Individual-based Population Model Research Articles

An integrated modeling approach for considering wildlife reintroduction in the face of climate uncertainty: A case for the North Cascades grizzly bear

As Earth faces a crisis of biodiversity loss, reintroduction of imperiled species has become an important tool toward mitigating extirpation. Current habitat quality for a reintroduced species may change dramatically under future climate scenarios, undermining or supporting species conservation efforts. Models designed to understand such change must consider the niche plasticity of a species to assess the costs and benefits of reintroduction. We integrated spatially-explicit individual-based population models with a dynamic vegetation model, using combinations of global climate models and greenhouse gas scenarios to better understand potential future carrying capacity for grizzly bears in the North Cascades Ecosystem (NCE). We estimated the ecosystem could support a grizzly bear population under several climate change scenarios through the 2080s, with the amount of high quality habitat increasing across all models, scenarios, and time periods, as compared to current conditions. Projected future habitat quality remained consistent or increased slightly along the eastern portion of the ecosystem, and increased along its central and western portions, for a net increase in high quality habitat through time. At the most plausible female home range size of 280 km2, we estimated carrying capacity would increase from a baseline of 139 female bears to 241–289 female bears. Estimated changes in habitat over time could increase grizzly bear density to 20–22 bears/1000 km2 (males and females) from the previous estimate of 17 bears/1000 km2. Species with broad ecological niches (i.e., generalists), such as grizzly bears, may be especially good candidates for reintroduction efforts in some ecosystems. Our integrated model structure provides an innovative tool for advancing reintroduction initiatives while considering some long-term risks for species.

Time reversal of spinal processes for linear and non-linear branching processes near stationarity

We consider a stochastic individual-based population model with competition, trait-structure affecting reproduction and survival, and changing environment. The changes of traits are described by jump processes, and the dynamics can be approximated in large population by a non-linear PDE with a non-local mutation operator. Using the fact that this PDE admits a non-trivial stationary solution, we can approximate the non-linear stochastic population process by a linear birth-death process where the interactions are frozen, as long as the population remains close to this equilibrium. This allows us to derive, when the population is large, the equation satisfied by the ancestral lineage of an individual uniformly sampled at a fixed time T, which is the path constituted of the traits of the ancestors of this individual in past times t≤T. This process is a time inhomogeneous Markov process, but we show that the time reversal of this process possesses a very simple structure (e.g. time-homogeneous and independent of T). This extends recent results where the authors studied a similar model with a Laplacian operator but where the methods essentially relied on the Gaussian nature of the mutations.

Condors in space: an individual-based population model for California condor reintroduction planning

ContextExisting demographic models of California condors have not simultaneously considered individual condor movement paths, the distribution and juxtaposition of release sites, habitat components, or the spatial distribution of threats.ObjectivesOur objectives were to develop a dynamic spatially explicit and individual-based model (IBM) of California condor demography and to evaluate its ability to replicate empirical data on demography and distribution from California (1995–2019).MethodsWe built a female-only spatially explicit California condor IBM in HexSim, using a daily timestep that allowed us to simulate the foraging behavior of condors, changes in food distribution and availability, and the ephemeral threat of lead in decaying food resources.ResultsSimulated population size was highly correlated with annual population census data once the population became established with > 50 females (r2 = 0.99). Mean simulated fecundity and mortality estimates were not significantly different from empirical trends (p > 0.05), although empirical data had higher interannual variability. The geographic distribution of modeled condors was similar to the empirical distribution with an overall accuracy of 79%, a commission error of 27%, and an omission error of 9%. Simulated movement density corresponded moderately well to the density of observed GPS locations (weighted kappa = 0.44).ConclusionsWe developed a spatially explicit California condor IBM that is well-calibrated to empirical data from California. Given its mechanistic underpinnings and flexibility to incorporate a variety of spatial and demographic inputs, we expect our model to be useful for assessing the relative risks and benefits of future condor reintroduction and management scenarios.

Density dependence can obscure nonlethal effects of disturbance on life history of medium-sized cetaceans.

Nonlethal disturbance of animals can cause behavioral and physiological changes that affect individual health status and vital rates, with potential consequences at the population level. Predicting these population effects remains a major challenge in ecology and conservation. Monitoring fitness-related traits may improve detection of upcoming population changes, but the extent to which individual traits are reliable indicators of disturbance exposure is not well understood, especially for populations regulated by density dependence. Here we study how density dependence affects a population’s response to disturbance and modifies the disturbance effects on individual health and vital rates. We extend an energy budget model for a medium-sized cetacean (the long-finned pilot whale Globicephala melas) to an individual-based population model in which whales feed on a self-replenishing prey base and disturbance leads to cessation of feeding. In this coupled predator-prey system, the whale population is regulated through prey depletion and the onset of yearly repeating disturbances on the whale population at carrying capacity decreased population density and increased prey availability due to reduced top-down control. In populations faced with multiple days of continuous disturbance each year, female whales that were lactating their first calf experienced increased mortality due to depletion of energy stores. However, increased prey availability led to compensatory effects and resulted in a subsequent improvement of mean female body condition, mean age at first reproduction and higher age-specific reproductive output. These results indicate that prey-mediated density dependence can mask negative effects of disturbance on fitness-related traits and vital rates, a result with implications for the monitoring and management of marine mammal populations.

Individual-based model of population dynamics in a sea urchin of the Kerguelen Plateau (Southern Ocean), Abatus cordatus, under changing environmental conditions

The Kerguelen Islands are part of the French Southern Territories, located at the limit of the Indian and Southern oceans. They are highly impacted by climate change, and coastal marine areas are particularly at risk. Assessing the responses of species and populations to environmental change is challenging in such areas for which ecological modelling can constitute a helpful approach. In the present work, a DEB-IBM model (Dynamic Energy Budget – Individual-Based Model) was generated to simulate and predict population dynamics in an endemic and common benthic species of shallow marine habitats of the Kerguelen Islands, the sea urchin Abatus cordatus. The model relies on a dynamic energy budget model (DEB) developed at the individual level. Upscaled to an individual-based population model (IBM), it then enables to model population dynamics through time as a result of individual physiological responses to environmental variations. The model was successfully built for a reference site to simulate the response of populations to variations in food resources and temperature. Then, it was implemented to model population dynamics at other sites and for the different IPCC climate change scenarios RCP 2.6 and 8.5. Under present-day conditions, models predict a more determinant effect of food resources on population densities, and on juvenile densities in particular, relative to temperature. In contrast, simulations predict a sharp decline in population densities under conditions of IPCC scenarios RCP 2.6 and RCP 8.5 with a determinant effect of water warming leading to the extinction of most vulnerable populations after a 30-year simulation time due to high mortality levels associated with peaks of high temperatures. Such a dynamic model is here applied for the first time to a Southern Ocean benthic and brooding species and offers interesting prospects for Antarctic and sub-Antarctic biodiversity research. It could constitute a useful tool to support conservation studies in these remote regions where access and bio-monitoring represent challenging issues.

A cohort projection method to follow deb-structured populations with periodic, synchronized and iteroparous reproduction

The need to follow structured populations, as opposed to unstructured ones, is well-recognized. The most detailed category of population models are the individual-based population models (IBMs), also called agent-based population models (ABMs). Their analysis is generally by simulation in time followed by statistical analysis of the numerical results. A less detailed method is the physiologically structured population model approach (PSPMs) leading originally to continuous-time partial differential equations (pdes) for the p(opulation)-states such as number(-density) with respect to time and i(ndividual)-state such as age and/or size and later to a delay equation formulation. Their mathematical analysis and computational methods are generally complex. Discrete-time matrix population models (MPMs) are much simpler to analyse in all respects, but the applicability is limited due to stringent modelling assumptions made. We discuss here a class of models we call the Cohort Projection Models (CPMs), which were formerly introduced as a special case of PSPMs with pulsed reproduction. CPMs follow cohorts of identical individuals in a Lagrangian way of which the changes of their i-states such as, size, energy reserves and maturity, are described by age dependent ordinary differential equations (ode)s from DEB theory. Simultaneously the p-states, such as number of individuals are described by time dependent odes obeying conservation laws. The population is subdivided in generations on the assumption that seasonal cycles synchronize reproduction events among cohorts and all eggs that are produced by different generations are the same. Feedback from the environment can be included via specification of food dynamics that accommodates competition. Temperature follows a specified periodic trajectory in time. This allows for the definition of a projection map of i-states and p-states, from one reproduction event to the next. The projection interval is typically one year for seasonal variability. The properties of the map can be studied using nonlinear dynamical system theory, such as existence and stability of fixed points and, thereby, the long-term dynamics of the food-population system. We demonstrate this using deb parameter values from the Add-my-Pet (AmP) collection for over 2000 animal species, which were estimated from empirical data. CPMs are meant to match the relative simplicity of the analysis of MPMs with the realism of the deb models for the dynamics of the population individuals.

Multiple stressors and data deficient populations; a comparative life-history approach sheds new light on the extinction risk of the highly vulnerable Baltic harbour porpoises (Phocoena phocoena)

Many endangered marine mammal populations are difficult to study, spread out over large areas, and capturing them for branding and research purposes would be unethical. Yet, they are in urgent need for assessment and conservation actions. We suggest collecting data from other more abundant populations of the same species, with careful consideration of body size, age at sexual maturity, and ecological conditions to produce scientifically sound best approximations of vital rates of data deficient endangered populations. The genetically distinct Baltic Sea harbour porpoise population amounts to about 500 animals and is classified as ‘Critically Endangered’ according to the IUCN red list. Data deficiency on nearly all demographic parameters have precluded systematic investigations of the relative importance of stressors affecting population viability. We took a comparative life history approach and investigated the phenotypic plasticity in somatic and demographic vital rates of seven larger, well studied North Atlantic harbour porpoise populations, enabling us to approximate the missing pieces of the life history of the Baltic population. We parameterized a stochastic, individual-based population model, and performed a population viability analysis for a range of biologically realistic parameter values and scenarios of environmental stressors. The baseline scenario was based on the most representative samples of healthy harbour porpoise populations and challenged with three different levels of bycatches. Due to high levels of endocrine disruptive contaminants observed in Baltic harbour porpoises, we also investigated the effect of a possible reduction in fecundity. Subsequently, the combined effects of bycatches and reduced fecundity were investigated in terms of population growth rate and quasi-extinction risk. The Baltic harbour porpoise population is viable in the baseline scenario without anthropogenic stressors. However, even the lowest estimated bycatch level of 7 individuals per year will lead to a population collapse to ≤50 animals with high probability (0.4–1.0) over the next century, assuming an intermediate or low (<73%) fecundity. Adult survival is of critical importance and mitigation of fishery impacts and reduction of anthropogenic disturbances in the identified main breeding areas are recommended.

Estimating inter-individual variability of dynamic energy budget model parameters for the copepod Nitocra spinipes from existing life-history data

The degree of biological variability within a population is an important factor for its ecological success. Yet, individual-based population models (IBMs) that utilize the Dynamic Energy Budget (DEB) theory as a mechanistic basis to simulate an individual's life history, largely rely on rule-of-thumb estimates of inter-individual variability of their parameter values. In this study, we explored how data from previous life-history experiments with the copepod Nitocra spinipes could be used to make realistic estimates of variability in DEB parameter values for this species. We specifically investigated to which one of 12 possible parameters variability should be added, and what shape and scale its probability distribution should have, to most accurately reproduce the experimental data's variation with model simulations. The best result was achieved by drawing the parameter ‘surface-area-specific maximum assimilation rate’ from a log-normal distribution with a standard deviation of 0.15 or by drawing the maturity thresholds for birth and puberty (assuming proportionality between the two) from a log-normal distribution with a standard deviation of 0.40. In both cases, the model not only accurately reproduced the variation observed in development time data (which had been used in the fitting process) but also gave adequate predictions of the variation observed in brood size data (which had not been used for fitting). Although the findings of this study apply to one species only, the methods presented here are a proof of principle for how the question of intra-specific variability in a DEB model, for any species, can be addressed in a simple, yet purposeful manner.

The boon and bane of boldness: movement syndrome as saviour and sink for population genetic diversity

BackgroundMany felid species are of high conservation concern, and with increasing human disturbance the situation is worsening. Small isolated populations are at risk of genetic impoverishment decreasing within-species biodiversity. Movement is known to be a key behavioural trait that shapes both demographic and genetic dynamics and affects population survival. However, we have limited knowledge on how different manifestations of movement behaviour translate to population processes. In this study, we aimed to 1) understand the potential effects of movement behaviour on the genetic diversity of small felid populations in heterogeneous landscapes, while 2) presenting a simulation tool that can help inform conservation practitioners following, or considering, population management actions targeting the risk of genetic impoverishment.MethodsWe developed a spatially explicit individual-based population model including neutral genetic markers for felids and applied this to the example of Eurasian lynx. Using a neutral landscape approach, we simulated reintroductions into a three-patch system, comprising two breeding patches separated by a larger patch of differing landscape heterogeneity, and tested for the effects of various behavioural movement syndromes and founder population sizes. We explored a range of movement syndromes by simulating populations with various movement model parametrisations that range from ‘shy’ to ‘bold’ movement behaviour.ResultsWe find that movement syndromes can lead to a higher loss of genetic diversity and an increase in between population genetic structure for both “bold” and “shy” movement behaviours, depending on landscape conditions, with larger decreases in genetic diversity and larger increases in genetic differentiation associated with bold movement syndromes, where the first colonisers quickly reproduce and subsequently dominate the gene pool. In addition, we underline the fact that a larger founder population can offset the genetic losses associated with subpopulation isolation and gene pool dominance.ConclusionsWe identified a movement syndrome trade-off for population genetic variation, whereby bold-explorers could be saviours - by connecting populations and promoting panmixia, or sinks - by increasing genetic losses via a ‘founder takes all’ effect, whereas shy-stayers maintain a more gradual genetic drift due to their more cautious behaviour. Simulations should incorporate movement behaviour to provide better projections of long-term population viability and within-species biodiversity, which includes genetic diversity. Simulations incorporating demographics and genetics have great potential for informing conservation management actions, such as population reintroductions or reinforcements. Here, we present such a simulation tool for solitary felids.

Ocean acidification and dynamic energy budget models: Parameterisation and simulations for the green-lipped mussel

Ocean acidification (OA), the change in ocean chemistry caused by carbon dioxide emissions, poses a serious imminent threat to marine organisms, especially those with calcium carbonate shells. The green-lipped mussel (Perna canaliculus), endemic to New Zealand, is common in coastal ecosystems and is an economically important aquaculture species. As a step towards supporting aquaculture management in a changing environment, we used a dynamic energy budget (DEB) model to investigate the potential influence of OA on growth and reproduction of the mussel. Zero-variate and growth data from local mussel farms were used to parameterise the model with the AmP method. The parameter estimation showed an acceptable goodness of fit, with a low mean relative error of 0.143 and the symmetric mean squared error of 0.125. The model was subsequently modified to estimate parameter values under OA conditions, based on data obtained from laboratory experiments where mussels were grown at future projected reduced pH (elevated pCO2) levels. The maintenance ([ṗM]) and volume-specific cost for growth ([EG]) were identified as the key parameters in response to OA. The model was then applied to simulate mussel energetics under pCO2 scenarios projected for 2050 and 2100. The model predicts that decreasing pH would cause reductions in shell length growth, flesh weight and reproductive capacity. As well as providing a quantitative tool for understanding the influence of OA on mussel physiology, this DEB model is also an important component of individual-based population and ecosystem models, enabling simulation of complex population and ecosystem level responses to OA.

Identification of cell‐to‐cell heterogeneity through systems engineering approaches

AbstractCells in a genetically homogeneous cell‐population exhibit a significant degree of heterogeneity in their responses to an external stimulus. To understand origins and importance of this heterogeneity, individual‐based population model (IBPM), where parameters follow probability density functions (PDFs) instead of being constants, has been previously developed. However, parameter identification for an IBPM is challenging as estimating PDFs is computationally expensive. Also, because of experimental limitations and nonlinearity of models, not all parameters' PDFs are identifiable. Motivated by the above considerations, a new methodology is proposed in this study. First, a subset of parameters whose PDFs is identifiable are determined through sensitivity analysis, and only these PDFs are estimated. Second, an artificial neural network model is developed to find an empirical relation between these parameter and output PDFs to reduce computational costs of the parameter identification. The proposed approach is validated by estimating PDFs of parameters of a tumor necrosis factor‐α signaling model.

Land use change and rodenticide exposure trump climate change as the biggest stressors to San Joaquin kit fox.

Animal and plant species often face multiple threats simultaneously. We explored the relative impact of three major threats on populations of the endangered San Joaquin kit fox. This species was once widely distributed across the southern San Joaquin Valley, California, USA, but agriculture and urban development have replaced much of its natural habitat. We modeled impacts of climate change, land-use change, and rodenticide exposure on kit fox populations using a spatially explicit, individual-based population model from 2000 to 2050 for the Central Valley, California. Our study indicates that land-use change will likely have the largest impact on kit fox populations. Land development has the potential to decrease populations by approximately 15% under a compact growth scenario in which projected population increases are accommodated within existing urban areas, and 17% under a business-as-usual scenario in which future population growth increases the developed area around urban centers. Plausible scenarios for exposure to pesticides suggest a reduction in kit fox populations by approximately 13%. By contrast, climate change has the potential to ameliorate some of these impacts. Climate-change induced vegetation shifts have the potential to increase total available kit fox habitat and could drive population increases of up to 7%. These vegetation shifts could also reduce movement barriers and create opportunities for hybridization between the endangered San Joaquin kit fox and the more widely distributed desert kit fox, found in the Mojave Desert. In contrast to these beneficial impacts, increasing climate extremes raise the probability of the kit fox population dropping below critical levels. Taken together, these results paint a complex picture of how an at-risk species is likely to respond to multiple threats.

Species-specific population dynamics and their link to an aquatic food web: A hybrid modeling approach

The Topeka shiner, a small cyprinid fish, is a seminal example of an endangered aquatic species in the Midwestern USA. Populations and their associated critical habitats may experience potential direct and/or indirect effects from anthropogenic activity. However, potential impacts on fish populations from alterations in the food web are difficult to predict because they are based on complex dynamics of food web interactions. In order to simulate Topeka shiner population dynamics under different food-web scenarios, a hybrid modeling approach was developed by linking an aquatic food web model (comprehensive aquatic systems model, CASMTS) with a species-specific, individual-based population model (TS-IBM). The CASMTS was parameterized and calibrated to represent the waterbody conditions and aquatic species community in a small headwater pool in Iowa, representative of key habitat for the Topeka shiner within its geographical range. In the TS-IBM, life history, growth, and diet are represented and based on data available from the literature for the Topeka shiner and/or surrogate species. The two models are linked by the transfer of daily biomasses of Topeka shiner diet items. We simulated the effects of alterations of the food web on the Topeka shiner populations by systematically reducing the available prey base biomass. Reductions in different food groups had varying impacts on the simulated Topeka shiner populations and were dependent on the species’ preference for detritus consumption. Simulations also included predation and identified predator densities to which Topeka shiner populations were vulnerable. The hybrid model provides a platform for the assessment of potential direct and food-web mediated indirect effects of stressors for the purposes of risk assessment, habitat management, and species recovery plans.

Conservation planning for species recovery under the Endangered Species Act: A case study with the Northern Spotted Owl.

The northern spotted owl (Strix occidentalis caurina) was listed as threatened under the U.S. Endangered Species Act (ESA) in 1990. We applied modern spatial conservation theory and models to evaluate several candidate critical habitat networks, and sought an efficient conservation solution that encompassed the highest value lands for spotted owl recovery rather than maximizing the total area of potential critical habitat. We created a map of relative habitat suitability, which served as input to the spatial conservation prioritization program Zonation. We used the spatially-explicit individual-based population model HexSim to estimate and compare simulated spotted owl population outcomes among a suite of candidate critical habitat networks that varied in size and spatial arrangement under alternative scenarios of future habitat suitability and barred owl (S. varia) effects. We evaluated simulated spotted owl population outcomes, including total population size, and extinction and quasi-extinction likelihoods for 108 combinations of candidate critical habitat networks by habitat change by barred owl scenarios, both range-wide and within 11 distinct portions of the owl’s range. Barred owl encounter rates and the amount and suitability of habitat had substantial effects on simulated spotted owl populations. When barred owl encounter rates were high, changes in the amount and suitability of habitat had minimal impacts on population performance. Under lowered barred owl encounter rates, candidate critical habitat networks that included most existing high suitability habitat supported a high likelihood of long-term population persistence. Barred owls are currently the primary driving force behind poor population performance of NSOs; however, our models demonstrated that a sufficient area of high suitability habitat remains essential for recovery when effects of barred owls can be reduced. The modeling approach we employed is sufficiently flexible to incorporate new information about spotted owls as it becomes available and could likely be applied to conservation planning for other species.

Identification of Heterogeneous Parameters in an Intracellular Reaction Network from Population Snapshot Measurements through Sensitivity Analysis and Neural Network

Cells in a clonal cell-population exhibit a significant degree of heterogeneity in their responses to an external stimulus. In order to model a heterogeneous intracellular process, the individual-based population model (IBPM) has been developed in the past. Specifically, the IBPM approach can represent the heterogeneous dynamics in a cell population with a system of differential equations, whose model parameters follow probability density functions (PDF) instead of being constants. Therefore, in order to accurately predict the heterogeneous cellular dynamics, it is important to infer the PDFs of the model parameters from available experimental measurements. In this study, we propose a methodology to estimate the PDFs of the model parameters from population snapshot measurements obtained from flow cytometry. First, the PDFs of the model parameters are assumed to be normal so that a finite dimensional vector will be inferred from the measurements instead of inferring PDFs. Second, the sensitivity analysis is performed to identify which PDFs of the model parameters are identifiable and should be estimated from the available measurements. Next, in order to reduce the excessive number of evaluations of the IBPM during the PDF estimation process, an NNM is developed so that the output PDFs can be computed for given parameter PDFs. Lastly, the NNM is used to estimate the PDFs of the model parameters by minimizing the difference between the measured and predicted PDFs of the output. To show the effectiveness of the proposed methodology, the PDFs of parameters of a TNFα signaling model were estimated from in silico measurements.

Assessing the sensitivity of bivalve populations to global warming using an individual-based modelling approach.

Climate change exposes benthic species populations in coastal ecosystems to a combination of different stressors (e.g., warming, acidification and eutrophication), threatening the sustainability of the ecological functions they provide. Thermal stress appears to be one of the strongest drivers impacting marine ecosystems, acting across a wide range of scales, from individual metabolic performances to geographic distribution of populations. Accounting for and integrating the response of species functional traits to thermal stress is therefore a necessary step in predicting how populations will respond to the warming expected in coming decades. Here, we developed an individual-based population model using a mechanistic formulation of metabolic processes within the framework of the dynamic energy budget theory. Through a large number of simulations, we assessed the sensitivity of population growth potential to thermal stress and food conditions based on a climate projection scenario (Representative Concentration Pathway; RCP8.5: no reduction of greenhouse gas emissions). We focused on three bivalve species with contrasting thermal tolerance ranges and distinct distribution ranges along 5,000km of coastline in the NE Atlantic: the Pacific oyster (Magallana gigas), and two mussel species: Mytilus edulis and Mytilus galloprovincialis. Our results suggest substantial and contrasting changes within species depending on local temperature and food concentration. Reproductive phenology appeared to be a core process driving the responses of the populations, and these patterns were closely related to species thermal tolerances. The nonlinear relationship we found between individual life-history traits and response at the population level emphasizes the need to consider the interactions resulting from upscaling across different levels of biological organisation. These results underline the importance of a process-based understanding of benthic population response to seawater warming, which will be necessary for forward planning of resource management and strategies for conservation and adaptation to environmental changes.

The power of hybrid modelling: An example from aquatic ecosystems

Planktonic communities in ponds and lakes show a high annual dynamic controlled by biotic interactions, nutrients and weather. In recent years, there has been an increase in demand for realistic and accurate lake models to improve ecological management of water bodies and to answer ecotoxicological questions in aquatic risk assessment. Most existing aquatic models are either ecosystem models aimed at describing the overall ecosystem dynamics, but which are incapable of including individual life-cycles and plasticity, or very detailed and realistic individual-based models lacking an appropriate level of environmental complexity. To reconcile these concepts, we present here a modelling approach using an individual-based population model (IBM), integrated within an ecosystem lake model, to link responses at the individual and population levels. We combine an IBM for Daphnia magna (IDamP) and a complex biogeochemical lake model (StoLaM), to create the DaLaM (Daphnia Lake Model). We use DaLaM to predict population dynamics of D. magna and phytoplankton within a simplified, daphnid-dominated food web under field conditions. In DaLaM, relevant variable environmental conditions such as underwater light climate, water temperature, turbulence, and nutrient availability are realistically simulated forced by weather conditions. For model testing we used data from aquatic mesocosm field studies exhibiting variable nutrient and weather conditions and lasting from several months to 2 years. DaLaM gave improved predictions of the overall population patterns of daphnids and phytoplankton in the mesocosms in contrast to its separate submodels. This study is an example of successfully merging individual-based population models with dynamic ecosystem models utilising the accuracy of the former and the dynamic environment of the latter to simulate more realistic field populations.

Modeling the Population Effects of Hypoxia on Atlantic Croaker (Micropogonias undulatus) in the Northwestern Gulf of Mexico: Part 2\u2014Realistic Hypoxia and Eutrophication

Quantifying the population-level effects of hypoxia on coastal fish species has been challenging. In the companion paper (part 1), we described an individual-based population model (IBM) for Atlantic croaker in the northwestern Gulf of Mexico (NWGOM) designed to quantify the long-term population responses to low dissolved oxygen (DO) concentrations during the summer. Here in part 2, we replace the idealized hypoxia conditions with realistic DO concentrations generated from a 3-dimensional water quality model. Three years were used and randomly arranged into a time series based on the historical occurrence of mild, intermediate, and severe hypoxia year types. We also used another water quality model to generate multipliers of the chlorophyll concentrations to reflect that croaker food can be correlated to the severity of hypoxia. Simulations used 100 years under normoxia and hypoxia conditions to examine croaker population responses to the following: (1) hypoxia with food uncoupled and coupled to the severity of hypoxia, (2) hypoxia reducing benthos due to direct mortality, (3) how much hypoxia would need to be reduced to offset decreased croaker food expected under 25 and 50% reduction in nutrient loadings, and (4) key assumptions about avoidance movement. Direct mortality on benthos had no effect on long-term simulated croaker abundance, and the effect of hypoxia (about a 25% reduction in abundance) was consistent whether chlorophyll (food) varied with hypoxia or not. Reductions in hypoxia needed with a 25% reduction in nutrient loadings to result in minimal loss of croaker is feasible, and the croaker population will likely do as well as possible (approach abundance under normoxia) under the 50% reduction in nutrient loadings. We conclude with a discussion of why we consider our simulation-based estimates of hypoxia causing a 25% reduction the long-term population abundance of croaker in the NWGOM to be realistic and robust.

The role of bivalves in the Balgzand: First steps on an integrated modelling approach

The present paper describes a process oriented modelling tool that integrates physical, biogeochemical, ecological and physiological factors governing bivalve populated marine ecosystems. This modelling tool is the result of the coupling between an individual-based population model for bivalves (based on the Dynamic Energy Budgets theory, DEB) and a hydrodynamic/biogeochemical model (MOHID Water Modelling System). The model was implemented in the Balgzand area (Wadden Sea, The Netherlands) in a fine resolution domain to study mussel population dynamics and to quantify the influence of mussel communities on the pelagic system. Model results for a reference scenario (2009/2010) are in agreement with observations, and provide a consistent quantitative description of local hydrodynamics and biogeochemical cycles. The Balgzand acts as a sink of phytoplankton due to bivalves’ filtration, and a source of ammonia, exporting about 40% more than the input flux. These results suggest significant ammonia regeneration. Results show that despite the long and almost continuous spawning season, only a few cohorts are able to survive. Early stage mortality (top-down), in particular cannibalism and shrimp predation, can control the persistence of new cohorts in the first month although starvation (bottom-up) represents the main cause of biomass loss in an overall. The tendency is that new mussel beds are formed in areas adjacent to already existing mussel beds and channel edges. Bivalves’ activity intensifies the seasonal patterns of food and nutrients in areas close to the mussel beds, though not changing their overall spatial distribution. This study not only confirms but also quantifies mussels’ potential to influence ecosystem functioning due to their role in nutrient cycling. As the first integrated modelling study that focus on the mussels’ beds in the Balgzand, the main difficulties on the model design, setup and results analysis were overcome. The model can now be used further, tested and improved in the same or other systems in order to serve as an effective and reliable scientific and management tool.

Temperature and food quantity effects on the harpacticoid copepod Nitocra spinipes: Combining in vivo bioassays with population modeling.

The harpacticoid copepod Nitocra spinipes has become a popular model species for toxicity testing over the past few decades. However, the combined influence of temperature and food shortage, two climate change-related stressors, has never been assessed in this species. Consequently, effects of three temperatures (15, 20 and 25°C) and six food regimes (between 0 and 5 × 105 algal cells/mL) on the life cycle of N. spinipes were examined in this study. Similarly to other copepod species, development times and brood sizes decreased with rising temperatures. Mortality was lowest in the 20°C temperature setup, indicating a close-by temperature optimum for this species. Decreasing food concentrations led to increased development times, higher mortality and a reduction in brood size. A sex ratio shift toward more females per male was observed for increasing temperatures, while no significant relationship with food concentration was found. Temperature and food functions for each endpoint were integrated into an existing individual-based population model for N. spinipes which in the future may serve as an extrapolation tool in environmental risk assessment. The model was able to accurately reproduce the experimental data in subsequent verification simulations. We suggest that temperature, food shortage, and potentially other climate change-related stressors should be considered in environmental risk assessment of chemicals to account for non-optimal exposure conditions that may occur in the field. Furthermore, we advocate combining in vivo bioassays with population modeling as a cost effective higher tier approach to assess such considerations.