Here we compare the applicability, the information provided and the cost-benefit of three sampling methods usually used in the study of rocky benthic assemblages. For comparative purposes, sampling was performed seasonally and along a depth gradient (0-50 m) in the Cabrera Archipelago (western Mediterranean). The destructive scraping (collection) method was the least cost-effective but provided the best qualitative and quantitative information. The in situ visual method was the most time-effective but provided low levels of taxonomic resolution and its accuracy decreased with depth due to the increasing difficulty of recognizing species in situ due to nitrogen narcosis, reduced light and cold. The photoquadrat method showed intermediate values of cost-effectiveness and information but was not suitable for multilayered assemblages, as it only accounted for the overstory. A canonical correspondence analysis showed that depth was highlighted as the main environmental gradient (16.0% of variance) by the three methods. However, differences due to the sampling method (7.9% of variance) were greater than differences due to temporal variability (5.8% of variance), suggesting that the three methods are valid but their selection has to be carefully assessed in relation to the targeted assemblages and the specific goals of each study.
Este trabajo compara la aplicabilidad, la calidad de la información, los costes y los beneficios de tres métodos de muestreo usados habitualmente en el estudio de comunidades en el bentos rocoso mediterráneo. Para fines comparativos, el muestreo se realizó estacionalmente y a lo largo de un gradiente de profundidad (0-50 m) en el Archipiélago de Cabrera (Mediterráneo Occidental). El método destructivo de raspado (recolección) tuvo altos costes, pero proporcionó la información de mejor calidad, tanto a nivel cualitativo como cuantitativo. El método visual in situ fue el más eficiente en cuanto a la obtención de información, pero proporcionó una baja resolución taxonómica y su exactitud decreció con la profundidad debido a la dificultad de reconocer especies in situ bajo condiciones de narcosis, falta de luz y frío. El método fotográfico obtuvo valores intermedios de coste-beneficio, pero no fue adecuado para caracterizar comunidades estratificadas ya que solo tuvo en cuenta el estrato superior. Un análisis CCA mostró que el principal gradiente ambiental resaltado en todos los métodos era la profundidad (16.0% de la varianza). Sin embargo, las diferencias debidas al método de muestreo (7.9% de la varianza) fueron más grandes que las debidas a la variabilidad estacional (5.8% de la varianza). En consecuencia, los tres métodos son válidos para el muestreo de comunidades rocosas mediterráneas, pero su selección debe basarse en un análisis minucioso de las comunidades a caracterizar y en los objetivos específicos de cada estudio.
The study of community organization patterns is essential in ecology as it provides a descriptive basis to further develop hypothesis, build models, design experiments or perform monitoring fieldwork. Using the appropriate methodology is fundamental to obtain an accurate description of the assemblages as well as qualitative and quantitatively representative samples (
SCUBA diving allows marine scientists to study benthic assemblages in situ by means of a wide variety of sampling methods. Dredging and other similar remote techniques used on soft bottoms are not appropriate for rocky bottoms as they provide incomplete information of the system (
The classic destructive method consists in scraping and collecting all existing organisms of a known area (
Non-destructive direct methods use quadrats of a specific area to estimate the species cover percentage or frequency. Data are estimated in situ using sub-quadrats (area,
Non-destructive indirect methods use photos or video to estimate species cover percentage or frequency. Diving time is short but the subsequent frame treatment is long. Cover data is finally estimated using sub-quadrats (
The different methods for studying rocky benthic assemblages also provide contrasting information and different cost-benefits. It is essential to assess the quality of the information obtained and the effort and cost of the sampling methodology used because sampling is the first information filter. The comparison of different methods helps researchers to select the best one for attaining their goals. Several methods for studying rocky benthic assemblages have been compared in different locations (e.g.
The study site was located at “Estell des Coll” (39°07′19″N, 2°56′09″E), a small islet within the Archipelago of Cabrera National Park (Balearic Islands, western Mediterranean). Five benthic assemblages were studied along a vertical transect at 0 (A0), 4 (A4), 12 (A12), 25 (A25) and 50 (A50) metres depth along a rocky cliff in four different seasons: winter, spring, summer and autumn.
Assemblage A0 was strongly multilayered with a dense canopy of the brown alga
Assemblage A4 was a photophilic algal assemblage covered by small erect algae, such as
Assemblage A12 was dominated by the canopy-forming alga
The brown erect alga
A coralligenous assemblage was present at the bottom of the cliff (A50), dominated by the crustose calcareous alga
Sample collection was performed by SCUBA diving, except for A0. Sampling time, especially at 50 m, was the main limiting factor. The five assemblages were sampled in each season with three different sampling methods: the collection (scraping) method, the in situ visual method and photoquadrats. Two dives by three divers were needed to obtain the samples in each season. The in situ relevés (visual method) were always made by the same experienced diver. The other two divers collected the samples and took the pictures.
The collection method is destructive and consists in scraping off all organisms from a 20×20 cm (400 cm2) quadrat with a hammer and a chisel. Two replicates were obtained per season and depth, which provide a sampling area large enough to be considered as representative of most Mediterranean rocky bottom assemblages (
The in situ visual method is direct and non-destructive. Species abundance was measured in situ with a 25×25 cm (625 cm2) quadrat divided into 25 sub-squares of 5×5 cm2. The presence or absence of each species was recorded within each sub-square and the total abundance was calculated as the percentage of sub-squares in which a species was present (
The photoquadrat method is indirect and non-destructive. Photos of the assemblages were used to estimate the coverage area of the different species. Pictures were taken with a Nikonos V camera equipped with a 28 mm UW Nikkor objective, a close-up Nikonos lens and a Nikon SB-105 flash (
The three sampling methods were compared through three descriptors of community structure (species richness, species diversity and quantitative similarity between samples) using abundance data. Descriptors were calculated using replicates, which vary with each method (collection method, 2 replicates =800 cm2; in situ visual method, 4 replicates =2500 cm2; photoquadrat method, 8 replicates =2480 cm2). Consequently, we compared the information obtained using the areas that were considered as representative for each sampling method, not the same areas, as it would be too time-consuming to collect, identify and quantify everything present in areas of e.g. 2500 cm2.
Species richness per assemblage (N) was calculated as the total number of species merging all replicates for each season and depth. Species diversity was estimated with the Shannon Index (H’,
The qualitative similarity among methods was calculated with the Jaccard Index (
The ordination of species in space (depth) and time (seasons) for each sampling method was analysed with canonical correspondence analysis (CCA) and the relevance of each factor was estimated with partial CCAs. The relevance of the method factor, as well as space and time, were analysed with a CCA and partial CCAs including all three data sets. Only the 68 species common to the three methods were included and the abundances were all transformed to a 0-100 scale. We selected unimodal methods because a preliminary detrended correspondence analysis showed that the gradient length (SD) was higher than would be the case for a complete species turnover (4.0 SD,
A total of 262 species were identified in the 40 samples obtained with the collection method (
A total of 114 species were identified in the 80 samples obtained with the in situ visual method (
A total of 160 different species or categories were identified in the 160 frames obtained with the photoquadrat method (
Species diversity, measured with the Shannon Index (H’), tended to increase along the depth gradient with the collection method (
The quantitative similarity between samples, measured with the Kulczynski Index, was calculated by comparing sampling areas of 400 cm2 (pairs of one replicate) for the collection method. This method yielded high similarity values (≥0.7) for A0 and A4 all year round, indicating high homogeneity between samples (
Method | Season | Group size | 0 m | 4 m | 12 m | 25 m | 50 m |
---|---|---|---|---|---|---|---|
Collection | spring | 1 | 0.78 | 0.74 | 0.44 | 0.76 | 0.44 |
summer | 1 | 0.80 | 0.75 | 0.52 | 0.66 | 0.32 | |
autumn | 1 | 0.84 | 0.85 | 0.46 | 0.25 | 0.46 | |
winter | 1 | 0.69 | 0.88 | 0.82 | 0.62 | 0.27 | |
In situ visual | spring | 1 | 0.71±0.06 | 0.74±0.06 | 0.66±0.08 | 0.65±0.06 | 0.69±0.06 |
2 | 0.78±0.02 | 0.82±0.04 | 0.75±0.02 | 0.73±0.01 | 0.75±0.06 | ||
summer | 1 | 0.48±0.09 | 0.67±0.09 | 0.72±0.04 | 0.58±0.05 | 0.79±0.03 | |
2 | 0.60±0.06 | 0.77±0.07 | 0.79±0.05 | 0.68±0.00 | 0.81±0.02 | ||
autumn | 1 | 0.77±0.04 | 0.76±0.02 | 0.71±0.06 | 0.63±0.09 | 0.59±0.12 | |
2 | 0.82±0.01 | 0.81±0.01 | 0.78±0.02 | 0.70±0.02 | 0.69±0.06 | ||
winter | 1 | 0.77±0.04 | 0.76±0.06 | 0.73±0.08 | 0.65±0.06 | 0.70±0.07 | |
2 | 0.83±0.05 | 0.82±0.01 | 0.80±0.03 | 0.73±0.03 | 0.78±0.05 | ||
Photoquadrat | spring | 1 | 0.92±0.08 | 0.48±0.16 | 0.51±0.15 | 0.70±0.13 | 0.36±0.26 |
2 | 0.93±0.04 | 0.58±0.11 | 0.61±0.10 | 0.77±0.09 | 0.50±0.17 | ||
3 | 0.94±0.02 | 0.64±0.09 | 0.67±0.07 | 0.81±0.08 | 0.56±0.13 | ||
4 | 0.94±0.02 | 0.68±0.08 | 0.70±0.06 | 0.84±0.08 | 0.61±0.11 | ||
summer | 1 | 0.97±0.02 | 0.47±0.15 | 0.67±0.07 | 0.63±0.16 | 0.43±0.20 | |
2 | 0.97±0.01 | 0.60±0.11 | 0.75±0.05 | 0.72±0.10 | 0.56±0.14 | ||
3 | 0.98±0.01 | 0.67±0.09 | 0.79±0.04 | 0.76±0.09 | 0.62±0.11 | ||
4 | 0.98±0.01 | 0.71±0.08 | 0.82±0.03 | 0.79±0.07 | 0.66±0.10 | ||
autumn | 1 | 0.85±0.06 | 0.52±0.16 | 0.58±0.13 | 0.65±0.09 | 0.48±0.26 | |
2 | 0.89±0.04 | 0.61±0.10 | 0.67±0.08 | 0.74±0.06 | 0.58±0.15 | ||
3 | 0.91±0.03 | 0.65±0.07 | 0.72±0.06 | 0.78±0.05 | 0.64±0.11 | ||
4 | 0.92±0.02 | 0.68±0.05 | 0.75±0.04 | 0.81±0.04 | 0.68±0.10 | ||
winter | 1 | 0.95±0.02 | 0.48±0.14 | 0.52±0.10 | 0.61±0.07 | 0.46±0.20 | |
2 | 0.96±0.02 | 0.60±0.09 | 0.64±0.07 | 0.70±0.05 | 0.58±0.12 | ||
3 | 0.97±0.01 | 0.66±0.06 | 0.70±0.06 | 0.75±0.04 | 0.64±0.09 | ||
4 | 0.97±0.01 | 0.69±0.05 | 0.73±0.05 | 0.77±0.03 | 0.68±0.08 |
To sum up, for the areas sampled in each methodology, the photoquadrat method detected a similar species richness per assemblage to the collection method (except for the strongly multilayered A0 assemblage), though the total number of species identified with the photoquadrat method was only 61% of that identified with the collection method. The in situ visual method detected much lower species richness than the collection and photoquadrat methods. Species diversity estimates were the highest with the in situ visual method and the lowest with the collection method, but differences decreased along the depth gradient and all three H’ converged at A50. Both the collection and photoquadrat methods were able to detect a higher seasonal variability of species diversity than the in situ visual method. The number of replicates and sampling area needed to obtain a good homogeneity level (Kulczynski Index ≥0.7) changed among methods and assemblages (
K≥0.7 | A0 | A4 | A12 | A25 | A50 | |||||
---|---|---|---|---|---|---|---|---|---|---|
n | cm2 | n | cm2 | n | cm2 | n | cm2 | n | cm2 | |
Collection | 1 | 400 | 1 | 400 | >1 | >400 | >1 | >400 | >1 | >400 |
In situ visual | >2 | >1250 | 2 | 1250 | 2 | 1250 | >2 | >1250 | >2 | >1250 |
Photoquadrat | 1 | 310 | >4 | >1240 | 4 | 1240 | 2 | 620 | >4 | >1240 |
The collection method has been used as a reference to calculate qualitative similarity (Jaccard Index) with the other two methods (
Partial CCAs showed that space (depth) explained about twice as much variance as time (season) for all three methods. Depth explained 21%, 22% and 18% of the variance and season explained 10%, 12% and 9% of the variance for the collection, in situ visual and photoquadrat methods, respectively. Shared variance between depth and season was really low (<0.1%) for all methods. When the three data sets were merged, partial CCA showed that space (depth) explained 16.0% of the variance, being the main factor explaining species distribution, and it was strongly associated with the CCA first axis (CCA1,
The costs of the equipment used in this study are relatively low when compared with those of other sampling underwater devices, such as remotely operated vehicles or submersibles. However, in marine environments, an important fraction of the cost is due to time used in sample collection and processing. Diving time is one of the main limiting factors when sampling benthic communities. This limitation mainly affects the collection and in situ visual methods, which are more time-consuming than the photoquadrat method (
Collection method | In situ visual method | Photoquadrat method | |
---|---|---|---|
Time cost | 33.5 h | 1.25 h | 6.25 h |
Replicates, total area | n=2, 800 cm2 | n=4, 2500 cm2 | n=8, 2480 cm2 |
Diving time | 30 min | 45 min | 15 min |
Lab time | 33 h | 30 min | 6 h |
Working time per 1000 cm2 | 42 h | 30 min | 2.5 h |
Equipment cost | Medium | Low | Medium |
Diver skill requirements | Experienced diver | Experienced diver and taxonomic specialist | Experienced diver |
Obtained information | |||
Objectivity | High | Medium | High |
Resolution | High | Low | Medium |
Adequacy | |||
Surface of study | Small | Medium | Medium |
Long-term studies | No | Yes | Yes |
Multilayered assemblages | Yes | Yes | No |
Deep assemblages (>50 m) | Yes | No | Yes |
Bathymetric studies | Yes | Yes | Yes |
Seasonal studies | Yes | Yes | Yes |
Permanent record | Yes | No | Yes |
The collection method is limited by both diving and laboratory time. Moreover, the sampled surface is smaller than in the other two methods, resulting in a high time cost per equivalent area and making this method suitable for working at small scales. However, the collection method is the only self-sufficient method, as the other two methods need to take extra samples to identify the unknown species in the field. In contrast, working effort for the in situ visual method is much lower and is mainly due to diving time (
The information quality and suitability of each method are summarized in
In contrast, the in situ visual method is fast but provides a much lower resolution. In fact it only detects 43% of the species detected with the collection method and the abundance data obtained are more discrete and homogeneous (ranging from 0 when a species is not present to 25 when a species is present in all sub-quadrats, which represent a two-fold range) than biomass or coverage data (a six-fold range). Consequently, information is simplified and diversity estimates are affected. A similar in situ visual method can be performed by estimating the percent cover of species in each sub-square (5 classes) and then summing scores across the 25 sub-squares (
The photoquadrat method has an intermediate time cost and resolution (it detects 60% of the species detected with the collection method). Photo digitalization is more objective than in any other direct method (
The quantification estimates of some organisms change between methods. For example, crustose species of the understory have higher estimates with the collection (biomass) and the in situ visual (abundance) method than with the photoquadrat (coverage) method. This may lead to contradictory results between methods, as observed with the specific diversity of assemblage A4. A4 assemblage was dominated by the crustose calcified coralline alga
All three methods similarly detect spatial and temporal variability of the data. Independently of the sampling method, the main species distribution and abundance pattern is always related to the depth gradient. The three methods are good and consistent for detecting species changes along the bathymetric axis. However, differences due to the sampling method are greater than differences due to temporal variability. The three methods are inconsistent in detecting the small-scale seasonal changes, probably because of the combined limitations discussed above (overestimation or underestimation of specific groups, reduction in information quality with depth or in multilayered communities, and homogeneous abundance data).
In conclusion, all three methods are valid for studying rocky benthic assemblages but their specific limitations must always be taken into account. The staff and resource availability, the assemblage type, the working scale and the objectives of each particular study are other aspects to consider when choosing the most appropriate sampling method. For instance, the collection method is the best when high accuracy is needed; the in situ visual method provides fast results; and photoquadrats provide a permanent record that can always be revisited and used for different objectives.
Xavier Turon provided the Turbo Pascal program to calculate similarities. Funding to NS was partially provided by the “Comissionat per a Universitat i Recerca de la Generalitat de Catalunya”. This paper is a contribution of the CSIC INTRAMURAL project reference 201330E065.
The following material is available through the online version of this article and at the following link:
Table S1. – Species total biomass (g of dry weight) estimated with the collection method (2 replicates of 400 cm2). Depth is expressed in meters.
Table S2. – Species abundance (%) estimated with the in situ visual method (4 replicates of 625 cm2). Depth is expressed in meters. See Table S3 for the “Complex 9” composition.
Table S3. – Species cover (cm2) estimated with the photoquadrat method (8 replicates of 310 cm2). Depth is expressed in meters.
Table S4. – Number of species (N) and species diversity (H’) for each depth, season and sampling method. CV is the seasonal coefficient of variation.