Convex Hull Area Research Articles

Research on an Improved Segmentation Recognition Algorithm of Overlapping Agaricus bisporus.

The accurate identification of overlapping Agaricus bisporus in a factory environment is one of the challenges faced by automated picking. In order to better segment the complex adhesion between Agaricus bisporus, this paper proposes a segmentation recognition algorithm for overlapping Agaricus bisporus. This algorithm calculates the global gradient threshold and divides the image according to the image edge gradient feature to obtain the binary image. Then, the binary image is filtered and morphologically processed, and the contour of the overlapping Agaricus bisporus area is obtained by edge detection in the Canny operator, the convex hull and concave area are extracted for polygon simplification, and the vertices are extracted using Harris corner detection to determine the segmentation point. After dividing the contour fragments by the dividing point, the branch definition algorithm is used to merge and group all the contours of the same Agaricus bisporus. Finally, the least squares ellipse fitting algorithm and the minimum distance circle fitting algorithm are used to reconstruct the outline of Agaricus bisporus, and the demand information of Agaricus bisporus picking is obtained. The experimental results show that this method can effectively overcome the influence of uneven illumination during image acquisition and be more adaptive to complex planting environments. The recognition rate of Agaricus bisporus in overlapping situations is more than 96%, and the average coordinate deviation rate of the algorithm is less than 1.59%.

Phenotyping Deficit Moisture Stress Tolerance in Tomato Using Image Derived Digital Features

Evaluation of tomato genotypes for the response to water stress using digital features assists in high-throughput phenotyping. A study was conducted on imaging tomato genotypes maintained at field capacity and water deficit stress at flowering stage during 2016 at the plant phenomics facility, ICAR-Indian Institute of Horticultural Research, Bengaluru, Karnataka, India. Image derived digital features were obtained through image analysis. Physiological parameters, relative water content, water potential and quantum efficiency of PSII were measured at field capacity and water deficit stress conditions. Study established the relationship between physiological functionality and digital features of tomato plants. Transition of plants from field capacity to water stress caused reduction in digital biomass and canopy top area (CTA) among nine tomato genotypes. Deficit moisture stress resulted in lower relative water content, water potential and quantum efficiency of PSII. We observed an apparent relationship between physiological functionality and digital features, convexhull area and compactness. Two genotypes, IIHR 2843 and IIHR 2195 displayed contrasting response under 100% and 50% FC moisture regimes. At 50% FC, the genotypes IIHR 2843 and IIHR 2195 showed lower and higher water use efficiency, respectively. Genotype IIHR 2195 not only continued to sustain biomass production under deficit moisture stress but showed lower water consumption and higher water use efficiency compared to IIHR 2843. Apparent relationship established between physiological functionality and the digital features in this study clearly indicated that the digital features could be employed to capture the response of tomato genotypes to water deficit stress.

CHARACTERIZATION OF DICHOTOMOUS EMOTIONAL STATES USING ELECTRODERMAL ACTIVITY BASED GEOMETRIC FEATURES

In this work, an attempt has been made to classify dichotomous emotional states using Electrodermal activity (EDA) and geometric features. For this, the annotated happy and sad EDA is obtained from the online public database. The EDA is subjected to discrete Fourier transform, and Fourier coefficients in the complex plane are obtained. The envelope of the complex plane is identified using the α-shape method. Five geometric features, namely center of gravity, eccentricity, convexity, rectangularity, and convex hull area are computed from the envelope and statistical analysis is performed. Two machine-learning algorithms, namely random forest (RF) and support vector machine, are considered for the classification. The results show that the proposed approach is able to classify the dichotomous emotional states. The rectangularity feature is found to be distinct and shows a statistically significant difference between the happy and sad emotional states (p<0.05). The RF classifier yields the highest F-m and AUC of 87.8% and 93.8%, respectively in differentiating emotional states. Thus, it appears that the proposed method could be used to understand the neurological, psychiatric, and biobehavioral mechanisms associated with happy and sad emotional states.

A novel region-based expansion rate obstacle detection method for MAVs using a fisheye camera

Expansion-based methods are among the fastest algorithms to detect obstacles for safe navigation of Micro-Aerial Vehicles (MAVs). These methods are based on estimating an enlarging rate which is mostly computed using point features. Using points alone may result in situations where obstacles are only partially identified. This paper presents a new technique that uses image regions, instead of points, for estimating the expansion rate. The proposed algorithm utilises a fisheye camera that can be installed in front of a drone to detect obstacles in all directions. The camera takes images on which obstacles are identified. At each point in time, we extract the regions on the latest fisheye image and check to determine whether or not its regions belong to an obstacle. This step is completed by matching region points with those within the previous image. If at least three points are matched, then the convex hulls of the matched points on both images are formed. The expansion ratio of the convex hull areas is then estimated. If this ratio is bigger than a certain threshold, the region on the latest image is determined as an obstacle; otherwise, it is disregarded. This process is repeated until all pixels of the image are labelled either as obstacle or non-obstacle. Experiments were carried out using 50 pairs of fisheye images that covered a variety of obstacles, including people, pillars, trees, walls, and so on. The findings showed between 74%, and 84% of pixels were labelled correctly. By comparing these results with those obtained by the method developed by Al-Kaff et al. (2017), it is clear that the proposed method produces more stable and accurate results.

Automatic scoring of postures in grouped pigs using depth image and CNN-SVM

Animal posture is a manifestation of animal behavior, and an animal’s behavior provides information about their health, welfare, and living environment. In recent years, machine vision and machine learning technologies have been widely used to detect individual or group behavior of pigs. The purpose of this study is to use machine vision and deep learning technologies to recognize and score multiple postures (standing, sitting, sternal recumbency, ventral recumbency and lateral recumbency) of pigs under commercial conditions based on depth images. In this study, the Azure Kinect DK depth camera with a top view was used to obtain the depth image of pigs, and the target pig image was obtained by GrabCut image segmentation and watershed segmentation of target object calibration. Then, based on the characteristics of the image, the convex hull, boundary, and the depth distance of the shoulder and the hip were obtained. The ratio of the convex hull perimeter to the boundary and the ratio of the convex hull area to the boundary, as well as the depth distance of the shoulder and the hip, and the depth distance ratio of the shoulder to the hip were obtained as the input of the Convolutional Neural Network-Support Vector Machine (CNN-SVM) classification model, and the model was trained and tested. In various classifier detection experiments, the performance of our pig posture classifier for standing posture and lateral recumbency posture was better, with the area under the receiver operating characteristic (AUC) values being 0.9969 and 0.9967, respectively. However, the performance of sitting posture, sternal recumbency posture and ventral recumbency posture classifier was slightly worse but still had good performance: AUC values were 0.9790, 0.9355 and 0.9795, respectively. The model in this article was used to detect the average postures of pigs in one day (taking the average for eight consecutive days), and it was found that the proportion of lying postures was higher than other postures (lying postures were 72%, standing postures were 20%, and sitting postures were 8%). The proportion of standing postures in the daytime was higher than that in the evening, and lying posture was the opposite. The proportion of the three lying postures also changes over time. This study compared the difference of posture recognition accuracy between the model in this paper (CNN-SVM), SVM and CNN; using the same training data and experimental data, the accuracy of posture recognition of the three models was 94.6368%, 92.2175% and 90.5396%, respectively. Therefore, the recognition accuracy of the model in this paper was improved greatly compared with CNN and SVM.

Assessing the Impact of Soil on Species Diversity Estimation Based on UAV Imaging Spectroscopy in a Natural Alpine Steppe

Grassland species diversity monitoring is essential to grassland resource protection and utilization. “Spectral variation hypothesis” (SVH) provides a remote sensing method for monitoring grassland species diversity at pixel scale by calculating spectral heterogeneity. However, the pixel spectrum is easily affected by soil and other background factors in natural grassland. Unmanned aerial vehicle (UAV)-based imaging spectroscopy provides the possibility of soil information removal by virtue of its high spatial and spectral resolution. In this study, UAV-imaging spectroscopy data with a spatial resolution of 0.2 m obtained in two sites of typical alpine steppe within the Sanjiangyuan National Nature Reserve were used to analyze the relationships between four spectral diversity metrics (coefficient of variation based on NDVI (CVNDVI), coefficient of variation based on multiple bands (CVMulti), minimum convex hull volume (CHV) and minimum convex hull area (CHA)) and two species diversity indices (species richness and the Shannon–Wiener index). Meanwhile, two soil removal methods (based on NDVI threshold and the linear spectral unmixing model) were used to investigate the impact of soil on species diversity estimation. The results showed that the Shannon–Wiener index had a better response to spectral diversity than species richness, and CVMulti showed the best correlation with the Shannon–Wiener index between the four spectral diversity metrics after removing soil information using the linear spectral unmixing model. It indicated that the estimation ability of spectral diversity to species diversity was significantly improved after removing the soil information. Our findings demonstrated the applicability of the spectral variation hypothesis in natural grassland, and illustrated the impact of soil on species diversity estimation.

Proline-Mediated Drought Tolerance in the Barley (Hordeum vulgare L.) Isogenic Line Is Associated with Lateral Root Growth at the Early Seedling Stage.

A vigorous root system in barley promotes water uptake from the soil under water-limited conditions. We investigated three spring barley genotypes with varying water stress responses using rhizoboxes at the seedling stage. The genotypes comprised two elite German cultivars, Barke and Scarlett, and a near-isogenic line, NIL 143. The isogenic line harbors the wild allele pyrroline-5-carboxylate synthase1-P5cs1. Root growth in rhizoboxes under reduced water availability conditions caused a significant reduction in total root length, rooting depth, root maximum width, and root length density. On average, root growth was reduced by more than 20% due to water stress. Differences in organ proline concentrations were observed for all genotypes, with shoots grown under water stress exhibiting at least a 30% higher concentration than the roots. Drought induced higher leaf and root proline concentrations in NIL 143 compared with any of the other genotypes. Under reduced water availability conditions, NIL 143 showed less severe symptoms of drought, higher lateral root length, rooting depth, maximum root width, root length density, and convex hull area compared with Barke and Scarlett. Within the same comparison, under water stress, NIL 143 had a higher proportion of lateral roots (+30%), which were also placed at deeper substrate horizons. NIL 143 had a less negative plant water potential and higher relative leaf water content and stomatal conductance compared with the other genotypes under water stress. Under these conditions, this genotype also maintained an enhanced net photosynthetic rate and exhibited considerable fine root growth (diameter class 0.05–0.35 mm). These results show that water stress induces increased shoot and root proline accumulation in the NIL 143 barley genotype at the seedling stage and that this effect is associated with increased lateral root growth.

Shaping the way from the unknown to the known: The role of convex hull shape in numerical comparisons

Various studies have shown that numerical processing is modulated by non-numerical physical properties. One such physical property is the convex hull – the smallest convex polygon surrounding all items in an array. The convex hull is usually discussed only in terms of its area. However, our group has shown that observers use the convex hull shape, as defined according to the number of vertices of the convex hull, to make numerical estimations (Katzin, Katzin, Rosén, Henik, & Salti, 2020). Yet, it is still unknown if and how the convex hull shape affects comparison tasks, and how it interacts with its counterpart, convex hull area. Here we re-examine the data collected by Katzin, Salti, and Henik (2019). Using image processing, we extracted the information on the convex hull shape and showed that the shape affects latency and accuracy of numerical comparisons. We found that both the convex hull shape and other physical properties (i.e., convex hull area, average diameter, density, total circumference, and total surface area) have distinct effects on performance. Finally, the convex hull shape effect was found in counting and estimation ranges, however its effect decreased with numerosities above the counting range. Our results indicate that the interplay between convex hull shape and other physical properties, including convex hull area and numerosity, plays an important role in numerical decisions. We suggest that the convex hull shape should be controlled for when designing non-symbolic numerical tasks.

Examining vocal attractiveness through articulatory working space.

Robust gender differences exist in the acoustic correlates of clearly articulated speech, with females, on average, producing speech that is acoustically and phonetically more distinct than that of males. This study investigates the relationship between several acoustic correlates of clear speech and subjective ratings of vocal attractiveness. Talkers were recorded producing vowels in /bVd/ context and sentences containing the four corner vowels. Multiple measures of working vowel space were computed from continuously sampled formant trajectories and were combined with measures of speech timing known to co-vary with clear articulation. Partial least squares regression (PLS-R) modeling was used to predict ratings of vocal attractiveness for male and female talkers based on the acoustic measures. PLS components that loaded on size and shape measures of working vowel space-including the quadrilateral vowel space area, convex hull area, and bivariate spread of formants-along with measures of speech timing were highly successful at predicting attractiveness in female talkers producing /bVd/ words. These findings are consistent with a number of hypotheses regarding human attractiveness judgments, including the role of sexual dimorphism in mate selection, the significance of traits signalling underlying health, and perceptual fluency accounts of preferences.

High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance.

Drought is one of the most severe and unpredictable abiotic stresses, occurring at any growth stage and affecting crop yields worldwide. Therefore, it is essential to develop drought tolerant varieties to ensure sustainable crop production in an ever-changing climate. High-throughput digital phenotyping technologies in tandem with robust screening methods enable precise and faster selection of genotypes for breeding. To investigate the use of digital imaging to reliably phenotype for drought tolerance, a genetically diverse safflower population was screened under different drought stresses at Agriculture Victoria’s high-throughput, automated phenotyping platform, Plant Phenomics Victoria, Horsham. In the first experiment, four treatments, control (90% field capacity; FC), 40% FC at initial branching, 40% FC at flowering and 50% FC at initial branching and flowering, were applied to assess the performance of four safflower genotypes. Based on these results, drought stress using 50% FC at initial branching and flowering stages was chosen to further screen 200 diverse safflower genotypes. Measured plant traits and dry biomass showed high correlations with derived digital traits including estimated shoot biomass, convex hull area, caliper length and minimum area rectangle, indicating the viability of using digital traits as proxy measures for plant growth. Estimated shoot biomass showed close association having moderately high correlation with drought indices yield index, stress tolerance index, geometric mean productivity, and mean productivity. Diverse genotypes were classified into four clusters of drought tolerance based on their performance (seed yield and digitally estimated shoot biomass) under stress. Overall, results show that rapid and precise image-based, high-throughput phenotyping in controlled environments can be used to effectively differentiate response to drought stress in a large numbers of safflower genotypes.

Large‐scale patterns of green turtle trophic ecology in the eastern Pacific Ocean

AbstractTrophic position and niche width are fundamental components of a species’ ecology, reflecting resource use, and influencing key demographic parameters such as somatic growth, maturation, and survival. Concepts about a species’ trophic niche space have important implications for local management and habitat protection, and can shed light about resilience to changing climate for species occurring over broad spatial scales. For elusive marine animals such as sea turtles, trophic niche is challenging to study, and researchers often rely on other metrics, such as isotopic niche, as a proxy. Here, stable isotope analysis (δ13C and δ15N values) was conducted on bulk skin tissue of 718 green turtles (Chelonia mydas) distributed among 16 foraging areas in the eastern Pacific from the USA to Chile, a range spanning ~10,000 km. Compound‐specific nitrogen isotope analysis of amino acids (CSIA‐AA) was applied to 21 turtles among seven sites. Isotopic niche space was determined via Bayesian ellipse area (BEA) and convex hull area (CHA) analyses of bulk isotope values, which were also used along with amino acid δ15N values to determine trophic position (TP). Substantial variability in bulk tissue δ13C and δ15N values was found within and among sites, and amino acid δ15N values confirmed this was largely due to spatial differences in baseline nitrogen isotopic compositions, but also to a lesser extent from TP differences among the green turtle foraging populations. Isotope niche space varied among sites, influenced by the diversity of prey types and relative input of terrestrial‐ vs. marine‐derived nutrients; BEAs were the most suitable measurement of isotopic niche space due to the larger influence of outlying values with the CHA approach. Amino acid isotope‐derived TP estimates that accounted for local habitat conditions (e.g., mixed seagrass/macroalgae diet) performed the best among several approaches; TP ranged from 2.3 to 3.6, which indicates an omnivorous diet for most populations. In addition to providing additional spatial resolution for δ13C and δ15N isoscapes in the eastern Pacific, especially in coastal habitats, this study further establishes CSIA‐AA as an effective tool to study the trophic ecology of sea turtles across a variety of food webs and habitats.

Signature Verification Using Support Vector Machine and Convolution Neural Network

Signature is used for recognition of an individual. Signature is considered as a mark that an individual write on a paper for his/her identity or proof. It is used as a unique feature for identifying an individual. It is highly used in social and business functions which gives rise to verification of signature. There are chances of signature getting forged. Hence, the need to identify signature as genuine of forged is utmost important. In this paper, identification of signature as genuine or forged is done using two approaches. First approach is using SVM and second is using CNN. For SVM, pre-processing of signature image is done and feature extraction is performed. Features extracted are histogram of gradient, shape, aspect ratio, bounding area, contour area and convex hull area. Further, SVM is applied to classify signature as genuine or forged and accuracy is determined. In the second approach, signature image is pre-processed, CNN is used to classify signature as genuine or forged and accuracy is determined. Dataset used here is ICDAR Dutch dataset along with 80 signatures taken from 4 people.Dutch dataset consists of 362 signature imagesand signature images taken from 4 people consists 10 genuine and 10 forged signatures which sums to 442 signature images. The proposed system provides accuracy of 86.39% using SVM and around 83.78% using CNN.

Intelligent-based decision support system for diagnosing glaucoma in primary eyecare centers using eye tracker

It is quite alarming that the increase of glaucoma is due to the lack of awareness of the disease and the cost for glaucoma screening. The primary eye care centers need to include a comprehensive glaucoma screening and include machine learning models to elaborate it as decision support system. The proposed system considers the state of art of eye gaze features to understand cognitive processing, direction and restriction of visual field. There is no significant difference in global and local ratio and skewness value of fixation duration and saccade amplitude, which suggest that there is no difference in cognitive processing. The significance value of saccadic extent along vertical axis, Horizontal Vertical ratio (HV ratio), convex hull area and saccadic direction show that there is restriction in vertical visual field. The statistical measures (p < 0.05) and Spearman correlation coefficient with class label validate the results. The proposed system compared the performance of seven classifiers: Naïve Bayes classifier, linear and kernel Support Vector classifiers, decision tree classifier, Adaboost, random forest and eXtreme Gradient Boosting (XGBoost) classifier. The discrimination of eye gaze features of glaucoma and normal is efficiently done by XGBoost with accuracy 1.0. The decision support system is cost-effective and portable.

Trophic niche and interspecific diet relationship of Gymnocypris in autumn from Langcuo Lake of Tibet, China.

Langcuo Lake is a typical plateau lake in the southern Tibet, with an altitude of 4300 m. Gymnocypris chui and Gymnocypris scleracanthus coexisted in this lake. In this study, we investigated the food composition and trophic niche of two Gymnocypris fishes in autumn by carbon and nitrogen stable isotopes technique in 2018. We further analyzed their interspecific diet relationship, including the diet similarity index and overlap coefficient. The results showed that G. chui mainly feed on ephemeroptera larvae, chironomidae larvae, periphytic algae, gammarus and plankton, while G. scleracanthus mainly feed on ephemeroptera larvae, periphytic algae, chironomidae larvae, gammarus and aquatic vascular plants. Two Gymnocypris fishes had trophic niche overlap. The δ13C range value (CR=1.60), δ15N range value (NR=1.89), and total area of convex hull (TA=1.80) of G. chui were lower than those of G. scleracanthus, with the difference values being 0.09, 0.57 and 1.29, respectively. The diet similarity index and overlap coefficient of those two species were higher, with a value of 94.2% and 91.5%, respectively. Our results suggested that the interspecific diet competition may be intense between the G. chui and G. scleracanthus in Langcuo Lake in autumn.

Lettuce growth stage identification based on phytomorphological variations using coupled color superpixels and multifold watershed transformation

Identifying the plant's developmental growth stages from seed leaf is crucial to understand plant science and cultivation management deeply. An efficient vision-based system for plant growth monitoring entails optimum segmentation and classification algorithms. This study presents coupled color-based superpixels and multifold watershed transformation in segmenting lettuce plant from complicated background taken from smart farm aquaponic system, and machine learning models used to classify lettuce plant growth as vegetative, head development and for harvest based on phytomorphological profile. Morphological computations were employed by feature extraction of the number of leaves, biomass area and perimeter, convex area, convex hull area and perimeter, major and minor axis lengths of the major axis length the dominant leaf, and length of plant skeleton. Phytomorphological variations of biomass compactness, convexity, solidity, plant skeleton, and perimeter ratio were included as inputs of the classification network. The extracted Lab color space information from the training image set undergoes superpixels overlaying with 1,000 superpixel regions employing K-means clustering on each pixel class. Six-level watershed transformation with distance transformation and minima imposition was employed to segment the lettuce plant from other pixel objects. The accuracy of correctly classifying the vegetative, head development, and harvest growth stages are 88.89%, 86.67%, and 79.63%, respectively. The experiment shows that the test accuracy rates of machine learning models were recorded as 60% for LDA, 85% for ANN, and 88.33% for QSVM. Comparative analysis showed that QSVM bested the performance of optimized LDA and ANN in classifying lettuce growth stages. This research developed a seamless model in segmenting vegetation pixels, and predicting lettuce growth stage is essential for plant computational phenotyping and agricultural practice optimization.

Variability of isotopic partitioning between sympatric brown trout (Salmo trutta) and European grayling (Thymallus thymallus) in the Loue River (France)

AbstractTrophic partitioning, defining how individuals or populations differ in their resource use, is expected to promote coexistence of sympatric species by reducing resource overlap. We used stable isotope measurements (δ13C and δ15N) to address niche characteristics (δ13C and δ15N ranges, isotope convex hull and standard ellipse areas, mean nearest neighbour distances and trophic positions) and trophic partitioning of sympatric brown trout (Salmo trutta,n = 110) and European grayling (Thymallus thymallus,n = 63) in two sampling locations of a French river. Aquatic resources predominantly fuelled both species, yet both terrestrial resources (TER) and trophic positions (TP) were higher for trout (~36 ± 13% TER, 3.6 ± 0.7 TP) than grayling (~26 ± 9% TER, 2.7 ± 0.6 TP) supporting difference in their trophic niches. Isotope analyses showed that trout had a larger isotope niche than grayling suggesting more opportunistic trophic behaviour. Their isotopic overlapping was higher at the upstream site (isotopic nestedness = 0.8 ± 0.1) than that at the downstream site (isotopic nestedness = 0.4 ± 0.2). Euclidian distances of stable isotopes and TP for the two species increased with salmonid size, while aquatic resource use decreased with salmonid size. These results demonstrate an increase in isotopic niche partitioning and change in trophic attributes with ontogeny. Our study showed that despite relying on similar resources, these two sympatric salmonids exhibited clear trophic differences that were amplified with ontogeny. The consideration of fish ontogenetic dietary shift would hence be a determinant driver of the trophic niche partitioning for these sympatric salmonids.

Non-destructive phenotyping for early seedling vigor in direct-seeded rice

BackgroundEarly seedling vigor is an essential trait of direct-seeded rice. It helps the seedlings to compete with weeds for water and nutrient availability, and contributes to better seedling establishment during the initial phase of crop growth. Seedling vigor is a complex trait, and phenotyping by a destructive method limits the improvement of this trait through traditional breeding. Hence, a non-invasive, rapid, and precise image-based phenotyping technique is developed to increase the possibility to improve early seedling vigor through breeding in rice and other field crops.ResultsTo establish and assess the methodology using free-source software, early seedling vigor was estimated from images captured with a digital SLR camera in a non-destructive way. Here, the legitimacy and strength of the method have been proved through screening seven diverse rice cultivars varying for early seedling vigor. In the regression analysis, whole-plant area (WPA) estimated by destructive-flatbed scanner (WPAs) and non-destructive imaging (WPAi) approaches was strongly related (R2 > 83%) and suggested that WPAi can be adapted in place of destructive methods to estimate seedling vigor. In addition, this study has identified a set of new geometric traits (convex hull and top view area) for screening breeding lines for early seedling vigor in rice, which decreased the time by 80% and halved the cost of labor in data observation.ConclusionsThe method demonstrated here is affordable and easy to establish as a phenotypic platform. It is suitable for most glasshouses/net houses for characterizing genotypes to understand the plasticity of shoots under a given environment at the seedling stage. The methodology explained in this experiment has been proven to be practical and suggested as a technique for researchers involved in direct-seeded rice. Consequently, it will help in the simultaneous screening of genotypes in large numbers, the identification of donors, and in gaining information on the genetic basis of the trait to design a breeding program for direct-seeded rice.

The relevance of ecoregions and mountainous environments in the diversity and endemism of land gastropods

Twenty-five sub-ecoregions make Argentina from southern South America a favored area to study the mutual correspondence between environments and biodiversity. Unfortunately, efforts devoted to study these environments are unbalanced, with the subtropical dry forests less studied than the tropical and subtropical humid ones. Since the limits of ecoregions are based on vegetation criteria, land gastropods represent an independent source of information to test the relevance of sub-ecoregions in different aspects of biodiversity. We ask if land gastropods mirror these traditional diversity patterns when their distributions are framed in the context of sub-ecoregions. Additionally, we want to test if short-range endemic species (SRE) are randomly scattered across the sub-ecoregions. We first built an updated taxonomic checklist and mapped all the valid records compiled to date. Taxonomic richness, taxonomic diversity, and beta-diversity between sub-ecoregions were calculated. We obtained a hierarchical grouping of sub-ecoregions and the respective list of species that significantly support each cluster. We also developed two new analytical resources: a radial plot for showing the species composition of clusters resolved at three taxonomic levels, and a mixed coefficient of distributional size useful to identify SRE from sparse point records. This dimensionless measure of spatial range combines information of both the convex hull area and the length of the minimum spanning tree connecting point localities of presence. The Southern Andean Yungas and Dry Chaco are the species-level richest ecoregions. Although the Paranaense Forest harbors half of the number of species found in the Chaco Serrano, it reaches the highest score of taxonomic diversity because of the eclectic nature of their genera. SRE species are not randomly distributed across the sub-ecoregions, but they broadly overlap with the orographic Peripampasic arc extended over Chaco Serrano and Yungas Forests. SREs are highly dependent on the physical nature of the landscape.

A convex cover for closed unit curves has area at least 0.1

We improve a lower bound for the smallest area of convex covers for closed unit curves from 0.0975 to 0.1, which makes it substantially closer to the current best upper bound 0.11023. We did this by considering the minimal area of convex hull of circle, line of length 12, and rectangle with side 0.1727×0.3273. By using geometric methods and the Box-search algorithm, we proved that this area is at least 0.1. We give informal numerical evidence that the obtained lower bound is close to the limit of current techniques, and substantially new idea is required to go significantly beyond 0.10044.

Fish isotopic niches associated with environmental indicators and human disturbance along a disturbed large subtropical river in China

Stable isotopes are increasingly used to detect and understand the impacts of environmental changes on riverine ecological properties. The δ13C and δ15N signatures of fish with different feeding habits were measured in a large subtropical river to evaluate how fish isotopic niches respond to environmental gradients and human disturbance. From basal resources to fish consumers, the high values of epilithic periphyton (biofilm) δ13C and suspended particulate organic matter δ15N concurrently determined the niche ranges and space (e.g., convex hull area) of fish communities. Along a longitudinal gradient (except in the industrial zone), the number of fish trophic guilds identified by Bayesian ellipses continuously increased; meanwhile, higher trophic diversity and less redundancy were observed near the lower reaches and estuary. Variance inflation factors were estimated to detect the multicollinearity of 40 environmental variables, 14 of which were selected as indicators. Relative importance (RI) analysis was used to evaluate the explanatory power of these indicators for the spatial variation in isotopic niche metrics; the results showed that riffle habitat area, water nitrate concentration, gravel-cobble substrate, and riparian buffer width were the 4 key environmental indicators (average RI > 12%) that determined the longitudinal pattern of fish isotopic niches. These findings suggested that community-level δ13C signatures are more responsive to changes in habitats (e.g., riffle) and substrates (e.g., gravel-cobble) supporting the productivity of autochthonous diatoms while δ15N signatures respond to water quality altered by nitrogen pollution from manure-fertilized farming and poultry livestock effluent. Furthermore, δ15N may be more robust and interpretable than δ13C as an isotopic indicator of ecosystem change in rivers exposed to multiple or complex anthropogenic stressors.