Modelling distribution of marine benthos from hydroacoustics and underwater video

Holmes, Karen; Niel, Kimberly P. Van; Radford, Ben; Kendrick, Gary A.; Grove, Simon L.

doi:10.1016/j.csr.2008.04.016

Cited by 112 publications

(57 citation statements)

References 44 publications

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“…Rugosity acts as a measure of terrain 'roughness', or structural complexity, and also serves as a surrogate for substrate type (Rinehart et al 2004;Dunn & Halpin 2009). Profile curvature measures convexity/concavity of the terrain perpendicular to the direction of the maximum slope, whilst plan curvature is the convexity/concavity of the terrain parallel to the direction of the maximum slope and combined curvature regroups the two dimensions in the same index (Holmes et al 2008). Along with slope, curvature also serves as a proxy for local-scale hydrology.…”

Section: Predictor Variablesmentioning

confidence: 99%

The impact of modelling method selection on predicted extent and distribution of deep-sea benthic assemblages

Piechaud

Downie

Stewart³

et al. 2014

Earth and Environmental Science Transactions of the Royal Socie

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Predictive modelling of deep-sea species and assemblages with multibeam acoustic datasets as input variables is now a key tool in the provision of maps upon which spatial planning and management of the marine environment can be based. However, with a multitude of methods available, advice is needed on the best methods for the task at hand. In this study, we predictively modelled the distribution and extent of three vulnerable marine ecosystems (VMEs) at the assemblage level ('Lophelia pertusa reef frameworks'; 'Stylasterids and lobose sponges'; and 'Xenophyophore fields') on the eastern flank of Rockall Bank, using three modelling methods: MaxEnt; RandomForests classification with multiple assemblages (gRF); and RandomForests classification with the presence/absence of a single VME (saRF). Performance metrics indicated that MaxEnt performed the best, but all models were considered valid. All three methods broadly agreed with regard to broad patterns in distribution. However, predicted extent presented a variation of up to 35 % between the different methods, and clear differences in predicted distribution were observed. We conclude that the choice of method is likely to influence the results of predicted maps, potentially impacting political decisions about deep-sea VME conservation.

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Section: Predictor Variablesmentioning

confidence: 99%

The impact of modelling method selection on predicted extent and distribution of deep-sea benthic assemblages

Piechaud

Downie

Stewart³

et al. 2014

Earth and Environmental Science Transactions of the Royal Socie

View full text Add to dashboard Cite

show abstract

“…Modelling the spatial distribution of megabenthos on the basis of empirical relationships between its biological composition and coinciding habitat properties as derived from hydroacoustic remote sensing techniques has been pioneered by Hewitt et al (2004), Holmes et al (2008), Buhl-Mortensen, Dolan & Buhl-Mortensen (2009) and Monk et al (2011). The current trend includes variations on a framework that involves at a minimum: (1) biological (response) data compiled by means of underwater video footage analysis and (2) geophysical (predictor) data collected through echosounder (multibeam or single-beam) or sidescan sonar, from which various proxies can be derived (Buhl-Mortensen, Buhl-Mortensen, Dolan, Dannheim & Kröger, 2009;Holmes et al, 2008;Ierodiaconou et al, 2007;Rattray et al, 2009).…”

Section: Biotope Mapping As Distribution Modelling Of Communitiesmentioning

confidence: 99%

“…The current trend includes variations on a framework that involves at a minimum: (1) biological (response) data compiled by means of underwater video footage analysis and (2) geophysical (predictor) data collected through echosounder (multibeam or single-beam) or sidescan sonar, from which various proxies can be derived (Buhl-Mortensen, Buhl-Mortensen, Dolan, Dannheim & Kröger, 2009;Holmes et al, 2008;Ierodiaconou et al, 2007;Rattray et al, 2009). Further, Ierodiaconou et al (2007) were the first to use backscatter (an acoustic property of the seabed obtained as a by-product of multibeam data) in combination with bathymetry to predict dominant biotic categories and, as shown in the recent GeoHab (Marine Geological and Biological Habitat Mapping) 2011 Conference, this is becoming established as a branch in its own right of predictive mapping of the benthos.…”

Section: Biotope Mapping As Distribution Modelling Of Communitiesmentioning

confidence: 99%

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Seabed Mapping and Marine Spatial Planning: A Case Study from a Swedish Marine Protected Area

Gonzalez-Mirelis¹,

Lundälv²,

Jonsson³

et al. 2012

Marine Ecosystems

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“…Conse-quently, this approach is rapidly gaining acceptance in remote areas, including in seabed environments (e.g. Holmes et al 2008, Buhl-Mortensen et al 2009a, where the cost of direct sampling is often prohibitive and the need for completed biotic maps continues. Clearly, a bottleneck in this process is the development of a comprehensive classification system of regional biodiversity sufficiently unambiguous to be readily incorporated into a model as the target pattern.…”

Section: Introductionmentioning

confidence: 99%

Interaction between classification detail and prediction of community types: implications for predictive modelling of benthic biotopes

Gonzalez-Mirelis¹,

Bergström²,

Lindegarth³

2011

Mar. Ecol. Prog. Ser.

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Ecological classifications lie, explicitly or implicitly, at the foundation of biotic maps, which are in turn an essential tool in ecology and conservation. Techniques are being developed that make efficient use of scarce biological data to create those maps, such as those based on modelling, or predictive mapping. However, little attention has been paid to just how sensitive models are to the pattern they are designed to model. We have examined the predictive accuracy of classification models (Random Forests) relative to the classification detail of community types. The data are from a recently established Marine National Park off the west coast of Sweden where a map of benthic communities has been commissioned. A total of 447 georeferenced, underwater video sequences constituted the sample base for this study. Samples were classified according to increasing values of a similarity threshold, based on faunal composition, in a hierarchical cluster analysis framework. A random forest was fitted at each level of classification detail to predict the class membership of an independent set of samples, based on environmental (terrain and substrate-related) variables. Predictive accuracy was high (multiclass area under the curve, M-AUC = 0.79 to 0.81) across intermediate levels of classification detail (similarity cut off, 40 to 50%). Predictive accuracy was moderate (M-AUC = 0.73 to 0.78) at both ends of the spectrum of classificatory resolutions tested (cut offs: 30%, i.e. very coarse; 60%, very fine). These findings have ramifications in the 'classify first, then predict' approach to predictive mapping at the community level, as they show that calibrating classes before modelling them greatly enhances model reliability.

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Modelling distribution of marine benthos from hydroacoustics and underwater video

Cited by 112 publications

References 44 publications

The impact of modelling method selection on predicted extent and distribution of deep-sea benthic assemblages

The impact of modelling method selection on predicted extent and distribution of deep-sea benthic assemblages

Seabed Mapping and Marine Spatial Planning: A Case Study from a Swedish Marine Protected Area

Interaction between classification detail and prediction of community types: implications for predictive modelling of benthic biotopes

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