seal#

… is a tool to explore the influence of spatial scale on ecological data, inspired by Palmer & White.
… helps reveal whether and how observational data is affected by spatial scale—grain and extent.
… shows whether properties of a researched community (e.g., turnover, dissimilarity) vary when sampled at different spatial scales.
… provides results as raw CSV files, with optional graphical outputs. However, plots are not the main outcome and users are encouraged to use their favourite plotting tool.
… can explore the influence of spatial scale on virtually any phenomenon, since it necessitates only a column containing object identity (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 statistical testing tool. Most outputs are descriptive, not inferential.
… does not separate the effect of spatial scale from other potential drivers of community structure. It indicates whether spatial scale matters but does not rule out alternative explanations.

Model situations#

When a new area is designated for long-term protection, research, or monitoring, permanent plots (quadrats or transects) are often established. seal can help optimize their size and spacing. Initially, the area should be sampled evenly (in a grid-like manner) to collect pilot data. For future monitoring, the number of samples will likely be reduced to use research resources efficiently. seal can analyse the pilot data to assess whether results are affected by spatial scale. Provided only limited cumulative area can be sampled in the future, seal can compare species–area relationships across different sample sizes and spacing, helping to select the most efficient design for long-term monitoring.

seal can be used to verify if properties of the sampled community, such as turnover or dissimilarity differ for different spatial scales. That helps to interpret community patterns at the right spatial resolution and not draw misleading conclusions based on scale-dependent effects.

Its usage is not limited to ecology either: any phenomenon where data collection happens in a way allowing distinction between grain and extent (so almost all studies with a spatial component) can be analysed. An example could be collecting water quality data in a body of water or studies characterising human settlements based on socio-economic factors.

seal can be used as a simple visual tool for teaching concepts like spatial scale, 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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