CS 380 Lab 3:  Species Distribution Modeling using MaxEnt  (Part II)

In the second part of the MaxEnt lab, we will build predictive models that combine information from multiple climate parameters to make stronger predictions.
  1. For each of your two species, now use multiple climate parameters to run a new model. Use your original two choices and any other climate variables that you identified in the last question from Part I.  When re-running your model, also check the box on the MaxEnt screen labeled “Do jackknife to measure variable importance.” Be sure to create two new output folders within your working directory.
  2. Re-evaluate the two new maps of predicted species presence. Label and paste them into your lab write-up. Did incorporating more climate variables improve the model's performance? If so, where and why? Where does the model still seem to be inaccurate? Why do you think this is?
Look at the response curves for each of your two new models. Note that the top and bottom rows of response curves look different even though they represent the same climate variables. The bottom curves represent what each response curve would look like if it were the only variable used to predict the probability of species presence. The top curves show the actual relationships between all climate variables and species presence in your new model.  The multivariate model may indicate a wider response range for a variable than was discovered using that variable alone.

Pick the single species and distribution model that interest you the most. Label and paste all response curves (top and bottom) from that new model into your lab write-up.
  1. What do the response curves from your chosen model tell you about the climate constraints on your species?
  2. Do any of the variables function very differently in this multi-variable model than they would alone (are any top curves very different than their counterparts on the bottom)? Why do you think this is?

Examining the Effects of Climate Change on Species Distributions

Much of the western United States became warmer during the 1900s. This warming is expected to continue for many years to come as a result of an increase in the amount of long-wave radiation emitted towards the ground by greenhouse gas molecules like CO2, CH3, and H2O. This is likely to affect forests substantially. Species living in hot, dry regions are likely to suffer as evapotranspiration rates (and thus drought) increase. Species living in cold regions may benefit as warmer temperatures may allow for photosynthesis earlier in the spring and later in the fall.

These changes are likely to impact forests most substantially at their boundaries, where trees stand on the front lines of a constant battle between survival and death. If temperatures warm, new seedlings and mature trees growing at the upper elevation tree line in the Sierra Nevadas will die less often and the upper tree line will rise. If evapotranspiration increases, new seedlings and mature trees growing on the lower elevation tree line between alpine forest above and desert scrub below will die more often and the lower tree line will also rise. This is how the edges of populations move when climate changes.

In this section you will use the last multivariable model that you created above, and apply it to new climate data that assume a hypothetical change of 4°C. While real temperature change will be very spatially, seasonally, and diurnally variable (warming should be most substantial near poles, during winter, and at night), this hypothetical temperature change is applied everywhere at all times. So, we are assuming that diurnal temperature range and annual temperature range are unchanged. We also assume that rainfall is unchanged.
 
  1. Set up MaxEnt to run the same model as the last multi-variable model you created above. However, before running the model, use the Browse button next to “Projection layers directory/file” to select the environmentIncrTemp folder described in the previous lab.
  2. Run the model, remembering to create a new output folder.
  3. Open the .html file in your output folder and scroll to the maps. The top map should be identical to the map produced by your last model run. Check to make sure it is. The bottom map shows the probabilities for species presence given the hypothetical warming of 4°C.  Include this map in your lab write-up, placing it side-by-side with a map of the predictions for the current temperature.  Label each map clearly.
  4. How do your predictions of species presence in a warmer climate differ from those in the current climate? Where are the regions where your species is no longer predicted to grow? Why do you think this is?
  5. Are there areas where the probability of species presence has increased? Where are they and why do you think this happened?
Place your lab write-up (just one per group, please!) in hardcopy into the submission box outside my office (Park 249) by Tuesday, Oct. 5th at 2:30pm (class time) or submit it in-class then.
This lab is based on the Species Distribution Modeling assignment developed by Park Williams, UCSB Geography.