To work as an environmental scientist means examining ecology, the economy, and society, all of which can impact or be impacted by the general environment. All are complex systems involving hundreds or thousands of individual components. For example, an ecological system could comprise many types of wildlife, plant species, fungi, weather patterns, geography, and much more.
Maintaining a holistic view of relevant systems can allow environmental professionals to make great insights through data modeling of those same systems. Data modeling is a process used to create a representation of something found in the real world, generally with the purpose of using the model for experimentation and research instead of its real world counterpart. By studying to become an environmental scientist, you can learn how to apply systems thinking to the data modeling process, and thereby discover useful information about the systems that affect the environment.
Here is a look at how graduates of environmental science training can apply systems thinking to data modeling.
Graduates of Environmental School Use Systems Thinking to Create Predictive Models
One of the primary uses for data models is as predictive tools used to anticipate how modifications to the modeled system could result in change. For example, a researcher could use data modeling to predict how global warming might result in rising sea levels around the globe.
Using a system-based approach to these models can offer greater accuracy than merely analyzing a few component parts. This is because a holistic model of a system will account for a far greater number of its many variables, some of which could prove surprisingly influential throughout the time that change is ongoing. Sea levels, for example, will be affected by ice melting in the Arctic and Antarctic and by the fact that warm water has a greater volume than cold water as well as other variables.
Failing to account for an influential variable will make a model less capable of accurate prediction. Therefore, the model will not be as valuable for analyzing the system that’s being researched. Professionals holding a Master of Environmental Management who can create useful models are therefore hugely important to conservation research.
Systems Thinking Helps Scientists Research Surprises in Models
Though introducing modifications to models often leads to predictable results, it is not uncommon for surprises to pop up, too. This can be because complex systems possess “emergent” properties. Emergent properties are not inherent to any one component of the system, but rather exist as a result of interactions between components. A sea turtle’s preferred migration route, for example, is not a quality of the animal’s structural system, but rather an emergent behavior arising from the animal’s interaction with outside influences (the environment, availability of food, etc.)
When a single component of a system changes, it can affect the alteration, creation, or elimination of emergent properties within this system. Waterway currents may weaken, a species of plant may begin to overrun its environment, etc. Being able to create, analyze, and understand system models will allow graduates of environmental schools to interpret surprise emergent behavior resulting from modifications to models, and use that information as a basis for further research.
Often, surprises in modeling will prove to be nothing more than error, but given the delicacy of environments the world over, it is vital that nothing is left to chance and that potentially dangerous changes can be anticipated and prevented.
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