Estimates Of Aboveground Net Primary Production Research Articles

Hybrid modeling for grassland productivity prediction: A parametric and machine learning technique for grazing management with applicability to digital twin decision systems

CONTEXTMonitoring Aboveground Net Primary Production (ANPP) is critical to assess not only the current ecosystem status but also its long-term dynamics. In rangelands, the seasonal dynamics of ANPP determines forage availability, stock density, and livestock productivity. OBJECTIVETo develop a hybrid model to be used as a prediction engine for ANPP in the native grasslands of Uruguay. The model combines a parametric component based on the seasonal dynamics of ANPP, and an artificial neural network (ANN) component used to model the remaining non-linearities, which are mainly related to precipitation and temperature variability. The output of hybrid model is proposed as the “virtual entity” of a digital twin support decision system where the “physical entity” is characterized by a collection of bi-weekly (fortnight) ANPP estimates. METHODSFortnight ANPP data were calculated from MODIS EVI for the 2001–2020 period. A sigmoidal functional response, having three parameters with an explicit biological interpretation, was fitted to the accumulated ANPP as a function of time. Forecasts were generated by extrapolating the sigmoidal functional response fit up to four fortnights ahead. From these fits, we obtained the fortnight ANPP values by differentiating the accumulated fortnight ANPP. Predictions (up to four fortnights) were generated for each fortnight and year. The residuals from these fits were modeled using a multilayer perceptron trained by backpropagation using climate variables as independent variables. RESULTS AND CONCLUSIONSThe sigmoidal functional response model fit was highly significant for the accumulated ANPP profile. This model also had a high explanatory power for the accumulated ANPP curve. The median of the percentage absolute residuals for forecasts made 1 to 4 fortnights ahead ranged from 17% to 18%. The ANN significantly reduced this unexplained variability in ANPP, showing a median reduction in residuals of 35%, 31%, 30%, and 30% for 1 to 4 fortnights ahead forecasts, respectively, when compared to predictions from the sigmoidal functional response fit. SIGNIFICANCEBy integrating both parametric and machine learning techniques, the hybrid model developed can make accurate predictions in a way that is both efficient and dependable. The hybrid model not only represents an advantage in terms of predictive power, but it also allows for a deeper understanding of the basic ecological processes involved in forage production.

Multiple traps compromise the sustainability of most livestock farmers in Sierras del Este, Uruguay

AbstractLivestock system structure is a relevant factor in determining the decision-making processes and the initiatives to reduce the vulnerability to external drivers. The definition of livestock stocking density (LSD) is a key factor as it determines the sustainability of the activity. LSD adjusted to grassland productivity in space, and time has been advocated by researchers and policy makers. However, most livestock farmers fail to adjust the LSD, and therefore, their vulnerability increases. This study explored the hypothesis that productive decisions in livestock systems include information that reinforced the vulnerability. The main objective of this study was to evaluate vulnerability of livestock farmers through the information that guides the decision-making process. We analyze information based on which farmers define the LSD, focusing on the relationship between the farmers’ perception of grassland productivity, the main reasons for their decisions, and grassland productivity using remote sensing techniques. The research strategy integrated interviews with farmers regarding LSD and grassland aboveground net primary productivity estimates. The results showed that 77% of the farmers use LSD higher than the carrying capacity, although 66% of them do so knowingly. Consequently, most farmers are in a situation of high vulnerability, especially to rainfall regime variability. Almost all farmers are affected by at least one trap: poverty, rigidity, or gilded. A clear contradiction is identified between the farmer’s decisions and the current recommendations suggested by researchers and public policy, which are mainly explained by the farmers’ optimism. Results highlight the access to information as a great barrier to the adoption of adaptive strategies. We show for the first time the main traps and its causal associated mechanisms, which limit the decision-making of Uruguayan livestock farmers. The proposed framework can be used in other agricultural systems to identify barriers and traps, as the first step toward increasing agroecosystem sustainability.

Aboveground biomass and seasonal patterns of aboveground net primary productivity in five bamboo species in northern Laos

Abstract Aims Accurate estimates of bamboo biomass and net primary productivity (NPP) are required to evaluate the carbon sequestration potential of bamboo forests. However, relevant data that are important for climate change mitigation, have rarely been collected in regions outside of East Asia and India. Information on seasonal patterns of NPP and its components will enable the quantification of factors that influence the carbon balance in bamboo forests. In this study, we quantified the aboveground biomass (AGB) and aboveground NPP of five major bamboo species in northern Laos using monthly data collected over a 12-month period. Methods All live culms in 10, 2 m × 2 m plots (for one monopodial bamboo species: Indosasa sinica) and 30 clumps per species (for four sympodial bamboo species: Bambusa tulda, Cephalostachyum virgatum, Dendrocalamus membranaceus and Gigantochloa sp.) were numbered and measured at breast height. We set 10 or 20 litter traps per species to collect litterfall. Censuses of dead and recruited culms and litterfall collection were performed once per month for 12 months. Important Findings The AGB was highest in I. sinica (59.87 Mg ha−1) and lowest in C. virgatum (11.54 Mg ha−1), and was mostly below the plausible global range for bamboos (32–256 Mg ha−1). The sympatric distribution of multiple bamboo species at the study sites may have suppressed the AGB in four of the five studied species. The aboveground NPP estimates were between 3.43 and 14.25 Mg ha−1 yr−1; those for D. membranaceus (8.20 Mg ha−1 yr−1) and I. sinica (14.25 Mg ha−1 yr−1) were comparable to mean global estimates for temperate evergreen forests (8.78 Mg ha−1 yr−1) and tropical moist forests (10.56 Mg ha−1 yr−1). High culm recruitment rates (15.20–23.39% yr−1) were major contributors to aboveground NPP estimates. Seasonal patterns of aboveground NPP were largely influenced by the phenology of the new culms. In the four sympodial bamboo species, new culms began to emerge following the onset of persistent rainfall, mainly in July and August. However, the sprouting of new culms in the monopodial species I. sinica followed a trend of increasing temperatures, mainly in March and April. Thus, our results indicate that bamboos have considerable potential for sequestering carbon in northern Laos, but that this potential may be affected by climate change.

A reality check for climate change experiments: Do they reflect the real world?

Experiments are widely used in ecology, particularly for assessing global change impacts on ecosystem function. However, results from experiments often are inconsistent with observations made under natural conditions, suggesting the need for rigorous comparisons of experimental and observational studies. We conducted such a "reality check" for a grassland ecosystem by compiling results from nine independently conducted climate change experiments. Each experiment manipulated growing season precipitation (GSP) and measured responses in aboveground net primary production (ANPP). We compared results from experiments with long-term (33-yr) annual precipitation and ANPP records to ask if collectively (n=44 experiment-years) experiments yielded estimates of ANPP, rain-use efficiency (RUE, grams per square meter ANPP per mm precipitation), and the relationship between GSP and ANPP comparable to observations. We found that mean ANPP and RUE from experiments did not deviate from observations. Experiments and observational data also yielded similar functional relationships between ANPP and GSP, but only within the range of historically observed GSP. Fewer experiments imposed extreme levels of GSP (outside the observed 33-yr record), but when these were included, they altered the GSP-ANPP relationship. This result underscores the need for more experiments imposing extreme precipitation levels to resolve how forecast changes in climate regimes will affect ecosystem function in the future.

Estimation of net primary productivity of forests by modified CASA models and remotely sensed data

ABSTRACTTo increase the accuracy of predicting net primary productivity (NPP), in this study, Carnegie–Ames–Stanford Approach (CASA) model was modified by developing new methods to estimate absorbed photosynthetically active radiation or fraction of photosynthetically active radiation (FPAR) and water stress coefficient (WSC). In the modified model, FPAR was derived based on its non-linear relationship with leaf area index. Moreover, WSC was estimated using leaf water potential from soil moisture instead of a traditional evapotranspiration-based method. This study was conducted in Baiyun District area of Guangzhou, China, using Gaofen-1 (GF-1), Landsat 7, and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. The predictions from the original and three modified CASA models and MODIS NPP product MOD17A3 were compared with field observations. The results showed that all the CASA-based models led to similar spatial distributions of forest aboveground NPP estimates. Overall, the estimates increased with elevation because the valley bottoms were dominated by developed or urbanized areas whereas the hillslopes and hilltops were largely vegetated. Based on root mean square error (RMSE) and relative RMSE between the observed and predicted values, the CASA model that integrated the modifications of both FPAR and WSC increased the estimation accuracy of NPP by 8.1% over the original one. The increase in accuracy was mainly contributed by the modification of FPAR. This suggested that the modification of FPAR provided greater potential than that of WSC for improving the predictions of CASA model. Compared to the CASA models, MOD17A3 had lower accuracy of aboveground NPP estimates. This study also showed that the fine spatial resolution GF-1 image provided a new source of data used to estimate NPP of forest ecosystems.

Regional-Scale High Spatial Resolution Mapping of Aboveground Net Primary Productivity (ANPP) from Field Survey and Landsat Data: A Case Study for the Country of Wales

This paper presents an alternative approach for high spatial resolution vegetation productivity mapping at a regional scale, using a combination of Normalised Difference Vegetation Index (NDVI) imagery and widely distributed ground-based Above-ground Net Primary Production (ANPP) estimates. Our method searches through all available single-date NDVI imagery to identify the images which give the best NDVI–ANPP relationship. The derived relationships are then used to predict ANPP values outside of field survey plots. This approach enables the use of the high spatial resolution (30 m) Landsat 8 sensor, despite its low revisit frequency that is further reduced by cloud cover. This is one of few studies to investigate the NDVI–ANPP relationship across a wide range of temperate habitats and strong relationships were observed (R2 = 0.706), which increased when only grasslands were considered (R2 = 0.833). The strongest NDVI–ANPP relationships occurred during the spring “green-up” period. A reserved subset of 20% of ground-based ANPP estimates was used for validation and results showed that our method was able to estimate ANPP with a RMSE of 15–21%. This work is important because we demonstrate a general methodological framework for mapping of ANPP from local to regional scales, with the potential to be applied to any temperate ecosystems with a pronounced green up period. Our approach allows spatial extrapolation outside of field survey plots to produce a continuous surface product, useful for capturing spatial patterns and representing small-scale heterogeneity, and well-suited for modelling applications. The data requirements for implementing this approach are also discussed.

Convergence between ANPP estimation methods in grasslands — A practical solution to the comparability dilemma

Aboveground net primary production (ANPP) is a key ecosystem characteristic and of fundamental importance for essentially all aspects of matter and energy fluxes in terrestrial ecosystems. Various methods for estimating ANPP are available and despite partial consensus on ‘best practice methods’ important methodological issues remain unresolved: ANPP data obtained with different methods differ in their magnitude, variability and their tendency to over- or underestimate primary production. Paradoxically, despite the large number of published ANPP data, the limited comparability of ANPP estimates across studies de facto leads to a scarcity of ANPP data for assembled large-scale studies. We aimed to overcome these problems by establishing conversion rates between the most commonly used ANPP methods, making the large body of published ANPP data more comparable and thus useful for assembled large-scale studies.Using seasonal biomass dynamics from 89 sites representing various biomes and climata, we established linear conversions for all 21 combinations between the seven most common ANPP estimation algorithms in grass-dominated vegetation. We also checked for confounding effects of environmental factors such as biome and climatic aridity. Aridity was the only factor with a clear influence on ANPP conversions, and in six cases we thus calculated separate relationships for dry and humid environments. In these cases, dryland ANPP was systematically underestimated by the respective methods. As these methods are insensitive to turn-over processes from live to senescent biomass, we assume this underestimation is related to climate-induced differences in biomass turn-over rates, with more arid sites having higher rates.The majority of the resulting 27 conversions had high (pseudo) R2 values (≥0.65; full range: 0.31–0.92), indicating clear linear relationships between most ANPP estimation methods. Given the large size of the dataset and the accuracy of statistical models, we assume that most conversion formulae are generally valid. We classified conversions with respect to their R2 values and their methodological comparability, and concluded that 16 conversions can be fully recommended. For those cases where a recalculation of ANPP on basis of original biomass data is not possible, our conversion formulae offer an easy and practical approach to synchronize ANPP estimates from divergent algorithms and sources.

Long-term tree productivity of a South Carolina coastal plain forest across a hydrology gradient

Aims In recent years, there has been an increased interest in examining changes in forest systems in response to drought, flooding, hurricanes and climate change. In the southern United States, forested wetlands are of special interest because of the extent of these forests. Coastal plain forested wetlands are among the most vulnerable to these climatic impacts. One of the problems in developing management practices for these coastal areas is the difficulty in adequately describing productivity relations and predicting how the structure and function of these communities might be affected by natural or anthropogenic disturbances. Community response to environmental change often occurs over a period of years, and the majority of reported studies are for 1–3 years in duration. This study documents long-term changes (10 years) in structure, composition and growth along a catena of high water table forested sites of an ancient beach ridge landscape in coastal South Carolina. Methods Aboveground net primary production (ANPP) of trees was monitored from 2000 to 2009 on three sites within a longleaf pine-swamp blackgum forest system on the southern end of the Waccamaw Neck area of Georgetown County, SC. Permanent study plots (20 3 25 m) were established across a moisture gradient (Dry, Intermediate, and Wet). Water levels were continuously monitored, litterfall was measured monthly and growth of trees >10 cm diameter at breast height was monitored on an annual basis. Annual litterfall and tree production values were summed to provide estimates of ANPP. Important Findings The study site was under severe drought conditions July 2001 through late summer 2002 and again in 2007. Diameter growth was affected in all three sites, but with different patterns. It seems that diameter growth in the Wet site was more sensitive to drought conditions in 2001–02 and 2007 than either Dry or Intermediate sites. While droughts did not seem to have a significant impact on litterfall in the Wet site, litterfall in the Intermediate site was more sensitive to the drought than either Dry or Wet sites. ANPP was significantly lower in both Intermediate and Wet sites in 2001 at 1 000 g/m) occurred in the Intermediate and Wet sites in 2003 following a return to more normal water levels at the end of the drought. Maximum tree production occurred on the Wet site in 2003 (657 g/m), which exceeded total ANPP of any site in 2001. In the Dry site, ANPP remained relatively consistent throughout the study when compared to Wet and Intermediate sites. While litterfall estimates are well defined with 3–5 years of data, data collection is continuing to assess impact of drought on stem growth across the gradient, which is still not clear with 10 years of data.

Carbon Stocks and Fluxes in Rangelands of the Río de la Plata Basin

Grasslands are one of the most modified biomes on Earth. Land use changes had a large impact on carbon (C) stocks of grasslands. Understanding the impact of land use/land cover changes on C stocks and fluxes is critical to evaluate the potential of rangeland ecosystem as C sinks. In this article we analyze C stocks and fluxes across the environmental gradients of one of the most extensive temperate rangeland areas: the Rio de la Plata Grasslands (RPG) in South America. The analysis summarizes information provided by field studies, remote sensing estimates, and modeling exercises. Average estimates of aboveground net primary production (ANPP) ranged from 240 to 316 g C ? m 22 ? yr 21 . Estimates of belowground NPP (BNPP) were more variable than ANPP and ranged from 264 to 568 g C ? m 22 ? yr 21 . Total Carbon ranged from 5 004 to 15 008 g C ? m 22 . Plant biomass contribution to Total Carbon averaged 13% and varied from 9.5% to 27% among sites. The largest plant C stock corresponded to belowground biomass. Aboveground green biomass represented less than 7% of the plant C. Soil organic carbon (SOC) was concentrated in the slow and passive compartments of the organic matter. Active soil pool represented only 6.7% of the SOC. The understanding of C dynamics and stocks in the RPG grasslands is still partial and incomplete. Field estimates of ANPP and BNPP are scarce, and they are not based on a common measurement protocol. Remotely sensed techniques have the potential to generate a coherent and spatially explicit database on ANPP. However, more work is needed to improve estimates of the spatial and temporal variability of radiation use efficiency. The absence of a flux tower network restricts the ability to track seasonal changes in C uptake and to understand fine-scale controls of C dynamics. Resumen

Radiation use efficiency in adjacent hardwood and pine forests in the southern Appalachians

The efficiency with which trees convert photosynthetically active radiation (PAR) to biomass has been shown to be consistent within stands of an individual species, which is useful for estimating biomass production and carbon accumulation. However, radiation use efficiency ( ɛ) has rarely been measured in mixed-species forests, and it is unclear how species diversity may affect the consistency of ɛ, particularly across environmental gradients. We compared aboveground net primary productivity (ANPP), intercepted photosynthetically active solar radiation (IPAR), and radiation use efficiency ( ɛ = ANPP/IPAR) between a mixed deciduous forest and a 50-year-old white pine ( Pinus strobus L.) plantation in the southern Appalachian Mountains. Average ANPP was similar in the deciduous forest (11.5 Mg ha −1 y −1) and pine plantation (10.2 Mg ha −1 y −1), while ɛ was significantly greater in the deciduous forest (1.25 g MJ −1) than in the white pine plantation (0.63 g MJ −1). Our results demonstrate that late-secondary hardwood forests can attain similar ANPP as mature P. strobus plantations in the southern Appalachians, despite substantially less annual IPAR and mineral-nitrogen availability, suggesting greater resource-use efficiency and potential for long-term carbon accumulation in biomass. Along a 260 m elevation gradient within each forest there was not significant variation in ɛ. Radiation use efficiency may be stable for specific forest types across a range of environmental conditions in the southern Appalachian Mountains, and thus useful for generating estimates of ANPP at the scale of individual watersheds.

Uncertainty in Calculations of Net Primary Production for Grasslands

Net primary production (NPP) is a fundamental characteristic of all ecosystems and foundational to understanding the fluxes of energy and nutrients. Because NPP cannot be measured directly, researchers use field-measured surrogates as input variables in various equations designed to estimate ‘true NPP’. This has led to considerable debate concerning which equations most accurately estimate ‘true NPP’. This debate has influenced efforts to assess NPP in grasslands, with researchers often advocating more complex equations to avoid underestimation. However, this approach ignores the increase in statistical error associated with NPP estimates as a greater number of parameters and more complex mathematical functions are introduced into the equation. Using published grassland data and Monte Carlo simulation techniques, we assessed the relative variability in NPP estimates obtained using six different NPP estimation equations that varied in both the number of parameters and intricacy of mathematical operations. Our results indicated that more complex equations may result in greater uncertainty without reducing the probability of underestimation. The amount of uncertainty associated with estimates of NPP was influenced by the number of parameters as well as the variability in the data and the nature of the mathematical operations. For example, due to greater variability in the field-measured belowground data than aboveground data, estimates of belowground NPP tended to have more uncertainty than estimates of aboveground NPP. An analysis in which the input data were standardized allowed us to isolate the details of the calculations from the variability in the data in assessing the propagation of uncertainty. This analysis made clear that equations with product terms have the potential to magnify the uncertainty of the inputs in the estimates of NPP although this relationship was complicated by interactions with data variability and number of parameters. Our results suggest that more complex NPP estimation equations can increase uncertainty without necessarily reducing risk of underestimation. Because estimates can never be tested by comparison to “true NPP”, we recommend that researchers include an assessment of propagation of statistical error when evaluating the ‘best’ estimation method.

Seasonal Variation in Aboveground Production and Radiation-use Efficiency of Temperate rangelands Estimated through Remote Sensing

Aboveground net primary production (ANPP) of grasslands varies spatially and temporally. Spectral information provided by remote sensors is a promising new tool that may be able to estimate ANPP in real time and at low cost. The objectives of this study were (a) to evaluate at a seasonal scale the relationship between ANPP and the normalized difference vegetation index (NDVI), (b) to estimate seasonal variations in the coefficient of conversion of absorbed radiation into aboveground biomass (ea), and (c) to identify the environmental controls on such temporal changes. We used biomass-based field determinations of ANPP for two grassland sites in the Flooding Pampa, Argentina, and related them with NDVI data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR) satellites using three different models. Results were compared with data obtained from the new Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at an additional site. The first model was based solely on NDVI; the second was based on the amount of photosynthetically active radiation absorbed by the green vegetation (APARg), which was derived from NDVI and incoming photosynthetically active radiation (PAR); the third was based on APARg and ea, which was in turn estimated from climatic variables. NDVI explained between 63 and 93% of ANPP variation, depending on the site considered. Estimates of ANPP were not improved by considering the variation in incoming PAR. At both sites, ea varied seasonally (from 0.2 to 1.2 g DM/MJ) and was significantly associated with combinations of precipitation and temperature. Combining ea variations with APARg increased our ability to account for seasonal ANPP variations at both sites. Our results indicate that NDVI produces good, direct estimates of ANPP only if NDVI, PAR, and ea are correlated throughout the seasons. Thus, in most cases, seasonal variations of ea associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.

Alternative method for estimating aboveground net primary productivity applied to balsam fir stands in eastern Canada

Aboveground net primary productivity (ANPP) was measured in three balsam fir (Abies balsamea (L.) Mill.) forests on a climatic transect extending from southern New Brunswick ("warm" study area) to central Quebec ("cool" study area). Annual foliar production was estimated with a relationship between cross-sectional area at breast height of the current-year annual xylem ring and the mass of current-year foliage, using data obtained by harvesting trees at the beginning of the study. This relationship differed among study areas. Annual branch production was determined from annual foliar production and the ratio of annual branch production to annual foliar production. The ratio of branch to foliar production was estimated from intensive measurement of a sample of branches collected at the end of the study period; it varied among years but was similar for all study areas. ANPP was 3.36 Mg C·ha–1·year–1 at the warm study area, 3.73 Mg C·ha–1· year–1 at the mid-transect study area, and 3.04 Mg C·ha–1·year–1 at the cool study area. These estimates of ANPP were greater than those estimated using a conventional method of summing up increment and litterfall. On average, the conventional estimate of ANPP was 83% of the estimate using relationships described above. Because net ecosystem productivity is the difference between NPP and heterotrophic respiration, a 17% underestimate of NPP can have a substantial effect on the estimate of carbon-sink activity of a forest.

Biomass, production and woody detritus in an old coast redwood (Sequoia sempervirens) forest

We examined aboveground biomass dynamics, aboveground net primary production (ANPP), and woody detritus input in an old Sequoia sempervirens stand over a three-decade period. Our estimates of aboveground biomass ranged from 3300 to 5800 Mg ha−1. Stem biomass estimates ranged from 3000 to 5200 Mg ha−1. Stem biomass declined 7% over the study interval. Biomass dynamics were patchy, with marked declines in recent tree-fall patches <0.05 ha in size. Larger tree-fall patches approaching 0.2 ha in size were observed outside the study plot. Our estimates of ANPP ranged from 6 to 14 Mg ha−1yr−1. Estimates of 7 to 10 Mg ha−1yr−1 were considered to be relatively accurate. Thus, our estimates based on long-term data corroborated the findings of earlier short-term studies. ANPP of old, pure stands of Sequoia was not above average for temperate forests. Even though production was potentially high on a per stem basis, it was moderate at the stand level. We obtained values of 797 m3 ha−1 and 262 Mg ha−1 for coarse woody detritus volume and mass, respectively. Fine woody detritus volume and mass were estimated at 16 m3 ha−1 and 5 Mg ha−1, respectively. Standing dead trees (or snags) comprised 7% of the total coarse detritus volume and 8% of the total mass. Coarse detritus input averaged 5.7 to 6.9 Mg ha−1yr−1. Assuming steady-state input and pool of coarse detritus, we obtained a decay rate constant of 0.022 to 0.026. The old-growth stand of Sequoia studied had extremely high biomass, but ANPP was moderate and the amount of woody detritus was not exceptionally large. Biomass accretion and loss were not rapid in this stand partly because of the slow population dynamics and low canopy turnover rate of Sequoia at the old-growth stage. Nomenclature: Hickman (1993).

Regional scale relationships between ecosystem structure and functioning: the case of the Patagonian steppes

ABSTRACTAims 1. To characterize ecosystem functioning by focusing on above‐ground net primary production (ANPP), and 2. to relate the spatial heterogeneity of both functional and structural attributes of vegetation to environmental factors and landscape structure. We discuss the relationship between vegetation structure and functioning found in Patagonia in terms of the capabilities of remote sensing techniques to monitor and assess desertification.Location Western portion of the Patagonian steppes in Argentina (39°30′ S to 45°27′ S).Methods We used remotely‐sensed data from Landsat TM and AVHRR/NOAA sensors to characterize vegetation structure (physiognomic units) and ecosystem functioning (ANPP and its seasonal and interannual variation). We combined the satellite information with floristic relevés and field estimates of ANPP. We built an empirical relationship between the Landsat TM‐derived normalized difference vegetation index (NDVI) and field ANPP. Using stepwise regressions we explored the relationship between ANPP and both environmental variables (precipitation and temperature surrogates) and structural attributes of the landscape (proportion and diversity of different physiognomic classes (PCs)).Results PCs were quite heterogeneous in floristic terms, probably reflecting degradation processes. Regional estimates of ANPP showed differences of one order of magnitude among physiognomic classes. Fifty percent of the spatial variance in ANPP was accounted for by longitude, reflecting the dependency of ANPP on precipitation. The proportion of prairies and semideserts, latitude and, to a lesser extent, the number of PCs within an 8 × 8 km cell accounted for an additional 33% of the ANPP variability. ANPP spatial heterogeneity (calculated from Landsat TM data) within an 8 × 8 km cell was positively associated with the mean AVHRR/NOAA NDVI and with the diversity of physiognomic classes.Main conclusions Our results suggest that the spatial and temporal patterns of ecosystem functioning described from ANPP result not only from water availability and thermal conditions but also from landscape structure (proportion and diversity of different PCs). The structural classification performed using remotely‐sensed data captured the spatial variability in physiognomy. Such capability will allow the use of spectral classifications to monitor desertification.

Tree damage, allometric relationships, and above-ground net primary production in central Amazon forest

The loss of tree mass over time from damage can lead to underestimates in above-ground net primary productivity (ANPP) if not accounted for properly. Bias in the allometric relationship between trunk base diameter ( D b, at 1.3 m height or above the buttresses) and mass can also lead to systematic errors in ANPP estimates. We developed an unbiased model of the relationship between D b and tree mass using data from 315 trees (≥5 cm D b) harvested in the central Amazon. This model was compared with other theoretical ( n=1) and empirical models ( n=4). The theoretical model, and one empirical model, made predictions that differed substantially form our central Amazon model. The other three empirical models made predictions that were consistent with our model despite being developed in different tropical forests. Models differed mostly in predicting large tree mass. Using permanent forest inventory plot data, our D b versus tree mass model, and a bole volume model, we estimated that tree damage amounts to 0.9 Mg ha −1 per year (dry mass) of litter production. This damage should be included as a mass loss term when calculating ANPP. Incorporating fine litter data from published studies, we estimated that average ANPP for central Amazon plateau forests is at least 12.9 Mg ha −1 per year (or 6.5 Mg C ha −1 per year). Additional sources of error as described in the text can raise this estimate by as much as 4 Mg ha −1 per year. We hypothesize that tree damage in old-growth forests accounts for a significant portion of age related decline in productivity.

Patterns and controls of ecosystem function in longleaf pine - wiregrass savannas. I. Aboveground net primary productivity

Longleaf pine - wiregrass (Pinus palustris Mill. - Aristida stricta Michx.) woodlands occupy sites ranging from deep, xeric sandhills to the edge of wetlands in the southeastern United States. Aboveground net primary productivity (ANPP) of the overstory and understory were determined for three replicate sites of three site types (xeric, intermediate, and wet-mesic) that span a wide environmental gradient. In addition, soil moisture (at 30 and 90 cm) and N mineralization (in situ buried bag incubations) were measured through an annual cycle. Longleaf pine - wiregrass ecosystems varied by nearly twofold in ANPP across complex gradients. Overstory and understory and total (overstory and understory) ANPP were positively correlated to soil moisture at 30 and 90 cm. The proportion of understory ANPP relative to the total ANPP did not increase across the environmental gradient as predicted by hypotheses that invoke niche differentiation in rooting habits of grasses and trees. Contrary to expectations, cumulative net N mineralization was negatively related to soil moisture. All ANPP estimates were significantly and negatively related to cumulative N-mineralization. Further work is needed to explore the mechanisms by which soil moisture regulates productivity across space, time, and for individual species. Additional experimentation through resource addition would allow for investigations into multiple resource limitations and how resource limitations vary depending on gradient position.

Grassland Precipitation-Use Efficiency Varies Across a Resource Gradient

Aboveground net primary production (ANPP) is positively related to mean annual precipitation, an estimate of water availability. This relationship is fundamental to our understanding and management of grassland ecosystems. However, the slope of the relationship between ANPP and precipitation (precipitation-use efficiency, PUE) has been shown to be different for temporal compared with spatial precipitation series. When ANPP and precipitation are averaged over a number of years for different sites, PUE is similar for grasslands all over the world. Studies for two US Long Term Ecological Research Sites have shown that PUE derived from a long-term dataset (temporal model) has a significantly lower slope than the value derived for sites distributed across the US central grassland region (spatial model). PUE differences between the temporal model and the spatial model may be associated with both vegetational and biogeochemical constraints. Here we use two independent datasets, one derived from field estimates of ANPP and the other from remote sensing, to show that the PUE is low at both the dry end and the wet end of the annual precipitation gradient typical of grassland areas (200–1200 mm), and peaks around 475 mm. The intermediate peak may be related to relatively low levels of both vegetational and biogeochemical constraints at this level of resource availability.

Canopy Dynamics, Light Interception, and Radiation Use Efficiency of Selected Loblolly Pine Families

Abstract Needlefall, aboveground net primary production (ANPP), canopy light interception and attenuation, and "radiation use efficiency" of five open-pollinated loblolly pine (Pinus taeda L.) families were investigated using 100-tree family block plots near Summerville, SC. Family variation in annual needlefall amounts was significant during the fourth (from 4796 to 6191 kg ha-1) and fifth growing seasons (from 4717 to 5721 kg ha-1); needlefall patterns for the poorest performing family were asynchronous from those of the others, with notably higher needlefall rates from April to July. Estimates of ANPP at age 4 yr varied by 16% (from 32.9 to 38.2 Mg ha-1) among families, and were generally commensurate with progeny test rankings based on height growth. The relationship between ANPP and leaf area index (LAI) was both significant and curvilinear, with an apparent optimum level of production (42.6 Mg ha-1) achieved at a LAI of about 13 (all-sided; February). The least productive families had the lowest mean LAI (e.g., 7.9), while the most productive families generally had higher levels of LAI (e.g., 13.6). Significant family differences in canopy light interception were present in every month sampled except June, when photosynthetically active radiation (PAR) interception exceeded 90% for all families. Light extinction coefficients (k) for February differed significantly among the five families, and ranged from 0.28 to 0.38; families with the largest LAIs generally had the lowest values of k and the highest levels of PAR interception. A strong linear relationship (r² = 0.74) was found between ANPP and intercepted PAR. Estimates of the dry matter:radiation quotient (ε "radiation use efficiency") differed significantly among families, and ranged from 1.33 g MJ-1 to 1.48 g MJ-1. Results of this study suggest that fundamental differences exist in production ecology among loblolly pine families, and that canopy-level characteristics such as LAI, PAR interception, and "radiation use efficiency" are important in conferring differential family performance. For. Sci. 44(1):64-72.

Plant Production and Soil Microorganisms in Late‐Successional Ecosystems: A Continental‐Scale Study

Annual C inputs from plant production in terrestrial ecosystems only meet the maintenance energy requirements of soil microorganisms, allowing for little or no net annual increase in their biomass. Because microbial growth within soil is limited by C availability, we reasoned that plant production should, in part, control the biomass of soil microorganisms. We also reasoned that soil texture should further modify the influence of plant production on soil C availability because fine—textured soils typically support more microbial biomass than coarse—textured soils. To test these ideas, we quantified the relationship between aboveground net primary production (ANPP) and soil microbial biomass in late—successional ecosystems distributed along a continent—wide gradient in North America. We also measured labile pools of C and N within the soil because they represent potential substrate for microbial activity. Ecosystems ranged from a Douglas—fir forest in the western United States to the grasslands of the mid—continent to the hardwood forest in the eastern U.S. Estimates of ANPP obtained from the literature ranged from 82 to 1460 g°m—2°yr—1. Microbial biomass C and N were estimated by the fumigation—incubation technique. Labile soil pools of C and N and first—order rate constants for microbial respiration and net N mineralization were estimated using a long—term (32 wk) laboratory incubation. Regression analyses were used to relate ANPP and soil texture with microbial biomass and labile soil C and N pools. Microbial biomass carbon ranged from 2 g/m2 in the desert grassland to 134 g/m2 in the tallgrass prairie; microbial N displayed a similar trend among ecosystems. Labile C pools, derived from a first—order rate equation, ranged from 115 g/m2 in the desert grassland to 491 g/m2 in the southern hardwood forest. First—order rate constants for microbial respiration (k) fell within a narrow range of values (0.180 to 0.357 wk—1), suggesting that labile C pools were chemically similar among this diverse set of ecosystems. Potential net N mineralization rates over the 32—wk incubation were linear in most ecosystems with first—order responses only in the alpine tundra, tallgrass prairie, and forests. Microbial biomass C displayed a positive, linear relationship with ANPP (r2 = 0.51), but was not significantly related to soil texture. Labile C also was linearly related to ANPP (r2 = 0.32) and to soil texture (r2 = 0.33). Results indicate that microbial biomass and labile organic matter pools change predictably across broad gradients of ANPP, supporting the idea that microbial growth in soil is constrained by C availability.