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An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations

机译:一种改进的中性景观模型用于重建真实景观并生成景观序列以进行空间生态模拟

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摘要

Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer‐generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape‐level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class‐ and patch‐level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user‐defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.
机译:许多研究通过模拟计算机生成的具有不同组成和配置的景观中的景观过程,评估了景观格局对空间生态过程的影响。为了产生这样的景观,已经开发了各种中性景观模型。但是,这些模型中包含的有限的景观级别模式变量集通常不足以生成反映真实景观的景观。为了在生成的景观模式中获得更大的灵活性和可变性,应该在这些模型中实现一组更完整的类和补丁级别的模式变量。这些增强功能已在“景观生成器(LG)”中实现,该软件使用优化算法生成与用户定义的目标值匹配的景观。专为小规模参与式空间规划而开发,我们提高了LG的可用性,并展示了LG可如何用于较大规模的生态研究。首先,我们使用LG从真实景观(即瑞士的山区)中重建景观图案。其次,我们生成了具有递增变化的格局变量的景观系列,可用于生态模拟研究。我们发现LG能够重新创建与真实景观近似的景观图案。此外,我们成功生成了传统的中性景观模型无法实现的景观系列。 LG是产生中性景观的一种有前途的新颖方法,可以测试有关景观格局对生态过程的影响的新假设。 LG可在线免费获得。

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