Small-scale renewable energy sources are being promoted at the global level to mitigate the effects of climate change and to meet increasing human demand for energy and food, despite a poor understanding in some cases of their potential ecological consequences. The Province of British Columbia (BC) has a mandate to work towards carbon-neutrality, and as such the majority of new power options are distributed energy technologies (wind, small hydropower). Individual small hydropower projects are considered to have only modest ecological footprints, but small amounts of power production at each location necessitate a large number of sites and (e.g., > 7200 potential locations identified in BC) which have the potential to generate substantial cumulative effects in both aquatic and terrestrial environments (e.g., reducing salmonid habitat and fragmenting old-growth forests). The primary challenge for conservation is the lack of a framework that simultaneously evaluates cumulative impacts on aquatic and terrestrial species and ecosystems, while considering alternative development scenarios. We are leading a large collaboration with academic colleagues (C. Wilmers, P. deValpine), non-profits (M. Ruckelshaus, C. Orr), the power industry (P. Kariya), power utilities (C. Matheson, D. Little), and government biologists and regulators to develop spatially explicit cumulative impacts models. The primary goal is to identify where there are the best opportunities for developing renewable energy while minimizing cumulative impacts to biodiversity and ecosystem services. This approach integrates the economics of power production (costs, revenue, power capacity), estimates the physical impacts of small hydropower for species and ecosystems, and articulates the tradeoffs that may exist between biodiversity conservation and renewable energies under different development scenarios. This project is aimed at identifying solutions for a future that will increasingly pit conservation of species and ecosystem services against development for energy, economic return, and urbanization. The expectation is that such a method may be adaptable and scalable to different case studies of distributed energy development (e.g. wind, solar, wave).