Nick Phelps, MS, PhD Director, Minnesota Aquatic Invasive Species Research Center Assistant Professor, Fisheries, Wildlife and Conservation Biology

Transcription

1 Nick Phelps, MS, PhD Director, Minnesota Aquatic Invasive Species Research Center Assistant Professor, Fisheries, Wildlife and Conservation Biology Dept University of Minnesota MAISRC Research and Management Showcase September 12, 2018

2 Dr. Eva Enns UMN SPH Dr. Luis Escobar Virginia Tech Dr. Ranjan Muthukrishnan MAISRC Dr. Meggan Craft UMN CVM Dr. Andres Perez UMN CVM Dr. Matteo Convertino Hakkaido University Megan Tomamichel MAISRC Zoe Kao UMN SPH Dr. Kaushi Kanankege UMN CVM Dr. Huijie Qiao Chinese Academy of Sciences Dr. Robert Haight US Forest Service Adam Doll MN DNR Dr. Dan Larkin UMN CFANS

3 Uninfested lakes Infested lakes

4 Uninfested lakes Infested lakes

5 Uninfested lakes Infested lakes

6 Uninfested lakes Infested lakes

7 Uninfested lakes Infested lakes

8 Uninfested lakes Infested lakes

9 Uninfested lakes Infested lakes

10 Uninfested lakes Infested lakes

11 Uninfested lakes Infested lakes

12 1. Determine lake suitability for AIS 2. Determine risk via watercraft movement 3. Determine risk via river connection Simulate future risk of AIS spread

13 Escobar et al Sci Reports. Escobar et al JFD. Romero-Alvarez et al PLoS. Escobar et al FVS. Muthukrishnan et al AB. Escobar et al. In prep.

14 1. Determine lake suitability for AIS 2. Determine risk via watercraft movement 3. Determine risk via river connection Predict future risk of AIS spread

15 Used responses from MN DNR Watercraft Inspection survey from Each survey generates up to two edges Aggregated over all responses to create network Adjustments: Data errors (lake/county matching) Sampling bias (random forest) Missing edges (various regressions) Previous lake visited (recalled) Location of inspection Next lake visited (planned)

16 1,336,146 inspections conducted across 769 lakes Complete network included: 2,529 lakes 1,687,224 movements ~46% were self-loops Includes direction and weight After adjustments much larger and more complex network Accuracy = 96.45%

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18 Exiting: Probability of watercraft leaving contaminated from an infested lake (unknown for starry stonewort) (inspection survey) Entering: Probability of contaminated watercraft resulting in infestation in a suitable waterbody (unknown)

19 Zebra mussel violations Starry stonewort violations

20 1. Determine lake suitability for AIS 2. Determine risk via watercraft movement 3. Determine risk via river connection Predict future risk of AIS spread

21 Network model Waterbody ID Verification

22 Complete network included: 10,105 lakes 133,952 river segments 5,672 lakes connected to 1 lake through rivers

23 Entering: Probability of species migration through river connection p/distance (unknown)

24 Zebra mussel migration Starry stonewort migration

25 Additional data collection needed? High risk/low frequency vs. low risk/high frequency

26 1. Determine lake suitability for AIS 2. Determine risk via watercraft movement 3. Determine risk via river connection Predict future risk of AIS spread

27 We want to simulate the AIS spread in MN (~25,900 lakes) using the boater movements and river connection zebra mussel 54 lakes starry stonewort 8 lakes Compare predicted # of infested lakes to the data Predicted infested risk in 2025 Year end Model validation/calibration period Prediction period 8 years x 10,000 simulations

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29 Predicted risk 2017 Predicted risk 2025

30 DOW # Lake Name Combined risk (2025) bertha hen arrowhead loon unnamed unnamed unnamed nisswa ox little ox upper gull roy middle cullen rat margaret hole-in-the-day ray spider

31 DOW # Lake Name Predicted Boater Risk (2025) lake of the woods badoura bog lac la croix nett roseau vermilion red heron mud namakan north branch kawishiwi rainy pelican rice shell kiwosay pool cedar ball club

32 DOW # Lake Name Predicted River Risk (2025) unnamed hen loon bertha arrowhead unnamed unnamed little ox ox rat spider upper gull margaret ray hole-in-the-day roy nisswa nelson

33 Lake Rank Combined risk Diamond East Loon Floyd Jewett Norway Reno Average risk for Minnesota lakes =

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35 Predicted risk 2017 Predicted risk 2025

36 DOW # Lake Name Combined risk (2025) east lake sylvia unnamed kitchi pike bay pug hole shemahgun little rice whitefish cross lake reservoir lower hay andrusia cedar island upper hay horseshoe island daggett amik beltrami

37 DOW # Lake Name Predicted Boater Risk (2025) leech mille lacs minnetonka u.s. lock & dam #3 pool rush mud pepin u.s. lock & dam #2 pool sand point shell heron cedar osakis pelican kiwosay pool jefferson lac qui parle gull

38 DOW # Lake Name Predicted River Risk (2025) unnamed east lake sylvia shemahgun little rice pug hole kitchi pike bay amik loon hen island arrowhead pig bertha thunder daggett ahlin burt

39 Lake Rank Combined risk Medicine Pleasant Wolf Average risk for Minnesota lakes =

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42 Model can easily be manipulated to reflect management decisions and risk tolerance Status quo Number of infested lakes Education Inspections Time

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44 zebra mussel 54 lakes starry stonewort 8 lakes Compare predicted # of infested lakes to the data Implement management scenarios Predicted infested risk in 2025 Year end Model validation/calibration period Prediction period 8 years x 10,000 simulations

45 Zebra mussels by 2025 Starry stonewort by 2025 Scenarios average # of infested lakes # of infested lakes averted % reduction Status quo 591 Education % Penalty % Decon % Scenarios average # of infested lakes # of infested lakes averted % reduction Status quo 143 Education % Penalty % Decon % HYPOTHETICAL MANAGEMENT SCENARIOS!!

46 Phase II: Understanding risk to optimize decision-making County Prevention Aid minnesotawaters.org Locating boat inspection stations on Minnesota lakes - Bob Haight 2:15 room 135AC 3:15 room 155A J. Johnson MN DNR

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