#patchiness

[bioayurveda-things]

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[abysmallyabyssal]

Patterns in Patchiness

Patterns in megafauna distribution are generally measured using photographic quadrats along a transect. The results were varied. Grassle et al. compared distribution among quadrats and found out that Ophiomusium lymani showed uniformity at low high densities but aggregation at low densities. Ophiophthalmus normani showed uniform distribution at small scales but random distribution at large scales. Echinus affinus and Phormosoma placenta aggregated. 

Macrofauna horizontal distribution points towards randomness, but because sampling efforts are low, non-random patterns cannot be detected. Macrofauna can also be disturbed by sampling with corers, and their distribution might be affected. Aggregation on the sediment surface can also be seen.

Meiofauna are less random at smaller scales and more random at larger scales. But as macrofauna, this result may be tampered with disturbance from sampling and low repetition of sampling.

Causes of patchiness

Patchiness can be caused by vectorial patterns - small scale habitat heterogeneity and variations in hydrodynamics caused by interference of biogenic structures, social patterns - mating, spawning, and feeding aggregates or spacing, species interactions such as predation and competition, and stochastic patterns, such as a benthic storm wiping out some patch of habitat (recolonization can be observed using azoic trays).

[datasciencegirl]

Measuring Phytoplankton Patchiness (and Who Won the Competition?)

So far, the descriptive analysis of the Wave Glider observations provided value in my effort to refine our estimates of phytoplankton in the ocean. But what about the replicated Wave Glider observations that I raved about earlier? When do those come into play?

I applied an autospectral analysis to replicate Wave Glider observations. Briefly described, this analysis is used to measure the covariance between the original data set and a lagged duplicate data set (which is carried out after cleansing the data set by removing temporal trends and interpolating data over equally spaced sampling distances). This correlation indicates how similar one data point is to its neighbor. This process is replicated with each lag of the data set (over a set distance)  until the original and lagged data sets are no longer correlated; the distance at which the data sets are no longer correlated, as determined by a set threshold (dotted line below), is known as the “Decorrelation Length Scale” (DLS). 

This DLS distance gives a measure of spatial “patchiness” or variability of chlorophyll in the ocean. I applied this analysis to each Wave Glider transect across the variety of ecosystem types (Coastal Upwelling, Transition Zone, Equatorial Upwelling, and Oligotrophic). Means and standard deviations of the replicate patchiness measures (i.e. autospectra or DLS) indicated how patchiness varied among ecosystem types. Results demonstrated a decrease in patchiness with distance offshore, that corresponded nicely to an increase the the spatial scale of eddies.

So what does this all mean? Why would this measure of patchiness be of interest to an oceanographer?

Although Wave Gliders can collect similar types of data as ships at a fraction of the cost (and with virtually zero environmental impact), ship time is still necessary to carry experiments and make more complex measurements. Saying this, the high costs and environmental impacts of ships emphasize the need to spend time at sea efficiently. Therefore knowing when, where, and how frequently to sample is of high value to oceanographers.

The autospectral analysis above suggests that sampling distances of 4 to 7 Km in the coastal upwelling zone off of CA is adequate for capturing phytoplankton patchiness. This distance increases to 30 Km in offshore waters. However in the transition zone between these two regions, one must sample at a minimum of every 8 Km to have any hope of capturing the phytoplankton variability in this highly dynamic region! And let us not forget that none of this applies to the daily variations in phytoplankton, which would require virtual stationary sampling on the order of less than every 24 hours in high growth systems.

Furthermore, Wave Gliders can serve as a scouts that locate the optimal sampling region for a ship, while collecting a continuous record of the conditions in that region before the ship arrives and until after it departs.

With regards to my own research, I see Wave Gliders as the ultimate observer for those hard-to-get-to regions. This type of data is extremely valuable for ecosystem modelers who wish to check the consistency of their models, and even assimilate data into their models, for regions where little is known or observations are highly variable. 

So how did my research fare in the PacX Challenge?  Well, the competition was stiff and all the finalists carried out some great research. But I was beat out in the end by the similar approach of Tracy Villareal of the University of Texas. I wish Tracy all the best in his future Wave Glider endeavours!

[mypatchiness]

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