BioBase Paper Published: Estimation of paddlefish (Polyodon spathula Walbaum, 1792) spawning habitat availability with consumer-grade sonar.

We’re excited to see another publication demonstrating another novel use of BioBase EcoSound technology for Fisheries Science. For a complete list of pubs see hereContact us to get a copy of any of these publications

Estimation of paddlefish (Polyodon spathula Walbaum, 1792) spawning habitat availability with consumer-grade sonar
 
Jason D. Schooley
Oklahoma Department of Wildlife Conservation
 
Ben C. Neely
Kansas Department of Wildlife, Parks, and Tourism
 
Journal of Applied Icthyology 2017
 
Summary
The paddlefish (Polyodon spathula Walbaum, 1792) is a springtime migrant that requires discrete abiotic conditions such as water temperature, discharge, and substrate composition for successful spawning and recruitment. Although population declines have prevailed throughout much of the species range, Oklahoma paddlefish are abundant and support popular recreational snag fisheries – most notably in Grand Lake. This stock utilizes the Grand Lake’s two primary headwaters, the Neosho and Spring rivers, with only episodic recruitment success. However, relationships between suitable spawning habitat and water level have not been evaluated in this system. Using consumer-grade sonar equipment, this study identified and quantified hard river substrates (such as cobble and bedrock) and investigated proportional habitat availability at a variety of simulated river conditions. Sonar data were used to construct 49-m2 grids of depth and bottom hardness (H) ranging from 0.0 (soft) -0.5 (hard). Ground-truthing samples of bottom composition were collected with a grab sampler and by visual identification. Substrate types were pooled into two categories: soft substrates (H < 0.386) and spawning substrates (H ≥ 0.386) allowing for estimation of available spawning habitat in each river. Spawning habitat comprised 69% of total available habitat for the Neosho River (6.5 ha/km) and 58% for the Spring River (7.9 ha/km). Estimated spawning habitat was simulated over a range of river stages and predictive models were developed to estimate proportional spawning habitat availability (PHA). Although the Spring River contains more concentrated spawning habitat in closer proximity to Grand Lake, the Neosho River contains a greater quantity over nearly twice the distance to the first migration barrier, has a larger watershed, and demonstrates greater PHA at lower river stages. Model results were validated in context of known high and low recruitment years, where a greater frequency and duration of days with ≥90% PHA were observed in good recruitment years, particularly in the Neosho River. In total, results suggest the Neosho River has greater value for paddlefish reproduction than the Spring River. Research-informed harvest management will remain critical to the conservation of wild-recruiting stocks for continued recreational use in Oklahoma.
Average Neosho and Spring river substrate hardness index (H) for substrate classification groups across pooled methods (grab samples and visual samples). Cobble/Rock includes fine, medium, and coarse cobble pooled with bedrock. Substrates represented by H ≥ 0.386 were regarded as paddlefish spawning habitat. Sample size is noted at the base of each column and error bars indicate 95% confidence intervals
Schooley JD, Neely BC. Estimation of paddlefish (Polyodon spathula Walbaum, 1792) spawning habitat availability with consumer-grade sonar. J Appl Ichthyol. 2017;00:1–9. https://doi.org/10.1111/jai.13565

Interpreting bottom hardness in shallow lakes and ponds: digging deeper into the data

BioBase’s EcoSound bottom composition (hardness) algorithm has become quite popular for researchers and lake/pond managers to determine where sedimentation from the watershed may be occurring.  However, interpreting sonar returns in shallow environments (e.g., less than 7 ft or 2 m) with off-the-shelf sonar is challenging, especially if aquatic vegetation is present.  Each situation is different and the objective of this blog is to inform you of how to interpret your EcoSound map in situations when you encounter counter intuitive bottom hardness results.

Here are some high level points to remember.

Continue reading “Interpreting bottom hardness in shallow lakes and ponds: digging deeper into the data”

Composition Algorithm Improved!

The centralized nature of BioBase cloud technologies coupled with sophisticated, yet low-cost consumer electronics like Lowrance or Simrad depth sounders/chartplotters have created fertile grounds for developing, testing, and verifying algorithms for typing aquatic environments.  The more users upload from a greater range of systems, the more refined algorithms can become addressing a wider range of conditions and use cases!

Early in 2014, we released a revision to our bottom composition (hardness) algorithm that is more sensitive and robust in a greater range of depths and bottom conditions.  Many outside researchers were involved with collecting important “ground truth” information while they logged their BioBase data.  This blog not only serves to describe the new Bottom Composition algorithm, but also publish the results and acknowledge the scientists that helped with this effort.

How it works and the outputs produced 
The Composition algorithm processes the 200 kHz Broadband downlooking signal and produces a data point for GPS signals (Typically 1 pt every 1-2 seconds). Algorithms estimate the acoustic reflectivity of the bottom. Signals bounce more on a hard bottom than a soft bottom where signal is absorbed. Hardness ratings are consistent across all mapped systems and not relative to a trip (e.g., a trip with muck and silt will show all light tan colors). GPS point data along tracks are sent to an interpolation (kriging) algorithm to predict hardness between sampled areas and create a uniform map

Figure 1. Continuous, unitless data are created with each GPS coordinate to reflect relative hardness from soft 0-0.25 (light tan), to medium 0.25 – 0.4 to hard 0.4 – 0.5 (dark red).

How does it compare with actual data?  Verification results from independent researchers
Unlike conventional models or software programs that use limited datasets in a narrow range of conditions to calibrate and verify model outputs, BioBase is able to draw from our central database and network of professionals using the system to develop new or improved algorithms.

For revisions to the composition algorithm, Navico technical staff worked with scientists from the University of Florida (Mark Hoyer), USGS in Little Rock AR and Reston VA and  (Drs. Reed Green, Nancy Rybicki and Elizabeth Striano), and across the pond with the Center for Ecology and Hydrology (Drs. Ian Winfield, Helen Miller, and Joey van Rijn) evaluating the agreement of their independently collected bottom composition data with companion BioBase hardness datasets.  Despite field error in the precise estimation of actual hardness and overlap with simultaneously collected BioBase data, we were encouraged by the high agreement of compared data sets.  See for yourself below!

Figure 2.  Sediment depth measured by a coring device as it relates to simultaneously collected Lowrance acoustic data which was processed for bottom hardness with BioBase.  Data were collected in Lochloosa Lake, Alchua Co. Florida USA by Mark Hoyer; Director of Florida Lakewatch at University of Florida – Gainsville.  Seventeen sites were also sampled in Lake Sampson (Bradford Co., FL).  Sediment thickness at those sites only averaged one inch.  All companion BioBase composition data indicated hard substrates, and thus complete agreement.  Lastly, in Newnan’s Lake (Alachua Co. FL) fewer agreements between sediment thickness and BioBase composition were found due to the acoustically reflective organic peat substrates in the lake.
Figure 3. Sediment depth measured by a 3/4 inch all thread pipe pushed to the “point of refusal” as it relates to simultaneously collected Lowrance acoustic data which was processed for bottom hardness with BioBase.  Data were collected in Millwood Lake, Ashdown Co., Arkansas USA by Dr. Reed Green USGS Arkansas Water Science Center.  Publication of sediment depth patterns on Millwood can be found at: Richards, J.M., and Green, W.R., 2013, Bathymetric map, area/capacity table, and sediment volume estimate for Millwood Lake near Ashdown, Arkansas, 2013: U.S. Geological Survey Scientific Investigations Map 3282, 1 sheet,http://dx.doi.org/10.3133/sim3282.
Table 1. Agreement between visually estimated substrate hardness while collecting Lowrance/BioBase composition data from 9 of 23 samples in coastal Back Bay, Virginia Beach VA, USA in 2012.  BioBase composition data at the remaining 13 sites were not generated due to depth or vegetation thresholds.  Bottoms cannot be typed where vegetation fills > 60% of the water column or in depths less than 2.4 feet from the transducer face.  Data were collected by Dr Nancy Rybicki and Elizabeth Striano, USGS – Reston VA as a component of a vegetation assessment study.

Figure 4A.  Hardness data from Windermere (Cumbria, England) as scored by visual estimation from underwater imagery as it relates to hardness from Lowrance and BioBase.  Data were collected in 2012 by Dr. Ian Winfield and Joey van Rijn and described in a previous blog post.  The biological context and other companion composition data are presented in Miller et al. 2014.

Figure 4B.  Bottom substrate (and Northern Pike) as viewed from a camera mounted on a Remote Operated Vehicle (ROV) in Windermere.  See Figure 5A for the Hardness Score and Corresponding BioBase Hardness data

Create your own sediment thickness models
The BioBase composition algorithm will not predict sediment depth, only whether the bottom is hard or soft based on the “echo” of the acoustic signal.  Still, what we show in Figures 2 and 3 are that sediment depth may correspond predictably with bottom hardness as estimated by BioBase.  The primary benefit of BioBase is to provide a full-system understanding of where hard and soft areas exist (Figure 5).  Investigators can follow up with a couple of dozen coring points in areas of interest (e.g., sedimentation deltas) to develop system-specific relationships like those in Figures 2 and 3.  The paradigm shift that BioBase has brought is a new way to focus more detailed sediment depth sampling, rather than using a coring probe as the sole mapping tool.

Figure 5.  Full system map of bottom composition.  Data were exported from a fully interactive free demo account.  Log in and view trips and data and how they correspond to the sonar log.

Guest Blog: ciBioBase and Arctic charr habitat in Windermere, U.K.

By Dr. Ian J. Winfield and Joey van Rijn

The Arctic charr (Salvelinus alpinus) is well appreciated as an important fisheries species in many northern areas of the world.  In addition, it is equally important to evolutionary biologists because of this species’ frequent development of ‘morphs’ or ‘types’ and their bearing on our understanding of mechanisms of speciation (Figure 1).  In the U.K., this fascinating fish is also recognised as having great nature conservation value.

Figure 1.  A female (top) and male (bottom) Arctic charr from Windermere, U.K.  Photo courtesy of the Center for Ecology and Hydrology)

Windermere is England’s largest lake and has been at the forefront of several areas of Arctic charr research for many decades, with the notable exception of studies of their spawning grounds (Figure 2).  Despite their long appreciated significance for the coexistence of autumn- and spring-spawning Arctic charr types, local spawning grounds have not been studied in any detail since their original brief description in the 1960s.  At that time, laborious and spatially-limited direct observations by divers showed that spawning requires the availability of gravel or other hard bottom habitat.  New information on these critical areas is needed by ecologists and evolutionary biologists and, more urgently, by fisheries and conservation organisations responsible for the management of Windermere.

Figure 2.  Breathtaking view of Windermere’s north basin; home to several spawning populations of Arctic charr.  Photo courtesy of Dr. Ian Winfield.

We are currently using the newly developed bottom hardness capability of ciBioBase to survey and characterise the spawning grounds of Arctic charr in Windermere.  Limited underwater video is being used for ground-truthing, but the combination of a Lowrance HDS-5 sounder with ciBioBase is allowing us to investigate the known spawning grounds with unprecedented speed (Figure 3).  For the first time, we have been able to document in detail the bathymetry and bottom features of a long-monitored (for spawning fish) spawning ground just north of the island of North Thompson Holme in the lake’s north basin.  ciBioBase is also enabling us to examine other known spawning grounds in Windermere and to expand our coverage to other potential areas previously unstudied.

Figure 3. An example ciBioBase output of bottom composition on and around the Arctic charr spawning ground of North Thompson Holme in the north basin of Windermere

The rapidity of the field component of hydroacoustic surveys is well known.  ciBioBase now offers us a similarly fast method of hydroacoustic data analysis for key environmental characteristics in relation to the spawning of Arctic charr.  This new approach helps us to dramatically increase our return on investment and also allows us to review results within hours of coming off the water, leading in some cases to us adapting our field plans on the basis of initial results.

Dr. Ian J Winfield is a Freshwater Ecologist at the Centre for Ecology & Hydrology in Lancaster, U.K.  He has over 30 years of research experience in fish and fisheries ecology, hydroacoustics, and lake ecosystem assessment and management.  Dr. Winfield sits on several regional, national and international advisory boards and is the current President of the Fisheries Society of the British Isles (FSBI).

Joey van Rijn is an undergraduate student currently following a BSc. degree course in Applied Biology at the University of Applied sciences, HAS Den Bosch, in the Netherlands. He is experienced in ecological and particularly phenological research including work on temperature-induced differences between urban and rural areas in the timing of blossoming and leaf unfolding in shrubs.  He has also been involved with the development of fish ways for standing waters in the Netherlands. Joey is currently undertaking a research internship at the Centre for Ecology & Hydrology in Lancaster, U.K., where his research mainly focuses on using hydroacoustics to investigate Arctic charr spawning grounds in Windermere.

Lake Bottom Depth Precision and Accuracy

In an addendum to an earlier post, we continue to evaluate the accuracy and precision of BioBase depth outputs.  Lowrance has been in the depth sounding business since 1957.  They have tight factory calibration standards whereby depth should never be more than 2% different than the actual depth.  Of course then we expect depths to be spot on on hard bottom surfaces where truth can be easily measured.  But what about in mucky bottoms which are common place in many lakes, ponds, backwaters throughout the US and abroad?  With this in mind, in late May of 2012, we traveled to Pool 8 of the Mississippi River near LaCrosse WI to do some testing in a mucky, moderately dense vegetated backwater (Figure 1).  At some point we have to step back and ask, “what is the bottom of a body of water?”

Figure 1.  Vegetation cover and biovolume (% of water column occupied with vegetation) in Pool 8 of the Mississippi R. in LaCrosse WI on 5/29/2012.  Average biovolume was 30% during the survey.

The most difficult aspect of this testing was to get an objective estimate of the true depth.  In other words, where exactly did the plants end and bottom start?  Typically, investigators use a survey rod like that seen in Figure 2 to estimate actual bottom based on where they feel resistance on the survey rod.  Piece of cake over sand.  Not so easy over flocculant silt and muck or vegetative areas.

Figure 2.  Measuring bottom with a survey rod in a mucky Minnesota Lake.  Typically, the survey rod will sink several inches into the bottom before the surveyor feels resistance and judges the depth to the bottom

Many experienced surveyors will tell you that the rod will sink into the muck some distance before you feel resistance.  There is a positive correlation in the distance it sinks and how mucky the bottom is.  So, we went into this investigation expecting deeper rod depths measured than ciBioBase outputs. 

Accurate and precise results in mucky, vegetated bottoms

After 30 points measured with the survey rod, we compared the results with the ciBioBase depths measured in the same location.  We were pleased to see very high precision with a Coefficient of Determination (R^2) of 0.94 and a systematic difference in depth of only 4.9″ (Figure 3).  The depth of 4.9″ was quite possibly the average depth where we first felt resistance of the survey rod.  The upshot here is that ciBioBase depth outputs are highly precise, consistent and accurate even in mucky vegetated bottoms.

Figure 3. Accuracy and precision of ciBioBase depths measured against depths collected with a survey rod in the mucky, vegetated backwaters of Pool 8 of the Mississippi River near LaCrosse, WI.