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This research presents HD-ClvR, which is a combination of three gene drives: homing, cleave-and-rescue and daisyfield. Our modelling indicates that HD-ClvR overcomes an important trade-off in current homing gene drive designs: the trade-off between resistance allele formation and gene drive efficiency. This strategy benefits from the efficiency of a homing gene drive and the evolutionary stability of cleave-and-rescue gene drive. Due to the inclusion of a daisyfield system, HD-ClvR is self-limiting and can be controlled by supplementation of gene drive animals.
HD-ClvR compared to other gene drives
Over recent years, many different gene drives have been published and developments have been geared towards both efficiency and safety38. An ongoing issue has been the development of resistance alleles. For CRISPR-based homing gene drive there are two fundamental approaches to combat resistance allele formation: careful gRNA targeting and gRNA multiplexing. When a gRNA targets a conserved sequence in a gene, resistance alleles are likely to disrupt gene function through NHEJ repair and will therefore reduce fitness39. Recently, population suppression was already shown to work with a carefully targeted homing gene drive in contained mosquito populations39, however, current data suggests that homing might be less efficient in mammals than in insects14. A recent paper has proposed the concept of ‘tethered homing gene drive’, which combines a threshold-dependent underdominance gene drive with a homing gene drive for improved suppression capabilities40. We use this concept in a different manner in HD-ClvR, by relying on a daisyfield rather than threshold-dependence for self-limitation. Very recently, two new papers have proposed a gene drive similar to HD-ClvR, but intented for population modification instead of suppression41,42. These studies also combine homing and cleave-and-rescue principles to combat resistance alleles and their modifications are able to persist stably in cage experiments, which is promising for HD-ClvR.
In addition to targeting conserved sequences, when gRNA multiplexing, resistant allele allele formation is reduced because multiple sites are targeted simultaneously. For homing gene drives, multiplexing has been shown to reduce homing efficiency when more than two gRNAs are used28. In contrast, cleave-and-rescue gene drives do not have this problem, as they do not use homing and can therefore multiplex gRNAs without any efficiency costs. HD-ClvR separates the elimination of resistance alleles and homing efficiency, and therefore gRNAs can be optimised for both goals separately.
To date, most gene drive research has focused on improving the efficiency, however, equally important is the development of strategies that allow for containment, or even reversibility, of the gene drives29,43. For contained gene drives, density dependence is often used, which requires large numbers of gene drive individuals to be released into a target population to spread44. Therefore, non-target populations are unlikely to be affected by this type of gene drive. However, a large single release of gene drive individuals can put significant pressure on the local ecosystem, and if a population is already at carrying capacity, it may lead to starvation or mass migration of the population. In contrast, HD-ClvR uses ongoing input in the form of gene drive animals to control the extent of population suppression and contain spread, while the total amount of gene drive animals necessary for release is similar to threshold-dependent gene drives. Therefore, the use of HD-ClvR seems more feasible than threshold-dependent gene drives. Although self-limitation comes with increased cost and labour relative to unlimited gene drives, we believe this is justified by the control and safety of HD-ClvR.
As stated above, the initial introduction frequency for a standard cleave-and-rescue gene drive in our randomly mating model was increased 10-fold over the other homing-based strategies. This increase is necessary due to the significant cost to the reproduction rate that is incurred when using a standard cleave-and-rescue gene drive. On average, cleave-and-rescue animals will produce 50% less offspring than wild-type animals21,24. This significantly slows the spread of the gene drive and due to density dependent dynamics, requires large initial releases of cleave-and-rescue animals for population suppression. With a homing-cleave-and-rescue drive, more offspring inherit the drive and there is less cost to the reproduction rate. Effectively, for homing-cleave-and-rescue, the reproduction rate of gene drive individuals is equal to the homing efficiency (plus half of the homing failure rate, where the gene drive is inherited by chance), which so far has been shown to range from 0.7 to 1 in different organisms14,39,45.
Supplementation
As animal supplementation is a critical component of HD-ClvR, our modelling investigated how daisyfield size and the level and placement of supplemented HD-ClvR animals effects efficiency and safety of population suppression. Optimisation of these parameters can significantly reduce cost and labour, as well as reduce the risk of unwanted impacts on non-target populations. We modelled our supplementation as a percentage of the total population size, therefore the number of individuals needed for supplementation increases linearly with population size. We also want to minimise the risk of non-target populations being impacted by the gene drive, and therefore, there is a trade-off between safety (size of the daisyfield) and cost and labour (level of supplementation required).
The least number of daisy elements that can suppress the population with a realistic level of supplementation, but does not cause any serious issues in non-target populations, should be objectively established through an in-depth risk assessment process. In a larger population however, the spread is slower than in a small one. Therefore, for improved safety and efficiency, gene drives are best applied in small sub-populations separately. The impact of a single introduction, such as a rogue deployment or migration, depends on the population size. The smaller the population, the bigger the impact. This it is a concern when the target population is much larger than the non-target population, but this is not the case for invasive UK grey squirrels and many other invasive species.
The appropriate daisyfield size also depends on the rate of NHEJ ((P_n)) of the gene drive system; the higher the ((P_n)), the more embryonic lethal offspring will arise and the sooner daisyfield burns out. To choose a safe number of daisy elements, we also need an estimate of how many animals a rogue party could obtain, potential breed and add into a non-target population for their own benefit. Overall, each target population and prospective gene drive strategy needs to be considered on a case-by-case basis and include an in-depth multidisciplinary risk assessment process.
When we consider the spatial aspects of a HD-ClvR supplementation programme, the picture becomes more complex. A key factor is the supplementation location of individuals. Obviously, supplementing individuals in a location where the population has already been suppressed will be ineffective. Therefore, different placement strategies can be adopted to keep placing individuals in a relevant area. A monitoring system where not only the size of the population is known, but also the location can significantly help HD-ClvR continue spreading and suppress a targeted population.
In this study, we modelled HD-ClvR using five different supplementation placement strategies in grey squirrel. These were: supplementation at the mean of population location, the mode of population location, randomly, randomly in 10 groups, and in a moving front (Fig. 6a). With supplementation at the mean of the population location, supplementation started in the middle of the population. After a few generations, a gap appears in the middle due to local suppression. The mean of the populations location still lies in the middle, as can be seen in Fig. 6c at 20 generations. Therefore, supplementation is not effective until the population is also suppressed in another location, thereby shifting the mean. Additionally, when there is a single large patch of the population left and additional smaller clusters, supplementation in the middle of the large patch allows the smaller clusters to recover, as can be seen in Fig. 6c after 64 generations.
With supplementation at the mode of the population location, we supplement in a location where there are many individuals. This placement strategy avoids the problem of supplementing in a location without individuals, either in a doughnut-like spatial population structure or in a multi-patch population. However, this placement strategy still allows small patches to form and recover. Supplementation at a random location theoretically means that supplementation happens uniformly, but in reality, this is not the case. Initially HD-ClvR spreads in multiple locations, but after the population is suppressed in certain regions, supplementation in those regions becomes ineffective. Therefore, at a later stage of population suppression this placement scheme becomes increasingly ineffective.
Supplementation at random locations is more effective when they are broken up into multiple groups (ten in our model). The gene drive spreads in many locations initially like the random single location placement scheme. After significant suppression of the population some but not all of the 10 groups supplemented are at ineffective locations. The groups that are placed at relevant locations are enough to keep the gene drive spreading. In our model supplementation in groups at random locations gets close to the speed at which a gene drive spreads in a non-spatial model.
The moving front placement scheme is very effective initially, as the gene drive spreads uniformly across the front. In this case, supplementation keeps ahead of where the populations is being suppressed. This placement strategy allows the population to recover behind the moving front after effective initial spread and near-complete suppression. To improve efficiency of the moving front strategy, it may be beneficial to include random supplementation behind the moving front to prevent animals from re-establishing.
Finally, in our spatial model, it was evident that there is more uncertainty in levels of population suppression than a randomly mating model leads us to believe. As can be seen in Fig. 6b, the 95% quantiles are broader than the quantiles in Fig. 3. Therefore, we conclude that to tailor the amount of supplementation, it is vital to closely monitor a population where a gene drive is used.
Assumptions and future work
Our model works under the following six assumptions. First, our model excludes some complexities of the optimal number of gRNAs for homing. Although our model suggests that multiplexing gRNAs for both the homing and cleave-and-rescue gene drives is most effective, a recent study using a more complex model and in vivo data shows that the optimal number of gRNAs to use for homing in Drosphilia melanogaster is two. They report a decrease in homing efficiency with more than two gRNAs due to reduced homology and Cas nuclease saturation28. Therefore, our gene drive with four gRNAs for both homing and cleave-and-rescue will likely be less efficient in such a complex model. We suggest using two homing gRNAs and four cleave- and-rescue gRNAs is likely most efficient, while still eliminating all resistance alleles28. It would be prudent to analyse our gene drive in this complex model as well to get a definitive estimate, as Cas saturation is thought to have an influence on gene drive efficiency when multiplexing is used28.
Second, we assumed there was no embryonic Cas-gRNA expression. Embryonic Cas-gRNA expression might be problematic as it leads to resistance allele formation and can interfere with the cleave-and-rescue mechanism by cleaving alleles from the wildtype parent. As our gene drive eliminates resistance alleles, embryonic Cas-gRNA expression may not inhibit spread, depending on the rate. Additionally, if the embryonic Cas-gRNA expression turns out to be more common in grey squirrel or other species, the cleave-and-rescue part of the gene drive can be harnessed with a double rescue mechanism to overcome this issue, as reported by Champer et al.24.
Third, we did not take other types of resistance alleles into account such as mutations rendering the CRISPR-Cas non-functional. As this is a universal assumption in gene drive research, we will have to await multigenerational studies to see if this is problematic.
Fourth, HD-ClvR has not been tested in vivo, which is our next step. The two recent papers testing a gene drive similar to HD-ClvR for population modification have performed in vivo tests in Drosophila melanogaster which showed very efficient conversion rates41. Proof-of-concept testing of HD-ClvR would likely initially occur in D. melanogaster and mouse models before progressing to squirrel studies. Recent reports have shown that the VASA promoter for Cas expression in homing gene drives is not optimal and further investigation to identify a meiosis-specific germline promoter is needed15. Furthermore, the integration of many daisies in a squirrel genome will be a molecular challenge and is a feat which has not yet been reported on in any species. This task could be achieved using either a random integration strategy, such as lentiviruses46 or a targeted integration strategy that exploits neutral repetitive sequences in the genome as target sites32. Also, non-model species might be difficult to genetically engineer, although grey squirrel embryology will likely follow the extensive knowledge on rodent and farmed animal embryology, and similar reagents and equipment could be used. An important consideration when engineering gene drive is that the modified animals maintain enough wild vigour to survive and breed in a wild population. Promising technologies for generating gene drive harbouring mammals with as little intervention as possible include in situ delivery of CRISPR reagents to the oviduct47.
Fifth, for our spatial modelling, we assumed that an estimation of population size could be made every year, although there is a significant amount of room for error in this estimate. Additionally, for some of our placement schemes, we assumed an accurate estimate of population location. As the random placement in groups scheme turned out most effective, this is not a problem so much as further potential for improvement. Another direction for future spatial work is the modelling of real landscapes, which are more complex than what we modelled in this study48. In complex landscapes, it might be that gene drive spread is slower or even regionally confined in some situations. Additionally, there might be spatial dynamics to gene drives in general such as ’chasing’, which is the perpetual escaping and chasing of wildtype and gene drive animals34. Further efforts are necessary to create a more realistic spatial model before we can consider using a gene drive.
A final consideration is that the ecological services the grey squirrel and other invasive species provide are largely unchartered. Ecologists need to investigate the ecological services that an invasive species performs and how an abrupt suppression of this invasive population might impact the ecosystem as a whole. We need to consider other restorative measures such as reintroducing native species to fragmented habitats, amongst other ecological interventions49. From a regulatory perspective, there is no tested legislative framework for the release of gene drive organisms; and with regard to our test animal it is currently illegal to breed grey squirrels in the UK. Developing these legislative frameworks alongside gene drive research is important. More importantly, the UK needs to continue to broaden public engagement and see whether the public is receptive to the deployment of gene drive technology in parallel to a financial overview of how much it would cost to apply gene drives reflecting our predicted need for supplementation.
Summary
HD-ClvR offers an efficient, self-limiting, and controllable gene drive strategy. We show that in the spatial model, complete population suppression is achieved approximately 5 years later than in the randomly mating population model. We then explored how the placement of supplemented animals could impact population suppression. Our results show that spatial dynamics of supplementation placement are not prohibitive to the spread of the gene drive, but that in fact, with an optimised strategy, spread at a rate equal to randomly mating population can be achieved. In our models, we have shown that grey squirrels have a spatial life history which facilitates the spread of a gene drive. Therefore, gene drives could be a valuable tool in the conservation toolbox. More

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In the present study, we used accelerometer enabled animal-borne biologging tags (recording temperature, pressure and three-axis accelerometry) to describe in high temporal resolution the variability and repeatability of 67 breaches made by three sharks over 41 cumulative days (Fig. 1; shark 5 m length (n = 1) 678 kg estimated mass; and sharks 6 m length (n = 2) 160 kg estimated mass). Approximately half (n = 28) of all breaches were single breaches, but we also recorded 13 double breaches, three triple breaches and one shark that breached four times in 47 s (Fig. 2A-D). Consecutive breaches were 18 s apart (mean value ± 6 s.d, range 12–47 s); i.e. sharks ascend from depth to the surface, propel themselves out of the water and swim to depth before commencing the subsequent ascent. Breaching frequency varied among individuals. Shark 1 breached 0.4 ± 0.9 times per day (mean ± 1 s.d.; range 0–2, n = 2 breaches), shark 3 breached 0.9 per day (± 0.9 s.d, range 0–2, n = 5) and shark 2 breached 1.9 times per day (± 1.8 s.d, range 0–6, N = 60) over 4.8, 4.9 and 31.8 tracking days respectively), during both day and night (peak hour of breaching 04:00 am). Multiple breaches by the same sharks have never been empirically demonstrated before, and breaching has not been described to take place at night.
Figure 1

Basking shark breaching. Breaching recorded by a towed camera tag deployed in 2018. These data are from a shark that was not instrumented with an accelerometer, they are included to aid visualisation of the breaching process from a point-of-view perspective. For sharks instrumented with accelerometers in 2017, tags where attached flush to the surface of the animal at the base of the dorsal fin. (A) Basking shark breaching (photo: Youen Jacob). The timing and depth associated with each image (C–H) are identified on the breaching depth profile (B). (C,D) the shark starts to ascend from 72 m depth at 0.94 m of vertical gain per second, reaching the surface (in view, E) in 77 s. The shark can be seen completely out of the water (F), before descending (G,H) to depth again.

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Figure 2

Characteristics of breaching. (A–D) A quadruple breach by a six-metre basking shark over 47 s showing changes in depth (A), tail beat amplitude (B), VeDBA (C) and speed (D) over the series of breaches. (E) Depth profiles of 16 single breaching events performed by a single shark, with time (in seconds) centred on the breach, overlaid on a common timescale showing repeatability of ascent angle and subsequent descent after breaching. (F) Dubai plot showing tri-axial acceleration data as a 3-dimensional histogram, with time spent by sharks in a particular posture on each facet of the sphere extruded as triangular bars, and colour scaling with the cumulative time in a given facet. Data show a right-handed breach of a single shark, where rapid rolling is indicated by short dark blue bars on the right face of the sphere.

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At the onset of a breach, sharks switched from slow swimming at 0.3 m.s-1 (mean value ± 0.16 s.d., range 0.17–0.4 m.s-1) at 14.8 m depth (± 5, range 4.6–28 m), to swimming towards the surface (Fig. 1C-E) at an angle of 38.9° (± 13.2, 23.08 to 81.6°), and an average (mean) ascent speed of 2.7 m.s-1 ± 0.5 (1.2–3.8 m.s-1). The peak ascent phase of a breach was observed when the rates of ascent and swimming speed rapidly increased. Breaching metrics were calculated separately for this peak ascent phase, where basking sharks reached the surface in 6 s (± 2.1, 2–17), before breaching near vertically at 76° (± 9, 43.3–87.9°), leaving the water at a mean exit speed of 3.9 m.s-1 ± 0.6. range: 2.2–5.6 m.s-1) (Fig. 1F). To contextualise our observations, an 8 m basking shark breached at 5.1 m.s-1 from 28 m depth10 and oceanic whitetip sharks (Carcharhinus longimanus)11 and great white sharks (Carcharodon carcharias)11,12 ambush-breach their prey at 4 and up to 6.5 m s−1 respectively, but from considerably deeper ( > 100 m11) and are smaller sharks. The peak forces generated by the three tagged basking sharks (which were estimated to weigh up to 1160 kg) were 20 G at the peak of breaching. Breaches could be further characterised by whether sharks exited the water on a particular side of their body. Sharks rolled to their right side in 45 of the 67 breaches (representing 67% of breaches), which may be suggestive of lateralisation (Fig. 2F), the preference for breaching on one side consistently across events13,14. Dynamic body acceleration (VeDBA) (linear mixed effects model; χ2 7.6, p = 0.006) along with tailbeat amplitude (linear mixed effects model; χ2 5.54, p = 0.019) increased with the sharks’ ascent pitch towards the surface. Breaching events were highly repeatable, both among and between sharks, following a similar ascent rate, speed and angle, and from a similar starting depth (Fig. 2E). Breaching was more energetically demanding than routine swimming (breaching VeDBA 7.7 m s−2 ± 4.5, range 0.4–14.7 vs routine swimming VeDBA 0.24 m.s-2 ± 0.04, 0.2–0.27), requiring double the tail beat frequency (breaching 0.49 Hz ± 0.12 vs routine swimming 1.08 Hz ± 0.51) and 15 times the tail beat amplitude (breaching 1.5 ± 1.1 Hz vs. routine swimming 0.1 ± 0.05 Hz). During multiple breaching events, the ascent rate, swimming speed and acceleration were similar for every subsequent breach, although the ascent starting depth was often shallower than for the initial breach. The relatively low field metabolic rate that comes with being ectothermic makes energetically demanding behaviour relatively more expensive for sharks. Therefore, the costs of performing multiple breaches may accumulate more rapidly compared to endothermic whales, such as humpback whales (Megaptera novaeangliae), which have been recorded breaching 17 times in a 6.5 h deployment15. On average, sharks required an estimated 11.5 kJ (range 3–22 kJ) of mechanical energy (({E}_{m})) to perform a breach, and expended the same ({E}_{m}) for each breach, regardless of whether they breached once or several times (Wilcoxon rank sum test, W = 198.5, p = 0.87; ({E}_{m} single) = 11.5 to 11.8 kJ, ({E}_{m} multi) = 9.98 to 10.3 kJ). Comparatively, the energetic cost of breaching for an 8 m basking shark weighing 2700 kg was estimated six times greater (63 to 72 kJ10). These differences may be attributed to the sharks in the present study being smaller, with the cost of breaching found to increase with increasing body mass15. A breach likely constitutes approximately 0.05 to 0.09% of their daily metabolic cost, which ranged from 12.8 to 21.5 MJ per day16. For comparison, the relative cost of performing a single breach in a 7.8 m (7000 kg) humpback whale represents 0.08 to 0.5% of its daily field metabolic rate15.
The question remains what the function of breaching is for basking sharks. We are still far from certain what the function of breaching is for many aquatic species, but spinner dolphins, blacktip sharks and humpback whales are known to breach to dislodge epiparasites17. Gore et al.9 noted that epiparasitic lampreys were not dislodged from basking sharks following breaching, suggesting that it may not function for parasite removal, or may require several breaches to dislodge such parasites. Breaching may be used to visually signal between spinner dolphins, and between humpback whales17. Basking sharks breached during the night-time as well as the daytime, and have small eyes, suggesting that breaching is unlikely to be a visual signal. However, breaching may play a role in acoustic communication between distant groups of sharks. Basking sharks can apparently detect weak electric signals produced by zooplankton18, and some elasmobranchs use electro-sensory cues during courtship19, suggesting that breaching could convey readiness to mate. It thus seems possible that the acoustic signal of breaching could be detectable and useful to basking sharks. We have no information in the present study about the presence of other sharks during breaching, although future work using animal-borne acoustic proximity receivers on large numbers of sharks, or aerial drones, could provide insight into the social networks of basking sharks, and whether they breach in proximity to conspecifics. We propose that in the absence of a better explanation and given the predictable and persistent aggregations of basking sharks in Scottish waters, that breaching may be more likely to be related to intra-specific signalling, than anything else yet described.
We show using repeated direct measurements from three individuals, that the mechanical forces required for basking sharks to breach are considerable, but that basking sharks can breach repeatedly in quick succession. The role of breaching seems most likely to be related to intra-specific signalling and may add to a weight of evidence suggesting that Scottish waters may be an important site for breeding for the species. More

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