Team Members:
Person Name | Person role on project | Affiliation |
---|---|---|
Christoph Nolte | Principal Investigator | Boston University, Boston, USA |
Paulo Arévalo | Co-Investigator | Boston University, Boston, USA |
Eric Bullock | Co-Investigator | U.S. Forest Service, Ogden, USA |
Ana Laura Reboredo Segovia | Graduate Student Researcher | Boston University, Boston, USA |
Luis Miguel Renjifo | Collaborator | Universida Pontificia Javeriana, Bogota, Colombia |
This project will combine remote sensing and social science methods to measure and predict the relative effectiveness of conservation instruments in protecting threatened forest habitat in the Colombian Andes. This ecoregion stands unequaled among global biodiversity hotspots in terms of species richness and endemism, but is also one of the most severely threatened by habitat loss, climate change, and post-conflict development. In response, Colombia has implemented a range of conservation policies, including protected areas (PAs), land acquisitions for conservation (LACs), and payments for environmental services (PES). Its central government now faces critical policy decisions about the importance given to each in a future policy mix to conserve Andean forests. These choices are currently undermined by limited evidence on the effectiveness of each instrument in the Colombian Andes. Remote sensing-based impact assessments can help narrow this gap, but applications in the tropical Andes have long been inhibited by cloud cover and topography. We will address this problem by combining continuous change detection and classification and a fusion of Landsat and Sentinel 1/2 data to develop the first temporally consistent map of 25-year forest change in the Colombian Andes that discriminates between forest types of different habitat value: mature forest, disturbed forest, secondary forests, dry forests, and forest plantations. We will quantify the impacts and cost-effectiveness of PAs, LACs, and PES by combining the forest change layers with nationwide parcel data and a unique dataset of thousands of publicly-financed conservation instruments. Using counterfactual inferential methods, novel machine learning methods, and field research, we will examine and predict how causal effects of each instrument vary across landscapes as a function of ecological, demographic, and institutional variables. Collaboration with Colombia's leading research institutes in land cover change mapping (IDEAM) and biodiversity research (IAVH, Universidad Javeriana) will ensure responsiveness to national priorities, exchange of expertise, and timely dissemination of results. This research combines the expertise of three early-career researchers to support Colombia's environmental institutions in the design of optimal and equitable policy strategies that conserve forest habitat under budget constraints. By producing the first evidence on conservation effects of LACs in the tropics, the project also makes a novel, unique contribution to the global evidence base on the effectiveness of conservation instruments.