Just a bit of context to help you understand what the game is about :
Welcome to the northern Colorado Plateau of Utah ! During this period of the year, you, a male mule deer, and your congeners are doing your reproductive migration. Currently, this area faces some major issues : diverse oil and gas wells are being created on the Plateau to collect fossil energies. What does it mean for your cohorte ? It means you encounter an ecological continuity interruption ! In fact, now, you may not cross this territory as easily as before, but you still need to reach your beloved female. This ecological continuity interruption signifies that you will use more energy to cross the Plateau compared to when the wells weren’t installed. The energy cost is detailed by scientists in a Dynamic Energy Budget (DEB) Model.
The game is completely free, you just have to print it ! All the documents mandatory to play are available just here. You will find some instructions for the game and its preparation, explanations on the article and pieces to play (checkerboarders, cards, pawns).
The use of pesticides is a major issue for freshwater ecosystems. A lot of different molecules exist and legal decisions are necessary to regulate and control their use. To make informed management decisions, experimental ecotoxicology studies are essential to assess the risks pesticides pose to ecosystems. Some key indicators provided by these studies are useful, such as the lowest concentration needed to cause an effect or the mortality of an organism.But every study has its biases: sometimes scientists cannot experiment on a large number of organisms, sometimes the tested concentrations are too high or too low, and sometimes studies only examine short-term effects rather than long- term exposure. Additionally, some studies focus solely on adult organisms or only on eggs.
But what if we could predict in advance the effect of a pesticide on a specific organism? What if we could test every concentration, every exposure duration… simply by modifying a line in a code? This is precisely what scientists from the UK and Germany explored in their 2018 study, investigating the modeling of the effects of the pesticide beta-cyfluthrin on the survival and growth of rainbow trout. But how reliable is it?
Methods
What is pyrethroid beta-cyfluthrin ?
(Beta)Cyfluthrin is a pyrethroid and common household pesticide. Its action relies on neurotoxic effects on insects. Like most pyrethroids, it is also highly toxic to fish and invertebrates, but it is far less toxic to humans. It is generally sprayed onto agricultural crops and outbuildings. Cyfluthrin also causes a problem of bioaccumulation in freshwater ecosystems: the concentration of this pesticide is generally higher inside living organisms compared with the external environment, and this concentration increases following the trophic chain. At the top of it, fish are therefore mainly threatened by these kinds of pollutants.
Why study rainbow trout?.
Rainbow trout was chosen for this study because, as a tasty fish, it has a great economic value. It is also one of the most sensitive species to (beta-)cyfluthrin, based on acute toxicity data, and the only cold water fish that is recommended for ecotoxicological testing by the OECD (“Organisation for Economic Co-operation and Development“). Rainbow trout life cycle includes different early life stages (ELS): egg, alevin, and swim-up fry. During the first two stages, the fish doesn’t feed in the environment, relying on reserves inside the egg or the yolk sac.
What is a model ? How does it work?
Let’s take a closer look at the models used in this study to predict how beta-cyfluthrin exposure patterns affect the early life stages of rainbow trout. The Dynamic Energy Budget (DEB) model is a bioenergetics framework that tracks how fish allocate energy along all their different life-stages for growth, maintenance, and reproduction, allowing researchers to assess how stressors like pesticides influence these key biological processes. By integrating a Toxicokinetic-Toxicodynamic (TKTD) module, the model also simulates how fish absorb, process, and respond metabolically to beta-cyfluthrin over time. This makes it possible to link internal pesticide concentrations to survival and sublethal effects. Here, the authors chose to test the hypothesis that the pesticide would affect the ability to feed, based on the known neurotoxic effects of beta-cyfluthrin. A key advantage of this approach is its ability to distinguish between fish biology and chemical effects, offering a clearer mechanistic explanation for observed impacts. Additionally, the combined DEB-TKTD model allows for time-dependent predictions, making it more relevant for assessing real-world pesticide exposure scenarios where concentrations vary over time.
Calibration and validation of the model (Fig 1)
Every modeling process includes a calibration step, where experimental data are used to adjust parameters so that the model behaves similarly to the object of study. Experiment 1 was used for calibration. In this experiment, fish were exposed to a constant concentration of beta-cyfluthrin for 9 weeks through all their early life stages (egg, alevin and swim-up fry). Different concentrations were tested, ranging from 10 to 160 ng/L. Significant effects on growth were found for concentrations of 17.7 ng/L and above, and significant effects on mortality were assessed for concentrations of 31.8 ng/L and above.
Fig1 :Comparison between Experiments 1 and 2. Dashed lines represent stage transitions from egg to alevin, and from alevin to swim-up stage. The blue bars represent the timing of exposure to the test substance (constant in Experiment 1, peaked in Experiment 2). Zimmer&Al, 2018.
Once calibrated, the model needs to be validated to assess the quality of its results for controlled factor values or on examples that were not used during the calibration phase. Experiment 2 was used for this purpose: in this experiment, fish were exposed to two pulsed exposures in 14 days of interval (mimicking agricultural runoffs). Different peak concentrations of 32, 48, 72, 180 and 450 ng/L during different life stages were tested : either the two exposures happened during the egg life stage (cohort C), either one during alevin and one during swim-up fry life stage (cohort B) or both during swim-up fry life stage (cohort A). The results assessed no effect on survival (no mortality) on the three cohorts, but significative effects on cohort A: impairment on feeding behaviour for peaks of 48 ng/L and above, which was reversible after stopping the exposure, and a significant decrease in growth for peaks of 72 ng/L and above.
Results: is this model reliable?
Fig 2: Representation of the main results found by DEB and TKTD models. Created in https://BioRender.com
The authors found out that… the model worked ! The mechanism of a neurotoxic effect impairing feeding behaviour and consequences on growth at early life stages fit the results given by the model and the experiments. This means that this model would be reliable to predict pesticides impact on fish populations under different exposure scenarios. In accordance with this hypothesis, as only swim-up fries start to feed in the environment, the effect is only assessed at this stage of life and not before.
Implications
There are many advantages to using mechanistic modeling to explore ecotoxicology issues. First, it reduces the need for animal testing to study the effects of pollutants on organisms, which in turn minimizes the impact on animal welfare and survival—typically low in toxicology experiments. Additionally, modeling allows for future predictions under different scenarios, such as rising water temperatures due to climate change. This technique also provides valuable insights for management and agricultural practices related to pesticide use, based on robust results. For instance, in this case, the effects of pulsed exposures appear to be less harmful and more reversible than constant exposures, offering potential guidelines for regulating these substances. However, no prediction can ever be perfect. Models will always be simplifications of complex biological processes. This is why any new model requires ongoing improvements and updated data for calibration, especially in relation to climate change and its interactions with fish physiology and toxicant dilution in freshwater ecosystems. Like animals and ecosystems, a model must be dynamic and constantly evolving!
Sources
Zimmer, E.I., Preuss, T.G., Norman, S. et al. Modelling effects of time-variable exposure to the pyrethroid beta-cyfluthrin on rainbow trout early life stages. Environ Sci Eur 30, 36 (2018). https:/ /doi.org/10.1186/s12302-018-0162-0
Cayo Corcellas, Ethel Eljarrat, Damià Barceló, First report of pyrethroid bioaccumulation in wild river fish: A case study in Iberian river basins (Spain), Environment International, Volume 75, 2015, Pages 110-116, ISSN 0160-4120, https:/ /doi.org/10.1016/j.envint.2014.11.007
Mousti-cartes est un jeu de plateau pour 2 à 6 joueurs menés par un maître du jeu. Il a pour but de vulgariser une étude scientifique sur les effets d’un biocide sur les différents taxons d’un réseau trophique. Ainsi, il permet de faire découvrir aux joueurs des notions de bioénergétique. Il est basé sur l’article suivant : Verena Gerstle, Eric Bollinger, Alessandro Manfrin, Sebastian Pietz, Sara Kolbenschlag, Alexander Feckler, Martin H. Entling & Carsten A. Brühl. Trophic effects of Bti-based mosquito control on two top predators in floodplain pond mesocosms. Environ Sci Pollut Res 31, 45485–45494 (2024). Les fichiers contiennent tout le matériel pour jouer sauf les ciseaux et la colle (notes explicatives, plateau et cartes). Pour jouer il suffit d’imprimer les feuilles suivantes (attention à ne pas vous spoiler), puis de découper et d’assembler les cartes. Le plateau doit être imprimé au format A3 et les cartes en A4.
The mackerel, a cornerstone of fishing in Northwest Asia, is central to a new bioenergetic model aimed at better understanding its growth dynamics and exploitation.
A Major Economical and Ecological Challenge.
In the northwest Pacific, chub mackerel (Scomber japonicus) plays a key role in the fishing industry. Its capture is an economic pillar for fishermen and a vital food source for millions of consumers. However, the sustainable management of this resource raises many questions : how can optimal extraction be ensured while preserving the population?
Recently, fishery results suggest a decline in populations, which is characterized by lower densities and a clear decrease in the average size of catches. For fishing activities to be sustainable, populations must have a sufficient number of breeders to allow the regeneration of harvested individuals. The age (or size) structure of a population thus serves as a key indicator of its ability to withstand long-term harvesting pressure.
Figure 1 : The left panel illustrates a low-density population. The central panel represents a high effective density, but with a structure mainly composed of juveniles and very few reproductive individuals, which concentrates the harvesting pressure on a small number of mature individuals. The right panel represents a stock that is sufficiently structured to sustainably support fishing focused on mature individuals.
These concerning trends urge a reconsideration of harvesting levels, and the implementation of adaptive management strategies to ensure a responsible and sustainable exploitation of this vital resource.
Figure 2. Illustration of the developmental stages of Scomber japonicus (larval, juvenile, adult) within the framework of the DEB model. The transition from larval stage to juvenile (metamorphosis) is a key period during which the mackerel leaves its larval diet(consumption of the yolk sac) for an autonomous diet typical of juvenile and adult stages.
New ways of understanding mackerel dynamics
Traditionally, fish stock estimation relies on approaches based on catch and mortality models (such as the Gompertz-Laird and Von-Bertalanffy Growth Models—GLGM & VBGM). However, these models do not account for energy flows and resource allocation across different life stages. This is where the innovative approach of the Dynamic Energy Budget (DEB) model comes into play, developed by Weiwei He et al. in their scientific article “Dynamic energy budget model for the complete life cycle of chub mackerel in the Northwest Pacific”, 2024.
The Dynamic Energy Budget (DEB) model describes how an organism allocates its energy throughout its life cycle. This energy is derived from ingested food, which is assimilated and stored in an energy reserve. This reserve is then mobilized to support essential biological functions: Growth, Maturation (transition from one life stage to another), Reproduction, and Maintenance (survival and functioning of the organism). Progression through life stages depends on the amount of energy available in this reserve. Food density plays a key role in replenishing this reserve, while temperature influences its utilization and the metabolic cost of energy conversions.
« Based on the DEB model, the biological processes and population dynamics of chub mackerel […] can be interrelated, providing support for studies of the species in the Northwest Pacific » – Weiwei He et al,. 2024.
Figure 3 : The energy derived from food (food density X) follows different pathways in DEB models : Assimilation rate (pA) : the rate of energy incorporation from food into the reserve Metabolic rate (pC) : rate at which the energy stored in the reserve is mobilized. Energy converted to growth (pG) : rate of energy conversion into biomass, inducing structural changes in the organism (growth). Energy converted to maturation and reproduction (pR) : rate of conversion for energy allocated to maturity and reproduction buffer. Maintenance (pS, pJ) : rate of conversion for energy required for the organism’s functioning and preservation. These parameters directly influence the development and survival of the fish depending on environnemental conditions (food availability, temperature, etc.).
Comparison between approaches
The VBGM and GLGM models were developed by Go et al. in 2020. There is a good fit of the equation of GLGM for the growth of larval and juvenile fish, while VBGM focuses on growth of juvenile and adult. They can be used to study the effect of temperature on growth, but without accounting for food density. What they can be reproached is the lack of a singular growth equation to fit the complete life cycle of chub mackerel from egg to adult, and the inability to study the effect of food density on the development of the fish.
The DEB model differs from classical models by allowing a more precise prediction of fish growth and reproduction trajectories. Unlike static approaches, it captures the individual dynamics of organisms throughout their lives. Therefore it is an agent-based approach that can be scaled up to the population level through growth, fecundity, and survival rate parameters. Another key advantage of DEB modeling is its ability to incorporate both temperature effects and food density effects.
The effects of environmental conditions on the individual
Environmental conditions were tested by incorporating various scenarios of food resource availability and different temperatures, reflecting changes in environmental conditions, partly driven by climate change. The objective is to predict—or at least assess—how environmental disturbances may impact the development of chub mackerel and to establish a population-level link to estimate stock dynamics and population structures. This aims to support adapted revisions of fisheries management policies.
The results from this study show that environmental conditions (temperature and food availability) have a major impact on the development and complete life cycle of chub mackerel.
Temperature Effect
Temperature is a crucial parameter for ectothermic species, as it largely determines their metabolic rate/functioning. Typically, a thermal performance curve defines an optimum where the organism is at the best of its performance. Deviating of this “thermal preferendum” – either below or above – may lead to detrimental physiological or behavioral effects on the individual.
In this context, an increase of temperature shortens the time to metamorphosis, accelerates the growth and increases the length/weight ratio, while also increasing annual fecundity and reducing survival. This reflects a state of stress for the organism, as an accelerated metabolism is costly for the fish.
On the contrary, when the temperature falls below the thermal preferendum, metamorphosis is delayed because of the slowdown of metabolism. Growth is also inhibited : at the same age, fish are smaller and gain weight less efficiently. As a result, they require more time to reach maturity or a critical size and may exhibit poor body condition if they remain in suboptimal conditions for an extended period.
For the effect on reproduction, excessively low temperatures can reduce fecundity by delaying maturation of gonads and limiting the energy available for reproduction. Moreover, thermal stress caused by suboptimal temperatures can increase the risk of mortality. Finally, while a slowed metabolism may, in some cases, slow down aging, it can also weaken the immune system, making individuals more vulnerable to various stressors.
Food density effect
Regarding food availability, an abundance of food is generally beneficial for the organism and is not governed by a strict optimum. In light of the DEB model proposed in the work of Weiwei He et al. (2023), an abundance of food leads to a shortening of metamorphosis time between developmental stages, an increase of the length/age ratio and an improvement in body condition (indiquée par un ratio longueur-poids plus élevé). (indicated by a higher length-weight ratio). Gametic production also benefits from this, enhancing the reproductive potential of the species. In terms of survival, the individual experiences less stress related to resource scarcity, suggesting a lower mortality rate.
On the other hand, when food resources become scarce, several negative effects emerge from this : inter-stage metamorphosis are delayed, growth is slowed down, and individuals show worse body condition (fish become thinner). As a result, the age at sexual maturity is delayed, and reproductive success becomes more uncertain due to a significant reduction in the energy allocated to gametic production. Furthermore, the increased stress caused by the food shortage weakens immune resistance and amplifies stress episodes, ultimately leading to a slight increase in mortality. In conclusion, while food availability primarily affects morphological parameters and reproductive capacity, it only moderately impacts the mortality rate.
Consequences for sustainable fisheries management
Results from this study lead to new avenues of reflexion : by integrating temperature variations and food availability throughout the whole life cycle of chub mackerel, the DEB approach may contribute to the adjustment of catch quotas in a dynamic environment. This is a promising perspective for reconciling the economic interests of fisheries with the sustainable preservation of marine ecosystems…
Perspectives : Towards a larger application ?
This model could be applied to other commercially important species, such as tuna or sardines, to optimize management strategies. With the increase in anthropogenic pressures on marine resources, more robust predictive tools like the DEB model appear like great solutions for the future —especially in a dynamic environment facing the full impact of current climate change.
Mackerel fishing, essential for many coastal economies, must rely on more precise models to ensure its sustainability. Integrating the DEB approach paves the way for more refined and adaptive fisheries management, marking a crucial step toward a more responsible exploitation of marine resources. Changes in temperature, along with shifts in oceanic communities, directly influence the future of exploited fish stocks.
Incorporating these key parameters provides a valuable tool for predicting and anticipating fishing practices allowing not only for a proactive adjustment of current methods but also for avoiding a collapse in stocks before revising exploitation strategies.
Cited study : Weiwei, H., Wenjiang, G., & Ruixing, C. (2024). Dynamic energy budget model for the complete life cycle of chub mackerel in the Northwest Pacific. *Aquatic Ecology*, 48(1), 24–35. https://doi.org/10.1016/aqeco.2024.007
Disclaimer: Although based on actual research (see the scientific article below), the facts and characters depicted in this interview are purely fictional.
On the advice of a committee of researchers, the municipality of Biarritz has closed the coastline to the public during the seabirds’ nesting period on the rocky coast. In this interview, we interviewed one of these researchers who studied the impact of corticosterone on parental activity in seabirds.
A podcast where we meet scientists specializing in endangered and critically threatened species. Each episode, we dive into the science behind their survival, the challenges they face, and the conservation efforts trying to save them.
Today’s episode: The Amur Tiger – A Predator on the Edge
Today, we meet a specialist in the Siberian tiger, also known as the Amur tiger. This majestic feline, the largest of all big cats, roams the icy forests of Russia, where every movement, every hunt, and every moment of rest is dictated by its energy balance. How does it manage to survive in one of the harshest environments on Earth? How much food does it need to sustain itself, and what happens when prey becomes scarce?
We’ll explore the fascinating world of bioenergetics, uncovering how this predator’s metabolism shapes its behavior, survival strategies, and ultimate fate in a rapidly changing world. Stay tuned for an in-depth journey into the life of one of the planet’s most endangered carnivores!
A deep dive into the hidden struggle of one of the world’s most endangered big cats! Stay tuned!
Rainbow trout Oncorhynchus mykiss – Timothy Knepp – U.S. Fish and Wildlife Service
Re-introduction of native species is far from being simple: many parameters must be accounted for! To illustrate that, we are going to take a closer look at a study made by Kalb and Huntsman (2017) on a stream in southcentral New Mexico which was deemed suitable for re-introduction of the native Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis). Before re-introducing this species, researchers wanted to know if the habitat was able to sustain it. Thus, they evaluated habitat using resource selection functions with a mechanistic drift-foraging model to explain rainbow trout distributions. They studied rainbow trouts because they are present on the stream and are close relative to the Rio grande cutthroat trout, consequently all the results of this study can be extended to this native species.
Each month, the available habitat and foraging locations were evaluated along the stream. Foraging locations were defined as the location where they could observe a foraging fish. For each foraging site, the length of the fish was estimated and physical characteristics such as discharge, focal velocity (current velocity at the head of the fish), depth, cover distance and temperature were measured on the exact fish location. These parameters were also measured on the available sites. Macroinvertebrate drift was estimated on all the locations (available and foraging). All these parameters were used in bioenergetic models which allow the researchers to estimate all the intakes of the fish (net energy intake, energy assimilated…) and all the costs associated with foraging (capturing a prey, swimming…).
First, they observed that macroinvertebrate drift was strongly season- and temperature-dependant with high values in summer and fall and low values in winter and spring. Moreover, as we must expect it, water temperature, depth and discharge were found to be seasonal parameters too. Secondly, models identified the depth as the most limiting factor for habitat selection: trout of all ages preferred habitat location with a greater depth. The most interesting thing about the models is that they can show the characteristics of the chosen habitat according to the age of the trout and the season. In fact, they showed that during the winter the smaller size-classes were more likely to choose a position closer to cover. Additionally, they highlighted that spring was the season with the greater energy intake for all the size-classes expect the 4+. Finally, drift-foraging models identified that 81% of observed trout selected positions could meet maintenance levels throughout the year and 40% of selected habitats could sustain maximum growth. Despite these last observations, the larger size-classes were energetically more limited throughout the year.
This study showed that trout population prefers deep pool habitats with slow moving water and that this stream was able to sustain a great population of rainbow trout and could consequently sustain a great native population of Rio grande cutthroat trout. However, authors warn us about the risk of hybridization and interspecific competition and suggest removing the non-native fishes first.
To answer the question in the title: yes, bioenergetic models can help to re-introduce a native species in a given environment. Nonetheless, this example is really specific: author had the chance to find and study a close relative to the native trout in the stream! The main thing to remember is that bioenergetic models give a lot of useful information on how a species uses an habitat and must be taken into account (if applicable) in the management of species.
Cited study : Kalb, B. W., Huntsman, B. M., Caldwell, C. A., & Bozek, M. A. (2018). A mechanistic assessment of seasonal microhabitat selection by drift-feeding rainbow trout Oncorhynchus mykiss in a Southwestern headwater stream. Environmental Biology of Fishes, 101(2), 257-273.
Bioenergetics for management and conservation 
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