Download BPI Predicted habitat suitability Currents Median grain size

ICES Cooperative Research Report No. 288
6.6
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Habitat suitability modelling
Wouter Willems
6.6.1
Introduction
To underpin management decisions scientifically, there is a growing need to have detailed
knowledge of the distribution of marine species. Predictive modelling is a time- and costeffective method to produce detailed distribution maps. Predictive modelling objectively
investigates the relationship between the occurrence of a species and the abiotic habitat. This
habitat is quantified by a number of environmental variables, measured directly at the time of
sampling (e.g. grain size), through remote sensing, or derived from other models (e.g.
currents). These physical variables are often available for a much wider area and sometimes
with full-scale coverage (e. g. depth rasters).
The most important goal of habitat suitability models (Guisan and Zimmerman, 2000) is to
define the niche of a species quantitatively, in order to improve on the often vague assertions
encountered in the literature, for example, that a species “prefers fine sand and shallow water
depths”. Habitat suitability models can assign a probability of occurrence for each location,
based on the local environmental variables. Additionally, the models can be applied to
construct full-cover species distributions maps. These maps are generated by feeding fullcover maps of each environmental variable into the model. The model will predict the
probability of occurrence for the species for each pixel of the raster (Figure 6.6.1). Species
distribution maps are important in nature conservation and spatial planning in general for the
North Sea (e.g. defining MPAs, fisheries closures, planning of wind farms).
Currents
Median
grain size
Predicted habitat
suitability
BPI
Figure 6.6.1. Application of a habitat suitability model to generate a full cover species distribution
map: BPI = Benthic Position Index (Lundblad et al., 2006).
This account will focus on Ophiodromus flexuosus (Chiaje, 1827), a free-living polychaete, as
an example of the approach applied on a North Sea-wide scale. The aims of this research were
(1) to identify the environmental variables determining the distribution of O. flexuosus; (2) to
construct the optimal model predicting the presence of O. flexuosus.
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Structure and dynamics of the North Sea benthos
6.6.2
Methodology
The methodology is explained in Figure 6.6.2. In the preparation phase, the dataset containing
both the species observations and environmental variables is loaded. A first visualization is
done by means of barplots that show the absolute and relative presence of the species along
the range of a variable (Figure 6.6.3). Next, the dataset is split up in a training set and a test
set. This process is repeated iteratively so that each sample has been in the training set or in
the test set at one time. This technique is called cross validation and is used to construct
replicas of the models. For each training/test set combination, a new model is created. From
the available variables, a subset is selected. This variable selection removes variables for two
reasons: (1) the variable is not relevant to the species; (2) there are redundant variables in the
dataset. Variable selection will lead to simpler models with fewer parameters.
Figure 6.6.2. Overview of the Habitat Suitability Toolbox.
A model is then constructed that can predict the presence of a species in the training set from
the environmental variables in the training set. Any modelling technique can be used (e.g.
GLM, Neural Networks, fuzzy logic). In this account, the technique Artificial Neural
Networks (ANN) is used. ANN is a technique from the field of artificial intelligence (Lek and
Guégan, 1999). The networks have a structure similar to the human brain: a network of
connected neurons. The neurons are the building blocks of ANN. Data enter a neuron from
several other neurons, are summed, then fed into an activation function, which generates the
output of the neuron. Neurons can pass on information because they are connected. The
importance of a connection is expressed as an interconnection weight. The adjustment of these
weights will influence the model output (Lek and Guégan, 1999). Through a learning
algorithm, the weights will be adjusted iteratively, increasing the agreement between the
observed and predicted presence of the species (Lek and Guégan, 1999). In this research, the
neurons in the ANNs are organized in three layers: environmental variables are presented at
the input layer, are passed on to the hidden layer (which processes the information), and
finally the output layer generates the prediction of the probability of the presence of O.
flexuosus.
ANN has several internal parameter settings (e.g. choice of number of interneurons, transfer
functions). A series of models with different parameter settings was created. To identify the
best model, performance indicators (Fielding and Bell, 1997) were used. These formulae
allow the generation of a one-number summary of the predictive power of a model. The
predictive power for the training set and the test set are stated separately. The test set is to be
regarded as a set to test the predictive power for “unseen” samples. The relative contribution
of each environmental variable in the prediction is also stated. This is done by calculating the
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correlation between the model output with normal environmental variables and the same
dataset with one variable ordered randomly. A low correlation between the model predictions
means that the variable has a high correlation to the model output. The model predictions can
then be visualized extensively. If full-scale rasters for the environmental variables are
available, the model can be applied to generate full-scale species distribution maps.
6.6.3
Results
Visualization and variable selection
An important visualization shows the presence of the species along the range of the variable.
Additionally, it is important to show the distribution of samples along the range of the
variable. The more samples that are available in a certain range, the more reliable the
conclusions and models will be for that part of the range. In Figure 6.6.3, two graphs are
plotted for each environmental variable. The left one shows the distribution of the samples
along the range of the variable, and the right one shows the relative occurrence of the species.
Depth, maximum temperature in June, and percentage of mud show clear relationships with
the presence of O. flexuosus. Depth shows a peak around 100 m, although few samples are
available at such depths for the North Sea dataset. Although most samples were taken in lowmud environments, percentage of mud still shows a pattern with the occurrence of the species.
O. flexuosus shows a relatively narrow environmental preference in response to variation in
sorting, tidal stress, d10, and a high percentage of sand values.
Figure 6.6.3. Absolute and relative presence of Ophiodromus flexuosus along the range of the
environmental variables in the final model. The left graphs show the number of samples in that
range of the graph. The right graphs show the relative occurrence of the species (black bars).
Continued on next page.
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Structure and dynamics of the North Sea benthos
Figure 6.6.3 continued. Absolute and relative presence of Ophiodromus flexuosus along the range
of the environmental variables in the final model. The left graphs show the number of samples in
that range of the graph. The right graphs show the relative occurrence of the species (black bars).
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Because related variables (e.g. maximum temperature in June and February) were expected to
be highly correlated, and thus potentially redundant, principal components analysis (PCA) was
used to examine the relationships between the variables for inclusion in the models. Based on
the PCA, we could identify groups of highly correlated variables (see PCA in Section 5.3;
Table 6.6.1).
Table 6.6.1. PCA components for the complete dataset.
PC1
PC2
Variables positively correlated with the PCA axes.
d10, d50, d90, ratio d10, d90, mode (mm), percentage of gravel
Average temperature June
Average temperature February
Tidal stress
Variables negatively correlated with the PCA axes.
Percentage of sand
Depth
Latitude
Average salinity June
Based on the plots (Figure 6.6.3) and the PCA, a subset of seven variables was retained (Table
6.6.2). This subset was iteratively split up in a training and test set, and models were
constructed.
Table 6.6.2. Environmental variables in the final model for Ophiodromus flexuosus.
depth
sorting (geometric)
Percentage of mud
Tidal stress
d10 (mm)
Maximum temperature June
Percentage of sand
In Figure 6.6.4, a new PCA was plotted, based only on the seven environmental variables in
the final model. The Channel samples are clearly delimited from the others, based only on
these seven variables. The samples where the species is present are a distinct cloud in the PCA
plot. Overall, the “species present” samples are well separated from the “species absent” and
Channel samples, which indicates that the chosen environmental variables allow a distinction
to be made between sites where the species will be present or absent.
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Figure 6.6.4. PCA ordination analysis. Only the environmental variables in the final model are
plotted.
Model
By varying the number of interneurons (6–9) and the transfer functions of the interneuron and
output neuron (logistic, pure linear and Gaussian transfer functions), 36 model parameter
combinations were tested. The best overall model had six interneurons, logistic interneuron
transfer functions, and Gaussian output neuron transfer functions. According to the
performance statistic, the model performed well, and the performance of the unseen data in the
test set was good (Table 6.6.3). The Kappa and NMI values were high. The AUC values close
to 1 indicated a good balance between specificity and sensitivity. The CCi indicated that
91.6% and 88.2% of the samples in the training and test set, respectively, were predicted
correctly.
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Table 6.6.3. Performance statistics of the best model for Ophiodromus flexuosus.
KAPPA
NMI
AUC
CCI
training
0.589529
0.338271
0.987066
91.59091
validation
0.489107
0.253546
0.962873
88.18182
The relative contribution of each environmental variable to the model output is shown in
Figure 6.6.5. Depth and maximum temperature in June are very important. d10 and sorting
make a moderate contribution, while tidal stress, percentage of sand, and percentage of mud
make a very small contribution.
In the PCA based on the seven chosen variables (Figure 6.6.4), the importance of depth and
maximum temperature in June is also clear. It is possible to delimit the cloud of samples
where the species is present by drawing lines perpendicular to the vectors depth and maximum
temperature in June.
% mud
% sand
tidalstress
d10
sorting
max temp
June
depth
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Figure 6.6.5. The relative contribution of each environmental variable to the model output.
Maps
Maps show the observed (Figure 6.6.6a) and predicted (Figure 6.6.6b) distribution of O.
flexuosus. The overall pattern is very similar. That the model is a habitat suitability model
(Guisan and Zimmerman, 2000) causes slight overestimation of the modelled species’
presence in some regions. It predicts the species’ presence, if the physical habitat is suitable.
At some sites, the physical habitat is suitable, but the species can be absent for other reasons,
e.g. sampling inefficiencies or biotic factors such as predation and patchy recruitment success.
Thus, the model predicts the potential area where the species might be found; clearly, it cannot
compensate for all circumstances (including sampling error) that determine the presence or
absence of the species in field samples.
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Structure and dynamics of the North Sea benthos
Figure 6.6.6a–b. Maps showing the distribution of Ophiodromus flexuosus. (Top) observed
distribution, green = species absent, red = species present; (Bottom) predicted distribution, values
indicated the habitat suitability, which is the probability of occurrence of the species.
6.6.4
Conclusions
This example of the application of a habitat suitability model demonstrates its utility in
predicting species occurrence on the scale of the whole North Sea. It also demonstrates the
importance of employing an array of relevant measures of environmental variability to
identify a subset with the greatest predictive power. The model, applied to a wider range of
species from NSBP 2000, should provide, inter alia, a useful tool for interpreting any changes
evident in a repeated survey.
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References
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errors in conservation presence/absence models. Environmental Conservation, 24(1): 38–
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Guisan, A., and Zimmerman, N. E. 2000. Predictive habitat distribution models in ecology.
Ecological Modelling, 135: 147–186.
Lek, S., and Guégan, J. F. 1999. Artificial neural networks as a tool in ecological modelling,
an introduction. Ecological Modelling, 120: 65–73.
Lundblad, E., Wright, D. J., Miller, J., Larkin, E. M., Rinehart, R., Naar, D. F., Donahue, B.
T., Anderson, S. M., and T. Battista. 2006. A benthic terrain classification scheme for
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