Open Collections

Cosgrove, Cameron

University of British Columbia

The Earth is facing a biodiversity crisis, with over one million species at risk of extinction. To inform recovery actions for threatened species, statistical models are used to predict and understand the habitat required for species survival. Airborne Laser Scanning (ALS) data (a lidar technology) is proving increasingly useful for modelling habitat, providing detailed 3D environmental information. In British Columbia, the prediction of marbled murrelet (Brachyramphus marmoratus, hereafter ‘murrelet’) nesting habitat is of considerable interest. Murrelets are threatened seabirds that nest in large trees along the Pacific coast. This habitat has severely declined due to industrial logging and fine-scale maps of remaining nesting areas are needed to inform recovery actions. This thesis is composed of two complementary research chapters. First, I investigate how ALS technology can be used to advance habitat modelling. I synthesize the available approaches to infer habitat characteristics with ALS and provide a non-technical overview of processing options and data considerations. I advocate for the use of ALS predictors that capture the specific resources, risks, and conditions that directly determine habitat suitability. In doing so, habitat models can be tailored to a species’ ecology and test a wider range of biologically realistic species-environment relationships. Second, I apply these approaches to model murrelet habitat. Two rare-species modelling approaches - ensembles of small models (ESMs) and maximum entropy modelling (MaxEnt) - were evaluated to link 58 nests with ALS and non-ALS predictors at a 100 m resolution in Desolation Sound (DS). An independent model transfer was conducted in Clayoquot Sound (CS), with 21 nests. A MaxEnt model using two ALS predictors (forest vertical complexity and the volume of internal forest gaps) was identified as the best model, achieving good performance in DS (AUC = 0.77, Boyce index = 0.99) and reasonable performance in CS (AUC = 0.63, Boyce index = 0.67). On average, the most suitable habitat was found in small clusters (< 2 ha) of older forest. This thesis addresses the need for fine-scale maps of murrelet nesting habitat, providing a quantitative, ecologically justified model that can be used for conservation decision-making.

Text

2023-09-28

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/86049

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/