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

Safeguarding tropical forest biodiversity requires solutions for monitoring ecosystem structure over time. In the Amazon, logging and fire reduce forest carbon stocks and alter habitat, but the long-term consequences for wildlife remain unclear, especially for lesser-known taxa. Here, we combined multiday acoustic surveys, airborne lidar, and satellite time series covering logged and burned forests (n = 39) in the southern Brazilian Amazon to identify acoustic markers of forest degradation. Our findings contradict expectations from the Acoustic Niche Hypothesis that animal communities in more degraded habitats occupy fewer “acoustic niches” defined by time and frequency. Instead, we found that aboveground biomass was not a consistent proxy for acoustic biodiversity due to the divergent patterns of “acoustic space occupancy” between logged and burned forests. Ecosystem soundscapes highlighted a stark, and sustained reorganization in acoustic community assembly after multiple fires; animal communication networks were quieter, more homogenous, and less acoustically integrated in forests burned multiple times than in logged or once-burned forests. These findings demonstrate strong biodiversity cobenefits from protecting burned Amazon forests from recurrent fire. By contrast, soundscape changes after logging were subtle and more consistent with acoustic community recovery than reassembly. In both logged and burned forests, insects were the dominant acoustic markers of degradation, particularly during midday and nighttime hours, which are not typically sampled by traditional biodiversity field surveys. The acoustic fingerprints of degradation history were conserved across replicate recording locations, indicating that soundscapes may offer a robust, taxonomically inclusive solution for digitally tracking changes in acoustic community composition over time.

Biological diversity is disappearing rapidly in response to human activity, especially in tropical forests, which are home to well over half of Earth’s terrestrial species (1). Global concern over greenhouse gas emissions from tropical forests (2) has led to international efforts to Reduce Emissions from Deforestation and Forest Degradation (REDD+) (3). Retention of diverse ecosystems supports climate change mitigation and adaptation (4); yet, carbon-focused conservation may not result in a commensurate win for tropical forest biodiversity (5). Longstanding data gaps on species distributions and uncertainty regarding the direct and indirect impacts of human activity on biodiversity complicate efforts to quantify the interplay between carbon and biodiversity (6, 7).Across the tropics, the Brazilian Amazon has the highest rates of deforestation (8), and forest degradation from fire and logging may double biodiversity loss from deforestation alone (9). However, the long-term impacts of human activity on Amazon biodiversity remain highly uncertain due, in part, to the spatial heterogeneity among degraded forests from differences in the timing, frequency, extent, and severity of disturbances (10). Time-varying heterogeneity in the biodiversity of degraded forests may also explain some of the apparent contradictions in previous studies of degradation impacts on birds, the most well-studied Amazonian taxa. Many nectarivorous birds, for example, increase in abundance immediately after logging but ultimately decline. Yet, many insectivorous birds show immediate sensitivity to changes in habitat from logging but continue to decline in abundance over time (11). Time dependence also complicates efforts to measure the effects of degradation on insects, a problem confounded by limited research (12).Addressing the tropical biodiversity extinction crisis, therefore, requires an efficient, distributed, long-term monitoring system to assess ecosystem structure (13). Traditional, ground-based biodiversity inventories are logistically prohibitive to conduct at scale, and limited taxonomic expertise perpetuates large data discrepancies for lesser-known taxa, such as insects, which constitute the bulk of tropical biodiversity (7). Advances in the emerging discipline of acoustic remote sensing, or ecoacoustics, may permit large-scale biodiversity monitoring for multiple taxa, including unidentifiable species, based on the aggregate sound signature of the animal community, or soundscape (14–16). Since multiple sites can be recorded simultaneously over time, sound surveys reduce the effort and cost associated with routine monitoring and facilitate standardized assessments of community variation and ecosystem recovery. Most previous efforts to utilize acoustic data for biodiversity monitoring have focused on detecting known vocalizations associated with individual species (17, 18), but there is increasing interest in evaluating the entire collection of signals in a given soundscape to derive measures of ecosystem intactness that include all sound-generating taxa without definitive species identification (15, 16, 19, 20).The Acoustic Niche Hypothesis (ANH) (21) is a core premise of ecoacoustics and the prevailing organizing principle for assessing diversity (16), community similarity (22), and human impacts (23, 24) using soundscape data. The ANH posits that more intact habitats support more biodiverse communities that occupy more “acoustic niches.” Greater niche partitioning of available acoustic space, defined by frequency and time of day, is posited to minimize communication interference among coexisting species. The ANH implies a positive linear relationship between habitat intactness (i.e., biomass) and acoustic niche infilling or acoustic space occupancy (ASO) by the “animal orchestra.” The corollary is that more degraded habitats support less acoustic infilling due to vacant acoustic niches from local species extirpations (25). Ecoacoustic approaches have great potential to extend monitoring capabilities in the hyperdiverse tropics, where competition for acoustic space is strongest (16, 26). Still, large uncertainties remain as to whether soundscape infilling can be used as a robust proxy for ecosystem intactness to monitor landscapes altered by human activity (27).Here, we test the ANH across logged and burned Amazon forests to identify acoustic markers of forest degradation (Fig. 1). We collected coincident high-density airborne lidar data and multiday acoustic recordings (214 24-h surveys) during September and October 2016 in 39 forests with different times since logging (4 to 23 y) and histories of fire activity (1 to 5 fires), stratified based on a 33-y time series of annual Landsat imagery (10). We used space-for-time substitution and two complementary analytic approaches to characterize threshold effects and time dependence for changes in the structure of animal soundscapes along gradients of degradation history (see Materials and Methods). First, we calculated ASO for each site at hourly and 1-min time steps to test the ANH and to quantify the magnitude, persistence, and variability in the infilling of acoustic space following forest degradation. Second, we developed a network-based approach to capture additional complexity from the soundscape data to track the composition and co-occurrences of “acoustic pseudotaxa” (defined as the community components that occupy the same acoustic niche) along degradation and recovery pathways. Our findings demonstrate that soundscapes encode digital markers of the history of degradation from human activity, revealing distinct patterns of community change following logging and fire. This study paves the way for more widespread use of ecoacoustics to benchmark and monitor changes in acoustic community composition in human-altered tropical forest landscapes, especially in remote regions with many unknown species.Open in a separate windowFig. 1.Acoustic recording sites in logged and burned forests (n = 39) were distributed across 9,400 km² in northern Mato Grosso, Brazil (Upper Left). Colored boxes identify subsets of the study domain to illustrate how the triplicate sampling scheme was designed to capture the heterogeneity in habitat structure and acoustic community composition in logged (yellow) and burned (black) forests. False-color composites of Landsat imagery (2014, 543-RGB) in each panel show deforested areas in magenta and gradients of forest cover in shades of green.