Analysing economic costs of invasive alien species with the invacost R package

Abstract

AimLarge-scale datasets are becoming increasingly available for macroecological research from different disciplines. However, learning their specific extraction and analytical requirements can become prohibitively time-consuming for researchers. We argue that this issue can be tackled with the provision of methodological frameworks published in open-source software. We illustrate this solution with the invacost R package, an open-source software designed to query and analyse the global database on reported economic costs of invasive alien species, InvaCost.InnovationsFirst, the invacost package provides updates of this dynamic database directly in the analytical environment R. Second, it helps understand the nature of economic cost data for invasive species, their harmonisation process, and the inherent biases associated with such data. Third, it readily provides complementary methods to query and analyse the costs of invasive species at the global scale, all the while accounting for econometric statistical issues.Main conclusionsThis tool will be useful for scientists working on invasive alien species, by (i) facilitating access and use to this multi-disciplinary data resource and (ii) providing a standard procedure which will facilitate reproducibility and comparability of studies, one of the major critics of this topic until now. We discuss how the development of this R package was designed as an enforcement of general recommendations for transparency, reproducibility and comparability of science in the era of big data in ecology.

Are the “100 of the world’s worst” invasive species also the costliest?

“…As cost estimates in InvaCost are made under different temporal scales, we annualized the data based on the difference between the ''Probable_start-ing_year_adjusted'' (i.e., the year the cost started) and ''Probable_ending_year_adjusted'' (i.e., the year the cost ended) columns using the expandYearlyCosts function of the 'invacost' package (v0.3-4) in R (v4.0.2) (Leroy et al 2020). Each expanded entry thus corresponded to a single year for which costs were available following this expansion process (i.e., costs spanning multiple years were divided among those same years).…”

Economic costs of biological invasions in protected areas worldwide - where do we stand?

“…Here, we expanded the Protected Area Subset, the Paired Subset and the WPDA Subset(Subsets i-iii in Fig. 1) to take into account the duration time (in years) of each cost estimate with the expandYearlyCosts function of the 'invacost' package version 0.3-4 (Leroy et al 2020). This function relies on information contained in the Probable_starting_year_adjusted and Probable_ending_year_adjusted columns to repeat each annualized cost as many times as years of cost occurrence between 1976 and 2020.…”

The magnitude, diversity, and distribution of the economic costs of invasive terrestrial invertebrates worldwide

“…The extraction and analysis of cost data from the InvaCost database were performed using the "invacost" package v0.3-4 (Leroy et al 2020) in R v4.0.2 (R Core Team 2020). To speci cally examine terrestrial invertebrates, a two-step ltering process was performed.…”

“…Collated data comprised a total of 1,109 entries (Online Resource 1, Tab_2 "DatasetTerrestrialInvertebrates"). However, because the temporal extent of these reported costs varied considerably across records (i.e., infra-year, single year and multi years), we used the expandYearlyCosts function of the "invacost" R package to obtain comparable annual costs for all cost estimates (Leroy et al 2020). In brief, this function provides annualised cost estimates for all entries, based upon the time range represented in the original cost data (Diagne et al 2020; https://doi.org/10.6084/m9.…”

“…Examination of quantiles from this relationship indicated that years following 2013 were still too incomplete (< 75 %), and we thus excluded those years from temporal analyses. Then, a range of regression modelling techniques was applied to examine the temporal dynamics of reported costs (modelCosts function in "invacost" R package, Leroy et al 2020), including linear/robust regressions (linear and quadratic terms), multivariate adaptive regression Splines (MARS), generalised additive models (GAM) and quantile regression (quantiles 0.1, 0.5, 0.9). This range of modelling approaches was selected to account for issues related to econometric data, such as heteroskedasticity and presence of outliers, allowing for selection among several linear and non-linear models.…”