Forecasting potential invaders to prevent future biological invasions worldwide, Supporting Information S1

Forecasting potential invaders to prevent future biological invasions worldwide, Supporting Information S1

## 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. (2024 -- Global Change Biology). The project repository containing the R code to run our quantitative invasion risk assessment tool can be accessed in: https://github.com/armanpili/ForecastingInvaders

Contained herein are the associated data of the project.

Tabl_S1_1.csv — Unintentionally transported and introduced amphibians and reptiles

TableS1_2_raw.xslx — Full raw and harmonised life-history and ecological traits of global amphibians and reptiles.

TableS1_2_1.csv — Life-history and ecological traits and macroecological patterns of frogs used in multiple imputation. This is a subset of the global amphibian dataset, reduced to contain a maximum of 60% data missingness in columns (variables) and rows (species).

TableS1_2_2.csv — Life-history and ecological traits and macroecological patterns of lizards used in multiple imputation. This is a subset of the global saurian reptile dataset, reduced to contain a maximum of 60% data missingness in columns (variables) and rows (species).

TableS1_2_3.csv — Life-history and ecological traits and macroecological patterns of snakes used in multiple imputation. This is a subset of the global serpentine reptile dataset, reduced to contain a maximum of 60% data missingness in columns (variables) and rows (species).

TableS1_3.csv — Evaluation scores of random forest models fitted with life-history and ecological traits, macroecological patterns, life-history and ecological traits and macroecological patterns, and optimal subset of life-history and ecological traits and macroecological patterns.

Table S1_4.csv — Predicted risk scores of unintentional transport, introduction, and establishment of frogs, lizards, and snakes.