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558 results for “Species interactions”
The latitudinal gradient in rates of evolution for bird beaks, a species interaction trait
<p>Where is evolution fastest? The biotic interactions hypothesis proposes that greater species richness creates more ecological opportunity, driving faster evolution at low latitudes, whereas the "empty niches" hypothesis proposes that ecological opportunity is greater where diversity is low, spurring faster evolution at high latitudes. We tested these contrasting predictions by analyzing rates of beak evolution for a global dataset of 1141 avian sister species. Rates of beak size evolution are similar across latitudes, with some evidence that beak shape evolves faster in the temperate zone, consistent with the empty niches hypothesis. The empty niches hypothesis is further supported by a meta-analysis showing that rates of trait evolution and recent speciation are generally faster in the temperate zone, whereas rates of molecular evolution are slightly faster in the tropics. Our results suggest that drivers of evolutionary diversification are either similar across latitudes or more potent in the temperate zone, thus calling into question multiple hypotheses that invoke faster tropical evolution to explain the latitudinal diversity gradient.</p>
Data and climate variable selection from: Effects of density, species interactions and environmental stochasticity on the dynamics of British bird communities
<p>Our knowledge of the factors affecting species abundances is mainly based on time-series analyses of a few well-studied species at single or few localities, but we know little about whether results from such analyses can be extrapolated to the community level. We apply a Joint Species Distribution Model to long-term time-series data on British bird communities to examine the relative contribution of intra- and interspecific density dependence at different spatial scales, as well as the influence of environmental stochasticity, to spatio-temporal interspecific variation in abundance. Intraspecific density dependence has the major structuring effect on these bird communities. In addition, environmental fluctuations affect spatiotemporal differences in abundance. In contrast, species interactions had a minor impact on variation in abundance. Thus, important drivers of single-species dynamics are also strongly affecting dynamics of communities in time and space.</p>
Giant sengi or elephant-shrew (Rhynchocyon species) interactions with Red-Capped Robin-Chat (Cossypha natalensis) and White-Chested Alethe (Chamaetylas fuelleborni) in Tanzania
<p>Using camera traps and direct observations, we recorded interactions between three species of giant sengi (<em>Rhynchocyon sp.</em>) and two insectivorous bird species from five forests in Tanzania. In all instances, the birds closely followed the giant sengis who were moving and foraging in the leaf litter. Given the two bird species are insectivorous and follow ant swarms to obtain flushed prey, these interactions suggest that following the sengi represents an adaptive foraging strategy. Our observations indicate that these behaviors and associations may be more pervasive than previously thought.</p>
Figure 9 from: Rousse P, Broad G, van Noort S (2016) Review of the genus Genaemirum Heinrich (Ichneumonidae, Ichneumoninae) with interactive identification keys to species. ZooKeys 635: 77-105. https://doi.org/10.3897/zookeys.636.10216
Figure 9 - Genaemirum varianum (Tosquinet). Holotype male. A habitus lateral view (inset: data labels) B head, mesosoma, dorsal view C head anterior view D head, mesosoma anterior-lateral view E metasomal tergites 1-4 dorsal view F propodeum, dorsal view.
Figure 10 from: Rousse P, Broad G, van Noort S (2016) Review of the genus Genaemirum Heinrich (Ichneumonidae, Ichneumoninae) with interactive identification keys to species. ZooKeys 635: 77-105. https://doi.org/10.3897/zookeys.636.10216
Figure 10 - Genaemirum vulcanicola Heinrich. Holotype female. A habitus lateral view (inset: data labels) B head, mesosoma, dorsal view C head anterior view D head, mesosoma anterior-lateral view E metasomal tergites 1-4 dorsal view F propodeum, dorsal view.
Figure 7 from: Rousse P, Broad G, van Noort S (2016) Review of the genus Genaemirum Heinrich (Ichneumonidae, Ichneumoninae) with interactive identification keys to species. ZooKeys 635: 77-105. https://doi.org/10.3897/zookeys.636.10216
Figure 7 - Genaemirum rhinoceros Heinrich. Holotype female. A habitus lateral view (inset: data labels) B head, mesosoma, dorsal view C head anterior view D head, pronotum anterior-lateral view E metasomal tergites 1-2 dorsal view F propodeum, dorsal view.
Figure 8 from: Rousse P, Broad G, van Noort S (2016) Review of the genus Genaemirum Heinrich (Ichneumonidae, Ichneumoninae) with interactive identification keys to species. ZooKeys 635: 77-105. https://doi.org/10.3897/zookeys.636.10216
Figure 8 - Genaemirum varianum (Tosquinet). Paratype female. A habitus lateral view (inset: data labels) B head, mesosoma, dorsal view C head anterior view D head, pronotum anterior-lateral view E fore-tibial armature F propodeum, dorsal view.
Figure 6 from: Rousse P, Broad G, van Noort S (2016) Review of the genus Genaemirum Heinrich (Ichneumonidae, Ichneumoninae) with interactive identification keys to species. ZooKeys 635: 77-105. https://doi.org/10.3897/zookeys.636.10216
Figure 6 - Genaemirum mesoleucum Heinrich. Holotype female. A habitus lateral view (inset: data labels) B head, mesosoma, dorsal view C head anterior view D head, pronotum lateral view E metasomal tergites 1-4 dorsal view F propodeum, dorsal view.
Figure 3 from: Rousse P, Broad G, van Noort S (2016) Review of the genus Genaemirum Heinrich (Ichneumonidae, Ichneumoninae) with interactive identification keys to species. ZooKeys 635: 77-105. https://doi.org/10.3897/zookeys.636.10216
Figure 3 - Genaemirum phacochoerus sp. n. Holotype female. A habitus lateral view (inset: data labels) B head, mesosoma, dorsal view C head anterior view D head, mesosoma anterior-lateral view E metasomal tergites 1-4 dorsal view F propodeum, dorsal view.
Figure 4 from: Rousse P, Broad G, van Noort S (2016) Review of the genus Genaemirum Heinrich (Ichneumonidae, Ichneumoninae) with interactive identification keys to species. ZooKeys 635: 77-105. https://doi.org/10.3897/zookeys.636.10216
Figure 4 - Genaemirum fumosum sp. n. Holotype female. A habitus lateral view (inset: data labels) B head, mesosoma, dorsal view C head anterior view D head, mesosoma anterior-lateral view E metasomal tergites 1-4 dorsal view F propodeum, dorsal view.
Figure 5 from: Rousse P, Broad G, van Noort S (2016) Review of the genus Genaemirum Heinrich (Ichneumonidae, Ichneumoninae) with interactive identification keys to species. ZooKeys 635: 77-105. https://doi.org/10.3897/zookeys.636.10216
Figure 5 - Genaemirum doryalidis Heinrich. Holotype female. A habitus lateral view (inset: data labels) B head, mesosoma, dorsal view C head anterior view D head, mesosoma anterior-lateral view E metasomal tergites 1-4 dorsal view F propodeum, dorsal view.
Figure 2 from: Rousse P, Broad G, van Noort S (2016) Review of the genus Genaemirum Heinrich (Ichneumonidae, Ichneumoninae) with interactive identification keys to species. ZooKeys 635: 77-105. https://doi.org/10.3897/zookeys.636.10216
Figure 2 - Genaemirum phagocossorum sp. n. Paratype male. A habitus lateral view (inset: data labels) B head, mesosoma, dorsal view C head anterior view D head, mesosoma anterior-lateral view E metasomal tergites 1-4 dorsal view F propodeum, dorsal view.
Figure 1 from: Rousse P, Broad G, van Noort S (2016) Review of the genus Genaemirum Heinrich (Ichneumonidae, Ichneumoninae) with interactive identification keys to species. ZooKeys 635: 77-105. https://doi.org/10.3897/zookeys.636.10216
Figure 1 - Genaemirum phagocossorum sp. n. Holotype female. A habitus lateral view (inset: data labels) B head, mesosoma, dorsal view C head anterior view D head, mesosoma anterior-lateral view E metasomal tergites 1-4 dorsal view F propodeum, dorsal view.
Refining species generality from a dynamical view on trophic interactions
<p>Dataset of "Refining species generality from a dynamical view on trophic interactions".</p>
Figure 2 from: Wati RK, van Vugt RR, Gravendeel B (2018) A Linnaeus NG interactive key to the species of Glomera (Orchidaceae, Coelogyninae) from Southeast Asia. PhytoKeys 110: 9-22. https://doi.org/10.3897/phytokeys.110.28435
Figure 2 Photographs of Glomera species collected from online platforms. 1Glomeraaurea (photograph by Mehd Halaouate) 2Glomeramacdonaldii (photograph by Benoit Henry) 3Glomeratubisepala (photograph by Gary Yong Gee) 4Glomeraglomeroides (photograph by S.A. James).
Figure 1 from: Wati RK, van Vugt RR, Gravendeel B (2018) A Linnaeus NG interactive key to the species of Glomera (Orchidaceae, Coelogyninae) from Southeast Asia. PhytoKeys 110: 9-22. https://doi.org/10.3897/phytokeys.110.28435
Figure 1 Illustrations of a selection of key characters used in the identification keys. 1Glomeraacutiflora (Schltr.) J.J.Sm. with green leaves (photograph by Rogier van Vugt) 2Glomera sp. with reddish-brown leaves (photograph by fotosynthesys deposited on FLICKR) 3Glomerapungens (Schltr.) J.J.Sm. with upright flowers (photograph by Rogier van Vugt) 4Glomerahamadryas (Schltr.) J.J.Sm. with flowers turned up-side-down (photograph by Rogier van Vugt) 5 Various shapes of the leaf blade, leaf tip, leaf sheath, leaf spathe, floral bract, entire flower, sepals, petals, lip and ovary present in Glomera and Glossorhyncha (illustrations by Esmée Winkel).
Figure 1 from: Schneider SA, Fizdale MA, Normark BB (2019) An online interactive identification key to common pest species of Aspidiotini (Hemiptera, Coccomorpha, Diaspididae), version 1.0. ZooKeys 867: 87-96. https://doi.org/10.3897/zookeys.867.34937
Figure 1 Aspidiotine general morphology. This diagram exemplifies a composite aspidiotine species, illustrating major anatomical features, body segmentation, and traits that a user would encounter in the key. The illustration orients users to the appearance of slide-mounted specimens and terminology used to describe their features. The illustration is based on a similar image presented by Miller and Davidson (2005), their Figure 3. Illustration by Taina Litwak.
Figure 3 from: Schneider SA, Fizdale MA, Normark BB (2019) An online interactive identification key to common pest species of Aspidiotini (Hemiptera, Coccomorpha, Diaspididae), version 1.0. ZooKeys 867: 87-96. https://doi.org/10.3897/zookeys.867.34937
Figure 3 Abdominal segmentation. This diagram shows pygidial segmentation as it is defined for the purposes of this key. The panels highlight (A) the pygidium (B) abdominal segment 8 (C) abdominal segment 7 (D) abdominal segment 6 and (E) abdominal segment 5. Illustrations by Taina Litwak.
Figure 2 from: Schneider SA, Fizdale MA, Normark BB (2019) An online interactive identification key to common pest species of Aspidiotini (Hemiptera, Coccomorpha, Diaspididae), version 1.0. ZooKeys 867: 87-96. https://doi.org/10.3897/zookeys.867.34937
Figure 2 Aspidiotine pygidial morphology. This diagram provides an enlarged view of the general pygidial morphology of aspidiotines. This serves as another guide to the appearance of anatomical features and their terminology. Illustration by Taina Litwak.
Data from: An a posteriori species clustering for quantifying the effects of species interactions on ecosystem functioning
1. Quantifying the effects of species interactions is key to understanding the relationships between biodiversity and ecosystem functioning but remains elusive due to combinatorics issues. Functional groups have been commonly used to capture the diversity of forms and functions and thus simplify the reality. However, the explicit incorporation of species interactions is still lacking in functional group-based approaches. Here we propose a new approach based on an a posteriori clustering of species to quantify the effects of species interactions on ecosystem functioning. 2. We first decompose the observed ecosystem function using null models, in which species diversity does not affect ecosystem function, to separate the effects of species interactions and species composition. This allows the identification of a posteriori functional groups that have contrasting diversity effects on ecosystem functioning. We then develop a formal combinatorial model of species interactions in which an ecosystem is described as a combination of co-occurring functional groups, which we call an assembly motif. Each assembly motif corresponds to a particular biotic environment. We demonstrate the relevance of our approach using datasets from a microbial experiment and the long-term Cedar Creek Biodiversity II experiment. 3. We show that our a posteriori approach is more accurate, more efficient and more parsimonious than a priori approaches. The discrepancy between a priori and a posteriori approaches results from the way each clustering is set up: a priori approaches are based on ecosystem or species properties, such as ecosystem size (number of species or functional groups) or species' functional traits, whereas our a posteriori approach is based only on the observed interaction and composition effects on ecosystem functioning. 4. Our findings demonstrate that an a posteriori approach is highly explanatory: it identifies who interacts with whom, and quantifies the effects of species interactions on ecosystem functioning. They also highlight that a combinatorial modelling of ecosystem functioning can predict the functioning of an ecosystem without any hypothesis about the biotic or environmental determinants or any information on species functional traits. It only requires the species composition of the ecosystem and the observed functioning of others that share the same assembly motif.
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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)
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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.