Predictive mapping of soft-bottom benthic biodiversity using a surrogacy approach

Huang, Zhi; McArthur, Matthew A.; Przeslawski, Rachel; Siwabessy, Justy; Nichol, Scott L.; Brooke, Brendan

doi:10.1071/mf13104

Cited by 17 publications

(12 citation statements)

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“…Tidal range is microtidal (~0.6–1.8 m) and tidal currents weak, but regional oceanographic currents (Leeuwin Current and Ningaloo Current) are strong enough to initiate sand transport. Seabed sediments were sampled across three survey areas in water depths of 30 to 250 m (Huang, McArthur, et al, ). In the northern survey area, the sediment ranges from gravelly sand on the inner shelf to sand and muddy sand on the middle and outer shelf.…”

Section: Study Areasmentioning

confidence: 99%

Using Multibeam Backscatter Data to Investigate Sediment‐Acoustic Relationships

et al. 2018

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Sediment properties are known to influence acoustic backscatter intensity. This sediment-acoustic relationship has been investigated previously through using physical geoacoustic models and empirical methods and found to be complex and nonlinear. Here we employ a robust machine-learning statistical model (random forest decision tree) to investigate the most likely nonlinear sediment-backscatter relationships. The analysis uses colocated sediment and acoustic backscatter data (collected from a 300-kHz multibeam sonar system) for 564 locations in four different areas on the Australian margin. Seven sediment grain size properties (%gravel, %sand, %mud, mean grain size, sorting, skewness, and kurtosis) were used to predict the acoustic backscatter responses at individual incidence angles. The modeling results demonstrate the effectiveness of this multivariate predictive approach for the investigation of sediment-acoustic relationship. Thus, we find that for incidence angles between 1°and 41°, the sediment variables explain around 70% of variance in the backscatter intensity. Sediment mud content was found to be the most important sediment variable in the model and has a significant negative relationship with backscatter intensity. Mean grain size was the second ranked sediment variable and found to have a positive relationship with backscatter intensity. The results also show that sediment mud content plays a key role in sorting-backscatter and sand-backscatter relationships. Using only two sediment properties, mud content and mean grain size, together it was possible to largely explain the sediment-acoustic relationship. The strongest backscatter return occurred with medium sediment mud content and large mean grain sizes (or muddy coarse sand).

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Section: Study Areasmentioning

confidence: 99%

Using Multibeam Backscatter Data to Investigate Sediment‐Acoustic Relationships

et al. 2018

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“…, Watling and Skinder , Huang et al. , Rees et al. , Gonzalez‐Mirelis and Buhl‐Mortensen ) and then to scale up robust ecological patterns using fitted model predictions and continuous layers of abiotic condition afterward (Brown et al.…”

Section: Introductionmentioning

confidence: 99%

“…Some have used ecological observations to improve abiotic classifications through ground-truthing (Greene et al 2010, Kerrigan et al 2010, Monteiro et al 2015. However, an increasingly preferred method is to examine abiotic-assemblage relationships first (e.g., Auster et al 2002, Watling and Skinder 2007, Huang et al 2014, Rees et al 2014, Gonzalez-Mirelis and Buhl-Mortensen 2015 and then to scale up robust ecological patterns using fitted model predictions and continuous layers of abiotic condition afterward (Brown et al 2011, Sutcliffe et al 2015. This method ensures that underlying complexities are preserved and that there is no loss of information through arbitrary classification.…”

Section: Introductionmentioning

confidence: 99%

Abiotic proxies for predictive mapping of nearshore benthic assemblages: implications for marine spatial planning

McHenry

Steneck

Brady

2017

Ecological Applications

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Marine spatial planning (MSP) should assist managers in guiding human activities toward sustainable practices and in minimizing user conflicts in our oceans. A necessary first step is to quantify spatial patterns of marine assemblages in order to understand the ecosystem's structure, function, and services. However, the large spatial scale, high economic value, and density of human activities in nearshore habitats often makes quantifying this component of marine ecosystems especially daunting. To address this challenge, we developed an assessment method that employs abiotic proxies to rapidly characterize marine assemblages in nearshore benthic environments with relatively high resolution. We evaluated this assessment method along 300 km of the State of Maine's coastal shelf (<100 m depth), a zone where high densities of buoyed lobster traps typically preclude extensive surveys by towed sampling gear (i.e., otter trawls). During the summer months of 2010-2013, we implemented a stratified-random survey using a small remotely operated vehicle that allowed us to work around lobster buoys and to quantify all benthic megafauna to species. Stratifying by substrate, depth, and coastal water masses, we found that abiotic variables explained a significant portion of variance (37-59%) in benthic species composition, diversity, biomass, and economic value. Generally, the density, diversity, and biomass of assemblages significantly increased with the substrate complexity (i.e., from sand-mud to ledge). The diversity, biomass, and economic value of assemblages also decreased significantly with increasing depth. Last, demersal fish densities, sessile invertebrate densities, species diversity, and assemblage biomass increased from east to west, while the abundance of mobile invertebrates and economic value decreased, corresponding mainly to the contrasting water mass characteristics of the Maine Coastal Current system (i.e., summertime current direction, speed, and temperature). Integrating modeled predictions with existing GIS layers for abiotic conditions allowed us to scale up important assemblage attributes to define key foundational ecological principles of MSP and to find priority regions where some bottom-disturbing activities would have minimal impact to benthic assemblages. We conclude that abiotic proxies can be strong forcing functions for the assembly of marine communities and therefore useful tools for spatial extrapolations of marine assemblages in congested (heavily used) nearshore habitats.

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“…One of the few examples are maps by Huang et al. (), who mapped infaunal benthic species of the Carnarvon shelf of western Australia using random forest decision tree model.…”

Section: Introductionmentioning

confidence: 99%

“…So far, very few abundance maps of marine benthic invertebrate have been published. One of the few examples are maps by Huang et al (2014), who mapped infaunal benthic species of the Carnarvon shelf of western Australia using random forest decision tree model.…”

Section: Introductionmentioning

confidence: 99%

Mapping species abundance by a spatial zero‐inflatedPoisson model: a case study in theWaddenSea, theNetherlands

Lyashevska

Brus

Meer

2016

Ecology and Evolution

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The objective of the study was to provide a general procedure for mapping species abundance when data are zero‐inflated and spatially correlated counts. The bivalve species Macoma balthica was observed on a 500×500 m grid in the Dutch part of the Wadden Sea. In total, 66% of the 3451 counts were zeros. A zero‐inflated Poisson mixture model was used to relate counts to environmental covariates. Two models were considered, one with relatively fewer covariates (model “small”) than the other (model “large”). The models contained two processes: a Bernoulli (species prevalence) and a Poisson (species intensity, when the Bernoulli process predicts presence). The model was used to make predictions for sites where only environmental data are available. Predicted prevalences and intensities show that the model “small” predicts lower mean prevalence and higher mean intensity, than the model “large”. Yet, the product of prevalence and intensity, which might be called the unconditional intensity, is very similar. Cross‐validation showed that the model “small” performed slightly better, but the difference was small. The proposed methodology might be generally applicable, but is computer intensive.

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Predictive mapping of soft-bottom benthic biodiversity using a surrogacy approach

Cited by 17 publications

References 50 publications

Using Multibeam Backscatter Data to Investigate Sediment‐Acoustic Relationships

Using Multibeam Backscatter Data to Investigate Sediment‐Acoustic Relationships

Abiotic proxies for predictive mapping of nearshore benthic assemblages: implications for marine spatial planning

Mapping species abundance by a spatial zero‐inflatedPoisson model: a case study in theWaddenSea, theNetherlands

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