Behind the scenes of landscape connectivity: Optimization, thresholding

Optimization is underlying technique of many graph-theoretic algorithms, including shortest paths and minimum spanning tree and hence - hidden component of landscape connectivity methods. Optimization solutions have different selectivity but it remains unexplored. Indeterminate solutions happen without warning and may be spatially biased. Errors are hidden. These issues may undermine connectivity measures and conclusions. Two optimization targets may be distinguished: mobile agent and connectivity provider, with different requirements. Review of how are they handled in literature finds them entangled and often misspecified, owing to inconclusive results, especially pronounced when conservation and explanatory goals are mixed. I categorize graph structures against targets and show how initial choice of structure introduces implicit optimization. Allowing for stochasticity of mobile agent resolves omniscience problem. Lastly, I advocate against creating landscape graphs with thresholds in favor of complete planar graphs. Thresholding is impractical, biased by design and yields non-optimal solutions due to simplistic partitioning.

Automated summary

The importance of optimization in graph-theoretic algorithms and landscape connectivity methods is explored in this article. It is pointed out that indeterminate solutions may introduce bias in the results, and two optimization targets are distinguished. The use of complete planar graphs is advocated over thresholding in landscape graph creation.