Animal soundscapes reveal key markers of Amazon forest degradation from fire and logging

Soundscape data from degraded Amazon forests did not support the ANH (Fig. 2). Instead, acoustic analyses revealed contrasting impacts of fire and logging on acoustic community structure. After fire, daily ASO increased linearly with estimated aboveground biomass, whereas daily ASO in logged forests did not increase with biomass (Fig. 2). Importantly, ASO–degradation relationships varied with time of day (Fig. 2 and SI Appendix, Fig. S1), highlighting the value of full-day measurements from autonomous recording devices for detailed investigations of the ANH and the dominant indicators of degradation.

Insects were the most obvious markers of changing acoustic community composition in burned forests. ASO during insect-dominated periods of the day (e.g., midmorning, noon, nighttime) strongly differentiated burned forests as a function of both biomass (max |r| = 0.9 at 2200 to 2300 hours) and fire frequency (max |r| = 0.82 at 2000 to 2100 hours), and these time periods governed the overall daily trend (Fig. 2). Notably, ASO relationships with biomass and fire frequency were weakest during the 0500 to 0600 dawn chorus typical of bird surveys (P > 0.05; Fig. 2). In logged forests, the only time interval that exhibited a moderately strong relationship with logging age (max |r| = 0.61 at 2200 to 2300) showed an unexpected decline in ASO with increasing time since logging. ASO and biomass in logged forests were not correlated for any time period. Relationships between ASO and degradation history were consistent when aggregating acoustic niches at both hourly and minute time scales (SI Appendix, Fig. S2).

The 24-h profile of ASO and peaks of acoustic activity varied markedly among burned forests as a function of fire frequency and aboveground biomass and less so among logged forests, despite a large range (50%) in aboveground biomass (Fig. 3 and SI Appendix, Fig. S3). The timing and magnitude of the dominant acoustic activity peaks were similar among logged forests (Fig. 3), which varied broadly in terms of time since logging (4 to 23 y) and potential impacts of logging infrastructure (e.g., skid trails, tree-fall gaps) (Fig. 1). Animal orchestras in both logged and once-burned forests exhibited greater daily variability and overall ASO than recurrently burned forests (Fig. 3 and SI Appendix, Fig. S3). Acoustic communities in recurrently burned forests occupied the smallest amount of frequency space during all time periods except dusk, which was the most heavily occupied time window for all degradation classes (1700 to 1800). Dawn hours had the lowest ASO across all 24-h soundscapes except in the most heavily degraded forests, burned three or more times.

We identified a breakpoint in acoustic community composition between forests burned once and forests burned two or more times. This threshold effect from recurrent fire activity manifested as a sustained reduction in ASO from late morning through midafternoon (1000 to 1500; Fig. 3 and SI Appendix, Fig. S3). Differences across levels of initial fire severity were also most evident during this time interval, reflecting localized impacts of burn intensity on acoustic communities, even at short length scales (300 m) within the same fire event (Fig. 4). In contrast to the nonlinear shifts observed midday, ASO declined linearly with increasing fire frequency after dusk (Fig. 3).

Network analyses confirmed that differences in ASO, the frequency-agnostic measure of soundscape intactness, reflected frequency-specific differences in the composition of animal communities following logging and fire. The majority of frequencies outside the midrange (∼3.7 to 5.2 kHz) were more prevalent in logged and once-burned forests than in forests burned multiple times. Evidence for this comes from the frequency-specific-measure, partner diversity (PD), which is the Shannon diversity of hours in which a given frequency band is detected; see Materials and Methods (Fig. 3 and SI Appendix, Fig. S3). We found strong suppression of PD for most spectra in burned forests after two or more fires. Importantly, the frequency bands that best differentiated burned habitat condition (3.5 kHz, 6.7 to 7.4 kHz) were not the same frequencies that best differentiated logged habitat condition (4.2 kHz; Fig. 3 and SI Appendix, Fig. S3). For most frequency bands, the greatest daily prevalence (PD) was observed in logged forests after 4 y of regeneration, followed by a sharp but short-term dip in PD between 4 and 10 y (Fig. 3 and SI Appendix, Fig. S3). Still, the relative dynamic range of network attributes was much more limited among logged classes than among burned classes.

Overall, fire resulted in more empty acoustic niches across the 24-h soundscape than logging, and recurrent burns led to a major restructuring of the acoustic community. Matrix subtraction provided complementary insight into the recovery of soundscape structure following logging and the reorganization of soundscape structure following multiple burns. After logging, several acoustic pseudotaxa appeared and disappeared with time since logging (e.g., 4.2 kHz), but most time-frequency niches that went silent between 4 and 10 y after logging were reoccupied between 10 and 23 y of recovery (Figs. 3 and 4). By contrast, the transition pathways after fire indicated a possible divergence following initial and repeated burns (Fig. 4). There were large pseudotaxa losses when comparing a forest burned once with a forest burned a second time. By contrast, comparing a twice-burned forest with a forest burned five times revealed large gains in pseudotaxa. Importantly, the colonizing assemblages of pseudotaxa gained between the second and fifth fires appear to be fundamentally distinct from the assemblages of pseudotaxa initially lost between the first and second fires (Fig. 4). In all transitions, losses and gains were clustered in time-frequency space. For example, a large cluster of losses during the transition from one to five fires was detected midday (1000 to 1500; Fig. 4), the same time period shown in Fig. 2 that clearly differentiated forests by fire frequency.

Animal communication networks also became more acoustically homogenous with increasing fire recurrence. Alatalo interaction evenness (AIE), a measure of the homogeneity of signal interactions across time-frequency space, increased linearly with increasing fire frequency (Fig. 5). The variance among replicates within each burn class declined linearly with increasing fire frequency. The evenness of the communication network further explained variation in successional recovery after fire: AIE was consistently lower in younger once-burned stands than older once-burned stands, reflecting an increased dominance of fewer sets of sounds.

The clustering of sounds provides further insight into the component processes that drive system-level patterns such as ASO and evenness. These local-scale metrics (e.g., clustering coefficient [CC]) measure the transitivity of co-occurrences of acoustic pseudotaxa over frequency or time. The large drop in the CC of frequency bands between one and two fires indicated a disintegration of time-synchronized groups of spectrally similar sounds (Fig. 5). The subsequent increase in the clustering tendency of acoustic pseudotaxa between two and five fires was of a fundamentally different character, as new acoustic pseudotaxa appeared at frequencies that had limited overlap with the acoustic pseudotaxa lost between one and two fires (Figs. 4 and 5).

By contrast, animal communication networks after logging suggested community-level recovery rather than the reorganization observed due to fires. We observed a short-term decline in AIE and the CC of sound hours and sound frequency bands between 4 and 10 y after logging (Fig. 5). However, this effect appeared transient, with soundscapes of logged forests far less variable than fire-affected soundscapes, based on the similarity in ASO and network patterns and the transition matrices of losses and gains of acoustic pseudotaxa (Figs. 3–5).

Acoustic soundscapes capture the full 24-h profile of animal activity in species-rich tropical forest ecosystems to provide a holistic digital assessment of acoustic community changes and reassembly following forest degradation. Results from this study expand upon previous field studies restricted to specific taxa over limited survey periods and narrow habitat ranges (28–30). Soundscape data highlighted distinct trajectories of acoustic community assembly after logging and fire, which did not correlate with aboveground biomass recovery. Differences in acoustic community composition between burned and logged forests underscore the need to track long-term changes in tropical forest biodiversity to complement carbon-centric conservation strategies such as REDD+. In particular, we found major restructuring of the acoustic community after two or more fires. Soundscape markers of forest degradation were strongest and most consistent during midday and midnight hours typically dominated by insect choruses, time periods rarely sampled in field-based inventories of birds, the most commonly studied taxa (7). By revealing the hidden structure of acoustic assemblages, the network-based methods in this study offer a window into the nature of animal communities in frontier Amazon forests. Network analyses of ecoacoustic remote sensing data represent a scalable approach to benchmark and track changes in ecosystem structure over space and time.

Patterns of ASO following logging and fire did not vary predictably with aboveground biomass, a common metric of tropical forest intactness (31) and part of the theoretical basis for the ANH. Contrary to expectation, we found that animal communities in less-intact forests did not contain more soundscape gaps (i.e., empty acoustic niches). One possible explanation is that the long-term evolutionary mechanisms that favor acoustic niche partitioning may lose relevance during transient periods as animal communities respond to or reorganize following disturbances (32). Further work that directly compares acoustic community structure before and after forest degradation may help to resolve this incongruency. Surveys in forests without a history of logging or fire may reveal additional insights about the ANH and baseline patterns of ASO, although intact forests are rare in older frontier Amazon landscapes, given the cumulative impact of decades of human activity. Longer-term surveys of ASO may also help interrogate the ANH across seasonal time scales, such as during the rainy season when there is greater acoustic sensitivity to amphibians. Independent of the lack of support for the ANH, the consistent acoustic fingerprints that we identified among replicate sites and surveys suggest that the application of network theory to soundscape data may help to better differentiate among animal community assemblages following forest degradation than measures of habitat structure alone. These results corroborate previous reports based on acoustic studies of birds that soundscapes function as memory banks of Amazon forest modification (33). This study goes further to demonstrate that soundscapes may be far more valuable when collected as 24-h records that capture all acoustically active taxa, beyond those that are readily identifiable, and when analyzed as system-level interaction networks.

Insects (e.g., orthopterans, hemipterans), not birds, were the strongest acoustic markers of threshold changes in degraded forest communities, highlighting the value of nighttime surveys of insect choruses for a more complete picture of the animal community in human-modified forests. By contrast, soundscape measures from bird-dominated dawn and dusk intervals did not differentiate degraded frontier forests, possibly because bird choruses decline rapidly in response to even moderate perturbations to forest cover (23). In Borneo, degraded forest soundscapes were also found to be noisier and more homogenous at night, which was attributed to an influx of generalist insect species following logging (22). The major contribution of insects to the soundscape record in this study, and their sensitivity to microhabitat changes following modification of forest structure, may further explain the observed differences across degraded Amazon forests.

The mechanisms for partitioning communication space to reduce interspecific competition and maximize signal transmission vary among the major sound producers in diverse terrestrial systems. For example, co-occurring assemblages of cricket species avoid masking by finely partitioning spectral space into nearly pure-tone frequency channels (34). By contrast, tropical cicadas emit broadband signals that occupy more frequencies (i.e., a larger “niche breadth”) such that neighboring species stratify acoustic space vertically—from the ground to canopy top—to avoid signal overlap. Consequently, cicadas have evolved to occupy extremely narrow thermal niches that make them immediately responsive to changes in forest structure from human disturbance. Importantly, the timing of the noisy cicada chorus, which is governed in part by temperature, affects the periods of acoustic activity by other species (35). This interplay among forest structure (36), microclimate (37), and animal habitat may contribute to the observed restructuring of the entire 24-h animal communication network in burned forests, given that fire has more substantial and long-lasting impacts on forest structure than selective logging in this region (10).

We found that animal communication networks in burned Amazon forests became quieter, less connected, and more homogenous after degradation from multiple fire events. The application of network theory to high-throughput acoustic surveys revealed a coordinated loss and gain of acoustic pseudotaxa suggestive of distinct trajectories of biotic reorganization following one to five burns. Acoustic evidence for successional divergence following repeated burning is consistent with experimental manipulation (38) and traditional species inventories (39). Because Amazonian forests are not adapted to fire, species that do survive initial or repeated burning can influence recovery pathways. For example, bird assemblages in Amazon forests burned multiple times are almost completely distinct from nearby unburned forests (40). In this study, the linear change in AIE after each recurrent fire event indicated increasing biotic homogenization, as reflected by the shifting dominance of fewer sets of sounds in the 24-h soundscape. The change in the CC following repeated fire indicated that the assemblage of acoustic pseudotaxa lost between one and two fires was distinct from the colonizing assemblage between two and five fires. The CC may be a particularly useful marker of forest degradation because it is sensitive to biological mechanisms that occupy conspicuous swaths of acoustic space (e.g., cricket choruses, cicada tymbal vibrations).

After selective logging, acoustic communication networks indicate a pattern of acoustic community recovery, rather than the reassembly observed following fire. Logging altered the grouping tendency of acoustic pseudotaxa and the PD per sound frequency band, but only in the short term. Smaller effect sizes in both ASO and network metrics after logging than after fire are consistent with the selective nature of logging in Amazon forests. Previous research suggests that the effect of logging on tropical forest biota is governed less by time since recovery and more by logging intensity (41), which may be less variable in the Amazon than other tropical forests. Overall, evidence for acoustic community recovery in this study is consistent with the retention of guild structure following selective logging based on species-level responses from field studies (28).

The introduction of network tools to interpret soundscape data opens avenues for monitoring biological communities at an intermediate level of complexity. This middle ground draws upon clusters of sound production to identify patterns of interaction and connectivity within the acoustic communication network that cannot be ascertained from simpler soundscape measures such as ASO or highly aggregated indices of diversity (19). These network measures of the acoustic community assemblage may enable long-term monitoring and change detection efforts, while circumventing the burden of individual species identification (42, 43). Importantly, acoustic communication networks also provide clues to help target specific time and frequency ranges in subsequent investigations of individual species. Traditional species presence/absence measurements or machine learning approaches to parse vocalizations (18) can use the same set of soundscape recordings, underscoring the flexibility of acoustic measurements.

Ecoacoustic surveys have the potential to accelerate our understanding of animal community dynamics, and the ecological resilience of Amazon forests, in response to a range of disturbance types and frequency regimes. In the Amazon, longstanding interest in island biogeography has spurred studies of forest fragmentation (44) and edge effects (45). Yet, the diversity of disturbance types across the Amazon region is large and not easily replicated by experimental manipulation studies (44, 46), given the observed differences in habitat structure (10) and acoustic activity within forests based on the specific timing, frequency, and severity of forest degradation from logging and fire. Ecoacoustic surveys complement traditional taxa-constrained field approaches with full-day measurements of all sound-producing taxa, providing an inclusive, objective, and scalable approach to monitor the acoustic community across disturbance types since monitoring can be implemented by experts and nonexperts alike, and measurements can be collected simultaneously across large spatial extents. The cumulative impact of human activity along the arc of deforestation gives rise to broad gradients of forest condition from cycles of logging, fire, and land abandonment, in addition to differences in fragment size or pressure from hunting, grazing, or harvesting. Ecoacoustic surveys offer a pathway to rapidly assess acoustic community composition in these dynamic, human-modified landscapes.

Long-term monitoring of the acoustic community may also reveal important time lags between disturbance and recovery for coevolved animal and floristic communities, as well as ecological thresholds from novel disturbance types or extreme events, such as drought. Despite broad recognition of the role of the animal community for mediating Amazon vegetation succession through seed dispersal (47) and pollination (44), the time scales and dependencies of vegetation and animal community recovery following forest disturbance remain uncertain. Soundscape data from ecoacoustic monitoring have the potential to define important time lags in animal community recovery that may delay or interrupt these critical mechanisms, potentially triggering persistent shifts in ecosystem composition and function. In grassland ecosystems where fires are a natural feature on the landscape, increasing fire frequency has been shown to result in long-term animal community stability despite temporally unstable plant communities (48). By contrast, frequent fires are a novel disturbance regime as a result of recent human occupation in Amazon forests, which are not evolutionarily adapted to fire, even low-intensity surface fires that primarily consume litter and downed woody debris (49–52). Our findings demonstrate that acoustic surveys, in tandem with data on habitat structure and land-use dynamics, can advance our understanding of the time scales for recovery of ecological structure in burned forests and the conditions under which burned forests may shift into persistent alternative ecosystem states. New extremes in fire activity may also alter the magnitude of impacts to vegetation and animal communities, even in fire-adapted ecosystems such as the neighboring Pantanal biome, given the unprecedented size and severity of recent burns. The ability to rapidly deploy networks of ecoacoustic monitoring devices opens the possibility for greater understanding of tipping points and the component processes of acoustic community composition following disturbance based on the coevolution of habitat use and habitat structure.

We anticipate several future avenues for building upon this work to bolster capabilities for rapid and continuous monitoring of ecosystem degradation in the species-rich tropics. Longer-term observations are needed to further constrain the time scales for recovery and confirm the results from space-for-time sampling. Importantly, when collected over longer time frames, acoustic data provide an opportunity to work with seasonal and annual datasets to account for variation from phenology. For example, future work could evaluate associations between habitat condition and animal communication networks outside of the breeding bird period frequently targeted in such studies. Studies testing the ANH during the wet season may allow greater insight into changes in anuran assemblages. Linking ecoacoustic observations with traditional species-level measurements, which could be ascertained through reanalysis of the same set of acoustic surveys (18), is an important next step for enabling biological attribution from changes in animal communication networks. Further elucidation of the links among soundscape structure, species composition, and habitat structure is needed to better understand the breakpoints associated with recurrent fire events and to target evidence for local species extirpations of seed dispersers and pollinators that could alter the time scales and trajectories of forest succession. The lack of an intact reference soundscape is a limitation of this investigation but a constraint that is, unfortunately, common across tropical frontier regions, where intact refugia are scarce in human-dominated landscapes. Amazon forests without a history of recent logging, fire, hunting, or fragmentation are, therefore, a priority for future ecoacoustic studies. Finally, we demonstrate that animal communication networks are a promising synthetic analytic framework for characterizing differences in biotic assembly based on the coordinated behavior of sound-producing species. Further development of these network approaches to analyze soundscape data may provide additional insights about the complex structure and dynamics of acoustic communities over time.

Acoustic measurements strongly differentiate between burned and logged Amazon forests and provide clear evidence for breakpoints in acoustic community structure following recurrent burning. Divergent patterns of acoustic activity were most pronounced during midday and nighttime hours, underscoring the unique insights from multiday recordings and the contributions from lesser-known taxa, such as insects. Ecoacoustic surveys are inclusive, capturing all sound-producing taxa, and they represent objective long-term records of ecosystem structure that support a range of analytic approaches from community network analyses to individual species detection. Given observed differences in acoustic communities within and between logged and burned forests, acoustic monitoring is an important complement to satellite and airborne remote sensing measures of forest structure to track ecosystem changes from human activity over time at the tropical forest frontier. Large and persistent changes in acoustic community composition following recurrent fires highlight the potential benefits of policies and management to limit further fire activity, particularly in frontier forest regions where recurrent burning is most concentrated. Given the potential biodiversity benefits of protecting burned Amazon forests from additional fires, the coordinated use of ecoacoustic and satellite fire monitoring data may improve biodiversity retention in frontier forests by supporting more robust biodiversity safeguards for REDD+ and related tropical forest conservation efforts.

We gratefully acknowledge the support from the NASA Earth and Space Science Fellowship program and NASA’s Biological Diversity and Ecological Forecasting Program to D.I.R. and NASA’s Carbon Monitoring System to D.C.M. We also recognize support from the National Science Foundation through the Doctoral Dissertation Research Improvement Grant to D.I.R. and through award DGE-1632976 that supported A.S. Lidar data were acquired by Sustainable Landscapes Brazil with support from the US State Department, US Agency for International Development, and USDA Forest Service. Marcos Longo, Maiza Dos-Santos, and Hyeungu Choi provided assistance with lidar data processing, and Eveline Salvático provided field support for soundscape surveys in this study. We would also like to acknowledge Michael Keller and Matthew Hansen for their feedback on the study design and analysis.