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Alert thresholds for monitoring environmental variables: A new approach applied to seagrass beds diversity in New Caledonia
Marine Pollution Bulletin 77 (2013) 300–307
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Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Alert thresholds for monitoring environmental variables: A new approach applied to seagrass beds diversity in New Caledonia Simon Van Wynsberge a,b,⇑, Antoine Gilbert c, Nicolas Guillemot d, Claude Payri a, Serge Andréfouët a a
UR-227 CoRéUs, IRD (Institut de Recherche pour le Développement), Laboratoire d’Excellence CORAIL, Noumea, New Caledonia UMR-241 EIO, UPF (Université de la Polynésie Française), Faa’a, Tahiti, French Polynesia c Ginger Soproner, Noumea, New Caledonia d Nicolas Guillemot Consultant, Noumea, New Caledonia b
a r t i c l e Keywords: Indicators Power analysis Alert threshold Seagrass Monitoring
i n f o
a b s t r a c t Monitoring ecological variables is mandatory to detect abnormal changes in ecosystems. When the studied variables exceed predefined alert thresholds, management actions may be required. In the past, alert thresholds have been typically defined by expert judgments and descriptive statistics. Recently, approaches based on statistical power were also used. In New Caledonia, seagrass monitoring is a priority given their vulnerability to natural and anthropic disturbances. To define a suitable monitoring strategy and alert thresholds, we compared a Percentile Based Approach (PBA) and a sensitivity analysis of power (SAP). Both methods defined statistically relevant alert thresholds, but the SAP approach was more robust to spatial and temporal variability of seagrass cover. Moreover, this method characterized the sensitivity of threshold values to sampling efforts, a useful knowledge for managers. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Resource exploitation, pollution, climate change, as well as natural disturbances, can affect ecosystemic variables of different types (Bellwood et al., 2004). Their monitoring is of critical importance to detect abnormal trends and environmental degradations (Hughes et al., 2003; Halpern et al., 2008). Before explaining causality of changes, monitoring plans have to detect changes. Recently, several management frameworks have called for fast and useable protocols to define ‘‘alert’’ or ‘‘quality’’ thresholds (e.g., European Water Framework Directive, 2009; Australian and New Zealand Guidelines for Fresh and Marine Water Quality, 2000). In practice, a portion of the available dataset is used to define reference values, and ‘‘alert state levels’’ are proclaimed as soon as the monitored variable exceeds these reference values. This monitoring scheme has been recently applied for benthic quality assessment in both temperate (Borja et al., 2003, 2007; Nilson and Rosenberg, 1997; Rosenberg et al., 2004) and tropical (Bigot et al., 2008) regions, and for monitoring seagrass beds in Australia (McKenzie, 2009). Many studies are based on descriptive statistics approaches (e.g., percentiles or medians), mostly because of their simplicity and robustness to extreme events. In the past decades, techniques using the power of statistical tests have also been developed. The statistical power reflects the ⇑ Corresponding author at: UMR-241 EIO, UPF (Université de la Polynésie Française), Faa’a, Tahiti, French Polynesia. Tel.: +689 76 36 35. E-mail address: [email protected] (S. Van Wynsberge). 0025-326X/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2013.09.035
capacity to detect a significant difference between ‘‘reference’’ and ‘‘tested’’ datasets when a change actually occurred between them. Power is a function of sample size (N), the probability of type I error (a), and the magnitude of the difference between the null hypothesis and reality (the ‘‘effect size’’ or ‘‘standardized effect size’’; Cohen, 1988, Fig. 1). When, for a given sampling effort, the difference between the ‘‘tested’’ dataset and the ‘‘reference’’ dataset is high compared to natural variability, the probability to be right by concluding to a difference between the ‘‘tested’’ and ‘‘reference’’ datasets, using a statistical test, is high (i.e. statistical power is high). A useful application of statistical power is the sensitivity analysis, which is commonly used in various science fields (e.g., Cohen, 1988; Faul et al., 2007, 2009). Sensitivity analyses of power aim at determining the effect size required to conclude on the reality of a difference for a given power. Guillemot and Ducrocq (2011) used a sensitivity analysis of power for commercial fisheries management in New Caledonia’s South Province to determine Catch Per Unit of Effort (CPUE) thresholds on the basis of historical dataset and related trends. Logically, Guillemot and Ducrocq (2011) defined afterwards for managers ‘‘pre-alert’’ and ‘‘alert’’ states in reference to historical levels of exploited fish stocks. In New Caledonia, fisheries are not the only source of concerns. Coastal environments pay a toll to ongoing mining projects throughout the country. Nickel mining is a large driver of the local economy, and massive projects lead to visible environmental impacts on terrestrial and marine realms (e.g., Fichez et al., 2010; Morat, 1993). The critical habitats
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and lagoons became part of the UNESCO World Heritage listing in 2008 (Andréfouët and Wantiez, 2010). The present study focused on the Voh-Koné-Pouembout area on the north western coast, where a major nickel mining site will take momentum in its extracting activity in 2013 (Fig. 2). A large navigation channel was dug in the lagoon in 2008/2009 near the mining site to give access to large boats to the new harbor. As the maritime traffic will increase through that channel in 2013, it is expected that resuspended sediments may affect nearby seagrass beds in the future (Fig. 2). Detecting abnormal trends and defining alert thresholds for seagrass cover is a high priority. 2.2. Seagrass beds sampling protocol Fig. 1. Definition of type I error (a) and power (1 b). a is the area under the tails of the null hypothesis H0 whereas power is the area under the alternative hypothesis H1 outside the tails of the null hypothesis. The standardized effect size is the distance between the mean of H0 and H1 divided by standard deviation. The greater the effect size and lower the intrinsic variability, the higher the power.
that are potentially disturbed by mining-induced sedimentation and pollution include coral reefs, mangroves and seagrass beds. The latter offers challenging configurations for monitoring given their large diversity in New Caledonia (Andréfouët et al., 2010). Seagrass beds are of particular concern as they play a major role (Collier and Waycott, 2009; Mellors, 2003; Cullen-Unsworth et al., 2013) but are exposed and vulnerable to many disturbances, both natural and anthropogenic (Erftemeijer and Lewis, 2006; Collier and Waycott, 2009; Mellors, 2003; Rasheed, 2004). In most monitoring schemes, the total seagrass cover has been used as indicator of seagrass status (e.g., Heidelbaugh and Nelson, 1996; Duarte et al., 2004; McDonald et al., 2006), due to its relative sampling simplicity and cost-effectiveness (Fontan et al., 2010). However, the natural variability associated with seagrass cover is high and may blur management recommendations (Mellors, 2003; Corbett et al., 2005). High temporal variability makes the definition of relevant alert thresholds difficult, and high spatial variability makes thresholds sensitive to sampling effort and design (Van Wynsberge et al., 2012). In fact, what is actually missing is a statistically relevant method to detect changes and define alert thresholds, taking into account both sampling effort and high variability of monitored variables. Hereafter, we compare the results and robustness of two approaches used to define alert thresholds for seagrass cover in New Caledonia. First, we considered the approach referenced as the ‘‘Percentile Based Approach’’ (PBA), which is similar to methods used for monitoring water quality in Australia and New Zealand (Australian and New Zealand Guidelines for Fresh and Marine Water Quality, 2000). This method is currently in use for monitoring seagrass meadow in Australia (see McKenzie, 2009). It defined ‘‘quality’’ states of seagrass beds in comparison with seagrass cover percentiles of reference and tested datasets. The PBA approach thus defined ‘‘quality’’ states of seagrass beds on the basis of descriptive statistics only, without involving statistical tests. Second, we implemented a sensitivity analysis of power (SAP) using a Wilcoxon–Mann–Whitney test to define ‘‘alert thresholds’’ for seagrass bed cover, and tested a wide range of sampling efforts. Both methods aim to detect and classify changes. We point out that they are not used here to identify the cause of observed changes, which is beyond the scope of this paper.
Seagrass density data were collected at 20 stations (Fig. 2), each year from 2007 to 2012 during both the summer and winter periods. As some species displayed lower and less variable coverage in the winter season (e.g., Cymodocea serrulata, Friedman test, p < 0.01), we restricted our study to the dataset sampled in the winter season only. Stations were distributed along a offshore–inshore gradient, throughout the distribution area of shallow seagrass (Fig. 2). For each station, cover of seagrass species were estimated using a set of ten 0.25 m2 randomly placed quadrats using the Braun-Blanquet cover abundance scale (Braun-Blanquet, 1932). Specifically, two divers estimated semi-quantitatively the cover of every seagrass species, using cover classes and indexes described in Table 1. 2.3. Defining monitoring station groups and reference dataset The sampled stations were clustered into four monitoring groups according to their spatial locations and exposure to potential mining impact and sediment resuspension (Fig. 2). Group 1 was composed of stations far from the mining site. Conversely, groups 2 and 4 were more likely to be exposed to potential mining impacts given their proximity to development sites. Group 3 was intermediate. Spatial and temporal variability of seagrass cover for any given g group (respectively SVg and TVg) were computed using the equations below: ni p X X
SV g ¼
i Þ2 ðyij y
i¼1 j¼1 ni p X XX
ð1Þ
i Þ2 ðyij y
g
i¼1 j¼1
p X i y Þ2 ni ðy
TV g ¼
i¼1 p XX g
ð2Þ i y Þ2 ni ðy
i¼1
2. Materials and methods
where p is the number of years, ni is the number of sampled stations for year i, and y is the seagrass cover. The 2007–2011 dataset was used as reference dataset. As unusual dredging activities occurred in 2008–2009 around stations of group 4, we first performed a Kruskall–Wallis test to verify that seagrass cover was not affected and did not bias the 2007–2011 reference period. This 2007–2011 reference period was used to evaluate seagrass cover spatial and temporal variability (SV and TV).
2.1. Study site
2.4. Monitoring seagrass bed cover, species richness and composition
New Caledonia is a large western Pacific group of islands, 1500 km east of Australia. Large coastal and offshore areas of reefs
For each group, seagrass state was assessed by the mean total cover of seagrasses (i.e. all species included), as recommended by
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Fig. 2. Location map of the Voh-Koné-Pouembout area (New Caledonia) and seagrass sampling stations. Numbers inside black circles indicate the affiliation of each station to groups 1, 2, 3 or 4.
Table 1 Braun-Blanquet cover abundance scale (Braun-Blanquet, 1932) used for seagrass cover estimates. Index
Seagrass cover (%)
0 0.1 0.5 1 2 3 4 5
0 <5 (solitary stolons) <5 (few stolons) <5 (several stolons) 5–25 25–50 50–75 75–100
Fontan et al. (2010). We tested if the 2012 seagrass cover was abnormally low compared to the 2007–2011 reference trends.
Comparison of seagrass cover between tested and reference datasets was assessed using two methods. First, according to the ‘‘Percentile Based Approach’’ (PBA), three quality states were defined by comparing the medians and percentiles of tested and reference datasets: The 2012 seagrass cover was considered ‘‘Poor’’ when its median was below the percentile 10 of the reference 2007–2011 dataset. The 2012 seagrass cover was considered ‘‘Fair’’ when its median was between the percentile 10 and percentile 50 of the 2007– 2011 reference dataset. The 2012 seagrass cover was considered in a ‘‘Good’’ state when its median was above the percentile 50 of the 2007–2011 reference dataset.
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Table 2 Ranges of values for sampling effort (both for the reference dataset ‘‘N1’’, and for the tested dataset ‘‘N2’’) and standardized effect size (Cohen, 1988) used to calculate power. Alpha type I error was set at 0.05. Sampling effort is expressed in quadrat (10 quadrats per station). Group
Sampling effort for reference dataset (N1)
Sampling effort for tested dataset (N2)
Standardized effect size
1 2 3 4
140 150 202 550
5–150 5–150 5–200 5–300
0.05–3 0.05–3 0.05–3 0.05–3
(in (in (in (in
step step step step
of of of of
5) 5) 5) 5)
(in (in (in (in
step step step step
of of of of
0.05) 0.05) 0.05) 0.05)
Second, a sensitivity analysis of power approach (SAP), derived from Guillemot and Ducrocq (2011), was applied. The difference of seagrass covers between the tested (2012) and reference (2007– 2011) periods was assessed using a Wilcoxon–Mann–Whitney test. Then a power analysis using the software G*Power 3.1 (Faul et al., 2009) provided the statistical power of this test, given a fixed type I error, but for various sampling efforts and effect sizes. All tested values are summarized in Table 2. In the SAP approach, three alert states were defined by comparing the means of tested and reference datasets: An ‘‘Alert state’’ was proclaimed when statistical power of the test was above the 95% threshold. This reflects a high probability to rightly identify a significant difference between ‘‘reference’’ and ‘‘tested’’ datasets. A ‘‘pre-alert state’’ was proclaimed when statistical power of the test was between the 70% and 95% thresholds. This reflects a medium probability to rightly identify a significant difference between ‘‘reference’’ and ‘‘tested’’ datasets. A ‘‘no-alert state’’ was proclaimed when the statistical power of the test was below the 70%. This reflects a low probability to rightly identify a significant difference between ‘‘reference’’ and ‘‘tested’’ datasets. Results were displayed using the filled contour function of R 2.13.0. to discriminate the effects on power of both effect size and sampling effort. For an easier interpretation, the values of effect sizes were converted to seagrass cover thresholds, using equation 3.
C%tested
dataset
¼ C%reference
dataset
ðeffect size SDreference
dataset Þ
ð3Þ
Where C% is the percentage of total seagrass cover, and SD is its standard deviation. The seagrass cover of tested dataset was finally evaluated according to these alert thresholds to define the state of the 2012 seagrass meadows. The SAP approach is synthesized in Fig. 3. 3. Results 3.1. Differences in seagrass cover Among the eleven seagrass species described in New Caledonia (Payri and Richer de Forges, 2007), seven were found in the Voh-Koné-Pouembout sampling area (i.e., Thalassia hemprichii, Halophila ovalis, Cymodocea rotundata, Syringodium isoetifolium, C. serrulata, Enhalus acoroides, Halodule uninervis), but with various occurrences among groups (Table 3). In addition to differences in seagrass species composition, the four groups displayed differences in mean total seagrass cover, as well as differences in temporal and spatial variability during the 2007–2011 reference period (Fig. 4, Table 3). Group 1 included five seagrass species, with low spatial and temporal variability in total cover. Group 2 was a monospecific (H. ovalis) seagrass bed with high temporal variability, but low spatial variability. Group 3
Fig. 3. General approach to determine alert thresholds from a reference dataset using a SAP approach, and then evaluating the state of the variable for a tested dataset.
Table 3 Spatial (SVg) and temporal (STg) variability of total seagrass cover, calculated for groups of stations used to monitor seagrass cover. Groups hold various seagrass species richness, and various spatial and temporal variability of total seagrass cover. Group
Seagrass species composition
SVg (%)
STg (%)
Group 1
Thalassia hemprichii Halophila ovalis Cymodocea rotundata Syringodium isoetifolium Cymodocea serrulata H. ovalis T. hemprichii C. rotundata Enhalus acoroides C. serrulata T. hemprichii S. isoetifolium C. serrulata E. acoroides C. rotundata Halodule uninervis
21.3
9.4
18.5 44.9
57.5 11.2
15.3
21.9
Group 2 Group 3
Group 4
included four species, with low temporal variability, but high spatial variability. Finally, group 4 yielded a mix of six species, with low spatial variability and medium temporal variability. Seagrass cover was not significantly different within the 2007– 2011 reference period for groups 3 (p = 0.55) and 4 (p = 0.058). Some differences were observed for groups 1 (p < 7 105) and 2 (4 109), but are likely due to natural variability as these two groups were not particularly exposed to any unusual disturbance (e.g., dredging, cyclone). This justified the use of the 2007–2011 period as reference period.
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Fig. 4. Total seagrass cover ± standard deviation for the 2007–2011 reference period, for station groups 1–4. No data variable for group 3 in 2007.
3.2. Alert thresholds for seagrass cover Threshold values of seagrass cover established using the reference datasets (i.e., 2007–2011 surveys) are presented in Fig. 5 for the PBA method. For groups 2 and 3, with respectively high temporal and spatial variability, the thresholds for alert states were almost unreachable for the median (i.e., percentile 10 respectively at 1.7% and 0.1% total seagrass cover).
According to the SAP approach, the power based thresholds which defined ‘‘alert states’’ are presented in Fig. 6 for various sampling effort. Thresholds remained into achievable ranges of values even for highly variable systems (respectively 12.5% and 14.5% total seagrass cover for groups 2 and 3). Using alert thresholds established with PBA, the 2012 seagrass cover for all monitoring groups corresponded to ‘‘Fair’’ states compared to their respective historical trends. The Wilcoxon–Mann Whitney test suggested that the mean seagrass cover of tested and reference data sets were statistically different for groups 1 and 2 (Table 4). This means that for these two groups, seagrass cover is abnormally low compared to their historical trends. When comparing the 2012 mean seagrass cover to alert thresholds established with the SAP approach, a ‘‘pre-alert state’’ could be raised for group 1, and an ‘‘alert state’’ for group 2. For groups 3 and 4, considering the low differences in mean seagrass cover between tested and reference datasets, no ‘‘alert’’ state was raised. 4. Discussion 4.1. Scope and robustness of percentile vs. power based approaches In most past studies, alert thresholds for seagrass cover were evaluated qualitatively, using expert judgment (e.g. Erftemeijer and Lewis, 2006). The PBA, widely used for water quality assessment (Australian and New Zealand Guidelines for Fresh and Marine Water Quality, 2000) and recently applied to seagrass beds
Fig. 5. Definition of alert thresholds (percentile 10 and 50) for seagrass cover using the 2007–2011 reference dataset (PBA method). Dashed lines separate reference and tested datasets. The state of total seagrass cover for the tested dataset (2012 dataset) is evaluated by locating the median relative to the alert thresholds. The green, yellow and red colors indicate respectively ‘‘good’’, ‘‘fair’’, and ‘‘poor’’ states of seagrass cover. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 6. Definition of alert thresholds (95% and 70% power in respectively red and yellow) for the 2012 seagrass cover using the 2007–2011 reference dataset (SAP approach). The state of the 2012 total seagrass cover is evaluated by locating the mean (black point) relative to these alert thresholds. The yellow and red colors also indicate respectively ‘‘pre-alert’’ and ‘‘alert’’ states compared to historical trends. The green color indicates an absence of alert state. Sampling effort is expressed in number of quadrats. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 4 Results of the Wilcoxon–Mann–Whitney tests. NS and *** respectively indicate a non-significant and a significant difference (a = 0.05) of total seagrass cover between the tested dataset (2012) and the reference dataset (2007–2011). Group
Seagrass mean cover ± SD of reference dataset (2007–2011)
Seagrass mean cover ± SD of tested dataset (2012)
p-Value
Significance
Power
1 2 3 4
30.7 ± 20.0 29.6 ± 25.3 29.2 ± 29.7 22.5 ± 18.8
18.8 ± 16.1 8.7 ± 6.9 23.1 ± 2.4 21.6 ± 16.8
8.4 104 1.1 105 0.12 0.56
***
0.89 0.99 0.37 0.11
(McKenzie, 2009) was a first step toward statistically relevant definition of alert thresholds. However, the choice of percentile values for alert thresholds remains a fairly subjective choice. Water quality assessment usually uses a percentile 20 as upper limit for ‘‘poor quality’’ (Australian and New Zealand Guidelines for Fresh and Marine Water Quality, 2000). In highly variable systems, however, a lower limit is recommended since low median values can still be the result of natural processes. Here, percentile 10 were used as the upper limit of ‘‘poor state’’ seagrass cover as recommended by
***
NS NS
McKenzie (2009). While suitable for transect-based sampling of seagrass cover in Australia (McKenzie, 2009), this was obviously unsuitable for our quadrat-based sampling strategy in New Caledonia since very low values for median seagrass cover were necessary to define a ‘‘poor’’ state. Obviously, appropriate percentile thresholds needs to account for the sampling method used and the profile of natural variability of local seagrass cover. Adapting the sensitivity analysis of power to define alert thresholds allowed developing a statistically sound and objective
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method for monitoring tropical coastal assemblages. Values of power thresholds to assess alert states (here 95% and 70%) are relevant to statistical significance only. A 95% power threshold represents a high probability to be right when concluding in a difference between the tested and reference datasets. We chose a 95% threshold value of power for lower limit of ‘‘alert state’’ since beta type II error is at least as important as alpha type I error in environmental management (Peterman, 1990; Field et al., 2004), the latter being set at 0.05 in most cases. The 70% threshold value of power for upper limit of ‘‘alert state’’ can be argued, as the lowest values accepted for power are usually between 70% and 80% in the literature (Peterman, 1990; Field et al., 2004; Heidelbaugh and Nelson, 1996). The groups 1, 2, 3 and 4 represented various systems of natural variability. They allowed the assessment of the robustness of both the PBA and SAP methods to spatial and temporal variability. Both methods were relevant when studying a system of low or medium variability (Groups 1 and 4). However, when spatial variability was high (Group 3), we found that the percentile 10 of the reference dataset was very low and that the ‘‘poor state’’ threshold was thus almost impossible to reach. A similar observation was made when temporal variability was high (Group 2). When studying means (i.e., with SAP method), the alert threshold remained into a realistic range of values and allowed a consistent definition of alert thresholds (Fig. 6). In many monitoring schemes, sampling design is of critical importance, as small sampling variations can result in contradictory conclusions (e.g., Van Wynsberge et al., 2012). In fact, one question left frequently unanswered is, ‘‘would management recommendations be the same if a greater sampling effort was implemented?’’ SAP provides some answers to this question, by testing robustness of alert thresholds to sampling design. The higher the sampling effort, the better the statistical test can discriminate a difference with certainty. In our seagrass example, managers can easily notice that for a similar degree of spatial and temporal variability, the ‘‘pre-alert state’’ observed for group 1 would have switched to an ‘‘alert-state’’ if sampling effort had increased from 30 replicates to 45 replicates. Such simulations are precious tools for managers, who can optimize their monitoring schemes accordingly. Interestingly, managers can also appreciate that for Groups 3 and 4, the statistical test could not discriminate a difference between tested and reference dataset with enough certainty to raise an ‘‘alert state’’ nor a ‘‘pre-alert state’’, regardless of the magnitude of the sampling effort. Therefore, the incapacities of the statistical test to detect a difference between the tested and reference datasets were not the result of a poor sampling effort, but rather due to a negligible decrease in seagrass cover in 2012 compared to the natural variability observed between 2007 and 2011. 4.2. Limitations and insights about power and percentile based approaches Despite the large scope and robustness of sensitivity analyses, we emphasize here several limitations when using this method for defining alert thresholds. These limitations are true for the PBA as well. First, alert thresholds are based on a statistical comparison between a tested dataset and a reference dataset. The definition of the reference dataset is therefore critical. When temporal dataset are available, it should be demonstrated that no disturbances have modified the properties of the studied ecosystem during the reference period. It is a difficult task since very few locations are pristine, but at least a period with demonstrated limited impact should be selected. Similarly, periods with some rare high magnitude natural perturbations (e.g., cyclones) should be avoided when defining the reference datasets, otherwise the ‘‘alert states’’ of the
tested dataset may be underestimated. In this study, differences of seagrass cover within the reference period were observed only for groups 1 and 2. These differences can reasonably be attributed to natural temporal variability since these two groups were not particularly exposed to unusual disturbances (either anthropogenic or natural) compared to groups 3 and 4. This does not mean that the 2007–2011 period was exempt of any disturbance, but that the impact of disturbances can be considered sufficiently low during this period compared to natural variability, to keep using this period for reference. Second, reaching an alert state threshold means that an unusually low cover of seagrass is observed but this does not mean that seagrass cover will not recover to acceptable states afterward. Therefore, crossing an ‘‘alert threshold’’ should not be interpreted as reaching an ecological point of no return. Resilience – or the ability to recover after a natural or anthropogenic disturbance (Bellwood et al., 2004; Hughes et al., 2003), can however be evaluated afterward by the SAP and PBA approaches according to the time required for an ‘‘alert state’’ to shift back to a ‘‘no-alert state’’. Finally and most importantly, the SAP and PBA approaches aim to detect and qualify changes of a given variable, but do not pretend to identify the cause of change, which may be anthropic or natural. Identifying causes of change mostly concern the choice of the variable itself (i.e., choice of indicator), which needs to be settled at the beginning of the management strategy. 4.3. On the status of the 2012 seagrass cover Seagrass total cover has been previously recommended for monitoring the quality of seagrass beds in New Caledonia (Fontan et al., 2010). However, this does not explicitly consider the variability in species composition, while seagrass species have different physiological and morphological properties and exert various responses to disturbances (Erftemeijer and Lewis, 2006). The choice of an indicator is also a tradeoff between its representativeness and the cost required for its assessment. Despite its suboptimal description of seagrass beds, the total seagrass cover is attractive for managers because it can be easily and cost-effectively sampled. Thus, managers should keep in mind that the selected indicator may be sensitive to a limited number of processes and that it may not reflect targeted changes. Considering the weak specificity of total seagrass cover, it is hazardous to attribute the pre-alert state of group 1 and alert state of group 2 obtained with the SAP approach to any specific disturbance (e.g., oversedimentation). To identify that a specific disturbance affects seagrass assemblages beyond total cover, analyses should consider a large panel of indicators (Caddy, 1999, 2002). Moreover, multiple other parameters would have to be monitored at the same time to explain the changes observed in seagrass characteristics (e.g. physical and chemical parameters). Caddy (1999, 2002) developed a ‘‘traffic light’’ approach which helps managers reporting alert state decisions based on indicators of various types. The concept is that for complex and highly variable ecosystems, unusual values for one indicator (e.g., total seagrass cover) can reflect a variety of processes (e.g., natural variability) but unusual values of several variables (e.g., both total seagrass cover and sediment resuspension) may reveal an unusually low quality state of the system (e.g., seagrass beds) and call for management actions. We suggest that the SAP approach could be used on a similar suite of indicators, which could be integrated in a traffic light system to follow a precautionary approach of ecosystem monitoring. SAP can be used for a wide range of indicators, provided that a statistical test is selected to compare datasets. It has first already been used in sociology, psychology, and ecology fields (Cohen, 1988; Gerrodette, 1987) but only recently as a tool to define alert
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thresholds (Guillemot and Ducrocq, 2011). Here, we found that the sensitivity analysis was particularly relevant for monitoring total seagrass cover, while considering different groups of seagrass beds, characterized by different exposure to disturbances, and by different ecological properties. This method is certainly applicable to a wide range of habitats and ecosystems, both marine and terrestrial. Acknowledgements This study was done thanks to the Environmental Department of the Koniambo Nickel SAS (KNS) who funded data acquisition and authorized its use here. EGLE and SOWEMAR societies, and Tom Heintz also helped during fieldwork. References Andréfouët, S., Wantiez, L., 2010. Characterizing the diversity of coral reef habitats and fish communities found in a UNESCO World Heritage Site: the strategy developed for lagoons of New Caledonia. Mar. Pollut. Bull. 61, 612–620. Andréfouët, S., Payri, C., Kulbicki, M., Scopélitis, J., Dalleau, M., Mellin, C., Scamps, M., Dirberg, G., 2010 Mesure, suivi et potentiel économique de la diversité de l’habitat récifo-lagonaire néo-calédonien : inventaire des herbiers, suivi des zones coralliennes et rôle des habitats dans la distribution des ressources en poissons de récifs. Rapport Conventions Sciences de la Mer – Biologie Marine n°31. IRD Centre de Nouméa/ZoNéCo., Nouméa. pp. 121. Australian and New Zealand Guidelines for Fresh and Marine Water Quality, vol. 2. Aquatic Ecosystems – Rationale and Background information, 2000. (Chapter 8). Bellwood, D.R., Hughes, T.P., Folke, C., Nyström, M., 2004. Confronting the coral reef crisis. Nature 429 (6994), 827–833. Bigot, L., Grémare, A., Amouroux, J.M., Frouin, P., Maire, O., Gaertner, J.C., 2008. Assessment of the ecological quality status of soft-bottoms in Reunion Island (tropical Southwest Indian Ocean) using AZTI marine biotic indices. Mar. Pollut. Bull. 56 (4), 704–722. Borja, A., Muxika, I., Franco, J., 2003. The application of a Marine Biotic Index to different impact sources affecting soft-bottom benthic communities along European coasts. Mar. Pollut. Bull. 46 (7), 835–845. Borja, A., Josefson, A.B., Miles, A., Muxica, I., Olsgard, F., Phillips, G., Rodriguez, J.G., Rygg, B., 2007. An approach to the intercalibration of benthic ecological status assessment in the North Atlantic ecoregion, according to the European Water Framework Directive. Mar. Pollut. Bull. 55 (1), 42–52. Braun-Blanquet, J., 1932. Plant Sociology (Translation Fuller GD and Conrad HS). McGraw-Hill, New York, pp. 539. Caddy, J.F., 1999. Deciding on Precautionary Management Measure for a Stock Based on a suite of Limit Reference Points (LRPs) as a Basis for a Multi-LRP Harvest Law. Scientific council studies, Northwest Atlantic Fisheries organization. Dartmouth NS 32, 55–68. Caddy, J.F., 2002. Limit reference points, traffic lights, and holistic approaches to fisheries management with minimal stock assessment input. Fish. Res. 56 (2), 133–138. Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences, second ed. Lawrence Erlbaum Associates Publishers, Hillsdale, New Jersey, pp. 273–405. Collier, C., Waycott, M., 2009. Drivers of change to seagrass distributions and communities on the great barrier reef: literature review and gaps analysis. Reef and Rainforest Research Centre, Cairns, QLD, Australia, pp. 58. Common implementation strategy for the water framework directive (2000/60/EC), 2009. Policy Summary of Guidance document N°23 on Eutrophication Assessment in the Context of European Water Policies. Technical, Report, 2009. http://dx.doi.org/10.2779/95341. Corbett, C.A., Doering, P.H., Madley, K.A., Ott, J.A., Tomasko, D.A., 2005 Using seagrass coverage as an indicator of ecosystem condition. In: Bortone SA et al. (Ed.). Estuarine Indicators CRC Marine Science Series, pp. 229–245. Cullen-Unsworth, L.C., Mtwana Nordlund, L., Paddock, J., Baker, S., McKenzie, L.J., Unsworth, R.K.F., 2013. Seagrass meadows globally as a coupled social-
307
ecological system: implications for human wellbeing. Marine Pollut. Bulletin. (in press). Duarte, C.M., Alvarez, E., Grau, A., Krause-Jensen, D., 2004. Which monitoring strategy should be choosen? In: Borum, J., Duarte, C.M., Krause-Jensen, D., Greve, T.M., (Eds.), vol. 41. European Seagrasses: An Introduction to Monitoring and Management, pp. 44. Erftemeijer, P.L., Lewis, R.R.R., 2006. Environmental impacts of dredging on seagrasses: a review. Mar. Pollut. Bull. 52 (12), 1553–1572. Faul, F., Erdfelder, E., Lang, A.G., Buchner, A., 2007. G⁄Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39 (2), 175–191. Faul, F., Erdfelder, E., Buchner, A., Lang, A.G., 2009. Statistical power analyses using G⁄Power 3.1: tests for correlation and regression analyses. Behav. Res. Methods 41 (4), 1149–1160. Fichez, R., Chifflet, S., Douillet, P., Gérard, P., Gutierrez, F., Jouon, A., Ouillon, S., Grenz, C., 2010. Biogeochemical typology and temporal variability of lagoon waters in a coral reef ecosystem subject to terrigeneous and anthropogenic inputs (New Caledonia). Mar. Pollut. Bull. 61 (7), 309–322. Field, S.A., Tyre, A.J., Jonzén, N., Rhodes, J.R., Possingham, H.P., 2004. Minimizing the cost of environmental management decisions by optimizing statistical thresholds. Ecol. Lett. 7 (8), 669–675. Fontan, E., Dumas, P., Payri, C., Ponton, D., 2010. Mise au point d’outils de caractérisation et de suivi des herbiers subtidaux de Nouvelle-Calédonie. Rapport ZoNeCo, Nouméa, Nouvelle-Calédonie, pp. 158. Gerrodette, T., 1987. A power analysis for detecting trends. Ecology 68 (5), 1364– 1372. Guillemot, N., Ducrocq, M., 2011. Developing a database and relevant management indicators for monitoring commercial fisheries. SPC Fish. Newslett. 134, 34–40. Halpern, B.S., Walbridge, S., Selkoe, K.A., Kappel, C.V., Micheli, F., D’Agrosa, C., Bruno, J.F., Casey, K.S., Ebert, C., Fox, H.E., Fujita, R., Heinemann, D., Lenihan, H.S., Madin, E.M.P., Perry, M.T., Selig, E.R., Spalding, M., Steneck, R., Watson, R., 2008. A global map of human impact on marine ecosystems. Science 319 (5865), 948– 952. Heidelbaugh, W.S., Nelson, W.G., 1996. A power analysis of methods for assessment of change in seagrass cover. Aquat. Bot. 53 (3), 227–233. Hughes, T.P., Baird, A.H., Bellwood, D.R., Card, M., Connolly, S.R., Folke, C., Grosberg, R., Hoegh-Guldberg, O., Jackson, J.B.C., Kleypas, J., Lough, J.M., Marshall, P., Nyström, M., Palumbi, S.R., Pandolfi, J.M., Rosen, B., Roughgarden, J., 2003. Climate change, human impacts, and the resilience of coral reefs. Science 301 (5635), 929–933. McDonald, J.I., Coupland, G.T., Kendrick, G.A., 2006. Underwater video as a monitoring tool to detect changes in seagrass cover. J. Environ. Manage. 80, 148–155. McKenzie, L., 2009. Marine and Tropical Sciences Research Facility (MTSRF) Project Milestone Report, Reef and Rainforest Research Centre, Australia, pp. 28. Mellors, J.E., 2003. Sediment and nutrient dynamics in coastal intertidal seagrass of north eastern tropical Australia, Doctoral dissertation, James Cook University, pp. 255. Morat, P., 1993. New Caledonia: a case study in biodiversity. Biodiver. Lett. 1 (3), 69–71. Nilson, H.C., Rosenberg, R., 1997. Benthic habitat quality assessment of an oxygen stressed fjord by surface and sediment profile images. J. Mar. Syst. 11, 249–264. Payri, C., Richer de Forges, B., 2007. Compendium of marine species of New Caledonia. IRD, Centre de Nouméa, Nouméa, pp. 435. Peterman, R.M., 1990. Statistical power analysis can improve fisheries research and management. Can. J. Fish. Aquat. Sci. 47, 2–15. Rasheed, M.A., 2004. Recovery and succession in a multi-species tropical seagrass meadow following experimental disturbance: the role of sexual and asexual reproduction. J. Exp. Mar. Biol. Ecol. 310 (1), 13–45. Rosenberg, R., Blomquist, M., Nilsson, H.C., Cederwall, H., Dimming, A., 2004. Marine quality assessment by use of benthic species–abundance distributions: a proposed new protocol within the European Union Water Framework Directive. Mar. Pollut. Bull. 49 (9), 728–739. Van Wynsberge, S., Andréfouët, S., Hamel, M.A., Kulbicki, M., 2012. Habitats as surrogates of taxonomic and functional fish assemblages in coral reef ecosystems: a critical analysis of factors driving effectiveness. PLoS ONE 7 (7), e40997. http://dx.doi.org/10.1371/journal.pone.0040997.
Contents lists available at ScienceDirect
Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Alert thresholds for monitoring environmental variables: A new approach applied to seagrass beds diversity in New Caledonia Simon Van Wynsberge a,b,⇑, Antoine Gilbert c, Nicolas Guillemot d, Claude Payri a, Serge Andréfouët a a
UR-227 CoRéUs, IRD (Institut de Recherche pour le Développement), Laboratoire d’Excellence CORAIL, Noumea, New Caledonia UMR-241 EIO, UPF (Université de la Polynésie Française), Faa’a, Tahiti, French Polynesia c Ginger Soproner, Noumea, New Caledonia d Nicolas Guillemot Consultant, Noumea, New Caledonia b
a r t i c l e Keywords: Indicators Power analysis Alert threshold Seagrass Monitoring
i n f o
a b s t r a c t Monitoring ecological variables is mandatory to detect abnormal changes in ecosystems. When the studied variables exceed predefined alert thresholds, management actions may be required. In the past, alert thresholds have been typically defined by expert judgments and descriptive statistics. Recently, approaches based on statistical power were also used. In New Caledonia, seagrass monitoring is a priority given their vulnerability to natural and anthropic disturbances. To define a suitable monitoring strategy and alert thresholds, we compared a Percentile Based Approach (PBA) and a sensitivity analysis of power (SAP). Both methods defined statistically relevant alert thresholds, but the SAP approach was more robust to spatial and temporal variability of seagrass cover. Moreover, this method characterized the sensitivity of threshold values to sampling efforts, a useful knowledge for managers. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Resource exploitation, pollution, climate change, as well as natural disturbances, can affect ecosystemic variables of different types (Bellwood et al., 2004). Their monitoring is of critical importance to detect abnormal trends and environmental degradations (Hughes et al., 2003; Halpern et al., 2008). Before explaining causality of changes, monitoring plans have to detect changes. Recently, several management frameworks have called for fast and useable protocols to define ‘‘alert’’ or ‘‘quality’’ thresholds (e.g., European Water Framework Directive, 2009; Australian and New Zealand Guidelines for Fresh and Marine Water Quality, 2000). In practice, a portion of the available dataset is used to define reference values, and ‘‘alert state levels’’ are proclaimed as soon as the monitored variable exceeds these reference values. This monitoring scheme has been recently applied for benthic quality assessment in both temperate (Borja et al., 2003, 2007; Nilson and Rosenberg, 1997; Rosenberg et al., 2004) and tropical (Bigot et al., 2008) regions, and for monitoring seagrass beds in Australia (McKenzie, 2009). Many studies are based on descriptive statistics approaches (e.g., percentiles or medians), mostly because of their simplicity and robustness to extreme events. In the past decades, techniques using the power of statistical tests have also been developed. The statistical power reflects the ⇑ Corresponding author at: UMR-241 EIO, UPF (Université de la Polynésie Française), Faa’a, Tahiti, French Polynesia. Tel.: +689 76 36 35. E-mail address: [email protected] (S. Van Wynsberge). 0025-326X/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2013.09.035
capacity to detect a significant difference between ‘‘reference’’ and ‘‘tested’’ datasets when a change actually occurred between them. Power is a function of sample size (N), the probability of type I error (a), and the magnitude of the difference between the null hypothesis and reality (the ‘‘effect size’’ or ‘‘standardized effect size’’; Cohen, 1988, Fig. 1). When, for a given sampling effort, the difference between the ‘‘tested’’ dataset and the ‘‘reference’’ dataset is high compared to natural variability, the probability to be right by concluding to a difference between the ‘‘tested’’ and ‘‘reference’’ datasets, using a statistical test, is high (i.e. statistical power is high). A useful application of statistical power is the sensitivity analysis, which is commonly used in various science fields (e.g., Cohen, 1988; Faul et al., 2007, 2009). Sensitivity analyses of power aim at determining the effect size required to conclude on the reality of a difference for a given power. Guillemot and Ducrocq (2011) used a sensitivity analysis of power for commercial fisheries management in New Caledonia’s South Province to determine Catch Per Unit of Effort (CPUE) thresholds on the basis of historical dataset and related trends. Logically, Guillemot and Ducrocq (2011) defined afterwards for managers ‘‘pre-alert’’ and ‘‘alert’’ states in reference to historical levels of exploited fish stocks. In New Caledonia, fisheries are not the only source of concerns. Coastal environments pay a toll to ongoing mining projects throughout the country. Nickel mining is a large driver of the local economy, and massive projects lead to visible environmental impacts on terrestrial and marine realms (e.g., Fichez et al., 2010; Morat, 1993). The critical habitats
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and lagoons became part of the UNESCO World Heritage listing in 2008 (Andréfouët and Wantiez, 2010). The present study focused on the Voh-Koné-Pouembout area on the north western coast, where a major nickel mining site will take momentum in its extracting activity in 2013 (Fig. 2). A large navigation channel was dug in the lagoon in 2008/2009 near the mining site to give access to large boats to the new harbor. As the maritime traffic will increase through that channel in 2013, it is expected that resuspended sediments may affect nearby seagrass beds in the future (Fig. 2). Detecting abnormal trends and defining alert thresholds for seagrass cover is a high priority. 2.2. Seagrass beds sampling protocol Fig. 1. Definition of type I error (a) and power (1 b). a is the area under the tails of the null hypothesis H0 whereas power is the area under the alternative hypothesis H1 outside the tails of the null hypothesis. The standardized effect size is the distance between the mean of H0 and H1 divided by standard deviation. The greater the effect size and lower the intrinsic variability, the higher the power.
that are potentially disturbed by mining-induced sedimentation and pollution include coral reefs, mangroves and seagrass beds. The latter offers challenging configurations for monitoring given their large diversity in New Caledonia (Andréfouët et al., 2010). Seagrass beds are of particular concern as they play a major role (Collier and Waycott, 2009; Mellors, 2003; Cullen-Unsworth et al., 2013) but are exposed and vulnerable to many disturbances, both natural and anthropogenic (Erftemeijer and Lewis, 2006; Collier and Waycott, 2009; Mellors, 2003; Rasheed, 2004). In most monitoring schemes, the total seagrass cover has been used as indicator of seagrass status (e.g., Heidelbaugh and Nelson, 1996; Duarte et al., 2004; McDonald et al., 2006), due to its relative sampling simplicity and cost-effectiveness (Fontan et al., 2010). However, the natural variability associated with seagrass cover is high and may blur management recommendations (Mellors, 2003; Corbett et al., 2005). High temporal variability makes the definition of relevant alert thresholds difficult, and high spatial variability makes thresholds sensitive to sampling effort and design (Van Wynsberge et al., 2012). In fact, what is actually missing is a statistically relevant method to detect changes and define alert thresholds, taking into account both sampling effort and high variability of monitored variables. Hereafter, we compare the results and robustness of two approaches used to define alert thresholds for seagrass cover in New Caledonia. First, we considered the approach referenced as the ‘‘Percentile Based Approach’’ (PBA), which is similar to methods used for monitoring water quality in Australia and New Zealand (Australian and New Zealand Guidelines for Fresh and Marine Water Quality, 2000). This method is currently in use for monitoring seagrass meadow in Australia (see McKenzie, 2009). It defined ‘‘quality’’ states of seagrass beds in comparison with seagrass cover percentiles of reference and tested datasets. The PBA approach thus defined ‘‘quality’’ states of seagrass beds on the basis of descriptive statistics only, without involving statistical tests. Second, we implemented a sensitivity analysis of power (SAP) using a Wilcoxon–Mann–Whitney test to define ‘‘alert thresholds’’ for seagrass bed cover, and tested a wide range of sampling efforts. Both methods aim to detect and classify changes. We point out that they are not used here to identify the cause of observed changes, which is beyond the scope of this paper.
Seagrass density data were collected at 20 stations (Fig. 2), each year from 2007 to 2012 during both the summer and winter periods. As some species displayed lower and less variable coverage in the winter season (e.g., Cymodocea serrulata, Friedman test, p < 0.01), we restricted our study to the dataset sampled in the winter season only. Stations were distributed along a offshore–inshore gradient, throughout the distribution area of shallow seagrass (Fig. 2). For each station, cover of seagrass species were estimated using a set of ten 0.25 m2 randomly placed quadrats using the Braun-Blanquet cover abundance scale (Braun-Blanquet, 1932). Specifically, two divers estimated semi-quantitatively the cover of every seagrass species, using cover classes and indexes described in Table 1. 2.3. Defining monitoring station groups and reference dataset The sampled stations were clustered into four monitoring groups according to their spatial locations and exposure to potential mining impact and sediment resuspension (Fig. 2). Group 1 was composed of stations far from the mining site. Conversely, groups 2 and 4 were more likely to be exposed to potential mining impacts given their proximity to development sites. Group 3 was intermediate. Spatial and temporal variability of seagrass cover for any given g group (respectively SVg and TVg) were computed using the equations below: ni p X X
SV g ¼
i Þ2 ðyij y
i¼1 j¼1 ni p X XX
ð1Þ
i Þ2 ðyij y
g
i¼1 j¼1
p X i y Þ2 ni ðy
TV g ¼
i¼1 p XX g
ð2Þ i y Þ2 ni ðy
i¼1
2. Materials and methods
where p is the number of years, ni is the number of sampled stations for year i, and y is the seagrass cover. The 2007–2011 dataset was used as reference dataset. As unusual dredging activities occurred in 2008–2009 around stations of group 4, we first performed a Kruskall–Wallis test to verify that seagrass cover was not affected and did not bias the 2007–2011 reference period. This 2007–2011 reference period was used to evaluate seagrass cover spatial and temporal variability (SV and TV).
2.1. Study site
2.4. Monitoring seagrass bed cover, species richness and composition
New Caledonia is a large western Pacific group of islands, 1500 km east of Australia. Large coastal and offshore areas of reefs
For each group, seagrass state was assessed by the mean total cover of seagrasses (i.e. all species included), as recommended by
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Fig. 2. Location map of the Voh-Koné-Pouembout area (New Caledonia) and seagrass sampling stations. Numbers inside black circles indicate the affiliation of each station to groups 1, 2, 3 or 4.
Table 1 Braun-Blanquet cover abundance scale (Braun-Blanquet, 1932) used for seagrass cover estimates. Index
Seagrass cover (%)
0 0.1 0.5 1 2 3 4 5
0 <5 (solitary stolons) <5 (few stolons) <5 (several stolons) 5–25 25–50 50–75 75–100
Fontan et al. (2010). We tested if the 2012 seagrass cover was abnormally low compared to the 2007–2011 reference trends.
Comparison of seagrass cover between tested and reference datasets was assessed using two methods. First, according to the ‘‘Percentile Based Approach’’ (PBA), three quality states were defined by comparing the medians and percentiles of tested and reference datasets: The 2012 seagrass cover was considered ‘‘Poor’’ when its median was below the percentile 10 of the reference 2007–2011 dataset. The 2012 seagrass cover was considered ‘‘Fair’’ when its median was between the percentile 10 and percentile 50 of the 2007– 2011 reference dataset. The 2012 seagrass cover was considered in a ‘‘Good’’ state when its median was above the percentile 50 of the 2007–2011 reference dataset.
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Table 2 Ranges of values for sampling effort (both for the reference dataset ‘‘N1’’, and for the tested dataset ‘‘N2’’) and standardized effect size (Cohen, 1988) used to calculate power. Alpha type I error was set at 0.05. Sampling effort is expressed in quadrat (10 quadrats per station). Group
Sampling effort for reference dataset (N1)
Sampling effort for tested dataset (N2)
Standardized effect size
1 2 3 4
140 150 202 550
5–150 5–150 5–200 5–300
0.05–3 0.05–3 0.05–3 0.05–3
(in (in (in (in
step step step step
of of of of
5) 5) 5) 5)
(in (in (in (in
step step step step
of of of of
0.05) 0.05) 0.05) 0.05)
Second, a sensitivity analysis of power approach (SAP), derived from Guillemot and Ducrocq (2011), was applied. The difference of seagrass covers between the tested (2012) and reference (2007– 2011) periods was assessed using a Wilcoxon–Mann–Whitney test. Then a power analysis using the software G*Power 3.1 (Faul et al., 2009) provided the statistical power of this test, given a fixed type I error, but for various sampling efforts and effect sizes. All tested values are summarized in Table 2. In the SAP approach, three alert states were defined by comparing the means of tested and reference datasets: An ‘‘Alert state’’ was proclaimed when statistical power of the test was above the 95% threshold. This reflects a high probability to rightly identify a significant difference between ‘‘reference’’ and ‘‘tested’’ datasets. A ‘‘pre-alert state’’ was proclaimed when statistical power of the test was between the 70% and 95% thresholds. This reflects a medium probability to rightly identify a significant difference between ‘‘reference’’ and ‘‘tested’’ datasets. A ‘‘no-alert state’’ was proclaimed when the statistical power of the test was below the 70%. This reflects a low probability to rightly identify a significant difference between ‘‘reference’’ and ‘‘tested’’ datasets. Results were displayed using the filled contour function of R 2.13.0. to discriminate the effects on power of both effect size and sampling effort. For an easier interpretation, the values of effect sizes were converted to seagrass cover thresholds, using equation 3.
C%tested
dataset
¼ C%reference
dataset
ðeffect size SDreference
dataset Þ
ð3Þ
Where C% is the percentage of total seagrass cover, and SD is its standard deviation. The seagrass cover of tested dataset was finally evaluated according to these alert thresholds to define the state of the 2012 seagrass meadows. The SAP approach is synthesized in Fig. 3. 3. Results 3.1. Differences in seagrass cover Among the eleven seagrass species described in New Caledonia (Payri and Richer de Forges, 2007), seven were found in the Voh-Koné-Pouembout sampling area (i.e., Thalassia hemprichii, Halophila ovalis, Cymodocea rotundata, Syringodium isoetifolium, C. serrulata, Enhalus acoroides, Halodule uninervis), but with various occurrences among groups (Table 3). In addition to differences in seagrass species composition, the four groups displayed differences in mean total seagrass cover, as well as differences in temporal and spatial variability during the 2007–2011 reference period (Fig. 4, Table 3). Group 1 included five seagrass species, with low spatial and temporal variability in total cover. Group 2 was a monospecific (H. ovalis) seagrass bed with high temporal variability, but low spatial variability. Group 3
Fig. 3. General approach to determine alert thresholds from a reference dataset using a SAP approach, and then evaluating the state of the variable for a tested dataset.
Table 3 Spatial (SVg) and temporal (STg) variability of total seagrass cover, calculated for groups of stations used to monitor seagrass cover. Groups hold various seagrass species richness, and various spatial and temporal variability of total seagrass cover. Group
Seagrass species composition
SVg (%)
STg (%)
Group 1
Thalassia hemprichii Halophila ovalis Cymodocea rotundata Syringodium isoetifolium Cymodocea serrulata H. ovalis T. hemprichii C. rotundata Enhalus acoroides C. serrulata T. hemprichii S. isoetifolium C. serrulata E. acoroides C. rotundata Halodule uninervis
21.3
9.4
18.5 44.9
57.5 11.2
15.3
21.9
Group 2 Group 3
Group 4
included four species, with low temporal variability, but high spatial variability. Finally, group 4 yielded a mix of six species, with low spatial variability and medium temporal variability. Seagrass cover was not significantly different within the 2007– 2011 reference period for groups 3 (p = 0.55) and 4 (p = 0.058). Some differences were observed for groups 1 (p < 7 105) and 2 (4 109), but are likely due to natural variability as these two groups were not particularly exposed to any unusual disturbance (e.g., dredging, cyclone). This justified the use of the 2007–2011 period as reference period.
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Fig. 4. Total seagrass cover ± standard deviation for the 2007–2011 reference period, for station groups 1–4. No data variable for group 3 in 2007.
3.2. Alert thresholds for seagrass cover Threshold values of seagrass cover established using the reference datasets (i.e., 2007–2011 surveys) are presented in Fig. 5 for the PBA method. For groups 2 and 3, with respectively high temporal and spatial variability, the thresholds for alert states were almost unreachable for the median (i.e., percentile 10 respectively at 1.7% and 0.1% total seagrass cover).
According to the SAP approach, the power based thresholds which defined ‘‘alert states’’ are presented in Fig. 6 for various sampling effort. Thresholds remained into achievable ranges of values even for highly variable systems (respectively 12.5% and 14.5% total seagrass cover for groups 2 and 3). Using alert thresholds established with PBA, the 2012 seagrass cover for all monitoring groups corresponded to ‘‘Fair’’ states compared to their respective historical trends. The Wilcoxon–Mann Whitney test suggested that the mean seagrass cover of tested and reference data sets were statistically different for groups 1 and 2 (Table 4). This means that for these two groups, seagrass cover is abnormally low compared to their historical trends. When comparing the 2012 mean seagrass cover to alert thresholds established with the SAP approach, a ‘‘pre-alert state’’ could be raised for group 1, and an ‘‘alert state’’ for group 2. For groups 3 and 4, considering the low differences in mean seagrass cover between tested and reference datasets, no ‘‘alert’’ state was raised. 4. Discussion 4.1. Scope and robustness of percentile vs. power based approaches In most past studies, alert thresholds for seagrass cover were evaluated qualitatively, using expert judgment (e.g. Erftemeijer and Lewis, 2006). The PBA, widely used for water quality assessment (Australian and New Zealand Guidelines for Fresh and Marine Water Quality, 2000) and recently applied to seagrass beds
Fig. 5. Definition of alert thresholds (percentile 10 and 50) for seagrass cover using the 2007–2011 reference dataset (PBA method). Dashed lines separate reference and tested datasets. The state of total seagrass cover for the tested dataset (2012 dataset) is evaluated by locating the median relative to the alert thresholds. The green, yellow and red colors indicate respectively ‘‘good’’, ‘‘fair’’, and ‘‘poor’’ states of seagrass cover. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 6. Definition of alert thresholds (95% and 70% power in respectively red and yellow) for the 2012 seagrass cover using the 2007–2011 reference dataset (SAP approach). The state of the 2012 total seagrass cover is evaluated by locating the mean (black point) relative to these alert thresholds. The yellow and red colors also indicate respectively ‘‘pre-alert’’ and ‘‘alert’’ states compared to historical trends. The green color indicates an absence of alert state. Sampling effort is expressed in number of quadrats. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 4 Results of the Wilcoxon–Mann–Whitney tests. NS and *** respectively indicate a non-significant and a significant difference (a = 0.05) of total seagrass cover between the tested dataset (2012) and the reference dataset (2007–2011). Group
Seagrass mean cover ± SD of reference dataset (2007–2011)
Seagrass mean cover ± SD of tested dataset (2012)
p-Value
Significance
Power
1 2 3 4
30.7 ± 20.0 29.6 ± 25.3 29.2 ± 29.7 22.5 ± 18.8
18.8 ± 16.1 8.7 ± 6.9 23.1 ± 2.4 21.6 ± 16.8
8.4 104 1.1 105 0.12 0.56
***
0.89 0.99 0.37 0.11
(McKenzie, 2009) was a first step toward statistically relevant definition of alert thresholds. However, the choice of percentile values for alert thresholds remains a fairly subjective choice. Water quality assessment usually uses a percentile 20 as upper limit for ‘‘poor quality’’ (Australian and New Zealand Guidelines for Fresh and Marine Water Quality, 2000). In highly variable systems, however, a lower limit is recommended since low median values can still be the result of natural processes. Here, percentile 10 were used as the upper limit of ‘‘poor state’’ seagrass cover as recommended by
***
NS NS
McKenzie (2009). While suitable for transect-based sampling of seagrass cover in Australia (McKenzie, 2009), this was obviously unsuitable for our quadrat-based sampling strategy in New Caledonia since very low values for median seagrass cover were necessary to define a ‘‘poor’’ state. Obviously, appropriate percentile thresholds needs to account for the sampling method used and the profile of natural variability of local seagrass cover. Adapting the sensitivity analysis of power to define alert thresholds allowed developing a statistically sound and objective
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method for monitoring tropical coastal assemblages. Values of power thresholds to assess alert states (here 95% and 70%) are relevant to statistical significance only. A 95% power threshold represents a high probability to be right when concluding in a difference between the tested and reference datasets. We chose a 95% threshold value of power for lower limit of ‘‘alert state’’ since beta type II error is at least as important as alpha type I error in environmental management (Peterman, 1990; Field et al., 2004), the latter being set at 0.05 in most cases. The 70% threshold value of power for upper limit of ‘‘alert state’’ can be argued, as the lowest values accepted for power are usually between 70% and 80% in the literature (Peterman, 1990; Field et al., 2004; Heidelbaugh and Nelson, 1996). The groups 1, 2, 3 and 4 represented various systems of natural variability. They allowed the assessment of the robustness of both the PBA and SAP methods to spatial and temporal variability. Both methods were relevant when studying a system of low or medium variability (Groups 1 and 4). However, when spatial variability was high (Group 3), we found that the percentile 10 of the reference dataset was very low and that the ‘‘poor state’’ threshold was thus almost impossible to reach. A similar observation was made when temporal variability was high (Group 2). When studying means (i.e., with SAP method), the alert threshold remained into a realistic range of values and allowed a consistent definition of alert thresholds (Fig. 6). In many monitoring schemes, sampling design is of critical importance, as small sampling variations can result in contradictory conclusions (e.g., Van Wynsberge et al., 2012). In fact, one question left frequently unanswered is, ‘‘would management recommendations be the same if a greater sampling effort was implemented?’’ SAP provides some answers to this question, by testing robustness of alert thresholds to sampling design. The higher the sampling effort, the better the statistical test can discriminate a difference with certainty. In our seagrass example, managers can easily notice that for a similar degree of spatial and temporal variability, the ‘‘pre-alert state’’ observed for group 1 would have switched to an ‘‘alert-state’’ if sampling effort had increased from 30 replicates to 45 replicates. Such simulations are precious tools for managers, who can optimize their monitoring schemes accordingly. Interestingly, managers can also appreciate that for Groups 3 and 4, the statistical test could not discriminate a difference between tested and reference dataset with enough certainty to raise an ‘‘alert state’’ nor a ‘‘pre-alert state’’, regardless of the magnitude of the sampling effort. Therefore, the incapacities of the statistical test to detect a difference between the tested and reference datasets were not the result of a poor sampling effort, but rather due to a negligible decrease in seagrass cover in 2012 compared to the natural variability observed between 2007 and 2011. 4.2. Limitations and insights about power and percentile based approaches Despite the large scope and robustness of sensitivity analyses, we emphasize here several limitations when using this method for defining alert thresholds. These limitations are true for the PBA as well. First, alert thresholds are based on a statistical comparison between a tested dataset and a reference dataset. The definition of the reference dataset is therefore critical. When temporal dataset are available, it should be demonstrated that no disturbances have modified the properties of the studied ecosystem during the reference period. It is a difficult task since very few locations are pristine, but at least a period with demonstrated limited impact should be selected. Similarly, periods with some rare high magnitude natural perturbations (e.g., cyclones) should be avoided when defining the reference datasets, otherwise the ‘‘alert states’’ of the
tested dataset may be underestimated. In this study, differences of seagrass cover within the reference period were observed only for groups 1 and 2. These differences can reasonably be attributed to natural temporal variability since these two groups were not particularly exposed to unusual disturbances (either anthropogenic or natural) compared to groups 3 and 4. This does not mean that the 2007–2011 period was exempt of any disturbance, but that the impact of disturbances can be considered sufficiently low during this period compared to natural variability, to keep using this period for reference. Second, reaching an alert state threshold means that an unusually low cover of seagrass is observed but this does not mean that seagrass cover will not recover to acceptable states afterward. Therefore, crossing an ‘‘alert threshold’’ should not be interpreted as reaching an ecological point of no return. Resilience – or the ability to recover after a natural or anthropogenic disturbance (Bellwood et al., 2004; Hughes et al., 2003), can however be evaluated afterward by the SAP and PBA approaches according to the time required for an ‘‘alert state’’ to shift back to a ‘‘no-alert state’’. Finally and most importantly, the SAP and PBA approaches aim to detect and qualify changes of a given variable, but do not pretend to identify the cause of change, which may be anthropic or natural. Identifying causes of change mostly concern the choice of the variable itself (i.e., choice of indicator), which needs to be settled at the beginning of the management strategy. 4.3. On the status of the 2012 seagrass cover Seagrass total cover has been previously recommended for monitoring the quality of seagrass beds in New Caledonia (Fontan et al., 2010). However, this does not explicitly consider the variability in species composition, while seagrass species have different physiological and morphological properties and exert various responses to disturbances (Erftemeijer and Lewis, 2006). The choice of an indicator is also a tradeoff between its representativeness and the cost required for its assessment. Despite its suboptimal description of seagrass beds, the total seagrass cover is attractive for managers because it can be easily and cost-effectively sampled. Thus, managers should keep in mind that the selected indicator may be sensitive to a limited number of processes and that it may not reflect targeted changes. Considering the weak specificity of total seagrass cover, it is hazardous to attribute the pre-alert state of group 1 and alert state of group 2 obtained with the SAP approach to any specific disturbance (e.g., oversedimentation). To identify that a specific disturbance affects seagrass assemblages beyond total cover, analyses should consider a large panel of indicators (Caddy, 1999, 2002). Moreover, multiple other parameters would have to be monitored at the same time to explain the changes observed in seagrass characteristics (e.g. physical and chemical parameters). Caddy (1999, 2002) developed a ‘‘traffic light’’ approach which helps managers reporting alert state decisions based on indicators of various types. The concept is that for complex and highly variable ecosystems, unusual values for one indicator (e.g., total seagrass cover) can reflect a variety of processes (e.g., natural variability) but unusual values of several variables (e.g., both total seagrass cover and sediment resuspension) may reveal an unusually low quality state of the system (e.g., seagrass beds) and call for management actions. We suggest that the SAP approach could be used on a similar suite of indicators, which could be integrated in a traffic light system to follow a precautionary approach of ecosystem monitoring. SAP can be used for a wide range of indicators, provided that a statistical test is selected to compare datasets. It has first already been used in sociology, psychology, and ecology fields (Cohen, 1988; Gerrodette, 1987) but only recently as a tool to define alert
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thresholds (Guillemot and Ducrocq, 2011). Here, we found that the sensitivity analysis was particularly relevant for monitoring total seagrass cover, while considering different groups of seagrass beds, characterized by different exposure to disturbances, and by different ecological properties. This method is certainly applicable to a wide range of habitats and ecosystems, both marine and terrestrial. Acknowledgements This study was done thanks to the Environmental Department of the Koniambo Nickel SAS (KNS) who funded data acquisition and authorized its use here. EGLE and SOWEMAR societies, and Tom Heintz also helped during fieldwork. References Andréfouët, S., Wantiez, L., 2010. Characterizing the diversity of coral reef habitats and fish communities found in a UNESCO World Heritage Site: the strategy developed for lagoons of New Caledonia. Mar. Pollut. Bull. 61, 612–620. Andréfouët, S., Payri, C., Kulbicki, M., Scopélitis, J., Dalleau, M., Mellin, C., Scamps, M., Dirberg, G., 2010 Mesure, suivi et potentiel économique de la diversité de l’habitat récifo-lagonaire néo-calédonien : inventaire des herbiers, suivi des zones coralliennes et rôle des habitats dans la distribution des ressources en poissons de récifs. Rapport Conventions Sciences de la Mer – Biologie Marine n°31. IRD Centre de Nouméa/ZoNéCo., Nouméa. pp. 121. Australian and New Zealand Guidelines for Fresh and Marine Water Quality, vol. 2. Aquatic Ecosystems – Rationale and Background information, 2000. (Chapter 8). Bellwood, D.R., Hughes, T.P., Folke, C., Nyström, M., 2004. Confronting the coral reef crisis. Nature 429 (6994), 827–833. Bigot, L., Grémare, A., Amouroux, J.M., Frouin, P., Maire, O., Gaertner, J.C., 2008. Assessment of the ecological quality status of soft-bottoms in Reunion Island (tropical Southwest Indian Ocean) using AZTI marine biotic indices. Mar. Pollut. Bull. 56 (4), 704–722. Borja, A., Muxika, I., Franco, J., 2003. The application of a Marine Biotic Index to different impact sources affecting soft-bottom benthic communities along European coasts. Mar. Pollut. Bull. 46 (7), 835–845. Borja, A., Josefson, A.B., Miles, A., Muxica, I., Olsgard, F., Phillips, G., Rodriguez, J.G., Rygg, B., 2007. An approach to the intercalibration of benthic ecological status assessment in the North Atlantic ecoregion, according to the European Water Framework Directive. Mar. Pollut. Bull. 55 (1), 42–52. Braun-Blanquet, J., 1932. Plant Sociology (Translation Fuller GD and Conrad HS). McGraw-Hill, New York, pp. 539. Caddy, J.F., 1999. Deciding on Precautionary Management Measure for a Stock Based on a suite of Limit Reference Points (LRPs) as a Basis for a Multi-LRP Harvest Law. Scientific council studies, Northwest Atlantic Fisheries organization. Dartmouth NS 32, 55–68. Caddy, J.F., 2002. Limit reference points, traffic lights, and holistic approaches to fisheries management with minimal stock assessment input. Fish. Res. 56 (2), 133–138. Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences, second ed. Lawrence Erlbaum Associates Publishers, Hillsdale, New Jersey, pp. 273–405. Collier, C., Waycott, M., 2009. Drivers of change to seagrass distributions and communities on the great barrier reef: literature review and gaps analysis. Reef and Rainforest Research Centre, Cairns, QLD, Australia, pp. 58. Common implementation strategy for the water framework directive (2000/60/EC), 2009. Policy Summary of Guidance document N°23 on Eutrophication Assessment in the Context of European Water Policies. Technical, Report, 2009. http://dx.doi.org/10.2779/95341. Corbett, C.A., Doering, P.H., Madley, K.A., Ott, J.A., Tomasko, D.A., 2005 Using seagrass coverage as an indicator of ecosystem condition. In: Bortone SA et al. (Ed.). Estuarine Indicators CRC Marine Science Series, pp. 229–245. Cullen-Unsworth, L.C., Mtwana Nordlund, L., Paddock, J., Baker, S., McKenzie, L.J., Unsworth, R.K.F., 2013. Seagrass meadows globally as a coupled social-
307
ecological system: implications for human wellbeing. Marine Pollut. Bulletin. (in press). Duarte, C.M., Alvarez, E., Grau, A., Krause-Jensen, D., 2004. Which monitoring strategy should be choosen? In: Borum, J., Duarte, C.M., Krause-Jensen, D., Greve, T.M., (Eds.), vol. 41. European Seagrasses: An Introduction to Monitoring and Management, pp. 44. Erftemeijer, P.L., Lewis, R.R.R., 2006. Environmental impacts of dredging on seagrasses: a review. Mar. Pollut. Bull. 52 (12), 1553–1572. Faul, F., Erdfelder, E., Lang, A.G., Buchner, A., 2007. G⁄Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39 (2), 175–191. Faul, F., Erdfelder, E., Buchner, A., Lang, A.G., 2009. Statistical power analyses using G⁄Power 3.1: tests for correlation and regression analyses. Behav. Res. Methods 41 (4), 1149–1160. Fichez, R., Chifflet, S., Douillet, P., Gérard, P., Gutierrez, F., Jouon, A., Ouillon, S., Grenz, C., 2010. Biogeochemical typology and temporal variability of lagoon waters in a coral reef ecosystem subject to terrigeneous and anthropogenic inputs (New Caledonia). Mar. Pollut. Bull. 61 (7), 309–322. Field, S.A., Tyre, A.J., Jonzén, N., Rhodes, J.R., Possingham, H.P., 2004. Minimizing the cost of environmental management decisions by optimizing statistical thresholds. Ecol. Lett. 7 (8), 669–675. Fontan, E., Dumas, P., Payri, C., Ponton, D., 2010. Mise au point d’outils de caractérisation et de suivi des herbiers subtidaux de Nouvelle-Calédonie. Rapport ZoNeCo, Nouméa, Nouvelle-Calédonie, pp. 158. Gerrodette, T., 1987. A power analysis for detecting trends. Ecology 68 (5), 1364– 1372. Guillemot, N., Ducrocq, M., 2011. Developing a database and relevant management indicators for monitoring commercial fisheries. SPC Fish. Newslett. 134, 34–40. Halpern, B.S., Walbridge, S., Selkoe, K.A., Kappel, C.V., Micheli, F., D’Agrosa, C., Bruno, J.F., Casey, K.S., Ebert, C., Fox, H.E., Fujita, R., Heinemann, D., Lenihan, H.S., Madin, E.M.P., Perry, M.T., Selig, E.R., Spalding, M., Steneck, R., Watson, R., 2008. A global map of human impact on marine ecosystems. Science 319 (5865), 948– 952. Heidelbaugh, W.S., Nelson, W.G., 1996. A power analysis of methods for assessment of change in seagrass cover. Aquat. Bot. 53 (3), 227–233. Hughes, T.P., Baird, A.H., Bellwood, D.R., Card, M., Connolly, S.R., Folke, C., Grosberg, R., Hoegh-Guldberg, O., Jackson, J.B.C., Kleypas, J., Lough, J.M., Marshall, P., Nyström, M., Palumbi, S.R., Pandolfi, J.M., Rosen, B., Roughgarden, J., 2003. Climate change, human impacts, and the resilience of coral reefs. Science 301 (5635), 929–933. McDonald, J.I., Coupland, G.T., Kendrick, G.A., 2006. Underwater video as a monitoring tool to detect changes in seagrass cover. J. Environ. Manage. 80, 148–155. McKenzie, L., 2009. Marine and Tropical Sciences Research Facility (MTSRF) Project Milestone Report, Reef and Rainforest Research Centre, Australia, pp. 28. Mellors, J.E., 2003. Sediment and nutrient dynamics in coastal intertidal seagrass of north eastern tropical Australia, Doctoral dissertation, James Cook University, pp. 255. Morat, P., 1993. New Caledonia: a case study in biodiversity. Biodiver. Lett. 1 (3), 69–71. Nilson, H.C., Rosenberg, R., 1997. Benthic habitat quality assessment of an oxygen stressed fjord by surface and sediment profile images. J. Mar. Syst. 11, 249–264. Payri, C., Richer de Forges, B., 2007. Compendium of marine species of New Caledonia. IRD, Centre de Nouméa, Nouméa, pp. 435. Peterman, R.M., 1990. Statistical power analysis can improve fisheries research and management. Can. J. Fish. Aquat. Sci. 47, 2–15. Rasheed, M.A., 2004. Recovery and succession in a multi-species tropical seagrass meadow following experimental disturbance: the role of sexual and asexual reproduction. J. Exp. Mar. Biol. Ecol. 310 (1), 13–45. Rosenberg, R., Blomquist, M., Nilsson, H.C., Cederwall, H., Dimming, A., 2004. Marine quality assessment by use of benthic species–abundance distributions: a proposed new protocol within the European Union Water Framework Directive. Mar. Pollut. Bull. 49 (9), 728–739. Van Wynsberge, S., Andréfouët, S., Hamel, M.A., Kulbicki, M., 2012. Habitats as surrogates of taxonomic and functional fish assemblages in coral reef ecosystems: a critical analysis of factors driving effectiveness. PLoS ONE 7 (7), e40997. http://dx.doi.org/10.1371/journal.pone.0040997.