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Soundscape studies are increasingly used to capture landscape-scale ecological patterns. Yet, several aspects of soundscape diversity remain unexplored. Although some processes influencing acoustic niche usage may operate in the 24-hr temporal domain, most acoustic indices only capture the diversity of sounds co-occurring in sound files at a specific time of day. Moreover, many indices do not consider the relationship between the spectral and temporal traits of sounds simultaneously. To provide novel insights into landscape-scale patterns of acoustic niche usage at broader temporal scales, we present a workflow to quantify soundscape diversity through the lens of trait-based ecology. Our workflow quantifies the diversity of sound in the 24-hr acoustic trait space. We introduce the Operational Sound Unit (OSU), a unit of diversity measurement that groups sounds by their shared acoustic properties. Using OSUs and building on the framework of Hill numbers, we propose three metrics that capture different aspects of acoustic trait space usage: (i) soundscape richness, (ii) soundscape diversity and (iii) soundscape evenness. We demonstrate the use of these metrics by (a) simulating soundscapes to assess whether the indices possess a set of desirable behaviours and (b) quantifying soundscape richness and evenness along a gradient in species richness. We demonstrate that (a) the indices outlined herein have desirable behaviours and (b) the soundscape richness and evenness are positively correlated with the richness of sound-producing species. This suggests that more acoustic niche space is occupied when the species richness is higher. Additionally, species-poor acoustic communities have a higher proportion of rare sounds and use the acoustic space less evenly. Our workflow generates novel insights into acoustic niche usage at a landscape scale and provides a useful tool for biodiversity monitoring. Moreover, Hill numbers can also be used to measure the taxonomic, functional and phylogenetic diversity. Using a common framework for diversity measurement gives metrics a common behaviour, interpretation and standardised unit, thus ensuring comparisons between soundscape diversity and other metrics represent real-world ecological patterns rather than mathematical artefacts stemming from different formulae.

Climate change is altering the spatial distribution of many species around the world. In response, we need to identify and protect suitable areas for a large proportion of the fauna so that they persist through time. This exercise must also evaluate the ability of existing protected areas to provide safe havens for species in the context of climate change. Here, we combined passive acoustic monitoring, semi-automatic species identification models, and species distribution models of 21 bird and frog species based on past (1980–1989), present (2005–2014), and future (2040–2060) climate scenarios to determine how species distributions relate to the current distribution of protected areas in Puerto Rico. Species detection/non-detection data were acquired across ~ 700 sampling sites. We developed always-suitable maps that characterized suitable habitats in all three time periods for each species and overlaid these maps to identify regions with high species co-occurrence. These distributions were then compared with the distribution of existing protected areas. We show that Puerto Rico is projected to become dryer by 2040–2060, and precipitation in the warmest quarter was among the most important variables affecting bird and frog distributions. A large portion of always-suitable areas (ASA) is outside of protected areas (> 80%), and the percent of protected areas that overlaps with always-suitable areas is larger for bird (75%) than frog (39%) species. Our results indicate that present protected areas will not suffice to safeguard bird and frog species under climate change; however, the establishment of larger protected areas, buffer zones, and connectivity between protected areas may allow species to find suitable niches to withstand environmental changes.

A major barrier to advancing ornithology is the systemic exclusion of professionals from the Global South. A recent special feature, Advances in Neotropical Ornithology, and a shortfalls analysis therein, unintentionally followed a long-standing pattern of highlighting individuals, knowledge, and views from the Global North, while largely omitting the perspectives of people based within the Neotropics. Here, we review current strengths and opportunities in the practice of Neotropical ornithology. Further, we discuss problems with assessing the state of Neotropical ornithology through a northern lens, including discovery narratives, incomplete (and biased) understanding of history and advances, and the promotion of agendas that, while currently popular in the north, may not fit the needs and realities of Neotropical research. We argue that future advances in Neotropical ornithology will critically depend on identifying and addressing the systemic barriers that hold back ornithologists who live and work in the Neotropics: unreliable and limited funding, exclusion from international research leadership, restricted dissemination of knowledge (e.g., through language hegemony and citation bias), and logistical barriers. Moving forward, we must examine and acknowledge the colonial roots of our discipline, and explicitly promote anti-colonial agendas for research, training, and conservation. We invite our colleagues within and beyond the Neotropics to join us in creating new models of governance that establish research priorities with vigorous participation of ornithologists and communities within the Neotropical region. To include a diversity of perspectives, we must systemically address discrimination and bias rooted in the socioeconomic class system, anti-Blackness, anti-Brownness, anti-Indigeneity, misogyny, homophobia, tokenism, and ableism. Instead of seeking individual excellence and rewarding top-down leadership, institutions in the North and South can promote collective leadership. In adopting these approaches, we, ornithologists, will join a community of researchers across academia building new paradigms that can reconcile our relationships and transform science. Spanish and Portuguese translations are available in the Supplementary Material.

Despite several efforts to quantify the effectiveness of forest certification in developing sustainable use of forest resources, there is little evidence that certified forests are more effective in conserving fauna than non‐certified managed forest. To evaluate the impact of forest certification on the fauna, we compared the biodiversity in reference sites (n = 23), Forest Stewardship Council (FSC) certified management sites (n = 24) and non‐FSC management sites (n = 20) in the Tahuamanu region of Peru, during the dry season of 2017. Specifically, we determined if the acoustic space used (ASU), soundscapes composition, and the bird richness and composition significantly varied among the three management types. Variation in ASU was best explained by management type and mean ASU in the FSC sites was significantly greater than the reference and non‐FSC sites, possibly suggesting greater richness of acoustically active species. An ordination analysis of the soundscapes showed that there was a significant difference among the three management types. There was greater dissimilarity in soundscape composition between the FSC and non‐FSC sites, and greater overlap between FSC and reference sites. Bird identifications resulted in 11,300 detections of 226 bird species. Bird species richness and composition were not significantly different among the management types, indicating, in this context, that birds may not be the best indicators of different management strategies. The weak discrimination by the bird community is likely due to their dispersal ability, undisturbed primary forest matrix, and the occurrence of bamboo patches. The differences in ASU among the management types were most likely due to differences in acoustically active insects, which may be more sensitive to changes in microhabitat differences. Our findings correspond with the conclusions of other studies that certified forests can maintain levels of fauna biodiversity similar to those of undisturbed primary forest in the Amazon region.

Knowledge that can be gained from acoustic data collection in tropical ecosystems is low‐hanging fruit. There is every reason to record and with every day, there are fewer excuses not to do it. In recent years, the cost of acoustic recorders has decreased substantially (some can be purchased for under US$50, e.g., Hill et al. 2018) and the technology needed to store and analyze acoustic data is continuously improving (e.g., Corrada Bravo et al. 2017, Xie et al. 2017). Soundscape recordings provide a permanent record of a site at a given time and contain a wealth of invaluable and irreplaceable information. Although challenges remain, failure to collect acoustic data now in tropical ecosystems would represent a failure to future generations of tropical researchers and the citizens that benefit from ecological research. In this commentary, we (1) argue for the need to increase acoustic monitoring in tropical systems; (2) describe the types of research questions and conservation issues that can be addressed with passive acoustic monitoring (PAM) using both short‐ and long‐term data in terrestrial and freshwater habitats; and (3) present an initial plan for establishing a global repository of tropical recordings.

An upward shift in elevation is one of the most conspicuous species responses to climate change. Nevertheless, downward shifts and, apparently, the absences of response have also been recently reported. Given the growing evidence of multiple responses of species distributions due to climate change and the paucity of studies in the tropics, we evaluated the response of a montane bird community to climate change, without the confounding effects of land‐use change. To test for elevational shifts, we compared the distribution of 21 avian species in 1998 and 2015 using occupancy models. The historical data set was based on point counts, whereas the contemporary data set was based on acoustic monitoring. We detected a similar number of species in historical (36) and contemporary data sets (33). We show an overall pattern of no significant change in range limits for most species, although there was a significant shift in the range limit of eight species (38%). Elevation limits shifted mostly upward, and this pattern was more common for upper than lower limits. Our results highlight the variability of species responses to climate change and illustrate how acoustic monitoring provides an easy and powerful way to monitor animal populations along elevational gradients.

Summary Conservation of threatened species relies on predictions about their spatial distribution; however, it is often difficult to detect species in the wild. The combination of acoustic monitoring to improve species detectability and statistical methods to account for false-negative detections can improve species distribution estimates. Here, we combine a novel automated species-specific identification approach with occupancy models that account for imperfect detectability to provide a more accurate species distribution map of the Elfin Woods Warbler Setophaga angelae, a rare, elusive and threatened bird species. We also compared three automated species identification/validation approaches to determine which approach provided occupancy estimates similar to manual validation of all recordings. Acoustic data were collected along three elevational gradients (95–1074 m a.s.l) in El Yunque National Forest, Puerto Rico. The detection matrices acquired through automated species-specific identification models and manual validations of all recordings were used to create occupancy models. Although this species has a wider distribution than previously reported, it depends on Palo Colorado forest cover and it mainly occurs between 600 and 900 m a.s.l. Unbiased and precise occupancy models were developed by using automated species identification models and only manually validating 4% of the recordings. Our approach draws on the strength of two active areas of ecological research: acoustic monitoring and occupancy modelling. Our methods provide an effective and efficient way to translate the enormous amount of acoustic information collected with passive acoustic monitoring devices into meaningful ecological data that can be applied to understand and map the distribution of rare, elusive and threatened species.

The rapidly developing field of ecoacoustics offers methods that can advance multi-taxa animal surveys at policy-relevant extents. While the field is promising, there remain foundational assumptions that need to be tested across different biomes before the methods can be applied widely. Here we test two of these assumptions in the Amazon: 1) that acoustic indices can be used to predict soundscapes of different habitat types, and 2) that acoustic indices are related to vegetation structure. We recorded soundscapes and collected vegetation data in 143 sites spanning six natural and two human-modified habitats in Virua National Park, Roraima, Brazil. We grouped the eight habitats into three categories based on vegetative characteristics and flooding regime: open habitats, flooded-forests, and non-flooded forests. Thirteen acoustic indices were calculated from 92,283 one-minute recordings to describe the soundscapes of the habitats. We found that each habitat type had unique and predictable soundscapes. Random forest models were 74% accurate at predicting the eight habitats types and 87% accurate at predicting the three broader habitats categories. The most important acoustic indices to distinguish habitats were the third quartile and centroid. Canopy cover significantly affected 11 of 13 acoustic indices, and while other vegetation variables (e.g., shrub cover and number of trees) appeared in top models for some indices, their effects were not significant. The best indices linking soundscapes to vegetation structure were the acoustic evenness index and skewness, with canopy cover explaining 81% and 52% of the variance in these indices, respectively. These results expand our knowledge regarding which acoustic indices best connect changes in habitats to changes in soundscapes. These findings are particularly important for diverse ecosystems, like the Amazon, which are known to have complex soundscapes with sound-producing animals that are difficult to detect with traditional survey methods (e.g., visual transects). Ultimately, our results suggest that soundscapes are able to track changes in biodiversity levels across major habitat types of the Amazon.

Automated acoustic recorders can collect long-term soundscape data containing species-specific signals in remote environments. Ecologists have increasingly used them for studying diverse fauna around the globe. Deep learning methods have gained recent attention for automating the process of species identification in soundscape recordings. We present an end-to-end pipeline for training a convolutional neural network (CNN) for multi-species multi-label classification of soundscape recordings, starting from raw, unlabeled audio. Training data for species-specific signals are collected using a semi-automated procedure consisting of an efficient template-based signal detection algorithm and a graphical user interface for rapid detection validation. A CNN is then trained based on mel-spectrograms of sound to predict the set of species present in a recording. Transfer learning of a pre-trained model is employed to reduce the necessary training data and time. Furthermore, we define a loss function that allows for using true and false template-based detections to train a multi-class multi-label audio classifier. This approach leverages relevant absence (negative) information in training, and reduces the effort in creating multi-label training data by allowing weak labels. We evaluated the pipeline using a set of soundscape recordings collected across 749 sites in Puerto Rico. A CNN model was trained to identify 24 regional species of birds and frogs. The semi-automated training data collection process greatly reduced the manual effort required for training. The model was evaluated on an excluded set of 1000 randomly sampled 1-min soundscapes from 17 sites in the El Yunque National Forest. The test recordings contained an average of ~3 present target species per recording, and a maximum of 8. The test set also showed a large class imbalance with most species being present in less than 5% of recordings, and others present in >25%. The model achieved a mean-average-precision of 0.893 across the 24 species. Across all predictions, the total average-precision was 0.975.

Natural resource extraction is increasing rapidly in tropical forests, but we lag behind in understanding the impacts of these disturbances on biodiversity. In high diversity tropical habitats, acoustic monitoring is an efficient tool for sampling a large proportion of the fauna across varied spatial and temporal scales. We used passive acoustic monitoring in a pre-montane forest in Peru to investigate how soundscape composition and richness of acoustic frequencies varied with distance from a natural gas exploratory well and with operational phase (construction and drilling). We also evaluated how anuran and avian species richness and vocal activity varied with distance and between phases. Soundscape analyses showed that acoustic frequency similarity was greatest among sites closer to (≤250 m) and farther from (≥500 m) the platform. Soundscapes revealed more frequencies were used during construction and showed a weak trend of increasing frequency richness with increasing distance from the disturbance. Avian species richness and detections increased with distance from the platform, but anuran richness and detections declined with distance. Operational phase did not play a significant role in overall richness or activity patterns of either group. Among birds, insectivore detections increased with distance from the platform, and nectarivores were detected more frequently during the drilling phase. Results demonstrate that acoustic monitoring and soundscape analyses are useful tools for evaluating the impact of development activity on the vocalizing community, and should be implemented as a best practice in monitoring biodiversity and for guiding specific mitigation strategies.