Discussion
Our model reveals that our chosen management objective – to maximise the remaining genetic diversity of the recipient population – is sensitive to the timing and size of a given translocation action. There is a clear trade-off between maintaining local allelic diversity, and population persistence. As a general rule, a greater number of introductees in the years surrounding the maximum rate of environmental change reduced the probability of population extinction, but these large cohorts tend to produce a lower retention of the recipient population’s alleles. This apparent trade off can be optimised, however, with the highest expected return when we introduce a greater number of pre-adapted individuals immediately prior to when selection is strongest, or by introducing a larger number of individuals earlier. These more optimal strategies give time for recombination to break apart the introduced genome before selection peaks and so retain almost all of the initial allelic diversity.
Our results fit with previous explorations of implementing TGF (with a step-wise threat) (Kelly et al. 2018) or assisted colonisation (that do not consider evolution) which show the timing and size of the introduced cohort to be primary considerations for conservation managers undertaking such actions (McDonald-Madden et al. 2011). Managing extinction risk in partnership with genetic diversity requires the consideration and integration of process such as recombination rate, outbreeding depression, trait heritability and so on, but these must all be considered with future environmental suitability in mind. While we found that it is possible to achieve a positive conservation outcome even under harsh levels of environmental change, these more dramatic environmental shifts had a drastically reduced window in which to act and required a much larger introduction size to achieve an optimal outcome.
Previous work on TGF has focused on maximising the expected proportion of the recipient genome remaining at the management horizon (Kellyet al . 2018). Here we deepen this idea by considering the allelic diversity rather than allelic richness, by incorporating a measure of diversity (D) into our management objective. This allows us to optimise our actions to ensure not only population persistence and a number of locally adapted alleles are present (richness), but importantly, that the relative abundance at which they occur is relatively even. This evenness can provide a buffer during periods of small population size, slowing the rate at which alleles are lost through drift, so it is important to account for this property. Given that heterozygosity is intrinsically linked to the additive genetic variance, or the ability to respond to environmental change (Falconer & Macaky 1996; Swindell & Bouzat 2005) our reformulated management objective provides an arguably more robust objective with which to optimise gene flow actions. When compared to the earlier metrics, we find that our optimal course did change, usually favouring a delayed course of action, or a larger introduced cohort (S2).
We modelled microevolutionary processes across various scenarios of environmental change to investigate the benefit gained by TGF actions across a range of threat profiles. The profiles explored (see S1) are relevant to threats such as invasive species and disease which may rapidly move into a population and alter it from one state to another (e.g. a near-stepwise change), or climate driven threats which are characterised by a more fluid state change. In general, the greater the rate of environmental shift per timestep, the lower the expected return on a given management action. In addition, the window in which to act is considerably narrower for more rapid threat profiles. Our results suggest that the optimum time of action is usually around the time of most rapid environmental change. While the optimum timing is largely unaffected by demographic parameters, these parameters did alter the maximum expected return we could expect.
The optimal strategy for implementing TGF is similar under varying carrying capacities and trait heritabilities. Carrying capacity does, however, have a large bearing on the effectiveness of TGF, with larger populations producing a greater return on investment when compared to small populations. This mirrors theoretical expectations that larger populations can evolve more rapidly because they are less affected by stochastic process, such as extinction and genetic drift (Fisher 1930; Wright 1931; Moran 1958). The heritability of the focal trait also affected returns, with less heritable traits causing an increased rate of extinction (particularly when coupled with a high level of outbreeding depression). Overall, TGF decreased extinction probability, and this is consistent across all scenarios.
There is however a risk when hybridising populations (Bell et al.2019; Haris et al . 2019): outbreeding depression can reduce the population fitness (Frankham et al. 2011). Reduced fitness in hybrids could result from the breakdown of local adaptation, or from genetic incompatibilities such as Dobzhansk-Muller incompatibilities (Fitzpatrick 2008). Both of which can be difficult to predict prior to any management action. Our model incorporated the possibility of genetic incompatibilities and showed outbreeding depression reduced the success of TGF. Although it was typically beneficial to act, we uncovered some scenarios with high levels of outbreeding depression in which it was not beneficial to implement TGF. These occur particularly in scenarios where the threat of extinction without intervention is low. Although every management decision has its own peculiarities and risks, recent review suggest that the detrimental effects of outbreeding depression are likely overstated in the literature, and in most cases, outbreeding should cause only minor and transitory effects ( Frankham et al . 2015). By contrast, crossing populations can mask deleterious alleles, and often leads to hybrid vigour, which in turn can lead to a decreased extinction probability (Weeks et al. 2017). The possibility of heterosis is probably as difficult to predict as outbreeding depression: if it occurs in a system its effects should be to improve our management objective. Although we don’t include heterosis in our model, it should not be forgotten that heterosis is often observed and should improve conservation outcomes under TGF. More generally, any fitness benefits conferred from carrying favourable alleles will likely outweigh transitory impacts of outbreeding depression, and as we found, given time, recombination will ensure that any maladaptive genetic combinations are rapidly lost.
Recombination, by affecting the independence of loci, can potentially lower the effectiveness of TGF. Despite this, we found that increasing the level of recombination had only a mild positive effect on the expected returns but did not change when or how to act. Although the populations survived under various recombination rates, a lower proportion of the allelic diversity was retained, because lower recombination rates cause selection to capture larger pieces of the introduced genome: the introduced cohorts’ neutral alleles are carried along with the threat-adapted (favoured) alleles. As a precaution, the introduction of threat-adapted individuals should occur as early as feasible to allow time for linkage disequilibrium to decay. Given this advice we found that acting earlier required the introduction or a larger cohort of individuals to obtain a comparable return on investment, which may become an issue if budgets are constrained. Although not explored here, an obvious extension to our work would be to consider multiple introduction events and their relative timing; outcomes in that setting may be quite sensitive to recombination rates.
We, of course, do not capture all possible complexities in our model. In reality, genes influence traits to varying degrees (i.e. there is a distribution of effect sizes, d ); loci are non-randomly linked; and there are interactions within and between loci (Gomulkiewicz & Holt 1995). Although we have incorporated recombination, and we have integrated over locus positions, reality is far more complex. Dominance in phenotypic loci may result in faster adaptive shifts if the dominance effects are in the direction of selection, and this might cause selection to fix larger parts of the introduced genome. Dominance may also cause heterosis, and the distribution of dominance effects at phenotypic loci will depend on recent selection pressures on the population. Dominance effects are very likely to be important in TGF, but the distribution of dominance effects is sufficiently uncertain that including them in a model such as ours would likely provide more complexity than clarity. In a real setting it may prove useful to examine composite dominance effects (using F1 crosses, for example) prior to large scale implementation.
Managers need to carefully define the accepted levels of final diversity and extinction risk prior to implementing TGF. In some cases, incremental gains in diversity or survival probability (for example, from 0.9 to 0.92) will often involve a large increase in effort. Indeed, we found that such gains in diversity or extinction risk do often involve large increases in the size of the introduced cohort. The question then arises, what return on a conservation action is good enough? Given unlimited resources, maximising the objective function makes sense, but given the very real constraints around conservation funding, and competing management actions (e.g., augmentation of existing populations; habitat management; abatement of threats such as predation in situ; fostering connectivity and dispersal; translocation), managers may wish to predefine an acceptable level of benefit that they consider good enough. There are also numerous cases where the need for conservation translocation is immediate (Soorae 2011). Our results suggest that in such cases, a greater number of individuals will be required to achieve even a semi-optimal outcome (E(Y ) >= 0.5).
Whilst studies examining the optimal implementation of TGF are scarce, one suitable objective function has already been proposed – to maximise the proportion of the recipient genome remaining post TGF action. We extend this earlier objective function into a more holistic measure: the allelic diversity remaining post action. Encouragingly, our optimal action sets broadly align with those proposed by the earlier management objective, although when optimising for allelic diversity, the window of action in which to act to affect a positive conservation return is greatly tightened (see S2).
Given this sensitivity it is imperative that management objectives are defined prior to instigating any action. Estimating the timing of intervention requires consideration not only of the biological aspect of conservation decision making but potential delays that result from socioeconomic issues such as budget cycles, permitting, and social licence. For example, a recurrent issue in translocation is that it often involves interagency collaboration, which can be fraught with pitfalls (Susskind 2012). Adding nonbiological issues, like conflict resolution, is likely to increase the urgency for action by increasing the time it takes to act (Wilson 1997; Ariza et al. 2012). In a TGF setting, this will have real costs, often increasing the amount of effort required to achieve a high expected return or missing completely the window of opportunity in which to act. Although we present a generalised model species here, we recognise that the biology of the species may preclude genetic translocation as a viable alternative. Clearly, a feasibility assessment is warranted early on in the planning process for any real species.
As the impacts of climate change increase, there will be an increased need to translocate populations outside their historic range boundaries and these managed relocation events will require very clear planning and justification. Although substantially safer than translocation outside the species range, TGF is not without risk. Where individuals are translocated, there is always a risk of translocating pathogens also (Sainsbury & Vaughan-Higgins 2012) that should be considered. Also, there are difficult-to-predict risks associated with placing traits into novel environments. Generally, balancing the needs of a presumptive conservation target against other risks and opportunities is a difficult task. Managers must treat TGF actions as an investment decision (Canessaet al. 2014; McDonald-Madden et al. 2012) and act accordingly. Based on what is known, would TGF be a wise investment of limited resources, or do alternative priorities take precedence? Including this cost-axis into future assessments of targeted gene flow presents an important avenue for exploration.