Maximizing the success of assisted colonizations


  • Editor: Matthew Gompper


Aliénor L. M. Chauvenet, Institute of Zoology, Zoological Society of London, London NW1 4RY, UK.



Climate change is causing spatio-temporal shifts in environmental conditions, and species that are not able to track suitable environments may face increased risks of extinction. Assisted colonization, a form of translocation, has been proposed as a tool to help species survive the impacts of climate change. Unfortunately, translocations generally have a low success rate, a well-documented fact that is not considered in most of the recent literature on assisted colonization. One of the main impediments to translocation success is inadequate planning. In this review, we argue that by using well-known analytical tools such as species distribution models and population dynamics modelling we can maximize the success of assisted colonization. In particular, we present guidelines as to which questions should be investigated when planning assisted colonization and suggest methods for answering them. Finally, we also highlight further implementation and research issues that remain to be solved for assisted colonizations to be efficient climate change adaptation tools.


Global biodiversity is under increasing threat from anthropogenic impact, and the unprecedented rate of species loss is a major concern to ecologists and wildlife managers (Secretariat of the Convention on Biological Diversity, 2010). One of these impacts is climate change, which has already affected the Earth's biota and is expected to have a profound effect on individuals and populations for years to come (Mooney et al., 2009). While climate change is often perceived as being synonymous with global warming, an increased average temperature on Earth is not the only expected consequence. Changes in seasonal patterns of rainfall and temperature, frequencies of extreme events and greenhouse gas concentrations are also to be expected (Foden et al., 2008). All of these consequences pose a threat to species globally. As environmental conditions are altered through climate change, spatial mismatches will develop between species locations and their optimal environments (Atkins & Travis, 2010). Responses of species to change in environmental conditions are expected to fall into one of the following four categories: (1) thrive, that is, species able to remain extant under the new set of conditions without having to change their ecology; (2) adapt, that is, species able to remain extant under the new set of conditions by changing their ecology; (3) shift range, that is, species whose range will contract or shift to remain under suitable conditions; (4) go extinct, that is, species not able to remain extant under changing conditions nor adapting or shifting distribution (Pettorelli, 2012).

Numerous species could fall into category (4), and many of these expected extinctions may be driven by topographic, physiological, behavioral or ecological constraints to dispersal (Foden et al., 2008). This is the case for species confined to offshore islands, isolated patches of habitats separated from suitable environments by physical barriers and species that are not mobile or disperse too slowly. To counteract the impact of climate change, human intervention, in the form of translocation, may be a solution. Translocation outside a species’ historic range for conservation purposes is traditionally called ‘conservation introduction’ (IUCN, 1998), but the term ‘assisted colonization’ has recently been coined in the literature (Seddon, 2010).

Assisted colonization is a controversial tool for reasons that are discussed below. However, in cases where it is the conservation action of choice, it makes sense to ensure that it succeeds. In this review, we highlight how assisted colonization can be planned to efficiently counteract the impact of climate change on biodiversity. To do so, we review the state of translocation as a conservation action and highlight its existing shortcomings, review the current state of knowledge on assisted colonization and propose a way to improve the overall chance of success of translocations and thus assisted colonizations.


Translocations are defined as ‘any movement of living organisms from one area to another’ (IUCN, 1987) and have been part of the conservation toolkit for decades (Seddon, Armstrong & Maloney, 2007). Conservation-orientated translocations have historically been poorly planned and monitored (Armstrong & Seddon, 2008) as they were likely to be a last resort solution, and decision makers often did not have the time or training to predict possible outcomes or plan necessary management beforehand. Guidelines for reintroduction that were published by the IUCN (1998) and several papers in the late 1980s and 1990s emphasized the need for an increase in research-oriented planning and monitoring (Scott & Carpenter, 1987; Griffith et al., 1989). Although the situation improved, a recent review suggests that the success rate of translocations is still generally low, with many attempts having resulted in either failure or partial success (see Box 1; Seddon et al., 2007). Moreover, Seddon et al. (2007) still found that more than half of the literature published in peer-reviewed journals on wildlife reintroduction were the result of retrospective evaluation, that is, analysis is done because data were generated as a by-product of a reintroduction instead of being generated as one of the aims of the project, thus highlighting the current need for better planning and more targeted monitoring (Ewen & Armstrong, 2007; Armstrong & Seddon, 2008).

Box 1. Measuring the success of translocation projects

One issue with evaluating translocation projects is that what constitutes a successful translocation is still not clear (see e.g. Table 1). A popular metric of success is whether or not the translocated population is self-sustaining. The term ‘self-sustaining’ is however vague and has not been properly defined even when used as a metric for success (Griffith et al., 1989; Dickens, Delehanty & Romero, 2010). It could, for example, imply that a translocation attempt is considered successful when the new population persists without needing further translocations or that it persists without any human intervention at all. Either of these definitions are probably a commendable aim for any translocation attempt. Unfortunately, translocated populations that are not subject to management actions, such as feeding or supplementation of individuals, at least for some years after translocation, are rare (see e.g. Chauvenet et al., 2012; Soorae, 2012), making this measure of success inadequate on the shorter term. One proposed solution to the lack of agreement about how to measure translocation success is to work within a decision-making framework (Rout, Hauser & Possingham, 2009). This means establishing explicit conservation objectives for the translocation against which success can be measured on the short and medium terms using targeted monitoring (Nichols & Armstrong, 2012). Structured decision making provides such a framework. By identifying objectives and available actions and incorporating the best knowledge about the species to be translocated, structured decision making allows decisions to be logical and transparent (Nichols & Armstrong, 2012). A well-known subset of structured decision making is adaptive management (AM). AM is an iterative process that allows decisions to be recurrent while taking into account the uncertainty surrounding the system's parameters (McCarthy et al., 2012). It is particularly relevant to translocation as they are long-term endeavours and objectives may have to be revised every few years based on monitoring and management results (McCarthy & Possingham, 2007; Seddon et al., 2007). Eventually, after short- and medium-term goals have been reached, managers should find themselves in a position where a self-sustaining population is achievable. Hence, the long-term goal can still be to establish a population that requires no intervention to persist, but using AM would ensure adequate decision making throughout the process and a regular assessment of progress towards this ultimate goal.

Table 1. Example of measure of success of species translocation
Measure against which success is assessedReference
Establishment of a self-sustaining populationGriffith et al. (1989); Dickens et al. (2010)
Surviving capture, transport and release, breeding and settling in new areaLetty, Marchandeau & Aubineau (2007)
Persistence time of populationSheller, Fagan & Unmack (2006)
Individuals survive and do as well or better in new areaStrum (2005)
Survival of released individual, recruitment and dispersalOro et al. (2011)
Yearly survival, number of individuals attempting to breed and fledgling success over 2 yearsReynolds et al. (2008)

Assisted colonization


The term ‘assisted migration’ was used by McLachlan, Hellmann & Schwartz (2007) to describe conservation introduction in response to climate change, with specific reference to the translocation of Florida torreya (Torreya taxifolia) seedlings from Florida to North Carolina. The alternative terms ‘assisted colonization’ (Hoegh-Guldberg et al., 2008) and ‘managed relocation’ (Richardson et al., 2009) have also been used in subsequent literature. Seddon (2010) proposed that the term ‘assisted colonization’ be adopted for describing conservation introductions carried out to address threats to species, as opposed to those carried out for the purpose of ecosystem restoration; this usage is currently being adopted in the revised IUCN reintroduction guidelines. Seddon (2010) also argued that Ricciardi & Simberloff (2009b) proposed the most sensible definition of assisted colonization as ‘translocation of a species to favourable habitat beyond their native range to protect them from human-induced threats, such as climate change’. The advantage of this definition is that it includes climate change explicitly but acknowledges the fact that assisted colonization can also be used to counteract other threats (Seddon, 2010), thus eliminating the need for more terminology.

In this paper, we focus on the use of assisted colonization as an adaptation tool to climate change. Following Füssel & Klein (2006), we define an ‘adaptation tool’ as a strategy for ‘moderating the adverse effects of unavoided climate change through a wide range of actions that are targeted at the vulnerable system’.

Concerns about assisted colonization

McLachlan et al. (2007) sparked a debate on the usefulness of assisted colonization as an adaptation tool for climate change. This debate has so far mainly focused on three points: ecological risks, costs and uncertainties (Hewitt et al., 2011). The main ecological risk associated with assisted colonization is the introduction of invasive alien species. Invasive alien species are introduced species (i.e. species occurring outside their native range due to human intervention) that cause damage to the environment, the economy and ultimately human well-being (Hulme et al., 2009). Invasive alien species are a significant threat to biodiversity (Mueller & Hellmann, 2008). It is therefore not surprising that conservationists, decision makers, managers and the public have grown wary of species introductions, including those associated with assisted colonizations (Ricciardi & Simberloff, 2009a,b). Mueller & Hellmann (2008) not only argued that the risk of invasion due to assisted colonization is small but also found that if any species were to become invasive, it would have a large negative impact on biodiversity. Moreover, it is difficult to predict which species will become invasive. All introduced species will have an impact on their recipient ecosystem but that impact can span from positive, to neutral, to negative, with invasiveness being the extreme (Blackburn et al., 2011). Not only is the probability of a species becoming invasive species specific, but this probability may also depend on the composition of the recipient community. In addition, the invasive behavior may be difficult to detect, especially in the beginning when the invasive species is in low numbers (Mehta et al., 2007). Nonetheless, the risk of losing species through not doing assisted colonization needs to be balanced against the risk of invasion. Those risks will be dependent, among other things, on how close the species is to extinction (Schlaepfer et al., 2009).

The precautionary principle, defined as ‘when an activity raises threats of harm to human health or the environment, precautionary measures should be taken even if some cause and effect relationships are not fully established scientifically’ (Raffensperger & Tickner, 1999), has been invoked as an argument against assisted colonization. Indeed, Ricciardi & Simberloff (2009b) argue that assisted colonization is not a viable management action because the risks to biodiversity cannot be entirely anticipated and consequences may be more damaging than inaction. Yet this reasoning has been declared as a weak argument by others (Sax, Smith & Thompson, 2009; Thomas, 2011). Being precautionary might mean not using assisted colonization to prevent a potential invasion but also using assisted colonization before the extinction of a given species due to other threats (Sax et al., 2009). Considering that the threat of climate change is global and that its effects are widely believed to be inevitable and considering that the risk of translocated species becoming invasive is small (Mueller & Hellmann, 2008; Thomas, 2011), it is likely that many species will need assisted colonization and that some of them may have benign enough impacts following translocation to be good candidates for it.

A step towards safe and efficient assisted colonization

One way to minimize the risks of negative ecological impacts and to maximize the net benefit of assisted colonization is to work within a well-defined and, ideally, widely accepted decision framework. Three laudable attempts have been made at devising decision frameworks for assisted colonization. Firstly, Hoegh-Guldberg et al. (2008) proposed a linear decision tree for deciding whether or not to perform assisted colonization. This decision tree's main advantage is that it is extremely simple to use and therefore may appeal to many stakeholders. However, one of its downfalls is the vagueness of several questions. For example, the first question is ‘Is there a high risk of decline or extinction under climate change?’, and the possible answers are ‘low’, ‘moderate’ or ‘high’. To answer this question, relevant stakeholders would have to decide what a high risk is, know the climate-change projections under all different scenarios and decide which one is most likely and assess threats to the species or habitat of interest under chosen projections. Subsequently, Richardson et al. (2009) proposed a decision framework for assisted colonization that is much more complex than that proposed by Hoegh-Guldberg et al. (2008). Described by the authors as a ‘multivariate framework’ that can be ‘conceptualized as N-dimensional set of criteria’ (Richardson et al., 2009), it is based on the concept of scoring and/or ranking different ecological (e.g. likelihood of extinction, potential for reversibility or the likelihood native species may go extinct in the recipient range) and social attributes (e.g. cultural importance of the target species and its community, financial loss if the focal species goes extinct) used to evaluate the need for, and feasibility of, assisted colonization from both the donor and recipient communities point of view. This decision framework's advantage is that it reflects the complexity of natural systems and the impact of such a level of complexity on how to make decisions. In addition, it is designed to be used by all categories of stakeholders, including the recipient community. However, Richardson et al. (2009) do not provide guidance on how to score attributes or even offer an exhaustive list of attributes to use. Therefore, it is difficult to see how this framework can or will be used by stakeholders and how it guarantees decisions to be transparent and repeatable. Most recently, McDonald-Madden et al. (2011) published a framework that attempts to optimize the decision of when to move a population if the aim is to maximize its size. They used an optimization algorithm whereby decisions are made based on the size of the candidate population for assisted colonization (i.e. source), the cost of translocation in terms of individual lives (i.e. survival rate of translocated individuals) and the dynamic state of the carrying capacity at both source and target sites (e.g. increasing or decreasing). The main drawback is the current lack of flexibility as the framework was designed to answer one specific question: what is the optimal timing of assisted colonization? However, it is arguably the most transparent of the set, as it is not only quantitative but also explicitly accounts for the uncertainty about the impact of climate change on the candidate population.

The core issue

Overall, the debate on assisted colonization has focused on questions about ethical issues (Minteer & Collins, 2010), feasibility (Hoegh-Guldberg et al., 2008; Richardson et al., 2009) and potential negative impact (Mueller & Hellmann, 2008). However, assisted colonization is first and foremost a type of translocation (Seddon, 2010) and, as pointed out before, translocations are suffering from overall poor planning and implementation. Thus, some of the issues that have been contributing to poor success rates in translocations (Seddon, 1999) will also reduce the success of assisted colonization. Yet, the question of how to achieve success and avoid problems associated with translocations has been largely absent from the assisted colonization debate. Moreover, published literature on translocation projects show that climate change has seldom been taken into account, even though it is bound to influence future translocation success. A search in ‘ISI Web of Knowledge’ (up to July 2010) for any paper combining conservation-oriented translocations (any kind) and climate change (even just a mention) revealed 142 published papers (Fig. S1). Within those 142 papers, only 5 (3.5%) mention climate change as a possible correlate of success/failure of a translocation after it took place, while only 11 (7.8%) mention climate change in the process of planning a conservation translocation (Table S1). The remainder (88.7%) mention conservation-oriented translocation and climate change in the same paper, but without linking an actual translocation outcome or planning to changes associated with climate.

We thus propose that the key to maximizing the success of assisted colonization in the face of climate change is to draw from the knowledge garnered from translocation research, as well as experience from past successes and failures, in order to address issues that can impede success.

Assisted colonization as an adaptation tool to climate change

Research on translocation, and particularly reintroduction, is extensive. As a result, there is much knowledge to be drawn from the literature for assisted colonization.

For example, Osborne & Seddon (2012) focus on the use and issues of habitat suitability models for reintroduction. They argue that habitat suitability modelling (also known as species distribution models) is an important tool for planning translocation but raise several issues regarding our ability to correctly identify and define suitable and unsuitable habitat. While these issues apply to reintroduction, selecting a translocation site that is, and will remain, suitable in the future is paramount to the success of assisted colonization. Therefore, being able to identify what makes habitat suitable is an issue that is at the heart of assisted colonization. Armstrong & Reynolds (2012) argue for the systematic use of population models when planning reintroductions and provide a detailed step-by-step guide for building them. Using population models when planning reintroduction can yield invaluable knowledge on how to plan and implement a translocation project. The benefits that using population models for reintroduction projects yield (e.g. investigating release and management strategies on the population's viability before taking action) are equally valuable in assisted colonization projects. In particular, as assisted colonization is introduction, rather than reintroduction, and by definition riskier, using population models before translocation may prevent irreparable mistakes. McCarthy, Armstrong & Runge (2012) and Nichols & Armstrong (2012) focus on explaining and promoting structured decision making (including adaptive management: see Box 1) in the context of reintroduction. Whether for reintroduction or assisted colonization projects, structured decision making guarantees those decisions are transparent and accountable. Furthermore, structured decision making ensures that the actions implemented are chosen based on the best available knowledge and that uncertainties in this knowledge are made explicit.

In the following section, we present and discuss guidelines on how to plan and implement assisted colonization as an adaptation tool to climate change. By relying on concepts that have been developed for reintroduction or translocation, we bridge the gap between our knowledge of translocation and performing successful assisted colonization.

Recommendations on planning and implementing assisted colonization

The guidelines for planning and implementing successful assisted colonization under climate change take the form of a list of questions along with a list of methods to answer them (Table 2). At the planning phase, the first priority is to identify whether a species is threatened by climate change and is thus a candidate for assisted colonization (Q1 in Table 2). The decision frameworks proposed by Hoegh-Guldberg et al. (2008) and Richardson et al. (2009) have been partly designed to address this issue by suggesting questions to investigate, but do not explicitly propose a method to do so. To assess whether or not a species is a candidate for assisted colonization, we suggest exploring whether the species is experiencing (or is projected to experience) an increased extinction risk associated with range contraction driven by climate change. Habitat suitability models (Hirzel & Le Lay, 2008; McRae et al., 2008; Elith & Leathwick, 2009; Osborne & Seddon, 2012) have been put forward as a way to identify conditions promoting species’ survival and project changes in their distribution under different climate-change scenarios (obtained from sources like the IPCC 2007 or other global or regional climate models: Wolf et al., 2010; Barbraud et al., 2011). To quantitatively assess changes in species’ risk of extinction associated with likely range contraction, habitat suitability models can be combined with a spatially explicit population viability analysis (see e.g. Keith et al., 2008; Brook et al., 2009). Habitat suitability models will also highlight populations that are not immediately threatened and could potentially be used as source populations for translocations (Q2 in Table 2), as well as potential translocation sites where the environmental conditions are projected to remain stable as climate changes (Q3 in Table 2). However, once potential translocation sites have been identified, the risk of the introduced species becoming invasive should be assessed (Q3 in Table 2). A risk assessment could be based on which of the traits known to promote invasiveness the species possesses (see e.g. Sakai et al., 2001; Van Kleunen, Weber & Fischer, 2010). Loss, Terwilliger & Peterson (2011) suggested using laboratory and field tests as a way to assess invasiveness likelihood. Quantitative models of community interactions could also be designed on a case-by-case basis to predict the impact of a new species assemblage on the introduced species’ behavior.

Table 2. Table summarizing the questions that need to be answered, and the methods that can be used to do so, to maximize the success of assisted colonization under climate change
  1. SDM, species distribution model; PVA, population viability analysis.
PlanningQ1. Is the species threatened by the impact of climate change?
  • Decision frameworks
  • SDM identifying future range contraction potentially followed by a spatially explicit PVA
Q2. Which population (if n > 1) can be the source for the translocated individuals?
  • SDM identifying populations not threatened by the range contraction
  • Scenario-based population dynamics modelling to project the source population's abundance under different harvesting scenarios (scenarios are defined by numbers of individual harvested e.g. 0, 10, 20, etc.)
Q3. Where can the species be translocated?
  • SDM to locate where the identified suitable environmental conditions will be spatially distributed in the future
  • Risk assessment of the likelihood the introduced species will become invasive using knowledge on intrinsic traits that promote invasiveness, laboratory and field tests, or community-based modelling
Q4. How many individuals and what sex ratio should be translocated?
  • Scenario-based population dynamics modelling to project the translocated population's dynamics under different founder population scenarios (scenarios are defined by e.g. founding numbers, sex and age composition, genetic composition, etc.)
Q5. What management should be applied to the translocated population?
  • Scenario-based population dynamics modelling to predict the abundance under different management scenarios (scenarios are defined by different management options, e.g. supplemental feeding, vaccination, doing nothing, etc.)
ImplementationQ6. Is the source population negatively affected by the removal of individuals?
  • Monitoring to determine source population's abundance and demographic parameters
  • Population dynamics modelling to project the source population in the future
Q7. Are the projections made for the translocated population correct?
  • Monitoring of the translocated population's abundance and demographic parameters
  • Comparison between projection and observed abundance
  • Population dynamics modelling to project the source population in the future
Q8. What adaptive management decision, if any, should be made?
  • Scenario-based population dynamics modelling to predict the abundance under new management scenario (scenarios are defined by different management options, e.g. supplemental feeding, vaccination, doing nothing, etc.)

The second priority at the planning stage is defining the logistics of the translocation by identifying the best source population (Q2 in Table 2), the optimal founder population (e.g. size, age composition, sex ratio and genetic composition; Q4 in Table 2) and the best management, if any, to be applied to the newly translocated population (Q5 in Table 2). We recommend a scenario-based approach in order to find the optimal answer to these questions. This involves building a population model and investigating the impact of different management decisions (i.e. the scenarios) on the population trajectory (Armstrong & Reynolds, 2012). The outcomes of the different runs are assessed against a specific objective set for the population. For example, managers may want to know the optimal founding population in order to maximize the growth rate of the translocated population and to minimize costs. When implementing the translocation, they would select the strategy given by the scenario that performed best against that objective, that is, that gave the most cost-efficient strategy. This scenario-based approach allows the identification of a case-specific optimal translocation strategy for both the source and translocated populations. In addition, it can yield predictions for the future dynamics of both populations if this optimal strategy is implemented. One caveat to this approach is the amount of data required to parameterize the models. In some cases, it may be possible to obtain estimates of expected demographic parameters from published meta-analyses (Brawn, Karr & Nichols, 1995; Falster, Moles & Westoby, 2008; McCarthy, Citroen & McCall, 2008).

Once the initial assisted colonization has taken place, in situ monitoring becomes the backbone of future decisions (Ewen & Armstrong, 2007; Armstrong & Seddon, 2008; Nichols & Armstrong, 2012). It should be done for both the source and the translocated populations. A key requirement, however, is that monitoring programs should be designed with specific questions in mind (Armstrong & Seddon, 2008). The monitoring data collected from the source population can, for example, be used to verify that the initial conclusion that the source population would not be negatively affected by the removal of individuals was correct (Q6 in Table 2). Similarly, the monitoring data collected for the translocated population can be used to match the projected population trajectory with the actual translocated population growth (Q7 in Table 2) and to increase the understanding of the translocated population dynamics to support future management decisions through adaptive management (Q8 in Table 2; McCarthy et al., 2012; Nichols & Armstrong, 2012).

By following the proposed systematic guidelines in Table 2, decision makers could be one step closer to securing the future of biodiversity with assisted colonization. Nevertheless, we have identified several issues at the implementation and research levels that have to be resolved before assisted colonization can become the most cost-efficient adaptation tool for species threatened by climate change.

Issues regarding implementation

There is an untold number of species that will be potential candidates for assisted colonization, that is, for which the answer to Q1 (Table 2) is ‘yes’ because they are projected to have an increased extinction risk due to climate-change-related range contraction. One pressing question is how to choose which species to move first? There are several prioritisation schemes and methods that currently attempt to guide conservation choices, for example, cost-benefit analysis (Bottrill et al., 2008), biodiversity hotspots (Joppa et al., 2011) and phylogenetic uniqueness (Isaac et al., 2007). These could all be useful to prioritize species for assisted colonization. However, once assisted colonization has been implemented (i.e. a species has been introduced to a new area), undoing this action and its consequences is very difficult, thus leaving little room for error when making decisions. As a result, concerns like the risks associated with species introduction, such as diseases or potential for invasiveness for example, could play a large role in deciding for which species to take action first.

Secondly, one of the possible future impediments to implementing assisted colonization will be the question of who will make the critical decisions of prioritization and implementation. Today, depending on the project, conservation decisions are made by various groups, from small organized groups of people (e.g. the Torreya Guardians; McLachlan et al., 2007) to Non-Governmental Organizations (e.g. Durrell Wildlife Conservation Trust, BirdLife International; Corry et al., 2010) and governments (e.g. New Zealand Department of Conservation; Cromarty et al., 2002). However, the need for assisted colonization will grow alongside the impact of climate change on everything else, increasing the chance of conflicts between groups. While no one entity can be in charge of making decisions regarding all assisted colonization projects, we hope that by using the same decision-making framework at least the decisions made will be transparent.

Further research required

There are also a number of challenging issues for research. Firstly, there are the issues of the accuracy and scale of climate-change projections that are to be used in habitat suitability models (e.g. to answer Q1–3 in Table 2). There are several models that can be used to project climate change (‘the multi-model dataset at PCMDI’;; these are mostly global climate models (GCMs) that originally had coarse resolution but have been downscaled to resolutions as small as 30 arc-second ( The issue is that GCMs do not always agree on predictions at the global scale, let alone at the scale needed for conservation, which is often much smaller than a country and can be sometimes as restricted as a field.

Secondly, there are four possible responses of species to climate change: thrive, adapt, shift or die. Although they are clear-cut, little is known of the true potential of species to tolerate or adapt to climate change. Often, the problem will lie in identifying which alternative outcome the species are moving towards, for example, species with long generation time and limited dispersal like the Florida torreya (McLachlan et al., 2007) will be both unable to adapt and unable to shift range. Unless there is evidence for range shifting, the only way to be really sure about the species’ fate would be to wait and observe. However, this is not an option for those species that are declining rapidly. Because conservation money is limited, it is inefficient to move species that would have survived had they been left in their original range. On the other hand, some species could be saved with assisted colonization but may be ignored because the skills necessary to identify their lack of adaptation are missing (Hill, Griffiths & Thomas, 2011). Although studies have started investigating the problem for plants (see Jump & Peňuelas 2005 for a review), one of the biggest challenges yet is to learn to predict plasticity and adaptation capacity for all species.

Thirdly, the relationship between environmental conditions and species’ demography is still not fully understood. So far, most published research has focused on identifying the relationship between some climate variables and the survival and reproduction of species based on current or past conditions (see e.g. Borrego et al., 2008; Frederiksen et al., 2008). However, our ability to predict how changes in baseline environmental conditions will affect future demographic rates is still limited. For example, difficulties arise when the future environmental conditions are outside the range of conditions experienced by species in the past, and models based on current conditions cannot be trusted to be informative (Berteaux et al., 2006). As a result, the quality and accuracy of predictive population models in the face of climate change, such as those that would be built to answer Q2 (Table 2), may be compromised. Lessons from historical introductions for reasons other than conservation may help in these circumstances (cf Cassey et al., 2008).

Finally, Armstrong & Reynolds (2012) suggest five topics related to population modelling that warrant further research. While those are offered in the context of reintroduction, advances on those subjects would benefit assisted colonization or indeed any form of translocation. In particular, they suggest focusing on the long-term genetic effect of translocating populations. Inbreeding and loss of genetic diversity could have a significant impact on the long-term viability of any population, but especially those established through translocation as they are generally small. However, genetic issues are seldom included into predictive population models (Armstrong & Reynolds, 2012), which may be an impediment to our ability to accurately predict the dynamics of translocated populations.


We are grateful to Tim Coulson and Patricia Brekke for their comments on an earlier version of this paper. We also would like to thank the three anonymous reviewers whose comments helped improve this paper greatly. A.L.M.C. was supported financially by an AXA fellowship.