Susana M Coelho

Olivier Godfroy

Alok Arun

Gildas Le Corguillé

Akira F Peters

J Mark Cock
email: cock@sb-roscoff.fr

Coelho Sm

Le Corguillé

Peters Af

Ouroboros

Genetic regulation of life cycle transitions in the brown alga Ectocarpus

Keywords: brown algae, Ectocarpus siliculosus, gametophyte, homeotic mutation, initial cell polarisation, life cycle, multicellularity, parthenogenesis, Phaeophyceae, sporophyte

The life cycle of an organism is one of its most elemental features, underpinning a broad range of phenomena including developmental processes, reproductive fitness, mode of dispersal and adaptation to the local environment. Life cycle modification may have played an important role during the evolution of several eukaryotic groups, including the terrestrial plants. Brown algae are potentially interesting models to study life cycle evolution because this group exhibits a broad range of different life cycles. Currently, life cycle studies are focused on the emerging brown algal model Ectocarpus. Two life cycle mutants have been described in this species, both of which cause the sporophyte generation to exhibit gametophyte characteristics. The ouroboros mutation is particularly interesting because it induces complete conversion of the sporophyte generation into a functional, gamete-producing gametophyte, a class of mutation that has not been described so far in other systems. Analysis of Ectocarpus life cycle mutants is providing insights into several life-cyclerelated processes including parthenogenesis, symmetric/asymmetric initial cell divisions and sex determination.

Multicellular organisms from diverse groups across the eukaryotic tree exhibit a variety of life cycles, differing essentially in the relative importance of the diploid and haploid phase [START_REF] Coelho | Complex life cycles of multicellular eukaryotes: new approaches based on the use of model organisms[END_REF] . For most animal species the diploid phase of the life cycle is the only multicellular stage, whereas flowering plants exhibit an alternation between two multicellular generations, the macroscopic, diploid sporophyte and the microscopic, haploid gametophyte. Modifications to life cycle structures may have played an important adaptive role during the evolution of some major eukaryotic groups. In the green lineage, for example, there appears to have been a gradual tendency towards increasing dominance of the diploid generation as plants adapted to the terrestrial environment. As a result, the gametophyte generation in angiosperms (i.e. the pollen grain or the embryo sac) has been reduced to a structure consisting of just a few cells.

Despite the importance of life cycles, from both an evolutionary and a developmental perspective, little is known about how life cycle transitions are regulated at the molecular level. Brown algae are a particularly interesting group for the study of life cycle regulation and evolution because they exhibit a broad range of different cycles, ranging from isomorphic, haploid-diploid (with two morphologically identical generations) to diploid (with only one multicellular generation). In addition, brown algal life cycles are often quite complex, sometimes including several variant pathways. The filamentous brown alga Ectocarpus has a haploid-diploid life cycle that involves alternation between two morphologically similar generations but a number of variations on this basic cycle are possible. For example, gametes that fail to fuse with a gamete of the opposite sex can germinate parthenogenetically to produce haploid parthenosporophytes [START_REF] Bothwell | Role of endoreduplication and apomeiosis during parthenogenetic reproduction in the model brown alga Ectocarpus[END_REF][START_REF] Müller | Kernphasenwechsel und Sexualität der Braunalge Ectocarpus siliculosus im Kulturversuch[END_REF] . Such variant life cycles provide valuable information about the influence of factors such as ploidy and meiosis/fertilisation on life cycle transitions.

Another, more direct, approach to elucidate the mechanisms that control life cycle progression is to screen for mutant individuals in which these processes are modified. This type of screen has been carried out with Ectocarpus and two life cycle mutants have been described so far. The first mutation, immediate upright (imm), causes partial conversion of the sporophyte generation into a gametophyte [START_REF] Peters | Life-cycle-generation-specific developmental processes are modified in the immediate upright mutant of the brown alga Ectocarpus siliculosus[END_REF] . The imm sporophyte exhibits several morphological features typical of the gametophyte, but nonetheless produces spores and not gametes. The more recently described ouroboros (oro) mutation causes conversion of the the sporophyte into a full-functional, gamete-producing gametophyte [START_REF] Coelho | OUROBOROS is a master regulator of the gametophyte to sporophyte life cycle transition in the brown alga Ectocarpus[END_REF] . In crosses, imm and oro behaved as two independent, single locus, recessive Mendelian factors [START_REF] Coelho | OUROBOROS is a master regulator of the gametophyte to sporophyte life cycle transition in the brown alga Ectocarpus[END_REF] . Microarray analyses of changes in mRNA abundances in the imm and oro mutants compared to wild type samples suggested that both loci act as master regulators, influencing the expression of a large number of downstream genes. This is a typical characteristic of homeotic genes and, in this respect, it is interesting to note that oro can be considered to belong to a novel class of homeotic gene, causing homeotic conversion at the level of the entire bodyplan rather than effecting just one organ or tissue.

Characterisation of the imm and oro mutations is expected to provide insights into several aspects of life cycle regulation. For example, gametes from Ectocarpus strains carrying the oro mutation germinate parthenogentically to give partheno-gametophytes rather than partheno-sporophytes. As the former is the wild type situation in several other species of brown algae, such as Myriotrichia clavaeformis for example [START_REF] Peters | Proposal of Ectocarpus siliculosus (Ectocarpales, Phaeophyceae) as a model organism for brown algal genetics and genomics[END_REF] , it will be interesting to determine whether variability with respect to this character in different brown algal species is linked to variability at the ORO locus.

Life cycle mutants have also been used to investigate the relationship between early development and life cycle progression. The two generations of the Ectocarpus life cycle exhibit radically different patterns of early development [START_REF] Peters | Life-cycle-generation-specific developmental processes are modified in the immediate upright mutant of the brown alga Ectocarpus siliculosus[END_REF] . The initial cell of the gametophyte (a meio-spore) undergoes an asymmetric division to produce a rhizoid and an upright filament, whereas the sporophyte initial cell (a zygote) divides symmetrically to produce a basal filament that is strongly attached to the substratum, upright filaments and rhizoids only being produced later in development. The phenotype of the imm mutant demonstrates that the pattern of initial cell division can be uncoupled from life cycle generation determination because functional sporophytes develop from an asymmetric initial cell division in this mutant.

The sex of Ectocarpus gametophytes is determined genetically by a single Mendelian locus. The oro mutation is not linked to the sex locus, so it was possible to produce both male and female strains carrying this mutation. Crosses between oro males and oro females resulted in diploid individuals that developed as gametophytes and produced diploid gametes [START_REF] Coelho | OUROBOROS is a master regulator of the gametophyte to sporophyte life cycle transition in the brown alga Ectocarpus[END_REF] . The diploid gametophytes produced by this cross provided additional evidence that the life cycle generation is not strictly determined by ploidy in Ectocarpus (as was already suggested by several observations, including the generation of haploid partheno-sporophytes from parthenogenic gamete germination, mentioned above). Sex determination in Ectocarpus is unusual in that sexuality is expressed during the haploid gametophyte generation as a result of the male and female haplotypes of the sex locus operating independently in male and female individuals. Interestingly, the oro homozygous, diploid gametophytes produced by the above cross were males, indicating that the male haplotype of the sex locus is dominant over the female haplotype when they occur in the same sexual individual [START_REF] Coelho | OUROBOROS is a master regulator of the gametophyte to sporophyte life cycle transition in the brown alga Ectocarpus[END_REF] .

Global analysis of gene expression in wild type strains and life cycle mutants allowed the identification of a large collection of genes whose transcripts accumulated to significantly different levels on the sporophyte and the gametophyte [START_REF] Coelho | OUROBOROS is a master regulator of the gametophyte to sporophyte life cycle transition in the brown alga Ectocarpus[END_REF] . Analysis of the predicted functions of these genes indicated a tendency for genes associated with primary metabolism (including carbohydrate and lipid metabolism) and related functions such as transport to be over-represented amongst the transcripts that accumulated to higher levels in the sporophyte, whereas genes associated with protein synthesis and modification and other basic cellular processes, such as cell cycle and DNA replication, were over-represented amongst the transcripts that accumulated to higher levels in the gametophyte, suggesting that there may be marked physiological differences between the two generations of the life cycle. This observation is consistent with one of the ideas that has been put forward to explain the stability of haploid-diploid life cycles over long timescales -that the two generations of the life cycle may be adapted to different ecological niches, allowing survival in a changing environment [START_REF] Coelho | Complex life cycles of multicellular eukaryotes: new approaches based on the use of model organisms[END_REF][START_REF] Mable | The evolution of life cycles with haploid and diploid phases[END_REF] . There is some evidence that the two generations of E. crouaniorum may be adapted to different environmental conditions [START_REF] Peters | Reinstatement of E. crouaniorum Thuret in Le Jolis as a third common species of Ectocarpus (Ectocarpales, Phaeophyceae) in western Europe, and its phenology at Roscoff, Brittany[END_REF] . The small sporophyte of this species, which grows on stones or shells, is present all year round, whereas the larger gametophyte is found only in the spring, suggesting that the sporophyte generation might be adapted to more extreme physical conditions whereas the role of the gametophyte would be to grow rapidly under favourable growth conditions. More information about the niches of individual generations of different Ectocarpus species in the wild is necessary to further investigate the ecological role of each life cycle generation in these organisms.

From a developmental point of view, the phenotypes of the imm and oro mutants could be due either to failure to initiate the sporophyte pathway or failure to repress the gametophyte pathway. Microarray analysis provided evidence that the transition from the sporophyte to the gametophyte generation involved predominantly gene repression [START_REF] Coelho | OUROBOROS is a master regulator of the gametophyte to sporophyte life cycle transition in the brown alga Ectocarpus[END_REF] , suggesting that the principal function of the imm and oro genes may be to repress the gametophyte developmental pathway. If this is the case, then the sporophyte developmental program could be considered to be the default pathway. This may have implications for evolutionary modifications of life cycles in the brown algae.

Current work aimed at identifying the genes affected by the imm and oro mutations by positional cloning should not only begin to elucidate the molecular mechanisms that regulate life cycle progression in Ectocarpus but should also shed light on other fundamentally important processes such as early development and parthenogenesis. Importantly, a growing number of genetic and genomic tools are being made available for Ectocarpus [START_REF] Heesch | A sequence-tagged genetic map for the brown alga Ectocarpus siliculosus provides large-scale assembly of the genome sequence[END_REF][START_REF] Dittami | Global expression analysis of the brown alga Ectocarpus siliculosus (Phaeophyceae) reveals large-scale reprogramming of the transcriptome in response to abiotic stress[END_REF][START_REF] Bail | Normalisation genes for expression analyses in the brown alga model Ectocarpus siliculosus[END_REF][START_REF] Gschloessl | HECTAR: a method to predict subcellular targeting in heterokonts[END_REF][START_REF] Ritter | Copper stress proteomics highlights local adaptation of two strains of the model brown alga Ectocarpus siliculosus[END_REF] , including the complete genome sequence [START_REF] Cock | The Ectocarpus genome and the independent evolution of multicellularity in brown algae[END_REF] .The availability of these tools will increasingly facilitate the use of this emerging model organism to study many aspects of brown algal biology [START_REF] Charrier | Development and physiology of the brown alga Ectocarpus siliculosus: two centuries of research[END_REF] including life cycle regulation.

Acknowledgements

This project was supported by the Centre National de Recherche Scientifique, the University Pierre and Marie Curie, the Groupement d'Interet Scientifique Génomique Marine, the Interreg program France (Channel)-England (project Marinexus) and the Agence Nationale de la Recherche (project Bi-cycle). AA was supported by a fellowship from the European Erasmus Mundus program.


Commentary The Ectocarpus genome sequence: insights into brown algal biology and the evolutionary diversity of the eukaryotes

Hidden beneath the ocean along many stretches of the coastline are luxuriant forests of brown seaweeds. These organisms have an atypical evolutionary history compared to the more commonly studied organisms in biology. The brown algae are members of the stramenopiles (or heterokonts), which diverged from other major eukaryotic groups such as green plants, animals and fungi well over a billion years ago [START_REF] Baldauf | An overview of the phylogeny and diversity of eukaryotes[END_REF][START_REF] Yoon | A molecular timeline for the origin of photosynthetic eukaryotes[END_REF]. As a result, the brown algae exhibit many unusual and interesting metabolic, developmental and cell biological features. The recent analysis of the complete genome sequence of the filamentous brown alga Ectocarpus silicilosus has provided some important clues about how these features were acquired and the molecular mechanisms that underlie them [START_REF] Cock | The Ectocarpus genome and the independent evolution of multicellularity in the brown algae[END_REF].

The brown algae are one of only a small number of eukaryotic groups that exhibit complex multicellular development. Comparison of the Ectocarpus genome sequence with those of related unicellular eukaryotes such as the diatoms, and with the genomes of other multicellular organisms has allowed the identification of a number of genome characteristics that may have been critical for the evolution of complex multicellularity in the brown algae [START_REF] Cock | The Ectocarpus genome and the independent evolution of multicellularity in the brown algae[END_REF]. For example, many new protein kinase families appear to have been acquired by the brown algae since they diverged from the diatoms, including a family of receptor kinases, which are particularly interesting because analogous families evolved independently in both the green plant and animal groups and these signalling molecules are thought to have played a key role in the emergence of multicellularity in both of these groups.

The Ectocarpus genome has also provided clues about how brown algae cope with the highly variable environment of the intertidal. Evidence was found for a complex photosynthetic system, diverse enzymes involved in the metabolism of reactive oxygen species, a flavonoid pathway and halogen metabolism enzymes, all of which are likely to contribute to combating the abiotic and biotic stresses encountered in the intertidal environment. This issue of New Phytologist includes a special feature devoted to the analysis of the Ectocarpus genome sequence and the development of this brown alga as a model organism. In the first paper, Peters et al. (2010a) have carried out an analysis of Ectocarpus strains isolated from the region of origin of the Peruvian strain that was used for genome sequencing. This study, which involved the isolation of more than a hundred strains from the region, has provided a solid basis for relating laboratory work on the sequenced strain to its ecology in the field. Until recently Ectocarpus strains have been classified into two species, E. siliculosus and E. fasiculatus, but it has become increasingly clear that this system underestimates the complexity of the genus. A third species, E. crouaniorum, has recently been reinstated (Peters et al., 2010b) and the new study suggests that additional species need to be defined, the sequenced strain itself probably representing a separate species.

Ectocarpus has long been studied as a model organism for the brown algae [START_REF] Coelho | Complex life cycles of multicellular eukaryotes: new approaches based on the use of model organisms[END_REF][START_REF] Charrier | Development and physiology of the brown alga Ectocarpus siliculosus: two centuries of research[END_REF] and one of the objectives of the genome project was to provide support for the application of genetic and genomic approaches to this organism. A number of additional tools have been developed recently, including for example an EST-based microarray [START_REF] Dittami | Global expression analysis of the brown alga Ectocarpus siliculosus (Phaeophyceae) reveals large-scale reprogramming of the transcriptome in response to abiotic stress[END_REF], classical genetic approaches [START_REF] Peters | Life-cycle-generation-specific developmental processes are modified in the immediate upright mutant of the brown alga Ectocarpus siliculosus[END_REF], quantitative PCR (Le [START_REF] Bail | Normalisation genes for expression analyses in the brown alga model Ectocarpus siliculosus[END_REF] and proteomic techniques [START_REF] Ritter | Copper stress proteomics highlights local adaptation of two strains of the model brown alga Ectocarpus siliculosus[END_REF]. Tool development is an ongoing process, with current efforts focusing on the development of genetic transformation and the construction of a mutant population suitable for screening by the TILLING approach. In this issue, [START_REF] Heesch | A sequence-tagged genetic map for the brown alga Ectocarpus siliculosus provides large-scale assembly of the genome sequence[END_REF] describe the construction of a highdensity genetic map that will be an invaluable tool for future genetic analyses in Ectocarpus. The genetic map consists of 26 large linkage groups plus eight smaller ones, consistent with the estimation of about 25 chromosomes reported by [START_REF] Müller | Untersuchungen zur Entwicklungsgeschichte der Braunalge Ectocarpus siliculosus aus Neapel[END_REF][START_REF] Müller | Generationswechsel, Kernphasenwechsel und Sexualität der Braunalge Ectocarpus siliculosus im Kulturversuch[END_REF] based on cytogenetic analysis. In addition to being an important tool for future genetic analyses, the genetic map also allowed the individual supercontigs of the genome assembly to be organised into pseudochromosomes (concatenations of supercontigs based on the order in which they have been mapped onto linkage groups) providing a chromosome-scale view of the genome [START_REF] Cock | The Ectocarpus genome and the independent evolution of multicellularity in the brown algae[END_REF].

The availability of complete genome sequences, not just for Ectocarpus but also for other stramenopiles, now opens up the possibility of carrying out exhaustive inventories of gene families and comparing them across the stramenopiles and with the equivalents in other major eukaryotic groups. [START_REF] Rayko | Transcription factor families inferred from genome sequences of photosynthetic stramenopiles[END_REF] have carried out this type of analysis for the transcription factor families of a broad range of stramenopiles and have identified several stramenopile-specific characteristics such as the abundance of Myb and C2H2 zinc finger proteins. The study also identified lineage-specific events, such as the proteins with two tandem bZIP domains that appear to be present only in stramenopile genomes.

Similar approaches have revealed unique aspects of stramenopile carbohydrate metabolism. Michel et al. (2010a,b) have carried out a comprehensive analysis of candidate genes underlying the biochemistry of metabolic, storage and cell wall carbohydrates. Earlier biochemical and physiological studies have pointed to several unique aspects of carbohydrate metabolism in the strameopiles. Key features are the use of D-mannitol rather than sucrose as a major product of photosynthetically-produced D-fructose-6-phosphate and the use of b-1,3glucans such as laminarin rather than starch as a primary storage carbohydrate. In several eukaryotes, enzymes involved in b-1,3-glucan synthesis are conserved but they have evolved to produce extracellular b-1,3-glucans. Bioinformatic analyses (Michel et al., 2010a) have revealed a complete set of enzymes involved in mannitol, laminarin and trehalose metabolism and a complete absence of genes involved in sucrose, starch and glycogen metabolism. Phylogenetic analyses indicate that genes involved in trehalose synthesis were acquired from an ancestral red algal symbiont whereas those underlying D-mannitol metabolism were acquired through horizontal gene transfer from Actinobacteria. The fundamental differences in metabolic and storage carbohydrate genes between stramenopiles and alveolates has led Michel et al. (2010a) to speculate that they arose from distinct eukaryotic hosts, which is not consistent with the chromalveolate hypothesis (Cavalier-Smith, 1999) of a single ancient eukaryotic host. Interestingly, two recent articles convergence towards similar conclusion, through independent approaches based on rigorous statistical testing of several explicit predictions of the chromalveolate hypothesis (Stiller et al. 2009;[START_REF] Baurain | Phylogenomic evidence separate acquisition of plastids in cryptophytes, haptophytes, and stramenopiles[END_REF].

In a companion paper, Michel et al. (2010b) present an analysis of candidate genes involved in cell wall biosynthesis and remodelling. Brown algal cell walls contain cellulose and certain unique polysaccharides, including alginates. Sulphated fucans also represent a significant unusual component of brown algal cell walls. Michel et al. (2010b) show the absence of genes encoding cellulase, expansins and alginate lyases. Phylogenetic analyses indicate the inheritance of cellulose synthesis genes from the ancestral red algal symbiont. In contrast, genes involved in alginate and hemicellulose synthesis were acquired through horizontal gene transfer with an Actinobacterial origin. Further complexity in the evolution of stramenopile cell walls is indicated by the conservation of genes involved in sulphated fucan synthesis with animals, suggesting an origin with the eukaryotic host.

Other studies featured in this issue illustrate how genome information is being integrated with experimental data obtained using Ectocarpus as a model organism to investigate various aspects of brown algal biology. [START_REF] Gravot | Diurnal oscillations of metabolite abundances and gene analysis provide new insights into central metabolic processes of the brown alga Ectocarpus siliculosus[END_REF] describe several aspects of primary metabolism in Ectocarpus during the diurnal cycle. Biochemical and transcriptional patterns are revealed that signify unique metabolic strategies. Intriguingly, Ectocarpus, like the stramenopile diatom Thallassiosira pseudonana [START_REF] Armbrust | The genome of the diatom Thalassiosira pseudonana: Ecology, evolution, and metabolism[END_REF], appears to have genes encoding enzymes characteristic of organic carbon concentrating mechanisms. Moreover, the multicellular nature of the Ectocarpus thallus has the potential to provide the functional compartmentalization necessary for such pathways. Nevertheless, the metabolic data do not support active C4 or CAM pathways under the conditions studied. Rather, inorganic carbon concentrating mechanisms relying on bicarbonate transport and carbonic anhydrase activity are indicated. Given the challenges of photosynthesis in the upper intertidal, it will be interesting to determine whether or not Ectocarpus is restricted to C3 photosynthesis or if a combination of carbon concentrating mechanisms may be utilized, as has recently been demonstrated in diatoms [START_REF] Roberts | C3 and C4 Pathways of Photosynthetic Carbon Assimilation in Marine Diatoms Are under Genetic, Not Environmental, Control[END_REF].

Reproductive and developmental plasticity is characteristic of brown macroalgae, and Ectocarpus, a photoautroph whose ecology demands alternative reproductive and developmental strategies, is no exception. With heteromorphic (but superficially similar) sporophtye and gametophyte generations, Ectocarpus can give rise to sporophyte clones, meiospores and gametes. This reproductive repertoire is expanded by parthenogenetic development of unfertilized gametes. What is remarkable about the resultant parthenosporophytes, is that a proportion exhibit reproductive competence that is essentially identical to that of a diploid heterozygous sporophyte, implying DNA replication without cell division at some stage of parthenosporophyte development [START_REF] Müller | Generationswechsel, Kernphasenwechsel und Sexualität der Braunalge Ectocarpus siliculosus im Kulturversuch[END_REF]. [START_REF] Bothwell | Endoreduplication and the life cycle of the brown alga Ectocarpus siliculosus[END_REF] examine the cell ploidy of Ectocarpus during the parthenogenetic cycle and provide evidence that it is during the first cell cycle of the parthenosporophyte that endoreduplication events occur. While endoreduplication is a common developmental strategy in a range of sessile organisms, and especially photoautotrophs [START_REF] Sugimoto-Shirasu | Big it up': endoreduplication and cell-size control in plants[END_REF], this is the first detailed description of how and when it occurs in a brown alga. The next challenge is to understand how the DNA of these parthenotes escapes the normal controls of ploidy during the cell cycle. The authors present the first step: a description of the genes that likely underpin the cell cycle and endoreduplication process. This genomic information, together with new genetic and genomic tools available in Ectocarpus, pave the way to new and important insights into the unique developmental strategies of multicellular heterokonts.

The seven companion papers presented in this issue illustrate how the Ectocarpus genome is being exploited to study many facets of brown algal biology and to investigate processes of fundamental importance to eukaryotes in general. The challenge now is to progress from analysis of the genome sequence to the application of approaches that will allow gene functions to be determined or confirmed experimentally. If this process is successful we can expect many new insights into the biology of these unusual and complex organisms in the coming years.

J. Mark Cock

FigureFig. 1 .

 1 Figure

1,2,* , Susana M. Coelho 1,2 , Colin Brownlee 3 , Alison Taylor

  4 1 UPMC Univ. Paris 06, The Marine Plants and Biomolecules Laboratory, UMR 7139, Station Biologique de Roscoff, Place Georges Teissier, BP74, 29682 Roscoff Cedex, France; 2 CNRS, UMR 7139, Laboratoire International Associé Dispersal and Adaptation in Marine Species, Station Biologique de Roscoff, Place Georges Teissier, BP74, 29682 Roscoff Cedex, France; 3 Marine Biological Association of the UK, The Laboratory, Citadel Hill, Plymouth, PL1 2PB, UK; 4 Department of Biology and Marine Biology, The University of North Carolina, Wilmington, 601 South College Road, Wilmington, NC 28403, USA ( * author for correspondence: tel +33 (0)2 98 29 23 60; email cock@sb-roscoff.fr)


Joanna Brebner
email: j.s.brebner@qmul.ac.uk

James Makinson

Olivia Bates

Natacha Rossi

Ka S Lim

Thibault Dubois

Tamara Gómez-Moracho

Mathieu Lihoreau

Lars Chittka

Joe Woodgate

Bumblebees strategically use ground-level linear features in navigation

Keywords: Bee, Bombus terrestris, Exploration flight, Harmonic radar, Insect navigation, Orientation flight, Pollination, Road following, Route formation, Search flight

Extended ground-level structures like roads or field edges can be important cues for navigating animals, seen for example in road-following pigeons. In a landscape devoid of skyline cues but with a rectangular grid of pathways and roads, we used harmonic radar to track free-flying bumblebees.

Individual bees consistently used ground-level linear features for navigation in a wide range of behavioural contexts. Bee exploration flights, search behaviour and foraging routes were shaped by linear features, with bees frequently flying along and parallel to pathways and roads. Comparisons of flight trajectories across these behavioural contexts show that individuals modulated their use of linear features strategically with respect to their individual goals and experience. Bees searching for a feeder used linear features to target their search, while foragers often followed pathways to return to their hive without overshooting. These findings on a major pollinator have important implications for the placements of bee colonies for agriculture and floral resources for conservation.

INTRODUCTION

Ground-level elongated linear structures, such as rivers, forest edges, paths and roads provide unique visual information which can affect animal dispersal and navigation [START_REF] Billington | Radio tracking study of greater horseshoe bats at Buckfastleigh Caves Site of Special Scientific Interest[END_REF][START_REF] Gesicki | Detoured flight direction responses along the southwest coast of Lake Erie by night-migrating birds[END_REF][START_REF] Schiffner | Pigeon navigation: different routes lead to Frankfurt[END_REF]. For instance, pigeons follow roads [START_REF] Guilford | Positional entropy during pigeon homing II: navigational interpretation of Bayesian latent state models[END_REF][START_REF] Lipp | Pigeon homing along highways and exits[END_REF], bats fly along hedgerows and tree-lines [START_REF] Billington | Radio tracking study of greater horseshoe bats at Buckfastleigh Caves Site of Special Scientific Interest[END_REF] and turtles follow coastlines [START_REF] Luschi | Long-distance migration and homing after displacement in the green turtle (Chelonia mydas): a satellite tracking study[END_REF]. Such features may be particularly relevant for pollinators that exploit agricultural terrains which often present sparse visual environments with prominent man-made linear features.

There is tentative evidence that bees use such linear structures for navigation. For example, von [START_REF] Von Frisch | Himmel und erde in konkurrenz bei der orientierung der bienen[END_REF] trained honeybees to fly along continuous forest edges, lake edges and roads and observed that they would prioritise this information over cues gained from their sun compass [START_REF] Von Frisch | The Dance Language and Orientation of Bees[END_REF]. [START_REF] Menzel | Guidance of navigating honeybees by learned elongated ground structures[END_REF] observed that some experimentally displaced honeybees tended to use gravel roads, field edges, hedgerows and irrigation channels to return to their hive. [START_REF] Collett | Insect navigation: Do honeybees learn to follow highways[END_REF], reviewing other radar studies [START_REF] Degen | Honeybees learn landscape features during exploratory orientation flights[END_REF][START_REF] Osborne | The ontogeny of bumblebee flight trajectories: From naïve explorers to experienced foragers[END_REF][START_REF] Wolf | So near and yet so far: Harmonic radar reveals reduced homing ability of nosema infected honeybees[END_REF], further observed that displaced honeybee foragers seemed to follow a pathway then a hedgerow back to their hive, while honeybees performing their first exploration flights appeared in some cases to follow field edges.

These observations suggest that bees use ground-level linear features for some navigational tasks. However, none of the studies were designed to examine this question in a quantitative and experimental manner, so cannot address the likelihood or degree of use of these features, or the variety of contexts in which they are used. Interpreting these observations is made more difficult still because the landscapes in which the bees were tracked contained potential landmarks such as trees and buildings that bees could have used for navigation. Thus, important questions remain about the likelihood of bees using ground-level linear features, the contexts in which they are used and whether their use depends on a bee's experience with the local landscape. If bees consistently use ground-level linear features for navigation, this may strongly affect the areas they are most likely to discover, pollinate and fly through.

Here we examined how and to what extent bees learn to use linear features during navigation using a harmonic radar to track free-flying bumblebees (Bombus terrestris) in a unique, visually sparse landscape, characterised by a ground-level rectangular grid of roads, pathways and field borders.

While compass cues remained, the horizon provided no skyline cues that bumblebees could exploit to indicate location (including the farm which was too far from the hive, Fig 1a ; for more detail see Appendix, Field site panorama). In the absence of skyline information, any influence of ground level cues can be isolated, allowing us to quantify bumblebees' use of linear features in exploration flights, searching flights, foraging flights and over route development.

METHODS

Bees and Field Site

Field work was carried out on a rice farm (Finca Casudis, La Puebla del Río; Latitude 37.13562305, Longitude: -6.080421855), South of Seville, Spain, between the 20 April and the 15 May 2018. The field site was composed of a series of unflooded rice paddies on flat terrain, with few to no above ground level features (Fig 1a;Appendix,Field site panorama). The rice paddies were all 420 m by 200 m, set out in a grid and separated by raised pathways of compacted earth (approximately 0.5 m higher that the fields). All pathways running WSW-ENE comprised two single-lane roads separated by a narrow irrigation channel. Every second pathway running WNW-ESE was a single lane road, while the others were narrow footpaths, approximately 1 m wide. A small packed earth runway ran along one of the WSW-ENE roads, from the main farm outward (thicker pale line, Fig 1b). To the north of this road is a footpath along which the hives were placed (Hive Location 1) for Experiments 1-4, while to the South, in a field were placed the hives (Hive Location 2) for an additional test (see Appendix, Experiment A3). All pathways (including roads, footpaths, field edges and the runway) are referred to as this environment's linear features.

Eight commercially bred bumblebee colonies (Bombus terrestris; colonies with 150-200 individuals on average; Koppert Biological Systems, Spain) were used. Each colony was transferred to a custom wooden hive box (30 × 21 × 16 cm high), with a Perspex tunnel (26 × 4 × 4 cm high) ending in a small Perspex platform (4 x approx. 6 cm), through which the bees could access the outside world. Small sliding barriers could be used to block the tunnel, allowing us to control which bees could go in and out. Two hives (placed side by side) were used at a time to maximise the number of active foragers.

Bees were allowed to feed freely from experimental feeders in a flight tent (3 x 3 x 3 m) for 2-4 days before the hive was transported to the field site. The flight tent was located more than 15 km from the field site, in a very different visual environment (either a walled garden, or bordered by a wall on one side and a dense grove of orange trees on all others. The horizon was not visible from either location). Bees were only able to fly within the flight tent, so that their visual experience prior to the field site was restricted to a small space walled with fine white mesh, while the fields and paths in our field site were generally uniformly coloured (mostly brown) during data collection (Fig 1a).

Individual bees were thus familiar with the feeder design but naïve to the field site and their location within it at the beginning of experiments. Feeders were made of a raised blue platform (20 x 20 cm), approximately 1 m above ground level, identical to those used in [START_REF] Woodgate | Continuous radar tracking illustrates the development of multi-destination routes of bumblebees[END_REF] supporting a gravity feeder (described in Chap 2, section 5, von Frisch, 1967). A 1 m tall feeder could have been visible to bees at a range of 25 m maximum (calculated from the smallest measured bee spatial resolution of 2.3°; [START_REF] Dyer | Comparative psychophysics of bumblebee and honeybee colour discrimination and object detection[END_REF]. This is a generous estimate, since in reality the feeders were thin, and located on the lower-level field beside the footpath so that they appeared less than 1 m above the horizon. We are therefore confident that feeders could not be used as landmarks for bee orientation outside of this 25 m range.

Natural foraging sources were present along the river Guadalquivir (West of the farm, over 1200 m from the hive site), but were largely absent within the rice paddies themselves. Experimental feeders providing ad libitum 30% (by weight) unscented sucrose solution were placed 160-250 m from the bumblebee hives, either along the linear features (Feeders F1,F2 and F3) or in the middle of a rice paddy (Feeder F4) (Fig 1b).

All tracked bees were given unique colour and letter identification using custom-made metal tags (Qualitech, March, Cambridgeshire, UK), attached to their thorax using superglue (Loctite Power Flex Gel, Henkel Ltd., Hemel Hempstead, UK). Bees were primarily tagged within the flight tent before the hive was transported to the field site, but this tagging process continued after experiments started so that newly emerged bees could also be tracked. What was known about individual experience was carefully noted for reference.

Harmonic Radar

Individual bees were tracked using a 32 mm harmonic radar described in previous studies [START_REF] Riley | Tracking bees with harmonic radar[END_REF], located approximately 310 m from the hive (Fig 1b). To allow radar tracking, a lightweight transponder (16 mm long, approximately 20 mg) was attached to the metal tag of a focal bee as it left the hive, using a small magnet at the base of the transponders. This allowed us to attach and remove it from the metal tags very quickly without capturing or handling the bee, thus minimizing stress due to manipulation.

The radar rotated constantly, scanning through 360° once every 3 seconds. When a bee with a transponder was within a line of sight of the radar up to approximately 1 km, the radar returned its distance and azimuth from the radar once every rotation. Flight path tracks were converted to GPS coordinates by triangulating the radar signals against two locations whose GPS coordinates were known, using a custom-written Matlab script (Mathworks Inc., Natrick, USA). These tracks were visualised by plotting them on top of a satellite image of the field site. We tracked a total of 180 flights from 83 individuals (Appendix, Table A1).

Ethical note

Bees were kept outside (first in a distant location within a flight tent and then in the field site), covered with tarp to protect them from rain and sun. The hive boxes kept the bee colonies in a dark environment, comparable to their natural underground hives. Bees were frequently given full access to the field site (as in a natural setting), except in specific circumstances, for up to a day at most, for the purposes of the experiment. They were never locked in for long enough to cause stress or harm.

Bumblebees store food (nectar and pollen) in their hive, and workers have intrinsic motivation to leave and forage for the colony. Supplementary pollen was provided every two days. Colonies were checked regularly for food stores and were provided with sugar solution if their stores were low. The bees were never starved. Handling was kept to a minimum. Individual foragers were tagged with metal tags, involving one short capture per individual, where tags were attached to the thorax using super glue. Metal tags allowed us to apply and remove our magnetised radar transponders without capturing them, involving only brief containment within the tunnel leading to their hive. This minimised stress and handling time to only a few seconds. Non-worker individuals were not manipulated. Radar transponders were small enough to allow for normal bumblebee flight.

Bumblebees regularly carry heavier loads of nectar and pollen than the weight of the transponder.

Eighty-three bees were tracked.

Once the colonies were no longer being used for experiments, they were humanely killed via freezing, so as to prevent commercially-bred bees from affecting local wild bumblebee populations.

The research described here aligns with the ASAB/ABS Guidelines for the use of animals in Research.

No license or permits were required for these experiments.

Experiments

Details of dates, bumblebee colonies, number of tracks and individuals for each experiment can be found in Appendix, Table A1. In all cases described below, the colonies were placed at the first hive

Experiment 1

Do bees use ground-level features during exploration flights? The first flights of bees in a new environment focus on exploring the surrounding landscape and, in the case of bumblebees, also the food sources within it [START_REF] Osborne | The ontogeny of bumblebee flight trajectories: From naïve explorers to experienced foragers[END_REF][START_REF] Woodgate | Life-long radar tracking of bumblebees[END_REF]. These 'exploration flights' usually consist of several loops in all directions around the hive, starting small and progressively widening [START_REF] Osborne | The ontogeny of bumblebee flight trajectories: From naïve explorers to experienced foragers[END_REF]. Individuals with no previous flight experience in this environment were tracked on their first exploration flights in the field site. This was done using the sliding barriers in the hive tunnel to allow access to the outside for only the focal bee and no others. These first flights were primarily recorded on the 24 April, when there were no artificial feeders present in the field site. Three more first flights were recorded on the 4 May, when several feeders were present in the field.

We also recorded flights displaying typical looping behaviour around the hive, characteristic of exploration flights [START_REF] Osborne | The ontogeny of bumblebee flight trajectories: From naïve explorers to experienced foragers[END_REF][START_REF] Woodgate | Life-long radar tracking of bumblebees[END_REF], between the 30 April and the 8 May, from bees in colonies whose tunnels were kept open to allow free access to the field site. We could not be certain whether these bees had previously flown, and so we call these tracks 'apparent exploration flights'. There were feeders present during these recordings (Appendix, Table A1).

Experiment 2

Do bees use linear features during repeated back and forth flights to forage locations? Once bees had completed their exploration flights, we opportunistically tracked foraging bumblebees while all individuals had free access to the field site. Two main feeder treatments were set up during this time. All colonies were given a day of exposure to the field before feeders were introduced, and different colonies were used for each treatment.

In the first treatment, we placed feeders in positions F1 and F2 (35 m apart) along linear features (Fig 1b). F1 was located next to a crossroads, connected to both the hive and F2 by linear features.

The hive and F2 were not placed on the same linear feature, so that the straightest path from the hive to F2 did not involve flight along a linear feature. The intention was to compare flights to F1 (where the most direct flight coincided with a flight over a linear feature) and to F2 (where the most direct flight crossed over open field). Since most of the foraging flights tracked in this condition (60%) involved individual bees visiting both F1 and F2 in the same foraging trip, we set up a second treatment.

In the second treatment, we placed feeders in positions F1 and F3 (168 m apart) along the same linear features. F1 remained next to the crossroads, connected to both the hive and F3, while F3 was located on the same path as F2 had been, but further from F1 to increase the cost of joint visits to F1 and F3. As with the previous condition, we aimed to compare tracks to F1, along a linear feature, with tracks to F3, where a flight following linear features would take the bee on a longer flight via the crossroads, and the most efficient flight would be across the field. Some of the bees we tracked did not visit our experimental feeders but flew instead to distant locations, primarily in the direction of the river. Many of these bees returned with pollen. We therefore categorised non-looping flights to distant locations as natural forage flights (Appendix, Experiment A2). Unlike the flights to feeders, the final goals of these natural forage flights were out of range of the radar. This means that the flight we tracked was the portions of outbound and inbound flight within range of the radar, up to 1 km from the hive location. Outbound flight portions therefore stop before bees reached their destination, while inbound flights begin when the bees pass into the radar's range.

Experiment 3

Does bee reliance on linear features change with experience? One bee was repeatedly tracked while habitually foraging at feeder F3 (Fig 1b). Here, the most direct route from the hive to this feeder would involve no flight along linear features (238 m), while an alternative route continuously following linear features would be 41% longer (336 m). The inbound and outbound portions of flight were manually categorised as follows: if the flight approaching the goal (feeder for outbound and hive for inbound portions of flight) followed the associated linear feature for at least the final 30 m, then it was labelled as 'using linear features' to reach the goal, while if the bee did not follow the associated linear feature on its way to the goal, it was labelled 'not using linear features'. Tracks in which the bee followed linear features after or not immediately before reaching the desired goal were classified as 'not using linear features'. We used 30 m as a cut-off point since it is larger than our generous calculated maximum range (25 m) at which bees might be able to discern a feeder and use it as a beacon. We assessed the frequency of each of these flight strategies as the bees gained experience.

Experiment 4

Do bees use linear features to search for a removed feeder? When a bee does not find a goal (e.g. hive or feeder) in its usual position, it searches using a distinctive flight pattern, performing looping flight in all directions around the goal's last known location [START_REF] Reynolds | Honeybees perform optimal scale-free searching flights when attempting to locate a food source[END_REF]. We identified focal bees from individuals who regularly visited a single feeder, either F1 or F3 (Fig 1b). When the focal bee returned from a flight to its preferred feeder, we used the tunnel dividers to confine all bees to the hive. All feeders were then removed from the field. The focal bee was released from the hive and then tracked until it returned to the hive. The focal bees tested searched either for F1 (N = 3 bees) or F3 (N = 3 bees).

Track Analyses Proximity Score:

To quantify use of ground level linear features by bees, we analysed all tracks for the bees' proximity to linear features. All tracks were reduced to non-stationary data by excluding points that showed less than 1 m of motion since the previously recorded bee position.

We used a satellite image of the field site, acquired from Google Earth Pro (Google, Mountain View, California, USA), which we converted to a black and white image (1116 x 632 px) showing the positions of all linear features. The pixel coordinates for all linear features in the image were extracted using custom written Matlab script (Mathworks Inc., Natrick, USA). The rest of the calculations were done using custom written C# code (Microsoft Visual Studio Community, 2017).

We translated all radar track coordinates into the same pixel coordinate system as the image, using the relative positions of the hive and feeder F1 for reference. We calculated the Euclidean distance between each bee track coordinate and the closest linear feature and rescaled it to real-world distance in meters. This allowed us to assess the minimum distance to any linear feature for every point in the bees' flight path. From this we calculated an 'initial score', the proportion of points in the track (excluding stationary points) which were less than 5 m from any linear feature.

Since flights were centred on the hive, which was positioned next to a linear feature for Experiments 1-4, and since the field site was a regular grid of linear features, it is to be expected that bees would often approach or cross linear features even if they do not use them in any way for navigation. To ask whether bees spent a greater proportion of their time in close proximity to linear features than expected by chance, we calculated a track-specific 'control score'. This control represented the average expected proximity score from the same track rotated multiple times around the hive. Specifically, we rotated every track around the hive position in 10° increments and calculated the initial score, as described above, for each rotated track. The control score is the average of all initial scores from the 35 rotations, and estimates the level of proximity we would expect by chance if the bee's flight path was independent of landscape features, while still centred on the same hive position. We tested whether bees followed linear features more than expected by chance, during exploration flights, using a series of paired-samples t-tests (after testing each dataset for normality using a Kolmogorov-Smirnov test; a Wilcoxon matched-pair signed-rank test was used as a non-parametric alternative if the data was not normally distributed), comparing the initial proximity scores to their paired control proximity scores. The null hypothesis was that there would be no significant difference between initial and control scores, suggesting that bees spent no more time in close proximity to linear features than they would by chance. The dependent variables for each test were the proximity scores (initial and control scores) from flights categorised as first flights or apparent exploration flights (Experiment 1). Our method of generating control scores assumes that flights have no specific destination, so is not suitable for assessing flights to feeders. We report initial proximity scores for foraging and search flight, but do not test them against a control. In all cases, repeated measures of individuals were averaged before statistical tests so that all tests were performed on data representing one data point per individual. Flights where the individual was unknown were not included in analyses.

Flight bearings:

Flying along linear features is not the only way in which bees might use linear features in navigation.

Bees might also fly parallel to linear features, or their internal representation of space may be influenced by the grid of features around them even when out of sight of linear features. We therefore calculated the bearings flown by bees in our experiments to determine whether they showed any biases matching the orientations of linear features.

Flight bearings were calculated as degrees anti-clockwise from East between every two consecutive points of non-stationary track. For each track, these angles were sorted into 10° bins, and the proportion of data points falling into each bin was calculated. This allowed tracks to be assessed together (with every track equally weighted) to explore group level effects. These data were plotted in circular bar charts, and the raw data were tested for uniformity for first and apparent exploration flights (Experiment 1) to assess initial biases in flight path; and for searching flights to removed feeders (Experiment 4), to assess biases during targeted searching. We tested whether the flight bearings in each experiment were uniformly distributed using Kuiper's test of uniformity, which is sensitive to multimodal distributions [START_REF] Birch | An Examination of the Kuiper Test[END_REF][START_REF] Landler | Circular data in biology: Advice for effectively implementing statistical procedures[END_REF], since flights parallel to linear features would be predicted to have four modes at 90° intervals. Kuiper's tests were performed using R (RStudio Team, 2016).

RESULTS

Experiment 1

Do bees use ground-level features during exploration flights? To examine if the structure of exploration flights was affected by linear features, we tracked bumblebees during their very first flights (N = 14 bees, N = 17 flight tracks; 3 of the flight tracks are portions of the 14 flights in which the precise bee identity is uncertain) and apparent exploration flights (N = 18, from 13 bees; see Appendix, Table A1). Exploration flights showed a visible bias for linear features, where bees frequently flew along and parallel to surrounding roads, field borders and pathways (see Fig 2a-d A bias toward following linear features during exploration flights can predict where individual foragers discover food sources and so may lead to colony-level biases in exploitation of resources. Some support for this is provided by a further experiment in which a feeder located along a linear feature received far more visits than an identical one in the middle of a field (Appendix, Experiment A1).

Experiment 2

Do bees use linear features during repeated back and forth flights to forage locations? In the first treatment, feeders F1 and F2 were presented together (Fig 3). Here, twelve bees out of 15 were observed flying to both feeders, for a total of 17 flights out of 28 to both feeders rather than only to F1 (5 flights from 5 bees) or only to F2 (6 flights from 5 bees). In the second treatment, feeder F1 remained in the same place, while F2 was removed and replaced by F3, 168 m from F1 along the same road as F2 had been. Only one bee visited both feeders in a single flight, compared to 28 flights of 3 bees to F3 only. No bee was recorded flying to F1 only during this time.

In both treatments, bees flying to feeders consistently spent a high proportion of flight within 5 m of linear features (flights to F1: Mean ± SD = 0.88 ± 0.18, N=10 bees; flights to F1 and F2: Mean ± SD = 0.80 ± 0.11, N=8 bees; flights to F2: Mean ± SD = 0.71 ± 0.09, N=5 bees; flights to F3: Mean ± SD = 0.59 ± 0.11, N=3 bees). When bees flew to feeders other than F1, where the shortest potential route would not follow linear features, they still frequently flew over linear features more than necessary (Fig 3a-d), particularly during their final approach to the feeder or hive. Some bees did not visit feeders and instead flew beyond the range of the radar, presumably to natural forage. The return portions of these flights almost always followed the hive's footpath on the final approach to the hive (see Appendix, Experiment A2). Nonetheless, even bees flying back and forth to feeders F2 and F3 never followed linear features for the entire route, instead cutting across the corner of the field, demonstrating that they were not entirely dependent on linear features. The use of linear features in the last approach then likely indicates a strategy to locate the exact hive position.

In a further experiment, where neither hive nor feeders were located on linear features, we still observed extensive flight along footpaths, field borders and roads, and more flight in close proximity to linear features than expected by chance (Appendix, Experiment A3). Linear features were therefore not salient in these experiments only because the hive and feeders were located on them.

Experiment 3

Does bees' reliance on linear features change with experience? One bee was recorded on 21 foraging trips over 4 days to feeder F3. The most direct route from the hive to this feeder would involve no flight along linear features while an alternative route continuously following linear features would be 41% longer. As the bee gained experience, we observed a switch in flight strategy (Fig 4). In the first 10 foraging trips, 60% of outbound and inbound flights went directly over linear features for at least the last 30 m approaching the goal (the feeder for outbound flights; the hive for inbound flights; Figs 4a-h). By contrast, in the last 11 foraging trips of the bumblebee, only 14% of outbound and inbound flights went directly over linear features during the final approach to the goal (Fig. 4i-p). This suggests that linear features initially played an important role in helping the bee precisely locate the goal, but that with experience the bee was able to form a more efficient route and approach the goal without such a guide.

Experiment 4

Do bees use linear features to search for a removed feeder? We investigated whether bees would use information from linear features to search efficiently by identifying repeat visitors to individual feeders and tracking their flight after removing all feeders from the field. Feeders and the hive were all placed along linear features for this experiment. Search flights (N=7 flights from 6 bees) showed consistently very high proportions of flight within 5m of linear features (Mean ± SD = 0.70 ± 0.17; Fig 5). These numbers are comparable to those from flights to feeders, despite the lack of looping behaviour in those cases, suggesting that searching bees largely restricted their search to linear features.

During their search, bees not only flew repeatedly over the stretch of road along which the feeder was usually found, but also along other linear features (Fig 5). Initially, bees returned to familiar locations such as the crossroads or even the hive itself before repeating their route to the feeder location. Later in their search, bees often scanned other parallel and perpendicular linear features (Fig 5a-d). This suggests that bees either widened their search space for the lost feeder or switched strategy and searched for novel feeders, while relying on their learnt association between the feeder and road to guide them.

Flight bearings of bees during searching flights were significantly non-uniformly distributed (Kuiper's test of uniformity, V=4.9107, N=7, P < 0.01), with more flight in directions associated with prevalent ground-level features (Fig 5e). These flights offer a striking contrast to the variety of azimuthal directions adopted by bees searching in landscapes less dominated by regular linear features [START_REF] Reynolds | Honeybees perform optimal scale-free searching flights when attempting to locate a food source[END_REF].

In contrast, when searching for unpredictably located feeders within a field, where the hive and feeders were located away from linear features, bees largely restricted their flight within the field, often approaching field borders without crossing them (Appendix, Experiment A3). The structure of searching flights thus appears to be flexible, with bees using linear features strategically, based on individual experience.

DISCUSSION

Our experimental manipulation of hive and feeder location in a landscape with a featureless, flat horizon and a regular grid of ground-level linear features demonstrates that bees made extensive use of these features during their exploration flights, foraging flights and searching flights. Their utilisation of ground information was strikingly strategic, directly linked to each individual's goals and experience.

Exploration flights performed by bees in our field site were vastly different to those observed in environments with a variety of skyline and ground level cues [START_REF] Osborne | The ontogeny of bumblebee flight trajectories: From naïve explorers to experienced foragers[END_REF][START_REF] Woodgate | Life-long radar tracking of bumblebees[END_REF]. Flights previously recorded in landscapes less dominated by linear features show distinct looping patterns in all directions (see Osborne et al., 2013, Fig 2A;Woodgate et al., 2016, Fig 2). Our flight tracks show, instead, high proportions of flight along and parallel to linear features (Fig 2 ), demonstrating that the shape of exploration flights is determined by the structure of the surrounding landscape. This could have important implications for bee spatial distribution, through potentially biasing the discoveries of suitable nest locations by new queens [START_REF] Makinson | Harmonic radar tracking reveals random dispersal pattern of bumblebee (Bombus terrestris) queens after hibernation[END_REF].

Moreover, since bumblebees use exploration flights to discover forage [START_REF] Osborne | The ontogeny of bumblebee flight trajectories: From naïve explorers to experienced foragers[END_REF][START_REF] Woodgate | Life-long radar tracking of bumblebees[END_REF], we predict that linear structures will bias floral resource discovery and pollination at a colony level. Support for this comes from our observations of bee visitation rates to feeders, where a huge majority of total visitations were to the feeder along a footpath rather than to the identical feeder in the centre of a field (Appendix, Experiment A1). This was all the more striking since bumblebees do not communicate forage locations with each other [START_REF] Dornhaus | Evolutionary origins of bee dances[END_REF], so that this was the result of individual-level exploration and foraging decisions. Furthermore, after simulating a foraging source no longer being available (for example, flowers running dry or a tree being cut down) by removing the feeders (Experiment 4), bees used their previous experience of linear features to limit their search, making it likely that the first discoveries made may shape the next floral resources discovered and pollinated long-term.

Bees flying to experimental feeders often flew distinct, detouring routes along linear features (Experiment 2). This was reminiscent of previous observations by Chittka et al. (1995), where bees detoured via a salient landmark to reach a feeder. One bee, observed over many flights gradually refined her route from cutting across the corner of a field but still following linear features, to eventually bypassing linear features altogether (Experiment 3). Likewise, the formation of efficient routes between multiple food sources (termed trap-lines) are refined over time and experience [START_REF] Woodgate | Continuous radar tracking illustrates the development of multi-destination routes of bumblebees[END_REF]. This suggests that while inexperienced, the bee relied on linear features for accuracy, switching to using vector memories and/or more detailed visual memory once she was sufficiently experienced [START_REF] Collett | Spatial memory in insect navigation[END_REF][START_REF] Menzel | Bees travel novel homeward routes by integrating separately acquired vector memories[END_REF]. If bees rely more on linear features when they have less certainty about their position, this could explain previous observations in which displaced honeybees [START_REF] Collett | Insect navigation: Do honeybees learn to follow highways[END_REF][START_REF] Menzel | Guidance of navigating honeybees by learned elongated ground structures[END_REF][START_REF] Wolf | So near and yet so far: Harmonic radar reveals reduced homing ability of nosema infected honeybees[END_REF] and pigeons [START_REF] Lipp | Pigeon homing along highways and exits[END_REF] showed extensive detouring along linear features.

Why do bees preferentially follow pathways, field edges and roads? Experiments with humans and rats have suggested that beacon cues (that indicate a nearby target location) are preferentially used over more complex visual cues (for example, cues which inform on the next direction in a route, but do not directly indicate the goal) [START_REF] Chan | From objects to landmarks: The function of visual location information in spatial navigation[END_REF][START_REF] Waller | Landmarks as beacons and associative cues: Their role in route learning[END_REF]. Following a linear feature to a desired goal may be similar to using a beacon cue, by providing an easier alternative to navigating directly to an otherwise isolated point, an example of 'cognitive offloading' [START_REF] Risko | Cognitive offloading[END_REF]. The last part of a bee's return to the hive often relies on beacons and other landmarks [START_REF] Turner | The homing of the burrowing-bees (Anthrophoridae)[END_REF], because path integration lacks small-scale accuracy [START_REF] Collett | How do insects use path integration for their navigation[END_REF][START_REF] Menzel | Bees travel novel homeward routes by integrating separately acquired vector memories[END_REF]. In our experiments, bees may have implemented a strategy in which they switched to following linear features before they expected to reach their hive. This could allow them to compensate for errors and imprecisions in their path integration and avoid missing their goal.

Path integration is only possible with a combination of distance estimation and heading information [START_REF] Collett | Memory use in insect visual navigation[END_REF]. The latter is derived from compass cues within the environment, either from celestial information (eg. the sun) or from the geomagnetic field [START_REF] Wajnberg | Magnetoreception in eusocial insects: An update[END_REF][START_REF] Wehner | Astronavigation in insects[END_REF]. Could linear features have had an effect on the use of compass cues and path integration? Bee distance estimation, derived from visual feedback, becomes more precise when paired with location-specific landmarks [START_REF] Srinivasan | Going with the flow: A brief history of the study of the honeybee's navigational 'odometer[END_REF]. Linear features however often extend over long distances and might instead be more indicative of direction. When bees are unable to use their sun compass, they rely on the visual landscape to infer direction information [START_REF] Dyer | Honey bee orientation: A backup system for cloudy days[END_REF]. When, experimentally, learnt celestial cues are put into conflict with the visual landscape, bees appear to rely first on their visual memory, and not compass cues, to locate food sources [START_REF] Von Frisch | Himmel und erde in konkurrenz bei der orientierung der bienen[END_REF][START_REF] Von Frisch | The Dance Language and Orientation of Bees[END_REF], similarly to what we suggest above. If our field site had not presented bees with a regular arrangement of linear features but instead given bees an irregular set of paths and roads, would this have caused bees to fly less efficient routes? Alternatively, if through experimental manipulation linear features could be added or removed, how would that affect bee orientation?

Bees rely primarily on celestial cues for compass information [START_REF] Collett | Insect navigation: visual panoramas and the sky compass[END_REF]. However, Fleischmann et al. (2018) showed that Cataglyphis nova ants appear to use geomagnetic cues to control the 'pirouettes' during which they are thought to learn views of the nest, during their first learning walks around the nest. This raises the question of whether our use of a small magnet at the base of the radar transponder may have hindered the use of magnetic cues by our bees. Previous experiments on honeybees have suggested that they may be able to detect geomagnetic fields but that they rely on them only when no other cues are available, if at all (REFS & CHECK LIT TO CONFIRM IF THIS IS ACCURATE). It is unknown if bees also make use of magnetic cues when they first exit their nest. If so, our use of magnets may have made them more reliant on visual cues, affecting the structure of their first flights. Bumblebees combine learning about the immediate surroundings of the hive with more extensive explorations, during which they search for food sources (REFS: OSBORNE PAPER, WOODGATE 2016). It is these longer-range explorations that show the greatest influence of linear features in our study, but there is no evidence from any species for any effect of magnetic cues on such exploration flight. Our bees showed no evidence of any difficulty in returning to the hive after their exploration flights.

Insects are thought to use visual-familiarity-based mechanisms to navigate, where the heading is determined by a measure of similarity between the current view and a relevant subset of memories [START_REF] Baddeley | A model of ant route navigation driven by scene familiarity[END_REF][START_REF] Collett | Spatial memory in insect navigation[END_REF][START_REF] Zeil | Visual homing: an insect perspective[END_REF]. In this linear-feature-rich landscape, we might predict that footpaths and roads might all present very similar visual cues. When returning or searching for a location on a linear feature, using familiarity to guide the bee's route might always direct the bee towards the closest linear features, causing the behaviours we observed. If this is the case, we would consequently also predict frequent aliasing errors, where one linear feature is mistaken for another. This was not the case: one single aliasing error was observed, from a bee on her first flight (Fig 2a), and experienced bees readily flew away from linear features, even reducing their flight along linear features with experience (Experiment 3,Fig 4). Instead, bees changed how they used linear features depending on their goal: following them to reach a feeder placed along a footpath (Experiment 2); relying on them more when inexperienced (Experiment 3); flying along them when searching for a feeder previously placed along a road (Experiment 4); using linear features as visual boundaries within which to focus their flight when searching for feeders within a field (Appendix, Experiment A3). This flexible, goal-dependent use of linear features strongly suggests that bees strategically use the information available to them for efficient navigation.

Bees and other insects are known to monitor their movements using visual feedback from their own motion, termed 'optic flow' [START_REF] Srinivasan | Honeybees as a model for the study of visually guided flight, navigation, and biologically inspired robotics[END_REF]. For example, bees control their position within flight tunnels by maintaining the speed of optic flow from their left and their right (either matching left and right inputs to position themselves in the centre, [START_REF] Srinivasan | Range perception through apparent image speed in freely flying honeybees[END_REF] or keeping the optic flow input constant and following one surface, [START_REF] Serres | A bee in the corridor: Centering and wall-following[END_REF]. If linear features were less visually rich, and thus provided less optic flow, than the surrounding fields, bees might move closer to linear features in order to increase the visual input from that direction. However, when using ventral optic flow to control position (as they must in such flat terrain), bees have been shown to prefer to fly over surfaces with rich feedback [START_REF] Linander | How bumblebees use lateral and ventral optic flow cues for position control in environments of different proximity[END_REF]. Furthermore, adjustments would only happen if there was extremely little visual feedback from linear features, since differences in frequency and visual contrast have no effect on bee position [START_REF] Srinivasan | Range perception through apparent image speed in freely flying honeybees[END_REF].

In our field site, the dirt paths and roads were visually rich, presenting an uneven surface full of visually contrasting elements (such as cracks and shadows; see Fig 1a). Moreover, this optic-flow driven positioning behaviour is characterised as a direct response to visual input [START_REF] Lecoeur | The role of optic flow pooling in insect flight control in cluttered environments[END_REF][START_REF] Srinivasan | Range perception through apparent image speed in freely flying honeybees[END_REF]. If flying closer to linear features was caused by this mechanism, bee flight trajectories should drift until they reached a visually balanced position. We observe instead sudden detours (Appendix,Fig A2), loops into open fields (Fig 2 ), and a gradual reduction in flight along linear features with experience (Experiment 3). We thus believe flight along linear features is not caused by a response to optic flow but instead reflects an adaptive use of available landmarks.

Ground-level linear features are of under-appreciated importance to animal navigation, especially flying insects. Understanding their utilisation by bees will have practical consequences of considerable importance for agriculture and conservation, in the context of the pollination crisis.

Insect pollinators are responsible for 35% of global food production [START_REF] Klein | Importance of pollinators in changing landscapes for world crops[END_REF][START_REF] Potts | Summary for policymakers of the assessment report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on pollinators, pollination and food production[END_REF] and bee hives are routinely transported for pollination services in intensive agricultural terrains characterised by regular man-made linear features that could be used to direct pollination outcomes [START_REF] Alger | Home sick: impacts of migratory beekeeping on honey bee (Apis mellifera) pests, pathogens, and colony size[END_REF]). We predict for example that placing a hive along a linear feature will predispose the bees to travelling along this particular linear feature whereas placing a hive in the centre of a field might instead predispose bees to concentrate their pollination efforts within the field's borders. Linear features could also be used for conservation purposes to influence the movements of bees in semi-urban and urban environments, and facilitate the localisation of suitable habitats or foraging areas by wild species.

ACKNOWLEDGMENTS

We gratefully thank Oscar Ramos Rodriguez for assisting with fieldwork preparation and communicating with our Spanish collaborators; Carlos Fernandez Bermejo for ensuring our fieldwork did not disrupt the day to day running of Finca Casudis farm; Marcos Perez Formigo for helping navigate government permissions for this experiment and the use of harmonic radar in Spain; Xim Cerda from Estación Biológica de Doñana for his generous hospitality and help in finding a suitable field site and obtaining authorisation for radar work in Spain; Manuel Cano López from the Federación de Arroceros de Sevilla and Jordi Figuerola from Estación Biológica de Doñana for helping identify the owners of suitable rice paddies; Phil Gould from Rothamsted Research for driving the harmonic radar from Hertfordshire to Seville; and Jose Eric Romero Gonzalez, Amelia Kowalewska and Cristian Pasquaretta for assisting with fieldwork. experience of the field site. Feeder F1 was located 168 m from the hive along a path, while feeder F4 was located 168 m from the hive in the middle of the adjacent field (100 m from the closest path; Fig 1b). Bees were allowed to come and go freely from the hive from 1735 to 2135 on the 7 May and from 1100 to 1740 on the 8 May 2018. Visitations at each feeder were filmed (Sony Handicam HDR-CX240, Sony Corporation, Tokyo, Japan) between those times, for a total of 10 hours and 45 min.

The footage was analysed to record the timing of every visit by a bee.

Bees visited F1 far more often than F4 (F1: 341 visits over 10 hours 45 min; F4: 8 visits over the same period; F1 received 97.7% of all visits, Fig A1). The visitation frequency to F1 was on average 3.22 visits over a 5 min period, with a highest frequency of 10 visits in any consecutive 5 min period, while F4 received on average 0.06 visits per 5 min and never more than 1 visit in any consecutive 10 min period. The first visit to F1 occurred only 10 min after bees were allowed access to the field site, while the first visit to F4 was 35 minutes after that (45 minutes since the hives were opened).

Experiment A2

Do bees use linear features when flying to distant natural forage sources? During experiment 2, not all bees used our experimental feeders. Some were tracked flying to distant locations, out of range of the radar (N= 39 tracks, 29 bees; 4 were from unknown individuals and were excluded from statistical analysis). These locations were presumed to be natural forage, since these individuals frequently returned with pollen and tended to fly towards the river (along which were natural foraging sources).

Bees flying to distant locations spent significantly more flight in close proximity to linear features than expected by chance (Kolmogorov-Smirnov test of normality, P = 0.009 for initial scores, P = 0.024 for control scores; Wilcoxon related-samples signed-rank test, P < 0.001). Within these flights, those that included clear inbound and outbound portions of flight showed lower initial scores in their outbound journeys compared to the corresponding inbound journeys (paired sample t-test, t19 = -2.391, P = 0.027). During the final approach (last 30 m) to a feeder or their hive, bees very often flew along the closest linear feature to reach their goal, suggesting that linear features play an important role in finding the precise location of a goal (Fig A2). We tracked a total of 39 flights to distant locations, from 29 bees, of which 22 flights were complete enough to examine the inbound portions of flight. Bees clearly followed the footpath adjacent to the hive during their final approach in 21 of these 22 flights (N=20 bees; Fig A2 ).

Experiment A3

Do bees use linear features when their hive is located in the centre of a field? We investigated whether linear features influenced feeder discovery when feeders were not associated with linear features. For this experiment, the hives were located in the centre of a field (Hive Location 2, Methods), 100 m from the closest linear features.

Bees were trained and tracked individually searching for an array of three feeders from a hive position in the centre of a field (Hive Location 2, Materials and Methods).

Pretraining: All bees were given free access to the field site and trained to feed from three feeders near the hive entrance, providing ad libitum 40% sucrose solution. Once bees were accustomed to this, the feeders were each placed 5 m from the hive in a triangle formation. Individual bees were then trained one by one on all three feeders, each containing 10µL 40% sucrose solution, refilled regularly using an electronic pipette (HandyStep, BrandTech Scientific Inc., Essex, CT, US).The average crop content (from 3 measures) was determined for each individual bee.

Experiment: The three feeders were then arranged in random arrays, where the feeder locations were determined by an algorithm, with the limitation that no two feeders could be closer than 5 m from each other, all feeders were located within the surrounding field and no new array positions used could be within 5 m of any previously used array. Individuals were then tracked searching for the three feeders, each containing one third of the focal bee's crop capacity of 40% sucrose solution.

Every bee (N=13) was tracked during several foraging trips searching for such arrays. Some flights were excluded from analysis due to missing data within the tracks, so that a total of 20 tracks from 13 bees were analysed.

Results: Even when searching for feeders placed randomly within a field from a hive located in the centre of that same field, bees showed a higher proportion of flight within 5 m of linear features Out of 20 tracks, the bee left the field in half of them. Of these 10, 5 only made a single flight loop outside the field borders while 3 showed extensive exploration beyond the field. Of the 10 tracks in which the bee remained within the field, the bee flew up to but not beyond the field borders in 7, suggesting that bees may have learned to use linear features to bound their search space and turned back when they encountered one (Fig A3c -d). The last three tracks remained in close proximity to the hive. Four of the 20 tracks (the 3 with extensive flight outside the field and 1 without flight extending beyond the field borders) were long flights showing sustained periods following linear features, suggesting that a learned association between the hive and a linear feature is not the sole cause of line following behaviour. Different feeder treatments were used for experiments as follows: F1 and F2 from the 20 to the 23 April, F1 and F3 from the 26 to the 30 April, F1, F3 and F4 on the 4 May, and F1 and F4 from the 5 to the 8 May. Flight paths were tracked using harmonic radar (represented by a white dot), located at the end of a packed earth runway which ran East from the farm.

The farm Finca Casudis is located to the West, next to the banks of the river Guadalquivir, where some natural forage sources (represented by red ellipses) could be found. The satellite image used is from Google Earth, ©2018 Google. Note that all rice paddies were dry (no vegetation, brown) at the time of the experiments.

  location 'Hive Location 1' (Fig 1b). Colonies were placed at location 'Hive Location 2' (Fig 1b) for an additional test (see appendix, Experiment A3).

  ). Bees showed significantly higher proportions of their flight in proximity to linear features than expected by chance during first flights (paired-samples t-test, t13 = 3.438, P = 0.004; Fig 2e) and apparent exploration flights (paired-samples t-test, t12 = 3.648, P = 0.003; Fig 2e). The distribution of flight bearings was significantly non-uniform (Kuiper's test of uniformity, V = 3.1921, N=35, P < 0.01), showing more flight in the directions of linear features aligned with the one associated with the hive than in any other direction (Fig 2f). This corresponded not only to bees flying along the footpath associated with the hive, but also to bees flying parallel to that linear feature and along other identically-oriented linear features nearby (Fig 2). One bee showed extensive flight along a field border parallel to the footpath along which the hive was located, suggesting an aliasing error (where the bee mistook one linear feature for another, similar one; Fig 2a). These flights are distinctly different to previously published examples of bumblebee exploration flights, which showed quasicircular loops in all directions (see[START_REF] Osborne | The ontogeny of bumblebee flight trajectories: From naïve explorers to experienced foragers[END_REF][START_REF] Woodgate | Life-long radar tracking of bumblebees[END_REF]).

  than expected by chance (Kolmogorov-Smirnov test of normality, P < 0.05 for initial scores, P = 0.061 for control score; Wilcoxon matched-pair signed-rank test, N=13, P = 0.006; Fig A3a). The bees' flight bearings were not uniformly distributed (Kuiper's test of uniformity, V= 3.5925, N=20, P<0.01), showing a bias in directions corresponding to ENE-WSW linear features (Fig A3b).

Table A1 :

 A1 Details of experimental data. Hive locations 'Hive Location 1' was along a linear feature and 'Hive Location 2' was in the centre of a field, see Methods Fig 1b. Individual colonies used were labelled C1 to C8. The number of flights tracked indicate tracks used solely for that experiment, and for none other, except in the case of Experiment 3, where that data was also included in Experiment 2. N = Individual bees tracked during each time period for each experiment. Some individuals were tracked during several periods. All data from flights tracked were averaged per individual within relevant categories irrespective of date tracked before statistical analysis. Where relevant, the number and position of feeders is indicated for each time period.

Figure 1 :

 1 Figure 1: Panorama and satellite view of the field site. (a): 360° panorama of the field site from the hive position. The hive, with a black tarp covering, is visible on the left of the image. To the right are the grain silos at the farm, barely visible as a small dark grey building above the horizon. The landscape lacks skyline cues (see Appendix). (b): Satellite image with relevant locations. Bumblebees were tracked from two hive locations (Hive Location 1 and Hive Location 2, represented by green circles). Hives were located at Hive Location 1 for Experiments 1-4 and at Hive Location 2 for Experiment A3. Bees inour experiments foraged at experimental feeders (F1, F2, F3 and F4, represented by light blue dots). F1 and F4 were both 168 m from the hive, with F1 along the footpath directly connected to the hive and F4 NE from the hive in the centre of a field. F2 and F3 were along a dirt road perpendicular to the footpath where the hive was placed and directly connected to F1, 172 m and 238 m from the hive respectively. F1 and F2 were 35 m apart, while feeders F1 and F3 were 168 m apart.

Figure 2 :

 2 Figure 2: Bees biased their first exploration flights towards linear features. (a-d): Example first flights of individual bees. Tracks begin dark blue, turn green then yellow by the end of the recorded track. Grey dashed lines indicate a period where the bee's position was unknown. The hive is represented by a green circle. Initial portions of flight were close to the hive and frequently followed the associated footpath. Subsequent loops took the bee further afield, often in directions along or parallel to linear features, showing distinctive right angle turns that matched the surrounding grid (b, d). (a): Example of aliasing error. The bee started with flight along the footpath associated with the hive, performed several loops outward which also scanned along other linear features, and flew away to a distant location. On its return flight, this bee spent a long time flying back and forth along a field border parallel to the footpath associated with the hive, before it successfully re-joined the correct linear feature, found the hive, and returned to distant forage. The satellite image used is from Google Earth, ©2018 Google. (e): Proximity of bee to linear features (initial score) compared to the track-specific control proximity (control score) for first flights (left, in blue; N = 14) and apparent exploration flights (right, in orange; N = 13). Each set of two linked points represents a single individual's scores. Asterisks indicate p-values from paired-samples t-tests: ** P < 0.01. (f): Circular bar chart representing the mean proportion of each first and apparent exploration flight spent flying in each direction (N = 35). Black lines represent standard error of the mean for each bar. Red dots on the perimeter show the orientations of linear features in the environment. Bees in first and apparent exploration flights showed a bias towards flying in directions matching the orientations of linear features, specifically those running WNW-ESE.

Figure 3 :

 3 Figure 3: Bees followed linear features during foraging flights to feeders. (a-d): Example flights of bees to experimental feeders F1, F2 and/or F3. Tracks begin dark blue, turn green then yellow by the end of the recorded track. Grey dashed lines indicate a period where the bee's position was unknown. The hive is represented by a green circle, and feeders are represented by light blue dots. (a): Example flight to F1, showing a direct, straight route along the path. (b): Example flight to F1 and F2. The bee followed the footpath to F1, followed the dirt road at a right angle to reach F2, zigzagged between the feeders multiple times before cutting across the corner of the field from F2 to return to the hive. (c): Example flight to F2. The bee followed the footpath connected to the hive for about a third of the distance before crossing over the corner of the field to the feeder in a curved route. (d): Example flight to F3. The bee flew directly to the feeder from the hive. On the return flight the bee joined and followed the footpath connected to the hive until it reached its goal. The satellite image used is from Google Earth, ©2018 Google.

Figure 4 :

 4 Figure 4: Development of direct route to feeders with experience. (a-p): Radar tracks of the first eight (a-h) and last eight (i-p) flights of a single bee foraging at F3. Flight bouts are indicated by white numbers at the bottom left of each track. Each flight is split into an outbound (blue to green) and inbound (green to yellow) portion of flight. Portions of flight are labelled 'LF' if the bee followed a linear feature for the last 30 m of flight approaching the goal (F3 or the hive); portions of flight without line following are marked 'n'. Outbound portions of flight are labelled in blue, next to the feeder. Inbound portions of flight are labelled in orange, next to the hive. The hive is represented by a green circle, and the feeder is represented by a light blue dot. (a-h): Six of eight outbound portions and four of eight inbound portions of flight show the bee following the appropriate linear feature on its final approach to the goal (LF). (i-p): Only one outbound and two inbound portions of flight show flight along linear features (LF). The satellite image used is from Google Earth, ©2018 Google.

Figure 5 :

 5 Figure 5: Bees searching for an experimentally removed feeder reduced their search space by concentrating on linear features. Every bee had prior experience foraging at the feeder (labelled F1 or F3) and was tracked searching after all feeders were removed from the field site. (a-d): Example flight paths of bees searching for the experimentally removed feeder. The location of this feeder before its removal is indicated by a white cross. Tracks begin dark blue, turn green then yellow by the end of the recorded track. Grey dashed lines indicate a period where the bee's position was unknown. The hive is represented by a green circle. Bees began by searching primarily along the linear feature connected to the feeder's location before expanding their search outwards. These larger loops frequently involved flight along other unrelated linear features (a, b). The satellite image used is from Google Earth, ©2018 Google. (e): Circular bar chart representing the mean proportion of each searching flight (N = 7) spent flying in each direction. Black lines represent standard error of the mean for each bar. Red dots on the perimeter show the orientations of linear features in the environment.

Figure A1 :

 A1 FigureA1: A feeder on a linear feature was far more visited than one not near a linear feature. Histogram showing the number of visits to two feeders in 5 m periods over two days. Feeder F1 (in green), was located along a linear feature adjacent to the hive location. Feeder F4 (in black) was placed 100 m away from the closest linear feature. Both feeders were otherwise identical and at an equal distance from the hive. Feeder visits were counted using video footage. Feeder F1 was visited a lot more frequently than F4.

Figure A2 :

 A2 Figure A2: Bees followed linear features to reach their hive from distant foraging locations. A-D: Example flight paths of bees flying to and from distant foraging sources. Tracks begin dark blue, turn green then yellow by the end of the recorded track. Tracks cut off when bees fly beyond the range of the radar, and restart when bees re-enter the range Grey dashed lines indicate a period where the bee's position was unknown. The hive is represented by a green circle. Most outbound flights (in blue) cut across linear features and did not follow them for extended periods. Return flights differed markedly: in almost all cases recorded, the bee joined the linear feature along which the hive was placed and followed it for some distance for the last stretch of flight. The satellite image used is from Google Earth, ©2018 Google.

Figure A3 :

 A3 Figure A3: Bees searching for unpredictably located feeders within a field used linear features. Bees were trained to a protocol in which three feeders were moved at random within the field between foraging bouts. (a): Proximity of bee to linear features (initial score) compared to the track-specific control proximity (control score) of bees performing searching flights for unpredictable feeders. Each set of two linked points represents a single individual's scores. Asterisks indicate pvalues from a Wilcoxon matched-pair signed-rank test: ** P < 0.01. (b): Circular bar chart representing the mean proportion of each searching flight spent flying in each direction. Black lines represent standard error of the mean for each bar. Red dots on the perimeter show the orientations of linear features in the environment. Bees showed a bias towards flying in ENE-WSW directions. (c-d): Example flight paths of bees searching for feeders in unpredictable locations. Tracks begin dark blue, turn green then yellow by the end of the recorded track. Grey dashed lines indicate a period where the bee's position was unknown. The hive is represented by a green circle. The satellite image used is from Google Earth,

(C7, C8) 20 9-17 May 2018 (N = 13), three feeders present within the field in random positions

We are grateful to the following organisations and programs for funding this research: EPSRC program grant Brains-on-Board (EP/P006094/1): supporting Lars Chittka, Joe Woodgate, Joanna Brebner, Natacha Rossi, and Ka S. Lim; E.R.C. Advanced Grant SpaceRadarPollinator, awarded to Lars Chittka: supporting Joe Woodgate, James Makinson, Ka S. Lim, and Olivia Bates; Smart Crop Protection (SCP) strategic programme (BBS/OS/CP/000001), funded through the Biotechnology and Biological Sciences Research Council's Industrial Strategy Fund: supporting Ka S. Lim; Agence Nationale de la Recherche (ANR-16-CE02-0002-01; ANR-19-CE37-0024; ANR-20-ERC8-0004-01): supporting Mathieu Lihoreau, Tamara Gómez-Moracho and Thibault Dubois.

APPENDIX FIELD SITE PANORAMA

The rice farm Finca Casudis was set in extremely level land, with an almost completely flat horizon and almost no above ground level landmarks. The highest point on the skyline was an installation with grain silos and grain elevator at the farm (dark shape to the right of the panoramic image, Fig 1a). We assessed whether this could provide a landmark for bees using a panoramic image from the location of Hive Location 1 (since Hive Location 1 was closer to the farm than Hive Location 2, see Methods,Fig 1b). A 360° panoramic photograph was taken using a Samsung Galaxy A3 mobile phone (Fig 1a). From this panorama, assuming pixels were square (so that the height of the image was scaled identically to the width), we calculated that the top of the grain silos was 1.86° above the adjacent ground level. The maximum resolution of bumblebees has been placed anywhere between 2.3° [START_REF] Dyer | Comparative psychophysics of bumblebee and honeybee colour discrimination and object detection[END_REF] and 4.8° [START_REF] Chakravarthi | Spatial vision in Bombus terrestris[END_REF]. Thus, it is unlikely that bumblebees could detect the grain silos or any other man-made structure from anywhere close to the hive. It is harder to determine whether there was enough variation around the skyline to provide useful cues, because there is some distortion in the panoramic image: the skyline was visibly flatter than it appears on the image. The maximum difference in horizon level on the panoramic image was 3.30°, but we believe this is an overestimation. We used heywhatsthat.com (Kosowsky, 2021) to calculate the projected skyline as seen from 6ft above ground level at the hive's GPS position (Hive Location 1), and estimated the maximum variation to be 1.03°, suggesting that there was little or no variation detectable by bumblebees.

DETAILS OF DATA COLLECTED ADDITIONAL EXPERIMENTS

Experiment A1

Do linear features affect feeder visitations? Between the 7 and 8 May 2018, we compared the visitation rates between two identical feeders by bees from two bumblebee colonies without prior