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Feature Issue: Some Thoughts on Resilience
What do you mean, ‘resilient’?
Dave Hodgson, Jenni L. McDonald, and David J. Hosken
Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Penryn, Cornwall TR10 9FE, UK
In a world beset by environmental disasters and anthropogenic disturbances, resilience might be the key to the
persistence of natural systems. Yet, the ‘measurement’
of resilience is hampered by the multiple (and often
conflicting) processes that yield the response of systems
to insult. We recommend the simultaneous consideration of ‘resistance’ and ‘recovery’ as measurable components that together represent resilience.
The definition and measurement of resilience
Resilience is the capacity of a system to persist or maintain
function in the face of exogenous disturbance [1,2]. Resilience resonates with the modern view that natural systems
are pushed, pulled, and sometimes battered by disturbances that vary in structure, amplitude, and frequency
[3]. Owing to the rise of this non-equilibrium paradigm in
biology, resilience has become the focus of a growing
proportion of ecological and evolutionary research, and
is popular at the interface among conservation, engineering, and the social sciences [2,4]. A search of the ISI Web of
Knowledge database reveals that the prevalence of ‘resilience’ as a keyword in peer-reviewed research papers has
risen from 0% in the early 1970s to over 1% of all papers in
the ‘Ecology’ scientific category. ‘Evolution’ lags behind at
0.2%, but in both categories the study of resilience is rising
fast. Unfortunately, with popularity comes confusion,
which hampers interdisciplinarity. Empiricists hope to
measure what they study, and this has yielded a profusion
of metrics and indices that are all called ‘resilience’
[5,6]. We argue that resilience cannot be captured in a
single metric. However, the plural features that make
some systems more resilient than others can be measured
and have well-established names.
The confusion of resilience
Holling’s [1] classic exposition defined resilience to be the
ability of a system to resist change in the face of disturbance,
and stability to be the ability of a system to return to a stable
state following disturbance. A contradictory view [5,7] is
that resilience is the process of recovery following disturbance, not the ability to resist disturbance in the first place.
Our own survey of recently published empirical studies
suggests that resilience is commonly used to represent
Corresponding author: Hodgson, D. ([email protected]).
Keywords: elasticity; latitude; precariousness; recovery; resilience; resistance.
0169-5347/
ß 2015 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tree.2015.06.010
resistance, or recovery, or both. Resilience has come to mean
so many different things that it must assume its broadest
definition.
The components of resilience
When exposed to disturbance, systems vary in their ‘resistance’ and in their ‘recovery’. ‘Resistance’ describes the
instantaneous impact of exogenous disturbance on system
state, while ‘recovery’ captures the endogenous processes
that pull the disturbed system back towards an equilibrium. The rate at which the disturbed system recovers is
called ‘elasticity’ [5]. The duration of the journey from
disturbed to stable state is ‘return time’. If alternative
stable states exist, then ‘latitude’ describes the distance
to a tipping point, past which the system will move, at a
different rate, to a new stable state [2]. ‘Precariousness’
measures the distance from the disturbed state to the
nearest tipping point [2].
Resilience of what, and to what?
We can measure ‘resistance’, ‘elasticity’, ‘return time’,
‘precariousness’, and ‘latitude’, as long as we understand
the dynamics of the system. One might measure the level of
disease exposure required to cause infection (‘resistance’);
the rate of evolution of an exaggerated display trait when
sexual selection is relaxed (‘elasticity’) [8]; how long it takes
for primary forest to recover from hurricane damage (‘return time’); the magnitude of cull that flips a population
beyond the Allee threshold [9] towards certain extinction
(‘latitude’); or how close a disturbed coral reef is to tipping
into an algae-dominated system (‘precariousness’) [10]. In
any study of resilience it is crucial to (i) define and model
the system; (ii) define and measure the system state that is
at risk; (iii) define the stable states to which the system
might recover; and (iv) define the magnitude, frequency,
and structure of disturbance [11].
If the metrics of resilience can be measured, then we can
compare resilience among systems. For a given exogenous
disturbance, we might find that one system is more resilient because it recovers with high ‘elasticity’ and therefore
low ‘return time’, while another is more resilient because it
is more ‘resistant’.
The representation of resilience
It is common to represent the broad concept of resilience
using a rolling ball analogy (Figure 1A) [2,12]. The state of
the system is a flat base, while the vertical axis describes
the potential of the system (ball) to move from one state to
another [12]. If the slope is steep, then the ball appears
Trends in Ecology & Evolution, September 2015, Vol. 30, No. 9
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(B)
Increase
0
elas
prec
elas
State−recovery phase space
Decline
Classical resilience landscape
Rate of change in system state
Potential energy
(A)
Trends in Ecology & Evolution September 2015, Vol. 30, No. 9
prec
lat
lat
Stable =m
Unstable =m
Stable =m
Disturbed system state
Stable
Unstable
Stable
Disturbed sytem state
TRENDS in Ecology & Evolution
Figure 1. Classical representations of resilience landscapes. (A) Resilience is often plotted conceptually using a rolling ball analogy: each ball represents a disturbed system,
and it rolls downhill owing to ‘gravity’. If not further disturbed, the ball settles into troughs, representing stable states of the x-axis. Tipping points, representing unstable
equilibrium states, are the peaks of the landscape. Each stable equilibrium (=m) has ‘latitude’ (lat: distance in state to the nearest tipping point), and each disturbed state has
‘precariousness’ (prec: distance in state to the nearest tipping point), and ‘elasticity’ (elas: rate of return to the local attractor equilibrium). (B) A more mathematical
representation measures the rate of change in state of any disturbed system, yielding a recovery phase space. All equilibria sit on the line y = 0, but only some are stable
attractors. Disturbed systems recover to their local stable attractor, and the axes allow simple measurement of ‘latitude’, ‘precariousness’, and ‘elasticity’. However, neither
representation describes the ‘resistance’ of the system to disturbance.
simultaneously resistant to disturbance and able, following disturbance, to recover quickly to its trough. We argue
that the analogy helps to conceptualise resilience, but is
not useful in application, for two reasons. First, while the
resilience ‘landscape’ that the ball rolls across has a clearly
defined base, the unit of measurement of the y-axis is much
less clear. The only label we have found, ‘potential energy’
[12], makes some sense, but we defy any biologist to
measure the ‘potential energy’ of a complex natural system. Another representation, familiar to engineers and
ecological modellers, is a phase plot showing the rate of
change of a system when disturbed away from an equilibrium (Figure 1B). Here, there is no ambiguity on the y-axis,
although it requires deep understanding of the system, and
its equilibria, to be modelled and parameterised.
Second, the use of a single resilience landscape suggests
that the post-disturbance, endogenous dynamic of a system
will mirror the instantaneous response to exogenous
events. But, just because a system recovers quickly from
disturbance does not mean it is resistant to disturbance in
the first place. For example, elephants persist at low
densities and have low reproductive potential. Their life
history makes them resistant to disturbances but, if disturbed, their populations recover very slowly, suggesting a
direct trade-off between ‘resistance’ and ‘elasticity’. The
‘latitude’ and ‘elasticity’ components of resilience could also
trade off against each other: if a system has large ‘latitude’,
then there is little risk of tipping into alternative stable
states, hence ‘elasticity’ can be sacrificed during evolution
504
of the system. For example, it requires a substantial
reduction in grazing to tip coral reefs into algal-dominated
systems [10], but coral reefs have slow rates of recovery.
There might also exist trade-offs between ‘resistance’ and
‘latitude’: the risk of tipping into an unfavourable state,
imposed by small ‘latitude’, might be so great that the
only systems that persist are those that are sufficiently
resistant to disturbance.
We therefore suggest that the resilience of a system
requires a new representation: first, of ‘resistance’; second,
of ‘recovery’. The change in state caused by a disturbance
will tend to rise monotonically with the magnitude of
disturbance (Figure 2B), irrespective of the existence
of alternative stable states. The instantaneous impact of
disturbance then maps onto the ‘recovery’ landscape
(Figure 2C): recovery is only possible if the system resists
disturbance within the limits set by ‘latitude’. If the system
does recover to its pre-disturbance state, then its ‘return
time’ will also increase with increasing impacts of disturbance on system state (Figure 2C). This yields a bivariate
resilience space (Figure 2D) in which systems and disturbances can be represented and compared. When systems
are disturbed beyond tipping points, ‘return time’ will cross
a natural breakpoint, but this can still be measured and
represented graphically (Figure 2C,D).
The future of resilience
Our aim here is to encourage better theoretical and empirical work on the topic of resilience. This requires recognition
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Trends in Ecology & Evolution September 2015, Vol. 30, No. 9
(B)
Classical resilience landscape
Resistance to disturbance
Change in state
Potenal energy
(A)
Disturbed system state
(C)
Magnitude of disturbance
(D)
Recovery from disturbance
Bivariate measure of resilience
X
X
X
Return me
Return me
X X
X
Distance from original equilibrium
Change in state
TRENDS in Ecology & Evolution
Figure 2. A new representation of resilience. (A) Classical resilience landscapes for three different natural systems (red, black, and blue). Each system has two stable
equilibria but differs in latitude and, for any given disturbed state, in elasticity, return time, and precariousness. We argue that the y-axis (‘potential energy’) is too vague to
be measurable in natural systems, and that this analogy does not represent the resistance of systems to exogenous disturbance. Instead we recommend the combined
representation of ‘resistance’ and ‘recovery’. In (B) we show how the states of the three different systems might respond to increasing magnitudes of disturbance: systems
are less resistant as they move up the y-axis. In (C) we show how these three different systems recover from changes to their state: systems take longer to recover as they
move up the y-axis. In each case, ‘X’ marks a tipping point, past which the system ‘recovers’, with new return time, to a different stable state. Vertical dashed lines tie the
tipping points to the x-axis. In (D) we map the resistance in panel (B) onto the recovery in panel (C), to show the bivariate resilience of the three states exposed to five
different magnitudes of disturbance (point sizes increase with increasing magnitude). For the second-largest disturbance magnitude, all three states occupy the same point
in resilience space, but the red system is the most precarious. For smaller magnitudes of disturbance, the red system displays poor resistance but fast recovery. The blue
system shows high resistance but slow recovery. The blue state has greatest latitude, but the precariousness of each disturbed system depends on the magnitude of the
disturbance. For the largest magnitude of disturbance, the red and blue systems both cross tipping points and enter new recovery phases while the black system, with high
resistance and high latitude, still recovers to the original equilibrium but with long return time.
of the multiple components of resilience and a reversal of
the current proliferation of metrics. The established measures of ‘resistance’ and ‘recovery’ should be embraced,
standardised, measured, and compared across systems
and fields of research. We recommend the adoption of bivariate measurement and analysis of ‘change in state’ and
‘return time’. This approach will help to determine which
natural systems are more resilient than others, but will
force us to consider whether resilience is achieved via
resistance or recovery. It will help to guide the management
of natural systems that provide humans with ecological
services: human values will determine whether we ‘want’
a system that resists disturbance, recovers quickly, or avoids
tipping points. Pervasive trade-offs might prevent achievement of all three.
Acknowledgements
The authors thank Katrina Brown, Alden Griffith, and four anonymous
reviewers for valuable insight into resilience. D.H., J.L.M., and D.J.H. are
supported by the Natural Environment Research Council.
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Seville, Spain
increasingly attracting attention [1–3]. Biodiversity is often thought to be a key feature underpinning the resilience of
ecosystems [4–7]. Importantly, one of
the primary focuses of biodiversity studies
has been to elucidate the mechanisms by
which biodiversity stabilizes ecosystem
functions under environmental changes
(disturbance) [4,5,8]. However, it is still
not feasible to incorporate knowledge of
diversity–stability relationships into the
real-world situations of ecosystem management [9] and thus the application of
this research remains severely limited.
The multiple meanings and measures of
resilience
currently in use make it difficult
Centre for Ecology and Conservation, College of Life
to determine whether and how biodiverand Environmental Sciences, University of Exeter,
Cornwall Campus, Penryn TR10 9EZ, UK
sity, or other system features, influence
52
Faculty of Arts and Sciences, Department of
resilience. According to the resilience theEnvironmental and Health Studies, Telemark University
ory of Holling [10], changes are ubiquitous
College, N-3800 Bø i Telemark, Norway
53
These authors contributed equally
in ecosystems and the resilience of a system
determines its capacity for absorbing
*Correspondence: [email protected] (J.A. Mills).
changes to maintain fundamental controls
http://dx.doi.org/10.1016/j.tree.2015.12.004
on function and structure [2,3]. An imporReferences
tant feature of this concept is the empha1. Mills, J.A. et al. (2015) Archiving primary data: solutions for
sis on possible alternative system
long-term studies. Trends Ecol. Evol. 30, 581–589
that are associated with
2. Moore, A.J. et al. (2010) The need for archiving data in properties
evolutionary biology. J. Evol. Biol. 23, 659–660
renewal and reorganization after distur3. Katzner, T. (2015) Do open access data policies inhibit
bance [2,3]. Although this perception of
innovation? Bioscience 65, 1037–1038
4. Fenichel, E.P. and Skelly, D.K. (2015) Why should data ‘ecological resilience’ [3] is widely used,
be free; don’t you get what you pay for? Bioscience 65,
many policy and management docu541–542
5. Whitlock, M.C. et al. (2016) A balanced data archiving policy ments, as well as academic literature,
for long-term studies. Trends Ecol. Evol. 31, 84–85
use another definition of resilience, ‘engi6. Roche, D.G. et al. (2014) Troubleshooting public data archivneering resilience’ [3], which is defined as
ing: suggestions to increase participation. PLoS Biol. 12,
the time taken to return to close to the pree1001779
disturbance state [11]. A recent synthesis
by Nimmo et al. [12] proposed that engiLetter
neering resilience has much to offer conservation science and practice. Other
syntheses by Oliver et al. [7] and Connell
and Ghedini [13] have similarly identified
numerous management implications
based on a focus on resistance and recovery (i.e., engineering resilience). Some of
these syntheses and others have concluded that biodiversity is crucial for resil1,
ience,
which is not necessarily consistent
Akira S. Mori *
with empirical evidence. A global syntheThe concept of ‘resilience’, which helps sis of plant diversity experiments found
describe system responses to change, is that biodiversity did not consistently
51
Resilience in the
Studies of
Biodiversity–
Ecosystem
Functioning
Trends in Ecology & Evolution, February 2016, Vol. 31, No. 2
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Box 1. Definition of Resilience in Ecology
‘Resilience’ has two common definitions in ecology [1–3]. Holling [10] introduced the concept of resilience, defining ‘ecological resilience’ as a system feature that
determines its capacity for absorbing changes while recognizing possible alternative states [3]. Pimm [13] introduced an alternative meaning, defining resilience as the
time needed for the system to return to close to its pre-disturbance state, now referred to as ‘engineering resilience’ [3]. Holling's resilience has precedence in ecology, but
Pimm's resilience is more consistent with the definition of resilience in other disciplines, including engineering.
As seen in the framework of the Resilience Alliance (http://www.resalliance.org/), there is a large body of literature on ecological resilience [10]. In parallel, engineering
resilience is extensively discussed in the biodiversity–ecosystem functioning literature in the context of stability of function in response to environmental changes [4,5].
Biodiversity–ecosystem functioning studies have begun to disentangle resistance and (engineering) resilience components of ecosystem stability (Figure I). However,
there remains little connection between diversity–stability studies and ecological resilience (Figure I). Bridging concepts of ecosystem stability and ecological resilience
requires careful consideration of the resilience concepts considered and their relationship to those in other areas of the ecological literature.
More interests in ...[1-3]
(A)
Ecological resilience (resilience) [10]
Management
The concept of resilience [1-3]
Engineering resilience (recovery) [11]
Synonymous?
When applied to ...
Ecosystem funconing
A main focus on ...
?
This flow may be an important
but not a sufficient condion
for resilience-based management.[2,3]
Rapid recovery
“Stability” [5,8]
Which can be realized by ...[4]
and/or
Resistance
?
A possible link with ecological resilience? [4]
(B)
Environmental change drivers
Biodiversity declines
(b)
(a)
(c)
WWF
(d)
(e)
(f)
(g)
Changes in the stability of funcon
[Changes in the mean (μ) / variance (σ)]
WWF
Due to changes in the ability to recover or resist?
Figure I. Resilience for Management. (A) A conceptual flow diagram surrounding the issue of resilience. Reference numbers to support each notion are indicated
[1–5,8,10,11]. (B) Biodiversity is an important mediator between environmental change drivers and the stability of ecosystem functioning. Examples of environmental change
drivers include: climate extremes such as (a) drought-induced fires in Utah, USA, (b) stand-replacing fires in California, USA, and (c) heavy rainfall and the associated floods in
India; biotic disturbance such as (d) insect outbreak in British Columbia, Canada and (e) over-browsing by mammalian herbivory in northern Japan; and anthropogenic
disturbance such as (f) over-grazing by livestock and (g) fertilization. These drivers often lead to declines in biodiversity, which in turn affect the stability of ecosystem functioning
through changes in the mean and variance of the function. (c) and (g) by World Wildlife Fund under Creative Commons license; other photographs by A.S.M.
influence the engineering resilience of ecosystem functioning (productivity) after climate extremes [4]. To implement the
results from these or other studies in management projects, it is necessary to disentangle the many meanings and
measures of resilience and related stability
concepts.
In the rich body of biodiversity–ecosystem
functioning studies, the roles of diversity in
stabilizing the focal function (e.g., productivity) are often cited as evidence that
88
biodiversity increases resilience. Recent
studies [4,5] have quantified how ecosystem stability, quantified as the invariability
in ecosystem function across years
(1/coefficient of variation), can be realized
through both resistance and recovery of
function during and after disturbances.
The recovery component of invariability
may be thus regarded as a measure of
engineering resilience (Box 1). However,
measures of engineering resilience have
differed even among papers that have a
common focus on the stability of
Trends in Ecology & Evolution, February 2016, Vol. 31, No. 2
ecosystem functioning [4,5]. Such discrepancies represent both progress in
the development of quantitative measures
and inconsistency in the terminology used
in ecology. Although challenging, it will be
fruitful to bridge concepts of ecosystem
stability and ecological resilience (Box 1).
Resilience and stability are complementary properties that describe system
dynamics [10,11]. Difficulty, however,
exists around these two terms, as they
are supported by different management
objectives and strategies. For example, a
recent synthesis by Oliver et al. [7]
described the resistance and recovery
aspects of biodiversity to support ecosystem functions. Their main focus on ecosystem functions, summarized as ‘while
species composition is typically the target
of conservation, it is ecosystem functions,
rather than species composition per se,
that need to be resilient, if ecosystem services are to be maintained’ [7], is intriguing. However, the synthesis may be at
odds with the specific description of resilience for an individual function such as
productivity or pollination. To avoid confusion, resilience in this case should be
explicitly termed as recovery or defined
as the analogy of engineering resilience
[1]. Despite progress in theoretical understanding of how and when the relative
contributions of resistance and engineering resilience vary in a changing environment (e.g., [14]), a narrow focus on
resilience may be insufficient to translate
such knowledge into practice. More specifically, because the term ‘resilience’ is
increasingly framed within the concept
of ecological resilience for management
applications [1], a whole-system perspective with a simultaneous focus on alternative stable states, system changeability,
and robustness [2,3] deserves to gain further attention over the perspective based
on recovery and resistance of individual
functions. Resilience-based management
does not typically seek to increase the rate
of return to an original state, which often
implicitly assumes the existence of a single
equilibrium, but instead recognizes that
many natural systems could have multiple
attractors [2,3]. Thus, although stability is
often beneficial for the reliable provision of
ecosystem functioning and services,
which contribute to some aspects of resilience that benefit society (e.g., crop production), it is not necessarily a sufficient
condition for achieving the objectives of
resilience-based management (Box 1).
Interestingly, the stabilizing effects of
biodiversity on ecosystem functioning
are often realized through variability,
asynchrony, and compensation among
different species [8]. Such mechanisms
underpinning ecosystem stability illustrate
the importance of instability and changeability in community dynamics. In other
words, stability itself is often (but not
always) supported by changes, which
are inherent in ecosystems.
Although the modern science of ecology
acknowledges the nonequilibrium nature
of ecosystems [3], the existence of single
equilibria is still often assumed in the literature, with critical implications for conservation and restoration. This has largely helped
management, but at the same time has
created some confusion. As discussed by
Nimmo et al. [12], a specific focus on resistance and engineering resilience (recovery)
may be widely feasible for some stakeholders. However, caution is necessary, as this
framework is relevant only where change
from the basin of attraction is unideal and
thus of interest [12]. Here, rather than continue to make progress in parallel, I would
encourage unification of the ecological and
engineering resilience frameworks [6,9].
Furthermore, these efforts should also be
integrated with the growing body of biodiversity–ecosystem functioning studies.
Such a synthesis could inform and benefit
society by supporting the vital functionality
of ecosystems that contributes to the longterm maintenance of ecological resilience.
Acknowledgments
A.S.M. developed the ideas for resilience under the
4. Isbell, F. et al. (2015) Biodiversity increases the resistance of
ecosystem productivity to climate extremes. Nature 526,
574–577
5. Wright, A.J. et al. (2015) Flooding disturbances increase
resource availability and productivity but reduce stability in
diverse plant communities. Nat. Commun. 6, 6092
6. Mori, A.S. et al. (2013) Response diversity determines the
resilience of ecosystems to environmental change. Biol.
Rev. 88, 349–364
7. Oliver, T.H. et al. (2015) Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684
8. Tilman, D. (1996) Biodiversity: population versus ecosystem stability. Ecology 77, 350–363
9. Baskett, M.L. et al. (2014) Response diversity can increase
ecological resilience to disturbance in coral reefs. Am. Nat.
184, E16–E31
10. Holling, C.S. (1973) Resilience and stability of ecological
systems. Annu. Rev. Ecol. Syst. 4, 1–23
11. Pimm, S.L. (1984) The complexity and stability of ecosystems. Nature 307, 321–326
12. Nimmo, D.G. et al. (2015) Vive la résistance: reviving resistance for 21st century conservation. Trends Ecol. Evol. 30,
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2646–2656
Letter
A Synthesis is
Emerging between
Biodiversity–
Ecosystem Function
and Ecological
Resilience Research:
Reply to Mori
support of the Organisation for Economic Co-operation and Development (OECD) Co-operative Research
Programme. He thanks Forest Isbell for critical inputs
on this commentary.
1
Graduate School of Environment and Information
Sciences, Yokohama National University, Yokohama,
Kanagawa, Japan
*Correspondence: [email protected] (A.S. Mori).
http://dx.doi.org/10.1016/j.tree.2015.12.010
References
1. Standish, R.J. et al. (2014) Resilience in ecology: abstraction,
distraction, or where the action is? Biol. Conserv. 177, 43–51
2. Chapin, F.S.I. et al. (2009) Principles of Ecosystem Stewardship. Resilience-Based Natural Resource Management
in a Changing World, Springer
3. Gunderson, L.H. et al. (2009) Foundation of Ecological
Resilience, Island Press
Tom H. Oliver,1,2,*
Matthew S. Heard,2
Nick J.B. Isaac,2
David B. Roy,2
Deborah Procter,3
Felix Eigenbrod,4
Rob Freckleton,5
Andy Hector,6
C. David L. Orme,7
Owen L. Petchey,8
Vânia Proença,9
David Raffaelli,10
K. Blake Suttle,11
Trends in Ecology & Evolution, February 2016, Vol. 31, No. 2
89
ARTICLE
Received 25 Feb 2015 | Accepted 3 Nov 2015 | Published 8 Dec 2015
DOI: 10.1038/ncomms10122
OPEN
Declining resilience of ecosystem functions under
biodiversity loss
Tom H. Oliver1,2, Nick J.B Isaac1, Tom A. August1, Ben A. Woodcock1, David B. Roy1 & James M. Bullock1
The composition of species communities is changing rapidly through drivers such as habitat
loss and climate change, with potentially serious consequences for the resilience of
ecosystem functions on which humans depend. To assess such changes in resilience,
we analyse trends in the frequency of species in Great Britain that provide key ecosystem
functions—specifically decomposition, carbon sequestration, pollination, pest control and
cultural values. For 4,424 species over four decades, there have been significant net declines
among animal species that provide pollination, pest control and cultural values. Groups
providing decomposition and carbon sequestration remain relatively stable, as fewer species
are in decline and these are offset by large numbers of new arrivals into Great Britain. While
there is general concern about degradation of a wide range of ecosystem functions, our
results suggest actions should focus on particular functions for which there is evidence of
substantial erosion of their resilience.
1 NERC Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK. 2 School of Biological
Sciences, University of Reading, Whiteknights, PO Box 217, Reading, Berkshire RG6 6AH, UK. Correspondence and requests for materials should be addressed
to T.H.O. (email: [email protected]).
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iological species are essential for the provision of ecosystem
services ranging from food production (including
direct food provision and the underpinning functions of
pollination, pest control and decomposition), climate regulation
(carbon sequestration) to intrinsic cultural values1. More
biodiverse systems, in particular those with higher species
richness, have often been found to provide higher levels of
ecosystem function under controlled experimental conditions2,3.
Perhaps more importantly, and our focus here, is the additional
role of biodiversity in maintaining ecosystem function flows in
the longer term under environmental perturbations3,4, that is,
promoting resilience in function provision5.
Although there is clear evidence of declines in biodiversity
(taxonomic, phylogenetic and functional) at the global level6, the
impact on the resilience of ecosystem functions on which humans
depend is not known. Understanding which functions are more
or less at risk is important for prioritizing conservation and
restoration efforts. In theory, long-term trends in species
occurrence can be linked to temporal change in the resilience
of ecosystem functions, in order to identify large-scale patterns
and help inform planning of national and international responses.
However, progress has been hampered through: (a) a lack of data
and robust methodology to calculate trends in the frequency of
occurrence of functionally important species from opportunistic
biological records (the most common source of ecological data for
species) and (b) a lack of information on ‘effect’ traits, which are
attributes that determine the contributions of species’ individuals
to ecosystem function7.
To assess trends in the occurrence of species, data availability is
often a limiting factor, with previous attempts being restricted
to a subset of species groups for which standardized monitoring
data are available—in Great Britain, this comprises a subset of
mammals, birds, butterflies and macro-moths. In this study, for
these four species groups with standardized monitoring schemes
in place (395 species), we calculated trends in individual species’
abundances over the last four decades. For an additional 4,029
species from 18 national recording schemes, we applied new
analytical methods to calculate trends in frequency of occurrence
from nonstandardized occurrence records, accounting for
spatiotemporal patterns in recorder effort. We used binomial
mixed effects models8 to estimate trends in frequency of
occurrence across 1 km grid cells for each species in Great
Britain between 1970 and 2009. This approach has been shown to
be robust to spatiotemporal variation in recorder effort in a
simulation study comparing different methods8. It produces
trends in species’ occurrence that reflect national and local
abundance trends, where data are available for comparison. For
each species’ model, we tested the null hypothesis of no trend in
occurrence over time, at three different thresholds of type 1 error:
0.05, 0.01 and 0.001.
Next, we grouped all species by the primary ecosystem
functions that they underpin, namely: decomposition, carbon
sequestration, pollination, pest control and cultural values. Our
assumption is that changes in the national frequency of species in
each functional group can provide an indication of trends in
resilience of those functions. Our argument is that when more
species are present in a functional group there is a ‘portfolio’
effect whereby the overall abundance of individuals providing the
function is more constant owing to a statistical averaging effect4,9,
meaning levels of function provision are less likely to fall below
some minimum acceptable threshold5,10. Furthermore, there is
often negative spatial and/or temporal covariance (asynchrony)
between species’ population sizes, driven by differing responses to
environmental change or competition4,9,10. These mechanisms
lead to an ‘insurance’ effect of biodiversity (also sometimes called
‘functional redundancy’) whereby higher species richness within a
2
functional group is more likely to maintain ecosystem function
provision under environmental perturbations, that is, it leads to
more resilient ecosystem functions.
Rather than compare absolute numbers of declining species
across functional groups, we assessed the balance of increasing
versus decreasing species using proportion tests and presented
results as log ratios, so that our tests were not biased by
differences in total species numbers or statistical power between
functional groups arising from differences in the mean numbers
of records per species. We repeated the tests at each threshold of
type 1 error to assess sensitivity to the level of statistical
significance for trends.
To put species into functional groups, we consulted taxon
experts and reviewed published literature to classify which higher
taxa are primary or secondary providers of pollination, pest
control, decomposition, carbon sequestration and experiential
cultural values. Although individual species vary in their
functional contributions11,12, our reasoning for allocating
ecosystem function provision at three broad levels (‘primary’,
‘secondary’ or ‘very limited/none’) for higher taxa, as opposed
to species-specific weightings, is twofold. First, functional
contribution measurements are often context specific as the
roles of species can change over space and time4,10,13. In different
environments, species’ relative frequencies vary, affecting their
functional contributions10,14,15, while there are also changes in
the per capita contributions of individual species to function
provision7. For example, the per capita roles of natural enemies
vary depending on which crop pest is dominant and which other
natural enemies are present13,16. Therefore the limited ‘effect
trait’ data that do exist17–19 may not accurately predict ecosystem
functions provided by individual species in new locations or time
periods. A second reason for our species grouping is that
environmental resource managers are tasked with ensuring the
continued provision of ecosystem functions under changing
environments, that is, their resilience5. This includes maintaining
ecosystem functions in the face of the challenges posed by climate
change, habitat degradation, invasive species and pollution.
Because the dominant species providing ecosystem functions
can switch under these perturbations, an assessment of the
resilience of functions must consider all of the species that can
potentially fulfil a function (that is, reflecting the ‘portfolio’
or ‘insurance’ effect of biodiversity2,3), rather than on the
small subset of species that are currently functionally
dominant20,21.
To evaluate the consequences of observed biodiversity change
for the resilience of ecosystem functions, we explored the balance
between increasing and decreasing species within each functional
group. We also calculated the frequency of new species arriving
in Great Britain since 1970, to assess the likelihood that
disproportionate declines of species in a given functional group
might be offset by new arrivals.
Many ecosystem functions are delivered at the local level (and,
therefore, their resilience is determined by regional species pools),
whereas our analysis reflects changes in the frequency of species
in functional groups at the national level. However, there are
strong reasons to believe that these national trends will also reflect
changes in average regional species richness. Each trend in the
frequency of occurrence of a species at the national level is
derived from local changes in occupancy at the 1 km scale, and
thus reflects average changes at this scale. Collating trends by
functional group, there may be 1 km cells which are the exception
and retain higher numbers of species (for example, protected
areas with higher-quality habitats) and, equally, cells which lose
species more rapidly (for example, intensive farmland). However,
on balance, national trends in species richness should also be
reflected by changes in average regional species richness,
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especially as the species richness at larger spatial scales forms the
species pool for smaller scales.
There are likely to be other factors besides regional species
richness which may also affect the resilience of ecosystem
functions. For example, mechanisms at the intraspecific level such
as genetic diversity and at the landscape scale such as habitat
diversity and connectivity may also have a role in mediating
ecosystem function resilience5. Notwithstanding these points,
species richness (and the functional redundancy it confers) is seen
as a key mechanism in promoting resilience of ecosystem
functions2–4,9,10 and the national species trends from 22 higher
taxonomic groups assembled here provide a key source of
evidence to indicate trends in the resilience of ecosystem
functions over the last four decades.
Our results show that the resilience of particular ecosystem
functions, such as pollination, is being eroded more rapidly than
others. This has implications for prioritizing ameliorative actions
to limit the likelihood of deficits in the provision of ecosystem
functions and the consequent negative impacts on human
well-being that these would have.
Results
Trends in species across functional groups. Trends in the
frequency of occurrence of 4,029 species from 1970 to 2009 were
analysed using binomial mixed effects models (see Methods), and
these were combined with data for an additional 395 species for
which abundance trends were already available. In the
Supplementary Information, we provide an example for butterflies showing how trends such as these estimated at the national
level are also reflective of trends at the regional level
(Supplementary Fig. 1).
Our overall analysis showed that species groups providing
decomposition and carbon sequestration functions appear
relatively robust. Only 7 and 10% of species (n ¼ 95 and 2,276)
in these respective groups have shown statistically significant
declines (assessed at Po0.05; proportion test), and there are a
greater number of increasing species (12 and 17% of species have
increased, respectively; Figs 1 and 2; Supplementary Table 2).
However, groups providing pest control or pollination functions
have shown greater declines of 16, and 27% of species,
respectively (n ¼ 1,447 and 720 species). For pest control, these
declines have been largely offset by increases in other species
(17%), but this is less so for pollinating species, of which only 23%
of species are increasing.
For cultural values, considering all species in this functional
grouping, declines were more than offset by increases (14 versus
19%; n ¼ 2,615; Figs 1 and 2; Supplementary Table 2). However,
considering only the animal species that provide cultural values,
the decreases and increases were much more balanced (27 versus
25%, n ¼ 590).
Comparison with newly arriving species. These trends are in
native species or those present within Great Britain before 1970.
A number of species have arrived in Great Britain since 1970,
principally through human introduction, and therefore analysis
of resident biodiversity change only tells half the story. We found
that a large number of species that can provide carbon sequestration, decomposition and cultural values (plant and animals
combined) have arrived in Great Britain since 1970. These
additions to national biodiversity, in combination with the large
number of increasing native species, offer further potential to
offset the relatively small numbers of declining species (Fig. 3).
This suggests that the resilience of carbon sequestration
and decomposition should be relatively robust despite wider
biodiversity decline. In contrast, species groups providing
pollination, pest control or animal-associated cultural values have
had far fewer species arriving relative to the numbers that are in
decline. Therefore, these ecosystem functions appear to be under
particular threat. Results remain qualitatively similar when
‘secondary’ function providers (groups considered to have only a
minor contribution to a function) are also included, except that
decomposers show a much greater proportion of species
increasing in frequency of occurrence (Supplementary Figs 2–4;
Supplementary Table 3).
Carbon sequestration
Cultural values
(animals only)
Cultural values
(plants and animals)
Decomposition
Pest control
Pollination
0.00
0.25
0.50
0.75
1.00
Proportion of species
Highly significant decline (P<0.001)
Nonsignificant increase
Significant decline (P<0.01)
Marginally significant increase (P <0.05)
Marginally significant decline (P <0.05)
Significant increase (P <0.01)
Nonsignificant decline
Highly significant increase (P<0.001)
Figure 1 | Trends in species grouped by ecosystem function. Shown are the proportion of species in different functional groupings showing
significant changes in frequency of occurrence in Great Britain between 1970 and 2010. Total sample sizes for respective rows are as follows: n ¼ 2,276;
590; 2,615; 95; 1,447; 720.
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log ratio of positive to negative trends
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0.40
***
All trends
0.30
***
Marginally significant trends (P<0.05)
Significant trends (P<0.01)
0.20
*
Highly significant trends (P <0.001)
*** ***
0.10
0.00
***
***
***
***
* * *
–0.10
–0.20
Pollination
Pest
control
Cultural
(animals only)
Decomposition
Cultural
(plants and animals)
Carbon
sequestration
Figure 2 | Net balance of species trends across ecosystem functions. Shown are the log ratio of numbers of increasing versus decreasing species in
different functional groups. The different bars indicate different significance levels for individual species trends. A positive ratio indicates more species in a
given functional group are increasing. Differences in the balance of increasing versus decreasing species is assessed using an exact binomial test for all
trends or a proportion test for significant trends. Asterisks indicate significantly different proportions (*Po0.05; ***Po0.001).
Pollination
***
***
Pest control
Decomposition
***
Carbon sequestration
***
Cultural (animal only)
***
Cultural (plant and animal)
***
–500
0
500
1,000
1,500
2,000
Number of species
Figure 3 | Balance of declining species versus new arrivals grouped by ecosystem function. Shown are numbers of species with declines in
frequency of occurrence in Great Britain between 1970 and 2010 (at Po0.05; black bars) versus the number of new species arriving into Great
Britain since 1970 (grey bars). Asterisks indicate significantly different proportions using an exact binomial test (***Po0.001).
Exploring finer resolution functional classifications. One
additional question that may be asked, which is relevant to
informing management of ecosystem functions, is whether trends
in the frequency of occurrence of all species in a broad functional
group (for example, ‘pest control providers’ or ‘pollinators’) also
reflect patterns in a subset of those species that are particularly
important for a specific ‘sub-function’ (for example, pest control
in wheat or pollination of oilseed rape—two of the most widespread crops in Great Britain). To explore this, we analysed
trends for the subsets of carabid beetles that provide pest control
in wheat and of bees that pollinate oilseed rape22 and compared
these with trends for all British carabids and bees, respectively
(Supplementary Figs 5 and 6). In both the cases, we found no
significant difference in the proportion of species showing
positive or negative trends (at Po0.05; proportion test). This
suggests that our results can be broadly indicative of a more
refined classification of crop-visiting species within these broad
functional groups.
Discussion
Our results show significant variation in biodiversity trends
between different broad functional groups. For those functional
groups which have suffered net declines of species, this does not
necessarily mean that there have been reductions in the provision
of these functions; the species that declined may have had
less dominant functional roles under recent environment
conditions20. Alternatively, the ‘functional redundancy’ effect of
biodiversity may have buffered these losses, whereby declines in
functionally important species are replaced by increases in
others4,5,9,10. However, our results do provide evidence for
4
erosion of the resilience of certain functions, increasing the risk of
failure in their delivery under future environmental
perturbations. Loss of species richness in functional groups
means that there is a weaker ‘portfolio’ effect (independent
fluctuations of multiple species leading to a more stable
ecosystem function provision4,9), as well as lower functional
redundancy4,5,9,10. Therefore, the ‘insurance’ capacity provided
by biodiversity is weakened leading to higher risk of ecosystem
function deficits. The extent of this risk is a function of both the
relative number of functionally important species that are
declining in combination with the magnitude and impact of
future environmental perturbations. Some perturbations such as
extreme weather events are predicted to continue to increase in
the future23. We do not address changes in perturbation
frequency here; our results inform on the extent of declines in
functionally important species, but the insurance value provided
by these species is likely to be even higher under the more
frequent and higher magnitude perturbations expected in the
future. Therefore, it is important to conserve biodiversity to
maintain resilient ecosystem functions.
Our results show some declines in species across all functional
groups (Fig. 1, Supplementary Table 2); but for carbon
sequestration and decomposition, the relative proportion of
declining species is small and more than offset by increasing
species and new arrivals into Great Britain. Therefore, these
functions are likely to remain relatively resilient. However, for
pest control and pollination, the arrivals into Great Britain are
not sufficient to offset declines, which suggests an erosion of
resilience of these ecosystem functions. Recent work has shown
that most crop pollination is carried out by just a few species that
are common and not particularly threatened20,21. However,
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Table 1 | Ecosystem functions provided by higher taxonomic groups in Great Britain.
Ecosystem function and service type
Species group
Ants
Bees
Birds
Butterflies
Carabid beetles
Centipedes
Cerambycid
beetles
Craneflies
Dragonflies and
damselflies
Crickets and
earwigs
Harvestmen
Hoverflies
Isopods
Ladybird beetles
Mammals
Millipedes
Mosses and
liverworts
Moths
Soldier beetles and
glowworms
Spiders
Vascular plants
Wasps
Number
of species
analysed
28
196
46
59
304
30
31
Provisioning Provisioning
Regulating
Pollination Pest control Decomposition
Regulating
Carbon
sequestration
Cultural
Experiential
value
1
0
0
0
1*
0
1*
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
1
1*
0
0
0
0
0
0
1*
0
0
0
1970–2009
1970–2009
1970–2009
1970–2009
1984–2009
1970–2009
1970–2009
0
1
0
0
0
0
0
1
1
0
1
1
0
0
1*
0
1
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
A*
2,080
1968–2007
1970–2009
1
1*
0
1
0
0
0
0
1
0
92,788
683,261
38,181
1970–2009
1970–2009
1970–2009
0
0
1*
1
0
1
0
0
0
0
1
0
0
1
0
Total number of
occurrence records
Analysis
year range
3,037
91,352
A*
A*
27,537
1,251
417
1970–2009
1970–2009
1966–2011
1976–2012
1970–2009
1970–2009
1970–2009
0
1
0
1
0
0
1*
1
0
1
0
1
1
0
67
37
1,208
343,996
1970–2009
1970–2009
0
0
10
2,898
1970–2009
19
206
27
30
30
40
251
1,247
207,053
3,781
14,016
A*
2,316
30,397
259
43
502
2,025
184
Taxonomic groups correspond to national recording schemes or societies http://www.brc.ac.uk/recording-schemes). Scores of ‘1’ indicate species in a group are primary ecosystem function providers;
scores of ‘1*’ indicate species in a group are secondary ecosystem function providers. Shown also are the total number of species and occurrence records analysed in each group after controls for
recording effort. A* indicates that abundance rather than occurrence data were analysed.
significantly different environmental conditions, such as those
expected under climate change, can force species outside of their
niches causing large declines24. In these cases, less abundant
species may replace dominant functional roles, but only if
sufficient species richness has been maintained to conserve this
functional ‘redundancy’21. Therefore, maintaining biodiversity is
essential to safeguard pollination of both crops and wildflower
plants.
For the cultural values arising from biodiversity, it is not yet
clear the extent to which plants and animals are complementary
functional groups with respect to their impacts on wellbeing25.
Nor is it known whether the cultural values of native versus
non-native species differ for most people. Cultural values assessed
across plants and animals combined appear to be resilient owing
to limited declines in native species, but the animal-associated
component has suffered greater proportional declines which are
not offset by new arrivals.
In this study, we took the approach of categorizing ecosystem
functions at broad taxonomic levels (Table 1). Notwithstanding
the current lack of knowledge of the relative functional roles of
many species, it is likely that the functional contributions
of species are context specific and vary between locations and
over time4,10,13. This is particularly likely to be the case where
environmental conditions show large temporal or spatial
variation, such as may occur under climate change14,24.
Therefore, allowing for potential shifts in ecological dominance
with environmental change, resource managers are best to
consider the full suite of species which can fulfill a given
functional role to assess the resilience of an ecosystem function5.
Future work could investigate the ideal taxonomic resolution at
which functional redundancy operates for different ecosystem
functions. For example, Kleijn et al.21 show that only a relatively
small subset of bee species are important for the pollination
of European crops under current environmental conditions,
although this primarily reflects species’ relative abundances20,
which could easily change in the future21. Here, we assessed
trends in a much broader range of pollinating species, but we also
repeated the analysis for the subset that are commonly found
pollinating oilseed rape, the most common insect-pollinated crop
in Great Britain. In this case, we did not find marked differences
in the balance of declining versus increasing species between the
two sets of functional categorization, although this is not always
guaranteed to be the case. Of course, as well as the time frame
over which resilience is assessed, the exact function of interest is
also pertinent. An interest in the resilience of pollination of
wildflowers and crops in general would lead to inclusion of a
much broader range of pollinating species than focus on the
resilience of pollination of a specific crop. Likewise, allowing for
flexibility in exact crop variety or species (for example, for
optimum crop choice varying over time owing to climate change
or fluctuations in global markets), one may wish to consider a
broader range of potential functional species.
The ecosystem functions we studied are associated positively
with ecosystem services, such as crop production or climate
regulation. We conducted a sensitivity analysis by excluding
species that may have negative impacts in relation to societal
needs (that is, they provide ‘disservices’, for example, by acting as
crop pests) and might negate or outweigh certain functions we
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considered (for example, excluding species that are pollinators in
the adult stage but crop pests in the larval stage). However, we did
not attempt to assess the resilience of such ecosystem disservices
(that is, ‘unhelpful’ resilience26). The reason for this is that
ecosystem disservices are more likely to be a result of the actions
of individual species, rather than suites of functionally similar
species. Therefore, other metrics besides species richness and
associated functional redundancy are more likely to be relevant in
assessing the resilience of these disservices (for example, genetic
diversity of pest species helping them to develop resistance to
pesticides, or changes in landscape habitat structures helping
disease vectors to spread5). Thus, it should be noted that some of
the species here may have negative impacts in some contexts, and
these may need to be managed on an individual basis.
With regards to the five ecosystem functions that we
considered, our overall results suggest that widespread concerns
that biodiversity declines will compromise ecosystem functions
and the services they underpin1,3 are well founded, but our results
suggest that certain functions are less resilient and at higher risk
than others. Efforts to reverse losses in biodiversity and ecosystem
services, such as ecological restoration, necessarily involve tradeoffs with different actions benefiting different taxa and services27.
Restoration actions can also take decades to become effective28.
By indicating which ecosystem functions are most at risk, this
study provides a possible approach to prioritizing ameliorative
actions. However, continued research into species’ functional
roles and monitoring of their status, especially the development
of monitoring schemes for less well-studied but functionally
important groups, such as soil invertebrates and microorganisms,
is critical for refining risk assessments and guiding sustainable
environmental management.
Methods
Statistics of species’ abundance and occurrence trends. Where standardized
abundance data were available for taxonomic groups we used these (birds: http://
www.bto.org/volunteer-surveys/bbs; butterflies: http://www.ukbms.org/; moths:
http://www.rothamsted.ac.uk/insect-survey/LTTrapSites.html; mammals: http://
jncc.defra.gov.uk/trackingmammals). For butterflies and moths, abundance trends
and associated confidence scores were available from log-linear Poisson models29
fitted to data across all sites for the dates 1976–2012 (ref. 30) and 1968–2007,
respectively31. For moths, these abundance data reflect a subset of all species in
Great Britain. Therefore, we multiplied the number of new moth arrivals identified
from occurrence data by the proportion of British moth species for which
abundance trends were available to ensure a fair comparison. For birds, trends were
derived from fitting a linear regression to annual combined indices from the
Breeding Bird Survey and Common Bird Census Schemes between 1970 and 2009
(ref. 32). For mammals, trends were only available over a 25-year period up to 2007
for 21 species. Precise statistics, beyond qualitative indication of significance at
Po0.05, are not published in the Tracking Mammals Partnership Update33, so any
trends were conservatively allocated as marginally significant at 0.01oPo0.05.
For a further 10-bat species, trends were only available from 10 years before 2007.
Because of the short timeframe relative to the rest of our analysis (1970–2010), any
significant 10-year trends were treated as having low confidence (P40.05) over the
entire timeframe.
For species groups without standardized abundance monitoring schemes,
geo-referenced species occurrence records with sighting dates were obtained from
18 data sets from national recording schemes and societies in Great Britain. For
each species, a binomial linear mixed-effects model was fitted to detection/nondetection data of species in selected 1 km cells across Great Britain, to assess
directional changes over time (increase or decrease) in the probability of species
occurrence per ‘site visit’. This probability of species occurrence relates to both the
number of cells occupied (that is, the distribution extent of a species) and to the
local abundance of species in the average cell (Supplementary Fig. 1). Across many
species, for any given cell, these changes will lead to a net change in the number of
function-providing species present and their abundances, with potential
consequences for resilience of ecosystem functions2–4,9,10.
A ‘site visit’ to each 1 km cell is defined as a unique combination of date, 1 km2
grid cell and taxonomic group (that is, those listed in Table 1). To reduce the
variation in recorder effort, we restricted analyses to well-sampled grid squares
with repeat visits by filtering data. This was done by first removing all visits where
the total number of species recorded was less than the median for the taxonomic
group in question. Second, we excluded all grid cells that had visits in fewer than
3 years between 1970 and 2009 (ref. 34). This determined the total sample size for
6
statistical analysis of each species. A mixed-effects model, with binomial error
structure, was then fitted to the detection/non-detection data of these 1 km cells
with year as the covariate and 1 km grid cell as a random effect34,35. For each
species’ model, we tested the null hypothesis of no trend in occurrence over time, at
three different thresholds of type 1 error: 0.05, 0.01 and 0.001 and collated results
for all the species in each functional group. We assessed the balance of increasing
versus decreasing species using proportion tests and presented results as log ratios,
repeating tests at each threshold of type 1 error.
Categorizing species groups by ecosystem function. For each of the five
ecosystem functions assessed, our sample sizes were determined by the availability
of species data. However, our analyses contained at least one or more of the broad
species groups of key importance as assessed by the UKNEA36 (Supplementary
Table 1). Notwithstanding this, microorganisms and fungi are also important for
the provision of some functions such as decomposition, carbon sequestration and
pest control, but lacked sufficient data for analysis36. Therefore, a caveat in our
analysis is that trends in these unstudied groups could affect the performance of
ecosystem functions. However, we do include a large component of the biodiversity
underpinning these ecosystem functions, and the effects of other taxa such as
microorganisms and fungi are likely to be either complementary or additive to the
species we analyse.
In total, we analysed 4,424 species across 22 groups defined by national
recording schemes. The taxonomic resolution of these varies (Table 1), but such
grouping was preferable to deal with spatiotemporal variation in recorder effort
(records on species are collated and validated within each recording scheme).
In addition, species within a recording scheme often share the same functional
roles (for example, all plants sequester carbon, all bees visit flowers and potentially
carry pollen), although average functional contributions may differ and be context
specific11,13,37. However, for pest control, strictly herbivorous or granivorous
species clearly do not deliver this function and so were excluded from this group.
The following literature resources were used to exclude six ladybird beetle
species (Coleoptera: Coccinellidae)38–43, 21 grasshopper and cricket species
(Orthoptera)44,45, four bird species (Aves)46 and nine mammal species
(Mammalia). In addition, some species may provide ecosystem disservices, such
as crop pests. These species may provide an ecosystem function such as pollination
in the adult stage, but larval stages may be pests, with a net negative effect on
ecosystem services of crop production. Although this study focuses on ecosystem
functions rather than final services (for example, food production), we investigated
the exclusion of temperate crop pest species (81 species identified from ref. 47), but
found negligible differences in our overall results (Supplementary Fig. 7).
For each ecosystem function, a combination of expert consultation and
published literature was used to classify species groups into primary or secondary
providers of an ecosystem function (Table 1; note that groups can provide multiple
functions). This process comprised the authors first allocating all species groups by
function based on our own ecological knowledge, then contacting experts to review
and suggest modifications to these groupings. A minimum of two experts per
ecosystem function, identified on the basis of relevant career history, contributed to
the process. To support and validate the evidence provided by expert opinion,
a subsequent literature review was then undertaken to highlight relevant references
for the final species groupings. Primary function providers are those groups where
the majority of species contribute in an important way to delivering that function.
Analyses were subsequently repeated including ‘secondary’ function providers,
which were groups whose species can potentially provide those ecosystem
functions, but to a more limited extent. Where experts did not agree or there was
only limited evidence for the functional role of a given species group, that group
was classed as a secondary function provider.
Although the species occurrence records analysed in this study represent an
unparalleled resource of millions of records (1,546,816 records analysed after strict
controls for recorder effort), a number of species groups did not have sufficient
data for analysis. For example, certain fly species (Diptera) may contribute to
pollination functions, but sufficient records for fly families other than Syrphidae
(hoverflies) were lacking. In many cases, this is due to the taxonomic intractability
of certain taxa that have precluded the long-term collection of biological records.
Nevertheless, for pollination and most of the other functions assessed in this study,
we are confident that key species groups are represented in our analysis. Significant
exceptions are earthworms (Clitellata: Oligochaeta) and microorganisms, which
include highly important decomposers48, but lack sufficient data to be included in
the analysis. Therefore, further monitoring might prioritize such important groups
to refine future analyses. A full list of species included in the analysis, the group to
which they belong and their category of ecosystem function contribution can be
found in Supplementary Data 1. A summary of the species groups included in each
function follows below.
Pollination. Primary pollinating species groups were identified as those
which repeatedly visit flowers of the same species and have body structures and
behaviours which could lead to a reasonable likelihood of pollen transfer. Bee
species (Hymenoptera: Apidae) are regarded as highly effective pollinators49,50, but
other groups such as hoverflies (Hymenoptera: Syrphidae), moths and butterflies
(Lepidoptera) also provide important outcrossing pollination functions50.
Secondary pollinating species groups included two beetle groups: soldier beetles
and glow-worms (Coleoptera: Cantharidae, Drilidae and Lampyridae) and
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NATURE COMMUNICATIONS | DOI: 10.1038/ncomms10122
cerambycid beetles (Coleoptera: Cerambycidae) that often interact with flowers but
are not thought to transfer much pollen owing to their relatively smooth bodies
when compared with bees49. Wasps (Hymenoptera: Apoidea, Chrysidoidea and
Vespoidea) were also included as secondary pollinators reflecting their frequent
visitations to flowering plants51.
Pest control. Species groups providing primary pest control functions were
those predators that are likely to act as natural enemies of crop pests. This includes
carabid beetles (Coleoptera: Carabidae)52,53, ladybird beetles52,54,55, spiders
(Araneae)52,54,56, centipedes52, wasps (for example, Ichneumonidae and
Brachonidae)57, dragonflies and damselflies (Odonata)58, harvestmen
(Opiliones)56,59, hoverflies 54,60, soldier beetles and glow-worms56, ants
(Hymenoptera: Formicidae)61; although some ant species can also protect aphid
crop pests62, birds63 and mammals (Mammalia)64,65. Omnivorous crickets
(Orthoptera) and earwigs (Dermaptera)44,45 may act as predators and were
included as providing secondary pest control.
Decomposition. Primary decomposer species groups were those which process
significant amounts of dead organic matter (DOM) by direct consumption or
through changing DOM structure to allow decomposition by other means
(e.g., aeration and introduction of fungi and bacteria). These groups included
ants61, isopods (Isopoda)48 and millipedes (Myriapoda: Diploploda)48. Secondary
decomposers included species groups which are omnivorous but likely to have a
lesser effect on decomposition rates. These included carabid and cerambycid
beetles, craneflies (Diptera: Tipuloidea and Ptychopteridae) and harvestmen66,67.
Carbon sequestration. This functional group includes species which are
capable of a net draw down and storage of CO2 from the atmosphere and storage
in tissues. They include the photosynthesizing species groups of vascular plants
(Tracheophyta), mosses (Bryophyta) and liverworts (Marchantiophyta)68,69.
Note that, although per capita contributions of tree species may be much greater
than smaller organisms such as mosses, net ecosystem functions depend on total
abundance, total biomass and turnover of species, all of which can be higher for
smaller organisms.
Cultural value. This category, rather than being based on biophysical functions
leading to provisioning and regulating services, relates to cultural ecosystem
services70. Here, we focused on a commonly used subset of cultural services
comprising humans experiencing species in their natural environment70. We made
the assumption that the rate of submission of biological records since 1970 in Great
Britain generally reflects the relative value of different species groups with respect
to the well-being benefits from experiencing them. We ranked species groups by
the median number of records per species per decade submitted to British
recording schemes and societies. We selected the top quartile of groups which
included birds, butterflies, moths, vascular plants, bees and mammals. Although
most biological records are submitted by a small proportion of the general public
dedicated to natural history recording, these six groups do represent those that are
most visible and popular with the general public (for example, regularly appearing
in news reports). For example, searching for these six groups (common name in
singular and plural) on Google gave rise to 3.49 billion hits in total, compared with
1.33 billion hits for all other 16 species groups combined. However, we prefer the
former approach to identify cultural value because people are directly experiencing
species before submitting their records. We conducted two analyses, one
combining all these culturally important groups and one separating out culturally
important animals from plants.
Note that the ecosystem service function categories described above are not
mutually exclusive. In addition, particular species providing ecosystem functions
may, in some cases, provide certain ecosystem disservices. For example, rabbits and
deer provide enjoyment for people experiencing them in the natural environment,
but can also cause damage to land at high abundances. These ecosystem disservices
may need to be balanced with ecosystem functions provided by species on a case by
case basis.
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Acknowledgements
We thank the thousands of recorders across the many natural history schemes and
societies for submitting records on which these analyses were based. We additionally
thank C. Carvell, F.K. Edwards, M. Heard, J. Murphy, N. Ostle, J.K. Pell, O. Pescott,
S.G. Potts, P. Richards and A. Vanbergen for providing expert advice. The CEH
Biological Records Centre is co-funded by the UK Joint Nature Conservation Committee
and the Natural Environment Research Council. T.H.O. and J.M.B. were funded through
a BESS consortium project (Wessex BESS, ref. NE/J014680/1). The BESS (Biodiversity
and Ecosystem Service Sustainability) programme is funded by the UK Natural Environment Research Council (NERC) and the Biotechnology and Biological Sciences
Research Council (BBSRC) as part of the UK’s Living with Environmental Change
(LWEC) programme.
Author contributions
T.H.O. conceived the study and analysed the data; N.J.B.I. and T.A.A. produced
species trends; B.A.W., J.M.B., D.B.R. helped interpret the results; all the authors contributed to writing the paper.
Additional information
Supplementary Information accompanies this paper at http://www.nature.com/
naturecommunications
Competing financial interests: The authors declare no competing financial interests.
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How to cite this article: Oliver, T. H. et al. Declining resilience of ecosystem functions
under biodiversity loss. Nat. Commun. 6:10122 doi: 10.1038/ncomms10122 (2015).
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objectives and strategies. For example, a
recent synthesis by Oliver et al. [7]
described the resistance and recovery
aspects of biodiversity to support ecosystem functions. Their main focus on ecosystem functions, summarized as ‘while
species composition is typically the target
of conservation, it is ecosystem functions,
rather than species composition per se,
that need to be resilient, if ecosystem services are to be maintained’ [7], is intriguing. However, the synthesis may be at
odds with the specific description of resilience for an individual function such as
productivity or pollination. To avoid confusion, resilience in this case should be
explicitly termed as recovery or defined
as the analogy of engineering resilience
[1]. Despite progress in theoretical understanding of how and when the relative
contributions of resistance and engineering resilience vary in a changing environment (e.g., [14]), a narrow focus on
resilience may be insufficient to translate
such knowledge into practice. More specifically, because the term ‘resilience’ is
increasingly framed within the concept
of ecological resilience for management
applications [1], a whole-system perspective with a simultaneous focus on alternative stable states, system changeability,
and robustness [2,3] deserves to gain further attention over the perspective based
on recovery and resistance of individual
functions. Resilience-based management
does not typically seek to increase the rate
of return to an original state, which often
implicitly assumes the existence of a single
equilibrium, but instead recognizes that
many natural systems could have multiple
attractors [2,3]. Thus, although stability is
often beneficial for the reliable provision of
ecosystem functioning and services,
which contribute to some aspects of resilience that benefit society (e.g., crop production), it is not necessarily a sufficient
condition for achieving the objectives of
resilience-based management (Box 1).
Interestingly, the stabilizing effects of
biodiversity on ecosystem functioning
are often realized through variability,
asynchrony, and compensation among
different species [8]. Such mechanisms
underpinning ecosystem stability illustrate
the importance of instability and changeability in community dynamics. In other
words, stability itself is often (but not
always) supported by changes, which
are inherent in ecosystems.
Although the modern science of ecology
acknowledges the nonequilibrium nature
of ecosystems [3], the existence of single
equilibria is still often assumed in the literature, with critical implications for conservation and restoration. This has largely helped
management, but at the same time has
created some confusion. As discussed by
Nimmo et al. [12], a specific focus on resistance and engineering resilience (recovery)
may be widely feasible for some stakeholders. However, caution is necessary, as this
framework is relevant only where change
from the basin of attraction is unideal and
thus of interest [12]. Here, rather than continue to make progress in parallel, I would
encourage unification of the ecological and
engineering resilience frameworks [6,9].
Furthermore, these efforts should also be
integrated with the growing body of biodiversity–ecosystem functioning studies.
Such a synthesis could inform and benefit
society by supporting the vital functionality
of ecosystems that contributes to the longterm maintenance of ecological resilience.
Acknowledgments
A.S.M. developed the ideas for resilience under the
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Letter
A Synthesis is
Emerging between
Biodiversity–
Ecosystem Function
and Ecological
Resilience Research:
Reply to Mori
support of the Organisation for Economic Co-operation and Development (OECD) Co-operative Research
Programme. He thanks Forest Isbell for critical inputs
on this commentary.
1
Graduate School of Environment and Information
Sciences, Yokohama National University, Yokohama,
Kanagawa, Japan
*Correspondence: [email protected] (A.S. Mori).
http://dx.doi.org/10.1016/j.tree.2015.12.010
References
1. Standish, R.J. et al. (2014) Resilience in ecology: abstraction,
distraction, or where the action is? Biol. Conserv. 177, 43–51
2. Chapin, F.S.I. et al. (2009) Principles of Ecosystem Stewardship. Resilience-Based Natural Resource Management
in a Changing World, Springer
3. Gunderson, L.H. et al. (2009) Foundation of Ecological
Resilience, Island Press
Tom H. Oliver,1,2,*
Matthew S. Heard,2
Nick J.B. Isaac,2
David B. Roy,2
Deborah Procter,3
Felix Eigenbrod,4
Rob Freckleton,5
Andy Hector,6
C. David L. Orme,7
Owen L. Petchey,8
Vânia Proença,9
David Raffaelli,10
K. Blake Suttle,11
Trends in Ecology & Evolution, February 2016, Vol. 31, No. 2
89
Georgina M. Mace,12
Berta Martín-López,13
Ben A. Woodcock,2 and
James M. Bullock2
A recent paper by Mori [1] states the need
for a unification of studies of ‘engineering’
and ‘ecological’ frameworks of resilience.
Engineering resilience focuses on the
capacity of a system to recover to equilibrium following some kind of perturbation, while ecological resilience (ER)
explicitly recognizes multiple stable states
and the capacity for systems to resist
‘regime shifts’ between alternative states.
We find Mori's argument somewhat surprising given the number of recent biodiversity–ecosystem functioning (B-EF)
studies that incorporate aspects of both
resistance and recovery (e.g., see references in [2,3]). We would argue that a
synthesis is well underway and that
apparent discrepancies are more due to
differences in the spatial, temporal, and
systems scale of focus and ambiguities in
defining this study context rather than any
fundamental incompatibilities in conceptual frameworks.
With regard to our recent review on the
mechanisms that underpin the resilience
of ecosystem functions [3], Mori states ‘To
avoid confusion, resilience in this case
should be explicitly termed as recovery
or defined as the analogy of engineering
resilience’. We clearly consider both
recovery and resistance mechanisms that
promote the resilience of ecosystem functions. It is unclear what would be the benefit of narrowing the focus to recovery or
engineering resilience.
Mori appears to feel that although there is
some consideration of resistance in recent
B-EF research (red text in his Box 1) it
does not adequately embrace some of
the concepts in the ‘ER’ definition, such
as the potential for alternative stable
states. We clearly define resilience at the
level of an individual function, specifically
as ‘the degree to which the ecosystem
90
function can resist or recover rapidly from
environmental perturbations, thereby
maintaining function above a socially
acceptable level’ [3]. This definition does
not preclude the existence of alternative
stable states of the underlying system
and, indeed, we include the potential to
shift to alternative states that provide
lower function delivery as one of several
mechanisms underpinning the provision
of resilient ecosystem functions. However,
there are many other factors that operate
at finer scales of biological organization,
such as the species level (e.g., genetic
variability, sensitivity to environmental
change, adaptive phenotypic plasticity,
Allee effects) and the community-level (e.
g., correlation between response and
effect traits, functional redundancy, network interaction structure). Most importantly, we feel that a focus on system
state (relative to an assumed equilibrium)
is not particularly helpful. The ER literature
is somewhat vague with regard to what
aspects of the system should be resistant
in the face of an environmental perturbation. The relevant response is varyingly
defined as the system ‘state’, the ‘persistence of relationships among state variables within the system’, or the ‘ways of
functioning’ [4]. In our review, we promote
a definition focusing on functions that are
delivered by a system because biological
systems are clearly dynamic, not least
because the environment is continually
changing. So even a system close to equilibrium would show changes in state, not
to mention that many systems of interest
(e.g., agroecosystems) are far from any
equilibrium or that an equilibrium may
not even exist [5]. Therefore, we feel it
does not make sense to focus on inconstancy of system state variables or their
interrelationships, not least because
changes in system state can actually
ensure that ecosystem functions are
maintained (the example we give is that
of species turnover in bee communities
under climate change, which allows resilient pollination functions). The ER literature
itself highlights the importance of internal
system reorganizations as a mechanism
Trends in Ecology & Evolution, February 2016, Vol. 31, No. 2
of maintaining resilience in the face of
perturbations (‘adaptive capacity’ [4]).
This clearly involves changes in system
state variables and their interrelationships.
Similarly, in the B-EF literature, as Mori
states, the stabilizing effects of biodiversity
on ecosystem functioning are often realized through dynamic processes such as
asynchrony and compensation among
species [6]. So both camps – the B-EF
and the ER research fields – seem to be in
agreement here: it is not invariance in the
system variables that is important but the
maintenance of the ecosystem functions
that the system provides.
Although Mori calls for greater synthesis,
we suggest that the two research fields of
B-EF and ER have already started to converge. Traditionally, B-EF research has
certainly adopted a more reductionist
(and empirical) approach in contrast to
the holistic systems thinking of ER. As a
consequence, the original B-EF studies
were conducted in small-scale experiments often focusing on a single function
(e.g., plant productivity) and over limited
timescales. However, recent research has
considered a wider range of ecosystem
functions and incorporated the study of
multiple functions simultaneously (e.g.,
[7]). Studies have moved from considering
simply the species richness of assemblages to the functional diversity and interactions between species in wider foodweb networks [8]. Empirical studies have
also been conducted over increasingly
larger spatial scales (e.g., [9]) and across
scales (e.g., [7]), moving B-EF increasingly
in the direction of a broader research
framework. Similarly, in the ER research
field, key developments have been made
from the original abstract theories of systems and simple analogies with real-world
examples to recent progress toward the
testing and implementation of these theories (e.g., through quantification of early
warning systems [10]).
To avoid further confusion, however,
reducing ambiguity in the study system
context is critical [11]. We propose that
Table 1. Perceived Discrepancies in B-EF versus ER Literature and Potential Reconciliationa
Perceived Discrepancy
Further Details
Clarification/Potential Reconciliation
B-EF literature has traditionally
focused primarily on single
ecosystem functions in
isolation (e.g., plant
productivity) while ER literature
comprises a more holistic view
of entire ecosystems (and even
socioecological systems).
In recent years B-EF research has rapidly
expanded beyond single ecosystem functions
such as plant productivity to consider a varied
range of functions in isolation as well as
multifunctionality (e.g., [7]). Similarly, attempts to
test and apply the abstract concepts of ER
literature have led to examination of specific
systems and ecosystem functions.
The two fields of research appear to be converging. To facilitate this bridging,
it remains essential for studies to be specific about the characteristics of the
system they are measuring, the disturbance regime, and the spatial and
temporal scale of interest (see main text).
B-EF literature focuses on
stability and equilibrium and
ignores the existence of
alternative stable states. The
existence of alternative stable
states is a requisite for ER.
ER definitions concern the likelihood of a system
crossing thresholds between alternative stable
states (‘regime shifts’). A system need not have
high constancy to be resilient: it may be dynamic
around a semistable equilibrium (i.e., staying
within a ‘domain of attraction’). Therefore, ER
authors have suggested that stability is not a
relevant measure of resilience and it may even
lead to contradictory management outcomes
(also see below).
The key point here is whether the focus is on system state variables or
ecosystem functions provided by the system. If the focus is the latter, studies
do not rely on quantifying the return to some equilibrium state nor, indeed, do
they need to posit the existence of alternative stable states as do ER studies
(and some authors have questioned the extent to which these really exist [5]).
With a focus on ecosystem functions, any system is suitable for study, even
those that are managed far from any stable equilibrium (i.e., most managed
ecosystems).
Managing for stability of
ecosystem functions (as
informed under a B-EF
framework) can be detrimental
in the longer term.
This issue is often highlighted in ER literature, a
frequently cited example being the management
of woodlands to prevent fires. If fires are regularly
suppressed (i.e., to provide stable ecosystem
functions from woodlands in the short term), this
leads to the accumulation of deadwood,
meaning that large fires eventually break out with
detrimental effects. By contrast, an ER
management perspective (adopting a wider
spatial- and temporal-scale view) would allow
frequent smaller fires in parts of the woodland
system [4].
Rather than a fundamental disagreement, the discrepancy here is simply a
result of a focus on different spatial and temporal scales. If both approaches
adopt a large-scale perspective, management recommendations would not
be at odds (i.e., the stability of functions across the whole woodland system
in the longer term is maintained by not continually suppressing fires locally).
As highlighted in the main text, clarification on the system type and spatial
and temporal scales of interest is critical to avoid researchers talking at
cross-purposes. Note also that under a more recent suggestion the focus of
management might not be for stability of ecosystem function per se but just
for provision consistently above some socially acceptable threshold,
although the two are likely to be correlated [3].
ER literature focuses on the
system state while BE-F
studies are concerned with the
ecosystem functions.
This statement does not hold true and in fact
research fields are guilty of ambiguity in what
variables are being measured (i.e., ‘resilience of
what to what’?). In ER literature the focus of
resilience is varyingly defined as the system state
(i.e., state variables), the relationships between
variables in a system, or the ways of functioning
(i.e., ecosystem functions) [4]. In BE-F literature
the focus has traditionally been on measuring
stability in ecosystem functions, but some more
recent studies (which might arguably be
included in ‘BE-F literature’) have focused on
measuring system states (e.g., species
composition) (e.g., [2]).
First, clarity is essential reduce confusion [11] and authors should be careful
to avoid ambiguity. Second, a conceptual framework needs internal
coherency. It is contradictory to think about system variables (such as
species composition) remaining constant as the definition of a resilient
system [2] while also defining resilience as the capacity to reorganize (e.g.,
through species turnover) to retain function [4]. Both research fields
recognize the truth in this. ER literature holds that systems are dynamic and
may operate away from equilibrium (i.e., they move around within a ‘domain
of stability’, also sometimes called the ‘normal operating range’), with
resilience as the tendency to remain in this domain. Thus, internal
reorganizations of system states may be essential in allowing a system to
absorb disturbances while remaining in a stability domain that delivers better
ecosystem function. Similarly, BE-F literature documents in detail both
empirically and theoretically [6] how changes in the composition of
communities promote the maintenance of functions provided by a system.
Therefore, resilience does not mean inconstancy of system state variables,
and dynamic systems are needed to provide resilient ecosystem functions.
a
To aid researchers a more extensively referenced version of this table is available online (Table S1 in the supplemental information online).
many of the apparent discrepancies
between the B-EF and ER research fields
are simply a result of researchers focusing
at different temporal or spatial scales
and talking at cross-purposes. We highlight
some of these apparent discrepancies and
their potential reconciliation in Table 1.
To conclude, both B-EF and ER
approaches had initial weaknesses, such
as the limited focus of empirical B-EF studies and the limited approach to quantification in more abstract, holistic ER theories.
However, researchers in both fields have
recognized this and, by increasing the
scope of B-EF studies and adopting a more
empirical perspective on ER theories, the
two fields are now beginning to merge. It is
hoped that this emerging synthesis will help
in understanding, predicting, and delivering
solutions for the management of resilient
ecosystem functions [12].
Trends in Ecology & Evolution, February 2016, Vol. 31, No. 2
91
Acknowledgments
The author group is part of the Tansley Working Groups
initiative sponsored by the UK Natural Environment
Research Council (NERC) (http://www.nerc.ac.uk/).
T.H.O. was supported by the Wessex Biodiversity Ecosystem Services Sustainability (BESS) project (ref. NE/
J014680/1) within the NERC BESS Programme. V.P.
was supported by Fundação para a Ciência e a Tecnologia (BPD/80726/2011). Contributions of O.L.P.
were supported by the University of Zurich Research
Priority Program on ‘Global Change and Biodiversity’
and SNSF Project 31003A_137921 ‘Predicting Com-
9. Karp, D.S. et al. (2011) Resilience and stability in bird guilds
across tropical countryside. Proc. Natl. Acad. Sci. U.S.A.
108, 21134–21139
10. Dai, L. et al. (2012) Generic indicators for loss of resilience
before a tipping point leading to population collapse. Science 336, 1175–1177
11. Grimm, V. and Wissel, C. (1997) Babel, or the ecological
stability discussions: an inventory and analysis of terminology and a guide for avoiding confusion. Oecologia 109,
323–334
12. Truchy, A. et al. (2015) Linking biodiversity, ecosystem
functioning and services, and ecological resilience: towards
an integrative framework for improved management. Adv.
Ecol. Res. Published online November 26, 2015. http://dx.
doi.org/10.1016/bs.aecr.2015.09.004
munity Responses to Environmental Change’.
Supplemental Information
Supplemental data associated with this article can be
found, in the online version, at http://dx.doi.org/10.
1016/j.tree.2015.12.008.
1
School of Biological Sciences, University of Reading,
Reading RG6 6AS, UK
NERC Centre for Ecology and Hydrology, Wallingford, UK
3
Joint Nature Conservation Committee, UK
4
University of Southampton, Southampton, UK
5
University of Sheffield, Sheffield, UK
6
Department of Plant Sciences, University of Oxford,
2
Oxford, UK
Imperial College London, London, UK
8
Institute of Evolutionary Biology and Environmental
Studies, University of Zurich, Zurich, Switzerland
9
Instituto Superior Técnico, University of Lisbon, Lisbon,
Portugal
10
University of York, York, UK
7
11
Department of Earth and Planetary Science, University
of California, Berkeley, CA, USA
12
University College London, London, UK
13
Faculty of Sustainability, Institute of Ethics and
Transdisciplinary Sustainability Research, Leuphana
University of Lüneburg, Lüneburg, Germany
*Correspondence: [email protected] (T.H. Oliver).
Letter
Consumptive
Tourism Causes
Timidity, Rather
Than Boldness,
Syndromes: A
Response to
Geffroy et al.
Robert Arlinghaus,1,2,*,@
Josep Alós,1
Thomas Klefoth,3
Kate Laskowski,1
Christopher T. Monk,1
Shinnosuke Nakayama,1,2 and
Arne Schröder1
http://dx.doi.org/10.1016/j.tree.2015.12.008
References
1. Mori, A.S. (2016) Resilience in the studies of biodiversityecosystem functioning. Trends Ecol. Evol. 31, 87–89
2. Nimmo, D.G. et al. (2015) Vive la résistance: reviving resistance for 21st century conservation. Trends Ecol. Evol. 30,
516–523
3. Oliver, T.H. et al. (2015) Biodiversity and the resilience of
ecosystem services. Trends Ecol. Evol. 30, 673–684
4. Gunderson, L. et al. (2010) Foundations of Ecological Resilience, Island Press
5. Newton, A. and Cantarello, E. (2015) Restoration of forest
resilience: an achievable goal? New Forests 46, 645–668
6. Loreau, M. and de Mazancourt, C. (2013) Biodiversity and
ecosystem stability: a synthesis of underlying mechanisms.
Ecol. Lett. 16, 106–115
7. Pasari, J.R. et al. (2013) Several scales of biodiversity affect
ecosystem multifunctionality. Proc. Natl. Acad. Sci. U.S.A.
110, 10219–10222
8. Reiss, J. et al. (2009) Emerging horizons in biodiversity and
ecosystem functioning research. Trends Ecol. Evol. 24,
505–514
92
Geffroy et al. [1] proposed that naturebased tourism reduces the fearfulness
and antipredator behavior of animals, leading towards a boldness syndrome that elevates natural predation rates and could
trigger cascading effects on populations
and communities. We agree with the
framework, hypotheses, and future
research needs proposed in [1], but they
apply strictly to nonthreatening human–
wildlife interactions. However, naturebased tourism is often consumptive, where
wild-living animals are chased, stressed,
and eventually harvested in activities such
as recreational fishing and hunting. No
threatening forms of human use of animals
Trends in Ecology & Evolution, February 2016, Vol. 31, No. 2
were elaborated in [1]. As a complementary
perspective, we here propose that consumptive nature-based tourism might lead
to opposite behavioral outcomes to those
proposed in [1] by inducing a timidity, rather
than a boldness, syndrome (Figure 1).
Human exploitation of wild-living animals
creates a ‘landscape of fear’ [2,3]. A commonly reported plastic behavioral
response of animals to human-induced
predation risk involves increased antipredator behavior and heightened timidity,
characterized by a greater use of refuges
and reduced activity [2–8]. For such
effects to happen, the experience of nonlethal, yet threatening stimuli caused by
humans are often sufficient. For example,
catch-and-release angling is increasingly
common in tourism-based fishing operations. Being hooked, physiologically
stressed, and eventually released promotes refuge-seeking behaviors that
reduce vulnerability to fishing, which
may also affect nonhooked conspecifics
through social learning [8,9]. In addition to
plastic effects within the behavioral reaction norm, lethal consumptive tourism may
also cause evolutionary responses in a
range of life-history and behavioural traits
that collectively increase the average
timidity levels of surviving individuals
[3,6,7,10–12]. For example, bold, explorative, aggressive, and active behavioral
types (aka ‘personalities’) within exploited
wildlife populations are often selectively
harvested [3,7,10–12]. The positive heritability characterizing most personality
traits in turn could facilitate an evolutionary
(i.e., genetic) response of timidity-related
behaviors [6,7,12]. Increased timidity due
to learning and/or evolutionary adaptation
can occur in both predator and prey populations when they are exposed to threatening stimuli by recreational fishers or
hunters. The net result for a prey species
should generally involve a reduction,
rather than an increase [1], in natural
mortality risk because either the prey
becomes shyer when they are exploited
or it benefits from an increased timidity of
the exploited predator, thereby being