Piospheres describe herbivore utilization gradients around watering points, as commonly found in grass-dominated ecosystems. Spatially explicit, dynamic models are ideal tools to study the ecological and economic problems associated with the resulting land degradation. However, there is a need for appropriate landscape input maps to these models that depict plausible initial vegetation patterns under a range of scenarios. Our goal was to develop a spatially-explicit piosphere landscape generator (PioLaG) for semi-arid savanna rangelands with a focus on realistic vegetation zones and spatial patterns of basic plant functional types around livestock watering points. We applied a hybrid modelling approach combining aspects of both process- and pattern-based modelling. Exemplary parameterization of PioLaG was based on literature data and expert interviews in reference to Kalahari savannas. PioLaG outputs were compared with piosphere formations identified on aerial images. PioLaG allowed to create rangeland landscapes with piospheres that can be positioned within flexible arrangements of grazing units (camps). The livestock utilization gradients showed distinct vegetation patterns around watering points, which varied according to the pre-set initial rangeland condition, grazing regime and management type. The spatial characteristics and zoning of woody and herbaceous vegetation were comparable to real piosphere patterns. PioLaG can provide important input data for spatial rangeland models that simulate site-specific savanna dynamics. The created landscapes can also be used as a direct decision support for land managers in attempts to maintain or restore landscape functionality and key ecosystem services such as forage production.

中文翻译：

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PioLaG：用于稀树草原牧场建模的 piosphere 景观生成器

Piospheres 描述了取水点周围的食草动物利用梯度，这在以草为主的生态系统中很常见。空间明确的动态模型是研究与土地退化相关的生态和经济问题的理想工具。但是，需要为这些模型提供适当的景观输入图，以描绘一系列情景下合理的初始植被模式。我们的目标是为半干旱稀树草原牧场开发一个空间明确的 piosphere 景观生成器 (PioLaG)，重点是现实植被区和牲畜饮水点周围基本植物功能类型的空间模式。我们应用了一种混合建模方法，结合了基于流程和基于模式的建模的各个方面。PioLaG 的示例性参数化基于参考 Kalahari 稀树草原的文献数据和专家访谈。将 PioLaG 输出与航拍图像上识别的电离层地层进行了比较。PioLaG 允许创建带有 piospheres 的牧场景观，这些 piospheres 可以定位在放牧单位（营地）的灵活安排内。牲畜利用梯度在饮水点附近表现出不同的植被模式，其根据预设的初始牧场条件、放牧方式和管理类型而变化。木本和草本植被的空间特征和分区与真实的海生圈模式相当。PioLaG 可以为模拟特定地点稀树草原动态的空间牧场模型提供重要的输入数据。