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132 results for “environmental detection”
Detecting local variations across metazoan communities in backreef depressions of Reunion Island (Mascarene Archipelago) through environmental DNA survey
<p>The back-reef depressions, or lagoons, of Reunion Island (western Indian Ocean) host a high abundance of organisms living amongst the coral reefs and are critical sites for artisanal fishing, tourism, and shoreline stability for the island. Over time, increasing degradation of Reunionese reefs has been observed due to overexploitation, beach erosion and eutrophication. Efforts to mitigate the impact of these pressures on aquatic organisms include biodiversity surveys primarily performed through visual censuses that can be logistically complex and may unintentionally overlook organisms. Surveys integrating environmental DNA (eDNA) collections have provided rapid biodiversity assessments, while helping to circumvent some limitations of visual surveys. The present study describes the results of an exploratory eDNA survey, which aims to characterize metazoan communities of four Reunionese lagoons located along the west coast of the island. As eDNA surveys first require deliberate study design and optimization for each new context, we sought to establish a modernized workflow implementing specialized equipment to collect and preserve samples to facilitate future studies in these lagoons. During the austral summer of 2023, samples were pumped directly from surface and bottom depths at each site through self-preserving filters which were then processed for DNA metabarcoding using regions of the 12S ribosomal RNA (12S), small ribosomal subunit 18S (18S) and Cytochrome Oxidase I (COI) genes. The survey detected high species richness that varied by site, and in a single collection period, recovered the presence of 60 teleost families and numerous invertebrate taxa, including members of the coral faunal community that are less studied in Reunion. Distinct biological communities were observed at each site, and within a single lagoon, suggesting that these differences are due to site-specific factors (e.g., environmental variables, geographic distance, etc.). Although continued protocol optimization is needed, the present findings demonstrate the successful application of an eDNA-based survey for biodiversity assessment within Reunionese lagoons.</p>
Detection of Submicron- and Nanoplastics Spiked in Environmental Fresh- and Saltwater with Raman Spectroscopy
<p>ABSTRACT</p> <p>Detection of small plastic particles in environmental water samples has been a topic of increasing interest in recent years. A multitude of techniques, such as variants of Raman spectroscopy, have been employed to facilitate their analysis in such complex sample matrices. However, these studies are often conducted for a limited number of plastic types in matrices with relatively little additional materials. Thus, much remains unknown about what parameters influence the detection limits of Raman spectroscopy for more environmentally relevant samples. To address this, this study utilizes Raman spectroscopy to detect six plastic particle types; 161 and 33 nm polystyrene, < 450 nm and 36 nm poly(ethylene terephthalate), 121 nm polypropylene, and 126 nm polyethylene; spiked into artificial saltwater, artificial freshwater, North Sea, Thames River, and Elbe River water. Overall, factors such as plastic particle properties, water matrix composition, and experimental setup were shown to influence the final limits of detection.</p>
Detecting small environmental differences: Risk-response curves for predator-induced behavior and morphology. 2008.
Most organisms possess traits that are sensitive to changes in the environment (i.e. plastic traits) which results in the expression of environmentally-induced polymorphisms. While most phenotypically plastic traits have traditionally been treated as threshold switches between induced and uninduced states, there is growing evidence that many traits can respond in a continuous fashion. In this experiment we exposed larval anurans (wood frog tadpoles, Rana sylvatica) to an increasing gradient of predation risk to determine how organisms respond to small environmental changes. We manipulated predation risk in two ways: by altering the amount of prey consumed by a constant number of predators (Dytiscus sp.) and by altering the number of predators that consume a constant amount of prey. We then quantified the expression of predator-induced behavior, morphology, and mass to determine the level of risk that induced each trait, the level of risk that induced the maximal phenotypic response for each trait, whether the different traits exhibited a plateauing response, and whether increasing risk via increasing predator number or via increasing prey consumption induced similar phenotypic changes. We found that all of the traits exhibited fine-tuned, graded responses and most of them exhibited a plateauing response with increased predation risk, suggesting either a limit to plasticity or the reflection of high costs of the defensive phenotype. For many traits, a large proportion of the maximum induction occurred at low levels of risk, suggesting that the chemical cues of predation are effective at extremely low concentrations. In contrast to earlier work, we found that behavioral and morphological responses to increased predator number were simply a response to increased total prey consumption. These results have important implications for models of plasticity evolution, models of optimal phenotypic design, expectations for how organisms respond to fine-grained changes (i.e. wi
Site environmental, climate, water levels and temperatures, vegetation cover, and GIS change detection for assessing permafrost change in fens on the Tanana Flats, central Alaska
This data package provides data used to assess the roles of climate extremes, ecological succession, and hydrology in repeated permafrost aggradation and degradation in fens on the Tanana Flats, central Alaska. The package provides data on site environmental information, Fairbanks climate, vegetation cover, water levels and temperatures, as well as GIS files for fen change detection. The Site data include information on observers, locations, geomorphology, hydrology, soils, vegetation, and disturbance. The table has numerous fields that uses coding for class characteristics and these codes are described in the metadata as well as compiled in the ELS_Arctic_Boreal_Site_Soil_Veg_Code_Sheet_2020.docx. Alaska Climate records for Fairbanks (UAF Experiment Station) from 1904 to 2019 were acquired from the National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/cdo-web/). Additional data were obtained for the Nenana station (about 70 km southwest of Fairbanks), to fill in small data gaps (particularly precipitation/snow depth ruler measurements) in the Fairbanks record. We attributed the data with fields for summer (May-September) and winter periods (November-March) and hydrologic year (October-September) and calculated mean air temperature, precipitation, and snow depth by seasonal period (average of daily values) and year. The broad summer and winter periods were of interest because warmer and wetter summers increase soil heat input and warmer and snowier winters reduce soil heat loss. Fen hydrology data include information on fen water level/pressure and temperatures collected every two hours at seven sites within fens from 2011 to 2014. Vegetation composition and cover of fens, scrub, and forests was sampled to assess effects of thermokarst on vegetation change. Plant cover was determined by point-sampling at 100 points (including repetitive “hits” for all layers) distributed along 5 equally spaced rows (4-m long, 20 points per row) across the 10-m l
Data From: Powerful detection of polygenic selection and environmental adaptation in US beef cattle
<p>GEMMA output containing summary statistics for generation proxy selection mapping (GPSM) and environmental GWAS (envGWAS) selection analyses from <br> Rowan et al. "Powerful detection of polygenic selection and environmental adaptation in US beef cattle" 2021<br> https://doi.org/10.1101/2020.03.11.988121 </p> <p>File names identify the analysis run, for example<br> "Gelbvieh_envgwas_desert_summary_stats.txt.gz"<br> Is the Gelbvieh dataset analyzed using the Desert ecoregion as the dependent variable <br> in a univariate envGWAS model. </p> <p>Files are formated according to GEMMA output.</p>
Odontocete detections and corresponding values of environmental variables in the Hawaiian Archipelago
<p>This dataset contains detections of echolocation clicks at two sites in the Hawaiian Archipelago. These sites are Hawaii and Manawai (also known as Pearl and Hermes Reef). Echolocation clicks have been labeled using a neural network classifier that was trained and tested on data from the Hawaiian Islands and can successfully identify many species of regionally present odontocetes. During the labeling process, clicks were grouped into five-minute bins and each bin was given a class label. The data provided here is further binned at a daily level, where counts of a given class represent the number of five-minute bins within a given day that were labeled as that class. One file is provided per site, and files are in .csv format that can be read using any desired coding language. </p> <p>In addition to acoustic counts, values for environmental variables considered in the corresponding manuscript are provided in the CSV files. The final file in this dataset contains satellite-derived chlorophyll-a concentration values from NASA MODIS for the Hawaiian region (used to create Supplementary Fig. 1). Details on all variables and how they were accessed can be found in the manuscript and in the README file accompanying this dataset. </p>
Fig. 4 in First report of successful Naegleria detection from environmental resources of some selected areas of Rawlakot, Azad Jammu and Kashmir, Pakistan
Fig. 4. (A) Primary sequence alignment was obtained with reference sequences already available in Gen Bank. Neighbour joining phylogenetic relationship between the partial sequences of 18S rRNA of Naegleria from isolates obtained in this study and reference sequences present in Gen Bank. (B). Neighbour joining phylogenetic relationship between the partial sequences of 18S rRNA of Naegleria from isolates obtained in this study and reference sequences present in Gen Bank. The tree was generated in CLC Main Workbench version 6.6.2 using 1000 bootstrap replications. Branch length is proportional to the calculated genetic distance (scale shown).
Fig. 3 in First report of successful Naegleria detection from environmental resources of some selected areas of Rawlakot, Azad Jammu and Kashmir, Pakistan
Fig. 3. To confirm the incidence of Naegleria populations in water and soil samples, DNA were extracted from amoebae retrieved from NNA plates with 2 weeks time and utilized for PCR examination as demonstrated in methods section. PCR products were obtained in all DNA samples verifying the existence of Naegleria. Lane 1: 250 bp DNA ladder; Lane 2: RAW STW4; Lane 3: RAW LW1; Lane 4: RAW PW8; Lane 5: RAW DS1; Lane 6: RAW STW7; Lane 7: RAW TW3; Lane 8: RAW DS2; Lane 9: RAW DS3; Lane 10: RAW DS4; Lane 11: RAW DS5; Lane 12: RAW DS6; Lane 13: RAW DS7; Lane 14: RAW DS8; Lane 15: RAW DS9; Lane 16: RAW DS10; Lane 17: +ve control; Lane 18: -ve control.
Fig. 2 in First report of successful Naegleria detection from environmental resources of some selected areas of Rawlakot, Azad Jammu and Kashmir, Pakistan
Fig. 2. Naegleria cysts detection on NNA under inverted microscope (×400). Water and soil samples were filtered and pored respectively and inoculated on NNA plate seeded with E. coli as demonstrated in methods section. Plates were monitored for amoebic outgrowth up to two weeks, and images were taken. Only representative samples of water (a) RAW STW7 and soil (b) RAW DS7 are shown here.
Figure 2 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
Figure 2. Mean posterior estimates of the probability of capturing grass carp eDNA from a site in a sample among sites (θ) from the model with the lowest WAIC score [ψ(Site)Θ(Site)p(.)]. Error bars represent 95% credible intervals. DR = Detroit River, HP = Hot Ponds, MB = Maumee Bay. All sites are located in western Lake Erie.
Figure 1 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
Figure 1. Map denoting all monthly grass carp eDNA sampling events in 2018 (A–C) and 2019 (D–F) aggregated at each sampling location (Hot Ponds, Detroit River, and North Maumee Bay) and acoustic receiver locations (grey circles) in the western basin of Lake Erie. Positive and negative eDNA detections, defined as at least one positive qPCR detection on one replicate among all markers (GCTM10, GCTM22, GCTM32) are denoted by orange crosses and pink triangles, respectively. The 3 grass carp captured from conventional gear (total sampling events = 451) in the Detroit River (October 2018), Hot Pond (July 2019) and North Maumee Bay (July 2019) are denoted by a yellow hexagon.
Figure 2 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)
Figure 2. The Rapid Environmental (e)DNA Assessment and Deployment Initiative & Network (READI-Net) project components being developed to support molecular detection of invasive species. Molecular tools, like eDNA sampling, are sensitive and costeffective for early detection of invasive species and are one component of the National Early Detection Rapid Response framework.
Figure 1 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)
Figure 1. The National Early Detection Rapid Response framework developed by the U.S. Department of the Interior Invasive Species Task Force. Open circles represent the components to be enacted for effective early detection and rapid response of invasive species. The associated commentary reflects the primary questions, observations, and directives that the process from one component to the next. At the core of the process, are the informational inputs necessary for management decision-making.
Figure 4 in Limitations of allometry, morphometry, and fluctuating asymmetry in detecting environmental stress caused by lead soil contamination in aphids under field conditions
Figure 4 Fluctuating asymmetry (mean and error deviation) observed in the antenna and tibia of Brevicoryne brassicae in the presence (Lead (Pb)) and absence (Control) of lead.
Figure 3 in Limitations of allometry, morphometry, and fluctuating asymmetry in detecting environmental stress caused by lead soil contamination in aphids under field conditions
Figure 3 Negative allometry represented by the allometric coefficients of both the antenna and tibia and their confidence intervals; the values are related to the body length ofBrevicoryne brassicae in the presence (Lead (Pb)) and absence (Control) of lead.
Figure 1 in Limitations of allometry, morphometry, and fluctuating asymmetry in detecting environmental stress caused by lead soil contamination in aphids under field conditions
Figure 1 Brevicoryne brassicae placed in a dorsal-ventral position for structure measurement. (a): Total body length (b): antenomer length (c): length of the posterior tibia. Source: the authors.
Figure 2 in Limitations of allometry, morphometry, and fluctuating asymmetry in detecting environmental stress caused by lead soil contamination in aphids under field conditions
Figure 2 Mean length and standard error of the antenna, tibia and body length of Brevicoryne brassicae in the presence (Lead (Pb)) and absence (Control) of lead.
Environmental DNA-based detection of Batrachochytrium salamandrivorans in captive settings
<p>Detecting pathogens in the live animal trade is critical for tracking and preventing their movement, introduction, and spillover into susceptible fauna. However, the scale of the live animal trade makes individually testing animals infeasible for all but the most economically important taxa. For instance, while the fungal pathogen, <em>Batrachochytrium salamandrivorans</em> (<em>Bsal</em>), threatens amphibian, particularly caudate diversity, in Europe and the Americas, screening even a fraction of the millions of live amphibians imported into the United States, alone, is impractically laborious and expensive. A promising alternative to individual-level sampling (e.g., swabbing the skin of salamanders) is to instead collect DNA from the animals' environment (e.g., housing container or water) which allows us to screen a whole group of animals at a time. </p> <p>We used a series of experiments with <em>Bsal</em>-spiked water and substrates and experimentally infected rough-skinned newts (<em>Taricha granulosa</em>) to determine how best to collect <em>Bsal</em> environmental DNA (eDNA) samples, that is, which methods yield the greatest recovery of <em>Bsal</em> eDNA, and evaluate the capacity of these methods to detect <em>Bsal</em>-infected animals in conditions that might be found in captive settings and trade.</p> <p>We found that filtering water housing infected animals for even an hour can consistently recover detectable levels of <em>Bsal</em> eDNA, that there is little evidence of <em>Bsal </em>eDNA being clumped in housing containers or being swamped or inhibited under realistically dirty conditions, and that eDNA-based methods achieves an equivalent or higher chance of detecting <em>Bsal</em> infections in a group of co-housed newts with fewer samples than traditional methods of individually swabbing.</p> <p>By sampling the genetic materials shed or produced by a whole group of animals, eDNA-based methods are a powerful means of detecting pathogens, such as <em>Bsal,</em> in shipment and captive population. These methods bring routine pathogen surveillance into reach in many more contexts and can thus be an important tool in conservation and disease control.</p>
Odontocete detections and corresponding values of environmental variables in the Hawaiian Archipelago
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How, what, and where you sample environmental DNA affects diversity estimates and species detection
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