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470 results for “spatial pattern”
Spatial patterns of seed removal by harvester ants in a seed tray experiment
<p>Using a selection of native grass and forb seeds commonly seeded in local restoration projects, we conducted a field experiment to evaluate the effects of seed species, distance of seed patches from nests, and distance between patches on patterns of seed removal by Owyhee harvester ants, <em>Pogonomyrmex salinus </em>(Olsen) (Hymenoptera: Formicidae). To provide context for ants' seed preferences, we evaluated differences in handling time among seed species. In addition, we assessed the influences of cheatgrass, <em>Bromus tectorum </em>(L.) (Poales: Poaceae), and Sandberg bluegrass, <em>Poa secunda </em>(J. Presl.), cover on seed removal. We found significant differences in removal rates among seed species. In general, seeds placed closer to nests were more vulnerable to predation than those placed farther away, and seeds in closely spaced patches were more vulnerable than seeds in widely spaced patches. However, the strength of these effects differed by seed species. Differences in handling time among seed species may help to explain these findings; the protective effect of from-nest distance was weaker for species which required less time to transport. For two of the seed species, there was an interaction between the distance of seed patches from nests and the distance between patches such that the protective effect of distance between patches decreased as distance from nests increased. Cheatgrass and bluegrass cover both had small protective effects on seeds. Taken together, these results offer insight into the spatial ecology of harvester ant foraging and may provide context for successful implementation of restoration efforts where harvester ants are present.</p>
Fig. 2 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. 2. Head of Satanoperca pappaterra (a) and Crenicichla britskii (b), showing differences in the mouth protrusion.
Fig. 4 in Seasonal and spatial dispersal patterns of select ambrosia beetles (Coleoptera: Curculionidae) from forest habitats into production nurseries
Fig. 4. Mean (± SE) captures of Cnestus mutilatus, Xylosandrus compactus, X. cra`ssiusculus, and X. germanus in ethanol-baited Baker traps deployed at various distances from the nursery–forest interface at 2 sites in South Carolina in 2011 and 2012.
Fig. 3 in Seasonal and spatial dispersal patterns of select ambrosia beetles (Coleoptera: Curculionidae) from forest habitats into production nurseries
Fig. 3. Mean captures of Cnestus mutilatus, Xylosandrus compactus, X. crassiusculus, and X. germanus in ethanol-baited Baker traps at 2 sites in Louisiana and Mississippi in 2013 and 2014.
Fig. 1 in Seasonal and spatial dispersal patterns of select ambrosia beetles (Coleoptera: Curculionidae) from forest habitats into production nurseries
Fig. 1. Satellite image of the Mississippi research site (Google, Mountain View, California, USA) with an overlay showing a randomized complete block design of 5 blocks. Representing the 2013 test, each block shown here had a trap placed at −25, 25, 50, 100, and 200 m from the nursery–forest interface.
Fig. 5 in Seasonal and spatial dispersal patterns of select ambrosia beetles (Coleoptera: Curculionidae) from forest habitats into production nurseries
Fig. 5. Mean (± SE) captures of Cnestus mutilatus, Xylosandrus compactus, X. crassiusculus, and X. germanus in ethanol-baited Baker traps deployed at various distances from the nursery–forest interface at 2 sites in Louisiana and Mississippi in 2013 and 2014.
Figure2. Generation of negative feedbacks gets tuned once a TCR completes stimulation beyond the threshold l. A TCell generates activation signal to BCell once it gets stimulation of its k-TCRs.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
Figure1. Static stimulation of a single TCR- The kinetic proofreading by the receptor on input x Є X forwards the receptor position p toward l. The receptor will generate negative feedback if p > β. The receptor will generate success signal when p== l.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
FIGURES 2 a-g -AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>The program was implemented in Matlab and tested with several patterns. The first, dataset1<br> consisted of 2 patterns each comprised of 100 points falling on two concentric circles of radii 10<br> and 20 respectively. The second, dataset2 consisted of 3 patterns each of 100 points falling on three<br> concentric circles of radii 10, 15, and 20 respectively. The model was further tested for its<br> capability to find clusters in patterns of open spatial form using dataset3 and dataset4 consisting of<br> 200 and 300 points falling on 2 and 3 concentric semi circles respectively. As shown in the<br> Figure2.a and Figure2.b, the algorithm is capable of determining spatial association of a data point<br> with other data points belonging to its appropriate circle only. The results successfully demonstrated<br> the capability of our model to automatically detect clean clusters of arbitrary shapes in the input<br> data represented in closed spatial form. The model was found even capable of determining clusters<br> of open spatial forms also, as shown in Figure2.c and Figure2.e. However, the output of the<br> algorithm was found affected by the values of the algorithm parameters k and a. In the present<br> experiment, k =8 and a =10 was sufficient for performing correct cluster associations. On the other<br> hand, correct clustering for the dataset2, could be obtained with 10NN estimation i.e. k =10, with<br> a.=15. Moreover setting k =15, with a.=15 was required for dataset4, as clustering error was<br> observed with k =10, with a.=15, as in Figure2.d. Figure2.f and Figure2.g show the correct<br> clustering even in presence of combination of open and closed form of input patterns. In each<br> figure, the first sub-plot shows the original data and the second sub-plot shows the clusters<br> identified by our program.</p>
Figure3. APCs A1-A4 connected within range of cohesion-factor-threshold form members of one ARB-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>Figure3 depicts this process. The<br> model with the above specification then effectively detects self or non-self pathogens. In terms of<br> its application to the task of clustering, this interpretation means making the affinities high within<br> clusters and low across clusters. A pathogen corresponding to an outlier would not stimulate a TCR<br> sufficiently and may not form part of any ARB.</p>
Spatial patterns of uncertainty in climate exposure metrics for North America at 1km resolution
<p>The data provided below represents the degree of uncertainty or variation between 8 individual general circulation models (GCM) for three metrics commonly used to assess the intensity of exposure to climate change. The three exposure metrics (forward and backward <a href="https://adaptwest.databasin.org/pages/adaptwest-velocitywna">climatic velocity</a> and <a href="https://adaptwest.databasin.org/pages/climatic-dissimilarity">local climatic dissimilarity</a>) were calculated based on the first two principal components (PC) scores derived from <a href="https://adaptwest.databasin.org/pages/climatic-dissimilarity">11 different climate variables</a>. Frameworks and heuristics supporting climate adaptation for conservation often rely on projections of climate change or climate exposure. However, projections of climate change vary among alternative GCM outputs, different emissions scenarios, and different future time periods. The potential for these model predictions to vary geographically presents a source of uncertainty in assigning climate-informed conservation strategies to landscapes. Regions with high agreement among predictions could be more confidently assigned a climate-informed strategy, whereas regions with less agreement among predictions may require a more cautious approach. More information on the data can be found at https://adaptwest.databasin.org/pages/uncertainty-climate-metrics.</p>
Figure 2 in Spatial and temporal nesting pattern of Sea Turtles in Alas Purwo National Park, and its implications for conservation management practices
Figure 2. Trend of sea turtle nesting in the past 40 years at APNP: (A) L.olivacea, (B) C. mydas, (C) E. imbricata, and (D) D. coriacea.
Figure 1 in Spatial and temporal nesting pattern of Sea Turtles in Alas Purwo National Park, and its implications for conservation management practices
Figure 1. Study location in Alas Purwo National Park's Pancur-Cungur Coast with six hypothetical stations (dot: sector benchmark point).
Figure 4 in Spatial and temporal nesting pattern of Sea Turtles in Alas Purwo National Park, and its implications for conservation management practices
Figure 4. Number of four sea turtles nesting in each month during survey period: (A) L.olivacea, (B) C. mydas, (C) E. imbricata, and (D) D. coriacea.
Fig. 3 in Spatial and seasonal patterns in fish assemblage in Córrego Rico, upper Paraná River basin
Fig. 3. Non-metric multidimensional scaling (NMDS) ordination based on fish abundance data from Córrego Rico: a) Stretches grouped in upper, middle and lower (S1 to S7; d- dry season and r- rainy season). b) Stretches (S1 to S7; d- dry season and r- rainy season); fish species with highest correlations with the axis (Ser-het: Serrapinnus heterodon; Par-oxy: Paravandellia oxyptera; Pia-arg: Piabina argentea; Geo-bra: Geophagus brasiliensis; Ser-not: Serrapinnus notomelas; Che-est: 'Cheirodon' stenodon); environmental variables with highest correlations with the fish assemblage (Dis: discharge; Wid: width; O%: dissolved oxygen; Vel: water velocity; Am: ammonia; Nit: Nitrate). Vectors show the direction and magnitude of correlations.
Measuring them all: individual-based functional spatial patterns in mountain grasslands
<p>Raw data, additional material and instructions for reproducibility for the article De Benedictis, L.L.M., Chelli, S., Canullo, R., Campetella, G., 2025. Measuring Them all: Individual-Based Functional Spatial Patterns in Mountain Grasslands. Journal of Vegetation Science 36, e70029. <a href="https://doi.org/10.1111/jvs.70029">https://doi.org/10.1111/jvs.70029</a>. <br>Scripts are found in the linked repository.</p>
Behavioral strategies and the spatial pattern formation of nesting
<p>This dataset contains data from a combined field and simulation study regarding spatial pattern formation of nesting, described in the paper: "Batsleer, F., Maes, D., Bonte, D. (2021) Behavioral strategies and the spatial pattern formation of nesting. The American Naturalist".</p> <p>The study investigates the relative importance of environmental and behavioral mechanisms in nest aggregations of the ground-nesting digger wasp <em>Bembix rostrata</em>. A field study was combined with an individual-based model that simulated the possible behaviors of spatial organisation of nesting.</p> <p>In the first analysis, a microhabitat model was built based on the location of the nests from a capture-mark-recapture study (CMR) and environmental variables NDVI (vegetation) and insolation (sun irradiance), derived from detailed remote sensing data from a drone flight.</p> <p>In the second analysis, an IBM was built that combined three possible mechanisms of nest choice to simulate the emerging spatial and network patterns found in the field. This was done by combining an environmental cue (based on the microhabitat model) and two relevant behavioral mechanisms related to local site fidelity and conspecific attraction. Strengths and combinations of the mechanisms could vary. Simulations were compared to the field data to find which combinations and strengths of mechanisms can best explain the emerging spatial and network patterns.</p> <p>The main results are that 1) the observed pattern in nature is best predicted by the simultaneous effect of a weak environmental cue and strong behavioral mechanisms. 2) individuals differ in their combination of mechanisms used and will either use local site fidelity (personal information) or conspecific attraction (inadvertent social information), but not both simultaneously. 3) We demonstrate that the nest pattern formation of a central place foraging insect cannot be considered as the sum of environmental and behavioral mechanisms.</p>
Fig. 7. a in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures
Fig. 7. a) Diagram of Canonical Discriminant Analysis for the ecomorphological indices of the fish assemblage grouping in habitat types in the upper Paraná River floodplain (rivers, channels, connected and disconnected lagoons). b) Histograms with the scores of the habitat types for Canonical axis 1.
Fig. 6 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures
Fig. 6. Diagram of Canonical Discriminant Analysis for the ecomorphological indices of the fish assemblage grouping in trophic guilds in the upper Paraná River floodplain (detritivores, insectivores, piscivores, invertivores, omnivores and herbivores).
Fig. 4 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures
Fig. 4. Distribution of scores centroids of the 35 species on the first two axes of the Principal Components Analysis (PC 1 and PC 2), applied to the correlation matrix (Pearson) formed by 22 ecomorphological indices. Each polygon defines the morphological space occupied by the species that compose the corresponding trophic guild.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.