Grant: Discovery Grants Program - Individual

To help alleviate Canada's biodiversity crisis, management agencies allocate their limited resources to a combination of direct conservation action and monitoring to inform management decisions. Determining the right balance between acting on current information and monitoring to gather more information is a difficult challenge. My proposed research will address this challenge by focusing on the following objectives: 1) identify key data gaps impeding decisions for biodiversity conservation in Canada; 2) provide new ways of filling data gaps through statistical techniques, community/citizen science, experiments, and data syntheses; and 3) design methods to strike the best balance between general surveillance monitoring and targeted monitoring to inform immediate decisions. For objective 1, we will combine efficient prioritization tools with data from conservation agencies to identify the types of data that are most consequential for biodiversity conservation decisions. We will then examine management plans to identify the gaps in these types of data. I predict that the most consequential gaps will occur for data such as species detectability and management uncertainty, which are not routinely collected by conservation agencies. For objective 2, we will determine where community science can supplement professionally collected biodiversity data. We will also produce hybrid species distribution models combining community science and professionally collected data, and will use Bayesian techniques to identify areas where targeted data collection can fill gaps that are most important for management decisions. In addition, we will conduct research to help fill two key data gaps we already know exist: a) detectability of plant species at risk, and b) long-term benefits of forest restoration for biodiversity. For 2a), we will conduct experiments to derive plant species at risk detectability estimates. For 2b), we will conduct a systematic review to identify the determinants of biodiversity outcomes for ecological restoration projects. For objective 3 we will design a new methodological framework to help determine the long-term conservation value of general surveillance monitoring, versus monitoring to inform immediate decisions. We will use simulated species at risk management scenarios where surveys and models iteratively improve each other. I anticipate that this work will demonstrate the conservation benefit of general surveillance monitoring of ecosystems, despite apparent inefficiency for informing immediate decisions. My proposed research will combine statistical modeling and ecological data with decision science in ways that have not been explored. In addition to pushing the boundaries of conservation science, this work will provide actionable lessons for management agencies, and make decisions regarding how to combine monitoring with direct management much easier.