Ecosystem model

An ecosystem model is an abstract, usually mathematical, representation of an ecological system (ranging in scale from an individual population, to an ecological community, or even an entire biome), which is studied to better understand the real system.[2]

Using data gathered from the field, ecological relationships—such as the relation of sunlight and water availability to photosynthetic rate, or that between predator and prey populations—are derived, and these are combined to form ecosystem models. These model systems are then studied in order to make predictions about the dynamics of the real system. Often, the study of inaccuracies in the model (when compared to empirical observations) will lead to the generation of hypotheses about possible ecological relations that are not yet known or well understood. Models enable researchers to simulate large-scale experiments that would be too costly or unethical to perform on a real ecosystem. They also enable the simulation of ecological processes over very long periods of time (i.e. simulating a process that takes centuries in reality, can be done in a matter of minutes in a computer model).[3]

Ecosystem models have applications in a wide variety of disciplines, such as natural resource management,[4] ecotoxicology and environmental health,[5][6] agriculture,[7] and wildlife conservation.[8] Ecological modelling has even been applied to archaeology with varying degrees of success, for example, combining with archaeological models to explain the diversity and mobility of stone tools.[9]

Fasham Ducklow McKelvie 1990
A structural diagram of the open ocean plankton ecosystem model of Fasham, Ducklow & McKelvie (1990).[1]

Types of models

There are two major types of ecological models, which are generally applied to different types of problems: (1) analytic models and (2) simulation / computational models. Analytic models are typically relatively simple (often linear) systems, that can be accurately described by a set of mathematical equations whose behavior is well-known. Simulation models on the other hand, use numerical techniques to solve problems for which analytic solutions are impractical or impossible. Simulation models tend to be more widely used, and are generally considered more ecologically realistic, while analytic models are valued for their mathematical elegance and explanatory power.[10][11][12] Ecopath is a powerful software system which uses simulation and computational methods to model marine ecosystems. It is widely used by marine and fisheries scientists as a tool for modelling and visualising the complex relationships that exist in real world marine ecosystems.[13][14][15][16][17][18][19]

Model design

Silver Spring Model
Diagram of the Silver Springs model (Odum, 1971). Note the aggregation into functional groups such as "herbivores" or "decomposers".[20]

The process of model design begins with a specification of the problem to be solved, and the objectives for the model.[21]

Ecological systems are composed of an enormous number of biotic and abiotic factors that interact with each other in ways that are often unpredictable, or so complex as to be impossible to incorporate into a computable model. Because of this complexity, ecosystem models typically simplify the systems they are studying to a limited number of components that are well understood, and deemed relevant to the problem that the model is intended to solve.[22][23]

The process of simplification typically reduces an ecosystem to a small number of state variables and mathematical functions that describe the nature of the relationships between them.[24] The number of ecosystem components that are incorporated into the model is limited by aggregating similar processes and entities into functional groups that are treated as a unit.[25][26]

After establishing the components to be modeled 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. However, for many ecological problems spatial dynamics are an important part of the problem, with different spatial environments leading to very different outcomes. Spatially explicit models (also called "spatially distributed" or "landscape" models) attempt to incorporate a heterogeneous spatial environment into the model.[27][28][29] A spatial model is one that has one or more state variables that are a function of space, or can be related to other spatial variables.[30]

Validation

After construction, models are validated to ensure that the results are acceptably accurate or realistic. One method is to test the model with multiple sets of data that are independent of the actual system being studied. This is important since certain inputs can cause a faulty model to output correct results. Another method of validation is to compare the model's output with data collected from field observations. Researchers frequently specify beforehand how much of a disparity they are willing to accept between parameters output by a model and those computed from field data.[31][32][33][34][35]

Examples

The Lotka–Volterra equations

Lotka Volterra dynamics
A sample time-series of the Lotka-Volterra model. Note that the two populations exhibit cyclic behaviour, and that the predator cycle lags behind that of the prey.

One of the earliest,[36] and most well-known, ecological models is the predator-prey model of Alfred J. Lotka (1925)[37] and Vito Volterra (1926).[38] This model takes the form of a pair of ordinary differential equations, one representing a prey species, the other its predator.

where,

  • is the number/concentration of the prey species;
  • is the number/concentration of the predator species;
  • is the prey species' growth rate;
  • is the predation rate of upon ;
  • is the assimilation efficiency of ;
  • is the mortality rate of the predator species

Volterra originally devised the model to explain fluctuations in fish and shark populations observed in the Adriatic Sea after the First World War (when fishing was curtailed). However, the equations have subsequently been applied more generally.[39] Although simple, they illustrate some of the salient features of ecological models: modelled biological populations experience growth, interact with other populations (as either predators, prey or competitors) and suffer mortality.

A credible, simple alternative to the Lotka-Volterra predator-prey model and its common prey dependent generalizations is the ratio dependent or Arditi-Ginzburg model.[40] The two are the extremes of the spectrum of predator interference models. According to the authors of the alternative view, the data show that true interactions in nature are so far from the Lotka-Volterra extreme on the interference spectrum that the model can simply be discounted as wrong. They are much closer to the ratio dependent extreme, so if a simple model is needed one can use the Arditi-Ginzburg model as the first approximation.[41]

Others

The theoretical ecologist Robert Ulanowicz has used information theory tools to describe the structure of ecosystems, emphasizing mutual information (correlations) in studied systems. Drawing on this methodology and prior observations of complex ecosystems, Ulanowicz depicts approaches to determining the stress levels on ecosystems and predicting system reactions to defined types of alteration in their settings (such as increased or reduced energy flow, and eutrophication.[42]

Conway's Game of Life and its variations model ecosystems where proximity of the members of a population are factors in population growth.

See also

References

  1. ^ Fasham, M. J. R.; Ducklow, H. W.; McKelvie, S. M. (1990). "A nitrogen-based model of plankton dynamics in the oceanic mixed layer". Journal of Marine Research. 48 (3): 591–639. doi:10.1357/002224090784984678.
  2. ^ Hall, Charles A.S. & Day, John W. (1990). Ecosystem Modeling in Theory and Practice: An Introduction with Case Histories. University Press of Colorado. pp. 7–8. ISBN 978-0-87081-216-3.
  3. ^ Hall & Day, 1990: pp. 13-14
  4. ^ Dale, Virginia H. (2003). "Opportunities for Using Ecological Models for Resource Management". Ecological Modeling for Resource Management. pp. 3–19. doi:10.1007/0-387-21563-8_1. ISBN 978-0-387-95493-6.
  5. ^ Pastorok, Robert A. (2002). "Introduction". Ecological modeling in risk assessment: chemical effects on populations, ecosystems, and landscapes. CRC Press. p. 7. ISBN 978-1-56670-574-5.
  6. ^ Forbes, Valery E. (2009). "The Role of Ecological Modeling in Risk Assessments Seen From an Academic's Point of View". In Thorbek, Pernille (ed.). Ecological Models for Regulatory Risk Assessments of Pesticides: Developing a Strategy for the Future. CRC Press. p. 89. ISBN 978-1-4398-0511-4.
  7. ^ Palladino, Paolo (1996). "Ecological Modeling and Integrated Pest Management". Entomology, ecology and agriculture: the making of scientific careers in North America, 1885-1985. Psychology Press. p. 153. ISBN 978-3-7186-5907-4.
  8. ^ Millspaugh, Joshua J.; et al. (2008). "General Principles for Developing Landscape Models for Wildlife Conservation". Models for planning wildlife conservation in large landscapes. Academic Press. p. 1. ISBN 978-0-12-373631-4.
  9. ^ Marwick, Ben (2013). "Multiple Optima in Hoabinhian flaked stone artefact palaeoeconomics and palaeoecology at two archaeological sites in Northwest Thailand". Journal of Anthropological Archaeology. 32 (4): 553–564. doi:10.1016/j.jaa.2013.08.004.
  10. ^ Jørgensen, Sven Erik (1996). Handbook of environmental and ecological modeling. CRC Press. pp. 403–404. ISBN 978-1-56670-202-7.
  11. ^ Grant, William Edward & Swannack, Todd M. (2008). Ecological modeling: a common-sense approach to theory and practice. John Wiley & Sons. p. 74. ISBN 978-1-4051-6168-8.
  12. ^ Hall & Day, 1990 p. 9
  13. ^ Pauly, D. (2000). "Ecopath, Ecosim, and Ecospace as tools for evaluating ecosystem impact of fisheries". ICES Journal of Marine Science. 57 (3): 697–706. doi:10.1006/jmsc.2000.0726.
  14. ^ Christensen, Villy; Walters, Carl J. (2004). "Ecopath with Ecosim: Methods, capabilities and limitations". Ecological Modelling. 172 (2–4): 109–139. doi:10.1016/j.ecolmodel.2003.09.003.
  15. ^ Christensen V (2009) "The future of Ecopath" In: Palomares, MLD, Morissette L, Cisneros-Montemayor A, Varkey D, Coll M, Piroddi C (Eds), Ecopath 25 Years Conference Proceedings: Extended Abstracts, Fisheries Centre Research Reports 17(3): 159–160. University of British Columbia.
  16. ^ Khan, M. F.; Preetha, P.; Sharma, A. P. (2015). "Modelling the food web for assessment of the impact of stock supplementation in a reservoir ecosystem in India". Fisheries Management and Ecology. 22 (5): 359–370. doi:10.1111/fme.12134.
  17. ^ Panikkar, Preetha; Khan, M. Feroz; Desai, V. R.; Shrivastava, N. P.; Sharma, A. P. (2014). "Characterizing trophic interactions of a catfish dominated tropical reservoir ecosystem to assess the effects of management practices". Environmental Biology of Fishes. 98: 237–247. doi:10.1007/s10641-014-0255-6.
  18. ^ Panikkar, Preetha; Khan, M. Feroz (2008). "Comparative mass-balanced trophic models to assess the impact of environmental management measures in a tropical reservoir ecosystem". Ecological Modelling. 212 (3–4): 280–291. doi:10.1016/j.ecolmodel.2007.10.029.
  19. ^ Feroz Khan, M.; Panikkar, Preetha (2009). "Assessment of impacts of invasive fishes on the food web structure and ecosystem properties of a tropical reservoir in India". Ecological Modelling. 220 (18): 2281–2290. doi:10.1016/j.ecolmodel.2009.05.020.
  20. ^ Odum, H.T. (1971). Environment, Power, and Society. Wiley-Interscience New York, N.Y.
  21. ^ Soetaert, Karline & Herman, Peter M.J. (2009). A practical guide to ecological modelling: using R as a simulation platform. Springer. p. 11. ISBN 978-1-4020-8623-6.
  22. ^ Gillman, Michael & Hails, Rosemary (1997). An introduction to ecological modelling: putting practice into theory. Wiley-Blackwell. p. 4. ISBN 978-0-632-03634-9.
  23. ^ Müller, Felix; et al. (2011). "What are the General Conditions Under Which Ecological Models Can Be Applied". In Jopp, Fred; et al. (eds.). Modeling Complex Ecological Dynamics. Springer. pp. 13–14. ISBN 978-3-642-05028-2.
  24. ^ Hall & Day, 1990: p. 21
  25. ^ Hall & Day, 1990: p. 19
  26. ^ Buschke, Falko T.; Seaman, Maitland T. (2011). "Functional Feeding Groups as a Taxonomic Surrogate for a Grassland Arthropod Assemblage". African Invertebrates. 52: 217–228. doi:10.5733/afin.052.0112.
  27. ^ McCallum, Hamish (2000). "Spatial Parameters". Population parameters: estimation for ecological models. Wiley-Blackwell. p. 184. ISBN 978-0-86542-740-2.
  28. ^ Tenhunen, John D.; et al., eds. (2001). Ecosystem approaches to landscape management in Central Europe. Springer. pp. 586–587. ISBN 978-3-540-67267-8.
  29. ^ Ball, George L. (1999). "Ecological modeling". Encyclopedia of environmental science. Springer. p. 154. ISBN 978-0-412-74050-3.
  30. ^ Sklar, Fred H. & Hunsaker, Carolyn T. (2001). "The Use and Uncertainties of Spatial Data for Landscape Models: An Overview with Examples from the Florida Everglades". In Hunsaker, Carolyn T. (ed.). Spatial uncertainty in ecology: implications for remote sensing and GIS applications. Springer. p. 15. ISBN 978-0-387-95129-4.
  31. ^ Jørgensen, Sven Erik & Bendoricchio, G. (2001). Fundamentals of ecological modelling. Gulf Professional Publishing. p. 79. ISBN 978-0-08-044028-6.
  32. ^ Pastorok, Robert A. (2002). "Introduction". Ecological modeling in risk assessment: chemical effects on populations, ecosystems, and landscapes. CRC Press. p. 22. ISBN 978-1-56670-574-5.
  33. ^ Shifley, S.R. (2008). "Validation of Landscape-Scale Decision Support Models That Predict Vegetation and Wildlife Dynamics". In Millspaugh, Joshua J.; Thompson, Frank Richard (eds.). Models for planning wildlife conservation in large landscapes. Academic Press. p. 419. ISBN 978-0-12-373631-4.
  34. ^ Voinov, Alexey (2008). Systems Science and Modeling for Ecological Economics. Academic Press. p. 131. ISBN 978-0-12-372583-7.
  35. ^ Reuter, Hauke; et al. (2011). "How Valid Are Model Results? Assumptions, Validity Range and Documentation". In Jopp, Fred; et al. (eds.). Modeling Complex Ecological Dynamics. Springer. p. 325. ISBN 978-3-642-05028-2.
  36. ^ Earlier work on smallpox by Daniel Bernoulli and human overpopulation by Thomas Malthus predates that of Lotka and Volterra, but is not strictly ecological in nature
  37. ^ Lotka, A. J. (1925). The Elements of Physical Biology. Williams & Williams Co., Baltimore, USA.
  38. ^ Volterra, Vito (1926). "Fluctuations in the Abundance of a Species considered Mathematically". Nature. 118 (2972): 558–560. doi:10.1038/118558a0.
  39. ^ Begon, M.; Harper, J. L.; Townsend, C. R. (1988). Ecology: Individuals, Populations and Communities. Blackwell Scientific Publications Inc., Oxford, UK.
  40. ^ Arditi, Roger; Ginzburg, Lev R. (1989). "Coupling in predator-prey dynamics: Ratio-Dependence". Journal of Theoretical Biology. 139 (3): 311–326. doi:10.1016/S0022-5193(89)80211-5.
  41. ^ Arditi, R. and Ginzburg, L.R. (2012) How Species Interact: Altering the Standard View on Trophic Ecology Oxford University Press. ISBN 9780199913831.
  42. ^ Ulanowicz, Robert E. (1997). Ecology, the Ascendent Perspective. Columbia University Press. ISBN 978-0-231-10829-4.

Further reading

  • Khan, M. F.; Preetha, P.; Sharma, A. P. (2015). "Modelling the food web for assessment of the impact of stock supplementation in a reservoir ecosystem in India". Fisheries Management and Ecology. 22 (5): 359–370. doi:10.1111/fme.12134.
  • Panikkar, Preetha; Khan, M. Feroz; Desai, V. R.; Shrivastava, N. P.; Sharma, A. P. (2014). "Characterizing trophic interactions of a catfish dominated tropical reservoir ecosystem to assess the effects of management practices". Environmental Biology of Fishes. 98: 237–247. doi:10.1007/s10641-014-0255-6.
  • Panikkar, Preetha; Khan, M. Feroz (2008). "Comparative mass-balanced trophic models to assess the impact of environmental management measures in a tropical reservoir ecosystem". Ecological Modelling. 212 (3–4): 280–291. doi:10.1016/j.ecolmodel.2007.10.029.
  • Feroz Khan, M.; Panikkar, Preetha (2009). "Assessment of impacts of invasive fishes on the food web structure and ecosystem properties of a tropical reservoir in India". Ecological Modelling. 220 (18): 2281–2290. doi:10.1016/j.ecolmodel.2009.05.020.

External links

Biogeochemistry

Biogeochemistry is the scientific discipline that involves the study of the chemical, physical, geological, and biological processes and reactions that govern the composition of the natural environment (including the biosphere, the cryosphere, the hydrosphere, the pedosphere, the atmosphere, and the lithosphere). In particular, biogeochemistry is the study of the cycles of chemical elements, such as carbon and nitrogen, and their interactions with and incorporation into living things transported through earth scale biological systems in space through time. The field focuses on chemical cycles which are either driven by or influence biological activity. Particular emphasis is placed on the study of carbon, nitrogen, sulfur, and phosphorus cycles. Biogeochemistry is a systems science closely related to systems ecology.

Cascade effect (ecology)

An ecological cascade effect is a series of secondary extinctions that is triggered by the primary extinction of a key species in an ecosystem. Secondary extinctions are likely to occur when the threatened species are: dependent on a few specific food sources, mutualistic (dependent on the key species in some way), or forced to coexist with an invasive species that is introduced to the ecosystem. Species introductions to a foreign ecosystem can often devastate entire communities, and even entire ecosystems. These exotic species monopolize the ecosystem's resources, and since they have no natural predators to decrease their growth, they are able to increase indefinitely. Olsen et al. showed that exotic species have caused lake and estuary ecosystems to go through cascade effects due to loss of algae, crayfish, mollusks, fish, amphibians, and birds. However, the principal cause of cascade effects is the loss of top predators as the key species. As a result of this loss, a dramatic increase (ecological release) of prey species occurs. The prey is then able to overexploit its own food resources, until the population numbers decrease in abundance, which can lead to extinction. When the prey's food resources disappear, they starve and may go extinct as well. If the prey species is herbivorous, then their initial release and exploitation of the plants may result in a loss of plant biodiversity in the area. If other organisms in the ecosystem also depend upon these plants as food resources, then these species may go extinct as well. An example of the cascade effect caused by the loss of a top predator is apparent in tropical forests. When hunters cause local extinctions of top predators, the predators' prey's population numbers increase, causing an overexploitation of a food resource and a cascade effect of species loss. Recent studies have been performed on approaches to mitigate extinction cascades in food-web networks.

Ecological forecasting

Ecological forecasting uses knowledge of physics, ecology and physiology to predict how ecological populations, communities, or ecosystems will change in the future in response to environmental factors such as climate change. The ultimate goal of the approach is to provide people such as resource managers and designers of marine reserves with information that they can then use to respond, in advance, to future changes, a form of adaptation to global warming.

One of the most important environmental factors for organisms today is global warming. Most physiological processes are affected by temperature, and so even small changes in weather and climate can lead to large changes in the growth, reproduction and survival of animals and plants. The scientific consensus

is that the increase in atmospheric greenhouse gases due to human activity caused most of the warming observed since the start of the industrial era. These changes are in turn affecting human and natural ecosystems.One major challenge is to predict where, when and with what magnitude changes are likely to occur so that we can mitigate or at least prepare for them. Ecological forecasting applies existing knowledge of how animals and plants interact with their physical environment to ask how changes in environmental factors might result in changes to the ecosystems as a whole.

Ecopath

Ecopath with Ecosim (EwE) is a free and open source ecosystem modelling software suite, initially started at NOAA by Jeffrey Polovina, but has since primarily been developed at the formerly UBC Fisheries Centre of the University of British Columbia. In 2007, it was named as one of the ten biggest scientific breakthroughs in NOAA's 200-year history. The NOAA citation states that Ecopath "revolutionized scientists' ability worldwide to understand complex marine ecosystems". Behind this lie more than two decades of development work in association with Villy Christensen, Carl Walters, Daniel Pauly, and other fisheries scientists, followed with the provision of user support, training and co-development collaborations. In 2013, development efforts were centralized under Ecopath International Initiative, Spain. Per January 2019 there are an estimated 8000+ users across academia, non-government organizations, industry and governments in 150+ countries.

Environmental modelling

Environmental modelling is the creation and use of mathematical models of the environment. Environmental modelling may be used purely for research purposes and improved understanding of environmental systems, or for providing an interdisciplinary analysis that can inform decision making and policy.

Functional response

A functional response in ecology is the intake rate of a consumer as a function of food density (the amount of food available in a given ecotope). It is associated with the numerical response, which is the reproduction rate of a consumer as a function of food density. Following C. S. Holling, functional responses are generally classified into three types, which are called Holling's type I, II, and III.

Knowledge ecosystem

The idea of a knowledge ecosystem is an approach to knowledge management which claims to foster the dynamic evolution of knowledge interactions between entities to improve decision-making and innovation through improved evolutionary networks of collaboration.In contrast to purely directive management efforts that attempt either to manage or direct outcomes, knowledge ecosystems espouse that knowledge strategies should focus more on enabling self-organization in response to changing environments. The suitability between knowledge and problems confronted defines the degree of "fitness" of a knowledge ecosystem. Articles discussing such ecological approaches typically incorporate elements of complex adaptive systems theory. Known implementation considerations of knowledge ecosystem include the Canadian Government.

Mesoamerican Society for Ecological Economics

The Mesoamerican Society for Ecological Economics (SMEE) is a regional chapter of the International Society for Ecological Economics (ISEE). After its foundation in 2008 at Guatemala City, the organization has already celebrated its first International Conference in 2010 at Mexico City and will carry out the second International Conference, EcoEco Alternatives, between March 4 and 8 2014 at the main campus of the University of Costa Rica.This branch of the ISEE has a unique emphasis within ecological economics. Topics like social justice and the human value in environmental conservation prevail in this region. As a consequence of the strong influence from Joan Martinez Alier's "environmentalism of the poor or social environmentalism", major attention is given to ecological-distributive conflicts. Alier insists that in the South a struggle exists against these conflicts generated by economic growth, mainly by the North. These endeavors "attempt to preserve the access of the communities to natural resources and services." On top of the negative effects on the environment by economic distribution, the cultural influence is also widely debated. For instance, the anthropologist Arturo Escobar suggests that culturally-driven preferences are one of the main factors degrading the environment. For example, society naturally gives privilege to the capitalist model that distributes natural resources with the purposes of production and profit, instead of endorsing the agroforestal ecosystem model, which is less harmful to the environment. As part of this alternate perception in Mesoamerica, Ecological economics doesn't consider that the economic valuation of natural resources nor environmental norms are effective solutions to these social-environmental conflicts. On the other hand, an alternative based on community-based conservation and the management of sustainability is more advocated upon. By adding the latter cultural perspective, the three pillars of sustainable development (the social, environmental, and economic) end up being addressed by these proponents.

Mesopredator release hypothesis

The mesopredator release hypothesis is an ecological theory used to describe the interrelated population dynamics between apex predators and mesopredators within an ecosystem, such that a collapsing population of the former results in dramatically-increased populations of the latter. This hypothesis describes the phenomenon of trophic cascade in specific terrestrial communities.

A mesopredator is a medium-sized, middle trophic level predator, which both preys and is preyed upon. Examples are raccoons, skunks, snakes, cownose rays, and small sharks.

Michael Fasham

Michael John Robert Fasham, FRS (29 May 1942 – 7 June 2008) was a British oceanographer and ecosystem modeller. He is best known for his pioneering work in the development of open ocean plankton ecosystem models.

PCDitch

PCDitch is a dynamic aquatic ecosystem model used to study eutrophication effects in ditches. PCDitch models the nutrient fluxes in the water, the sediment and the vegetation, as well as the competition between different groups of vegetation. PCDitch is used both by scientists and water quality managers.

PCLake

PCLake is a dynamic, mathematical model used to study eutrophication effects in shallow lakes and ponds. PCLake models explicitly the most important biotic groups and their interrelations, within the general framework of nutrient cycles. PCLake is used both by scientist and water managers. PCLake is in 2019 extended to PCLake+ which can be applied to stratifying lakes.

Pattern-oriented modeling

Pattern-oriented modeling (POM) is an approach to bottom-up complex systems analysis that was developed to model complex ecological and agent-based systems. A goal of POM is to make ecological modeling more rigorous and comprehensive.A traditional ecosystem model attempts to approximate the real system as closely as possible. POM proponents posit that an ecosystem is so information-rich that an ecosystem model will inevitably either leave out relevant information or become over-parameterized and lose predictive power. Through a focus on only the relevant patterns in the real system, POM offers a meaningful alternative to the traditional approach.

An attempt to mimic the scientific method, POM requires the researcher to begin with a pattern found in the real system, posit hypotheses to explain the pattern, and then develop predictions that can be tested. A model used to determine the original pattern may not be used to test the researcher’s predictions. Through this focus on the pattern, the model can be constructed to include only information relevant to the question at hand.POM is also characterized by an effort to identify the appropriate temporal and spatial scale at which to study a pattern, and to avoid the assumption that a single process might explain a pattern at multiple temporal or spatial scales. It does, however, offer the opportunity to look explicitly at how processes at multiple scales might be driving a particular pattern.A look at the trade-offs between model complexity and payoff can be considered in the framework of the Medawar zone. The model is considered too simple if it addresses a single problem (e.g., the explanation behind a single pattern), whereas it will be considered too complex if it incorporates all the available biological data. The Medawar zone, where the payoff in what is learned is greatest, is at an intermediate level of model complexity.

Productivity (ecology)

In ecology, productivity refers to the rate of generation of biomass in an ecosystem. It is usually expressed in units of mass per unit surface (or volume) per unit time, for instance grams per square metre per day (g m−2 d−1). The mass unit may relate to dry matter or to the mass of carbon generated. Productivity of autotrophs such as plants is called primary productivity, while that of heterotrophs such as animals is called secondary productivity.

Rapoport's rule

Rapoport's rule is an ecogeographical rule that states that latitudinal ranges of plants and animals are generally smaller at lower latitudes than at higher latitudes.

Sustainable gardening

Sustainable gardening includes the more specific sustainable landscapes, sustainable landscape design, sustainable landscaping, sustainable landscape architecture, resulting in sustainable sites. It comprises a disparate group of horticultural interests that can share the aims and objectives associated with the international post-1980s sustainable development and sustainability programs developed to address the fact that humans are now using natural biophysical resources faster than they can be replenished by nature.Included within this compass are those home gardeners, and members of the landscape and nursery industries, and municipal authorities, that integrate environmental, social, and economic factors to create a more sustainable future.

Organic gardening and the use of native plants are integral to sustainable gardening.

System dynamics

System dynamics (SD) is an approach to understanding the nonlinear behaviour of complex systems over time using stocks, flows, internal feedback loops, table functions and time delays.

Systems psychology

Systems psychology is a branch of both theoretical psychology and applied psychology that studies human behaviour and experience in complex systems. It is inspired by systems theory and systems thinking, and based on the theoretical work of Roger Barker, Gregory Bateson, Humberto Maturana and others. Groups and individuals are considered as systems in homeostasis. Alternative terms here are "systemic psychology", "systems behavior", and "systems-based psychology".

Villy Christensen

Villy Christensen is an ecosystem modeller with a background in fisheries science. He is known for his work as a project leader and core developer of Ecopath, an ecosystem modelling software system widely used in fisheries management. Ecopath was initially an initiative of the NOAA, but since primarily developed at the UBC Fisheries Centre of the University of British Columbia. In 2007, it was named as one of the ten biggest scientific breakthroughs in NOAA’s 200-year history. The citation states that Ecopath “revolutionized scientists’ ability worldwide to understand complex marine ecosystems".

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