DOI: 10.5061/dryad.wh70rxwq0
title: Data from: Tree growth responses to extreme drought after mechanical thinning and prescribed fire in a Sierra Nevada mixed-conifer forest, USA

description: An estimated 128 M trees died during the 2012-2016 California drought,
 largely in the southern Sierra Nevada Range. Prescribed burning and
 mechanical thinning are widely used to reduce fuels and restore ecosystem
 properties, but it is unclear if these treatments improve tree growth and
 vigor during extreme drought. This study examined tree growth responses
 after thinning, prescribed burning, and extreme drought at the Teakettle
 Experimental Forest, a historically frequent fire mixed-conifer forest in
 the southern Sierra Nevada of California, USA. Mechanical thinning (no
 thin, understory thin, and overstory thin) and prescribed burning
 (unburned, fall burning) were implemented in 2000-2001. Using annual
 growth data from increment cores, over 10,000 mapped and measured trees,
 and lidar-derived metrics of solar radiation and topographic wetness, we
 had two primary questions. First, what were the growth responses to
 thinning and prescribed burning treatments, and did these responses
 persist during the 2012-2016 drought? Second, what tree-level attributes
 and environmental conditions influenced growth responses to treatments and
 drought? Thinning increased residual tree growth and that response
 persisted through extreme drought 10 -15 years after treatments. Growth
 responses were higher in overstory versus understory thinning, with
 differences between thinning types more pronounced during drought.
 Species-specific growth responses were strongest with overstory thinning,
 with sugar pine (Pinus lambertiana) and incense-cedar (Calocedrus
 decurrens) having higher growth responses compared to white fir (Abies
 concolor) and Jeffery pine (Pinus jeffreyi). For individual trees, factors
 associated with higher growth responses were declining pretreatment growth
 trend, smaller tree size, and post-treatment low neighborhood basal area.
 Growth responses were initially not influenced by topography, but
 topographic wetness became important during extreme drought. Mechanical
 thinning resulted in durable increases in residual tree growth rates
 during extreme drought over a decade after thinning occurred, indicating
 treatment longevity in mitigating drought stress. In contrast, tree growth
 did not improve after prescribed burning, likely due to fire effects that
 reduced surface fuels, but had little effect on reducing tree density.
 Thinning treatments promoted durable growth responses, but focusing on
 stand-level metrics may ignore important tree-level attributes such as
 localized competition and topography associated with higher water
 availability. Mechanical thinning was effective at improving growth in
 trees that had been experiencing declining growth trends, but was less
 effective in improving growth responses in large old higher ecological
 importance.
description: See methods section of Zald et al.
 2022. Tree growth responses to extreme drought after mechanical thinning
 and prescribed fire in a Sierra Nevada mixed-conifer forest, USA.
 <em>Forest Ecology and Management</em>. https://doi.org/10.1016/j.foreco.2022.120107.
description: <strong>TEF_Dendro.RData</strong>
 RData file of data used for analysis in Zald et al.
 2022. RData file contains three dataframes (tk.rwl, tk.lookup, and
 tk.comp.topo). tk.rwl contains raw ring width data for all sampled trees.
 tk.lookup contains tree attribute data for all sampled trees. tk.comp.topo
 contains local neighborhood basal area values, potential solar radiation
 values, and topographic wetness values for censused trees.
 
 <strong>TEF_Dendro.html</strong>
 R Markdown html uses data
 frames in TEF_Dendro.Rdata to conducting the following data processing and
 analyses steps: 1. Plot sampled trees in relation to all live tree
 diameters and local (10 m radius) neighborhood basal area within each
 treatment combination. 2. Plot relationship between diameter and age by
 species for sampled trees. 3. Calculate tree growth metrics. 4. Plot
 annual growth trends over time by treatment combination. 5. Stand level
 growth responses using linear mixed effects models, with diagnostics,
 estimated marginal means and post-hoc comparison tests. 6. Tree level
 growth responses using random forest with multi-stage model
 selection.