Ecological niche modelling using cluster analysis to determine suitable environments of Giant Hogweed occurrence.

Simon Fonji

Presentation OutlineBackgroundObjectiveResearch questionsMethodsResultsConclusion

Giant HogweedGeneral Physiology:Why is it such a successfulInvader in Latvia?

Mature height of 6 to 21 feet makes ittaller than any other herbaceous plant orgrass in Latvia- Leaves up to 1.5 meters acrosson lower 1/3 of stalk readily shade out competitors in the understory- Compound umbel (flower) canreach 2 meters in diameter and produce up to 100,000 seeds with over 90%viability rate Huge taproot serves as nitrogen storage device and provides tremendous regenerative potential

Problems: Human Health RiskExtreme Phototoxic Dermatitis

Native Region: The CaucasusIncludes:SW RussiaGeorgiaNE TurkeyArmeniaAzerbaijanNorthern Iran

Introduction to LatviaIn the 1960s, Giant Hogweed was plantedin Latvia in places like Madona and Cesis as a silage crop to feed cattle

Objectiveuse cluster analysis to group the most common classes of conditions where Giant Hogweed is present use the results from cluster analysis to create a Giant Hogweed suitability map Determine priority areas for conservation planning

Research QuestionsCan cluster analysis be used to accurately predict where Giant Hogweed will likely grow?

Data Two Landsat TM images obtained in the summers of 1992 and 2007.PPGIS data and other GPS data of Giant HogweedLatvian demographic data from 1992 & 2007Digital topographic layers and municipal boundaries.

MethodologyUse presence-only data to group cluster of conditions of environmental variablesHierarchical-cluster analysis was used to determine the number of clustersK-cluster analysis was used to group the most common classes of conditions where Giant Hogweed is present.Variables used in cluster analysis are: slope, elevation, distance to roads/rivers/urban centers, land cover, land cover change, population density.

Hierarchical-cluster and K-cluster analyses ResultsHierarchical cluster resultsK-cluster resultsK-cluster results

StageCluster CombinedCoefficientsStage Cluster First AppearsNext StageCluster 1Cluster 2Cluster 1Cluster 23671361935.399351364371368103775.4423630369369103035.77636835337237011915.83036036537137111366.0183703673723721106.47137136937337311257.10837236637437412409.326373362375375123910.5543740376376132711.80037500

Standardized Cluster Means1234Zscore(agricultural change)-.24007.90767-.07773-.16594Zscore(elevation).08065-.05849-.33569-.07463Zscore(dist_road_km).03159-.42826.33472-.25259Zscore(dist_urbancenter_km).21010-.43524-.45707-.55315Zscore(dist_water_km).04965-.11011-.03520-.19479Zscore(forest change)-.23607-.313131.91316-.23138Zscore(agriculture land cover).48235-.67534-1.93205.12605Zscore(forest land cover)-.32168-.298582.35100-.12066Zscore(urban land cover)-.349581.78076-.44222.01226Zscore(nochange).47631-1.24443-1.09161.27748Zscore(population density)-.15205.08813.61235.49152Zscore(slope)-.23951-.20978-.182953.12492Zscore(urban change)-.348241.84792-.36147-.05113

ANOVAClusterErrorFSig.Mean SquaredfMean SquaredfZscore(agricchange)21.4773.77137327.855.000Zscore(dem)2.1253.9713732.189.089Zscore(dist_road_km)5.6503.9143736.178.000Zscore(dist_urbancenter_km)12.8393.89437314.358.000Zscore(dist_water_km0).79431.055373.753.521Zscore(forestchange)54.6063.526373103.717.000Zscore(lcagric)77.2583.366373211.123.000Zscore(lcforest)82.3593.288373285.990.000Zscore(lcurban)76.2283.441373172.950.000Zscore(nochange)66.0993.461373143.285.000Zscore(pop_den2)9.05431.0313738.785.000Zscore(slope)90.7643.330373274.725.000Zscore(urbanchange)80.1523.484373165.553.000Table 5, F-test and significance of variables in the model

Four clusters were produced from K-cluster analysis characterized by specific environmental conditionsExamples of conditions: Cluster 1:Elevation 64 and 183 metersSlopes 0 and 0.89 degreeIn agricultural areasNear low population centersCluster 4:Elevation 46.8 and 172.5 metersSlopes 3.8 and 12.4 degreeNear urban centers In high population centersThese conditions were used to build a Giant Hogweed suitability map

Combine clusterFor each cluster find range of variable values where the majority of Giant Hogweed pixels fall

map shows all pixels whose environmental variables resemble any of the four clusters, and are therefore likely candidates for future Giant Hogweed colonization.

Habitat suitability mapFor each cluster, this shows the area where all 13 of the utilized variables fall between the 10th and 90th percentile.27% of the Giant Hogweed testing points fall within this zone.85% of the Giant Hogweed testing points fall with the zone where at least 12 of the 13 utilized variables fall between the 10th and 90th percentiles.

ResultsCluster 1 occurs in mostly agricultural areas where the population density is low. These areas are mostly rural areas where agricultural abandonment is common. Cluster 2: occurs mostly in urban areas close to roads and urban centers. This is probably near big cities where urban expansion is taking place and where people have gardens or small farms in their compounds. Cluster 3: occurs near urban centers where land cover type and land cover change to forest. These are probably remote areas near cities that are far from roads and along forest edges.Cluster 4 occurs mostly in urban areas near roads where the population density is high. These are most likely small farm areas and gardens in big cities. Cluster 4 is very similar to cluster 2.

ConclusionCluster analysis can be used as an effective tool to select sites with favourable conditions for Giant Hogweed occurrence based on environmental factorsLULCC, demographic and geographic factors influence the spread of Giant Hogweed in Latvia.Giant Hogweed suitability map to predict potential spreaduseful for managing and controlling Giant Hogweed in LatviaApplicable to other regions with high Giant Hogweed occurrence: Larger Baltic region and RussiaCentral and Western EuropeNorthern USA and Canada

Thank you for listening.

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