Assessing and improving the transferability of current global spatial prediction models
Published 2023 View Full Article
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Title
Assessing and improving the transferability of current global spatial prediction models
Authors
Keywords
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Journal
GLOBAL ECOLOGY AND BIOGEOGRAPHY
Volume -, Issue -, Pages -
Publisher
Wiley
Online
2023-01-26
DOI
10.1111/geb.13635
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- (2022) Hanna Meyer et al. Nature Communications
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- (2021) Zander S. Venter et al. Remote Sensing
- National climate and biodiversity strategies are hamstrung by a lack of maps
- (2021) Guido Schmidt-Traub Nature Ecology & Evolution
- The global distribution and environmental drivers of aboveground versus belowground plant biomass
- (2021) Haozhi Ma et al. Nature Ecology & Evolution
- Conservation needs to break free from global priority mapping
- (2021) Carina Wyborn et al. Nature Ecology & Evolution
- Areas of global importance for conserving terrestrial biodiversity, carbon and water
- (2021) Martin Jung et al. Nature Ecology & Evolution
- Spatial cross-validation is not the right way to evaluate map accuracy
- (2021) Alexandre M.J.-C. Wadoux et al. ECOLOGICAL MODELLING
- Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
- (2021) Nico Lang et al. REMOTE SENSING OF ENVIRONMENT
- The current and future uses of machine learning in ecosystem service research
- (2021) Matthew Scowen et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance
- (2020) Kenneth D. Roe et al. PLoS One
- Copernicus Global Land Cover Layers—Collection 2
- (2020) Marcel Buchhorn et al. Remote Sensing
- Spatial validation reveals poor predictive performance of large-scale ecological mapping models
- (2020) Pierre Ploton et al. Nature Communications
- Soil nematode abundance and functional group composition at a global scale
- (2019) Johan van den Hoogen et al. NATURE
- The global tree restoration potential
- (2019) Jean-Francois Bastin et al. SCIENCE
- Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data
- (2019) Patrick Schratz et al. ECOLOGICAL MODELLING
- Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction
- (2019) Hanna Meyer et al. ECOLOGICAL MODELLING
- Comment on “The global tree restoration potential”
- (2019) Joseph W. Veldman et al. SCIENCE
- Comment on “The global tree restoration potential”
- (2019) Andrew K. Skidmore et al. SCIENCE
- TRY plant trait database – enhanced coverage and open access
- (2019) Jens Kattge et al. GLOBAL CHANGE BIOLOGY
- An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems
- (2019) Roger Sayre et al. Global Ecology and Conservation
- Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation
- (2018) Hanna Meyer et al. ENVIRONMENTAL MODELLING & SOFTWARE
- A methodology to derive global maps of leaf traits using remote sensing and climate data
- (2018) Álvaro Moreno-Martínez et al. REMOTE SENSING OF ENVIRONMENT
- Outstanding Challenges in the Transferability of Ecological Models
- (2018) Katherine L. Yates et al. TRENDS IN ECOLOGY & EVOLUTION
- Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review
- (2018) Jacinta Holloway et al. Remote Sensing
- block CV: An r package for generating spatially or environmentally separated folds for k -fold cross-validation of species distribution models
- (2018) Roozbeh Valavi et al. Methods in Ecology and Evolution
- WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas
- (2017) Stephen E. Fick et al. INTERNATIONAL JOURNAL OF CLIMATOLOGY
- ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
- (2017) Marvin N. Wright et al. Journal of Statistical Software
- Quantifying Forest Biomass Carbon Stocks From Space
- (2017) Pedro Rodríguez-Veiga et al. Current Forestry Reports
- SoilGrids250m: Global gridded soil information based on machine learning
- (2017) Tomislav Hengl et al. PLoS One
- Machine learning in geosciences and remote sensing
- (2016) David J. Lary et al. Geoscience Frontiers
- Building Predictive Models inRUsing thecaretPackage
- (2015) Max Kuhn Journal of Statistical Software
- What do we gain from simplicity versus complexity in species distribution models?
- (2014) Cory Merow et al. ECOGRAPHY
- Spatial leave-one-out cross-validation for variable selection in the presence of spatial autocorrelation
- (2014) Kévin Le Rest et al. GLOBAL ECOLOGY AND BIOGEOGRAPHY
- Spatial bias in the GBIF database and its effect on modeling species' geographic distributions
- (2013) Jan Beck et al. Ecological Informatics
- Mapping where ecologists work: biases in the global distribution of terrestrial ecological observations
- (2012) Laura J Martin et al. FRONTIERS IN ECOLOGY AND THE ENVIRONMENT
- Assessing transferability of ecological models: an underappreciated aspect of statistical validation
- (2012) Seth J. Wenger et al. Methods in Ecology and Evolution
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