Distribution Map Research Articles

From unwanted to wanted: Blending functional weed traits into weed distribution maps

AbstractSite‐specific weed management (SSWM) is increasingly employed to reduce herbicide inputs. Incorporating functional traits of weed species allows for the selection of SSWM methods that effectively reduce the abundance of weeds with a high competitive potential (disservice) while preserving weeds that provide beneficial ecosystem services (service). In this study, we aim to assess relevant weed functional traits and translate this information into a spatial trait distribution map for weed (dis‐)service provision. The distribution of weed abundance in a field was recorded using a spatial grid. Data on functional traits for the recorded weed species were extracted from published datasets and combined into the two variables, service and disservice. Individual traits (service/disservice) were weighted for each pixel of the weed distribution map based on the number of individual plants per species. Principal component analysis was employed to generate independent variables to describe the potential for service and disservice provision. As a result, two (dis‐)service trait‐based distribution maps were generated: one highlights field areas that provide enhanced ecological services, while the other displays areas with a high disservice potential. The results show that around 61% of the area in the field had a high service potential. The area with a high disservice was slightly higher than the half of the area with a high service, while about 32% of the field has both high service and disservice potential in the same area. This study presents a spatially explicit approach to incorporate information on weed functional traits into SSWM approaches targeted at reducing weed competition while at the same time enhancing weed functional diversity.

Mangrove mapping in China using Gaussian mixture model with a novel mangrove index (SSMI) derived from optical and SAR imagery

As an important shoreline vegetation and highly productive ecosystem, mangroves play an essential role in the protection of coastlines and ecological diversity. Accurate mapping of the spatial distribution of mangroves is crucial for the protection and restoration of mangrove ecosystems. Supervised classification methods rely on large sample sets and complex classifiers and traditional thresholding methods that require empirical thresholds, given the problems that limit the feasibility and stability of existing mangrove identification and mapping methods on large scales. Thus, this paper develops a novel mangrove index (spectral and SAR mangrove index, SSMI) and Gaussian mixture model (GMM) mangrove mapping method, which does not require training samples and can automatically and accurately map mangrove boundaries by utilizing only single-scene Sentinel-1 and single-scene Sentinel-2 images from the same time period. The SSMI capitalizes on the fact that mangroves are differentiated from other land cover types in terms of optical characteristics (greenness and moisture) and backscattering coefficients of SAR images and ultimately highlights mangrove forest information through the product of three expressions (f(S) = red egde/SWIR1, f(B) = 1/(1 + e-VH), f(W)=(NIR-SWIR1)/(NIR+SWIR1)). The proposed SSMI was tested in six typical mangrove distribution areas in China where climatic conditions and mangrove species vary widely. The results indicated that the SSMI was more capable of mapping mangrove forests than the other mangrove indices (CMRI, NDMI, MVI, and MI), with overall accuracys (OA) higher than 0.90 and F1 scores as high as 0.93 for the other five areas except for the Maowei Gulf (S5). Moreover, the mangrove maps generated by the SSMI were highly consistent with the reference maps (HGMF_2020、LASAC_2018 and IMMA). In addition, the SSMI achieves stable performance, as shown by the mapping results of the other two classification methods (K-means and Otsu’s algorithm). Mangrove mapping in six typical mangrove distribution areas in China for five consecutive years (2019–2023) and experiments in three Southeast Asian countries with major mangrove distributions (Thailand, Vietnam, and Indonesia) demonstrated that the SSMIs constructed in this paper are highly stable across time and space. The SSMI proposed in this paper does not require reference samples or predefined parameters; thus, it has great flexibility and applicability in mapping mangroves on a large scale, especially in cloudy areas.

Research on Leaf Area Index Inversion Based on LESS 3D Radiative Transfer Model and Machine Learning Algorithms

The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productivity, water cycle, and carbon balance. In this study, we propose to combine high-resolution GF-6 2 m satellite images with the LESS three-dimensional RTM and employ different machine learning algorithms, including Random Forest, BP Neural Network, and XGBoost, to achieve LAI inversion for forest stands. By reconstructing real forest stand scenarios in the LESS model, we simulated reflectance data in blue, green, red, and near-infrared bands, as well as LAI data, and fused some real data as inputs to train the machine learning models. Subsequently, we used the remaining measured LAI data for validation and prediction to achieve LAI inversion. Among the three machine learning algorithms, Random Forest gave the highest performance, with an R2 of 0.6164 and an RMSE of 0.4109, while the BP Neural Network performed inefficiently (R2 = 0.4022, RMSE = 0.5407). Therefore, we ultimately employed the Random Forest algorithm to perform LAI inversion and generated LAI inversion spatial distribution maps, achieving an innovative, efficient, and reliable method for forest stand LAI inversion.

Does Urban Green Space Pattern Affect Green Space Noise Reduction?

The effect of urban green spaces on traffic noise reduction has been extensively studied at the level of single vegetation, hedges, etc., but there is a lack of corresponding studies at the scale of spatial patterns of urban green spaces. Therefore, this study aims to analyze the relationship between the spatial pattern of urban green space and the change in green space’s noise reduction capacity. Through the morphology spatial pattern analysis method, this analysis divides the urban green space in the Fuzhou high-tech zone into seven types of elements with different ecological definitions and simulates the noise condition of the urban environment with the presence of green space as well as without the presence of green space by computer simulation, calculates the distribution map of the noise reduction produced by the urban green space, and analyzes the correlation between the seven types of green space elements and the noise reduction with the geographically weighted regression modeling analysis. The study finds that (1) Urban green space patterns can significantly affect the net noise reduction of green space. Areas with high green coverage can produce a stronger green space noise reduction effect. (2) More complex green space shapes and more fragmented urban green space can produce higher noise reduction. (3) The green space close to the source of noise can exert a stronger noise reduction effect. Therefore, in the process of planning and design, from the perspective of improving the urban acoustic environment, the configuration of high-quality green spaces in areas with higher levels of noise pollution should be given priority, which may have better noise reduction effects.

Spatial distribution, ecological and ecotoxicity evaluation of heavy metals in agricultural soils along Lala-Manjo Highway, Cameroon Volcanic Line

This study aims at assessing the spatial distribution, ecological and toxicity levels of heavy metals in agricultural soils of Lala-Manjo Highway in Cameroon. The soil samples were collected and analyzed by inductively coupled plasma-mass spectrometry. The result shows that Co, Cr, Cu, Hg, Mn, Ni, Sr, V and Zn concentrations are higher than those of the upper continental crust reference values except those of As, Cd and Pb. Correlation coefficient indicate similar input sources governed by same geochemical characteristics. Spatial distribution maps show high concentration of heavy metals in east, south and western part of the study area. Pollution indicators show that enrichment factor varied from moderate to high enrichment of heavy metals. The contamination degree (27.12–35.59), geo-accumulation index (0.01–4.94), and potential ecological risk index (RI = < 150) revealed a low to moderate levels of metallic pollution. The majority of stations have pollution load index and Nemerow integrated pollution index values greater than one, indicating serious pollution. Modified hazard quotient and toxic risk index suggest significant severity of pollution and low toxicity risk of heavy metals, pointing low toxicity effects of heavy metals to fauna and flora in the soil ecosystem. Considering the factors governing soils accumulation such as geology, climate and anthropogenic activities as well as the correlation with commonly used environmental background, it is proposed that the data from this study can be considered as a baseline of pollution to plan major agricultural developments in the area. The study underlines the importance of continuous monitoring and adaptive management for the preservation of soil ecosystem. The results may assist companies and local population in setting realistic targets for monitoring and remediation programs.

Construction and assessment of a fire risk index system for typical grasslands in Xinjiang, China

BackgroundFire hazards have a substantial impact on grassland ecosystems, and they are becoming more frequent and widespread because of global changes and human activities. However, there is still a lack of a widely accepted or practical method to evaluate grassland fire risk. In our study of typical grasslands in northern Xinjiang, we selected 18 evaluation indicators for grassland fires from three aspects of hazard, exposure, and vulnerability. Employing the analytic hierarchy process, weighted comprehensive evaluation method, and standard deviation classification, we determined the fire risk level thresholds, aiming to develop efficient and precise methods for assessing grassland fire risks, and ultimately created a grid-based map of grassland fire risk levels.ResultsThe risk level of grassland fires is determined by the combined spatial heterogeneity of fire-causing factors’ hazard and fire hazard-bearing bodies’ vulnerability and exposure. The hazard of grassland fire and fire hazard-bearing bodies’ vulnerability and exposure are dominated by medium level and medium–low level. Most areas of grassland fire risk levels are medium–low, medium, or medium–high risk, with few areas being high risk or low risk. The grassland fire risk exhibits a spatial distribution characterized by higher risks in the western and lower in the eastern; high and medium–high risk areas are primarily distributed in the western and some northeastern regions of the study area. The simulate result effectively represents the spatial distribution of grassland fire in the research area.ConclusionWe established a grassland fire risk index system and model, creating a spatial distribution map of grassland fire risk levels based on grid. Few grassland areas have fire risks and show a patchy distribution. The results generally reflect the spatial distribution pattern of grassland fire risks in the study area. This research provides technical support for scientifically formulating local grassland fire disaster prevention and relief strategies.

DSANet: Dual-path segmentation-guided attention network for radiotherapy dose prediction from CT images only

In radiation therapy, an ideal treatment plan always takes the treatment planner a couple of days to tweak repeatedly, owing to the complex dose distribution which requires the prescribed dose in the tumor area but significantly reduced radiation dose in the surrounding normal tissues. To facilitate this process, many deep learning-based studies have been devoted to the automatical prediction of dose distribution. However, besides the computed tomography (CT) image, these methods usually require extra segmentation masks such as tumor target and organs at risk (OARs) as prior knowledge, and acquiring such annotations are quite time-consuming. In this paper, we propose a Dual-path Segmentation-guided Attention Network (DSANet) to directly predict the dose distribution map solely from the CT images. Specifically, considering the high correlation between the tumor anatomical structure and the dose distribution, we develop a dual-path architecture which consists of 1) a primary dose prediction path aiming at generating accurate dose distribution maps, and 2) an auxiliary segmentation path applied to provide attention maps with anatomical information for the primary dose prediction task. Moreover, we design an attention map-guided feature decoupling (AMFD) module to enforce the primary network to focus not only on the high-dose distribution in the tumor area but also on the low-dose distribution in the surrounding regions. Experiments on two in-house datasets demonstrate the effectiveness and generalization of our method.

Revision and transfer of Neotropical genera of the tribe Morimopsini Lacordaire, 1869 (Coleoptera: Cerambycidae): new species, redescription, taxonomic notes, and species key

The transfer of Neotropical genera from Morimopsini to Apomecynini is proposed. Two new species are described, Tuberolamia santossilvai sp. nov. and Polytuberotum boteroi sp. nov. The unknown female of Tuberolamia andicola Breuning, 1940 is described and illustrated. Keys to species, distribution map, and taxonomic considerations for the group are provided.

Revealing the Dynamic Recrystallization Mechanism and Hot Workability of Fe–0.15C–10Mn Medium‐Mn Steel through Grain Size Distribution and 3D Processing Maps

Revealing the Dynamic Recrystallization Mechanism and Hot Workability of Fe–0.15C–10Mn Medium‐Mn Steel through Grain Size Distribution and 3D Processing Maps

Effects of stamp material and restoration depth on the accuracy of direct composite resin restorations using stamp technique

ObjectivesTo evaluate the effects of different stamp materials and restoration depths on the accuracy of direct composite resin restorations using stamp technique. MethodsEighty standard resin teeth were divided into four groups based on different stamp materials: flowable composite resin (FR), vinyl polydimethyl siloxane (VPS) for bite registration (VB), VPS for impression (VI) and transparent VPS (TV). Each material group was further divided into two subgroups based on restoration depth (1 and 2 mm; n = 10). Standardized Class I cavities were prepared and restored with the corresponding stamps. Pre- and post-treatment scans of each tooth were fitted using Geomagic Control X software, generating deviation distribution maps of the occlusal surface. The accuracy indicators, including mean height variation (MHV), root mean square (RMS), and proportions of height variation with different ranges (PHVrange), were recorded to evaluate the accuracy of restorations. Analysis of variance (ANOVA) were used for statistical analysis (α=0.05). ResultsThe type of stamp material significantly affected all accuracy indicators, while restoration depth did not show a significant effect on any of the indicators. MHV values ranged from 29.70 ± 5.88 μm to 63.52 ± 9.58 μm, and RMS values ranged from 62.78 ± 8.76 μm to 101.79 ± 13.17 μm, displaying a trend of FR<VB<VI<TV. The FR groups had the highest PHV<0.05mm values (72.24 ± 3.40 % for 1 mm, 71.47 ± 8.54 % for 2 mm) and the lowest PHV>0.2mm values (0.70 ± 0.50 % for 1 mm, 0.94 ± 0.47 % for 2 mm). In contrast, the TV groups exhibited the lowest PHV<0.05mm values (59.69 ± 10.23 % for 1 mm, 59.67 ± 5.70 % for 2 mm), while the VI groups showed the highest PHV>0.2mm values (7.34 ± 1.58 % for 1 mm, 8.20 ± 3.16 % for 2 mm). ConclusionsThe accuracy of direct composite resin restorations using stamp made of flowable composite resin was higher than that of all tested VPS materials, thereby reducing the need for occlusal adjustment and improving clinical efficiency. Besides, restoration depth had no significant impact on the accuracy of stamp technique, regardless of the stamp material used. Clinical significanceThe accuracy of direct composite resin restorations using stamp technique with different stamp materials varies. The flowable composite resin stamp exhibits higher accuracy compared with VPS stamps, reducing the need for occlusal adjustments.

Four new species of the lanternfly genus Zanna Kirkaldy, 1902 from Cambodia and Vietnam (Hemiptera: Fulgoromorpha: Fulgoridae)

Four new species of Zanna Kirkaldy, 1902 are described: two from Cambodia, Z. chartieri Constant sp. nov. from Tatai in Koh Kong Province and Z. limbourgi Constant sp. nov. from Phnom Aural Wildlife Sanctuary in Kampong Speu Province and Kbal Spean in Siem Reap Province, and two from Vietnam: Z. bidoupana sp. nov. from Bidoup-Nui Ba National Park in Lam Dong Province and Z. kusamae sp. nov. from Dong Nai Biosphere Reserve in Dong Nai Province. Illustrations of the holotypes and male genitalia, photographs of live specimens and nymphs, a distribution map and host plants records are provided. The type of Zanna chinensis (Distant, 1893) is also illustrated for comparison. The genus Zanna now contains 37 species.

Shutdown dose rate calculation for fission reactors: An application of the MS-CADIS method to OPAL

There have been considerable advances of shutdown dose rate (SDDR) calculation methods for fusion related problems, however applications of these methods in the fission reactor field have hitherto been sparse. The present study attempts to bridge this gap by investigating the applicability of SDDR calculation methods for fission reactor problems. Specifically, we aim to assess and validate whether recent advances in SDDR methods can be successfully applied in fission research reactors. To this end, we estimate the shutdown dose rate distribution at the Open Pool Australian Light water reactor (OPAL) using the rigorous two step (R2S) computational method, and we compare the calculated results with the experimental data. This method utilizes a 3D reactor model implemented in the Monte Carlo N-Particle (MCNP) transport code, the AutomeD VAriaNce reduction Generator (ADVANTG) code for geometry discretization and variance reduction calculations, and the Oak Ridge Isotope GENeration (ORIGEN) inventory code for activation calculations. To ensure robustness, we employ two variance reduction techniques, Forward Weighted Consistent Adjoint Driven Importance Sampling (FW-CADIS) and Multi-Step Consistent Adjoint Driven Importance Sampling (MS-CADIS). To the best of our knowledge, this is the first MS-CADIS method implementation for fission reactor problems. The SDDR is estimated at ten locations within the experimental hall, all situated more than 4 m away from the reactor core.The paper shows that, the experimental observations are within the lower and upper bounds of the simulation results for 4 out of 10 locations, while the remaining observations are within a factor of 7, with one significant outlier. The calculated average dose rate is within 5% of the nominal values of the experimental observations for 3 locations. The computational results are within statistical uncertainty by using two different variance reduction techniques, with significant computational advantage of MS-CADIS over FW-CADIS for SDDR calculations. The results indicate that the combination of SDDR distribution maps, estimated dose rate energy dependance, and activation information are powerful tools in identifying the radioisotopes and reactor components dominating the SDDR. These results can contribute to better radiation safety practices in contaminated areas, by enabling the minimal dose path planning or by improving radiation shielding.

Distribution mapping of deadly scorpions in Iran

Scorpion sting is a problem all over the world and becomes an acute problem when it is associated with death. Iran is known as a region with a large number of scorpions and, of course, with many cases of scorpion stings per year. So far, 11 scorpion species in Iran have been identified as dangerous, of which there are only three species for which deaths have been reported. Due to the importance of these three species, we prepared a distribution map of these three types of scorpions and discuss the implications of these findings in the larger context of dangerous scorpion stings in Iran.

A 3D imaging system for dosimetry in FLASH radiotherapy

This study examines the feasibility of reconstructing the spatial dose distribution delivered by a 9 MeV electron beam within a plastic scintillator block using optical tomography. The system is designed for use in 3D-dosimetry for clinical applications in quality assurance and beam characterization, especially with FLASH-beam sources. The proposed detector reconstructs the 3D dose distribution map employing a monolith with 10 cm side, imaged in four different projections by scientific cameras. The 3D dose distribution simulated with Geant4 was reconstructed using a maximum likelihood expectation maximization algorithm applied to the lateral projection, resulting in a longitudinal image with a standard deviation of 0.2 Gy. Furthermore, a preliminary experimental test has been performed.

A Multi-Scale Content-Structure Feature Extraction Network Applied to Gully Extraction

Black soil is a precious soil resource, yet it is severely affected by gully erosion, which is one of the most serious manifestations of land degradation. The determination of the location and shape of gullies is crucial for the work of gully erosion control. Traditional field measurement methods consume a large amount of human resources, so it is of great significance to use artificial intelligence techniques to automatically extract gullies from satellite remote sensing images. This study obtained the gully distribution map of the southwestern region of the Dahe Bay Farm in Inner Mongolia through field investigation and measurement and created a gully remote sensing dataset. We designed a multi-scale content structure feature extraction network to analyze remote sensing images and achieve automatic gully extraction. The multi-layer information obtained through the resnet34 network is input into the multi-scale structure extraction module and the multi-scale content extraction module designed by us, respectively, obtained richer intrinsic information about the image. We designed a structure content fusion network to further fuse structural features and content features and improve the depth of the model’s understanding of the image. Finally, we designed a muti-scale feature fusion module to further fuse low-level and high-level information, enhance the comprehensive understanding of the model, and improve the ability to extract gullies. The experimental results show that the multi-scale content structure feature extraction network can effectively avoid the interference of complex backgrounds in satellite remote sensing images. Compared with the classic semantic segmentation models, DeepLabV3+, PSPNet, and UNet, our model achieved the best results in several evaluation metrics, the F1 score, recall rate, and intersection over union (IoU), with an F1 score of 0.745, a recall of 0.777, and an IoU of 0.586. These results proved that our method is a highly automated and reliable method for extracting gullies from satellite remote sensing images, which simplifies the process of gully extraction and provides us with an accurate guide to locate the location of gullies, analyze the shape of gullies, and then provide accurate guidance for gully management.

Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea.

The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple points and non-point sources. Monitoring chlorophyll-a (Chl-a) concentrations, a proxy for algal biomass is essential for assessing the trophic status of the river and managing its ecological health. This study aimed to improve the accuracy and reliability of Chl-a estimation in the Nakdong River using machine learning models (MLMs) and simultaneous use of multiple remotely sensed datasets. This study compared the performances of four MLMs: multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and eXetreme Gradient Boosting (XGB) using three different input datasets: (1) two remotely sensed datasets (Sentinel-2 and Landsat-8), (2) standalone Sentinel-2, and (3) standalone Landsat-8. The results showed that the MLP model with multiple remotely sensed datasets outperformed other MLMs with 0.43 - 0.86 greater in R2 and 0.36 - 5.88 lower in RMSE. The MLP model demonstrated the highest performance across the range of Chl-a concentrations and predicted peaks above 20 mg/m3 relatively well compared to other models. This was likely due to the capacity of MLP to handle imbalanced datasets. The predictive map of the spatial distribution of Chl-a generated by MLP well captured the areas with high and low Chl-a concentrations. This study pointed out the impacts of imbalanced Chl-a concentration observations (dominated by low Chl-a concentrations) on the performance of MLMs. The data imbalance likely led to MLMs poorly trained for high Chl-a values, producing low prediction accuracy. In conclusion, this study demonstrated the value of multiple remotely sensed datasets in enhancing the accuracy and reliability of Chl-a estimation, mainly when using the MLP model. These findings would provide valuable insights into utilizing MLMs effectively for Chl-a monitoring.

A new nothospecies of Prosthechea (Orchidaceae: Laeliinae) from the Diamantina Plateau, Minas Gerais, Brazil

Based on morphological evidence found in some specimens recently collected in the Diamantina Plateau region, Minas Gerais State, Brazil, a new natural hybrid between Prosthechea calamaria and P. faresiana is described and illustrated as P. ×riopretensis. Both putative parental species are known for the region and their flowering period overlaps. The new nothospecies carries most of the morphological features of P. calamaria, such as short plants (≤ 19 cm height) and inflorescences (≤ 7 cm long), and flowers with a small ovate lip (≤ 1 cm long), but the floral color pattern are remarkably like P. faresiana – sepals and petals covered by longitudinal vinaceous stripes. In addition to a digital composite plate of the taxonomic novelty, further images and a distribution map of the new nothospecies and its putative parents are provided, as well as notes on morphology, phenology, and ecology.

First national mapping of cadmium in cacao beans in Colombia

Cacao crops in Colombia play a fundamental role in its economy. Cacao bean exports have a rising tendency due to increased area and production. However, cadmium (Cd) in cacao beans has affected market in Colombia. The aim of this study is to assess Cd distribution hotspots for cacao beans, Cd in beans and soil relationships, geological ages and soil chemical parameters, overall Cd dynamics, soil-to-plant translocation, and EU regulation impacts on Cd presence in Colombian cacao production. A hot and cold spot analysis combined with a cluster and outlier analysis was used to build the geographic Cd distribution in cacao beans in Colombia. Cadmium in the subsurface and the relationship with Cd in the soil surface of cacao farms were analysed using the geoaccumulation index (Igeo). The bioconcentration factor (BCF) and soil chemical features in cacao beans were quantified. Positive correlations were observed within all the studied variables except for Mn and Fe. The highest correlation was observed within Ca, pH, and Cd in soil. The Valanginian-Albian and Albian-Maastrichtian geological ages are directly related to Cd in beans, Zn, P, soil Cd, and Fe. Regions with the highest cacao productivity and export registered an average Cd concentration in beans of 2.39_ ± _2.82 mg kg−1. Two municipalities in Santander were ‘partially contaminated’ according to Igeo. The BCF was not directly proportional to soil and bean Cd level and did not fully coincide with hot spots. This evidence reinforces the idea that even areas with apparently low Cd levels may register a latent problem associated with genetic/ontogenetic, climatic, or management factors. This first distribution map of Cd in cacao beans constitutes a tool to implement actions to minimize risks and face challenges posed by the imposed regulations that may limit raw cacao bean exports in Colombia.

Equity in the geographical distribution of general practitioners in Iran.

The equitable distribution of workforce is imperative for achieving public health goals. This study was conducted to evaluate the equity of the distribution of general practitioners (GPs) in Iran. In this descriptive and cross-sectional study, data from the Information System of Benefits for Iranians and the Iranian Statistics Center were utilized. The study population encompasses all GPs operating in Iran in 2022. To assess equity in the geographical distribution of physicians, the Gini coefficient and Lorenz curve were calculated using Excel software. Additionally, ArcGIS software was used to create a distribution map. The population of Iran and active GPs in 2022 were 85,874,000 and 67,852 respectively, resulting in an average of 7.9 active GPs per 10,000 people. The provinces of Yazd, Tehran, Mazandaran, and Fars exhibited the highest concentration of active GPs per capita, collectively representing more than 28% of the population and 37% of the active GPs. Fifty percent of the active GPs were women. The Gini coefficients for the provincial and intercity distributions of GPs were 0.08 and 0.2, respectively. The distribution of GPs among provinces and cities was deemed fair and relatively fair, respectively. Iran has a lower ratio of GPs per capita than the European nations. Policymakers should pay more attention to the implementation of appropriate policies with the aim of increasing equity in the geographical distribution of GPs in the country.

Scrophularia anatolica (Scrophulariaceae): A new species from eastern Anatolia (Turkey)

Scrophularia anatolica sp. nov. (Scrophulariaceae) is described from Eastern Anatolia (Bingöl‒Turkey). The new species is related to S. erzincanica and S. lucida, but it differs from S. erzincanica by scarious bracts and bracteole margins, broad calyx lobes with scarious and dentate margins, and from S. lucida by life duration, plant length, staminode shape, calyx lobes ovate to orbicular, dentate scarious margin and stamens long exerted from corolla. Ecological data and conservation status of the new species are also presented. A distribution map of the new taxon and related species is provided.