Tropical forest is increasingly a landscape of fragments: patches of a few hectares to a few thousand, set in pasture, oil palm, tea or smallholder farms. Deciding which patches still hold a rich bird community, and whether corridors or replanting change that, requires surveys at many sites and repeated over years. Point counts by expert observers remain the reference method, but observers able to identify several hundred species by voice are few, and their time is the limiting cost of most tropical surveys.

Passive acoustic monitoring removes part of that constraint. Small programmable recorders such as the AudioMoth can be left at a site for weeks and cost little enough to deploy by the dozen (Hill et al., 2018). The difficulty moves to analysis, since no one can listen to thousands of hours of sound. Acoustic indices were devised as a shortcut: summary statistics of the distribution of sound energy across time and frequency, intended to rise and fall with the diversity of animals calling (Sueur et al., 2008; Sueur et al., 2014).

This article sets out a protocol for using such indices to estimate bird species richness across forest fragments in the tropics. It takes the view that an index is a predictor to be calibrated locally against known richness, and builds the calibration and its validation into the procedure. The protocol is a proposal drawn from published methods. It is not applied to recordings here and has not been validated.

Soundscape ecology treats the sound of a place as the sum of biological sources (biophony), physical sources such as wind and rain (geophony) and human sources (anthrophony) (Pijanowski et al., 2011). Indices try to isolate properties of the first. Table 1 lists the six in most common use, with what each measures and what is known to distort it.

Tests of the indices against bird richness have given mixed results. In a comparison of temperate sites in England with tropical sites in Ecuador, indices predicted avian richness in the former and not in the latter (Eldridge et al., 2018). Work in two regions of Yunnan found correlations with bird diversity that were weak and differed between regions (Mammides et al., 2017). A meta-analysis of the published tests reported a positive overall relation between indices and diversity that was only moderate in strength and highly variable among studies (Alcocer et al., 2022).

The reasons are understood in outline. In tropical forest, cicadas, katydids and frogs often produce more acoustic energy than birds, and they do so in bands that overlap bird song. Heavy rain saturates every index. A single loud species near the microphone can dominate. Indices also respond to the settings used to calculate them, and default settings were chosen for other ecosystems. Metcalf et al. (2021) showed for Amazonian forest that indices tracked biodiversity better when computed within frequency bands and times of day that matched the animals of interest. Several studies indicate that combinations of indices fitted with flexible models predict biological variables better than any one index (Buxton et al., 2018). In fragmented forest in eastern Australia, indices differed widely in how well they reflected landscape configuration and ecological condition (Fuller et al., 2015), a further sign that calibration cannot be skipped.

The protocol has eight steps, shown in Figure 1. The first three concern how recordings are gathered and cleaned, the fourth the indices, and the last four the calibration against reference data and its use.

The reference data are the costly element, and their scale should be decided first. A workable division is to collect reference richness at roughly a third of the recorder points, chosen to span all fragment groups, and to use the remainder for prediction. Annotation effort is better spent on many short samples from many mornings than on long stretches of a few. Where an expert listener is available only briefly, recordings allow the annotation to be done later and elsewhere, and a second listener can check a sample to measure agreement. Raw audio should be archived, since learned acoustic features and species classifiers are improving quickly and old recordings can be reanalysed.

The protocol has not been tested, and the central uncertainty is whether calibrated indices will predict richness in a given tropical landscape at all. The published record gives grounds for doubt (Eldridge et al., 2018; Alcocer et al., 2022). The decision rule in the seventh step exists for that reason, and a negative result from it is a legitimate and publishable outcome. Even a successful calibration estimates the richness of species that vocalise during the recording windows. Quiet, rare and seasonally silent species are missed by indices and point counts alike.

Two further matters are unresolved. Fragmentation alters the soundscape through routes other than bird richness: edges are windier, closer to roads and often richer in generalist insects, so an index may track edge effects that happen to correlate with bird loss in the calibration set and fail elsewhere. Also, richness is a blunt target, because a fragment can keep its species count while forest specialists are replaced by common open-country birds. Indices cannot detect that turnover. Species-level classifiers can, and as they mature for tropical faunas the role of indices may narrow to rapid screening.

Acoustic indices are cheap to compute and tempting to over-interpret. For fragmented tropical forest, the published evidence supports their use only as locally calibrated predictors, restricted to the bands and hours in which birds dominate, combined in a multi-index model, and tested on fragments the model has not seen. The eight-step protocol proposed here arranges those requirements in a sequence that a survey team can follow and document. Whether it yields useful estimates is an empirical matter for each landscape, and the protocol is designed so that a team finds out before relying on the numbers.

To be declared by the author(s) before publication.

To be declared by the author(s) before publication.