A fine-scale lidar-based habitat suitability mapping methodology for the marbled murrelet (Brachyramphus marmoratus) on Vancouver Island, British Columbia




Clyde, Georgia Emily

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The marbled murrelet (Brachyramphus marmoratus) is a Threatened seabird with very particular nesting requirements. They choose to nest almost exclusively on mossy platforms, provided by large branches or deformities, in the upper canopies of coniferous old-growth trees located within 50 km of the ocean. Due primarily to a loss of this nesting habitat, populations in B.C. have seen significant decline over the past several decades. As such, reliable spatial habitat data are required to facilitate efficient management of the species and its remaining habitats. Current habitat mapping methodologies are limited by their qualitative assessment of habitat attributes and the large, stand-based spatial scale at which they classify and map habitat. This research aimed to address these limitations by utilizing light detection and ranging (lidar) technologies to develop an object-based habitat mapping methodology capable of quantitatively mapping habitat suitability at the scale of an individual tree on Northern Vancouver Island, British Columbia (B.C.). Using a balanced random forest (BRF) classification algorithm and in-field habitat suitability data derived from low-level aerial surveys (LLAS), a series of lidar-derived terrain and canopy descriptors were used to predict the habitat suitability (Rank 1: Very High Suitability – Rank 6: Nil Suitability) of lidar-derived individual tree objects. The classification model reported an overall classification accuracy of 71%, with Rank 1 – Rank 5 reporting individual class accuracies of 90%, 86%, 74%, 67%, and 98%, respectively. Evaluation of the object-based predictive habitat suitability maps provided evidence that this new methodology is capable of identifying and quantifying within-stand habitat variability at the scale of an individual tree. This improved quantification provides a superior level of habitat differentiation currently unattainable using existing habitat mapping methods. As the total amount of suitable nesting habitat in B.C. is expected to continue to decline, this improved quantification is a critical advancement for strategic managers, facilitating improved habitat and species management.



lidar, marbled murrelet, balanced random forest, habitat classification, habitat suitability, single tree detection