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Scholar Van Den Bussche, R. A., and Hoofer, S. R. (2004). Phylogenetic relationships among recent chiropteran families and the importance of choosing appropriate out-group taxa. J. Mammal. 85: 321.2.0.CO;2" data-track-item_id="10.1644/1545-1542(2004)0852.0.CO;2" data-track-value="article reference" data-track-action="article reference" href=" aria-label="Article reference 103" data-doi="10.1644/1545-1542(2004)0852.0.CO;2">Article Google Scholar Van Den Bussche, R. A., Hoofer, S. R., and Hansen, E. W. (2002). Characterization and phylogenetic utility of the mammalian protamine P1 gene. Mol. Phylogenet. Evol. 22: 333.PubMed CAS Google Scholar Van Den Bussche, R. A., Reeder, S. A., Hansen, E. W., and Hoofer, S. R. (2003). Utility of the dentin matrix protein 1 (DMP1) gene for resolving mammalian intraordinal relationships. Mol. Phylogenet. Evol. 26: 89.PubMed CAS Google Scholar Wible, J. R., and Novacek, J. M. (1988). Cranial evidence for the monophyletic origin of bats. Am. Mus. Novit. 2911: 1. Google Scholar Yang, J. (1977). On some Salientia and Chiroptera from Shanwang, Linqu Shandong. Vert. PalAs. 15: 76 (in Chinese).Download references

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And bloom samples from Jiaozhou Bay. China Harmful Algae 96:101821. CAS Google Scholar Zhang T, Fan X, Gao F et al (2019) Further analyses on the phylogeny of the subclass Scuticociliatia (Protozoa, Ciliophora) based on both nuclear and mitochondrial data. Mol Phylogenet Evol 139:106565. CAS Google Scholar Zhao Y, Yi Z, Gentekaki E et al (2016) Utility of combining morphological characters, nuclear and mitochondrial genes: An attempt to resolve the conflicts of species identification for ciliated protists. Mol Phylogenet Evol 94:718–729. Google Scholar Stern RF, Andersen RA, Jameson I et al (2012) Evaluating the ribosomal internal transcribed spacer (ITS) as a candidate dinoflagellate barcode marker. PLoS ONE 7:e42780. CAS Google Scholar Choi J, Park JS (2020) Comparative analyses of the V4 and V9 regions of 18S rDNA for the extant eukaryotic community using the Illumina platform. Sci Rep 10:6519. CAS Google Scholar Mordret S, Piredda R, Vaulot D et al (2018) dinoref: a curated dinoflagellate (Dinophyceae) reference database for the 18S rRNA gene. Mol Ecol Resour 18:974–987. CAS Google Scholar Zimmermann J, Jahn R, Gemeinholzer B (2011) Barcoding diatoms: evaluation of the V4 subregion on the 18S rRNA gene, including new primers and protocols. Org Divers Evol 11:173. Google Scholar Forster D, Filker S, Kochems R et al (2019) A comparison of different ciliate metabarcode genes as bioindicators for environmental impact assessments of salmon aquaculture. J Eukaryot Microbiol 66:294–308. CAS Google Scholar Hamsher SE, Evans KM, Mann DG et al (2011) Barcoding diatoms: exploring alternatives to COI-5P. Protist 162:405–422. CAS

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JMODELTEST 2.1.10:: DESCRIPTIONjModelTest is a tool to carry out statistical selection of best-fit models of nucleotide substitution. It implements five different model selection strategies: hierarchical and dynamical likelihood ratio tests (hLRT and dLRT), Akaike and Bayesian information criteria (AIC and BIC), and a decision theory method (DT). It also provides estimates of model selection uncertainty, parameter importances and model-averaged parameter estimates, including model-averaged phylogenies. Please note that the DT weights are very gross.jModelTest2 includes High Performance Computing (HPC) capabilities and additional features like new strategies for tree optimization, model-averaged phylogenetic trees (both topology and branch length), heuristic filtering and automatic logging of user activity.::DEVELOPERPosada Lab:: SCREENSHOTS:: REQUIREMENTSWindows / Linux / MacOSJava:: DOWNLOAD jMODELTEST:: MORE INFORMATIONCitationPosada D. 2008.jModelTest: Phylogenetic Model Averaging. Molecular Biology and Evolution 25: 1253-1256.Darriba D, Taboada GL, Doallo R, Posada D. 2012.jModelTest 2: more models, new heuristics and parallel computing. Nature Methods 9(8), 772. ModelTest-NG 0.1.6:: DESCRIPTIONModelTest-NG (ModelTest next generation) is a tool for selecting the best-fit model of evolution for DNA and protein alignments. ModelTest-NG supersedes jModelTest and ProtTest in one single tool, with graphical and command console interfaces.::DEVELOPERthe Exelixis Lab:: SCREENSHOTSN/A:: REQUIREMENTSLinux / MacOs:: DOWNLOAD ModelTest-NG:: MORE INFORMATIONCitationMol Biol Evol. 2019 Aug 20. pii: msz189. doi: 10.1093/molbev/msz189.ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models.Darriba D, Posada D, Kozlov AM, Stamatakis A, Morel B, Flouri T. About EVOL Album. EVOL is a English album released on . This album is composed by Adnan Aziz. EVOL Album has 1 song sung by Mischief. Listen to EVOL song in high quality download EVOL song on Gaana.com. Related Tags - EVOL, EVOL Songs, EVOL Songs Download, Download EVOL Songs, Listen EVOL Songs, EVOL MP3 Songs

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Bioclimatic modelling. Glob. Ecol. Biogeogr. 2018, 27, 277–284. [Google Scholar] [CrossRef]Tozer, B.; Sandwell, D.T.; Smith, W.H.F.; Olson, C.; Beale, J.R.; Wessel, P. Global Bathymetry and Topography at 15 Arc Sec: SRTM15+. Earth Space Sci. 2019, 6, 1847–1864. [Google Scholar] [CrossRef]Davies, A.J.; Guinotte, J.M. Global Habitat Suitability for Framework-Forming Cold-Water Corals. PLoS ONE 2011, 6, e18483. [Google Scholar] [CrossRef] [PubMed]Steinacher, M.; Joos, F.; Frölicher, T.L.; Plattner, G.K.; Doney, S.C. Imminent ocean acidification in the Arctic projected with the NCAR global coupled carbon cycle-climate model. Biogeosciences 2009, 6, 515–533. [Google Scholar] [CrossRef]Cutler, D.R.; Jr, E.T.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. Random forests for classification in ecology. Ecol. A Publ. Ecol. Soc. Am. 2007, 88, 2783–2792. [Google Scholar] [CrossRef] [PubMed]Elith, J.; Leathwick, J.R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 2009, 40, 677–697. [Google Scholar] [CrossRef]Vignali, S.; Barras, A.G.; Arlettaz, R.; Braunisch, V. SDMtune: An R package to tune and evaluate species distribution models. Ecol. Evol. 2020, 10, 11488–11506. [Google Scholar] [CrossRef]Dullo, W.C.; Flögel, S.; Rüggeberg, A. Cold-water coral growth in relation to the hydrography of the Celtic and Nordic European continental margin. Mar. Ecol. Prog. 2008, 371, 165–176. [Google Scholar] [CrossRef]Xia, Y.; Liu, C.; Li, Y.; Liu, N. A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst. Appl. 2017, 78, 225–241. [Google Scholar] [CrossRef]Allouche, O.; Tsoar, A.; Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic

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(TSS). J. Appl. Ecol. 2006, 43, 1223–1232. [Google Scholar] [CrossRef]Thuiller, W.; Lafourcade, B.; Engler, R.; Araújo, M.B. BIOMOD—A platform for ensemble forecasting of species distributions. Ecography 2009, 32, 369–373. [Google Scholar] [CrossRef]Breiner, F.T.; Nobis, M.P.; Bergamini, A.; Guisan, A. Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods Ecol. Evol. 2018, 9, 802–808. [Google Scholar] [CrossRef]Ingram, M.; Vukcevic, D.; Golding, N. Multi-output Gaussian processes for species distribution modelling. Methods Ecol. Evol. 2020, 11, 1587–1598. [Google Scholar] [CrossRef]Marchetto, E.; Da Re, D.; Tordoni, E.; Bazzichetto, M.; Zannini, P.; Celebrin, S.; Chieffallo, L.; Malavasi, M.; Rocchini, D. Testing the effect of sample prevalence and sampling methods on probability-and favourability-based SDMs. Ecol. Model. 2023, 477, 110248. [Google Scholar] [CrossRef]Marmion, M.; Luoto, M.; Heikkinen, R.K.; Thuiller, W. The performance of state-of-the-art modelling techniques depends on geographical distribution of species. Ecol. Model. 2009, 220, 3512–3520. [Google Scholar] [CrossRef]Godsoe, W.; Harmon, L.J. How do species interactions affect species distribution models? Ecography 2012, 35, 811–820. [Google Scholar] [CrossRef]Kinlan, B.P.; Poti, M.; Drohan, A.F.; Packer, D.B.; Dorfman, D.S.; Nizinski, M.S. Predictive modeling of suitable habitat for deep-sea corals offshore the Northeast United States. Deep Sea Res. Part I Oceanogr. Res. Pap. 2020, 158, 103229. [Google Scholar] [CrossRef]Doherty, B.; Cox, S.P.; Rooper, C.N.; Johnson, S.D.; Kronlund, A.R. Species distribution models for deep-water coral habitats that account for spatial uncertainty in trap-camera fishery data. Mar. Ecol. Prog. Ser. 2021, 660, 69–93. [Google Scholar] [CrossRef]Chu, J.W.; Nephin, J.; Georgian, S.; Knudby, A.; Rooper, C.; Gale,

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Google Scholar Cuypers B, Domagalska MA, Meysman P et al (2017) Multiplexed spliced-leader sequencing: a high-throughput, selective method for RNA-seq in trypanosomatids. Sci Rep 7:3725. CAS Google Scholar Abraham JS, Sripoorna S, Maurya S et al (2019) Techniques and tools for species identification in ciliates: a review. Int J Syst Evol Microbiol 69:877–894. CAS Google Scholar Strüder-Kypke MC, Lynn DH (2010) Comparative analysis of the mitochondrial cytochrome c oxidase subunit I (COI) gene in ciliates (Alveolata, Ciliophora) and evaluation of its suitability as a biodiversity marker. Syst Biodivers 8:131–148. Google Scholar Wang P, Gao F, Huang J et al (2015) A case study to estimate the applicability of secondary structures of SSU-rRNA gene in taxonomy and phylogenetic analyses of ciliates. Zool Scr 44:574–585. Google Scholar Shazib SUA, Vďačný P, Kim JH et al (2016) Molecular phylogeny and species delimitation within the ciliate genus Spirostomum (Ciliophora, Postciliodesmatophora, Heterotrichea), using the internal transcribed spacer region. Mol Phylogenet Evol 102:128–144. Google Scholar Sun P, Clamp JC, Xu D et al (2013) An ITS-based phylogenetic framework for the genus Vorticella: finding the molecular and morphological gaps in a taxonomically difficult group. Proc R Soc B 280:20131177. Google Scholar Wylezich C, Nies G, Mylnikov AP et al (2010) An evaluation of the use of the LSU rRNA D1–D5 domain for DNA-based taxonomy of eukaryotic protists. Protist 161:342–352. CAS Google Scholar Santoferrara LF, McManus GB, Alder VA (2013) Utility of genetic markers and morphology for species discrimination within the order Tintinnida (Ciliophora, Spirotrichea). Protist 164:24–36. Google

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M.; Hocine, H.; Benammar, L.; Ayachi, A.; Bachir, A.S.; Monteoliva-Sánchez, M.; Dekak, A. Diversity and bioprospecting of extremely halophilic archaea isolated from Algerian arid and semi-arid wetland ecosystems for halophilic-active hydrolytic enzymes. Microbiol. Res. 2018, 207, 289–298. [Google Scholar] [CrossRef]Benhadj, M.; Gacemi-Kirane, D.; Toussaint, M.; Hotel, L.; Bontemps, C.; Duval, R.E.; Leblond, P.; Aigle, B. Diversity and antimicrobial activities of Streptomyces isolates from Fetzara Lake, north eastern Algeria. Ann. Biol. Clin. 2018, 76, 81–95. [Google Scholar] [CrossRef]Shirling, E.T.; Gottlieb, D. Methods for characterization of Streptomyces species. Int. J. Syst. Evol. 1966, 16, 313–340. [Google Scholar] [CrossRef]Gordon, R.E.; Barnett, D.A.; Handerhan, J.E.; Pang, C.H.N. Nocardia coeliaca, Nocardia autotrophica, and the nocardin strain. Int. J. Syst. Evol. Microbiol. 1974, 24, 54–63. [Google Scholar] [CrossRef]Cassagne, C.; Normand, A.C.; Bonzon, L.; L’Ollivier, C.; Gautier, M.; Jeddi, F.; Ranque, S.; Piarroux, R. Routine identification and mixed species detection in 6,192 clinical yeast isolates. Med. Mycol. 2016, 54, 256–265. [Google Scholar] [CrossRef]Kieser, T.B.M.J.; Bibb, M.J.; Buttner, M.J.; Chater, K.F.; Hopwood, D.A. Practical Streptomyces Genetics. A Laboratory Manual; The John Innes Foundation: Norwich, UK, 2000. [Google Scholar]Weisburg, W.G.; Barns, S.M.; Pelletier, D.A.; Lane, D.J. 16S ribosomal DNA amplification for phylogenetic study. J. Bacteriol. 1991, 173, 697–703. [Google Scholar] [CrossRef] [PubMed]Yoon, S.H.; Ha, S.M.; Lim, J.; Kwon, S.; Chun, J. A large-scale evaluation of algorithms to calculate average nucleotide identity. Antonie Van Leeuwenhoek 2017, 110, 1281–1286. [Google Scholar] [CrossRef] [PubMed]Larkin, M.A.; Blackshields, G.; Brown, N.P.; Chenna, R.; McGettigan, P.A.; McWilliam, H.; Valentin, F.; Wallace, I.M.; Wilm, A.; Lopez,. About EVOL Album. EVOL is a English album released on . This album is composed by Adnan Aziz. EVOL Album has 1 song sung by Mischief. Listen to EVOL song in high quality download EVOL song on Gaana.com. Related Tags - EVOL, EVOL Songs, EVOL Songs Download, Download EVOL Songs, Listen EVOL Songs, EVOL MP3 Songs

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Feio MJ, Filipe AF, Garcia-Raventós A et al (2020) Advances in the use of molecular tools in ecological and biodiversity assessment of aquatic ecosystems. Avanços no uso de ferramentas moleculares na avaliação ecológica e biodiversidade dos ecossistemas aquáticos. Limnetica. Google Scholar Schweiger AK, Cavender-Bares J, Townsend PA et al (2018) Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nat Ecol Evol 2:976–982. Google Scholar Rico-Sánchez AE, Sundermann A, López-López E et al (2020) Biological diversity in protected areas: not yet known but already threatened. Glob Ecol Conserv 22:e01006. Google Scholar Chantangsi C, Lynn DH, Brandl MT et al (2007) Barcoding ciliates: a comprehensive study of 75 isolates of the genus Tetrahymena. Int J Syst Evol Microbiol 57:2412–2423. CAS Google Scholar Park M-H, Jung J-H, Jo E et al (2019) Utility of mitochondrial CO1 sequences for species discrimination of Spirotrichea ciliates (Protozoa, Ciliophora). Mitochondrial DNA Part A 30:148–155. CAS Google Scholar Xu J (2017) Fungal DNA barcoding. In: The 6th international barcode of life conference 01:913–932. T, Sandionigi A, Viard F, Casiraghi M (2015) DNA (meta)barcoding of biological invasions: a powerful tool to elucidate invasion processes and help managing aliens. Biol Invasions 17:905–922. Google Scholar Hebert PDN, Cywinska A, Ball SL, deWaard JR (2003) Biological identifications through DNA barcodes. Proc R Soc Lond B 270:313–321. CAS Google Scholar Yao H, Song J, Liu C et al (2010) Use of ITS2 region as the universal DNA barcode for plants and animals. PLoS ONE 5:e13102. CAS Google

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The early stage of WSSV infection. BMC Genomics 20, 247. (2019).Article PubMed PubMed Central Google Scholar Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548. (2018).Article CAS PubMed Google Scholar Parthier, C. et al. Structure of the Toll-Spätzle complex, a molecular hub in Drosophila development and innate immunity. Proc. Natl. Acad. Sci. 111, 6281. (2014).Article ADS CAS PubMed PubMed Central Google Scholar Lou, F., Gao, T. & Han, Z. Transcriptome analyses reveal alterations in muscle metabolism, immune responses and reproductive behavior of Japanese mantis shrimp (Oratosquilla oratoria) at different cold temperature. Comp. Biochem. Physiol. Part D Genomics Proteomics 32, 100615. (2019).Article CAS PubMed Google Scholar Chandhini, S. & Rejish Kumar, V. J. Transcriptomics in aquaculture: Current status and applications. Rev. Aquac. (2018).Article Google Scholar Sittikankaew, K. et al. Transcriptome analyses reveal the synergistic effects of feeding and eyestalk ablation on ovarian maturation in black tiger shrimp. Sci. Rep. 10, 3239. (2020).Article ADS CAS PubMed PubMed Central Google Scholar Innis, J. W. Role of HOX genes in human development. Curr. Opin. Pediatr. 9, 617–622. (1997).Article CAS PubMed Google Scholar Negre, B., Ranz, J. M., Casals, F., Cáceres, M. & Ruiz, A. A new split of the hox gene complex in Drosophila: relocation and evolution of the gene labial. Mol. Biol. Evol. 20, 2042–2054. (2003).Article CAS PubMed Google Scholar Pavlopoulos, A. & Akam, M. Hox gene Ultrabithorax regulates distinct sets of target genes at successive stages of Drosophila haltere morphogenesis. Proc. Natl. Acad. Sci. 108, 2855. (2011).Article ADS CAS PubMed PubMed Central Google Scholar Gąsiorowski, L. & Hejnol, A. Hox gene expression during development of the phoronid Phoronopsis harmeri. EvoDevo 11, 2. (2020).Article CAS PubMed PubMed Central Google Scholar Gao, Y. et al. Transcriptome analysis on the exoskeleton formation in early. About EVOL Album. EVOL is a English album released on . This album is composed by Adnan Aziz. EVOL Album has 1 song sung by Mischief. Listen to EVOL song in high quality download EVOL song on Gaana.com. Related Tags - EVOL, EVOL Songs, EVOL Songs Download, Download EVOL Songs, Listen EVOL Songs, EVOL MP3 Songs

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Social–ecological systems. Ecol Soc 9(3):7Article Google Scholar Friess DA, Rogers K, Lovelock CE et al (2019) The state of the world’s mangrove forests: past, present, and future. Annu Rev Environ Resour 44:89–115. Google Scholar Gavin MC, McCarter J, Berkes F et al (2018) Effective biodiversity conservation requires dynamic, pluralistic, partnership-based approaches. Sustainability 10:1846. Google Scholar Gómez-Ruiz PA, Betancourth-Buitrago RA, Arteaga-Cote M et al (2022) Fostering a participatory process for ecological restoration of mangroves in Pantanos De Centla Biosphere Reserve (Tabasco, Mexico). Ecosyst People 18:112–118. Google Scholar Greening H, Janicki A, Sherwood ET et al (2014) Ecosystem responses to long-term nutrient management in an urban estuary: Tampa Bay, Florida, USA. Estuar Coast Shelf Sci 151:A1–A16. CAS Google Scholar Katon BM, Pomeroy RS, Garces LR et al (2000) Rehabilitating the mangrove resources of Cogtong Bay, Philippines: a co-management perspective. Coast Manage 28:29–37. Google Scholar Mangrove Action Project (2023) About MAP. [Accessed May 13, 2023]Nesbitt TH (2016) Increasing cooperation and advancing reconciliation in the cooperative management of protected areas in Canada’s north. In: Herrmann TM, Martin T (eds) Indigenous peoples’ governance of Land and Protected territories in the Arctic. Springer, New York, NY, pp 43–67Chapter Google Scholar Redpath SM, Young J, Evely A et al (2013) Understanding and managing conservation conflicts. Trends Ecol Evol 28(2):100–109. PubMed Google Scholar Renzi JJ, He Q, Silliman BR (2019) Harnessing positive species interactions to enhance coastal wetland restoration. Front Ecol Evol 7:131. Google Scholar RStudio Team, RStudio (2023) RStudio: Integrated Development for R. PBC, Boston, MA URL. Google Scholar Sievers M, Brown CJ, Buelow CA et al (2022) Greater consideration of animals will enhance coastal restoration outcomes. Bio Sci 72(11):1088–1098. Google Scholar Stephenson J, Berkes F, Turner NJ et al (2014) Biocultural conservation of marine ecosystems: examples from New Zealand and Canada. Indian J Tradit Knowl 13:257–265 Google Scholar Sterling EJ, Betley E, Sigouin A et al (2017) Assessing the evidence for stakeholder engagement in biodiversity conservation. Biol Cons 209:159–171. Google Scholar Zaldívar-Jiménez A, Ladrón de Guevara-Porras P, Pérez-Ceballos R et al (2017) US-Mexico joint Gulf of Mexico large marine ecosystem based assessment and management: experience in

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Scholar Van Den Bussche, R. A., and Hoofer, S. R. (2004). Phylogenetic relationships among recent chiropteran families and the importance of choosing appropriate out-group taxa. J. Mammal. 85: 321.2.0.CO;2" data-track-item_id="10.1644/1545-1542(2004)0852.0.CO;2" data-track-value="article reference" data-track-action="article reference" href=" aria-label="Article reference 103" data-doi="10.1644/1545-1542(2004)0852.0.CO;2">Article Google Scholar Van Den Bussche, R. A., Hoofer, S. R., and Hansen, E. W. (2002). Characterization and phylogenetic utility of the mammalian protamine P1 gene. Mol. Phylogenet. Evol. 22: 333.PubMed CAS Google Scholar Van Den Bussche, R. A., Reeder, S. A., Hansen, E. W., and Hoofer, S. R. (2003). Utility of the dentin matrix protein 1 (DMP1) gene for resolving mammalian intraordinal relationships. Mol. Phylogenet. Evol. 26: 89.PubMed CAS Google Scholar Wible, J. R., and Novacek, J. M. (1988). Cranial evidence for the monophyletic origin of bats. Am. Mus. Novit. 2911: 1. Google Scholar Yang, J. (1977). On some Salientia and Chiroptera from Shanwang, Linqu Shandong. Vert. PalAs. 15: 76 (in Chinese).Download references

2025-04-11
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And bloom samples from Jiaozhou Bay. China Harmful Algae 96:101821. CAS Google Scholar Zhang T, Fan X, Gao F et al (2019) Further analyses on the phylogeny of the subclass Scuticociliatia (Protozoa, Ciliophora) based on both nuclear and mitochondrial data. Mol Phylogenet Evol 139:106565. CAS Google Scholar Zhao Y, Yi Z, Gentekaki E et al (2016) Utility of combining morphological characters, nuclear and mitochondrial genes: An attempt to resolve the conflicts of species identification for ciliated protists. Mol Phylogenet Evol 94:718–729. Google Scholar Stern RF, Andersen RA, Jameson I et al (2012) Evaluating the ribosomal internal transcribed spacer (ITS) as a candidate dinoflagellate barcode marker. PLoS ONE 7:e42780. CAS Google Scholar Choi J, Park JS (2020) Comparative analyses of the V4 and V9 regions of 18S rDNA for the extant eukaryotic community using the Illumina platform. Sci Rep 10:6519. CAS Google Scholar Mordret S, Piredda R, Vaulot D et al (2018) dinoref: a curated dinoflagellate (Dinophyceae) reference database for the 18S rRNA gene. Mol Ecol Resour 18:974–987. CAS Google Scholar Zimmermann J, Jahn R, Gemeinholzer B (2011) Barcoding diatoms: evaluation of the V4 subregion on the 18S rRNA gene, including new primers and protocols. Org Divers Evol 11:173. Google Scholar Forster D, Filker S, Kochems R et al (2019) A comparison of different ciliate metabarcode genes as bioindicators for environmental impact assessments of salmon aquaculture. J Eukaryot Microbiol 66:294–308. CAS Google Scholar Hamsher SE, Evans KM, Mann DG et al (2011) Barcoding diatoms: exploring alternatives to COI-5P. Protist 162:405–422. CAS

2025-04-08
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Bioclimatic modelling. Glob. Ecol. Biogeogr. 2018, 27, 277–284. [Google Scholar] [CrossRef]Tozer, B.; Sandwell, D.T.; Smith, W.H.F.; Olson, C.; Beale, J.R.; Wessel, P. Global Bathymetry and Topography at 15 Arc Sec: SRTM15+. Earth Space Sci. 2019, 6, 1847–1864. [Google Scholar] [CrossRef]Davies, A.J.; Guinotte, J.M. Global Habitat Suitability for Framework-Forming Cold-Water Corals. PLoS ONE 2011, 6, e18483. [Google Scholar] [CrossRef] [PubMed]Steinacher, M.; Joos, F.; Frölicher, T.L.; Plattner, G.K.; Doney, S.C. Imminent ocean acidification in the Arctic projected with the NCAR global coupled carbon cycle-climate model. Biogeosciences 2009, 6, 515–533. [Google Scholar] [CrossRef]Cutler, D.R.; Jr, E.T.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. Random forests for classification in ecology. Ecol. A Publ. Ecol. Soc. Am. 2007, 88, 2783–2792. [Google Scholar] [CrossRef] [PubMed]Elith, J.; Leathwick, J.R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 2009, 40, 677–697. [Google Scholar] [CrossRef]Vignali, S.; Barras, A.G.; Arlettaz, R.; Braunisch, V. SDMtune: An R package to tune and evaluate species distribution models. Ecol. Evol. 2020, 10, 11488–11506. [Google Scholar] [CrossRef]Dullo, W.C.; Flögel, S.; Rüggeberg, A. Cold-water coral growth in relation to the hydrography of the Celtic and Nordic European continental margin. Mar. Ecol. Prog. 2008, 371, 165–176. [Google Scholar] [CrossRef]Xia, Y.; Liu, C.; Li, Y.; Liu, N. A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst. Appl. 2017, 78, 225–241. [Google Scholar] [CrossRef]Allouche, O.; Tsoar, A.; Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic

2025-03-29
User1719

(TSS). J. Appl. Ecol. 2006, 43, 1223–1232. [Google Scholar] [CrossRef]Thuiller, W.; Lafourcade, B.; Engler, R.; Araújo, M.B. BIOMOD—A platform for ensemble forecasting of species distributions. Ecography 2009, 32, 369–373. [Google Scholar] [CrossRef]Breiner, F.T.; Nobis, M.P.; Bergamini, A.; Guisan, A. Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods Ecol. Evol. 2018, 9, 802–808. [Google Scholar] [CrossRef]Ingram, M.; Vukcevic, D.; Golding, N. Multi-output Gaussian processes for species distribution modelling. Methods Ecol. Evol. 2020, 11, 1587–1598. [Google Scholar] [CrossRef]Marchetto, E.; Da Re, D.; Tordoni, E.; Bazzichetto, M.; Zannini, P.; Celebrin, S.; Chieffallo, L.; Malavasi, M.; Rocchini, D. Testing the effect of sample prevalence and sampling methods on probability-and favourability-based SDMs. Ecol. Model. 2023, 477, 110248. [Google Scholar] [CrossRef]Marmion, M.; Luoto, M.; Heikkinen, R.K.; Thuiller, W. The performance of state-of-the-art modelling techniques depends on geographical distribution of species. Ecol. Model. 2009, 220, 3512–3520. [Google Scholar] [CrossRef]Godsoe, W.; Harmon, L.J. How do species interactions affect species distribution models? Ecography 2012, 35, 811–820. [Google Scholar] [CrossRef]Kinlan, B.P.; Poti, M.; Drohan, A.F.; Packer, D.B.; Dorfman, D.S.; Nizinski, M.S. Predictive modeling of suitable habitat for deep-sea corals offshore the Northeast United States. Deep Sea Res. Part I Oceanogr. Res. Pap. 2020, 158, 103229. [Google Scholar] [CrossRef]Doherty, B.; Cox, S.P.; Rooper, C.N.; Johnson, S.D.; Kronlund, A.R. Species distribution models for deep-water coral habitats that account for spatial uncertainty in trap-camera fishery data. Mar. Ecol. Prog. Ser. 2021, 660, 69–93. [Google Scholar] [CrossRef]Chu, J.W.; Nephin, J.; Georgian, S.; Knudby, A.; Rooper, C.; Gale,

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