ArticlePDF Available

Towards a different attitude to uncertainty



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 (
Academic editor: Yrjö Haila|Received6 August 2014|Accepted 5 September 2014|Published 9 October 2014
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
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.
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
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.
Launched to accelerate biodiversity conservation
A peer-reviewed open-access journal
Guy Pe’er et al. / Nature Conservation 8: 95–114 (2014)
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 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)
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)
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)
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
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)
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
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
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)
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
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.
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)
(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
Abou Chakra M, Traulsen A (2014) Under high stakes and uncertainty the rich should lend
the poor a helping hand. Journal of eoretical Biology 341: 123–130. doi: 10.1016/j.
Ackerman F, Finlayson IJ (2006) e economics of inaction on climate change: a sensitivity
analysis. Climate Policy 6: 509–526. doi: 10.1080/14693062.2006.9685617
Armstrong DP, Seddon PJ (2008) Directions in reintroduction biology. Trends in Ecology and
Evolution 23: 20–25. doi: 10.1016/j.tree.2007.10.003
Bakkes JA, Brauer I, Ten Brink P, Gorlach B, Kuik OJ, Medhurst J (2007) Cost of Policy Inac-
tion. Netherlands Environmental Assessment Agency MNP, National Institute of Public
Health and the Environment RIVM Bilthoven, Netherlands, 136 pp.
Balvanera P, Siddique I, Dee L, Paquette A, Isbell F, Gonzalez A, Byrnes J, O’Connor MI,
Hungate BA, Grin JN (2014) Linking biodiversity and ecosystem services: Current un-
certainties and the necessary next steps. Bioscience 64: 49–57. doi: 10.1093/biosci/bit003
Bar-David S, Saltz D, Dayan T (2005) Predicting the spatial dynamics of a reintroduced
population: the Persian fallow deer. Ecological Applications 15: 1833–1846. doi:
Beale CM, Lennon JJ (2012) Incorporating uncertainty in predictive species distribution mod-
elling. Philosophical Transactions of the Royal Society B: Biological Sciences 367: 247–
258. doi: 10.1098/rstb.2011.0178
Bogale A, Korf B (2007) To share or not to share? (Non-)violence, scarcity and resource ac-
cess in Somali Region, Ethiopia. Journal of Development Studies 43: 743–765. doi:
Bosetti V, Carraro C, Sgobbi A, Tavoni M (2009) Delayed action and uncertain stabilisation
targets. How much will the delay cost? Climatic Change 96: 299–312. doi: 10.1007/
Boyd L, Houpt KA (1994) Przewalski’s Horse: e History and Biology of an Endangered Spe-
cies. SUNY Press, 313 pp.
Burgman MA, Lindenmayer DB, Elith J (2005) Managing landscapes for conservation under
uncertainty. Ecology 86: 2007–2017. doi: 10.1890/04-0906
Carpenter SR, Bennett EM, Peterson GD (2006) Scenarios for ecosystem services: an overview.
Ecology and Society 11: 1–29.
Carwardine J, Wilson KA, Hajkowicz SA, Smith RJ, Klein CJ, Watts M, Possingham HP
(2010) Conservation planning when costs are uncertain. Conservation Biology 24: 1529–
1537. doi: 10.1111/j.1523-1739.2010.01535.x
Towards a dierent attitude to uncertainty 109
CBD – Convention on Biological Diversity (2004) Decisions Adopted by the Conference of the
Parties to the Convention on Biological Diversity at its Seventh Meeting.
doc/meetings/cop/cop-07/ocial/cop-07-21-part2-en.pdf [access date 2 September 2014]
Chee YE (2004) An ecological perspective on the valuation of ecosystem services. Biological
Conservation 120: 549–565. doi: 10.1016/j.biocon.2004.03.028
Cohen D (1966) Optimizing reproduction in a randomly varying environment. Journal of
eoretical Biology 12: 119–129. doi: 10.1016/0022-5193(66)90188-3
Coltman DW, O’Donoghue P, Jorgenson JT, Hogg JT, Strobeck C, Festa-Bianchet M (2003)
Undesirable evolutionary consequences of trophy hunting. Nature 426: 655–658. doi:
Conroy MJ, Runge MC, Nichols JD, Stodola KW, Cooper RJ (2011) Conservation in the face
of climate change: e roles of alternative models, monitoring, and adaptation in confront-
ing and reducing uncertainty. Biological Conservation 144: 1204–1213. doi: 10.1016/j.
Descartes R (1637) Discourse on the Method of Rightly Conducting the Reason and of Seek-
ing Truth in the Sciences. Original version in French, translated by Donald A. Cress, 1998.
Hackett Publishing, Indianapolis, USA, 65 pp.
EC (2000) Communication from the Comission on the precautionary principle. European
Commission, Brussels, Belgium.
EEA (2010) Biodiversity Baseline EU 2010. European Environment Agency, Copenhagen,
EEA (2013) e European Grassland Buttery Indicator: 1990–2011. European Environment
Agency, Luxembourg.
Ernande B, Dieckmann U, Heino M (2004) Adaptive changes in harvested populations: plas-
ticity and evolution of age and size at maturation. Proceedings of the Royal Society of
London Series B: Biological Sciences 271: 415–423. doi: 10.1098/rspb.2003.2519
Evans MR (2012) Modelling ecological systems in a changing world. Philosophical Transactions
of the Royal Society B: Biological Sciences 367: 181–190. doi: 10.1098/rstb.2011.0172
Fischer J, Lindenmayer DB (2000) An assessment of the published results of animal reloca-
tions. Biological Conservation 96: 1–11. doi: 10.1016/s0006-3207(00)00048-3
Flather CH, Hayward GD, Beissinger SR, Stephens PA (2011) Minimum viable populations: is
there a ‘magic number’ for conservation practitioners? Trends in Ecology & Evolution 26:
307–316. doi: 10.1016/j.tree.2011.03.001
Francis RA, Goodman MK (2010) Post-normal science and the art of nature conservation.
Journal for Nature Conservation 18: 89–105. doi: 10.1016/j.jnc.2009.04.002
Frank SA, Slatkin M (1990) Evolution in a variable environment. American Naturalist 136:
244–260. doi: 10.1086/285094
Froeb LM, Kobayashi BH (2001) Evidence production in adversarial vs. inquisitorial regimes.
Economics Letters 70: 267–272. doi: 10.2139/ssrn.179182
Funtowicz S, Ravetz J (1991) A new scientic methodology for global environmental issues.
In: Costanza R (Ed.) Ecological economics: the science and management of sustainability.
Columbia University Press, New York, 137–152.
Gärdenfors P (2004) Conceptual spaces. e geometry of thought. MIT Press, Cambridge, 317 pp.
Guy Pe’er et al. / Nature Conservation 8: 95–114 (2014)
Garud R, Gehman J, Karunakaran A (2014) Boundaries, breaches, and bridges: e case of
Climategate. Research Policy 43: 60–73. doi: 10.1016/j.respol.2013.07.007
Gilpin ME, Soulé ME (1986) Minimum viable populations: Process of species extinctions. In:
Soulé ME (Ed.) Conservation biology: the science of scarcity and diversity. Sinauer, Sun-
derland, Massachusetts, 19–34.
Gould Sj, Eldredge N (1993) Punctuated equilibrium comes of age. Nature 366: 223–227. doi:
Grechi I, Chades I, Buckley YM, Friedel MH, Grice AC, Possingham HP, van Klinken RD,
Martin TG (2014) A decision framework for management of conicting production and
biodiversity goals for a commercially valuable invasive species. Agricultural Systems 125:
1–11. doi: 10.1016/j.agsy.2013.11.005
Haila Y, Henle K (2014) Uncertainty in biodiversity science, policy and management: a con-
ceptual overview. Nature Conservation 8: 27–43. doi: 10.3897/natureconservation.8.5941
Haila Y, Henle K, Apostolopoulou E, Cent J, Framstad E, Görg C, Jax K, Klenke R, Magnus-
son WE, Matsinos Y, Müller B, Paloniemi R, Pantis J, Rauschmayer F, Ring I, Settele
J, Similä J, Touloumis K, Tzanopoulos J, Pe’er G (2014) Confronting and coping with
uncertainty in biodiversity research and praxis. Nature Conservation 8: 45–75. doi:
Hardin G (1968) e tragedy of the commons. Science 162: 1243–1248. doi: 10.1126/sci-
Hertwich E (2012) Biodiversity: Remote responsibility. Nature 486: 36–37. doi:
Higgins JV, Ricketts TH, Parrish JD, Dinerstein E, Powell G, Palminteri S, Hoekstra JM, Mor-
rison J, Tomasek A, Adams J (2004) Beyond Noah: Saving species is not enough. Conser-
vation Biology 18: 1672–1673. doi: 10.1111/j.1523-1739.2004.0421b.x
Hsiang SM, Burke M, Miguel E (2013) Quantifying the inuence of climate on human con-
ict. Science 341. doi: 10.1126/science.1235367
Ibisch PL, Geiger L, Cybulla F (2012) Global Change Management: Knowledge Gaps, Blinds-
pots and Unknowables. Nomos, Baden-Baden, Germany, 254 pp.
IPCC (2002) Climate change and biodiversity. Intergovernmental Panel of Climate Change
Technical Paper V.
IPCC (2013) Climate Change 2013: e Scientic Basis. Contribution of Working Group I
to the ird Assessment Report of the Intergovernmental Panel on Climate Change. Cam-
bridge University Press, Cambridge.
Jetz W, Rubinstein DR (2011) Environmental uncertainty and the global biogeogra-
phy of cooperative breeding in birds. Current Biology 21: 1–7. doi: 10.1016/j.
Kanongdate K, Schmidt M, Krawczynski R, Wiegleb G (2012) Has implementation of the
precautionary principle failed to prevent biodiversity loss at the national level? Biodiversity
and Conservation 21: 3307–3322. doi: 10.1007/s10531-012-0375-2
Keith DA, Martin TG, McDonald-Madden E, Walters C (2011) Uncertainty and adaptive
management for biodiversity conservation. Biological Conservation 144: 1175–1178. doi:
Towards a dierent attitude to uncertainty 111
Knights AM, Culhane F, Hussain SS, Papadopoulou KN, Piet GJ, Raakaer J, Rogers SI, Rob-
inson LA (2014) A step-wise process of decision-making under uncertainty when imple-
menting environmental policy. Environmental Science & Policy 39: 56–64. doi: 10.1016/j.
Koetz T, Farrell KN, Bridgewater P (2012) Building better science-policy interfaces for inter-
national environmental governance: assessing potential within the Intergovernmental Plat-
form for Biodiversity and Ecosystem Services. International Environmental Agreements-
Politics Law and Economics 12: 1–21. doi: 10.1007/s10784-011-9152-z
Larkin PA (1977) An epitaph for the concept of Maximum Sustained Yield. Transactions of
e American Fisheries Society 106: 1–11. doi: 10.1577/1548-8659(1977)106<1:AEFT-
Le Billon P (2001) e political ecology of war: natural resources and armed conicts. Political
Geography 20: 561–584. doi: 10.1016/s0962-6298(01)00015-4
Lenton TM (2011) Early warning of climate tipping points. Nature Climate Change 1: 201–
209. doi: 10.1038/nclimate1143
Lenzen M, Moran D, Kanemoto K, Foran B, Lobefaro L, Geschke A (2012) International trade
drives biodiversity threats in developing nations. Nature 486: 109–112. doi: 10.1038/na-
Levin SA (1999) Fragile Dominion: Complexity and the Commons. Perseus Publishing, 256 pp.
Lin N, Michener CD (1972) Evolution of sociality in insects. Quarterly Review of Biology 47:
131–159. doi: 10.1086/407216
Lindsey PA, Roulet PA, Romanach SS (2007) Economic and conservation signicance of the
trophy hunting industry in sub-Saharan Africa. Biological Conservation 134: 445–469.
doi: 10.1016/j.biocon.2006.09.005
MA (2005) Millennium Ecosystem Assessment: Ecosystems and Human Well-being: Biodiver-
sity Synthesis. Island Press, Washington D.C., 100 pp.
Marzetti S, Scazzieri R (2011) Fundamental uncertainty: Rationality and plausible reasoning.
Palgrave Macmillan, London, 304 pp.
McAllister RRJ, Tisdell JG, Reeson AF, Gordon IJ (2011) Economic behavior in the face of re-
source variability and uncertainty. Ecology and Society 16. doi: 10.5751/es-04075-160306
McDonald-Madden E, Probert WJM, Hauser CE, Runge MC, Possingham HP, Jones ME,
Moore JL, Rout TM, Vesk PA, Wintle BA (2010) Active adaptive conservation of threat-
ened species in the face of uncertainty. Ecological Applications 20: 1476–1489. doi:
Mech LD (1995) e challenge and opportunity of recovering wolf populations. Conservation
Biology 9: 270–278. doi: 10.1046/j.1523-1739.1995.9020270.x
Mitchell SD (2009) Unsimple truths: Science, complexity, and policy. e University of Chi-
cago Press, Chicago. doi: 10.7208/chicago/9780226532653.001.0001
Müller B, Quaas MF, Frank K, Baumgartner S (2011) Pitfalls and potential of institutional
change: Rain-index insurance and the sustainability of rangeland management. Ecological
Economics 70: 2137–2144. doi: 10.1016/j.ecolecon.2011.06.011
Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB, Kent J (2000) Biodiversity hot-
spots for conservation priorities. Nature 403: 853–858. doi: 10.1038/35002501
Guy Pe’er et al. / Nature Conservation 8: 95–114 (2014)
Ostrom E (1999) Coping with tragedies of the commons. Annual Review of Political Science
2: 493–535. doi: 10.1146/annurev.polisci.2.1.493
Ostrom E (2009) A general framework for analyzing sustainability of Social-Ecological Sys-
tems. Science 325: 419–422. doi: 10.1126/science.1172133
Palazy L, Bonenfant C, Gaillard JM, Courchamp F (2011) Cat Dilemma: Too protected to
escape trophy hunting? PLoS ONE 6. doi: 10.1371/journal.pone.0022424
Pe’er G, Dicks LV, Visconti P, Arlettaz R, Báldi A, Benton TG, Collins S, Dieterich M, Gregory
RD, Hartig F, Henle K, Hobson PR, Kleijn D, Neumann RK, Robijns T, Schmidt JA,
Shwartz A, Sutherland WJ, Turbé A, Wulf F, Scott AV (2014a) EU agricultural reform fails
on biodiversity. Science 344: 1090–1092. doi: 10.1126/science.1252254
Pe’er G, Matsinos YG, Johst K, Franz KW, Turlure C, Radchuk V, Malinowska AH, Curtis
JMR, Naujokaitis-Lewis I, Wintle BA, Henle K (2013a) A protocol for better design, ap-
plication and communication of population viability analyses. Conservation Biology 27:
644–656. doi: 10.1111/cobi.12076
Pe’er G, McNeely JA, Dieterich M, Jonsson BG, Selva N, Fitzgerald JM, Nesshover C (2013b)
IPBES: opportunities and challenges for SCB and other learned societies. Conservation
Biology 27: 1–3. doi: 10.1111/cobi.12000
Pe’er G, Tsianou MA, Franz KW, Matsinos GY, Mazaris AD, Storch D, Kopsova L, Verboom
J, Baguette M, Stevens VM, Henle K (2014b) Toward better application of Minimum
Area Requirements in conservation planning. Biological Conservation 170: 92–102. doi:
Perry N (2010) e ecological importance of species and the Noahs Ark problem. Ecological
Economics 69: 478–485. doi: 10.1016/j.ecolecon.2009.09.016
Polasky S, Carpenter SR, Folke C, Keeler B (2011) Decision-making under great uncertainty:
environmental management in an era of global change. Trends in Ecology & Evolution 26:
398–404. doi: 10.1016/j.tree.2011.04.007
Quaas MF, Requate T, Ruckes K, Skonhoft A, Vestergaard N, Voss R (2013) Incentives for
optimal management of age-structured sh populations. Resource and Energy Economics
35: 113–134. doi: 10.1016/j.reseneeco.2012.12.004
Ravetz J (2004) e post-normal science of precaution. Futures 36: 347–357. doi: 10.1016/
Ravetz JR (2011) ‘Climategate’ and the maturing of post-normal science. Futures 43: 149–157.
doi: 10.1016/j.futures.2010.10.003
Rayfuse R (2012) Precaution and the Protection of Marine Biodiversity in Areas beyond Na-
tional Jurisdiction. International Journal of Marine and Coastal Law 27: 773–781. doi:
Regan HM, Colyvan M, Burgman MA (2002) A taxonomy and treatment of uncertain-
ty for ecology and conservation biology. Ecological Applications 12: 618–628. doi:
Richardson AJ, Bakun A, Hays GC, Gibbons MJ (2009) e jellysh joyride: causes, con-
sequences and management responses to a more gelatinous future. Trends in Ecology &
Evolution 24: 312–322. doi: 10.1016/j.tree.2009.01.010
Towards a dierent attitude to uncertainty 113
Rockstrom J, Steen W, Noone K, Persson A, Chapin FS, Lambin EF, Lenton TM, Scheer M,
Folke C, Schellnhuber HJ, Nykvist B, de Wit CA, Hughes T, van der Leeuw S, Rodhe H,
Sorlin S, Snyder PK, Costanza R, Svedin U, Falkenmark M, Karlberg L, Corell RW, Fabry
VJ, Hansen J, Walker B, Liverman D, Richardson K, Crutzen P, Foley JA (2009) A safe
operating space for humanity. Nature 461: 472–475. doi: 10.1038/461472a
Rutz C, Dwyer J, Schramek J (2013) More New Wine in the Same Old Bottles? e Evolving
Nature of the CAP Reform Debate in Europe, and Prospects for the Future. Sociologia
Ruralis 54: 266–284. doi: 10.1111/soru.12033
Salomon Y, McCarthy MA, Taylor P, Wintle BA (2013) Incorporating uncertainty of manage-
ment costs in sensitivity analyses of matrix population models. Conservation Biology 27:
134–144. doi: 10.1111/cobi.12007
Scheer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR, Dakos V, Held H, van Nes EH,
Rietkerk M, Sugihara G (2009) Early-warning signals for critical transitions. Nature 461:
53–59. doi: 10.1038/nature08227
Schneider SH (2006) Climate change: Do we know enough for policy action? Science and
Engineering Ethics 12: 607–636. doi: 10.1007/s11948-006-0061-4
Schneider SH, Kuntz-Duriseti K, Azar C (2000) Costing non-linearities, surprises and irrevers-
ible events. Pacic and Asian Journal of Energy 10: 81–106.
Scott JM, Csuti B (1997) Noah worked two jobs. Conservation Biology 11: 1255–1257. doi:
Smith L (2007) Chaos: A very short introduction. Oxford University Press, Oxford, 176 pp.
doi: 10.1093/actrade/9780192853783.001.0001
Stanley-Price MR (1989) Animal reintroductions: the Arabian oryx in Oman. Cambridge Uni-
versity Press, New York, 291 pp.
Stewart RR, Possingham HP (2005) Eciency, costs and trade-os in marine reserve system de-
sign. Environmental Modeling & Assessment 10: 203–213. doi: 10.1007/s10666-005-9001-y
Sutherland WJ (2006) Predicting the ecological consequences of environmental change: a
review of the methods. Journal of Applied Ecology 43: 599–616. doi: 10.1111/j.1365-
Taleb NN (2008) e black swan: the impact of the highly improbable. Penguin Books, Lon-
don, 366 pp.
TEEB (2010) e Economics of Ecosystems and Biodiversity: Ecological and Economic Foun-
dations. Earthscan, London and Washington, 456 pp.
Tilman D, May RM, Lehman CL, Nowak MA (1994) Habitat destruction and the extinction
debt. Nature 371: 65-66. doi: 10.1038/371065a0
Tisdell CA (2011) Core issues in the economics of biodiversity conservation. In: Costanza R,
Limburg K, Kubiszewski I (Eds) Ecological Economics Reviews. Wiley-Blackwell, Malden,
99–112. doi: 10.1111/j.1749-6632.2010.05889.x
Traill LW, Bradshaw CJA, Brook BW (2007) Minimum viable population size: A meta-analysis
of 30 years of published estimates. Biological Conservation 139: 159–166. doi: 10.1016/j.
Tudge C (1992) Last Animals at the Zoo. Island Press, Washington D.C., 266 pp.
Guy Pe’er et al. / Nature Conservation 8: 95–114 (2014)
Tzanopoulos J, Mouttet R, Letourneau A, Vogiatzakis IN, Potts SG, Henle K, Mathevet R,
Marty P (2013) Scale sensitivity of drivers of environmental change across Europe. Global
Environmental Change-Human and Policy Dimensions 23: 167–178. doi: 10.1016/j.
UN (1997) Glossary of Environment Statistics. Studies in Methods, Series F, No. 67. New York.
van der Sluijs JP, van Est R, Riphagen M (2010) Beyond consensus: reections from a demo-
cratic perspective on the interaction between climate politics and science. Current Opinion
in Environmental Sustainability 2: 409–415. doi: 10.1016/j.cosust.2010.10.003
Walker BH, Salt DA (2006) Resilience inking: Sustaining Ecosystems and People in a
Changing World. Island Press, 192 pp.
Walters JR, Derrickson SR, Fry DM, Haig SM, Marzlu JM, Wunderle JM (2010) Status
of the California Condor (Gymnogyps californianus) and Eorts to Achieve Its Recovery.
eAuk 127: 969–1001. doi: 10.1525/auk.2010.127.4.969
Weinzettel J, Hertwich EG, Peters GP, Steen-Olsen K, Galli A (2012) Auence drives the glob-
al displacement of land use. Global Environmental Change 23: 433–438. doi: 10.1016/j.
Westgate MJ, Likens GE, Lindenmayer DB (2013) Adaptive management of biologi-
cal systems: A review. Biological Conservation 158: 128–139. doi: 10.1016/
Wiegleb G, Broring U, Choi G, Dahms HU, Kanongdate K, Byeon CW, Ler LG (2013) Eco-
logical restoration as precaution and not as restitutional compensation. Biodiversity and
Conservation 22: 1931–1948. doi: 10.1007/s10531-013-0518-0
Wilson HB, Joseph LN, Moore AL, Possingham HP (2011) When should we save the most en-
dangered species? Ecology Letters 14: 886–890. doi: 10.1111/j.1461-0248.2011.01652.x
Ye YM, Cochrane K, Bianchi G, Willmann R, Majkowski J, Tandstad M, Carocci F (2013)
Rebuilding global sheries: the World Summit Goal, costs and benets. Fish and Fisheries
14: 174–185. doi: 10.1111/j.1467-2979.2012.00460.x
... There has been a lack of integration of uncertainty information and limitations of certain models into planning results. Pe'er et al. (2014) believe that one main reason for this dearth is the simplification of model outputs for decision makers. This may be attributed to the belief that there is a considerable mismatch between information outcomes that scientists and practitioners produce and the expertise that policy and decision makers have. ...
... If this does not occur, there is a risk that information will be assumed to be part of reality even if this is virtually impossible. In conventional planning systems such uninformed decisions could result in long-term risks to both the environment and humans (Pe'er et al. 2014). In environmental assessment results uncertainties are usually not described. ...
Full-text available
Context Although uncertainties are ubiquitous in landscape planning, so far, no systematic understanding exists regarding how they should be assessed, appropriately communicated and what impacts they yield on decision support. With increasing interest in the role of uncertainties in science and policy, a synthesis of relevant knowledge is needed to further promote uncertainty assessment in landscape planning practice. Objectives The aim of this paper is to synthesize knowledge about types of uncertainties in landscape planning, of methods to assess these uncertainties, and of approaches for appropriately coping with them. Methods The paper is based on a qualitative literature review of relevant papers identified in the ISI Web of Knowledge and supplemented by frequently cited publications. The identification and synthesis of relevant information was guided by a developed framework concerning uncertainty in landscape planning. Results The main types of uncertainties identified in landscape planning are data-, model-, projection- and evaluation uncertainty. Various methods to address these uncertainties have been identified, including statistical methods for the assessment of uncertainties in planning approaches that help to cope with uncertainties. The integration of uncertainty assessments into landscape planning results is lacking. Conclusions The assessment of uncertainties in landscape planning have been addressed by science, but what is missing are considerations and ideas on how to use this knowledge to foster uncertainty analysis in landscape planning practice. More research is needed on how the application of identified approaches into landscape planning practice can be achieved and how these results might affect decision makers.
... Pe'er et al. (2014) list overconfidence, ignoring of early-warning signs, policy-or societal-stagnation and irresponsible behaviour as potential pitfalls of perceived certainty. Alternatively, acknowledging uncertainty as inevitable can yield positive responses such as increased stakeholder cooperation, flexibility and adaptability and knowledge improvement (Pe'er et al. 2014). ...
Full-text available
Beyond measurement of the ecological and economic impacts of invasive species and pest control on conservation, agricultural productivity and ecosystem services, there are multiple social and human dimensions which influence how stakeholders perceive and respond to research and management strategies. Given the increasing global attention on interdisciplinarity to enhance conservation research and management outcomes, we present a multidimensional framework to inform stakeholder engagement which consolidates current social sciences contributions to invasion science. Beyond unpacking the multiple drivers for considering the social dimensions of knowledge, emotions, trust and risk, we identify the ethical considerations for including social perspectives in research planning and decision-making. The framework captures the multiple personal, individual, institutional and governance dimensions of invasive species control and demonstrates how these dimensions relate. The paper concludes by discussing the implications for invasion science, policy and practice.
... Biological Conservation 226 (2018) [101][102][103][104][105][106][107][108][109][110] recommend that scientists should put a necessary stress on a good and relevant communication to increase the use of science-based policy advice in policy formulation. There are scientific uncertainties in research fields like biodiversity conservation (Conroy et al., 2011;Keith et al., 2011;Pe'er et al., 2014). Thus, scientists need to promote different research strategies to tackle these challenges. ...
Red lists of threatened species have been a powerful instrument to interact loss of biodiversity in many countries. However, there have been growing concerns over the scientific basis of red lists and the influence of red lists on conservation policy formulation. This article explores science–policy interface in the development and use of the Vietnamese Red Data Book 2007 by applying the Research – Integration – Utilization (RIU) model of scientific knowledge transfer. Our study has shown the scientific weaknesses of the Vietnamese Red Data Book 2007, which arise from limited availability of updated data on rare and threatened species in Vietnam and unknown factors influencing them. Despite the existing limitations, the science-based policy advice of the Vietnamese Red Data Book 2007 has achieved certain political influence due to successful integration. Our study also reveals that good and actor-relevant communication could help to win powerful allies in conservation policy formulation, which contributes to a successful transfer of scientific knowledge. Based on our results, we recommend that the improvement of the scientific basis of the red lists is essential to enhance science-based policy support in biodiversity conservation.
... He concludes that to be successful monitoring programs need to take these uncertainties into account already at the early conceptual stage. Pe'er et al. (2014) take up another attitude to uncertainty and elaborate upon the possibility that uncertainty could be embraced. They spell out several ways in which the effort exclusively to reduce uncertainty may be counterproductive, and demonstrate that well articulated uncertainty can have positive effects in knowledge production. ...
Full-text available
The protection of biodiversity is a complex societal, political and ultimately practical imperative of current global society. The imperative builds upon scientific knowledge on human dependence on the life-support systems of the Earth. This paper aims at introducing main types of uncertainty inherent in biodiversity science, policy and management, as an introduction to a companion paper summarizing practical experiences of scientists and scholars (Haila et al. 2014). Uncertainty is a cluster concept: the actual nature of uncertainty is inherently context-bound. We use semantic space as a conceptual device to identify key dimensions of uncertainty in the context of biodiversity protection; these relate to [i] data; [ii] proxies; [iii] concepts; [iv] policy and management; and [v] normative goals. Semantic space offers an analytic perspective for drawing critical distinctions between types of uncertainty, identifying fruitful resonances that help to cope with the uncertainties, and building up collaboration between different specialists to support mutual social learning.
... Uncertainty may play an important role. It is a question of communication, how we can use uncertainty instead of blowing it up all the time to levels where we just do not communicate at all (see Pe'er et al. 2014b). ...
Full-text available
This paper summarises discussions in a workshop entitled “exploring uncertainties in biodiversity science, policy and management”. It draws together experiences gained by scientists and scholars when encountering and coping with different types of uncertainty in their work in the field of biodiversity protection. The discussion covers all main phases of scientific work: field work and data analysis; methodologies; setting goals for research projects, taking simultaneously into account the agency of scientists conducting the work; developing communication with policy-makers and society at large; and giving arguments for the societal relevance of the issues. The paper concludes with a plea for collaborative learning that would build upon close cooperation among specialists who have developed expertise in different fields in research, management and politics.
This article explores a dark side of the current enthusiasm for compiling large datasets in support of evidence-based conservation, at various scales. We use a series of concrete examples to show how data gathered in biodiversity databases can be poorly informative for the design and implementation of effective conservation strategies and actions. This is due to two mechanisms. The first is the use of ill-advised formats, compelling contributors to fill in data which are at odds with the ecological information that they are truly able to capture. The second corresponds to the fact that, unless knowledge gaps are explicitly and emphatically highlighted, the elaboration of databases bestows visibility on knowledge and invisibility on knowledge gaps. Given these risks, we call for a cultural shift among conservation practitioners, consultants and others, to embrace the idea that documenting and acknowledging knowledge gaps and uncertainties is just as important as compiling data and taking known information into account. We also propose a series of concrete reforms to address these risks. In particular, we point the need for procedural improvements in the process through which conservation databases are elaborated and we suggest introducing differentiated data sharing policies for conservation databases.
Stakeholder cooperation can be vital in managing conservation conflicts. Laboratory experiments show cooperation is less likely in the presence of uncertainty. Much less is known about how stakeholders in real‐life conservation conflicts respond to different types of uncertainty. We tested the effects of different sources of uncertainty on cooperative behaviour using a framed field experiment and interviews. The experiment compared a baseline scenario of perfect certainty with scenarios including either: i) scientific uncertainty about the effectiveness of a conflict‐reduction intervention; ii) administrative uncertainty about intervention funding; or iii) political uncertainty about the extent of community support. We applied these scenarios to a conservation conflict in the Outer Hebrides, Scotland, involving the management of geese to simultaneously meet both conservation and farming objectives. We asked 149 crofters (small scale farmers) if they would commit to cooperate with others by helping fund a goose management plan given the three sources of uncertainty. On average, intention to cooperate was highest (99%) in scenarios without uncertainty, and lowest under administrative uncertainty (77%). Scientific uncertainty and political uncertainty both had less of an effect, with over 95% of crofters predicted to be willing to cooperate in these scenarios. Crofters who indicated concern for other crofters suffering the impact of geese were more likely to cooperate. The longer an individual had been a crofter, the less likely they were to cooperate. Synthesis and applications. Crofters’ intention to cooperate is high but lessened by uncertainty, especially over the commitment from other stakeholders such as government, to cooperate on goose management. Existing cooperation on goose management may be at risk if uncertainty isn't reduced outright or commitments between parties are not strengthened. This has wide applicability, supporting the need for researchers and government advisers to: i) determine how uncertainty will impact intention of stakeholders to cooperate; and ii) take steps (such as uncertainty reduction, communication, or acceptance) to reduce the negative impact of uncertainty on cooperation. This article is protected by copyright. All rights reserved.
There is a growing recognition that scientific and social conflict pervades invasive species management, but there is a need for empirical work that can help better understand these conflicts and how they can be addressed. We examined the tensions and conflicts facing invasive Asian carp management in Minnesota by conducting 16 in-depth interviews with state and federal agency officials, academics, and stakeholders. Interviewees discussed the tensions and conflicts they saw impacting management, their implications, and what could be done to address them. We found three key areas of conflict and tension in Asian carp management: 1) scientific uncertainty concerning the impacts of Asian carp and the efficacy and non-target effects of possible management actions; 2) social uncertainty concerning both the lack of societal agreement on how to respond to Asian carp and the need to avoid acting from apathy and/or fear; and 3) the desired approach to research and management – whether it is informed by “political need” or “biological reality”. Our study of these tensions and conflicts reveals their importance to Asian carp management and to invasive species management, more broadly. We conclude with a discussion of possible ways to address these areas of tension and conflict, including the potential of deliberative, participatory approaches to risk-related decision making and the need to productively engage with apathy and fear.
Full-text available
The protection of biodiversity is a complex societal, political and ultimately practical imperative of current global society. The imperative builds upon scientific knowledge on human dependence on the life-support systems of the Earth. This paper aims at introducing main types of uncertainty inherent in biodiversity science, policy and management, as an introduction to a companion paper summarizing practical experiences of scientists and scholars (Haila et al. 2014). Uncertainty is a cluster concept: the actual nature of uncertainty is inherently context-bound. We use semantic space as a conceptual device to identify key dimensions of uncertainty in the context of biodiversity protection; these relate to [i] data; [ii] proxies; [iii] concepts; [iv] policy and management; and [v] normative goals. Semantic space offers an analytic perspective for drawing critical distinctions between types of uncertainty, identifying fruitful resonances that help to cope with the uncertainties, and building up collaboration between different specialists to support mutual social learning.
Full-text available
The California Condor (Gymnogyps californianus; hereafter “condor”; Fig. 1) has long been symbolic of avian conservation in the United States. Its large size, inquisitiveness, and association with remote places make it highly charismatic, and its decline to the brink of extinction aroused a continuing public interest in its plight. By 1982, only 22 individuals remained of this species whose range once encompassed much of North America. The last wild bird was trapped and brought into captivity in 1987, which rendered the species extinct in the wild (Snyder and Snyder 1989). In the 1980s, some questioned whether viable populations could ever again exist in the natural environment, and whether limited conservation funds should be expended on what they viewed as a hopeless cause (Pitelka 1981). Nevertheless, since that low point, a captive-breeding and release program has increased the total population by an order of magnitude, and condors fly free again in California, Arizona, Utah, and Baja California, Mexico (Fig. 2). At this writing (summer 2009), more than 350 condors exist, 180 of which are in the wild (J. Grantham pers. comm.). The free-living birds face severe challenges, however, and receive constant human assistance. The intensive management applied to the free-living populations, as well as the ongoing monitoring and captive-breeding programs, are tremendously expensive and become more so as the population grows. Thus, the program has reached a crossroads, caught between the financial and logistical pressures required to maintain an increasing number of condors in the wild and the environmental problems that preclude establishment of naturally sustainable, free-ranging populations. Recognizing this dilemma, in November 2006, Audubon California requested that the American Ornithologists’ Union (AOU) convene an independent panel to evaluate the California Condor Recovery Program. The National Audubon Society (NAS) and the AOU have a long history of interest and involvement in condor recovery. The NAS helped fund Carl Koford’s pioneering studies of condor biology in the 1940s (Koford 1953). A previous panel jointly appointed by the NAS and AOU examined the plight of the condor in the late 1970s, and their report (Ricklefs 1978) laid the groundwork for the current conservation program. The NAS was a full partner with the U.S. Fish and Wildlife Service (USFWS) in the early days of the program, from 1980 through 1988. Ricklefs (1978) recommended that the program “be reviewed periodically by an impartial panel of scientists,” and this was done annually by an AOU committee for several years after the release of the report, but the condor program has not been formally and thoroughly reviewed since the mid-1980s. Audubon California believed that the recovery program was operating with a recovery plan (USFWS 1996) widely acknowledged to be outdated, and that issues that were impeding progress toward recovery needed outside evaluation in order for the USFWS, which administers the program, and other policy makers to make the best decisions about the direction of the program (G. Chisholm pers. comm.). Such an evaluation would also help funding organizations better invest in the program.
Uncertainty and rationality are closely related features of human decision making. Many practical decisions are traditionally reconstructed as attempts to frame uncertain outcomes within the domain of rule-constrained reasoning, and much established literature explores the manifold ramifications of rationality when choice among uncertain outcomes has to be made (as with choice criteria associated with maximization of expected utility). However, this overall picture is changing rapidly as a result of recent work in a variety of related disciplines. Research in cognitive science, artificial intelligence, philosophy and economics has called attention to the open-ended structure of rationality. This point of view stresses the active role of the human mind in developing conceptual possibilities relevant to problem solving under contingent sets of constraints. Rationality is conceived of as a pragmatic attitude that is nonetheless conducive to rigorous investigation of decision making. In particular, conditions for rational decision are moved back to its cognitive frame (the collection of concepts and predicates that makes any given representation of problem space possible), and the cognitive frame is associated with the context-dependent utilization of cognitive abilities. This view of rationality distances itself from received conceptions of deductive and inductive inference as it is related to a situational conception of reasoning.
The primary purpose of this paper is to highlight issues that are crucial when costing climatic impacts, particularly when the possibility is allowed for non-linearities, surprises, and irreversible events. The assumptions made when carrying out such exercises largely explain why different authors obtain different policy conclusions. Uncertainties become more significant when projections of climatic impacts are considered. There is uncertainty about how the biosphere will respond to human-induced climate change. However, it is clear that life, biogeochemical cycles, and climate are linked components of a highly interactive system. Non-linearities and the likelihood of rapid, unanticipated events (surprises) require that costing methods use a wide range of estimates for key parameters or structural formulations, and that, when possible, results be cast in probabilistic terms rather than central tendencies since the latter mask the policy-relevant wide range of potential results such a diversity of approaches implies. Costs need also to be presented in more numeraires than just monetary ones. This paper recommends that key for authors of scientific assessments is transparency of assumptions and the use of as wide a range of eventualities (and their attendant probabilities) as possible to help decision makers become aware of the arguments for flexibility of policy options.
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