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Animals can be important in nutrient cycling in particular ecosystems, but few studies have examined how this importance varies along environmental gradients. In this study we quantified the nutrient cycling role of an abundant detritivorous fish species, the gizzard shad (Dorosoma cepedianum), in reservoir ecosystems along a gradient of ecosystem productivity. Gizzard shad feed mostly on sediment detritus and excrete sediment-derived nutrients into the water column, thereby mediating a cross-habitat translocation of nutrients to phytoplankton. We quantified nitrogen and phosphorus cycling (excretion) rates of gizzard shad, as well as nutrient demand by phytoplankton, in seven lakes over a four-year period (16 lake-years). The lakes span a gradient of watershed land use (the relative amounts of land used for agriculture vs. forest) and productivity. As the watersheds of these lakes became increasingly dominated by agricultural land, primary production rates, lake trophic state indicators (total phosphorus and chlorophyll concentrations), and nutrient flux through gizzard shad populations all increased. Nutrient cycling by gizzard shad supported a substantial proportion of primary production in these ecosystems, and this proportion increased as watershed agriculture (and ecosystem productivity) increased. In the four productive lakes with agricultural watersheds (>78% agricultural land), gizzard shad supported on average 51% of phytoplankton primary production (range 27-67%). In contrast, in the three relatively unproductive lakes in forested or mixed-land-use watersheds (>47% forest, <52% agricultural land), gizzard shad supported 18% of primary production (range 14-23%). Thus, along a gradient of forested to agricultural landscapes, both watershed nutrient inputs and nutrient translocation by gizzard shad increase, but our data indicate that the importance of nutrient translocation by gizzard shad increases more rapidly. Our results therefore support the hypothesis that watersheds and gizzard shad jointly regulate primary production in reservoir ecosystems.  相似文献   
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Extant sirenians are restricted to warm waters, presumably due to their low metabolism and poor thermoregulatory capacity, including thin blubber. When subjected to winter waters, Florida manatees (Trichechus manatus latirostris) migrate to warm areas, but dugongs (Dugong dugon) do not and instead live year-round in winter waters as cool as 15–18 °C. Dugongs appear to be more active than manatees and may have higher metabolic rates, but little is known about thermal energetics or the insulative properties of their integument. This study investigated the physical and thermal properties of whole samples of dugong integument, i.e. epidermis, dermis and hypodermis (blubber) sampled from fresh dugong carcasses collected from 2004 to 2012 in Moreton Bay (27.21°S, 153.25°E). Physico-chemical properties (thickness, density and lipid content) of each component tissue layer were measured. Thermal conductance (C) and conductivity (k) were measured for each tissue layer through in vitro temperature flux experiments within an insulated chamber. C and k were higher for dermis (25.7 ± 1.2 W m?2 K?1, 0.43 ± 0.02 W m?1 K?1, respectively, n = 21) than blubber (24.3 ± 2.4 W m?2 K?1, 0.31 ± 0.01 W m?1 K?1, n = 21), suggesting that blubber, with higher density and lipid content, affords better insulation. However, because the dermis contributes 65 % of integumentary thickness, both layers contribute significantly to insulation. The integument of dugongs is a poorer insulator compared to many cold-water marine mammals, but the greater thickness of its dermal layer means that despite its relatively thin blubber, its integumentary insulation is similar to warm-water dolphins of similar body size.  相似文献   
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Regional Environmental Change - This study examines the potential for ecological engineering to enhance the beneficial ecosystem services provided by birds in tropical rice fields. Bird activities...  相似文献   
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The objective is to summarize the current use of artificial intelligence (AI) in obstetric ultrasound. PubMed, Cochrane Library, and ClinicalTrials.gov databases were searched using the following keywords “neural networks”, OR “artificial intelligence”, OR “machine learning”, OR “deep learning”, AND “obstetrics”, OR “obstetrical”, OR “fetus”, OR “foetus”, OR “fetal”, OR “foetal”, OR “pregnancy”, or “pregnant”, AND “ultrasound” from inception through May 2022. The search was limited to the English language. Studies were eligible for inclusion if they described the use of AI in obstetric ultrasound. Obstetric ultrasound was defined as the process of obtaining ultrasound images of a fetus, amniotic fluid, or placenta. AI was defined as the use of neural networks, machine learning, or deep learning methods. The authors’ search identified a total of 127 papers that fulfilled our inclusion criteria. The current uses of AI in obstetric ultrasound include first trimester pregnancy ultrasound, assessment of placenta, fetal biometry, fetal echocardiography, fetal neurosonography, assessment of fetal anatomy, and other uses including assessment of fetal lung maturity and screening for risk of adverse pregnancy outcomes. AI holds the potential to improve the ultrasound efficiency, pregnancy outcomes in low resource settings, detection of congenital malformations and prediction of adverse pregnancy outcomes.  相似文献   
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