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Mangrove wetlands span broad geographical gradients, resulting in functionally diverse tree communities. We asked whether latitudinal variation, allometric scaling relationships and species composition influence mangrove forest structure and biomass allocation across biogeographical regions and distinct coastal morphologies.
The scale effects of increased gravitational loading by chronic centrifugation on metabolic rate and body composition in metabolically mature mammals were investigated. Individual oxygen consumption rates in groups of 12 each, 8-month-old, hamster, rats, guinea pigs, and rabbits were measured at weekly intervals at 1.0 g, then 2.0 g for 6 weeks. Metabolic rate was increased significantly in all species, and stabilized after 2 weeks at 2.0 g. Statistical analysis of the data revealed that the larger the animal the greater was the increase in mass-specific metabolic rate, or metabolic intensity, over the 1.0 g value for the same animal, with the result that the interspecies allometric scaling relationship between metabolic rate and total body mass is different at 2.0 g compared 10 1.0 g. Analysis of covariance shows that the postioning constant at 2.0 g is increased by 17% at 2.0 g at the P .001 level, and the exponent is increased by 8% at the P = 0.008 level. Thus, the hypothesis that augmented gravitational loading should shift the allometric relationship between metabolic rate and body size by an increase in both parameters is supported.
Accurately quantifying forest biomass is of crucial importance for climate change studies. By quantifying the amount of above and below ground biomass and consequently carbon stored in forest ecosystems, we are able to derive estimates of carbon sequestration, emission and storage and help close the carbon budget. Mangrove forests, in addition to providing habitat and nursery grounds for over 1300 animal species, are also an important sink of biomass. Although they only constitute about 3% of the total forested area globally, their carbon storage capacity -- in forested biomass and soil carbon -- is greater than that of tropical forests (Lucas et al, 2007). In addition, the amount of mangrove carbon -- in the form of litter and leaves exported into offshore areas is immense, resulting in over 10% of the ocean's dissolved organic carbon originating from mangroves (Dittmar et al, 2006) The measurement of forest above ground biomass is carried out on two major scales: on the plot scale, biomass can be measured using field measurements through allometric equation derivation and measurements of forest plots. On the larger scale, the field data are used to calibrate remotely sensed data to obtain stand-wide or even regional estimates of biomass. Currently, biomass can be calculated using average stand biomass values and optical data, such as aerial photography or satellite images (Landsat, Modis, Ikonos, SPOT, etc.). More recent studies have concentrated on deriving forest biomass values using radar (JERS, SIR-C, SRTM, Airsar) and/or lidar (ICEsat/GLAS, LVIS) active remote sensing to retrieve more accurate and detailed measurements of forest biomass. The implementation of a generation of new active sensors (UAVSar, DesdynI, Alos/Palsar, TerraX) has prompted the development of new tecm'liques of biomass estimation that use the combination of multiple sensors and datasets, to quantify past, current and future biomass stocks. Focusing on mangrove forest biomass estimation, this book chapter has 3 main objectives: a) To describe in detail the field methodologies used to derive accurate estimates of biomass in mangrove forests b) To explain how mangrove forest biomass can be measured using several remote sensing techniques and datasets c) To give a detailed explanation of the measurement challenges and errors that arise in each estimate of forest biomass
Consideration is given to the possibility of using Landsat TM data to detect differences and changes in the leaf area index (LAI) of closed canopy pine plantations in central Massachusetts. Field measurements of the LAI were obtained using locally-derived allometric relationships between leaf area and trunk diameter. The relationship between the Landsat TM Band 3/Band 4 ratio and regional differences in LAI is examined. A thinning treatment was conducted to demonstrate the ability of TM to detect a known reduction in LAI. It is concluded that, for the detection of moderate changes and differences in the LAI of closed canopy pine plantations at local scales, the TM sensor is better than field measurements involving allometric equations.
Human heart rate, controlled by complex feedback mechanisms, is a vital index of systematic circulation. However, it has been shown that beat-to-beat values of heart rate fluctuate continually over a wide range of time scales. Herein we use the relative dispersion, the ratio of the standard deviation to the mean, to show, by systematically aggregating the data, that the correlation in the beat-to-beat cardiac time series is a modulated inverse power law. This scaling property indicates the existence of long-time memory in the underlying cardiac control process and supports the conclusion that heart rate variability is a temporal fractal. We argue that the cardiac control system has allometric properties that enable it to respond to a dynamical environment through scaling.
Mangroves are ecologically and economically important forested wetlands with the highest carbon (C) density of all terrestrial ecosystems. Because of their exceptionally large C stocks and importance as a coastal buffer, their protection and restoration has been proposed as an effective mitigation strategy for climate change. The inclusion of mangroves in mitigation strategies requires the quantification of C stocks (both above and belowground) and changes to accurately calculate emissions and sequestration. A growing number of countries are becoming interested in using mitigation initiatives, such as REDD+ (reducing emissions from deforestation and forest degradation), in these unique coastal forests. However, it is not yet clear how methods to measure C traditionally used for other ecosystems can be modified to estimate biomass in mangroves with the precision and accuracy needed for these initiatives. Airborne Lidar (ALS) data has often been proposed as the most accurate way for larger scale assessments but the application of ALS for coastal wetlands is scarce, primarily due to a lack of contemporaneous ALS and field measurements. Here, we evaluated the variability in field and Lidar-based estimates of aboveground biomass (AGB) through the combination of different local and regional allometric models and standardized height metrics that are comparable across spatial resolutions and sensor types, the end result being a simplified approach for accurately estimating mangrove AGB at large scales and determining the uncertainty by combining multiple allometric models.We then quantified wall-to-wall AGB stocks of a tall mangrove forest in the Zambezi Delta, Mozambique. Our results indicate that the Lidar H100 height metric correlates well with AGB estimates, with R(exp 2) between 0.80 and 0.88 and RMSE of 33% or less. When comparing Lidar H100 AGB derived from three allometric models, mean AGB values range from 192 Mg ha(exp −1) up to 252 Mg ha(exp −1).We suggest the best model to predict AGB was based on the East Africa specific allometry and a power-based regression that used Lidar H100 as the height input with an R(exp 2) of 0.85 and an RMSE of 122 Mg ha(exp −1) or 33%. The total AGB of the Lidar inventoried mangrove area (6654 ha) was 1 350 902 Mg with a mean AGB of 203 Mg ha(exp −1) +/-166 Mg ha(exp −1). Because the allometry suggested here was developed using standardized height metrics, it is recommended that the models can generate AGB estimates using other remote sensing instruments that are more readily accessible over other mangrove ecosystems on a large scale, and as part of future carbon monitoring efforts in mangroves.
It is postulated that augmentation of gravitational loading should produce a shift in the classic Kleiber mammalian allometric relationship between metabolic rate and total body mass by an increase in both these parameters. Oxygen consumption rate and body mass measurements of 10 male rabbits 8 months of age were obtained initially for 1.0 g, and then over a 9-week period of chronic centrifugation at 2.0 g. Analysis of covariance showed that the positioning constant at 2.0 g is increased by 17 percent from that at 1.0 g at the P less than 0.001 level, and the exponent is increased by 8 percent at the P = 0.008 level. It is concluded that abatement of gravitational loading in spaceflight will result in a lowering of both allometric parameters.
Procedures have been developed for studying the effect of changes in gravitational loading on skeletal muscle mass through measurements of the body creatine content. These procedures were developed for studies of gravitational scale effects in a four-species model, comprising the hamster, rat, guinea pig, and rabbit, which provides a sufficient range of body size for assessment of allometric parameters. Since intracellular muscle creatine concentration varies among species, and with age within a given species, the concentration values for metabolically mature individuals of these four species were established. The creatine content of the carcass, skin, viscera, smooth muscle, and skeletal muscle was determined for each species. In addition, the skeletal muscle mass of the major body components was determined, as well as the total and fat-free masses of the body and carcass, and the percent skeletal muscle in each. It is concluded that these procedures are particularly useful for studying the effect of gravitational loading on the skeletal muscle content of the animal carcass, which is the principal weight-bearing organ of the body.
It is well known that skeletal muscle intrinsic maximal shortening velocity is inversely related to species body mass. However, there is uncertainty regarding the relationship between the contractile properties of muscle fibers obtained from commonly studied laboratory animals and those obtained from humans. In this study we determined the contractile properties of single chemically skinned fibers prepared from rat, rhesus monkey, and human soleus and gastrocnemius muscle samples under identical experimental conditions. All fibers used for analysis expressed type I myosin heavy chain as determined by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Allometric coefficients for type I fibers from each muscle indicated that there was little change in peak tension (force/fiber cross-sectional area) across species. In contrast, both soleus and gastrocnemius type I fiber maximal unloaded shortening velocity (Vo), the y-intercept of the force-velocity relationship (Vmax), peak power per unit fiber length, and peak power normalized for fiber length and cross-sectional area were all inversely related to species body mass. The present allometric coefficients for soleus fiber Vo (-0.18) and Vmax (-0.11) are in good agreement with published values for soleus fibers obtained from common laboratory and domesticated mammals. Taken together, these observations suggest that the Vo of slow fibers from quadrupeds and humans scale similarly and can be described by the same quantitative relationships. These findings have implications in the design and interpretation of experiments, especially those that use small laboratory mammals as a model of human muscle function.
Polarimetric and multifrequency data from the NASA/JPL airborne synthetic aperture radar (AIRSAR) have been used in a multi-tier estimation algorithm to calculate a comprehensive set of forest canopy properties including branch layer moisture and thickness, trunk density, trunk water content and diameter, trunk height, and subcanapy soil moisture. The estimation algorithm takes advantage of species-specific allometric relations, and is applied to a 100Km x 100Km area in the Canadian boreal region containing many different vegetation species types. The results show very good agreement with ground measurements taken at several focused and auxiliary study sites. This paper expands on the results reported in [1] and applies the algorithm on the regional scale.
The accurate quantification of forest 3-D structure is of great importance for studies of the global carbon cycle and biodiversity. These studies are especially relevant in Africa, where deforestation rates are high and the lack of background data is great. Mangrove forests are ecologically significant and it is important to measure mangrove canopy heights and biomass. The objectives of this study are to estimate: 1. The total area, 2. Canopy height distributions and 3. Aboveground biomass of mangrove forests in Africa. To derive mangrove 3-D structure and biomass maps, we used a combination of mangrove maps derived from Landsat ETM+, LiDAR canopy height estimates from ICEsat/GLAS (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System) and elevation data from SRTM (Shuttle Radar Topography Mission) for the African continent. More specifically, we extracted mangrove forest areas on the SRTM DEM using Landsat based landcover maps. The LiDAR (Light Detection and Ranging) measurements from the large footprint GLAS sensor were used to derive local estimates of canopy height and calibrate the Interferometric Synthetic Aperture Radar (InSAR) data from SRTM. We then applied allometric equations relating canopy height to biomass in order to estimate above ground biomass (AGB) from the canopy height product. The total mangrove area of Africa was estimated to be 25 960 square kilometers with 83% accuracy. The largest mangrove areas and greatest total biomass was 29 found in Nigeria covering 8 573 km2 with 132 x10(exp 6) Mg AGB. Canopy height across Africa was estimated with an overall root mean square error of 3.55 m. This error also includes the impact of using sensors with different resolutions and geolocation error which make comparison between measurements sensitive to canopy heterogeneities. This study provides the first systematic estimates of mangrove area, height and biomass in Africa. Our results showed that the combination of ICEsat/GLAS and SRTM data is well suited for vegetation 3-D mapping on a continental scale.
In three sites of boreal and temperate forests, P band HH, HV, and VV polarization data combined estimate total aboveground dry woody biomass within 12 to 27% of the values derived from allometric equations, depending on forest complexity. Biomass estimates derived from HV-polarization data only are 2 to 14% less accurate. When the radar operates at circular polarization, the errors exceed 100% over flooded forests, wet or damaged trees and sparse open tall forests because double-bounce reflections of the radar signals yield radar signatures similar to that of tall and massive forests. Circular polarizations, which minimize the effect of Faraday rotation in spaceborne applications, are therefore of limited use for measuring forest biomass. In the tropical rain forest of Manu, in Peru, where forest biomass ranges from 4 kg/sq m in young forest succession up to 50 kg/sq m in old, undisturbed floodplain stands, the P band horizontal and vertical polarization data combined separate biomass classes in good agreement with forest inventory estimates. The worldwide need for large scale, updated, biomass estimates, achieved with a uniformly applied method, justifies a more in-depth exploration of multi-polarization long wavelength imaging radar applications for tropical forests inventories.