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.