How does the landscape context of occurrence data influence models of invasion risk? A comparison of independent datasets in Massachusetts, USA
How does the landscape context of occurrence data influence models of invasion risk? A comparison of independent datasets in Massachusetts, USA
复制标题
发生数据的景观背景如何影响入侵风险模型？
DOI：
10.1007/s10980-014-0080-5
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发表时间：
2014
影响因子：
5.2
通讯作者：
B. Bradley
中科院分区：
文献类型：
--
作者：
R. Vieira;J. Finn;B. Bradley
非原生入侵植物在景观上的空间分布受人类活动的强烈影响。人们将非本地物种引入新的景观和地区(繁殖压力)，并通过干扰本地生态系统来增加生态系统的入侵性。然而，导致入侵的不同景观驱动因素的相对重要性可能因景观背景而异(即周围土地覆盖和土地利用的类型和数量)。如果是这样的话，在一种情况下收集的数据可能不适合预测更广泛的环境中的入侵风险。为了测试独立的发生数据集是否表明入侵的景观驱动因素相似，我们比较了基于新英格兰入侵植物地图集(IPANE)数据的景观模型和基于森林管理计划(FSP)的模型，前者由训练有素的公民科学家机会性地贡献，后者位于私有和相对未受干扰的森林中。我们评估了16个与繁殖压力和/或干扰有关的景观变量，作为入侵植物存在的显著预测因子，基于存在/不存在和计数回归模型。林区内入侵植物的存在和丰富度受空地比例和居民区附近的影响最大，这两个因素都是森林内部繁殖的来源。相比之下，同一地区的IPANE入侵植物的存在和丰富度受到到道路和溪流的距离的影响。这些结果表明，景观入侵的驱动因素在很大程度上取决于景观环境，而发生数据集的选择将强烈影响模型的结果。
The spatial distribution of non-native, invasive plants on the landscape is strongly influenced by human action. People introduce non-native species to new landscapes and regions (propagule pressure) as well as increase ecosystem invasibility through disturbance of native ecosystems. However, the relative importance of different landscape drivers of invasion may vary with landscape context (i.e., the types and amounts of surrounding land cover and land use). If so, data collected in one context may not be appropriate for predicting invasion risk across a broader landscape. To test whether independent occurrence datasets suggest similar landscape drivers of invasion, we compared landscape models based on data compiled by the Invasive Plant Atlas of New England (IPANE), which are contributed opportunistically by trained citizen scientists, to models based on Forest Stewardship plans (FSPs), which are located in privately owned and relatively undisturbed forests. We evaluated 16 landscape variables related to propagule pressure and/or disturbance for significant predictors of invasive plant presence based on presence/absence and count regression models. Presence and richness of invasive plants within FSPs was most influenced by proportion of open land and proximity to residential areas, which are both sources of propagules in forest interiors. In contrast, IPANE invasive plant presence and richness for the same area was influenced by distance to roads and streams. These results suggest that landscape drivers of invasion vary considerably depending on landscape context, and the choice of occurrence dataset will strongly influence model results.