Recent publication on curly-leaf pondweed

Just a quick post to announce the recent publication of a paper authored by Contour Innovations Chief Aquatic Biologist Ray Valley documenting recent short-term declines of the invasive curly-leaf pondweed potentially due to heavy winter snowfall.  You can access the article here or email Ray at and ask for a pdf copy.


Curlyleaf pondweed (Potamogeton crispus) is a long-established, nonnative aquatic plant common throughout southern and central Minnesota that is thought to be expanding northward. Curlyleaf pondweed typically grows abundantly in spring in productive lakes and then senesces in midsummer, often followed by algae blooms. We report observations of widespread, short-term declines in curlyleaf pondweed cover that appear linked to winter snow depth on frozen lakes. These findings suggest that climate change may greatly affect habitat suitability for curlyleaf pondweed. As Minnesota lakes warm with less snow cover limiting light penetration, curlyleaf pondweed growth will likely increase. These observations form the foundation for targeted follow up studies that more precisely describe conditions limiting the growth and expansion of curlyleaf pondweed in north-temperate, North American lakes.


Later this winter, Ray will post a blog that goes in more detail about this long-established invasive aquatic plant and the potential for its management to positively affect water quality by reducing internal nutrient loadingIn a nutshell, the jury is still out and more robust monitoring and research is needed if Minnesota is to efficiently and wisely invest tax payer dollars dedicated to clean water work in the state.

What to do with all this Lake Habitat Data!?

Fifteen data points per second, four hours on Lake X today, several more tomorrow.  Lake Y and Z to follow.  Repeat next year and the year after.  Since no one has to process the data, it can be collected during non-dedicated mapping time by hitting record on your Lowrance HDS each time  your on the water.  Simple math tells you that this is going to lead to A LOT of data.  What are you going to do with it all?

This “problem” is new to biologists and lake management practitioners in the 21st Century.  Decision making in a data “poor” environment has been much more common and indeed is still a real problem.  The “problem” of too much data, really isn’t a problem at all.  Modern computing technology can return only information that is important to you and archive the rest for safe keeping.

With regards to aquatic plant assessment and monitoring in lakes, never before have we been able to rapidly collect and interpret information about how much plants are growing and where.   So, we spend three hours going back and forth on our favorite 230 acre, upload our data to ciBioBase and get a pretty map and some statistics on the density of the vegetation (Figure 1).  So what?  What does it mean?

Figure 1.Example automated summary report from ciBioBase.

Well, admittedly it is difficult to judge whether 78% of the lake being covered with vegetation (PAC) is normal.  What is normal?  This exemplifies the importance of collecting baseline information to judge whether changes from time A or B are significant.

The invasive aquatic plant, Eurasian watermilfoil has a tendency to grow to the surface of lakes, displace native plant species, and impede navigation.  The extent of surface growth and overall cover of Eurasian watermilfoil and other invasive plant species are typically the conditions that lake managers and citizens want to reduce.  ciBioBase provides a rapid and objective way to monitor how cover and surface growth of vegetation is changing as the lake is affected by various stressors and our responses to them (e.g., herbicide treatments).  For instance, often a water resource agency or citizen group will state objectives in a lake management plan something to the effect of “Objective 1: reduce the abundance of Eurasian watermilfoil by 80%.”  What should be asked next is 80% of what? What is our yardstick?  We can’t expect to be successful at water and fisheries resource conservation without clearly defining management targets and evaluating whether we’re getting there.

Furthermore, there is a tight link between water quality and aquatic plant growth.  Clear lakes with all native plant species often have high cover of vegetation, but relatively little surface-growing vegetation (except near shore or in shallow bays).  As more nutrients run into the lake from lawns and parking lots, aquatic plants initially increase in abundance and grow closer to the surface to get sunlight from the clouding water.  If we continue to mow our lawns down to the lake edge, over fertilize, and route water from parking lots and roofs into our lakes unabated, then aquatic plants crash because the water is too turbid to support plant growth.  Next thing you know, largemouth bass, bluegill, and northern pike disappear and you find your lake on the EPA’s Impaired Water’s List and now you need to spend million’s to clean it up.  ciBioBase can be used to prevent you from getting to that point.

One precise way of doing so is to monitor the maximum depth that vegetation grows in your lake.  There is a tight link between water clarity and the depth that plants grow in lakes (Figure 2).  The extent of plant growth integrates the short-term dynamic nature of water clarity and gives a measure of the overall water clarity conditions for the year.  The conventional water clarity monitoring routine involves citizens and lake managers taking a dozen trips a season to the middle of the lake to drop a Secchi disk down and measure the distance where the disk disappears from sight.  With one 3-hr mid-summer ciBioBase survey, you can get a measure of water clarity conditions for the entire season.  This depth should remain relatively consistent from year to year in stable watershed and lake conditions.  A change of two feet over the course of a couple of years should raise a flag that conditions in the lake may be changing and initiate some initial investigation into possible causes.

Figure 2. Relationship between the maximum depth of vegetation growth as a function of water clarity from 33 Minnesota lakes where lakes were mapped with sonar and water clarity data was collected with a Secchi disk.

To bring this discussion full circle, we should ask: how do we know the change in point A or B is due to a real change in lake conditions and not an artifact of our sampling?  This question plagued the 20th Century Lake Manager to the point of gridlock.  In the 21st century, we can overwhelm the question with data to get almost a census of the current conditions rather than a small statistical sample fraught with error.  Lake Managers don’t have to physically wade through all this data to find the answer.  High-powered computers and processing algorithms can do the heavy lifting, the lake manager or concerned citizen can focus on implementing practices that will result in clean water and healthy lake habitats.