Historical ecology: using unconventional data sources to test for effects of global environmental change.
Historical ecology: using unconventional data sources to test for effects of global environmental change.
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历史生态学：使用非常规数据源来测试全球环境变化的影响。
DOI：
10.3732/ajb.1200503
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发表时间：
2013
影响因子：
3
通讯作者：
I. Myers
中科院分区：
文献类型：
--
作者：
M. Vellend;Carissa D Brown;H. Kharouba;J. L. McCune;I. Myers
预测全球变化驱动因素对未来生态的影响需要了解这些驱动因素在过去是如何产生我们今天看到的植物种群和群落的。历史生态数据源作出了贡献的核心重要性，全球变化生物学，但仍然在大多数生态学家的工具包。在这里，我们回顾了历史生态数据的四个非传统来源的优势和劣势：土地调查记录，“遗产”植被数据，历史地图和照片，植物标本馆标本。我们讨论了最近的贡献，利用这些数据源来了解栖息地干扰和气候变化对植物种群和群落的影响，以及景观变化的防御殖民时间滞后的持续时间。历史数据经常支持使用传统生态研究（例如，随着温度的升高，暖适应物种的增加），但在有些情况下，添加不同的数据源会导致不同的结论（例如，时间植被变化不像时序研究预测的那样）。历史和当代数据来源的明确结合是揭示全球变化的多种驱动因素的长期后果的一种特别有力的方法。尽管历史数据的局限性，其中包括斑点和可能有偏见的空间和时间的覆盖范围，他们往往代表了唯一的手段，在过去的生态现象的特点，并已被证明是必不可少的性质，规模和全球变化对植物种群和社区的影响的一般性。
Predicting the future ecological impact of global change drivers requires understanding how these same drivers have acted in the past to produce the plant populations and communities we see today. Historical ecological data sources have made contributions of central importance to global change biology, but remain outside the toolkit of most ecologists. Here we review the strengths and weaknesses of four unconventional sources of historical ecological data: land survey records, "legacy" vegetation data, historical maps and photographs, and herbarium specimens. We discuss recent contributions made using these data sources to understanding the impacts of habitat disturbance and climate change on plant populations and communities, and the duration of extinction-colonization time lags in response to landscape change. Historical data frequently support inferences made using conventional ecological studies (e.g., increases in warm-adapted species as temperature rises), but there are cases when the addition of different data sources leads to different conclusions (e.g., temporal vegetation change not as predicted by chronosequence studies). The explicit combination of historical and contemporary data sources is an especially powerful approach for unraveling long-term consequences of multiple drivers of global change. Despite the limitations of historical data, which include spotty and potentially biased spatial and temporal coverage, they often represent the only means of characterizing ecological phenomena in the past and have proven indispensable for characterizing the nature, magnitude, and generality of global change impacts on plant populations and communities.