**This research was conducted at the Department of Marine
Sciences, University of the Aegean, Greece, supported by the European
Union’s Horizon 2020 research and innovation programme
HORIZON-CL6–2021-BIODIV-01–12, under grant agreement No 101059407,
“MarinePlan – Improved transdisciplinary science for effective
ecosystem-based maritime spatial planning and conservation in European
Seas”.**

The **priorCON** package offers an innovative tool-set
that incorporates graph community detection methods into systematic
conservation planning. This package is designed to enhance spatial
prioritization by focusing on the protection of areas with high
ecological connectivity. Unlike traditional approaches that prioritize
individual planning units, **priorCON** focuses on clusters
of features that exhibit strong ecological linkages. The
**priorCON** package is built upon the
**prioritizr** package (Hanson et
al., 2024), using commercial and open-source exact algorithm solvers
that ensure optimal solutions to prioritization problems.

**Features of priorCON**

**Graph Community Detection**: The package utilizes connectivity metrics to identify clusters of habitat patches with high connectivity values.**Spatial prioritization**: The package uses the connectivity metrics as inputs in prioritizr to identify areas with high connectivity.**Connectivity Estimation**: The package estimates the connectivity protection level by calculating the protected connections.**Post-processing**: The package can be used to produce and download the outputs of the analysis as shapefiles, matrices and tmap plots.

This workflow is shown on Fig. 1.

This tutorial will guide you through the key functions of the package, from data preparation to generating informative outputs to address conservation connectivity challenges in diverse ecosystems.

The package provides 4 steps to perform the analysis:

Preprocessing (Optional): Function

`preprocess_graphs()`

is built in order to create edge lists from various inputs formats.Connectivity metrics estimation: Function

`get_metrics()`

is built to calculate the following graph metrics: degree, eigenvector centrality, betweenness centrality, louvain clustering, walktrap clustering, s-core, PageRank.Prioritization: Functions

`basic_scenario()`

and`connectivity_scenario()`

are used to insert the data to**prioritizr**run the prioritization problem and obtain the optimum solutions.Post-processing: Function

`get_outputs()`

is used in order to plot interactive maps and export tables, shapefiles and rasters of the results

The functions of steps 1 and 2 can be used within the spatial conservation planning workflow as described here but also independently to create and obtain the graph metrics that might be also useful within other research contexts. In the next sections we provide an illustrative example to explain in more detail how the functions operate.

Let us consider the following dataset as an illustrative example. The area examined is Thermaikos gulf in Greece. In this example, a Lagrangian model has been run to the area, to estimate connectivity. This connectivity will be incorporated into the analysis to obtain the areas that need to be protected to maximize the connectivity protection. Preprocessing is necessary in this case in order to transform the Lagrangian data in an edge list.

The input of an edge list is required in most graph theoretic
approaches. Fig. 2 represents a typical
directed weighted graph. In the edge list, the first two columns utilize
the nodes IDs to represent edges and the last column corresponds to the
value of the edges. This value actually represents the connectivity
value between the corresponding nodes and may represent for instance
values resulting from particle drift models, migration probabilities or
other. This preprocessing step can be conducted using the function
`preprocess_graphs()`

and serves in reading and transforming
the initial input data into an edge list. Further details on the initial
data formats are given below. In case that an edge list is available,
this step can be skipped.

Function `preprocess_graphs()`

takes as input a list of
.txt/.csv objects. Each object represents the connections between a node
and all the other nodes. For the model to read the data, it is necessary
to have all the .txt/.csv objects in one folder. There are two ways to
incorporate connectivity data, based on their linkage to features:

Case 1: the connectivity data correspond to specific biodiversity features. If a biodiversity feature has its own connectivity dataset then the file including the edge lists needs to have the same name as the corresponding feature. For example, consider having 5 species (f1, f2, f3, f4, f5) and 5 connectivity datasets. Then the connectivity datasets need to be in separate folders named: f1,f2,f3,f4,f5 and the algorithm will understand that they correspond to the species.

Case 2: the connectivity dataset represents a spatial pattern that is not directly connected with a specific biodiversity feature. Then the connectivity data need to be included in a separate folder named in a different way than the species. For example consider having 5 species (f1,f2,f3,f4,f5) and 1 connectivity dataset. This dataset can be included in a separate folder (e.g. “Langragian_con”).

In our example we use connectivity values that are not directly connected with a specific species, therefore we illustrate Case 2. Fig. 3 represents the structure used in the tutorial. The data need to be stored in this way in order for the algorithm to read them properly.

A typical Lagrangian output is a set of files representing the likelihood of a point moving from an origin (source) to a destination (target). This can be represented using a list of .txt files (as many as the origin points) including information for the destination probability. The .txt files need to be named in an increasing order. The name of the files need to correspond to the numbering of the points, in order for the algorithm to match the coordinates with the points (Fig. 4).

As long as the data are set in this way, the preprocessing algorithm can run and transform the format of the inputs to an edge list. If an edge list dataset is available, this preprocessing step can be skipped.

Function `get_metrics()`

is used to calculate graph
metrics values. The edge lists created from the previous step, or
inserted directly from the user are used in this step to create graphs.
The directed graphs are transformed to undirected. The function is based
on the **igraph** package (Csárdi and Nepusz, 2006; Csárdi et al., 2024) which is used to create
clusters using Louvain and Walktrap and calculate the following metrics:
Eigenvector Centrality, Betweenness Centrality and Degree and PageRank.
S-core is calculated using the package **brainGraph** (Watson, 2024). The user can choose between
these options to create the respective outputs. `'s_core'`

,
`'louvain'`

, `'walktrap'`

, `'eigen'`

,
`'betw'`

, `'deg'`

or `'page_rank'`

.

Detailed information on the theory and equations of the used graph metrics are provided in Nagkoulis et al.(2024; subm Methods in Ecology and Evolution).

Two alternative functions can be used for the prioritization step: i)
the `connectivity_scenario()`

function, which includes
connectivity into the optimization procedure and ii) the
`basic_scenario()`

function, which does not include
connectivity. The two functions can be run separately. Users may use
both functions, if they wish to compare the results obtained from the
two scenarios, i.e. with and without connectivity. Alternatively, only
the `connectivity_scenario()`

function can be run to obtain
the prioritization outputs of the connectivity scenario. Both functions
are based on the **priorititizr** package (Hanson et al., 2024). The connectivity
metrics are first transformed to rasters, following an approach similar
to Marxan Connect (Daigle et
al., 2020). Then **priorititizr** maximizes the utility
obtained from protecting both features and connections. Mathematically,
the target of the optimization is to maximize U under the budget (B)
limitations (eq. 2 and 3 of Nagkoulis et al.(2024)):

\[ U = \sum_{\boldsymbol{PU_i} \, \in \,SA} -\lambda c_i + \sum_{\boldsymbol{PU_i} \, \in \,SA} \sum_{j=1}^J \mu_j f_j + \sum_{\boldsymbol{PU_i} \, \in \,SA} \sum_{j=1}^J \mu_j M_j, \]

\[ \sum_{\boldsymbol{PU_i} \, \in \,SA} c_i \leq B \]

A set of planning units \(\boldsymbol{A}=(\boldsymbol{PU}_1, \boldsymbol{PU}_2, ..., \boldsymbol{PU_i}, ..., \boldsymbol{PU_i})\) is considered to be protected when \(\boldsymbol{PU_i} \, \in \, SA\). The protection of any \(\boldsymbol{PU_i}\) results in a cost \(c_i\). A finite set of features \(\boldsymbol{F}=(F_1, F_2, ... , F_j, ..., F_J)\) is also distributed in \(A\), suggesting that these features can be spatially mapped within the PUs. Each \(PU\) is thus defined as a spatial object containing the following properties: \(\boldsymbol{PU_i}=(f_1, f_2, ..., f_j, ..., f_J, c_i)\), where \(f_j\) indicates the quantity of \(F_j\) located in \(PU_i\). We use the annotation \(M_j\) for the values of the metric for every feature \(j\). By inserting metrics into the analysis, each PU’s (\(\boldsymbol{PU_i}\)) properties are extended and can be expressed as follows: \(\boldsymbol{PU_i} = (f_1, f_2, ..., f_j, ..., f_J, M_1, M_2, ..., M_j, ..., M_J, c_i)\).

The first input that can be given to the algorithm is a typical cost
layer representing the cost of protecting each single planning unit.
This cost layer is inserted to **prioritizr**. If such a
cost layer is not available, a cost layer with all planning units having
equal cost can be inserted (Fig. 5. The
algorithm needs a cost layer, in order to determine the planning
units.

```
# Set tmap to “view” mode
tmap_mode("view")
# Read the cost raster
cost_raster <- get_cost_raster()
# Plot the cost raster with tmap
tm_shape(cost_raster) +
tm_raster(title = "cost")
```

The second input are the features rasters. In case such rasters are not available, a raster with equal values can be given to the algorithm. In this case we have used such a pseudo-raster, adding some noise to improve the performance of the algorithm.

```
# Read the features raster
features <- get_features_raster()
# Plot the features raster with tmap
tm_shape(features) +
tm_raster(title = "f1")
```

```
# Solve an ordinary prioritizr prioritization problem
basic_solution <- basic_scenario(
cost_raster = cost_raster,
features = features,
budget_perc = 0.1
)
# Solve a prioritizr prioritization problem,
# by incorporating graph connectivity of the features
connectivity_solution <- connectivity_scenario(
cost_raster = cost_raster,
features = features,
budget_perc = 0.1,
pre_graphs = pre_graphs
)
```

The results obtained from **prioritizr** are presented
using matrices and plots, allowing the user to compare the outcomes of
incorporating connectivity metrics in the analysis. Function
`get_outputs()`

takes as input a prioritization solution. The
user can also get the outputs as shapefiles and raster to use them in
their analysis.

```
# Get outputs from basic_scenario function for feature “f1”
basic_outputs <- get_outputs(solution = basic_solution,
feature = "f1",
pre_graphs = pre_graphs)
basic_outputs$tmap
```

```
# Print summary of features and connections held
# percentages for basic scenario
print(basic_outputs$connectivity_table)
```

```
# Get outputs from connectivity_scenario function for feature “f1”
connectivity_outputs <- get_outputs(solution = connectivity_solution,
feature = "f1",
pre_graphs = pre_graphs)
connectivity_outputs$tmap
```

Csárdi, Gábor, and Tamás Nepusz. 2006. “The igraph software package for
complex network research.” *InterJournal* Complex Systems:
1695. https://igraph.org.

Csárdi, Gábor, Tamás Nepusz, Vincent Traag, Szabolcs Horvát, Fabio
Zanini, Daniel Noom, and Kirill Müller. 2024. * igraph: Network Analysis and Visualization in
R*. https://doi.org/10.5281/zenodo.7682609.

Daigle, Rémi M., Anna Metaxas, Arieanna C. Balbar, Jennifer McGowan,
Eric A. Treml, Caitlin D. Kuempel, Hugh P. Possingham, and Maria Beger.
2020. “
Operationalizing ecological connectivity in spatial conservation
planning with Marxan Connect.” *Methods in Ecology and
Evolution* 11 (4): 570–79. https://doi.org/10.1111/2041-210X.13349.

Hanson, Jeffrey O, Richard Schuster, Nina Morrell, Matthew
Strimas-Mackey, Brandon P M Edwards, Matthew E Watts, Peter Arcese,
Joseph R Bennett, and Hugh P Possingham. 2024. * prioritizr: Systematic Conservation
Prioritization in R*. https://CRAN.R-project.org/package=prioritizr.

Watson, Christopher G. 2024. *brainGraph: Graph Theory Analysis of Brain MRI
Data*. https://doi.org/10.32614/CRAN.package.brainGraph.