Traditional Survey Methods Research Articles

Optimizing Ship Draft Observation with Wave Energy Attenuation and PaddlePaddle-OCR in an Anti-Fluctuation Device

With the development and application of artificial intelligence (AI) in the shipping industry, using AI to replace traditional draft survey methods in bulk carriers can significantly reduce manpower, lower the risks associated with visual observations, improve measurement accuracy, and minimize the impact of human subjective factors. Ultimately, the integration of software and hardware technologies will replace human visual observations with automated draft measurement calculations. A similar anti-fluctuation device described in this article has been used in ship draft observation based on AI-assisted proving, which can ease the fluctuation of the wave inside the pipe. Observers can directly read the water surface inside the pipe and compare it to the ship’s draft mark to obtain the final draft, effectively improving draft observation accuracy. However, some surveyors refuse to accept the readings obtained from this device, citing a lack of theoretical basis or the absence of accreditation from relevant technical authorities, leading to the rejection of results. To address these issues, this paper integrates wave energy attenuation theory with PaddlePaddle-OCR recognition to further validate the anti-fluctuation device for accurate ship draft observation. The experimental results are as follows: first, the pipe effectively suppresses the amplitude of external water surface fluctuations by 75%, explaining the fundamental theory that wave heights within the anti-fluctuation device are consistent with external swell heights. When taking a draft measurement, the system dynamically adjusts the position of the main tube in response to the ship’s movements, maintaining the stability of the measurement section and significantly reducing the difficulty of observations. Due to the reduction in fluctuation amplitude, there is a noticeable improvement in observation accuracy.

3D characterization of the Mila 18 archaeological site in Warsaw, Poland: From imaging to excavation

Archaeological site investigations in urban environments are often beset with challenges such as (1) an absence of buried artifacts due to recent disturbance from infrastructure development or (2) community concerns about potential site impacts from excavations. Noninvasive geophysical surveys that use a combination of methods can help mitigate the risks of uncertain outcomes by identifying areas where culturally significant features are more likely to be uncovered. We show how new technology and traditional geophysical survey methods were used to characterize the subsurface of the Mila 18 Memorial site in Warsaw, Poland. This site is one of the most important places of remembrance for the Holocaust and coincides with the location of an underground bunker that was used by Jewish resistance groups during the 1943 Warsaw Ghetto Uprising. In this case study, we showcase the use of drone multispectral imaging and handheld lidar scanning in conjunction with other geophysical techniques including electrical resistivity tomography, ground-penetrating radar, magnetic gradiometer, twin-probe resistance, and fixed-frequency electromagnetic surveying. The geophysical results were included in an interactive 3D site model to help identify a suitable site for excavation. To document the excavation and to validate and further interrogate the geophysical survey results, we used lidar-based photo-textured scans of the excavation that were incorporated into the 3D site model.

Crowdsourced data as a strategic approach to include the human dimension in outdoor environmental quality assessments

Outdoor environments extend living spaces as venues for various activities. Comfortable open public spaces can positively impact citizens' health and well-being, thereby improving the livability and resilience of cities. Considering the visitors' perception of these environments in comfort studies is crucial for ensuring their well-being and promoting the use of these spaces. However, traditional survey methods may be time- and resource-consuming to gather significant sample sizes, usually focusing on selected homogeneous samples. Crowdsourced data, then, has emerged as an alternative for assessing human perception, as it eases the collection of subjective feedback and potentially amplifies impact and inclusivity. This study presents a strategic approach for analyzing publicly available and willingly reported crowdsourced data from a digital mapping platform in outdoor comfort evaluations, aiming to verify whether these data are informative regarding environmental quality perception and to identify the environmental factors that people are most sensitive to. Urban parks located in New York City served as a case study. A multi-source, interdisciplinary information framework combined crowdsourced reviews with environmental data used to determine prevailing thermal conditions. Overall perception of parks was well-rated, revealing that their attractions and activities are probably the most appealing characteristics for park attendance. Regarding environmental perception, acoustic and thermal factors are clearly the most influential. Acoustics were well-rated, while the main aspect regarding the thermal domain is the recognition of shading as a mitigator for hot conditions. Environmental data provided complementary insights, particularly concerning the range of thermal sensations experienced in urban parks. The findings confirm that willingly reported crowdsourced data can provide valuable insights into urban crowd environmental perception, presenting a potentially suitable and effective method to include the human perspective in environmental quality assessments, as well as to evaluate and predict environmental-related risks.

Development and Application of a Portable Environmental DNA Test for the Detection of Rafetus swinhoei in Viet Nam

ABSTRACTSwinhoe's (or Yangtze) giant softshell turtle (Rafetus swinhoei) is a large, critically endangered freshwater turtle and considered among the rarest species in the world. As of 2024, only two individuals have been confirmed to remain alive, one at the Suzhou Zoo in China and one in Xuan Khanh Lake, Viet Nam. The only hope for the long‐term survival of R. swinhoei is finding additional, as yet undiscovered, animals that have thus far eluded detection by traditional survey methods. In recent years, numerous studies have been published on the use of environmental DNA (eDNA) for species detection and monitoring. This method takes advantage of the persistence of DNA in the environment, such as in water, soil, and air. An organism's DNA is shed into the environment through urine, feces, and the sloughing of skin. Species‐specific quantitative polymerase chain reaction (qPCR) testing can be used to detect eDNA in samples collected from the environment. eDNA trials to detect R. swinhoei were initiated by the Asian Turtle Program and Washington State University in 2013. To expand the use of eDNA for species detection by conservationists, we developed and validated a first‐of‐its‐kind innovative point‐of‐detection (POD) qPCR platform for the rapid, onsite detection of R. swinhoei from water samples. Here we show that the portable eDNA test kit can be used for the successful detection of R. swinhoei in a large body of water and that pooling filters may be a useful strategy to reduce test costs and improve detection efficiency. Use of this test can expand the search for R. swinhoei in unexplored and understudied lakes, reservoirs, and other bodies of water where this species may be present and could inform field surveys utilizing eDNA for other threatened species that are rare in nature.

Research on Urban Street Spatial Quality Based on Street View Image Segmentation

Assessing the quality of urban street space can provide suggestions for urban planning and construction management. Big data collection and machine learning provide more efficient evaluation methods than traditional survey methods. This study intended to quantify the urban street spatial quality based on street view image segmentation. A case study was conducted in the Second Ring Road of Changsha City, China. Firstly, the road network information was obtained through OpenStreetMap, and the longitude and latitude of the observation points were obtained using ArcGIS 10.2 software. Then, corresponding street view images of the observation points were obtained from Baidu Maps, and a semantic segmentation software was used to obtain the pixel occupancy ratio of 150 land cover categories in each image. This study selected six evaluation indicators to assess the street space quality, including the sky visibility index, green visual index, interface enclosure index, public–facility convenience index, traffic recognition, and motorization degree. Through statistical analysis of objects related to each evaluation indicator, scores of each evaluation indicator for observation points were obtained. The scores of each indicator are mapped onto the map in ArcGIS for data visualization and analysis. The final value of street space quality was obtained by weighing each indicator score according to the selected weight, achieving qualitative research on street space quality. The results showed that the street space quality in the downtown area of Changsha is relatively high. Still, the level of green visual index, interface enclosure, public–facility convenience index, and motorization degree is relatively low. In the commercial area east of the river, improvements are needed in pedestrian perception. In other areas, enhancements are required in community public facilities and traffic signage.

A technological biodiversity monitoring toolkit for biocredits

Abstract Biodiversity is in crisis globally, and we consistently fail to hit global targets to stem its loss. Inspired by the Kunming‐Montreal Global Biodiversity Framework, the biodiversity credit market offers an avenue for vital funding for biodiversity conservation projects around the world. Various biodiversity credit methodologies and standards are becoming available, and most will require the measurement and monitoring of biodiversity at scale. Private investment in conservation through biodiversity credits entails specific needs for biodiversity data collection, including the need for biodiversity claims to be verifiable. We conceptualise these requirements around ‘SAGED’ criteria: Scalable, Accessible, Granular (data of appropriate spatial, taxonomic and temporal resolution), Evidenceable and Directly measured (where possible). Measuring and monitoring biodiversity across ecosystems, ecoregions and taxa is expensive and time‐consuming with traditional survey methods. These methods often rely on access to experts with sufficient taxonomic and survey expertise, which is challenging in many parts of the world. Accordingly, we review biodiversity monitoring technologies and assess their readiness to fulfil key requirements of assessments for the purpose of nature accounting for biodiversity credits (particularly SAGED criteria). We focus on monitoring technologies that are commonly cited by biodiversity credit methodologies, including (e)DNA metabarcoding, passive acoustic monitoring and various other remote sensing methods. We also explore the current limits of these techniques in obtaining appropriate biodiversity measures and metrics for biodiversity finance. Synthesis and applications. Technological solutions for biodiversity monitoring are not (yet) a panacea but are key for evidenceable monitoring at scale. For current use in biocredit markets, we advise these are combined with ground validation and human‐collected ecological data. Developments in automation and machine learning will rapidly make these technologies more accessible and efficient.

Integrating Traditional and Modern Survey Methods: A Comparative Study of East African Practices

As East Africa experiences rapid urbanization and development, the integration of traditional and modern survey methods emerges as a crucial strategy for enhancing land surveying practices. This review article explores the complementary strengths of traditional methods, rooted in local knowledge and cultural practices, and modern technologies, such as Geographic Information Systems (GIS) and drone-based surveys. The study examines the importance of integrating these approaches to improve data accuracy, cultural relevance, and community acceptance. Through comparative analysis and case studies from Kenya and Tanzania, the article highlights the benefits and challenges of combining these methodologies. The review is based on an extensive examination of existing literature and field studies to provide a comprehensive understanding of the topic. Addressing technological, financial, regulatory, and social barriers is essential for realizing the potential of this integrated approach, which can significantly enhance urban planning, infrastructure development, and sustainable land management in East Africa. Keywords: Traditional Survey Methods, Modern Survey Technologies, GIS Integration, Drone Surveys, Land Management

A 10-m scale chemical industrial parks map along the Yangtze River in 2021 based on machine learning

Strengthening industrial pollution control in the Yangtze River is a fundamental national policy of China. There is a lack of detailed distribution of chemical industrial parks (CIPs). This Study utilized random forest (RF) and active learning to generate the distribution map of CIPs along the Yangtze River at 10-m resolution. Based on Sentinel-2 imagery, spectral and texture features are extracted. Combined with the Points of Interest (POI), a multidimensional feature space is constructed. By employing partitioned training, classification of CIPs map is achieved on Google Earth Engine (GEE). Technical validation along the entire Yangtze River demonstrates a model accuracy of 80%. Compared to traditional manual survey methods, this approach saves significant time and economic costs while also being timelier. As the first publicly available CIPs map within a 5-km range along the Yangtze River, this research will provide a scientific basis for the fine governance of chemical industries in the region. Additionally, it offers a model guide for the accurate identification of the chemical industry.

The lexical divide: propositive modes and non-agentic attitudes define the progressive left in Chile

Internal factors-such as psychological traits or individual attitudes-relate to and explain political cleavages. Yet, little is known about how locus of control, agency, and modal attitudes impact political ideology. Utilizing textual analysis within the context of the Chilean 2015 constituent process, we go beyond traditional survey methods to explore community clusters in “Values” and “Rights” networks built upon the deliberation of 106,000 people. Our findings reveal distinct attitudinal patterns across political orientations: the progressive left generally exhibits a more propositive and non-agentic attitude, the traditional left adopts an evaluative stance towards values, and the right-wing community leans towards a factual attitude but shifts to an evaluative stance when discussing rights. These results underscore the role of psychological constructs in shaping political ideologies and introduce textual analysis as a robust tool for psychological and political inquiry. The study offers a comprehensive understanding of the complexities of political behavior and provides a new lens through which to examine the psychology of political ideology.

Testing multiple environmental DNA substrates for detection of the cryptic and Critically Endangered burrowing freshwater crayfish Engaewa pseudoreducta

AbstractEffective conservation of endangered species depends on knowledge of their distributions, but species detection can often be challenging. An example of this is provided by the Critically Endangered Margaret River burrowing crayfish (Engaewa pseudoreducta), which is highly cryptic. Due to the burrowing habit of this crayfish, detection of this species currently requires a great deal of effort, the results are often non‐conclusive, and, as it involves manual excavation of their burrows, the habitat of this and other species is destroyed in the detection process. In response to these challenges, this study developed and optimized a species‐specific probe‐based qPCR assay targeting the 16S gene region to detect the target species from environmental samples. Three test substrates—chimney pellets (soil expelled by a crayfish as it digs its burrow), burrow scrapes (soil lining the inside of a burrow), and burrow water (water that is filling the burrow space)—were tested from 11 crayfish burrows thought to have been constructed by the target species to see if eDNA could be detected. DNA from the target species was successfully amplified from both chimney pellets (6/11 samples) and burrow scrapes (3/11 samples); however, E. pseudoreducta was not detected in any water samples. As previously stated, sampling techniques to confirm the presence of this species have relied on burrow excavation (resulting in habitat destruction) and were often not definitive; therefore, replacing the traditional survey method with a noninvasive eDNA‐based technique could be of enormous benefit to the management and conservation of this (and similar) species.

BiFPN-KPointNet-CBAM: Application of 3D Point Cloud Technology Based on Deep Learning in Measuring Vegetation

The results of traditional vegetation-measuring methods are mostly two-dimensional data, which can only convey limited information. The greening situation of many cities or regions in the world cannot be fully assessed by these results. In this regard, this paper proposes the use of the air–ground integrated point cloud data acquisition mode for measuring vegetation. This mode combines a backpack-mounted laser scanning system, a vehicle-mounted laser scanning system, and UAV tilt photography technology to collect greening data in a comprehensive park and along a municipal road in Guangzhou, China. To classify the collected greening data, we propose the BiFPN-KPointNet-CBAM model, which was derived from PointNet. The model was introduced to analyze the distribution of green plants in study areas. The experimental findings indicate that our model achieved a notable enhancement in the overall accuracy by approximately 8% compared with other state-of-the-art models. Compared with the traditional greening survey method, this method obtained three-dimensional and more accurate greening data, and thus, provides higher quality greening data for urban managers.

Biofouling sponges as natural eDNA samplers for marine vertebrate biodiversity monitoring

Environmental DNA (eDNA) analysis has now become a core approach in marine biodiversity research, which typically involves the collection of water or sediment samples. Yet, recently, filter-feeding organisms have received much attention for their potential role as natural eDNA samplers. While the indiscriminate use of living organisms as ‘sampling tools’ might in some cases raise conservation concerns, there are instances in which highly abundant sessile organisms may become a nuisance as biofouling on artificial marine structures. Here we demonstrate how a sea sponge species that colonizes the moorings of the world's largest curtain of hydroacoustic receivers can become a powerful natural collector of fish biodiversity information. By sequencing eDNA extracted from Vazella pourtalesii retrieved from moorings during routine biofouling maintenance, we detected 23 species of marine fish and mammals, compared to 19 and 15 species revealed by surface and bottom water eDNA respectively, and 28 species captured by groundfish survey in the surrounding area, which are more ecologically impactful and involve higher additional costs. Sponge-based species inventories proved at least as informative as those obtained by traditional survey methods, and are also able to detect seasonal differences in fish assemblages. We conclude that opportunistic sampling of marine sponge biofouling may become an efficient way to document and monitor biodiversity in our rapidly changing oceans.

Assessing Outcomes in HIV Prevention and Treatment Programs With Female Sex Workers and Men Who Have Sex With Men: Expanded Polling Booth Survey Protocol.

Assessing HIV outcomes in key population prevention programs is a crucial component of the program cycle, as it facilitates improved planning and monitoring of anticipated results. The Joint United Nations Programme on HIV and AIDS recommends using simple, rapid methods to routinely measure granular and differentiated program outcomes for key populations. Following a program science approach, Partners for Health and Development in Africa, in partnership with the Nairobi County Government and the University of Manitoba, aims to conduct an outcome assessment using a novel, expanded polling booth survey (ePBS) method with female sex workers and men who have sex with men in Nairobi County, Kenya. This study aims to (1) estimate the incidence and prevalence of HIV; (2) assess biomedical, behavioral, and structural outcomes; and (3) understand barriers contributing to gaps in access and use of available prevention and treatment services among female sex workers and men who have sex with men in Nairobi. The novel ePBS approach employs complementary data collection methods, expanding upon the traditional polling booth survey (PBS) method by incorporating additional quantitative, qualitative, and biological data collection components and an improved sampling methodology. Quantitative methods will include (1) PBS, a group interview method in which individuals provide responses through a ballot box in an unlinked and anonymous way, and (2) a behavioral and biological survey (BBS), including a face-to-face individual interview and collection of linked biological samples. Qualitative methods will include focus group discussions. The ePBS study uses a 2-stage, population- and location-based random sampling approach involving the random selection of locations from which random participants are selected at a predetermined time on a randomly selected day. PBS data will be analyzed at the group level, and BBS data will be analyzed at an individual level. Qualitative data will be analyzed thematically. Data were collected from April to May 2023. The study has enrolled 759 female sex workers (response rate: 759/769, 98.6%) and 398 men who have sex with men (response rate: 398/420, 94.7%). Data cleaning and analyses are ongoing, with a focus on assessing gaps in program coverage and inequities in program outcomes. The study will generate valuable HIV outcome data to inform program improvement and policy development for Nairobi County's key population HIV prevention program. This study served as a pilot for the novel ePBS method, which combines PBS, BBS, and focus group discussions to enhance its programmatic utility. The ePBS method holds the potential to fill an acknowledged gap for a rapid, low-cost, and simple method to routinely measure HIV outcomes within programs and inform incremental program improvements through embedded learning processes.

Accuracy Assessment of UAV LiDAR Compared to Traditional Total Station for Geospatial Data Collection in Land Surveying Contexts

Abstract. Accurate surveying of vegetated areas presents significant challenges due to obstructions that obscure visibility and compromise the precision of measurements. This paper introduces a methodology employing the DJI Zenmuse L2 Light Detection and Ranging (LiDAR) sensor, which is mounted on a Matrice 350 RTK drone. The DJI Zenmuse L2 sensor excels at capturing detailed terrain data under heavy foliage, capable of collecting 1.2 million points per second and offering five returns, thus enhancing the sensor's ability to detect multiple surface responses from a single laser pulse. In a case study conducted near a creek heavily obscured by tree coverage, traditional aerial imaging techniques are found insufficient for capturing critical topographic features, such as the creek banks. Employing LiDAR, the study aims to map these obscured features effectively. The collected data is processed using DJI Terra software, which supports the accurate projection and analysis of the LiDAR data. To validate the accuracy of the data collected from the LiDAR sensor, traditional survey methods are deployed to ground truth the data and provide an accuracy assessment. Ground control points (GCPs) are established using a GNSS receiver to provide geodetic coordinates, which then assist in setting up a total station. This total station measures vertical and horizontal angles, as well as the slope distance from the instrument to positions underneath the tree coverage on the ground. These measurements serve as checkpoints to validate the accuracy of the LiDAR data, thus ensuring the reliability of the survey. This paper discusses the potential of integrating LiDAR data with traditional surveying data, which is expected to enhance the ability of surveyors to map environmental features efficiently and accurately in complex and vegetated terrains. Through detailed procedural descriptions and expected outcomes, the study aims to provide valuable insights into the strategic application of geospatial technologies to overcome common surveying challenges.

Speaking of reform: Experimental insights into influencing police executives' perspectives on civilian oversight

AbstractResearch summaryThis study investigates how information about public opinion and peer practices influences police executives' views on civilian review boards. We applied structural topic modeling in an experimental paradigm, a novel approach diverging from traditional experimental survey methods, to the open‐ended responses of 1331 police executives collected in an original survey experiment. This technique enables the capture of subtle shifts in belief directly from the executives' own words. The experiment systematically varied the information provided to police executives, including state‐level public opinion data from a representative sample of 16,840 U.S. residents, and peer practices in major city police agencies. Our findings reveal that police executives, although generally aligned in their views, demonstrate a readiness to update their beliefs when presented with cohesive local public opinion and information about peer practices in policing.Policy implicationsIn a democratic society, the core policy beliefs of police executives critically shape the existence and efficacy of civilian oversight mechanisms. Our findings demonstrate the adaptability of police executives in updating their policy positions, reflecting their commitment to informed decision making in response to the dynamic nature of police governance. We find that police executives are willing to update their beliefs related to the public's ability to oversee policing in an unbiased and qualified manner when presented with information about reforms adopted by peer agencies. In contrast, information about public demand for more aggressive forms of oversight can lead to a backlash, increasing the likelihood of police executives to favor existing oversight mechanisms and to doubt the public's qualification. For executives, reformers, and scholars, these findings highlight the limitations of public opinion and benefits of peer influence as mechanisms for policy learning in policing.

Exploring climate change discourse on social media and blogs using a topic modeling analysis

Climate change is one of the most pressing global issues of our time, and understanding public perception and awareness of the topic is crucial for developing effective policies to mitigate its effects. While traditional survey methods have been used to gauge public opinion, advances in natural language processing (NLP) and data visualization techniques offer new opportunities to analyze user-generated content from social media and blog posts. In this study, a new dataset of climate change-related texts was collected from social media sources and various blogs. The dataset was analyzed using BERTopic and LDA to identify and visualize the most important topics related to climate change. The study also used sentence similarity to determine the similarities in the comments written and which topic categories they belonged to. The performance of different techniques for keyword extraction and text representation, including OpenAI, Maximal Marginal Relevance (MMR), and KeyBERT, was compared for topic modeling with BERTopic. It was seen that the best coherence score and topic diversity metric were obtained with OpenAI-based BERTopic. The results provide insights into the public's attitudes and perceptions towards climate change, which can inform policy development and contribute to efforts to reduce activities that cause climate change.

Thematic Analysis of Using Visual Methods to Understand Healthcare Teams

In healthcare, teams are essential in solving today’s toughest challenges. However, narrow disciplinary perspectives have limited our current understanding of how teams function in various healthcare contexts. Researchers often rely on traditional survey methods as their primary tool, which can prevent them from gathering comprehensive data. To overcome this limitation, sharing stories and narratives visually allows participants to create representations of their perceptions of team experiences and complex encounters using a variety of approaches. Despite the potential of this method, there is little empirical guidance on how to use it in health sciences research. To address this gap and provide guidance on using visual methods, particularly for analysing team function, we contacted researchers worldwide specializing in qualitative research methods. These researchers had published experience using visual methods. Over a year, three virtual, asynchronous brainstorming sessions were conducted with 16 researchers. The brainstorming sessions consisted of respondents receiving a survey with open-ended prompts to gather insight into the best practices for using visual methods. Researchers agreed that visual methods can gather implicit data, break down researcher-participant power dynamics, and improve a study’s accessibility to under-represented groups. It was highlighted, when interpreting drawings, co-analysis with the creator should be facilitated to mitigate biases and support interpretation. This use of visual methods can aid the creation of tacit and more nuanced descriptions of complex phenomena and improve team function through a deeper understanding and respect of each members’ needs/experiences/perspectives. Future studies should pilot visual methods with different healthcare teams to further investigate team dynamics and how findings can be used to optimize team function.

Estimation of Surface Soil Nutrient Content in Mountainous Citrus Orchards Based on Hyperspectral Data

Monitoring soil conditions is of great significance for guiding fruit tree production and increasing yields. Achieving a rapid determination of soil physicochemical properties can more efficiently monitor soil conditions. Traditional sampling and survey methods suffer from slow detection speeds, low accuracy, limited coverage, and require a large amount of manpower and resources. In contrast, the use of hyperspectral technology enables the precise and rapid monitoring of soil physicochemical properties, playing an important role in advancing precision agriculture. Yuxi City, Yunnan Province, was selected as the study area; soil samples were collected and analyzed for soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and available nitrogen (AN) contents. Additionally, soil spectral reflectance was obtained using a portable spectroradiometer. Hyperspectral characteristic bands for soil nutrients were selected from different spectral preprocessing methods, and different models were used to predict soil nutrient content, identifying the optimal modeling approach. For SOM prediction, the second-order differentiation-multiple stepwise regression (SD-MLSR) model performed exceptionally well, with an R2 value of 0.87 and RMSE of 6.61 g·kg−1. For TN prediction, the logarithm of the reciprocal first derivative-partial least squares regression (LRD-PLSR) model had an R2 of 0.77 and RMSE of 0.37 g·kg−1. For TP prediction, the logarithmic second-order differentiation-multiple stepwise regression (LTSD-MLSR) model had an R2 of 0.69 and RMSE of 0.04 g·kg−1. For AN prediction, the logarithm of the reciprocal second derivative-partial least squares regression (LRSD-PLSR) model had an R2 of 0.83 and RMSE of 24.12 mg·kg−1. The results demonstrate the high accuracy of these models in predicting soil nutrient content.

Evaluating the predictors of habitat use and successful reproduction in a model bird species using a large‐scale automated acoustic array

The emergence of continental to global scale biodiversity data has led to growing understanding of patterns in species distributions, and the determinants of these distributions, at large spatial scales. However, identifying the specific mechanisms, including demographic processes, determining species distributions remains difficult, as large‐scale data are typically restricted to observations of only species presence. New remote automated approaches for collecting data, such as automated recording units (ARUs), provide a promising avenue towards direct measurement of demographic processes, such as reproduction, that cannot feasibly be measured at scale by traditional survey methods. In this study, we analyze data collected by ARUs from 452 survey points across an approximately 1500 km long study region to compare patterns in adult and juvenile distributions in great horned owl Bubo virginianus. We specifically examine whether habitat associated with successful reproduction is the same as that associated with adult presence. We postulated that congruence between these two distributions would suggest that all areas of the species' range contribute equally to maintenance of the population, whereas significant differences would suggest more specificity in the species' requirements for successful reproduction. We filtered adult and juvenile calls of the species for manual review using automated classification and constructed single season occupancy models to compare land cover and vegetation covariates which significantly predicted presence of each life stage. We found that habitat use by adults was significantly predicted by increasing amounts of forest cover, reduced forest basal area, and lower elevations; whereas juvenile presence was significantly predicted only by decreasing amounts of forest cover, a pattern opposite that of adults. These results show that presence of adult great horned owls is not a sufficient proxy for locations at which reproduction occurs, and also demonstrate a highly scalable workflow that could be used for similar analyses in other sound‐producing species.

Validation and development of eDNA metabarcoding primers for comprehensive assessment of Chinese amphibians.

Environmental DNA (eDNA) metabarcoding has emerged as a powerful, non-invasive tool for biodiversity assessments. However, the accuracy and limitations of these assessment techniques are highly dependent on the choice of primer pairs being used. Although several primer sets have been used in eDNA metabarcoding studies of amphibians, there are few comparisons of their reliability and efficiency. Here, we employed lab- and field-tested sets of publicly available and de novo-designed primers in amplifying 83 species of amphibian from all three orders (Anura, Caudata, and Gymnophiona) and 13 families present in China to evaluate the versatility and specificity of these primers sets in amphibian eDNA metabarcoding studies. Three pairs of primers were highly effective, as they could successfully amplify all the major clades of Chinese amphibians in our study. A few non-amphibian taxa were also amplified by these primers, which implies that further optimization of amphibian-specific primers is still needed. The simultaneous use of three primer sets can completely cover all the species obtained by conventional survey methods and has even effectively distinguished quite a number of species (n = 20) in the Wenshan National Nature Reserve. No single primer set could individually detect all of the species from the studied region, indicating that multiple primers might be necessary for a comprehensive survey of Chinese amphibians. Besides, seasonal variations in amphibian species composition were also revealed by eDNA metabarcoding, which was consistent with traditional survey methods. These results indicate that eDNA metabarcoding has the potential to be a powerful tool for studying spatial and temporal community changes in amphibian species richness.