Publication type: Thesis (Doctor of Philosophy of Science, Zoology)
Publisher: The University of British Columbia
Author: Xiong Zhang
Securing a healthy and biodiverse ocean is vital to our human wellbeing. However, marine conservation is challenging, especially for data-poor species, whose habitats and threats are understudied. My dissertation explored how to address such challenges at two large spatial scales (in China and globally), with a focus on the little studied seahorses (Hippocampus spp.). In the first two data chapters, I explored the utility of various sources of information about species and habitat covariates in species distribution models (SDMs). My results from the first chapter showed that local ecological knowledge provided useful biogeographic data of five Chinese seahorse species to predict their distributions, which were mainly associated with ocean temperature. My second chapter at the global scale indicated that integrating citizen sciences, museum collections, and research-grade data with continuous predictors derived the best SDM models; these models predicted reliable habitat maps for 33 out of 42 species that were primarily associated with depths, proximity to macrohabitats (e.g., sponges), pH, and ocean temperature. In the third analytical chapter, I explored global threat patterns and conservation status for 42 seahorse species with two cumulative-human-impact (CHI) models (spatial and non-spatial) and random forest (RF) models. I found that human-impact indices (from the CHI models) can be used to predict conservation status at high accuracies (87% and 96%) in RF models. Applying a non-spatial CHI model derived indices better predicted conservation status, while using a spatial CHI model identified distribution patterns of threats. In the fourth data chapter, I integrated the derived biogeographic and threat maps in a novel framework to set conservation priorities for seahorse habitats in China and globally, using Marxan software. I found that the two major outputs of Marxan (i.e., selection frequency and best solution) were useful to determine feasible priority solutions at large spatial scales. My results identified valuable datasets and approaches to advancing ecological and conservation knowledge for data-poor marine species, an essential precursor to action for the ocean.