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Perspectives in Ecology and Conservation 15 (2017) 257–265

Supported by Boticário Group Foundation for Nature Protection

www.perspectecolconserv.com

ssays  and  Perspectives  -  Rewilding  South  American  Landscapes

ewilding  ecological  communities  and  rewiring  ecological  networks

athias  Mistretta  Piresa,b

Departamento de Biologia Animal, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Rua Monteiro Lobato 255, 13083-862 Campinas, SP, Brazil
Departamento de Ecologia, Instituto de Biociências, Universidade Estadual Paulista (UNESP-Rio Claro), Av. 24-A 1515, 13506-900 Rio Claro, SP, Brazil

 r  t i  c  l  e  i  n  f  o

rticle history:
eceived 6 June 2017
ccepted 6 September 2017
vailable online 27 September 2017

eywords:
onservation
iological invasion
cological networks
ood web
leistocene

a  b  s  t  r  a  c  t

Rewilding  encompasses  management  actions  such  as reintroductions  and  translocations  with  the  pur-
pose  of  restoring  ecological  processes  and  ecosystem  functions  that  were  lost  when  species  were  locally
extirpated.  The  success  of  a species  introduction  is  conditioned  by multiple  factors,  in  particular,  ecolog-
ical  interactions.  To  predict  the  fate  of  the  introduced  population  and  the  community-level  outcomes  of
the introduction,  species  interaction  patterns  need  to be  considered.  Here  I  propose  that  ecological  net-
work models  can  help  in  rewilding  projects  in  at least  three  ways.  First,  combining  ecological  information
and  probabilistic  models  it is possible  to infer  the  most  likely  ways  whereby  the introduced  species  will
integrate  the  community  and  which  will  be its  role  in  the  topology  of  the  food  web.  Second,  by  determin-
ing  the  species  more  likely  to  interact  directly  or indirectly  with  the  introduced  species,  it  is possible  to
identify  those  species  that may  affect  the success  of  the introduction  and those  that  are  more  likely to be
affected.  Third,  by constructing  potential  interaction  networks  representing  the  rewilding  scenario,  one

can  infer  the  possible  ways  by  which  the  overall  structure  of  the network  will  change  and  thus  devise
more  efficient  plans  to monitor  the community.  Network  models  can  be an  important  asset  in  rewilding,
helping  in  feasibility  and risk  assessment  as  well  as  in  monitoring  the  consequences  after  species  release.

©  2017  Associação  Brasileira  de Ciência  Ecológica  e Conservação.  Published  by Elsevier  Editora  Ltda.
This  is  an  open  access  article  under  the  CC  BY-NC-ND  license  (http://creativecommons.org/licenses/by-

nc-nd/4.0/).
ntroduction

Over the past decades, conservation biology underwent a shift
rom a field whose main mission was to evaluate extinction risk
nd halt diversity loss, with particular focus on threatened species
Meine, 2010), to a more process-centered view, whose focus is
he conservation of functional ecosystems (Tylianakis et al., 2010).
he functioning of ecological systems depends on the integrity of
he ecological networks formed by the multiple interactions that
pecies establish with each other (McCann, 2007; Molnar et al.,
004). The main strategy for the conservation of ecosystems is
rguably the establishment and management of large intercon-
ected reserves (Lovejoy, 2006). Larger areas can harbor a greater
iversity of habitats, organisms and thus of ecological processes
Peres, 2005). However, maintaining large areas does not guaran-
ee ecological processes will be preserved. In fact, most regions of
he planet are already largely defaunated (Dirzo et al., 2014), and

ithout large stocks of organisms in neighboring areas, reserves
ay  be nothing more than large patches of empty forests (Redford,

992) amidst the urban and agricultural landscape matrix.

E-mail address: mathiasmpires@gmail.com

https://doi.org/10.1016/j.pecon.2017.09.003
530-0644/© 2017 Associação Brasileira de Ciência Ecológica e Conservação. Published by
http://creativecommons.org/licenses/by-nc-nd/4.0/).
The extirpation of large vertebrates began when humans
expanded their distribution in the Pleistocene and continued in his-
torical times, having profound consequences for ecological systems
(Malhi et al., 2016). Large bodied vertebrates are often key players
in ecosystems, participating in several processes such as nutrient
cycling (Doughty et al., 2013), long-distance seed dispersal (Pires
et al., 2017) and exerting top-down control on species on lower
trophic levels (Ripple et al., 2015; Terborgh, 2001). Moreover even
smaller-sized vertebrates, which might be able to compensate to
some extent the absence of large-bodied species, are now declin-
ing in most areas (Donatti et al., 2009). This scenario calls for more
active restoration approaches in order to reestablish animal popu-
lations in the wild (rewilding) and their ecological interactions
(rewiring), thus reinstating ecological processes and ecosystem
functions (Seddon et al., 2014).

Soulé and Noss (1998) proposed the use of the Pleistocene
as a baseline for ecosystem restoration in North America and
introduced the rewilding concept. Rewilding was originally defined
as “the restoration and protection of big wilderness and wide-

ranging large animals – particularly carnivores” (Soulé and Noss,
1998). The use of the Pleistocene fauna as a baseline implies in
the introduction of taxon substitutes, such as large felids, feral
horses, cattle and elephants, that would be able perform the

 Elsevier Editora Ltda. This is an open access article under the CC BY-NC-ND license

https://doi.org/10.1016/j.pecon.2017.09.003
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mailto:mathiasmpires@gmail.com
https://doi.org/10.1016/j.pecon.2017.09.003
http://creativecommons.org/licenses/by-nc-nd/4.0/
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2 y and 

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58 M.M. Pires / Perspectives in Ecolog

oles that vacated since the native megafauna, like saber-toothed
ats, lions, camels, sloths and mastodons died out (Donlan, 2005;
aletti, 2004). More recently the definition has been loosened
nd rewilding now encompasses both the reintroduction of locally
xtinct species (Galetti et al., 2017; Svenning et al., 2016) and
onservation translocations using surrogate species whose eco-
ogical roles would be equivalent to the species that have been
ost (Seddon et al., 2014). Different from traditional reintroduction,

hich focuses on recovering declining populations, the ultimate
im of rewilding is to restore ecosystem processes that were lost
ue to local extirpation, generating a self-regulated community,
ithout the need of continued management (Sandom et al., 2013;

venning et al., 2016).
To reestablish ecological processes, a rewilding project envis-

ges rewiring an emptied food web with the desired links. Rewiring
s the reconfiguration of the interaction patterns of network ele-

ents (Watts and Strogatz, 1998). I refer to network rewiring
s the establishment of novel ecological interactions, which will
enerally involve the introduced species, but also reconfigurations
f the interaction network that occur as an indirect effect of a
pecies introduction.

If the goal in rewilding is to restore certain ecological processes,
he viability of the introduced population over time has to be
ecured. However, a number of examples from accidental intro-
uctions and biological invasions show that the introduction of

 species into a local food web may  trigger cascading effects via
irect and indirect pathways that can result in diversity decline
nd changes in ecosystem properties (Lodge, 1993). Thus, a rewild-
ng program should also be able to ensure that the negative impact
n other populations will be minimal. Considering the amount of
esources a rewilding initiative demands and the potential mishaps,
t is compulsory to use techniques that allow foreseeing the suite
f possible outcomes after an introduction.

The “Guidelines for Reintroductions and Other Ecological
ranslocations” (IUCN/SSC, 2013) from the IUCN Reintroduction
pecialist Group (RSG) highlight the need of assessing the match
etween the abiotic and biotic needs of the candidate species and
eatures of the target area. Basic information about the abiotic
onditions determining species occurrence are available for many
pecies, especially the ones that are often considered as poten-
ial rewilding candidates. Distribution modeling techniques allow
redicting, sometimes with a very high level of confidence, where
re the suitable regions for a species (Elith and Leathwick, 2009).
et, without careful consideration of how biotic interactions affect
he dynamics of the managed population and how the introduced
pecies will affect other organisms, a reintroduction program is
entenced to failure. In short, the main challenge of any reintroduc-
ion is how to guarantee that the introduced species will subsist in

 biotic context where it is able to sustain its population and will
ot harm the others.

Here I first review a set of cases where success and failure of
pecies introduction was related to the effects of biotic interactions.
ext, I argue that approaches derived from network science can be
n asset for planning, assessing the viability, and for monitoring the
uccess of rewilding.

iotic interactions and rewilding success

As soon as individuals of the candidate species are released they
ill create novel interactions with several other species, which
ill influence the likelihood that the population establishes and

ill have consequences to the local community. Looking at the

utcomes of past reintroductions and translocations is the key to
nderstand how biotic interactions affected their success or failure.
ost of the early attempts of reintroduction were ill-planned, with
Conservation 15 (2017) 257–265

no post-introduction monitoring (Seddon et al., 2007). Available
data regarding introductions in the 70s and the 80s show many pro-
grams were unsuccessful in reestablishing populations, although
the causes of failure were mostly unaddressed (Griffith et al., 1989;
Seddon et al., 2007). Examining more than 500 cases of reintroduc-
tion to understand what aspects were being reported, Seddon et al.
(2007), found that only 7% of the studies addressed the ecologi-
cal effects of the introduction, i.e., the interactions of the released
species with the environment and other organisms.

The RSG (Reintroduction Specialist Group) reports include
detailed analyses on the consequences of introductions, but tend to
report mainly the successful attempts. Actually, this is a trend in the
restoration literature (Fischer and Lindenmayer, 2000; Moehren-
schlager et al., 2013), which may  be harmful to our understanding
on the reasons underlying failure or negative impacts of introduc-
tions and translocations. Yet, most of the reports on introductions
and translocations covered by the RSG list interactions with preda-
tors, pathogens, competitors and resource availability (prey or
plants) as potential causes determining the success of introduction
attempts.

Predation, either by native or invasive predators, is often identi-
fied as a major determinant of post-release mortality, limiting the
success of released individuals to form a viable population (Innes
et al., 1999; Moseby et al., 2011; Seddon et al., 2007). High mortal-
ity due to predation after release has been associated mainly with
the naivety of captive-bred individuals (Aaltonen et al., 2009; Big-
gins et al., 2011). Exposure to predators during captivity has been
shown to reduce predation-related mortality for different species
(e.g., Heezik et al., 1999). A careful assessment of the predator–prey
interactions the rewilding candidate establishes in its location of
origin may  allow determining the potential predators in the tar-
get area and how the introduced species will integrate the local
interaction network. Such knowledge may  help devising strategies
that minimize loss due to predation, including rearing schemes that
foster anti-predator behavior.

Large-bodied rewilding candidates are presumably less likely to
be victims of predation, especially in already defaunated areas. Still,
other natural enemies such as pathogens may  induce high mor-
tality in released individuals. The stress produced during capture
and transportation may  affect the immune system of introduced
individuals making them more susceptible to pathogen infection,
a phenomenon described for different species such as beavers
(Castor fiber; Nolet et al., 1997) and the Eurasian lynx (Lynx lynx;
Schmidt-Posthaus et al., 2002). Knowledge of the pathogens carried
by resident organisms and their potential to infect the introduced
species is essential to the development of countermeasures that
reduce mortality risk.

Another important source of mortality related to the biotic com-
ponent is starvation. Examples include introductions of the river
otter (Lontra canadensis; Day et al., 2013), the Arabian oryx (Oryx
leucoryx; Mésochina et al., 2003), and the Canadian lynx (Lynx
canadensis; Devineau et al., 2010). Failure of several reintroduc-
tion attempts of African predators has also been associated with
reduced prey availability in the target area or inefficient hunting
skills of captive-bred individuals (Hayward et al., 2007). In a compi-
lation of carnivore introduction success, Jule et al. (2008) found that
starvation was the second cause of mortality after human related
causes such as shooting and vehicle collision. Competition with res-
ident species for resources may  also hinder the establishment of the
introduced population (Hayward et al., 2007; Jule et al., 2008).

Starvation may  only become a prevalent mortality cause when
populations grow unchecked due to low top-down control. In the

Oostvaardersplassen, a fenced nature reserve in the Netherlands
where cattle, red deer and horses were introduced, populations
undergo die-offs as the availability and quality of the forage
drops during the winter (Vera, 2009). In the absence of predators,



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M.M. Pires / Perspectives in Ecolog

ntroduced species may  also cause undesirable impacts on the
egetation, affecting several other species, such as happened with
eintroduced elk (Cervus canadensis)  in Kentucky (Cox, 2011),
ntroduced red deer (Cervus elaphus) in Scotland and mustang
orses in North America (Sandom et al., 2013). Yet, the effects of

ntroduced herbivores on vegetation are likely complex, including
esirable and undesirable outcomes (Johnson and Cushman, 2007).
eing able to predict in which species the rewilding candidate will

eed on, which are the potential competitors in the target area, and
hether it will be able to shift to alternate resources in case the
ain resource declines is important to foretell not only the success

f the introduction, but also the consequences it will have for other
embers of the community.
A number of examples illustrate the potential of introductions

o produce adverse effects (Moehrenschlager et al., 2013; Ricciardi
nd Simberloff, 2009). The introduction of foxes (Vulpes vulpes) in
ustralia in the nineteenth century for recreational hunting still

hreatens several native species (Saunders et al., 2010). Beavers
Castor canadensis) introduced in the 1940s in Southern South
merica for fur trading have devastated the endemic Nothofagus

orests and constructed dams produce a number of effects on ripar-
an communities, water chemistry, and nutrient cycling (Vázquez,
002). Introduction of the Nile perch (Lates niloticus) in Lake Victo-
ia resulted in population decline and extinctions of several2 native
pecies of fishes (Harrison and Stiassny, 1999). In all these cases
he introduced species impacted resident populations via negative
irect and indirect effects, causing diversity loss and changes in
cosystem functions.

Despite the conceivable risks, well-planned and well-monitored
ewilding has the potential to produce the desired effects. The
eplacement of extinct endemic giant tortoises in Mauritius by an
cologically similar tortoise (Aldabrachelys gigantea) has restored
cological functions such as seed dispersal and control of invasive
eeds (Griffiths et al., 2010). The release of wolves in Yellowstone

ecame the paramount example of the overarching effects that
ewilding can produce. The introduction of wolves is thought to
ave regulated the population of elks, numerically or by inducing
ehavioral changes, allowing woody plants such as aspen (Populus
remuloides) and willows (Salix spp.) to recover, but also benefitted
eavers and songbirds that depended on the trees and bears who
enefit from carcasses (Ripple and Beschta, 2012).

Collectively the examples discussed above highlight a salient
eature of rewilding, the introduction of a novel species in a com-

unity will create a suite of novel direct and indirect interactions,
hich determine the fate of the introduced population and the con-

equences of the introduction for the community. The interactions
stablished by an introduced species are part of a larger network of
nteractions. The network approach offers the tools to characterize
nteraction patterns and thus infer how species affect each other
n the community. Using a network approach in the rewilding con-
ext might allow determining which other species may  represent
otential threats or assets to the establishment of the introduced
opulation, affecting the outcome of a rewilding project.

 network approach for rewilding

If the motivation for rewilding is to restore ecological processes,
he proximate goal is to restore past interactions or establish novel
nes that will help reinstating community function (Seddon et al.,
014). The multiple biotic interactions species establish in ecolog-

cal communities make it hard to predict how an introduction will
lay out. Ideally, a rewilding program should be able to foresee: (i)

hich interactions will be established once a species is introduced;

ii) how the introduced population will react, taking into account
he positive and negative interactions; (iii) the impacts of the intro-
uction on the multiple species in the community and how these
Conservation 15 (2017) 257–265 259

impacts will feedback to the introduced population. I argue that
network modeling may  not only help devising such predictions but
also monitoring the outcomes of an introduction.

The network approach has a long history in ecology, from
the diagrammatic representation of feeding interactions between
species in communities or energy flow in ecosystems, to more
sophisticated quantitative analysis of network structure in search
for general patterns and underlying processes (Dunne, 2006). The
overall architecture of the network is determined by the ecology
of its constituent species. The number and strength of interactions
of a given species in the network are determined by its degree of
ecological specialization (Blüthgen et al., 2008). Thus, the level of
ecological specialization of the species in the studied community
determines network properties such as the distribution of the num-
ber of links, the distribution of interaction strengths and the overall
density of links in the network. Complex network patterns such
as compartmentation, the presence of groups of species densely
connected to each other, can also be mapped into ecological mecha-
nisms such as habitat preferences or morphofunctional constraints
(Dunne, 2006; Baskerville et al., 2011). Therefore, examining the
structure of ecological networks offers insights on the mechanisms
underlying community assembly and functioning.

The main information required to study interaction networks
is basically the information on who interacts with whom in a
given locality. Ideally we  wish to know what are the demographic
consequences of each interaction, but this information is often
unavailable (Vázquez et al., 2005). The inherent complexity of real
ecological networks makes the outcome of interactions in terms of
their demographic effects hard to predict (Vucetich et al., 2011).
Even the dynamics of apparently simple systems where a preda-
tor and prey seem to control each other’s densities, are actually
more complicated and involve several trophic levels (Krebs et al.,
2001). In this sense predicting the demographic responses of all
community elements to such a drastic intervention as a species
introduction is unfeasible. However, networks can help us predict
how the introduced species will integrate in the community and
point in the direction of what are the most likely ways a community
is expected to change.

The network approach offers the toolset to quantitatively assess
the overall architecture formed by species interactions and the role
of each species in the network. A rewilding candidate will usu-
ally be chosen based on the expectation that it will be able to
restore key processes lost after species extirpation. In this sense
the introduced species is expected to display the characteristics of
keystone species, those whose influence on ecological processes
are disproportionate in relation to abundance (Power et al., 1996).
Although identifying keystone species is fraught with challenges
and requires experiments or manipulation in the field (Power
et al., 1996), the network approach offers ways to detect poten-
tial keystone species based on the species connection patterns
and its degree of topological centrality (Jordán et al., 2006). If the
introduced species take on a central position in the network, inter-
acting directly and indirectly with many other species, it is very
likely to become a key species in the community (Soulé et al., 2003).

One of the main shortcomings of the network approach is that
obtaining detailed information on interactions to build high resolu-
tion ecological networks is demanding (McCann, 2007). Moreover,
ecological systems are dynamic adaptive systems where species
compositions and interaction patterns can change over short and
long timescales. For instance, Olesen et al. (2008) have shown that
interaction turnover in plant–pollinator networks may be high,
changing from day to day. Therefore, the best way to think on

an ecological network is not as a snapshot, which is often how
networks are represented and studied. Instead, predictions should
incorporate the uncertainty on interaction patterns, which is inher-
ent to any real ecological network.



2 y and Conservation 15 (2017) 257–265

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Box 1: Constructing probabilistic predator–prey
networks using body mass data

In previous studies we used the network modeling
approach to reconstruct predator–prey interactions between
large mammals that occurred in the past (Yeakel et al., 2014;
Pires et al., 2015). First, we compiled information on the inter-
action patterns of mammalian predators and their prey in
Africa to investigate how body size affected the probability
that we observed an interaction between two species in the
dataset. To determine the relationship between body mass and
predator–prey interactions, we used a statistical model (Rohr
et al., 2010) where the logit of the probability of an interaction
(aij = 1 in matrix A) is a function of the ratio between the body
mass of predator (mi) and prey species (mj):

log

[
P(aij = 1)
P(aij = 0)

]
=  ̨ +  ̌ log

(
mi

mj

)
+ � log2

(
mi

mj

)
.

Using model fitting techniques we estimated the param-
eters of the model (˛, ˇ, �) that yielded the best fit to the
observed data. The expected fraction of interactions (and non-
interactions) correctly predicted by the model can be computed
by summing the estimated probability assigned to each pair-
wise interaction (and absences of interactions) and dividing it
by the total number of interactions.

fc
(
A|�

)
=

∑
i

∑
jaijP(i, j|�) + (1 − aij)(1 − P(i, j|�))∑

i

∑
jaij + (1 − aij)

This metric allows characterizing the performance of the
model in a straightforward way, and it showed that the model
was able to correctly predict between 70% and 83% of the
observed interactions in different datasets (Pires et al., 2015).

For a given a set of parameters the probability of interac-
tions can be computed as:

P(i, j) = e˛+  ̌ log(mi/mj)+� log2(mi/mj)

1 + e˛+  ̌ log(mi/mj)+� log2(mi/mj)

We then used the model and estimated parameters to
infer the probability of interactions and reconstruct potential
interaction networks between species known to occur in past
assemblages in Africa (Yeakel et al., 2014) and in the Ameri-
cas during the Pleistocene (Pires et al., 2015). The underlying
assumption here is that the rules governing the relationship
between body mass and predator–prey interaction patterns
would be similar in past and present assemblages.

In these examples we only used qualitative information on
interactions and used only body–mass relationship to predict
predator–prey interactions. If more information were avail-
able for the assemblages we wanted to reconstruct, such as
abundance estimates, it would have been possible to weight
pairwise interaction probabilities and obtain more realistic esti-
mates. The purpose of this type of model is not to obtain highly
accurate inferences on the probability that two species will
interact in a given place, but to generate an approximation
that shows which interactions are more likely or less likely to
60 M.M. Pires / Perspectives in Ecolog

In the same ways that considering stochastic processes helped
o devise more representative models for population viability
nalyses (Morris and Doak, 2002), by dealing with ecological inter-
ctions under a probabilistic framework it is possible to encompass
he uncertainty in the outcomes of rewiring after a species is
ntroduced. The toolset to analyze the structure of probabilistic
nteraction networks is now available (Poisot et al., 2016), and prob-
bilistic models that predict species interaction patterns based on
pecies traits are becoming more popular in the literature of eco-
ogical networks (e.g., Pires and Guimarães, 2013; Rohr et al., 2010;

illiams and Purves, 2011).
Probabilistic network models are statistical or rule-based mod-

ls in which the probability of pairwise species interactions is
redicted from a set of parameters, which can often be mapped

nto one or a small set of species traits (Williams and Purves, 2011).
here is a growing body of studies showing a large part of the inter-
ction patterns in real ecological networks can be predicted based
n basic biological information, such as habitat use (Baskerville
t al., 2011), body mass relationships (Brose et al., 2006; Gravel
t al., 2013) and abundance (Krishna et al., 2008).

Interactions between any pair of species can only occur if species
o-occur in space and time. For instance, diverging habitat prefer-
nces may  result in low encounter rates diminishing the likelihood
f interactions (Baskerville et al., 2011). Species may  also miss
ach other due to phenological mismatches, as happens in many
easonal plant–pollinator systems where the activity patterns of
ollinators are decoupled from the flowering periods of certain
lants (Olesen et al., 2011). If individuals from different species do
ome into contact, some level of trait matching may  be required
or interactions to take place. For instance, body size ratio has been
ound to be a good predictor of predator–prey interactions (Brose
t al., 2006). Fruit size and gape-width are often found as rele-
ant traits conditioning seed-dispersal interactions between plants
nd frugivores (Dehling et al., 2016). Likewise, the match between
he depth of the corolla and the length of the pollinator proboscis
r bird beak constrains interactions in plant–pollinator networks
Stang et al., 2009; Vizentin-Bugoni et al., 2014). In fact, the over-
ll structure of interaction networks seems to be predicted by a
mall number of species traits (Eklöf et al., 2013; Pires et al., 2011).
inally, abundance determines the likelihood that individuals from
ifferent species will run into each other. Thus, high interaction
requency may  simply reflect high local abundance, whereas two
are species are unlikely to interact (Vázquez et al., 2007).

The first step to build predictive network models is to compile
nformation on the interactions between the species in the tar-
et area, and interaction patterns of the candidate species with
ther species across its distribution (Fig. 1). Although any species is
nvolved in multiple types of interactions, both as consumer (preda-
or, herbivore, seed disperser, pollinator) and as resource (prey,
ost), studied networks usually focus on a certain interaction type,
hich presumably is the most relevant for the phenomena of inter-

st. Accordingly, different types of interactions can be modeled
eparately. To model trophic interactions such as predator–prey or
erbivore–plant interactions, for instance, the required informa-
ion can be drawn from dietary assessments, which are available
or a large number of species, especially those that are often con-
idered for rewilding. Next, one should gather information on the
raits likely to affect interaction patterns such as morphology, habi-
at preferences, activity patterns and behavior (Fig. 1). Of course
he lack of information for certain traits will be the main limita-
ion at this point. However, one or a few trait dimensions may  be
nough to build networks models that are able to predict a reason-

ble share of species interaction patterns (see Box 1). Moreover,
f a species is being introduced in a given locality it is expected
hat basic information on morphology and behavior, for instance,
s known.
happen in a given assemblage.

Morales-Castilla et al. (2015) suggest a hierarchical framework
for combining these different layers of biological information in a
single model and build theoretical ecological networks that encom-
pass the effects of multiple factors (Fig. 2). This approach starts by
identifying species unlikely to interact based on basic biological
information such as trophic level or guilds. This coarse catego-

rization allows identifying forbidden links (e.g., herbivores are not
allowed to feed on carnivores). The next step is to refine the infer-
ence on the interactions by establishing the relationship between
interaction probability and species traits. Using information on



M.M. Pires / Perspectives in Ecology and Conservation 15 (2017) 257–265 261

Fig. 1. An outline for building ecological networks to inform rewilding programs. Data on the traits and interaction patterns of the candidate species can be used to test the
relationship between specific traits and the probability of interactions with other species. Similar tests can be performed using data on the traits and interactions among the
s n be u
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pecies  occurring in the target area. The estimated parameters and trait data can the
he  rewilding scenario. The position of the introduced species in the network (as h
nd  network structure can be examined.

nteraction patterns for the species occurring in the target area
nd assuming interaction sampling was thorough, it is possible
o use statistical approaches such as generalized models to test
or the effectiveness of certain traits in predicting interactions
nd estimate the parameters governing the relationship (Eklöf
t al., 2013; Rohr et al., 2010; Sebastián-González et al., 2017).
nce the important traits are identified, the estimated parameters
f the fitted models can be used to compute interaction proba-
ilities (Figs. 1 and 2; Morales-Castilla et al., 2015). Combining

nformation on the traits of the species in the target area and the
stimated parameters, interaction probabilities can be computed
roviding a picture of which are the species likely to interact with
he introduced species (Figs. 1 and 2; Box 1).

The interaction probabilities estimated from different traits can
e combined in multidimensional models resulting in joint proba-
ilities, which reflect the combined effects of different independent
raits on the likelihood that an interaction occurs (Fig. 2; Eklöf et al.,
013; Williams and Purves, 2011). Further layers depicting habitat
se or abundance can be used to weight the estimated probabil-

ties (Fig. 2). Actually, the use of a hierarchical framework allows
onsidering any modifier known to affect interactions, including

ehavior, natural history information or abiotic characteristics of
he environment (Fig. 2). Layers representing mechanisms known
o affect the efficiency of interactions, the actual effects of inter-
ctions on individual fitness or population dynamics, may  also
sed to compute interaction probabilities and build ecological networks simulating
ted by the node with a different color in the model network) can then be inferred

be considered, delivering more functionally meaningful networks
(Vidal et al., 2013).

The relationship between interaction probability and species
traits can also be analyzed under a Bayesian approach, which
uses information on the observed interactions and empirical trait
distribution to obtain posterior probability distributions for the
parameters (Bartomeus et al., 2016). This approach also allows
weighting the trait distribution by species abundances, thus tak-
ing into account the neutral component of the interaction. Multiple
traits can be considered at the same time by computing the joint
probabilities under the assumption that the considered traits are
independent (Bartomeus et al., 2016). Even in the absence of infor-
mation on specific traits governing interactions it might be possible
to obtain information on interaction probabilities using informa-
tion on the patterns of interactions of other species, as done using
the framework of latent traits (Rohr et al., 2016) or by using proxies
that encompass a combination of traits such as phylogenetic data
(Mouquet et al., 2012).

The final outcome of the outlined modeling approaches is an
array of pairwise interaction probabilities. The pairwise interac-
tion probabilities can be represented as a matrix or as a weighted

network where the weights represent the inferred probabilities
(Fig. 2). This network has the desirable property of encompassing
the uncertainty on interaction patterns. The interaction proba-
bilities obtained using any of the approaches listed above, allow



262 M.M. Pires / Perspectives in Ecology and Conservation 15 (2017) 257–265

Fig. 2. Building a probabilistic network. Each hypothetical matrix contains information on the probability of pairwise interactions between predator (rows, labeled P#)
and  prey (columns, labeled p#) species. Darker colors depict higher probabilities. Species are ordered according to decreasing body size from top to bottom and left to
r -occur
m Overa
p d ens

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C

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ight.  Interaction probabilities can be estimated according to different variables: co
atching (body–mass ratio is depicted in the example), and relative abundances. 

robabilities computed according to each variable and a probabilistic network or an

onstructing an ensemble of potential networks, each representing
 hypothesis about how species interact with each other in the com-
unity. The properties of the whole network and of each species

an be analyzed using metrics designed specifically for probabilis-
ic networks (Poisot et al., 2016) or by applying the conventional

etrics to the networks constructed from the interaction proba-
ilities. From these interaction networks other networks depicting

ndirect interactions, such as competition, can also be inferred.
Having inferred networks for the rewilding scenario, it is pos-

ible to identify those species that are more likely to be affected
y the introduction, e.g., by quantifying the distances separating
he species in the food web. Furthermore, using basic descriptors
f network structure it is possible to infer what are the most likely
esponses of the community as a whole to rewilding. Properties
uch as connectance, the proportion of interactions that actually
ccur relative to the theoretical maximum, and compartimentaliza-
ion, are related to the vulnerability of a network to change (Dunne
t al., 2002; Stouffer and Bascompte, 2011). Thus, given the straight
elationship between network structure and community dynamics,
nalyzing the structure of the local interaction networks before and
fter management actions such as rewilding should be a standard
rocedure.

hallenges

The main challenge to the implementation of network methods
n rewilding projects is data availability. Obtaining information on
cological interactions in the wild is expensive and time consuming
Tylianakis et al., 2010). In the best scenario, interactions among

pecies in the target area will be well known and a model will
nly be required to fit the candidate species in the network. Nev-
rtheless, in the proposed framework a first approximation on the
nteraction networks can be obtained even in the absence of data
rence, behavioral or natural history information known to affect interactions, trait
ll interaction probabilities can be obtained from the element-wise product of the
emble of theoretical networks based on probabilities may be built and analyzed.

on the local interactions patterns. Using the data already available
in the literature, one may  be able to infer the relationship between
interaction patterns and traits in a different locality and generate
potential interaction networks for the species in the area of inter-
est under the assumption that the relationship between traits and
interaction occurrence is consistent.

Interaction patterns can vary over time and across space. One
way of dealing with this variation is to perform separate analyses on
the relationship between interaction probability and species traits
for all locations from where good quality information on inter-
action patterns is available. By conducting separate analyses for
different locations one can evaluate the consistency of the rela-
tionship between interaction patterns and traits and estimate a
range for the parameter values. Different networks could then be
generated using a range of parameters to account for such context
dependence.

Another potential source of variation in the interaction pat-
terns is intrapopulation variation. Interactions take place at the
individual level (Poisot et al., 2015) and the traits of individuals
affect how exactly they will be behave in a novel ecological context.
Using population averages to predict interactions is reasonable if
intrapopulation variation is small or if the sample of individuals
to be introduced is not a biased one. Otherwise variation among
individuals should also be taken into account. Some of the trait-
informed network models discussed above can also incorporate
trait information at the individual level when this information is
available (Bartomeus et al., 2016).

Any species participate in different types of interactions, playing
multiple ecological roles as consumers, resources, hosts, competi-

tors and mutualists. Networks are often constructed based on a
single type of interaction, but the ideal scenario would be to gener-
ate predictions on multiple interaction types, so as to anticipate the
consequences of an introduction for different groups of organisms



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M.M. Pires / Perspectives in Ecolog

nd different ecological processes. Obtaining information on all the
ossible interactions of species in a community is unrealistic. Yet,
ome information on the main interactions is better than none. The
tudied networks will always include only a subset of the innu-
erable interactions that species participate. The main assumption

hared by any community ecology study is that the subset of stud-
ed interactions is representative of the interactions governing the
ynamics of the studied system. Network ecologists have recently
egun to merge different types of interactions in a single network
nd study its properties (Pilosof et al., 2017). As novel methods
re devised for the analysis of these multilayered networks a more
omplete assessment of the impacts of the introduction of a new
pecies in the community will be possible.

oncluding remarks

The assessment of the results of species introductions and
ranslocations often focus on population aspects of the target
pecies or on a single or a few pairwise interactions (Svenning
t al., 2016). However, to fully understand the impact of such inter-
entions, variables related to the whole community and ecological
rocesses should be monitored over time. In this sense using a net-
ork approach to monitor the aftermath of the release may  help
nraveling broader consequences and the need for further manage-
ent. The main risk is that the new community with the introduced

pecies have unanticipated emergent properties of its own  (Seddon
t al., 2014). Although network approaches cannot replace the need
f detailed information on demographic trends of species to assess
ow community functioning was impacted, the use of networks
ay  allow detecting which are the species that should be moni-

ored closely. Moreover the network approach is not intended to
eplace any other tool already established in conservation practice,
ut to be used as an ancillary approach that combined with others,
ay  assist conservation planning and monitoring.
Monitoring a rewilding program using networks may  also help

n adaptive management actions, where management options are
eassessed based on results from previous implementation steps
o guarantee efficient establishment of the population or reduce
mpacts (Moehrenschlager et al., 2013). After release the several
ossible hypothesis on interaction patterns generated by network
odels can be refined according to observation and the interac-

ion networks can be constantly reevaluated. Depending on the
bserved changes, the need for interventions, such as resource pro-
isioning or predator control (in the case of exotics) (e.g., Innes et al.,
999), may  be anticipated.

Network science can also benefit from the use of network mod-
ls in rewilding. Rewilding episodes may  allow testing theoretical
redictions on the relationship between network structure and
ommunity dynamics (e.g., Ripple and Beschta, 2012) and guide
heoreticians on how to make models more accurate. With each
pecies introduction instance where the changes in networks struc-
ure are not evaluated we miss a unique opportunity of learning

ore about how ecological communities respond to change.
The success of any introduction depend on several factors, such

s the number of released individuals (Fischer and Lindenmayer,
000), variation in habitat quality (Wolf et al., 1998), and human

nterference (Jule et al., 2008). Yet, a key component in a rewild-
ng program is how biotic interactions affect and are affected
y a species introduction. Because biotic interactions can affect
he success of introduction in multiple ways, using probabilistic
pproaches and network models in the different stages of program

mplementation can offer important insights on community-
evel responses to rewilding while incorporating uncertainties,
hus helping evaluating the balance between the benefits and
isks.
Conservation 15 (2017) 257–265 263

When rewilding involves species that are not originally from
the target region the risks are potentially greater and less pre-
dictable and meticulous assessment of the these risks are necessary
(IUCN/SSC, 2013). Although introductions of species outside its
range would better be avoided (Ricciardi and Simberloff, 2009) the
severity of the ongoing biodiversity crisis asks for proactive meas-
ures to maintain functional ecosystems (Svenning et al., 2016). As
highlighted by Jepson (2016) rewilding is already happening and
we need to employ the right tools to minimize negative impacts
and strengthen the desired effects. Being able to foresee how inter-
actions will rewire the food web  is critical to predict the success of
an introduction and its potential outcomes.

Funding

MMP  was supported by São Paulo Research Foundation (FAPESP:
grant #2013/22016-6) and Coordenaç ão de Aperfeiç oamento de
Pessoal de Nível Superior (CAPES).

Acknowledgments

I thank Paulo R. Guimarães and Tiago B. Quental for comments
and suggestions.

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	Rewilding ecological communities and rewiring ecological networks
	Introduction
	Biotic interactions and rewilding success
	A network approach for rewilding
	Challenges
	Concluding remarks
	Funding
	Acknowledgments
	References