55 research outputs found
Starvation resistance of gypsy moth, Lymantria dispar (L.) (Lepidoptera: Lymantriidae): tradeoffs among growth, body size, and survival
Survival and body composition of starving gypsy moth larvae initially reared on aspen foliage or artificial diet differeing in nitrogen (N) and carbohydrate concentration were examined under laboratory conditions. Diet nitrogen concentration strongly affected starvation resistance and body composition, but diet carbohydrate content had no effects on these. Within any single diet treatment, greater body mass afforded greater resistance to starvation. However, starving larvae reared on 1.5% N diet survived nearly three days longer than larvae reared on 3.5% N diet. Larvae reared on artificial diet survived longer than larvae reared on aspen. Differences in survival of larvae reared on artificial diet with low and high nitrogen concentrations could not be attributed to variation in respiration rates, but were associated with differences in body composition. Although percentage lipid in larvae was unaffected by diet nitrogen concentration, larvae reared on 1.5% N diet had a higher percentage carbohydrate and lower percentage protein in their bodies prior to starvation than larvae reared on 3.5% N diet. Hence, larger energy reserves of larvae reared on low nitrogen diet may have contributed to their greater starvation resistance. Whereas survival under food stress was lower for larvae reared on high N diets, growth rates and pupal weights were higher, suggesting a tradeoff between rapid growth and survival. Larger body size does not necessarily reflect larger energy reserves, and, in fact, larger body size accured via greater protein accumulation may be at the expense of energy reserves. Large, fast-growing larvae may be more fit when food is abundant, but this advantage may be severely diminished under food stress. The potential ecological and evolutionary implications of a growth/survival tradeoff are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47792/1/442_2004_Article_BF00317588.pd
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Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data
© 2015 Elsevier Inc. Spatially explicit predictions of fuel moisture content are crucial for quantifying fire danger indices and as inputs to fire behaviour models. Remotely sensed predictions of fuel moisture have typically focused on live fuels; but regional estimates of dead fuel moisture have been less common. Here we develop and test the spatial application of a recently developed dead fuel moisture model, which is based on the exponential decline of fine fuel moisture with increasing vapour pressure deficit (D). We first compare the performance of two existing approaches to predict D from satellite observations. We then use remotely sensed D, as well as D estimated from gridded daily weather observations, to predict dead fuel moisture. We calibrate and test the model at a woodland site in South East Australia, and then test the model at a range of sites in South East Australia and Southern California that vary in vegetation type, mean annual precipitation (129-1404mmyear-1) and leaf area index (0.1-5.7). We found that D modelled from remotely sensed land surface temperature performed slightly better than a model which also included total precipitable water (MAE<1.16kPa and 1.62kPa respectively). D calculated with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite was under-predicted in areas with low leaf area index. Both D from remotely sensed data and gridded weather station data were good predictors of the moisture content of dead suspended fuels at validation sites, with mean absolute errors less than 3.9% and 6.0% respectively. The occurrence of data gaps in remotely sensed time series presents an obstacle to this approach, and assimilated or extrapolated meteorological observations may offer better continuity
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Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data
© 2015 Elsevier Inc. Spatially explicit predictions of fuel moisture content are crucial for quantifying fire danger indices and as inputs to fire behaviour models. Remotely sensed predictions of fuel moisture have typically focused on live fuels; but regional estimates of dead fuel moisture have been less common. Here we develop and test the spatial application of a recently developed dead fuel moisture model, which is based on the exponential decline of fine fuel moisture with increasing vapour pressure deficit (D). We first compare the performance of two existing approaches to predict D from satellite observations. We then use remotely sensed D, as well as D estimated from gridded daily weather observations, to predict dead fuel moisture. We calibrate and test the model at a woodland site in South East Australia, and then test the model at a range of sites in South East Australia and Southern California that vary in vegetation type, mean annual precipitation (129-1404mmyear-1) and leaf area index (0.1-5.7). We found that D modelled from remotely sensed land surface temperature performed slightly better than a model which also included total precipitable water (MAE<1.16kPa and 1.62kPa respectively). D calculated with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite was under-predicted in areas with low leaf area index. Both D from remotely sensed data and gridded weather station data were good predictors of the moisture content of dead suspended fuels at validation sites, with mean absolute errors less than 3.9% and 6.0% respectively. The occurrence of data gaps in remotely sensed time series presents an obstacle to this approach, and assimilated or extrapolated meteorological observations may offer better continuity
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A semi-mechanistic model for predicting the moisture content of fine litter
© 2015 Elsevier B.V. The moisture content of vegetation and litter (fuel moisture) is an important determinant of fire risk, and predictions of dead fine fuel moisture content (fuel with a diameter <25.4. mm) are particularly important. A variety of indices, as well as empirical and mechanistic models, have been proposed to predict fuel moisture, but these approaches have seldom been validated across temporally extensive datasets, or widely contrasting vegetation types. Here, we describe a semi-mechanistic model, based on the exponential decline of fuel moisture content with atmospheric vapor pressure deficit, that predicts daily minimum fuel moisture content. We calibrated the model at one site in New South Wales, Australia, and validated it at three contrasting ecosystem types in California, USA, where 10-h fuel moisture content was continuously measured every 30. min over a year. We found that existing drought indices did not accurately predict fuel moisture, and that empirical and equilibrium models provided biased estimates. The mean absolute error (MAE) of the fuel moisture content predicted by our model across sites and years was 3.7%, which was substantially lower than for other, commonly used models. Our model's MAE dropped to 2.9% when fuel moisture was below 20%, and to 1.8% when fuel moisture was below 10%. Our model's MAE was comparable to instrumental MAE (3.1-2.5%), indicating that further improvement may be limited by measurement error. The simplicity, accuracy and precision of our model makes it suitable for a range of applications, such as operational fire management and the prediction of fire risk in vegetation models, without the need for site-specific calibrations
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