Estimates Of Diversity Research Articles

Structural variability of protist assemblages in surface sediments across Italian Mediterranean marine subregions

Marine sediments host heterogeneous protist communities consisting of both living benthic microorganisms and planktonic resting stages. Despite their key functions in marine ecosystem processes and biogeochemical cycles, their structure and dynamics are largely unknown. In the present study, with a spatially intensive sampling design we investigated benthic protist diversity and function of surface sediment samples from three subregions of the Mediterranean Sea, through an environmental DNA metabarcoding approach targeting the 18S V4 region of rRNA gene. Protists were characterized at the taxonomic level and trophic function, both in terms of alpha diversity and community composition, testing for potential differences among marine subregions and bathymetric groups. Overall, Alveolata and Stramenopiles were the two divisions that dominated the communities. These dominant groups exhibited significant differences among the three Mediterranean subregions in the alpha diversity estimates based on the detected ASVs, for all computed indices (ASV richness, Shannon and Simpson indices). Protist communities were also found to be significantly different in terms of composition at the order rank in the three subregions p-value < 0.01). These differences were mainly driven by Anoecales, Peridiniales, Borokales, Paraliales and Gonyaulacales, which together contributed almost 80% of the average dissimilarity. Anoecales was the dominant order in the Ionian – Central Mediterranean and Adriatic Sea, but with considerably different relative abundances (52% and 36%, respectively), while Borokales was the dominant order in the Western Mediterranean Sea (33%). Similarly, significant differences among the three marine subregions were also highlighted when protist assemblages were examined in terms of trophic function, both in terms of alpha diversity (calculated on the ASVs for each trophic group) and community composition p-value < 0.01. In particular, the Adriatic Sea stood out for having the highest relative abundance of autotrophic/mixotrophic components in the surface sediments analyzed. Conversely, no significant differences in protist assemblages were found among depth groups. This study provided new insights into the taxonomic and trophic composition of benthic protist communities found in Mediterranean surface sediments, revealing geographical differences among regional seas. The results were discussed in relation to the Mediterranean environmental features that could generate the differences among benthic protist communities.

POLO: Pose estimation with one-stage location-sensitive coding

The precise monitoring of fish keypoints and behavioral patterns is crucial in fish farming, influencing decisions on feeding schedules and assessing fish health. Traditional approaches to multi-object pose estimation often lean towards either Bottom-up or Top-down methods. Our innovative solution introduces a streamlined single-stage multi-object pose estimation framework, utilizing a unique approach to instance assignment based on location. By incorporating position encoding as an index for candidate pose heatmap groups, we achieve end-to-end multi-object pose estimation with reduced redundancy through non-maximum suppression. Our framework, named POLO, has been validated on a meticulously annotated fish keypoint dataset, demonstrating outstanding performance with a remarkable 65.34% OKS AP at 71.4 FPS on Tesla v100. With its real-time capabilities, POLO is highly adaptable, making it suitable for deployment on various edge computing devices and addressing real-world challenges effectively. We believe our framework can serve as a solid baseline for diverse pose estimation tasks across different domains.

High-precision estimation of plant alpha diversity in different ecosystems based on Sentinel-2 data

At present, the accuracy of remote sensing estimation models of plant alpha diversity is generally low, and high-precision estimation models in deciduous broadleaved forest (DBF), deciduous coniferous forest (DCF) and evergreen coniferous forest (ECF) are still lacking. The main purpose of this study is to construct high-precision remote sensing models for plant alpha diversity in multiple ecosystems at global scale. Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data. NDVI and NDVI based spectral diversity/heterogeneity indices were selected as predictive variables, and alpha diversity indices were selected as response variables. Simple linear regression (SLR), partial linear regression (PLSR), and random forest (RF) models were used to evaluate the predictive ability of the predictive variables against the response variables under six ecosystems (evergreen broadleaved forest (EBF), DBF, ECF, DCF, shrub, and grassland), and to compare the estimated robustness of various spectral diversity indices. In terms of prediction accuracy, the SLR models were the worst, and the PLSR model were average. RF performed best, outperforming most current models. Especially in DBF, ECF, shrub and grassland, the determination coefficient R2 of RF models can be as high as 0.9. In terms of the prediction of α-diversity, the prediction effect of species richness was better than that of Shannon index, Simpson index and Pielou index. The higher the vegetation complexity, the more accurate the assessment of vegetation α-diversity tends to be, especially in DBF, shrub and grassland. According to the importance of predictive variables and model stability evaluation results, NDVI, standard deviation of NDVI (SD), and NDVI derived Shannon’s diversity index (Sha), Cumulative Residual Entropy (CRE), Pielou’s evenness index (Pie), Hill’s numbers (Hill), Berger-Parker’s diversity index (Ber), Parametric Rao’s index of quadratic entropy(paRao) are all powerful indicators for predicting plant alpha diversity. Among them, the prediction performance of NDVI and SD is better. This study is not only an exploration of the practicability of R package “rasterdiv”, but also an attempt to construct high-precision remote sensing estimation models of plant alpha diversity at global scale.

An experimental analysis of outdoor UAV localisation through diverse estimators and crowd-sensed data fusion

Motivated by the challenge of achieving precise 3D outdoor localisation for unmanned aerial vehicles (UAVs) in global navigation satellite system (GNSS)-denied environments, this paper introduces an innovative technique. Integrating crowd-sensed data fusion to counter inertial navigation system (INS) drift during GNSS signal outages, the proposed method exploits diverse estimators to enhance its efficacy. A micro lightweight frequency modulated continuous wave (FMCW) radar mounted on the UAV captures ground scatterer reflections, processed via fast Fourier transform (FFT) to generate a range-Doppler map. This map facilitates forward velocity estimation during GNSS signal loss. This approach employs adaptive thresholding, image binarisation, and connected components-based techniques for target detection from a computer vision standpoint. The derived radar-based velocity fuses with magnetometer, barometer, and inertial measurement unit (IMU) data using diverse estimators like extended Kalman filter (EKF) and particle filter (PF). Real-time flight data evaluation and simulated outage periods using EKF and PF validate the outdoor localisation system. Experimental analyses demonstrate substantial improvements, enhancing 3D positioning accuracy by 99.89% and 99.83% for the initial and subsequent flights, respectively, leveraging PF to fortify the standalone INS mode during GNSS signal loss. This approach significantly enhances UAV localisation precision, particularly in challenging GNSS-denied scenarios, showcasing the potential for real-world applications.

Assessing the impact of heavy metals on bacterial diversity in coastal regions of Southeastern India.

Globally, there is growing concern over the environmental contamination of coastal ecosystems caused by anthropogenic activities. Here,we performed a study to evaluate the degree of heavy metal contamination in 5 different sediment samples collected from five sites along the Southeastern coast of India. Additionally, the research aims to explore the potential ecological implications of heavy metal contamination on the bacterial diversity, a crucial factor in upholding a sustainable ecosystem. A total of seven heavy metals, i.e., chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), mercury (Hg), cadmium (Cd) and arsenic (As), were assessed and quantifiedusing inductively coupled plasma mass spectrometry. Targeted amplicon sequencing revealed that phylum Proteobacteria (36.9%) was the most dominating followed by Halobacterota (25.5%), Actinobacteriota (15%), Firmicutes (6.7%), Bacteroidota (4.0%), Thermoplasmatota (2.3%), Acidobacteriota (2.0%), Chloroflexi (1.6%), Planctomycetota (1.2%) and Crenarchaeota (1.1%). According to the alpha diversity estimate, lesser bacterial diversity was observed in areas with high pollution levels. Moreover, the physicochemical parameters of the sediments were analyzed. The contamination levels of the sediments were evaluated using the geo-accumulation index (Igeo), contamination factor (CF) and pollution loading index (PLI) to ascertain the comprehensive toxicity status of the sediments. TheIgeo values revealed sediment pollution with metals such as Hg and Cd. The sediments obtained from the sampling site PU-01 showed the highest concentration of Hg pollution. Considering the ecotoxicological aspect, theestimated risk index (RI) values indicated a range from low to significant ecological risk.

Tissue determinants of the human T cell receptor repertoire.

98% of T cells reside in tissues, yet nearly all human T cell analyses are performed from peripheral blood. We single-cell sequenced 5.7 million T cells from ten donors' autologous blood and tonsils and sought to answer key questions about T cell receptor biology previously unanswerable by smaller-scale experiments. We identified distinct clonal expansions and distributions in blood compared to tonsils, with surprisingly low (1-7%) clonal sharing. These few shared clones exhibited divergent phenotypes across bodily sites. Analysis of antigen-specific CD8 T cells revealed location as a main determinant of frequency, phenotype, and immunodominance. Finally, diversity estimates from the tissue recalibrates current repertoire diversity estimates, and we provide a refined estimate of whole-body repertoire. Given the tissue-restricted nature of T cell phenotypes, functions, differentiation, and clonality revealed by this dataset, we conclude that tissue analyses are crucial for accurate repertoire analysis and monitoring changes after perturbing therapies.

Classifying Cockroaches According to Forewings: Pitfalls and Implications for Fossil Systematics

A reliable character system is crucial to taxonomy and systematics, and it promises valid downstream inferences, e.g., estimates of diversity and disparity, reconstruction of evolutionary history, and even stratigraphic correlations. Modern taxonomy and systematics of extant cockroaches requires an integrative study involving multiple lines of evidence with emphasis on genital and reproductive characteristics and molecular data. In contrast, many fossil cockroach taxa published recently are based solely on forewings. Many studies have shown that forewing-based taxa are questionable. In order to find out how much of the phylogenetic signal we could ascertain from venational similarity, and how confident we could be, this study used forewing characters to reconstruct phylogenies of the genera of well-recognized family-group taxa. The intuitively reconstructed phylogeny of 75 extant genera failed to recover those taxa or their relationships. Parsimony analyses of various datasets all yielded strong polyphyly and chaotic relationships. In conclusion, the forewing of cockroaches is not a universally competent character system. The underlying causes are the complicated nature of veins and the limitations of current analytical techniques. The uncertainty in forewing-based taxonomy and systematics has been underestimated in the literature. Forewing-based fossil taxa warrant re-evaluation; some of them are herein deemed nomina dubia in their current state.

Bait type affects the diversity assessment of cetoniid beetles in the Brazilian Cerrado

AbstractCetoniidae (Coleoptera), known as flower and fruit beetles, form a diverse group, feed on nectar, pollen, plant exudates, and are highly attracted to fermented fruits. To evaluate their diversity in natural and agricultural areas, traps baited with fermented fruits are used. The objective of this study was to compare the effectiveness of various types of fruit used as bait, with or without the addition of sugarcane juice to accelerate fruit fermentation, on the assessment of Cetoniidae diversity in the Brazilian Cerrado. The study was conducted in two conservation units in Brasília (DF, Brazil). Two experiments were conducted, the first aiming to select the most attractive fruits (banana, pineapple, mango, grape, or cagaita), and the second to verify whether there is interference of sugarcane juice in the fruit fermentation process and, consequently, in the attractiveness to Cetoniidae. The chemical profiles of the types of bait were evaluated by identifying and quantifying the main volatile organic compounds released by the fruit traps. Insect collections were conducted weekly using fruit‐baited traps for 1 month, without interruption. We found that the diversity estimate of Cetoniidae was higher for banana‐based baits relative to grape and cagaita fruit, but not relative to pineapple, mango, and sugarcane juice. Additionally, no increase in attractiveness was observed with the addition of sugarcane juice to banana, pineapple, and mango bait. Most compounds in the fruit extracts belong to alcohol, ester, and terpene functional groups. Our results suggest that banana, pineapple, mango, and sugarcane juice, used alone, may be used for Cetoniidae diversity studies, and that banana bait seems to be the most suitable for Cetoniidae collection in the Cerrado, because it enables the collection of a high diversity of species and also of species considered rare.

Genetic Features of the Bream Abramis brama Population from the Lake System of Sebezhsky National Park

The genetic variability of microsatellite DNA has been studied for the first time for five loci in the common bream Abramis brama (L.) from lakes Sebezhskoye and Necheritsa of the Sebezhsky National Park lake system (Pskov oblast). The results demonstrate the uniformity of estimates of the genetic variability of the bream in the lake system. Estimates of the allelic diversity of microsatellite loci and the expected heterozygosity of local bream groups do not differ significantly. The total genetic differentiation of bream is θ = 0.004, 95% CI (–0.01; 0.01), and is statistically insignificant. The Bayesian analysis of the studied multilocus genotypes has not revealed any population–genetic structure. The results indicate a high level of gene flow between local bream groupings and make it possible to assume the presence of a genetically unified panmixed population in the Sebezhsky National Park lake system.

Recent secondary contact, genome-wide admixture, and asymmetric introgression of neo-sex chromosomes between two Pacific island bird species.

Secondary contact between closely related taxa represents a "moment of truth" for speciation-an opportunity to test the efficacy of reproductive isolation that evolved in allopatry and to identify the genetic, behavioral, and/or ecological barriers that separate species in sympatry. Sex chromosomes are known to rapidly accumulate differences between species, an effect that may be exacerbated for neo-sex chromosomes that are transitioning from autosomal to sex-specific inheritance. Here we report that, in the Solomon Islands, two closely related bird species in the honeyeater family-Myzomela cardinalis and Myzomela tristrami-carry neo-sex chromosomes and have come into recent secondary contact after ~1.1 my of geographic isolation. Hybrids of the two species were first observed in sympatry ~100 years ago. To determine the genetic consequences of hybridization, we use population genomic analyses of individuals sampled in allopatry and in sympatry to characterize gene flow in the contact zone. Using genome-wide estimates of diversity, differentiation, and divergence, we find that the degree and direction of introgression varies dramatically across the genome. For sympatric birds, autosomal introgression is bidirectional, with phenotypic hybrids and phenotypic parentals of both species showing admixed ancestry. In other regions of the genome, however, the story is different. While introgression on the Z/neo-Z-linked sequence is limited, introgression of W/neo-W regions and mitochondrial sequence (mtDNA) is highly asymmetric, moving only from the invading M. cardinalis to the resident M. tristrami. The recent hybridization between these species has thus enabled gene flow in some genomic regions but the interaction of admixture, asymmetric mate choice, and/or natural selection has led to the variation in the amount and direction of gene flow at sex-linked regions of the genome.

MetaBakery: a Singularity implementation of bioBakery tools as a skeleton application for efficient HPC deconvolution of microbiome metagenomic sequencing data to machine learning ready information.

In this study, we present MetaBakery (http://metabakery.fe.uni-lj.si), an integrated application designed as a framework for synergistically executing the bioBakery workflow and associated utilities. MetaBakery streamlines the processing of any number of paired or unpaired fastq files, or a mixture of both, with optional compression (gzip, zip, bzip2, xz, or mixed) within a single run. MetaBakery uses programs such as KneadData (https://github.com/bioBakery/kneaddata), MetaPhlAn, HUMAnN and StrainPhlAn as well as integrated utilities and extends the original functionality of bioBakery. In particular, it includes MelonnPan for the prediction of metabolites and Mothur for calculation of microbial alpha diversity. Written in Python 3 and C++ the whole pipeline was encapsulated as Singularity container for efficient execution on various computing infrastructures, including large High-Performance Computing clusters. MetaBakery facilitates crash recovery, efficient re-execution upon parameter changes, and processing of large data sets through subset handling and is offered in three editions with bioBakery ingredients versions 4, 3 and 2 as versatile, transparent and well documented within the MetaBakery Users' Manual (http://metabakery.fe.uni-lj.si/metabakery_manual.pdf). It provides automatic handling of command line parameters, file formats and comprehensive hierarchical storage of output to simplify navigation and debugging. MetaBakery filters out potential human contamination and excludes samples with low read counts. It calculates estimates of alpha diversity and represents a comprehensive and augmented re-implementation of the bioBakery workflow. The robustness and flexibility of the system enables efficient exploration of changing parameters and input datasets, increasing its utility for microbiome analysis. Furthermore, we have shown that the MetaBakery tool can be used in modern biostatistical and machine learning approaches including large-scale microbiome studies.

Unveiling errors in soil microbial community sequencing: a case for reference soils and improved diagnostics for nanopore sequencing

The sequencing platform and workflow strongly influence microbial community analyses through potential errors at each step. Effective diagnostics and experimental controls are needed to validate data and improve reproducibility. This cross-laboratory study evaluates sources of variability and error at three main steps of a standardized amplicon sequencing workflow (DNA extraction, polymerase chain reaction [PCR], and sequencing) using Oxford Nanopore MinION to analyze agricultural soils and a simple mock community. Variability in sequence results occurs at each step in the workflow with PCR errors and differences in library size greatly influencing diversity estimates. Common bioinformatic diagnostics and the mock community are ineffective at detecting PCR abnormalities. This work outlines several diagnostic checks and techniques to account for sequencing depth and ensure accuracy and reproducibility in soil community analyses. These diagnostics and the inclusion of a reference soil can help ensure data validity and facilitate the comparison of multiple sequencing runs within and between laboratories.

Computational estimation of clonal diversity in autoimmunity.

Diversity is the cornerstone of the adaptive immune system, crucial for its effectiveness against constantly evolving pathogens that pose threats to higher vertebrates. Accurately measuring and interpreting this diversity presents challenges for immunologists, as changes in diversity and clonotype composition can tip the balance between protective immunity and autoimmunity. In this review, we present the current methods commonly used to measure diversity from single-cell T-cell receptor and B-cell receptor sequencing. We also discuss two case studies where single-cell sequencing and diversity estimations have led to breakthroughs in autoimmune disease discovery and therapeutic innovation, and reflect upon the necessity andimportance of accurately defining and measuring lymphocyte diversity in these contexts.

Genetic diversity and connectivity of the invasive gastropod, Callinina georgiana (Caenogastropoda: Viviparidae) across a fragmented riverscape: A mitonuclear perspective

Abstract Aquatic invasive species are a significant threat to global freshwater biodiversity. This study focuses on the banded mystery snail, Callinina georgiana, an invasive species in the Adirondack region of northern New York—an important section of the New York Great Lakes Basin. This project aims to explore the genetic connectivity of C. georgiana within its invasive range using a combination of mitochondrial and nuclear markers. Sampling was conducted in the Raquette River and adjacent waterways, with a total of 229 snails collected from 16 distinct populations distributed across eight different waterbodies. Also included were two populations from the species' native range in the southern U.S.A. DNA was extracted, and a 710‐bp fragment of the mitochondrial DNA marker cytochrome c oxidase 1 and a 351‐bp fragment of nuclear marker histone‐3 were amplified. Population genetic analyses including haplotype patterning, AMOVA and genetic diversity estimates, neutrality tests and tests for isolation by distance were performed to assess connectivity patterns. Results showed moderate to high levels of genetic admixture within the snail's invasive range as indicated by the lack of geographic patterning of haplotypes and low to moderate levels of genetic differentiation across multiple sites. Demographic analyses combined with high numbers of private haplotypes indicate historic population expansion. Interestingly, a case of mitonuclear discordance was detected for native and invasive populations as evident by incongruent haplotype patterns for the cytochrome c oxidase 1 and histone‐3 markers. Callinina georgiana exhibits a high level of genetic connectivity in its invasive range. The presence of dams does not significantly affect apparent gene flow, indicating that anthropogenic activities, such as boat traffic might be key in dispersing the snails across this fragmented freshwater system. This study offers new insights into the dispersal and genetic structure of an invasive freshwater snail. It highlights the importance of considering anthropogenic factors when confronting complex patterns of genetic diversity. The findings are significant for biodiversity conservation and provide a basis for developing strategies to manage and contain the spread of aquatic invasive species such as C. georgiana, especially in regions with high human activity.

Multi-season-based estimation of genetic diversity, correlation and path analysis in spearmint (Mentha spicata L.) half-sib populations

Spearmint (Mentha spicata L.) is popular for its medicinal benefits and industrial uses, in this study forty-three half-sib progenies were evaluated over three seasons at CSIR-CIMAP, Research Centre Pantnagar to study genetic variability, correlation, path coefficient analysis and Mahalanobis D2 diversity by using five morphological and two biochemical traits. These forty-three half-sib populations were derived from nine commercially existing spearmint varieties through an intra-population improvement method. Results of correlation and path coefficient studies indicated the paramount importance of certain key traits in the selection process. Notably, traits such as herb yield per plot (kg), plant height (cm) and oil content (%) emerged as pivotal, exhibiting positive correlations, high heritability and moderate to high genetic advance. These attributes, therefore need primacy in the selection of superior lines. Furthermore, genotypes OPSP-106, OPSP-92, OPSP-47, OPSP-35 and OPSP-76 showed significant genetic divergence. These accessions hold promise as parent candidates for hybridization experiments aimed at finding transgressive segregants in later generations. Additionally, this study highlighted the presence of specific oil components, such as carvone and limonene, offering insights into potential avenues for targeted breeding efforts. This study provides a holistic view of the genetic makeup of Mentha spicata genotypes, enabling informed decisions in breeding programs. By prioritizing traits and maximizing genetic diversity, the results of this investigation will contribute to the development of improved spearmint varieties with enhanced morphological and biochemical characters, thus advancing the medicinal and industrial applications of spearmint.

Hitac: a hierarchical taxonomic classifier for fungal ITS sequences compatible with QIIME2

BackgroundFungi play a key role in several important ecological functions, ranging from organic matter decomposition to symbiotic associations with plants. Moreover, fungi naturally inhabit the human body and can be beneficial when administered as probiotics. In mycology, the internal transcribed spacer (ITS) region was adopted as the universal marker for classifying fungi. Hence, an accurate and robust method for ITS classification is not only desired for the purpose of better diversity estimation, but it can also help us gain a deeper insight into the dynamics of environmental communities and ultimately comprehend whether the abundance of certain species correlate with health and disease. Although many methods have been proposed for taxonomic classification, to the best of our knowledge, none of them fully explore the taxonomic tree hierarchy when building their models. This in turn, leads to lower generalization power and higher risk of committing classification errors.ResultsHere we introduce HiTaC, a robust hierarchical machine learning model for accurate ITS classification, which requires a small amount of data for training and can handle imbalanced datasets. HiTaC was thoroughly evaluated with the established TAXXI benchmark and could correctly classify fungal ITS sequences of varying lengths and a range of identity differences between the training and test data. HiTaC outperforms state-of-the-art methods when trained over noisy data, consistently achieving higher F1-score and sensitivity across different taxonomic ranks, improving sensitivity by 6.9 percentage points over top methods in the most noisy dataset available on TAXXI.ConclusionsHiTaC is publicly available at the Python package index, BIOCONDA and Docker Hub. It is released under the new BSD license, allowing free use in academia and industry. Source code and documentation, which includes installation and usage instructions, are available at https://gitlab.com/dacs-hpi/hitac.

Low genetic differentiation despite high habitat fragmentation in an endemic and endangered species of Iridaceae from South America: implications for conservation

Abstract To conserve threatened species effectively, it is crucial to map the genetic variation of the remaining populations. Thus, using 15 microsatellites markers, from which 10 were specially developed for this study, we investigated genetic structure and gene flow patterns of Herbertia zebrina Deble, a critically endangered species endemic from grasslands of southern Brazil. We also investigated the degree of habitat fragmentation and the impacts on levels of genetic diversity, mating system and pollinators of the species. STRUCTURE and discriminant analysis of principal components identified the existence of three genetic clusters. Populations were not isolated by distance, and genetic differentiation among populations was low (7.0%). Migration rates were also low, but no evidence of genetic bottlenecks was found. However, effective population-scaled mutation rates (Θ) were < 1, suggesting that populations could be experiencing genetic drift, but the reason remains unknown. The results indicate that measurements of habitat fragmentation were not significantly correlated with genetic diversity estimates, which tend to increase with fragment size. H. zebrina was identified as an outcrossing species and specialized pollinators, such as Chalepogenus goeldianus and Lanthanomelissa betinae were rarely observed. Our findings suggest that genetic differentiation across multiple populations within the entire geographic distribution of H. zebrina is very low and populations may struggle to adapt to the current environmental and pollination changes. However, habitat fragmentation is still too recent to detect significant impacts on the levels of genetic variation. Thus, conservation plans are necessary to avoid further declines of this species.

Influence of dams on sauger population structure and hybridization with introduced walleye.

Dams have negatively affected freshwater biodiversity throughout the world. These negative effects tend to be exacerbated for aquatic taxa with migratory life histories, and for taxa whose habitat is fundamentally altered by the formation of large reservoirs. Sauger (Sander candadensis; Percidae), large-bodied migratory fishes native to North America, have seen population declines over much of the species' range, and dams are often implicated for their role in blocking access to spawning habitat and otherwise negatively affecting river habitat. Furthermore, hybridization appears to be more frequent between sauger and walleye in the reservoirs formed by large dams. In this study, we examine the role of dams in altering sauger population connectivity and facilitating hybridization with introduced walleye in Wyoming's Wind River and Bighorn River systems. We collected genomic data from individuals sampled over a large spatial scale and replicated sampling throughout the spawning season, with the intent to capture potential variation in hybridization prevalence or genomic divergence between sauger with different life histories. The timing of sampling was not related to hybridization prevalence or population divergence, suggesting limited genetic differences between sauger spawning in different time and places. Overall, there was limited hybridization detected, however, hybridization was most prevalent in Boysen Reservoir (a large impounded section of the Wind River). Dams in the lower Wind River and upper Bighorn River were associated with population divergence between sauger upstream and downstream of the dams, and demographic models suggest that this divergence has occurred in concordance with the construction of the dam. Sauger upstream of the dams exhibited substantially lower estimates of genetic diversity, which implies that disrupted connectivity between Wind River and Bighorn River sauger populations may already be causing negative demographic effects. This research points towards the importance of considering the evolutionary consequences of dams on fish populations in addition to the threats they pose to population persistence.

High‐resolution spectral data predict taxonomic diversity in low diversity grasslands

Abstract Mitigating impacts of global change on biodiversity is a pressing goal for land managers, but understanding impacts is often limited by the spatial and temporal constraints of traditional in situ data. Advances in remote sensing address this challenge, in part, by enabling standardized mapping of biodiversity at large spatial scales and through time. In particular, hyperspectral imagery can detect functional and compositional characteristics of vegetation by measuring subtle differences in reflected light. The spectral variance hypothesis (SVH) expects spectral diversity, or variability in reflectance across pixels, to predict vegetation diversity. However, the majority of research testing the SVH to date has been conducted in systems with controlled conditions or spatially homogenous assemblages, with little generalizability to heterogeneous real‐world systems. Here, we move the field forward by testing the SVH in a species‐rich system with high heterogeneity resulting from variable species composition and a recent fire. We use very high spatial resolution (~1 mm) hyperspectral imagery to compare spectrally derived estimates of vegetation diversity with in situ measures collected in Boulder, CO, USA. We find that spectral diversity and taxonomic diversity are positively correlated only for low to moderate diversity transects, or in transects that were recently burned where vegetation diversity is low and composed primarily of C3 grasses. Additionally, we find that the relationship between spectral and taxonomic diversity depends on spatial resolution, indicating that pixel size should remain a priority for biodiversity monitoring. Practical implication: The context dependency of this relationship, even with high spatial resolution data, confirms previous work that the SVH does not hold across landscapes and demonstrates the necessity for repeated, high‐resolution data in order to tease apart the biological conditions underpinning the SVH. With refinement, however, the remote sensing techniques described here will offer land managers a cost‐effective approach to monitor biodiversity across space and time.

Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble

AbstractSelecting an appropriate model for a catchment is challenging, and choosing an inappropriate model can yield unreliable results. The Automatic Model Structure Identification (AMSI) method simultaneously calibrates model structural choices and model parameters, which reduces the workload of comparing different models. In this study we benchmark AMSI's capabilities in two ways, using 12 hydro‐climatically diverse Model Parameter Estimation Experiment catchments. First, we calibrate parameter values for 7,488 different model structures and test AMSI's ability to find the best‐performing models in this set. Second, we compare the performance of these 7,488 models and AMSI's selection to the performance of 45 commonly used, structurally more diverse, conceptual models. In both cases, we quantify model accuracy (through the Kling‐Gupta Efficiency) and model adequacy (through various hydrologic signatures). AMSI effectively identifies high‐accuracy models among the 7,488 options, with Kling‐Gupta‐Efficiency scores comparable to the best among the 45 models. However, model adequacy remains poor for the accurate models, regardless of the selection method. In nine of the tested catchments, none of the most accurate models replicate observed signatures with less than 50% errors; in the remaining three catchments, only a handful of models do so. This paper thus provides strong empirical evidence that relying on aggregated efficiency metrics is unlikely to result in hydrologically adequate models, no matter how the models themselves are selected. Nevertheless, AMSI has been shown to effectively search the model hypothesis space it was given. Combined with an improved calibration approach it can therefore offer new ways to address the challenges of model structure selection.