An Introduction to CLIMEX - WordPress.com · 2017. 8. 31. · DPD > 0 is equivalent to obligate...

Presenter:Darren Kriticos

An Introduction to CLIMEX

Bob Sutherst, Gunter MaywaldDarren Kriticos and Tania Yonow

Personal Use Only - Not for Further Distribution

SELAMAT DATANGKuala Lumpur

Housekeeping

• Exits• Fire assembly point• Mobile Telephones (silent please)• Dinner ??

– Attendees– Venue

• Daily Programme

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

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

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

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

Matching Months

Log Regression

Matching Extremes

BIOCLIM

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

Toolbox

Match Climates CLIMEX Model

MetManager

MapManager

Compare

Locations

Compare

YearsPersonal Use Only - Not for Further Distribution

Introduction to CLIMEX

Exercise 1Becoming familiar with CLIMEX

Flythrough the package

Starting CLIMEX

CLIMEX“applications”

Stored settings for the currently selected model

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

Match Climates‘poor person’s climate‐matching’

Match Climates

Introduction to Match Climates

• ‘Home’ and ‘Away’• Weighting• Masking (Match Period)• Scenarios

Away LocationsHome Location

Match Climates

Away LocationsHome Region

Match Climates (Regional)

Match Climates Screen

Tutorial 1Comparing Climates

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

Cool / Wet

Hot / Dry

The Direction of Climate Differences Matters

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)

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)

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

New Orleans

Mount Tamborine

Averages Don’t Tell the Whole Story

Brisbane (Australia) Match With Thomasville (USA)

Is Winter Too Coldor

Not Warm Enough?

Exercise: Match Tmin & Tmax

How Cold Are You, Then?

erage Tempe

ratures (oC)

Mount Tamborine

Ipswich

Using Nature’s Water Storage

Flow of Water

Evapotranspiration

Exercise: Match Soil Moisture

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

Applications

Biological Control

Exercise: Discussion

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.

CLIMEX MODELCompare Locations

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

• Doing the possible

• ‘Data’ availability

• ‘Intelligent’ results cf. Match Climates

CLIMEX ‐ Being Pragmatic ‘Garbage’ In‐‘Pure Wisdom’ Out

‘Where’ is as important as ‘When’

Living at the margin is more exciting but dangerous

Compare Locations Screen

Cane Toad

Tiger Snake

Tutorial 2Getting started on CLIMEX modelling

CLIMEX: Axe or scalpel?

View from an aeroplane

Back‐to‐Front

Reductionist Modelling

Inferential Modelling

Taking the Bad With the Good

Time of Year

Relative change in

ulation grow

th Growth Season Survival Season

• Statistical description versustransparent mechanisms

• Hypothesis for species

What’s In a Model?

Some CLIMEX Theory…

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

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.

Growth Index

Weekly Growth Index

GIw = Instantaneous intrinsic rate of natural increase ‘r’ in relation to climate

Grow or Perish

Living in Week‐tight CompartmentsWeekly Snapshots of ‘r’

Week 1, n

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

Medfly: USA, Oklahoma

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

rature o C

75Rainfall (m

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

CLIMEX Parameter Values

DV0 DV1 DV2 DV3

rature In

However…

Temperature Index

Temperature

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

Temperature Index (Ih)

DV2 DV3

Maximum Temperature

Weekly Temperature Index

Annual Temperature Index52

i=1TIA = 100(TIW)/52

Temperature Index

DV2 DV3

Maximum Temperature

TIW = Iq x Ih

Flow of Water

Evapotranspiration

Moisture

SM0 SM1 SM2 SM3

Moisture Index

Soil Moisture

Medfly – Western Oklahoma

Soil MoistureMedfly Moisture Index

J F M A M J J A S O N D

Soil Moisture vs Moisture Index

LT1 LT0

Light Index

Daylength

Diapause Parameters

• DPD0 Entry Daylength• DPT0 Entry Temperature• DPT1 Exit Temperature• DPD Min. days in diapause• DPSW Winter/Summer switch

Diapause

InDiapause

(no stress accumulation)

DPD0DPT0

DPT1(DPD)

DPSW0 1

DiapauseInduction

DPSW0 1

DiapauseTermination

at least DPD days

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

SV0 SV1 SV2 SV3

Substrate Index

Substrate Variable

Substrate Index(Physical or Biotic)

Biotic Index

• Competition (SIP0 < 0)• Synergy (SIP0 > 0)

Allows modelling of interacting species

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

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

Stresses

What Happens at the Edge of the Range?

DRY WET

Moisture GradientLow High

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

Accumulating Stress[Mild + Slow] or [Severe + Fast]

Temperature oC

Accumulating Stress[Mild + Slow] or [Severe + Fast]

00.20.40.60.8

11.21.41.6

0 2 4 6Week

ssTotal Accumulated StressWeekly Stress

How Cold Are You, Then?

Mount Tamborine

Ipswich

14 percentiles

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

Tutorial 3Climate Change and Irrigation

Scenarios

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

The MetManager

The MapManager

CLIMEX Explained

Temperature Index

DV2 DV3

Maximum Temperature

DV0 DV1 DV2 DV3

rature In

SM0 SM1 SM2 SM3

Moisture Index

Soil Moisture

LT1 LT0

Light Index

Daylength

Cold StressToo cold (minimum temperatures)

TTCSTHCS

Not warm enough (degree‐days)

DTCS (above DVCS)DHCS

Too cold (average temperatures)

TTCSATHCSA

Cold Stress (1)

Minimum Temperature

Cold Stress (2)

Day-degrees above DVCS

Cold Stress (3)

Average Temperature

Heat Stress

Too hot (maximum temperatures)

TTHSTHHS

Not cool enough (degree‐days)

DTHS (above DV3)DHHS

Heat Stress (1)

Maximum Temperature

Degree-days above DV3

Heat Stress (2)

Dry Stress

Soil Moisture

Wet Stress

Soil Moisture

Stress vs Growth Thresholds

Soil Moisture/Temperature

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

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

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

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.

Hot‐Dry Stress

Weekly Stress Rate =

(Tmax – TTHD) x (MTHD – SM) x PHD

while Tmax > TTHD and SM < MTHD

Tutorial 4Stress Indices

Scenarios

Generation Time Parameter

Minimum day‐degrees above DV0 needed to complete a generation

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

• Pest Risk Analysis

• Quarantine (Tutorials 6, 7)

• Biological Control (Tutorial 5)

CLIMEX Applications

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

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

• 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

Darwin

Launceston

Rockhampton

Coober Pedy

Canberra

Temperature Ranges (0C) Australia

200100

DarwinLauncestonRockhampton

Coober PedyCanberra

Rainfall Ranges (mm) Australia

• Distributions are dynamic

• What quality is your data on species’ distributions?

• How complete was the sampling?

Data Quality

Key Assumptions

• Climate is the only factor limiting the distribution

• Species interactions & other barriers are detectable as internal inconsistencies if significant

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

• What do you sense?

• How do you feel?

Your Personal Comfort Index

Species Parameter Fitting

ExerciseCodling Moth Distribution

Codling Moth ‐ Distribution

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

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

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

Codling Moth Map 1

Codling Moth Parameter Fitting

Examine locationsCold Stress ?Heat Stress ?Dry Stress ?

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?

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?

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.

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

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 Map 2

Examine locationsCold Stress ?Degree‐days ?

Northern locations have a positive GI,but degree‐days < 600

Try reducing PDD to (say) 450

Codling Moth Map 3

Examine locationsHeat Stress ?Dry Stress ?

Adjust the following parameters:

Heat Stress

TTHS - 33ºCTHHS – 0.0003

Heat Stress

Moisture Index

SM0 – 0.02SM1 – 0.08SM2 – 1.0SM3 – 1.2

Heat Stress

Moisture Index

SM0 – 0.02SM1 – 0.08SM2 – 1.0SM3 – 1.2

Dry Stress

SMDS – 0.02HDS – -0.003

Heat Stress

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 Map 4

Codling Moth World Distribution

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

CliMond 500th user!

• www.climond.org• As of this morning 497 registered users• 2 weeks to go to our first birthday!

Species X

Parameter Fitting Exercise

Parameter Validation

Projection

• Capture all positive locations cfstatistics

• Use CLIMEX to set context for every new species study

• Listen to CLIMEX when fitting parameter values

Conclusions

• Feedback – Discussion (10 min)

• Problems?

Parameter Fitting

Importing point distribution data into CLIMEX

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

Species Information

• Evidence for Non‐Climatic Factors • Stress• Constraints• Growth• Goodness of Fit• Validation• Source and Destination Risks• Conclusions

Template for CLIMEX Analysis

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

Dymex population dynamics modelling

• Mechanistic modelling • Modular• Discrete timestep• Life history processes• Cohorts• Integrate multiple taxa

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

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

The DYMEX Builder

Module Library

Data File Reader

Expression

Evaporation Model

Soil Moisture Model

Lifecycle

Function Library

User Interface

Model DescriptionFile

Structure of a DYMEX Model

File Reader

Daylength

Latitude

Evaporation

Lifecycle

Soil Moisture

File Reader

Daylength

Latitude

Evaporation

Lifecycle

Soil Moisture

File Reader

Daylength

Latitude

Evaporation

Lifecycle

Soil Moisture

Keyboard

Growing a DYMEX Model

Model Complexity

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]

Add an adult lifestage

Add transfer to adult

Add fecundity and reproduction

Fecundity fixed at establishment at 25

Some DYMEX examples

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.

QLD Fruit Fly

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.

SPAnDX

Interacting Lifecycles

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.

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!

Buddleja‐Cleopus Model Lifecycle schematic

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.

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

Bitou Bush lifecycle in B2MP

Dormant Seed

Seedling Juvenile Adult

Ray Floret

Sterile Fruit

Standing Dead Plant

Immature Fruit

Germinable Seed

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

“Automatic” Species Parameter Fitting

• Genetic algorithm in Version 3

• Automatic fitting is not a panacea

Discussion

Case study: Anastrepha fraterculus

Species Coeff.-1

Legend#* Known distribution

Species Convex Hull

World CountriesNative

CX Species range Distribution Toolbox

(set of models and scripts)

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

Genetic Algorithm In ActionKnown Distribution

Reference File

ModelledCore Distribution

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

Regional Climate Match Index Results –New Zealand

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

Exercise

CLIMEX Manual Diapause

EI: Run 1 (14:44) North Americadat <Species 1>

No Climate ChangeVariable: EI

0 1,000

Mercator projection

No Diapause (23)

Obligate Winter Diapause (24)

0 1,000

Mercator projection

Obligate Summer Diapause (25)

0 1,000

Mercator projection

Extracting Phenology from Geographic Distribution

Exercise

Space and Time

Convert Tsetse (Glossina morsitans)Distribution to Seasonal Phenology

Glossinamorsitans

Core Range Western Uganda

Range Margin South Africa

Compare Years

• Life’s ups and downs: GIw• Annual Waves• Raw is dangerous

Compare Years

B. microplus with Amberley met data

Raw is Dangerous

Weekly

AnnualCompare Locations

Compare Years

rature

Why Compare Locations and Compare Years Don’t Talk to Each Other

• 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?

Character Building: The Gold in Failure

When Wrong is Exciting

Exercise

Getting it Wrong for Good Reasons

Discussion & Tutorial 8

When Equal is Not the Same

Variances

Exercise

Cane Toads in Florida

When There Aren’t Enough Days in the Year

Exercise

Haemaphysalis

What to do about Hiccups –When the Rain Comes Back?

Bimodal Rainfall

Exercise

East Africa

How to Make your Results Look (Too) Good

Validation against Independent Data

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

• 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

Tutorial 7

DPD0 10 hours

DPT0 10oC

DPT1 0oC

DPD 90 days

DPSW 0

Obligate Winter Diapause Parameters

DPD0 10 hours

DPT0 30oC

DPT1 30oC

DPD 30 days

DPSW 1

Obligate Summer Diapause Parameters

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

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.

An Introduction to CLIMEX - WordPress.com · 2017. 8. 31. · DPD > 0 is equivalent to obligate...

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