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Hemidactylus frenatus is an Asian gecko species that has invaded many tropical regions to become one of the most widespread lizards worldwide. This species has dispersed across the Pacific Ocean to reach Hawaii and subsequently Mexico and other Central American countries. More recently, it has been reported from northwestern South America. Using 12S and cytb mitochondrial DNA sequences I found that South American and Galápagos haplotypes are identical to those from Hawaii and Papua New Guinea, suggesting a common Melanesian origin for both Hawaii and South America. Literature records suggest that H. frenatus arrived in Colombia around the mid-‘90s, dispersed south into Ecuador in less than five years, and arrived in the Galápagos about one decade later.
The impacts of invasive alien species are greatest when they become dominant members of a community, introduce novel traits, and displace native species. Invasions by alien mollusks represent a novel context by which to compare trait differences between generalist native and introduced herbivores in terrestrial ecosystems. Here, we determined the abundance, habitat, feeding preferences, as well as the metabolic rate of the native Pacific banana slug (Ariolimax columbianus) and the alien black slug (Arion rufus) in the coastal forests of British Columbia, Canada. Through a series of observational and experimental studies, we found that alien slugs are more abundant, differ in their habitat preferences, and consumed more fungi (mushrooms) than native banana slugs. Conversely, in an enclosures experiment we found that herbivory damage by native slugs was higher compared to enclosures with alien only and control enclosures. Finally, metabolic rates were similar for both slug species. These results suggest that alien black slugs possess a suite of traits that make them functionally different from native banana slugs.
This study introduces a simple generic model, the Generic Pest Forecast System (GPFS), for simulating the relative populations of non-indigenous arthropod pests in space and time. The model was designed to calculate the population index or relative population using hourly weather data as influenced by evelopmental rate, high and low temperature mortalities and wet soil moisture mortality. Each module contains biological parameters derived from controlled experiments. The hourly weather data used for the model inputs were obtained from the National Center of Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) at a 38 km spatial resolution. A combination of spatial and site-specific temporal data was used to validate the GPFS models. The oriental fruit fly, Bactrocera dorsalis (Hendel), was selected as a case study for this research because it is climatically driven and a major pest of fruit production. Results from the GPFS model were compared with field B. dorsalis survey data in three locations: 1) Bangalore, India; 2) Hawaii, USA; and 3) Wuhan, China. The GPFS captured the initial outbreaks and major population peaks of B. dorsalis reasonably well, although agreement varied between sites. An index of agreement test indicated that GPFS model simulations matched with field B. dorsalis observation data with a range between 0.50 and 0.94 (1.0 as a perfect match). Of the three locations, Wuhan showed the highest match between the observed and simulated B. dorsalis populations, with indices of agreement of 0.85. The site-specific temporal comparisons implied that the GPFS model is informative for prediction of relative abundance. Spatial results from the GPFS model were also compared with 161 published observations of B. dorsalis distribution, mostly from East Asia. Since parameters for pupal overwintering and survival were unknown from the literature, these were inferred from the distribution data. The study showed that GPFS has promise for estimating suitable areas for B. dorsalis establishment and potentially other non-indigenous pests. It is concluded that calibrating prediction models with both spatial and sitespecific temporal data may provide more robust and reliable results than validations with either data set alone.