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Corrigendum to " Unexpected absence of exo-erythrocytic merogony during high gametocytaemia in two species of Haemoproteus (Haemosporida: Haemoproteidae), including description of Haemoproteus angustus n. sp. (lineage hCWT7) and a report of previously unknown residual bodies during in vitro gametogenesis" " [Int. J. Parasitol.: Parasites and Wildlife 23 (April 2024) 100905] /
No full textThe authors regret the error in the second GenBank accession number, which was written incorrectly in the published article. The error is present on page 3 at the end of the paragraph 2.6 of the article. The corrected sentence is: “They were deposited in GenBank (OQ383418, OR823203).” The authors would like to apologise for any inconvenience caused
Checklist of the aphyllophoroid species from Lithuania.
No full textT he article contains a list of the aphyllophoroid fungi species found in Lithuania. The species are listed in alphabetical order according to the Latin alphabet. All names of the aphyllophoroid fungi in Lithuania have been approved by the State Lithuanian Language Commission. By 2024, a total of 608 aphyllophoroid species from 185 genera have been identified in Lithuania
Memoirs of Erikas Purvinas and Kazys Brundza about research into Šepeta and other bogs of Lithuania in the interwar and World War II years.
No full textT he article presents the edited memoirs of Erikas Purvinas (1908–1999) and Kazys Brundza (1903–1991) about mire research in Lithuania in the interwar and World War II years. The memoir manuscript by E. Purvinas is currently stored in the library of the Lithuanian Society of Plant Physiologists, whose collections are housed at the Nature Research Centre, Akademijos St. 2, Vilnius. Meanwhile, the manuscripts of the memoirs authored by K. Brundza, wherein he looks back on research into various Lithuanian bogs, i.e., Šepeta, Amalva, Kamanos, Gabijauriškis etc., are kept in the Brundzos family document collection, stored in the Lithuanian Central State Archive
First report of Sarcocystis halieti (Apicomplexa) in bearded vulture (Gypaetus barbatus) /
No full textAt least three Sarcocystis species ( S. falcatula, S. halieti and S. wobeseri –like) have been detected infecting raptorial birds. By histopathology and PCR-sequencing of the ITS1 marker, S. halieti was detected in a bearded vulture ( Gypaetus barbatus ) and a black kite ( Milvus migrans ) from the Catalonia region in North Spain. The 241 bp-long sequences obtained from the Sarcocystis organisms detected in both raptors showed 97.5–99.6% and 97.9–100% similarity with those of previously identified S. halieti ; also, the phylogenetic trees generated placed the identified sequences together with other sequences of S. halieti available in GenBank. In sum, the description of the bearded vulture as a new intermediate host for S. halieti adds new insights on the complex epidemiology of the genus involving avian hosts
Low genetic variability of the tundra vole in Lithuania /
No full textThe distribution and spread of the tundra vole (Alexandromys oeconomus) in Lithuania have been documented over the last 70 years, but the genetic diversity of the species has not been studied. In this study, we examined A. oeconomus trapped in three sites in northern and western Lithuania using mtDNA sequence analysis of the cytb and control region. The western and northern sites are separated by anthropogenic landscape barriers. The western site is subject to regular spring flooding. Phylogenetic analyses of the studied individuals placed them in the Central European phylogroup, suggesting that Lithuanian A. oeconomus originated from northeastern Poland. In Lithuania, the genetic diversity of A. oeconomus at both mtDNA loci was relatively low (Hd 0.15, p < 0.05). The genetic divergence between the western and northern samples of A. oeconomus in Lithuania, together with the low genetic variability among the voles studied, provides new insights into the phylogeography of the species and the influence of barriers on the colonization of the country
Sexual dimorphism in subterranean amphipod crustaceans covaries with subterranean habitat type /
No full textSexual dimorphism can evolve in response to sex-specific selection pressures that vary across habitats. We studied sexual differences in subterranean amphipods Niphargus living in shallow subterranean habitats (close to the surface), cave streams (intermediate), and cave lakes (deepest and most isolated). These three habitats differ because at greater depths there is lower food availability, reduced predation, and weaker seasonality. Additionally, species near the surface have a near-even adult sex ratio (ASR), whereas species from cave lakes have a female-biased ASR. We hypothesized (a) a decrease in sexual dimorphism from shallow subterranean habitats to cave lake species because of weaker sexual selection derived from changes in the ASR and (b) an increase in female body size in cave lakes because of stronger fecundity selection on account of oligotrophy, reduced predation, and weaker seasonality. We measured body size and two sexually dimorphic abdominal appendages for all 31 species and several behaviours related to male competition (activity, risk-taking, exploration) for 12 species. Species with an equal ASR that live close to the surface exhibited sexual dimorphism in all three morphological traits, but not in behaviour. The body size of females increased from the surface to cave lakes, but no such trend was observed in males. In cave lake species, males and females differed neither morphologically nor behaviourally. Our results are consistent with the possibility that sexual and fecundity selection covary across the three habitats, which indirectly and directly, respectively, shape the degree of sexual dimorphism in Niphargus species
Phragmatobia amurensis edita, a new subspecies from Shaanxi Province, China (Lepidoptera: Erebidae: Arctiinae: Arctiini) /
No full textIsospora and Lankesterella parasites (Eimeriidae, Apicomplexa) of passeriform birds in Europe: Infection rates, phylogeny, and pathogenicity /
No full textWild birds are common hosts to numerous intracellular parasites such as single-celled eukaryotes of the family Eimeriidae (order Eucoccidiorida, phylum Apicomplexa). We investigated the infection rates, phylogeny, and pathogenicity of Isospora and Lankesterella parasites in wild and captive passerine birds. Blood and tissue samples of 815 wild and 15 deceased captive birds from Europe were tested using polymerase chain reaction and partial sequencing of the mitochondrial cytochrome b and cytochrome c oxidase I and the nuclear 18S rRNA gene. The infection rate for Lankesterella in wild birds was 10.7% compared to 5.8% for Isospora. Chromogenic in situ hybridization with probes targeting the parasites’ 18S rRNA was employed to identify the parasites’ presence in multiple organs, and hematoxylin–eosin staining was performed to visualize the parasite stages and assess associated lesions. Isospora parasites were mainly identified in the intestine, spleen, and liver. Extraintestinal tissue stages of Isospora were accompanied by predominantly lymphohistiocytic inflammation of varying severity. Lankesterella was most frequently detected in the spleen, lung, and brain; however, infected birds presented only a low parasite burden without associated pathological changes. These findings contribute to our understanding of Isospora and Lankesterella parasites in wild birds
Gravel-pebble paradox of the Curonian Spit: genetic, lithodynamic and evolutionary contexts /
No full textA deep-learning framework for enhancing habitat identification based on species composition /
No full textAims: The accurate classification of habitats is essential for effective biodiversity conservation. The goal of this study was to harness the potential of deep learning to advance habitat identification in Europe. We aimed to develop and evaluate models capable of assigning vegetation-plot records to the habitats of the European Nature Information System (EUNIS), a widely used reference framework for European habitat types. Location: The framework was designed for use in Europe and adjacent areas (e.g., Anatolia, Caucasus). Methods: We leveraged deep-learning techniques, such as transformers (i.e., models with attention components able to learn contextual relations between categorical and numerical features) that we trained using spatial k-fold cross-validation (CV) on vegetation plots sourced from the European Vegetation Archive (EVA), to show that they have great potential for classifying vegetation-plot records. We tested different network architectures, feature encodings, hyperparameter tuning and noise addition strategies to identify the optimal model. We used an independent test set from the National Plant Monitoring Scheme (NPMS) to evaluate its performance and compare its results against the traditional expert systems. Results: Exploration of the use of deep learning applied to species composition and plot-location criteria for habitat classification led to the development of a framework containing a wide range of models. Our selected algorithm, applied to European habitat types, significantly improved habitat classification accuracy, achieving a more than twofold improvement compared to the previous state-of-the-art (SOTA) method on an external data set, clearly outperforming expert systems. The framework is shared and maintained through a GitHub repository. Conclusions: Our results demonstrate the potential benefits of the adoption of deep learning for improving the accuracy of vegetation classification. They highlight the importance of incorporating advanced technologies into habitat monitoring. These algorithms have shown to be better suited for habitat type prediction than expert systems. They push the accuracy score on a database containing hundreds of thousands of standardized presence/absence European surveys to 88.74%, as assessed by expert judgment. Finally, our results showcase that species dominance is a strong marker of ecosystems and that the exact cover abundance of the flora is not required to train neural networks with predictive performances. The framework we developed can be used by researchers and practitioners to accurately classify habitats