An Introduction to CLIMEX - WordPress.com · 2017. 8. 31. · DPD > 0 is equivalent to obligate...
Transcript of An Introduction to CLIMEX - WordPress.com · 2017. 8. 31. · DPD > 0 is equivalent to obligate...
© CSIRO
Presenter:Darren Kriticos
An Introduction to CLIMEX
Bob Sutherst, Gunter MaywaldDarren Kriticos and Tania Yonow
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SELAMAT DATANGKuala Lumpur
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Housekeeping
• Exits• Fire assembly point• Mobile Telephones (silent please)• Dinner ??
– Attendees– Venue
• Daily Programme
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Daily Programme
• 8:30 – 10.00 Morning Session 1• 10:00 – 10:30 Morning Coffee/Tea• 10:30 – 12:30 Morning Session 2• 12:30 – 1:15 pm Lunch• 1:15 – 3:00 Afternoon session 1• 3:00 – 3:30 Coffee/Tea• 3:30 – 5:00 Afternoon Session 2
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Outcomes of Training
• Appreciation of the conceptual basis of CLIMEX
• Provision of a context for plot studies
• Inference of species’ climatic requirements from their distributions
• Inference of geographical and seasonal climatic suitability, length of growing season, number of generations pa, nature of limiting effects
• Skills in operating CLIMEX software
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CLIMEX Course Programme• Overview Friday
• Match Climates Friday
• The CLIMEX Model Friday
–Compare Locations Friday
–Compare Years Saturday
– Species Fitting Saturday
• MetManager Saturday
• MapManager Saturday
• Own Species Fitting Sunday
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Approaches to Climate‐Matching
• Pattern‐matching of meteorological and/‐environmental Data
• (Eg. BIOCLIM, DOMAIN, CLIMATE, Floramap, GAMs, GARP, GRASP, HABITAT, Logistic Regression / Discriminant Functions)
• See Kriticos, D. J. & Randall, R. P. (2001). A comparison of systems to analyse potential weed distributions. In: Groves, R.H., Panetta, F.D., and Virtue, J.G., Eds. Weed Risk Assessment. Melbourne, Australia: CSIRO Publishing pp. 61‐79.
• Process‐oriented modelling • CLIMEX, NAPPFAST
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Matching Months
Log Regression
Matching Extremes
BIOCLIM
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Log RegressionCLIMEX
Comparison of ResultsCLIMEX & Logistic Regression
Sutherst, R. W. & Bourne, A. S. (2009) Modelling non-equilibrium distributions of invasive species: a tale of two modelling paradigms. Biological Invasions, 11, 1231-1237. Personal Use Only - Not for Further Distribution
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Toolbox
Match Climates CLIMEX Model
MetManager
MapManager
Compare
Locations
Compare
YearsPersonal Use Only - Not for Further Distribution
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Introduction to CLIMEX
Exercise 1Becoming familiar with CLIMEX
Flythrough the package
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Starting CLIMEX
CLIMEX“applications”
.GMD
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Stored settings for the currently selected model
.DXS
CLIMEX“applications”
.GMD
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Stored settings for the currently selected model
.DXS
CLIMEX“applications”
.GMD
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Simulation File (*.dxs)
• Model name
• Parameter sets in use
• Model and module settings
(Preferences dialog)
• Formats for tables, maps and graphs
• SequencesText file – you can edit in Notepad,but best to let CLIMEX update it
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Match Climates‘poor person’s climate‐matching’
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Match Climates
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Introduction to Match Climates
• ‘Home’ and ‘Away’• Weighting• Masking (Match Period)• Scenarios
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Away LocationsHome Location
Match Climates
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Away LocationsHome Region
Match Climates (Regional)
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Match Climates Screen
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Tutorial 1Comparing Climates
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Match with the World
• No weighting or masking; weight and mask• Brisbane (Sub‐Tropics)• Amsterdam (Temperate)• Athens (Mediterranean)• Beijing (Continental Temperate)• Singapore (Tropical)• Cairo (Arid Zone)• Your Home Towns
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Cool / Wet
Hot / Dry
AB
C
The Direction of Climate Differences Matters
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At the Edge of the Range
Nairobi is marginal for R. appendiculatus
Match Nairobi with Kenya (use Africa)Compare Tmax for Kibos (0.68), Muguga (0.76)
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At the Edge of the Range
Kibos is more favourable for R. appendiculatusMuguga is less favourable
Nairobi is marginal for R. appendiculatus
Match Nairobi with Kenya (use Africa)Compare Tmax for Kibos (0.68), Muguga (0.76)
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Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
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pera
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-5
-10
New Orleans
Mount Tamborine
Averages Don’t Tell the Whole Story
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Brisbane (Australia) Match With Thomasville (USA)
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Is Winter Too Coldor
Not Warm Enough?
Exercise: Match Tmin & Tmax
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How Cold Are You, Then?
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erage Tempe
ratures (oC)
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Mount Tamborine
Ipswich
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Using Nature’s Water Storage
Flow of Water
Water
Evapotranspiration
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Exercise: Match Soil Moisture
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Masking ExerciseMatch Temperate & Tropical
Match Nairobi with European locations in summer.
Nairobi with Equatorial zone set to 10, including April 30 ‐ September 2.
Map CMI for Europe to see the similarity between Nairobi and European summer
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Applications
Biological Control
Exercise: Discussion
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Useful References• Csurhes, S. M. & Kriticos, D. J. (1994) Gleditsia triacanthos L.
(Caesalpiniaceae), another thorny, exotic fodder tree gone wild. Plant Protection Quarterly, 9, 101‐105.
• Dhileepan, K., Senaratne, K. A. D. W. & Raghu, S. (2006) A systematic approach to biological control agent exploration and prioritisation for prickly acacia (Acacia nilotica ssp. indica). Australian Journal of Entomology, 45, 303‐307.
• Robertson, M. P., Kriticos, D. J. & Zachariades, C. (2008) Climate matching techniques to narrow the search for biological control agents. Biological Control, 46, 442‐452.
• Kriticos, D. J. (2012) Regional climate‐matching to estimate current and future biosecurity threats. Biological Invasions.
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CLIMEX MODELCompare Locations
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Point Distribution Data
• Assumption that if a species has been recorded at a location, the climate is suitable
• GBIF, MOBOT, etc.• Varying precision, meaning, time of capture
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• Doing the possible
• ‘Data’ availability
• ‘Intelligent’ results cf. Match Climates
CLIMEX ‐ Being Pragmatic ‘Garbage’ In‐‘Pure Wisdom’ Out
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‘Where’ is as important as ‘When’
Living at the margin is more exciting but dangerous
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Compare Locations Screen
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Cane Toad
Tiger Snake
Tutorial 2Getting started on CLIMEX modelling
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CLIMEX: Axe or scalpel?
View from an aeroplane
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Back‐to‐Front
Reductionist Modelling
HCDW
Inferential Modelling
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Taking the Bad With the Good
Time of Year
Relative change in
pop
ulation grow
th Growth Season Survival Season
1.0
-1.0
0
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• Statistical description versustransparent mechanisms
• Hypothesis for species
What’s In a Model?
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Some CLIMEX Theory…
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EI = GIA x SI x SX,
where GIA is the Annual Growth Index,
SI is the combined Annual Stress, and
SX is the product of interaction terms involving each stress.
Eco‐climatic Index
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Annual GIAGIA = 100(GIW)/52,
where GIW is the Weekly Growth Index,scaled between 0‐1, and
GIA is the Annual Growth Indexscaled between 0‐100.
52
i=1
Growth Index
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Weekly Growth Index
GIw = Instantaneous intrinsic rate of natural increase ‘r’ in relation to climate
Grow or Perish
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Living in Week‐tight CompartmentsWeekly Snapshots of ‘r’
Week 1, n
GI N
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Weekly GIWGIW = TI x MI (x DI x LI x RI x SV x BI)
where TI is the weekly Temperature IndexMI is the weekly Moisture IndexDI is the weekly Diapause IndexLI is the weekly Light IndexRI is the weekly Radiation IndexSV is the weekly Substrate IndexBI is the weekly Biotic Index
Growth Index
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Medfly: USA, Oklahoma
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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rature o C
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m)
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Index
MI
GITI
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Exercise
Run Compare Locations in Australia forRussian wheat aphid (Diuraphis noxia)
Look at detailed Growth Charts at a few locations
Note the relationship between:Temperature IndexMoisture IndexGrowth Index
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CLIMEX Parameter Values
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DV0 DV1 DV2 DV3
Tempe
rature In
dex
However…
Temperature Index
Temperature
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Temperature Index (Iq)
A = accumulated degree‐days above DV0 assuming a constant temperature of DV1Q = area under the temperature curve above DV0
Iq = Q/A, if Q A, then Iq = 1
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Temperature Index (Ih)
DV2 DV3
1
Ih
Maximum Temperature
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Weekly Temperature Index
Annual Temperature Index52
i=1TIA = 100(TIW)/52
Temperature Index
DV2 DV3
1
Ih
Maximum Temperature
X
TIW = Iq x Ih
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Flow of Water
Water
Evapotranspiration
Moisture
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SM0 SM1 SM2 SM3
Moisture Index
Moisture Index
Soil Moisture
© CSIRO
Medfly – Western Oklahoma
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Moi
stur
e / M
oist
ure
Inde
x
Soil MoistureMedfly Moisture Index
J F M A M J J A S O N D
Soil Moisture vs Moisture Index
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LT1 LT0
Ligh
t Ind
ex
Light Index
Daylength
1
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Diapause Parameters
• DPD0 Entry Daylength• DPT0 Entry Temperature• DPT1 Exit Temperature• DPD Min. days in diapause• DPSW Winter/Summer switch
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Diapause
InDiapause
(no stress accumulation)
DPD0DPT0
DPT1(DPD)
DPSW0 1
DiapauseInduction
DPSW0 1
DiapauseTermination
at least DPD days
© CSIRO
DPD > 0 is equivalent to obligate diapause
EI is set to 0 if induction conditions not met
Diapause
InDiapause
(no stress accumulation)
DPD0DPT0
DPT1(DPD)
DPSW0 1
DiapauseInduction
DPSW0 1
DiapauseTermination
at least DPD days
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SV0 SV1 SV2 SV3
Substrate Index
Substrate Variable
Substrate Index(Physical or Biotic)
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Biotic Index
• Competition (SIP0 < 0)• Synergy (SIP0 > 0)
Allows modelling of interacting species
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BIW(1) = 1 + SIP01 X TGIW(2)
BIW(2) = 1 + SIP02 X TGIW(1)
• Competition (SIP0 < 0)• Synergy (SIP0 > 0)
Biotic Index
Allows modelling of interacting species
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EI = GIA x SI x SX
where GIA is the Annual Growth Index
SI is the combined Annual Stress
SX is the product of interaction terms involving each stress
Eco‐climatic Index
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Stresses
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What Happens at the Edge of the Range?
N
DRY WET
Moisture GradientLow High
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SI = (1‐CS/100)(1‐DS/100)(1‐HS/100)(1‐WS/100),
where SI is the Annual Stress Index, scaled between 0‐1, and CS, DS, HS and WS are Cold, Dry, Hot and Wet stress respectively.
Stress InteractionsSX is the product of interaction terms involving a temperature and a moisture stress, i.e. CDX, CWX, HDX and HWX are the annual cold–dry, cold–wet, hot–dry and hot–wet stress interaction indices.
Stresses
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Accumulating Stress[Mild + Slow] or [Severe + Fast]
Temperature oC
Stre
ss R
ate
0 15
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Time
Accu
mul
ated
Stre
ss
100
0
Accumulating Stress[Mild + Slow] or [Severe + Fast]
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00.20.40.60.8
11.21.41.6
0 2 4 6Week
Stre
ssTotal Accumulated StressWeekly Stress
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How Cold Are You, Then?
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onth
ly A
vera
ge T
empe
ratu
re (o
C)
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Mount Tamborine
Ipswich
14 percentiles
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Exercise
Run Compare Locations in Australia forRussian wheat aphid (Diuraphis noxia)
Look at detailed Growth Charts at a few locations
Note the relationship between:Eco‐climatic IndexGrowth IndexStress Indices
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Tutorial 3Climate Change and Irrigation
Scenarios
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Days 3 and 4• The MetManager• The MapManager• Review of CLIMEX functions• Some notes on parameters• Parameter Fitting• Compare Years• Using the Grid Database• What would you like to know?
• Fitting your own species
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The MetManager
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The MapManager
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CLIMEX Explained
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Temperature Index
DV2 DV3
1
Ih
Maximum Temperature
X
DV0 DV1 DV2 DV3
Tempe
rature In
dex
© CSIRO
SM0 SM1 SM2 SM3
Moi
stur
e In
dex
Moisture Index
Soil Moisture
© CSIRO
LT1 LT0
Ligh
t Ind
ex
Light Index
Daylength
1
© CSIRO
Cold StressToo cold (minimum temperatures)
TTCSTHCS
Not warm enough (degree‐days)
DTCS (above DVCS)DHCS
Too cold (average temperatures)
TTCSATHCSA
© CSIRO
TTCS
THCS
Cold Stress (1)
Minimum Temperature
© CSIRO
DTCS
DHCS
Cold Stress (2)
Day-degrees above DVCS
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TTCSA
THCSA
Cold Stress (3)
Average Temperature
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Heat Stress
Too hot (maximum temperatures)
TTHSTHHS
Not cool enough (degree‐days)
DTHS (above DV3)DHHS
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TTHS
THHS
Heat Stress (1)
Maximum Temperature
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DTHS
DHHS
Degree-days above DV3
Heat Stress (2)
© CSIRO
SMDS
HDS
Dry Stress
Soil Moisture
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SMWS
HWS
Wet Stress
Soil Moisture
© CSIRO
Stress vs Growth Thresholds
Soil Moisture/Temperature
© CSIRO
Cold‐Wet Stress
•DTCW
•MTCW
•PCW
Stress accumulates only when this number of degree‐days above DV0 is accumulated per week.
Stress accumulates only when this level of soil moisture is exceeded
Rate of stress accumulation
© CSIRO
Hot‐Wet Stress
•TTHW
•MTHW
•PHW
Stress accumulates only when the weekly maximum temperature exceeds this parameter.
Stress accumulates only when this level of soil moisture is exceeded
Rate of stress accumulation
© CSIRO
Cold‐Dry Stress
•DTCD
•MTCD
•PCD
Stress accumulates only when this number of degree‐days above DV0 is not achieved in any week.
Stress accumulates only when this level of soil moisture is not reached
Rate of stress accumulation
© CSIRO
Hot‐Dry Stress
•TTHD
•MTHD
•PHD
Stress accumulates only when the weekly maximum temperature exceeds this parameter.
Stress accumulates only when this level of soil moisture is not reached.
Rate of stress accumulation
© CSIRO
Hot‐Dry Stress
Weekly Stress Rate =
(Tmax – TTHD) x (MTHD – SM) x PHD
while Tmax > TTHD and SM < MTHD
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Tutorial 4Stress Indices
Scenarios
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Generation Time Parameter
PDD
Minimum day‐degrees above DV0 needed to complete a generation
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PDD Example
• Prickly Acacia (Acacia nilotica)• Run Acnil, set PDD to 0 temporarily
– Note EI pattern– explain the deficiencies
• Add PDD back in and run– Note difference– Note how positive GI exceeds range of EI– Note high EI at range boundary
• Run climate change scenario
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• Pest Risk Analysis
• Quarantine (Tutorials 6, 7)
• Biological Control (Tutorial 5)
CLIMEX Applications
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How do we judge the quality of a CLIMEX model?
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How do we judge the quality of a CLIMEX model?
• Agrees with all qualified location data– EI >= 1
• Parameters are biologically reasonable• No excessive climate suitability
• AUC/ROC is INAPPROPRIATE– Measures aspects of the model that are irrelevant to invasive species, and most other modelling questions
– Says as much or more about sampling biases as it does about model behaviour
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How do we decide when to stop modelling?
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How do we decide when to stop modelling?
• When we can’t improve one aspect of the model, without sacrificing another
• When our research question has been answered
• When we have a publishable model– Defendable– Useful
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• Effective data‐base ‐ close to infinity
50 Locs x 6+ Factors x 52 Weeks = 15,000
• Parameter range – wide domain
• Visual fitting – concealed rigour
Seeing Is Believing
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-100
400
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100
00
Darwin
Launceston
Rockhampton
Coober Pedy
Canberra
Temperature Ranges (0C) Australia
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0
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300
200100
DarwinLauncestonRockhampton
Coober PedyCanberra
Rainfall Ranges (mm) Australia
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• Distributions are dynamic
• What quality is your data on species’ distributions?
• How complete was the sampling?
Data Quality
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Key Assumptions
• Climate is the only factor limiting the distribution
• Species interactions & other barriers are detectable as internal inconsistencies if significant
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Species Parameters
• Accessible from Parameter Grid Window or Parameter Tree Window
• Parameter Grid more suitable for CLIMEX
• Don’t forget Parameter Comments
• Parameter limits pre‐set by CLIMEX
• There is inter‐dependence between parameters
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• What do you sense?
• How do you feel?
Your Personal Comfort Index
Species Parameter Fitting
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Species Parameter Fitting
ExerciseCodling Moth Distribution
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Codling Moth ‐ Distribution
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Codling Moth ‐ Parameter Fitting
• Create convenient Location Selection that includes the required meteorological data
• Create a map region that covers the area considered
• Select a starting template parameter set
Some preparation
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Codling Moth ‐ Parameter Fitting
Examine the known distribution
• Survives in the cold weather of northern Europe• Survives in the warmer, dry Mediterranean region• Absent from wet, tropical regions of Africa
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Codling Moth ‐ Parameter Fitting
Examine the known distribution
• Survives in the cold weather of northern Europe• Survives in the warmer, dry Mediterranean region• Absent from wet, tropical regions of Africa
Choose Temperate Template to start, but set DTCS to 25oC
© CSIRO
Codling Moth Map 1
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Codling Moth Parameter Fitting
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Codling Moth Parameter Fitting
Examine locationsCold Stress ?Heat Stress ?Dry Stress ?
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Codling Moth Parameter Fitting
Western Asia is indicated as being too hot and dry for the moth.Northern Europe and eastern European countries are too cold.
As the known range of the moth includes these areas,the corresponding parameters need to be adjusted.
© CSIRO
Codling Moth Parameter Fitting
Western Asia is indicated as being too hot and dry for the moth.Northern Europe and eastern European countries are too cold.
As the known range of the moth includes these areas,the corresponding parameters need to be adjusted.
First line of attack might be to reduce the cold stress. How?
© CSIRO
Western Asia is indicated as being too hot and dry for the moth.Northern Europe and eastern European countries are too cold.
As the known range of the moth includes these areas,the corresponding parameters need to be adjusted.
However, knowledge of the insect tells us that it has anobligate winter diapause.
First line of attack might be to reduce the cold stress. How?
Codling Moth Parameter Fitting
© CSIRO
From literature sources (see User’s Guide p45) we know:
Diapause is initiated with decreasing daylength, somewhere between 13 and 18 hours of daylight, and when summer temperatures drop below 15oC.
About 3 months below the developmental threshold are requiredfor successful completion of diapause.
Diapause is terminated when the necessary period of coolinghas been completed, and temperatures begin to rise between 0oC and 10oC.
Codling Moth Parameter Fitting
© CSIRO
These findings indicate that the diapause parameters be set as follows:
DPD0: 14 hoursDPT0: 11oCDPT1: 6oCDPD: 90 daysDPSW: 0 (winter diapause)
See User’s Guide for more explanation
Codling Moth Parameter Fitting
© CSIRO
Also adjust the following parameters:
DV0: 10oC TTCS ‐ 0oCDV1: 20oC THCS ‐ 0DV2: 30oC DTCS ‐ 0 degree‐daysDV3: 33oC DHCS ‐ 0
PDD ‐ 600 degree‐days
Codling Moth Parameter Fitting
© CSIRO
Codling Moth Map 2
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© CSIRO
Examine locationsCold Stress ?Degree‐days ?
Codling Moth Parameter Fitting
© CSIRO
Northern locations have a positive GI,but degree‐days < 600
Try reducing PDD to (say) 450
Codling Moth Parameter Fitting
© CSIRO
Codling Moth Map 3
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Codling Moth Parameter Fitting
Examine locationsHeat Stress ?Dry Stress ?
© CSIRO
Adjust the following parameters:
Heat Stress
TTHS - 33ºCTHHS – 0.0003
Codling Moth Parameter Fitting
© CSIRO
Adjust the following parameters:
Heat Stress
TTHS - 33ºCTHHS – 0.0003
Moisture Index
SM0 – 0.02SM1 – 0.08SM2 – 1.0SM3 – 1.2
Codling Moth Parameter Fitting
© CSIRO
Adjust the following parameters:
Heat Stress
TTHS - 33ºCTHHS – 0.0003
Moisture Index
SM0 – 0.02SM1 – 0.08SM2 – 1.0SM3 – 1.2
Dry Stress
SMDS – 0.02HDS – -0.003
Codling Moth Parameter Fitting
© CSIRO
Adjust the following parameters:
Heat Stress
TTHS - 33ºCTHHS – 0.0003
Moisture Index
SM0 – 0.02SM1 – 0.08SM2 – 1.0SM3 – 1.2
Dry Stress
SMDS – 0.02HDS – -0.003
Wet Stress
SMWS – 1.2HWS – 0.0005
Codling Moth Parameter Fitting
© CSIRO
Codling Moth Map 4
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Codling Moth World Distribution
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Realistically, it could take up to a month to create a good species parameter file that will give a good match to the known distribution and be consistent with any known biological data.
Parameter Fitting
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© CSIROPersonal Use Only - Not for Further Distribution
© CSIRO
CliMond 500th user!
• www.climond.org• As of this morning 497 registered users• 2 weeks to go to our first birthday!
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Species X
Parameter Fitting Exercise
© CSIRO
Parameter Validation
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Projection
?
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• Capture all positive locations cfstatistics
• Use CLIMEX to set context for every new species study
• Listen to CLIMEX when fitting parameter values
Conclusions
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• Feedback – Discussion (10 min)
• Problems?
Parameter Fitting
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Importing point distribution data into CLIMEX
© CSIRO
Walking the Talk – a Procedure
• Collate species distribution and other data• Fit model to species native range (save the model)
– Start with stresses– Move on to growth indices
• Check, and if necessary refit model to a sub‐set of species invaded range
• Validate (test) model with independent data from species invaded range elsewhere
© CSIRO
Species Information
• Evidence for Non‐Climatic Factors • Stress• Constraints• Growth• Goodness of Fit• Validation• Source and Destination Risks• Conclusions
Template for CLIMEX Analysis
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© CSIRO
Dymex – for when you crave more detail
• Insect population dynamics– Queensland Fruit Fly (Bactrocera tryoni)
• Weed landscape metapopulation dynamics– Acacia nilotica
• Weed biocontrol– Cleopus japonicus on Buddleja davidii
• Integrated weed management– Bitou bush, seed fly (Mesoclanis polana), fire, herbicide
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Dymex population dynamics modelling
• Mechanistic modelling • Modular• Discrete timestep• Life history processes• Cohorts• Integrate multiple taxa
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Why Dymex?
DYMEX automates much of the model building and simulation procedures:
• Generation of computer code• Support system with modules for routine functions:
– Output tables, graphs and now also maps – Meteorological data inputs– Optimisation and sensitivity analysis
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Why Dymex?
• Re ‐useable & exchangeable modules • Environmental management drivers and their interactions
• Biological processes and attributes to associate with lifecycle stages
• 'Inherit' / enhance properties • Library of functions• Spatial modelling platform
© CSIRO
The DYMEX Builder
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Module Library
Timer
Data File Reader
Expression
Event
Evaporation Model
Soil Moisture Model
Lifecycle
Function Library
User Interface
Model DescriptionFile
© CSIRO
Structure of a DYMEX Model
Personal Use Only - Not for Further Distribution
Timer
File Reader
Daylength
Latitude
Evaporation
Lifecycle
Soil Moisture
© CSIRO
Structure of a DYMEX Model
Personal Use Only - Not for Further Distribution
Timer
File Reader
Daylength
Latitude
Evaporation
Lifecycle
Soil Moisture
Mo
de
l Ou
tpu
t
© CSIRO
Structure of a DYMEX Model
Personal Use Only - Not for Further Distribution
Timer
File Reader
Daylength
Latitude
Evaporation
Lifecycle
Soil Moisture
Mo
de
l Ou
tpu
t
File
Keyboard
© CSIRO
Growing a DYMEX Model
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Model Complexity
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The modules• Timer
– Days since start– Day of year– Simulation date
• Meterological data [MetBase]– Minimum Temperature– Maximum Temperature
• Average daily temperature [Expression]• Bug lifecycle [Lifecycle]
© CSIRO
Add an adult lifestage
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Add transfer to adult
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Add fecundity and reproduction
Fecundity fixed at establishment at 25
© CSIRO
Some DYMEX examples
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© CSIRO
Insect population dynamics
• Yonow, T., Zalucki, M. P., Sutherst, R. W., Dominiak, B., Maywald, G. F., Maelzer, D. A. & Kriticos, D. J. (2004) Modelling the population dynamics of the Queensland Fruit Fly, Bactrocera (Dacus) tryoni: a cohort‐based approach incorporating the effects of weather.Ecological Modelling, 173, 9‐30.
© CSIRO
QLD Fruit Fly
© CSIRO
Weed landscape metapopulationdynamics
• Kriticos, D. J., Brown, J. R., Maywald, G. F., Radford, I. D., Nicholas, D. M., Sutherst, R. W. & Adkins, S. A. (2003) SPAnDX: a process‐based population dynamics model to explore management and climate change impacts on an invasive alien plant, Acacia nilotica. Ecological Modelling, 163, 187‐208.
© CSIRO
SPAnDX
© CSIRO
Interacting Lifecycles
© CSIRO
Weed biocontrol
• Buddleja davidii and Cleopus japonicus• Kriticos, D. J., Watt, M. S., Withers, T. M., Leriche, A. & Watson, M. (2009) A process‐based population dynamics model to explore target and non‐target impacts of a biological control agent. Ecological Modelling, 220, 2035‐2050.
© CSIRO
Buddleja‐Cleopus Major Model Components
• Plant– Germination
• Hydrothermal– Growth
• Temperature• Soil moisture• LAI• Plant Competition• Age• Herbivory
– Survival– Reproduction
• Insect– Development– Survival
• Age• Temperature• Host resource
– Reproduction• Temperature
– Dispersal• Host resource• Poorly understood!
© CSIRO
Buddleja‐Cleopus Model Lifecycle schematic
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© CSIRO
Integrated weed management• Kriticos, D. J., Stuart, R. M. & Ash, J. E. (2003) Exploring interactions between cultural and biological control techniques: Modelling bitou bush (Chrysanthemoides monilifera ssp. monilifera) and a seed fly (Mesoclanis polana). Proceedings of the XI International Symposium on Biological Control of Weeds (eds J. M. Cullen, D. T. Briese, D. J. Kriticos, W. M. Lonsdale, L. Morin & J. K. Scott), pp. 559‐573. Canberra, Australia.
© CSIRO
B2MP Description• Climate‐driven & cohort‐based• Daily timestep• Temperature, rainfall and Pot Evap. inputs• Plant and animal growth and development driven by growth indices
• Dormancy, rotting, dispersal and germination of seedbank
• Density dependence seedlings, eggs, larvae• Simulates herbicide and fire effects• Built within DYMEX
© CSIRO
Bitou Bush lifecycle in B2MP
Dormant Seed
Seedling Juvenile Adult
Ray Floret
Sterile Fruit
Standing Dead Plant
Immature Fruit
Germinable Seed
© CSIRO
Mesoclanis polana
Chrysanthemoides monilifera
Egg Gravid Female
Teneral Female
Ray Florets
Sterile Fruit
Fruits not attacked
Attackedfruits
Dormant Seed
Immature Fruit
Eggs laid onto ray florets.
Larvae & pupae
attack fruits.
Larvae & Pupae
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© CSIRO
“Automatic” Species Parameter Fitting
• Genetic algorithm in Version 3
• Automatic fitting is not a panacea
Discussion
© CSIRO
Case study: Anastrepha fraterculus
Species Coeff.-1
0
1
Legend#* Known distribution
Species Convex Hull
World CountriesNative
0
1
CX Species range Distribution Toolbox
(set of models and scripts)
© CSIRO
Genetic Algorithm ResultsGeneration 1, Best value: 99.083 Average: 94.789 StdDev: 7.9087*** Best Genotype: Values: ‐19.72 ‐0.0523 20 ‐0.0942 31.585
0.0303 76 0.0357 0.0972 ‐0.0523 1.303 0.0113 38.305 12.04 45.5; Fitness 99.0826
Generation 2, Best value: 99.083 Average: 95.503 StdDev: 5.1778*** Best Genotype: Values: ‐19.72 ‐0.0523 20 ‐0.0942 31.585
0.0303 76 0.0357 0.0972 ‐0.0523 1.303 0.0113 38.305 12.04 45.5; Fitness 99.0826
Generation 57, Best value: 99.185 Average: 97.946 StdDev: 0.96942*** Best Genotype: Values: ‐12.12 ‐0.0744 60.6 ‐0.0627 30.52
0.0527 57.8 0.0616 0.1545 ‐0.0381 4.08175 0.055 26.11 5.88 35.84; Fitness 99.1845
© CSIRO
Genetic Algorithm In ActionKnown Distribution
Reference File
ModelledCore Distribution
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Regional Climate Matching
• Compare the climate of a (home) region to that of a selected set of (away) locations
• Independent of species biology• Overall level of similarity given by the ‘Composite Match Index’–product of selected component indices:
e.g. Tmax, Tmin, Rain Total, RH, Rain Pattern
© CSIRO
Regional Climate Match Index Results –New Zealand
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© CSIRO
Climate Datasetso Enhanced point locations database (~ 3 000 points)o CliMond 0.5 degree and 10’ grids for terrestrial areas
– Historical (1950‐2000) from Worldclim and CRU– IPCC AR4 datasets
o Based on CRU 0.5 degree gridded dataseto Selected periods to 2100o GCMs: CSIRO3 and Miroco Scenarios: A1B, A2
– Köppen‐Geiger climate zones– Raw monthly climate variables– 36 Bioclim variables– CLIMEX Metman, ASCII Grid, ESRI Grid
© CSIRO
Exercise
CLIMEX Manual Diapause
Species Parameter Fitting
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© CSIRO
EI: Run 1 (14:44) North Americadat <Species 1>
No Climate ChangeVariable: EI
0
25
50
75
100
0 1,000
Mercator projection
No Diapause (23)
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© CSIRO
Obligate Winter Diapause (24)
EI: Run 1 (16:41) North Americadat <Species 1>
No Climate ChangeVariable: EI
0
25
50
75
100
0 1,000
Mercator projection
Personal Use Only - Not for Further Distribution
© CSIRO
Obligate Summer Diapause (25)
EI: Run 3 (16:46) North Americadat <Species 1>
No Climate ChangeVariable: EI
0
25
50
75
100
0 1,000
Mercator projection
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© CSIRO
Extracting Phenology from Geographic Distribution
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© CSIRO
Exercise
Space and Time
Convert Tsetse (Glossina morsitans)Distribution to Seasonal Phenology
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© CSIRO
Glossinamorsitans
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© CSIRO
Core Range Western Uganda
© CSIRO
Range Margin South Africa
© CSIRO
Compare Years
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• Life’s ups and downs: GIw• Annual Waves• Raw is dangerous
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© CSIRO
Compare Years
B. microplus with Amberley met data
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© CSIRO
Raw is Dangerous
Daily
Weekly
AnnualCompare Locations
Compare Years
Tempe
rature
Why Compare Locations and Compare Years Don’t Talk to Each Other
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© CSIRO
• Take monthly averages of daily meteorological data and then convert it back to weekly averages to introduce some smoothing
• Stress values often exceed 100 so you need to compare relative values
What Can You Do About It?
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© CSIRO
Character Building: The Gold in Failure
When Wrong is Exciting
Exercise
Getting it Wrong for Good Reasons
Discussion & Tutorial 8
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© CSIRO
When Equal is Not the Same
Variances
Exercise
Cane Toads in Florida
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© CSIRO
When There Aren’t Enough Days in the Year
PDD
Exercise
Haemaphysalis
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© CSIRO
What to do about Hiccups –When the Rain Comes Back?
Bimodal Rainfall
Exercise
East Africa
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© CSIRO
How to Make your Results Look (Too) Good
Validation against Independent Data
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© CSIRO
Aladdin’s Den
Treasure Trove Awaiting to be Discovered out there:
• Exploitation of spatial information
• Biological responses on geographical scales
• Detect role of climate vs other factors
Personal Use Only - Not for Further Distribution
© CSIRO
• Amblyomma ticks in Zimbabwe
• Old World Screw‐worm fly in Lybiao Irrigation
• Boophilus ticks in Africao Hybrid zone
• Gum leaf skeletoniser in Tasmaniao Extended native range
Some New Insights
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© CSIRO
Species Parameter Fitting
Tutorial 7
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© CSIRO
DPD0 10 hours
DPT0 10oC
DPT1 0oC
DPD 90 days
DPSW 0
Obligate Winter Diapause Parameters
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© CSIRO
DPD0 10 hours
DPT0 30oC
DPT1 30oC
DPD 30 days
DPSW 1
Obligate Summer Diapause Parameters
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© CSIRO
Websites
• CABI sponsored Demo of CLIMEX at: http://www.ento.csiro.au/climex/demo/climexdemowww.htm
• CLIMEX software & Patches from Hearne Scientifichttp://www.hearne.com.au
Resources
Personal Use Only - Not for Further Distribution
© CSIRO
CLIMEX Pathogen Models• Ekins, M. G., Aitken, E. A. B. & Goulter, K. C. (2002) Carpogenicgermination of Sclerotinia minor and potential distribution in Australia. Australasian Plant Pathology 31: 259‐265.• Lanoiselet, V., Cother, E.J. & Ash, G.J. (2002) Climex and Dymexsimulations of the potential occurrence of rice blast disease in south‐eastern Australia. Australasian Plant Pathology 31, 1‐7.• Yonow, T., Kriticos, D.J. & Medd, R.W. (2004) The potential geographic range of Pyrenophora semeniperda. Phytopathology 94, 805‐812.• Watt, M. S., Kriticos, D. J., Alcaraz, S., Brown, A. & Leriche, A. (2009) The hosts and potential geographic range of Dothistroma needle blight. Forest Ecology and Management, 257, 1505‐1519.• Pinkard, E. A., Kriticos, D. J., Wardlaw, T. J. & Carnegie, A. J. (2010) Estimating the spatio‐temporal risk of disease epidemics using a bioclimatic niche model. Ecological Modelling, 221, 2828‐2838.• Yonow, T., Hattingh, V. & De Villiers, M. (Submitted) CLIMEX Modelling of the potential global distribution of the citrus black spot disease caused by Guignardia citricarpa and the risk posed to Europe. Crop Protection.