Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
58
datasets available to search
ShareScore release 0.7.1
Dataset results
58 results for “Population assignment”
Genetic assignments for Spring Evolutionary Significant Unit reanalysis, Central Valley Chinook Salmon populations, CA, 2011-2024
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is difficult to visually distinguish individuals from the different Evolutionarily Significant Units (ESU). As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to genetic lineage; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program compliance monitoring programs. The genetic lineage was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central V
Data from: Population genomics and morphometric assignment of western honey bees (Apis mellifera L.) in the Republic of South Africa
Backgrounds: Apis mellifera scutellata and A.m. capensis (the Cape honey bee) are western honey bee subspecies indigenous to the Republic of South Africa (RSA). Both bees are important for biological and economic reasons. First, A.m. scutellata is the invasive "African honey bee" of the Americas and exhibits a number of traits that beekeepers consider undesirable. They swarm excessively, are prone to absconding (vacating the nest entirely), usurp other honey bee colonies, and exhibit heightened defensiveness. Second, Cape honey bees are socially parasitic bees; the workers can reproduce thelytokously. Both bees are indistinguishable visually. Therefore, we employed Genotyping-by-Sequencing (GBS), wing geometry and standard morphometric approaches to assess the genetic diversity and population structure of these bees to search for diagnostic markers that can be employed to distinguish between the two subspecies. Results: Apis mellifera scutellata possessed the highest mean number of polymorphic SNPs (among 2,449 informative SNPs) with minor allele frequencies >0.05 (Np = 88%). The RSA honey bees generated a high level of expected heterozygosity (Hexp = 0.24). The mean genetic differentiation (FST; 6.5%) among the RSA honey bees revealed that approximately 93% of the genetic variation was accounted for within individuals of these subspecies. Two genetically distinct clusters (K = 2) corresponding to both subspecies were detected by Model-based Bayesian clustering and supported by Principal Coordinates Analysis (PCoA) inferences. Selected highly divergent loci (n = 83) further reinforced a distinctive clustering of two subspecies across geographical origins, accounting for approximately 83% of the total variation in the PCoA plot. The significant correlation of allele frequencies at divergent loci with environmental variables suggested that these populations are adapted to local conditions. Only 17 of 48 wing geometry and standard morphometric parameters were useful for clustering A.m. capensis, A.m. scutellata, and hybrid individuals. Conclusions: We produced a minimal set of 83 SNP loci and 17 wing geometry and standard morphometric parameters useful for identifying the two RSA honey bee subspecies by genotype and phenotype. We found that genes involved in neurology/behavior and development/growth are the most prominent heritable traits evolved in the functional evolution of honey bee populations in RSA.
Population assignment tests uncover rare long-distance larval dispersal events
<p>Long-distance dispersal (LDD) is consequential to metapopulation ecology and evolution. In systems where dispersal is undertaken by small propagules, such as larvae in the ocean, documenting LDD is especially challenging. Genetic parentage analysis has gained traction as a method for measuring larval dispersal, but such studies are generally spatially limited, leaving LDD understudied in marine species. We addressed this knowledge gap by uncovering LDD with population assignment tests in the coral reef fish <i>Elacatinus lori</i>—a species whose short-distance dispersal has been well-characterized by parentage analysis. When adults (<i>n</i> = 931) collected throughout the species' range were categorized into three source populations, assignment accuracy exceeded 99%, demonstrating low rates of connectivity between populations in the adult generation. After establishing high assignment confidence, we assigned settlers (<i>n</i> = 3,828) to source populations. Within the settler cohort, < 0.1% of individuals were identified as long-distance dispersers from other populations. These results demonstrate an exceptionally low level of connectivity between <i>E. lori</i> populations, despite the potential for ocean currents to facilitate LDD. More broadly, these findings illustrate the value of combining genetic parentage analysis and population assignment tests to uncover short- and long-distance dispersal, respectively.</p>
Low-coverage whole genome sequencing for highly accurate population assignment: Mapping migratory connectivity in the American Redstart (Setophaga ruticilla)
<p>Understanding the geographic linkages among populations across the annual cycle is an essential component for understanding the ecology and evolution of migratory species and for facilitating their effective conservation. While genetic markers have been widely applied to describe migratory connections, the rapid development of new sequencing methods, such as low-coverage whole genome sequencing (lcWGS), provides new opportunities for improved estimates of migratory connectivity. Here, we use lcWGS to identify fine-scale population structure in a widespread songbird, the American Redstart (<em>Setophaga</em> <em>ruticilla</em>), and accurately assign individuals to genetically distinct breeding populations. Assignment of individuals from the nonbreeding range reveals population-specific patterns of varying migratory connectivity. By combining migratory connectivity results with demographic analysis of population abundance and trends, we consider full annual cycle conservation strategies for preserving numbers of individuals and genetic diversity. Notably, we highlight the importance of the Northern Temperate-Greater Antilles migratory population as containing the largest proportion of individuals in the species. Finally, we highlight valuable considerations for other population assignment studies aimed at using lcWGS. Our results have broad implications for improving our understanding of the ecology and evolution of migratory species through conservation genomics approaches.</p>
Development of a SNP panel for geographic assignment and population monitoring of jaguars (Panthera onca)
Open the record for dataset details and reuse information.
Population assignment tests uncover rare long-distance larval dispersal events
Open the record for dataset details and reuse information.
Data from: Population genomics and morphometric assignment of western honey bees (Apis mellifera L.) in the Republic of South Africa
Open the record for dataset details and reuse information.
Low-coverage whole genome sequencing for highly accurate population assignment: Mapping migratory connectivity in the American Redstart (Setophaga ruticilla)
Open the record for dataset details and reuse information.
Genetic assignment of individuals to source populations using network estimation tools
Open the record for dataset details and reuse information.
Data from: SNPs reveal a genetic cline across the northeast Atlantic and enable powerful population assignment in the European lobster
Resolving stock structure is crucial for fisheries conservation to ensure that the spatial implementation of management is commensurate with that of biological population units. To address this in the economically important European lobster (Homarus gammarus), genetic structure was explored across the species' range using a small panel of single nucleotide polymorphisms (SNPs) previously isolated from restriction-site associated DNA sequencing; these SNPs were selected to maximise differentiation at a range of both broad- and fine-scales. After quality control and filtering, 1,278 lobsters from 38 sampling sites were genotyped at 79 SNPs. The results revealed a pronounced phylogeographic break between the Atlantic and Mediterranean basins, while structure within the Mediterranean was also apparent, partitioned between lobsters from the central Mediterranean and the Aegean Sea. In addition, a genetic cline across the northeast Atlantic was revealed using both putatively neutral and outlier SNPs, but the precise driver(s) of this clinal pattern –isolation-by-distance, secondary contact, selection across an environmental gradient, or a combination of these factors– remains undetermined. Putatively neutral markers differentiated lobsters from Oosterschelde, an estuary on the Dutch coast, a finding likely explained by past bottlenecks and limited gene flow with adjacent North Sea populations. Building on the findings of our spatial genetic analysis, we were able to test the accuracy of assigning lobsters at various spatial scales, including to basin of origin (Atlantic or Mediterranean), region of origin and sampling location. The predictive model assembled using 79 SNPs correctly assigned 99.7 % of lobsters not used to build the model to their basin of origin, but accuracy decreased to region of origin and again to sampling location. These results are of direct relevance to managers of lobster fisheries and hatcheries, and provide the basis for a genetic tool for tracing the origin of European lobsters in the food supply chain.
Data from: An empirical comparison of SNPs and microsatellites for parentage and kinship assignment in a wild sockeye salmon (Oncorhynchus nerka) population
Because of their high variability, microsatellites are still considered the marker of choice for studies on parentage and kinship in wild populations. Nevertheless, single nucleotide polymorphisms (SNPs) are becoming increasing popular in many areas of molecular ecology, owing to their high-throughput, easy transferability between laboratories and low genotyping error. An ongoing discussion concerns the relative power of SNPs compared to microsatellites – that is, how many SNP loci are needed to replace a panel of microsatellites? Here, we evaluate the assignment power of 80 SNPs (HE=0.30, 80 independent alleles) and 11 microsatellites (HE =0.85, 194 independent alleles) in a wild population of about 400 sockeye salmon with two commonly used software packages (Cervus3, Colony2) and, for SNPs only, a newly developed software (SNPPIT). Assignment success was higher for SNPs than for microsatellites, especially for parent pairs, irrespective of the method used. Colony2 assigned a larger proportion of offspring to at least one parent than the other methods, though Cervus and SNPPIT detected more parent pairs. Identification of full sib groups without parental information from relatedness measures was possible using both marker systems, though explicit reconstruction of such groups in Colony2 was impossible for SNPs because of computation time. Our results confirm the applicability of SNPs for parentage analyses and refute the predictability of assignment success from the number of independent alleles.
Data from: Applications of random forest feature selection for fine-scale genetic population assignment
Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest, and guided regularized random forest) compared with FST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP dataset for Atlantic salmon (Salmo salar) and a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha). In each species, we identified the minimum panel size required to obtain a self-assignment accuracy of at least 90% using each method to create panels of 50-700 markers Panels of SNPs identified using random forest-based methods performed up to 7.8 and 11.2 percentage points better than FST-selected panels of similar size for the Atlantic salmon and Chinook salmon data, respectively. Self-assignment accuracy ≥90% was obtained with panels of 670 and 384 SNPs for each dataset, respectively, a level of accuracy never reached for these species using FST-selected panels. Our results demonstrate a role for machine-learning approaches in marker selection across large genomic datasets to improve assignment for management and conservation of exploited populations.
FIGURE 3 in Genetic and morphological variability among the populations assigned to the genus Tropiocolotes Peters, 1880 (Squamata: Gekkonidae) in south Iran
FIGURE 3. Bayesian inference phylogenetic tree of Tropiocolotes populations in southern Iran using two mtDNA genes (COI and 16S). Tropiocolotes steudneri sensu stricto from Egypt was used as the outgroup. Numbers next to the nodes are the MP and ML bootstrap values and BI posterior probabilities (MP/ML/BI).
FIGURE 1 in Genetic and morphological variability among the populations assigned to the genus Tropiocolotes Peters, 1880 (Squamata: Gekkonidae) in south Iran
FIGURE 1. Map of southern Iran showing sampling localities for the populations of Tropicolates. Blue circles denote T. naybandensis and red circles denote Tropiocolotes cf. steudneri. The type locality of T. naybandensis is marked with a star.
FIGURE 2 in Genetic and morphological variability among the populations assigned to the genus Tropiocolotes Peters, 1880 (Squamata: Gekkonidae) in south Iran
FIGURE 2. Ordination of principal component 1 (PC1) against principal component 2 (PC2) for differentiated characters of the genus Tropiocolotes in southern Iran.
Subspecies and Distribution. S. s. scrofa Linnaeus, 1758 — W Europe, from Denmark, Germany, Poland, and Czech Republic to N Italy and N Iberian Peninsula; possibly also Albania. The taxonomic status of animals in Austria, Switzerland, Slovenia, and Slovakia is unclear but presumably these populations are included in scrofa, as are the populations of Sweden, Finland, and the Baltic states. However, restocking of once depleted populations, for example in Italy, has likely involved the introduction and mixing of this subspecies with other subspecies, such as attila. S. s. affinis Gray, 1847 — S India and Sri Lanka. S. s. algirus Loche, 1867 — Tunisia, Algeria, and Morocco, on the coastal side of the mountains or in the low montane areas. S. s. attila Thomas, 1912 — Hungary, Ukraine, C & S Belarus, Romania, Moldova, and S Russia towards the N flank of the Caucasus, but not including the Transcaucasian countries of Georgia, Armenia, and Azerbaijan. The range possibly extends as far S as the Mesopotamian Delta in Iraq, in which case it would likely include W & SW Iran, and possibly E Turkey and Syria, where it borders with lybicus. Such a range could not be easily reconciled with a statement by Groves that "the difference between pigs from N and S of the Caucasus is quite striking; Transcaucasian boars are certainly not attila." This subspecies may also extend into C Asia and include Kazakhstan, Uzbekistan, and Turkmenistan, but no data exist to support this. S. s. baeticus Thomas, 1912 — originally described from Coto Donana, S Spain, and later merged with meridionalis; also S Portugal. Unless evidence is found that these Italian and Iberian populations are the relics of a much larger formerly contiguous range, this subspecies should be kept as distinct. S. s. coreanus Heude, 1897 — Korean Peninsula. S. s. eristatus Wagner, 1839 — Himalayas S to C India and E to Indochina (N of the Kra Isthmus). S. s. davidi Groves, 1981 — the arid zone from E Iran to Gujarat, including Pakistan and NW India, and perhaps N to Tajikistan. S. s. leucomystax Temminck, 1842 — main Is ofJapan (Honshu, Shikoku, Kyushu, Nakadori, Hiburijima, Tojima, Kushima, and other smaller Is). S. s. lybicus Gray, 1868 — Bulgaria, Greece, Turkey, Syria, Jordan, Israel, Palestine, in the past also in Lybia, and Egypt. The former Yugoslavia was included in its range, which would suggest that now Slovenia, Serbia, Croatia, Bosnia and Herzegovina, Montenegro, and Kosovo are within the range of this subspecies, although the exact boundaries are unclear. Pigs from Albania have been assigned to S. s. scrofa. S. s. majori De Beaux & Festa, 1927 — C & S Italian Peninsula. S. s. menidionalis Forsyth Major, 1882 — Corsica and Sardinia, with the proviso that the two populations are very likely to be introduced or feral. S. s. moupinensis Milne-Edwards, 1871 — China, S to Vietnam and W to Sichuan. S. s. nigripes Blanford, 1875 — the flanks of the Tianshan mountains in Kyrgyzstan and NW China (Xinjiang). An animal photographed in NE Iran (Golestan) looked like this subspecies. S. s. nukiuanus Kuroda, 1924 — Iriomote, Ishigaki, Okinawa, Tokunoshima, Amamioshima, and Kakerome Is in the Ryukyu chain in extreme S Japan, though some of these populations have hybridized with introduced domesticates. S. s. sibiricus Staffe, 1922 — Mongolia and Transbaikal (S & E of Lake Baikal). S. s. tawvanus Swinhoe, 1863 — Taiwan. S. s. ussuricus Heude, 1888 — far E Russia and the Manchurian region (China). Korean populations were previously included in this subspecies, but based on new evidence, the Korean taxon seems more similar to moupinensis. S. s. vittatus Boie, 1828 — Malay Peninsula, S of the Isthmus of Kra, the offshore islands of Terutai and Langkawi, Sumatra, Riau Archipelago, Java, Bali, and a range of smaller islands around these, including Babi, Bakong, Batam, Bawean, Bengkalis, Bintan, Bulan, Bunguran, Cuyo, Deli, Durian, Enggano, Galang, Jambongan, Karimon (Riau Is), Kundur, Lagong, Laut, Lingga, Lingung, Mapor, Moro Kecil, North Pagai, Nias, Panaitan, Payong, Penang, Pinie, Rupat, Siantan, Siberut, Simeulue, Singkep, Sugi, Sugi Bawa, Telibon, Tinggi, Tuangku, and the Tambelan Is. This species was originally present from the British Is in the extreme W, through Eurasia from S Scandinavia to S Siberia, extending as far E as Korea and Japan, and SE into some of the Sunda Is and Taiwan. In the S the species ranged along the Nile Valley to Khartoum, and N of the Sahara in Africa, more orless following the continental coasts of S, E, and SE Asia. Within this range it was absent only from extremely dry deserts, e.g. the driest regions of Mongolia and in China W of Sichuan; and alpine zones, such as the high altitudes of Pamir and Tien Shan. In recent centuries, the range of S. scrofa has changed dramatically because of hunting and changes in available habitat. The species disappeared from the British Is in the 17" century, from Denmark in the 19" century, and was greatly reduced in range and numbers in the 20" century from areas as distant as Tunisia, Sudan, Germany, and Russia. Following these severe declines, there were some slight population recoveries in Russia, Italy, Spain, and Germany in the mid-20™ century, and natural and assisted range expansions in Denmark and Sweden. The species has also been inadvertently reintroduced in various locations in the Great Britain via escapees of mixed origin from commercial farming enterprises. Ex-S. scrofa stocks also occur as introduced feral populations in various other parts of the world, including Australia, New Zealand, the eastern Malay Archipelago, and in North, Central, and South America. In all of these areas they are now generally recognized as a major pest. in Suidae
Subspecies and Distribution. S. s. scrofa Linnaeus, 1758 — W Europe, from Denmark, Germany, Poland, and Czech Republic to N Italy and N Iberian Peninsula; possibly also Albania. The taxonomic status of animals in Austria, Switzerland, Slovenia, and Slovakia is unclear but presumably these populations are included in scrofa, as are the populations of Sweden, Finland, and the Baltic states. However, restocking of once depleted populations, for example in Italy, has likely involved the introduction and mixing of this subspecies with other subspecies, such as attila. S. s. affinis Gray, 1847 — S India and Sri Lanka. S. s. algirus Loche, 1867 — Tunisia, Algeria, and Morocco, on the coastal side of the mountains or in the low montane areas. S. s. attila Thomas, 1912 — Hungary, Ukraine, C & S Belarus, Romania, Moldova, and S Russia towards the N flank of the Caucasus, but not including the Transcaucasian countries of Georgia, Armenia, and Azerbaijan. The range possibly extends as far S as the Mesopotamian Delta in Iraq, in which case it would likely include W & SW Iran, and possibly E Turkey and Syria, where it borders with lybicus. Such a range could not be easily reconciled with a statement by Groves that "the difference between pigs from N and S of the Caucasus is quite striking; Transcaucasian boars are certainly not attila." This subspecies may also extend into C Asia and include Kazakhstan, Uzbekistan, and Turkmenistan, but no data exist to support this. S. s. baeticus Thomas, 1912 — originally described from Coto Donana, S Spain, and later merged with meridionalis; also S Portugal. Unless evidence is found that these Italian and Iberian populations are the relics of a much larger formerly contiguous range, this subspecies should be kept as distinct. S. s. coreanus Heude, 1897 — Korean Peninsula. S. s. eristatus Wagner, 1839 — Himalayas S to C India and E to Indochina (N of the Kra Isthmus). S. s. davidi Groves, 1981 — the arid zone from E Iran to Gujarat, including Pakistan and NW India, and perhaps N to Tajikistan. S. s. leucomystax Temminck, 1842 — main Is ofJapan (Honshu, Shikoku, Kyushu, Nakadori, Hiburijima, Tojima, Kushima, and other smaller Is). S. s. lybicus Gray, 1868 — Bulgaria, Greece, Turkey, Syria, Jordan, Israel, Palestine, in the past also in Lybia, and Egypt. The former Yugoslavia was included in its range, which would suggest that now Slovenia, Serbia, Croatia, Bosnia and Herzegovina, Montenegro, and Kosovo are within the range of this subspecies, although the exact boundaries are unclear. Pigs from Albania have been assigned to S. s. scrofa. S. s. majori De Beaux & Festa, 1927 — C & S Italian Peninsula. S. s. menidionalis Forsyth Major, 1882 — Corsica and Sardinia, with the proviso that the two populations are very likely to be introduced or feral. S. s. moupinensis Milne-Edwards, 1871 — China, S to Vietnam and W to Sichuan. S. s. nigripes Blanford, 1875 — the flanks of the Tianshan mountains in Kyrgyzstan and NW China (Xinjiang). An animal photographed in NE Iran (Golestan) looked like this subspecies. S. s. nukiuanus Kuroda, 1924 — Iriomote, Ishigaki, Okinawa, Tokunoshima, Amamioshima, and Kakerome Is in the Ryukyu chain in extreme S Japan, though some of these populations have hybridized with introduced domesticates. S. s. sibiricus Staffe, 1922 — Mongolia and Transbaikal (S & E of Lake Baikal). S. s. tawvanus Swinhoe, 1863 — Taiwan. S. s. ussuricus Heude, 1888 — far E Russia and the Manchurian region (China). Korean populations were previously included in this subspecies, but based on new evidence, the Korean taxon seems more similar to moupinensis. S. s. vittatus Boie, 1828 — Malay Peninsula, S of the Isthmus of Kra, the offshore islands of Terutai and Langkawi, Sumatra, Riau Archipelago, Java, Bali, and a range of smaller islands around these, including Babi, Bakong, Batam, Bawean, Bengkalis, Bintan, Bulan, Bunguran, Cuyo, Deli, Durian, Enggano, Galang, Jambongan, Karimon (Riau Is), Kundur, Lagong, Laut, Lingga, Lingung, Mapor, Moro Kecil, North Pagai, Nias, Panaitan, Payong, Penang, Pinie, Rupat, Siantan, Siberut, Simeulue, Singkep, Sugi, Sugi Bawa, Telibon, Tinggi, Tuangku, and the Tambelan Is. This species was originally present from the British Is in the extreme W, through Eurasia from S Scandinavia to S Siberia, extending as far E as Korea and Japan, and SE into some of the Sunda Is and Taiwan. In the S the species ranged along the Nile Valley to Khartoum, and N of the Sahara in Africa, more orless following the continental coasts of S, E, and SE Asia. Within this range it was absent only from extremely dry deserts, e.g. the driest regions of Mongolia and in China W of Sichuan; and alpine zones, such as the high altitudes of Pamir and Tien Shan. In recent centuries, the range of S. scrofa has changed dramatically because of hunting and changes in available habitat. The species disappeared from the British Is in the 17" century, from Denmark in the 19" century, and was greatly reduced in range and numbers in the 20" century from areas as distant as Tunisia, Sudan, Germany, and Russia. Following these severe declines, there were some slight population recoveries in Russia, Italy, Spain, and Germany in the mid-20™ century, and natural and assisted range expansions in Denmark and Sweden. The species has also been inadvertently reintroduced in various locations in the Great Britain via escapees of mixed origin from commercial farming enterprises. Ex-S. scrofa stocks also occur as introduced feral populations in various other parts of the world, including Australia, New Zealand, the eastern Malay Archipelago, and in North, Central, and South America. In all of these areas they are now generally recognized as a major pest.
Subspecies and Distribution. M. n. nattereriKuhl, 1817 — broadly extended in N, C & E Europe until 60° N and W Turkey. M.n.hoveliD.L.Harrison,1964—SCTurkey,WSyria,Lebanon,Israel,andWJordan. M. n. tschuliensis Kuzyakin, 1935 — Crimea S to Caucasus region. Isolated populations from W Russia, Crete, Cyprus, and Iraq, Iran, and Turkmenistan are not currently assigned to a subspecies. in Vespertilionidae
Subspecies and Distribution. M. n. nattereriKuhl, 1817 — broadly extended in N, C & E Europe until 60° N and W Turkey. M.n.hoveliD.L.Harrison,1964—SCTurkey,WSyria,Lebanon,Israel,andWJordan. M. n. tschuliensis Kuzyakin, 1935 — Crimea S to Caucasus region. Isolated populations from W Russia, Crete, Cyprus, and Iraq, Iran, and Turkmenistan are not currently assigned to a subspecies.
Subspecies and Distribution. M.h.hasselti:Temminck,1840—MalayPeninsula(includingoffshoreLangkawiI),RiauArchipelago,SWSumatra(BukitBarisanSelatanNationalPark),Java,andSumbawaI. M.h.abbottiLyon,1916—MentawaiIs,offWSumatra. M.h.continentisShamel,1942—patchilydistributedinSEAsia. M. h. macellus Temminck, 1840 — Borneo, also E India (West Bengal) and Sri Lanka; but S Asian populations are assigned to the nominate subspecies by some authors. One record from S China (Yunnan), subspecies not known. in Vespertilionidae
Subspecies and Distribution. M.h.hasselti:Temminck,1840—MalayPeninsula(includingoffshoreLangkawiI),RiauArchipelago,SWSumatra(BukitBarisanSelatanNationalPark),Java,andSumbawaI. M.h.abbottiLyon,1916—MentawaiIs,offWSumatra. M.h.continentisShamel,1942—patchilydistributedinSEAsia. M. h. macellus Temminck, 1840 — Borneo, also E India (West Bengal) and Sri Lanka; but S Asian populations are assigned to the nominate subspecies by some authors. One record from S China (Yunnan), subspecies not known.
FIGURE 8 in Redescription of the goby Glossogobius tenuiformis Fowler, 1934 (Teleostei: Gobiidae) and assignment of Oman Glossogobius populations: a morpho-molecular approach
FIGURE 8. Sensory papillae in Glossogobius tenuiformis; ZM-CBSU O002.Gt144, 36 mm SL; Oman: Wadi Shab. Lines are numbered following Akihito & Meguro (1975). Pores abbreviations are PNP: posterior nasal pores, AIO: anterior interorbital pore, PIO: posterior interorbital pore, PO: postorbital pore, IFO: infraorbital pore, LC: lateral canal pore above pre-operculum, TLC: terminal lateral canal pore, LCT: lateral canal tube detached from main lateral canal.
FIGURE 7. Glossogobius tenuiformis a, Glossogobius tenuiformis a in Redescription of the goby Glossogobius tenuiformis Fowler, 1934 (Teleostei: Gobiidae) and assignment of Oman Glossogobius populations: a morpho-molecular approach
FIGURE 7. Glossogobius tenuiformis a, Glossogobius tenuiformis a, ZM-CBSU O001.Gt101, 75 mm SL; b, ZM-CBSU O001. Gt104, 59 mm SL; c, ZM-CBSU O001.Gt102, 52 mm SL; Oman: Wadi Hasik.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.