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1.
The hunter-fly genus Limnophora (Muscidae: Coenosiinae) is an important component of running water assemblages. Both adults and larvae are predators, the adults mostly feeding on blackflies (Simuliidae) and other small aquatic insects. This study was conducted at two tufa barriers in Plitvice Lakes National Park (NP) (tufa barrier Labudovac and tufa barrier Kozjak-Milanovac) and at two tufa barriers in Krka National Park (NP) (tufa barrier Roški slap and tufa barrier Skradinski buk). Adults were collected monthly from February 2007 until December 2013 at Plitvice Lakes NP and from September 2013 to October 2014 at Krka NP, using pyramid-type emergence traps. Over the 7-year study period at the Plitvice Lakes tufa barriers, a total of 193 specimens belonging to 6 species were collected, while during the 1-year study period at the Krka tufa barriers a total of 848 specimens belonging to 4 species were collected. Abundance of Limnophora specimens at the Krka NP sites was up to 30 times higher than at the Plitvice Lakes NP sites, which could be attributed to higher levels of potential food and higher water temperatures. The dominant species at the Plitvice Lakes sites were Limnophora pulchriceps and Limnophora riparia. At the Krka tufa barriers, the dominant species at site Roški slap was Limnophora croatica and at site Skradinski buk Limnophora riparia. The highest numbers of emerging specimens at all sites were present in the summer months. At the Plitvice Lakes sites most species were univoltine or bivoltine, while at the Krka sites most species were multivoltine. Water temperature was the main factor influencing the timing of emergence and the duration of the flight period. The highest abundance of Limnophora species was recorded over moss substrate. A significant positive correlation emerged between the numbers of emerging prey and the numbers of emerging specimens of Limnophora. These results give a new insight into the microhabitat preferences and prey-predator relationships of Limnophora.  相似文献   
2.
植被动力学模式中物候方案的研究进展   总被引:3,自引:0,他引:3  
植物物候是指植物生长过程中呈现出的季节性现象,一般与植物所处的气候与环境变化密切相关。植被动力学模式研究的物候主要表现为叶面积指数变化,直接影响陆气间的碳通量与水热交换,同时影响物种间的竞争,从而间接地影响生态系统的结构组成。按照建模方法的差别,目前模式中使用的物候方案可分为使用卫星观测资料的物候方案、基于物候——气候关系的统计模型和基于叶碳平衡(周转)的动力学模型三大类。将植物物候分为物候期的触发和物候期叶片的发育过程两部分,分别对国际上广泛使用的八种全球植被动力学模式进行分类描述,对比其优缺点。最后探讨了植被动力学模式中物候方案的进一步发展方向。  相似文献   
3.
Neutron probe soil moisture measurements obtained biweekly during the growing season between 1982 and 1991 from multiple depths under grass-covered plots at 17 Illinois Climate Network sites are used to forecast crop yields. A Soil Moisture Index (SMIX) that combines the effect of intensity, duration, and timing of drought or excessively wet conditions was computed by integrating the quantity of available soil moisture throughout the rooting zone over the growing season. Relationships between the SMIX values and crop yields are evaluated at county, regional, and statewide scales. Coefficients of determination (r 2) for relationships between the SMIX values and maize, soybeans, and hay yields at the statewide level are 0.88, 0.74, and 0.81, respectively, when the period of integration is terminated at the end of the growing season. This new soil index can be employed to forecast yields as early as 12 weeks before harvest for the state of Illinois. However, predictions with RMSE ≤ 10% of the mean yield can be achieved only for SMIX integration periods ending 5, 9, and 6 weeks before harvest for maize, soybeans, and hay, respectively. Nomograms are presented for using the relationships between the SMIX values and crop yields to forecast Illinois's major crops well before harvest.  相似文献   
4.
Aquatic dance flies (Empididae; Clinocerinae and Hemerodromiinae) are important components of freshwater assemblages, especially in running waters. They are predators as larvae and adults and thus essential for understanding aquatic food webs. This study was conducted in Plitvice lakes National Park (Croatia) representing a wide variety of freshwater habitats (springs, streams, lakes and tufa barriers). Adults were collected monthly from March 2007 until March 2009 using pyramid-type emergence traps at 13 locations. A total of 3865 specimens comprising 18 species were collected. The dominant genus was Chelifera, while the most abundant species was Hemerodromia unilineata. All species were univoltine except Chelifera precabunda, Chelifera pyrenaica and Chelifera stigmatica that were bivoltine. Considerable differences in composition and structure of aquatic dance flies assemblages were recorded along a longitudinal gradient of studied sites, primarily related to differences in physical and chemical parameters of water. Water temperature was the main factor influencing the timing of emergence. Hemerodromia species preferred variable water temperature throughout the year while the majority of the Chelifera species preferred stable water temperature characteristic of spring sites. Furthermore, discharge affected assemblage composition of aquatic dance flies. The highest abundance of aquatic dance flies was recorded in lotic habitats with fast water current over substrates of moss, gravel and particulate tufa with detritus. These results give a new insight on microhabitat preference and their distribution on unique karstic habitats.  相似文献   
5.
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.  相似文献   
6.
浮游植物水华作为近海重要的生物过程,其动态变化对生态系统内的能童传递、生产力水平和各生源要素的循环等均有重要影响.随着气候变化对生态系统影响研究的深入,浮游植物水华生物气候学研究已成为当前生物海洋学研究的热点.综述了浮游植物水华的研究历史、研究方法及其发生发展的动力学机制,重点评述了气候变化对浮游植物水华动态的影响及国...  相似文献   
7.
National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux between land and atmosphere. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale as well as national and continental scales. Existing satellite-based NPP products tend to underestimate NPP on croplands. An Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP over large multi-state regions. The method is documented here and evaluated for corn (Zea mays L.) and soybean (Glycine max L. Merr.) in Iowa and Illinois in 2006 and 2007. The method includes a crop-specific Enhanced Vegetation Index (EVI), shortwave radiation data estimated using the Mountain Climate Simulator (MTCLIM) algorithm, and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that corresponds to the Cropland Data Layer (CDL) land cover product. Results from the modeling framework captured the spatial NPP gradient across croplands of Iowa and Illinois, and also represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 917 g C m−2 yr−1 and 409 g C m−2 yr−1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Site comparisons with flux tower data show AgI-LUE NPP in close agreement with tower-derived NPP, lower than inventory-based NPP, and higher than MOD17A3 NPP. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.  相似文献   
8.
通过对物候资料与气象资料的对比分析找出与农业界限温度对应的物候指标,以物候指标为依据,就可掌握农业四季的初终期和农业界限温度的出现时间,并以物候指标预告农时,简便易行,对指导农业生产有广泛的应用价值。  相似文献   
9.
The accurate and timely estimates of crop physiological growth stages are essential for efficient crop management and precise modeling of agricultural systems. Satellite remote sensing has been widely used to retrieve vegetation phenology metrics at local to global scales. However, most of these phenology metrics (e.g., green-up) are different from crop growth stages (e.g., emergence) used in crop management and modeling. As such, an integrated framework referred to as PhenoCrop was developed to: 1) establish a connection between remote sensing-derived phenology metrics and key crop growth stages based on Wang and Engle plant phenology model and 2) use fused MODIS-Landsat 30 m 8-day reflectance data generated using Kalman Filter-based data fusion technique to produce onset dates of key growth stages of corn (Zea mays L.) and soybeans (Glycine max L.) at 30 m spatial resolution. In this paper, we described the PhenoCrop framework, and tested its performance for the State of Nebraska for 2012–2016 by comparison to observations of estimated key growth stages at four experimental sites, and state-level statistical data from Crop Progress Reports (CPRs) published by the United States Department of Agriculture’s (USDA) National Agricultural Statistical Services (NASS). In addition, to evaluate the suitability of using coarse or high spatial resolution satellite imagery, fused MODIS-Landsat-based estimates were compared with those produced using EOS MODIS 250 m (MOD9Q1) reflectance data.The PhenoCrop estimates captured the typical spatial trends of gradual delay in the progression of the growing season from southeast to northwest Nebraska. Also inter-annual differences due to factors such as weather fluctuations and change in management strategies (e.g., early season in 2012) were evident in the estimates. Validation results revealed that average root mean square error (RMSE) of the state-level estimates of corn and soybean growth stages ranged from 1.10 to 4.20 days and from 3.81 to 7.89 days, respectively, while pixel level estimates had a RMSE ranging from 3.72 to 8.51 days for corn and 4.76–9.51 days for soybean growth stages. Although MODIS 250 m based estimates showed similar general spatial patterns observed in the fused MODIS-Landsat based estimates, the accuracy and ability to capture field scale variations was improved with fused MODIS-Landsat data. Overall, results showed the ability of PhenoCrop framework to provide reliable estimates of crop growth stages that can be highly useful in crop modeling and crop management during the growing season.  相似文献   
10.
Vegetation phenology is a sensitive indicator that reflects the vegetation–atmosphere interactions and vegetation processes under global atmospheric changes. Fast-developing remote sensing technologies that monitor the land surface at high spatial and temporal resolutions have been widely used in vegetation phenology retrieval and analysis at a large scale. While researchers have developed many phenology retrieving methods based on remote sensing data, the relationships and differences among the phenology retrieving methods are unclear, and there is a lack of evaluation and comparison with the field phenology recoding data. In this study, we evaluated and compared eight phenology retrieving methods using Moderate Resolution Imaging Spectroradiometer (MODIS) and the USA National Phenology Network data from across North America. The studied phenology retrieving methods included six commonly used rule-based methods (i.e., amplitude threshold, the first-order derivative, the second-order derivative, the third-order derivative, the relative change curvature, and the curvature change rate) and two newly developed machine learning methods (i.e., neural network and random forest). At the large scale, the start of the season (SOS) values, derived by all methods, had similar spatial distributions; however, the retrieved values had large uncertainties in each pixel, and the end of the season (EOS) inverted values were largely different among methods. At the site scale, the SOS and EOS values extracted by the rule-based methods all had significant positive correlations with the field phenology observations. Among the rule-based methods, the amplitude threshold method performed the best. The machine learning methods outperformed the rule-based methods in terms of retrieving the SOS when assessed using the field observations. Our study highlighted that there were large differences among the methods in retrieving the vegetation phenology from satellite data and that researchers must be cautious in selecting an appropriate method for analyzing the satellite-retrieved phenology. Our results also demonstrated the importance of field phenology observations and the usefulness of the machine learning methods in understanding the satellite-based land surface phenology. These findings provide a valuable reference for the future development of global and regional phenology products.  相似文献   
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