Journal of Applied Ecology 2010, 47, 1110–1118
doi: 10.1111/j.1365-2664.2010.01850.x
Environmental impact assessment of offshore wind farms: a simulation-based approach Blanca Pe´rez Lapen˜a1*, Kathelijne M. Wijnberg1, Suzanne J. M. H. Hulscher1 and Alfred Stein2 1
Water Engineering and Management, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands; and International Institute for Geo-Information Science and Earth Observation (ITC), PO Box 6, 7500 AA Enschede, The Netherlands
2
Summary 1. Assessing and monitoring the impact of offshore wind farms on marine fauna is vital if we want to achieve ecologically sustainable development of this renewable energy resource. Given the complexity of the marine environment, a method capable of accommodating spatio-temporal behaviour of specific species and their interrelation with other marine phenomena is an essential prerequisite for investigating whether or not there has been any measurable impact to date. 2. This paper presents a method based on geostatistical simulation to assess whether pre- and postconstruction collected bird count data suggest displacement of birds due to the wind farm. The method takes into account spatial autocorrelation in species abundance at various scales, pre- and post-construction differences in environmental conditions and in survey effort and design. 3. We demonstrate that taking these factors into account influences the conclusions about a wind farm’s impact on bird life. In particular, incorporating spatial autocorrelation in seabird numbers is an important factor in reducing the risk of wrongly identifying an effect of a wind farm on bird abundance. 4. Synthesis and applications. The development of offshore wind farms is often in conflict with nature conservation interests. Environmental impact assessment and monitoring is essential to protect and manage the marine environment. The method described here will allow scarce data to be utilized effectively as a basis for well-informed environmental decisions. In addition, the method will assist in the design of optimal monitoring procedures at a given site, balancing costs and effectiveness in detecting potentially harmful impacts. Key-words: autocorrelation, geostatistics, impact assessment, offshore construction, seabirds, spatial simulation, survey design
Introduction Marine waters have wide economical value including oil and gas reserves, sand extraction, fisheries and more recently offshore wind farms. The marine environment is also home to diverse marine fauna. Synergy between these economical and ecological functions is essential to ecologically sustainable development. The impact of human activities on the marine fauna must be assessed (Gill 2005), particularly with respect to offshore wind farms since construction is a relatively recent development (Inger et al. 2009). Therefore, little is known about the impact on marine fauna (Garthe & Hu¨ppop 2004; Merck 2006), including seabirds (Drewitt & Langston 2006). Under the BACI (Before-After-Control-Impact) framework, the assessment of impact is based on comparisons *Correspondence author. E-mail:
[email protected]
between data (e.g. species counts) collected pre- and post-construction, in the potentially impacted area and in a control area (Stewart-Oaten, Murdoch & Parker 1986; McDonald, Erickson & McDonald 2000; Petersen et al. 2006). In the case of wind farms, the impact area is the location(s) where construction will take place. Ideally, the control area should have similar environmental conditions to the impact area but should be far enough away to be unaffected. Assessment of the impact of offshore wind farms through comparisons of pre- and post-construction species counts at impact and control areas is generally not sufficient for three main reasons: 1. Spatial autocorrelation in seabird numbers. 2. The dynamic nature of environmental factors affecting the spatial distribution of seabirds. 3. Differences in survey effort and design between pre- and post-construction periods.
2010 The Authors. Journal compilation 2010 British Ecological Society
Impact assessment and offshore wind farms 1111 Spatial autocorrelation may occur as a consequence of seabirds responding to variation over time in environmental factors such as water temperature, salinity and water transparency (Garthe 1997) that are in turn spatially autocorrelated. Spatial autocorrelation in species abundance data at such coarse scales can be addressed by building a deterministic model relating the expected (or mean) number of birds to environmental factors. However, the residuals of the fitted model may still be spatially autocorrelated (Pebesma, Duin & Bio 2000). This may occur, for example, if other environmental factors affecting seabirds were omitted or due to the flock behaviour of birds. Where there is unexplained positive spatial structure to the data, pairs of species observations at given distances will tend to have similar values, hence cannot be considered as independent observations. Traditional (non-spatial) statistical tests for calculating significant differences in the number of birds between the pre- and post-construction periods are not suitable, as the standard errors will be underestimated. This would result in inflation of Type I errors, i.e. an increase in the risk of wrongly identifying an effect of the wind farm on the number of birds. The dynamic nature of environmental factors will affect the spatial distribution of seabirds because conditions during pre- and post-construction surveys may not be the same. It will therefore be difficult to isolate the effect of the wind farm. Finally, differences in survey effort and design (number and location of observations) between the pre- and post-construction periods may also generate differences in the number of birds observed that may be unrelated to wind farm construction. The objective of this paper is to present a transparent and flexible method to detect differences in the number of a given species due to the presence of an offshore wind farm. In statistical terms, this means testing the null hypothesis of ‘no change’ in bird numbers between pre- and post-wind farm construction. The method explicitly takes into account the effect of differences in the spatial structures of species abundance at various scales as well as the effect of variation in post-construction survey effort and design.
Materials and methods We test the null hypothesis that the construction of an offshore wind farm had no effect on the number of birds using the area with the wind turbines (hereafter referred to as wind farm area). A model relating species abundance to environmental factors is constructed to account for spatial autocorrelation in bird counts over large distances (hereafter referred to as coarse scale). Using this deterministic model, the number of birds that are expected at survey locations is characterized. To account for variation in environmental conditions between pre- and post-construction periods, the pre-construction situation is re-defined (hereafter referred to as ‘reference’ situation) to match the actual environmental conditions of the post-construction survey. The deterministic model then predicts the expected (or mean) number of birds at post-construction survey locations. To define the null hypothesis we use the difference between the mean number of birds in the wind farm (lwf) and control area (lc),
hereafter referred to as indicator. We use this difference to accommodate temporal variation in bird numbers unrelated to wind farm construction. The null hypothesis and alternative hypothesis are stated as follows: H0 : ½lwf lc ref ½lwf lc post ¼ 0 Ha : ½lwf lc ref ½lwf lc post 6¼ 0 where [lwf - lc]ref is the expected difference in the ‘reference’ situation and [lwf - lc]post is the expected difference in the post-construction survey. We refer to the difference [lwf - lc]ref - [xwf xc]post as the test statistic, where xwf and xc are an estimate of lwf and lc, respectively. Due to stochastic processes, [xwf - xc]post will deviate from [lwf lc]ref. Therefore, the probability distribution of the test statistic under H0 is derived using geostatistical simulations. This probability distribution is hereafter referred to as the null distribution of the test statistic. Values [xwf - xc]post that could arise from [lwf - lc]ref are based on simulated bird count surveys, also referred to as realizations. The xwf and xc in each realization of [lwf - lc]post is calculated using simulated counts having the required spatial autocorrelation. Then, by comparing the observed difference [lwf - lc]ref - [xwf - xc]post against the null distribution, H0 is either non-rejected or rejected in favour of Ha (Parkhurst 2001). A step-flow diagram of the method is presented in Fig. 1. In the following sections, the method is described in more detail.
PROPERTIES OF A SEABIRD COUNT DATA SET
The artificial data set arose from an on-going impact assessment of an offshore wind farm development on the Dutch North Sea Continental Shelf. The study area is located between 10 and 18 km off the coast of Egmond aan Zee (The Netherlands). It covers an area of approximately 900 km2. Within this area, we distinguished the wind farm area (approximately 39 km2) from the zone around it (control area). Survey transects were approximately 3 km apart, providing 10 equidistant transects over the total study area in each survey (Leopold et al. 2004). Seabird count data was collected from a ship using the strip-transect methodology as described by Tasker et al. (1984). Birds were counted during 5 min periods from each side of the ship over approximately 0Æ5 km2 of surface area. Bird counts were georeferenced to the central position of the counting strips (Fig. 2). Effectively, this yields to a relative measure of abundance if detection probabilities were not considered. Variation in the data could result from several processes, e.g. changes in population size (van Eerden & Gregersen 1995), species’ seasonal rhythms (Leopold et al. 2004), location of breeding colonies (Suryan & Irons 2001), meteorological conditions on the day of the survey, water temperature and salinity, water transparency (Garthe 1997), food availability (Hunt 1997), fishing activity (Camphuysen 1995; Skov & Durinck 2001), the observer and time of the day (Certain & Bretagnolle 2008). These sources of variability can be accounted for in the model.
THE GEOSTATISTICAL MODEL
In this paper, the focus is on variation in species counts in space. We adopt the view of Fortin & Dale (2005) in which ecological data may include spatial structure at different scales: gradients at coarser scales and spatial autocorrelation at intermediate scales. Following the approach in Pebesma, Duin & Burrough (2005), spatial structure in seabird counts is incorporated by means of two components in a sta-
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1112 B. Pe´rez Lapen˜a et al.
Fig. 1. Step-flow diagram of the developed method. Numbers refer to the steps described in section ‘Simulation’.
sion and, therefore, we assume the data is described by a Poisson distribution. The count at a given location Y(i) is treated as the outcome of a Poisson random variable which is modelled as: YðiÞ ¼ lðiÞ þ eðiÞ
Fig. 2. Observed density (birds km–2) of common guillemots Uria aalge during November 2003. Each circle is scaled to represent the observed seabird density. The coordinates of the circle centre represent the central position of the survey counting strips.
tistical model: a deterministic component l(i) and a stochastic component e(i), where i is the spatial location of the sampling unit. The deterministic component includes variability in species counts due to known sources, and the stochastic component accounts for variability that we cannot explain. From a statistical point of view, the spatial distribution of species counts is bounded by zero and does not have a distinct upper limit. For simplicity, we consider that bird counts do not exhibit overdisper-
eqn 1
If a relationship exists between the distribution of seabirds and coarse scale environmental factors (e.g. water temperature), this relationship can be analysed using regression methods. Given that bird counts are assumed to be Poisson distributed variables, we use a Generalized Linear Model (GLM) with a Poisson link function. With the resulting deterministic model, expected (or mean) bird counts l(i) for values of environmental factors can be predicted. Once we have modelled spatial autocorrelation in bird counts at coarse scales, the remaining spatial autocorrelation is modelled within the residuals e(i) calculated from equation 1. The chosen model for the spatial autocorrelation is the variogram. As this model requires the residual to be a stationary random variable, the autocorrelation is modelled in the standardized residuals as described in Pebesma et al. (2005). In the case of a Poisson variable Y, whenever overdispersion is not present, the standardized Pearson residuals equal: pc ðiÞ ¼
YðiÞ lðiÞ pffiffiffiffiffiffiffiffi lðiÞ
eqn 2
where pc(i) is a normal random variable pc(i) N(0, 1). Once the experimental variogram has been determined, the theoretical variogram is fitted. This provides the semi-variances for pairs of observation locations separated by a distance h. The exponential variogram model, for example, takes the form:
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Applied Ecology, 47, 1110–1118
Impact assessment and offshore wind farms 1113 h cðhÞ ¼ c0 þ c1 1 exp a
eqn 3
where c0 is the so-called nugget (residual semi-variance at distance h = 0), c1 is the sill (residual semi-variance at distance h where spatial autocorrelation is no longer present), and the effective range is 3a (distance h over which spatial autocorrelation is no longer present). Once the spatial autocorrelation in bird counts has been modelled, the model parameters are used to simulate realizations of seabird counts at post-construction survey locations. The simulated counts have therefore similar statistical properties as the ‘reference’ situation.
SIMULATION
In order to investigate impact, we test for statistical differences in the chosen indicator between the ‘reference’ situation and the post-construction survey data. The distribution of possible differences [lwf - lc]ref - [xwf - xc]post under H0 is derived using geostatistical simulation. The simulation procedure consists of the following steps: 1. Using the parameters of the environmental model, the value of the mean l is predicted at each of the post-construction survey locations. The predicted means represent expected bird counts at post-construction survey locations and define the ‘reference’ situation. Using the predicted means, the indicator for the ‘reference’ situation [lwf - lc]ref is calculated. 2. The spatially autocorrelated standardized residuals pc(i) are simulated at each post-construction location using the fitted variogram model. This is achieved by creating a matrix pu with each row representing a spatial location and each column containing a value sampled from a standard Normal distribution pu(i) N(0,1). The expectation of the product of matrix pu by its transpose puT would result in the correlation matrix, which for uncorrelated standard normal variables, reduces to the identity matrix I. Eðpu pTu Þ ¼ I
HYPOTHESIS TESTING
Testing the null hypothesis of ‘no change’ in the number of birds between the ‘reference’ and post-construction situations involves assessing whether the observed difference [lwf lc]ref - [xwf - xc]post falls into the (user-specified) critical region. We define the critical region for rejection of H0 using percentiles of the null distribution rather than from a fitted ‘theoretical’ distribution. The critical region is the set of differences that causes H0 to be rejected in favour of Ha. Therefore, rejection or nonrejection of H0 allows us to investigate impact or no impact of the wind farm, respectively.
Results In this study we demonstrate the method using an artificial seabird data set. Artificial data has been chosen in favour of actually observed data as this allows us to apply the method with varying parameters for four aspects: spatial structure, survey effort and design, values for environmental factors and assumptions regarding the statistical distributional properties of the data. This section illustrates (i) the need to incorporate these aspects, (ii) their effect on the null distribution and (iii) their effect on the conclusions drawn in the context of impact assessment. Three cases are considered (SP1, SP2 and SP3) for the spatial structure in the residuals obtained in equation 2. In the three cases, the exponential variogram model (described in equation 3) is used with specific parameter values as:
eqn 4
Next, the uncorrelated random variables are transformed to satisfy the spatial correlation function (derived from equation 3). This requires finding a matrix U that, when entered in equation 4, would result in the correlation matrix C as opposed to the identity matrix: T
a transformation between the Normal and the Poisson distribution is carried out. 4. For each realization r, the difference between [lwf - lc]ref and [xwf - xc]post(r) is calculated. With the calculated differences over all realizations, we derive the null distribution to be used in hypothesis testing.
T
Eð½Upu ½ pu U Þ ¼ C
eqn 5
8 < nugget SP1 sill : eff. range
c0 ¼ 0 c1 ¼ 1 3a ¼ 500 m
eqn 8
8 < nugget SP2 sill : eff. range
c0 ¼ 0 c1 ¼ 1 3a ¼ 4000 m
eqn 9
8 < nugget SP3 sill : eff. range
c0 ¼ 0 c1 ¼ 1 3a ¼ 12 000 m
Therefore, we need to find matrix U satisfying: UUT ¼ C
eqn 6
where UUT is the so-called Cholesky decomposition of the correlation matrix C. Multiplying U by the matrix of uncorrelated variables, results in autocorrelated standardized Pearson residuals pc. pc ¼ Upu
eqn 7
3. The count Y(i) at each location is derived from pc(i) using equation 2. The resulting counts are still normally distributed with the specified mean l(i) and variance r2(i) = l(i). As the process generating the seabird counts is assumed to be a Poisson process,
eqn 10
This choice of values for the effective range parameter 4000 and 12 000 m aims to illustrate the effect of spatial structure in seabird counts in relation to the size of the wind farm (of approximately 12 km in the major diagonal). Four cases are considered for the survey effort and design over the area studied for impact. In the first case (SED1), the area is surveyed at spatial locations 1 km apart (Fig. 3a). In
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1114 B. Pe´rez Lapen˜a et al. (a)
(b)
(c)
(d)
Fig. 3. Cases for survey effort and design: (a) case SED1, (b) case SED2, (c) case SED3, and (d) case SED4. Each dot represents the spatial location where data on the number of birds is assumed to be available. The polygon represents the wind farm area.
the second case (SED2), survey transects are 3 km apart and with a distance of 1 km between consecutive observation locations within transects (Fig. 3b). In the third case (SED3), the number of observations equals the number from case SED2 with a different spatial arrangement within the wind farm area (Fig. 3c). In the fourth case (SED4), the number of observations equals the number from case SED2 and SED3 but it has a different ratio of the wind farm area to survey area (Fig. 3d). We explore the effect of external factors on the null distribution by considering two different parameter values for the mean l in equation 1. In the first case (EF1), it is assumed that the deterministic model predicts in the post-construction survey a constant mean count at each observation location l(i) = 5 over the entire study area. In the second case (EF2) the mean bird count at each location is related to a linear predictor of a given explanatory variable affecting the number of birds observed (e.g. sea water temperature as a proxy for feeding conditions) and it is therefore variable amongst observation locations. The generalized linear model that is used for illustrating the case EF2 is presented in equation 11. lðiÞ ¼ expð34 þ 04 wtempðiÞÞ
eqn 11
Finally, we explore the effect of assumptions regarding the distributional properties of the seabird data on the null distribution (case NB). Instead of assuming a Poisson distribution to describe the data, we assume a Negative Binomial distribution. In this manner, additional variation from that described by a Poisson process can be incorporated using a dispersion parameter. To illustrate the case NB, we impose that the variance of the Negative Binomial distribution is twice the variance of the Poisson distribution. For example, for case EF1, the variance of the Negative Binomial distribution is set to rNB2 = 10. The dispersion parameter ø of the Negative Binomial distribution
is therefore ø = 5. The calculation of the Pearson residuals (equation 2) is modified in order to account for overdispersion in the data. Pearson residuals are calculated as: pc ðiÞ ¼
YðiÞ lðiÞ qffiffiffiffiffiffiffiffi r2NB
eqn 12
Given the new calculation of the Pearson residuals, step 3 in section ‘Simulation’ is accordingly modified: the count Y(i) at each location is derived from pc(i) using equation 12. As the process generating the seabird counts is assumed to be a Negative Binomial process, a transformation between the Normal and the Negative Binomial distribution is carried out. This particular combination of cases SP2-EF1-SED2 (hereafter referred to as the main case), serves as a reference for investigating the effect of spatial structure, survey effort and design, environmental factor values, and the distributional properties of the data. The resulting null distribution for the main case is shown in Fig. 4a, and is based on 20,000 realizations. All implementation of the method has been done using the software R (R 2008) and related packages (see Appendix S1, Supporting Information).
EFFECT OF SPATIAL STRUCTURE
Neglecting spatial structure when simulating bird counts in impact and control areas has an important effect on the null distribution. This effect is observed by comparing the main case vs. SP1-EF1-SED2. Figure 4a, the main case, shows the null distribution when spatial structure is taken into account. Figure 4b shows the null distribution under the assumption of independent observations.
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Applied Ecology, 47, 1110–1118
Impact assessment and offshore wind farms 1115
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Fig. 4. Null distributions of the test statistic [lwf - lc]ref - [xwf - xc]post for the different cases investigated in section ‘Demonstration’. For each null distribution, the mean (l) and variance (r2) is provided.
Both distributions have the same mean and the difference lies in the variance. This difference is caused when adding spatial dependency in the observations used to calculate the means xwf and xc of the indicator [xwf - xc]post, leading to an increase in the variance of the null distribution. We explore the effect of varying spatial structures on the null distribution, by comparing the main case vs. SP3-EF1SED2. This effect is isolated by using the same survey effort and design (SED2) and the same value of the environmental factor at each location (EF1). Figure 4c shows the null distribution when the effective range is three times larger than the effective range used in the main case. Both null distributions in Figs 4a and c have the same mean, with a larger variance for the distribution in Fig. 4c. This increase in the variance is caused by the distance over which spatial dependency between the observations is present. Spatial dependency over larger distances (e.g. range of 12 000 m as compared to a range of 4000 m) causes pairs of neighbouring observations over larger distances to have similar bird counts in each realization. As each realization is independent from any other, differences [xwf - xc]post present a larger variability over all realizations than when the counts are less correlated. This larger variability, in turn has an effect on the variability of the test statistic over all realizations, leading to an increase in the variance of the null distribution.
EFFECT OF SURVEY EFFORT AND DESIGN
We explore the effect of varying survey design and the ratio of the wind farm area to survey area by comparing the main case vs. SP2-EF1-SED3 (Fig. 4d) and the main case vs. SP2-EF1SED4 (Fig. 4e).
The effect of survey design is isolated by using the same spatial structure (SP2) and the same value of the environmental factor at each location (EF1). We use a different spatial arrangement of survey locations in the wind farm area and apply our method to derive the null distribution. Although both cases use the same number of observations to derive the null distributions in Figs 4a and d, there is a difference is the calculated variance. This difference is caused by the configuration of the survey locations, as the observations for case SED3 are on average closer together than for case SED2. This causes a higher spatial dependency amongst observations and therefore, an increase in the variance of the derived null distribution in Fig. 4d. We isolate the effect of the ratio of the wind farm area to the survey area by using the same number of observations, both in the wind farm and in the control area, in case SED2 and in case SED4. The difference between these two cases is the ratio of the wind farm area to the survey area which for case SED2 is 1 ⁄ 30 and for case SED4 is 1 ⁄ 10. The null distributions in Figs 4a and e have the same mean, with a larger variance for the distribution in Fig. 4e. This increase in variance is caused by the configuration of survey locations. Therefore, when reducing the survey area in this context, it becomes more difficult to reject H0, and thus to investigate impact. On the other hand, we may look at the same effect but under different conditions. This time, cases SP2-EF1-SED4 (Fig. 4e) and cases SP2EF1-SED1 (Fig. 4f) are compared. The ratio of the wind farm to the survey area in both cases is 1 ⁄ 10 and 1 ⁄ 30, respectively, but the density of observations remains the same. Comparing the null distributions in Fig. 4e to the one in Fig. 4f, we observe that the means in both distributions remain the same with a decrease in variance when a larger area is surveyed. This
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Applied Ecology, 47, 1110–1118
1116 B. Pe´rez Lapen˜a et al. is due to an increase in the number of observations that are not spatially autocorrelated. As a result, there is an increase in the number of observations that can be considered as independent which allows for a better estimation of the mean in each realization.
EFFECT OF ENVIRONMENTAL FACTOR VALUES
Table 1. Values of the test statistic [lwf - lc]ref - [xwf - xc]post at 2Æ5 and 97Æ5 percentiles using the null distributions presented in section ‘Demonstration’ Figure
2Æ5 percentile
97Æ5 percentile
Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.
)2Æ03 )1Æ27 )3Æ00 )2Æ36 )2Æ22 )1Æ77 )2Æ1 )3Æ01
1Æ87 1Æ20 2Æ63 2Æ09 2Æ00 1Æ61 1Æ88 2Æ48
4a 4b 4c 4d 4e 4f 4g 4h
In our method, we account for the fact that variability in environmental factors affecting seabird counts may change between pre- and post-construction periods. This is achieved by re-defining the pre-construction situation into the ‘reference’ situation and predicting the expected bird count at postconstruction survey locations using the environmental model and the actual survey conditions. Moreover, if the mean number of birds at observation locations changes, the variance also changes. This, in turn, has an effect on the null distribution, here leading to a slight increase in the variance of the null distribution (Fig. 4g). In the simulation, the spatial structure in bird counts Y(i) at local scale is modelled in the standardized residuals (equation 2). Since the standardized residuals have by definition a variance of one, back transformation of pc(i) results in counts Y(i) with a non-stationary variance. Therefore, when applying the method to real data, residuals should be standardized to derive an estimate of the variogram for input in the simulation procedure.
the critical values. In the case of a two-sided test (it is unknown whether the impact of the wind farm will be positive or negative) and for example a significance level of a = 0Æ05, the critical values are calculated as the 2Æ5 and 97Æ5 percentiles of the 20 000 simulated [lwf - lc]ref - [xwf - xc]post. Table 1 shows these percentiles for the null distributions derived in previous sections. The calculated critical values determine whether we reject or fail to reject H0, and as such will influence our conclusion regarding impact of the wind farm on seabirds. For example, if the observed difference in the post-construction survey [lwf - lc]ref - [xwf - xc]post was 1Æ75, it would lead to a rejection of H0 using the null distributions presented in Figs 4b and f and non-rejection of H0 in all other cases of Fig. 4.
ROBUSTNESS: EFFECT OF DATA DISTRIBUTIONAL
Discussion
PROPERTIES
We have assumed that seabird data are well described by a Poisson distribution. We now explore the effect of the assumptions regarding the distributional properties of the data on the null distribution by comparing the main case (Fig. 4a) vs. SP2EF1-SED2-NB (Fig. 4h). Both null distributions have the same mean while the variance is larger for the null distribution derived under the assumption that seabird count data follows a Negative Binomial distribution (Fig. 4h). The reason is that the Negative Binomial distribution allows for larger variation than given by the mean. In each realization larger count values are allowed and, given the presence of spatial autocorrelation in the simulated counts, differences [xwf - xc]post present a larger variability over all realizations as compared to differences calculated assuming Poisson distributed data. Therefore, when using a Negative Binomial distribution to describe the data, it becomes more difficult to reject H0 and consequently to investigate impact. Given the large difference in variance between the null distributions in Figs 4a and h, it is important to check that the data is consistent with the model assumptions used to generate the null distribution.
TESTING H0
Determining the set of differences [lwf - lc]ref - [xwf - xc]post that causes H0 to be rejected in favour of Ha requires choosing
This study proposes a method that aims to enhance environmental management, with the following properties: (i) It is transparent and easily understood by ecologists who need to apply this method in their impact assessment procedures. (ii) It is flexible, allowing site-specific and species-specific information to be accommodated in the analysis, as well as increasing knowledge over time about species behaviour. (iii) It allows the data to be utilized effectively as a basis for well-informed environmental decisions. The cases adopted in the Results section have been simplified for reasons of clarity, whereas more complicated cases can be accommodated. Several studies have analysed the relationship between seabirds and environmental factors (e.g. Camphuysen 1995; van Eerden & Gregersen 1995; Garthe 1997; Hunt 1997; Skov & Durinck 2001; Suryan & Irons 2001). Accommodating these relationships would result in a more complex deterministic model that predicts expected bird counts in the ‘reference’ situation. In addition, although we have focused on the spatial component of the data, knowledge on changes in seabird numbers over time could be readily incorporated in the statistical model. Another aspect that we have not addressed in the current work, but which can be readily incorporated, is how to deal with the larger number of zero count observations than would be expected from a Poisson process. This effect is often encountered in species data (Potts & Elith 2006). A solution has been proposed by Pebesma et al. (2000)
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Impact assessment and offshore wind farms 1117 which uses both Logistic and Poisson models for predicting expected bird abundance. The first model is applied to positive species counts that are first converted into a binary variable. The Poisson model is then applied for the non-zero observation counts. Finally, in those situations where l(i) is less than 10, a transformation of the standardized residuals so that these are normally distributed may be required. The reason for a transformation would be two-fold. On the one hand, standardized residuals having a skewed distribution would produce an erratic variogram (Gringarten & Deutsch 2001), inflating the semi-variance for pairs of observations containing extreme values. On the other hand, the simulation procedure presented in this paper simulates standardized residuals from a standard normal distribution. Our method is presented within a hypothesis testing framework. We look at the chances of obtaining the post-construction collected data if the null hypothesis of ‘no change’ in bird abundance is true. By using all available knowledge to construct the null distribution through simulation, instead of a priori assuming a distribution, the decision about impact is better informed. When following a null hypothesis approach, as presented in this work, one either rejects H0 or not, suggesting that a wind farm had either impact or no impact on seabirds, respectively. If the outcome of the analysis suggests that an impact has occurred, there is the chance that one rejects H0 when H0 was actually true (type I error). Committing type I errors in environmental decision making may cause, for example, wind farm operations to be halted when in fact, these were not causing any impact (Underwood & Chapman 2003). Therefore, type I errors should be minimized as much as possible. In our case, we have demonstrated that where there is spatial autocorrelation in bird abundance, it becomes more difficult to reject H0 when this is incorporated in the method than when it is neglected. Therefore, our method contributes to the reduction of type I errors. Conversely, there is also the possibility that one fails to reject H0 when H0 is in fact false (type II error). The consequences of committing type II errors in environmental decision making are even more serious than for type I errors, as a potential impact may continue without being detected (Underwood & Chapman 2003). So far, we have not explicitly addressed how our method may be applied to reduce type II errors. Reducing type II errors would imply maximizing the probability that the method (correctly) rejects H0 when it is indeed false, that is, maximizing statistical power. In this regard, statistical power calculations may be achieved with a slight modification to the method. Instead of testing H0 using the (single) observed difference [lwf - lc]ref - [xwf - xc]post, power calculations require the specification of a pre-defined decline in the expected bird abundance within the wind farm area for the post-construction period (Maclean, Skov & Rehfisch 2007). Subsequently, multiple values for the difference [lwf - lc]ref - [xwf - xc]decline are obtained from simulated realizations and the power to detect change in bird abundance, when a decline has occurred, is then calculated as the number of times that H0 is, correctly, rejected. Under the proposed method, statistical power can be consid-
ered a function of survey effort and design, spatial distribution of environmental factors, spatial autocorrelation in species abundance and the ratio of the wind farm area to survey area. Today, offshore wind is widely recognized in Europe as an important method of meeting renewable energy targets. Within Europe, the offshore wind energy is expected to increase from 2 GW now to 40 GW in 2020 and 150 GW in 2030 (EWEA 2009). Europe is currently leading in offshore wind development, but North America and Asia are close behind. Apart from the technical challenges of offshore wind technology, the tension between development of offshore wind farms and nature conservation interests is also important. Installation locations that are good from a technical point of view (shallow and ⁄ or nearshore) often tend to be of high ecological interest for diverse marine fauna, and seabirds in particular. The development of many offshore wind farms, which will be needed to realize the ambitions for offshore wind energy, carries the risk of unforeseen cumulative impacts. Therefore, it is important to apply sound impact assessment methods and to design suitable monitoring programmes in order to protect and manage the marine environment. Our method allows better interpretation of the available marine data, providing a good basis for well-informed environmental decisions. In addition, the Precautionary Principle has been recognized as a guiding tool upon which the development of the European Union’s environmental policy should be based (Sanderson & Petersen 2002). Under this guidance, an environmental impact assessment study that shows ‘there is no evidence that an impact has occurred’ does not provide sufficient information, since we also run the risk that an impact did occur but remained undetected. In that case, the necessary mitigating actions will not be taken. Similarly, any positive effects of offshore wind farms on marine fauna might be missed (Inger et al. 2009), which could also affect policy decisions. At present it is not clear how to implement the Precautionary Principle in practice (Underwood & Chapman 2003). Applying the method described here in a power analysis framework (as described above) would be a first step, as it would provide probabilities of impacts of a predefined size to have remained undetected in the available survey data. With this information, environmental managers can be explicit about which risk they are willing to take to let a certain size impact continue without protective measures. Since our method accounts for the effects of survey effort and design, it can also be used to provide environmental managers with information about the design of optimal monitoring procedures at a given site. This will lead to transparent decision-making both for developers in the offshore wind industry and for policymakers who have to balance the need to reach targets for renewable energy at sea against the need to protect the environment.
Acknowledgements This work is part of the project PhD@Sea, which is substantially funded under the BSIK-programme of the Dutch Government and supported by the consor-
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1118 B. Pe´rez Lapen˜a et al. tium WE@Sea. We acknowledge NoordzeeWind and IMARES for the baseline impact study data set; Mardik Leopold, Jan de Leeuw, Henk Kouwenhoven, Jan Tjalling van der Wal and Fredrik Huthoff for the helpful discussions during various stages of the work.
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Supporting Information Additional Supporting Information may be found in the online version of this article. Appendix S1. Implementation details of the method. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.
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