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opencc-by-4.0Oct 2017View details →

Fig. 1 in Interspecific Interactions as a Factor of Limitation of Geographical Distribution: Evidence Obtained by Modeling Home Ranges of Vole Twin Species Microtus Arvalis – M. Levis (Rodentia, Microtidae)

Fig. 1. Potential distribution of the Common vole Microtus arvalis. White circles are georeferenced occurrences of genetically identified individuals; black indicates areas of maximum habitat suitability, white are areas of lowest suitability.

Fig. 5 in Factors limiting the current distribution of the introduced Java sparrow (Lonchura oryzivora) in Bangkok, Thailand

Fig. 5. Four nest localities of Java sparrow in the study area; nurse dormitory of Bhumipol Hospital (1), Air Force Youth Club (2), Air Force Museum (3) and Phaholyothin 69/1 alley (4). Java sparrows were mostly found nesting in (1) and (3). All sites were located <1.5 km from Don Muang Airport. (1), (2) and (3) were located on the eastern side of Don Muang Airport and (4) was located to the south-west.

Fig. 6. There were 39 in Factors limiting the current distribution of the introduced Java sparrow (Lonchura oryzivora) in Bangkok, Thailand

Fig. 6. There were 39 nest sites at Bhumibol Hospital, 22 sites at the Royal Thai Air Force Museum, 3 at the Thai Air Force Youth Club and 3 at Phahonyothin Soi 69/1. Birds mostly located their nests in cavities in the ceilings of buildings or in openings of air ducts.

Fig. 3. Within the 15 occupied cells, 6 cells had a in Factors limiting the current distribution of the introduced Java sparrow (Lonchura oryzivora) in Bangkok, Thailand

Fig. 3. Within the 15 occupied cells, 6 cells had a percentage of paddyfield cover <1%, 6 cells had 1–20% paddyfield cover and 3 cells had 20–40% paddyfield cover. For the 26 cells with no detections, 16 had a percentage of paddyfield cover <1%, 7 cells had 1–20% paddyfield cover and 3 cells had 20–40% paddyfield cover.

Fig. 2. Forty-one grid cells randomly set within a 10 in Factors limiting the current distribution of the introduced Java sparrow (Lonchura oryzivora) in Bangkok, Thailand

Fig. 2. Forty-one grid cells randomly set within a 10-km radius around Don Muang airport, Bangkok, Thailand, used for surveying Java sparrows. Don Muang was the site where Java sparrows were first recorded in Thailand in 1924. The study area includes three provinces; Bangkok, Pathum Thani, and Nonthaburi. The primary land use was urban, while paddyfields were mostly located on the eastern side of the study area. Different symbols (circles, squares and triangles) represent the number of occasions (out of 5 possible visits to a location) on which the sparrow was detected. Size of symbols of Java sparrow detection points was related to the number of birds detected; smallest size indicated only 1 bird was detected, medium size indicated 2–10 birds were detected, and largest size indicated a group of more than 40 birds was detected.

Fig. 1. 101 in Factors limiting the current distribution of the introduced Java sparrow (Lonchura oryzivora) in Bangkok, Thailand

Fig. 1. 101 grid cells (400 ha each) were overlaid on the study area (40,000 ha). We randomly selected approximately 40% (41) (green) of 98 accessible grid cells in total. Access to three grid cells (red) in the middle of study area including Don Muang International Airport were restricted by the Thai Air Force.

Fig. 4 in Factors limiting the current distribution of the introduced Java sparrow (Lonchura oryzivora) in Bangkok, Thailand

Fig. 4. Number of Java sparrows counted at the two main roosting sites in Bangkok, Thailand, each site was counted once per month from August 2016 to July 2017. Lowest and highest counts (33 and 2132 individuals) were obtained from the Kan Kheha Thung Song Hong (KKTSH) roost (dashed line). Lowest counts at KKTSH were in August–September 2016. The roosting site at Chaeng Wattana (solid line) had a lower number of birds but may have received some birds from KKTSH during the first two months of the count.

opencc-by-4.0Feb 2023View details →

Data for Measurement report: Air pollution emission factors of inland river ships under compliance with the 10 parts per million limit for sulfur content in fuel

Since July 1, 2019, China’s domestic diesel fuel has been limited to 10 ppm of sulfur. Hence, to explore the applicability of the “sniffer” method and the distribution and level of inland river ships (IRSs) emission factors (EFs) under this limitation, we installed “sniffer” monitoring equipment, from August 2020 to June 2022, at the Gezhou Dam of the Yangtze River in China and monitored emissions from 8,238 IRSs in total passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oilsamples for direct fuel sulfur content detection, which determined the true fuel sulfur content of the passing ships. fuel sulfur content. The “sniffer” monitoring equipment included SO2, CO2, NO, and NO2 gas sensors, PM2.5 and PM10 particulate matter sensors, as well as wind speed, wind direction, temperature, humidity, and pressure sensors.

zenodo36/100

Dataset for the study:"Driving and limiting factors of CH4 and CO2 emissions from coastal brackish-water wetlands in temperate regions"

Dataset used for statistical analysis of the manuscript "Chiapponi, E., Silvestri, S., Zannoni, D., Antonellini, M., and Giambastiani, B. M. S.: Driving and limiting factors of CH4 and CO2 emissions from coastal brackish-water wetlands in temperate regions, EGUsphere, https://doi.org/10.5194/egusphere-2023-605, 2023." The dataset include: <ul> <li>CO2 and CH4 fluxes retrived with a portable fluximeter from soils and standing waters</li> <li>environemntal parameters ( T of air and water, Electrical Conductivity (EC), irradiance and water depth </li> </ul> To cite content from this repository: "Chiapponi, E., Silvestri, S., Zannoni, D., Antonellini, M., and Giambastiani, B. M. S.: Dataset for the study:"Driving and limiting factors of CH4 and CO2 emissions from coastal brackish-water wetlands in temperate regions", EGUsphere, 10.5281/zenodo.10390803."

opencc-by-4.0Dec 2023View details →

zenodo36/100

Fig. 2 in Interspecific Interactions as a Factor of Limitation of Geographical Distribution: Evidence Obtained by Modeling Home Ranges of Vole Twin Species Microtus Arvalis – M. Levis (Rodentia, Microtidae)

Fig. 2. Potential distribution of the East European vole (Microtus levis). Captions as in fig.1.

opencc-by-4.0Oct 2017View details →

opencc-zeroApr 2022View details →

Data from: Demographic and ecogeographic factors limit wild grapevine spread at the southern edge of its distribution range - wild grapevine sampling locations, Maxent input files, morphological and microsatellite data

This dataset contains raw data described in the paper: "Rahimi O., Ohana-Levi N., Brauner H., Inbar N., Hübner S. and Drori E. (2021) "Demographic and ecogeographic factors limit wild grapevine spread at the southern edge of its distribution range", accepted for publication in "Ecology and Evolution". The spatial distribution of plants is constrained by demographic and eco-geographic factors that determine the range and abundance of the species. In this study, we performed genetic and morphological analyzes based on SSR and OIV datasets. In addition, according to the spatial distribution model performed by Maxent software we found that distance to water sources, Normalized difference vegetation index, and precipitation are the main environmental factors constraining V.v. sylvestris distribution at its southern distribution range. All raw data used for this study can be found in this deposit which contains a table with grapevine locations, Maxent input files, morphological and microsatellite data.

opencc-zeroOct 2022View details →

Spatial distribution and its limiting environmental factors of native orchid species diversity in the Beipan River Basin of Guizhou Province, China

Understanding the distribution of biodiversity and its determinants, particularly that of ecologically sensitive ones, has long been intriguing to the science community and will help formulate conservation strategies under future climate changes. To this end, we conducted extensive field surveys on the distribution of orchid flora in the Beipan River Basin in Guizhou Province, which is one of the biodiversity conservation priorities in China. The data we acquired, together with those published previously, were converted into orchid species richness for each of the 3km × 3km grid cells covering the study region. Redundancy analysis (RDA) and Geographically Weighted Regression (GWR) were then applied to determine which of the 30 environmental factors are potentially critical for the spatial distribution of orchid flora we have observed. Despite a moderate spatial extent, we found that the Beipan River Basin harbors about 249 native orchid species belonging to 74 genera, equivalent to 14.5% of orchid flora of China. Orchid species richness in this area follows a descending gradient from the southeast to the northwest, 70.41% of its variation among grid cells can be explained by environmental factors and spatial variables, and spatial variables accounted for 63.90% of the spatial variation of orchid distribution, indicating that spatial variables played a dominant role in the distribution of wild orchidaceae species richness. In addition, the main environmental driver is the mean temperature of the wettest quarter. Our study provides a good example for revealing the main drivers of orchid distribution characteristics, and has a certain reference value for the development of orchid conservation strategies.

opencc-zeroMar 2023View details →

Seed limitation interacts with biotic and abiotic factors to constrain novel species' impact on community biomass and richness

Seed limitation can narrow down the number of coexisting plant species, limit plant community productivity, and can also constrain community responses to changing environmental and biotic conditions. In a 10-year full-factorial experiment of seed addition, fertilisation, warming, and herbivore exclusion, we tested how seed addition alters community richness and biomass, and how its effects depend on seed origin and biotic and abiotic context. We found that seed addition increased species richness in all treatments, and increased plant community biomass depending on nutrient addition and warming. Novel species, originally absent from the communities, increased biomass the most, especially in fertilised plots and in the absence of herbivores, while adding seeds of local species did not affect biomass. Our results show that seed limitation constrains both community richness and biomass, and highlight the importance of considering trophic interactions and soil nutrients when assessing novel species immigrations and their effects on community biomass.