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1. Tracking individual Bactrocera tryoni : Wind effects and natural movement

   https://doi.org/10.1111/eea.13578  

   Moses, Ethan R; Lehman, Meredith_G M; Johnson, Adesola J; Welty_Peachey, Allysen M; Yoder, James M; De_Faveri, Stefano G; Cheesman, Jodie; Manoukis, Nicholas C; Siderhurst, Matthew S (August 2025, Entomologia Experimentalis et Applicata)

   Abstract Determining the movement characteristics under real‐world conditions of insect pests, such as tephritid fruit flies, is critical to increase the effectiveness of detection, response, and control strategies. In this study, we conducted two experiments using harmonic radar to track wild‐caught male Queensland fruit flies (Qflies),Bactrocera tryoni(Froggatt) (Diptera: Tephritidae), a major horticultural pest in Australia. In Experiment 1, we continuously tracked individual Qflies, which were prodded to induce movement in a high‐density papaya (Carica papaya, L., Caricaceae) field. We conducted Experiment 2 in a field with lower papaya density and tracked flies were allowed to move without disturbance. This latter natural movement experiment showed that Qflies move at a rate of (mean ± SE) 19 ± 3 m h⁻¹. In both experiments, overall and between‐tree flight directions were found to be correlated with wind direction, whereas within‐tree movement directions were not. Further, the effect of wind direction on fly trajectories varied by step distance but not strongly with wind speed, whereas step‐distance distributions were consistent with Lévy walks (i.e., short random steps with occasional larger steps). Qfly movements were well fitted by two‐state hidden Markov models, further supporting the observation that Qflies move differently within (short steps with random direction) and between (longer more directional steps) trees. Data on flight directionality, step distances, and movement speed determined in this study provide parameters that may help enhance current surveillance, control, and eradication methods, such as optimizing trap placements and pesticide applications, determining release sites for parasitoids, and setting quarantine boundaries after incursions. 

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2. High-speed whole-brain imaging in Drosophila

   https://doi.org/10.1101/2025.06.18.660371  

   Gauthey, Wayan; Lin, Albert; Ahmed, Osama M; Leifer, Andrew M; Murthy, Mala; Thiberge, Stephan Y (June 2025, bioRxiv)

   Abstract Recent advances in brain-wide recordings of small animals such as worms, fish, and flies have revealed complex activity involving large populations of neurons. In theDrosophilabrain, with about 140,000 neurons, brain-wide recordings have been critical to uncovering widespread sensory and motor activity. However, current limitations in volumetric imaging rates hinder the accurate capture of fast neural dynamics. To improve the speed of volumetric imaging inDrosophila, we leverage the recently introduced light beads microscopy (LBM) method. We built a microscope and a LBM module tailored to fly brain experiments and used it to record brain-wide calcium signals in adult behaving flies at either 28 volumes per second or at 60 volumes per second (when selecting the central brain alone). We uncover fast-timescale auditory responses that are missed with standard volumetric imaging. We also demonstrate how temporal super-resolution can be combined with LBM data to uncover responses to singleDrosophilacourtship song pulses. This establishes LBM as a viable tool for capturing whole-brain activity at high spatial and temporal resolution in the fly. 

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3. The DrosophilaSIFamide Receptor Regulates Sleep and Feeding in a Time‐Of‐Day Specific Manner

   https://doi.org/10.1111/gbb.70043  

   Velazquez, Anayatzi; Cusick, Madelyn R; De, Siddarth; Beaudouin, Andi A; Stepankovskaya, Ariel; Tidaback, Justina A; Tsibere, Oleksandra; Cavanaugh, Daniel J (December 2025, Genes, Brain and Behavior)

   ABSTRACT To optimize health, organisms must coordinate energy intake and expenditure and apportion related behaviors to appropriate times of day. In the fruit fly,Drosophila melanogaster, the SIFamide (SIFa) neuropeptide impacts multiple behavioral outputs important for energy regulation, including reproductive activity, sleep, and feeding. SIFa‐expressing neurons receive convergent inputs from circadian and homeostatic brain regions and extend elaborate projections throughout the central nervous system. Consistent with this distribution pattern, the SIFa receptor (SIFaR) is widely expressed in the brain and ventral nerve cord, providing the anatomical substrate for SIFa signaling to influence a broad range of neuronal functions. To further explore the pleiotropic role of SIFa signaling in behavioral control, we have assessed survival, locomotor activity, sleep, and feeding inSIFaRmutant flies, as well as in flies with RNA interference‐induced reduction ofSIFaRexpression. We find that loss of SIFaR has a complex effect on fly survival that is background‐ and allele‐specific. However, outcrossedSIFaRmutant flies are viable, enabling monitoring of adult behavior. These flies exhibit elevated locomotor activity, reduced sleep, and increased feeding at specific times of day. We also find thatSIFaRmutations drastically decrease starvation resistance. These results suggest a prominent role for SIFaR in integrating homeostatic and circadian information to coordinate the magnitude and timing of energy balance‐related behaviors. 

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4. Experimental assessment of 3D-printed traps and chemical attractants for the collection of wild Drosophila melanogaster

   https://doi.org/10.1101/2025.01.28.635319  

   Keene-Snickers, Alexandra H; Dunham, Tillie J; Stenglein, Mark D (January 2025, bioRxiv)

   ABSTRACT Drosophila melanogaster, the common fruit fly, has been instrumental to our understanding of evolution, genetics and disease. There are benefits to studying these flies in the wild, including assessment of their naturally occurring microbiota. To facilitate efforts to catch wildD. melanogaster, we designed two fly traps and evaluated several candidate attractants. The first trap utilized a stable food substrate that can be used to catch live flies to establish new lab colonies. The second trap was designed to be reusable and easy to ship to enable the collection of flies over time from diverse locations. We evaluated several chemical attractants derived from banana and from marula fruit, which is the proposed ancestral food host ofD. melanogaster. We found that wild flies were preferentially attracted to banana-based odorants over marula-derived ones. Overall, these traps and attractants represent an inexpensive and simple option for the collection of wildD. melanogasterand related species for sampling or colony establishment. 

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5. Statistical structure of locomotion and its modulation by odors

   https://doi.org/10.7554/eLife.41235  

   Tao, Liangyu; Ozarkar, Siddhi; Beck, Jeffrey M; Bhandawat, Vikas (January 2019, eLife)

   Many behaviors that we perform everyday, including something as familiar as making a peanut-butter sandwich, consist of a sequence of recognizable acts. These acts may include, for example, holding a knife and opening a jar. Yet often neither the sequence nor the individual acts are always performed in the exact same way. For example, there are many ways to hold a knife and there are many ways to open a jar, meaning neither of these actions could be called “stereotyped”. A lack of stereotypy makes it difficult for a computer to automatically recognize the individual acts in a sequence. This same problem would apply to other common behaviors, such as walking around somewhere you have not visited before. While we easily recognize it when we see it, walking is not a stereotyped behavior. It consists of a series of movements that differ between individuals, and even in the same individual at different times. So how can someone automatically recognize the individual acts in a non-stereotyped behavior like walking? To begin to find out, Tao et al. developed a mathematical model that can recognize the walking behavior of a fruit fly. Existing recordings of fruit flies walking were analyzed using a type of mathematical model called a Hierarchical Hidden Markov Model (often shortened to HHMM). Such models assume that there are hidden states that influence the behaviors we can see. For example, someone’s chances of going skiing (an observable behavior) depend on whether or not it is winter (a hidden state). The HHMM revealed that the seemingly random wanderings of a fly consist of ten types of movement. These include the “meander”, the “stop-and-walk”, as well as right turns and left turns. Exposing the flies to a pleasant odor – in this case, apple cider vinegar – altered how the flies walked by changing the time they spent performing each of the different types of movement. All flies in the dataset used the same ten movements, but in different proportions. This means that each fly showed an individual pattern of movement. In fact, the differences between flies are so great that Tao et al. argue that there is no such thing as an average walk for a fruit fly. The model represents a complete description of how fruit flies walk. It thus provides clues to the processes that transform an animal’s sensory experiences into behavior. But it also has potential clinical applications. Similar models for human behaviors could help reveal behaviors that are abnormal because of disease. Normal behaviors also show variability, and some diseases increase or decrease this variability. By making it easier to detect these changes, mathematical models could support earlier diagnosis of medical conditions. 

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