Table S2.
Summary of predictor variables in three broad categories of enabling conditions
| Variable | Description | Survey question | References | |
| Attributes of knowledge | ||||
| Salience | The relevance and timeliness of scientific knowledge to the needs of decision-makers | Q1 | Cash et al. (16); Hirsch and Luzadis (52) | |
| Credibility | The degree to which scientific and technical arguments are trustworthy and expert-based | Q2 | Cash et al. (16) | |
| Legitimacy | Whether knowledge considers stakeholders’ different points of view, as evidenced by the representation of diverse views in decision-making processes | Q3 | Cash et al. (16) | |
| Local knowledge | Whether local knowledge and experience was included in ES assessment and planning | Q4 | Watson (53) | |
| Aspects of the process | ||||
| Coproduction | Scientists, stakeholders, and decision-makers working together to produce ES information; | Q5 | Cash et al. (16); Karl et al. (23); Rowe and Lee (19) | |
| Interactions PC* | Amount of interaction between decision-makers and scientists by phone/email, or in person | Q6 and Q7 | Karl et al. (23); Rowe and Lee (19) | |
| Represent PC** | Proportion of stakeholders represented; concentrated or shared distribution of decision-making power | Q8 and Q14 | Beierle and Konisky (54); Young et al. (46); Lee (55); Reed (43), The Natural Capital Project | |
| Dissent | Level of conflict or disagreement among stakeholders | Q9 | Karl et al.(23) | |
| Trust | Amount that trust between stakeholders increased during project | Q10 | Pretty and Smith (56); Pretty and Ward (57) | |
| Length | Length of project in years | The Natural Capital Project | ||
| Contextual conditions | ||||
| Inst. capacity PC*** | Capacity before the project to: measure baseline ES and human activities; monitor changes to ES and human activities; implement policy | Q11, Q12, and Q13 | Ferraro et al. (58); Smit and Wandel (59); Haas et al. (26) | |
| Year | Year project began | The Natural Capital Project | ||
See SI Text for survey questions. Multimodel inference was conducted with the following first principal components based on the variables noted below: *Interactions PC; **Represent PC; ***Inst. Capacity PC.