Small-scale Patterns Research Articles

Wavelet analysis of discharge and sediment load for the Subarnarekha, Brahmani, and Mahanadi Rivers of India

ABSTRACT This study aims to identify distinct small-scale temporary patterns between the river discharge and suspended sediment load for the main tropical rivers using continuous wavelet transform. The strong annual, and irregular semiannual with few quarterly and interdecadal variabilities are observed in river discharge and sediment load. The wavelet coherence discloses that the strongest in-phase coherence relationships exist between total suspended sediment and streamflow. The methodology for identifying the primary climate factors that integrate the Partial wavelet coherence (PWC), Multiple wavelet coherence (MWC), and Quadruple wavelet coherence (QMWC) techniques is presented. The results show that the ENSO is the major regulator of streamflow followed by IOD and PDO. The chosen rivers show a statistically significant ( p ≤ 0.05 ) descending trend in sediment load but no significant positive or negative trend in river discharge. This could be due to regional sediment storage and large-scale stream events affecting the sediment transport dynamics.

Classifying the clouds of Venus using unsupervised machine learning

Because Venus is completely shrouded by clouds, they play an important role in the planet’s atmospheric dynamics. Studying the various morphological features observed on satellite imagery of the Venusian clouds is crucial to understanding not only the dynamic atmospheric processes, but also interactions between the planet’s surface structures and atmosphere. While attempts at manually categorizing and classifying these features have been made many times throughout Venus’ observational history, they have been limited in scope and prone to subjective bias. We therefore present and investigate an automated, objective, and scalable approach for their classification using unsupervised machine learning that can leverage full datasets of past, ongoing, and future missions.To achieve this, we introduce a novel framework to generate nadir observation patches of Venus’ clouds at fixed consistent scales from satellite imagery data of the Venus Express and Akatsuki missions. Such patches are then divided into classes using an unsupervised machine learning approach that consists of encoding the patch images into feature vectors via a convolutional neural network trained on the patch datasets and subsequently clustering the obtained embeddings using hierarchical agglomerative clustering.We find that our approach demonstrates considerable accuracy when tested against a curated benchmark dataset of Earth cloud categories, is able to identify meaningful classes for global-scale (3000km) cloud features on Venus and can detect small-scale (25km) wave patterns. However, at medium scales (∼500km) challenges are encountered, as available resolution and distinctive features start to diminish and blended features complicate the separation of well defined clusters.

AerialIRGAN: unpaired aerial visible-to-infrared image translation with dual-encoder structure

Due to the high cost of equipment and the constraints of shooting conditions, obtaining aerial infrared images of specific targets is very challenging. Most methods using Generative Adversarial Networks for translating visible images to infrared greatly depend on registered data and struggle to handle the diversity and complexity of scenes in aerial infrared targets. This paper proposes a one side end-to-end unpaired aerial visible-to-infrared image translation algorithm, termed AerialIRGAN. AerialIRGAN introduces a dual-encoder structure, where one encoder is designed based on the Segment Anything Model to extract deep semantic features from visible images, and the other encoder is designed based on UniRepLKNet to capture small-scale patterns and sparse patterns from visible images. Subsequently, AerialIRGAN constructs a bridging module to deeply integrate the features of both encoders and their corresponding decoders. Finally, AerialIRGAN proposes a structural appearance consistency loss to guide the synthetic infrared images to maintain the structure of the source image while possessing distinct infrared characteristics. The experimental results show that compared to the existing typical infrared image generation algorithms, the proposed method can generate higher-quality infrared images and achieve better performance in both subjective visual description and objective metric evaluation.

Steel Surface Defect Detection Algorithm Based on Improved YOLOv8n

The traditional detection methods of steel surface defects have some problems, such as a lack of feature extraction ability, sluggish detection speed, and subpar detection performance. In this paper, a YOLOv8-based DDI-YOLO model is suggested for effective steel surface defect detection. First, on the Backbone network, the extended residual module (DWR) is fused with the C2f module to obtain C2f_DWR, and the two-step approach is used to carry out the effective extraction of multiscale contextual information, and then fusing feature maps formed from the multiscale receptive fields to enhance the capacity for feature extraction. Also based on the above, an extended heavy parameter module (DRB) is added to the structure of C2f_DWR to make up for the lack of C2f’s ability to capture small-scale pattern defects between training to enhance the training fluency of the model. Finally, the Inner-IoU loss function is employed to enhance the regression accuracy and training speed of the model. The experimental results show that the detection of DDI-YOLO on the NEU-DET dataset improves the mAP by 2.4%, the accuracy by 3.3%, and the FPS by 59 frames/s compared with the original YOLOv8n.Therefore, this paper’s proposed model has a superior mAP, accuracy, and FPS in identifying surface defects in steel.

Small-scale hydrological patterns in a Siberian permafrost ecosystem affected by drainage

Abstract. Climate warming and associated accelerated permafrost thaw in the Arctic lead to a shift in landscape patterns, hydrologic conditions, and release of carbon. In this context, the lateral transport of carbon and shifts therein following thaw remain poorly understood. Crucial hydrologic factors affecting the lateral distribution of carbon include the depth of the saturated zone above the permafrost table with respect to changes in water table and thaw depth and the connectivity of water-saturated zones. Landscape conditions are expected to change in the future due to rising temperatures and polygonal or flat floodplain Arctic tundra areas in various states of degradation; hydrologic conditions will also change. This study is focused on an experimental site near Chersky, northeast Siberia, where a drainage ditch was constructed in 2004 to simulate landscape degradation features that result in drier soil conditions and channeled water flow. We compared water levels and thaw depths in the drained area (dry soil conditions) with those in an adjacent control area (wet soil conditions). We also identified the sources of water at the site via stable water isotope analysis. We found substantial spatiotemporal changes in the water conditions at the drained site: (i) lower water tables resulting in drier soil conditions, (ii) quicker water flow through drier areas, (iii) larger saturation zones in wetter areas, and (iv) a higher proportion of permafrost meltwater in the liquid phase towards the end of the growing season. These findings suggest decreased lateral connectivity throughout the drained area. Shifts in hydraulic connectivity in combination with a shift in vegetation abundance and water sources may impact carbon sources and sinks as well as transport pathways. Identifying lateral transport patterns in areas with degrading permafrost is therefore crucial.

Experimental Examination of Additively Manufactured Patterns on Structural Nuclear Materials for Digital Image Correlation Strain Measurements

BackgroundThere are a limited number of commercially available sensors for monitoring the deformation of materials in-situ during harsh environment applications, such as those found in the nuclear and aerospace industries. Such sensing devices, including weldable strain gauges, extensometers, and linear variable differential transformers, can be destructive to material surfaces being investigated and typically require relatively large surface areas to attach (> 10 mm in length). Digital image correlation (DIC) is a viable, non-contact alternative to in-situ strain deformation. However, it often requires implementing artificial patterns using splattering techniques, which are difficult to reproduce.ObjectiveAdditive manufacturing capabilities offer consistent patterns using programmable fabrication methods.MethodsIn this work, a variety of small-scale periodic patterns with different geometries were printed directly on structural nuclear materials (i.e., stainless steel and aluminum tensile specimens) using an aerosol jet printer (AJP). Unlike other additive manufacturing techniques, AJP offers the advantage of materials selection. DIC was used to track and correlate strain to alternative measurement methods during cyclic loading, and tensile tests (up to 1100 µɛ) at room temperature.ResultsThe results confirmed AJP has better control of pattern parameters for small fields of view and facilitate the ability of DIC algorithms to adequately process patterns with periodicity. More specifically, the printed 100 μm spaced dot and 150 μm spaced line patterns provided accurate measurements with a maximum error of less than 2% and 4% on aluminum samples when compared to an extensometer and commercially available strain gauges.ConclusionOur results highlight a new pattern fabrication technique that is form factor friendly for digital image correlation in nuclear applications.

Lagrangian surface drifter observations in the North Sea: an overview of high-resolution tidal dynamics and surface currents

Abstract. A dataset of 85 Lagrangian surface drifter trajectories covering the central North Sea area and the Skagerrak from 2017–2021 of 17 deployments is presented. The data have been quality-controlled, uniformly structured, and assimilated in a standard NetCDF format (https://doi.org/10.1594/PANGAEA.963166, Meyerjürgens et al., 2023a). Using appropriate methods presented in detail here, surface currents were calculated from the drifter position data. Based on a drifter deployment in the Skagerrak, it is demonstrated that the Lagrangian measurements can be converted into an Eulerian representation by calculating mean current velocities. Tidal energy spectra were analyzed separately for the southern and northern areas of the North Sea, and tidal ellipses were calculated to determine the tidal impact on surface currents. Significant differences between the shallow shelf and the deeper areas of the North Sea are evident. While the shallow nearshore areas are dominated by tidal currents, deeper areas such as the Skagerrak record a high mean residual circulation driven by high-density gradients. Measurements using Eulerian approaches and remote sensing methods are restricted in temporal and spatial coverage, in particular, to capture fine-scale dynamics. For this reason, Lagrangian measurements, to a large extent, provide new insights into the complex submesoscale dynamics of the North Sea. Exemplarily, the Skagerrak region is used to demonstrate that high-resolution drifter observations capture both mesoscale and small-scale current patterns. This unique dataset, covering the entire southeastern North Sea and the Skagerrak, offers further analysis possibilities and can be used for the investigation of various hydrodynamic and environmental issues, e.g., the analysis of submesoscale current dynamics at ocean fronts, the determination of the kinetic eddy energy, and the propagation of pollutants in the North Sea.

Bacterial and fungal communities in sub-Arctic tundra heaths are shaped by contrasting snow accumulation and nutrient availability.

Climate change is affecting winter snow conditions significantly in northern ecosystems but the effects of the changing conditions for soil microbial communities are not well-understood. We utilized naturally occurring differences in snow accumulation to understand how the wintertime subnivean conditions shape bacterial and fungal communities in dwarf shrub-dominated sub-Arctic Fennoscandian tundra sampled in mid-winter, early, and late growing season. Phospholipid fatty acid (PLFA) and quantitative PCR analyses indicated that fungal abundance was higher in windswept tundra heaths with low snow accumulation and lower nutrient availability. This was associated with clear differences in the microbial community structure throughout the season. Members of Clavaria spp. and Sebacinales were especially dominant in the windswept heaths. Bacterial biomass proxies were higher in the snow-accumulating tundra heaths in the late growing season but there were only minor differences in the biomass or community structure in winter. Bacterial communities were dominated by members of Alphaproteobacteria, Actinomycetota, and Acidobacteriota and were less affected by the snow conditions than the fungal communities. The results suggest that small-scale spatial patterns in snow accumulation leading to a mosaic of differing tundra heath vegetation shapes bacterial and fungal communities as well as soil carbon and nutrient availability.

Predator-prey interactions in a coastal setting: Linking crab feeding rates to small scale distribution of clams

A coastal predator-prey system, juvenile green crabs (Carcinus maenas) preying upon juvenile hard clams (Mercenaria mercenaria), was used to explore the link between crab predation rates and clam density and small-scale distribution patterns. The channel working area of a racetrack flume was adapted to form a sedimentary arena in a flowing seawater system (5 cm s−1) to assess crab predation rates in relation to clam density and distribution patterns (clams clustered in one patch vs two nearby vs two farther apart). The trials detected significant differences in relation to clam initial density and distribution with strong (∼50%) declines in clam mortality levels among spatial arrangements (one patch > two nearby > two farther apart). Feeding of clams was associated with the time taken by crabs to handle the first clam (first patch), and the frequency of three distinct types of crab behavior (eating, resting, and searching). Altogether these results suggest that small-scale changes in number and distribution of juvenile clams matter and may have unexpectedly strong effects on the outcome of predator-prey interactions.

The Flexibility of Sengon Agroforestry in Small-Scale Forest Development in Indonesia

Small-scale forests (SSF) are essential to the community’s ecological, social, and economic aspects. The tree species that many people choose to develop in small-scale forests is sengon. Sengon has various advantages and can be developed in agroforestry with various plants. The research question in this study is how flexible the sengon agroforestry pattern is in Small-Scale Forest Development in Indonesia? The study aimed to determine the flexibility of sengon agroforestry in small-scale forests and the added value of small-scale forests from the economic and ecological aspects. The study was conducted using a systematic review. The results show that small-scale forest development with an agroforestry pattern can diversify income sources from the same land management, thus providing a sustainable income. The sengon agroforestry pattern in small-scale forests can be made with various types of plants (a) woody trees, (b) food crops, (c) plantation crops, (d) horticultural crops, (e) medicinal plants and (f) animal feed crops. Small-scale forest agroforestry patterns can provide economic, ecological, and social benefits. By knowing various alternative agroforestry cropping patterns, communities will be more motivated to improve their small-scale forest management to increase their economy.

A Practical Approach to Automatic Earthquake Catalog Compilation in Local OBS Networks Using Deep-Learning and Network-Based Algorithms

Abstract In land-based seismology, modern automatic earthquake detection and phase picking algorithms have already proven to outperform classic approaches, resulting in more complete catalogs when only taking a fraction of the time needed for classic methods. For marine-based seismology, similar advances have not been made yet. For ocean-bottom seismometer (OBS) data, additional challenges arise, such as a lower signal-to-noise ratio and fewer labeled data sets available for training deep-learning models. However, the performance of available deep-learning models has not yet been extensively tested on marine-based data sets. Here, we apply three different modern event detection and phase picking approaches to an ∼12 month local OBS data set and compare the resulting earthquake catalogs and location results. In addition, we evaluate their performance by comparing different subcatalogs of manually detected events and visually revised picks to their automatic counterparts. The results show that seismicity patterns from automatically compiled catalogs are comparable to a manually revised catalog after applying strict location quality control criteria. However, the number of such well-constrained events varies between the approaches and catalog completeness cannot be reliably determined. We find that PhaseNet is more suitable for local OBS networks compared with EQTransformer and propose a pick-independent event detection approach, such as Lassie, as the preferred choice for an initial event catalog compilation. Depending on the aim of the study, different schemes of manual repicking should be applied because the automatic picks are not yet reliable enough for developing a velocity model or interpreting small-scale seismicity patterns.

Arthropod diversity in the alpine tundra using metabarcoding: Spatial and temporal differences in alpha- and beta-diversity.

All ecosystems face ecological challenges in this century. Therefore, it is becoming increasingly important to understand the ecology and degree of local adaptation of functionally important Arctic-alpine biomes by looking at the most diverse taxon of metazoans: the Arthropoda. This is the first study to utilize metabarcoding in the Alpine tundra, providing insights into the effects of micro-environmental parameters on alpha- and beta-diversity of arthropods in such unique environments. To characterize arthropod diversity, pitfall traps were set at three middle-alpine sampling sites in the Scandinavian mountain range in Norway during the snow-free season in 2015. A metabarcoding approach was then used to determine the small-scale biodiversity patterns of arthropods in the Alpine tundra. All DNA was extracted directly from the preservative EtOH from 27 pitfall traps. In order to identify the controlling environmental conditions, all sampling locations were equipped with automatic data loggers for permanent measurement of the microenvironmental conditions. The variables measured were: air temperature [°C] at 15 cm height, soil temperature [°C] at 15 cm depth, and soil moisture [vol.%] at 15 cm depth. A total of 233 Arthropoda OTUs were identified. The number of unique OTUs found per sampling location (ridge, south-facing slope, and depression) was generally higher than the OTUs shared between the sampling locations, demonstrating that niche features greatly impact arthropod community structure. Our findings emphasize the fine-scale heterogeneity of arctic-alpine ecosystems and provide evidence for trait-based and niche-driven adaptation. The spatial and temporal differences in arthropod diversity were best explained by soil moisture and soil temperature at the respective locations. Furthermore, our results show that arthropod diversity is underestimated in alpine-tundra ecosystems using classical approaches and highlight the importance of integrating long-term functional environmental data and modern taxonomic techniques into biodiversity research to expand our ecological understanding of fine- and meso-scale biogeographical patterns.

Small-scale spatial distribution of ghost shrimp and macrobenthic fauna in an Amazon macrotidal dissipative sandy beach

ABSTRACT Spatial changes in environmental characteristics strongly influence the structure of benthic communities on sandy beaches, where the patterns of occurrence are mainly controlled by morphodynamic characters and biological interactions. Despite being very abundant and extensive, the Amazonian sandy beaches are among the coastal environments less well known to science. On these beaches, the ghost shrimp Lepidophthalmus siriboia Felder and Rodrigues, 1993 is an important component of the intertidal macrofauna. The present study describes the small-scale distribution patterns of L. siriboia and of the macrobenthic communities in a dissipative macrotidal Amazonian sandy beach. Samples were collected in the intertidal zone of two areas: Area 1, next to a tidal channel; and Area 2, away from the tidal channel. In Area 1, with finer sediments, there was a higher burrow density of L. siriboia and higher density and species richness of macrofauna. In both areas, the burrow density of L. siriboia increased towards the sea. The faunal distribution patterns can be explained by changes in flooding and the type of substratum across the shore and alongshore. These findings add to our knowledge of tropical sandy beach ecology and reinforce the idea that tropical macrotidal beaches have a complexity of morphological features, which are drivers of macrofaunal structure.

On the propeller wake evolution using large eddy simulations and physics-informed space-time decomposition

A novel modal analysis methodology, denoted as the physics informed sparsity-promoting dynamic mode decomposition (pi-SPDMD) model, was introduced for the reduction and reconstruction analysis of intricate propeller wake flows, aiming to provide insight into the inherent flow structures spanning diverse temporal and spatial scales. Large-Eddy Simulation (LES) was employed to numerically model the wake dynamics of a four-bladed propeller, providing a comprehensive resolution from the proximate to the distant wake regions. The findings indicate that the pi-SPDMD model enhances the efficiency of the sparse-promoting algorithm, producing modes that gravitate towards stability, and the resulting decomposition maintains commendable physical fidelity. Integrating the results from the LES solution and the modal decomposition of pi-SPDMD, the tip vortex exhibits a uniform topological configuration with notable coherence in the proximate domain. In this region, the large-scale vortex is the dominant feature of the propeller wake, and there is a marked intermittency in the turbulence. In the mid-field, the tip vortex system transitions into fine-scale vortices, rapidly diminishing in coherence due to the onset of elliptic instability and subsequent secondary vortex generation. As the tip vortex structures related to physical quantities become fully discretized, the small-scale turbulent patterns quickly intermingle, leading to a more homogeneous distribution in the distant wake.

The sandpaper theory of flow–topography interaction for homogeneous shallow-water systems

Recent studies reveal the dramatic impact of seafloor roughness on the dynamics and stability of broad oceanic flows. These findings motivate the development of parameterizations that concisely represent the effects of small-scale bathymetric patterns in theoretical and coarse-resolution numerical circulation models. The previously reported quasi-geostrophic ‘sandpaper’ theory of flow–topography interaction a priori assumes gentle topographic slopes and weak flows with low Rossby numbers. Since such conditions are often violated in the ocean, we now proceed to formulate a more general model based on shallow-water equations. The new version of the sandpaper model is validated by comparing roughness-resolving and parametric simulations of the flow over a corrugated seamount.

Impact of different nucleic acid testing scenarios on COVID-19 transmission

In the past three years, waves of COVID-19 infections have emerged one after another, and may enter a small-scale wave-like recurrent epidemic pattern in the future. When COVID-19 infections occur in small-scale, how to efficiently detect and prevent the disease has become the main problem. In this study, based on the characteristics of the Omicron variant and China's pandemic prevention and control strategies, the following three nucleic acid testing scenarios were simulated: scenario 1 (baseline scenario) included conducting nucleic acid testing at administrative region; scenario 2 included conducting nucleic acid testing at the community; and scenario 3 included conducting nucleic acid testing at the health facility closest to households. The model calibration showed that the baseline scenario was consistent with the actual transmission scenario of the disease. The simulation results revealed that compared with scenario 1, the cumulative cases in scenarios 2 and 3 were reduced by 9.52 % and 46.83 %, respectively. Compared with scenario 2, the cumulative cases in scenario 3 were reduced by 41.23 %. Thus, adopting nucleic acid testing measures at the household level can effectively limit the spread of COVID-19 and should be given a priority when local emergency occurs in the future.

Exact and sampling methods for mining higher-order motifs in large hypergraphs

Network motifs are recurrent, small-scale patterns of interactions observed frequently in a system. They shed light on the interplay between the topology and the dynamics of complex networks across various domains. In this work, we focus on the problem of counting occurrences of small sub-hypergraph patterns in very large hypergraphs, where higher-order interactions connect arbitrary numbers of system units. We show how directly exploiting higher-order structures speeds up the counting process compared to traditional data mining techniques for exact motif discovery. Moreover, with hyperedge sampling, performance is further improved at the cost of small errors in the estimation of motif frequency. We evaluate our method on several real-world datasets describing face-to-face interactions, co-authorship and human communication. We show that our approximated algorithm allows us to extract higher-order motifs faster and on a larger scale, beyond the computational limits of an exact approach.

Sustainable Maize Crop Production in Owena Basin, Nigeria

To improve maize production, efforts must be doubled to expand the current extent of land suitability profiling. Lingering questions hinged on whether maize is presently grown on land that supports its optimal growth and if farmers’ knowledge of their farmland suitability informs their decision to cultivate improved maize varieties. However, attaining the level of food self-subsistence largely requires optimum land use and the adoption of innovative advancement to double farmers' yields. Unfortunately, land suitability profiling and farmers’ cultivation of Improved Maize Varieties (IMV) still require more attention in the scheme of initiatives to revitalize agriculture. Previous research addressed other crops rather than maize staples and assessed areas exclusive of the current study area. This paper used Geographical Information Systems (GIS) and Multicriteria Analysis (MCA) to map out land areas suitable for maize cultivation and further used frequency, counts, and parametric estimator to data from 466 maize farmers to profile farmers who cultivated IMV, examine the effect of cultivating IMV and its determinants. Results showed the suitability of AHP for land evaluation, revealing the highly, moderately, marginally, and least suitable areas of land for maize cultivation occupying 5.09%, 56.53%, 37.47%, and 0.89% of the total land. The result also divulges the dominance of old respondents where the age of farmers who did not cultivate IMV ranged between 50 and 61 years while the age of respondents who cultivated IMV ranged between 52 and 72 years. A small-scale production pattern was observed as the area of land under cultivation varied between 1.9 and 2.67 acres for respondents who grew IMV and those who did not. The Potential Outcome mean (POmean) estimation for respondents who did not grow IMV was 8127.70kg and 11695.8 kg for respondents who cultivated IMV, and the size of land cultivated and access to extension services significantly (at P>|z|=0.000) contributed to the likelihood of farmers growing IMV. The study highlights the importance of harnessing the comparative advantage of land by cultivating the crop it best supports suggesting the need to embrace IMV cultivation and practices including but not limited to the cultivation of legumes to help maintain the soil integrity and further advocate for improved communication between farmers and agricultural services developers.

Gene-rich plastid genomes of two parasitic red algal species, Laurencia australis and L. verruciformis (Rhodomelaceae, Ceramiales), and a taxonomic revision ofJanczewskia.

Parasitic red algae are an interesting system for investigating the genetic changes that occur in parasites. These parasites have evolved independently multiple times within the red algae. The functional loss of plastid genomes can be investigated in these multiple independent examples, and fine-scale patterns may be discerned. The only plastid genomes from red algal parasites known so far are highly reduced and missing almost all photosynthetic genes. Our study assembled and annotated plastid genomes from the parasites Janczewskia tasmanica and its two Laurencia host species (Laurencia elata and one unidentified Laurencia sp. A25) from Australia and Janczewskia verruciformis, its host species (Laurencia catarinensis), and the closest known free-living relative (Laurencia obtusa) from the Canary Islands (Spain). For the first time we show parasitic red algal plastid genomes that are similar in size and gene content to free-living host species without any gene loss or genome reduction. The only exception was two pseudogenes (moeB and ycf46) found in the plastid genome of both isolates of J. tasmanica, indicating potential for future loss of these genes. Further comparative analyses with the three highly reduced plastid genomes showed possible gene loss patterns, in which photosynthetic gene categories were lost followed by other gene categories. Phylogenetic analyses did not confirm monophyly of Janczewskia, and the genus was subsumed into Laurencia. Further investigations will determine if any convergent small-scale patterns of gene loss exist in parasitic red algae and how these are applicable to other parasitic systems.

Temporal effects of sugar intake on fly local search and honey bee dance behaviour.

Honey bees communicate flight navigational information of profitable food to nestmates via their dance, a small-scale walking pattern, inside the nest. Hungry flies and honey bee foragers exhibit a sugar-elicited search involving path integration that bears a resemblance to dance behaviour. This study aimed to investigate the temporal dynamics of the initiation of sugar-elicited search and dance behaviour, using a comparative approach. Passive displacement experiments showed that feeding and the initiation of search could be spatially and temporally dissociated. Sugar intake increased the probability of initiating a search but the actual onset of walking triggers the path integration system to guide the search. When prevented from walking after feeding, flies and bees maintained their motivation for a path integration-based search for a duration of 3min. In flies, turning and associated characters were significantly reduced during this period but remained higher than in flies without sugar stimulus. These results suggest that sugar elicits two independent behavioural responses: path integration and increased turning, with the initiation and duration of path integration system being temporally restricted. Honey bee dance experiments demonstrated that the motivation of foragers to initiate dance persisted for 15min, while the number of circuits declined after 3min following sugar ingestion. Based on these findings, we propose that food intake during foraging increases the probability to initiate locomotor behaviours involving the path integration system in both flies and honey bees, and this ancestral connection might have been co-opted and elaborated during the evolution of dance communication by honey bees.