Metabarcoding reveals the dietary diversity and food web structure of spider functional guilds in a highly diverse subtropical forest.

Occupational noise exposure remains one of the most common and preventable causes of acquired hearing loss. In hearing conservation programs, surveillance has traditionally centered on pure-tone audiometry at conventional frequencies from 0.5 to 8 kHz, because threshold elevation at these frequencies provides a clinically interpretable marker of noise-induced hearing loss (NIHL). Such threshold shifts reflect established cochlear dysfunction, including sensory-cell damage and outer hair cell-related deficits, and are essential for identifying clinically meaningful NIHL. However, conventional pure-tone audiometry is not optimized to detect auditory changes that may occur before workers reach threshold criteria for hearing impairment. This limitation is clinically relevant because workers can report hearing-related difficulties, particularly in challenging listening environments, even when conventional audiometric thresholds remain within normal limits (), and because noise-related cochlear changes may involve frequency regions, cochlear mechanisms, or suprathreshold auditory processes that are only partly represented by standard threshold testing. This is a prevention problem rather than only a measurement problem: once a standard threshold shift is evident, the opportunity for earlier intervention has already been reduced. This surveillance gap has motivated increasing interest in earlier indicators of noise-related auditory dysfunction that can be assessed in vivo and deployed at scale in occupational settings. Occupational health programs need measures that are more sensitive to early auditory change than conventional audiometry, yet feasible enough for use in exposed working populations. Thus, the key question for occupational hearing surveillance is not whether hazardous noise can cause NIHL, which is already well established, but whether a practical test battery can detect early auditory differences among workers who would otherwise pass routine audiometric monitoring. A useful early-detection framework should move beyond reliance on any single putative biomarker. Experimental and human evidence indicates that noise exposure can affect multiple components of the auditory system, including outer hair cell-dependent cochlear amplification, basal cochlear regions represented by extended high frequencies, and suprathreshold auditory processing (; ; ; ; ; ; , ). However, in living humans, non-invasive tests cannot be interpreted as pathology-specific assays. Associations among exposure history, electrophysiological measures, and functional listening outcomes have varied across cohorts and protocols (; ; ; ). For occupational surveillance, an appropriate strategy is to evaluate whether complementary measures converge on a pattern of early noise-related auditory change at the group level. Several clinically accessible measures are well suited to this broader surveillance approach. Distortion-product otoacoustic emissions (DPOAEs) provide objective information about the cochlear amplifier and outer hair cell-related function. Extended high-frequency (EHF) audiometry can detect basal-cochlear vulnerability that may precede, accompany, or exceed changes in the conventional 0.5-8 kHz range. Acoustic reflex thresholds (ARTs), obtained using standard clinical immittance procedures, provide information about the sound level needed to elicit the middle-ear muscle reflex and may capture additional aspects of auditory input and reflex-arc function, although they are not specific to a single cochlear lesion. Speech-in-noise (SiN) testing adds a functional outcome that is directly relevant to workers’ listening complaints. Auditory brainstem response (ABR) measures, particularly suprathreshold wave metrics, remain important comparator measures because they have been widely used in studies of early noise-related auditory dysfunction, even though their clinical translation has been limited by protocol dependence, equipment requirements, and inconsistent findings across studies (; ; ; ; , ; ; ; ).