Modelling the influence of abiotic and biotic factors on plankton distribution in the Bay of Biscay, during three consecutive years (2004–06)

Three surveys were carried out in the Bay of Biscay during the springs of 2004, 2005 and 2006. Hydrographic, nano-microplankton (diatoms, ciliates and unidentified particles) and mesozooplankton biomass data were collected at mesoscale spatial resolution. Generalized additive models (GAMs) based on a combination of hydrographic, geographic and biological terms were used to understand the factors affecting distribution. The final models accounted for 66% of the variability in the biomass distribution of unidentified particles, 60% for diatoms, 44% for mesozooplankton and 23% for ciliates. The contribution of hydro-geographical terms was greater than the information described by the biological variables. Geographical location (latitude and longitude) was the main explanatory factor for all of the plankton groups identified, revealing that the presence of mesoscale fronts related to geographical structures is more relevant than the hydrographic variables per se, to describe plankton distribution.

[1]  Trevor Hastie,et al.  Generalized linear and generalized additive models in studies of species distributions: setting the scene , 2002 .

[2]  M. Alcaraz,et al.  Estimating zooplankton biomass through image analysis , 2003 .

[3]  K. Flynn.  Modelling multi-nutrient interactions in phytoplankton; balancing simplicity and realism , 2003 .

[4]  Philippe Grosjean,et al.  Enumeration, measurement, and identification of net zooplankton samples using the ZOOSCAN digital imaging system , 2004 .

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

[6]  X. Irigoien,et al.  Mapping plankton distribution in the Bay of Biscay during three consecutive spring surveys , 2007 .

[7]  G. Daskalov,et al.  Relating fish recruitment to stock biomass and physical environment in the Black Sea using generalized additive models , 1999 .

[8]  David L. Borchers,et al.  Spatiotemporal modelling for the annual egg production method of stock assessment using generalized additive models , 1998 .

[9]  D. Borchers,et al.  Changes in the distribution of sardine eggs and larvae off Portugal, 1985-2000 , 2003 .

[10]  J. Le Fèvre,et al.  Aspects of the Biology of Frontal Systems , 1987 .

[11]  H. D. Buf,et al.  Automatic diatom identification , 2002 .

[12]  X. Irigoien,et al.  Relationships between frontal structures and zooplankton communities along a cross-shelf transect in the Bay of Biscay (1995 to 2003) , 2004 .

[13]  K. Mann,et al.  Dynamics of Marine Ecosystems , 1991 .

[14]  Allen R. Hanson,et al.  Automatic In Situ Identification of Plankton , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[15]  S. Wood,et al.  GAMs with integrated model selection using penalized regression splines and applications to environmental modelling , 2002 .

[16]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[17]  Phil F. Culverhouse,et al.  Automatic image analysis of plankton: future perspectives , 2006 .

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

[19]  Qiao Hu,et al.  Accurate automatic quantification of taxa-specific plankton abundance using dual classification with correction , 2006 .

[20]  P. Culverhouse,et al.  Do experts make mistakes? A comparison of human and machine identification of dinoflagellates , 2003 .

[21]  Paul J. Harrison,et al.  Estimating carbon, nitrogen, protein, and chlorophyll a from volume in marine phytoplankton , 1994 .

[22]  L. Segel,et al.  Hypothesis for origin of planktonic patchiness , 1976, Nature.

[23]  J. Mauchline.  The biology of calanoid copepods , 1998 .

[24]  P. Ward,et al.  Do habitat models accurately predict the depth distribution of pelagic fishes , 2006 .

[25]  C. Folt,et al.  Biological drivers of zooplankton patchiness. , 1999, Trends in ecology & evolution.

[26]  C. Devey,et al.  Introduction to the InterRidge Special Issue , 2007 .

[27]  P. Utgoff,et al.  RAPID: Research on Automated Plankton Identification , 2007 .

[28]  C. Davis,et al.  A three‐axis fast‐tow digital Video Plankton Recorder for rapid surveys of plankton taxa and hydrography , 2005 .

[29]  R. Tibshirani,et al.  Generalized Additive Models , 1991 .

[30]  Jorge M. Lobo,et al.  Searching for a Predictive Model for Species Richness of Iberian Dung Beetle Based on Spatial and Environmental Variables , 2002, Conservation biology : the journal of the Society for Conservation Biology.

[31]  R. Denno,et al.  Predator diversity and the functioning of ecosystems: the role of intraguild predation in dampening trophic cascades , 2005 .

[32]  M. Yallop.  Distribution patterns and biomass estimates of diatoms and autotrophic dinoflagellates in the NE Atlantic during June and July 1996 , 2001 .

[33]  Norman L. Guinasso,et al.  COMBINING NEW TECHNOLOGIES FOR DETERMINATION OF PHYTOPLANKTON COMMUNITY STRUCTURE IN THE NORTHERN GULF OF MEXICO 1 , 2005 .

[34]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[35]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[36]  M. Fasham,et al.  Accounting for grazing dynamics in nitrogen‐phytoplankton‐zooplankton (NPZ) models , 2007 .

[37]  D. Beare,et al.  Assessing long-term changes in early summer zooplankton communities around Iceland , 2000 .

[38]  R. Pingree,et al.  Slope turbulence, internal waves and phytoplankton growth at the Celtic Sea shelf-break , 1981, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[39]  A. Hirzel,et al.  Evaluating the ability of habitat suitability models to predict species presences , 2006 .

[40]  B. Planque,et al.  Modelling potential spawning habitat of sardine (Sardina pilchardus) and anchovy (Engraulis encrasicolus) in the Bay of Biscay , 2007 .

[41]  R. Denno,et al.  Predator diversity dampens trophic cascades , 2004, Nature.

[42]  Charles S. Yentsch,et al.  An imaging-in-flow system for automated analysis of marine microplankton , 1998 .

[43]  G. Swartzman,et al.  Modelling Spatio-Temporal Effects of Environment on Atlantic Herring, Clupea harengus , 2000, Environmental Biology of Fishes.

[44]  S. Wood,et al.  Generalized Additive Models: An Introduction with R , 2006 .

[45]  Adrian P. Martin.  Phytoplankton patchiness: the role of lateral stirring and mixing , 2003 .

[46]  P. Lehodey,et al.  Potential changes in skipjack tuna (Katsuwonus pelamis) habitat from a global warming scenario: modelling approach and preliminary results , 2003 .

[47]  D. Karentz,et al.  Temporal patterns and variations in phytoplankton community organization and abundance in Narragansett Bay during 1959–1980 , 1998 .

[48]  Edward R. Abraham,et al.  The generation of plankton patchiness by turbulent stirring , 1998, Nature.

[49]  P. Wiebe,et al.  Patterns and Processes in the Time-Space Scales of Plankton Distributions , 1978 .

[50]  M. Sebastián,et al.  Seasonal and inter-annual variability of the phytoplankton communities in an upwelling area of the Alborán Sea (SW Mediterranean Sea) , 2005 .

[51]  C. García-Soto,et al.  Mesoscale variability in a high-resolution grid in the Cantabrian Sea (southern Bay of Biscay), May 1995 , 2002 .

[52]  C. Duarte.  Marine ecology warms up to theory. , 2007, Trends in ecology & evolution.

[53]  Scott M. Gallager,et al.  Distribution of plankton, particles, and hydrographic features across Georges Bank described using the Video Plankton Recorder , 2001 .

[54]  A. New,et al.  Propagation of internal tides from the upper slopes of the Bay of Biscay , 1986, Nature.

[55]  S. Wood.  Generalized Additive Models: An Introduction with R , 2006 .

[56]  C. Davis,et al.  Real-time observation of taxa-specific plankton distributions: an optical sampling method , 2004 .

[57]  A. New.  Internal tidal mixing in the Bay of Biscay , 1988 .