# Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series
## 7 Conclusions  
The methodology proposed to infer long-term vegetation changes (MRA-WT) from 15-year daily GPP and NPP 1 km spatial resolution series over Peninsular Spain demonstrated its effectiveness in a multilevel decomposition time series at different temporal scales.  
The main key findings of our study are as follows:  
1. Daily GPP and NPP time series demonstrated to be a high quality input for the temporal analysis, specifically for water stress characterization in sparse vegetation areas (obtained the same precision, 10%, as in dense vegetation areas).  
* A higher temporal resolution offered an opportunity to improve the accuracy in the trend estimates since more significant pixels (72%) were obtained as compared to annual resolution studies (17%).
* A negative clear agreement between GPP and precipitation was observed, particularly in southeastern Spain, eastern Mediterranean coastland, and central Spain. An increase in temperature was shown to favor the carbon assimilation of deciduous broadleaved forest in the north of Spain.
* Evidence of forest vulnerability was confirmed, particularly in Mediterranean conifer species located at low elevations.  
As further research, the quantification of limiting factors, such as water stress in the vegetation response and the production of biomass, is particularly relevant. In this sense, the interannual CUE characterization of this region would be of great interest, as would the water used efficiency (WUE), which is defined as the ratio of carbon gain to water consumption (i.e., evapotranspiration) and quantifies the rate of carbon uptake per unit of water loss.  
Conceptualization, methodology, software, formal analysis, investigation, resources, data curation, writing, visualization, supervision, project administration, and funding acquisition: B.M., S.S.-R., M.C.-T., F.J.G.-H. and M.A.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported thanks to grant CGL2016-75238-R (project ESCENARIOS) funded by Ministerio de Ciencia e Innovacion (MCIN) and grant PID2020-118036 RB-I00 (project ECCE EO) funded by MCIN/AEI/10.13039/501100011033 and by \"ERDF A way of making Europe\". The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with grant number LSA SAF CDOP-3 has also supported this study.  
Part of the data was kindly provided by the Spanish Meteorological Agency (www.aemte.es). The Third Spanish National Forest Inventory data were freely downloaded from the Spanish Ministry of Ecological Transition ([[https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datosnaturaleza/informacion-disponible/ifn3.aspx](https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datosnaturaleza/informacion-disponible/ifn3.aspx)]([https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datosnaturaleza/informacion-disponible/ifn3.aspx](https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datosnaturaleza/informacion-disponible/ifn3.aspx)), accessed on 13 January 2022). We thank [PERSON] for implementing the code to compute Mann-Kendall test with the Theil-Sen slope in MATLAB. We thank [PERSON] for implementing the code to compute SPI in MATLAB.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.