Traditional Survey Methods Research Articles

The advantage of mobile technologies in crowdsourcing landscape preferences: Testing a mobile app to inform planning decisions

Urban green spaces are vital for cities to achieve sustainability. Optimal design and management of green space is necessary to counter pressure from increased use due to urban densification and therefore needs to account for the preferences of its potential users. However, preference is inherently subjective and difficult to measure. This study tested a crowdsourcing method using a mobile app for on-site data collection to study the use of, perception of, and preferences for different green configurations in the Rembrandtpark, a large urban park in Amsterdam. Responses obtained through the mobile app were compared to those obtained through more traditional on-site and postal surveys to identify differences in sampled population and results. We found clear differences in respondent samples and in stated preferences when comparing the mobile app and the traditional survey methods. Mobile app respondents (n = 154) were younger and more willing to become more involved with park management. They were also less satisfied with most aspects of locations and found them more noisy. On-site survey respondents (n = 146) appeared more susceptible to response bias in their answers to certain questions. Employing different recruitment methods is important to engage different park user types and account for multiple perceptions.

The efficiency of LiDAR HMLS scanning in monitoring forest structure parameters: implications for sustainable forest management

PurposeThis article aims to compare the LiDAR handheld mobile laser scanner (HMLS) scans with traditional survey methods, as the tree gauge and the hypsometer, to study the efficiency of the new technology in relation to the accuracy of structural forest attributes estimation useful to support a sustainable forest management.Design/methodology/approachA case study was carried out in a high forest located in Tuscany (Italy), by considering 5 forest types, in 20 different survey plots. A comparative analysis between two survey methods will be shown in order to verify the potential limits and the viability of the LiDAR HMLS in the forest field.FindingsThis research demonstrates that LiDAR HMLS technology allows to obtain a large amount of valuable data on forest structural parameters in a short span of time with a high level of accuracy and with obvious impact in terms of organisational efficiency.Practical implicationsFindings could be useful for forest owners highlighting the importance of investing in science and technology to improve the overall efficiency of forest resources management.Originality/valueThis article adds to the current knowledge on the precision forestry topic by providing insight on the feasibility and effectiveness of using precision technologies for monitoring forest ecosystems and dynamics. In particular, this study fills the gap in the literature linked to the need to have practical examples of the use of innovative technologies in forestry.

Optimizing aerial imagery collection and processing parameters for drone‐based individual tree mapping in structurally complex conifer forests

Abstract Recent advances in remotely piloted aerial systems (‘drones’) and imagery processing enable individual tree mapping in forests across broad areas with low‐cost equipment and minimal ground‐based data collection. One such method involves collecting many partially overlapping aerial photos, processing them using ‘structure from motion’ (SfM) photogrammetry to create a digital 3D representation and using the 3D model to detect individual trees. SfM‐based forest mapping involves myriad decisions surrounding methods and parameters for imagery acquisition and processing, but it is unclear how these individual decisions or their combinations impact the quality of the resulting forest inventories. We collected and processed drone imagery of a moderate‐density, structurally complex mixed‐conifer stand. We tested 22 imagery collection methods (altering flight altitude, camera pitch and image overlap), 12 imagery processing parameterizations (image resolutions and depth map filtering intensities) and 286 tree detection methods (algorithms and their parameterizations) to create 7,568 tree maps. We compared these maps to a 3.23‐ha ground reference map of 1,775 trees >5 m tall that we created using traditional field survey methods. The accuracy of individual tree detection (ITD) and the resulting tree maps was generally maximized by collecting imagery at high altitude (120 m) with at least 90% image‐to‐image overlap, photogrammetrically processing images into a canopy height model (CHM) with a twofold upscaling (coarsening) step and detecting trees from the CHM using a variable window filter after applying a moving window mean smooth to the CHM. Using this combination of methods, we mapped trees with an accuracy exceeding expectations for structurally complex forests (for canopy‐dominant trees >10 m tall, sensitivity = 0.69 and precision = 0.90). Remotely measured tree heights corresponded to ground‐measured heights with R2 = 0.95. Accuracy was higher for taller trees and lower for understorey trees and would likely be higher in less dense and less structurally complex stands. Our results may guide others wishing to efficiently produce broad‐extent individual tree maps of conifer forests without investing substantial time tailoring imagery acquisition and processing parameters. The resulting tree maps create opportunities for addressing previously intractable ecological questions and informing forest management.

KAZNET: An Open-Source, Micro-Tasking Platform for Remote Locations

Field surveys are the workhorse of social and environmental research, but conventional collection through monitors or enumerators are cost prohibitive in many remote or otherwise difficult settings, which can lead to a poor understanding of those environments and an underrepresentation of the people living in them. In such cases, micro-tasking can offer a promising alternative. By activating in-situ data collectors, micro-tasking avoids many of the large expenses related to conventional field survey processes. In addition to relaxing resource constraints, crowd-sourcing can be flexible and employ data quality protocols unheard-of for conventional methods. This study assesses the potential of using micro-tasking to monitor socioeconomic and environmental indicators in remote settings using a new platform called KAZNET. KAZNET leverages the network of people with smartphones, which are becoming ubiquitous even in the remote rural settings, to execute both long-term and short-term data collection activities, with flexibility to adjust or add tasks in real-time. It also allows for multiple projects, requiring different data types, to be rolled out in the same platform simultaneously. For the data-collector, KAZNET is effectively a wrapper for the commonly used and open source, Open Data Kit (ODK) software, which specializes in offline data collection. A web interface allows administrators to calibrate, deploy, and validate tasks performed by contributors. KAZNET has been used in several projects to collect data in remote pastoral regions of East Africa since its inception in 2017. KAZNET has shown to be effective for collecting high frequency and repeated measures from markets, households and rangelands in remote regions at relatively low cost compared to traditional survey methods. While the successes of micro-tasking are promising, there are clear trade-offs and complementarities between micro-tasking and standard surveys methods, which researchers and practitioners need to consider when implementing either approach.

Research on urban spatial structure based on the dual constraints of geographic environment and POI big data

A large number of cities in China have proposed the spatial development strategy of polycentric in their planning, but whether the evolution of polycentric structure meets the planning expectation and whether the polycentric structure and function can be identified through effective ways has become a hot topic of academic research at present. Compared with the traditional survey and identification methods of population, land use and industry, the emergence of urban big data provides an opportunity for polycentric identification, especially the analysis based on POI (Point of Interest) big data is more accurate and efficient than the traditional methods. Chongqing, as a typical representative of mountainous cities, has chosen the development model of polycentric clusters at an early stage and strengthened it in the planning, forming a real polycentric city with both form and function, which makes it typical and representative among polycentric cities in China. Based on the POI data of Chongqing's main urban area, the paper identifies the polycentric structure of the city as a whole and different functions and its influence range based on the spatial distribution characteristics and aggregation degree of the overall and different types of POI data. The results show that Chongqing, as a mountainous city, exhibits an obvious “polycentric and clustered” structure under natural constraints and planning guidance. The study of Chongqing can reveal the differences between the polycentric characteristics of mountain cities and plain cities, summarize the effectiveness and shortcomings of polycentric development, and provide useful reference for urban planning. This paper collects all kinds of geospatial data, network big data and all kinds of historical statistical yearbook data in the main urban area of Chongqing. The experimental results show that by using the double constraints of POI big data and geographical environment, Chongqing is in urban polycentric structure, different functional types of urban centers, and employment function space and leisure and recreation function space have many advantages.

Fish community surveys in eelgrass beds using both eDNA metabarcoding and seining: implications for biodiversity monitoring in the coastal zone

Marine Protected Areas (MPAs) have been adopted globally as a tool to combat biodiversity loss and restore marine ecosystems. Successful application of MPAs will be predicated on the ability to monitor biodiversity in a synoptic and noninvasive manner. Environmental DNA (eDNA) methods have important advantages over traditional biodiversity survey methods for monitoring conservation areas. To evaluate the efficacy of eDNA metabarcoding for fish biodiversity monitoring, we sampled 19 coastal eelgrass (Zostera marina) beds in Canada, as eelgrass beds are known for high biodiversity and significant conservation value. At each site, beach seines were used to survey fish and water samples were collected contemporaneously for eDNA metabarcoding. In total, beach seining caught 32 672 individuals across 59 fish taxa, and eDNA detected 129 fish taxa. eDNA captured site-level variation and detected higher species richness at both site and regional levels compared to seining. eDNA abundance had a positive association with capture abundance. Collectively these results highlight how eDNA metabarcoding offers an efficient approach for monitoring fish biodiversity in coastal eelgrass beds, thus providing a valuable and noninvasive tool for MPA planning and coastal monitoring.

Combined Use of eDNA Metabarcoding and Bottom Trawling for the Assessment of Fish Biodiversity in the Zhoushan Sea

With the increase in fishing intensity and the intensification of marine pollution, the fishery resources in the Zhoushan Sea are seriously degraded, and the difficulty of censusing fish diversity hampers effective management in marine fishes. Environmental DNA metabarcoding and bottom trawl methods were used to determine the ability of the methods to distinguish fish assemblages in the Zhoushan Sea. The species composition and diversity of the Zhoushan Sea were assessed via high-throughput sequencing analysis of eDNA coupled with bottom trawl fishery survey data, after which the two methods were compared. eDNA screening identified 38.2% more fish species than bottom trawls. Combining these two methods, 33 orders, 65 families, and 130 species of fishes were identified. Perciformes and Clupeiformes, the most abundant orders in the catch, represented 31.5 and 10.0% of the total fish abundance, respectively. The results of ANOSIM and redundancy analyses indicated that the fish community structure varied significantly between summer and winter, however depth and temperature being the main environmental factors influencing fish distribution. The biodiversity index was higher in summer than in winter. Thus, our work provides more detailed seasonal data on biodiversity in the Zhoushan Sea, which is essential for the long-term management and conservation of coastal biodiversity. Compared with traditional survey methods, eDNA determination is highly sensitive, accurate, cost-efficient, and suitable for fish diversity studies in relevant sea areas. Although this approach cannot completely replace traditional methods, our findings demonstrate that it provides a reliable complementary method for assessing fish diversity in marine ecosystems.

Monitoring, Prediction, and Evaluation of Mountain Geological Hazards Based on InSAR Technology

When traditional geological hazard survey methods are used for deformation monitoring in mountainous areas, it often shows the disadvantages of low applicability of monitoring methods and limited accuracy of detection results. In recent years, synthetic aperture radar interferometry (InSAR) technology has incomparable advantages in surface deformation monitoring, such as all-weather detection, wide detection range, high detection accuracy, and low detection cost. At the same time, InSAR technology can also provide data and technical support for the subsequent task of potential geological disaster point identification and geological disaster risk zoning in the study area. Alos-2 radar is selected; in this paper the satellite image is the research data, and the InSAR technology is used to complete the surface deformation detection. Then, based on the previous surface deformation monitoring results, the potential geological disaster points in the study area are extracted, and the distribution law and incubation conditions of the disaster points are analyzed and described. According to the field conditions of a certain area, the surface distribution, development causes, and inducing mechanism of the potential geological disaster points are explored; the results show that the development of geological disasters in the study area is affected by many factors such as landform, geological environment, climate, hydrology, and human activities. Based on this, 11 factors such as formation lithology, slope, and river are used as evaluation factors for mountain geological disaster monitoring, prediction, and evaluation analysis. Finally, the improved analytic hierarchy process information model is used to complete the monitoring, prediction, evaluation, and analysis of regional geological hazards in the study area. In this paper, the improved AHP-information method is used to classify the risk of mountain geological disasters in the study area. Finally, the evaluation results are verified, which proves that the improved AHP-information method is reliable, and its mountain geological disaster monitoring and prediction evaluation effect is better than the traditional AHP-information method.

相机陷阱法在潮间带底栖蟹类群落生态学中的应用研究

PDF HTML阅读 XML下载 导出引用 引用提醒 相机陷阱法在潮间带底栖蟹类群落生态学中的应用研究 DOI: 10.5846/stxb202102080404 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 科技基础资源调查专项（2017FY100701）；厦门大学研究生田野调查基金项目（2020FG022）；国家自然科学基金项目（42076161） Feasibility study on the application of camera trapping method in the study of benthic crab community ecology in the intertidal zone Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:潮间带大型底栖动物调查是滨海湿地生物多样性监测的重要环节。目前普遍应用的传统侵入性调查方法（挖掘法、手捕法和陷阱诱捕法）存在干扰强、耗时久和劳动密集等问题，无法客观反映活动性较强的蟹类等重要功能类群的群落结构和物种多样性。对陆地野生动物研究中常用的相机陷阱法进行了符合潮间带应用条件的便携式设计，并以红树林蟹类为例，通过与3种传统侵入性调查方法进行对比，探讨相机陷阱法在潮间带底栖蟹类群落生态学研究中的应用。结果表明：（1）相机陷阱法在种群密度测定方面比挖掘法提高2.1倍，比手捕法提高10.3倍，比陷阱诱捕法提高16.3倍；（2）相机陷阱法在调查物种数方面均高于3种传统侵入性调查方法，适用于沙蟹科（Ocypodidae）和方蟹科（Grapsidae）蟹类调查；（3）相机陷阱法对个体大小选择性弱，采样更加全面；（4）相机陷阱法在群落alpha多样性（Shannon-Wiener指数）、beta多样性（样方-多度矩阵总方差Var （Y））和功能多样性（Rao二次熵和功能离散度）调查方面整体不弱于侵入性调查方法。此外，相较于侵入性调查方法，相机陷阱法对沉积物结构破坏小、不干扰蟹类群落结构，在兼顾数据丰富性的同时又具有高效性。然而，由于相机陷阱法基于蟹类底表活动性，无法适用于冬季调查、夜行性蟹类和游泳型蟹类调查。未来，相机陷阱法的发展将与基于深度学习的图像处理技术结合以满足数据处理需求。研究揭示了相机陷阱法在潮间带底栖蟹类群落中的应用优越性与局限性，为潮间带大型底栖动物群落生态学的研究提供技术支撑。 Abstract:The investigation of macro-benthic fauna in the intertidal zone is a crucial part of biodiversity monitoring in coastal wetlands. However, there are still some critical gaps, such as the great effects of human disturbance, time-consuming and labor-intensive, that exist in the widely used traditional invasive survey methods (e.g., excavation, hand catch, and pitfall trapping) at present. Thus, it is hard for us to objectively investigate the community structure and biodiversity of important functional groups with intense activity such as crabs. The camera trap is commonly used in the study of terrestrial wildlife. A camera trapping method was designed with the character of portable to meet the application conditions of the intertidal zone. We took crabs in mangrove wetlands as the survey object. Then, the application of the camera trapping method in the study of intertidal benthic crab community ecology was discussed by comparing it with the three traditional invasive survey methods. The results showed that:(1) The camera trapping method was 2.1 times higher than the excavation method, 10.3 times higher than the hand catch method, and 16.3 times higher than the pitfall trapping method in population density determination. (2) The results of the number of species of camera trapping method was higher than the three traditional invasive survey methods. And camera trapping method was suitable for the investigation of Grapsidae and Ocypodidae. (3) The camera trapping method had weak selectivity on individual size and was more comprehensive in sampling. (4) The camera trapping method is no less than invasive survey methods on community alpha diversity (Shannon-Wiener index), beta diversity (total variance of the quadrat-abundance matrix, Var(Y)), and functional diversity (Rao quadratic entropy and functional dispersion). In addition, compared with the invasive survey methods, the camera trapping method has minor damage to the structure of sediment, does not interfere with the crab community structure, and has high efficiency while taking the data richness into account. However, because the camera trapping method is based on the surface activity of crabs, it cannot be applied to winter surveys and surveys of nocturnal crabs and swimming crabs. In the future, the development of camera trapping will be combined with image processing technology based on deep learning to meet data processing needs. This study revealed the advantages and limitations of the camera trapping method in the investigation of the intertidal benthic crab community. It also provided technical support for the study of intertidal macro-benthic community ecology. 参考文献 相似文献 引证文献

Creation and Spatial Analysis of 3D City Modeling based on GIS Data

The 3D city model is one of the crucial topics that are still under analysis by many engineers and programmers because of the great advancements in data acquisition technologies and 3D computer graphics programming. It is one of the best visualization methods for representing reality. This paper presents different techniques for the creation and spatial analysis of 3D city modeling based on Geographical Information System (GIS) technology using free data sources. To achieve that goal, the Mansoura University campus, located in Mansoura city, Egypt, was chosen as a case study. The minimum data requirements to generate a 3D city model are the terrain, 2D spatial features such as buildings, landscape area and street networks. Moreover, building height is an important attribute in the 3D extrusion process. The main challenge during the creation process is the dearth of accurate free datasets, and the time-consuming editing. Therefore, different data sources are used in this study to evaluate their accuracy and find suitable applications which can use the generated 3D model. Meanwhile, an accurate data source obtained using the traditional survey methods is used for the validation purpose. First, the terrain was obtained from a digital elevation model (DEM) and compared with grid leveling measurements. Second, 2D data were obtained from: the manual digitization from (30 cm) high-resolution imagery, and deep learning structure algorithms to detect the 2D features automatically using an object instance segmentation model and compared the results with the total station survey observations. Different techniques are used to investigate and evaluate the accuracy of these data sources. The procedural modeling technique is applied to generate the 3D city model. TensorFlow & Keras frameworks (Python APIs) were used in this paper; moreover, global mapper, ArcGIS Pro, QGIS and CityEngine software were used. The precision metrics from the trained deep learning model were 0.78 for buildings, 0.62 for streets and 0.89 for landscape areas. Despite, the manual digitizing results are better than the results from deep learning, but the extracted features accuracy is accepted and can be used in the creation process in the cases not require a highly accurate 3D model. The flood impact scenario is simulated as an application of spatial analysis on the generated 3D city model. Doi: 10.28991/CEJ-2022-08-01-08 Full Text: PDF

Monitoring of honey bee floral resources with pollen DNA metabarcoding as a complementary tool to vegetation surveys

Abstract Monitoring biodiversity is a growing and pressing challenge, particularly as climate change threatens species with extinction and leads to widespread shifts in plant distribution and phenology. Tracking changes via ground vegetation surveys is costly and time‐consuming, hence monitoring of complex and heterogenous communities remains an ongoing challenge. Molecular DNA methods are rapidly being developed to provide fast and reproducible results for environmental monitoring, including diet and ecosystem assessments. Here, we used DNA metabarcoding of pollen foraged by European honey bees (Apis mellifera) to investigate their floral resource use in an urban reserve. We collected three different pollen samples from hives: individual bees, raw honey and pollen traps, and identified plants using two metabarcoding markers (ITS2 and trnL). We then compared the results to a ground vegetation survey of surrounding flowering taxa. Pollen DNA metabarcoding detected 74 taxa (48.6% identified to species) across all pollen sources, compared to 44 taxa recorded by the survey (93% identified to species). Within the metabarcoding results, we identified 25% of the genera and 9% of the species found during the survey, with three of the top 10 flowering genera represented. While honey was the most taxon‐rich pollen source (mean = 8.5, SD = 3.5), followed by honey bees (mean = 5.8, SD = 6.1) and pollen traps (mean = 4.2, SD = 1.7), combining the results of six individual bees could detect similar taxa numbers to honey, while 20 bees were required to detect as many taxa as the survey. We demonstrate how DNA metabarcoding of the pollen foraged by honey bees can detect more flowering taxa than traditional survey methods, and how different pollen sources and genetic markers affect the level of detection of plant taxa. The foraging choices of honey bees matched few species detected by the vegetation survey, therefore pollen metabarcoding is recommended as a complementary approach to ground surveys. Rigorous validation and stringent filtering of metabarcoding results were also required to exclude potential false positives. Altogether, this molecular approach can be used to augment vegetation surveys, while tracking the floral resources used by bees.

Transient Electromagnetic 1‐Dimensional Inversion Based on the Quantum Particle Swarms Optimization‐Smooth Constrained Least Squares Joint Algorithm and Its Application in Karst Exploration

Before the construction of the bridge bored pile in the karst area, geological conditions of the excavation area should be investigated. In order to avoid the karst caves in underground space making adverse impacts on the construction, bearing capacity, and stability of pile foundation, in this paper, we use the transient electromagnetic method to detect the karst development in the bearing layer of the pile foundation, which is different from the traditional karst survey method. To improve the interpretation accuracy of transient electromagnetic detection for karst caves, the quantum particle swarm optimization (QPSO) algorithm was combined with the smooth constrained least squares (CLS) algorithm, and the transient electromagnetic inversion based on the QPSO‐CLS joint algorithm was generated. Better inversion results were achieved by the proposed method in this study. Based on the inversion calculation results of simulation data and field test data, it is further demonstrated that the QPSO‐CLS joint algorithm has high optimization efficiency without manually setting the initial model. The interpretation results are consistent with the theoretical model and drilling logging results, which proves the adaptability of the proposed algorithm.

Acoustic and Genetic Data Can Reduce Uncertainty Regarding Populations of Migratory Tree-Roosting Bats Impacted by Wind Energy.

Simple SummaryAlthough wind energy provides valuable environmental and economic benefits, it has unintended negative consequences for wildlife, particularly birds and bats that collide with the rotating blades. In North America, for example, there are increasing concerns that wind turbines threaten the persistence of populations of migratory tree-roosting bats, such as the hoary bat (Lasiurus cinereus). Because it is not possible to monitor population sizes for these solitary and cryptic species using traditional techniques, we must rely on other methods, such as acoustic and genetic monitoring, to provide information regarding population status and trends. Using these approaches, we can build a weight of evidence to assess whether mortality associated with wind turbines is sustainable and determine if and when mitigation measures should be implemented to reduce wind turbine mortality. To this end, we recommend that acoustic data be collected using the North American Bat Monitoring Program (NABat) protocols, and that genetic diversity be monitored at repeated time intervals to document species trends. There are no short-term measures to resolve population-level questions for migratory tree-roosting bats. Nonetheless, we discuss opportunities for relatively short-term investments that will lead to long-term success in reducing uncertainty for these species.Wind turbine-related mortality may pose a population-level threat for migratory tree-roosting bats, such as the hoary bat (Lasiurus cinereus) in North America. These species are dispersed within their range, making it impractical to estimate census populations size using traditional survey methods. Nonetheless, understanding population size and trends is essential for evaluating and mitigating risk from wind turbine mortality. Using various sampling techniques, including systematic acoustic sampling and genetic analyses, we argue that building a weight of evidence regarding bat population status and trends is possible to (1) assess the sustainability of mortality associated with wind turbines; (2) determine the level of mitigation required; and (3) evaluate the effectiveness of mitigation measures to ensure population viability for these species. Long-term, systematic data collection remains the most viable option for reducing uncertainty regarding population trends for migratory tree-roosting bats. We recommend collecting acoustic data using the statistically robust North American Bat Monitoring Program (NABat) protocols and that genetic diversity is monitored at repeated time intervals to show species trends. There are no short-term actions to resolve these population-level questions; however, we discuss opportunities for relatively short-term investments that will lead to long-term success in reducing uncertainty.

From Coastal to Montane Forest Ecosystems, Using Drones for Multi-Species Research in the Tropics

Biodiversity monitoring is crucial in tackling defaunation in the Anthropocene, particularly in tropical ecosystems. However, field surveys are often limited by habitat complexity, logistical constraints, financing and detectability. Hence, leveraging drones technology for species monitoring is required to overcome the caveats of conventional surveys. We investigated prospective methods for wildlife monitoring using drones in four ecosystems. We surveyed waterbird populations in Pulau Rambut, a community of ungulates in Baluran and endemic non-human primates in Gunung Halimun-Salak, Indonesia in 2021 using a DJI Matrice 300 RTK and DJI Mavic 2 Enterprise Dual with additional thermal sensors. We then, consecutively, implemented two survey methods at three sites to compare the efficacy of drones against traditional ground survey methods for each species. The results show that drone surveys provide advantages over ground surveys, including precise size estimation, less disturbance and broader area coverage. Moreover, heat signatures helped to detect species which were not easily spotted in the radiometric imagery, while the detailed radiometric imagery allowed for species identification. Our research also demonstrates that machine learning approaches show a relatively high performance in species detection. Our approaches prove promising for wildlife surveys using drones in different ecosystems in tropical forests.

Differencing strategies for SLR observations at the Wettzell observatory

The precise estimation of geodetic parameters using single- and double-differenced SLR observations is investigated. While the differencing of observables is a standard approach for the GNSS processing, double differences of simultaneous SLR observations are practically impossible to obtain due to the SLR basic principle of observing one satellite at a time. Despite this, the availability of co-located SLR telescopes and the use of the alternative concept of quasi-simultaneity allow the forming of SLR differences under certain assumptions, thus enabling the use of these processing strategies. These differences are in principle almost free of both, satellite- and station-specific error sources, and are shown to be a valuable tool to obtain relative coordinates and range biases, and to validate local ties. Tested with the two co-located SLR telescopes at the Geodetic Observatory Wettzell (Germany) using SLR observations to GLONASS and LAGEOS, the developed differencing approach shows that it is possible to obtain single- and double-difference residuals at the millimetre level, and that it is possible to estimate parameters, such as range biases at the stations and the local baseline vector with a precision at the millimetre level and an accuracy comparable to traditional terrestrial survey methods. The presented SLR differences constitute a valuable alternative for the monitoring of the local baselines and the estimation of geodetic parameters.

Genotyping-in-Thousands by sequencing panel development and application to inform kokanee salmon (Oncorhynchus nerka) fisheries management at multiple scales.

The ability to differentiate life history variants is vital for estimating fisheries management parameters, yet traditional survey methods can be inaccurate in mixed-stock fisheries. Such is the case for kokanee, the freshwater resident form of sockeye salmon (Oncorhynchus nerka), which exhibits various reproductive ecotypes (stream-, shore-, deep-spawning) that co-occur with each other and/or anadromous O. nerka in some systems across their pan-Pacific distribution. Here, we developed a multi-purpose Genotyping-in-Thousands by sequencing (GT-seq) panel of 288 targeted single nucleotide polymorphisms (SNPs) to enable accurate kokanee stock identification by geographic basin, migratory form, and reproductive ecotype across British Columbia, Canada. The GT-seq panel exhibited high self-assignment accuracy (93.3%) and perfect assignment of individuals not included in the baseline to their geographic basin, migratory form, and reproductive ecotype of origin. The GT-seq panel was subsequently applied to Wood Lake, a valuable mixed-stock fishery, revealing high concordance (>98%) with previous assignments to ecotype using microsatellites and TaqMan® SNP genotyping assays, while improving resolution, extending a long-term time-series, and demonstrating the scalability of this approach for this system and others.

High-density Yellow Rail (Coturnicops noveboracensis) Population Beyond Purported Range Limits in the Northwest Territories, Canada

The Yellow Rail (Coturnicops noveboracensis) is a secretive marsh bird of conservation concern in Canada. However, the status of this species in northern boreal regions remains largely unknown given uncertainty about population abundance and distribution. This knowledge gap is mainly due to limitations of traditional survey methods to detect this species. In this study, avian point count data collected from autonomous recording units and augmented by detections from a machine-learning recognizer were used to generate a species distribution model to provide habitat-specific density estimates and population size estimates for Yellow Rail breeding in the Edéhzhíe Dehcho Protected Area, Northwest Territories. This protected area is ∼150 km beyond the currently established northern range limit. A large population estimated at 906 (± 146) pairs was discovered. Yellow Rail were found at high densities in marshes (0.063 ± 0.004 males/ha), but were also observed in fens and bogs, albeit at much lower densities (0.003 ± 0.002 males/ha and < 0.001 ± 0.002 males/ha). Our results suggest both the range and the population size of Yellow Rail are much larger than currently reported. Further studies are required to provide better population size and distribution estimates to conserve this species at risk in Canada.

Evaluation of a digital food scanner to monitor food intake in a clinical setting

Rationale: Monitoring of food intake in clinical settings is important for health monitoring and to prevent malnutrition. Digital food recording with depth detection capability can be a viable alternative to traditional survey methods of food intake, saving time and human resources.

Development of an eDNA metabarcoding tool for surveying the world’s largest amphibian

Due to the overexploitation of farming, as well as habitat destruction, the wild population of Chinese giant salamander (CGS) Andrias davidianus, a species with seven genetically distinct lineages, has decreased by over 80% in the past 70 years. Traditional survey methods have proven to be unsuitable for finding this rare and elusive species. We evaluated the efficacy of environmental DNA (eDNA) sampling to detect CGS indirectly from its aquatic environment. We developed several species-specific primer sets; validated their specificity and sensitivity; and assessed their utility in silico, in the laboratory, and at two field sites harboring released farm-bred CGS. We detected the presence of CGS DNA by using polymerase chain reaction and Sanger sequencing. We also sequenced an amplicon mixture of seven haplotype-represented samples using high-throughput sequencing. Our eDNA methods could detect the presence of CGS at moderate densities reported across its range, proving them as a cost-effective way to establish broad-scale patterns of occupancy for CGS. In addition, our primers enabled the detection of mitochondrial lineage mixture or introduced individuals from geographically isolated populations of CGS.

High-resolution combined building stock and building energy modeling to evaluate whole-life carbon emissions and saving potentials at the building and urban scale

As global and local actors seek to address climate concerns, municipalities, regions, and countries are developing policies for the built environment to reach carbon neutrality. In most cases, however, current policies target new construction and operational carbon emissions only, thus omitting the significant carbon emission saving potential resulting from the reactivation of embodied carbon in existing buildings. This article describes the development of a high-resolution combined building stock model (BSM) and building energy model (BEM) on both building and urban scale using all residential buildings of Ithaca, NY, USA as a case study. The model offers a holistic, detailed and local perspective on operational and embodied carbon emissions, associated saving potentials at both the building and urban scale, and the linkages, trade-offs and synergies between buildings and energy use as a basis for decision-making.A circular economy (CE) in construction posited on the reuse and recycling of existing building materials, necessitates a detailed material inventory of the current building stock. However, the scale and nature of this endeavor preclude traditional survey methods. The modeling process described in this article instead engages a bottom-up data aggregation and analysis approach that combines detailed construction archetypes (CAs) and publicly available, higher-level municipal geospatial data with building metadata defining occupancy and systems to create an autogenerated, detailed 3D geometry. The resulting BSM and BEM can simulate both embodied carbon content and operational carbon emissions of individual buildings within a municipal study with minimal required input data and a feasible computational effort. This provides modelers with a new spatial and geometric fidelity to simulate holistic renewal efforts, and inform carbon neutrality policies and incentives towards the decarbonization of the built environment.