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opencc-by-4.0Dec 2023View details →

Data for the paper Large-Scale Tropical Circulation Intensification by Aerosol Effect on Clouds

Data for the paper Large-Scale Tropical Circulation Intensification by Aerosol Effect on Clouds.   Please see the README for more information.

opencc-by-4.0Dec 2023View details →

Experimental measurements of the effects of surface roughness on large-scale downburst-like impinging jets at the WindEEE Dome laboratory

Thunderstorm downbursts originate as negatively buoyant currents of cold air descending from cumulonimbus clouds. Upon impacting the ground, a strong radial outflow develops with maximum wind velocities occurring at the near-ground level. These types of flows pose serious hazard to the natural and built environment. Their restricted time and spatial extent as well as their intermittent and non-Gaussian fluctuating nature make them extremely challenging to be recorded and analyzed through classic full-scale measurements in nature. Alongside synoptic-scale extra tropical cyclones, downbursts govern the wind climate at the mid-latitude areas around the globe. Recent trends in climate change studies suggest both an increased intensity as well as frequency of occurrence of these events. Therefore, their scientific comprehension urges serious consideration. In the context of the project THUNDERR “Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures”, financed by the European Research Council (ERC) Advanced Grant 2016 (grant No. 741273, P.I. Prof. Giovanni Solari, University of Genoa), an extensive experimental campaign was recently conducted at the WindEEE Dome wind chamber. This campaign focused on measuring downburst-like flows (DLFs) generated by large-scale impinging jets. The dataset presented here encompasses a portion of the measurements collected during this comprehensive experimental initiative. Specifically, this series of tests aimed to unravel the role of surface roughness in potentially altering the configuration and dynamics of the overall radial outflow. The term "surface roughness" pertains to the ground patch under examination, encompassing both the natural features of the terrain and any obstacles, such as buildings, present on the ground. While surface roughness plays a decisive role in changing the shape and magnitudes of the wind speed vertical profiles for extra-tropical cyclones, the scientific literature has not yet thoroughly addressed its impact on downburst winds, which are different being dominated by intense vortex dynamics. Impinging jets, considered representative for the simulation of downburst like flow (DLF), are here simulated as transient phenomena through the opening and closing of the bell-mouth that connects the test chamber and the upper plenum of the dome, the latter being pressurized before releasing the jet. As a result, the velocity records exhibit a distinct pattern, featuring a sudden ramp-up of velocity, followed by a velocity peak, a statistically-stationary phase, and ultimately, a gradual velocity deceleration—mirroring the behavior observed in real-world scenarios. The database consists of six ASCII tab-delimited text files, denoted as ‘windspeedDB89z0eq007.txt’, ‘windspeedDB89z0eq020.txt’, ‘windspeedDB89z0eq320.txt’, ‘windspeedDB124z0eq007.txt’, ‘windspeedDB124z0eq020.txt’, and ‘windspeedDB124z0eq320.txt’, aligning with the two jet intensities and three rough surfaces employed in the experiments. These filenames correspond to: (i) centerline jet velocities at the nozzle outlet section, with values of Wjet = 8.9 and 12.4 m/s (indicated as “Wjet” in the database files); (ii) equivalent full-scale roughness lengths z0eq = 0.007, 0.020, 0.32 m (“z0eq” in the database files, see details below). Each file encompasses wind speed timeseries, detailed as follows: The three-component velocity measurements were recorded by means of 11 Cobra probes (sampling frequency 2,500 Hz) mounted on a stiff mast. The heights (z) of the probes were z = 0.040, 0.070, 0.100, 0.125, 0.150, 0.200, 0.300, 0.400, 0.500, 0.700, 1.000 m above the surface. Within the database files, the wind speed linked to various heights is labeled as “v_zXXXXmm”. In this notation, 'v' designates the velocity component: longitudinal ‘U’ (along the horizontal axis of the probe), corresponding to the radial outflow of the downburst, with a positive value when the flow is directed toward the probe. Transversal, ‘V’, represents the velocity component transverse to the probe's centerline axis, having a positive value when the flow is directed right-to-left concerning an observer facing the probe's head. The vertical component is denoted as ‘W’ with a positive value indicating an upward direction. The term “XXXX” signifies the height of the probe, specified in millimeters (mm). The mast with the Cobra probes was subsequently positioned at ten radial r distances with respect to the jet impingement position in the range r/D (D = 3.2 m is the jet diameter) between 0.2–2.0 with an increment of 0.2. Note that the position r/D = 0.8 was adjusted to r/D = 0.75. This modification was necessary due to irregularities on the chamber floor at r/D = 0.8, which could have otherwise introduced bias into the measurements. The radial distance is identified with “r/D_distance” in the dataset files. The ceiling height of the testing chamber is H = 3.75 m, which leads to H/D > 1 allowing for a full vertical development of the downburst radial outflow. For every r/D position, each experiment with the same initial condition (i.e., Wjet) was repeated 10 times (“repetition#” in the database files) to inspect the repeatability of the tests and their variance. Each velocity record lasted 12 s (12 × 2,500 = 30,000 samples) and the duration of the downburst-like part of the record varied between 3–5 s. Overall, 6,600 total time series (2 Wjet × 3 rough surface × 10 repetitions × 10 r/D positions × 11 heights z) of downburst-like outflows were recorded during this set of experimental tests. The reported accuracy of Cobra probes from the manufacturer is +/- 0.5 m/s and +/- 1° for velocity and yaw/pitch angles respectively, up to approximately 30% of turbulence intensity. All velocity magnitudes below 1 m/s were removed and converted to NaN (Not a Number) in the database due to the poor accuracy of Cobra probes for velocities below this threshold. In addition, some velocity values were reported as null in the instrument readings due to the incoming flow being outside the probe's spatial cone of measurement (+/- 45° in respect to the probe horizontal axis). These values are flagged as NULL values in the database. This notation aligns with that utilized in a preceding database of measurements collected within the same experimental campaign at the WindEEE Dome (Canepa et al., 2021; <a href="https://doi.org/10.1594/PANGAEA.931205">https://doi.org/10.1594/PANGAEA.931205</a>). DLFs were tested on three different surfaces: (i) WindEEE Dome bare floor; (ii) Carpet; (iii) Artificial grass. A 1 m × 8 m rectangular section was selected from each of the three surfaces for testing purposes. Each surface was positioned with a 1 m offset relative to the geometric location of the jet impingement. It was identified by an equivalent full-scale roughness length, “z0eq”based on matching atmospheric boundary layer profiles measured in WindEEE in boundary layer mode with standard ESDU (Engineering Science Data Unit) profiles. A total of 15 different Atmospheric Boundary Layer (ABL)-like profiles were tested inside the chamber by varying the rotation-per-minute (rpm) of the fans across the 4 rows of the 60-fan wall—a peripheral wall of the hexagonal WindEEE Dome chamber comprising a matrix of 4 × 15 (rows × columns) fans that is used to produce ABL -like flows. A specific configuration of the 60-fan wall and a length scale of 1:200 were chosen based on correlation analysis between physically reproduced ABL profiles and curve fitting through Eq. A1.8 of the ESDU 82026. This scale is deemed suitable for both ABL and downburst winds produced at the laboratory. Through a linear fitting of the measured data on the U – ln(z) chart, employing the logarithmic law-of-the-wall (dependent on roughness length z0 and friction velocity u*), the equivalent roughness lengths for the three surfaces were determined: z0eq = 0.007, 0.020, 0.320 m for the WindEEE Dome bare floor, carpet, and artificial grass, respectively. In summary, each experimental velocity time series consists of 30,000 rows, with 33 columns detailing the three velocity components (U, V, W) across the 11 Cobra probe heights. Columns 34 to 37 provide information on the repetition number, radial position of measurement, equivalent full-scale roughness length, and jet intensity. The subsequent timeseries within the dataset refer to the parameters in columns 34 to 37, each one spanning its entire range in the specified order. Researchers can leverage this database to validate and calibrate numerical and analytical models of thunderstorm winds, in addition to interpreting full-scale measurements of the phenomenon. It also serves as a valuable resource for the fluid dynamics community, particularly those interested in the physical comprehension of downscaled flows or the surface flow dynamics of large Reynolds number impinging jets.

opencc-zeroFeb 2022View details →

Long-term monitoring in endangered woodlands shows effects of multi-scale drivers on bird occupancy

Occupancy predictor data, detection predictor data, and species detections from sites in remnant Box Gum Grassy Woodland patches in south-eastern Australia. Only sites, species, and predictors used in our statistical analysises included. For privacy, predictors have been standardised (mean = 0, standard deviation = 1) and latitude and longitude have been offset by random vectors.

opencc-zeroFeb 2022View details →

Data from: How far is enough? Prediction of the scale of effect for wild bees

A crucial issue for landscape ecologists is identifying the spatial extents at which a landscape affects species occurrence. Multi-scale analyses are usually conducted to identify the "scale of effect", that is, the spatial extent associated with the best relationship between landscape variables and species occurrence, which is assumed to be related to species traits. However, few guidelines exist to determine the range of distances to be investigated. Based on the foraging distances of wild bee species, our main goal was to estimate the maximum distance of effect, that is, the distance beyond which the scale of effect for wild bee species is unlikely to be detected. Using the InVEST pollination model, we (i) modelled bee categories with distinct foraging distances and identified the scale of effect on their simulated abundance (ii) defined an index, noted λ, that estimates the distance beyond which landscape composition has only negligible effects on simulated abundances. We validated our results by identifying the scale of effect on the abundances of 16 bee species collected in south-western France. We detected a significant positive relationship between the average foraging distance (α) of the modelled bees and their scale of effect. The λ index was linearly related to the average foraging distances of bees (λ=5.4 α+253) and was above the identified scale of effect for the modelled bees. The λ was also found to be above the scale of effect for 93% of the observed bee species. Our results suggest that the λ index is a good estimator of the upper limit of the scale of effect for wild bees. The λ index could be used to identify the minimum distance between sampling sites before setting up an experiment and the maximum buffer size required in multi-scale analysis to detect the scale of effect.

opencc-by-4.0Jul 2024View details →

Continental scale α- and β-diversity patterns of terrestrial eukaryotic microbes: effect of climate and microhabitat on testate amoeba assemblages in Eurasian peatlands

Open the record for dataset details and reuse information.

opencc-by-nc-nd-4.0Mar 2018View details →

Quantifying the large-scale electrification equilibrium effects in dust storms using field observations at Qingtu Lake Observatory

The measured divergence of the electric field, PM10 concentration, ambient temperature and relative humidity time series during dust storms (at Qingtu Lake observatory) are given in these datasets.

FIG. 8 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success

FIG. 8. Daily rainfall (mm) during the 2018 incubation period and average 15-minute soil saturation (%) at the bottom (solid line) and top (dashed line) of turtle nests (red, n ¼ 6) and haphazard sites (gray, n ¼ 6).

FIG. 6 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success

FIG. 6. Mean (6 range) daily soil temperature (8C) at the depth of the nest chamber center during the 2018 incubation season for turtle nests (n ¼ 6, red) and paired haphazard sites (n ¼ 6, light gray).

FIG. 7 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success

FIG. 7. Diel soil temperature (8C) pattern for turtle nests (n ¼ 6, red line) and paired haphazard sites (n ¼ 6, gray line) measured hourly (points) at depths equivalent to the bottom (A) and top (B) of the nest chambers during the 2018 summer incubation period.

FIG. 2 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success

FIG. 2. Mean (695% confidence interval) hourly soil temperature (8C) at the depth of the nest chamber top (A) and bottom (B) for turtle nests during the 2018 (n ¼ 6) and 2019 (n ¼ 6) incubation period. Nest were laid in sites with a crevice (red line, n ¼ 3), ledge (gray line, n ¼ 5), or flat (black line, n ¼ 4) bedrock morphology.

FIG. 5 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success

FIG. 5. Mean (6 SE) soil saturation (%) recession curves after rainfall events for sections of the turtle nest cavities with 100% hatch success (red line, n ¼ 8) and 0% hatch success (gray line, n ¼ 9) during the 2018 and 2019 incubation periods (A). Mean (6 SE) soil saturation (%) recession curves after rainfall events for turtle nests (red line, n ¼ 6) and paired haphazard sites (gray line, n ¼ 6) during the 2018 incubation period (B).

FIG. 3 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success

FIG. 3. Predicted probability (695% confidence intervals) of turtle egg hatch success (n ¼ 105) in relation to mean daily soil temperature (8C) when variance of percent soil saturation during incubation was high (standard deviation of 20% saturation, gray) compared to low (standard deviation of 10% saturation, red). Mean daily incubation temperature is shown for each turtle egg and black circles represent sample size (1–3 eggs [small circle], 4–6 eggs [medium circle], or 7þ eggs [large circle]).

FIG. 1 in Multi-scale Assessment of Rock Barrens Turtle Nesting Habitat: Effects of Moisture and Temperature on Hatch Success

FIG. 1. In a rock barrens landscape in the eastern Georgian Bay region (A), turtles nest in shallow soil deposits underlain by bedrock which can be classified as having either a crevice (B), ledge (C), or flat (D) morphology.

opencc-zeroDec 2013View details →

Data from: Scale-dependent effects of landscape variables on gene flow and population structure in bats

Aim: A common pattern in biogeography is the scale-dependent effect of environmental variables on the spatial distribution of species. We tested the role of climatic and land cover variables in structuring the distribution of genetic variation in the grey long-eared bat, Plecotus austriacus, across spatial scales. Although landscape genetics has been widely used to describe spatial patterns of gene flow in a variety of taxa, volant animals have generally been neglected because of their perceived high dispersal potential.Location: England and Europe. Methods: We used a multiscale integrated approach, combining population genetics with species distribution modelling and geographical information under a causal modelling framework, to identify landscape barriers to gene flow and their effect on population structure and conservation status. Genotyping involved 23 polymorphic microsatellites and 259 samples from across the species' range. Results: We identified distinct population structure shaped by geographical barriers and evidence of population fragmentation at the northern edge of the range. Habitat suitability (as captured by species distribution models, SDMs) was the most important landscape variable affecting genetic connectivity at the broad spatial scale, while at the fine scale, lowland unimproved grasslands, the main foraging habitat of P. austriacus, played a pivotal role in promoting genetic connectivity. Main conclusions: The importance of lowland unimproved grasslands in determining the biogeography and genetic connectivity in P. austriacus highlights the importance of their conservation as part of a wider landscape management for fragmented edge populations. This study illustrates the value of using SDMs in landscape genetics and highlights the need for multiscale approaches when studying genetic connectivity in volant animals or taxa with similar dispersal abilities.

opencc-zeroDec 2018View details →

Data from: Genetic and environmental effects on the scaling of metabolic rate with body size

Metabolic rate (MR) often scales with body mass (BM) following a power function of the form MR = aBMb, where b is the allometric exponent (i.e. slope on a log-log scale). The variational properties of b have been debated, but very few studies have tested for genetic variance in b, and none have tested for a genotype-by-environment (GxE) interaction in b. Consequently, the short-term evolutionary potentials of both b and its phenotypic plasticity remain unknown. Using 10 clones of a population of Daphnia magna, we estimated the genetic variance in b and assessed whether a GxE interaction affected b. We measured metabolic rate on juveniles of different sizes reared and measured at three temperatures (17, 22 and 28&[deg]C). Overall, b decreased with increasing temperature. We found no evidence of genetic variance in b at any temperature, and thus no GxE interaction in b. However, we found a significant GxE interaction in size-specific metabolic rate. Using simulations, we show how this GxE interaction can generate genetic variation in the ontogenetic allometric slopes of animals experiencing directional changes in temperature during growth. This suggests that b can evolve despite having limited genetic variation at constant temperatures.

opencc-zeroDec 2017View details →

Data from: The effects of spatial scale and isoscape on consumer isotopic niche width

1. The mean and variance of ecological variables are dependent on sampling attributes such as the coverage of environmental heterogeneity (sampling extent) and spatial scale. Trophic niche width is often approximated by bulk tissue stable isotopes of C and N, i.e. the population isotopic niche. However, recent studies suggest that environmental heterogeneity (experienced by individuals) may be more important in defining the isotopic niche width than trophic variability. We hypothesised that isotopic niche width will increase monotonically with spatial scale, largely produced by environmental variation, e.g. nutrient source. 2. To refine this hypothesis, by describing the shapes of isotope scaling curves, we explored a previously published dataset describing three Chilean intertidal species representing different feeding guilds (grazing snails, suspension feeding mussel). We tested these hypotheses on a new, larger dataset describing three functionally-analogous intertidal species from Northern Ireland. We generated isotopic variance-area curves from a spatially-explicit bootstrap and investigated the scale-dependency of environment-isotope relationships, including wave exposure and sub-habitat heterogeneity. 3. Spatial scale explained 50% of the variance in population isotopic niche widths (bivariate C-N ellipse area) by simple, non-linear relationships. Finer scales (< 1 to 10 km lag) accounted for most variance. Scale dependence was strong for ẟ15N variance, of which > 40% was explained by modelling linear coefficients. A ẟ15N baseline gradient, or isoscape, dominated ẟ15N variance scaling patterns, from sheltered, terrestrially-influenced embayments to exposed, pelagic-dominated coastline. Consumer ẟ13C variance had a weaker scale-dependence, plateauing at mesoscales (> 20 km lag). 4. We show that isotopic niche width is strongly dependent on sampling spatial extent, which controls the environmental heterogeneity experienced by individual consumers. Environmental heterogeneity must be accounted for before isotopic niche width can be considered to accurately represent trophic niche width. Studies conducted at different spatial scales are likely to identify different environment-isotope relationships. 5. We recommend that spatial scale should be incorporated into sampling designs explicitly, easiest by maintaining a consistent lag distance or area within which populations are sampled. Identified isoscapes can be de-trended, where necessary.

opencc-zeroDec 2017View details →

Data from: Short-term microbial effects of a large-scale mine-tailing storage facility collapse on the local natural environment

We investigated the impacts of the Mount Polley tailings impoundment failure on chemical, physical, and microbial properties of substrates within the affected watershed, comprised of 70 hectares of riparian wetlands and 40 km of stream and lake shore. We established a biomonitoring network in October of 2014, two months following the disturbance, and evaluated riparian and wetland substrates for microbial community composition and function via 16S and full metagenome sequencing. A total of 234 samples were collected from substrates at 3 depths and 1,650,752 sequences were recorded in a geodatabase framework. These data revealed a wealth of information regarding watershed-scale distribution of microbial community members, as well as community composition, structure, and response to disturbance. Substrates associated with the impact zone were distinct chemically as indicated by elevated pH, nitrate, and sulphate. The microbial community exhibited elevated metabolic capacity for selenate and sulfate reduction and an abundance of chemolithoautotrophs in the Thiobacillus thiophilus/T. denitrificans/T. thioparus clade that may contribute to nitrate attenuation within the affected watershed. The most impacted area (a 6km stream connecting two lakes) exhibited 30% lower microbial diversity relative to the remaining sites. The tailings impoundment failure at Mount Polley Mine has provided a unique opportunity to evaluate functional and compositional diversity soon after a major catastrophic disturbance to assess metabolic potential for ecosystem recovery.

opencc-zeroJul 2021View details →

Data from: Scale-dependent effects of terrestrial habitat on genetic variation in the great crested newt (Triturus cristatus)

Context Terrestrial landscapes surrounding aquatic habitat influence the persistence of amphibian spatially structured populations (SSPs) via their crucial role in providing estivation and overwintering sites, facilitating or hampering dispersal and colonisation, and consequently the maintenance or loss of genetic diversity. Objectives To highlight the landscape drivers of genetic variation, we investigated the relationship between the level of genetic variation measured within ponds of the great crested newt (Triturus cristatus), and the composition of the surrounding landscape at various spatial scales. Methods Based on the sampling of 40 ponds in thirteen SSPs, the influence of landscape features on several estimators of genetic variation was investigated via linear mixed models, with effects within and between SSPs incorporated. Results The best models depended on the spatial scale, with more significant associations within radii of 50 and 100 m of core ponds, particularly for allelic richness. Responses within and between SSPs were mostly similar. The availability of aquatic habitat in the landscape had a positive effect, while woodland, arable land and pasture had different effects depending on scale and response variable. Total length of roads within a 250 m radius influenced effective population size negatively. Conclusions Our results stress the need to investigate the influence of environmental predictors at multiple spatial scales for an adequate understanding of ongoing processes. Generally, the landscape affected genetic variation similarly within and between SSPs. This allowed us to provide general guidelines for the persistence of great crested newt populations, with an emphasis on the importance of the aquatic habitat.

opencc-zeroDec 2020View details →

Focusing on individual plants to understand community scale biodiversity effects: the case of root distribution in grasslands

Spatial resource partitioning between species via differences in rooting depth is one of the main explanations for the positive biodiversity-productivity relationship. However, evidence for the importance of this mechanism is limited. This may be due to the community scale at which these interactions are often investigated. Community measures represent net outcomes of species interactions and may obscure the mechanisms underlying belowground interactions. Here, we assess the performance of ~1700 individual plants and their heterospecific neighbours over three growing seasons in experimental grassland plots containing one, four or 16 different plant species and tested whether their performance in mixtures compared to monocultures was related to their own rooting depth vs. the rooting depth of their heterospecific neighbours. Overall, individuals of deep-rooting species performed better in mixtures and this effect significantly increased when surrounded by more shallow-rooting species. This effect was not apparent for the shallow rooting species. Together, including both deep and shallow rooting species increased mixture performance. Our results show that taking the perspective of the individual rather than the community can elucidate the interactions between species that contribute to positive biodiversity effects, emphasizing the need for studies at different scales to disentangle the myriad interactions that take place in diverse communities.