Repository with annotations from the OBIS/VLIZ SDMs study group
Qiao, H., Feng, X., Escobar, L.E., Peterson, A.T., Soberón, J., Zhu, G. and Papeş, M. (2019), An evaluation of transferability of ecological niche models. Ecography, 42: 521-534. https://doi.org/10.1111/ecog.03986
Guerreiro, M.F., Borges, F.O., Santos, C.P. et al. Impact of climate change on the distribution and habitat suitability of the world’s main commercial squids. Mar Biol 170, 129 (2023). https://doi.org/10.1007/s00227-023-04261-w
Fernandez, M., Sillero, N., Yesson, C. (2022). To be or not to be: the role of absences in niche modelling for highly mobile species in dynamic marine environments. Ecological Modelling, 471. https://doi.org/10.1016/j.ecolmodel.2022.110040
Pennino, M. G., Paradinas, I., Illian, J. B., Muñoz, F., Bellido, J. M., López‐Quílez, A., & Conesa, D. (2018). Accounting for preferential sampling in species distribution models. In Ecology and Evolution (Vol. 9, Issue 1, pp. 653–663). Wiley. https://doi.org/10.1002/ece3.4789
El‐Gabbas, A., & Dormann, C. F. (2017). Improved species‐occurrence predictions in data‐poor regions: using large‐scale data and bias correction with down‐weighted Poisson regression and Maxent. In Ecography (Vol. 41, Issue 7, pp. 1161–1172). Wiley. https://doi.org/10.1111/ecog.03149
Varela, S., Anderson, R. P., García-Valdés, R., & Fernández-González, F. (2014). Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. Ecography, 37(11), 1084–1091. https://doi.org/10.1111/j.1600-0587.2013.00441.x
Vollering, J., Halvorsen, R., Auestad, I., & Rydgren, K. (2019). Bunching up the background betters bias in species distribution models. Ecography, 42(10), 1717–1727. https://doi.org/10.1111/ecog.04503
Gregg, W. & Casey, N. (2007) Sampling biases in MODIS SeaWiFS ocean chlorophyll data. In Remote Sensing of Environment (Vol. 111, Issue 1, pp. 25-35) https://doi.org/10.1016/j.rse.2007.03.008
Gregg, W. & Rousseaux, C. (2019) Global ocean primary production trends in the modern ocean color sattelite product (1998-2015). In Environmental Research Letters (Vol. 14, Issue 12) https://doi.org/10.1088/1748-9326/ab4667
Isaac, N. J. B., Jarzyna, M. A., Keil, P., Dambly, L. I., Boersch-Supan, P. H., Browning, E., Freeman, S. N., Golding, N., Guillera-Arroita, G., Henrys, P. A., Jarvis, S., Lahoz-Monfort, J., Pagel, J., Pescott, O. L., Schmucki, R., Simmonds, E. G., & O’Hara, R. B. (2020). Data Integration for Large-Scale Models of Species Distributions. Trends in Ecology & Evolution, 35(1), 56–67. https://doi.org/10.1016/j.tree.2019.08.006
Miller, D. A., Pacifici, K., Sanderlin, J. S., & Reich, B. J. (2019). The recent past and promising future for data integration methods to estimate species’ distributions. Methods in Ecology and Evolution, 10(1), 22-37. https://doi.org/10.1111/2041-210X.13110
de Oliveira, G., Rangel, T. F., Lima-Ribeiro, M. S., Terribile, L. C., & Diniz-Filho, J. A. F. (2014). Evaluating, partitioning, and mapping the spatial autocorrelation component in ecological niche modeling: a new approach based on environmentally equidistant records. Ecography, 37(7), 637–647. https://doi.org/10.1111/j.1600-0587.2013.00564.x
Bohl, C. L., Kass, J. M., & Anderson, R. P. (2019). A new null model approach to quantify performance and significance for ecological niche models of species distributions. In Journal of Biogeography (Vol. 46, Issue 6, pp. 1101–1111). Wiley. https://doi.org/10.1111/jbi.13573 (also available at https://www.researchgate.net/publication/333028975_A_new_null_model_approach_to_quantify_performance_and_significance_for_ecological_niche_models_of_species_distributions)
Grimmett, L., Whitsed, R., & Horta, A. (2020). Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics. Ecological Modelling, 431. https://doi.org/10.1016/j.ecolmodel.2020.109194
Valavi, R., Guillera‐Arroita, G., Lahoz‐Monfort, J. J., & Elith, J. (2022). Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code. Ecological Monographs, 92(1), e01486. https://doi.org/10.1002/ecm.1486
Brazdil, P., van Rijn, J. N., Soares, C., & Vanschoren, J. (2022). Metalearning: applications to automated machine learning and data mining (p. 346). Springer Nature. (Free e-book available at https://library.oapen.org/bitstream/handle/20.500.12657/53319/1/978-3-030-67024-5.pdf)
Cawood, P., & Van Zyl, T. (2022). Evaluating state-of-the-art, forecasting ensembles and meta-learning strategies for model fusion. Forecasting, 4(3), 732-751. https://doi.org/10.48550/arXiv.2203.03279
Shlesinger, T., & van Woesik, R. (2023). Oceanic differences in coral-bleaching responses to marine heatwaves. In Science of The Total Environment (Vol. 871, p. 162113). Elsevier BV. https://doi.org/10.1016/j.scitotenv.2023.162113
Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E. D., Robinson, B. S., Hodgson, D. J., & Inger, R. (2018). A brief introduction to mixed effects modelling and multi-model inference in ecology. In PeerJ (Vol. 6, p. e4794). PeerJ. https://doi.org/10.7717/peerj.4794
Woods, B. L., Van de Putte, A. P., Hindell, M. A., Raymond, B., Saunders, R. A., Walters, A., & Trebilco, R. (2023). Species distribution models describe spatial variability in mesopelagic fish abundance in the Southern Ocean. In Frontiers in Marine Science (Vol. 9). Frontiers Media SA. https://doi.org/10.3389/fmars.2022.981434
Morera‐Pujol, V., Mostert, P. S., Murphy, K. J., Burkitt, T., Coad, B., McMahon, B. J., ... & Ciuti, S. (2023). Bayesian species distribution models integrate presence‐only and presence–absence data to predict deer distribution and relative abundance. Ecography, 2023(2), e06451.
Monnier-Corbel, A., Robert, A., Hingrat, Y., Benito, B. M., & Monnet, A. C. (2023). Species Distribution Models predict abundance and its temporal variation in a steppe bird population. Global Ecology and Conservation, 43, e02442. https://doi.org/10.1016/j.gecco.2023.e02442
Bowler, D. E., Callaghan, C. T., Bhandari, N., Henle, K., Benjamin Barth, M., Koppitz, C., Klenke, R., Winter, M., Jansen, F., Bruelheide, H., & Bonn, A. (2022). Temporal trends in the spatial bias of species occurrence records. Ecography, 2022(8). https://doi.org/10.1111/ecog.06219
Crispim-Mendes, T., Valerio, F., Marques, A.T. et al. High-resolution species distribution modelling reveals spatio-temporal variability of habitat suitability in a declining grassland bird. Landsc Ecol 39, 49 (2024). https://doi.org/10.1007/s10980-024-01848-6
Tredennick, A. T., Hooker, G., Ellner, S. P., & Adler, P. B. (2021). A practical guide to selecting models for exploration, inference, and prediction in ecology. In Ecology (Vol. 102, Issue 6). Wiley. https://doi.org/10.1002/ecy.3336
Rew, J.; Cho, Y.; Moon, J.; Hwang, E. Habitat Suitability Estimation Using a Two-Stage Ensemble Approach. Remote Sens. 2020, 12, 1475. https://doi.org/10.3390/rs12091475
Frans, V.F., Liu, J. Gaps and opportunities in modelling human influence on species distributions in the Anthropocene. Nat Ecol Evol 8, 1365–1377 (2024). https://doi.org/10.1038/s41559-024-02435-3
Peiffer, F., Lima, A. R. A., Henriques, S., Pardal, M. A., Martinho, F., Gonçalves, J. M., ... & Silva, G. J. F. (2024). Habitat suitability of two flagship species, Hippocampus hippocampus and Hippocampus guttulatus, in the Atlantic coast of the Iberian Peninsula-implications for conservation. Global Ecology and Conservation, 53, e02993. https://doi.org/10.1016/j.gecco.2024.e02993
Lawlor, J.A., Comte, L., Grenouillet, G. et al. Mechanisms, detection and impacts of species redistributions under climate change. Nat Rev Earth Environ 5, 351–368 (2024). https://doi.org/10.1038/s43017-024-00527-z
Stuart-Smith, R.D., Edgar, G.J. & Bates, A.E. Thermal limits to the geographic distributions of shallow-water marine species. Nat Ecol Evol 1, 1846–1852 (2017). https://doi.org/10.1038/s41559-017-0353-x
Harrison XA, Donaldson L, Correa-Cano ME, Evans J, Fisher DN, Goodwin CED, Robinson BS, Hodgson DJ, Inger R. 2018. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6:e4794 https://doi.org/10.7717/peerj.4794
Nolan, V., Gilbert, F., & Reader, T. (2021). Solving sampling bias problems in presence–absence or presence‐only species data using zero‐inflated models. In Journal of Biogeography (Vol. 49, Issue 1, pp. 215–232). Wiley. https://doi.org/10.1111/jbi.14268
Online resources:
- A classic: https://r-graph-gallery.com/
- Addition to the classic: https://r-charts.com/
- Flowchart to choose graph: https://www.data-to-viz.com/
- Inspiration: https://www.dataviz-inspiration.com/
- Inspiration: https://www.cedricscherer.com/
- Machine learning visuals: https://dagshub.com/blog/best-tools-for-machine-learning-model-visualization/
- Overview of data vis tools: https://data.europa.eu/apps/data-visualisation-guide/tag/data-visualisation-tools
- The chartmaker directory: https://chartmaker.visualisingdata.com/
- Creating Quarto websites: https://ucsb-meds.github.io/creating-quarto-websites/
- RAW graphs: https://www.rawgraphs.io/
- Colour palette generator: https://coolors.co/
Interesting R packages:
- GGplotly & Plotyly
- Mapview
- Viridis (color blind friendly!)
In addition:
- Book: Powerful Charts - Koen van Eeckhout (Baryon) --> available in VLIZ library
- Paper: Nature Methods: https://mcmanuslab.ucsf.edu/sites/mcmanuslab.ucsf.edu/files/event/file-attachments/data-visualization-nature-methods-selected.pdf
- Online course: https://data.europa.eu/en/academy/introducing-data-visualisation
- Paper: Midway, S. R. (2020). Principles of Effective Data Visualization. In Patterns (Vol. 1, Issue 9, p. 100141). Elsevier BV. https://doi.org/10.1016/j.patter.2020.100141
- Book: Data Visualization: A practical introduction, Kieran Healy. https://socviz.co/
And tables:
- Tables with R: https://r-graph-gallery.com/table.html
- Examples of interactive tables: https://flourish.studio/blog/how-make-interactive-table/
- Species distributions models may predict accurately future distributions but poorly how distributions change: A critical perspective on model validation (https://onlinelibrary.wiley.com/doi/full/10.1111/ddi.13687)
- blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models (https://doi.org/10.1111/2041-210X.13107) and step-by-step tutorial (https://cran.r-project.org/web/packages/blockCV/vignettes/tutorial_2.html)
- Flexible species distribution modelling methods perform well on spatially separated testing data (https://doi.org/10.1111/geb.13639)
- A review of regularised estimation methods and cross-validation in spatiotemporal statistics (https://doi.org/10.48550/arXiv.2402.00183)
- Machine learning algorithm validation with a limited sample size (https://doi.org/10.1371/journal.pone.0224365)
- Study the study area
- Provide multiple evaluation metrics
- Transparency is important