Biodiversity Estimates Research Articles

Ecosystem service indicators on military-managed drylands in the Western United States.

Lands devoted to military use are globally important for the production of ecosystem services and for the conservation of biodiversity. The United States has one of the largest military land estates in the world, and most of these areas occur in water-limited landscapes. Despite many of these areas receiving intense or sustained disturbance from military training activities, the structure and function of ecosystems contained within their boundaries continue to provide critical benefits to people across spatial scales. The land owned and managed by the Department of Defense is subject to regulation across local, state, and federal governing bodies, constraining and shaping both how land management is conducted and how ecosystem services are prioritized. Here, we explored the supply of ecosystem services from military lands in dryland areas of the United States using key indicators of ecosystem services: biodiversity estimates derived from range maps, ecosystem productivity estimates from satellite observations, and spatially explicit, hierarchical ecosystem classifications. Additionally, we utilized content analysis of the environmental management plans of these areas to describe the unique set of demands and regulatory constraints on these areas. We found that the US military land estate in drylands contains many types of ecosystems and provides a large and diverse supply of ecosystem services, comparable to the sum of services from public lands in these areas. Additionally, the degree to which the ecosystem services concept is captured in environmental management plans is strongly shaped by the language of the governing legislation that mandated the use of environmental management plans in these areas, although these plans do not explicitly address land management using the concept of ecosystem services. Collectively, our findings suggest that military use and management of land represents an important source of ecosystem services, that military land use can be considered a cultural ecosystem service unto itself, and that top-down regulation can affect how these services are identified and valued. Our work highlights the need for the research and conservation communities to quantify ecosystem services from individual military installations so that both services and biodiversity can be safeguarded in an era of military conflict across the globe.

Cryptic diversity patterns of subterranean estuaries.

Subterranean estuaries are coastal ecosystems characterized by vertically stratified groundwater. The biota within these ecosystems is relatively understudied due to the inherent difficulty of accessing such extreme environments. The fauna inhabiting these ecosystems is considered vulnerable to extinction, and the presence of cryptic species has major implications for research and conservation efforts. Most species lack molecular data; however, the evaluation of genetic data for some taxa has revealed that undocumented species are common. This study employs molecular species delimitation methods and DNA barcoding through the analysis of publicly and newly generated sequences, including individuals from type localities and non-crustacean phyla; the latter are typically overlooked in biodiversity assessments of subterranean estuaries. We analysed 376 cytochrome c oxidase subunit I (COI) gene sequences and 154 16S rRNA gene sequences. The COI sequences represented 32% of previously described species and 50% of stygobiont species from the Yucatan Peninsula and Cozumel Island, while sequences of the 16S rRNA represented 14% of described species and 22% of stygobionts. Our results revealed cryptic genetic lineages and taxonomic misidentification of species. As several species from these ecosystems are recognized as endangered, the use of molecular approaches will improve biodiversity estimates and highlight overlooked cryptic lineages in need of evaluation of conservation status.

Impacts of counting protocols for light microscopy on estimates of biodiversity and algal density of phytoplankton

AbstractKnowledge on the biodiversity and abundance of phytoplankton is key for many ecological and societal (e.g., blue growth) questions. Gathering temporal variation and spatial patterns on key indicators requires reliable and standardized protocols on sampling, species identification and counting. Numerous methods are used but consequences for comparing the biodiversity and abundance of phytoplankton of these different techniques are not well known. We evaluated the consequences of different counting protocols using light microscopy (i.e., subsampling transects or wedges within counting chambers) for these indices using samples collected weekly to bi‐weekly (n = 398, 2009–2018) from the Wadden Sea (southern North Sea). Phytoplankton cells were counted (by one person under similar conditions) in a fixed number of viewing fields (58, 70, and 29) at three respective magnifications (10 × 100, 10 × 40, and 10 × 10). Patterns in the spatial distribution of phytoplankton cells varied among species and clustering of cells occurred in more than one‐fifth of the samples. This will induce error in the conversion from counts (per viewing field) to abundance (cells mL−1). Our present effort resulted in a high accuracy (95%) in overall cell abundances. This was not the case for species richness, for example, capturing 90% of all species present in the sample would require an almost threefold increase in effort for the 10 × 40 and 10 × 10 magnifications. We recommend that counting methods be tailored to the main research objectives and that counting protocols should quantify uncertainty as well as potential bias to provide an estimation of the error in phytoplankton abundance and species composition.

Gaps in legislation and communication identified as stakeholders reflect on 30×30 policy in Icelandic waters

Other Effective Conservation Measures (OECMs) recognize that various forms of management actions can contribute to biodiversity conservation. OECMs have been criticized for ambiguity, lack of criteria for evaluation and, thereby, inconsistent implementation. Nevertheless, many coastal states aim to evaluate current management actions as candidates for OECMs and count them toward numerical biodiversity conservation goals. Successful implementation requires careful examination of biodiversity benefits and estimation of the social impacts of such actions. There are numerous fisheries restricted areas within the Icelandic Exclusive Economic Zone, some with designated biodiversity conservation goals, whereas the Icelandic Act of Nature Conservation (60/2913) has rarely been used in marine waters. In the current study, complementary methods are used to broadly examine stakeholder views on the efficiency of current legislation, and future policy, to achieve numerical goals on biodiversity conservation. Despite documenting broad willingness of stakeholders to protect ocean space, gaps in legislation and communication are identified both through survey responses and in interviews with key informants. As a first study to document stakeholder perceptions on using fisheries legislation for the purpose of biodiversity conservation, the results have relevance for future policy on reporting fisheries legislation as OECMs, understanding stakeholder perceptions on different actions and, finally, to inform policy on stakeholder involvement, and outreach campaigns, as governments move forward to meet marine conservation goals.

Plant Species Classification and Biodiversity Estimation from UAV Images with Deep Learning

Biodiversity is a characteristic of ecosystems that plays a crucial role in the study of their evolution, and to estimate it, the species of all plants need to be determined. In this study, we used Unmanned Aerial Vehicles to gather RGB images of mid-to-high-altitude ecosystems in the Zao mountains (Japan). All the data-collection missions took place in autumn so the plants present distinctive seasonal coloration. Patches from single trees and bushes were manually extracted from the collected orthomosaics. Subsequently, Deep Learning image-classification networks were used to automatically determine the species of each tree or bush and estimate biodiversity. Both Convolutional Neural Networks (CNNs) and Transformer-based models were considered (ResNet, RegNet, ConvNeXt, and SwinTransformer). To measure and estimate biodiversity, we relied on the Gini–Simpson Index, the Shannon–Wiener Index, and Species Richness. We present two separate scenarios for evaluating the readiness of the technology for practical use: the first scenario uses a subset of the data with five species and a testing set that has a very similar percentage of each species to those present in the training set. The models studied reach very high performances with over 99 Accuracy and 98 F1 Score (the harmonic mean of Precision and Recall) for image classification and biodiversity estimates under 1% error. The second scenario uses the full dataset with nine species and large variations in class balance between the training and testing datasets, which is often the case in practical use situations. The results in this case remained fairly high for Accuracy at 90.64% but dropped to 51.77% for F1 Score. The relatively low F1 Score value is partly due to a small number of misclassifications having a disproportionate impact in the final measure, but still, the large difference between the Accuracy and F1 Score highlights the complexity of finely evaluating the classification results of Deep Learning Networks. Even in this very challenging scenario, the biodiversity estimation remained with relatively small (6–14%) errors for the most detailed indices, showcasing the readiness of the technology for practical use.

Addressing multiple facets of bias and uncertainty in continental scale biodiversity databases

The availability of biodiversity databases is expanding at unprecedented rates. Nevertheless, species occurrence data can be intrinsically biased and contain uncertainties that impact the accuracy and reliability of biodiversity estimates. In this study, we developed a reproducible framework to assess three dimensions of bias—taxonomic, spatial, and temporal—as well as temporal uncertainty associated with data collections. We utilized the vegetation plot data located in Europe, from sPlotOpen, an open-access database, as a case study. The metrics proposed for estimating bias include completeness of the species richness for taxonomic bias, Nearest Neighbor Index for spatial bias, and Pielou’s index for temporal bias. Additionally, we introduced a new method based on a negative exponential curve to model the temporal decay in biodiversity data, aiming to quantify temporal uncertainty. Finally, we assessed the sampling bias considering the influence of various spatial variables (i.e, road density, human population count, Natura 2000 network and topographic roughness). We discovered that the facets of bias and the temporal uncertainty varied throughout Europe, as did the different roles played by spatial variables in determining biases. sPlotOpen showed a clustered distribution of the vegetation plots, and an uneven distribution in sampling completeness, year of sampling and temporal uncertainty. The facets of bias were significantly explained mainly by the presence of Natura 2000 network and marginally by the human population count. These results suggest that employing an efficient procedure to examine biases and uncertainties in data collections can enhance data quality and provide more reliable biodiversity estimates.

Performance of DNA metabarcoding, standard barcoding and morphological approaches in the identification of insect biodiversity.

For two decades, DNA barcoding and, more recently, DNA metabarcoding have been used for molecular species identification and estimating biodiversity. Despite their growing use, few studies have systematically evaluated these methods. This study aims to evaluate the efficacy of barcoding methods in identifying species and estimating biodiversity, by assessing their consistency with traditional morphological identification and evaluating how assignment consistency is influenced by taxonomic group, sequence similarity thresholds and geographic distance. We first analysed 951 insect specimens across three taxonomic groups: butterflies, bumblebees and parasitic wasps, using both morphological taxonomy and single-specimen COI DNA barcoding. An additional 25,047 butterfly specimens were identified by COI DNA metabarcoding. Finally, we performed a systematic review of 99 studies to assess average consistency between insect species identity assigned via morphology and COI barcoding and to examine the distribution of research effort. Species assignment consistency was influenced by taxonomic group, sequence similarity thresholds and geographic distance. An average assignment consistency of 49% was found across taxonomic groups, with parasitic wasps displaying lower consistency due to taxonomic impediment. The number of missing matches doubled with a 100% sequence similarity threshold and COI intraspecific variation increased with geographic distance. Metabarcoding results aligned well with morphological biodiversity estimates and a strong positive correlation between sequence reads and species abundance was found. The systematic review revealed an 89% average consistency and also indicated taxonomic and geographic biases in research effort. Together, our findings demonstrate that while problems persist, barcoding approaches offer robust alternatives to traditional taxonomy for biodiversity assessment.

Shallow coverage in shallow waters: the incompleteness of intertidal species inventories in biodiversity database records

The availability of online biodiversity data has increased in recent decades, aiding our understanding of diversity patterns and species richness–environment relationships across temporal and spatial scales. However, even the most exhaustive databases are prone to sampling biases, which create knowledge gaps in species distributions and increase uncertainty in model predictions. Regarding marine environments, intertidal zones are globally distributed and considered early warning systems for climate change impacts and species' range shifts. Owing to their relative accessibility, intertidal records should – supposedly – be less incomplete and biased compared to open‐ocean and deep‐sea areas. Yet, the extent and coverage of intertidal records available in global biodiversity databases remains unknown. In this study, we used a high‐resolution worldwide tidal flat map to identify intertidal records of 11 563 benthic species from the OBIS (Ocean Biodiversity Information System) portal. Following a thorough data‐cleaning process, we evaluated geographic patterns in observed species richness, site accessibility, sampling effort, and inventory completeness across latitudes. We demonstrate that observed species richness has mid‐latitudinal peaks while the tropics accumulate species with missing records, similar to patterns described for the entire marine realm. These patterns correlate with disproportionate mid‐latitude sampling efforts and poor tropical sampling coverage. Sixty‐five percent of the mapped intertidal sites are located within 3 hours of a city, but sampling records remain almost absent along African Atlantic, South American Pacific, and Indo‐Pacific coasts. Thus, even for the accessible and well‐studied intertidal shorelines, database records are not free from geographical biases and their associated implications for biodiversity estimates. Our results highlight the need for a better data‐sharing culture, and we hope to encourage initiatives promoting more and better‐distributed research efforts on intertidal biodiversity, which could improve global scale detection and prediction of climate change impacts at regional and global scales.

Three-dimensional conservation planning of fish biodiversity metrics to achieve the deep-sea 30×30 conservation target.

Accelerating rate of human impact and environmental change severely affects marine biodiversity and increases the urgency to implement the Convention on Biological Diversity (CBD) 30×30 plan for conserving 30% of sea areas by 2030. However, area-based conservation targets are complex to identify in a 3-dimensional (3D) ocean where deep-sea features such as seamounts have been seldom studied mostly due to challenging methodologies to implement at great depths. Yet, the use of emerging technologies, such as environmental DNA combined with modern modeling frameworks, could help address the problem. We collected environmental DNA, echosounder acoustic, and video data at 15 seamounts and deep island slopes across the Coral Sea. We modeled 7 fish community metrics and the abundances of 45 individual species and molecular operational taxonomic units (MOTUs) in benthic and pelagic waters (down to 600-m deep) with boosted regression trees and generalized joint attribute models to describe biodiversity on seamounts and deep slopes and identify 3D protection solutions for achieving the CBD area target in New Caledonia (1.4millionkm2). We prioritized the identified conservation units in a 3D space, based on various biodiversity targets, to meet the goal of protecting at least 30% of the spatial domain, with a focus on areas with high biodiversity. The relationship between biodiversity protection targets and the spatial area protected by the solution was linear. The scenario protecting 30% of each biodiversity metric preserved almost 30% of the considered spatial domain and accounted for the 3D distribution of biodiversity. Our study paves the way for the use of combined data collection methodologies to improve biodiversity estimates in 3D structured marine environments for the selection of conservation areas and for the use of biodiversity targets to achieve area-based international targets.

Sandy bottoms have limited species richness but substantially contribute to the regional coastal fish β-diversity: A case study of the Central Mediterranean Sea

Global marine biodiversity loss impairs entire ecosystems and their stability. Robust biodiversity estimates are key to inform policies and management strategies, and need to consider the contribution of diverse habitats, including those for which estimates of biodiversity are scattered or totally absent. This study assessed the fish diversity associated with three main coastal habitats (rocky bottoms, Posidonia oceanica meadows, sandy bottoms), and their role in shaping the overall coastal fish diversity, also in relation to potential environmental and anthropogenic drivers affecting patterns of fish diversity in coastal areas. Using underwater visual census, we sampled 62 sites distributed on the three habitats, for a total of 496 replicates. We assessed the contribution of each habitat to β-diversity, divided into Local Contribution to β-diversity (LCBD), a comparative indicator of the contributions to β-diversity of each habitat, and Species Contribution to β-diversity (SCBD), which measures the relative importance of each species in affecting β-diversity. Finally, we modelled species diversity in relation to potential environmental and anthropogenic drivers. Overall, 72 species were recorded, with the highest species richness observed on rocky bottoms (56 species, 16 unique to this habitat), followed by P. oceanica (38 species, 0 unique) and sandy bottoms (32 species, 14 unique). Sandy bottom assemblages had a significantly higher contribution to LCBD than P. oceanica meadows and rocky bottoms, and two of the five species with the highest contribution to SCBD are exclusively associated with sandy bottoms. Finally, sea surface temperature, sea surface salinity, and habitat were highlighted as significant predictors of species richness. Our findings, aside from highlighting the environmental drivers of coastal fish diversity in the Mediterranean Sea, unravel the potential key role of sandy bottoms in contributing to overall coastal fish diversity and can inform conservation planning.

Optimising species detection probability and sampling effort in lake fish eDNA surveys

Environmental DNA (eDNA) metabarcoding is transforming biodiversity monitoring in aquatic environments. Such an approach has been developed and deployed for monitoring lake fish communities in Great Britain, where the method has repeatedly shown a comparable or better performance than conventional approaches. Previous analyses indicated that 20 water samples per lake are sufficient to reliably estimate fish species richness, but it is unclear how reduced eDNA sampling effort affects richness, or other biodiversity estimates and metrics. As the number of samples strongly influences the cost of monitoring programmes, it is essential that sampling effort is optimised for a specific monitoring objective. The aim of this project was to explore the effect of reduced eDNA sampling effort on biodiversity metrics (namely species richness and community composition) using algorithmic and statistical resampling techniques of a data set from 101 lakes, covering a wide spectrum of lake types and ecological quality. The results showed that reliable estimation of lake fish species richness could, in fact, usually be achieved with a much lower number of samples. For example, in almost 90% of lakes, 95% of complete fish richness could be detected with only 10 water samples, regardless of lake area. Similarly, other measures of alpha and beta-diversity were not greatly affected by a reduction in sample size from 20 to 10 samples. We also found that there is no significant difference in detected species richness between shoreline and offshore sampling transects, allowing for simplified field logistics. This could potentially allow the effective sampling of a larger number of lakes within a given monitoring budget. However, rare species were more often missed with fewer samples, with potential implications for monitoring of invasive or endangered species. These results should inform the design of eDNA sampling strategies, so that these can be optimised to achieve specific monitoring goals.

Enhancing metabarcoding of freshwater biotic communities: A new online tool for primer selection and exploring data from 14 primer pairs

AbstractFreshwater ecosystems are complex, diverse, and face multiple imminent threats that have led to changes in both structure and function. It is urgent that we develop and standardize monitoring tools that allow for rapid and comprehensive assessment of freshwater communities to understand their changing dynamics and inform conservation. Environmental DNA surveys offer a means to inventory and monitor aquatic diversity, yet most studies focus on one or a few taxonomic groups because of technical challenges. In this study, we (1) create an eDNA metabarcoding dataset (natural water bodies) with 14 validated primer pairs; (2) create a free online, user‐friendly tool for primer selection that can be used for any metabarcoding data (SNIPe); and (3) using SNIPe, explore our dataset to derive subsets of informative, cost‐effective primer pairs that maximize detection of freshwater diversity. We first evaluated the completeness of public reference sequence databases and the efficiency of 14 primer pairs in silico, in vitro on five mock communities (mix of DNA from tissues of select taxa), in vivo on water samples from aquarium samples with known taxonomic composition, and finally in vivo on water samples from freshwater systems in Eastern Canada. Results from analyses using SNIPe revealed that 13 or 14 primer pairs are necessary to recover 100% of the species in water samples (natural systems), but that four primer pairs are sufficient to recover almost 75% of taxa with little overlap. Our work highlights the power of eDNA metabarcoding for reconstructing freshwater communities, including prey, parasite, pathogen, invasive, and declining species. It also emphasizes the importance of marker choice on species resolution, and primer characteristics and filtering parameters on detection success and accuracy of biodiversity estimates. Together, these results highlight the usefulness of eDNA for freshwater monitoring and should prompt more studies of tools to survey all communities.

Evaluating eDNA and eRNA metabarcoding for aquatic biodiversity assessment: From bacteria to vertebrates

The monitoring and management of aquatic ecosystems depend on precise estimates of biodiversity. Metabarcoding analyses of environmental nucleic acids (eNAs), including environmental DNA (eDNA) and environmental RNA (eRNA), have garnered attention for their cost-effective and non-invasive biomonitoring capabilities. However, the accuracy of biodiversity estimates obtained through eNAs can vary among different organismal groups. Here we evaluate the performance of eDNA and eRNA metabarcoding across nine organismal groups, ranging from bacteria to terrestrial vertebrates, in three cross-sections of the Yangtze River, China. We observe robust complementarity between eDNA and eRNA data. The relative detectability of eNAs was notably influenced by major taxonomic groups and organismal sizes, with eDNA providing more robust signals for larger organisms. Both eDNA and eRNA exhibited similar cross-sectional and longitudinal patterns. However, the detectability of larger organisms declined in eRNA metabarcoding, possibly due to differential RNA release and decay among different organismal groups or sizes. While underscoring the potential of eDNA and eRNA in large river biomonitoring, we emphasize the need for differential interpretation of eDNA versus eRNA data. This highlights the importance of careful method selection and interpretation in biomonitoring studies.

Unraveling community assembly in temperate desert ecosystems: Insights from multi‐seasonal surveys along the expressway

AbstractFor temperate desert ecosystems, understanding the maintenance mechanisms of biodiversity has become a central concern of policymakers, practitioners, and academicians, especially in the context of human disturbances. However, most of the past desert research is based on a single survey and single functional traits or phylogeny, and may lead to incomplete estimates of community assembly. To assess climate factors, soil factors, and human interference in the processes of dispersal assembly, filtering, and limiting similarity in community assembly, we selected 50 sites along the Beijing‐Xinjiang Expressway and conducted plant surveys at sites 0.5, 1.0, and 1.5 km away from the expressway twice, one in spring and one in autumn, and collected 31 functional traits and phylogenetic information of 74 species. We found that incomplete detection can lead to bias in biodiversity estimation due to seasonal surveys. Road disturbance at different distances had no significant effect on species composition and community assembly processes. The trade‐off between dispersal limitation and environmental filtering is the process of maintaining desert plant diversity. Precipitation directly determined the dispersal limitation and environmental filtering. Soil pH mediated the effect of temperature on biotic interaction in the dimension of phylogeny, while soil pH and organic carbon acted as intermediate factors affecting biotic interaction in functional traits. Our findings suggest that climate change, notably precipitation, has a more pronounced impact on desert plant communities than human disturbances. A multifaceted approach considering functional traits and phylogeny across seasons is essential for understanding species coexistence in desert ecosystems.

Seasonal freeze‒thaw processes impact microbial communities of soil aggregates associated with soil pores on the Qinghai–Tibet Plateau

BackgroundSeasonal freeze‒thaw (FT) processes alter soil formation and cause changes in soil microbial communities, which regulate the decomposition of organic matter in alpine ecosystems. Soil aggregates are basic structural units and play a critical role in microbial habitation. However, the impact of seasonal FT processes on the distribution of microbial communities associated with soil pores in different aggregate fractions under climate change has been overlooked. In this study, we sampled soil aggregates from two typical alpine ecosystems (alpine meadow and alpine shrubland) during the seasonal FT processes (UFP: unstable freezing period, SFP: stable frozen period, UTP: unstable thawing period and STP: stable thawed period). The phospholipid fatty acid (PLFA) method was used to determine the biomass of living microbes in different aggregate fractions.ResultsThe microbial biomass of 0.25–2 mm and 0.053–0.25 mm aggregates did not change significantly during the seasonal FT process while the microbial biomass of > 2 mm aggregates presented a significant difference between the STP and UTP. Bacterial communities dominated the microbes in aggregates, accounting for over 80% of the total PLFAs. The microbial communities of soil aggregates in the surface layer were more sensitive to the seasonal FT process than those in other soil layers. In the thawing period, Gram positive bacteria (GP) was more dominant. In the freezing period, the ratio of Gram-positive to Gram-negative bacterial PLFAs (GP/GN) was low because the enrichment of plant litter facilitated the formation of organic matter. In the freezing process, pores of 30–80 μm (mesopores) favored the habitation of fungal and actinobacterial communities while total PLFAs and bacterial PLFAs were negatively correlated with mesopores in the thawing process.ConclusionsThe freezing process caused a greater variability in microbial biomass of different aggregate fractions. The thawing process increased the differences in microbial biomass among soil horizons. Mesopores of aggregates supported the habitation of actinobacterial and fungal communities while they were not conducive to bacterial growth. These findings provide a further comprehension of biodiversity and accurate estimation of global carbon cycle.

DeepDive: estimating global biodiversity patterns through time using deep learning

Understanding how biodiversity has changed through time is a central goal of evolutionary biology. However, estimates of past biodiversity are challenged by the inherent incompleteness of the fossil record, even when state-of-the-art statistical methods are applied to adjust estimates while correcting for sampling biases. Here we develop an approach based on stochastic simulations of biodiversity and a deep learning model to infer richness at global or regional scales through time while incorporating spatial, temporal and taxonomic sampling variation. Our method outperforms alternative approaches across simulated datasets, especially at large spatial scales, providing robust palaeodiversity estimates under a wide range of preservation scenarios. We apply our method on two empirical datasets of different taxonomic and temporal scope: the Permian-Triassic record of marine animals and the Cenozoic evolution of proboscideans. Our estimates provide a revised quantitative assessment of two mass extinctions in the marine record and reveal rapid diversification of proboscideans following their expansion out of Africa and a >70% diversity drop in the Pleistocene.

Substantial unrealised global biodiversity of continental microcrustaceans

Abstract Freshwater ecosystems provide essential services for human well-being, with their conservation success reliant on the precise quantification of biodiversity. Diplostraca (= Cladocera) and Copepoda are diverse groups of microcrustaceans in aquatic ecosystems, important for a multitude of these services. We examined biodiversity trends of these groups, utilizing a comprehensive dataset, approximately 2341 studies, spanning from the early 19th Century to the present day, aiming at predicting the total number of microcrustacean species that will have been described by 2100. Descriptions increased until the 1900s, surged, and then declined after 1975. The predictive models indicate that by the year 2100, an additional 16–68% of cladoceran species and 37–126% of copepod species are likely to be identified, representing a significant portion of currently unrealised biodiversity. Eighteen prolific authors contributed to a quarter of these descriptions, with the rest spread among numerous authorities. Based on our extrapolation, the total number of microcrustacean species could potentially exceed 6114 by 2050 and 9046 by 2100, notwithstanding extinctions. These findings, which point to a significant amount of unrealised biodiversity, underscore the need to refine biodiversity estimates beyond conventional expert opinion. Such accuracy is crucial for addressing the underappreciated scale of the current biodiversity crisis.

Nomenclatural stability and the longevity of helminth species names

Although most Latin binomial names of species are valid, many are eventually unaccepted when they are found to be synonyms of previously described species, or superseded by a new combination when the species they denote are moved to a different genus. What proportion of parasite species names become unaccepted over time, and how long does it take for incorrect names to become unaccepted? Here, we address these questions using a dataset comprising thousands of species names of parasitic helminths from four higher taxa (Acanthocephala, Nematoda, Cestoda, and Trematoda). Overall, among species names proposed in the past two-and-a-half centuries, nearly one-third have since been unaccepted, the most common reason being that they have been superseded by a new combination. A greater proportion of older names (proposed pre-1950) have since been unaccepted compared to names proposed more recently, however most taxonomic acts leading to species names being unaccepted (through either synonymy or reclassification) occurred in the past few decades. Overall, the average longevity of helminth species names that are currently unaccepted was 29 years; although many remained in use for over 100 years, about 50% of the total were invalidated within 20 years of first being proposed. The patterns observed were roughly the same for all four higher helminth taxa considered here. Our results provide a quantitative illustration of the self-correcting nature of parasite taxonomy, and can also help to calibrate future estimates of total parasite biodiversity.

Harnessing Deep Learning for Underwater plastic Trash Identification

Abstract—Machine and deep learning (DL) offer significant opportunities for exploring and monitoring oceans and for tackling important problems ranging from litter and oil spill detection to marine biodiversity estimation. Reasonably priced hardware platforms, in the form of autonomous (AUV) and remote operated (ROV) underwater vehicles, are also becoming available, fuelling the growth of data and offering new types of ap- plication areas. This article presents a research vision for DL in the oceans, collating applications and use cases, identifying opportunities, constraints, and open research challenges. We conduct experiments on underwater marine litter detection to demon- strate the benefits DL can bring to underwater envi- ronments. Our results show that integrating DL in underwater explorations can automate and scale-up monitoring, and highlight practical challenges in enabling underwater operations. This project introduces a refined YOLOv8-based algorithm tailored for the en- hanced detection of small-scale underwater debris, to mitigate the prevalent challenges of high miss and false detection rates . The research presents the YOLOv8 algorithm, which optimizes the backbone, neck layer, and C2f module for underwater characteristics and incorporates an effective attention mech- anism. This algorithm improves the accuracy of underwater trash detection while simplifying the computational model. Empirical evidence underscores the superiority of this method over the other conventional network, manifesting in a significant uplift in detection performance. Notably, the proposed method realized a 63% mean average precision (mAP50), a 60% surge in recall (R). Transcending its foundational utility in marine conservation, this methodology harbors potential for subsequent integra- tion into remote sensing ventures. Such an adaptation could substantially enhance the precision of detection models, particularly in the realm of localized surveillance, thereby broad- ening the scope of its applicability and impact.

Different approaches to estimate benthic metazoan diversity associated with free-living macroalgae (Fucus vesiculosus) on shallow soft sediments

Habitat complexity can boost biodiversity by providing a wide range of niches allowing species co-existence. Baltic Sea benthic communities are characterised by low species diversity. Thus the occurrence of the habitat forming macroalga Fucus vesiculosus may influence benthic communities and promote diversity. Here biodiversity estimates were obtained through conventional and eDNA approaches for the benthic assemblages associated with free-living Fucus and the adjacent bare-sediment habitats at six sites from the Northern Baltic Proper and the Gulf of Finland. Free-living F. vesiculosus habitats are heterogeneous with biodiversity estimates varying considerably among sites. The additional habitat complexity provided by F. vesiculosus tended to improve taxa richness as a result of additional epifauna assemblages, although macroinfaunal taxa richness and abundance was often reduced. Consequently the complex habitats provided by free-living F. vesiculosus often improve biodiversity, yet alters the composition of assemblages in soft sediment habitats and consequential ecosystem functioning. The study emphasised the disparity in biodiversity estimates achieved when employing different biodiversity approaches. Biodiversity estimates were more similar within approaches compared to between habitat types, with each approach detecting exclusive taxa. Consequently, biodiversity estimates can benefit from a multi-approach design where both conventional and eDNA approaches are employed in complement.