The State of Knowledge of the Molecular Biology, Population Genetics, and Ecology of Gene-Drive Modified Organisms
For more than 50 years, biologists, geneticists, entomologists, and other scientists have explored approaches to harness gene drives to control or alter natural populations. Scientists have observed gene drives, systems of biased inheritance in which the ability of a genetic element to pass from a parent to its offspring through sexual reproduction is enhanced, in many organisms, including nematodes, plants, rodents (e.g., mice and lemmings), yeast, insects (e.g., fruit flies and mosquitoes) and fish (Boveri, 1887; Dobrovolskaia-Zavadskaia and Kobozieff, 1927; Gershenson, 1928; Rhoades, 1942; Ephrussi et al., 1955; Schultz, 1961; Hickey and Craig, 1966; Bengtsson, 1977; Beeman et al., 1992). Such observations led to proposals to develop gene-drive modified organisms for public health, conservation, agriculture, and other societal purposes, for example, by suppressing populations of mosquito species that transmit human diseases such as malaria, dengue, Zika, and chikungunya among others (Craig et al., 1960; Hamilton, 1967; Esvelt et al., 2014; Campbell et al., 2015).
Two essential components of a gene drive are a silenced (turned-off) or engineered genetic trait (or genetic element that enables the trait to be expressed) and a mechanism to drive the modified genetic element through a population by sexual reproduction. The deployment of cheaper and more user-friendly tools, such as transcription activator-like effector nucleases and CRISPR/Cas9, have facilitated insertion and deletion genetic engineering in many organisms from a single cell to complex multicellular organisms (Sander and Joung, 2014). Such tools, when coupled with driving genetic elements such as transposable elements or homing endonucleases, may enable researchers to mimic naturally occurring gene drive mechanisms. Indeed, recent advances in genome editing techniques using CRISPR/Cas9 as homing endonucleases have enabled researchers to develop gene drives in laboratory populations of yeast, fruit flies, and mosquitoes (DiCarlo et al., 2015; Gantz and Bier, 2015; Gantz et al., 2015; Hammond et al., 2016). The advent of CRISPR/Cas9-enabled gene drives or other gene drive technologies could in principle provide novel approaches to suppress populations or modify the genotypes of populations for pest control, conservation, or other purposes, throughout the world (Esvelt et al., 2014). This chapter has two aims:
- Outline the state of knowledge on genetic elements and their drive mechanisms; and
- Describe primary evolutionary and ecological considerations for the development and potential release of gene-drive modified organisms
The committee discusses the potential for developing gene drives from both molecular and population biology stand points. The discussions include examining how species’ dispersal can influence the spread of genetic elements through populations, and how ecological impacts can follow from the release of a gene-drive modified organism, particularly one that is designed to reduce or eliminate a population.
As briefly described in Chapter 1, selfish genetic elements are sequences of DNA, such as genes or their fragments, all or parts of chromosomes, or noncoding DNA, for which inheritance
is biased in their favor. Selfish genetic elements can “achieve drive” through one or more of three primary mechanisms: overreplication, interference, and gonotaxis (Burt and Trivers, 2006; see Box 2-1). One important particularity of these types of mechanisms is that they do not need to make any contribution to the reproductive success of the host organism in order to drive successfully. Genes in Conflict (Burt and Trivers, 2006) provides an in-depth discussion of selfish genetic elements and their drive mechanisms. Here, the committee briefly describes the main types of genetic elements that researchers are using to develop gene-drive modified organisms in the laboratory, and potentially for release into the environment.
Transposable elements (TEs), also referred to as transposons or jumping genes, small DNA segments can move from one part of the genome to another by excising themselves and randomly inserting elsewhere in the genome. In the context of a gene drive, TEs typify an overreplication mechanism. Multiple copies of the same TE often amass in the genome (i.e., increase in copy number) due to DNA repair or gene replication mechanisms that operate in eukaryotic cells. Thus, the copy number of TEs typically exceeds what would be expected under Mendelian inheritance.
Plant geneticist Barbara McClintock1 discovered TEs in 1952. She observed that some DNA sequences in maize could occasionally change their location in the genome, and suggested these “controlling elements” could potentially turn genes on and off (McClintock, 1951, 1956). Since then, scientists have found that TEs are ubiquitous among eukaryotes and often constitute a major part of the genome (Wicker et al., 2007).
The P-element transposon is a well-documented TE in the fruit fly Drosophila melanogaster. The P-element transposon has long been used to create genetically modified Drosophila melanogaster in the laboratory (Rubin and Spradling, 1982). Meister and Grigliatti (1993) first showed that a P-element transposon could rapidly spread a specific gene into an experimental Drosophila melanogaster population. Although P-elements are specific to Drosophila melanogaster, the piggyBac and Hermes TEs have been used for transformation in mosquitoes with varying degrees of success (Fraser, 2012). The use of TEs as vectors for a gene drive has several disadvantages, however, including insertion into random locations, relatively low transforming frequency, limited cargo gene size, and low stability of the integrated sequence (Fraser, 2012).
1Barbara McClintock shared the 1983 Nobel Prize in Physiology or Medicine for her discovery of mobile genetic elements.
Meiotic drive is an interference gene drive mechanism that refers to genetic alterations that cause a distortion of allelic segregation compared to expected Mendelian inheritance frequencies (McDermott and Noor, 2010).
A well-studied meiotic drive is the Segregation Distorter (SD) autosomal gene complex in Drosophila melanogaster (Hiraizumi and Crow, 1960). The SD autosomal gene complex has three elements: an allele of the gene SD, an enhancer of segregation distorter E(SD), and a responder (Rsp) locus that is the target of the SD gene. The SD interacts with the Rsp in ways still not well understood in order for its effects to be manifest, and, and the E(SD) magnifies these effects (see Larracuente and Presgraves, 2012 for details). When the SD autosomal gene complex is present in the male, wild-type2Drosophila melanogaster sperm do not complete development and only sperm carrying the SD autosomal gene complex survive, thus increasing the frequency of the SD complex in the population. Yet, the SD autosomal gene complex is present in the Drosophila melanogaster population at a relatively low frequency (1-5%) for reasons that are not well understood. Natural meiotic drives have also been found in mosquitoes (Hickey and Craig, 1966; Sweeny and Barr, 1978). In this case, the meiotic drive gene is linked to the male-determining locus (M), which is on an autosome, and the responder gene to the female-determining locus (m) is on the homologous chromosome. The meiotic drive product causes the breakage of the female-determining autosome. When the allele is present in the male, no females are produced, leading to a highly biased sex ratio in favor of males as long as the local population has no resistance alleles.
In vertebrates, the most studied natural meiotic drive is the t-haplotype in the house mouse Mus musculus (Silver, 1993; Ardlie, 1998). The t-haplotype consists of a series of linked, independent T complex distorter genes and a T complex responder gene that are inherited together. When present in the heterozygous (Tt) condition in the male, the wild-type sperm show motility defects and are functionally inactive, so more than 90% of the progeny receive the t-haplotype. The sterility of the Tt males, the presence of recessive lethal mutations within the t-haplotype, and a number of non-genetic factors, such as multiple matings and population size, serve to maintain the t-haplotype at a low frequency in a population (Ardlie, 1998).
Meiotic drive also occurs in plants. For example, the Abnormal 10 (Ab10) chromosome of maize (Zea mays ssp. mays) is a modified version of chromosome 10 linked to factors that cause segregation distortion (Rhoades and Dempsey, 1985). Ab10 affects the segregation of chromosome 10 and also affects unlinked chromosomes if they contain chromosomal knobs (small heterochromatic regions that sometimes act as neocentromeres during meiosis to allow chromosomes to be pulled apart). In the presence of Ab10, a knobbed chromosome of a heterozygous chromosomal pair segregates into about 70%, instead of the expected 50%, of viable megaspores (Rhoades, 1942). In theory, the Ab10 system can drive itself to fixation while simultaneously causing unlinked maize chromosomes to have ever-increasing chromosomal knobs. However, the Ab10 chromosome tends to be rare in natural populations, perhaps because its spread is constrained by the size and architecture of chromosomes during segregation (Buckler et al., 1999). Additional segregation distorters have been identified in other plant species, such as skewed sex ratios in Silene (Correns, 1906; Delph and Carroll, 2001) and skewed chromosomal segregation in monkeyflower hybrids (Fishman and Saunders, 2008). Generally, the formation of neocentromeres in plants and other organisms often appears to be a product of meiotic drive (Dawe and Hiatt, 2004), perhaps reflecting rapidly changing interactions among centromeric components (Henikoff et al., 2001).
2The collection of genotypes or alleles found in a natural populations. Natural populations harbor substantial amounts of genetic variation, so there is rarely a single wild-type genotype or allele.
Underdominance, or heterozygous disadvantage, occurs when the heterozygous progeny “have a lower relative fitness than both [parental] homozygotes” (Altrock et al., 2011). Curtis (1968) proposed that fertile chromosomal translocation homozygotes could be used to drive a gene into a pest population since the heterozygote is semi-sterile (as evidenced by the fact it produces about 50% of the expected progeny). Researchers attempted this approach but met with little success for various technical reasons (Curtis, 1985; Sinkins and Gould, 2006). In the past 15 years several models for using engineered underdominance for pest control were proposed, including those of Davis et al. (2001), Magori and Gould (2006), and Altrock et al. (2010). One approach that has been tested in laboratory populations is the maternal-effect lethal underdominance system (UDMEL) in Drosophila melanogaster (Akbari et al., 2013). The UDMEL system includes two maternal toxins targeting maternal genes essential for embryonic development and two antidotes (Akbari et al., 2013). The maternal toxin A is linked to the antidote B, and maternal toxin B is linked to the antidote A. The two constructs can be situated at the same position on homologous chromosomes or on different chromosomes, and the offspring must receive both constructs to survive. This requirement will only be met if the number of transgenic organisms released is above a certain threshold; otherwise, the transgenes will be lost from the population. In Drosophila, this method has been used both to drive a transgene to fixation through males carrying the transgenes and to remove a transgene from the population by increasing the ratio of wild-type males and females relative to the ratio of transgenic flies (Akbari et al., 2013). Similar methods have been proposed and modeled but not tested in the laboratory, including Semele (Marshall et al., 2011) and Medusa (Marshall and Hay, 2014). Semele is a toxin-antidote system in which a semen-specific toxin is carried in transgenic males and an antidote is carried in transgenic females. Wild-type females that meet with the transgenic males are either killed or unable to produce offspring, which leads to population suppression (Marshall et al., 2011). When both transgenic males and females are released, the transgenes and any cargo gene that they contain will become fixed in the population. In the Medusa system, maternal toxin A and zygotic antidote B are on the X-chromosome whereas zygotic toxin B and zygotic antidote A are on the Y chromosome. At least two releases of males bearing both transgenic chromosomes are needed for suppression of the female population. Both of these methods require a high release threshold to be driven into the population.
Other approaches for establishing underdominance are also being tested. For example, Reeves et al. (2014) are using an RNA interference (RNAi) approach in Drosophila melanogaster to suppress an endogenous gene that is haploinsufficient (that is, the gene must be present in two copies for normal development) coupled with an RNAi-insensitive rescue version of the gene.
Maternal-Effect Dominant Embryonic Arrest
Maternal-effect dominant embryonic arrest (Medea) is a natural genetic element that was first discovered in the flour beetle (Tribolium castaneum) and causes maternal-effect lethality in all offspring that lack the Medea-bearing chromosome (Beeman et al., 1992). Synthetic Medea elements, consisting of a microRNA that targets and silences a maternal gene necessary for embryonic development (maternal toxin) linked to a zygotic antidote gene that rescues that function, have been inserted in the Drosophila melanogaster genome using the P-element transposon (Chen et al., 2007; Akbari et al., 2014). In these instances the chromosome carrying the Medea element replaced the wild-type chromosome in about 16 generations. This element can carry a cargo gene into the population and potentially can be used for population suppression (Akbari et al., 2014).
Homing Endonuclease Genes
Homing endonuclease genes (HEGs) are situated on a chromosome within a specific sequence that they recognize and cut. These genes encode enzymes that work by cutting the recognition sequence on the chromosome that is homologous to the one originally containing the HEG. After the sequence is cut, homologous recombination is used to then copy the HEG into the cut homologous chromosome. When “this process occurs in the germline, the proportion of gametes that contain the HEG is greater than 50%” (Fraser, 2012), and therefore the HEG could theoretically drive itself through the population. HEGs are present in eukaryotic organisms, archaea, and bacteria, where their recognition sequences are found at low frequencies in the genome (Jasin, 1996).
Austin Burt (2003) first proposed the idea of using HEGs to develop a gene drive. Windbichler et al. (2011) later described the use of an HEG in the creation of a gene drive in mosquitoes. In this instance, a transgenic mosquito was created with a cleavage site near a fluorescence gene, and, upon expression of the HEG from a donor DNA plasmid, the site was cut, allowing for copying of the HEG into the target site through gene repair and homologous recombination. One limitation of this system is that the DNA recognition and cleavage functions of these HEGs are very much intertwined (Sander and Joung, 2014). Furthermore, this method requires the ability to easily generate an HEG cleavage site in the target gene of interest, limiting the use of HEGs for editing purposes (unless the site is found naturally in the target gene). Building on the concept of meiotic drive described earlier, Galizi et al. (2014) used a specific HEG called the “X-shredder” to distort artificially the sex ratio in Anopheles gambiae by targeting a specific sequence on the X chromosome for disruption. This “X-shredder” mechanism, in turn, led to the loss of females (population suppression) and the subsequent bias toward male progeny. This mechanism, however, can only work if the sequence of interest is found on the X chromosome and is (ideally) repetitive in nature, due to the mechanism of repair employed by the cell.
In addition, the T complex distorter genes are now being considered as a means of introducing the sex-determining Sry gene into genetic (XX) females so they develop as males but are sterile (Campbell et al., 2015). Case Study 4 of this report (see Chapter 3) summarizes the use of this type of gene drive to eradicate invasive rodents on islands.
Zinc Finger Nucleases
Zinc finger nucleases (ZFNs), an alternative to HEGs, are engineered DNA binding proteins that facilitate targeted editing of the genome (Pratt et al., 2012; Figure 2-1). ZFNs combine a nuclease domain derived from a specific restriction enzyme (typically FokI) with a DNA binding domain mediated by zinc fingers and can be used to target user-defined DNA sequences (Kim et al., 1996). The ZFNs function as pairs because the enzymatic domains must form dimers in order to cleave DNA (Urnov et al., 2010). However, ZFNs can cleave other sequences besides the intended one, are sometimes toxic to cells (Cornu et al., 2008), and must be custom-made, making them a more expensive method for editing (Koo et al., 2015).
Transcription Activator-Like Effector Nucleases
Like ZFNs, Transcription Activator-Like Effector Nucleases (TALENs) utilize the same nuclease domain and function as dimers but instead rely on a DNA binding domain called a TAL effector derived from the plant pathogenic bacterium Xanthomonas (Boch and Bonas, 2010). These TAL effector binding sites recognize single bases such that four different sites (unique to each of the four bases that constitute DNA) can be generated (Boch et al., 2009). Their creation can be quite time-consuming and labor-intensive, as TALENs require a new protein pair to be
created for every DNA sequence to be edited. Choosing sequences that differ by at least seven base pairs from I other sites and using software to generate site-specific TALENs3 has been helpful in creating functional TALENs (reviewed in Koo et al., 2015). Simoni et al. (2014) showed that ZFNs and TALENs could be used as gene drives, with homing frequencies of 34% and 49% to available target loci, respectively, in Drosophila melanogaster. In many instances, though, TALENs are not transmitted faithfully due to the number of repetitive elements required and their subsequent tendency to recombine, leading to their loss of function (reviewed in Koo et al., 2015).
CRISPR/Cas is a genetic engineering tool developed from an adaptive immune system like response observed in bacteria and archaea. The CRISPR/Cas9 system requires a target-specific guide RNA (gRNA) and a CRISPR associated protein (Cas9), which is an enzyme that cleaves DNA (Jinek et al., 2012; Bolukbasi et al., 2015; Sternberg and Doudna, 2015; see Figures 2-1 and 2-2).
Compared to ZFNs and TALENs, the CRISPR/Cas system is a less expensive and less laborious method for genetic engineering, and also can be effectively used to target multiple genes at once through the introduction of relevant gRNAs (Bono et al., 2015).
Scientists have used the CRISPR/Cas9 system to developed a gene drive in the laboratory in several organisms, including fruit flies, mosquitoes, and yeast (DiCarlo et al., 2015; Gantz and Bier, 2015; Gantz et al., 2015; Hammond et al., 2016). The CRISPR/Cas9 system can insert a particular gene into a chromosome, resulting in one copy of the gene drive in the genome. The inserted gene drive then “cuts” the wild-type homologous chromosome. Using the inserted gene drive as the template, the DNA repair machinery inserts a copy of the gene drive into the wildtype homologous chromosome, resulting in two copies of the gene drive in the genome (Sander and Joung, 2014). Thus, all of the gene-drive modified organism’s offspring will inherit one copy of the gene drive (see Box 2-2). The CRISPR/Cas9 system therefore increases the likelihood that an organism will pass on a particular gene, and could be used for engineering a gene-drive modified organism to drive a gene through a population (Webber et al., 2015).
In 2015, researchers published four proof-of-concept studies demonstrating the use of CRISPR/Cas9 to develop gene drives in the yeast Saccharomyces cerevisiae (DiCarlo et al., 2015), the fruit fly Drosophila melanogaster (Gantz and Bier, 2015) and two mosquito species, Anopheles stephensi (Gantz et al., 2015) and Anopheles gambiae (Hammond et al., 2016).
For their development of a gene drive in yeast, DiCarlo et al. (2015) used a split-gene drive4 in which Cas9 and the guide RNA used for targeting were physically separated. Only when Cas9 was present was the targeted gene disrupted. Using this technique, the researchers achieved a highly efficient disruption that is capable of carrying a cargo gene into the site with the same high efficiency. The drive also was highly efficient in various genetic backgrounds. Their experiments also showed that the edited gene sequence could be restored with an overwriting drive that contained an intact copy of the gene although the Cas9 and guide RNA remained in the genome. In addition, DiCarlo et al. (2015) showed that using a much larger construct containing both Cas9 and the guide RNA was also highly efficient. The purpose of this research was to find safer ways to develop gene drives in various organisms.
In the fruit fly, Drosophila melanogaster, Gantz and Bier (2015) created a gene drive construct containing Cas9 under the control of DNA sequences (promoters) that would cause its expression in both germline and somatic cells linked to a guide RNA. The guide RNA targeted a particular site in the yellow body color gene of Drosophila. Injection of this construct into wildtype embryos yielded flies that, when mated to wild-type fruit flies, produced yellow (y- y-) female progeny rather than the expected females with a darker body color (y+ y-), showing that the
gene on both chromosomes had been disrupted. When these y- y- females were mated to wildtype males, 97% of the female progeny were y- y-, indicating that the insertion was transmitted for at least two generations. However, phenotypic mosaicism was found in some of these females.
Gantz et al. (2015) used the same basic strategy as Gantz and Bier (2015) to drive two anti-parasite genes along with a fluorescent eye color marker into Anopheles stephensi, a mosquito vector of the malaria parasite. They found a 98.8% gene conversion rate in the third generation of both gene-drive modified males and females mated with wild-type mosquitoes, and that the anti-parasite genes were transcriptionally active. However, they noted maternal effects due to activity of Cas9 in the embryo so that inheritance of the gene drives was decreased resulting in near-Mendelian ratios of the progeny. There were also fewer progeny, indicating that the chosen insertion site (in the eye color gene) may not be the optimal site for use in making transgenic mosquitoes for release. Gantz et al. (2015) concluded that the gene drive should be restricted to the germ line and that additional work is needed to find the best site for insertion and the most efficacious anti-parasite genes to use.
In their research on the mosquito Anopheles gambiae, Hammond et al. (2016) used a CRISPR/Cas9-based gene drive to disrupt three different putative female-fertility genes. The construct was similar to that of Gantz et al. (2015) except that it lacked the anti-parasite genes. For all three genes, the inserted gene drive construct efficiently copied itself into the gene on the homologous chromosome causing sterility of female homozygotes, but also severely decreased the fertility of the heterozygous females. Modeling showed that this reduced reproductive capability of the heterozygotes would lead to the disappearance of the gene drive from the population over time for two of the three inserted genes. Population cage studies with the gene that showed the highest insertion efficiency and higher heterozygous female fertility revealed an increase in the frequency of the insertion from 50% to 75% over four generations.
In addition to the need to refine methods to develop CRISPR/Cas9-based gene drives in yeast, fruit flies, and mosquitoes, another important consideration is whether these scientific findings will be applicable to other organisms. For example, could a CRISPR/Cas9-based gene drive be developed in vertebrate animals (e.g., fish and rodents), or for use in plants?
Researchers have made considerable progress in understanding the genome and how it might be manipulated using a gene drive. However, such research is in an early stage. While high quality laboratory work demonstrates the application of gene drives in the laboratory, additional research is needed to refine gene drive technology and understand its effects before gene-drive modified organisms can be release in the environment.
Although molecular biology research on gene drives is rapidly advancing, extensive research on population dynamics, evolutionary processes, and ecology of gene-drive modified organisms has not yet taken place. Releasing gene drives into the environment means that complex molecular systems will be introduced into complex ecological systems, setting off a cascade of eco-evolutionary dynamics. Key considerations include fitness, species dispersal, gene flow, ecosystem dynamics, and evolution. Changes in population dynamics will influence evolutionary processes and vice versa. Advances in theory, modeling, and empirical studies will be needed to understand and better understand the effect of gene-drive modified organisms on these complex processes.
The Role of Evolutionary Fitness
The success or failure of a gene-drive modified organism will depend on the evolutionary fitness of the organism. Fitness is, most simply, the number of offspring that an individual contributes to the next generation. When discussing the fitness of individuals with different geno-
types (in the context of this report, the individuals that do and do not carry a gene drive) is the average number of offspring contributed by each genotype, which tells us how many of each type of gene (the gene drive or its alternative, wild-type form) will populate the next generation. The average fitness of a genotype is measured by combining the rate at which different genotypes survive to reproduce with the number of offspring contributed by those that do survive to reproduce (Orr, 2009).
The fitness of an individual organism may be measured absolutely, as the total number of surviving offspring that it produces during its lifetime, or relatively, as a proportion of the highest value absolute fitness seen in another individual. Relative fitness is the usual standard for comparing genotypes; a genotype whose carriers leave only 80% as many offspring, on average, as those left by the genotype with the highest absolute fitness is said to have a relative fitness of 0.80. A variety of empirical methods have been used to estimate the relative fitness of a particular genotype compared to other genotypes in a population, especially by tracking their comparative ability to produce offspring in future generations (Prout, 1965; Burt, 1995; Mueller, 2009).
A final important quantity is the mean fitness of a population. When describing absolute fitness, the mean fitness is, approximately, the ecological replacement rate: How many offspring are, on average, left behind by one individual? If the mean absolute fitness is greater than 1.0, the population will grow in size in the next generation and if the mean absolute fitness is less than 1.0, the population will decrease in size. It is important to distinguish relative and absolute fitness in gene drive applications because measures of relative fitness may not reveal how a gene drive will affect the actual numbers of individuals. When population suppression is the goal of deploying a gene drive, it is essential to understanding mean fitness in absolute terms.
The fitness of an individual can be affected by small genetic changes, such as the introduction of a point mutation or a gene drive. Introduced mutations may have a positive effect on fitness or a negligible effect, but more often they are expected to decrease the fitness of their carriers. However, the magnitude and direction of the fitness effect caused by a mutation at one gene or the insertion of a gene drive at one location can also depend on the other genes carried by that individual. This is because interactions between the mutation and other loci in the genome can affect the phenotype of the organism and its fitness (de Visser and Krug, 2014). Evidence of such interactions can be found when the fitness of a mutation or genetic modification varies among genetic stocks or lines derived from a target population (e.g., Amenya et al., 2010). These interactions are known as epistatic effects. Thus, a rigorous examination of the fitness consequences of introduced genetic material requires measurement of its effects across multiple genetic backgrounds. For this reason, it is sometimes useful to measure the mean fitness of a population with a mutation or gene drive because that mean will be based on the total collection of genotypes in the population. If one or more new genotypes are introduced into a population, mean fitness may increase, decrease, or remain the same.
The measure of fitness effects is relevant to gene drive applications because it is the basis for estimating the rate of spread of the gene drive through a population. The conceptual foundation for these estimates comes from the population genetics literature, particularly the models of natural meiotic drives developed by Hartl (1970), who in turn built on previous models for the t-allele system in mice (Lewontin, 1968). In these models, the fitness of an organism that contains the gene drive is one key parameter, but there is another important parameter: the rate at which the drive allele converts the other, non-drive allele in a heterozygous individual. For example, when a heterozygous individual always produces gametes with only the drive allele, the conversion rate is 100%.
These population genetic models illustrate that the basic dynamics of gene drives are propelled by the conversion rate of the gene drive and the fitness of individuals that have the drive. When the drive has no effect on fitness and only acts through the conversion process, the gene drive spreads rapidly through a population until all individuals are homozygous for the drive. This can happen in as few as a dozen generations, assuming that enough gene drive individuals are released initially to drive the process deterministically (Unckless et al., 2015). The rate of
spread of the gene drive is even faster if the drive is beneficial—that is, if the gene drive increases the fitness of its carriers. Speed of gene drive spread is also strongly influenced by generation time; the shorter the generation time of a species, the faster the spread.
When the gene drive decreases the fitness of the organism (that is, when it carries a cost), the results depend on the balance between the conversion rate (which increases the frequency of the drive) and the cost of the drive (which decreases its frequency) (Burt, 2003). In the simplest case, when the drive is lethal in the homozygous condition but has no effect on the fitness of heterozygote carriers, the drive reaches an equilibrium frequency equal to its conversion rate. When the conversion rate is very low and the fitness cost to homozygotes carrying the drive is very high, this equilibrium frequency is itself very low, and the drive will not spread through the population and might even be lost. When the gene drive affects the fitness of heterozygote carriers as well as the fitness of homozygote carriers, the cost to the heterozygous individuals can determine whether that equilibrium is stable or unstable (Deredec et al., 2008; Unckless et al., 2015). If it is unstable, then introductions of the drive must be done at frequencies that exceed that equilibrium value if the drive is to spread, a situation not unlike the population genetics of control systems using Wolbachia strains (Turelli and Hoffmann, 1999).
These models show an important characteristic of a gene drive; namely, it can spread throughout a population even if it reduces the fitness of individuals that carry it. This is an especially important property when the goal of deploying a gene drive is population suppression (e.g., reducing the population density of a disease vector). In many cases, the goal of deploying a gene drive will be to modify the genetic constitution of a population, for example, to prevent a disease vector from acquiring or transmitting a pathogen. For either goal, the approach requires that the altered genotype can survive in the environment and contribute to sexual reproduction; otherwise the introduced gene cannot spread into the target population. If suppression is the goal, the fitness effect of the introduced gene may be as extreme as lethality (fitness of zero), and preliminary experiments can be conducted to confirm that this effect occurs regardless of the genetic background of individuals that inherit the gene in the target population. If replacement is the goal, the fitness effect of the introduced gene must be non-lethal, because replacement of individuals in the target population is the desired outcome. However, even in the case of modification, low fitness of the engineered genotype and those inheriting the gene may be desirable in order to facilitate creating a “self-limiting” gene drive that would either be very restricted in its spatial dissemination or lost after a certain number of generations (Gould et al., 2008; Legros et al., 2013).
Species Dispersal and Gene Flow Among Populations
The models of Hartl (1970) and others (Deredec et al., 2008; Unckless et al., 2015) are important for generating expectations about the spread of gene drives through a population, and similar models will be useful for risk assessment. However, like most population models, these contain simplifying assumptions for mathematical tractability, such as the assumption that there is only one population of constant size. In reality, populations are often spatially structured with some genetic migration among them.
Understanding the patterns of a species’ spatial structure and how genes move among populations are important components to understand when preparing to release a gene-drive modified organism. Researchers can develop prospective simulations that model the target species and help estimate the number of gene-drive modified individuals to introduce or guide the spatial distribution of introductions. However, data on movement patterns and their effects on spatial structure may not always be available. Thus, models can also be informed by what is known about spatial structure for a variety of other organisms. The following sections focus on some of the properties of gene dispersal and its potential effects—both beneficial and detrimental—that can inform the application of gene drives and aid in planning their release.
Types of Dispersal Among Populations
The promise of gene drives is based on the potential spread of the desired gene through an entire area occupied by a species or population. The spread itself occurs via the movement of individuals or gametes from one location to another, with subsequent mating and reproduction. The spread of genes via movement between populations is called gene flow (Slatkin, 1987). Understanding the role of gene flow is critical for determining how rapidly a gene drive will spread among populations, whether the goal is to move the drive into additional populations or, conversely to limit its spread. Understanding gene flow is also vital for estimating the likelihood that the gene drive may move into a non-target population.
The diversity of gene flow patterns is influenced by three main factors: the stage of the life cycle in which the movement of individual organisms among populations is most likely, the type of movement through which individuals carry genes among populations, and the spatial scale over which movement typically occurs.
Gene flow may occur by the movement of either whole organisms or gametes. For many species, “typical” movement of an individual occurs in specific life cycle stages. For example, in many organisms, movement occurs via dispersal of fertilized eggs (especially in marine animals, e.g., D’Aloia et al., 2015), seeds (as in vascular plants, e.g., Picard et al., 2015; Shao et al., 2015), or spores (as in fungi, ferns, and mosses, for example). By contrast, in many animals, movement among populations is most likely when juveniles or young adults of one gender disperse from the area of their birth to establish themselves elsewhere (Graw et al., 2016). In these cases, social interactions can play a critical role in determining individual movement, where an individual settles, and whether movement results in breeding and actual gene flow (Booth et al., 2009; Wey et al., 2016). The stage of the life cycle in which gene flow occurs can influence the rate at which genes move from one population into another. For example, the passive dispersal of fertilized eggs and seeds can introduce substantial numbers of genes from one population into another (Ceron-Souza et al., 2015), whereas the dispersal of juvenile or adult individuals in search of new habitat will generate much lower rates of gene exchange (Craig et al., 2015).
In contrast, many plants and some marine invertebrates disperse primarily through the movement of gametes rather than whole organisms. The most familiar example is wind-borne pollen, which can transport genes across long distances (Huang et al., 2015). In many cases, especially when pollen movement is facilitated by insect pollinators, the movement of genes can be quite circumscribed (Tambarussi et al., 2015). Gene flow via gametes is fundamentally different from gene flow via movement of individual organisms in two ways. First, it represents sexual transfer of a haploid genome rather than the movement of a diploid genome. Second, it offers a greater possibility of gene flow among closely related species. For example, gamete dispersal can move engineered genes from a target organism into a wild or domesticated relative more quickly and at a higher rate than might occur in hybridization via the movement of seeds among locations (O’Connor et al., 2015).
There are four broad types of movement that produce gene flow. First, individuals move via human-assisted dispersal. Human-assisted dispersal is well-recognized as a common avenue for the introduction of unwanted invasive species (Fonzi et al., 2015), but humans also move genotypes from one area to another. This can be accidental, as in the transport of marine organisms in ballast (Hershler et al., 2015) or purposeful, as in the enhancement of game or fishery populations (Anderson et al., 2014). Human-assisted movement can produce high or low rates of gene flow, depending upon the numbers of individuals transported. Second, individuals move in response to disruptive events. These can include evacuation in response to wildfires or other sources of rapid habitat destruction or fragmentation (Crosby et al., 2009; McElroy et al., 2011). Individuals in aquatic systems can also be transported among locations by flooding events such as flash flooding of streams or sheet flows across large areas (Apodaca et al., 2013). Gene flow from disruptive events can occur at a high rate if the event does not also cause high mortality. Third, the life history of many species includes a significant probability of normal movement
from one population to another without human assistance or a disruptive event (Graw et al., 2016). Rates of movement are highly variable, from cases in which it is rare for individuals to move to a different population to cases in which a significant fraction of the population disperses during every generation. Fourth, individuals can move in response to their perceptions of the quality of their current environment and that of nearby locations. For example, some animals will emigrate from a population in response to crowding, a shortage of breeding sites, or other indicators of habitat unsuitability (Clobert et al., 2004). If local habitats vary in quality, gene flow rates will be asymmetrical, with more animals leaving some populations than others and, conversely, some populations receiving more immigrants than others (Kawecki, 2004). A particularly important situation occurs when individuals emigrate from a population with a high density of individuals to a neighboring population with a low density, or into an area of suitable habitat that was previously not occupied by the species (Gauffre et al., 2014). For example, colonists may come from several different local populations and rapid recolonize an area in which a local population has been driven close to extinction (McCauley et al., 1995).
The spatial scale of movement is highly variable (Bohonak, 1999). Clearly, human-assisted dispersal can transport individuals for long distances and thereby link populations that might never exchange migrants via the typical movement patterns of individuals (Fonzi et al., 2015). Similarly, movements in response to disruptive events can also involve long distances. “Normal” movements have patterns and characteristic distances that are specific to individual species and their life histories (Ronce and Olivieri, 2004), and these can differ even among species occupying the same habitat (Nidiffer and Cortes-Ortiz 2015). At one end of the spectrum, there are species in which individuals move only very short distances in their lifetimes; when this is so, gene flow is restricted to low rates of exchange only among adjacent populations (Baer, 1998). At the opposite extreme, there are species in which individuals move considerable distances in a lifetime, which can create extensive dispersal to many other populations regardless of the distance separating them (Jue et al., 2015).
The Implications of Gene Flow for Gene Drives
Regardless of the type or movement, the spatial scale, or the life stage in which it occurs, gene flow at a sufficient rate can cause populations to converge in gene frequencies (Slatkin, 1985). Of course, complete convergence will not occur because populations of limited size will experience random changes in gene frequencies that act counter to gene flow’s otherwise homogenizing influence. It is important to note, too, that gene flow may cause maladapted genes to move between the subpopulations. If dispersal is a relatively weak force compared to selection, maladaptive genes will be removed by selection, similar to the removal of spontaneously occurring deleterious mutations that appear in the local gene pool. However, a distinctly different evolutionary outcome will occur if the rate of dispersal exceeds the strength of selection. Here, dispersal can cause a population decline because maladaptive genes are introduced into a subpopulation faster than they can be purged by selection (Bolnick and Nosil, 2007).
These concepts have ramifications for gene drives. As discussed above, gene drive mechanisms may be specifically designed to introduce maladaptive or even lethal genes into a target population, and the mechanism may itself override the effects of natural selection. Therefore, if a gene drive construct is introduced into one population, dispersal may facilitate its entry into another population. This spread may be beneficial if the intent is for the gene drive to affect multiple populations. However, if the intent of the gene drive is to affect a single target population, then gene flow may spread the gene drive to non-target populations, thereby creating unintended evolutionary and ecological consequences. If a gene drive construct reaches a non-target population, its fate will be governed in part by the fitness it imparts across genetic backgrounds and by its conversion rate. Conversely, if a gene drive is deployed for conservation goals, for example, to suppress the population of an invasive rodent on an island, the social system of the rodents
may limit the ability of the introduced organisms carrying the gene drive to establish territories, obtain mates, and spread the desired gene through the population.
It is clear that knowing the amount and pattern of gene flow among populations will be crucial for predicting the spatial dynamics of a gene drive that is released in the environment (North et al., 2013). Some studies have begun to model more complex scenarios of population history (Deredec et al., 2008, 2011), but many features of gene drives can be modeled more explicitly. These include, but are not limited to the effects of mixed mating systems (e.g., plants that self-fertilize and outcross at varying rates); the effects of spatial structure and gene flow; the potential for selection to act against migrants from another population if a drive is meant to spread spatially (Nosil et al., 2005); the evolution of resistance to the gene drive allele, which may lose effectiveness over time; the population dynamics of off-target effects (unintended editing of genes within the organism) that could lead to unexpected and undesired genetic changes; and the capacity for pathogens to overcome engineered resistance (as in the case of malarial resistance in mosquitoes). Additional modeling is necessary both for a more nuanced view of the capabilities and promise of gene drive, as well as for risk assessment (see Chapter 6 for further discussion).
Nonetheless, even the simplest models highlight important empirical shortcomings. For example, although empirical evidence indicates suggest that conversion rates for gene drives are high for specific wild-type alleles in the laboratory (Gantz and Bier, 2015; Gantz et al., 2015; Hammond et al., 2016), there are, as of yet, no estimates of gene drive conversion rates in larger and more genetically variable populations. There are additional challenges awaiting the study of the fitness benefits or costs because estimates based on assays of edited genes may not always reflect the benefits and costs created by the gene drive constructs, even in the laboratory. For example, Hammond et al. (2016) found that while heterozygotes for three genes edited to drive female fertility to zero in Anopheles gambiae showed no differences from the fertility of wild-type homozygotes, the heterozygotes for two of the edited genes formed by gene drive constructs had fertility rates so low that a gene drive construct using them would fail to increase in frequency. Heterozygotes for the third gene also had reduced fertility but Hammond et al. (2016) showed that the gene drive construct would still increase in frequency. It is difficult at present to model the spread of a gene drive without estimates of important model parameters, including fitness, conversion rate, population structure, gene flow, and ecological interactions among others. Empirical measurements of all of these important parameters are important prerequisites for the release of gene-drive modified organisms.
The Potential for Effects on Non-Target Species: Horizontal Gene Transfer
A related concern is that the release of gene-drive modified organisms may affect the evolution of species that are entirely distinct from the intended target species. Horizontal gene transfer (HGT; sometimes called lateral gene transfer) is similar to gene flow, but it refers to the movement of genes between populations of otherwise distinct species. There is increasing evidence that HGT has profoundly impacted the evolution of prokaryotes, because of multiple mechanisms that allow genes to be transferred between unrelated bacterial species (Koonin et al., 2001). This transfer facilitates introduction of novel DNA into the chromosomes of bacterial cells via infection of genetic elements (plasmids or phages) or simple uptake of DNA from the environment. In addition, HGT can allow genes to cross between biological domains (Bacteria, Archaea, Eukaryota), which constitute the highest taxonomic levels in biology and that are separated by billions of years of evolution (Hilario et al., 1993; Aravind et al., 1998; Klotz and Loewen, 2003). This possibility is exemplified by Agrobacterium tumefaciens bacterial infection of plants that can permit genes to move from bacteria into the host plant genome (Krenek et al., 2015).
The existence of HGT creates the concern that gene drive mechanisms, or their individual component parts, may spread into non-target species. Although HGT may occur more slowly in an evolutionary sense than the production of genetic variation within a species, it has also been
argued that HGT can exact more profound changes in natural populations, perhaps contributing to major evolutionary transitions (Gogarten and Townsend, 2005; Keeling and Palmer, 2008; Syvanen, 2012). There is also a growing appreciation that the likelihood of HGT events may vary among eukaryotic lineages, with the historical occurrence of these events perhaps being more common in plants than in other eukaryotes (Andersson, 2005). Moreover, separate but closely-related species of plants often hybridize (Rieseberg and Carney, 1998), suggesting that the possibility of the horizontal exchange of gene drives between species should need to be evaluated prior to environmental release.
Removal or Substantial Reduction of a Target Species
One possible goal of release a gene-drive modified organism is to cause the extinction of the target species or a drastic reduction in its abundance. Whether this outcome produces undesirable ecological consequences or not will depend upon factors that will vary from case to case.
The fundamental issue at the crux of ecological consequences of releasing a gene-drive modified organism is the fact that species do not exist in an ecological vacuum. Individual species are connected to other species in the community through direct trophic links (e.g., species A preys on species B) and through indirect trophic links (e.g., species C competes with species D for the same resource, or species E and F are both preyed on by species G). These links create dynamic feedbacks that affect the relative abundances of different species (Wootton, 1994). The feedback loops and their associated nonlinear dynamics can create a system of considerable complexity (Scheffer, 2009; Leroux and Loreau, 2010). This complexity makes accurate prediction difficult in the abstract because individual situations will vary; however, theory and empirical results offer insights about the issues that could come into play.
First, removing a species or substantially reducing its abundance can alter the community in which it is embedded. This is most obvious when a so-called keystone species5 is removed. The most well-known examples are keystone predators, which are predators at the top of a food chain whose loss triggers a dramatic change in the abundance of species at all lower levels of the food chain (Paine, 1966; Estes et al., 2011).
Second, the impact of removing a species can depend on whether there are ecological equivalents in the community. A target species may be abundant because it out-competes its ecological equivalents and keeps their abundances low (Klatt et al., 2015). In such cases, removing the target species may produce a competitive release of the other species, and the increase in their abundance may compensate for the loss of the target species in terms of any wider effects on the community that might otherwise radiate through the food web.
Third, there is increasing evidence that communities have tipping points at which they change rapidly from one configuration to another (Scheffer, 2009; Travis et al., 2013). Tipping points and alternative stable states are characteristic of systems, including ecological communities that include non-linear dynamics and that contain multiple feedback loops. A system can move past a tipping point when the abundance of a critical species passes a threshold value; complete removal of a species is not necessary to send an ecosystem across a tipping point into a new mix of species and abundances (Bundy and Fanning, 2005). A critical feature of these alternative states is that, in some cases, it may be very difficult to push the system back to its previous configuration, even with active restoration efforts (Burkepile and Hay, 2008; Mumby and Steneck, 2008). To be sure, there have been successful restorations of ecosystems (Shapiro and Wright, 1984) but a successful reversal cannot be assumed possible and, even if probable, could require many years of sustained effort (Jyvasjarvi et al., 2013).
Whether the ecological consequences of species removal or reduction through releasing a gene-drive modified organism are considered “desirable” or “undesirable” will depend on the context. For example, in the most straightforward case, removing or reducing the abundance of a recent invasive species may facilitate the recovery of endangered populations and the restoration of much of a community that has been disrupted by the invader.
In another example, when suppressing a target species releases ecologically equivalent species that, in effect, replace the target species’ role in the ecosystem, it is unlikely that there will be substantial additional effects that would be considered “undesired.” However, it is possible that the release of ecological equivalents may vitiate the effect of suppressing a target species. This seems most likely when the target species is a vector for a pathogen that can also be transmitted by the ecologically equivalent competitors that may be released (Rey and Lounibos, 2015). For example, several species of Aedes can transmit dengue and chikungunya, and suppressing the numerically dominant species may induce the release of the others (Alto et al., 2015).
Ecological principles suggest that the most likely scenario for creating an undesired ecological consequence via population suppression would be if a gene drive were to be deployed on a native keystone species (i.e., not a disruptive invasive species). At this time, few, if any, of the candidates for the deployment of gene drives represent known keystone species. Perhaps the most prominent candidates are mosquitos, the larvae of which are eaten by a variety of aquatic predators (Kumar and Hwang, 2006; Shaalan and Canyon, 2009) and the adults of which are considered by some to be a resource for bats (Salinas-Ramos et al., 2015). While there is evidence that some species of bats will alter habitat use to capitalize on swarms of adult mosquitoes (Gonsalves et al., 2013a), mosquitoes in general do not appear as an important component of bat diets except perhaps for very small bodied species (Jones et al., 2009; Gonsalves et al., 2013b).
While present discussions do not focus on native keystone species, future proposals may do so. There is also the possibility that a gene drive could have a non-target effect by moving into a species for which it was not intended via hybridization (Rieseberg and Ellstrand, 1993; Ellstrand, 2014; Kraus, 2015). In this light, it will be important to consider prospectively and carefully the likelihood of an undesired outcome. The biggest challenge is the rapidity with which gene drives can spread, because consequences could occur too quickly for any adaptive management scheme to halt them.
Many of these points were made in the Ecological Society of America’s most recent report on genetically modified organisms in the environment (Snow et al., 2005). The conclusions and recommendations of that report can be applied to many of the ecological issues surrounding the release of gene-drive modified organisms, with the added emphasis on the speed with which a gene drive can spread and the possibility of rapid development harmful ecological consequences.
Evolutionary biology suggests two additional considerations about assessing the potential ecological effects of gene drives, particularly when used to remove a a target species or reduce its abundance. The first is evolutionary history. Species interactions are often not merely ecological processes but evolutionary results (Kerr et al., 2015). This is most obviously true in pathogen-host systems (Duffy and Hall, 2008) and predator-prey systems (Brodie et al., 2002) in which the features of one population have been molded by its coevolution with a population of another species (Thompson, 2005). Disrupting a coevolved system by removing one species can produce a dramatic effect in the other species. Whether this is considered undesirable depends, again, on context. In some cases, this is precisely the goal of deploying a gene drive construct: Suppressing a disease vector will have an adverse effect on the pathogen carried by that vector. On the other hand, if a predator has evolved specialized features that improve its ability to capture and consume an individual prey species, at a cost to its ability to consume other species, then removing the prey will have an adverse effect on the predator because it cannot readily switch its consumption to other
species. While at present, there is no proposal for deploying a gene drive in such a context, it is possible that a gene drive could have a non-target effect of this type. This might be of particular concern in plant groups in which gene flow across species is possible and the effects of a non-target suppressor could translate into undesired effects on specialized insect pollinators and herbivores.
The second consideration is evolutionary future. Species that have been the targets of control mechanisms have often evolved some form of resistance that has allowed the recovery from the reductions in abundance produced by the initial application of those control mechanisms. The classic cases are antibiotic resistance (Perron et al., 2015), pesticides (Georghiou, 1990), herbicides (Busi et al., 2013), and viral control agents (Kerr et al., 2015). It is possible that resistance to a gene drive will arise. Resistance may evolve rapidly enough to impair the effectiveness of a gene drive for either population suppression or population modification, such as has been proposed for interfering with transmission of viral pathogens. Indeed, the lower the equilibrium population mean fitness becomes after the introduction of a gene drive, the stronger the selection pressure will be on any beneficial resistance mutant that arises even though the rate of these mutations will be lower as well. For a gene drive, the resistance could be systemic (i.e., to Cas9) or could depend on the target gene (i.e., gRNAs). The evolution of resistance is not guaranteed because resistance might depend on specific characteristics of individual species such as the frequency of end-joining (NHEJ) DNA repair, or its timing, or the overall mutation rate, which can vary widely among species and even lineages within a species (Denver et al., 2012).
A wide variety of gene drives occur naturally in many types of organisms that cause genes or other genetic elements to spread throughout populations to varying degrees. To date, most gene drive research focuses on insects, although some research has also been conducted on yeast and mice. Preliminary evidence from research using mosquitoes, fruit flies, and yeast suggests that gene drives developed in the laboratory with CRISPR/Cas9 could spread a targeted gene through nearly 100% of a given population. Cell types and species are likely to differ in their capacity to carry a gene drive, and therefore the effects and efficacy of gene drives are expected to be largely species-dependent. Additional laboratory research is needed on CRISPR/Cas9 and other gene drive mechanisms, both to refine these approaches and to understand how they might work under different environmental conditions and in a diversity of organisms.
Research on the molecular biology of gene drives has outpaced research on population genetics and ecosystem dynamics, two fields of study that are essential to determining the efficacy of gene drives and their biological and ecological outcomes. There are considerable gaps in knowledge regarding the implications of gene drives for an organism’s fitness, gene flow in and among populations, and the dispersal of individuals, and how factors such as mating behavior, population sub-structure, and generation time might influence a gene drive’s effectiveness. Addressing knowledge gaps about gene drives will require the convergence of multiple fields of study including molecular biology, genome editing, population genetics, evolutionary biology, and ecology.
Akbari, O.S., K.D. Matzen, J.M. Marshall, H. Huang, C.M. Ward, and B.A. Hay. 2013. A synthetic gene drive system for local, reversible modification and suppression of insect populations. Curr. Biol. 23(8):671-677.
Akbari, O.S., C.H. Chen, J.M. Marshall, H. Huang, I. Antoshechkin, and B.M. Hay. 2014. Novel synthetic Medea selfish genetic elements drive population replacement in Drosophila; A theoretical exploration of Medea-dependent population suppression. ACS Synth. Biol. 3(12):915-923.
Alto, B.W., D.J. Bettinardi, and S. Ortiz. 2015. Interspecific larval competition differentially impacts adult survival in Dengue vectors. J. Med. Entomol. 52(2):163-170.
Altrock, P.M., A. Traulsen, R.G. Reeves, and F.A. Reed. 2010. Using underdominance to bi-stably transform local populations. J. Theor. Biol. 267(1):62-75.
Altrock, P.M., A. Traulson, and F.A. Reed. 2011. Stability properties of underdominance in finite subdivide populations. PLoS Comp. Biol. 7(11):e1002260.
Amenya, D.A., M. Bonizzoni, A.T. Isaacs, N. Jasinskiene, H. Chen, O. Marinotti, G. Yan, and A.A. James. 2010. Comparative fitness assessment of Anopheles stephensi transgenic lines receptive to site-specific integration. Insect Mol. Biol. 19(2):263-269.
Anderson, A.P., M.R. Denson, and T.L. Darden. 2014. Genetic structure of striped bass in the southeastern United States and effects from stock enhancement. N. Am. J. Fish. Manage. 34(3):653-667.
Andersson, J.O. 2005. Lateral gene transfer in eukaryotes. Cell Mol. Life Sci. 62(11):1182-1197.
Apodaca, J.J., J.C. Trexler, N. Jue, M. Schrader, and J. Travis. 2013. Large-scale natural disturbance alters genetic population structure of the sailfin molly, Poecilia latipinna. Am. Nat. 181(2):254-263.
Aravind L, R.L. Tatusov, Y.I. Wolf, D.R. Walker, and E.V. Koonin. 1998. Evidence for massive gene exchange between archaeal and bacterial hyperthermophiles. Trends Genet. 14(11):442-4.
Ardlie, K.G. 1998. Putting the brake on drive: Meiotic drive of t haplotypes in natural populations of mice. Trends Genet. 14(5):189-193.
Baer, C.F. 1998. Species-wide population structure in a southeastern U.S. freshwater fish, Heterandria formosa: Gene flow and biogeography. Evolution 52(1):183-193.
Beeman, R.W., K.S. Friesen, and R.E. Dennell. 1992. Maternal-effect selfish genes in flour beetles. Science 256(5053):89-92.
Bengtsson, B.O. 1977. Evolution of the sex ratio in the wood lemming, Myopus schisticolor. Pp. 333-343 in Measuring Selection in Natural Populations, T.M. Fenchel, and F.B. Christiansen, eds. Berlin: Springer.
Boch, J., and U. Bonas. 2010. Xanthomonas AvrBs3 family-type III effectors: Discovery and function. Annu. Rev. Phytopathol. 48:419-436.
Boch, J., H. Scholze, S. Schornack, A. Landgraf, S. Hahn, S. Kay, T. Lahaye, A. Nickstadt, and U. Bonas. 2009. Breaking the code of DNA binding specificity of TAL-type III effectors. Science 326(5959):1509-1512.
Bohonak, A.J. 1999. Dispersal, gene flow, and population structure. Quart. Rev. Biol. 74:21-45.
Bolnick, D.I., and P. Nosil. 2007. Natural selection in populations subject to a migration load. Evolution 61:2229-2243.
Bolukbasi, M.F., A. Gupta, S. Oikemus, A.G. Derr, M. Garber, M.H. Brodsky, L.J. Zhu, and S.A. Wolfe. 2015. DNA-binding-domain fusions enhance the targeting range and precision of Cas9. Nat. Methods 12(12):1150-1156.
Bono, J.M., E.C. Olesnicky, and L.M. Matzkin. 2015. Connecting genotypes, phenotypes and fitness: Harnessing the power of CRISPR/Cas9 genome editing. Mol. Ecol. 24(15):3810-3822.
Booth, W., W.I. Montgomery, and P.A. Prodoehl. 2009. Spatial genetic structuring in a vagile species, the European wood mouse. Journal of Zoology 279:219-228.
Boveri, T. 1887. Ueber Differenzierung der Zellkerne wahrend der Furchung des Eies von Ascaris megalocephala. Anat Anz. 2:688-693.
Brodie, E.D., B.J. Ridenhour, and E.D. Brodie, III. 2002. The evolutionary response of predators to dangerous prey: Hotspots and coldspots in the geographic mosaic of coevolution between garter snakes and newts. Evolution 56(10):2067-2082.
Brown, V.A., E.B. de Torrez, and G.F. McCracken. 2015. Crop pests eaten by bats in organic pecan orchards. Crop Prot. 67:66-71.
Buckler, E.S., T.L. Phelps-Durr, C.S. Buckler, R.K. Dawe, J.F. Doebley, and T.P. Holtsford. 1999. Meiotic drive of chromosomal knobs reshaped the maize genome. Genetics 153:415-426.
Bundy, A., and L.P. Fanning. 2005. Can Atlantic cod (Gadus morhua) recover? Exploring trophic explanations for the non-recovery of the cod stock on the eastern Scotian Shelf, Canada. Can. J. Fish. Aquat. Sci. 62(7):1474-1489.
Burkepile, D.E., and M.E. Hay. 2008. Herbivore species richness and feeding complementarity affect community structure and function on a coral reef. Proc. Natl. Acad. Sci. 105(42):16201-16206.
Burt, A. 1995. Perspective–The Evolution of fitness. Evolution 49(1):1-8.
Burt, A. 2003. Site-specific selfish genes as tools for the control and genetic engineering of natural populations. Proc. Biol. Soc. 270(1518):921-928.
Burt, A., and R. Trivers. 2006. Genes in Conflict: The Biology of Selfish Genetic Elements. Cambridge, MA: The Belknap Press of Harvard University Press.
Busi, R., M.M. Vila-Aiub, H.J. Beckie, T.A. Gaines, D.E. Goggin, S.S. Kaundun, M. Lacoste, P. Neve, S.I. Nissen, and J.K. Norsworthy. 2013. Herbicide-resistant weeds: From research and knowledge to future needs. Evol. Appl. 6:1218-1221.
Campbell, K.J., J. Beek, C.T. Eason, A.S. Glen, J. Godwin, F. Gould, N.D. Holmes, G.R. Howald, F.M. Madden, J.B. Ponder, D.W. Threadgill, S.A. Wegmann, and G.S. Baxter. 2015. The next generation of rodent eradications: Innovative technologies and tools to improve species specificity and increase their feasibility on islands. Biol. Conserv. 185:47-58.
Ceron-Souza, I., E.G. Gonzalez, A.E. Schwartzbach, D.E. Salas-Leiva, E. Rivera-Ocasio, N. Toro-Perea, E. Bermingham, and W.O. McMillan. 2015. Contrasting demographic history and gene flow patterns of two mangrove species on either side of the Central American isthmus. Ecol. Evol. 5(16):3486-3499.
Chen, C-H., H. Huang, C.M. Ward, J.T. Su, L.V. Schaeffer, M. Guo, and B.A. Hay. 2007. A synthetic maternal-effect selfish genetic element drives population replacement in Drosophila. Science 316(5824):597-600.
Clobert, J., R.A. Ims, and F. Rousset. 2004. Causes, mechanisms, and consequences of dispersal. Pp. 307-335 in Ecology, Genetics, and Evolution of Metapopulations, I. Hanski, and O.E. Gaggiotti, eds. Boston: Elsevier.
Cornu, T.I., S. Thibodeau-Beganny, E. Guhl, S. Alwin, M. Eichtinger, J. Joung, and T. Cathomen. 2008. DNA-binding specificity is a major determinant of the activity and toxicity of zinc-finger nucleases. Mol. Ther. 16(2):352-358.
Correns, C. 1906. Die vererbung der Geshlechstsformen bei den gynodiöcischen Pflanzen. Ber. Dtsch. Bot. Ges. 24:459-474.
Craig, G.B., Jr., W.A. Hickey, and R.C. Vandehey. 1960. An inherited male-producing factor in Aedes aegypti. Science 132(3443):1887-1889.
Craig, H.R., S. Kendall, T. Wild, and A.N. Powell. 2015. Dispersal and survival of a polygynandrous passerine. Auk 132(4):916-925.
Crosby, M.K.A., L.E. Licht, and J. Fu. 2009. The effect of habitat fragmentation on finescale population structure of wood frogs (Rana sylvatica). Conserv Genet. 10:1707-1718.
Curtis, C.F. 1968. Possible use of translocations to fix desirable genes in insect populations. Nature 218(5129):368-369.
Curtis, C.F. 1985. Genetic control of insect pests: Growth industry or lead balloon? Biol. J. Linn. Soc. Lond. 26(4):359-374.
D’Aloia, C.C., S.M. Bogdanowicz, R.K. Franic, J.E. Majoris, R.G. Harrison, and P.M. Buston. 2015. Patterns, causes, and consequences of marine larval dispersal. Proc. Natl. Acad. Sci. 112:13940-13945.
Davis, S., N. Bax, and P. Grewe. 2001. Engineered underdominance allows efficient and economical introgression of traits into pest populations. J. Theor. Biol. 212(1):83-98.
Dawe, R.K., and E.N. Hiatt. 2004. Plant neocentromeres: Fast, focused, and driven. Chromosome Res. 12(6):655-669.
de Visser, J.A., and J. Krug. 2014. Empirical fitness landscapes and the predictability of evolution. Nat. Rev. Genet. 15(7):480-490.
Delph, L.F., and S.B. Carroll. 2001. Factors affecting relative seed fitness and female frequency in a gynodioecious species, Silene acaulis. Evol. Ecol. Res. 3:487-505.
Denno, R.F., and D. Lewis. 2009. Predator-prey interactions. Pp. 202-212 in The Princeton Guide to Ecology, S.A. Levin, ed. Princeton, NJ: Princeton University Press.
Denver, D.R., L.J. Wilhelm, D.K. Howe, K. Gafner, P.C. Dolan, and C.F. Baer. 2012. Variation in base-substitution mutation in experimental and natural lineages of Caenorhapditis nematodes. Genome Biol. Evol. 4(4):513-522.
Deredec, A., A. Burt, and H.C. Godfray. 2008. The population genetics of using homing endonuclease genes in vector and pest management. Genetics 179:2013-2026.
Deredec, A., H.C. Godfray, and A. Burt. 2011. Requirements for effective malaria control with homing endonuclease genes. Proc. Natl. Acad. Sci. U.S.A. 108(43):E874-E880.
DiCarlo, J.E., A. Chavez, S.L. Dietz, K.M. Esvelt, and G.M. Church. 2015. Safeguarding CRISPR-Cas9 gene drives in yeast. Nat. Biotech. 33(12):1250-1255.
Dobrovolskaia-Zavadskaia, N., and N. Kobozieff. 1927. Sur la reproduction des souris anoures. C.R. Soc. Biol. 97:116-119.
Duffy, M.A., and S.R. Hall. 2008. Selective predation and rapid evolution can jointly dampen effects of virulent parasites on Daphnia populations. Am. Nat. 171(4):499-510.
Ellstrand, N.C. 2014. Is gene flow the most important evolutionary force in plants? Am. J. Bot. 101(5):737-753.
Ephrussi, B., H. de Margerie-Hottinguer, and H. Roman. 1955. Suppressiveness: A new factor in the genetic determinism of the synthesis of respiratory enzymes in yeast. Proc. Natl. Acad. Sci. 41(12):1065-1071.
Estes, J.A., J. Terborgh, J.S. Brashares, M.E. Power, J. Berger, W.J. Bond, S.R. Carpenter, T.E. Essington, R.D. Holt, and J.B. Jackson. 2011. Trophic downgrading of planet Earth. Science 333(6040):301-306.
Esvelt, K.M., A.L. Smidler, F. Catteruccia, and G.M. Church. 2014. Concerning RNA-guided gene drives for the alteration of wild populations. eLife 3:e03401.
Fishman, L., and A. Saunders. 2008. Centromere-associated female meiotic drive entails male fitness costs in monkeyflowers. Science 322(5907):1559-1562.
Fonzi, E., Y. Higa, A.G. Bertuso, K. Futami, and N. Minakawa. 2015. Human-mediated marine dispersal influences the population structure of Aedes aegypti in the Philippine archipelago. PLoS Negl. Trop. Dis. 9(6):e0003829.
Fraser, M.J., Jr. 2012. Insect transgenesis: Current applications and future prospects. Annu. Rev. Entomol. 57:267-289.
Galizi, R., L.A. Doyle, M. Menichelli, F. Bernardini, A. Deredec, A. Burt, B.L. Stoddard, N. Windbichler, and A. Crisanti. 2014. A synthetic sex ratio distortion system for the control of the human malaria mosquito. Nat. Commun. 5:3977.
Gantz, V.M., and E. Bier. 2015. Genome editing. The mutagenic chain reaction: A method for converting heterozygous to homozygous mutations. Science 348(6233):442-444.
Gantz, V.M., N. Jasinskiene, O. Tatarenkova, A. Fazekas, V.M. Macias, E. Bier, and A.A. James. 2015. Highly efficient Cas9-mediated gene drive for population modification of the malaria vector mosquito Anopheles stephensi. Proc. Natl. Acad. Sci. U.S.A. 112:E6736-E6743.
Gauffre, B., K. Berthier, P. Inchausti, Y. Chaval, V. Bretagnolle, and J.F. Cosson. 2014. Short-term variations in gene flow related to cyclic density fluctuations in the common vole. Mol. Ecol. 23(13):3214-3225.
Georghiou, G.P. 1990. Overview of insecticide resistance. ACS Symposium Series 421:18-41.
Gershenson, S. 1928. A new sex-ratio abnormality in Drosophila obscura. Genetics 13(6):488-507.
Godwin, J. 2015. Gene Drives in Rodents for Invasive Species Control Webinar, October 15, 2015. Available at: http://nas-sites.org/gene-drives/2015/10/02/webinar-gene-drive-research-in-different-organisms/ [accessed April 22, 2016].
Gogarten, P., and J.P. Townsend. 2005. Horizontal gene transfer, genome innovation and evolution. Nat. Rev. Microbiol. 3(9):679-687.
Gonsalves, L., S. Lamb, C. Webb, B. Law, and V. Monamy. 2013a. Do mosquitoes influence bat activity in coastal habitats? Wildlife Res. 40(1):10-24.
Gonsalves, L., B. Bicknell, B. Law, C. Webb, and V. Monamy. 2013b. Mosquito consumption by insectivorous bats: does size matter? PLoS ONE 8(10):e77183.
Gould, F., Y. Huang, M. Legros, and A.L. Lloyd. 2008. A killer-rescue system for self-limiting gene drive of anti-pathogen constructs. Proc. Biol. Sci. 275(1653):2823-2829.
Graw, B., A.K. Lindholm, and M.B. Manser. 2016. Female biased dispersal in the solitarily foraging slender mongoose, Galerella sanguinea, in the Kalahari. Anim. Behav. 111:69-78.
Hamilton, W.D. 1967. Extraordinary sex ratios. A sex-ratio theory for sex linkage and inbreeding has new implications in cytogenetics and entomology. Science 156(3774):477-488.
Hammond, A., R. Galizi, K. Kyrou, A. Simoni, C. Siniscalchi, D. Katsanos, M. Gribble, D. Baker, E. Marois, S. Russell, A. Burt, N. Windbichler, A. Crisanti, and T. Nolan. 2016. A CRISPR-Cas9 gene drive system targeting female reproduction in the malaria mosquito vector Anopheles gambiae. Nat. Biotechnol. 34(1):78-83.
Hartl, D. 1970. Analysis of a general population genetic model of meiotic drive. Evolution 24(3):538-545.
Henikoff, S., K. Ahmad, and H.S. Malik. 2001. The centromere paradox: Stable inheritance with rapidly evolving DNA. Science 293(5532):1098-1102.
Hershler, R., H.P. Liu, J.T. Carlton, A.N. Cohen, C.B. Davis, J. Sorensen, and D. Weedman. 2015. New discoveries of introduced and cryptogenic fresh and brackish water gastropods (Caenogastropoda: Cochliopidae) in the western United States. Aquat. Invasions 10(2):147-156.
Hickey, W.A., and G.B. Craig, Jr. 1966. Genetic distortion of sex ratio in a mosquito, Aedes aegypti. Genetics 53(6):1177-1196.
Hilario E., J.P. Gogarten. 1993. Horizontal transfer of ATPase genes—the tree of life becomes a net of life. Biosystems 31(2-3):111-9.
Hiraizumi, Y., and J.F. Crow. 1960. Heterozygous effects on viability, fertility, rate of development, and longevity of Drosophila chromosomes that are lethal when homozygous. Genetics 45(8):1071-1083.
Huang, H., R.J. Ye, M.L. Qi, X.Z. Li, D.R. Miller, C.N. Stewart, D.W. DuBois, and J.M. Wang. 2015. Wind-mediated horseweed (Conyza canadensis) gene flow: Pollen emission, dispersion, and deposition. Ecol. Evol. 5(13):2646-2658.
Jasin, M. 1996. Genetic manipulation of genomes with rare-cutting endonucleases. Trends Genet. 12(6):224-228.
Jinek, M., K. Chylinski, I. Fonfara, M. Hauer, J.A. Doudna, and E. Charpentier. 2012. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337(6096):816-821.
Jones, G., D.S. Jacobs, T.H. Kunz, M.R. Willig, and P.A. Racey. 2009. Carpe noctum: The importance of bats as bioindicators. Endang. Species Res. 8:93-115.
Jue, N., T. Brule, F.C. Coleman, and C.C. Koenig. 2015. From shelf to shelf: Assessing historical and contemporary genetic differentiation and connectivity across the Gulf of Mexico in Gag, Mycteroperca microlepsis. PLoS ONE 10(4):e0120676.
Jyvasjarvi, J., H. Immonen, P. Hogmander, H. Hogmander, H. Hamalainen, and J. Karjalainen. 2013. Can lake restoration by fish removal improve the status of profundal macroinvertebrate assemblages? Freshwater Biol. 58(6):1149-1161.
Kawecki, T. 2004. Ecological and evolutionary consequences of source-sink population dynamics. Pp. 387-414 in Ecology, Genetics, and Evolution of Metapopulations, I. Hanski, and O.E. Gaggiotti, eds. Boston: Elsevier.
Keeling, P.J., and J.D. Palmer. 2008. Horizontal gene transfer in eukaryotic evolution. Nat. Rev. Genet. 9(8):605-618.
Kerr, P.J., J. Liu, I. Cattadori, E. Ghedin, A.F. Read, and E.C. Holmes. 2015. Myxoma virus and the Leporipoxviruses: An evolutionary paradigm. Viruses 7(3):1020-1061.
Kim, Y.G., J. Cha, and S. Chandrasegaran. 1996. Hybrid restriction enzymes: Zinc finger fusions to Fok I cleavage domain. Proc. Natl. Acad. Sci. 93(3):1156-1160.
Klatt, B.J., L.L. Getz, and B. McGuire. 2015. Interspecific interactions and habitat use by prairie voles (Microtus ochrogaster) and meadow voles (M. pennsylvanicus). Am. Midl. Nat. 173:242-252.
Klotz, M.G., and P.C. Loewen. 2003. The molecular evolution of catalatic hydroperoxidases: evidence for multiple lateral transfer of genes between prokaryota and from bacteria into eukaryota. Mol Biol Evol. 20(7):1098-112.
Koo, T., J. Lee, and J.S. Kim. 2015. Measuring and reducing off-target activities of programmable nucleases including CRISPR-Cas9. Mol. Cells 38(6):475-481.
Koonin, E.V., K.S. Makarova, and L. Aravind. 2001. Horizontal gene transfer in prokaryotes: Quantification and classification. Annu. Rev. Microbiol. 55:709-742.
Kraus, F. 2015. Impacts from invasive reptiles and amphibians. Annu. Rev. Ecol Evol. Syst. 46:75-97.
Krenek, P., O. Samajova, I. Luptovciak, A. Doskocilova, G. Komis, and J. Samaj. 2015. Transient plant transformation mediated by Agrobacterium tumefaciens: Principles, methods and applications. Biotechnol. Adv. 33(6):1024-1042.
Kumar, R. and J.S. Hwang. 2006. Larvicidal efficiency of aquatic predators: a perspective for mosquito control. Zoological Studies 45(4):447-466.
Larracuente, A.M., and D.C. Presgraves. 2012. The selfish segregation distorter gene complex of Drosophila melanogaster. Genetics 192(1):33-53.
Legros, M., C. Xu, A. Morrison, T.W. Scott, A.L. Lloyd, and F. Gould. 2013. Modeling the dynamics of a non-limited and a self-limited gene drive system in structured Aedes aegypti populations. PLoS ONE 8(12):e83354.
Leroux, S.J., and M. Loreau. 2010. Consumer-mediated recycling and cascading trophic interactions. Ecology 91:2162-2171.
Lewontin, R.C. 1968. The effect of differential viability on the population dynamics of t alleles in the house mouse. Evolution 22(2):262-273.
Magori, K., and F. Gould. 2006. Genetically engineered underdominance for manipulation of pest populations: A deterministic model. Genetics 172(4):2613-2620.
Marshall, J.M., and B.A. Hay. 2014. Medusa: A novel gene drive system for the suppression of insect populations. PloS ONE 9(7):e102694.
Marshall, J.M., G.W. Pittman, A.B. Buchman, and B.A. Hay. 2011. Semele: A killer-male, rescue-female system for suppression and replacement of insect disease vector populations. Genetics 187(2):535-551.
McCauley, D.E., J. Raveill, and J. Antonovics. 1995. Local founding events as determinants of genetic structure in a plant population. Heredity 75:630-636.
McClintock, B. 1951. Chromosome organization and genic expression. Cold Spring Harb. Symp. Quant. Biol. 16:13-47.
McClintock, B. 1956. Intranuclear systems controlling gene action and mutation. Brookhaven Symp. Biol. 8:58-74.
McDermott, S.R., and M.A. Noor. 2010. The role of meiotic drive in hybrid male sterility. Philos. Trans. R. Soc. Lond B. Biol. Sci. 365(1544):1265-1272.
McElroy, T.C., K.L. Kandl, and J.C. Trexler. 2011. Temporal population genetic structure of eastern mosquitofish in a dynamic aquatic landscape. J. Hered. 102(6):678-687.
Meister, G.A., and T.A. Grigliatti. 1993. Rapid spread of a P-element/Adh gene construct through experimental populations of Drosophila melanogaster. Genome 36(6):1169-1175.
Mueller, L.D. 2009. Fitness, demography, and population dynamics. Pp. 197-216 in Experimental Evolution, T. Garland, and M.R. Rose, eds. Berkley, CA: University of California Press.
Mumby, P.J., and R.S. Steneck. 2008. Coral reef management and conservation in light of rapidly evolving ecological paradigms. Trends Ecol. Evol. 23(10):555-563.
Nidiffer, M., and L. Cortes-Ortiz. 2015. Intragroup genetic relatedness in two howler monkey species (Alouatta pigra and A. palliata): Implications for understanding social systems and dispersal. Am. J. Primatol. 77(12):1333-1345.
North, A., A. Burt, and C.J. Godfray. 2013. Modelling the spatial spread of a homing endonuclease gene in a mosquito population. J. Appl. Ecol. 50(5):1216-1225.
Nosil, P., T.H. Vines, and D.J. Funk. 2005. Perspective: Reproductive isolation caused by natural selection against immigrants from divergent habitats. Evolution 59(4):705-719.
O’Connor, K., M. Powell, C. Nock, and A. Shapcott. 2015. Crop to wild gene flow and genetic diversity in a vulnerable Macadamia (Proteaceae) species in New South Wales, Australia. Biol. Conserv. 191:504-511.
Orr, H.A. 2009. Fitness and its role in evolutionary genetics. Nat. Rev. Genet. 10(8):531-539.
Pagel, M. 2002. Encyclopedia of Evolution. Vol 1. Oxford University Press, Oxford.
Paine, R.T. 1966. Food web complexity and species diversity. Am. Nat. 100(910):65-75.
Pennisi, E. 2013. The CRISPR craze. Science 341(6148):833-836.
Perron, G.G., R.F. Inglis, P.S. Pennings, and S. Cobey. 2015. Fighting microbial drug resistance: A primer on the role of evolutionary biology in public health. Evol. Appl. 8(13):211-222.
Picard, M., J. Papaix, F. Gosselin, D. Picot, E. Bideau, and C. Baltzinger. 2015. Temporal dynamics of seed excretion by wild ungulates: Implications for plant dispersal. Ecol. Evol. 5(13):2621-2632.
Pratt, J., N. Venkatraman, A. Brinker, Y. Xiao, J. Blasberg, D.C. Thompson, and M. Bourner. 2012. Use of zinc finger nuclease technology to knock out efflux transporters in C2BBe1 cells. Curr. Protoc. Toxicol. Chapter 23, Unit 23.2.
Prout, T. 1965. The estimation of fitnesses from genotypic frequencies. Evolution 19(4):546-551.
Reeves, R.G., J. Bryk, P.M. Altrock, J.A. Denton, and F.A. Reed. 2014. First steps towards underdominant genetic transformation of insect populations. PLoS ONE 9(5):e97557.
Rey, J.R., and P. Lounibos. 2015. Ecology of Aedes aegypti and Aedes albopictus in the Americas and disease transmission. Biomedica 35:177-185.
Rhoades, M.M. 1942. Preferential segregation in maize. Genetics 27(4):395-407.
Rhoades, M.M., and E. Dempsey. 1985. Structural heterogeneity of chromosome 10 in races of maize and teosinte. Pp. 1-18 in Plant Genetics, M. Freeling, ed. New York: Alan R. Liss.
Rieseberg, L.H., and S.E. Carney. 1998. Tansley Review No. 102: Plant hybridization. New Phytol. 140(4):599-624.
Rieseberg, L.H., and N.C. Ellstrand. 1993. What can molecular and morphological markers tell us about plant hybridization? Crit. Rev. Plant Sci. 12(3):213-241.
Ronce, O., and I. Olivieri. 2004. Life history evolution in metapopulations. Pp. 227-257 in Ecology, Genetics, and Evolution of Metapopulations, I. Hanski, and O.E. Gaggiotti, eds. Boston: Elsevier.
Rubin, G.M., and A.C. Spradling. 1982. Genetic transformation of Drosophila with transposable element vectors. Science 218(4570):348-353.
Saey, T.H. 2015. Gene drives spread their wings. Science News 188(12):16.
Salinas-Ramos, V.B., L.G. Herrera-Montalvo, V. Leon-Regagnon, A. Arrizabalaga-Escudero, and E.L. Clare. 2015. Dietary overlap and seasonality in three species of mormopid bats from a tropical dry forest. Mol. Ecol. 24(20):5296-5307.
Sander, J.D., and J.K. Joung. 2014. CRISPR-Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 32(4):347-355.
Shaalan, E.A.S., and D.V. Canyon. 2009. Aquatic insect predators and mosquito control. Tropical Biomedicine, 26 (3): 223-261.
Shapiro, J., and D.I. Wright. 1984. Lake restoration by biomanipulation - Round Lake, Minnesota, the 1st two years. Freshwater Biol. 14(4):371-383.
Scheffer, M. 2009. Critical Transitions in Nature and Society. Princeton: Princeton University Press.
Schultz, R.J. 1961. Reproductive mechanisms of unisexual and bisexual strains of viviparous fish Poeciliopsis. Evolution 15(3):302-325.
Shao, J.W., J. Wang, Y.N. Xu, Q. Pan, Y. Shi, S. Kelso, and G-S. Lv. 2015. Genetic diversity and gene flow within and between two different habitats of Primula merrilliana (Primulaceae), and endangered distylous forest herb in eastern China. Bot. J. Linn. Soc. 179:172-189.
Silver, L.M. 1993. The peculiar journey of a selfish chromosome: Mouse t haplotypes and meiotic drive. Trends Genet. 9(7):250-254.
Simoni, A., C. Siniscalchi, Y.S. Chan, D.S. Huen, S. Russell, N. Windbichler, and A. Crisanti. 2014. Development of synthetic selfish elements based on modular nucleases in Drosophila melanogaster. Nucleic Acids Res. 42(11):7461-7472.
Sinkins, S.P., and F. Gould. 2006. Gene drive systems for insect vectors. Nat. Rev. Genet. 7(6):427-435.
Slatkin, M. 1985. Gene flow in natural populations. Annu. Rev. Ecol. Syst. 16:393-430.
Slatkin, M. 1987. Gene flow and the geographic structure of natural populations. Science 236(4803):787-792.
Snow, A.A., D.A. Andow, P. Gepts, E.M. Hallerman, A. Power, J.M. Tiedje, and L.L. Wolfenbarger. 2005. Genetically engineered organisms and the environment: Current status and recommendations. Ecol. Appl. 15(2):377-404.
Sternberg, S.H., and J.A. Doudna. 2015. Expanding the biologist's toolkit with CRISPR-Cas9. Mol. Cell 58(4):568-574.
Sweeny, T.L., and A.R. Barr. 1978. Sex ratio distortion caused by meiotic drive in a mosquito, Culex pipiens L. Genetics 88(3):427-446.
Syvanen, M. 2012. Evolutionary implications of horizontal gene transfer. Annu. Rev. Genet. 46:341-358.
Tambarussi, E.V., D. Boshier, R. Vencovsky, M.L. Freitas, and A.M. Sebbenn. 2015. Paternity analysis reveals significant isolation and near neighbor pollen dispersal in small Cariniana legalis Mart. Kuntze populations in the Brazilian Atlantic forest. Ecol. Evol. 5(23):5588-5600.
Thompson, J.N. 2005. The Geographic Mosaic of Coevolution. Chicago: University of Chicago Press.
Travis, J., F.C. Coleman, P.J. Auster, P.M. Cury, J.A. Estes, J. Orensanz, C.H. Peterson, M.E. Power, R.S. Steneck, and J.T. Wootton. 2013. Integrating the invisible fabric of nature into fisheries management. Proc. Nat. Acad. Sci. 111(2):581-584.
Turelli, M., and A.A. Hoffmann. 1999. Microbe-induced cytoplasmic incompatibility as a mechanism for introducing transgenes into arthropod populations. Insect Mol. Biol. 8(2):243-255.
Unckless, R.L., P.W. Messer, T. Connallon, and A.G. Clark. 2015. Modeling the manipulation of natural populations by the mutagenic chain reaction. Genetics 201(2):425-431.
Urnov, F.D., E.J. Rebar, M.C Holmes, H.S. Zhang, and P.D. Gregory. 2010. Genome editing with engineered zinc finger nucleases. Nat. Rev. Genet. 11(9):636-646.
Webber, B.L., S. Raghu, and O.R. Edwards. 2015. Opinion: Is CRISPR-based gene drive a biocontrol silver bullet or global conservation threat? Proc. Natl. Acad. Sci. 112(34):10565-10567.
Wey, T.W., O. Spiegel, P.O. Montiglio, and K.E. Mabry. 2015. Natal dispersal in a social landscape: considering individual behavioral phenotypes and social environment in dispersal ecology. Current Zoology 61:543-556.
Wicker, T., F. Sabot, A. Hua-Van, J.L. Bennetzen, P. Capy, B. Chalhoub, A. Flavell, P. Leroy, M. Morgante, O. Panaud, E. Paux, P. SanMiguel, and A.H. Schulman. 2007. A unified classification system for eukaryotic transposable elements. Nat. Rev. Genet. 8(12):973-982.
Windbichler, N., M. Menichelli, P.A. Papathanos, S.B. Thyme, H. Li, U.Y. Ulge, B.T. Hovde, D. Baker, R.J. Monnat, Jr., A. Burt, and A. Crisanti. 2011. A synthetic homing endonuclease-based gene drive system in the human malaria mosquito. Nature 473(7346):212-215.
Wootton, J.T. 1994. The nature and consequences of indirect effects in ecological communities. Annu. Rev. Ecol. Syst. 25:443-466.
Yin, H., R.L. Kanasty, A.A. Eltoukhy, A.J. Vegas, J.R. Dorkin, and D.G. Anderson. 2014. Non-viral vectors for gene-based therapy. Nat. Rev. Genet. 15(8):541-555.