Abstract
Recent studies have demonstrated the potential of combining molecular technologies with environmental sampling to detect various vertebrate species in aquatic ecosystems. The European pond turtle (Emys orbicularis) is a threatened and elusive aquatic reptile with shy behaviour. We aimed to develop and evaluate a methodology to detect the presence of this secretive aquatic reptile in ponds from environmental water samples. First, we determined that reptilian DNA can be isolated and amplified from water samples in artificial and natural ponds with known turtle density. Then we compared the potential of two water sampling methods (through filtration or precipitation) and found no significant differences between these approaches. Finally, we demonstrated that the eDNA concentration detected is not correlated with the number of E. orbicularis individuals or biomass. Detection of eDNA was higher in artificial ponds with small volumes of water or in the shallow waters of natural ponds. The eDNA-based methodology aims to detect the presence of specific species, even at low density, with better accuracy than visual observation. However, our study indicates that this method of population monitoring should be applied with caution to aquatic reptiles.
Introduction
Effective management of endangered native species and recently introduced invasive species requires efficient detection methods, especially for populations at low density. Population detection in freshwater habitats and marine ecosystems have recently been conducted from environmental DNA (eDNA): this approach is a rapid, cost-effective tool for applied conservation biology and could be used as an early-warning system to monitor endangered species (Wilcox et al., 2013; Sigsgaard et al., 2015) and to detect the presence of invasive species (Ficetola et al., 2008; Jerde et al., 2011; Goldberg et al., 2013; Piaggio et al., 2014). This method has been used for mammals (Foote et al., 2012; Thomsen et al., 2012a), amphibians (Ficetola et al., 2008; Goldberg et al., 2011; Thomsen et al., 2012a), invertebrates (Tréguier et al., 2014), fish (Jerde et al., 2011; Takahara et al., 2012; Kelly et al., 2014a) and reptiles (Piaggio et al., 2014).
However, persistence of DNA in the environment is an important element to evaluate in order to confirm the recent or old presence of focal species. Consequently, it has been intensely researched over the last decade (e.g. Willerslev and Cooper, 2005). It has been shown to persist for days or weeks in aquatic environments (Dejean et al., 2011; Thomsen et al., 2012b; Piaggio et al., 2014; Pilliod et al., 2014), and it may persist in soil for centuries or millennia (Willerslev et al., 2003; Haile et al., 2007; Yoccoz et al., 2012). Low persistence in aquatic freshwater and marine ecosystems may be explained by fragmentation (e.g. Willerslev and Cooper, 2005; Deagle, Eveson and Jarman, 2006; Taberlet et al., 2012). Biochemical processes and DNases as well as UV-B light, temperature, salinity or pH (Lindahl, 1993; Barnes et al., 2014; Strickler, Fremier and Goldberg, 2015) can all cause fragmentation, which leads to difficulties with PCR amplification. As eDNA rapidly fragments in freshwater ecosystems, positive detection of a species indicates the recent presence of that species, which makes eDNA an important tool for species detection (Dejean et al., 2011; Foote et al., 2012; Thomsen et al., 2012a, 2015).
In the same way, eDNA amounts should theoretically be related to the biomass or number of individuals of the focal species. Recent surveys have successfully established quantitative relationships between the biomass of fish species and the relative abundance of their eDNA detected in the environment (Takahara et al., 2012; Thomsen et al., 2012a). Moreover, quantification of biomass or the number of individuals were estimated by quantifying the initial eDNA concentration from positive replicates using quantitative PCR (Ellison et al., 2006; Bustin et al., 2009). However, eDNA concentration does not always reflect the real biomass or species composition in an ecosystem (Kelly et al., 2014a). Consequently, eDNA-based methodologies seem reliable for high secreting taxa (i.e. secreting high DNA quantities) but little is known about its applicability to lower secreting taxa like reptiles. Additionally, problematic issues, such as low-quality and low-quantity DNA as well as contamination and inhibition from co-extracted substances may lead to false positive and false negative results (John, 1992; Matheson et al., 2010; McKee, Spear and Pierson, 2015); impacts of such problematic issues would have even a higher influence on lower secreting taxa. Consequently, such biases may lead to over- or under-estimates of species occurrence and thus impact conservation efforts (Kelly et al., 2014b; Thomsen and Willerslev, 2015).
Environmental DNA comes from excreted cells or tissues and as free extracellular DNA from secretions, hairs and epidermal tissues (Beja-Pereira et al., 2009 and references therein). Despite the fact that amphibians and fish are reliably detected with eDNA, little is known about the applicability of this technique to detect the presence of reptiles in aquatic ecosystems (Kelly et al., 2014a; Piaggio et al., 2014). The European pond turtle (Emys orbicularis) is a threatened aquatic reptile, whose distribution area has decreased dramatically during the last few centuries due to the loss of aquatic habitat and nesting sites (Fritz, 2003), to consumption by humans (Schneeweiss, 1997) and, locally, to competition from exotic species (Cadi and Joly, 2004). Current conservation and reintroduction programmes benefit to this species in several European countries (Fritz and Chiari, 2013). Due to the cryptic behaviour of this species, traditional monitoring techniques with boats and nets are time-consuming, difficult to set up, prone to false-negative detection rates (Jerde et al., 2011), and could have a strong impact on ecosystems by disturbing local fauna and flora (Tyre et al., 2003; Biber, 2011). In some cases, visual observation can be an alternative approach to determine the presence of turtles in ponds, but this approach may lead to a large underestimation of the distribution of this species. Consequently, detecting the presence of this cryptic species by developing alternative approaches should improve the efficiency of monitoring programmes for this high priority conservation species and for aquatic reptiles in general.
We evaluated the potential of molecular techniques to detect the presence of an aquatic reptile species in environmental water samples from artificial and natural ponds, and we compared two different water sample collection methods (filtration and precipitation). In addition, we attempted to determine if eDNA concentration reflects the relative abundance or biomass of pond turtles to determine whether eDNA-based methodologies may be accurate to detect and evaluate the density of an aquatic reptile species.
Materials and methods
Sampling and extraction from artificial sites
To collect eDNA from artificial ponds, 90 ml of water from five outdoor sites (two different locations) in Switzerland with a known number and biomass of captive E. orbicularis (table 1) was filtered through a Millipore® Sterivex™ 0.22 μm filter (Sigma-Aldrich, St. Louis, USA) in the first 30 cm water depth. Filters were then immediately stored at −80°C for further processing. To recover cellular remains and DNA from the filters, membranes were cut into small pieces and transferred into Eppendorf tubes for extraction with the QIAamp Tissue Extraction kit (Qiagen, Hombrechtikon, Switzerland) as per the manufacturer’s standard protocol.
Rates of detection, visual observations and eDNA concentrations of Emys orbicularis in artificial and natural ponds with filtration and precipitation water sampling methods. The natural ponds are closed to each other and belong to the same natural reserve.
Citation: Amphibia-Reptilia 39, 2 (2018) ; 10.1163/15685381-17000025
Rates of detection, visual observations and eDNA concentrations of Emys orbicularis in artificial and natural ponds with filtration and precipitation water sampling methods. The natural ponds are closed to each other and belong to the same natural reserve.
Citation: Amphibia-Reptilia 39, 2 (2018) ; 10.1163/15685381-17000025
Rates of detection, visual observations and eDNA concentrations of Emys orbicularis in artificial and natural ponds with filtration and precipitation water sampling methods. The natural ponds are closed to each other and belong to the same natural reserve.
Citation: Amphibia-Reptilia 39, 2 (2018) ; 10.1163/15685381-17000025
Sampling and extraction from natural sites
To collect eDNA from natural ponds, an additional 90 ml of water from 15 sites spread over 4 different natural ponds with known turtle density (table 1) was filtered following the procedure described above. The number of eDNA samples per site relied on the size of ponds. In order to compare the efficiency of sampling between filtration and precipitation methods, additional 90 ml water was collected simultaneously from the same locations and was uniformly distributed in six Falcon tubes each with 33.5 ml ethanol and 1.5 ml sodium acetate (Ficetola et al., 2008). Samples were then immediately stored at −80°C until further processing. To recover DNA and cellular remains from water samples, tubes were centrifuged at 13,200 × g for 30 min at 6°C and the supernatant was discarded (Ficetola et al., 2008). Then 360 μl ATL buffer and 40 μl Proteinase K (Qiagen, Hombrechtikon, Switzerland) were used to resuspend the pellet in the first tube. This solution was then used to resuspend the pellet from the second tube. This procedure was repeated for all tubes from the same site until pellets from all the six tubes were dissolved and gathered together in the 6th tube as described by Ficetola et al. (2008). To extract DNA from the cellular remains, the solution was incubated for 3 h at 56°C. Finally, 400 μl buffer AL (Qiagen) and 400 μl ethanol were added to samples prior to elution. Further extraction steps were performed following the manufacturer’s standard protocol (QIAamp Tissue Extraction; Qiagen).
Specificity of primers
Specifically designed primers eDNA_1f (5′-CCAAATATCCTTCTGAGGTGC-3′) and eDNA_1r (5′-GCGTTATCTACTGAGAATCC-3′) were used to amplify a 115 bp fragment of the mitochondrial cyt b gene. The alignment search tool BLAST (Basic Local Alignment Search Tool; Genbank, www.ncbi.nlm.nih.gov/blast) showed that these primers do not match with high scores to any published European species sequence on GenBank (www.ncbi.nlm.nih.gov/blast) except for E. orbicularis. Pond turtle DNA from one positive control was extracted from buccal swabs after an initial incubation at 56°C in ATL lysis buffer (Qiagen, Hombrechtikon, Switzerland) for 12 h following the DNeasy Blood and Tissue Handbook manufacturer’s handbook. The positive control was then quantified with a Nanodrop, 1000 Spectrophotometer (ThermoScientific, Wilmington, USA) and diluted from 5 × 10−1 to 5 × 10−15 μg/μl to obtain positive control dilutions. Primer specificity was tested by PCR amplification of the positive control E. orbicularis DNA dilutions and water samples containing pond turtle DNA. PCR was performed with 55 cycles of denaturation at 95°C for 30 s, annealing at 56°C for 30 s and elongation at 72°C for 30 s. Positive PCR products were sent to Macrogen Europe for sequencing and cyt b sequences were compared to reference sequences on GenBank.
Amplification by quantitative PCR
To improve the sensitivity of eDNA amplification, quantitative PCR (qPCR) using SYBR Green PCR Master Mix (Roche, Basel, Switzerland) was carried out with an ABI 7000 Sequence Detection System (Applied Biosystems, Foster City, USA) including an initial 10 min denaturation step at 95°C, 55 cycles of denaturation at 95°C for 30 s and annealing at 56°C for 30 s on both water samples and on positive controls to set the detection threshold. Amplification of water samples as well as positive and negative controls were repeated 6 times following the multi-tube approach to account for stochasticity in low quantity or quality DNA amplification (Taberlet et al., 1996). Amplification in any of the six replicates was considered to be positive detection when the initial DNA concentration was above the detection threshold and when sigmoidal amplification curves and melting temperatures were identical to that of positive controls. Crossing point (Cp) values of all positive replicates were determined with ABI Prism 7000 SDS software 1.1 (Applied Biosystems).
All DNA procedures were conducted using precautions required for ancient DNA and for analysing fragmented DNA at low concentrations (Taberlet et al., 1996; Willerslev and Cooper, 2005; Knapp and Hofreiter, 2010). For instance, the extraction and amplification steps were performed in laboratories where no genetic work on turtles had previously been conducted. Moreover, to detect putative contamination of samples with exogenous DNA during the study, negative controls (field, DNA extraction and PCR blanks) were processed throughout the whole procedure. Negative field controls were obtained from ponds without turtles, while DNA extraction negative controls and PCR negative controls were processed without turtle DNA in the laboratory procedures.
Statistical analyses
In artificial ponds, we tested if the detected DNA concentration was correlated with the known number of individuals and the biomass of turtles using a Spearman correlation test in R 3.3.0 (R Core Team, 2016) using the average of all qPCR quantifications for each sample. In natural ponds, we tested if the detected DNA concentration was correlated with the number of turtles present at that site by the same statistical method. The number of individuals was previously estimated by traditional monitoring techniques with nets. As natural ponds have unknown depths and volumes, we did not investigate any correlation between biomass and the detected DNA concentration in natural ponds. Additionally, we visually searched for E. orbicularis for 5 min at each location point before sampling the water on a sunny and hot day. This search was conducted only once for each site. Finally, the difference between sampling methods (filtration on membranes vs. precipitation in tubes) was tested by comparing occupancy models (MacKenzie et al., 2006) with and without the impact of sampling methods using the package unmarked 0.12-2 with R. Comparison with the direct observation was conducted using the same approach (separated for filter and precipitation).
Results
All PCR products from the 20 positive controls and from 46 water samples reported only E. orbicularis sequences, confirming that the primers developed for our study were highly specific to this species. We succeeded in amplifying turtle DNA from water samples collected in tubes or on filters with a detection limit of 5 × 10−11 μg/μl.
In artificial ponds with high turtle biomass, the eDNA-based methodology identified the presence of turtles in all five ponds with detection probabilities (the proportion of positive PCRs per water sample) ranging from 83.3% to 100%, whereas visual observations identified the presence of turtles in only one pond. The concentration of DNA detected in water samples ranged from 5.39 × 10−8 to 2.97 × 10−2 μg/μl (table 1).
In natural ponds, the eDNA-based methodology identified the presence of turtles with detection probabilities ranging from 25-100% (based on both sampling methods) in seven of the 15 natural sites. The detection using filter was lower (5 cases) than using the precipitation method (7 cases) and the model including the detection method was better than the model without (AIC = 129.52 compared to 138.83; detection level: 0.405 ± 0.0757 for the filter method and 0.762 ± 0.0657 for the precipitation method). For positive amplification, mean DNA concentrations ranged from 1.68 × 10−7 to 9.87 × 10−4 μg/μl (table 1). No turtle DNA was detected in the other eight sites, even though all of them are known to be inhabited by E. orbicularis (table 1). No correlation was found between the detected DNA concentration in the 5 artificial ponds and number of turtles indicated in table 1 (
Discussion
We successfully detected the presence of a reptile with an eDNA-based methodology in aquatic ecosystems. In artificial ponds, turtles were found with high detection probabilities. In natural ponds, turtles were detected at less than half of the sites, even though all of them are known to be occupied by a high density population. Interestingly, turtle DNA was only detected in shallow waters with aquatic vegetation. Detecting the presence of turtles in these areas may be due to the dilution of DNA in water or may reflect the microhabitat used by the species: shallow waters are warmer and richer in vegetation than deeper waters, and these habitats are known to be used by turtles for feeding and refuging (Fritz, 2003). Pond turtles are easily captured with nets in deep water as they move along the banks, but they do not use deep waters for feeding, basking or refuging. They use shallow waters for their main activities and deep waters just to move between optimal shallow waters. At the 15 natural sites, we found no correlation between the estimated number of turtles per site and DNA concentration. Consequently, Emys orbicularis eDNA probably does not reflect density evaluated by the traditional capture technique: effectiveness of the eDNA method may be influenced by temporal and spatial variability within the sampling area, by non-homogenous mixing or heterogeneity in the distribution of the sample within the water column. Additionally, it may be influenced by variations in the rates of dilution and diffusion in the environment and by variables affecting DNA fragmentation such as temperature, pH or UV-B light (Goldberg et al., 2011; Mahon et al., 2013; Bohmann et al., 2014; Strickler, Fremier and Goldberg, 2015).
In natural ponds, visual observation revealed the presence of turtles in only one site while traditional capture surveys found turtles in all 15 sites. As our eDNA-based methodology detected turtles in less than half of the natural sites, our results contain a high proportion of false negative results. False negatives are frequent in environmental studies and are present in all detection methodologies. When eDNA is fragmented and at low concentration (Darling and Mahon, 2011; Ficetola et al., 2015), or when water samples contain high quantities of environmental particles, the efficiency of filtration and precipitation methods as well as extraction and PCR procedures may be decreased (Volkmann et al., 2007). When eDNA is at low concentration, using filters may solve part of the problem by concentrating eDNA. Unfortunately for water samples in ponds, the amount of phytoplankton and other particles makes the use of such filters complicated (but see Turner et al., 2014). Moreover, statistical methodologies, like site occupancy models, have been developed, to counterbalance the impact of false negatives in studies where no precise information about the presence of a species in a population is available (Schmidt et al., 2013). Based on our results, we suggest that aquatic turtle monitoring using eDNA may be more efficient than surveys based on direct observation, but may be less efficient than traditional net-based surveys. However, the performance of the three methods may vary across the seasons and the period of the day and, thus, may be affected by population densities and site features. In order to counterbalance small DNA quantities released by turtles into the environment, larger water volumes may be collected in ponds. Even if our results demonstrated that filters may be less efficient in ponds, the filtration method may be used to strongly increase the amount of sampled water. However, this relies on the condition that filters may not be rapidly obstructed by phytoplankton and environmental particles.
Aquatic reptiles are likely to be more difficult to detect with an eDNA-based methodology than other higher secreting aquatic taxa such as amphibians and fish, which can be detected even at low density (Ficetola et al., 2008; Jerde et al., 2011; Thomsen et al., 2012b). Lower detection probabilities for turtles than for fish or amphibians may be due to the presence of scutes rather than epithelial cells or mucus, and to their different excretion systems (as urine of fish and amphibians is highly diluted in comparison to that of turtles). Consequently, turtles may harbour lower DNA shedding rates (see discussion in Kelly et al., 2014a). Similar results were found in other low secreting taxa like crayfish and invertebrates, harbouring a chitin envelope rather than epithelial cells (Tréguier et al., 2014; Roussel et al., 2015). Moreover, biomass and surface area are known to be influential factors, as a group of small individuals sheds more genetic material than a single large individual (Foote et al., 2012; Thomsen et al., 2012b; Kelly et al., 2014a). Finally, surveys establishing quantitative relationships between species biomass and relative abundance of detected eDNA (Takahara et al., 2012; Thomsen et al., 2012a; Pilliod et al., 2014) have been applied to taxa other than reptiles and to a reptile species under controlled conditions. However, even if even for fish, the relationship between eDNA and biomass is often very weak (Evans et al., 2016).
To our knowledge, our study is the first to compare two different water sampling methods (filtration and precipitation) for reptile eDNA collection from natural ponds. We found significant differences in their efficiency, as we observed a lower efficiency of filters with the same amount of water. Vörös et al. (2017) demonstrated the opposite result on Proteus anguinus in rivers, but the amount of filtered water was 26 bigger than with the precipitation method. Eichmiller et al. (2016) also demonstrated a higher detection with filters, but again with a higher amount of water. Consequently, filtering higher water volumes may improve the efficiency of the filtration method. On the contrary, large amounts of particles in pond water readily precipitate during the precipitation method and may provide a useful source of eDNA. Consequently, filters may be more appropriate for stream waters (that generally contain a very limited amount of particles) as they allow eDNA to be collected from larger volumes of water more easily than the precipitation method, as also suggested by Vörös et al. (2017). Therefore, we recommend using the precipitation method for ponds and the filtration method for stream waters.
Conclusion
Genetic markers are valuable tools for monitoring biodiversity, detecting and identifying species, and can provide valuable information for the management and conservation of species and ecosystems (Schwartz, Luikart and Waples, 2007; Foote et al., 2012). However, species detection in eDNA-based surveys and traditional monitoring surveys are likely to be imperfect, which can lead to an underestimation of species distribution (Schmidt et al., 2013) and poor management decisions. Surveys based on eDNA require careful attention due to biases in detection. Differential DNA shedding rates and/or preferential amplification of species and the use of microhabitats need to be considered when planning environmental studies and interpreting eDNA results. Despite these potential drawbacks, eDNA-based monitoring surveys are predicted to have a promising role in the future of environmental management (Kelly et al., 2014b). However, this promising tool may be less suitable for low secreting taxa, such as reptiles, than for higher secreting taxa. As a consequence, we argue that eDNA methods should be used to complement, rather than replace, traditional monitoring approaches for aquatic reptiles.
Acknowledgements
We thank the University of Basel for the financial support (Forschungsfonds), Prof. W. Salzburger for giving us the opportunity to work in a turtle-DNA free laboratory, Francesco Ficetola and two anonymous reviewers for their constructive comments.
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Footnotes
Associate Editor: Francesco Ficetola.