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Dispersal and species’ responses to climate change

TL;DR: Evidence for direct and indirect influences that climate change may have on dispersal, some ecological and others evolutionary, is compiled across the fields of dispersal ecology and evolution, species distribution modelling and conservation biology.
Abstract: Dispersal is fundamental in determining biodiversity responses to rapid climate change, but recently acquired ecological and evolutionary knowledge is seldom accounted for in either predictive methods or conservation planning. We emphasise the accumulating evidence for direct and indirect impacts of climate change on dispersal. Additionally, evolutionary theory predicts increases in dispersal at expanding range margins, and this has been observed in a number of species. This multitude of ecological and evolutionary processes is likely to lead to complex responses of dispersal to climate change. As a result, improvement of models of species’ range changes will require greater realism in the representation of dispersal. Placing dispersal at the heart of our thinking will facilitate development of conservation strategies that are resilient to climate change, including landscape management and assisted colonisation. Synthesis This article seeks synthesis across the fields of dispersal ecology and evolution, species distribution modelling and conservation biology. Increasing effort focuses on understanding how dispersal influences species' responses to climate change. Importantly, though perhaps not broadly widely-recognised, species' dispersal characteristics are themselves likely to alter during rapid climate change. We compile evidence for direct and indirect influences that climate change may have on dispersal, some ecological and others evolutionary. We emphasise the need for predictive modelling to account for this dispersal realism and highlight the need for conservation to make better use of our existing knowledge related to dispersal.

Summary (3 min read)

1. Introduction

  • Semiconductor lasers are sensitive to feedback effects, which occur when a portion of the emitted light re-enters the active laser cavity [1,2].
  • Techniques to improve feedback immunity include surface relief gratings [3], increased cavity mirror reflectivity [4,5], and optical isolators.
  • Numerical solution of this equation provides important insight into the way a self-mixing sensor functions.
  • Lasers under feedback exhibit complicated phenomena, including hysteresis effects and the presence of multiple possible laser oscillation modes, some of which are unstable [28].
  • Because of this, a series of considered steps must be followed to obtain physically meaningful solutions from the excess-phase equation.

2. Theory

  • The self-mixing model developed here is based on the work by Petermann [30].
  • The authors then provide a description of important considerations and relevant additional equations.
  • The laser’s terminal voltage also varies in response to feedback [41] providing an alternative signal source that contains equivalent information to the optical power signal [13,42].
  • This approximation often holds in practice, especially when light is scattered from rough surfaces (e.g., [45] reports an intensity ratio of 10−5).

A. Weak Feedback, C ≤ 1

  • When the feedback parameter is less than or equal to one, the right-hand side (r.h.s.) of Eq. (6) is monotonic, and a unique solution can be found for ϕ.
  • The solution can be found in a robust manner using a bounded root finding algorithm, such as bisection, between known bounds that the authors denote ϕmin and ϕmax.
  • The bounds are obtained by considering the periodicity of the sine function that has a maximum value of 1 andminimum value of −1.
  • Therefore ϕmin is found by substituting 1 for the sine function in Eq. (6), and ϕmax by substituting −1.

B. Moderate/Strong Feedback, C > 1

  • When the feedback parameter is greater than one, there may be multiple solutions that satisfy Eq. (6).
  • Figure 3 shows ϕmin and ϕmax corresponding to the trough and peak locations for the left-most solution.
  • Solutions corresponding to possible lasing modes are indicated by circles and crosses in Fig. 3, but the modes indicated with crosses are not stable; thus their solutions are not contained here.
  • The modal stability also can be formally verified though linear stability analysis [28].).
  • The next step is to find the possible values of m where a valid solution exists.

1. Lower Bound

  • The authors obtain mlower by finding the left-most peak position that is greater than zero.
  • This is obtained by substituting the equation for the peak from Eq. (11) into Eq. (6) and rearranging form.

2. Upper Bound

  • The authors obtain mupper in a similar fashion to the lower bound.
  • It is obtained by finding the right-most trough position that is less than zero.

C. Path Dependence (Hysteresis)

  • Initially, any valid solution interval could contain ϕ.
  • Moreover, with a periodic stimulus (such as harmonic motion), ϕ will behave periodically from the second period onward.
  • These solutions are plotted using the thin lines in Fig. 5(b); the thick lines in the figure will be used to illustrate the path dependence of the solutions.
  • The path of solutions to the excess-phase equation are then plotted in Fig. 5(b).
  • Relating these assumptions on the laser behavior to the equations derived above, if a solution previously existed for a given integer value of m, the solution will remain in the same region until m falls outside of mlower;mupper .

E. Pseudocode for Solving Self-Mixing Equations

  • The values at the bounds must differ in sign.
  • Algorithm 2 presents pseudocode to synthesize self-mixing signal waveforms for given round-trip phase samples in vector ϕ0, which demonstrates how to use the selmixpower function from Algorithm 1.

4. Applications

  • This section demonstrates how to apply Algorithms 1 and 2 developed in the previous section to typical self-mixing sensor configurations.
  • The examples presented aim to provide a starting point for self-mixing laser modeling that can be extended to other selfmixing sensor applications with little effort.

A. Target Displacement

  • Measuring target displacement is an application often presented in the published self-mixing literature, as it is easy to understand andmakes use of simple stimuli.
  • This section will describe how to generate synthetic self-mixing target displacement signals.
  • This process permits the parameters associated with experimental signals to be estimated, such as the feedback parameter C [36].
  • The authors can observe trends in the evolution of the self-mixing signal for increasing values of the feedback parameter C; the hysteresis effect becomes greater, waveform asymmetry becomes more pronounced, and the number of fringes is decreased.

B. Absolute Distance

  • The distance to a fixed target can be obtained from the self-mixing sensor by frequency modulating the laser.
  • The frequency modulation is typically achieved by modulating the laser bias current with an ostensibly triangular waveform [6].
  • Δν tn ΔFTri tn ; (18) where Tri represents a triangle function, and ΔF is the frequency modulation coefficient.
  • MATLAB code implementing the algorithm appears in absolute_distance.m with the resulting signal plotted in Fig. 8 for a target distance of 24 mm and a laser frequency sweep over a range of 46 GHz with a base frequency of ν00 c∕ 845 nm .
  • The authors can perform a rough check on the synthetic signal using the result derived by Beheim and Fritsch [6]: L Nf c∕ 2ΔF with an uncertainty of c∕ 2ΔF where.

C. Absolute Distance and Velocity

  • It is also possible to consider the previous absolute distance measurement with a target in motion.
  • Substituting Eq. (23) into Eq. (21), the authors obtain the expression for the external phase, ϕ0 tn 4π L0 vtn 1 λ0 ΔFTri tn c : (24) The inclusion of the velocity term accounts for the Doppler effect caused by the target motion.
  • The self-mixing sensor can also provide useful signals for nondeterministic stimuli, which is of interest, for example, for sensing the velocity of a rough moving target [12,45].
  • The stimulus signal time samples, ψ tn , are in the complex domain and represent the amplitude and phase fluctuations of the stimulus.
  • Nevertheless, this procedure provides means for extracting parameter values, such as the feedback parameter, C, by fitting experimentally acquired signals to the synthetic velocimetry signal.

E. Other Applications

  • The procedure for solving the excess-phase equation presented in this article also can be applied to a range of other applications where various parameters change over time.
  • One example is the selfmixing imaging sensor where an optical chopper is used to modulate the self-mixing signal [13].
  • The chopper can be modeled by considering two states: the chopper obstructing the beam, and the chopper allowing the beam to pass through.
  • These states would correspond to changing the feedback parameter (when the chopper obstructs the beam, the feedback would be reduced) and the optical path length (which would be equal to the distance between the laser and the chopper when the beam is obstructed by the chopper).
  • Again, Algorithm 2 can be used to generate the synthetic signal for this case if the time series of the round-trip phase and feedback parameters are provided.

5. Conclusions

  • This article presents a simple, systematic method for solving the excess-phase equation numerically to generate synthetic self-mixing signals for a range of feedback levels.
  • The ability to synthesize self-mixing sensor signals can provide insight into the operation and performance of self-mixing sensors under different experimental conditions.
  • Moreover, such synthetic signals can be fitted to experimentally observed signals, enabling the extraction of independent experimental system parameters.

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Dispersal and species’ responses to climate change
Justin M. J. Travis, Maria Delgado, Greta Bocedi, Michel Baguette, Kamil Barton´,
Dries Bonte, Isabelle Boulangeat, Jenny A. Hodgson, Alexander Kubisch,
Vincenzo Penteriani, Marjo Saastamoinen, Virginie M. Stevens and James M. Bullock
J. M. J. Travis (justin.travis@abdn.ac.uk) and G. Bocedi, School of Biological Sciences, Univ. of Aberdeen, Zoology Building, Tillydrone Avenue,
Aberdeen, AB24 2TZ, UK. – M. Delgado and M. Saastamoinen, Dept of Biosciences, Univ. of Helsinki, Viikinkaari 1, FI-00014 Helsinki,
Finland. – M. Baguette, Museum National dHistoire Naturelle, Dept Ecologie et Gestion de la Biodiversité, and CNRS USR2936, Station
dEcologie Expérimentale, route du CNRS, FR-09200 Moulis, France. – K. Bartoń and A. Kubisch, Field Station Fabrikschleichach, Univ. of
Wuerzburg, Glashuettenstrasse 5, DE-96181 Rauhenebrach, Germany. – D. Bonte, Dept of Biology, Ghent Univ., K. L. Ledeganckstraat 35,
BE-9000 Ghent, Belgium. – I. Boulangeat, Laboratoire dEcologie Alpine, UMR CNRS 5553, Univ. Joseph Fourier, BP 53, FR-38041
Grenoble Cedex 9, France. – J. A. Hodgson, Dept of Biology, Wentworth Way, Univ. of York, York, YO10 5DD, UK. – V. Penteriani, Dept of
Conservation Biology, Estación Biológica de Doñana, CSIC, c/Américo Vespucio s/n, ES-41092 Seville, Spain. VP also at: Finn. Mus. of Nat.
Hist., Zool. Mus. Pohjoinen Rautatiekatu 13, Univ. of Helsinki, FI-00100 Helsinki, Finland. – V. M. Stevens, CNRS USR2936, Station
dEcologie Expérimentale, route du CNRS, FR-09200 Moulis, France. VMS also at: F.R.S.-FNRS, Unité de Biologie du Comportement, Univ.
de Liège, 22 Quai van Beneden, BE-4020 Liège, Belgium. – J. M. Bullock, Centre for Ecology and Hydrology, Maclean Building, Benson Lane,
Crowmarsh Gifford, Wallingford, OX10 8BB, UK.
Dispersal is fundamental in determining biodiversity responses to rapid climate change, but recently acquired ecological
and evolutionary knowledge is seldom accounted for in either predictive methods or conservation planning. We empha-
sise the accumulating evidence for direct and indirect impacts of climate change on dispersal. Additionally, evolutionary
theory predicts increases in dispersal at expanding range margins, and this has been observed in a number of species. is
multitude of ecological and evolutionary processes is likely to lead to complex responses of dispersal to climate change. As a
result, improvement of models of species range changes will require greater realism in the representation of dispersal. Plac-
ing dispersal at the heart of our thinking will facilitate development of conservation strategies that are resilient to climate
change, including landscape management and assisted colonisation.
Ongoing rapid climate change is resulting in the geographic
shifting of species suitable environmental conditions (IPCC
2007, Chen et al. 2011). Species might survive this rapid
change by shifting their distributions or through evolution
such that populations become adapted to the new local cli-
matic conditions (Berg et al. 2010, Bellard et al. 2012). For
both these responses, dispersal is a central process; it deter-
mines the potential spread rate of a population and, as the
process by which genes are moved between populations, it
influences the rate of adaptation to changing conditions
and the potential for evolutionary rescue (Bell and Gonzalez
2011). us, understanding, predicting and managing bio-
diversity responses to rapid climate change demands a full
consideration of species dispersal characteristics and how
these characteristics may themselves change under climate
change. Despite this, the great majority of studies project-
ing future species distributions do not explicitly account for
dispersal (omas et al. 2004, uiller et al. 2006). A signifi-
cant number of review and perspectives papers considering
climate change and biodiversity responses have been pub-
lished recently, covering both conceptual and methodologi-
cal aspects of modelling range shifts (uiller et al. 2008,
e review and decision to publish this paper has been taken by the above noted SE.
e decision by the handling SE is shared by a second SE and deputy EiC.
This article seeks synthesis across the fields of dispersal ecology and evolution, species distribution model-
ling and conservation biology. Increasing effort focuses on understanding how dispersal influences species
responses to climate change. Importantly, though perhaps not broadly widely-recognised, species’ dispersal
characteristics are themselves likely to alter during rapid climate change. We compile evidence for direct and
indirect influences that climate change may have on dispersal, some ecological and others evolutionary. We
emphasise the need for predictive modelling to account for this dispersal realism and highlight the need for
conservation to make better use of our existing knowledge related to dispersal.
Synthesis

Elith and Leathwick 2009, Dawson et al. 2011, McMahon
et al. 2011, Bellard et al. 2012, Bocedi et al. 2012, Schurr
et al. 2012). Although these reviews tackle the issue of how
dispersal is important in governing responses to climate
change, importantly they do not examine the role of climate
change-driven changes in dispersal.
Here, we demonstrate that climate change is likely to
have direct and indirect impacts on the dispersal of individu-
als, and will exert new selection pressures leading to dispersal
evolution. We argue for the incorporation of the emerging
synthesis concerning the ecology and evolution of disper-
sal into models of species spread under climate change,
with an explicit consideration of the resulting uncertainties.
Moreover, we stress that the development of climate change
resilient conservation strategies has seldom benefited from
the improved ecological and evolutionary knowledge about
dispersal, and believe that including dispersal details will
help resolve current heated debates about future conser-
vation strategies (Hodgson et al. 2009, Doerr et al. 2011,
omas 2011, Webber et al. 2011). e conceptual scope of
this perspectives article is therefore purposely broad, cover-
ing a number of topics such as observed ecological and evo-
lutionary patterns, theory, models and conservation.
Will climate change reduce or enhance individual
dispersal abilities?
e initiation of dispersal (emigration) by an individual, its
subsequent movements (transfer) and its settlement deci-
sions (immigration), are influenced by local conditions, and
so climate change may affect an individuals dispersal. Pre-
dicted impacts of climate change on means and variabilities
of temperatures, rainfall, storm events, wind speed, snow and
ice cover, CO
2
concentrations, etc. (IPCC 2007) could affect
the dispersal process directly, and also indirectly by changing
the biophysical environment (e.g. habitat quality, availability
of food resources, etc.) and the state of individuals (body
size and morphology, body condition and rate of develop-
ment). Table 1 summarises empirical evidence for such
effects among different taxa (including vascular plants, algae,
insects, reptiles, birds, fish and mammals) in both terrestrial
and aquatic (marine and continental) systems. e decision
to disperse can be affected directly by changes in temperature
(Battisti et al. 2006, Pärn et al. 2011, Delattre et al. 2013), in
windspeed (omas et al. 2003), in storms (Lea et al. 2009),
in flooding (Roche et al. 2012), and in snow cover (Schwartz
et al. 2009). Changes in climatic factors can also have a
direct impact on organisms during the transfer phase of dis-
persal, either by increasing (Dickison et al. 1986, Censky
et al. 1998, Peirson et al. 2008, Kuparinen et al. 2009, Cor-
mont et al. 2011, Monzón-Argüello et al. 2012) or decreas-
ing the dispersal distance (Geffen et al. 2007, Bullock et al.
2012). In one recent case that highlights a further potential
complexity, the impact of temperature on dispersal distance
was shown to interact with the degree of habitat fragmenta-
tion (Delattre et al. 2013): dispersal distance was greater at
lower temperatures in fragmented landscape while, in more
continuous landscapes, dispersal distance was greater under
warmer conditions.
Multiple indirect effects of climate change on dispersal
are also possible. For example, smaller individuals often
have reduced dispersal ability, and climate-induced dete-
rioration in habitat quality or faster development during
higher temperatures can both reduce body size in a variety
of species (Gibbs et al. 2011, McCauley and Mabry 2011).
However, poor conditions in the biophysical environment
can also increase the probability of emigration in some
other species (Figuerola 2007). Species which rely on other
biota for dispersal, such as seeds carried by ants, will suf-
fer if the phenology of the dispersal agent becomes asyn-
chronous under climate change (Warren et al. 2011). e
phenology of American redstart on breeding grounds shows
a climate-driven latitudinal gradient, such that natal disper-
sal distances decrease when the growing season starts earlier
(Studds et al. 2008).
Our survey indicates multiple and varied climate impacts
on dispersal and that the predicted changes in specific climatic
factors may both increase and decrease dispersal abilities
depending on the system and species considered (Table 1).
For example, non-optimal temperatures may induce flo-
tation behaviour and thus dispersal in aquatic molluscs
( Correia Rosa et al. 2012), but can decrease the probability
of dispersal in spiders (Bonte et al. 2008) and lizards (Massot
et al. 2008). In addition, opposing consequences may arise
in a single species. For example, dispersal of wind-dispersed
thistles should decrease with predicted reductions in wind
speed (Bullock et al. 2012), but should increase as plants
grow taller in warmer conditions (Zhang et al. 2011).
Such variation in the impact of climate change on dis-
persal will become better understood and more predictable
as dispersal mechanisms themselves are better elucidated
and incorporated into dispersal models see below. Climate
change will also affect other aspects of life history such as
fecundity and mortality, which will determine the effective-
ness of dispersal at a population level. For example, if climate
change results in a local population having a higher fecun-
dity it may indirectly increase the number of individuals
dispersing, while if there is higher mortality in new habitat
colonised at the range edge, it will decrease the number of
effective colonists.
How will dispersal evolve under climate change?
A classic study related the dispersal characteristics of lodge-
pole pine seeds to its post-glacial spread (Cwynar and
MacDonald 1987); seeds in populations towards the still
expanding colonisation front were more dispersive than
those in longer established populations. We now have accu-
mulating evidence of similar increases in dispersal ability in
populations shifting their ranges in response to contempo-
rary environmental change. For example, at expanding range
margins the frequency of long-winged morphs of bush crick-
ets is increased (Simmons and omas 2004) and speckled
wood butterflies invest more in thorax size, which increases
their flight power (Hill et al. 1999).
ese observations of increased dispersal at expand-
ing margins conform to theoretical expectations. Models
have demonstrated that, at expanding margins, selection
can: favour increased emigration rates, even when local
populations are at low densities (Travis et al. 2009); pro-
mote risky movement behaviours, enhancing the possibil-
ity of long distance dispersal even if mortality is increased

Table 1. Effects of climate change on individual dispersal. Climate change is predicted to lead to lower windspeeds (A), higher temperatures (B), increased frequency of storms (C), flooding (D), reduced
snow (E) and ice (F) cover, and changed rainfall (G). Each of these climatic factors has been shown to affect dispersal in a range of organisms from all taxa, either through a direct impact on the indi-
vidual during dispersal, or indirectly by altering the biophysical environment or the state of the dispersing organism. Key empirical examples in both marine and terrestrial ecosystems of these effects
are described (red arrows depicting decreases, blue arrows increases an increase). The arrows indicate how predicted changes in specific climatic factors would alter the propensity to emigrate or the
distance dispersed during transfer. *Windspeed changes are projected, but are not well characterised and vary among climate models and geographic regions (Pryor and Barthelmie 2011, Bullock
et al. 2012).
1
IPCC 2007,
5
Delattre et al. 2013.

between species due to disparate generation times, and this,
together with variability in standing genetic variation within
populations, is likely to determine their potential for local
adaptation (Berg et al. 2010). Phenotypic plasticity can thus
play a central role by providing the potential for organisms
to respond rapidly and effectively to environmental change.
Ultimately, changes due to phenotypic plasticity may be
advantageous because it is possible that a changing environ-
ment may select for differing patterns of plasticity among
individuals (i.e. plasticity itself can evolve by natural selec-
tion; Sultan et al. 2012). However, many species may lack
both the plasticity that would allow them to better cope
with climate change and the genetic variation that would
allow them to evolve in response to climate change. eir
long generation times and relatively small population sizes
make evolutionary adaptation unlikely. And it is unclear if
they have enough phenotypic plasticity to successfully adapt
to new climate conditions.
How is dispersal included in predicting species’
future distributions?
e last decade has seen a massive effort in developing spe-
cies distribution models (SDMs) to project where species are
likely to be found under future scenarios of environmental
change (omas et al. 2004, uiller et al. 2006). e most
widely used approach relates species location data to envi-
ronmental variables including climate. By using the outputs
of global climate models, these so-called habitat suitability
models can project species distributions onto future climatic
conditions. Amongst the well-understood limitations of this
approach (Franklin 2010, Dawson et al. 2011), the com-
plete lack of, or incomplete treatment of dispersal is key
(Fig. 1). Indeed, this lack is also apparent in more mecha-
nistic approaches, which use an understanding of species
climatic tolerances to construct process-based distribution
models (Dormann et al. 2012).
An initial method used to establish bounds of uncertainty
in species range changes has been to run models assuming
that species exhibit either unlimited or no dispersal. Such
studies demonstrate huge differences between the two sce-
narios in projections of, for example, regional species extinc-
tions (omas et al. 2004) and functional diversity of trees
across Europe (uiller et al. 2006). e resulting apprecia-
tion of the importance of dispersal has stimulated a recent
move towards more integrated models that seek to account
for dispersal, population dynamics and habitat dynamics, as
well as climate and habitat suitability (Franklin 2010).
Recent studies have proposed hybrid models that couple
habitat suitability models with more mechanistic models
accounting for dispersal in a variety of ways (Fig. 1). A sim-
ple way to incorporate dispersal has been to couple habitat
suitability models with colonisation models that are based
on nearest-neighbour dispersal whereby landscape grid cells
that become climatically suitable can be colonised if a neigh-
bouring cell is already occupied (Midgley et al. 2006, Roura-
Pascual et al. 2009, Willis et al. 2009). A more sophisticated
and increasingly used approach for including dispersal in
predictive species distribution models is to fit a statistical
function (i.e. dispersal kernels) to observed dispersal data
(Pagel and Schurr 2012, Schurr et al. 2012). Dispersal kernels
(Bartoń et al. 2012); and foster investment in dispersal
traits at the expense of other life-history attributes (Burton
et al. 2010). While the same selective forces that act on
dispersal in stationary ranges, including kin competition
and inbreeding depression, may still play a role at expand-
ing margins, selection will now favour dispersal strategies
which maximise the likelihood that some descendants fol-
low the expanding margin (Travis et al. 2009, Bartoń et al.
2012, Henry et al. 2013).
Most theory exploring dispersal evolution during range
expansions has assumed that species spread across homoge-
neous landscapes (Travis et al. 2009, Burton et al. 2010).
While this provides some generic insights, it is clearly not
realistic for most biogeographic ranges. Recent modelling
has begun to shed light on how dispersal will evolve, and
influence the spatial structure of species ranges, when range
expansions occur across environmental gradients (Kubisch
et al. 2010, Phillips 2011). e evolution of increased dis-
persal at expanding range margins may have unexpected
consequences in heterogeneous landscapes (Travis et al.
2010, Phillips 2011). It can allow a species to cross large
gaps between habitat patches, and this is more likely when
the population has been expanding for long enough to
have evolved greater dispersal ability (Travis et al. 2010).
Counter-intuitively, evolutionary rescue might be pro-
moted under increased rates of experienced climate change
(Boeye et al. 2013). However, increased dispersal at expand-
ing range margins may not always result in species spread-
ing more readily across heterogeneous landscapes, as it can
diminish the ability to adapt to local environmental condi-
tions and ultimately lead to reduced spread rate (Phillips
2011). ese initial theoretical studies serve to highlight
the likely complexity of eco-evolutionary dispersal dynam-
ics at expanding range margins. Increased eco-evolutionary
understanding and subsequently improved prediction of
biogeographic range-shifting requires integration of ecol-
ogy and evolution.
Whether dispersal is evolving in reality, or if organisms
show phenotypic plasticity is a challenging question due to
the complex and poorly understood interactions between
plastic and genetic processes. Yet, the difference is impor-
tant for understanding the current and likely future con-
sequences of climate change, because it informs us about
the extent to which populations are subject to natural selec-
tion resulting from a changing environment (Visser 2008).
Recent studies present evidence that we can expect global
warming to impact species in both ways (Balanyá et al. 2006,
Bradshaw and Holzapfel 2006, Charmantier et al. 2008).
However, while the evidence for climate change driven
phenotypic responses in wild populations is strong, empiri-
cal data allowing us to evaluate whether or not any genetic
response and hence evolution has actually occurred are
still rather scarce (Bradshaw and Holzapfel 2006, Reusch
and Wood 2007, Gienapp et al. 2008). Indeed, it is still
questionable whether for most organisms evolution can be
rapid enough to allow adaptation to changes in climatic
conditions, especially as evidence suggest that adaptation
is favoured under gradual environmental change (Bell
and Gonzalez 2011) and that it can impose demographic
costs (Lynch and Lande 1993, Bürger and Lynch 1995).
e impact of a given rate of climate change may differ

that represent the dispersal process more mechanistically,
allowing dispersal distances to vary in space and time. For
instance, a mechanistic model of seed dispersal by wind,
based on a mathematical simplification of a complex sto-
chastic model of seed transport in turbulent air (Katul et al.
2005), has been applied to project the spread rates of plant
populations under a changing climate (Nathan et al. 2011,
Bullock et al. 2012). Similarly, simple animal movement tra-
jectories across a landscape have been simulated with indi-
viduals also able to select an optimal patch from all those
within a maximum dispersal radius (McRae et al. 2008).
is shift towards mechanistic modelling offers the oppor-
tunity to incorporate potentially crucial details related to the
ecology and evolution of dispersal and promises to yield new
tools that can inform the development of improved conser-
vation strategies for a changing environment.
have already been integrated with habitat suitability models
in few studies, through coupling with a simple migration
model (Engler et al. 2009) or spatially explicit metapopula-
tion models (RAMAS-GIS; Keith et al. 2008, Anderson et al.
2009). is last kind of hybrid model offers the possibility
to consider not only the transfer (as dispersal distance) phase
but also the emigration and immigration ones by incorporat-
ing the population dynamics. Yet, the major challenge of this
method is to represent the influential, but rare, long distance
dispersal events that are often poorly described by standard
statistical distributions. One way of achieving this is to use a
mixture of two statistical distributions, obtaining fat tail dis-
tributions that include long distance dispersal events (Engler
et al. 2009, Pagel and Schurr 2012).
Because different internal (e.g. individual condition, sex)
and external (e.g. the local environment) factors can alter
individual dispersal processes (Clobert et al. 2009), the dis-
tribution of dispersal distances is unlikely to be a xed prop-
erty of a species. Including this complexity requires models
Figure 1. Dispersal will be the heart of a new generation of process-based models developed to predict, and inform the management of,
species responses to environmental change. By incorporating dispersal together with an explicit representation of population dynamics,
models will become much better able to simulate the spatio-temporal dynamics of species under alternative future climate and land-use
scenarios. To date, most projections of biodiversity responses to climate change have been made using all or nothing dispersal with fewer
examples of nearest-neighbour dispersal or statistical dispersal kernels. While more detailed mechanistic dispersal models have been devel-
oped both for animal and plant dispersal, they have yet to be used extensively in the climate change field. In part this is due to the substan-
tial challenges faced with these more sophisticated models, both in terms of the data needs for parameterisation and the greater computation
needs of these more complex approaches. We argue that incorporating greater realism in the dispersal process will result in improved predic-
tive capability, particularly when there are likely to be synergistic impacts of climate and land use change.
1
omas et al. 2004,
2
uiller
et al. 2006,
3
Pitt et al. 2009,
4
Iverson et al. 2011,
5
Carey 1996,
6
Engler and Guisan 2009,
7
Bullock et al. 2012,
8
Nathan et al. 2011,
9
McRae
et al. 2008,
10
Travis et al. 2012.

Citations
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Journal ArticleDOI
25 Jul 2014-Science
TL;DR: The full spectrum of conservation translocations is reviewed, from reinforcement and reintroduction to controversial conservation introductions that seek to restore populations outside their indigenous range or to introduce ecological replacements for extinct forms.
Abstract: The rate of biodiversity loss is not slowing despite global commitments, and the depletion of animal species can reduce the stability of ecological communities. Despite this continued loss, some substantial progress in reversing defaunation is being achieved through the intentional movement of animals to restore populations. We review the full spectrum of conservation translocations, from reinforcement and reintroduction to controversial conservation introductions that seek to restore populations outside their indigenous range or to introduce ecological replacements for extinct forms. We place the popular, but misunderstood, concept of rewilding within this framework and consider the future role of new technical developments such as de-extinction.

493 citations

Journal ArticleDOI
TL;DR: The diagnostic features of evolutionary rescue are outlined and this phenomenon is distinguished from demographic and genetic rescue, highlighting the demographic, genetic, and extrinsic factors that affect the probability of rescue.
Abstract: Evolutionary rescue occurs when adaptive evolutionary change restores positive growth to declining populations and prevents extinction. Here we outline the diagnostic features of evolutionary rescue and distinguish this phenomenon from demographic and genetic rescue. We then synthesize the rapidly accumulating theoretical and experimental studies of evolutionary rescue, highlighting the demographic, genetic, and extrinsic factors that affect the probability of rescue. By doing so, we clarify the factors to target through management and conservation. Additionally, we identify several putative cases of evolutionary rescue in nature, but conclude that compelling evidence remains elusive. We conclude with a horizon scan of where the field might develop, highlighting areas of potential application, and suggest areas where experimental evaluation will help to evaluate theoretical predictions.

475 citations

Journal ArticleDOI
TL;DR: In this paper, the authors use case studies to explore marine ecological engineering in practice, and introduce a conceptual framework for designing artificial structures with multiple functions, and show that current and future marine developments could be designed to reduce negative ecological impacts while promoting ecosystem services.
Abstract: Underwater cities have long been the subject of science fiction novels and movies, but the “urban sprawl” of artificial structures being developed in marine environments has widespread ecological consequences. The practice of combining ecological principles with the planning, design, and operation of marine artificial structures is gaining in popularity, and examples of successful engineering applications are accumulating. Here we use case studies to explore marine ecological engineering in practice, and introduce a conceptual framework for designing artificial structures with multiple functions. The rate of marine urbanization will almost certainly escalate as “aquatourism” drives the development of underwater accommodations. We show that current and future marine developments could be designed to reduce negative ecological impacts while promoting ecosystem services.

291 citations

Journal ArticleDOI
TL;DR: Good dispersal ability was consistently associated with high fecundity and survival, and in aerial dispersers it was associated with early maturation, highlighting the complex role of dispersal in the evolution of species life-history strategies.
Abstract: Dispersal, the behaviour ensuring gene flow, tends to covary with a number of morphological, ecological and behavioural traits. While species-specific dispersal behaviours are the product of each species’ unique evolutionary history, there may be distinct interspecific patterns of covariation between dispersal and other traits (‘dispersal syndromes’) due to their shared evolutionary history or shared environments. Using dispersal, phylogeny and trait data for 15 terrestrial and semi-terrestrial animal Orders (> 700 species), we tested for the existence and consistency of dispersal syndromes across species. At this taxonomic scale, dispersal increased linearly with body size in omnivores, but decreased above a critical length in herbivores and carnivores. Species life history and ecology significantly influenced patterns of covariation, with higher phylogenetic signal of dispersal in aerial dispersers compared with ground dwellers and stronger evidence for dispersal syndromes in aerial dispersers and ectotherms, compared with ground dwellers and endotherms. Our results highlight the complex role of dispersal in the evolution of species life-history strategies: good dispersal ability was consistently associated with high fecundity and survival, and in aerial dispersers it was associated with early maturation. We discuss the consequences of these findings for species evolution and range shifts in response to future climate change.

199 citations


Cites background from "Dispersal and species’ responses to..."

  • ...Habitat fragmentation, climate change and their interactions create new evolutionary pressures on dispersal behaviour by altering its cost–benefit balance (Kokko & Lopez-Sepulcre 2006; Berg et al. 2010; Le Galliard et al. 2012a; Baguette et al. 2013; Travis et al. 2013)....

    [...]

  • ...…on the shape and strength of selection on dispersal is now particularly relevant because organisms are facing new selective pressures on dispersal due to habitat fragmentation (Baguette et al. 2012) and climate change (Le Galliard et al. © 2014 John Wiley & Sons Ltd/CNRS 2012a; Travis et al. 2013)....

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Journal ArticleDOI
TL;DR: This work reviews the empirical literature on the genetic basis of dispersal, explores how theoretical investigations of the evolution of dispersed populations have represented the genetics of disperseal, and discusses how the geneticBased on the results, theoretical predictions of the Evolution of Dispersal and potential consequences are discussed.
Abstract: Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography. It is subject to evolutionary change, which begs the question, what is the genetic basis of this potentially complex trait? To address this question, we (i) review the empirical literature on the genetic basis of dispersal, (ii) explore how theoretical investigations of the evolution of dispersal have represented the genetics of dispersal, and (iii) discuss how the genetic basis of dispersal influences theoretical predictions of the evolution of dispersal and potential consequences. Dispersal has a detectable genetic basis in many organisms, from bacteria to plants and animals. Generally, there is evidence for significant genetic variation for dispersal or dispersal-related phenotypes or evidence for the micro-evolution of dispersal in natural populations. Dispersal is typically the outcome of several interacting traits, and this complexity is reflected in its genetic architecture: while some genes of moderate to large effect can influence certain aspects of dispersal, dispersal traits are typically polygenic. Correlations among dispersal traits as well as between dispersal traits and other traits under selection are common, and the genetic basis of dispersal can be highly environment-dependent. By contrast, models have historically considered a highly simplified genetic architecture of dispersal. It is only recently that models have started to consider multiple loci influencing dispersal, as well as non-additive effects such as dominance and epistasis, showing that the genetic basis of dispersal can influence evolutionary rates and outcomes, especially under non-equilibrium conditions. For example, the number of loci controlling dispersal can influence projected rates of dispersal evolution during range shifts and corresponding demographic impacts. Incorporating more realism in the genetic architecture of dispersal is thus necessary to enable models to move beyond the purely theoretical towards making more useful predictions of evolutionary and ecological dynamics under current and future environmental conditions. To inform these advances, empirical studies need to answer outstanding questions concerning whether specific genes underlie dispersal variation, the genetic architecture of context-dependent dispersal phenotypes and behaviours, and correlations among dispersal and other traits.

185 citations


Cites background from "Dispersal and species’ responses to..."

  • ...…are however a handful of examples looking at the evolution of settlement behaviours (typically probability of settling) based on habitat selection (Stamps, Krishnan & Reid, 2005), mate finding (Shaw & Kokko, 2014, 2015), prey (Travis et al., 2013a) and conspecific density (Poethke et al., 2011b)....

    [...]

  • ...…rules that may depend, for example, on environmental conditions, local density of conspecifics (McPeek & Holt, 1992; Travis, Murrell & Dytham, 1999; Poethke & Hovestadt, 2002; Kun & Scheuring, 2006), and local density of prey/parasites/predators (Travis et al., 2013a; Iritani & Iwasa, 2014)....

    [...]

  • ...Dispersal has a central role in life history and its evolution is fundamental in determining the consequences of land use change, habitat degradation, and climate change for species persistence, or a species’ invasive potential (Clobert et al., 2012; Travis et al., 2013a)....

    [...]

References
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08 Jan 2004-Nature
TL;DR: Estimates of extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.
Abstract: Climate change over the past approximately 30 years has produced numerous shifts in the distributions and abundances of species and has been implicated in one species-level extinction. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15-37% of species in our sample of regions and taxa will be 'committed to extinction'. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction ( approximately 18%) than mid-range ( approximately 24%) and maximum-change ( approximately 35%) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.

7,089 citations

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TL;DR: Species distribution models (SDMs) as mentioned in this paper are numerical tools that combine observations of species occurrence or abundance with environmental estimates, and are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time.
Abstract: Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.

5,076 citations


"Dispersal and species’ responses to..." refers background in this paper

  • ...Sy nt he si s Elith and Leathwick 2009, Dawson et al. 2011, McMahon et al. 2011, Bellard et al. 2012, Bocedi et al. 2012, Schurr et al. 2012)....

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Journal ArticleDOI
19 Aug 2011-Science
TL;DR: A meta-analysis shows that species are shifting their distributions in response to climate change at an accelerating rate, and that the range shift of each species depends on multiple internal species traits and external drivers of change.
Abstract: The distributions of many terrestrial organisms are currently shifting in latitude or elevation in response to changing climate Using a meta-analysis, we estimated that the distributions of species have recently shifted to higher elevations at a median rate of 110 meters per decade, and to higher latitudes at a median rate of 169 kilometers per decade These rates are approximately two and three times faster than previously reported The distances moved by species are greatest in studies showing the highest levels of warming, with average latitudinal shifts being generally sufficient to track temperature changes However, individual species vary greatly in their rates of change, suggesting that the range shift of each species depends on multiple internal species traits and external drivers of change Rapid average shifts derive from a wide diversity of responses by individual species

3,986 citations

Journal ArticleDOI
TL;DR: Overall, this review shows that current estimates of future biodiversity are very variable, depending on the method, taxonomic group, biodiversity loss metrics, spatial scales and time periods considered.
Abstract: Many studies in recent years have investigated the effects of climate change on the future of biodiversity. In this review, we first examine the different possible effects of climate change that can operate at individual, population, species, community, ecosystem and biome scales, notably showing that species can respond to climate change challenges by shifting their climatic niche along three non-exclusive axes: time (e.g. phenology), space (e.g. range) and self (e.g. physiology). Then, we present the principal specificities and caveats of the most common approaches used to estimate future biodiversity at global and sub-continental scales and we synthesise their results. Finally, we highlight several challenges for future research both in theoretical and applied realms. Overall, our review shows that current estimates are very variable, depending on the method, taxonomic group, biodiversity loss metrics, spatial scales and time periods considered. Yet, the majority of models indicate alarming consequences for biodiversity, with the worst-case scenarios leading to extinction rates that would qualify as the sixth mass extinction in the history of the earth.

2,834 citations

Frequently Asked Questions (18)
Q1. What are the contributions mentioned in the paper "Dispersal and species’ responses to climate change" ?

Travis et al. this paper presented a survey of the state-of-the-art work in the field of bioinformatics and applied it to the area of ecology and hydrology. 

Most connectivity models consider spatial dispersal processes as a simple function of distance instead of considering the dynamics of emigration, movement between patches and settlement decisions that together result in colonisation. 

Climate change will also affect other aspects of life history such as fecundity and mortality, which will determine the effectiveness of dispersal at a population level. 

An increased understanding of dispersal under climate change is critical to inform the deployment of effective climate change resilient conservation strategies. 

While the same selective forces that act on dispersal in stationary ranges, including kin competition and inbreeding depression, may still play a role at expanding margins, selection will now favour dispersal strategies which maximise the likelihood that some descendants follow the expanding margin (Travis et al. 

Recognition that contemporary conservation needs to facilitate the shifting of species’ biogeographic ranges and promote local adaptation has resulted in a number of potential interventions being suggested, including landscape management, assisted colonisation and genetic reinforcement (assisted adaptation) (Loss et al. 2011). 

An initial method used to establish bounds of uncertainty in species’ range changes has been to run models assuming that species exhibit either unlimited or no dispersal. 

Predicted impacts of climate change on means and variabilities of temperatures, rainfall, storm events, wind speed, snow and ice cover, CO2 concentrations, etc. (IPCC 2007) could affect the dispersal process directly, and also indirectly by changing the biophysical environment (e.g. habitat quality, availability of food resources, etc.) and the state of individuals (body size and morphology, body condition and rate of development). 

Species which rely on other biota for dispersal, such as seeds carried by ants, will suffer if the phenology of the dispersal agent becomes asynchronous under climate change (Warren et al. 2011). 

A more sophisticated and increasingly used approach for including dispersal in predictive species’ distribution models is to fit a statistical function (i.e. dispersal kernels) to observed dispersal data (Pagel and Schurr 2012, Schurr et al. 2012). 

Because different internal (e.g. individual condition, sex) and external (e.g. the local environment) factors can alter individual dispersal processes (Clobert et al. 2009), the distribution of dispersal distances is unlikely to be a fixed property of a species. 

a mechanistic approach also reduces the requirement for direct measurements of the rare long distance dispersal events that have a disproportionate impact on rates of spread (Neubert and Caswell 2000, Clark et al. 2001). 

By using the outputs of global climate models, these so-called habitat suitability models can project species distributions onto future climatic conditions. 

A simple way to incorporate dispersal has been to couple habitat suitability models with colonisation models that are based on nearest-neighbour dispersal whereby landscape grid cells that become climatically suitable can be colonised if a neighbouring cell is already occupied (Midgley et al. 

Dispersal kernels(Bartoń et al. 2012); and foster investment in dispersal traits at the expense of other life-history attributes (Burton et al. 2010). 

In one recent case that highlights a further potential complexity, the impact of temperature on dispersal distance was shown to interact with the degree of habitat fragmentation (Delattre et al. 2013): dispersal distance was greater at lower temperatures in fragmented landscape while, in more continuous landscapes, dispersal distance was greater under warmer conditions. 

The conceptual scope of this perspectives article is therefore purposely broad, covering a number of topics such as observed ecological and evolutionary patterns, theory, models and conservation. 

evolution of egg desiccation resistance has been incorporated in a biophysical model to predict climate impacts on the range of the dengue fever vector Aedes aegypti (Kearney et al. 2009).