Understanding and predicting how human activities impact biodiversity is challenging given the often large number of species in any given location. Importantly, despite the increasing amount of data generated by the remote-sensing revolution currently underway in Ecology (e.g., photos from satellites and camera traps), the use and integration of this information with field data on biodiversity is limited by the absence of modeling methods that can integrate multiple data streams and that can properly account for the characteristics of the data generated by these sensors. The long-term goal of this project is to advance cyberinfrastructure by creating broadly applicable methods for biodiversity datasets and by training the next generation of quantitative environmental scientists. This project will focus on substantially improving Mixed Membership (MM) models. These models were originally developed for text-mining purposes but have been widely used for biodiversity research in a wide range of ecosystems. Unfortunately, the current formulation of these models still has important limitations. This project will develop improved MM models that can account for the characteristics of the data generated by these sensors, can integrate multiple sources of data, and enable biodiversity predictions to be made. Ultimately, these improved MM models will be critical to enhance our ability to quantify and predict impacts on biodiversity. This project will also increase the awareness of the impact of climate change on biodiversity among high-school teachers and students.
Evaluating and forecasting how species composition has been and will be altered by anthropogenic stressors is key to sustaining biodiversity and ecosystem functioning, but existing methods to quantify biodiversity change have important limitations. Biodiversity data are highly multivariate (e.g., an assemblage can contain hundreds of species in tropical forests) but many of the dimension-reduction methods typically used to interpret these data often generate results that are not easily interpretable (e.g., nonmetric multidimensional scaling axis scores), rely on unrealistic assumptions (e.g., hard clustering of sites), and are ill suited for wildlife studies because they do not account for imperfect detection. Critically, many of these methods do not allow for formal inference and/or predictions to be made and these methods do not leverage multiple data streams. To circumvent these limitations, this project will develop methods to generate new insights on the drivers of spatial and temporal variation of biodiversity. The overall objective of this project is to significantly improve MM models for biodiversity research. The specific objectives of this project consist of a) creating MM models that can generate reliable inference and predictions, integrate disparate data streams, and account for detection issues; and b) disseminate and train scientists on the developed models; and increase awareness of the impact of climate change on biodiversity among high-school students while addressing important science, math, and statistics standards. The results of this project will be stored in the stable URL https://denisvalle.weebly.com/mm-models.html
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.