title: Consensus forecasting of species distributions: the effects of niche model performance and niche properties.
abstract: 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.
authors.affiliation: Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Forest Silviculture of the State Forestry Administration, Beijing, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, China.
authors.forename: Shirong
authors.initials: S
authors.lastname: Liu
authors.affiliation: Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, China.
authors.forename: Pengsen
authors.initials: P
authors.lastname: Sun
authors.affiliation: Centre for Forest Conservation Genetics, Department of Forest Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Tongli
authors.initials: T
authors.lastname: Wang
authors.affiliation: Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Guangyu
authors.initials: G
authors.lastname: Wang
authors.affiliation: Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Forest Silviculture of the State Forestry Administration, Beijing, China.
authors.forename: Xudong
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Beijing University of Agriculture, Beijing, China.
authors.forename: Linlin
authors.initials: L
authors.lastname: Wang
mesh_terms: D002681:China; D002980:Climate; D057231:Climate Change; D003198:Computer Simulation; D032921:Consensus; D017753:Ecosystem; D065928:Forests; D006801:Humans; D015233:Models, Statistical; D063148:Plant Dispersal / Q000502:physiology*; D012044:Regression Analysis; D013045:Species Specificity; D014197:Trees / Q000502:physiology*; D035501:Uncertainty