Spatial distributions, environmental drivers and co-existence patterns of key cold-water corals in the deep sea of the Azores (NE Atlantic)

Taranto, Gerald H.; González-Irusta, J.M.; Domínguez-Carrió, Carlos; Pham, Christopher K.; Tempera, Fernando; Ramos, Manuela; Gonçalves, Guilherme; Carreiro-Silva, Marina; Morato, Telmo

doi:10.1016/j.dsr.2023.104028

Cited by 10 publications

(3 citation statements)

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“…Other modelling studies have shown the importance of salinity for the distribution of both A. arbuscula (Buhl-Mortensen et al, 2015;Yesson et al, 2012) and E. rostrata Anderson, Guinotte, Rowden, Tracey, et al, 2016). Indeed, water physical-chemical boundaries (Davies & Guinotte, 2011;Dullo et al, 2008;Georgian et al, 2020) and, consequently, different water masses (Auscavitch et al, 2020;Buhl-Mortensen et al, 2015;Lapointe et al, 2020;Mosquera Giménez et al, 2019;Puerta et al, 2020Puerta et al, , 2022Quattrini et al, 2017;Taranto et al, 2023) are known to influence the distribution of CWCs. Interestingly, our results showed a disagreement between the values of mean bottom salinity for the maximum probability of presence of the two species, with the salinity peak for A. arbuscula around 34.85 psu and over 35.00 psu for E. rostrata (see Figure 5).…”

Section: Environmental Conditions For the Presence Of Vme Indicator T...mentioning

confidence: 99%

“…Species distribution models (SDMs) have been widely used to predict the distribution of deep-sea benthic communities (e.g. Beazley et al, 2018;Howell et al, 2022;Kenchington et al, 2016;Knudby et al, 2013;Rooper et al, 2016;Taranto et al, 2023;Tittensor et al, 2009). However, in deep-sea ecosystems, including CWC habitats on seamounts, species data are limited due to constraints in sampling accessibility and/or are biased due to the ad hoc nature of deep-sea surveys, for example, video surveys with remotely operated vehicles (ROVs).…”

Section: Introductionmentioning

confidence: 99%

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Ensemble modelling to predict the distribution of vulnerable marine ecosystems indicator taxa on data‐limited seamounts of Cabo Verde (NW Africa)

Vinha,

Murillo,

Schumacher

et al. 2024

Diversity and Distributions

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AimSeamounts are conspicuous geological features with an important ecological role and can be considered vulnerable marine ecosystems (VMEs). Since many deep‐sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold‐water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data are scarce.LocationSeamounts around the Cabo Verde archipelago (NW Africa).MethodsWe used species presence–absence data obtained from remotely operated vehicle (ROV) footage collected during two research expeditions. Terrain variables calculated using a multiscale approach from a 100‐m‐resolution bathymetry grid, as well as physical oceanographical data from the VIKING20X model, at a native resolution of 1/20°, were used as environmental predictors. Two modelling techniques (generalized additive model and random forest) were employed and single‐model predictions were combined into a final weighted‐average ensemble model. Model performance was validated using different metrics through cross‐validation.ResultsTerrain orientation, at broad scale, presented one of the highest relative variable contributions to the distribution models of all CWC taxa, suggesting that hydrodynamic–topographic interactions on the seamounts could benefit CWCs by maximizing food supply. However, changes at finer scales in terrain morphology and bottom salinity were important for driving differences in the distribution of specific CWCs. The ensemble model predicted the presence of VME taxa on all seamounts and consistently achieved the highest performance metrics, outperforming individual models. Nonetheless, model extrapolation and uncertainty, measured as the coefficient of variation, were high, particularly, in least surveyed areas across seamounts, highlighting the need to collect more data in future surveys.Main ConclusionsOur study shows how data‐poor areas may be assessed for the likelihood of VMEs and provides important information to guide future research in Cabo Verde, which is fundamental to advise ongoing conservation planning.

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Section: Environmental Conditions For the Presence Of Vme Indicator T...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble modelling to predict the distribution of vulnerable marine ecosystems indicator taxa on data‐limited seamounts of Cabo Verde (NW Africa)

Vinha,

Murillo,

Schumacher

et al. 2024

Diversity and Distributions

View full text Add to dashboard Cite

show abstract

“…Assessments and predictions of spatial distribution patterns have been conducted extensively for several deep-sea sponge ( 1;2 ) and deep-sea coral species ( 3;4;5;6;7;8;9 ). Although microbial ( 16S amplicon) data exist and the main driving factors have been identiﬁed for both deep-sea sponge ( 10 ; 11 ) and coral ( 12–14 ) microbiomes, those existing datasets represent only snapshots in space and time, and microbial information have not been incorporated into host distribution models.…”

Section: Introductionmentioning

confidence: 99%

Past, present, and future spatial distributions of deep-sea coral and sponge microbiomes revealed by predictive models

Busch,

Murillo,

Lirette

et al. 2024

Preprint

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Knowledge of the spatial distribution patterns of biodiversity in the ocean is key to evaluate and ensure ocean integrity and resilience. Especially for the deep ocean, wherein situmonitoring requires sophisticated instruments and considerable ﬁnancial investments, modelling approaches are crucial to move from scattered data points to predictive continuous maps. Those modelling approaches are commonly run on the macrobial level, but spatio-temporal predictions of host-associated microbiomes are not being targeted. This is especially problematic as previous research has highlighted that host-associated microbes (microbiomes) may display distribution patterns that are not perfectly correlated with host animal biogeographies, but also with other factors such as prevailing environmental conditions. We here establish a new simulation approach and present predicted spatio-temporal distribution patterns of deep-sea sponge and coral microbiomes, making use of a combination of environmental data, host data and microbiome data to advance our understanding of deep-sea microbiomes. This approach allows predictions of microbiome spatio-temporal distribution patterns on scales that are currently not covered by classical sampling approaches at sea. This includes both predictions in space within regional oceanic provinces off eastern North America, and also in time, with predictions into the past and future, covering a time span of 214 years. In summary, our presented predictions allow (i) identiﬁcation of microbial biodiversity hotspots in the past, present, and future, (ii) evaluation of microbial-macrobial connections at case-study sites through trait-based predictions, and (iii) identiﬁcation of shifts in microbial community composition (key taxa) across environmental gradients and shifting environmental conditions.

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High‐Resolution Ensemble Modelling of Coral Distributions in the Northern Gulf of Mexico Based on Geomorphometry: Coral Diversity and Benthic Habitat Fragmentation From Oil and Gas Infrastructure to Inform Spatial Planning

Lecours,

Oxton,

Khor

et al. 2024

Aquatic Conservation

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The northern Gulf of Mexico is home to several species of corals that provide a wide range of ecosystem services to other organisms. Oil and gas infrastructure, such as platforms and pipelines, form an extensive network throughout the northern Gulf of Mexico. Detrimental impacts associated with oil and gas exploration and extraction have been recorded in this area at depths where corals are found. Due to these ecosystems' vulnerability to long‐term impacts, it is necessary to determine areas of interest that would benefit from further exploration and informed spatial planning. This study aimed to identify potential areas of interest for coral studies in the northern Gulf of Mexico. Ensemble species distribution models for 13 species of corals including scleractinians, black corals, and octocorals were produced based on seafloor characteristics and combined to identify areas with relatively higher coral diversity potential than others. The ensemble modelling approach produced robust outputs, as evaluated by the area under the curve, Cohen's kappa coefficient, sensitivity, specificity and the proportion of correct predictions. The proximity of suitable habitat to active and proposed oil and gas infrastructure was evaluated; this spatial analysis showed that oil and gas infrastructures potentially impact 23.5% of all predicted suitable coral habitat in the study area and contribute to benthic habitat fragmentation. Twelve areas of interest greater than 100 km2 and located outside a 4‐km zone of potential influence from oil and gas infrastructure were delineated and deemed of interest for further exploration and spatial planning, and hypothetical prioritization scenarios for spatial planning are presented. The maps produced can inform discussions among stakeholders to reach the best spatial planning outcomes while considering other ecological, social, economic and governance factors.

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Spatial distributions, environmental drivers and co-existence patterns of key cold-water corals in the deep sea of the Azores (NE Atlantic)

Cited by 10 publications

References 123 publications

Ensemble modelling to predict the distribution of vulnerable marine ecosystems indicator taxa on data‐limited seamounts of Cabo Verde (NW Africa)

Ensemble modelling to predict the distribution of vulnerable marine ecosystems indicator taxa on data‐limited seamounts of Cabo Verde (NW Africa)

Past, present, and future spatial distributions of deep-sea coral and sponge microbiomes revealed by predictive models

High‐Resolution Ensemble Modelling of Coral Distributions in the Northern Gulf of Mexico Based on Geomorphometry: Coral Diversity and Benthic Habitat Fragmentation From Oil and Gas Infrastructure to Inform Spatial Planning

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