DOI: 10.25338/b80642
title: Spatiotemporal data for studying the dry forest-rainforest ecotone in Guanacaste, Costa Rica

description: This data package contains data supporting the manuscript "Climate
 and topography control variation in the tropical dry forest–rainforest
 ecotone." The contents include 1) compositeMODIS_ALL_20220713.tif: a
 GeoTIFF image file of biweekly composite MODIS NDVI from 2000 to 2021.
 This was acquired using GoogleEarthEngine and subsetted to the study area.
 2) guanClimate.csv: a spreadsheet of climate variables averaged for the
 study area. These were derived from the CHELSA interpolated climate
 dataset and the Multivariate ENSO Index v2
 (https://psl.noaa.gov/enso/mei/). 3) guan_DEM.tif: a GeoTIFF digital
 elevation model for the study area. 4) guan_TWI.tif: a GeoTIFF of the
 Topographic Wetness Index (TWI) for the study area.  5)
 guanacaste.shp, .shx, .dbf, .sbx, .sbn: a shapefile of the Guanacaste
 administrative boundary, which is also the study area boundary. 5)
 ImageMetadata_20220714.csv: a spreadsheet of information about the bands
 contained in compositeMODIS_ALL_20220613.tif. These reflect derived
 datasets developed from publicly available sources. Methods and data
 sources are described briefly below and in detail in the paper. Processing
 and analysis code are archived in a linked Zenodo repository.
description: MODIS NDVI data (L3 product MOD13Q1) were obtained using
 GoogleEarthEngine and subset to the study area. Ecotone areas were
 delineated using spatial synchrony analyses. We measured synchrony between
 all pairs of pixels and used network modularity analyses to identify
 groups of pixels with high within-group and low between-group synchrony.
 Comparison with field plots and known vegetation distributions
 corroborated that major groupings distinguished dry forest from
 rainforest. Ecotone areas were then pixels that were weakly associated
 with either group. We analyzed how the probability of a pixel being
 ecotone depended on elevation and topographic wetness index (TWI). TWI was
 calculated using WhiteBox software in R. Applying the analysis to
 individual years of data, we used thresholds on group association strength
 to discretize the ecotone area and measured the area, median elevation,
 elevation range, and perimeter:area ratio of the ecotone. We then analyzed
 inter annual variation in ecotone characteristics for long-term trends and
 the influence of precipitation, potential evapotranspiration, and the
 Multivariate El Niño Index (MEI v2).
description: # Spatiotemporal data for studying the dry forest-rainforest ecotone in
 Guanacaste, Costa Rica --- This data package contains data supporting the
 manuscript "Climate and topography control variation in the tropical
 dry forestrainforest ecotone." The contents include 1)
 compositeMODIS_ALL_20220713.tif: a GeoTIFF image file of biweekly
 composite MODIS NDVI from 2000 to 2021. This was acquired using
 GoogleEarthEngine and subsetted to the study area. 2\) guanClimate.csv: a
 spreadsheet of climate variables averaged for the study area. These were
 derived from the CHELSA interpolated climate dataset and the Multivariate
 ENSO Index v2 (). 3\) guan_DEM.tif:
 a GeoTIFF digital elevation model for the study area. 4\) guan_TWI.tif: a
 GeoTIFF of the Topographic Wetness Index (TWI) for the study area. 5\)
 guanacaste.shp, .shx, .dbf, .sbx, .sbn: a shapefile of the Guanacaste
 administrative boundary, which is also the study area boundary. 5\)
 ImageMetadata_20220714.csv: a spreadsheet of information about the bands
 contained in compositeMODIS_ALL_20220613.tif. These reflect derived
 datasets developed from publicly available sources. Methods and data
 sources are described briefly below and in detail in the paper. Processing
 and analysis code are archived in a linked Zenodo repository. MODIS NDVI
 data (L3 product MOD13Q1) were obtained using GoogleEarthEngine and subset
 to the study area. Ecotone areas were delineated using spatial synchrony
 analyses. We measured synchrony between all pairs of pixels and used
 network modularity analyses to identify groups of pixels with high
 within-group and low between-group synchrony. Comparison with field plots
 and known vegetation distributions corroborated that major groupings
 distinguished dry forest from rainforest. Ecotone areas were then pixels
 that were weakly associated with either group. We analyzed how the
 probability of a pixel being ecotone depended on elevation and topographic
 wetness index (TWI). TWI was calculated using WhiteBox software in R.
 Applying the analysis to individual years of data, we used thresholds on
 group association strength to discretize the ecotone area and measured the
 area, median elevation, elevation range, and perimeter:area ratio of the
 ecotone. We then analyzed inter annual variation in ecotone
 characteristics for long-term trends and the influence of precipitation,
 potential evapotranspiration, and the Multivariate El Nio Index (MEI v2).
 We found that topography and climate, through their influence on moisture
 availability, shaped spatiotemporal variation in the dry forest-rainforest
 ecotone. ## Description of the data and file structure 1.
 compositeMODIS_ALL_20220713.tif: a multi band GeoTIFF image file of
 biweekly composite MODIS NDVI from 2000 to 2021. Information on the time
 period each band corresponds to is given in ImageMetadata_20220714.csv. 2.
 guanClimate.csv: a spreadsheet of climate variables averaged for the study
 area. These were derived from the CHELSA interpolated climate dataset and
 the Multivariate ENSO Index v2 ().
 There are 6 columns: * years: (numeric) the year corresponding to the
 climate measure * totalPr: (numeric) total (over months) precipitation, kg
 m^-2 * meanPET: (numeric) mean (over months) potential evapotranspiration,
 kg m^-2 * drySeasonL: (numeric) length of dry season (months), where dry
 season has PET &gt; precipitation * mei: (numeric) the mean (over months)
 Multivariate ENSO Index. 1. guan_DEM.tif: a GeoTIFF digital elevation
 model for the study area. 2. guan_TWI.tif: a GeoTIFF of the Topographic
 Wetness Index (TWI) for the study area. 3. guanacaste.shp, .shx, .dbf,
 .sbx, .sbn: a shapefile of the Guanacaste administrative boundary, which
 is also the study area boundary. 4. ImageMetadata_20220714.csv: a
 spreadsheet of temporal information about the bands contained in
 compositeMODIS_ALL_20220613.tif. There is 1 column giving the date. ##
 Sharing/Access information Data was derived from the following sources: *
 MEI: * CHELSA:
 * MODIS:
 ## Code/Software
 There are two code files: 1. guanacaste_sync_allYears.R: the main script
 for conducting synchrony-based analyses of the ecotone 2.
 ecotoneDelineationStats.R: the main script for analyzing spatial and
 temporal patterns in the ecotone and their relationship to climate and
 topography The scripts were developed in R version 4.2.1. Major packages
 used in data analysis are raster (v 3.5-21), rgdal (v 1.5-32), mgcv (v
 1.8-40), and wsyn (v 1.0.4).