Validation of species–climate impact models under climate change

Increasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climate‐change scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present‐day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding‐bird species over the past ～20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision‐planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models' predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species–climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.     

Uncertainty in ensemble forecasting of species distribution

Species distribution modelling has been widely applied in order to assess the potential impacts of climate change on biodiversity. Many methodological decisions, taken during the modelling process and forecasts, may, however, lead to a large variability in the assessment of future impacts. Using measures of species range change and turnover, the potential impacts of climate change on French stream fish species and assemblages were evaluated. Our main focus was to quantify the uncertainty in the projections of these impacts arising from four sources of uncertainty: initial datasets (Data), statistical methods [species distribution models (SDM)], general circulation models (GCM), and gas emission scenarios (GES). Several modalities of the aforementioned uncertainty sources were combined in an ensemble forecasting framework resulting in 8400 different projections. The variance explained by each source was then extracted from this whole ensemble of projections. Overall, SDM contributed to the largest variation in projections, followed by GCM, whose contribution increased over time equalling almost the proportion of variance explained by SDM in 2080. Data and GES had little influence on the variability in projections. Future projections of range change were more consistent for species with a large geographical extent (i.e., distribution along latitudinal or stream gradients) or with restricted environmental requirements (i.e., small thermal or elevation ranges). Variability in projections of turnover was spatially structured at the scale of France, indicating that certain particular geographical areas should be considered with care when projecting the potential impacts of climate change. The results of this study, therefore, emphasized that particular attention should be paid to the use of predictions ensembles resulting from the application of several statistical methods and climate models. Moreover, forecasted impacts of climate change should always be provided with an assessment of their uncertainty, so that management and conservation decisions can be taken in the full knowledge of their reliability.     

用组合模型综合比较的方法分析气候变化对朱鹮潜在生境的影响

气候变化的不确定性和物种与环境关系的不确定性使气候变化生物学的研究充满变数。为了降低不确定性,人们开始用组合模型综合比较的方法研究物种对气候变化的响应。以朱鹮(Nipponia nippon)为研究对象,介绍组合模型综合比较方法的特点。朱鹮曾经高度濒危,目前种群大小在迅速恢复中;然而其分布区依旧狭小,气候变化可能是朱鹮面临的新威胁。应用BIOMOD模型中的9种模型,选择了每年的最低温和最高温、温度的季节性变异、每年的总降水量和降水的季节性变异共5个气候因子,依据WorldClim气候数据的CGCM2气候模型的A2a排放情形,计算了朱鹮当前(1950—2000年)的适宜生境和2020年、2050年、2080年3个阶段的潜在生境范围。结果表明朱鹮潜在生境将逐渐北移,生境中心脱离现在的保护区。因此,制定朱鹮的长期保护策略是必要的。9个模型在预测结果上、变量权重上和拟合优度的指标上都有差异,反映了模型本身的不确定性。气候变化的生物学效应比较复杂,应用多个模型进行综合比较,可以尽可能地减少模型所导致的误差。     

Revisiting projected shifts in the climate envelopes of North American trees using updated general circulation models

Global climate models are constantly being upgraded, but it is often not clear what these changes have on climate change impact projections. We used difference maps to directly compare downscaled projections of temperature and precipitation across North America for two versions (or generations) of three different Atmospheric‐Ocean General Circulation Models (AOGCM)s. We found that AOGCM versions differed in their projections for the end of the current century by up to 4 °C for annual mean temperature and 60% for annual precipitation. To place these changes in an ecological context, we reanalyzed our work on shifts in tree climate envelopes (CEs) using the newer‐generation AOGCM projections. Based on the updated AOGCMs, by the 2071–2100 period, tree CEs shifted up to 2.4 degrees further north or 2.6 degrees further south (depending on the AOGCM) and were about 10% larger in size. Despite considerable differences between versions of a given AOGCM, projections made by the newer version of each AOGCM were in general agreement, suggesting convergence across the three models studied here. Assessing the AOGCM outputs in this way provides insight into the magnitude and importance of change associated with AOGCM upgrades as they continue to evolve through time.