Regional classification using gradients of marine species assemblages: a data-driven approach to modelling marine ecosystems

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2017-11-14
Authors/Contributors
Abstract
Marine management and conservation efforts often rely on predictive modelling of species observations, the output of such models can be influenced by their regional extent. This study proposes a data-driven classification of marine regions by clustering modelled gradients of species assemblages. Two clustering methods are considered, the CLARA algorithm and mean-shift segmentation, and compared with depth and geographically stratified regions. Regional classification was applied to models using three methods: Regional indices as categorical predictors, regional ensemble models, and a pre-calibration regional data-filter. Regional influence was measured in changes of MSE and R2 values. Large changes in model output were restricted to a small number of anomalous species models. Mean-shift clustered regions produced moderately improved MSE and R2 values compared to the other methods. Regional influence in the species distribution models were shown to be species dependent, necessitating an assessment of relevant species included in regional classification.
Document
Identifier
etd10499
Copyright statement
Copyright is held by the author.
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Schuurman, Nadine
Member of collection
Attachment Size
etd10499_AMcMillan.pdf 2.92 MB