English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The difficulty to measure or predict species community composition at finespatio-temporal resolution and over large spatial scales severely hampers ourability to understand species assemblages and take appropriate conservationmeasures. Despite the progress in species distribution modeling (SDM) over thepast decades, SDM have just begun to integrate high resolution remote sensingdata and their predictions are still entailed by many biases due toheterogeneity of the available biodiversity observations, most oftenopportunistic presence only data. We designed a European scale dataset coveringaround ten thousand plant species to calibrate and evaluate SDM predictions ofspecies composition in space and time at high spatial resolution (~ten meters),and their spatial transferability. For model training, we extracted andharmonized five million heterogeneous presence-only records from selected GBIFdatasets and 6 thousand exhaustive presence-absence surveys both sampled during2017-2021. We associated species observations to diverse environmental rastersclassically used in SDMs, as well as to 10 m resolution RGB and Near-Infra-Redsatellite images and 20 years-time series of climatic variables and satellitepoint values. The evaluation dataset is based on 22 thousand standardizedpresence-absence surveys separated from the training set with a spatial blockhold out procedure. The GeoLifeCLEF 2023 dataset is open access and the firstbenchmark for researchers aiming to improve the prediction of plant speciescomposition at a very fine spatial grain and at continental scale. It is aspace to explore new ways of combining massive and diverse species observationsand environmental information at various scales. Innovative AI-basedapproaches, in particular, should be among the most interesting methods toexperiment with on the GeoLifeCLEF 2023 dataset.

The GeoLifeCLEF 2023 Dataset to evaluate plant species distribution models at high spatial resolution across Europe