Population Dynamics Model Research Articles

Manipulation of natural transformation by AbaR-type islands promotes fixation of antibiotic resistance in Acinetobacter baumannii

The opportunistic pathogen Acinetobacter baumannii, carries variants of A. baumannii resistance islands (AbaR)-type genomic islands conferring multidrug resistance. Their pervasiveness in the species has remained enigmatic. The dissemination of AbaRs is intricately linked to their horizontal transfer via natural transformation, a process through which bacteria can import and recombine exogenous DNA, effecting allelic recombination, genetic acquisition, and deletion. In experimental populations of the closely related pathogenic Acinetobacter nosocomialis, we quantified the rates at which these natural transformation events occur between individuals. When integrated into a model of population dynamics, they lead to the swift removal of AbaRs from the population, contrasting with the high prevalence of AbaRs in genomes. Yet, genomic analyses show that nearly all AbaRs specifically disrupt comM, a gene encoding a helicase critical for natural transformation. We found that such disruption impedes gene acquisition, and deletion, while moderately impacting acquisition of single nucleotide polymorphism. A mathematical evolutionary model demonstrates that AbaRs inserted into comM gain a selective advantage over AbaRs inserted in sites that do not inhibit or completely inhibit transformation, in line with the genomic observations. The persistence of AbaRs can be ascribed to their integration into a specific gene, diminishing the likelihood of their removal from the bacterial genome. This integration preserves the acquisition and elimination of alleles, enabling the host bacterium-and thus its AbaR-to adapt to unpredictable environments and persist over the long term. This work underscores how manipulation of natural transformation by mobile genetic elements can drive the prevalence of multidrug resistance.

Revisiting Fishery Sustainability Targets

Density-dependent population dynamic models strongly influence many of the world’s most important harvest policies. Nearly all classic models (e.g. Beverton-Holt and Ricker) recommend that managers maintain a population size of roughly 40–50 percent of carrying capacity to maximize sustainable harvest, no matter the species’ population growth rate. Such insights are the foundational logic behind most sustainability targets and biomass reference points for fisheries. However, a simple, less-commonly used model, called the Hockey-Stick model, yields very different recommendations. We show that the optimal population size to maintain in this model, as a proportion of carrying capacity, is one over the population growth rate. This leads to more conservative optimal harvest policies for slow-growing species, compared to other models, if all models use the same growth rate and carrying capacity values. However, parameters typically are not fixed; they are estimated after model-fitting. If the Hockey-Stick model leads to lower estimates of carrying capacity than other models, then the Hockey-Stick policy could yield lower absolute population size targets in practice. Therefore, to better understand the population size targets that may be recommended across real fisheries, we fit the Hockey-Stick, Ricker and Beverton-Holt models to population time series data across 284 fished species from the RAM Stock Assessment database. We found that the Hockey-Stick model usually recommended fisheries maintain population sizes higher than all other models (in 69–81% of the data sets). Furthermore, in 77% of the datasets, the Hockey-Stick model recommended an optimal population target even higher than 60% of carrying capacity (a widely used target, thought to be conservative). However, there was considerable uncertainty in the model fitting. While Beverton-Holt fit several of the data sets best, Hockey-Stick also frequently fit similarly well. In general, the best-fitting model rarely had overwhelming support (a model probability of greater than 95% was achieved in less than five percent of the datasets). A computational experiment, where time series data were simulated from all three models, revealed that Beverton-Holt often fit best even when it was not the true model, suggesting that fisheries data are likely too small and too noisy to resolve uncertainties in the functional forms of density-dependent growth. Therefore, sustainability targets may warrant revisiting, especially for slow-growing species.

Local population dynamics of gray wolves Canis lupus and Eurasian lynx Lynx lynx exhibit consistency with intraspecific contest competition models

AbstractIn Europe, the gray wolf and Eurasian lynx populations are recovering after various levels of persecution. The two species differ in their social structure and spatial patterns of aggregation. Using model selection, we investigated the consistency of the available time series data on local wolf and lynx sub‐populations with a number of single‐species population growth models that pertain to two types of intraspecific competition, namely, scramble (SC) and contest competition (CC), and reflect random (R) or aggregated (A) distribution of individuals. The applied models of population growth—the Ricker (SCR), Skellam (CCR), Hassell (SCA), and Beverton–Holt (CCA) models—were all parameterized in terms of intrinsic growth rate and carrying capacity with unified definitions. The projected carrying capacity was allowed to show a temporal trend, which was justified by an observed increase in prey abundance in recent decades. For both species, the models pertaining to contest competition outperformed the scramble competition models, and the Beverton–Holt model had the greatest weight. However, for the lynx, the difference of performance between the scramble and contest competition models was considerably smaller than that for the wolves. In most of the models, when it was meaningful, an optional time lag operator was added to account for a delay in individual maturity and reproduction. However, the models with a time lag had a worse fit than the models without it. This study promotes the application of population models that reflect intraspecific competition for modeling population dynamics in a single‐ or multi‐species framework.

Modeling Population Dynamics in the Indian Context: A Differential Difference Equation Approach

The study of population dynamics is of paramount importance in comprehending the evolving demographics and socioeconomic structure of a country. The task of modelling population dynamics in the Indian context poses distinct challenges and opportunities owing to the extensive and heterogeneous nature of the country's population. This research paper utilises a differential difference equation (DDE) methodology to comprehensively model population dynamics in India. The study seeks to enhance comprehension of population dynamics by considering various factors, including birth rates, death rates, migration patterns, and seasonal variations. The study employs Delay Differential Equations (DDEs) as a mathematical framework to effectively model the dynamic characteristics of population systems. These equations account for time delays, historical events, and discrete factors that play a significant role in shaping population dynamics, whether it be growth or decline. Numerical techniques, such as Euler's method and the Runge-Kutta method, are utilised in order to solve Delay Differential Equation (DDE) models and effectively simulate the dynamics of populations over a given time period. The evaluation of each method involves a comparative analysis of its accuracy, smoothness, convergence properties, and computational efficiency. The research findings presented in this study enhance our comprehension of population dynamics in India and offer significant insights that can be utilised by policymakers, urban planners, and researchers in the fields of demography, public health, and social sciences. The results have the potential to provide valuable insights for evidence-based decision-making and the development of effective policies aimed at addressing the various challenges and opportunities related to population dynamics in the Indian context.

Small populations of Palaeolithic humans in Cyprus hunted endemic megafauna to extinction.

The hypothesized main drivers of megafauna extinctions in the late Quaternary have wavered between over-exploitation by humans and environmental change, with recent investigations demonstrating more nuanced synergies between these drivers depending on taxon, spatial scale, and region. However, most studies still rely on comparing archaeologically based chronologies of timing of initial human arrival into naïve ecosystems and palaeontologically inferred dates of megafauna extinctions. Conclusions arising from comparing chronologies also depend on the reliability of dated evidence, dating uncertainties, and correcting for the low probability of preservation (Signor-Lipps effect). While some models have been developed to test the susceptibility of megafauna to theoretical offtake rates, none has explicitly linked human energetic needs, prey choice, and hunting efficiency to examine the plausibility of human-driven extinctions. Using the island of Cyprus in the terminal Pleistocene as an ideal test case because of its late human settlement (~14.2-13.2 ka), small area (~11 000 km2), and low megafauna diversity (2 species), we developed stochastic models of megafauna population dynamics, with offtake dictated by human energetic requirements, prey choice, and hunting-efficiency functions to test whether the human population at the end of the Pleistocene could have caused the extinction of dwarf hippopotamus (Phanourios minor) and dwarf elephants (Palaeoloxodon cypriotes). Our models reveal not only that the estimated human population sizes (n = 3000-7000) in Late Pleistocene Cyprus could have easily driven both species to extinction within < 1000 years, the model predictions match the observed, Signor-Lipps-corrected chronological sequence of megafauna extinctions inferred from the palaeontological record (P. minor at ~12-11.1 ka, followed by P. cypriotes at ~10.3-9.1 ka).

Modeling population dynamics of beluga whales in the Eastern High Arctic – Baffin Bay population

AbstractBeluga whales (Delphinapterus leucas) have a long history of subsistence harvest that has continued into the present and a history of exploitation through commercial harvest. The Eastern High Arctic – Baffin Bay (EHA) population of beluga whales has the northernmost distribution of any beluga whale population in Canada. Beluga whales from the EHA population have a single complete abundance estimate from 1996 and 3 partial abundance estimates in 1981, 2010, and 2012 from aerial surveys; reliable estimates from surveys are lacking in recent years, limiting the ability to determine population dynamics. In 2020 satellite imagery was used to estimate abundance for beluga whales in the EHA population for estuaries in the whales' summering area. We built a stochastic stock production model to estimate population dynamics from the start of reliably compiled harvest history data in 1977 to 2022, using abundance estimates from 1981, 1996, 2010, 2012, and 2020. We also extended the model 10 years into the future under 7 potential annual harvest scenarios (within the range of annual reported harvests) to calculate the probability of decline. We estimated the starting population in 1977 as 29,615 whales (95% CI = 20,765–46,251), and the estimate from 2022 was 16,495 whales (95% CI = 6,731–33,504). Landed catch of 0, 100, 200, 300, 400, 500, and 600 beluga whales annually resulted in &lt;1%, 3%, 23%, 50%, 70%, 83%, and 90% probabilities of decline, respectively. There was a notable decrease in abundance over the time series, likely caused by harvest pressure. Harvest in recent years has ranged from 155–553 catches per year, and our results suggest that 108 landed catches per year equates to a 5% risk of decline, the low risk goal for precautionary fisheries management, which aligns with population conservation goals. Our model is a first step in understanding the EHA beluga whale population dynamics using what data are available and suggests that current harvests in some years may result in a population decline and should be monitored.

Developing a fine-scale spatial operating model of eastern oyster population dynamics in Chesapeake Bay, Maryland, U.S.A.

Management decisions for the eastern oyster (Crassostrea virginica) fishery in Maryland are made at a finer spatial scale than the spatial data resolution of the current stock assessment. This mismatch creates concerns that the consequences of management actions and fishing activities are not being adequately represented when assessing fishery status. To produce a model that could support a participatory modeling process intended to differentiate results of fine-scale management actions we developed a method for conditioning a down-scaled oyster stock assessment model to produce a spatially-explicit operating model at the scale of individual oyster bars for eastern oysters in Chesapeake Bay, Maryland. To ensure that parameter values of the operating model were consistent with data at multiple scales we fitted the model to bar-specific harvest data during 2004–2020 and regional abundance estimates from the current Maryland Oyster Stock Assessment during 1999–2020. The operating model can then predict effects of different management actions, such as planting hatchery-reared oysters, addition of substrate, or modifying fishing regulations, at a bar-specific scale and compare outcomes among different scenarios. Model outputs included a suite of management performance metrics, including but not limited to oyster abundance and fishery harvest, that were important to stakeholders. These bar-specific scenarios would not have been possible using an operating model with the same spatial resolution as the current Maryland Oyster Stock Assessment. Accounting for fine scale spatial processes can be important to engaging participants and our model provides an expedient option to develop an operating model that downscales stock assessment models.

A DISCRETE-TIME POPULATION DYNAMICS MODEL FOR THE INFORMATION SPREAD UNDER THE EFFECT OF SOCIAL RESPONSE

In this paper, we construct and analyze a mathematically reasonable and simplest population dynamics model based on Mark Granovetter’s idea for the spread of a matter (rumor, innovation, psychological state, etc.) in a population. The model is described by a one-dimensional difference equation. Individual threshold values with respect to the decision-making on the acceptance of a spreading matter are distributed throughout the population ranging from low (easily accepts it) to high (hardly accepts). Mathematical analysis on our model with some general threshold distributions (uniform; monotonically decreasing/increasing; unimodal) shows that a critical value necessarily exists for the initial frequency of acceptors. Only when the initial frequency of acceptors is beyond the critical, the matter eventually spreads over the population. Further, we give the mathematical results on how the equilibrium acceptor frequency depends on the nature of threshold distribution.

Dynamics of a goshawk population across half a century is driven by the variation of first-year survival.

Population dynamics are driven by stochastic and density-dependent processes acting on demographic rates. Individuals differ demographically, and to capture these differences, models of population dynamics are usually structured by age and stage, rarely by sex. An effect of sex on population dynamics is expected if the dynamics of males and females differ, requiring an unequal sex ratio at birth and/or sex-specific survival probabilities. Goshawks (Accipiter gentilis) show large sexual size dimorphism and differential survival, but it is unknown whether males and females contribute differently to population dynamics. We studied a goshawk population in northern Germany over 47 years using brood monitoring data, collected feathers and nestling ringing data. We jointly analyzed the data using a two-sex integrated population model and performed retrospective and prospective population analyses to understand whether the demographic drivers of population change differ between the sexes. The population showed large fluctuations, during which the number of breeding pairs doubled, but the long-term trend of the population was slightly negative. Female survival exceeded male survival during the first year of life. Females started to reproduce at a younger age than males, productivity increased with female age, the sex ratio of nestlings was male biased and there was moderate male immigration. Despite these differences, temporal variation in sex ratio did not contribute to population dynamics and the contribution of temporal variation in survival was similar for both sexes. Variation in first-year survival was the strongest driver in this population, regulated by a weak density-dependent feedback acting through female first-year survival. Overall, the contributions of the two sexes to population dynamics were similar in this monogamous species with strong sexual size dimorphism.

GIS-ODE: linking dynamic population models with GIS to predict pathogen vector abundance across a country under climate change scenarios.

Mechanistic mathematical models such as ordinary differential equations (ODEs) have a long history for their use in describing population dynamics and determining estimates of key parameters that summarize the potential growth or decline of a population over time. More recently, geographic information systems (GIS) have become important tools to provide a visual representation of statistically determined parameters and environmental features over space. Here, we combine these tools to form a 'GIS-ODE' approach to generate spatiotemporal maps predicting how projected changes in thermal climate may affect population densities and, uniquely, population dynamics of Ixodes ricinus, an important tick vector of several human pathogens. Assuming habitat and host densities are not greatly affected by climate warming, the GIS-ODE model predicted that, even under the lowest projected temperature increase, I. ricinus nymph densities could increase by 26-99% in Scotland, depending on the habitat and climate of the location. Our GIS-ODE model provides the vector-borne disease research community with a framework option to produce predictive, spatially explicit risk maps based on a mechanistic understanding of vector and vector-borne disease transmission dynamics.

MULTI-AGENT SIMULATION MODEL OF INFECTIOUS DISEASE SPREAD

The aim of the research is to develop a multi-agent simulation model for predicting the spread of infectious diseases, particularly COVID-19. In the context of the COVID-19 pandemic, there emerged an urgent need to create tools for forecasting and analyzing the dynamics of epidemics, as well as for evaluating the effectiveness of management decisions. The use of mathematical models in this process allows for an adequate description of the infection spread dynamics, which is essential for making informed decisions. The article discusses traditional approaches to epidemic modeling, such as the predator-prey model and the compartmental SIR (Susceptible-Infectious-Recovered) model. The predator-prey model describes the interaction between two species in an ecosystem using differential equations, which allows for modeling population dynamics. The compartmental SIR model divides the population into three groups: susceptible, infected, and recovered, which enables the analysis of the spread of infectious diseases. However, these models have limitations, particularly due to assumptions about population homogeneity and constant parameters. To more accurately model complex epidemic processes, a multi-agent simulation model was developed. In this model, agents interact within a defined area, mimicking real conditions of infection spread. Agents are divided into three classes: healthy, infected, and recovered. The movement of agents is modeled using random walk in a two-dimensional space, taking into account the possibility of contact between them, which can lead to infection. Infected agents transition to the recovered class after a certain period of illness and can no longer be infected. Modeling results showed that the multi-agent model allows for more accurate prediction of infection spread dynamics. Numerous experiments were conducted, demonstrating the model's adequacy in replicating the infection process, peak infection rates, and recovery periods. The influence of various parameters, such as the duration of illness, on the epidemic dynamics was investigated. The obtained results confirm that considering individual characteristics and behavioral traits of agents improves the accuracy of modeling. This allows the multi-agent simulation model to be used for developing effective control strategies and predicting the spread of infectious diseases, which can be useful for making management decisions in real pandemic conditions.

Optimal Solutions for a Class of Impulsive Differential Problems with Feedback Controls and Volterra-Type Distributed Delay: A Topological Approach

In this paper, the existence of optimal solutions for problems governed by differential equations involving feedback controls is established for when the problem must account for a Volterra-type distributed delay and is subject to the action of impulsive external forces. The problem is reformulated within the class of impulsive semilinear integro-differential inclusions in Banach spaces and is studied by using topological methods and multivalued analysis. The paper concludes with an application to a population dynamics model.

Information theory unification of epidemiological and population dynamics

We reformulate models in epidemiology and population dynamics in terms of probability distributions. This allows us to construct the Fisher information, which we interpret as the metric of a one-dimensional differentiable manifold. For systems that can be effectively described by a single degree of freedom, we show that their time evolution is fully captured by this metric. In this way, we discover universal features across seemingly very different models. This further motivates a reorganisation of the dynamics around zeroes of the Fisher metric, corresponding to extrema of the probability distribution. Concretely, we propose a simple form of the metric for which we can analytically solve the dynamics of the system that well approximates the time evolution of various established models in epidemiology and population dynamics, thus providing a unifying framework.

Crown-of-thorns starfish complete their larval phase eating only nitrogen-fixing Trichodesmium cyanobacteria.

Cyanobacteria of the genus Trichodesmium form extensive blooms that supply new N to nutrient-poor marine ecosystems. Yet little is known about what eats Trichodesmium. In this laboratory study, we show that one of the greatest threats to coral reefs, predatory crown-of-thorns starfish (CoTS), Acanthaster sp., completes their larval phase feeding solely on Trichodesmium. We observed Trichodesmium erythraeum CMP1985 in the stomachs of larvae using florescence microscopy and traced the assimilation of nitrogen from labeled trichomes into larval tissues using stable isotopes. Some larvae fed T. erythraeum were morphologically ready to become benthic juveniles after 19 days. Given that Trichodesmium can be food for CoTS, reported increases in Trichodesmium could be a driving factor in the heightened frequency of CoTS population irruptions that have devastated coral reefs in past decades. Future studies could test this through investigating the diets of wild larvae and incorporating Trichodesmium abundance into models of CoTS population dynamics.

Smoothly vanishing density in the contact process by an interplay of disorder and long-distance dispersal

Realistic modeling of ecological population dynamics requires spatially explicit descriptions that can take into account spatial heterogeneity as well as long-distance dispersal. Here, we present Monte Carlo simulations and numerical renormalization group results for the paradigmatic model, the contact process, in the combined presence of these factors in both one and two-dimensional systems. Our results confirm our analytic arguments stating that the density vanishes smoothly at the extinction threshold, in a way characteristic of infinite-order transitions. This extremely smooth vanishing of the global density entails an enhanced exposure of the population to extinction events. At the same time, a reverse order parameter, the local persistence displays a discontinuity characteristic of mixed-order transitions, as it approaches a non-universal critical value algebraically with an exponent \beta_p'&lt;1βp′&lt;1.

Mathematical model of mosquito population dynamics with constants and periodic releases of Wolbachia-infected males

In this article, we propose three mathematical models of mosquito dynamics. The first model describes the dynamics of wild mosquitoes in combination with mechanical control and larvicide. For this model, we derive a threshold parameter N , called the basic number of offspring, and show that the trivial equilibrium is globally asymptotically stable when N ≤ 1 , while if N > 1 , the non-trivial equilibrium is globally asymptotically stable. In the second model, we evaluated a constant release of Wolbachia-infected males in the wild mosquito population. Our analysis identified a critical threshold for releasing Wolbachia-infected male mosquitoes M i crit to reduce or eliminate the wild mosquito population. In the third model, we assessed wild mosquito dynamics by periodically releasing Wolbachia-infected male mosquitoes. The analysis of this model produced two thresholds, T ∗ the threshold on the release period and Λ ∗ the threshold on the release quantity. The existence of trivial periodic solutions is obtained, and the corresponding local and global stability conditions are proved by Floquet's theory and Lyapunov's stability theorem, respectively. We also prove the existence conditions for non-trivial periodic solutions and their local stability. Finally, we perform some numerical simulations to illustrate our theoretically pertinent results.

Genome resequencing reveals population divergence and local adaptation of blacklegged ticks in the United States.

Tick vectors and tick-borne disease are increasingly impacting human populations globally. An important challenge is to understand tick movement patterns, as this information can be used to improve management and predictive modelling of tick population dynamics. Evolutionary analysis of genetic divergence, gene flow and local adaptation provides insight on movement patterns at large spatiotemporal scales. We develop low coverage, whole genome resequencing data for 92 blacklegged ticks, Ixodes scapularis, representing range-wide variation across the United States. Through analysis of population genomic data, we find that tick populations are structured geographically, with gradual isolation by distance separating three population clusters in the northern United States, southeastern United States and a unique cluster represented by a sample from Tennessee. Populations in the northern United States underwent population contractions during the last glacial period and diverged from southern populations at least 50 thousand years ago. Genome scans of selection provide strong evidence of local adaptation at genes responding to host defences, blood-feeding and environmental variation. In addition, we explore the potential of low coverage genome sequencing of whole-tick samples for documenting the diversity of microbial pathogens and recover important tick-borne pathogens such as Borrelia burgdorferi. The combination of isolation by distance and local adaptation in blacklegged ticks demonstrates that gene flow, including recent expansion, is limited to geographical scales of a few hundred kilometres.

Hierarchical Bayesian Integrated Modeling of Age- and Sex-Structured Wildlife Population Dynamics

AbstractBiodiversity of large wild mammals is declining at alarming rates worldwide. It is therefore imperative to develop effective population conservation and recovery strategies. Population dynamics models can provide insights into processes driving declines of particular populations of a species and their relative importance. But there are insufficient tools, namely population dynamics models for wild herbivores, for characterizing their decline and for guiding conservation and management actions. Therefore, we have developed a model which can serve as a tool to fill that void. Specifically, we develop an integrated Bayesian state-space population dynamics model for wildlife populations and illustrate it using a topi population inhabiting the Greater Mara-Serengeti Ecosystem in Kenya and Tanzania. The model integrates ground demographic survey with aerial survey monitoring data. It incorporates population age and sex structure and life history traits and strategies and relates birth rates, age-specific survival rates and sex ratios with meteorological covariates, prior population density, environmental seasonality and predation risk. It runs on a monthly time step, enabling accurate characterization of reproductive seasonality, phenology, synchrony and prolificacy of births, juvenile and adult recruitments. Model performance is evaluated using balanced bootstrap sampling and by comparing model predictions with empirical aerial population size estimates. The hierarchical Bayesian model is implemented using MCMC methods for parameter estimation, prediction and inference and reproduces several well-known features of the Mara topi population, including striking and persistent population decline, seasonality of births, juvenile and adult recruitments. It is general and can be readily adapted for other wildlife species and extended to incorporate several additional useful features. Supplementary materials accompanying this paper appear on-line.

A multi-tier methodology for the estimation of individual-specific parameters of DEB models

Intraspecific variability plays a key role in modeling population dynamics and ecological interactions. It is also a fundamental issue for agriculture, livestock production, and aquaculture. Dynamic Energy Budget (DEB) theory is a metabolic theory that can provide insights for these applications, as it can accurately predict the response of an organism to changes in environmental conditions. However, DEB model parameters are typically estimated for the species using mean trait values, neglecting individual differences. With advancing data collection technologies, there is a growing need to leverage individual data to calibrate DEB models. In this paper, we propose a multi-tier methodology for estimating individual-specific DEB parameters based on techniques and principles of the standard DEB estimation procedure. Individual data is often not sufficiently complete, therefore we compliment it available data on organisms of the same species. To avoid estimation bias, we employ a hierarchical tier structure. Data is categorized into tiers, depending on how general it is. In each tier, only a subset of the parameters is estimated depending on available data. The estimation proceeds in a top-down approach, starting with the most general tier and culminating in the individual tier. Values obtained in previously estimated tiers are used as pseudo-data to anchor the parameter estimation. In this way, we obtain biologically sensible individual parameter estimates in an efficient manner. As a case study, we apply our methodology to a dataset that contains weight and feed intake measurements for 52 individuals of the Mertolenga cattle breed from two performance trials. We conclude that variability in the surface-specific maximum assimilation rate and the digestion efficiency is needed to accurately model the individual data. Furthermore, the analysis of tier parameters and individual parameter distributions uncovers differences in the diets fed to the animals.

Modelling thermal reaction norms for development and viability in Drosophila suzukii under constant, fluctuating and field conditions

Phenological models for insect pests often rely on knowledge of thermal reaction norms. These may differ in shape depending on developmental thermal conditions (e.g. constant vs. fluctuating) and other factors such as life-stages. Here, we conducted an extensive comparative study of the thermal reaction norms for development and viability in the invasive fly, Drosophila suzukii, under constant and fluctuating thermal regimes. Flies, were submitted to 15 different constant temperatures (CT) ranging from 8 to 35 °C. We compared responses under CT with patterns observed under 15 different fluctuating temperature (FT) regimes. We tested several equations for thermal performance curves and compared various models to obtain thermal limits and degree-day estimations. To validate the model's predictions, the phenology was monitored in two artificial field-like conditions and two natural conditions in outdoor cages during spring and winter. Thermal reaction norm for viability from egg to pupa was broader than that from egg to adult. FT conditions yielded a broader thermal breadth for viability than CT, with a performance extended towards the colder side, consistent with our field observations in winter. Models resulting from both CT and FT conditions made accurate predictions of degree-day as long as the temperature remained within the linear part of the developmental rate curve. Under cold artificial and natural winter conditions, a model based on FT data made more accurate predictions. Model based on CT failed to predict adult's emergence in winter. We also document the first record of development and adult emergence throughout winter in D. suzukii. Population dynamics models in D. suzukii are all based on summer phenotype and CT. Accounting for variations between seasonal phenotypes, stages, and thermal conditions (CT vs. FT) could improve the predictive power of the models.