DOI: 10.25338/b86k9m
title: California subalpine forest post-fire diversity and productivity

description: High severity fire may promote or reduce plant understory diversity in
 forests. However, few empirical studies test long-standing theoretical
 predictions that productivity may help to explain observed variation in
 post-fire plant diversity. Support for the influence of productivity on
 disturbance-diversity relationships is found predominantly in experimental
 grasslands, while tests over large areas with natural disturbance and
 productivity gradients are few and have yielded inconsistent results.
 Here, we measured the response of post-fire understory plant diversity to
 natural gradients of fire severity and productivity in a large-scale
 observational study in California’s subalpine forests. We found that plant
 species richness increased with increasing fire severity and that this
 trend was stronger at high productivity. We used plant traits to
 investigate whether release from competition might contribute to
 increasing diversity and found that short-lived and far-dispersing species
 benefited more from high severity fire than their long-lived and
 near-dispersing counterparts. For far-dispersing species only, the benefit
 from high severity fire was stronger in high productivity plots where
 unburned species richness was lowest. Our results support theoretical
 connections between fire severity, productivity and plant communities that
 are key to predicting the consequences of increasing fire severity and
 frequency on diversity in the coming decades.
description: <b><i>Fire and plot
 selection</i></b> Over the course of three
 summers (July – September, 2017 – 2019), we sampled 248 plots across 13
 fires in subalpine forest (see Appendix S1: Table S1 for fire
 information). We identified fires that contained each of six fire severity
 classes (unburned, low, low-moderate, moderate, high-moderate, and high)
 corresponding to fire-caused basal area mortality (0%, 0-25%, 25-50%,
 50-75%, 75-90%, and 90-100% respectively) using the remotely sensed
 relativized differenced normalized burn ratio (RdNBR; Miller and Thode
 2007). Relativized fire severity measures such as RdNBR report relative
 change such that areas of both sparser and denser vegetation can
 experience the full fire severity spectrum. This helps to decouple the
 correlation between fire severity and productivity that can arise with
 absolute measures of biomass change or mortality (Pausas and Bradstock
 2007). We sampled all 10 subalpine fires in the study region that
 contained six fire severity classes and were within two days hike of a
 trailhead as well as 3 smaller fires with fewer severity classes. Time
 since fire at the time of sampling ranged relatively evenly from 2-17
 years. We used a stratified random sampling design,
 placing 405 square meter circular plots at the crosshairs of a 200x200
 meter grid in upland areas with no recorded history of previous fire or
 logging. We stratified plots across preliminary fire severity classes
 (from RdNBR) as well as aspect (see Appendix S1: Supplemental Methods
 & Figure S1) and estimated final plot-level fire severity based on
 fire-caused basal area mortality as described above as well as relative
 fuel consumption (see Appendix S1: Table S2 for fire severity class
 descriptions). We subsequently censused all vascular plant species in the
 plot by systematically searching the plot area. Species richness was low
 in our study area (median 10), making it easy to locate all
 species. <b><i>Remotely sensed
 environmental variables</i></b> We used
 NDVI, a remotely sensed measure of photosynthetic activity, to represent
 plot-level productivity. NDVI has strong theoretical and empirical links
 to Gross Primary Productivity (GPP; Glenn et al. 2008), and robust
 correlations have been found between NDVI and tree radial growth
 measurements in other energy-limited systems like boreal forest (Beck et
 al. 2011).  NDVI derived from 30x30m Landsat imagery is commonly used to
 represent productivity in montane forest (Burkle et al. 2015) and a recent
 study in Sierra Nevada subalpine forest found that it declined with
 mortality in whitebark pine stands and that inter-annual variability in
 NDVI tracked snow accumulation throughout a 30 year study period (Potter
 and Dolanc 2016). Using Google Earth Engine, we removed snow and cloud
 pixels from Landsat V imagery and calculated the average of maximum annual
 NDVI for the years 1995 through 2000 (Pettorelli et al. 2005, Gorelick et
 al. 2017). This six-year time period predates sampled fires, represents
 both wet and dry years in the study region, and its average correlates
 well with total aboveground tree biomass calculated for 37 unburned plots
 (Appendix S1: Fig. S1). We calculated Heat Load Index, a single metric
 accounting for slope, aspect, and some elements of slope position and
 shadow (essentially total plot-level solar radiation), using the R package
 spatialEco (McCune and Keon, 2002).
 <b><i>Plant trait data</i></b>
 We compiled available information on plant life history and
 dispersal syndrome for species in our dataset using the University of
 California Jepson Herbarium (http://ucjeps.berkeley.edu), the USDA Forest
 Service Fire Effects Information System (feis-crs.org), JSTOR Global
 Plants (https://plants.jstor.org/), and our own familiarity with the species. We characterized species life history as either annual/biennial, short-lived perennial (non-woody perennial without rhizomes, stolons, bulbs or root storage organs) or long-lived perennial (woody perennials and/or perennials with rhizomes, stolons, bulbs, or other storage organs), and species dispersal syndrome as either far-dispersing (vertebrate or wind dispersal syndrome) or near-dispersing (unassisted dispersal syndrome) (Supplement S1: Table S3). Species dispersal syndrome was classified as vertebrate-dispersing if the reproductive structures were fleshy or adhesive, wind-dispersing if the reproductive structure included a pappus, and unassisted if the reproductive structure had none of the previously described characteristics.
description: See README file.