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Quantitative Invasion Risk Assessment for Forecasting Pontential Invaders

Research rationale

The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species, but robust and defensible forecasts of potential invaders, especially species worldwide with no invasion history, are rare.

The tool

Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e. attributes of a typical invasive alien species). We implemented a multiple imputation with chain equation workflow to estimate invasion syndromes from imputed datasets of species’ life-history and ecological traits (e.g., body size, reproductive traits, microhabitat) and macroecological patterns (e.g., geographic range size, commonness, habitat generalism, tolerance to disturbance).

The tool is run under R computing program. And this repository contains the R scripts and sample files to run the tool.

The description and application of tool can be read in full in Pili et al. (XXXX).

The repository

There are four core R scripts.

  1. macroeclogicalPatterns.Rmd --- for quantifying macroecological patterns.
  2. PhylogeneticEigenvectorMapping.Rmd --- for mapping phylogenetic eigenvectors and determining optimal number of phylogenetic eigenvectors for phylogenetic imputation.
  3. Multipleimputation.Rmd --- for multiple imputation of life-history and ecological traits and macroecological patterns.
  4. invasionSyndromesModels.Rmd --- for modelling invasion syndromes.

The file structure of your .Rproject

should ideally have:

  • ./data/
  • ./data/ecoregions/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/habitats/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/humanFootprint/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/invasionHistory/ # provided
  • ./data/occurrenceData/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/phylogeneticTree/ # retrieve from source. See PhylogeneticEigenvectorMapping.Rmd
  • ./data/ports/ # retrieve from source. See macroecologicalPatterns.Rmd
  • /data/rangeMaps/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./input/ #example data to run Multipleimputation.Rmd and invasionSyndromesModels.Rmd are included
  • ./output/
  • ./output/miceRangeError/
  • ./output/miceRangeImp/
  • ./output/miceRangeImportance/
  • ./output/rf_fineTune/
  • ./output/rf_pred/
  • ./figures/

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Quantitative invasion risk assessment of species worldwide

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var __re = new RegExp('^' + "github\\.com" + '

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Quantitative Invasion Risk Assessment for Forecasting Pontential Invaders
Research rationale
The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species, but robust and defensible forecasts of potential invaders, especially species worldwide with no invasion history, are rare.
The tool
Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e. attributes of a typical invasive alien species). We implemented a multiple imputation with chain equation workflow to estimate invasion syndromes from imputed datasets of species’ life-history and ecological traits (e.g., body size, reproductive traits, microhabitat) and macroecological patterns (e.g., geographic range size, commonness, habitat generalism, tolerance to disturbance).
The tool is run under R computing program. And this repository contains the R scripts and sample files to run the tool.
The description and application of tool can be read in full in Pili et al. (XXXX).
The repository
There are four core R scripts.
  1. macroeclogicalPatterns.Rmd --- for quantifying macroecological patterns.
  2. PhylogeneticEigenvectorMapping.Rmd --- for mapping phylogenetic eigenvectors and determining optimal number of phylogenetic eigenvectors for phylogenetic imputation.
  3. Multipleimputation.Rmd --- for multiple imputation of life-history and ecological traits and macroecological patterns.
  4. invasionSyndromesModels.Rmd --- for modelling invasion syndromes.
The file structure of your .Rproject
should ideally have:
  • ./data/
  • ./data/ecoregions/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/habitats/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/humanFootprint/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/invasionHistory/ # provided
  • ./data/occurrenceData/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/phylogeneticTree/ # retrieve from source. See PhylogeneticEigenvectorMapping.Rmd
  • ./data/ports/ # retrieve from source. See macroecologicalPatterns.Rmd
  • /data/rangeMaps/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./input/ #example data to run Multipleimputation.Rmd and invasionSyndromesModels.Rmd are included
  • ./output/
  • ./output/miceRangeError/
  • ./output/miceRangeImp/
  • ./output/miceRangeImportance/
  • ./output/rf_fineTune/
  • ./output/rf_pred/
  • ./figures/
About
Quantitative invasion risk assessment of species worldwide
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Quantitative Invasion Risk Assessment for Forecasting Pontential Invaders
Research rationale
The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species, but robust and defensible forecasts of potential invaders, especially species worldwide with no invasion history, are rare.
The tool
Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e. attributes of a typical invasive alien species). We implemented a multiple imputation with chain equation workflow to estimate invasion syndromes from imputed datasets of species’ life-history and ecological traits (e.g., body size, reproductive traits, microhabitat) and macroecological patterns (e.g., geographic range size, commonness, habitat generalism, tolerance to disturbance).
The tool is run under R computing program. And this repository contains the R scripts and sample files to run the tool.
The description and application of tool can be read in full in Pili et al. (XXXX).
The repository
There are four core R scripts.
  1. macroeclogicalPatterns.Rmd --- for quantifying macroecological patterns.
  2. PhylogeneticEigenvectorMapping.Rmd --- for mapping phylogenetic eigenvectors and determining optimal number of phylogenetic eigenvectors for phylogenetic imputation.
  3. Multipleimputation.Rmd --- for multiple imputation of life-history and ecological traits and macroecological patterns.
  4. invasionSyndromesModels.Rmd --- for modelling invasion syndromes.
The file structure of your .Rproject
should ideally have:
  • ./data/
  • ./data/ecoregions/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/habitats/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/humanFootprint/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/invasionHistory/ # provided
  • ./data/occurrenceData/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/phylogeneticTree/ # retrieve from source. See PhylogeneticEigenvectorMapping.Rmd
  • ./data/ports/ # retrieve from source. See macroecologicalPatterns.Rmd
  • /data/rangeMaps/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./input/ #example data to run Multipleimputation.Rmd and invasionSyndromesModels.Rmd are included
  • ./output/
  • ./output/miceRangeError/
  • ./output/miceRangeImp/
  • ./output/miceRangeImportance/
  • ./output/rf_fineTune/
  • ./output/rf_pred/
  • ./figures/
About
Quantitative invasion risk assessment of species worldwide
Resources
Readme
Activity
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Watchers
1 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Quantitative Invasion Risk Assessment for Forecasting Pontential Invaders
Research rationale
The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species, but robust and defensible forecasts of potential invaders, especially species worldwide with no invasion history, are rare.
The tool
Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e. attributes of a typical invasive alien species). We implemented a multiple imputation with chain equation workflow to estimate invasion syndromes from imputed datasets of species’ life-history and ecological traits (e.g., body size, reproductive traits, microhabitat) and macroecological patterns (e.g., geographic range size, commonness, habitat generalism, tolerance to disturbance).
The tool is run under R computing program. And this repository contains the R scripts and sample files to run the tool.
The description and application of tool can be read in full in Pili et al. (XXXX).
The repository
There are four core R scripts.
  1. macroeclogicalPatterns.Rmd --- for quantifying macroecological patterns.
  2. PhylogeneticEigenvectorMapping.Rmd --- for mapping phylogenetic eigenvectors and determining optimal number of phylogenetic eigenvectors for phylogenetic imputation.
  3. Multipleimputation.Rmd --- for multiple imputation of life-history and ecological traits and macroecological patterns.
  4. invasionSyndromesModels.Rmd --- for modelling invasion syndromes.
The file structure of your .Rproject
should ideally have:
  • ./data/
  • ./data/ecoregions/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/habitats/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/humanFootprint/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/invasionHistory/ # provided
  • ./data/occurrenceData/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/phylogeneticTree/ # retrieve from source. See PhylogeneticEigenvectorMapping.Rmd
  • ./data/ports/ # retrieve from source. See macroecologicalPatterns.Rmd
  • /data/rangeMaps/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./input/ #example data to run Multipleimputation.Rmd and invasionSyndromesModels.Rmd are included
  • ./output/
  • ./output/miceRangeError/
  • ./output/miceRangeImp/
  • ./output/miceRangeImportance/
  • ./output/rf_fineTune/
  • ./output/rf_pred/
  • ./figures/
About
Quantitative invasion risk assessment of species worldwide
Resources
Readme
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Stars
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Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Quantitative Invasion Risk Assessment for Forecasting Pontential Invaders
Research rationale
The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species, but robust and defensible forecasts of potential invaders, especially species worldwide with no invasion history, are rare.
The tool
Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e. attributes of a typical invasive alien species). We implemented a multiple imputation with chain equation workflow to estimate invasion syndromes from imputed datasets of species’ life-history and ecological traits (e.g., body size, reproductive traits, microhabitat) and macroecological patterns (e.g., geographic range size, commonness, habitat generalism, tolerance to disturbance).
The tool is run under R computing program. And this repository contains the R scripts and sample files to run the tool.
The description and application of tool can be read in full in Pili et al. (XXXX).
The repository
There are four core R scripts.
  1. macroeclogicalPatterns.Rmd --- for quantifying macroecological patterns.
  2. PhylogeneticEigenvectorMapping.Rmd --- for mapping phylogenetic eigenvectors and determining optimal number of phylogenetic eigenvectors for phylogenetic imputation.
  3. Multipleimputation.Rmd --- for multiple imputation of life-history and ecological traits and macroecological patterns.
  4. invasionSyndromesModels.Rmd --- for modelling invasion syndromes.
The file structure of your .Rproject
should ideally have:
  • ./data/
  • ./data/ecoregions/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/habitats/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/humanFootprint/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/invasionHistory/ # provided
  • ./data/occurrenceData/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/phylogeneticTree/ # retrieve from source. See PhylogeneticEigenvectorMapping.Rmd
  • ./data/ports/ # retrieve from source. See macroecologicalPatterns.Rmd
  • /data/rangeMaps/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./input/ #example data to run Multipleimputation.Rmd and invasionSyndromesModels.Rmd are included
  • ./output/
  • ./output/miceRangeError/
  • ./output/miceRangeImp/
  • ./output/miceRangeImportance/
  • ./output/rf_fineTune/
  • ./output/rf_pred/
  • ./figures/
About
Quantitative invasion risk assessment of species worldwide
Resources
Readme
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Stars
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Watchers
1 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Quantitative Invasion Risk Assessment for Forecasting Pontential Invaders
Research rationale
The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species, but robust and defensible forecasts of potential invaders, especially species worldwide with no invasion history, are rare.
The tool
Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e. attributes of a typical invasive alien species). We implemented a multiple imputation with chain equation workflow to estimate invasion syndromes from imputed datasets of species’ life-history and ecological traits (e.g., body size, reproductive traits, microhabitat) and macroecological patterns (e.g., geographic range size, commonness, habitat generalism, tolerance to disturbance).
The tool is run under R computing program. And this repository contains the R scripts and sample files to run the tool.
The description and application of tool can be read in full in Pili et al. (XXXX).
The repository
There are four core R scripts.
  1. macroeclogicalPatterns.Rmd --- for quantifying macroecological patterns.
  2. PhylogeneticEigenvectorMapping.Rmd --- for mapping phylogenetic eigenvectors and determining optimal number of phylogenetic eigenvectors for phylogenetic imputation.
  3. Multipleimputation.Rmd --- for multiple imputation of life-history and ecological traits and macroecological patterns.
  4. invasionSyndromesModels.Rmd --- for modelling invasion syndromes.
The file structure of your .Rproject
should ideally have:
  • ./data/
  • ./data/ecoregions/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/habitats/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/humanFootprint/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/invasionHistory/ # provided
  • ./data/occurrenceData/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/phylogeneticTree/ # retrieve from source. See PhylogeneticEigenvectorMapping.Rmd
  • ./data/ports/ # retrieve from source. See macroecologicalPatterns.Rmd
  • /data/rangeMaps/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./input/ #example data to run Multipleimputation.Rmd and invasionSyndromesModels.Rmd are included
  • ./output/
  • ./output/miceRangeError/
  • ./output/miceRangeImp/
  • ./output/miceRangeImportance/
  • ./output/rf_fineTune/
  • ./output/rf_pred/
  • ./figures/
About
Quantitative invasion risk assessment of species worldwide
Resources
Readme
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Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Quantitative Invasion Risk Assessment for Forecasting Pontential Invaders
Research rationale
The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species, but robust and defensible forecasts of potential invaders, especially species worldwide with no invasion history, are rare.
The tool
Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e. attributes of a typical invasive alien species). We implemented a multiple imputation with chain equation workflow to estimate invasion syndromes from imputed datasets of species’ life-history and ecological traits (e.g., body size, reproductive traits, microhabitat) and macroecological patterns (e.g., geographic range size, commonness, habitat generalism, tolerance to disturbance).
The tool is run under R computing program. And this repository contains the R scripts and sample files to run the tool.
The description and application of tool can be read in full in Pili et al. (XXXX).
The repository
There are four core R scripts.
  1. macroeclogicalPatterns.Rmd --- for quantifying macroecological patterns.
  2. PhylogeneticEigenvectorMapping.Rmd --- for mapping phylogenetic eigenvectors and determining optimal number of phylogenetic eigenvectors for phylogenetic imputation.
  3. Multipleimputation.Rmd --- for multiple imputation of life-history and ecological traits and macroecological patterns.
  4. invasionSyndromesModels.Rmd --- for modelling invasion syndromes.
The file structure of your .Rproject
should ideally have:
  • ./data/
  • ./data/ecoregions/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/habitats/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/humanFootprint/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/invasionHistory/ # provided
  • ./data/occurrenceData/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/phylogeneticTree/ # retrieve from source. See PhylogeneticEigenvectorMapping.Rmd
  • ./data/ports/ # retrieve from source. See macroecologicalPatterns.Rmd
  • /data/rangeMaps/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./input/ #example data to run Multipleimputation.Rmd and invasionSyndromesModels.Rmd are included
  • ./output/
  • ./output/miceRangeError/
  • ./output/miceRangeImp/
  • ./output/miceRangeImportance/
  • ./output/rf_fineTune/
  • ./output/rf_pred/
  • ./figures/
About
Quantitative invasion risk assessment of species worldwide
Resources
Readme
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Watchers
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Forks
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Quantitative Invasion Risk Assessment for Forecasting Pontential Invaders
Research rationale
The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species, but robust and defensible forecasts of potential invaders, especially species worldwide with no invasion history, are rare.
The tool
Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e. attributes of a typical invasive alien species). We implemented a multiple imputation with chain equation workflow to estimate invasion syndromes from imputed datasets of species’ life-history and ecological traits (e.g., body size, reproductive traits, microhabitat) and macroecological patterns (e.g., geographic range size, commonness, habitat generalism, tolerance to disturbance).
The tool is run under R computing program. And this repository contains the R scripts and sample files to run the tool.
The description and application of tool can be read in full in Pili et al. (XXXX).
The repository
There are four core R scripts.
  1. macroeclogicalPatterns.Rmd --- for quantifying macroecological patterns.
  2. PhylogeneticEigenvectorMapping.Rmd --- for mapping phylogenetic eigenvectors and determining optimal number of phylogenetic eigenvectors for phylogenetic imputation.
  3. Multipleimputation.Rmd --- for multiple imputation of life-history and ecological traits and macroecological patterns.
  4. invasionSyndromesModels.Rmd --- for modelling invasion syndromes.
The file structure of your .Rproject
should ideally have:
  • ./data/
  • ./data/ecoregions/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/habitats/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/humanFootprint/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/invasionHistory/ # provided
  • ./data/occurrenceData/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./data/phylogeneticTree/ # retrieve from source. See PhylogeneticEigenvectorMapping.Rmd
  • ./data/ports/ # retrieve from source. See macroecologicalPatterns.Rmd
  • /data/rangeMaps/ # retrieve from source. See macroecologicalPatterns.Rmd
  • ./input/ #example data to run Multipleimputation.Rmd and invasionSyndromesModels.Rmd are included
  • ./output/
  • ./output/miceRangeError/
  • ./output/miceRangeImp/
  • ./output/miceRangeImportance/
  • ./output/rf_fineTune/
  • ./output/rf_pred/
  • ./figures/
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Quantitative invasion risk assessment of species worldwide
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