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411 results for “spatial variation”
Data from: Population genomic analysis suggests strong influence of river network on spatial distribution of genetic variation in invasive saltcedar across the southwestern US
Understanding the complex influences of landscape and anthropogenic elements that shape the population genetic structure of invasive species provides insight into patterns of colonization and spread. The application of landscape genomics techniques to these questions may offer detailed, previously undocumented insights into factors influencing species invasions. We investigated the spatial pattern of genetic variation and the influences of landscape factors on population similarity in the invasive riparian shrub saltcedar (Tamarix L.) by analyzing 1,997 genome-wide SNP markers for 259 individuals from 25 populations collected throughout the southwestern US. Our results revealed a broad-scale spatial genetic differentiation of saltcedar populations between the Colorado and Rio Grande river basins and identified potential barriers to population similarity along both river systems. River pathways most strongly contributed to population similarity. In contrast, low temperature and dams likely served as barriers to population similarity. We hypothesize that large-scale geographic patterns in genetic diversity resulted from a combination of early introductions from distinct populations, the subsequent influence of natural selection, dispersal barriers, and founder effects during range expansion.
Data from: Spatially variable habitat quality contributes to within-population variation in reproductive success
Variation in habitat quality is common across terrestrial, freshwater, and marine habitats. We investigated how habitat quality influenced the reproductive potential of mud crabs across 30 oyster reefs that were degraded to different extents. We further coupled this field survey with a laboratory experiment designed to mechanistically determine the relationship between resource consumption and reproductive performance. We show a >10-fold difference in average reproductive potential for crabs across reefs of different quality. Calculated consumption rates for crabs in each reef, based on a type II functional response, suggest that differences in reproductive performance may be attributed to resource limitation in poor quality reefs. This conclusion is supported by results of our laboratory experiment where crabs fed a higher quality diet of abundant animal tissue had greater reproductive performance. Our results demonstrate that spatial variation in habitat quality can be a considerable contributor to within-population individual variation in reproductive success (i.e., demographic heterogeneity). This finding has important implications for assessing population extinction risk.
Data from: Spatial variation in bidirectional pollinator-mediated interactions between two co-flowering species in serpentine plant communities
<p>Pollinator-mediated competition and facilitation are two important mechanisms mediating co-flowering community assembly. Experimental studies, however, have mostly focused on evaluating outcomes for a single interacting partner at a single location. Studies that evaluate spatial variation in the bidirectional effects between co-flowering species are necessary if we aim to advance our understanding of the processes that mediate species coexistence in diverse co-flowering communities. Here, we examine geographic variation (i.e., at landscape level) in bidirectional pollinator-mediated effects between co-flowering <em>Mimulus guttatus</em> and <em>Delphinium uliginosum</em>. We evaluated effects on pollen transfer dynamics (conspecific and heterospecific pollen deposition) and plant reproductive success. We found evidence of asymmetrical effects (one species is disrupted and the other one is facilitated) but the effects were highly dependent on geographical location. Furthermore, effects on pollen transfer dynamics did not always translate to effects on overall plant reproductive success (i.e., pollen tube growth) highlighting the importance of evaluating effects at multiple stages of the pollination process. Overall, our results provide evidence of a spatial mosaic of pollinator-mediated interactions between co-flowering species and suggest that community assembly processes could result from competition and facilitation acting simultaneously. Our study highlights the importance of experimental studies that evaluate the prevalence of competitive and facilitative interactions in the field, and that expand across a wide geographical context, in order to more fully understand the mechanisms that shape plant communities in nature.</p>
Spatial and temporal genetic variation in Ethiopian barley (Hordeum vulgare L.) landraces as revealed by simple sequence repeat (SSR) markers
<p>Ethiopia is a center of diversity for barley (<i>Hordeum vulgare </i>L.) and it is grown across different agro-ecologies of the country. Unraveling population structure and gene flow status on temporal scales assists an evaluation of the consequences of physical, demographic as well as overall environmental changes on the stability and persistence of populations. Here, we examine spatial and temporal genetic variation within and among barley landrace samples collected over a period of four decades (1976-2017), using simple sequence repeat (SSR) markers. Our objective was to evaluate spatial and temporal changes in barley population connectivity associated with the closure of geographic origin and time periods. Low to strong genetic diversity was observed among the landraces and STRUCTURE, Neighbour joining tree and Discriminant Analysis of Principal Component analysis revealed three clusters. The cluster analysis revealed a close relationship between landraces along geographic proximity with genetic distance increases along with geographic distance. The grouping of landraces based on altitudinal classes was influenced by geographic proximity. From AMOVA year categories, it was observed that within population genetic diversity much higher than between population genetic diversity and that the temporal differentiation is considerably smaller. The low to strong genetic differentiation between landraces from various geographic origins could be attributed to gene flow across the region as a consequence of seed exchange among farmers. Nevertheless, we found some connectivity between changes in population dynamics as well as contemporary gene flow. The results demonstrate that this set of SSRs was highly informative and was useful in generating a meaningful classification of barley germplasms. Furthermore, our data also suggest that landraces are a source of valuable germplasm for sustainable agriculture in the context of future climate change, and that <i>in-situ</i> conservation strategies based on farmers use can conserve the genetic identity of landraces while allowing adaptation to local-environments.</p>
Spatial variation in antler investment of Apennine red deer
<p>Heterogeneity in resource availability and quality can trigger spatial patterns in the expression of sexually selected traits such as body mass and weaponry. While relationships between habitat features and phenotypic quality are well established at a broad geographical scale, information is scanty on spatial patterns at a finer, intra-population scale. We used data collected on 1965 male red deer Cervus elaphus over 20 years from a non-migratory population living on two sides of a mountainous ridge with substantial differences in land cover and habitat quality but similar climate and population density. We investigate spatial patterns in (i) body mass, (ii) antler mass, (iii) antler investment. We also tested for site- and age-specific patterns in allometric relationship between body mass and antler mass. Statistically significant fine-scale spatial variations in body mass, antler mass and, to a lesser extent, antler allocation matched spatial differences in land cover. All three traits were greater in the northern slope, characterized by higher habitat heterogeneity and greater availability of open habitats, than in the southern slope. Moreover, the allometric relationship between body mass and antler mass differed among age classes, in a pattern that was consistent between the two mountain slopes. Our results support the occurrence of spatial patterns in the expression of individual attributes also at a fine, intra-population scale. Our findings emphasize the role of environmental heterogeneity in shaping spatial variations of key life-history traits, with potential consequences for reproductive success.</p>
FIGURES 20, 21 in Complementary description of three species of Steneotarsonemus (Acari: Tarsonemidae) from rice agroecosystems of Eastern India with notes on their taxonomic status, spatial distribution, intraspecific variation and species composition
FIGURES 20, 21. Steneotarsonemus furcatus (male). 20. Dorsal surface. 21. Ventral surface.
FIGURES 26, 27 in Complementary description of three species of Steneotarsonemus (Acari: Tarsonemidae) from rice agroecosystems of Eastern India with notes on their taxonomic status, spatial distribution, intraspecific variation and species composition
FIGURES 26, 27. Steneotarsonemus subfurcatus (female). 26. Dorsal surface. 27. Ventral surface.
FIGURES 16.—19 in Complementary description of three species of Steneotarsonemus (Acari: Tarsonemidae) from rice agroecosystems of Eastern India with notes on their taxonomic status, spatial distribution, intraspecific variation and species composition
FIGURES 16.—19. Steneotarsonemus furcatus (female). 16.—leg I, 17.—leg II, 18.—leg III, 19.—leg IV.
FIGURES 10.—13 in Complementary description of three species of Steneotarsonemus (Acari: Tarsonemidae) from rice agroecosystems of Eastern India with notes on their taxonomic status, spatial distribution, intraspecific variation and species composition
FIGURES 10.—13. Steneotarsonemus spinki (male). 10.—leg I, 11.—leg II, 12.—leg III, 13.—leg IV.
FIGURES 8, 9 in Complementary description of three species of Steneotarsonemus (Acari: Tarsonemidae) from rice agroecosystems of Eastern India with notes on their taxonomic status, spatial distribution, intraspecific variation and species composition
FIGURES 8, 9. Steneotarsonemus spinki (male). 8. Dorsal surface. 9. Ventral surface.
FIGURES 4.—7 in Complementary description of three species of Steneotarsonemus (Acari: Tarsonemidae) from rice agroecosystems of Eastern India with notes on their taxonomic status, spatial distribution, intraspecific variation and species composition
FIGURES 4.—7. Steneotarsonemus spinki (female). 4.—leg I, 5—leg II, 6—leg III, 7—leg IV.
FIGURES 14, 15 in Complementary description of three species of Steneotarsonemus (Acari: Tarsonemidae) from rice agroecosystems of Eastern India with notes on their taxonomic status, spatial distribution, intraspecific variation and species composition
FIGURES 14, 15. Steneotarsonemus furcatus (female). 14. Dorsal surface. 15. Ventral surface.
FIGURES 32, 33 in Complementary description of three species of Steneotarsonemus (Acari: Tarsonemidae) from rice agroecosystems of Eastern India with notes on their taxonomic status, spatial distribution, intraspecific variation and species composition
FIGURES 32, 33. Steneotarsonemus subfurcatus (male). 32. Dorsal surface. 33. Ventral surface.
Fig. 5 in Spatial variation of summer microphytoplankton and zooplankton communities related to environmental parameters in the coastal area of Djerba Island (Tunisia, Eastern Mediterranean) Abstract
Fig. 5: Spatial variations of zooplankton abundance, zooplankton groups, dominant species, species richness and species diversity index along the west and east coasts of Djerba Island.
Fig. 1 in Spatial Variation In Prey Composition And Its Possible Effect On Reproductive Success In An Expanding Eastern Imperial Eagle (Aquila Heliaca) Population
Fig. 1. Breeding distribution of the imperial eagle in Hungary between 1995 and 2004. Data are presented in a 10 km × 10 km UTM grid. Regions: (1) Eastern Zemplén Mts, (2) Western Zemplén Mts, (3) Cserehát Mts, (4) Bükk Mts, (5) Mátra Mts, (6) Börzsöny Mts, (7) West-Heves Plain, (8) East-Heves Plain, (9) Borsodi Mezőség Plain, (10) Jászság Plain, (11) Nagykunság Plain, (12) Dévaványa Plain, (13) West-Békés Plain, (14) South-Békés Plain, (15) Hortobágy Plain, (16) Aggtelek Mts, (17) Gerecse Mts, (18) Vértes Mts, (19) Eastern Bakony Mts. Dark grey: two sample areas, where diet composition and reproductive success were compared. Grey: data on prey composi-
Fig. 1 in Relationships between morphology, diet and spatial distribution: testing the effects of intra and interspecific morphological variations on the patterns of resource use in two Neotropical Cichlids
Fig. 1. Dispersion of the scores of the first two PCA axes, calculated with the variance matrix of 22 ecomorphological indices. a) Scores classified by the type of environment; b) Scores classified by food resources, where: Emp = empty, Cru = crustacean, Aqu = aquatic insect, Fis = fish, Mol = mollusk, Hig = higher plant, Det = detritus. Dashed line: Crenicichla britskii; dotted line: Satanoperca pappaterra. ARA = Aspect ratio of the anal fin; ARC = Aspect ratio of the caudal fin; ARPt = Aspect ratio of the pectoral fin; ARPv = Aspect ratio of the pelvic fin; PI = Protrusion index; RAA = Relative area of the anal fin; RAD = Relative area of the dorsal fin; RAE = Relative area of the eye; RAPt = Relative area of the pectoral fin; RAPv = Relative area of the pelvic fin; RHM = Relative height of the mouth; RHPd = Relative width of the caudal peduncle; RWPd = Relative width of the caudal peduncle.
Figure 11 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 11. – Diagram of eigenvalues (A) and the factorial discriminant analysis performed on the clusters (n = 3) established by the self-organizing map and the environmental variables (n = 17) (B). The barycentre of samples from the same cluster are marked with the Roman numeral (I, IIa, IIb) of that cluster's name.
Figure 8 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 8. – Classification of samples using occurrence data through the learning process of the self-organizing map. The defined clusters are numbered I, IIa, and IIb. Dj, To, Co, Ad, Ma, Ds, At, Ak, Al, and Ac are the site codes (see Tab. I). Numbers 1 through 4 correspond to the samples. Numbers 1 through 20 in the corner represent the node numbers.
Figure 10 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 10. – Boxplot comparing fish species richness in the three clusters defined by the self-organizing map.
Figure 9 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 9. – Fish species distribution patterns in each cluster defined by the hierarchical clustering applied to the self-organizing map units. Dark colour represents a high probability of occurrence, and light colour indicates a lower probability.
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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.