Macroscale intraspecific variation and environmental heterogeneity: analysis of cold and warm zone abundance, mortality, and regeneration distributions of four eastern US tree species

Abstract

I test for macroscale intraspecific variation of abundance, mortality, and regeneration of four eastern US tree species (Tsuga canadensis,Betula lenta,Liriodendron tulipifera, and Quercus prinus) by splitting them into three climatic zones based on plant hardiness zones (PHZs). The primary goals of the analysis are to assess the differences in environmental heterogeneity and demographic responses among climatic zones, map regional species groups based on decision tree rules, and evaluate univariate and multivariate patterns of species demography with respect to environmental variables. I use the Forest Inventory Analysis (FIA) data to derive abundance, mortality, and regeneration indices and split the range into three climatic zones based on USDA PHZs: (1) cold adapted, leading region; (2) middle, well‐adapted region; and (3) warm adapted, trailing region. I employ decision tree ensemble methods to assess the importance of environmental predictors on the abundance of the species between the cold and warm zones and map zonal variations in species groups. Multivariate regression trees are used to simultaneously explore abundance, mortality, and regeneration in tandem to assess species vulnerability. Analyses point to the relative importance of climate in the warm adapted, trailing zone (especially moisture) compared to the cold adapted, leading zone. Higher mortality and lower regeneration patterns in the warm trailing zone point to its vulnerability to growing season temperature and precipitation changes that could figure more prominently in the future. This study highlights the need to account for intraspecific variation of demography in order to understand environmental heterogeneity and differential adaptation. It provides a methodology for assessing the vulnerability of tree species by delineating climatic zones based on easily available PHZ data, and FIA derived abundance, mortality, and regeneration indices as a proxy for overall growth and fitness. Based on decision tree rules, ecologically meaningful variations in species abundance among the climatic zones can be related to environmental variability and mapped.

Evaluation of four clinical laboratory parameters for the diagnosis of myalgic encephalomyelitis

“…CART analysis is well-suited for this investigation, as it produces an algorithm that is amenable for diagnostic data purposes. It is also able to classify systems that differ due to natural causes and determine the relative importance of different variables for identifying homogeneous groups within a data set [ 37 ].…”

Vulnerability of eastern US tree species to climate change

“…Some studies have detected evidence for higher mortality, lower regeneration, and/or range contraction at species' lagging edges (Murphy et al, 2010;Zhu et al, 2012;Prasad, 2015), as well as northward migration (Woodall et al, 2009;Treyger & Nowak, 2011;Delzon et al, 2013;Boisvert-Marsh et al, 2014;Monleon & Lintz, 2015). Some studies have detected evidence for higher mortality, lower regeneration, and/or range contraction at species' lagging edges (Murphy et al, 2010;Zhu et al, 2012;Prasad, 2015), as well as northward migration (Woodall et al, 2009;Treyger & Nowak, 2011;Delzon et al, 2013;Boisvert-Marsh et al, 2014;Monleon & Lintz, 2015).…”

“…It is by far the country's most visited National Park (National Park Service, 2016). However, GRSM lies at the southern range of many northern and mountainous species, making them particularly vulnerable to warming (Prasad, 2015;Reich et al, 2015).…”

“…Demographic and range dynamics of trees in the eastern United States and similar regions have likely already begun to respond to climate change. Some studies have detected evidence for higher mortality, lower regeneration, and/or range contraction at species' lagging edges (Murphy et al, 2010;Zhu et al, 2012;Prasad, 2015), as well as northward migration (Woodall et al, 2009;Treyger & Nowak, 2011;Delzon et al, 2013;Boisvert-Marsh et al, 2014;Monleon & Lintz, 2015). The latter does not appear to be keeping pace with climate warming, which is only projected to intensify (Corlett & Westcott, 2013).…”

“…How adaptation will influence species' responses to climate change remains highly uncertain (Parmesan, 2006;Hof et al, 2011;Kremer et al, 2012). Although genotypic evolution is unlikely to facilitate tree adaptation during the 21st century (Savolainen et al, 2004;Jump & Penuelas, 2005), trees tend to display high levels of phenotypic plasticity in locally adapted populations (Davis & Shaw, 2001;Jump & Penuelas, 2005;Nicotra et al, 2010;Prasad, 2015). This can increase adaptive capacity because (i) stand-level phenotypes may evolve with climate warming (e.g., Kelly et al, 2003), and (ii) subpopulations are better suited to their local conditions.…”