Aquatic environments encompass the world’s most extensive habitats, rich with sounds produced by a diversity of animals. Passive acoustic monitoring (PAM) is an increasingly accessible remote sensing technology that uses hydrophones to listen to the underwater world and represents an unprecedented, non-invasive method to monitor underwater environments. This information can assist in the delineation of biologically important areas via detection of sound-producing species or characterization of ecosystem type and condition, inferred from the acoustic properties of the local soundscape. At a time when worldwide biodiversity is in significant decline and underwater soundscapes are being altered as a result of anthropogenic impacts, there is a need to document, quantify, and understand biotic sound sources–potentially before they disappear. A significant step toward these goals is the development of a web-based, open-access platform that provides: (1) a reference library of known and unknown biological sound sources (by integrating and expanding existing libraries around the world); (2) a data repository portal for annotated and unannotated audio recordings of single sources and of soundscapes; (3) a training platform for artificial intelligence algorithms for signal detection and classification; and (4) a citizen science-based application for public users. Although individually, these resources are often met on regional and taxa-specific scales, many are not sustained and, collectively, an enduring global database with an integrated platform has not been realized. We discuss the benefits such a program can provide, previous calls for global data-sharing and reference libraries, and the challenges that need to be overcome to bring together bio- and ecoacousticians, bioinformaticians, propagation experts, web engineers, and signal processing specialists (e.g., artificial intelligence) with the necessary support and funding to build a sustainable and scalable platform that could address the needs of all contributors and stakeholders into the future.
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A framework for near-real time monitoring of diversity patterns based on indirect remote sensing, with an application in the Brazilian Atlantic rainforest
Monitoring biodiversity change is key to effective conservation policy. While it is difficult to establish in situ biodiversity monitoring programs at broad geographical scales, remote sensing advances allow for near-real time Earth observations that may help with this goal. We combine periodical and freely available remote sensing information describing temperature and precipitation with curated biological information from several groups of animals and plants in the Brazilian Atlantic rainforest to design an indirect remote sensing framework that monitors potential loss and gain of biodiversity in near-real time. Using data from biological collections and information from repeated field inventories, we demonstrate that this framework has the potential to accurately predict trends of biodiversity change for both taxonomic and phylogenetic diversity. The framework identifies areas of potential diversity loss more accurately than areas of species gain, and performs best when applied to broadly distributed groups of animals and plants.
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- Award ID(s):
- 1745562
- NSF-PAR ID:
- 10355719
- Date Published:
- Journal Name:
- PeerJ
- Volume:
- 10
- ISSN:
- 2167-8359
- Page Range / eLocation ID:
- e13534
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Advances in remote sensing technologies offer new means to monitor habitats of importance on large scales. Florida rosemary scrub is one such threatened habitat, found in patches across the landscape in relatively elevated areas, and is often characterized by shrub‐less areas (gaps) among the dominant shrubs, which provide favorable microhabitats for many endemic and endangered plants and animals. However, gaps are difficult and time‐consuming to characterize, especially across large areas, using traditional ground‐based field methods. We developed and tested a method for rapidly classifying gaps using an unmanned aerial vehicle (UAV or drone). Aerial data were collected by a UAV‐mounted camera in April 2018, and stratified, random ground surveys to verify UAV data were conducted March through April 2018 at Archbold Biological Station in south‐central Florida, USA. We used mosaicked and georeferenced digital surface and terrain models to calculate vegetation height across 33 rosemary scrub sites (~230,000 m2at 0.064 m2pixel resolution). Gaps were defined as >1 m2areas where vegetation height was <10 cm. We found that gap areas from UAV models and field surveys were significantly correlated across varying gap sizes, times‐since‐fire, and relative elevations. We also observed a significant decrease in mean gap area and percent gap space with increasing time‐since‐fire, a pattern consistent with smaller‐scale, ground‐based sampling, and a marginally significant increase in gap area with relative elevation. This remote sensing method lends itself to better exploration of how gap areas, their spatiotemporal patterns, and associated fire history, elevation, soil, and other geographic data affect structural vegetation dynamics across the landscape. This study illustrates the success of UAV modeling of gap space in Florida rosemary scrub, a result of regional consequence for the southeastern United States, but more broadly, it encourages the use of UAV technology as a tool to enhance traditional field‐based methods in systems globally. As habitat fragmentation and loss become increasingly problematic for the conservation of threatened habitats, understanding these complex spatial dynamics is crucial to the conservation and management of vegetation communities and their biodiversity.
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Background The proliferation of mobile health (mHealth) applications is partly driven by the advancements in sensing and communication technologies, as well as the integration of artificial intelligence techniques. Data collected from mHealth applications, for example, on sensor devices carried by patients, can be mined and analyzed using artificial intelligence–based solutions to facilitate remote and (near) real-time decision-making in health care settings. However, such data often sit in data silos, and patients are often concerned about the privacy implications of sharing their raw data. Federated learning (FL) is a potential solution, as it allows multiple data owners to collaboratively train a machine learning model without requiring access to each other’s raw data. Objective The goal of this scoping review is to gain an understanding of FL and its potential in dealing with sensitive and heterogeneous data in mHealth applications. Through this review, various stakeholders, such as health care providers, practitioners, and policy makers, can gain insight into the limitations and challenges associated with using FL in mHealth and make informed decisions when considering implementing FL-based solutions. Methods We conducted a scoping review following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched 7 commonly used databases. The included studies were analyzed and summarized to identify the possible real-world applications and associated challenges of using FL in mHealth settings. Results A total of 1095 articles were retrieved during the database search, and 26 articles that met the inclusion criteria were included in the review. The analysis of these articles revealed 2 main application areas for FL in mHealth, that is, remote monitoring and diagnostic and treatment support. More specifically, FL was found to be commonly used for monitoring self-care ability, health status, and disease progression, as well as in diagnosis and treatment support of diseases. The review also identified several challenges (eg, expensive communication, statistical heterogeneity, and system heterogeneity) and potential solutions (eg, compression schemes, model personalization, and active sampling). Conclusions This scoping review has highlighted the potential of FL as a privacy-preserving approach in mHealth applications and identified the technical limitations associated with its use. The challenges and opportunities outlined in this review can inform the research agenda for future studies in this field, to overcome these limitations and further advance the use of FL in mHealth.more » « less
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Abstract 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 < 0.05) 14 year linear trend (positive—forest gain, negative—forest loss) within each hexagon. Of the 1,308 hexagons with significant trends, 36.6% (n = 479) lost forests and 63.4% (n = 829) gained forests. We estimated an overall net gain of ~500,000 ha in woody vegetation. Forest loss dominated the 1,000–1,499 m elevation zone and forest gain dominated above 1,500 m. The most important transitions were forest loss at lower elevations for pastures and croplands, forest gain in abandoned pastures and cropland in mid‐elevation areas, and shrub encroachment into highland grasslands. Expert validation confirmed the observed trends, but some areas of apparent forest gain were associated with new shade coffee, pine, or eucalypt plantations. In addition, after controlling for elevation and country, forest gain was associated with a decline in the rural population. Although we document an overall gain in forest cover, the recent reversal of forest gains in Colombia demonstrates that these coupled natural‐human systems are highly dynamic and there is an urgent need of a regional real‐time land‐use, biodiversity, and ecosystem services monitoring network. -
BACKGROUND The Republic of Madagascar is home to a unique assemblage of taxa and a diverse set of ecosystems. These high levels of diversity have arisen over millions of years through complex processes of speciation and extinction. Understanding this extraordinary diversity is crucial for highlighting its global importance and guiding urgent conservation efforts. However, despite the detailed knowledge that exists on some taxonomic groups, there are large knowledge gaps that remain to be filled. ADVANCES Our comprehensive analysis of major taxonomic groups in Madagascar summarizes information on the origin and evolution of terrestrial and freshwater biota, current species richness and endemism, and the utilization of this biodiversity by humans. The depth and breadth of Madagascar’s biodiversity—the product of millions of years of evolution in relative isolation —is still being uncovered. We report a recent acceleration in the scientific description of species but many remain relatively unknown, particularly fungi and most invertebrates. DIGITIZATION Digitization efforts are already increasing the resolution of species richness patterns and we highlight the crucial role of field- and collections-based research for advancing biodiversity knowledge in Madagascar. Phylogenetic diversity patterns mirror that of species richness and endemism in most of the analyzed groups. Among the new data presented, our update on plant numbers estimates 11,516 described vascular plant species native to Madagascar, of which 82% are endemic, in addition to 1215 bryophyte species, of which 28% are endemic. Humid forests are highlighted as centers of diversity because of their role as refugia and centers of recent and rapid radiations, but the distinct endemism of other areas such as the grassland-woodland mosaic of the Central Highlands and the spiny forest of the southwest is also important despite lower species richness. Endemism in Malagasy fungi remains poorly known given the lack of data on the total diversity and global distribution of species. However, our analysis has shown that ~75% of the fungal species detected by environmental sequencing have not been reported as occurring outside of Madagascar. Among the 1314 species of native terrestrial and freshwater vertebrates, levels of endemism are extremely high (90% overall)—all native nonflying terrestrial mammals and native amphibians are found nowhere else on Earth; further, 56% of the island’s birds, 81% of freshwater fishes, 95% of mammals, and 98% of reptile species are endemic. Little is known about endemism in insects, but data from the few well-studied groups on the island suggest that it is similarly high. The uses of Malagasy species are many, with much potential for the uncovering of useful traits for food, medicine, and climate mitigation. OUTLOOK Considerable work remains to be done to fully characterize Madagascar’s biodiversity and evolutionary history. The multitudes of known and potential uses of Malagasy species reported here, in conjunction with the inherent value of this unique and biodiverse region, reinforce the importance of conserving this unique biota in the face of major threats such as habitat loss and overexploitation. The gathering and analysis of data on Madagascar’s remarkable biota must continue and accelerate if we are to safeguard this unique and highly threatened subset of Earth’s biodiversity. Emergence and composition of Madagascar’s extraordinary biodiversity. Madagascar’s biota is the result of over 160 million years of evolution, mostly in geographic isolation, combined with sporadic long distance immigration events and local extinctions. (Left) We show the age of the oldest endemic Malagasy clade for major groups (from bottom to top): arthropods, bony fishes, reptiles, flatworms, birds, amphibians, flowering plants, mammals, non-flowering vascular plants, and mollusks). Humans arrived recently, some 10,000 to 2000 years (top right) and have directly or indirectly caused multiple extinctions (including hippopotamus, elephant birds, giant tortoises, and giant lemurs) and introduced many new species (such as dogs, zebu, rats, African bushpigs, goats, sheep, rice). Endemism is extremely high and unevenly distributed across the island (the heat map depicts Malagasy palm diversity, a group characteristic of the diverse humid forest). Human use of biodiversity is widespread, including 1916 plant species with reported uses. The scientific description of Malagasy biodiversity has accelerated greatly in recent years (bottom right), yet the diversity and evolution of many groups remain practically unknown, and many discoveries await.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7717/peerj.13534
