Most temperate forests in U.S. are recovering from heavy exploitation and are in intermediate successional stages where partial tree harvest is the primary disturbance. Changes in regional forest composition in response to climate change are often predicted for plant functional types using biophysical process models. These models usually simplify the simulation of succession and harvest and may not consider important species‐specific demographic processes driving forests changes. We determined the relative importance of succession, harvest, and climate change to forest composition changes in a 125‐million ha area of the Central Hardwood Forest Region of U.S. We used a forest landscape modeling approach to project changes in density and basal area of 23 tree species due to succession, harvest, and four climate scenarios from 2000 to 2300. On average, succession, harvest, and climate change explained 78, 17, and 1% of the variation in species importance values (IV) at 2050, respectively, but their contribution changed to 46, 26, and 20% by 2300. Climate change led to substantial increases in the importance of red maple and southern species (e.g., yellow‐poplar) and decreases in northern species (e.g., sugar maple) and most of widely distributed species (e.g., white oak). Harvest interacted with climate change and accelerated changes in some species (e.g., increasing southern red oak and decreasing American beech) while ameliorated the changes for others (e.g., increasing red maple and decreasing white ash). Succession was the primary driver of forest composition change over the next 300 years. The effects of harvest on composition were more important than climate change in the short term but climate change became more important than harvest in the long term. Our results show that it is important to model species‐specific responses when predicting changes in forest composition and structure in response to succession, harvest, and climate change.

Species distribution models require selection of species, study extent and spatial unit, statistical methods, variables, and assessment metrics. If absence data are not available, another important consideration is pseudoabsence generation. Different strategies for pseudoabsence generation can produce varying spatial representation of species. We considered model outcomes from four different strategies for generating pseudoabsences. We generating pseudoabsences randomly by 1) selection from the entire study extent, 2) a two-step process of selection first from the entire study extent, followed by selection for pseudoabsences from areas with predicted probability <25%, 3) selection from plots surveyed without detection of species presence, 4) a two-step process of selection first for pseudoabsences from plots surveyed without detection of species presence, followed by selection for pseudoabsences from the areas with predicted probability <25%. We used Random Forests as our statistical method and sixteen predictor variables to model tree species with at least 150 records from Forest Inventory and Analysis surveys in the Laurentian Mixed Forest province of Minnesota. Pseudoabsence generation strategy completely affected the area predicted as present for species distribution models and may be one of the most influential determinants of models. All the pseudoabsence strategies produced mean AUC values of at least 0.87. More importantly than accuracy metrics, the two-step strategies over-predicted species presence, due to too much environmental distance between the pseudoabsences and recorded presences, whereas models based on random pseudoabsences under-predicted species presence, due to too little environmental distance between the pseudoabsences and recorded presences. Models using pseudoabsences from surveyed plots produced a balance between areas with high and low predicted probabilities and the strongest relationship between density and area with predicted probabilities ≥75%. Because of imperfect accuracy assessment, the best assessment currently may be evaluation of whether the species has been sufficiently but not excessively predicted to occur.