diff --git a/docs/src/assets/tutorials/adapt_line.png b/docs/src/assets/tutorials/adapt_line.png new file mode 100644 index 0000000000000000000000000000000000000000..8fbe439a4285862b2b764e63d51c41928bb4674a Binary files /dev/null and b/docs/src/assets/tutorials/adapt_line.png differ diff --git a/docs/src/assets/tutorials/line.png b/docs/src/assets/tutorials/line.png new file mode 100644 index 0000000000000000000000000000000000000000..5fc90bd8fe02d504017b183a8d0de9dc29cb7eb3 Binary files /dev/null and b/docs/src/assets/tutorials/line.png differ diff --git a/docs/src/examples/delta_competition_example.md b/docs/src/examples/delta_competition_example.md index 574dafff1335bf3264aacfd329e8bc2fcd895724..b0ceb6fe6ab468569da0b725e822d61ec1eb6b8c 100644 --- a/docs/src/examples/delta_competition_example.md +++ b/docs/src/examples/delta_competition_example.md @@ -34,6 +34,7 @@ Here is how you can visualise the landscape. using GraphPlot gplot(g, collect(1:nodes), collect(1:nodes))  +![delta_comp_pos](../assets/tutorials/line.png) ## Defining competition processes We propose that any individual have a constant birth rate, and competes with all the individuals present in the same patch. Let i \in \N,x_i \in \{1,2,\dots,9\}. diff --git a/docs/src/examples/gradient.md b/docs/src/examples/gradient.md index 0519a4ce384b5fcab8fec7d7b3ba39399aca5f9b..c50fc6cf5e52968c1cc39a110394d5dd724f775f 100644 --- a/docs/src/examples/gradient.md +++ b/docs/src/examples/gradient.md @@ -2,6 +2,12 @@ In this tutorial, we model agents evolving on a discrete segment, where each patch favours an optimal adaptive trait, along a gradient. This is typically the case along an altitudinal gradient. +Here is what the geographical space looks like. + +![delta_comp_pos](../assets/tutorials/adapt_line.png) + +Every colour stands for a different optimal trait. + ## Run the world julia using Revise,ABMEv,Plots,UnPack