Predicting Species Diversity of Benthic Communities within Turbid Nearshore Using Full-Waveform Bathymetric LiDAR and Machine Learners.

Browman, Howard; Collin, Antoine; Archambault, Phillippe et Long, Bernard (2011). Predicting Species Diversity of Benthic Communities within Turbid Nearshore Using Full-Waveform Bathymetric LiDAR and Machine Learners. PLoS ONE , vol. 6 , nº 6. e21265. DOI: 10.1371/journal.pone.0021265.

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Résumé

Epi-macrobenthic species richness, abundance and composition are linked with type, assemblage and structural complexity of seabed habitat within coastal ecosystems. However, the evaluation of these habitats is highly hindered by limitations related to both waterborne surveys (slow acquisition, shallow water and low reactivity) and water clarity (turbid for most coastal areas). Substratum type/diversity and bathymetric features were elucidated using a supervised method applied to airborne bathymetric LiDAR waveforms over Saint-Siméon–Bonaventure's nearshore area (Gulf of Saint-Lawrence, Québec, Canada). High-resolution underwater photographs were taken at three hundred stations across an 8-km² study area. Seven models based upon state-of-the-art machine learning techniques such as Naïve Bayes, Regression Tree, Classification Tree, C 4.5, Random Forest, Support Vector Machine, and CN2 learners were tested for predicting eight epi-macrobenthic species diversity metrics as a function of the class number. The Random Forest outperformed other models with a three-discretized Simpson index applied to epi-macrobenthic communities, explaining 69% (Classification Accuracy) of its variability by mean bathymetry, time range and skewness derived from the LiDAR waveform. Corroborating marine ecological theory, areas with low Simpson epi-macrobenthic diversity responded to low water depths, high skewness and time range, whereas higher Simpson diversity relied upon deeper bottoms (correlated with stronger hydrodynamics) and low skewness and time range. The degree of species heterogeneity was therefore positively linked with the degree of the structural complexity of the benthic cover. This work underpins that fully exploited bathymetric LiDAR (not only bathymetrically derived by-products), coupled with proficient machine learner, is able to rapidly predict habitat characteristics at a spatial resolution relevant to epi-macrobenthos diversity, ranging from clear to turbid waters. This method might serve both to nurture marine ecological theory and to manage areas with high species heterogeneity where navigation is hazardous and water clarity opaque to passive optical sensors.

Type de document:	Article
Mots-clés libres:	accuracy; Canada; classification; coastal waters; hydrodynamics; machine learning; macrobenthos; marine biology; nonhuman; population abundance; species diversity; species richness; theory; animal; biodiversity; geography; invertebrate; marine biology; methodology
Centre:	Centre Eau Terre Environnement
Date de dépôt:	29 nov. 2019 14:40
Dernière modification:	29 nov. 2019 14:40
URI:	https://espace.inrs.ca/id/eprint/9519

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