BMCC Ecology Species Distribution Modeling Conclusion Summary

Lab 2:
Species distribution modeling
Overview of Lab activities
1. Overview of key topics in Species Distribution Modeling
2. Instructor-led Wallace tutorial, modeling distributions in a conservation context
a. Modeling the distribution & habitat suitability of the spectacled bear (Tremarctos ornatus)
b. For a written tutorial see page 5
3. Create a model, using Wallace, for one of the species that you are studying for your group project.
In a future Writing Practice, you will write-up the results of this model into your group project.
Lab objectives
1. Discuss the relationship between environments and species’ distributions
2. Build a species distribution model based on locality and environmental data
3. Explore distribution changes with changing environments (in both time and space)
Key concepts and calculations
1. Species distribution model (SDM): an estimate of habitat suitability for a species given occurrence
information and associated environmental data
2. BAM diagram: the conceptual framework of Biotic, Abiotic, and Movement (or Mobility, or
Migration) factors that determine where a species can exist and where it does exist
3. Geographic range vs. potential distribution
Background
Geographic ranges of species are influenced by three main factors. First, they
are a reflection of the abiotic (environmental) conditions that are suitable for a
species’ persistence (“A” in figure to the right). Second, biotic interactions can
limit the areas where species can be present; for example, strong competition
may exclude a focal species (“B” in figure). Finally, even if abiotic and biotic
conditions are suitable, a species must be able to access a region, or migrate to
it, for the species to be present (“M” in figure).
Lab exercise
For a brief background on Species Distribution Models and their uses, read through the abstract of this
paper:
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Warren, DL, Dornburg, A, Zapfe, K, Iglesias, TL. The effects of climate change on Australia’s only endemic
Pokémon: Measuring bias in species distribution models. Methods Ecol Evol. 2021; 00: 1– 11.
https://doi.org/10.1111/2041‐210X.13591
https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13591
Wallace is a Graphical User Interface (also known as GUI, think “point-and-click”) application for
ecological modeling programmed in the computer language R. Its current version focuses on building,
evaluating, and visualizing models of species niches and distributions. We will refer to these models as
Species Distribution Models (SDMs). Know that there is continuous debate about what these models
actually show the user, but you can think of them as hypotheses of the geographical distribution of a
species based on environmental conditions of the areas where the species is known to occur. As you
read through sections of the online app (called components and modules), you will be pointed to some
sources of detailed info for reference.
***Fun Fact: Wallace was largely developed within the CCNY Biology Department, led by the lab of
Prof. Robert Anderson. Ph.D. and Master’s students in the Anderson and Carnaval labs have contributed
to its development.
We will be using a cloud-based system to run our SDMs. This way, we will not need to download any
software or rely upon the processing power of our computers. Instead, all the coding, programming, and
functionality of Wallace is stored in the cloud, and we can draw upon this functionality via the internet.
First, you must go to the RStudio Cloud site (https://rstudio.cloud/) and Sign Up for a new account (you
can also sign in with a google account).
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After creating your account, follow this link https://rstudio.cloud/project/3505328. This will bring you to
a functional space from which you can access Wallace, but it is not yet linked to your RStudio Cloud
account (signaled by the red, flashing “TEMPORARY COPY” along the top of the page). To have your
own modifiable version, click on Save a Permanent Copy (right next to the red, flashing words).
With this personal copy, you must run the following commands in the R Console (the big, central box on
your screen), which will bring you to Wallace. Simply type them in as written here, and press enter for
each of the two commands.
> library(wallace)
> run_wallace()
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After running the run_wallace() command, a new tab will open with the Wallace GUI. You may proceed
to follow your instructor’s tutorial on modeling the spectacled bear’s distribution (you can follow along
here: (https://wallaceecomod.github.io/vignettes/wallace_vignette.html). For this lab, we will be creating
models using an algorithm called Bioclim.
*Along the top are the 8 components of Wallace, each with one or more modules, or functions, to perform.*
When the tutorial is finished, proceed with modeling the species selected for your group project. First,
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choose one species in your group to model- we recommend choosing the species with the most occurrence
points. Now, develop a model representative of this species’ current environmental niche (limited to
where it exists now and using current climatic conditions).
**If the Wallace screen turns gray – close window and start over in R Studio cloud with run_wallace()**
Before you leave the session, be sure to show and discuss your results with your instructor, should
you have questions regarding interpretation.
****
Wallace SDM Tutorial
BIO228 – Fall 2021
1. Access this project link (https://rstudio.cloud/project/3505328) and save a permanent copy to your
RStudio Cloud user (or, if you have already done that, just go to https://rstudio.cloud/ and open
the saved project).
2. After opening the project, in R, type the following commands: library(wallace) and
run_wallace(). Remember to press enter after every command. The Wallace GUI will open in
another tab.
3. On module 1 Occ Data, enter the name of the species to be modeled. Set the maximum number
of occurrences to 1000, and click query database. The points should show up in the map.
4. On module 2 Process Occs, create a polygon around the points you want (in case there are points
clearly wrong, or points in the water, make sure to keep them out of the polygon). After drawing
the polygon, click on select occurrences. The map will zoom towards your points.
5. Still on module 2, click on Spatial Thin. Select a distance for spatial thinning in order to remove
points that are too clustered together (between 5 and 30km, depending on how spread out the data
is).
6. On module 3 Env Data, select the resolution of the climatic variables. Start working at a relatively
low spatial resolution (2.5 arcmin). If your analysis is running well, give it a try with a a higher
resolution (e.g. 30 arcsec). Click on Load Env Data.
7. In module 4 Process Envs, click on minimum convex polygon to choose the background extent.
Keep 0.5 degrees, and click select. Then change the “No of background points’ to 5000 and click
on samples.
a. Note: You can play around with the size and shape of the background area depending on
your project. For example, if you are in an island system, a minimum convex polygon
might not be the best choice (you don’t want too many background points in the ocean)
and point buffers might be better. Just remember to make a note of what you actually did
so you can write up your methods later on.
8. On module 5 Partition occs, select jackknife if you have less than 20 points, or random k-fold if
more than 20 points. If random k-fold was chosen, choose 2 as number of folds if you have less
than 50 points. If more than 50 points, use 4 as number of folds.
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9. On module 6 Model, select BIOCLIM as your algorithm. Click on Run.
10. On module 7 Visualize, click on Map Predictions and click on plot to show the results of the
model in the map. Screenshot and download as a GeoTIFF file type.
11. On module 8 Project, create a polygon of the area you want your model to try and predict the
probability of occurrence of the species (can be in the same area you used to model but larger, like
all of South America or Africa, for example, or it can be an area entirely separate area that is distant
from the one where you created your model). Screenshot and download as a GeoTIFF file type.
Homework:
Report on your model here:
1. Record the settings that you have used in Wallace:
2. After running Wallace for your focal taxon, take screenshots of your mapped results and
paste here. This will be used for your term project write-up.
3. According to your model, what areas have high predicted suitability and which have low
suitability?
(Optional) Turning your TIFF files into a map image for your paper:
1. Download QGIS for free here (https://qgis.org/en/site/forusers/download.html).
2. Download a vector layer of the world from Natural Earth: www.naturalearthdata.com > Get the
Data > Medium Scale Data; Cultural > Download countries.
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3. Launch QGIS and open a new project. Go to layer > add layer > add vector layer. Navigate to the
file path where you downloaded the Natural Earth vector layer and add the .shp file. You can
change the fill color and line style of the map in QGIS by clicking the box next to the vector
layer.
4. Add your TIFF file to the map: Go to layer > add layer > add raster layer. Navigate to where you
downloaded your tiff file from Wallace and add. You can edit the color gradients of the image by
clicking the box next to the layer name.
5. Export the map from QGIS as an image. Click New Print Layout on QGIS, name it something,
and press ok. In the popup, click Add New Map
. Then, drag a square to fit the white canvas.
Your map view will be put into this square.
6. You can use the toolbar to select the area that you want to map: the zoom tools move in and out,
and Move item content
moves the image. It is also easy to add a legend, a north arrow and a
scale bar from the side toolbar. Use the Layout tab for adding items to the map. For a north
arrow: Layout > Add image > drag a small rectangle> Item Properties > Search directories >
Select north arrow from the list.
7. To export the map and save it as an image file (e.g., jpg), use Composer>Export Image. You now
have a map image external to the GIS.
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Map
Occs Tb!
Results
Component Guidance
Module Guidance
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82 m
93 m
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Predicted Suitability en
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