Dominguez Almela, V., 2022. Modelling and empirical approaches for predicting the invasiveness of alien species. Doctoral Thesis (Doctoral). Bournemouth University.
Full text available as:
|
PDF
DOMINGUEZ ALMELA, Victoria_Ph.D._2021.pdf Available under License Creative Commons Attribution Non-commercial. 4MB | |
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
Abstract
Biological invasions are a pervasive aspect of global change that result from the release of alien species into novel environments. Where an introduced alien establishes a sustainable population, disperses and impacts native species then it is considered as invasive. Predicting the outcomes of alien introductions is critical for conservation management, with predictions of dispersal rates a critical component within this. In riverine ecosystems, dispersal can be inhibited by barriers and unidirectional flow in channels that are primarily linear channels, with establishment, dispersal and colonisation also affected by biological interactions with native biota. Here, modelling approaches were applied to predicting: (1) invasion outcomes when the species-specific traits are poorly known, but a robust time- series of the dispersal of an invading species is available; (2) optimal scenarios and trade-offs for managing an invasion in a riverine system; and (3) influences of the physical environment on the introduction and invasion. This was followed by empirical approaches that investigated (4) the ecological interactions between analogous invasive and native species; and (5) the probability that an alien invasion could be resisted by the native community. Individual-based models (IBMs) were used, with approximate Bayesian computation (ABC) successfully applied in Chapter 2 to recreate an ongoing invasion and then predict the range expansion of the species. This model was then used in Chapter 3 to predicting the outcomes for the invasion of different species using a range of management strategies. In these invasion outcomes, the main source of variation in dispersal and establishment rates related to the life history (r- to K-selected) and dispersal (slow to fast) traits. The final model in Chapter 4 then identified that the complexity of the physical environment also influenced the invasion outcomes in freshwater fish, with the location of the initial introduction and the quality of the invaded area being the two most important factors. The initial set of empirical approaches in Chapter 5 predicted the trophic impacts of a globally invasive fish on a threatened native fish, with aquaria experiments and experimental ponds predicting that the global invader will substantially impact the trophic ecology of the native species (trophic niche shifts, niche expansion), with the patterns detected in the controlled environments matching those detected in wild, uncontrolled conditions. The second empirical study (Chapter 6) revealed that biological resistance to the Ponto-Caspian invader zebra mussel Dreissena polymorpha has been minimal in England, and this was likely to contribute to its invasive success. In conclusion, these results suggest that a combination of modelling and empirical approaches for predicting the outcomes of biological invasions provide substantially improved understandings of invasion dynamics and processes, leading to more informed policy and practice.
Item Type: | Thesis (Doctoral) |
---|---|
Additional Information: | If you feel that this work infringes your copyright please contact the BURO Manager. |
Uncontrolled Keywords: | invasive species; freshwater; individual-based model; stable isotope analysis; integrated thesis |
Group: | Faculty of Science & Technology |
ID Code: | 36817 |
Deposited By: | Symplectic RT2 |
Deposited On: | 04 Apr 2022 14:30 |
Last Modified: | 23 Jan 2024 09:41 |
Downloads
Downloads per month over past year
Repository Staff Only - |