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133 results for “Moose”
Fig. 3 in Patterns of parasite eggs, oocysts and larvae shedding by moose in the Biebrza marshland (NE Poland)
Fig. 3. The relationship between the median EPG of Parafasciolopsis fasciolaemorpha, the median LPG of Elaphostrongylus sp. and the presence of snow cover.
Fig. 2 in New geographic records for Echinococcus canadensis in coyotes and moose from Nova Scotia, Canada
Fig. 2. Protoscolices contained in cyst fluid released upon dissection of a unilocular cyst from the lungs of a moose from Cape Breton Island, Nova Scotia.
Fig. 1. Echinococcus canadensis recovered from a in New geographic records for Echinococcus canadensis in coyotes and moose from Nova Scotia, Canada
Fig. 1. Echinococcus canadensis recovered from a gastrointestinal flush of a coyote at necropsy from Cape Breton, Nova Scotia. Note that the genital pore (circled) is located in the posterior half of the segment.
Fig. 3 in Distribution, prevalence and intensity of moose nose bot fly (Cephenemyia ulrichii) larvae in moose (Alces alces) from Norway
Fig. 3. The predicted parasite intensity of moose nose bot fly larvae for harvested calves (red), yearlings (blue) and adult (green) moose in central and southern Norway. Predictions from the highest ranked intensity model with study area and age group. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Distribution, prevalence and intensity of moose nose bot fly (Cephenemyia ulrichii) larvae in moose (Alces alces) from Norway
Fig. 2. The predicted infection prevalence of moose nose bot fly larvae with increasing moose (host) density. The shaded area shows the 95% confidence interval. Predictions from the highest ranked model with moose density. In the plot we used the function "jitter" in the R package ggeffects (Lüdecke, 2018), which adds small random variation to the data points to better reflect the amount of data for moose densities. Hence, the points do not reflect exact values as they are binomial.
Fig. 1 in Distribution, prevalence and intensity of moose nose bot fly (Cephenemyia ulrichii) larvae in moose (Alces alces) from Norway
Fig. 1. Study areas in southern (Oslo, AurskogHøland and Kongsvinger) and central Norway (Selbu, Tydal, Malvik, Stjørdal and Meråker) with location and moose density (moose density, see Materials and methods) in sampling municipalities. Red filled circle indicate where the moose nose bot fly (Cephenemyia ulrichii) was first found in Norway, and open circles show where moose heads were examined without detection of the moose nose bot fly in 1987 (Nilssen and Haugerud, 1994). Blue circles indicate where the moose nose bot fly were found in Sweden in the late 1970s and 1980s (Steen et al., 1988). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Elucidating nematode diversity and prevalence in moose across a wide latitudinal gradient using DNA metabarcoding
Fig. 2. Nematode component community in winter with a) the number of nematode taxa detected at each study area and b) the number of nematode taxa shared among study areas.
Fig. 3 in Elucidating nematode diversity and prevalence in moose across a wide latitudinal gradient using DNA metabarcoding
Fig. 3. Prevalence in each study area of the six most common nematodes detected. Whiskers indicate 95% confidence intervals.
Fig. 1. A in Elucidating nematode diversity and prevalence in moose across a wide latitudinal gradient using DNA metabarcoding
Fig. 1. A map showing the distribution of the five study areas across Norway ranging from 59.6◦N to 70.5◦N.
Fig. 2 in DNA sequencing confirms meningeal worm (Parelaphostrongylus tenuis) and muscle worm (Parelaphostrongylus andersoni) in white-tailed deer (Odocoileus virginianus): Implications for moose (Alces alces) management
Fig. 2. Summary of Parelaphostrongylus spp. infection in white-tailed deer (Odocoileus virginianus) fecal samples collected in Western Manitoba. Partial CO1 and ITS-2 genetic sequence results found white-tailed deer fecal samples with Parelaphostrongylus andersoni and Parelaphostrongylus tenuis in game hunting area (GHA) 13, 18 and 27 and only P. tenuis infected fecal pellets in GHA 22. GHA 13 and 18 (blue) are areas where moose populations are a management concern whereas GHAs 22 and 27 (yellow) are areas where moose populations are not a management concern. GHAs 18 and 22 were sampled in 2020 (light colors) while GHAs 13 and 27 were sampled in 2021 (dark colors). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in DNA sequencing confirms meningeal worm (Parelaphostrongylus tenuis) and muscle worm (Parelaphostrongylus andersoni) in white-tailed deer (Odocoileus virginianus): Implications for moose (Alces alces) management
Fig. 1. Average dorsal-spined larvae (DSL) prevalence in white-tailed deer (Odocoileus virginianus) fecal samples in each game hunting area (GHA) sampled in Manitoba in 2020 and 2021. For trips, early summer collection was in June, mid-summer was July–August and late summer was August–September. GHA 13 and 18 (blue) are areas where moose populations are a management concern whereas GHAs 22 and 27 (yellow) are areas where moose populations are not a management concern. GHAs 18 and 22 were sampled in 2020 (light colors) while GHAs 13 and 27 were sampled in 2021 (dark colors). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Molecular identification of Trypanosoma theileri complex in Eurasian moose Alces alces (L.)
Fig. 2. Phylogenetic tree of Trypanosoma sp. 18S rRNA partial gene. Maximum-likelihood tree computed with the GTR + I + G model of sequence evolution. Trypanosoma sp. found in our study (haplotype H1 and H2 marked with red color) and downloaded from GenBank. Hosts were listed after GenBank numbers and country of origin. Numbers listed at nodes represent percent support for that node from 1000 bootstrap replicates. The ML tree has been rooted with sequences of Trypanosoma cyclops. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1. The trypanosomes from European moose. A, B in Molecular identification of Trypanosoma theileri complex in Eurasian moose Alces alces (L.)
Fig. 1. The trypanosomes from European moose. A, B. light microscope images; C. drawing scheme. Scale bar 10 μm.
Calving season habitat selection of maternal and non-maternal female moose in Southwest Alaska, U.S.A
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Winter browsing by moose (Alces alces) in a forested mountainous landscape of West-Central Sweden
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Data for: Temporal variations in female moose responses to roads and logging in the absence of wolves
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Soil Ammonium and Nitrate rates in and out of the Moose Exclosures on the Tanana River Floodplain , Fall 2001
Soil ammonium and nitrate rates were sampled both inside and out of the moose exclosures located on the Tanana River Floodplain. Samples were collected in the fall of 2001.
MOOSE Structural Mechanics FEM Demonstration
<p>Demonstration of <a href="https://www.mooseframework.org/">MOOSE</a> finite element workflow for a simple structural mechanics problem in Windows 10 using Windows Subsystem Linux (WSL). Targeted for Windows-based MOOSE users who wish to simply use existing functionality of MOOSE -- not focused on developing / contributing to MOOSE. Datafiles to replicate are provided.</p>
Microbial associations and spatial proximity predict North American moose (Alces alces) gastrointestinal community composition
<ol> <li>Microbial communities are increasingly recognised as crucial for animal health. However, our understanding of how microbial communities are structured across wildlife populations is poor. Mechanisms such as interspecific associations are important in structuring free-living communities, but we still lack an understanding of how important interspecific associations are in structuring gut microbial communities in comparison to other factors such as host characteristics or spatial proximity of hosts.</li> </ol> <p> </p> <ol> <li>Here we ask how gut microbial communities are structured in a population of North American moose (<i>Alces alces</i>). We identify key microbial interspecific associations within the moose gut and quantify how important they are relative to key host characteristics, such as body condition, for predicting microbial community composition.</li> </ol> <p> </p> <ol> <li>We sampled gut microbial communities from 55 moose in a population experiencing decline due to a myriad of factors, including pathogens and malnutrition. We examined microbial community dynamics in this population utilizing novel graphical network models that can explicitly incorporate spatial information.</li> </ol> <p> </p> <ol> <li>We found that interspecific associations were the most important mechanism structuring gut microbial communities in moose and detected both positive and negative associations. Models only accounting for associations between microbes had higher predictive value compared to models including moose sex, evidence of previous pathogen exposure, or body condition. Adding spatial information on moose location further strengthened our model and allowed us to predict microbe occurrences with ~90% accuracy.</li> </ol> <p> </p> <ol> <li>Collectively, our results suggest that microbial interspecific associations coupled with host spatial proximity are vital in shaping gut microbial communities in a large herbivore. In this case, previous pathogen exposure and moose body condition were not as important in predicting gut microbial community composition. The approach applied here can be used to quantify interspecific associations and gain a more nuanced understanding of the spatial and host factors shaping microbial communities in non-model hosts.</li> </ol>
Seasonal release from competition explains partial migration in European moose
Partial migration, whereby a proportion of a population migrates between distinct seasonal ranges, is common throughout the animal kingdom. However, studies linking existing theoretical models of migration probability, with empirical data are lacking. The competitive release hypothesis for partial migration predicts that due to density-dependent habitat selection, the proportion of migrants increases as the relative quality and size of the seasonal range increases, but decreases with increasing migration cost and population density. To test this prediction, we developed a quantitative framework to predict the proportion of migrants, using empirical data from 545 individually GPS-marked moose (Alces alces) from across Fennoscandia, spanning latitudes of 56° to 68°N. Moose contracted their ranges to common and spatially limited winter areas (typically at lower elevation), but expanded them during summer due to an increase in suitable habitat (at highland ranges). As predicted from our model, a better and larger highland range relative to the lowland range corresponded to a higher proportion of migrants in an area. Quantitative predictions coupling the balance of habitat availability of seasonal ranges with the probability of migrating in a large herbivore is a necessary step towards an enhanced understanding of the mechanisms underlying migration at the population level.
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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.