10.6084/m9.figshare.3546852.v1 Geoffrey A. Fricker Geoffrey A. Fricker Jeffrey A. Wolf Jeffrey A. Wolf Sassan S. Saatchi Sassan S. Saatchi Thomas W. Gillespie Thomas W. Gillespie Supplement 1. The spatial and non-spatial data sets as well as the R code to perform Ordinary Least Squares and Generalized Least Squares regression analysis. Wiley 2016 multiple regression models tropical forests Barro Colorado Island, Panama remote sensing tree species richness high-resolution satellite imagery lidar alpha diversity spatial scale Environmental Science Ecology 2016-08-09 02:44:04 Dataset https://wiley.figshare.com/articles/dataset/Supplement_1_The_spatial_and_non-spatial_data_sets_as_well_as_the_R_code_to_perform_Ordinary_Least_Squares_and_Generalized_Least_Squares_regression_analysis_/3546852 <h2>File List</h2><div> <p><a href="TABLES_CODE.zip">TABLES_CODE.zip</a> (MD5: bb78bef8ed07b50f56555a594dc9ad74)</p> <p><a href="200.csv">200.csv</a> (MD5: f3dd49b436b548e88cb366831697f346)</p> <p><a href="100.csv">100.csv</a> (MD5: 45342b9bd19db40c8f4c4480c618d613)</p> <p><a href="10.csv">10.csv</a> (MD5: 302504c6368dd71845d2302421f3f466)</p> <p><a href="10_100.csv">10_100.csv</a> (MD5: 5a76d3d07dfa88d08d0323875db55a49)</p> <p><a href="10_200.csv">10_200.csv</a> (MD5: 7d2c830a789b2b84c5764de1988967d5)</p> <p><a href="d100.csv">d100.csv</a> (MD5:f2c2bdc846d38b27f4c7dc0c31c8771f )</p> <p><a href="d50.csv">d50.csv</a> (MD5: 12863fe859f675c87899531cefad2a85)</p> <p><a href="d20.csv">d20.csv</a> (MD5: 60e8a63bc51ec2ae19b0fd08552673f6)</p> <p><a href="QB_BCI_predictions_grid_rsvars.csv">QB_BCI_predictions_grid_rsvars.csv</a> (MD5: fd5584e3971f69ba1f2d7a26b6747992)</p> <p><a href="2015_fricker_predictions_fid.csv">2015_fricker_predictions_fid.csv</a> (MD5: 73f69524912cec54b7c0a860de6aff80)</p> <p><a href="2015_02_03_bci_main.r">2015_02_03_bci_main.r</a> (MD5: c40a6770943c7106be689ddc94408154)</p> <p><a href="2015_02_03_ols_gls_optimization_Fig5.r">2015_02_03_ols_gls_optimization_Fig5.r</a> (MD5: b4b9417dfe1350d9d5e2e2bf14a40e40)</p> <p><a href="2015_02_03_bci_ols_predictions.r">2015_02_03_bci_ols_predictions.r</a> (MD5: adb311fff7ffb47a225e1ab2b53be5ba)</p> <p><a href="2015_02_03_multiscale_analysis.r">2015_02_03_multiscale_analysis.r</a> (MD5: 2d32b9e8804d75b047c7b9d65d1b60f8)</p> <p><a href="bci50ha_100m.shp">bci50ha_100m.shp</a> (MD5: 109b6fe73fee786b168fa48271d92ff6)</p> <p><a href="bci50ha_50m.shp">bci50ha_50m.shp</a> (MD5: 36b610e1cb4589787b26226a2e01b9e9)</p> <p><a href="bci50ha_20m.shp">bci50ha_20m.shp</a> (MD5: 30e23f05c86a56acebc1dad921162422)</p> <p><a href="c7_t_10mm.shp">c7_t_10mm.shp</a> (MD5: 565f38ecdbcc55e9db4e2529aaf3c7f2)</p> <p><a href="QB_BCI_prediction_grid.shp">QB_BCI_prediction_grid.shp</a> (MD5: 702cd736606b196fc4a0b6b2b3818609)</p> <p><a href="QB_BCI_gls_ols_predictions.shp">QB_BCI_gls_ols_predictions.shp</a> (MD5: 702cd736606b196fc4a0b6b2b3818609)</p> </div><h2>Description</h2><div> <p>TABLES_CODE.zip is an archive containing all data and code described below (20 files)</p> <p>200.csv is a comma separated file which includes all response and predictor variables at the 1-ha spatial scale for stems greater than 200 mm dbh.</p> <p>100.csv is a comma separated file which includes all response and predictor variables at the 1-ha spatial scale for stems greater than 100 mm dbh.</p> <p>10.csv is a comma separated file which includes all response and predictor variables at the 1-ha spatial scale for all stems.</p> <p>10_100.csv is a comma separated file which includes all response and predictor variables at the 1-ha spatial scale for stems smaller than 100 mm dbh.</p> <p>10_200.csv is a comma separated file which includes all response and predictor variables at the 1-ha spatial scale for stems smaller than 200 mm dbh.</p> <p>d100.csv is a comma separated file for the multi-scale analysis which includes all response and predictor variables at the 100 × 100 m subplot scale (same contents as 10.csv). </p> <p>d50.csv is a comma separated file for the multi-scale analysis which includes all response and predictor variables at the 50 × 50 m subplot scale.</p> <p>d20.csv is a comma separated file for the multi-scale analysis which includes all response and predictor variables at the 20 × 20 m subplot scale. </p> <p>QB_BCI_predictions_grid_rsvars.csv is a comma separated file which contains a value for remote sensing variables (used in the predictive models) for each 1-ha grid cell (‘fid’ corresponds with the QB_BCI_prediction_grid.shp).</p> <p>2015_fricker_predictions_fid.csv is a comma separated file which contains a prediction for each tree size class for the prediction grid over BCI.</p> <p>2015_02_03_bci_main.r is the r-code for reading in all data files and creating correlation matrices. Also contains the base linear model used for the OLS regression models.</p> <p>2015_02_03_ols_gls_optimization_Fig5.r is the r-code for performing the Generalize Least Squares (GLS) spatial model optimization.</p> <p>2015_02_03_bci_ols_predictions.r is the r-code for making the Ordinary Least Squares (and one GLS) prediction across BCI.</p> <p>2015_02_03_multiscale_analysis.r is the r-code for the multi-scale analysis</p> <p>Bci50ha_100m.shp is the GIS shapefile of the 50 ha forest dynamics plot at the 100 × 100 m subplot scale.</p> <p>Bci50ha_50m.shp is the GIS shapefile of the 50 ha forest dynamics plot at the 50 × 50 m subplot scale.</p> <p>Bci50ha_20m.shp is the GIS shapefile of the 50 ha forest dynamics plot at the 20 × 20 m subplot scale.</p> <p>C7_t_10mm.shp is the GIS shapefile of the tree data for all stems greater than 10 mm dbh for the 50 ha forest dynamics plot.</p> <p>QB_BCI_prediction_grid.shp is the GIS shapefile which was used to make predictions across BCI (1 ha scale).</p> <p>QB_BCI_gls_ols_predictions.shp is the GIS shapefile is the GIS shapefile containing the predictions across BCI (1 ha scale).</p> </div>