Abstract：
BACKGROUND: Selection of climate-change adapted ecotypes of commercially valuable species to date relies on DNA-assisted screening followed by growth trials. For trees, such trials can take decades, hence any approach that supports focussing on a likely set of candidates may save time and money. We use a non-stationary statistical analysis with spatially varying coefficients to identify ecotypes that indicate first regions of similarly adapted varieties of Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) in North America. For over 70,000 plot-level presence-absences, spatial differences in the survival response to climatic conditions are identified.
RESULTS: The spatially-variable coefficient model fits the data substantially better than a stationary, i.e. constant-effect analysis (as measured by AIC to account for differences in model complexity). Also, clustering the model terms identifies several potential ecotypes that could not be derived from clustering climatic conditions itself. Comparing these six identified ecotypes to known genetically diverging regions shows some congruence, as well as some mismatches. However, comparing ecotypes among each other, we find clear differences in their climate niches.
CONCLUSIONS: While our approach is data-demanding and computationally expensive, with the increasing availability of data on species distributions this may be a useful first screening step during the search for climate-change adapted varieties. With our unsupervised learning approach being explorative, finely resolved genotypic data would be helpful to improve its quantitative validation.
RESULTS: The spatially-variable coefficient model fits the data substantially better than a stationary, i.e. constant-effect analysis (as measured by AIC to account for differences in model complexity). Also, clustering the model terms identifies several potential ecotypes that could not be derived from clustering climatic conditions itself. Comparing these six identified ecotypes to known genetically diverging regions shows some congruence, as well as some mismatches. However, comparing ecotypes among each other, we find clear differences in their climate niches.
CONCLUSIONS: While our approach is data-demanding and computationally expensive, with the increasing availability of data on species distributions this may be a useful first screening step during the search for climate-change adapted varieties. With our unsupervised learning approach being explorative, finely resolved genotypic data would be helpful to improve its quantitative validation.
摘要：
背景：迄今为止，对具有商业价值的物种的适应气候变化的生态型的选择依赖于DNA辅助筛选，然后进行生长试验。对于树木，这样的试验可能需要几十年，因此，任何支持关注一组可能的候选人的方法都可以节省时间和金钱。我们使用具有空间变化系数的非平稳统计分析来识别生态型，这些生态型表明北美花旗松（Pseudotsugamenziesii（Mirbel）Franco）的类似适应品种的第一个区域。超过70,000个情节级别的缺席，确定了对气候条件的生存响应的空间差异。
结果：空间可变系数模型比平稳的数据拟合得更好，即恒定效应分析(由AIC测量以说明模型复杂性的差异)。此外，聚类模型术语可以识别几种潜在的生态型，这些生态型不能从聚类气候条件本身中得出。将这六种已识别的生态型与已知的遗传差异区域进行比较显示出一些一致性，还有一些不匹配。然而,相互比较生态型，我们发现它们的气候生态位存在明显差异。
结论：虽然我们的方法对数据要求很高，而且计算成本很高，随着物种分布数据的增加，这可能是寻找适应气候变化的品种的有用的第一步。我们的无监督学习方法是探索性的，精细解析的基因型数据将有助于提高其定量验证。
结果：空间可变系数模型比平稳的数据拟合得更好，即恒定效应分析(由AIC测量以说明模型复杂性的差异)。此外，聚类模型术语可以识别几种潜在的生态型，这些生态型不能从聚类气候条件本身中得出。将这六种已识别的生态型与已知的遗传差异区域进行比较显示出一些一致性，还有一些不匹配。然而,相互比较生态型，我们发现它们的气候生态位存在明显差异。
结论：虽然我们的方法对数据要求很高，而且计算成本很高，随着物种分布数据的增加，这可能是寻找适应气候变化的品种的有用的第一步。我们的无监督学习方法是探索性的，精细解析的基因型数据将有助于提高其定量验证。
