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Spatial dataset for ecological niche and spatial distribution modeling of Herichthys bartoni (Cichliformes: Cichlidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic cichlid <em>Herichthys bartoni</em> in the Media Luna spring, Mexico. This data includes occurrences records by species life stage (adult, juvenile and fry), in three field sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>For more information about the codes where the previous datasets could be used, visit the following repository with URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Likewise, the UC and WDp variables used to run the ecological niche and spatial distribution model, by summer period, can be found in the following repository wirh URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in African American and European American patients with TNBC (Spatial Transcriptomic 10X Visium portion)
<p>Racial disparities in triple-negative breast cancer (TNBC) outcomes have been reported. However, the biological mechanisms underlying these disparities remain unclear. We integrated imaging mass cytometry and spatial transcriptomics, to characterize the tumor microenvironment (TME) of African American (AA) and European American (EA) patients with TNBC. The TME in AA patients was characterized by interactions between endothelial cells, macrophages, and mesenchymal-like cells, which were associated with poor patient survival. In contrast, the EA TNBC-associated niche is enriched in T-cells and neutrophils suggestive of an exhaustion and suppression of otherwise active T cell responses. Ligand-receptor and pathway analyses of race-associated niches found AA TNBC to be “immune cold” and hence immunotherapy resistant tumors, and EA TNBC as ‘inflamed’ tumors that evolved a distinctive immunosuppressive mechanism. Our study revealed the presence of racially distinct tumor-promoting and immunosuppressive microenvironments in AA and EA patients with TNBC, which may explain the poor clinical outcomes.</p> <p> </p> <p>This dataset contains the 10X Visium Spatial Transcriptomic data of TNBC patients. There are two cohorts.</p> <p> </p> <p><strong>Baylor Scott and White (BSW) cohort</strong>: <strong>10x.visium.tar.gz</strong>, containing 10 patients with TNBC from Baylor Scott and White affiliated Hospital. </p> <p>Each sample is made of Space Ranger processed spot-separated gene expression data (processed to HDF5 AnnData file). There are also H&E images, and spot coordinate files available. </p> <p> </p> <p>For <strong>Georgia validation cohort</strong>, 400 genes used for validation of ESG signatures (associated with BA-Community 1 and WA-Community-1) were obtained and provided by Ritu Aneja's lab. These 400 genes' spot-based expression data across Black and White TNBC patients are provided. See file <strong>georgia.validation.visium.tar.gz</strong>. Expression was normalized by total counts per spot, followed by log-normalization by Giotto.</p> <p> </p> <p>As well in our paper, we integrated a published racial TNBC cohort for deriving some of initial results in the paper. This refers to the Bassiouni et al (Cancer Research) paper in Carpten's group. <strong>GSM_giotto_processed.tar.gz</strong> refers to this dataset, which we deposit here. The data were normalized by Giotto using standard procedure.</p>
Data for: Biomechanical adaptations enable phoretic mite species to occupy distinct spatial niches on host burying beetles
<p>Niche theory predicts that ecologically similar species coexist by minimising interspecific competition through niche partitioning. Therefore understanding the mechanisms of niche partitioning is essential for predicting interactions and coexistence between competing organisms. Here we study two phoretic mite species, <em>Poecilochirus carabi, </em>and <em>Macrocheles nataliae</em> that coexist on the same host-burying beetle <em>Nicrophorus vespilloides </em>and use it to 'hitchhike' between reproductive sites. Field observations revealed clear spatial partitioning between species in distinct host body parts. <em>P. carabi</em> preferred the ventral side of the thorax, whereas <em>M. nataliae </em>were exclusively found ventrally at the hairy base of the abdomen. Experimental manipulations of mite density showed that each species preferred these body parts, largely regardless of the density of the other mite species on the host beetle. Force measurements indicated that this spatial distribution is mediated by biomechanical adaptations, because each mite species required more force to be removed from their preferred location on the beetle. While <em>P. carabi</em> attached with large adhesive pads to the smooth thorax cuticle, <em>M. nataliae</em> gripped abdominal setae with their chelicerae. Our results show that specialist biomechanical adaptations for attachment can mediate spatial niche partitioning among species sharing the same host.</p>
Fig. 4 in Determining Spatial Parameters Of The Ecological Niche Of Parus Major (Passeriformes, Paridae) On The Base Of Remote Sensing Data
Fig. 4. Distribution of resources (light bars) and distribution of resources used by P. major (grey bars).
Fig. 5 in Determining Spatial Parameters Of The Ecological Niche Of Parus Major (Passeriformes, Paridae) On The Base Of Remote Sensing Data
Fig. 5. Distribution of pseudo absence cells: a — the distance to the presence cells is not less than 1000 meters; b — the distance to the presence cells is not less than 500 meters; c — the distance to the presence cells is not less than 250 meters; d — distance to the presence cells is not less than 100 meters.
Figure 5 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)
Figure 5. Correlation between the environment variables and the axes selected as a result of FANTER analysis. A – marginality axes 1 and 2; B - specialization axes 44 and 45. Type_1 - type_6 - the proportion of physiognomic types of vegetation cover; temp_05 - top soil temperature (3-5 cm) May 3, 2012; temp_06 - temperature of the top layer of soil (3-5 cm) June 20, 2012; Tm - thermoclimate; Kn - continentality; Om - ombroclimate; Kr - cryoclimate; Hd - humidity; Tr - salt regime; Nt - nitrogen nutrition; Rc - acidity; Lc - lighting; St - stepants; Pr - pratants; Humus – humus comtant; EC – soil electrical conductivity, imp_05 - imp_50 - soil mechanical impedance at a depth of 5, ..., 50 cm, Agr_10 - Agr_025 - aggregate fractions of size> 10, ..., <0.25 mm, g_Vlag - hygroscopic humidity,%; Compact – soil shrinkage, in %.
Figure 8 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)
Figure 8. Spatial distribution of the habitat preference index (HSI) for Vallonia pulchella within the experimental site on red-brown clays based on ENFA (top) and MADIFA (bottom) procedures. The arrow indicates the zones of greatest difference.
Figure 4 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)
Figure 4. An histogram of the available resource units. Resource allocation (black bars) and an histogram of the used resource units distribution of resource use (gray bars) of Vallonia pulchella. Type_1 - type_6 - the proportion of physiognomic types of vegetation cover; temp_05 - top soil temperature (3-5 cm) May 3, 2012; temp_06 - temperature of the top layer of soil (3-5 cm) June 20, 2012; Tm - thermoclimate; Kn - continentality; Om - ombroclimate; Kr - cryoclimate; Hd - humidity; Tr - salt regime; Nt - nitrogen nutrition; Rc - acidity; Lc - lighting; St - stepants; Pr - pratants; Humus – humus comtant; EC – soil electrical conductivity, imp_05 - imp_50 - soil mechanical impedance at a depth of 5, ..., 50 cm, Agr_10 - Agr_025 - aggregate fractions of size> 10, ..., <0.25 mm, g_Vlag - hygroscopic humidity,%; Compact – soil shrinkage, in %.
Figure 2 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)
Figure 2. Soil surface and physiognomic characteristics of the vegetation cover. 1 – type_1 (Bromus sguarrosus L.); 2 – type_2 (Seseli tortuosum L.); 3 – type_3 (Lactuca tatarica (L.) C.A. Mey.); 4 – type_4 (Medicago sativa L.); 5 – type_5 (dead plant residue); 6 – type_6 (open soil cover).
Figure 1 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)
Figure 1. Research Centre of the Dnipro Agrarian and Economic University in Pokrov (Ukraine). A – satellite image of the study area (1 – reclaimed land; 2 – mining quarry); B – technosoils profile; C – quarry panorama view.
Figure 1 in Spatial distribution and dietary niche breadth of the leopard Panthera pardus (Carnivora: Felidae) in the northeastern Himalayan region of Pakistan
Figure 1. Distribution of the leopard (Panthera pardus) in and around Pir Lasura National Park, northeastern Himalayan region, Pakistan, as indicated by various direct and indirect signs of the species.
Niche suitability and spatial distribution patterns of anurans in a unique Ecoregion mosaic of Northern Pakistan
<p><span>The lack of information regarding biodiversity states hampers designing and implementation conservation strategies and future targets. </span><span>Northern Pakistan </span><span>consists</span><span> of a unique ecoregion mosaic which supports a myriad of environmental niches for anuran diversity to flourish in comparison to the deserts and xeric shrublands throughout the rest of the country. In order to study the niche suitability, overlap and distribution patterns</span><span> </span><span>in Pakistan, we collected observational data for nine amphibian species across several distinct ecoregions by surveying 87 randomly selected locations </span><span> </span><span>from 2016 to 2018 in District Rawalpindi and Islamabad Capital Territory. Our model showed that the precipitation of the warmest and coldest quarter, distance to rivers and vegetation were the greatest drivers of anuran distribution, expectedly indicating that the presence of humid forests and proximity to waterways greatly influences the habitable range of anurans in Pakistan. Sympatric overlap between species occurred at significantly higher density in tropical and subtropical coniferous forests than in other ecoregion types. We </span><span>found species </span><span>such as </span><span><em>Minervarya</em> spp.</span><span>, <em>Hoplobatrachus</em> <em>tigerinus</em> and <em>Euphlyctis</em> spp. showed preference for the lowlands in proximal, central and southern parts of the study area proximal to urban settlements, little vegetation and higher average temperatures. The toads <em>Duttaphrynus</em> </span><em><span>bengalensis</span></em><span> </span><span>and </span><em><span>D. </span><span>stomaticus</span></em><span> had </span><span> </span><span>scattered distribution</span><span>s</span><span> throughout the study area with no clear preference for elevation. <em>Sphaerotheca</em> <em>pashchima</em> </span><span>showed a patchy distribution in the midwestern extent of the study area as well as the foothills to the north. <em>Microhyla</em> <em>nilphamariensis</em> also showed a wide distribution throughout the study area with a preference for both lowlands and montane terrain. Endemic frogs (<em>Nanorana</em> <em>vicina</em> and <em>Allopaa</em> <em>hazarensis</em>) were observed only in locations with higher elevations, higher density of streams and lower average temperatures as compared to the other seven species sampled.</span></p>
Data for: Biomechanical adaptations enable phoretic mite species to occupy distinct spatial niches on host burying beetles
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Niche suitability and spatial distribution patterns of anurans in a unique Ecoregion mosaic of Northern Pakistan
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Effects of phylogenetic distance, niche overlap and habitat alteration on spatial co-occurrence patterns in Neotropical bats and birds
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How competitive intransitivity and niche overlap affect spatial coexistence
<p>Competitive intransitivity is mostly considered outside the main body of coexistence theories that rely primarily on the role of niche overlap and differentiation. How the interplay of competitive intransitivity and niche overlap jointly affects species coexistence has received little attention. Here, we consider a rock-paper-scissors competition system where interactions between species can represent the full spectra of transitive-intransitive continuum and niche overlap/differentiation under different levels of competition asymmetry. By comparing results from pair approximation that only considers interference competition between neighbouring cells in spatial lattices, with those under the mean-field assumption, we show that (1) species coexistence under transitive competition is only possible at high niche differentiation; (2) in communities with partial or pure intransitive interactions, high levels of niche overlap are not necessary to beget species extinction; and (3) strong spatial clustering can widen the condition for intransitive loops to facilitate species coexistence. The two mechanisms, competitive intransitivity and niche differentiation, can support species persistence and coexistence, either separately or in combination. Finally, the contribution of intransitive loops to species coexistence can be enhanced by strong local spatial correlations, modulated and maximised by moderate competition asymmetry. Our study, therefore, provides a bridge to link intransitive competition to other generic ecological theories of species coexistence.</p>
Spatial and temporal niche overlap of aardwolves and aardvarks in Serengeti National Park, Tanzania
<p>Species interactions can influence species distributions, but mechanisms mitigating competition or facilitating positive interactions between ecologically similar species are often poorly understood. Aardwolves (<em>Proteles cristata</em>) and aardvarks (<em>Orycteropus afer</em>) are nocturnal, insectivorous mammals that co-occur in eastern and southern Africa, and knowledge of these species is largely limited to their nutritional biology. We used aardwolf and aardvark detections from 106 remote cameras during 2016–2018 to assess their spatial and temporal niche overlap in the grasslands of Serengeti National Park, Tanzania. Using a multispecies occupancy model, we identified a positive interaction between occupancy probabilities for aardwolves and aardvarks. Slope, proportion of grassland, and termite mound density did not affect occupancy probabilities of either species. Probability of aardwolf, but not aardvark, occupancy increased with distance to permanent water sources, which may relate to predation risk avoidance. Diel activity overlap between aardwolves and aardvarks was high during wet and dry seasons, with both species being largely nocturnal. Aardwolves and aardvarks have an important ecological role as termite consumers, and aardvarks are suggested to be ecosystem engineers. Our results contribute to a better understanding of the spatial and temporal niche of insectivores like aardwolves and aardvarks, suggesting high spatial and temporal niche overlap in which commensalism occur, whereby aardwolves benefit from aardvark presence through increased food accessibility.</p>
Figure 7 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)
Figure 7. Results of MADIFA-mapping of Vallonia pulchella ecological niche.
Dataset for the manuscript: "Three-dimensional species distribution modeling reveals the realized spatial niche for coral recruitment on contemporary Caribbean reefs"
<p>Whether the three-dimensional (3D) structure of habitats influences and partition recruitment niches of corals is unknown. We developed a new method that combined Species Distribution Modeling and Structure from Motion to characterize and map the three-dimensional recruitment niches of two ecosystem engineers on Caribbean coral reefs, scleractinian corals and octocorals. </p> <p>In this repository, we include 48 3D models of small areas of the reef (i.e., within ~ 0.25 m<sup>2</sup> quadrats) reconstructed with Structure-from-Motion, as well as the geospatial data used to characterize and map the realized recruitment niche for scleractinian corals and octocorals on Caribbean coral reefs. We conducted the study at two shallow, fringing reefs off the south shore of St. John, US Virgin Islands, named Grootpan and Europa Bays (18° 18.360’N, 64° 43.140’W, and 18° 19.016’N, 64° 43.798’W, respectively). Within each 0.25 m<sup>2</sup> quadrat, we counted and marked all recruits (octocorals ≤ 5 cm height, and scleractinians ≤ 4 cm wide).</p> <p><em>DATASET DESCRIPTIONS:</em></p> <ul> <li><strong>"Quadname_data.zip":</strong> In each of this folders we included all the data calculated within a quadrat: <ul> <li>ASCII files (.txt).</li> <li>The annotated dense point cloud (.las) for each quadrat.</li> <li>The quadrat 3D model texture (.jpg).</li> <li>The quadrat 3D polygon mesh (.ply).</li> <li>The quadrat 2.5D Digital Elevation Model (i.e., DEM; .tif).</li> <li>Shape files with recruits local coordinates within each quadrat (.dbf, .prj, .shp, .shx).</li> </ul> </li> <li><strong>"datawide.rds": </strong>This is the file needed to run the analyses performed in Martínez-Quintana et al., 2023. This file is obtained after processing all the raw data calculated within each quadrat. All code associated with the workflow used to obtain the datawide.rds file and run the analyses performed in Martínez-Quintana et al., 2023 is available at <a href="https://github.com/AdamWilsonLab/meshSDM">github.com/AdamWilsonLab/meshSDM</a>.</li> </ul> <p><strong>IMPORTANT NOTES: </strong></p> <ul> <li>Quadrat names starting with the letters “eu” indicate the data were collected at Europa Bay, whereas those starting with the letters “ec” indicate that data were collected at Grootpan Bay (commonly named East Cabritte).</li> <li>Each ASCII file (quadname_ASCII_subsampled_X.txt) contains the slope and roughness of the quadrat calculated on the point cloud at 5, 10, 20, and 100 mm scales, and the smooth point cloud used to calculate the topographic exposure index (TEI) described in Martínez-Quintana et al., 2023. Calculations were performed and ASCII files were created with CloudCompare.</li> <li>Each dense point cloud, mesh, texture, and DEM were calculated with Agisoft Metashape.</li> <li>Agisoft Metashape allows the user to classify and annotate groups of points in the dense point cloud. However, the list of classes provided by the software corresponds to the standard list used for terrestrial LiDAR data; these classes cannot be renamed within the software. Thus, for the present study, we coded the automatic semantic classifications available in Metashape as follows: <ul> <li>Ground = Calcareous rock.</li> <li>Building = Igneous rock.</li> <li>High noise = Sand.</li> <li>Low vegetation = Adult Scleractinian corals.</li> <li>Medium vegetation = Adult Octocoral base.</li> <li>High vegetation = Sponge.</li> <li>Water = Octocoral recruit (named also ocr).</li> <li>Road Surface = Scleractinian recruit (named also scr).</li> <li>Unclassified = created points but never classified (excluded from the analyses).</li> <li>Low Point = noise (unreliable points).</li> <li>Transmission tower and Rail = Points outside the quadrat and excluded from the analysis.</li> </ul> </li> </ul>
Spatial and temporal niche overlap of aardwolves and aardvarks in Serengeti National Park, Tanzania
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OpenNeuro
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