Nick Phelps, MS, PhD - maisrc.umn.edu · Dr. Eva Enns. UMN SPH. Dr. MegganCraft. UMN CVM. Dr....
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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
Transcript of Nick Phelps, MS, PhD - maisrc.umn.edu · Dr. Eva Enns. UMN SPH. Dr. MegganCraft. UMN CVM. Dr....
Nick Phelps, MS, PhDDirector, Minnesota Aquatic Invasive Species Research Center
Assistant Professor, Fisheries, Wildlife and Conservation Biology DeptUniversity of Minnesota
MAISRC Research and Management ShowcaseSeptember 12, 2018
Dr. Eva EnnsUMN SPH
Dr. Meggan CraftUMN CVM
Dr. Andres PerezUMN CVM
Dr. Matteo ConvertinoHakkaido University
Dr. Huijie QiaoChinese Academy of
Sciences
Dr. Luis EscobarVirginia Tech
Zoe KaoUMN SPH
Dr. Kaushi KanankegeUMN CVM
Dr. Dan LarkinUMN CFANS
Dr. Ranjan MuthukrishnanMAISRC
Adam DollMN DNR
Dr. Robert HaightUS Forest Service
Megan TomamichelMAISRC
Uninfested lakes
Infested lakes
Presenter
Presentation Notes
The predicted total arriving boats to inform the total number of total traffic arrived on each lake in the simulation. Inspection survey inform the number of leaving boats from one lake to another lake.
Uninfested lakes
Infested lakes
Uninfested lakes
Infested lakes
Uninfested lakes
Infested lakes
Uninfested lakes
Infested lakes
Uninfested lakes
Infested lakes
Uninfested lakes
Infested lakes
Uninfested lakes
Infested lakes
Uninfested lakes
Infested lakes
1. Determine lake suitability for AIS
2. Determine risk via watercraft movement
3. Determine risk via river connection
Simulate future risk of AIS spread
Escobar et al. 2017. FVS.
Escobar et al. 2016. JFD.
Romero-Alvarez et al. 2017. PLoS.
Muthukrishnan et al. 2018. AB.
Escobar et al. 2016. Sci Reports.
Escobar et al. In prep.
1. Determine lake suitability for AIS
2. Determine risk via watercraft movement
3. Determine risk via river connection
Predict future risk of AIS spread
• Used responses from MN DNR Watercraft Inspection survey from 2014-2017– 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)
Next lake visited(planned)
Location ofinspection
• 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 largerand more complex network– Accuracy = 96.45%
Presenter
Presentation Notes
Infested infested (70%) Uninfested infested/uninfested (50/50%)
Entering: Probability of contaminated
watercraft resulting in infestation in a suitable
waterbody (unknown)
Exiting: Probability of watercraft
leaving contaminated from an infested lake(unknown for starry
stonewort) (inspection survey)
Zebra mussel violations Starry stonewort violations
1. Determine lake suitability for AIS
2. Determine risk via watercraft movement
3. Determine risk via river connection
Predict future risk of AIS spread
Waterbody ID Verification
Network model
• Complete network included:– 10,105 “lakes”– 133,952 river segments
• 5,672 lakes connected to ≥ 1 lake through rivers
Entering: Probability of species migration
through river connectionp/distance(unknown)
Zebra mussel migration Starry stonewort migration
• Additional data collection needed?• High risk/low frequency vs. low risk/high frequency
1. Determine lake suitability for AIS
2. Determine risk via watercraft movement
3. Determine risk via river connection
Predict future risk of AIS spread
• We want to simulate the AIS spread in MN (~25,900 lakes) using the boater movements and river connection
x 10,000 simulations
starry stonewort
8 lakeszebra mussel
54 lakes
Model validation/calibration
period
Prediction period 8 years
Compare predicted # of infested lakes
to the data
Predicted infested risk in
2025
Year end ............
Predicted risk 2017 Predicted risk 2025
DOW # Lake Name Combined risk(2025)
18035500 bertha 0.940718027000 hen 0.938918036600 arrowhead 0.937218026800 loon 0.935618068000 unnamed 0.935018064400 unnamed 0.908318063900 unnamed 0.906418039900 nisswa 0.867718028800 ox 0.856218030200 little ox 0.854411021800 upper gull 0.846018039800 roy 0.843118037700 middle cullen 0.842518041000 rat 0.838911022200 margaret 0.838318040100 hole-in-the-day 0.835911022000 ray 0.835611022100 spider 0.821373013300 cedar island 0 8095
DOW # Lake Name Predicted Boater Risk(2025)
39000200 lake of the woods 0.361029032000 badoura bog 0.318669022400 lac la croix 0.316436000100 nett 0.316368000300 roseau 0.305369037800 vermilion 0.30264003500 red 0.302332005700 heron 0.294445000200 mud 0.292869069300 namakan 0.291538073800 north branch kawishiwi 0.290469069400 rainy 0.289569084100 pelican 0.28651006700 rice 0.28623010200 shell 0.285715024500 kiwosay pool 0.284843011500 cedar 0.283731081200 ball club 0.282621037500 christina 0 2791
DOW # Lake Name Predicted River Risk(2025)
18068000 unnamed 0.932518027000 hen 0.931718026800 loon 0.930618035500 bertha 0.925418036600 arrowhead 0.924818064400 unnamed 0.908318063900 unnamed 0.905418030200 little ox 0.847718028800 ox 0.833318041000 rat 0.827011022100 spider 0.820511021800 upper gull 0.819011022200 margaret 0.818911022000 ray 0.815018040100 hole-in-the-day 0.813218039800 roy 0.810618039900 nisswa 0.787118041100 nelson 0.755973008200 schneider 0 7453
Lake Rank Combined riskDiamond 395 0.2831East Loon 306 0.3576Floyd 446 0.2573Jewett 884 0.1741Norway 615 0.2135Reno 289 0.3875
Average risk for Minnesota lakes = 0.1784
Predicted risk 2017 Predicted risk 2025
DOW # Lake Name Combined risk(2025)
86028900 east lake sylvia 0.544131089500 unnamed 0.46424000700 kitchi 0.345011041500 pike bay 0.31964000300 pug hole 0.29724026900 shemahgun 0.29524001500 little rice 0.294418031000 whitefish 0.222718031200 cross lake reservoir 0.220418037800 lower hay 0.21694003800 andrusia 0.205473013300 cedar island 0.185618041200 upper hay 0.181773015700 horseshoe 0.178818026900 island 0.178318027100 daggett 0.177731086500 amik 0.17684013500 beltrami 0.176818036600 arrowhead 0.1747
Presenter
Presentation Notes
Wolf Lake is rank #21 and Lake Bemidji is rank #42. (See excel table)
DOW # Lake Name Predicted Boater Risk(2025)
11020300 leech 0.159448000200 mille lacs 0.158827013300 minnetonka 0.142025001700 u.s. lock & dam #3 pool 0.138756014100 rush 0.137745000200 mud 0.133525000100 pepin 0.133019000500 u.s. lock & dam #2 pool 0.129869061700 sand point 0.12953010200 shell 0.128732005700 heron 0.128343011500 cedar 0.127177021500 osakis 0.126318030800 pelican 0.126315024500 kiwosay pool 0.126240009200 jefferson 0.126137004600 lac qui parle 0.125511030500 gull 0.125110005900 waconia 0.1247
DOW # Lake Name Predicted River Risk(2025)
31089500 unnamed 0.460486028900 east lake sylvia 0.45014026900 shemahgun 0.29354001500 little rice 0.28744000300 pug hole 0.28684000700 kitchi 0.263611041500 pike bay 0.206431086500 amik 0.176318026800 loon 0.170618027000 hen 0.169518026900 island 0.167618036600 arrowhead 0.165818035400 pig 0.162818035500 bertha 0.16254027500 thunder 0.160518027100 daggett 0.16024034000 ahlin 0.13804028400 burt 0.131873008200 schneider 0.1296
Lake Rank Combined riskMedicine 174 0.1210Pleasant 211 0.1181Wolf 32 0.1738
Average risk for Minnesota lakes = 0.0056
https://www.maisrc.umn.edu/modeling-ais
https://www.maisrc.umn.edu/modeling-ais
Model can easily be manipulated to reflect management decisions and risk tolerance
Time
Number of infested
lakes
Status quo
Education
Inspections
x 10,000 simulations
Implement management
scenariosstarry
stonewort8 lakes
zebra mussel54 lakes
Model validation/calibration
period
Prediction period 8 years
Compare predicted # of infested lakes
to the data
Predicted infested risk in
2025
Year end ............
Scenarios average # of infested
lakes
# of infested
lakes averted
% reduction
Status quo 591Education 528 63 10.7%Penalty 464 127 21.5%Decon 442 150 25.3%
Scenarios average # of infested
lakes
# of infested
lakes averted
% reduction
Status quo 143Education 93 50 35.0%Penalty 56 88 61.0%Decon 45 98 68.4%
Zebra mussels by 2025 Starry stonewort by 2025
HYPOTHETICAL MANAGEMENT SCENARIOS!!
J. Johnson
minnesotawaters.org
County Prevention Aid
MN DNR
Phase II: Understanding risk to optimize decision-making
“Locating boat inspection stations on Minnesota lakes”
- Bob Haight2:15 room 135AC3:15 room 155A
