关键词: 气候变化, 组合集成学习技术, 模型比较, 马尾松, 物种分布模拟

Abstract:

Aims New, powerful statistical techniques and GIS tools have resulted in a plethora of methods for modelling species distribution. However, little is known about the relative performance of different models in simulating and projecting species’ distributions under future climate. Our objective is to compare novel ensemble learning models with other conventional models by modelling the potential distribution of Masson pine (Pinus massoniana) and identifying and quantifying differences in model outputs.
Methods We simulated Masson pine potential distribution (baseline 1961-1990) and projected future potential distributions for three time periods (2010-2039, 2040-2069 and 2070-2099) using three global circulation models (GCM) (MIROC32_medres, JP; CCCMA_CGCM3, CA, and BCCR-BCM2.0, NW), one pessimistic SRES emissions scenario (A2), three ensemble learning models (random forest, RF; generalized boosted model, GBM, and NeuralEnsembles) and three conventional models (generalized linear model, GLM; generalized additive models, GAM, and classification and regression model, CART). The environmental envelope method was used to select absence of species. The area under the curve (AUC) values of receiver operator characteristic (ROC) curve, Kappa and true skill statistic (TSS) were used to objectively assess the predictive accuracy of each model. National standards for seed zone of Masson pine (GB 8822.6-1988) was employed to intuitively assess model performance. We developed ClimateChina software to downscale current and future GCM climate data and calculate seasonal and annual climate variables for specific locations based on latitude, longitude and elevation.
Important findings Ensemble learning models (GBM, NeuralEnsembles and RF) achieved a higher predictive success in simulating the distribution of Masson pine compared to other conventional models (CART, GAM and GLM). RF had the highest predictive accuracy, and CART had the lowest. Masson pine shows a globally consistent pattern in response to climate change for the three GCMs and six models, i.e., Masson pine will likely gradually shift northward and expand its distribution under altered future climate, with the magnitude of range changes dependent on model classes and GCMs. RF predicts a greater magnitude of range changes than other models. Projections of Masson pine distribution by NW are more conservative than JP and CA climate scenarios. In the case of Masson pine, range changes are mainly attributed to the colonization of newly available suitable habitat in high-latitudes and unchanging habitat suitability in the south-central part of its baseline range. Differences among 18 projections (6 models × 3 GCMs) increase with increasing time, and the greatest spatial uncertainty in projections is mainly in the north and west borders of the potential distribution range.

Key words: climate change, ensemble learning model, model comparision, Pinus massoniana, species distribu- tion modelling