Conceptual figure on how to determine an ELF using the principle of limiting factors.

<p>a) Biological response data is plotted against potentially limiting resources (adapted from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114648#pone.0114648.g003" target="_blank">Fig. 3</a> in Cade and Noon [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114648#pone.0114648.ref016" target="_blank">16</a>]). b) Boundary models (<i>LF</i>_<i>V</i>) are fit to the upper hull of each scatterplot. c) To produce a prediction of the maximum biological response as well as the identity of the most limiting factor at a given site with a set of measured resource values, the <i>LF</i>_<i>V</i> models are applied to these resource values, and d) the resource that predicts the lowest maximum biological response is chosen as the most limiting factor, and its prediction is that site’s maximum biological response.</p

Summary of the percent area constrained by each bioclimate variable for sites that are within 10% of the predicted maximum tree cover for the entire study area as well as by forest type.

<p>Summary of the percent area constrained by each bioclimate variable for sites that are within 10% of the predicted maximum tree cover for the entire study area as well as by forest type.</p

In panel a), Map of the predicted maximum tree cover for the study area under mean temperature, minimum temperature, potential evapotranspiration, and PET-P limitations.

<p>b) Map of the predicted maximum tree cover for the study under only mean temperature limitations. c) Map of the standard deviation of the all-factor maximum tree cover predictors generated from an N = 500 bootstrap. d) Map of the stability of the limiting factor identity predictions from an N = 500 bootstrap. Values of 1.0 indicate a single factor was chosen as the limiting factor in all bootstraps (most stable).</p

Summary of total percent area constrained by each bioclimate variable for the entire study area as well as by forest type.

<p>Summary of total percent area constrained by each bioclimate variable for the entire study area as well as by forest type.</p

Map of which environmental limiting factors constrain maximum tree cover at a given location.

<p>Map of which environmental limiting factors constrain maximum tree cover at a given location.</p

Tree cover (y axis) vs. bioclimatic variables. The scatterplot is density shaded.

<p>A-D. The colored line represents the 99% quantile (the environmental limiting factor) of that bioclimatic variable. The horizontal black line represents the study area 99% quantile of tree cover.</p

The study area for this analysis are the eastern slopes of the Lake Tahoe Basin, CA/NV.

<p>The study area for this analysis are the eastern slopes of the Lake Tahoe Basin, CA/NV.</p

Detection of Salt Marsh Vegetation Stress and Recovery after the Deepwater Horizon Oil Spill in Barataria Bay, Gulf of Mexico Using AVIRIS Data

<div><p>The British Petroleum Deepwater Horizon Oil Spill in the Gulf of Mexico was the biggest oil spill in US history. To assess the impact of the oil spill on the saltmarsh plant community, we examined Advanced Visible Infrared Imaging Spectrometer (AVIRIS) data flown over Barataria Bay, Louisiana in September 2010 and August 2011. Oil contamination was mapped using oil absorption features in pixel spectra and used to examine impact of oil along the oiled shorelines. Results showed that vegetation stress was restricted to the tidal zone extending 14 m inland from the shoreline in September 2010. Four indexes of plant stress and three indexes of canopy water content all consistently showed that stress was highest in pixels next to the shoreline and decreased with increasing distance from the shoreline. Index values along the oiled shoreline were significantly lower than those along the oil-free shoreline. Regression of index values with respect to distance from oil showed that in 2011, index values were no longer correlated with proximity to oil suggesting that the marsh was on its way to recovery. Change detection between the two dates showed that areas denuded of vegetation after the oil impact experienced varying degrees of re-vegetation in the following year. This recovery was poorest in the first three pixels adjacent to the shoreline. This study illustrates the usefulness of high spatial resolution airborne imaging spectroscopy to map actual locations where oil from the spill reached the shore and then to assess its impacts on the plant community. We demonstrate that post-oiling trends in terms of plant health and mortality could be detected and monitored, including recovery of these saltmarsh meadows one year after the oil spill.</p></div

Vegetation indexes used for stress detection due to oil contamination.

<p>Vegetation indexes used for stress detection due to oil contamination.</p

Color-infrared view of study site showing oiled shoreline.

<p>Location of Barataria Bay in the Mississippi Delta in the Gulf of Mexico, AVIRIS images of Barataria Bay depicted in color infrared bands with oiled shoreline shown in yellow.</p