Spatial ecology can be defined as the study of the relationship between landscapes and organisms and life forms in that space. It focuses on how the specific spatial arrangements of organisms, populations, and landscapes affect ecological dynamics.
Figure 1: An image extracted from the spatial ecology simulation from Labster. The simulation is useful for High School and University/College course.
Although diversity indices are very useful for assessing the diversity of an area, they can hide some information. The importance of endemic species is easily obscured in such an index. Endemic species are species with a very limited geographic distribution so that they can be found in a certain area and nowhere else in the world. For the Index, an endemic species is just another species in the area, but more than that, because removing it from the area means the extinction of that species. Therefore, endemic status must be taken into account when making decisions.
Read on for some thoughts on why this can be a challenging topic for teachers and students, five suggestions for changing it, and thoughts on why virtual labs can make things easier.
There are three reasons in particular why spatial ecology can be difficult, even for the most diligent of students.
It is difficult for high school students to understand the study of spatial ecology because ecosystems are highly integrated sites with many organisms that have many relationships with each other. This complexity means that it is often difficult to understand the actual consequences of an event or action.
The interpolation technique is a mathematical method to create new data points from an existing data set. This technique uses the information from the variables in the data set to predict values of the same variables in other locations within the sampling area (Figure 2). There are many different types of interpolation techniques, and they can go from simply assigning the value of the nearest existing data point to the new data point, to complex models that take into account all the datasets every time that they need to predict one new data point.
For example, in Figure 2 the blue circles are the sampled data points. Using the interpolation technique, a new data point (blue square) can be estimated. The size of the blue circles represents the influence of the original data points on the new data point generated. The closer the original data point is (blue circle) to the newly generated one(blue square), the more influence it will have on the new data point. Note that all previous data points will have some influence on that new data point, no matter how far away they are.
Figure 2: Example of how interpolation works.
The diversity index is a quantitative measure used to assess an area in terms of the number and distribution of species found in it. There are many different types of indexes. Some indices are very simple, considering only the number of species found in the area (richness), while others also consider community uniformity (Shannon and Simpson index).
The Shannon index considers the number and uniformity of species across communities.
The Shannon index is calculated from the ratio of individuals of a species to the total number of individuals of all species combined (pi) and multiplied by the natural logarithm of the same number. This is done for each type, and all the numbers are added. Shannon Index values in natural systems typically range from 0.5 to 5. In general, areas with a Shannon Index value below 2 are considered areas of low diversity, while areas with a Shannon Diversity Index above 3 are considered areas of high diversity.
With these points in mind, here are five things you can consider introducing into your spatial ecology lessons to make them more engaging, approachable, and enjoyable to teach for you and to learn for your students.
Spatial ecology has largely emerged from important empirical and theoretical developments in the 1950s and 1970s that highlighted how spatial heterogeneity can drive population persistence and how dispersal can profoundly impact populations and communities.
Temperature can be measured in any environment with a thermometer. Some species have different tolerances to different temperature ranges. Under certain conditions, the temperature can determine the range of some species because (among other effects) it can slow down or speed up enzymatic processes. Some animal and plant species may use different strategies to adapt to extreme temperatures. In cold temperatures, the presence of a thick layer of fur or fat in animals helps them stay warm, whereas the use of photosynthesis of CAM (Crassulacean Acid Metabolism) in plants allows them to store water in their tissues during drought.
Salinity is defined as the amount of salt dissolved in water or contained in soil/sediment. Salinity is usually caused by cations and anions such as Ca2+, K+, Mg2+, and Cl-. The salinity of certain areas may increase due to their proximity to the ocean where water can seep through the terrain, but dissolved minerals can also cause this in the substrate. High salinity can be toxic to organisms that are not adapted because it can reduce the ability of organisms to maintain homeostasis and retain water. Salinity can be measured with a conductometer or refractometer. Conductometers usually use electrical resistance to estimate salinity, which is why the unit for measuring salinity is micro siemens per meter.
An additional environmental variable is water. Moisture content is the amount of water contained in a material, in this case, the substrate. Water is an essential element for life and low water levels are detrimental to species that do not have proper adaptations. Moisture content can be measured by the difference in weight between the wet substrate (without treatment) and the dry substrate (after drying in the oven for 48 hours). For this reason, one of the most common units for measuring soil moisture content is the volume percent or cubic centimeters of water per cubic centimeter of soil.
A sample is a selection from a subset of the population (sample) that is considered representative of the group to which they belong. This is done to study or determine the characteristics of the entire population. Sampling is also about determining the location of a particular area to be sampled. In "sampling" different methods can be used to select the sampling point:
Random: Samples were taken randomly from the research area.
Systematic: Measuring points are selected according to a defined pattern (grid).
Stratified: Before selecting a location, the area is divided into groups according to certain variables/parameters (for example if we are only interested in areas above a certain height).
Subjective: The area was selected for non-objective reasons.
Note that the random and systematic sampling method is useful when knowledge of the learning system is limited. However, systematic sampling is usually the best option when interpolation techniques or creating gradient maps of an area are required. Importantly, stratified and subjective methods require a high level of prior knowledge of the research area.
Figure 4: An image extracted from the spatial ecology simulation from Labster. The simulation is useful for High School and University/College courses
A unique way to teach spatial ecology is through a virtual laboratory simulation. At Labster, we strive to provide highly interactive lab presentations using game-based elements such as storytelling and scoring systems in an immersive 3D world.
Check out the Labster spatial ecology simulation that allows students to learn about spatial ecology through active, inquiry-based learning. In the simulation, students will go on a mission to help the residents of the exoplanet Astakos IV choose the location for the new research center while learning how environmental factors can influence species distribution.
Learn more about the spatial ecology simulation here, or get in touch to find out how you can start using virtual labs with your students.
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