Traditional Survey Methods Research Articles

Aquatic insect community structure revealed by eDNA metabarcoding derives indices for environmental assessment.

Environmental DNA (eDNA) analysis provides an efficient and objective approach for monitoring and assessing ecological status; however, studies on the eDNA of aquatic insects, such as Ephemeroptera, Plecoptera, and Trichoptera (EPT), are limited despite its potential as a useful indicator of river health. Here, we investigated the community structures of aquatic insects using eDNA and evaluated the applicability of eDNA data for calculating assessment indices. Field surveys were conducted to sample river water for eDNA at six locations from upstream to downstream of two rivers in Japan in July and November 2016. Simultaneously, aquatic insects were collected using the traditional Surber net survey method. The communities of aquatic insects were revealed using eDNA by targeting the cytochrome oxidase subunit I gene in mitochondrial DNA via metabarcoding analyses. As a result, the eDNA revealed 63 families and 75 genera of aquatic insects, which was double than that detected by the Surber net survey (especially for families in Diptera and Hemiptera). The seasonal differences of communities were distinguished by both the eDNA and Surber net survey data. Furthermore, the total nitrogen concentration, a surrogate of organic pollution, showed positive correlations with biotic environmental assessment indices (i.e., EPT index and Chironomidae index) calculated using eDNA at the genus-level resolution but the indices calculated using the Surber net survey data. Our results demonstrated that eDNA analysis with higher taxonomic resolution can provide as a more sensitive environmental assessment index than the traditional method that requires biotic samples.

In Situ Block Size Distribution Aimed at the Choice of the Design Block for Rockfall Barriers Design: A Case Study along Gardesana Road

When studying rockfall phenomena, a single value of the block volume is not sufficient to take into account the natural variability of the geometrical features (orientation, spacing, persistence) of the discontinuity sets. Different approaches for obtaining cumulative distributions of potentially detachable block volumes are compared. A highly fractured rock mass outcropping along the western Lake Garda (Italy), consisting of prevailing limestone and interbedded marls, is studied in detail from geological and geostructural points of view. Then, a representative rock face has been selected and analyzed with traditional and non-contact survey methods to identify the main discontinuity sets and to collect spacing samples. Based on these data, in situ block size distributions for different combinations of sets are built following statistically-based approaches, without the use of a Discrete Fracture Network (DFN) generator. The validation of the obtained distributions is attempted based on the detached block surveyed at the foot of the slope. However, in this particular case study, the detached blocks cover only a minimal volume range compared to both theoretical values and visible rockfall scars. The fallen rock blocks have a marginal role in design block determination, since their volume depends on geological discontinuities (bedding and fractures) and could be affected by other processes after the detachment (e.g., fragmentation). The procedure here described should be standard practice in the study of rockfall events, and it should be uniform in European standards such as Eurocodes. Future developments should involve the scientific community for setting the percentiles of the probability distribution to be considered for block design definition.

Estimating numerical scale ratings from text-based service reviews

PurposeThis paper develops a generalizable, machine-learning-based method for measuring established marketing constructs using passive analysis of consumer-generated textual data from service reviews. The method is demonstrated using topic and sentiment analysis along dimensions of an existing scale: lodging quality index (LQI).Design/methodology/approachThe method induces numerical scale ratings from text-based data such as consumer reviews. This is accomplished by automatically developing a dictionary from words within a set of existing scale items, rather a more manual process. This dictionary is used to analyze textual consumer review data, inducing topic and sentiment along various dimensions. Data produced is equivalent with Likert scores.FindingsPairedt-tests reveal that the text analysis technique the authors develop produces data that is equivalent to Likert data from the same individual. Results from the authors’ second study apply the method to real-world consumer hotel reviews.Practical implicationsResults demonstrate a novel means of using natural language processing in a way to complement or replace traditional survey methods. The approach the authors outline unlocks the ability to rapidly and efficiently analyze text in terms of any existing scale without the need to first manually develop a dictionary.Originality/valueThe technique makes a methodological contribution by outlining a new means of generating scale-equivalent data from text alone. The method has the potential to both unlock entirely new sources of data and potentially change how service satisfaction is assessed and opens the door for analysis of text in terms of a wider range of constructs.

Using Citizen Science to Collect Coastal Monitoring Data

Hart, J.D. and Blenkinsopp, C.E., 2020. Using citizen science to collect useful coastal data. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 824–828. Coconut Creek (Florida), ISSN 0749-0208.Coastal monitoring is becoming increasingly important as coastal hazard risks increase due to factors such as climate change. Traditional survey methods are often expensive and require technical skills and special equipment which restricts the amount of data that can reasonably be collected. Results from two citizen science projects are presented to assess what data can be extracted from imagery collected by the public. Schemes which incorporate members of the public in the data collection phase of a project offer the opportunity to engage local groups/communities with important coastal issues, while collecting valuable scientific data which can be used by coastal managers to assess the vulnerability of the coast to coastal hazards.

A Methodology to Assess the Morphological Change of a Multilevel Beach Cusp System and their Hydrodynamics: Case Study of Long Strand, Ireland

Nuyts, S.; Murphy, J.; Li, Z., and Hickey, K., 2020. A methodology to assess the morphological change of a multilevel beach cusp system and their hydrodynamics: Case study of Long Strand, Ireland. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 593-598. Coconut Creek (Florida), ISSN 0749-0208.Characterising the behaviour of highly dynamic and small-scale coastal features is challenging, for lack of a non-intrusive and cost-effective survey methodology that can map coastal features with high spatial and temporal resolution. Unmanned Aerial Vehicles (UAV) are already widely used in coastal research but to date, they have primarily been used as an auxiliary approach for traditional survey methods (e.g. beach profiles). In this study, a methodology is proposed to fully capture the dynamic behaviour of a multilevel beach cusp system. The aim of the research is to establish a method that links the morphological change of these rhythmic coastal features with detailed information of the hydrodynamics in the study area. This is achieved with a UAV system in combination with MIKE 21 Spectral Waves FM Model. Long Strand, Co. Cork in Ireland is used as a case study to evaluate the proposed methodology. Results show that the proposed approach allows to accurately determine beach cusp parameters (i.e. spacing, amplitude, elevation, and depth) and link their high spatial topographical data with the hydrodynamic forces at any given location. Using this survey method in future, coastal research will allow for a better understanding of complex dynamic coastal features and their behaviour over time.

Comparison of Canopy Closure Estimation of Plantations Using Parametric, Semi-Parametric, and Non-Parametric Models Based on GF-1 Remote Sensing Images

Canopy closure (CC) is an important parameter in forest ecosystems and has diverse applications in a wide variety of fields. Canopy closure estimation models, using a combination of measured data and remote sensing data, can largely replace traditional survey methods for CC. However, it is difficult to estimate the forest CC based on high spatial resolution remote sensing images. This study used China Gaofen-1 satellite (GF-1) images, and selected China’s north temperate Wangyedian Forest Farm (WYD) and subtropical Gaofeng Forest Farm (GF) as experimental areas. A parametric model (multiple linear regression (MLR)), non-parametric model (random forest (RF)), and semi-parametric model (generalized additive model (GAM)) were developed. The ability of the three models to estimate the CC of plantations based on high spatial resolution remote sensing GF-1 images and their performance in the two experimental areas was analyzed and compared. The results showed that the decision coefficient (R2), root mean square error (RMSE), and relative root mean square error (rRMSE) values of the parametric model (MLR), semi-parametric model (GAM), and non-parametric model (RF) for the WYD forest ranged from 0.45 to 0.69, 0.0632 to 0.0953, and 9.98% to 15.05%, respectively, and in the GF forest the R2, RMSE, and rRMSE values ranged from 0.40 to 0.59, 0.0967 to 0.1152, and 16.73% to 19.93%, respectively. The best model in the two study areas was the GAM and the worst was the RF. The accuracy of the three models established in the WYD was higher than that in the GF area. The RMSE and rRMSE values for the MLR, GAM, and RF established using high spatial resolution GF-1 remote sensing images in the two test areas were within the scope of existing studies, indicating the three CC estimation models achieved satisfactory results.

Distribution of the acoustic occurrence of dolphins during the summers 2011 to 2015 in the Upper Gulf of California, Mexico.

Baseline knowledge of spatial and temporal distribution patterns is essential for cetacean management and conservation. Such knowledge is particularly important in areas where gillnet fishing occurs, as the Upper Gulf of California, which increases the probability of bycatch of cetaceans. In this area, the vaquita porpoise (Phocoena sinus) has been widely studied, but the knowledge of other cetaceans is scarce and based on traditional visual survey methods. We used data collected by an array of acoustic click detectors (C-PODs) during the summers 2011 to 2015 to analyze the distribution of dolphins in the Vaquita Refuge in the Upper Gulf of California. We recorded 120,038 echolocation click trains of dolphins during 12,371 days of recording effort at 46 sampling sites. Based on simultaneous visual and acoustic data, we estimated a false positive acoustic detection rate of 19.4%. Dolphin acoustic activity varied among sites, with higher activity in the east of the Vaquita Refuge. Acoustic activity was higher at night than during the day. We used negative binomial generalized linear models to study the count of clicks of dolphins in relation to spatial, temporal, physical, biological and anthropogenic explanatory variables. The best model selected for the response variable included sampling site, day-night condition, and vertical component of tide speed. Patterns in the spatial distribution of predicted acoustic activity of dolphins were similar to the acoustic activity observed per sampling season. Higher acoustic activity was predicted at night, but the tide speed variable was not relevant under this condition. Acoustic activity patterns could be related to the availability of prey resources since echolocation click trains are associated with foraging activities of dolphins. This is the first study of the distribution of dolphins in Mexico using medium-term systematic passive acoustic monitoring, and the results can contribute to better management to the natural protected area located in the Upper Gulf of California.

UAV-mounted hyperspectral mapping of intertidal macroalgae

Intertidal macroalgal communities mark the boundary of the marine realm and are faced with many direct and indirect anthropogenic pressures. The effective and sustainable management of these resources must be underpinned by accurate, efficient and cost-effective environmental data collection. Traditional field survey methods, whilst accurate, are time-consuming and limited in the area that can be covered. Remote sensing permits large areas to be rapidly surveyed but the effectiveness of satellites and aircraft for mapping fine-scale intertidal macroalgal mapping is limited by their coarse spatial resolution and restricted operational flexibility. The rapid development of unoccupied aerial vehicle (UAV) and sensor technology can address these issues and provide a potential alternative to established remote sensing platforms. Here, a detailed methodology is presented for the assessment of the commercially and ecologically important intertidal brown macroalga Ascophyllum nodosum using a multirotor UAV and pushbroom hyperspectral sensor. Two different classifiers, Maximum Likelihood Classifier (MLC) and Spectral Angle Mapper (SAM), were compared along with two different sources of spectral profiles, one collected in-situ with a spectral radiometer and the other derived from hyperspectral imagery. Of the classifiers compared, both trained using image-derived spectra, MLC more accurately classified A. nodosum, and other common intertidal species and substratum (Overall Accuracy (OA) 94.7%) than SAM (OA 81.1%). In addition, SAM, trained using in-situ spectra, was the least accurate of the three classifier workflows used (OA 71.4%). The low accuracy of the spectral radiometer approach was likely due to high levels of noise present in the hyperspectral data, a result of the relative instability of the UAV platform causing vibration. The accurate mapping of non-target species also highlights the applicability of this methodology for a broader range of intertidal macroalgal species and communities. This research clearly demonstrates the potential of UAV-mounted hyperspectral remote sensing for mapping the spatially and spectral complex macroalgal habitats found within the intertidal zone.

The Use of the Net Promoter Score (NPS) in an Outpatient Allergy and Pulmonary Clinic: An Innovative Look into Using Tablet-Based Tool vs Traditional Survey Method.

IntroductionPatient satisfaction has become an essential metric in addition to the type of care they receive. Phone calls, emails, and text to patients after their healthcare visit are the typical way of obtaining the data reflecting patient satisfaction. The purpose of this retrospective quality improvement study is to compare the traditional post-outpatient clinic survey method with an onsite concise two-question survey using a tablet method immediately after the patient visit using Net Promoter Score (NPS) questions.MethodsData were collected retrospectively from February to August 2018 from an outpatient subspecialty clinic in southern California using an existing database from two different sources: the traditional method (TM) and the tablet-based tool (TBT), using NPS. The TM data were obtained from a third-party company using two questions via phone, email, and text collected 2–4 weeks after the patient’s visit. The TBT has only two questions that were given to patients upon their visit check-out. These two questions assessed both provider and clinic’s performance using the NPS method.ResultsIn total, there were 1708 patients seen from February to August 2018. In the TM, the total outgoing messages during this period were 580 (34.0%) with 156 responses (27%). In the TBT, 648 out of 1708 (37.9%) surveys were collected with a 100% response rate. The NPS score showed that 99.2% of the providers were promoters. The NPS score for the clinic was 96% which reflects a promoter score.ConclusionOur results indicate that when using the TBT immediately after their visit to the clinic, a higher response rate was noted. In addition, both methods had similar outcomes in terms of patient satisfaction NPS scores. Future prospective studies with a larger sample size are warranted to evaluate the effectiveness of the TBT tool in assessing patient satisfaction.

Application of airborne lidar technology in transmission line survey

In the survey of transmission line, the traditional aerial survey method is difficult to get the surface elevation through the vegetation, corn field and other surface attachments, while the manual survey method has the disadvantages of low efficiency and high cost in mountainous and hilly areas. In this paper, the airborne lidar surveying and mapping technology is used, combined with a 110kV transmission line engineering survey example, generating high-precision surface DEM based on the point cloud classification results and extracting the single wood information based on the point cloud to accurately count the amount of tree felling. A panoramic simulation line corridor engineering geographic information system is established, which provides technical support for the route scheme optimization and plane section extraction, and is applied to the project. It is of practical significance to evaluate the effect.

A comparison of supervised machine learning algorithms for mosquito identification from backscattered optical signals

The surveillance of mosquito populations is paramount in the fight against mosquito-borne diseases that affect millions of people every year. Evaluating the efficiency of mitigation methods requires extensive and long-term surveys which can be costly and time consuming. The recent development of optical sensors give access to alternative methods for entomological monitoring but require efficient classification algorithms to be successful. In this contribution, supervised machine learning algorithms such as Linear Discriminant Analysis, Decision Trees, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes are compared for the identification of mosquitoes through optical signals. Based on predictor variables derived from the wing beat frequency and optical cross section of mosquitoes, these algorithms were trained to perform different classification tasks: the identification of species, sex and/or gravidity of mosquitoes present in New Jersey, USA. Results shows that the most polyvalent machine learning algorithm for mosquito identification is Support Vector Machine that performs on average over all tasks between 0.65 and 7.3% better than other algorithms. Moreover, Support Vector Machine is the algorithm that best performed for the most complex tasks, more than 2% above the second best, it is therefore the most suited for real-world study where several species of mosquito can be expected at a single location. A close second is Linear Discriminant Analysis that is only outperformed by Support Vector Machine by 0.65% over all tasks and is the most performant when studying mosquito gravidity. Finally, Decision Trees algorithm has reached almost perfection in identifying the sex of a single mosquito species with 99.9% accuracy which is 1.3% more than the second best performing algorithm on this task. These results demonstrate that optical sensors, coupled with machine learning, can be a viable alternative or complementary methodology for the monitoring of mosquito populations. Furthermore, this methodology can perform non-intrusive, automatic, time-resolved measurements of insect population dynamics over extended periods of time without the need for laboratory analysis of captured specimens as in most traditional survey methods.

Environmental DNA Sampling Informs Fish Eradication Efforts: Case Studies and Lessons Learned

AbstractWorldwide, freshwater ecosystems are threatened by invasive species, resulting in adverse effects on infrastructure, economy, recreation, and native aquatic communities. In stream settings, chemical piscicides can be an effective tool for eradicating invasive fishes. However, chemical treatments are expensive and time consuming, and they do not discriminate between invasive and native species in a system. Therefore, managers would ideally limit treatment to only the area occupied by the invasive species. Because traditional survey methods may not accurately detect individuals in low abundance (e.g., at the edge of their distribution, or following an eradication effort), chemical treatments may be applied more broadly and more often than is necessary to ensure complete coverage. Furthermore, inadequate posttreatment sampling can fail to detect survivors of a treatment. As a result, managers may erroneously conclude that eradication was successful, leaving the ecosystem vulnerable to reestablishment by the invader. More sensitive sampling tools should allow for more precise definition of the treatment area and more accurate evaluation of project success. This would reduce project costs and overall effects on native species. Here, we illustrate how environmental DNA (eDNA) sampling addressed these challenges through three case studies, each of which used eDNA sampling to inform the removal of Brook Trout Salvelinus fontinalis in small streams. We found that eDNA methods can be informative throughout all stages of eradication projects in stream settings. It can assist with delimiting the population prior to treatment, provide detailed location data on surviving target individuals, and serve as an efficient and relatively inexpensive monitoring tool to assess long‐term treatment efficacy. When combined with traditional survey tools, such as electrofishing, eDNA sampling may help reduce the size and number of treatments that are necessary to reach project goals. This translates directly to increased efficacy of treatments, reduced labor and cost, and reduced adverse effects on the native community.

Environmental DNA and Specific Primers for Detecting the Invasive Species Ectopleura crocea (Hydrozoa: Anthoathecata) in Seawater Samples

In marine environments, environmental DNA (eDNA) can be effectively detected and possibly quantified when combined with molecular techniques, as demonstrated by several recent studies. In this study, we developed a species-specific primer set and a probe to detect the distribution and biomass of an invasive hydrozoan in South Korea, Ectopleura crocea. These molecular markers were designed to amplify a 187 bp region based on mitochondrial cytochrome c oxidase subunit I (COI) of E. crocea and were tested on seawater samples from 35 Korean harbors in 2017. Of the 35 sites we investigated, only nine harbors returned positive detections when using traditional survey methods, while surveys based on the use of eDNA techniques detected E. crocea DNA in all seawater samples. These results suggest that eDNA surveys based on molecular techniques are more effective at identifying species distribution and estimating biomass than traditional surveys based on visual assessment of morphology.

Surveying frogs from the bellies of their parasites: Invertebrate-derived DNA as a novel survey method for frogs

Biodiversity conservation is reliant on thorough species occurrence data. However, the collection of such data is often difficult for species with poor detectability. Recently, invertebrate-derived DNA (iDNA) has emerged as a potentially powerful survey method to increase the detectability of terrestrial vertebrates. Parasitic invertebrates are effective samplers of some vertebrate diversity and can be used to detect and identify their host species by sequencing the DNA contained in their meals. Among taxa that can be very difficult to detect using traditional survey methods are many frog species, which are often of a high conservation priority. However, the potential for frog-specific iDNA surveys has not been explored; most iDNA surveys have targeted mammals. We carried out traditional audio-visual and iDNA frog surveys in eastern Australia to determine whether frog-biting midges (Corethrella and Sycorax spp.) can be used as an effective survey method for frogs. The frog species detected through each method differed. Five species were detected only during traditional surveys, three by both methods, and one exclusively via iDNA. We demonstrate for the first time that iDNA can be used in targeted surveys of frog diversity and that it may increase the detectability of some species compared to traditional surveys. iDNA holds particular promise in the search for rare species, including those that are missing, feared extinct, and may also be useful in biosecurity, detecting invasive species.

In Situ Shear Wave Velocity From Multichannel Analysis of Surface Waves (MASW) Tests at Six Sites of Delhi Technological University

In the mid-1980s, the surface wave method was initially exhibited as an instrument for the geotechnical engineering field. This non-obtrusive seismic method of multi-channel examination of surface waves is prominently used to draw out the shear-wave velocity profile (variation of soil with respect to its depth). In an attempt to increase the sureness in the construed shear wave velocity (Vs) profile as a result of the ambiguity in the analysed dispersion characteristics, the multichannel method is used in this research to characterize six test sites on the campus of Delhi Technological University, Delhi. The multi-channel analysis of surface waves (MASW) method commenced from the traditional seismic survey method that engages twenty-four receivers positioned all along a linear survey track. The current study implies results from MASW survey at six sites within the DTU campus, for which the MASW data acquisition comprise of the use of a 24-channel PASI seismograph and 24 numbers of 4.5 Hz Vertical geo-phones set apart at 2 m each and source offset (2 m,4 m). The seismic power source was a steel sledgehammer (15 lbs). In order to develop and invert the surface-wave seismic data in addition to creating the one-dimensional depth versus Shear Wave Velocity (Vs) profiles WinMasw software was used. The mean shear wave velocity of the current study area is 285 m/sec. The mean shear wave velocity for Delhi city is in a range of 110 to 350 m/s.

CHINA FOREST COVER EXTRACTION BASED ON GOOGLE EARTH ENGINE

Abstract. Forest cover rate is the principal indice to reflect the forest acount of a nation and region. In view of the difficulty of accurately calculating large-scale forest area by traditional statistical survey methods, it is proposed to extract China forest area based on Google Earth Engine platform. Trained by the enough samples selected through the Google Earth software, there are nine different random forest classifiers applicable to their corresponding zones. Using Landsat 8 surface reflectance data of 2018 year and the modified forest partition map, China forest cover is generated on the Google Earth Engine platform. The accuracy of China's forest coverage achieves 89.08%, while the accuracy of Global Forest Change datasets of Maryland university and Japan’s ALOS Forest/Non-Forest forest product reach 87.78% and 84.57%. Besides, the precision of tropical/subtropical forest, temperate coniferous forest as well as nonforest region are 83.25%, 87.94% and 97.83%, higher than those of other’s accuracy. Our results show that by means of the random forest algorithm and enough samples, tropical and subtropical broadleaf forest, temperate coniferous forest and nonforest partition can be extracted more accurately. Through the computation of forest cover, our result shows that China has a area of 220.42 million hectare in 2018.

Monitoring Fungal Communities With the Global Spore Sampling Project

The kingdom Fungi is a megadiverse group represented in all ecosystem types. The global diversity and distribution of fungal taxa are poorly known, in part due to the limitations related to traditional fruit-body survey methods. These previous hurdles are now being overcome by rapidly developing DNA-based surveys. Past fungal DNA surveys have predominantly examined soil samples, which capture high species diversity but represent only the local soil community. Recent work has shown that DNA samples collected from the air with cyclone samplers provide information on fungal diversity at the scale of some tens of kilometres around the sampling location. To test the feasibility of air sampling for investigating global patterns of fungal diversity, we established a new initiative called the Global Spore Sampling Project (GSSP). The GSSP currently involves 50 sampling locations distributed on all continents, with each location collecting two 24-hr samples per week. Here we describe the GSSP methodology, including the sampling, DNA extraction and sequencing protocols, and the bioinformatics pipeline. We further report results based on 75 pilot samples from five locations, of which three in Europe, one in Australia, and one in Greenland. The results show highly consistent patterns, suggesting that GSSP holds much promise for systematic global fungal monitoring. The GSSP provides highly standardized sampling across space and time, enabling much-improved estimation of total fungal diversity, the global distribution of different fungal groups, fungal fruiting phenology, and the extent of long-distance dispersal in fungi.

A critical overview of driver recording tools

Introduction: Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal. Method: In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed. Results: A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters. Conclusions: New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.

Using environmental DNA (eDNA) to detect the endangered Spectaclecase Mussel (Margaritifera monodonta)

Margaritifera monodonta, or the Spectaclecase Mussel, is a federally endangered freshwater mussel species that has experienced a 55% reduction in range and is currently concentrated in 3 rivers in the Midwest region of the United States (Gasconade and Meramec rivers, Missouri, and St Croix River, Wisconsin). The detection of new populations by traditional survey methods has been limited because these mussels tend to occur under large rocks and boulders. Environmental DNA (eDNA) technology has been used to detect invasive and rare species, but its use for detection of rare, benthic-dwelling species in large flowing systems has been limited. Here, we propose using eDNA to assess known populations of M. monodonta. We designed a M. monodonta-specific quantitative polymerase chain reaction (qPCR) assay and tested it using water samples from multiple M. monodonta housing tanks, water samples from 2 known mussel beds on the St Croix River, and water samples from 3 known mussel beds on the Mississippi River. We observed higher overall eDNA detection rates on the St Croix River (30.2%) compared to the upper Mississippi River (0.60%). We also observed higher eDNA detection rates (73.3–93.1%) in 2018 for samples collected during the larval release period in May compared to samples collected in August after the reproductive period had ended (55.6–70.8%) on the St Croix River. We tested samples collected at 3 distances downstream from the 2 mussel beds found in the St Croix River, but we did not observe a substantial effect of distance on our detection rates. However, we did observe greater detection rates for samples collected near the bottom compared to at the surface. Our results indicate that this novel qPCR assay can successfully detect M. monodonta eDNA and could be used to rapidly screen locations to guide intensive physical searches for populations in riverine systems.

A Pedagogical Framework for Integrating Laser Scanning into Traditional Built Heritage Documentation Coursework

This study explores how historic preservation educators can effectively integrate 3D terrestrial laser scanning technology with traditional field survey methods as part of built heritage documentation coursework. Multi-phase, qualitative research was applied. With Phase 1, cross-analysis of interviews with historic preservation educators and digital documentation experts helps establish pedagogical objectives. An action research approach is then used during Phase 2 to examine two revisions to conventional curricula and assess which better helps achieve the objectives by comparing the deliverables and workflows of the two courses. The revised curriculum, where a digital-data-centered process was employed from the beginning, rather than treated as a supplement, enhanced the complementation of the digital and field tools to facilitate students' research and representation of the historic building. Based on this finding, Phase 3 includes the formulation of a theoretical framework, titled Integrated Virtual and Field Experience for Interacting with the Built Environment, with its four complementary tasks—virtual measurement, virtual experience, field measurement, and field experience—that historic preservation instructors can consider when organizing built heritage documentation pedagogy that integrates digital technologies such as laser scanning.