Physical - Biological Interactions Influencing Marine Plankton Production

Physical - Biological Interactions Influencing Marine Plankton Production
Kendra L. Daly, Walker O. Smith, Jr.
Annual Review of Ecology and Systematics, Vol. 24 (1993) pp 555-585

An excellent review article.
Daly and Smith(1993) review biological and physical interactions in oceans to see how they influence plantonik growth (mostly phytoplankton, but zooplanktons are also discussed) . As the paper talks only about marine environment the discussion is difficult to apply to a much smaller estuarine system; the currents in the oceans and the light gradient due to a much much greater depth are not applicable. also the freshwater influence, which will be significant for esturies, is not accounted for.
The similarity however is that they are both fluid and the organisms of interests are the same. Some questions are applicable to both ecological systems. It is also interesting to see how coastal estuarine systems interacts with the larger scale marine environment, from the latter's perspective. the discussion of estuarine systems is too peripheral and too generic to be of much direct use.

The paper is well organised; it is divided into 2 sections-

a) Physical interactions
which talk about physical processes like motion and light etc.
large scale - 1,000 to >10,000 km & years to centuries
mesoscale - 100 m to 100 km & days to months
smallscale - mm to meters & seconds to hours

b) Biological interactions
which talk about interactions between biological entities and between biological entities and their physical environment. these are further classified into large scale; mesoscale and small scale - (i found this classification system fuzzier).
large scale interactions
e.g. large scale heat absorbtion at a global level that impacts global temperature.
mesoscale interactions
at this scale plankton appear to temprarily adapt
??
small scale interactions
phytoplankton - nutrient interactions.
interactions with environment due to physiological response in the cells.

The paper, then, presents two case studies to discuss complexity of interactions. these are marine examples and did not interest me considerably.

interesting quotes from the paper are classified under following headings -

1. Scales of Interaction
2. Aquatic ecosystem
3. Physical indicators and their influence
4. Primary Production

Quotes on Modeling Techniques from Kanal

interesting quotes from Kanal (1993)
1. Paul Werbos had talked about error back propagation in his doctoral thesis "Beyond regression: new tools for prediction and analysis in behavioral sciences" (1974) before Rumelhart et al (1986).

2.

A basic problem of statistical pattern recognition, viz., the dimensionality -sample size problem also arises in artificial neural systems. In the design of multilayer feedforward networks one question is how many hidden units to use.

A few techniques are reviewed - but they appear an over-kill (in the best case) and clearly inapplicable (in the worst case) because in my experiments the size is not crucial - the order of free parameters in the network remains constant. there is only one hidden layer - and no. of hidden units are of order of 1 to 10.

3.

While the generation of artificial neural networks excite us, we should keep in mind that:

(1) As has been shown by [Comparing hierarchical statistical classifiers with error back propagation neural network; Kanal et al (1989)], often fairly simple statistical decision tree methods give equivalent or better results;

(2) the various neural network paradigms for pattern classification introduced in recent years have close connections with stochastic approximation, estimation and classification procedures known in statistical pattern recognition; and

(3) rather good algorithms have been developed in recent years for large combinatorial optimization problems whereas neural networks have so far only been demonstrated on much smaller problems. It remains to be shown that combinatorial optimization is a good area for artificial neural networks.

4.

...the problem of scalability remains one of the basic concerns for employing various pattern recognition, parallel processing, and machine intelligence tools on real world problems.

5. "They were AI as long as it was unclear how to make them work." After a very interesting discussion on what is AI, based on AI Magazine, Roger Shank (1991)

6. Theorem of the Ugly Duckling
(by Watanabe)
separate post.

Pattern Recognition - review paper 1992

On pattern, categories and alternate realities
Laveen N Kanal
Pattern Recognition letters 14 (1993) 241 - 255

A review on pattern recognition presented at the 11th international conference of pattern recognition on reception of an award; the author is from dept of computer science.

The review having been presented in 1992 is, now, dated. The good thing is that certain questions are so fundamental that they cannot be dated. Some of such questions are presented in an informal language, which makes it very good. Tho there is some history also described - it is so intertwined with the personal history of the author that apart from giving an interesting perspective, it does little else. The quotes (also picked from general philosophy) are excellent and i have posted them separately.

Finally an excellent read for anyone interested in pattern recognition or one of the techniques used for them.

Neural Network - Statistical perspective

Neural Networks: A Review from a statistical perspective
Bing Cheng and D.M. Titterington
Statistical Science
1994, Vol 9, No. 1, pgs 2-54

A excellent paper that introduces the connections between statistical methods and neural networks.

* Introduces NN jargon and, to some extent, statistical jargon to the reader. Good as a reference for and introduction to FFNN, i.e., multi layer perceptron.

* Mentions concerns with back propagation algorithm, namely, speed and debates relevance of various quasi-newton techniques - which by not evaluating second derivative speed up the training (relevant to me because that is what i am using).

* Gives good examples of successful NN - in one case of NN that did not need any training. other examples are way too complicated as opposed to generalised techniques.

* Section 4 'Multilayer Perceptron' is very relevant tho sometimes decends to gibberish considering that I am not so well acquinted with the statistical jargon. A few revisits would be able to improve that situation - which would be very much worth it.

* Section 5 discusses Hopfield network - for associative memories (i.e. cluster analysis) but this is too much of jargon and gibberish for me - at this point.

* Section 6 discusses 'Associative networks with unsupervised learning' in lesser detail; but I have not dwelled too much with this section either.
Section 7 talks about the 'Future' - raises some good questions.

* The paper also references to a few really good papers.

Note: use this paper while introducing terms like multi layer perceptron; training algorithm. you would find these terms being defined from the more conservative statistical background useful.

Scale

Determining natural Scales of Ecological Systems
RL Habeeb; J Treilco; S Wotherspoon and CR Johnson
(UTAS)
Ecological Monographs 75(4) 2005 pp 267-287

The paper does not seem to include much introductory material. The problem, as explained in the abstract, is very exciting. The rest of the paper, more or less, gives a feeling as being a part of a continuing discussion. Might revisit later - not thrilled. note also - the paper is sent from utas.

Characteristic length scale (CLS): The characteristic length is a natural scale of a system at which the underlying deterministic dynamics are most clearly observed.
A key issue in ecology is to identify the appropriate scale(s) at which to observe trends in ecosystem behaviour.