Introduced weed richness across altitudinal gradients in Hawai’i: humps, humans and water-energy dynamics
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- Jakobs, G., Kueffer, C. & Daehler, C.C. Biol Invasions (2010) 12: 4019. doi:10.1007/s10530-010-9816-6
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Native species richness commonly declines with increasing altitude, but patterns of introduced species richness across altitudinal gradients have been less frequently studied. We surveyed introduced roadside weeds along altitudinal transects ranging from 30 to 4,100 m in Hawai’i, with the objectives of (1) testing the hypothesis that a mass effect due to mixing of tropical and temperate species at mid-elevation promotes a hump-shaped pattern of introduced species richness with altitude, and (2) testing the potential roles of anthropogenic activity, energy (temperature) and water-energy dynamics (productivity-diversity hypothesis) in determining introduced weed richness. A total of 178 introduced weeds were recorded. Introduced weed richness does not decline monotonically with altitude. Rather, mixing of tropical and temperate species helps to maintain high mean richness up to 2,000 m, suggesting a mass effect, but without a distinct richness peak. Patchy occurrence of a transformer species, Pennisetum clandestinum, introduced high variance in richness at mid-elevations. General linear models considering estimated actual evapotranspiration (AET, a measure of energy-water dynamics) together with an index of human activity (distance from urban area or length of major roads) accounted for more variance in introduced weed richness than models with energy alone (temperature) and human activity. Native Hawaiian species richness along roadsides was also weakly correlated with AET but negatively associated with human activity. Our observed association between introduced species richness and AET mirrors patterns reported for native species richness around the world, indicating that AET-richness patterns can develop on a short time scale (on the order of 100 years). To test the generality of introduced weed richness patterns, we tried using the Hawai’i island model to predict weed richness on the neighboring island of Maui. Although weed richness on Maui was under-predicted, the same predictors (human activity and AET) were important on Maui. Scaling for differences in regional human population density or economic activity (both higher on Maui) may allow more accurate and transferable quantitative predictions of introduced weed richness patterns.