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

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 envi- ronmental 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 out- comes, 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.

[1]  F. G. Stehli,et al.  Taxonomic Diversity of Recent Bivalves and Some Implications for Geology , 1967 .

[2]  R. Whittaker.  Evolution and measurement of species diversity , 1972 .

[3]  P. E. Roberts,et al.  Mesopelagic faunal transition across the Subtropical Convergence east of New Zealand , 1978 .

[4]  Erwin Suess,et al.  Particulate organic carbon flux in the oceans—surface productivity and oxygen utilization , 1980, Nature.

[5]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[6]  David H. Wright,et al.  Species-energy theory: an extension of species-area theory , 1983 .

[7]  R. A. Heath.  A review of the physical oceanography of the seas around New Zealand ― 1982 , 1985 .

[8]  R. Tibshirani,et al.  Generalized additive models for medical research , 1986, Statistical methods in medical research.

[9]  T. M. Smith,et al.  A new model for the continuum concept , 1989 .

[10]  D. Currie.  Energy and Large-Scale Patterns of Animal- and Plant-Species Richness , 1991, The American Naturalist.

[11]  T. Yee,et al.  Generalized additive models in plant ecology , 1991 .

[12]  B. Stanton,et al.  Advances in New Zealand oceanography, 1967–91 , 1991 .

[13]  K. Rohde.  Latitudinal gradients in species diversity: the search for the primary cause , 1992 .

[14]  J. Grassle,et al.  Deep-Sea Species Richness: Regional and Local Diversity Estimates from Quantitative Bottom Samples , 1992, The American Naturalist.

[15]  A. Clarke.  Is there a latitudinal diversity cline in the sea? , 1992, Trends in ecology & evolution.

[16]  R. May,et al.  A dip into the deep seas , 1993, Nature.

[17]  Jeffrey S. Levinton,et al.  Marine Biology: Function, Biodiversity, Ecology , 1995 .

[18]  R. Collier,et al.  Export production of particles to the interior of the equatorial Pacific Ocean during the 1992 EqPac experiment , 1995 .

[19]  K. I. Ugland,et al.  Coastal and deep-sea benthic diversities compared , 1997 .

[20]  K. Banse.  Patterns in the ocean: Ocean processes and marine population dynamics , 1997 .

[21]  S. McClatchie,et al.  Demersal fish community diversity off New Zealand: Is it related to depth, latitude and regional surface phytoplankton? , 1997 .

[22]  K Roy,et al.  Marine latitudinal diversity gradients: tests of causal hypotheses. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Mike P. Austin,et al.  The potential contribution of vegetation ecology to biodiversity research , 1999 .

[24]  M. Zacharias,et al.  Large scale characterization of intertidal communities using a predictive model , 1999 .

[25]  A. O. Nicholls,et al.  SELECTING MARINE RESERVES USING HABITATS AND SPECIES ASSEMBLAGES AS SURROGATES FOR BIOLOGICAL DIVERSITY , 1999 .

[26]  M. Uddstrom,et al.  On the use of high‐resolution satellite data to describe the spatial and temporal variability of sea surface temperatures in the New Zealand region , 1999 .

[27]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[28]  M. Livingston.  Links between climate variation and the year class strength of New Zealand hoki (Macruronus novaezelandiae) Hector , 2000 .

[29]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[30]  S. Sarkar,et al.  Systematic conservation planning , 2000, Nature.

[31]  Simon Ferrier,et al.  Using abiotic data for conservation assessments over extensive regions : quantitative methods applied across New South Wales, Australia , 2000 .

[32]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[33]  Brian J. Bett,et al.  Temporal variability in phytodetritus and megabenthic activity at the seabed in the deep northeast Atlantic , 2001 .

[34]  J. Manderson,et al.  Spatially explicit analysis of estuarine habitat for juvenile winter flounder: combining generalized additive models and geographic information systems , 2001 .

[35]  J. Gray.  Marine diversity: the paradigms in patterns of species richness examined* , 2001 .

[36]  W. Ponder,et al.  Evaluation of Museum Collection Data for Use in Biodiversity Assessment , 2001 .

[37]  P. Boyd,et al.  Phytoplankton distributions around New Zealand derived from SeaWiFS remotely‐sensed ocean colour data , 2001 .

[38]  B. Bull,et al.  Upper‐slope fish communities on the Chatham Rise, New Zealand, 1992–99 , 2001 .

[39]  Roy A. Walters,et al.  Ocean tides around New Zealand , 2001 .

[40]  J. Friedman.  Stochastic gradient boosting , 2002 .

[41]  R. Hurst,et al.  Demersal fish assemblages along the continental shelf and upper slope of the east coast of the South Island, New Zealand , 2002 .

[42]  Julian D. Olden,et al.  A comparison of statistical approaches for modelling fish species distributions , 2002 .

[43]  F. Mueter,et al.  Spatial and temporal patterns in the demersal fish community on the shelf and upper slope regions of the Gulf of Alaska , 2002 .

[44]  M. Austin.  Spatial prediction of species distribution: an interface between ecological theory and statistical modelling , 2002 .

[45]  Fish assemblages in the Hauraki Gulf, New Zealand , 2002 .

[46]  J. S. Gray,et al.  Species richness of marine soft sediments , 2002 .

[47]  Richard Field,et al.  ENERGY, WATER, AND BROAD‐SCALE GEOGRAPHIC PATTERNS OF SPECIES RICHNESS , 2003 .

[48]  M. Cardinale,et al.  Pattern of distribution and diversity of demersal assemblages in the central Mediterranean Sea , 2003 .

[49]  K. Mengersen,et al.  Bridging the Gap between Different Statistical Approaches: An Integrated Framework for Modelling , 2003 .

[50]  Wave hindcast for the New Zealand region: Deep‐water wave climate , 2003 .

[51]  Jerome H Friedman,et al.  Multiple additive regression trees with application in epidemiology , 2003, Statistics in medicine.

[52]  K. Frank,et al.  Marine fish diversity on the Scotian Shelf, Canada , 2003 .

[53]  J. Hall,et al.  Variability in benthic biomass and activity beneath the Subtropical Front, Chatham Rise, SW Pacific Ocean , 2003 .

[54]  R. Valente,et al.  A review of approaches for classifying benthic habitats and evaluating habitat quality. , 2004, Journal of environmental management.

[55]  D. Tracey,et al.  Fish species composition on seamounts and adjacent slope in New Zealand waters , 2004 .

[56]  S. Ferrier,et al.  Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling , 2004, Biodiversity & Conservation.

[57]  M. Mangel,et al.  Examining the importance of consistency in multi-vessel trawl survey design based on the U.S. west coast groundfish bottom trawl survey , 2004 .

[58]  M. Kendall,et al.  The influence of bottom type and shelf position on biodiversity of tropical fish inside a recently enlarged marine reserve , 2004 .

[59]  Jesús Muñoz,et al.  Comparison of statistical methods commonly used in predictive modelling , 2004 .

[60]  M. Francis,et al.  New Zealand Demersal Fish Assemblages , 2002, Environmental Biology of Fishes.

[61]  Michael Drielsma,et al.  Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling , 2002, Biodiversity & Conservation.

[62]  Ralph Charles Mac Nally,et al.  A Successful Predictive Model of Species Richness Based on Indicator Species , 2004 .

[63]  Rick L. Lawrence,et al.  Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis , 2004 .

[64]  S. Eguchi,et al.  An introduction to the predictive technique AdaBoost with a comparison to generalized additive models , 2005 .

[65]  G. De’ath,et al.  Development of a robust classifier of freshwater residence in barramundi (Lates calcarifer) life histories using elemental ratios in scales and boosted regression trees , 2005 .

[66]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .