Baseline biogeochemical data from Australia's continental margin links seabed sediments to water column characteristics

Radke, Lynda; Nicholas, Tony; Thompson, Peter A.; Li, Jin; Raes, Eric J.; Carey, Matthew; Atkinson, Ian; Huang, Zhi; Trafford, Janice M.; Nichol, Scott L.

doi:10.1071/mf16219

Cited by 14 publications

(13 citation statements)

References 113 publications

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“…Many of our samples off SE Australia were collected from directly under this phytoplankton bloom (Figure 1). Conversely, the GAB samples were located to the north of this band and our samples from the NE were from relatively oligotrophic subtropical waters (Radke et al, 2017). It must be emphasized that our carbon flux data are modelled from surface chlorophyll data and depth Previous studies have shown that the bathymetric diversity gradient (BDG) for seafloor fauna is generally unimodal, with a diversity peak in the mid-bathyal (~2,000 m) in the North Atlantic…”

Section: F I G U R E 3 Key Variable Responses Using Final Rad Modelsmentioning

confidence: 95%

Regional‐scale patterns of deep seafloor biodiversity for conservation assessment

O'Hara

Williams

Althaus

et al. 2020

Diversity and Distributions

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Aim Mining and petroleum industries are exploring for resources in deep seafloor environments. Lease areas are often spatially aggregated and continuous over hundreds to thousands of kilometres. Sustainable development of these resources requires an understanding of the patterns of biodiversity at similar scales, yet these data are rarely available for the deep sea. Here, we compare biodiversity metrics and assemblage composition of epibenthic megafaunal samples from deep‐sea benthic habitats from the Great Australian Bight (GAB), a petroleum exploration zone off southern Australia, to similar environments off eastern Australia. Location The Great Australian Bight (34–36°S, 129–134°E) and south‐eastern (SE) and north‐eastern (NE) Australian continental margins (23–42°S, 149–155°E) in depths of 1,900–5,000 m. Methods A species–sample matrix was constructed from invertebrate and fish megafauna collected from beam trawl samples across regions at lower bathyal (1,900–3,200 m) and abyssal (>3,200 m) depths, and analysed using multivariate, rarefaction and model‐based statistics. We modelled rank abundance distributions (RAD) against environmental factors to identify drivers of abundance, richness and evenness. Results Multivariate analyses showed regional and bathymetric assemblage structure across the region. There was an almost complete turnover of sponge fauna between the GAB and SE. SE samples had the highest total faunal abundance and species richness. RAD models linked total abundance and species richness to levels of carbon flux. Evenness was associated with seasonality of net primary production. Conclusions Significant assemblage structure at regional scales is reported for the first time at lower bathyal and abyssal depths in the southern Indo‐Pacific region along latitudinal and longitudinal gradients. The GAB fauna was distinct from other studied areas. Relatively high species richness, previously reported from the GAB continental shelf, did not occur at lower bathyal or abyssal depths. Instead, the abundance, richness and evenness of the benthic fauna are linked to surface primary production, which is elevated off SE Australia.

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Section: F I G U R E 3 Key Variable Responses Using Final Rad Modelsmentioning

confidence: 95%

Regional‐scale patterns of deep seafloor biodiversity for conservation assessment

O'Hara

Williams

Althaus

et al. 2020

Diversity and Distributions

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“…The non-random sampling design can be ad hoc sampling based on expert knowledge, purely opportunistic when a certain type of environmental condition becomes available, or systematic sampling. This type of sampling design was applied to many surveys [27][28][29]. For spatial predictive modeling, this method is not recommended for future studies.…”

Section: Sampling Designmentioning

confidence: 99%

A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R

2019

Applied Sciences

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Spatial predictive methods are increasingly being used to generate predictions across various disciplines in environmental sciences. Accuracy of the predictions is critical as they form the basis for environmental management and conservation. Therefore, improving the accuracy by selecting an appropriate method and then developing the most accurate predictive model(s) is essential. However, it is challenging to select an appropriate method and find the most accurate predictive model for a given dataset due to many aspects and multiple factors involved in the modeling process. Many previous studies considered only a portion of these aspects and factors, often leading to sub-optimal or even misleading predictive models. This study evaluates a spatial predictive modeling process, and identifies nine major components for spatial predictive modeling. Each of these nine components is then reviewed, and guidelines for selecting and applying relevant components and developing accurate predictive models are provided. Finally, reproducible examples using spm, an R package, are provided to demonstrate how to select and develop predictive models using machine learning, geostatistics, and their hybrid methods according to predictive accuracy for spatial predictive modeling; reproducible examples are also provided to generate and visualize spatial predictions in environmental sciences.

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“…In survey areas A-D, sediment sampling sites were selected to represent all geomorphic seabed features across the range of mapped water depths. In the survey areas E-H, sampling sites were selected using a spatially-balanced random stratified method [15][16][17] and further detailed in subsequent studies [6,29,43,44]. In total, 195 seabed sediment samples were collected and processed in the laboratory using wet sieve separation to determine percentage gravel, sand, and mud content.…”

Section: Study Region and Sediment Samplesmentioning

confidence: 99%

Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

Siwabessy

Huang

et al. 2019

Geosciences

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Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry-related variables due to the lack of backscatter-derived data. In this study, we applied random forests (RFs), hybrid methods of RF and geostatistics, and generalized boosted regression modelling (GBM), to seabed sand content point data and acoustic multibeam data and their derived variables, to develop an accurate model to predict seabed sand content at a local scale. We also addressed relevant issues with variable selection. It was found that: (1) backscatter-related variables are more important than bathymetry-related variables for sand predictive modelling; (2) the inclusion of highly correlated predictors can improve predictive accuracy; (3) the rank orders of averaged variable importance (AVI) and accuracy contribution change with input predictors for RF and are not necessarily matched; (4) a knowledge-informed AVI method (KIAVI2) is recommended for RF; (5) the hybrid methods and their averaging can significantly improve predictive accuracy and are recommended; (6) relationships between sand and predictors are non-linear; and (7) variable selection methods for GBM need further study. Accuracy-improved predictions of sand content are generated at high resolution, which provide important baseline information for environmental management and conservation.

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Baseline biogeochemical data from Australia's continental margin links seabed sediments to water column characteristics

Cited by 14 publications

References 113 publications

Regional‐scale patterns of deep seafloor biodiversity for conservation assessment

Regional‐scale patterns of deep seafloor biodiversity for conservation assessment

A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R

Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

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