Download Spatial probability modelling of eelgrass (Zostera marina

1093
Spatial probability modelling of eelgrass (Zostera marina)
distribution on the west coast of Norway
Trine Bekkby, Eli Rinde, Lars Erikstad, Vegar Bakkestuen, Oddvar Longva, Ole Christensen,
Martin Isæus, and Pål Erik Isachsen
Bekkby, T., Rinde, E., Erikstad, L., Bakkestuen, V., Longva, O., Christensen, O., Isæus, M., and Isachsen, P. E. 2008. Spatial probability modelling of
eelgrass (Zostera marina) distribution on the west coast of Norway. – ICES Journal of Marine Science, 65: 1093– 1101.
Based on modelled and measured geophysical variables and presence/absence data of eelgrass Zostera marina, we developed a spatial
predictive probability model for Z. marina. Our analyses confirm previous reports and show that the probability of finding Z. marina is
at its highest in shallow, gently sloping, and sheltered areas. We integrated the empirical knowledge from field samples in GIS and
developed a model-based map of the probability of finding Z. marina using the model-selection approach Akaike Information
Criterion (AIC) and the spatial probability modelling extension GRASP in S-Plus. Spatial predictive probability models contribute
to a better understanding of the factors and processes structuring the distribution of marine habitats. Additionally, such models
provide a useful tool for management and research, because they are quantitative and defined objectively, extrapolate knowledge
from sampled to unsurveyed areas, and result in a probability map that is easy to understand and disseminate to stakeholders.
Keywords: Akaike’s information criterion (AIC), eelgrass, GIS, habitat mapping, predictive modelling, seagrass, Zostera marina.
Received 8 November 2007; accepted 25 April 2008; advance access publication 4 June 2008.
T. Bekkby and E. Rinde: Norwegian Institute for Water Research, Gaustadalléen 21, N-0349 Oslo, Norway. L. Erikstad and V. Bakkestuen: Norwegian
Institute for Nature Research, Gaustadalléen 21, N-0349 Oslo, Norway. V. Bakkestuen: Department of Botany, NHM, University of Oslo, PO Box 1172
Blindern, N-0318 Oslo, Norway. O. Longva: Geological Survey of Norway, N-7491 Trondheim, Norway. O. Christensen: Electromagnetic Geoservices
(EMGS), Stiklestadveien 1, N-7041 Trondheim, Norway. M. Isæus: AquaBiota Water Research, Svante Arrhenius väg 21A, SE-10405
Stockholm, Sweden. P. E. Isachsen: Norwegian Meteorological Institute, Gaustadalléen 21, N-0349 Oslo, Norway. Correspondence to T. Bekkby:
tel: þ47 22185100; fax: þ47 22185200; e-mail: [email protected].
Introduction
Zostera marina meadows are highly productive (Duarte and
Chiscano, 1999), have several associated faunal groups (Baden
and Pihl, 1984; Baden and Boström, 2000; Boström and
Bonsdorff, 2000; Fredriksen and Christie, 2003; Fredriksen et al.,
2004), and are regarded as of great ecological importance (den
Hartog, 1970; Boström and Mattila, 1999). The Rio declaration
(1992/93:13) lists seagrass meadows as being in need of protection, and Z. marina meadows are included as a key element in
the Habitat Directive Annex I and as part of the Norwegian
mapping programme on marine biodiversity (Rinde et al., 2006).
In countries with a long and convoluted coastline, such as
Norway, detailed mapping of all areas is practically and economically difficult. Moreover, simply mapping seagrass meadows does
not capture the dynamic nature of the species or give managers
the knowledge needed for planning. Aerial photography is considered by some to be the optimal method for mapping and monitoring of seagrass (Dobson et al., 1995). However, this method is
hampered by observation noise when conditions are wavy and
windy, as is often the case along the west Norwegian coast. Also,
the method is more suitable in areas of dense Z. marina
meadows than for the sparse and patchy occurrences often
found in Norway.
Habitat distribution models include the influence of spatial
variability of geophysical factors, measured in the field or
# 2008
modelled. Hence, these models are independent of the windand wave-induced noise associated with aerial photography, and
may represent a broader temporal scale when it comes to
Z. marina occurrence. Habitat distribution models have been
applied in several studies (e.g. Guisan and Zimmermann, 2000;
Kelly et al., 2001; Bekkby et al., 2002; Lehmann et al., 2002; Elith
et al., 2006; Wilson et al., 2007), and have contributed to a
better understanding of the factors and processes structuring the
distribution of marine habitats. The models are useful as tools
for extrapolating statistical relationships from sampled to unsurveyed areas, and the approach is increasingly used by managers,
for instance as part of the Norwegian mapping programme on
marine biodiversity (Rinde et al., 2006), and the management of
Gullmarsfjord, Sweden (Bekkby and Rosenberg, 2006).
Our study focuses on the distribution of Z. marina along geophysical gradients, using variables (depth, “enclosedness” as an
indicator of inlets and bays, wave exposure, and current speed)
believed to be structurally important at a landscape scale.
Knowledge of the factors determining Z. marina distribution
exists from other studies (Dennison, 1987; Narumalani et al.,
1997; Duarte and Kallf, 1990; Duarte, 1991; Nielsen et al., 2002;
Krause-Jensen et al., 2003; Ralph et al., 2007). However, in contrast
to these studies, we have used quantitative geophysical variables
that are defined objectively. These variables are elaborated as GIS
layers covering the whole study area.
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The aim of the study was to use the geophysical variables
together with field observations to develop a spatial predictive
probability map of Z. marina distribution.
Methods
Study site and field sampling
Information was collected in Sandøy municipality (628N 68E),
Møre and Romsdal, Norway (Figure 1), from 19 September to
1 October 2003. The area is typical of the outer central west
coast of Norway, with small islands, underwater shallows and
rocks, and high tidal amplitude (1.80 m). In all, 695 stations
were visited (Figure 2), and Z. marina presence and absence
T. Bekkby et al.
were recorded using a water glass or an underwater camera. Any
coverage of Z. marina was defined as presence. Depth was recorded
using a rigidly mounted echosounder (with a dual frequency 200/
83 kHz sonar system, showing bottom definition with a 208 beam).
The stations were selected manually, to represent the variability in
terrain, wave exposure, and current speed within the study area as
part of a project mapping several habitats within the region (both
soft and rocky seabed habitats). The minimum distance between
stations was 15 m.
To be able to detect the possible effects of constant wind
pressure and storm events (Marba and Duarte, 1995; Bell et al.,
1999; Fonseca et al., 2000), values of average and maximum
wave exposure were included in the analyses. To detect possible
Figure 1. Location of the study area (area encircled) in Sandøy municipality, 628N 68E, Møre and Romsdal, Norway. The map shows the
central and southern part of Norway only.
Spatial probability modelling of Zostera marina distribution on the west coast of Norway
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Figure 2. The study area (in Sandøy municipality, 628N 68E, Møre and Romsdal, Norway) and the 695 sampled stations. Triangles show field
stations with eelgrass (Zostera marina) presence, circles show field stations with eelgrass absence. Note that some data points may be hidden,
because the size of the study area is 25 25 km. The depths are shown using a DTM with a spatial resolution of 10 m.
effects of environmental factors operating on other temporal scales
than that at which seagrass occurrence was measured (e.g. Greve
and Krause-Jensen, 2005), values of wave exposure based on
wind statistics from different periods were tested. Different
measures of current speed were included in the analyses (mean,
median, 90th percentile, and maximum amplitude of tidal
currents).
Predictor variables
For the predictive probability modelling, the variables depth, slope,
“enclosedness”, wave exposure, and current speed were available as
GIS layers (as described below). For the statistical analyses, we used
field-measured depth rather than the GIS depth layer.
Field-measured depth (measured using an echosounder) was
adjusted for temporal tidal differences (the nautical chart zero, i.e.
the coastline, is defined by the Norwegian Mapping Authority as
the lowest astronomical tide). For the predictive probability modelling, an interpolated land/sea digital terrain model (DTM) was
developed using the linear algorithm of the software Surfer
(version 6.1; Keckler, 1996; see Bekkby et al., 2002, for more
details), based on point data and contour intervals purchased
from the Norwegian Mapping Authority. The method is based on
the assumption of spatial autocorrelation, is an exact interpolator
(i.e. the interpolated grid will maintain the digitized data integrity),
and uses a semivariogram. The interpolation was based on point
data (both land and sea), land contours at 20 m intervals, and the
0- (coastline), 3-, 5-, 6-, 10-, 20-, 50-, 100-, and 200-m depth contours. The dataset used for interpolation consisted of 14–91 map
readings per 100 m2. Consequently, a spatial (horizontal) DTM
resolution of 10 m was considered appropriate.
Slope (10 m grid resolution) was calculated from the DTM
using the function available in ArcView 3.3, identifying the
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maximum rate of change from each cell to its neighbours (in
degrees).
“Enclosedness” (Bekkby and Isæus, 2008), i.e. the degree to
which an area is situated in an inlet or bay, was modelled at a
spatial resolution of 10 m. The midpoint in each grid cell was
given eight radiation lines 400-m long (and 458 apart).
The number of lines crossing land was counted. The higher the
number, the more “enclosed” the area. For example, 8 (the
maximum value) means that the point is surrounded by land
within 400 m in all eight directions, and 0 (the minimum value)
means land is not present within a radius of 400 m.
For wave exposure, data on fetch (distance to nearest shore,
island, or coast), and wind strength and direction were used for
modelling wave exposure at a grid resolution of 10 m (“simplified
wave model”, SWM; detailed description in Isæus, 2004). This
wave exposure model has been validated in the Stockholm archipelago (Isæus, 2004), has been applied in the Norwegian
mapping programme on marine biodiversity (Rinde et al.,
2006), and has been used in studies of the effect of boating activities on aquatic vegetation and fish recruitment in the Baltic
(Eriksson et al., 2004; Sandström et al., 2005). The delineation
of SWM into classes of wave exposure was made according to
Rinde et al. (2006). The effects of wave exposure were analysed
at different temporal scales: mean and maximum values for the
previous year (1 September 2002–31 August 2003), for the last 5
years (1 September 1998– 31 August 2003), for the previous
winter period (1 September 2002–30 April 2003), and for the
growth season just before sampling (1 May–31 August 2003).
Current speed was estimated by the numerical ocean model
ROMS (Schepetkin and McWilliams, 2005) operated in a twodimensional (one layer) mode with 100 m spatial resolution.
The model was driven by time –mean currents at the open boundaries, and time –mean atmospheric winds from summer of 2007
compiled from coarser operational models run by the
Norwegian Meteorological Institute. In addition, tide elevation
and currents for the two dominating constituents (M2, K1) were
downscaled from the fes2004 global tidal analysis (Lyard et al.,
2006). From the currents modelled, we estimated the mean,
median, 90th percentile, and maximum amplitude of tidal currents in each grid cell.
Analyses: generalized additive model and Akaike’s
information criterion model selection
We analysed the statistical influence of depth, slope, “enclosedness” (inlet or bay), wave exposure, and current speed using generalized additive models (GAMs; Hastie and Tibshirani, 1990;
occurrence as a binomial dataset, 2 d.f. for the smoothing spline
function) in S-PLUS 2000. GAMs permit the response probability
distribution to be any member of the exponential family of distributions, and the method is commonly used when there are many
independent variables in the model.
As a tool for model selection, we used the Akaike’s information
criterion (AIC; see Burnham and Anderson, 2001) in GRASP (an
extension to S-PLUS 2000; Lehmann et al., 2002, 2004). The AIC
method implies a non-traditional way of thinking in ecological
research (although the method has existed for several decades
and is implemented in several statistical packages), because it
does not include traditional H0 testing and interpretation of
p-values. The candidate models were tested and ranked relative
to each other. The best model according to AIC is the model
receiving most support from the data, and which at the same
T. Bekkby et al.
time uses a small number of explanatory factors (the principle
of parsimony, i.e. the trade-off between squared bias and variance
against the number of parameters in the model). We used the AICc
calculations (as recommended by Burnham and Anderson, 2004),
which is the AIC adjusted to fit small sample sizes (AIC and AICc
being equal at large sample sizes).
Spatial probability prediction and validation
A predictive model of the probability of Z. marina distribution
based on presence/absence data was developed in the S-PLUS
2000 extension GRASP (Lehmann et al., 2002). GRASP has
solved one of the large problems with spatial modelling (as
described by Lehmann, 1998), because it has introduced a way
of exporting the statistical models to GIS software. The probability
model was validated using a cross-validation method (a ROC test;
Fielding and Bell, 1997), based on the presence/absence data. The
cross-validation was made with five subsets (folds) of the entire
dataset (fivefold cvROC).
Results
Zostera marina was recorded at 35 of the 695 stations (Figure 2).
Table 1 summarizes the environmental conditions at the
sampled stations. Our analyses show that the probability of
finding Z. marina may be predicted using the variables depth,
slope, and wave exposure (Table 2). The probability was greatest
in shallow, gently sloping, sheltered areas (Model 19 in Table 2;
Figures 3 and 4; fivefold cvROC ¼ 0.89). Z. marina was found
close to the surface (at 0.5-m depth) down to 10 m deep. We
did not find it in areas steeper than 5.58. Z. marina was found in
sheltered locations, and not in exposed areas. Using wave exposure
averaged over the past 5 years provided a better model than any of
the other temporal alternatives of wave exposure (previous year,
growth season, and winter average and maximum). Using 5
years maximum was equally good (Model 20 in Table 2; D, the
discrepancy from the best model, was ,2).
We found no effect of current speed or “enclosedness” (inlet or
bay). The factors depth, slope, and wave exposure (5-year average)
were used for predictive probability modelling of Z. marina
distribution (Figure 5).
Discussion and conclusions
Three geophysical variables, depth, slope, and wave exposure
(based on wind statistics averaged over the past 5 years), can
predict Z. marina distributions in a reliable way and with a high
degree of certainty (fivefold cvROC ¼ 0.89). The relationship is
consistent with literature findings of factors that regulate the
species composition in marine vegetated areas: exposure to air
(Lewis et al., 1985; Duarte, 1991), light (Backman and Barilotti,
1976; Dennison, 1987; Duarte, 1991; Nielsen et al., 2002;
Krause-Jensen et al., 2003), wave exposure (Robertson and
Mann, 1984; Lewis et al., 1985; Thom, 1990; Fonseca et al.,
2002; Krause-Jensen et al., 2003), and slope (Duarte and Kallf,
1990; Narumalani et al., 1997).
Zostera marina showed a strong preference for sheltered areas
and was not recorded in exposed areas. Physical disturbance
created by wave action is known to reduce survival and affect
the average biomass, cover, and distribution of seagrass
meadows (Fonseca and Bell, 1998; Fonseca et al., 2002). Our
area has large wave-exposure gradients, so an effect of wave
exposure was expected.
Spatial probability modelling of Zostera marina distribution on the west coast of Norway
1097
Table 1. Statistics for the predictor variables depth, slope, “enclosedness” (part of an inlet or bay), wave exposure (wind statistics averaged
over the past 5 years), and mean current speed at the sampling stations.
Presence or absence
Parameter
Depth
(m)
5.1
Slope
(88 )
1.9
“Enclosedness” Wave exposure, past 5 years
(0 –8)
average
1.69
64 140 (very sheltered)
Mean current speed
(m s – 1)
0.01
Z. marina presence
Mean
(n
¼
35)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Standard
2.3
1.2
1.23
62 505
0.02
deviation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maximum
10
5.5
4
225 496 (sheltered)
0.1
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minimum
0.5
0.4
0
8
716
(very
sheltered)
0.01
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Z. marina absence
Mean
11.0
4.8
1.34
564 630 (exposed)
0.05
(n
¼
660)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Standard
10.7
5.9
1.39
590 371
0.04
deviation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maximum
71.0
46.6
8
1 880 479 (very exposed)
0.23
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minimum
0.0
0.0
0
13 982 (sheltered)
0.01
Depth was measured in the field, and the other variables were modelled as described in text. For “enclosedness”, 8 (the maximum value) means that the
point is surrounded by land within 400 m in all eight directions, 0 (the minimum value) means land is not present within a radius of 400 m. The wave
exposure classes are based on Rinde et al. (2006).
Table 2. The results of the AICc model selection (sorted with
descending AICc values).
Model Predictors
AICc D
Wi
19
Depth
þ
Slope
þ
SWM
(5
years
average)
195.62
0.00
0.64
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
20
Depth
þ
Slope
þ
SWM
(5
years
maximum)
196.95
1.24
0.34
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
17
Depth
þ
SWM
(5
years
average)
202.54
5.59
0.02
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
18
Slope þ SWM (5 years average)
218.52 21.57 0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
9
SWM
(5
years
average)
221.43 24.49 0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
8
SWM
(previous
year
average)
222.41
25.47 0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
11
SWM
(winter
average)
222.94
26.00 0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
16
Depth
þ
Slope
223.06
26.11
0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
10
SWM (growth season maximum)
225.43 28.49 0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
6
SWM
(growth
season
average)
226.08
29.14 0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
7
SWM
(winter
maximum)
226.40
29.46
0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
4
SWM
(previous
year
maximum)
227.27
30.33
0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
5
SWM (5 years maximum)
227.38 30.44 0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
1
Depth
249.47
52.53 0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
2
Slope
258.51
61.57 0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
14
Current
speed,
90th
percentile
260.13
63.19 0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
15
Maximum
amplitude
of
tidal
currents
265.04
68.10 0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
13
Median
current
speed
271.65
74.71
0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
12
Mean
current
speed
272.47
75.53
0.00
. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . .
3
“Enclosedness”
278.04 81.10 0.00
The response variable was Zostera marina occurrence (presence/absence).
Predictor variables were depth, slope, “enclosedness” (part of inlet or bay),
average and maximum wave exposure (SWM), and current speed measures.
D is the discrepancy from the best model. Wi is the probability that the
model is the most appropriate (the Akaike weight). For the factors that did
not improve the model, only the AICc value for the simple factor effect is
shown.
We found Z. marina only in areas deeper than 0.5 m (which in
our study is 0.5 m below the lowest astronomical tide) and down
to 10 m deep. The main influence of depth on algae and seagrass is
generated through its effect on light and hence available irradiance
for photosynthetic activity. The observed lower depth limit of
10 m in the study area most likely represents the compensation
depth of Z. marina (Abe et al., 2003; Duarte et al. 2007).
Probability profiles (Figure 5) were bell-shaped from land out
to the sea, with low probability in inner areas, an increase
farther out to sea, and low probability in outer areas. The low
probability in the innermost areas may be caused by the high
levels of wave exposure in our area, because the effect of waves
on the seabed (given similar surface wave exposure levels) is
larger in shallow than in deep areas (Krause-Jensen et al., 2003).
High levels of wave exposure may result in coarser sediment and
sediment instability, which may be unsuitable for Z. marina
growth.
Zostera marina occurred more often in flat than in steep
terrain. Slope has also been identified previously as an important
regulating factor for the distribution of macrophytes (Duarte
and Kallf, 1990; Narumalani et al., 1997). However, many study
areas have small slope variation in a generally gentle terrain,
and consequently have not demonstrated any effects of slope
(Krause-Jensen et al., 2003). Our study area has large terrain
variations and steep slopes, which have higher probabilities for
rocky substratum and sediment instability (which is unsuitable
for seagrasses).
Using maximum values of wave exposure (i.e. focusing on
storm events) did not improve the model. This was unexpected,
because storm events may cause disturbance such as resuspension
of sediments, movements of sand dunes, and the subsequent burial
of seagrasses (Marba and Duarte, 1995; Bell et al., 1999; Fonseca
et al., 2000). However, in our area, the wind is generally so
strong that the average value of wave exposure may be above the
limit at which these disturbances have an influence.
Consequently, including maximum wave exposure will not add
any information or improve the model.
Including wave exposure values at different temporal scales did
not improve the model, and wave exposure averaged over the past
5 years provided a better model than those including wave
exposure averaged for the previous year, for the growth season,
and for winter (including the additive effect by combinations
of these factors). Vegetative growth or seeding can be a slow
process (Hemminga and Duarte, 2000), and Z. marina presence
may depend on environmental factors at a different temporal
scale than that measured (Greve and Krause-Jensen, 2005).
However, similar to the discussion on maximum wave exposure
1098
T. Bekkby et al.
Figure 3. Histograms of eelgrass (Zostera marina) occurrence against depth, slope, and wave exposure [SWM, based on wind statistics
averaged for the past 5 years, numbers presented as 1/1000 (see Rinde et al., 2006)]. The depth x-axis is in metres, the slope x-axis in degrees.
The height of the bars represents the total number of observations (presences and absences) and the light grey on the histograms represents
the number of absences. The dark grey on the histograms and the number on the top of each bar is the number of eelgrass observations (in all,
35). Dashed lines represent the overall proportion of the presences compared with the total number of observations; solid lines show whether
the proportion of presences in each bar is higher or lower than the overall proportion.
Figure 4. Response curve of eelgrass (Zostera marina) occurrence against depth, slope, and wave exposure (SWM, based on wind statistics
averaged for the past 5 years, numbers presented as 1/1000, Rinde et al., 2006). The depth x-axis is presented in metres, the slope x-axis in
degrees. The y-axis represents the additive contribution of each variable. Note that the range of the y-axes differs between the panels. Dashed
lines represent the upper and lower pointwise 2 s.e. curves.
above, the reason for the lack of effect of exposure values at
different temporal scales may be that the patterns in wave exposure
structuring the distribution of Z. marina are included in the
averaged models.
We found no effect of current speed, using mean, median,
90th percentile, and maximum amplitude. A possible explanation
is that the current speed model is too coarse (100 m spatial
resolution). However, Figure 4 indicates that the habitat
structures modelled are large, and we expected the resolution of
the current speed model to be sufficient. A second possible
explanation is that the model is two-dimensional (one layer)
and that we consequently do not have a good representation of
the seabed current.
“Enclosedness” (inlet or bay) did not have any effect, and the
influence of this factor (indicating soft sediment) is most likely
covered by the other factors being included in the model (e.g.
slope and wave exposure).
Among the stations visited in this study, Z. marina was quite
rare (found at just 5% of the 695 stations). This is caused by
both the distribution pattern of the species and the sampling
design. The area belongs to the outer archipelago of the west
coast of Norway, and many of the locations are too exposed to
waves for Z. marina growth. Also, the stations were selected to
cover a range of marine habitats, and consequently included
areas where Z. marina was not expected to be abundant (see
Figure 4 and Table 1 for information on stations and predictor
statistics). As a result, there were few instances of Z. marina
presence compared with its absence.
The cross-validation value of the probability model was high
(0.89). However, the calculated probability of Z. marina occurrence (Figure 5) was low, a maximum of 44%. This model result
is reliable and reflects the field information, in particular the
spatial pattern, because Z. marina requires environmental conditions that are rare compared with those generally found in this
outer coast area. The low level of probability may also be caused
by both Z. marina absence and presence being recorded in areas
of similar depth, slope, and conditions of wave exposure. These
areas may consist of sediment types unsuitable for Z. marina
growth (e.g. coarse and/or unstable sediment), something that
may not be completely captured by the predictors at the given
spatial scale. It may also be caused by the patchiness and temporal
variability of Z. marina. As for the patchiness, the resolution of
our bathymetric data (10 m) may not be sufficient to detect small
terrain structures of relevance for Z. marina occurrence. As for temporal variability, a lot of the absence from shallow, sheltered, gently
sloping areas is likely a result of our snapshot sampling, i.e. only
including data from 1 year. We believe that sampling data over
several years would lead to more recorded presences in the area,
higher model certainty, and consequently greater probability.
The validation of the model shows a high degree of precision,
and the results reflect the influence of the large gradients of the
physical factors within the area. The geographical distribution
pattern of Z. marina is consistent over large areas, indicating
that the recorded probability differences between areas are reliable
even if the actual probability value is low. The spatial patterns are
more consistent with field observations and literature than we
expected, based on the rather coarse temporal and spatial resolution of the predictor variables.
Nutrient level is generally an important variable for Z. marina
growth and occurrence (see Lee et al., 2007; review by Burkholder
et al., 2007). Additionally, incidences of oxygen depletion may
explain the discrepancies between models and actual observations
of eelgrass (Holmer and Bondgaard, 2001). These variables have
not been available for our study, neither as point measurements
Spatial probability modelling of Zostera marina distribution on the west coast of Norway
1099
Figure 5. Predicted probability of finding eelgrass (Zostera marina) within the study area. The darker the area, the greater the probability.
The spatial resolution of the model is 10 m.
at the selected stations nor as GIS layers for use in the probability
modelling. As the study area is in an archipelago in the outer
exposed coast, oxygen depletion and spatial variation in nutrient
level was not expected. However, year-on-year variations in Z.
marina occurrence may be explained by these factors, and
follow-up studies should take them into account.
Grazing (see Hemminga and Duarte, 2000), bioturbation
(Philippart, 1994; Townsend and Fonseca, 1998), interactions
with epiphytes and free-floating macroalgae (Hauxwell et al.,
2001), and temperature (Lee et al., 2007) are other factors that
may influence the distribution of Z. marina. No information on
these factors was available for this study.
Spatially autocorrelated data may pose problems for validation
tests in GAMs owing to violations of the assumption of independence (Edwards et al., 2006). However, in ecological datasets, the
assumption of independence is often unrealistic (Økland, 2007).
To reduce the effect of autocorrelation, the stations of this study
were selected to avoid multiple registrations within the same
grid cell (the minimum distance between stations was 15 m).
In our opinion, spatial predictive probability modelling is
potentially a very useful approach for management purposes,
because it is quantitative and defined objectively, extrapolates
knowledge from sampled to unsurveyed areas, and results in a
probability map that is easy to understand and disseminate to
various stakeholders. The approach provides users with the
choice of applying the precautionary principle approach or a
more focused approach. Including all possible habitats for the
given species (i.e. also areas with low probabilities) is a relevant
starting point for a precautionary principle approach in the
mapping, monitoring, or research activity. However, if one
needs to give priority to areas with respect to, for instance, establishing marine protected areas, it will be useful in identifying areas
with the highest probabilities only. Probability maps may also
be used for restoration purposes, e.g. finding areas suitable for
1100
seagrass planting (Kelly et al., 2001), and as a basis for measuring
area for integrated coastal-zone management and planning
purposes.
Evaluation of how environmental patterns change with focal
scale is a basic step in understanding ecosystems (Wiens, 1989;
Holling, 1992; Levin, 1992). There is no optimal scale at which
ecosystems should be described, classified, modelled, and
mapped, and a land-/seascape might appear heterogeneous at
one scale, but quite homogeneous at another (Forman and
Godron, 1986). Further studies will need to focus on understanding how a change of spatial scale affects the predictability of models
and the information obtained from the results.
Acknowledgements
The project was funded by the Research Council of Norway, the
Norwegian Institute for Nature Research, and the Norwegian
Institute for Water Research (NIVA). Thanks to H. Christie
(NIVA), J. Ekebom (Metsähallitus, Natural Heritage Services,
Finland) and the anonymous referee for valuable comments, as
well as to everyone who participated in the fun and hard-working
field survey at the windy and wavy Møre coast.
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