Traditional Population Research Articles

Population Digital Health: Continuous Health Monitoring and Profiling at Scale.

This paper introduces population digital health (PDH)-the use of digital health information sourced from health internet of things (IoT) and wearable devices for population health modeling-as an emerging research domain that offers an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. PDH combines health data sourced from health IoT devices, machine learning, and ubiquitous computing or networking infrastructure to increase the scale, coverage, equity, and cost-effectiveness of population health. This contrasts with the traditional population health approach, which relies on data from structured clinical records (eg, electronic health records) or health surveys. We present the overall PDH approach and highlight its key research challenges, provide solutions to key research challenges, and demonstrate the potential of PDH through three case studies that address (1) data inadequacy, (2) inaccuracy of the health IoT devices' sensor measurements, and (3) the spatiotemporal sparsity in the available digital health information. Finally, we discuss the conditions, prerequisites, and barriers for adopting PDH drawing on from real-world examples from different geographic regions.

Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning

Population mobility between cities significantly affects traffic congestion, disease spread, and societal well-being. As globalization and urbanization accelerate, understanding the dynamics of population mobility becomes increasingly important. Traditional population migration models reveal the factors influencing migration, while machine learning methods provide effective tools for creating data-driven models to handle the nonlinear relationships between origin and destination characteristics and migration. To deepen the understanding of population mobility issues, this study presents GraviGBM, an expandable population mobility simulation model that combines the gravity model with machine learning, significantly enhancing simulation accuracy. By employing SHAPs (SHapley Additive exPlanations), we interpret the modeling results and explore the relationship between urban characteristics and population migration. Additionally, this study includes a case analysis of COVID-19, extending the model’s application during public health emergencies and evaluating the contribution of model variables in this context. The results show that GraviGBM performs exceptionally well in simulating inter-city population migration, with an RMSE of 4.28, far lower than the RMSE of the gravity model (45.32). This research indicates that distance emerged as the primary factor affecting mobility before the pandemic, with economic factors and population also playing significant roles. During the pandemic, distance remained dominant, but the significance of short distances gained importance. Pandemic-related indicators became prominent, while economics, population density, and transportation substantially lost their influence. A city-to-city flow analysis shows that when population sizes are comparable, economic factors prevail, but when economic profiles match, living conditions dictate migration. During the pandemic, residents from hard-hit areas moved to more distant cities, seeking normalcy. This research offers a comprehensive perspective on population mobility, yielding valuable insights for future urban planning, pandemic response, and decision-making processes.

Coexistence through sustainable conservation strategies for sitatunga Tragelaphus spekii in African ecosystems

Sitatunga Tragelaphus spekii is an antelope species adapted to the dense swamps and marshes of Sub‐Saharan Africa, where traditional population survey techniques have been ineffective and encountered difficulties in making estimations. The species formerly occurred alongside waterways throughout the lowland forest zone of West and central Africa, extending into swamp systems in the savanna zones of central, East, and southern Africa. In most parts of Africa, the sitatunga population is declining, and attracting the attention of conservationists. Furthermore, its geographical range has been recorded to have shrunk. The present study reviewed major threats to sitatunga, assessed previous and current management approaches, and proposed new approaches to effectively manage its declining populations in Africa. To achieve the study objectives, published literature, reports, online information, expert knowledge, and personal experience were reviewed to acquire relevant information. Results indicated that sitatunga are threatened due to increased habitat loss, population isolation, political instability, water level changes, habitat fragmentation, illegal hunting, and diseases. Current wildlife management approaches raise many doubts as to their effectiveness. National‐level management may unsustainably segment management actions while the protected area approach manages only part of the range of wildlife. The current tenure system in most parts of Africa discourages human–wildlife co‐existence, whereas human–wildlife conflict management approaches only treat the symptoms and not the root cause of the problems. If wildlife, including sitatunga, are to persist in Africa, management approaches should be changed and include re‐focusing of the management context at the ecosystems and landscape level; assessing the genetic diversity of sitatunga; promoting better wetland management, including the aspect of human dimension in management; using non‐invasive techniques to genetically estimate the minimum population size; assessing inbreeding; and enhancing the implementation strategy of wildlife policy in African countries. Changing the attitude of the local community may take time, but it is a pivotal point if humans and wildlife are to coexist.

Variety is the spice of life: nongenetic variation in life histories influences population growth and evolvability.

Individual vital rates, such as mortality and birth rates, are key determinants of lifetime reproductive success, and variability in these rates shapes population dynamics. Previous studies have found that this vital rate heterogeneity can influence demographic properties, including population growth rates. However, the explicit effects of the variation within and the covariance between vital rates that can also vary throughout the lifespan on population growth remain unknown. Here, we explore the analytical consequences of nongenetic heterogeneity on long-term population growth rates and rates of evolution by modifying traditional age-structured population projection matrices to incorporate variation among individual vital rates. The model allows vital rates to be permanent throughout life ("fixed condition") or to change over the lifespan ("dynamic condition"). We reduce the complexity associated with adding individual heterogeneity to age-structured models through a novel application of matrix collapsing ("phenotypic collapsing"), showing how to collapse in a manner that preserves the asymptotic and transient dynamics of the original matrix. The main conclusion is that nongenetic individual heterogeneity can strongly impact the long-term growth rate and rates of evolution. The magnitude and sign of this impact depend heavily on how the heterogeneity covaries across the lifespan of an organism. Our results emphasize that nongenetic variation cannot simply be viewed as random noise, but rather that it has consistent, predictable effects on fitness and evolvability.

An improve crested porcupine algorithm for UAV delivery path planning in challenging environments

With the rapid advancement of drone technology and the growing applications in the field of drone engineering, the demand for precise and efficient path planning in complex and dynamic environments has become increasingly important. Traditional algorithms struggle with complex terrain, obstacles, and weather changes, often falling into local optima. This study introduces an Improved Crown Porcupine Optimizer (ICPO) for drone path planning, which enables drones to better avoid obstacles, optimize flight paths, and reduce energy consumption. Inspired by porcupines' defense mechanisms, a visuo-auditory synergy perspective is adopted, improving early convergence by balancing visual and auditory defenses. The study also employs a good point set population initialization strategy to enhance diversity and eliminates the traditional population reduction mechanism. To avoid local optima in later stages, a novel periodic retreat strategy inspired by porcupines' precise defenses is introduced for better position updates. Analysis on the IEEE CEC2022 test set shows that ICPO almost reaches the optimal value, demonstrating robustness and stability. In complex mountainous terrain, ICPO achieved optimal values of 778.1775 and 954.0118; in urban terrain, 366.2789 and 910.1682 and ranked first among the compared algorithms, proving its effectiveness and reliability in drone delivery path planning. Looking ahead, the ICPO will provide greater efficiency and safety for drone path planning in navigating complex environments.

Preliminary Analysis of Simple Novelty Search.

Novelty search is a powerful tool for finding diverse sets of objects in complicated spaces. Recent experiments on simplified versions of novelty search introduce the idea that novelty search happens at the level of the archive space, rather than individual points. The sparseness measure and archive update criterion create a process that is driven by a two measures: (1) spread out to cover the space while trying to remain as efficiently packed as possible, and (2) metrics inspired by k nearest neighbor theory. In this paper, we generalize previous simplifications of novelty search to include traditional population (μ,λ) dynamics for generating new search points, where the population and the archive are updated separately. We provide some theoretical guidance regarding balancing mutation and sparseness criteria and introduce the concept of saturation as a way of talking about fully covered spaces. We show empirically that claims that novelty search is inherently objectiveless are incorrect. We leverage the understanding of novelty search as an optimizer of archive coverage, suggest several ways to improve the search, and demonstrate one simple improvement-generating some new points directly from the archive rather than the parent population.

Genetic network analysis uncovers spatial variation in diversity and connectivity of a species presenting a continuous distribution

The conservation of genetic diversity and connectivity is essential for the long-term persistence and adaptive ability of a species. Recent calls have been made for the inclusion of genetic diversity and differentiation measures in the assessment, management, and conservation of species. However, the literature often lacks direction on how to do so for species with continuous distributions or no distinct breaks in genetic connectivity. There are many considerations to overcome when investigating genetic diversity and connectivity of such species. We combine multiple genetic network methodologies with more traditional population genetic analyses within a single framework to address the challenges of investigating population structure and quantifying variation in genetic diversity and connectivity of wide-ranging species with continuous distributions. We demonstrate the efficacy and applicability of our framework through a study on woodland caribou (Rangifer tarandus) occupying the boreal forest of Canada; a species of significant conservation concern. The dataset consisted of 4911 unique individuals genotyped at 9 microsatellite loci, which were subsequently partitioned into 103 spatial nodes to create a population-based genetic network. The Walktrap community detection algorithm was used to detect hierarchical population genetic structure within the study area and node-based network metrics such as mean inverse edge weight and clustering coefficient were used to quantify the variation in genetic connectivity across the range. Lastly, genetic diversity was assessed by calculating allelic richness and heterozygosity of the nodes making up the network. The community detection analysis identified two communities at the coarsest scale to nine communities at the optimal partition. A strong pattern of Isolation by Distance (IBD) was found across the range at multiple scales. Furthermore, signs of genetic erosion along the study area’s southern boundaries were depicted by nodes presenting low genetic diversity and low centrality values. These results are important to the species status assessments in providing previously unavailable information on connectivity and diversity within and beyond the current local population units used in management. Our approach to quantify the patterns and extent of connectivity across the boreal range is comprehensive and could easily be adapted to other species. The results are robust and provide a solid foundation for the continued monitoring and recovery of the species.

Exploring Fertility Dynamics and Factors Shaping Russia’s Demographic Prospects

The research examines fertility patterns in Russia by using current calendar birth rates and their implications for actual generations. Birth rates for these generations are derived from the 2020 Census data and are further refined through calculations employing one-year age-specific rates. This methodology allows us to estimate parameters for age-specific fertility models in real generations, offering a different approach compared to traditional population census methods. Given the variance in fertility rates by birth order, the analysis favors a segmented approach, separately considering trends for first, second, third, and subsequent births. Factors influencing fertility are examined both statistically, with a focus on the impact of population age–sex structure transformations on overall fertility rates, and through insights from Vyborochnoe nabliudenie reproduktivnykh planov naseleniia [Sample Observation of Population Reproductive Plans] performed by Rosstat [Federal State Statistics Service] in 2022. Value orientations are the primary determinant in reproductive behavior, which means that a key goal of demographic policy is to influence these orientations. However, it is also possible to influence fertility rates through social and demographic policies creating favorable conditions for families to fulfill their desire for children.

Implementasi Awig-Awig Terhadap Krama Tamiu dan Tamiu dalam Hukum Adat Bali

This study aims to analyze the implementation and inhibiting factors in the implementation of awig-awig towards krama tamiu and tamiu. This study uses descriptive empirical legal research methods. Awig-awig in the Pedungan Traditional Village has been applied to krama tamiu and tamiu. The application of awig-awig to krama tamiu and tamiu is contained in Pawos 6 Awig-Awig of Pedungan Traditional Village which explains the recognition of krama tamiu and tamiu as part of the traditional village population which must comply with all applicable regulations. Inhibiting factors in the implementation of awig-awig towards krama tamiu and tamiu include the low level of understanding of krama tamiu and tamiu towards traditional village awig-awig, lack of socialization of awig-awig to the community, as well as village officials' lack of understanding of awig-awig which causes discrepancies in the implementation of village regulations.

Measuring urban shrinkage in India using night-light data from DMSP-OLS and VIIRS-NPP satellite sensors

Urban shrinkage, an emerging phenomenon marked by a decline in city population, is experienced by both developed and developing countries. In Asia, India hosts a significant number of shrinking cities. Over traditional population measures, night-light data emerges as a great replacement for advanced measurement of socio-economic and demographic parameters without any constraint of time and administrative boundaries. This study aims to measure urban shrinkage in India using night-light data captured by DMSP/OLS and VIIRS satellite sensors, examine spatial patterns, and verify them using population-based estimates. Despite increased economic development and availability of electricity compared to 1990, there has been a reduction in night light reflection in many parts. The results show that 20 % of cities experienced urban shrinkage in India during 1992–2021. The shrinking cities cluster in eastern, southern, and northern zones, and the spatial pattern matches with population-based estimates. The paradox of urban shrinkage in India signals a need for the enhancement of urban policies and programs. Striving for balanced urban growth is crucial for fostering economic prosperity and reducing regional inequality. An inclusive development approach for all urban settlements may help address the premature issue of urban shrinkage in India and achieve sustainable development goals.

Stratifying macrophages based on their infectious burden identifies novel host targets for intervention during Crohn's disease associated adherent-invasive Escherichia coli infection.

Bacterial infection is a dynamic process resulting in a heterogenous population of infected and uninfected cells. These cells respond differently based on their bacterial load and duration of infection. In the case of infection of macrophages with Crohn's disease (CD) associated adherent-invasive Escherichia coli (AIEC), understanding the drivers of pathogen success may allow targeting of cells where AIEC replicate to high levels. Here we show that stratifying immune cells based on their bacterial load identifies novel pathways and therapeutic targets not previously associated with AIEC when using a traditional homogeneous infected population approach. Using flow cytometry-based cell sorting we stratified cells into those with low or high intracellular pathogen loads, or those which were bystanders to infection. Immune cells transcriptomics revealed a diverse response to the varying levels of infection while pathway analysis identified novel intervention targets that were directly related to increasing intracellular AIEC numbers. Chemical inhibition of identified targets reduced AIEC intracellular replication or inhibited secretion of tumour necrosis factor alpha (TNFα), a key cytokine associated with AIEC infection. Our results have identified new avenues of intervention in AIEC infection that may also be applicable to CD through the repurposing of already available inhibitors. Additionally, they highlight the applicability of immune cell stratification post-infection as an effective approach for the study of microbial pathogens.

Estimation of Population Mean in an Infinite Population Using Samples by a Systematic Manner

Systematic sampling method has been used in a finite population for estimation of the population characteristic. In this article, we are trying to extend this method when the population size is countably infinite. In finite population, a unit has equal probability of being chosen in most of the cases except PPS. However, this cannot be so in infinite population. In that case,we approximate infinite population by large finite population and then use the traditional finite population method. It is true that the original population have some distribution, but without knowing that we are using systematic sampling method and based on that we are estimating population mean. We want to see the effectiveness of this systematic manner if the population has some specified distribution. Here, based on Poisson assumption, we provide some approximation methods and see performances in each case. However, this is to be done for some important distributions also. Here, the problem has been viewed in three ways and out of that the first method performs well, where the Poisson probabilities has been assigned to all possible samples rather than any truncation of the infinite population by finite population. . KEYWORDS :Systematic sampling, Risk function, Poisson distribution, Truncation, Approximation methods.

A novel strategy for detecting mutations from circulating tumor DNA with blood tissue only.

e20031 Background: Molecular Residual Disease (MRD) test detects tumor-specific mutations in circulating tumor DNA (ctDNA), which can indicate the risk of tumor recurrence. However, without tumor-specific mutations, detecting mutations in blood tissue alone is extremely challenging due to the low concentration of ctDNA in blood. To address this challenge, we proposed an innovative mutation filtering and calling strategy by simulating noise’s distribution and quality assessment to greatly boost the performance. Methods: We performed 406 pairs of MRD tests on both tumor and blood tissue of postoperative patients with mixed cancer types, including lung, gastrointestinal (GI), hepatobiliary, and pancreatic. Patients that possess one or more tumor-specific mutations in their blood tissue were classified as recurrence high-risk (n = 66), others were classified as recurrence low-risk (n = 340). All sequence data were analyzed using both traditional and proposed method. In the traditional method, VAF threshold, end-reads, multiple-align reads and population frequencies were used as filtering criteria. While in the proposed method, VAF threshold was excluded and two new parameters were introduced: The distribution of variant allele frequency (VAF) of sequencing noise, which simulated 42 healthy individuals’ background noise with conf > 0.995; The normalized proportion of supported unique and total variants, to assess the likelihood of mutations originated from ctDNA. Results: Among 406 patients of mixed tumor types, the traditional approach barely reached a sensitivity of 39.4% and a specificity of 89.1%, while our strategy achieved a sensitivity of 71.2% and a specificity of 80.9%. Particularly among the 92 patients with gastrointestinal tumor, by filtering specific noise caused by the MRD panel (e.g. multi-alignment error of MUC gene family), we further boost the sensitivity from 47.1% to 82.4% with a loss of specificity from 93.3% to 90.7%. Moreover, among 248 patients with lung tumors, the sensitivity and specificity between traditional approach and proposed method were 15.8%, 93.9%, and 52.6%, 90.0% respectively, which might be caused by the low recurrence rate in the patient cohort (19 out of 248). Conclusions: The novel strategy based on simulated noise distribution and read quality assessment surpasses the traditional threshold-based approaches in mutation detection, especially in deep sequencing like MRD tests. The findings of the study indicate that the recurrence monitoring strategy is applicable across various tumor types and exhibits superior performance in GI tumor, albeit with certain limitations. In conclusion, this study boosts the feasibility of MRD as a non-invasive alternative for tissue biopsy by providing a novel mutation filtering strategy, which further strengthen the potential of MRD testing in enhancing cancer patients’ long-term prognosis and treatment.

Deep bed filtration and formation damage by particles with distributed properties

Current models for deep bed filtration describe particles with uniform properties. Yet, the sizes, densities, and mineral composition of particles vary significantly in the same injection well. The aim of this work is to provide an effective mathematical model for water injection of particles with distributed properties and formation damage prediction. We average the set of traditional population balance equations for single-property particles and obtain one upscaled equation. The upscaled equation for particle retention rate contains a non-linear function of suspended concentration, which we call the 'suspension function'. We derive analytical solutions for the upscaled equation for linear (coreflood) and radial (well injectivity) flows. Then we treat lab coreflood data to determine the model suspension function and provide a model for well injectivity prediction. The retention profile for the flow of uniform particles has an exponential form. Frequently reported in the literature, hyper-exponential forms have been hypothetically explained by multiple particle properties. The inverse solution allows revealing the individual filtration coefficients for binary mixtures from total breakthrough concentrations during coreflood. Treatment of the data from lab experiments reveals individual filtration coefficients that belong to common intervals. For the first time, deep bed filtration of particles with distributed properties is upscaled and presented using a single equation that reflects the particle property distribution. This equation provides an effective mathematical model for tuning lab coreflood data, determines the model function, and uses it for injectivity decline prediction.

A Dynamic Fitting Strategy for Physiological Models: A Case Study of a Cardiorespiratory Model for the Simulation of Incremental Aerobic Exercise.

Using mathematical models of physiological systems in medicine has allowed for the development of diagnostic, treatment, and medical educational tools. However, their complexity restricts, in most cases, their application for predictive, preventive, and personalized purposes. Although there are strategies that reduce the complexity of applying models based on fitting techniques, most of them are focused on a single instant of time, neglecting the effect of the system's temporal evolution. The objective of this research was to introduce a dynamic fitting strategy for physiological models with an extensive array of parameters and a constrained amount of experimental data. The proposed strategy focused on obtaining better predictions based on the temporal trends in the system's parameters and being capable of predicting future states. The study utilized a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects undergoing aerobic exercise were used for fitting and validation. The model predictions obtained in a steady state using the proposed strategy and the traditional single-fit approach were compared. The most successful outcomes were primarily linked to the proposed strategy, exhibiting better overall results regarding accuracy and behavior than the traditional population fitting approach at a single instant in time. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.

Integrating social vulnerability into high-resolution global flood risk mapping

High-resolution global flood risk maps are increasingly used to inform disaster risk planning and response, particularly in lower income countries with limited data or capacity. However, current approaches do not adequately account for spatial variation in social vulnerability, which is a key determinant of variation in outcomes for exposed populations. Here we integrate annual average exceedance probability estimates from a high-resolution fluvial flood model with gridded population and poverty data to create a global vulnerability-adjusted risk index for flooding (VARI Flood) at 90-meter resolution. The index provides estimates of relative risk within or between countries and changes how we understand the geography of risk by identifying ‘hotspots’ characterised by high population density and high levels of social vulnerability. This approach, which emphasises risks to human well-being, could be used as a complement to traditional population or asset-centred approaches.

Genomic ancestry and cancer among Latin Americans

Latin American populations, characterized by intricate admixture patterns resulting from the intermingling of ancestries from European, Native American (NA) Asian, and African ancestries which result in a vast and complex genetic landscape, harboring unique combinations of novel variants. This genetic diversity not only poses challenges in traditional population genetics methods but also opens avenues for a deeper understanding of its implications in health. In cancer, the interplay between genetic ancestry, lifestyle factors, and healthcare disparities adds a layer of complexity to the varying incidence and mortality rates observed across different Latin American subpopulations. This complex interdependence has been unveiled through numerous studies, whether conducted on Latin American patients residing on the continent or abroad, revealing discernible differences in germline composition that influence divergent disease phenotypes such as higher incidence of Luminal B and Her2 breast tumors, EGFR and KRAS mutated lung adenocarcinomas in addition to an enrichment in BRCA1/2 pathogenic variants and a higher than expected prevalence of variants in colorectal cancer associated genes such as APC and MLH1. In prostate cancer novel risk variants have also been solely identified in Latin American populations. Due to the complexity of genetic divergence, inputs from each individual ancestry seem to carry independent contributions that interplay in the development of these complex disease phenotypes. By understanding these unique population characteristics, genomic ancestries hold a promising avenue for tailoring prognostic assessments and optimizing responses to oncological interventions.

The effectiveness of breeding work on the improvement of the main economically important traits of traditional variety population Banannaya in Primorsky kray

Relevance. Although there is notable success in breeding vegetable crops to obtain heterotic F1 hybrids, traditional selection methods have been gaining in popularity as a means of improving the economically important traits of local traditional varieties in Russia today. These varieties suffered the reduction in their valuable starting parameters (yield, the uniformity of marketable produce and biochemical composition) over the many years of reproduction. The Department of Potato Breeding and Horticulture (FSBSI “FSC of Agricultural Biotechnology of the Far East named after A. K. Chaiki”) have been working on the creation of new squash variety-populations with large fruits using the variety-population Banannaya 42, bred by V. Ya. Smolei at the Far Eastern experimental station of the All-Union Scientific Research Institute of Plant Breeding in the 1940s, as the starting material. We carried out a multi-year research on the selection of promising families belonging to this variety to increase yield, improve the uniformity of fruits and the main biochemical parameters. Results. The breeding work conducted on the families of variety-population 945 in 2011-2022 resulted in the improvement of some economically important traits (the uniformity of fruit shape, yield, and biochemical parameters) compared to the started form. The eleven years of research allowed us to achieve high uniformity of fruit shape in the selection of the ninth year (PIP 2) – 90.0-90.7 % (increased by 34.9-36.0 %). The yield increased from 28.4 t/ha to 38.2-45.3 t/ha or by 34.5-59.5 %. The following biochemical parameters were improved through selection: the content of dry mater from 8.5 to 12.9 % or by 51.8 % on average, the content of sugars from 6.1 to 9.1 or by 49.2 %, the content of carotene from 1.7 to 3.8 or by 123.5 %, and the content of vitamin C from 9.6 to 21.1 or by 119.8 %. We selected the families of the same type from variety-population 945 (variety-population Banata) with improved and uniform economically important traits for submission to the State variety testing. The yield was 38.2-45.3 t/ha, fruits were heart-shaped, skin was greyish green, pulp was orange, and the content of sugars was 9.0-13.2 %. Large-fruited variety population Banata was included in the State register of breeding achievements admitted to use in the Russian Federation in 2024.

Enhancing conservation and management of mountain area through a biocultural diversity evaluation approach:A case study of Taishan Mountain Area, China

As hotspots of biocultural diversity (BCD), mountain areas contain the richest ecosystems with cultural characteristics. However, in the context of cultural homogenization and ecological decline, some high-value mountain areas are still not adequately protected, and some protected areas (PAs) even suffer from an imbalance between natural and cultural protection. To improve the coverage and conservation effectiveness of PAs in mountain areas, we propose a BCD evaluation approach to identify conservation gaps and prioritization, taking the Taishan Mountain Area (TMA) as a study case: first, we used four biodiversity factors (topographic diversity, geological diversity, ecosystem services, bird species distribution) and four cultural diversity factors (cultural landscape, intangible cultural heritage, traditional production modes, population and economy) to evaluate the BCD values; second, we generated a map of BCD hotspots and graded them into three levels; and finally, we compared the evaluation result with the PAs’ layout in the TMA and determined the conservation gap and prioritization. We find that 43.92% of the high BCD areas and 84.39% of the medium BCD areas within the mountain area are inadequately protected, and 10.79% of the low BCD areas may be overprotected, classifying the above conservation gaps as three priority levels of conservation. Our study indicates that the current PAs in mountain areas are insufficiently protected and urgently need to be expanded; (2) by analyzing the coupling relationship between biodiversity and cultural diversity, it is found that only 23.90% of the relationships between the two were coordinated or above in the TMA; (3) we propose the optimization strategies of four typical conservation gaps in the TMA; (4) we finally develop an integrated conservation model of biological and cultural values for different PA categories in mountain areas based on a gap analysis. Optimizing mountain conservation and management based on the BCD evaluation results is conducive to promoting the sustainable development of human beings and nature while guaranteeing the unbiased allocation of conservation resources in mountain areas.

Molecular Characterisation of Traditional Cashew (Anacardium occidentale L.) Populations from Nachingwea and Newala Districts in Tanzania

This study assessed the genetic diversity of 120 selected cashew plants from Nachingwea and Newala traditional cashew populations, comprising of 60 cashew plants from each population. Eight Simple Sequence Repeats (SSR) makers were used to ascertain the extent of genetic variation within and between the two traditional cashew populations. Genomic DNA extraction and Polymerase Chain Reaction (PCR) were performed using standard protocols. The results showed that all SSR markers were polymorphic. Average Polymorphic Information Content (PIC), heterozygosity, number of alleles per locus, allelic frequency and Shannon’s Index were 0.540, 0.721, 3.391, 0.666 and 0.649, respectively. Analysis of molecular variance results showed that genetic variation within populations was 88% while genetic variation between populations was 12%, implying gene flow between the populations. The results further showed that the two cashew populations are genetically diverse and that Nachingwea traditional cashew population was genetically more diverse than Newala population. High genetic variation observed in the cashew populations shows that they are suitable for use in cashew breeding programmes. Thus, conservation of traditional cashew populations is recommended