A framework for the quantification of soundscape diversity using Hill numbers

1. Soundscape studies are increasingly common to capture landscape-scale ecological patterns. Yet, several aspects of soundscape diversity quantification remain unexplored. Although some processes influencing acoustic niche usage may operate in the 24h 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 functional ecology. 2. Our workflow quantifies the functional diversity of sound in the 24-hour acoustic trait space. We put forward an entity, the Operational Sound Unit (OSU), which groups sounds by their shared functional properties. Using OSUs as our unit of diversity measurement, 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; (iii) soundscape evenness. We demonstrate the use of these metrics by (a) simulating soundscapes to assess if the indices possess a set of desirable behaviours; and (b) quantifying the soundscape richness and evenness along a gradient in species richness to illustrate how these metrics can be used to shed unique insights into patterns of acoustic niche usage. 3. 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 soniferous species. This suggests that the acoustic niche space is more filled where taxonomic richness is higher. Moreover, species-poor acoustic communities have a higher proportion of rare sounds and use the acoustic space less effectively. As the correlation between the soundscape and taxonomic richness is strong (>0.8) and holds at low sampling intensities, soundscape richness could serve as a proxy for taxonomic richness. 4. Quantifying the soundscape diversity through the lens of functional ecology using the analytical framework of Hill numbers generates novel insights into acoustic niche usage at a landscape scale and provides a useful proxy for taxonomic richness measurement.

Edaphic invertebrate diversity around the reservoir of a small hydroelectric power station in southern Brazil

To meet the growing demand for electricity, considerable effort has been invested in the construction of hydroelectric stations in Brazil. The environmental impacts caused by these projects have been significant, especially on fauna. To evaluate the biodiversity of edaphic invertebrates, a study was performed in an area surrounding a small hydroelectric station of Flor do Sertão, in the Santa Catarina state, Brazil, covering three distances (5, 15 and 30 meters) from the flooded region of the reservoir. From December 2010 to June 2011, surveys of edaphic invertebrates were performed by pitfall traps. The invertebrates collected at each distance were compared and their diversity was calculated through Hill numbers. The dynamics were analyzed through the Shannon index exponential and the inverse Simpson index, and correlated climatic variables to invertebrate diversity. A total of 14,074 specimens were collected from 24 taxonomic groups (Order). The analyses according to size and sample coverage showed few differences in invertebrate diversity between the distances. Values of richness and diversity of common groups are similar for all distances. There is a trend decrease in diversity dynamics for common individuals, with a strong decline in June. The diversity of edaphic invertebrates exhibited high correlation with temperature and no correlation with pluviosity.

Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance

Background Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks. Results Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation. Conclusions Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.

Comparison of Soil Biology Quality in Organically and Conventionally Managed Agro-Ecosystems Using Microarthropods

Since management practices profoundly influence soil characteristics, the adoption of sustainable agro-ecological practices is essential for soil health conservation. We compared soil health in organic and conventional fields in the Abruzzi region (central Italy) by using (1) the soil biology quality (QBS) index (which expresses the level of specialisation in soil environment shown by microarthropods) and (2) microarthropod diversity expressed by Hill numbers. QBS values were calculated using both the original formulation based on only presence/absence data and a new abundance-based version. We found that organic management improves soil biology quality, which encourages the use of organic farming to maintain soil health. Including arthropod abundance in QBS calculation does not change the main outcomes, which supports the use of its original, speedier formulation. We also found that agricultural fields included in protected areas had greater soil health, which shows the importance of the matrix in determining agricultural soil health and highlights the importance of land protection in preserving biodiversity even in managed soils. Finally, we found that soil biology quality and microarthropod community structure are distinctly influenced by certain physical and chemical characteristics of the soil, which supports the use of microarthropods as biological indicators.

Climate and human induced 2000-year vegetation diversity change in Yunnan, southwestern China

Understanding long-term vegetation diversity patterns and their potential responses to climate and/or human driven processes are important for ecosystem modeling and conservation. Late-Holocene fossil pollen assemblage and associated vegetation diversity estimates provide an opportunity to explore the interactions among vegetation, climate, and human activities. A continuous 2000-year palynological record was obtained from the Beihai Wetland, southwestern China, to represent regional vegetation history, particularly the vegetation diversity changes. The results indicate that regional vegetation was dominated by deciduous broadleaved forest components (e.g. Alnus, deciduous Quercus), which showed a gradual decrease accompanied by expansion of herbaceous taxa (mainly Poaceae) after AD 800. Such progressive decline of forest was attributed to regional deforestation driven by intensified human activities, which was further confirmed by the increasing non-pollen polymorph abundance, particularly an abrupt rise after AD 1350. Vegetation diversity based on the Hill numbers ( N0, N1, and N2) showed a dramatic decline between ca. AD 200–400, which was triggered by regional fire events as shown by increased charcoal abundance from a nearby lake. The vegetation diversity reduced gradually after AD 800, especially the vegetation richness reflected by N0, revealing the transitional process from climate-driven to human-dominated vegetation changes. Minor increases of vegetation diversity occurred during Chinese dynastical transitions, probably due to reduced human activities following war-induced population crises. On the multidecadal scale, variations in vegetation diversity correlated significantly with climate fluctuations (revealed by synthesized temperature of China and stable oxygen isotope record from Dongge Cave) before AD 800, indicating a climate dominant condition. Then, the correlation between vegetation diversity and climate declined after AD 800, representing a progressive transition to human-dominant condition. In addition, the compositional turnover based on DCCA of the fossil pollen assemblage revealed a stepwise decrease, indicating reduced vegetation turnovers under anthropogenic influences.

Information-based summary statistics for spatial genetic structure inference

Inference of spatial patterns of genetic structure often relies on parameter estimation and model evaluation using a set of summary statistics (SS) that summarise the information present in the data. An important subset of these SS is best described as diversity indices, which are based on information theory principles that can be classified as belonging to three different ‘families’ encompassing a spectrum of information measures, qH. These include the richness family of order q = 0, ArSS; the Shannon family of order q = 1, HSS; and the heterozygosity family of order q = 2, HeSS. Although commonly used by ecologists, the Shannon family has been rather neglected by population geneticists and evolutionary biologists. However, recent population genetic studies have advocated their use, yet the power of these SS for spatial structure discrimination has not been systematically assessed.In this study, we performed a comprehensive assessment of the three families of SS, as well as a fourth family consisting of SS belonging to the Shannon family but expressed in terms of Hill numbers , for spatial structure inference using simulated microsatellites data under typical spatial scenarios. To give an unbiased evaluation, we used three machine learning methods, Kernel Local Fisher discriminant analysis (KLFDA), random forest classification (RFC), and deep neural network (DL), to test the performance of different SS to discriminate between spatial scenarios, and then identified the most informative metrics for discriminatory power.Results showed that the SS family of order q = 1 expressed in terms of Hill numbers, , outperformed the other two families (ArSS, HeSS) as well as the untransformed Shannon entropy (HSS) family. Jaccard dissimilarity (J) and its Mantel’s r showed the highest discriminatory power to discriminate all spatial scenarios, followed by Shannon differentiation ΔD and its Mantel’s r.Information-based summary statistics, especially the diversity of order q = 1 and Shannon differentiation measures, can increase the power of spatial structure inference. In addition, different sets of SS provide complementary power for discriminating between spatial scenarios.

Emerging SARS-CoV-2 diversity revealed by rapid whole genome sequence typing

Background Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events. Methods We developed a tool (GNUVID) that integrates whole genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to all high-quality genomes available from GISAID. STs were clustered into clonal complexes (CCs), and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events, and to estimate effective viral diversity across locations and over time in 16 US states. Results GNUVID is a highly scalable tool for viral genotype classification (https://github.com/ahmedmagds/GNUVID) that can quickly classify hundreds of thousands of genomes in a way that is consistent with phylogeny. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states, an average of 20.6 putative introductions and 7.5 exportations for each state over the time period analyzed. We introduce the use of effective diversity metrics (Hill numbers) that can be used to estimate the impact of interventions (eg., travel-restrictions, vaccine uptake, mask mandates) on the variation in circulating viruses. Conclusions Our classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. GNUVID classification lends itself to measures of ecological diversity, and, with systematic genomic sampling, it could be used to track circulating viral diversity and identify emerging clones and hotspots.

Late Quaternary Climate Reconstruction and Lead-Lag Relationships of Biotic and Sediment-Geochemical Indicators at Lake Bolshoe Toko, Siberia

Millennial-scale climate change history in eastern Siberia and relationships between diatom diversity, paleoclimate, and sediment-geochemical lake system trajectories are still poorly understood. This study investigates multi-proxy time series reaching back to the Late Pleistocene derived from radiocarbon dated Lake Bolshoe Toko sediment cores, southeastern Yakutia, Russia. We analyzed diatoms, elements (XRF), minerals (XRD), grain-size, organic carbon, and included chironomid analyses and published pollen-data for quantitative paleoclimate reconstruction. Changes in diatom species abundances reveal repeated episodes of thermal stratification indicated by shifts from euplanktonic Aulacoseira to Cyclotella species. Chironomid and pollen-inferred temperature reconstruction reveal that the main shift between these diatom species is related to the onset of Holocene Thermal Maximum (HTM) at 7.1 cal ka BP. Comparison to other paleoclimate records along a north-south transect through Yakutia shows that the HTM was delayed as far south as the Stanovoy mountains. Relationships between sediment-geochemistry, paleoclimate variability and diatom species richness (alpha diversity) was tested in a moving temporal offset approach to detect lead-lag relationships. Sediment-geochemical data, mainly uniform during the Holocene, revealed strongest positive or negative correlations ahead of species richness changes. Mean July air temperature (TJuly) reconstructions correlate with both Hill numbers and relative assemblage changes indicated by sample scores of multidimensional scaling analysis (MDS) over the entire time series. We found that sediment organic carbon revealed distinct positive correlations, i.e., centennial-scale delay to increases in diatom effective richness (Hill numbers N0 and N2). We conclude that a lag of deposited organic carbon concentrations behind changes in diatom alpha diversity reveals that species richness can augment the production and thus sequestration of organic matter in comparable lake systems.

Local-scale impact of wind energy farms on rare, endemic, and threatened plant species

Background Wind energy farms have become a popular solution to produce green energy worldwide. Their development within protected areas has increased dramatically in the past decade, and the effects on the rare, endemic and threatened plant species (i.e., protected plant species), essential for habitat conservation and management, are little known. Only a few studies directly quantify the impacts of wind energy farms on them. Our study analyzes the impact of wind energy farms on rare, endemic, and threatened plant species in steppic habitats and their recovery potential over a ten-year period on a wind energy farm within the Dealurile Agighiolului Natura 2000 site (Dobrogea Region, SE Romania). Methods We surveyed the rare, endemic, and threatened plant species within a radius of approximately 50 m around each of the 17 wind towers during the wind farm operational phase. We selected 34 plots to allow the investigation of two types of areas: (1) a disturbed area overlapping the technological platform, where the vegetation was removed before construction, and (2) an adjacent undisturbed area. To understand the effects of the wind energy farm on the rare, endemic, and threatened plant species diversity and the differences between the disturbed and undisturbed areas, we calculated under both conditions: (1) plant species richness; (2) sample-size-based rarefaction and extrapolation with Hill numbers parameterized by species richness; (3) non-metric multidimensional scaling of Jaccard dissimilarity index; (4) functional diversity; (5) beta-diversity (including replacement and nestedness of species). Results As a result of the disturbances caused by the wind energy farm’s development, we identified a sharp contrast between the diversity of rare, endemic, and threatened plants inhabiting disturbed and undisturbed areas near the wind towers. Our research showed that less than 40% of the total inventoried rare, endemic, and threatened species colonized the disturbed sites. Species turnover within undisturbed plots was higher than disturbed plots, implying that the plant community’s heterogeneity was high. However, a higher richness in rare, endemic, and threatened plant species was found in the plots around the wind towers in grasslands of primary type. Sample-size-based rarefaction and extrapolation with Hill numbers by observed species richness indicated an accurate estimation of species richness in disturbed habitats, demonstrating that recovery after wind energy farm construction was incomplete after ten years of low-intensity plant restoration and conservation activities. Thus, we consider that operating activities must be reconfigured to allow the complete recovery of the communities with rare, endemic, and threatened plant species.

A Combined Application of Molecular Microbial Ecology and Elemental Analyses Can Advance the Understanding of Decomposition Dynamics

Introducing animal carbon-source to soil initiates biochemical and microbial processes that lead to its decomposition and recycling, which subsequently cause successional shifts in soil microbial community. To investigate the use of soil microbial community to inform criminal investigation, this study was designed to mimic clandestine graves. It compared the decomposition of stillborn piglets (Sus scrofa domesticus), as human analogues, to oak (Quercus robur) leaf litter and soil-only controls outdoors for 720 days. Environmental and edaphic parameters were monitored and showed soil microbial community alignment with temperature seasonality, which highlighted the importance of this abiotic factor. Denaturing gradient gel electrophoresis (DGGE) data were used to calculate Hill numbers and diversity indices of the bacterial 16S rRNA community did not distinguish mammalian- from plant-based decomposition consistently during the first or second year of the study. In contrast, the fungal 18S rRNA community allowed clear differentiation between different treatments (beta diversity) throughout the 720-day experiment and suggested the moment of the decomposing mammalian skin rupture. 16S rRNA-based NGS facilitated the identification of e.g., Pirellulaceae, Acidobacteria ii1-15_order and Candidatus xiphinematobacter as Year 2 bacterial markers of gravesoil at family, order and species taxonomic levels, respectively, and confirmed the similarity of the calculated Hill diversity metrics with those derived from DGGE profiling. Parallel soil elemental composition was measured by portable X-ray Fluorescence where calcium profiles for the piglet-associated soils were distinct from those without carrion. Also, soil calcium content and PMI correlated positively during the first year then negatively during the second. This study is one of the first to apply a multidisciplinary approach based on molecular and physicochemical analytical techniques to assess decomposition. It highlights the recognised potential of using soil microbial community in forensic investigations and provides a proof-of-concept for the application of a combined molecular and elemental approach to further understand the dynamics of decomposition. In addition, it sets the scene for further research in different conditions based on Hill numbers metrics instead of the classic ecological indices for soil necrobiome richness, diversity and evenness.