Hasil untuk "Ecology"

Nicholas A. Bokulich, Sathish Subramanian, J. Faith et al.

High-throughput sequencing has revolutionized microbial ecology, but read quality remains a considerable barrier to accurate taxonomy assignment and α-diversity assessment for microbial communities. We demonstrate that high-quality read length and abundance are the primary factors differentiating correct from erroneous reads produced by Illumina GAIIx, HiSeq and MiSeq instruments. We present guidelines for user-defined quality-filtering strategies, enabling efficient extraction of high-quality data and facilitating interpretation of Illumina sequencing results.

C. Huttenhower, D. Gevers, R. Knight et al.

Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.

Ø. Hammer, D. Harper, P. Ryan

Ø. Hammer, D. Harper, P. Ryan et al.

R. Welcomme

G. Nikolʹskiĭ

H. Walter

Prof. Dr. Walter Tranquillini

Bill Devall, George Sessions

A. Marshall

W. J. Bell, R. Cardé

Miguel Ángel Muñoz-Bañón, Nived Chebrolu, Sruthi M. Krishna Moorthy et al.

Saplings are key indicators of forest regeneration and overall forest health. However, their fine-scale architectural traits are difficult to capture with existing 3D sensing methods, which make quantitative evaluation difficult. Terrestrial Laser Scanners (TLS), Mobile Laser Scanners (MLS), or traditional photogrammetry approaches poorly reconstruct thin branches, dense foliage, and lack the scale consistency needed for long-term monitoring. Implicit 3D reconstruction methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) are promising alternatives, but cannot recover the true scale of a scene and lack any means to be accurately geo-localised. In this paper, we present a pipeline which fuses NeRF, LiDAR SLAM, and GNSS to enable repeatable, geo-localised ecological monitoring of saplings. Our system proposes a three-level representation: (i) coarse Earth-frame localisation using GNSS, (ii) LiDAR-based SLAM for centimetre-accurate localisation and reconstruction, and (iii) NeRF-derived object-centric dense reconstruction of individual saplings. This approach enables repeatable quantitative evaluation and long-term monitoring of sapling traits. Our experiments in forest plots in Wytham Woods (Oxford, UK) and Evo (Finland) show that stem height, branching patterns, and leaf-to-wood ratios can be captured with increased accuracy as compared to TLS. We demonstrate that accurate stem skeletons and leaf distributions can be measured for saplings with heights between 0.5m and 2m in situ, giving ecologists access to richer structural and quantitative data for analysing forest dynamics.

Кузнецова О.А., Негашева М.А., Хафизова А.А. et al.

Введение. Формирование дефинитивной длины тела определяется генетическими факторами и факторами окружающей среды. Актуальной задачей является межсистемный анализ связей секулярной динамики длины тела с изменениями во времени социально-экономических и демографических показателей. Подобная модель взаимосвязей была разработана для изменений длины тела населения России во второй половине XX в. на основе флуктуаций во времени социально-экономических и демографических показателей. Цель данной работы – проверка работоспособности общероссийской модели на региональных материалах – анализ общих тенденций и выявление особенностей на примере четырех крупных городов России. Материалы и методы. Материалами послужили временные ряды антропометрических, социально-экономических и демографических показателей из открытых источников. Использованы данные по длине тела, собранные авторами в 2020-2024 гг. в результате обследования молодежи 17-23 лет в четырех городах России: Барнаул, Москва, Петрозаводск и Краснодар. Длина тела для демографических когорт с 1930-39 гг. до начала 2000-х гг. рождения получена из источников литературы. Результаты и обсуждение. Показано, что изменчивость длины тела в отдельных городах и федеральных округах сходна с общероссийской. В первой половине XX в. наблюдается резкое увеличение дефинитивной длины тела с последующим снижением прироста и выходом на плато. Для современной когорты обследованных (2000-2006 гг. рождения) в некоторых крупных городах отмечено уменьшение длины тела. Заключение. Проверка регрессионной модели общероссийского секулярного тренда длины тела в связи с изменениями во времени социально-экономических и демографических показателей на региональных данных показала близкие результаты. Выявленные региональные особенности могут быть обусловлены разновременной динамикой изменений социально-экономических условий. Незначительное уменьшение длины тела в некоторых городах России у молодежи 2000-2006 гг. рождения может свидетельствовать об изменении направления секулярного тренда, что подтверждает общебиологическую гипотезу о волнообразном характере трансэпохальной динамики дефинитивной длины тела. Финансирование. Работа выполнена при поддержке гранта РНФ № 23-18-00086 «Региональные особенности влияния социально-экономических и социокультурных факторов на секулярный тренд размеров тела современной молодёжи на рубеже XX-XXI веков».

Aude Vuilliomenet, Santiago Martínez Balvanera, Oisin Mac Aodha et al.

1. Passive acoustic monitoring (PAM) coupled with artificial intelligence (AI) is becoming an essential tool for biodiversity monitoring. Traditional PAM systems require manual data offloading and impose substantial demands on storage and computing infrastructure. The combination of on-device AI-based processing and network connectivity enables local data analysis and transmission of only relevant information, greatly reducing storage needs. However, programming these devices for robust operation is challenging, requiring expertise in embedded systems and software engineering. Despite the increase in AI-based models for bioacoustics, their full potential remains unrealized without accessible tools to deploy them on custom hardware and tailor device behaviour to specific monitoring goals. 2. To address this challenge, we develop acoupi, an open-source Python framework that simplifies the creation and deployment of smart bioacoustic devices. acoupi integrates audio recording, AI-based data processing, data management, and real-time wireless messaging into a unified and configurable framework. By modularising key elements of the bioacoustic monitoring workflow, acoupi allows users to easily customise, extend, or select specific components to fit their unique monitoring needs. 3. We demonstrate the flexibility of acoupi by integrating two bioacoustic classifiers: BirdNET, for the classification of bird species, and BatDetect2, for the classification of UK bat species. We test the reliability of acoupi over a month-long deployment of two acoupi-powered devices in a UK urban park. 4. acoupi can be deployed on low-cost hardware such as the Raspberry Pi and can be customised for various applications. acoupi standardised framework and simplified tools facilitate the adoption of AI-powered PAM systems for researchers and conservationists. acoupi is on GitHub at https://github.com/acoupi/acoupi.

Yujiang Chen, Lin Li, Lingling Liu et al.

In this paper, we delve into the dynamical properties of a class of three-dimensional logistic ecological models. By using the complete discriminant theory of polynomials, we first give a topological classification for each fixed point and investigate the stability of corresponding system near the fixed points. Then employing the bifurcation and normal form theory, we discuss all possible codimension-1 bifurcations near the fixed points, i.e., transcritical, flip, and Neimark-Sacker bifurcations, and further prove that the system can undergo codimension-2 bifurcations, specifically 1:2, 1:3, 1:4 strong resonances and weak resonance Arnold tongues. Additionally, chaotic behaviors in the sense of Marotto are rigorously analyzed. Numerical simulations are conducted to validate the theoretical findings and illustrate the complex dynamical phenomena identified.

Maïri Souza Oliveira, Maxime Lenormand, Sandra Luque et al.

Secondary forests now dominate tropical landscapes and play a crucial role in achieving COP15 conservation objectives. This study develops a replicable national approach to identifying and characterising forest ecosystems, with a focus on the role of secondary forests. We hypothesised that dominant tree species in the forest canopy serve as reliable indicators for delineating forest ecosystems and untangling biodiversity complexity. Using national inventories, we identified in situ clusters through hierarchical clustering based on dominant species abundance dissimilarity, determined using the Importance Variable Index. These clusters were characterised by analysing species assemblages and their interactions. We then applied object-oriented Random Forest modelling, segmenting the national forest cover using NDVI to identify the forest ecosystems derived from in situ clusters. Freely available spectral (Sentinel-2) and environmental data were used in the model to delineate and characterise key forest ecosystems. We finished with an assessment of distribution of secondary and old-growth forests within ecosystems. In Costa Rica, 495 dominant tree species defined 10 in situ clusters, with 7 main clusters successfully modelled. The modelling (F1-score: 0.73, macro F1-score: 0.58) and species-based characterisation highlighted the main ecological trends of these ecosystems, which are distinguished by specific species dominance, topography, climate, and vegetation dynamics, aligning with local forest classifications. The analysis of secondary forest distribution provided an initial assessment of ecosystem vulnerability by evaluating their role in forest maintenance and dynamics. This approach also underscored the major challenge of in situ data acquisition.

Mehmet Hadi Suzer, Ferit Kiray, Emrah Ramazanoglu et al.

Sustainable nitrogen (N) management in arable crops requires the real-time assessment of crop growth and N uptake, particularly in water-limited environments. In the present study, we conducted two large-scale field experiments with rainfed and irrigated wheat in South-East Turkey to evaluate the effectiveness of drone- and satellite-based spectral indices, in combination with neural network models, for estimating biomass and nitrogen uptake. Four N fertilizer rates in the irrigated fields (N₀: 0, N₆: 60, N₁₂: 120, and N₁₆: 160 kg N ha⁻¹) and five N rates in the rainfed fields (N₀: 0, N₂: 20, N₄: 40, N₅: 50, and N₆: 60 kg N ha⁻¹) were tested. Highest fresh biomass was 57.7 ± 1.1 and 15.9 ± 1.0 t/ha⁻¹ for irrigated and rainfed treatments, respectively, with 2.5-fold higher grain yield in irrigated (8.2 ± 1.2 t/ha⁻¹) compared to rainfed (2.9 ± 0.9 t/ha⁻¹) wheat. Drone-based spectral indices, especially those based on the red-edge region (CL_{Red_edge}), correlated strongly with biomass (R² > 0.9 in irrigated wheat) but failed to explain crop N concentration throughout the vegetation period. This limitation was attributed to the nitrogen dilution effect, where increasing biomass during crop growth leads to a decline in the concentration of nitrogen, complicating its accurate estimation via remote sensing. To address this, we employed a two-layer feed-forward neural network model and used SPAD and plant height values as supplementary input parameters to enhance estimations based on vegetation indices. This approach substantially enhanced the predictions of N uptake (R² up to 0.95), while even simplified model version using only NDVI and plant height parameters achieved significant performance (R² = 0.84). Overall, our results showed that spectral indices are reliable predictors of biomass but insufficient for estimating nitrogen concentration or uptake. Integrating indices with complementary crop traits in nonlinear models provides acceptable estimates of N uptake, supporting more precise fertilizer management and sustainable wheat production under water-limited conditions.

Mussakhan Ryskul , Borankulova Dina , Bülent Aksoy et al.

Fieldwork plays a crucial role in developing students' research skills by bridging theoretical knowledge with practical application. Despite its importance, challenges remain in effectively integrating fieldwork into geography education to maximize student learning outcomes. This study aims to address this gap by identifying effective strategies for enhancing research skills through field-based activities. A mixed-methods approach was employed, utilizing both quantitative and qualitative research techniques to analyze survey responses from 94 students. Research skill development activities were conducted both in and out of the classroom, and students' performance was assessed using survey-based evaluation tasks. The findings indicate a significant difference in research skill development between the control and experimental groups (U=385.00; p<.05), demonstrating the positive impact of structured fieldwork. Additionally, male students exhibited a faster and more accurate progression in research-related activities compared to female students. Standard deviation values of development indicators further supported these findings, highlighting key differences in skill acquisition. These results provide a foundation for refining fieldwork-based instructional methods, contributing to both theoretical advancements in geography education and practical improvements in teaching approaches.

Phillip Ormeño Vásquez, Naysha Rojas Villa, Cristian Rojas Villa

Tourism and hospitality education in Peru is evolving to incorporate a stronger focus on environmental conservation. In this context, this study aims to assess environmental conservation attitudes of students enrolled in tourism and hospitality programs offered by Peruvian universities. A descriptive, cross-sectional study was carried out among students pursuing a degree in Tourism and Hospitality Management at the National University of Cañete, in Lima, Peru. The research focused on the period from July to December 2018. The final report was completed in 2020. A sample of 45 students was taken from a total population of 281, based on specific selection criteria. The demographic variables considered for this study were: age group, gender, and class shift, alongside the following dimensions: cognitive, affective and reactive. The majority of participants were female (73.3%), within the 20- to 22-year-old group (80.0%), and attended morning classes (64.4%). The highest level of agreement among students was observed for the statement that local hotels should implement appropriate waste management systems (item 2). Furthermore, 86.7% of respondents strongly agreed that plants and animals have the same right to life as human beings (item 20). Similarly, 60.0% of them indicated they would be willing to mobilize others in support of public space conservation (item 26). Overall, the findings suggest that students possess sound cognitive and affective attitudes towards environmental conservation. However, a lower reactive performance requires strategies to foster a deep emotional connection.

Emre Anakok, Pierre Barbillon, Colin Fontaine et al.

Citizen science monitoring programs can generate large amounts of valuable data, but are often affected by sampling bias. We focus on a citizen science initiative that records plant-pollinator interactions, with the goal of learning embeddings that summarize the observed interactions while accounting for such bias. In our approach, plant and pollinator species are embedded based on their probability of interaction. These embeddings are derived using an adaptation of variational graph autoencoders for bipartite graphs. To mitigate the influence of sampling bias, we incorporate the Hilbert-Schmidt Independence Criterion (HSIC) to ensure independence from continuous variables related to the sampling process. This allows us to integrate a fairness perspective, commonly explored in the social sciences, into the analysis of ecological data. We validate our method through a simulation study replicating key aspects of the sampling process and demonstrate its applicability and effectiveness using the Spipoll dataset.