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Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts.

机译:使用记录中心数据预测物种分布:蝙蝠栖息地栖息地适宜性的多尺度建模。

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摘要

Conservation increasingly operates at the landscape scale. For this to be effective, we need landscape scale information on species distributions and the environmental factors that underpin them. Species records are becoming increasingly available via data centres and online portals, but they are often patchy and biased. We demonstrate how such data can yield useful habitat suitability models, using bat roost records as an example. We analysed the effects of environmental variables at eight spatial scales (500 m - 6 km) on roost selection by eight bat species (Pipistrellus pipistrellus, P. pygmaeus, Nyctalus noctula, Myotis mystacinus, M. brandtii, M. nattereri, M. daubentonii, and Plecotus auritus) using the presence-only modelling software MaxEnt. Modelling was carried out on a selection of 418 data centre roost records from the Lake District National Park, UK. Target group pseudoabsences were selected to reduce the impact of sampling bias. Multi-scale models, combining variables measured at their best performing spatial scales, were used to predict roosting habitat suitability, yielding models with useful predictive abilities. Small areas of deciduous woodland consistently increased roosting habitat suitability, but other habitat associations varied between species and scales. Pipistrellus were positively related to built environments at small scales, and depended on large-scale woodland availability. The other, more specialist, species were highly sensitive to human-altered landscapes, avoiding even small rural towns. The strength of many relationships at large scales suggests that bats are sensitive to habitat modifications far from the roost itself. The fine resolution, large extent maps will aid targeted decision-making by conservationists and planners. We have made available an ArcGIS toolbox that automates the production of multi-scale variables, to facilitate the application of our methods to other taxa and locations. Habitat suitability modelling has the potential to become a standard tool for supporting landscape-scale decision-making as relevant data and open source, user-friendly, and peer-reviewed software become widely available.
机译:保护越来越多地在景观尺度上进行。为了使此方法有效,我们需要有关物种分布及其基础环境因素的景观尺度信息。通过数据中心和在线门户网站越来越多地获得种类记录,但是种类记录经常是零散的和有偏见的。我们以蝙蝠栖息记录为例,演示了这些数据如何产生有用的栖息地适应性模型。我们分析了八种蝙蝠(Pipistrellus pipistrellus,P。pygmaeus,Nyctalus noctula,Myotis mystacinus,M。brandtii,M。nattereri,M。daubentonii)在八个空间尺度(500 m-6 km)上环境变量对栖息地选择的影响。以及Plecotus auritus)。对来自英国湖区国家公园的418个数据中心栖息记录进行了建模。选择目标组假缺以减少抽样偏差的影响。多尺度模型结合了在其最佳性能空间尺度上测得的变量,被用于预测栖息地栖息地的适宜性,从而产生具有有用的预测能力的模型。小面积的落叶林地不断提高栖息地栖息地的适应性,但其他栖息地的关联在物种和规模之间也有所不同。 pi蝶与小规模的建筑环境呈正相关,并取决于大规模的林地可用性。另一种更专业的物种对人为改变的景观高度敏感,甚至避免了小的乡村城镇。大规模的许多关系的强度表明,蝙蝠对栖息地的改变很敏感,而不是栖息地本身。高分辨率,大范围的地图将有助于保护主义者和规划者进行有针对性的决策。我们提供了一个ArcGIS工具箱,该工具箱可自动生成多尺度变量,以方便将我们的方法应用于其他分类单元和位置。随着相关数据和开放源代码,用户友好型和经过同行评审的软件的广泛使用,人居适应性模型有可能成为支持景观尺度决策的标准工具。

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    Bellamy C; Altringham J;

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  • 年度 2015
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