Drivers of woody canopy water content responses to drought in a Mediterranean-type ecosystem

<p>Severe droughts increase physiological stress in woody plant species, which can lead to mortality, fundamentally altering the composition, structure, and biogeography of forests in many regions. Little is known, however, about the factors determining the physiological response of woody plants to drought at landscape scales. Our objective was to understand woody plant species responses to ongoing changes in climate, using remotely sensed canopy water content (CWC) as an indicator of plant physiological and phenological status. We used fused imaging spectroscopy and light detection and ranging (LiDAR) from the Carnegie Airborne Observatory (CAO) to quantify the factors affecting species compositional changes in CWC in a diverse Mediterranean-type ecosystem (Jasper Ridge Biological Preserve, CA) between 2013 and 2015. Mapped CWC was spatially variable in both of the observation years, and proved to be most closely tied to species composition and distribution across the landscape. The secondary predictors of CWC were elevation and soil substrate. In contrast, we found that CWC change was much more related to environmental factors than to the species composition. We suggest that the effect of environment on CWC change is mediated through species resistance and resilience to drought. Monitoring CWC change with imaging spectroscopy is a powerful approach to identifying species-level responses to climatic events and long-term change, which may provide support for policy decisions and conservation at large spatial scales.</p

Appendix D. Extended comparison of methods developed for the estimation of ɑ-diversity and based on the spectral variation hypothesis.

Extended comparison of methods developed for the estimation of ɑ-diversity and based on the spectral variation hypothesis

Appendix B. Pseudo-code for mapping biodiversity using the spectral species distribution.

Pseudo-code for mapping biodiversity using the spectral species distribution

Appendix C. Optimal component selection for biodiversity estimation using spectral species distribution.

Optimal component selection for biodiversity estimation using spectral species distribution

Appendix A. Field plot network description.

Field plot network description

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

Appendix A. A list of species-specific and generic allometric equations used to calculate aboveground biomass.

A list of species-specific and generic allometric equations used to calculate aboveground biomass

Appendix C. Full results of modeling aboveground biomass using landscape variables with ordinary least-squares regression and simultaneous autoregressive approaches.

Full results of modeling aboveground biomass using landscape variables with ordinary least-squares regression and simultaneous autoregressive approaches

Hydrological Networks and Associated Topographic Variation as Templates for the Spatial Organization of Tropical Forest Vegetation

<div><p>An understanding of the spatial variability in tropical forest structure and biomass, and the mechanisms that underpin this variability, is critical for designing, interpreting, and upscaling field studies for regional carbon inventories. We investigated the spatial structure of tropical forest vegetation and its relationship to the hydrological network and associated topographic structure across spatial scales of 10–1000 m using high-resolution maps of LiDAR-derived mean canopy profile height (MCH) and elevation for 4930 ha of tropical forest in central Panama. MCH was strongly associated with the hydrological network: canopy height was highest in areas of positive convexity (valleys, depressions) close to channels draining 1 ha or more. Average MCH declined strongly with decreasing convexity (transition to ridges, hilltops) and increasing distance from the nearest channel. Spectral analysis, performed with wavelet decomposition, showed that the variance in MCH had fractal similarity at scales of ∼30–600 m, and was strongly associated with variation in elevation, with peak correlations at scales of ∼250 m. Whereas previous studies of topographic correlates of tropical forest structure conducted analyses at just one or a few spatial grains, our study found that correlations were strongly scale-dependent. Multi-scale analyses of correlations of MCH with slope, aspect, curvature, and Laplacian convexity found that MCH was most strongly related to convexity measured at scales of 20–300 m, a topographic variable that is a good proxy for position with respect to the hydrological network. Overall, our results support the idea that, even in these mesic forests, hydrological networks and associated topographical variation serve as templates upon which vegetation is organized over specific ranges of scales. These findings constitute an important step towards a mechanistic understanding of these patterns, and can guide upscaling and downscaling.</p></div