Economic impact of invasive alien species in Argentina: a first national synthesis

Invasive alien species (IAS) affect natural ecosystems and services fundamental to human well-being, human health and economies. However, the economic costs associated with IAS have been less studied than other impacts. This information can be particularly important for developing countries such as Argentina, where monetary resources for invasion management are scarce and economic costs are more impactful. The present study provides the first analysis of the economic cost of IAS in Argentina at the national level, using the InvaCost database (expanded with new data sources in Spanish), the first global compilation of the reported economic costs of invasions. We analyzed the temporal development of invasions costs, distinguishing costs according to the method reliability (i.e. reproducibility of the estimation methodology) and describing the economic costs of invasions by invaded environment, cost type, activity sector affected and taxonomic group of IAS. The total economic cost of IAS in Argentina between 1995 and 2019 was estimated at US$ 6,908 million. All costs were incurred and 93% were highly reliable. lhe recorded costs were mainly related to terrestrial environments and the agricultural sector, with lack of costs in other sectors, making it difficult to discuss the actual distribution of invasion costs in Argentina. Nevertheless, the reported costs of IAS in this country are very high and yet likely much underestimated due to important data gaps and biases in the literature. Con-sidering that Argentina has an underdeveloped economy, costs associated with biological invasions should be taken into consideration for preventing invasions, and to achieve a more effective use of available resources.

Automated summary

This study provides the first analysis of the economic cost of invasive alien species (IAS) in Argentina at the national level, using the InvaCost database. It found that the reported costs of IAS in Argentina are high, yet likely underestimated due to important data gaps and biases in the literature.