects traits (reflecting different functional potential), although maps derived using single traits or combinations of a range of traits are possible. Models predicting variations in response traits expression provide greater confidence than those predicting effects traits. We discuss how such maps may be used to assist in the decision‐making process for the licensing of anthropogenic activities and as part of marine spatial planning approaches. The confidence in such maps to reflect spatial variations in marine benthic trait expression may, in the future, inherently be improved through (1) the inclusion of more empirical macrofaunal assemblage field data; (2) an improved knowledge of marine benthic taxa trait expression; and (3) a greater understanding of the traits responsible for determining a taxon's response to an anthropogenic pressure and a taxon's functional potential.

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Section: Discussionmentioning

confidence: 99%

Mapping marine benthic biological traits to facilitate future sustainable development

Bolam

Cooper

Downie

2023

Ecological Applications

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“…When modeling these features, MLC will pick up the local changes in slope and associated rugosity but has difficulty linking these features into the larger unit that captures the full complexity of the bedrock unit. This indicates that MLC may not be the preferred method of mapping bedrock units compared with methods such as machine learning–based modeling (Jackson-Bue et al , 2022). However, as long as each of these unit boundaries is well delineated, it is simple for the user of the model outputs to merge these polygons while finalizing their interpretations.…”

Section: Discussionmentioning

confidence: 99%

A Generalized Semiautomated Method for Seabed Geology Classification Using Multibeam Data and Maximum Likelihood Classification

Parkinson,

Douglas,

et al. 2023

Journal of Coastal Research

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“…To incorporate relevant BNAM output into the modeling, each tow was joined with the bottom temperature, stress, and salinity values, which corresponded spatiotemporally (i.e., to the month and year the tow was conducted). Bottom stress was of particular interest because it is believed to exert a mechanistic influence on benthic communities and has shown utility in mapping some benthic habitats (Jackson-Bué et al, 2022). Further, it can serve as a proxy for sediment grain size (Ward et al, 2015); this is a useful trait given that C. frondosa seems to prefer harder substrate types on the Scotian Shelf (Harper, 2020).…”

Section: Environmental Covariatesmentioning

confidence: 99%

Comparing spatiotemporal species distribution models: A case study of a Scotian Shelf sea cucumber (Cucumaria frondosa)

Hebert,

Sameoto,

Keith

et al. 2024

Ecosphere

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Numerous spatiotemporal species distribution modeling frameworks are now available to the ecological practitioner. This study compared three such frameworks accessible in the R programming language: generalized additive models with spatiotemporal smooths as implemented by mgcv, spatiotemporal generalized linear mixed models based on nearest neighbor Gaussian processes as implemented by starve, and spatiotemporal generalized linear mixed models based on the stochastic partial differential equations approach as implemented by sdmTMB. The primary focus was to compare the inferences obtained from applying these frameworks to the case study of the orange‐footed sea cucumber, Cucumaria frondosa, on the Scotian Shelf off Nova Scotia, Canada. Each model was fit to catch data (2000–2019) from Fisheries and Oceans Canada's annual Research Vessel and Snow Crab surveys. Environmental covariates were sourced from high‐resolution data layers, including physical oceanographic, bathymetric, and seafloor morphometric datasets. The three models captured variability in sea cucumber distribution that would have been overlooked without a spatiotemporal approach. Although their predictions were similar, including within C. frondosa spatial reserves, the models provided different inferences regarding covariate effects. This suggests that while practitioners primarily interested in mapping species distributions need only apply the most familiar framework, those most concerned with identifying predictive environmental covariates may benefit from comparing the output from multiple approaches. Employing multiple approaches can also serve as a validation technique.

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Seabed morphology and bed shear stress predict temperate reef habitats in a high energy marine region

Cited by 8 publications

References 74 publications

Mapping marine benthic biological traits to facilitate future sustainable development

Mapping marine benthic biological traits to facilitate future sustainable development

A Generalized Semiautomated Method for Seabed Geology Classification Using Multibeam Data and Maximum Likelihood Classification

Comparing spatiotemporal species distribution models: A case study of a Scotian Shelf sea cucumber (Cucumaria frondosa)

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