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  "Title": "Workflow to visualise trait dispersion and assess invasibility",
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  "Authors@R": "person(\"Sandra\", \"MacFadyen\", email = \"macfadyen@sun.ac.za\", role = c(\"aut\", \"cre\"))",
  "Description": "Utilities for assembling invader–resident interaction\ntensors, computing trait-based competition kernels from\ndistance matrices, deriving environmental suitability kernels,\ncalculating invasion fitness, and summarising trait dispersion\nmetrics. Includes helpers to predict site × species responses\nfor residents and simulated invaders.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/b-cubed-eu/invasimapr,\nhttps://b-cubed-eu.github.io/invasimapr/",
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  "Date/Publication": "2026-01-30 13:25:28 UTC",
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    "build_model_formula",
    "compute_centrality_hull",
    "compute_establishment_probability",
    "compute_invasion_fitness",
    "compute_resident_crowding",
    "compute_site_saturation",
    "compute_trait_space",
    "derive_sensitivities",
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    "get_trait_data",
    "invasimapr",
    "learn_sensitivities",
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    "predict_establishment",
    "predict_invaders",
    "prep_resident_glmm",
    "prepare_inputs",
    "prepare_trait_space",
    "simulate_invaders",
    "site_varying_alpha_beta_gamma",
    "standardise_by_site",
    "standardise_model_inputs",
    "summarise_invasiveness_invasibility",
    "summarise_results"
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    {
      "page": "assemble_matrices",
      "title": "Assemble community matrices for invasion-fitness workflows",
      "topics": [
        "assemble_matrices"
      ]
    },
    {
      "page": "build_invader_predictors",
      "title": "Build standardized invader predictors r_is_z, C_is_z, S_is_z (expects env/traits frames already projected to model columns)",
      "topics": [
        "build_invader_predictors"
      ]
    },
    {
      "page": "build_model_formula",
      "title": "Flexible formula constructor for residents-only trait–environment models",
      "topics": [
        "build_model_formula"
      ]
    },
    {
      "page": "compute_centrality_hull",
      "title": "Compute trait-space centrality (robust Mahalanobis) and hull status, with visuals",
      "topics": [
        "compute_centrality_hull"
      ]
    },
    {
      "page": "compute_establishment_probability",
      "title": "Probabilistic establishment from invasion fitness",
      "topics": [
        "compute_establishment_probability"
      ]
    },
    {
      "page": "compute_invasion_fitness",
      "title": "Compute invasion fitness lambda_{is} for multiple model options",
      "topics": [
        "compute_invasion_fitness"
      ]
    },
    {
      "page": "compute_resident_crowding",
      "title": "Resident crowding C_{js} via Hellinger composition and Gower–Gaussian trait kernel",
      "topics": [
        "compute_resident_crowding"
      ]
    },
    {
      "page": "compute_site_saturation",
      "title": "Compute site-only saturation S_{s} and global z-score S_{s}^{(z)}",
      "topics": [
        "compute_site_saturation"
      ]
    },
    {
      "page": "compute_trait_space",
      "title": "Shared trait-space construction (Gower + PCoA), resident hull, centroid, and density plot",
      "topics": [
        "compute_trait_space"
      ]
    },
    {
      "page": "derive_sensitivities",
      "title": "Derive trait-varying sensitivities (αᵢ, βᵢ) and abiotic slope (θ, Γᵢ)",
      "topics": [
        "derive_sensitivities"
      ]
    },
    {
      "page": "fit_auxiliary_residents_glmm",
      "title": "Auxiliary GLMM for trait-varying and site-varying slopes",
      "topics": [
        "fit_auxiliary_residents_glmm"
      ]
    },
    {
      "page": "get_trait_data",
      "title": "Scrape and Analyze Wikipedia & Trait Data for a Species",
      "topics": [
        "get_trait_data"
      ]
    },
    {
      "page": "learn_sensitivities",
      "title": "Learn sensitivities (alpha_i, beta_i, theta_i/gamma_i) and optional site-varying alpha_is, Gamma_is",
      "topics": [
        "learn_sensitivities"
      ]
    },
    {
      "page": "model_residents",
      "title": "Resident GLMM and construction of standardized predictors",
      "topics": [
        "model_residents"
      ]
    },
    {
      "page": "predict_establishment",
      "title": "Compute invasion fitness (lambda) and optional establishment probability (P)",
      "topics": [
        "predict_establishment"
      ]
    },
    {
      "page": "predict_invaders",
      "title": "Build standardized invader predictors (r_is_z, C_is_z, S_is_z)",
      "topics": [
        "predict_invaders"
      ]
    },
    {
      "page": "prep_resident_glmm",
      "title": "Build residents-by-sites long table and fit the residents-only GLMM",
      "topics": [
        "prep_resident_glmm"
      ]
    },
    {
      "page": "prepare_inputs",
      "title": "Prepare inputs (assemble and align core tables)",
      "topics": [
        "prepare_inputs"
      ]
    },
    {
      "page": "prepare_trait_space",
      "title": "Prepare trait space and resident crowding (no site-z)",
      "topics": [
        "prepare_trait_space"
      ]
    },
    {
      "page": "simulate_invaders",
      "title": "Simulate hypothetical invader trait profiles from a resident trait pool",
      "topics": [
        "simulate_invaders"
      ]
    },
    {
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    },
    {
      "page": "standardise_by_site",
      "title": "Standardize a site-by-species matrix by site (row-wise z) with optional robust SD",
      "topics": [
        "standardise_by_site"
      ]
    },
    {
      "page": "standardise_model_inputs",
      "title": "Standardise model inputs (no leakage) for residents and invaders",
      "topics": [
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      ]
    },
    {
      "page": "summarise_invasiveness_invasibility",
      "title": "Summaries of invasion fitness: species invasiveness, trait effects, and site invasibility",
      "topics": [
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      ]
    },
    {
      "page": "summarise_results",
      "title": "Summarise invasiveness and invasibility (tables, maps, rankings)",
      "topics": [
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      ]
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      "filename": "clusters_3.html",
      "title": "Clustering and risk scenarios",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Install and load invasimapr and other core packages",
        "Clustering and risk scenarios",
        "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)",
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        "Build distances: profile (D1) + geographic (D0)",
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        "Works for Euclidean distances (e.g., dist() on scaled data or coordinates).",
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        "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",
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        "Plot (relative values 0–1 are easiest to compare)",
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        "Session information"
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      "modified": "2026-01-30 13:25:28",
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      "title": "Introduction",
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      "headings": [
        "invasimapr",
        "A novel framework to visualise trait dispersion and assess species invasiveness and site invasibility",
        "Introduction",
        "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)"
      ],
      "created": "2026-01-30 13:25:28",
      "modified": "2026-01-30 13:25:28",
      "commits": 1
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      "title": "Invasion fitness synthesis",
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      "headings": [
        "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"
      ],
      "created": "2026-01-30 13:25:28",
      "modified": "2026-01-30 13:25:28",
      "commits": 1
    },
    {
      "source": "workflow_2.Rmd",
      "filename": "workflow_2.html",
      "title": "Step-by-step Workflow",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Step-by-step Workflow",
        "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"
      ],
      "created": "2026-01-30 13:25:28",
      "modified": "2026-01-30 13:25:28",
      "commits": 1
    },
    {
      "source": "tutorial_5.Rmd",
      "filename": "tutorial_5.html",
      "title": "Tutorial on computing invasion fitness",
      "engine": "knitr::rmarkdown",
      "headings": [
        "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"
      ],
      "created": "2026-01-30 13:25:28",
      "modified": "2026-01-30 13:25:28",
      "commits": 1
    }
  ],
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  "_indexed": true,
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