Population Dynamics
Table of Contents
Lab 1 - Population Dynamics
Introduction
Analyzing the population trends of an ecosystem is a fundamental skill ecologists use to understand the interaction between organisms in their environment. Plotting population data is necessary to both analyze and communicate these trends. Students will demonstrate their understanding of population dynamics by plotting invasive species data using MATLAB.
Learning Objectives
Students are able to create vectors and the naming constraints in MATLAB
Students understand how to create a plot in MATLAB using two vectors
I Do
Going online and identifying a data set, downloading, preparing/cleaning data, and creating the data visualization.
We Do
Step 1 - Each student chooses a different species and follows the same procedure, with assistance if needed.
Step 2 - Students are assigned different time frames for a particular species and the individual plots are compared to the full dataset
You Do
Plot the weight in metric tons of the native white catfish and invasive blue catfish species in Virginia from 2009 to 2020.
Population Dynamics
Population dynamics is the study of changes in populations over time and can be affected by many biological and physical factors. One of the greatest of these factors is the energy transfers between organisms in an ecosystem shown through a food chain. The constant interaction between predators and prey act as a limiting factor of growth. As the population of a predator increases, the population of prey will decrease since they are being consumed more. Less available prey for predator consumption will cause a decrease in predator population and an increase in prey. This is known as population cycling where populations rise and fall in response to resource availability.
Invasive species are organisms introduced into a non-native ecosystem and a lot of times cause economic and ecological harm. If the limiting factors of invasive species are not present in the ecosystem they are introduced to, they can grow uncontrolled and out-compete native species for resources.
Data Source
There are plenty of sources of wildlife data that track population dynamics throughout the years including the National Oceanic and Atmospheric Administration (NOAA) for oceanic wildlife. The NOAA fisheries data extraction tool collects commercial and recreation landings data throughout the United States by year, state, and species. This data gives insight into the populations of oceanic wildlife over time.
Using the tool, let's pull some data; follow the link below and choose a species, states, and a range of years. For example, data for the blue crab in Virginia from 1950-2020 are shown in the table.
https://www.fisheries.noaa.gov/foss/f?p=215:200:7609940163857:Mail:NO:::
Visualization
Looking at the table of metric tons of blue crabs caught each year, are you able to identify any trends?
Probably not. Data visualization is important in highlighting trends and organizing data in an easy-to-view format. Visualization can include charts, maps, infographics, etc. and is a necessary skill for all scientists to communicate their findings.
Sampling choices
When creating data visualizations, one consideration we have to make is the effect of sampling choices on the accuracy of a model as the representation of a whole system. This is especially important for biologists since we design experiments by choosing the duration of a study, frequency of data collection, and times of collection to collect data we use for analysis. If we don't make the right choices on how we collect our data, we might miss the bigger picture of our study subject. Let's use the blue crab data from the table above to visualize a couple of examples with different sampling choices.
First, lets say we only collected data from 1950-1964 instead of 1950-2020 and used that to predict the outlook of blue crab populations in Virginia. Looking at the table below, there is an upward spike towards the end of the graph, so we may assume the population continues upwards.
Instead of using the first 15 years of data we have, let's say we only have the last 15, from 2006 to 2020. The trend we notice here is a lot different than the first graph we made.
Now, instead of collecting data yearly, let's plot data collected every 10 years. The trend is clear and more complete, and we can start to predict and analyze the population dynamics. Now, we can see a downward trend overall despite the spikes up in some years.
By plotting yearly data, we are able to see the same decreasing population while also seeing the cycling occurring. This could be helpful in analyzing influences of specific yearly factors that could not be seen in 10 year data.
Making the right data collection and plotting choices is an essential part of data visualization and should always be considered with the goal in mind.
Code Sample
