There may be several mechanisms that drive observed interactions
between plants and pollinators in an ecosystem, many of which may
involve trait matching or trait complementarity. Hence a model of
insect species activity on plant species should be represented as
a mixture of these linkage rules. Unfortunately, ecologists do not
always know how many, or even which, traits are the main contributors
to the observed interactions. This thesis proposes the Latent Dirichlet
Allocation (LDA) model from artificial intelligence for modelling
the observed interactions in an ecosystem as a finite mixture of
(latent) interaction groups in which plant and pollinator pairs that
share common linkage rules are placed in the same interaction group.
Several model selection criteria are explored for estimating how many
interaction groups best describe the observed interactions. This thesis
also introduces a new model selection score called ``penalized perplexity".
The performance of the model selection criteria, and of LDA in general,
are evaluated through a comprehensive simulation study that consider
networks of various size along with varying levels of nesting and numbers of
interaction groups. Results of the simulation study suggest that LDA
works well on networks with mild-to-no nesting, but loses accuracy with
increased nestedness. Further, the penalized perplexity tended to
outperform the other model selection criteria in identifying the correct
number of interaction groups used to simulate the data. Finally, LDA was
demonstrated on a real network, the results of which provided insights
into the functional roles of pollinator species in the study region.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OGU.10214/5252 |
| Date | 08 January 2013 |
| Creators | Callaghan, Liam |
| Contributors | Ali, Ayesha, Umphrey, Gary |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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