Distribution Map Of Species Research Articles

Additional information on the taxonomy of genus Asclerobia Roesler, 1969 (Lepidoptera; Pyralidae).

The current contribution aims to supplement a previous one by the author (2021) with information on new taxonomic changes in the systematics of genus Asclerobia, and to correct some diagnosis errors. Two new species-group taxa are established as synonyms: Asclerobia flavitinctella (Ragonot, 1893) = Salebria tchahabarella Amsel, 1950 syn. nov., and Asclerobia sinensis (Caradja in Caradja & Meyrick, 1937) = Etiella yangtseella Caradja, 1939 syn. nov. The hitherto unknown female genitalia of Asclerobia flavitinctella are described, and the improved diagnosis of this species as well as Asclerobia sinensis are given. The updated keys based on external features and genitalia of both sexes, as well as a revised distribution map for Asclerobia species are provided.

A new cave-dwelling spider of the genus Speleoticus (Araneae, Nesticidae) from Sichuan, China.

Speleoticus Ballarin & Li, 2016 is a relatively small spider genus of the family Nesticidae, currently including five cave-dwelling species, distributed exclusively in East Asia, four of which are known from China. A new troglobitic spider of the genus Speleoticus from the limestone Cave Hanwang Dong, north-eastern Sichuan, China, is described under the name of S.hei Yu & Lin, sp. n. Detailed descriptions, photographs and a distribution map of the new species are provided.

Combining environmental DNA with remote sensing variables to map fish species distributions along a large river

AbstractBiodiversity loss in river ecosystems is much faster and more severe than in terrestrial systems, and spatial conservation and restoration plans are needed to halt this erosion. Reliable and highly resolved data on the state of and change in biodiversity and species distributions are critical for effective measures. However, high‐resolution maps of fish distribution remain limited for large riverine systems. Coupling data from global satellite sensors with broad‐scale environmental DNA (eDNA) and machine learning could enable rapid and precise mapping of the distribution of river organisms. Here, we investigated the potential for combining these methods using a fish eDNA dataset from 110 sites sampled along the full length of the Rhone River in Switzerland and France. Using Sentinel 2 and Landsat 8 images, we generated a set of ecological variables describing both the aquatic and the terrestrial habitats surrounding the river corridor. We combined these variables with eDNA‐based presence and absence data on 29 fish species and used three machine‐learning models to assess environmental suitability for these species. Most models showed good performance, indicating that ecological variables derived from remote sensing can approximate the ecological determinants of fish species distributions, but water‐derived variables had stronger associations than the terrestrial variables surrounding the river. The species range mapping indicated a significant transition in the species occupancy along the Rhone, from its source in the Swiss Alps to outlet into the Mediterranean Sea in southern France. Our study demonstrates the feasibility of combining remote sensing and eDNA to map species distributions in a large river. This method can be expanded to any large river to support conservation schemes.

Two new species and faunistic records of the genus Mannerheimia Mäklin, 1880 from India, Nepal and China, and additional notes on some eastern Palaearctic species of Omaliinae Macleay, 1825 (Coleoptera: Staphylinidae).

New taxonomic and faunistic data for four species of the genus Mannerheimia Mäklin, 1880 from India, Nepal and China are provided. Two new species from the Himalayan Region are described and illustrated: M. prolongata sp.n. (India, Uttarakhand) and M. punctatissima sp.n. (Eastern Nepal, Taplejung). The distribution map for both new species is given. Faunistic and diagnostic data for several species of Omaliinae Macleay, 1825 from the Eastern Palaearctic Region are also given. New diagnostic data for Acrolocha rogeri Shavrin, 2017 and Amphichroum telnovi Shavrin, 2021 are provided. Depositories of the type material for three species of the genus Anthobium Leach, 1819 are clarified. The modified distributional maps for Amphichroum pindarense Champion, 1920, A. telnovi and A. anthobioides Champion, 1925 are provided. Several species are recorded from the certain area for the first time: Acidota crenata (Fabricius, 1792) from Kyrgyzstan, Acrulia inflata (Gyllenhal, 1813) from Maritime Province of Russia, Amphichroum pindarense from Nepal, A. telnovi from India, and M. hartmanni Shavrin, 2018 from the eastern part of Nepal (Solukhumbu).

Predicting current and future high-risk areas for vectors and reservoirs of cutaneous leishmaniasis in Iran

Climate change will affect the distribution of species in the future. To determine the vulnerable areas relating to CL in Iran, we applied two models, MaxEnt and RF, for the projection of the future distribution of the main vectors and reservoirs of CL. The results of the models were compared in terms of performance, species distribution maps, and the gain, loss, and stable areas. The models provided a reasonable estimate of species distribution. The results showed that the Northern and Southern counties of Iran, which currently do not have a high incidence of CL may witness new foci in the future. The Western, and Southwestern regions of the Country, which currently have high habitat suitability for the presence of some vectors and reservoirs, will probably significantly decrease in the future. Furthermore, the most stable areas are for T. indica and M. hurrianae in the future. So that, this species may remain a major reservoir in areas that are present under current conditions. With more local studies in the field of identifying vulnerable areas to CL, it can be suggested that the national CL control guidelines should be revised to include a section as a climate change adaptation plan.

Bioclimatic inference based on mammal community using machine learning regression models: perspectives for paleoecological studies

Climate has played a significant role in shaping the distribution of mammal species across the world. Mammal community composition can therefore be used for inferring modern and past climatic conditions. Here, we develop a novel approach for bioclimatic inference using machine learning (ML) algorithms, which allows for accurate prediction of a set of climate variables based on the composition of the faunal community. The automated dataset construction process aggregates bioclimatic variables with modern species distribution maps, and includes multiple taxonomic ranks as explanatory variables for the predictions. This yields a large dataset that can be used to produce highly accurate predictions. Various ML algorithms that perform regression have been examined. To account for spatial dependence in our data, we employed a geographical block validation approach for model validation and selection. The random forest (RF) outperformed the other evaluated algorithms. Ultimately, we used unseen modern mammal surveys to assess the high predictive performances and extrapolation abilities achieved by our trained models. This contribution introduces a framework and methodology to construct models for developing models based on neo-ecological data, which could be utilized for paleoclimate applications in the future. The study aimed to satisfy specific criteria for interpreting both modern and paleo faunal assemblages, including the ability to generate reliable climate predictions from faunal lists with varying taxonomic resolutions, without the need for published wildlife inventory data from the study area. This method demonstrates the versatility of ML techniques in climate modeling and highlights their promising potential for applications in the fields of archaeology and paleontology.

A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs

Wildland conservation efforts require accurate maps of plant species distribution across large spatial scales. High-resolution species mapping is difficult in diverse, dense plant communities, where extensive ground-based surveys are labor-intensive and risk damaging sensitive flora. High-resolution satellite imagery is available at scales needed for plant community conservation across large areas, but can be cost prohibitive and lack resolution to identify species. Deep learning analysis of drone-based imagery can aid in accurate classification of plant species in these communities across large regions. This study assessed whether drone-based imagery and deep learning modeling approaches could be used to map species in complex chaparral, coastal sage scrub, and oak woodland communities. We tested the effectiveness of random forest, support vector machine, and convolutional neural network (CNN) coupled with object-based image analysis (OBIA) for mapping in diverse shrublands. Our CNN + OBIA approach outperformed random forest and support vector machine methods to accurately identify tree and shrub species, vegetation gaps, and communities, even distinguishing two congeneric shrub species with similar morphological characteristics. Similar accuracies were attained when applied to neighboring sites. This work is key to the accurate species identification and large scale mapping needed for conservation research and monitoring in chaparral and other wildland plant communities. Uncertainty in model application is associated with less common species and intermixed canopies.

Erythroxylum confertifolium, a new species from Chapada dos Veadeiros National Park, Goiás, Brazil, unveiled by morphology and leaf anatomy

Erythroxylum confertifolium, a new species from the Chapada dos Veadeiros National Park, Goiás, Brazil, is described and illustrate here. It differs from E. campestre and E. parvistipulatum, its most similar congeners, by a set of morphological (e.g. branching appearance of the stem, leaf shape and consistency, numbers of setules of stipules and cataphylls, aspect of the pedicel, size of flower parts and fruits, and aspect of auricles of petals and styles), and leaf anatomical characters (e.g. number of main and accessory vascular bundles and their appearance on the petiole and midrib, aspect of the sheath that surrounds the vascular bundle, presence or absent of papillae, palisade parenchyma/spongy parenchyma ratio, presence or absence of sheath extension in vascular bundles, as well as composition of tissues of the cortex, and fibers types in midrib). Images, conservation assessments, and a distribution map of the new species are presented, as well as images for their morphologically correlated.

First Experience with Zhuhai-1 Hyperspectral Data for Urban Dominant Tree Species Classification in Shenzhen, China

An accurate spatial distribution map of the urban dominant tree species is crucial for evaluating the ecosystem service value of urban forests and formulating urban sustainable development strategies. Spaceborne hyperspectral remote sensing has been utilized to distinguish tree species, but these hyperspectral data have a low spatial resolution (pixel size ≥ 30 m), which limits their ability to differentiate tree species in urban areas characterized by fragmented patches and robust spatial heterogeneity. Zhuhai-1 is a new hyperspectral satellite sensor with a higher spatial resolution of 10 m. This study aimed to evaluate the potential of Zhuhai-1 hyperspectral imagery for classifying the urban dominant tree species. We first extracted 32 reflectance bands and 18 vegetation indices from Zhuhai-1 hyperspectral data. We then used the random forest classifier to differentiate 28 dominant tree species in Shenzhen based on these hyperspectral features. Finally, we analyzed the effects of the classification paradigm, classifier, and species number on the classification accuracy. We found that combining the hyperspectral reflectance bands and vegetation indices could effectively distinguish the 28 dominant tree species in Shenzhen, obtaining an overall accuracy of 76.8%. Sensitivity analysis results indicated that the pixel-based classification paradigm was slightly superior to the object-based paradigm. The random forest classifier proved to be the optimal classifier for distinguishing tree species using Zhuhai-1 hyperspectral imagery. Moreover, reducing the species number could slowly improve the classification accuracy. These findings suggest that Zhuhai-1 hyperspectral data can identify the urban dominant tree species with accuracy and holds potential for application in other cities.

Mapping hydrothermal alterations and lineaments associated with epithermal and massive sulphides deposits of Tifraouine (northwest Algerian coast): Use of Landsat 8 OLI data and remote sensing

Within the context of studying mineralizations related to the Miocene volcanic rocks from the northern part of Algeria, Landsat 8 OLI data and remote sensing techniques are carried out to characterize and map hydrothermal alteration outcrops in the Miocene volcanic rocks of Tifraouine on the northwestern Algerian coast, where several indications of mineralization are known. Short-wave infrared (SWIR) bands allowed for the identification of clay alterations, while the visible and near-infrared bands help in the detection of iron alterations. Red-green-blue color combination images of 6/7, 4/2 and 5/6 ratios were produced. Principal component analysis (PCA) was computed to characterise altered zones and extract lineaments. Matched filtering technique (MF) allows direct detection after applying minimum noise fraction (MNF) transformation and pixel purity index to the spectra in order to obtain a distribution map of the target mineral species in studied area. The remote sensing results combined with the structural geological data determine a relationship between mineralization and structural features and show that hydrothermal alterations are clearly controlled by tectonic. They are highly concentrated in the calderas zone and extend towards the Bouzedjar area which allowed checking the map performed according to the field studies. This will contribute to preliminary studies related to mineral exploration and enhance the mining potential of Tifraouine. The acquisition of supplementary data would allow us to extend this study in coming works.

Field validation as a tool for mitigating uncertainty in species distribution modeling for conservation planning

AbstractDespite the success of species distribution models (SDMs) in rare species conservation planning, inherent uncertainty in predictions can facilitate misuse or misapplication. Field validation is important as model predictions can vary greatly due to low species prevalence or spatial bias; traits frequently encountered in rare species data. This study outlines an effective SDM approach to map the potential distribution of a Florida endemic plant species, Macbridea alba, which is transferable to other rare species that: (1) utilize presence data to create species distribution maps, (2) conduct field validation to gather independent data, (3) and use independent data for model evaluation and improvement. Independent data was used to assess each model's ability to predict the occurrence of M. alba and identify habitat for conservation and reintroduction efforts. Our study documents that for low prevalence species, models can produce significant variation in optimized threshold and evaluation metrics, which impact outcomes when converting continuous predictions to binary predictions, a common practice in model application. Our species distribution models were useful in finding new populations of M. alba. Field validation is vital to improving model performance, threshold selection, and evaluating model design and should be carefully considered when suggesting model use and application.

Data on the distribution of Osmoderma barnabita Motschulsky, 1845 (Coleoptera: Cetoniidae) in Bulgaria from 1904 to 2022

The hermit beetle, Osmoderma eremita s.l. (Scopoli 1763), is a species complex of scarab saproxylic beetles listed in the IUCN Red List of Threatened Species and protected in Bulgaria by Bern Convention, the Habitats Directive, and the Bulgarian Biodiversity Act. The taxonomic status of the species complex in Bulgaria is not fully known. It is believed that Osmoderma barnabita Motschulsky, 1845 is the main species present in the country, but it is also possible that Osmoderma lassalei Baraud and Tauzin, 1991 exists in the southernmost parts, where is the northern limit of its distribution. Data collected over the years on the presence of O. barnabita are very scarce due to its hidden lifestyle and lack of interest from the scientists. The present study aims to summarise all available data on O. barnabita in Bulgaria (literature, National Natural History Museum - Bulgarian Academy of Sciences collection, field work, and citizen science) for the period from 1904 to 2022. A map of the current species distribution was generated. The altitudinal distribution ranged from 0 to 1700 m, mainly in mountainous areas with well-preserved forests. Eighty-three percent of the records with exact coordinates were from forests aged above 50 years, and 35% were from forests aged above 100 years. There were also individual records from cities, showing the need to protect urban green spaces and the old trees within them. Based on the results obtained, O. barnabita is newly registered for six Natura 2000 sites, confirmed in 18 sites; and for the remaining 18, where it is included in the standard data forms based on modelling its habitat, additional studies are needed to confirm or exclude it.

Macrolobium paulobocae (Leguminosae, Detarioideae), a new species from seasonally inundated black-water floodplain forests in the Brazilian Amazon.

Macrolobium paulobocae is presented as a new species of the legume subfamily Detarioideae. It is restricted to seasonally flooded igapó forests in the Central Amazon. We provide a description, illustration, photographs, and a distribution map of the new species, as well as a table of comparative morphology with similar, likely phylogenetically related species. The epithet is in honor of Paulo Apóstolo Costa Lima Assunção, or Paulo Boca, a great Amazonian botanist, victim of COVID-19 in January 2021.

Two new species of the genus Hoplopeza Bezzi (Diptera, Hybotidae, Ocydromiinae) from the high Andean forests of Colombia.

Two new Andean species of Hoplopeza Bezzi are described and illustrated: Hoplopeza colombiana sp. nov. and Hoplopeza rafaeli sp. nov. These species are the first record from the Andean ecosystem of Colombia. In addition, the genus is diagnosed, the first description of the female terminalia, as well as a key and distribution map of the Andean species of the genus are presented.

A new species of Strotarchus Simon, 1888 from Honduras (Araneae, Cheiracanthiidae).

A new species, Strotarchus chamevazquezi sp. nov., is described based on both sexes from Department Francisco Morazán, Honduras. A detailed description, diagnosis, photographs, and distribution map of the new species are given. This is the first species of this genus recorded from Honduras.

Living in the darkness: a new cave cricket species of the genus Endecous Saussure (Orthoptera, Grylloidea, Phalangopsidae) from Serra da Bodoquena in Mato Grosso do Sul state, Brazil.

We describe a new species of the genus Endecous Saussure (1878), recorded at the Dente de Cão and Urubu Rei caves, which are located in the Bodoquena municipality, state of Mato Grosso do Sul, Brazil. Species of Endecous are the most common of crickets in Brazilian hypogean environments. Endecous comprises 24 species described and is widely distributed throughout South America, occurring also in Argentina, Bolivia and Uruguay. We also present a brief discussion about the presence of the genus in subterranean environments and a distribution map of Endecous troglobitic species through the regions of the Brazilian karst.

Gelasia attariana (Scorzonerinae, Cichorieae, Asteraceae), a new species from NW of Iran, inferred from morphological and molecular data

Gelasia attariana is described as a new species from northwest of Iran, near the border to Azerbaijan. The new species is morphologically similar to G. cinerea and G. wendelboi, mainly because of sharing caulescent perennial life form, entire lanceolate leaves with 3–7 parallel veins, tomentose indumentum of phyllaries and glabrous achenes without carpopodium, but differs in plant height, indumentum type of stems and leaves, length/width ratio of leaves, leaves color, apex of leaves and length of achenes and pappus. Morphological comparisons and taxonomic relationships of G. attariana with its closely related taxa are discussed. Consistent with morphological data, molecular evidences based on nrDNA ITS sequences confirmed that the new species can be recognized as a distinct taxon from its close relatives. Description, images of living plants and distribution map of the new species are also provided.

Ecological niche modelling and population genetic analysis of Indian temperate bamboo Drepanostachyum falcatum in the western Himalayas.

The present study was conducted to understand the key ecological and biological questions of conservation importance in Drepanostachyum falcatum which aimed to map potential distribution in the western Himalayas and decipher spatial genetic structure. Eco-distribution maps were generated through ecological niche modelling using the Maximum Entropy (MaxEnt) algorithm implemented with 228 geocoordinates of species presence and 12 bioclimatic variables. Concomitantly, 26 natural populations in the western Himalayas were genetically analysed using ten genomic sequence-tagged microsatellite (STMS) markers. Model-derived distribution was adequately supported with appropriate statistical measures, such as area under the 'receiver operating characteristics (ROC)' curve (AUC; 0.917 ± 0.034)", Kappa (K; 0.418), normalized mutual information (NMI; 0.673) and true skill statistic (TSS; 0.715). Further, Jackknife test and response curves showed that the precipitation (pre- and post-monsoon) and temperature (average throughout the year and pre-monsoon) maximize the probabilistic distribution of D. falcatum. We recorded a wide and abundant (4096.86 km2) distribution of D. falcatum in the western Himalayas with maximum occurrence at 1500 to 2500m asl. Furthermore, marker analysis exemplified high gene diversity with low genetic differentiation in D. falcatum. Relatively, the populations of Uttarakhand are more genetically diverse than Himachal Pradesh, whereas within the Uttarakhand, the Garhwal region captured a higher allelic diversity than Kumaon. Clustering and structure analysis indicated two major gene pools, where genetic admixing appeared to be controlled by long-distance gene flow, horizontal geographical distance, aspect, and precipitation. Both the species distribution map and population genetic structure derived herein may serve as valuable resources for conservation and management of Himalayan hill bamboos.

CACTUS – Vegetation database of the Dutch Caribbean Islands

The vegetation database CACTUS (registered in GIVD under SA-00-004) aims to bring together all plot-based relevés from the Dutch Caribbean Islands that are available from literature, unpublished resources, and recent field surveys. The database currently contains 2,701 vegetation descriptions. The database is used for vegetation classification, to investigate vegetation change over time, to assist in the planning of vegetation surveys, as a source for plant species distribution maps, and to inform nature conservation and policy. Taxonomic references: Van Proosdij (2012) for the Leeward Islands, Axelrod (2017, 2021) for the Windward Islands, except for St. Martin (Howard 1974-1989).

Seeing Trees from Drones: The Role of Leaf Phenology Transition in Mapping Species Distribution in Species-Rich Montane Forests

The complex topography of subtropical montane forests favors the coexistence of diverse plant species, making these species-rich forests a high priority for biodiversity monitoring, prediction, and conservation. Mapping tree species distribution accurately in these areas is an essential basis for biodiversity research and is often challenging due to their complex structure. Remote sensing has widely been used for mapping tree species, but relatively little attention has been paid to species-rich montane forests. In this study, the capability of high-resolution UAV remote sensing imagery for mapping six tree species, standing dead trees, and canopy gaps was tested in a subtropical montane forest at an elevation of 816~1165 m in eastern China. Spectral, spatial geometrical, and textural features in a specific phenological period when obvious color differences among the leaves of different species were extracted, and four object-based classification algorithms (K-nearest neighbor (KNN), classification and regression tree (CART), support vector machine (SVM), and random forest (RF)) were used for tree species classification. We found that: (1) mapping tree species distribution using low-cost UAV RGB imagery in a specific leaf phenological period has great application potential in subtropical montane forests with complex terrain. (2) Plant spectral features in the leaf senescence period contributed significantly to species classification, while the contribution of textural features was limited. The highest classification accuracy was 83% using KNN with the combination of spectral and spatial geometrical features. (3) Topographical complexity had a significant impact on mapping species distribution. The classification accuracy was generally higher in steep areas, especially in the low slope area.