Movement rules determine nomadic species' responses to resource supplementation and degradation
Abstract
- In environments that vary unpredictably, many animals are nomadic, moving in an irregular pattern that differs from year to year. Exploring the mechanisms of nomadic movement is needed to understand how animals survive in highly variable environments, and to predict behavioural and population responses to environmental change.
- We developed a network model to identify plausible mechanisms of nomadic animal movement by comparing the performance of multiple movement rules along a continuum from nomadism to residency. Using simulations and analytical results, we explored how different types of habitat modifications (that augment or decrease resource availability) might affect the abundance and movement rates of animals following each of these rules.
- Movement rules for which departure from patches depended on resource availability and/or competition performed almost equally well and better than residency or uninformed movement under most conditions, even though animals using each rule moved at substantially different rates. Habitat modifications that stabilized resources, either by resource supplementation or degradation, eroded the benefits of informed nomadic movements, particularly for movements based on resource availability alone.
- These results suggest that simple movement rules can explain nomadic animal movements and determine species’ responses to environmental change. In particular, landscape stabilization and supplementation might be useful strategies for promoting populations of resident animals, but would be less beneficial for managing highly mobile species, many of which are threatened by habitat disruption and changes in climate.
1 INTRODUCTION
Long-distance animal movements influence population dynamics and species interactions, and can transport energy and nutrients between distant sites (Bauer & Hoye, 2014; Lundberg & Moberg, 2003). Resource distributions are a key driver of animal movement patterns, with animals moving to track abundant resources and avoid resource-scarce environments. Seasonally migratory species often make repeated, round-trip journeys to track changes in resources over predictable, annual cycles (Fryxell, Greever, & Sinclair, 1988; Holdo, Holt, & Fryxell, 2009; Levey & Stiles, 1992). In other environments, different movement patterns can emerge, such as range residency (including territoriality) and nomadism (Johnson, Wiens, Milne, & Crist, 1992; Mueller et al., 2011); these patterns depend on the underlying landscape, resource variability, and species' movement and navigation capacities (Gautestad, 2011; Mettke-Hofmann, 2014; Winkler et al., 2014).
Nomadism, which is characterized by irregular movements in both time and space, occurs in landscapes that vary unpredictably on large spatial scales (Jonzén, Knudsen, Holt, & Saether, 2011; Mueller & Fagan, 2008; Teitelbaum & Mueller, 2019). Nomadic species are geographically widespread and taxonomically diverse, and include species such as banded stilts Cladorhynchus leucocephalus, which move between saltwater ponds in Australia (Pedler, Ribot, & Bennett, 2014), and Thompson's gazelle Eudorcas thomsonii in the Serengeti, whose movements track grass availability (Fryxell, Wilmshurst, & Sinclair, 2004). Although nomadic movements can appear to be random or complex (Roshier, Klomp, & Asmus, 2006; Roshier & Reid, 2003), simple environmental and social conditions probably cue departure events and movement patterns (e.g. McEvoy, Roshier, Ribot, & Bennett, 2015; Roshier et al., 2006). Nomadism can be characterized by search behaviour (i.e. exploration of unfamiliar sites), navigation (i.e. movement to a known destination) and revisitation of previously occupied patches (Teitelbaum & Mueller, 2019). Because many nomadic species are declining (Runge, Martin, Possingham, Willis, & Fuller, 2014), identifying mechanisms that underlie nomadic animal movements, and their responses to environmental change, are needed to better predict and conserve this widespread movement behaviour.
Models of animal movements in response to resources (i.e. foraging theory) have historically assumed that animals have complete information about their environments (e.g. the ideal free distribution: Charnov, 1976; Fagan et al., 2017; Fretwell & Lucas, 1969; McNamara, 1982). This assumption is often unrealistic, so more recent models of foraging and migration have incorporated memory and perception to understand more realistic movement patterns (e.g. Avgar, Deardon, & Fryxell, 2013; Bateman, Lewis, Gall, Manser, & Clutton-Brock, 2015; Bracis & Mueller, 2017; Fagan et al., 2017; Tao, Börger, & Hastings, 2016). For nomadic species inhabiting unpredictable environments, however, memory might not help in navigating to profitable patches (Berbert & Fagan, 2012). Furthermore, resource patches can be far apart in nomadic systems (McEvoy et al., 2015), which means that nomads’ movements can depend more strongly on decisions to depart a patch (Van Moorter, Rolandsen, Basille, & Gaillard, 2016) than on using perception to move up a continuous resource gradient (Fagan et al., 2017). As a result, predicting nomadic movements requires understanding the cues that animals use when deciding when to leave their current patch.
Humans are currently altering resource dynamics across the globe (Rowe, Terry, & Rickart, 2011). Many anthropogenic habitat modifications decrease resource variability (Oro, Genovart, Tavecchia, Fowler, & Martínez-Abraín, 2013), either by providing supplemental resources for wildlife, or by degrading habitats leading to the loss of resources (Fensholt & Proud, 2012). Such habitat modifications can have dramatic effects on animal population sizes and movement patterns (Iwamura et al., 2013; Rode et al., 2014; Wiegand, Revilla, & Moloney, 2005). Some species that are highly mobile in their natural habitats have recently adopted resident behaviours in urban, suburban and agricultural areas that offer consistent year-round access to resources (e.g. at bird feeders, garbage dumps, and gardens). Examples include decreased migration propensity and smaller foraging ranges in Spanish white storks using landfills and establishment of resident populations of nomadic grey-headed flying foxes in urban areas in Australia (Gilbert et al., 2015; Plowright et al., 2011; van der Ree, McDonnell, Temby, Nelson, & Whittingham, 2006). However, these studies have largely focused on changes in resource quantity, rather than changes in variability (Shochat, Warren, Faeth, McIntyre, & Hope, 2006); because nomadic movements tend to occur in environments that are highly variable in both space and time (Teitelbaum & Mueller, 2019), decreased spatiotemporal variability in resource availability due to human development could reduce the benefits of this unique movement behaviour.
Here we propose simple departure rules for nomadic animal movements, explore the dynamical outcomes of these departure rules in variable landscapes, and investigate how departure rules determine nomadic animals' responses to habitat modification. We develop a network model of nomadic animal movements across discrete patches that vary in resource availability over space and time. To identify movement mechanisms that best promote survival, we compare the population sizes of nomadic animals using departure rules based on resource availability and/or conspecific density to randomly moving nomadic animals. We also explore different movement propensities within each of these rules, based on the observation that movement types exist along a spectrum of movement frequency (Teitelbaum & Mueller, 2019). When movement propensities are extremely low, animals become range residents, which we analyse as another movement rule. Because nomadic animals tend to live in unpredictable environments with high variability (Jonzén et al., 2011; Mueller et al., 2011), we predict that nomadic departure rules will perform better than residency in variable environments. Given that human habitat modifications can decrease resource variability, we also explore the effects of resource supplementation, stabilization and degradation on populations of animals moving according to different rules. This experiment can help predict how nomadic species' abundances and movement patterns will respond to human-mediated landscape change, and whether conservation strategies established for range residents can also benefit nomadic species.
2 MATERIALS AND METHODS
2.1 Model development
We model animal movement on a network of interconnected patches similar to a migratory network, which consists of nodes of habitat patches connected by edges that represent migration routes (Taylor & Norris, 2010). Unlike a migratory network, our nomadic network is fully connected, similar to a classic metapopulation model (Hanski & Ovaskainen, 2000; Levins, 1969). In other words, movement can occur between any pair of patches, consistent with the multidirectional movements of nomadic animals (Abrahms et al., 2017; Teitelbaum & Mueller, 2019). Because of the long-distance nature of nomadic movements, we assume that patches in the nomadic network are large and far apart. We explicitly model only long-distance interpatch movements; shorter-distance movements are implicitly possible but occur within patches. Movement between patches depends on departure rules (described below), and departing animals redistribute equally to all other habitat patches. We thus model navigation as an asocial process, which is consistent with expectations for nomadic movements in environments where multiple patches can be resource-rich simultaneously (Mueller & Fagan, 2008). We consider movements over the nomad's nonbreeding season, so that survival depends on current resource availability at the patch and movement costs during inter-patch movements, and no reproduction occurs.
2.2 Landscape structure
Resource availability varies across both space and time in P patches. Resource availability in patch i and time t, Ai(t), is a quantity scaled between 0 (no resources) and 1 (maximum resource availability). There is no spatial autocorrelation in resource availability, based on the observation that there is little autocorrelation in the environments usually inhabited by nomadic animals (Jonzén et al., 2011) and our assumption that patches are far apart. Landscape-level variability (V) controls the minimum and maximum values of resource availability at all patches in the landscape, where higher values of V correspond to more variable landscapes (Figure 1a,b). At each patch, resource availability is drawn from the uniform distribution 
. We also simulate landscapes with different levels of temporal stability, where resources remain stable for E time steps (Figure 1c,d). Changing resource variability (V) does not affect temporal autocorrelation; changing E affects temporal autocorrelation but not spatial variance or mean resource availability (Figure S1). Each simulation consists of 52 time steps, designed to represent weeks in a year. We simulate 100 landscapes of for each of five values of V between 0.2 and 1, two values of E (1 and 3) and three values of P (3, 10 and 15).
2.3 Animal movement
We investigate departure rules based on current or past conditions at the occupied patch, and assume that departing animals distribute equally to all other patches. Though some nomadic animals move to known destinations (e.g. Roshier et al., 2006), our assumption that destinations are random is realistic for animals that use non-oriented movement or where patches are far enough apart that sensory mechanisms cannot inform a destination (Mueller & Fagan, 2008). Using a fully connected network allows us to investigate the importance of the departure mechanism alone for nomadic populations, independent of the roles of spatial structure or destination mechanisms.
(1)
is the number of individuals at patch i at time t.
(2)
(3)
(Figure 2c); in essence, this rule assumes that individuals can sense patch carrying capacity, either directly or if competition reduces patch suitability by reducing intake rates (Fretwell & Lucas, 1969; Hopcraft et al., 2014). The parameter 
represents the number of individuals supported by one unit of resource availability:
(4)We also consider the equivalent departure rule that incorporates memory (Figure 2e).
.2.4 Demographics
. Mortality occurs twice during each time step: during movement and at a patch. During movement, the number of animals departing patch i that survive movement is 
, where c is the cost of movement (0 ≤ c ≤ 1), assumed to be equal between any two patches (Table 1). The number of individuals at patch i following departure and immigration is therefore:
| Parameter | Description | Range | Baseline value |
|---|---|---|---|
| Landscape parameters | |||
| V | Controls minimum and maximum value of resource availability | 0.2–1 | 1 |
| E | Number of time steps where resource availability remains constant | 1, 3 | 1 |
| P | Number of patches | 3, 10, 15 | 10 |
| Movement parameters | |||
| W | Resource threshold (value of resource availability below which movement starts to occur) | 0.1–1 | 0.5 |
| T | Density threshold (population size above which movement occurs) | 0.1–1 | 0.5 |
| p | Movement rate for uninformed movement (individuals/time step) | 0.3 | 0.05 |
| Species parameters | |||
| μ 1 | Strength of density-dependent mortality | 0.0433, 0.0229, 0.0132 | 0.0229 |
| μ 0 | Strength of density-independent mortality; weekly per capita mortality rate when A = 0 | 0.0433, 0.0229, 0.0132 | 0.0229 |
| c | Cost of movement; proportion dying during one movement event | 0–0.05 | 0.01 |
| α | Scaling parameter; number of individuals supported by one unit of resources at carrying capacity | 1 | 1 |
and μ0 in patches where 
. Density-dependent mortality only occurs when the number of animals exceeds carrying capacity (Figure S2).
(5)
is the number of individuals at patch i after movement and α is as described above.2.5 Parameters and outputs
We used parameters that represent realistic movement and mortality rates for a long-lived vertebrate species (Table 1), and assume that animals update their movement decisions on a weekly basis (e.g. as has been modelled previously in ungulates: Holdo et al., 2009; López-Alfaro, Estades, Aldridge, & Gill, 2012) and steelhead (Satterthwaite et al., 2012). The mortality rates selected correspond to ~50% annual survival in the lowest quality habitat and 100% survival in the highest quality habitat at or below carrying capacity, based on studies of ungulates during harsh conditions (Appendix 1; Clutton-Brock & Coulson, 2002; Coulson, Milner-Gulland, & Clutton-Brock, 2000; Milner-Gulland, 1997; Owen-Smith, Mason, & Ogutu, 2005).
We examined two variables at the end of the simulation as a metric of the performance of each rule in a given landscape: (a) population size at the end of the simulation and (b) annual survival; this second variable standardizes population sizes to account for differences in initial population size across landscapes simulated with the same parameters. We examined the sensitivity of these outputs to our different parameter sets. We also calculated these output metrics halfway through the simulation period to test whether results were sensitive to the duration of the nonbreeding season. When comparing between rules, we used the threshold resource level (W) or density value (T) that produced the largest population size for each combination of other parameters (Appendix 2).
2.6 Resource supplementation, stabilization and degradation
We modelled anthropogenic habitat modification by changing the per-patch resource availability to a constant value, rather than one that changes over time. We explored three potential scenarios of habitat modification relative to average resource availability of 0.5 in the unmodified landscape: resource supplementation (resource availability at modified patches = 0.7), stabilization with no change in the mean (modified patches = 0.5), and resource degradation (modified patches = 0.3; Figure S3). For each value, we considered the effect of 0%–100% of the patches in the network being modified. At its extreme, this modification produces stable resource availability at all patches and time points (i.e. V = 0). Because portions of our model become analytically tractable under this condition, we solved equations for the quantity 
for each movement rule, which can be interpreted as the realized cost of movement (Appendix 3). Based on these solutions, we expected that resource supplementation would favour informed movement over uninformed movement and residency, but that informed movement would no longer provide any benefit or would even be detrimental in degraded landscapes.
All simulations for habitat modification were based on a single parameterization of the nomadic network model for both landscape and movement parameters (Table 1, baseline value column). These parameters represent a highly variable landscape (i.e. one likely to be inhabited by nomads), a low cost of movement, and moderate density dependence. We used the same process as above to simulate movement and population dynamics, and the parameters in each departure rule (e.g. W) remained unchanged regardless of the number of modified patches.
All simulations and analyses were implemented in R Version 3.5.1 (R Development Core Team, 2018).
3 RESULTS
3.1 Performance of departure rules in variable environments
Animals using informed nomadic departure rules (resource-based and competition-based) performed better than residents, animals using a density-only rule, or uninformed nomadic animals in variable environments (Figure 3a; Figure S4). Population sizes and survival rates almost always declined with increasing landscape variability; however, this decline was less dramatic for nomadic than resident animals (Figure 3a; Figure S5). Specifically, population sizes of residents were 21% smaller in highly variable landscapes as compared to less variable landscapes. In comparison, this decrease in population size was 18% for animals using the density-only rule, 17% for animals using uninformed movement, 14% for animals using resource-based movement and 12% for animals using competition-based movement. When density-independent mortality rates were high, population sizes of animals using resource- and competition-based movement rules were unaffected or even increased with increasing landscape variability, but residents or populations using uninformed movement declined (Figure S6). Higher landscape variability generally favoured higher movement propensities for animals using resource-based and uninformed movement (Figures S7 and S8). For example, the optimal value of the departure threshold W was 0.01 (the lowest value tested) when V = 0.2 and W = 0.5 when V = 1 (Table 1; Figure S7). For the density-only rule, the optimal value of T was usually 0.5 and had no consistent relationship with V (Figure S9).
The relative performance of the different movement rules was qualitatively insensitive to the duration of the simulation (Figure S10), the number of patches in the system, or mortality rates.
Departure rules that incorporated short-term memory generally performed slightly worse than simpler rules that did not incorporate memory when there was no temporal autocorrelation in the landscape, but performed slightly better in landscapes with some temporal autocorrelation (Figure S11). However, the benefits of memory were small relative to the benefits of informed movement over residency. For example, movement rules based on competition conferred a 20% benefit to population size relative to residency, but memory conferred only a 2% additional benefit relative to competition alone (when V = 1 and E = 3).
Higher costs of movement negatively affected all mobile animals, with the strongest negative effect on animals moving according to resource-based rules. For animals using resource-based movement, those with higher movement propensities (i.e. a higher movement threshold, W) performed better only when movement costs were low, density-dependent mortality was strong and landscape variability was high (Figures S7 and S12). When the cost of movement between patches was low, resource-based and competition-based departure rules performed similarly in variable environments (Figure 3a,b). However, as the cost of movement increased, residency began to outperform informed movement rules (Figure 3b; Figure S12), owing to higher movement rates for animals using the informed departure rules (Figure 3c). The relative performance of the competition-based and resource-based rules also depended on density-dependent and density-independent mortality rates; when density-independent mortality rates were high, resource-based movement performed better, and when density-dependent mortality rates were high, competition-based movement performed better (Figure S6).
3.2 Habitat modification
All three types of habitat modification reduced or eliminated the benefits of informed movement strategies (Figure 4). Resource supplementation increased survival rates for all departure rules (Figure 4a), with the largest increase for residents. Rules that incorporated memory performed no better than those that did not, and even performed worse when the proportion of modified habitat was low (Figure S13).
Similarly, habitat modification that stabilized resources (without changing the landscape-level mean) increased survival rates, with the strongest positive effects for residents (Figure 4b). However, for animals using competition-based movement, these benefits appeared only when at least 30% of habitat was stabilized, whereas residents and animals using uninformed movement benefitted from any level of modification (Figure 4b). Survival rates of animals using resource-based movement declined steeply when the proportion of patches modified was low, possibly because the movement threshold W was 0.5, equal to resource availability in the stabilized patches, meaning that animals never departed these patches even when densities were high.
When resources were degraded, residents and animals using uninformed movement still benefitted from increased stability (Figure 4c). In contrast, population sizes of animals using resource-based movement declined, and those of animals using competition-based movement remained unchanged or declined slightly, as we expected from our analytical solutions (Appendix 3). For all types and levels of modification, the competition-based rule performed at least as well as any other rule, even though its benefit (especially relative to residency) declined to zero with increasing modification. Though resource-based movement performed well in highly variable environments, it was the worst-performing rule when a large proportion of habitat was degraded.
Differences in the performance of each departure rule under habitat degradation resulted partially from differences in movement rates of animals using each rule. For the competition-based rule, movement rates decreased with increasing resource stability, and movement eventually ceased in 100% modified landscapes (Figure S14). In contrast, animals using the resource-based rule increased their movement rates in degraded landscapes, even at high levels of modification.
4 DISCUSSION
Our network-based model showed that simple informed nomadic rules (for which departure was based on resources and/or competition cues) outperformed both residency and uninformed movement in variable environments. Animals using resource-based movement usually moved more than animals using competition-based movement, and the relative performance of competition-based and resource-based movement strategies depended on the mortality costs of movement, resource availability, and competition. Not surprisingly, the benefits of informed movement rules declined in more stable environments, and movement responses to human-altered resources depended on a population's movement strategy. Residents derived the greatest benefit from habitat modification that stabilized resources. In contrast, populations using resource-based rules declined the most under resource degradation. This result suggests that animals using resources as departure cues could be particularly vulnerable to declines under habitat degradation, as a result of moving more than is optimal in homogenized landscapes.
4.1 Mechanisms of nomadic movement in highly variable landscapes
Our model results confirmed the adaptive value of nomadic movements in variable and unpredictable environments (Jonzén et al., 2011). More variable landscapes also favoured higher movement propensities for resource-based movement, further supporting that, along a spectrum from residency to nomadism, increasing landscape variability favours more nomadic movements. In contrast, uninformed movement and density-only movement rules performed poorly in all environments, suggesting that nomadism is unlikely to be random wandering or purely social, but rather must be based on environmental cues (Roshier et al., 2006). That both resource-based and competition-based departure rules performed well also suggests that both are plausible mechanisms for nomadic movement; the existence of multiple mechanisms is consistent with observations of nomadic animals (Table S1). For instance, resource-based movements have been observed in loggerhead turtles, (Schofield et al., 2010), grey-headed flying foxes (Parry-Jones & Augee, 1992) and some desert birds (Dean, 2004). Competition-based movements are well described in locusts (Bazazi et al., 2011) and birds (Dean, 2004) as well as in planthoppers, where the highly mobile state is more common in animals living in more variable habitats and at high densities (Denno, Roderick, Olmstead, & Dobel, 2011). Our modelling results confirm that both of these mechanisms could be adaptive in variable landscapes.
Incorporating memory into movement decisions improved performance only in landscapes with some temporal autocorrelation. In other words, while informed nomadic movement outperforms uninformed movement, the identity of this information is also important (Krebs & Inman, 2006; Stephens, 1989). In environments with no spatial or temporal autocorrelation, memory is unlikely to be useful because the past is a poor predictor of the future (Mueller & Fagan, 2008). In environments with low or moderate autocorrelation, memory could become useful for making navigation decisions, but the scale of memory must match the scale of autocorrelation (Mettke-Hofmann, 2014). For example, autocorrelation could occur because the mechanisms driving resource dynamics create temporal autocorrelation (e.g. vegetation green-up or drying) or from spatial patterns in abiotic drivers such as rainfall, each of which can occur at a different spatiotemporal scale in different environments. Future studies could investigate the interactions between the type and scale of autocorrelation and the types of memory that are beneficial for making nomadic movement decisions (Fagan et al., 2013).
Lower movement costs favoured higher movement propensities (i.e. more nomadic behaviour) in animals using resource-based movement (see also McNamara, 1982). This result suggests that resource-based nomadic movements might be less likely to occur in species or landscapes where movement is costly. Because the energetic cost of locomotion is lowest for animals that swim (Schmidt-Nielsen, 1972), resource-based movements could be more common in swimming than in walking or flying animals (particularly walking animals: Hein, Hou, & Gillooly, 2012). Among non-aquatic species, turtles, flying foxes and birds all use relatively low-cost locomotion strategies and have been hypothesized to follow resource-based rules (see above), whereas walking ungulates have been hypothesized to move based on conspecific density (Morelle et al., 2015). Similarly, resource-based movement performed better when density-dependent mortality was lower than density-independent mortality, suggesting that these mechanisms could be more common in systems where intraspecific competition is weak. For example, if food availability is controlled by environmental stochasticity, not by depletion by consumers (Fahrig, 2007; Newton, 2006), or the nomadic species is a less successful competitor than local heterospecifics (Dahbi, Retana, Lenoir, & Cerdá, 2008), then density-independent mortality could dominate.
4.2 Effects of resource supplementation, stabilization and removal on nomadic animals
Humans are modifying resources for countless mobile species. In many cases, these changes stabilize resources, whether through subsidy or degradation, by making habitat patches more uniform over time (Oro et al., 2013; Shochat et al., 2006). When we examined the effects of habitat modification on nomadic animals, we found that competition-based movement was more resilient than resource-based movement and that residents benefitted most from habitat modification. Animals using competition-based movement were resilient because they moved less as more patches became stabilized, eventually becoming resident at high levels of modification. In contrast, animals using resource-based and uninformed rules continued moving as habitat degradation continued, thus incurring a cost even when movement was no longer beneficial. It is important to note that such movements could have benefits we do not consider here, such as identification of new, suitable patches (Bennetts & Kitchens, 2000), or maintaining genetic connectivity (Fischer & Lindenmayer, 2007). Our modelling results suggest that departure rules that incorporate information on both resources and density perform slightly better both in variable environments and under environmental change, compared to departure rules that are based on resources alone.
Consistent with these modelling results, empirical studies show that movement rates can change following habitat modification. Increased food availability can reduce movements, for example in spiders (Kreiter & Wise, 2001), goshawks (Kennedy & Ward, 2003) and even humans in hunter-gatherer societies (Testart et al., 1982). In nomadic white ibis Eudocimus albus inhabiting urban areas, a higher proportion of provisioned food is associated with higher site fidelity (Maureen Murray, pers. comm.), and nomadic grey-headed flying foxes have established permanent camps in urban areas, where food is more stable (van der Ree et al., 2006; Williams, McDonnell, Phelan, Keim, & Van Der Ree, 2006). At the same time, some species may continue moving even in altered landscapes where movement is no longer as adaptive. Increases in winter temperatures are predicted to promote residency in partially migratory populations, but the proportion of blue tits migrating has remained steady as temperatures have increased, probably because they respond to non-temperature cues (Nilsson, Lindström, Jonzén, Nilsson, & Karlsson, 2006). Our results highlight the importance of considering not just changes in mean resource availability, but also changes in resource dynamics, when studying how habitat modifications impact animal populations.
Different movement modes are likely to respond differently to various forms of landscape change (e.g. fragmentation vs. loss, Fahrig, 2007). Although we lack empirical evidence that discriminates the effects of habitat modification on animals moving according to different rules, our model showed that each departure rule responded differently to landscape stabilization. In addition, even without a change in the departure rule in our model, movement rates changed alongside habitat modification; this behavioural adaptation suggests that management that preserves or restores dynamic landscapes could also restore natural movement patterns quickly, even in the absence of natural selection.
4.3 Implications and future directions
Modelling is a powerful way to explore multiple scenarios in cases where field studies may be too time- or extent-limited to draw useful conclusions. Here we build the first mechanistic model of departure rules underlying nomadic movements in variable and modified landscapes; we show that the responses of nomadic animals to habitat modification are linked to their movement mechanisms, and that residents benefit more from habitat modification than do mobile animals. These results and this modelling framework provide an opportunity for future investigations of nomadic species in changing landscapes. For instance, future models could explore whether more complex movement mechanisms (e.g. destination rules or exploratory movements, Bennetts & Kitchens, 2000) allow nomadic animals to better respond to environmental change, particularly in regions where climate models predict increased environmental variability (Meehl & Tebaldi, 2004). In addition, if mortality rates increase with habitat modification (e.g. due to movement costs of roads and other barriers, Nandintsetseg et al., 2019 or novel species interactions, Stojanovic, Webb, Alderman, Porfirio, & Heinsohn, 2014), then mobile animals might suffer, even under landscape changes that increase resource availability. A spatially explicit version of this model could help elucidate whether variation in movement costs across the landscape affects the benefits of nomadic movements and populations’ responses to interacting environmental changes.
Future empirical studies should focus on evidence for differential effects of habitat modification on species using different movement strategies. These studies could come in the form of comparative analyses of the relative abundance of more and less mobile species living in the same areas (‘species pairs’), as has been done for regionally co-occurring migratory and resident species (Fryxell et al., 1988). Alternatively, investigating intraspecific variation in movement strategies would reveal whether population abundances and growth rates are related to their movement strategy and/or characteristics of the landscapes in which they live. Future models could incorporate additional aspects of landscape change (e.g. climate extremes) and social behaviour, since some nomadic species move in groups rather than making independent movement decisions (Teitelbaum & Mueller, 2019). In addition, our simple model considers only a single species, but in reality, animals exist in communities. Future studies should explore how nomadic movements affect species interactions (e.g. with competitors or parasites) and how changes in movement patterns could alter these interactions.
We saw that the effect of habitat modification differed depending on both the type of modification (e.g. resource degradation vs. supplementation) and a population's movement strategy, where residents benefitted most from these changes. In migratory species, phenological mismatches can cause population declines when migrants do not track environmental change (Winkler et al., 2014). These declines may be less dramatic in nomads because their movement patterns are more flexible. In this model, animals using a competition-based rule adapted to modified landscapes by changing their movement patterns (i.e. decreasing their movement rates). Though this reduction in movement increased their performance in modified landscapes, it could also reduce the ecosystem-level benefits of nomadism (e.g. seed dispersal in fragmented landscapes or trophic effects: Bauer & Hoye, 2014; Mueller, Lenz, Caprano, Fiedler, & Böhning-Gaese, 2014; Teitelbaum & Mueller, 2019). Thus, management goals for mobile animals should consider not only population sizes, but also maintaining historical movement patterns.
Our study provides insight into movement mechanisms that promote the survival of nomadic species in highly variable landscapes. There are probably multiple mechanisms operating simultaneously, each of which will respond differently to habitat modification. We urge researchers and managers to consider the cues for movement when considering study design and conservation actions, since these behavioural mechanisms ultimately mediate the responses of species to their environments.
ACKNOWLEDGEMENTS
We thank T. Mueller, R.M. Holdo, V.O. Ezenwa, J. Hepinstall-Cymerman and the Hall and Altizer laboratory groups at UGA for helpful discussion. We thank M.V. Evans, R.S. Smith and C.A. Sanchez for comments on an earlier version of the manuscript and K. Sabey for assistance with the graphical abstract. This research was supported by a UGA Presidential Fellowship and an NSF Graduate Research Fellowship to C.S.T. and NSF grants DEB-1518611 and DEB-1754392 to S.A. and R.J.H.
AUTHORS' CONTRIBUTIONS
C.S.T. conceived the study; all authors participated in designing project goals; C.S.T. and R.J.H. developed the model and derived analytical solutions; C.S.T. implemented simulations and designed figures and tables; C.S.T. wrote the first draft of the manuscript and all authors contributed to subsequent revisions.
Open Research
DATA AVAILABILITY STATEMENT
Data are available from the Dryad Digital Repository https://doi.org/10.5061/dryad.dbrv15dzg (Teitelbaum, Altizer, & Hall, 2020).
