Optimizing an Environmental Observatory Network Design Using Publicly Available Data

Abstract

There is a need to optimize resources for large‐scale environmental monitoring efforts, especially in developing countries. We tested a flexible framework to optimize the design (i.e., selection of study sites) of an environmental observatory network (EON) using publicly available data for Mexico. This country represents a challenge for designing EONs because of its megadiversity and large climate and ecological heterogeneity. We address three pervasive challenges for designing EONs: (1) How to characterize and delineate ecologically similar areas, (2) how to set geographic priorities to establish new representative study sites, and (3) how to assess the representativeness of current and potential new study sites. We used unsupervised cluster analysis to spatially delineate ecologically similar sampling domains. We identified the most representative sites within each domain using a conditioned Latin Hypercube‐sampling strategy. Finally, we demonstrated the applicability of this approach by assessing the spatial representativeness of the eddy covariance network in Mexico (i.e., MexFlux). At least 84 distributed sampling sites are needed to represent >45% of the spatial heterogeneity of gross primary productivity (GPP) dynamics (i.e., GPP_mean and GPP_cv) and evapotranspiration (ET) dynamics (i.e., ET_mean and ET_cv) at the national level. The current array of MexFlux only represents 3% of GPP and 5% of ET dynamics spatial variability at the national‐level, while the same number of sites organized under an optimal framework nearly doubled these estimates. Our framework is based on a data‐driven approach and publicly available sources of information, so it could be applied anywhere in the world.

The Case for a Critical Zone Science Approach to Research on Estuarine and Coastal Wetlands in the Anthropocene

“…Harnessing the potential of environmental observatories to improve Earth system models also necessitates better integration between sites (Baatz et al 2018), with the added benefit of optimizing resources (Hinckley et al 2016). Using Mexico as an example, Villareal et al (2019) propose a framework to improve the efficiency and effectiveness of environmental observatories and suggest that such a model could be applied more generally. Not only do such networks encourage researchers to keep pace with technological and conceptual scientific advances (Huang et al 2020), they also foster interaction and communication with society at large which is essential to retaining their relevance and, accordingly, funding (Richter et al 2018).…”

Representativeness of FLUXNET Sites Across Latin America

“…Absence data points were generated by random selection, as randomly selected points usually produce reliable distribution models (Barbet‐Massin et al., 2012). Also, the uncertainty of each model was assessed by repeating 10 times each model with only one iteration and calculate their mean and standard deviation, as it was performed in a previous study (Villarreal et al., 2019). We assessed the 95% confidence interval to determine differences between represented and nonrepresented areas for the different environmental variables.…”

“…Most terrain parameters and soil resources variables were downloaded from worldgrids.org (accessed May 2018), but soil organic carbon was downloaded from www.fao.org (accessed May 2018) and soil phosphorus from data.nasa.gov (accessed May 2018). Respectively, NASA-MODIS products MOD17A2 and MOD16A2 from 2001 to 2014 were used to characterize GPP and ET as previously done for assessment VILLARREAL AND VARGAS (Villarreal et al, 2018(Villarreal et al, , 2019. The statistic parameters used to characterize GPP and ET dynamics were the mean (GPP_mean, ET_mean) and the coefficient of variation (GPP_CV, ET_CV), since they have been used as proxies to represent ecosystem productivity and seasonality, respectively (Alcaraz-Segura et al, 2017;Villarreal et al, 2018Villarreal et al, , 2019.…”

“…A common approach to assess the representativeness of EONs has been the estimation of minimum distances within a multivariate space (Hargrove et al, 2003;Sulkava et al, 2011). An alternative approach is the use of machine learning techniques, which estimate the spatial distribution of the environmental range monitored by the EON's study sites (i.e., nodes) across the spatial domain of the network (Villarreal et al, 2018(Villarreal et al, , 2019. We propose that it is possible to assess the representativeness of EONs based on concepts derived from species distributions models (SDMs).…”

Observing system simulation experiments double scientific return of surface-atmosphere synthesis

“…Previous studies employed data-intensive scientific discovery for post-field OSD assessments (e.g., Kumar et al, 2016;Chu et al, 2017;Mahecha et al, 2017;Koffi et al, 2013;Montanari et al, 2012;Loescher et al, 2014;Villarreal et al, 2019). In 85 comparison, one innovation of the presented approach is that it provides design information prior to testing OSDs in the field.…”