Listening to cities during the COVID-19 lockdown: How do human activities and urbanization impact soundscapes in Colombia?

Noise is one of the fastest growing and most ubiquitous type of environmental pollution, with prevalence in cities. The COVID-19 confinement in 2020 in Colombia led to a reduction in human activities and their associated noise. We used this unique opportunity to measure the impacts of noise on urban soundscapes, and explore the effects of urbanization intensity independently of human activity. We launched a community science initiative inviting participants to collect audio recordings from their windows using smartphones. Recordings were taken during severe mobility restrictions (April), and during a period of lightened restrictions (May-June). From the data collected, we measured changes in sound pressure levels (SPL), acoustic structure (soundscape spectro-temporal characteristics), and human perception between the two periods. A 12% increase in human activities had a detectable acoustic footprint, with a significant increase of SPL (2.15 dB, 128% increase), a shift towards dominance of low-frequency broadband signals, and a perceived dominance of human-made over wildlife sounds. Measured changes in SPL and acoustic structure were directly proportional to urbanization; however, perception of these changes was not. This gap may be associated with a masking effect generated by noise or a disconnect of humans from nature in large cities. The mobility restrictions created a chance to better understand the impacts of urbanization and human activities on the soundscape, while raising public awareness regarding noise pollution effects on people and wildlife. Information analyzed here might serve in urban planning in developing countries where urban expansion is occurring in a rapid, unplanned fashion.

One hundred and six years of change in a Sonoran Desert plant community: Impact of climate anomalies and trends in species sensitivities.

A major restriction in predicting plant community response to future climate change is a lack of long-term data needed to properly assess species and community response to climate and identify a baseline to detect climate anomalies. Here, we use a 106-year dataset on a Sonoran Desert plant community to test the role of extreme temperature and precipitation anomalies on community dynamics at the decadal scale and over time. Additionally, we tested the climate sensitivity of 39 desert plant species and whether sensitivity was associated with growth form, longevity, geographic range, or local dominance. We found that desert plant communities had shifted directionally over the 106 years, but the climate had little influence on this directional change primarily due to nonlinear shifts in precipitation anomalies. Decadal-scale climate had the largest impact on species richness, species relative density, and total plant cover, explaining up to 26%, 45%, and 55% of the variance in each, respectively. Drought and the interaction between the frequency of freeze events and above-average summer precipitation were among the most influential climate factors. Increased drought frequency and wetter periods with frequent freeze events led to larger reductions in total plant cover, species richness, and the relative densities of dominant subshrubs Ambrosia deltoidea and Encelia farinosa. More than 80% of the tested species were sensitive to climate, but sensitivity was not associated with a species' local dominance, longevity, geographic range, or growth form. Some species appear to exhibit demographic buffering, where when they have a higher sensitivity to drought, they also tend to have a higher sensitivity to favorable (i.e., wetter and hotter) conditions. Overall, our results suggest that, while decadal-scale climate variation substantially impacts these desert plant communities, directional change in temperature over the last century has had little impact due to the relative importance of precipitation and drought. With projections of increased drought in this region, we may see reductions in total vegetation cover and species richness due to the loss of species, possibly through a breakdown in their ability to demographically buffer climatic variation, potentially changing community dynamics through a change in facilitative and competitive processes.

Systematic review of machine learning methods applied to ecoacoustics and soundscape monitoring.

Soundscape ecology is a promising area that studies landscape patterns based on their acoustic composition. It focuses on the distribution of biotic and abiotic sounds at different frequencies of the landscape acoustic attribute and the relationship of said sounds with ecosystem health metrics and indicators (e.g., species richness, acoustic biodiversity, vectors of structural change, gradients of vegetation cover, landscape connectivity, and temporal and spatial characteristics). To conduct such studies, researchers analyze recordings from Acoustic Recording Units (ARUs). The increasing use of ARUs and their capacity to record hours of audio for months at a time have created a need for automatic processing methods to reduce time consumption, correlate variables implicit in the recordings, extract features, and characterize sound patterns related to landscape attributes. Consequently, traditional machine learning methods have been commonly used to process data on different characteristics of soundscapes, mainly the presence-absence of species. In addition, it has been employed for call segmentation, species identification, and sound source clustering. However, some authors highlight the importance of the new approaches that use unsupervised deep learning methods to improve the results and diversify the assessed attributes. In this paper, we present a systematic review of machine learning methods used in the field of ecoacoustics for data processing. It includes recent trends, such as semi-supervised and unsupervised deep learning methods. Moreover, it maintains the format found in the reviewed papers. First, we describe the ARUs employed in the papers analyzed, their configuration, and the study sites where the datasets were collected. Then, we provide an ecological justification that relates acoustic monitoring to landscape features. Subsequently, we explain the machine learning methods followed to assess various landscape attributes. The results show a trend towards label-free methods that can process the large volumes of data gathered in recent years. Finally, we discuss the need to adopt methods with a machine learning approach in other biological dimensions of landscapes.

Multidimensional tropical forest recovery.

Tropical forests disappear rapidly because of deforestation, yet they have the potential to regrow naturally on abandoned lands. We analyze how 12 forest attributes recover during secondary succession and how their recovery is interrelated using 77 sites across the tropics. Tropical forests are highly resilient to low-intensity land use; after 20 years, forest attributes attain 78% (33 to 100%) of their old-growth values. Recovery to 90% of old-growth values is fastest for soil (<1 decade) and plant functioning (<2.5 decades), intermediate for structure and species diversity (2.5 to 6 decades), and slowest for biomass and species composition (>12 decades). Network analysis shows three independent clusters of attribute recovery, related to structure, species diversity, and species composition. Secondary forests should be embraced as a low-cost, natural solution for ecosystem restoration, climate change mitigation, and biodiversity conservation.

A policy-driven framework for conserving the best of Earth's remaining moist tropical forests.

Tropical forests vary in composition, structure and function such that not all forests have similar ecological value. This variability is caused by natural and anthropogenic disturbance regimes, which influence the ability of forests to support biodiversity, store carbon, mediate water yield and facilitate human well-being. While international environmental agreements mandate protecting and restoring forests, only forest extent is typically considered, while forest quality is ignored. Consequently, the locations and loss rates of forests of high ecological value are unknown and coordinated strategies for conserving these forests remain undeveloped. Here, we map locations high in forest structural integrity as a measure of ecological quality on the basis of recently developed fine-resolution maps of three-dimensional forest structure, integrated with human pressure across the global moist tropics. Our analyses reveal that tall forests with closed canopies and low human pressure typical of natural conditions comprise half of the global humid or moist tropical forest estate, largely limited to the Amazon and Congo basins. Most of these forests have no formal protection and, given recent rates of loss, are at substantial risk. With the rapid disappearance of these 'best of the last' forests at stake, we provide a policy-driven framework for their conservation and restoration, and recommend locations to maintain protections, add new protections, mitigate deleterious human impacts and restore forest structure.

Global humid tropics forest structural condition and forest structural integrity maps.

Remotely sensed maps of global forest extent are widely used for conservation assessment and planning. Yet, there is increasing recognition that these efforts must now include elements of forest quality for biodiversity and ecosystem services. Such data are not yet available globally. Here we introduce two data products, the Forest Structural Condition Index (SCI) and the Forest Structural Integrity Index (FSII), to meet this need for the humid tropics. The SCI integrates canopy height, tree cover, and time since disturbance to distinguish short, open-canopy, or recently deforested stands from tall, closed-canopy, older stands typical of primary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar. The FSII overlays a global index of human pressure on SCI to identify structurally complex forests with low human pressure, likely the most valuable for maintaining biodiversity and ecosystem services. These products represent an important step in maturation from conservation focus on forest extent to forest stands that should be considered "best of the last" in international policy settings.

Episodic death across species of desert shrubs.

Extreme events shape population and community trajectories. We report episodic mortality across common species of thousands of long-lived perennials individually tagged and monitored for 20 years in the Colorado Desert of California following severe regional drought. Demographic records from 1984 to 2004 show 15 years of virtual stasis in populations of adult shrubs and cacti, punctuated by a 55-100% die-off of six of the seven most common perennial species. In this episode, adults that experienced reduced growth in a lesser drought during 1984-1989 failed to survive the drought of 2002. The significance of this event is potentially profound because population dynamics of long-lived plants can be far more strongly affected by deaths of adults, which in deserts potentially live for centuries, than by seedling births or deaths. Differential mortality and rates of recovery during and after extreme climatic events quite likely determine the species composition of plant and associated animal communities for at least decades. The die-off recorded in this closely monitored community provides a unique window into the mechanics of this process of species decline and replacement.