Quadrats are standard sampling units of relatively small, uniformly defined areas that divide a habitat of interest into subregions of equal size. They are used in spatial ecology analysis to (i) estimate population sizes when it is not feasible to measure their abundance in entirety, (ii) define the patterns of their spatial distribution, and (iii) compute species-area and distance-decay curves that define how spatial patterns of diversity and other categories of observations differ as a function of spatial scale. All of these uses have important implications in biogeography, the conservation of biodiversity, and ecophysiological adaptations. In addition, defining these patterns contributes to our understanding of community structure and function within a complex landscape.
The results of quadrat-based spatial analyses are heavily dependent on quadrat size. This is because the size of quadrats will significantly influence the extent of edge effects that can cause counting errors, the recognition of spatially discrete clusters of organisms, and the resolution of void areas that separate them. Thus, a major non-trivial challenge in the quadrat-based analysis of spatial patterns is how to optimize quadrat dimensions for the particular study in order to maximize the precision, accuracy and usefulness of the extracted data and the conclusions derived from them.
A major benefit of the quadrat–based approach in spatial ecology analyses is that it forces the analysis to take into account possible scales within which statistically significant inhomogeneous events may be occurring. Its major limitation of ignoring the precise location of each individual foreground object is nicely complemented when combined with other point pattern and geostatistical methods.
We are pleased to announce the release of CMEIAS Quadrat Maker v1.0, a free, improved computing technology that is uniquely designed to alleviate the important but cumbersome, time-consuming tasks of preparing digital images for quadrat-based spatial distribution analysis. The application performs 3 major routines: 1) significantly facilitates the task of optimizing the grid dimensions of the raster that divides digital landscape images into contiguous quadrats with optimal spatial resolution of foreground objects by avoiding their over- or under-sampling of points, 2) produces and saves an indexed image of the original landscape overlaid with the optimized grid-lattice that is annotation to indicate each quadrat’s location; and 3) produces individual images of each quadrat sample saved with column-row location in their file name that are now ready for plot-based spatial pattern analysis of the landscape domain. Input landscape images can be 8-bit grayscale or 24-bit RGB color in tiff, bmp, jpg or png format. Grid specifications, quadrat size in pixels, and percentage of the landscape area represented by each quadrat are automatically displayed in the GUI as the grid optimization procedure is interactively performed, and this information is also provided as a saved, output txt table.
Figures 1A-D show how the grid dimensions influence resolution of spatial patterns of the foreground objects (black circles) and how CMEIAS Quadrat Maker can facilitate the important evaluation of multiple grid-lattice dimensions to establish which one is optimal.
Figure 1. Evaluation of grid dimensions to optimize quadrat sizes. Shown are index images of the landscape with annotated lattices of A) 4x4, B) 6x6, C) 8x8 and D) 10x10 grid overlays. The 8x8 grid produced the optimized quadrat dimensions.
Figures 2A-B show an example of an indexed image and the output table reporting the pixel dimensions of the original landscape and proportions of each individual quadrat in the indexed image.
Figure 2. A) Indexed image of a landscape domain containing 33 foreground objects overlaid with a 3x2 grid-lattice raster and annotated labels of the column-row location of each of the six quadrats. B) Table output of image information reporting the pixel dimensions of the original landscape and proportions of each individual quadrat in the indexed image.
Details of our publication describing CMEIAS Quadrat Maker are provided in:
Frank Dazzo and Colin Gross (2013). Cmeias Quadrat Maker: a digital software tool to optimize grid dimensions and produce quadrat images for landscape ecology spatial analysis. Journal of Ecosystem & Ecography 3(136): 1-4. DOI: 10.4172/2157-7625.1000136
Click here to download CMEIAS Quadrat Maker.