Integrated assessment of river health based on water quality, aquatic life and physical habitat

The health conditions of Liao River were assessed using 25 sampling sites in April 2005, with water quality index, biotic index and physical habitat quality index. Based on the method of cluster analysis (CA) for water quality indices, it revealed that heavily polluted sites of Liao River are located at estuary and mainstream. The aquatic species surveyed were attached algae and benthic invertebrates. The result showed that the diversity and biomass of attached algae and benthic index of biotic integrity (B-IBI) were degrading as the chemical and physical quality of water bodies deteriorating. Physiochemical parameters, BOD5, CODCr, TN, TP, NH3-N, DO, petroleum hydrocarbon and conductivity, were statistically analyzed with principal component analysis and correlation analysis. The statistical results were incorporated into the integrated assessing water quality index, combining fecal coliform count, attached algae diversity, B-IBI and physical habitat quality score. A comprehensive integrated assessing system of river ecological health was established. Based on the systimetic assesment, the assessed sites are categorized into 9 “healthy” and “sub-healthy” sites and 8 “sub-sick” and “sick” sites.     

Biological Connectivity of Seasonally Ponded Wetlands across Spatial and Temporal Scales

Many species that inhabit seasonally ponded wetlands also rely on surrounding upland habitats and nearby aquatic ecosystems for resources to support life stages and to maintain viable populations. Understanding biological connectivity among these habitats is critical to ensure that landscapes are protected at appropriate scales to conserve species and ecosystem function. Biological connectivity occurs across a range of spatial and temporal scales. For example, at annual time scales many organisms move between seasonal wetlands and adjacent terrestrial habitats as they undergo life‐stage transitions; at generational time scales, individuals may disperse among nearby wetlands; and at multigenerational scales, there can be gene flow across large portions of a species’ range. The scale of biological connectivity may also vary among species. Larger bodied or more vagile species can connect a matrix of seasonally ponded wetlands, streams, lakes, and surrounding terrestrial habitats on a seasonal or annual basis. Measuring biological connectivity at different spatial and temporal scales remains a challenge. Here we review environmental and biological factors that drive biological connectivity, discuss implications of biological connectivity for animal populations and ecosystem processes, and provide examples illustrating the range of spatial and temporal scales across which biological connectivity occurs in seasonal wetlands.     

太子河流域硅藻群落与驱动因子的定量关系

定量研究水生生物对水环境参数的适宜值是评估栖息地质量和维持生物完整性的主要途径. 以辽宁省太子河流域为研究范例,选择Y(优势度指数)大于0.000 1的硅藻为研究对象,结合水环境参数,采用CCA(典范对应分析)、CART(分类回归树)和WA(加权平均回归分析)等方法,分析硅藻与水环境因子的关系,并计算硅藻对驱动因子的最适值. CCA结果表明,IOS(底质指数)、ρ(TDS)(TDS为总溶解固体)和ρ(COD_Mn)是硅藻群落的驱动因子;CART预测结果表明,IOS高的水环境硅藻密度高于IOS低的水环境,ρ(TDS)和ρ(COD_Mn)低的水环境硅藻密度高于ρ(TDS)和ρ(COD_Mn)高的水环境;WA结果显示,96种硅藻对IOS、ρ(TDS)和ρ(COD_Mn)的最适值范围分别为1.00~6.44、60.29~820.30 mg/L和0.46~2.89 mg/L. 钝端菱形藻解剖刀变种和尖端菱形藻适宜栖息于IOS较低而ρ(COD_Mn)较高的水环境, Gomphonema trancatum和肿大桥弯藻则适宜栖息于IOS较高的水环境;缠结异极藻二叉变种和尖细异极藻适宜栖息于ρ(TDS)较高的水环境,弧形峨眉藻和克洛钝脆杆藻则适宜栖息于ρ(TDS)较低的水环境;弧形峨眉藻和隐头舟形藻威蓝变种适宜栖息于ρ(COD_Mn)较低的水环境. 针杆藻和桥弯藻对IOS的最适值高于舟形藻和菱形藻以及其他藻种,96种硅藻对ρ(TDS)和ρ(COD_Mn)的最适值均表现为菱形藻和异极藻较高、针杆藻和桥弯藻较低.      

Case Study Comparing Multiple Irrigated Land Datasets in Arizona and Colorado,USA

While there are currently a number of irrigated land datasets available for the western United States (U.S.), there is uncertainty regarding in how they relate to each other. To help understand the characteristics of available irrigated datasets, we compared (1) the Cropland Data Layer (CDL), (2) Moderate Resolution Imaging Spectroradiometer Irrigated Agriculture Dataset (IAD), (3) Digitized Irrigated Land (DIL), and (4) Consumptive Use for Irrigation (CUI) data in Arizona and Colorado, U.S. These datasets were derived from multiple sources at various spatial resolutions and temporal scales. We found spatial and temporal trends among all of them. The datasets showed decreases in irrigated land area in Arizona during the 2000–2010 time period. The change ranges and ratios were similar in all Arizona datasets. Irrigated land in Colorado decreased in DIL and CUI but increased in IAD and CDL. The agreement within the same type of dataset during different time periods was from 60% to 80% (R² from 0.35 to 0.72) in Arizona and from 50% to 80% (R² from 0.23 to 0.68) in Colorado. DIL had the highest agreement (80%) in both states. The agreement among different datasets acquired at approximately the same time frame ranged from 51% to 63% (R² from 0.14 to 0.31) in Arizona and from 47% to 69% (R² from 0.32 to 0.40) in Colorado. The results from this study support a greater understanding of the multiresolution and multitemporal nature of these datasets for various applications.     

Effects of Recent Volcanic Eruptions on Aquatic Habitat in the Drift River, Alaska, USA: Implications at Other Cook Inlet Region Volcanoes

/ Numerous drainages supporting productive salmon habitat are surrounded by active volcanoes on the west side of Cook Inlet in south-central Alaska. Eruptions have caused massive quantities of flowing water and sediment to enter the river channels emanating from glaciers and snowfields on these volcanoes. Extensive damage to riparian and aquatic habitat has commonly resulted, and benthic macroinvertebrate and salmonid communities can be affected. Because of the economic importance of Alaska's fisheries, detrimental effects on salmonid habitat can have significant economic implications. The Drift River drains glaciers on the northern and eastern flanks of Redoubt Volcano. During and following eruptions in 1989-1990, severe physical disturbances to the habitat features of the river adversely affected the fishery. Frequent eruptions at other Cook Inlet region volcanoes exemplify the potential effects of volcanic activity on Alaska's important commercial, sport, and subsistence fisheries. Few studies have documented the recovery of aquatic habitat following volcanic eruptions. The eruptions of Redoubt Volcano in 1989-1990 offered an opportunity to examine the recovery of the macroinvertebrate community. Macroinvertebrate community composition and structure in the Drift River were similar in both undisturbed and recently disturbed sites. Additionally, macroinvertebrate samples from sites in nearby undisturbed streams were highly similar to those from some Drift River sites. This similarity and the agreement between the Drift River macroinvertebrate community composition and that predicted by a qualitative model of typical macroinvertebrate communities in glacier-fed rivers indicate that the Drift River macroinvertebrate community is recovering five years after the disturbances associated with the most recent eruptions of Redoubt Volcano. KEY WORDS: Aquatic habitat; Volcanoes; Lahars; Lahar-runout flows; Macroinvertebrates; Community structure; Community composition; Taxonomic similarity     

基于文献计量分析的珊瑚礁生态修复技术研究现状及发展趋势

珊瑚礁是海洋生态系统的重要组成部分,具有食物资源、药用价值、海岸防护等生态功能.近年来受到自然气候及人类活动的影响,珊瑚礁生态系统发生了大规模的退化,因此,珊瑚礁生态修复技术逐渐成为研究热点.本文从Web of Science核心合集、CNKI、德温特数据库等获得相关文献,对全球及国内珊瑚礁生态修复技术研究热点进行全面...     

Validating graph-based connectivity models with independent presence–absence and genetic data sets

Habitat connectivity is a key objective of current conservation policies and is commonly modeled by landscape graphs (i.e., sets of habitat patches [nodes] connected by potential dispersal paths [links]). These graphs are often built based on expert opinion or species distribution models (SDMs) and therefore lack empirical validation from data more closely reflecting functional connectivity. Accordingly, we tested whether landscape graphs reflect how habitat connectivity influences gene flow, which is one of the main ecoevolutionary processes. To that purpose, we modeled the habitat network of a forest bird (plumbeous warbler [Setophaga plumbea]) on Guadeloupe with graphs based on expert opinion, Jacobs’ specialization indices, and an SDM. We used genetic data (712 birds from 27 populations) to compute local genetic indices and pairwise genetic distances. Finally, we assessed the relationships between genetic distances or indices and cost distances or connectivity metrics with maximum-likelihood population-effects distance models and Spearman correlations between metrics. Overall, the landscape graphs reliably reflected the influence of connectivity on population genetic structure; validation R² was up to 0.30 and correlation coefficients were up to 0.71. Yet, the relationship among graph ecological relevance, data requirements, and construction and analysis methods was not straightforward because the graph based on the most complex construction method (species distribution modeling) sometimes had less ecological relevance than the others. Cross-validation methods and sensitivity analyzes allowed us to make the advantages and limitations of each construction method spatially explicit. We confirmed the relevance of landscape graphs for conservation modeling but recommend a case-specific consideration of the cost-effectiveness of their construction methods. We hope the replication of independent validation approaches across species and landscapes will strengthen the ecological relevance of connectivity models.     

Effects of climate and land-cover change on the conservation status of gibbons

Species shift their distribution in response to climate and land-cover change, which may result in a spatial mismatch between currently protected areas (PAs) and priority conservation areas (PCAs). We examined the effects of climate and land-cover change on potential range of gibbons and sought to identify PCAs that would conserve them effectively. We collected global gibbon occurrence points and modeled (ecological niche model) their current and potential 2050s ranges under climate-change and different land-cover-change scenarios. We examined change in range and PA coverage between the current and future ranges of each gibbon species. We applied spatial conservation prioritization to identify the top 30% PCAs for each species. We then determined how much of the PCAs are conserved in each country within the global range of gibbons. On average, 31% (SD 22) of each species’ current range was covered in PAs. PA coverage of the current range of 9 species was <30%. Nine species lost on average 46% (SD 29) of their potential range due to climate change. Under climate-change with an optimistic land-cover-change scenario (B1), 12 species lost 39% (SD 28) of their range. In a pessimistic land-cover-change scenario (A2), 15 species lost 36% (SD 28) of their range. Five species lost significantly more range under the A2 scenario than the B1 scenario (p = 0.01, SD 0.01), suggesting that gibbons will benefit from effective management of land cover. PA coverage of future range was <30% for 11 species. On average, 32% (SD 25) of PCAs were covered by PAs. Indonesia contained more species and PCAs and thus has the greatest responsibility for gibbon conservation. Indonesia, India, and Myanmar need to expand their PAs to fulfill their responsibility to gibbon conservation. Our results provide a baseline for global gibbon conservation, particularly for countries lacking gibbon research capacity.     

Boosting large-scale river connectivity restoration by planning for the presence of unrecorded barriers

Conservation decisions are invariably made with incomplete data on species’ distributions, habitats, and threats, but frameworks for allocating conservation investments rarely account for missing data. We examined how explicit consideration of missing data can boost return on investment in ecosystem restoration, focusing on the challenge of restoring aquatic ecosystem connectivity by removing dams and road crossings from rivers. A novel way of integrating the presence of unmapped barriers into a barrier optimization model was developed and applied to the U.S. state of Maine to maximize expected habitat gain for migratory fish. Failing to account for unmapped barriers during prioritization led to nearly 50% lower habitat gain than was anticipated using a conventional barrier optimization approach. Explicitly acknowledging that data are incomplete during project selection, however, boosted expected habitat gains by 20–273% on average, depending on the true number of unmapped barriers. Importantly, these gains occurred without additional data. Simply acknowledging that some barriers were unmapped, regardless of their precise number and location, improved conservation outcomes. Given incomplete data on ecosystems worldwide, our results demonstrate the value of accounting for data shortcomings during project selection.     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.