Additionally, the package provides many utility functions to visualize, extract, and sample landscape metrics. Lastly, we provide building-blocks to motivate the development and integration of new metrics in the future. We demonstrate the usage and advantages of landscapemetrics by analysing the influence of different sampling schemes on the estimation of landscape metrics. In so doing, we demonstrate the many advantages of the package, especially its easy integration into large workflows. These new developments should help with the integration of landscape analysis in ecological research, given that ecologists are increasingly using R for the statistical analysis, modelling and visualization of spatial data. Understanding how landscape characteristics affect ecological processes and the spatial distribution of species and communities is central to ecology (Turner 1989, 2005, Kupfer 2012). Thereby, one major challenge is how to describe and quantify landscape characteristics (Turner 2005, Lausch et al. Typically, landscapes are characterized as discrete patches of different land-cover classes (i.e. a landscape mosaic, Forman and Godron 1986, Forman 1995, Wiens 1995) which has several benefits. These include a straightforward application and communication (McGarigal et al. 2015), especially in human-dominated landscapes where the distinction between different land-cover classes is rather clear-cut (Lausch et al. While other landscapes may be better described by a gradient-based description of landscape structure (McGarigal et al. 2010), the landscape-mosaic model remains the dominant paradigm (Kupfer 2012, With 2019).
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