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Bark and ambrosia beetles and pinhole borers (Coleoptera: Curculionidae: Scolytinae and Platypodinae) are two subfamilies of weevils that use acoustic communication within plant tissue. These insects transmit and detect sound in a medium that is neither air nor water and they are among the smallest animals with sound-producing organs. Nevertheless, their sound production is sorely understudied, mostly due to the difficulties associated with acoustically monitoring individuals inside plants. We analysed the stridulatory sounds from 55 bark and ambrosia beetle species within 15 subtribes collected in four countries, making this the largest acoustic dataset of these taxa to date. We characterized and compared the amplitude and spectro-temporal parameters of the distress airborne signals produced by the beetles, in conjunction with phenology and life history data. Sound production was present in 33% of the collected species, of which 60% of these sounds had not been previously reported. Depending on species, either both sexes stridulated or only one. Some species had calls with different acoustic morphotypes (one, two, or three notes), and when both sexes stridulated, sounds generally differed. Our data suggest that type of mating system and size play an important role in determining the acoustic communicatory capacity of most species.

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Bark beetles (Coleoptera: Curculionidae: Scolytinae) are a speciose subfamily of weevils that primarily live in bark and consequently largely communicate using sound. Having colonized multiple countries outside its native range, Hylurgus ligniperda (Fabricius) is considered to be a successful invader, yet little is known about its acoustic communication. In the present study, we investigate individual sound production and dyadic interactions among males and females of H. ligniperda. Two temporal parameters (duration and inter-note interval) and three spectral parameters (minimum, maximum and centroid frequencies) are used as descriptors to quantify call variations depending on behavioural context. We also present a method for automatically extracting and analyzing these calls, which allows acoustic discrimination amongst individuals. Hylurgus ligniperda exhibits sexual dimorphism in its stridulatory organ. Females do not produce stridulatory sounds, whereas males produce single-noted calls and modify their spectro-temporal parameters in accordance with context. Acoustic stimulation from nearby males does not appear to be a causative factor in such modification. Instead, hierarchical clustering analysis shows that physical interactions play a more important role in affecting call parameters than acoustic signals. Centroid and maximum frequencies are the largest contributors to the variability of the data, suggesting that call variations in H. ligniperda mainly occur in the spectral domain.

Rainfall is one of the most predominant geophonic sources in nature, and the major climatic phenomenon influencing species biology in tropical ecosystems. Although its effects on acoustic indices have been studied, rainfall is recognized as a nuisance factor affecting their estimation. Consequently, files with rainfall sounds are typically removed from ecoacoustic analyses. In tropical rainforests, where rainfall is a common and unpredictable event, its influence on acoustic indices needs to be explicitly examined before implementing acoustic passive monitoring. Using mixed‐effects models we assessed the effect of different rainfall conditions on the direction and magnitude of the values of eight commonly used acoustic indices. We obtained 18336 1‐min recordings from 28 sampling sites in a montane forest on the northern Andes of Colombia between May‐July 2018. We identified 2867 1‐min recordings containing light to heavy rainfall. We found that both rainfall occurrence and its variation in intensity were associated with increases in ACI, ADI, H, and M index values, and decreases in AEI, BI, NDSI, and NP values. The estimated indices exhibited differential sensitivity to rainfall, with M, NDSI, and NP showing higher differences associated with increasing frequency and intensity of rainfall. Regardless the direction of change in index values caused by rainfall, we found that the magnitude of variation depended on the index. For instance, ACI and BI indices showed low sensitivity and can be considered as reliable acoustic metrics, even during heavy intensity rainfall. In contrast, M, NDSI, and NP might lead to misleading inferences, if rainfall events are not considered during calculation. We stress the importance of careful interpretation of biological inferences based on these sensitive indices and encourage an explicit assessment of rainfall, particularly in short‐term acoustic surveys in highly pluvious regions where rainfall is a conspicuous component of the soundscape.

The inability to maintain signal detection performance with time on task, or vigilance decrement, is widely studied in people because of its profound implications on attention-demanding tasks over sustained periods of time (e.g., air-traffic control). According to the resource depletion (overload) theory, a faster decrement is expected in tasks that are cognitively demanding or overstimulating, while the underload theory predicts steeper decrements in tasks that provide too little cognitive load, or understimulation. Using Trite planiceps, a jumping spider which is an active visual hunter, we investigated vigilance decrement to repetitive visual stimuli. Spiders were tethered in front of two stimulus presentation monitors and were given a polystyrene ball to hold. Movement of this ball indicates an attempt to turn towards a visual stimulus presented to a pair of laterally facing (anterior lateral) eyes for closer investigation with high acuity forward-facing (anterior median) eyes. Vigilance decrement is easily measured, as moving visual stimuli trigger clear optokinetic responses. We manipulated task difficulty by varying the contrast of the stimulus and the degree of 'noise' displayed on the screen over which the stimulus moved, thus affecting the signal:noise ratio. Additionally, we manipulated motivation by paired testing of hungry and sated spiders. All factors affected the vigilance decrement, but the key variable affecting decrement was stimulus contrast. Spiders exhibited a steeper decrement in the harder tasks, aligning with the resource depletion theory.

Abstract. The Learning Algorithm for Multivariate Data Analysis (LAMDA) is an unsupervised fuzzy-based classification methodology. The operating principle of LAMDA is based on finding the datum-cluster relationship obtained by means of the Global Adequacy Degrees (GADs) of the Marginal Adequacy Degrees (MADs) of all the data attributes. In comparison with other unsupervised clustering algorithms, LAMDA does not require the number of classes as input parameter; however, in some applications, the quantity of obtained clusters does not correspond with the number of desired classes. Typically, this issue is overcome by merging interrelated clusters within the same class; nevertheless, in some applications the number of generated clusters related to the same class reaches a non-desired and impractical number. In LAMDA, the number of generated clusters is controlled by using a linear mixed connective with an exigency index α. This connective is an unnatural aggregation operator of the MADs, which adds an additional parameter to set up. In this paper, a full reinforcement operator (Yager-Rybalov Triple Π) is used as aggregation operator for merging the information contained in the MADs. This approach significantly reduces the number of generated classes and suppresses the LAMDA dependence of the parameter α. The proposed approach was tested in a case study related to unsupervised anuran vocalization recognition. A database of advertisement calls of six anuran (frog) species for testing this proposal was selected. All 102 vocalizations were correctly identified (100% of accuracy) and solely the desired classes were generated by the algorithm (establishing a cluster-class bijection).