seal#

… is a tool to explore how ecological patterns change with spatial scale, inspired by Palmer & White (1994).
… systematically modifies two components of spatial scale—grain and extent—and evaluates how observed patterns respond.
… reveals whether properties of a sampled community (e.g., species richness, turnover, dissimilarity) vary across spatial scales.
… treats spatial scale as a variable to be systematically manipulated rather than as a fixed property of the dataset.
… provides results as raw CSV files, with optional graphical outputs. Numerical results are the primary outputs. Built-in plots are intended mainly for rapid inspection, and users are encouraged to use their preferred plotting tools.
… can explore the influence of spatial scale on other spatially distributed phenomena. It requires an object identity column (e.g., species, element, category), and optionally a quantitative value (e.g., units, individuals, count, concentration). However, to keep the documentation concise, we will focus on the analysis of a community of species.

… is not a hypothesis-testing tool. Most outputs are descriptive, not inferential. This is a limitation of spatially point-based studies as sampling units are not independent. Moreover, data present in one observational scale contribute to data of higher scales, making inferential statistics biased.
… does not isolate the effect of spatial scale from other potential drivers of community structure. It indicates whether an observed pattern changes with grain and/or extent, but cannot by itself identify the underlying cause or rule out alternative explanations.

Model situations#

When a new area is designated for long-term protection, research, or monitoring, permanent sampling units (quadrats or transects) are often established. seal can help inform decisions about their size and spatial arrangement. During an initial survey, the area can be sampled evenly in a relatively fine spatial scale forming a grid to obtain pilot data. As subsequent monitoring will often involve fewer samples, seal can be used to examine how reducing the sampled area, changing grain, or increasing spacing between sampling units may affect the observed results. Provided only a limited cumulative area can be sampled, seal can compare species–area relationships across alternative sampling configurations (sample sizes and spacing), helping to select the most efficient design that retain the most relevant information while using available resources efficiently.

seal can also be used to assess whether properties of a sampled community, such as species richness, species dominance, turnover or dissimilarity change with grain or extent. That helps to interpret community patterns at an appropriate spatial resolution and avoid misleading conclusions based on the scale at which the data were collected.

The applications of seal are not restricted to ecological communities either: it can be used for other spatially structured data where observations can be assigned to sampling units and where grain and extent can be distinguished and modified (so almost all studies with a spatial component). An example includes spatial surveys of water quality or studies comparing composition of human settlement using quantitative socio-economic variables. However, interpretation of individual analyses must remain appropriate to the phenomenon being studied.

seal can also serve as a visual teaching tool for concepts like spatial scale, species–area relationship, turnover, and distance decay. As it does not focus on the complexity of other variables, it offers a clear way to explore these ideas. Students can even use their data, making the learning process more engaging and relevant.

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