Multiscale selection in spatially structured populations

The spatial structure of natural populations is key to many of their evolutionary processes. Formal theories analysing the interplay between natural selection and spatial structure have mostly focused on populations divided into distinct, non-overlapping groups. Most populations, however, are not structured in this way, but rather (self-)organise into dynamic patterns unfolding at various spatial scales. Here, we present a mathematical framework that quantifies how patterns and processes at different spatial scales contribute to natural selection in such populations. To that end, we define the Local Selection Differential (LSD): a measure of the selection acting on a trait within a given local environment. Based on the LSD, natural selection in a population can be decomposed into two parts: the contribution of local selection, acting within local environments, and the contribution of interlocal selection, acting among them. Varying the size of the local environments subsequently allows one to measure the contribution of each length scale. To illustrate the use of this new multiscale selection framework, we apply it to two simulation models of the evolution of traits known to be affected by spatial population structure: altruism and pathogen transmissibility. In both models, the spatial decomposition of selection reveals that local and interlocal selection can have opposite signs, thus providing a mathematically rigorous underpinning to intuitive explanations of how processes at different spatial scales may compete. It furthermore identifies which length scales - and hence which patterns - are relevant for natural selection. The multiscale selection framework can thus be used to address complex questions on evolution in spatially structured populations.

Antagonism between killer yeast strains as an experimental model for biological nucleation dynamics

Antagonistic interactions are widespread in the microbial world and affect microbial evolutionary dynamics. Natural microbial communities often display spatial structure, which affects biological interactions, but much of what we know about microbial warfare comes from laboratory studies of well-mixed communities. To overcome this limitation, we manipulated two killer strains of the budding yeast Saccharomyces cerevisiae, expressing different toxins, to independently control the rate at which they released their toxins. We developed mathematical models that predict the experimental dynamics of competition between toxin-producing strains in both well-mixed and spatially structured populations. In both situations, we experimentally verified theory's prediction that a stronger antagonist can invade a weaker one only if the initial invading population exceeds a critical frequency or size. Finally, we found that toxin-resistant cells and weaker killers arose in spatially structured competitions between toxin-producing strains, suggesting that adaptive evolution can affect the outcome of microbial antagonism in spatial settings.

Spatial scale of transmission explains how acute infections circulate at low prevalence

Fundamental questions remain about the regulation of acute pathogens in the absence of acquired immunity. This is especially true for canine rabies, a universally fatal zoonosis. From tracing rabies transmission in a population of 50,000 dogs in Tanzania between 2002-2016 we unravel the processes through which rabies is regulated and persists, fitting individual-based models to spatially-resolved data to investigate the mechanisms modulating transmission and the scale over which they operate. We find that while prevalence never exceeds 0.15%, we detect significant susceptible depletion at local scales commensurate with rabid dog movement, reducing transmission through clustering of rabies deaths and individuals incubating infection. Individual variation in rabid dog behaviour facilitates virus dispersal and co-circulation of lineages, enabling metapopulation persistence. These mechanisms likely operate in many pathogens circulating in spatially structured populations, with important implications for prediction and control, yet are unobservable unless the scale of host interactions is identified.

Town population size and structuring into villages and households drive infectious disease risks in pre-healthcare Finland

Social life is often considered to cost in terms of increased parasite or pathogen risk. However, evidence for this in the wild remains equivocal, possibly because populations and social groups are often structured, which affects the local transmission and extinction of diseases. We test how the structuring of towns into villages and households influenced the risk of dying from three easily diagnosable infectious diseases—smallpox, pertussis and measles—using a novel dataset covering almost all of Finland in the pre-healthcare era (1800–1850). Consistent with previous results, the risk of dying from all three diseases increased with the local population size. However, the division of towns into a larger number of villages decreased the risk of dying from smallpox and to some extent of pertussis but it slightly increased the risk for measles. Dividing towns into a larger number of households increased the length of the epidemic for all three diseases and led to the expected slower spread of the infection. However, this could be seen only when local population sizes were small. Our results indicate that the effect of population structure on epidemics, disease or parasite risk varies between pathogens and population sizes, hence lowering the ability to generalize the consequences of epidemics in spatially structured populations, and mapping the costs of social life, via parasites and diseases.

Regional and local effects drive changes in spawning stream occupancy in a sockeye salmon metapopulation

There is a need to explicitly consider metapopulation dynamics in the development of conservation strategies for spatially-structured populations. We examined the spatial dynamics of sockeye salmon (Oncorhynchus nerka) that spawn in 36 streams of the Stuart River watershed in British Columbia, Canada, using a 69-year record of spawner abundance and a demographically-based Bayesian dynamic occupancy model. We identified a set of 12 streams with good habitat conditions that were occupied >90% of years despite large year-to-year changes in abundance. Over 85% of spawners were concentrated in these streams. Many other streams with poorer habitat had small populations that were not persistent over time and were periodically recolonized by dispersers from other streams. Although it is often assumed population diversity and resiliency is maximized when all available habitats are used, for this salmon metapopulation, resiliency is due to the core streams of higher habitat quality. Currently other streams make only small contributions to population abundance, however, some may have conservation value if their habitats become more suitable for spawning in the future.

Fragmentation helps evolutionary rescue in highly connected habitats

Genetic variation and population sizes are critical factors for successful adaptation to novel environmental conditions. Gene flow between sub-populations is a potent mechanism to provide such variation and can hence facilitate adaption, for instance by increasing genetic variation or via adaptive introgression. On the other hand, if gene flow between different habitats is too strong, locally beneficial alleles may not be able to establish permanently. In the context of evolutionary rescue, intermediate levels of gene flow are therefore often optimal for maximizing a species chance for survival in meta-populations without spatial structure. To which extent and under which conditions gene flow facilitates or hinders evolutionary rescue in spatially structured populations remains unresolved. We address this question and show that detrimental effects of gene flow can become negligible in spatially structured populations subject to a gradual deterioration of environmental conditions. If the number of sub-populations is sufficiently large, we find a positive relationship between the amount of gene flow and the survival chance of the population. A counter-intuitive conclusion is that increased fragmentation can facilitate species survival in the face of severe environmental change if migration is common but limited to neighboring sub-populations.

Vector dynamics influence spatially imperfect genetic interventions against disease

Background and objectives Genetic engineering and similar technologies offer promising new approaches to controlling human diseases by blocking transmission from vectors. However, in spatially structured populations, imperfect coverage of the vector will leave pockets in which the parasite may persist. Movement by humans may disrupt this local persistence and facilitate eradication when these pockets are small, spreading parasite reproduction outside unprotected areas and into areas that block its reproduction. Here we consider the sensitivity of this process to biological details: do simple generalities emerge that may facilitate interventions? Methodology We develop formal mathematical models of this process similar to standard Ross-Macdonald models, but (i) specifying spatial structure of two patches, with vector transmission blocked in one patch but not in the other, (ii) allowing temporary human movement (travel instead of migration), and (iii) considering two different modes of mosquito biting. Results We find that there is no invariant effect of disrupting spatial structure with travel. For both biting models, travel out of the unprotected patch has different consequences than travel by visitors into the patch, but the effects are reversed between the two biting models. Conclusions and implications Overall, the effect of human travel on the maintenance of vector-borne diseases in structured habitats must be considered in light of the actual biology of mosquito abundances, biting dynamics, and human movement patterns. Lay summary Genetic interventions against pathogens transmitted by insect vectors are promising methods of controlling infectious diseases. These interventions may be imperfect, leaving pockets where the parasite persists. How will human movement between protected and unprotected areas affect persistence? Mathematical models developed here show that the answer is ecology-dependent, depending on vector biting behavior.