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Keywords:

  • Conservation costs;
  • Florida;
  • invasion;
  • invasive plants;
  • management costs;
  • protected areas

Abstract

Aim

Invasive species management is an expensive priority on many protected areas but the magnitude of invasion can vary drastically from site to site. Conservation planners must consider this variability when they plan for treatment across multiple protected areas. We examine the scope for predicting site invadedness and management costs from common protected area characteristics, a method that could be used to estimate the future management needs of a protected area network.

Location

Three hundred and sixty-five protected areas across the state of Florida, USA.

Methods

We use data on invasive plant cover and protected area features to predict invadedness and invasive species management funding allocation in a multiple regression framework. We then examine the relationship between invadedness and funding on a subset of 46 of the protected areas.

Results

Invadedness (relative proportion of a protected area that is covered by invasive plants) was related to the size of a protected area and the number of surrounding households. However, the explained variation (9–50%) depended on the type of species occurrence data used; with models using approximated data on the area infested able to explain more of the variation than those that included data with GIS-calculated area infested. Cumulative funding investment at a protected area was also predicted by the number of surrounding households and protected area size. Yet, funding and invadedness were not correlated with one another.

Main conclusions

Readily available data on protected area features were statistically related to variation in the invadedness of a protected area and were also associated with past management expenditures. This does not translate into a clear relationship between current invadedness and past expenditures, however. Our results suggest that basing predictions of future costs on current funding may not accurately represent budgetary needs.