Experimental Study of Invasion and Biofouling of Freshwater Mussel <i>Limnoperna fortunei</i>

Golden mussel Limnoperna fortunei(Dunker 1857) is a filter-collector species of fresh water mussel originating from southern China. In the water transfer tunnels from the East River to Shenzhen and Hong Kong, golden mussels attach to the walls of pipelines and gates, causing serious biofouling, increased flow resistance, and resulted in corrosion of the tunnel wall. Golden mussel has very high environmental adaptability and may colonize habitats with low dissolved oxygen and a wide range of trophic levels. The colonization process of the species on solid surface was studied in the Xizhijiang River, a tributary of the East River and the main water resource of Shenzhen from March 2010 to April 2011. The results showed that the golden mussel completed three generations and reproduced six cohorts per year in the tropic zone. Water temperature was the controlling factor for the growth rate and maturity of each cohort. Based on the results, an ecological method for controlling the invasion of golden mussels in water transfer tunnels was proposed.

A new multi-source remote sensing image sample dataset with high resolution for flood area extraction:GF-FloodNet

Deep learning algorithms show good prospects for remote sensingflood monitoring.They mostly rely on huge amounts of labeled data.However,there is a lack of available labeled data in actual needs.In this paper,we propose a high-resolution multi-source remote sensing dataset forflood area extraction:GF-FloodNet.GF-FloodNet contains 13388 samples from Gaofen-3(GF-3)and Gaofen-2(GF-2)images.We use a multi-level sample selection and interactive annotation strategy based on active learning to construct it.Compare with otherflood-related datasets,GF-FloodNet not only has a spatial resolution of up to 1.5 m and provides pixel-level labels,but also consists of multi-source remote sensing data.We thoroughly validate and evaluate the dataset using several deep learning models,including quantitative analysis,qualitative analysis,and validation on large-scale remote sensing data in real scenes.Experimental results reveal that GF-FloodNet has significant advantages by multi-source data.It can support different deep learning models for training to extractflood areas.There should be a potential optimal boundary for model training in any deep learning dataset.The boundary seems close to 4824 samples in GF-FloodNet.We provide GF-FloodNet at https://www.kaggle.com/datasets/pengliuair/gf-floodnet and https://pan.baidu.com/s/1vdUCGNAfFwG5UjZ9RLLFMQ?pwd=8v6o.

Predicted trends of soil erosion and sediment yield from future land use and climate change scenarios in the Lancang-Mekong River by using the modified RUSLE model

Soil erosion and sediments in the Lancang-Mekong River Basin as a result of climate change and changes in land use pose a threat to the existence of the riparian people,biodiversity and ecosystems.This study seeks to assess the annual soil erosion in terms of spatial distribution and the trends of sediment yield with the climate and land changes in future scenarios in 2030 and 2040 through the modified RUSLE model.Future lands were simulated by using the MLP artificial neural network and the Markov chain analysis.The future climate was examined by using the Max Planck Institute model,which showed a corrected bias and downscaled grid size under the Representative Concentration Pathways(RCPs).The simulated land use indicated that the forest areas were converted mostly to agricultural lands and urban areas.In the future,the average rainfall under all RCP scenarios is higher than that from the historical period.The R and C factors changed constantly,thereby affecting the soil erosion rate and sediment yield.The maximum erosion was estimated at approximately 21,000 and 21,725 t/km2/y under RCP8.5 in both years.Meanwhile,the results of sediment yield in 2030 and 2040 under RCP scenarios were much higher when compared to historical sediment data around 66.3%and 71.2%,respectively.Thailand's plateau,some parts of Cambodia and Laos PDR and the Mekong Delta are vulnerable to increase soil erosion and sediment yield.Measures to address these issues need to be planned to prepare and mitigate the possible effects,especially the loss of storage capacity in dams.

The assemblage characteristics of benthic macroinvertebrates in the Yalutsangpo River, the highest major river in the world

Aquatic ecosystems of highland rivers are different from those of low altitude rivers because of the specific topography and environmental parameters associated with high altitudes. Yalutsangpo, the upper course of the Brahmaputra River, is the highest major river in the world, flowing from west to east across Tibet, China and pouring into India. Macroinvertebrates were sampled from Yalutsangpo and its tributaries, the Lhasa, Niyang, and Parlong Tsangpo Rivers, from October 2009 to June 2010, to study characters of the highland aquatic ecosystem. Altogether, 110 macroinvertebrate taxa belonging to 57 families and 102 genera were identified from the basin. The biodiversity and composition of macroinvertebrate assemblages were strongly affected by altitude gradients. Local diversity represented by taxa richness and the improved Shannon-Wiener index were high at altitudes of 3,300-3,700 m, among which suitability of habitat was higher due to the better integrated environmental condi- tions of water temperature, dissolved oxygen, and aquatic vegetation, etc. Macroinvertebrates were grouped into shredders, scrapers, predators, collector-filterers, and collector-gatherers according to their feeding behaviors. It was found that the distributions of the functional feeding groups varied with habitat altitudes. Shredders were present at altitudes of 2,900-4,400m, while scrapers mainly inhabited altitudes of 3,500-4,500 m, and collector-filterers preferred 3,500-4,000 m. Even though the local taxa richness was not high at each site, the taxonomic composition and density of the assemblages varied greatly among the different sites, resulting in much higher regional diversity compared to thelowland river with similar flow and substrate conditions. The regional cumulative taxa richness of Yalutsangpo decreased and more families were lost as the altitude increased. However, some families that were newly present as the altitude increased were essential for sustaining the high regional biodiversity. The ordination diagram obtained from Detrended Correspondence Analysis indi- cated that altitude, fiver pattern, riverbed structures, bank structures, and flow conditions were the main factors that influenced the macroinvertebrate assemblages in the Yalutsangpo basin.

Macroinvertebrate distribution and aquatic ecology in the Ruoergai （Zoige） Wetland, the Yellow River source region

The Ruoergai （Zoige） Wetland, the largest plateau peatland in the world, is located in the Yellow River source region. The discharge of the Yellow River increases greatly after flowing through the Ruoergai Wetland. The aquatic ecosystem of the Ruoergai Wetland is crucial to the whole Yellow River basin. The Ruoergai wetland has three main kinds of water bodies： rivers, oxbow lakes, and marsh wetlands. In this study, macro- invertebrates were used as indicators to assess the aquatic ecological status because their assemblage structures indicate long-term changes in environments with high sensitivity. Field investigations were conducted in July, 2012 and in July, 2013. A total of 72 taxa of macroinvertebrates belonging to 35 families and 67 genera were sampled and identified. Insecta was the dominant group in the Ruoergai Basin. The alpha diversity of macroinvertebrates at any single sampling site was low, while the alpha diversity on a basin-wide scale was much higher. Macroinvertebrate assemblages in rivers, oxbow lakes, and marsh wetlands differ markedly. Hydrological connectivity was a primary factor causing the variance of the bio-community. The river channels had the highest alpha diversity of macroinvertebrates, followed by marsh wetlands and oxbow lakes. The density and biomass of Gastropoda, collector filterers, and scrapers increased from rivers to oxbow lakes and then to marsh wetlands. The fiver ecology was particular in the Ruoergai Wetland with the high beta diversity ofmacroinvertebrates, the low alpha diversity of macroinvertebrates, and the low taxa richness, density, and biomass of EPT （Ephemeroptera, Plecoptera, Trichoptera）. To maintain high alpha diversity of macro-invertebrates in the Ruoergai Wetland, moderate connec- tivity of oxbow lakes and marsh wetlands with rivers and measures to control headwater erosion are both crucial.

北方半干旱草原生态系统光合参数的季节和年际变异

生态系统表观量子效率(α)、最大光合速率(P_(max))和暗呼吸速率(R_(d))不仅反映了生态系统水平光合生理特征,同时也是碳循环模型中光合过程模拟的关键参数。气候和植被因子都会影响光合参数的季节和年际变异,但二者在光合参数调控过程中的相对贡献和作用途径尚不清晰。本研究基于连续12年(2006-2017)的涡度相关观测数据,分析了内蒙古半干旱典型草原光合参数的季节和年际变化规律利用回归分析和结构方程模型(SEM)方法明晰了环境和生理调控的作用途径及相对贡献。结果发现,光合参数(α、P_(max)和R)均表现出单峰的季节变化趋势,并呈现明显的年际波动。温度(T_(a))和土壤含水量(SWC)的变化共同影响光合参数的季节变化,而SWC主导了其年际变异。α和R_(d)的变化主要由T_(a)决定,而P_(max)的变化主要受SWC的影响。SEM模型分析表明,除了直接作用外,环境因子主要通过影响冠层水平气孔导度(gc)对光合参数和碳同化生理过程进行调控。此外,叶面积指数对光合参数特别是Pmax的季节和年际变异起主要调控作用。以上结果明确了环境和植被共同决定了生态系统水平光合参数的季节和年际变异,并强调了在水分受限的草原生态系统中,植被生理调控在光合碳同化能力和碳汇功能评估中的重要作用。

Optical pulse repetition rate multiplication based on series-coupled double-ring resonator

In this paper, optical pulse repetition rate multiplication based on a series-coupled double-ring resonator is proposed. First, the spectral characteristic of the series-coupled double-ring resonator is simulated and the optimum coupling coefficients to achieve a periodic flat-top passband are obtained. Then, high-quality pulse repetition rate multiplication is realized by periodically filtering out spectral lines of the input pulse train. Different multiplication factors N 2, 3, 4, 5 can be obtained by adjusting the ring radii. In addition, compared with a single-ring resonator, the multiplied output pulse train by a series-coupled double-ring resonator exhibits much better power uniformity.