Population Dynamics Model Research Articles

Fusing time-varying mosquito data and continuous mosquito population dynamics models

Climate change is arguably one of the most pressing issues affecting the world today and requires the fusion of disparate data streams to accurately model its impacts. Mosquito populations respond to temperature and precipitation in a nonlinear way, making predicting climate impacts on mosquito-borne diseases an ongoing challenge. Data-driven approaches for accurately modeling mosquito populations are needed for predicting mosquito-borne disease risk under climate change scenarios. Many current models for disease transmission are continuous and autonomous, while mosquito data is discrete and varies both within and between seasons. This study uses an optimization framework to fit a non-autonomous logistic model with periodic net growth rate and carrying capacity parameters for 15 years of daily mosquito time-series data from the Greater Toronto Area of Canada. The resulting parameters accurately capture the inter-annual and intra-seasonal variability of mosquito populations within a single geographic region, and a variance-based sensitivity analysis highlights the influence each parameter has on the peak magnitude and timing of the mosquito season. This method can easily extend to other geographic regions and be integrated into a larger disease transmission model. This method addresses the ongoing challenges of data and model fusion by serving as a link between discrete time-series data and continuous differential equations for mosquito-borne epidemiology models.

Regional growth rates and growth synchronicity between two populations of Arctica islandica in the western Mid-Atlantic (US)

Arctica islandica supports an important fishery in the US Mid-Atlantic. This species is extremely slow growing and long-lived, characteristics that may make this species particularly vulnerable to fishing pressure and climate change. Understanding regional growth dynamics over time and growth responses to changing environments will improve current fishery assessments to maintain the sustainability of this stock. Two populations of A. islandica from Georges Bank and off Long Island on the US continental shelf with observed ages between 17 and 310 y were evaluated for age-at-length relationships and growth trends over time. Growth rates have been increasing at Georges Bank and Long Island since the 1700s. Growth rates at Long Island have been accelerating and have exceeded those at Georges Bank since the 1980s. Growth rates from this study support previous research that this species is sexually dimorphic, and females grow faster than males within a population post-maturation. Positive growth index periods at both sites may be synchronous with 32-y harmonics of the Atlantic Multi-Decadal Oscillation. A modification to the traditional Tanaka growth model that included a growth term to further increase growth at old age proved the best-fit growth model to not only each population, but also to birth-year cohorts. Both the classic and modified Tanaka models used in this study are vast improvements over the von Bertalanffy models currently applied to this species in assessment models and population dynamics models. Increasing growth rates over time resulted in fewer years of reproduction prior to recruiting to the fishery as A. islandica are reaching fishery size in an increasingly short period of time. The impact that reduced years of reproduction has on A. islandica population resiliency is yet unknown.

Hierarchical forecasting models of stink bug population dynamics for pest management

In recent decades, the intensification of agricultural production, accompanied by an increasing pressure from pests in various crops, has resulted in a substantial increase in the use of synthetic pesticides. Integrated pest management (IPM) provides a framework for the development and use of sustainable control strategies, which include the monitoring of the crop pest complex and the use of decision tools such as predictive models. In this study, an empirical modeling approach based on a hierarchical Bayesian model with a state-space structure was developed to perform stink bug (Pentatomidae) density forecasts to assist in deciding when to carry out pest control interventions, thus increasing the efficacy and efficiency of IPM. Using stink bug abundance and crop phenology data, along with meteorological data from eight different sites in Argentina, we made 1-week forecasts of population density, evaluated the predictive capacity of different models using Leave-One-Out-Cross-Validation, and analyzed how the uncertainty in the predictions vary as a function of the number of vertical beat sheet samples. The forecasts made with our best model showed a reasonable degree of accuracy. We found that i) the observation error of the vertical beat sheet method was much larger than expected, and ii) the uncertainty analysis suggested a sample size of 40 to obtain a good balance between precision and sampling effort, which is in stark contrast to the average sample size usually taken by advisors. Our approach provides advisors with a tool to make better-informed decisions about when and if to carry out pest control interventions.

Understanding the state-dependent impact of species correlated responses on community sensitivity to perturbations.

Understanding how communities respond to perturbations requires us to consider not only changes in the abundance of individual species but also correlated changes that can emerge through interspecific effects. However, our knowledge of this phenomenon is mostly constrained to situations where interspecific effects are fixed. Here, we introduce a framework to disentangle the impact of species correlated responses on community sensitivity to perturbations when interspecific effects change over time due to cyclic or chaotic population dynamics. We partition the volume expansion rate of perturbed abundances (community sensitivity) into contributions of individual species and of species correlated responses by converting the time-varying Jacobian matrix containing interspecific effects into a time-varying covariance matrix. Using population dynamics models, we demonstrate that species correlated responses change considerably across time and continuously alternate between reducing and having no impact on community sensitivity. Importantly, these alternating impacts depend on the abundance of particular species and can be detected even from noisy time series. We showcase our framework using two experimental predator-prey time series and find that the impact of species correlated responses is modulated by prey abundance-as theoretically expected. Our results provide new insights into how and when species interactions can dampen community sensitivity when abundances fluctuate over time.

Smaller is better in competition for space.

Body size is a prominent morphological trait which affects many aspects of an organism's life. Although large body size is generally considered to be advantageous, ecologists have wondered about the benefits of being small. Many studies of body size depend on the metabolic theory of ecology since body size is an irremovable part of an organism's energy budget. Body size is also a spatial quantity and therefore is linked to spatial processes. Here, I show that competition for space leads to a benefit of being small and hence selects for increasingly smaller body size. I build a deterministic population dynamics model and a stochastic model of birth, death and dispersal in a population of individuals with two different body sizes and show that only the smaller individuals survive. I also extend the population dynamics model to continuously varying body sizes and include a stabilizing natural selection for an intermediate body size. I find that the intrinsic advantage of smaller body size in competition for space can only be overcome when natural selection for a large body size is sufficiently strong. Overall, my results point to a novel benefit of being small.

GARRAFÃO PROJECT: ORIGIN, HISTORY AND MAIN ASPECTS OF THE DEVELOPMENT OF THE LONGEST LONG-TERM STUDY OF ECOLOGY OF SMALL MAMMALS IN BRAZIL

This article introduces the special volume dedicated to one of the longest wildlife monitoring programs ever carried out in Brazil: the long-term study on small mammals developed by the Laboratório de Vertebrados at UFRJ from 1996 to 2019 in Garrafão, Municipality of Guapimirim, RJ. The guiding ideas of this study emerged from a previous project developed at the Restinga de Barra de Maricá, RJ, in the 1980s on the ecology of small mammal populations and communities. The main research objectives were related to community structure, population growth and population dynamics, aiming to understand the ecological processes and the mechanisms explaining the fluctuation of population size in several species over time. Initially, models of genetic population structure and population dynamics were tested. Further developments in the project allowed further aspects to be studied, such as diet and nutritional aspects, population genetics, use of space, habitat, biogeography, taxonomy and systematics. These approaches, based on relevant ecological questions, have provided advances in knowledge in several areas of biology, especially in population ecology. The study resulted in dozens of articles, book chapters, dissertations and theses and was interrupted by conjuncture factors and abrupt changes in the funding policy for the development of scientific research in Brazil.

The evolution of infectious transmission promotes the persistence of mcr-1 plasmids.

Conjugative plasmids play a vital role in bacterial evolution and promote the spread of antibiotic resistance. They usually cause fitness costs that diminish the growth rates of the host bacteria. Compensatory mutations are known as an effective evolutionary solution to reduce the fitness cost and improve plasmid persistence. However, whether the plasmid transmission by conjugation is sufficient to improve plasmid persistence is debated since it is an inherently costly process. Here, we experimentally evolved an unstable and costly mcr-1 plasmid pHNSHP24 under laboratory conditions and assessed the effects of plasmid cost and transmission on the plasmid maintenance by the plasmid population dynamics model and a plasmid invasion experiment designed to measure the plasmid's ability to invade a plasmid-free bacterial population. The persistence of pHNSHP24 improved after 36 days evolution due to the plasmid-borne mutation A51G in the 5'UTR of gene traJ. This mutation largely increased the infectious transmission of the evolved plasmid, presumably by impairing the inhibitory effect of FinP on the expression of traJ. We showed that increased conjugation rate of the evolved plasmid could compensate for the plasmid loss. Furthermore, we determined that the evolved high transmissibility had little effect on the mcr-1-deficient ancestral plasmid, implying that high conjugation transfer is vital for maintaining the mcr-1-bearing plasmid. Altogether, our findings emphasized that, besides compensatory evolution that reduces fitness costs, the evolution of infectious transmission can improve the persistence of antibiotic-resistant plasmids, indicating that inhibition of the conjugation process could be useful to combat the spread of antibiotic-resistant plasmids. IMPORTANCE Conjugative plasmids play a key role in the spread of antibiotic resistance, and they are well-adapted to the host bacteria. However, the evolutionary adaptation of plasmid-bacteria associations is not well understood. In this study, we experimentally evolved an unstable colistin resistance (mcr-1) plasmid under laboratory conditions and found that increased conjugation rate was crucial for the persistence of this plasmid. Interestingly, the evolved conjugation was caused by a single-base mutation, which could rescue the unstable plasmid from extinction in bacterial populations. Our findings imply that inhibition of the conjugation process could be necessary for combating the persistence of antibiotic-resistance plasmids.

Non-Standard and Null Lagrangians for Nonlinear Dynamical Systems and Their Role in Population Dynamics

Non-standard Lagrangians do not display any discernible energy-like terms, yet they give the same equations of motion as standard Lagrangians, which have easily identifiable energy-like terms. A new method to derive non-standard Lagrangians for second-order nonlinear differential equations with damping is developed and the limitations of this method are explored. It is shown that the limitations do not exist only for those nonlinear dynamical systems that can be converted into linear ones. The obtained results are applied to selected population dynamics models for which non-standard Lagrangians and their corresponding null Lagrangians and gauge functions are derived, and their roles in the population dynamics are discussed.

Dynamic Population Models with Temporal Preferential Sampling to Infer Phenology

Dynamic Population Models with Temporal Preferential Sampling to Infer Phenology

Salmon lice dispersal and population model for management strategy evaluation

Modern marine salmon aquaculture includes management of salmon lice (Lepeophtheirus salmonis) infestations. Moving from a reactive delousing treatment effort to a proactive preventive strategy requires a simulation tool integrating salmon lice spread between farms with a population dynamic model at individual farms. However, such predictions have proven challenging. Here, we propose a mechanistic cohort-based model of salmon lice that explicitly accounts for the development of lice in relation to temperature coupled with hydrodynamic particle simulations of lice infections between farms. The model was validated against observed salmon lice counts and is able to produce realistic patterns of salmon lice epidemic development, but has a limited ability to resolve realistic temporal salmon lice dynamics on a per-farm basis. The model can, however, be used to evaluate general regional and national management strategies e.g. level of treatment threshold. Results shown that decreasing the treatment threshold has no significant impact the total number of treatments indicating that there is no argument against lowering the treatment threshold in a connected farm network which will eventually benefit the wild salmonid populations.

Hierarchical modelling approach to estimate the abundance of data-limited cetacean species and its application to fishery-targeted and rarely seen delphinid species off Japan

AbstractAn assessment of the abundances and their trends is urgently needed for the conservation and management of fishery-targeted and rarely seen cetacean species (FTCS and RSCS, respectively); however, such assessment is often challenging because of the paucity of available data. In particular, the number of sightings is smaller than the general requirement for the reliable estimation of a detection function, and the spatial coverage of many cetacean surveys is insufficient. To address these issues, we propose a Bayesian approach that uses the previous abundance estimation of the same species or a species with similar biological traits as prior information. Therefore, we obtained the latest abundance estimates for six FTCS and two RSCS. For FTCS, we also estimated abundance trends by fitting an exponential population dynamics model with random effects accounting for interannual changes in animal distributions to the posterior samples of the Bayesian abundance estimates. Our approach enables us to (1) facilitate stakeholders’ consensus by maintaining previously agreed abundances while updating the conservation information; (2) identify the species of greater concern and prioritize conservation efforts towards those species; and (3) monitor the abundance and trends of data-limited cetacean species.

Selection-Mutation dynamics with spatial dependence

We study the limit of many small mutations for a model of population dynamics. The population is structured by phenological traits and is spatially inhomogeneous. The various sub-populations compete for the same nutrient which diffuses through the spatial environment, with the possibility of creating fast time oscillations in the dynamics. We control those oscillations by the stability of an auxiliary system (without mutations) which guides the dynamics over fast time scales.

Combining multiple data sources with different biases in state-space models for population dynamics.

The resolution at which animal populations can be modeled can be increased when multiple datasets corresponding to different life stages are available, allowing, for example, seasonal instead of annual descriptions of dynamics. However, the abundance estimates used for model fitting can have multiple sources of error, both random and systematic, namely bias. We focus here on the consequences of, and how to address, differing and unknown observation biases when fitting models.State-space models (SSMs) separate process variation and observation error, thus providing a framework to account for different and unknown estimate biases across multiple datasets. Here we study the effects on the inference of including or excluding bias parameters for a sequential life stage population dynamics SSM using a combination of theory, simulation experiments, and an empirical example.When the data, that is, abundance estimates, are unbiased, including bias parameters leads to increased imprecision compared to a model that correctly excludes bias parameters. But when observations are biased and no bias parameters are estimated, recruitment and survival processes are inaccurately estimated and estimates of process variance become biased high. These problems are substantially reduced by including bias parameters and fixing one of them at even an incorrect value. The primary inferential challenge is that models with bias parameters can show properties of being parameter redundant even when they are not in theory.Combining multiple datasets into a single analysis by using bias parameters to rescale data can offer significant improvements to inference and model diagnostics. Because their estimability in practice is dataset specific and will likely require more precise estimates than might be expected from ecological datasets, we outline some strategies for characterizing process uncertainty when it is confounded by bias parameters.

An exploratory assessment of the Bering-Chukchi-Beaufort Seas stock of bowhead whales using a stochastic population dynamics model

The stochastic population dynamics model used by Aboriginal Whaling Management Procedure developers is revised to correct weaknesses related to uncertainty parameterisation and replacement yield estimation. Two variants of this model, along with the standard deterministic version, are used to assess the Bering-Chukchi-Beaufort Seas stock of bowhead whales. The variants differ with respect to the magnitude and complexity of the stochastic variation they introduce into natural mortality and birth/calf survival processes. An allowable catch statistic, E (Q0), is defined for appropriate use with stochastic model assessments. Using the same assessment methods, likelihood and priors as IWC (1999a), 5th percentiles of E (Q0) were found to be 117, 106 and 91 for the deterministic, simpler stochastic and extreme stochastic models, respectively. Bayes factor results show that there is no evidence suggesting that either stochastic model should be favoured over any simpler alternative, and the deterministic model yielded the best fit overall. The E (Q0) estimates confirm and strengthen past IWC Scientific Committee inference that under current bowhead subsistence hunting levels the stock should continue to increase towards stabilisation above its maximum sustainable yield level.

A Bayesian stock assessment of the eastern Pacific gray whale using abundance and harvest data from 1967-1996

Abundance and harvest data since 1966/67 were used to assess the eastern Pacific stock of gray whales. A Bayesian statistical method was used to estimate probability distributions for the parameters of both a simple and an age and sex structured population dynamics model, as well as output quantities of interest. Model comparisons using the Bayes Factor provided conclusive evidence that an additional parameter should be used to account for unexplained variation in the abundance time series. Incorporating the additional variance parameter decreased the precision of the estimates of the other parameters. Point estimates of carrying capacity ranged from 24,640-31,840 for the different models, but the posterior distributions from the selected models were very broad and excluded few values. The current depletion level (population size as a fraction of carrying capacity) was estimated to be about 0.75, with a lower 2.5th percentile of 0.36. The probability that the population was still below one-half of its carrying capacity was estimated to be 0.21, with a corresponding probability of 0.28 that the population was still below its maximum sustainable yield level. Quantities from which catch limits could potentially be calculated were estimated, including current replacement yield, maximum sustainable yield and the quantity Q1 (described in Wade and Givens, 1997).

An examination of certain of the assumptions made in the Bayesian approach used to assess the eastern North Pacific stock of gray whales (Eschrichtius robustus)

An assessment of the eastern North Pacific stock of gray whales (Eschrichtius robustus) is conducted using a variant of the Bayesian stock assessment method of Wade (2002). This variant is based on the BALEEN II population dynamics model and uses parameters whose values are more familiar to members of the International Whaling Commission’s Scientific Committee. The sensitivity of the results to changes to some of the specifications used in the assessment is examined. The results are shown to be relatively insensitive to the first year considered in the analysis and the year for which a prior on absolute abundance is specified. An alternative Bayesian assessment method which involves projecting the population forward from pre-exploitation equilibrium in 1600 is also considered. As expected from previous assessments, results from this method are unable to mimic the recent trends in absolute abundance obtained from shore counts and are inconsistent with the fact that the fishery was commercially extinct by the end of the 19th Century. Allowing for underestimation of historical commercial and aboriginal catches provides improved consistency with recent trends in abundance but does not resolve these problems completely. The impact of process error (in the form of temporally correlated fluctuations in calf survival) on the dynamics of the population is found to be largely inconsequential in terms of resolving the inconsistency between historical catches and recent estimates of abundance.

Quantifying and Disentangling the Competition Effect of a Weed Community in a Long-Term Biennial Cereal-Legume Rotation

Weeds are a permanent constraint on crop productivity in agriculture. Due to the importance of the effect of weeds on the crop, there has been great interest in establishing the competitive ability of each species to optimize its control. This work presents a new methodology approach to determining the relative competitiveness of weed species based on population dynamics theory, which is applied to establish the competitiveness of Papaver rhoeas L. (PAP), Veronica hederifolia L. (VER), Descurainia sophia L. (DES) and Fumaria spp. (FUM) infesting a biennial cereal-legume rotation under conventional tillage. Data to fit the nonlinear population dynamic models were obtained from a long-term experiment (32 years) in Mediterranean drylands. The results showed asymmetric competitive interactions, and the competitive ability of weeds was crop specific. In cereals, the competitiveness ranking order was FUM > PAP > VER > DES, with strong interspecific competition; in legumes, it was VER > FUM > DES > PAP, with weak interspecific competition intensity. Overall, intraspecific competition was stronger than interspecific competition in the rotation system. The information gained in these studies can provide insights into the role of the intraspecific and interspecific competition in weed communities and help identify weed species that are relatively poor competitors in given crops.

Population modelling for pest management: A case study using a pest land snail and its fly parasitoid in Australia

We built a DYMEX population dynamics model of the invasive land snail, Cochlicella acuta, and one of its dipteran parasitoids, Sarcophaga villeneuveana. The snail lifecycle consists of different size classes, based on shell height, as both parasitism and reproduction are influenced by snail height. The model reveals the likely role of small, cryptic snails in maintaining populations in the face of biological control and suggests that suppression of snail populations may only be possible with unrealistically high parasitism rates and with repeated annual spring releases of flies. The current model appears to describe the phenology of both the snail and fly adequately; however, construction of the model identified numerous knowledge gaps. Snail control would benefit from a thorough understanding of its basic biology and what drives its population dynamics – aspects which, surprisingly, have been largely ignored. Otherwise, any control measures will likely be unpredictable, with no real understanding of how or why management options may or may not work. We highlight the importance of modelling, preferably at an early stage, to synthesise available knowledge and data into a framework that helps target research at an early stage, and the benefits of experimentation via modelling.

Model design for nonparametric phylodynamic inference and applications to pathogen surveillance.

Inference of effective population size from genomic data can provide unique information about demographic history and, when applied to pathogen genetic data, can also provide insights into epidemiological dynamics. The combination of nonparametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for nonparametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on nonparametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. Our methodology is implemented in a new R package entitled mlesky. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the methodology to a dataset of HIV-1 in the USA. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.

Estimation of fish biomass around the Sangihe Islands waters based on the acoustic data and numerical model of spatial ecosystem and population dynamics

The Sangihe Islands is located in the Republic of Indonesia Fisheries Management Area (WPPNRI) 716, which has an area of about 52.5 million hectares. The potential productions for small and large pelagic fish in WPP NRI 716 are 332,635 tons/year and 181,491 tons/year, respectively, with utilization rates of 0.48 and 0.63. The dominant fish species caught were skipjack, mackerel, yellowfin tuna, and anchovies. In order to estimate fish biomass around the Sangihe Islands waters, acoustic data has been collected in several locations using a 120 kHz BIOSONIC DT-X echo sounder mounted on fishing vessels with purse seine gear. In addition, an analysis of the data from the results of the Spatial Ecosystem and Population Dynamics Model (SEAPODYM) from INDESO Project was also carried out. This model has a resolution of 1/12° and applied in Indonesian waters for 3 types of tuna, namely yellowfin, bigeye, and skipjack. Acoustic data showed that the largest fish biomass along the survey tracks was found at depths between 126 and 150 m, with a total biomass of 21,418 tons. Meanwhile, from the model results, it was found that skipjack biomass was the largest. The average biomass around the Sangihe Islands waters was around 1,340 tons.