thermal stratification biology discussion
Many cold‐water stenotherms, such as Salvelinus namaycush (lake trout), Coregonids and Cottus spp. Brief periods of whole water column mixing occur prior to and after stratification in dimictic lakes during spring and autumn (Wetzel, 2001). bacteria, birds and mammals, which had low sequence abundances, Figure S1). Thermal energy storage systems help to couple thermal energy generation and process demand in cogeneration facilities. Alberdi et al., 2017; Clare, Chain, Littlefair, & Cristescu, 2016; Deiner et al., 2015). The sampling points were distributed at six evenly spaced intervals, but because the lakes were different depths, absolute measurements differ between the lakes. For example, eDNA from cold‐water stenotherms could only be detected in large proportions at the bottom of the lakes during lake stratification (S. namaycush and Cottus cognatus, slimy sculpin). pumps, van Dorn bottles, or the use of a boat to sample at the centre of a lake). During summer, the upper warm layer (epilimnion) is separated from a deep, cold layer of the lake (hypolimnion) by the formation of a thermocline (a temperature‐dependent density gradient) between these layers. Thermal stratification and mixing could affect the vertical gradients of physical and chemical processes in the water body (Chimney et al. Funders did not have a role in study design; collection, analysis, or interpretation of data; writing of the paper; or decision to submit for publication. We chose not to use a correction for the low numbers of sequences which appear in blank samples because PCR amplification dynamics occur differently in samples which have extremely low amounts of template DNA when compared with positive template samples, resulting in compositional shifts of OTUs (Castle et al., 2018; Chandler, Fredrickson, & Brockman, 1997). Here, we tested how seasonal variation in thermal stratification and animal habitat preferences influence the distribution of eDNA in lakes. We validated our results by simultaneously collecting detailed acoustic telemetry data to define fine‐scale habitat preferences of an obligate cold‐water stenothermic fish, S. namaycush. The design of field sampling campaigns provides the foundation on which other methods build, including timing and duration of sampling, location and replication of samples, power of experimental design and even choice of sampling equipment. We sampled eDNA depth profiles of five dimictic lakes during both summer stratification and autumn turnover, each containing warm- and cool-water fishes as well as the cold-water stenotherm, lake trout (Salvelinus namaycush). A number of data logging receivers (VR2W, 69 kHz; Vemco, Innovasea, Bedford, NS) were deployed under water at specific locations in the lake such that the “listening radius” of each receiver (spherical volume ~350 m diameter) overlapped slightly with the other receivers, resulting in maximum coverage of the lake. This primer pair was able to amplify and discriminate between the greatest number of fish species, prioritizing species known to exist in our study area. In the mock community, we made 19/27 correct detections at species level (Tables S4 and S5). Thus, they have different thermal preferences according to their bioenergetic and foraging requirements. Our results reflect those of Handley et al., (2019), who found greater heterogeneity in community composition of samples at three depth points during summer sampling when compared with winter sampling in their study of a single deep lake (1,480 ha, depth of 44 m/64 m in two basins), and that eDNA from a cold‐water stenotherm (S. alpinus) was only detectable in midwater and deep‐water habitats. Environmental DNA (eDNA) is increasingly being used to conduct biodiversity surveys, species occupancy studies, and detect endangered and invasive species (Deiner et al., 2017; Taberlet, Coissac, Pompanon, Brochmann, & Willerslev, 2012). The episodes were caused by rise and decline of solar irradiance reaching the lake surface. A full list of the reduced models that we tested and their AIC scores appears in Table S8. Thermocline depths are also strongly influenced by lake clarity—specifically, the concentration of dissolved organic carbon. Final models were evaluated for overdispersion. The tags randomly emitted signals every 120–300 s (lakes 373, 626 and 239) or every 110–250 s (lakes 223 and 224). Within the affected range (2.5–4.5 °C), the rate of earlier development of strong stratification increased with increasing T 20–80 ( Fig. Between five and ten tagged adults were monitored in each lake during the study period. In total, 336 samples were taken throughout the entire study (24 samples × two lake states × seven lake replicates). All filtrations were completed within eight hours of sample collection. For example, Klobucar, Rodgers, and Budy, (2017) found that surface sampling points had lower Salvelinus alpinus (arctic char) eDNA concentrations than the deeper sampling point during summer stratification in North Alaskan lakes, probably due to limited thermal habitat for the deep‐water species. Of those not detected at species level, four were detected at genus level (i.e. Lake Stratification and Mixing Many of our Illinois lakes and reservoirs are deep enough to stratify, or form "layers" of water with different temperatures. Point 1 is the shallowest measurement near the surface of the lake. Other taxonomic groups appeared at very low frequencies when our ASVs were matched against the NCBI database, such as bacterial, mammalian and bird taxa, but as they were not the focus of our study they were excluded. There are 14 species of fish across all the study lakes (mean 8, range 6–10 species per lake, Table S2). An important seasonal feature of many temperate lakes is stratification, where isolated layers of water are formed. We implemented negative binomial mixed effects models with lake identity as a random effect in glmmTMB (Brooks et al., 2017), again using the total library size (DNA sequence counts for each sample) as a log offset in the model. the last common ancestor algorithm assigned a match of the correct genus with no species name), two were detected at family level (i.e. Since many of the variables, including ceiling height, people and processes, solar gain, and outside weather conditions cannot be controlled, the most common technologies used are related to the building's HVAC (heating, ventilation, and air conditioning) system. This is an important finding for the design of eDNA sampling studies, given that our study lakes are some of the smallest capable of supporting S. namaycush habitat. Despite this, eDNA studies often involve the collection of surface samples only, without considering the important seasonal forces which shape thermal stratification and the distinct thermal preferences of fish occupying these ecosystems. Nonbiological nucleotides were removed (primers, indices and adapters) using cutadapt (Martin, 2011). NOTE: Your email address is requested solely to identify you as the sender of this article. As a result, thermal stratification can be established during the warm season if a There were also five other models within two AIC counts of the top ranked model, which could be considered as having equal explanatory power (all models are listed in Table S10). Scripts to process bioinformatic data are available from https://github.com/CristescuLab/YAAP. The laboratory and equipment were thoroughly cleaned with 10% bleach before and after use (e.g. The filters were immediately stored in screw‐cap tubes at −20°C and then shipped on dry ice to McGill University, Montréal for molecular analysis. Our study was designed to test the influences of lake stratification and mixing on eDNA distribution within the framework of a replicated, whole‐lake experimental design. Stratification is a desirable strategy to provide efficient room air conditioning with much less effort than using the piston strategy. The full pipeline is available from https://github.com/CristescuLab/YAAP. We implemented negative binomial mixed effects models with lake identity as a random effect in glmmTMB (Brooks et al., 2017), using the total library size (DNA sequence counts for each sample) as a log offset in the model (Zurr, Ieno, Walker, Saveliev, & Smith, 2009). Similarly, there was a slight decrease in the sequences of minnow and perch species at deeper depths in the water column (Perca flavescens (yellow perch), M. margarita, P. promelas), but minnows could still be detected at the deepest depths in greater proportions than during stratification. Sampling dates were chosen based on decades‐long records of the timing of seasonal stratification and turnover (mixing) in these lakes. Early eDNA studies examined the effects of single environmental factors on shedding and degradation in controlled environments such as aquaria or mesocosms, either with or without organisms present (Andruszkiewicz, Sassoubre, & Boehm, 2017; Klymus, Richter, Chapman, & Paukert, 2015; Lance et al., 2017; Mächler, Osathanunkul, & Altermatt, 2018). Yet, this knowledge is essential for adequate survey design and correct interpretation of results as we move into the genomic era of assessing eukaryotic biodiversity (Bohmann et al., 2014). During fieldwork, we used a dedicated boat and separate tubing for each lake to prevent between‐lake transfer of DNA. Xiangxi River is a typical tributary of Three Gorges Reservoir (TGR) in China. We sampled eDNA depth profiles of five dimictic lakes during both summer stratification and autumn turnover, each containing warm‐ and cool‐water fishes as well as the cold‐water stenotherm, lake trout (Salvelinus namaycush). Many seed species have an embryonic dormancy phase, and generally will not sprout until this dormancy is broken. After removing adapters, discarding low‐quality sequences, merging paired‐end sequences and length filtering, we retained 76,734 ± 5,954 sequences per sample. One negative control of 500 ml distilled water was stored in the cooler and filtered in the same way as the field samples for each lake. 85 A short-term study of vertical and horizontal distribution of zooplankton during thermal stratification in Lake Kinneret, Israel? These findings contribute to our overall understanding of the “ecology” of eDNA within lake ecosystems, illustrating how the strong interaction between seasonal thermal structure in lakes and thermal niches of species on very localised spatial scales influences our ability to detect species.
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