Multispecies deep learning using citizen science data produces more informative plant community models.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
24 May 2024
24 May 2024
Historique:
received:
09
05
2023
accepted:
03
05
2024
medline:
25
5
2024
pubmed:
25
5
2024
entrez:
24
5
2024
Statut:
epublish
Résumé
In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. Here, we map fine-grained spatiotemporal distributions for thousands of species, using deep neural networks (DNNs) and ubiquitous citizen science data. Based on 6.7 M observations, we jointly model the distributions of 2477 plant species and species aggregates across Switzerland with an ensemble of DNNs built with different cost functions. We find that, compared to commonly-used approaches, multispecies DNNs predict species distributions and especially community composition more accurately. Moreover, their design allows investigation of understudied aspects of ecology. Including seasonal variations of observation probability explicitly allows approximating flowering phenology; reweighting predictions to mirror cover-abundance allows mapping potentially canopy-dominant tree species nationwide; and projecting DNNs into the future allows assessing how distributions, phenology, and dominance may change. Given their skill and their versatility, multispecies DNNs can refine our understanding of the distribution of plants and well-sampled taxa in general.
Identifiants
pubmed: 38789424
doi: 10.1038/s41467-024-48559-9
pii: 10.1038/s41467-024-48559-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4421Subventions
Organisme : Swiss National Science Foundation | National Center of Competence in Research Affective Sciences - Emotions in Individual Behaviour and Social Processes (National Centre of Competence in Research Affective Sciences)
ID : 20BD21_193907
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
ID : 20BD21_193907
Informations de copyright
© 2024. The Author(s).
Références
Wüest, R. O. et al. Macroecology in the age of Big Data – Where to go from here? J. Biogeogr. jbi.13633, https://doi.org/10.1111/jbi.13633 (2019).
Waller, J. Will citizen science take over? GBIF Data Blog https://data-blog.gbif.org/post/gbif-citizen-science-data/ (2021).
Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Conserv. 213, 280–294 (2017).
doi: 10.1016/j.biocon.2016.09.004
Isaac, N. J. B., Strien, A. J., August, T. A., Zeeuw, M. P. & Roy, D. B. Statistics for citizen science: extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060 (2014).
doi: 10.1111/2041-210X.12254
Díaz, S. et al. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. https://doi.org/10.5281/zenodo.3553579 (2019).
Isaac, N. J. B. & Pocock, M. J. O. Bias and information in biological records. Biol. J. Linn. Soc. 115, 522–531 (2015).
doi: 10.1111/bij.12532
Mair, L. & Ruete, A. Explaining Spatial Variation in the Recording Effort of Citizen Science Data across Multiple Taxa. PLoS One 11, e0147796 (2016).
pubmed: 26820846
pmcid: 4731209
doi: 10.1371/journal.pone.0147796
Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R. & Legendre, F. Taxonomic bias in biodiversity data and societal preferences. Sci. Rep. 7, 9132 (2017).
pubmed: 28831097
pmcid: 5567328
doi: 10.1038/s41598-017-09084-6
Pagel, J. et al. Quantifying range-wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records. Methods Ecol. Evol. 5, 751–760 (2014).
doi: 10.1111/2041-210X.12221
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).
pubmed: 30746437
pmcid: 6357756
doi: 10.1126/sciadv.aat4858
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Modell. 135, 147–186 (2000).
doi: 10.1016/S0304-3800(00)00354-9
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).
pubmed: 19323182
doi: 10.1890/07-2153.1
Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Modell. 275, 73–77 (2014).
doi: 10.1016/j.ecolmodel.2013.12.012
Chauvier, Y. et al. Novel methods to correct for observer and sampling bias in presence‐only species distribution models. Glob. Ecol. Biogeogr. 30, 2312–2325 (2021).
doi: 10.1111/geb.13383
Botella, C., Joly, A., Monestiez, P., Bonnet, P. & Munoz, F. Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection. PLoS One 15, e0232078 (2020).
pubmed: 32433677
pmcid: 7239389
doi: 10.1371/journal.pone.0232078
Descombes, P. et al. Strategies for sampling pseudo-absences for species distribution models in complex mountainous terrain. Preprint at https://doi.org/10.1101/2022.03.24.485693 (2022).
Alzubaidi, L. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 53 (2021).
pubmed: 33816053
pmcid: 8010506
doi: 10.1186/s40537-021-00444-8
Rademaker, M., Hogeweg, L. & Vos, R. Modelling the niches of wild and domesticated Ungulate species using deep learning. Preprint at https://doi.org/10.1101/744441 (2019).
de Lutio, R. et al. Digital taxonomist: Identifying plant species in community scientists’ photographs. ISPRS J. Photogramm. Remote Sens. 182, 112–121 (2021).
doi: 10.1016/j.isprsjprs.2021.10.002
Aodha, O. Mac, Cole, E. & Perona, P. Presence-Only Geographical Priors for Fine-Grained Image Classification. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV) 9595–9605, https://doi.org/10.1109/ICCV.2019.00969 (2019).
Estopinan, J., Servajean, M., Bonnet, P., Munoz, F. & Joly, A. Deep Species Distribution Modeling From Sentinel-2 Image Time-Series: A Global Scale Analysis on the Orchid Family. Front. Plant Sci. 13, 839327 (2022).
Deneu, B. et al. Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLOS Comput. Biol. 17, e1008856 (2021).
pubmed: 33872302
pmcid: 8084334
doi: 10.1371/journal.pcbi.1008856
Botella, C., Joly, A., Bonnet, P., Monestiez, P. & Munoz, F. A Deep Learning Approach to Species Distribution Modelling. In Multimedia Tools and Applications for Environmental & Biodiversity Informatics 169–199 (Springer International Publishing, 2018) https://doi.org/10.1007/978-3-319-76445-0_10 .
Rew, J., Cho, Y. & Hwang, E. A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks. Remote Sens. 13, 1495 (2021).
doi: 10.3390/rs13081495
Fithian, W., Elith, J., Hastie, T. & Keith, D. A. Bias correction in species distribution models: pooling survey and collection data for multiple species. Methods Ecol. Evol. 6, 424–438 (2015).
pubmed: 27840673
doi: 10.1111/2041-210X.12242
Botella, C. et al. The GeoLifeCLEF 2023 dataset to evaluate plant species distribution models at high spatial resolution across Europe. Preprint at arXiv https://doi.org/10.48550/arXiv.2308.05121 (2023).
Good, I. J. Rational Decisions. J. R. Stat. Soc. Ser. B 14, 107–114 (1952).
doi: 10.1111/j.2517-6161.1952.tb00104.x
Gneiting, T. & Raftery, A. E. Strictly Proper Scoring Rules, Prediction, and Estimation. J. Am. Stat. Assoc. 102, 359–378 (2007).
doi: 10.1198/016214506000001437
Cole, E. et al. Multi-label learning from single positive labels. Preprint at arXiv https://doi.org/10.48550/arXiv.2106.09708 (2021).
Clémençon, S., Robbiano, S. & Vayatis, N. Ranking data with ordinal labels: optimality and pairwise aggregation. Mach. Learn. 91, 67–104 (2013).
doi: 10.1007/s10994-012-5325-4
Werner, T. A review on instance ranking problems in statistical learning. Mach. Learn. 111, 415–463 (2022).
doi: 10.1007/s10994-021-06122-3
Järvelin, K. & Kekäläinen, J. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20, 422–446 (2002).
doi: 10.1145/582415.582418
Wang, N. et al. Rank4Class: a ranking formulation for multiclass classification. Preprint at arXiv https://doi.org/10.48550/arXiv.2112.09727 (2022).
Holm, S. A Simple Sequentially Rejective Multiple Test Procedure. Scand. J. Stat. 6, 65–70 (1979).
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).
pubmed: 3287615
doi: 10.1126/science.3287615
Tang, J. et al. Emerging opportunities and challenges in phenology: a review. Ecosphere 7, e01436 (2016).
Basler, D. Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe. Agric. Meteorol. 217, 10–21 (2016).
doi: 10.1016/j.agrformet.2015.11.007
Welle, T., Aschenbrenner, L., Kuonath, K., Kirmaier, S. & Franke, J. Mapping Dominant Tree Species of German Forests. Remote Sens. 14, 3330 (2022).
doi: 10.3390/rs14143330
Braun-Blanquet, J. Über den Deckungswert der Arten in den Pflanzengesellschaften der Ordnung Vaccinio-Piceetalia. Jahresber. Naturforschenden Ges. Graubündens 130, 115–119 (1946).
Wohlgemuth, T. Swiss Forest Vegetation Database. Biodivers. Ecol. 4, 340–340 (2012).
doi: 10.7809/b-e.00131
Brändli, U.-B., Abegg, M. & Allgaier Leuch, B. Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009–2017. https://doi.org/10.16904/envidat.146 (2020).
van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).
doi: 10.1007/s10584-011-0148-z
Dipner, M. et al. Trockenwiesen und -weiden von nationaler Bedeutung. Vollzugshilfe zur Trockenwiesenverordnung. Umwelt-Vollzug (2010).
Chen, D., Xue, Y., Chen, S., Fink, D. & Gomes, C. Deep multi-species embedding. Preprint at arXiv https://doi.org/10.48550/arXiv.1609.09353 (2017).
Lorieul, T., Cole, E., Servajean, M., Bonnet, P. & Joly, A. Overview of GeoLifeCLEF 2022: predicting species presence from multi-modal remote sensing, bioclimatic and pedologic data. In CLEF 2022 - Working Notes of the Conference and Labs of the Evaluation Forum, 1940-1956 (CLEF, 2022).
Carlisle, D. Phenological and Cultural Studies of Common Dandelion (Taraxacum officinale Weber) (Western Kentucky University, 1973).
Rutishauser, T., Luterbacher, J., Jeanneret, F., Pfister, C. & Wanner, H. A phenology‐based reconstruction of interannual changes in past spring seasons. J. Geophys. Res. Biogeosci. 112, G04016 (2007).
doi: 10.1029/2006JG000382
Thuiller, W., Brotons, L., Araújo, M. B. & Lavorel, S. Effects of restricting environmental range of data to project current and future species distributions. Ecography 27, 165–172 (2004).
doi: 10.1111/j.0906-7590.2004.03673.x
Katal, N., Rzanny, M., Mäder, P. & Wäldchen, J. Deep Learning in Plant Phenological Research: A Systematic Literature Review. Front. Plant Sci. 13, 805738 (2022).
Yamamoto, S. & Sota, T. Incipient allochronic speciation by climatic disruption of the reproductive period. Proc. R. Soc. B Biol. Sci. 276, 2711–2719 (2009).
doi: 10.1098/rspb.2009.0349
Fisogni, A. et al. Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient. Landsc. Urban Plan. 226, 104512 (2022).
doi: 10.1016/j.landurbplan.2022.104512
Wüest, R. O., Bergamini, A., Bollmann, K., Brändli, U.-B. & Baltensweiler, A. Modellierte Verbreitungskarten für die häufigsten Gehölzarten der Schweiz. Schweizerische Z. fur Forstwes. 172, 226–233 (2021).
doi: 10.3188/szf.2021.0226
Waser, L., Ginzler, C. & Rehush, N. Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys. Remote Sens. 9, 766 (2017).
doi: 10.3390/rs9080766
Scherrer, D. et al. Low naturalness of Swiss broadleaf forests increases their susceptibility to disturbances. Ecol. Manag. 532, 120827 (2023).
doi: 10.1016/j.foreco.2023.120827
Ellenberg, H. H. Vegetation Ecology of Central Europe (Cambridge University Press, 1988).
Booth, T. H. Species distribution modelling tools and databases to assist managing forests under climate change. Ecol. Manag. 430, 196–203 (2018).
doi: 10.1016/j.foreco.2018.08.019
Chang, J. et al. Future productivity and phenology changes in European grasslands for different warming levels: implications for grassland management and carbon balance. Carbon Balance Manag. 12, 11 (2017).
pubmed: 28474332
pmcid: 5418182
doi: 10.1186/s13021-017-0079-8
Zettlemoyer, M. A. & Peterson, M. L. Does phenological plasticity help or hinder range shifts under climate change? Front. Ecol. Evol. 9, 689192 (2021).
Gérard, M., Vanderplanck, M., Wood, T. & Michez, D. Global warming and plant–pollinator mismatches. Emerg. Top. Life Sci. 4, 77–86 (2020).
pubmed: 32558904
pmcid: 7326340
doi: 10.1042/ETLS20190139
Elith, J. & Leathwick, J. R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
doi: 10.1146/annurev.ecolsys.110308.120159
Sánchez-Fernández, D., Lobo, J. M. & Hernández-Manrique, O. L. Species distribution models that do not incorporate global data misrepresent potential distributions: a case study using Iberian diving beetles. Divers. Distrib. 17, 163–171 (2011).
doi: 10.1111/j.1472-4642.2010.00716.x
Scherrer, D., Esperon‐Rodriguez, M., Beaumont, L. J., Barradas, V. L. & Guisan, A. National assessments of species vulnerability to climate change strongly depend on selected data sources. Divers. Distrib. 27, 1367–1382 (2021).
doi: 10.1111/ddi.13275
Shen, X. & Meinshausen, N. Engression: extrapolation for nonlinear regression? Preprint at arXiv https://doi.org/10.48550/arXiv.2307.00835 (2023).
Brun, P. et al. Model complexity affects species distribution projections under climate change. J. Biogeogr. 47, 130–142 (2020).
doi: 10.1111/jbi.13734
R Development Core Team. R: A Language and Environment for Statistical Computing. http://www.r-project.org (2008).
Couture-Beil, A. rjson: JSON for R. https://cran.r-project.org/package=rjson (2022).
Münkemüller, T. et al. Scale decisions can reverse conclusions on community assembly processes. Glob. Ecol. Biogeogr. 23, 620–632 (2014).
pubmed: 24791149
pmcid: 4001086
doi: 10.1111/geb.12137
Delarze, R., Gonseth, Y., Eggenberg, S. & Vust, M. Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten. (Ott Verlag, 2015).
Hintermann, U., Weber, D. & Zangger, A. Biodiversity monitoring in Switzerland. Schriftenr. Landschaftspfl. und Naturschutz 62, 47–58 (2000).
Descombes, P. et al. Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscape. Ecography https://doi.org/10.1111/ecog.00119 (2020).
Wüest, R. O., Bergamini, A., Bollmann, K. & Baltensweiler, A. LiDAR data as a proxy for light availability improve distribution modelling of woody species. For. Ecol. Manag. 456, 117644 (2020).
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
doi: 10.1002/qj.3803
Robinson, N., Regetz, J. & Guralnick, R. P. EarthEnv-DEM90: A nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data. ISPRS J. Photogramm. Remote Sens. 87, 57–67 (2014).
doi: 10.1016/j.isprsjprs.2013.11.002
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).
pubmed: 28872642
pmcid: 5584396
doi: 10.1038/sdata.2017.122
Ginzler, C. & Hobi, M. Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory. Remote Sens. 7, 4343–4370 (2015).
doi: 10.3390/rs70404343
Drusch, M. et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens. Environ. 120, 25–36 (2012).
doi: 10.1016/j.rse.2011.11.026
Chatenoux, B., Giuliani, G. & Rodila, D. Enhanced Vegetation Index (EVI) - Switzerland [2018, Sentinel-2]. https://doi.org/10.26037/yareta:hapbjzl6dvbwnb5modewqozbfm (2022).
Chatenoux, B., Giuliani, G. & Rodila, D. Enhanced Vegetation Index (EVI) - Switzerland [2019, Sentinel-2]. https://doi.org/10.26037/yareta:tilf3ibfnrafjpj6xpnea3vhpm (2022).
Chatenoux, B., Giuliani, G. & Rodila, D. Enhanced Vegetation Index (EVI) - Switzerland [2020, Sentinel-2]. https://doi.org/10.26037/yareta:of5ddowrxvbtjjurioduueopey (2022).
Chatenoux, B., Giuliani, G. & Rodila, D. Enhanced Vegetation Index (EVI) - Switzerland [2021, Sentinel-2]. https://doi.org/10.26037/yareta:hgw56omleveiplgftnd5ugwpja (2022).
Chatenoux, B. et al. The Swiss data cube, analysis ready data archive using earth observations of Switzerland. Sci. Data 8, 295 (2021).
pubmed: 34750391
pmcid: 8575969
doi: 10.1038/s41597-021-01076-6
Giuliani, G., Rodila, D., Külling, N., Maggini, R. & Lehmann, A. Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System. Land 11, 615 (2022).
doi: 10.3390/land11050615
Broennimann, O. CHclim25 - sum of precipitation (Prec). https://doi.org/10.5281/zenodo.7868382 (2023).
Broennimann, O. CHclim25 - average temperature (Tave). https://doi.org/10.5281/zenodo.7859251 (2023).
Külling, N. et al. SWECO25: a cross-thematic raster database for ecological research in Switzerland. Sci. Data 11, 21 (2024).
pubmed: 38172116
pmcid: 10764791
doi: 10.1038/s41597-023-02899-1
Conrad, O. et al. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci. Model Dev. 8, 1991–2007 (2015).
doi: 10.5194/gmd-8-1991-2015
Hijmans, R. J. terra: Spatial Data Analysis. https://cran.r-project.org/package=terra (2022).
Bottou, L. Stochastic Gradient Descent Tricks. 421–436. https://doi.org/10.1007/978-3-642-35289-8_25 . (2012).
Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).
Paszke, A. et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32, 8024–8035 (Curran Associates, Inc., 2019).
Jagerman, R. & de Rijke, M. Accelerated Convergence for Counterfactual Learning to Rank. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Association for Computing Machinery, 2020) https://doi.org/10.1145/3397271.3401069 .
pandas development team, T. pandas-dev/pandas: Pandas. https://doi.org/10.5281/zenodo.3509134 (2020).
Harris et al. Array programming with NumPy. Nature 585, 357–362 (2020).
pubmed: 32939066
pmcid: 7759461
doi: 10.1038/s41586-020-2649-2
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).
pubmed: 17011070
doi: 10.1016/j.tree.2006.09.010
McCullagh, P. Generalized linear models. Eur. J. Oper. Res. 16, 285–292 (1984).
doi: 10.1016/0377-2217(84)90282-0
Hastie, T. J. & Tibshirani, R. J. Generalized additive models (Chapman & Hall/CRC, 1990).
Friedman, J. H. Greedy Function Approximation: A Gradient Boosting Machine. Ann. Stat. 29, 1189–1232 (2001).
doi: 10.1214/aos/1013203451
Ridgeway, G. The State of Boosting. Comput. Sci. Stat. 31, 172–181 (1999).
Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001).
doi: 10.1023/A:1010933404324
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).
doi: 10.1016/j.ecolmodel.2005.03.026
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol. Evol. 3, 327–338 (2012).
doi: 10.1111/j.2041-210X.2011.00172.x
Wisz, M. S. & Guisan, A. Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data. BMC Ecol. 9, 8 (2009).
pubmed: 19393082
pmcid: 2680809
doi: 10.1186/1472-6785-9-8
Liu, C., Newell, G. & White, M. The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo-absences or background sites. Ecography 42, 535–548 (2019).
doi: 10.1111/ecog.03188
Wood, S. Generalized Additive Models: An Introduction with R (CRC Press, 2006).
Liaw, A. & Wiener, M. Classification and Regression by randomForest. R. N. 2, 18–22 (2002).
Greenwell, B., Boehmke, B., Cunningham, J. & Developers, G. B. M. gbm: Generalized Boosted Regression Models. https://cran.r-project.org/package=gbm (2018).
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling. https://cran.r-project.org/package=dismo (2017).
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
doi: 10.1111/j.1600-0587.2012.07348.x
Harrell, F. E. et al. Development of a clinical prediction model for an ordinal outcome. Stat. Med. 17, 909–944 (1998).
pubmed: 9595619
doi: 10.1002/(SICI)1097-0258(19980430)17:8<909::AID-SIM753>3.0.CO;2-O
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models (Cambridge University Press, 2017) https://doi.org/10.1017/9781139028271 .
Breiner, F. T., Guisan, A., Bergamini, A. & Nobis, M. P. Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol. Evol. 6, 1210–1218 (2015).
doi: 10.1111/2041-210X.12403
Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. https://cran.r-project.org/package=rstatix (2023).
Chinchor, N. MUC-4 evaluation metrics. In Proceedings of the 4th conference on Message understanding - MUC4 ’92 22 (Association for Computational Linguistics, 1992) https://doi.org/10.3115/1072064.1072067 .
CH2018. CH2018 – Climate Scenarios for Switzerland. https://www.nccs.admin.ch/nccs/de/home/klimawandel-und-auswirkungen/schweizer-klimaszenarien.html (2018).
Cleveland, W. S., Grosse, E. & Shyu, W. M. Local regression models. In Statistical Models In S (eds. Chambers, J. M. & Hastie, T. J.) (Wadsworth & Brooks/Cole, 1992).
Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21, 7881 (2005).
doi: 10.1093/bioinformatics/bti623
Ushey, K., Allaire, J. J. & Tang, Y. reticulate: Interface to ‘Python’. https://rstudio.github.io/reticulate/ (2024).
Neuwirth, E. RColorBrewer: ColorBrewer Palettes. https://cran.r-project.org/package=RColorBrewer (2022).
Ooms, J. magick: Advanced Graphics and Image-Processing in R. https://cran.r-project.org/package=magick (2020).
Brun, P. et al. Multispecies deep learning using citizen science data produces more informative plant community models. Zenodo https://doi.org/10.5281/zenodo.10869585 (2024).