Marine Species Distributions: From Data to Predictive Models
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Transcript of Marine Species Distributions: From Data to Predictive Models
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Marine Species Distributions: From data to predictive models
Samuel Bosch
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Topics
• Introduction
• Invasive seaweeds
• Marine species distribution modelling
• Some future perspectives
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Oceans • 70% of area • 40% of ecosystem value • 25% of species richness • > 200,000 registered species
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Threats
Pollution
Overexploitation
Invasive species
Global climate change
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© Hugo Ahlenius, UNEP/GRID-Arenda, 2008
Invasive marine species
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Invasive seaweeds
Undaria pinnatifida Sargassum muticum Codium fragile
Caulerpa taxifolia Asparagopsis armata Dasysiphonia japonica
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Introduction rate
Curated list of 153 introduced seaweed species in Europe
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Introduction rate
Species Records
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Introduction rate
Species Records
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Invasive seaweeds: Vectors
Hull Fouling
Aquaculture
Suez Canal
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a tale from
Monaco
Aquaria ?
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and its ecological
conse-quence
Aquaria ?
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Aquaria ?
Sampling
• 217 samples • 135 species
• 6 invasive or introduced • 40 possibly invasive
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Present 2055
• Rich species diversity • Invasive species • Potential for new introductions
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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.
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Marine species distribution modelling
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Image credit: Université de Lausanne
Species distribution modelling (SDM)
Species field observations
Environmental data Model fitting Predicted species
distributions
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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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Environmental data
Occurrences
SDM algorithm
Model
Absences
Output
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Environmental data
Occurrences
SDM algorithm
Model
Absences
Output
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Occurrences: Database
701 million occurrences
48.4 million occurrences of 123,287 marine species
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Occurrences
But:
• Spatially uneven sampling and reporting
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Occurrences
Himanthalia elongatha
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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.
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Occurrences
But:
• Spatially uneven sampling and reporting
• Errors
– Taxonomic
• Misidentifications
• [cryptic] species complexes
– Geographic
• Typo’s, 0,0, generated coordinates, ….
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Occurrences: (Eur)OBIS QC
Indicate the completeness and correctness
• Taxonomic
• Geographic
• Outliers
• Additional fields such as abundance
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Occurrences: (Eur)OBIS QC
Outlier analysis on the dataset ‘ICES Biological community’
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Environmental data
Occurrences
SDM algorithm
Model
Absences
Output
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Absences
• Presence-only SDM
– Only presences
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Absences
• Presence-only SDM
1. Only presences
2. Pseudo-absences
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Environmental data
Occurrences
SDM algorithm
Model
Absences
Output
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Environmental data
Salinity Bathymetry
Temperature Chlorophyll a
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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)
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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
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library(marinespeed) # list all 514 species species <- list_species() view(species) help(marinespeed)
MarineSPEED
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Predictor relevance
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Predictor relevance
0
25
50
75
100
Sh
ore
dis
tan
ce
Ba
thym
etr
y
SS
T (
ran
ge
)
Sa
linity
Ca
lcite
pH
Ch
loro
ph
yll
a (
me
an
)
Ch
loro
ph
yll
a (
min
)
Ch
loro
ph
yll
a (
ma
x)
Ch
loro
ph
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ran
ge
)
Diffu
se
atte
nu
atio
n (
me
an
)
Diffu
se
atte
nu
atio
n (
min
)
Diffu
se a
ttenuation (
max)
SS
T (
mean)
PA
R (
me
an
)
PA
R (
ma
x)
Ph
osp
ha
te
Nitra
te
Sili
ca
te
In s
pe
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s to
p 5
(%
)
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Statistical variation
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Biological variation
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Environmental data
Occurrences
SDM algorithm
Model selection
Absences
Output
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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
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Environmental data
Occurrences
SDM algorithm
Model
Absences
Output
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Output
• Maps
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Output
• Response curves
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Can we predict invasive seaweeds?
Abiotic
Movement
Biotic
GO
GI
Geographic area
Sargassum muticum
Codium fragile
Dictyota cyanoloma
Grateloupia turuturu
Undaria pinnatifida
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Can we predict invasive seaweeds?
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Can we predict invasive seaweeds?
Native Invasive
European Invasive non-European
1971
1941
Sargassum muticum
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Can we predict invasive seaweeds?
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Modelling in 1970
Sargassum muticum model fitted only with native records
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Can we predict invasive seaweeds?
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Modelling in 1970
Sargassum muticum model fitted with native records and Californian invasive records from before the European introduction
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Europe in 2100 ?
Predicted changes in the range of 15 invasive seaweeds in Europe by 2100
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Uncertainty
Uncertainty in the predicted ranges of 15 invasive seaweeds
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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
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Future perspectives
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Future perspectives
• Traits data in WoRMS
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Future perspectives
• New data in OBIS
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Future perspectives
• Bio-ORACLE 2: including benthic layers
Surface layer
Difference between surface and benthic layer
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Future perspectives
• Biotic interactions and knowledge transfer
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Future perspectives
• Use MarineSPEED to study other aspects of SDM
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Acknowledgement
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The Great Wave off Kanagawa
“All models are wrong, but some are useful” – George Box