Evolution of dispersal under variable connectivity

The pattern of connectivity between local populations or between microsites supporting individuals within a population is a poorly understood factor affecting the evolution of dispersal. We modify the well-known Hamilton May model of dispersal evolution to allow for variable connectivity between microsites. For simplicity, we assume that the microsites are either solitary, i.e., weakly connected through costly dispersal, or part of a well-connected cluster of sites with low-cost dispersal within the cluster. We use adaptive dynamics to investigate the evolution of dispersal, obtaining analytic results for monomorphic evolution and numerical results for the co-evolution of two dispersal strategies. A monomorphic population always evolves to a unique singular dispersal strategy, which may be an evolutionarily stable strategy or an evolutionary branching point. Evolutionary branching happens if the contrast between connectivities is sufficiently high and the solitary microsites are common. The dimorphic evolutionary singularity, when it exists, is always evolutionarily and convergence stable. The model exhibits both protected and unprotected dimorphisms of dispersal strategies, but the dimorphic singularity is always protected. Contrasting connectivities can thus maintain dispersal polymorphisms in temporally stable environments.Peer reviewe

Joint evolution of dispersal and connectivity

Functional connectivity, the realized flow of individuals between the suitable sites of a heterogeneous landscape, is a prime determinant of the maintenance and evolution of populations in fragmented habitats. While a large body of literature examines the evolution of dispersal propensity, it is less known how evolution shapes functional connectivity via traits that influence the distribution of the dispersers. Here, we use a simple model to demonstrate that, in a heterogeneous environment with clustered and solitary sites (i.e., with variable structural connectivity), the evolutionarily stable population contains strains that are strongly differentiated in their pattern of connectivity (local vs. global dispersal), but not necessarily in the fraction of dispersed individuals. Also during evolutionary branching, selection is disruptive predominantly on the pattern of connectivity rather than on dispersal propensity itself. Our model predicts diversification along a hitherto neglected axis of dispersal strategies and highlights the role of the solitary sites-the more isolated and therefore seemingly less important patches of habitat-in maintaining global dispersal that keeps all sites connected.Peer reviewe

Evolution of dispersal : adaptive dynamics of one- and two-dimensional strategies

Dispersal is a significant characteristic of life history of many species. Dispersal polymorphisms in nature propose that dispersal can have significant effect on species diversity. Evolution of dispersal is one probable reason to speciation. I consider an environment of well-connected and separate living sites and study how connectivity difference between different sites can affect the evolution of a two-dimensional dispersal strategy. Two-dimensionality means that the strategy consists of two separate traits. Adaptive dynamics is a mathematical framework for analysis of evolution. It assumes small phenotypic mutations and considers invasion possibility of a rare mutant. Generally invasion of a sufficiently similar mutant leads to substitution of the former resident. Consecutive invasion-substitution processes can lead to a singular strategy where directional evolution vanishes and evolution may stop or result in evolutionary branching. First I introduce some fundamental elements of adaptive dynamics. Then I construct a mathematical model for studying evolution. The model is created from the basis of the Hamilton-May model (1977). Last I analyse the model using tools I introduced previously. The analysis predicts evolution to a unique singular strategy in a monomorphic resident population. This singularity can be evolutionarily stable or branching depending on survival probabilities during different phases of dispersal. After branching the resident population becomes dimorphic. There seems to be always an evolutionarily stable dimorphic singularity. At the singularity one resident specializes fully to the well-connected sites while the other resides both types of sites. Connectivity difference of sites can lead to evolutionary branching in a monomorphic population and maintain a stable dimorphic population

Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal

Dispersal is a key ecological process, but it remains difficult to measure. By recording numbers of dispersed individuals at different distances from the source, one acquires a dispersal gradient. Dispersal gradients contain information on dispersal, but they are influenced by the spatial extent of the source. How can we separate the two contributions to extract knowledge about dispersal? One could use a small, point-like source for which a dispersal gradient represents a dispersal kernel, which quantifies the probability of an individual dispersal event from a source to a destination. However, the validity of this approximation cannot be established before conducting measurements. This represents a key challenge hindering progress in characterization of dispersal. To overcome it, we formulated a theory that incorporates the spatial extent of sources to estimate dispersal kernels from dispersal gradients. Using this theory, we re-analyzed published dispersal gradients for three major plant pathogens. We demonstrated that the three pathogens disperse over substantially shorter distances compared to conventional estimates. This method will allow the researchers to re-analyze a vast number of existing dispersal gradients to improve our knowledge about dispersal. The improved knowledge has potential to advance our understanding of species' range expansions and shifts, and inform management of weeds and diseases in crops

Positive fitness effects help explain the broad range of Wolbachia prevalences in natural populations

The bacterial endosymbiont Wolbachia is best known for its ability to modify its host’s reproduction by inducing cytoplasmic incompatibility (CI) to facilitate its own spread. Classical models predict either near-fixation of costly Wolbachia once the symbiont has overcome a threshold frequency (invasion barrier), or Wolbachia extinction if the barrier is not overcome. However, natural populations do not all follow this pattern: Wolbachia can also be found at low frequencies (below one half) that appear stable over time. Wolbachia is known to have pleiotropic fitness effects (beyond CI) on its hosts. Existing models typically focus on the possibility that these are negative. Here we consider the possibility that the symbiont provides direct benefits to infected females (e.g. resistance to pathogens) in addition to CI. We discuss an underappreciated feature of Wolbachia dynamics: that CI with additional fitness benefits can produce low-frequency (< 1/2) stable equilibria. Additionally, without a direct positive fitness effect, any stable equilibrium close to one half will be sensitive to perturbations, which make such equilibria unlikely to sustain in nature. The results hold for both diplodiploid and different haplodiploid versions of CI. We suggest that insect populations showing low-frequency Wolbachia infection might host CI-inducing symbiotic strains providing additional (hidden or known) benefits to their hosts, especially when classical explanations (ongoing invasion, source-sink dynamics) have been ruled out.Peer reviewe

Measuring splash-dispersal of a major wheat pathogen in the field

Capacity for dispersal is a fundamental fitness component of plant pathogens. Characterization of plant pathogen dispersal is important for understanding how pathogen populations change in time and space. We devised a systematic approach to measure and analyze rain splash-driven dispersal of plant pathogens in field conditions, using the major fungal wheat pathogen Zymoseptoria tritici as a case study. We inoculated field plots of wheat (Triticum aestivum) with two distinct Z. tritici strains. Next, we measured disease intensity as counts of fruiting bodies (pycnidia) using automated image analysis. These measurements characterized primary disease gradients, which we used to estimate effective dispersal of the pathogen population. Genotyping of re-isolated pathogen strains with strain-specific PCR-reaction confirmed the conclusions drawn from phenotypic data. Consistently with controlled environment studies, we found that the characteristic scale of dispersal is tens of centimeters. We analyzed the data using a spatially-explicit mathematical model that incorporates the spatial extent of the source, rather than assuming a point source, which allows for a more accurate estimation of dispersal kernels. We employed bootstrapping methods for statistical testing and adopted a two-dimensional hypotheses test based on kernel density estimation, enabling more robust inference compared to standard methods. We report the first estimates of dispersal kernels of the pathogen in field conditions. However, repeating the experiment in other environments would lead to more robust conclusions. We put forward advanced methodology that paves the way to further measurements of plant pathogen dispersal in field conditions, which can inform spatially targeted plant disease management

Positive fitness effects help explain the broad range of Wolbachia prevalences in natural populations

The bacterial endosymbiont Wolbachia is best known for its ability to modify its host’s reproduction by inducing cytoplasmic incompatibility (CI) to facilitate its own spread. Classical models predict either near-fixation of costly Wolbachia once the symbiont has overcome a threshold frequency (invasion barrier), or Wolbachia extinction if the barrier is not overcome. However, natural populations do not all follow this pattern: Wolbachia can also be found at low frequencies (below one half) that appear stable over time. Wolbachia is known to have pleiotropic fitness effects (beyond CI) on its hosts. Existing models typically focus on the possibility that these are negative. Here we consider the possibility that the symbiont provides direct benefits to infected females (e.g. resistance to pathogens) in addition to CI. We discuss an underappreciated feature of Wolbachia dynamics: that CI with additional fitness benefits can produce low-frequency (< 1/2) stable equilibria. Additionally, without a direct positive fitness effect, any stable equilibrium close to one half will be sensitive to perturbations, which make such equilibria unlikely to sustain in nature. The results hold for both diplodiploid and different haplodiploid versions of CI. We suggest that insect populations showing low-frequency Wolbachia infection might host CI-inducing symbiotic strains providing additional (hidden or known) benefits to their hosts, especially when classical explanations (ongoing invasion, source-sink dynamics) have been ruled out

Improved control of Septoria tritici blotch in durum wheat using cultivar mixtures

Mixtures of cultivars with contrasting levels of disease resistance are capable of suppressing infectious diseases in wheat, as demonstrated in numerous field experiments. Most studies focused on airborne pathogens in bread wheat, while splash-dispersed pathogens have received less attention, and no studies have been conducted in durum wheat. We conducted a two-year field experiment in Tunisia, a major durum wheat producer in the Mediterranean region, to evaluate the performance of cultivar mixtures in controlling the polycyclic, splash-dispersed disease Septoria tritici blotch (STB) in durum wheat. To measure STB severity, we used a novel, high-throughput method based on digital analysis of images captured from 3074 infected leaves collected from 42 and 40 experimental plots on the first and the second year, respectively. This method allowed us to quantify pathogen reproduction on wheat leaves and to acquire a large dataset that exceeds previous studies with respect to accuracy and statistical power. Our analyses show that introducing only 25% of a disease-resistant cultivar into a pure stand of a susceptible cultivar provides a substantial reduction of almost 50% in disease severity. However, adding a second resistant cultivar to the mixture did not further improve disease control, contrary to predictions of epidemiological theory. Susceptible cultivars can be agronomically superior to resistant cultivars or be better accepted by growers for other reasons. Hence, if mixtures with only a moderate proportion of the resistant cultivar provide similar degree of disease control as resistant pure stands, as our analysis indicates, such mixtures are more likely to be accepted by growers

Precision phenotyping reveals novel loci for quantitative resistance to septoria tritici blotch

Accurate, high-throughput phenotyping for quantitative traits is the limiting factor for progress in plant breeding. We developed automated image analysis to measure quantitative resistance to septoria tritici blotch (STB), a globally important wheat disease, enabling identification of small chromosome intervals containing plausible candidate genes for STB resistance. 335 winter wheat cultivars were included in a replicated field experiment that experienced natural epidemic development by a highly diverse but fungicide-resistant pathogen population. More than 5.4 million automatically generated phenotypes were associated with 13,648 SNP markers to perform a GWAS. We identified 26 chromosome intervals explaining 1.9-10.6% of the variance associated with four independent resistance traits. Sixteen of the intervals overlapped with known STB resistance intervals, suggesting that our phenotyping approach can identify simultaneously (i.e. in a single experiment) many previously defined STB resistance intervals. Seventeen of the intervals were less than 5 Mbp in size and encoded only 173 genes, including many genes associated with disease resistance. Five intervals contained four or fewer genes, providing high priority targets for functional validation. Ten chromosome intervals were not previously associated with STB resistance, perhaps representing resistance to pathogen strains that had not been tested in earlier experiments. The SNP markers associated with these chromosome intervals can be used to recombine different forms of quantitative STB resistance that are likely to be more durable than pyramids of major resistance genes. Our experiment illustrates how high-throughput automated phenotyping can accelerate breeding for quantitative disease resistance