geo/igarss workshop barcelona, 22 july 2007 - … · the word biodiversity, deemed more appealing...

GEO/IGARSS Workshop Barcelona, 22 July 2007

The word biodiversity, deemed more appealing to communicators than biological diversity, first appeared as the title of the Proceedings of the National Forum on Biological Diversity organized by the U.S. National Research Council (NRC) held in 1986.

(Wilson, E.O. (Ed.), 1988).


The following definition of Biodiversity was adopted by the 1992 United Nations Earth Summit in Rio de Janeiro:

•  The variability among living organisms from all sources, including, 'inter alia', terrestrial, marine, and other aquatic ecosystems, and the ecological complexes of which they are part: this includes diversity within species, between species and of ecosystems".


•  Biodiversity may be considered as the taxonomic variation of life forms within a given ecosystem, biome or the entire Earth.

•  Biological diversity has been generated by the conjunction of genetic evolution and habitat heterogeneity.

•  Biodiversity is often used as a measure of the health of biological systems.


Biodiversity is often treated by ecologists working at the species level to express species richness or species evenness depending on which of the following indices is used:

•  Species richness •  Simpson index •  Shannon index

Other indices also used by ecologists are: •  Alpha diversity •  Beta diversity •  Gamma diversity


•  In what follows we shall use the term biodiversity in its simplest inception: the number of taxonomically, or otherwise, defined marine species.

•  We shall also constrain our scope to the marine plankton.


•  The Competitive Exclusion Principle which was formulated by Georgii Frantsevitch Gause (1934) says that if two species are absolutely the same (i.e. they are competing for the same habitat, nutrients, etc.) then, over time, one of them must outcompete the other.

•  Following this principle, a problem arose which was coined “The Paradox of the Plankton” by G. Evelyn Hutchinson (1961). Simply stated, he could not find enough limitations in the ocean to support the number of species observed.

•  There are over 200,000 species of algae recorded at the University of California Index Nominum Algarum , many of them belonging to the marine phytoplankton biocenosis (http://ucjeps.berkeley.edu/index.html)

•  There are over 120,000 species recorded in Algaebase, the University of Galway (Irl) data base (http://www.algaebase.org)

•  In the NW Mediterranean alone we have an Inventory of observed phytoplankton with over 1400 entries including species, synonyms and varieties (Velasquez, 1997)


•  Bacterioplankton (includes viruses and archaea mostly heterotrophic)

•  Picophytoplankton (includes cyanobacteria and other pro- and eucariotic photosynthetic organisms)

•  Phytoplankton (mostly eucariotic photosynthetic and mixotrophic organisms)

•  Zooplankton (heterotrophic metazoa)


•  Femtoplankton (0.02 – 0.2 µm) •  Picoplankton (0.2 – 2 µm) •  Nanoplankton (2 – 20 µm) •  Microplankton (20 – 200 µm) •  Mesoplankton (0.2 – 20 mm) •  Macroplankton (20 – 200 mm) •  Megaloplankton (0.2 – 2 m)


• Bacterioplankton (culture, DAPI) • Picoplankton (flow cytometry, SEM) • Phytoplankton:

– Nanoplankton (Uttermohl, SEM) – Microplankton (Uttermohl, SEM)

• Zooplankton: – Mesoplankton (binocular, cameras) – Macrozooplankton (direct observation)

* Also specialized taxonomists group mostly according to technique


Although many of the planktonic species are common to all parts of the Earth’s oceans, taxonomic description is usually made for specific regions:

• Tropical plankton • Subtropical plankton • Arctic/Antarctic plankton • Coastal/Neritic plankton • Mediterranean plankton


The Mediterranean System

There are two general approaches to ecological modeling: 1.  Coastal ecosystems models, very complex in

structure, with relatively simple hydrodynamics.

2.  Ocean biogeochemical, NPZD-type models, very simple in structure but coupled to highly structured hydrodynamic models.

Most models presently running belong to one of these two categories.


Other options are: 1.  PFT models running off-line using

velocity and diffusion fields generated by higher resolution hydrodynamic models.

2.  Integrated hydrodynamic/biological models.

Efforts are being done to develop strategies for making these two options practical.


Some authors maintain that PFTs can be defined by specific sets of physiological traits (i.e. a measurable or observable property of individual PFT):

1.  Pico-heterotrophs 2.  Pico-autotrophs 3.  Phytoplankton N2-fixers 4.  Phytoplankton calcifiers 5.  Phytoplankton DMS-producers 6.  Phytoplankton silicifiers 7.  Mixed-phytoplankton 8.  Proto-zooplankton 9.  Meso-zooplankton 10. Macro-zooplankton


However, there are a number of difficulties inherent in deriving PFT-specific values for each of the relevant traits (LeQuéré et al.2004): •  When values are based on laboratory experiments, they

are usually conducted on individual species easy to culture and that are not necessarily characteristic of the PFT as a whole.

•  When trait values are based on empirical observation, the sparse measurements may not represent average responses adequately.

•  In all cases, diversity within PFT is neglected.


•  Kemp and Mitsch modeled the development of a three species system using a simple P-limited model with uptake, respiration and death as the major processes and turbulent diffusion to transport nutrient onto the cell wall.

•  As a result of their simulations, only one species survived at slow diffusion rates, while, at high turbulent diffusion rates, all three species could be made to survive over long periods of time.


•  Kemp and Mitsch used 10 different parameters each with one to three different rank values.

•  They did not exploit all the capabilities of their model because they used the parameter values only in three sets, each corresponding to one theoretical species.

•  Had they used the 3 rank values in a 10th-order matrix, they could have generated 310 different especies of which only a subset would have subsisted through the simulation.


•  Hydrodynamic and environmental variability, makes all the plankton species in a given biocenosis to be exposed to changing conditions.

•  Accepting the Competitive Exclusion Principle, we may assume that each individual species occupies, at least temporarily, a single niche to which it is best adapted.

•  On the other hand, we know the most important processes in which all individual species are involved (light/nutrient limitation, grazing, mortality, settling, etc.) and their formulation.


Functionally generated Biodiversity (Inductive model)

•  Even if all plankton species present at one time or another in that biocenosis were known, it would be impossible to know all the rate parameter values controlling the development of individual species.

•  By ranking all these parameter values we are defining a number of virtual species each with one of the possible sets of values.

•  Biotic and abiotic variability generates conditions in which some of the species will take over all others.


Functionally generated Biodiversity (Inductive model)

• The prototype model presented here is based on the generation of virtual species by attributing ranked values to the various parameters intervening in the general biological model. For example:

• Uptake • Grazing • Mortality


Run Model

• The number of species generated in our model equals nm where n is the number of ranked values and m the number of parameters.

•  In our case, the species richness is S= 35

= 243


• After 8 hours running, the model goes through a maximum of 111 species having biomass above the threshold.

• After 480 hours (20 days) of running, however, the number of species having biomass above the threshold declines to only 12.


Run Model

•  The model has a simple NPZD structure thus not having large memory requirements. Even so, a total number of 10 parameters with 3 or 4 ranked values brings the total number of species to values comparable with real world numbers (57K-1M).

•  The vector containing the number (biomass) of individuals for each species can be much reduced if only those with values above threshold are retained.

•  Eventually, most abundant species, with numbers adding up to, say, 90% of the total biomass may be retained, the rest being considered below the threshold.

•  Packing techniques should be developed in order to carry on the information through the time integration.


•  Different plankton communities are to be expected under such diverse conditions as tropical and polar environments or between upwelling zones and central gyres.

•  Also, ecological analyses carried out on the simulated data should allow comparison between virtual and real communities for validation.

•  Concerted efforts should be made to get a Biodiversity model up and running.


geo/igarss workshop barcelona, 22 july 2007 - … · the word biodiversity, deemed more appealing...

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