Improving the predictive capability of benthic species distribution models by incorporating oceanographic data – Towards holistic ecological modelling of a submarine canyon

Pearman, Tabitha R. R.; Robert, Katleen; Callaway, Alexander; Hall, Rob; Iacono, Claudio Lo; Huvenne, Veerle

doi:10.1016/j.pocean.2020.102338

Cited by 58 publications

(69 citation statements)

References 83 publications

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“…All terrain and geological variables were less important. This aligns well with the findings of other studies (e.g., Beazley et al, 2015;Pearman et al, 2020) that oceanographic variables are more important than terrain variables for defining benthic species distributions. This points to a more mechanistic relationship existing between oceanographic conditions and benthic species composition (described in Young et al, 1996 for the case of sponges), which is emerging thanks to the fact that oceanographic models are becoming more accessible to researchers engaged in benthic SDM, while previous studies utilized only topographic variables and were relying upon the terrain characteristics as proxies for the oceanographic parameters (Wilson et al, 2006).…”

Section: Discussionsupporting

confidence: 91%

Modeling the Distribution of Habitat-Forming, Deep-Sea Sponges in the Barents Sea: The Value of Data

et al. 2021

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The use of species occurrence as a proxy for habitat type is widespread, probably because it allows the use of species distribution modeling (SDM) to cost-effectively map the distribution of e.g., vulnerable marine ecosystems. We have modeled the distribution of epibenthic megafaunal taxa typical of soft-bottom, Deep-Sea Sponge Aggregations (DSSAs), i.e., “indicators,” to discover where in the Barents Sea region this habitat is likely to occur. The following taxa were collectively modeled: Hexadella cf. dedritifera, Geodia spp., Steletta sp., Stryphnus sp. The data were extracted from MarVid, the video database for the Marine AREAl database for NOrwegian waters (MAREANO). We ask whether modeling density data may be more beneficial than presence/absence data, and whether using this list of indicator species is enough to locate the target habitat. We use conditional inference forests to make predictions of probability of presence of any of the target sponges, and total density of all target sponges, for an area covering a large portion of the Norwegian Barents Sea and well beyond the data’s spatial range. The density models explain <31% of the variance, and the probability models have high classificatory power (AUC > 0.88), depending on the variables/samples used to train the model. The predicted surfaces were then classified on the basis of a probability threshold (0.75) and a density threshold (13 n/100 m2) to obtain polygons of “core area” and “hotspots” respectively (zones). The DSSA core area comprises two main regions: the Egga shelf break/Tromsøflaket area, and the shelf break southwest of Røst bank in the Træna trench. Four hotspots are detected within this core area. Zones are evaluated in the light of whole-community data which have been summarized as taxon richness and density of all megafauna. Total megafaunal density was significantly higher inside the hotspots relative to the background. Richness was not different between zones. Hotspots appeared different to one another in their richness and species composition although no tests were possible. We make the case that the effectiveness of the indicator species approach for conservation planning rests on the availability of density data on the target species, and data on co-occurring species.

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Section: Discussionsupporting

confidence: 91%

Modeling the Distribution of Habitat-Forming, Deep-Sea Sponges in the Barents Sea: The Value of Data

et al. 2021

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“…Clear examples evidencing this association can be found in Rebesco and Taviani 41 . This is supported by species distribution modelling studies that reveal in further detail that CWC distribution is influenced by current velocities 33 , 40 , 42 – 44 .…”

Section: Introductionmentioning

confidence: 53%

“…Thus, predictive habitat suitability modelling is required and have previously been applied to cold-water coral habitats using terrain and environmental data in the north east Atlantic 83 , south Pacific oceans 84 and on a global scale 85 . Pearman et al 42 and Bargain et al 43 show that inclusion of hydrographic variables such as current speed in predictive mapping clearly improves model performance. Our dataset endorses the inclusion of high-resolution hydrodynamic variables to improve predictive model performance, as clear linkages between flow strength and reef surface coverage (live, dead coral and coral rubble) was observed.…”

Section: Resultsmentioning

confidence: 99%

Influence of benthic currents on cold-water coral habitats: a combined benthic monitoring and 3D photogrammetric investigation

Wheeler

Price

et al. 2020

Sci Rep

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Strong currents are a key component of benthic habitats by supplying food and nutrients to filter-feeding organisms such as cold-water corals. Although field measurements show that cold-water coral habitats exist in areas of elevated bottom currents, flume studies show that cold-water corals feed more effectively at lower flow speeds. This research aims to explore this disconnect in situ by utilising high spatial resolution ROV photogrammetric data coupled with high temporal resolution in situ acoustic doppler current profile measurements at seven study sites within the upper Porcupine Bank Canyon (uPBC), NE Atlantic. Object-based image analysis of photogrammetric data show that coral habitats vary considerably within the upper canyon. Although there is a regional hydrodynamic trend across the uPBC, this variation is likely driven locally by topographic steering. Although live coral tends not to face directly into the prevailing current direction, preferring lower local flows speeds, they can tolerate exposure to high-flow speeds of up to 114 cm s−1, the highest recorded in a Desmophyllum pertusum habitat. Not only do these high flow speeds supply food and nutrients, they may also help contribute to coral rubble production through physical erosion. These results can be incorporated into simulations of future deep-water habitat response to changing environmental conditions while extending the upper current speed threshold for cold-water corals.

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“…Several recent studies have highlighted the importance of including variables from oceanographic models in benthic habitat distribution models (HDMs) (e.g., [1,2]). Although such variables are generally recognised as important and have been included in some earlier HDMs (e.g., [3][4][5][6]), oceanographic data are all too often excluded from published HDM studies due to lack of availability at relevant resolutions and/or extent (e.g., [7]).…”

Section: Introductionmentioning

confidence: 99%

Using Spatial Validity and Uncertainty Metrics to Determine the Relative Suitability of Alternative Suites of Oceanographic Data for Seabed Biotope Prediction. A Case Study from the Barents Sea, Norway

et al. 2021

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The use of habitat distribution models (HDMs) has become common in benthic habitat mapping for combining limited seabed observations with full-coverage environmental data to produce classified maps showing predicted habitat distribution for an entire study area. However, relatively few HDMs include oceanographic predictors, or present spatial validity or uncertainty analyses to support the classified predictions. Without reference studies it can be challenging to assess which type of oceanographic model data should be used, or developed, for this purpose. In this study, we compare biotope maps built using predictor variable suites from three different oceanographic models with differing levels of detail on near-bottom conditions. These results are compared with a baseline model without oceanographic predictors. We use associated spatial validity and uncertainty analyses to assess which oceanographic data may be best suited to biotope mapping. Our results show how spatial validity and uncertainty metrics capture differences between HDM outputs which are otherwise not apparent from standard non-spatial accuracy assessments or the classified maps themselves. We conclude that biotope HDMs incorporating high-resolution, preferably bottom-optimised, oceanography data can best minimise spatial uncertainty and maximise spatial validity. Furthermore, our results suggest that incorporating coarser oceanographic data may lead to more uncertainty than omitting such data.

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Improving the predictive capability of benthic species distribution models by incorporating oceanographic data – Towards holistic ecological modelling of a submarine canyon

Cited by 58 publications

References 83 publications

Modeling the Distribution of Habitat-Forming, Deep-Sea Sponges in the Barents Sea: The Value of Data

Modeling the Distribution of Habitat-Forming, Deep-Sea Sponges in the Barents Sea: The Value of Data

Influence of benthic currents on cold-water coral habitats: a combined benthic monitoring and 3D photogrammetric investigation

Using Spatial Validity and Uncertainty Metrics to Determine the Relative Suitability of Alternative Suites of Oceanographic Data for Seabed Biotope Prediction. A Case Study from the Barents Sea, Norway

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