Hasil untuk "Ecology"

C. S. Holling

B. Latour, C. Porter

Travis Longcore, Catherine Rich

C. S. Holling

Laban Kayitete, Elie Sinayitutse, Matthew Dennis

The Hooded Vulture (Necrosyrtes monachus) plays a vital role in environmental cleaning and disease control. However, its population is rapidly declining across its range, especially in East Africa. Despite conservation efforts invested in its protection, its spatial distribution in East Africa remains understudied. Utilising ensemble Species Distribution Models, this study leverages the response of N. monachus to bioclimatic factors, elevation, and land cover to predict the species' current distribution across Kenya, Rwanda, Tanzania, and Uganda, and assess potential climate change impacts. Findings reveal that only 11.813 % of the study area represents suitable habitat for N. monachus, with 35.954 % of this falling within protected areas. The Hooded Vulture exhibited strong dependence on climatic conditions, with variables of large influence to its distribution being isothermality, annual mean temperature, precipitation seasonality, elevation, and annual precipitation, while the urban land exhibited moderate influence. Climate change projections indicate regional habitat stability, but varying spatial and climatic pathway-based trajectories, with habitat expansions under sustainable development pathways (SSP126) and mixed outcomes under fossil-fuelled scenarios (SSP585), particularly affecting Kenya with consistent declines, while Tanzania, Uganda, and Rwanda showed expansions. The proportion of suitable habitat within protected areas remained stable across scenarios, though substantial national disparities persist. This research underscores the role of modelling in informed conservation and urgency in transboundary conservation strategies extending beyond currently protected areas and provides critical insights for adaptive conservation planning to safeguard the Hooded Vulture's future in East Africa.

Yupeng LI, Yaning CHEN, Fei WANG et al.

Tajikistan, a mountainous country and a vital water tower for Central Asia, is becoming increasingly vulnerable to snow drought under climate change, threatening its snow- and glacier-fed streamflow. Yet, the impacts of snow drought on the regional hydrology remain insufficiently understood. In this study, we integrated multisource data, including the Fifth Generation European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis for Land Applications (ERA5-Land) data and hydrological station data, to systematically assess the snow drought patterns and their impacts on streamflow during 1950–2023. We identified snow drought events based on precipitation and snow fraction anomalies relative to climatological means and classified them into warm snow drought, dry snow drought, and warm&dry snow drought. The results revealed that snow drought was a recurrent phenomenon, occurring in 51.70% of the years during the study period, with warm&dry snow drought accounting for 21.90% of the total events. Both the frequency and severity exhibited pronounced spatial variability, largely governed by the elevation and snowfall fraction. Specifically, the frequency of warm snow drought was negatively correlated with the snowfall fraction, decreasing on average by 0.20 per unit increase in snowfall fraction, whereas the frequency of dry snow drought was positively correlated, increasing by 0.07 per unit increase. The streamflow analysis results demonstrated that snow drought typically reduced the warm-season discharge by 5.00%–18.00% in certain rivers, thereby exacerbating the water stress during the dry season. The results of this study advance our understanding by explicitly linking the types of snow drought to hydrological responses in Central Asia’s high mountains, providing a scientific basis for climate adaptation and sustainable water resource management in Tajikistan.

Edoardo Zelli, Océane Legatelois, Laura Calvet-Mir et al.

The escalating human-driven pressures, such as coastal development, climate change, and overfishing, have led to habitat degradation and declines in marine resource abundance. These impacts pose significant threats to the livelihoods of coastal communities, particularly in the Mediterranean Sea. In situations where stock assessment data is scarce, fishers’ ecological knowledge (FEK) has proven valuable for understanding the ecological status of marine resources and their historical transformations. However, FEK can be affected by memory bias, reducing its reliability, a phenomenon known as the "shifting baseline syndrome” (SBS). This study investigates the presence of SBS within Barcelona’s fishing community by comparing fishers’ perceptions of abundance changes in six commercially significant marine species in Catalonia with data from scientific stock assessments. A mixed-methods approach was employed, incorporating semi-structured interviews and the analysis of fishery-independent biological data. The findings reveal no significant discrepancies between scientific assessments and fishers’ perceptions, suggesting that SBS is not present in this fishery. However, variations in how fishers perceive these changes were identified, pointing to potential future challenges. This research provides novel evidence of fishers’ ability to offer consistent quantitative insights into the status of commercial marine species within Barcelona’s fishing sector.

Hui Wang, Wan Duan, Qianqian Dong et al.

With continuous increases in nitrogen (N) deposition in the future, its impacts on terrestrial ecosystems are attracting growing concern. Arbuscular mycorrhiza (AM) fungi play a crucial role in shaping both soil microbial and plant communities. AM fungi play a crucial role in shaping the soil microbial and plant communities, yet their patterns of influence under increased N deposition scenarios remain unclear, particularly in desert ecosystems. Therefore, we conducted a field experiment simulating increased N deposition and AM fungal suppression to assess the effects of increased N deposition and AM fungi on soil microbial communities, employing phospholipid fatty acid (PLFA) biomarker technology in the Gurbantunggut Desert of Xinjiang. We found that increased N deposition promoted soil microbial biomass, including AM fungi, fungi, Actinomycetes (Act), Gram-positive bacteria (G⁺), Gram-negative bacteria (G⁻), and Dark Septate Endophyte (DSE). AM fungal suppression significantly increased the content of soil Act and G⁺. There were clearly and significantly interactive effects of increased N deposition and AM fungi on soil microbial contents. Both increased N deposition and AM fungi caused significant changes in soil microbial community structure. Random forest analysis revealed that soil nitrate N (NO₃⁻-N), Soil Organic Carbon (SOC), and pH were main factors influencing soil microorganisms; soil AM fungi, G⁺, and Act significantly affected plant Shannon diversity; soil G⁻, Act, and fungi posed significant effects on plant community biomass. Finally, the structure equation model results indicated that soil fungi, especially AM fungi, were the main soil microorganisms altering the plant community diversity and biomass under increased N deposition. This study reveals the crucial role of AM fungi in regulating soil microbial responses to increased N deposition, providing experimental evidence for understanding how N deposition affects plant communities through soil microorganisms.

Charles C. Elton

Yishi Shen, Shi Zhang, Weimin Huang et al.

Birds have remarkable flight capabilities due to their adaptive wing morphology. However, studying live birds is time-consuming and laborious, and obtaining information about the complete wingbeat cycle is difficult. To address this issue and provide a complete dataset, we recorded comprehensive motion capture wing trajectory data from five free-flying pigeons (Columba livia). Five key motion parameters are used to quantitatively characterize wing kinematics: flapping, sweeping, twisting, folding and bending. In addition, the forelimb skeleton is mapped using an open-chain three-bar mechanism model. By systematically evaluating the relationship of joint degrees of freedom (DOFs), we configured the model as a 3-DOF shoulder, 1-DOF elbow and 2-DOF wrist. Based on the correlation analysis between wingbeat kinematics and joint movement, we found that the strongly correlated shoulder and wrist roll within the stroke plane cause wing flap and bending. There is also a strong correlation between shoulder, elbow and wrist yaw out of the stroke plane, which causes wing sweep and fold. By simplifying the wing morphing, we developed three flapping wing robots, each with different DOFs inside and outside the stroke plane. This study provides insight into the design of flapping wing robots capable of mimicking the 3D wing motion of pigeons.

S. Barrett, G. E. Hutchinson

Zhimin Liu, Quanlai Zhou, Qun Ma et al.

Changes in grassland soil organic carbon stock (SOCS) may significantly affect the regional climate and carbon cycle of terrestrial ecosystems. However, how the impact of climate factors on SOCS and the dominant climate factors are regulated by the area scale of grasslands remains unclear. To understand the scale effects of climate on SOCS and how to accurately estimate SOCS at different scales, three area scales were defined by extending grassland types on the basis of meadow, typical and desert grasslands (Scale I (average area 37.22 × 104 km2) included each of these three types of grasslands, Scale II (average area 74.45 × 104 km2) was achieved by a pairwise combination of these three types of grasslands. Scale III (area 111.67 × 104 km2) was an aggregate of these three types of grasslands), the relationship between climate factors (i.e., mean annual precipitation, mean annual temperature, annual maximum temperature, annual minimum temperature, mean annual ground temperature, mean annual humidity, annual sunshine duration, annual maximum depth of accumulated snow, and the number of snow-covered days) and SOCS at the three scales were explored in the grasslands of northern China. Our results indicated that the total SOCS in grasslands at the three scales was ordered as desert grassland < meadow grassland < typical grassland. Of the nine climate factors, mean annual precipitation, positively correlated with SOCS, was the most significant climatic factor for all three scales. The dominant climatic factors of the SOCS differed across grassland area scales (i.e., MAP and MAH for meadow grassland, AMAT, MAP, NSD, and MAH for typical grassland, MAP, NSD, MAH, AMAT, and ASD for meadow-typical grassland scale, MAP, MAT, and MAGT for typical-desert grassland scale, MAP and MAT for meadow-typical-desert grassland scale). The impact of climate factors on the SOCS decreased as the scale increased. It is essential to screen appropriate climate predictors according to a given area scale when assessing regional SOCS. Multiple climate factors are better predictors for accessing SOCS at a small scale. At a large scale, however, dominant climatic factors are predictors that are more efficient.

Bo Peters, Gesche Blume-Werry, Alexander Gillert et al.

Abstract Plant roots influence many ecological and biogeochemical processes, such as carbon, water and nutrient cycling. Because of difficult accessibility, knowledge on plant root growth dynamics in field conditions, however, is fragmentary at best. Minirhizotrons, i.e. transparent tubes placed in the substrate into which specialized cameras or circular scanners are inserted, facilitate the capture of high-resolution images of root dynamics at the soil-tube interface with little to no disturbance after the initial installation. Their use, especially in field studies with multiple species and heterogeneous substrates, though, is limited by the amount of work that subsequent manual tracing of roots in the images requires. Furthermore, the reproducibility and objectivity of manual root detection is questionable. Here, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise. Our minirhizotron data come from various wetlands on organic soils, i.e. highly heterogeneous substrates consisting of dead plant material, often times mainly roots, in various degrees of decomposition. This may be seen as one of the most challenging soil types for root segmentation in minirhizotron images. RootDetector showed a high capability to correctly segment root pixels in minirhizotron images from field observations (F1 = 0.6044; r2 compared to a human expert = 0.99). Reproducibility among humans, however, depended strongly on expertise level, with novices showing drastic variation among individual analysts and annotating on average more than 13-times higher root length/cm2 per image compared to expert analysts. CNNs such as RootDetector provide a reliable and efficient method for the detection of roots and root length in minirhizotron images even from challenging field conditions. Analyses with RootDetector thus save resources, are reproducible and objective, and are as accurate as manual analyses performed by human experts.

Hamed Javadi, Seyed Gholam Reza Moosavi, Nasrin Farahmandrad

IntroductionHarsh ecological conditions, including water scarcity, have limited vegetation life in desert areas. Consequently, the cultivation of drought-resistant plants compatible with desert areas and their expansion, while creating suitable vegetation, increases biodiversity, controls desertification and is oriented towards the sustainability of desert ecosystems. Cannabis is a drought-tolerant plant which, because of its great genetic diversity, has the ability to grow in different climates, particularly in semi-desert areas. Appropriate agricultural management enhances the vegetation, production and productivity of agricultural products. In this context, it is important to study planting date and plant density as factors impacting production. Planting dates must be chosen to allow sufficient time for each stage of growth and development. The use of optimal plant density may improve plant growth and increase plant yield by reducing intra-plant competition. Results of search on two densities of cannabis plants of 8 and 16 plants per m-2 in Birjand, the highest seed yield was obtained from a density of 16 plants per m-2. Finding on densities of 50, 150, and 250 plants per m-2 in Mashhad, and 30, 90, and 150 plants per m-2 in Shirvan reported that as the density of cannabis plants increased, the flowering date decreased in both regions. Given the arid and semi-arid climate of South Khorasan, planting plants compatible with the climate of the region, such as cannabis, can increase vegetation cover while producing an acceptable yield. The objectives of the current research are to study the effect of agricultural management on the growth characteristics of the forgotten cannabis plant in semi-arid climate of Birjand. Material and MethodsThe current research was carried out in Center of Agriculture and Natural Resources Research if South Khorasan, located at 59′ 13° east longitude and 53° 32′north latitude, and 1491m above sea level. South Khorasan province has a desert and semi-desert climate. Before preparing the soil to determine the required amount of chemical and organic fertilizers, the soil in the field was analyzed. Data on temperature changes and the total number of sunny hours of various months during the cannabis growing period were received from the Birjand weather station. The experiment was conducted as a split plot based on a randomized complete block design with three replications. Treatments investigated included planting date on three levels of May 12, May 27 and June 11 as the main plot and plant density at three levels of 22.2, 11.1 and 7.4 plants per m-2 as the sub plot. In this research, the phenological characteristics including the number of days to emergency, days to flowering, days to seed filling, days to physiological maturity, length of vegetative period, length of reproductive period, length of flowering period, and morphological characteristics including plant height, number of main stem branches, stem diameter and seed yield were investigated. Statistical analysis of the data was done using SAS software and the comparison of averages was done based on Duncan's 5% multiple range test. Results and DiscussionThe results showed that the impact of planting date on all morphophenological traits was significant, with the exception of stem diameter. The delay in planting between May 12 and June 11 significantly reduced the length of phenological stages, and vegetative growth of cannabis, and ultimately caused a 48% decrease in seed yield. Late cultivation, due to the increase in temperature, the plant completes its vegetative growth faster. The delay in planting by shortening the period of effective growth, reducing the photosynthetic potential of the plant, and coinciding with the period of seed filling with low temperatures and shortening of the day has led to a decrease in the quantity and the filling speed of the seeds, and subsequently the yield of the seeds decreases. It has been reported that a 20-day delay in seeding from 10 May led to a 46% decrease in seed yield under climatic conditions in Azerbaijan. The effect of plant density on morphological traits, number of days until flowering of female plants, days until seed set, days until physiological maturity, length of vegetative period, length of flowering period and seed yield were significant. The increase in density from 7.4 to 22.2 plants per m-2, while delaying flowering, increased seed yield by 15.4%. Increased plant density due to higher plant height and increased number of plants per unit area increased seed yield. Results of search on two densities of cannabis plants of 8 and 16 plants per m-2 in Birjand, the highest seed yield was obtained from a density of 16 plants per m-2. To achieve proper yield performance, and develop cannabis cultivation- as a plant compatible with the semi-desert region- the planting date of May 12 and the density of 22.2 plants per m-2 can be used.

Yiyu Li, Qingxu Huang, Ling Zhang et al.

As a proxy for human activity, per capita urban land has great significance for urban planning. We still lack a comprehensive understanding of per capita urban land from the perspective of urban–rural gradients. Thus, based on the concentric buffering method and the dynamic-time-warp clustering method, this research analyzes the urban–rural gradient of the per capita urban land of 345 cities in China in 2000, 2010, and 2016. We find that the per capita urban land in China grew from 110.2 m²/person in 2000 to 118.9 m²/person, increasing by 7.9%. The urban–rural gradient of the per capita urban land can be classified into six types: (1) large city with a mono peak; (2) large city with a fluctuating increase; (3) medium city with a mono peak; (4) medium city with a declining trend; (5) small city with a mono peak, and (6) small city with a declining trend. In addition, most cities shifted from a mono-peak type to a declining type, which suggested that the low-density, sprawling development was intensifying. The dynamic-time-warp clustering method used in this research can effectively compare trends of the urban–rural gradient of per capita urban land across cities, which can be applied to the analysis of the urban–rural gradient of air pollution, urban green space, and urban heat islands.

Sebastian Dederichs, Peter Dannenberg

Nicht erst seit der Covid-19-Pandemie nimmt der Online-Lebensmitteleinzelhandel in Deutschland zu und bringt neue, teilweise hybride, Betriebsformen und Vertriebsmodelle hervor. Hiermit gehen bisher kaum untersuchte räumliche Veränderungen der einzelnen Wertschöpfungsschritte einher, beispielsweise in den Bereichen vorgelagerte Logistik, Filialstruktur und Warenübergabe. Anhand von drei ausgewählten Fallbeispielen (Picnic, Wochenmarkt24 und Rewe) wurden neuere Betriebsformen und deren räumliche Logistik- und Vertriebsstrukturen identifiziert und unterschiedliche Standortfaktoren aufgeführt. Diese beinhalten neben den typischen Faktoren der Standortwahl für Distributionslager (Nähe zu Kunden, Arbeitskräften und Lieferanten) auch spezifische betriebsformen- und vertriebsmodellabhängige Faktoren, wie eine stärkere Verkürzung der ,letzten Meile‘, eine Mindest- oder Maximalverdichtung von Haushalten im Einzugsgebiet oder die Nähe zu einer (landwirtschaftlichen) Erzeugerstruktur.

Masumeh Ahmadipari, Ahmadreza Yavari, Morteza Ghobadi

Habitat assessment of species is one of the most important strategies to conserve biodiversity in the protected areas. The main objective of this study is to present an ecological assessment model for habitat management of brown bears using the MaxEnt algorithm in Oshtorankooh protected area, Lorestan. 55 presence points of brown bear and seven environmental variables including slope, elevation, distance from river, distance from road, distance from forest and grassland, distance from cropland and vegetation, and distance from rural area were applied for habitat assessment process. The importance of these variables was investigated by the Jaknikfe test and their predictive rate was assessed by response curves. The distance from the rural area and elevation were respectively the most important factors for modeling the distribution of brown bears in Oshtorankooh protected area. The final suitability map of habitat for brown bear species was classified into four categories: more suitable, suitable, less suitable and unsuitable. An area of 22566.7 ha was determined as a more suitable habitat for brown bears in the study area. The result indicates that the southern and central areas of the study area are more suitable for the species. The result of the model validity was obtained as 0.92, showing that the integrated model was very efficient in the habitat assessment process.

Clemens Schwingshackl, Jana Sillmann, Ana Maria Vicedo‐Cabrera et al.

Abstract Global warming is leading to increased heat stress in many regions around the world. An extensive number of heat stress indicators (HSIs) has been developed to measure the associated impacts on human health. Here we calculate eight HSIs for global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We compare their future trends as function of global mean temperature, with particular focus on highly populated regions. All analyzed HSIs increase significantly (p < 0.01) in all considered regions. Moreover, the different HSIs reveal a substantial spread ranging from trends close to the rate of global mean temperature up to an amplification of more than a factor of two. Trends change considerably when normalizing the HSIs by accounting for the different scales on which they are defined, but the large spread and strong trends remain. Consistently, exceedances of impact‐relevant thresholds are strongly increasing globally, including in several densely populated regions, but also show substantial spread across the selected HSIs. The indicators with the highest exceedance rates vary for different threshold levels, suggesting that the large indicator spread is associated both to differences in trend magnitude and the definition of threshold levels. These results highlight the importance of choosing indicators and thresholds that are appropriate for the respective impact under consideration. Additionally, further validation of HSIs regarding their capability to quantify heat impacts on human health on regional‐to‐global scales would be of great value for assessing global impacts of future heat stress more reliably.

A. Łomnicki