Search

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.

The mangroves of Puerto Rico occupy a gradient of urbanization that offers a chance to test hypotheses on urban faunal communities. These hypotheses state that urban avifaunal communities have greater representation by generalists and that certain mangrove specialists can utilize urban landscapes. Much of this is said to be driven by food resources, with frugivores and nectarivores benefiting from abundant residential flowers and fruits, while insectivores are driven away by low food resources. This study used passive acoustic monitoring to identify the audible anuran and avifaunal species in mangroves across an urban gradient of Puerto Rico. Five anurans and thirty-one avian species were detected across all sites, with twenty-three species found at the most species rich site, and eight at the least rich site. Analyses on urban effects were conducted at an island-wide scale as well as a local scale with different results between the two. Island wide, the most urban faunal communities were more similar to each other in species composition relative to the least urban communities, and there was a significant difference in the community composition between the two. However, there were no differences in avian or anuran species richness between the least and most urban sites. Minimum canopy height was the strongest predictor of overall avian richness and avian invertivore richness, while the extent of mixed forest cover was the strongest predictor for increasing anuran richness. Some urban metrics, such as street density and the percent of surrounding urban and developed open space were strong predictors of certain avian feeding guilds and distribution groups. At the local scale, sites of maximum urbanness held 2–3 more bird species on average than corresponding sites of minimum urbanness at the same location, although there was no difference in anuran species and no differences in avian or anuran community composition between the two. Further, avian richness did increase significantly from the minimum to maximum urbanness site at six of the nine locations. These findings highlight that higher mangrove canopies are the strongest predictor of higher avian richness, but depending on the scale of observations, urbanness also plays a limited role in shaping mangrove faunal communities.

The effects of forest degradation, fragmentation, and climate change occur over long time periods, yet relatively few data are available to evaluate the long-term effects of these disturbances on tropical species occurrence. Here, we quantified changes in occupancy of 50 bird species over 17 years on Barro Colorado Island (BCI), Panama, a model system for the long-term effects of habitat fragmentation. The historical data set (2002&ndash, 2005) was based on point counts, whereas the contemporary data set (2018) was based on acoustic monitoring. For most species, there was no significant change in occupancy, however, the occupancy of four species (Tinamus major, Polioptila plumbea, Myiarchus tuberculifer, and Ceratopipra mentalis) increased significantly, and the occupancy of three species (Saltator grossus, Melanerpes pucherani, and Cyanoloxia cyanoides) decreased significantly. Forest age explained the majority of occupancy variation and affected the occupancy of more bird species than survey period or elevation. Approximately 50% of the species seem to favor old-growth forest, and 15 species (30%) had a significantly higher occupancy in old-growth forest sites. Elevation had no significant impact on the occupancy of the majority of bird species. Although BCI has been a protected reserve for approximately 100 years, land-use legacies (i.e., forest age) continue to influence bird distribution.

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.

The interactions between climate and land‐use change are dictating the distribution of flora and fauna and reshuffling biotic community composition around the world. Tropical mountains are particularly sensitive because they often have a high human population density, a long history of agriculture, range‐restricted species, and high‐beta diversity due to a steep elevation gradient. Here we evaluated the change in distribution of woody vegetation in the tropical Andes of South America for the period 2001–2014. For the analyses we created annual land‐cover/land‐use maps using MODIS satellite data at 250 m pixel resolution, calculated the cover of woody vegetation (trees and shrubs) in 9,274 hexagons of 115.47 km2, and then determined if there was a statistically significant (p, The interactions between climate and land‐use change are dictating the distribution of flora and fauna and reshuffling biotic community composition around the world. In the Andes of South America, we found a net increase in woody vegetation above 1000 m. While climate change has likely contributed to this increase, especially at higher elevations, land‐use change is the primary factor altering the contemporary distributions of many species.

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.

Acoustic ecology, or ecoacoustics, is a growing field that uses sound as a tool to evaluate animal communities. In this manuscript, we evaluate recordings from eight tropical forest sites that vary in species richness, from a relatively low diversity Caribbean forest to a megadiverse Amazonian forest, with the goal of understanding the relationship between acoustic space use (ASU) and species diversity across different taxonomic groups. For each site, we determined the acoustic morphospecies richness and composition of the biophony, and we used a global biodiversity dataset to estimate the regional richness of birds. Here, we demonstrate how detailed information on activity patterns of the acoustic community (8000 Hz), which contributed to greater ASU. This novel approach for analyzing the presence and acoustic activity of multiple taxonomic groups contributes to our understanding of ecological community dynamics and provides a useful tool for monitoring species in the context of restoration ecology, climate change and conservation biology.

Elevational gradients influence the distribution and composition of animal species and can provide useful information for the development of conservation strategies in the context of climate change. Despite an increase in studies of species diversity along elevational gradients, there is still a lack of information about community responses to environmental gradients, in part because of the logistical limitations of sampling multiple taxa simultaneously. One solution is to use passive acoustic monitoring (PAM) to acquire and analyze information from different animal taxa simultaneously along an entire elevational gradient. To improve our understanding of how environmental gradients influence patterns of animal communities and to test the relationship between soundscapes and animal composition we investigated how variation in bird and anuran composition affect the acoustic structure and composition of the soundscapes along an elevation gradient. We used PAM deploying portable acoustic recorders along three elevational transects in the Luquillo Mountains (LM), Puerto Rico. We found that elevation plays a major role in structuring the acoustic community and that the soundscape composition reflected the same patterns of anuran and bird distribution and composition along the elevational gradient. This study shows how different animal taxa respond to environmental gradients and provide strong evidence for the use of soundscapes as a tool to describe and compare species distribution and composition across large spatial scales.

BackgroundClimate change and infectious diseases threaten animal and plant species, even in natural and protected areas. To cope with these changes, species may acclimate, adapt, move or decline. Here, we test for shifts in anuran distributions in the Luquillo Mountains (LM), a tropical montane forest in Puerto Rico by comparing species distributions from historical (1931–1989)and current data (2015/2016).MethodsHistorical data, which included different methodologies, were gathered through the Global Biodiversity Information Facility (GBIF) and published literature, and the current data were collected using acoustic recorders along three elevational transects.ResultsIn the recordings, we detected the 12 native frog species known to occur in LM. Over a span of ∼25 years, two species have become extinct and four species suffered extirpation in lowland areas. As a consequence, low elevation areas in the LM (DiscussionWe hypothesize that these extirpations are due to the effects of climate change and infectious diseases, which are restricting many species to higher elevations and a much smaller area. Land use change is not responsible for these changes because LM has been a protected reserve for the past 80 years. However, previous studies indicate that (1) climate change has increased temperatures in Puerto Rico, and (2)Batrachochytrium dendrobatidis (Bd)was found in 10 native species and early detection of Bd coincides with anurans declines in the LM. Our study confirms the general impressions of amphibian population extirpations at low elevations, and corroborates the levels of threat assigned by IUCN.

Traditionally, animal species diversity and abundance is assessed using a variety of methods that are generally costly, limited in space and time, and most importantly, they rarely include a permanent record. Given the urgency of climate change and the loss of habitat, it is vital that we use new technologies to improve and expand global biodiversity monitoring to thousands of sites around the world. In this article, we describe the acoustical component of the Automated Remote Biodiversity Monitoring Network (ARBIMON), a novel combination of hardware and software for automating data acquisition, data management, and species identification based on audio recordings. The major components of the cyberinfrastructure include: a solar powered remote monitoring station that sends 1-min recordings every 10 min to a base station, which relays the recordings in real-time to the project server, where the recordings are processed and uploaded to the project website (arbimon.net). Along with a module for viewing, listening, and annotating recordings, the website includes a species identification interface to help users create machine learning algorithms to automate species identification. To demonstrate the system we present data on the vocal activity patterns of birds, frogs, insects, and mammals from Puerto Rico and Costa Rica.

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.

Artisanal and small-scale gold mining (ASM) is becoming a significant cause of environmental degradation in tropical ecosystems. In this study, we conducted a rapid assessment on the impact of an ASM gold mine on the vocalizing avian and anuran communities in the buffer zone of the Tambopata National Reserve in Peru. We used seven audio recorders (three near an active mine, two in an abandoned mine, and two in an adjacent forest) to collect 2900 recordings to generate soundscapes and compare acoustic activity patterns of birds and anurans among sites. We identified 56 bird species during the morning chorus (05:00-07:00) and 9 anurans species in the evening chorus (18:00-20:00). Bird species richness was similar between the forest (28 bird species), the abandoned mine (25 species) and the active mine (24 species), but species richness of birds sensitive to disturbance was much lower in the active mine. In contrast, anuran species richness was highest in the active mine (5 species) and lowest in the forest (2 species). Results indicate that acoustic monitoring and soundscape analysis can be effective tools for evaluating the impact of mining activities on vocalizing species, and could become useful tools in rapid environmental impact assessments for mitigation and conservation strategies in ASM mining regions.