POLLINATORS POLLINATION AND FOOD PRODUCTION

THE ASSESSMENT REPORT ON POLLINATORS, POLLINATION AND FOOD PRODUCTION
of involving end users in design and implementation is
repeatedly emphasized, and the development of agricultural
DSSs has tended to shift towards participatory approaches
to both design and implmentation (Jakku and Thorburn,
2010; Valls-Donderis et al., 2013).
6.6 DEALING WITH
ECOLOGICAL UNCERTAINTY
Knowledge about the natural world and its complex
relationships is inherently uncertain. Decision-makers
faced with uncertain information need to know as much as
possible about how much uncertainty there is and why it
exists, in order to choose a course of action.
For scientific information, there has been considerable
effort to clarify and manage uncertainty across different
research fields (e.g., Elith et al., 2002; Regan et al., 2002;
Walker et al., 2003; Norton et al., 2006; Li and Wu, 2006;
Beale and Lennon, 2012; Kujala et al., 2013; Riveiro et al.,
2014). Among the proposed taxonomies, frameworks, and
modelling approaches, there is neither a commonly shared
terminology (Walker et al., 2003) nor a comprehensive
framework (see Mastrandrea et al., 2011 and Moss, 2011
for general uncertainties guidance). We therefore take a
pluralist view and use all the available information to suggest
how to improve the treatment of uncertainty in pollination
research and management strategies.
Uncertainty assessment is not something to be added only a
posteriori to interpret scientific results, management decisions
or policy options. It is better to recognize it from the outset
(Refsgaard et al., 2007). Perceiving, defining and analysing
different sources of ecological uncertainty can increase the
accuracy of risk estimation, improve models and predictions,
and consequently improve control over the system. Although
future drivers, effects or events cannot always be anticipated,
environmental management or restoration of pollinators and
pollination services can be performed in ways that tolerate
ecological and economic uncertainty.
Table 6.6.1 summarises a general view of uncertainty. It
is divided into four main sources: linguistic, stochastic,
scientific and epistemic. Two or more types of uncertainty
are identifiable within each source. This list of sources and
types of uncertainty is not exhaustive.
For each type of uncertainty, we use examples from
pollinator and pollination research to illustrate how its
extent can be monitored, and/or how it can be reduced.
For instance, incomplete knowledge of the ecological
system (a type of epistemic uncertainty) and mistakes in
observations (a type of scientific uncertainty) will always lead
to uncertainty in predictions, but the extent of these types of
uncertainty can be accounted for and potentially reduced in
different ways. Table 6.6.2 suggests policy responses and
applicable tools for the different sources of uncertainty.
The sources of uncertainty in Table 6.6.1 help to explain
why there is uncertainty, rather than how much uncertainty
there is. The overall amount of uncertainty, or level of
confidence in a particular finding, combines different sources
together and does not distinguish among them. This
report defines the amount of uncertainty with consistent,
well-defined terms based on authors’ evaluations of the
quantity, quality and consistency of the evidence and level
of agreement for each finding (see IPBES Guidance on
a Common Approach to Applying Uncertainty Terms, in
preparation). These terms (well established, established
but incomplete, unresolved, and inconclusive) are generally
selected using expert judgement, although probabilistic or
statistical information would be used if it were available.
Table 6.6.1 clearly shows that the study of pollinators
and pollination is a multi-dimensional social construct,
and includes dimensions that involve the entire process
(generation and communication) of the production of
scientific knowledge.
The major area of discussion about uncertainty in the
scientific literature concerns modelling processes and
model selection, just one of the sources of uncertainty in
Table 6.6.1 (e.g., Walker et al., 2003; Wintle et al., 2003; Li
and Wu, 2006; Pappenberger and Beven, 2006; Rivington
et al., 2006; Refsgaard et al., 2007; Ascough II et al.,
2008; Cressie et al., 2009; Reilly and Willenbockel, 2010;
Hildebrandt and Knoke, 2011; Keenan et al., 2011; Beale
and Lennon, 2012; Rinderknecht et al., 2012; Mosadeghi et
al., 2013; Riveiro et al., 2014; Sileshi, 2014).
Other sources of uncertainty are prominent in the use of
pollinator and pollination science for policy and decisionmaking.
For example, uncertainty surrounding the impact
of sublethal effects of pesticides on pollinators might be
considered an example of data uncertainty (a type of scientific
uncertainty), because the true levels of field exposure are
poorly known and the sublethal effects are only characterised
for a small selection of pollinator species (see section 2.2.1.4).
Maxim and Van der Sluijs (2007) also demonstrated epistemic
uncertainty in the debate surrounding the insecticide
imidacloprid in France, through the use of ‘contradictory
expertise’ leading to different interpretations; epistemic
uncertainty includes variations in the interpretation of
scientists about concepts, methodologies, data sets, and
ethical positions that may come from different epistemological
positions or understandings of the world.
Another area of uncertainty is the extent to which crop yields
depend on pollination. There is stochastic uncertainty at
local scales, because both yield and pollination, and their
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6. RESPONSES TO RISKS AND OPPORTUNITIES ASSOCIATED
WITH POLLINATORS AND POLLINATION