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opencc-by-4.0Dec 2018View details →

Supplementary data to: Importance and vulnerability of the world's water towers

This archive contains data produced for a study assessing the importance and vulnerability of the world’s water towers. Code (R-scripts) used to process these files is available on the <a href="https://github.com/mountainhydrology/pub_ngs-watertowers">MountainHydrology Github page</a> The archive is organized in directories with specific topics. Each directory contains input files (optional) and output/processed files. The input files can be used in combination with the R-scripts published on <a href="https://github.com/mountainhydrology/pub_ngs-watertowers">Github</a> to generate the processed files included in this archive. In many cases external published data is used as input data for the calculations. In that case the data is not included in this archive but literature references and links to the specific files are provided in the description below. Files which have been preprocessed before use in the R-scripts are included in this archive. For calculation details please see the publication, in particular Extended Data Tables 3 and 4. Archive contents The archives contents are organized in eight separate directories, which are listed here, along with their contents: <ul> <li>ERA5</li> </ul> Precipitation and evaporation data are extracted from ERA5 reanalysis available online in the Copernicus Climate Data Store at https://cds.climate.copernicus.eu This directory includes: Input <pre><code>ERA5_evaporation_avgannual_2001_2017.nc - Average annual evaporation (mm) for 2001-2017 ERA5_evaporation_ymonmean_2001_2017.nc - Multi-year mean monthly evaporation (mm) for 2001-2017 era5_total-precipitation_ymonmean_2001-2017_global.tif - Multi-year mean monthly precipitation (mm) for 2001-2017 era5_total-precipitation_yearsum_2001-2017.tif - Average annual precipitation (mm) for 2001-2017</code></pre> Output <pre><code>P_avg_annual_basin_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to basins P_avg_annual_DS_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to downstream basins P_avg_annual_mm.tif - Average annual precipitation 2001-2017 (mm) P_avg_annual_WT_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to Water Tower Units P_var_interannual.tif - Interannual variablity in precipitation 2001-2017 P_var_interannual_basin.tif - Interannual variablity in precipitation 2001-2017 aggregated to basins P_var_interannual_DS.tif - Interannual variablity in precipitation 2001-2017 aggregated to downstream basins P_var_interannual_WT.tif - Interannual variablity in precipitation 2001-2017 aggregated to Water Tower Units P_var_intraannual.tif - Intra-annual variablity in precipitation 2001-2017 P_var_intraannual_basin.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to basins P_var_intraannual_DS.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to downstream basins P_var_intraannual_WT.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to Water Tower Units WTU_P_indicators.csv - Table listing all calculated precipition indicators per Water Tower Unit</code></pre> <ul> <li>Glaciers</li> </ul> Glacier volume and mass balance are derived from published datasets. This directory includes: Output <pre><code>Glac_area_WT_km2.tif - Glacier area (km2) aggregated for Water Tower Units Glac_volume_WT_km3.tif - Glacier volume (km3) aggregated for Water Tower Units WTU_Glacier_indicators.csv - Table listing all derived glacier indicators per Water Tower Unit WTU_MB.shp - shapefile of Water Tower Units including the glacier mass balance per Water Tower Units as attribute</code></pre> External data Glacier volume data published in Farinotti et al., 2019, Nature Geoscience, were used. Reference: Farinotti, D. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019). Glacier volume (km3) and glacier area (km2) at 0.05 degrees spatial resolution were used, which are available <a href="https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/315707/global_fraction-of-degree_grids.zip?sequence=60&isAllowed=y">here</a>. The used files are p05_degree_glacier_area_km2.tif and p05_degree_glacier_volume_km3.tif Glacier mass balance data published by the World Glacier Monitoring Service were used to derive an average glacier mass balance per Water Tower Unit. References: Zemp, M. et al. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature 568, 382–386 (2019). World Glacier Monitoring Service. Fluctuations of Glaciers (FoG) Database. (2018). doi:10.5904/wgms-fog-2018-06 <ul> <li>HydroLAKES</li> </ul> Surface lake and water storage per Water Tower Unit was calculated. This directory includes: Output <pre><code>WTU_lake_storage_volume.csv - Table listing lake and reservoir volume (km3) per Water Tower Unit WTU_surface_water_storage_km3.tif - Lake and reservoir storage volume (km3) aggregated to Water Tower Units</code></pre> External data For surface water lakes and reservoirs the HydroLAKES dataset is used. The shapefile HydroLAKES_polys_v10.shp can be downloaded from <a href="http://https://97dc600d3ccc765f840c-d5a4231de41cd7a15e06ac00b0bcc552.ssl.cf5.rackcdn.com/HydroLAKES_polys_v10_shp.zip">HydroSheds</a> Reference: Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 1–11 (2016). <ul> <li>Indicators</li> </ul> All indicators and subindicators calculated for the Water Tower Index calculation are stored per Water Tower Unit. This directory includes: <pre><code>indicators.csv - Table with all indicators and subindicators per Water Tower Unit</code></pre> <ul> <li>Snow</li> </ul> The MODIS MOD10CM006 snow cover product was used to derive snow persistence. Reference: Hall, D. K. & Riggs, G. A. MODIS/Terra Snow Cover Monthly L3 Global 0.05Deg CMG, Version 6. (2015). doi:10.5067/MODIS/MOD10CM.006 This archive includes: Input <pre><code>MOD10CM006_yearmean_2001-2017.tif - Annual mean snow cover 2001-2017 MOD10CM006_ymonmean_2001-2017.tif - Multi-year mean monthly snow cover 2001-2017</code></pre> Output <pre><code>Snow_persistence_avg_annual.tif - Average annual snow persistence 2001-2017 Snow_persistence_avg_annual_WT.tif - Average annual snow persistence 2001-2017 aggregated to Water Tower Units Snow_persistence_var_interannual.tif - Interannaul variability in snow persistence 2001-2017 Snow_persistence_var_interannual_WT.tif - Interannaul variability in snow persistence 2001-2017 aggregated to Water Tower Units Snow_persistence_var_intraannual.tif - Intra-annaul variability in snow persistence 2001-2017 Snow_persistence_var_intraannual_WT.tif - Intra-annaul variability in snow persistence 2001-2017 aggregated to Water Tower Units WTU_Snow_indicators.csv - Table listing all derived snow indicators per Water Tower Unit</code></pre> <ul> <li>Uncertainty</li> </ul> The directory contains the uncertainty ranges used in the uncertainty analysis The directory includes: <pre><code>ET_uncertainty_per_downstream.csv - Table listing SD in evaporation per downstream basin ET_uncertainty_per_WTU.csv - Table listing SD in evaporation per Water Tower Unit P_uncertainty_per_downstream.csv - Table listing SD in precipitation per downstream basin P_uncertainty_per_WTU.csv - Table listing SD in precipitation per Water Tower Unit WTU_IceVol_uncertainty.csv - Table listing uncertainty in ice volume per Water Tower Unit</code></pre> <ul> <li>Water demands</li> </ul> Net water demands for irrigation, industrial and domestic water use, as well as the environmental flow requirement are extracted from PCR-GLOBWB hydrological model output. Reference: Wada, Y., De Graaf, I. E. M. & van Beek, L. P. H. High-resolution modeling of human and climate impacts on global water resources. J. Adv. Model. Earth Syst. 8, 735–763 (2016). The directory includes: Input <pre><code>Dom_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net domestic water demand 2001-2014 at 0.05 degrees resolution (km3) Ind_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net industrial water demand 2001-2014 at 0.05 degrees resolution (km3) Irr_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net irrigation water demand 2001-2014 at 0.05 degrees resolution (km3) Tot_use_ymonmean_2001_2014_005.tif - Sum of the three above global_historical_riverdischarge_ymonmean_m3second_5min_2001_2014.nc4 - Multi-year mean monthly natural discharge (m3/s) 2001-2014</code></pre> Output <pre><code>Domestic_use_avg_annual_basin_km3.tif - Average annual net domestic water demand 2001-2014 aggregated to basins Domestic_use_avg_annual_km3.tif - Average annual net domestic water demand 2001-2014 Industrial_use_avg_annual_basin_km3.tif - Average annual net industrial water demand 2001-2014 aggregated to basins Industrial_use_avg_annual_km3.tif - Average annual net industrial water demand 2001-2014 Irrigation_use_avg_annual_basin_km3.tif - Average annual net irrigation water demand 2001-2014 aggregated to basins Irrigation_use_avg_annual_km3.tif - Average annual net irrigation water demand 2001-2014 Natural_demand_avg_annual_basin_km3.tif - Average annual natural water demand 2001-2014 aggregated to basins Total_human_demand_avg_annual_basin_km3.tif - Average annual net human (sum of domestic, industrial and irrigation) water demand 2001-2014 aggregated to basins Water_gap_average_annual_basin.tif - Average annual water gap 2001-2014 aggregated to basins WTU_Demand_DS_P_available.csv - Table listing dowstream water availability per sector per basin WTU_Demand_indicators.csv - Table listing demand per sector per basin WTU_Domestic_Water_Gap_monthly.csv - Table listing multi-year average monthly domestic water gap per basin WTU_Industrial_Water_Gap_monthly.csv - Table listing multi-year average monthly industrial water gap per basin WTU_Irrigation_Water_Gap_monthly.csv - Table listing multi-year average monthly irrigation water gap per basin WTU_Natural_Water_Gap_monthly.csv - Table listing multi-year average monthly natural water gap per basin WTU_Total_Water_Gap_monthly.csv - Table listing multi-year average monthly water gap per basin</code></pre> <ul> <li>WTU units</li> </ul> The spatial units for all calculations are the Water Tower Units, their downstream basins, and the entire basins (Water Tower Unit + downstream basin). They are extracted using definitions of basins and mountain ranges. This directory includes: Output <pre><code>basins.tif - Definition of basins with Water Tower Units at 0.05 degrees spatial resolution basins_downstream.tif - Definition of downstream basins at 0.05 degrees spatial resolution basins_vector.shp - Definition of basins with Water Tower Units as vector data downstream_vector.shp - Definition of downstream basins as vector data gmba_all.shp - All GMBA mountain ranges including glacier volume and snow persistence gmba_ss.shp - GMBA mountain ranges included in Water Tower Units WTU.tif - Definition of Water Tower Units as 0.05 degrees spatial resolution WTU_specs.csv - Table with set of specifications of Water Tower Units WTU_vector.shp - Definition of Water Tower Units as vector data</code></pre> External data FAO's classification of major hydrological basins and FAO's classification of subbasins per continent are used. These are based on HydroSheds and are available as shapefiles at <a href="http://www.fao.org/nr/water/aquamaps/">FAO Aquamaps</a> The specific shapefiles used are: <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=38047&fname=Major_hydrological_basins.zip&access=private">major_hydrobasins.shp</a> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37039&fname=hydrobasins_asia.zip&access=private">hydrobasins_asia.shp</a> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37174&fname=hydrobasins_southam.zip&access=private">hydrobasins_southam.shp</a> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=38044&fname=hydrobasins_northam.zip&access=private">hydrobasins_northam.shp</a> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37299&fname=hydrobasins_neareast.zip&access=private">hydrobasins_neareast.shp</a> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37250&fname=hydrobasins_europe.zip&access=private">hydrobasins_europe.shp</a> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37173&fname=hydrobasins_centralam.zip&access=private">hydrobasins_centralam.shp</a> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37251&fname=hydrobasins_austpacific.zip&access=private">hydrobasins_austpacific.shp</a>:

opencc-by-4.0Sep 2020View details →

European Building Vulnerability Data Repository

A repository for the European vulnerability database developed as part of the European Seismic Risk Model 2020 (ESRM20). More information available in the following paper: Crowley et al. (2021) “Open models and software for assessing the vulnerability of the European building stock,” COMPDYN 2021, 8th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Greece.

opencc-by-4.0Feb 2022View details →

Data and Material for 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'

Data and Material supporting the paper 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'.

opencc-by-4.0Mar 2022View details →

Data from: Elevated fires during COVID-19 lockdown and the vulnerability of protected areas

Related article: Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x. In this dataset: This dataset contains information about monthly fire incidence and precipitation for the protected areas of Madagascar from January 2012 to December 2020. The fire data is sourced from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product and the precipitation data from the Global Precipitation Measurement (GPM) mission (for years 2016-2020) and its predecessor The Tropical Rainfall Measuring Mission (TRMM) (for years 2011-2015) at spatial resolution 10 km. The fire and precipitation data was overlayed with the protected area polygons of the June 2020 release of the World Database of Protected Areas. For sources and more details on how the data was compiled see the related article. The data can be used to inspect temporal dynamics of wildfires inside protected areas and for informing adaptive protected area management and planning. Please cite this dataset as: Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x. Column names NAME: Name of protected area Fires_sum: Number of observed fires (VIIRS) Month: Month Year: Year Precipitation: Precipitation (mm) Plag_1:Plag_12: Precipitation during previous month; 2 months ago; 3 months ago…12 months ago YEAR_CREAT: Year of establishment of protected area Biome: Biome REP_AREA: Area of protected area (km2) Fires_per_km2: Fires per km2 Prec_acc_12m: Accumulated precipitation during the last 12 months fBiome: Biome as factor fNAME: Name as factor sPrecipitation: Precipitation (scaled; see Methods section of article) sPlag_1: Precipitation in previous month (scaled; see Methods section of article) sPrec_acc_12m: Accumulated precipitation during the last 12 months (scaled; see Methods section of article) Pred_Zinb_1a: Predicted fires (see Methods section of article) Diff_Zinb_1a: Difference: Observed fires - predicted fires Year_pred: Year for prediction License Creative Commons Attribution 4.0 International.

opencc-by-4.0Jun 2022View details →

Data and code of "Post-trauma behavioral phenotype predicts the degree of vulnerability to fear relapse after extinction in male rats"

This dataset contains behavioral and transcriptomic data, and the original code related to the following article: Post-trauma behavioral phenotype predicts the degree of vulnerability to fear relapse after extinction in male rats. Fanny Demars, Ralitsa Todorova, Gabriel Makdah, Antonin Forestier, Marie-Odile Krebs, Bill P Godsil, Thérèse M Jay, Sidney I Wiener, & Marco N Pompili (2022) Current Biology 32. https://doi.org/10.1016/j.cub.2022.05.050

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Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)

Aim: Seamounts are conspicuous geological features with an important ecological role and can be considered Vulnerable Marine Ecosystems (VMEs). Since many deep-sea regions remain largely unexplored, investigating the occurrence of VME taxa on seamounts is challenging. Our study aimed to predict the distribution of four cold-water coral (CWC) taxa, indicators for VMEs, in a region where occurrence data is scarce. Location: Seamounts around the Cabo Verde Archipelago (NW Africa). Methods: We used species presence-absence data obtained from Remotely Operated Vehicle (ROV) footage collected during two research expeditions. Terrain variables calculated using a multiscale approach from a 100 m resolution bathymetry grid, as well as physical oceanographical data from the VIKING20X model, at a native resolution of 1/20°, were used as environmental predictors. Two modelling techniques (Generalized Additive Model (GAM) and Random Forest (RF)) were employed and single-model predictions were combined into a final weighted-average ensemble model. Model performance was validated using different metrics through cross-validation. Results: Terrain orientation, at broad-scale, presented one of the highest relative variable contributions to the distribution models of all CWC taxa, suggesting that hydrodynamic-topographic interactions on the seamounts could benefit CWCs by maximizing food supply. However, changes at finer scales in terrain morphology and bottom salinity were important for driving differences in the distribution of specific CWCs. The ensemble model predicted the presence of VME taxa on all seamounts and consistently achieved the highest performance metrics, outperforming individual models. Nonetheless, model extrapolation and uncertainty, measured as the coefficient of variation, were high, particularly, in least surveyed areas across seamounts, highlighting the need to collect more data in future surveys. Main conclusions: Our study shows how data-poor areas may be assessed for the likelihood of VMEs and provides important information to guide future research in Cabo Verde, which is fundamental to advise ongoing conservation planning.

opencc-zeroMay 2024View details →

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Data from: Agricultural specialisation increases the vulnerability of pollination services for smallholder farmers

Smallholder farms make up 84% of all farms worldwide and feed two billion people. These farms are heavily reliant on ecosystem services and vulnerable to environmental change, yet under-represented in the ecological literature. The high diversity of crops in these systems makes it challenging to identify and manage the best providers of an ecosystem service, such as the best pollinators to meet the needs of multiple crops. It is also unclear whether ecosystem service requirements change as smallholders transition towards more specialised commercial farming – an increasing trend worldwide. Here, we present a new metric for predicting the species providing ecosystem services in diverse multi-crop farming systems. Working in 10 smallholder villages in rural Nepal, we use this metric to test whether key pollinators, and the management actions that support them, differ based on a farmers' agricultural priority (producing nutritious food to feed the family versus generating income from cash crops). We also test whether the resilience of pollination services changes as farmers specialise on cash crops. We show that a farmers' agricultural priority can determine the community of pollinators they rely upon. Wild insects including bumblebees, solitary bees, and flies provided the majority of the pollination service underpinning nutrient production, whilst income generation was much more dependent on a single species - the domesticated honeybee Apis cerana. The significantly lower diversity of pollinators supporting income generation leaves cash crop farmers more vulnerable to pollinator declines. Regardless of a farmers' agricultural priority, the same collection of wild plant species (mostly herbaceous weeds and shrubs) were important for supporting crop pollinators with floral resources. Promoting these wild plants is likely to enhance pollination services for all farmers in the region. Synthesis and applications: We highlight the increased vulnerability of pollination services when smallholders transition to specialised cash crop farming and emphasise the role of crop, pollinator, and wild plant diversity in mitigating this risk. The method we present could be readily applied to other smallholder settings across the world to help characterise and manage the ecosystem services underpinning the livelihoods and nutritional health of smallholder families.

opencc-zeroJul 2024View details →

opencc-by-4.0Oct 2017View details →

MADFORWATER: WP1: Water and water-related vulnerabilities in Egypt, Morocco and Tunisia: Task1.2: Analysis and mapping of water stress, water vulnerability and potential for water reuse in Egypt, Morocco and Tunisia: Subtask1.2.b: Data collection on water stress and vulnerability: Souss-Massa Region Subset

This folder contains the dataset that I used to write my conference paper "Groundwater Resources Scarcity in Souss-Massa Region and Alternative Solutions for Sustainable Agricultural Development"

opencc-by-4.0Sep 2019View details →

Bahamas National Hazard Analysis. Data Inputs and Outputs for the InVEST Coastal Vulnerability Model.

The following folders contain the model inputs and outputs for the InVEST Coastal Vulnerability model that were used in the analysis discussed in: Silver JM, Arkema KK, Griffin RM, Lashley B, Lemay M, Maldonado S, Moultrie SH, Ruckelshaus M, Schill S, Thomas A, Wyatt K and Verutes G (2019) Advancing Coastal Risk Reduction Science and Implementation by Accounting for Climate, Ecosystems, and People. Front. Mar. Sci. 6:556. doi: 10.3389/fmars.2019.00556 The readme.txt file contains information about data layers.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov..

Natural history specimen data linked to collectors and determiners held within, "A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9">https://bionomia.net/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9">https://gbif.org/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Sep 2021View details →

Data from: Detecting the effects of rapid tectonically-induced subsidence on Mayotte Island since 2018 on beach and reef morphology, and implications for coastal vulnerability to marine flooding

This dataset contains data from the monitoring morphological evolution of beaches and coral reefs in Mayotte island.  Mayotte, part of the coral reef-fringed Comoro archipelago in the SW Indian Ocean, experienced in 2018 and 2019 an intense seismic crisis. The repeated earthquake activity since May 2018 has been associated with deformation of the surface of Mayotte, resulting in land subsidence. The earlier 2006-2008 profiles were realized using a Leica TC 407® total station, and referenced to local IGN 50 benchmarks. The more recent 2019, 2020, and 2021 surveys were carried out using a GNSS differential Trimble R8S® system. Given the rapid subsidence that has affected Mayotte, the benchmarks used in this study, like others in Mayotte, need to be recalibrated by the IGN (French Institut Géographique National) and SHOM. This has still not yet been done, as the final outcome of the vertical island movements is still not clear.

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Data for: Genomic vulnerability to climate change in Quercus acutissima, a dominant tree species in East Asian deciduous forests

Understanding the evolutionary processes that shape the landscape of genetic variation and influence the response of species to future climate change is critical for biodiversity conservation. Here, we sampled 27 populations across the distribution range of a dominant forest tree, Quercus acutissima, in East Asia, and applied genome-wide analyses to track the evolutionary history and predict the fate of populations under future climate. We found two genetic groups (East and West) in Q. acutissima that diverged during the Pliocene. We also found a heterogeneous landscape of genomic variation in this species, which may have been shaped by population demography and linked selections. Using genotype-environment association analyses, we identified climate-associated SNPs in a diverse set of genes and functional categories, indicating a model of polygenic adaptation in Q. acutissima. We further estimated three genetic offset metrics to quantify genomic vulnerability of this species to climate change due to the complex interplay between local adaptation and migration. We found that marginal populations are under higher risk of local extinction because of future climate change, and may not be able to track suitable habitats to maintain the gene-environment relationships observed under the current climate. We also detected higher reverse genetic offsets in northern China, indicating that genetic variation currently present in the whole range of Q. acutissima may not adapt to future climate conditions in this area. Overall, this study illustrates how evolutionary processes have shaped the landscape of genomic variation, and provides a comprehensive genome-wide view of climate maladaptation in Q. acutissima.

opencc-zeroJan 2023View details →

opencc-by-4.0Jan 2023View details →

Supplementary Data and Code: Determinants of range sizes pinpoint vulnerability of groundwater species to climate change: a case study on subterranean amphipods from the Dinarides

Supplementary Data and R code for phylogenetic analyses for manuscript entitled Determinants of range sizes pinpoint vulnerability of groundwater species to climate change: a case study on subterranean amphipods from the Dinarides. The dataset contains beast.tree → data for import into R: maximum credibility phylogeny data_lambert.csv → data for import into R: data on habitat and distribution for 52 Niphargus species morpho.csv → data for import into R: morphometric data (body length) for 52 Niphargus species niphargus_ranges.Rmd → fully reproducible R markdown file niphargus_ranges.html → html output of Rmd file To be able to run the analysis put the data files into folder <data> and run the Rmd script.

Data and code for paper "Freihardt (2024): Perceptions of environmental changes among a climate-vulnerable population from Bangladesh. Climatic Change. DOI 10.1007/s10584-024-03678-6"

This dataset contains the temperature, precipitation, erosion, and perception data, as well as the analysis code in R necessary to replicate the results of the paper: Freihardt, J. (2024): Perceptions of environmental changes among a climate-vulnerable population from Bangladesh. Climatic Change, 177, 25. DOI: 10.1007/s10584-024-03678-6.

FIG. 7 in A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.

FIG. 7. — Median-joining networks based on trnSGG and rps4-trnS sequences: A, the Ptisana attenuata clade; B, the P. salicina/P. soluta comb. nov., stat. nov./P. smithii clade. The size of each circle is proportional to the haplotype frequency. Undetected intermediate haplotypes on nodes are shown as black circles and hatch marks represent mutational steps separating haplotypes.

FIG. 6 in A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.

FIG. 6. — Phylogram from the Bayesian phylogenetic analysis of the chloroplast DNA sequence data for Ptisana Murdock. Support values for branches are given in the order of Bayesian inference posterior probability; maximum parsimony bootstrap support; and maximum likelihood bootstrap support. Only values>0.80 PP and 60% BS are shown.

FIG. 4 in A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.

FIG. 4. — Distribution map for the New Caledonian endemic species of Ptisana attenuata (Labill.) Murdock (), P. rolandi-principis (Rosenst.) Christenh. (Δ), and P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov. (, with unvouchered field observations indicated by a broken outline). Shaded areas are ultramafic substrates. The collecting sites of the sequenced P. attenuata samples are indicated.

FIG. 5 in A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.

FIG. 5. — The holotype of Ptisana soluta (Compton) Murdock & Perrie, comb. nov., stat. nov. (Compton 1674, Ignambi, 1914, BM[BM000787128]) showing how the lamina transitions from 3-pinnate proximally to 2-pinnate distally. CC BY The Trustees of the Natural History Museum, London.