Introduction
Widespread and rapid environmental change is forcing many species to
drastically alter how they interact with and respond to the environment
(Hoffman & Sgro 2011). As these changes become harder to mitigate and
manage, imperilled populations may survive by shifting their geographic
range, through phenotypic plasticity, or via genetic adaptation (Nunney
2015). It is, however, increasingly difficult for populations to shift
their range because many plant and animal species are now in fragmented
habitat and do not possess the dispersal ability to navigate between
suitable patches (Tingley et al . 2009). It is also unclear how
often plasticity will provide a long term advantage since plasticity may
or may not be aligned in an adaptive direction, and may also reduce the
effectiveness of natural selection in driving adaptation to changing
conditions (Ghalambor et al. 2007; Chevin & Hoffman 2017; Nobleet al . 2019). Genetic adaptation is clearly the most robust
solution to directional environmental change, and for populations with
suitable standing genetic variation, rapid adaptation may forestall
extinction through evolutionary rescue (Bell et al. 2019; Harriset al. 2019). But for many species, necessary traits are either
locally absent or at low frequencies, slowing the evolutionary response
and priming populations for extinction (Lacey 1997; Hoffman et
al. 2017).
One way to increase the chance of evolutionary rescue is to provide
populations with the genetic variation necessary for adaptation. Some
strategies advocate simply increasing genetic variation, in a
non-directional manner. Such “genetic rescue” is particularly powerful
when populations have low diversity and are suffering inbreeding
depression (Lande & Shannon 1996; Hedrick & Fredrickson 2010). Other
strategies take a more targeted approach, seeking to increase genetic
variation in the direction needed to adapt. This idea of introducing
individuals with pre-adapted traits into a population was first proposed
as a possible response to the impact of climate change, where the idea
was termed “assisted gene flow”. It is, however, a strategy that can
be applied to broad suite of conservation problems, and in recognition
of this broader application we refer to it here as “targeted gene
flow”. Conservation managers have already begun to employ targeted gene
flow (hereafter TGF) with the aim of increasing the frequency of
pre-adapted traits in threatened populations (Aitken et al. 2013;
Kelly & Phillips 2016, 2018; Weeks et al. 2017; Indigo et
al. 2018).
As with any conservation action, TGF carries both risk and cost.
Relative to other conservation actions, TGF will tend to be very cost
effective, but it is not without risk: outbreeding depression (Frankhamet al. 2011), genetic swamping, and disease transmission
(Cunningham 1996; Sainsbury & Vaughan-Higgins 2012) are all
possibilities to be considered. Because of risks and costs, any
conservation action needs to be characterized to allow scenario-testing,
cost-benefit analysis, and to provide managers with realistic
expectations (Knight et al. 2006a; Weeks et al. 2011)
While conservation managers regularly use population models to assess
alternative scenarios, adaptive evolutionary processes are rarely
included in models of population viability (Lacey 2019) or in
cost-benefit exercises (Klein et al. 2009). By its nature, TGF
requires models that incorporate evolution into population viability and
cost-benefit analyses.
The stated aims of conservation translocations are usually to create or
maintain viable populations of a single, focal species, with measures of
success based on abundance, extent, resilience, persistence, or any
combination of the above (Pavlik 1996; Vallee et al. 2004). With
TGF we also want a viable population, but we want to avoid swamping the
local genome in the process. Swamping the local genome is akin to
extinction and reintroduction, and one of the great promises of TGF is
that we might both prevent extinction and conserve local genetic
diversity in the process: the aim being to manipulate populations so
that they are not only locally adapted but carry genes that allow them
to survive under future environmental shifts (Harris et al.2019). Given the complexity of prioritizing management actions across
multiple measures of success, we need a clear statement of our
management objective (Regan et al. 2005). Here we propose a
robust objective: to keep the recipient population extant and to achieve
this whilst maintaining the genetic diversity currently present. While
extinction is straightforward, diversity is a rich concept that admits a
wide range of possible definitions (see Morris et al. (2014)). We
focus here on the maintenance of genetic diversity through maintaining,
as far as possible, the set of alleles that are initially present in the
recipient population. This provides an objective that considers not only
the richness of genetic material remaining but the evenness at which
this material occurs. Aside from the total number of alleles present in
a population the distribution of their abundances is also an important
component of diversity. If an allele is represented in only a tiny
percentage of individuals, it should be clear that it contributes less
to the population’s diversity than an allele represented in 50% of the
population. The importance of allelic evenness has received less
attention than that of richness but its value seems inarguable.
We equate our management objective to a gambler’s return on investment:
the probability of winning (avoiding extinction) multiplied by the
payout (the remaining allelic diversity). To achieve this, we
incorporate our probability of ‘winning’ (1 – x ), where xis the extinction probability, with a common measure of genetic
diversity, the Gini-Simpson Index . The Gini-Simpson Index of
diversity (D ) is equivalent to the expected heterozygosity under
Hardy-Weinberg equilibrium and is a common measure of diversity (Guiasu
& Guiasu 2012; Morris et al. 2014), where 1 represents maximum
diversity, and 0, no diversity. Thus our objective is to maximise the
expected return:
Where we calculate D using only alleles initially present in the
recipient population. The problem we address is a general one: how does
varying key management levers (the timing and size of the introduced
cohort) influence the expected return of a TGF action? We explore this
question across a range of scenarios of environmental change ranging
from near step changes to a much more gradual environmental shift. We
explore the influence of a continuous gradual shift in the environment,
similar to climate change projections (IPCC ARC6 Climate Change 2018),
as well as threats that constitute a shorter, more drastic change in
environmental suitability, such as the introduction of a wildlife
disease or the invasion of a pest species.
We utilize a discrete-time individual-based population model with the
goal of exploring the optimal timing and size of a TGF action across
various scenarios of environmental change. Our model is structured such
that it is flexible across study species and various projections of
environmental change. Against our new management objective, we explore
the sensitivity of the optimal choice of management strategy across a
wide range of demographic, evolutionary and environmental parameter
values.