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7,081 results for “Habitats”
Habitat features and performance interact to determine the outcomes of terrestrial predator-prey pursuits
<p>1. Animals are responsive to predation risk, often seeking safer habitats at the cost of foraging rewards. Although previous research has examined how habitat features affect detection by predators, little is known about how the interaction of habitat features, sensory cues, and physical performance capabilities affect prey escape performance once detected.</p> <p>2. To investigate how specific habitat features affect predation risk, we developed an individual-based model of terrestrial predator–prey pursuits in habitats with programmable features.</p> <p>3. We ran simulations varying the relative performance capabilities of predator and prey as well as the availability and abundance of refuges and obstacles in the habitat.</p> <p>4. Prey were more likely to avoid detection in complex habitats containing a higher abundance of obstacles; however, if detected, prey escape probability was dependent on both the abundance of refuges and obstacles and the predator's relative performance capabilities. Our model accurately predicted the relative escape success for impala escaping from cheetah in open savanna versus acacia thicket habitat, though escape success was consistently underestimated.</p> <p>5. Our model provides a mechanistic explanation for the differential effects of habitat on survival for different predator–prey pairs. Its flexible nature means that our model can be refined to simulate specific systems and could have applications toward management programs for species threatened by habitat loss and predation.</p>
A layer of global potential habitats
<p>Potential global distribution, e.g. void of human influence, of habitat types following the IUCN habitat classification system at level 1. To create this layer data on the <a href="https://zenodo.org/record/3631254">potential distribution of land cover</a> was intersected with data on climate, elevation and topography. In total 12 classes are mapped based on the decision tree by <a href="https://www.nature.com/articles/s41597-020-00599-8">Jung et al. (2020)</a>, with the version number of this layer matching the version of the decision tree by Jung et al. Style file for use in QGIS are supplied.</p> <p>Future versions will include the potential distribution of biomes (https://zenodo.org/record/3526620) as well as potential fractional cover estimates per class.</p> <p><strong>This layer depicts predictions of potential habitats for a contemporary reference climatology (1970-2015) and not historical habitats lost to land cover and land use change! </strong>An explanation can be found in <a href="https://peerj.com/articles/5457/">Hengl et al. (2018)</a>.</p> <p>Note that the underlying potential land cover distributions are initial predictions for testing purposes only. A publication explaining all processing steps is pending. Furthermore mapping discrete habitat classes ignores the predicted uncertainty in mapped classes. More details can be found <a href="https://zenodo.org/record/3631254">here</a> .</p> <p><strong>Fileformat:</strong> geoTiff | <strong>Projection:</strong> WGS84<strong> | Resolution:</strong> ~250m | <strong>Extent:</strong> Global</p>
NÓS OS BICHOS | Habitat
<p>O curta-metragem da série "Nós, os bichos" enfatiza a importância do equilíbrio ambiental e da vida selvagem na Saúde Única, prevenindo o surgimento de doenças re-emergentes e emergentes, como a COVID-19.</p>
WE, THE ANIMALS | Habitat
<p>During this pandemic, “We, the animals” are back to claim our rights and remind all of society and its governments that the balance of the planet and, consequently, the health of all depends on urgent changes, not only in principle but also in attitudes. Say no to deforestation and habitat loss, trafficking, hunting, consumption and possession of wild animals. Say yes to ecosystem health and animal welfare. Nature thanks you!</p>
Dataset for: African manatee (Trichechus senegalensis) habitat suitability at Lake Ossa, Cameroon using trophic state models and predictions of submerged aquatic vegetation
<p>See research article here: https://onlinelibrary.wiley.com/doi/epdf/10.1002/ece3.8202</p> <p>Aim: The present study aims at investigating the past and current trophic status of Lake Ossa and evaluating its potential impact on African manatee health.</p> <p>Location: Lake Ossa is known as a refuge for the threatened African manatees in Cameroon. Little information exists on the water quality and health of the ecosystem as reflected by its chemical and biological characteristics.</p> <p>Methods: Aquatic biotic and abiotic parameters including water clarity, nitrogen, phosphorous and chlorophyll concentrations were measured monthly during four months at each of 18 water sampling stations evenly distributed across the lake. These parameters were then compared with historical values obtained from the literature to examine the dynamic trophic state of Lake Ossa.</p> <p>Results: Results indicate that Lake Ossa’s trophic state parameters doubled in only three decades (from 1985 to 2016), moving from a mesotrophic to a eutrophic state. The decreasing nutrient gradient moving from the mouth of the lake (in the south) to the north indicates that the flow of the adjacent Sanaga River is the primary source of nutrient input. Further analysis suggests that the poor transparency of the lake is not associated with chlorophyll concentrations but rather with the suspended sediments brought-in by the Sanaga River. Consequently, our model demonstrated that despite nutrient enrichment, less than 5% of the lake bottom surface sustained submerged aquatic vegetation. Thus, shoreline emergent vegetation is the primary food available for the local manatee population. During the dry season, water recedes drastically and disconnects from the dominant shoreline emergent vegetation, decreasing accessibility for manatees.</p> <p>Main conclusions: The current study revealed major environmental concerns (eutrophication and sedimentation) that may negatively impact habitat quality for manatees. Efficient land use and water management across the entire watershed may be necessary to mitigate such issues.</p>
Dataset of host, habitat and vegetation parameters in relation to gastrointestinal parasite infections of small mammalian hosts in Madagascar
<p>The dataset contains information on the endoparasite infection status of 903 individuals of four small mammal species (<em>Microcebus murinus</em>, <em>M. ravelobensis</em>, <em>Eliurus myoxinus</em>, <em>Rattus rattus</em>) in relation to host-specific (sex, body condition, population density) and habitat-specific factors (degree of habitat fragmentation, fragment size, distance to the edge of the fragment, percentage of edge habitat, vegetation structure).</p>
Latitudinal core habitat prediction data for the manuscript: "Seascape topography slows predicted range shifts in fish under climate change"
<p>Latitudinal locations of core environmental habitat for yellowtail kingfish (<em>Seriola lalandi</em>), Australian bonito (<em>Sarda australis</em>), Australian spotted mackerel (<em>Scomberomorus munroi</em>), narrow-barred Spanish mackerel (<em>Scomberomorus commerson</em>) and common dolphinfish (<em>Coryphaena hippurus</em>) nearshore of the continental shelf break (i.e. 200-m isobath) within 145 – 160°E, 15 – 45°S and between years 1998 – 2018.</p>
Proactive conservation to prevent habitat losses to agricultural expansion
<p>The projected loss of millions of square kilometres of natural ecosystems to meet future demand for food, animal feed, fibre, and bioenergy crops is likely to massively escalate threats to biodiversity. Reducing these threats requires a detailed knowledge of how and where they are likely to be most severe. We developed a geographically explicit model of future agricultural land clearance based on observed historic changes and combine the outputs with species-specific habitat preferences for 19,859 species of terrestrial vertebrates. We project that 87.7% of these species will lose habitat to agricultural expansion by 2050, with 1,280 species projected to lose ≥25% of their habitat. Proactive policies targeting how, where, and what food is produced could reduce these threats, with a combination of approaches potentially preventing almost all these losses while contributing to healthier human diets. As international biodiversity targets are set to be updated in 2021, these results highlight the importance of proactive efforts to safeguard biodiversity by reducing demand for agricultural land.</p>
Code and data for: Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?
<p>This repository is a companion to the manuscript "<em>Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?</em>" and is linked to <a href="https://github.com/CBeardsworth/Pheasant_OrientStrat_Habitat">Github</a>.</p> <p>For any questions about the code please contact Christine at <a href="mailto:c.e.beardsworth@gmail.com">c.e.beardsworth@gmail.com</a></p> <p>To use any data contained in this repository contact Joah at <a href="mailto:j.r.madden@exeter.ac.uk">j.r.madden@exeter.ac.uk</a> for permission.</p> <p>In this repository, we have included a run-through of the R analysis <a href="https://cbeardsworth.github.io/Pheasant_OrientStrat_Habitat/">here</a> to show the outputs of the analysis without the need to run the code. For those that might want to run the code themselves, we have included three R scripts (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/R">/R</a>) and their accompanying datasets (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a>). A description of the code and the data needed to run them is below:</p> <p><em>Cognition analysis and figs.R</em> = Run the cognition analysis for the first section of the manuscript and create the figures. For this, the datasets mazeData.csv (the learning trials) and mazeRotationResults.csv (the probe trial) are required. </p> <p><em>iSSA analysis and bootstrapping.R</em> = Run iSSA models and bootstrapping. This produces the datasets required for the next stage of analysis. For this code, the datasets habitat.grd (habitat information), atlas2018-strategy.csv (atlas data + id and strategy data for each bird) and FeederCoords2017_27700.csv (coordinates of feeder locations from 2017-2018) are required. The produced datasets are included in <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a> therefore to run subsequent analyses, this code does not need to be run. To develop this code we relied heavily on the code included in the supplementary material of <a href="https://doi.org/10.1002/ece3.4823">Signer et al. (2019)</a> as well as an <a href="https://bsmity13.github.io/log_rss">online tutorial</a> from Brian J. Smith for calculating log-RSS.</p> <p><em>Habitat analysis and Figs.R</em> = Run the statistical models for the final section of the manuscript and create the figures. For this code, the datasets produced in the previous R script are required (habitatOrientation_coefs.csv and habitatOrientation_avail.csv). We have included <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">these datasets</a> so users do not need to run the iSSA analysis and bootstrapping.R script themselves. </p>
FIGURE 2. Tiganophyton karasense. A. Plant habit and habitat. B in From the frying pan: an unusual dwarf shrub from Namibia turns out to be a new brassicalean family
FIGURE 2. Tiganophyton karasense. A. Plant habit and habitat. B. Part of an old long shoot showing short shoots with their rosettes of foliage leaves (mainly) and bracts. C. Young, actively elongating long shoots with short shoots not yet fully developed in leaf axils; arrows indicate where a long shoot emerges from the apex of a short shoot. D. Long shoot densely covered with short shoots, the latter bearing flowers. Photographs: W. Swanepoel.
Figs 14–16 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Figs 14–16. Mumetopia interfeles Roháček sp. nov., female paratypes (SMOC MIT076–MIT078). 14. Body of aberrant specimen, lateral view. 15. Right wing. 16. Posterior part of thorax and abdomen, dorsal view. Scale bars = 0.5 mm. Photos by P. Krásenský.
Figs 1–3 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Figs 1–3. Mumetopia interfeles Roháček sp. nov., ♂. 1. Holotype (SMOC MIT001), lateral view. 2. Right wing, paratype (SMOC MIT003). 3. Head and thorax, paratype (SMOC MIT002), laterodorsal view. Scale bars = 0.5 mm. Photos by P. Krásenský.
Fig. 32 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Fig. 32. Bayesian hypothesis for relationships of 64 species of Anthomyzidae based on DNA sequence data (12S, 16S, 28S, COI, COII, CytB, ITS2) representing 4583 characters. Numbers above nodes = posterior probability values if> 0.5, and below nodes = bootstrap support values for RAxML.
Figs 17–21 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Figs 17–21. Mumetopia interfeles Roháček sp. nov., female paratypes (SMOC MIT080–MIT083). 17. Abdomen, dorsal view. 18, 21. Spermatheca. 19. Genital chamber and S8, lateral view. 20. Same, ventral view (setosity of S8 omitted). Scale bars: 17 = 0.2 mm; 18, 21= 0.05 mm; 19–20 = 0.1 mm. For abbreviations see Material and methods.
Figs 9–13 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Figs 9–13. Mumetopia interfeles Roháček sp. nov., male paratypes (SMOC MIT005, MIT006). 9. Gonostylus, posterolateroventral view (widest extension view). 10. Hypandrial complex, lateral view. 11. Transandrium, caudal view. 12. Filum of distiphallus, anteroventral view. 13. Aedeagal complex, lateral view. Scale bars: 9, 12 = 0.05 mm; 10–11, 13 = 0.1 mm. For abbreviations see Material and methods.
Figs 29–31. Maps and habitat. 29 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Figs 29–31. Maps and habitat. 29. Map of Chile with position of type locality of Mumetopia interfeles Roháček sp. nov. (blue open square). 30. Satellite view of part of Valparaíso city, with collecting site indicated by red arrow. 31. Habitat of M. interfeles sp. nov., northern end of grassy area between houses visited by cats. Photo by J. von Tschirnhaus (31), other sources: Vemaps.com (29), Google Earth Pro (30).
Figs 4–8 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Figs 4–8. Mumetopia interfeles Roháček sp. nov., male paratypes (SMOC MIT004–MIT007). 4. Postabdomen with fifth abdominal segment, dorsal view. 5. Same, ventral view. 6. External genitalia, caudal view. 7. Postabdomen with fifth abdominal segment, lateral view. 8. Genitalia, lateral view. Scale bars: 4–5, 7 = 0.2 mm; 6, 8 = 0.1 mm. For abbreviations see Material and methods.
Fig. 34 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Fig. 34. Most parsimonious tree (L = 34 steps, CI = 0.85, RI = 0.82) resulting from cladistic morphological analysis of the Chamaebosca clade, ACCTRAN optimization. Full circles = non-homoplasious character transformations; empty circles = homoplasious character transformations. Numbers below branches indicate change to apomorphic (1, 2) or plesiomorphic (0) states of characters (reversals in case of (0)). Adapted from Roháček & Barber (2009: fig. 38).
Fig. 33 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Fig. 33. RAxML hypothesis for relationships of Anthomyzidae based on DNA sequence data (12S, 16S, 28S, COI, COII, CytB, ITS2) representing 4583 characters, without the Anthomyza group of genera clade displayed. Numbers below nodes = bootstrap support values.
Figs 22–28 in Mumetopia interfeles sp. nov., a new species of Anthomyzidae (Diptera) occurring en masse in an urban grassy habitat in Chile: its taxonomy, phylogeny and biology
Figs 22–28. Mumetopia interfeles Roháček sp. nov., paratypes (SMOC MIT002–MIT006, MIT081– MIT083). 22. ♂, f 1, posterior view. 23. Female postabdomen, lateral view. 24. Same, dorsal view. 25. Same, ventral view. 26. Apex of male t2 and base of mid basitarsus, anterior view. 27. Apex of male t 3 and hind basitarsus with 3 thickened ventrobasal setulae (black arrow), anterior view. 28. Male f 3, anterior view. Scale bars: 22, 26–28 = 0.2 mm; 23–25 = 0.1 mm. For abbreviations see Material and methods.
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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)
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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.