Next Article in Journal
Effects of Biotic and Abiotic Factors on the Spatiotemporal Distribution of Round Scad (Decapterus maruadsi) in the Hainan Island Offshore Area
Next Article in Special Issue
Dead Shells Bring to Life Baselines for Conservation: Case Studies from The Bahamas, Southern California, and Wisconsin, USA
Previous Article in Journal
Artificial Seaweed Substrates Complement ARMS in DNA Metabarcoding-Based Monitoring of Temperate Coastal Macrozoobenthos
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diversity
Volume 15
Issue 5
10.3390/d15050658
diversity-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Communication

Sewage Pipe Waters Affect Colour Composition in Palaemon Shrimp from the Intertidal in the Canary Islands: A New Non-lethal Bioindicator of Anthropogenic Pollution

by
Enrique Lozano-Bilbao
1,* and
Jesús Alcázar-Treviño
2,3,*
1
Grupo Interuniversitario de Toxicología Alimentaria y Ambiental, Facultad de Medicina, Universidad de La Laguna (ULL), Campus de Ofra, San Cristóbal de La Laguna, 38071 Santa Cruz de Tenerife, Spain
2
Department of Industrial Engineering, University of La Laguna, 38200 La Laguna, Spain
3
Canary Islands Oceanographic Centre, Spanish Institute of Oceanography (IEO), Spanish National Research Council (CSIC), 38180 Santa Cruz de Tenerife, Spain
*
Authors to whom correspondence should be addressed.
Diversity 2023, 15(5), 658; https://doi.org/10.3390/d15050658
Submission received: 28 March 2023 / Revised: 8 May 2023 / Accepted: 10 May 2023 / Published: 12 May 2023
(This article belongs to the Special Issue Anthropogenic Impacts on Marine Ecosystems: A Biodiversity Conservation Emergency)
Browse Figures
Review Reports Versions Notes

Abstract

:
Marine pollution through anthropic outfalls like sewage pipes is a growing concern since point-source pollution can affect many organisms. Investigating pollutant concentrations in organisms usually requires sacrificing the organisms, but here we propose a new method to infer anthropic pollution in the intertidal by measuring colour levels in Palaemon elegans rockpool shrimp. We took pictures of live shrimp from pools near sewage pipes and control zones in three of the Canary Islands (Gran Canaria, Lanzarote and Tenerife), and measured their RGB (red, green and blue) abdominal colour composition. We then statistically compared colours from the control zone and sewage pipe and between islands. We found a clear differentiation in colour composition between the control zone and areas with a sewage pipe. Our results supported the hypothesis that pollution affects colouration in these invertebrates. We, therefore, suggest the use of darker colourations in P. elegans as a bioindicator of anthropic pollution, a first sign that should spur more in-depth studies in the affected area. This methodology is pollutant unspecific but non-extractive, so we propose its use as a citizen science tool to inform scientists and technicians of possible illegal and/or untreated wastewater that could affect intertidal biota.

1. Introduction

Intertidal pollution is related to human pollution, i.e., the contamination of oceans, rivers, and lakes [1,2,3]. This pollution occurs through industrial discharges, the plastic and agrochemical waste that humans dump into bodies of water, pesticides and fertilizer residues from crops, and the discharge of oil and other chemicals into the sea [4,5,6]. The effects of pollution on the intertidal are varied. Spills, oil, and chemicals have been found to have a direct effect on marine organisms, causing disease and mortality [7,8,9]. Plastic can also cause disease in marine organisms, as it accumulates in the intertidal and may contain toxic substances [10]. In addition, as contaminants enter the intertidal, ecological balances are altered and this, in turn, affects the diversity and health of marine ecosystems [11,12,13]. This can have long-term effects on the local economy and culture, as fishing and tourism are two important sources of income in many coastal areas [14,15,16]. Therefore, it becomes necessary to control intertidal pollution to avoid environmental damage and maintain marine biodiversity. This can be achieved through the adoption of preventive measures, such as proper treatment of waste and regulation of industrial discharges. Environmental education should also be promoted to raise awareness of the need to protect the marine environment [17,18,19,20].
Bioindicators constitute a large group of plant, fungal, and animal species, whose presence or physiological status in each ecosystem provide information on certain ecological characteristics of the ecosystem or the possible environmental impact of certain practices on that ecosystem [21,22,23]. These organisms are mainly used to evaluate the environmental quality of ecosystems. All bioindicators must meet a series of requirements for their use, such as dispersion and abundance in the territory, sedentary lifestyle, and toleration of contaminating agents in concentrations similar to those of the contaminated ecosystem but without lethal effects. The use of bioindicators to detect anthropic pollution traditionally required an extractive survey, killing the specimens in the process of measuring pollutants within their tissues [24,25,26]. These methods can be problematic when studying endangered species, as well as sparsely distributed populations, for which a small reduction in specimen numbers could cause dramatic effects [27]. Non-invasive survey techniques are needed for these cases, but also in general monitoring studies, to reduce human impacts on populations that are already affected by anthropogenic activities, like rockpool species of anemone and barnacles in the intertidal of the Canary Islands [5,28,29,30]. Since changes in colouration reflect the physiological state of several animals in different clades—e.g., acting as an honest signal like well-fed flamingos, among other species [31]—it could be possible for some species to reflect higher pollutant concentrations in their body colours.
Here, we measure the colouration of Palaemon elegans shrimp from the intertidal of the Canary Islands, comparing RGB colour composition in shrimp found near sewage pipes and in more pristine areas in order to assess their colouration as a new, non-invasive technique that could be used to detect anthropic pollution in these habitats.

2. Materials and Methods

Three islands of the Canary Islands (Gran Canaria, Lanzarote and Tenerife) were chosen for the study (Figure 1), and for each island, an area affected by a sewage pipe and a control area were determined. For each area, eight Palaemon elegans pond shrimp were sampled, for a total of 48 samples. Shrimp measuring 3.5 ± 0.2 cm were used to avoid the variable ‘length’ of the study. We started all the surveys at the low tide and keep this timing for the three locations in order for the water ponds to present similar temperatures and salinity.
Each sample was processed in situ in the intertidal during the surveys. The shrimp were fished and measured with a calliper. If the shrimp was within the chosen size, gauze was passed over the shrimp to remove the water from the body. The photographs were taken in a custom-made white hood made from standard lab coat fabric to eliminate the sun’s glare. The shrimp was placed lying on its right side facing to the left and three photos were taken without flash, and so on with each of the eight shrimp in each zone. The photos were taken at 15 cm from the shrimp, focusing on the first segment of the abdomen for the photo, using a black background so as not to reflect the light. Once the photos were taken, the shrimp were deposited in a bucket with water from the pool and then returned alive to the pools of origin.
For the processing of the photos, the Image Processing and Analysis in Java (Image J) program was chosen, selecting the left part of the first abdominal segment of the shrimp using the tool ‘Colour image processing’. Resultant colour data was obtained in ImageJ through a combination of image analysis tools, i.e., the colour histogram and colour measurement tools, obtaining quantitative values of red, green and blue (RGB) for each shrimp. We repeated this process for the three pictures per shrimp and calculated the mean values of RGB coordinates for each shrimp. Using this methodology, a total black colour would have (0,0,0) coordinates in RGB composition, while pure white would be (255,255,255).

Statistical Analyses

To study the existence of differences in colourimetry between the samples analysed in the tissue, a permutational multivariate analysis of variance (PERMANOVA) with Euclidean distances was performed. A two-way design was used with the factor “Zone” with two levels of variation (Sewage pipe and Control Zone) and another factor “Location” with three levels of variation (Tenerife, Gran Canaria, and Lanzarote). The following variables were included in the analysis: red, green, and blue colours of the colourimetry panel ranging from 0 to 255. Relative dissimilarities between areas were studied by principal coordinate analysis (PCoA) in which the colourimetry (RGB) that best explained the variability of the data was represented as vectors. One-way permutational analyses of variance with Euclidean distances were performed on the raw data. The analysis used 9999 permutations of interchangeable units and a posteriori pairwise comparisons to check for differences between significant factor levels (p-value < 0.05). The statistical packages PRIMER 7 and PERMANOVA þ v.1.0.1 [32,33] were used for statistical analyses.

3. Results

A total of 48 samples were analysed, as planned in the survey design. From these, RGB quantitative data were extracted from the pictures (Figure 2), and we calculated basic metrics comparing each colour (red, green and blue) and zone (control vs. sewage) for every island (Table 1 and Figure 3). There is a clear trend of higher RGB values in the samples from the Control Zone vs. lower RGB values near the sewage pipe (Figure 3).
We found significant differences between RGB values of shrimp from the control zone and the sewage pipe zone for the three islands (Table 2).
We found no significant differences in these RGB values between shrimp taken from the sewage pipes of Tenerife and Gran Canaria, and the same happened for the control zone of both islands (Table 3). However, we found differences in the same analyses when comparing both islands with Lanzarote.
Performed PCoA (Figure 4) showed a clear distinction between control zones and sewage pipe zones, as well as the separation of samples taken in Lanzarote from the ones taken in Tenerife and Gran Canaria. These results were clearly visible in the PCoA analysis, which accounted for approximately 99.4% of the total variability of the data (Figure 4). The distribution of the samples showed a clear difference in the RGB content of P. elegans in the Lanzarote control zone compared to the other zones. The clusters created in Tenerife and Gran Canaria overlapped each other according to the control zone and sewage pipe. The colours that best explained the variability found in the data are represented as vectors in the PCoA, which showed a positive pattern for red and green in the Lanzarote control zone.

4. Discussion and Conclusions

All the performed analyses show clear differentiation in shrimp RGB colouration when comparing the control zone and the sewage pipe areas in the three studied islands (Table 1, Table 2 and Table 3, Figure 2, Figure 3 and Figure 4). Thus, our main hypothesis is supported; human pollution which is being poured through these pipes to the coastal waters is affecting shrimp colouration. Specific pollutants concentrations were not measured in the present work, but there are several previous studies in the same sewage pipe of Tenerife that endorse our results since there is a clear gradient of disturbance in other invertebrates inhabiting the intertidal around this sewage pipe, and it happens the same for pollutant concentrations in rockpools seawater for the area [5,30,34,35].
The results obtained in the pairwise tests (Table 3) showed that there were no significant differences between Tenerife and Gran Canaria, both in the control area and the sewage pipe area. This is because the areas chosen for the study have a notable anthropic presence, since they are the two most populated islands within the archipelago [36], so the areas chosen without the presence of sewage pipes could have another type of contamination affecting the colouration of the shrimp, although not as strongly as seems to happen near the sewage pipes. Table 1 shows that red, green and blue colours have higher values in Lanzarote than in Tenerife and Gran Canaria. When any RGB values are high with the described methodology it means that they tend to be white. Being so, we propose that the specimens of P. elegans from the island of Lanzarote have less contamination due to less anthropic pressure in the chosen area [36,37,38,39,40,41] and present lighter colourations, while more contaminated shrimp from Tenerife and Gran Canaria present darker colours.
This is, to our knowledge, the first description of the use of colourimetry in shrimp as a bioindicator of potential human pollution. We find it extremely relevant to explore non-lethal methodologies to monitor bioindicators of pollution in the intertidal since many invertebrates of this habitat are endangered [42] or have a special protection level, with 12 molluscs, four cnidarians, and one Porifera species from the Centinela database of protected species in the Canary Islands [42] and their populations cannot sustain extractive methods if they are to survive. Our results provide a first assessment of the use of colourimetry in P. elegans shrimp to detect pollution, and although it will not provide detailed information on which precise contaminants are in the water, it will enable scientists to have a first sign of danger for this species and the whole intertidal ecosystems. Then, more precise, in-depth and costly studies could be conducted to assess which pollutant is responsible for the contamination. Our methodology is interesting in the light of the citizen science [43,44], so non-scientists following the described procedure of picture-taking could inform scientists of shrimp presenting colours associated with a more polluted intertidal, and this way inform authorities to solve human water outfall issues. There already exists a citizen science project in the Canary Islands that reports marine biota presence within the archipelago, and our proposed methodology could represent a further step to increase this monitoring of pollutants’ presence within the archipelago so scientists and technicians could carry out surveys in the reported places in order to assess the presence of pollutants, its possible source and inform the authorities to propose (a) correctly treat wastewaters to reduce pollutant input to the sea and (b) conservation efforts in the area. Therefore, in light of our results (Figure 3) we propose for a citizen science approach using our methodology as a first threshold for considering shrimp coloration indicative of potential pollution. This would consist of RGB coordinates from the ‘Colour image processing’ tool of ImageJ of (<110, <110, <100). For other areas outside the Canary Islands archipelago, a first study comparing polluted vs. non-polluted zones should be carried out to validate this threshold.
Human pollution can affect invertebrate colouration, i.e.,: British peppered moths that presented white colours and were camouflaged when perched on birch trees. During the Industrial Revolution in Britain, air pollution caused by coal burning darkened the bark of the trees were British peppered moths lived, causing a shift in the predator detection of the whiter vs. darker phenotypes. This in turn resulted in an increase in numbers of previously rare dark-coloured moths while the white ones were heavily reduced, in a so-called industrial adaptation [45]. In our study, we are observing the phenotypic plasticity of shrimp due to exposure to pollutants, and we do not know of any genetic adaptation effect on our studied shrimp. However, a detailed study in that direction should be performed to evaluate the risk of human-induced pollution affecting the genetic variation of this species on a similar scale as happened in British peppered moths.
In summary, in view of our results and the growing evidence of impacts from human wastewater on intertidal biota in these islands [5,30,34,35] along with the yearly increase in local population plus tourists in all the archipelago [36], we urge both community and local governments to take action to reduce these outputs and the effects on marine biota. Darker colourations in P. elegans suggested to be a bioindicator of anthropic pollution could become more predominant in rockpool shrimp on all the coasts of the Canary Islands and alert us of the increase in pollutant outputs to our coast.
Future works should include exploring possible correlations between these colour differences and specific pollutants (i.e., heavy metals like lead and cadmium) in order to further explore the proposed relationship between these two variables.

Author Contributions

Conceptualization, E.L.-B.; Methodology, E.L.-B.; Validation, E.L.-B. and J.A.-T.; Formal analysis, E.L.-B.; Investigation, E.L.-B. and J.A.-T.; Resources, E.L.-B.; Data curation, E.L.-B. and J.A.-T.; Writing—original draft, E.L.-B. and J.A.-T.; Writing—review & editing, E.L.-B. and J.A.-T.; Visualization, E.L.-B. and J.A.-T.; Project administration, E.L.-B.; Funding acquisition, E.L.-B. and J.A.-T. All authors have read and agreed to the published version of the manuscript.

Funding

Enrique Lozano-Bilbao thanks the University of La Laguna (ULL) and the Spanish Ministry of Universities for the Margarita Salas postdoctoral fellowship, granted by the UNI/551/2021 Order and funded by Next Generation EU Fund. JAT is currently funded by the postdoctoral program Catalina Ruiz (APCR2022010008) from Consejería de Economía, Conocimiento y Empleo of the Canary Islands Government.

Institutional Review Board Statement

For the capture of the specimens, a capture permit with reference SGBTM/BDM/AUTSPP/27/2022 was granted from the Ministry for Ecological Transition and the Demographic Challenge of the Government of Spain. All specimens were returned alive to their respective areas.

Data Availability Statement

All data has been given in tables within the manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the study’s design; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Genthe, B.; Kapwata, T.; Le Roux, W.; Chamier, J.; Wright, C.Y. The reach of human health risks associated with metals/metalloids in water and vegetables along a contaminated river catchment: South Africa and Mozambique. Chemosphere 2018, 199, 1–9. Available online: http://www.sciencedirect.com/science/article/pii/S0045653518301772 (accessed on 15 February 2023). [CrossRef] [PubMed]
  2. Abdel Ghani, S.A. Trace metals in seawater, sediments and some fish species from Marsa Matrouh Beaches in north-western Mediterranean coast, Egypt. Egypt. J. Aquat. Res. 2015, 41, 145–154. Available online: http://www.sciencedirect.com/science/article/pii/S1687428515000199 (accessed on 23 January 2023). [CrossRef]
  3. Irabien, M.J.; Velasco, F. Heavy metals in Oka river sediments (Urdaibai National Biosphere Reserve, northern Spain): Lithogenic and anthropogenic effects. Environ. Geol. 1999, 37, 54–63. [Google Scholar] [CrossRef]
  4. Goutte, A.; Barbraud, C.; Herzke, D.; Bustamante, P.; Angelier, F.; Tartu, S.; Clément-Chastel, C.; Moe, B.; Bech, C.; Gabrielsen, G.W.; et al. Survival rate and breeding outputs in a high Arctic seabird exposed to legacy persistent organic pollutants and mercury. Environ. Pollut. 2015, 200, 1–9. [Google Scholar] [CrossRef]
  5. Lozano-Bilbao, E.; Alcázar-Treviño, J.; Fernández, J.J. Determination of δ15N in Anemonia sulcata as a pollution bioindicator. Ecol. Indic. 2018, 90, 179–183. [Google Scholar] [CrossRef]
  6. Ruilian, Y.; Xing, Y.; Zhao, Y.; Hu, G.; Tu, X. Heavy metal pollution in intertidal sediments from Quanzhou Bay, China. J. Environ. Sci. 2008, 20, 664–669. [Google Scholar]
  7. Laurent, O.; Bard, D.; Filleul, L.; Segala, C. Effect of socioeconomic status on the relationship between atmospheric pollution and mortality. J. Epidemiol. Community Health 2007, 61, 665–675. [Google Scholar] [CrossRef]
  8. Handoh, I.C.; Kawai, T. Modelling exposure of oceanic higher trophic-level consumers to polychlorinated biphenyls: Pollution ‘hotspots’ in relation to mass mortality events of marine mammals. Mar. Pollut. Bull. 2014, 85, 824–830. [Google Scholar] [CrossRef]
  9. Ferretti, F.; Worm, B.; Britten, G.L.; Heithaus, M.R.; Lotze, H.K. Patterns and ecosystem consequences of shark declines in the ocean. Ecol. Lett. 2010, 13, 1055–1071. [Google Scholar] [CrossRef]
  10. Hipfner, J.M.; Galbraith, M.; Tucker, S.; Studholme, K.R.; Domalik, A.D.; Pearson, S.F.; Good, T.P.; Ross, P.S.; Hodum, P. Two forage fishes as potential conduits for the vertical transfer of microfibres in Northeastern Pacific Ocean food webs. Environ. Pollut. 2018, 239, 215–222. Available online: http://www.sciencedirect.com/science/article/pii/S0269749117351047 (accessed on 8 February 2023). [CrossRef]
  11. Lozano-Bilbao, E.; Lozano, G.; Gutiérrez, J.; Hardisson, A.; Rubio, C.; Paz, S.; Weller, D.G. The influence of the degassing phase of the Tagoro submarine volcano (Canary Islands) on the metal content of three species of cephalopods. Mar. Pollut. Bull. 2022, 182, 113964. Available online: https://www.sciencedirect.com/science/article/pii/S0025326X22006464 (accessed on 10 February 2023). [CrossRef] [PubMed]
  12. Bendell, L.I.; LeCadre, E.; Zhou, W. Use of sediment dwelling bivalves to biomonitor plastic particle pollution in intertidal regions; A review and study. PLoS ONE 2020, 15, e0232879. [Google Scholar] [CrossRef] [PubMed]
  13. Hwang, D.W.; Kim, P.J.; Kim, S.G.; Sun, C.I.; Koh, B.S.; Ryu, S.O.; Kim, T.H. Spatial distribution and pollution assessment of metals in intertidal sediments, Korea. Environ. Sci. Pollut. Res. 2019, 26, 19379–19388. [Google Scholar] [CrossRef]
  14. Frédou, F.L.; Frédou, T.; Gaertner, D.; Kell, L.; Potier, M.; Bach, P.; Travassos, P.; Hazin, F.; Ménard, F. Life history traits and fishery patterns of teleosts caught by the tuna longline fishery in the South Atlantic and Indian Oceans. Fish. Res. 2016, 179, 308–321. [Google Scholar] [CrossRef]
  15. Lozano-Bilbao, E.; Lozano, G.; Jiménez, S.; Jurado-Ruzafa, A.; Hardisson, A.; Rubio, C.; Weller, D.-G.; Paz, S.; Gutiérrez, J. Seasonal and ontogenic variations of metal content in the European pilchard (Sardina pilchardus) in northwestern African waters. Environ. Pollut. 2020, 266, 115113. Available online: http://www.sciencedirect.com/science/article/pii/S0269749120329407 (accessed on 10 February 2023). [CrossRef]
  16. Karunasagar, D.; Krishna, M.V.B.; Anjaneyulu, Y.; Arunachalam, J. Studies of mercury pollution in a lake due to a thermometer factory situated in a tourist resort: Kodaikkanal, India. Environ. Pollut. 2006, 143, 153–158. [Google Scholar] [CrossRef]
  17. Aydin-Önen, S.; Öztürk, M. Investigation of heavy metal pollution in eastern Aegean Sea coastal waters by using Cystoseira barbata, Patella caerulea, and Liza aurata as biological indicators. Environ. Sci. Pollut. Res. 2017, 24, 7310–7334. [Google Scholar] [CrossRef]
  18. Wang, X.; Zhang, C.; Zhang, Z. Pollution haven or porter? The impact of environmental regulation on location choices of pollution-intensive firms in China. J. Environ. Manag. 2019, 248, 109248. [Google Scholar] [CrossRef]
  19. Anand, K.S.; Giraud-Carrier, F.C. Pollution regulation of competitive markets. Manag. Sci. 2020, 66, 4193–4206. [Google Scholar] [CrossRef]
  20. Neves, S.A.; Marques, A.C.; Patrício, M. Determinants of CO2 emissions in European Union countries: Does environmental regulation reduce environmental pollution? Econ. Anal. Policy 2020, 68, 114–125. [Google Scholar] [CrossRef]
  21. Lionetto, M.G.; Caricato, R.; Giordano, M.E.; Pascariello, M.F.; Marinosci, L.; Schettino, T. Integrated use of biomarkers (acetylcholinesterase and antioxidant enzymes activities) in Mytilus galloprovincialis and Mullus barbatus in an Italian coastal marine area. Mar. Pollut. Bull. 2003, 46, 324–330. [Google Scholar] [CrossRef]
  22. Ding, J.; Sun, C.; He, C.; Li, J.; Ju, P.; Li, F. Microplastics in four bivalve species and basis for using bivalves as bioindicators of microplastic pollution. Sci. Total. Environ. 2021, 782, 146830. [Google Scholar] [CrossRef]
  23. Manickavasagam, S.; Sudhan, C.; Aanand, S. Bioindicators in aquatic environment and their significance. J. Aquac. Trop. 2019, 34, 73–79. [Google Scholar] [CrossRef]
  24. Thorne-Bazarra, T.; Lozano-Bilbao, E.; Triay-Portella, R.; Hardisson, A.; Paz, S.; Rubio-Armendariz, C.; Martín, V.; Gutiérrez, A.J. Metallic Study of the Invasive Species Cronius ruber—Assessment of Toxic Risk. Appl. Sci. 2022, 12, 3217. [Google Scholar] [CrossRef]
  25. Chandurvelan, R.; Marsden, I.D.; Glover, C.N.; Gaw, S. Assessment of a mussel as a metal bioindicator of coastal contamination: Relationships between metal bioaccumulation and multiple biomarker responses. Sci. Total. Environ. 2015, 511, 663–675. [Google Scholar] [CrossRef]
  26. Tokatli, C. Comparisons of diatoms and fishes as toxic metal bioindicator: A case study of an A-class wetland in northwest Turkey under effect of an intensive paddy cultivation stress. Environ. Sci. Pollut. Res. 2022, 29, 87231–87244. [Google Scholar] [CrossRef]
  27. McCauley, D.J.; Pinsky, M.L.; Palumbi, S.R.; Estes, J.A.; Joyce, F.H.; Warner, R.R. Marine defaunation: Animal loss in the global ocean. Science 2015, 347, 1255641. [Google Scholar] [CrossRef]
  28. Lozano-Bilbao, E.; Espinosa, J.M.; Lozano, G.; Hardisson, A.; Rubio, C.; González-Weller, D.; Gutiérrez, A.J. Determination of metals in Anemonia sulcata (Pennant, 1777) as a pollution bioindicator. Environ. Sci. Pollut. Res. 2020, 27, 21621–21627. [Google Scholar] [CrossRef]
  29. Lozano-Bilbao, E.; Herranz, I.; González-Lorenzo, G.; Lozano, G.; Hardisson, A.; Rubio, C.; González-Weller, D.; Paz, S.; Gutiérrez, A.J. Limpets as bioindicators of element pollution in the coasts of Tenerife (Canary Islands). Environ. Sci. Pollut. Res. 2021, 28, 42999–43006. [Google Scholar] [CrossRef]
  30. Lozano-Bilbao, E.; González-Delgado, S.; Alcázar-Treviño, J. Use of survival rates of the barnacle Chthamalus stellatus as a bioindicator of pollution. Environ. Sci. Pollut. Res. 2021, 28, 1247–1253. [Google Scholar] [CrossRef]
  31. Amat, J.A.; Rendón, M.A. Flamingo coloration and its significance. In Flamingos, Behavior, Biology, and Relationship with Humans; Nova Science Publishers, Inc.: New York, NY, USA, 2017; pp. 77–95. [Google Scholar]
  32. Woolf, M.S.; Dignan, L.M.; Scott, A.T.; Landers, J.P. Digital postprocessing and image segmentation for objective analysis of colorimetric reactions. Nat. Protoc. 2021, 16, 218–238. [Google Scholar] [CrossRef] [PubMed]
  33. Anderson, M.; Braak, C.T. Permutation tests for multi-factorial analysis of variance. J. Stat. Comput. Simul. 2003, 73, 85–113. [Google Scholar] [CrossRef]
  34. Anderson, M.R. The Resource for the Power Industry Professional. Proc. ASME Power 2004, 32. [Google Scholar]
  35. Herrera, E.T.; Lozano-Bilbao, E.; Lozano, G.; Hardisson, A.; Rubio, C.; González-Weller, D.; Gutiérrez, Á.J. Influencia metálica de un emisario submarino de Punta del Hidalgo, en el norte de Tenerife, Islas Canarias, España. Majorensis 2020, 16, 12–19. [Google Scholar]
  36. González-Delgado, S.; Lozano-Bilbao, E.; Alcázar-Treviño, J. Habitat preference of Anemonia sulcata in intertidal pools in the North-East Atlantic. Majorensis 2018, 14, 7–11. [Google Scholar]
  37. Canary Islands Institute of Statistics (ISTAC). Available online: https://datos.canarias.es/catalogos/estadisticas/ (accessed on 15 February 2023).
  38. López, E.P.; García, F.C. Agrotourism, sustainable tourism and ultraperipheral areas: The case of Canary Islands. PASOS Rev. Tur. Patrim. Cult. 2006, 4, 85–97. [Google Scholar]
  39. Bianchi, R.V. Tourism restructuring and the politics of sustainability: A critical view from the European periphery (The Canary Islands). J. Sustain. Tour. 2004, 12, 495–529. [Google Scholar] [CrossRef]
  40. Carrillo, J.; González, A.; Pérez, J.C.; Expósito, F.J.; Díaz, J.P. Projected impacts of climate change on tourism in the Canary Islands. Reg. Environ. Chang. 2022, 22, 61. [Google Scholar] [CrossRef]
  41. García-Romero, L.; Carreira-Galbán, T.; Rodríguez-Báez, J.Á.; Máyer-Suárez, P.; Hernández-Calvento, L.; Yánes-Luque, A. Mapping Environmental Impacts on Coastal Tourist Areas of Oceanic Islands (Gran Canaria, Canary Islands): A Current and Future Scenarios Assessment. Remote. Sens. 2023, 15, 1586. [Google Scholar] [CrossRef]
  42. Martín Martín, J.M.; Guaita Martínez, J.M.; Molina Moreno, V.; Sartal Rodríguez, A. An analysis of the tourist mobility in the island of Lanzarote: Car rental vs. more sustainable transportation alternatives. Sustainability 2019, 11, 739. [Google Scholar] [CrossRef]
  43. Silvertown, J. A new dawn for citizen science. Trends Ecol. Evol. 2009, 24, 467–471. [Google Scholar] [CrossRef]
  44. Vohland, K.; Land-Zandstra, A.; Ceccaroni, L.; Lemmens, R.; Perelló, J.; Ponti, M.; Samson, R.; Wagenknecht, K. The Science of Citizen Science; Springer Nature: Berlin, Germany, 2021; 520p. [Google Scholar]
  45. Hof, A.E.V.T.; Campagne, P.; Rigden, D.J.; Yung, C.J.; Lingley, J.; Quail, M.A.; Saccheri, I.J. The industrial melanism mutation in British peppered moths is a transposable element. Nature 2016, 534, 102–105. [Google Scholar] [CrossRef]
Diversity 15 00658 g001 550
Figure 1. Location of the surveys in the Canary Islands, NW Africa. (Asterisk symbol represents the sampling area of the island of Tenerife, cross symbol represents the sampling area of the island of Gran Canaria and black dot symbol represents the sampling area of the island of Lanzarote).
Figure 1. Location of the surveys in the Canary Islands, NW Africa. (Asterisk symbol represents the sampling area of the island of Tenerife, cross symbol represents the sampling area of the island of Gran Canaria and black dot symbol represents the sampling area of the island of Lanzarote).
Diversity 15 00658 g001
Diversity 15 00658 g002 550
Figure 2. Shrimp picture examples taken at the control zone [left] and near the sewage pipe [right] of Tenerife. In the first image, we indicate the main anatomical division of shrimp: C is for cephalothorax, A for abdomen, and T for telson. In the second image, the first abdominal segment is shown as ‘1’, as it was the one we used for the colourimetry analyses.
Figure 2. Shrimp picture examples taken at the control zone [left] and near the sewage pipe [right] of Tenerife. In the first image, we indicate the main anatomical division of shrimp: C is for cephalothorax, A for abdomen, and T for telson. In the second image, the first abdominal segment is shown as ‘1’, as it was the one we used for the colourimetry analyses.
Diversity 15 00658 g002
Diversity 15 00658 g003 550
Figure 3. Barplots for the RGB mean values of each zone per island. Each bar represents the colour coordinates values of a sample for Red, Green and Blue, respectively, for each sampling location. Using this methodology, a total black colour would have (0,0,0) coordinates in RGB composition, while pure white would be (255,255,255).
Figure 3. Barplots for the RGB mean values of each zone per island. Each bar represents the colour coordinates values of a sample for Red, Green and Blue, respectively, for each sampling location. Using this methodology, a total black colour would have (0,0,0) coordinates in RGB composition, while pure white would be (255,255,255).
Diversity 15 00658 g003
Diversity 15 00658 g004 550
Figure 4. Principal coordinate analysis (PCoA) showing the first two axes (99.4% of variability) based on Euclidean distances of square-root-transformed data of RGB data.
Figure 4. Principal coordinate analysis (PCoA) showing the first two axes (99.4% of variability) based on Euclidean distances of square-root-transformed data of RGB data.
Diversity 15 00658 g004
Table
Table 1. Descriptive statistics of all the RGB analysed in each sampling zone with sewage pipe and control zone [mean, standard deviation, minimum and maximum].
Table 1. Descriptive statistics of all the RGB analysed in each sampling zone with sewage pipe and control zone [mean, standard deviation, minimum and maximum].
Colour Control ZoneSewage Pipe
Gran CanariaLanzaroteTenerifeGran CanariaLanzaroteTenerife
RedMean1401681388210485
SD347273
Min134161127819780
Max1471761498711588
GreenMean1321581308510385
SD3343114
Min129155123818879
Max1381651349012889
BlueMean106127105769272
SD454348
Min100120100698353
Max113135115809679
Table
Table 2. One-way PERMANOVA analyses between control and sewage pipe zones for each island. * p-value < 0.05.
Table 2. One-way PERMANOVA analyses between control and sewage pipe zones for each island. * p-value < 0.05.
Gran CanariaLanzaroteTenerife
Sewage pipe vs. Control zone0.001 *
F = 10.69		0.001 *
F = 11.01		0.001 *
F = 10.61
Table
Table 3. Results of pairwise tests examining the significant factor “Zone” for each zone between islands were obtained in a one-way ANOVA. * p-value < 0.05.
Table 3. Results of pairwise tests examining the significant factor “Zone” for each zone between islands were obtained in a one-way ANOVA. * p-value < 0.05.
Sewage PipeControl Zone
Tenerife vs. Gran Canaria0.2830.563
Tenerife vs. Lanzarote0.003 *0.001 *
Gran Canaria vs. Lanzarote0.002 *0.002 *
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lozano-Bilbao, E.; Alcázar-Treviño, J. Sewage Pipe Waters Affect Colour Composition in Palaemon Shrimp from the Intertidal in the Canary Islands: A New Non-lethal Bioindicator of Anthropogenic Pollution. Diversity 2023, 15, 658. https://doi.org/10.3390/d15050658

AMA Style

Lozano-Bilbao E, Alcázar-Treviño J. Sewage Pipe Waters Affect Colour Composition in Palaemon Shrimp from the Intertidal in the Canary Islands: A New Non-lethal Bioindicator of Anthropogenic Pollution. Diversity. 2023; 15(5):658. https://doi.org/10.3390/d15050658

Chicago/Turabian Style

Lozano-Bilbao, Enrique, and Jesús Alcázar-Treviño. 2023. "Sewage Pipe Waters Affect Colour Composition in Palaemon Shrimp from the Intertidal in the Canary Islands: A New Non-lethal Bioindicator of Anthropogenic Pollution" Diversity 15, no. 5: 658. https://doi.org/10.3390/d15050658

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop