2021
DOI: 10.1371/journal.pone.0250427
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Mesophotic fish communities of the ancient coastline in Western Australia

Abstract: Marine diversity across the Australian continental shelf is shaped by characteristic benthic habitats which are determined by geomorphic features such as paleoshorelines. In north-western Australia there has been little attention on the fish communities that inhabit an ancient coastline at ~125 m depth (the designated AC125), which is specified as a key ecological feature (KEF) of the region and is thought to comprise hard substrate and support enhanced diversity. We investigated drivers of fish species richne… Show more

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Cited by 7 publications
(12 citation statements)
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“…Proximity to these features was the primary split in the MRT analysis, followed by depth, with strong alignment in patterns observed by the MRT with DISTLM analyses. Depth is often among the most common predictors of fish distributions (Moore et al, 2009;Monk et al, 2010;Galaiduk et al, 2017b), assemblage composition (Cappo et al, 2007;Moore et al, 2010;Harvey et al, 2013) and fish species richness (Young & Carr, 2015;McLean et al, 2021;Currey-Randall et al, 2021). Proximity to infrastructure, such as platform jackets and/or pipelines, featured in both statistical analyses but were ranked low as predictors each explaining <2% of variation in the fish abundance data in both instances.…”
Section: Discussionmentioning
confidence: 99%
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“…Proximity to these features was the primary split in the MRT analysis, followed by depth, with strong alignment in patterns observed by the MRT with DISTLM analyses. Depth is often among the most common predictors of fish distributions (Moore et al, 2009;Monk et al, 2010;Galaiduk et al, 2017b), assemblage composition (Cappo et al, 2007;Moore et al, 2010;Harvey et al, 2013) and fish species richness (Young & Carr, 2015;McLean et al, 2021;Currey-Randall et al, 2021). Proximity to infrastructure, such as platform jackets and/or pipelines, featured in both statistical analyses but were ranked low as predictors each explaining <2% of variation in the fish abundance data in both instances.…”
Section: Discussionmentioning
confidence: 99%
“…These techniques quantify the relationships among depth values in small neighbourhoods to reveal textural differences. Calculations are run on a small number of neighbourhood cells surrounding each pixel and the value assigned to the central cell in the output, thus creating a derivative dataset (see Cure et al, 2021;Currey-Randall et al, 2021;McLean et al, 2021) for additional information on secondary rasters). This produced a set of 23 secondary rasters that describe the structure and complexity of the seafloor (Supplementary Data Table S1).…”
Section: Depth and Geomorphology Gradientsmentioning
confidence: 99%
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“…This variable was a strong predictor of abundance of target species (including lutjanids and serranids) with numbers declining as depth increased. Depth has also been shown to be a strong predictor of fish communities in natural ecosystems of this region (Abdul Wahab et al, 2018;Currey-Randall et al, 2021). In this case, shorter wells were found in the shallowest depths, which may have confounded the relationship between surface area and abundance.…”
Section: Lutjanus Argentimaculatusmentioning
confidence: 90%
“…), yet assemblages were similarly dominated by lutjanids, serranids, and carangids (total 46% species in these families). These families are known to be ubiquitous in natural reef ecosystems across the NW (McLean et al, 2016;Abdul Wahab et al, 2018;Currey-Randall et al, 2021) and, excluding tropical snappers, also in the SE marine region (Williams and Bax, 2001;Butler et al, 2002). As richness and abundance counts are intrinsically linked to sampling effort, and with disproportionate sampling effort across pipelines (Table S2), it is not possible to compare these measures across pipelines.…”
Section: Fishery Target Species On Subsea Pipelines In Australiamentioning
confidence: 99%