Assessing ecological niche shift for the Caspian Kutum (Rutilus frisii) in southern waters of the Caspian Sea over a decadal period

Document Type : Research Article

Authors

1 Post-doctoral Researcher, Iran’s National Elites Foundation and Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Associate Professor, Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj, Iran

10.22059/ije.2024.385183.1848

Abstract

Objective: Assessment of influencing environmental fluctuations on organisms in aquatic ecosystems is of high importance in the management and conservation of them. The present study aimed to investigate ecological niche shifts of Caspian Kutum (Rutilus frisii) under the effects of environmental condition changes in southern waters of the Caspian Sea during a decadal period (catch seasons 2002/03 and 2011/12).
Method: The ecological niche modeling was applied using commercial catch data and remotely-sensed environmental data. The random forest method was used to evaluate ecological niche relationships.
Results: The results showed significant (P < 0.001) decreases in day-time sea surface temperature (SST) and near-surface chlorophyll-a concentration (Chl-a) during the study period. The importance levels of SST, slope, and distance to riverine entrance locations in defining fish ecological niche were increased over the decadal period, while Chl-a and particulate organic carbon (POC) content had lower importance levels at the end of the period. The estimations of optimum ecological ranges of SST indicated considerable decreases over the period, but for other parameters, there were increasing patterns of optimum levels with extending their ranges compared to the initial catch season. Also, spatial shifts were obtained in the occurrence of the ecological niche conditions over the coastal regions.
Conclusions: The findings of this study indicated considerable changes in the ecological niche of the Caspian Kutum during the decadal period and its spatial distribution over the southern coastal waters of the Caspian Sea.

Keywords

Main Subjects

References

Afonso, P., McGinty, N. & Machete, M. (2014). Dynamics of whale shark occurrence at their fringe oceanic habitat. PLoS One, 9. http://doi.org/10.1371/journal.pone.0102060
Breiman, I. (2001). Random forests. Machine Learning., 45, 5-32. http://doi.org/10.1023/A:1010933404324
Buisson, L., Thuiller, W., Casajus, N. & Lek, S. (2010). Grenouillet G. Uncertainty in ensemble forecasting of species distribution. Global Change Biology, 16(4), 1145-1157. http://doi.org/10.1111/j.1365-2486.2009.02000.x
Chen, S., Xiao, Y., Xiao, Z., Li, J. & Herrera-Ulloa, A. (2024a). Suitable habitat shifts and ecological niche overlay assessments among benthic Oplegnathus species in response to climate change. Environmental Research, 252, 119129. http://doi.org/10.1016/j.envres.2024.119129
Chen, S., Xiao, Y., Xiao, Z., Ma, D., Li, J. & Herrera-Ulloa, A. (2024b). Prediction of suitable habitat shifts and assessment of ecological niche overlaps for three Tridentiger species with intertidal and subtidal characteristics under future climate changes. Marine Pollution Bulletin, 198, 115827. http://doi.org/10.1016/j.marpolbul.2023.115827
Cutler, D.R., Edwards, Jr. T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J. & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783-2792. http://doi.org/10.1890/07-0539.1
Eagderi, S., Mouludi-Saleh, A., Esmaeili, H. R., Sayyadzadeh, G. & Nasri, M. (2022). Freshwater lamprey and fishes of Iran: a revised and updated annotated checklist-2022. Turkish Journal of Zoology, 46(6), 500-522. http://doi.org/10.55730/1300-0179.3104
Elith. J., Graham, H. C., Anderson, P. R., Dudik, M., Ferrier, S., Guisan, A., … & Zimmermann, E. N. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129-151. http://doi.org/10.1111/j.2006.0906-7590.04596.x
Esmaeili, H. R., Gholamhoseini, A., Mohammadian-Kalat, T. & Aliabadian, M. (2018). Predicted changes in climatic niche of Alburnus species (Teleosti: Cyprinidae) inIran until 2050. Turkish Journal of Fisheries and Aquatic Sciences, 18, 995-1003. http://doi.org/10.4194/1303-2712-v18-8-08
Esmaeili, H. R., Brian, W. C., Mehraban, H. R., Masoudi, M., Khaefi, R., Abbasi, K., Mostafavi, H. & Vatandoust, S. (2015). An updated checklist of fishes of the Caspian Sea basin of Iran with a note on their zoogeography. Iranian Journal of Ichthyology, 1(3), 152-184. http://doi.org/10.22034/iji.v3.18
Fazli, H., Behrouz Khoshghalb, M. R., Tavakoli, M., Ghodrati Shojaei, H., Daryanabard, G. R. & Soleimani Roudi, A. (2021). Spatial distribution and diversity of commercial demersal fish species in the shelf waters of the Caspian Sea. Journal of Applied Ichthyology, 37(6), 857-867. http://doi.org/10.1111/jai.14256
Fromentin, J. M. & Lopuszanski, D. (2014). Migration, residency, and homing of Bluefin tuna in the wastern Mediterranean Sea. ICES Journal of Marine Science, 71(3), 510-518. http://doi.org/10.1093/icesjms/fst157
Froeschle, B. F., Stunz, G. W., Robillard, M. M. R., Williams, J. & Froeschke, J. T. (2013). A modeling and field approach to identify essential fish habitat for juvenile Bay Whiff (Citharichthys spilopterus) and southern Flounder (Paralichthys lethostigma) within the Aransas Bay Complex, TX. Estuaries and Coasts, 36(5), 881-892. http://doi.org/10.1007/s12237-013-9600-9
Greenwell, B., Boehmke, B. & Cunningham, J. (2020). GBM Developers. gbm: Generalized Boosted Regression Models. R package version 2.1.8. http://CRAN.R-project.org/packages=gbm
Griffiths, J. R., Kadin, M., Nascimento, F. J., Tamelander, T., Tornroos, A., Bonaglia, S., Bonsdorff, E., Bruchert, V., Gardmark, A., Jarnstrom, M. & Kotta, J. (2017). The importance of benthic-pelagic coupling for marine ecosystem functioning in a changing world. Global Change Biology, 23(6), 2179-2196. http://doi.org/10.1111/gcb.13642
Guisan, A. & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135(2-3), 147-186. http://doi.org/10.1016/50304-3800(00)00354-9
Guisan, A., Tingley, A., Baumgartner, J. B., Naujokaitis-Lewis, I., Sutcliffe, P. R., Tulloch, A. L. T., Regan, T. J., Brotons, L., Mcdonald-Madden, E., Mantyka-Pringle, C., Martin, T. G., Rhodes, J.R., Maggini, R., Setterfield, S. A., Elith, J., Schwartz. M. W., Wintle, B. A., Broennimann, O., Austin, M., Ferrier, S., Kearney, M. R., Possingham, H. P. & Buckley, Y. M. (2013). Predicting species distributions for conservation decisions. Ecology Letters, 16(12), 1424-1435. http://doi.org/10.1111/ele.12189
Haghi Vayghan, A., Fazli, H., Ghorbani, R., Lee, M-A. & Nasrollahzadeh Saravi, H. (2016). Temporal habitat suitability modelling of Caspian Shad (Alosa spp.) in the southern Caspian Sea. Journal of Limnology. 2016; 75(1), 210-223. http://doi.org/10.4081/jlimnol.2015.1215 
He, M., Chen, L., Luo, G., Gu, X., Wang, G. & Ran, J. (2018). Suitable habitat prediction and overlap analysis of two sympatric species, giant panda (Ailuropoda melanoleuca) and Asiatic black bear (Ursus thibetanus) in Liangshan Mautains. Biodiversity Science, 26(11), 1180-1189. http://doi.org/10.17520/biods.2018167
Hua, C. Li. F., Zhu, Q., Zhu, G. & Meng, L. (2020). Habitat suitability of Pacific saury (Cololabis saira) based on a yield-density model and weighted analysis. Fisheries Research, 221, 105408. http://doi.org/10.1016/j.fishres.2019.105408
Jimenez-Valvarde, A., Peterson, A.T., Soberon, J., Overton, J.M., Aragon, P. & Lobo, J. M. (2011). Use of niche models in invasive species risk assessments. Biological Invasions, 13, 2785-2797. http://doi.org/10.1007/s10530-011-9963-4
Liaw, A. & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
Luan, J., Zhang, C., Xu, B., Xue, Y. & Ren, Y. (2018). Modelling the spatial distribution of three Portunidae crabs in Haizbou Bay, China. PloS One, 13(11), p.e0207457. http://doi.org/10.1371/journal.pone.0207457
Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, H. (2020). Ecological niche models and species distribution models in marine environments: a literature review and spatial analysis of evidence. Ecological Modelling, 415, 108837. http://doi.org/10.1016/j.ecolmodel.2019.108837.
Moëzzi, F., Poorbagher, H., Eagderi, S., Feghhi, J., Dormann, C. F., Khorshidi Nergi, S. & Amiri, K. (2022). Modelling habitat preference of Caspian Kutum, Rutilus Kutum, using non-linear habitat suitability indices and generalized additive models. Regional Studies in Marine science, 55, 101715. http://doi.org/10.1016/j.rsma.2022.102715
Moëzzi, F., Poorbagher, H., Eagderi, S., Feghhi, J., Dormann, C. F., Khorshidi Nergi, S. & Amiri, K. (2024). The importance of temporal scale in distribution modeling of migratory Caspian Kutum, Rutilus frisii. Ecology and Evolution, 14(9), e70259. http://doi.org/10.1002/ece3.70259
Moore, C., Drazen, J. C., Radford, B. T., Kelly, C. & Newman, S. J. (2016). Improving essential fish habitat designation to support sustainable ecosystem-based fisheries management. Marine Policy, 69, 32-41. http://doi.org/10.1016/j.marpol.2016.03.021
Olaya-Marin, E. J., Martinez-Capel, F. & Vezza, P. (2013). A comparison of artifician neural networks and random forests to predict native fish species richness in Mediterranean rivers. Knowledge and Management of Aquatic Ecosystems, 409, p07. http://doi.org/10.1890/07-0539.1
Olsen, Z. (2019). Quantifying nursery habitat function: variation in habitat suitability linked to mortality and growth for juvenile Black Drum in a hypersaline estuary. Marine and Coastal Fisheries, 11(1), 86-96. http://doi.org/10.1002/mcf2.10064
Parra, H. E., Pham, C. K., Menezes, G. M. & Rosa, A. (2017). Tempera F, Morato T. Predictive modeling of deep-sea fish distribution in the Azores. Deep Sea Research Part II: Topical Studies in Oceanography, 145, 49-60. http://doi.org/10.1016/j.dsr2.2016.01.004
Perea-Blazquez, A., Davy, S. K. & Bell, J. J. (2012). Estimates of particulate organic carbon flowing from the pelagic environment to the benthos through sponge assemblages. PLoS One, 7(1), e29569. http://doi.org/10.1371/journal.pone.0029569
Peterson, A. T. & Soberon, J. (2012). Species distribution modeling and ecological niche modelling: getting the concepts right. Natureza and Conservacao, 10(2), 102-107. http://doi.org/10.4322/natcon.2012.019
Peterson, A. T., Soberon, J., Pearson, R. G., Anderson, R. P., Martinez-Meyer, E., Nakamura, M. & Araujo, M. B. (2011). Ecological niches and geographic distributions. Princeton University Press.
Peterson, A. T., Papes, M. & Soberon, J. (2015). Mechanistic and correlative models of ecological niches. European Journal of Ecology, 1(2), 28-38. http://doi.org/ 10.1515/eje-2015-0014
Rabazanov, N. I., Orlov, A. M., Abdusamadov, A. S. & Barkhalov, R. M. (2019). Caspian Kutum Rutilus Kutum: A story of exploitation, survival, and revival. In Kruger, C.H.C. and Taylor, W.W. (eds.), From catastrophe to recovery: Stories of fishery management success. (pp: 485-508). American Fisheries Society. http://doi.org/10.47886/9781934874554
Reznic, D. N., Losos, J. & Travis, J. (2019). From low to high gear: there has been a paradigm shift in our understanding of evolution. Ecology Letters, 22(2), 233-244. http://doi.org/10.1111/ele.13189
Schming, M., Diogo, H., Serrao Santos, R. & Afonso, P. (2014). Assessing hotspots within hotspots to conserve biodiversity and support fisheries management. Marine Ecology Progress Series, 513, 187-199. http://doi.org/10.3354/meps10924.
Szucs, M., Vahsen, M., Melbourne, B., Hoover, C., Weiss-Lehman, C. & Hufbauer, R. (2017). Rapid adaptive evolution in novel environments acts as an architect of population range expansion. Proceedings of the National Academy of Science, 114(51), 13501-13506. http://doi.org/10.1073/pnas.1712934114
Vayghan, A. H., Poorbagher, H., Shahraini, H. T., Fazli, H., Saravi, H. N. (2013). Suitability indices and habitat suitability index model of Caspian Kutum (Rutilus frisii Kutum) in the southern Caspian Sea. Aquatic Ecology, 47(4), 441-451. http://doi.org/10.1007/s10452-013-9457-9
Xhsng, Z., Xu, S., Capinha, C., Weterings, R. & Gao, T. (2019). Using species distribution model to predict the impact of climate change on the potential distribution of Japanese whiting Sillago japonica. Ecological Indicators, 104, 333-340. http://doi.org/10.1016/j.ecolind.2019.05.023
Iranian journal of Ecohydrology
Volume 11, Issue 3
October 2024
Pages 395-410

Files

History

  • Receive Date: 23 July 2024
  • Revise Date: 18 August 2024
  • Accept Date: 27 August 2024
  • First Publish Date: 22 September 2024
  • Publish Date: 22 September 2024

Share

How to cite

Statistics