Does spatial distribution of tree size account for spatial variation in soil respiration in a tropical forest?
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Plant litter
Litter
Soil respiration
Tropical forest
Although significant advances have been made in understanding terrestrial carbon cycling, there is still a large uncertainty about the variability of carbon (C) fluxes at local scales. Using a carbon mass-balance approach, I investigated the relationships between fine detritus production and soil respiration for five tropical tree species established on 16-year-old plantations. Total fine detritus production ranged from 0.69 to 1.21 kg C·m –2 ·year –1 with significant differences among species but with no correlation between litterfall and fine-root growth. Soil CO 2 emissions ranged from 1.61 to 2.36 kg C·m –2 ·year –1 with no significant differences among species. Soil respiration increased with fine-root production but not with litterfall, suggesting that soil C emissions may depend more on belowground inputs or that both fine root production and soil respiration are similarly influenced by an external factor. Estimates of root + rhizosphere respiration comprised 52% of total soil respiration on average, and there was no evidence that rhizosphere respiration was associated with fine-root growth rates among species. These results suggest that inherent differences in fine-root production among species, rather than differences in aboveground litterfall, might play a main role explaining local-scale, among-forest variations in soil C emissions.
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Soil carbon
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Intertidal ecology
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A central question in ecology is explaining spatial variation in the abundance of species. Patterns of spatial variation in abundance can often be explained by spatial variation in the environment, including resource availability, climatic variables, and other factors that influence a species' reproduction and survival. We show that spatial patterns in abundance may also be driven by temporal environmental variation, in the absence of any fixed spatial environmental variation. To illustrate this, we build on work by J. H. Brown, D. W. Mehlman, and G. C. Stevens, who demonstrated spatial patterns in bird abundances that can be explained by fixed spatial variation in the environment. Using a pair of simple stochastic models of bird population dynamics, we show that similar patterns can be generated through temporal environmental variation that has no fixed spatial com- ponent. This occurs when population dynamics are characterized by very weak density dependence, so that population densities exhibit near-random-walk behavior. Because sim- ilar patterns of spatial variation in species' abundances can be produced by either fixed spatial environmental variation or spatiotemporal environmental variation, we argue that interpreting spatial variation in abundance may sometimes require understanding temporal variation in abundance.
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Understanding spatial and temporal variability patterns of crop yield and their relationship with soil properties can provide decision support to optimize crop management. The objectives of this study were to (1) determine the spatial and temporal variability of cotton (Gossypium hirsutum L.) lint yield over different growing seasons; (2) evaluate the relationship between spatial and temporal yield patterns and apparent soil electrical conductivity (ECa). This study was conducted in eight production fields, six with 50 ha and two with 25 ha, on the Southern High Plains (SHP) from 2000 to 2003. Cotton yield and ECa data were collected using a yield monitor and an ECa mapping system, respectively. The amount and pattern of spatial and temporal yield variability varied with the field. Fields with high variability in ECa exhibited a stronger association between spatial and temporal yield patterns and ECa, indicating that soil properties related to ECa were major factors influencing yield variability. The application of ECa for site-specific management is limited to fields with high spatial variability and with a strong association between yield spatial and temporal patterns and ECa variation patterns. For fields with low variability in yield, spatial and temporal yield patterns might be more influenced by weather or other factors in different growing seasons. Fields with high spatial variability and a clear temporal stability pattern have great potential for long-term site-specific management of crop inputs. For unstable yield, however, long-term management practices are difficult to implement. For these fields with unstable yield patterns, within season site-specific management can be a better choice. Variable rate application of water, plant growth regulators, nitrogen, harvest aids may be implemented based on the spatial variability of crop growth conditions at specific times.
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From published data of mature forests worldwide, Raich and Nadelhoffer suggested that total belowground carbon allocation (TBCA) could be estimated from the difference between annual rates of soil respiration and aboveground litterfall. Here we analyze new measurements of IRGA-based soil respiration and litterfall of natural mature oak forests dominated by Quercus mongolica in Korea. Rates of in situ soil respiration and aboveground litter production are highly and positively correlated. Our results disagree with the Raich and Nadelhoffer model far world forests. A regression analysis of the data from Q. mongolica forests produced the following relationship: annual soil respiration : 141 + 2.08 annual litterfall. The least squares regression line has a more gentle slope (2.08) than the slope (2.92) described by Raich and Nedelhoffer for mature forests worldwide. The regression slope of our study indicates that, on average, soil respiration is about two times the aboveground litterfall-C, which further implies that TBCA is similar with annual aboveground litterfall-C at natural Q. mongolica forests in Korea. The non-zero Y-intercept (141) of the regression indicates that TBCA may be greater than litterfall-C where litterfall rate are relativery low. Over a gradient of litterfall-C ranging from 200-370 g C , TBCA increased from 350-530 g C .
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Soil respiration
Plant litter
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Soil carbon
Terrestrial ecosystem
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A central question in ecology is explaining spatial variation in the abundance of species. Patterns of spatial variation in abundance can often be explained by spatial variation in the environment, including resource availability, climatic variables, and other factors that influence a species' reproduction and survival. We show that spatial patterns in abundance may also be driven by temporal environmental variation, in the absence of any fixed spatial environmental variation. To illustrate this, we build on work by J. H. Brown, D. W. Mehlman, and G. C. Stevens, who demonstrated spatial patterns in bird abundances that can be explained by fixed spatial variation in the environment. Using a pair of simple stochastic models of bird population dynamics, we show that similar patterns can be generated through temporal environmental variation that has no fixed spatial component. This occurs when population dynamics are characterized by very weak density dependence, so that population densities exhibit near-random-walk behavior. Because similar patterns of spatial variation in species' abundances can be produced by either fixed spatial environmental variation or spatiotemporal environmental variation, we argue that interpreting spatial variation in abundance may sometimes require understanding temporal variation in abundance.
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A central question in ecology is explaining spatial variation in the abundance of species. Patterns of spatial variation in abundance can often be explained by spatial variation in the environment, including resource availability, climatic variables, and other factors that influence a species’ reproduction and survival. We show that spatial patterns in abundance may also be driven by temporal environmental variation, in the absence of any fixed spatial environmental variation. To illustrate this, we build on work by J. H. Brown, D. W. Mehlman, and G. C. Stevens, who demonstrated spatial patterns in bird abundances that can be explained by fixed spatial variation in the environment. Using a pair of simple stochastic models of bird population dynamics, we show that similar patterns can be generated through temporal environmental variation that has no fixed spatial component. This occurs when population dynamics are characterized by very weak density dependence, so that population densities exhibit near-random-walk behavior. Because similar patterns of spatial variation in species’ abundances can be produced by either fixed spatial environmental variation or spatiotemporal environmental variation, we argue that interpreting spatial variation in abundance may sometimes require understanding temporal variation in abundance.
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Spatio-temporal variation in European starling reproductive success at multiple small spatial scales
Understanding population dynamics requires spatio-temporal variation in demography to be measured across appropriate spatial and temporal scales. However, the most appropriate spatial scale(s) may not be obvious, few datasets cover sufficient time periods, and key demographic rates are often incompletely measured. Consequently, it is often assumed that demography will be spatially homogeneous within populations that lack obvious subdivision. Here, we quantify small-scale spatial and temporal variation in a key demographic rate, reproductive success (RS), within an apparently contiguous population of European starlings. We used hierarchical cluster analysis to define spatial clusters of nest sites at multiple small spatial scales and long-term data to test the hypothesis that small-scale spatio-temporal variation in RS occurred. RS was measured as the number of chicks alive ca. 12 days posthatch either per first brood or per nest site per breeding season (thereby incorporating multiple breeding attempts). First brood RS varied substantially among spatial clusters and years. Furthermore, the pattern of spatial variation was stable across years; some nest clusters consistently produced more chicks than others. Total seasonal RS also varied substantially among spatial clusters and years. However, the magnitude of variation was much larger and the pattern of spatial variation was no longer temporally consistent. Furthermore, the estimated magnitude of spatial variation in RS was greater at smaller spatial scales. We thereby demonstrate substantial spatial, temporal, and spatio-temporal variation in RS occurring at very small spatial scales. We show that the estimated magnitude of this variation depended on spatial scale and that spatio-temporal variation would not have been detected if season-long RS had not been measured. Such small-scale spatio-temporal variation should be incorporated into empirical and theoretical treatments of population dynamics.
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