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Institution

University of New Hampshire

EducationDurham, New Hampshire, United States
About: University of New Hampshire is a education organization based out in Durham, New Hampshire, United States. It is known for research contribution in the topics: Population & Solar wind. The organization has 9379 authors who have published 24025 publications receiving 1020112 citations. The organization is also known as: UNH.


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Journal ArticleDOI
TL;DR: In this paper, the effects of vegetation change on the depth and distribution of plant roots are examined and compared with other factors and their treatment in models, and it is shown that changes in vegetation may influence the distribution of soil carbon and nutrients over time.
Abstract: The extent and consequences of global land-cover and land-use change are increasingly apparent. One consequence not so apparent is the altered structure of plants belowground. This paper examines such belowground changes, emphasizing the interaction of altered root distributions with other factors and their treatment in models. Shifts of woody and herbaceous vegetation with deforestation, afforestation, and woody plant en- croachment typically alter the depth and distribution of plant roots, influencing soil nutrients, the water balance, and net primary productivity (NPP). For example, our analysis of global soil data sets shows that the major plant nutrients C, N, P, and K are more shallowly distributed than are Ca, Mg, and Na, but patterns for each element vary with the dominant vegetation type. After controlling for climate, soil C and N are distributed more deeply in arid shrublands than in arid grasslands, and subhumid forests have shallower nutrient dis- tributions than do subhumid grasslands. Consequently, changes in vegetation may influence the distribution of soil carbon and nutrients over time (perhaps decades to centuries). Shifts in the water balance are typically much more rapid. Catchment studies indicate that the water yield decreases 25-40 mm for each 10% increase in tree cover, and increases in transpiration of water taken up by deep roots may account for as much as 50% of observed responses. Because models are increasingly important for predicting the consequences of vegetation change, we discuss the treatment of belowground processes and how different treatments affect model outputs. Whether models are parameterized by biome or plant life form (or neither), use single or multiple soil layers, or include N and water limitation will all affect predicted outcomes. Acknowledging and understanding such differences should help constrain predictions of vegetation change.

360 citations

Journal ArticleDOI
TL;DR: The 3XMM-DR5 dataset as mentioned in this paper contains 565,962 X-ray detections from the European Space Agency X-Ray observatory XMM-Newton.
Abstract: Context. Thanks to the large collecting area (3 ×~1500 cm2 at 1.5 keV) and wide field of view (30′ across in full field mode) of the X-ray cameras on board the European Space Agency X-ray observatory XMM-Newton , each individual pointing can result in the detection of up to several hundred X-ray sources, most of which are newly discovered objects. Since XMM-Newton has now been in orbit for more than 15 yr, hundreds of thousands of sources have been detected. Aims. Recently, many improvements in the XMM-Newton data reduction algorithms have been made. These include enhanced source characterisation and reduced spurious source detections, refined astrometric precision of sources, greater net sensitivity for source detection, and the extraction of spectra and time series for fainter sources, both with better signal-to-noise. Thanks to these enhancements, the quality of the catalogue products has been much improved over earlier catalogues. Furthermore, almost 50% more observations are in the public domain compared to 2XMMi-DR3, allowing the XMM-Newton Survey Science Centre to produce a much larger and better quality X-ray source catalogue.Methods. The XMM-Newton Survey Science Centre has developed a pipeline to reduce the XMM-Newton data automatically. Using the latest version of this pipeline, along with better calibration, a new version of the catalogue has been produced, using XMM-Newton X-ray observations made public on or before 2013 December 31. Manual screening of all of the X-ray detections ensures the highest data quality. This catalogue is known as 3XMM.Results. In the latest release of the 3XMM catalogue, 3XMM-DR5, there are 565 962 X-ray detections comprising 396 910 unique X-ray sources. Spectra and lightcurves are provided for the 133 000 brightest sources. For all detections, the positions on the sky, a measure of the quality of the detection, and an evaluation of the X-ray variability is provided, along with the fluxes and count rates in 7 X-ray energy bands, the total 0.2–12 keV band counts, and four hardness ratios. With the aim of identifying the detections, a cross correlation with 228 catalogues of sources detected in all wavebands is also provided for each X-ray detection.Conclusions. 3XMM-DR5 is the largest X-ray source catalogue ever produced. Thanks to the large array of data products associated with each detection and each source, it is an excellent resource for finding new and extreme objects.

360 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed an interdisciplinary approach for analyzing tropical deforestation in the Brazilian Amazon and proposed an explanatory model, considering the relationship among deforestation and large scale social, economic, and institutional factors.
Abstract: In the Brazilian Amazon, regional trends are influenced by large scale external forces but mediated by local conditions. Tropical deforestation has a large influence on global hydrology, climate and biogeochemical cycles, but understanding is inadequate because of a lack of accurate measurements of rate, geographic extent and spatial patterns and lack of insight into its causes including interrelated social, economic and environmental factors. This article proposes an interdisciplinary approach for analyzing tropical deforestation in the Brazilian Amazon. The first part shows how deforestation can be measured from satellite remote sensing and sociodemographic and economic data. The second part proposes an explanatory model, considering the relationship among deforestation and large scale social, economic, and institutional factors. 43 refs., 8 figs.

359 citations

Journal ArticleDOI
TL;DR: Mental health and educational consequences of physical and sexual abuse by peers in a convenience sample of adolescents found dating violence was associated with higher levels of depression, suicidal thoughts, and poorer educational outcomes.
Abstract: Increasing attention has been given to the problem of teen dating violence with more research needed on mediating and moderating factors in the relationship between victimization and negative consequences. This article explores mental health and educational consequences of physical and sexual abuse by peers in a convenience sample of adolescents. Dating violence was associated with higher levels of depression, suicidal thoughts, and poorer educational outcomes. The use of alcohol and depression complicated the relationship between victimization and outcomes. Sex differences in patterns of perceived social support as a moderator were also examined with more significant effects for girls.

359 citations

Journal ArticleDOI
TL;DR: In this article, a synthesis of the greenhouse gas methane from lakes and ponds is presented in the boreal region and northwards of the United States, where almost half of these waters are located.
Abstract: Lakes and ponds represent one of the largest natural sources of the greenhouse gas methane. By surface area, almost half of these waters are located in the boreal region and northwards. A synthesis ...

359 citations


Authors

Showing all 9489 results

NameH-indexPapersCitations
Derek R. Lovley16858295315
Peter B. Reich159790110377
Jerry M. Melillo13438368894
Katja Klein129149987817
David Finkelhor11738258094
Howard A. Stone114103364855
James O. Hill11353269636
Tadayuki Takahashi11293257501
Howard Eichenbaum10827944172
John D. Aber10720448500
Andrew W. Strong9956342475
Charles T. Driscoll9755437355
Andrew D. Richardson9428232850
Colin A. Chapman9249128217
Nicholas W. Lukacs9136734057
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202351
2022183
20211,148
20201,128
20191,140
20181,089