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Simple Process-Based Simulators for Generating Spatial Patterns of Habitat Loss and Fragmentation: A Review and Introduction to the G-RaFFe Model

机译：基于简单的基于过程的模拟器来生成栖息地丧失和破碎的空间格局：G-RaFFe模型的回顾与介绍

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Landscape simulators are widely applied in landscape ecology for generating landscape patterns. These models can be divided into two categories: pattern-based models that generate spatial patterns irrespective of the processes that shape them, and process-based models that attempt to generate patterns based on the processes that shape them. The latter often tend toward complexity in an attempt to obtain high predictive precision, but are rarely used for generic or theoretical purposes. Here we show that a simple process-based simulator can generate a variety of spatial patterns including realistic ones, typifying landscapes fragmented by anthropogenic activities. The model “G-RaFFe” generates roads and fields to reproduce the processes in which forests are converted into arable lands. For a selected level of habitat cover, three factors dominate its outcomes: the number of roads (accessibility), maximum field size (accounting for land ownership patterns), and maximum field disconnection (which enables field to be detached from roads). We compared the performance of G-RaFFe to three other models: Simmap (neutral model), Qrule (fractal-based) and Dinamica EGO (with 4 model versions differing in complexity). A PCA-based analysis indicated G-RaFFe and Dinamica version 4 (most complex) to perform best in matching realistic spatial patterns, but an alternative analysis which considers model variability identified G-RaFFe and Qrule as performing best. We also found model performance to be affected by habitat cover and the actual land-uses, the latter reflecting on land ownership patterns. We suggest that simple process-based generators such as G-RaFFe can be used to generate spatial patterns as templates for theoretical analyses, as well as for gaining better understanding of the relation between spatial processes and patterns. We suggest caution in applying neutral or fractal-based approaches, since spatial patterns that typify anthropogenic landscapes are often non-fractal in nature.
机译：景观模拟器广泛应用于景观生态学中以生成景观图案。这些模型可以分为两类：基于模式的模型，该模型生成与空间形状无关的过程的空间模式；以及基于过程的模型，试图基于对其进行成型的过程来生成模式。为了获得较高的预测精度，后者往往趋于复杂，但很少用于一般或理论目的。在这里，我们展示了一个简单的基于过程的模拟器可以生成各种空间模式，包括逼真的模式，以人为活动所代表的景观为代表。 “ G-RaFFe”模型生成道路和田地，以重现将森林转化为耕地的过程。对于选定水平的栖息地覆盖率，其结果主要取决于三个因素：道路数量（可及性），最大田间面积（考虑土地所有权模式）和最大田野断开连接（使田野与公路分离）。我们将G-RaFFe的性能与其他三个模型进行了比较：Simmap（中性模型），Qrule（基于分形）和Dinamica EGO（四个模型版本的复杂性不同）。基于PCA的分析表明，G-RaFFe和Dinamica版本4（最复杂）在匹配现实空间模式方面表现最佳，但是另一种考虑模型可变性的分析则将G-RaFFe和Qrule视为表现最佳。我们还发现模型的性能会受到栖息地覆盖和实际土地利用的影响，后者反映在土地所有权模式上。我们建议，可以使用简单的基于过程的生成器（例如G-RaFFe）来生成空间模式，作为用于理论分析的模板，以及更好地理解空间过程与模式之间的关系。我们建议在应用基于中性或分形的方法时要谨慎，因为代表人为景观的空间格局在本质上通常不是分形的。

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