Appendix A. A table and seven figures illustrating model fit and model limitations.

A table and seven figures illustrating model fit and model limitations

Appendix C. A table and 14 figures showing the effect of climate change on tree species.

A table and 14 figures showing the effect of climate change on tree species

Proportions of variance explained (<i>R</i> square values) by local linear regression in total variation among the nine neighbor pixels for monthly minimum and maximum temperatures, and monthly precipitation across the entire North America.

<p>The extent of the box indicates the 25^th and 75^th percentiles. The horizontal solid lines inside the boxes indicate the medians.</p

Distribution of 4891 weather stations and the baseline data sources (PRISM and WorldClim) within the coverage of ClimateNA.

<p>Distribution of 4891 weather stations and the baseline data sources (PRISM and WorldClim) within the coverage of ClimateNA.</p

Comparisons in prediction standard errors between ClimateNA and the baseline climate data for primary monthly climate variables based on evaluations against observations from 4891 weather stations in North America.

<p>Comparisons in prediction standard errors between ClimateNA and the baseline climate data for primary monthly climate variables based on evaluations against observations from 4891 weather stations in North America.</p

The amount of variance in observed climate variables explained by ClimateNA derived variables and their prediction standard errors.

<p>The amount of variance in observed climate variables explained by ClimateNA derived variables and their prediction standard errors.</p

Sources of climate data used to generate the baseline climate normal (1961–1990) grids for the ClimateNA software package.

<p>Sources of climate data used to generate the baseline climate normal (1961–1990) grids for the ClimateNA software package.</p

Consensus Forecasting of Species Distributions: The Effects of Niche Model Performance and Niche Properties

<div><p>Ensemble forecasting is advocated as a way of reducing uncertainty in species distribution modeling (SDM). This is because it is expected to balance accuracy and robustness of SDM models. However, there are little available data regarding the spatial similarity of the combined distribution maps generated by different consensus approaches. Here, using eight niche-based models, nine split-sample calibration bouts (or nine random model-training subsets), and nine climate change scenarios, the distributions of 32 forest tree species in China were simulated under current and future climate conditions. The forecasting ensembles were combined to determine final consensual prediction maps for target species using three simple consensus approaches (average, frequency, and median [PCA]). Species’ geographic ranges changed (area change and shifting distance) in response to climate change, but the three consensual projections did not differ significantly with respect to how much or in which direction, but they did differ with respect to the spatial similarity of the three consensual predictions. Incongruent areas were observed primarily at the edges of species’ ranges. Multiple stepwise regression models showed the three factors (niche marginality and specialization, and niche model accuracy) to be related to the observed variations in consensual prediction maps among consensus approaches. Spatial correspondence among prediction maps was the highest when niche model accuracy was high and marginality and specialization were low. The difference in spatial predictions suggested that more attention should be paid to the range of spatial uncertainty before any decisions regarding specialist species can be made based on map outputs. The niche properties and single-model predictive performance provide promising insights that may further understanding of uncertainties in SDM.</p></div

Species traits, model accuracy, and map correlation.

<p>Coefficient and <i>P</i>-values of F-statstic for variables retained in the multiple stepwise regression models of map correlation for baseline and future time periods.</p><p>Species traits, model accuracy, and map correlation.</p

Box-whisker plot of differences in model performance (AUC, Kappa, and TSS) among model classes when data were pooled for all species and split-sample bouts.

<p>Dots show the mean predictive accuracy across species and split-sample bouts.</p