This was the motivation behind recent research by collaborators from Portugal's University of Trás-os-Montes e Alto Douro. They used modeling techniques to predict the effects of anthropogenic disturbance on the richness of invasive plants. Their models were based on data collected in 89 municipalities across northern Portugal. The region contains wide variation in both socioeconomic status and environmental variables, and is known to have been invaded by 68 species of invasive exotic plants.
(Acer pseudoplatanus L., a.k.a. the sycamore maple, is one of the 68 invasive species considered in this study.)
The first step of the modeling process was to determine which of many potential environmental variables were most closely related to the richness of invasive species. By relating the presence/absence of exotic plants to the socioeconomic and environmental variables in each municipality, the researchers were able to identify 6 environmental variables that appeared to be particularly closely related to invasion: altitude (both maximum and minimum), maximum longitude, population size, population density, energy consumption, and variation in number of buildings; each of these variables was assessed at the level of individual municipalities. This analysis also identified whether these variables impacted invasiveness in a positive or a negative way: Building variation and both altitude traits exerted negative influences, while longitude, human population size and density, and energy use all exerted positive influences.
(Matosinhos Municipality, Portugal--an urbanizing area for which the researchers created simulations investigating likely increases in invasive species over the next 20 years.)
These data were then used in the development of a more complex model that could be used to run simulations investigating how fluctuations in all 6 of these characteristics might impact invasive species richness over time. The researchers ran simulations for 2 scenarios: increased settlement of an already urbanized area, and depopulation of a rural area. Both simulations were based on datasets from actual municipalities in which these trends have been forecasted: Matosinhos and Sernancelhe, respectively. For both municipalities, the simulations covered a 20-year period of time.
(Sernancelhe Municipality, Portugal--a depopulating area for which the researchers created a simulation to investigate changes in invasive plants over the next 20 years.)
During their initial analysis, the scientists found that urban and rural sites could clearly be differentiated from each other on the basis of both geophysical and socioeconomic variables. Although this was a predicted result, the identification of 6 focal variables that clearly distinguish between these 2 types of habitat may prove useful for future analyses for which scientists, conservationists, or planners need to know how they can reliably classify sites as either rural or urban. There were also striking differences in the patterns produced by simulations for the urbanizing and depopulating municipalities. In Matosinhos, increased urbanization was predicted to be associated with increasing population density and energy consumption, but a fairly constant rate of building construction. Species richness was predicted to differ dramatically depending on whether it was driven more by geophysical or human population characteristics. An average of the two patterns suggests that the number of invasive species will rise from a current level of 10 species to a predicted level of approximately 15. In Sernancelhe, on the other hand, energy consumption was predicted to remain steady while energy consumption and, especially, population density, would decline. This was predicted to cause a cycling of species richness. Unlike in Matosinhos, geophysical and human population variables, when considered separately, produced similar patterns for species richness. When averaged together, these predicted fluctuations every ~5 years between approximately 1.15 and 1.20 species, until a final decline to about 1.05 species by the end of the 20-year period.
Cumulatively, the results of the simulations indicate that human activities can have strong impacts on patterns of invasion, but that the strength of these anthropogenic factors will be influenced by environmental variables. This suggests that it may be possible to develop a series of models predicting potential invasion dynamics of different species in different areas. These could be used to identify particularly susceptible habitats where preventative measures should be used to minimize the risk of exotic introductions. The specificity--and, therefore, usefulness--of these models could be even further improved by including additional interactions and variables, such as life history information about particular exotic pests, or wide-scale environmental processes such as climate change.
For supplemental images associated with this post, please visit the Anthrophysis pin board at Pinterest.
Santos, M., Freitas, R., Crespí, A.L., Hughes, S.J., Cabral, J.A. 2011. Predicting trends of invasive plants richness using local socio-economic data: an application in North Portugal. Environmental Research 111:960-966.
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