Using habitat suitability models to identify ecoregions sensitive to invasion by invasive terrestrial plants invading Wisconsin

Transcription

1 Using habitat suitability models to identify ecoregions sensitive to invasion by invasive terrestrial plants invading Wisconsin Niels Jorgensen and Mark Renz

2 Terrestrial Invasive Species Pose risk to natives, ecosystems Economic impacts; costs Control, monitoring Require resources, tools in order to effectively control Difficult to monitor for all species Prioritize Identify suitable habitats based on biology; environment, climatic factors

3 all models are wrong, some are useful. - George E. P. Box Statistician, UW-Madison

4 Habitat Suitability Models Concept has been around for decades Typically use presence data Not necessarily predicting presence Tool to help identify a suitable habitat Variety of techniques available Previously would investigate individual statistical method at a time Now generally accepted to investigate multiple techniques; find similarities

5 VisTrails: Software for Assisted Habitat Modeling Open-source scientific workflow Easily shared among collaborators Simple interface allows for data exploration and visualization SAHM used to explore habitat suitability modeling across 5 different modeling approaches Uses the statistical software package R

6 Objectives Run a suite of five models to determine probability of suitable habitat for wild parsnip and Japanese barberry 1. Determine top significant predictors driving suitable habitats across Wisconsin that are useful to land managers 2. Identify Level III Ecoregions in WI with high percentage of total area of predicted suitable habitat for each species

7 Five Types of Models 1. Boosted Regression Tree (BRT) Ensemble machine learning technique for regression 2. Generalized Linear Model (GLM) Regression technique 3. Multivariate Adaptive Regression Splines (MARS) Non-parametric regression technique 4. Random Forests (RF) Ensemble machine learning technique for regression 5. Maximum Entropy (MaxEnt) Machine learning technique based on the principle of maximum entropy

8 Layers Used in Models Landscape Attributes Aspect, slope, vegetation continuous fields (percent tree cover), land cover classes, EVI, available water capacity (to 150cm), soil ph, soil OM, soil %clay, crop yield index Climate Data Average minimum and maximum temperatures Average spring, summer, autumn, winter precipitation Dispersal Corridors Roads, urban areas, waterways

9 Terrestrial Invasives Studied Wild Parsnip (Pastinaca sativa) Herbaceous biennial/monocarpic perennial Japanese Barberry (Berberis thunbergii) Deciduous perennial shrub Models performed at 30m resolution (pixels) Many more species with sufficient presence points ready to study

10 Wild Parsnip 1,414 presence points used from various sources GLEDN, EDDMapS, GLIFWC, MISIN, DNR Other groups included

11 AUC = AUC = AUC = AUC = 0.912

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13 Model Results Wild Parsnip BRT GLM MARS RF MAXENT Predictor 1 Roads Summer Precipitation Roads Roads Summer Precipitation Predictor 2 Summer Precipitation Roads Wisconsin Ecoregion Summer Precipitation Roads Predictor 3 Urban Areas Percent Tree Cover Summer Precipitation Urban Areas Average Minimum Temp

14 Model Results Wild Parsnip BRT GLM MARS RF MAXENT Predictor 1 Roads Summer Precipitation Roads Roads Summer Precipitation Predictor 2 Summer Precipitation Roads Wisconsin Ecoregion Summer Precipitation Roads Predictor 3 Urban Areas Percent Tree Cover Summer Precipitation Urban Areas Average Minimum Temp

15 Probability Roads Wild Parsnip Model Type Response BRT m GLM m MARS m RF 0-500m MaxEnt m Distance from Roadway (m)

16 Probability Summer Precipitation Wild Parsnip Model Type BRT GLM MARS RF MaxEnt Response inches inches inches inches inches Summer precipitation (inches)

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19 Level III Ecoregions in Wisconsin

20 0-69% >70%

21 0-69% >70%

22 Model Percentage of Ecoregions with 0.70 or Greater Probability of Suitable Habitat Western Corn Belt Northern Lakes & Forests Northern Central Hardwood Forests Driftless Area Southeastern WI Till Plains Central Corn Belt Plains BRT <10% <10% 19% 51% <10% <10% GLM 57% <10% 27% 60% 26% <10% MARS <10% <10% 53% 17% <10% <10% RF 15% 47% <10% <10% <10% <10% <10% of Ecoregion 10-49% of Ecoregion >50% of Ecoregion

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24 Japanese Barberry 99 presence points used Many more points recently became available Many more extend into Michigan s UP

25 Model Results Japanese Barberry BRT GLM MARS RF MAXENT Predictor 1 Available Water Capacity Wisconsin Ecoregion Wisconsin Ecoregion Aspect Wisconsin Ecoregion Predictor 2 Urban Areas Available Water Capacity Winter Precipitation Available Water Capacity Percent Tree Cover Predictor 3 Water Minimum Temperature Percent Tree Cover Percent Tree Cover Available Water Capacity Predictor 4 Percent Tree Cover Water Available Water Capacity Water Summer Precipitation

26 Model Results Japanese Barberry BRT GLM MARS RF MAXENT Predictor 1 Available Water Capacity Wisconsin Ecoregion Wisconsin Ecoregion Aspect Wisconsin Ecoregion Predictor 2 Urban Areas Available Water Capacity Winter Precipitation Available Water Capacity Percent Tree Cover Predictor 3 Water Minimum Temperature Percent Tree Cover Percent Tree Cover Available Water Capacity Predictor 4 Percent Tree Cover Water Available Water Capacity Water Summer Precipitation

27 Probability Available Water Capacity Japanese Barberry Model BRT GLM MARS RF MaxEnt Response 0-20cm 0-40cm 0-40cm 0-20cm 0-35cm AWC (cm of water)

28 Probability Water Japanese Barberry Model Response BRT 0-750m GLM m MARS m RF 0-750m MaxEnt 0-800m water (m)

29 Probability Percent Tree Cover Japanese Barberry Model Response BRT 50% GLM 80% MARS 50% RF 50% MaxEnt 45% % Tree Cover

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31 Comments All 5 models performed well, provided different probability levels for same exact data inputs Environmental data is only as good as the methods of collection Often times outdated (e.g. soils) Climate changing Not all data layers to 30m resolution

32 Future Explorations Choose incoming species that pose significant risk to native systems Consider habitat suitability models under climate change Can apply regionally, not just in WI Provide useful information to land managers for these terrestrial invasives

33 Questions and/or Suggestions Are there factors that would be useful to you that we could potentially include in our models to help guide surveys?