Time Series and Postglacial Forest Ecology

1981 ◽  
Vol 15 (3) ◽  
pp. 265-277 ◽  
Author(s):  
David G. Green
Keyword(s):  
Time Series ◽  
Forest Ecology ◽  
Forest Succession ◽  
Fire Frequency ◽  
Forest Composition ◽  
Mixed Composition ◽  
Individual Tree ◽  
Tree Communities ◽  
Pollen Records

AbstractForest ecology suffers from a lack of long-term community records. Preserved pollen data are richer in such information than is generally realized. By applying suitable statistical techniques to pollen records, one can learn much about competition, succession, and population dynamics in past tree communities. In this study, preserved pollen records from Everitt Lake, Nova Scotia, are analyzed as time series. Time domain studies reveal the post-fire responses of individual tree taxa. Correlograms yield models of past forest succession patterns. The models explain some effects of changing fire frequency, thus suggesting mechanisms by which fire, competition, and climate combine to produce long-term forest composition changes. Frequency domain studies suggest relationships between disturbance cycles, stand composition, and forest mosaics. Fire frequencies are seen to be highest where fire-dependent species abound and most regular where tree stands have uniform, not mixed, composition.

scholarly journals Postglacial Fire Frequency and its Relation to Long Term Vegetational and Climatic Changes in Yellowstone Park

1991 ◽  
Vol 15 ◽  
pp. 276-279
Author(s):  
Cathy Whitlock ◽  
Sarah Millspaugh
Keyword(s):  
Environmental Change ◽  
Climate Changes ◽  
Fire Frequency ◽  
Climatic Changes ◽  
Term Effect ◽  
Environmental Perturbations ◽  
Long Term Effect ◽  
Primary Agent ◽  
Pollen Records

The paleoecologic record provides unique insights into the response of coinmunities to environmental perturbations of different duration and intensity. Climate is a primary agent of environmental change and its long-term effect on the vegetation of the Yellowstone region is revealed in a network of pollen records. Fire frequency is controlled by climate, and as climate changes so too does the importance of fire in shaping and maintaining spatia\l patterns of vegetation. The prehistoric record of Yellowstone's northern range, for example, shows the response of vegetation to an absence of major fires in the last 150 years (Whitlock et al. 1991; Engstrom et al. 1991). In longer records spanning the last 14,000 year8, periods of frequent fires are suggested • by sediments containing high percentages of fire-adapted trees and high amounts of charcoal (Bamosky et al. 1987).

Modeling the long-term effects of disturbances on forest succession, Olympic Peninsula, Washington

1986 ◽  
Vol 16 (1) ◽  
pp. 56-67 ◽  
Author(s):  
Virginia H. Dale ◽  
Miles Hemstrom ◽  
Jerry Franklin
Keyword(s):  
Leaf Area ◽  
Pacific Northwest ◽  
Forest Succession ◽  
Douglas Fir ◽  
Western Hemlock ◽  
Forest Composition ◽  
Silver Fir ◽  
Long Term Effects ◽  
Olympic Peninsula

A model of forest development has been adapted for the Pacific Northwest. The regeneration, growth, and death of individual trees are tracked for simulated 0.2 ha plots and tree attributes are aggregated to provide stand measures. The model includes the influence of temperature, soil moisture, light tolerance, and competition on tree growth. Long-term simulations for Douglas-fir dominated forests on the western Olympic Peninsula show that the stand is eventually dominated by western hemlock with silver fir being codominant. Even after 1200 years of subsequent stand development, silver fir fails to replace western hemlock indicating that this is a self-replicating and stable community. Fire, windthrows, insect disturbance, and clear-cut logging followed by replanting are incorporated into the model as single-event disturbances to a 500-year-old forest. For those cases where large Douglas-fir survive the disturbance, stand biomass and leaf area patterns are not significantly impacted until the death of the last large Douglas-fir. The projections were all carried out to the time when the forest is dominated by western hemlock and silver fir. At that time, the differential effect of the earlier disturbance is not apparent from the forest composition, biomass, or leaf area patterns except for the insect disturbance. Following the removal of all Douglas-fir by an insect, leaf area fluctuates regularly with a period of 600 years.

Predicting long-term sapling dynamics and canopy recruitment in northern hardwood forests

2011 ◽  
Vol 41 (5) ◽  
pp. 903-919 ◽  
Author(s):  
Jacob J. Hanson ◽  
Craig G. Lorimer ◽  
Corey R. Halpin
Keyword(s):  
Growth Rates ◽  
Forest Composition ◽  
Calibration Data ◽  
Gap Size ◽  
Tree Model ◽  
Individual Tree ◽  
Northern Hardwood ◽  
Wide Range ◽  
Sapling Density

Prediction of forest composition and structure over multiple generations of trees is often hampered by limited data on understory tree dynamics and the highly variable process of canopy recruitment in forest openings. In this paper, we describe a model of sapling dynamics and overstory recruitment for CANOPY, a spatially explicit, crown-based, individual-tree model of gap dynamics. The model incorporates gap size as a predictor of sapling recruitment and height growth, and it mimics the processes of sapling release, gap capture, and lateral gap closure. Calibration data were derived from 12 data sets with a wide range of stand ages and disturbance history in northern hardwood stands in the Great Lakes region, USA. The model accounted for 30%–62% of the variation in sapling density, composition, and growth rates. Predicted effects of increasing gap size on growth rate were similar to observed trends. Growth equations that included gap size as an independent variable generally gave better predictions of sapling density, species composition, and growth rates than equations based on conventional plot-level competition metrics. Long-term, 1000-year simulations produced estimates of stand basal areas and tree density in each size class that are close to the mean observed values for old-growth stands in the region.

Browse selection by beaver: effects on riparian forest composition

1990 ◽  
Vol 20 (7) ◽  
pp. 1036-1043 ◽  
Author(s):  
Carol A. Johnston ◽  
Robert J. Naiman
Keyword(s):  
Forest Succession ◽  
Riparian Forest ◽  
Basal Area ◽  
Forest Composition ◽  
Maximum Diameter ◽  
Long Term Effects ◽  
Selective Foraging ◽  
Biomass Structure ◽  
Preferred Species

Beaver (Castorcanadensis) herbivory has both immediate and long-term effects on biomass, structure, and composition of riparian forests. Intense beaver foraging of trembling aspen (Populustremuloides Michx.) decreased tree density and basal area by as much as 43% within ~ 1-ha forage zones surrounding two beaver ponds in northern Minnesota. Maximum diameter of trees cut was 43.5 cm; average aspen stem diameter cut was 13.9 and 10.2 cm at the two ponds. Woody biomass harvested per beaver averaged 1.4 Mg•ha−1•year−1 over a 6-year foraging period. Most wood harvested was left on site or used in dam construction, rather than consumed. Selective foraging by beaver decreased the relative importance of preferred species (i.e., P. tremuloides) and increased the importance of avoided species (i.e., Alnusrugosa (Du Roi) Spreng., Piceaglauca (Moench) Voss), with long-term implications to forest succession and dynamics.

scholarly journals Decadal changes in fire frequencies shift tree communities and functional traits

2020 ◽  
Author(s):  
Adam F. A. Pellegrini ◽  
Tyler Refsland ◽  
Colin Averill ◽  
César Terrer ◽  
A. Carla Staver ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Fire History ◽  
Vegetation Type ◽  
Basal Area ◽  
Fire Regimes ◽  
Fire Frequency ◽  
Tree Communities ◽  
In Fire ◽  
The Impact

Global change has resulted in chronic shifts in fire regimes, increasing fire frequency in some regions and decreasing it in others. Predicting the response of ecosystems to changing fire frequencies is challenging because of the multi-decadal timescales over which fire effects emerge and the variability in environmental conditions, fire types, and plant composition across biomes. Here, we address these challenges using surveys of tree communities across 29 sites that experienced multi-decadal alterations in fire frequencies spanning ecosystems and environmental conditions. Relative to unburned plots, more frequently burned plots had lower tree basal area and stem densities that compounded over multiple decades: average fire frequencies reduced basal area by only 4% after 16 years but 57% after 64 years, relative to unburned plots. Fire frequency had the largest effects on basal area in savanna ecosystems and in sites with strong wet seasons. Analyses of tree functional-trait data across North American sites revealed that frequently burned plots had tree communities dominated by species with low biomass nitrogen and phosphorus content and with more efficient nitrogen acquisition through ectomycorrhizal symbioses (rising from 85% to nearly 100%). Our data elucidate the impact of long-term fire regimes on tree community structure and composition, with the magnitude of change depending on climate, vegetation type, and fire history. The effects of widespread changes in fire regimes underway today will manifest in decades to come and have long-term consequences for carbon storage and nutrient cycling.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

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