Optimizing restoration: A holistic spatial approach to deliver Nature's Contributions to People with minimal tradeoffs and maximal equity.

Ecosystem restoration is inherently a complex activity with inevitable tradeoffs in environmental and societal outcomes. These tradeoffs can potentially be large when policies and practices are focused on single outcomes versus joint achievement of multiple outcomes. Few studies have assessed the tradeoffs in Nature's Contributions to People (NCP) and the distributional equity of NCP from forest restoration strategies. Here, we optimized a defined forest restoration area across India with systematic conservation planning to assess the tradeoffs between three NCP: i) climate change mitigation NCP, ii) biodiversity value NCP (habitat created for forest-dependent mammals), and iii) societal NCP (human direct use of restored forests for livelihoods, housing construction material, and energy). We show that restoration plans aimed at a single-NCP tend not to deliver other NCP outcomes efficiently. In contrast, integrated spatial forest restoration plans aimed at achievement of multiple outcomes deliver on average 83.3% (43.2 to 100%) of climate change mitigation NCP, 89.9% (63.8 to 100%) of biodiversity value NCP, and 93.9% (64.5 to 100%) of societal NCP delivered by single-objective plans. Integrated plans deliver NCP more evenly across the restoration area when compared to other plans that identify certain regions such as the Western Ghats and north-eastern India. Last, 38 to 41% of the people impacted by integrated spatial plans belong to socioeconomically disadvantaged groups, greater than their overall representation in India's population. Moving ahead, effective policy design and evaluation integrating ecosystem protection and restoration strategies can benefit from the blueprint we provide in this study for India.

A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia.

Here we present a geographically diverse, temporally consistent, and nationally relevant land cover (LC) reference dataset collected by visual interpretation of very high spatial resolution imagery, in a national-scale crowdsourcing campaign (targeting seven generic LC classes) and a series of expert workshops (targeting seventeen detailed LC classes) in Indonesia. The interpreters were citizen scientists (crowd/non-experts) and local LC visual interpretation experts from different regions in the country. We provide the raw LC reference dataset, as well as a quality-filtered dataset, along with the quality assessment indicators. We envisage that the dataset will be relevant for: (1) the LC mapping community (researchers and practitioners), i.e., as reference data for training machine learning algorithms and map accuracy assessment (with appropriate quality-filters applied), and (2) the citizen science community, i.e., as a sizable empirical dataset to investigate the potential and limitations of contributions from the crowd/non-experts, demonstrated for LC mapping in Indonesia for the first time to our knowledge, within the context of complementing traditional data collection by expert interpreters.