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Data set for the manuscript "Development of Hydropower and the Environmental Impacts of Hydroelectric Dam Construction: A Case Study of the Three Gorges Dam"

This document provides the data set supplementary to the manuscript "Development of Hydropower and the Environmental Impacts of Hydroelectric Dam Construction: A Case Study of the Three Gorges Dam" Data content: Tables 1-3 <ul> <li>Table 1. The global annual data of GDP, surface temperature anomalies, carbon dioxide emissions and different kinds of renewable energy during 2000-2020. The renewable energy includes hydropower, wind, solar, geothermal, biomass and others (unit: TWh).</li> <li>Table 2.  The annual mean temperature, CO2 emissions, and renewable energy data in China from 2000 to 2020. The renewable energy data include hydro, solar, and wind electricity generation (unit: kWh).</li> <li>Table 3. Annual variation of precipitation and biodiversity in the TGD area. Annual mean precipitation data over China and the TGD are provided. Biodiversity data include the number of Yangtze Finless Porpoise, Carp Egg and Larvae, and the number of spawners in spawning ground for Acipenser_sinensis.</li> </ul>

opencc-by-4.0Oct 2022View details →

Modelling compound flooding: A case study from Indonesia

Flood-prone areas can be vulnerable to a range of flooding mechanisms, with the associated hazards likely to increase due to the impacts of global climate change. Here, we investigate compound flooding from waves, sea surge and river flow in northern Jakarta, Indonesia, which is a global hotspot of flooding, by combining process-based coastal and river models. The coastal hydrodynamic modelling of Jakarta Bay shows that, coastal storms can lead to substantial increase in sea water level due to wind and wave setup in the nearshore areas. The compound flood hazard in the low-lying coastal areas surrounding the West Flood Canal in the northern Jakarta, which drains the Cilliwung river, from a range of statistically significant compound flood scenarios, was simulated and analysed. The results reveal that low-lying areas around the river estuary are prone to flooding even during regular, low-intensity storm events while rarer storms caused extensive floods. Floods were not caused by direct overwashing of sea defences but by overspill of riverbanks due to high sea water level caused by wind and wave setup and sea surge obstructing high river flows during compound events. Potential changes to future flooding in the northern Jakarta were investigated by taking into account the regional sea level rise and land subsidence in Jakarta in numerical flood simulations. It was found that the sea level rise combined with rapid land subsidence will inundate the existing coastal flood defences during storms. As vast areas of the city will be below mean sea level by 2100, overwashing of coastal defences will lead to extensive flooding in the northern, western, and eastern Jakarta even during more frequent low-intensity storms.

opencc-by-4.0Jan 2022View details →

Dataset used in "Comment on "Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam" by Kim-Anh Nguyen, Yuei-An Liou, Ha-Phuong Tran, Phi-Phung Hoang and Thanh-Hung Nguyen"

The Excel file provides all the data included in Tab.4 of Nguyen et al. 2020 plus reflectances extracted from the Landsat 8 OLI image acquired on 14 February 2017 and downloaded from the USGS Earth Explorer website. Observations on the number of pixels falling of water, land and mixed water/land surfaces are provided as well as water percentage cover estimated using regular spaced points.  The dataset includes vector files (kml format) of the grids corresponding to the selected L8 pixels as well as the regularly spaced points generated within the selected pixels. These files can be imported in QGIS, Google Earth Pro and other free GIS software.

Figure 7 in Phylogenetic biome conservatism as a key concept for an integrative understanding of evolutionary history: Galliformes and Falconiformes as study cases

Figure 7. Biome transitions in Falconiformes. The number of recent species is indicated inside the circles. Arrow thickness is proportional to the number of colonizations. The dashed lines indicate only one colonization event. The number of transitions that did not imply colonization (niche conservatism) is indicated as different areas of the circles, classified in four categories. For more details about absolute scores, see Table 3.

Figure 5 in Phylogenetic biome conservatism as a key concept for an integrative understanding of evolutionary history: Galliformes and Falconiformes as study cases

Figure 5. Ancestral biome reconstruction for Falconiformes. Coloured circles represent the ten different biomes implemented in the model (Walter, 1970; Hernández Fernández, 2001); those at the nodes represent the inferred ancestral biome(s); those at the tips correspond to the recent biome distribution of species. Along the time scale, geological and climatic histories are shown, in addition to intercontinental biotic interchanges. Abbreviations: Af, Africa; Au, Australia; EAs, Eurasia; LB, land bridge; NA, North America; SA, South America.

Figure 6 in Phylogenetic biome conservatism as a key concept for an integrative understanding of evolutionary history: Galliformes and Falconiformes as study cases

Figure 6. Colonization dynamics of Falconiformes. Each graph represents the rate of colonization by new lineages for each biome throughout the Cenozoic.

Figure 4 in Phylogenetic biome conservatism as a key concept for an integrative understanding of evolutionary history: Galliformes and Falconiformes as study cases

Figure 4. Biome transitions in Galliformes. The number of recent species is indicated inside the circles. Arrow thickness is proportional to the number of colonizations. The dashed lines indicate only one colonization event. The number of transitions that did not imply colonization (niche conservatism) is indicated as different areas of the circles, classified in five categories. For more details about absolute scores, see Table 2.

Figure 2 in Phylogenetic biome conservatism as a key concept for an integrative understanding of evolutionary history: Galliformes and Falconiformes as study cases

Figure 2. Ancestral biome reconstruction for Galliformes. Coloured circles represent the ten different biomes implemented in the model (Walter, 1970; Hernández Fernández, 2001); those at the nodes represent the inferred ancestral biome(s) occupancy; those at the tips correspond to the recent biome distribution of species. Along the time scale, geological and climatic histories are shown, in addition to intercontinental biotic interchanges. Abbreviations: Af, Africa; Au, Australia; EAs, Eurasia; LB, land bridge; NA, North America; SA, South America.

Figure 3 in Phylogenetic biome conservatism as a key concept for an integrative understanding of evolutionary history: Galliformes and Falconiformes as study cases

Figure 3. Colonization dynamics of Galliformes. Each graph represents the rate of colonization by new lineages for each biome throughout the Cenozoic.

Figure 1 in Phylogenetic biome conservatism as a key concept for an integrative understanding of evolutionary history: Galliformes and Falconiformes as study cases

Figure 1. Schematic explanatory example for transition categories considered in this study: transition with biome conservatism; transition with colonization; and transition with loss of ancestral biome occupation. Note that ancestral biome occupation (for node A) is the same above and below, whereas biome occupations for derived nodes B (above) and C (below) differ.

Figure 4 in Characterizing Environmental Change and Species' Histories from Stratified Faunal Records in Southeastern Australia: A Regional Review and a Case Study for the Early to Middle Holocene

Figure 4. Selected species representations at WJ99. Species are sorted by those that show decline into the Holocene (blue), those whose values generally do not show particular decline or increase during the Holocene Climatic Optimum (gold), and those whose numbers appear to be affected—decline or increase—during the Holocene Climatic Optimum (red). Grey shading indicates approximate timing of peak Holocene Climatic Optimum conditions.

Figure 1 in Characterizing Environmental Change and Species' Histories from Stratified Faunal Records in Southeastern Australia: A Regional Review and a Case Study for the Early to Middle Holocene

Figure 1. Map of SE Australian Alps and region, indicating locations of sites with faunal material mentioned in text.

Figure 3 in Characterizing Environmental Change and Species' Histories from Stratified Faunal Records in Southeastern Australia: A Regional Review and a Case Study for the Early to Middle Holocene

Figure 3. Summary of known environmental shifts during the early to middle Holocene from SE Australia, chronologically related to notable temporal changes within (and environmental inferences from) the faunal composition of WJ99. * Approximate calibrated date ranges inferred from original uncalibrated dates.

Figure 2 in Characterizing Environmental Change and Species' Histories from Stratified Faunal Records in Southeastern Australia: A Regional Review and a Case Study for the Early to Middle Holocene

Figure 2. Representative (east and south) sections of WJ99 Square 10B, showing locations of AMS radio-

Data from "Higher mortality is not a universal cost of dispersal: A case study in African wild dogs"

Mortality is considered one of the main costs of dispersal. A reliable evaluation of mortality, however, is often hindered by a lack of information about the fate of individuals that disappear under unexplained circumstances (i.e. missing individuals). Here, we addressed this uncertainty by applying a Bayesian mortality analysis that inferred the fate of missing individuals based on information from individuals with known fate. Specifically, we tested the hypothesis that mortality during dispersal is higher than mortality among non-disperser using 32 years of mark-resighting data from a free-ranging population of the endangered African wild dog (Lycaon pictus) in northern Botswana. Contrary to expectations, we found that mortality during dispersal was lower than mortality among non-dispersers, indicating that higher mortality is not a universal cost of dispersal. Our findings suggest group living can incur costs for certain age classes, such as limited access to resources as group density increases, that exceed the mortality costs associated with dispersal. By challenging the accepted expectation of higher mortality during dispersal, we urge for further investigations of this key life-history trait, and propose a robust statistical approach to reduce bias in mortality estimates.

Input data for the case study reported in "DREAM: an R package for druggability evaluation of human complex diseases".

The data included in this record constituted the input for the case study reported in the manuscript "DREAM: an R package for druggability evaluation of human complex diseases", by Antonio Federico, Michele Fratello, Alisa Pavel, Lena Möbus, Giusy del Giudice, Angela Serra, Dario Greco. The data derive from transcriptomics experiments executed on lesional skin from atopic dermatitis patients and unaffected skin counterparts. The data consists of two files in ".txt" format reporting gene expression data in tabular format, where on the rows are reported genes and on the columns are reported samples. The data is an aggregated and batch-corrected collection of datasets originally downloaded by Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). The file "GE_Mic_AD_Pamr_MAARS.txt" reports gene expression estimates of lesional skin of atopic dermatitis patients, while the file "GE_Mic_AD_Pamr_nl_MAARS.txt" reports gene expression estimates of non-lesional skin of atopic dermatitis patients.

Rural Landscape Simplification and Provision of Cultural Ecosystem Services. A Case Study in the Argentine Pampas

This supplementary material consists of the survey questionnaire and the data set that gave rise to the article: Rural Landscape Simplification and Provision of Cultural Ecosystem Services. A Case Study in the Argentine Pampas publicado en el journal EARN Economía Agraria y Recursos Naturales. Agricultural and Resource Economics <a href="https://economiaagroalimentaria.es/en/earn-journal/">https://economiaagroalimentaria.es/en/earn-journal/</a> <a href="https://doi.org/10.7201/earn.2023.01.01">DOI: https://doi.org/10.7201/earn.2023.01.01</a> .

Fig. 2 in Why many Indonesian marine species remain undescribed: a case study using polychaete species discovery

Fig. 2. The composition of active local taxonomists. Blue, black, white, and grey bars indicate marine, faunal, floral, and microbial taxa, respectively (the last three taxa are non-marine). A list of the taxonomists is provided in Appendix 1.

Fig. 3 in Why many Indonesian marine species remain undescribed: a case study using polychaete species discovery

Fig. 3. Distributional map of Indonesian marine species exclusively described by local taxonomists. Grey and white land areas indicate Indonesia and the neighbouring countries, respectively.

Fig. 1 in Why many Indonesian marine species remain undescribed: a case study using polychaete species discovery

Fig. 1. The cumulative number of Indonesian polychaete species described (the black solid line), projected to the end of the 21st century with a 95% probability. The upper and lower red dashed lines, as well as the red solid line, are the projected species discovery trends assuming great, little, and medium taxonomic efforts, respectively, being made to shift the pre-existing trend.