Table 1.

Conservation relevance of assumptions and selected strategies to deal with assumptions

Assumption	Conservation relevance	Strategies to deal with assumptions for conservation science	Strategies to deal with assumptions for conservation policy and practice
(1) Worldview implicit normative preconceptions of the human–nature relationship	– Potentially neglecting importance of other arguments for conservation (intrinsic, relational)	– Address different values in ES assessments – Carry out assessments for biodiversity and ES separately	– Acknowledge the incompleteness of ES assessments with respect to different worldviews
(2) Preconception ecosystems being good per se	– Service-providing species might cause disservices (invasive species/large predators)	– Assess negative contributions/disservices next to services	– Acknowledge value conflicts when managing populations of service/disservice-providing species
(3) Ontology appropriate use of the terms “ecosystem” and “services”	– People connect to “nature”, not to “ecosystems” – Strict wording might cause rejections among stakeholders	– Adopt the conceptual language to specific contexts – Apply context-specific perspectives for place-based assessments	– Make use of local knowledge – When communicating to stakeholders adopt the language and step away from conceptual-scientific considerations
(4) Components ES components used interchangeably	– Misinterpretations from ES assessments, e.g. no actual use, but only potential provision in access-restricted areas – Hiding potential overuse when actual use is higher than sustainable capacity	– Explicitly name assessed components – Reduce conceptual fuzziness through generally accepted framework and shared common language, e.g. standardised essential variables of ES	– Consider actual use of ES, as well as potential (sustainable) provision when managing protected areas
(5) Representativeness secondary data, time, space are representative	– Credibility and robustness limited when transferring ES data from different context to e.g. a protected area	– Ask local community about their knowledge for context-specific place-based assessments – Use adjusted value transfers and meta-analytic value functions – Collect field data to evaluate uncertainties in transferred data	– Reconsider the applicability of transferred data from outside protected areas, in particular with respect to management and access restrictions – Locally adjusted values might more strongly stress the necessity for conservation
(6) Interactions ES are independent entities	– High multifunctionality does not tell about conservation values, e.g. rare ecosystems are not the most multifunctional – Trade-offs between provisioning and regulating services, including provision of habitat, and biodiversity and between provisioning and cultural services, such as recreational use of cultural landscapes of high conservation value	– Assess overuse and negative indirect effects related to joint use of ES (e.g. agriculture, forestry) – Study interactions over time and across space – Consider functional mechanisms relating ES and biodiversity (e.g. functional trait-based models)	– Consider rarity next to multifunctionality/number of ES – Conservation-compatibility of ES: where do (access) restrictions apply, e.g. for cultural or non-material uses e.g. recreation
(7) Expert judgement estimation of ES quantities is appropriate	– Results depend on panel of experts involved, in which conservation aspects or ES may be misjudged or overlooked	– Ensure that conservation experts are involved – Validate with field data, assess and report uncertainties – Test effects of different scoring approaches – Do not consider expert judgements as perfect substitute for empirical evidence and regard that they may change over time	– Consider different types of expertise, including lay expertise, indigenous and local knowledge, technical knowledge – Validate/reconsider prior conservation efforts if empirical data are available
(8) Validity ES indicators are credible	– Proxies do not convey the whole picture of an ES and might neglect aspects such as ecological relations	– Build scientific consensus on validity of different indicators – Discuss uncertainties of approximations transparently	– Critically check the conservation relevance of indicators, e.g. their capacity to monitor service-providing species
(9) Economic rationality maximising individual utility and well-informed preferences	– Preferences may not be well-established for unfamiliar goods like biodiversity, respondents might not oversee complex consequences for biodiversity	– To ensure well-informed preferences, inform people about ecological complexity underlying ES and include discussions among heterogeneous groups before eliciting values	– Ensure broad inclusion of knowledge on the diversity of values and preferences to avoid undervaluation of ecosystems and biodiversity
(10) Monetary valuation approximation of preferences through monetary measures	– Focus on monetary values might exclude other values of biodiversity – Willingness-to-pay is not equal to ability-to-pay for conservation	– Allow for expression of plural values by using various metrics besides monetary measures – Focus not only on monetary value outcomes but also on motives behind preferences	– Consider outcomes of different valuation methods, and be aware that monetary values are only one form of values – Consider relational values that might be of higher conservation relevance
(11) Aggregation summing up welfare across individuals, ES and time	– Interpretation of conservation relevance, e.g. the value of a service-providing species over time depends strongly on aggregation rule	– Test robustness of results by using different aggregation procedures – Be explicit about aggregation and discounting choices (e.g. weighting and social discount rates)	– Recognise the diversity of indicators, benefits and values that are attributed to nature and take minority values into account – Consider alternatives to aggregation (multi-criteria approaches)
(12) Relevance importance for conservation decisions	– Impact of a stronger integration of ES in conservation policy and practice on classic conservation goals still unclear – Actual uptake unclear	– Ask stakeholders/decision-makers about needs for, e.g. planning or instrument development to inform acceptable uncertainty levels – Analyse post-study uptake of ES assessment results	– Collaborate with scientists on monitoring the actual effect of ES-based management on other conservation goals (e.g. number of species)