DOI: 10.5061/dryad.1c59zw3tk
title: Data and code from: Spring phenological escape is critical for the survival of temperate tree seedlings

description:   Understory plants in deciduous forests often rely on access to
 ephemeral light availability before the canopy closes in spring and after
 the canopy reopens in fall, a strategy commonly referred to as
 phenological escape. Although there is evidence for a relationship between
 understory plant phenology and demographic performance, a mechanistic link
 is still missing. In this study, we bridged this gap by estimating annual
 carbon assimilation as a function of foliar phenology and photosynthetic
 capacity for seedlings of two temperate tree species that commonly
 co-occur across eastern North America. We then modeled the relationship
 between estimated carbon assimilation and observed seedling survival and
 growth. Our results indicate that seedlings of both species strongly
 depend on spring phenological escape to assimilate the majority of their
 annual carbon budget and that this mechanism significantly affects their
 likelihood of survival (but not growth). Foliar desiccation also played a
 strong role in driving patterns of seedling survival, suggesting that
 water availability will also help shape seedling recruitment dynamics. We
 found only weak associations between seedling senescence in fall and
 annual carbon assimilation, suggesting that phenological escape in fall
 plays a relatively minor role in seedling demographic performance. Our
 results indicate that spring phenological escape is critical for survival
 of these temperate tree species, and thus any changes to this dynamic
 associated with climate change could strongly impact these species’
 recruitment.  
description: These data were collected between 2014-2019 by Benjamin R. Lee
 and include model code for the photosynthesis, survival, and growth models
 outlined in the corresponding article. Models were conducted in OpenBUGS
 which is free to download online. Data for all models
 are included in the code documents in collapsible data-frames that are
 clearly labeled. Initial values are also recorded at the bottom of each
 model. Posterior parameter estimates are available in
 the supplemental materials of the corresponding article.
description: All models were run separately for each species.
 Parameter definitions for the photosynthesis model are provided
 in the supplemental materials for this article along with an in-depth
 description of the model structure. 
 <strong>Curve-level variables for photosynthesis
 models</strong>: plant = individual replicate identifier, curveid =
 individual curve replicate identifier, AQ = 1 indicates ACi curve and 2
 indicates AQ curve, PS = centered soil moisture value (see notes on soil
 moisture sub-model in supplement), VS = centered Vapor pressure deficit
 values. <strong>Observation-level variables for
 photosynthesis models:</strong> curve = curve identifier (same as
 curveid above), Obs = individual gas exchange measurement (multiple in
 each curve), Aobs = observed photosynthetic rate (A), Q = light level
 (PPFD) inside the cuvette, Pressure = atmospheric pressure, CiP =
 interstitial pressure, O = partial pressure of O2
 <strong>Survival/growth model
 variables:</strong> Site = (1 = George Reserve, 2 = Radrick Forest,
 3 = Saginaw), surv = recorded as either alive (1) or dead (0), growth =
 centered annual growth value, AnnC = centered estimate of annual carbon
 assimilation, PctDam = percent of leaf recorded as damaged by end of
 growing season, Des = binary desiccation damage indicator (1 = present, 0
 = absent), Deer = binary deer herbivory indicator (1 = present, 0 =
 absent), SdlgA = individual identifier for Acer seedlings, SdlgQ =
 individual identifier for Quercus seedlings, Year starts at 1 indicating
 data collected in 2015, GSF = Global Site Factor (centered around mean),
 Cohort = Seedling cohort (1-3 = seedlings planted in 2014-2016,
 respectively), Age = age of seedling in years at time of
 measurement