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A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19
<p><strong>Overview</strong></p> <p>This dataset is the repository for the following paper submitted to <em>Data in Brief</em>:</p> <p>Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. <em>Data in Brief</em> (submitted: December 2023).</p> <p>The <em>Data in Brief</em> article contains the supplement information and is the related data paper to:</p> <p>Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. <em>Journal of Arid Environments</em> (revision submitted: December 2023).</p> <p><strong>Description/abstract</strong></p> <p>The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant. </p> <p><strong>Folder structure</strong></p> <p>The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files: </p> <p>“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.</p> <p>“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.</p> <p>“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).</p> <p>“yield_productivity” contains .csv files of yield information for all countries listed above.</p> <p>“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).</p> <p>“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.</p> <p>“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”. </p> <p><strong>Code structure</strong></p> <p>1_MODIS_NDVI_hdf_file_extraction.R </p> <p><br>This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.</p> <p><br>2_MERGE_MODIS_tiles.R</p> <p><br>In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory__MODIS/merged").</p> <p><br>3_CROP_MODIS_merged_tiles.R</p> <p><br>Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS. <br>The repository provides the already clipped and merged NDVI datasets.</p> <p><br>4_TREND_analysis_NDVI.R</p> <p><br>Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.<br>To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique. </p> <p><br>5_BUILT_UP_change_raster.R</p> <p><br>Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022. </p> <p><br>6_POPULATION_numbers_plot.R</p> <p><br>For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration. </p> <p><br>7_YIELD_plot.R</p> <p><br>In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R. </p> <p><br>8_GLDAS_read_extract_trend</p> <p><br>The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection). <br>Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.<br>From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).<br>From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables. </p> <p><br>(9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis.</p> <p>___</p> <p>Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael Kempf</p> <p>___</p> <p><strong>Acknowledgements</strong></p> <p><span><span><span><span>I would like to thank three </span></span></span></span><span><span><span><span><span>anonymous</span></span></span></span></span><span><span><span><span> reviewers for their constructive comments and suggestions that sharpened the paper in the Journal of Arid Environments. I am particularly grateful to the Swiss National Science Foundation (SNSF/SNF) to fund my research project </span></span></span></span><span><span><span><span><em><span>EXOCHAINS - Exploring Holocene Climate Change and Human Innovations across Eurasia</span></em></span></span></span></span><span><span><span><span> at the University of Basel under grant number </span></span></span></span><span><span><span><span>TMPFP2_217358.</span></span></span></span></p> <p> </p> <p><span><span><span><span>__</span></span></span></span></p> <p><br>All data underlying the results of this article are publicly available on the internet:</p> <p>GLDAS Noah Land Surface Model L4 data: NASA's Earth Science Data Systems (ESDS) Program, https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 09th December 2023); </p> <p><br>Country borders: https://www.geoboundaries.org (last accessed 7th of March 2023) and Natural Earth https://www.naturalearthdata.com/ (last accessed 5th of December 2023);</p> <p><br>FAOstats (Food and Agriculture Organisation of the United Nations: https://www.fao.org/faostat/en/#data/QCL (last accessed 7th of March 2023);</p> <p><br>Global Human Settlement Layer datasets (GHSL): https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 7th of March 2023);</p> <p><br>Population development: <br>FAO, https://www.fao.org/countryprofiles/index/en/?iso3=JOR (last accessed 4th of March 2023); <br>the Worldbank, https://www.worldbank.org/en/home (last accessed: 04th of March 2023); <br>Worlddata.info, https://www.worlddata.info/asia/palestine/populationgrowth.php (last accessed 4th of March 2023);</p> <p><br>Water demand and population numbers (Tab. 1): https://www.fao.org/faostat/en/#data/OA; https://databank.worldbank.org/reports.aspx?source=world-development-indicators# (last accessed 13th of December 2023);</p> <p><br>MODIS: Earthdata server of the United States Geological Survey (USGS), MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006, https://lpdaac.usgs.gov/products/mod13q1v061/ (last accessed 7th of March 2023).</p> <p><br>Competing interests statement:<br>The author declares no conflict of interest.<br>The author has no relevant financial or non-financial interests to disclose.<br>Data availability: All data underlying the analyses are freely available on the internet and where applicable, sources are cited in the text.<br>Ethical approval: This article does not contain any studies with human participants performed by any of the authors.<br>Informed consent: This article does not contain any studies with human participants performed by any of the authors.</p>
Data from Davison et al. (2024) Changes in Danish bird communities over four decades of climate and land-use change
<p>Environmental and biodiversity data associated with the article: <br><strong>Davison, C. W., Rahbek, C., & Morueta-Holme, N. (2024) Changes in Danish bird communities over four decades of climate and land-use change. <em>Oikos. </em></strong>https://doi.org/10.1111/oik.10697</p> <p>Data on local bird species richness, functional diversity, temporal and spatial turnover (beta diversity), abundance, and biomass at volunteer led survey routes across Denmark. Matched habitat data (from volunteers) and historical climate data (E-OBS). Bird observations are a subset of the Common Bird Monitoring programme (DOF – BirdLife Denmark) that include routes surveyed in the summer season, spanning ≥10 years, and with full GPS coordinates. This excel document contains all of the derived (and anomysied) data used in the final analyses and includes metadata describing the variables.</p> <p>Climate and trait data were obtained from open-access databases (see references). Metadata is included in the excel file.</p> <ul> <li><strong>Danish Common Bird Monitoring programme</strong> – Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. <em>Dansk Ornitologisk Forening</em>.</li> <li><strong>E-OBS European gridded climate data</strong> – Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., & New, M. (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. <em>Journal of Geophysical Research Atmospheres</em>, <em>113</em>(20). https://doi.org/10.1029/2008JD010201</li> <li><strong>AVONET bird traits data</strong> – Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. <em>Ecology Letters</em>, <em>25</em>(3), 581–597. https://doi.org/10.1111/ele.13898</li> </ul> <p> </p>
Data for: A severe landslide event in the Alpine foreland under possible future climate and land-use changes
<p>Data underlying manuscript and supplementary figures of the corresponding publication, as well as the scripts to conduct the final analyses.</p>
GLM2_modified and Results as used in Ma et al: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 2020
<p>Code modified GLM2, scripts and result as used in Ma et al 2019, Ma et al 2019, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-146</p>
Model run and scenario data for study "Bioenergy-induced land-use change emissions with sectorally fragmented policies"
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Bioenergy-induced land-use change emissions with sectorally fragmented policies</strong></p> <p>by <em>Leon Merfort, Nico Bauer, Florian Humpenöder, David Klein, Jessica Strefler, Alexander Popp, Gunnar Luderer, Elmar Kriegler</em></p> <p>published in <em>Nature Climate Change </em>(2023).</p> <p><em><strong>ModelRuns_remind </strong></em>(directory) contains all REMIND model runs of the scenarios underlying the paper.</p> <p><em><strong>ModelRuns_magpie </strong></em>(directory) contains all MAgPIE model runs of the scenarios underlying the paper.</p> <p><em><strong>DataAnalysis </strong></em>(directory) contains an RStudio Project that was used for the data analysis and the generation of the figures of the paper. It additionally contains all figures and figure data that are shown in the paper.</p> <p><em><strong>ScenarioMapping.pdf</strong></em> contains the mapping from scenario names used in the paper to the model experiment names (in the model run directories).</p>
Supporting data: Land-use change alters the mechanisms assembling rainforest mammal communities in Borneo
<p>These supporting data files were used in the analyses for a forthcoming<em> </em>paper (DOI to be confirmed). The two files consist of: </p> <p>1. Combined camera trap and live trap species-abundance matrix. Each row corresponds to a separate location, with species in different columns. Old-growth forest, logged forest and oil palm plantation locations have the prefixes "Old", "Log" and "Palm", respectively. Values in each cell are the number of independent captures (as defined in the paper) per seven days summed over the camera- and live-trapping protocols.</p> <p>2. Covariate data for each location, covering habitat structure, topography and local landscape context (covariates as defined in the paper). </p>
Figure S4 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure S4. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Gower distance measure indicating annual forb plant functional types (PFTs).
Figure S5 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure S5. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Gower distance measure indicating perennial forb plant functional types (PFTs).
Figure S3 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure S3. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Gower distance measure indicating perennial grass plant functional types (PFTs).
Figure S1 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure S1. Principal Co-ordinate Analysis (PCoA) scatter diagram of the species-trait matrix revealing a strong clustering based on life history.
Figure 3 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure 3. Herbaceous species (left) and trait (right) diversity measures benchmarked against the mean value calculated for the untransformed (protected) area (----) across transformed land-use types. Vertical bars denote 0.95 confidence intervals. Significant deviations from the protected area (Sidak posthoc pairwise comparison; p<0.05) are denoted by (*).
Figure S2 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure S2. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Gower distance measure indicating annual grass plant functional types (PFTs).
Figure 4 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure 4. Principal Component Analysis (PCA) ordination of land-use type sampling plots correlated with plant functional types (PFT's). CAF (Communal abandoned fields); CR (Communal rangelands); NRSM (Naturally restored strip mine); RASM (Recently active strip mine); UMV (Untransformed Mopaneveld).
Figure 2 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure 2. Multidimensional Scaling (NMDS) ordination of sampling plots representing herbaceous species assemblages across land-use types. Broad groupings are encircled.
Figure 1 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure 1. Study area and locality of sampled sites. Strip mines and untransformed Mopaneveld is located at Pompeye (top) and communal areas at Lulekani (bottom).
FIGURE 4 in Land-use changes affect the functional structure of stream fish assemblages in the Brazilian Savanna
FIGURE 4 | Structural equation model diagrams showing the effects of landscape degradation (CDI) on the functional structure of stream fish assemblages from the Araguari River basin. CDI influenced functional diversity mediated by alterations in habitat heterogeneity and stability (A. Model fit: X2 = 32.8, df = 25, p = 0.51). CDI also influenced functional identity, mediated by changes in habitat type (B. Model fit: X2 = 44.9, df = 18, p = 0.13). Arrows indicate positive (black) and negative (gray) significant direct effects (p <0.05; *p <0.10), with thickness proportional to their power (standardized path coefficients along arrows). Biodiversity metrics – FRic: Functional Richness; FDiv: Functional Divergence; FEve: Functional Evennes; FSpe: Functional Specialization; FOri: Functional Originality; CWM1-3: Functional Identity. For physical-habitat codes, calculation and ecological meaning, see Tab. 1.
FIGURE 5 in Land-use changes affect the functional structure of stream fish assemblages in the Brazilian Savanna
FIGURE 5 | Ecomorphological space showing the position of each fish species (36) from the Araguari River basin. Each plot represents two axes of a principal component analysis (PCA), where species are plotted according to their respective trait values. Codes at the ends of the arrows are the most important ecomorphological traits for each PCA axis. For trait and species codes, see Tab. 2 and Tab. S3, respectively).
FIGURE 2 in Land-use changes affect the functional structure of stream fish assemblages in the Brazilian Savanna
FIGURE 2 | Examples of landscape and local-habitat conditions of the streams sampled across a degradation gradient in the Cerrado: A. Streams with a small strip of riparian forests in landscapes dominated by mechanized agriculture; B. Stream with relatively well-preserved local conditions, including forest on both banks; C. Stream surrounded by intermediate riparian cover; and D. Stream running in pasture areas without any forest.
FIGURE 3 in Land-use changes affect the functional structure of stream fish assemblages in the Brazilian Savanna
FIGURE 3 | Predictions tested using structural equation modeling, indicating the expected pathways (arrows) for the effects of catchment degradation (CDI) on stream physical habitat and, consequently, on the functional structure of the fish assemblages. Land use is expected to influence the functional diversity mediated by alterations in habitat heterogeneity and stability (A), and to influence functional identity, mediated by changes in habitat type (B). Arrows indicate expected effects between predictive and response variables, which can be positive (black continuous line), negative (gray continuous line) or non-directional (dashed lines). For physical-habitat codes, calculation and ecological meaning, see Tab. 1.
FIGURE 1 in Land-use changes affect the functional structure of stream fish assemblages in the Brazilian Savanna
FIGURE 1 | Headwater streams (N = 40; black dots) sampled for fish and local physical habitat. All sites drain to Nova Ponte Reservoir in the Araguari River basin, Upper Paraná River, Minas Gerais, Brazil.
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