Simulating the long-term dynamics of slug populations: a process-based modelling approach for pest control

Citation
Mdf. Shirley et al., Simulating the long-term dynamics of slug populations: a process-based modelling approach for pest control, J APPL ECOL, 38(2), 2001, pp. 401-411
Citations number
53
Categorie Soggetti
Environment/Ecology
Journal title
JOURNAL OF APPLIED ECOLOGY
ISSN journal
00218901 → ACNP
Volume
38
Issue
2
Year of publication
2001
Pages
401 - 411
Database
ISI
SICI code
0021-8901(200104)38:2<401:STLDOS>2.0.ZU;2-Z
Abstract
1. An individual-based simulation model was developed to investigate the lo ng-term dynamics of slug populations using the field slug Deroceras reticul atum as the test species. 2. The model consisted of two components: a model to provide meteorological data and simulate crop growth; and a model to simulate slug life histories . The patterns of slug population dynamics produced by the model matched th e characteristic bivoltine life cycle of D. reticulatum observed in field s tudies. 3. A sensitivity analysis showed that the output of the model was significa ntly influenced by soil moisture, air temperature and leaf area index. Thes e three environmental factors relate directly to important features in the biology of slugs. Of the life-history variables used as inputs to the model , growth rate, environmental mortality and the mortality of sexually mature slugs had a significant effect on the population dynamics predicted by the model. 4. The model output was compared with the observed dynamics of slug populat ions collected in the field. Predicted slug dynamics and observed slug numb ers showed a high degree of fidelity. 5. The simulation model was used to investigate the effects of varying the timing of control methods used to combat slug damage to crops. This result suggested that there is a 'window of opportunity', immediately post-harvest , when slug control is likely to have a significant effect on both the curr ent slug population and the slug numbers in the following spring. 6. Modelling approaches that link crop growth and population dynamics of pe sts have potential in assessing their agricultural impacts and in evaluatin g alternative strategies for their management.