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Towards a theory of optimal biodiversity monitoring networks
Our understanding of ecology is inherently constrained to the information we have collected about Earth's ecosystems. To best understand how life on Earth is changing, and to implement programs to conserve biodiversity and mitigate the negative consequences biodiversity loss has on human well-being, we need a massive scaling up of biodiversity monitoring programs. This need has resulted in calls to develop a Global Biodiversity Observation System (GBiOS). However, given the massive challenge of monitoring the entirety of Earth's biosphere, and the inherent inability to monitoring everywhere and everything, we need a robust theory of where and when to collect data to best inform conservation action. Although there is a wide body of theory on the general theory sampling design, this is not easily adaptable into a framework for biodiversity observation network design because of the many practical challenges imposed when planning and implementing monitoring programs. This talk will focus on the challenges for developing a theory of biodiversity monitoring that enables robust detection and attribution of biodiversity change, and actionable information for conservation. Specifically, it will advocate a theory of biodiversity monitoring based on in-silico testing to optimize monitoring design in face of uncertainty about the ground-truth dynamics governing ecosystem processes.
