Name: automatic image processing to determine the community size structure of riverine macroinvertebrates
Uploaded: 2023-01-13T12:50:00
Description: Watch this Scientific Journal Video about Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates at JoVE.com

This work was supported by the Spanish Ministry of Science, Innovation and Universities (grant number RTI2018-095363-B-I00). We thank the CERM-UVic-UCC members &#200;lia Bretxa, Anna Costarrosa, Laia Jim&#233;nez, Mar&#237;a Isabel Gonz&#225;lez, Marta Jutglar, Francesc Llach, and N&#250;ria Sellar&#232;s for their work in macroinvertebrate field sampling and laboratory sorting and David Albesa for collaborating in the sample scanning. We finally thank Josep Maria Gili and the Institut de Ci&#232;ncies del Mar (ICM-CSIC) for the use of the laboratory facilities and scanner device.

NOTE: The protocol described here is based on the system developed by Gorsky et al.27 for marine mesozooplankton. A specific description of the scanner (ZooSCAN), scanning software (VueScan 9x64 [9.5.09]), image processing software (Zooprocess, ImageJ), and automatic identification software (Plankton Identifier) steps can be found in previous references32,33. To best adjust the sizes of the benthic macroinvertebrates with respect to the me...

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The adaptation of the methodology described by Gorsky et al. 2010 for riverine macroinvertebrates allows for high classification accuracy in estimating the community size structure in freshwater macroinvertebrates. The results suggest that the protocol can reduce the time for estimating the individual size structure in a sample to about 1 hour. Thus, the proposed protocol is intended to promote the routine use of macroinvertebrate size spectra as a fast and integrative bioindicator to assess the impact of perturbations i...

Benthic macroinvertebrates are broadly used as bioindicators to determine the ecological status of water bodies1. Most indices to describe macroinvertebrate communities focus on taxonomic metrics. However, new bioassessment tools that integrate body size are encouraged to provide an alternative or complementary perspective to taxonomic approaches2,3.
Body size is considered a metatrait that is related to other vital traits such as metabolism, growth, respiration, and movement4. Furthermore, body size can determine trophic position and interactions5. The relationship between individual body size and the normalized biomass (or abundance) by size class in a community is defined as the size spectrum6 and follows the general pattern of a linear decrease in normalized biomass as individual size increases on a logarithmic scale7. The slope of this linear relationship has been extensively studied theoretically, and empirical studies across ecosystems have used it as an ecological indicator of the community size structure4. Another synthetic indicator of community size structure that has been successfully used in biodiversity-ecosystem functioning studies is community size diversity, which is represented as the Shannon index of the size classes of the size spectrum or its analog, which is calculated based on the individual size distributions8.
In freshwater ecosystems, the size structure of different faunal groups is used as an ataxic indicator to assess the response of biotic communities to environmental gradients9,10,11 and to anthropogenic perturbations12,13,14,15,16. Macroinvertebrates are not an exception, and their size structure also responds to environmental changes17,18 and anthropogenic perturbations, such as mining19, land use20, or&#160;nitrogen (N) and phosphorus (P) enrichment20,21,22. However, measuring hundreds of individuals to describe the community size structure is a tedious and time-consuming task that is often avoided as a routine measurement in laboratories due to a lack of time. Thus, several semi-automatic or automatic imaging methods to classify and measure specimens have been developed23,24,25,26. However, most of these methods are focused on taxonomic classification more than on the individual size of the organisms and are not ready to use for all kinds of macroinvertebrates. In marine plankton ecology, a scanning image analysis system has been extensively used to determine the size and taxonomic composition of zooplankton communities27,28,29,30,31. This instrument can be found in several marine institutes worldwide, and it is used to scan preserved zooplankton samples to obtain high-resolution digital images of the entire sample. The present protocol adapts the use of this instrument to estimate the macroinvertebrate community size spectrum in rivers in a rapid automatic manner without investing in creating a new device.
The protocol consists of scanning a sample and processing the whole image to automatically obtain single images (i.e., vignettes) of the objects in the sample. Several measures of shape, size, and grey-level features characterize each object and allow for the automatic classification of the objects into categories, which are then validated by an expert. The individual size of each organism is calculated using the ellipsoidal biovolume (mm3), which is derived from the area of the organism measured in pixels. This allows for obtaining the size spectrum of the sample in a rapid manner. To the best of our knowledge, this scanning imaging system has only been used to process mesozooplankton samples, but the device may potentially allow for working with freshwater benthic macroinvertebrates.
The overall goal of this study is, therefore, to introduce a method to rapidly obtain the individual size of preserved river macroinvertebrates by adapting an existing protocol previously used with marine mesozooplankton27,32,33. The procedure consists of&#160;using a semi-automatic approach that operates with a scanning device to scan water samples and three open software to process the scanned images. An adapted protocol to scan, detect, and identify digitized river macroinvertebrates to automatically acquire the community size structure and related size metrics is herein presented. The assessment of the procedure and guidelines to enhance the efficiency are also presented based on 42 scanned images of riverine macroinvertebrate samples collected from three basins in the North-Eastern (NE) Iberian Peninsula (Ter, Segre-Ebre, and Bes&#242;s).
The samples were collected at 100 m river stretches following the protocol for field sampling and laboratory analysis of benthic river macroinvertebrates in fordable rivers from the Spanish Government34. The samples were collected with a surber sampler (frame: 0.3 m x 0.3 m, mesh: 250 &#181;m) following a multi-habitat survey. In the laboratory, the samples were cleaned and sieved through a 5 mm and a 500 &#181;m mesh to obtain two subsamples: a coarse subsample (5 mm mesh) and a fine subsample (500 &#181;m mesh), which were stored in separate vials and preserved in 70% ethanol. Separating the sample into two size fractions allows for a better estimation of the community size structure, since large organisms are rarer and fewer than the small organisms. Otherwise, the scanned sample has a biased representation of the large size fraction.

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	<tbody>
		<tr>
			<td>Beaker</td>
			<td>Labbox</td>
			<td></td>
			<td>Other containers could be used</td>
		</tr>
		<tr>
			<td>Dionized water</td>
			<td>Icopresa</td>
			<td>&#160;8420239600123</td>
			<td>To dilute the ethanol</td>
		</tr>
		<tr>
			<td>Funnel</td>
			<td>Vitlab</td>
			<td>41094</td>
			<td></td>
		</tr>
		<tr>
			<td>Glass vials 8 ml</td>
			<td>Labbox</td>
			<td>SVSN-C10-195</td>
			<td>1 vial/subsample</td>
		</tr>
		<tr>
			<td>ImageJ Software&#160;</td>
			<td>Free access</td>
			<td></td>
			<td>Version 4.41o/ Image processing software</td>
		</tr>
		<tr>
			<td>Large frame</td>
			<td>Hydroptic</td>
			<td>&#160;Provided by ZooScan</td>
			<td>24.5 cm x 15.8 cm</td>
		</tr>
		<tr>
			<td>Monalcol 96 (Ethanol 96)</td>
			<td>Montplet</td>
			<td>1050JE001</td>
			<td></td>
		</tr>
		<tr>
			<td>Plankton Identifier Software</td>
			<td>Free access</td>
			<td></td>
			<td>Version 1.2.6/ Automatic identification software</td>
		</tr>
		<tr>
			<td>Sieve</td>
			<td>Cisa</td>
			<td>26852.2</td>
			<td>Nominal aperture 500&#181; and nominal aperture 0,5 cm</td>
		</tr>
		<tr>
			<td>Tweezers</td>
			<td>Bondline</td>
			<td>B5SA</td>
			<td>Stainless, anti-magnetic, anti-acid</td>
		</tr>
		<tr>
			<td>VueScan 9 x 64 (9.5.09) Software</td>
			<td>Hydroptic</td>
			<td></td>
			<td>Version 9.0.51/ Sacn software</td>
		</tr>
		<tr>
			<td>Wooden needle</td>
			<td></td>
			<td></td>
			<td>Any plastic or wood needle can be used</td>
		</tr>
		<tr>
			<td>Zooprocess Software&#160;</td>
			<td>Free access</td>
			<td></td>
			<td>Version 7.14/Image processing software</td>
		</tr>
		<tr>
			<td>ZooScan&#160;</td>
			<td>Hydroptic</td>
			<td>54</td>
			<td>Version III/ Scanner</td>
		</tr>
	</tbody>
</table>

automatic image processing to determine the community size structure of riverine macroinvertebrates

Body size is an important functional trait that can be used as a bioindicator to assess the impacts of perturbations in natural communities. Community size structure responds to biotic and abiotic gradients, including anthropogenic perturbations across taxa and ecosystems. However, the manual measurement of small-bodied organisms such as benthic macroinvertebrates (e.g., &gt;500 &#181;m to a few centimeters long) is time-consuming. To expedite the estimation of community size structure, here, we developed a protocol to semi-automatically measure the individual body size of preserved river macroinvertebrates, which are one of the most commonly used bioindicators for assessing the ecological status of freshwater ecosystems. This protocol is adapted from an existing methodology developed to scan marine mesozooplankton with a scanning system designed for water samples. The protocol consists of three main steps: (1) scanning subsamples (fine and coarse sample size fractions) of river macroinvertebrates and processing the digitized images to individualize each detected object in each image; (2) creating, evaluating, and validating a learning set through artificial intelligence to semi-automatically separate the individual images of macroinvertebrates from detritus and artifacts in the scanned samples; and (3) depicting the size structure of the macroinvertebrate communities. In addition to the protocol, this work includes the calibration results and enumerates several challenges and recommendations to adapt the procedure to macroinvertebrate samples and to consider for further improvements. Overall, the results support the use of the presented scanning system for the automatic body size measurement of river macroinvertebrates and suggest that the depiction of their size spectrum is a valuable tool for the rapid bioassessment of freshwater ecosystems.

Acquisition of digital images of macroinvertebrate samples 
Scanning nuances: Ethanol deposition in the scan tray 
While testing the system for macroinvertebrates, several scans were of poor quality. A dark saturated area in the background prevented normal processing of the image and the measurement of the individual sizes of the macroinvertebrates (Figure 2). Several reasons have been given for the appearance of saturated areas in the background o...

Watch this Scientific Journal Video about Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates at JoVE.com

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

The article is based on the creation of an adapted protocol to scan, detect, sort, and identify digitized objects corresponding to benthic river macroinvertebrates using a semi-automatic imaging procedure. This procedure allows the acquisition of the individual size distributions and size metrics of a macroinvertebrate community in about 1 h.

Body size is an important functional trait that can be used as a bioindicator to assess the impacts of perturbations in natural communities. Community ...

Body size has been prioritized as a key trait for monitoring. Yet measuring individual size is time consuming. Our method allows the routine use of macroinvertebrate size spectrum to assess impacts on freshwater ecosystems.Our protocol provides a standardized way to automatically determine individual size distribution of river macroinvertebrates in a sample in about an hour. Demonstrating the procedure will be Rosa Guri from the University of Vic. To begin, turn on the scanner.And switch on the light in the dual position. To project white light from top and bottom. Next, clean and rinse the scan tray with tap water.To create the blanks, pour 110 milliliters of tap water at room temperature into the scan tray until the glass is covered. Place the large frame on the scan tray with the corner at the top left part. And fill it with tap water until it covers the step of the frame to avoid a meniscus effect which would alter scanned images.Next, open the image processing software, select the working project, and click on scan convert background image. Next, open the scanning software and click on preview. Check the image for no lines or spots and wait for at least 30 seconds before starting the scan.Then press okay in the instructions window before the second scan to send the data from the scanning software to the image processing software. Pour 110 milliliters of 70%ethanol into the scan tray until the glass is covered. And place the large frame.Then pour the macroinvertebrates sample into the scan tray edged by the frame and cover it with more ethanol if needed. Then, using a wooden needle, homogenize the sample throughout the frame area. Placing the largest individuals in the center of the tray for proper image processing.And sink the floating organisms. Also, separate apart the clustered organisms. And pull in the organisms touching the frame edges to the center.Next, proceed to scan by clicking on scan sample with zoo scan for archive no process. Selecting the sample and following the instructions. Preview the image in the scanning software for no lines or spots.After 30 seconds, click on the scan button in the scanning software. And verify that the raw scanned image is correct. Remove the frame and wash it above the scan tray with a 70%ethanol filled squeeze bottle to recover any attached macroinvertebrates.Lift the upper part of the scanner to retrieve all of the organisms and ethanol through the scan retrieval funnel into a beaker. With the upper part of the scanner still lifted, clean the tray with the squeeze bottle to sweep along any remaining organisms. After recovering all of the specimens, clean the tray with tap water.To predict the identity, click on data analysis in the automatic identification software. In select learning file, locate PID_Process, then select learning_set to choose the learning set file to be used. Next, in select sample files, choose the sample to be predicted from the PID_results folder.In select a method choose the random forest method and then tick the save detailed results for each sample button. In original variables, untick the position variables. Lastly, in customized variables, tick only ESD.Click on start analysis and save the results as analysis_name. txt in the PID_process prediction folder. To manually validate, copy the analysis sample_dat_1 txt files from the PID_process prediction folder to the PID_process, PID_results folder.Select extract vignettes in folders according to prediction or validation in the image processing software. Then select used predicted files from PID_results folder. And with the default settings, pressing okay creates a new folder.Now go to the PID_process sorted vignettes folder and copy the newly created folder named with the sample name, date, and time to validate. Rename the folder to validate with validated. To manually validate the automatic classification, open the renamed folder sample name, date, and time validated.Review all the vignettes from each subfolder to identify misclassified objects if any. When an object is misclassified, drag the vignette to the correct folder. Next, select load identifications from sorted vignettes.Keeping the default settings, select the file named date, time, name validated to be processed. Then go to PID_process, PID_results, and then dat1_validated. And open the file named ID_from_sorted_vignettes date and time txt to verify that the last column, prediction, validated ID, date and time, which specifies the expert classification of each object, has been created.While testing the system for macroinvertebrates, some scans were of poor quality in the processed images. Despite this, a fine sub-sample with good scan quality of the raw and the processed image looks as depicted. In the set of analyzed vignettes, 86.1%corresponded to debris.Including detritus, fibers, body parts, or scanning artifacts. And the remaining 13.9%of the detected objects corresponded to the invertebrate organisms. Automatic recognition followed by manual validation of objects exhibited high recall for all the categories.While the contamination one precision was rather low except for the other invertebrates. The comparison of automatic versus validated macroinvertebrate abundance showed a high correlation with a slight overestimation by the automatic performance due to contamination from debris. The probability density functions of the individual size distributions of the automatic prediction strongly occurred with the validated predictions for the fine and coarse subsamples.The subsamples validated after the separation of touching objects from selected natural samples showed increased abundance. But the mean ellipsoidal volume was close to the validated samples. The size distributions of corrected samples differed slightly from the validated ones.But a strong correlation was observed. The normalized bio volume size spectrum was similar between the treatments. Except for a few size classes in a couple spectra.Take the required time to separate touching organs to obtain a fine scan and clean with water this scan tray after every scan with ethanol to avoid precipitation. We have adopted this procedure created for plankton to river macroinvertebrates. Thus, it could potentially be adapted to obtain individual body sizes of other finer groups.for example, terrestrial invertebrates. This method will allow a systematic estimation of macroinvertebrate community size structure over large, special, and temporal gradients. And investigate the role of body size in river ecosystem functioning and by assessment.

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