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

Scales of Interaction

Quotes from Daly and Smith, 1993
1.

2. The scale of a varying property is defined as the distance (or time) over which its quantity remains the same before significantly changing. The physical processes most likely to influence biological activity in the ocean are those that occur in the space/time domain intrinsic to specific organisms.
For example, the doubling time of phytoplankton ranges from about 0.5 to 10 days, in which time an individual cell may be transported several kilometers by currents. Thus, small- to meso-scale physical processes are relevant to the study of phytoplankton.

3. This review indicates that biological processes may be more important at smaller scales where behavior such as vertical migration and predation may control the location and production of plankton. Physical processes may be more important at larger scales in structuring biological communities and determining particle flow, but the magnitude of biological distributions also is determined by biological interactions.

4. Non-linearities in biological dynamics over different scales often confound interpretation of patterns. Thus, understanding and prediction of ecosystem function must derive from the study of fundamental processes in conjunction with the coupling of circulation and biological models.

5. The variability of physical forcing, particularly unpredictable fluctuations, may be more important to understanding biological activity than the mean conditions. Spectral analysis provides information on the scales of variability and is useful for comparing the effects of external forcing on different ecosystems.

Physical Indicators and their influence

From Daly and Smith, 2003
Influence of temperature
1. may constrain the maximum growth rate of phytoplankton
2. suggested that temperature, rather than food limitation, drive zooplankton production in temperate coastal regions
3. may be an important determinant of trophic structure
4. effect of temperature may be more important as a covariate with other factors than as a direct control of productivity.
...influence of temperature on marine productivity remains equivocal.

Hydrodynamic processe are believed to control primary productivity in the ocean by governing irradiance and nutrients. (see below, keep in mind that marine ecology is being discussed - and therefore processes are very different than at an estuarine systm)
... the scale at which vertical mixing is a function of wind velocity, duration of wind event, and fetch, and is generally confined to the upper 100m of the ocean.

Nutrient uptake and phytoplankton

Ecology

Ecology is the study of patterns in nature, of how those patters came to be, how they change in space and time, why some are more fragile than others. Population ecology is concerned with how populations interact with the environment and how these interactions give rise to patterns of community and ecosystems.

Sharon E Kingland
Page 1
Modeling Nature

Descriptive vs Explanatory Laws

[William Robin Thompson] was most concerned that entomologists should understand that mathematical "laws" merely expressed natural processes, they did not govern them. The distinction he made between a law as description versus law as explanation was similar to that made by Karl Pearson in The Grammer of Science.But where Pearson regarded such laws as having predictive value in physics, Thompson denied it could ever do that in biology. This was not just because mathematics were too simple in comparison with nature. Mathematics no matter how intricate, could never help biologists to understand causal relations, because natural events were made up of unique causes which were necessarily unpredictable:

"The tremendous multiplicity of factors acting on the real world has not merely the complexity of an elaborate mathematical equation, which is theoretically but not practically manageable; but implies a genuine unpredictability because the actual combination of factors has never been observed to operate and until it has, we cannot really be sure what its effect will be. Much less can we see this effects in its causes."

pg 140
Modelling Nature

On the position of ANN models in population ecology

Black boxes: If models that are not designed to reflect the actual processes in the under-lying systems are called black boxes. Then, in that respect ANN are indeed black boxes. ??check

Models or Laws: Model is a relation or a set of relations that relate the various components of a system. There are two ways to reach a model
- one is to study the physical and chemical processes and then use mathematical statements to express these processes. The mathematical statements would be called models; and
- two is by studying various components and finding the statistical relationships between them. The statistical relationships would be called models.

could both the methods be called empirical?

in any case, from kingsland's book pg. 100 Thompson's hypothesis is presented

If ecological interaction were found to display an underlying regularity, and if this regularity could be described mathematically, then mathematics might serve as a theoretical basis for population ecology.

Also, the book mentions on pg 85 that the term laws (and may i add models, as well) has been used in more than one way -
Specifically, Pearl used the term 'the law of population growth' for the logistic curve to possess universal applicability. Where as, Lotka used the same term

to mean an empirical relation between events having no apparent connection to principles of a more general nature. By this criteria, any other equation fitting the observations would be qualified equally as a law.

Lotka perceived that an empirical law of this sort imposed limits in two ways. First, because the fundamental principles underlying the curve were unknown (MARK1), the exact form of the equation had to be determined anew for each examples. Second, it was not possible to extrapolate much beyond the observed events, because unknown factors might come into play outside the observed range and cause departure from the law

Talking about the logistic curve as the law of population growth - the value lay not in universality or predicting but

in the fact that it could be so easily derived from first principles, and that its constants r and K were biologically meaningful. The equation represented in other words an argument about the population which could be useful as tool of research.- MARK2

Lotka finds use of such a law in this way

"An empirical formula is therefore not so much the solution of a problem as the challenge to such solution. It is a point of interrogation, an animated question mark."

This is later (Pg 87) explained as -

by looking at how a population departs from the law, one may get a more realistic idea of the actual mechanism underlying the population growth... Knowing how a population deviates from the law tells one how to refine the initial assumptions to get more accurate understanding of how populations behave.

By reading MARK1 and MARK2 above, what I am trying to do is see if there is a fundamental similarity between the logistic curve and ANN model as population law. If so, then we know exactly where to place the ANN models in the study called population ecology or population dynamics of phytoplankton [Note to self - there needs to be a discussion on exactly which term would I be using]. And, thus how far can the results be extrapolated.

My current hypothesis (??) is -
By developing a series of empirical models and using those empirical models to
- evaluate the technique (the model is objectively evaluated by having a large percentage of testing data)
- study how the relationship varies (by performing Sensitivity analysis on model that have been found to be good approximation using objective measures)

the next things -
1) look at what exactly is Thompson above is talking about.
2) See exactly what assumption and limitation are associated with the SA techniques that I have used.
3) A very brief discussion on which term (phytoplankton, blue-green algae, any other) would be used and why.

Research Interests

Since I am looking for a job, I would like to work, or rather continue working but at a larger scale in the area that I am already working in, Population ecology.

I am looking at the population dynamics of phytoplankton in an estuary in Victoria, using bio-physical time series. I have tried to understand the short time scale dynamics, varying from the order of hours to days. I have used an iterative non-linear statistical modelling tool which in other words is called feed-forward neural network. (neural networks have been given the bad name of being a black box, however I think it has got more to do with they way they are used).

Using partial-derivative sensitivity analysis (I have come across at least one paper which gets upset with this terminology) I have tried to evaluate the strength of the relation between each of the bio-physical parameter and chlorophyll. (chlorophyll is used an indicator of phytoplankton population.) And to see how the relationships change as the time interval between chlorophyll and bio-physical parameters is increased.

Now what I would like to do further is

1. Study non-linear time-series and see how does chaos (which appears in weather time series and water flows) appear in ecological time-series.
2. Study oceanography, my approach to my project was as a mathematician. I have, hopefully, learned some decent amount of biology in the process. However, I am sure there are quite a few things out there – so, I would like to do a short course in oceanography. (Note: I did NOT say I wanted to do a short course in chaotic time series analysis)
3. Study Bayesian: Bayesian is used to quantify uncertainty. It is being used to make ecological models more accessible to decision makers who can use them as decision support systems.