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

Invasive alien species (IAS) are a major driver of global biodiversity loss, hampering conservation efforts and disrupting ecosystem functions and services. While accumulating evidence documented ecological impacts of IAS across major geographic regions, habitat types and taxonomic groups, appraisals for economic costs remained relatively sparse. This has hindered effective cost-benefit analyses that inform expenditure on management interventions to prevent, control, and eradicate IAS. Terrestrial invertebrates are a particularly pervasive and damaging group of invaders, with many species compromising primary economic sectors such as forestry, agriculture and health. The present study provides synthesised quantifications of economic costs caused by invasive terrestrial invertebrates on the global scale and across a range of descriptors, using the InvaCost database. Invasive terrestrial invertebrates cost the global economy US$ 712.44 billion over the investigated period (up to 2020), considering only high-reliability source reports. Overall, costs were not equally distributed geographically, with North America (73%) reporting the greatest costs, with far lower costs reported in Europe (7%), Oceania (6%), Africa (5%), Asia (3%), and South America (< 1%). These costs were mostly due to invasive insects (88%) and mostly resulted from direct resource damages and losses (75%), particularly in agriculture and forestry; relatively little (8%) was invested in management. A minority of monetary costs was directly observed (17%). Economic costs displayed an increasing trend with time, with an average annual cost of US$ 11.40 billion since 1960, but as much as US$ 165.01 billion in 2020, but reporting lags reduced costs in recent years. The massive global economic costs of invasive terrestrial invertebrates require urgent consideration and investment by policymakers and managers, in order to prevent and remediate the economic and ecological impacts of these and other IAS groups.

Predictability analysis of the Pound's Brexit exchange rates based on Google Trends data

During the last decade, the use of online search traffic data is becoming popular in examining, analyzing, and predicting human behavior, with Google Trends being a popular tool in monitoring and analyzing the users' online search patterns in several research areas, like health, medicine, politics, economics, and finance. Towards the direction of exploring the Sterling Pound's predictability, we employ Google Trends data from the last 5 years (March 1st, 2015 to February 29th, 2020) and perform predictability analysis on the Pound's exchange rates to Euro and Dollar. The period selected includes the 2016 UK referendum as well as the actual Brexit day (January 31st, 2020), with the analysis aiming at analyzing the Pound's relationships with Google query data on Pound-related keywords and topics. A quantile dependence method is employed, i.e., cross-quantilograms, to test for directional predictability from Google Trends data to the Pound's exchange rates for lags from zero to 30 (in weeks). The results indicate that statistically significant quantile dependencies exist between Google query data and the Pound's exchange rates, which point to the direction of one of the main implications in this field, that is to examine whether the movements in one economic variable can cause reactions in other economic variables.