- Open Access
The physiology of movement
Movement Ecology volume 8, Article number: 5 (2020)
Movement, from foraging to migration, is known to be under the influence of the environment. The translation of environmental cues to individual movement decision making is determined by an individual’s internal state and anticipated to balance costs and benefits. General body condition, metabolic and hormonal physiology mechanistically underpin this internal state. These physiological determinants are tightly, and often genetically linked with each other and hence central to a mechanistic understanding of movement. We here synthesise the available evidence of the physiological drivers and signatures of movement and review (1) how physiological state as measured in its most coarse way by body condition correlates with movement decisions during foraging, migration and dispersal, (2) how hormonal changes underlie changes in these movement strategies and (3) how these can be linked to molecular pathways.
We reveale that a high body condition facilitates the efficiency of routine foraging, dispersal and migration. Dispersal decision making is, however, in some cases stimulated by a decreased individual condition. Many of the biotic and abiotic stressors that induce movement initiate a physiological cascade in vertebrates through the production of stress hormones. Movement is therefore associated with hormone levels in vertebrates but also insects, often in interaction with factors related to body or social condition. The underlying molecular and physiological mechanisms are currently studied in few model species, and show –in congruence with our insights on the role of body condition- a central role of energy metabolism during glycolysis, and the coupling with timing processes during migration. Molecular insights into the physiological basis of movement remain, however, highly refractory. We finalise this review with a critical reflection on the importance of these physiological feedbacks for a better mechanistic understanding of movement and its effects on ecological dynamics at all levels of biological organization.
An individual-based view on organismal movement as propounded by the Movement Ecology Paradigm (MEP) has provoked a breakthrough in movement ecology as it links the biomechanical and behavioural basis of movement to fitness . The MEP puts three environmentally dependent components of movement forward: motion capacity, navigation capacity, and internal state. As movement operates across different spatiotemporal scales, it can be dissected into its underlying building blocks .
The Fundamental Movement Elements (FME) form the smallest unit of organismal movement and include for instance step size and wing beat frequency. The FMEs hence depend directly on the motion capacity and internal state components (Fig. 1) and mechanistically integrate into different distinct movement modes , referred to as Canonical Activity Modes (CAMs) that are characterized by a distinct movement speed, directionality and correlations of the movement angles. Examples of CAMs include routine foraging, dispersal and migration. Routine movements occur at small temporal and spatial scales with the aim of resource intake, and include displacements at the same scale in response to the same or other species (mate location, predator escape,..). We refer to dispersal as any specific movement during an individual’s lifespan, that make individuals leave the place they were born to a new location where they produce offspring. At short temporal, but usually large spatial scales, individuals can move recurrently between areas in response to environmental cues that predict environmental change. We refer to these movements as migration, and note that despite large distances covered, migration should not result in dispersal as breeding locations may be identical or very closeby among years.
Individuals make decisions to switch between CAMs in response to both the environmental context and internal state. As the sequence and variation in FME’s are strung into an organism’s CAM, any decision made regarding shifts in these CAM’s will depend on an individual’s internal state (and navigation capacity). Because resources are rarely homogeneously distributed in the environment and often continuously changing in time as well, movement will be essential to gain access to resources (such as food, mates and shelter) and will directly impact the individual’s internal state. This feedbacks between an individual’s immediate environment and its internal state will therefore shape its lifetime movement trajectory and fitness .
The maximisation of energy balances forms the basis of optimal foraging theory and directly links an individual’s energetic state (body condition) to routine foraging activities [3, 4]. While straightforward from its most fundamental perspective (i.e., the marginal value theorem), we now appreciate that optimal foraging is modulated by environmental factors that have equal or stronger fitness effects, namely predation and disease risk perception and its translation to landscapes of fear and disgust [5, 6]. Foraging movement will thus directly influence energy gain and shape temporal variation in an individual’s internal state. Maximising body condition does, however, not maximise fitness as individuals also have to deal with unpredictable environmental changes at larger spatiotemporal scales. Organisms therefore need to disperse and expose themselves to costs largely exceeding those experienced during routine movements [7, 8]. Movement is thus a fundamental behaviour in life history and the result of a continuous decision making process in terms of how, when and where to displace [1, 9]. Since an individual’s internal state will determine movement, while movement as such will reciprocally affect the individual’s internal state , they are tightly connected in a closed feedback loop. Because internal state is closely connected to life histories and behaviour , we follow Jachowski and Singh's suggestion to use physiological state as a more accurate term for this internal state .
Understanding the causes and consequences of the variation in movement trajectories has been identified as an important knowledge gap in movement ecology . As a first step to integrate feedbacks between movement and physiological state into a formal movement theory, we here provide a view on the current state of the art. More specifically, we synthesise the available evidence on the physiological drivers and signatures of movement. As our aim is to link this condition-dependence to ecology, we do not review the current neurobiological basis of movement decisions as in [13,14,15,16], nor the physiology behind wing development in insects [17, 18] but instead provide a synthesis on (1) how physiological state as measured in its most coarse way by body condition correlates with movement decisions related to foraging, migration and dispersal, (2) how changes in stress hormones underlie changes in these movement strategies and (3) whether these can be related to alternative physiological pathways. We finally critically integrate these insights to advance our understanding of the importance of eco-physiological feedbacks in movement ecology and close this review by formulating some unresolved questions.
From routine movements to dispersal
There is an abundant body of literature on how different movement strategies are related to metrics of body condition. Body condition is predominantly measured in a coarse way by residual or absolute body mass. The efficiency and pace of foraging movements are mostly positively related to a better body condition [19,20,21]. A good body condition does, however, not necessarily result in longer foraging trips . Rather on the contrary, when foraging costs are substantial, individuals in better body conditions are able to handle prey more efficiently and may show reduced foraging distances [22,23,24]. Parasites are documented to directly decrease foraging performance by depleting energy reserves and causing physiological damage .
Dispersal is a three-stage process, encompassing decision making in terms of departure, displacement and settlement [7, 26]. The social dominance hypothesis predicts emigration of individuals in an inferior physiological state . In house sparrows (Passer domesticus), lower ranked individuals leave natal areas earlier than their conspecifics that occupy higher positions in the social hierarchy . Many empirical studies on non-social species report variable relationships between body condition and dispersal . We argue that these different patterns of body condition dependence arise from different levels of spatiotemporal variation of habitat quality. Indeed, theory has shown that costly dispersal is undertaken by individuals in the best body condition in heterogeneous environments where individuals experience variation in fitness prospects [27, 29,30,31]. This pattern has been widely documented in nature and by means of controlled experiments [32,33,34,35,36,37,38,39,40,41,42]. Interestingly, in metapopulations where local relatedness is high because of low evolved dispersal , the opposite Evolutionary Stable Strategy emerges. This has been documented in apterous aphids (Acyrthosiphon pisum), where individuals with a decreased energy content dispersed earlier than their siblings in better condition .
The eventual dispersal distance and speed is positively associated with a better body condition in insects [44, 45], salamanders , fish , birds [48,49,50] and mammals . In two group-living bird species, however, individuals in the best condition remained closest to their place of birth [51, 52]. In a saproxylic beetle (Osmoderma eremita) species, flight speed and take-off completion were negatively condition dependent . The unexpected associations are explained by increased advantages of philopatry as familiarity and, hence, fitness prospects in terms of mate finding decrease with distance from the natal range. In a study using money spiders (Erigone atra) as a model, emigration has been demonstrated to be positively body condition dependent, with settlement improving under competition in those phenotypes that previously engaged in dispersal . Similar strategies were found in meerkats (Suricata suricatta), where individuals in better conditions were found to engage more in prospecting and thereby increased settlement probability .
Migrations and stop-over events
Migration is, like dispersal, a decision making process. If only a certain fraction of the individuals engage in migration, while others remain resident, the strategy is referred to as partial migration. Here, body condition is expected to vary within and among populations and to steer variation in migratory tendency . The three main hypotheses that have been put forward on how body condition may modulate the decision to migrate, are (i) The arrival time hypothesis stating that a migration decision is made when residents have fitness gains by prioritising territory establishment, whereas (ii) the dominance hypothesis states that individuals migrate to escape competition by dominant conspecifics, and lastly (iii), the body-size hypothesis states that a high body condition reduces costs during migration . Both the arrival time and dominance hypothesis predict subordinate individuals to engage in migration, and was found in trout (Salmo trutta) . However, other studies focusing on a fish (Rutilis rutilis), bird (Otus elegans botelensis) and a large mammal herbivore (Odocoileus hemionus), did not find an association between migration and within-population heterogeneity in body condition [58,59,60]. An excellent overview of these hypotheses may be found in Chapman et al.  and we refer to Hegemann et al.  for a more physiological perspective on partial migration.
Not surprisingly, a body of literature shows that migration trajectories are strongly impacted by the individual’s energetic state. Our insights so far are primarily dominated by research on birds and to a lesser degree migratory fish. Migratory trajectories comprise distances that are magnitudes beyond the daily routine movements and are typically segmented in several migratory movement episodes and stopovers where individuals engage in foraging for refuelling. As energy demands are high to cross these long distances, time spent for stopover activities is higher for individuals in lower body condition [59,60,61,62,63,64,65,66,67,68,69,70,71], and hence leads to increased foraging of to allow refuelling [59, 65]. Integrated over the entire migration trajectory, individuals that start migration in better body condition will therefore migrate faster, more directionally and arrive earlier at breeding sites [72,73,74,75,76]. In two anadromous fish species, migration is also negatively related to body condition, [77, 78], but here this correlation is determined by local adaptation to freshwater and hence the upstream breeding grounds.
A threshold-view on movement decision making
As outlined above, a positive correlation between body condition and the efficiency of routine movements, dispersal and migration has been mostly documented. Efficient movements, do not always translate into longer and faster movements, but instead, evidence is pointing at cost-reducing strategies being the rule for individuals in a good body condition (e.g., [22,23,24]). Individuals in poor body condition are therefore anticipated to either invest their energy in extended movements or to follow energy-saving strategies by reducing further energy expenditure. Movement-decision making can thus be considered as a threshold trait [79, 80] with individual shifting CAMs when body condition is reaching a specific value. Individuals may adopt in this respect more endurance (thresholds to engage in costly movements at relative high body condition), or conservative (thresholds at low body condition) strategies. Under frequency dependence, both strategies may stably coexist in single populations. While theoretically established , it remains to be studied whether such a within-population heterogeneity in movement decision making is effectively related to different strategies adopted in response to body conditions, and whether such fitness stabilising strategies eventually affect population dynamics. Additionally, it remains unclear to which degree physiological constraints overrule this decision-making. Individuals in poor condition might be energetically so depleted that any engagement in extended and beneficial movements might simply not be possible. In kangaroo rats (Dipodomys spectabilis), for instance, the timing of emigration is strongly body condition dependent, and only initiated when male individuals reach a critical mass . Feedbacks between movement as both an energy-consuming and an energy-gaining process are thus likely key to spatial behaviours in the wild, but to date poorly understood despite the increase of biologging studies across a wide variety of taxa . Moreover, most insights on such conditional-dependent strategies come from studies that focussed on the active departure phases and neglected decision making in terms of settlement . Given the link between body-condition and competitive ability, it remains to be studied to which degree presumed maladaptive departure decisions may eventually be compensated by facilitated settlement in new environments – especially when demographic and environmental conditions are strongly different between locations.
Body-condition dependent strategies are often overruled by hormonal changes in response to acute biotic and abiotic stressors . We here review the current state-of-the art in order to facilitate the integration of these endogenous processes within a mechanistic movement ecology .
Glucocorticoids in vertebrates
In vertebrates, external triggers of movement decisions such as food shortage, fear and antagonistic interactions with conspecifics are known to initiate a physiological cascade through the hypothalamic-pituitary-adrenal (HPA) axis by which stress hormones (glucocorticoids; abbreviated here as CORT) are released from the adrenal cortex. Creel et al.  provide an extensive review on the environmental triggers of this HPA axis activity in social and territorial species. As the main environmental cues of CORT production are known to trigger movement, especially dispersal, it is not surprising that movement is strongly associated with CORT levels, often in interaction with factors related to body or social condition .
Food shortage and social interactions attenuate foraging activity through hormonal regulation in birds [86,87,88]. Elevated CORT levels will equally determine the timing of dispersal in birds and reptiles [86, 88,89,90]. These elevated hormone levels can be maternally determined [90, 91] and the duration of exposure to maternal CORT amplitudes determines whether individuals stay or disperse . In social vertebrates, increased CORT levels are associated with elevated extra territorial forays, hence with prospecting prior to pre- and dispersal behaviour [93, 94] or with settlement .
Baseline plasma CORT levels are elevated in migrating birds to facilitate migratory fattening while protecting skeletal muscle from catabolism, but they also induce health costs [96,97,98,99,100]. The migration modulation hypothesis is brought forward as an explanation of their repressed levels in relation to acute stress during long-distance migration . Studies on partial migration however do not confirm this general pattern . Instead, CORT levels are found to be elevated during landing , and increase during stop-over events, where it is positively correlated with fuel loading and behavioural restlessness when active migration is resumed [103, 104]. In nightingales (Luscinia luscinia), elevated CORT-levels are modulated by geomagnetic information ; and in The European robin (Erithacus rubecula) CORT-levels differ between spring and autumn migration . CORT-levels are thus to a large degree externally induced. In dark-eyed juncos (Junco hyemalis), genetic variation in these responses was found among two populations overwintering in areas that varied in the level of environmental predictability . More specifically, birds wintering in less predictable and more extreme environments showed a higher amplitude corticosterone response, which may enable them to adjust their behaviour and physiology more rapidly in response to environmental stressors such as storms . Although most studies have targeted CORT, other hormones like ghrelin and melatonin are also known to influence food intake and lipid storage dependent on body condition in migrating birds and other vertebrates [108,109,110].
Hormones in insects
Octopamine and adipokinetic hormones are known to regulate energy supply, the oxidative capacity of the flight muscles, heart rate, and probably also a general stimulation of the insect nervous system during periods of intense flight . Octopamine can be considered as the insect counterpart of adrenaline . Although no insect equivalents of corticosteroids have been identified it seems that the adipokinetic hormones perform similar functions . In invertebrates and insects in particular, Juvenile hormone (JH) regulates development, reproduction, diapause, polyphenism, and behaviour . While JH production has been predominantly associated with wing development  it has also been shown that lower JH titers advance and increase the duration of flight in corn rootworms (Diabrotica virgifera)  and milkweed bugs (Oncopeltus fasciatus) . In migrant Monarch butterflies (Danaus plexippus), migration necessitates the persistence through a long winter season. This prolonged survival has been shown to result from suppressed JH synthesis .
The molecular and physiological basis underlying body condition dependent movement
As outlined above, the dependency of movement strategies on body condition is highly complex and multidimensional, rendering the characterization of the underlying molecular and physiological mechanisms highly refractory. Traditionally, the contribution of candidate genes to foraging, dispersal, and migration behavior has been studied in isolation. We briefly discuss genes of major effect on different movement strategies and subsequently attempt to unify the molecular drivers of movement.
The usual suspects: genes that greatly influence animal movement
Phosphoglucose isomerase (PGI) is an important metabolic enzyme that catalyzes the reversible second step within the glycolytic pathway. In a series of pioneering studies, Watt and colleagues discovered that different allozymes (different alleles, separable by electrophoresis) of PGI have different thermostabilities in Colias butterflies and that their frequencies change in response to heat stress [118,119,120,121,122]. Polymorphisms in the pgi gene have subsequently been detected in many insect populations and species [123,124,125]. Its close association with flight performance rendered pgi the ideal candidate gene to study the genetic underpinnings of dispersal ability [123, 126, 127], as for instance in the Glanville fritillary (Melitaea cinxia) metapopulation on the Åland island group [128,129,130,131]. Currently, a body of work (see review in ) identifies PGI and other central metabolic enzymes as prime targets of natural selection via traits related to metabolic rate but also the ability of these enzymes to act as signaling molecules. Collectively, this strongly indicates that a diverse set of central metabolic enzymes determine body condition dependent movement .
The central role of a cGMP-activated protein kinase (PKG) in foraging behavior, adult dispersal and perception of nutrient stress in a wide diversity of insect species was initially discovered in the fruit fly Drosophila melanogaster where differences in food searching behavior of larvae were mapped to a locus on chromosome-2 called the foraging (for) gene [133,134,135,136,137,138,139]. It explains the genetic coupling between foraging and conditional dispersal. Since its discovery, homologs of the for gene have been studied as a potential causal factor in behavioral transitions in the nematode Caenorhabditis elegans, honeybee Apis mellifera, and two ant species [140,141,142,143]. For instance, upon manipulating the expression of Amfor and egl-f, orthologs in honeybees and C. elegans, respectively, food dependent movement is significantly altered in both species [140, 144].
Clock genes are involved in the timing and onset of migration in birds, fish and butterflies [145,146,147]. Allelic differences in clock genes like OtsClock1b and Adcyap1 are not only associated with differences in timing and distance of migration but also affect morphology, hormone production and timing of reproduction [146, 148, 149]. Recent work showed that migratory and non-migratory butterflies (Danaus plexippus) differ in the Collagen IV alpha-1 gene, which participates in muscle development, metabolism and circadian rhythm pathways [150, 151]. This indicates that a limited number of genes regulate multidimensional traits associated with condition-dependent migration.
The transcriptomic signature of movement
Although these candidate genes seem to be key regulators for movement behavior, they fail to provide us with a complete insight into the often complex genetic architecture of common traits underlying movement. To overcome this limitation, more pathway-oriented and genome-wide methodologies are now being applied in movement ecology. Advances in –omics technologies not only provide biologists with knowledge concerning the genome-wide gene content of many non-model species, but also the unbiased quantification of transcription by transcriptomics.
Using a transcriptomic approach, Somervuo et al.  found a large difference in gene expression profiles between populations of the Glanville fritillary (Melitaea cinxia) that inhabit either fragmented or continuous landscapes. These different expression profiles may indicate selection for certain variants in genetic pathways that are involved in successful dispersal in fragmented landscapes . Notably, they found a strong up-regulation in the immune response and down-regulation in the hypoxia response in more dispersive butterflies. The authors attributed this latter transcriptonal shift in dispersive butterflies to a lower sensitivity to changes in oxygen levels, allowing for higher peak metabolic performance during flight before the hypoxia response sets in . Other transcriptomic studies on lepidopterans show similar adaptations to long distance flight on a physiological level, including mobilization of energy, coping with stress (hypoxia) and hormonal control . Transcriptome analysis on adult D. melanogaster showed that the for gene at least partially operates through the insulin/Tor signaling pathways, which are regulatory pathways that control animal growth, metabolism, and differentiation [137, 154]. In line with the different movement strategies, individual D. melanogaster larvae with a long movement path (called rovers) store energy reserves mainly as lipids while individuals with shorter movement paths (sitters) store energy as carbohydrates [136, 137]. In other dipterans with variation in their flight capacity, differential gene expression analysis revealed that the insulin signaling pathway, lipid metabolism, and JH signaling regulate energy during flight . While JH-mediated signaling appears to be an important regulator for migratory behavior in Monarch butterflies (Danaus plexippus), no differential expression of the for gene was observed .
In birds and mammals, transcriptomics offers a new approach to study migration and dispersal by extracting blood from individuals before and after the movement type of interest and comparing RNA profiles. Although this analysis likely excludes important signals from other organs such as the liver and brain, it can offer key insights into molecular mechanisms related to the behavioral decision making of movement. In blackbirds (Turdus merula) it was shown that, prior to departure, many genes rapidly change their transcription and these genes are predicted to participate in cholesterol transport and lipid metabolism . In marmots (Marmota flaviventris), transcriptomic data shows that the differences between dispersers and resident individuals lie in the upregulation of the metabolism and immunity .
Using metabolomics and gene-editing to find and validate key regulators of movement
Transcriptomic analyses hold great promise to find common underlying molecular pathways that relate to certain types of movement behaviors, but it remains difficult to connect different transcriptomic profiles to the exact levels of metabolite production . In plant-feeding spider mites (Tetranychus urticae) that show genetic variation in dispersal along a latitudinal gradient, metabolomic profiling indicated that an allocation of energy could be linked to a dispersal-foraging trade-off, with more dispersive mites evolving to cope with lower essential amino acid concentrations thereby allowing them to survive with lower amounts of food [159, 160]. This finding is consistent with the theory that individuals of a population that forage on the same resources can differ on the genetic level in how these resources are metabolized and that these differences influence their movement behavior . In Drosophila that were artificially selected for increased dispersal, higher amounts of octopamine and serotonin were detected . These neurotransmitters are associated with an elevated exploratory behavior in animals, while octopamine is also known to be important when energy reserves have to be mobilized [162, 163]. Octapamine regulates the activation of catabolic enzymes, such as lipases and is the functional equivalent of mammalian norepinephrine [163,164,165].
No individual genes or single pathway clearly stand out from these metabolomics and transcriptomics studies. To causally link genes to movement, novel gene-editing techniques such as CRISPR/Cas9 technology has now made it possible to modify specific loci within the genomes of many organisms in a stable manner . Gene-editing is not commonly used in ecological research because methodologies are currently time-consuming and highly impractical, especially for complex traits such as movement behavior . Recently, pioneering work of Markert et al.  succeeded to efficiently generate and screen heritable clock gene knockout lines in monarch butterflies (Danaus plexippus) and recorded changes in migration behavior. Future work needs to incorporate similar gene-editing approaches to advance our understanding of the genetic architecture underlying movement behavior .
Closing the loop
Environmental change imposes physiological changes, but as these determine movement and hence suceptability to these environmental stressors, emerging feedbacks are expected at different levels of biological organisation. First, our synthesis made clear that carry-over effects between the movement modes (CAMs) are very likely. Environmental conditions constraining local foraging will eventually impose physiological changes that limit the efficiency of dispersal and migration events, and reciprocally, any excessive energy expenditure or exposure to additional stressors (if translated into endocrinal reactions) during these long-distance journeys can carry over to foraging movements in the subsequent resident stages .
As these physiological changes are anticipated to be correlated with demographic traits and behaviours, hence forming behavioural syndromes [26, 81, 170] they can eventually impact equilibrium population sizes and their fluctuations , as mediated by costs during movement and changes in local growth rates (e.g., [172, 173]). Such feedbacks can even be lagged if physiological responses are mediated through maternal effects, as for instance the case by induced hormonal effects . Ultimately, the physiological capacity will determine population dynamic consequences associated with climate change and the persistence of species in an altered environment , as for instance demonstrated in the Glanville fritillary [174, 175]. Here, feedbacks between colonisation, extinction and the PGI-related dispersal phenotypes maintained (genetically based) physiological heterogeneity in a metapopulation but since the different genotypes perform differently under different temperatures, gene-flow and metapopulation viability were shown to be vulnerable under climate change [176, 177]. In dendritic systems, body-condition dependent dispersal of a salamander (Gyrinophilus porphyriticus) was found to maintain positive growth in putative sinks, hence, contributing to form of self-organisation in these linear habitats .
The impact of body-condition dependent movement on community structure has been mainly studied from a co-dispersal perspective, i.e., when hosts in a specific physiological state are moving symbionts. The best-documented consequences of such physiological-induced individual differences are related to the quantity and quality of endozoochorously dispersed seeds by vertebrates . At the other extreme, parasitic symbionts are able to directly modify their host’s physiological state  in such a way to manipulate their own spread. Gut bacteria have in this respect been found to steer elementary cell-physiological and hormonal processes along the gut-brain axis that directly modify animal behaviours . Such behavioural modifications are, however, not restricted to gut microbiomes. Presumed commensal Ricketsia endosymbionts are for instance found to constrain spider dispersal behaviour , while the dispersal limitation in Borellia-infected ticks has been linked to physiological changes that eventually facilitate host transmission of their Lyme-causing bacterial symbiont .
A critical end-reflection
Our understanding of the relative importance of movement-physiology feedbacks in population and community dynamics is still developing. It is nevertheless clear from our review that human-induced rapid environmental changes will affect this eco-physiological nexus, and that the integration of multiple theoretical frameworks may be required to explain the observed variation in movements in nature . Understanding and predicting the responses of animals to environmental change and the potential for solving diverse conservation problems using physiological knowledge is key to the field of conservation physiology . While an extended discussion and speculation on how different anthropogenic pressures affect movement by directly impacting physiological processes is beyond the scope of this review (but see  for an excellent contribution with focus on vertebrate migration), we see direct links between spatiotemporal changes in resource quantity and quality, diseases and microbiomes, pollution, invasive species and habitat fragmentation affecting all movement strategies by their impact on body conditions and physiological state. The development of accurate forecasting models is one of the most urgent tasks to guide the effective conservation of biodiversity in the light of global changes. To date, however, models do not provide sufficiently accurate predictions because of an inherent lack of key biological processes, such as physiology and dispersal. We here show that the movement-physiological nexus is such a neglected important mechanism because the direct feedbacks impact connectivity and hence the persistence of metapopulation  and the potential for invasions .
Unbiased genome-wide transcriptomics using RNA-seq has become very popular in the last couple of years to study movement phenomena [147, 152, 157, 189,190,191]. In the near future, Next Generation Sequencing (NGS) will allow advanced comparisons of differentially expressed genes across species, movement type, and conditions [192,193,194]. It is in this respect not unlikely that a new generation of molecular techniques will eventually put the classical classification of dispersal, foraging, and migration aside, while providing a more condition and energy-dependent classification of movement with possible generic molecular responses that unify many types of movement. With the rise of novel molecular tools that allow gene editing [167, 195] and those allowing non-invasive monitoring in wild populations , it can also be anticipated that the physiology of movement will be studied at an unprecedented level of detail, especially given the central role of all movement types in species conservation .
We obviously applaud this direction as it will advance our understanding of spatial population dynamics substantially from an individual, mechanistic perspective. However, an open question, remains to what extent such highly detailed studies of the physiology of movement are needed or desirable to transform the field of movement ecology towards a more predictive science. It is clear from our review that physiological control mechanisms constrain and dictate variation in how animals with different movement strategies respond to its surrounding environment. The physiological control of movement should therefore be treated as a reaction norm, and as for models including feedbacks between evolution and ecology , we expect a realistic but simplified consideration of feedbacks between environmental cues, resources and physiological processes to improve the predictive power of the available models. The integration of simple allometric and metabolic rules offers in this respect promising avenues [171, 198,199,200,201], as do dynamic energy budget models [202,203,204]. It is less obvious to which degree any hormonal feedbacks need to be integrated. There is some evidence that endocrine processes impact direct costs of movement which will potentially impact connectivity at rates that cannot be predicted from metabolic processes alone. Independent of the empirical progress made in understanding the physiological coupling of movement and environmental change, theory is only marginally following this direction. We argue that such a parallel theoretical development is constrained by the added level of complexity, but to date, this has not even been put on the research agenda. Since the few available theoretical studies demonstrated that even the addition of simple movement reaction norms or metabolic rules, can largely change the emerging ecological dynamics, we advocate that a more mechanistic based movement theory is needed more than ever in light of generating synthesis in species responses to global change.
Whether such a theory needs to extend into the molecular pathways underlying the physiology of movement, is more questionable. While this perspective has been recently brought forward within the framework of a predictive ecology in response to climate change , our review showed that the needed insights into the principal physiological and genetic drivers of movement are largely lacking. Hence, no theory can be developed without an advanced empirical research agenda.
Modelling approaches that explicitly account for metabolic costs associated with movement and costs associated with risk-taking might already provide general insights on how feedbacks between the environment and physiology eventually shape movement strategies and their coexistence across and within populations (see e.g .[171, 206, 207]). One key area where further insights would benefit ecological forecasting is the study of the putative key- hormones and -genes that are central to the ecophysiological molecular network. If detected, such hormones or genes may serve as master-traits in predictive modelling and improve the accuracy and robustness of mechanistic models by restricting the number of free parameters. We additionally propose theory to integrate movement at lifetime scales, and to focus primarily on behavioural switches between routine movements, dispersal and migration in response to local demographic conditions, body condition and general physiological states (see e.g .) as linkers between local and regional demography. New generations of statistical tools now allow the detection of such discontinuities in movement trajectories  and therefore open avenues to use inverse modelling approaches  to test the relevance and importance of detailed physiological feedbacks for large-scale individual movement patterns and their impact on population-level processes in a wide array of animals in nature.
Environments are spatiotemporally heterogeneous, either because of external abiotic drivers or because of internal biotic dynamics. As organisms need to maximise fitness, their movement behaviour should be optimised. Failing to do so might lead to physiological states that constrain such adaptive shifts. Our review demonstrates the central importance of body condition or energetic state as a driver of movements at different spatiotemporal scales, from foraging to dispersal and migration. Overall, as body condition is determined by carry-over effects from early life, we show the importance of these early conditions for physiology and subsequent movement decision making. Negative relationships between movement and body condition become more common with increasing costs of movement. As a decision-making process, the onset of movement at these different spatiotemporal scales is associated with hormonal and gene-expression changes as well. These insights are merely derived from classical model systems and allow a profound insight into the physiological pathways, and the putative correlated responses on other traits and performance. It is, however, clear that much more work is needed to achieve sufficient progress in the field to develop a unifying synthesis on the link between environmental change, physiology and the resulting feedbacks on ecological dynamics. We encourage endeavours in this direction and are hopeful because of the accelerating rate at which new methodologies are developed. However, given the infancy of a physiological movement ecology and the urgency to develop a predictive model of biodiversity in response to environmental change, we advocate a cost-based modelling approach that considers movement decision thresholds in relation to basic physiological states as an important step forward. Ideally, such modelling approaches are centred on physiological dynamics caused by key-molecular pathways, that link environmental change to the condition-dependency of movement across the relevant spatiotemporal scales.
Synthesis of the outstanding questions
Knowledge caveats hinder the development of a movement ecology that integrates detailed physiological feedbacks in terms of the underlying molecular networks. It remains to be investigated whether and how much the integration of first principles that underlie physiological changes of movement, as to be developed by the next generation of theory, improve the predictive power of ecological forecasting models. Here we summarise the outstanding questions related to the main topics that are discussed in this paper.
Questions related to movement-decisions that depend on body condition.
How variable are body-condition dependent thresholds across contexts and environments, and to which degree do they underlie heterogeneity in movement strategies within and across populations?
What is the impact of these threshold responses on population dynamics and vice-versa?
When are these body-condition dependent movements overruled by hormonal processes (e.g., related to predation pressure, fear, social status and other stressors that are mechanistically decoupled from energetic condition)?
As metabolic processes and movement allometrically scale to body size, are condition-dependent strategies variable among species of different size, or even trophic levels (e.g. see )?
Questions related to movement syndromes
How are physiological processes that are central in life history and behavior at the basis of movement syndromes, i.e. how do movement strategies correlate with life histories and other behaviour?
How are these correlations shaped by the inter- and intraspecific interactions?
To what extent can microbial symbionts influence and shape these correlations and movement strategies?
Questions related to genes underlying movement-decisions
Are there generic molecular pathways that underly many different movement strategies and are they regulated by the same genes and hormones in different species?
Is there a common genetic background for movement syndromes, and strategies across all life stages?
Availability of data and materials
All references are listed in the paper
Canonical Activity Modes
Costs of transport
Fundamental Movement Elements
Movement Ecology Paradigm
Neuropeptide pigment dispersing factor
Single Nucleotide Polymorphism
Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, et al. A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci U S A. 2008;105:19052–9.
Getz WM, Saltz D. A framework for generating and analyzing movement paths on ecological landscapes. PNAS. 2008;105:19066–71.
Charnov EL. Optimal foraging, the marginal value theorem. Theor Popul Biol. 1976;9:129–36.
Pyke GH, Pulliam HR, Charnov EL. Optimal Foraging: A Selective Review of Theory and Tests. Q Rev Biol. 1977;52:137–54.
Gallagher AJ, Creel S, Wilson RP, Cooke SJ. Energy Landscapes and the Landscape of Fear. Trends Ecol Evol. 2017;32:88–96.
Weinstein SB, Buck JC, Young HS. A landscape of disgust. Science. 2018;359:1213–4.
Bonte D, Van Dyck H, Bullock JM, Coulon A, Delgado M, Gibbs M, et al. Costs of dispersal. Biol Rev. 2012;87:290–312.
Reid JM, Travis JMJ, Daunt F, Burthe SJ, Wanless S, Dytham C. Population and evolutionary dynamics in spatially structured seasonally varying environments. Biol Rev. 2018;93:1578–603.
Bonte D, Dahirel M. Dispersal: A central and independent trait in life history. Oikos. 2017;126:472–9.
Jachowski DS, Singh NJ. Toward a mechanistic understanding of animal migration: incorporating physiological measurements in the study of animal movement. Conserv Physiol. 2015;3:cov035.
Ricklefs RE, Wikelski M. The physiology/life-history nexus. Trends Ecol Evol. 2002;17:462–8.
Holyoak M, Casagrandi R, Nathan R, Revilla E, Spiegel O. Trends and missing parts in the study of movement ecology. Proc Natl Acad Sci USA. 2008;105:19060–5.
Borst A, Haag J, Reiff DF. Fly Motion Vision. Annu Rev Neurosci. 2010;33:49–70.
Maimon G, Straw AD, Dickinson MH. Active flight increases the gain of visual motion processing in Drosophila. Nat Neurosci. 2010;13:393–9.
Wang ZJ. Insect Flight: From Newton’s Law to Neurons. 2016. https://doi.org/10.1146/annurev-conmatphys-031113-133853.
Nishida S, Kawabe T, Sawayama M, Fukiage T. Motion Perception: From Detection to Interpretation. Annu Rev Vis Sci. 2018;4:501–23.
Zera AJ. The Endocrine Regulation of Wing Polymorphism in Insects: State of the Art, Recent Surprises, and Future Directions. Integr Comp Biol. 2003;43:607–16.
Zera A, Denno R. Physiology and ecology of dispersal polymorphism in insects. Annu Rev Entomol. 1997;42:207–30.
Laskowski KL, Pearish S, Bensky M, Bell AM. Predictors of Individual Variation in Movement in a Natural Population of Threespine Stickleback (Gasterosteus aculeatus). In: Pawar S, Woodward G, Dell AI, editors. Advances in ecological research, vol 52: trait-based ecology - from structure to function; 2015. p. 65–90.
Powell LL, Dobbs RC, Marra PP. Habitat and body condition influence American Redstart foraging behavior during the non-breeding season. J Field Ornithol. 2015;86:229–37.
Mattila ALK. Thermal biology of flight in a butterfly: genotype, flight metabolism, and environmental conditions. Ecol Evol. 2015;5:5539–51.
Dorhout DL, Sappington TW, Lewis LC, Rice ME. Flight behaviour of European corn borer infected with Nosema pyrausta. J Appl Entomol. 2011;135:25–37.
Sommerfeld J, Kato A, Ropert-Coudert Y, Garthe S, Hindell MA. The individual counts: within sex differences in foraging strategies are as important as sex-specific differences in masked boobies Sula dactylatra. J Avian Biol. 2013;44:531–40.
Walter ST, Leberg PL, Dindo JJ, Karubian JK. Factors influencing Brown Pelican (Pelecanus occidentalis) foraging movement patterns during the breeding season. Can J Zool. 2014;92:885–91.
Luong LT, Penoni LR, Horn CJ, Polak M. Physical and physiological costs of ectoparasitic mites on host flight endurance. Ecol Entomol. 2015;40:518–24.
Clobert J, Le Galliard J-F, Cote J, Meylan S, Massot M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol Lett. 2009;12:197–209.
Bonte D, de la Pena E. Evolution of body condition-dependent dispersal in metapopulations. J Evol Biol. 2009;22:1242–51.
Altwegg R, Ringsby T, Saether B. Phenotypic correlates and consequences of dispersal in a metapopulation of house sparrows Passer domesticus. J Anim Ecol. 2000;69:762–70.
Gyllenberg M, Kisdi E, Utz M. Evolution of condition-dependent dispersal under kin competition. J Math Biol. 2008;57:285–307.
Gyllenberg M, Kisdi E, Utz M. Body condition dependent dispersal in a heterogeneous environment. Theor Popul Biol. 2011;79:139–54.
Kisdi E, Utz M, Gyllenberg M. Evolution of condition-dependent dispersal - Oxford Scholarship. In: Clobert J, Baguette M, Benton T, Bullock J, editors. Dispersal Ecol Evol. Oxford: Oxford University Press; 2013. p. 131–51.
Lena J, Clobert J, de Fraipont M, Lecomte J, Guyot G. The relative influence of density and kinship on dispersal in the common lizard. Behav Ecol. 1998;9:500–7.
Meylan S, Belliure J, Clobert J, de Fraipont M. Stress and body condition as prenatal and postnatal determinants of dispersal in the common lizard (Lacerta vivipara). Horm Behav. 2002;42:319–26.
Barbraud C, Johnson A, Bertault G. Phenotypic correlates of post-fledging dispersal in a population of greater flamingos: the importance of body condition. J Anim Ecol. 2003;72:246–57.
Bonte D, Travis JMJ, De Clercq N, Zwertvaegher I, Lens L. Thermal conditions during juvenile development affect adult dispersal in a spider. Proc Natl Acad Sci USA. 2008;105:17000–5.
Debeffe L, Morellet N, Cargnelutti B, Lourtet B, Bon R, Gaillard J-M, et al. Condition-dependent natal dispersal in a large herbivore: heavier animals show a greater propensity to disperse and travel further. J Anim Ecol. 2012;81:1327-1327.
Saino N, Romano M, Scandolara C, Rubolini D, Ambrosini R, Caprioli M, et al. Brownish, small and lousy barn swallows have greater natal dispersal propensity. Anim Behav. 2014;87:137–46.
Baines CB, McCauley SJ, Rowe L. Dispersal depends on body condition and predation risk in the semi-aquatic insect, Notonecta undulata. Ecol Evol. 2015;5:2307–16.
Moore MP, Whiteman HH. Natal philopatry varies with larval condition in salamanders. Behav Ecol Sociobiol. 2016;70:1247–55.
Vanpe C, Debeffe L, Galan M, Hewison AJM, Gaillard J-M, Gilot-Fromont E, et al. Immune gene variability influences roe deer natal dispersal. OIKOS. 2016;125:1790–801.
Baines CB, McCauley SJ. Natal habitat conditions have carryover effects on dispersal capacity and behavior. Ecosphere. 2018;9.
Mishra A, Tung S, Sruti VRS, Sadiq MA, Srivathsa S, Dey S. Pre-dispersal context and presence of opposite sex modulate density dependence and sex bias of dispersal. OIKOS. 2018;127:1596–604.
Tabadkani SM, Ahsaei SM, Hosseininaveh V, Nozari J. Food stress prompts dispersal behavior in apterous pea aphids: Do activated aphids incur energy loss? Physiol Behav. 2013;110:221–5.
Latty TM, Reid ML. Who goes first? Condition and danger dependent pioneering in a group-living bark beetle (Dendroctonus ponderosae). Behav Ecol Sociobiol. 2010;64:639–46.
Wong JS, Cave AC, Lightle DM, Mahaffee WF, Naranjo SE, Wiman NG, et al. Drosophila suzukii flight performance reduced by starvation but not affected by humidity. J Pest Sci. 2018;91:1269–78.
Ousterhout BH, Semlitsch RD. Effects of conditionally expressed phenotypes and environment on amphibian dispersal in nature. Oikos. 2018;127:1142–1151.
Myles-Gonzalez E, Burness G, Yavno S, Rooke A, Fox MG. To boldly go where no goby has gone before: boldness, dispersal tendency, and metabolism at the invasion front. Behav Ecol. 2015;26:1083–90.
Vitz AC, Rodewald AD. Movements of fledgling ovenbirds (Seiurus Aurocapilla) and worm-eating warblers (Helmitheros Vermivorum) within and beyond the natal home range. AUK. 2010;127:364–71.
Maria d MD, Maria M, Alvarez Silvia J, Eliezer G, Fagan William F, Vincenzo P, et al. The importance of individual variation in the dynamics of animal collective movements. Philos Trans R Soc B: Biol Sci. 2018;373:20170008.
Azpillaga M, Real J, Hernandez-Matias A. Effects of rearing conditions on natal dispersal processes in a long-lived predator bird. Ecol Evol. 2018;8:6682–98.
Hardouin LA, Nevoux M, Robert A, Gimenez O, Lacroix F, Hingrat Y. Determinants and costs of natal dispersal in a lekking species. Oikos. 2012;121:804–12.
Tarwater CE. Influence of phenotypic and social traits on dispersal in a family living, tropical bird. Behav Ecol. 2012;23:1242–9.
Dubois GF, Le Gouar PJ, Delettre YR, Brustel H, Vernon P. Sex-biased and body condition dependent dispersal capacity in the endangered saproxylic beetle Osmoderma eremita (Coleoptera: Cetoniidae). J Insect Conserv. 2010;14:679–87.
Bonte D, De Meester N, Matthysen E. Selective integration advantages when transience is costly: immigration behaviour in an agrobiont spider. Anim Behav [Internet]. 2011;81:837–41.
Mares R, Bateman AW, English S, Clutton-Brock TH, Young AJ. Timing of predispersal prospecting is influenced by environmental, social and state-dependent factors in meerkats. Anim Behav. 2014;88:185–93.
Chapman BB, Brönmark C, Nilsson J-Å, Hansson L-A. The ecology and evolution of partial migration. Oikos. 2011;120:1764–75.
Peiman KS, Birnie-Gauvin K, Midwood JD, Larsen MH, Wilson ADM, Aarestrup K, et al. If and when: intrinsic differences and environmental stressors influence migration in brown trout (Salmo trutta). Oecologia. 2017;184:375–84.
Hegemann A, Fudickar AM, Nilsson J-Å. A physiological perspective on the ecology and evolution of partial migration. J Ornithol. 2019;160:893–905.
Loria DE, Moore FR. Energy demands of migration on red-eyed vireos, Vireo olivaceus. Behav Ecol. 1990;1:24–35.
Ydenberg R, Butler R, Lank D, Guglielmo C, Lemon M, Wolf N. Trade-offs, condition dependence and stopover site selection by migrating sandpipers. J Avian Biol. 2002;33:47–55.
Battley P, Piersma T, Rogers D, Dekinga A, Spaans B, Van Gils J. Do body condition and plumage during fuelling predict northwards departure dates of Great Knots Calidris tenuirostris from north-west Australia? IBIS. 2004;146:46–60.
Lehnen SE, Krementz DG. The Influence of Body Condition on the Stopover Ecology of Least Sandpipers in the Lower Mississippi Alluvial Valley during Fall Migration. AVIAN Conserv Ecol. 2007;2.
Arizaga J, Barba E, Belda EJ. Fuel management and stopover duration of Blackcaps Sylvia atricapilla stopping over in northern Spain during autumn migration period. Bird Stud. 2008;55:124–34.
Seewagen CL, Guglielmo CG. Effects of fat and lean body mass on migratory landbird stopover duration. Wilson J Ornithol. 2010;122:82–7.
Hatch MI, Smith RJ, Owen JC. Arrival timing and hematological parameters in Gray Catbirds (Dumetella carolinensis). J Ornithol. 2010;151:545–52.
Seewagen CL, Slayton EJ, Guglielmo CG. Passerine migrant stopover duration and spatial behaviour at an urban stopover site. Acta Oecologica-Int J Ecol. 2010;36:484–92.
Warnock N, Handel CM, Gill RE Jr, Mccaffery BJ. Residency Times and Patterns of Movement of Postbreeding Dunlin on a Subarctic Staging Area in Alaska. Arctic. 2013;66:407–16.
Smith AD, McWilliams SR. What to do when stopping over: behavioral decisions of a migrating songbird during stopover are dictated by initial change in their body condition and mediated by key environmental conditions. Behav Ecol. 2014;25:1423–35.
Sjoberg S, Alerstam T, Akesson S, Muheim R. Ecological factors influence timing of departures in nocturnally migrating songbirds at Falsterbo, Sweden. Anim Behav. 2017;127:253–69.
Schmaljohann H, Eikenaar C. How do energy stores and changes in these affect departure decisions by migratory birds? A critical view on stopover ecology studies and some future perspectives. J Comp Physiol A-Neuroethol Sensory Neural Behav Physiol. 2017;203:411–29.
Lupi S, Maggini I, Goymann W, Cardinale M, Mora AR, Fusani L. Effects of body condition and food intake on stop-over decisions in Garden Warblers and European Robins during spring migration. J Ornithol. 2017;158:989–99.
Izhaki I, Maitav A. Blackcaps Sylvia atricapilla stopping over at the desert edge; inter- and intra-sexual differences in spring and autumn migration. Ibis. 1998;140:234–43.
Deutschlander ME, Muheim R. Fuel reserves affect migratory orientation of thrushes and sparrows both before and after crossing an ecological barrier near their breeding grounds. J Avian Biol. 2009;40:85–9.
Smolinsky JA, Diehl RH, Radzio TA, Delaney DK, Moore FR. Factors influencing the movement biology of migrant songbirds confronted with an ecological barrier. Behav Ecol Sociobiol. 2013;67:2041–51.
Robertsen G, Armstrong JD, Nislow KH, Herfindal I, McKelvey S, Einum S. Spatial variation in the relationship between performance and metabolic rate in wild juvenile Atlantic salmon. J Anim Ecol. 2014;83:791–9.
Duijns S, Niles LJ, Dey A, Aubry Y, Friis C, Koch S, et al. Body condition explains migratory performance of a long-distance migrant. Proc R Soc B-Biol Sci. 2017;284.
Edeline E, Lambert P, Rigaud C, Elie P. Effects of body condition and water temperature on Anguilla anguilla glass eel migratory behavior. J Exp Marine Biol Ecol. 2006;331:217–25.
Persson L, Kagervall A, Leonardsson K, Royan M, Alanara A. The effect of physiological and environmental conditions on smolt migration in Atlantic salmon Salmo salar. Ecol Freshwater Fish. 2019;28:190–9.
Roff DA, Fairbairn DJ. The Evolution and Genetics of Migration in Insects. BioScience. 2007;57:155–64.
Roff DA. The Evolution of Threshold Traits in Animals. Q Rev Biol. 1996;71:3–35.
Spiegel O, Leu ST, Bull CM, Sih A. What’s your move? Movement as a link between personality and spatial dynamics in animal populations. Ecol Lett. 2017;20:3–18.
Edelman AJ. Sex-specific effects of size and condition on timing of natal dispersal in kangaroo rats. Behav Ecol. 2011;22:776–83.
Williams HJ, Taylor LA, Benhamou S, Bijleveld AI, Clay TA, Grissac S, et al. Optimizing the use of biologgers for movement ecology research. Gaillard J, editor. J Anim Ecol. 2019; 00: 1– 21.
Mortier F, Jacob S, Vandegehuchte ML, Bonte D. Habitat choice stabilizes metapopulation dynamics by enabling ecological specialization. Oikos. 2019;128:529–39.
Creel S, Dantzer B, Goymann W, Rubenstein DR. The ecology of stress: effects of the social environment. Funct Ecol. 2013;27:66–80.
Belthoff J, Dufty A. Corticosterone, body condition and locomotor activity: a model for dispersal in screech-owls. Anim Behav. 1998;55:405–15.
Breuner C, Hahn T. Integrating stress physiology, environmental change, and behavior in free-living sparrows. Horm Behav. 2003;43:115–23.
Cornelius JM, Breuner CW, Hahn TP. Under a neighbour’s influence: public information affects stress hormones and behaviour of a songbird. Proc R Soc B-Biol Sci. 2010;277:2399–404.
Hamann M, Jessop TS, Schauble CS. Fuel use and corticosterone dynamics in hatchling green sea turtles (Chelonia mydas) during natal dispersal. J Exp Marine Biol Ecol. 2007;353:13–21.
Pakkala JJ, Norris DR, Sedinger JS, Newman AEM. Experimental effects of early-life corticosterone on the hypothalamic-pituitary-adrenal axis and pre-migratory behaviour in a wild songbird. Funct Ecol. 2016;30:1149–60.
Meylan S, Miles DB, Clobert J. Hormonally mediated maternal effects, individual strategy and global change. Philos Trans R Soc B-Biol Sci. 2012;367:1647–64.
Vercken E, de Fraipont M, Dufty AM Jr, Clobert J. Mother’s timing and duration of corticosterone exposure modulate offspring size and natal dispersal in the common lizard (Lacerta vivipara). Horm Behav. 2007;51:379–86.
Young AJ, Monfort SL. Stress and the costs of extra-territorial movement in a social carnivore. Biol Lett. 2009;5:439–41.
Marty PR, Hodges K, Heistermann M, Agil M, Engelhardt A. Is social dispersal stressful? A study in male crested macaques (Macaca nigra). Horm Behav. 2017;87:62–8.
Maag N, Cozzi G, Clutton-Brock T, Ozgul A. Density-dependent dispersal strategies in a cooperative breeder. Ecol. 2018;99:1932–41.
Holberton RL. Changes in Patterns of Corticosterone Secretion Concurrent with Migratory Fattening in a Neotropical Migratory Bird. Gen Comp Endocrinol. 1999;116:49–58.
Long JA, Holberton RL. Corticosterone Secretion, Energetic Condition, and a Test of the Migration Modulation Hypothesis in the Hermit Thrush (Catharus guttatus), a Short-Distance Migrant. Auk. 2004;121:1094–102.
Bonier F, Martin PR, Moore IT, Wingfield JC. Do baseline glucocorticoids predict fitness? Trends Ecol Evol. 2009;24:634–42.
Wagner DN, Green DJ, Cooper JM, Love OP, Williams TD. Variation in Plasma Corticosterone in Migratory Songbirds: A Test of the Migration-Modulation Hypothesis. Physiol Bioch Zool. 2014;87:695–703.
Midwood JD, Larsen MH, Aarestrup K, Cooke SJ. Stress and food deprivation: linking physiological state to migration success in a teleost fish. J Exp Biol. 2016;219:3712–8.
Falsone K, Jenni-Eiermann S, Jenni L. Corticosterone in migrating songbirds during endurance flight. Horm Behav. 2009;56:548–56.
Nilsson ALK, Sandell MI. Stress hormone dynamics: an adaptation to migration? Biol Lett. 2009;5:480–3.
Eikenaar C, Klinner T, Stoewe M. Corticosterone predicts nocturnal restlessness in a long-distance migrant. Horm Behav. 2014;66:324–9.
Eikenaar C, Mueller F, Rueppel G, Stoewe M. Endocrine regulation of migratory departure from stopover: Evidence from a longitudinal migratory restlessness study on northern wheatears. Horm Behav. 2018;99:9–13.
Henshaw I, Fransson T, Jakobsson S, Jenni-Eiermann S, Kullberg C. Information from the geomagnetic field triggers a reduced adrenocortical response in a migratory bird. J Exp Biol. 2009;212:2902–7.
Loshchagina J, Tsvey A, Naidenko S. Baseline and stress-induced corticosterone levels are higher during spring than autumn migration in European robins. Horm Behav. 2018;98:96–102.
Holberton R, Able K. Differential migration and an endocrine response to stress in wintering dark-eyed juncos (Junco hyemalis). Proc R Soc B-Biol Sci. 2000;267:1889–96.
Goymann W, Lupi S, Kaiya H, Cardinale M, Fusani L. Ghrelin affects stopover decisions and food intake in a long-distance migrant. Proc Natl Acad Sci U S A. 2017;114:1946–51.
Fusani L, Coccon F, Rojas Mora A, Goymann W. Melatonin reduces migratory restlessness in Sylvia warblers during autumnal migration. Front Zool. 2013;10:79.
Higgins SC, Gueorguiev M, Korbonits M. Ghrelin, the peripheral hunger hormone. Ann Med. 2007;39:116–36.
Lorenz MW, Gäde G. Hormonal regulation of energy metabolism in insects as a driving force for performance. Integr Comp Biol. 2009;49:380–92.
Orchard I, Ramirez JM, Lange AB. A Multifunctional Role for Octopamine in Locust Flight. Annu Rev Entomol. 1993;38:227–49.
Adams ME. Chapter 146 - Juvenile Hormones. In: Resh VH, Cardé RT, editors. Encyclopedia of Insects. 2nd ed. San Diego: Academic Press; 2009. p. 541–6.
Dingle HH. Migration: The Biology of Life on the Move. 2nd ed. Oxford and New York: Oxfored University press; 2014.
Coats SA, Mutchmor JA, Tollefson JJ. Regulation of Migratory Flight by Juvenile Hormone Mimic and Inhibitor in the Western Corn Rootworm (Coleoptera: Chrysomelidae). Ann Entomol Soc Am. 1987;80:697–708.
Rankin MA, Riddiford LM. Significance of haemolymph juvenile hormone titer changes in timing of migration and reproduction in adult Oncopeltus fasciatus. J Insect Physiol. 1978;24:31–8.
Herman WS, Tatar M. Juvenile hormone regulation of longevity in the migratory monarch butterfly. Proc Biol Sci. 2001;268:2509–14.
Watt WB. Adaptation at specific loci. I. natural selection on phosphoglucose isomerase of colias butterflies: biochemical and population aspects. Genetics. 1977;87:177.
Watt WB, Wheat CW, Meyer EH, Martin J-F. Adaptation at specific loci. VII. Natural selection, dispersal and the diversity of molecular-functional variation patterns among butterfly species complexes (Colias: Lepidoptera, Pieridae). Mol Ecol. 2003;12:1265–75.
Wheat CW, Watt WB, Pollock DD, Schulte PM. From DNA to Fitness Differences: Sequences and Structures of Adaptive Variants of Colias Phosphoglucose Isomerase (PGI). Mol Biol Evol. 2006;23:499–512.
Wang B, Mason DePasse J, Watt WB. Evolutionary Genomics of Colias Phosphoglucose Isomerase (PGI) Introns. J Mol Evol. 2012;74:96–111.
Watt WB. Mechanistic Studies of Butterfly Adaptations. Butterflies: ecology and evolution taking flight. Chicago: The University of Chicago Press; 2003. p. 756.
Niitepõld K, Saastamoinen M. A Candidate Gene in an Ecological Model Species: Phosphoglucose Isomerase ( Pgi ) in the Glanville Fritillary Butterfly ( Melitaea cinxia ). Ann Zool Fennici. 2017;54:259–73.
Dahlhoff EP, Rank NE. Functional and physiological consequences of genetic variation at phosphoglucose isomerase: Heat shock protein expression is related to enzyme genotype in a montane beetle. Proc Natl Acad Sci. 2000;97:10056–61.
Wheat CW, Hill J. Pgi: the ongoing saga of a candidate gene. Curr Opin Insect Sci. 2014;4:42–7.
Saastamoinen M, Bocedi G, Cote J, Legrand D, Guillaume F, Wheat CW, et al. Genetics of dispersal: Genetic of dispersal. Biol Rev. 2018;93:574–99.
Wheat CW: Dispersal Genetics: Emerging Insights from Fruitflies, Butterflies and Beyond. In Dispersal Ecology and Evolution. Edited by Clobert J, Baguette M, Benton TG, Bullock JM. Oxford: Oxford University Press; 2012:95–107.
Niitepold K, Mattila ALK, Harrison PJ, Hanski I. Flight metabolic rate has contrasting effects on dispersal in the two sexes of the Glanville fritillary butterfly. Oecologia. 2011;165:847–54.
Hanski I, Schulz T, Wong SC, Ahola V, Ruokolainen A, Ojanen SP. Ecological and genetic basis of metapopulation persistence of the Glanville fritillary butterfly in fragmented landscapes. Nat Commun. 2017;8:14504.
Hanski I, Saccheri I. Molecular-Level Variation Affects Population Growth in a Butterfly Metapopulation. Barton N, editor. PLoS Biol. 2006;4:e129.
Orsini L, Wheat CW, Haag CR, Kvist J, Frilander MJ, Hanski I. Fitness differences associated with Pgi SNP genotypes in the Glanville fritillary butterfly (Melitaea cinxia): Reduced fitness in a butterfly metapopulation. J Evol Biol. 2009;22:367–75.
Marden JH. Nature’s inordinate fondness for metabolic enzymes: why metabolic enzyme loci are so frequently targets of selection. Mol Ecol. 2013;22:5743–64.
de Belle JS, Hilliker AJ, Sokolowski MB. Genetic localization of foraging (for): a major gene for larval behavior in Drosophila melanogaster. Genetics. 1989;123:157–63.
Sokolowski MB. Foraging strategies ofDrosophila melanogaster: A chromosomal analysis. Behav Genet. 1980;10:291–302.
Pereira HS, Sokolowski MB. Mutations in the larval foraging gene affect adult locomotory behavior after feeding in Drosophila melanogaster. Proc Natl Acad Sci. 1993;90:5044–6.
Allen AM, Anreiter I, Neville MC, Sokolowski MB. Feeding-Related Traits Are Affected by Dosage of the foraging Gene in Drosophila melanogaster. Genetics. 2017;205:761–73.
Kent CF, Daskalchuk T, Cook L, Sokolowski MB, Greenspan RJ. The Drosophila foraging gene mediates adult plasticity and gene-environment interactions in behaviour, metabolites, and gene expression in response to food deprivation. PLoS Genet. 2009;5:1–13.
Anreiter I, Kramer JM, Sokolowski MB. Epigenetic mechanisms modulate differences in Drosophila foraging behavior. Proc Natl Acad Sci USA. 2017;114:12518–23.
Osborne KA, Robichon A, Burgess E, Butland S, Shaw RA, Coulthard A, et al. Natural behavior polymorphism due to a cGMP-dependent protein kinase of Drosophila. Science. 1997;277:834–6.
Fujiwara M, Sengupta P, McIntire SL. Regulation of Body Size and Behavioral State of C. elegans by Sensory Perception and the EGL-4 cGMP-Dependent Protein Kinase. Neuron. 2002;36:1091–102.
Ingram KK, Oefner P, Gordon DM. Task-specific expression of the foraging gene in harvester ants. Mol Ecol. 2005;14:813–8.
Lucas C, Sokolowski MB. Molecular basis for changes in behavioral state in ant social behaviors. Proc Natl Acad Sci USA. 2009;106:6351–6.
Ben-Shahar Y. The foraging gene, behavioral plasticity, and honeybee division of labor. J Comp Physiol A. 2005;191:987–94.
Ben-Shahar Y, Robichon A, Sokolowski MB, Robinson GE. Influence of Gene Action Across Different Time Scales on Behavior. Science. 2002;296:741–4.
Merlin C, Liedvogel M. The genetics and epigenetics of animal migration and orientation: birds, butterflies and beyond. J Exp Biol. 2019;222:jeb191890.
O’Malley KG, Ford MJ, Hard JJ. Clock polymorphism in Pacific salmon: evidence for variable selection along a latitudinal gradient. Proc Biol Sci. 2010;277:3703–14.
Zhu H, Casselman A, Reppert SM. Chasing Migration Genes: A Brain Expressed Sequence Tag Resource for Summer and Migratory Monarch Butterflies (Danaus plexippus). PLoS One. 2008;3.
Mueller JC, Pulido F, Kempenaers B. Identification of a gene associated with avian migratory behaviour. Proc Biol Sci. 2011;278:2848–56.
Mettler R, Segelbacher G, Schaefer HM. Interactions between a Candidate Gene for Migration (ADCYAP1), Morphology and Sex Predict Spring Arrival in Blackcap Populations. PLoS ONE. 2015;10:e0144587.
Scotton C, Bovolenta M, Schwartz E, Falzarano MS, Martoni E, Passarelli C, et al. Deep RNA profiling identified CLOCK and molecular clock genes as pathophysiological signatures in collagen VI myopathy. J Cell Sci. 2016;129:1671–84.
Zhan S, Zhang W, Niitepõld K, Hsu J, Haeger JF, Zalucki MP, et al. The genetics of monarch butterfly migration and warning colouration. Nature. 2014;514:317–21.
Somervuo P, Kvist J, Ikonen S, Auvinen P, Paulin L, Koskinen P, et al. Transcriptome Analysis Reveals Signature of Adaptation to Landscape Fragmentation. Salzburger W, editor. PLoS ONE. 2014;9:e101467.
Jones CM, Papanicolaou A, Mironidis GK, Vontas J, Yang Y, Lim KS, et al. Genomewide transcriptional signatures of migratory flight activity in a globally invasive insect pest. Mol Ecol. 2015;24:4901–11.
Grewal SS. Insulin/TOR signaling in growth and homeostasis: A view from the fly world. Int J Biochem Cell Biol. 2009;41:1006–10.
Guo S, Zhao Z, Liu L, Li Z, Shen J. Comparative Transcriptome Analyses Uncover Key Candidate Genes Mediating Flight Capacity in Bactrocera dorsalis (Hendel) and Bactrocera correcta (Bezzi) (Diptera: Tephritidae). Int J Mol Sci. 2018;19,396.
Franchini P, Irisarri I, Fudickar A, Schmidt A, Meyer A, Wikelski M, et al. Animal tracking meets migration genomics: transcriptomic analysis of a partially migratory bird species. Mol Ecol. 2017;26:3204–16.
Armenta TC, Cole SW, Geschwind DH, Blumstein DT, Wayne RK. Gene expression shifts in yellow-bellied marmots prior to natal dispersal. Behav Ecol. 2019;30:267–77.
Valcu C-M, Kempenaers B. Proteomics in behavioral ecology. Behav Ecol. 2015;26:1–15.
Petegem KHPV, Renault D, Stoks R, Bonte D. Metabolic adaptations in a range-expanding arthropod. Ecol Evol. 2016;6:6556–64.
Dahirel M, Masier S, Renault D, Bonte D. The distinct phenotypic signatures of dispersal and stress in an arthropod model: from physiology to life history. Journal of Experimental Biology. 2019;222.
Tung S, Mishra A, Gogna N, Aamir Sadiq M, Shreenidhi PM, Shree Sruti VR, et al. Evolution of dispersal syndrome and its corresponding metabolomic changes. Evolution. 2018;72:1890–903.
Tao J, Ma Y-C, Yang Z-S, Zou C-G, Zhang K-Q. Octopamine connects nutrient cues to lipid metabolism upon nutrient deprivation. Sci Adv. 2016;2:e1501372.
Selcho M, Pauls D. Linking physiological processes and feeding behaviors by octopamine. Curr Opin Insect Sci. 2019;36:125–30.
Li Y, Hoffmann J, Li Y, Stephano F, Bruchhaus I, Fink C, et al. Octopamine controls starvation resistance, life span and metabolic traits in Drosophila. Sci Rep. 2016;6:35359.
Arrese EL, Soulages JL. Insect Fat Body: Energy, Metabolism, and Regulation. Annu Rev Entomol. 2010;55:207–25.
Sun D, Guo Z, Liu Y, Zhang Y. Progress and Prospects of CRISPR/Cas Systems in Insects and Other Arthropods. Front Physiol. 2017;8:608.
Phelps MP, Seeb LW, Seeb JE. Transforming ecology and conservation biology through genome editing. Conserv Biol. 0.
Markert MJ, Zhang Y, Enuameh MS, Reppert SM, Wolfe SA, Merlin C. Genomic Access to Monarch Migration Using TALEN and CRISPR/Cas9-Mediated Targeted Mutagenesis. G3. 2016;6:905–15.
Andersson N, Piha M, Meller K, Valimaki K, Lehikoinen A. Variation in body condition of songbirds during breeding season in relation to sex, migration strategy and weather. Ornis Fennica. 2018;95:70–81.
Stevens VMVM, Whitmee S, Le Galliard J-F, Clobert J, Böhning-Gaese K, Bonte D, et al. A comparative analysis of dispersal syndromes in terrestrial and semi-terrestrial animals. Ecol Lett. 2014;17:1039–52.
Hillaert J, Hovestadt T, Vandegehuchte ML, Bonte D. Size-dependent movement explains why bigger is better in fragmented landscapes. Ecol Evol. 2018;8:10754–67.
Tennessen JB, Parks SE, Langkilde T. Traffic noise causes physiological stress and impairs breeding migration behaviour in frogs. Conserv Physiol. 2014;2.
Martin AE, Jorgensen D, Gates CC. Costs and benefits of straight versus tortuous migration paths for Prairie Rattlesnakes (Crotalus viridis viridis) in seminatural and human-dominated landscapes. Can J Zool. 2017;95:921–8.
Hanski I, Mononen T. Eco-evolutionary dynamics of dispersal in spatially heterogeneous environments. Ecol Lett. 2011;14:1025–34.
Hanski I. Eco-evolutionary dynamics in a changing world. Ann N Y Acad Sci. 2012;1249:1–17.
DiLeo MF, Husby A, Saastamoinen M. Landscape permeability and individual variation in a dispersal-linked gene jointly determine genetic structure in the Glanville fritillary butterfly. Evol Lett. 2018;2:544–56.
Kahilainen A, van Nouhuys S, Schulz T, Saastamoinen M. Metapopulation dynamics in a changing climate: Increasing spatial synchrony in weather conditions drives metapopulation synchrony of a butterfly inhabiting a fragmented landscape. Glob Change Biol. 2018;24:4316–29.
Lowe WH, Likens GE, Cosentino BJ. Self-organisation in streams: the relationship between movement behaviour and body condition in a headwater salamander. Freshwater Biology. 2006;51:2052–62.
Zwolak R. How intraspecific variation in seed-dispersing animals matters for plants. Biol Rev. 2018;93:897–913.
Lopes PC. Why are behavioral and immune traits linked? Horm Behav. 2017;88:52–9.
Johnson KV-A, Foster KR. Why does the microbiome affect behaviour? Nat Rev Microbiol. 2018;16:647–55.
Goodacre SL, Martin OY, Bonte D, Hutchings L, Woolley C, Ibrahim K, et al. Microbial modification of host long-distance dispersal capacity. BMC Biol. 2009;7:32.
Gaitan J, Millien V. Stress level, parasite load, and movement pattern in a small-mammal reservoir host for Lyme disease. Can J Zool. 2016;94:565–73.
Rozen-Rechels D, van Beest FM, Richard E, Uzal A, Medill SA, McLoughlin PD. Density-dependent, central-place foraging in a grazing herbivore: competition and tradeoffs in time allocation near water. Oikos. 2015;124:1142–50.
Cooke SJ, Sack L, Franklin CE, Farrell AP, Beardall J, Wikelski M, et al. What is conservation physiology? Perspectives on an increasingly integrated and essential science. Conserv Physiol. 2013;1.
Lennox RJ, Chapman JM, Souliere CM, Tudorache C, Wikelski M, Metcalfe JD, et al. Conservation physiology of animal migration. Conserv Physiol. 2016;4.
Travis JMJ, Delgado M, Bocedi G, Baguette M, Bartoń K, Bonte D, et al. Dispersal and species’ responses to climate change. Oikos. 2013;122:1532–40.
Renault D, Laparie M, McCauley SJ, Bonte D. Environmental Adaptations, Ecological Filtering, and Dispersal Central to Insect Invasions. In: Berenbaum MR, editor. Annual Review Of Entomology, vol. 63; 2018. p. 345–68.
Richardson MF, Sequeira F, Selechnik D, Carneiro M, Vallinoto M, Reid JG, et al. Improving amphibian genomic resources: a multitissue reference transcriptome of an iconic invader. Gigascience. 2018;7.
Kvist J, Mattila ALK, Somervuo P, Ahola V, Koskinen P, Paulin L, et al. Flight-induced changes in gene expression in the Glanville fritillary butterfly. Mol Ecol. 2015;24:4886–900.
Jax E, Wink M, Kraus RHS. Avian transcriptomics: opportunities and challenges. J Ornithol. 2018;159:599–629.
Breschi A, Gingeras TR, Guigó R. Comparative transcriptomics in human and mouse. Nat Rev Genet. 2017;18:425–40.
Roux J, Rosikiewicz M, Robinson-Rechavi M. What to compare and how: Comparative transcriptomics for Evo-Devo: COMPARATIVE TRANSCRIPTOMICS FOR Evo-Devo. J Exp Zool (Mol Dev Evol). 2015;324:372–82.
Crow M, Lim N, Ballouz S, Pavlidis P, Gillis J. Predictability of human differential gene expression. Proc Natl Acad Sci USA. 2019;116:6491–500.
Marti A F i, Dodd RS. Using CRISPR as a Gene Editing Tool for Validating Adaptive Gene Function in Tree Landscape Genomics. Front Ecol Evol. 2018;6.
Madliger CL, Love OP, Hultine KR, Cooke SJ. The conservation physiology toolbox: status and opportunities. Conserv Physiol. 2018;6.
Govaert L, Fronhofer EA, Lion S, Eizaguirre C, Bonte D, Egas M, et al. Eco-evolutionary feedbacks-Theoretical models and perspectives. Funct Ecol. 2019;33:13–30.
Barnes AD, Spey I-K, Rohde L, Brose U, Dell AI. Individual behaviour mediates effects of warming on movement across a fragmented landscape. Funct Ecol. 2015;29:1543–52.
Kalinkat G, Jochum M, Brose U, Dell AI. Body size and the behavioral ecology of insects: Linking individuals to ecological communities. Curr Opin Insect Sci. 2015;9:24–30.
Hillaert J, Vandegehuchte ML, Hovestadt T, Bonte D. Information use during movement regulates how fragmentation and loss of habitat affect body size. Proc Royal Soc B-Biol Sci. 2018;285.
Hirt MR, Grimm V, Li Y, Rall BC, Rosenbaum B, Brose U. Bridging Scales: Allometric Random Walks Link Movement and Biodiversity Research. Trends Ecol Evol. 2018;33:701–12.
Nisbet RM, Jusup M, Klanjscek T, Pecquerie L. Integrating dynamic energy budget (DEB) theory with traditional bioenergetic models. J Exp Biol. 2012;215:892–902.
Smallegange IM, Caswell H, Toorians MEM, de RAM. Mechanistic description of population dynamics using dynamic energy budget theory incorporated into integral projection models. Methods Ecol Evol. 2017;8:146–54.
Campos-Candela A, Palmer M, Balle S, Álvarez A, Alós J. A mechanistic theory of personality-dependent movement behaviour based on dynamic energy budgets. Ecol Lett. 2019;22:213–32.
Urban MC, Bocedi G, Hendry AP, Mihoub J-B, Pe’er G, Singer A, et al. Improving the forecast for biodiversity under climate change. Science. 2016;353:aad8466.
Delgado MM, Bartoń KA, Bonte D, Travis JMJ. Prospecting and dispersal: their eco-evolutionary dynamics and implications for population patterns. Proc R Soc B: Biol Sci. 2014;281:20132851.
Hillaert J, Vandegehuchte ML, Hovestadt T, Bonte D. Habitat loss and fragmentation increase realized predator-prey body size ratios. Funct Ecol. 0.
Budaev S, Jørgensen C, Mangel M, Eliassen S, Giske J. Decision-Making From the Animal Perspective: Bridging Ecology and Subjective Cognition. Front Ecol Evol. 2019;7:164.
Gurarie E, Bracis C, Delgado M, Meckley TD, Kojola I, Wagner CM. What is the animal doing? Tools for exploring behavioural structure in animal movements. J Anim Ecol. 2016;85:69–84.
Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, et al. Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology. Science. 2005;310:987–91.
S.G., D.B., T.V.L. were supported by the Research Foundation - Flanders (FWO) project INVADED G018017N. N.W. was supported by a postdoctoral fellowship (12T9818N).
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Goossens, S., Wybouw, N., Van Leeuwen, T. et al. The physiology of movement. Mov Ecol 8, 5 (2020). https://doi.org/10.1186/s40462-020-0192-2
- Body condition
- Eco-physiological nexus