UNIVERSITY OF THE WESTERN CAPE
October 2005
SUBJECT: NISL CONSERVATION FOR CLIMATE CHANGE COURSE
MARKS: 100
TIME: 5 DAYS
Examiner Dr Richard Knight
GENERAL INSTRUCTIONS AND ACCEPTANCE OF
CONDITIONS
All STEPS ARE COMPULSORY
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WHERE IS THE DATA?
http://planet.uwc.ac.za/nisl/aiacc/practical/download/
File Names –
R.exe
R_additional_packages.exe
Climate_Change_Course_Practical.exe
DATA DESCRIPTION
You’ll need to run each of these executables. Install the software in the default directories that these
programmes specify.
R.exe – This will install the R statistical package that you will use for running the GAM model and
evaluating its accuracy.
R_additional_packages.exe – This will install the additional package Hmisc into the R directory. This
package contains a library that is needed in order to run the model.
Climate_Change_Course_Practical.exe – This installs all the data and additional files that you will
use in the practical to the C:\Climate Change Practical Folder. When unzipped, this folder should have
the following files:
a2_2050.dbf
AMD_DATA.dbf
Climate_Change_practical.doc
Climate_Change_Practical_Template.doc
countries.dbf
countries.sbn
countries.sbx
countries.shp
countries.shx
Functions.R
national_iucn1to6_poly.dbf
national_iucn1to6_poly.sbn
national_iucn1to6_poly.sbx
national_iucn1to6_poly.shp
national_iucn1to6_poly.shx
R 2.0.1 (shortcut)
SACIC_WMS_help.doc
Script.R
All the data for modelling the species are contained within the AMD_DATA.dbf. This database file
comprises XY locational data for Central and Southern Africa, at a resolution of 10’ by 10’. For each
point, data on the average ecoregion is specified, as well as presence data for 27 species of African
mammals, and each of six environmental variables.
The environmental variables are:
Table 1.
VARIABLE
DESCRIPTION
EVTR0112
GDD10_0112
MTC
MTW
PREC0112
TMEAN0112
Net evapotranspiration (mm)
Growth days over 10ºC (number of days)
Mean temperature of the warmest month (ºC)
Mean temperature of the coldest month (ºC)
Mean annual precipitation (mm)
Mean annual temperature (ºC)
The species for which data are provided are detailed on the following page (Table 2).
Table 2.
Countries in which
species is found
ALL
ALL
ALL
ALL
Botswana
Burundi
DRC
DRC
Kenya
Kenya
Kenya
Kenya/Moçambique/Tanzania
Malawi
Malawi
Moçambique
Namibia
Namibia
Namibia/Zimbabwe
RSA
RSA
RSA
RSA
Tanzania
Tanzania
Tanzania
Uganda
Zimbabwe
Common name
African weasel
Giraffe
Blue, sky or diademed monkey
Tsessebe
Black-footed or small spotted cat
L'Hoest's guenon (monkey)
Dryas guenon (monkey)
Aquatic or fishing genet
Grant's gazelle
Plain zebra
Puku
Suni
Bushy-tailed mongoose
Natal red duiker
Dusky or Peters’ short-snouted elephantshrew
Small grey mongoose
Mountain zebra
Jameson's red rockhare
Cape grysbok
Cape or small grey mongoose
Natal red hare
Vaal or grey rhebok
Abbott's duiker
Black-and-rufous elephant-shrew
Harvey's red duiker
Short-nosed or dusky-footed elephant-shrew
Nyala
Scientific name
Indicator in
DBF file
Poecilogale albinucha
Giraffa camelopardalis
Cercopithecus mitis
Damaliscus lunatus
Felis nigripes
Cercopithecus lhoesti
Cercopithecus dryas
Osbornictis piscivora
Gazella granti
Equus grevyi
Kobus vardonii
Neotragus moschatus
Bdeogale crassicauda
Cephalophus natalensis
AFR_WEAS
GIRAFFE
SKY_MONK
TSESSEBE
BLACK_FT_C
LHO_GUENO
DRYAS_GUEN
FISH_GENET
GRANT_GAZE
PLAIN_ZEBR
PUKU
SUNI
BUSH_MONGO
NATAL_DUIK
Elephantulus fuscus
Galerella flavescens
Equus zebra
Pronolagus randensis
Raphicerus melanotis
Galerella pulverulenta
Pronolagus crassicaudatus
Pelea capreolus
Cephalophus spadix
Rhynchocyon petersi
Cephalophus harvey
Elephantulus fuscipes
Tragelaphus angasii
DUSK_SHREW
GREY_MONGO
MOUNT_ZEBR
JAMES_RHAR
CAPE_GRYSB
CAPE_MONGO
NATAL_HARE
VAAL_RHEBO
ABBOT_DUIK
BL_RUF_SHR
HARV_DUIKR
SH_N_SHREW
NYALA
ANSWER 1- [10 marks]
Question 1
Verify that the species you are planning to model does in fact fall within the
limits of your selected country. Show the distribution of the species within your study country by
changing the legend, (display the presence values over the countries shapefile) and discuss whether
the distribution is suitable for modelling from the selected country. If it is not, specify any additional
or alternative areas that you will include in the modelling process to ensure an appropriate model.
Use “Print Screen” and paste the screen capture into Windows Paint® to capture the images from
ArcView® but save them in an image directory as *.jpg images. From this image directory you
should paste the images into your MS Word® document (this will prevent accidental loss of images
in the final document).
Insert your picture for Figure 1a here…
Figure. 1 (Caption for distribution of species X in country Y using point data at a
resolution of (specify))
Table 1. (insert a suitable caption)
(Mammal species)
Present
Absent
Number of cells of (specify country)
Total
Discussion of the results from the above table and image (ensure it is unbolded and
normal text)
Start your answer here…
ANSWER 2- [15 marks]
Question 2
Write a short description of the species you are modelling, including as many
details of the ecology of the species (habitat, diet, breeding habits, rarity) as you can. Include a
photograph of a specimen of the species (attribute your sources of information).
Insert your picture for Figure 2 here…
Figure. 2 (Caption for a picture of your chosen study species)
Source: (insert source or URL of webpage from which you obtained the image)
Describe the distribution and ecology of the species
Start your answer here…
ANSWER 3- [20 marks]
Question 3
Specify for each of your three models the environmental characteristics you
used in the GAM, their p-values, the overall p-value for the model, and the amount of the deviation in
distribution the models explain. All of these values should be obtainable from the console once you
have run a summary of the GAM. Explain whether these variables are direct or indirect descriptors of
the distribution of your species, and what the implications of this might be. For the single variable
model, explain why you chose your variable, and whether you think it is a good descriptor of the
species’ distribution.
Table 2. (insert a suitable caption - 1 variable model)
Variable name
p-value
Total model Pr (>|t|)
Deviance explained
Explain why you used this variable, and whether you think it is a good descriptor of the species
distribution.
Start your answer here…
Table 3. (insert a suitable caption – 3 variable model)
Variable name
p-value
Total model Pr (>|t|)
Deviance explained
Table 4. (insert a suitable caption – 5 variable model)
Variable name
p-value
Total model Pr (>|t|)
Deviance explained
Explain whether the variables are direct determinants of the species distribution, and the
implications of this for modelling:
Start your answer here…
ANSWER 4- [5 marks]
Question 4
Using one of the variables that is used in at least two of your models, show
the plots of its response curves in each model. These should be screenshots from R. If you do not
have the response curves available, you can run the command again from the relevant script, in
order to capture the curves. Explain why these curves are different in different models, and why the
margin of error (dotted lines) is higher at the edges of the distribution than in the middle.
Insert your picture for Figure 3a here…this is just an example pic!
Figure. 3a (Insert caption - Insert a plot of the response curve for the first model using
the chosen variable.)
Insert your picture for Figure 3b here…
Figure. 3b (Insert caption - Insert a plot of the response curve for the second model
using the chosen variable.)
Insert your picture for Figure 3c here…
Figure. 3c (Insert caption - Insert a plot of the response curve for the third model using
the chosen variable. If there is not third model, delete this section.)
Interpret the differences between the curves, and explain why the margin of error is greater at
the edges of the distribution:
Start your answer here…
ANSWER 5- [12 Marks]
Question 5
By comparing the modelled and actual distributions of your species, as well as
by comparing the receiver operating characteristic values of the respective models, describe how
you decided on the most appropriate model to use for the determination of the future niche for your
species. You should display the modelled distributions in ArcView using each of the models, as well
as the actual distribution.
Table 5. (Insert a suitable caption)
Number of variables used
in model
1
3
5
AUC (calibration dataset)
AUC (evaluation dataset)
AUC (total
Insert your picture for Figure 4 here…
Figure. 4 (The actual distribution of the species within your study area)
Insert your picture for Figure 5 here…
Figure. 5 (Insert caption - output of model 1)
Insert your picture for Figure 6 here…
Figure. 6 (Insert caption - output of model 2)
Insert your picture for Figure 7 here…
Figure. 7 (Insert caption - output of model 3)
Discuss the above results and differences, and explain which model you decided was best and
why.
Start your answer here…
ANSWER 6- [5 Marks]
Question 6
What is the cutoff value determined by the function as appropriate for
estimating species presence for your study species (using your final model)? This can be obtained
from the R console window. Once again, if you cannot find it, rerun the relevant command from the
script:
#Print out cutoff values in Console window
What, if anything, does this cutoff point imply about the model’s accuracy?
Start your answer here…
ANSWER 7- [20 Marks]
Question 7 Describe the likely impact of the climate change induced transformation of habitat
for the conservation of this species. Where possible, combine your outputs with the IUCN National
Parks and designated conservation areas (national_IUCN1to6_poly.shp) shapefile to illustrate
possible complications in terms of conservation areas. Using the attributes table for the IUCN parks
shapefile, calculate the extent of conservation areas (in ha) in both present and future scenarios in
which the species can be found. This value can be found in the “Gis_ha” column of the database for
each park that falls within the distribution area.
Insert your picture for Figure 8 here…
Figure. 8 (The modelled future niche for your mammal species for the whole of Africa
(binary representation) )
Insert your picture for Figure 9 here…
Figure. 9 (The current distribution for your mammal species for the whole of Africa
(binary representation) )
Insert your picture for Figure 10 here…
Figure. 10 (A map of conservation areas overlapped by the future species distribution
(may be a shapefile or an event layer output). For some species you may need to
prepare more than one map. If the species is predicted not to overlap any conservation
areas in 2050, overlay the conservation areas layer with your modelled distribution to
illustrate this.)
Discuss the implications for the conservation of your chosen mammal species under conditions
of climate change by 2050 (under the A2 emissions scenario).
Start your answer here…
ANSWER 8- [13 Marks]
Question 8 Prepare a shape file from the modelled future distribution of your species, and post
it on the UWC mapserver site:
http://mapserver.uwc.ac.za/imf-aims50/imf.jsp?site=Biota
You will need to set up your own account. You must save the session to the server, and this will give
you a response URL that can be used to access the session at any time. Report the URL of your
submission, and create a PDF of the distribution. You can either submit ht. The PDF should have an
appropriate title, and in the map notes you should include your name, the date, and the model details
used for preparing the future distribution of your chosen species. Detailed instructions on this
process are available in the South African Coastal Information Centre Web Mapping Service help file
(SACIC_WMS.doc), which can be found in the practical folder on your C: drive
Mapserver session URL :
PDF output URL:
An electronic copy of this MS WORD® document can be found at
http://planet.uwc.ac.za/nisl/aiacc/practical/docs