Eco-evolutionary Theory Research Articles

SIF-based GPP modeling for evergreen forests considering the seasonal variation in maximum photochemical efficiency

Solar-induced chlorophyll fluorescence (SIF) has shown great potential in estimating gross primary production (GPP). However, their quantitative relationship is not invariant, which undermines the reliability of empirical SIF-based GPP estimation at fine spatiotemporal scales, especially under extreme conditions. In this study, we developed a parsimonious mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) by employing the Mechanistic Light Response framework and Eco-Evolutionary theory to describe the light and dark reactions during photosynthesis, respectively. Specifically, we found that considering the seasonal variation in a key parameter of the MLR framework, the maximum photochemical efficiency of photosystem II (ΦPSIImax), can avoid the GPP overestimation in winter and early spring due to the relatively low environmental sensitivity of SIF. Compared to the estimates from other benchmark models, our GPP estimates were closer to the 1: 1 line and had higher accuracy (average R2 = 0.86, RMSE=1.99 μmol m−2 s−1) across sites. Furthermore, the changes in the relationship between SIF and J (refers to the electron transport rate) contribute a lot to the dynamic SIF–GPP relationship in this study, while the J–GPP relationship is less variant when the temperature drops. The seasonal variation in the SIF–J relationship, especially the reduction in its slope at low temperatures, is found largely explained by the ΦPSIImax. These results indicate the importance of the uncertainty caused by the variation in the SIF–J relationship for SIF-based GPP estimation, and the consideration of changes in ΦPSIImax under extreme conditions (such as severe winter in this study) is crucial for the improvement of GPP estimation via SIF.

The evolution of parasite virulence under targeted culling and harvesting in wildlife and livestock.

There is a clear need to understand the effect of human intervention on the evolution of infectious disease. In particular, culling and harvesting of both wildlife and managed livestock populations are carried out in a wide range of management practices, and they have the potential to impact the evolution of a broad range of disease characteristics. Applying eco-evolutionary theory we show that once culling/harvesting becomes targeted on specific disease classes, the established result that culling selects for higher virulence is only found when sufficient infected individuals are culled. If susceptible or recovered individuals are targeted, selection for lower virulence can occur. An important implication of this result is that when culling to eradicate an infectious disease from a population, while it is optimal to target infected individuals, the consequent evolution can increase the basic reproductive ratio of the infection, , and make parasite eradication more difficult. We show that increases in evolved virulence due to the culling of infected individuals can lead to excess population decline when sustainably harvesting a population. In contrast, culling susceptible or recovered individuals can select for decreased virulence and a reduction in population decline through culling. The implications to the evolution of virulence are typically the same in wildlife populations, that are regulated by the parasite, and livestock populations, that have a constant population size where restocking balances the losses due to mortality. However, the well-known result that vertical transmission selects for lower virulence and transmission in wildlife populations is less marked in livestock populations for parasites that convey long-term immunity since restocking can enhance the density of the immune class. Our work emphasizes the importance of understanding the evolutionary consequences of intervention strategies and the different ecological feedbacks that can occur in wildlife and livestock populations.

Adding pattern and process to eco-evo theory and applications.

Eco-evolutionary dynamics result when interacting biological forces simultaneously produce demographic and genetic population responses. Eco-evolutionary simulators traditionally manage complexity by minimizing the influence of spatial pattern on process. However, such simplifications can limit their utility in real-world applications. We present a novel simulation modeling approach for investigating eco-evolutionary dynamics, centered on the driving role of landscape pattern. Our spatially-explicit, individual-based mechanistic simulation approach overcomes existing methodological challenges, generates new insights, and paves the way for future investigations in four focal disciplines: Landscape Genetics, Population Genetics, Conservation Biology, and Evolutionary Ecology. We developed a simple individual-based model to illustrate how spatial structure drives eco-evo dynamics. By making minor changes to our landscape's structure, we simulated continuous, isolated, and semi-connected landscapes, and simultaneously tested several classical assumptions of the focal disciplines. Our results exhibit expected patterns of isolation, drift, and extinction. By imposing landscape change on otherwise functionally-static eco-evolutionary models, we altered key emergent properties such as gene-flow and adaptive selection. We observed demo-genetic responses to these landscape manipulations, including changes in population size, probability of extinction, and allele frequencies. Our model also demonstrated how demo-genetic traits, including generation time and migration rate, can arise from a mechanistic model, rather than being specified a priori. We identify simplifying assumptions common to four focal disciplines, and illustrate how new insights might be developed in eco-evolutionary theory and applications by better linking biological processes to landscape patterns that we know influence them, but that have understandably been left out of many past modeling studies.

Extending eco-evolutionary theory with oligomorphic dynamics.

Understanding the interplay between ecological processes and the evolutionary dynamics of quantitative traits in natural systems remains a major challenge. Two main theoretical frameworks are used to address this question, adaptive dynamics and quantitative genetics, both of which have strengths and limitations and are often used by distinct research communities to address different questions. In order to make progress, new theoretical developments are needed that integrate these approaches and strengthen the link to empirical data. Here, we discuss a novel theoretical framework that bridges the gap between quantitative genetics and adaptive dynamics approaches. 'Oligomorphic dynamics' can be used to analyse eco-evolutionary dynamics across different time scales and extends quantitative genetics theory to account for multimodal trait distributions, the dynamical nature of genetic variance, the potential for disruptive selection due to ecological feedbacks, and the non-normal or skewed trait distributions encountered in nature. Oligomorphic dynamics explicitly takes into account the effect of environmental feedback, such as frequency- and density-dependent selection, on the dynamics of multi-modal trait distributions and we argue it has the potential to facilitate a much tighter integration between eco-evolutionary theory and empirical data.

Experimental Evidence Questions the Relationship between Stress and Fluctuating Asymmetry in Plants

The eco-evolutionary theory of developmental instability predicts that small, non-directional deviations from perfect symmetry in morphological traits (termed fluctuating asymmetry, FA) emerge when an individual is unable to buffer environmental or genetic stress during its development. Consequently, FA is widely used as an index of stress. The goal of the present study was to experimentally test a seemingly trivial prediction derived from the theory of developmental instability—and from previous observational studies—that significant growth retardation (which indicates stress) in plants is accompanied by an increase in FA of their leaves. We induced stress, evidenced by a significant decrease in biomass relative to control, in cucumber (Cucumis sativus), sweet pepper (Capsicum annuum), and common bean (Phaseolus vulgaris) by applying water solutions of copper and nickel to the soil in which plants were grown. Repeated blind measurements of plant leaves revealed that leaf FA did not differ between stressed and control plants. This finding, once again, demonstrated that FA cannot be seen as a universal indicator of environmental stress. We recommend that the use of FA as a stress index is discontinued until the scope of the developmental instability theory is clarified and its applicability limits are identified.

Rapid evolution of diet choice in an introduced population of Trinidadian guppies.

Eco-evolutionary theory has brought an interest in the rapid evolution of functional traits. Among them, diet is an important determinant of ecosystem structure, affecting food web dynamics and nutrient cycling. However, it is largely unknown whether diet, or diet preference, has a hereditary basis and can evolve on contemporary timescales. Here, we study the diet preferences of Trinidadian guppies Poecilia reticulata collected from directly below an introduction site of fish transplanted from a high-predation environment into a low predation site where their densities and competition increased. Behavioural assays on F2 common garden descendants of the ancestral and derived populations showed that diet preference has rapidly evolved in the introduced population in only 12 years (approx. 36 generations). Specifically, we show that the preference for high-quality food generally found in high-predation guppies is lost in the newly derived low-predation population, who show an inertia toward the first encountered food. This result is predicted by theory stating that organisms should evolve less selective diets under higher competition. Demonstrating that diet preference can show rapid and adaptive evolution is important to our understanding of eco-evolutionary feedbacks and the role of evolution in ecosystem dynamics.

The ephemeral resource patch concept.

Ephemeral resource patches (ERPs) - short lived resources including dung, carrion, temporary pools, rotting vegetation, decaying wood, and fungi - are found throughout every ecosystem. Their short-lived dynamics greatly enhance ecosystem heterogeneity and have shaped the evolutionary trajectories of a wide range of organisms - from bacteria to insects and amphibians. Despite this, there has been no attempt to distinguish ERPs clearly from other resource types, to identify their shared spatiotemporal characteristics, or to articulate their broad ecological and evolutionary influences on biotic communities. Here, we define ERPs as any distinct consumable resources which (i) are homogeneous (genetically, chemically, or structurally) relative to the surrounding matrix, (ii) host a discrete multitrophic community consisting of species that cannot replicate solely in any of the surrounding matrix, and (iii) cannot maintain a balance between depletion and renewal, which in turn, prevents multiple generations of consumers/users or reaching a community equilibrium. We outline the wide range of ERPs that fit these criteria, propose 12 spatiotemporal characteristics along which ERPs can vary, and synthesise a large body of literature that relates ERP dynamics to ecological and evolutionary theory. We draw this knowledge together and present a new unifying conceptual framework that incorporates how ERPs have shaped the adaptive trajectories of organisms, the structure of ecosystems, and how they can be integrated into biodiversity management and conservation. Future research should focus on how inter- and intra-resource variation occurs in nature - with a particular focus on resource×environment×genotype interactions. This will likely reveal novel adaptive strategies, aid the development of new eco-evolutionary theory, and greatly improve our understanding of the form and function of organisms and ecosystems.

Horizontal gene transfer and ecological interactions jointly control microbiome stability.

Genes encoding resistance to stressors, such as antibiotics or environmental pollutants, are widespread across microbiomes, often encoded on mobile genetic elements. Yet, despite their prevalence, the impact of resistance genes and their mobility upon the dynamics of microbial communities remains largely unknown. Here we develop eco-evolutionary theory to explore how resistance genes alter the stability of diverse microbiomes in response to stressors. We show that adding resistance genes to a microbiome typically increases its overall stability, particularly for genes on mobile genetic elements with high transfer rates that efficiently spread resistance throughout the community. However, the impact of resistance genes upon the stability of individual taxa varies dramatically depending upon the identity of individual taxa, the mobility of the resistance gene, and the network of ecological interactions within the community. Nonmobile resistance genes can benefit susceptible taxa in cooperative communities yet damage those in competitive communities. Moreover, while the transfer of mobile resistance genes generally increases the stability of previously susceptible recipient taxa to perturbation, it can decrease the stability of the originally resistant donor taxon. We confirmed key theoretical predictions experimentally using competitive soil microcosm communities. Here the stability of a susceptible microbial community to perturbation was increased by adding mobile resistance genes encoded on conjugative plasmids but was decreased when these same genes were encoded on the chromosome. Together, these findings highlight the importance of the interplay between ecological interactions and horizontal gene transfer in driving the eco-evolutionary dynamics of diverse microbiomes.

Abstract PR010: Evolutionary Tumor Board: Implementing dynamic personalized therapy using evolutionary theory and mathematical modeling for clinical decision support

Abstract The era of big data in oncology has led to the promise of precision medicine for individual patients. However, many therapy decisions continue to be made based on “one size fits most” approaches, primarily since there exist few theoretical and practical tools to deal with a patient’s data over time. In parallel with this growing interest in personalized medicine, cancer is being increasingly recognized as an eco-evolutionary system that adapts to treatments, suggesting that static therapy regimens are often doomed to eventual failure. Here, we present preliminary results from a novel pilot clinical trial (NCT04343365), the Evolutionary Tumor Board (ETB), which uses eco-evolutionary theory (based on experiments and modeling) to assist with clinical decision making for each patient. We developed an informational and computational framework for applying evolutionary therapy approaches to individual patients in a dynamic fashion, using their clinical data in real time. The framework relies on detailed data curation and imaging measurements for each patient, as well as a mathematical modeling approach that accounts for multi-lesion tumor growth, treatment-induced death, and the evolution of resistance. The models are calibrated by historical datasets of similar patients, as well as the patient’s own temporal data. We use a “Phase i trial” approach to account for prediction uncertainty and provide decision support for therapy options available to the patient at any given time point. Crucially, this is presented in a way that harmonizes with the treating oncologist’s intuition. Fifteen patients at Moffitt have been enrolled into the ETB, many of whom have proceeded through the entire process, including follow-up analysis. The ETB generated outcome predictions and therapy recommendations for each case, and subsequent follow-up predictions and recommendations. Our current results demonstrate that the ETB approach has provided both novel and useful decision support for the clinicians. At the same time, numerous opportunities for further research and development have been identified. Our efforts show that there are both challenges and opportunities in the area of personalized therapy, particularly in the context of real-time clinical care. Early results from the ETB show great promise for improving patient outcomes in cancer using mathematical modeling and evolutionary therapy. Citation Format: Mark Robertson-Tessi, Joel Brown, Maria Poole, Kimberly Luddy, Andriy Marusyk, Jill Gallaher, Jeffrey West, Matthew Johnson, Heiko Enderling, Rikesh Makanji, Joaquim Farinhas, Robert Gatenby, Damon Reed, Christine Chung, Alexander Anderson. Evolutionary Tumor Board: Implementing dynamic personalized therapy using evolutionary theory and mathematical modeling for clinical decision support [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr PR010.

Rethinking the Roles of Pathogens and Mutualists: Exploring the Continuum of Symbiosis in the Context of Microbial Ecology and Evolution

Systems of classification are important for guiding research activities and providing a common platform for discussion and investigation. One such system is assigning microbial taxa to the roles of mutualists and pathogens. However, there are often challenges and even inconsistencies in reports of research findings when microbial taxa display behaviors outside of these two static conditions (e.g., commensal). Over the last two decades, there has been some effort to highlight a continuum of symbiosis, wherein certain microbial taxa may exhibit mutualistic or pathogenic traits depending on environmental contexts, life stages, and plant host associations. However, gaps remain in understanding how to apply the continuum approach to host–microbe pairs across a range of environmental and ecological factors. This commentary presents an alternative framework for evaluating the continuum of symbiosis using dominant archetypes that define symbiotic ranges. We focus particularly on fungi and bacteria, though we recognize that archaea and other microeukaryotes play important roles in host–microbe interactions that may be described by this approach. This framework is centered in eco-evolutionary theory and aims to enhance communication among researchers, as well as prioritize holistic consideration of the factors shaping microbial life strategies. We discuss the influence of plant-mediated factors, habitat constraints, coevolutionary forces, and the genetic contributions which shape different microbial lifestyles. Looking to the future, using a continuum-of-symbiosis paradigm will enable greater flexibility in defining the roles of target microbes and facilitate a more holistic view of the complex and dynamic relationship between microbes and plants. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Revisiting the rules of life for viruses of microorganisms.

Viruses that infect microbial hosts have traditionally been studied in laboratory settings with a focus on either obligate lysis or persistent lysogeny. In the environment, these infection archetypes are part of a continuum that spans antagonistic to beneficial modes. In this Review, we advance a framework to accommodate the context-dependent nature of virus-microorganism interactions in ecological communities by synthesizing knowledge from decades of virology research, eco-evolutionary theory and recent technological advances. We discuss that nuanced outcomes, rather than the extremes of the continuum, are particularly likely in natural communities given variability in abiotic factors, the availability of suboptimal hosts and the relevance of multitrophic partnerships. We revisit the 'rules of life' in terms of how long-term infections shape the fate of viruses and microbial cells, populations and ecosystems.

Coevolution, diversification and alternative states in two-trophic communities.

Single-trait eco-evolutionary models of arms races between consumers and their resource species often show inhibition rather than promotion of community diversification. In contrast, modelling arms races involving multiple traits, we found that arms races can promote diversification when trade-off costs among traits make simultaneous investment in multiple traits either more beneficial or more costly. Coevolution between resource and consumer species generates an adaptive landscape for each, with the configuration giving predictable suites of consumer and resource species. Nonetheless, the adaptive landscape contains multiple alternative stable states, and which stable community is reached depends on small stochastic differences occurring along evolutionary pathways. Our results may solve a puzzling conflict between eco-evolutionary theory that predicts community diversification via consumer-resource interactions will be rare, and empirical research that has uncovered real cases. Furthermore, our results suggest that these real cases might be just a subset of alternative stable communities.

Focused Identification of Germplasm Strategy (FIGS): polishing a rough diamond.

Focused Identification of Germplasm Strategy (FIGS) has been advocated as an efficient approach to predict and harness variation in adaptive traits in genebanks or wild populations of plants. However, a weakness of the current FIGS approach is that it only utilizes a priori knowledge of one evolutionary factor: natural selection. Further optimization is needed to capture elusive traits, and this review shows that nonadaptive evolutionary processes (gene flow and genetic drift) should be incorporated to increase precision. Focusing on plant resistance to insect herbivores, we also note that historic selection pressures can be difficult to disentangle, and provide suggestions for successful mining based on eco-evolutionary theory. We conclude that with such refinement FIGS has high potential for enhancing breeding efforts and hence sustainable plant production.

The problem of mediocre generalists: population genetics and eco-evolutionary perspectives on host breadth evolution in pathogens.

Many of our theories for the generation and maintenance of diversity in nature depend on the existence of specialist biotic interactions which, in host–pathogen systems, also shape cross-species disease emergence. As such, niche breadth evolution, especially in host–parasite systems, remains a central focus in ecology and evolution. The predominant explanation for the existence of specialization in the literature is that niche breadth is constrained by trade-offs, such that a generalist is less fit on any particular environment than a given specialist. This trade-off theory has been used to predict niche breadth (co)evolution in both population genetics and eco-evolutionary models, with the different modelling methods providing separate, complementary insights. However, trade-offs may be far from universal, so population genetics theory has also proposed alternate mechanisms for costly generalism, including mutation accumulation. However, these mechanisms have yet to be integrated into eco-evolutionary models in order to understand how the mechanism of costly generalism alters the biological and ecological circumstances predicted to maintain specialism. In this review, we outline how population genetics and eco-evolutionary models based on trade-offs have provided insights for parasite niche breadth evolution and argue that the population genetics-derived mutation accumulation theory needs to be better integrated into eco-evolutionary theory.

The target of selection matters: An established resistance-development-time negative genetic trade-off is not found when selecting on development time.

Trade-offs are fundamental to evolutionary outcomes and play a central role in eco-evolutionary theory. They are often examined by experimentally selecting on one life-history trait and looking for negative correlations in other traits. For example, populations of the moth Plodia interpunctella selected to resist viral infection show a life-history cost with longer development times. However, we rarely examine whether the detection of such negative genetic correlations depends on the trait on which we select. Here, we examine a well-characterized negative genotypic trade-off between development time and resistance to viral infection in the moth Plodia interpunctella and test whether selection on a phenotype known to be a cost of resistance (longer development time) leads to the predicted correlated increase in resistance. If there is tight pleiotropic relationship between genes that determine development time and resistance underpinning this trade-off, we might expect increased resistance when we select on longer development time. However, we show that selecting for longer development time in this system selects for reduced resistance when compared to selection for shorter development time. This shows how phenotypes typically characterized by a trade-off can deviate from that trade-off relationship, and suggests little genetic linkage between the genes governing viral resistance and those that determine response to selection on the key life-history trait. Our results are important for both selection strategies in applied biological systems and for evolutionary modelling of host-parasite interactions.

Plant Root-Exudates Recruit Hyperparasitic Bacteria of Phytonematodes by Altered Cuticle Aging: Implications for Biological Control Strategies.

Phytonematodes are globally important functional components of the belowground ecology in both natural and agricultural soils; they are a diverse group of which some species are economically important pests, and environmentally benign control strategies are being sought to control them. Using eco-evolutionary theory, we test the hypothesis that root-exudates of host plants will increase the ability of a hyperparasitic bacteria, Pasteuria penetrans and other closely related bacteria, to infect their homologous pest nematodes, whereas non-host root exudates will not. Plant root-exudates from good hosts, poor hosts and non-hosts were characterized by gas chromatography-mass spectrometry (GC/MS) and we explore their interaction on the attachment of the hyperparasitic bacterial endospores to homologous and heterologous pest nematode cuticles. Although GC/MS did not identify any individual compounds as responsible for changes in cuticle susceptibility to endospore adhesion, standardized spore binding assays showed that Pasteuria endospore adhesion decreased with nematode age, and that infective juveniles pre-treated with homologous host root-exudates reduced the aging process and increased attachment of endospores to the nematode cuticle, whereas non-host root-exudates did not. We develop a working model in which plant root exudates manipulate the nematode cuticle aging process, and thereby, through increased bacterial endospore attachment, increase bacterial infection of pest nematodes. This we suggest would lead to a reduction of plant-parasitic nematode burden on the roots and increases plant fitness. Therefore, by the judicious manipulation of environmental factors produced by the plant root and by careful crop rotation this knowledge can help in the development of environmentally benign control strategies.

Predicting population responses to environmental change from individual-level mechanisms: towards a standardized mechanistic approach.

Animal populations will mediate the response of global biodiversity to environmental changes. Population models are thus important tools for both understanding and predicting animal responses to uncertain future conditions. Most approaches, however, are correlative and ignore the individual-level mechanisms that give rise to population dynamics. Here, we assess several existing population modelling approaches and find limitations to both 'correlative' and 'mechanistic' models. We advocate the need for a standardized mechanistic approach for linking individual mechanisms (physiology, behaviour, and evolution) to population dynamics in spatially explicit landscapes. Such an approach is potentially more flexible and informative than current population models. Key to realizing this goal, however, is overcoming current data limitations, the development and testing of eco-evolutionary theory to represent interactions between individual mechanisms, and standardized multi-dimensional environmental change scenarios which incorporate multiple stressors. Such progress is essential in supporting environmental decisions in uncertain future conditions.

Evolutionary change in the human gut microbiome: From a static to a dynamic view.

Our intestine is a melting pot of interactions between microbial and human cells. This gene-rich ecosystem modulates our health, but questions remain unanswered regarding its genetic structure, such as, “How rapid is evolutionary change in the human gut microbiome? How can its function be maintained?” Much research on the microbiome has characterized the species it contains. Yet the high growth rate and large population sizes of many species, and the mutation rate of most microbes (approximately 10−3 per genome per generation), could imply that evolution might be happening in our gut along our lifetime. In support of this view, Garud and colleagues present an analysis that begins to unravel the pattern of short- and long-term evolution of dozens of gut species. Even with limited longitudinal short-read sequence data, significant evolutionary dynamics—shaped by both positive and negative selection—can be detected on human microbiomes. This may only be the tip of the iceberg, as recent work on mice suggests, and its full extent should be revealed with dense time series long-read sequence data and new eco-evolutionary theory.

A comprehensive database of thermal developmental plasticity in reptiles

How temperature influences development has direct relevance to ascertaining the impact of climate change on natural populations. Reptiles have served as empirical models for understanding how the environment experienced by embryos can influence phenotypic variation, including sex ratio, phenology and survival. Such an understanding has important implications for basic eco-evolutionary theory and conservation efforts worldwide. While there is a burgeoning empirical literature of experimental manipulations of embryonic thermal environments, addressing widespread patterns at a comparative level has been hampered by the lack of accessible data in a format that is amendable to updates as new studies emerge. Here, we describe a database with nearly 10, 000 phenotypic estimates from 155 species of reptile, collected from 300 studies manipulating incubation temperature (published between 1974–2016). The data encompass various morphological, physiological, behavioural and performance traits along with growth rates, developmental timing, sex ratio and survival (e.g., hatching success). This resource will serve as an important data repository for addressing overarching questions about thermal plasticity of reptile embryos.

Population and evolutionary dynamics in spatially structured seasonally varying environments.

ABSTRACTIncreasingly imperative objectives in ecology are to understand and forecast population dynamic and evolutionary responses to seasonal environmental variation and change. Such population and evolutionary dynamics result from immediate and lagged responses of all key life‐history traits, and resulting demographic rates that affect population growth rate, to seasonal environmental conditions and population density. However, existing population dynamic and eco‐evolutionary theory and models have not yet fully encompassed within‐individual and among‐individual variation, covariation, structure and heterogeneity, and ongoing evolution, in a critical life‐history trait that allows individuals to respond to seasonal environmental conditions: seasonal migration. Meanwhile, empirical studies aided by new animal‐tracking technologies are increasingly demonstrating substantial within‐population variation in the occurrence and form of migration versus year‐round residence, generating diverse forms of ‘partial migration’ spanning diverse species, habitats and spatial scales. Such partially migratory systems form a continuum between the extreme scenarios of full migration and full year‐round residence, and are commonplace in nature.Here, we first review basic scenarios of partial migration and associated models designed to identify conditions that facilitate the maintenance of migratory polymorphism. We highlight that such models have been fundamental to the development of partial migration theory, but are spatially and demographically simplistic compared to the rich bodies of population dynamic theory and models that consider spatially structured populations with dispersal but no migration, or consider populations experiencing strong seasonality and full obligate migration. Second, to provide an overarching conceptual framework for spatio‐temporal population dynamics, we define a ‘partially migratory meta‐population’ system as a spatially structured set of locations that can be occupied by different sets of resident and migrant individuals in different seasons, and where locations that can support reproduction can also be linked by dispersal. We outline key forms of within‐individual and among‐individual variation and structure in migration that could arise within such systems and interact with variation in individual survival, reproduction and dispersal to create complex population dynamics and evolutionary responses across locations, seasons, years and generations. Third, we review approaches by which population dynamic and eco‐evolutionary models could be developed to test hypotheses regarding the dynamics and persistence of partially migratory meta‐populations given diverse forms of seasonal environmental variation and change, and to forecast system‐specific dynamics. To demonstrate one such approach, we use an evolutionary individual‐based model to illustrate that multiple forms of partial migration can readily co‐exist in a simple spatially structured landscape. Finally, we summarise recent empirical studies that demonstrate key components of demographic structure in partial migration, and demonstrate diverse associations with reproduction and survival. We thereby identify key theoretical and empirical knowledge gaps that remain, and consider multiple complementary approaches by which these gaps can be filled in order to elucidate population dynamic and eco‐evolutionary responses to spatio‐temporal seasonal environmental variation and change.