Reimagining infrastructure for a biodiverse future.

Civil infrastructure will be essential to face the interlinked existential threats of climate change and rising resource demands while ensuring a livable Anthropocene for all. However, conventional infrastructure planning largely neglects the contributions and maintenance of Earth's ecological life support systems, which provide irreplaceable services supporting human well-being. The stability and performance of these services depend on biodiversity, but conventional infrastructure practices, narrowly focused on controlling natural capital, have inadvertently degraded biodiversity while perpetuating social inequities. Here, we envision a new infrastructure paradigm wherein biodiversity and ecosystem services are a central objective of civil engineering. In particular, we reimagine infrastructure practice such that 1) ecosystem integrity and species conservation are explicit objectives from the outset of project planning; 2) infrastructure practices integrate biodiversity into diverse project portfolios along a spectrum from conventional to nature-based solutions and natural habitats; 3) ecosystem functions reinforce and enhance the performance and lifespan of infrastructure assets; and 4) civil engineering promotes environmental justice by counteracting legacies of social inequity in infrastructure development and nature conservation. This vision calls for a fundamental rethinking of the standards, practices, and mission of infrastructure development agencies and a broadening of scope for conservation science. We critically examine the legal and professional precedents for this paradigm shift, as well as the moral and economic imperatives for manifesting equitable infrastructure planning that mainstreams biodiversity and nature's benefits to people. Finally, we set an applied research agenda for supporting this vision and highlight financial, professional, and policy pathways for achieving it.

Principles for data management, visualization, and communication to improve disaster response management: Lessons from the Hurricane Maria response mission.

Data visualization and communication are important components in disaster response management. Data management should be a basic part of emergency preparation in the same way as prepositioning essential supplies. For this preparation to be effective, well-conceived data structures and data collection systems must be in place before disasters happen, and required hardware should be designed to operate in contingency environments. However, due to challenges in disaster complexities and data management, there is still a pressing need for improvement. This paper identifies key principles to assist practitioners and software developers in designing and implementing data collection and reporting systems that can be used for data visualization during a disaster response. The authors reviewed existing literature on data and disaster management and incorporated their personal experiences as first responders with the US Army Corps of Engineers Hurricane Maria response mission to develop principles for improving data management and visualization during a disaster response. These principles are illustrated by two case studies from the Task Force Power and Operation Blue Roof mission efforts in Puerto Rico during 2017-2018. Suggested principles include considering data management as part of disaster preparedness, having flexible data tools resilient to unprecedented disaster outcomes, eg, interruption of telecommunications networks, and using diverse graphics and tools that are appropriate to their communication purpose and audience.

A strategic monitoring approach for learning to improve natural infrastructure.

Natural infrastructure (NI) development, including ecosystem restoration, is an increasingly popular approach to leverage ecosystem services for sustainable development, climate resilience, and biodiversity conservation goals. Although implementation and planning for these tools is accelerating, there is a critical need for effective post-implementation monitoring to accumulate performance data and evidence for best practices. The complexity and longer time scales associated with NI, compounded by differing disciplinary definitions and concepts of monitoring necessitate a deliberate and strategic approach to monitoring that encompasses different timeframes and objectives. This paper outlines a typology of monitoring classes differentiated by temporal scale, purpose of data collection, the information benefits of monitoring, and the responsible party. Next, we provide a framework and practical guidelines for designing monitoring plans for NI around learning objectives. In particular, we emphasize conducting research and development monitoring, which provides scientifically rigorous evidence for methodological improvement beyond the project scale. Wherever feasible, and where NI tools are relatively new and untested, such monitoring should avoid wasted effort and ensure progress and refinement of methodology and practice over time. Finally, we propose institutional changes that would promote greater adoption of research and development monitoring to increase the evidence base for NI implementation at larger scales.