Presentation of my PhD as used at the public defence in June 2017. Thesis outline: Chapter 1: General Introduction Chapter 2: Fishing for data and sorting the catch: assessing the data quality, completeness and fitness for use of data in marine biogeographic databases Chapter 3: sdmpredictors: an R package for species distribution modelling predictor datasets Chapter 4: In search of relevant predictors for marine species distribution modelling using the MarineSPEED benchmark dataset Chapter 5: Spatio-temporal patterns of introduced seaweeds in European waters, a critical review Chapter 6: A risk assessment of aquarium trade introductions of seaweed in European waters Chapter 7: Modelling the past, present and future distribution of invasive seaweeds in Europe Chapter 8: General discussion

1. Marine Species Distributions:
From data to predictive models
Samuel Bosch

2. Topics
• Introduction
• Invasive seaweeds
• Marine species distribution modelling
• Some future perspectives

3. Oceans
• 70% of area
• 40% of ecosystem value
• 25% of species richness
• > 200,000 registered species

4. Threats
Pollution
Overexploitation
Invasive species
Global climate change

5. © Hugo Ahlenius, UNEP/GRID-Arenda, 2008
Invasive marine species

6. Invasive seaweeds
Undaria pinnatifida Sargassum muticum Codium fragile
Caulerpa taxifolia Asparagopsis armata Dasysiphonia japonica

7. Introduction rate
Curated list of 153 introduced seaweed species in Europe

8. Introduction rate
Species Records

9. Introduction rate
Species Records

10. Invasive seaweeds: Vectors
Hull Fouling
Aquaculture
Suez Canal

11. a tale
from
Monaco
Aquaria ?

12. and its
ecological
conse-
quence
Aquaria ?

13. Aquaria ?
Sampling
• 217 samples
• 135 species
• 6 invasive or introduced
• 40 possibly invasive

14. Present 2055
• Rich species diversity
• Invasive species
• Potential for new introductions

15. More …
• Chapter 5
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 distribution modelling

17. Image credit: Université de 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

19. Environmental
data
Occurrences
SDM algorithm
Model
Absences
Output

20. Environmental
data
Occurrences
SDM algorithm
Model
Absences
Output

21. Occurrences: Database
701 million occurrences
48.4 million occurrences of 123,287 marine species

22. Occurrences
But:
• Spatially uneven sampling and reporting

23. Occurrences
Himanthalia
elongatha

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 uneven sampling and reporting
• Errors
– Taxonomic
• Misidentifications
• [cryptic] species complexes
– Geographic
• Typo’s, 0,0, generated coordinates, ….

26. Occurrences: (Eur)OBIS QC
Indicate the completeness and correctness
• Taxonomic
• Geographic
• Outliers
• Additional fields such as abundance

27. Occurrences: (Eur)OBIS QC
Outlier analysis on
the dataset ‘ICES
Biological
community’

28. Environmental
data
Occurrences
SDM algorithm
Model
Absences
Output

29. Absences
• Presence-only SDM
– Only presences

30. Absences
• Presence-only SDM
1. Only presences
2. Pseudo-absences

31. Environmental
data
Occurrences
SDM algorithm
Model
Absences
Output

32. Environmental data
Salinity Bathymetry
TemperatureChlorophyll a

33. sdmpredictors
library(sdmpredictors)
# view all available 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 all 514 species
species <-
list_species()
view(species)
help(marinespeed)
MarineSPEED

37. Predictor relevance

38. Predictor relevance
0
25
50
75
100
Shoredistance
Bathymetry
SST(range)
Salinity
Calcite
pH
Chlorophylla(mean)
Chlorophylla(min)
Chlorophylla(max)
Chlorophylla(range)
Diffuseattenuation(mean)
Diffuseattenuation(min)
Diffuseattenuation(max)
SST(mean)
PAR(mean)
PAR(max)
Phosphate
Nitrate
Silicate
Inspeciestop5(%)

39. Statistical variation

40. Biological variation

41. Environmental
data
Occurrences
SDM algorithm
Model selection
Absences
Output

42. SDM algorithm
Model selection
metric
Validation
dataset
Random Spatial
AUC Boyce
Kappa AIC
MaxEnt
Random
forests
GRaF
GLM
GAM
GARP
Visual
BIOCLIM
Ensemble
BRT
MARS
Temporal

43. Environmental
data
Occurrences
SDM algorithm
Model
Absences
Output

44. Output
• Maps

45. Output
• Response
curves

46. Can we predict invasive seaweeds?
Abiotic
Movement
Biotic
GO
GI
Geographic area
Sargassum muticum
Codium fragile
Dictyota
cyanoloma
Grateloupia turuturu
Undaria pinnatifida

47. Can we predict invasive seaweeds?

48. Can we predict invasive seaweeds?
Native
Invasive
EuropeanInvasive
non-European
1971
1941
Sargassum muticum

49. Can we predict invasive seaweeds?

50. Modelling in 1970
Sargassum muticum
model fitted only
with native records

51. Can we predict invasive seaweeds?

52. Modelling in 1970
Sargassum muticum
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
predicted ranges of
15 invasive seaweeds

55. More …
• Chapter 2
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

56. Future perspectives

57. Future perspectives
• Traits data in WoRMS

58. Future perspectives
• New data in OBIS

59. Future perspectives
• Bio-ORACLE 2: including benthic layers
Surface layer
Difference
between
surface and
benthic layer

60. Future perspectives
• Biotic interactions and knowledge transfer

61. Future perspectives
• Use MarineSPEED to study other aspects of
SDM

62. Acknowledgement

63. The Great Wave off Kanagawa
“All models are wrong,
but some are useful”
– George Box