Estimates Of Net Primary Production Research Articles

Estimation and Climate Impact Analysis of Terrestrial Vegetation Net Primary Productivity in China from 2001 to 2020

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments.

Spatial patterns of China's carbon sinks estimated from the fusion of remote sensing and field-observed net primary productivity and heterotrophic respiration

Accurately assessing the carbon sink and spatial distribution pattern of China's terrestrial ecosystems is of great significance to the implementation of climate change and carbon neutrality strategy. However, the views of various studies are still very controversial due to the differences in carbon sink estimation methods and data sources. In this study, vegetation net primary productivity (NPP) and ecosystem heterotrophic respiration (Rh) estimation models were constructed based on machine learning methods by fusing multisource data, such as remote sensing and ground observation data. The magnitude and spatial pattern of carbon sink in China from 2000 to 2018 were then revealed, and the carbon sink capacity of various ecosystems was quantitatively assessed. The main conclusions include the following: (1) The use of scale-matched carbon input and output data can help reduce the system error in carbon sink estimation. (2) China's terrestrial ecosystem carbon sink since the twenty-first century is approximately 0.458 Pg C/yr, which is equivalent to 22.72% of China's anthropogenic carbon emissions. (3) Deciduous forest has a higher carbon sink capacity than evergreen forest, while coniferous forest has a more stable carbon sink capacity than broad-leaved forest. The magnitude and spatial distribution of carbon sink in China reported in this study provides a scientific reference for achieving carbon neutrality and sustainable development.

Estimation of Biomass Storage and Net Primary Productivity of Five Dominant Forests of Kumaun Himalaya along the Altitudinal Gradient

Estimation of Biomass Storage and Net Primary Productivity of Five Dominant Forests of Kumaun Himalaya along the Altitudinal Gradient

Net Primary Productivity Estimation of Terrestrial Ecosystems in China with Regard to Saturation Effects and Its Spatiotemporal Evolutionary Impact Factors

The net primary productivity (NPP) of vegetation holds a pivotal character for the global carbon balance as a key parameter for characterizing terrestrial ecological processes. The most commonly used indices for estimating vegetation NPP, for instance, the normalized difference vegetation index (NDVI), often suffer from saturation issues that can compromise the accuracy of NPP estimation. This research utilizes a new vegetation index based on the radial basis function (RBF) to estimate vegetation NPP in Chinese terrestrial ecosystems over the past two decades (2001–2020) and investigates the spatiotemporal variation characteristics of NPP and the driving mechanisms. The results indicate that the kernel vegetation index (kNDVI) can effectively alleviate the saturation problem and significantly improve the accuracy of NPP estimation compared to NDVI. Over the past two decades, the NPP of Chinese terrestrial vegetation ranged from 64.13 to 79.72 g C/m2, with a mean value of 72.75 g C/m2, showing a fluctuating upward trend. Changes in the NPP of terrestrial ecosystems in China are mainly affected by precipitation. The dominant factors influencing NPP changes varied over time and had different impacts. For instance, in the period of 2001–2005 the climate had a positive effect on NPP changes, with the dominant factors being evaporation and precipitation. However, in the period of 2010–2015 the dominant climate factors shifted to evaporation and temperature, and their effect on NPP changes became negative. The outcomes of this research aim to serve as a foundation for carbon cycle research and ecosystem environment construction in China.

The challenge of estimating kelp production in a turbid marine environment.

Coastal kelp forests produce substantial marine carbon due to high annual net primary production (NPP) rates, but upscaling of NPP estimates over time and space remains difficult. We investigated the impact of variable underwater photosynthetically active radiation (PAR) and photosynthetic parameters on photosynthetic oxygen production of Laminaria hyperborea, the dominant NE-Atlantic kelp species, throughout summer 2014. Collection depth of kelp had no effect on chlorophyll a content, pointing to a high photoacclimation potential of L. hyperborea towards incident light. However, chlorophyll a and photosynthesis versus irradiance parameters differed significantly along the blade gradient when normalized to fresh mass, potentially introducing large uncertainties in NPP upscaling to whole thalli. Therefore, we recommend a normalization to kelp tissue area, which is stable over the blade gradient. Continuous PAR measurements revealed a highly variable underwater light climate at our study site (Helgoland, North Sea) in summer 2014, reflected by PAR attenuation coefficients (Kd ) between 0.28 and 0.87 m-1 . Our data highlight the importance of continuous underwater light measurements or representative average values using a weighted Kd to account for large PAR variability in NPP calculations. Strong winds in August increased turbidity, resulting in a negative carbon balance at depths >3-4m over several weeks, considerably impacting kelp productivity. Estimated daily summer NPP over all four depths was 1.48 ± 0.97 g C · m-2 seafloor · d-1 for the Helgolandic kelp forest, which is in the range of other kelp forests along European coastlines.

Subsistence of early anatomically modern humans in Europe as evidenced in the Protoaurignacian occupations of Fumane Cave, Italy

Documenting the subsistence strategies developed by early modern humans is relevant for understanding the success of their dispersal throughout Eurasia. Today, we know that there was not a single colonization event and that the process was progressive while coping with the MIS3 abrupt climatic oscillations. Modern humans expanded into the continent by adapting to different topographic situations and by exploiting resources in diverse ecological niches. The northern part of Italy is one of the first European regions where early modern humans are documented. Here, we present the subsistence regimen adopted by the Protoaurignacian groups in two different levels in Fumane Cave based on archaeozoological data. New radiocarbon dates confirm an overlap between Uluzzian and Protoaurignacian occupations, around 42 and 41,000 cal BP, and reveal that modern humans occupied the cave from GI10 to GS9, the last level coinciding with the Heinrich Event 4. The data indicate seasonal site occupations during late spring/summer and that prey exploitation was focused mostly on ibex and chamois, killed in nearby areas. The whole faunal assemblage suggests the presence of early modern humans in a cold environment with mostly open landscapes and patchy woodlands. The estimation of net primary productivity (NPP) in Fumane, compared with other contemporaneous Italian sites, reflects how the NPP fluctuations in the Prealpine area, where Fumane is located, affected the biotic resources in contrast to known Mediterranean sites. From a pan-European perspective, the spatiotemporal fluctuation of the NPP versus the subsistence strategies adopted by Protoaurignacian groups in the continent supports rapid Homo sapiens dispersal and resilience in a mosaic of environments that were affected by significant climate changes.

Net Primary Productivity Estimation Using a Modified MOD17A3 Model in the Three-River Headwaters Region

Remote sensing (RS) models can easily estimate the net primary productivity (NPP) on a large scale. The majority of RS models try to couple the effects of temperature, water, stand age, and CO2 concentration to attenuate the maximum light use efficiency (LUE) in the NPP models. The water effect is considered the most unpredictable, significant, and challenging. Because the stomata of alpine plants are less sensitive to limiting water vapor loss, the typically employed atmospheric moisture deficit or canopy water content may be less sensitive in signaling water stress on plant photosynthesis. This study introduces a soil moisture (SM) content index and an alpine vegetation photosynthesis model (AVPM) to quantify the RS NPP for the alpine ecosystem over the Three-River Headwaters (TRH) region. The SM content index was based on the minimum relative humidity and maximum vapor pressure deficit during the noon, and the AVPM model was based on the framework of a moderate resolution imaging spectroradiometer NPP (MOD17) model. A case study was conducted in the TRH region, covering an area of approximately 36.3 × 104 km2. The results demonstrated that the AVPM NPP greatly outperformed the MOD17 and had superior accuracy. Compared with the MOD17, the average bias of the AVPM was −9.8 gCm−2yr−1, which was reduced by 91.8%. The average mean absolute percent error was 57.0%, which was reduced by 68.2%. The average Pearson’s correlation coefficient was 0.4809, which was improved by 30.0%. The improvements in the NPP estimation were mainly attributed to the decreasing estimation of the water stress coefficient on the NPP, which was considered the higher constraint of water impact on plant photosynthesis. Therefore, the AVPM model is more accurate in estimating the NPP for the alpine ecosystem. This is of great significance for accurately assessing the vegetation growth of alpine ecosystems across the entire Qinghai–Tibet Plateau in the context of grassland degradation and black soil beach management.

Changes in the Spatiotemporal of Net Primary Productivity in the Conventional Lake Chad Basin between 2001 and 2020 Based on CASA Model

Accurate estimation of vegetation Net Primary Productivity (NPP) has important theoretical and practical significance for ecological environment governance, carbon cycle research, and the rational development and utilization of natural resources. In this study, the spatial characteristics, temporal changes, and driving factors of NPP in the Conventional Lake Chad Basin (CLCB) were based on MODIS data by constructing a Carnegie Ames Stanford Approach (CASA) model and using a combination of Residual trends (RESTREND) and correlation analysis. The results showed that from 2001 to 2020, the NPP of the CLCB decreased annually (1.14 g C/m2), mainly because of overgrazing, deforestation, and large-scale irrigation. We conducted a driving factor analysis and found that the main influencing factor of the NPP of the CLCB is high-intensity human activities, including farmland reclamation and animal husbandry. Although the impact of climate change on NPP is not obvious in the short term, climate change may help recover NPP in the long term. The continued reduction in NPP has greatly increased the difficulty of regreening the Sahel; the increase in population density and rapid urbanization have led are major contributing factors to this. Our findings have important implications for the continued implementation of stringent revegetation policies. However, owing to limited data and methods, only the overall change trend of NPP was obtained, and comprehensive follow-up studies are needed.

Biometric-Based Net Primary Production (NPP) and the Effects of a Masting Event on Production Allocation in a Secondary Lucidophyllous Forest in Central Japan

Lucidophyllous (evergreen broad-leaved) forests are the dominant forests in human-dominated subtropical/warm-temperate regions in East Asia. Biometric-based estimates of net primary production (NPP) were conducted in a secondary lucidophyllous forest on Mt. Kinka (35°26′ N, 136°47′ E) near the northern limit of their distribution in central Japan for three years, including the masting event. The forest stand mainly consists of Castanopsis cuspidata (Thunb.) Schottky and Cleyera japonica Thunb. in the canopy and subtree layers, respectively. In 2018, the total NPP of the masting year was 14.53 ± 2.03 ton ha−1 yr−1, including woody NPP (above: 2.63 ± 0.35 ton ha−1 yr−1; below: 0.57 ± 0.08 ton ha−1 yr−1), foliage NPP (4.07 ± 0.23 ton ha−1 yr−1), reproductive NPP (4.81 ± 0.77 ton ha−1 yr−1), and fine root production (Pfr) (2.46 ± 1.84 ton ha−1 yr−1). Pfr and belowground production comprised 16.9% and 20.9%, respectively, of the total NPP. The nut production of C. cuspidata in 2018 (4.31 ± 0.75 ton ha−1 yr−1) was significantly higher than that in 2017 (0.77 ± 0.13 ton ha−1 yr−1) and 2019 (0.23 ± 0.06 ton ha−1 yr−1). No significant change was observed for the three years of foliage NPP and total NPP without Pfr. However, the woody NPP in 2018 (3.20 ± 0.43) was lower than in 2017 (5.37 ± 0.33 ton ha−1 yr−1) and 2019 (4.71 ± 0.38 ton ha−1 yr−1). This suggests that nut production in the masting years compensated by decreasing woody production in the Castanopsis forest.

Estimation of Net Primary Production of Rhizophora mucronata Plantation Using Biomass and Leaf Phenological Characteristics in a Coastal Area at Bangpu, Samut Prakarn Province

Estimation of Net Primary Production of Rhizophora mucronata Plantation Using Biomass and Leaf Phenological Characteristics in a Coastal Area at Bangpu, Samut Prakarn Province

Above- and belowground net-primary productivity: A field-based global database of grasslands.

Net primary productivity (NPP) over global grasslands is crucial for understanding the terrestrial carbon cycling and for the assessments of wild herbivores food security. During the past few decades, numerous field investigations have been conducted to estimate grassland NPP since the measuring criterion released by the International Biological Program. However, a comprehensive NPP database, particularly for belowground NPP (BNPP), in global grasslands is rare to date. Here, field NPP measurements from 438 publications (1957-2018) in global grasslands were collected, critically filtered, and incorporated in a comprehensive global database with observations for aboveground NPP (ANPP), BNPP, total NPP (TNPP), and BNPP fraction (fBNPP ). Associated information on geographical locations, climatic records, grassland types, land use patterns, manipulations subjected to manipulative experiments, sampling year of study sites, as well as NPP measurement methods are also documented. This database included 2985 entries from 1785 study sites. Among them, 806 entries contained paired data of ANPP and BNPP, resulting in the 806 fBNPP data. The study sites encompassed global grasslands with latitudinal range of 54.5° S~78.9° N, longitudinal range of 157.4° W~175.8° E, and altitudes from 0 to 5168 m above sea level, covering broad climatic gradients (-17.6 to 28.8°C in mean annual temperature and 63-2052 mm in mean annual precipitation). This global database is the world's largest paired data of ANPP and BNPP field measurements in grasslands. It can be used to study the spatio-temporal patterns of NPP and its allocation, evaluate the responses of above- and below-ground carbon components to future global changes, and validate the NPP estimation by empirical or process-based models in global grasslands. The database can be freely used for noncommercial applications. We kindly request users cite this data paper when using the database, respecting all the hard work during data compilation.

Hotspots and drivers of compound marine heatwaves and low net primary production extremes

Abstract. Extreme events can severely impact marine organisms and ecosystems. Of particular concern are multivariate compound events, namely when conditions are simultaneously extreme for multiple ocean ecosystem stressors. In 2013–2015 for example, an extensive marine heatwave (MHW), known as the Blob, co-occurred locally with extremely low net primary productivity (NPPX) and negatively impacted marine life in the northeast Pacific. Yet, little is known about the characteristics and drivers of such multivariate compound MHW–NPPX events. Using five different satellite-derived net primary productivity (NPP) estimates and large-ensemble-simulation output of two widely used and comprehensive Earth system models, the Geophysical Fluid Dynamics Laboratory (GFDL) ESM2M-LE and Community Earth System Model version 2 (CESM2-LE), we assess the present-day distribution of compound MHW–NPPX events and investigate their potential drivers on the global scale. The satellite-based estimates and both models reveal hotspots of frequent compound events in the center of the equatorial Pacific and in the subtropical Indian Ocean, where their occurrence is at least 3 times higher (more than 10 d yr−1) than if MHWs (temperature above the seasonally varying 90th-percentile threshold) and NPPX events (NPP below the seasonally varying 10th-percentile threshold) were to occur independently. However, the models show disparities in the northern high latitudes, where compound events are rare in the satellite-based estimates and GFDL ESM2M-LE (less than 3 d yr−1) but relatively frequent in CESM2-LE. In the Southern Ocean south of 60∘ S, low agreement between the observation-based estimates makes it difficult to determine which of the two models better simulates MHW–NPPX events. The frequency patterns can be explained by the drivers of compound events, which vary among the two models and phytoplankton types. In the low latitudes, MHWs are associated with enhanced nutrient limitation on phytoplankton growth, which results in frequent compound MHW–NPPX events in both models. In the high latitudes, NPPX events in GFDL ESM2M-LE are driven by enhanced light limitation, which rarely co-occurs with MHWs, resulting in rare compound events. In contrast, in CESM2-LE, NPPX events in the high latitudes are driven by reduced nutrient supply that often co-occurs with MHWs, moderates phytoplankton growth, and causes biomass to decrease. Compound MHW–NPPX events are associated with a relative shift towards larger phytoplankton in most regions, except in the eastern equatorial Pacific in both models, as well as in the northern high latitudes and between 35 and 50∘ S in CESM2-LE, where the models suggest a shift towards smaller phytoplankton, with potential repercussions on marine ecosystems. Overall, our analysis reveals that the likelihood of compound MHW–NPPX events is contingent on model representation of the factors limiting phytoplankton production. This identifies an important need for improved process understanding in Earth system models used for predicting and projecting compound MHW–NPPX events and their impacts.

Improvement of evapotranspiration estimates for grasslands in the southern Great Plains: Comparing a biophysical model (SWAT) and remote sensing (MODIS)

Study regionMixed- and tall-grass prairies in the southern Great Plains, USA Study focusEstimates of evapotranspiration (ET) are widely available from remote sensing and commonly used for water management. However, this approach is limited by prolonged satellite revisit periods and prominent algorithm-induced bias. We compared ET estimates from MODIS (Moderate Resolution Imaging Spectroradiometer) products and those based on the biophysically-based Soil and Water Assessment Tool (SWAT) to measurements using the eddy covariance (EC) technique for a subhumid, tall-grass prairie site near Stillwater, Oklahoma (OK) and a semiarid, mixed-grass prairie site near Clinton, OK in the southern Great Plains, USA. New hydrological insights for the regionSWAT and MODIS produced ET estimates closer to EC measurements for the tall-grass prairie (calibration site) than for the mixed-grass prairie (validation site), with a better performance from SWAT. For the tall-grass prairie, the R2 values were relatively high and comparable (0.77 and 0.87), and the biases were relatively small (−0.40% and 5.04%) for the SWAT and MODIS comparisons to EC. SWAT performed much better than MODIS for the mixed-grass prairie with R2 values of 0.68 vs. 0.13 and bias of − 1.87% vs. − 45.71%, respectively. The SWAT simulation also reproduced better estimates of aboveground net primary productivity than the MODIS products. This study suggests that site-specific SWAT simulations can produce better ET estimates than MODIS products, especially in the water-limited mixed-grass prairie.

Unravelling net primary productivity dynamics under urbanization and climate change in the western Himalaya

Over the past few decades, climate change and urban expansion have strongly affected vegetation dynamics and overall carbon sink capacity of Himalayan ecosystem. However, the contribution of these two key factors on varying spatio-temporal scales in Himalayan landscapes still lacks in profound analyses. The present study takes Dharamsala and Pithoragarh urban landscapes as examples and uses the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) as the image fusion technique to generate highly resolved, both spatially (30 m) and temporally (monthly), NDVI images. These are used as inputs for Carnegie-Ames-Stanford Approach (CASA) model for Net Primary Productivity (NPP) estimation over the past two decades (2000–2020). During the study period, the NPP loss due to urbanization was 2065.43 kg C in Dharamsala and 401.07 kg C in Pithoragarh. Climate change, measured in terms of interseasonal changes in temperature, rainfall and solar radiation, had considerably contributed to the NPP variations of vegetated areas in both the study sites, however its contribution ratio was relatively lower than residual factors. There was a significant distribution gradient between how and where urban expansion and climate change influenced the NPP. Urban expansion impacts NPP more in plain areas corresponding to new urban land developments while climate change impacts NPP in high elevation mountainous regions dominated by Oak species. The observed declining trend of NPP under the current threat of urban expansion and climate change in the Himalaya highlights and invites the need of attributing importance to ecological issues. The findings of our study encompassing complex Himalayan landscapes could offer scientific perspectives for the management of ecological environment of highly fragile and vulnerable landscapes of the Himalaya.

New tree-ring data from Canadian boreal and hemi-boreal forests provide insight for improving the climate sensitivity of terrestrial biosphere models

Understanding boreal/hemi-boreal forest growth sensitivity to seasonal variations in temperature and water availability provides important basis for projecting the potential impacts of climate change on the productivity of these ecosystems. Our best available information currently comes from a limited number of field experiments and terrestrial biosphere model (TBM) simulations of varying predictive accuracy. Here, we assessed the sensitivity of annual boreal/hemi-boreal forest growth in Canada to yearly fluctuations in seasonal climate variables using a large tree-ring dataset and compared this to the climate sensitivity of annual net primary productivity (NPP) estimates obtained from fourteen TBMs. We found that boreal/hemi-boreal forest growth sensitivity to fluctuations in seasonal temperature and precipitation variables changed along a southwestern to northeastern gradient, with growth limited almost entirely by temperature in the northeast and west and by water availability in the southwest. We also found a lag in growth climate sensitivity, with growth largely determined by the climate during the summer prior to ring formation. Analyses of NPP sensitivity to the same climate variables produced a similar southwest to northeast gradient in growth climate sensitivity for NPP estimates from all but three TBMs. However, analyses of growth from tree-ring data and analyses of NPP from TBMs produced contrasting evidence concerning the key climate variables limiting growth. While analyses of NPP primarily indicated a positive relationship between growth and seasonal temperature, tree-ring analyses indicated negative growth relationships to temperature. Also, the positive effect of precipitation on NPP derived from most TBMs was weaker than the positive effect of precipitation on tree-ring based growth: temperature had a more important limiting effect on NPP than tree-ring data indicated. These mismatches regarding the key climate variables limiting growth suggested that characterization of tree growth in TBMs might need revision, particularly regarding the effects of stomatal conductance and carbohydrate reserve dynamics.

In situ estimates of net primary production in the open‐ocean Gulf of Mexico

AbstractNet primary production (NPP) measurements in the open‐ocean Gulf of Mexico are scarce and mostly limited to satellite‐derived surface estimates. Profiling floats can obtain depth‐resolved chlorophyll (Chl) and backscattering (bbp) data for NPP estimates, but historical float data in this region lacked field calibrations. We adjusted the float data with satellite Chl, generating a consistent dataset for deriving the first year‐round depth‐resolved NPP estimates in this region. The results revealed distinct seasonal patterns in surface NPP and depth‐integrated (0–100 m) total NPP, with the peak values in the winter and summer, respectively. The results showed that for most of the year subsurface (20–100 m) contributes more to the total (0–100 m) NPP than the surface layer (0–20 m), and the major controls of NPP in these two layers were light level and nutrient supply, respectively.

Contrasting strategies of nutrient demand and use between savanna and forest ecosystems in a neotropical transition zone

Abstract. The total demand for and uptake of nutrients by vegetation is rarely quantified or compared across vegetation types. Here, we describe different nutrient use and allocation strategies in neotropical savanna (cerrado) and transitional forest (cerradão) tree communities composed of different species, report leaf nutrient resorption and calculate ecosystem-level nutrient use efficiency. We couple net primary productivity (NPP) estimates with nutrient stoichiometry to quantify nutrient demand and nutrient flows at the whole-stand scale for different components of vegetation biomass. Species from the two vegetation communities showed similar mean nutrient concentrations and nutrient resorption efficiency, except for wood P concentration that was fourfold higher in cerrado than cerradão species. The cerradão showed higher canopy NPP, while fine roots and wood NPP were similar for the two vegetation types. Nutrient requirement in the two vegetation types was dominated by the demands of the canopy, with canopy resorption generally contributing more than 50 % of the total canopy demand for nutrients, while less than 35 % of N, P, K, Ca and Mg were allocated to wood or fine roots. Proportionally, cerrado showed higher nutrient demand from fine roots (over 35 % of the total nutrient demand) and for the wood component (over 13 % of the total nutrient demand), while ∼ 60 %–70 % of the cerradão nutrient demand was allocated to the canopy. The proportional difference in nutrient allocation to the different biomass components suggests cerrado species allocate less nutrients to a given fine root biomass, but more nutrients to a given wood biomass. Our findings suggest that cerradão species are more limited in P and K than cerrado species, inducing higher resorption to compensate for low uptake. Moreover, we found that N uptake for cerradão was higher with lower N use efficiency, i.e. the amount of production per nutrient unit, leading higher N demand compared to the cerrado. This difference in nutrient dynamics explains how similar soils and the same climate dominated by savanna vegetation can also support forest-like formations. Tree species composition is likely the major factor regulating nutrient use, limiting vegetation transitions and influencing nutrient demand at landscape scales.

An absorption‐based approach to improved estimates of phytoplankton biomass and net primary production

AbstractThe abundance and productivity of phytoplankton constrain energy transfer through marine food webs and the export of organic carbon to the deep ocean. Bio‐optical measurements correlate well with phytoplankton carbon (Cphyto), but the effect of taxonomic variability on this relationship is still uncertain. Here, we explore how changes in phytoplankton community composition influence the relationship between the particulate backscatter coefficient (bbp) and Cphyto and present a new approach to estimate phytoplankton biomass more accurately using bbp. We found that using a fixed scaling factor for the conversion of bbp to Cphyto could lead to the underestimation or overestimation of biomass, depending on the dominant taxonomic group in the phytoplankton community. In addition, we demonstrate how a simple ratio of absorption at two wavelengths can be used to provide a coarse approximation of phytoplankton community composition when scaling bbp to Cphyto, thereby improving the estimation of net primary production.

Primary production dynamics on the Agulhas Bank in autumn

The Agulhas Bank is a productive shelf sea, supporting important fish stocks, nursery grounds, and spawning sites. Few studies have examined the dynamics of primary production and the physio-chemical conditions that support this productivity during autumn. We report from a 14-day, 51-station survey of the central and eastern (21-27°E) Agulhas Bank in March 2019, during which we examined water-column structure, macronutrients, chlorophyll-a (total and size-fractionated), diatom cell counts and Net Primary Production (NPP). East to west trends were observed, with surface mixed layers (SML) and stratification increasing to the west. Euphotic zones were deeper than the SML, with SML irradiance conditions indicative of favorable light conditions for NPP. On average, surface waters contained ∼1.2 μmol N L−1 of nitrate (nitrate + nitrite; NO3) and ∼3 μmol Si L−1 of silicic acid, which contrasts with nutrient deficient subtropical source waters. Surface chlorophyll-a ranged from 0.3 to 5.1 mg m−3, with high values inshore and near the shelf break. Nanoplankton (2–20 μm) dominated size-fractionated chlorophyll-a, with microplankton (>20 μm) contributions increasing to the west. Measurements of NPP were collected at seven stations, ranging from 0.3 to 1.1 g C m−2 d−1, with a statistically significant relationship between integrated NPP and surface chlorophyll-a allowing further estimates of NPP (0.1–1.1 g C m−2 d−1). We estimated nitrogen-demand to support NPP, with a comparison to surface NO3 indicating ample nutrients to support daily NPP. Around half of the stations possessed a Subsurface Chlorophyll Maximum (SCM), with chlorophyll-a ranging from 1.7 to 10.3 mg m−3. Characteristics of the SCM (depth, light level, chlorophyll-to-carbon ratios) showed east to west variability, implying that the mechanisms of SCM formation ranged from in-situ growth (east) to photo-acclimation (west).

Vertical Structure in Phytoplankton Growth and Productivity Inferred From Biogeochemical‐Argo Floats and the Carbon‐Based Productivity Model

AbstractEstimates of marine net primary production (NPP) commonly rely on limited in situ 14C incubations or satellite‐based algorithms mainly constrained to the surface ocean. Here we combine data from biogeochemical Argo floats with a carbon‐based productivity model (CbPM) to compute vertically resolved estimates of NPP. Inferred NPP profiles derived by informing the CbPM with float‐based, depth‐resolved, bio‐optical data are able to qualitatively reproduce the vertical structure in NPP inferred from in situ 14C incubations at various ocean regions. At station ALOHA, float‐based estimates agree within uncertainty with productivity observations at depth, but underestimate surface NPP. We test the ability of the CbPM to infer the depth‐resolved structure in NPP from bio‐optical properties in the mixed layer, in similar fashion as how remote sensing algorithms of ocean productivity operate. In Southern Ocean waters, the depth‐reconstructing implementation of the CbPM overestimates phytoplankton division rates and Chl:C below the mixed layer, resulting in artificially high subsurface NPP when compared with the fully float‐informed implementation of the model. The CbPM subsurface extrapolation of phytoplankton Chl, Chl:C, division rates, and NPP improves by accounting for deep nutrient (iron) stress impacts on photoacclimation in the Southern Ocean. This improvement is also observed in vertically integrated NPP, where the mean bias between model implementations in depth‐integrated productivity south of 30°S is reduced by 62% when account for deep iron limitation. Our results demonstrate that profiling data from biogeochemical Argo floats can serve to inform regional adjustments that lead to the improvement of marine productivity algorithms.