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42 results for “Species absence”
Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species
<p>Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.</p>
Presence or absence of marsh plant species along transects through a nutrient enriched marsh receiving wastewater effluent and a reference (unenriched) marsh, Plum Island Ecosystems LTER.
Presence or absence of marsh plant species along transects through a nutrient enriched marsh receiving wastewater effluent and a reference (unenriched) marsh. Nutrient enrichment comes from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich. The marsh around Clubhead Creek, Rowley, MA was used as a reference.
Presence-Absence Points for Tree Species Distribution Modelling for Europe
<p>The dataset is a collection of presence and absence points for forest tree species for Europe. Each unique combination of longitude, latitude and year was considered as an independent sample. Presence data was obtained from the harmonized tree species occurrence dataset by <a href="https://zenodo.org/record/5524611">Heisig and Hengl (2020)</a> and absence data from the <a href="https://ec.europa.eu/eurostat/web/lucas">LUCAS</a> (in-situ source) dataset.</p> <p>A set of <strong>50</strong> different forest tree species was selected from the harmonized tree species dataset and data lacking a temporal observation was overlaid with yearly forest masks derived from land cover maps produced by <a href="https://zenodo.org/record/4725429">Parente et al. (2021)</a>. We overlaid the points with the probability maps for the classes:</p> <ul> <li>311: Broad-leaved forest,</li> <li>312: Coniferous forest,</li> <li>313: Mixed forest,</li> <li>323: Sclerophyllous forest,</li> <li>324: Transitional woodland-shrub,</li> <li>333: Sparsely vegetated area.</li> </ul> <p>Points were included in the dataset only if the probability value extracted for at least one of the above classes was <strong>≥ 50%</strong> for all the years considered. An additional quality flag was added to distinguish points coming from this operation and the points with original year of observation coming from source datasets.</p> <p>The final dataset contains <strong>4,359,999</strong> observations for and a total of <strong>630 </strong>columns. <br> <br> The first <strong>8 </strong>columns of the dataset contain metadata information used to uniquely identify the points:</p> <ul> <li><strong>id</strong>: unique point identifier,</li> <li><strong>year</strong>: year of observation,</li> <li><strong>postprocess</strong>: quality flag to identify if the temporal reference of an observation comes from the original dataset or is the result of spatiotemporal overlay with forest masks,</li> <li><strong>Tile_ID</strong>: contains the tile id from the eu_tiling_system (30 km grid),</li> <li><strong>easting</strong>: longitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li><strong>northing</strong>: latitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li><strong>Atlas_class</strong>: name of the tree species according to the European Atlas of Forest Tree Species or NULL in case of absence point,</li> <li><strong>lc1</strong>: contains original LUCAS land cover class or NULL if it's a presence point.</li> </ul> <p>The remaining columns contain the extracted values of a series of predictor variables (temperature, precipitation, elevation, topographical information, spectral reflectance) useful for species distribution modeling applications. These points were used to model the potential and realized distribution of a series of <strong>16 target species </strong>for the period 2000 - 2020. The approach involved training three ML models to predict probability of presence (<em>i.e.</em> <a href="http://link.springer.com/article/10.1023/A:1010933404324">Random Forest</a>, <a href="http://dl.acm.org/doi/abs/10.1145/2939672.2939785">XGBoost</a>, <a href="https://rss.onlinelibrary.wiley.com/doi/abs/10.2307/2344614">GLM</a>), which served as input to train a linear meta-model (<em>i.e.</em> <a href="http://papers.nips.cc/paper/2014/file/ede7e2b6d13a41ddf9f4bdef84fdc737-Paper.pdf">Logistic regression classifier</a>), responsible for predicting the final probability of presence for each species.</p> <p>The <em>RDS </em>file is created from a data.table object and suitable for fast reading in the R-programming environment. The <em>CSV.GZ</em> file contains records as a table with easting and northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.</p> <p>We provide <em>RDS </em>files for a 30km tile as an example containing raster stacks at 30m resolution of all the covariates included in the regression matrix. You can find the specific geographical location of the tile in Europe using the attached <em>GeoPackage </em>("eu_tiling_system_30km"): open it in QGIS and filter by "ID".</p> <p>In our approach we considered both static and dynamic covariates: dynamic covariates are calculated as averages of a 4 years time window (example: 2004 contains averages from 2002 to 2006). To get the predictions for a specific year, covariates contained in the <em>static</em> RDS file need to be bound with the respective year.</p> <p>To access our predictions (probabilities and uncertainties) produced for the target species access:</p> <ul> <li><strong>Open Data Science Europe viewer: <a href="https://maps.opendatascience.eu">https://maps.opendatascience.eu</a></strong></li> <li>Check the <strong>Related identifiers </strong>section of this repository to access each species individually</li> </ul> <p>If you instead would like to know more about the creation of this dataset and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_tree.species_anv.pnv.eml">GitLab</a>)</li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Broad-scale patterns of geographic avoidance between species emerge in the absence of fine-scale mechanisms of coexistence
<p>Aim: The need to forecast range shifts under future climate change has motivated an increasing interest in better understanding the role of biotic interactions in driving diversity patterns. The contribution of biotic interactions to shaping broad-scale species distributions is however, still debated, partly due to the difficulty of detecting their effects. We aim to test whether spatial exclusion between potentially competing species can be detected at the species range scale, and whether this pattern relates to fine-scale mechanisms of coexistence.</p> <p>Location: Western Palearctic</p> <p>Time period: Anthropocene</p> <p>Taxa: bats (Chiroptera)</p> <p>Methods: We develop and evaluate a measure of geographic avoidance that uses outputs of species distribution models to quantify geographic exclusion patterns expected if interspecific competition affects broad-scale distributions. We apply the measure to 10 Palearctic bat species belonging to four morphologically similar cryptic groups in which competition is likely to occur. We compare outputs to null models based on pairs of virtual species and to expectations based on ecological similarity and fine-scale coexistence mechanisms. We project changes in range suitability under climate change taking into account effects of geographic avoidance.</p> <p>Results: Values of geographic avoidance were above null expectations for two cryptic species pairs, suggesting that interspecific competition could have contributed to shaping their broad-scale distributions. These two pairs showed highest levels of ecological similarity and no trophic or habitat partitioning. Considering the role of competition modified predictions of future range suitability.</p> <p>Conclusions: Our results support the role of interspecific competition in limiting the geographic ranges of morphologically similar species in the absence of fine-scale mechanisms of coexistence. This study highlights the importance of incorporating biotic interactions into predictive models of range shifts under climate change, and the need for further integration of community ecology with species distribution models to understand the role of competition in ecology and biogeography.</p>
Fig. 3 - a-c in Ant diversity on the largest Mediterranean islands: on the presence or absence of 28 species in Sicily (Hymenoptera, Formicidae)
Fig. 3 - a-c) Plagiolepis schmitzii. Workers / operaie. Mondello (ESC). d-e) Ponera testacea. Worker / operaia. Pizzo di Pietralunga (ESC). f-j) Solenopsis orbula. Queens / regine. Messina. A specimen from De Stefani's collection / esemplare dalla collezione De Stefani (f,h,j) and a recently collected one / ed uno recentemente raccolto (g,i) (ESC). Scale bar / scala grafica: 0.5 mm.
Fig. 4 - a-b in Ant diversity on the largest Mediterranean islands: on the presence or absence of 28 species in Sicily (Hymenoptera, Formicidae)
Fig. 4 - a-b) Temnothorax clypeatus. Worker / operaia. Bosco di Santo Pietro (AAC). c-d) Temnothorax nylanderi. Worker / operaia. Lago Maulazzo (ESC). e-f) Temnothorax ravouxi. Queen / regina. Madonna delle Grazie (AAC). Scale bar / scala grafica: 0.5 mm.
Fig. 2 - a-d in Ant diversity on the largest Mediterranean islands: on the presence or absence of 28 species in Sicily (Hymenoptera, Formicidae)
Fig. 2 - a-d) Camponotus ruber. a-b) major worker / operaia maggiore; c-d) minor worker / operaia minore. Monte Pellegrino. Scale bar / scala grafica: 1 mm (ESC). e-f) Lasius myops. Worker / operaia. Pineta di Linguaglossa. Scale bar / scala grafica: 0.5 mm (AAC). g-h) Lasius platythorax. Worker / operaia. Monte Cofano. Scale bar / scala grafica: 0.5 mm (AAC).
Fig. 1 in Ant diversity on the largest Mediterranean islands: on the presence or absence of 28 species in Sicily (Hymenoptera, Formicidae)
Fig. 1 - Sampling effort across Sicily (circum-Sicilian islands excluded) according to the number of samples from AAC and ESC. / Sforzo di campionamento in Sicilia (isole circumsiciliane escluse) basato sul numero di campioni in AAC e ESC.
Fig. 5 - Solenopsis latro sicula. a-b,e in Ant diversity on the largest Mediterranean islands: on the presence or absence of 28 species in Sicily (Hymenoptera, Formicidae)
Fig. 5 - Solenopsis latro sicula. a-b,e) Queen / regina; c-d,f) male / maschio. Monte Pellegrino. Scale bar / scala grafica: 0.5 mm (ESC).
Fig. 4 in Unexpected absence of exo-erythrocytic merogony during high gametocytaemia in two species of Haemoproteus (Haemosporida: Haemoproteidae), including description of Haemoproteus angustus n. sp. (lineage hCWT7) and a report of previously unknown residual bodies during in vitro gametogenesis
Fig. 4. Comparison of fully grown gametocytes of Haemoproteus angustus n. sp. (lineage hCWT7) from the blood of Curruca communis (A, B), Haemoproteus sittae (unknown lineage) from the blood of Sittae europaea (C, D) and Haemoproteus dolniki (unknown lineage) from the blood of Fringilla coelebs (E, F). Note: the elongate and markedly attenuated nuclei of infected erythrocytes containing advanced gametocytes of the new species (A, B), which is not the case in other Haemoproteus parasites (C–F). All images are from type specimens of these species. Symbols are the same as in Fig. 2. Giemsa-stained thin blood films. Scale bar = 10 µm.
Fig. 7 in Unexpected absence of exo-erythrocytic merogony during high gametocytaemia in two species of Haemoproteus (Haemosporida: Haemoproteidae), including description of Haemoproteus angustus n. sp. (lineage hCWT7) and a report of previously unknown residual bodies during in vitro gametogenesis
Fig. 7. High gametocytaemia of Haemoproteus palloris (lineage hWW1) from the blood of a willow warbler Phylloscopus trochilus. Note that young gametocytes are absent, and the parasitaemia consists exclusively of mature fully grown macro- and microgametocytes, indicating a synchronous parasite development and probable absence of recent maturation of tissue meronts, which are the only source of merozoites for young gametocyte development during Haemoproteus infections. Triangle arrowhead – macrogametocytes; triangle wide arrowheads – microgametocytes. Long arrows – parasite nuclei. Giemsa-stained preparations. Scale bar = 10 µm.
Fig. 2 in Unexpected absence of exo-erythrocytic merogony during high gametocytaemia in two species of Haemoproteus (Haemosporida: Haemoproteidae), including description of Haemoproteus angustus n. sp. (lineage hCWT7) and a report of previously unknown residual bodies during in vitro gametogenesis
Fig. 2. Gametocytes of Haemoproteus angustus n. sp. (lineage hCWT7) from the blood of its type host, the common whitethroat Curruca communis: A-H – macrogametocytes, I-L – microgametocytes. Note: the markedly attenuated width of infected erythrocytes (D–G) containing advanced gametocytes, compared to uninfected erythrocytes, and the presence of predominantly oval or elongate pigment granules in fully grown gametocytes (F–H, J-L). All images are from the hapantotype. Long arrows – parasite nuclei. Short arrows – vacuoles. Arrowheads – pigment granules. Short simple wide arrow – nucleolus. Long triangle wide arrows – unfilled spaces between erythrocyte nuclei and gametocytes. Giemsa-stained thin blood films. Scale bar = 10 µm.
Fig. 6 in Unexpected absence of exo-erythrocytic merogony during high gametocytaemia in two species of Haemoproteus (Haemosporida: Haemoproteidae), including description of Haemoproteus angustus n. sp. (lineage hCWT7) and a report of previously unknown residual bodies during in vitro gametogenesis
Fig. 6. Bayesian Inference tree (A) based on partial (478 bp) cytb sequences of Haemoproteus angustus n. sp. (lineage hCWT7) and the 20 closest related Haemoproteus lineages. Bayesian posterior probabilities and Maximum Likelihood bootstrap values were indicated above and below nodes, respectively. For each lineage, representative GenBank accession numbers and MalAvi lineage codes (if available) are indicated as well as the most common bird host. The scale bar indicates the expected mean number of substitutions per site according to the model of sequence evolution applied. Images B and C show the Median-Joining DNA haplotype network of partial (478 bp) cytb sequences of H. angustus hCWT7 and the 20 closest related Haemoproteus lineages. The upper image (B) shows the host distribution, and the lower image (C) depicts the geographic distribution according to the United Nations geoscheme. Each circle represents a unique haplotype/lineage. The frequency of each lineage is indicated for all haplotypes with more than one record and roughly corresponds to the size of circles. Bars on branches indicate the number of substitutions between two haplotypes. Small white circles represent median vectors, which are hypothetical (often ancestral or unsampled) sequences required to connect existing haplotypes with maximum parsimony.
Fig. 3 in Unexpected absence of exo-erythrocytic merogony during high gametocytaemia in two species of Haemoproteus (Haemosporida: Haemoproteidae), including description of Haemoproteus angustus n. sp. (lineage hCWT7) and a report of previously unknown residual bodies during in vitro gametogenesis
Fig. 3. Comparison of fully grown gametocytes of Haemoproteus angustus n. sp. (lineage hCWT7) from the blood of Curruca communis (A–D), H. belopolskyi (lineage hHIICT1) from the blood of Hippolais icterina (E–H) as well as H. parabelopolskyi (hSYAT2) (I–L), H. pallidulus (hSYAT3) (M–P), H. homogeneae (hSYAT16) (Q–T) and H. majoris (hWW2) (U–X) from the blood of Sylvia atricapilla. Note: the markedly attenuated gametocytes of the new species (A–D), which are not present in other Haemoproteus parasites (E–X); the fully grown gametocytes of H. belopolskyi (F, H) and H. parabelopolskyi (J, L) are bigger than those of H. angustus n. sp.; the pigment granules are predominantly roundish and small in H. pallidulus (M–P); the fully grown gametocytes are small and do not reach poles of infected erythrocytes in H. homogeneae (R, T); the erythrocyte nuclei are displaced laterally by H. majoris (V, X) – all these feature are not characteristic of H. angustus sp. nov. Symbols are the same as in Fig. 2. Giemsa-stained thin blood films. Scale bar = 10 µm.
Fig. 1. Bayesian phylogenetic tree constructed using partial cytochrome b in Unexpected absence of exo-erythrocytic merogony during high gametocytaemia in two species of Haemoproteus (Haemosporida: Haemoproteidae), including description of Haemoproteus angustus n. sp. (lineage hCWT7) and a report of previously unknown residual bodies during in vitro gametogenesis
Fig. 1. Bayesian phylogenetic tree constructed using partial cytochrome b sequences of 61 lineages of Haemoproteus, 4 lineages of Plasmodium, and Leucocytozoon sp. lSISKIN2 as outgroup. Posterior probabilities higher than 0.8 are indicated close to the respective nodes. Red font indicates the parasite lineage described in this publication. Vertical bars (A–D) show groups of closely related lineages, which complete development and produce gametocytes only in non-passerines (A, D), both non-passerines and passerines (B), and only passerines (C). Blue font indicates Haemoproteus species, which develop in non-passerine avian hosts, which are indicated by symbols (● – Psittaciformes; ∎ - Coraciiformes; ▴ - Strigiformes; ◆ - Anseriformes; ★ - Charadriiformes; ♥ - Pelecaniformes; ⋄ - Piciformes; ⊠ - Sphenisciformes; Ω - Musophagiformes; § - Trochiliformes; Ψ – Falconiformes; Σ – Columbiformes; Φ - Galliformes). Lineage names were provided (according to MalAvi database), followed by parasite species names and sequence GenBank accession numbers.
Fig. 5. Haemoproteus angustus n in Unexpected absence of exo-erythrocytic merogony during high gametocytaemia in two species of Haemoproteus (Haemosporida: Haemoproteidae), including description of Haemoproteus angustus n. sp. (lineage hCWT7) and a report of previously unknown residual bodies during in vitro gametogenesis
Fig. 5. Haemoproteus angustus n. sp. (lineage hCWT7) gametogenesis (A–H) and ookinete development (I–L) in vitro: A, B – initial stages of rounding up of gametocytes after the exposure of infected blood to air; C, E – rounded up macrogametocyte (C) and microgametocyte (E); F – the exflagellation; G – microgamete; H – fertilization; I – initial stage of ookinete development; J – medium differentiated ookinete; K – nearly mature ookinete with a residual body; L – mature ookinete without residual body. Note: the presence of several small residual bodies in erythrocytes containing gametocytes, which were preparing to escape from infected erythrocytes (A-C, E) – a unique character of this species during the initial stage of gametogenesis. Short triangle wide arrows – residual bodies; triangle arrowhead – macrogamete; triangle wide arrowheads – microgametes; short barbed arrows – finger-like outgrowth; long barbed arrows – apical end of developing ookinete; simple wide arrowhead – residual body of ookinete. Other symbols as in Fig. 2. Giemsa-stained thin blood films. Scale bar = 10 µm.
Text-fig. 4. Occurrence of P3 in maxillae from Deninger bears and cave bears, data after Table 1 (presence = P3 or alveoli observed, absence = no P3 developed, broken = caudal part of maxilla broken). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 4. Occurrence of P3 in maxillae from Deninger bears and cave bears, data after Table 1 (presence = P3 or alveoli observed, absence = no P3 developed, broken = caudal part of maxilla broken).
Text-fig. 2. Occurrence of P3 in maxillae from brown bears, data after Table 1 (presence = P3 or alveoli observed, absence = no P3 developed, broken = caudal part of maxilla broken). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 2. Occurrence of P3 in maxillae from brown bears, data after Table 1 (presence = P3 or alveoli observed, absence = no P3 developed, broken = caudal part of maxilla broken).
Text-fig. 8. Occurrence of p3 in mandibles from Deninger bears, data after Table 3 (presence = p3 or alveoli observed, absence = no p3 developed, broken = diastema fragmented). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 8. Occurrence of p3 in mandibles from Deninger bears, data after Table 3 (presence = p3 or alveoli observed, absence = no p3 developed, broken = diastema fragmented).
Text-fig. 6. Occurrence of p3 in mandibles from brown bears, data after Table 3 (presence = p3 or alveoli observed, absence = no p3 developed, broken = diastema fragmented). in Anterior Premolar Variability In Pleistocene Cave And Brown Bears And Its Significance In Species Determination
Text-fig. 6. Occurrence of p3 in mandibles from brown bears, data after Table 3 (presence = p3 or alveoli observed, absence = no p3 developed, broken = diastema fragmented).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.