results of ais predictive modeling in a mn county...growing strong industries ~ developing new ideas...

Growing Strong Industries ~ Developing New Ideas ~ Nurturing Natural Resources

Josh DumkeKristi Nixon

https://data.nrri.umn.edu/ais/

Research guiding action: results of AIS predictive modeling in a MN County

St. Louis County Introduction Risk Assessment

Putting St. Louis County into Perspective

1. Connection to waters with most AIS in state: St. Louis River and Lake Superior

2. Lots of lakes; a rich network for secondary introductions because neighbors have lots of lakes too

3. Some well-known lakes, but many are ‘under the radar’ regarding AIS risk and prevention (132 lakes have boat access)

4. Is the recipient of the greatest AIS Prevention Aid due to high numbers of boat launches and parking spaces (>$710,000 for 2019)


Question: How to guide St. Louis County AIS Prevention Aid resources to have greatest impact preventing more invasions?

?MPR photo

https://whatcomboatinspections.com/


Introduction Risk Defined: the risk a lake has to receive AIS using 23 species from 2015 plan

We used models to identify lakes at greatest risk, but models need lots of data• Public AIS databases• Regional DNR offices• Spatial data downloads• Data from collaborators

Data – Boater SurveysBoaters surveys from MN DNR (2015-16) and 1854 Treaty Authority (2012-15) used to create a travel network and determine most frequent distance driven

Risk that AIS will remain alive as hitch-hikers diminishes as drying time of watercraft increases.

Data – Boater SurveysBoaters surveys from MN DNR (2015-16) and 1854 Treaty Authority (2012-15) used to create a travel network and determine most frequent distance driven

Risk that AIS will remain alive as hitch-hikers diminishes as drying time of watercraft increases.

~62% within 20 miles

AIS presence – AIS presence information for 23 species

Launches – Access type (trailer/carry-in), # ramps, docks, and parking spaces.

Recreation– Presence of popular game fish like Lake Trout, Walleye and Muskellunge, and average number of registered fishing tournaments.

Lake attributes - Max depth, surface area, shoreline distance, littoral area, and a calculated shoreline development index (SLD)

Proximity attributes – Land parcel type (residential, undeveloped, etc.), population density (ppl/acre), distance to towns, and road densities

Buffer analysis – Counts of individual AIS, launches, and lakes within 20 miles of every lake in the dataset (n=1,139; lakes >5 acres and assigned a Basin ID)

Data - Building the Dataset

73 boat launches w/in 20 mi of

East Vermilion

Data – AIS Summaries

Nearly 88% of lakes are AIS-free146 lakes have at least 1 of the 23 AIS considered

Most records are for invasive vegetation

Dissemination of Results via Web Tool

Main page featuresEssentially a data-dump of accesses, survey points, game fish, and individual AIS species



Spatial Analysis page featuresDisplay of travel network from boater surveys, advanced filters, and access spatial queries



Spatial Analysis page featuresAdvanced filter example• Conveys Vermilion gets lots of distant

visitors in the riskiest <24 hr period • Some prior lakes infested with AIS not

already in Vermilion

Lake Vermilion <24 hr travel network

Upper Red Lake: Starry Stonewort

Modeling - Methods

Data Screening: removed border waters, variables with missing data, “outlier” lakes, and highly correlated variables. Thinned to about 20 unique variables.

A Zero-inflated Poisson (ZIP) model was used to predict AIS Count.❖ Good at handling lots of zeros in a regression (88% of AIS count were 0)

A Random Forest model was used to identify which variables were most important in making correct AIS Presence/Absence predictions.

❖ Machine learning algorithm; randomly splits the dataset and randomly selects variables many times to ‘learn’ how to make correct classifications

Modeling - Results

ZIP model to predict AIS count (number of AIS expected)• More AIS predicted in 43 lakes than presently observed (R2 =59%) – These lakes would be good candidates for early detection surveys

Random Forest model to predict AIS presence (whether any AIS present)• 5.6% out-of-bag error

Modeling – Random Forest Results

Assigning Relative Risk to St. Louis County Lakes

SumRank criteria for top 5 predictive variables1. Presence of a trailer boat launch2. 90th percentile for road distance w/in 30 m of shoreline3. 90th percentile for shoreline complexity index (SLD)4. Presence of either Walleye, Muskellunge, or Lake Trout5. 90th percentile for surrounding population density

❖ By ranking the entire dataset with these variables, we can assign AIS introduction risk categories to every lake in the County.


Risk Assessment page featuresAIS introduction risk displayed for all lakes in the CountyCategories classified as Lowest Risk (0), Moderate Risk (1-3), and Highest Risk (4-5)



Risk Assessment page featuresGuides for early detection activities – 43 lakes based on ZIP modelPlus a calcium query tool and flowlines


Summary

The Web Tool provides:

• …baseline risk of any AIS arriving, and places all lakes of St. Louis County on a relative scale to inform decisions

• …access to advanced filters to drill down into AIS, lake, and landscape data to help interpret AIS introduction risk

• Live version online now at https://data.nrri.umn.edu/ais/

Rusty Crayfish Presence

Josh Dumke: [email protected] Nixon: [email protected]


Thank You

mailto:[email protected]

mailto:[email protected]

Additional Slides

Webmap Link


Additional Slides

Data - Building the Database

Data derived from (mostly) public sources:❖ Inclusion of collaborator survey data❖ Spatial data downloads (GIS layers and attributes,

water chemistry, land parcel designations)❖ MN DNR Lakefinder; infested waters lists❖ AIS presence websites (USGS NAS, MISIN,

GLANSIS, EDDMapS)❖ Inquires to MN DNR and SWCD regional offices

Resulting in:❖ 1,139 lakes >5 acres with a DOW❖ Comprehensive AIS presence information of

the 23 species listed in 2015 County Plan❖ 170 columns of information for each lake

(about 100 descriptive)

Rusty Crayfish Presence

Modeling – Random Forest Results

r = 0.65**

r = 0.40**

r = 0.37**r = 0.44**

r = 0.12**r = -0.19**

r = 0.12**

r = 0.23**

r = -0.23**

r = -0.06*

r = 0.08*

r = 0.14**r = 0.12**

Correlation of each variable to AIS Count (Pearson’s r)

Web Tool SummaryWhat the web tool doesn’t do Why?

It does not consider habitat suitability or probability of successful AIS establishment

Most lakes have no information on variables of ecological significance (e.g. [Ca], secchi, max depth, % littoral), and many AIS have unknown ecological thresholds

It does not make timeline predictions (e.g. 2 new AIS present by 2025)

Would require data on propagule loading, # of boats per season, and habitat suitability to make projections – beyond project scope

Not a predictor for individual AIS Web Tool assesses baseline risk of any AIS arriving, and places all lakes of St. Louis County on a relative scale.

Is not a new AIS mapping system Other databases are designed for conveying AIS sightings already (USGS NAS, MISIN, EddMapS, GLANSIS).

Live version online now at https://data.nrri.umn.edu/ais/

Collaborators – Boater Surveys

Watercraft Type:Majority of vessels recorded were classified as fishing boats ~17’ length regardless of use group

What this Web Tool Isn’t

• This does not consider habitat suitability or predict whether individual AIS will establish

• This web tool does not make timeline predictions (e.g. Zebra Mussel will be present by 2025)

• This is not a new AIS mapping system; there are already several databases that do that:


Risk Assessment page featuresSLRE high-risk designation: meets all 5 risk-based criteria

Doesn’t the SLRE already have all the AIS it could get?? Well, no. It is still at high-risk of receiving new AIS (e.g. starry stonewort), due to all the risk factors (lots of people and lots of access options)

Pop-up for SLRE

Data – AIS Survey Data From CollaboratorsInvasive Plants-NRRI

Spiny Waterflea-USFS and 1854 TA

Rusty Crayfish-USFS and 1854 TA


Risk Assessment page featuresFlowlines – consider lake position in a watershed; many are naturally connected


Risk Assessment page featuresCalcium data – only available for some lakes, but could be useful in determining lake habitat suitability for zebra mussel. User-defined limits can be set with the query tool.

All Calcium (n = 75) Results for minimum limit of 20 mg/L (n = 13)

Data – Building Connectivity Buffers

There was no difference in distance between 24 hr and 1-4 day users traveling between lakes, so travel is pooled.

A 20 mile buffer captures over 60% of the connections between different lakes within 4 days.

Buffers used to collect information on lake surroundings

results of ais predictive modeling in a mn county...growing strong industries ~ developing new ideas...

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