Plant traits are both responsive to local climate and strong predictors of primary productivity. We hypothesized that future climate change might promote a shift in global plant traits resulting in changes in Gross Primary Productivity (GPP). We characterized the relationship between key plant traits, namely Specific Leaf Area (SLA), height, and seed mass, and local climate and primary productivity. We found that by 2070, tropical and arid ecosystems will be more suitable for plants with relatively lower canopy height, SLA and seed mass, while far northern latitudes will favor woody and taller plants than at present. Using a network of tower eddy covariance CO2 flux measurements and the extrapolated plant trait maps, we estimated the global distribution of annual GPP under current and projected future plant community distribution. We predict that annual GPP in northern biomes (≥45 °N) will increase by 31% (+8.1 ± 0.5 Pg C), but this will be offset by a 17.9% GPP decline in the tropics (−11.8 ± 0.84 Pg C). These findings suggest that regional climate changes will affect plant trait distributions, which may in turn affect global productivity patterns.

Satellite remote sensing observations show an increased greenness trend over land in recent decades. While greenness observations can indicate increased productivity, estimation of total annual productivity is highly dependent on vegetation response to climate and environmental conditions. Models have been struggling to determine how much carbon is taken up by plants as a result of increased atmospheric CO₂ fertilization. Current remote sensing light use efficiency (LUE) models contain considerable uncertainty due to the lack of spatial and temporal variability in maximum LUE parameter and climate sensitivity defined for global plant functional types (PFTs). We used the optimum LUE (LUE_opt) previously derived from the global FLUXNET network to improve estimation of global gross primary productivity (GPP) for the period 1982–2016. Our results indicate increasing GPP in northern latitudes owing to reduced cold temperature constraints on plant growth, thereby suggesting increasing negative carbon‐climate feedback in high latitudes. In the tropics, by contrast, our results indicate an emerging positive climate feedback, mainly due to increasing atmospheric vapor pressure deficit (VPD). Further pervasive VPD increase is likely to continue to reduce global GPP and amplify carbon emissions.

One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus) in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear’s population.

The National Aeronautics and Space Administration's Soil Moisture Active Passive (SMAP) mission Level 4 Carbon (L4C) product provides model estimates of the Net Ecosystem CO ₂ exchange (NEE) incorporating SMAP soil moisture information. The L4C product includes NEE, computed as total ecosystem respiration less gross photosynthesis, at a daily time step posted to a 9-km global grid by plant functional type. Component carbon fluxes, surface soil organic carbon stocks, underlying environmental constraints, and detailed uncertainty metrics are also included. The L4C model is driven by the SMAP Level 4 Soil Moisture data assimilation product, with additional inputs from the Goddard Earth Observing System, Version 5 weather analysis, and Moderate Resolution Imaging Spectroradiometer satellite vegetation data. The L4C data record extends from March 31, 2015 to present with ongoing production and 8-12 day latency. Comparisons against concurrent global CO ₂ eddy flux tower measurements, satellite solar-induced canopy florescence, and other independent observation benchmarks show favorable L4C performance and accuracy, capturing the dynamic biosphere response to recent weather anomalies. Model experiments and L4C spatiotemporal variability were analyzed to understand the independent value of soil moisture and SMAP observations relative to other sources of input information. This analysis highlights the potential for microwave observations to inform models where soil moisture strongly controls land CO ₂ flux variability; however, skill improvement relative to flux towers is not yet discernable within the relatively short validation period. These results indicate that SMAP provides a unique and promising capability for monitoring the linked global terrestrial water and carbon cycles.