Purpose
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We developed a new, spatially extensive, fully quantitative, and taxonomically consistent dataset of benthic invertebrate
occurrence by merging data from five seabed photographic surveys on Chatham Rise (Bowden et al. 2019) and two on Campbell
Plateau (Bowden et al. in press). We then used this dataset to inform development of improved predictive species-distribution
models (SDM) for the entire south-eastern sector of New Zealand’s EEZ in depths from 100 to 1500 m. Predictions were developed
both for a range of individual taxa and for variations in whole-community composition, yielding maps of predicted population
densities, beta-diversity (rate of change of community composition), and hard-boundary community classifications. Modelling
methods for individual taxa included Boosted Regression Trees (BRT, De’ath 2007), Random Forests (RF, Breiman 2001), and
Generalised Additive Modelling (GAM), which were combined in ensembles, and Joint Species Distribution Modelling (jSDM, Ovaskainen
et al. 2017), while for communities we used Regions of Common Profile (RCP, Foster et al. 2013) and Gradient Forests (GF,
Ellis et al. 2012). For single-taxon ensemble models, the ‘hurdle’ technique was used, combining predictions from presence-absence
and abundance models to reduce bias associated with zero-inflated data. Explanatory environmental variables were selected
from an initial set of 58 candidate layers and the total of 354 invertebrate taxa identified from the seabed image surveys
were condensed into a set of 69 taxa by aggregation to higher taxonomic levels and exclusion of rarer and non-benthic taxa.
Outputs from the single-taxon models are presented as maps showing predicted occurrences as abundances (individuals 1000
m-2) with associated estimates of model precision (CV). Outputs from the community models are presented as either continuous
gradients of predicted change in community composition, or spatial classifications of the study area at a range of class-levels
(e.g., GF outputs range from 5 to 75 classes across the study area).
All model predictions are downloadable as raster files in geotiff format at 1000 m grid cell size and and have their relevant
projections written in their files.
These predictions are the best-informed representations of seabed distributions at regional scales in the New Zealand Exclusive
Economic Zone to date and provide a resource that will have applications in marine environmental management and ecosystem
research. Potential applications include quantification of benthic impacts from bottom-contact fishing gear and other anthropogenic
agencies, informing spatial management of biodiversity through, for example, the design of marine protected areas, and informing
research into ecosystem linkages between water-column and seabed processes. The dataset developed here has also enabled objective
evaluation of the credibility of predictions from earlier SDM initiatives for the region, which were based on museum and
bycatch data (see Bowden et al. 2021).
Acknowledgements
This study was funded by Fisheries New Zealand (FNZ) under projects ZBD2016-11 and ZBD2019-01, with governance at FNS by
Mary Livingston. The Principal investigator is David Bowden ([email protected]) and the full team includes: Owen Anderson; Ashley Rowden; Fabrice Stephenson; Caroline Chin; Malcolm Clark; Niki Davey;
Alan Hart, and Brent Wood.
References
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17 May - 7 June 2020. No. 44 p.
Bowden, D.A.; Rowden, A.A.; Anderson, O.F.; Clark, M.R.; Hart, A.; Davey, N., . . . Chin, C. (2019a). Quantifying Benthic
Biodiversity: developing a dataset of benthic invertebrate faunal distributions from seabed photographic surveys of Chatham
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Bowden, D.A.; Anderson, O.F.; Escobar-Flores, P.; Rowden, A.A.; Clark, M.R. (2019b). Quantifying benthic biodiversity: using
seafloor image data to build single-taxon and community distribution models for Chatham Rise, New Zealand. Aquatic Environment
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Bowden, D.A.; Anderson, O.F.; Rowden, A.A.; Stephenson, F.; Clark, M.R. (2021). Assessing Habitat Suitability Models for
the Deep Sea: Is Our Ability to Predict the Distributions of Seafloor Fauna Improving? Frontiers in Marine Science 8(239).
<http://dx.doi.org/10.3389/fmars.2021.632389>
Bowden, D.A.; Anderson, O.F.; Rowden, A.A.; Stephenson, F. (in press). Assessing the utility of habitat suitability models
developed for Chatham Rise when applied to Campbell Plateau. Aquatic Environment and Biodiversity Report No. p.
Breiman, L. (2001). Random forests. Machine Learning 45(1): 5-32
De'ath, G. (2007). Boosted trees for ecological modelling and prediction. Ecology 88(1): 243-251.
Ellis, N.; Smith, S.J.; Pitcher, C.R. (2012). Gradient forests: calculating importance gradients on physical predictors.
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Foster, S.D.; Givens, G.H.; Dornan, G.J.; Dunstan, P.K.; Darnell, R. (2013). Modelling biological regions from multi-species
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Ovaskainen, O.; Tikhonov, G.; Norberg, A.; Blanchet, F.G.; Duan, L.; Dunson, D.; Roslin, T.; Abrego, N. (2017). How to make
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Credit
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NIWA
See:
Bowden, D.A.; Anderson, O.F.; Rowden, A.A.; Stephenson, F.; Clark, M.R. (2021). Assessing Habitat Suitability Models for
the Deep Sea: Is Our Ability to Predict the Distributions of Seafloor Fauna Improving? Frontiers in Marine Science 8(239).
http://dx.doi.org/10.3389/fmars.2021.632389
Bowden, D.A.; Anderson, O.F.; Rowden A.A.; Stephenson F. (in press). Assessing the utility of habitat suitability models
developed for Chatham Rise when applied to Campbell Plateau. New Zealand Aquatic Environment and Biodiversity Report.
Stephenson, F.; Bowden, D.A.; Finucci, B.; Anderson, O.F.; Rowden, A.A. (in press). Developing updated predictive models
for benthic taxa and communities across Chatham Rise and Campbell Plateau using photographic survey data. New Zealand Aquatic
Environment and Biodiversity Report.
Anderson, O.F.; Pallentin, A.; Bowden, D.A.; Chin, C.; Davey, N.; Eton, N.; Fenwick, M.; George, S.; Macpherson, D. (in press).
Quantifying Benthic Biodiversity – Phase II: a factual voyage report from RV Tangaroa voyage TAN2004 to Campbell Plateau
17 May – 7 June 2020. New Zealand Aquatic Environment and Biodiversity Report.
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