Supplement 2. A listing containing code to perform the minimum span computation in C syntax.

<h2>File List</h2><div> <a href="minspan_code.html">minspan_code.html</a> (MD5: 38998dd468e7d8b2a3d89754b7a04863)</div><h2>Description</h2><div> <p>A function to compute minimum span (to a given radial resolution) written in ANSI-C syntax. For each of a given number of angles in [0,180), this code rotates the points around the given point and computes the nearest points at which the polygon crosses the x-axis (at least twice if point is inside polygon). The distance between these points is stored if minimal across all angles. </p> </div

Supplement 1. A vector file of polygon fragment boundaries used in this study in KML format.

<h2>File List</h2><div> <p><a href="CAO_kipuka_boundaries_20131204.kml">CAO_kipuka_boundaries_20131204.kml</a> (MD5: 9cd3a0afdd95797b21e41515140be36d) A polygon vector GIS layer of the fragment boundaries.</p> </div><h2>Description</h2><div> <p><b>Description</b></p> <p>These boundaries were computed using utilities packaged with the GDAL library (<a href="http://www.gdal.org/">http://www.gdal.org</a>) under the following methodology:</p> <ol> <li>We used gdalwarp and gdal_translate to stack the computed vegetation height and NDVI images onto the same grid at 2.0m resolution. Cubic spline interpolation as used.</li> <li>We used gdal_calc.py create a binary mask of cells meeting the following thresholds: Canopy height > 3.0 and NDVI > 0.7.</li> <li>We used gdal_sieve.py to groups less than 50 cells (0.02ha) with 8-connectedness.</li> <li>The remaining groups were polygonized using the utility gdal_polygonize.py</li> <li>Finally, the boundaries of these groups were rounded slightly using the -simplify flag of the ogr2ogr utility. Tolerance value (maximum distance segment can move when removing a node) was 2.0.</li> </ol> </div

Map of the four focal watersheds.

<p>The four study watersheds in Kruger National Park. A LiDAR-derived ground elevation map is shown for each site, demonstrating the hydrologic and geologic differences between the watersheds.</p

Spatial covariance of the vegetation structure metrics.

<p>Variograms showing the extent of spatial covariance of the six vegetation structure metrics for each of the four watersheds. In each of the variograms, semi-variance (SV) is scaled to span the interval [0,1] for better comparison of the range of spatial dependence between watersheds. Proportion vegetation is defined as the proportion of area in a 16.8 x 16.8 m window of the vegetation height map that is in the given height class: 0.5 to 2.5 m (shrub), 2.5 to 5.0 m (small tree), 5.0 to 10.0 m (medium tree), and >10.0 m (large tree).</p

Variance explained by the models for the four watersheds.

<p>The proportion of variation, measured as R², for all combinations of response variable and watershed. Values are given both before and after an autocovariate (ACV) was added to a full model with all environmental variables and one-way interactions. For the southern granite watershed, the total proportion of variance explained with soil class (STP) in the model—prior to the addition of and ACV term—is also shown. Bars represent the R² values given on the right side. The lag distance used to compute the ACV term for each model is given after each bar. One row is missing because there was too little large tree vegetation on the northern basalt site to fit a model. P(Shrub), P(SmTree), P(MedTree) and P(LgTree) refer to proportion of area in a 16.8 x 16.8 m window of the vegetation height map that is in the given height class: 0.5 to 2.5 m (shrub), 2.5 to 5.0 m (small tree), 5.0 to 10.0 m (medium tree), and >10.0 m (large tree).</p

Distributions of the environmental factors by watershed.

<p>Probability density functions of environmental drivers of woody vegetation cover showing the large differences between watersheds. The distributions are computed using a Gaussian kernel on 5000 randomly located samples selected from each watershed (without stratification), and they are scaled to fit all four watersheds on a single plot. The abbreviations O1, O2 and O3 refer to order 1, 2 and 3 streams, respectively.</p

Mean values of the vegetation structure metrics for the four watersheds.

<p>The mean and 95% confidence interval for each of the six vegetation structure metrics across 5000 randomly located samples selected from each watershed (without stratification). The vegetation structure within the four watersheds can differ by an order of magnitude. Proportion vegetation is defined as the proportion of area in a 16.8 x 16.8 m window of the vegetation height map that is in the given height class: 0.5 to 2.5 m (shrub), 2.5 to 5.0 m (small tree), 5.0 to 10.0 m (medium tree), and >10.0 m (large tree).</p

What mediates tree mortality during drought in the southern Sierra Nevada?

<p>We used high-fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) from the Carnegie Airborne Observatory (CAO) to estimate the effect of forest dieback on species composition in response to drought stress in Sequoia National Park. Our aims were: (1) to quantify site-specific conditions that mediate tree mortality along an elevation gradient in the southern Sierra Nevada Mountains; (2) to assess where mortality events have a greater probability of occurring; and (3) to estimate which tree species have a greater likelihood of mortality along the elevation gradient. The data-set include dead trees and highly stressed trees that were identified in 2015 in the forests of Sequoia National Park (SNP), along an elevation gradient in California’s southern Sierra. <br></p><p><br></p><p><br></p