Evaluating niche changes during invasion with seasonal models in Capsella bursa‐pastoris

Premise

Researchers often use ecological niche models to predict where species might establish and persist under future or novel climate conditions. However, these predictive methods assume species have stable niches across time and space. Furthermore, ignoring the time of occurrence data can obscure important information about species reproduction and ultimately fitness. Here, we assess and compare ecological niche models generated from full-year averages to seasonal models 

Methods

In this study, we generate full-year and monthly ecological niche models for Capsella bursa-pastoris in Europe and North America to see if we can detect changes in the seasonal niche of the species after long-distance dispersal. 

Key Results

We find full-year ecological niche models have low transferability across continents and there are continental differences in the climate conditions that influence the distribution of C. bursa-pastoris. Monthly models have greater predictive accuracy than full-year models in cooler seasons but no monthly models are able to predict North American summer occurrences very well.

Conclusions

The relative predictive ability of European monthly models compared to North American monthly models suggests a change in the seasonal timing between the native range to the non-native range. These results highlight the utility of ecological niche models at finer temporal scales in predicting species distributions and unmasking subtle patterns of evolution.

Methods
Here, we present the mean Partial Area Under the Curve (pAUC) value for comparisons of European monthly models on North American occurrences. We evaluated the performance of models using a pAUC analysis to focus on the most informative metrics of predictive ability for our study. We chose an admissible omission error rate of 0.15 for each model and 300 bootstrapped iterations. We evaluated the European models’ predictive ability on North American occurrences of C. bursa-pastoris to test for a seasonal niche shift. Each month of North American occurrences was evaluated against all twelve models resulting in 144 evaluations of the European model's predictive ability on North American occurrence records.

Premise
研究人员常借助生态位模型（ecological niche models）预测物种在未来或全新气候条件下的潜在定居与存续区域。然而此类预测方法默认物种在不同时空下的生态位保持稳定。此外，忽略物种发生时间数据会掩盖其繁殖乃至适合度相关的重要信息。本研究中，我们对基于全年平均值构建的生态位模型与季节型生态位模型进行评估与对比。

Methods
本研究针对欧洲与北美地区的荠菜（Capsella bursa-pastoris）构建全年与月度生态位模型，以探究能否检测到该物种经长距离扩散（long-distance dispersal）后其季节生态位的变化。

Key Results
我们发现，全年生态位模型在洲际间的跨区域迁移性能较差，且影响荠菜分布的气候条件存在洲际差异。相较于全年模型，月度模型在凉爽季节的预测精度更高，但无一款月度模型能较好地预测北美地区的夏季物种发生记录。

Conclusions
欧洲月度模型与北美月度模型的相对预测能力表明，该物种在原生分布区与非原生分布区之间的季节发生时序存在差异。本研究结果凸显了细时间尺度生态位模型在预测物种分布、揭示细微演化模式方面的应用价值。

Methods
本研究给出用于对比欧洲月度模型与北美物种发生记录的平均曲线下部分面积（Partial Area Under the Curve, pAUC）值。我们采用pAUC分析评估模型性能，以聚焦本研究中最具信息量的预测能力指标。我们为每个模型设定0.15的可接受漏报误差率，并开展300次自举迭代。我们通过将欧洲模型应用于北美荠菜的发生记录，以检验其季节生态位是否发生偏移。将北美各月份的物种发生记录与全部12个月度模型分别进行比对，最终得到144次欧洲模型对北美物种发生记录的预测能力评估。