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ABSTRACT The African trypanosome has evolved mechanisms to adapt to changes in nutrient availability that occur during its life cycle. During transition from mammalian blood to insect vector gut, parasites experience a rapid reduction in environmental glucose. Here we describe how pleomorphic parasites respond to glucose depletion with a focus on parasite changes in energy metabolism and growth. Long slender bloodstream form parasites were rapidly killed as glucose concentrations fell, while short stumpy bloodstream form parasites persisted to differentiate into the insect-stage procyclic form parasite. The rate of differentiation was lower than that triggered by other cues but reached physiological rates when combined with cold shock. Both differentiation and growth of resulting procyclic form parasites were inhibited by glucose and nonmetabolizable glucose analogs, and these parasites were found to have upregulated amino acid metabolic pathway component gene expression. In summary, glucose transitions from the primary metabolite of the blood-stage infection to a negative regulator of cell development and growth in the insect vector, suggesting that the hexose is not only a key metabolic agent but also an important signaling molecule. IMPORTANCE As the African trypanosome Trypanosoma brucei completes its life cycle, it encounters many different environments. Adaptation to these environments includes modulation of metabolic pathways to parallel the availability of nutrients. Here, we describe how the blood-dwelling life cycle stages of the African trypanosome, which consume glucose to meet their nutritional needs, respond differently to culture in the near absence of glucose. The proliferative long slender parasites rapidly die, while the nondividing short stumpy parasite remains viable and undergoes differentiation to the next life cycle stage, the procyclic form parasite. Interestingly, a sugar analog that cannot be used as an energy source inhibited the process. Furthermore, the growth of procyclic form parasite that resulted from the event was inhibited by glucose, a behavior that is similar to that of parasites isolated from tsetse flies. Our findings suggest that glucose sensing serves as an important modulator of nutrient adaptation in the parasite. 

The interface between field biology and technology is energizing the collection of vast quantities of environmental data. Passive acoustic monitoring, the use of unattended recording devices to capture environmental sound, is an example where technological advances have facilitated an influx of data that routinely exceeds the capacity for analysis. Computational advances, particularly the integration of machine learning approaches, will support data extraction efforts. However, the analysis and interpretation of these data will require parallel growth in conceptual and technical approaches for data analysis. Here, we use a large hand‐annotated dataset to showcase analysis approaches that will become increasingly useful as datasets grow and data extraction can be partially automated.

We propose and demonstrate seven technical approaches for analyzing bioacoustic data. These include the following: (1) generating species lists and descriptions of vocal variation, (2) assessing how abiotic factors (e.g., rain and wind) impact vocalization rates, (3) testing for differences in community vocalization activity across sites and habitat types, (4) quantifying the phenology of vocal activity, (5) testing for spatiotemporal correlations in vocalizations within species, (6) among species, and (7) using rarefaction analysis to quantify diversity and optimize bioacoustic sampling.

To demonstrate these approaches, we sampled in 2016 and 2018 and used hand annotations of 129,866 bird vocalizations from two forests in New Hampshire, USA, including sites in the Hubbard Brook Experiment Forest where bioacoustic data could be integrated with more than 50 years of observer‐based avian studies. Acoustic monitoring revealed differences in community patterns in vocalization activity between forests of different ages, as well as between nearby similar watersheds. Of numerous environmental variables that were evaluated, background noise was most clearly related to vocalization rates. The songbird community included one cluster of species where vocalization rates declined as ambient noise increased and another cluster where vocalization rates declined over the nesting season. In some common species, the number of vocalizations produced per day was correlated at scales of up to 15 km. Rarefaction analyses showed that adding sampling sites increased species detections more than adding sampling days.

Although our analyses used hand‐annotated data, the methods will extend readily to large‐scale automated detection of vocalization events. Such data are likely to become increasingly available as autonomous recording units become more advanced, affordable, and power efficient. Passive acoustic monitoring with human or automated identification at the species level offers growing potential to complement observer‐based studies of avian ecology.