Parameters
This page describes every parameter that controls a ekokrati.graph network analysis.
All parameters are set in the submission form or, for scripted access, in a
parameters.json file submitted alongside the input files.
Dispersal parameters
Distances
Type: list of positive integers (metres)
Example: 300, 1000, 3000
The dispersal distances to analyse. ekokrati.graph runs one full connectivity analysis per distance and returns all results together.
Each distance represents a separate dispersal scenario — a species for which the probability of dispersing that far equals the Dispersal probability value below. Comparing EC(PC) across distances gives a connectivity profile across a spectrum of dispersal abilities, from sedentary to wide-ranging species.
Choosing distances: start with the range that brackets the focal species or species guild. Distances below the median nearest-patch distance will produce sparse, fragmented networks; distances above the 90th percentile will produce nearly complete graphs. The pre-submission edge histogram shows the distribution of pairwise distances in your patch layer and helps calibrate this choice.
Dispersal probability
Type: float in (0, 1)
Default: 0.5
Field label: Probability of dispersal
The probability that an individual disperses the stated Distance. This value, together with the distance, determines the dispersal kernel: a species that has a 50% probability of crossing 1 000 m has θ = −ln(0.5)/1000 ≈ 0.000693 m⁻¹.
The same probability value applies to all distances in the list. Because θ is derived per distance from (prob, d), each distance represents a different species scenario — see Dispersal kernel.
Default guidance: 0.5 (the threshold is the median dispersal distance) is the conventional choice following the Conefor standard and makes dPC values comparable across studies that use the same convention.
Graph construction parameters
Minimum edge probability
Type: float in (0, 1)
Default: 0.001
Field label: Min. edge probability
Edges (potential dispersal connections) with a dispersal probability below this threshold are excluded from the graph before the analysis runs. This is a computational cutoff — it reduces graph size and speeds up analysis without affecting ecologically meaningful results, since connections with p < 0.001 have negligible influence on dPC.
This is not the same as Dispersal probability. Dispersal probability parameterises the kernel (what species are we modelling); minimum edge probability prunes the graph (which connections are negligible). See the Progressive refinement workflow for how to use both.
Adjusting: increase to 0.01–0.05 for a faster first run on large networks (100 000+ edges). Decrease below 0.001 only if your landscape is very sparsely connected and you are concerned about missing long-distance stepping-stone routes.
Input format
Distance method
Type: enum — edge or centroid
Default: edge
How pairwise distances between patches are measured.
| Method | Description | When to use |
|---|---|---|
edge |
Nearest point on each patch boundary | Preferred — ecologically correct for most species |
centroid |
Patch centroid to centroid | Faster; appropriate for small patches relative to dispersal distance; matches Conefor default |
For most landscapes, edge and centroid produce similar rankings but
different absolute dPC values. If replicating Conefor results use centroid.
Analysis scope
Include all metrics
Type: boolean
Default: true
When true, ekokrati.graph computes the full decomposition: dPC, dPCflux, dPCconnector, dPCintra, dIIC, EC(PC), and betweenness centrality (dBC_PC).
When false, only EC(PC) and dPC (without decomposition) are computed — roughly 50% faster. Use for exploratory runs on large networks.
Scenario parameters
These parameters apply only to scenario analyses (Edit tab → Run as scenario,
or /jobs/from-layer). They are not present in the standard submission form.
Edge overrides
Type: object with remove list and optional force_include list
Manual edge overrides applied on top of the computed graph:
remove— centroid-index pairs to exclude regardless of dispersal probabilityforce_include— centroid-index pairs to include regardless ofmin_edge_prob
These are set interactively in the Edit tab and do not need to be specified manually in normal use.