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...

<ol>
	<li>Birk, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three+hundred+ways+to+assess+Europe's+surface+waters:+An+almost+complete+overview+of+biological+methods+to+implement+the+Water+Framework+Directive.">Three hundred ways to assess Europe's surface waters: An almost complete overview of biological methods to implement the Water Framework Directive.</a> Ecological Indicators. 18, 31-41 (2012).</li><li>Basset, A., Sangiorgio, F., Pinna, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+with+benthic+macroinvertebrates:+advantages+and+disadvantages+of+body+size+descriptors.">Monitoring with benthic macroinvertebrates: advantages and disadvantages of body size descriptors.</a> Aquatic Conservation: Marine and Freshwater Ecosystems. 14, S43-S58 (2004).</li><li>Reyjol, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+the+ecological+status+in+the+context+of+the+European+Water+Framework+Directive:+Where+do+we+go+now.">Assessing the ecological status in the context of the European Water Framework Directive: Where do we go now.</a> Science of the Total Environment. 497-498, 332-344 (2014).</li><li>Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M., West, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toward+a+metabolic+theory+of+ecology.">Toward a metabolic theory of ecology.</a> Ecology. 85 (7), 1771-1789 (2004).</li><li>Woodward, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Body+size+in+ecological+networks.">Body size in ecological networks.</a> Trends in Ecology &amp; Evolution. 20 (7), 402-409 (2005).</li><li>Sprules, W. G., Barth, L. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Surfing+the+biomass+size+spectrum:+Some+remarks+on+history,+theory,+and+application.">Surfing the biomass size spectrum: Some remarks on history, theory, and application.</a> Canadian Journal of Fisheries and Aquatic Sciences. 73 (4), 477-495 (2016).</li><li>White, E. P., Ernest, S. K. M., Kerkhoff, A. J., Enquist, B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Relationships+between+body+size+and+abundance+in+ecology.">Relationships between body size and abundance in ecology.</a> Trends in Ecology &amp; Evolution. 22 (6), 323-330 (2007).</li><li>Quintana, X. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+nonparametric+method+for+the+measurement+of+size+diversity+with+emphasis+on+data+standardization.">A nonparametric method for the measurement of size diversity with emphasis on data standardization.</a> Limnology and Oceanography - Methods. 6 (1), 75-86 (2008).</li><li>Blanchard, J. L., Heneghan, R. F., Everett, J. D., Trebilco, R., Richardson, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=From+bacteria+to+whales:+Using+functional+size+spectra+to+model+marine+ecosystems.">From bacteria to whales: Using functional size spectra to model marine ecosystems.</a> Trends in Ecology &amp; Evolution. 32 (3), 174-186 (2017).</li><li>Petchey, O. L., Belgrano, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Body-size+distributions+and+size-spectra:+Universal+indicators+of+ecological+status.">Body-size distributions and size-spectra: Universal indicators of ecological status.</a> Biology Letters. 6 (4), 434-437 (2010).</li><li>Emmrich, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Geographical+patterns+in+the+body-size+structure+of+European+lake+fish+assemblages+along+abiotic+and+biotic+gradients.">Geographical patterns in the body-size structure of European lake fish assemblages along abiotic and biotic gradients.</a> Journal of Biogeography. 41 (12), 2221-2233 (2014).</li><li>Arranz, I., Brucet, S., Bartrons, M., Garc&#237;a-Comas, C., Benejam, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fish+size+spectra+are+affected+by+nutrient+concentration+and+relative+abundance+of+non-native+species+across+streams+on+the+NE+Iberian+Peninsula.">Fish size spectra are affected by nutrient concentration and relative abundance of non-native species across streams on the NE Iberian Peninsula.</a> Science of the Total Environment. 795, 148792 (2021).</li><li>Vila-Mart&#237;nez, N., Caiola, N., Ib&#225;&#241;ez, C., Benejam, L. l., Brucet, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Normalized+abundance+spectra+of+the+fish+community+reflect+hydropeaking+on+a+Mediterranean+large+river.">Normalized abundance spectra of the fish community reflect hydropeaking on a Mediterranean large river.</a> Ecological Indicators. 97, 280-289 (2019).</li><li>Benejam, L. l., Tobes, I., Brucet, S., Miranda, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Size+spectra+and+other+size-related+variables+of+river+fish+communities:+systematic+changes+along+the+altitudinal+gradient+on+pristine+Andean+streams.">Size spectra and other size-related variables of river fish communities: systematic changes along the altitudinal gradient on pristine Andean streams.</a> Ecological Indicators. 90, 366-378 (2018).</li><li>Sutton, I. A., Jones, N. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measures+of+fish+community+size+structure+as+indicators+for+stream+monitoring+programs.">Measures of fish community size structure as indicators for stream monitoring programs.</a> Canadian Journal of Fisheries and Aquatic Sciences. 77 (5), 824-835 (2019).</li><li>Murry, B. A., Farrell, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Resistance+of+the+size+structure+of+the+fish+community+to+ecological+perturbations+in+a+large+river+ecosystem.">Resistance of the size structure of the fish community to ecological perturbations in a large river ecosystem.</a> Freshwater Biology. 59, 155-167 (2014).</li><li>Townsend, C. R., Thompson, R. M., Hildrew, A. G., Raffaelli, D. G., Edmonds-Brown, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Body+size+in+streams:+Macroinvertebrate+community+size+composition+along+natural+and+human-induced+environmental+gradients.">Body size in streams: Macroinvertebrate community size composition along natural and human-induced environmental gradients.</a> In Body Size: The Structure and Function of Aquatic Ecosystems. , (2007).</li><li>Gjoni, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Patterns+of+functional+diversity+of+macroinvertebrates+across+three+aquatic+ecosystem+types,+NE+Mediterranean.">Patterns of functional diversity of macroinvertebrates across three aquatic ecosystem types, NE Mediterranean.</a> Mediterranean Marine Science. 20 (4), 703-717 (2019).</li><li>Pomeranz, J. P. F., Warburton, H. J., Harding, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Anthropogenic+mining+alters+macroinvertebrate+size+spectra+in+streams.">Anthropogenic mining alters macroinvertebrate size spectra in streams.</a> Freshwater Biology. 64 (1), 81-92 (2019).</li><li>Garc&#237;a-Gir&#243;n, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Anthropogenic+land-use+impacts+on+the+size+structure+of+macroinvertebrate+assemblages+are+jointly+modulated+by+local+conditions+and+spatial+processes.">Anthropogenic land-use impacts on the size structure of macroinvertebrate assemblages are jointly modulated by local conditions and spatial processes.</a> Environmental Research. 204, 112055 (2022).</li><li>Demi, L. M., Benstead, J. P., Rosemond, A. D., Maerz, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+N+and+P+additions+alter+stream+macroinvertebrate+community+composition+via+taxon-level+responses+to+shifts+in+detrital+resource+stoichiometry.">Experimental N and P additions alter stream macroinvertebrate community composition via taxon-level responses to shifts in detrital resource stoichiometry.</a> Functional Ecology. 33 (5), 855-867 (2019).</li><li>Basset, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+benthic+macroinvertebrate+size+spectra+index+for+implementing+the+Water+Framework+Directive+in+coastal+lagoons+in+Mediterranean+and+Black+Sea+ecoregions.">A benthic macroinvertebrate size spectra index for implementing the Water Framework Directive in coastal lagoons in Mediterranean and Black Sea ecoregions.</a> Ecological Indicators. 12 (1), 72-83 (2012).</li><li>&#196;rje, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+image-based+identification+and+biomass+estimation+of+invertebrates.">Automatic image-based identification and biomass estimation of invertebrates.</a> Methods in Ecology and Evolution. 11 (8), 922-931 (2020).</li><li>Raitoharju, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Benchmark+database+for+fine-grained+image+classification+of+benthic+macroinvertebrates.">Benchmark database for fine-grained image classification of benthic macroinvertebrates.</a> Image and Vision Computing. 78, 73-83 (2018).</li><li>Lytle, D. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+processing+and+identification+of+benthic+invertebrate+samples.">Automated processing and identification of benthic invertebrate samples.</a> Journal of the North American Benthological Society. 29 (3), 867-874 (2010).</li><li>Serna, J. P., Fern&#225;ndez, D. S., V&#233;lez, F. J., Aguirre, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+image+processing+method+for+recognition+of+four+aquatic+macroinvertebrates+genera+in+freshwater+environments+in+the+Andean+region+of+Colombia.">An image processing method for recognition of four aquatic macroinvertebrates genera in freshwater environments in the Andean region of Colombia.</a> Environmental Monitoring and Assessment. 192, 617 (2020).</li><li>Gorsky, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+zooplankton+image+analysis+using+the+ZooScan+integrated+system.">Digital zooplankton image analysis using the ZooScan integrated system.</a> Journal of Plankton Research. 32 (3), 285-303 (2010).</li><li>Marcolin, C. R., Schultes, S., Jackson, G. A., Lopes, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Plankton+and+seston+size+spectra+estimated+by+the+LOPC+and+ZooScan+in+the+Abrolhos+Bank+ecosystem+(SE+Atlantic).">Plankton and seston size spectra estimated by the LOPC and ZooScan in the Abrolhos Bank ecosystem (SE Atlantic).</a> Continental Shelf Research. 70, 74-87 (2013).</li><li>Silva, N., Marcolin, C. R., Schwamborn, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+image+analysis+to+assess+the+contributions+of+plankton+and+particles+to+tropical+coastal+ecosystems.">Using image analysis to assess the contributions of plankton and particles to tropical coastal ecosystems.</a> Estuarine, Coast and Shelf Science. 219, 252-261 (2019).</li><li>Vandromme, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+biases+in+computing+size+spectra+of+automatically+classified+zooplankton+from+imaging+systems:+A+case+study+with+the+ZooScan+integrated+system.">Assessing biases in computing size spectra of automatically classified zooplankton from imaging systems: A case study with the ZooScan integrated system.</a> Methods in Oceanography. 1-2, 3-21 (2012).</li><li>Naito, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Surface+zooplankton+size+and+taxonomic+composition+in+Bowdoin+Fjord,+north-western+Greenland:+A+comparison+of+ZooScan,+OPC+and+microscopic+analyses.">Surface zooplankton size and taxonomic composition in Bowdoin Fjord, north-western Greenland: A comparison of ZooScan, OPC and microscopic analyses.</a> Polar Science. 19, 120-129 (2019).</li><li>. Zooprocess/Plankton Identifier protocol for computer assisted zooplankton sorting Available from: <a target="_blank" href="https://manualzz.com/doc/43116355/zooprocess—plankton-identifier-protocol-for">https://manualzz.com/doc/43116355/zooprocess—plankton-identifier-protocol-for</a> (2013)</li><li>Protocolo de muestreo y laboratorio de fauna bent&#243;nica de invertebrados en r&#237;os vadeables. C&#211;DIGO: ML-Rv-I-2013. Ministerio de Agricultura, Alimentaci&#243;n y Medio Ambiente Available from: <a target="_blank" href="https://www.miteco.gob.es/es/agua/temas/estado-y-calidad-de-las-aguas/ML-Rv-I-2013_Muestreo%20y%20laboratorio_Fauna%20bent%C3%B3nica%20de%20de%20invertebrado_%20R%C3%Ados%20vadeables_24_05_2013_tcm30-175284.pdf">https://www.miteco.gob.es/es/agua/temas/estado-y-calidad-de-las-aguas/ML-Rv-I-2013_Muestreo%20y%20laboratorio_Fauna%20bent%C3%B3nica%20de%20de%20invertebrado_%20R%C3%Ados%20vadeables_24_05_2013_tcm30-175284.pdf</a> (2013)</li><li>Garc&#237;a-Comas, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prey+size+diversity+hinders+biomass+trophic+transfer+and+predator+size+diversity+promotes+it+in+planktonic+communities.">Prey size diversity hinders biomass trophic transfer and predator size diversity promotes it in planktonic communities.</a> Proceedings of the Royal Society Biological Sciences. 283 (1824), 20152129 (2016).</li><li>Garc&#237;a-Comas, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mesozooplankton+size+structure+in+response+to+environmental+conditions+in+the+East+China+Sea:+How+much+does+size+spectra+theory+fit+empirical+data+of+a+dynamic+coastal+area.">Mesozooplankton size structure in response to environmental conditions in the East China Sea: How much does size spectra theory fit empirical data of a dynamic coastal area.</a> Progress in Oceanography. 121, 141-157 (2014).</li><li>Marquina, D., Buczek, M., Ronquist, F., Lukasik, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effect+of+ethanol+concentration+on+the+morphological+and+molecular+preservation+of+insects+for+biodiversity+studies.">The effect of ethanol concentration on the morphological and molecular preservation of insects for biodiversity studies.</a> PeerJ. 9, 10799 (2021).</li><li>Bell, J. L., Hopcroft, R. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessment+of+ZooImage+as+a+tool+for+the+classification+of+zooplankton.">Assessment of ZooImage as a tool for the classification of zooplankton.</a> Journal of Plankton Research. 30 (12), 1351-1367 (2008).</li><li>Colas, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ZooCAM,+a+new+in-flow+imaging+system+for+fast+onboard+counting,+sizing+and+classification+of+fish+eggs+and+metazooplankton.">The ZooCAM, a new in-flow imaging system for fast onboard counting, sizing and classification of fish eggs and metazooplankton.</a> Progress in Oceanography. 166, 54-65 (2018).</li><li>Bachiller, E., Fernandes, J. A., Irigoien, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improving+semiautomated+zooplankton+classification+using+an+internal+control+and+different+imaging+devices.">Improving semiautomated zooplankton classification using an internal control and different imaging devices.</a> Limnology and Oceanography Methods. 10 (1), 1-9 (2012).</li></ol>

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.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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.

automatic-image-processing-to-determine-community-size-structure

Research

JoVE Journal

Environment

Optimized Negative Staining: a High-throughput Protocol for Examining Small and Asymmetric Protein Structure by Electron Microscopy

Structural determination of proteins is rather challenging for proteins with molecular masses between 40 - 200 kDa. Considering that more than half of natural proteins have a molecular mass between 40 - 200 kDa1,2, a robust and high-throughput method with a nanometer resolution capability is needed. Negative staining (NS) electron microscopy (EM) is an easy, rapid, and qualitative approach which has frequently been used in research laboratories to examine protein structure and protein-protein interactions. Unfortunately, conventional NS protocols often generate structural artifacts on proteins, especially with lipoproteins that usually form presenting rouleaux artifacts. By using images of lipoproteins from cryo-electron microscopy (cryo-EM) as a standard, the key parameters in NS specimen preparation conditions were recently screened and reported as the optimized NS protocol (OpNS), a modified conventional NS protocol 3 . Artifacts like rouleaux can be greatly limited by OpNS, additionally providing high contrast along with reasonably high&#8208;resolution (near 1 nm) images of small and asymmetric proteins. These high-resolution and high contrast images are even favorable for an individual protein (a single object, no average) 3D reconstruction, such as a 160 kDa antibody, through the method of electron tomography4,5. Moreover, OpNS can be a high&#8208;throughput tool to examine hundreds of samples of small proteins. For example, the previously published mechanism of 53 kDa cholesteryl ester transfer protein (CETP) involved the screening and imaging of hundreds of samples 6. Considering cryo-EM rarely successfully images proteins less than 200 kDa has yet to publish any study involving screening over one hundred sample conditions, it is fair to call OpNS a high-throughput method for studying small proteins. Hopefully the OpNS protocol presented here can be a useful tool to push the boundaries of EM and accelerate EM studies into small protein structure, dynamics and mechanisms.

More than half of proteins are small proteins (molecular mass &#60;200 kDa) that are challenging for both electron microscope imaging and three-dimensional reconstructions. Optimized negative staining is a robust and high-throughput protocol to obtain high contrast and relatively high resolution (~1 nm) images of small asymmetric proteins or complexes under different physiological conditions.

Structural determination of proteins is rather challenging for proteins with molecular masses between 40 - 200 kDa. Considering that more than half of ...

Automated Gel Size Selection to Improve the Quality of Next-generation Sequencing Libraries Prepared from Environmental Water Samples

Next-generation sequencing of environmental samples can be challenging because of the variable DNA quantity and quality in these samples. High quality DNA libraries are needed for optimal results from next-generation sequencing. Environmental samples such as water may have low quality and quantities of DNA as well as contaminants that co-precipitate with DNA. The mechanical and enzymatic processes involved in extraction and library preparation may further damage the DNA. Gel size selection enables purification and recovery of DNA fragments of a defined size for sequencing applications. Nevertheless, this task is one of the most time-consuming steps in the DNA library preparation workflow. The protocol described here enables complete automation of agarose gel loading, electrophoretic analysis, and recovery of targeted DNA fragments. 
In this study, we describe a high-throughput approach to prepare high quality DNA libraries from freshwater samples that can be applied also to other environmental samples. We used an indirect approach to concentrate bacterial cells from environmental freshwater samples; DNA was extracted using a commercially available DNA extraction kit, and DNA libraries were prepared using a commercial transposon-based protocol. DNA fragments of 500 to 800 bp were gel size selected using Ranger Technology, an automated electrophoresis workstation. Sequencing of the size-selected DNA libraries demonstrated significant improvements to read length and quality of the sequencing reads.

This manuscript describes an automated gel size selection approach for purifying DNA fragments for next-generation sequencing. The Ranger Technology provides complete automation of the entire process of agarose gel loading, electrophoretic analysis, and recovery of targeted DNA fragments allowing for high-throughput and high quality next-generation sequencing libraries.

Next-generation sequencing of environmental samples can be challenging because of the variable DNA quantity and quality in these samples. High quality DNA ...

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

The morphology and structure of plumbing systems can provide key information on the eruption rate and style of basalt lava fields. The most powerful way to study subsurface geo-bodies is to use industrial 3D&#160;reflection seismological imaging. However, strategies to image subsurface volcanoes are very different from that of oil and gas reservoirs. In this study, we process seismic data cubes from the Northern Tarim Basin, China, to illustrate how to visualize sills through opacity rendering techniques and how to image the conduits by time-slicing. In the first case, we isolated probes by the seismic horizons marking the contacts between sills and encasing strata, applying opacity rendering techniques to extract sills from the seismic cube. The resulting detailed sill morphology shows that the flow direction is from the dome center to the rim. In the second seismic cube, we use time-slices to image the conduits, which corresponds to marked discontinuities within the encasing rocks. A set of time-slices obtained at different depths show that the Tarim flood basalts erupted from central volcanoes, fed by separate pipe-like conduits.

Three-dimensional (3D) reflection seismology is a powerful method for imaging subsurface volcanoes. By using industrial 3D seismological data from the Tarim Basin, we illustrate how to extract the sills and the conduits of subsurface volcanoes from seismic data cubes.

The morphology and structure of plumbing systems can provide key information on the eruption rate and style of basalt lava fields. The most powerful way ...

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Underwater imaging has long been used in the field of marine ecology but decreasing costs of high-resolution cameras and data storage have made the approach more practical than in the past. Image-based surveys allow for initial samples to be revisited and are non-invasive compared to traditional survey methods that typically involve nets or dredges. Protocols for image-based surveys can vary greatly but should be driven by target species behavior and survey objectives. To demonstrate this, we describe our most recent methods for an Atlantic sea scallop (Placopecten magellanicus) drop camera survey to provide a procedural example and representative results. The procedure is divided into three critical steps that include survey design, data collection, and data products. The influence of scallop behavior and the survey goal of providing an independent assessment of the U.S. sea scallop resource on the survey procedure are then discussed in the context of generalizing the method. Overall, the broad applicability and flexibility of the University of Massachusetts Dartmouth School for Marine Science and Technology (SMAST) drop camera survey demonstrates the method could be generalized and applied to a variety of sessile invertebrates or habitat focused research.

Image based surveying is an increasingly practical, non-invasive method to sample the marine environment. We present the protocol of a drop camera survey that estimates the abundance and distribution of the Atlantic sea scallop (Placopecten magellanicus). We discuss how this protocol can be generalized for application to other benthic macroinvertebrates.

Underwater imaging has long been used in the field of marine ecology but decreasing costs of high-resolution cameras and data storage have made the ...

Experimental Study of the Relationship Between Particle Size and Methane Sorption Capacity in Shale

The amount of adsorbed shale gas is a key parameter used in shale gas resource evaluation and target area selection, and it is also an important standard for evaluating the mining value of shale gas. Currently, studies on the correlation between particle size and methane adsorption are controversial. In this study, an isothermal adsorption apparatus, the gravimetric sorption analyzer, is used to test the adsorption capacity of different particle sizes in shale to determine the relationship between the particle size and the adsorption capacity of shale. Thegravimetric method requires fewer parameters and produces better results in terms of accuracy and consistency than methods like the volumetric method. Gravimetric measurements are performed in four steps: a blank measurement, preprocessing, a buoyancy measurement, and adsorption and desorption measurements. Gravimetric measurement is presently considered to be a more scientific and accurate method of measuring the amount of adsorption; however, it is time-consuming and requires a strict measuring technique. A Magnetic Suspension Balance (MSB) is the key to verify the accuracy and consistency of this method. Our results show that adsorption capacity and particle size are correlated, but not a linear correlation, and the adsorptions in particles sieved into 40 - 60 and 60 - 80 meshes tend to be larger. We propose that the maximum adsorption corresponding to the particle size is approximately 250 &#181;m (60 mesh) in the shale gas fracturing.

We use an isothermal adsorption apparatus, the gravimetric sorption analyzer, to test the adsorption capacity of different particle sizes of shale, in order to find out the relationship between particle size and the adsorption capacity of shale.

The amount of adsorbed shale gas is a key parameter used in shale gas resource evaluation and target area selection, and it is also an important standard ...

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize (Zea mays L.)

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole grain composition and processing characteristics vary among maize hybrids and can impact the quality of the final processed product. Therefore, in order to produce healthier processed food products from maize, it is necessary to know how to optimize processing parameters for particular sets of germplasm to account for these differences in grain composition and processing characteristics. This includes a better understanding of how current processing techniques impact the nutritional quality of the final processed food product. Here, we describe a microscale protocol that both simulates the processing pipeline to produce cornflakes from large flaking grits and allows for the processing of multiple grain samples simultaneously. The flaking grits, the intermediate processed products, or final processed product, as well as the corn grain itself, can be analyzed for nutritional content as part of a high-throughput analytical pipeline. This procedure was developed specifically for incorporation into a maize breeding research program, and it can be modified for other grain crops. We provide an example of the analysis of insoluble-bound ferulic acid and p-coumaric acid content in maize. Samples were taken at five different processing stages. We demonstrate that sampling can take place at multiple stages during microscale processing, that the processing technique can be utilized in the context of a specialized maize breeding program, and that, in our example, most of the nutritional content was lost during food product processing.

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Here, we present a microscale protocol for processing grain samples and for incorporating this microscale approach into a high-throughput analytical pipeline. This is a higher throughput adaptation of currently available protocols.

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole ...

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Variable microorganisms in particulate matter (PM) under different environmental conditions may have significant impacts on human health. In this study, we described a protocol for multiple analyses of the biological compositions in environmental PM. Five experiments are presented: (1) PM number monitoring by using a laser particle counter; (2) PM collection by using a cyclonic aerosol sampler; (3) PM collection by using a high-volume air sampler with filters; (4) culturable microbes collection by the Andersen six-stage sampler; and (5) detection of biological composition of environmental PM by bacterial 16SrDNA and fungal ITS region sequencing. We selected hazy days and a livestock farm as two typical examples of the application in this protocol. In this study, these two sampling methods, cyclonic aerosol sampler and filter sampler, showed different sampling efficiency. The cyclonic aerosol sampler performed much better in terms of collecting bacteria, while these two methods showed the same efficiency in collecting fungi. Filter samplers can work under low temperature conditions while cyclonic aerosol samplers have a sampling limitation for temperature. A solid impacting sampler, such as an Andersen six-stage sampler, can be used to sample bioaerosols directly into the culture medium, which increases the survival rate of culturable microorganisms. However, this method mainly relies on culture while more than 99% of microbes cannot be cultured. DNA extracted from the culturable bacteria collected by the Andersen six-stage sampler and samples collected by cyclonic aerosol sampler and filter sampler were detected by bacterial 16S rDNA and fungal ITS region sequencing.All the methods above may have wide application in many fields of study, such as environmental monitoring and airborne pathogen detection. From these results, we can conclude that these methods can be used under different conditions and may help other researchers further explore the health impacts of environmental bioaerosols.

Understanding the biological composition of environmental particulate matter is important for the study of its significant impacts on human health and disease spread. Here, we used three types of bioaerosol sampling methods and a biological analysis of airborne microbes to better explore airborne microbial communities under different environmental conditions.

Variable microorganisms in particulate matter (PM) under different environmental conditions may have significant impacts on human health. In this study, ...

Combined Size and Density Fractionation of Soils for Investigations of Organo-Mineral Interactions

Combined size and density fractionation (CSDF) is a method used to physically separate soil into fractions differing in particle size and mineralogy. CSDF relies on sequential density separation and sedimentation steps to isolate (1) the free light fraction (uncomplexed organic matter), (2) the occluded light fraction (uncomplexed organic matter trapped in soil aggregates) and (3) a variable number of heavy fractions (soil minerals and their associated organic matter) differing in composition. Provided that the parameters of the CSDF (dispersion energy, density cut-offs, sedimentation time) are properly selected, the method yields heavy fractions of relatively homogeneous mineral composition. Each of these fractions is expected to have a different complexing ability towards organic matter, rendering this a useful method to isolate and study the nature of organo-mineral interactions. Combining density and particle size separation brings an improved resolution compared to simple size or density fractionation methods, allowing the separation of heavy components according to both mineralogy and size (related to surface area) criteria. As is the case for all physical fractionation methods, it may be considered as less disruptive or aggressive than chemically-based extraction methods. However, CSDF is a time-consuming method and furthermore, the quantity of material obtained in some fractions can be limiting for subsequent analysis. Following CSDF, the fractions may be analyzed for mineralogical composition, soil organic carbon concentration and organic matter chemistry. The method provides quantitative information about organic carbon distribution within a soil sample and brings light to the sorptive capacity of the different, naturally-occurring mineral phases, thus providing mechanistic information about the preferential nature of organo-mineral interactions in soils (i.e., which minerals, what type of organic matter).

Combined size and density fractionation (CSDF) is a method to physically separate soil into fractions differing in texture (particle size) and mineralogy (density). The purpose is to isolate fractions with different reactivities towards soil organic matter (SOM), in order to better understand organo-mineral interactions and SOM dynamics.

Combined size and density fractionation (CSDF) is a method used to physically separate soil into fractions differing in particle size and mineralogy. CSDF ...

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Experimental bioreactors, such as those treating wastewater, contain particles whose size and shape are important parameters. For example, the size and shape of activated sludge flocs can indicate the conditions at the microscale, and also directly affect how well the sludge settles in a clarifier.
Particle size and shape are both misleadingly &#39;simple&#39; measurements. Many subtle issues, often unaddressed in informal protocols, can arise when sampling, imaging, and analyzing particles. Sampling methods may be biased or not provide enough statistical power. The samples themselves may be poorly preserved or undergo alteration during immobilization. Images may not be of sufficient quality; overlapping particles, depth of field, magnification level, and various noise can all produce poor results. Poorly specified analysis can introduce bias, such as that produced by manual image thresholding and segmentation.
Affordability and throughput are desirable alongside reproducibility. An affordable, high throughput method can enable more frequent particle measurement, producing many images containing thousands of particles. A method that uses inexpensive reagents, a common dissecting microscope, and freely-available open source analysis software allows repeatable, accessible, reproducible, and partially-automated experimental results. Further, the product of such a method can be well-formatted, well-defined, and easily understood by data analysis software, easing both within-lab analyses and data sharing between labs.
We present a protocol that details the steps needed to produce such a product, including: sampling, sample preparation and immobilization in agar, digital image acquisition, digital image analysis, and examples of experiment-specific figure generation from the analysis results. We have also included an open-source data analysis pipeline to support this protocol.

The size and shape of particles in activated sludge are important parameters that are measured using varying methods. Inaccuracies arise from non-representative sampling, suboptimal images, and subjective analysis parameters. To minimize these errors and ease measurement, we present a protocol specifying every step, including an open source software pipeline.

Experimental bioreactors, such as those treating wastewater, contain particles whose size and shape are important parameters. For example, the size and ...

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

The size spectrum is an inverse, allometric scaling relationship between average body mass (M) and the density (D) of individuals within an ecological community or food web. Importantly, the size spectrum assumes that individual size, rather than species&#8217; behavioral or life history characteristics, is the primary determinant of abundance within an ecosystem. Thus, unlike traditional allometric relationships that focus on species-level data (e.g., mean species&#8217; body size vs. population density), size spectra analyses are &#8216;ataxic&#8217; &#8211; individual specimens are identified only by their size, without consideration of taxonomic identity. Size spectra models are efficient representations of traditional, complex food webs and can be used in descriptive as well as predictive contexts (e.g., predicting responses of large consumers to changes in basal resources). Empirical studies from diverse aquatic ecosystems have also reported moderate to high levels of similarity in size spectra slopes, suggesting that common processes may regulate the abundances of small and large organisms in very different settings. This is a protocol to model the community-level size spectrum in wadable streams. The protocol consists of three main steps. First, collect quantitative benthic fish and invertebrate samples that can be used to estimate local densities. Second, standardize the fish and invertebrate data by converting all individuals to ataxic units (i.e., individuals identified by size, irrespective of taxonomic identity), and summing individuals within log2 size bins. Third, use linear regression to model the relationship between ataxic M and D estimates. Detailed instructions are provided herein to complete each of these steps, including custom software to facilitate D estimation and size spectra modeling.

This is a protocol to model the size spectrum (scaling relationship between individual mass and population density) for combined fish and invertebrate data from wadable streams and rivers. Methods include: field techniques to collect quantitative fish and invertebrate samples; lab methods to standardize the field data; and statistical data analysis.

The size spectrum is an inverse, allometric scaling relationship between average body mass (M) and the density (D) of individuals within an ecological ...