Modeling invasive plant spread: the role of plant-environment interactions and model structure

• Published in: Ecology (Durham) 1996-10, Vol.77 (7), p.2043-2054
• Main Authors: Higgins, Steven I., Richardson, David M., Cowling, Richard M.
• Format: Article
• Language: English
• Subjects: alien plants; Animal and plant ecology; Animal, plant and microbial ecology; Biological and medical sciences; Biological invasions; Biotic communities; Demecology; Ecological invasion; Ecological modeling; Ecology; Ecosystems; ENVIRONMENTAL IMPACT; FORET CLAIRE; FORMACION BOSCOSA; Fundamental and applied biological sciences. Psychology; FYNBOS; General aspects; IMPACT SUR L'ENVIRONNEMENT; IMPACTO AMBIENTAL; Invasive species; MALEZAS; MAUVAISE HERBE; Metapopulation ecology; MODELE DE SIMULATION; Modeling; MODELOS DE SIMULACION; Parametric models; PINUS; Plant populations; Population biology; SABANAS; SAVANE; SAVANNAS; SIMULATION MODELS; Simulations; Spatial models; Standard deviation; Trees; WEEDS; WOODLANDS
• ISSN: 0012-9658; 1939-9170
• DOI: 10.2307/2265699

Alien plants invade many ecosystems worldwide and often have substantial negative effects on ecosystem structure and functioning. Our ability to quantitatively predict these impacts is, in part, limited by the absence of suitable plant-spread models and by inadequate parameter estimates for such models. This paper explores the effects of model, plant, and environmental attributes on predicted rates and patterns of spread of alien pine trees (Pinus spp.) in South African fynbos (a mediterranean-type shrubland). A factorial experimental design was used to: (1) compare the predictions of a simple reaction-diffusion model and a spatially explicit, individual-based simulation model; (2) investigate the sensitivity of predicted rates and patterns of spread to parameter values; and (3) quantify the effects of the simulation model's spatial grain on its predictions. The results show that the spatial simulation model places greater emphasis on interactions among ecological processes than does the reaction-diffusion model. This ensures that the predictions of the two models differ substantially for some factor combinations. The most important factor in the model is dispersal ability. Fire frequency, fecundity, and age of reproductive maturity are less important, while adult mortality has little effect on the model's predictions. The simulation model's predictions are sensitive to the model's spatial grain. This suggests that simulation models that use matrices as a spatial framework should ensure that the spatial grain of the model is compatible with the spatial processes being modeled. We conclude that parameter estimation and model development must be integrated procedures. This will ensure that the model's structure is compatible with the biological processes being modeled. Failure to do so may result in spurious predictions.