The overall aim of our research is to advance the understanding of causal processes determining biodiversity patterns and change in the Anthropocene and inform conservation decisions to mitigate the biodiversity crisis. Specifically, we seek to quantify the effect of humans relative to natural drivers on “what grows where” for plants and birds, attribute biotic change to individual drivers, particularly land use and climate change, and apply new knowledge to evidence-based conservation.
We approach these areas with a combination of ecoinformatics, fieldwork, modelling, historical datasets, citizen science, and remote sensing. We also work on developing new methods to improve fine-grain, large extent measures of land cover/use combining remote sensing data and ground-based habitat imagery with deep learning.
Land use and climate change impacts
Disentangling and quantifying the effect of human versus natural drivers and land use versus climate change are hard problems due to the correlations, synergies, feedback effects between drivers, and mismatches in the temporal and spatial scales at which each driver operates. We use reviews and meta-analyses of the existing research literature to map gaps and synthesize what we know, perform vegetation resurveys of historical datasets to quantify biotic change along climate and land use gradients, and analyze drivers of changes in distribution, community composition, and diversity through time.
An improved understanding of biodiversity responses to natural and human drivers will help create a more synthetic understanding of fundamental macroecological processes, and enable more robust predictions.
Methods for quantifying biotic change and its drivers
One challenge in the attribution of human drivers of biotic change is that there is no consensus on how to best define and measure ecologically relevant land use change. Coarse land cover classifications of the landscape (“urban”, “forest”, “agriculture”, etc.) lose important information on land use intensity and the heterogeneity in habitat structure and resources available within each class. We are exploring methods to improve land use and land cover information and monitor habitat changes, taking advantage of recent advances in remote sensing, ground based imagery, and deep learning for automatizing image classification.
Applications to evidence-based conservation
We are interested in the translation of new knowledge into conservation recommendations, and explore the application of bioclimatic models and scenario planning to climate change adaptation in conservation management. Besides participating in collaborative efforts to inform land conservation for biodiversity protection under climate change, we are exploring new projects in translational ecology, in which conservation managers are involved from the project design stages.
Morueta-Holme Lab 