Understanding partial migration: linking genetic, developmental, and environmental drivers

Understanding partial migration: linking genetic, developmental, and environmental drivers | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 December 2025 V1 Latest version Share on Understanding partial migration: linking genetic, developmental, and environmental drivers Authors : Dongmin Kim 0000-0002-1508-1590 [email protected] , Ellen Aikens , Teresa Pegan , Paul Moorcroft , David Wolfson , and Jesús Pinto-Ledezma Authors Info & Affiliations https://doi.org/10.22541/au.176463855.58122192/v1 492 views 135 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Partial migration, where some individuals in a population migrate while others remain resident, arises from the dynamic interplay of multiple non-exclusive eco-evolutionary mechanisms. These mechanisms vary because migratory patterns can be shaped by genetics, individual experience, social learning, and fluctuating environmental pressures. Yet there is no clear link between these mechanisms and the behavioral patterns observed across species and environments. As a result, studies often focus on isolated examples without a shared structure for comparison or synthesis. Here, we integrate mechanisms, scales, and patterns to guide the study of partial migration. This synthesis emphasizes three core components: (1) identifying and linking developmental, plastic, and genetic drivers of partial migration, (2) incorporating processes from individual decisions to population-level patterns across spatial and temporal scales, and (3) providing a roadmap to help researchers choose an appropriate framing for their research questions. It also stresses that careful consideration of these scales is essential before developing models or analyses that address eco-evolutionary questions. This holistic approach enables researchers to generate testable hypotheses about the ecological and evolutionary mechanisms governing partial migration, thereby capturing complexity in terrestrial systems more effectively and supporting the selection of appropriate methods. 1. Introduction Populations of many species include some individuals that migrate and others that do not, a behavior known as partial migration (Box 1). This phenomenon arises when individuals in a population react to the same exogenous cues in starkly different ways, and its underlying causes have intrigued ecologists and evolutionary biologists for decades (Chapman et al. 2011). In recent years, advances in tracking technologies have revealed that partial migration is widespread across diverse taxonomic groups (Beltran et al. 2025; Berg et al. 2019; Chapman et al. 2011), motivating renewed efforts to identify its ecological and evolutionary drivers. Although often considered a population-level process, partial migration is shaped by variations in individual behaviors and decisions (Chapman et al. 2011). Studies have highlighted that mechanisms involving genetics, individual experience, social learning, and fluctuating environmental pressures may contribute to this variation (Aikens et al. 2024; Delmore et al. 2020; Pulido 2011; Eggeman et al. 2016). Recent evidence further suggests that partial migration may result from combinations of these mechanisms (Acker et al. 2023; Joly et al. 2025; Ranck et al. 2023). Despite this progress, it remains unclear how different mechanisms interact and shape individual-level behaviors that can translate into population-level outcomes (Buchan et al. 2020; Soriano-Redondo et al. 2023). To address these challenges, we first clarify our use of the term partial migration since it can be interpreted differently by biologists (Box 1). In this synthesis, we define partial migration as the behavioral phenomenon in which some individuals in a single breeding population migrate in a given year, while other individuals from the same population remain resident (Chapman et al. 2011). Here, we synthesize existing knowledge that links ecological and evolutionary mechanisms with testable hypotheses in partial migration (Sections 2-4). We first explore potential interactions among the different mechanisms driving partial migration and their outcomes, with a primary focus on terrestrial systems (Figure 1). We review the history of partial migration research and offer researchers practical guidance on identifying a research question and the data needed for their study (Figures 1 and 2). Finally, we discuss how emerging tools, such as high-resolution tracking, remote sensing, and machine learning, can aid in improving our understanding of the drivers and consequences of partial migration (Section 5). Our overarching goal is to synthesize diverse strands of partial migration research to enable comparative insights across systems, and to provide biologists with a road map for framing their research questions based on the mechanisms and hypotheses they have identified as being of interest. 2. Mechanisms of partial migration Before we can understand why partial migration varies across species, we must first identify the underlying individual-level mechanisms that drive migratory patterns in animals. Here, we consider partial migration as a behavior that emerges from a combination of three mechanisms: (1) genetic polymorphism, (2) ontogenetic canalization, and (3) phenotypic plasticity (Figure 1). Earlier theoretical and experimental research on animal migration identified that in some species, migration is genetically heritable (Berthold 1995; Delmore et al. 2020; Pulido 2011; Figure 2). For example, in birds, individuals may exhibit the same movement pattern over their lifetime, regardless of the conditions they are exposed to, because they are genetically predisposed to migrate or not to migrate (i.e., genetic polymorphism; Berthold and Pulido 1994; Justen et al. 2024). However, individuals' migratory behaviors, may also depend on how they acquire and process information, individually or socially (i.e., ontogenetic canalization; Abrahms et al. 2021; Byholm et al. 2022; Mueller et al. 2013). Researchers have also found that the migratory strategies of some animals exhibit phenotypic plasticity: the ability of a genotype to produce different phenotypes in various environments, depending on factors such as resource availability, individual state (e.g., body size or sex), or interactions among individuals (Eggeman et al. 2016; Sanz‐Aguilar et al. 2012; see Figure 2). Despite the different mechanisms that shape partial migration being known, it remains unclear how fixed or flexible these mechanisms are, and how intertwined they are within individuals. On the one hand, migratory behaviors controlled by genetic polymorphisms are the most fixed within an individual’s lifetime (Berthold 1994). Suppose an animal’s decision on whether and where to migrate is solely or primarily determined by heredity. In that case, the expression of this behavior within populations must change across generations due to natural selection or other evolutionary processes. Ontogeny is intermediate, where information gathering happens via between- and within-individual learning (Aikens et al. 2022; Aikens et al. 2024; Mueller et al. 2013). If the learning is social (considered a second inheritance system besides genetics), there is flexibility in what can be learned within a lifetime (Knudsen et al. 2025; Teitelbaum et al. 2016). Conditions experienced early in life may set the stage for later migratory patterns that are canalized when the animal reaches sexual maturity and begins breeding (Aikens et al. 2024; Brønnvik et al. 2024). Yet, behavioral flexibility tends to decline with age, becoming more difficult and costly, which limits mature animals’ ability to explore or adopt alternative strategies. In this sense, the early life environment and the types of information available (e.g., individual experience, learning from social expertise existing in the population, and heterospecific information) should affect the expression of the migratory behavior. On the other hand, some animals respond dynamically to environmental conditions (i.e., phenotypic plasticity; see also Witczak et al. 2024), showing flexibility across their entire lifetime. Under these assumptions, the individuals' environmental conditions and competitive ability are the main contributors to partial migration. 2.1. Genetic polymorphism For many species, the incidence of migratory behaviour is fixed within an individual’s lifetime and persists through generations due to genetic inheritance (Akesson and Hedenstrom 2007; Berthold 1991; Berthold 1999; Berthold and Querner 1981). Many avian species are obligate migrants, in which every individual in the population migrates each year. These species tend to breed in places where the non-breeding season presents severe challenges to survival, such as temperate latitudes with harsh winters. Some species of obligate migrants undergo their first long-distance migration without the aid of parents or older conspecifics, demonstrating that the migratory program—the timing, distance, and direction of the journey—is likely innate in these species (Akesson and Helm 2020). A history of captive breeding experiments, most notably on Eurasian blackcaps (Sylvia atricapilla) during the 1980s (Delmore et al. 2020; Liedvogel et al. 2011), has demonstrated that innate aspects of the migratory program can have an inherited genetic basis. Experimental methods from these studies included raising individuals from populations with varying migratory patterns without parental care and then quantifying their migratory behavior, which maintained population-specific migratory trends (Berthold and Querner 1981, Helbig et al. 1989; Pulido and Berthold 2010). Researchers also showed hereditary connections by breeding hybrids from different populations (i.e., resident and migrant, or migrants from populations traveling in different directions), showing that hybrids had intermediate phenotypes in orientation, distance, and the propensity to migrate. The extent to which these results can be generalized across diverse species is unclear, but they demonstrate that variation in migration between separate populations can be influenced by genetic differences between the populations. It is less clear how genetic inheritance contributes to variation in migratory behavior within comingling partially migratory populations. Some populations of partial migrants, such as those of blackcaps and the European Shag (Gulosus aristotelis), have been the subject of quantitative genetic studies, confirming that individual migratory status is influenced by heritable genetic variance (Acker et al. 2023; Berthold 1988). However, the heritability of migratory status in the European Shag was found to be only 9%. By contrast, much more variation in the migratory phenotype was explained by “permanent individual variance,” a metric that potentially reflects effects of ontogenetic canalization (see next subsection) or non-additive genetic effects. Acker et al. (2023) suggested that evaluating heritability in captive settings, as has been done in blackcaps, may artificially reduce effects of early-life environmental variation (i.e., through ontogenetic canalization) on individual migratory behaviors, thereby inflating apparent genetic heritability. Indeed, much of the variation in migration in partially migratory European Shags arises from a three-way interaction between genetic variance, environmental effects, and permanent individual variance, emphasizing the importance of studying populations in natural conditions and considering multiple potential mechanisms of partial migration (Acker et al. 2023). Although the contribution of genetic variation to migratory variation in partially migratory populations is known to differ across species, the requirement for long-term analyses of pedigreed populations makes it challenging to apply quantitative genetic approaches to diverse species. Another drawback is that quantitative genetic approaches do not provide information about which specific genes are involved and how these genes influence phenotypes. Since the breeding experiments of the 1980s, advances in sequencing technologies have allowed researchers to attempt to identify specific genes that might play a role in migratory inheritance (reviewed in Gu et al. 2024), including in partial migrants. Genetic variants that correlate with variation in migratory phenotype can be identified as candidate genes that may influence the phenotype. This approach is feasible in diverse wild species because it requires only the collection of DNA sequence data and information about the phenotypes. However, these studies have produced mixed results, highlighting that there is a growing consensus that the genetic basis of migratory phenotypes may be highly complex, rather than being controlled by a few genes of large effect (Caballero-Lopez and Bensch 2024; Weissensteiner et al. 2025). For example, in the partially migratory European Blackbird (Turdus merula), migratory status was associated with variation in loci that overlap with the gene PER2, which is known to play a role in circadian rhythm (Weissensteiner et al. 2025). However, no variants of PER2 were fixed between migrants and nonmigrants, indicating that this gene alone does not control migratory phenotype. Other studies evaluating genetic differences associated with migration often find signals in circadian genes such as Clock and ADCYAP1 (Le Clercq et al. 2023; Mueller et al. 2011; Saino et al. 2015). As sequencing becomes more logistically feasible with decreasing sequencing costs, the understanding of functional expression with transcriptomics promises greater insight into the genetic influences of partial migration (Caballero-Lopez and Bensch 2024; Cavedon et al. 2019; Stapley et al. 2010). 2.2. Ontogenetic canalization For long-lived species that perform multiple migrations over their lifetime, early life experiences can shape decisions about whether, where, and when to migrate later in life (Aikens et al. 2022; Sergio et al. 2014). According to the exploration-refinement hypothesis, long-lived birds are expected to explore and prioritize information gain during early life when they are not constrained by pressures to arrive early on the breeding grounds during the later reproductive phase of life (Campioni et al. 2020; Guilford et al. 2011). As a result, young individuals are expected to exhibit varied and wide-ranging behaviors that enable them to explore different environments; however, as adults, this flexibility becomes increasingly constrained, and routes are expected to be refined to increase reproductive success (Aikens et al. 2024; Campioni et al. 2020). Although this hypothesis originated to explain the development of migration routes and tactics in birds, it may also apply more generally to other long-lived migratory species that are constrained by dependent young and extensive parental care. Thus, it is often assumed that early life is a sensitive developmental period when key information about whether, where, and when to migrate is acquired through individual experience or social learning (Aikens et al. 2022; Bontekoe et al. 2025). Evidence of ontogenetic shifts in migration (e.g., changes in migration over a lifetime) is growing, especially in avian species ( e.g., Abrahms et al. 2021; Aikens et al. 2024; Campioni et al. 2020; Chan et al. 2024; Sergio et al. 2014; Witczak et al. 2024). For example, white storks explored new routes during their first fall and spring migrations, which allowed them to innovate faster and more direct migrations later in life (Aikens et al. 2024). Key information about the benefits and risks associated with migratory decision-making (e.g., suitable resource availability) can be acquired through individual or social learning (Bontekoe et al. 2025; Byholm et al. 2022; Loonstra et al. 2023; Sergio et al. 2014). Reintroduced whooping cranes, for instance, relied on social information to appropriately time migration during early life, but shifted to using personal information later in life (Abrahms et al. 2021). Ontogenetic shifts in migration patterns have also been observed in ungulates, where migratory plasticity is relatively common (Berg et al, 2019). In some cases, the ontogenetic shifts observed are consistent with ontogenetic canalization and the exploration-refinement hypothesis. A recent example of this migratory pattern was published for the female mule deer (Jacopak et al. 2025). The authors found that as yearlings, a fraction of young female mule deer developed entirely new migration routes that had little or no overlap with the migration paths they had taken as fawns when they followed the migratory paths of their mothers. These novel migration routes were subsequently often maintained as adults (Jacopak et al. 2025). However, in other species, rather than canalization, migration becomes more variable with age (see plasticity section below). For example, a study of moose migratory patterns in Sweden found that while young moose (less than five years old) always migrated, older moose (five to fifteen years) appeared to shift towards a conditional strategy mediated by snow conditions, that is, older individuals still generally migrated in low snow depth areas, but in areas of deep snow older individuals were less likely to migrate (Singh et al. 2012). Beyond influencing where and when to migrate, individual-level learning and social learning can also affect if, and how far, an animal migrates and has been attributed to population-level shifts in migratory propensity and migration distance across a range of taxa, over both rapid and more prolonged timescales (Brown and Webster 2025; Garland et al. 2025; Jesmer et al. 2018; Teitelbaum et al. 2016). 2.3. Phenotypic Plasticity Although genetic polymorphisms have been shown to play a role in migratory behavior for many species, empirical studies suggest that most individual decisions involving migration are primarily influenced by phenotypic plasticity, also known as ‘conditional strategies’ (Chapman et al. 2011). Phenotypic plasticity offers more flexibility in the prevalence, timing, and route of migratory behaviors than genetic polymorphism or ontogenic canalization because individual reactions can be triggered by real-time interactions between environmental conditions and internal states (e.g., sex, physical condition, social hierarchy, breeding status; Dingle 2014; Hebblewhite and Merrill 2011). Understanding what factors lead to the development of phenotypic plasticity as a primary mechanism of partial migration is an active topic of research, but the degree of inter-annual environmental predictability is likely a major influence (Lundberg 1988, Riotte-Lambert et al. 2020). If uncertainty in cyclical environmental conditions (or more importantly, the biological ability to tolerate conditions via residency) is low, selective pressure may favor an increased frequency of engrained behavior through genetic polymorphism (Riotte-Lambert et al. 2020). In addition, for species whose migratory habits are primarily influenced by phenotypic plasticity, intrinsic differences in physiology and life experience likely interact with extrinsic factors such as social dynamics and density dependence to shape the interaction between environmental conditions and the cues that dictate whether an individual is a resident or migrant for a given season (Berg et al. 2019; Cagnacci et al. 2011; Mysterud et al. 2011). Furthermore, the physiological state of an individual also plays an important role in determining the role of plasticity regarding an individual’s response to environmental conditions. For example, Monteith et al. (2001) found that migration timing reflected a risk-reward tradeoff that varied for different segments of a montane mule deer population. Older females and those in good nutritional states were better able to handle the risk of delaying autumn migration (and the concurrent dangers of severe weather and deep snow) and therefore benefited from the increasing foraging opportunities present on the summer range. Ultimately, the relative contribution of factors (both exogenous and endogenous) affecting phenotypic plasticity likely change throughout an individual’s lifetime, especially as their social hierarchy and reproductive status shift as they age (Gauthreaux 1982, Kokko 1999). 3. Ecological and evolutionary theories and hypotheses of partial migration Here, we argue that these underlying mechanisms and traits are rooted in shared evolutionary and ecological processes, and can serve as the basis for connecting the existing theories and hypotheses to explain the causes of partial migration (Figure 1; Boxes 2 and 3). We begin by outliting theories and hypotheses from Berg et al. (2019), Chapman et al. (2011), and Lundberg (1988), which account for different underlying mechanisms and demographic traits across various study systems (Boxes 2 and 3). These theories and hypotheses describe pathways (involving genetics, individual interactions, and environmental variability) that lead to partial migration. We then conducted a systematic review of the partial migration literature published between 2015 and 2024 to detect emerging trends in the recent partial migration literature and identify what are the under-explored hypotheses (Appendix S1). We focused on this period because it reflects rapid growth in the use of biologging and tracking technologies, which have greatly expanded researchers’ ability to study partial migration in birds, mammals, and other taxa by providing fine-scale movement data. Our literature review revealed that most studies have focused on easily quantifiable factors of partial migration, such as remotely sensed environmental layers, bands, and GPS tags on animals. We found that (1) foraging maturation, (2) thermal tolerance or body size, and (3) arrival time are the most studied hypotheses over the last decade (Figure 4a). These recent trends in partial migration research highlights that direct observations of animal movement help researchers test hypotheses related to timing of migration under environmental variability. We also identified that the effects of the social environment and density dependence on partial migration are still hard to study over a long period and across vast spatial scales. Because long-term tracking of individuals is often limited by the lifespan of monitoring technologies and the difficulty of repeatedly recapturing the same animals to replace or maintain tracking devices over multiple years. Notably, we found that only one study within the last 10 years has been briefly conducted on the social fence hypothesis (Figure 3a; Barker et al., 2019). We also found different trends in partial migration studies by bird and mammal systems (Figure 4b). For example, the most studied hypothesis is the thermal tolerance or body size hypothesis in bird literature (Figure 4b). In mammal literature, most partial migration studies focused on the foraging maturation hypothesis (Figure 4b). Furthermore, no research was found regarding sexual conflict, terminal investment, and trophic polymorphism in mammals’ partial migration (Figure 4a). 4. Scales and the study of partial migration To address the gaps we identified in the partial migration literature, we propose a conceptual roadmap that advances research through two complementary paths (Figure 1). One approach begins with an eco-evolutionary theory of interest (Box 3) to frame a research question to understand how different mechanisms shape partial migration. The alternative approach starts with empirically grounded hypotheses (Box 2) derived from relationships between mechanisms and patterns of partial migration in a focal system. In both cases, researchers should identify the data and/or model required to test these hypotheses, clarifying how specific mechanisms (genetic polymorphism, ontogenetic canalization, and phenotypic plasticity) contribute to individual migratory decisions. Previous studies suggest that most partially migratory species are likely influenced by all three mechanisms and that variation within and across species determines which mechanisms dominate individual migratory decisions (Figure 1; Gómez-Bahamón et al. 2020; Jahn et al. 2013). Because these mechanisms and the patterns are scale-dependent (Levin 1992; Spake et al. 2021; Brian and Catford 2023), careful attention to spatial and temporal scales of data and model is essential when evaluating the individual and interactive effects of the mechanisms influencing partial migration. Given that this scale-dependence has strong theoretical and modeling implications, we suggest that, going forward, the mechanisms driving partial migration should be examined from an integrative perspective in which the effect of each mechanism varies across time and space (Figure 3). For example, from a temporal lens, evidence indicates that heritable changes in migration within populations involve genetic changes over long evolutionary timescales (Gómez-Bahamón et al. 2020). Conversely, genetic polymorphism might not explain variation in migratory behavior across individual lifespans. On shorter timescales, many populations may exhibit a strong propensity for migratory behaviors to evolve rapidly, often shifting from migration to partial migration or from residency to partial migration (Berthold 1999). This apparent rapidity with which migration can evolve may be a consequence of the joint influence of genetics and phenotypic plasticity in most populations (Able and Belthoff 1998; Gómez-Bahamón et al. 2020). From a spatial lens, at macroecological scales, changes in the patterns of migratory behavior are linked to long-term environmental variation in which the species are distributed (Winger et al. 2012; Zink and Gardner 2017). For example, some studies support that migration is an ancestral behavior present in most lineages (Zink 2011) and that migration patterns can shift – from full migratory to partial migratory to sedentary states – when environmental changes alter the benefits of seasonal movement, allowing species to track their realized niches and maximize their survival on their potential geographic distribution and over macroevolutionary time (Gómez-Bahamón et al. 2020, Winger et al. 2019). At local scales, the interaction between ecological mechanisms (e.g., competitive interactions) and biological differences between individuals (e.g., sex and age) are more relevant than macroecological factors or evolutionary mechanisms in dictating whether animals decide to migrate or not. Last, under our suggested perspective, the relative contribution of each mechanism to partial migratory behavior changes depending on the temporal and spatial scale of evaluation (Figure 3). On the one hand, the expected outcomes resulting from phenotypic plasticity may be more relevant at local scales and over short periods of time. On the other hand, at macro-ecological and evolutionary scales, and depending on the environmental conditions, these environmental changes and ecological pressures may trigger the re-expression of inherited traits or behavioral tendencies that allow animals to shift their migratory behavior. 5. Future research directions and needs 5.1. Data integration to reveal partial migration The study of animal migration has advanced considerably towards understanding its causes and consequences, mainly from an ecological perspective. Despite this progress, knowledge gaps still exist that need to be filled to answer the elusive questions of why animals migrate and whether migration (partial or full) provides animals with ecological and evolutionary advantages relative to non-migrating individuals and species. Data integration, the combination of multiple datasets under a unified probabilistic framework, is increasingly being used to augment the breadth of data sources and rigor of statistical analyses (Kays and Wikelski 2023; Lapatas et al. 2015). Integrating standardized metadata across technologies, from traditional animal-banding to light-level geolocators and GPS tracking systems, can help better understand intraspecific variation in migratory timing and routes, breeding and wintering sites, and the selection of stopover sites across the animal Tree of Life for multiple species. Combining historical animal-banding programs (e.g., EURING, du Feu et al. 2016) and GPS tracking systems (e.g., reverse-GPS system ATLAS, Beardsworth et al. 2022) may also unveil unknown patterns of breeding and nonbreeding site selection, reveal factors that cause changes in migratory routes, and the species' responses to factors of global change. Partial migration encompasses multiple ecological and evolutionary hypotheses to explain its causes, operating across distinct spatial and temporal scales (see Section 4; Figures 1 and 3). However, mismatches between the biological mechanisms underpinning these hypotheses and the resolution of currently available datasets can limit our ability to test mechanisms rigorously (Nathan et al. 2022). For example, evaluating the arrival time hypothesis may require multi-year, high-resolution tracking of migratory animals across regional to global domains, whereas hypotheses involving body size or thermal tolerance may be shaped by generational genetic variation and physiological development, requiring decadal datasets at relatively consistent regional scales, potentially coupled with phylogenetic approaches. Yet, such datasets are often sparse or patchy (Winker and Delmore 2025). Similarly, the social fence hypothesis (operating on a local or regional scale over short seasonal windows) may require dense tagging of social migratory individuals, as well as integration of movement data with social network metrics. However, such data are not often available in standard ecological monitoring programs (Bontekoe et al. 2025; Farine and Whitehead 2015). Our systematic literature review revealed that social fences and density dependence were rarely quantified (Figure 4), suggesting that quantifying the social landscape and conspecific density may be a limiting factor in simultaneously evaluating multiple hypotheses to explain partial migration. Because migration can involve the splitting and merging of multiple populations and groups across spatial scales—from landscapes to bioregions (Cohen et al. 2018), accurately capturing the social environment and density of conspecifics across the full annual cycle of migration remains a major challenge to overcome (Bontekoe et al. 2025). Addressing this challenge will require leveraging environmental sensors, such as radar (Van Doren and Horton 2018; Giuntini et al. 2025), acoustic recorders (Rhinehart et al. 2020) and high-resolution imagery from drones (Koger et al. 2023a) or low-orbit satellites (Wu et al. 2023), that can estimate animal presence and abundance, in combination with advances in machine learning to automate processes, classify, and interpret the large volumes of sensor data. The deployment of these sensors at scale will enable the quantification of social and density information at the correct spatial and temporal resolutions (Koger et al. 2023a, b). For example, using drones combined with computer vision and deep learning has successfully tracked individuals simultaneously in group-living species, allowing researchers to reconstruct spatial distribution and social structure of social animals (Keger et al. 2023a; Weinstein 2018; White et al. 2025). Similarly, deep learning applied to aerial imagery has been used not just to count animals but to identify individuals and infer social context, offering a path toward quantifying social fences and density dependence at fine scales (Aliane 2025). Beyond developing infrastructure to link traditionally disparate data sources and developing better tools to quantify key ecological factors (e.g., density), disentangling the relative contributions of genetics, ontogenetic development, and conditional strategies will require creative approaches from multiple fields across ecological and evolutionary biology. For example, research focusing on the regulatory mechanisms underlying gene expression, as opposed to more commonly studied candidate gene approaches, could provide important insights into how the environmental conditions where species occur and life-stage requirements interact to shape gene expression and emergent migratory behavior (Caballero‐Lopez and Bensch 2024). Furthermore, combining long-term monitoring with careful experimental design or genomic studies holds promise to unveil how shifting environmental conditions, selective pressures, and stochasticity interact to influence genetic change and behavioral shifts that scale up to affect partial migration (Bontekoe et al. 2025; Clutton-Brock and Sheldon 2010). To overcome data mismatches and improve inference across nested scales, strategies should include combining complementary datasets (e.g., satellite remote sensing, animal-borne sensors, genetic sampling, citizen science records) to improve spatial and temporal resolution, and applying hierarchical or multi-scale modeling frameworks that allow inference across nested scales (Allen and Singh 2016; Cagnacci et al. 2010). In addition, adopting standardized data management and sharing practices – such as following FAIR (Findable, Accessible, Interoperable, Reusable) data principles (Wilkinson et al. 2016; Vogt et al. 2025) and using established biodiversity and trait data standards like Darwin Core (Wieczorek et al. 2012) and MoveTraits (Beumer et al. 2025) – can reduce incompatibility among datasets originating from different sources or formats. These approaches facilitate interoperability and data reuse across platforms and institutions, and support transparent workflows for integrating heterogeneous data. By aligning data collection with the processes under investigation and adopting integrative approaches, researchers can better capture the complexity of partial migration across ecological contexts. 5.2. Modeling multi-causality in partial migration Inferring causality in ecology remains challenging because ecological phenomena emerge from the interaction of multiple, intertwined drivers (Franks et al. 2025; Siegel and Dee 2025). For example, individual movement decisions are influenced by multiple mechanisms (discussed in Sections 2 and 4), such as environmental cues, physiological constraints, developmental experiences, genetic predispositions, and social influences (Chapman et al. 2011; Kaitala et al. 1993). To disentangle causality in partial migration, tools like graphical models (Jordan 2004) and directed acyclic graphs (DAGs) offer a promising approach for formalizing hypotheses about these causal relationships by explicitly representing multiple pathways and confounding variables before experimental designs and further modeling analysis (Laubach et al. 2021). By structuring these pathways in advance, DAGs can help disentangle potential informative or correlated variables and clarify whether observed partial migration patterns are consistent with the researchers’ proposed causal models. For example, Pulido’s (2011) model of partial migration highlights the importance of integrating physiological and environmental thresholds to understand the causes of partial migration. Additional pathways influencing partial migration can be added to Pulido’s model, such as individual ontogeny and social context. These additions will allow researchers to investigate whether partial migration patterns change over time as new drivers or new data are included. 5.3. Synthesizing the multiple definitions of partial migration Here, we highlight the variation in partial migration definitions and encourage researchers to explore potential interactions between our proposed mechanisms and investigate the outcomes based on the multiple definitions described in Box 1. For example, under geospatial partial migration, where migratory and resident populations occupy different parts of a species’ range, the arrival-time hypothesis is interpreted differently. Under this definition, the advantage of early arrival operates between populations rather than within a single, mixed population. Migratory individuals may reach the breeding grounds later than residents, but selection acts primarily at the level of geographically distinct populations. Differences in arrival time may reflect long-term genetic polymorphism and phenotypic plasticity playing a larger role. These examples highlight that the relative roles of the mechanisms differ to support the same hypothesis, like arrival time, and it depends on how researchers define what partial migration is. We encourage researchers to consider how their conceptualization of partial migration influences hypotheses, expected outcomes, and the interpretation of underlying mechanisms. 5.4. Different pathways to study partial migration In our roadmap, we provide a list of theories specific to partial migration studies alongside empirically grounded hypotheses (Figure 1). We find that exploring general evolutionary game theories and/or life history theories (Box 3), which describe broad pathways leading to partial migration, is particularly helpful when researchers prefer to think about the causes of partial migration from evolutionary perspectives (Lundberg 1988). For example, researchers draw on the concept of an evolutionary stable strategy, which describes migrants and residents coexisting when both groups have an equal average fitness payoff. Researchers can then examine possible ecological mechanisms (e.g., competitive interactions) and biological differences among individuals (e.g., age and sex) to test whether an evolutionary stable strategy explains the causes of partial migration. For instance, the arrival times of partially migratory individuals may support the evolutionary stable strategy: residents tolerate harsh wintering conditions for early access to mates or territories, while migrants avoid winter costs but may settle in poorer habitats once they are back in the breeding grounds. The arrival time hypothesis thus illustrates how empirically derived hypotheses can align with eco-evolutionary theory to explain the cause of partial migration. Additionally, researchers may have available datasets before identifying a theory or a hypothesis they are interested to test. In this case, we suggest to first investigate the scales of the data and what theories or hypotheses can be tested based on the scales of their data. If the data tracks multiple years of full migratory paths of the same individuals, hypotheses like arrival time can be tested. If the phylogenetic data of migratory animals are available, hypotheses like body size can be tested that require decadal tracks of the traits to explain the evolution of partial migration. Conclusion Partial migration is a widespread and complex behavioral phenomenon shaped by the interplay of genetic, developmental, and environmental factors. By synthesizing the diverse mechanisms, scales, and patterns that influence partial migration, we provide an approach to understand why some individuals migrate while others remain resident. Recognizing the importance of spatial and temporal context is critical for accurately linking individual-level decisions to population-level outcomes as partial migration. This integrative perspective not only clarifies the sources of variation in migratory strategies across species and ecosystems but also helps researchers with a structured approach to generate testable hypotheses and develop biologically relevant eco-evolutionary models. With our synthesis, we encourage researchers to further explore the interactions between the proposed mechanisms beyond partial migration, such as seasonal migration. Reference Able, K. P., & Belthoff, J. R. (1998). Rapid ‘evolution’of migratory behaviour in the introduced house finch of eastern North America. Proceedings of the Royal Society of London. Series B: Biological Sciences, 265(1410), 2063-2071. Abrahms, B., Teitelbaum, C.S., Mueller, T. & Converse, S.J. (2021). Ontogenetic shifts from social to experiential learning drive avian migration timing. Nat Commun, 12, 7326. Acker, P., Daunt, F., Wanless, S., Burthe, S.J., Newell, M.A., Harris, M.P., et al. (2023). Additive genetic and environmental variation interact to shape the dynamics of seasonal migration in a wild bird population. Evolution, 77, 2128–2143. Adriaensen, F. & Dhondt, A.A. (1990). Population Dynamics and Partial Migration of the European Robin (Erithacus rubecula) in Different Habitats. The Journal of Animal Ecology, 59, 1077. Aikens, E.O., Bontekoe, I.D., Blumenstiel, L., Schlicksupp, A. & Flack, A. (2022). Viewing animal migration through a social lens. Trends in Ecology & Evolution, 37, 985–996. Aikens, E.O., Nourani, E., Fiedler, W., Wikelski, M. & Flack, A. (2024). Learning shapes the development of migratory behavior. Proc. Natl. Acad. Sci. U.S.A., 121, e2306389121. Åkesson, S. & Helm, B. (2020). Endogenous Programs and Flexibility in Bird Migration. Front. Ecol. Evol., 8. Aliane, N. (2025). Drones and AI-Driven Solutions for Wildlife Monitoring. Drones, 9(7), 455. Allen, A.M. & Singh, N.J. (2016). Linking Movement Ecology with Wildlife Management and Conservation. Front. Ecol. Evol., 3. Alonso, J.C., Morales, M.B. & Alonso, J.A. (2000). Partial Migration, and Lek and Nesting Area Fidelity in Female Great Bustards. The Condor, 102, 127–136. Barker, K.J., Mitchell, M.S. & Proffitt, K.M. (2019). Native forage mediates influence of irrigated agriculture on migratory behaviour of elk. Journal of Animal Ecology, 88, 1100–1110. Beardsworth, C.E., Gobbens, E., van Maarseveen, F., Denissen, B., Dekinga, A., Nathan, R., Toledo, S. and Bijleveld, A.I. (2022). Validating ATLAS: A regional‐scale high‐throughput tracking system. Methods in Ecology and Evolution, 13(9), pp.1990-2004. Beltran, R.S., Kilpatrick, A.M., Picardi, S., Abrahms, B., Barrile, G.M., Oestreich, W.K., et al. (2025). Maximizing biological insights from instruments attached to animals. Trends in Ecology & Evolution, 40, 37–46. Berg, J.E., Hebblewhite, M., St. Clair, C.C. & Merrill, E.H. (2019). Prevalence and Mechanisms of Partial Migration in Ungulates. Front. Ecol. Evol., 7, 325. Berthold, P. (1991). Genetic control of migratory behaviour in birds. Trends in Ecology & Evolution, 6, 254–257. Berthold, P. (1995). Microevolution of migratory behaviour illustrated by the Blackcap Sylvia atricapilla: 1993 Witherby Lecture. Bird Study, 42, 89–100. Berthold, P. & Pulido, F. (1994). Heritability of migratory activity in a natural bird population. Proc. R. Soc. Lond. B, 257, 311–315. Berthold, P. & Querner, U. (1981). Genetic Basis of Migratory Behavior in European Warblers. Science, 212, 77–79. Berthold, P. & Querner, U. (1982). Partial migration in birds: experimental proof of polymorphism as a controlling system. Experientia, 38, 805–806. Beumer, L.T., Hertel, A.G., Royauté, R., Tucker, M.A., Albrecht, J., Beltran, R.S., Cagnacci, F., Davidson, S.C., Dejid, N., Kays, R. and Kölzsch, A., 2025. MoveTraits–A database for integrating animal behaviour into trait-based ecology. bioRxiv, pp.2025-03. Bontekoe, I.D., Aikens, E.O., Schlicksupp, A., Blumenstiel, L., Honorato, R.G., Jorzik, I., et al. (2025). The study of social animal migrations: a synthesis of the past and guidelines for future research. Proc. R. Soc. B., 292, 20242726. Boyle, W.A. (2008). Partial migration in birds: tests of three hypotheses in a tropical lekking frugivore. Journal of Animal Ecology, 77, 1122–1128. Brønnvik, H., Nourani, E., Fiedler, W. & Flack, A. (2024). Experience reduces route selection for conspecifics by the collectively migrating white stork. Current Biology, 34, 2030-2037.e3. Brown, C. & Webster, M. (2025). Fishy culture in a changing world. Phil. Trans. R. Soc. B, 380, 20240130. Brian, J. and Catford, J., 2023. Ecological scale and context dependence. In Effective Ecology (pp. 63-79). CRC Press. Buchan, C., Gilroy, J.J., Catry, I. & Franco, A.M.A. (2020). Fitness consequences of different migratory strategies in partially migratory populations: A multi‐taxa meta‐analysis. Journal of Animal Ecology, 89, 678–690. Byholm, P., Beal, M., Isaksson, N., Lötberg, U. & Åkesson, S. (2022). Paternal transmission of migration knowledge in a long-distance bird migrant. Nat Commun, 13, 1566. Caballero‐Lopez, V. & Bensch, S. (2024). The regulatory basis of migratory behaviour in birds: different paths to similar outcomes. Journal of Avian Biology, 2024, e03238. Cagnacci, F., Boitani, L., Powell, R.A. & Boyce, M.S. (2010). Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges. Phil. Trans. R. Soc. B, 365, 2157–2162. Cagnacci, F., Focardi, S., Heurich, M., Stache, A., Hewison, A.J.M., Morellet, N., et al. (2011). Partial migration in roe deer: migratory and resident tactics are end points of a behavioural gradient determined by ecological factors. Oikos, 120, 1790–1802. Campioni, L., Dias, M.P., Granadeiro, J.P. & Catry, P. (2020). An ontogenetic perspective on migratory strategy of a long‐lived pelagic seabird: Timings and destinations change progressively during maturation. Journal of Animal Ecology, 89, 29–43. Cavender‐Bares, J., Kozak, K. H., Fine, P. V., & Kembel, S. W. (2009). The merging of community ecology and phylogenetic biology. Ecology letters, 12(7), 693-715. Cavedon, M., Gubili, C., Heppenheimer, E., vonHoldt, B., Mariani, S., Hebblewhite, M., et al. (2019). Genomics, environment and balancing selection in behaviourally bimodal populations: The caribou case. Molecular Ecology, 28, 1946–1963. Chan, Y., Kormann, U.G., Witczak, S., Scherler, P. & Grüebler, M.U. (2024). Ontogeny of migration destination, route and timing in a partially migratory bird. Journal of Animal Ecology, 93, 1316–1327. Chapman, B.B., Brönmark, C., Nilsson, J. & Hansson, L. (2011). The ecology and evolution of partial migration. Oikos, 120, 1764–1775. Clutton-Brock, T. & Sheldon, B.C. (2010). Individuals and populations: the role of long-term, individual-based studies of animals in ecology and evolutionary biology. Trends in Ecology & Evolution, 25, 562–573. Cohen, E.B., Hostetler, J.A., Hallworth, M.T., Rushing, C.S., Sillett, T.S. & Marra, P.P. (2018). Quantifying the strength of migratory connectivity. Methods Ecol Evol, 9, 513–524. Delmore, K., Illera, J.C., Pérez-Tris, J., Segelbacher, G., Lugo Ramos, J.S., Durieux, G., et al. (2020). The evolutionary history and genomics of European blackcap migration. eLife, 9, e54462. Dingle, H. (2014). Migration: the biology of life on the move. Oxford University Press. Eggeman, S.L., Hebblewhite, M., Bohm, H., Whittington, J. & Merrill, E.H. (2016). Behavioural flexibility in migratory behaviour in a long‐lived large herbivore. Journal of Animal Ecology, 85, 785–797. Farine, D.R. & Whitehead, H. (2015). Constructing, conducting and interpreting animal social network analysis. Journal of Animal Ecology, 84, 1144–1163. du Feu, C.R., Clark, J.A., Schaub, M., Fiedler, W. & Baillie, S.R. (2016). The EURING Data Bank – a critical tool for continental-scale studies of marked birds. Ringing & Migration, 31, 1–18. Fortuna, R., Acker, P., Ugland, C.R., Burthe, S.J., Harris, M.P., Newell, M.A., et al. (2024). Season-specific genetic variation underlies early-life migration in a partially migratory bird. Proc. R. Soc. B., 291, 20241660. Franks, D.W., Ruxton, G.D. and Sherratt, T., 2025. Ecology needs a causal overhaul. Biological Reviews. Fudickar, A.M., Jahn, A.E. & Ketterson, E.D. (2021). Animal Migration: An Overview of One of Nature’s Great Spectacles. Annu. Rev. Ecol. Evol. Syst., 52, 479–497. Garland, E.C., Corkeron, P., Noad, M.J., Abrahms, B., Allen, J.A., Constantine, R., et al. (2025). Culture and conservation in baleen whales. Phil. Trans. R. Soc. B, 380, 20240133. Gauthreaux, S.A. (1982). Age-Dependent Orientation in Migratory Birds. In: Avian Navigation, Proceedings in Life Sciences (eds. Papi, F. & Wallraff, H.G.). Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 68–74. Giuntini, S., Burt, C. S., Abbott, A. L., Adams, C. A., Belotti, M. C. T., Deng, Y., ... & Horton, K. G. (2025). Structuring the skies: Diel dynamics of migratory animal movement in the lower atmosphere. Ecology, 106(11), e70247. Gómez-Bahamón, V., Tuero, D.T., Castaño, M.I., Jahn, A.E., Bates, J.M. & Clark, C.J. (2020). Sonations in Migratory and Non-migratory Fork-tailed Flycatchers ( Tyrannus savana ). Integrative and Comparative Biology, 60, 1147–1159. Gu, Z., Dixon, A. & Zhan, X. (2024). Genetics and Evolution of Bird Migration. Annu. Rev. Anim. Biosci., 12, 21–43. Guilford, T., Freeman, R., Boyle, D., Dean, B., Kirk, H., Phillips, R., et al. (2011). A Dispersive Migration in the Atlantic Puffin and Its Implications for Migratory Navigation. PLoS ONE, 6, e21336. Hebblewhite, M. & Merrill, E.H. (2011). Demographic balancing of migrant and resident elk in a partially migratory population through forage–predation tradeoffs. Oikos, 120, 1860–1870. Helbig, A.J., Berthold, P. & Wiltschko, W. (1989). Migratory Orientation of Blackcaps (Sylvia atricapilla): Population-specific Shifts of Direction during the Autumn. Ethology, 82, 307–315. Jahn, A.E., Levey, D.J., Cueto, V.R., Ledezma, J.P., Tuero, D.T., Fox, J.W., et al. (2013). Long-distance bird migration within South America revealed by light-level geolocators. The Auk, 130, 223–229. Jahn, A.E., Levey, D.J., Hostetler, J.A. & Mamani, A.M. (2010). Determinants of partial bird migration in the Amazon Basin. Journal of Animal Ecology, 79, 983–992. Jakopak, R.P., LaSharr, T.N., Rafferty, R.T., Dwinnell, S.P.H., Randall, J., Kaiser, R., et al. (2025). Migratory routes are inherited primarily from mother in a terrestrial herbivore. Current Biology, 35, 3523-3529.e3. Jesmer, B., Fugate, J. & Kauffman, M. (2025). On the interface between cultural transmission, phenotypic diversity, demography and the conservation of migratory ungulates. Phil. Trans. R. Soc. B, 380, 20240131. Joly, K., Cameron, M.D. & White, R.G. (2025). Behavioral adaptation to seasonal resource scarcity by Caribou ( Rangifer tarandus ) and its role in partial migration. Journal of Mammalogy, 106, 96–104. Jordan, M.I. (2004). Graphical Models. Statist. Sci., 19. Justen, H.C., Easton, W.E. & Delmore, K.E. (2024). Mapping seasonal migration in a songbird hybrid zone -- heritability, genetic correlations, and genomic patterns linked to speciation. Proc. Natl. Acad. Sci. U.S.A., 121, e2313442121. Kaitala, A., Kaitala, V. & Lundberg, P. (1993). A Theory of Partial Migration. The American Naturalist, 142, 59–81. Kays, R. & Wikelski, M. (2023). The Internet of Animals: what it is, what it could be. Trends in Ecology & Evolution, 38, 859–869. Ketterson, E.D. & Nolan, V. (1983). The Evolution of Differential Bird Migration. In: Current Ornithology (ed. Johnston, R.F.). Springer US, New York, NY, pp. 357–402. Knudsen, T.E., MacKenzie, B.R., Thygesen, U.H. & Mariani, P. (2025). Evolution and stability of social learning in animal migration. Mov Ecol, 13, 43. Koger, B., Deshpande, A., Kerby, J.T., Graving, J.M., Costelloe, B.R. & Couzin, I.D. (2023a). Quantifying the movement, behaviour and environmental context of group‐living animals using drones and computer vision. Journal of Animal Ecology, 92, 1357–1371. Koger, B., Hurme, E., Costelloe, B.R., O’Mara, M.T., Wikelski, M., Kays, R., et al. (2023b). An automated approach for counting groups of flying animals applied to one of the world’s largest bat colonies. Ecosphere, 14. Kokko, H. (1999). Competition for early arrival in migratory birds. Journal of Animal Ecology, 68, 940–950. Kokko, H. (2011). Directions in modelling partial migration: how adaptation can cause a population decline and why the rules of territory acquisition matter. Oikos, 120, 1826–1837. Laland, K.N., Sterelny, K., Odling-Smee, J., Hoppitt, W. & Uller, T. (2011). Cause and effect in biology revisited: is Mayr’s proximate-ultimate dichotomy still useful? Science, 334, 1512–1516. Lapatas, V., Stefanidakis, M., Jimenez, R.C., Via, A. & Schneider, M.V. (2015). Data integration in biological research: an overview. J of Biol Res-Thessaloniki, 22, 9. Laubach, Z.M., Murray, E.J., Hoke, K.L., Safran, R.J. & Perng, W. (2021). A biologist’s guide to model selection and causal inference. Proc. R. Soc. B., 288, 20202815. Le Clercq, L., Bazzi, G., Cecere, J.G., Gianfranceschi, L., Grobler, J.P., Kotzé, A., et al. (2023). Time trees and clock genes: a systematic review and comparative analysis of contemporary avian migration genetics. Biological Reviews, 98, 1051–1080. Levin, S.A., 1992. The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology, 73(6), pp.1943-1967. Liedvogel, M., Åkesson, S. & Bensch, S. (2011). The genetics of migration on the move. Trends in Ecology & Evolution, 26, 561–569. Loonstra, A.H.J., Verhoeven, M.A., Both, C. & Piersma, T. (2023). Translocation of shorebird siblings shows intraspecific variation in migration routines to arise after fledging. Current Biology, 33, 2535-2540.e3. Lundberg, P. (1988). The evolution of partial migration in Birds. Trends in Ecology & Evolution, 3, 172–175. Monteith, K.L., Bleich, V.C., Stephenson, T.R., Pierce, B.M., Conner, M.M., Klaver, R.W., et al. (2011). Timing of seasonal migration in mule deer: effects of climate, plant phenology, and life-history characteristics. Ecosphere, 2, art47. Morrison, M., Cohen, J.M., Gurarie, E. & Van Deelen, T.R. (2024). Environmental Drivers and Fitness Consequences of Partial Migration under Climate Change. Journal of Fish and Wildlife Management, 15, 216–227. Mueller, J.C., Pulido, F. & Kempenaers, B. (2011). Identification of a gene associated with avian migratory behaviour. Proceedings of the Royal Society B: Biological Sciences, 278, 2848–2856. Mueller, T., O’Hara, R.B., Converse, S.J., Urbanek, R.P. & Fagan, W.F. (2013). Social Learning of Migratory Performance. Science, 341, 999–1002. Mysterud, A., Loe, L.E., Zimmermann, B., Bischof, R., Veiberg, V. & Meisingset, E. (2011). Partial migration in expanding red deer populations at northern latitudes – a role for density dependence? Oikos, 120, 1817–1825. Nathan, R., Monk, C.T., Arlinghaus, R., Adam, T., Alós, J., Assaf, M., et al. (2022). Big-data approaches lead to an increased understanding of the ecology of animal movement. Science, 375, eabg1780. Nilsson, A.L.K., Lindström, Å., Jonzén, N., Nilsson, S.G. & Karlsson, L. (2006). The effect of climate change on partial migration – the blue tit paradox. Global Change Biology, 12, 2014–2022. Pereira, A., Hazell, M. & Fryxell, J.M. (2024). Flexible migration by woodland caribou in Ontario, Canada. J Wildl Manag, 88, e22645. Pulido, F. (2007). The Genetics and Evolution of Avian Migration. BioScience, 57, 165–174. Pulido, F. (2011). Evolutionary genetics of partial migration–the threshold model of migration revis (it) ed. Oikos, 120(12), 1776-1783. Pulido, F. & Berthold, P. (2010). Current selection for lower migratory activity will drive the evolution of residency in a migratory bird population. Proceedings of the National Academy of Sciences, 107, 7341–7346. Pulido, F., Berthold, P., Mohr, G. & Querner, U. (2001). Heritability of the timing of autumn migration in a natural bird population. Proc. R. Soc. Lond. B, 268, 953–959. Ranck, S.C., Garsvo, C.M., Schwartz, D.M., Reynard, L.M., Kohn, M.J. & Heath, J.A. (2023). Sex, body size, and winter weather explain migration strategies in a partial migrant population of American Kestrels. Ornithology, 140, ukad019. Rhinehart, T.A., Chronister, L.M., Devlin, T. & Kitzes, J. (2020). Acoustic localization of terrestrial wildlife: Current practices and future opportunities. Ecology and Evolution, 10, 6794–6818. Riotte-Lambert, L. & Matthiopoulos, J. (2020). Environmental Predictability as a Cause and Consequence of Animal Movement. Trends in Ecology & Evolution, 35, 163–174. Saino, N., Bazzi, G., Gatti, E., Caprioli, M., Cecere, J.G., Possenti, C.D., et al. (2015). Polymorphism at the Clock gene predicts phenology of long-distance migration in birds. Molecular Ecology, 24, 1758–1773. Sanz‐Aguilar, A., Béchet, A., Germain, C., Johnson, A.R. & Pradel, R. (2012). To leave or not to leave: survival trade‐offs between different migratory strategies in the greater flamingo. Journal of Animal Ecology, 81, 1171–1182. Schroeder, M.A. & Braun, C.E. (1993). Partial migration in a population of greater prairie-chickens in northeastern colorado. The Auk, 110(1), 21-28. Sergio, F., Tanferna, A., De Stephanis, R., Jiménez, L.L., Blas, J., Tavecchia, G., et al. (2014). Individual improvements and selective mortality shape lifelong migratory performance. Nature, 515, 410–413. Shaw, A.K. & Levin, S.A. (2011). To breed or not to breed: a model of partial migration. Oikos, 120, 1871–1879. Siegel, K. & Dee, L.E. (2025). Foundations and Future Directions for Causal Inference in Ecological Research. Ecology Letters, 28, e70053. Singh, N.J., Börger, L., Dettki, H., Bunnefeld, N. & Ericsson, G. (2012). From migration to nomadism: movement variability in a northern ungulate across its latitudinal range. Ecological Applications, 22, 2007–2020. Shaw, A. K., Bisesi, A. T., Wojan, C., Kim, D., Torstenson, M., Narayanan, N., ... & Shao, C. (2024). Six personas to adopt when framing theoretical research questions in biology. Proceedings of the Royal Society B, 291(2031), 20240803. Spake, R., Mori, A.S., Beckmann, M., Martin, P.A., Christie, A.P., Duguid, M.C. and Doncaster, C.P., 2021. Implications of scale dependence for cross‐study syntheses of biodiversity differences. Ecology Letters, 24(2), pp.374-390. Soriano‐Redondo, A., Franco, A.M.A., Acácio, M., Payo‐Payo, A., Martins, B.H., Moreira, F., et al. (2023). Fitness, behavioral, and energetic trade‐offs of different migratory strategies in a partially migratory species. Ecology, 104, e4151. Stapley, J., Reger, J., Feulner, P.G.D., Smadja, C., Galindo, J., Ekblom, R., et al. (2010). Adaptation genomics: the next generation. Trends in Ecology & Evolution, 25, 705–712. Teitelbaum, C.S., Converse, S.J., Fagan, W.F., Böhning-Gaese, K., O’Hara, R.B., Lacy, A.E., et al. (2016). Experience drives innovation of new migration patterns of whooping cranes in response to global change. Nat Commun, 7, 12793. Van Doren, B.M. & Horton, K.G. (2018). A continental system for forecasting bird migration. Science, 361, 1115–1118. Vogt, L., Strömert, P., Matentzoglu, N., Karam, N., Konrad, M., Prinz, M. and Baum, R., 2025. Suggestions for extending the FAIR Principles based on a linguistic perspective on semantic interoperability. Scientific Data, 12(1), p.688. Weinstein, B. G. (2018). A computer vision for animal ecology. Journal of Animal Ecology, 87(3), 533-545. Weissensteiner, M.H., Delmore, K., Peona, V., Lugo Ramos, J.S., Arnaud, G., Blas, J., et al. (2025). Combining Individual‐Based Radio‐Tracking With Whole‐Genome Sequencing Data Reveals Candidate for Genetic Basis of Partial Migration in a Songbird. Ecology and Evolution, 15, e70800. White, E. P., Garner, L., Weinstein, B. G., Senyondo, H., Ortega, A., Steinkraus, A., ... & Ernest, S. M. (2025). Near real‐time monitoring of wading birds using uncrewed aircraft systems and computer vision. Remote Sensing in Ecology and Conservation, 11(3), 255-265. Wieczorek, J., Bloom, D., Guralnick, R., Blum, S., Döring, M., Giovanni, R., Robertson, T. and Vieglais, D., 2012. Darwin Core: an evolving community-developed biodiversity data standard. PloS one, 7(1), p.e29715. Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.W., da Silva Santos, L.B., Bourne, P.E. and Bouwman, J., 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1), pp.1-9. Winger, B.M., Auteri, G.G., Pegan, T.M. and Weeks, B.C. (2019). A long winter for the Red Queen: rethinking the evolution of seasonal migration. Biological Reviews, 94(3), pp.737-752. Winger, B.M., Lovette, I.J. & Winkler, D.W. (2012). Ancestry and evolution of seasonal migration in the Parulidae. Proc. R. Soc. B., 279, 610–618. Winker, K. & Delmore, K. (2025). Seasonally migratory songbirds have different historic population size characteristics than resident relatives. eLife, 12, RP90848. Witczak, S., Kormann, U.G., Catitti, B., Scherler, P., Van Bergen, V. & Grüebler, M.U. (2024). Temporal and spatial variation in reproductive benefits in a partial migrant. Ecology, 105, e4451. Xu, W., Barker, K., Shawler, A., Van Scoyoc, A., Smith, J.A., Mueller, T., et al. (2021). The plasticity of ungulate migration in a changing world. Ecology, 102, e03293. Zink, R.M. (2011). The evolution of avian migration: The evolution of avian migration Biological Journal of the Linnean Society, 104, 237–250. Zink, R.M. & Gardner, A.S. (2017). Glaciation as a migratory switch. Sci. Adv., 3, e1603133. Figure 1. Roadmap to help researchers choose a framing for their research question in partial migration studies. The roadmap illustrates two complementary entry points for studying the mechanisms shaping partial migration. One path begins with selecting an eco-evolutionary theory of interest, which then guides the development of empirically grounded hypotheses about how specific mechanisms influence individual migratory decisions. The other path starts from empirically grounded hypotheses derived from observed relationships between mechanisms and patterns of partial migration in a focal system. In both pathways, researchers identify the mechanisms (genetic polymorphism, ontogenetic canalization, and phenotypic plasticity) that may jointly contribute to migratory strategies, acknowledging that most partially migratory species are shaped by some combination of all three. The process ultimately leads researchers to determine the spatial and temporal scales of analysis and the data or models required to test these hypotheses, recognizing that both mechanisms and resulting patterns are scale-dependent (see Figure 3 for more details). Figure 2. History of advances in partial migration research in terrestrial mammals and birds over the last five decades (1980s - 2020s). Listed papers are key reviews and research articles (above 100 citations) focusing on the evolution (left panel) and ecology (right panel) of partial migration. The center boxes represent key themes of partial migration research by evolutionary biologists and ecologists. Figure 3. The hypotheses proposed to explain partial migration operate across different spatial and temporal scales (see Section 4). Color gradients indicate the variation in spatial and temporal scales from short period and small space (white) to longer period and larger space (purple). For example, the competitive release hypothesis (lower left panel) functions at a local spatial scale and over very short time periods, where direct interactions among individuals occur. In contrast, the arrival-time hypothesis operates at broader spatial scales and over longer durations (weeks). Some hypotheses may also interact to influence others (indicated by faded green colors from light green to darker green); for instance, sexual conflict and fasting endurance may shape decisions about when and where individuals depart and arrive (i.e., arrival time). The illustration of the figure is adapted from Cavender-Bares et al. (2009). Figure 4. Existing hypotheses and mechanisms to explain partial migration in birds and mammals. 121 Peer-reviewed studies published between 2015 and 2024 on partial migration. (a) Hypotheses tested by the selected literature (note: some papers tested more than one hypothesis on multiple species). (b) Hypotheses tested by the selected literature per taxa group. Box 1. Multiple definitions of partial migration. Partial migration is commonly defined as “some individuals migrate and others stay” (Chapman et al. 2011; Fortuna et al. 2024; Lundberg 1988; Morrison et al. 2024). While this core idea is widely shared among biologists, the nuances of how partial migration is conceptualized vary across disciplines. These variations often reflect differences in the level of organization (population vs. species) and ecological context. 1. Population-level partial migration Evolutionary theorists and ecologists often define partial migration as a fraction of individuals migrating within a single, commingled population while others remain resident (Lundberg 1988; Kokko 2011; Shaw & Levin, 2011). 2. Geospatial partial migration Ornithologists frequently use partial migration to describe species that contain separate migratory and resident populations in different parts of their range (Pulido 2007). This pattern, termed geospatial partial migration, is typical of species whose breeding ranges span temperate/tropical transition zones. Individuals breeding in the temperate zone migrate after breeding, while those breeding in the tropics remain resident. Under this definition, mechanisms such as the arrival-time hypothesis operate between populations rather than within a single, mixed population. Differences in arrival time may reflect long-term genetic polymorphism and phenotypic plasticity. 3. Population-level plasticity in mammals In mammalogy, partial migration is sometimes viewed as population-level plasticity in migratory behavior. For example, herds of ungulate species (e.g., reindeer) may switch between migratory and resident behaviors across years: the entire population may migrate in one year but remain resident the next (Pereira et al. 2024; Xu et al. 2021). Box 2. Definitions of hypotheses related to the causes of partial migration. The details of the hypotheses are summarized in Berg et al. (2019), Boyle (2008), and Chapman et al. (2011). Arrival Time: Intrasexual competition for high-quality breeding areas keeps individuals residents, even when little food is available. Competitive release; Dominance: Migrants avoid intraspecific competition for limited resources. Fasting endurance: Migrants avoid a greater risk of starvation during periods of food scarcity. Foraging maturation: Migrants gain advantages by securing high-quality resources Predation vulnerability: Migrants avoid the high risk of dying by escaping predation. Sexual conflict: Males and females experience different costs and benefits of migrating. Social fence: Residents with secure territory force other incoming individuals to migrate. Terminal investment: Residents invest heavily in reproduction instead of migrating. Thermal tolerance; Body size: Individuals with great thermal tolerance remain as residents, while those with lower thermal tolerance migrate. Trophic polymorphism: Difference in foraging morphology drives individuals’ migratory strategies. Box 3. Definitions of evolutionary theories in terms of understanding the causes of partial migration. The details of the theories are summarized in Lundberg (1988). Evolutionary stable strategy: Partial migration persists because neither migrating nor staying provides a consistent advantage over the other.Bet-hedging:Both migrating and staying are risky, but becoming one or the other helps the population cope with unpredictable environments.State-dependent selection:An individual’s decision to migrate depends on its own traits (e.g., age, body condition, sex).Frequency-dependent selection:The success of migrating versus staying depends on how many individuals use each strategy. Supplementary Material File (20251201_partial_migration.pdf) Download 1.49 MB Information & Authors Information Version history V1 Version 1 02 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords genetic polymorphism hypothesis mechanisms ontogenic canalization partial migration patterns phenotypic plasticity theory Authors Affiliations Dongmin Kim 0000-0002-1508-1590 [email protected] Department of Ecology, Evolution, and Behavior, University of Minnesota Department of Organismic and Evolutionary Biology, Harvard University View all articles by this author Ellen Aikens School of Computing, University of Wyoming Haub School of Environment and Natural Resources, University of Wyoming View all articles by this author Teresa Pegan Department of Organismic and Evolutionary Biology, Harvard University View all articles by this author Paul Moorcroft Department of Organismic and Evolutionary Biology, Harvard University View all articles by this author David Wolfson Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota View all articles by this author Jesús Pinto-Ledezma Department of Ecology, Evolution, and Behavior, University of Minnesota View all articles by this author Metrics & Citations Metrics Article Usage 492 views 135 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Dongmin Kim, Ellen Aikens, Teresa Pegan, et al. Understanding partial migration: linking genetic, developmental, and environmental drivers. Authorea . 02 December 2025. DOI: https://doi.org/10.22541/au.176463855.58122192/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176463855.58122192/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fea508f4c3de2c5',t:'MTc3OTI2OTM0Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();