DOI: 10.5061/dryad.hmgqnk9vf
title: Lebanon biogeography outlined by tree and shrub species distribution pattern

description: This dataset contains environmental study data on various tree and shrub
 species in Lebanon. The research examines multiple environmental factors
 influencing species distribution and presence. Occurrence points were
 collected from field surveys under various studies, covering Lebanon. We
 used all the presence points and consistent environmental variables called
 predictors to conduct ecological niche modelling and further use the
 outcomes of the predicted distribution of tree and shrub species to
 outline Lebanon’s biogeography. The data referred to as
 predictors for ecological niche modelling are the environmental factors
 used for the study, retrieved from open sources. These include climatic,
 topographic, and biogeographic factors and predictors retrieved from
 remote sensing data such as cloud coverage, normalized difference
 vegetation index, and land surface temperature. This information
 is used to delineate suitability maps for each species and understand the
 factors driving their distribution. In the next step, species were
 classified based on their biogeographical appurtenance and grouped into
 regional chorotypes. The produced heat maps in addition to dissimilarity
 analyses allowed us to delineate the biogeographical zones and their
 transition areas in Lebanon. Moreover, this data is being analyzed to
 study vegetation variation across an elevation gradient.
description: The data set is a compilation of presence points of phanerophytes
 across Lebanon to conduct ecological niche modelling, grouping species
 into regional and global chorotypes, to further define based on their
 biographic appartunance. In a second step we used dissimilarity indices to
 define the limits of biogeographic regions and their transition zones. The
 indices were applied using R package plug ins into GIS tools. 
description: # Lebanon biogeography outlined by tree and shrub species distribution
 pattern ## Description of the data files The dataset consists of
 structured tabular data across three sheets originating from three
 articles: - Stephan et al 2020 - Hammoud and Stephan 2022 - Stephan and
 Korban 2025 From the list of the predictors used in each of the three
 publications, we used those listed in the table below: #### Table 1.
 Predictors: Data source and calculation tools Code Predictor Source Tool
 DEM Digital elevation model (m) Contour line(a) Topo to raster Aspect
 Aspect (°) DEM Aspect Slope Slope (%) DEM Slope DFS Distance from the sea
 (m) DEM Fishnet - Near CC Mean Cloud Coverage (May -July) (%) Cloud
 Raster(b) Mean Calculation Tmin Mean of Minimal Temperature of the coldest
 month (°C) Mean of Minimal Temperature of the coldest month (c) Mean
 Calculation Tmax Mean of Maximum Temperature of the hottest month (°C)
 Mean of Maximum Temperature of the hottest month (c) Mean Calculation P
 Cumulative Annual Precipitation (mm) Precipitation Isohyets(d) - EQ
 Emberger Quotient Cumulative annual precipitation; Raster Calculator ```
 Mean Minimal Temperature of the Coldest Month; Mean Maximal Temperature of
 the Hottest Month ``` AWC Available water content (mm) Soil maps - Flow
 ``` Flow Accumulation DEM Fill; Flow direction; Flow Accumulation ``` NDVI
 Normalized Difference Vegetation Index Mosaic of Landsat 8-Operational
 Land Imager images Raster Calculator LST Land Surface Temperature (°C)
 average for summer months Mosaic of Landsat 8 Images Raster Calculator LD
 Land Degradation Land Productivity(e) SDG Land degradation plugin ``` Land
 Cover(e) Soil Organic Carbon(e) ``` Soil type Geze Soil Geze
 classification(f) Feature to Raster (a) The Lebanese Army Geographic
 Department; (b) National Scientific Research Center (2002 – 2013); (c)
 WorldClim (1970 – 2000); (d) National Scientific Research Center; (e)
 Trends.Earth Data, Sustainable Development Goals Plugin.; (f) National
 Scientific Research Center, Soil Maps. #### Each file contains a list of
 columns including: 1. Species Latin name (and code) 2. Geospatial
 coordinates marking exact species locations in meters (X, Y using Lambert
 projection, with Deir el Zor as a reference), and decimal degree (global
 reference). We used the latter for all presence points of the three
 publications. 3. Environmental variables (predictors) are listed in Table
 1, with the detailed sources of these variables, means of their
 calculation, and units whenever valid. The predictors used in our study
 are among the variables listed in the columns (in bold) of the three
 papers and are detailed as follows: FID presence point code Species_Name
 species name code Species species Latin name X Longitude in meters using
 Lambert projection Y Latitude in meters using Lambert projection Longitude
 Longitude in decimal degrees, using WG84 projection Latitude Latitude in
 decimal degrees, using WG84 projection Aspect slope direction to the
 north, in degrees AWC estimated in mm from soil depth and texture as
 extracted from CNRS soil maps cumul cumulative average annual
 precipitations in mm extracted from worldlim (omitted from last model fit)
 Curvature inverse length in meters, taken from 10m topographic maps
 (omitted from last model fit) DEM digital elevation model taken in meters
 from 10m topographic maps Distance (DFS) distance from the sea, estimated
 in meters from the closest point to the location Emb_max (Q) Emberger
 quotient calculated from cumulative annual precipitation (mm) of the
 highest isohyet, divided by the square of the Mean of Minimum Temperature
 of The coldest Month and the square of the Mean Maximum temperature of the
 warmest month (converted from Kelvin to degrees) Emb_min Emberger quotient
 calculated from cumulative annual precipitation (mm) of the lowest
 isohyet, divided by the square of the Mean of Minimum Temperature of The
 coldest Month and the square of the Mean Maximum temperature of the
 warmest month (converted from Kelvin to degrees); (omitted from the last
 model fit) Flow_acc calculated in number of cells, using digital elevation
 models to determine how much flow is directed to each cell in a raster
 grid. Hillshade derived from the digital elevation model through altitude,
 slope, and aspect, based on the sun-defined position (angle from the
 north, which is 34 degrees in the case of Lebanon; omitted from the last
 model fit) IMI Integrated moisture index derived from hill shade,
 curvature, flow accumulation, and available water content (omitted from
 the last model fit) Max08 (Tmax) Mean maximum temperature of the warmest
 month in degree Celsius Mean5_7 (Coverage) Cloud coverage (in percentage
 of the sky) between May and July Min02 (Tmin) Mean minimum temperature of
 the coldest month in degree Celsius Mother_roc bedrock type derived from
 the geological map of Lebanon NDVI Normalized Difference Vegetation Index
 from mosaic Landsat images PDIR Potential Direct Incident Radiation
 (MJ/cm2 year-1) calculated based on latitude, folded aspect (22-degree
 north is the used azimuth), and slope (extracted from DEM) (omitted from
 the last model fit) rain_max_p (P) cumulative average annual
 precipitations in mm extracted from Atlas climatic du Liban (using the
 highest isohyet for each presence point) Slope derived from DEM in percent
 Geze_Soil derived from the geological map of Lebanon (Raymond Geze) LST
 Land Surface temperature in degrees Celsius Land Degradation using change
 in land cover and land use, land productivity, and soil organic carbon
 data using the Land Degradation Plug-in ## Notes Species data with
 corresponding observational and measurement records are displayed in rows
 and sorted by species. Missing values are replaced with `"n/a"`
 to indicate unavailability. These are points where data for the
 environmental variables are absent. This dataset is intended for
 researchers studying environmental influences on species distribution and
 ecosystem interactions. Users unfamiliar with the dataset should refer to
 the original study documentation for variable definitions and
 interpretation. ## Sharing/Access Information Data was derived from the
 following sources, in which we further detail our methodology: 1. Stephan,
 J., Hammoud, Y., Korban, M., & Ferro, I. (2025). Lebanon biogeography
 outlined by tree and shrub species distribution pattern. Ecology and
 Evolution (in process). 2. Stephan, J., Bercachy, C., Bechara, J.,
 Charbel, E., & López-Tirado, J. (2020). Local ecological niche
 modelling to provide suitability maps for 27 forest tree species in edge
 conditions. iForest-Biogeosciences and Forestry, 13(3),
 230.[https://doi.org/10.3832/ifor3331-013](https://doi.org/10.3832/ifor3331-013) 3. Hammoud, Y., & Stephan, J. (2022). Novelty predictors for shrub (and climbers) ecological niche modeling, based on their successional stage. Ecological Informatics, 71, 101771.[https://doi.org/10.1016/j.ecoinf.2022.101771](https://doi.org/10.1016/j.ecoinf.2022.101771) 4. Stephan, J., & Korban, M. (2025). Ecological niche modelling using MaxEnt for riparian species in a Mediterranean context. Ecological Indicators, 171, 113167. [https://doi.org/10.1016/j.ecolind.2025.113167](https://doi.org/10.1016/j.ecolind.2025.113167) ## Code/Software This dataset is provided in a cleaned Excel format (.xlsx) and can be accessed using any spreadsheet software (e.g., Microsoft Excel, Google Sheets, LibreOffice Calc) or programmatically via Python (pandas) or R (readr). For further analysis, users may employ Python or R scripts for data processing and visualization. Recommended packages include Pandas, Numpy, and Matplotlib for Python, as well as tidyverse and ggplot2 for R.