Procreating with a relative is taboo in most human societies for many reasons, but they all stem from avoiding one thing in particular — inbreeding increases the risk of genetic disorders that can seriously compromise a child’s health, life prospects, and survival.
While we all inherit potentially harmful mutations from our parents, the effects of these mutations are often partially or completed masked if we possess two alternative variants of a gene — one from each parent. However, the children of closely related parents are more likely to inherit the same copies of harmful mutations. This is known as ‘inbreeding depression’.
But inbreeding depression can happen in any species, with the risk increasing as populations become smaller. Because many species are rapidly declining in abundance and becoming isolated from one another predominantly due to habitat destruction, invasive species, and climate change, the chances of inbreeding are also increasing.
Not only are such populations more susceptible to random disturbances, they are also victim of reduced population growth rates arising from inbreeding depression. This produces what is generally known as the ‘extinction vortex‘ — the smaller your population, the more you inbreed and produce sub-optimal offspring, leading to even more population decline and eventually extinction.
One emergency intervention that can ‘rescue’ such inbred populations from extinction (at least in the short term) is to introduce unrelated individuals from other populations in an attempt to increase genetic diversity, and therefore, the rate of population growth. While somewhat controversial because some fear introducing diseases or eroding local-area specialisation (so-called ‘outbreeding depression’), the risk-benefit ratio of this ‘genetic rescue’ is now widely considered to be worth it.
Australia has the worst record of mammal extinction of any nation, with 110 marsupial species (~ 65% of extant species) listed as threatened under the Environment Protection and Biodiversity Conservation Act 1999. Many of these species now only occur in small (< 1000 individuals) and isolated populations occupying < 10% of their former geographic ranges. The effect of inbreeding depression on extinction risk is now recognised as immediate threat to endangered marsupials (and other species), now making the prospect of genetic rescue a serious management consideration.
Nature plays dice
Populations in need of genetic rescue are not only at risk from overly close familial relations. The problems facing small populations include many interacting threats that arise due to chance events. The discrete and binary nature of birth and death (an individual is born, or not; is alive, or not) introduces randomness into the number of individuals in a population — we call this ‘demographic stochasticity’, and the number of different gene variants in the gene pool — we call this ‘genetic drift’.
Due to the strong influence of randomness in small populations, population size can suddenly collapse in the absence of further degradation of their habitat, and harmful mutations can accumulate before they are purged by natural selection. And to make matters worse, the mutual reinforcement of stochastic demographic and genetic processes that prevail in small populations creates a positive feedback loop (demo-genetic feedback) that heightens extinction risk as populations decline — a phenomenon referred to as the ‘extinction vortex’.
While genetic rescue can immediately improve fitness, these gains might be short-lived if the risks of demographic instability and genetic drift remain, counteracting the benefits of genetic intervention, and drawing the population back into the extinction vortex. Maximising the effectiveness of genetic-rescue interventions therefore requires understanding ‘demo-genetic feedback’ and its role in shaping the outcomes of genetic rescue.
In our recent paper published in Evolutionary Applications (which we’re also pleased to report made the journal’s ‘Editor Pick‘), we highlight the relevance of demo-genetic feedback to genetic rescue and suggest an approach for building simulation models that can be applied to evaluate different scenarios of genetic rescue.
Our paper is aimed at conservation practitioners and applied ecologists seeking to do genetic rescue in populations of threatened species.
Theory into practice
Rapid advances in computational power, sequencing technology, and modelling software are facilitating the sophisticated simulation models built and validated with more data or based on more-realistic assumptions than was previously possible. These enable genetically explicit, individual-based models needed to make more accurate predictions of the dynamics of wildlife populations under proposed management interventions such as genetic rescue.
Open-source software capable of simulating the influence of demo-genetic feedback on the outcome of genetic rescue includes SLiM, quantiNemo, CDMetaPOP, RangeShifter, and HexSim (not an exhaustive list, but the main ones).
We compared the capabilities of these five software programs for incorporating demo-genetic feedback into simulations of genetic rescue. We contend that the implementation of genetic rescue in conservation should be guided by models that replicate the mutual reinforcement of demographic stochasticity, genetic drift, and inbreeding. Building such models is aimed at helping decision-makers to choose optimally among a set of competing potential interventions.
To demonstrate our approach, we developed simulations of a heuristic model using SLiM to show how demo-genetic feedback influences extinction dynamics and the outcomes of genetic rescue of small populations.
We give the model details in the paper, but in summary, we calculated probability of extinction at an arbitrary time horizon of 1500 years in a no-intervention scenario and four alternative genetic-rescue scenarios. Genetic-rescue scenarios differed in two main ways: the number of individuals translocated into the target population (50 or 100 individuals), and the number of translocation events (once, or three times).
Compared to the no-intervention scenario, we found that genetic rescue decreased the probability of extinction by 3–9 %, with the largest effect in the scenario where 100 individuals were translocated three times.
The goal of the modelling exercise is not to predict specific outcomes of genetic rescue or proscribe details of its implementation. Rather, forward-projection models can be used to rank the relative success of virtual genetic-rescue scenarios and prioritise real-world strategies based on the relative probability of reducing inbreeding depression.
Our heuristic model was purposefully simple in terms of the variables we examined. Alternative scenarios facing decision-makers might differ for a range of variables, for example: population abundance of the target and source populations, or degree of genetic differentiation of the target and source populations, as well as many other potentially important things (some of which we list in our paper).
Big data for hungry models
Rapid advances in sequencing technology and bioinformatics have increased the feasibility of obtaining high-resolution (and lower-cost) genomic data for threatened species. These genomic data, as well as the many studies based on microsatellite DNA, can be leveraged to develop and apply simulation models of genetic rescue.
We focussed on Australian threatened marsupials to demonstrate the types of genetic data that are available and suggest a set of modelling strategies and how data can be used to develop and apply them. Of the 21 species for which we found published genetic data, all had microsatellite data, 13 species had mitochondrial DNA (mtDNA), 9 species had genome-wide single-nucleotide polymorphisms (SNPs), and 2 species had whole-genome sequences — koala (Phascolarctos cinereus) and Tasmanian devil (Sarcophilus harrisii). There was also one species in which ancient DNA had been sequenced.
Most species (15) were represented by one or two types of sequence data, while fewer (6) had three or more data types. Species with the most genetic data included woylie (Bettongia penicillata), northern quoll (Dasyurus hallucatus), western barred bandicoot (Perameles bougainville), Leadbeater’s possum (Gymnobelideus leadbeateri), koala, and Tasmanian devil.
Genetic data can be used at different stages of model development and can be applied at one or all stages of model building, calibration, and/or validation. Most populations needing genetic rescue are unlikely to have the necessary data to estimate all mechanistic parameters, such as mutation rates, so estimates can be used from (in order of preference) other populations of the same species, related species, or unrelated species to fill the gap.
For example, a mutation rate for koalas has recently been estimated, which should supersede estimates for Drosophila or humans if simulating genetic rescue of a marsupial population. Parameters for which there are no data can be estimated from allometric relationships, or based on other reasonable assumptions informed by theoretical predictions, with cautious interpretation of model outputs guided by global sensitivity analyses to quantify and highlight uncertainty.
More commonly available data for target populations include sequence-based estimates of genetic diversity and inbreeding (e.g., allelic richness, heterozygosity/homozygosity, inbreeding or relatedness coefficients), and less commonly, genetic load. Such information could be used to calibrate (and ideally, validate) the mechanistic parameters in the model that give rise to virtual sequence variation.
Known as ‘pattern-oriented modelling’, this approach is widely used to calibrate and validate individual-based models. Once the genetic mechanisms that give rise to virtual sequence variation are calibrated, users could then run simulations of genetic rescue and compare them based on how much they affect virtual genetic diversity, inbreeding, or genetic load.
Another strategy involves simulating genetic rescue of populations in which allele frequencies have been initialised from sequence (marker) data (‘empirical alleles’). Here, sequence data from the target population can be imported into the simulated populations at a user-defined time based on when the data were collected.
Why it’s important
We are in the midst of an extinction crisis that is no longer solely a moral challenge to humanity, but also an existential one. The plants, animals and other organisms on Earth today are a living repository of millions of years of evolution and diversification — encoded genetically — that has produced the living forms and functions that underpin the integrity of the biosphere.
Due to global declines in species abundance and distribution, the loss of genetic and functional diversity already far exceed safe limits within which humanity can continue to develop and thrive for generations to come. In an attempt to stem the bleeding, so to speak, genetic rescue is not only necessary, but must be applied efficiently and effectively.
We simply do not have the time (or money) to waste.