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Water Scarcity

WATER SCARCITY AFFECTS EVERY CONTINENT

2.7 billion people experience water scarcity at least one month a year. This is expected to grow to two-thirds of the world’s population by 2025 (Falkenmark Water Stress Indicator).[17]

PUBLIC WATER: THE GLASS IS HALF EMPTY

Water scarcity affects every continent and was listed in 2015 by the World Economic Forum as the largest global risk in terms of potential impact over the next decade.[16][7]

We become more and more dependant on surface water for drinking, which requires more filtration infrastructure, and more monitoring of surface water sources. Currently 63% of public water (serving a population of 169 million) in the USA is from surface water.[13] This concept applies globally as well.

Wetlands provide surface water filtration, however more than half of the world’s wetlands have disappeared, requiring more filtration infrastructure.[17]

 

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Study Structure & Methods

 

HYPOTHESIS & CONTROLS

Hypothesis: A novel DNA Barcoding process utilizing Chironomidae (Diptera) will provide more accurate and precise waterway health measures than manual taxonomic identification by morphology.

 

Phase I:   DNA Barcoding Method Development
Independent Variables: DNA extraction methods, primers
Dependent Variable: Percent amplification
Controls: DNA ladder

 

Phase II:  Chironomidae as Index of Health

A statistical sampling plan was designed that represents variation in geological, ecological, and land use factors


Independent Variable: Freshwater bodies with a variety of geological, ecological, and anthropogenic differences (per statistical plan)
Dependent Variable: Genera and species present
Positive Controls: Healthy location, from historical analysis
Negative Control: Unhealthy location, from historical analysis


Research Questions:
1) Can DNA Barcoding be used as a means of monitoring surface water sources?
2) How do Chironomidae genera and species vary in response to variation in geological, ecological, and land use factors?
3) How do Chironomidae genera and species vary in response to nutrient pollution?
4) Will this project add new species to the Chironomidae data sets in genetic sequence databases used by the scientific community?
5) What is the effect of different methods of PCR on the amplification of Chironomidae DNA? 

METHODS

Phase I:

Four molecular analysis approaches were evaluated: eDNA Metabarcoding Extraction and eDNA Metabarcoding Primer, Rapid Method (chromatography paper) Extraction and PCR Bead, Silica Resin Extraction and PCR Bead, Silica Resin Extraction and MM Primer.

 

Phase II:

Sample sites were chosen according to a statistical sampling plan to capture a variety of geological, ecological, and anthropogenic factors: high gradient vs coastal plain, stream vs. pond, healthy ecosystem vs. unhealthy ecosystem. 

Benthic Macroinvertebrate Sampling: Freshwater macroinvertebrate samples were collected with D-frame net. The percentage of net jabs taken in each habitat type corresponded to the percentage of each habitat type’s presence in the stream reach per Department of Environmental Protection procedure. 

Macroinvertebrates were identified, and those from the Chironomidae family (order Diptera) were identified under a microscope and removed for DNA Barcode analysis. 

DNA Isolation: The membrane-bound organelles such as the nucleus and mitochondria were dissolved with lysis solution. Silica resin was used to bind DNA. The nucleic acids were eluted from the silica resin. 

Polymerase Chain Reaction (PCR) Amplification was performed with COI primers LCO1490 and HC02198. Samples were thermal cycled with the appropriate temperature profile programmed. 

 

Gel Electrophoresis was used to verify DNA amplification.

 

Samples were then sent for DNA Sequencing. Bioinformatic analysis was completed by trimming and analyzing the Chironomidae genetic sequences. The final sequences were submitted and compared to multiple genetic sequence databases to determine the genus and species of each sample. Software tools were programmed and developed to easily calculate biological health scores. The appropriate index was selected (High Gradient or Coastal Plain Macroinvertebrate Index). 

Water quality chemical analysis certifications relevant to this project were up to date. Chemical sampling was performed with LaMotte water test kit and procedure. 

 

 

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Results

 

From: Taxonomic macroinvertebrate sample identified to order and family level by volunteer or professional taxonomist.

To: DNA Barcoding used to identify macroinvertebrate sample identified to species level. Reveals the additional taxonomic resolution provided by DNA Barcoding.

RESULTS OF METHOD SELECTION  

Four molecular analysis methods were evaluated, and had very different results in terms of the percent of samples that successfully amplified. 

Silica resin and PCR bead successfully amplified 100% of the samples.

 

MACROINVERTEBRATE FAMILIES COMPARED: CHIRONOMIDAE CHOSEN 

The Chironomidae samples showed the least undetermined nucleotides, best peak quality, and best Phred sequence quality.

The Gammaridae also responded very well to barcoding, However, the gammaridae do not have the range of geography and biotic indices that the Chironomidae do.  
Phred scores were compared using two-sample t-tests (0.01 significance level).

Chironomidae vs. Physidae p = 1.01 x 10-6 indicating a statistically significant difference.  

Chironomidae vs. Haliplidae p = 7.37 x 10-8 indicating a statistically significant difference.

Chironomidae vs. Gammaridae p = .053 indicating a difference that is not significant. However Gammaridae were not chosen due to their more limited number of species, geographic range, and biotic index range. 


 

CHIRONOMIDAE HAVE A SURFACE GEOLOGY PREFERENCE

The Chironomidae sampled here aligned by genera with either high gradient streams in piedmont geology, or sandy soils and coastal plain geology. Only 13% of the genera sampled were found evenly in both geologies.

Coastal Plain bottom composition: silt
Piedmont bottom composition: cobble, pebble, bedrock

 

DISTRIBUTION BY NUTRIENT POLLUTION

A moderate positive linear association was found between the weighted average Hilsenhoff scores of the Chironomidae genera sampled at each site and nutrient pollution.

 

Comparison of Genera Tolerance Values and Nutrient Pollution: This analysis compared the relationship between the weighted average Hilsenhoff tolerance scores of the Chironomidae genera sampled at each site, with the nutrient pollution. The value for nutrient pollution was calculated from the average ppm of nitrate and orthophosphate sampled at each site, which was normalized to a value between 0 and 10. When nutrient pollution data for sites are graphed with the weighted average Hilsenhoff tolerance scores of the Chironomidae genera sampled, a moderate positive linear association is noted. There is a statistically significant relationship with p<0.05. In a linear regression ran, R2 =.67 indicating that 67% of the variation in the Hilsenhoff tolerance scores of the Chironomidae genera sampled were accounted for by overall nutrient pollution data. This means that 33% of the variation in tolerance score is influenced by factors other than nutrient pollution.

 

 

CHIRONOMIDAE DNA DATA SHOW A STRONG POSITIVE LINEAR RELATIONSHIP TO HISTORICAL HEALTH DATA

A strong positive linear association was found between the weighted average tolerance values of the Chironomidae genera sampled at each site, and the overall historical health based on sampling at each site.

Comparison of Genera Tolerance Values and Overall Historical Health Values: The Chironomidae health data correlates to historical health measurements. When historical health data for sites are graphed with the weighted average Hilsenhoff tolerance scores of the Chironomidae genera sampled, a strong positive linear relationship is noted. There is a statistically significant relationship with p<0.05. In a linear regression, R2=.79 indicating that 79% of the variation in the Hilsenhoff tolerance scores of the Chironomidae genera sampled were accounted for by overall historical waterway health data. This means that 21% of the variation in tolerance score is influenced by factors other than overall waterway health. Bottom composition is likely a part of that 21% as there are some Chironomidae genera that prefer a healthy pebble-bottomed stream over a healthier mud-bottomed stream.


 

CHIRONOMIDAE DNA DATA COMPARE FAVORABLY TO CURRENT MANUAL METHOD 

A Bland-Altman analysis showed limits of agreement of -0.853 and 0.868 between the weighted average tolerance values of the Chironomidae genera barcoded and the standard method that uses manual taxonomic identification by morphology.

This indicates that the new method proposed here of DNA barcoding Chironomidae is in agreement with the current method to within 1.72 on a zero to ten health
scale.

 

RESULTS OF PHYLOGENETIC TREE ANALYSIS

The following phylogenetic trees were used to analyze the genetic relationships between selections of the
Chironomidae sampled with respect to site, taxa level identified, and biotic index.

Phylogenetic tree which diagrams the genetic relationship between the Chironomidae samples from six sites with the most variation:

 

Phylogenetic tree which diagrams the genetic relationship between Chironomidae samples with the taxa level identified (e.g, subfamily, genus, species):

Identification down to species level indicates a match in the sequence databases. Identification to genus or subfamily indicates gaps in the sequence database that can be filled with a widespread barcoding initiative. The gaps could also allude to potential novel species.

 

Phylogenetic tree which diagrams the genetic relationship between Chironomidae samples with the Hilsenhoff tolerance value for each genera:

The family biotic index for Chironomidae is 6. This masks an underlying variability as the genera sampled for this study range in biotic index from 2 to 10 on a scale of 0 to 10 health scale.

 

 

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Discussion

 
PHASE I: 
MICROBIOLOGY APPROACH

Four molecular analysis approaches were evaluated: eDNA Metabarcoding Extraction and eDNA Metabarcoding Primer, Rapid Method (chromatography paper) Extraction and PCR Bead, Silica Resin Extraction and PCR Bead, Silica Resin Extraction and MM Primer. Silica resin and PCR bead successfully amplified 100% of the samples. This result also verified that the appropriate laboratory and field practices and techniques had been used, and that the techniques and methods were not excessively cumbersome.

PHASE II: CHIRONOMIDAE AS INDEX OF HEALTH

Statistical Analysis: The Bland-Altman test was selected for analysis, as this is a common statistical tool used to compare two different methods of measurement when a true value or calibration standard is not available, and measurements must be made indirectly.[2]

Waterway ecosystem bioassessment by weighted average tolerance values of the Chironomidae genera barcoded were compared with the current method that uses manual taxonomic identification by morphology.

Comparing two measurement systems by running a regression and calculating a correlation coefficient R value is not sufficient to compare measurement systems, as two methods of measuring the same value are nearly guaranteed to be correlated.[2]

Bland-Altman analysis determines the level of agreement between two measurement systems.[2] This comparison showed limits of agreement of -0.853 and 0.868 between the weighted average tolerance values of the Chironomidae genera barcoded and the current method that uses manual taxonomic identification by morphology. This indicates that the new method proposed here of DNA barcoding Chironomidae is in agreement with the current method to within 1.72 out of 10. This finding is significant, especially considering that waterway health data is often reported as good / fair / poor, and leads to the conclusion that waterway ecosystem bioassessment by DNA Barcoding of Chironomidae is a viable option for bioassessment globally.

 

Statistical power is the sensitivity of a test, or the ability of a test to find an effect if there is one to be found, or in other words the probability that the test will correctly reject a false null hypothesis. Statistical power = 1 – β, where β is the probability of making a Type II error and alpha α is the probability of making a Type I error. Statistical power is also a function of the sample size, alpha, and effect size. Increasing the sample size increases statistical power, but there is typically a cost or challenge to obtaining more samples. Increasing alpha also increases statistical power, however this merely exchanges this risk of a Type II error (β-risk) for the risk of a Type I error (α-risk). Where statistical significance determines if there is a difference between the two groups, effect size quantifies the difference between the two groups. Bigger effects are easier to detect than smaller effects. If the data being sampled has a large amount of variance, both from the value being measured and the noise in the data, this will decrease the statistical power.[3] Measurement error is also a source of noise. The goal of using DNA Barcoding to resolve taxa in more detail to the genus and species level, is to reduce variability and therefore increase statistical power. Increasing the precision of the measurement increases the statistical power and/or decreases sample size. A statistical power of 0.80 is typical, and indicates a 4:1 trade off between β-risk and α-risk. Highly consistent systems in engineering and physical sciences, as well as medical tests where the risk of a false negative (not detecting a disease) can have higher statistical power such as 0.90.

 

DNA Barcoding increases resolution from family level, to genus and species, as well as reducing errors from manual taxonomic identification by morphology. In the case of Chironomidae this means that genus level tolerance values ranging from 0 to 10 can be used instead of the family level tolerance value of 6. This increases the statistical power of the bioassessment method. 

 

DNA Barcoding for Bioassessment: Highly detailed genus and species level data is more accurate and precise but difficult to obtain manually due to cost, specimen condition, incomplete taxonomic knowledge, poor taxonomic keys, lack of trained taxonomists. Error rates of genus and species in samples identified manually by experts have been found to be as high as 65%.[5] DNA Barcoding is especially valuable for identifying such versatile and phenotypically similar specimens as Chironomidae.

 

Selection of Chironomidae as a Global Common Denominator: Various macroinvertebrate families were identified by DNA Barcoding with silica resin and PCR beads. Selecting one family to focus on provided a natural limit that allowed effects of differences in extraction and amplification of DNA to be minimized, for example macroinvertebrates with tough exoskeletons or shells can be more difficult to extract DNA from, and many mollusks contain PCR inhibitors. The Chironomidae were identified as the best option with the best sequence quality, as they had the best Phred score, least undetermined nucleotides, and best peak quality. The Gammaridae also responded very well to barcoding, with a Phred score of 98% vs 99% for Chironomidae, however the gammaridae do not have the range of geography and biotic indices that the Chironomidae do. 

 

Comparison of Genera Tolerance Values and Nutrient Pollution: In a linear regression ran, R2 =.67 indicating that 67% of the variation in the Hilsenhoff tolerance scores of the Chironomidae genera sampled were accounted for by overall nutrient pollution data. This means that 33% of the variation in tolerance score is influenced by factors other than nutrient pollution. Not all variation in health as measured by Genera Tolerance Values can be explained by nutrient pollution as there are many other factors that contribute to a healthy waterway. 

 

Comparison of Genera Tolerance Values and Historical Health Values: In a linear regression, R2=.79 indicating that 79% of the variation in the Hilsenhoff tolerance scores of the Chironomidae genera sampled were accounted for by overall historical waterway health data. This means that 21% of the variation in tolerance score is influenced by factors other than overall waterway health. Bottom composition is likely a part of that 21% as there are some Chironomidae genera that prefer a healthy pebble-bottomed stream over a healthier mud-bottomed stream. Future plans for this study include finding out more about how bottom composition affects the residential Chironomid. The overall historical health score explains more of the variation in Hilsenhoff tolerance scores of the Chironomidae genera sampled. 

 

Phylogenetic Tree Analysis: Phylogenetic trees were used to analyze the genetic relationships between selections of the Chironomidae sampled with respect to site, taxa level identified, and biotic index. Identification to genus or subfamily indicates either gaps in the sequence database, or a potential novel species encountered

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How We Monitor

 
BIOASSESSMENT: WHY MACROINVERTEBRATES?

Macroinvertebrates spend their larval lives in a small area of water, showing the cumulative effects of all stressors in a way that chemical testing and field sensors do not: from nonpoint source nutrient and heavy metal pollution, to temperature and dissolved oxygen, to flow alteration. Measures of taxa richness and relative abundance provide valuable information on trends in ecosystem health.

Macroinvertebrates are a significant part of the food web, preyed upon by fish, birds, reptiles, and amphibians.

WHY DNA BARCODING?

Current waterway assessment methods are based on a procedure defined and popularized by Hilsenhoff: a minimum 100 organism sub-sample is obtained from a Stratified Random Sample taken in the field. Organisms are identified to the lowest practical taxonomic level with a microscope and taxonomic keys.[9][12]

Highly detailed genus and species level data is more accurate and precise but difficult to obtain with the current methods using manual taxonomic identification by morphology due to cost, specimen condition, incomplete taxonomic knowledge, poor taxonomic keys, and lack of trained taxonomists.

Error rates of genus and species in samples identified by experts have been found to be as high as 65%.[1][5]

Molecular methods, such as DNA Barcoding from a region of the mitochondrial gene COI (cytochrome c oxidase subunit 1), are now available. In 2003, Hebert et. al. started working on improving bioassessment with DNA Barcoding, and currently there are many open investigations in this area, including an EU COST Action.[8][11] 

DNA Barcoding overcomes limitations of manual taxonomic identification and significantly improves the accuracy, precision, and statistical power of bioassessment tools.[18] However, there is currently no standard bioassessment method that leverages the power of DNA Barcoding.[1]

WHY CHIRONOMIDAE?

Chironomidae are versatile, free living, holometabolous benthic macroinvertebrates and a global common denominator among most aquatic sites:[5]

  • Species have been observed on every continent including Antarctica and in a great range of altitudes[5]
  • In fresh water, from millimeter-thick films to 1360 meters below the surface of Lake Baikal
  • In glacial meltwater just above freezing to hot springs over 40°C
  • In arid regions requiring survival through drought
  • In fully marine environments and even in algae on sea turtle shells

The Hilsenhoff Family Tolerance Value for Chironomids is 6. This is an average of the Genera Tolerance Values which have been shown to range greatly (ex. from 2 to 10).

Chironomidae include all functional feeding groups: collector/gatherers, shredders, scrapers, filter-feeders, and predators.[5]

Phenotypic similarities make morphological identification difficult. However I found Chironomidae to barcode well since they lack inhibitors that impede amplification using the silica resin isolation method and PCR primer beads.

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Solution: DNA Barcoding of Chironomidae

 

 

Hilsenhoff tolerance scores of the Chironomidae genera sampled and identified using the DNA Barcoding method developed here were used with GIS software to provide a water quality overview map.[6]

 

The learnings from these data were offered and are being applied to fund and build a microbiology capability at a nonprofit scientific water study institute. Laboratory space, scientific staff, and capital equipment are established. Currently groups are being scheduled for DNA Barcode citizen science training.

 

Visualizations from this project’s data were used in community land use decision making such as preservation of wetlands, headwaters, and wildlife corridors.


 

 

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Better H2O Management

 
A SIGNIFICANTLY IMPROVED OPTION FOR MANAGEMENT OF WATERWAYS

Waterway ecosystem bioassessment by DNA Barcoding of Chironomidae is a significantly improved option for bioassessment globally, providing more accuracy, precision, and higher statistical power than the standard method of using manual taxonomic identification by morphology.

  • Analysis shows a statistically significant agreement between methods. (p<0.05)
  • The family tolerance value for Chironomidae is 6, however identification to the genus level revealed Hilsenhoff tolerance values ranging from 2 to 10.
  • Geography and historical health is represented in phylogenetic tree groupings.
  • Samples from the healthiest sites are nearly genetically identical.
  • The most sensitive genus of Chironomid was only found in the healthiest sites.
  • DNA Barcoding of Chironomidae is faster and lower cost than manual taxonomic identification by morphology.

The method is robust, reproducible, and suitable for augmenting citizen science initiatives.

  • All samples barcoded using the optimized method of silica resin isolation and PCR beads as was observed by percent amplification in gel electrophoresis.
  • The phylogenetic tree shows potential novel species where closely related samples correspond to gaps in the sequence database, and new sequences were added to databases used by the scientific community.
  • In analyzing the distribution of Chironomidae genera between streams with urban vs. open space catchment areas, there was not a statistical correlation. This may require further study with more detailed land use data. (Not statistically significant p>.05)

The investigation into the Chironomidae family shows that DNA Barcode analysis can result in waterway health data that is both more accurate and more precise, and therefore add significant value for managing an increasingly scarce water resource.  

 

 

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