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Hi Sarah – there are several ways to vary transition probabilities over space and time – with increasing degree of specificity (and thus complexity):
1. The simplest way to vary transition probabilities over time is to use the “Transition Multiplier” feature. This is a very popular way to get started with variability in transition probabilities. If you are using the Windows user interface you can access this feature using the “Transitions-Multipliers” screen (under the Advanced tab) from within your Scenario. We call this the “Transitions Multiplier” scenario datasheet. The trick with all ST-Sim datasheets is that by default only the required columns are displayed. If you right click on the empty grid in the user interface, however, a list of optional fields will be displayed for each datasheet. After right-clicking you can select the “Timestep” column for display. You can then enter a series of multipliers in this grid for each combination of Transition Group and Timestep. The values entered here for multipliers are then used to scale your base probabilities for each Transition Group up and down for each timestep. So for example a row with Transition Group=Fire, Timestep=1, Multiplier=0.5 would scale the base probability for Fire by 0.5 in Timestep 1. The simplest way to do this is to set your base transition probabilities all to 1 (in your Pathway Diagram), and then to enter the actual value of your probabilities as multipliers.
2. Using the Transition-Multipliers scenario datasheet you can also vary your probabilities spatially by displaying a second optional “Stratum” column. If you’ve defined various Strata for your model (under Project Definitions), then you can use this screen to also varyyour base probabilities by these strata (in addition to varying them by timestep).
3. If you want to vary your probabilities spatially at the level of a cell, you can also use the “Transition Spatial Multipliers” scenario datasheet. This datasheet allows you to specify a multiplier for your probabilities – similar to the way described above – except that here the multiplier is not specified as a single value by timestep (and strata), but rather as a raster by timestep. This allows you to then vary the probability of any transition, for any cell and any timestep. Note that you can also use the Transition Multiplier and Transition Spatial Multiplier datasheets together – the final probability is the combined multiplier from both.
As for sampling probabilities from a distribution, there are several ways to do this also (with again increasing complexity):
1. One of the optional columns on the Transition Multiplier scenario datasheet is “Multiplier Distribution”. If you display this then you can choose from one of the built-in ST-Sim distributions. For example if you select “Normal” for the distribution, the “Multiplier” column now represents the mean while the “Multiplier SD” column is used to specify the Standard Deviation. ST-Sim then samples from this distribution throughout the simulation. The column “Multiplier Sampling Frequency” tells ST-Sim how often to sample from the distribution. You can also vary the parameters of the distribution for every Stratum and/or Timestep if you like, using the Stratum and Timestep columns.
2. If you would like to sample from a distribution that is not built-in to ST-Sim, then there are again a couple of ways to do this. The first is to enter your own custom distributions directly into ST-Sim. Basically you first provide a name for your distribution (under Project Definitions), and then use the “Distributions” scenario datasheet to specify the frequency distribution from which to sample. Any distributions defined this way get added to the list of built-in distributions and can thus be used as described above in the Transition Multiplier scenario datasheet.
3. The most complicated (yet most flexible) way to sample transition probabilities from a distribution is to do sampling yourself before you start the simulation, and then feed to sampled values (e.g. for Stratum, Iteration and Timestep) into either the “Transition Multipliers” or “Transition Spatial Multipliers” scenario datasheets. This gives you full control over the sampling, but can generate quite large input datasets. Generally we use a script in either R or Python – or in simple cases even an Excel file – to generate the set of such multipliers, save them (e.g. as a CSV or XLSX), and then import them into the appropriate scenario datasheet. The one constraint with this approach is that it requires all the sampling to be done before the ST-Sim simulation begins. Very soon, however, we hope to add a feature that will also allow you to call scripts (e.g. in Python or R) to do the sampling within the simulation.
A final note is that much of what is described above often relies on one or more scripts to generate the appropriate model inputs. Our language of choice at the moment for preparing such inputs is R (although other languages work equally well) – as such we have been working on an R package that will facilitate the movement of data back and forth between ST-Sim and R, which we hope to release in beta form in the next few weeks. If you are interested in a copy of the package in its early release form let us know and we can make sure you are notified when it comes out.
Hope this helps – Colin