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• W3100738394 abstract "‘Resource tracking’ describes how animals move to exploit resource availability that changes across space and time, a behavior that should be beneficial and ubiquitous in many systems and taxa.However, when and how animals track resources will depend on the spatiotemporal configuration of resources in their environment.We present a unifying framework to quantify resource dynamics, which allows for testing predictions on emergent movement behaviors in different resource configurations.Resource tracking has important consequences for individual fitness, population dynamics, community interactions, and ecosystem services.The reliance of animals on phenological diversity in resource availability, and on their ability to move without restrictions to track resources, is a significant concern for biodiversity conservation with human-induced rapid environmental change. Resource tracking, where animals increase energy gain by moving to track phenological variation in resources across space, is emerging as a fundamental attribute of animal movement ecology. However, a theoretical framework to understand when and where resource tracking should occur, and how resource tracking should lead to emergent ecological patterns, is lacking. We present a framework that unites concepts from optimal foraging theory and landscape ecology, which can be used to generate and test predictions on how resource dynamics shape animal movement across taxa, systems, and scales. Consideration of the interplay between animal movement and resource dynamics not only advances ecological understanding but can also guide biodiversity conservation in an era of global change. Resource tracking, where animals increase energy gain by moving to track phenological variation in resources across space, is emerging as a fundamental attribute of animal movement ecology. However, a theoretical framework to understand when and where resource tracking should occur, and how resource tracking should lead to emergent ecological patterns, is lacking. We present a framework that unites concepts from optimal foraging theory and landscape ecology, which can be used to generate and test predictions on how resource dynamics shape animal movement across taxa, systems, and scales. Consideration of the interplay between animal movement and resource dynamics not only advances ecological understanding but can also guide biodiversity conservation in an era of global change. The spatiotemporal distribution of resources shapes ecological processes, from individual fitness to population dynamics and species interactions [1.Guégan J.-F. et al.Energy availability and habitat heterogeneity predict global riverine fish diversity.Nature. 1998; 391: 382-384Crossref Scopus (242) Google Scholar,2.Questad E.J. Foster B.L. Coexistence through spatio-temporal heterogeneity and species sorting in grassland plant communities.Ecol. Lett. 2008; 11: 717-726Crossref PubMed Scopus (0) Google Scholar]. Animal movement is the primary link between these processes, allowing individuals to respond dynamically to ever changing, heterogeneous environments [3.Nathan R. et al.A movement ecology paradigm for unifying organismal movement research.Proc. Natl. Acad. Sci. U. S. A. 2008; 105: 19052-19059Crossref PubMed Scopus (1325) Google Scholar,4.Morales J.M. et al.Building the bridge between animal movement and population dynamics.Phil. Trans. R. Soc. Lond. B. Biol. Sci. 2010; 365: 2289-2301Crossref PubMed Scopus (0) Google Scholar]. Research over the last decade has revealed how both the spatial and temporal configuration of resources, termed the ‘resource landscape’ (see Glossary), can profoundly underpin animal movement strategies [5.Mueller T. et al.How landscape dynamics link individual- to population-level movement patterns: a multispecies comparison of ungulate relocation data.Glob. Ecol. Biogeogr. 2011; 20: 683-694Crossref Scopus (107) Google Scholar]. More recently, the concept of ‘resource tracking’ describes how fine-scale movements in response to ‘phenological variation’ in resource availability can scale up to emergent movement patterns such as migration [6.Armstrong J.B. et al.Resource waves: phenological diversity enhances foraging opportunities for mobile consumers.Ecology. 2016; 97: 1099-1112Crossref PubMed Scopus (60) Google Scholar]. Drawing from the fields of behavioral and spatial ecology, the central tenet of resource tracking is that mobile consumers can benefit by moving to exploit phenological variation in resources across space. From ungulates tracking fleeting plant phenology [7.Aikens E.O. et al.The greenscape shapes surfing of resource waves in a large migratory herbivore.Ecol. Lett. 2017; 65: 502-510Google Scholar, 8.Holdo R.M. et al.Opposing rainfall and plant nutritional gradients best explain the wildebeest migration in the Serengeti.Am. Nat. 2009; 173: 431-445Crossref PubMed Scopus (147) Google Scholar, 9.Singh N.J. et al.Tracking greenery across a latitudinal gradient in central Asia - the migration of the saiga antelope.Divers. Distrib. 2010; 16: 663-675Crossref Scopus (41) Google Scholar, 10.Merkle J.A. et al.Large herbivores surf waves of green-up during spring.Proc. Biol. Sci. 2016; 283: 20160456Crossref PubMed Scopus (105) Google Scholar, 11.Middleton A.D. et al.Green-wave surfing increases fat gain in a migratory ungulate.Oikos. 2018; 20: 741-749Google Scholar] to whales tracking prey aggregations [12.Abrahms B. et al.Memory and resource tracking drive blue whale migrations.Proc. Natl. Acad. Sci. U. S. A. 2019; 116: 5582-5587Crossref PubMed Scopus (38) Google Scholar] to raptors tracking updrafts [13.Shepard E.L.C. et al.Energy landscapes shape animal movement ecology.Am. Nat. 2013; 182: 298-312Crossref PubMed Scopus (146) Google Scholar, 14.Harel R. et al.Adult vultures outperform juveniles in challenging thermal soaring conditions.Sci. Rep. 2016; 6: 1-8Crossref PubMed Scopus (43) Google Scholar, 15.Duerr A.E. et al.Flight response of slope-soaring birds to seasonal variation in thermal generation.Funct. Ecol. 2014; 29: 779-790Crossref Scopus (38) Google Scholar], resource tracking has been documented worldwide among a diverse range of systems and taxa (Figure 1). Though resource tracking is fundamental to animal movement ecology [6.Armstrong J.B. et al.Resource waves: phenological diversity enhances foraging opportunities for mobile consumers.Ecology. 2016; 97: 1099-1112Crossref PubMed Scopus (60) Google Scholar], its theoretical foundation has not yet been articulated. Optimal foraging theory (OFT) and landscape ecology both offer classical ecological theory that underlie resource tracking. OFT is concerned with predicting the fine-scale behavioral decisions of animals based on the idea that animals make foraging decisions to maximize their energy intake and fitness [16.Pyke G.H. Optimal foraging theory: a critical review.Annu. Rev. Ecol. Evol. Syst. 1984; 15: 523-575Crossref Google Scholar, 17.MacArthur R.H. Pianka E.R. On optimal use of a patchy environment.Am. Nat. 1966; 100: 603-609Crossref Google Scholar, 18.Schoener T.W. Theory of feeding strategies.Annu. Rev. Ecol. Evol. Syst. 1971; 2: 369-404Crossref Google Scholar]. Among other things, OFT has provided theoretical background on how animals make behavioral decisions under uncertainty [19.Dall S.R. et al.Information and its use by animals in evolutionary ecology.Trends Ecol. Evol. 2005; 20: 187-193Abstract Full Text Full Text PDF PubMed Scopus (820) Google Scholar], particularly when the environment varies in space and time [20.Schmidt K.A. et al.The ecology of information: an overview on the ecological significance of making informed decisions.Oikos. 2010; 119: 304-316Crossref Scopus (163) Google Scholar]. Importantly, the manner in which animals forage under uncertainty sparked interest in the scale that animals perceive their environment [21.Abrahams M.V. Patch choice under perceptual constraints: a cause for departures from an ideal free distribution.Behav. Ecol. Sociobiol. 1986; 19: 409-415Crossref Scopus (0) Google Scholar,22.Lima S.L. Zollner P.A. Towards a behavioral ecology of ecological landscapes.Trends Ecol. Evol. 1996; 11: 131-135Abstract Full Text PDF PubMed Scopus (654) Google Scholar]. Whereas OFT is rooted in classical ethology, which considers animal behavior at the scale of seconds to hours [23.Owen-Smith N. et al.Foraging theory upscaled: the behavioural ecology of herbivore movement.Phil. Trans. R. Soc. Lond. Biol. Sci. 2010; 365: 2267-2278Crossref PubMed Scopus (0) Google Scholar], resource tracking can be considered a manifestation of OFT played out over longer periods across large landscapes. Specifically, individual movement decisions intended to maximize energy intake in a variable environment lead to movement patterns at broader temporal scales [24.Mueller T. Fagan W.F. Search and navigation in dynamic environments - from individual behaviors to population distributions.Oikos. 2008; 117: 654-664Crossref Scopus (221) Google Scholar]. At the other end of the spatial scale continuum, landscape ecology is concerned with the causes and consequences of spatial heterogeneity within mosaics of habitat patches (i.e., the landscape scale) [25.Turner M.G. Landscape ecology: the effect of pattern on process.Annu. Rev. Ecol. Evol. Syst. 1989; 20: 171-197Crossref Scopus (1927) Google Scholar]. Concepts derived from landscape ecology offer insight into a range of ecological processes [26.Dunning J.B. et al.Ecological processes that affect populations in complex landscapes.Oikos. 1992; 65: 169-175Crossref Google Scholar]. For instance, landscape ecology highlighted the idea of landscape connectivity, whereby an animal’s use of a habitat patch is dependent on its distance from the animal, the biophysical nature along the way, and mobility of the animal [27.Taylor P.D. et al.Connectivity is a vital element of landscape structure.Oikos. 1993; 68: 571-573Crossref Google Scholar,28.Tischendorf L. Fahrig L. On the usage and measurement of landscape connectivity.Oikos. 2000; 90: 7-19Crossref Google Scholar]. This framework has informed our understanding of conservation biology [29.Wiens J.A. Landscape ecology as a foundation for sustainable conservation.Land Ecol. 2008; 24: 1053-1065Crossref Scopus (95) Google Scholar] and dispersal and invasion ecology, among others [30.Turner M.G. Landscape ecology: what is the state of the science?.Annu. Rev. Ecol. Evol. Syst. 2005; 36: 319-344Crossref Scopus (587) Google Scholar]. While landscape ecology has classically considered resource configurations as static [25.Turner M.G. Landscape ecology: the effect of pattern on process.Annu. Rev. Ecol. Evol. Syst. 1989; 20: 171-197Crossref Scopus (1927) Google Scholar], particularly at subannual timescales (but see [31.Zaccarelli N. et al.Order and disorder in ecological time-series: introducing normalized spectral entropy.Ecol. Indic. 2013; 28: 22-30Crossref Scopus (42) Google Scholar]), studies of resource tracking explicitly consider how the spatial patterning of resources changes through time, typically at finer temporal extents of weeks to months. As landscape ecology has offered ecologists a framework for consistently defining and quantifying spatial heterogeneity in resources [32.Li H. Reynolds J.F. On definition and quantification of heterogeneity.Oikos. 1995; 73: 280-284Crossref Google Scholar], a similar framework is needed for characterizing and quantifying spatiotemporal heterogeneity that incorporates the critical element of time. The concept of resource tracking provides a theoretical bridge between: (i) the temporally explicit, often spatially implicit, field of OFT focused on animal behavior; and (ii) the spatially explicit, often temporally implicit, field of landscape ecology focused on the physical environment. Additionally, resource tracking serves as a lens to unify these two fields across spatial scales to better understand the drivers of animal movement. At the broadest spatial scale, Earth’s tilt and orbit generate seasonal variation in photoperiod and climate. Geographic variation in seasonality sets the stage for biological variation at nested spatial scales (Figure 2A,B ). For example, latitudinal variation in photoperiod, atmospheric patterns, and temperature generate corresponding gradients in the phenology of primary productivity, including phytoplankton blooms in aquatic and marine ecosystems [33.Strub P.T. James C. Atmospheric conditions during the spring and fall transitions in the coastal ocean off western United States.J. Geophys. Res. Oceans. 1988; 93: 15561-15584Crossref Scopus (37) Google Scholar,34.Checkley D.M. Barth J.A. Patterns and processes in the California Current System.Prog. Oceanogr. 2009; 83: 49-64Crossref Scopus (271) Google Scholar] and spring green-up and flowering in terrestrial ecosystems [35.Hopkins A. The bioclimatic law.J. Wash. Acad. Sci. 1920; 10: 34-40Google Scholar, 36.Prevéy J. et al.Greater temperature sensitivity of plant phenology at colder sites: implications for convergence across northern latitudes.Glob. Change Biol. 2017; 23: 2660-2671Crossref PubMed Scopus (64) Google Scholar, 37.O’Leary D. et al.Snowmelt velocity predicts vegetation green-wave velocity in mountainous ecological systems of North America.Int. J. Appl. Earth Obs. Geoinf. 2020; 89: 102110Crossref Google Scholar]. Nested within latitudinal or continental gradients, geomorphic processes that generate topography and bathymetry shape finer scales of phenology. For example, elevation mediates the growing season for plants [35.Hopkins A. The bioclimatic law.J. Wash. Acad. Sci. 1920; 10: 34-40Google Scholar], such that low elevation sites within mountain ranges can green-up several months earlier than high elevation sites [7.Aikens E.O. et al.The greenscape shapes surfing of resource waves in a large migratory herbivore.Ecol. Lett. 2017; 65: 502-510Google Scholar]. In riverine systems, water temperatures often vary longitudinally and generate corresponding gradients in the phenology of insects and fishes [38.Anderson H.E. et al.Water temperature drives variability in salmonfly abundance, emergence timing, and body size.River Res. Appl. 2019; 35: 1013-1022Crossref Scopus (4) Google Scholar,39.Deacy W.W. et al.Variation in spawning phenology within salmon populations influences landscape-level patterns of brown bear activity.Ecosphere. 2019; 10e02575Crossref Scopus (6) Google Scholar]. Within-site variation in phenology results from microclimate and hydrological variation. For example, equator-facing slopes experience more solar insolation, and depressions in topography (e.g., valley bottoms or marine canyons) accumulate cooler temperatures [40.Dobrowski S.Z. A climatic basis for microrefugia: the influence of terrain on climate.Glob. Change Biol. 2011; 17: 1022-1035Crossref Scopus (471) Google Scholar]. These topoclimatic mechanisms can generate phenological variation at small spatial scales. While the physical environment exhibits heterogeneity at multiple spatial scales, the biological response to phenology is patterned at multiple organizational levels, including individual, population, and species. Coupling of environmental heterogeneity with variable biological responses act to prolong the overall availability of resources to consumers [41.Diez J.M. et al.Forecasting phenology: from species variability to community patterns.Ecol. Lett. 2012; 15: 545-553Crossref PubMed Scopus (123) Google Scholar] (Figure 2C). Individuals may vary in responses to the same environmental cues (i.e., different reaction norms [42.Inouye B.D. et al.Phenology as a process rather than an event: from individual reaction norms to community metrics.Ecol. Monogr. 2019; 89e01352Crossref Scopus (12) Google Scholar]), or individuals may have a common response but experience different microhabitats [39.Deacy W.W. et al.Variation in spawning phenology within salmon populations influences landscape-level patterns of brown bear activity.Ecosphere. 2019; 10e02575Crossref Scopus (6) Google Scholar]. These individual-level differences cause broad phenological variation within a population. For example, in salmon, genetics give rise to individual-level variation in arrival at spawning sites [43.Kovach R.P. et al.Genetic change for earlier migration timing in a pink salmon population.Proc. Biol. Sci. 2012; 279: 3870-3878Crossref PubMed Scopus (0) Google Scholar,44.Hendry A.P. et al.Adaptive variation in senescence: reproductive lifespan in a wild salmon population.Proc. Biol. Sci. 2004; 271: 259-266Crossref PubMed Scopus (70) Google Scholar]. The magnitude of individual-level variation in arrival date subsequently determines how long a population of salmon is available to predators [39.Deacy W.W. et al.Variation in spawning phenology within salmon populations influences landscape-level patterns of brown bear activity.Ecosphere. 2019; 10e02575Crossref Scopus (6) Google Scholar]. Across salmon populations, variation in water temperature among spawning sites leads to population-level variation in average spawn timing [45.Lisi P.J. et al.Association between geomorphic attributes of watersheds, water temperature, and salmon spawn timing in Alaskan streams.Geomorphology. 2013; 185: 78-86Crossref Scopus (59) Google Scholar]. This population-level variation across the landscape prolongs foraging opportunities for consumers that can move among spawning sites [46.Schindler D.E. et al.Riding the crimson tide: mobile terrestrial consumers track phenological variation in spawning of an anadromous fish.Biol. Lett. 2013; 9: 20130048Crossref PubMed Scopus (74) Google Scholar]. Furthermore, many water bodies have multiple species of salmon and these species often have different relationships between water temperature and spawning phenology. Together, this variation can increase the duration of salmon foraging opportunities for consumers such that spawning salmon can be available for several months in some basins, though individuals are only present and spawning for days or weeks [47.Service C.N. et al.Salmonid species diversity predicts salmon consumption by terrestrial wildlife.J. Anim. Ecol. 2019; 88: 392-404Crossref PubMed Scopus (7) Google Scholar]. The resource landscape determines the availability of energy to mobile consumers, which in turn shapes animal movement. The spatial scale of the resource landscape depends on the ecological process, time period, and mobility or activity of the study species in question [48.Wiens J.A. Spatial scaling in ecology.Funct. Ecol. 1989; 3: 385-397Crossref Google Scholar]. While most studies of resource tracking focus on food resources, animals also track non-food resources that reduce energetic output or increase survival. For example, soaring birds track seasonal emergence of wind thermals [15.Duerr A.E. et al.Flight response of slope-soaring birds to seasonal variation in thermal generation.Funct. Ecol. 2014; 29: 779-790Crossref Scopus (38) Google Scholar]; migrating waterfowl and ungulates track snow or ice melt [49.Xu F. Si Y. The frost wave hypothesis: how the environment drives autumn departure of migratory waterfowl.Ecol. Indic. 2019; 101: 1018-1025Crossref Scopus (5) Google Scholar,50.Rickbeil G.J.M. et al.Plasticity in elk migration timing is a response to changing environmental conditions.Glob. Change Biol. 2019; 25: 2368-2381Crossref PubMed Scopus (13) Google Scholar]; ectotherms such as tuna, salmon, and tortoises track thermal landscapes over large distances [51.Bastille-Rousseau G. et al.Migration triggers in a large herbivore: Galápagos giant tortoises navigating resource gradients on volcanoes.Ecology. 2019; 100e02658Crossref PubMed Scopus (2) Google Scholar, 52.Baldock J.R. et al.Juvenile coho salmon track a seasonally shifting thermal mosaic across a river floodplain.Freshw. Biol. 2016; 61: 1454-1465Crossref Scopus (14) Google Scholar, 53.Boustany A.M. et al.Movements of pacific bluefin tuna (Thunnus orientalis) in the Eastern North Pacific revealed with archival tags.Prog. Oceanogr. 2010; 86: 94-104Crossref Scopus (70) Google Scholar]; and vertically migrating fish track seasonal changes in light environments to avoid predation [54.Scheuerell M.D. Schindler D.E. Diel vertical migration by juvenile sockeye salmon: empirical evidence for the antipredation window.Ecology. 2003; 84: 1713-1720Crossref Google Scholar]. Given the incredibly broad diversity of resource landscapes on Earth, a generalizable framework for their characterization and quantification is necessary in order to test hypotheses across systems. We expand upon Mueller et al. [24.Mueller T. Fagan W.F. Search and navigation in dynamic environments - from individual behaviors to population distributions.Oikos. 2008; 117: 654-664Crossref Scopus (221) Google Scholar] to characterize the resource landscape according to six axes (Figure 3). Abundance, timing, ephemerality, and predictability are defined as patch-level characteristics. The spatial configuration and variance of patch-level characteristics constitute emergent landscape-level characteristics. All of these axes interact with one another to produce resource dynamics that influence animal movement. By applying these axes, several predictions about resource tracking and animal movement emerge that often challenge traditional conceptions. For example, two patches that differ in total resource abundance might be classified as low- or high-quality habitat, with the assumption being that animals will use the high-quality (i.e., higher resource abundance) habitat more frequently than the lower-quality habitat (i.e., lower resource abundance), or that high-quality habitats will support a greater density of animals [55.Fretwell S.D. Lucas H.L. On territorial behavior and other factors influencing habitat distribution in birds.Acta Biotheor. 1970; 19: 16-36Crossref Scopus (0) Google Scholar] (Figure 3A). However, if the timing of peak abundance across patches varies, then moving across patches would extend the time window when resources are available (Figure 3B). Thus, habitats with relatively low resource abundance may contribute disproportionately to consumers if their phenological patterns provide temporally unique foraging opportunities (Figure 4A ). Ephemerality describes how long resources are accessible at a given point on the landscape (Figure 3C). For example, fleeting resources that are only available for a short period of time are likely to favor close tracking of peak resource availability, whereas resources that are more prolonged might still facilitate some tracking, but deviating from peak resource availability is less costly [7.Aikens E.O. et al.The greenscape shapes surfing of resource waves in a large migratory herbivore.Ecol. Lett. 2017; 65: 502-510Google Scholar] (Figure 4B). Resource tracking is less likely in places with constant resource availability [5.Mueller T. et al.How landscape dynamics link individual- to population-level movement patterns: a multispecies comparison of ungulate relocation data.Glob. Ecol. Biogeogr. 2011; 20: 683-694Crossref Scopus (107) Google Scholar]. However, resource tracking is beneficial if the resource is constant but of low quality, or animals move to track an alternative resource pulse available elsewhere (e.g., predators moving to intercept migrant prey [56.Furey N.B. et al.Migratory coupling between predators and prey.Nat. Ecol. Evol. 2018; 2: 1846-1853Crossref PubMed Scopus (10) Google Scholar]). Resource predictability can be described in terms of temporal ‘constancy’ and ‘contingency’ (Figure 3D) [57.Colwell R.K. Predictability, constancy, and contingency of periodic phenomena.Ecology. 1974; 55: 1148-1153Crossref Google Scholar]. Greater predictability can occur through either little variation in a resource through time (i.e., high temporal constancy) or periodic variation in the resources through time (i.e., high temporal contingency) [58.Riotte-Lambert L. Matthiopoulos J. Environmental predictability as a cause and consequence of animal movement.Trends Ecol. Evol. 2019; 35: 163-174Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar]. A seasonal environment is characterized by low constancy but high contingency, whereas a completely unpredictable landscape would have low constancy and low contingency (Figure 3D) [24.Mueller T. Fagan W.F. Search and navigation in dynamic environments - from individual behaviors to population distributions.Oikos. 2008; 117: 654-664Crossref Scopus (221) Google Scholar]. More repeatable movement behaviors and greater fidelity to certain patches are more likely to emerge in patches with greater predictability [24.Mueller T. Fagan W.F. Search and navigation in dynamic environments - from individual behaviors to population distributions.Oikos. 2008; 117: 654-664Crossref Scopus (221) Google Scholar,59.Teitelbaum C.S. Mueller T. Beyond migration: causes and consequences of nomadic animal movements.Trends Ecol. Evol. 2019; 34: 569-581Abstract Full Text Full Text PDF PubMed Scopus (23) Google Scholar] (Figure 4D). Likewise, moderate constancy and contingency, characterized by predictable peaks in resources that vary in absolute abundance across space and time, may result in animals that rely on memory or tracking of long-term averages [12.Abrahms B. et al.Memory and resource tracking drive blue whale migrations.Proc. Natl. Acad. Sci. U. S. A. 2019; 116: 5582-5587Crossref PubMed Scopus (38) Google Scholar]. Heterogeneity in patch-level characteristics leads to emergent properties at the landscape level that profoundly shape the broad-scale movement patterns of animals [5.Mueller T. et al.How landscape dynamics link individual- to population-level movement patterns: a multispecies comparison of ungulate relocation data.Glob. Ecol. Biogeogr. 2011; 20: 683-694Crossref Scopus (107) Google Scholar,24.Mueller T. Fagan W.F. Search and navigation in dynamic environments - from individual behaviors to population distributions.Oikos. 2008; 117: 654-664Crossref Scopus (221) Google Scholar] (Figure 3E,F). Environments can vary in the spatial configuration of resource phenology, from a completely random distribution, to patches of areas with similar phenology, to ordered gradients (Figure 3E, Box 1). Increasing spatial autocorrelation should favor longer distance animal movements to track spatial heterogeneity in resource phenology [5.Mueller T. et al.How landscape dynamics link individual- to population-level movement patterns: a multispecies comparison of ungulate relocation data.Glob. Ecol. Biogeogr. 2011; 20: 683-694Crossref Scopus (107) Google Scholar, 6.Armstrong J.B. et al.Resource waves: phenological diversity enhances foraging opportunities for mobile consumers.Ecology. 2016; 97: 1099-1112Crossref PubMed Scopus (60) Google Scholar, 7.Aikens E.O. et al.The greenscape shapes surfing of resource waves in a large migratory herbivore.Ecol. Lett. 2017; 65: 502-510Google Scholar,24.Mueller T. Fagan W.F. Search and navigation in dynamic environments - from individual behaviors to population distributions.Oikos. 2008; 117: 654-664Crossref Scopus (221) Google Scholar] (Figure 4D). When resource phenology is highly ordered across space (i.e., is highly spatially autocorrelated), it can be described as a ‘resource wave’ [7.Aikens E.O. et al.The greenscape shapes surfing of resource waves in a large migratory herbivore.Ecol. Lett. 2017; 65: 502-510Google Scholar]. Tracking of resource waves, which results in directional and long-distance movement such as migration, is a specific case of resource tracking and is prevalent in systems with wave-like resource phenology (Box 1) [7.Aikens E.O. et al.The greenscape shapes surfing of resource waves in a large migratory herbivore.Ecol. Lett. 2017; 65: 502-510Google Scholar,60.Aikens E.O. et al.Wave-like patterns of plant phenology determine ungulate movement tactics.Curr. Biol. 2020; 30: 1-6Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar]. The total variance in patch-level characteristics in a landscape can also vary substantially, ranging from homogeneous to highly variable (Figure 3F). Increasing variance should also favor resource tracking to exploit phenological variation in the landscape.Box 1A Generalizable Framework to Quantify Spatial Configuration and Variance in Resource LandscapesSemivariograms are a tool from geostatistics that examine the degree of autocorrelation between a variable of interest at different spatial scales [101.Clark I. Practical Geostatistics. Applied Science, 1979Google Scholar]. Spatial and temporal resource dynamics can be examined using variograms by modeling the spatial autocorrelation of the date of peak resource availability at different scales (Figure I). If there is no spatial structure in peak resources (Figure IA), then the semivariance plotted across different distance lags will be a flat line (Figure IE). The patch size of areas that peak in resource availability at similar times increase with spatial autocorrelation (Figure IB,C), resulting in increasing semivariance values that plateau at larger distance lags (Figure IF,G). Wave-like resources are characterized by resource phenology that progresses sequentially across large landscapes (Figure ID). In an environment with a resource wave, the difference in the date of resource peaks increases with greater distances between points (Figure ID), resulting in a continued increase in the semivariance at larger distance lags (Figure IH). Several key metrics can be extracted from the semivariogram, including the landscape-level variance in phenology, or strength of the wave (i.e., the maximum semivariance, also called the sill; horizontal dark grey broken line in Figure IG) and the length of the wave (i.e., the distance lag where the maximum semivariance is reached, also called the range; vertical light grey broken line in Figure IG). It is useful to fi" @default.
• W3100738394 created "2020-11-23" @default.
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• W3100738394 date "2021-04-01" @default.
• W3100738394 modified "2023-10-15" @default.
• W3100738394 title "Emerging Perspectives on Resource Tracking and Animal Movement Ecology" @default.
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