Purpose
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Distributions of individual species, patterns of variability in species richness and abundance, and locations of sensitive
or vulnerable habitats are essential inputs into marine spatial planning and risk assessment processes. Chatham Rise is
an important deep-sea fishing region in New Zealand. Lying at the convergence of Sub-Tropical and Sub-Antarctic water masses,
it has a highly diverse and dynamic physical environment, supporting high levels of biological production and encompassing
a broad range of benthic habitats and fauna. Existing knowledge about seabed faunal distributions on Chatham Rise comes
from records of museum specimens, fisheries and research trawl bycatch, and increasing from photographic surveys. Data from
museum and trawl databases have been used to build models that predict species and community distributions in unsampled
space but because the models are based on presence-only data from disparate sources and do not incorporate population density
data, their predictions are considered uncertain.
To reduce uncertainty in predictions, we developed a new, spatially extensive, fully quantitative, and taxonomically consistent
dataset of benthic invertebrate occurrence by merging data from five seabed photographic surveys, including an extensive dedicated
survey (TAN1701) as part of this project (Bowden et al., 2019a). We then used this dataset to inform development of improved
predictive models for Chatham Rise at both single-taxon and community levels, yielding maps of predicted population densities,
beta-diversity (rate of change of community composition), and community classifications (Bowden et al., 2019b). Two independent
modelling methods were used for each level: Boosted Regression Trees (BRT, De’ath 2007) and Random Forests (RF, Breiman
2001) for single-taxa, and Regions of Common Profile (RCP, Foster et al. 2013) and Gradient Forests (GF, Ellis et al. 2012)
for communities, enabling ensemble model predictions for single taxa and comparison between classification methods for communities.
For single-taxon models, the ‘hurdle’ model technique was used, combining predictions from presence-absence and abundance
models to reduce bias associated with zero-inflated data. Sets of explanatory environmental variables (12 for single-taxon
models, 18 for GF, and 9 for RCP) were selected from an initial set of 58 candidate layers and the 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. Single-taxon models were produced for 20 taxa, selected according to their sensitivity
or vulnerability to human-induced environmental impacts, while all 69 taxa were included in community models.
Outputs from the single-taxon models are presented as maps showing predicted occurrences as densities (individuals 1000 m-2)
with associated estimates of model precision (CV) and cross-validation metrics. All models performed well by these criteria
but a comparison using invertebrate bycatch data from the trawl database was inconclusive for most taxa modelled because
of inadequate abundance information in the test data. While predictions for most of the taxa modelled have clear similarities
with those of previous models, they also show differences, often driven by inclusion of density data. Outputs from the
community models are presented as spatial classifications of the study area, analogous to existing spatial classifications
such as the Marine Environments Classification (MEC, Snelder et al. 2007, Leathwick et al. 2012) and derivatives. RCP divided
the area into 7 classes, whereas a hierarchical clustering method allowed GF results to be assessed at class levels from
7 to 50 classes and compared visually against existing classifications.
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. A further obvious application and test of the
predictions will be to use the modelled relationships developed here to predict faunal distributions across seabed areas
beyond Chatham Rise.
This study is 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; Caroline Chin; Malcolm Clark; Niki Davey; Alan Hart; Andrea Mari; Andrew Miller;
Ashley Rowden and Brent Wood.
References:
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
Rise. Aquatic Environment and Biodiversity Report No. 221. 35 p.
Bowden, D.; Anderson, O.; Escobar-Flores, P.; Rowden, A.; Clark, M. (2019b). Quantifying benthic biodiversity: using seafloor
image data to build single-taxon and community distribution models for Chatham Rise, New Zealand. Aquatic Environment and
Biodiversity Report No. 235. 67 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.
Ecology 93(1): 156-168.
Foster, S.D.; Givens, G.H.; Dornan, G.J.; Dunstan, P.K.; Darnell, R. (2013). Modelling biological regions from multi-species
and environmental data. Environmetrics 24(7): 489-499.
Leathwick, J.; Rowden, A.; Nodder, S.D.; Gorman, A.R.; Bardsley, S.; Pinkerton, M., . . . Goh, A. (2012). A Benthic-Optimised
Marine Environment Classification (BOMEC) for New Zealand waters. New Zealand Aquatic Environment and Biodiversity Report
No. 88. 54 p.
Snelder, T.H.; Leathwick, J.R.; Dey, K.L.; Rowden, A.A.; Weatherhead, M.A.; Fenwick, G.D., . . . Zeldis, J.R. (2007). Development
of an ecologic marine classification in the New Zealand region. Environmental Management 39(1): 12-29.
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Credit
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NIWA
See:
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
Rise. Aquatic Environment and Biodiversity Report No. 221. 35 p.
Bowden, D.; Anderson, O.; Escobar-Flores, P.; Rowden, A.; Clark, M. (2019b). Quantifying benthic biodiversity: using seafloor
image data to build single-taxon and community distribution models for Chatham Rise, New Zealand. Aquatic Environment and
Biodiversity Report No. 235. 67 p.
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