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8,119 results for “species distribution”
Factors influencing transferability in species distribution models
<p>Species distribution models (SDMs) provide insights into species' ecology and distributions and are frequently used to guide conservation priorities. However, many uses of SDMs require model transferability, which refers to the degree to which a model built in one place or time can successfully predict distributions in a different place or time. If a species' model has high spatial transferability, the relationship between abundance and predictor variables should be consistent across a geographical distribution. We used Breeding Bird Surveys, climate and remote sensing data, and a novel method for quantifying model transferability to test whether SDMs can be transferred across the geographic ranges of 129 species of North American birds. We also assessed whether species' traits are correlated with model transferability. We expected that prediction accuracy between modeled regions should decrease with 1) geographical distance, 2) degree of extrapolation, and 3) were affected by a 'core-boundary' effect, which assesses distances to the boundary of a distribution. Our results suggest that very few species have a high model transferability index (<em>MTI</em>). Species with large distributions, with distributions located in areas with low topographic relief, and with short lifespans are more likely to exhibit low transferability. Transferability between modeled regions also decreased with geographical distance and degree of extrapolation. We expect that low transferability in SDMs potentially resulted from both ecological non-stationarity (i.e., biological differences within a species across its range) and over-extrapolation. Accounting for non-stationarity and extrapolation should substantially increase prediction success of species distribution models, therefore enhancing the success of conservation efforts.</p>
Fig. 10. Distribution records for Spanglerelmis Polizei & Bispo gen. nov. A in Spanglerelmis, a new genus of Elmidae (Insecta: Coleoptera) from Brazil with new species and biological notes
Fig. 10. Distribution records for Spanglerelmis Polizei & Bispo gen. nov. A. Map of Brazil highlighting the São Paulo and Santa Catarina States, showing the localities of Spanglerelmis gen. nov. B–D. Locality of Spanglerelmis xiririca gen. et sp. nov. B. Parque Estadual Caverna do Diabo, Eldorado, São Paulo State, Brazil (type locality) (24°38′00.7ʺ S, 048°24′32.7ʺ W). C. Parque Estadual de Intervales, Ribeirão Grande, São Paulo State, Brazil (24°16′16ʺ S, 048°25′31ʺ W). D. Echaporã, São Paulo State, Brazil (22°25′06.9ʺ S, 050°12′0.9ʺ W). E. Type locality of Spanglerelmis timburi gen. et sp. nov. Timburi, São Paulo State, Brazil (23°11′01.6ʺ S, 049°37′49.2ʺ W). F. Type locality of Spanglerelmis femoralis gen. et comb. nov. Nova Teutônia (currently county of Seara), Santa Catarina State, Brazil (27°09′42.5′′ S, 052°25′28.5′′ W).
Code and data for "Species traits associated with rapid shifts in elevational distributions of Swiss birds"
<p><strong>Abstract</strong><br> Global change in climate and land use have profound effects on species’ geographic and elevational distributions. In European birds, while species are predicted to track their climatic niches upslope, lowland agricultural intensification and high elevation land abandonment can drive elevational shifts. Species traits that can predict response to change in climate and land use can inform conservation, but thorough examination of their relationships with elevational shifts in European birds are lacking. We estimate change in the elevational distributions of 71 species from 1996 to 2016 in a region of the western Palearctic with wide elevational gradients (approximately 3,000 m) and large changes in temperature. We model the relationships between elevational shifts and species traits associated with resource preference and adaptive capacity at five reference points including the cool edge, warm edge, and the core of species’ elevational distributions. When intermediate reference points were removed changes to the results were negligible, indicating that three reference points are likely sufficient. We found significant upslope and downslope shifts in 56% and 23% of our study species, respectively. Asymmetric rates of shifts in the cool and warm edges caused significant contractions in elevational extent in 30% of our study species. The effect of elevational preference (i.e. midpoint elevation) was habitat dependent. Movement in alpine birds was unidirectionally upslope, with nearly half displaying significant or apparent elevational range contractions. In woodland birds, asymmetries of shifts in reference points led to expansions in extent in low elevation species and contractions in high elevation species. Generally, migrants, species with smaller mass, smaller relative brain size, smaller hand-wing index, and generalists in diet, habitat, and elevation had greater upslope shifts. While elevational shifts in European birds were heterogenous and species-specific, many were rapid, and species traits associated with resource preference and adaptive capacity were associated with common patterns of elevation.</p>
Fig. 4 in Review of the genus Caucaseuma Strasser, 1970, with the description of a new cavernicolous species from the Western Caucasus and an updated key and distribution (Diplopoda, Chordeumatida, Anthroleucosomatidae)
Fig. 4. Caucaseuma strasseri Antić sp. nov., paratype, ♂ (IZB). Legs in anterior view. A. Leg-pair 1. B. Leg-pair 2. C. Leg-pair 3. D. Leg-pair 4. E. Leg-pair 5. F. Leg-pair 6. G. Leg-pair 7. H. Leg-pair 10. I. Leg-pair 11. Scale bars = 0.5 mm.
Fig. 3 in Review of the genus Caucaseuma Strasser, 1970, with the description of a new cavernicolous species from the Western Caucasus and an updated key and distribution (Diplopoda, Chordeumatida, Anthroleucosomatidae)
Fig. 3. Caucaseuma strasseri Antić sp. nov., paratype, ♂ (NHMW MY10260). SEM images of some habitual structures. A. Anterior part of the body, anterolateral view. B. Head, anterior view. C. Head, right side, lateral view. D. Left antennomeres 6 and 7, detail. E. Tip of right antenna. F. Right legs 1 and 2, anteroventral view. G. Ring 15, dorsal view. H. Posterior macrochaeta. Scale bars: A–B, G = 0.2 mm; C, E–F = 0.1 mm; D = 0.02 mm; H = 0.05 mm.
Fig. 7 in Review of the genus Caucaseuma Strasser, 1970, with the description of a new cavernicolous species from the Western Caucasus and an updated key and distribution (Diplopoda, Chordeumatida, Anthroleucosomatidae)
Fig. 7. Anterior gonopods in the genus Caucaseuma Strasser, 1970, anterior views. A. C. elephantum Antić & Makarov, 2016. B. C. fanagoriyskaya Antić & Makarov, 2016. C. C. minellii Antić & Makarov, 2016. D. C. glabroscutum Antić & Makarov, 2016. E. C. kelasuri Antić & Makarov, 2016. F. C. lohmanderi Strasser, 1970. G. C. variabile Antić & Makarov, 2016. H. C. strasseri Antić sp. nov., paratype, ♂ (IZB). A–E, G after Antić & Makarov (2016), F after Strasser (1970). Scale bar = 0.3 mm for all, except for F (not to scale).
Fig. 6 in Review of the genus Caucaseuma Strasser, 1970, with the description of a new cavernicolous species from the Western Caucasus and an updated key and distribution (Diplopoda, Chordeumatida, Anthroleucosomatidae)
Fig. 6. Caucaseuma strasseri Antić sp. nov., paratype, ♂ (NHMW MY10260). SEM images of gonopods. A–F. Anterior gonopods, anterior, posterolateral, anterodistolateral, posterior, lateral and distal views, respectively. G–I. Posterior gonopods, anterior, lateral and posterior views, respectively. Abbreviations: A = angiocoxite; aA = anterior part of angiocoxite; bp = bone-like process of angiocoxite; Cv = coxal vesicle; Cx = coxite; pA = posterior part of angiocoxite; pp = posterior projection of angiocoxite; S = gonopodal sternum; Sp = sternal process; T = telopodite; tp = triangular process of angiocoxite. Scale bars = 0.1 mm.
Fig. 5 in Review of the genus Caucaseuma Strasser, 1970, with the description of a new cavernicolous species from the Western Caucasus and an updated key and distribution (Diplopoda, Chordeumatida, Anthroleucosomatidae)
Fig. 5. Caucaseuma strasseri Antić sp. nov., paratype, ♂ (IZB). Gonopods. A–C. Anterior gonopods, anterior, posterior and lateral views, respectively. D. Posterior gonopods, posterior view. Abbreviations: A = angiocoxite; aA = anterior part of angiocoxite; bp = bone-like process of angiocoxite; Cv = coxal vesicle; Cx = coxite; pA = posterior part of angiocoxite; pp = posterior projection of angiocoxite; S = gonopodal sternum; Sp = sternal process; T = telopodite; tp = triangular process of angiocoxite. Scale bar = 0.3 mm.
Fig. 7 in A new species of Acantholeberis (Crustacea, Branchiopoda) suggests an ancient geographic distribution of the genus in South America
Fig. 7. Acantholeberis accolismaris Sousa, Elmoor-Loureiro & Álvarez-Silva sp. nov., parthenogenetic female from Jurubatiba National Park.A–C. First limb. A. Stiff seta on first endite. B. Stiff setae on second and third endites. C. Setae d–f on endite 2. D–G. Second limb. D. Partial view of the endite. E. Scraper 8. F–G. Gnathobase, arrow shows the position of fourth element. H–K. Third limb. H–I. Exopodite. J–K. Distal endite, arrows indicate the sensilla. L–N. Fourth limb. L. General view. M. Exopodite. N. Endite and gnathobase. O–P. Fifth limb. Q. Sixth limb. Scale bars: 10 µm.
Fig. 6 in A new species of Acantholeberis (Crustacea, Branchiopoda) suggests an ancient geographic distribution of the genus in South America
Fig. 6. Acantholeberis accolismaris Sousa, Elmoor-Loureiro & Álvarez-Silva sp. nov., parthenogenetic female from Jurubatiba National Park. A. Valve. B–F. Close view of selected regions of valve margin, as defined in subfigure A (arrows indicate some fine details). G–H. Carapace ventral view. I. Postabdomen. J. Postabdominal claw. K. Basal spines of the claw (arrow). Scale bars: A, G, I = 100 µm; B–F, H, J–K = 10 µm.
Fig. 2 in A new species of Acantholeberis (Crustacea, Branchiopoda) suggests an ancient geographic distribution of the genus in South America
Fig. 2. Acantholeberis accolismaris Sousa, Elmoor-Loureiro & Álvarez-Silva sp. nov., parthenogenetic female from the Planície Costeira, Rio Grande do Sul (FDRS0696). A. Habitus, holotype. B–C. Lateral view, shape variation of carapace. D. Ventral view. E. Dorsal view. F. Outwardly directed setae. G. Plumose and ventrally directed setae. H. Horn-like outgrowth anterior to labrum. I. Dorsal organ. J. Antennule. K. Idem, inner surface. L. Antenna. M. Idem, first segment of exopodite, distal outer spine and inner sensory seta on basal segment. N. Idem, lateral seta of first segment of endopodite, armature detail. O–P. Morphology of apical setae of endopodite and exopodite. Q. Maxilla. R. Idem, short crown-like seta.
Fig. 4 in A new species of Acantholeberis (Crustacea, Branchiopoda) suggests an ancient geographic distribution of the genus in South America
Fig. 4. Acantholeberis accolismaris Sousa, Elmoor-Loureiro & Álvarez-Silva sp. nov., parthenogenetic female from the Planície Costeira, Rio Grande do Sul. A. Lateral view of postabdomen. B. Apical view of postabdomen. C. Basal spines variation. D. Detail of postabdominal setae showing difference between proximal and distal segment.
Fig. 1 in A new species of Acantholeberis (Crustacea, Branchiopoda) suggests an ancient geographic distribution of the genus in South America
Fig. 1. Geographic distribution of species of Acantholeberis Lilljeborg, 1853 in South America. Black triangles represent previous records of A. smirnovi Paggi & Herrera-Martinez, 2020 (Paggi & Herrera-Martinez 2020). Asterisks show records of A. accolismaris Sousa, Elmoor-Loureiro & Álvarez-Silva sp. nov.
Fig. 18 in Taxonomy and distribution of recent species of the subfamily Nodosariinae (Foraminifera) in Icelandic waters
Fig. 18. Kernel density smoothed distribution (violin plots) of species along physical gradients. A. Depth (m). B. Temperature (°C). C. Salinity (ppt); omitted are two outlier salinity records for N. haliensis Eiland & Guðmundsson, 2004 (33.98 and 34.34 ppt) and one for P. subannulata (Cushman, 1923) (34.3 ppt). Based on BIOICE samples of the RP-epibenthic and detritus sledges.
Fig. 22 in Taxonomy and distribution of recent species of the subfamily Nodosariinae (Foraminifera) in Icelandic waters
Fig. 22. Results of non-metric multidimensional scaling (NMDS) ordination of community composition based on Bray-Curtis dissimilarities on presence/absence data from BIOICE samples of the RPepibenthic and detritus sledges. A. Ordination plot showing site scores as grey dots and species positions in red. Level curves for depth (pale blue) are fitted to NMDS scores with a generalized additive model (vegan function ordisurf). Blue vectors represent linear effects of environmental gradients in latitude, longitude, and depth fitted with the vegan function envfit. B. Map of Icelandic waters with sites colour coded by their scores on the first NMDS axis.
Fig. 21 in Taxonomy and distribution of recent species of the subfamily Nodosariinae (Foraminifera) in Icelandic waters
Fig. 21. Species distributions based on BIOICE samples (Icelandic waters). A. Dentalina filiformis (d'Orbigny, 1826). B. Grigelis pyrula (d'Orbigny, 1826).
Fig. 15 in Taxonomy and distribution of recent species of the subfamily Nodosariinae (Foraminifera) in Icelandic waters
Fig. 15. Pseudonodosaria subannulata (Cushman, 1923). A, C. Megalosphere (BIOICE 2869, IIHN40054), specimen with nearly horizontal sutures (A) and aperture (C). B, D. Megalosphere (BIOICE 2856, IIHN40017), specimen with irregularly slanted sutures (B) and aperture (D). Light source combination of incident light and dark field. Scale bars = 0.25 mm.
Fig. 12. Nodosaria subsoluta Cushman, 1923. A in Taxonomy and distribution of recent species of the subfamily Nodosariinae (Foraminifera) in Icelandic waters
Fig. 12. Nodosaria subsoluta Cushman, 1923. A. Megalosphere side view (BIOICE 2868, IINH 40218). B, D. Microsphere (BIOICE 2219, IINH 40182), side view of specimen containing cytoplasm (B) and aperture (D). C, E. Megalosphere (BIOICE 2978, IINH 40347), side view (C) and aperture (E) stained with indigo blue. Light source combination of incident light and dark field. Scale bars = 0.25 mm.
Fig. 9 in Taxonomy and distribution of recent species of the subfamily Nodosariinae (Foraminifera) in Icelandic waters
Fig. 9. Grigelis semirugosus? (d'Orbigny, 1846). A. Specimen with missing initial end (BIOICE 3669, IINH 40403). B. Detail of aperture (BIOICE 2465, IINH 40402). Light source combination of incident light and dark field. Scale bars = 0.25 mm.
Fig. 11 in Taxonomy and distribution of recent species of the subfamily Nodosariinae (Foraminifera) in Icelandic waters
Fig. 11. Grigelis guttifera (d'Orbigny, 1846) comb. nov. A. Microsphere (BIOICE 3522, IINH 40126), base of chambers hispid; initial end missing. B, D. Megalosphere (BIOICE 2708, IINH 40107), side view, base of chambers mostly with knobs and faint striations (A) and aperture (D) stained with indigo blue. C. Megalosphere (BIOICE 2853, IINH 40236b), base of chambers hispid and with knobs. E. Megalosphere (BIOICE 3166, IINH 40116), aperture stained with indigo blue. Light source combination of incident light and dark field. Scale bars = 0.25 mm.
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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.