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4

Either do what Spacedman suggests (rasterize and sum) or work directly on the polygons: library(raster) sp1 <- spPolygons(rbind(c(-180,-20), c(-140,55), c(10, 0), c(-140,-60), c(-180,-20)), attr=data.frame(sp=1)) sp2 <- spPolygons(rbind(c(-10,0), c(140,60), c(160,0), c(140,-55), c(-10,0)), attr=data.frame(sp=2)) sp3 <- spPolygons(rbind(c(-125,-20), ...


2

If you are wanting to extract the pixel values for a polygon boundary rather than the interior area, then you need to convert your polygons into polylines. You don't actually state what GIS system you are using so I shall assume you are using an Advanced licensed ArcGIS. Use the feature to line to convert your polygons to polylines, then convert this to a ...


2

Did you try importing it as a raster directly from the svg format? If that doesn't work, try converting it to another type of image file, such as jpg or pdf. You should be able to import either of those using the "Add Raster Layer" button on the Layer toolbar. Once the image is imported into QGIS, you should georeference it so that any additional data you ...


2

You could use the Join Attributes by Location tool from the toolar (Vector > Data Management Tools > Join Attributes by Location) and select your polygon layer as the Target layer and your grid layer as the Join layer. Then choose to take a summary and select any of the options (doesn't matter which if you're only interested in the count which is default): ...


1

The issue you have is that your data covers a very small area, such that you couldn't possibly make any inference about the species response to different climate variables. Were there any other attributes collected with the species data relating to physical habitat? This could be slope, elevation, soil type, vegetation type etc. You might be able to find ...


1

Getting temperature estimates at finer resolution than 1 km is not likely to happen without you placing your own temperature loggers around your study site. You're going to have to look into using different explanatory variables. If you hope to be able to determine any drivers of distribution in an area that small, especially for wildlife (vs. plants because ...


1

First make sure your fishnet layer has a unique ID field, we'll assume it's called Id. I would assume that each feature in your fishnet layer has the same area, but just in case calculate the area for each feature in a new field called AREA. Run the Intersection tool under the Vector -> Geoprocessing Tools menu. Choose your fishnet layer for the input layer ...


1

Create a vector grid with required settings(extent, spacing) and choose output type as polygon vector > research tools > vector grid perform an intersection with the species distribution layer. vector > geoprocessing tools > intersection Calculate the area of each polygon of the newly generated layer. join a area attribute to grid layer create the ...


1

Interesting method to use; have you considered running the model with equal numbers of pseudo absences and then increasing or decreasing the proportion of known absences to pseudo absences to see if a trend appears in how it effects the model output? This approach may also give you insight into the uncertainty of the model as well.


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