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7,081 results for “Habitats”
Fig. 2 in Can artificial retreat sites help frogs recover after severe habitat devastation? Insights on the use of "coqui houses" after Hurricane Maria in Puerto Rico
Fig. 2. Change in forest structure in the Palo Colorado forest transect (El Yunque) due to Hurricane Maria and corresponding damage/recovery stages according to Table 1. (A) Before the hurricane. (B) Same site after the hurricane, stage 1. (C) Moderate recuperation, stage 3. (D–E) Canopy dominated by Sierra Palm fronds showing signs of further recuperation of original understory vegetation, stage 4.
Fig. 9 in Can artificial retreat sites help frogs recover after severe habitat devastation? Insights on the use of "coqui houses" after Hurricane Maria in Puerto Rico
Fig. 9. Different uses ascribed to the two types of artificial habitats (=coqui houses) placed in the forest. (A) Coqui frog using bamboo house as retreat site during the day. (B) Bamboo house used as nesting site with a double clutch. Note that eggs are observed but the guarding male jumped away as the photo was taken. (C) PVC house used by a coqui as a nocturnal perching site. (D) PVC house used by a coqui as a calling site during the night.
Fig. 1 in Can artificial retreat sites help frogs recover after severe habitat devastation? Insights on the use of "coqui houses" after Hurricane Maria in Puerto Rico
Fig. 1. Map showing the location of El Yunque National Forest in Puerto Rico, and the location of the study transects.
Рис. 1. Пионерский пруΑ, место нахоΑки Brachytron pratense Fig. 1. Pionersky Pond, the habitat of Brachytron pratense in Brachytron pratense (Müller, 1764) (Odonata: Aeshnidae): a new species in the fauna of Chuvashia
Рис. 1. Пионерский пруΑ, место нахоΑки Brachytron pratense Fig. 1. Pionersky Pond, the habitat of Brachytron pratense
Data from: Semi-natural habitat, but not aphid amount or continuity, predicts lady beetle abundance across agricultural landscapes
<p>The amount of semi-natural habitat surrounding farm fields is a common but inconsistent predictor of natural enemy populations and predation services. Standard land cover metrics may not accurately capture the actual availability of limiting resources for natural enemies and can miss important dynamics across space and time. Theory from animal movement and landscape ecology predicts that regions with more, spatio-temporally continuous resources (i.e. food, shelter) should have larger predator populations and enhanced biological control. To test these predictions empirically, we designed a study measuring aphids, lady beetles, and predation services in agricultural landscapes in Wisconsin, USA. In two study years, we sampled lady beetles and aphids in 336 crop fields (corn, soybean, alfalfa, and small grains) and adjacent semi-natural habitat patches (grasslands and woodlands) across 24 1.5 km buffer landscapes at 4–7 time points each, and in one year we assessed predation rates with sentinel egg cards. We used aphid counts to model habitat-specific aphid phenologies, from which we calculated landscape indices of prey amount and continuity. These indices, along with semi-natural habitat area, were used to predict lady beetle abundance. While there were strong differences in the abundance and timing of aphids by habitat, semi-natural habitat amount was still a better predictor of lady beetle counts and sentinel egg predation than either aphid amount or continuity indices in these landscapes.</p> <p>Synthesis and application: Our findings confirm the robust relationship between lady beetles and semi-natural habitat in agricultural landscapes, and highlight the complexities of measuring fine-scale resource heterogeneity in real landscapes. Retaining or adding woodland and grassland patches in agricultural landscapes is likely to support larger lady beetle populations and enhance predation in crop fields. Our results suggest that these habitats may be more important for shelter than prey continuity, though this mechanism warrants further investigation. Future work should continue to refine experimental methods for the successful integration of landscape ecology and animal behavior to support conservation goals.</p>
Stream temperature data for Alaska Sustainable Salmon Fund project 53007 Assessing Thermal Habitat Variability to Identify Refugia in SE Alaska Subsistence Salmon Watersheds
<p>Hourly stream temperature data were collected in eight watersheds in southeast Alaska to better understand within-watershed thermal heterogeneity. Watersheds include: Chilkat, Chilkoot, Klag, Cowee, Peterson (Juneau road system), Saltery, Kadashan, and Klawock. Site latitude and longitude are recorded in the metadata file. Data were collected with HOBO Onset Pro V2 or HOBO TidbiT MX 400 temperature loggers following the protocols in <a href="https://doi.org/10.1016/j.ejrh.2015.07.008">Mauger et al, 2015</a>. Data collection dates range from January 1, 2020 to November 2, 2023, although not all sites cover this entire date range. </p>
Data from: "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal"
<p>Processed datasets used for analysis in "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal" by Hooven et al. published in <em>Mammal Research</em>. R scripts used to process and analyze these data are available from: <a href="https://github.com/nhooven/elk-individual-habitat">https://github.com/nhooven/elk-individual-habitat</a></p> <p>WS_sampled.csv, SU_sampled.csv, UA_sampled.csv, AW_sampled.csv - Processed telemetry datasets (with relocation data removed), resultant files from script "01 - Pre-processing.R".</p> <p>WS_HRs.csv, SU_HRs.csv, UA_HRs.csv, AW_HRs.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by individual. </p> <p>WS_groups.csv, UA_groups.csv, AW_groups.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by groups. </p> <p>Note: Raw telemetry data and home range polygons are not available due to the sensitive nature of providing animal locations publicly. Please direct any questions or concerns to the corresponding author (nathan.d.hooven@gmail.com). </p>
Habitats labelled data
<p><span>This dataset has been created within the EU H2020 Natural Intelligence project (ID </span><span> 101016970</span><span>).</span></p> <p><span>The dataset contains labeled pictures of typical and early warning species of four different habitats, divided into four subfolders. </span></p> <p><span>Each subfolder contains:</span></p> <p><span>1) species data images (.jpg files)</span></p> <p><span>2) .txt files for each image containing the labelling information. The first value indicates the species (defined in the .yaml file), the remaining values describe the box vertices.</span></p> <p><span>3) a single .yaml file containing the parameter used in the labelling and detection AI training code:</span></p> <p><span> - path to the dataset root directory</span></p> <p><span> - path to the the train images directory</span></p> <p><span> - path to the validation images directory</span></p> <p><span> - path to the test images directory</span></p> <p><span> - number of classes (species)</span></p> <p><span> </span></p> <p><span>The four subfolders are named after the four habitats, i.e.,</span></p> <p><span>- </span><span>Dunes. This refers to EU habitats 2110 and 2120 in Italian dunes. The included species are:</span></p> <p><span> o </span><span>Achillea maritima – typical species</span></p> <p><span> o </span><span>Calamagrotis arenaria – typical species</span></p> <p><span> o </span><span>Carpobrotus acinaciformis – invasive species</span></p> <p><span> o </span><span>Eryngium maritimum – typical species</span></p> <p><span> o </span><span>Pancratium maritimum – typical species</span></p> <p><span> o </span><span>Thinopyrum junceum – typical species</span></p> <p><span>- </span><span>Grasslands. This refers to EU habitat 6210* in the Italian Central Apennines. The included species are:</span></p> <p><span> o </span><span>Asphodelus macrocarpus – early warning species</span></p> <p><span> o </span><span>Dactylorhiza sambucina – typical species</span></p> <p><span> o </span><span>Orchis morio – typical species</span></p> <p><span>- </span><span>Forests. This refers to EU habitat 9210* in the Italian Apennines. The included species are:</span></p> <p><span> o </span><span>Anemonoides nemorosa – typical species</span></p> <p><span> o </span><span>Corydalis cava – typical species</span></p> <p><span> o </span><span>Doronicum columnae – early warning species</span></p> <p><span> o </span><span>Anemonoides ranunculoides – typical species</span></p> <p><span>- </span><span>Screes. This refers to EU habitat 8110 and 8120 in the Italian Alps. The included species are:</span></p> <p><span> o </span>Cerastium sp.<span> – typical species</span></p> <p><span> o </span><span>Luzula alpino-pilosa – early warning species</span></p> <p><span> o </span><span>Saxifraga – typical species</span></p> <p><span> o </span><span>Ranunculus glacialis – typical species</span></p> <p><span> o </span><span>Geum reptans – typical species</span></p> <p><span> o </span><span>Papaver alpinum – typical species</span></p> <p><span> </span></p> <p><span>Researchers from a variety of disciplines can benefit from using this dataset because of its multidisciplinary scope. Botanists could evaluate the accuracy of this data as well as the habitat's conditions using the plant images that the robot captured, robotic engineers could test their AI algorithms for identifying and classifying different species using these data. The code used to label the images can be found here: <a href="https://github.com/ivangrov/ModifiedOpenLabelling">https://github.com/ivangrov/ModifiedOpenLabelling</a></span></p> <p><span> </span></p>
Fig. 1. Study area and surrounding intertidal habitats. A in Two new species of Perinereis Kinberg, 1865 (Annelida: Nereididae) from the rocky shore of Maharashtra, India, including notes and an identification key to Group 1
Fig. 1. Study area and surrounding intertidal habitats. A. Coastal region of Maharashtra (western India) and collecting localities. B. Aerial view of the rocky intertidal habitat of the study area. C. Intertidal area with rocks covered with seaweed. D. Close-up view of the intertidal area with rocks covered with seaweed and oyster shells. E. Scraped-out seabed (oyster shells, algae, and sediment), burrows of polychaetes and other small invertebrates can be seen. F. Close-up view of burrowing nereidid dwelling among the seabed. Red arrows point to burrowing worms. Scale bars: B = 20 m; C = 1 m; D = 50 cm; E–F = 3 cm.
Improving the application of Important Plant Areas to conserve threatened habitats: a case study of Uganda
<p><strong>This data set relates to the publication: Richards, S. L., Kalema, J., Ojelel, S., Williams, J., & Darbyshire, I. (2024). Improving the application of Important Plant Areas to conserve threatened habitats: A case study of Uganda. Conservation Science and Practice, e13246. https://doi.org/10.1111/csp2.13246<br></strong></p> <p><strong>Abstract:</strong></p> <p>Important Plant Areas (IPAs) are a successful method of identifying priority areas for plant conservation. Assessment of IPAs, however, often relies on criteria related to species, while incorporation of habitats has been less consistent. Using Uganda as a case study, we test the application of the threatened habitat criterion – criterion C. We identified nationally threatened habitats using Red List of Ecosystems criteria and assess, for the first time, how differing application of thresholds under IPA criterion C can influence IPA network outcomes. Eleven threatened habitats were identified, with declines switching from predominantly forest to savanna after the mid-20<sup>th</sup> century. Significantly, we found current IPA guidance on use of criterion C needlessly limits the number of sites that qualify as IPAs. The “five best sites” IPA threshold is reserved for countries where quantitative data is unavailable, however, the application of the relevant numerical thresholds (site contains ≥10% of national resource or site is among the best quality examples required to collectively prioritisie up to 20% of the national resource) to quantitative data largely generated fewer than five IPAs, comparably limiting conservation opportunities identified. We recommend, therefore, that the “five best” threshold is available for application on both qualitative and quantitative data. This will bolster the value of IPAs in conserving and restoring threatened and ecologically important habitats under the Kunming-Montreal Global Biodiversity Framework.</p> <p><strong>Dataset:</strong></p> <p>Within this dataset is a shapefile of the estimated extent of threatened habitats in Uganda. Each polygon represents a single "site" for each threatened habitat, with methodology for site identification given in the manuscript. Feature area and percentage national resource are given for each site, enabling users to identify those that trigger the different IPA criterion C thresholds.</p> <p><strong>In this study, we have preliminarily identified the threatened habitats and IPAs for Uganda. However, it is important to seek the expertise and views of stakeholders, consider other IPA criteria met and any complementarity between sites when identifying IPAs. In addition, ground-truthing or more localised data could validate the threat status of a vegetation type as well as identifying which sites are best to conserve these habitats. </strong></p>
Fig. 6 in Decadal status of Acanthaster planci (Linnaeus, 1758) along the coral reef habitat of Andaman and Nicobar Islands
Fig. 6 — Substrate specificity of A. planci (CoTS) in Andaman and Nicobar Islands (a - Dorsal view of A. planci; b - Ventral view of A. planci; c & d - Animal grazing on Acroporidae corals; e - Animal grazing on Poritidae corals; f - Animal in coral crevice; g - Animal grazing on sponges and algae; and h - Feeding scar on aroporid corals due to A. planci
Fig. 4 in Decadal status of Acanthaster planci (Linnaeus, 1758) along the coral reef habitat of Andaman and Nicobar Islands
Fig. 4 — PCA of A. planci (CoTS) population in Andaman and Nicobar Islands (N&MA- North & Middle Andaman, SA- South Andaman, N-Nicobar)
Patterns of island fox habitat use in sand dune habitat on San Clemente Island
<p>On San Clemente Island (SCI), the island fox subspecies (<em>Urocyon littoralis</em><em> clementae</em>) has been monitored annually since 1988 to track long-term population trends. Annual density estimates in most habitat types across the island range from 2–13 foxes/km<sup>2</sup>, yet unusually high estimates have repeatedly approached 50 foxes/km<sup>2 </sup>in a unique sand dune habitat area. Although sand dune habitat is restricted to one small area on the island, these estimates suggest sand dune habitat supports one of the highest population densities of any fox species in the world, and it may support > 5% of the SCI fox population. This finding prompted our investigation to determine if SCI foxes captured in the sand dunes habitat area maintained home ranges within this habitat type. Between January–July 2018, we used Global Positioning System collars to track the movements of 12 island foxes captured in the sand dune habitat area. Contrary to our initial predictions, we found that island foxes captured in the sand dune habitat area do maintain home ranges and core areas centralized in sand dune habitat. All 12 island fox home ranges estimated contained >50% sand dune habitat in either their 50% or 95% fixed kernel density estimate (KDE) home range, and island foxes were 3.14 times more likely to use active sand dune habitat when compared to the second most abundant habitat type, maritime desert scrub (Adjusted = 3.14, 95% CI = 3.07–3.12). We also found that island foxes in sand dune habitat maintained much smaller home ranges than reported estimates in other habitat types, with an average 95% KDE home range size of 0.42 km<sup>2</sup> (95% CI = 0.20–0.63 km<sup>2</sup>). Although sand dune habitat comprises just 2% of available habitat on SCI, our research highlights the importance of this unique habitat area for island foxes.</p>
Figure 2 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 2. Maxent predictions of suitable zebra mussel (Dreissena polymorpha) habitat in Texas. Shading indicates the logistic output of the Maxent model. Polygons represent state and national borders as well as major river basins within Texas.
Figure 1 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 1. Physicochemical data survey lakes. Sites categorized by TPWD (at the time of this study in 2016) as "infested" (the water body has an established, reproducing population) or "positive" (zebra mussels or their larvae have been detected on more than one occasion despite lack of evidence of a fully established, reproducing population) are indicated by red triangles and included: Lakes Austin, Belton, Bridgeport, Dean Gilbert, Lavon, Lewisville, Ray Roberts, Stillhouse Hollow, Texoma, Travis, and Waco. Sites categorized by TPWD as zebra mussel "negative" are indicated by green circles and included: Lakes Aquilla, Buchanan, Georgetown, Granbury, Granger, Hubbard Creek, Inks, Lady Bird, LBJ, Limestone, Marble Falls, Palo Pinto, Pflugerville, Possum Kingdom, Proctor, and Whitney.
Figure 4 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 4. Biplot of components 1 and 2 (top) and 1 and 3 (bottom) from Principal Component Analysis of water quality variables in 27 study lakes. Variables that predominated in each component (|factor loading| ≥ 0.50) are shown on the appropriate axes. Individual lake data are represented by symbols, with open circles representing lakes without previously reported incidences of zebra mussels (absent, 16 lakes), and solid circles those known to harbor the invasive species (present, 11 lakes) at the time of sampling (October 2016). No separation between the two lake groups is evident in either of the biplots. Ca, calcium, N, nitrogen; P, phosphorous.
Figure 3 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 3. Maxent predictions of suitable quagga mussel (Dreissena bugensis) habitat in Texas. Shading indicates the logistic output of the Maxent model. Polygons represent state and national borders as well as major river basins within Texas.
Figure 3. Structurally complex high rugosity coral-dominated reef habitat. Image shows fixed transect 01 from the start pin looking toward a 180 in Fishes of War in the Pacific National Historic Park
Figure 3. Structurally complex high rugosity coral-dominated reef habitat. Image shows fixed transect 01 from the start pin looking toward a 180° heading in the Asan Beach unit (NPS photo).
Figure 2. Typical low rugosity algal-dominated pavement habitat. Image shows fixed transect 09, looking towards a 90 in Fishes of War in the Pacific National Historic Park
Figure 2. Typical low rugosity algal-dominated pavement habitat. Image shows fixed transect 09, looking towards a 90° heading from the start pin at the Agat Bay unit (NPS photo).
Data from: Rapid shift in benthic assemblages following coral bleaching at an upper mesophotic habitat in Taiwan
<p>Mesophotic coral ecosystems (MCEs; typically, 30–150 m depths) have traditionally been considered potential refuges for shallow-water organisms, but recent evidence suggests that this role is context-dependent. Here, we document a singular habitat at the upper mesophotic depth (- 30 m) in Xiaoliuqiu, Taiwan, and assess the changes in the benthic assemblage one year after a heatwave affected the reefs. In the habitat studied, abundant branching depth-specialist corals were found free-living and thriving on the sandy-rubble bottom amidst dense filamentous turf algae. In 2022, 63.6% of the corals were observed bleached, which was associated with severe heat stress affecting shallow reefs. The dominant coral at the time, <em>Acropora tenella</em>, suffered the most from the bleaching, with only 9.7% of its population remaining healthy. After one year, there was a noticeable shift in dominance from <em>A. tenella</em> to the previously cryptic <em>Anacropora</em> spp. without an obvious change in coral cover. We hypothesize that this rapid shift is driven by <em>Anacropora</em> spp., benefiting from the dense canopy provided by <em>A. tenella</em>. This suggests that the composition of understory organisms may play an important role in the resilience of some reefs affected by disturbance. The characteristics of this habitat, which consists mainly of deep-water specialists, and its susceptibility to stressors indicate that this habitat is unlikely to serve as a refuge for most shallow-water taxa. Our findings reinforce that the effectiveness of MCEs as refuges is not universal, and emphasize the importance of incorporating these unique habitats into conservation strategies and gaining a deeper understanding of their functions before they are lost.</p>
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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.