In the light of unprecedented change in global biodiversity, realtime and accurate ecosystem and biodiversity assessments are becoming increasingly essential. Nevertheless, estimation of biodiversity using ecological field data can be difficult for several reasons. For instance, for very large areas, it is challenging to collect data that provide reliable information. Some of these restrictions in Earth observation can be avoided through the use of remote sensing approaches.Various studies have estimated biodiversity on the basis of the Spectral Variation Hypothesis (SVH). According to this hypothesis, spectral heterogeneity over the different pixel units of a spatial grid reflects a higher niche heterogeneity, allowing more organisms to coexist. Recently, the spectral species concept has been derived, following the consideration that spectral heterogeneity at a landscape scale corresponds to a combination of subspaces sharing a similar spectral signature. With the use of high resolution remote sensing data, on a local scale, these subspaces can be identified as separate spectral entities, the so called "spectral species". Our approach extends this concept over wide spatial extents and to a higher level of biological organization. We applied this method to MODIS imagery data across Europe.

Background Obtaining instantaneous gas exchanges data is fundamental to gain information on photosynthesis. Leaf level data are reliable, but their scaling up to canopy scale is difficult as they are acquired in standard and/or controlled conditions, while natural environments are extremely dynamic. Responses to dynamic environmental conditions need to be considered, as measurements at steady state and their related models may overestimate total carbon (C) plant uptake. Results In this paper, we describe an automatic, low-cost measuring system composed of 12 open chambers (60 × 60 × 150 cm; around 400 euros per chamber) able to measure instantaneous CO2 and H2O gas exchanges, as well as environmental parameters, at canopy level. We tested the system’s performance by simulating different CO2 uptake and respiration levels using a tube filled with soda lime or pure CO2, respectively, and quantified its response time and measurement accuracy. We have been also able to evaluate the delayed response due to the dimension of the chambers, proposing a method to correct the data by taking into account the response time ($${t}_{0}$$ t 0 ) and the residence time (τ). Finally, we tested the system by growing a commercial soybean variety in fluctuating and non-fluctuating light, showing the system to be fast enough to capture fast dynamic conditions. At the end of the experiment, we compared cumulative fluxes with total plant dry biomass. Conclusions The system slightly over-estimated (+ 7.6%) the total C uptake, even though not significantly, confirming its ability in measuring the overall CO2 fluxes at canopy scale. Furthermore, the system resulted to be accurate and stable, allowing to estimate the response time and to determine steady state fluxes from unsteady state measured values. Thanks to the flexibility in the software and to the dimensions of the chambers, even if only tested in dynamic light conditions, the system is thought to be used for several applications and with different plant canopies by mimicking different environmental conditions.

Photosynthesis has been mainly studied under steady-state conditions even though this assumption results inadequate for assessing the biochemical responses to rapid variations occurring in natural environments. The combination of mathematical models with available data may enhance the understanding of the dynamic responses of plants to fluctuating environments and can be used to make predictions on how photosynthesis would respond to non-steady-state conditions. In this study, we present a leaf level System Dynamics photosynthesis model based and validated on an experiment performed on two soybean varieties, namely, the wild type Eiko and the chlorophyll-deficient mutant MinnGold, grown in constant and fluctuating light conditions. This mutant is known to have similar steady-state photosynthesis compared to the green wild type, but it is found to have less biomass at harvest. It has been hypothesized that this might be due to an unoptimized response to non-steady-state conditions; therefore, this mutant seems appropriate to investigate dynamic photosynthesis. The model explained well the photosynthetic responses of these two varieties to fluctuating and constant light conditions and allowed to make relevant conclusions on the different dynamic responses of the two varieties. Deviations between data and model simulations are mostly evident in the non-photochemical quenching (NPQ) dynamics due to the oversimplified combination of PsbS- and zeaxanthin-dependent kinetics, failing in finely capturing the NPQ responses at different timescales. Nevertheless, due to its simplicity, the model can provide the basis of an upscaled dynamic model at a plant level.