Building a Habitat Suitability Model Part 3.
Back in August/September, I started a series of blogs about my work here at the ASLC on habitat modeling. Since it has been a while, when I sat down to write this next installment I figured I should start with a recap. You can read the original posts here and here, but I think there is a better way to find out what has been going on between episodes…
Now that we are caught up, you may be wondering: “What exactly is a ‘habitat use model and why is the math awakening?”
Think back to elementary school–were you ever asked to make a model of the solar system? Of a volcano? I remember in sixth grade we were asked to make models of DNA or of a cell using edible materials. There were double helices of DNA made up of Twizzlers and gum drops (red, green, blue, and yellow for A-T-C-G); and bowls of cytoplasmic Jell-o with assorted candy structures representing the nucleus, Golgi apparatus, and mitochondria (the powerhouse of the cell). None of these ‘models’ were 100% accurate to real life, but they took a very complicated system and distilled the important parts into a simplified version that conveyed something about the relationships and structure.
Mathematical models, of which habitat use models are a specific type, are just like that golf-ball model solar system: they look at relationships between components of a system but instead of conveying them with distances, colors or sizes, mathematical models describe the relationships using equations. This allows mathematical models to not only describe the pattern, but to predict how parts of the system will respond as other parts change.
I was going to go into more about how models work, and why they are so important– but then I found this great video from the International Arctic Research Center that explains it WAY better than I could.
If you don’t have 13 minutes to learn something new, some of the main take-away points from the video are:
- The world is messy–we use models to help understand the world and plan
- We need data! Models need data to help represent the real world and explain patterns.
- Scientists are ALWAYS going back to the data and validating models to make sure that they are still good representations of what is happening in the real world.
- The math involved in models can often be understood with basic algebra (y=mx+b) so those skills are important to teach to young students!
- A more complicated model is not always a better model
- Models are not crystal balls
Hopefully this video helps demystify some of the ‘scariness’ of models and helps explain why they are such important tools for scientists and for our interested in understanding the relationship between animal movement and the environment. The marine world is very complicated, but mathematical models can help us tease apart these patterns. As we move forward with our models on the Juvenile Steller sea lion project, we will share our results here with you!
Written by: Amy Bishop
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