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![Occurrences
But:
• Spatially uneven sampling and reporting
• Errors
– Taxonomic
• Misidentifications
• [cryptic] species complexes
– Geographic
• Typo’s, 0,0, generated coordinates, ….](https://image.slidesharecdn.com/presentation-180124095448/85/Marine-Species-Distributions-From-Data-to-Predictive-Models-25-320.jpg)
Uploaded bySamuel Bosch
Marine Species Distributions: From Data to Predictive Models
The document discusses the modeling of marine species distributions, particularly focusing on invasive seaweeds in European waters. It highlights the challenges posed by data quality, geographical variability, and the predictive modeling of species distributions using various environmental and biological predictors. Future perspectives include improving data accessibility and understanding biotic interactions to enhance marine species modeling.
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Marine Species Distributions: From Data to Predictive Models
- 1. Marine Species Distributions: Fromdata to predictive models Samuel Bosch
- 2. Topics • Introduction • Invasiveseaweeds • Marine species distribution modelling • Some future perspectives
- 3. Oceans • 70% ofarea • 40% of ecosystem value • 25% of species richness • > 200,000 registered species
- 4.
- 5. © Hugo Ahlenius,UNEP/GRID-Arenda, 2008 Invasive marine species
- 6. Invasive seaweeds Undaria pinnatifidaSargassum muticum Codium fragile Caulerpa taxifolia Asparagopsis armata Dasysiphonia japonica
- 7. Introduction rate Curated listof 153 introduced seaweed species in Europe
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- 10. Invasive seaweeds: Vectors HullFouling Aquaculture Suez Canal
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- 13. Aquaria ? Sampling • 217samples • 135 species • 6 invasive or introduced • 40 possibly invasive
- 14. Present 2055 • Richspecies diversity • Invasive species • Potential for new introductions
- 15. More … • Chapter5 Bosch, S., De Clerck, O. and Frédéric Mineur, F. Spatio-temporal patterns of introduced seaweeds in European waters, a critical review. • Chapter 6 Vranken, S., Bosch, S., Peña, V., Leliaert, F., Mineur, F. and De Clerck, O. A risk assessment of aquarium trade introductions of seaweed in European waters.
- 16. Marine species distributionmodelling
- 17. Image credit: Universitéde Lausanne Species distribution modelling (SDM) Species field observations Environmental data Model fitting Predicted species distributions
- 18. Ecological Niche Hutchinson (1957) “…the hypervolume defined by the environmental dimensions within which that species can survive and reproduce.” Abiotic Movement Biotic GO GI Geographic area
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- 21. Occurrences: Database 701 millionoccurrences 48.4 million occurrences of 123,287 marine species
- 22. Occurrences But: • Spatially unevensampling and reporting
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- 24. Aiello-Lammens, M. E.et al. 2015. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. - Ecography (Cop.). 38: 541–545.
- 25. Occurrences But: • Spatially unevensampling and reporting • Errors – Taxonomic • Misidentifications • [cryptic] species complexes – Geographic • Typo’s, 0,0, generated coordinates, ….
- 26. Occurrences: (Eur)OBIS QC Indicatethe completeness and correctness • Taxonomic • Geographic • Outliers • Additional fields such as abundance
- 27. Occurrences: (Eur)OBIS QC Outlieranalysis on the dataset ‘ICES Biological community’
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- 29. Absences • Presence-only SDM –Only presences
- 30. Absences • Presence-only SDM 1.Only presences 2. Pseudo-absences
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- 33. sdmpredictors library(sdmpredictors) # view allavailable layers View(list_layers()) # load SST mean from Bio-ORACLE and # bathymetry from MARSPEC as lat/lon data x <- load_layers(c("BO_sstmean","MS_bathy_5m"), equalarea = FALSE)
- 34. Which one ? •Calcite • Chlorophyll A • Cloud fraction • Diffuse attenuation coefficient at 490 nm • Dissolved oxygen • Nitrate • Photosynthetically available radiation • pH • Phosphate • Salinity • Silicate • Sea surface temperature • Bathymetry • East/West aspect • North/South Aspect • Plan curvature • Profile curvature • Distance to shore • Bathymetric slope • Concavity
- 35. library(marinespeed) # list all514 species species <- list_species() view(species) help(marinespeed) MarineSPEED
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- 42. SDM algorithm Model selection metric Validation dataset RandomSpatial AUC Boyce Kappa AIC MaxEnt Random forests GRaF GLM GAM GARP Visual BIOCLIM Ensemble BRT MARS Temporal
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- 46. Can we predictinvasive seaweeds? Abiotic Movement Biotic GO GI Geographic area Sargassum muticum Codium fragile Dictyota cyanoloma Grateloupia turuturu Undaria pinnatifida
- 47. Can we predictinvasive seaweeds?
- 48. Can we predictinvasive seaweeds? Native Invasive EuropeanInvasive non-European 1971 1941 Sargassum muticum
- 49. Can we predictinvasive seaweeds?
- 50. Modelling in 1970 Sargassummuticum model fitted only with native records
- 51. Can we predictinvasive seaweeds?
- 52. Modelling in 1970 Sargassummuticum model fitted with native records and Californian invasive records from before the European introduction
- 53. Europe in 2100? Predicted changes in the range of 15 invasive seaweeds in Europe by 2100
- 54. Uncertainty Uncertainty in the predictedranges of 15 invasive seaweeds
- 55. More … • Chapter2 Vandepitte, L. et al. 2015. Fishing for data and sorting the catch: assessing the data quality, completeness and fitness for use of data in marine biogeographic databases. - Database • Chapter 3 Bosch, S., Tyberghein, L., De Clerck, O. sdmpredictors: an R package for species distribution modelling predictor datasets • Chapter 4 Bosch, S., Tyberghein, L., Deneudt, K., Hernandez, F., De Clerck, O. In search of relevant predictors for marine species distribution modelling using the MarineSPEED benchmark dataset • Chapter 7 Bosch, S., Gomez Giron, E., Martínez, B., De Clerck, O. Modelling the past, present and future distribution of invasive seaweeds in Europe
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- 57. Future perspectives • Traitsdata in WoRMS
- 58. Future perspectives • Newdata in OBIS
- 59. Future perspectives • Bio-ORACLE2: including benthic layers Surface layer Difference between surface and benthic layer
- 60. Future perspectives • Bioticinteractions and knowledge transfer
- 61. Future perspectives • UseMarineSPEED to study other aspects of SDM
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- 63. The Great Waveoff Kanagawa “All models are wrong, but some are useful” – George Box