Variation in demersal fish species richness in the oceans surrounding New Zealand: An analysis using boosted regression trees

  • Leathwick J
  • Elith J
  • Francis M
 et al. 
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We analysed relationships between demersal fish species richness, environment and trawl characteristics using an extensive collection of trawl data from the oceans around New Zealand. Analyses were carried out using both generalised additive models and boosted regression trees (sometimes referred to as 'stochastic gradient boosting'). Depth was the single most important environmental predictor of variation in species richness, with highest richness occurring at depths of 900 to 1000 m, and with a broad plateau of moderately high richness between 400 and 1100 m. Richness was higher both in waters with high surface concentrations of chlorophyll a and in zones of mixing of water bodies of contrasting origins. Local variation in temperature was also important, with lower richness occurring in waters that were cooler than expected given their depth. Variables describing trawl length, trawl speed, and cod-end mesh size made a substantial contribution to analysis outcomes, even though functions fitted for trawl distance and cod-end mesh size were constrained to reflect the known performance of trawl gear. Species richness declined with increasing cod-end mesh size and increasing trawl speed, but increased with increasing trawl distance, reaching a plateau once trawl distances exceed about 3 nautical miles. Boosted regression trees provided a powerful analysis tool, giving substantially superior predictive performance to generalized additive models, despite the fitting of interaction terms in the latter.

Author-supplied keywords

  • Boosted regression trees
  • Demersal fish
  • Species richness
  • Statistical model

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