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701 results for “distribution patterns”
Dataset from: Tolerance to aerial exposure influences distributional patterns in multi-species intertidal seagrass meadows
<p>This is the dataset for an article published in Marine Environmental Research titled, 'Tolerance to aerial exposure influences distributional patterns in multi-species intertidal seagrass meadows', in October 2023. Following is the abstract for the paper for which this was the primary data:</p><p>Multi-specific seagrass meadow assemblages dominate most tropical intertidal regions but the relative role of environmental stress in determining distribution patterns is still uncertain. Here we combine observational and experimental approaches to examine aerial exposure as a factor driving species occurrence patterns in intertidal meadows of the Andaman archipelago, where up to 6 seagrass species co-occur. In the studied meadow, patterns of exposure did not map onto distance from the coast, instead creating a patchy matrix of exposure, based on fine-scale bathymetric differences. Distributional surveys showed that seagrass species were similarly patchy, often tracking the degree of aerial exposure during low tide. While some species (<i>Halophila ovalis, Halophila minor,</i> and <i>Thalassia hemprichii</i>) frequently occurred in submerged or subtidal areas and were rarely found in completely exposed areas, other species (<i>Cymodocea rotundata</i>, <i>Halophila beccarii,</i> and <i>Halodule uninervis</i>) also occupied areas that were subject to partial or complete aerial exposure during low tide. To confirm this pattern, we used field-based transplant experiments, employing a natural gradient of tidal exposure to subject six seagrass species to different desiccation exposure times. After a month, <i>H. beccarii</i> and <i>H. uninervis</i> transplants survived in areas that sustained more than 3 h of aerial tidal exposure without significant mortality, compared with other species (<i>H. ovalis, H. minor, T. hemprichii, C. rotundata</i>) that showed dramatic shoot mortality at the same exposure regimes. For all species, 4 h represented the upper limit of exposure, in both experimental and distributional studies. However, despite their wider tolerance of exposure to air, <i>H. beccarii</i> and <i>H. uninervis</i> did not dominate the entire meadow. This could be a result either of their poor tolerance to other environmental factors or their lower competitive abilities among other mechanisms. This suggests that in tropical multi-specific meadows, strong environmental filters could override clear intertidal zonation to create patchy matrices based on species tolerances.</p>
Modelling the carbon balance in bryophytes and lichens: Presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects
<p><strong>Premise </strong></p> <p>Bryophytes and lichens have important functional roles in many ecosystems. Insight into how their CO<sub>2</sub> exchange responds to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time-scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO<sub>2</sub> exchange rates of these poikilohydric organisms change through time as a function of weather conditions.</p> <p><strong>Methods</strong></p> <p>PoiCarb simulates diel fluctuations of CO<sub>2</sub> exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes are net photosynthesis, dark respiration, evaporation and water uptake. Measured CO<sub>2</sub>-exchange responses to light, temperature, atmospheric CO<sub>2</sub> concentration, and thallus water content (calculated in a separate module) are used to parameterise the model's carbon module. We validated the model by comparing modelled diel courses of net CO<sub>2</sub> exchange to such courses from field measurements on the tropical lichen <em>Crocodia aurata</em>. To demonstrate the model's usefulness, we simulated potential climate-change effects.</p> <p><strong>Results </strong></p> <p>Diel patterns were reproduced well and modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration.</p> <p><strong>Conclusions</strong></p> <p>Being able to reproduce the weather-dependent variation in diel carbon balances is a clear improvement compared to simple extrapolations of short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding species' conservation.</p>
Figure 8 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 8. Hypothesized life cycle of Plesionika edwardsii in the Azorean region. After the incubation period of shrimp eggs, (1) larvae are released into the water column and (2) juveniles develop in shallow waters. Mature females and males are distributed up to 600 m with a sexual segregation by depth: (3) non-ovigerous females are mainly found up to 200 m, (4) ovigerous females between 200 and 300 m, and (5) males from 400 to 500 m deep. Females are bigger than males, and ovigerous females are bigger than nonovigerous females. A bigger-deeper trend is observed up to 400 m. (6) Long larval stages of P. edwardsii increases its potential for dispersal (Landeira et al., 2009), favoring connectivity and stock homogeneity between adjacent areas.
Figure 5 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 5. Sex ratio of Plesionika edwardsii by depth stratum in the Azorean region during the period 1999–2000.
Figure 2 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 2. Seasonal predicted mean catch per unit effort (CPUE, g trap-1) by depth stratum for males, non-ovigerous and ovigerous females of Plesionika edwardsii in the Azorean region for the period 1999–2000. Light-colored symbols represent raw data. Detailed parameter estimates are in Tab. S4.
Figure 7 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 7. Size at which 50 % of the shrimps are mature (L 50) estimated for Plesionika edwardsii in the Azorean region fitting a logistic curve to the proportion of ovigerous females. Logistic curve was estimated combining all data obtained during the period 1999–2000.
Figure 4 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 4. Seasonal predicted mean cephalothorax length (CL) by depth stratum for males, non-ovigerous and ovigerous females of Plesionika edwardsii in the Azorean region for the period 1999–2000. Light-colored symbols represent raw data. Detailed parameter estimates are in Tab. S4.
Figure 1 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 1. Sampling areas of Plesionika edwardsii in the mid-North Atlantic Ocean, Azorean region (ICES Subdivision 10a2) between 1999 and 2000. Orange dots represent each site sampled by a trap.
Figure 6 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 6. Sex ratio of Plesionika edwardsii by size class in the Azorean region during the period 1999–2000.
Figure 3 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 3. Size frequency distribution of males, non-ovigerous and ovigerous females Plesionika edwardsii in the Azorean region during the period 1999-2000.
Fig. 3 in Waterbird Distribution Patterns And Environmentally Impacted Factors In Reclaimed Coastal Wetlands Of The Eastern End Of Nanhui County, Shanghai, China
Fig. 3. Non-metricmulti-dimensionalscaling(NMDS) ordinationplotsshowingwaterbird communitystructurefromsixstudysites.
Acaulescence promotes speciation and shapes the distribution patterns of palms in Neotropical seasonally dry habitats
<p>Rainforests have been a source of lineages to open and seasonally dry habitats throughout Angiosperm evolution, especially in the Neotropics. However, the underlying mechanisms that allow such shifts remain poorly understood at large spatial scales. Here, we test whether acaulescence (an underground stem or a very short stem concealed in the ground) has affected the colonization and speciation in Neotropical seasonally dry habitats by <span>cocosoid palms</span> (Cocoseae). Acaulescent species maintain their growth underground, which increases their chances of survival from prolonged seasonal dry season and frequent fires. We use an integrative approach based on trait‐dependent diversification models, phylogenetic comparative methods, and ecological niche models. We found that shifts towards acaulescent growth form were accompanied by evolutionary transitions to seasonally dry habitats. Acaulescent lineages had higher speciation rates than non-acaulescent ones.<i> </i>However, the interaction between acaulescence and seasonally dry habitats had no significant effect on Cocoseae speciation rates. Acaulescent palms are primarily distributed in Neotropical seasonally dry habitats and non-acaulescent palms are concentrated in Amazonian rainforests. Our results suggest that an underground stem, with high carbohydrate and water storage capacity, is a preadaptation by which rainforest lineages were able to colonize and diversify in new fire-prone, increasingly seasonal and drier adaptive zones. The projected global expansion of dry seasonal habitats requires an understanding of how drought-avoidance functional traits evolve and how they are linked to seasonally dry habitats. Our results are, thus, a step forward in determining plant response mechanisms to drier and seasonal conditions.</p>
Fig. 1 in Distribution Pattern, Nest-Tree Features And Breeding Performance Of Population Of The Black Stork, Ciconia Nigra (Ciconiiformes, Ciconiidae), In Northwestern Serbia
Fig. 1. The proportion of tree species picked for nest placement by the Black Stork (Ciconia nigra) in Northwestern Serbia (n = 44).
Text-fig. 3. Outcrop cross section of the turbidite facies distribution in the Majalengka, correlated northwest to southeast. The progradation pattern indicated by thickening of sandstone into the basin area are shown. F1 – heterolithic sandstone-mudstone 1; F2 – heterolithic sandstone-mudstone 2; F3 – mudstone facies; F4 – heterolithic fine sand and mudstone; F5 – conglomeratic to massive sandstone facies (Muljana 2012). in Lithofacies And Ichnofacies Of Turbidite Deposits, West Java, Indonesia
Text-fig. 3. Outcrop cross section of the turbidite facies distribution in the Majalengka, correlated northwest to southeast. The progradation pattern indicated by thickening of sandstone into the basin area are shown. F1 – heterolithic sandstone-mudstone 1; F2 – heterolithic sandstone-mudstone 2; F3 – mudstone facies; F4 – heterolithic fine sand and mudstone; F5 – conglomeratic to massive sandstone facies (Muljana 2012).
Figure 5 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 5. Moving colonies to imperialist in culture and language axes (AtashpazGargari et al. 2008).
Figure 2 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 2. Generalized semivariogram showing the range of spatial dependence, nugget effect (C0) variability associated with spatial dependence (C), and sill (C + C0).
Figure 5 in Hybrid neural network with genetic algorithms for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Iran
Figure 5. Tetranychus urticae distribution maps in actual (b, d and f) and classified conditions by MLPNN (c, e and a). The maps of a, c, e and b, d, f have been drawn according to economic threshold of 4, 8 and 12, respectively.
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters).
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012. in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012.
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs).
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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.