Marine Species Distributions: From Data to Predictive Models

Marine Species Distributions: From data to predictive models

Samuel Bosch

Topics

• Introduction

• Invasive seaweeds

• Marine species distribution modelling

• Some future perspectives

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

Threats

Pollution

Overexploitation

Invasive species

Global climate change

© Hugo Ahlenius, UNEP/GRID-Arenda, 2008

Invasive marine species

Invasive seaweeds

Undaria pinnatifida Sargassum muticum Codium fragile

Caulerpa taxifolia Asparagopsis armata Dasysiphonia japonica

Introduction rate

Curated list of 153 introduced seaweed species in Europe

Introduction rate

Species Records

Introduction rate

Species Records

Invasive seaweeds: Vectors

Hull Fouling

Aquaculture

Suez Canal

a tale from

Monaco

Aquaria ?

and its ecological

conse-quence

Aquaria ?

Aquaria ?

Sampling

• 217 samples • 135 species

• 6 invasive or introduced • 40 possibly invasive

Present 2055

• Rich species diversity • Invasive species • Potential for new introductions

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.

Marine species distribution modelling

Image credit: Université de Lausanne

Species distribution modelling (SDM)

Species field observations

Environmental data Model fitting Predicted species

distributions

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

Environmental data

Occurrences

SDM algorithm

Model

Absences

Output

Environmental data

Occurrences

SDM algorithm

Model

Absences

Output

Occurrences: Database

701 million occurrences

48.4 million occurrences of 123,287 marine species

Occurrences

But:

• Spatially uneven sampling and reporting

Occurrences

Himanthalia elongatha

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.

Occurrences

But:

• Spatially uneven sampling and reporting

• Errors

– Taxonomic

• Misidentifications

• [cryptic] species complexes

– Geographic

• Typo’s, 0,0, generated coordinates, ….

Occurrences: (Eur)OBIS QC

Indicate the completeness and correctness

• Taxonomic

• Geographic

• Outliers

• Additional fields such as abundance

Occurrences: (Eur)OBIS QC

Outlier analysis on the dataset ‘ICES Biological community’

Environmental data

Occurrences

SDM algorithm

Model

Absences

Output

Absences

• Presence-only SDM

– Only presences

Absences

• Presence-only SDM

1. Only presences

2. Pseudo-absences

Environmental data

Occurrences

SDM algorithm

Model

Absences

Output

Environmental data

Salinity Bathymetry

Temperature Chlorophyll a

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)

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

library(marinespeed) # list all 514 species species <- list_species() view(species) help(marinespeed)

MarineSPEED

Predictor relevance

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

yll

a (

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

cie

s to

p 5

(%

)

Statistical variation

Biological variation

Environmental data

Occurrences

SDM algorithm

Model selection

Absences

Output

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

Environmental data

Occurrences

SDM algorithm

Model

Absences

Output

Output

• Maps

Output

• Response curves

Can we predict invasive seaweeds?

Abiotic

Movement

Biotic

GO

GI

Geographic area

Sargassum muticum

Codium fragile

Dictyota cyanoloma

Grateloupia turuturu

Undaria pinnatifida

Can we predict invasive seaweeds?

Can we predict invasive seaweeds?

Native Invasive

European Invasive non-European

1971

1941

Sargassum muticum

Can we predict invasive seaweeds?

Modelling in 1970

Sargassum muticum model fitted only with native records

Can we predict invasive seaweeds?

Modelling in 1970

Sargassum muticum model fitted with native records and Californian invasive records from before the European introduction

Europe in 2100 ?

Predicted changes in the range of 15 invasive seaweeds in Europe by 2100

Uncertainty

Uncertainty in the predicted ranges of 15 invasive seaweeds

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

Future perspectives

Future perspectives

• Traits data in WoRMS

Future perspectives

• New data in OBIS

Future perspectives

• Bio-ORACLE 2: including benthic layers

Surface layer

Difference between surface and benthic layer

Future perspectives

• Biotic interactions and knowledge transfer

Future perspectives

• Use MarineSPEED to study other aspects of SDM

Acknowledgement

The Great Wave off Kanagawa

“All models are wrong, but some are useful” – George Box