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首页> 外文期刊>Ecological Applications >Fire frequency in the interior Columbia River Basin: Building regional models from fire history data
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Fire frequency in the interior Columbia River Basin: Building regional models from fire history data

机译：哥伦比亚流域内部的火灾频率：根据火灾历史数据建立区域模型

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Fire frequency affects vegetation composition and successional pathways; thus it is essential to understand fire regimes in order to manage natural resources at broad spatial scales. Fire history data are lacking for many regions for which fire management decisions are being made, so models are needed to estimate past fire frequency where local data are not yet available. We developed multiple regression models and tree-based (classification and regression tree, or CART) models to predict fire return intervals across the interior Columbia River basin at l-km resolution, using georeferenced fire history, potential vegetation, cover type, and precipitation databases. The models combined semiqualitative methods and rigorous statistics. The fire history data are of uneven quality; some estimates are based on only one tree, and many are not cross-dated. Therefore, we weighted the models based on data quality and performed a sensitivity analysis of the effects on the models of estimation errors that are due to lack of cross-dating. The regression models predict fire return intervals from 1 to 375 yr for forested areas, whereas the tree-based models predict a range of 8 to 150 yr. Both types of models predict latitudinal and elevational gradients of increasing fire return intervals. Examination of regional-scale output suggests that, although the tree-based models explain more of the variation in the original data, the regression models are less likely to produce extrapolation errors. Thus, the models serve complementary purposes in elucidating the relationships among fire frequency, the predictor variables, and spatial scale. The models can provide local managers with quantitative information and provide data to initialize coarse-scale fire-effects models, although predictions for individual sites should be treated with caution because of the varying quality and uneven spatial coverage of the fire history database. The models also demonstrate the integration of qualitative and quantitative methods when requisite data for fully quantitative models are unavailable. They can be tested by comparing new, independent fire history reconstructions against their predictions and can be continually updated, as better fire history data become available. [References: 87]
机译：火灾频率影响植被组成和演替路径;因此，必须了解火灾情况，以便在广阔的空间尺度上管理自然资源。许多地区都缺乏制定消防管理决策的火灾历史数据，因此需要模型来估算过去尚无本地数据的火灾发生频率。我们开发了多个回归模型和基于树的模型（分类和回归树或CART）模型，使用地理参考的火灾历史记录，潜在植被，覆盖类型和降水量数据库，以l-km的分辨率预测整个哥伦比亚河盆地的回火间隔。 。这些模型结合了半定性方法和严格的统计数据。火灾历史数据的质量参差不齐;一些估计仅基于一棵树，而许多估计却没有过时。因此，我们基于数据质量对模型进行加权，并对由于缺乏跨日期的估计误差对模型误差的影响进行了敏感性分析。回归模型预测林区的回火间隔为1至375年，而基于树的模型预测为8至150年。两种类型的模型都可以预测回火间隔增加的纬度和纬度梯度。对区域规模产出的检验表明，尽管基于树的模型解释了原始数据中的更多差异，但是回归模型不太可能产生外推误差。因此，这些模型在阐明火灾发生频率，预测变量和空间尺度之间的关系时起补充作用。这些模型可以为当地管理人员提供定量信息，并提供数据以初始化粗略的火灾效应模型，尽管由于火灾历史数据库的质量变化和空间覆盖范围不均匀，因此应谨慎对待各个场所的预测。当无法获得完全定量模型所需的数据时，这些模型还演示了定性和定量方法的集成。可以通过将新的独立火灾历史重建图与其预测进行比较来进行测试，并且可以在获得更好的火灾历史数据时对其进行不断更新。 [参考：87]

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