Ecology and genetics can influence the fate of individuals and populations in multiple ways. However, to date, few studies consider them when modelling the evolutionary trajectory of populations faced with admixture with non-local populations. For the Atlantic salmon, a model incorporating these elements is urgently needed because many populations are challenged with gene-flow from non-local and domesticated conspecifics. We developed an Individual-Based Salmon Eco-genetic Model (IBSEM) to simulate the demographic and population genetic change of an Atlantic salmon population through its entire life-cycle. Processes such as growth, mortality, and maturation are simulated through stochastic procedures, which take into account environmental variables as well as the genotype of the individuals. IBSEM is based upon detailed empirical data from salmon biology, and parameterized to reproduce the environmental conditions and the characteristics of a wild population inhabiting a Norwegian river. Simulations demonstrated that the model consistently and reliably reproduces the characteristics of the population. Moreover, in absence of farmed escapees, the modelled populations reach an evolutionary equilibrium that is similar to our definition of a ‘wild’ genotype. We assessed the sensitivity of the model in the face of assumptions made on the fitness differences between farm and wild salmon, and evaluated the role of straying as a buffering mechanism against the intrusion of farm genes into wild populations. These results demonstrate that IBSEM is able to capture the evolutionary forces shaping the life history of wild salmon and is therefore able to model the response of populations under environmental and genetic stressors.

Throughout their native range, wild Atlantic salmon populations are threatened by hybridization and introgression with escapees from net‐pen salmon aquaculture. Although domestic–wild hybrid offspring have shown reduced fitness in laboratory and field experiments, consequential impacts on population abundance and genetic integrity remain difficult to predict in the field, in part because the strength of selection against domestic offspring is often unknown and context‐dependent. Here, we follow a single large escape event of farmed Atlantic salmon in southern Newfoundland and monitor changes in the in‐river proportions of hybrids and feral individuals over time using genetically based hybrid identification. Over a three‐year period following the escape, the overall proportion of wild parr increased consistently (total wild proportion of 71.6%, 75.1% and 87.5% each year, respectively), with subsequent declines in feral (genetically pure farmed individuals originating from escaped, farmed adults) and hybrid parr. We quantify the strength of selection against parr of aquaculture ancestry and explore the genetic and demographic consequences for populations in the region. Within‐cohort changes in the relative proportions of feral and F1 parr suggest reduced relative survival compared to wild individuals over the first (0.15 and 0.81 for feral and F1, respectively) and second years of life (0.26, 0.83). These relative survivorship estimates were used to inform an individual‐based salmon eco‐genetic model to project changes in adult abundance and overall allele frequency across three invasion scenarios ranging from short‐term to long‐term invasion and three relative survival scenarios. Modelling results indicate that total population abundance and time to recovery were greatly affected by relative survivorship and predict significant declines in wild population abundance under continued large escape events and calculated survivorship. Overall, this work demonstrates the importance of estimating the strength of selection against domestic offspring in the wild to predict the long‐term impact of farmed salmon escape events on wild populations.

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Genetic interaction between domesticated escapees and wild conspecifics represents a persistent challenge to an environmentally sustainable Atlantic salmon aquaculture industry. We used a recently developed eco‐genetic model (IBSEM) to investigate potential changes in a wild salmon population subject to spawning intrusion from domesticated escapees. At low intrusion levels (5%–10% escapees), phenotypic and demographic characteristics of the recipient wild population only displayed weak changes over 50 years and only at high intrusion levels (30%–50% escapees) were clear changes visible in this period. Our modeling also revealed that genetic changes in phenotypic and demographic characteristics were greater in situations where strayers originating from a neighboring wild population were domestication‐admixed and changed in parallel with the focal wild population, as opposed to nonadmixed. While recovery in the phenotypic and demographic characteristics was observed in many instances after domesticated salmon intrusion was halted, in the most extreme intrusion scenario, the population went extinct. Based upon results from these simulations, together with existing knowledge, we suggest that a combination of reduced spawning success of domesticated escapees, natural selection purging maladapted phenotypes/genotypes from the wild population, and phenotypic plasticity, buffer the rate and magnitude of change in phenotypic and demographic characteristics of wild populations subject to spawning intrusion of domesticated escapees. The results of our simulations also suggest that under specific conditions, natural straying among wild populations may buffer genetic changes in phenotypic and demographic characteristics resulting from introgression of domesticated escapees and that variation in straying in time and space may contribute to observed differences in domestication‐driven introgression among native populations.

African Oryza glaberrima and Oryza sativa landraces are considered valuable resources for breeding traits due to their adaptation to local environmental and soil conditions. They often possess superior resistance to endemic pests and tolerance to drought and nutrient deficiencies when compared to the "imported" high production Asian rice varieties. In contrast, "domestication traits" such as seed shattering, lodging, and seed yield are not well established in these African landraces. Therefore, the use of these African varieties for high production agriculture is limited by unpredictable yield and grain quality. We are addressing this shortcoming by developing protocols for genetically transforming African landraces to allow the use of CRISPR-Cas mediated breeding approaches. Here we use as proof of concept the cultivated African landrace Kabre to target selected known "domestication loci" and improve the agronomic potential of Kabre rice. Stable genetic transformation with CRISPR-Cas9-based vectors generated single and simultaneous multiple gene knockouts. Plants with reduced stature to diminish lodging were generated by disrupting the HTD1 gene. Furthermore, three loci shown to control seed size and/or yield (GS3, GW2 and GN1A) were targeted using a multiplex CRISPR-Cas9 construct. This resulted in mutants with significantly improved seed yield. Our study provides an example of how new breeding technologies can accelerate the development of highly productive African landrace rice varieties, an important advancement considering that Africa is a hotspot for worldwide population growth and therefore prone to food shortage. OPEN ACCESS Citation: Lacchini E, Kiegle E, Castellani M, Adam H, Jouannic S, Gregis V, et al. (2020) CRISPRmediated accelerated domestication of African rice landraces. PLoS ONE 15(3): e0229782. https://doi.

This paper presents a novel version of the bees algorithm. This version is characterized by an extended set of search operators, and a mechanism that protects the most recently generated solutions from competition with more evolved individuals. Compared to the standard implementation of the bees algorithm, the new procedure requires the selection of an additional set of parameters. A new statistical method is proposed to tune these extra parameters. The proposed tuning method was used to determine a unique set of learning parameters for the modified bees algorithm on eight popular function optimization benchmarks. When tested against the standard bees algorithm and two other well-known optimization procedures, the new algorithm attained top performances on nearly all the benchmarks. The experimental results also proved that, tested on a search space much larger than that where it was tuned, the modified bees algorithm still outperformed the standard method, and the degradation of the performance of the two algorithms was comparable. These results prove the effectiveness of the modified bees algorithm, and show that the proposed tuning procedure is a valuable alternative to the complex and subjective trial-and-error methods that are often used.

This paper proves the capability of the bees algorithm to solve complex parameter optimization problems for robot manipulator control. Two applications are presented. The first case considers the modelling of the inverse kinematics of an articulated robot arm using neural networks. The weights of the connections between the nodes need to be set so as to minimize the difference between the neural network model and the desired behaviour. In the proposed example, the bees algorithm is used to train three multilayer perceptrons to learn the inverse kinematics of the joints of a three-link manipulator. The second case considers the design of a hierarchical proportional–integral–derivative (PID) controller for a flexible single-link robot manipulator. The six gains of the PID controller need to be optimized so as to minimize positional inaccuracies and vibrations. Experimental tests demonstrated the validity of the proposed approach. In the first case, the bees algorithm proved very effective at optimizing the neural network models. Compared with the results obtained employing the standard back-propagation rule and an evolutionary algorithm, the bees algorithm obtained superior results in terms of training accuracy and robustness. In the second case, the proposed method demonstrated remarkable efficiency and consistency in the tuning of the PID controller parameters. In 50 independent optimization trials, the PID controllers designed using the bees algorithm consistently outperformed a robot controller designed using a standard manual technique.

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