Microscopic versus qPCR (quantitative polymerase chain reaction) analyses were compared for the detection of Didymosphenia geminata in biofilms and water filtrates from seven Gaspésie rivers (Canada). For the qPCR approach, two DNA extraction kits (QIAamp DNA Micro Kit, Qiagen and PowerSoil DNA Isolation Kit, Mo Bio Laboratories) and two pairs of primers were considered. The pair of primers D602F/D753Rext did not amplify D. geminata DNA whereas the pair of primers D602F/D753R was specific for D. geminata. Presence-absence diagnosis based on qPCR and microscopic analyses were consistent: D. geminata was detected in six of the seven rivers, both in the biofilm and filtrate samples. However, technical replications were needed at certain sites to observe the presence of D. geminata cells by microscopy. This underscores the necessity of replicate analyses, which is cost-effective to achieve when using qPCR due to the capacity to process tens of samples in a single PCR run in the context of a large scale assessment.

INTRODUCTION

Didymosphenia geminata is a stalk-forming freshwater diatom ranging in size from 65 to 161 μm (Diatoms of the United States; available at https://westerndiatoms.colorado.edu). This alga is native to North America and northern Europe (Patrick & Reimer 1975). In recent years, D. geminata has become more prevalent in rivers and streams across large biogeographical scales (Spaulding & Elwell 2007). Moreover, D. geminata has been reported as an exotic species in the southern hemisphere (Kilroy & Unwin 2011; Sastre et al. 2013). In Quebec, paleolimnological work was carried out to assess the historical presence and abundance of D. geminata cells in the Matapedia River catchment. Results showed that cells, albeit rare, were present at least since the early 1970s but that there was a recent 200-fold increase in prevalence of cells within the last decade (Lavery et al. 2014). Under low nutrient and stable hydrological conditions, D. geminata can produce large benthic mats (Cullis et al. 2012). Thick mats have the potential to alter the aquatic food base and consequently have disruptive impacts to higher trophic levels (Larned et al. 2007; Gillis & Chalifour 2010; Anderson et al. 2014; James & Chipps 2016). It was not until 2006 that D. geminata mats were first reported in eastern Canada (Gillis & Chalifour 2010). Thick mats covering 100% of the riverbed spanning over 35 km of river were registered (Gillis & Chalifour 2010). Between 2007 and 2010, D. geminata mats were reported in multiple rivers across the Gaspésie Peninsula region (Gillis & Chalifour 2010) and concurrently observed throughout rivers in the northeastern United States (Khan-Bureau et al. 2014) raising concerns for managers and angler groups. The presence of D. geminata cells in a river does not always result in visual observations of dense proliferations on the river bed. In fact, surveys conducted in New Zealand by Kilroy & Unwin (2011) highlighted that cells may be microscopically identified in benthic samples at sites showing no evidence of D. geminata. Research efforts to understand potential drivers of D. geminata prevalence and severity in Gaspésie rivers are currently underway (Gillis 2017). Robust and detailed surveys assessing presence–absence of cells in water and biofilm as well as the presence of mats are needed to test hypotheses to further identify water chemistry parameters that may control this species biogeography and severity. Limited regional scale monitoring efforts to detect D. geminata in rivers across the Gaspésie Peninsula were made by the Quebec government from 2006 to 2009 (MDDEP 2009). Detection surveys have not been made since and therefore currently limit our ability to understand factors limiting D. geminata presence and mat severity. A more precise and recent species distribution survey is therefore needed.

The aim of this study was to evaluate the efficiency of a molecular-based technique (real-time quantitative polymerase chain reaction, qPCR) for detection of D. geminata cells in Quebec rivers in comparison to traditional microscopy-based assessments.

METHODS

Study sites

This study was conducted in seven rivers of the northeastern Gaspésie Peninsula in Quebec, Canada (Figure 1): Cap-Chat, Madeleine, Grande-Vallée, Sainte-Anne, York, Dartmouth and Saint-Jean. Mean values of water chemistry parameters, sampled between 2012 and 2014, for five of the sampled rivers were obtained via the Quebec government water quality monitoring program (MDDELCC 2016) (Table 1). Benthic and drift samples were collected at seven sites between 13 and 16 July 2015 following the methods described later in the section entitled ‘Biofilm and water column sampling’.
Table 1

Sampling location, catchment description, water chemistry (±SD) and history of D. geminata presence

SiteRiverCatchment size (km2)River lengtha (km)Year of first cell detectionbVisual presence of didymo mats during field surveySampling site distance from the mouth of the riverNH3 (mg/l)DOC (mg/l)Conductivity (μS/cm)pHTotal P (mg/l)
Saint-Jean 1,109 118.2 2009 No 7.73 0.01 ± 0.00 1.0 ± 0.5 233 ± 27 8.1 0.002 ± 0.002 
York 1,028 110.2 2008 Yes 21.58 0.01 ± 0.01 2.1 ± 1.2 218 ± 39 8.0 0.003 ± 0.002 
Dartmouth 962 85.5 Undetected Yes 6.23 n.a. n.a. n.a. n.a. n.a. 
Grande-Vallée 172 25.1 Not surveyed No 3.67 n.a. n.a. n.a. n.a. n.a. 
Madeleine 1,232 126.3 2008 No 4.40 0.01 ± 0.00 2.0 ± 1.8 254 ± 75 8.1 0.003 ± 0.004 
Sainte-Anne 822 67.1 2006 Yes 7.91 0.01 ± 0.01 1.9 ± 1.0 150 ± 34 8.0 0.003 ± 0.004 
Cap-Chat 739 62.8 2008 No 3.55 0.01 ± 0.00 2.6 ± 1.4 156 ± 40 8.0 0.005 ± 0.007 
SiteRiverCatchment size (km2)River lengtha (km)Year of first cell detectionbVisual presence of didymo mats during field surveySampling site distance from the mouth of the riverNH3 (mg/l)DOC (mg/l)Conductivity (μS/cm)pHTotal P (mg/l)
Saint-Jean 1,109 118.2 2009 No 7.73 0.01 ± 0.00 1.0 ± 0.5 233 ± 27 8.1 0.002 ± 0.002 
York 1,028 110.2 2008 Yes 21.58 0.01 ± 0.01 2.1 ± 1.2 218 ± 39 8.0 0.003 ± 0.002 
Dartmouth 962 85.5 Undetected Yes 6.23 n.a. n.a. n.a. n.a. n.a. 
Grande-Vallée 172 25.1 Not surveyed No 3.67 n.a. n.a. n.a. n.a. n.a. 
Madeleine 1,232 126.3 2008 No 4.40 0.01 ± 0.00 2.0 ± 1.8 254 ± 75 8.1 0.003 ± 0.004 
Sainte-Anne 822 67.1 2006 Yes 7.91 0.01 ± 0.01 1.9 ± 1.0 150 ± 34 8.0 0.003 ± 0.004 
Cap-Chat 739 62.8 2008 No 3.55 0.01 ± 0.00 2.6 ± 1.4 156 ± 40 8.0 0.005 ± 0.007 

n.a., not available.

aSource for watershed area and river length data: Conseil de l'eau du Nord de la Gaspésie.

bSource:MDDEP (2009) results from filtered river water analysed in microscopy from surveys carried out between 2006 and 2009.

Figure 1

Study area showing the seven sampled rivers in the Gaspésie region, Québec, Canada. 1: Saint-Jean, 2: York, 3: Dartmouth, 4: Grande-Vallée, 5: Madeleine, 6: Sainte-Anne, 7: Cap-Chat.

Figure 1

Study area showing the seven sampled rivers in the Gaspésie region, Québec, Canada. 1: Saint-Jean, 2: York, 3: Dartmouth, 4: Grande-Vallée, 5: Madeleine, 6: Sainte-Anne, 7: Cap-Chat.

Biofilm and water column sampling

As suggested in Kilroy (2004), drift-net samples from the water column may provide an additional integrated assessment of the presence of cells in upstream portions of the watershed. For this reason, biofilm and water filtrate samples were collected to evaluate the ability of qPCR to detect D. geminata cells in Quebec rivers in comparison to traditional microscopy-based assessments within these two matrices. Biofilms sampled at each site were obtained by scraping eight rocks of similar size (composite sample; total surface scraped reaching about 64 cm2 at each site) selected in fast and slow-flowing environments within the reach. The biofilm was collected using a toothbrush and by carefully rinsing each rock with 70% ethanol. Drifting D. geminata cells in the water column were sampled using a plankton net (Wisconsin net; mesh size = 40 μm) submerged for 10 minutes at a depth of about 50 cm from the bed of the river. Water velocity was estimated using a float method, based on the average time for a floating object to travel a known distance. The volume of water filtered during sampling was estimated by multiplying the water velocity estimate by the surface area of the net opening. The net was then rinsed multiple times with 70% ethanol to remove all collected drifting particles. All material potentially in contact with D. geminata cells was soaked in 5% hydrochloric solution between each site to kill live cells and to denature DNA as suggested in Cary et al. (2007).

Didymosphenia geminata detection by microscopy

Samples preserved in 70% ethanol were vigorously shaken and 1 mL aliquots were transferred into 2 mL tubes. An aliquot of 100 μL of each sub-sample was placed on a microscope slide (after homogenization using the pipette to avoid clumps being collected) and dried on a hot plate at low heat. Two replicate slides were mounted per sample. D. geminata cells were counted under a Reichert-Jung Polyvar light microscope under 100× magnification. The observation and identification of D. geminata was relatively simple as the cells were easily identifiable from other particles due to their large size and distinctive shape (Figure 2).
Figure 2

Didymosphenia geminata cells (scale bar = 10 microns) and mat development stages in Gaspésie rivers.

Figure 2

Didymosphenia geminata cells (scale bar = 10 microns) and mat development stages in Gaspésie rivers.

Didymosphenia geminata detection by qPCR

Selection of primers

Two pairs of primers (D602F/D753Rext and D602F/D753R) developed specifically for detection of the 18S ribosomal DNA sequence of D. geminata obtained from Cary et al. (2014) and Cary et al. (2006) respectively (Table 2), were tested.

Table 2

Sequence of the primers tested in the study for the detection of D. geminata and lambda DNA as a positive control

Gene nameOrganismAssociated primersPrimer (5′-3′)Peak of fusion (°C)
18s Didymosphenia geminata D602F GTT GGA TTT GTG ATG GAA TTT GAAa 80.5 
18s Didymosphenia geminata D753Rext GAC TTA CGT CGA TGA ATG TAT TAG CAb 
18s Didymosphenia geminata D753R AAT ACA TTC ATC GAC GTA AGT Cb 
 Bacteriophage lambda isolated from the heat-inducible lysogen E. coli (cI857 S7) CALF GCC AGG TCA TCT GAA ACA Ga 90 
 Bacteriophage lambda isolated from the heat-inducible lysogen E. coli (cI857 S7) CALR TCT GCG ATG CTG ATA CCGb 
Gene nameOrganismAssociated primersPrimer (5′-3′)Peak of fusion (°C)
18s Didymosphenia geminata D602F GTT GGA TTT GTG ATG GAA TTT GAAa 80.5 
18s Didymosphenia geminata D753Rext GAC TTA CGT CGA TGA ATG TAT TAG CAb 
18s Didymosphenia geminata D753R AAT ACA TTC ATC GAC GTA AGT Cb 
 Bacteriophage lambda isolated from the heat-inducible lysogen E. coli (cI857 S7) CALF GCC AGG TCA TCT GAA ACA Ga 90 
 Bacteriophage lambda isolated from the heat-inducible lysogen E. coli (cI857 S7) CALR TCT GCG ATG CTG ATA CCGb 

18S – ribosomal DNA 18S.

aForward primer.

bReverse primer.

D. geminata DNA extraction methods

Two commercial DNA extraction kits were considered for this study: QIAamp DNA Micro Kit (Qiagen) and PowerSoil DNA Isolation Kit (Mo Bio Laboratories Inc). The QIAamp DNA Micro Kit is suitable for samples with a small amount of genetic material and was chosen because cells of D. geminata were likely to represent a small fraction of the total biomass, especially in biofilms where mucilaginous stalks are abundant. The PowerSoil DNA Isolation Kit is especially designed to minimize inhibitors effects in soil such as humic acids, also present in aquatic environments. Samples preserved in 70% ethanol were vigorously shaken and 1 mL aliquots were transferred into 2 mL tubes and centrifuged for 4 min at 8,000 g at room temperature. The pellet was re-suspended in 1 mL milliQ water for DNA extraction.

QIAamp DNA micro kit

A 100 μL aliquot was transferred into clean tubes containing 100 μL of glass beads (<106 μm-diameter, Sigma), as well as 180 μL of ATL buffer and 1 μL of Reagent DX (Qiagen). Tubes were vortexed for 20 min and cell disruption was ensured by the use of a mechanical cell disruption device for 2 min (Disruptor Genie, Scientific Industries). Then, 20 μL of proteinase K (Qiagen) and 1 μL of lambda DNA (100 pg/μL) (New England BioLabs) were added to the tubes and mixed by vortexing for 15 s. Lambda DNA was used as an internal control. Tubes were incubated at 56 °C overnight and mixed by vortexing for 10 s each 10 min for the first hour of incubation. The end of the extraction was performed according to the manufacturer's instructions in the section ‘Isolation of genomic DNA from tissue samples’. The DNA was stored at −20 °C until use for qPCR.

Powersoil DNA isolation kit

A 100 μL aliquot was transferred into PowerBead tubes (Mo Bio) containing 60 μL of solution C1 (Mo Bio), 20 μL of proteinase K (Qiagen), 1 μL of lambda DNA (100 pg/μL) (New England BioLabs) and glass beads (<106 μm-diameter, Qiagen). Samples were homogenized by vortexing for 20 min and cells were mechanically broken as described above. Tubes were incubated at 56 °C overnight and mixed by vortexing for 10 s each 10 min for the first hour of incubation. The last steps of the extraction was completed according to the manufacturer's instructions in the section ‘Isolation of genomic DNA from tissue samples’. The DNA was stored at −20 °C until use for qPCR.

Real-time quantitative PCR

Real-time quantitative PCR reactions were performed in a C1000™ Thermal Cycler (BIO-RAD) using a CFX96 Touch™ Real-Time quantitative PCR Detection System (BIO-RAD) with one cycle at 95 °C for 2 min and 50 amplification cycles at 95 °C for 10 s, 55 °C for 15 s and 68 °C for 15 s. Reactions were conducted in multiplate low-profile 96-well unskirted PCR Plates (BIO-RAD). Each well contained 10 μL of SsoAdvanced universal SYBR Green Supermix (2X, BIO-RAD), 2 μL of DNA extract, D. geminata 18S ribosomal DNA primer pair at a final concentration of 300 nM for each primer for a final volume of 20 μL. For negative controls, DNA extract was replaced by diethylpyrocarbonate-treated water (DEPC-treated water). For positive controls, the wells contained 10 μL of SsoAdvanced universal SYBR Green Supermix (2X, BIO-RAD), 2 μL of lambda DNA (100 pg/μL) (New England BioLabs), the primers CALF and CALR at a final concentration of 500 nM for each primer and DEPC-treated water for a final volume of 20 μL. Each sample was analysed with technical duplicates. Specificity was determined for each reaction from the dissociation curve and the sequencing of the PCR product. This dissociation curve was obtained with the SYBR Green fluorescence level during gradual heating of the PCR products from 60 to 99 °C. These PCR products were sent for sequencing, and the sequences obtained were treated with a blast analysis.

DNA recovery calculation

For DNA recovery calculation, the samples were spiked with 100 femtograms of Lambda DNA as an internal standard at the beginning of the extraction procedure. For the PCR conditions described above, the addition of the internal standard gave a threshold cycle (Ct) of 21.5 (Tanguay, personal communication) for a recovery of 100% and corresponds to 2,000 copies of the target gene. The recovery of each sample was calculated according to the following equation: 
formula
where Ct, IS is the Ct of the internal standard Lambda DNA.

A recovery of 1 corresponds to the maximal recovery and indicates that all Lambda DNA is recovered in the DNA extract and that PCR inhibitors are absent in the PCR reaction.

RESULTS AND DISCUSSION

Primers selection

Amplifications with high Ct values were recorded (Ct values >36) using the primer pair D602F/D753Rext (Cary et al. 2014), suggesting a non-specific amplification of D. geminata. Melting curves also presented multiple peaks in each sample. These results led us to conclude that the primer pair D602F/D753Rext does not amplify the 18S ribosomal DNA sequence of D. geminata. Amplifications with Ct values between 21.5 and 36.5 were recorded using the primer pair D602F/D753R (Cary et al. 2006), and all samples presented the same fusion temperature of 80.5 °C, characteristic of the 18S ribosomal DNA sequence of D. geminata. Final qPCR products were sent for sequencing to the Plateforme de séquençage et de génotypage des génomes, Centre Hospitalier Universitaire de Québec. The obtained sequences were blasted on the NCBI database (available at http://blast.ncbi.nlm.nih.gov/), and the correspondence with the 18S ribosomal DNA sequence of D. geminata was confirmed for all samples. We concluded that the primer pair D602F/D753Rext published in Cary et al. (2014) was not specific to D. geminata. The results presented in the following sections were obtained with the pair of primers D602F/D753R (Cary et al. 2006).

DNA recovery efficiencies

Mean recovery efficiencies calculated with the lambda DNA as an internal standard are shown in Table 3. The results revealed low recovery using the Qiagen kit (11.5 ± 3.4%). For certain samples this kit even failed at recovering any lamdba DNA (Table 4). The Mo Bio extraction kit yielded a higher recovery efficiency (17.5 ± 3.1%) and null values were not encountered. The low performance of the Qiagen kit was in part due to the recovery values of zero in the water filtrate matrix for Saint-Jean, Cap-Chat, Grande-Vallée, and Sainte-Anne rivers (Table 4).

Table 3

Recovery calculated using lambda DNA as an internal standard for all samples, samples extracted with MoBio or Qiagen kit for biofilm or water filtrates (mean ± SE, n = the number of samples)

SamplesMean recovery (%)
All samples (n = 28) 14.5 ± 2.3 
Biofilm (n = 14) 16.4 ± 3.3 
Water (n = 14) 12.6 ± 3.3 
MoBio (n = 14) 17.5 ± 3.1 
MoBio, biofilm (n = 7) 21.3 ± 5.0 
MoBio, water (n = 7) 13.7 ± 3.4 
Qiagen (n = 14) 11.5 ± 3.4 
Qiagen, biofilm (n = 7) 11.6 ± 3.9 
Qiagen, water (n = 7) 11.4 ± 5.6 
SamplesMean recovery (%)
All samples (n = 28) 14.5 ± 2.3 
Biofilm (n = 14) 16.4 ± 3.3 
Water (n = 14) 12.6 ± 3.3 
MoBio (n = 14) 17.5 ± 3.1 
MoBio, biofilm (n = 7) 21.3 ± 5.0 
MoBio, water (n = 7) 13.7 ± 3.4 
Qiagen (n = 14) 11.5 ± 3.4 
Qiagen, biofilm (n = 7) 11.6 ± 3.9 
Qiagen, water (n = 7) 11.4 ± 5.6 
Table 4

Recovery calculated using lambda DNA as an internal standard for samples from the seven Gaspésie rivers in the biofilm and water filtrates using MoBio and Qiagen

RiverMatrixRecovery (%)Recovery (%)
QiagenMoBio
Saint-Jean Biofilm 8.8 28.1 
Water 0.0 33.0 
York Biofilm 6.7 46.3 
Water 17.7 9.2 
Cap-Chat Biofilm 27.4 10.7 
Water 0.0 10.3 
Dartmouth Biofilm 20.7 17.8 
Water 31.2 16.4 
Madeleine Biofilm 0.7 15.9 
Water 31.0 6.3 
Sainte-Anne Biofilm 0.0 14.8 
Water 0.0 11.2 
Grande-Vallée Biofilm 16.7 15.2 
Water 0.0 9.7 
RiverMatrixRecovery (%)Recovery (%)
QiagenMoBio
Saint-Jean Biofilm 8.8 28.1 
Water 0.0 33.0 
York Biofilm 6.7 46.3 
Water 17.7 9.2 
Cap-Chat Biofilm 27.4 10.7 
Water 0.0 10.3 
Dartmouth Biofilm 20.7 17.8 
Water 31.2 16.4 
Madeleine Biofilm 0.7 15.9 
Water 31.0 6.3 
Sainte-Anne Biofilm 0.0 14.8 
Water 0.0 11.2 
Grande-Vallée Biofilm 16.7 15.2 
Water 0.0 9.7 

Recovery efficiencies are affected by two main causes. First, low recovery may result from low DNA yield which is particularly encountered in the case of complex matrices such as environmental samples. Low DNA concentrations have also been reported by Uyua et al. (2014) for biofilm extractions in a study evaluating several protocols, and DNA concentrations equal to zero were even obtained for one of the tested approaches. Decreasing recovery efficiencies with increasing matrix complexity was reported by Yáñez et al. (2005). The authors spiked different water matrices with Legionella pneumophila and obtained recovery efficiencies of 59.9% for distilled water, 42.0% for potable water and 36.0% for cooling tower water. It seems reasonable to assume that recovery efficiency will be even lower in environmental samples, as observed in this study. Bletz et al. (2015) also pointed out that differential efficiency among DNA extraction methods influenced the detection of an amphibian pathogen (Batrachochytrium dendrobatidis). This result underscores the importance of taking into account recovery efficiencies, particularly to avoid false negatives. Secondly, recovery can be affected by the presence of PCR inhibitors, which represent an important issue because their occurrence may lead to false negatives or quantitative errors of several orders of magnitude (Bar et al. 2012; Jones et al. 2015). Inhibitors naturally found in the environment, such as humic acids, polysaccharides or metals, are designated as intrinsic inhibitors. Substances such as phenols, alcohols or salts introduced during sample preparation are known as extrinsic inhibitors. Our results suggest that the extraction procedure led to the recovery of low percentages of the environmental DNA or that the extraction protocol did not successfully remove PCR inhibitors (or PCR inhibitors were introduced during extraction procedures).

DNA recovery efficiencies were higher for samples from the biofilm matrix compared to the water filtrate matrix (21.3 ± 5.0 and 13.7 ± 3.7% mean respectively). This result was surprising since lower recovery rates were expected for biofilms than for water filtrates; benthic samples have been described as recalcitrant DNA sources in terms of both DNA yield and the potential persistence of PCR inhibitors (Uyua et al. 2014). DNA extraction was anticipated to be less effective for biofilms due to their dense tri-dimensional structure composed of a variety of organisms embedded in an exo-polysaccharide matrix compared with free-living organisms (or colonies) from the water column. This thick matrix can be strongly cohesive and particle size has been shown to influence the extraction where lower particle size offers a higher surface for the chemical to react during DNA extraction (Demeke & Jenkins 2010). High amounts of PCR inhibitors were also expected to be present in the biofilms samples due to the exo-polysaccharides component, particularly in the biofilms containing D. geminata and long polysaccharides stalks (Whitton et al. 2009; Bothwell et al. 2012). As mentioned earlier, polysaccharides may be potent enzymatic inhibitors (Pandey et al. 1996; Monteiro et al. 1997), and may adsorb significant amounts of metals (Kaplan et al. 1987), which are also known as PCR inhibitors affecting the enzymatic activity or damaging DNA.

D. geminata in Gaspésie rivers

Detection of D. geminata cells

The presence of D. geminata was detected by qPCR in six of the seven rivers monitored in July 2015 (Table 5), both in the biofilm and water filtrate samples (cells were not detected in the Grande-Vallée River). The results from the microscopy analyses were consistent with the molecular-based approach, both for biofilm and water filtrate samples; D. geminata cells were observed at all sites except in the Grande-Vallée River.

Table 5

Didymosphenia geminata detection results obtained by qPCR and microscopy analyses for biofilm and water filtrate samples for the seven rivers sampled in July 2015

SiteRiverBiofilm
Water
qPCRMicroscopyqPCRMicroscopy
Saint-Jean Detected Detected Detected Detected 
York Detected Detected Detected Detected 
Dartmouth Detected Detected Detected Detected 
Grande-Vallée Non detected Non detected Non detected Non detected 
Madeleine Detected Detected Detected Detected 
Sainte-Anne Detected Detected Detected Detected 
Cap-Chat Detected Detected Detected Detected 
SiteRiverBiofilm
Water
qPCRMicroscopyqPCRMicroscopy
Saint-Jean Detected Detected Detected Detected 
York Detected Detected Detected Detected 
Dartmouth Detected Detected Detected Detected 
Grande-Vallée Non detected Non detected Non detected Non detected 
Madeleine Detected Detected Detected Detected 
Sainte-Anne Detected Detected Detected Detected 
Cap-Chat Detected Detected Detected Detected 

D. geminata concentrations in biofilm and water

In the biofilms samples, the highest cell densities were recorded in the York, Madeleine and Sainte-Anne rivers (1,200, 750 and 630 cells/cm2, respectively) whereas the lowest cell densities were observed in Cap-Chat and Dartmouth rivers (20 and 30 cells/cm2, respectively) (Figure 3). These results provide an interesting overview of the abundance of D. geminata cells in the biofilm of the seven rivers sampled. Moreover, D. geminata mats were observed in the York, Sainte-Anne and Dartmouth rivers. In the Madeleine River, high cell densities were recorded in the biofilm but mats were not present; on the contrary in Dartmouth River where cell densities in biofilms were low, mats were observed. This emphasises the fact that the presence of D. geminata cells in a river does not always result in visual observations of dense proliferations on the river bed (Kilroy & Unwin 2011). Further, it also reiterates the importance of distinguishing mat growth from cell division, where optimal nutrient conditions for cell division differ from optimal nutrient conditions for mat growth (Kilroy & Bothwell 2011; Cullis et al. 2012).
Figure 3

Didymosphenia geminata cell density estimated by microscopy analyses on biofilms (a) and water filtrates (b) for the seven Gaspésie rivers sampled in July 2015. The underlined numbers denote the mean (n = 2) D. geminata density in biofilm sample in cells/cm2 and in water samples in cells/m3. M: presence of mats.

Figure 3

Didymosphenia geminata cell density estimated by microscopy analyses on biofilms (a) and water filtrates (b) for the seven Gaspésie rivers sampled in July 2015. The underlined numbers denote the mean (n = 2) D. geminata density in biofilm sample in cells/cm2 and in water samples in cells/m3. M: presence of mats.

In water filtrates, the highest cell densities were recorded in the Cap-Chat and Sainte-Anne rivers (28,430 and 17,430 cells/m3, respectively) and the lowest cell densities were observed in the Dartmouth and Saint-Jean rivers (100 and 120 cells/m3, respectively). High densities of D. geminata cells in the water filtrates, as observed in the Cap-Chat and Sainte-Anne rivers, provide an integrated assessment of the presence of cells in upstream portions of the watershed, which could possibly indicate extensive mats or widespread growth upstream because mat coverage can be positively correlated with cell density (Ellwood & Whitton 2007). The high propagule pressure from the water column in Cap-Chat could result in mat formation further downstream if local conditions are suitable.

An interesting result to note is the fact that the presence of D. geminata could have been overlooked in the biofilm (Cap-Chat and Dartmouth rivers) and in the water filtrates (Dartmouth and St-Jean rivers) if only one technical replicate had been processed under the microscope (presence observed in only one of the two replicates) (Table 6). This emphasises the necessity of replicate analyses, which are cost-effective to achieve using qPCR in the context of a large scale assessments compared to microscopy-based assessments.

Table 6

Didymosphenia geminata cells counted by microscopy in a 100 μL aliquot of biofilm or water for the seven Gaspésie rivers sampled in July 2015

SiteRiverNumber of D. geminata cells counted in a 100 μL biofilm aliquot
Number of D. geminata cells counted in a 100 μL water aliquot
Technical replicate #1Technical replicate #2Technical replicate #1Technical replicate #2
Saint-Jean 
York 30 47 10 25 
Dartmouth 
Grande-Vallée 
Madeleine 23 25 18 28 
Sainte-Anne 14 26 200 281 
Cap-Chat 330 591 
SiteRiverNumber of D. geminata cells counted in a 100 μL biofilm aliquot
Number of D. geminata cells counted in a 100 μL water aliquot
Technical replicate #1Technical replicate #2Technical replicate #1Technical replicate #2
Saint-Jean 
York 30 47 10 25 
Dartmouth 
Grande-Vallée 
Madeleine 23 25 18 28 
Sainte-Anne 14 26 200 281 
Cap-Chat 330 591 

Samples were counted in duplicates. At each site, biofilms sample is a composite sample (total surface scraped reaching about 64 cm2); biofilm was then transferred into a tube filled up to 50 mL with water from the site. Drifting D. geminata cells in the water column were sampled using a plankton net (Wisconsin net; mesh size = 40 μm) submerged for 10 minutes at a depth of about 50 cm from the bed of the river, cells were then transferred into a tube and filled up to 50 mL with water from the site.

CONCLUSIONS

To enhance our knowledge of D. geminata distribution across broad geographical scales, it is necessary to gain a better understanding of regional drivers of cell presence and mat severity (of mat growth and extracellular stalk growth). While D. geminata mats are typically assessed visually, the absence of dense mat formation is not an indicator of this species absence. Cells can be present in the water column and in the biofilm without producing thick benthic mats. In the absence of these macroscopic accruals, presence–absence diagnosis is traditionally performed by microscopy-based assessments. At present, information is lacking for predicting occurrence, severity and persistence of nuisance mats towards facilitating management and mitigation measures (Cullis et al. 2012). By yielding precise distribution maps of D. geminata occurrence in rivers we can more effectively test hypotheses for identifying threshold values for presence–absence and mat development. Hence, future management of water resources could be strategically focused on vulnerable and suitable habitats where cells have not yet been found.

Two DNA extraction kits (QIAamp DNA Micro Kit; Qiagen and PowerSoil DNA Isolation Kit; Mo Bio Laboratories) and two pairs of primers were considered in this study. The pair of primers D602F/D753Rext (Cary et al. 2014) did not amplify D. geminata DNA whereas the D602F/D753R (Cary et al. 2006) was specific to D. geminata. The Mo Bio kit yielded better recovery efficiencies than the Qiagen kit. Additionally, recovery efficiency was null for certain samples extracted with the Qiagen kit. qPCR revealed the presence of D. geminata cells in six of the seven studied rivers sampled in July 2015. These results are consistent with the data obtained by microscopy analyses and thus confirm the ability of the qPCR based assay for the presence–absence diagnosis of D. geminata in environmental samples. The small volume of sample used for qPCR or microscopy analyses decreased the probability of detecting D. geminata cells. The main benefit of a molecular detection of D. geminata is the rapidity, sensitivity and low cost of the approach, allowing for the possibility of analysing multiple replicate samples for a site and therefore increasing the confidence in negative results.

Upgrading the method to be able to provide a quantitative estimate of the D. geminata cell density is an interesting avenue to consider. Before this can be accomplished, higher recovery efficiencies would have to be obtained. Evaluating and untangling the contribution of both extraction efficiency and PCR inhibition on DNA recovery is necessary. For example, the influence of PCR inhibition on recovery could be estimated by adding an internal standard after the extraction procedure step instead of only before. Also, analyses based on detecting amplification curve anomalies by comparing kinetics parameters of test and reference reactions (kinetics outlier detection) could be used to help distinguishing PCR inhibition from DNA recovery problems, as suggested in Jones et al. (2015). It could also be recommended to conduct more than one extraction per sample (technical replicate at the extraction step) in order to avoid false negatives due to extraction problems or sub-sampling effects. qPCR estimates of cell density would provide valuable information to more precisely characterize the degree of colonisation of the species in terms of propagule pressure. While the use of a calibrator sample (where D. geminata cell density in the calibrator sample is estimated under the microscope) has been suggested by Cary et al. (2014) (relative quantification), calibration curves may also be developed based on samples containing different densities of D. geminata cells (absolute quantification) manually isolated as described by Khan-Bureau et al. (2016).

To conclude based on our findings, we propose the flow chart presented in Figure 4 for the survey of D. geminata. We recommend the use of the qPCR approach on water filtrate samples in downstream sections of rivers for initial screening surveys to detect the presence of D. geminata at the watershed scale and pinpoint areas of watersheds that might have algal accrual. Upon positive diagnosis, we recommend an on-site visual assessment (i.e. Standing Crop Index (SCI)) as described in Kilroy & Bothwell (2011) to assess the severity of D. geminata within a given reach. Upon negative diagnosis regarding the visual assessment, additional molecular based investigations of D. geminata cells in biofilms can be conducted, if these results are positive, then further microscopic assessments can follow in order to determine the cell concentration.
Figure 4

Flow chart proposed for the survey of D. geminata. Water filtrates for the initial screening survey could be sampled at determined frequencies and during a specific period for example every month during ice-off.

Figure 4

Flow chart proposed for the survey of D. geminata. Water filtrates for the initial screening survey could be sampled at determined frequencies and during a specific period for example every month during ice-off.

These combined data would yield an accurate map of D. geminata distribution and mat severity, and would help identify reach scale and broad scale controlling factors for cellular presence and mat accrual. Overall, D. geminata presence-absence assessment by qPCR has a great potential to assist in sound management and conservation strategies of vulnerable rivers and may help maintain the ecological goods and services they provide.

ACKNOWLEDGEMENTS

Field surveys and laboratory analyses were funded in part by the contribution of the Fonds Eau Nord Gaspésie and the Ministère du Développement Durable, de l'Environnement et de la Lutte aux Changements Climatiques (MDDELCC). We wish to thank J. Madore and T. Ratté from the Conseil de l'eau du Nord de la Gaspésie for their assistance in enabling this endeavor and P. Gosselin for mapping services. We would also like to thank P. Tanguay and D. Stewart from Natural Resources Canada, Canadian Forest Service, and Laurentian Forestry Centre for their laboratory support and technical advice. S. Kim Tiam was supported by the Mine of Knowledge CREATE program (www.mine.umontreal.ca). C. Fortin is supported by the Canada Research Chair program.

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