Broad Extent Research Articles

Globus automation services: Research process automation across the space–time continuum

Research process automation–the reliable, efficient, and reproducible execution of linked sets of actions on scientific instruments, computers, data stores, and other resources–has emerged as an essential element of modern science. We report here on new services within the Globus research data management platform that enable the specification of diverse research processes as reusable sets of actions, flows, and the execution of such flows in heterogeneous research environments. To support flows with broad spatial extent (e.g., from scientific instrument to remote data center) and temporal extent (from seconds to weeks), these Globus automation services feature: (1) cloud hosting for reliable execution of even long-lived flows despite sporadic failures; (2) a simple specification and extensible asynchronous action provider API, for defining and executing a wide variety of actions and flows involving heterogeneous resources; (3) an event-driven execution model for automating execution of flows in response to arbitrary events; and (4) a rich security model enabling authorization delegation mechanisms for secure execution of long-running actions across distributed resources. These services permit researchers to outsource and automate the management of a broad range of research tasks to a reliable, scalable, and secure cloud platform. We present use cases for Globus automation services, describe their design and implementation, present microbenchmark studies, and review experiences applying the services in a range of applications.

Impacts of spatial scale and resolution on species distribution models of American chestnut (Castanea dentata) in Pennsylvania, USA

American chestnut (Castanea dentata) was once a foundational tree species of eastern North American hardwood forests before its functional extinction due to an invasive chestnut blight first described at the beginning of the 20th century. Ongoing research to develop a blight-resistant American chestnut through traditional breeding or transgenic means is promising. However, restoration of American chestnut will require a thorough understanding of its spatial distribution and habitat suitability to both find surviving mature trees to enhance the genetic diversity of experimental blight-resistant trees and to identify optimal restoration sites. While many studies have modeled the spatial distribution of American chestnut trees, few have addressed how adjusting spatial scale and resolution impact the predictive ability of species distribution models (SDMs) for the tree species. In this study, we used Maxent to model American chestnut distribution at coarse resolution (238 m) across the state of Pennsylvania and fine resolution (3 m) within French Creek State Park in the southeastern portion of the state. Pennsylvania was chosen as our study area due to its central location in American chestnut’s pre-blight range. Results indicate that the spatial extent of the study area and ranges of environmental variables included in the models influence which variables are most predictive of chestnut distribution. This study suggests that environmental variables such as soil content, soil pH, and elevation are all strong predictors of American chestnut habitat, but their importance varies by resolution. This study also investigates how spatial scale and resolution influence the predictive ability of American chestnut SDMs and offers a workflow for locating restoration sites by first modeling species distribution at a coarse scale and broad extent to identify patches of suitable habitat, then focusing resources on fine scale modeling of species distribution local to those suitable patches.

Arctic Climate Extremes

There are multiple extreme events underway in the Arctic that are beyond previous records: rain in Greenland, Alaska weather variability, and ecosystem reorganizations in the Barents and the northern Bering Sea associated with climate change and sea-ice loss. Such unique extreme events represent a philosophical challenge for interpretation, i.e., a lack of statistical basis, as well as important information for regional adaptation to climate change. These changes are affecting regional food security, human/wildlife health, cultural activities, and marine wildlife conservation. Twenty years ago, the Arctic was more resilient to climate change than now, as sea ice had a broader extent and was three times thicker than today. These new states cannot be assigned probabilities because one cannot a priori conceive of these states. They often have no historical analogues. A way forward for adaptation to future extremes is through scenario/narrative approaches; a recent development in climate change policy is through decision making under deep uncertainty (DMDU).

Quantifying Lidar Elevation Accuracy: Parameterization and Wavelength Selection for Optimal Ground Classifications Based on Time since Fire/Disturbance

Pre- and post-fire airborne lidar data provide an opportunity to determine peat combustion/loss across broad spatial extents. However, lidar measurements of ground surface elevation are prone to uncertainties. Errors may be introduced in several ways, particularly associated with the timing of data collection and the classification of ground points. Ground elevation data must be accurate and precise when estimating relatively small elevation changes due to combustion and subsequent carbon losses. This study identifies the impact of post-fire vegetation regeneration on ground classification parameterizations for optimal accuracy using TerraScan and LAStools with airborne lidar data collected in three wavelengths: 532 nm, 1064 nm, and 1550 nm in low relief boreal peatland environments. While the focus of the study is on elevation accuracy and losses from fire, the research is also highly pertinent to hydrological modelling, forestry, geomorphological change, etc. The study area includes burned and unburned boreal peatlands south of Fort McMurray, Alberta. Lidar and field validation data were collected in July 2018, following the 2016 Horse River Wildfire. An iterative ground classification analysis was conducted whereby validation points were compared with lidar ground-classified data in five environments: road, unburned, burned with shorter vegetative regeneration (SR), burned with taller vegetative regeneration (TR), and cumulative burned (both SR and TR areas) in each of the three laser emission wavelengths individually, as well as combinations of 1550 nm and 1064 nm and 1550 nm, 1064 nm, and 532 nm. We find an optimal average elevational offset of ~0.00 m in SR areas with a range (RMSE) of ~0.09 m using 532 nm data. Average accuracy remains the same in cumulative burned and TR areas, but RMSE increased to ~0.13 m and ~0.16 m, respectively, using 1550 nm and 1064 nm combined data. Finally, data averages ~0.01 m above the field-measured ground surface in unburned boreal peatland and transition areas (RMSE of ~0.19 m) using all wavelengths combined. We conclude that the ‘best’ offset for depth of burn within boreal peatlands is expected to be ~0.01 m, with single point measurement uncertainties upwards of ~0.25 m (RMSE) in areas of tall, dense vegetation regeneration. The importance of classification parameterization identified in this study also highlights the need for more intelligent adaptative classification routines, which can be used in other environments.

Intuitively visualizing spatial data from biogeographic assessments: A 3-dimensional case study on remotely sensing historic shipwrecks and associated marine life

Biogeographic assessments aim to determine spatial and temporal distributions of organisms and habitats to help inform resource management decisions. In marine systems, rapid technological advances in sensors employed for biogeographic assessments allow scientists to collect unprecedented volumes of data, yet it remains challenging to visually and intuitively convey these sometimes massive spatial or temporal data as actionable information in geographically relevant maps or virtual models. Here, we provide a case study demonstrating an approach to bridge this data visualization gap by displaying coastal ocean data in a 3D, interactive online format. Our case study documents a workflow that provides resource managers, stakeholders, and the general public with a platform for direct exploration of and interaction with 3D data from hydrographically mapping shipwrecks and marine life on the continental shelf of North Carolina, USA. We simultaneously mapped shipwrecks and their associated fish using echosounders. A multibeam echosounder collected high-resolution multibeam bathymetry of the shipwrecks and detected the broad extent of fish schools. A calibrated splitbeam echosounder detected individual fish and fish schools. After processing the echosounder data, we built an interactive, online 3D data visualization web application complemented by multimedia and story text using ESRI geographic information systems. The freely available visual environment, called “Living Shipwrecks 3D,” allows direct engagement with the biogeographic assessment data in a customizable format. We anticipate that additional interactive 3D data applications can be constructed using a similar workflow allowing seamless exploration of complex spatial data used in biogeographic assessments.

Vegetation height and structure drive foraging habitat selection of the lesser kestrel (Falco naumanni) in intensive agricultural landscapes.

Habitat selection in animals is a fundamental ecological process with key conservation implications. Assessing habitat selection in endangered species and populations occupying the extreme edges of their distribution range, or living in highly anthropized landscapes, may be of particular interest as it may provide hints to mechanisms promoting potential range expansions. We assessed second- and third-order foraging habitat selection in the northernmost European breeding population of the lesser kestrel (Falco naumanni), a migratory falcon of European conservation interest, by integrating results obtained from 411 direct observations with those gathered from nine GPS-tracked individuals. The study population breeds in the intensively cultivated Po Plain (northern Italy). Direct observations and GPS data coincide in showing that foraging lesser kestrels shifted their habitat preferences through the breeding cycle. They positively selected alfalfa and other non-irrigated crops during the early breeding season, while winter cereals were selected during the nestling-rearing phase. Maize was selected during the early breeding season, after sowing, but significantly avoided later. Overall, vegetation height emerged as the main predictor of foraging habitat selection, with birds preferring short vegetation, which is likely to maximise prey accessibility. Such a flexibility in foraging habitat selection according to spatio-temporal variation in the agricultural landscape determined by local crop management practices may have allowed the species to successfully thrive in one of the most intensively cultivated areas of Europe. In the southeastern Po Plain, the broad extent of hay and non-irrigated crops is possibly functioning as a surrogate habitat for the pseudo-steppe environment where most of the European breeding population is settled, fostering the northward expansion of the species in Europe. In intensive agricultural landscapes, the maintenance of alfalfa and winter cereals crops and an overall high crop heterogeneity (deriving from crop rotation) is fundamental to accommodate the ecological requirements of the species in different phases of its breeding cycle.

Regional models do not outperform continental models for invasive species

Aim: Species distribution models can guide invasive species prevention and management by characterizing invasion risk across space. However, extrapolation and transferability issues pose challenges for developing useful models for invasive species. Previous work has emphasized the importance of including all available occurrences in model estimation, but managers attuned to local processes may be skeptical of models based on a broad spatial extent if they suspect the captured responses reflect those of other regions where data are more numerous. We asked whether species distribution models for invasive plants performed better when developed at national versus regional extents. Location: Continental United States. Methods: We developed ensembles of species distribution models trained nationally, on sagebrush habitat, or on sagebrush habitat within three ecoregions (Great Basin, eastern sagebrush, and Great Plains) for nine invasive plants of interest for early detection and rapid response at local or regional scales. We compared the performance of national versus regional models using spatially independent withheld test data from each of the three ecoregions. Results: We found that models trained using a national spatial extent tended to perform better than regionally trained models. Regional models did not outperform national ones even when considerable occurrence data were available for model estimation within the focal region. Information was often unavailable to fit informative regional models precisely in those areas of greatest interest for early detection and rapid response. Main conclusions: Habitat suitability models for invasive plant species trained at a continental extent can reduce extrapolation while maximizing information on species’ responses to environmental variation. Standard modeling methods can capture spatially varying limiting factors, while regional or hierarchical models may only be advantageous when populations differ in their responses to environmental conditions, a condition expected to be relatively rare at the expanding boundaries of invasive species’ distributions.

Laserfarm – A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds

Quantifying ecosystem structure is of key importance for ecology, conservation, restoration, and biodiversity monitoring because the diversity, geographic distribution and abundance of animals, plants and other organisms is tightly linked to the physical structure of vegetation and associated microclimates. Light Detection And Ranging (LiDAR) — an active remote sensing technique — can provide detailed and high resolution information on ecosystem structure because the laser pulse emitted from the sensor and its subsequent return signal from the vegetation (leaves, branches, stems) delivers three-dimensional point clouds from which metrics of vegetation structure (e.g. ecosystem height, cover, and structural complexity) can be derived. However, processing 3D LiDAR point clouds into geospatial data products of ecosystem structure remains challenging across broad spatial extents due to the large volume of national or regional point cloud datasets (typically multiple terabytes consisting of hundreds of billions of points). Here, we present a high-throughput workflow called ‘Laserfarm’ enabling the efficient, scalable and distributed processing of multi-terabyte LiDAR point clouds from national and regional airborne laser scanning (ALS) surveys into geospatial data products of ecosystem structure. Laserfarm is a free and open-source, end-to-end workflow which contains modular pipelines for the re-tiling, normalization, feature extraction and rasterization of point cloud information from ALS and other LiDAR surveys. The workflow is designed with horizontal scalability and can be deployed with distributed computing on different infrastructures, e.g. a cluster of virtual machines. We demonstrate the Laserfarm workflow by processing a country-wide multi-terabyte ALS dataset of the Netherlands (covering ∼34,000 km2 with ∼700 billion points and ∼ 16 TB uncompressed LiDAR point clouds) into 25 raster layers at 10 m resolution capturing ecosystem height, cover and structural complexity at a national extent. The Laserfarm workflow, implemented in Python and available as Jupyter Notebooks, is applicable to other LiDAR datasets and enables users to execute automated pipelines for generating consistent and reproducible geospatial data products of ecosystems structure from massive amounts of LiDAR point clouds on distributed computing infrastructures, including cloud computing environments. We provide information on workflow performance (including total CPU times, total wall-time estimates and average CPU times for single files and LiDAR metrics) and discuss how the Laserfarm workflow can be scaled to other LiDAR datasets and computing environments, including remote cloud infrastructures. The Laserfarm workflow allows a broad user community to process massive amounts of LiDAR point clouds for mapping vegetation structure, e.g. for applications in ecology, biodiversity monitoring and ecosystem restoration.

High resolution thermal remote sensing and the limits of species' tolerance.

Extinction risks for many insect species, particularly across very broad spatial extents, have been linked to the growing frequency and severity of temperatures that exceed the boundaries of their realized niches. Measurement and mitigation of such impacts is hindered by the availability of high-resolution measurements of species-specific severity of extreme weather, especially temperature. While techniques enabling interpolation of broad-scale remote sensing metrics are vital for such efforts, direct remote sensing measurements of thermal conditions could improve habitat management by providing detailed insights that interpolative approaches cannot. Advances in unmanned aerial vehicle (UAV) technology have created opportunities to better evaluate the role of microclimates in local species extinctions. Here, we develop a method to create high-resolution maps of microclimates using UAV and thermal imaging technology that use species’ realized niche boundaries to assess potential effects of severity of extreme temperatures. We generated air temperature maps (5 cm resolution) and canopy height maps (1 cm resolution) for 15 sites in a rare alvar ecosystem in eastern Ontario. We validated these remote sensing observations against independent, in situ temperature observations using iButtons. Temperature observations were accurate and related to physical heterogeneity in alvar habitats. We converted temperature measures into estimates of proximity of thermal niche boundaries for three butterfly species found during field surveys. This is the first time that this method has been applied to high resolution remote sensing observations and offers potential to assess the availability and adequacy of microclimates within habitats at resolutions relevant for conservation management.

Investigating chemical and microbial functional indicators of nutrient retention capacity in greenhouse stormwater retention ponds in southwestern Ontario, Canada

The tributaries flowing through Leamington, Ontario are unique in the Canadian Lake Erie watershed due to the broad spatial extent of greenhouse operations, which more than doubled in size and density from 2011 to 2022. These greenhouse operations are considered to be potential nutrient point sources with respect to observed nutrient concentrations in tributaries adjacent to greenhouse stormwater retention ponds (GSWPs). Identifying causal factors of nutrient release, whether this be chemical or biological, within these ponds may be critical for mitigating their impact on the watershed and ultimately the receiving waters of Lake Erie. Specifically, phosphorus and nitrogen accumulation in freshwater ponds can contribute to environmental damage proximal to adjacent streams, serving as a potential catalyst for algal blooms and eutrophication. This study compared correlations between the water column N:P stoichiometry, sediment nutrient retention capacity, and drivers of microbial metabolism within GSWP sediments. Correlations between water column TN:TP ratios and sediment nutrient retention capacity were observed, suggesting an interplay between N and P in terms of nutrient limitation. Further, clear shifts were observed in the bacterial metabolic pathways analyzed through metatranscriptomics. Specifically, genes related to nitrogen fixation, nitrification and denitrification, and other metabolic processes involving sulfur and methane showed differential expression depending on the condition of the respective pond (i.e., naturalized wetland vs. dredged, eutrophic pond). Collectively, this research serves to highlight the interconnected role of chemical-biological processes particularly as they relate to significant ecosystem processes such as nutrient loading and retention dynamics in impaired freshwater systems.

Consistent trait-temperature interactions drive butterfly phenology in both incidental and survey data

Data availability limits phenological research at broad temporal and spatial extents. Butterflies are among the few taxa with broad-scale occurrence data, from both incidental reports and formal surveys. Incidental reports have biases that are challenging to address, but structured surveys are often limited seasonally and may not span full flight phenologies. Thus, how these data source compare in phenological analyses is unclear. We modeled butterfly phenology in relation to traits and climate using parallel analyses of incidental and survey data, to explore their shared utility and potential for analytical integration. One workflow aggregated “Pollard” surveys, where sites are visited multiple times per year; the other aggregated incidental data from online portals: iNaturalist and eButterfly. For 40 species, we estimated early (10%) and mid (50%) flight period metrics, and compared the spatiotemporal patterns and drivers of phenology across species and between datasets. For both datasets, inter-annual variability was best explained by temperature, and seasonal emergence was earlier for resident species overwintering at more advanced stages. Other traits related to habitat, feeding, dispersal, and voltinism had mixed or no impacts. Our results suggest that data integration can improve phenological research, and leveraging traits may predict phenology in poorly studied species.

Plant diversity reduces satellite-observed phenological variability in wetlands at a national scale.

Plant diversity may enhance stability of ecosystem function and its satellite-derived indicators. However, its potential to stabilize phenology, or seasonal changes in plant function, is little understood, especially in understudied systems with high biodiversity potential such as wetlands. Using a large sample of U.S. wetlands and a new satellite-based indicator of phenological stability, we found that plant diversity was negatively associated with interannual phenological variability after controlling for covariates representing climate, site conditions, and spectral fluctuations. Furthermore, plant diversity and covariates better explained phenological variability than stability in annually summarized satellite-based biomass indicators used by earlier studies. Last, a subsequent path analysis indicated that phenological variability could mediate plant diversity relationship with the latter stability. Our results suggest that contributions of plant diversity to seasonality of ecosystems may have a stabilizing role in their functioning and offer a new basis for assessing biodiversity-stability relationships across broad geographic extents.

The medical importance of scorpions venom: Review

Scorpio poison may reason intense medicinal complexities and early doom whether inserted inside the human being's body. Nerves venoms are the major ingredients of Scorpio poison that are recognized to be accountable for the sick appearances of intoxication. In addition, Nerves venoms, a broad extent of other bio vigorous particles ability are discovered in Scorpio poisons. Improvement in segregation, description, and biotechnical processes may be not solitary to the evolution of extra efficient handlings versus Scorpio intoxicates, however, have as well drove the detection of numerous Scorpio poison peptides through attention curative characteristics. Thus, Scorpio poison may not lone be a medicinal menace to a human being's health, nonetheless could demonstrate to be a worthy origin of bio vigorous particles that may assist as drives for the evolution of novel curatives versus existing and arising illnesses. This presentation submits together the harmful and useful characteristics of Scorpio poison venoms and debates the modern progress inside the evolution of new medication versus Scorpio intoxicates and the curative viewpoint for Scorpio poisons in medicine detection.

The relative influence of history, climate, topography and vegetation structure on local animal richness varies among taxa and spatial grains.

Understanding the spatial scales at which environmental factors drive species richness patterns is a major challenge in ecology. Due to the trade-off between spatial grain and extent, studies tend to focus on a single spatial scale, and the effects of multiple environmental variables operating across spatial scales on the pattern of local species richness have rarely been investigated. Here, we related variation in local species richness of ground beetles, landbirds and small mammals to variation in vegetation structure and topography, regional climate, biome diversity and glaciation history for 27 sites across the USA at two different spatial grains. We studied the relative influence of broad-scale (landscape) environmental conditions using variables estimated at the site level (climate, productivity, biome diversity and glacial era ice cover) and fine-scale (local) environmental conditions using variables estimated at the plot level (topography and vegetation structure) to explain local species richness. We also examined whether plot-level factors scale up to drive continental scale richness patterns. We used Bayesian hierarchical models and quantified the amount of variance in observed richness that was explained by environmental factors at different spatial scales. For all three animal groups, our models explained much of the variation in local species richness (85%-89%), but site-level variables explained a greater proportion of richness variance than plot-level variables. Temperature was the most important site-level predictor for explaining variance in landbirds and ground beetles richness. Some aspects of vegetation structure were the main plot-level predictors of landbird richness. Environmental predictors generally had poor explanatory power for small mammal richness, while glacial era ice cover was the most important site-level predictor. Relationships between plot-level factors and richness varied greatly among geographical regions and spatial grains, and most relationships did not hold when predictors were scaled up to the continental scale. Our results suggest that the factors that determine richness may be highly dependent on spatial grain, geography, and animal group. We demonstrate that instead of artificially manipulating the resolution to study multiscale effects, a hierarchical approach that uses fine grain data at broad extents could help solve the issue of scale selection in environment-richness studies.

Relaxin Inhibits the Cardiac Myofibroblast NLRP3 Inflammasome as Part of Its Anti-Fibrotic Actions via the Angiotensin Type 2 and ATP (P2X7) Receptors.

Chronic NLRP3 inflammasome activation can promote fibrosis through its production of interleukin (IL)-1β and IL-18. Conversely, recombinant human relaxin (RLX) can inhibit the pro-fibrotic interactions between IL-1β, IL-18 and transforming growth factor (TGF)-β1. Here, the broader extent by which RLX targeted the myofibroblast NLRP3 inflammasome to mediate its anti-fibrotic effects was elucidated. Primary human cardiac fibroblasts (HCFs), stimulated with TGF-β1 (to promote myofibroblast (HCMF) differentiation), LPS (to prime the NLRP3 inflammasome) and ATP (to activate the NLRP3 inflammasome) (T+L+A) or benzoylbenzoyl-ATP (to activate the ATP receptor; P2X7R) (T+L+Bz), co-expressed relaxin family peptide receptor-1 (RXFP1), the angiotensin II type 2 receptor (AT2R) and P2X7R, and underwent increased protein expression of toll-like receptor (TLR)-4, NLRP3, caspase-1, IL-1β and IL-18. Whilst RLX co-administration to HCMFs significantly prevented the T+L+A- or T+L+Bz-stimulated increase in these end points, the inhibitory effects of RLX were annulled by the pharmacological antagonism of either RXFP1, AT2R, P2X7R, TLR-4, reactive oxygen species (ROS) or caspase-1. The RLX-induced amelioration of left ventricular inflammation, cardiomyocyte hypertrophy and fibrosis in isoproterenol (ISO)-injured mice, was also attenuated by P2X7R antagonism. Thus, the ability of RLX to ameliorate the myofibroblast NLRP3 inflammasome as part of its anti-fibrotic effects, appeared to involve RXFP1, AT2R, P2X7R and the inhibition of TLR-4, ROS and caspase-1.

Interannual variation in climate contributes to contingency in post‐fire restoration outcomes in seeded sagebrush steppe

AbstractInterannual variation, especially weather, is an often‐cited reason for restoration “failures”; yet its importance is difficult to experimentally isolate across broad spatiotemporal extents, due to correlations between weather and site characteristics. We examined post‐fire treatments within sagebrush‐steppe ecosystems to ask: (1) Is weather following seeding efforts a primary reason why restoration outcomes depart from predictions? and (2) Does the management‐relevance of weather differ across space and with time since treatment? Our analysis quantified range‐wide patterns of sagebrush (Artemisia spp.) recovery, by integrating long‐term records of restoration and annual vegetation cover estimates from satellite imagery following thousands of post‐fire seeding treatments from 1984 to 2005. Across the Great Basin, sagebrush growth increased in wetter, cooler springs; however, the importance of spring weather varied with sites' long‐term climates, suggesting differing ecophysiological limitations across sagebrush's range. Incorporation of spring weather, including from the “planting year,” improved predictions of sagebrush recovery, but these advances were small compared to contributions of time‐invariant site characteristics. Given extreme weather conditions threatening this ecosystem, explicit consideration of weather could improve the allocation of management resources, such as by identifying areas requiring repeated treatments; but improved forecasts of shifting mean conditions with climate change may more significantly aid the prediction of sagebrush recovery.

Comparison of methods for estimating density and population trends for low-density Asian bears

Populations of bears in Asia are vulnerable to extinction and effective monitoring is critical to measure and direct conservation efforts. Population abundance (local density) or growth (λ) are the most sensitive metrics to change. We discuss the value in implementing spatially explicit capture-recapture (SCR), the current gold standard for density estimation, and open population SCR (OPSCR) to monitor changes in density over time. We provide guidance for designing studies to provide estimates with sufficient power to detect changes. Because of the wide availability of camera traps and interest in their use, we consider six density estimation methods and their extensions developed for use with camera traps, with specific consideration of assumptions and applications for monitoring Asian bears. We conducted a power analysis to calculate the precision in estimates needed to detect changes in populations with reference to IUCN Red List criteria. We performed a systematic review of empirical studies implementing camera trap abundance estimation methods and considered sample sizes, effort, and model assumptions required to achieve adequate precision for population monitoring. We found SCR and OPSCR, reliant on “marked” individuals, are currently the only methods with enough power to reliably detect even moderate to major (20–80%) declines. Camera trap methods with unmarked individuals rarely achieved precision sufficient to detect even large declines (80–90%), although with some exceptions (e.g., situations with moderate population densities, large number of sampling sites, or inclusion of ancillary local telemetry data. We describe additional estimation options including line transects, direct observations, monitoring age-specific survival and reproductive rates, and hybrid/integrated methodologies that may have potential to work for some Asian bear populations. We conclude monitoring changes in abundance or density is possible for most Asian bear populations but will require collaboration among researchers over broad spatial extents and extensive financial investment to overcome biological and logistical constraints. We strongly encourage practitioners to consider study design and sampling effort required to meet objectives by conducting simulations, power analyses, and assumption checks prior to implementing monitoring efforts, and reporting standardized dispersion measures such as coefficients of variation to allow for assessment of precision. Our guidance is relevant to other low-density and wide-ranging species.

Determination of friction factor and heat transfer rate in laminar flow in an agitated vessel

Helical coils are commonly employed in a variety of process industries due to their superior fluid mixing capabilities, the flexibility of fabrication, and simple design. Frictional pressure losses occur in flow-through helically coiled tubes, affecting the heat transfer rate across the coils. An inquiry is carried out in the current experimental study to discover friction factors and heat transfer rates within the coil tubes of test fluids flowing in the laminar flow area. Non-Newtonian test fluids comprised ichorous solutions of Carboxy Methyl Cellulose (CMC) at concentrations of 0.5 percent, 1 percent, and 2 percent, whereas Newtonian test fluids consisted of water. With the use of an appropriate viscosity expression, Newtonian consonances are demonstrated to apply to several non-Newtonian fluids with certain constraints. In a laminar flow, the shear stress in a helical coil is greater than in a straight conduit. It has been proved that the experimental data can be fruitfully correlated across a broad extent of fluid data and coil radius stretch. The investigated data obtained has been correlated with Standard a deviation of 15.6% with the range 25<D_e < 2000 and 40<P_r < 226.

Notebook‐as‐a‐VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment

AbstractVirtual research environments (VREs) provide user‐centric support in the lifecycle of research activities, for example, discovering and accessing research assets or composing and executing application workflows. A typical VRE is often implemented as an integrated environment, including a catalog of research assets, a workflow management system, a data management framework, and tools for enabling user collaboration. In contrast, notebook environments like Jupyter allow researchers to rapidly prototype scientific code and share their experiments as online accessible notebooks. Jupyter can support several popular languages used by data scientists, such as Python, R, and Julia. However, such notebook environments do not have seamless support for running heavy computations on remote infrastructure or finding and accessing collaborative software code inside notebooks. This article investigates the gap between a notebook environment and a VRE and proposes an embedded VRE solution for the Jupyter environment called Notebook‐as‐a‐VRE (NaaVRE). The NaaVRE solution provides functional components via a component marketplace and allows users to create a customized VRE on top of the Jupyter environment. From the VRE, a user can search research assets (data, software, and algorithms), compose workflows, manage the lifecycle of an experiment, and share the results among users in the community. We demonstrate how such a solution can enhance a legacy workflow that uses Light Detection and Ranging (LiDAR) data from country‐wide airborne laser scanning surveys for deriving geospatial data products of ecosystem structure at high resolution over broad spatial extents. This enables users to scale out the processing of multi‐terabyte LiDAR point clouds for ecological applications to more data sources in a distributed cloud environment. Similar applications could be developed for workflows producing other essential biodiversity variables.

How to Define the Elephant: Towards a Novel Conceptualization of Populism

In recent decades, a serious number of studies have been dedicated to defining and conceptualizing populism in order to enable proper and comparative analyses of the phenomenon. They usually studied populism by reducing it to an ideology, discourse, or strategy and provided analytical approaches accordingly although some other approaches (style of communication, political project, etc.) are possible to come across in the relevant literature. Critically engaging with the most influential ones, this article ascertains two principal deficiencies in these bodies of work. Firstly, the minimal and generic definitions presented in these studies empty the concept to a broad extent. Secondly, most scholars assume the concentrated dimension of populism as the whole of the phenomenon and undertake its conceptualization with this assumption to a large extent. As a result of a comprehensive discussion on these main and also some secondary deficiencies, this article offers two ways to treat them. Scholars who examine populism and make comparisons between different cases can either carry out their analyses by taking the multi-layered nature of the phenomenon into account or by clearly stating the dimension of populism which they study and, in this way, limit their work to this dimension without further claims. In addition to all these, showing the inadequacy of the Sartorian approaches that dominate the literature, the article discusses that Wittgensteinian approaches can provide appropriate alternative frameworks for the conceptualization of populism.