Introduction | Method | Ocurrences data | Impact data | Transforming EICAT impact categories to numerical values | Species impact indicator | Site impact indicator | Regional impact indicator

Introduction | Package Installation | Step 1: Data Ingestion with process_cube() | Key process_cube() Arguments | Example: Import Data and Filter by Time | Step 2: Indicator Calculation | Available Indicators | Core Arguments for Wrapper Functions | Example: Observed Species Richness Map | Example: Total Occurrences Time Series | Step 3: Visualization with plot() | Plotting the Map | Plotting the Time Series

Install and load invasimapr and other core packages | Hierarchical clustering of invaders and sites | Setup helper functions | Assemble the invader × site fitness matrix ($\lambda$) | Cluster sites (columns as observations) | Cluster invaders (rows as observations) | Attach cluster labels for downstream use | Quick diagnostic plots | Mapping site-level risk categories | Setup (packages, inputs) | Assemble a long table of ($\lambda$) (one row per site × invader) | Prepare site coordinates (x,y) | Build distances: profile (D1) + geographic (D0) | Run ClustGeo (blend profile + spatial cohesion) | Within-cluster sum of squares from a distance matrix | Works for Euclidean distances (e.g., dist() on scaled data or coordinates). | W(C) = 0.5 / n_C * sum_{i,j in C} d(i,j)^2 ; W = sum_C W(C) | Total sum of squares from a distance matrix: T = 0.5/n * sum_{i,j} d(i,j)^2 | Evaluate a grid of alpha to visualize within-cluster inertia trade-off | Precompute totals for normalization (optional) | Plot (relative values 0–1 are easiest to compare) | Map the categories | Cluster-wise summaries for reporting | Session information

invasimapr | A novel framework to visualise trait dispersion and assess species invasiveness and site invasibility | Network Invasibility Cube | Shared Trait-Environment Space | Invasion Fitness | Invasiveness and Invasibility | Overview of invasimapr workflow | Discussion and conclusion | References | Appendices | Expanded formula and links to model components | Trait-conditioned environmental suitability $r^{(z)}_{is}$ | Trait-space crowding $C^{(z)}_{is}$ | Performance-weighted resident filtering $S^{(z)}_{is}$ | Trait-dependent slopes $\gamma_i, \alpha_i, \beta_i$ | Predictive distribution | Glossary (objects and equation components)

Install and load invasimapr and other core packages | Synthesising invasion-fitness insights | Distribution of invasion fitness values | Top and bottom invaders and sites | Functional correlates of invasion success | Faceted maps for key invaders | Select key invaders (top & bottom) | Compute z-MAD standardization of ($\lambda$) | Order facets by mean ($\lambda$) and build labels | Ordered faceted maps (centered palette) | Alternative gradient (viridis/magma) | Invader advantage relative to site mean | Session information

Setup | Install and load invasimapr | Load other R libraries | Data access and preparation | Data access and preparation using dissmapr | Install dissmapr | Import and harmonise biodiversity-occurrence data | Format biodiversity records to long/wide formats | Generate spatial grid and gridded summaries | Retrieve, crop, resample, and link environmental rasters to sampling sites | Remove highly correlated predictors (Optional) | Data access and preparation using invasimapr | Retrieve and link trait and metadata for each species | Alternatively, load a local combined site-environment-trait file | Prepare residents' community data with prepare_inputs() | Generate hypothetical invaders: $\textit{invader} \times \textit{traits}$ | Shared trait space and resident crowding | Standardise model inputs (optional) | Compute and plot the shared trait space | Determine centrality and convex-hull membership | Resident crowding $C_{js}$ from composition × trait similarity | Resident predictors (standardised): $r^{(z)}{js}, C^{(z)}{js}, S^{(z)}_{js}$ | Build the model frame and formula | Fit the residents model (e.g., GLMM) | Site-standardised resident predictors | Site-only saturation $S_s$ and global z-score | Learn trait- and site-varying sensitivities ($\alpha, \beta, \Gamma/\gamma$) | Auxiliary residents-only model on standardised predictors | Trait-varying sensitivities and $\gamma$ | Site-varying penalties and slopes $\alpha_{is}$ and $\Gamma_{is}$ | Invader predictors $r^{(z)}{is}, C^{(z)}{is}, S^{(z)}_{is}$ | Invasion fitness ($\lambda$) and establishment probability ($P$) | Summaries: species, traits, and sites | Common pitfalls & quick fixes | Session information

Install and load invasimapr and other core packages | Tutorial: Computing different invasion fitness options and predicting establishment methods | Different invasion fitness options, linked back to the base formula | Compute different forms of invasion fitness | Option A: Baseline ($\gamma$ = 1, $k$ = 0) | Option B: Parsimonious abiotic scaling ($\gamma$ = $\theta_0$) | Option C: Trait-varying abiotic scaling ($\gamma_i$ = $\theta_i$) | Option D: Site-varying abiotic and crowding ($\gamma_{is}$, $\alpha_{is}$) | Option E: Signed saturation effect ($\beta$ may be ±) | Predict establishment probability using different methods | Method A: Probit $P=\Phi(\lambda/\sigma)$ | Method B: Logistic $P=\text{logit}^{-1}(\lambda/\tau)$ | Method C: Hard rule $P=\mathbb{I}{\lambda>0}$ | Method D: Probit with predictive SD (uncertainty-aware) | Minimal fallback when you only have $\lambda$ (Optional) | :bulb: Section takeaway | Session information

Load data | Time of introduction | Number of introductions per year | Cumulative number of alien species | Native range | Pathways of introduction | Pathway data | Visualize pathways at level 1 | Visualize pathways over time at level 1 | Visualize pathways at level 2 | Visualize pathways over time at level 2 | Pathway count table | Additional resources

Introduction | Decision rules approach | Example data | Apply decision rules | Understanding the results | GAM approach | Apply GAM | Understanding GAM results | Correcting for research effort | Choosing between approaches | Additional resources