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389 results for “woodlands”
Ground Arthropod Community Survey in Grassland, Shrubland, and Woodland at the Sevilleta National Wildlife Refuge, New Mexico (1992-2004)
This data set contains records for the numbers of selected groups of ground-dwelling arthropod species and individuals collected from pitfall traps at 4 sites on the Sevilleta NWR, including creotostebush shrubland, both black and blue grama grasslands, and a pinyon/juniper woodland. Data collections begin in May of 1989, and are represented by subsequent sample collections every 2 months. One site (Goat Draw/Cerro Montosa) was discontinued in 2001, and a new site (Blue Grama) was initiated . Only three sites, creosotebush, black grama, and blue grama were continued between 2001-2004.
Figure 1 in Assessing high compositional differences of beetle assemblages across vertical woodland strata in the New Forest, Hampshire, England
Figure 1. Correspondence analysis ordination of subfamily/family level showing separation between the sampling methods. Eigenvalue axis 1: 0.4953, variation 45.14; axis 2: 0.2668, variation 69.47. Key to subfamily/family abbreviations – Carabida: Carabidae, Hydrophl: Hydrophilidae, Leiodida: Leiodidae, Omaliina: Omaliinae, Pselaphn: Pselaphinae, Phloeocr: Phloeocharinae, Tachypor: Tachyporinae, Habrocer: Habrocerinae, Aleochar: Aleocharinae, Oxytelin: Oxytelinae, Scaphidi: Scaphidiinae, Scydmaen: Scydmaeninae, Paederin: Paederinae, Staphyln: Staphylininae, Geotrupd: Geotrupidae, Scirtida: Scirtidae, Throscid: Throscidae, Elaterid: Elateridae, Canthard: Cantharidae, Ptiliida: Ptiliidae, Anobiida: Anobiinae, Malachii: Malachiidae, Sphindid: Sphindidae, Nitiduld: Nitidulidae, Cryptoph: Cryptophagidae, Coccinel: Coccinellidae, Coryloph: Corylophidae, Latridii: Latridiidae, Melandry: Melandryidae, Tenebrio: Tenebrionidae, Salpingd: Salpingidae, Scraptii: Scraptiidae, Cerambyc: Cerambycidae, Crytocp: Cryptocephalinae, Chrysoml: Chrysomelinae, Galerucn: Galerucinae, Rhynchit: Rhynchitidae, Apionida: Apionidae, Curculio: Curculioninae, Cossonin: Cossninae, Entimina: Entiminae, Molytina: Molytinae, Scolytin: Scolytinae.
Figure 8 in Five new species of Enchytraeidae (Annelida: Clitellata) from Mediterranean woodlands of Italy and reaffirmed validity of Achaeta etrusca, Fridericia bulbosa and F. miraflores
Figure 8. Fridericia bulbosa (Rosa, 1887) sensu stricto. (A) Anterior body segments (dorsal view); (B) lateral view of segments I–V. (C and D) Clitellum in dorsal (C) and ventrolateral views (D); the elliptical contour in (D) shows the midventral granular field behind the male pores; (E) chylus cells in segment XIV; (F) shallow dorsolateral view of segments IV–V, showing the conspicuous epidermal gland cells; (G) midventral close-up of clitellum revealing the I-shaped male slits and the granular field behind them (contour); (H) dorsal view of segments IV–VI, showing the bent tail of a peptonephridium, the spermathecae and the pharyngeal glands. I, coelomocytes. (A) to (D) from permanent whole-mounted specimens, (E) to (I) from live specimens. Anterior to the right, except in (G) and (H) where anterior is to the top.
Figure 1 in Five new species of Enchytraeidae (Annelida: Clitellata) from Mediterranean woodlands of Italy and reaffirmed validity of Achaeta etrusca, Fridericia bulbosa and F. miraflores
Figure 1. Achaeta borbonica sp. nov. (A) Anterior body half in a dorsal view. Note the intestinal loop in IX; (B) lateral view of segments IV–V, showing the right spermatheca; asterisks indicate the oesophageal dorsal ridge; (C) ventral view of segment V, revealing the closely spaced spermathecal pores; (D) sperm funnel in segment X; (E) lateral view of nephridium in segment VIII; (F) coelomic cavity of caudal segments (lateral view), showing the coarse granulation of the chloragogenous cells and the small size of coelomocytes as compared with the flask-shaped glands. (G and H) Lateral views of clitellum, in vivo (G) and after fixation (H). All except (H) from live specimens. In (A), (D) and (E) anterior to the top; in all others, anterior to the right.
Figure 3 in Five new species of Enchytraeidae (Annelida: Clitellata) from Mediterranean woodlands of Italy and reaffirmed validity of Achaeta etrusca, Fridericia bulbosa and F. miraflores
Figure 3. Achaeta giustii sp. nov. (A) Cephalic region (lateral view); (B) dorsolateral view of segments I–VIII, showing the well-developed pharyngeal glands; (C) dorsal view of clitellum. Note the granular cells bordering the middorsal interruption (dg); (D) shallow lateral view of body wall (segment IX), revealing the lozenge pattern of the longitudinal muscle fibres; (E) lateral view of clitellum, showing the reticulate pattern of the gland cells; (F) lateral view of same clitellum in a deeper optical section, documenting its midventral continuity and middorsal gap; (G) ectal portion of the spermatheca; (H) sperm funnel. All from permanent wholemounted specimens, anterior to the left.
Figure 7 in Five new species of Enchytraeidae (Annelida: Clitellata) from Mediterranean woodlands of Italy and reaffirmed validity of Achaeta etrusca, Fridericia bulbosa and F. miraflores
Figure 7. Fridericia meridiana sp. nov. (A) Anterior body segments (dorsal view). The white arrow points to the small spur of the last pharyngeal glands into VII; (B) clitellum (dorsal view); (C) male pores and midventral interruption of clitellum between and before them; (D) lateral view of clitellum and male opening after fixation; (E) coelomic cavity of segments XI–XII, showing the minute sperm funnel; (F) dorsolateral view of segments IV–V, showing first two pairs of pharyngeal glands and the right spermatheca; (G) coelomocytes; (H) peptonephridium. (A) and (D) from permanent whole-mounted specimens; all others from live specimens. In all, anterior to the right.
Plant life history data as evidence of an historical mixed-severity fire regime in Banksia woodlands
<p><i><strong>Context:</strong></i> The concept of the fire regime serves as an agreed upon template by which to inform understanding and management of fire-prone ecosystems globally. While observations from satellite imagery or palaeoecological proxy data can provide direct evidence of past fire regimes, they may be limited in temporal and/or spatial scale and are not available for all ecosystems. However, fire-related plant trait and demographic data offers an alternative approach to understand species-fire regime associations at the ecosystem scale. </p><p><i><strong>Aims:</strong></i> We aimed to quantify the life history strategies and associated fire regimes for six co-occurring shrub and tree species from fire-prone, Mediterranean climate Banksia woodlands in southwestern Australia. </p><p><i><strong>Methods:</strong></i> We collected static demographic data on size structure, seedling recruitment, and plant mortality across sites of varying time since last fire. We combined demographic data with key fire-related species traits to define plant life history strategies. We then compared observed life histories with <i>a priori</i> expectations for surface, stand-replacing, and mixed-severity fire regime types to infer historical fire regime associations.</p><p><i><strong>Key results:</strong></i> Fire-killed shrubs and weakly serotinous trees had abundant post-fire seedling recruitment, but also developed multi-cohort populations during fire-free periods via inter-fire seedling recruitment. Resprouting shrubs had little seedling recruitment at any time, even following fire, and showed no signs of decline in the long absence of fire likely due to their very long lifespans. </p><p><i><strong>Conclusions:</strong></i> The variation in life history strategies for these six co-occurring species is consistent with known ecological strategies to cope with high variation in fire intervals in a mixed-severity fire regime. While resprouting and strong post-fire seedling recruitment indicate a tolerance of frequent fire, inter-fire recruitment and weak serotiny is interpreted as a bet-hedging strategy to cope with occasional long fire-free periods that may otherwise exceed adult and seed bank lifespans. </p><p><i><strong>Implications:</strong></i> Our findings suggest that Banksia woodlands have evolved with highly variable fire intervals in a mixed-severity fire regime. Further investigations of species adaptations to varying fire size and patchiness can help extend our understanding of fire regime tolerances.</p>
Fig. 2 in Besnoitia tarandi in Canadian woodland caribou - Isolation, characterization and suitability for serological tests
Fig. 2. Growth of Besnoitia besnoiti Bb-EvoraCl2 (triangles) and Besnoitia tarandi Bt-CA-Quebec1 (crosses) as assessed by real-time PCR on in-vitro cultivated MARC-145 cells 24, 48 or 72 h p.i. Linear regression revealed that B. tarandi BtCA-Quebec1 grew faster than B. besnoiti BbEvoraCl2.
Fig.5 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.5. Jamesoniella autumnalis distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig.3 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.3. Neckera pennata distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzemes, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig.1 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.1. Anomodon longifolius distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical reģions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumnkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J-upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N-upland Latgale, O-Austrumlatgale.
Fig.4 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.4. Lejeunea cavifolia distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig.2 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.2. Homalia trichomanoides distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Eastern bettong (Bettongia gaimardi) reintroduced to Mulligan's Flat Woodland Sanctuary and Tidbinbilla Nature Reserve: DArT SNPs + individual information
<p>Incorporating genetic data into conservation programmes improves management outcomes, but the impact of different sample-grouping methods on genetic diversity analyses is poorly understood. To this end, the multi-source reintroduction of the eastern bettong (<em>Bettongia gaimardi</em>) was used as a long-term case study to investigate how sampling regimes may affect common genetic metrics, and hence management decisions. The dataset comprised 5307 SNPs sequenced across 263 individuals. Samples included 45 founders from five genetically distinct Tasmanian source regions, and 218 of their descendants captured during annual monitoring at Mulligan's Flat Woodland Sanctuary (MFWS; 121 samples across eight generations), and Tidbinbilla Nature Reserve (TNR; 97 samples across nine generations). The most management-informative sampling regime was found to be generational cohorts, providing detailed long-term trends in genetic diversity. When these generation-specific trends were not investigated, recent changes in population genetics were masked, and it became apparent that management recommendations would be less appropriate. The results also illuminated the importance of considering establishment and persistence as separate phases of a multi-source reintroduction. The establishment phase (useful for informing early adaptive management) should consist of no less than two generations, and continue until admixture is achieved (admixture defined here as >80% of individuals possessing >60% of source genotypes, with no one source composing >70% of >20% individuals' genotype) is achieved. This ensures that the persistence phase analyses of population trends remain minimally biased. Based on this case study, we recommend that emphasis be given to the value of generationally specific analyses, and that conservation programmes collect DNA samples throughout the establishment and persistence phases, and avoid collecting genetic samples only when analysis is imminent. We also recommend that population genetic analyses for multi-source reintroductions consider whether admixture has been achieved when calculating descriptive genetic metrics. </p>
Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands
<p>Increasing extreme wildfire occurrence globally is boosting demand to understand the fuel dynamics and fire risk of fire-prone areas. This is particularly pressing in fire-prone, Mediterranean climate-type vegetation, such as the Banksia woodlands surrounding metropolitan Perth, southwestern Australia. Despite an extensive wildland-urban interface and frequent fire occurrence, fuel accumulation and the spatial variation in fuel risk is not well quantified across the broad extent of this ecosystem. Using a space for time sampling approach to generate a chronosequence of time since fire, we selected sites that spanned across two distinct sandy soil types (Spearwood and Bassendean sands) and a rainfall gradient (550 to 750 mm north–south). We examined 82 sites in Banksia woodlands, southwestern Australia. Of the 82 sites, 44 burnt during the measurement period (2016 to 2021), which provided the opportunity for fuel measurements following fire (resulting in total N = 126). We wanted to answer two key questions: 1) How do measures of fuel load (mass) and arrangement (structure and continuity) vary across space and time, particularly with respect to time since the last fire? 2) How do biophysical drivers, such as soil type and rainfall, influence fuel accumulation and arrangement, and do these covariates improve litter fuel modelling beyond traditional asymptotic models? We found that fine surface fuel loads (litter and small twigs) differed between sand types, accumulating faster and reaching a higher peak on Spearwood sands (7–9 Mg ha−1) compared to Bassendean sands (6–7 Mg ha−1). Shrub layer fuel loads also accumulated faster on Spearwood sands than on Bassendean sands. While shrub layer fuels on Spearwood sands peaked at 14 years and declined thereafter, those on Bassendean sand did not decline over time but have lower overall connectivity. Total fine fuels (fine surface plus fine shrub layer fuels) had no significant decline over the same time period, on either sand type. Total fine fuel loads reached a peak of 9–10 Mg ha−1 between 13- and 20-years following fire, depending on the underlying sand type. Our quantitative fuel accumulation models confirmed the strength of time since fire as a predictor of hazard, but nonetheless included up to 40% unexplained variance. Importantly, while components fluctuated over time, the combined total of fine fuels did not decline with the long absence of fire, suggesting fire risk does not necessarily decrease in long unburned vegetation.</p>
A low-dimensional, mechanistic water balance model for piñon pine-juniper woodlands in southern Nevada, USA
<p>This is a low-dimensional water balance model developed for pinon pine-juniper woodlands in southern Nevada. It may require extensive revision for use in other similar or dissimilar woodland ecosystems. The model will require parameterization before use in any capacity.</p> <p>This model is mechanistic, but is driven from randomized precipitation. This framework allows the user to estimate the mean and standard deviation of water balance variables by simulating the same average meteorological year 1000s of times, each with randomized precipitation. This technique approximates a normal distribution of precipitation, and will need to be modified for locations/sites that have a non-normal precipitation distribution. It is not recommended to use the model in any other way without first testing and validating its output.</p> <p>I have provided documentation throughout the wrapper, model and parameter files to assist with your use of the model. You are encouraged to learn from, build on, and improve this model. You will need to set your own pathways and build your own meteorological inputs and site parameterizations. You may need to update, revise and add/remove different submodules depending on your intended use.</p>
Text-fig. 10. Extant pans east of Inhaminga (18°26′28″'S: 35°35′45″E) surrounded by woodland. The pans typically have an arid, vegetation-free, marginal zone and a water-logged sump. Some pans are connected to each other by shallow overflow valleys. Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 10. Extant pans east of Inhaminga (18°26′28″'S: 35°35′45″E) surrounded by woodland. The pans typically have an arid, vegetation-free, marginal zone and a water-logged sump. Some pans are connected to each other by shallow overflow valleys. Image modified from Google Earth.
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Ungulates mitigate the effects of drought and shrub encroachment on the fire hazard of Mediterranean oak woodlands
<p>Dataset included: Shrub density; Shrub biomass; Fuel load of <em>Cistus ladanifer</em>; Fuel load herbs; Fuel load litter</p>
Supplementary material 1 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Supplementary material 1 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
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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.