cphst tools: nappfastlangif.uaslp.mx/documentos/presentaciones... · 4 the purpose of nappfast to...

Roger MagareyRoger MagareyCPHST/PERALCPHST/PERAL

Raleigh, NCRaleigh, NC

Special thanks to Special thanks to Dan Dan BorchertBorchert, Jessica Engle and , Jessica Engle and

Kathryn Kathryn EcherdEcherdCPHST/PERALCPHST/PERAL

Raleigh, NCRaleigh, NC

3

Workshop OutlineWorkshop Outline

i) System overviewi) System overviewii) Model creation using templatesii) Model creation using templates

weather dataweather dataiii) Map creationiii) Map creationiv) Climate matchingiv) Climate matchingv) Map interpretationv) Map interpretationvi) Collaboration and data sharingvi) Collaboration and data sharing

4

The Purpose of NAPPFASTThe Purpose of NAPPFASTTo assist the CAPS Program determine where To assist the CAPS Program determine where and when to survey for invasive pest speciesand when to survey for invasive pest species

To provide information on pests (potential To provide information on pests (potential distribution, etc.) for risk assessment purposesdistribution, etc.) for risk assessment purposes

To assist APHIS by providing relevant To assist APHIS by providing relevant information in Emergency Response situationsinformation in Emergency Response situations

5

The Development of NAPPFASTThe Development of NAPPFAST

Idea originated at NCSU: Dr. Jack BaileyIdea originated at NCSU: Dr. Jack Bailey-- method method for forecasting peanut diseases in NC and the for forecasting peanut diseases in NC and the flexible template for disease modelingflexible template for disease modeling-- 19901990’’ssDan Dan FieselmannFieselmann and Glenn Fowler worked with and Glenn Fowler worked with Dr. Bailey for several years on project Dr. Bailey for several years on project development development Roger Magarey developed the connection Roger Magarey developed the connection between between ZedXZedX Inc and NAPPFAST in 2002Inc and NAPPFAST in 2002Dan Borchert joins the NAPPFAST Team in 2003Dan Borchert joins the NAPPFAST Team in 2003

6

NAPPFAST System OverviewNAPPFAST System OverviewInternetInternet--based Pest Prediction Systembased Pest Prediction SystemBiological model (Degree day, Disease Infection, Biological model (Degree day, Disease Infection, or Multior Multi--function) templates paired with large function) templates paired with large climate database climate database Produce geoProduce geo--referenced output mapsreferenced output mapsDesigned to assist pest Designed to assist pest survey detection efforts: survey detection efforts: predict when and wherepredict when and where

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NAPPFASTNAPPFAST

NAPPFAST NAPPFAST Map ViewMap View

Public AccessJB Site

Sirex Site

Cactoblastis

Site

Insect Specific Reporting

Insect Specific Phenology

NAPPFAST-OBS

Canned maps

Pest Models and Observations

Top 50 PestsTop 50 Pests

8

NAPPFAST PROJECTSNAPPFAST PROJECTSOctober 2005 to presentOctober 2005 to present

Survey and detection ~ 4Survey and detection ~ 4Risk maps for CAPS top 50 pestsRisk maps for CAPS top 50 pests

Pathway/organism risk assessment ~ 13Pathway/organism risk assessment ~ 13WeatherWeather--based analysis of Citrus cankerbased analysis of Citrus cankerBactroceraBactrocera invadensinvadens

Emergency response ~ 9Emergency response ~ 9Light brown apple moth risk mappingLight brown apple moth risk mapping

Parte 2Parte 2

Part 2Part 2 Model CreationModel Creation

11

Log into NAPPFAST at www.nappfast.org

.

Then select the Nappfast

tool.

http://www.nappfast.org/

12

From the ‘Action’

drop down menu you can:

Edit:

Make changes to an existing model.Add:

Create a new

model.Rename:

Change the

name of an existing model.

Delete:

Delete an existing model.Copy:

Copy an existing model, give it a new name, and

create a new model.

13

The dates when the model will collect data.

Template for the model.

The variables of the model.

14

Values in the legend indicating number of accumulative days where conditions will favor the development of the pest.

Values differentiating between infection and non-infection.

Name to be displayed on the maps and/or any comments about the construction of the model.

Hit ‘Save’

to make any changes to the model

One can test the model.

15

NAPPFAST MAPVIEWNAPPFAST MAPVIEW Important PointsImportant Points

What models are used to make the What models are used to make the predictive maps in NAPPFAST?predictive maps in NAPPFAST?

Degree DayDegree DayInfection Infection GenericGenericClimate matchingClimate matching

16

Degree Day Model: TheoryDegree Day Model: Theory

““Phenology and development of most Phenology and development of most organisms follow a temperature dependent organisms follow a temperature dependent time scaletime scale”” (Allen 1976)(Allen 1976)Attempts to integrate temperature and Attempts to integrate temperature and time started 250 + years ago time started 250 + years ago Development is widely believed to follow a Development is widely believed to follow a sigmoid shapesigmoid shape

Dev.

Tem

p.

17

Degree Day Model: TheoryDegree Day Model: Theory

Organisms have base developmental Organisms have base developmental temperaturetemperature-- minimum temperature below minimum temperature below which no development occurswhich no development occursOrganisms have set number of units to Organisms have set number of units to complete development complete development -- physiological time: physiological time: measured in developmental units (DU) or measured in developmental units (DU) or degree days (DD) degree days (DD) Parameters established from lab or field Parameters established from lab or field studiesstudies

18

Degree Day ModelDegree Day ModelExample: Light Brown Apple Moth Example: Light Brown Apple Moth base temperature base temperature 7.5 C7.5 Crequires ~requires ~640 DD640 DD

to complete developmentto complete development

(egg, larvae, pupae, adult to egg)(egg, larvae, pupae, adult to egg)

Degree days are typically calculated from Degree days are typically calculated from average of high and low temperature for a 24 average of high and low temperature for a 24 hour period above the base temperaturehour period above the base temperature

http://www.hortnet.co.nz/key/keys/info/lifecycl/lba-desc.htm



19

Degree Day ModelDegree Day ModelLight Brown Apple MothLight Brown Apple Moth: : Base temperature Base temperature 7.5 C7.5 C

640 DD640 DD

for generation developmentfor generation development

If average daily temp was If average daily temp was 11C: 3.5 DD (1111C: 3.5 DD (11--7.5)7.5)

are are accumulated and it would take accumulated and it would take 182182

days at that days at that

temperature to complete developmenttemperature to complete development

If average daily temp was If average daily temp was 20C: 12.5 DD (2020C: 12.5 DD (20--7.5)7.5)

are are accumulated and it would take accumulated and it would take 51.251.2

days at that days at that

temperature to complete developmenttemperature to complete development

20

P. japonica P. japonica general informationgeneral information

UnivoltineUnivoltine-- one generation per yearone generation per yearOverwinters typically as a third instar larvaeOverwinters typically as a third instar larvae

http://www.invasive.org/images/768x512/1150134.jpg

21

Insect Development DatabaseInsect Development Database

22

Model ParametersModel Parameters

Japanese BeetleJapanese Beetle

StageStage DD in stageDD in stage First entryFirst entry second entrysecond entry

Overwintering stageOverwintering stage 3rd instar3rd instar 400400 00 400400

PupaePupae 124124 401401 525525

Low 10 CLow 10 C AdultAdult 117117 526526 643643

Upper 34 CUpper 34 C egg egg 140140 644644 784784

first instarfirst instar 222222 785785 10071007

Second instarSecond instar 419419 10081008 14271427

third instarthird instar 720720 14281428

26

Graphing toolsGraphing tools

27

Disease Infection ModelDisease Infection Model

Plant pathologist Plant pathologist describe describe interactions interactions between pathogen, between pathogen, host and host and environmental environmental conditions as the conditions as the disease triangle.disease triangle.

28

Infection is often the rate limiting step in an epidemic because it requires moisture which is often limited in terrestrial environments

Infection can be modeled by a temperature /moisture response function - a mathematical function that describes the response of an organism to temperature and moisture

Disease infection modelDisease infection model

29

Disease Infection ParametersDisease Infection ParametersTTminmin = Min. temperature for infection, = Min. temperature for infection, ooCC,,TTmaxmax = Max. temperature for infection, = Max. temperature for infection, ooCC,,TTopt opt = Opt. temperature for infection, = Opt. temperature for infection, ooCC,,WWmin min = Minimum wetness duration requirement, h = Minimum wetness duration requirement, h

Parameters established in laboratory studiesParameters established in laboratory studies

30

Temperature response functionTemperature response function

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30 35

Tem

pera

ture

Res

pons

e

Temperature C

High ToptLow Topt

31

Temperature moisture response Temperature moisture response functionfunction

Low ToptHigh Wmin

High ToptLow Wmin

32

Sudden Oak Death, Sudden Oak Death, PhytophthoraPhytophthora ramorumramorum

Fungal disease in cool Fungal disease in cool wet weatherwet weather..

Currently in Western US: Currently in Western US: California and OregonCalifornia and Oregon

Source Ventana Wilderness Society

33

Model ParametersModel Parameters

Temperature requirement Temperature requirement 33--28 C, 20 C optimum (28 C, 20 C optimum (WerresWerres, 2001; , 2001; OrlikowskiOrlikowski, 2002), 2002)..

Moisture requirement Moisture requirement 12 hours for zoospore infection (Huberli,2003)12 hours for zoospore infection (Huberli,2003)

Model description Model description Unpublished infection model uses Wang et al. Unpublished infection model uses Wang et al.

(1998 ) temperature response function scaled to a (1998 ) temperature response function scaled to a wetness duration requirementwetness duration requirement..

36

MultiMulti--function Modelfunction Model

Allows for construction of many different Allows for construction of many different models using simple logical and models using simple logical and mathematical equations: mathematical equations: (X>A, X and Y, X or Y, X and (Y or Z), (X>A, X and Y, X or Y, X and (Y or Z), XX≥≥A and XA and X≤≤B, A* B, A* exp(Bexp(B

* X), etc.)* X), etc.)

Some examples used to date are: Some examples used to date are: temperature exclusions (high and or low temperature exclusions (high and or low lethal temperatures), frost free days, and lethal temperatures), frost free days, and emergence dates emergence dates

Parte 3Parte 3

Part 3Part 3 Map CreationMap Creation

Select the Select the ‘‘MapMap’’

feature from the drop down feature from the drop down menu in the menu in the ‘‘HistoryHistory’’

section.section.

40

Requesting a map.Requesting a map.

Select which Select which ‘‘MapMap’’

feature from the drop down menu one wishes to feature from the drop down menu one wishes to request and hit the request and hit the ‘‘GoGo’’

button.button.

41

Requesting a map.Requesting a map.Model:

Which model one

wishes to request a map for.Begin:

When one wants the

map request to begin collecting data ( Jan 1 to Dec 31)

.

End:

When one wants the map request to stop collecting data ( Jan 01 to Dec 31)

.

Num Years:

10, 20

or 30

yrs.When finished, hit ‘Request’.

Region:

What are of the world one wants the map request to collect data from (Africa, Asia, Australia,

Europe,

South

America, North America, World, China). Email:

Email address for map confirmation.

42

Requesting a map.Requesting a map.Data Type:

Station

is

used for the North America where 2000+ stations have reported data. Grid

is used

for the rest of the world. Dev Data:

Just ignore.

Interpolation:

2D can be used with either Station or Grid Data Type. 3D

can

only be used with Station Data Type.

When finished, hit ‘Request’.

43

View the legend. View descriptions of data

used to make map.

View comments made in the model construction.

Name of the parameterand (dates map covers).

Press anywhere on map to open a larger interactive window of the map.

Name of the current model.

See the entire list of models.

44

All average history maps will be displayed in this area under the heading ‘Average History’

by each variable.

To view a map, press ‘Load’. ‘Delete’

will

remove the map from the website.

All probability maps will be displayed in this area under the heading ‘Probability’

by each variable or parameter and the accumulative of

the variable(s).

45

An average history map in An average history map in NAPPFASTNAPPFAST

46Whomever creates the model chooses the values for the legend.

47

Probability Maps in NAPPFASTProbability Maps in NAPPFAST

48

Variable:

Which one of the variables created by the model one wishes to map. Search Value:

Favorable or

Unfavorable.Search Occur:

How many

days that have the selected search value starting with the beginning date you selected .

Probability map differences from average history requests.

Map Type:

Percent displays the probability of the selected

variable of occurring. Freq. of occurr. (X

Years) (X=10, 20, or

30)

displays the probability of how frequently out of X

yrs the conditions of the model will be met.

49

Probability of the frequency of occurrence in 10 yrs of over 120 days favorable for development of an insect.

Percent probability of the occurrence of over 120 days favorable for the development of an insect.

50

Historical maps in NAPPFASTHistorical maps in NAPPFAST

51

History requests are for mapping selected historical periods i.e. day or year. The history request lets you pick an end date (and a beginning date for accumulated variables).

52

Historical map

53

Viewing the NAPPFAST maps in Viewing the NAPPFAST maps in ArcMapArcMap

54

Have the map you wish to export loaded. In output, have ‘Geo-

Tif’

selected and press the ‘Go’

button.

Save the compressed folder.

Extract the compressed folder.

Open ArcCatalog.

55

Right click on the thumbnail of the data file and select the properties.

Press the ‘Edit’ button beside the

‘Spatial Reference’ section.

56

Double click ‘Select’

Double click ‘WGS 1984.prj’

Double click ‘World’

Double click ‘Geographic Coordinate System’

Click ‘Add’Click ‘OK’

Click ‘OK’

57

Open ‘ArcMap’.

Choose an empty map, a template, or browse for existing maps.

Click the ‘Add’

button.Navigate to the folder where the NAPPFAST file is located and select it.

Parte 4Parte 4

59

Part 4Part 4Climate MatchingClimate Matching

60

Climate MatchingClimate Matching

Unlike other NAPPFAST models, climate Unlike other NAPPFAST models, climate matching creates models from the observed matching creates models from the observed distribution of the pest not from biological or distribution of the pest not from biological or experimental data.experimental data.Climate matching works well for weeds but for Climate matching works well for weeds but for arthropods or pathogens there is insufficient arthropods or pathogens there is insufficient observations.observations.Observations can be obtained from taxonomic Observations can be obtained from taxonomic portals such as the Global Biodiversity portals such as the Global Biodiversity Information Facility. Information Facility.

61

Climate MatchingClimate Matching

Simple climate matching procedure Simple climate matching procedure similar to BIOCLIM. similar to BIOCLIM. Climate envelope defines habitat Climate envelope defines habitat space within 1.28 standard space within 1.28 standard deviations of average for each deviations of average for each variable.variable.Weather data is based on a 32 K Weather data is based on a 32 K NCEP grid includes 12+ climate NCEP grid includes 12+ climate variables variables The climate match is expressed as a The climate match is expressed as a percentage of the total years x total percentage of the total years x total variables for each pixel. variables for each pixel.

Beaumont et al 2005Ecological modeling

62

Climate Matching VariablesClimate Matching Variables(1) annual values of extreme minimum (1) annual values of extreme minimum

temperature, temperature, (2) extreme maximum temperature, (2) extreme maximum temperature, (3) annual number of frost free days, (3) annual number of frost free days, (4) annual degree days, (4) annual degree days, (5) annual precipitation, (5) annual precipitation, (6) annual Potential (6) annual Potential EvapotraspirationEvapotraspiration (PET), (PET), (7) average temperature of the three warmest (7) average temperature of the three warmest

months in the year, months in the year, (8) PET for the 3(8) PET for the 3--month period found in 7, and month period found in 7, and (9) precipitation for the period found in 7.(9) precipitation for the period found in 7.

63

Climate Matching ProcedureClimate Matching ProcedureSearch for species observations in GBIFSearch for species observations in GBIFFormat the observations dataFormat the observations dataCompile a configuration file to select Compile a configuration file to select variablesvariablesSubmit to Submit to ZedXZedX (offline)(offline)Wait for productsWait for products

64

GBIF SearchGBIF Search

65

Configuration FileConfiguration File

66

ProductsProducts

Map of observed locationsMap of observed locationsMap of predicted zonesMap of predicted zonesQuality control statisticsQuality control statistics

Percent accuracy (all, withheld)Percent accuracy (all, withheld)Number of observations/ Number of Number of observations/ Number of continental regionscontinental regionsAverage date of observations Average date of observations

Area projections for US Area projections for US

67

Acacia Acacia decurrensdecurrens

home.vicnet.net.au

68

Antarctica

RussiaCanada

China

Brazil

Australia

United States

Greenland

Iran

India

Sudan

Algeria

Kazakhstan

Argentina

LibyaMexico

Peru

Mongolia

Congo, DRC

Saudi Arabia

Indonesia

Created by Roger Magarey,and Dan Borchert,

USDA-APHIS-PPQ-CPHST-PERALusing NAPPFAST Climate Matchingand Global Biodiversity Information

Facility Data

0 4,900 9,8002,450Kilometers

4


69



Facility Data

0 4,900 9,8002,450Kilometers

4


70



Facility Data

0 4,900 9,8002,450Kilometers

4


71


Australian herbarium

72

Kikuyu grassKikuyu grass

Australian Herbaria

Calflora www.tropicalgrasslands.asn.au

73


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Kazakhstan

Argentina

LibyaMexico

Peru

Mongolia

Congo, DRC

Saudi Arabia

Indonesia



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0 4,900 9,8002,450Kilometers

4

Australian Herbaria

Calflora

74



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77


Parte 5Parte 5

79

Part 5Part 5Climate MatchingClimate Matching

80

P. japonica P. japonica general informationgeneral information

UnivoltineUnivoltine-- one generation per yearone generation per yearOverwinters typically as a third instar larvaeOverwinters typically as a third instar larvae

http://www.invasive.org/images/768x512/1150134.jpg

81

Frequency of Occurrence (30year)

00 66

66 1212

1212 1818

1818 2424

2424 3030

82


00 66

66 1212

1212 1818

1818 2424

2424 3030

Adult beetles begin to emerge in central NC 3rd

week in May (Fleming 1972)

83


00 66

66 1212

1212 1818

1818 2424

2424 3030

Beetles appear in central Virginia in last week of May- first week of June.(Fleming 1972)

84


00 66

66 1212

1212 1818

1818 2424

2424 3030

Mountainous Eastern TN beetles appear first week of June (Fleming 1972)

Beetles appear in central Virginia in last week of May- first week of June.(Fleming 1972)

85


00 66

66 1212

1212 1818

1818 2424

2424 3030

Adult beetles begin to emerge in Maryland & Delaware mid June (Fleming 1972)

86


00 66

66 1212

1212 1818

1818 2424

2424 3030

Adult beetles begin to emerge in Southern NJ and Southeastern PA in 3rd week of June (Fleming 1972)

87


00 66

66 1212

1212 1818

1818 2424

2424 3030

Emergence in mountainous regions of NJ and PA 1-2 weeks later (Fleming 1972)

Emergence in Southeastern NY, CT, RI and Southern MA in last week of June (Fleming 1972)

88


00 66

66 1212

1212 1818

1818 2424

2424 3030

Emergence begins in Southern NH and VT in first week of July (Fleming 1972)

89


00 66

66 1212

1212 1818

1818 2424

2424 3030

90


00 66

66 1212

1212 1818

1818 2424

2424 3030

91

Chrysanthemum white rust (Chrysanthemum white rust (PucciniaPuccinia horianahoriana))Overwinters when min T > Overwinters when min T > --66ooCCInfection requirements Infection requirements TminTmin = 4, = 4, ToptTopt = = 17, 17, TmaxTmax = 23= 23ooC, C, WminWmin = 6 h, precipitation = 6 h, precipitation > 2 mm per day (Water 1981).> 2 mm per day (Water 1981).

Chrysanthemum White RustChrysanthemum White Rust

92

Chrysanthemum White RustChrysanthemum White Rust

Frequency of years without overwintering

93

Chrysanthemum white rustChrysanthemum white rust

Average number of days suitable for infection

94

Pine Shoot Beetle (PSB), Pine Shoot Beetle (PSB), TomicusTomicus piniperdapiniperda

Overwinters as adult, can Overwinters as adult, can emerge as soon as emerge as soon as temperatures reach 50temperatures reach 50--54 F 54 F Emerges over a relatively Emerges over a relatively short period of timeshort period of timeImportant to have traps out Important to have traps out in time but not too earlyin time but not too early

http://www.ncrs.fs.fed.us/4401/focus/climatology/tomicus/

http://www.ncrs.fs.fed.us/4401/focus/climatology/tomicus/insects/psbfingers.jpg

95

00 66

66 1212

1212 1818

1818 2424

2424 3030

Frequency of Occurrence (30 year)

> 40 F

> 45 F

> 50 F

January 1-15PSB

Parte 6Parte 6

99

Part 6Part 6Collaboration and Data Collaboration and Data

SharingSharing

100

NAPPFAST SharingNAPPFAST Sharing

NAPPFASTNAPPFASTCAPS TOP 50 Risk Mapping ProjectCAPS TOP 50 Risk Mapping ProjectNAPPFAST MAPVIEWNAPPFAST MAPVIEWNAPPFASTNAPPFAST--OBSOBS

101

NAPPFASTNAPPFAST

NAPPFASTNAPPFASTRestricted access available to selected Restricted access available to selected cooperatorscooperatorsSteep learning curveSteep learning curveData can not be exported but model output Data can not be exported but model output can becan be

102

Risk Maps for Top 50 CAPS PestsRisk Maps for Top 50 CAPS Pests

Developed standard, transparent, method Developed standard, transparent, method for map creationfor map creationCan be used as building blocks for Can be used as building blocks for mapping new pestsmapping new pestsCan be easily modifiedCan be easily modified

103

Risk Maps for Top 50 CAPS PestsRisk Maps for Top 50 CAPS Pests

104

Host Commodities for

Helicoverpa armigera (Ac)

105

Primary and Secondary Host Commodities for

Helicoverpa armigera (Ac)

106

Primary Hosts Acreage Divided by Acres per County

Secondary Hosts Acreage Divided by Acres per County

*0.66 *0.34

+

Relative Proportion of Hosts per County

0= No Hosts per county

5= 0.05-0.075 of each acre in that county is composed of host crop

10= 0.75-1.0 of each acre in that county is composed of host crop

107

Host Map Host Map SpodopteraSpodoptera lituralitura

108

NAPPFAST Map: NAPPFAST Map: SpodopteraSpodoptera lituralitura Five Generations and Inverse of Cold DaysFive Generations and Inverse of Cold Days

109

Risk Map Risk Map SpodopteraSpodoptera lituralitura Equal Weighting of Host and NAPPFASTEqual Weighting of Host and NAPPFAST

110

NAPPFAST Map ViewNAPPFAST Map ViewEasy, rapid site navigation Easy, rapid site navigation designed for users interested designed for users interested in quick access to information in quick access to information Contains series of Contains series of ““Canned Canned Probability MapsProbability Maps”” for approx. for approx. 30 pests30 pestsUser can zoom in, add User can zoom in, add commodity overlays, county commodity overlays, county outlines, interstates and cities outlines, interstates and cities to map to map Converts the custom map to Converts the custom map to PDF for easy printingPDF for easy printingMap View site can be Map View site can be automatically accessed automatically accessed through the GPDD for through the GPDD for coincidental pestscoincidental pests

Username:aphismap

Password:maps2004

111

NAPPFASTNAPPFAST--OBSOBS

New tool in development!New tool in development!NAPPFASTNAPPFAST--OBS will allow different organizations OBS will allow different organizations including PPQ, states, seed industry companies, crop including PPQ, states, seed industry companies, crop consultants, and universities to share data and products in consultants, and universities to share data and products in a secure system. a secure system. NAPPFASTNAPPFAST--OBS will use a common interface architecture OBS will use a common interface architecture and database structure with the industry Pest Information and database structure with the industry Pest Information Platform (Platform (iPIPEiPIPE). Data from APHIS will be stored in ). Data from APHIS will be stored in separate data tables.separate data tables.Each organization will control the sharing of their own data Each organization will control the sharing of their own data and products with other organizations in the system. and products with other organizations in the system.

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NAPPFASTNAPPFAST--OBSOBSUpgrades NAPPFAST MAPVIEW and incorporates the Upgrades NAPPFAST MAPVIEW and incorporates the CAPS Top 50 risk matrixCAPS Top 50 risk matrixBased on commodity/pest designBased on commodity/pest designInteractive maps and ability to view data across yearsInteractive maps and ability to view data across yearsStatic risk maps available as overlays for selected pestsStatic risk maps available as overlays for selected pestsDynamic risk maps (e.g. weekly Dynamic risk maps (e.g. weekly phenologiesphenologies) provided as ) provided as backgroundsbackgroundsTools for generic data upload and linkage to ISIS Tools for generic data upload and linkage to ISIS RoleRole--based based accessaccessAbility to provide commentary and documentsAbility to provide commentary and documentsReportsReports

114

JB Reporting SiteJB Reporting Site

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Sirex Reporting SiteSirex Reporting Site

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CactoblastisCactoblastis

Phenology SitePhenology Site

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