Animals adapt their movement for fitness-seeking activities like foraging, mating, and escaping predators that transpire on fine time and space scales (Fig. 1, section 1). Randomly walking (RW) animals, for example, can increase foraging success under uncertainty (Duffy 2011), but underperform in changing habitats where more advanced Correlated Random Walk (CRW) strategies may dominate (Morales et al. 2004; Duffy 2011). However, assuming CRW can be naïve, especially with complex vertebrate behaviour. Theory can help define movement strategy across taxa, such as classic foraging theory (Optimal Foraging Theory [OFT]; (Pyke et al. 1977; Charnov 1976; Wajnberg et al. 2013)) that argues animals uniformly optimise their space use in response to resources. However, it neglects key behaviour, such as movement capacity and habitat-dependent movement, and movement drivers, such as energy use and predator presence. Recent trait-based approaches, such as IBMs, can integrate these key behaviours and drivers and thus represent more ecologically real scenarios, shifting the OFT paradigm to the ecological stone age (Börger et al. 2008; Holyoak et al. 2008; Mueller & Fagan 2008; Giuggioli & Bartumeus 2010; Bauer & Klaassen 2013; Fagan et al. 2013).

Fronhofer, Hovestadt & Poethke (Fronhofer et al. 2013) claim small-scale movement ought to be based on behaviour and physiology of the individual, not random. Foraging IBMs often involve multiple, interacting traits, i.e. turning angle relative to resources, habitat type (Buchmann et al. 2011; Earl & Zollner 2014; Kanagaraj et al. 2013), or predators in space (Morales et al. 2004; Duffy 2011; Kranstauber et al. 2012; Bode & Delcourt 2013; Wajnberg et al. 2013; Wilson et al. 2013; Latombe et al. 2014), step length (Leising 2001; Morales et al. 2004; Kranstauber et al. 2012), and behavioural states or rules (Railsback et al. 1999; Morales et al. 2004; Giske et al. 2013; Pauli et al. 2013; Ringelman 2014), as well as physiology, i.e. biomass (Roese et al. 1991; Buchmann et al. 2011) and energy reserves, including metabolism and gut capacity (Roese et al. 1991; Guensch et al. 2001; Peck & Daewel 2007; Giske et al. 2013; Pauli et al. 2013; Wood et al. 2013) that together define the movement process. These direct links between individual and environment suggest, for animals, the environment becomes a sandbox for combined traits to persist, dominate, or adapt (Roese et al. 1991; Railsback et al. 1999; Guensch et al. 2001; Mueller et al. 2011; Pauli et al. 2013; Shepard et al. 2013; Wood et al. 2013; Ringelman 2014). As a consequence, we can assess movement under different conditions, such as patchy landscapes and within populations, using a common framework, particularly for conservation (Fahrig 2003). Estimating foraging success in space also involves multiple, cooperating behaviours like individual responses to food and group members (Giske et al. 2013; Bonnell et al. 2013), resource attractiveness (Dumont & Hill 2004; Bode & Delcourt 2013; Kułakowska et al. 2014; Earl & Zollner 2014), and individual to group movement (Dumont & Hill 2004; Couzin et al. 2005; Bonnell et al. 2013). Spatial IBMs also help bridge the gap between model and real movement patterns (Guensch et al. 2001; Bonnell et al. 2013; Mooij & DeAngelis 2003) under environmental change (Roese et al. 1991; Börger et al. 2011) and highlight traits often overlooked in classic foraging models, such as handling time and food size (Roese et al. 1991; Guensch et al. 2001; Pauli et al. 2013).

Process-based approaches, such as foraging IBMs, ought to emphasise not only movement detail at the immediate scale of survival (Roese et al. 1991), but also evolutionary processes such as fitness-seeking (Giske et al. 2013; Ayllón et al. 2016), including risky decision-making (Railsback & Harvey 2002; Laundré et al. 2014). Animals trade off activities like searching and sheltering against energy reserves, where wrong decisions inherently cost energy, or life; however, the movement trade-off against immediate mortality from a predator must consider future risks such as starvation (risk allocation hypothesis; (Lima et al. 1999)). Thus, physiology and behaviour adapted to mediate energy costs of maintenance, growth, and fitness, i.e. reproduction, may impose further, more vital constraints than simply optimising short-term foraging gain, emphasising how the individual energy budget can be pragmatic in predicting fitness outcomes from movement behaviour.

Despite their usefulness as foraging models, IBMs are surprisingly unfamiliar with the core underlying principle of feeding and movement, i.e. energetics. Movement capacity is often imposed, e.g. through a maximum number of steps, but not emerging directly from energetic principles (see Table 1 for overview). These limitations probably exist because energetics models can be species specific, data hungry, and parameter heavy (Sibly et al. 2013). However, incorporating energy reserves allow models to conveniently assess basic movement costs sensitive to behaviour (Roese et al. 1991; Guensch et al. 2001; Pauli et al. 2013; Bonnell et al. 2013). For example, combinations of turning angle and step length (Wilson et al. 2013), as well as movement speed (Shepard & Lambertucci 2013; Wilson et al. 2015), can generate diverse movement patterns that accumulate different costs depending on the scale of movement. Movement speed can also include additional detail, such as step frequency and length, when estimating movement costs. Miwa et al. (Miwa et al. 2015) suggest speed shares a stronger relationship with heart rate than step length alone as a proxy for energy costs (overall dynamic body acceleration, (Gleiss et al. 2011)); however, this method still only indirectly approximates energy costs from this relationship using previous data (Miwa et al. 2015). Animals trade-off energy costs against their physiology, motor control, and manoeuvre capabilities, where speed choice can play a key role when negotiating the movement task at hand, e.g. foraging versus escaping predators, and the current environmental conditions, e.g. open habitat versus proximity to refuge, which ultimately determines fitness (Wilson et al. 2015).

Food size, bite size, and intake rate are also key, but often-overlooked, energetics traits influencing movement (Roese et al. 1991; Guensch et al. 2001; Peck & Daewel 2007; Pauli et al. 2013). These detailed measures help estimate important functions such as feeding rates and digestive capacity (Peck & Daewel 2007; Giske et al. 2013) for growing animals (Roese et al. 1991; Peck & Daewel 2007) that in turn contribute to different activity states (Pauli et al. 2013; Louzao et al. 2014) and the overall time budget (Bergman, Carita et al. 2001; Merkle et al. 2014). Ultimately, this detail helps reveal functional movement limits via the ways animals adapt to changing habitat quality (Guensch et al. 2001; Pauli et al. 2013), different temperatures (Peck & Daewel 2007; Ayllón et al. 2016), or to paths of least physical resistance (Shepard et al. 2013; Wilson et al. 2012), as shown for soaring birds and costs of high-altitude migration (Hawkes et al. 2013; Sachs et al. 2013).

Energetics IBMs suggest animals use energy to connect habitat choice with survival probability under different risks in space. Guensch et al. (2001) show stream fish prefer habitats of higher energy gain and low movement costs. Railsback et al. (1999) use body condition to show stream fish trade off energy intake against habitat type and choose patches of low energy gain that maximise survival to escape fast-flowing water currents, while Pauli et al. (2013) use energetics to capture individual growth changes in response to changing resources and habitat type, and different predation and starvation risks. Energy reserves are a useful reference for animals to assess trade-offs in behaviour, such as negotiating predator and foraging risks within a landscape of fear vs. food (Laundré et al. 2014) or complementing perception ranges when resource density changes (Zollner & Lima 2005). As spatial models, energetics IBMs connect individuals with habitat and social information by using energy as a currency against direct risks, such as predators (Railsback et al. 1999; Giske et al. 2013; Pauli et al. 2013) and competing for food (Bode & Delcourt 2013; Bonnell et al. 2013). Energetics models also connect movement with time. Intake rate and bite size are intrinsically linked, suggesting handling time budgets help animals trade off energy costs and gains (Guensch et al. 2001) and shape foraging success (Roese et al. 1991). Energy drives metabolic functions on longer time scales like starvation (Railsback et al. 1999) and reproduction (Railsback et al. 1999; Guensch et al. 2001), as well as intake and growth rates (Guensch et al. 2001) as they vary under temperature and prey constraints (Peck & Daewel 2007). These trade-offs become amenable on longer time scales by affecting size classes within populations, thus influencing evolutionary adaptation to future environmental change (Ayllón et al. 2016). Models, such as IBMs, capable of capturing these vital interacting processes are thus useful frameworks using general mechanisms like energetics to link local actions, such as how animals absorb information and assess risk, with evolutionary ones, such as balancing fitness outcomes.

Energetics IBMs teach us foraging is a complex relationship between costs and gains sensitive to the scale of movement, ranging from basic step length and turning angles (Wilson et al. 2013) to complex social behaviour (Bode & Delcourt 2013; Bonnell et al. 2013). Wilson et al. (Wilson et al. 2013) measured turn costs as individual energy output depending on turn angle across distance travelled, which increases at finer scales; models assuming the environment absorb these costs thus should be treated with caution. By building from the individual level, IBMs help us understand movement constraints in space and time with changing environments (Giske et al. 2013; Pauli et al. 2013) and body size (Roese et al. 1991; Railsback et al. 1999; Peck & Daewel 2007; Fiksen & Carlotti 1998), identify the subtle interface between resource availability and movement in space (Earl & Zollner 2014; Bode & Delcourt 2013), and generate sensible outcomes at the population level (Roese et al. 1991; Ayllón et al. 2016). Individual rates of food intake provide clues on how behaviour reflects energy gain as resources vary across space (Bode & Delcourt 2013). Similarly, interpreting energy gain from physiology, such as stomach capacity, connects individual traits directly to how animals perceive individual risk and thus modify movement decisions (Giske et al. 2013). The type of system also determines how to appropriately interpret energy exchange between individuals and the environment, where energy uptake is either from surfaces or volumes in terrestrial versus aquatic habitats. That is, calculating metabolic rates becomes more uniform in oceans because prey is measured in biomass. This argument also suggests energetics models are sensitive to species and scale; here, static parameters, such as maximum feeding rate, may underestimate intake rates as prey density varies in space and time (Peck & Daewel 2007). The stability of an ecosystem depends on its individual parts, so connecting crucial individual behaviour like foraging to movement should translate across scales, particularly in space (Hosseini 2006). Behaviour and ecology at the population and ecosystem levels thus relies on real estimates of cues driving individual behavior (Buchmann et al. 2011; Buchmann et al. 2012; Buchmann et al. 2013; Earl & Zollner 2014), where basic and general individual mechanisms like energy use are useful and translate to appropriate scales.

Models concerned with predicting only single outcomes, such as growth, may suffer by ignoring cooperating constraints to movement in space, such as trade-offs to predation (Railsback et al. 1999; Fiksen & Carlotti 1998) and risk-taking in landscapes of food vs. fear (Giske et al. 2013; Laundré et al. 2014). Energetics models should also consider metabolic processes other than simply energy intake (Louzao et al. 2014), such as maintenance costs (Kooijman 2010; Sibly et al. 2013) and the consequences of metabolism over longer time scales related to fitness consequences (Ayllón et al. 2016; Malishev et al. in review). Incorporating energetics in IBMs at small scales creates opportunities to connect movement with time, specifically the activity budget of animals, such as food handling rates, as well as providing a mechanism directly linked to survival probability from which animals make their activity and habitat choices. Individual traits like physiology link time budgets directly to energy intake on fine movement scales, which translates simply to behaviour. Despite this straightforward relationship, movement models at this scale commonly make energetics subjective, probably because they are easier to develop and more convenient (Holyoak et al. 2008). Advanced technology, such as GPS units with accelerometers, can simplify how we estimate energy use for different behaviours when collecting data from the field (Gleiss et al. 2011; Louzao et al. 2014; Nams 2014; Wilson et al. 2014; Miwa et al. 2015; Scharf et al. 2016). Using metabolic rules from energetics theory to formulate the individual energy budget provides a general approach to drive movement decisions and estimate movement costs, thus filling in the gaps from field data estimates of energy use as well as escaping the demands and limitations of formulating species-specific models (Grimm et al. 2005; Louzao et al. 2014). Packaging a theory-driven energy budget into an individual-based model (Malishev et al. in review) thus supplies an underlying, unifying mechanism to individual movement across taxa and scales within a general movement framework.

Memory controls the ability to revisit previous patches (Nabe-Nielsen et al. 2013) to evolve movement strategies and yield more spatially aware animals for memorising previous gains or predicting new ones (Fagan et al. 2013). Movement may involve different types of memory, i.e. for remembering sites (reference memory) and assessing current gain from previous experience (working memory) (Van Moorter et al. 2009) to generate a localised understanding of habitats (Avgar et al. 2013; Fronhofer et al. 2013), so exploiting this ability is likely to expedite movement and probably infers selective advantages. Memory can also help predict seasonally-driven food availability across larger time and space scales, e.g. how mobile prey arranges itself in space may come from earlier decisions to settle or nest (Ringelman 2014). As food changes across scales, remembering previous food sites can outperform random foraging (Nabe-Nielsen et al. 2013); animals combining memory with habitat and social information (Huse et al. 2002; Couzin et al. 2005; Bonnell et al. 2013; Latombe et al. 2014), e.g. seabirds using fellows to find resources (Grünbaum & Veit 2003), use less space and improve foraging gain (Merkle et al. 2014), suggesting memory also depends on scale.

Memory can build more efficient movement paths, improving the ability to exploit landscapes as food becomes more predictable in space and time (Mueller et al. 2011; Avgar et al. 2013). However, remembering food location is more sustainable (and potentially improves fitness) with better quality food. Bison, for example, are better at remembering food location than quality (Merkle et al. 2014), but better food is a stronger incentive than location to re-visit profitable patches. For central place foragers or migrants, memory can become less effective in disturbed environments, i.e. when barriers are introduced (Mueller et al. 2011). Selection may then favour memory when high quality habitat translates simply to a location or under more stable environments. Therefore, cognitive-based IBMs sensitive to scale, spatially informed, and responsive to environmental change appear to be most sensible for predicting individual movement.

Memory and energy reserves can concurrently drive movement, but both vary among individuals in how they define the movement process, suggesting other individual traits, such as movement strategy, may dominate. For example, predators modelled with a correlated random walk algorithm are more efficient at finding random prey and can exploit spatial memory to improve foraging success when prey is clustered (Ringelman 2014); however, being spatially aware can be less efficient when resources are random (Fronhofer et al. 2013). This relationship also tells us the benefits of memory vary with how food is distributed in space. In changing environments, the turnover of remembering experiences should be rapid, as long-term memory can diminish the probability of gathering future, valuable information (Eliassen et al. 2009). The expected energy intake of animals over time can depend heavily on previous foraging experience to locate larger and closer food patches (Merkle et al. 2014). However, animals relying on estimates of future food yield from current working knowledge would probably benefit by combining traits and behaviours, such as assessing current energy reserves prior to revisiting food sites, particularly when food changes in time (Kułakowska et al. 2014) and against predator risk (Eliassen et al. 2007).

Memory allows animals to be more spatially informed about resources without assuming food levels in their habitat (Nabe-Nielsen et al. 2013). However, can animals really know the yield of future, unpredictable food before departing? The ability to make prescient movement decisions based on internal, limiting cues, such as energy reserves, offers a useful mechanism for movement shared among taxa. We argue individual-based movement models tackling complex cognitive processes become more useful when standardized by a general, shared trait like energy use to improve predictive power under multiple behaviours. Cognitive-based models also need to forecast more than one movement step into the future, consider the value of long-term memory when information on habitat quality is low, especially over large spatial changes (Eliassen et al. 2009), and, for realism, incorporate restricted and imprecise memory (Fronhofer et al. 2013). Having some ability to forecast shares similar tenets with other mechanisms of movement. It is this. Mechanisms at the individual level tell us movement decisions ought to consider not only immediate benefits but be prescient. Memory infers movement evolves from internal traits using external feedback in the same way past energy use motivates future decisions. Finally, complementary individual traits tell us movement must be sensitive to changes in scale. Because exploiting memory creates more spatially aware consumers, linking resource use with individual mechanisms like energetics provides useful constraints upon which to base the memory and decision-making parts of movement.

We can define dispersal as fine scale movements of foraging, mating, or avoiding predators translated to larger scales, such as landscapes, linking these behaviours to habitat selection, range expansion, regional dispersal, and species distribution (Fig 1, section 2). Therefore, dispersal ability is useful for estimating how populations (Fahrig 2003; Latombe et al. 2014; Aben et al. 2014) and communities (Buchmann et al. 2011) respond to fragmented habitats; however, assessing dispersal ability and range expansion often involves species responses to resource availability and interacting individuals, such as avoiding predators, curbing mortality risk, and group social information, using metrics to assess habitat suitability (Kramer-Schadt et al. 2004; Kramer-Schadt et al. 2005; Bocedi, Palmer, et al. 2014; Aben et al. 2014; Mooij & DeAngelis 2003; Bocedi, Zurell, et al. 2014; Hayes & Thompson 2013; Henry et al. 2014). Therefore, habitat suitability indices to predict animal occupancy and range dynamics may ignore important behavioural detail, thereby diluting subtle, but decisive, movement mechanisms (Schurr et al. 2012; Kubisch et al. 2014; Mooij & DeAngelis 2003). For example, dispersal is a by-product of recurring, frequent movement linked to common behaviour connecting animals to space, such as resource use (van Dyck & Baguette 2005), competition (Moorcroft et al. 2006), mating opportunities (Wang & Grimm 2007), and mortality risk (Kramer-Schadt et al. 2005; Bocedi, Zurell, et al. 2014; Kramer-Schadt et al. 2004), in turn forming home ranges. Home range size and dispersal can then vary under combinations of these constraints, which influences future dispersal potential of individuals and thus population structure (Wang & Grimm 2007; Bocedi, Palmer, et al. 2014) over different time and space scales (Bocedi, Zurell, et al. 2014; Henry et al. 2014; Bowler & Benton 2005; Kramer-Schadt et al. 2011; Kanagaraj et al. 2013).

Home range behaviour is often complemented by individual traits such as cognition and thus directly related to memory, as animals occupy home ranges by revisiting previous sites. Animals can stabilise their home ranges by relying on memory over exploring new resources (Nabe-Nielsen et al. 2013) and using complementary types of memory e.g. reference and working (Van Moorter et al. 2009). However, because home range stability depends on scale (Lyons et al. 2013), home range models must balance the cost of animals requiring too much information (see (Börger et al. 2008)). Other key movement traits linked to home range behaviour, such as duration and frequency of patch visits, may include mechanisms that support biologically relevant movement rules and share direct trade-offs with the environment (Lyons et al. 2013; Van Moorter et al. 2015). Models that capture individual responses to resource, competitor, predator, or mate distribution, for example, are useful at predicting home range behaviour because they define the spatial limits of animal movement (Mitchell & Powell 2004; Moorcroft et al. 2006; Buchmann et al. 2011; Lyons et al. 2013; Bocedi, Zurell, et al. 2014; Bocedi, Palmer, et al. 2014). Therefore, future avenues where movement IBMs are well suited involve scenarios where home ranges are defined by individual preferences and how they change across different environments (Börger et al. 2008).

Home range IBMs linked to energetics generally use resource intake as a proxy, i.e. resource sites visited (Mitchell & Powell 2004; Nabe-Nielsen et al. 2013) or patches consumed (Van Moorter et al. 2009; Buchmann et al. 2011), rather than individual energy reserves, highlighting a disparity in how we interpret individual-based energetics with home range models. These works propose that direct links animals share with their environment help animals form home ranges: selecting resource sites rather than simply movement to define home ranges (Mitchell & Powell 2004), using attraction rules to nearby, previously visited food (Nabe-Nielsen et al. 2013) or shelter (Moorcroft et al. 2006) to generate more stable home ranges. Because energy intake depends on patch residence time, visiting fewer patches but for longer can stabilise home range size (Nabe-Nielsen et al. 2013) depending on food renewal rates (Van Moorter et al. 2009). Home range models assuming animals optimise resource use based on their dispersal in the landscape can accurately predict real home range size in black bears (Mitchell & Powell 2007) when animals use less area and fewer resources (Mitchell & Powell 2012), despite not strictly including individual energetics. Further, foraging strategy can shift with body mass, which can generate different home range sizes based on efficiency of resource use (Buchmann et al. 2012; Buchmann et al. 2011). Therefore, behavioural strategy can determine the potential movement costs required to optimise space use and generate stable home ranges. Mechanistic home range models based on movement rules (Van Moorter et al. 2009) could be more general and realistic by using mechanisms like energetics, rather than relying on random walks to drive movement in space.

A common, but abstract way to integrate energetics into movement is depicting decision-making surfaces based on resource availability or mortality risk (e.g. (Fronhofer et al. 2013; Pauli et al. 2013), which can translate small-scale movement models to the larger home range and regional scale by unifying energy uptake and use from the individual level (Fig. 1). Supplementing energetics with other traits like memory can make movement models more predictive and realistic (Latombe et al. 2014). For example, the restricted movement of individuals by energy costs may override the strength of reference memory, producing less sustainable home ranges. Energy intake changes in space; irrelevant patch use by animals visiting empty patches in a landscape can shape how we think animals forge home ranges (Mitchell & Powell 2012). This highlights the intimacy energetics share with other traits that models ought to balance depending on the species or system modelled. Home range behaviour and dispersal involve multiple cues (Pe’er & Kramer-Schadt 2008; Schick et al. 2008), so models assuming animals ignore time for other important activities in addition to finding resources (Mitchell & Powell 2004) can be misleading and inaccurate. Energy use that animals abide by across time and space thus represents useful logic when reasoning what general mechanism could usefully summarise complex movement, such as home range behaviour. This complexity teaches us exploring home range behaviour using mechanisms is simplified when movement decisions stem from cues directly reflecting the processes shaping home range behaviour, such as when internal energy reserves influence animals to compromise or exploit resources or mates in a landscape.

Individual-based movement models are less common at the large scale of dispersal and migration, where birds and fish dominate (see (Bauer et al. 2009)). The large time and space scale of dispersal and migration means migrants must adapt to long physical travel and uncertain habitat and resource availability at destination sites (Pettifor et al. 2000; Duriez et al. 2009). Because large-scale movement is often group-based (Merkle et al. 2015), social input can dilute the migration process (Shaw & Couzin 2013) that, together with large-scale environmental change (Boone et al. 2006), can make migration less predictable (Cote et al. 2016). Therefore, using both internal and external cues can help animals make informed movement decisions, such as when to depart based on available food (McNamara et al. 2011) and climate (Boone et al. 2006) together with assessing energy reserve to forecast distances to future stopover sites (Duriez et al. 2009; Pettifor et al. 2000). Individual-based models typically use behaviour rules to model group movement during migration (Huse & Giske 1998; Pettifor et al. 2000; Duriez et al. 2009; Huse et al. 2004; Huse et al. 2002; Shaw & Couzin 2013). Dominant and experienced individuals can influence group behaviour through naïve or new migrants, but learning new migration paths depends on the demography of dominants and its timing with migration events (Huse et al. 2002).

Migration often involves two distinct movement phases: large scale to and small scale within a destination. External cues, such as season (Pettifor et al. 2000; Boone et al. 2006) and resource patterns (Duriez et al. 2009) regulate when animals move both between and within migration sites. Small-scale environmental change can help animals negotiate changing food availability or predation risk within foraging sites, e.g. geese navigating daily tidal cycles (Pettifor et al. 2000) or pelagic fish in diurnal migration (Giske et al. 2013). Over larger scales, migrants generally prefer seasonal cues between sites (Pettifor et al. 2000) but switch to favour localised cues, such as resources, within sites (Shaw & Couzin 2013).

External cues, such as temperature, alter resources density and distribution in space and time, so adapting migration decisions to these cues may improve their success (Huse et al. 2004). However, adapting to short daily temperature profiles to assess habitat suitability can drive migrating birds such as geese to depart prematurely and risk starvation (Duriez et al. 2009), suggesting large-scale dispersal is sensitive to the rate and scale of change in external cues (Boone et al. 2006). Similarly, the immediate environment provides physical handicaps for animals to exploit, such as wind currents for migrating birds (Safi et al. 2013), e.g. dynamic soaring (Sachs et al. 2013; Shepard & Lambertucci 2013) that facilitates faster flying speeds and improves access to food sources, especially for individuals and species with greater wingspans (Shepard & Lambertucci 2013).

Conversely, internal cues, such as energy reserves, allow animals to make movement decisions from individual physical mechanisms. For example, given animals improve their energy use by exploiting different physical handicaps in their environment suggests energy use is a limiting mechanism across larger scales; however, it also constrains the potential frequency of small-scale repetitive movement within the larger, more steady migration path, which must be negotiated together with the total movement path across long time periods (Sachs et al. 2013). Categorising the migration path into distinct movement phases based on how migrating birds exploit their immediate environment, i.e. wind currents, identifies vital points in time and space animals ought to exploit to optimise energy usage (Sachs et al. 2013). However, migrants will not always capitalise paths of least resistance, such as bar-headed geese that, despite available tail winds, on average flew under conditions sub-optimal for reduced energy costs (Hawkes et al. 2013). The conclusions from these findings are twofold: 1) although migrating birds sometimes utilise low altitude areas along migratory paths to fly faster and reduce movement costs, over longer time periods, this type of behaviour may not always be possible or sustainable (Hawkes et al. 2013; Sachs et al. 2013; Shepard & Lambertucci 2013); and 2) despite their availability, decisions to exploit physical handicaps vary among individuals, which is particularly evident in group-based migration (Cote et al. 2016), thus the general limiting constraint to movement in lieu of or dominant to the external physical environment is likely to be internal and individual-based, such as energy reserves.  

Calculating food intake rate from gut capacity, feeding response to prey density (functional response), and food availability, as well as metabolic activities of respiration, egestion, and excretion, generates realistic metrics of how animal might behave in nature, linking large-scale dispersal to reasonable fitness outcomes, such as growth (Huse et al. 2004) and reproduction (Pettifor et al. 2000). Geese use external cues such as spring plant emergence to inform migration decisions, but combined with internal cues like energy reserves based on gut capacity, metabolic rates, and thermoregulatory costs allows individuals to select the best departure times (Duriez et al. 2009). Data on bite size, ingestion rate, and foraging efficiency allow models to specify when migrants depart based on energy levels (Pettifor et al. 2000). Because energy reserves at any given time vary among individuals, estimating movement from internal mechanisms retains individual-level responses to environmental change by assigning different movement rules among individuals. This has two advantages: it improves how we interpret movement costs in different ecosystems, i.e. terrestrial versus aquatic (Hein et al. 2012), particularly as energy use can become more abstract with increasing scales (Pauli et al. 2013; Wilson et al. 2015); and it addresses the common problem of such models neglecting key between-individual differences (Holyoak et al. 2008).

Energy reserves act as a movement engine to underpin movement across different migration phases, from small to large spatial scales, by a common physical mechanism. Because the costs of migration can be substantial and difficult to interpret across species and systems, using energetics, such as metabolic rules to interpret the relationship between body mass and movement costs (Hein et al. 2012) (see also (Buchmann et al. 2011; Buchmann et al. 2012; Buchmann et al. 2013; Fiksen & Carlotti 1998; Giske et al. 2013; Duriez et al. 2009; Roese et al. 1991)), unifies the migration process with general principles. Migration also often involves group movement, where the benefit of using general model traits such as energetics sorts individuals into common assemblages (see (Purves et al. 2013)); this satisfies the concept of the super-individual—one type of individual representing a suite of individuals with similar attributes—across large space and time scales (Huse et al. 2004), e.g. smaller juveniles with high metabolic rates than larger adults that require different handling times (time) and incur greater movement costs (space).

The ability for spatial models to more accurately reflect real movement patterns under large-scale habitat change (Pettifor et al. 2000; Shaw & Couzin 2013; Huse & Giske 1998), the importance of fine scale space on food encounter rates (Huse et al. 2004; Bode & Delcourt 2013; Wajnberg et al. 2013), and the response of highly mobile prey to environmental change (Huse & Giske 1998) reinforce the need for explicitly modelling space at the migration scale. Habitat loss also increases the required travel distance to new sites, so spatial models need to also clearly define what makes habitats attractive for migrants (Pettifor et al. 2000). Due to the subtlety of spatial movement, however, IBMs should be vigilant when applying movement rules at large scales (Huse et al. 2004). For example, over long time periods, slow moving animals can incur greater movement costs across large scales compared to faster animals by accumulating more non-movement costs per distance travelled (Wilson et al. 2015). We suggest internal cues to estimate the success of large-scale movement are probably more reliable for animals because e.g. energy intake is the product of individual behaviour, which can be less fickle than external cues.

The broad time and space scales IBMs address reveal the complexity of animal movement is universal across scales. Individual-based models, useful for studying processes, help dissect the subtle interface between movement and different constraints, such as resource density. As movement decisions become more complex, internal mechanisms like memory aid animals in locating resources and structuring home ranges, while cooperating internal and external cues facilitating movement reinforces the need for modelling space at increasing scales. The sensitivity of movement to different scales, species, and constraints suggests the need for general, simplified mechanisms to unify the way we interpret movement. Our review suggests energetics models are useful candidates by showing, at the individual level, they underpin essential processes that drive movement, such as intake rate, prey handling, and growth across different scales with general physical principles. Movement research is beginning to embrace the same principles we argue in this review by explicitly addressing energetic costs (Shepard et al. 2013; Bonte et al. 2012; Bonnell et al. 2013; Hawkes et al. 2013; Sachs et al. 2013; Pauli et al. 2013; Wilson et al. 2013; Wood et al. 2013). The field has identified what it needs to progress: explicit focus on mechanisms (Holyoak et al. 2008; Giuggioli & Bartumeus 2010) and scales (Benhamou 2014) simplified by exploiting the strengths of process-based approaches such as IBMs (Bauer & Klaassen 2013).

We thank V. Grimm, M. Burgman, M. Bode, I. Veltheim, L. Rose, and two anonymous reviewers for helpful comments, discussion, and revisions.

Funding

MM was funded by an Australian Postgraduate Award (APA) and the Centre of Excellence for Biosecurity Risk Analysis.

Authors’ contributions

MM conceived the idea and wrote the manuscript, figures, and tables. SKS wrote the manuscript, figures, and tables. All authors read and approved the final manuscript.

Competing interests

The authors declare they have no competing interests.

Ethics approval and consent to participate

Not applicable.

Aben, J. et al., 2014. Simple individual-based models effectively represent Afrotropical forest bird movement in complex landscapes. Journal of Applied Ecology, 51(3), pp.693–702.

Avgar, T., Deardon, R. & Fryxell, J.M., 2013. An empirically parameterized individual based model of animal movement, perception, and memory. Ecological Modelling, 251, pp.158–172.

Ayllón, D. et al., 2016. InSTREAM-Gen: Modelling eco-evolutionary dynamics of trout populations under anthropogenic environmental change. Ecological Modelling, 326, pp.36–53.

Bacher, C. & Gangnery, A., 2006. Use of dynamic energy budget and individual based models to simulate the dynamics of cultivated oyster populations. Journal of Sea Research, 56(2), pp.140–155.

Bauer, S. et al., 2009. Animal migration: linking models and data beyond taxonomic limits. Biology letters, 5(4), pp.433–435.

Bauer, S. & Hoye, B.J., 2014. Migratory animals couple biodiversity and ecosystem functioning worldwide. Science (New York, N.Y.), 344(6179), p.1242552.

Bauer, S. & Klaassen, M., 2013. Mechanistic models of animal migration behaviour - their diversity, structure and use. Journal of Animal Ecology, 82(3), pp.498–508.

Benhamou, S., 2014. Of scales and stationarity in animal movements. Ecology Letters, 17(3), pp.261–272.

Bergman, Carita, M. et al., 2001. Ungulate foraging strategies : energy maximizing or time minimizing ? Journal of Animal Ecology, 70, pp.289–300.

Bocedi, G., Zurell, D., et al., 2014. Mechanistic modelling of animal dispersal offers new insights into range expansion dynamics across fragmented landscapes. Ecography, 37(12), pp.1240–1253.

Bocedi, G., Palmer, S.C.F., et al., 2014. RangeShifter: a platform for modelling spatial eco-evolutionary dynamics and species’ responses to environmental changes. Methods in Ecology and Evolution, 5(4), pp.388–396.

Bode, N.W.F. & Delcourt, J., 2013. Individual-to-Resource Landscape Interaction Strength Can Explain Different Collective Feeding Behaviours. PLoS ONE, 8(10).

Bonnell, T.R. et al., 2013. Emergent Group Level Navigation: An Agent-Based Evaluation of Movement Patterns in a Folivorous Primate. PLoS ONE, 8(10), pp.1–12.

Bonte, D. et al., 2012. Costs of dispersal. Biological Reviews, 87(2), pp.290–312.

Boone, R.B., Thirgood, S.J. & Hopcraft, J.G.C., 2006. Serengeti wildebeest migratory patterns modeled from rainfall and new vegetation growth. Ecology, 87(8), pp.1987–1994.

Börger, L. et al., 2011. Migration quantified: constructing models and linking them with data. In Animal Migration. Oxford: Oxford University Press.

Börger, L., Dalziel, B.D. & Fryxell, J.M., 2008. Are there general mechanisms of animal home range behaviour? A review and prospects for future research. Ecology Letters, 11(6), pp.637–650.

Bowler, D.E. & Benton, T.G., 2005. Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics. Biological reviews of the Cambridge Philosophical Society, 80(2), pp.205–225.

Brown, J.H. et al., 2004. Toward a metabolic theory of ecology. Ecology, 85(7), pp.1771–1789.

Buchmann, C.M. et al., 2011. An allometric model of home range formation explains the structuring of animal communities exploiting heterogeneous resources. Oikos, 120(1), pp.106–118.

Buchmann, C.M. et al., 2013. Habitat loss and fragmentation affecting mammal and bird communities-The role of interspecific competition and individual space use. Ecological Informatics, 14, pp.90–98.

Buchmann, C.M. et al., 2012. Movement upscaled - the importance of individual foraging movement for community response to habitat loss. Ecography, 35(5), pp.436–445.

Charnov, E.L., 1976. Optimal foraging, the marginal value theorem. Theoretical Population Biology, 9(2), pp.129–136.

Cote, J. et al., 2016. Behavioural synchronization of large-scale animal movements - disperse alone, but migrate together? Biological reviews of the Cambridge Philosophical Society, 33.

Couzin, I.D. et al., 2005. Effective leadership and decision-making in animal groups on the move. Nature, 433(7025), pp.513–516.

DeAngelis, D.L. & Mooij, W.M., 2005. Individual-Based Modeling of Ecological and Evolutionary Processes. Annual Review of Ecology, Evolution, and Systematics, 36(1), pp.147–168.

Delgado, M.D.M. et al., 2014. A statistical framework for inferring the influence of conspecifics on movement behaviour. Methods in Ecology and Evolution, 5(2), pp.183–189.

Duffy, K.J., 2011. Simulations to investigate animal movement effects on population dynamics. Natural Resource Modeling, 24(1), pp.48–60.

Dumont, B. & Hill, D.R.C., 2004. Spatially explicit models of group foraging by herbivores: what can Agent-Based Models offer? Animal Research, 53(5), pp.419–428.

Duriez, O. et al., 2009. What decision rules might pink-footed geese use to depart on migration? An individual-based model. Behavioral Ecology, 20(3), pp.560–569.

van Dyck, H. & Baguette, M., 2005. Dispersal behaviour in fragmented landscapes: Routine or special movements? Basic and Applied Ecology, 6(6), pp.535–545.

Earl, J.E. & Zollner, P.A., 2014. Effects of animal movement strategies and costs on the distribution of active subsidies across simple landscapes. Ecological Modelling, 283, pp.45–52.

Eliassen, S. et al., 2007. Exploration or exploitation: Life expectancy changes the value of learning in foraging strategies. Oikos, 116(3), pp.513–523.

Eliassen, S. et al., 2009. Quantifying the adaptive value of learning in foraging behavior. The American Naturalist, 174(4), pp.478–489.

Ellis, R.D., McWhorter, T.J. & Maron, M., 2012. Integrating landscape ecology and conservation physiology. Landscape Ecology, 27(1), pp.1–12.

Fagan, W.F. et al., 2013. Spatial memory and animal movement. Ecology Letters, 16(10), pp.1316–1329.

Fahrig, L., 2003. Effects of Habitat Fragmentation on Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 34(1), pp.487–515.

Fiksen, O. & Carlotti, F., 1998. A model of optimal life history and diel vertical migration in Calanus finmarchicus. Sarsia, 83, pp.129–147.

Fronhofer, E.A., Hovestadt, T. & Poethke, H.J., 2013. From random walks to informed movement. Oikos, 122(6), pp.857–866.

Giske, J. et al., 2013. Effects of the emotion system on adaptive behavior. The American Naturalist, 182(6), pp.689–703.

Giuggioli, L. & Bartumeus, F., 2010. Animal movement, search strategies and behavioural ecology: A cross-disciplinary way forward. Journal of Animal Ecology, 79(4), pp.906–909.

Gleiss, A.C., Wilson, R.P. & Shepard, E.L.C., 2011. Making overall dynamic body acceleration work: On the theory of acceleration as a proxy for energy expenditure. Methods in Ecology and Evolution, 2(1), pp.23–33.

Grimm, V. et al., 2005. Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology. Science, 310(5750), pp.987–991.

Grimm, V. & Railsback, S.F., 2005. Individual-based Modeling and Ecology: STU-Stud., Princeton: Princeton University Press.

Grimm, V. & Railsback, S.F., 2011. Pattern-oriented modelling: a “multi-scope” for predictive systems ecology. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 367(1586), pp.298–310.

Grünbaum, D. & Veit, R.R., 2003. Black-browed albatrosses foranging on antarctic krill: Denesty-dependence trough local enhancement? Ecology, 84(12), pp.3265–3275.

Guensch, G.R., Hardy, T.B. & Addley, R.C., 2001. Examining feeding strategies and position choice of drift-feeding salmonids using an individual-based, mechanistic foraging model. Canadian Journal of Fisheries and Aquatic Sciences, 58(3), pp.446–457.

Gustafson, E.J., 2013. When relationships estimated in the past cannot be used to predict the future: Using mechanistic models to predict landscape ecological dynamics in a changing world. Landscape Ecology, 28(8), pp.1429–1437.

Hawkes, C., 2009. Linking movement behaviour, dispersal and population processes: Is individual variation a key? Journal of Animal Ecology, 78(5), pp.894–906.

Hawkes, L.A. et al., 2013. The paradox of extreme high-altitude migration in bar-headed geese Anser indicus. Proceedings of the Royal Society B: Biological Sciences, 280(1750), p.20122114.

Hayes, D.B. & Thompson, B.E., 2013. Movement rules for juvenile steelhead: Dynamic linking of movement behaviour to habitat and density. Ecology of Freshwater Fish, pp.1–13.

Hein, A.M., Hou, C. & Gillooly, J.F., 2012. Energetic and biomechanical constraints on animal migration distance. Ecology Letters, 15(2), pp.104–110.

Henry, R.C. et al., 2014. Inter-annual variability influences the eco-evolutionary dynamics of range-shifting. PeerJ, 2, pp.e228–e228.

Holyoak, M. et al., 2008. Trends and missing parts in the study of movement ecology. Proceedings of the National Academy of Sciences of the United States of America, 105(49), pp.19060–19065.

Hosseini, P.R., 2006. Pattern formation and individual-based models: The importance of understanding individual-based movement. Ecological Modelling, 194(4), pp.357–371.

Huse, G. et al., 2004. Studying spatial and trophic interactions between capelin and cod using individual-based modelling. ICES Journal of Marine Science, 61(7), pp.1201–1213.

Huse, G. & Giske, J., 1998. Ecology in mare pentium: An individual-based spatio-temporal model for fish with adapted behaviour. Fisheries Research, 37(1–3), pp.163–178.

Huse, G., Railsback, S. & Fernö, A., 2002. Modelling changes in migration pattern of herring: collective behaviour and numerical domination. Journal of Fish Biology, 60(3), pp.571–582.

Jeltsch, F. et al., 2013. Integrating movement ecology with biodiversity research - exploring new avenues to address spatiotemporal biodiversity dynamics. Movement Ecology, 1(1), p.6.

Kanagaraj, R. et al., 2013. Using individual-based movement models to assess inter-patch connectivity for large carnivores in fragmented landscapes. Biological Conservation, 167, pp.298–309.

Kokko, H. & López-Sepulcre, A., 2006. From individual dispersal to species ranges: perspectives for a changing world. Science, 313(5788), pp.789–791.

Kooijman, S.A.L.M., 2010. Dynamic Energy Budget Theory, Cambridge, UK: Cambridge University Press.

Kramer-Schadt, S. et al., 2011. Analyzing the effect of stepping stones on target patch colonisation in structured landscapes for Eurasian lynx. Landscape Ecology, 26(4), pp.501–513.

Kramer-Schadt, S. et al., 2004. Fragmented landscapes, road mortality and patch connectivity: modelling influences on the dispersal of Eurasian lynx. Journal of Applied Ecology, 41(4), pp.711–723.

Kramer-Schadt, S., Revilla, E. & Wiegand, T., 2005. Lynx reintroductions in fragmented landscapes of Germany: Projects with a future or misunderstood wildlife conservation? Biological Conservation, 125(2), pp.169–182.

Kranstauber, B. et al., 2012. A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement. Journal of Animal Ecology, 81(4), pp.738–746.

Kubisch, A. et al., 2014. Where am I and why? Synthesizing range biology and the eco-evolutionary dynamics of dispersal. Oikos, 123(1), pp.5–22.

Kułakowska, K. et al., 2014. Using an individual-based model to select among alternative foraging strategies of woodpigeons: Data support a memory-based model with a flocking mechanism. Ecological Modelling, 280, pp.89–101.

Langrock, R. et al., 2014. Modelling group dynamic animal movement. Methods in Ecology and Evolution, 5(2), pp.190–199.

Latombe, G. et al., 2014. Uniting Statistical and Individual-Based Approaches for Animal Movement Modelling. PLoS ONE, 9(6), p.e99938.

Laundré, J.W. et al., 2014. The landscape of fear: The missing link to understand top-down and bottom-up controls of prey abundance? Ecology, 95(5), pp.1141–1152.

Leising, A.W., 2001. Copepod foraging in patchy habitats and thin layers using a 2-d individual-based model. Marine Ecology Progress Series, 216, pp.167–179.

Lima, S.L., Bednekoff, P.A. & Sih, A.E.A., 1999. Temporal Variation in Danger Drives Antipredator Behavior: The Predation Risk Allocation Hypothesis. The American Naturalist, 153(6), pp.649–659.

Louzao, M. et al., 2014. Coupling instantaneous energy-budget models and behavioural mode analysis to estimate optimal foraging strategy: an example with wandering albatrosses. Movement Ecology, 2(1), p.8.

Lyons, A., Turner, W. & Getz, W., 2013. Home range plus: a space-time characterization of movement over real landscapes. Movement Ecology, 1(1), p.2.

Malishev, M., Bull, C.M. & Kearney, M.R., An individual-based model of animal movement constraints for ectotherms based on food and microclimates in space and time. In review.

Martin, B.T. et al., 2012. Dynamic Energy Budget theory meets individual-based modelling: a generic and accessible implementation. Methods in Ecology and Evolution, 3(2), pp.445–449.

Martin, B.T. et al., 2013. Predicting Population Dynamics from the Properties of Individuals: A Cross-Level Test of Dynamic Energy Budget Theory. The American Naturalist, 181(4), pp.506–519.

McNamara, J.M. et al., 2011. Cues and the optimal timing of activities under environmental changes. Ecology Letters, 14(12), pp.1183–1190.

van der Meer, J., 2006. An introduction to Dynamic Energy Budget (DEB) models with special emphasis on parameter estimation. Journal of Sea Research, 56(2), pp.85–102.

Merkle, J.A., Fortin, D. & Morales, J.M., 2014. A memory-based foraging tactic reveals an adaptive mechanism for restricted space use. Ecology Letters, 17(8), pp.924–931.

Merkle, J.A., Sigaud, M. & Fortin, D., 2015. To follow or not? How animals in fusion-fission societies handle conflicting information during group decision-making. Ecology Letters, 18(8), pp.799–806.

Mitchell, M.S. & Powell, R.A., 2004. A mechanistic home range model for optimal use of spatially distributed resources. Ecological Modelling, 177(1–2), pp.209–232.

Mitchell, M.S. & Powell, R.A., 2012. Foraging optimally for home ranges. Journal of Mammalogy, 93(4), pp.917–928.

Mitchell, M.S. & Powell, R.A., 2007. Optimal use of resources structures home ranges and spatial distribution of black bears. Animal Behaviour, 74(2), pp.219–230.

Miwa, M. et al., 2015. Application of overall dynamic body acceleration as a proxy for estimating the energy expenditure of grazing farm animals: Relationship with heart rate. PLoS ONE, 10(6), pp.1–20.

Mooij, W.M. & DeAngelis, D.L., 2003. Uncertainty in spatially-explicit animal dispersal models. Ecological Applications, 13(3), pp.794–805.

Moorcroft, P.R., Lewis, M.A. & Crabtree, R.L., 2006. Mechanistic home range models capture spatial patterns and dynamics of coyote territories in Yellowstone. Proceedings of the Royal Society of London B: Biological Sciences, 273(1594), pp.1651–1659.

Van Moorter, B. et al., 2009. Memory keeps you at home: A mechanistic model for home range emergence. Oikos, 118(5), pp.641–652.

Van Moorter, B. et al., 2015. Movement is the glue connecting home ranges and habitat selection. Journal of Animal Ecology, 85(1), pp.21–31.

Van Moorter, B. et al., 2013. Understanding scales of movement: Animals ride waves and ripples of environmental change. Journal of Animal Ecology, 82(4), pp.770–780.

Morales, J.M. et al., 2004. Extracting More out of Relocation Data: Building Movement Models as Mixtures of Random Walks. Ecology, 85(9), pp.2436–2445.

Mueller, T. & Fagan, W.F., 2008. Search and navgation in dynamic environments - from individual behaviours to population distributions. Oikos, 117(December 2007), pp.654–664.

Mueller, T., Fagan, W.F. & Grimm, V., 2011. Integrating individual search and navigation behaviors in mechanistic movement models. Theoretical Ecology, 4(3), pp.341–355.

Nabe-Nielsen, J. et al., 2013. How a simple adaptive foraging strategy can lead to emergent home ranges and increased food intake. Oikos, 122(9), pp.1307–1316.

Nams, V.O., 2014. Combining animal movements and behavioural data to detect behavioural states. Ecology Letters.

Nathan, R. et al., 2008. A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences of the United States of America, 105(49), pp.19052–19059.

Nathan, R. et al., 2012. Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures. The Journal of Experimental Biology, 215(Pt 6), pp.986–96.

Nisbet, R.M. et al., 2012. Integrating dynamic energy budget (DEB) theory with traditional bioenergetic models. Journal of Experimental Biology, 215(7), p.1246.

Patterson, T. a. et al., 2008. State-space models of individual animal movement. Trends in Ecology and Evolution, 23(2), pp.87–94.

Pauli, B.P. et al., 2013. SEARCH: Spatially Explicit Animal Response to Composition of Habitat. PLoS ONE, 8(5).

Pe’er, G. & Kramer-Schadt, S., 2008. Incorporating the perceptual range of animals into connectivity models. Ecological Modelling, 213(1), pp.73–85.

Peck, M. a. & Daewel, U., 2007. Physiologically based limits to food consumption, and individual-based modeling of foraging and growth of larval fishes. Marine Ecology Progress Series, 347, pp.171–183.

Pettifor, R.A. et al., 2000. Spatially explicit, individual-based, behavioural models of the annual cycle of two migratory goose populations. Journal of Applied Ecology, 37, pp.103–135.

Purves, D. et al., 2013. Time to model all life on Earth. Nature, 493(7432), pp.295–297.

Pyke, G.H., 2015. Understanding movements of organisms: it’s time to abandon the Levy foraging hypothesis. Methods in Ecology and Evolution, 6(1), pp.1–16.

Pyke, G.H., Pulliam, H.R. & Charnov, E.L., 1977. Optimal Foraging: A Selective Review of Theory and Tests. The Quarterly Review of Biology, 52(2), pp.137–154.

Railsback, S.F., 2001. Concepts from complex adaptive systems as a framework for individual-based modelling. Ecological Modelling, 139(1), pp.47–62.

Railsback, S.F. et al., 1999. Movement rules for individual-based models of stream fish. Ecological Modelling, 123(2–3), pp.73–89.

Railsback, S.F. & Harvey, B.C., 2002. Analysis of Habitat-Selection Rules Using an Individual-Based Model. Ecology, 83(7), pp.1817–1830.

Ringelman, K.M., 2014. Predator foraging behavior and patterns of avian nest success: What can we learn from an agent-based model? Ecological Modelling, 272, pp.141–149.

Rittenhouse, T.A.G. & Semlitsch, R.D., 2006. Grasslands as movement barriers for a forest-associated salamander: Migration behavior of adult and juvenile salamanders at a distinct habitat edge. Biological Conservation, 131(1), pp.14–22.

Roese, J.H., Risenhoover, K.L. & Folse, L.J., 1991. Habitat heterogeneity and foraging efficiency: an individual-based model. Ecological Modelling, 57(1–2), pp.133–143.

Sachs, G. et al., 2013. Experimental verification of dynamic soaring in albatrosses. The Journal of Experimental Biology, 216(4), pp.4222–32.

Safi, K. et al., 2013. Flying with the wind: scale dependency of speed and direction measurements in modelling wind support in avian flight. Movement Ecology, 1(1), p.4.

Scharf, A.K. et al., 2016. Acceleration data reveal highly individually structured energetic landscapes in free-ranging fishers (Pekania pennanti). PLoS ONE, 11(2), p.e0145732.

Schick, R.S. et al., 2008. Understanding movement data and movement processes: Current and emerging directions. Ecology Letters, 11(12), pp.1338–1350.

Schurr, F.M. et al., 2012. How to understand species’ niches and range dynamics: A demographic research agenda for biogeography. Journal of Biogeography, 39(12), pp.2146–2162.

Shaw, A.K. & Couzin, I.D., 2013. Migration or residency? The evolution of movement behavior and information usage in seasonal environments. The American Naturalist, 181(1), pp.114–124.

Shepard, E.L.C. et al., 2013. Energy landscapes shape animal movement ecology. The American Naturalist, 182(3), pp.298–312.

Shepard, E.L.C. & Lambertucci, S.A., 2013. From daily movements to population distributions: weather affects competitive ability in a guild of soaring birds. Journal of The Royal Society Interface, 10(88), p.20130612.

Sibly, R.M. et al., 2013. Representing the acquisition and use of energy by individuals in agent-based models of animal populations. Methods in Ecology and Evolution, 4(2), pp.151–161.

Singer, A. et al., 2016. Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions? Ecological Modelling, 326, pp.63–74.

Sousa, T. et al., 2010. Dynamic energy budget theory restores coherence in biology. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 365(1557), pp.3413–3428.

Sousa, T., Domingos, T. & Kooijman, S.A.L.M., 2008. From empirical patterns to theory: a formal metabolic theory of life. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 363(1502), pp.2453–2464.

Stillman, R.A. et al., 2015. Making predictions in a changing world: The benefits of individual-based ecology. BioScience, 65(2), pp.140–150.

Sueur, C. et al., 2011. Collective decision-making and fission-fusion dynamics: A conceptual framework. Oikos, 120(11), pp.1608–1617.

Tang, W. & Bennett, D., 2010. Agent based Modeling of Animal Movement: A Review. Geography Compass, 7, pp.1–30.

Travis, J.M.J., Brooker, R.W. & Dytham, C., 2005. The interplay of positive and negative species interactions across an environmental gradient: insights from an individual-based simulation model. Biology letters, 1(1), pp.5–8.

Travis, J.M.J. & Dytham, C., 1998. The evolution of dispersal in a metapopulation: a spatially explicit, individual-based model. Proceedings of the Royal Society B: Biological Sciences, 265(1390), pp.17–23.

Turchin, P., 1998. Quantitative analysis of movement : measuring and modeling population redistribution in animals and plants, Sunderland: Sinauer.

Wajnberg, E., Hoffmeister, T.S. & Coquillard, P., 2013. Optimal within-patch movement strategies for optimising patch residence time: An agent-based modelling approach. Behavioral Ecology and Sociobiology, 67(12), pp.2053–2063.

Wang, M. & Grimm, V., 2007. Home range dynamics and population regulation: An individual-based model of the common shrew Sorex araneus. Ecological Modelling, 205(3–4), pp.397–409.

Wilson, R.P. et al., 2013. Turn costs change the value of animal search paths. Ecology Letters, 16(9), pp.1145–1150.

Wilson, R.P. et al., 2014. Wild state secrets: ultra-sensitive measurement of micro-movement can reveal internal processes in animals. Frontiers in Ecology and the Environment, 12(10), pp.582–587.

Wilson, R.P., Quintana, F. & Hobson, V.J., 2012. Construction of energy landscapes can clarify the movement and distribution of foraging animals. Proceedings of the Royal Society B: Biological Sciences, 279(1730), pp.975–980.

Wilson, R.S. et al., 2015. Predicting the Movement Speeds of Animals in Natural Environments. Integrative and Comparative Biology, 55(6), pp.1125–1141.

Wilson, W.G., 1998. Resolving discrepancies between deterministic population models and individual-based simulations. The American Naturalist, 151(2), pp.116–134.

Wood, K.A. et al., 2013. Go with the flow: Water velocity regulates herbivore foraging decisions in river catchments. Oikos, 122(12), pp.1720–1729.

Zollner, P.A. & Lima, S.L., 2005. Behavioral tradeoffs when dispersing across a patchy landscape. Oikos, 108(2), pp.219–230.