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SDMs study group

Repository with annotations from the OBIS/VLIZ SDMs study group

Articles from the meetings

Meeting 1 (2023/09/20)

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

Meeting 2 (2023/10/04) - sampling bias

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

Remote sensing bias:

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

Meeting 3 (2023/10/25)

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

Meeting 4 (2023/12/13)

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

Meeting 5 (2024/01/10)

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

Meeting 6 (2024/02/07)

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

Meeting 7 (2024/04/24)

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

Meeting 8 (2024/05/08)

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

Meeting 11[?] (2024/08/06)

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

Meeting 12 (2024/08/20)

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

Meeting 13 (2024/09/11)

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

Meeting 14 (2024/09/25)

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

Meeting 15 (2024/10/09)

Meeting 16 (2024/10/22)

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

Meeting 17 (2024/11/20): data visualization

Online resources:

Interesting R packages:

  • GGplotly & Plotyly
  • Mapview
  • Viridis (color blind friendly!)

In addition:

And tables:

Meeting 18 (2024/12/04): GAMs

Meeting 19 (2024/12/18): model validation

Meeting 20 (2025/01/08): R packages for data analysis/exploration/wrangling

Questions and hot-topics

Interesting resources

To remember

  • Study the study area
  • Provide multiple evaluation metrics
  • Transparency is important

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