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Towards a different attitude to uncertainty

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Abstract

The ecological literature deals with uncertainty primarily from the perspective of how to reduce it to accept-able levels. However, the current rapid and ubiquitous environmental changes, as well as anticipated rates of change, pose novel conditions and complex dynamics due to which many sources of uncertainty are difficult or even impossible to reduce. These include both uncertainty in knowledge (epistemic uncertainty) and societal responses to it. Under these conditions, an increasing number of studies ask how one can deal with uncertainty as it is. Here, we explore the question how to adopt an overall alternative attitude to uncertainty, which accepts or even embraces it. First, we show that seeking to reduce uncertainty may be counterpro-ductive under some circumstances. It may yield overconfidence, ignoring early warning signs, policy-and societal stagnation, or irresponsible behaviour if personal certainty is offered by externalization of environ-mental costs. We then demonstrate that uncertainty can have positive impacts by driving improvements in knowledge, promoting cautious action, contributing to keeping societies flexible and adaptable, enhanc-ing awareness, support and involvement of the public in nature conservation, and enhancing cooperation and communication. We discuss the risks of employing a certainty paradigm on uncertain knowledge, the potential benefits of adopting an alternative attitude to uncertainty, and the need to implement such an attitude across scales – from adaptive management at the local scale, to the evolving Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) at the global level.
Towards a dierent attitude to uncertainty 95
Towards a different attitude to uncertainty
Guy Pe’er1, Jean-Baptiste Mihoub1, Claudia Dislich2,3, Yiannis G. Matsinos4
1 UFZ – Helmholtz Centre for Environmental Research, Dept. Conservation Biology, Permoserstr. 15, Leipzig,
Germany 2 University of Göttingen, Dept. Ecosystem Modelling, Göttingen, Germany 3 UFZ – Helmholtz
Centre for Environmental Research, Dept. Ecological Modelling, Leipzig, Germany 4 University of the Aegean,
Dept. Environment, Mytilini, Greece
Corresponding author: Guy Pe’er (Guy.peer@ufz.de)
Academic editor: Yrjö Haila|Received6 August 2014|Accepted 5 September 2014|Published 9 October 2014
http://zoobank.org/34C97C3C-A5BA-4E00-968F-B5677DD2A2F4
Citation: Pe’er G, Mihoub J-B, Dislich C, Matsinos YG (2014) Towards a dierent attitude to uncertainty. Nature
Conservation 8: 95–114. doi: 10.3897/natureconservation.8.8388
Abstract
e ecological literature deals with uncertainty primarily from the perspective of how to reduce it to accept-
able levels. However, the current rapid and ubiquitous environmental changes, as well as anticipated rates of
change, pose novel conditions and complex dynamics due to which many sources of uncertainty are dicult
or even impossible to reduce. ese include both uncertainty in knowledge (epistemic uncertainty) and
societal responses to it. Under these conditions, an increasing number of studies ask how one can deal with
uncertainty as it is. Here, we explore the question how to adopt an overall alternative attitude to uncertainty,
which accepts or even embraces it. First, we show that seeking to reduce uncertainty may be counterpro-
ductive under some circumstances. It may yield overcondence, ignoring early warning signs, policy- and
societal stagnation, or irresponsible behaviour if personal certainty is oered by externalization of environ-
mental costs. We then demonstrate that uncertainty can have positive impacts by driving improvements in
knowledge, promoting cautious action, contributing to keeping societies exible and adaptable, enhanc-
ing awareness, support and involvement of the public in nature conservation, and enhancing cooperation
and communication. We discuss the risks of employing a certainty paradigm on uncertain knowledge, the
potential benets of adopting an alternative attitude to uncertainty, and the need to implement such an
attitude across scales – from adaptive management at the local scale, to the evolving Intergovernmental
Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) at the global level.
Keywords
Biodiversity conservation, communication, externalization, adaptive management, risk management,
policy inaction, science-policy dialogue, IPBES
Nature Conservation 8: 95–114 (2014)
doi: 10.3897/natureconservation.8.8388
http://natureconservation.pensoft.net
Copyright Guy Pe’er et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Introduction
e rapid growth in human population, combined with a steep increase in resource-
and energy-demands, exert unprecedented pressures on Earth’s natural resources
(Rockstrom et al. 2009). Natural and semi-natural habitats continue being rapidly
converted or degraded in response to humanity’s growing needs. ese rapid changes
raise uncertainties about the future of biodiversity, ecosystem functioning and services.
Additional sources of uncertainty emerge from rapid social, economic, political, and
technological changes (to name just a few). e conservation of biodiversity is thus
subject to an exceptional range of challenges and sources of uncertainty.
e topic of uncertainty in biodiversity research and conservation practice has tra-
ditionally focused on the realms of knowledge, also referred to as epistemic uncertainty
(Regan et al. 2002). e literature often focuses on three main origins of such uncer-
tainty: i) data, ii) models and iii) predictions - as well as their propagation along the sci-
entic process (e.g. Regan et al. 2002; Burgman et al. 2005; Sutherland 2006; McDon-
ald-Madden et al. 2010; Conroy et al. 2011; Polasky et al. 2011; Beale and Lennon
2012; Evans 2012). Important distinctions were made between imperfect knowledge
(Funtowicz and Ravetz 1991) and inherent, or ontological uncertainty, due to stochas-
ticity or randomness (Regan et al. 2002; Evans 2012; Haila and Henle 2014). Yet, the
literature is very limited in consideration of other sources of uncertainty (Funtowicz
and Ravetz 1991; Regan et al. 2002; Smith 2007; Mitchell 2009; Haila and Henle
2014). Particularly, sources of uncertainty pertaining to the “societal sphere” (as op-
posed to the “knowledge sphere”) have received little attention. ey emerge as soon as
knowledge has to be transferred, translated, shared, and implemented in the decision-
making process (Ibisch et al. 2012). For instance, linguistic uncertainty emerging from
vagueness and ambiguity can add confusion independently of epistemic uncertainty
(Regan et al. 2002). Additionally, uncertainty may originate from societal response,
ranging from social vindication to public consent, scepticism, or rejection.
It is important to realize that all dimensions of uncertainty strongly interact: sub-
jective judgements surrounding the knowledge sphere are shaped by uncertainty lev-
els belonging to cognitive processes (i.e. pre-conceptual (data), conceptual (proxy) or
symbolic levels (concepts) (Gärdenfors 2004; Haila and Henle 2014)). Ultimately, un-
certainty arising from the societal context aects decision-making (Marzetti and Sca-
zzieri 2011), and human preferences or ckleness create complex feedbacks among the
components of socio-ecological systems (Levin 1999; Francis and Goodman 2010).
Traditional approaches focusing mostly on reducing (epistemic) uncertainty, e.g.
through narrowing it within frequencies and quantity intervals or gathering further ev-
idence, are likely to be insucient (Sutherland 2006; Conroy et al. 2011; Evans 2012).
Besides, some aspects of uncertainty remain intrinsically irreducible (e.g. “unknowa-
bles”; Ibisch et al. 2012). Discussions conducted within two workshops on the topic
of uncertainty in biodiversity conservation, held in November and December 2011 in
Leipzig, Germany, identied three alternative approaches to dealing with uncertainty:
reducing it, accepting it, or embracing it (Haila et al. 2014). In accordance with these
Towards a dierent attitude to uncertainty 97
discussions, here we seek to explore how accepting and embracing uncertainty can
promote progress in biodiversity research and conservation practice. While some re-
cent studies addressing uncertainty in ecology have called for accepting the limits of
knowledge and the realms of non-knowledge (Beale and Lennon 2012; Ibisch et al.
2012), this paper attempts to break the unspoken assumption that “certainty is good”
while “uncertainty is bad”. To this end, we rst illustrate cases where seeking certainty
may have undesired eects. We then exemplify circumstances where uncertainty, or
the attitude to it, can yield positive outcomes. Our subjectively collected examples
do not attempt to provide a comprehensive coverage of the literature, but rather aim
to facilitate a constructive discussion toward a new and more exible attitude toward
uncertainty. Not all examples come from the biodiversity conservation realm, but we
believe that all of them have relevant implications for this eld.
Perverse effects of seeking certainty
A main problem with uncertainty may be the exaggerated pursuit of certainty. Seeking
certainty can pervade knowledge gathering and use, potentially leading to overcon-
dence, ignoring the uncertain, stagnation or inaction while awaiting stronger evidence
and irresponsible behaviours originating from the seeming certainty oered by exter-
nalizing the environmental consequences of our actions. In the following, we elaborate
on each of these circumstances.
Overcondence
Overcondence can be dened as using incomplete knowledge as if it was absolute
truth. To exemplify how overcondence relates to uncertainty, we focus on the use
of simplied metrics (e.g. threshold values) for ensuring species’ viability under an-
thropogenic pressure, or maintaining the sustainability of utilized natural resources.
Identifying such thresholds is achived through a long cognitive process of simplica-
tion, including the use of models. For instance, Population Viability Analyses (PVAs)
are commonly used to identify critical thresholds below which populations would
collapse. PVAs employ models ranging from simple mathematical or statistical for-
mulations, to complex, parameter-rich, individual-based models. Model outputs are
then aggregated to deliver understandable and digestible (but decisive) information for
decision makers, while often evicting the communication of model details, assump-
tions, limitations, and associated uncertainties. Policy-makers may continue the chain
of simplication, e.g. by utilizing even simpler measures as elaborated below.
A rst example is the concept of Minimum Viable Population size (MVP) under
which populations are assumed to be non-viable. Factors aecting this value for a given
species include taxonomy, life history or environmental conditions (Flather et al. 2011),
yet the demand for simple rules of thumb have led some ecologists to propose that popu-
Guy Pe’er et al. / Nature Conservation 8: 95–114 (2014)
98
lations (of any species) “require sizes to be at least 5000 adult individuals” (Traill et al.
2007). e use of such ‘magic numbers’ can be misleading or even wrong (Flather et al.
2011). Another important metric is the Minimum Area Requirement (MAR), dening
the minimum habitat area for a viable population. While oering policy-relevant informa-
tion, especially for spatial planning, it is notable that alternative scenarios, explored within
a given study, may oer MAR values diering by as much as two orders of magnitude for
the same species and site (Pe’er et al. 2014b). Under such uncertainty, the MAR values
nally communicated to stakeholders may reect primarily subjective decisions.
e third example is the Maximum Sustainable Yield (MSY), which denes the
largest yield (or catch) that can be removed from a stock over an indenite period
without causing a population or species’ collapse (UN 1997). MSY thresholds have
been long criticised for being over-simplistic (Larkin 1977), especially in the sheries
context (Quaas et al. 2013). Yet for policy-support, even simpler metrics are used that
focus merely on quotas, such as Individual Fishing Quotas (IFQs) or (trophy) hunting
quotas. e application of these metrics is are known ti support overshing, driving
declines in population sizes and biomass, as well as evolutionary changes in harvested
species (e.g. Coltman et al. 2003; Ernande et al. 2004; Palazy et al. 2011). Oversh-
ing further leads to marine biodiversity declines (Ye et al. 2013) and potentially even
ecosystem collapses (Richardson et al. 2009). Nonetheless, these metrics remain the
general norm in hunting and sheries’ policies.
ese examples illustrate widely used practices in biodiversity management, where
trying to reduce uncertainty can generate overcondence or misguidance. Simple
and clear metrics might ease communication between scientists and decision-makers,
but can lure judgement if inadequately designed or lacking sucient information on
wildlife populations (Flather et al. 2011). At times, these values reect nothing but
guesswork (Lindsey et al 2007). In addition, a range of uncertainties remain poorly
considered or communicated (Pe’er et al. 2014b). Communicated values and con-
dence intervals are subject to judgment interpretation, often dictated by societal as-
pects: thresholds that are over-restrictive may be rejected by civil society or policymak-
ers (Pe’er et al. 2014b), promote misreporting and thereby enhance uncertainty with
respect to population status (Quaas et al. 2013), or are simply posing goals that are
too challenging to meet (e.g. Palazy et al. 2011; Quaas et al. 2013). ese examples
therefore demonstrate the perverse outcomes of a demand on scientists to support
policy by maximising the (seeming) certainty with respect to the recommendations
provided to policymakers. is attitude dictates the use of over-simplied thresholds,
oering overcondence rather than a true characterisation of ecological knowledge and
its limits.
Ignoring the uncertain
Seeking certainty at all costs can hinder knowledge seeking and distort its interpreta-
tion, thereby slowing down the learning process. It remains an implicit goal of scientic
Towards a dierent attitude to uncertainty 99
research to obtain ‘perfect knowledge’ of Earth’s systems. To reach this goal, scien-
tists simplify, transform, and aggregate evidence to identify and understand patterns
and their underlying processes. Yet in the quest for understanding general patterns,
the importance of outliers is often underestimated (Ibisch et al. 2012). Rare and ex-
treme events may be exceptionally meaningful in revealing the capacities that individu-
als, species or ecosystems may exhibit. ey are known to shape species distribution
ranges and range shifts, as these are largely determined by rare long-distance dispersal
events. Likewise, rapid evolutionary changes are proposed to occur during rare and
rapid branching speciation events, known as “punctuated equilibrium” in evolution-
ary ecology (Gould and Eldredge 1993). However, because rare events are dicult to
measure and analyse statistically, they remain under-explored. For instance, while PVAs
frequently indicate that catastrophes and environmental stochasticity exert strong ef-
fects on simulation outcomes, a recent review could not detect an increase over time in
the proportion of studies examining their eects, or the number of studies incorporat-
ing several concomitant sources of stochasticity (Pe’er et al. 2013a). PVAs therefore
continue under-exploring, and likely underestimating, the impacts of rare, extreme or
complex events.
Disregarding the unexpected can lead to ‘black swan’ situations where events that
were considered highly improbable and irrelevant turn out to be both real and incur-
ring signicant impacts (Taleb 2008). In ecology, the risk of black swans emerges from
the vast range of environmental processes that are either non-linear or complex, such
as feedback loops leading to tipping-points (Richardson et al. 2009; Lenton 2011),
extinction debt (Tilman et al. 1994) followed by a spiral of ecosystem impoverishment
(Carpenter et al. 2006) or vortex of extinction (Gilpin and Soulé 1986). It is true that
such processes remain dicult to analyse with current decision-making tools (Polasky
et al. 2011), but compulsively targeting perfect knowledge may lead to neglecting
critical evidence (Evans 2012), ignoring early warning signs, or underestimating the
potential eects of such incidences (Ibisch et al. 2012). Such an attitude can further
weaken the ability to reconsider current understanding, and can paradoxically support
the preservation of imperfect knowledge.
Awaiting certainty as a driver of stagnation
Seeking complete certainty may delay action until strong(er) evidence can be obtained.
In the meantime, however, habitat loss, fragmentation and degradation, as well as cli-
mate change, continue unabated. A prominent example of societal demand for greater
certainty, accompanied by inaction, is represented by the debate over climate change,
and the work of the Intergovernmental Panel on Climate Change (IPCC). Discussions
over the last decades revolve primarily around two core questions: whether climate
change is occurring (including speed and severity), and whether it is caused, or signi-
cantly facilitated, by anthropogenic factors such as greenhouse gas emissions (IPCC
2013). While there is by now general acceptance that global warming is taking place,
Guy Pe’er et al. / Nature Conservation 8: 95–114 (2014)
100
the exact contribution of humans remains under debate. Combined with uncertainties
around questions of governance and best actions – namely, who should do what (e.g.
Ackerman and Finlayson 2006; Bosetti et al. 2009), societies and policymakers show
great resistance to take an action. e Costs of Policy Inaction (COPI; Bakkes et al.
2007), however, is likely to increase over time.
While biodiversity is aected by various forms of policy inaction in the climate
change context (IPCC 2002), an example for policy stagnation with more direct rel-
evance to biodiversity loss is the recent reform of the Common Agricultural Policy
(CAP) in the European Union. Following a complex negotiation process (Rutz et al.
2013), the CAP reform failed to oer eective measures to halt ongoing declines in
farmland biodiversity (Pe’er et al. 2014a). e link between agricultural intensication
and biodiversity loss is well established (MA 2005; EEA 2010, 2013), and there is also
growing evidence that the benets accrued from maintaining biodiversity exceed the
inclusive, long-term and larger-scale costs of losing biodiversity and ecosystem services
(TEEB 2010). However, farmers and the food industry can see short-term, measurable
economic gains from intensifying agricultural productivity, whereas the monetary and
societal costs incurred by biodiversity loss and ecosystem degradation are complex,
poorly quantied or even unquantiable (Pe’er et al. 2014a). Consequently, argu-
ments in favour of biodiversity conservation were either weakened by uncertainty, or
put aside in face of a stronger focus on food security and food production. Retaining
the CAP largely unchanged (see Rutz et al. 2013) therefore oers a good example
where policy stagnation emerges, at least in part, from a societal attitude that puts
higher weight on certain, short term benets than on long-term benets (or costs) that
are associated with higher uncertainty.
A third example of how the quest for certainty can lead to stagnation is the “cau-
tionary silence”, where experts may avoid engaging in a science-policy dialogue out of
the fear of making seemingly-uninformed statements (Pe’er et al. 2013b). In the case
of the Norwegian Nature Index, it was paradoxically the experts “… working with the
most accurate and precise population data [who] were also the ones most reluctant to
use their presumably excellent expert knowledge to extrapolate beyond their observa-
tions” (Haila et al. 2014).
Personal certainty allows ignoring negative environmental eects
Environmental externalities occur when an action produces environmental costs or
benets to a third party that was not involved in the action. Externalities can be
spatial, aecting dierent locations or acting at a larger spatial scale; or temporal,
i.e., acting at a dierent point in time and aecting, for instance, future generations.
Prominent examples for negative externalities include air, water or soil pollution,
which put a range of costs on humans and the environment, usually at larger scale
than the actions of single individuals; or externalization of environmental costs to
poorer societies (MA 2005).
Towards a dierent attitude to uncertainty 101
In today’s globalized world, where international trade chains often put large dis-
tances between production areas and consumers, environmental externalities often oc-
cur across continents (Lenzen et al. 2012). Displacement of land-use, where land-use
changes emerge from consumption elsewhere, largely acts from high-income to low-
income countries while putting pressure on ecosystems in the latter (Weinzettel et al.
2012). Lenzen et al. (2012) estimated that 30% of red-listed species are threatened due
to internationally traded commodities like coee, tea, sugar, textiles or sh. One might
argue that end-consumers may not be aware of the negative environmental consequences
of their action, partly due to complex causal relationships (Hertwich 2012). However,
it can also be asserted that consumers often act under the assumption of "personal cer-
tainty" regarding their own security. Globalization of markets and externalization of en-
vironmental costs render consumers, especially in high-income countries, immune to the
(immediate) consequences of their consumption attitudes. Resource shortage or price
uctuations can be easily buered at the consumer level by shifting markets, but can gen-
erate poverty or local food-scarcity at the area of production, often located in low-income
countries. e certainty that one’s actions will not expose oneself to environmental or
societal costs, thereby promotes unsustainable or even irresponsible behaviours.
A local scale example in which personal security can lead to unsustainable behav-
iour is risk avoidance oered by insurance. In dryland pastoral systems, where envi-
ronmental uncertainty is an inherent property of the ecosystem, farmers historically
developed approaches such as mobility, reliance on social networks for building up
herds after catastrophic events, and setting aside open grasslands as grazing reserves
for emergency times (Müller et al. 2011). Apart from their usefulness to deal with
uncertainty (with respect to income), these strategies often have positive ecological and
social by-eects. Nowadays, farmers can reduce their risks by contracting insurances,
which compensate them in the case of reduced rainfalls below a certain level. Reducing
the economic risks, however, replaces the necessity for ecosystem-based buers. is
potentially leads to a modication in farmers’ behaviour, up to abandoning traditional
sustainable strategies such as the protection of parts of the pasture in rainy years to use
it as a reserve for dry years (Müller et al. 2011).
ese examples demonstrate that, across scales, seeming certainty oered by exter-
nalizing environmental costs may promote irresponsibility or unsustainable practices
– thus laying the foundations of the tragedy of the commons (Hardin 1968, Ostrom
1999, 2009).
Positive outcomes of uncertainty
In the following sections we oer illustrative examples of circumstances where un-
certainty, or the attitude to it, can yield positive outcomes: driving improvements in
knowledge, promoting cautious actions, enhancing a more exible and adaptive soci-
etal behaviour, raising public awareness and engagement in nature conservation, en-
hancing cooperation, and promoting communication.
Guy Pe’er et al. / Nature Conservation 8: 95–114 (2014)
102
Driver for improving knowledge
Research is driven by the quest for improved understanding and certainty in knowl-
edge. Yet one could also assert that science and scientists thrive on uncertainty: open
questions make the world interesting and exciting, and motivate our quest for knowl-
edge. Uncertainty not only guides the starting point of learning processes, but is also a
key element at the closing of learning iterations. Descartes’ “philosophy of the doubt”,
upon which science still greatly relies, does not build on removing uncertainty but
rather on clearly identifying it en-route to so-called “perfect knowledge” (Descartes
1637). is entails identifying gaps, imprecision, inaccuracy, or any weakness associ-
ated with the process of understanding; excluding all questionable beliefs in the pur-
suit of scientic truth; and, at the end of any learning step, explicitly identifying and
acknowledging the remaining uncertainty. ereby, one obtains relevance and con-
dence in the outcomes of the scientic exploration, compared to leaving uncertainty
inextricable.
Promoting caution in action
Uninformed decisions taken by policy-makers and decision-makers could result in
long-term risks to humans, the environment, or both. Insucient scientic evidence
could, in such cases, promote cautious and responsible actions if a precautionary ap-
proach is taken (see also Haila et al. 2014). Specically, the precautionary principle has
the power to promote decisions on the basis of uncertainty itself: to this end, it is re-
quired to a) use currently available data, b) indicate uncertainty, c) identify potentially
adverse eects and d) evaluate the potential consequences of inaction (EC 2000). e
precautionary principle hence enables avoiding policy inaction when knowledge is in-
sucient. It explicitly adopts an attitude that accommodates uncertainty into decision
making and “…enables rapid response in the face of a possible danger to human, ani-
mal or plant health…” (EC 2000). is principle is well established in the European
Unions law, including the Habitats Directive, and was adopted by the Convention
on Biological Diversity (CBD 2004). A particularly interesting examination of the
precautionary principle in biodiversity conservation relates to ecological restoration:
restored ecosystems might prevent or reduce the impacts of environmental catastro-
phes (Wiegleb et al. 2013). e precautionary principle hence demonstrates that an
alternative attitude to uncertainty can promote both reactive and proactive conserva-
tion actions.
Promoting societal exibility, responsiveness and adaptability
Social acceptance of unknowns may allow societies to stay attentive to early warning
signs, and maintain sucient conceptual and practical exibility for an eective re-
Towards a dierent attitude to uncertainty 103
sponse. It may reduce the risks of disregarding “black swans”, as societies may be better
prepared to accept that the unexpected is likely to occur in a period of unforeseen,
rapid changes. It may further allow quick adoption of alternative reaction paradigms,
should current ones fail (Carpenter et al. 2006; Polasky et al. 2011). In conservation
practice, an example of a more exible decision-making process is the employment of
adaptive management, dened as “an iterative decision-making process under uncer-
tainty that is designed to learn and incorporate new information and thereby improve
future decision-making” (Polasky et al. 2011). is approach views management deci-
sions as experiments, whose impacts need to be tested, monitored and assessed within a
“learning by doing” process (Keith et al. 2011; Westgate et al. 2013; Haila et al. 2014).
Adaptive management can gain from embracing uncertainty, as this entails viewing
learning in a positive light, and welcoming the opportunity to experiment.
Raising public awareness and engagement
Uncertainty can be used to call for conservation actions, with direct benets for species
as well as promoting public awareness and engagement. Particularly, risks of species
extinction often confront scientists and practitioners with a conict known as “Noahs
Arch dilemma”: which species should we save rst? (Scott and Csuti 1997; Higgins et
al. 2004; Perry 2010). Dierent conservation schools suggest we should maximise the
number of species to be protected (Wilson et al. 2011), safeguard irreplaceable eco-
logical functions (Perry 2010), or seek to maximise cost-eectiveness of conservation
eorts in light of uncertainty (Salomon et al. 2013). By contrast, translocations, rein-
troductions and assisted colonisations of focal threatened species are characterised by
high costs and low chances of success. Nonetheless, they receive strong societal support
and substantial investments (Fischer and Lindenmayer 2000; Armstrong and Seddon
2008). Such eorts face various uncertainties, due to limited knowledge, high stochas-
ticity, and little room for mistakes. One can justify such eorts by ethical arguments,
the importance of specic cultural services provided by such species (Mech 1995) or
their key contribution to the functioning of ecosystems. Yet note that the appeal of
such actions lies especially in spectacular success stories, where species were rescued
from extinction from just a few remaining individuals. Some prominent examples
are the Arabian Oryx (Stanley-Price 1989), Californian condor (Walters et al. 2010),
Przewalski horse (Boyd and Houpt 1994), wisent (Tudge 1992) and Persian Fellow
deer (Bar-David et al. 2005).
e relation of such successes to uncertainty can be viewed in two ways. First, on
the choice between uncertain chances to save a species versus high risk of extinction if
no action is taken, the choice for uncertainty is a choice for hope. Secondly, the natu-
ral uncertainty around such emergency actions, and the ambition behind them, help
raising public attention, awareness and engagement, and attracts important funding to
nature conservation. Hence, uncertainty can be an important driver of action in situa-
tions where inaction could lead to irreversible, undesired losses.
Guy Pe’er et al. / Nature Conservation 8: 95–114 (2014)
104
A driver of cooperation
Uncertainty can enhance, or even drive, cooperation among animals and humans alike.
eories on the evolution of sociality have long suggested that resource scarcity or un-
predictability, or enhanced risks for individuals, can be key drivers toward cooperation
(Cohen 1966; Lin and Michener 1972; Frank and Slatkin 1990; Jetz and Rubinstein
2011). While some recent studies demonstrated that resource scarcity or unpredict-
ability (e.g. in relation to climate change) can enhance conicts and violence among
humans (Le Billon 2001; Hsiang et al. 2013), other, less prominent studies point out
that resource variability can also promote cooperation (Bogale and Korf 2007; McAl-
lister et al. 2011). An interesting example on the emergence of cooperation examined
local versus large-scale social conicts originating from heterogeneity in wealth and re-
sources (Abou Chakra and Traulsen 2014). is study examined social dilemmas with
tension between individual incentives to optimize personal gain versus social benets.
An additional cause of conict was the uneven allocation of resources between rich
and poor. Using a simulation model which assumes a collective-risk dilemma, Abou
Chakra and Traulsen (2014) found that enhanced uncertainty may lead to increased
cooperation where the rich assist the poor. However, the poor contributed only when
early contributions were made by the rich players. is study therefore points out that
uncertainty can indeed lead to cooperation, even at large scales, but this requires that
relevant players acknowledge their responsibility for this to happen. is example war-
rants attention in the context of the global biodiversity crisis, because global hotspots
of biodiversity and its loss are concentrated especially in low-income countries (Myers
et al. 2000).
Promoting communication and trust
In the scientic world, explicit consideration of limitations promotes credibility when
communicating knowledge. In the same way that a scientic paper gains credibility
by explicitly discussing its limitations, scientists communicating their knowledge to
the public are anticipated to exhibit honesty with respect to uncertainty. is is well
exemplied through the “ClimateGate” event: internal discussions over uncertainty,
which were not communicated transparently, have eased the case for those seeking to
distrust the work of the IPCC (van der Sluijs et al. 2010; Ravetz 2011; Garud et al.
2014). Ravetz (2011) suggested three main take-home messages from this incident:
“1) quantify uncertainty, 2) building scientic consensus […and retain] 3) openness
about ignorance”. Garud et al. (2014) suggested that the tension between “normal
science” - as perceived by scientists - and “post-normal science” constellations, where
high stakes meet high uncertainty, requires an alternative approach altogether. Accord-
ingly, the fourth assessment of IPCC has indeed adopted a new approach to uncer-
tainty, where comments are documented and dealt with in a completely transparent
way (IPCC 2013).
Towards a dierent attitude to uncertainty 105
Discussion
Using some illustrative examples, we have shown that seeking to reduce uncertainty
by all means can produce a range of adverse outcomes, including oversimplication
and overcondence, or policy stagnation due to awaiting greater certainty. On the
other hand, accepting and embracing uncertainty can have positive impacts such as
favouring cautionary actions, exible solutions, greater cooperation and transparent
communication.
As our focus is biodiversity conservation, many examples focus on conicts be-
tween humans and nature, and involve uncertainties originating from the complexity
of integrating the interests of multiple actors. While predictive ecology continues to
evolve towards better understanding of such dynamic processes (Evans 2012), our un-
derstanding of socio-ecological systems is only starting to develop, and the eld retains,
and will likely continue retaining, large degrees of unpredictability (Walker and Salt
2006; Scheer et al. 2009; Polasky et al. 2011).
A range of novel approaches can now integrate multiple sources of uncertainty,
oering promising frameworks to aid policy-makers and practitioners in dening ef-
fective strategies and solutions under uncertainty. ese include decision theory and
scenario-planning (reviewed by Polasky et al. 2011; Grechi et al. 2014; Knights et al.
2014), as well as the approaches proposed within the realms of post-normal science
(Ravetz 2004; Francis and Goodman 2010).
Notwithstanding, biodiversity research still focuses primarily on reducing Type
1 errors: failing to reject a wrong hypothesis (Schneider 2006). is entails a strong
preference for reducing uncertainty. Decision makers, by contrast, are usually more
concerned about committing Type 2 errors, namely, rejecting a correct hypothesis
(Schneider 2006), probably because their governance responsibilities make them more
prone to avoid taking decision only if risks might exceed acceptable thresholds. is
creates a dichotomy where scientists may adopt a “precautionary silence” while await-
ing better evidence, whereas policy-makers continue taking decisions within a “busi-
ness as usual” framework. Such dynamics maintain or even increase the pressures on
biodiversity. We therefore assert that the dominating certainty paradigm brings re-
searchers, practitioners, decision-makers and the public alike to share the common as-
sumption that ecological research can, and should, support policy by seeking to reduce
uncertainty. ereby, we maintain overcondence and policy stagnation, discard of
early-warning signs, or adopt irresponsible behaviours. We see this attitude as un-
necessary because policymakers are surely aware of, and obviously accept, uncertainty
in other elds. For example, economic decisions and negotiation processes not only
incorporate and accept uncertainty, but often even maintain it deliberately in order to
allow some freedom in interpretation or implementation. An alternative is therefore to
enhance the acceptance, by all parties, that biodiversity research and conservation act
largely in the realms of uncertainty. We do not perceive such an alternative attitude
as a replacement to the quest for knowledge and certainty, but as an expansion of the
range of potential responses to uncertain conditions.
Guy Pe’er et al. / Nature Conservation 8: 95–114 (2014)
106
Implications across scales
e need for a new attitude to uncertainty can be demonstrated across scales, from lo-
cal to global. Locally, adaptive management is already mentioned by thousands of eco-
logical studies, yet surprisingly few really adopt this principle, and even fewer can show
documented successes (Westgate et al. 2013). Among the key reasons are insucient
monitoring, and insucient addressing of social aspects (Westgate et al. 2013). ese
challenges indicate that, for adaptive management to become successful, a change in
attitude to uncertainty is needed among all parties.
At larger scales, the precautionary principle has only rarely been successfully ap-
plied in biodiversity conservation, partly due to the lack of sucient guidance to move
from awareness to implementation (Tisdell 2011; Kanongdate et al. 2012; Rayfuse
2012). Greater acceptance of uncertainty and its implications would likely reduce the
risk of societal resistance if the principle is used.
Global eorts to understand and address the biodiversity crisis, especially through
the evolving Intergovernmental Science-Policy Platform on Biodiversity and Ecosys-
tem Services (IPBES), need to tackle key questions on how to scale up ecological pro-
cesses, pressures and solutions from local to global. Scaling up, however, entails propa-
gation of uncertainty. Standing issues include the relationship between biodiversity
and ecosystem services (Balvanera et al. 2014); the multitude of drivers acting across
scales (MA 2005; Tzanopoulos et al. 2013); complex production-consumption chains
(Hertwich 2012; Lenzen et al. 2012); and rapid political and socioeconomic changes,
within which responsibilities need to be identied and decisions made. How IPBES
will accommodate uncertainty in its decision making processes, thus remains an open
and important question to resolve (Koetz et al. 2012; Pe’er et al. 2013b; Balvanera et
al. 2014).
Developing an alternative attitude to uncertainty could start among scientists, ac-
knowledging and communicating that the eld of biodiversity research largely lies in
the realms of uncertainty and therefore the demand for high condence cannot always
be fullled. Yet the x of environmental decision-making on condence intervals and
signicance levels, cannot be broken by scientists alone: it requires that stakeholders learn
to accept a diversity of knowledge and non-knowledge inputs into the science-policy and
science-society dialogue. In the process, the nature of the dialogue itself may change.
A cautionary point
While the main goal of this paper is to promote a broader range of attitudes to uncer-
tainty, we do not wish to suggest that uncertainty should be always perceived as posi-
tive or welcome. ere are numerous cases where uncertainty is clearly undesired, both
in terms of associated risks and negative societal responses to it. A particular reason for
caution should be given to circumstances where stakeholders or parties benet from
uncertainty or use it to achieve own goals. While in biodiversity conservation research
Towards a dierent attitude to uncertainty 107
we are only starting to understand the dierent aspects of uncertainty, other elds, e.g.
economics, politics, or insurance, have gained far more experience in this area. us,
how we deal with (and communicate) uncertainty may need caution depending on
circumstances and parties involved. However, there are plenty of opportunities for
learning.
Outlook
is paper focused on subjectively-collected examples to bring about a specic opinion.
While we did not attempt to oer a comprehensive coverage of such cases, we recog-
nize a need for an extended review. Elements of such a review would include mapping
circumstances in which certainty, versus uncertainty, may promote or impede eec-
tive management of natural resources. A meta-analysis or quantication of the impacts
could thus direct a better “choice of attitude” towards dierent forms of uncertainty.
To make these alternative attitudes operational in biodiversity conservation, it
could also be desirable to examine attitudes toward uncertainty within legislative or ju-
diciary frameworks in dierent parts of the world. For instance, it is worthy to explore
dierences between the European Union and the United States of America in terms
of evidence-provision in court (i.e. respectively inquisitorial vs adversarial (Froeb and
Kobayashi 2001)), or compare the precautionary principle, which is generally adopted
by the EU, against the “burden of proof” approach applied in North America. e way
uncertainty aects legislative systems may reect the general attitude of societies to it.
Better understanding of this relation may aid in developing operational alternatives in
biodiversity practice.
Finally, we call for stronger trans-disciplinary research on the feedbacks between
societal and scientic components in decision-making – e.g. in terms of “cost eective”
or “best” conservation eorts given societal perception of “success”. While we did not
explore in depth any economic criteria for decision-making, one should acknowledge
that it is primarily in economy that multi-dimensional approaches are adopted to ad-
dress multiple sources of uncertainty. ese are already increasingly adopted in eco-
logical decisions in consideration of the societal sphere (Schneider et al. 2000; Polasky
et al. 2011), as well as in analyses of trade-os between competing decisions under un-
certainty (Chee 2004; Stewart and Possingham 2005; Carwardine et al. 2010; TEEB
2010). Building on the experience gained through such studies, an iterative feedback
process could be achieved between facilitating the development of alternative attitudes
towards uncertainty, and integrating them into the science-policy dialogue.
Acknowledgements
e authors wish to thank Klaus Henle, Yrjö Haila and Birgit Müller for useful com-
ments, tips and ideas. GP and YGM acknowledge support from FP7 project SCALES
Guy Pe’er et al. / Nature Conservation 8: 95–114 (2014)
108
(contract 226852), JBM and GP acknowledge project EU BON (contract 308454),
and CD acknowledges nancial support from the Deutsche Forschungsgemeinschaft
(DFG) in the framework of the collaborative German-Indonesian project EFFORTS
(CRC990).
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Chapter
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Book
Preface 1. The Emergence of Chaos 2. Exponential Growth, Nonlinearity, Common Sense 3. Chaos in Context: Determinism Randomness and Noise 4. Chaos in Mathematical Models 5. Fractals, Strange Attractors, and Dimension(s) 6. Quantifying the Dynamics of Uncertainty 7. Real numbers, Real Observations and Computers 8. Sorry, Wrong Number: Statistics and Chaos 9. Predictability: Does Chaos Constrain Our Forecasts? 10. Applied Chaos: Can We See Through Our Models? 11. Philosophy in Chaos Glossary Further Reading