Predicting Invasion Dynamics of Four Alien Pinus Species in a Highly Fragmented Semi-Arid Shrubland in South Africa

• Published in: Plant ecology 2001-01, Vol.152 (1), p.79-92
• Main Authors: Rouget, Mathieu, Richardson, David M., Milton, Sue J., Polakow, Daniel
• Format: Article
• Language: English
• Subjects: Altitude; Anthropogenic factors; Biological invasions; Drought resistance; Ecological invasion; Environmental factors; Geographic information systems; Invasive plants; Invasive species; Land use; Logistic regression; Modeling; Pinus; Plant ecology; Plants; Regression analysis; Remote sensing; Shrublands; Soil pH; South Africa; Spatial distribution; Trees; Vegetation; Vegetation structure
• ISSN: 1385-0237; 1573-5052
• DOI: 10.1023/A:1011412427075

This study explored the determinants of spread of four alien Pinus species and the ability of models to predict invasion dynamics in a complex fragmented landscape. The role of environmental factors, natural and anthropogenic disturbance in relation to invasion history was assessed for different stages in the invasion process using a Geographic Information System. Pines escaped from plantations over the past 30 years and spread into the natural semi-arid shrubland (renosterveld). The pattern of spread was compared with a simulated random distribution using two different techniques, a standard logistic regression, and a new recursive modelling approach (Formal Inference-based Recursive Modelling; FIRM). FIRM analysis improved the accuracy of predictions and revealed interactive effects of variables hidden by the logistic regression analysis. More than 80% of isolated pine individuals were found in 20% of the habitat classified as suitable by the models. Soil pH was the most important predictor for the distribution of isolated trees, whereas the establishment of dense pine stands was largely determined by fire history. Differences in invasive behaviour could be explained by species attributes such as limited dispersal for P. canariensis, and better drought-tolerance for P. halepensis. Sixty-five percent of the current pine distribution was accurately predicted by the spatial distribution of the first trees to have invaded. Such models could be used to predict potential spread of invasive plants and gain a better understanding of the main factors driving the invasion process. However, the spread of invasive species in fragmented landscapes, strongly modified by human activities, is very complicated, and the spread remains difficult to predict in the long term. The dynamics of invasion are discussed in relation to changes in land use and disturbance regime.