How does agricultural pollution affect estuarine health in the United Kingdom?by Sven Mellaza and Matia Pavkovic
Published by the October 4, 2021 on 4:39 PM
Estuaries are among the most productive ecosystems on the planet. These habitats deliver many services to humankind. They are characterized by wide ranges of water salinity, currents power and turbidity. To their natural stressors are added human disturbances that affect the natural system.
The rivers, from their sources to the sea, follow a long path through land fields. In a period of important rainfall, a large part of the land pollution defined by Nitrates and Phosphates is collected by the rivers and transported to estuaries. Consequently, the enrichment of the system modifies the relation equilibrium in the food chain; a rich and complex link between the organisms is the base of a healthy resilient ecosystem. The management application is crucial, so estuarine ecosystems can continue to deliver services and host rich life diversity.
Impact analyses of the pollution on the ecosystem
In this study, the scientist aimed to asses estuarine health by considering the relation between all the organisms. They have analyzed two estuaries systems from the United Kingdom. Tamara estuary is a medium-size complex located on the south-west coast of England; it stretches from Gunnislake weir to Plymouth sound. The second estuary, Eden, is smaller compare to the first one. It is positioned between the village of Guardbrige and the town of St Andrews on the East coast of Scotland.
Since the 90’s, the two ecosystems have experienced major nutrient enrichment from the arable and livestock production. This pollution led to the ecosystem “eutrophication”, a biological phenomenon that can cause an increase of algal bloom and a decrease of oxygen concentration in habitats.
For this research, ecologists are using mathematical software called “Ecopath model”. The model is a widely used tool to identify and quantify major energy flow in the ecosystem, to visualize the important interaction between species, evaluate pollution and climate changes in aquatic ecosystems. This approach allows them to examine systems in their entirety to adjust management application.
The aim of this study is to analyze and compare food network structure of these two estuaries at different historical period of nutrient concentration. The results shows that Tamar estuary is 25% more active than Eden estuary in periods of high nutrients concentrations. The activity is defined by the number of matters flowing in the system. The scientists explain here the difference by the greater size and greater freshwater inputs from the rivers in Tamar estuary. The second indicator was the total biomass of the estuary. The productivity of the system decreases with the reduction of nutrients enrichment. Primary producer’s growth is stimulated by the extra nutriments, which improve their development and finally impact the upper trophic levels.
During pollution event, flow of nutrients increases, leading to degradation of the food-chain organization and structure. In addition, wider the estuaries are, slower it recovers from perturbations, especially looking at the trophic structure.
The Ecopath, Ecosim and Ecospace models are tools primely used to evaluate ecosystem impact of fisheries (Pauly et al. 2000). As it gets popular, these models are useful to evaluate trophic systems like estuaries. These well-known models can be useful to predict and analyze the effects of actual global changing. Nevertheless, a bunch of other models exist and are used to analyze trophic system like Bayesian-mixing model. Using different tools to study the same problematic permit comparison and can lead to different approaches.
A little bit of salt and heat... a good recipe for goby metabolism?by Maxime Deau, Quentin Garreau and Dorian Raoux
Published by the June 1, 2020 on 2:18 PM
As the literature shows, a variety of factors influence the well-being of fish populations. For example, we know that some fish may or may not be very sensitive to changes in the conditions of their living environment (water temperature or salinity). These changes can affect their metabolism (reduced fertility, growth, etc.) or even, in the worst case, lead to the death of individuals. The goby (Pomatoschistusmicrops) (Figure 1), a relatively tolerant species and an essential central link in the food web is one of the species studied in the observation of the impact of these changes on the fish fauna in the Minho estuary in Portugal (Figure 2).
In this study, the researchers were able to model the evolutionary dynamics of p.microps populations based on models that take into account different parameters of goby's life cycle like fertility, mortality, migration rate and the effect of environmental parameters such as salinity and temperature. The aim of these models is to describe the evolution of the different life stages of this fish by establishing the possible impacts of climate change on their metabolism. In this framework, they studied both the impacts of temperature and salinity and combined the impact of both.
It has been noted that salinity directly influences the metabolism of individuals. Indeed, it plays a particular role in the survival of many aquatic organisms but also on their growth (strong allocation of energy to osmoregulation; Rigal, F. and al.,2008). Therefore, it plays a role in the growth of the goby as well as indirectly on its prey. The latter will be less available, which implies a higher energy expenditure for predation. However, this species resists large variations in salinity (0 to 51 psu). For temperatures, the impact is more diverse. Since the goby does not thermoregulate, its metabolism is directly influenced by the temperature of the environment. In addition to its significant effect on pregnancy, it also has an impact on migration, reproduction, recruitment and mortality. (Sogard, 1997; Hurst et al., 2000; Hales and Able,2001; Hurst, 2007; Jones and Miller, 1966; Claridge et al.,1985; Wiederholm, 1987).
Regarding the goby’s responses to these parameters, the research team has implemented them in the model, running different scenarios (salinity and temperature variations). Temperature and salinity variations studied separately led to population crash, except for a salinity lower than the current state ( -5psu). However, the combination of the two variables gave scenarios showing an increase in the population when the salinity was -5 psu, with temperatures ranging from +1 to +3°C, with an optimum at +2°C (see figure 5).
For example, in extreme temperatures, the fish activity will be greatly reduced, which will imply a decrease in the search for preys or sexual partners, causing feeding and mating problems (predation of eggs by males (Magnhagen, 1992). However, a slight increase in temperature could cause a longer reproduction period, allowing for a greater number of offspring to be generated. It has also been noted that with an increase in temperature, there is a delay in the breeding period, leading to the appearance of offspring in a period that may be less favorable for their proper development (early winter/lower metabolism).
In conclusion, climate change, through its effects on water temperature and salinity, will have a significant impact on common goby populations. Indeed, these parameters have a great influence on the metabolism of these fish (whatever their stage of development).In many scenarios, increases in temperature and salinity can cause crash populations. But beware, in some cases (increase in temperature and decrease in salinity) the population of Pomatoschistus microps would tend to increase. Even if this scenario seems favorable for this species, some others will suffer. in fact, a study conducted on Arctic fish species has confirmed these trends
It is therefore clear that climate change affects population dynamics by changing fish environment and impacting their metabolism. It’s therefore important to continue this kind of study to have a better idea of these repercussion on a global scale. We are largely responsible for climate change, so it is up to us to make sure that we limit our impacts. Here is a link that will teach you how to reduce your carbon footprint through 20 examples of simple everyday actions: http://www.globalstewards.org/reduce-carbon-footprint.htm
Other cited studies:
Claridge, P.N., Hardisty,M.W., Potter, I.C., Williams, C.V., 1985. Abundance, life history and ligulosis in the Gobies (Teleostei) of the inner Severn Estuary. J.Mar. Biol. Assoc. U. K. 65, 951–968.
Hales, L.S., Able, K.W., 2001.Winter mortality, growth, and behavior of young-of-the-year of four coastal fishes in New Jersey (USA) waters. Mar. Biol. 139, 45–54.
Hurst, T., 2007. Causes and consequences of winter mortality in fishes. J. Fish Biol. 71, 315–345.
Hurst, T.P., Schultz, E.T., Conover, D.O., 2000. Seasonal energy dynamics of young of the year Hudson River striped bass. Trans. Am. Fish. Soc. Taylor & Francis 129, 145–157.
Jones, D., Miller, P.J., 1966. Seasonal migrations of the common Goby, Pomatoschistus microps (Kroyer), in Morecambe Bay and elsewhere. Hydrobiologia 27, 515–528.
Magnhagen, C., 1992. Alternative reproductive behaviour in the common goby, Pomatoschistus microps: an ontogenetic gradient? Anim. Behav. 44, 182–184.
Rigal, F., Chevalier, T., Lorin-Nebel, C., Charmantier, G., Tomasini, J.-A., Aujoulat, F., Berrebi, P., 2008. Osmoregulation as a potential factor for the differential distribution of two cryptic gobiid species, Pomatoschistus microps and P. marmoratus in French Mediterranean lagoons. Sci. Mar. 72, 469–476.
Sogard, S.M., 1997. Size-selective mortality in the juvenile stage of teleost fishes: a review. Bull. Mar. Sci. 60, 1129–1157.
Wiederholm, A.-M., 1987. Distribution of Pomatoschistus minutus and P. microps (Gobiidae, Pisces) in the Bothnian Sea: importance of salinity and temperature.Memoranda Societatis pro fauna et flora Fennica 63, 56–62.
This post is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.Use of dynamic energy budget and individual based models to simulate the dynamics of cultivated oyster populationsby Maxime Rochet and Jean-Baptiste Valerdi
Published by the October 8, 2018 on 12:45 PM
This paper deals with a test of Dynamic Energy Budget (DEB) apply for predictions of the oyster Crassostrea gigas production in Thau Lagoon. The DEB model is based on physiological and environmental parameters, he predict the growth at indivual level. In the case of oyster production the prediction must be applicate at the cohort levels, its why they choose to integrate the DEB model into a population dynamics concept. Population model choose its the IBM (Indivifual Based Model) method, the equations are used for the predict the harvested production and the stocks in place (total number of individuals). The advantage of this integration its to assess the effect of ecosystem changes on oyster production.
Oyster farming in the Thau Lagoon - Olivier Pessin - CC BY-SA 3.0The models recently used (DEB) have been compared with a more common prediction tool. The partial differential equation (PDE) are empirical equations used for the growth prediction between different class and simulate by individual total mass. This equation are more straightforward than the DEB-IBM models but they use only a single variable to represent individual growth. The DEB model integrate two variable of calibration, the other parameters of the differents equations were estimated from independent datasets using comprehensive studies of oyster growth and ecophysiology under controled conditions. The calibrated parameters are the chlorophyll a concentration proxy of the phytoplacton biomass (principal food of oysters) and the température linked to assimilation and maintenance rates. This technique modelise by this way the indivdual capatcity of food assimilation and the allocation of energy between energical reserve, structural tissues ans reproductive structure and maintenance. To be more likelihood a growth variability showing variability between individuals have been implanted. Some variability have been implanted into the prediction of PDE method and the DEB-IBM model. This variability was integrate by diffusion to reproduce the variability between individual growth in the PDE and by Xk (half saturation coefficient) variability in the DEB-IBM case.
The results of the differents simulations have proved a good capacity for the DEB-IBM model to predict the stocks and the harvest productions. The data estimated are close to the observed. He have to advantages to be generic, easy to etablish by the low number of measurables parameters. With the results showed in the study (see figure below) his capacity to take account of the environmment variables have been proved too . The limits are detectable in his sensivity to the variability and the large number of parameters estimated can induce in error.
From Bacher & Gangnery 2006.The comparison of the two models have show the effect of the variability in the predictions values. The values predicted by the DEB-IBM model look closer to the observation than the PDE predictions. For exemple the harvestes productions have been estimated earlier by the PDE method than the DEB-IBM, so the modelisation of DEB parameters can influe strongly the dynamic population and the production previsions.
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