Population Model Research Articles

The simple analytics of the environmental Kuznets curve: a reformulation

Purpose This paper aims to review the relationship between per capita income and aggregate emission in an economy populated by rational agents. According to the environmental Kuznets curve (EKC) hypothesis, pollution–income relationship is an inverted U-shaped curve. This paper aims to derive that relationship in an endogenous way and extends the relevant literature in an important way. Design/methodology/approach This paper formulated a general equilibrium model of homogenous population with identical tastes and preferences. Production side is modelled with firms operating in a monopolistically competitive environment. The approach is modelling the economy in an analytical way so that closed form solutions can be achieved. Model simulations have also been performed to get a clear view of results. Findings This study shows that increasing returns to scale in abatement technology ceases to be a sufficient condition for the generation of the EKC hypothesis. The general equilibrium structure of the model allows studying the endogenous evolution of income, emissions and prices of the abatement goods in a unified setting. Originality/value The paper is novel and original in nature. The results are new in the literature. These results extend and generalise the previous research work in this area in an important way. The sufficient condition that is obtained in this study limits the applicability of EKC in an otherwise identical economy as used in previous literature. Therefore, this paper adds value to the ongoing research related to EKC.

Development and validation of the post-CAR prognostic index for large B-cell lymphoma patients after CAR-T progression in third or later line treatment

Chimeric antigen receptor (CAR) T-cell therapy fails to achieve durable responses in over 60% of relapsed/refractory (R/R) large B-cell lymphoma (LBCL) patients in the third or later line setting. After CAR-T failure, survival outcomes are heterogeneous and a prognostic model in this patient population is lacking. A training cohort of 216 patients with progressive disease (PD) after CAR-T from 12 Spanish centers was used to develop the Post-CAR Prognostic Index (PC-PI); primary endpoint was overall survival (OS) from CAR-T progression. Validation was performed in an external cohort from three different European centers (n = 204). The prognostic score incorporated five variables, assessed at time of PD to CAR-T: ECOG (> 0), hemoglobin (< 10 g/dL), LDH (≥ 2xULN), number of extranodal sites (> 1) and time from CAR-T to PD (< 4 months). Patients were classified in four risk groups with distinct OS (p-value < 0.05 in all comparisons). In the validation cohort, median OS in the low (31%), intermediate-low (26%), intermediate-high (17%) and high risk (26%) were 15.7, 7.1, 1.8 and 1.0 months, respectively (p < 0.05 in all comparisons). Results were consistent following adjustment for subsequent treatment. In the external cohort, the PC-PI showed a C-statistic of 0.79 (95%CI 0.76–0.82), outperforming IPI and R-IPI. In conclusion, the PC-PI score is a novel tool for OS prediction and could facilitate risk-adapted management of LBCL patients relapsing after CAR T-cells. Additionally, these results will help stratification and interpretation of trials and real-world data incorporating CART-exposed patients.

Cardiovascular risk prediction using metabolomic biomarkers and polygenic risk scores: a cohort study and modelling analyses

Abstract Background/Introduction Metabolomic biomarker scores and polygenic risk scores (PRS) have shown promise for improving cardiovascular disease (CVD) prediction, but have not yet been evaluated in the context of current prediction models (SCORE2) and ESC recommendations for 10-year prediction of fatal and non-fatal CVD. Purpose To assess whether metabolomics biomarkers and PRSs can improve 10-year CVD risk prediction when added to SCORE2, and whether improvements are meaningful at ESC 2021 recommended risk thresholds for treatment consideration. Methods Metabolomics biomarker scores were constructed and compared to PRS and SCORE2 in 170,000 UK Biobank participants (5,096 incident CVD cases) aged 40–69 without previous CVD, diabetes, or lipid-lowering treatment. Improvement in risk discrimination when added to SCORE2 was assessed using Harrel’s C-index. Improvement in risk stratification following ESC guideline risk thresholds was assessed using categorical net reclassification. Population modelling was subsequently applied to estimate the impact on CVD prevention if applied at scale. Results Risk discrimination provided by SCORE2 (C-index: 0.718) was similarly improved by addition of metabolomic biomarker scores (ΔC-index: 0.011 [0.009–0.014]) and PRSs (ΔC-index 0.009; [0.007–0.012]). Addition of both metabolomic biomarker scores and PRSs to SCORE2 yielded the largest improvement risk discrimination, with ΔC-index 0.019 (0.016–0.022). Concomitant improvements in risk stratification were observed in categorical net reclassification index, with net case reclassification of 13.04% (11.67–14.41%). Modelling metabolic biomarker scores and PRSs for targeted risk-reclassification increased the number of CVD events prevented per 100,000 screened from 201 to 370 (ΔCVDprevented: 170 [158–182]) while essentially maintaining the number of statins prescribed per CVD event prevented. Conclusions Combining metabolomic biomarker scores and PRSs with SCORE2 moderately enhances prediction of first-onset CVD, and could have substantial population health benefit if applied at scale.

Clinicians' Perspectives and an Ethical Analysis of Safer Supply Opioid Prescribing.

In British Columbia, Canada, many physicians providing care to individuals with high-risk opioid use disorder adopted safer supply (SS) opioid prescribing in the spring of 2020 with the goal of facilitating public health measures for COVID-19. This prescribing practice continued after measures were lifted. This study aimed to explore prescribers' perspectives following several years of local experience in prescribing SS opioids, primarily in the form of hydromorphone tablets, and to apply ethical concepts to explore current challenges and ongoing sources of provider distress. Addiction medicine SS prescribers participated in individual or small group semi-structured interviews. Each interview was transcribed and analysed for recurrent themes. Themes were then integrated into a narrative ethics discussion. Eleven addiction medicine physicians practicing in various settings within Vancouver participated in this study. Six themes were identified: clinical assessment, clinician distress, gaps in care, models of safer supply, research, and special populations. Ethical dilemmas in prescribing SS are identified and explored through a discussion of biomedical ethics principles and the physician role.

Transferability of vaccination interventions from a community perspective: One size does not fit all

Abstract Background Removing health system barriers to childhood vaccination is an urgent matter in the migrant community in Greece, the Turkish and Moroccan communities in the Netherlands, the Ukrainian minority in Poland, and the marginalized Roma community in Slovakia. The objective of this study was to select and adapt potentially transferable evidence-based interventions for HPV and MMR vaccine uptake in partnership with the underserved communities. Methods Using a participatory approach, we assessed transferability of six pre-selected interventions with the Population, Intervention, Environment and Transfer (PIET-T) models: All countries investigated two interventions with trained community members to support vaccination as health promoters. Greece and the Netherlands included a school-based vaccination program. The Netherlands added two interventions addressing providers’ HPV vaccine communication, and Slovakia a multilevel intervention. Results We identified barriers and facilitators for successful intervention transfer in each of the target contexts. The transferability analysis showed that an intervention with health promoters is potentially transferable with adaptations in all countries. Principles of trust in the educator, tailored and trustworthy information, help in navigating the health system for vaccination and easy access at no cost are transferable key elements in all contexts. Adaptations refer to the intervention elements that need to be tailored to specific contextual needs. School-based vaccination was nowhere transferable. Health provider training needs to be culturally sensitive and adapted to the Dutch system. The multilevel intervention in Slovakia is not fully transferable. Conclusions Participatory transferability analysis was essential for decision-making and intervention adaptation. All countries independently selected a health promoter intervention as having potential to remove the identified health system barriers in the respective context.

Heat-related risk in outdoor workers in the context of climate change: a systematic review

Abstract Background Climate change is one of the paramount challenges in the current global health setting. From an occupational health perspective, outdoor workers are amongst the most vulnerable populations to climate change and heat-related risk. A systematic review was performed, aiming to assess heat-related health outcomes in outdoor workers. Methods The research query was structured using the Population, Intervention, Comparison and Outcome (PICO) model, and including synonyms and Medical Subject Headings (MeSH) terms for the following keywords: “outdoor workers”, “climate change”, and “occupational risk”. The systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements, screening three databases (PubMed, Web of Science, and Scopus). Results After duplicate removal and title and abstract screening, the final screening by full-text resulted in 10 included studies. The included studies were performed mostly on construction (included in 5 studies, 50.0%) and agriculture (in 5 studies, 50.0%) workers. Eight studies (80.0%) had a majority of male workers, while one (10.0%) had a majority of female workers, and one study (10.0%) did not report on the gender ratio. Four of the included studies (40.0%) reported a high prevalence of heat-related symptoms, ranging from 64.0% to 90.3% of workers. Three studies (30.0%) showcased a prevalence of heat-related symptoms in less than 50% of workers (ranging from 11.6% to 47.5%). Three studies (30.0%) evaluated odds ratio, highlighting an increased odds ratio of heat-related symptoms in outdoor workers. Conclusions This systematic review highlights heat-related stress in outdoor workers as an important occupational risk. Mitigation and prevention strategies should be developed and implemented at a company, national, and supra-national level, to ensure the safety and wellbeing of outdoor workers worldwide. Key messages • A systematic review was performed to assess risks related to climate change in outdoor workers. Heat-related symptoms were reported with a high prevalence, ranging from 64.0% to 90.3% of workers. • Mitigation and prevention strategies should be developed and implemented at a company, national, and supra-national level, to ensure the safety and wellbeing of outdoor workers worldwide.

Within-chain parallelization-Giving Stan Jet Fuel for population modeling in pharmacometrics.

Stan is a powerful probabilistic programming language designed mainly for Bayesian data analysis. Torsten is a collection of Stan functions that handles the events (e.g., dosing events) and solves the ODE systems that are frequently present in pharmacometric models. To perform a Bayesian data analysis, most models in pharmacometrics require Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution. However, MCMC is computationally expensive and can be time-consuming, enough so that people will often forgo Bayesian methods for a more traditional approach. This paper shows how to speed up the sampling process in Stan by within-chain parallelization through both multi-threading using Stan's reduce_sum() function and multi-processing using Torsten's group ODE solver. Both methods show substantial reductions in the time necessary to sufficiently sample from the posterior distribution compared with a basic approach with no within-chain parallelization.

1:2 Strong Resonance and Hybrid Control of Discrete Conformable Fractional Order Bacteria Population Model

1:2 Strong Resonance and Hybrid Control of Discrete Conformable Fractional Order Bacteria Population Model

Dynamics of a Discrete Population Model

In order to explore the influences of the strong Allee effect on population evolution in two-patch environments, this paper constructs a population model with discrete diffusion and analyzes its dynamics. Using the center manifold theorem, we discuss the codimension-one bifurcation, such as flip bifurcation, of the model. The qualitative structures and stability of the model at the degenerate fixed point are obtained from the approximation of flow. Based on the theoretical analysis results, we investigate the influences of the strong Allee effect and diffusion on population evolution. The qualitative structures of degenerate fixed point and numerical simulation show that the strong Allee effect will make the population tend to a stable size when the two initial population patch sizes are equal and exceed a threshold. But the stable size of population does not exceed the carrying capacity, depending on the constant Allee effect and carrying capacity, which differ from the conclusion of logistic model that population size tends to the carrying capacity. However, if the two initial population patches are equal in size and below this threshold, or if the sizes of the initial population patches are unequal, the population size will decrease. The fixed point becomes degenerate with respect to the strong Allee effect causing the population tend to a stable size below carrying capacity, which exacerbates the complexity of population evolution.

A Clinical and Economic Comparison of Cell-Based Versus Recombinant Influenza Vaccines in Adults 18–64 Years in the United States

Background: This analysis compares the cost-effectiveness of a cell-based influenza vaccine to a recombinant influenza vaccine, and each to no vaccination. The analysis is based on United States (US) commercial and societal perspectives. Methods: A Susceptible–Exposed–Infectious–Recovered (SEIR) transmission model of the total US population followed with a cost-effectiveness model for 18–64-year-olds was used to estimate the clinical and economic impact of vaccination over one influenza season (2018–2019). Deterministic and probabilistic sensitivity analyses were conducted. Results: Both enhanced vaccines prevented a substantial number of influenza cases and influenza-related deaths compared to no vaccination. The cell-based vaccine was associated with higher quality-adjusted life years (QALYs) gained compared to the recombinant vaccine or no vaccination. The cell-based vaccine had a 36% lower vaccination cost, amounting to $2.8 billion in cost savings, compared to the recombinant vaccine. The incremental cost-effectiveness ratios (ICERs) for the cell-based vaccine, compared to the recombinant vaccine or no vaccination, were dominant from all payer perspectives, regardless of risk groups. Conclusions: Overall, the cell-based vaccine was cost-saving compared to the recombinant vaccine for subjects aged 18–64 years in the US, achieving comparable health outcomes with a significant reduction in associated costs.

Improved Malaria Therapy with Cationic Nanocapsules Demonstrated in Plasmodium berghei-Infected Rodents Using Whole Blood Surrogate Population PK/PD Modeling

Objectives: Investigating how nanoparticle systems interact in whole blood (WB) is critical to evaluating the effectiveness of malaria therapy. Methods: We decided to establish a pharmacokinetic/pharmacodynamic (PK/PD) model of the quinine population in WB using Plasmodium berghei-infected mice, with a subsequent model comparison for nanocapsules coated with polysorbate (NCP80) or prepared with Eudragit® RS (NCEUD). The WB quinine population pharmacokinetic model in rats was developed using plasma and partition coefficients for rat erythrocytes. Mouse WB quinine population PK/PD modeling was developed using allometrically scaled literature-free mouse quinine pharmacokinetic data and covariate values to obtain a WB population pharmacokinetic model for quinine and nanocapsules in mice. This allowed for PK/PD modeling of the quinine population with the WB concentration and parasitemia data in mice. All models were built in NONMEN. Results: The WB quinine concentration profiles in rats were characterized using a two-compartment model. Nanoencapsulation reduced clearance and central compartment volume and increased peripherical compartimental volume. A maximum effect model described the PK/PD of the quinine WB population in mice, demonstrating that NCEUD enhances the antimalarial effect. Conclusions: Quinine WB is a good surrogate for describing the response to exposure in malaria. NCEUD outperformed NCP80 and free quinine, suggesting that cationic surfaces improve the potential for treating malaria.

Public Health Benefits of Applying Evidence-Based Best Practices in Managing Patients Hospitalized for COVID-19.

As COVID-19-related mortality remains a concern, optimal management of patients hospitalized for COVID-19 continues to evolve. We developed a population model based on real-world evidence to quantify the clinical impact of increased utilization of remdesivir, the effectiveness of which has been well established in hospitalized patients with COVID-19. The PINC AI healthcare database records for patients hospitalized for COVID-19 from January to December 2023 were stratified by those treated with or without remdesivir ("RDV" and "No RDV") and by supplemental oxygen requirements: no supplemental oxygen charges (NSOc), low-flow oxygen (LFO), and high-flow oxygen/non-invasive ventilation (HFO/NIV). Key vulnerable subgroups such as elderly and immunocompromised patients were also evaluated. The model applied previously published hazard ratios (HRs) to 28-day in-hospital mortality incidence to determine the number of potential lives saved if additional "No RDV" patients had been treated with remdesivir upon hospital admission. Of 84,810 hospitalizations for COVID-19 in 2023, 13,233 "No RDV" patients were similar in terms of characteristics and clinical presentation to the "RDV" patients. The model predicted that initiation of remdesivir in these patients could have saved 231 lives. Projected nationally, this translates to >800 potential lives saved (95% CI: 469-1,126). Eighty-nine percent of potential lives saved were elderly and 19% were immunocompromised individuals. Seventy-one percent were among NSOc or LFO patients. This public health model underscores the value of initiating remdesivir upon admission in patients hospitalized for COVID-19, in accordance with evidence-based best practices, to minimize lives lost due to SARS-CoV-2 infection.

The role of indirect interspecific effects in the stochastic dynamics of a simple trophic system.

Indirect interspecific effects (IIEs) occur when one species affects another through a third intermediary species. Understanding the role of IIEs in population dynamics is key for predicting community-level impacts of environmental change. Yet, empirically teasing apart IIEs from other interactions and population drivers has proven challenging and data-demanding, particularly in species-rich communities. We used stochastic population models parameterized with long-term time series of individual data to simulate population trajectories and examine IIEs in a simple high-arctic vertebrate food chain consisting of the wild Svalbard reindeer, its scavenger (the Arctic fox) and the barnacle goose, a migratory prey of the fox. We used the simulated population trajectories to explore co-fluctuations between the species within the food chain. Additionally, we adjusted the model in two ways: first, to isolate the impact of fluctuations in the abundance of a species by keeping its abundance constant; and second, to isolate the impact of a trophic interaction on the dynamics of other species by setting the abundance of the influencing species to zero. We found that fluctuations in reindeer carcasses shaped fox abundance fluctuations, which subsequently affected goose population dynamics. Reindeer and goose population growth rates were nevertheless only weakly correlated, probably in part due to demographic and environmental stochasticity, density dependence and lagged dynamics in the geese. However, removing the fluctuations in reindeer abundance or setting reindeer abundance to zero indeed demonstrated strong underlying IIEs on goose population dynamics and extinction probability. This study thus highlights the importance of species interactions, including IIEs, on species coexistence and communities in the long-term, that is beyond immediate effects and covariation in short-term fluctuations.

Dental age estimation in adults using the Lamendin criteria: Validation of a bayesian model in the Brazilian population

Age estimation provides information for biological profile, which is used as an important data during the process of to aid in the identification of bodies of deceased persons. Lamendin et al. proposed a simplified dental age estimation technique for adults based on the analysis of periodontal recession height, root height, and root translucency height. These characteristics were used to build a multi-population database that served in the development of a Bayesian model, known as the Forensic International Dental Database (FIDB). However, before this research, this model did not incorporate dental information from the Brazilian population. The aim of this article was to validate the application of the FIDB Bayesian statistical model for estimating dental age in samples from the Brazilian population and compare it to Lamendin's technique. A cross-sectional study was conducted using 230 dental elements extracted for therapeutic reasons and stored in a biobank. Two independent and calibrated examiners collected the data, considering the randomization of dental samples. Intra- and inter-examiner reliability analysis included the re-evaluation of 20 % (n = 40) of the sample. For statistical analyses, the data were organized in Microsoft© Excel© 365 version 2204 (Microsoft Corp., Redmond, WA, USA) spreadsheets, and R version 4.0.2 (R Foundation, Vienna, Austria) and IBM® SPSS® version 23.0 (SPSS Inc., IBM, Chicago, IL, USA) software were used. After applying inclusion and exclusion criteria, the final sample comprised 200 intact dental elements from 84 male and 116 female individuals. According to the Kolmogorov-Smirnov test, the data did not show a normal distribution, and the Intraclass Correlation Coefficient indicated that intra- and inter-examiner agreements were adequate. Through the Mann-Whitney U test, statistically significant differences between sexes were not observed. In terms of overall accuracy, the Bayesian model and the original Lamendin´s technique showed a mean absolute error of 9.39 years and 9.95, respectively. Regarding bias, it was generally 0.15 years for the Bayesian model and 5.62 years for the original Lamendin´s technique. The Wilcoxon classification test revealed no statistically significant difference between chronological and estimated ages, but only for the Bayesian model. It can be concluded that the developed Bayesian statistical model was able to estimate the age of Brazilian adults satisfactorily, and overall, the error rates were acceptable.

Evaluation of the Steno Type 1 Risk Engine in predicting cardiovascular events in an ethnic mixed population of type 1 diabetes mellitus and its association with chronic microangiopathy complications

BackgroundThe Steno Type 1 Risk Engine (ST1RE) was developed to aid clinical decisions in primary prevention for individuals with type 1 diabetes (T1D), as existing cardiovascular (CV) risk models for the general population and type 2 diabetes tend to underestimate CV risk in T1D. However, the applicability of ST1RE in different populations remains uncertain, as prediction models developed for one population may not accurately estimate risk in another. This study aimed to evaluate the performance of the ST1RE in predicting CV events among ethnically mixed T1D individuals and its association with the progression of microangiopathy complications.MethodsA retrospective survey of 435 adults with T1D who were free of CV events at baseline was assessed by ST1RE and chronic diabetes complications at 5 and 10 years of follow-up. The estimated CV risk rates were compared with the observed rates at 5 and 10 years using statistical analyses, including Receiver Operating Characteristic (ROC) curve analysis, Hosmer-Lemeshow test, Kaplan-Meier curves analysis and Cox-regression models.ResultsAmong 435 patients (aged 25 years; interquartile range [IQR]: 21–32) with a median T1D duration of 13 years (IQR: 9–18), only 5% were categorized into the high ST1RE group. Within a median follow-up of 9.2 years (IQR 6.0-10.7), 5.5% of patients experienced a CV event (1.6%, 14.9%, and 50% from the low, moderate, and high-risk groups, respectively). The hazard ratios (HRs) for CV events were greater in the high-risk group (HR 52.02; 95% CI 18.60–145.51, p < 0.001) and in the moderate-risk group (HR 8.66; 95% CI 2.90–25.80, p < 0.001) compared to the low-risk group. The ST1RE estimated CV events were similar to the observed at 5 years (3.4% vs. 3.5%; χ2 = 10.12, p = 0.899) and 10 years (6.8% vs. 9.9%; χ2 = 14.80, p = 0.676) of follow-up. The progression of microangiopathies was greater in the high vs. low for retinopathy (p = 0.008), diabetic kidney disease (p < 0.001), peripheral neuropathy (p = 0.021), and autonomic neuropathy (p = 0.008).ConclusionsST1RE performed well in predicting CV events at 5 and 10 years of follow-up. Moreover, higher ST1RE scores were associated with the progression of microangiopathy complications in this genetically heterogeneous T1D population.Graphical abstract

Uncertainty of the white dwarf astrophysical gravitational wave background

The astrophysical gravitational wave background (AGWB) is a stochastic gravitational wave (GW) signal emitted by different populations of in-spiralling binary systems containing compact objects throughout the Universe . In the frequency range between 10$^ $ and 10$^ $ hertz (Hz), it will be detected by future space-based gravitational wave detectors, such as Laser Interferometer Space Antenna (LISA). In a recent work, we concluded that the white dwarf (WD) contribution to the AGWB dominates that of black holes (BHs) and neutron stars (NSs) We aim to investigate the uncertainties of the WD AGWB that arise from the use of different stellar metallicities, star formation rate density (SFRD) models, and binary evolution models. We used the code we previously developed to determine the WD component of the AGWB. We used a metallicity-dependent SFRD based on an earlier work to construct five different SFRD models. We used four different population models based on a range of common-envelope treatments and six different metallicities for each model. For all possible combinations, the WD component of the AGWB is dominant over other populations of compact objects. The effects of metallicity and population model are less significant than the effect of a (metallicity dependent) SFRD model. We find a range of about a factor of 5 in the level of the WD AGWB around a value of $ WD $ at 1 mHz and a shape that is weakly dependent on the model. We find the uncertainty for the WD component of the AGWB to be about a factor of 5. We note that there are other uncertainties that have an effect on this signal as well. We discuss whether the turnover of the WD AGWB at 10 mHz will be detectable by LISA and find it to be likely. We confirm our previous findings asserting that the WD component of the AGWB dominates over other populations, in particular, BHs.

New Results Regarding Positive Periodic Solutions of Generalized Leslie–Gower-Type Population Models

In this paper, we focus on the existence of positive periodic solutions of generalized Leslie–Gower-type population models. Using the topological degree, we provide sufficient conditions to demonstrate the existence of positive periodic solutions to the considered models. It is interesting that the positive periodic solutions in this paper are general positive functions, not e exponential functions, which generalizes and improves the existing results. We note that due to the symmetrical property of periodic solutions, the results of this paper provide a deeper understanding of the periodic behavior of biological populations. Two numerical examples show the effectiveness of our main results.

Fractional Caputo Operator and Takagi–Sugeno Fuzzy Modeling to Diabetes Analysis

Diabetes is becoming more and more dangerous, and the effects continue to grow due to the population’s ignorance of the seriousness of this phenomenon. The studies that have been carried out have not been able to follow the phenomenon more precisely, which has led to the use of the fractional derivative tool, which has a very great capability to study real problems and phenomena but is somewhat limited on nonlinear models. In this work, we will develop a new fractional derivative model of a diabetic population, the Takagi–Sugeno fractional fuzzy model, which will enable us to study the phenomenon with these nonlinear terms in order to obtain greater precision in the results. We will study the existence and uniqueness of the solution using the Lipschizian theorem and then turn to the new fuzzy model, which leads us to four dynamical systems. The interpretation results show the quality of fuzzy membership in tracking the malleable phenomena of nonlinear terms existing in the system.

Modelling Tree Allometries to Understand the Impact of African Savannah Elephant Herbivory Dynamics on the Vegetation Structure and Tree Cover Change in a Protected Area

ABSTRACTIn landscapes with high elephant density, trees often exhibit more open canopies with fewer branches and foliage due to browsing pressure. This can result in altered tree morphology, with trees exhibiting stunted growth, multiple stems or unusual branching patterns in response to repeated damage from browsing. The objectives of this research were to (i) model the vegetation structure allometries, (ii) assess the impact of African savannah elephant (Loxodonta africana) herbivory on the vegetation structure and (iii) assess tree cover change and vegetation performance over time in Mana Pools National Park in Zimbabwe. We established 26 plots of 30 × 30 m size. Selection of sampling plots was done following several steps. First, a fish net grid with 30 × 30 m polygons was created and projected on the polygon of Mana Pools National Park. The polygons for exclusion zones were then clipped from the fish net grid using the clip tool in ArcGIS Pro 3.0. Then, selection of sampling plots was done initially by stratified random sampling using the Sampling Design Tool add in for ArcGIS Pro 3.0. Landsat images for the years 2003, 2013 and 2023 were used to assess land use land cover (LULC) time series and to calculate Normalised Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) for the period. A generalised linear model (GLM) was used to analyse tree allometries. Further statistical investigations were performed using Bayesian piecewise regression (BPR) and Bayesian regression modelling (BRM). Basal area, number of stems, height, long canopy, diameter and basal circumference were all significantly different (p &lt; 0.05) across all sampled plots. The change in growing conditions occurring as a tree grows beyond the reach of the African savannah elephant browsing indicates a natural system breakpoint. The best‐fitting models were a simple linear model and a two breakpoint model for the plant population exposed to elephant herbivory. LULC, NDVI and SAVI confirm evidence of high tree regeneration over 2 decades. Understanding the dynamics in vegetation and LULC changes is critical for effective conservation and management of the habitats for African savannah elephants, as well as for maintaining the health and resilience of forest ecosystems.

Resource‐explicit interactions in spatial population models

Abstract Continuous‐space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological or population genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact. While these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes. Here, we present a novel modelling method in which the resources available to a population are abstractly represented as an additional layer of the simulation. Instead of interacting directly with one another, individuals interact indirectly via this resource layer. We find that this method closely matches other spatial models, yet can dramatically increase the speed of the model, allowing the simulation of much larger populations. In addition to improved runtimes, models structured in this manner exhibit other desirable characteristics, including more explicit control over population density near the edge of the simulated area, and an efficient route for modelling complex heterogeneous landscapes.