GBIF Occurrence Download

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GBIF biodiversity species occurrences

DOI：

doi:10.15468/dl.ds614y

摘要：

A dataset containing 867 species occurrences available in GBIF matching the query: TaxonKey: Merops apiaster Linnaeus, 1758 Geometry: POLYGON((2 46

Data from: A full annual perspective on sex-biased migration timing in long-distance migratory birds

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long-distance migrant;Ficedula albicollis;Ficedula hypoleuca;Merops apiaster;Upupa epops;Lanius collurio;annual cycle;sexual selection;Apus apus;Acrocephalus arundinaceus;Ficedula semitorquata;Hirundo rustica;protandry;Cuculus canorus;Coracias garrulus;Tachymarptis melba;Oenanthe oenanthe;Riparia riparia;migration phenology;geolocator

DOI：

doi:10.5061/dryad.t78400r

摘要：

In many taxa, the most common form of sex-biased migration timing is protandry – the earlier arrival of males at breeding areas. Here we te

Data from: Mapping beta diversity from space: Sparse Generalized Dissimilarity Modelling (SGDM) for analysing high-dimensional data

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Merops apiaster;Luscinia megarhynchos;Galerida sp.;Cuculus canorus;Anthus campestris;Sylvia cantillans;Carduelis canabinna;Sturnus unicolor;Lanius meridionalis;Passer hispaniolensis;Cisticola juncidis;Tetrax tetrax;Upupa epops;Pica pica;Remote sensing;time series;EnMAP;Turdus merula;Falco naumanni;Cyanopica cookii;Gelochelidon nilotica;Clamator glandarius;Ciconia ciconia;Carduelis carduelis;Circus pygargus;Sylvia melanocephala;sparse canonical correlation analysis;Elanus caeruleus;Circus aeruginosus;hyperspectral data;Melanocorypha calandra;Falco tinnunculus;Lanius senator;Serinus serinus;Bubulcus ibis;Athene noctua;turnover;Miliaria calandra;Alectoris rufa;Saxicola torquata;biodiversity;Landsat;generalised dissimilarity modelling;Sylvia undata;Calandrella brachydactyla;Otis tarda;Columba palumbus;Coturnix coturnix;Picus viridis;community modelling;Burhinus oedicnemus;Oenanthe hispanica

DOI：

doi:10.5061/dryad.ns7pv

摘要：

ies of Landsat data as well as simulated EnMAP hyperspectral data. 3. The proposed approach always outperformed GDM models when fit on high-dimensional data