Aquatic Plant Abundance Mapping and Resilience!

Merriam-Webster Defines resilience as an ability to recover from or adjust easily to misfortune or change.  Eminent University of Wisconsin-Madison Ecologist Dr. Steve Carpenter further adds that resilience is the ability for a system to withstand a “shock” without losing its basic functions, http://www.youtube.com/watch?v=msiIV5NdLVs

Resilience is a relatively easy concept to understand, but it can be difficult to measure in lakes without monitoring subtle changes over time.  This stresses the importance of long-term monitoring and being on guard for new changes to water quality, aquatic plants, and fish.  Volunteer networks and agencies across the country are making great strides in monitoring water quality by dropping a disk in the water and scooping up some water and sending it to a lab for analysis.  In essence, taking the lake’s “blood” sample.  Indeed, water quality samples can be very telling.  But what is happening to the rest of the lake “body”?  How is it changing in relation to its liquid diet of runoff or medication to treat invasive species?  Unfortunately, until now, natural resource agencies, lake managers, and volunteers have not had the capabilities to objectively and efficiently assess these changes without time-intensive, coarse surveys of vegetation cover.

Your body’s immune system is the engine of resilience.  When your immune system becomes compromised, you become vulnerable to a wide range of ailments that may not be a threat to someone with a healthy immune system.  The same goes for lakes.  In the glaciated region of the Upper Midwestern US and Canada, healthy lakes are those that have intact watersheds where the hydrologic cycle is in balance.  Without going into great depth, keeping water where it falls (or at least slowing it down), goes a long way in keeping the hydrologic cycle in balance.  Healthy glacial lakes also have clear water, a diverse assemblage of native aquatic plants, and balanced fish communities.  When humans or the environment alter any one of these components, the lake must adjust in order to compensate for those alterations and remain in a healthy state.  The ability of the lake to do so is this concept of resilience (Figure 1).

Figure 1.  Conceptual diagram of a resilient system.  The height of the slope and the deepness of the valley are the compensatory mechanisms that bring a lake back to some resilient baseline condition after a short-term “shock” like a flood or a temporary septic failure.  Lakes with forested watersheds, clear water, native aquatic plants, and balanced fish communities are typically in this condition.

Slowly, as more curb and gutter goes in, green lawns replace native grasses, personal swimming beaches replace marshes, fish are overharvested or overstocked, or invasive species are introduced, the lake slowly loses its ability to compensate (Figure 2).  All of a sudden you hear “I’ve never seen that before” become more common when people describe a phenomenon on the lake that well, they’ve never seen before.   You may start to observe more algae blooms, more attached algae on rocks and plants, plants growing where they’ve never grown before, invasive species taking hold and thriving.  This is an example of the lake losing resilience and succumbing to the vagaries of the environment.  Under these circumstances, the lake can’t compensate anymore and you never know what you will see from year to year.  With no baseline, objective assessment of aquatic plant abundance and no monitoring of change in abundance and cover from year to year, it makes it even harder to know how much the lake has actually changed and what you need to try to get back to with implemented best management practices .

Figure 2.  An example of the consequences of the cumulative impacts of environmental and human stressors on lake resilience.  As lakes become more impacted by various watershed and in lake practices and invasive species, resilience is slowly worn away.  The valley becomes more shallow and a new “domain” enters the picture.  Lake conditions slosh around from one state to the next depending on the vagaries of weather and other disturbances.  Not knowing to expect from one year to the next becomes the norm.

A demonstration of the difference between a resilient lake and one that is losing resilience can be found in a paper published by Valley and Drake in Aquatic Botany in 2007 entitled “What does resilience of a clear-water state in lakes mean for the spatial heterogeneity of submersed macrophyte biovolume?”  Valley and Drake found very consistent patterns of vegetation growth from one sampling period to the next over three years in a clear lake (Square Lake, Washington Co. MN USA; Figure 3).  Each survey in Figure 3 took two days to survey and another week to make these plots.  Not including time on the water, ciBioBase produces these same plots in an hour.
 

Figure 3.  Submerged aquatic plant biovolume (% of water column inhabited by plants) as a function of depth in Square Lake, Washington Co., MN USA.  Notice the consistency of the pattern of vegetation growth from one time period to the next (study took place for 3 years from 2002-2004; Valley and Drake 2007).  Water clarity in Square Lake is high with diverse aquatic plants.

In contrast, patterns of vegetation growth were quite variable in a moderately turbid lake with abundant Eurasian watermilfoil; West Auburn Lake, Carver Co. MN USA; Figure 4).  For example, in summer 2003, a bloom of attached algae formed on Eurasian watermilfoil stems and effectively weighed down the stems and prevented them from reaching the surface.  This bloom was unique to 2003 and was not observed at any other time during the study.

Figure 4.  Plant growth as a function of depth in a moderately turbid Minnesota Lake with abundant Eurasian watermilfoil (West Auburn Lake, Carver Co. MN USA; Valley and Drake 2007).  Plants grew shallower and more variable in this more disturbed lake. 

If stressors continue unabated, then the lake can “tip” into a new, highly resilient domain of poor health (Figure 5).  The feedback mechanisms that used to keep the lake in a healthy state have now switched to new feedback mechanisms that are keeping it in an unhealthy state.  Algae begets more algae, carp beget more carp, stunted bluegill beget more stunted bluegill, if invasive plants are lucky enough to grow, they beget more invasive plants.  Getting the lake back to the original state is nearly impossible at this point.  It’s like Sisyphus rolling the rock uphill only to have it roll right back down again!  Although controversial, at some point, citizens, regulators, and lake managers need to start rethinking expectations and adapting management approaches in highly degraded systems.  Rather than trying to restore a lake to a Pre-European settlement condition through expensive, risky, and Draconian measures, it may be more reasonable to ask: “How can we have good enough water quality to support naturally reproducing stocks of game fish?”  “Can we manage invasive plants in a way that maintains fish habitat AND recreational opportunities?”  After the wailing and gnashing of teeth subsides and some agreement is reached on objectives and management strategies, then it becomes essential to determine whether implemented management practices are having their desired effect.  It doesn’t take two weeks and $10’s of thousands of dollars to do a vegetation survey.  Volunteers can do it, lake consultants can do it, state agencies can do it and they’ll all do it the same objective way with ciBioBase and they can all work together!

Figure 5.  Example of a lake that has flipped into a degraded regime regulated by new feedback mechanisms that keep it in the degraded state. 

The Upshot

Resilience is an easy concept to understand on a basic level, but hard to measure in lakes and changes slowly over time.  This stresses the importance of long-term monitoring and being on guard for those things “you’ve never seen before.”  Uploading data to ciBioBase every time you are on the water gives an objective and quantitative snapshot of the current conditions in your lake of interest.  Be watchful for anomalies in monitored areas.  Vegetation growth should follow a relatively predictable pattern from year to year and if it doesn’t, that may be the first indication that the lake is losing resilience and precautionary conservation measures should be taken.  Conservation measures may include better onsite storm water infiltration (e.g., rain gardens, nearshore vegetation buffers), maintaining a modest amount of aquatic plant growth in the lake, maintaining a balanced fish community in terms of species, size, and abundance.  These efforts will go a long way in protecting the long-term integrity of our beloved lakes!

Suggested Readings:

Carpenter, S.R., 2003. Regime shifts in lake ecosystems: pattern and variation. In: Excellence in Ecology, vol. 15, Ecology Institute Oldendorf/Luhe, Germany.

Scheffer, M., 1998. Ecology of Shallow Lakes. Chapman and Hall, London.

Valley, R.D. and M.T. Drake 2007.  What does resilience of a clear-water state in lakes mean for the spatial heterogeneity of submersed macrophyte biovolume? Aquatic Botany 87: 307-319.


Contour Innovations Welcomes Jesse Amo

Contour Innovations, LLC is proud to announce the addition of Jesse Amo to the position of Aquatic Biologist and Technical Sales.  Jesse will focus his efforts on sales, education and in-person demos for local government units and lake associations.  “Jesse’s enthusiasm, passion and wealth of knowledge in aquatic habitats make him a great steward for executing Contour Innovations vision of changing the way we assess aquatic environments using cloud computing and acoustics” commented Ray Valley, Contour Innovations’ Chief Aquatic Biologist.

Jesse brings a diverse mix of education and experience to the Contour Innovations team.  Jesse holds a Bachelors of Science degree in Zoology (fish and wildlife emphasis) from North Dakota State University with particular interest in aquatic ecosystems.   He is currently pursuing a graduate certificate in Geographic Information Science and Cartography from the University of North Dakota’s online program.   He draws on experience gathered while working with the Minnesota Department of Natural Resources, National Park Service, US Forest Service, US Fish & Wildlife Service, Minnesota lake associations, Minnesota Soil and Water Conservation Districts and various academic research programs.  He is also active in the Minnesota Association of Conservation Professionals and the American Fisheries Society.

In addition to his work in aquatic biology, Jesse  served 6 years in the Army National Guard and quickly adapted to several military occupational specialties.  He is a combat veteran who served a 12 month tour of duty in Iraq in 2004-2005.  His personal interests include many outdoor endeavors such as canoeing, fishing, hiking, cycling, and research and observation of ecosystems.  Jesse is driven to find innovative ways in increase our understanding and management of aquatic ecosystems.

Jesse’s motivation and understanding of aquatic systems made him a great addition to our rapidly growing team.  We’re excited to have him on board!

You can contact Jesse at JesseA@ContourInnovations.com

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.

Assessing Fish Habitat in Rivers

BioBase is not just a lake vegetation mapping tool, it also can help Fisheries managers and researchers assess, monitor, and simulate fish habitat conditions in large rivers.  We demonstrated this application on a trip to the Mississippi River Pool 2 in St. Paul, MN on 4/27/2012.  Just downstream of the Lock and Dam, we used a Lowrance HDS sounder and the automated processing of BioBase to map the bathymetry of a pool where a range of fish species often congregate (Figure 1).

Figure 1.  Bottom mapping with a Lowrance HDS-5 on Pool 2 of the Mississippi R. just downstream of the Lock and Dam on 4/27/2012.

 

The raw pool elevation on 4/27/2012 was 4.27 feet; still within the range of moderate drought according to the US Drought Monitor but 1.7 feet higher than the most recent low on 12/10/2011. Coincidentally, these drought levels follow historic flood levels just one year earlier (Figure 2). To demonstrate BioBase’s utility as a fish habitat assessment tool, we compared sizes and volumes of our mapped pool under the hydrologic conditions experienced on Pool 2 during the last year.

Figure 2. Hydrograph for the Mississippi River at St. Paul, MN (DNR ID# 20088002; USGS ID# 05331000; Data and figure courtesy of the MN DNR).


On 4/27/2012, we mapped and analyzed a 15-ft pool using the ciBioBase polygon creation tool and determined that the max depth was 17 ft, surface area was 317 m2 and the volume was 1508 m3 (Figure 3).

Figure 3.  Diagnostics of a pool of interest using BioBase’s polygon tool.

In order to reconstruct changes to this pool under the recent low flow on December 10th 2011, we used the Z-depth Offset feature iniBioBase to drop the elevation down 1.7 feet.  In Figure 4, you can see the striking difference this reduction has on the size of this pool and consequently the amount of available fish habitat.  The area on December 10th 2011 was estimated to be 3.1 m2 and volume was 9.4 m3; 100 times smaller in size and 161 times smaller in volume than on 4/27/2012. If we increase the offset by the peak flood elevation on March 30th 2011, the 15-foot hole becomes a 30-foot hole (Figure 5).

 

Figure 4. Polygon overlay in BioBase demonstrating the difference in size and volume of a 15-ft deep hole between the yearly low elevation on 12/10/2011 (pink) and during data collection on 4/27/12 (green).

 

Figure 5. Polygon overlay of drought elevations in 2012 (green and pink) overlain onto simulated peak flood bathymetry on 3/30/2011.
This demonstrates one potential application of BioBase for fish habitat studies in large rivers.  We presented three striking contrasts in fish habitat conditions within one year’s time with data that took 20 minutes to collect and an hour to analyze in BioBase. Different hydrological scenarios can be modeled in BioBase and thus could be used in predictive fisheries habitat models or to reconstruct habitat conditions over some period of time.

Analysis of Alternative Mapping Methods

Budgets are tight, time is short, labor resources and technical know-how are scarce.  These truths are the motivating force behind the ciBioBase system.  Recently, we ran an analysis that demonstrates the cost-effectiveness of ciBioBase.  We selected 3 peer-reviewed studies that demonstrated three alternative methods for whole lake assessments of vegetation abundance and compared the costs of producing a vegetation biovolume map with ciBioBase.  The first two studies Valley and Drake (2007) and Sabol et al. (2009) used a scientific-grade echosounder, associated software, and required expertise in hydroacoustics and Geographic Information Systems (GIS).  Hardware and software costs were adjusted to 2012 dollars which actually brought costs down to a total of $18,400.  These costs were amortized over 5 years at 5% interest and scaled to daily costs assuming use in a season would not typically exceed 45 days.  For both methods, hardware and software costs amounted to approximately $84 a day.  We did not factor in time on the water for any of these analyses, or the cost of training in hydroacoustics, geostatistics, and GIS.

Labor costs were relatively large in the Valley and Drake (2007) study because the authors were working in environments that exceeded the capability of the vegetation-detecting algorithm they were using.  Specifically, noisy signals generated in surface-growing vegetation canopies were thrown out and thus biasing biovolume (i.e., percent of the water column occupied by vegetation) downward.  Consequently,  Valley and Drake did ping-by-ping verification and reclassification where signals were obscured by surface-growing vegetation.  Summing the modest hardware and high labor costs to manually verify thousands of pings, the cost of producing a vegetation map in a 500-acre lake using methods described in Valley and Drake (2007) was approximately $1,288.

Labor costs were significantly lower in the Sabol et al. (2009) investigation because we assume vegetation did not grow to the surface in the Wisconsin study lake during the investigation and thus the vegetation algorithm processed individual files relatively quickly.  Taking the labor costs (10 hrs @ $25/hr) in Sabol et al. (2009) and adding in adjusted amortized hardware and annual maintenance costs, the costs of producing a map on a 500-acre lake was a much lower $357 compared with Valley and Drake (2007).
The third study evaluated the LAKEWATCH volunteer lake monitoring program administered by the University of Florida.  LAKEWATCH utilizes commercial-grade Lowrance sonar units to log data on bathymetry and vegetation height/biovolume (otherwise known as percent volume inhabited; Hoyer 2009).  Entry-level technicians analyze 100 random points from pooled transect files and record depth and estimate plant height to get a lake-wide estimate of percent area covered by vegetation and percent volume inhabited with aquatic plants.  Although the objective of LAKEWATCH is not to create high resolution vegetation maps, in order to make apples-to-apples comparisons, we had to scale-up the Hoyer (2009) method to reflect the same survey resolution (16,383 points) of the previous two methods.  This resulted in an incredibly high cost of $6,884 to produce the same type of vegetation map as described with the previous two methods.
ciBioBase 
Because we automate the analysis and mapping of vegetation, there is very little labor outside of conducting the survey, save for a recommended hour of reviewing the data after a trip and verifying the output.  Also, the hardware and software costs are minimal because we analyze data from Lowrance HDS-line sonar systems that are coupled with differentially corrected GPS systems and retail for $700-$2200.  Running the same calculations as the other methods, we estimated the per survey day cost of mapping a 500-acre lake was a very low $125; 2.8 times cheaper than the next lowest described by Sabol et al. (2009).

Daily Costs
Method Amortized Hardware Maint-enance Labor Subscription Cost Total      Cost
Valley and Drake (2007) $84 $23 $1,181  NA  $1,288
Sabol et al. (2009) $84 $23 $250  NA  $357
Hoyer (2009)* $3 $0 $6,881  NA  $6,884
ciBioBase $3 $0 $25 $97 $125
*High resolution vegetationmapping was not an objective of Hoyer (2009) and thus the following scaled-up cost estimates should be viewed as a hypothetical scenario for an equal comparison to other methods

The low rate of ciBioBase doesn’t consider any of the value-added features of ciBioBase such as:

·       Automation: No training needed in hydroacoustics, geostatistics, or GIS.  Our cloud-based software analyzes patterns in the acoustic signal and uses standard geostatistical techniques to produce accurate maps.
·       Centralization: As data from more systems is uploaded, algorithm performance is continually verified and enhanced.  These enhancements are constantly refined in the cloud and are pushed universally to all users, free of charge.

·       Crowd-sourcing: Multiple subscribers from an organization can contribute their data to an optional shared repository.  Organization members can leverage each other’s efforts and data to produce a single output.

·       Speed: Lowrance sonar units occupy little space on board (and actually are portable!) and come with a skimmer transducer that allows data collection of up to speeds of 10 mph.  As such a 500-acre lake may take half the time to traverse 25 mi of transect compared with methods 1 and 2.

·       Efficiency: Because there’s no “set-up and break down” with our method, hitting “record” is the extent of the effort you need to do to start logging data.  While doing so, you can be collecting other important fisheries, aquatic plant, or water quality data on the lake.
·       Data Visualization and Verification: We offer visual, geospatial tools to replay your trip and verify the automated output.

Log in and see for yourself! Go here and type demo@cibiobase.com for the login email and for the password enter “demo.”  You’ll first need Microsoft Silverlight, click here to check to see if you already have it installed on your PC or Mac or need to download it.
Literature Cited
Hoyer, M.V. 2009. Calculations for successful planning. Lakeline Spring 2009: 39-42.

Sabol, B.M., Kannenberg, J., and Skogerboe, J.G. 2009. Integrating acoustic mapping into
              operational aquatic plant management : a case study in Wisconsin. Journal of Aquatic Plant
              Management 44-52.

Valley, R.D. and M.T. Drake 2007. What does resilience of a clear-water state in lakes mean for the spatial heterogeneity of submersed macrophyte biovolume? Aquatic Botany 87: 307-319.

BioBase Polygon Management Tool!

Contour Innovations (CI) has just launched a new game changing feature with its polygon management tool, currently in Beta!   Anyone that has logged into their account lately may have noticed another tab at the top labeled ‘GIS Management.’   With this tab, BioBase users can take their data analysis and pretreatment assessments to the next level.

Although in development for quite some time, our team has determined that this powerful polygon tool is ready to launch for our users.  The polygon management tool allows subscribers to create a polygon within their data for automated processing and assessment of specific boundaries within an upload.  After you gather Lowrance sonar data and upload it to your ciBioBase account to create a bathymetric and vegetation abundance map, you can determine water volumes, acres, and max and average depths to perform analysis of specific areas of your coverage zone.  You’ll know where to focus your management efforts and have accurate details to help with the process.  By using the data you’ve already collected, the power of ciBioBase, and our TIN bathymetry output, you can create polygons and water volume estimates right in your account.  You draw the polygon lines, BioBase does the rest!  And the best part . . there are never additional charges once your data is in the System!

The days of estimating water volumes are over.  Even though we were already providing detailed water volume analysis of the area covered, ciBioBase will now allow you to create and manage your treatment zones and areas of concern in greater detail.  CI is taking lake management and habitat analysis to the next level and automating everything you need to take your collected data full circle.  ciBioBase is all you need!


Log into your account now to check it out!  This feature is available for any trip you’ve already uploaded to the System.  Not a BioBase user yet???  Give us a call to get started!  This feature is a perfect example of how we continue to innovate and make BioBase the best aquatic mapping and habitat software on the Planet.   There are no added costs or upgrade charges, just amazing feature updates.

We’re always interested in your feedback on the polygon analysis tool . . .

Verification of ciBioBase Depth Output

At Contour Innovations we are our own skeptics and constantly perform verification investigations of BioBase output for accuracy. 

As Chief Aquatic Biologist, I’ve been comparing bottom depths sampled with a survey rod with its corresponding depth derived from the automated depth outputs from the BioBase System.  In the figure below depths from Elk Lake (Clearwater Co. MN) are color coded from 1 – 50 ft with blue becoming more intense as depth increases.  The circles are depths recorded with a survey rod while the squares are ciBioBase depths.  Below is one visual representation of the high agreement between true depths and BioBase depths. This visual shows the symbol color agreement demonstrating accuracy in the output! 

True depth data come courtesy of Minnesota Dept. Natural Resources Fisheries Research Biologist Donna Dustin and are copyright of Minnesota DNR.

Ray Valley Joins Contour Innovations as Aquatic Biologist

Please join Contour Innovations in welcoming Ray Valley (RayV@ContourInnovations.com) to our team as Chief Aquatic Biologist. 

Previously employed by the Minnesota Department of Natural Resources as a Senior Research Biologist in the Section of Fisheries, Ray developed aquatic plant mapping protocols with acoustic technology and GIS, researched the link between aquatic plants and fish populations, and most recently chaired the successful launch of a collaborative and comprehensive long-term lake monitoring program called Sustaining Lakes in a Changing Environment (SLICE), Ray holds a B.S. degree in Fisheries from the University of Minnesota and a M.S. degree in Fisheries Ecology from Michigan State.

Ray brings a wide range of expertise to our team specifically related to aquatic vegetation mapping, GIS, and fisheries.   Our team is excited to have his deep technical background in aquatic habitat mapping using acoustics.  “We’ve only scratched the surface of what our platform can do both as a direct output and the benefit our users receive from a collaborative and uniform mapping effort,” said Matt Johnson, CEO of Contour Innovations.  “We will continue to add resources to ensure that Contour Innovations continues to push the boundaries in automated temporal and spatial mapping and Ray brings the expertise to go to the next level.”

Ray will be responsible for aquatic research using the ciBioBase System and providing technical mapping and research support for our empowered customers.    He will also be a keystone piece in designing and evaluating new features and valuable tools provided by the BioBase automated mapping system.  Ray will use his expertise to develop SOPs for and design mapping protocols for our customers’ unique mapping needs and to help maximize time on the water.
Ray has published the following selected list of articles related to submerged aquatic plant mapping and links to fish:

  • Valley, R.D. 2000. Effects of macrophyte structural heterogeneity and fish prey availability on age-0 largemouth bass foraging and growth. M.S. Thesis. Michigan State University, East Lansing.
  • Valley, R.D. and M.T. Bremigan. 2002.  Effects of macrophyte bed architecture on largemouth bass foraging: implications of exotic macrophyte invasions. Transactions of the American Fisheries Society 131(2):234-244
  • Valley, R.D. and M.T. Bremigan. 2002. Effects of selective removal of Eurasian watermilfoil on age-0 largemouth bass piscivory and growth in southern Michigan lakes. Journal of Aquatic Plant Management 40(2):79-87.
  • Valley, R.D., T.K. Cross, and P. Radomski 2004. The role of submersed aquatic vegetation as habitat for fish in Minnesota lakes, including the implications of non-native plant invasions and their management.  MN DNR, Division of Fish and Wildlife, Special Publication No. 160.
  • Valley, R.D., M.T. Drake, and C.S. Anderson. 2005. Evaluation of alternative interpolation techniques for the mapping of remotely-sensed submersed vegetation abundance. Aquatic Botany 81:13-25.
  • Valley, R.D., and M.T. Drake. 2005. Accuracy and precision of hydroacoustic estimates of aquatic vegetation and the repeatability of whole-lake surveys: field tests with a commercial echosounder. MN DNR, Division of Fisheries and Wildlife, Investigational Report No. 527.
  • Valley, R.D., W. Crowell, C. Welling, N. Proux. 2006. Effects of low dose applications of fluridone on submersed aquatic vegetation in a eutrophic Minnesota lake dominated by Eurasian watermilfoil and coontail. Journal of Aquatic Plant Management 44:19-25.
  • Valley, R.D. and M.T. Drake. 2007. What does resilience of a clear-water state in lakes mean for the spatial heterogeneity of macrophyte biovolume? Aquatic Botany 87:307-319.
  • Valley, R.D., M.D. Habrat, E. D. Dibble, and M.T. Drake. 2010. Movement patterns and habitat use of three declining littoral fish species in a north-temperate mesotrophic lake. Hydrobiologia 644:385-399.
  •  Beck, M.W., L. Hatch, B. Vondracek, and R.D. Valley. 2010. Development of a macrophyte-based index of biotic integrity for Minnesota lakes. Ecological Indicators 5:968-979.
  • Heiskary, S and Valley, R.D. In press. Curly-leaf pondweed and interrelationships with water quality. MN DNR Division of Fish and Wildlife, Investigational Report No. 557.
  • Valley, R.D. and Heiskary, S. In preparation. Short-term declines in curly-leaf pondweed across a network of sentinel lakes in Minnesota: potential influences of snow depth and water temperature. To be submitted to Lake and Reservoir Management.

Ray’s most research interests include lake ecology with specific emphasis on the interaction between aquatic plants and water quality regimes. 

He can be contacted at RayV@ContourInnovations.com

New Z-offset (depth offset) Feature

Some of our customers have requested the ability to make their maps even more accurate by eliminating the distance between their transducer and the bottom.  We listened! 
Depth calculations (z) using hydro acoustics are calculated from the source (transducer) to the bottom. Because a depth finder transducer is typically mounted below the water surface, depth readings are always off by the distance between the bottom of the transducer and the surface of the water . . . not anymore!   With the new z-offset feature, any user can now recalculate depths by entering this distance and reprocessing the trip.  For example, if your transducer is 6 inches below the surface, all of your depth readings should have a half foot added to them.  A 10 foot z should actually be 10.5” deep.  With a .5” z-offset, all of your depths will be reprocessed for better accuracy.  This is very important when calculating water volumes! 
The z-offset feature can also be used for calculations to high water marks or draw downs.  By using the z-offset for a 5 foot draw down scenario, our users can identify which bottom structures will be exposed as land (see below).  In addition, lake and pond managers can determine total water volumes at a high water mark by measuring this distance.  By simply offsetting all depth readings with a single z-coordinate offset, your trip will be reprocessed the way you want it.  Water volumes, blue scale, and plant biovolume will all be recalculated in your account.  Simple!
Below is an example of the z-offset in action for a simulated draw down.  We took an accurate trip from Trout Lake in Wisconsin and offest the z-coordinate by 20 feet to simulate a 20 foot draw down.  The new blue scale reflects the changes and displays the new land in green: