The Dynamics of a Grassland Community: A Simultaneous Investigation of Spatial and Temporal Heterogeneity at Various Scales

Thórhallsdóttir, Thóra Ellen

doi:10.2307/2260941

Cited by 89 publications

(48 citation statements)

References 28 publications

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“…Our results confirm earlier observations (e.g., Thorhallsdottir 1990) suggesting that the impact of climate on yield and income variability is especially important at higher aggregation levels. At the farm type level, farm characteristics are more important.…”

Section: Scope and Methods Of Analysissupporting

confidence: 92%

Vulnerability and adaptation of European farmers: a multi-level analysis of yield and income responses to climate variability

et al. 2008

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Climate change will affect crop yields and consequently farmers' income. The underlying relationships are not well understood, particularly the importance of crop management and related factors at the farm and regional level. We analyze the impacts of trends and variability in climatic conditions from 1990 to 2003 on trends and variability in yields of five crops and farmers' income at farm type and regional level in Europe considering farm characteristics and other factors. While Mediterranean regions are often characterized as most vulnerable to climate change, our data suggest effective adaptation to variable and changing conditions in these regions largely attributable to the characteristic farm types in these regions. We conclude that for projections of climate change impacts on agriculture, farm characteristics influencing management and adaptation should be considered, as they largely influence the potential impacts.

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Section: Scope and Methods Of Analysissupporting

confidence: 92%

Vulnerability and adaptation of European farmers: a multi-level analysis of yield and income responses to climate variability

et al. 2008

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“…Parsons and others (2010) simulated fine scale variations in fuel characteristics in a small area using a computer model called FUEL3D, and then input the simulated fuel distributions into highly mechanistic computational fluid dynamics models to simulate fire behavior. Spatial variations of grasslands have been described in the context of population dynamics and restoration potential (Peters and others 2006;Thorhallsdottir 1990). Fuel loadings have been manipulated at fine scales (1 to 5 m) to investigate the influence of fine fuel mosaics on fire intensity and effects (Rocca 2009;Thaxton and Platt 2006).…”

Section: Fuel Variabilitymentioning

confidence: 99%

Spatial variability of wildland fuel characteristics in northern Rocky Mountain ecosystems

Keane¹,

Gray²,

Bacciu³

2012

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We investigated the spatial variability of a number of wildland fuel characteristics for the major fuel components found in six common northern Rocky Mountain ecosystems. Surface fuel characteristics of loading, particle density, bulk density, and mineral content were measured for eight fuel components-four downed dead woody fuel size classes (1, 10, 100, 1000 hr), duff, litter, shrub, and herb-on nested plots located within sampling grids to describe their variability across spatial scales. We also sampled canopy bulk density, biomass, and cover for each plot in the grid. The spatial distribution and variability of surface and canopy fuel characteristics are described using the variance, spatial autocorrelation, semi-variograms, and Moran's I. We found that all fuels had high variability in loading (two to three times the mean), and this variability increased with the size of fuel particle. We also found that fuel components varied at different scales, with fine fuels varying at scales of 1 to 5 m, coarse fuels at 10 to 150 m, and canopy fuels at 100 to 500 m. Findings and data from this study can be used to sample, describe, and map fuel characteristics, such as loading, at the appropriate spatial scales to accommodate the next generation of fire behavior prediction models.Keywords: wildland fire, biomass, landscape ecology, woody debris, spatial scale, mineral content, particle density, bulk density, loading AuthorsRobert E. Keane is a Research Ecologist with the U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station at the Missoula Fire Sciences Laboratory in Montana. Since 1985, Keane has developed various ecological computer models for research and management applications. His recent research includes (1) developing ecological computer models for exploring landscape, fire, and climate dynamics; (2) describing, classifying, and mapping fuel characteristics; (3) investigating the ecology and restoration of whitebark pine; and (4) conducting fundamental wildland fuel science. He

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“…The information theory used by Bartha et al is after Juhasz-Nagy (1984) and is related to BQC. Analyses that relate to the plant's-eye view are in bold Amarasinghe & Pemadasa (1982) 1.0 Transect of cells BQV, BQC Zamfir et al (1999) 1.0 Transect of cells DCA De Pablo et al (1982) 0.64 Transect of cells H(E.P) Mahdi & Law (1987) 0.28, 0.13, 0.03 Separate plant-centred areas Densities within areas Kershaw (1962) 0.25 Transect of cells BQV, BQC Gibson & Greig-Smith (1986) 0.25 Transect of cells BQV Gibson (1988) 0.25 Transect of cells TTLQV, 3TLQV, TTLCA, DCA Bartha et al (1995) 0.25 Transect of cells Information theory Edwards et al (1996) 0.25 Transect of cells TTLQV Stowe & Wade (1979) 0.16 Transect of points J Agnew & Gitay (1990) 0.16 Transect of cells TTLQV Kershaw (1958Kershaw ( , 1959 0.05 Transect of points BQV, interspecific correlations Kershaw & Tallis (1958) 0.05 Transect of points BQV, interspecific correlations Turkington & Harper (1979) 0.05 Transect of points BQC Galiano (1985Galiano ( , 1986 0.04 Transect of cells BQV Wilson et al (1987) 0.04 Transect of cells Variance in species richness Van der Hoeven et al (1990) 0.04 Grid of cells Lagged Moran's I Van Rijnberk & During (1990) 0.016 Grid of points Z-correlograms Watkins & Wilson (1992) 0.016 Grid of cells Variance in species richness Thórhallsdóttir (1990b) 0.0078 Transect of points J Law et al (1993) 0.0078 Grid of points Neighbour identities…”

Section:     mentioning

confidence: 99%

Fine‐scale spatial structure in a grassland community: quantifying the plant’s‐eye view

2002

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Summary 1The fine-scale spatial patterns of Agrostis stolonifera , Holcus lanatus and Lolium perenne were recorded in an English lowland grassland as presence/absence maps from 400-cell quadrats at two different scales (2 × 2 cm or 8 × 8 cm cells). 2 Local spatial structure in these patterns was quantified using spatial covariance functions. Distance-and direction-dependent components were examined separately for both intra-and interspecific patterns. The significance of departures from randomness was determined using Monte Carlo techniques. 3 The smaller-scale data showed that all three species were significantly aggregated, Agrostis to a greater distance (8 cm) than Holcus or Lolium ( 4 cm). The intensity of aggregation decreased in the order Lolium > Holcus > Agrostis . The larger-scale data suggested that this aggregation extended to greater distances, and that it was most intense in Agrostis . 4 Despite the lack of visual directionality in the environment, Agrostis showed a directional pattern at both scales, with Lolium varying in the same direction at the larger scale. 5 Only Agrostis and Lolium showed a significant interspecific relationship (segregated to 2 cm at the small scale, but aggregated to 8 cm at the larger scale). There was no evidence of directionality in the interspecific components of pattern. 6 The nature of spatial structure appears to depend on the scale of observation, but the smaller-scale data are more likely to provide a biologically interpretable measure of local spatial structure in this community.

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The Dynamics of a Grassland Community: A Simultaneous Investigation of Spatial and Temporal Heterogeneity at Various Scales

Cited by 89 publications

References 28 publications

Vulnerability and adaptation of European farmers: a multi-level analysis of yield and income responses to climate variability

Vulnerability and adaptation of European farmers: a multi-level analysis of yield and income responses to climate variability

Spatial variability of wildland fuel characteristics in northern Rocky Mountain ecosystems

Fine‐scale spatial structure in a grassland community: quantifying the plant’s‐eye view

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