Relationship between home range and density?

[Chavoux Luyt]

Hi all,

It makes sense to me that for territorial animals, it should be relatively easy to determine their density using territory size (i.e. with 0% overlap): The density is simply the reciprocal of the (mean) home range size (excluding non-territorial sub-adults and juveniles). 

My question is if there exists any way to estimate the density from home ranges, where an average home range size and average overlap between home ranges are used? It makes sense to me that it should be possible and I have thought about ways of doing it (it is more complicated than I thought at first). I just don't want to re-invent the wheel if there are already tools that do this? A quick google search did not find anything, but might be my google-fu lacking, since all results I saw were about Kernel Density Estimates for determining home range and not about using home range size to determine the number of animals in an area (= density). I suspect that this might be more accurate way to determine the real density (at least of resident individuals, rather than those just moving through), than spatially explicit capture-recapture (SECR) methods using camera traps.

Any advice or pointers much appreciated!

Regards,

Chavoux

[GIS in Ecology]

Hi Chavoux,

This is an interesting question. I guess the issue here is over what area would you want to calculate the density of individuals from their home ranges. For example, would you want to estimate the density of individuals in a large area, like a polygon representing a specific reserve or protected areas? Or would you want to estimate the density of individuals within each small grid cell across a large area?

For the former, the best way to do it would be to create a polygon for each individual's home range (whether by MCP, or using a 95% PVC from a KDE, that would be up to you). Next, merge the data layers containing these individual polygons together so that you have one layer with multiple polygons, where each one represents the home range of an individual animal. Finally, take the polygon representing your area of interest (i.e. a reserve or protected area), and use it in a 'SELECT BY LOCATION' tool to select all the polygons in your home ranges data layer that intersect with this selection polygon. The number of selected polygons will be the number of individuals which use that specific area. Divide this number by the area of the selection polygon, and you'll have an estimate of the density of resident individuals who use it. Note: This will only work if each home range is represented by a single part polygon. If any are represented by multi-part polygons (as is common with 50% PVCs from KDEs), you will need to deal with this somehow first.

For the second option, where you want to know the density of individuals per grid cell based on their home ranges, you create your home range polygons as before, but then you turn each of them into an individual raster data layer of the preferred grid cell size (using a RASTERIZE tool). When you do this, ensure that grid cells within the home range polygon have a value of 1, and all others have a value of zero. Also, you need to ensure that the raster data layers for each individual's home range have the same cell size and extent so they overlay each other directly.  Once you have these raster data layers for all of your individuals, you can use a RASTER CALCULATOR tool to add them all together to get the number of individuals which use each grid cell. Again, divide this number by the area of each grid cell, and you will have a user density per grid cell. This is essentially how species richness is calculated from species range polygons. Note: This works with multi-part home ranges, however, you cannot use it to calculate the total density for the whole area because you cannot tell which individuals overlap in which grid cells.

So these are my two suggestions for doing this. They may not give you exactly the information you are looking for, depending on exactly what measure of density you wish to extract from the home range information, but they should at least give you a starting point to work from

Does anyone else have any other suggestions or approaches?

All the best,

Colin

[James Grecian]

Hi Chavoux,

The output from a kernel density estimate is a probability/ proportion occupancy per grid cell. We then typically extract contours for the 50% or 95% UD.

However, you could estimate similarity in multiple probability surfaces using something like Bhattacharrya’s affinity. There’s a tool to estimate overlap using this metric in the adehabitat series of packages.

Best,

James

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