Determining objective biophysical provinces from multiple satellite sensor observations

1. NMS Final stress= 11.6 MRPP A-stat=0.21 p<0.001 1 3 5 9 NMS Final stress= 7.11 MRPP A-stat=0.42 p<0.001 4 7 6 10 2 8 NMS Final stress= 14.1 MRPP A-stat=0.14 p<0.002 PAPA Determining objective biophysical provinces from multiple satellite sensor observations Maria T. Kavanaugh*, Ricardo M. Letelier, Yvette H. Spitz College of Oceanic and Atmospheric Sciences, Oregon State University *mkavanau@coas.oregonstate.edu Regional scale biophysical provinces maintain basin scale features and describe unique ecological communities. Introduction Satellite-derived biophysical boundaries are seasonally dynamic…. 8a 2 • If the structure of marine ecological systems is constrained by physical forcing, biophysical provinces, defined as regions displaying coherent patterns in physical forcing and biological response, may emerge as quasi-stable properties of regional and global oceans. The objective classification of coherent water masses in regional oceans is critical to: • Optimizing satellite-derived productivity algorithms and biogeochemical models, • Ecosystem process studies, • Spatially extrapolating long-term time series, • Creating appropriate sampling designs for ocean observing networks. • We have employed a classification scheme based on the Kohonen Self Organizing Map[1], a neural network algorithm. We combine satellite-derived ocean color and physical data of the surface ocean to create biophysical provinces in the North Pacific that can accommodate seasonal changes and different spatial scales. Here we show the effect of sensor and spatial scale in determining patterns of coherence for the North Pacific. chl sst par 1 6 3 6 2 5 JUN JUN JUN 3 5 8b 1 4 PAPA * 7 8 7 1 4 9 AUG AUG AUG FEB FEB FEB Axis 2 Figure 1. Normalized monthly climatologies of MODIS-Aqua derived chlorophyll (log-transformed), sea surface temperature, and SeaWiFS photosynthetically active radiation. PAPA * Axis 1 Figure 6. NMS and MRPP analysis for microbial assemblages during 2007/08 Line P cruises. Each point represents the sampling station in the five dimensional “taxa” space. Points that parse out adjacent in the ordination are similar in community structure. Figure 5. PRSOM-derived biophysical provinces the subarctic Pacific overlaid by the cruise track of the June 2007, August 2007, and February Line P cruises. Numbers and boundaries of provinces vary by season. Figure 2. PRSOM derived provinces for the North Pacific. ……reflecting differences in biophysical interactions. Choice of sensor may be important for determining boundaries. Using derived products allows for mechanistic understanding of provincial boundaries and seasonal dynamics. However we know that sensors differ in their bias and error in their estimation of, for example, chlorophyll, that may be more pronounced depending on region and time of year. For the NE subarctic Pacific, choice of sensor may be less critical than in the western subtropical gyre. Methods chl-mg m-3 Objective determination of biophysical provinces Like other ordination or classification techniques, the Probabilistic Self Organizing Map algorithm [2,3], or PRSOM condenses relevant multidimensional information (i.e. SST, chl-a, PAR etc) into 2-dimensional physical space. The unsupervised classification is based on a maximum likelihood optimization of variables within pixels and pixels in adjacent physical space per neuron. A Hierarchical Ascending clustering (HAC) algorithm is then used to reduce the number of classes defined at each neuron. The class identities are then remapped into physical space. Figure 3. PRSOM derived provinces using annual climatologies of the 3 fields described in Figure1. SST-degrees chl- log10(mg m-3) Figure 9. Comparison of satellite derived chlorophyll products and in situ measurements for the NE subarctic Pacific. In situ data are from the Line P cruises during 2006 and 2007. chl-mg m-3 chl-mg m-3 Figure7. PRSOM defined regions of coherence for annual climatologies of SeaWiFS-derived and MODIS-Aqua- derived chlorophyll. Note spatial heterogeneity of both the eastern and western subarctic. chl- log10(mg m-3) N-dimensional data i.e. SST, CHLO, PAR etc P classes P max= neural map dimension- i.e. 10x10= 100 HAC Q classes Defined by reduction in interclass inertia PRSOM SST-degrees SST-degrees Figure 8. Comparison of SeaWiFs and MODIS derived chlorophyll concentrations (mg m-3) from 2003-2007 for two regions in the North Pacific. Figure 4. Sea surface temperature and chlorophyll relationships through time (60 months) for 3 of the provinces defined for the North Pacific. Data reflect spatially-averaged provincial values for each month. Summary In situ validation • Satellite derived biophysical provinces reveal seasonal structural changes. • Regional analyses reveal spatial heterogeneities, increased number of discrete provinces, and variation in biophysical interactions- an important consideration for planning ocean observing networks. • PRSOM derived provinces reflect in situ differences in phytoplankton assemblages for the eastern subarctic Pacific. Validation of the model in other regions is currently in progress. • Depending on scale and region of interest, choice of sensor may impact provincial boundaries and interpretation of biophysical relationships within provinces. As part of a collaborative effort with the Canadian Institute for Ocean Sciences, several in situ biological measurements were collected during the regular Line P cruises in the eastern subarctic Pacific. Here we report validation of the PRSOM derived biophysical provinces with extracted pigments and flow cytometric measurements of Synechococcus, pico-eukaryotic algae, diatoms, and heterotrophic bacterioplankton abundances. Multivariate analysis of the abundances was conducted using Non-metric Multidimensional Scaling (NMS); grouping affinity of microbial populations in provinces was then assessed using a Multi-Response Permutation Procedure (MRPP). Acknowledgements This work was supported by funding from a NASA Earth and Space Science Graduate Fellowship (MTK), the National Science Foundation (RML) and the Cooperative Institute for Ocean and Satellite Studies. We thank the following people for their assistance with this project: M. Saraceno, T. Fitchett, J. Nahorniak, C. Vandetta, M.J. Zirbel, A. E. White. [1] Kohonen, T. 1990. The Self-Organizing Map. Proceedings of IEEE 78, 1464 -1480; [2] Anouar F., F. Badran, S. Thiria. 1998. Probabilistic self-organizing map and radial basis function networks. Neurocomputing 20, 83-96; [3] Saraceno M., C. Provost and M. Lebbah.2006. Biophysical regions identification using an artificial neuronal network: A case study in the South Western Atlantic. Adv. Space Res. 37:793-805 CCGS JP Tully image from: http://www.ccg-gcc.gc.ca/vessels-navires/photos/Tully4.jpg