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2,721 results for “CONNECTIVITY”
Data from: Habitat connectivity and local conditions shape taxonomic and functional diversity of arthropods on green roofs
Summary 1. Increasing development of urban environments creates high pressure on green spaces with potential negative impacts on biodiversity and ecosystem services. There is growing evidence that green roofs – rooftops covered with vegetation – can contribute mitigate the loss of urban green spaces by providing new habitats for numerous arthropod species. 2. Whether green roofs can contribute to enhance taxonomic and functional diversity and increase connectivity across urbanized areas remains, however, largely unknown. Furthermore, only limited information is available on how environmental conditions shape green roof arthropod communities. 3. We investigated the community composition of arthropods (Apidae, Curculionidae, Araneae and Carabidae) on 40 green roofs and 40 green sites at ground level in the city of Zurich, Switzerland. We assessed how the site's environmental variables (such as area, height, vegetation, substrate and connectivity among sites) affect species richness and functional diversity using generalized linear models. We used an extension of co-inertia analysis (RLQ) and fourth-corner analysis to highlight the mechanism underlying community assemblages across taxonomic groups on green roof and ground communities. 4. Species richness was higher at ground-level sites, while no difference in functional diversity was found between green roofs and ground sites. Green roof arthropod diversity increased with higher connectivity and plant species richness, irrespective of substrate depth, height and area of green roofs. The species trait analysis reviewed the mechanisms related to the environmental predictors that shape the species assemblages of the different taxa at ground and roof sites. 5. Our study shows the important contribution of green roofs in maintaining high functional diversity of arthropod communities across different taxonomic groups, despite their lower species richness compared to ground sites. Species communities on green roofs revealed to be characterized by specific trait assemblages. The study also provides details on the environmental conditions that influence arthropod diversity and gives new perspectives on how the design of green roofs can be improved to increase their ecological value. Furthermore, the study highlights the importance of integrating green roofs in planning policies which aim to enhance urban habitat connectivity.
Data from: Carrying capacity in a heterogeneous environment with habitat connectivity
A large body of theory predicts that populations diffusing in heterogeneous environments reach higher total size than if non-diffusing, and, paradoxically, higher size than in a corresponding homogeneous environment. However, this theory and its assumptions have not been rigorously tested. Here, we extended previous theory to include exploitable resources, proving qualitatively novel results, which we tested experimentally using spatially diffusing laboratory populations of yeast. Consistent with previous theory, we predicted and experimentally observed that spatial diffusion increased total equilibrium population abundance in heterogeneous environments, with the effect size depending on the relationship between r and K. Refuting previous theory, however, we discovered that homogeneously distributed resources support higher total carrying capacity than heterogeneously distributed resources, even with species diffusion. Our results provide rigorous experimental tests of new and old theory, demonstrating how the traditional notion of carrying capacity is ambiguous for populations diffusing in spatially heterogeneous environments.
Data from: Algal toxin impairs sea lion memory and hippocampal connectivity, with implications for strandings
Domoic acid (DA) is a naturally occurring neurotoxin known to harm marine animals. DA-producing algal blooms are increasing in size and frequency. Although chronic exposure is known to produce brain lesions, the influence of DA toxicosis on behavior in wild animals is unknown. We showed, in a large sample of wild sea lions, that spatial memory deficits are predicted by the extent of right dorsal hippocampal lesions related to natural exposure to DA and that exposure also disrupts hippocampal-thalamic brain networks. Because sea lions are dynamic foragers that rely on flexible navigation, impaired spatial memory may affect survival in the wild.
Data from: Contributions of local speech encoding and functional connectivity to audio-visual speech perception
Seeing a speaker's face enhances speech intelligibility in adverse environments. We investigated the underlying network mechanisms by quantifying local speech representations and directed connectivity in MEG data obtained while human participants listened to speech of varying acoustic SNR and visual context. During high acoustic SNR speech encoding by temporally entrained brain activity was strong in temporal and inferior frontal cortex, while during low SNR strong entrainment emerged in premotor and superior frontal cortex. These changes in local encoding were accompanied by changes in directed connectivity along the ventral stream and the auditory-premotor axis. Importantly, the behavioral benefit arising from seeing the speaker's face was not predicted by changes in local encoding but rather by enhanced functional connectivity between temporal and inferior frontal cortex. Our results demonstrate a role of auditory-frontal interactions in visual speech representations and suggest that functional connectivity along the ventral pathway facilitates speech comprehension in multisensory environments.
Data from: Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations
Network models are routinely downscaled compared to nature in terms of numbers of nodes or edges because of a lack of computational resources, often without explicit mention of the limitations this entails. While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics are conserved, here we show that limitations already arise if also second-order statistics are to be maintained. The temporal structure of pairwise averaged correlations in the activity of recurrent networks is determined by the effective population-level connectivity. We first show that in general the converse is also true and explicitly mention degenerate cases when this one-to-one relationship does not hold. The one-to-one correspondence between effective connectivity and the temporal structure of pairwise averaged correlations implies that network scalings should preserve the effective connectivity if pairwise averaged correlations are to be held constant. Changes in effective connectivity can even push a network from a linearly stable to an unstable, oscillatory regime and vice versa. On this basis, we derive conditions for the preservation of both mean population-averaged activities and pairwise averaged correlations under a change in numbers of neurons or synapses in the asynchronous regime typical of cortical networks. We find that mean activities and correlation structure can be maintained by an appropriate scaling of the synaptic weights, but only over a range of numbers of synapses that is limited by the variance of external inputs to the network. Our results therefore show that the reducibility of asynchronous networks is fundamentally limited.
Data from: Quantitative analysis of connectivity in populations of a semi-aquatic mammal using kinship categories and network assortativity
Analyzing the impact of anthropogenic and natural river barriers on the dispersal of aquatic and semi-aquatic species may be critical for their conservation, but no adequate genetic methods have been developed for quantifying the effect of specific barriers on current connectivity. Knowledge of kinship relationships between individuals and reconstructions of pedigrees obtained using genomic data can be extremely useful, not only for studying the social organization of animals, but also inferring how the last few generations of offspring have dispersed. In this study, we used kinship data to analyze connectivity patterns in a small semi-aquatic mammal, the Pyrenean desman, in an area comprising two river systems with close headwaters and dams of various sizes. Using a large SNP dataset from 70 specimens, we obtained kinship categories and reconstructed pedigrees. To quantify the barrier effect of specific obstacles, we constructed kinship networks and devised a method based on the assortativity coefficient, which measures the proportion between observed and expected kinship relationships across a barrier. The estimation of this parameter enabled us to infer that the most important barrier in the area was the watershed divide between the rivers, followed by a dam on one of the rivers. Other barriers did not significantly reduce the expected number of kinship relationships across them. This strategy and the information obtained with it may be crucial in determining the most important connectivity problems in an area and help develop conservation plans aimed at improving genetic exchange between populations of threatened species.
FIGURE 4 in Three decades to connect the sexes: Calatola microcarpa (Icacinaceae), a new species from the Southwestern Amazon
FIGURE 4. Distribution map of Calatola microcarpa Duno & J. Janovec (●).
FIGURE 1 in Three decades to connect the sexes: Calatola microcarpa (Icacinaceae), a new species from the Southwestern Amazon
FIGURE 1. Type specimen of Calatola microcarpa Duno & J. Janovec (R. Vásquez 12300, CICY).
FIGURE 4 in Biogeography of Glandulocaudinae (Teleostei: Characiformes: Characidae) revisited: phylogenetic patterns, historical geology and genetic connectivity
FIGURE 4. Area cladogram based on the current phylogeny of the Glandulocaudinae.
FIGURE 2 in Biogeography of Glandulocaudinae (Teleostei: Characiformes: Characidae) revisited: phylogenetic patterns, historical geology and genetic connectivity
FIGURE 2. Cladogram showing the phylogenetic relationship of the Glandulocaudinae.
FIGURE 6 in Fishes as living tracers of connectivity in the tropical western North Atlantic: I. Distribution of the neon gobies, genus Elacatinus (Pisces: Gobiidae)
FIGURE 6. Geographic distribution of Suite 3 Elacatinus: shallow water sponge-dwelling species.
FIGURE 8 in Fishes as living tracers of connectivity in the tropical western North Atlantic: I. Distribution of the neon gobies, genus Elacatinus (Pisces: Gobiidae)
FIGURE 8. Geographic distribution of Suite 4 Elacatinus: deep water sponge-dwelling species.
Fig. 1 in Fish larvae assemblages in two floodplain lakes with different degrees of connection to the Paraná River, Brazil
Fig. 1. Locations of the sampling sites in Ilha Grande National Park, upper Paraná River. X1 - Xambrê stream; X2 - Xambrê middle; S1 - Saraiva channel; and S2- Saraiva middle.
Fig. 3 in Fish larvae assemblages in two floodplain lakes with different degrees of connection to the Paraná River, Brazil
Fig. 3. Variations in mean fish larvae abundance per sampling for Xambrê Lake and Saraiva Lake sites (S1 - Saraiva channel, S2- Saraiva middle, X1 - Xambrê stream and X2 - Xambrê middle) in Ilha Grande National Park, upper Paraná River, during the three studied spawning seasons (s.d. = standard deviation).
Fig. 3 in Fish assemblages of tropical floodplain lagoons: exploring the role of connectivity in a dry year
Fig. 3. Average values (± standard error) of species richness (a), density (b), and biomass (c) in connected () and disconnected () lagoons.
One-dimensional non-LTE time-dependent radiative transfer of an He-detonation model and the connection to faint and fast-decaying supernovae
<p>Model spectra from <a href="https://ui.adsabs.harvard.edu/abs/2015MNRAS.447.1370D">Dessart & Hillier 2015, MNRAS, 447, 1370</a></p>
Figs 150–157 in Simulium (Trichodagmia) (Diptera, Simuliidae) phylogeny revisited: the Neotropical and Afrotropical connection
Figs 150–157. Larva (last instar). Hypostoma. 150–151. Simulium (Anasolen) Enderlein, 1930. 150. ambositrae Grenier & Grjebine, 1959. 151. nili Gibbins, 1934. — 152. S. (Freemanellum) manense Elsen & Escaffre, 1976. — 153–154. S. (Hemicnetha) Enderlein, 1934. 153. brachycladum Lutz & Pinto, 1932. 154. rubrithorax Lutz, 1909. — 155. S. (Obuchovia) galloprovinciale Giudicelli, 1963. — 156–157. S. (Trichodagmia) Enderlein, 1934. 156. hirtipupa Lutz, 1910. 157. lahillei (Paterson & Shannon, 1927). Scale bars = 0.1 mm.
Figure 2 from: Carvalho-Batista A, Negri M, Pileggi LG, Castilho AL, Costa RC, Mantelatto FL (2014) Inferring population connectivity across the range of distribution of the stiletto shrimp Artemesia longinaris Spence Bate, 1888 (Decapoda, Penaeidae) from DNA barcoding: implications for fishery management. In: Wehrtmann IS, Bauer RT (Eds) Proceedings of the Summer Meeting of the Crustacean Society and the Latin American Association of Carcinology, Costa Rica, July 2013. ZooKeys 457: 271-288. https://doi.org/10.3897/zookeys.457.6569
Figure 2 - Dendrogram based on Neighbor-Joining distance method of COI gene sequences of individuals of Artemesia longinaris. Localities represent the analyzed specimens. Numbers are bootstrap support values (1000 replicates); values below 50% are not shown.
Figure 4 from: Carvalho-Batista A, Negri M, Pileggi LG, Castilho AL, Costa RC, Mantelatto FL (2014) Inferring population connectivity across the range of distribution of the stiletto shrimp Artemesia longinaris Spence Bate, 1888 (Decapoda, Penaeidae) from DNA barcoding: implications for fishery management. In: Wehrtmann IS, Bauer RT (Eds) Proceedings of the Summer Meeting of the Crustacean Society and the Latin American Association of Carcinology, Costa Rica, July 2013. ZooKeys 457: 271-288. https://doi.org/10.3897/zookeys.457.6569
Figure 4 - Haplotype network of Artemesia longinarisaccording to Median-Joining analysis. Each circle represent one haplotype found in the localities (53 haplotypes in 60 specimens). The size of the circle of each haplotype is proportional to its frequency in the sample. Each small dash represents a mutational step.
Figure 1 from: Carvalho-Batista A, Negri M, Pileggi LG, Castilho AL, Costa RC, Mantelatto FL (2014) Inferring population connectivity across the range of distribution of the stiletto shrimp Artemesia longinaris Spence Bate, 1888 (Decapoda, Penaeidae) from DNA barcoding: implications for fishery management. In: Wehrtmann IS, Bauer RT (Eds) Proceedings of the Summer Meeting of the Crustacean Society and the Latin American Association of Carcinology, Costa Rica, July 2013. ZooKeys 457: 271-288. https://doi.org/10.3897/zookeys.457.6569
Figure 1 - Southwest Atlantic collection sites. Map showing the localities of the specimens of Artemesia longinaris analyzed: 1 Macaé, Brazil 2 Ubatuba, Brazil 3 Santos, Brazil 4 Cananéia, Brazil 5 São Francisco do Sul, Brazil 6 Rio Grande, Brazil 7 Mar del Plata, Argentina. The gray band indicates the complete geographical distribution of Artemesia longinaris.
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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.