Comment on "The global tree restoration potential".

Bastin et al (Reports, 5 July 2019, p. 76) neglect considerable research into forest-based climate change mitigation during the 1980s and 1990s. This research supports some of their findings on the area of land technically suitable for expanding tree cover, and can be used to extend their analysis to include the area of actually available land and operational feasibility.

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.

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.

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.