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896 results for “distributional ranges”
Fig. 1 in New data on distribution of Montana striata (Kittary, 1849) (Orthoptera: Tettigoniidae: Platycleidini) in the eastern part of the range
Fig. 1. Known localities and years of collecting of Montana striata in the Asian part of
Data for: Prey resources are equally important as climatic conditions for predicting the distribution of a broad-ranged apex predator
<p>Aim: A current biogeographic paradigm states that climate regulates species distributions at continental scales and that biotic interactions are undetectable at coarse-grain extents. However, advances in spatial modelling show that incorporating food resource distributions are important for improving model predictions at large distribution scales. This is particularly relevant to understand the factors limiting distribution of widespread apex predators whose diets are likely to vary across their range.</p> <p>Location: Neotropical Central and South America</p> <p>Methods: The harpy eagle (<em>Harpia harpyja</em>) is a large raptor, whose diet is largely comprised of arboreal mammals, all with broad distributions across Neotropical lowland forest. Here, we used a hierarchical modelling approach to determine the relative importance of abiotic factors and prey resource distribution on harpy eagle range limits. Our hierarchical approach consisted of the following modelling sequence of explanatory variables: (a) abiotic covariates, (b) prey resource distributions predicted by an equivalent modelling for each prey, (c) the combination of (a) and (b), and (d) as in (c) but with prey resources considered as a single prediction equivalent to prey species richness.</p> <p>Results: Incorporating prey distributions improved model predictions but using solely biotic covariates still resulted in a high performing model. In the Abiotic model, Climatic Moisture Index (CMI) was the most important predictor, contributing 76 % to model prediction. Three-toed sloth (<em>Bradypus</em> spp.) was the most important prey resource, contributing 64 % in a combined Abiotic-Biotic model, followed by CMI contributing 30 %. Harpy eagle distribution had high environmental overlap across all individual prey distributions, with highest coincidence through Central America, eastern Colombia, and across the Guiana Shield into northern Amazonia. </p> <p>Main conclusions: With strong reliance on prey distributions across its range, harpy eagle conservation programs must therefore consider its most important food resources as a key element in the protection of this threatened raptor.</p>
Giant panda distribution ranges in the Liangshan Mountains
<p><span>Comprehending the population trend and understanding the distribution range dynamics of species</span><span> is necessary for global species protection. Recognizing what causes dynamic distribution change is crucial for identifying species' environmental preferences and formulating protection policies. Here, we studied </span><span>the rear-edge population of the flagship species, giant pandas (</span><span><em>Ailuropoda</em> <em>melanoleuca</em></span><span>), </span><span>to 1) assess their population trend using their distribution patterns, 2) evaluate their distribution dynamics change from the 2nd (1988) to the 3rd (2001) surveys (2–3 Interval) and 3rd to the 4th (2013) survey (3–4 Interval) using a machine learning algorithm (The Extremely Gradient Boosting), and 3) decode model results to identify driver factors in the first known use of SHapley Additive exPlanations. Our results showed that the population trends in Liangshan Mountains were worst in the 2<sup>nd</sup> survey (k = 1.050), improved by the 3<sup>rd</sup> survey (k = 0.97), but got worse by the 4<sup>th</sup> survey (k = 0.996), which indicates a worrying population future. We found that precipitation had the most significant influence on distribution dynamics among several potential environmental factors, showing a negative correlation between precipitation and giant panda expansion. We recommend that more study is required to understand the micro-environment and animal distribution dynamics. We provide a fresh perspective on the dynamics of Giant Panda distribution, highlighting novel focal points for ecological research on this species. Our study offers theoretical underpinnings that could inform the formulation of more effective conservation policies. Also, we emphasize the uniqueness and importance of the Liangshan Mountains giant pandas as the rear-edge population, which is at a high risk of population extinction. </span></p>
Data for: Using distribution models to identify range shifts of four Acroneuria Pictet, 1841 (Plecoptera: Perlidae) species in the Midwest USA
<p><span></span></p> <p>Regional faunal assessments of stoneflies in the United States Midwest (herein defined as Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio, and Wisconsin) indicate increasing imperilment resulting from human disturbance and climate change. Large-bodied Perlid stoneflies with multivoltine life cycles are among the most at risk for regional extirpation, with losses reported in several midwestern states. Species distribution modeling was undertaken to describe distribution shifts for four widespread riverine species: <em>Acroneuria</em> <em>abnormis</em> (Newman, 1838), <em>A. frisoni </em>Stark & Brown, 1991, <em>A. internata </em>(Walker, 1852) and <em>A. lycorias</em> (Newman, 1839). The distribution modeling algorithm MaxEnt was selected to predict both the historical (i.e., pre-1960) and contemporaneous distributions for each species using separate occurrence datasets. These models permit the identification of suitable habitat loss through range contractions associated with human disturbance. Predictions of suitable habitat losses were recorded for multiple species but were greatest for<em> A. abnormis</em> and <em>A</em>. <em>internata</em>. These models serve to guide future collection efforts and to further describe patterns of regional biodiversity loss. The data presented within this dataset contain the occurrence data used for modeling with distinction between temporal periods modeled.</p>
Data from: Regional variation in climate change alters the range-wide distribution of colour polymorphism in a wild bird
<p><span>According to Gloger's rule animal colouration is expected to be darker in wetter and warmer climates. Such environmental clines are predicted to occur in colour polymorphic species and to be shaped by selection if colour morphs represent adaptations to different environments. We studied if the distribution of the colour polymorphic tawny owl (<em>Strix aluco</em>) morphs (a pheomelanic brown and a pale grey) across Europe follow the predictions of Gloger's rule and if there is a temporal change in the geographical patterns corresponding to regional variations in climate change. We used data on tawny owl museum skin specimen collections. First, we investigated long-term spatiotemporal variation in the probability of observing the colour morphs in different climate zones. Second, we studied if the probability of observing the colour morphs was associated with general climatic conditions. Third, we studied if weather fluctuations prior the finding year of an owl explains colour morph in each climate zone. The brown tawny owl morph was historically more common than the grey morph in every studied climate zone. Over time the brown morph has become rarer in the temperate and Mediterranean zone, whereas it has first become rarer but then again more common in the boreal zone. Based on general climatic conditions winter and summer temperature were positively and negatively associated with proportion of brown morph, respectively. Winter precipitation was negatively associated with proportion of brown morph. The effects of five-year means of weather on the probability to observe a brown morph differed between climate zones, indicating region dependent effect of climate change and weather on tawny owl colouration. To conclude, tawny owl colouration does not explicitly follow Gloger's rule, implying a time and space dependent complex system shaped by many factors. We provide novel insights in how the geographic distribution of pheomelanin-based colour polymorphism is changing.</span></p>
Fig. 1 in Distributional range of the South African maritime spider-egg parasitoid wasp, Echthrodesis lamorali (Hymenoptera: Platygastridae: Scelioninae)
Fig. 1. Lateral view of the habitus of a female Echthrodesis lamorali Masner specimen.
Data from: Rapoport’s rule explains the range size distribution of butterflies along the Eastern Himalayan elevation gradient
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Data for: Rapid shifts in Arctic tundra species’ distributions and inter-specific range overlap under future climate change
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Data from: Is there a disease-free halo at species range limits? The co-distribution of anther-smut disease and its host species
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Data from: Forecasting range shifts using abundance distributions along environmental gradients
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Data from: Lineage-specific trait variations and plasticity of obligate parthenogenetic animals following the expansion of distribution range to a continental archipelago.
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The best of two worlds: using stacked generalisation for integrating expert range maps in species distribution models
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Population expansion, divergence, and persistence in western fence lizards (Sceloporus occidentalis) at the northern extreme of their distributional range
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Data for: Prey resources are equally important as climatic conditions for predicting the distribution of a broad-ranged apex predator
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Evolution in response to climate in the native and introduced ranges of a globally distributed plant
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Assessing the links between pollinators and the genetic and epigenetic features of plant species with contrasting distribution ranges
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Data for: Using distribution models to identify range shifts of four Acroneuria Pictet, 1841 (Plecoptera: Perlidae) species in the Midwest USA
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Publication release: How well do species distribution models predict occurrences in exotic ranges?
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Global warming pushes the distribution range of the two alpine ‘glasshouse’ Rheum species north- and upwards in the Eastern Himalayas (EH) and the Hengduan Mountains (HM)
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Data from: Demographic and ecogeographic factors limit wild grapevine spread at the southern edge of its distribution range - wild grapevine sampling locations, Maxent input files, morphological and microsatellite data
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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.