Increased nitrogen in runoff and soil following 13 years of experimentally increased nitrogen deposition to a coniferous-forested catchment at Gårdsjön, Sweden

2006 ◽  
Vol 144 (2) ◽  
pp. 610-620 ◽  
Author(s):  
F. Moldan ◽  
O.J. Kjønaas ◽  
A.O. Stuanes ◽  
R.F. Wright
Keyword(s):  
Nitrogen Deposition ◽  
Forested Catchment

Negative effects of nitrogen deposition on Swiss butterflies

2021 ◽  
Author(s):  
Tobias Roth ◽  
Lukas Kohli ◽  
Beat Rihm ◽  
Reto Meier ◽  
Valentin Amrhein
Keyword(s):  
Nitrogen Deposition ◽  
Negative Effects

scholarly journals Selecting Biomonitors of Atmospheric Nitrogen Deposition: Guidelines for Practitioners and Decision Makers

Nitrogen ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 308-320
Author(s):  
D. Nayeli Martínez ◽  
Edison A. Díaz-Álvarez ◽  
Erick de la Barrera
Keyword(s):  
Environmental Pollution ◽  
Nitrogen Deposition ◽  
Biodiversity Loss ◽  
Decision Makers ◽  
Economic Losses ◽  
Atmospheric Nitrogen Deposition ◽  
Atmospheric Nitrogen ◽  
Nitrogen Emissions ◽  
Vascular Epiphytes ◽  
Ecophysiological Response

Environmental pollution is a major threat to public health and is the cause of important economic losses worldwide. Atmospheric nitrogen deposition is one of the most significant components of environmental pollution, which, in addition to being a health risk, is one of the leading drivers of global biodiversity loss. However, monitoring pollution is not possible in many regions of the world because the instrumentation, deployment, operation, and maintenance of automated systems is onerous. An affordable alternative is the use of biomonitors, naturally occurring or transplanted organisms that respond to environmental pollution with a consistent and measurable ecophysiological response. This policy brief advocates for the use of biomonitors of atmospheric nitrogen deposition. Descriptions of the biological and monitoring particularities of commonly utilized biomonitor lichens, bryophytes, vascular epiphytes, herbs, and woody plants, are followed by a discussion of the principal ecophysiological parameters that have been shown to respond to the different nitrogen emissions and their rate of deposition.

scholarly journals Environmental Drivers and Age Trends in Site Productivity for Oak in Southern Poland

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 209
Author(s):  
Luiza Tymińska-Czabańska ◽  
Jarosław Socha ◽  
Marek Maj ◽  
Dominika Cywicka ◽  
Xo Viet Hoang Duong
Keyword(s):  
Nitrogen Deposition ◽  
Management Practices ◽  
Additive Model ◽  
Climatic Conditions ◽  
Environmental Indicators ◽  
Environmental Drivers ◽  
Site Productivity ◽  
Site Factors ◽  
Wide Range ◽  
Si Model

Site productivity provides critical information for forest management practices and is a fundamental measure in forestry. It is determined using site index (SI) models, which are developed using two primary groups of methods, namely, phytocentric (plant-based) or geocentric (earth-based). Geocentric methods allow for direct site growth modelling, in which the SI is predicted using multiple environmental indicators. However, changes in non-static site factors—particularly nitrogen deposition and rising CO2 concentration—lead to an increase in site productivity, which may be visible as an age trend in the SI. In this study, we developed a geocentric SI model for oak. For the development of the SI model, we used data from 150 sample plots, representing a wide range of local topographic and site conditions. A generalized additive model was used to model site productivity. We found that the oak SI depended predominantly on physicochemical soil properties—mainly nitrogen, carbon, sand, and clay content. Additionally, the oak SI value was found to be slightly shaped by the topography, especially by altitude above sea level, and topographic position. We also detected a significant relationship between the SI and the age of oak stands, indicating the long-term increasing site productivity for oak, most likely caused by nitrogen deposition and changes in climatic conditions. The developed geocentric site productivity model for oak explained 77.2% of the SI variation.

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