Everything about Ecosystem Model totally explained
Ecosystem models, or
ecological models, are
mathematical representations of
ecosystems. Typically they simplify complex
foodwebs down to their major components or
trophic levels, and quantify these as either numbers of
organisms,
biomass or the
inventory/
concentration of some pertinent
chemical element (for instance,
carbon or a
nutrient species such as
nitrogen or
phosphorus).
Overview
Complexity
Ecosystem models are a development of
theoretical ecology that aim to characterise the major
dynamics of ecosystems, both to synthesise the understanding of such systems and to allow
predictions of their behaviour (in general terms, or in response to particular changes).
Because of the
complexity of ecosystems (in terms of numbers of species/ecological interactions), ecosystem models typically simplify the systems they're studying to a limited number of
pragmatic components. These may be particular species of interest, or may be broad
functional types such as
autotrophs,
heterotrophs or
saprotrophs. In
biogeochemistry, ecosystem models usually include representations of non-living "resources" such as nutrients, which are consumed by (and may be depleted by) living components of the model.
This simplification is driven by a number of factors:
- Ignorance: while understood in broad outline, the details of a particular foodweb may not be known; this applies both to identifying relevant species, and to the functional responses linking them (which are often extremely difficult to quantify)
- Computation: practical constraints on simulating large numbers of ecological elements; this is particularly true when ecosystem models are embedded within other models (such as physical models of terrain or ocean bodies, or idealised models such as cellular automata or coupled map lattices)
- Understanding: depending upon the nature of the study, complexity can confound the analysis of an ecosystem model; the more interacting components a model has, the less straightforward it's to extract and separate causes and consequences; this is compounded when uncertainty about components obscures the accuracy of a simulation
Structure
The process of simplification described above typically reduces an ecosystem to a small number of
state variables. Depending upon the system under study, these may represent ecological components in terms of numbers of
discrete individuals or quantify the component more
continuously as a measure of the total biomass of all organisms of that type, often using a common model currency (for example mass of carbon per unit area/volume).
The components are then linked together by
mathematical functions that describe the nature of the relationships between them. For instance, in models which include predator-prey relationships, the two components are usually linked by some function that relates total prey captured to the populations of both predators and prey. Deriving these relationships is often extremely difficult given
habitat heterogeneity, the details of component
behavioral ecology (including issues such as
perception,
foraging behaviour), and the difficulties involved in unobtrusively studying these relationships under field conditions.
Typically relationships are derived
statistically or
heuristically. For example, some standard functional forms describing these relationships are
linear,
quadratic,
hyperbolic or
sigmoid functions. The latter two are known in ecology as type II and type III responses, named by
C. S. Holling in early, groundbreaking work on predation in
mammals. Both describe relationships in which a linkage between components saturates at some maximum rate (for example above a certain concentration of prey organisms, predators can't catch any more per unit time). Some ecological interactions are derived explicitly from the
biochemical processes that underlie them; for instance, nutrient processing by an organism may
saturate because of either a limited number of
binding sites on the organism's exterior surface or the rate of
diffusion of nutrient across the
boundary layer surrounding the organism (see also
Michaelis-Menten kinetics).
After establishing the components to be modelled and the relationships between them, another important factor in ecosystem model structure is the representation of
space used. Historically, models have often ignored the confounding issue of space, utilising
zero-dimensional approaches, such as
ordinary differential equations. With
increases in computational power, models which incorporate space are increasingly used (for example
partial differential equations, cellular automata). This inclusion of space permits
dynamics not present in non-spatial frameworks, and illuminates processes that lead to
pattern formation in ecological systems.
Examples
One of the earliest, and most well-known, ecological models is the
predator-prey model of
Alfred J. Lotka (1925) and
Vito Volterra (1926). This model takes the form of a pair of
ordinary differential equations, one representing a prey
species, the other its predator.
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