Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB-II for 135 eastern U.S. trees.

Species distribution models (SDMs) provide useful information about potential presence or absence, and environmental conditions suitable for a species; and high-resolution models across large extents are desirable. A primary feature of SDMs is the underlying spatial resolution, which can be chosen for many reasons, though we propose that a hybrid lattice, in which grid cell sizes vary with the density of forest inventory plots, provides benefits over uniform grids. We examine how the spatial grain size affected overall model performance for the Random Forest-based SDM, DISTRIB, which was updated with recent forest inventories, climate, and soil data, and used a hybrid lattice derived from inventory densities.Modeled habitat suitability was compared between a uniform grid of 10 × 10 and a hybrid lattice of 10 × 10 and 20 × 20 km grids to assess potential improvements. The resulting DISTRIB-II models for 125 eastern U.S. tree species provide information on individual habitat suitability that can be mapped and statistically analyzed to understand current and potential changes.Model performance metrics were comparable among the hybrid lattice and 10-km grids; however, the hybrid lattice models generally had higher overall model reliability scores and were likely more representative of the inventory data.Our efforts to update DISTRIB models with current information aims to produce a more representative depiction of recent conditions by accounting for the spatial density of forest inventory data and using the latest climate data. Additionally, we developed an approach that leverages a hybrid lattice to maximize the spatial information within the models and recommend that similar modeling efforts be used to evaluate the spatial density of response and predictor data and derive a modeling grid that best represents the environment.

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

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.

Exploring tree species colonization potentials using a spatially explicit simulation model: implications for four oaks under climate change.

Climate change impacts tree species differentially by exerting unique pressures and altering their suitable habitats. We previously predicted these changes in suitable habitat for current and future climates using a species habitat model (DISTRIB) in the eastern United States. Based on the accuracy of the model, the species assemblages should eventually reflect the new quasi-equilibrium suitable habitats (~2100) after accounting for the lag in colonization. However, it is an open question if and when these newly suitable habitats will be colonized under current fragmented landscapes and realistic migration rates. To evaluate this, we used a spatially explicit cell-based model (SHIFT) that estimates colonization potentials under current fragmented habitats and several estimates of historical migration rates at a 1 km resolution. Computation time, which was previously the biggest constraint, was overcome by a novel application of convolution and Fast Fourier Transforms. SHIFT outputs, when intersected with future suitable habitats predicted by DISTRIB, allow assessment of colonization potential under future climates. In this article, we show how our approach can be used to screen multiple tree species for their colonization potentials under climate change. In particular, we use the DISTRIB and SHIFT models in combination to assess if the future dominant forest types in the north will really be dominated by oaks, as modelled via DISTRIB. Even under optimistic scenarios, we conclude that only a small fraction of the suitable habitats of oaks predicted by DISTRIB is likely to be occupied within 100 years, and this will be concentrated in the first 10-20 km from the current boundary. We also show how DISTRIB and SHIFT can be used to evaluate the potential for assisted migration of vulnerable tree species, and discuss the dynamics of colonization at range limits.

Regional scale patterns of fine root lifespan and turnover under current and future climate.

Fine root dynamics control a dominant flux of carbon from plants and into soils and mediate potential uptake and cycling of nutrients and water in terrestrial ecosystems. Understanding of these patterns is needed to accurately describe critical processes like productivity and carbon storage from ecosystem to global scales. However, limited observations of root dynamics make it difficult to define and predict patterns of root dynamics across broad spatial scales. Here, we combine species-specific estimates of fine root dynamics with a model that predicts current distribution and future suitable habitat of temperate tree species across the eastern United States (US). Estimates of fine root lifespan and turnover are based on empirical observations and relationships with fine root and whole-plant traits and apply explicitly to the fine root pool that is relatively short-lived and most active in nutrient and water uptake. Results from the combined model identified patterns of faster root turnover rates in the North Central US and slower turnover rates in the Southeastern US. Portions of Minnesota, Ohio, and Pennsylvania were also predicted to experience >10% increases in root turnover rates given potential shifts in tree species composition under future climate scenarios while root turnover rates in other portions of the eastern US were predicted to decrease. Despite potential regional changes, the average estimates of root lifespan and turnover for the entire study area remained relatively stable between the current and future climate scenarios. Our combined model provides the first empirically based, spatially explicit, and spatially extensive estimates of fine root lifespan and turnover and is a potentially powerful tool allowing researchers to identify reasonable approximations of forest fine root turnover in areas where no direct observations are available. Future efforts should focus on reducing uncertainty in estimates of root dynamics by better understanding how climate and soil factors drive variability in root dynamics of different species.

Predicting extinctions as a result of climate change.

Widespread extinction is a predicted ecological consequence of global warming. Extinction risk under climate change scenarios is a function of distribution breadth. Focusing on trees and birds of the eastern United States, we used joint climate and environment models to examine fit and climate change vulnerability as a function of distribution breadth. We found that extinction vulnerability increases with decreasing distribution size. We also found that model fit decreases with decreasing distribution size, resulting in high prediction uncertainty among narrowly distributed species. High prediction uncertainty creates a conservation dilemma in that excluding these species under-predicts extinction risk and favors mistaken inaction on global warming. By contrast, including narrow endemics results in over-predicting extinction risk and promotes mistaken inaction on behalf of individual species prematurely considered doomed to extinction.