Biodiversity Analysis Research Articles

Passive eDNA sampling facilitates biodiversity monitoring and rare species detection

Environmental DNA (eDNA) technology has revolutionized biomonitoring, but challenges remain regarding water sample processing. The passive eDNA sampler (PEDS) represents a viable alternative to active, water filtration-based eDNA enrichment methods, but the effectiveness of PEDS for surveying biodiverse and complex natural water bodies is unknown. Here, we collected eDNA using filtration and glass fiber filter-based PEDS (submerged in water for 1 d) from 27 sites along the final reach of the Yangtze River and the coast of the Yellow Sea, followed by eDNA metabarcoding analysis of fish biodiversity and quantitative PCR (qPCR) for a critically endangered aquatic mammal, the Yangtze finless porpoise. We ultimately detected 98 fish species via eDNA metabarcoding. Both eDNA sampling methods captured comparable local species richness and revealed largely similar spatial variation in fish assemblages and community partitions between the river and sea sites. Notably, the Yangtze finless porpoise was detected only in the metabarcoding of eDNA collected by PEDS at five sites. Also, species-specific qPCR revealed that the PEDS captured porpoise eDNA at more sites (7 vs. 2), in greater quantities, and with a higher detection probability (0.803 vs. 0.407) than did filtration. Our results demonstrate the capacity of PEDS for surveying fish biodiversity, and support that continuous eDNA collection by PEDS can be more effective than instantaneous water sampling at capturing low abundance and ephemeral species in natural waters. Thus, the PEDS approach can facilitate more efficient and convenient eDNA-based biodiversity surveillance and rare species detection.

Mapping Wildland-urban interfaces to support wildfire management over fire-prone forest outskirts of the Zhytomyr region

Recent wildfire events in Ukraine caused considerable economic and human losses, drawing the attention of public opinion in Ukraine to further research the issue related to the management of the risks of forest fires, specially, in the current context of climate change and due to the growing frequency of critical fire weather conditions. Current approaches to fighting wildfires in Ukraine are focused on fire extinction, currently omitting the management of vegetation fuels and their effect on wildfire behaviour to facilitate its mitigation. Due to current wildfire risks to the population and forests, and insufficient research on this issue in Ukraine, it is needed to further develop and test new approaches to reduce wildfire risk. For that purpose, it is required a deep understanding of the fire resilience of vegetation as well as the factors that make the communities vulnerable. In this manuscript, a method for assessing and mapping the Wildland-urban interface with a focus on fire risks for part of the Ukrainian Polissya is suggested. Wildland-urban interface zones were delineated for settlements in the study area and used to identify areas for wildfire risk remediation and silvicultural practices to increase forest resilience to fire. A biodiversity analysis of the main tree species, undergrowth, and understory of the study region, produced a list of local deciduous species that could be used to reduce fire intensity by increasing their proportion in pure pine forests. The volume of silvicultural efforts to increase forest resilience to fire and reduce wildfire risks to human settlements was assessed for one of the most forested regions of Ukraine. Moreover, the first comprehensive assessment of wildlands, which can potentially contribute to wildfire impacts on communities, was provided, making recommendations to reduce wildfire risks for the settlements. In this study, feasible and effective methods to assess Wildland-Urban-Interfaces, and reduce fire risks are suggested, suggesting a methodology concerning wildfire risks for a heavily forested region of Ukraine. Moreover, the suggested approaches that could be used in Ukrainian Forest Management to mitigate wildfire risks in the context of climate change, urbanization, and low resistance of pure pine stands to fires as well as pests and diseases

Archaeozoology Supports a Holistic View on Fish Assessments in Large Rivers—A Case Study from the Volga River: From Quantitative Data and Ancient DNA to Biodiversity Analysis

Knowledge of the historical and present dynamics of populations of migratory fish can promote our understanding of factors affecting their recruitment and abundance. Taxonomic identification of 23,802 bone remains and 13,539 scales of fish from 30 archaeological sites along Volga River revealed that they belonged to 41 different fish species. These data allow for retrospective comparisons and highlight the potential of archaeozoology in conservation biology. Sturgeons and salmonids are vulnerable to the impacts of fishery and climatic change. The sharp decline in the numbers of Starry sturgeon (Acipenser stellatus), Caspian trout (Salmo caspius), and Caspian Inconnu (Stenodus leucichthys) from the Volga in the 17th and 18th centuries was likely related to a cooling period (“Little Ice Age”). At present, the population numbers of all anadromous sturgeons and salmonids of the Volga River are critically low. In the Volga basin over the past two millennia, the sterlet (Acipenser ruthenus) has had a very large population number, high genetic (haplogroups and haplotypes) diversity, and large body sizes. Genetic analysis (aDNA and eDNA) have great potential to expand the knowledge of fish populations along large rivers and to improve long-term biomonitoring. Therefore, analyses of historical data, conventional surveys, as well as the inclusion of genetic approaches complement each other in the development of effective conservation strategies.

To what extent can we predict variation of bryophyte and tracheophyte community composition at fine spatial scale along an elevation gradient?

Mounting evidence points to the need for high-resolution climatic data in biodiversity analyses under global change. As we move to finer resolution, other factors than climate, including other abiotic variables and biotic interactions play, however, an increasing role, raising the question of our ability to predict community composition at fine scales. Focusing on two lineages of land plants, bryophytes and tracheophytes, we determine the relative contribution of climatic, non-climatic environmental drivers, spatial effects, community architecture and composition of one lineage to predict community composition of the other lineage, and how our ability to predict community composition varies along an elevation gradient. The relationship between community composition of one lineage and 68 environmental variables at 2-25 m spatial resolution, architecture and composition of the other lineage, and spatial factors, was investigated by hierarchical and variance partitioning across 413 2x2m plots in the Swiss Alps. Climatic data, although significant, contributed less to the model than any other variable considered. Community composition of one lineage, reflecting both direct interactions and unmeasured (hidden) abiotic factors, was the best predictor of community composition of the other lineage. Total explained variance substantially varied with elevation, underlining the fact that the strength of the species composition-environment relationship varies depending on environmental conditions. Total variance explained increased towards high elevation up to 50 %, with an increasing importance of spatial effects and vegetation architecture, pointing to increasing positive interactions and aggregated species distribution patterns in alpine environments. In tracheophytes, an increase of the contribution of non-climatic environmental factors was also observed at high elevation, in line with the hypothesis of a stronger environmental control under harsher conditions. Further improvements of our ability to predict changes in plant community composition may involve the implementation of historical variables and higher-resolution climatic data to better describe the microhabitat conditions actually experienced by organisms.

Wild Animals in Captivity: An Analysis of Parasite Biodiversity and Transmission among Animals at Two Zoological Institutions with Different Typologies.

We have conducted a 10-year-long coprological study of the animals housed in two zoological institutions (ZooAquarium and Faunia, Madrid, Spain) to assess the parasite biodiversity, prevalence, and their relation with host class, diet, and enclosure type (soil type and level of isolation from wild fauna). A total of 4476 faecal samples from 132 mammal species and 951 samples from 86 avian species were examined. The results indicated that only 12.8% of avian species had parasites at least once during the study period, whereas 62.1% of mammal species tested positive. Predominantly, protists (Entamoeba, flagellates, and ciliates) and nematodes (mainly Trichuris) were identified in the findings. Carnivorous species were primarily infected by nematodes, while herbivorous and omnivorous species were mainly infected by protists. The number of infected herbivorous and omnivorous species was significantly greater than carnivorous species. Differences were observed based on soil type (artificial, natural, mixed) and isolation level (isolated/accessible), but these differences were not statistically significant. Several parasites (Entamoeba spp., Giardia spp., Balantidoides coli, Trichuris spp.) could potentially be transmitted between humans and some mammals and birds. Regular animal analyses and a personnel health program in the institutions would minimise transmission risks between zoo animals, wildlife, and humans.

Comparative analysis of soil nematode biodiversity from five different fruit orchards in Osmaneli district, Bilecik, Türkiye.

Nematode population densities were determined in 60 soil and root samples collected from 6 fruit orchards in the Bilecik province (western Turkey), between April 2022 and June 2022. The total number of identified nematodes have reached up to 2418 individuals (number of female: 1036; male: 154; and juvenile: 1228). They belong to 54 species, 54 genera, 33 families and 11 orders. Plant parasitic nematodes that were detected mostly are listed as follows: Helicotylenchus (6,12 %), Pratylenchus (5,74 %), Paratylenchus (4.83 %), Xiphinema (3,06 %), Tylenchorhynchus (2,19 %), Malenchus (1.94 %) and Tylenchus (1.19 %). According to the maturity index analysis, mean values showed the highest maturity level at peach trees (MI value: 3,52), followed by; walnut trees (MI value: 2.49), cherry trees (MI value: 2.15), nectarine trees (MI value: 1.86), plum trees (MI value: 1.57), and olive trees (MI value: 1.42). Mostly the diverse group in terms of species richness was within the order Dorylaimida. The nematodes associated with peach and walnut trees here showed the most stable environments in terms of soil nematode community structure.

DECIPHERING THE DEEP: MACHINE LEARNING APPROACHES TO UNDERSTANDING OCEANIC ECOSYSTEMS

This paper presents a detailed exploration of the transformative role of Machine Learning (ML) in oceanographic research, encapsulating the paradigm shift towards more efficient and comprehensive analysis of marine ecosystems. It delves into the multifaceted applications of ML, ranging from predictive modeling of ocean currents to in-depth biodiversity analysis and deciphering the complexities of deep-sea ecosystems through advanced computer vision techniques. The discussion extends to the challenges and opportunities that intertwine with the integration of AI and ML in oceanography, emphasizing the need for robust data collection, interdisciplinary collaboration, and ethical considerations. Through a series of case studies and thematic discussions, this paper underscores the profound potential of ML to revolutionize our understanding and preservation of oceanic ecosystems, setting a new frontier for future research and conservation strategies in the realm of oceanography.

Particle-attached bacteria act as gatekeepers in the decomposition of complex phytoplankton polysaccharides

BackgroundMarine microalgae (phytoplankton) mediate almost half of the worldwide photosynthetic carbon dioxide fixation and therefore play a pivotal role in global carbon cycling, most prominently during massive phytoplankton blooms. Phytoplankton biomass consists of considerable proportions of polysaccharides, substantial parts of which are rapidly remineralized by heterotrophic bacteria. We analyzed the diversity, activity, and functional potential of such polysaccharide-degrading bacteria in different size fractions during a diverse spring phytoplankton bloom at Helgoland Roads (southern North Sea) at high temporal resolution using microscopic, physicochemical, biodiversity, metagenome, and metaproteome analyses.ResultsProminent active 0.2–3 µm free-living clades comprised Aurantivirga, “Formosa”, Cd. Prosiliicoccus, NS4, NS5, Amylibacter, Planktomarina, SAR11 Ia, SAR92, and SAR86, whereas BD1-7, Stappiaceae, Nitrincolaceae, Methylophagaceae, Sulfitobacter, NS9, Polaribacter, Lentimonas, CL500-3, Algibacter, and Glaciecola dominated 3–10 µm and > 10 µm particles. Particle-attached bacteria were more diverse and exhibited more dynamic adaptive shifts over time in terms of taxonomic composition and repertoires of encoded polysaccharide-targeting enzymes. In total, 305 species-level metagenome-assembled genomes were obtained, including 152 particle-attached bacteria, 100 of which were novel for the sampling site with 76 representing new species. Compared to free-living bacteria, they featured on average larger metagenome-assembled genomes with higher proportions of polysaccharide utilization loci. The latter were predicted to target a broader spectrum of polysaccharide substrates, ranging from readily soluble, simple structured storage polysaccharides (e.g., laminarin, α-glucans) to less soluble, complex structural, or secreted polysaccharides (e.g., xylans, cellulose, pectins). In particular, the potential to target poorly soluble or complex polysaccharides was more widespread among abundant and active particle-attached bacteria.ConclusionsParticle-attached bacteria represented only 1% of all bloom-associated bacteria, yet our data suggest that many abundant active clades played a pivotal gatekeeping role in the solubilization and subsequent degradation of numerous important classes of algal glycans. The high diversity of polysaccharide niches among the most active particle-attached clades therefore is a determining factor for the proportion of algal polysaccharides that can be rapidly remineralized during generally short-lived phytoplankton bloom events.9xVFsdNaK4F9f5nWnV_ZPhVideo

The Raw Material Challenge of Creating a Green Economy

Clean technologies and infrastructure for our low-carbon, green future carry intense mineral demands. The ambition remains to recycle and reuse as much as we can; however, newly mined resources will be required in the near term despite the massive improvements in the reuse and recycling of existing end-of-use products and wastes. Growth trends suggest that mining will still play a role after 2050 since the demand for metals will increase as the developing world moves toward a per capita usage of materials comparable to that of the developed world. There are sufficient geological resources to deliver the required mineral commodities, but the need to mine must be balanced with the requirement to tackle environmental and social governance issues and to deliver sustainable development goals, ensuring that outcomes are beneficial for both the people and planet. Currently, the lead time to develop new mines following discovery is around 16 years, and this needs to be reduced. New approaches to designing and evaluating mining projects embracing social, biodiversity, and life cycle analysis aspects are pivotal. New frontiers for supply should include neglected mined wastes with recoverable components and unconventional new deposits. New processing technologies that involve less invasive, lower energy and cleaner methodologies need to be explored, and developing such methodologies will benefit from using nature-based solutions like bioprocessing for both mineral recovery and for developing sustainable landscapes post mining. Part of the new ambition would be to seek opportunities for more regulated mining areas in our own backyard, thinking particularly of old mineral districts of Europe, rather than relying on sources with potentially and less controllable, fragile, and problematic supply chains. The current debate about the potential of mining our deep ocean, as an alternative to terrestrial sources needs to be resolved and based on a broader analysis; we can then make balanced societal choices about the metal and mineral supply from the different sources that will be able to deliver the green economy while providing a net-positive deal for the planet and its people.

Biodiversity and Structural Analysis of Woody Plant Species of Home Gardens in Basona Worana District, North Shoa Zone of Central Ethiopia.

The purpose of this study was to investigate the biodiversity and structure of woody plants of HGs in the Basona Worana district (BWD). For this, six kebeles and 138 HGs from three agroecologies of BWD were selected using various sampling techniques. The plot size used per HG was 400 m2. Diversity and important value indices (IVIs) were computed. For their structural analysis, the diameter at breast height (DBH) and height were also measured for trees and shrubs fulfilling the standards (diameters at breast height (DBH) ≥2.5 cm and height >2 m). Thus, a total of 42 woody species belonging to 37 genera and 26 families were identified. Fabaceae was the most abundant family, followed by Euphorbiaceae. Trees were the dominant habit. The richness in Kola HGs (33) was higher than Dega (14) and Woinadega (19) HGs, with an overall mean richness of 4.14 per HG of BWD. The Shannon, Simpson, and evenness indices for HGs in the district were 1.05, 0.55, and 0.75, respectively, showing their moderate diversity with even distribution. The Sorenson's similarity of HGs between Dega and Woinadega, Kola and Dega, and Kola and Woinadega were 40%, 28%, and 32%, respectively. The multiple site similarities of woody species among three ecological zones (36%) were still <50%, showing no similarity. The overall DBH and height classes' patterns of the woody species individuals abruptly decreased towards their highest classes. Similar DBH and height classes' patterns of woody species individuals were also observed in Kola and Woinadega agroecologies, indicating their selective tree cutting. The mean values of DBH, height, basal, and crown areas of the woody species in the district were ∼14 cm, ∼6 m, ∼29 m2·ha-1, and 5 m2, respectively. Based on IVI, most of the HGs were dominated by Eucalyptus globules (93.35), followed by Eucalyptus camaldulensis (45.04), Rhamnus prinoides (22.4), and Cupressus lusitanica (22.33). Hence, actions should be taken to promote the diversity and managing of HGs' woody species of BWD.

Assessing adequacy of citizen science datasets for biodiversity monitoring.

Tracking the state of biodiversity over time is critical to successful conservation, but conventional monitoring schemes tend to be insufficient to adequately quantify how species' abundances and distributions are changing. One solution to this issue is to leverage data generated by citizen scientists, who collect vast quantities of data at temporal and spatial scales that cannot be matched by most traditional monitoring methods. However, the quality of citizen science data can vary greatly. In this paper, we develop three metrics (inventory completeness, range completeness, spatial bias) to assess the adequacy of spatial observation data. We explore the adequacy of citizen science data at the species level for Australia's terrestrial native birds and then model these metrics against a suite of seven species traits (threat status, taxonomic uniqueness, body mass, average count, range size, species density, and human population density) to identify predictors of data adequacy. We find that citizen science data adequacy for Australian birds is increasing across two of our metrics (inventory completeness and range completeness), but not spatial bias, which has worsened over time. Relationships between the three metrics and seven traits we modelled were variable, with only two traits having consistently significant relationships across the three metrics. Our results suggest that although citizen science data adequacy has generally increased over time, there are still gaps in the spatial adequacy of citizen science for monitoring many Australian birds. Despite these gaps, citizen science can play an important role in biodiversity monitoring by providing valuable baseline data that may be supplemented by information collected through other methods. We believe the metrics presented here constitute an easily applied approach to assessing the utility of citizen science datasets for biodiversity analyses, allowing researchers to identify and prioritise regions or species with lower data adequacy that will benefit most from targeted monitoring efforts.

Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet

The rapid increase in human activities is causing significant damage to our planet's ecosystems, necessitating innovative solutions to preserve biodiversity and counteract ecological threats. Artificial Intelligence (AI) has emerged as a transformative force, providing unparalleled capabilities for environmental monitoring and conservation. This research paper explores the applications of AI in ecosystem management, including wildlife tracking, habitat assessment, biodiversity analysis, and natural disaster prediction. AI's role in environmental monitoring and conservation includes wildlife tracking, habitat assessment, resource conservation, biodiversity analysis, and species identification. AI algorithms analyze camera trap footage, drone imagery, and GPS data to identify and estimate population sizes, leading to improved anti-poaching efforts and enhanced protection of diverse species. Habitat assessment and resource conservation involve AI-powered image analysis, which aids in assessing forest health, detecting deforestation, and identifying areas in need of restoration. Biodiversity analysis and species identification are achieved through AI algorithms that analyze acoustic recordings, environmental DNA (eDNA), and camera trap footage. These innovations identify different species, assess biodiversity levels, and even discover new or endangered species. AI-powered flood prediction systems provide early warnings, empowering communities with better preparedness and evacuation efforts. Challenges, such as data quality and availability, algorithmic bias, and infrastructure limitations, are acknowledged as opportunities for growth and improvement. In policy and regulation, the paper advocates for clear frameworks prioritizing data privacy and security, algorithmic transparency, and equitable access. Responsible development and ethical use of AI are emphasized as foundational pillars, ensuring that the integration of AI into environmental conservation aligns with principles of fairness, transparency, and societal benefit.

Using remote-sensing and citizen-science data to assess urban biodiversity for sustainable cityscapes: the case study of Athens, Greece

ContextUrban biodiversity is an important and growing research area as cities continue to expand and human populations concentrate in urban centers. In order to effectively conserve urban biodiversity and inform future urban planning, a thorough understanding of the patterns and underlying factors affecting biodiversity is essential. However, a methodology of assessing urban biodiversity that would be replicable to different cities has been challenging, primarily due to data limitation on habitats and species in urban areas.ObjectivesIn response to these challenges, this work implements a biodiversity analysis framework, adapted for the municipality of Athens, Greece, a city that is facing its own unique challenges in preserving biodiversity while accommodating urban growth. The analysis granularity is at the zipcode level.MethodsA k-means clustering scheme that leads to theclassification of urban habitats is incorporated using earth observation data, while citizen science-generated species occurrence data from the Global Biodiversity Information Facility (GBIF) platform is used for biodiversity analysis and validation. This integrative approach allows us to account for fine-scale environmental variation, which plays a crucial role in species' abundances and distributions in urban settings.ResultsOur analysis shows that the fact that almost 80% of the Athens land cover is impervious and lacks vegetation has serious effects on biodiversity in the city, which is very limited and is only abundant in Urban Green areas. The city’s dense urban fabric, followed by intense cementification, lack of open streams and noisy large arterial roads take a toll on urban species occurrences, which are up to 100 times less than the green and blue areas.ConclusionsThis work highlights the significance of zipcode-level analyses in understanding the complex interplay between urbanization and biodiversity. Furthermore, it shows that it is possible to assess urban biodiversity using free and open satellite data, without previously acquiring a high-resolution species occurrence dataset through field surveys, thus providing a comprehensive understanding of urban biodiversity patterns in cities like Athens.

Recent Developments and Challenges in Projecting the Impact of Crop Productivity Growth on Biodiversity Considering Market-Mediated Effects.

The effect of an increase in crop productivity (output per unit of inputs) on biodiversity is hitherto poorly understood. This is because increased productivity of a crop in particular regions leads to increased profit that can encourage expansion of its cultivated area causing land use change and ultimately biodiversity loss, a phenomenon also known as "Jevons paradox" or the "rebound effect". Modeling such consequences in an interconnected and globalized world considering such rebound effects is challenging. Here, we discuss the use of computable general equilibrium (CGE) and other economic models in combination with ecological models to project consequences of crop productivity improvements for biodiversity globally. While these economic models have the advantage of taking into account market-mediated responses, resource constraints, endogenous price responses, and dynamic bilateral patterns of trade, there remain a number of important research and data gaps in these models which must be addressed to improve their performance in assessment of the link between local crop productivity changes and global biodiversity. To this end, we call for breaking the silos and building interdisciplinary networks across the globe to facilitate data sharing and knowledge exchange in order to improve global-to-local-to-global analysis of land, biodiversity, and ecosystem sustainability.

Screening and biodiversity analysis of cultivable inorganic phosphate-solubilizing bacteria in the rhizosphere of Hydrilla verticillata.

The inorganic phosphate-solubilizing bacteria (IPB) in the rhizosphere of Hydrilla verticillata can convert insoluble inorganic phosphorus in the environment into soluble phosphorus that can be directly absorbed and utilized by Hydrilla verticillata. In this research, the roots and rhizosphere sediments of Hydrilla verticillata were collected from high-organic matter urban landscape water. The National Botanical Research Institute's Phosphate growth medium (NBRIP medium) was used to screen for efficient cultivable IPB. The 16S rRNA gene sequence analysis was used to determine the taxonomic affiliation of the strains, and ammonium molybdate spectrophotometry was used to detect the phosphate-solubilizing ability of the strains. The results show that a total of 28 IPB strains with good phosphate-solubilizing effect are obtained from the roots and rhizosphere sediments of Hydrilla verticillata. These IPB strains belong to two phyla, four orders, seven classes, nine families, and nine genera. Among these, Bacillus and Acinetobacter are the dominant genera, and the strains SWIH-7, SWIP-6, SWIP-7, SWIP-13, SWIP-15 and SWIP-16 are potential new species. The IPB strains isolated and screened in this research are rich in diversity, with potential new species and stable phosphate-solubilizing characteristic. These IPB strains are suitable for further development as microbial bacterial agents, which can be applied to promote the recovery of submerged plants in polluted water with high-organic matter, treatment of polluted water and ecological restoration of water.

Exploring biodiversity patterns at the landscape scale by linking landscape energy and land use/land cover heterogeneity

Agricultural Biodiversity dynamics has been evaluated by social metabolism or by landscape structure-function analysis. In this study, by using ELIA modeling, we used both methods in combination to understand how the interplay between social metabolism and landscape structure-function can affect biodiversity pattern distribution. We used energy reinvestment (E) as an indicator of social metabolism and landscape heterogeneity (Le) as an indicator of landscape structure-function. We propose a research hypothesis to analyze biodiversity patterns considering four different clusters identified based on high or low E or Le. As cluster 1, we defined E as high and Le as low and associated natural ecosystems to it. These ecosystems are expected to contain high species abundance but low richness. As cluster 2, both E and Le were defined as high and semi-natural ecosystems were associated to it, where nature friendly farm system developed. In these ecosystems, high species abundance and richness are expected. Cluster 3 with low E and Le was associated intensive farmland, which is due to the simplification of the landscape. Here, low energy reinvestment and landscape heterogeneity confirm that ecosystem services related to biodiversity have been drastically reduced. Lastly, cluster 4 with low E but high Le refers to intensive mosaics of farmland and pasture. In this cluster, the biodiversity richness index is high due to spatial landscape diversity, but the biodiversity abundance index is low due to the lack of energy reinvestment. We evaluate the proposed hypothesis for biodiversity analysis in the Qazvin province, emphasizing the interplay between energy availability and landscape heterogeneity in shaping ecological communities. This study highlights the importance of understanding biodiversity patterns at spatial scale and emphasizes the need for interdisciplinary research to address conservation and sustainability challenges. Our approach would be very useful where there is lack of biodiversity data.

Mantodea (Insecta) in the Invertebrate Collection of Instituto Nacional de Pesquisas da Amaznia (INPA), Manaus, Brazil.

Increasingly, scientific collections play a fundamental role in the conservation and analysis of biodiversity as a witness and repository of ecosystem organisms at risk of losing their species. The Invertebrate Collection of the Instituto Nacional de Pesquisas da Amaznia (INPA) safeguards biological information, mainly from the Amazon biome, one of the most diverse and threatened ecosystems on the planet. Mantodea specimens deposited dry in the INPA Collection were reviewed. A total of 2,721 specimens were found deposited until the year 2019. A total of 194 species and 81 genera were found with representatives of all the neotropical and several "Old-World" families. Families such as Mantidae, Photinaidae, and Thespidae represented the highest percentages of specimens collected within the sample, with 30%, 11%, and 9%, respectively. Four genera and fourteen species are recorded for the first time for Brazil, bringing the number of species to 247. This officially makes Brazil the most diverse country in terms of Mantodea fauna on the planet. The Collection stands out for being the most diverse reported in Latin America, even with several species yet to be described. Presently it has seven primary type specimens and 26 secondary type specimens. The excellent state of conservation of the specimens, the number of specimens, and the diversity represented have made the INPA Collection an international benchmark for Amazonian and Neotropical studies on Mantodea.

Yeast Biodiversity of Karst Waters: Interest of Four Culture Media and an Improved MALDI-TOF MS Database

Karst aquifers are a significant source of drinking water and highly vulnerable to pollution and microbial contamination. Microbiological regulations for the quality of drinking water mostly focus on bacterial levels and lack guidance concerning fungal contamination. Moreover, there is no standardised microbial analysis methodology for identifying fungi in water. Our main objective was to establish the most effective culture and identification methodology to examine yeast diversity in karst waters. We assessed the comparative efficacy of four culture media (CHROMagar Candida, dichloran glycerol 18% [DG18], dichloran rose Bengal chloramphenicol [DRBC], and SYMPHONY agar) for yeast isolation from karst water samples. Furthermore, we investigated the comprehensiveness of databases used in MALDI-TOF mass spectrometry (MALDI-TOF MS) for identifying environmental yeast species. In total, we analysed 162 water samples, allowing the identification of 2479 yeast isolates. We demonstrate that a combination of four culture media, each with distinct specifications, more efficiently covers a wide range of yeast species in karst water than a combination of only two or three. Supplementation of a MALDI-TOF MS database is also critical for analysing environmental microbial samples and improved the identification of yeast biodiversity. This study is an initial step towards standardising the analysis of fungal biodiversity in karst waters, enabling a better understanding of the significance of this environmental reservoir in relation to public health.

RFLP-inator: interactive web platform for in silico simulation and complementary tools of the PCR-RFLP technique.

Polymerase chain reaction - Restriction Fragment Length Polymorphism (PCR-RFLP) is an established molecular biology technique leveraging DNA sequence variability for organism identification, genetic disease detection, biodiversity analysis, etc. Traditional PCR-RFLP requires wet-laboratory procedures that can result in technical errors, procedural challenges, and financial costs. With the aim of providing an accessible and efficient PCR-RFLP technique complement, we introduce RFLP-inator. This is a comprehensive web-based platform developed in R using the package Shiny, which simulates the PCR-RFLP technique, integrates analysis capabilities, and offers complementary tools for both pre- and post-evaluation of in vitro results. We developed the RFLP-inator's algorithm independently and our platform offers seven dynamic tools: RFLP simulator, Pattern identifier, Enzyme selector, RFLP analyzer, Multiplex PCR, Restriction map maker, and Gel plotter. Moreover, the software includes a restriction pattern database of more than 250,000 sequences of the bacterial 16S rRNA gene. We successfully validated the core tools against published research findings. This new platform is open access and user-friendly, offering a valuable resource for researchers, educators, and students specializing in molecular genetics. RFLP-inator not only streamlines RFLP technique application but also supports pedagogical efforts in genetics, illustrating its utility and reliability. The software is available for free at https://kodebio.shinyapps.io/RFLP-inator/.

Natural tea polyphenol functionalized graphene anode for simultaneous power production and degradation of methyl orange dye in microbial fuel cells.

The microbial fuel cell (MFCs) has dual functions, capable of achieving dye decolorization and synchronous power generation. Despite these advantages, the MFCs have faced challenges related to low electron transfer efficiencies and limited dye treatment capacity in wastewater applications. This work introduces an innovative approach by employing reduced graphene oxide-modified carbon cloth (TP-RGO@CC) anodes, utilizing tea polyphenols as the reducing agent. This modification significantly enhances the hydrophilicity and biocompatibility of the anodes. The MFC equipped with the TP-RGO@CC anode demonstrated a remarkable increase in the maximum power density, reaching 773.9 mW m-2, representing a 22% improvement over the plain carbon cloth electrode. The decolorization rate of methyl orange (50 mg L-1, pH 7) reached 99% within 48 h. Biodiversity analysis revealed that the TP-RGO@CC anode selectively enriched electrogens producing and organic matter-degrading bacteria, promoting a dual mechanism of dye decolorization, degradation, and simultaneous electro-production at the anode. This work highlights advanced anode materials that excel in effective pollutant removal, energy conversion, and biomass reuse.