Estimates Of Species Diversity Research Articles

Kernel density estimation of allele frequency including undetected alleles.

Whereas undetected species contribute to estimation of species diversity, undetected alleles have not been used to estimated genetic diversity. Although random sampling guarantees unbiased estimation of allele frequency and genetic diversity measures, using undetected alleles may provide biased but more precise estimators useful for conservation. We newly devised kernel density estimation (KDE) for allele frequency including undetected alleles and tested it in estimation of allele frequency and nucleotide diversity using population generated by coalescent simulation as well as well as real population data. Contrary to expectations, nucleotide diversity estimated by KDE had worse bias and accuracy. Allele frequency estimated by KDE was also worse except when the sample size was small. These might be due to finity of population and/or the curse of dimensionality. In conclusion, KDE of allele frequency does not contribute to genetic diversity estimation.

Acoustic monitoring of anurans and birds in tropical biomes

AbstractPassive acoustic monitoring (PAM) is increasingly popular in ecological research, but recording and analyzing large amounts of data is still a critical bottleneck for the long‐term monitoring of multiple species. We evaluated how temporal and spatial sampling effort affects species diversity estimates using a set of 14,045 1‐min recordings from various neotropical birds and anuran communities. Our goals were to evaluate (i) the daily vocal activity cycle of birds and anurans, (ii) the effect of temporal structure (e.g., number of minutes listened each hour; continuous versus intermittent recordings) on determining the species composition, and (iii) the species–area relationship, and how the number of recorders affects species richness estimates. Based on sampling coverage and completeness, we (iv) evaluate manual inspection schedules for birds and anurans across four biomes of Brazil. We found marked diel variation in vocal activity between taxonomic groups, indicating that birds and anurans are more efficiently detected during early periods of the day and night, respectively. For proper diversity estimates, biomes with higher biodiversity required longer inspecting periods and a larger number of replicates, irrespective of taxa. Although fewer recordings per hour are less informative than full‐hour sampling, species diversity is better estimated when inspected minutes are interspersed over longer periods than inspecting minutes recorded over shorter timespans. Based on our findings, we recommend how to set PAM programs over highly diverse ecosystems.Abstract in Portuguese is available with online material.

Chromosomal barcodes for simultaneous tracking of near-isogenic bacterial strains in plant microbiota

DNA-amplicon-based microbiota profiling can estimate species diversity and abundance but cannot resolve genetic differences within individuals of the same species. Here we report the development of modular bacterial tags (MoBacTags) encoding DNA barcodes that enable tracking of near-isogenic bacterial commensals in an array of complex microbiome communities. Chromosomally integrated DNA barcodes are then co-amplified with endogenous marker genes of the community by integrating corresponding primer binding sites into the barcode. We use this approach to assess the contributions of individual bacterial genes to Arabidopsis thaliana root microbiota establishment with synthetic communities that include MoBacTag-labelled strains of Pseudomonas capeferrum. Results show reduced root colonization for certain mutant strains with defects in gluconic-acid-mediated host immunosuppression, which would not be detected with traditional amplicon sequencing. Our work illustrates how MoBacTags can be applied to assess scaling of individual bacterial genetic determinants in the plant microbiota.

Assessing Forest Species Diversity in Ghana’s Tropical Forest Using PlanetScope Data

This study utilized a remotely sensed dataset with a high spatial resolution of 3 m to predict species diversity in the Bobiri Forest Reserve (BFR), a moist semi-deciduous tropical forest in Ghana. We conducted a field campaign of tree species measurements to achieve this objective for species diversity estimation. Thirty-five field plots of 50 m × 20 m were established, and the most dominant tree species within the forest were identified. Other measurements, such as diameter at breast height (DBH ≥ 5 cm), tree height, and each plot’s GPS coordinates, were recorded. The following species diversity indices were estimated from the field measurements: Shannon–Wiener (H′), Simpson diversity index (D2), species richness (S), and species evenness (J′). The PlanetScope surface reflectance data at 3 m spatial resolution was acquired and preprocessed for species diversity prediction. The spectral/pixel information of all bands, except the coastal band, was extracted for further processing. Vegetation indices (VIs) (NDVI—normalized difference vegetation index, EVI—enhanced vegetation index, SRI—simple ratio index, SAVI—soil adjusted vegetation index, and NDRE—normalized difference red edge index) were also calculated from the spectral bands and their pixel value extracted. A correlation analysis was then performed between the spectral bands and VIs with the species diversity index. The results showed that spectral bands 6 (red) and 2 (blue) significantly correlated with the two main species diversity indices (S and H′) due to their influence on vegetation properties, such as canopy biomass and leaf chlorophyll content. Furthermore, we conducted a stepwise regression analysis to investigate the most important spectral bands to consider when estimating species diversity from the PlanetScope satellite data. Like the correlation results, bands 6 (red) and 2 (blue) were the most important bands to be considered for predicting species diversity. The model equations from the stepwise regression were used to predict tree species diversity. Overall, the study’s findings emphasize the relevance of remotely sensed data in assessing the ecological condition of protected areas, a tool for decision-making in biodiversity conservation.

INDIVIDUAL TREE-BASED FOREST SPECIES DIVERSITY ESTIMATION USING UAV-BORNE HYPERSPECTRAL AND LIDAR DATA

Abstract. Forest biodiversity is essential in maintaining ecosystem functions and services. Recently, unmanned aerial vehicle (UAV) remote sensing technology has emerged as a cost-effective and flexible tool for biodiversity monitoring. In this study, we compared the optimal clustering algorithm, classification method (spectral angle mapper, SAM), spectral diversity metric and structural heterogeneity index for forest species diversity estimation in two complex subtropical forests, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves in China, using UAV-borne hyperspectral and LiDAR data. The results showed that the SAM classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (MZL: 0.62 > 0.46 and GGS: 0.55 > 0.46) and Shannon-Wiener index (MZL: 0.64 > 0.58 and GGS: 0.52 > 0.47), while the optimal clustering algorithm had the highest prediction accuracy for the Simpson index, followed by the SAM classification method, spectral diversity metric and structural heterogeneity index (MZL: 0.83>0.44>0.31>0.12, GGS: 0.62>0.44>0.38>0.00). Our study indicated that the SAM classification method had the advantage of identifying rare species and estimating species richness, while the clustering method could capture forest diversity patterns rapidly without distinguishing the specific tree species and predict the Simpson index more accurately. Overall, both clustering and classification methods exhibited superior performance compared to spectral or structural diversity indices. Our findings highlight the applicability of UAV remote sensing in monitoring forest species diversity in complex subtropical forests.

Phylogenomic species delimitation of the twisted‐winged parasite genus Stylops (Strepsiptera)

AbstractThe twisted‐winged parasite genus Stylops has a history of different species concepts with varying host specificity resulting in diverse species diversity estimates in different regions of the Holarctic. The adoption of a supergeneralist species concept in Europe, proposing synonymization of all Western Palaearctic Stylops species, did not facilitate taxonomic clarity and obscured the available life‐history data in the region for decades. Lack of molecular data has allowed divergent opinions on species hypotheses and little opportunity for evaluating them in this morphologically challenging genus. To solve these discrepancies and gain novel information about host associations, we applied whole‐genome sequencing to 163 specimens, representing a significant portion of putative European species. We evaluate the existing and conflicting species hypotheses with molecular species delimitation using Species bOundry Delimitation using Astral (SODA) and use a maximum likelihood phylogeny to investigate host associations of the species. Furthermore, we evaluate the effect of a number of loci used in SODA for the number of inferred species. We find justification for synonymization of multiple species and indications of undescribed species, as well as new host–parasite relationships. We show that the number of inferred species in SODA is exceedingly and positively correlated with the number of loci used, urging for cautious application. The results of our study bring clarity to the Western Palaearctic species diversity of Stylops. Furthermore, the comprehensive molecular dataset generated in this study will be a valuable resource for future studies on Stylops and the evolution of parasites in general.

Deep learning with citizen science data enables estimation of species diversity and composition at continental extents.

Effective solutions to conserve biodiversity require accurate community- and species-level information at relevant, actionable scales and across entire species' distributions. However, data and methodological constraints have limited our ability to provide such information in robust ways. Herein we employ a Deep-Reasoning Network implementation of the Deep Multivariate Probit Model (DMVP-DRNets), an end-to-end deep neural network framework, to exploit large observational and environmental data sets together and estimate landscape-scale species diversity and composition at continental extents. We present results from a novel year-round analysis of North American avifauna using data from over nine million eBird checklists and 72 environmental covariates. We highlight the utility of our information by identifying critical areas of high species diversity for a single group of conservation concern, the North American wood warblers, while capturing spatiotemporal variation in species' environmental associations and interspecific interactions. In so doing, we demonstrate the type of accurate, high-resolution information on biodiversity that deep learning approaches such as DMVP-DRNets can provide and that is needed to inform ecological research and conservation decision-making at multiple scales.

DNA barcodes provide insights into the diversity and biogeography of the non-biting midge Polypedilum (Diptera, Chironomidae) in South America.

South America, particularly within its tropical belt, is renowned for its unparalleled high levels of species richness, surpassing other major biomes. Certain neotropical areas harbor fragmented knowledge of insect diversity and face imminent threats from biodiversity loss and climate change. Hence, there is an urgent need for rapid estimation methods to complement slower traditional taxonomic approaches. A variety of algorithms for delimiting species through single-locus DNA barcodes have been developed and applied for rapid species diversity estimates across diverse taxa. However, tree-based and distance-based methods may yield different group assignments, leading to potential overestimation or underestimation of putative species. Here, we investigate the performance of different DNA-based species delimitation approaches to rapidly estimate the diversity of Polypedilum (Chironomidae, Diptera) in South America. Additionally, we test the hypothesis that significant differences exist in the community structure of Polypedilum fauna between South America and its neighboring regions, particularly the Nearctic. Our analysis encompasses a dataset of 1492 specimens from 598 locations worldwide, with a specific focus on South America. Within this region, we analyzed a subset of 247 specimens reported from 37 locations. Using various methods including the Barcode Index Number (BIN), Bayesian Poisson tree processes (bPTP), multi-rate Poisson tree processes (mPTP), single-rate Poisson tree processes (sPTP), and generalized mixed Yule coalescent (sGMYC), we identify molecular operational taxonomic units (MOTUs) ranging from 267 to 520. Our results indicate that the sGMYC method is the most suitable for estimating putative species in our dataset, resulting in the identification of 75 species in the Neotropical region, particularly in South America. Notably, this region exhibited higher species richness in comparison to the Palearctic and Oriental realms. Additionally, our findings suggest potential differences in species composition of Polypedilum fauna between the Neotropical and the adjacent Nearctic realms, highlighting high levels of endemism and species richness in the first. These results support our hypothesis that there are substantial differences exist in species composition between the Polypedilum fauna in South America and the neighboring regions.

Diversity and abundance of birds in dumpsites of Afar region, Ethiopia: implication for conservation

BackgroundDumpsites play key role in conservation of birds. A study was conducted to assess the diversity and abundance of birds in dumpsites of Afar region, Ethiopia from August, 2019 to March, 2020, covering both the wet and dry seasons. A total of nine dumpsites that contain domestic wastes found in different towns of Afar region were selected. Point count technique was employed to identify and count the birds. Shannon-Weiner diversity index was used to estimate species diversity and Two-way ANOVA was used to test birds’ species richness and abundance variation across dumpsites and seasons.ResultsA total of 48 bird species consisting of one endemic and 10 globally threatened species were recorded. Red-billed Quelea, Marabou Stork and Egyptian Vulture were the most abundant species. There was a significant difference in bird species richness and abundance among dumpsites (F = 8.44, df = 8, P < 0.05) and F = 15.507, df = 8, P < 0.05), respectively. Moreover, a significant difference in abundance was also observed between the two seasons (F = 19.339, df = 1, P < 0.05). The highest species diversity (H’ = 3.18) was observed during wet season in Abala and Afambo dumpsites and the highest species evenness (E = 0.86) was observed during dry season in Afdera.ConclusionBird species diversity and abundance among dumpsites was high, and man-made disturbance are main threats for conservation of birds in the area. Therefore, conservation measures are needed to maintain survival of birds mainly the globally threatened species. Moreover, proper management of dumpsites is vital to support the waste dependent birds.

Estimation of Species Diversity and Carbon Storage in 5-, 15- and 22-year-old Mango Agroforest (Cameroon)

Estimation of Species Diversity and Carbon Storage in 5-, 15- and 22-year-old Mango Agroforest (Cameroon)

Mayfly Larvae (Ephemeroptera) in Thailand: Diversity and Science Communication

Mayflies (Ephemeroptera) are one of the most common components of aquatic assemblages in freshwater environments and contribute to ecosystem services. Mayflies have been widely used as indicators of water quality and frequently play an important role in biomonitoring protocols. Mayflies exhibit a decrease in taxa richness with increases in stream or river pollution and degradation. A series of taxonomic studies on Thai mayflies reported 19 families, 73 genera, and approximately 176 species. The families Baetidae and Heptageniidae are the most diverse and widespread groups of Thai mayflies. Our understanding of the diversity of Thai mayflies has steadily increased in recent years. New genera are being described: Cymbalcloeon Suttinun, Gattolliat &amp; Boonsoong, 2020; Elatosara Malzacher, 2020; Megabranchiella Phlai-ngam &amp; Tungpairojwong, 2022; Mekongellina Malzacher, 2019; Sangpradubina Boonsoong &amp; Sartori, 2016; and Thainis Malzacher, 2020. New species and new country records are constantly being discovered in Thailand. Nevertheless, the number of genera and species seems to be lower than the species diversity estimation, and several genera and species remain undescribed. Research on the systematics and ecology of mayflies has been increasing in recent years. DNA barcoding can provide a powerful supplement to the traditional morphological approach to species delimitation. The book entitled “Mayfly Larvae in Thailand” (Thai version) and the boardgame “Thai Mayflies” were developed for environmental science communication. Further efforts are required to assess the conservation status of mayfly species. Thus, data on mayfly diversity and ecological requirements could be used as tools to evaluate environmental impacts on water resources and to drive future research on biodiversity conservation management strategies.

Species diversity of forest floor biota in non‐native Douglas‐fir stands is similar to that of native stands

AbstractCultivation of non‐native tree species is a promising option to adapt managed forests to climate change. However, consequences of non‐native tree species on flora, fauna, and microorganisms that occur in forest ecosystems (forest associates) need to be considered when managing forests. We lack a solid understanding of how cultivating non‐native tree species in pure stands and in mixture with native tree species impacts abundance and species diversity of forest associates. We compared abundance, alpha‐, beta‐ and gamma‐diversity of eight forest‐floor‐associated taxa that are relevant for ecosystem functioning (including fungi, plants, arthropods, and small mammals) between different forest ecosystems. We addressed pure stands of non‐native coniferous Douglas‐fir (Pseudotsuga menziesii) and two native species, broad‐leaved European beech (Fagus sylvatica) and coniferous Norway spruce (Picea abies), as well as mixed stands of European beech with each conifer in two regions with differing site conditions in temperate Northwest Germany. Diversity indices revealed differences among species groups. Gamma‐diversity and alpha‐diversity of forest associates in Douglas‐fir and spruce stands were usually higher than in beech stands. Estimates of species diversity of mixed stands are intermediate between estimates for the respective pure stands. Differences in the diversity between the two study regions were highly taxon specific with no clear support for a general trend toward a higher or lower species diversity of forest associates depending on site quality. Abundance values show a pattern similar to our diversity estimates, but with a higher statistical uncertainty. Non‐native Douglas‐fir stands provided habitats to support associated species communities of equally high or higher diversity than stands of native beech or spruce. Mixed stands of non‐native and native tree species may be a management option to achieve different goals, that is, to provide habitats for species that are strictly associated with beech and to increase resilience to climate change. However, the overall diversity of forest‐floor‐associated biota is not improved by cultivating Douglas‐fir or spruce in mixture with beech.

Rodent Surveys reveal good insights into the Snake Fauna in an Agro-Pastoral Ecosystem in Southern India

Rats and mice are among the foremost of agricultural pests that are plentiful in farmlands. Snakes are one of the most specialized natural rat-controlling predatory species, also equally prevalent in farmlands. We studied the snake fauna of agroecosystem in south-eastern India by gathering concomitant data on snakes, during rodent surveys wherein rodents were the main target taxa. From a one year long day-and-night survey focusing on rodents, we obtained 151 sightings of snakes belonging to 22 species (four of which are venomous). A total of 66 sightings representing 14 species were obtained in diurnal surveys, and 21 sightings representing 7 species were obtained in night surveys. As much as 64 sightings representing 20 species were obtained by chance encounters in the study site, but outside of the designated rodent survey duration. An uncommon snake Sibynophis subpunctatus was recorded. The colubrid Ptyas mucosa was the commonest encountered species during the day while at night it was the viper Echis carinatus. Estimated species diversity in Shannon-Wiener index was H = 1.218.

Difficult times for amphibians: Effects of land-use change at the local and landscape scales in the Iberá Wetlands

Land-use change and management practices have led to habitat loss, one of the greatest factors in biodiversity decline. Particularly, amphibians comprise the highest number of threatened vertebrate species at the global scale. In this work, amphibian communities were analysed in three differing landscapes: a protected wetland, a livestock-impaired rangeland and a pine afforestation. In each landscape, amphibian species were sampled. Land-cover type and composition, as well as primary vegetation types were characterised at the landscape and local scale, respectively. The relationship between these environmental variables and the amphibian communities was analysed. Twenty-one amphibian species were identified; the protected and afforested landscapes were the richest, whereas the rangeland showed the lowest species richness and diversity estimates. At the local scale, vegetation, water coverage and land-use category explained the higher presence of amphibian species and their abundance. These results show how different land-uses, especially livestock farming, modify the composition of amphibian communities. This work constitutes a foundation for the development of sustainable management practices for conserving amphibians in landscape-level altered habitats.

Applicability and Advantage of Mitochondrial Metagenomics and Metabarcoding in Spider Biodiversity Survey

Spiders are an extraordinary animal group with extremely high diversity in species, morphology, and behavior. Accurate estimation of species diversity and community composition is essential in spider ecological studies as well as applications of biodiversity surveys and monitoring. However, spider biodiversity surveys still largely utilize the morphology-based approach, which is often time-consuming and highly dependent on taxonomic experts. In this study, we tested the applicability of mitochondrial metagenomic and metabarcoding methods in the biodiversity survey of spiders. Local mitogenome and barcode databases of 62 reference species were built using next-generation sequencing and Sanger sequencing technologies. The performances of mitochondrial metagenomics, metabarcoding, and morphological methods were compared for five plot samples of spiders. The results show that the molecular methods (mitochondrial metagenomics and metabarcoding) have a higher species detection rate compared with the traditional morphological biodiversity method, which is largely due to their capability of incorporating the large proportion of immature specimens in each plot into the biodiversity assessment. Our study demonstrates the great potential of molecular approaches in advancing spider biodiversity and community ecology studies and suggests that by combining both mitochondrial metagenomic and metabarcoding methods we could provide more accurate and reliable biodiversity assessment for spiders.

Convergence of dominance and neglect in flying insect diversity

Most of arthropod biodiversity is unknown to science. Consequently, it has been unclear whether insect communities around the world are dominated by the same or different taxa. This question can be answered through standardized sampling of biodiversity followed by estimation of species diversity and community composition with DNA barcodes. Here this approach is applied to flying insects sampled by 39 Malaise traps placed in five biogeographic regions, eight countries and numerous habitats (>225,000 specimens belonging to >25,000 species in 458 families). We find that 20 insect families (10 belonging to Diptera) account for >50% of local species diversity regardless of clade age, continent, climatic region and habitat type. Consistent differences in family-level dominance explain two-thirds of variation in community composition despite massive levels of species turnover, with most species (>97%) in the top 20 families encountered at a single site only. Alarmingly, the same families that dominate insect diversity are ‘dark taxa’ in that they suffer from extreme taxonomic neglect, with little signs of increasing activities in recent years. Taxonomic neglect tends to increase with diversity and decrease with body size. Identifying and tackling the diversity of ‘dark taxa’ with scalable techniques emerge as urgent priorities in biodiversity science.

Widespread coexistence of genetically distinct morphotypes in theSatyrium longicaudacomplex (Orchidaceae)

AbstractSpecies-level taxonomy is traditionally based on herbarium collections that typically include few, or even single, representatives per site. This can lead to underestimation of diversity when there are sympatric populations of superficially similar plants belonging to different lineages. Satyrium longicauda (Orchidaceae) represents a taxonomic challenge for the delimitation of species boundaries due to the high degree of morphological variation detected within and among populations. Currently, just two varieties are accepted based mainly on length differences of the lateral sepal and nectar spur. However, there is extensive morphological variation within South African populations and evidence for several pollination ecotypes, indicating that this taxon represents an actively diverging species complex. Here, we evaluate intraspecific morphological variation through uni- and multivariate morphometrics and analyse internal transcribed spacer sequences for individuals sampled from 36 sites, including 14 sites where divergent morphotypes occur sympatrically. Morphometric analyses of 1802 individuals revealed the presence of eight morphotypes based on vegetative and floral characters. Up to six morphologically and genetically distinct morphotypes can coexist in sympatry. Morphological and genetic distances among populations were significantly correlated. Phylogenetic analyses of 120 accessions indicated that neither of the two varieties nor S. longicauda as a species is monophyletic, and provided evidence for the monophyly of some of the morphotypes including the newly described S. cernuiflorum. The presence of distinct morphological and genetic sympatric variants, which in several cases scale up to distinct evolutionary lineages, is consistent with the existence of different taxa according to morphological and biological species concepts. Our results therefore confirm that taxonomy based mainly on herbarium collections can grossly under-estimate actual diversity of disparate lineages, although further work is required to finalize taxonomic decisions. These findings have implications for efforts to estimate species diversity in groups that are in the process of diversifying and for conservation practice.

LiDAR GEDI derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems

The “Height Variation Hypothesis” is an indirect approach used to estimate forest biodiversity through remote sensing data, stating that greater tree height heterogeneity (HH) measured by CHM LiDAR data indicates higher forest structure complexity and tree species diversity. This approach has traditionally been analyzed using only airborne LiDAR data, which limits its application to the availability of the dedicated flight campaigns. In this study we analyzed the relationship between tree species diversity and HH, calculated with four different heterogeneity indices using two freely available CHMs derived from the new space-borne GEDI LiDAR data. The first, with a spatial resolution of 30 m, was produced through a regression tree machine learning algorithm integrating GEDI LiDAR data and Landsat optical information. The second, with a spatial resolution of 10 m, was created using Sentinel-2 images and a deep learning convolutional neural network. We tested this approach separately in 30 forest plots situated in the northern Italian Alps, in 100 plots in the forested area of Traunstein (Germany) and successively in all the 130 plots through a cross-validation analysis. Forest density information was also included as influencing factor in a multiple regression analysis. Our results show that the GEDI CHMs can be used to assess biodiversity patterns in forest ecosystems through the estimation of the HH that is correlated to the tree species diversity. However, the results also indicate that this method is influenced by different factors including the GEDI CHMs dataset of choice and their related spatial resolution, the heterogeneity indices used to calculate the HH and the forest density. Our finding suggest that GEDI LIDAR data can be a valuable tool in the estimation of forest tree heterogeneity and related tree species diversity in forest ecosystems, which can aid in global biodiversity estimation.

Individual tree-based forest species diversity estimation by classification and clustering methods using UAV data

Monitoring forest species diversity is essential for biodiversity conservation and ecological management. Currently, unmanned aerial vehicle (UAV) remote sensing technology has been increasingly used in biodiversity monitoring due to its flexibility and low cost. In this study, we compared two methods for estimating forest species diversity indices, namely the spectral angle mapper (SAM) classification approach based on the established species-spectral library, and the self-adaptive Fuzzy C-Means (FCM) clustering algorithm by selected biochemical and structural features. We conducted this study in two complex subtropical forest areas, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves using UAV-borne hyperspectral and LiDAR data. The results showed that the classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (0.62 &amp;gt; 0.46 for MZL and 0.55 &amp;gt; 0.46 for GGS) and Shannon-Wiener index (0.64 &amp;gt; 0.58 for MZL, 0.52 &amp;gt; 0.47 for GGS). However, the Simpson index estimated by the classification method correlated less with the field measurements than the clustering algorithm (R2 = 0.44 and 0.83 for MZL and R2 = 0.44 and 0.62 for GGS). Our study demonstrated that the classification method could provide more accurate monitoring of forest diversity indices but requires spectral information of all dominant tree species at individual canopy scale. By comparison, the clustering method might introduce uncertainties due to the amounts of biochemical and structural inputs derived from the hyperspectral and LiDAR data, but it could acquire forest diversity patterns rapidly without distinguishing the specific tree species. Our findings underlined the advantages of UAV remote sensing for monitoring the species diversity in complex forest ecosystems and discussed the applicability of classification and clustering methods for estimating different individual tree-based species diversity indices.

Large biodiversity datasets conform to Benford's law: Implications for assessing sampling heterogeneity

Inadequate sampling can cause biased estimates of species diversity, as species occurrence generally follows a log-normal distribution with a long tail. Understanding this sampling bias is fundamental to inform biodiversity conservation actions. However, currently available tests to assess data quality, such as fitting species abundance distribution (SAD) models and rarefaction curves are computationally costly and can still lead to erroneous conclusions.We evaluated Benford's law (first digit distribution) as a complementary method to assess data heterogeneity and survey coverage in large biodiversity datasets, including eBird data for 157 countries and three non-avian GBIF datasets. We also tested conformity to Benford's law of four simulated communities with different SAD models and four corrupted datasets with log-normal SAD. Finally, we evaluated the effect of including rare species in three datasets on the conformity to Benford's law and also compared Benford fit to the results of traditional methods to estimate survey completeness in seven datasets.Species-rich datasets with a large number of observations tended to obtain a good fit. Benford conformity can be a simple and sensitive measure of sampling evenness, complementing traditional methods to assess quality data in large-scale studies. Benford's test can reflect species abundance heterogeneity, especially in log-normally distributed data, but was not ideal to evaluate surveys completeness, as its results diverged from those of traditional methods.As the contribution of citizen science continues to increase in biodiversity monitoring, this fast and efficient method can play a critical role to assess the quality of datasets.