Enhancing interoperability to facilitate implementation of REDD+: case study of Mexico

Vargas, Rodrigo; Alcaraz‐Segura, Domingo; Birdsey, Richard A.; Brunsell, Nathaniel A.; Cruz-Gaistardo, Carlos Omar; Jong, Bernardus Hendricus jozeph De; Etchevers, J.; Guevara, Mário; Hayes, D. J.; Johnson, Kristofer D.; Loescher, Henry W.; Paz, Fernando; Ryu, Youngryel; Sánchez-Mejía, Z. M.; Toledo-Gutierrez, Karla P.

doi:10.1080/17583004.2017.1285177

Cited by 37 publications

(33 citation statements)

References 38 publications

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“…Gulf of Mexico and Caribbean Sea). Consequently, there is a need to develop reference frameworks for long-term monitoring projects of carbon stocks in mangroves of Mexico and implementation of REDD+initiatives (Vargas et al 2017).…”

Section: Carbon Stocks and Greennessmentioning

confidence: 99%

“…AGC and SOCD respectively) in mangrove forests across Mexico. Mexico is a megadiverse country and a global hotspot for conservation priorities (Myers et al 2000) with high potential for implementation of REDD +initiatives (Vargas et al 2017). It has 755 555 ha of mangroves (Valderrama-Landeros et al 2017) where trees can be taller than 35 m or smaller than 1 m as a consequence of gradients of local biophysical factors (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Greenness trends and carbon stocks of mangroves across Mexico

Vázquez‐Lule

Colditz

Herrera-Silveira³

et al. 2019

Environ. Res. Lett.

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Mangroves cover less than 0.1% of Earth's surface, store large amounts of carbon per unit area, but are threatened by global environmental change. The capacity of mangroves productivity could be characterized by their canopy greenness, but this property has not been systematically tested across gradients of mangrove forests and national scales. Here, we analyzed time series of Normalized Difference Vegetation Index (NDVI), mean air temperature and total precipitation between 2001 and 2015 (14 years) to quantify greenness and climate variability trends for mangroves not directly influenced by land use/land cover change across Mexico. Between 2001 and 2015 persistent mangrove forests covered 432 800 ha, representing 57% of the total current mangrove area for Mexico. We found a temporal greenness increase between 0.003 [0.001-0.004] and 0.004 [0.002-0.005] yr −1 (NDVI values±95%CI) for mangroves located over the Gulf of California and the Pacific Coast, with many mangrove areas dominated by Avicennia germinans. Mangroves developed along the Gulf of Mexico and Caribbean Sea did not show significant greenness trends, but site-specific areas showed significant negative greenness trends. Mangroves with surface water input have above ground carbon stocks (AGC) between 37.7 and 221.9 Mg C ha −1 and soil organic carbon density at 30 cm depth (SOCD) between 92.4 and 127.3 Mg C ha −1 . Mangroves with groundwater water input have AGC of 12.7 Mg C ha −1 and SOCD of 219 Mg C ha −1 . Greenness and climate variability trends could not explain the spatial variability in carbon stocks for most mangrove forests across Mexico. Site-specific characteristics, including mangrove species dominance could have a major influence on greenness trends. Our findings provide a baseline for national-level monitoring programs, carbon accounting models, and insights for greenness trends that could be tested around the world.

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Section: Carbon Stocks and Greennessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Greenness trends and carbon stocks of mangroves across Mexico

Vázquez‐Lule

Colditz

Herrera-Silveira³

et al. 2019

Environ. Res. Lett.

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“…Because iterative near-term forecasting requires a dynamic integration of models, predictions, and data, Dietze et al (2018) highlight approaches to data management, model construction and evaluation, and cyberinfrastructure that are necessary to effectively implement this type of forecasting (Box 1). Data needs to be released quickly under open licenses (Vargas et al, 2017; Dietze et al, 2018) and structured so that it can be used easily by a variety of researchers and in multiple modeling approaches (Borer et al, 2009; Strasser et al, 2011). Models need to be able to deal with uncertainty, in both the predictors and the predictions, to properly convey uncertainty in the resulting forecasts (Diniz-Filho et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Developing an automated iterative near-term forecasting system for an ecological study

White

Yenni

Taylor

et al. 2018

Preprint

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131. Most forecasts for the future state of ecological systems are conducted once and 14 never updated or assessed. As a result, many available ecological forecasts are not 15 based on the most up-to-date data, and the scientific progress of ecological 16 forecasting models is slowed by a lack of feedback on how well the forecasts 17 perform. 18 2. Iterative near-term ecological forecasting involves repeated daily to annual scale 19 1 forecasts of an ecological system as new data becomes available and regular 20 assessment of the resulting forecasts. We demonstrate how automated iterative 21 near-term forecasting systems for ecology can be constructed by building one to 22 conduct monthly forecasts of rodent abundances at the Portal Project, a long-term 23 study with over 40 years of monthly data. This system automates most aspects of 24 the six stages of converting raw data into new forecasts: data collection, data 25 sharing, data manipulation, modeling and forecasting, archiving, and presentation 26 of the forecasts. 273. The forecasting system uses R code for working with data, fitting models, making 28 forecasts, and archiving and presenting these forecasts. The resulting pipeline is 29 automated using continuous integration (a software development tool) to run the 30 entire pipeline once a week. The cyberinfrastructure is designed for long-term 31 maintainability and to allow the easy addition of new models. Constructing this 32 forecasting system required a team with expertise ranging from field site 33 experience to software development. 34 4. Automated near-term iterative forecasting systems will allow the science of 35 ecological forecasting to advance more rapidly and provide the most up-to-date 36 forecasts possible for conservation and management. These forecasting systems 37 will also accelerate basic science by allowing new models of natural systems to 38 be quickly implemented and compared to existing models. Using existing 39 technology, and teams with diverse skill sets, it is possible for ecologists to build 40 automated forecasting systems and use them to advance our understanding of 41 natural systems. 42 Forecasting the future state of ecological systems is important for management, 45 conservation, and evaluation of how well models capture the processes governing 46 ecological systems (Clark et al., 2001; Tallis & Kareiva, 2006; Díaz et al., 2015; Dietze, 47 2017). In 2001, Clark et al. (2001 called for a more central role of forecasting in 48 ecology. Since then, an increasing number of ecological forecasts are being published 49 that focus on societally important questions from daily to decadal time scales (Dietze et 50 al., 2018). At daily scales, ecological forecasts predict the occurrence of environmental 51 issues like toxic algal blooms (Stumpf et al., 2009) and pollen (Prank et al., 2013). At 52 monthly scales, forecasts are used to predict the stocks of fisheries (NOAA, 2016) and 53 the probability of coral bleaching events (Liu et al., 2018). At decada...

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“…Challenges related to conceptual barriers include syntactic and semantic differences in the types of information (Madin, Bowers, Schildhauer, & Jones, ); technological barriers such as incompatibility of information technologies (e.g., methods to acquire, process, store, exchange, and communicate data; Peters, Loescher, SanClements, & Havstad, ); organizational barriers related to current institutional responsibility and authority such as with institutions, networks, or governments (Supplemental Table S3); and cultural barriers that can be country‐specific but must be considered to increase interoperability of networks (Vargas et al., ).…”

Section: The Iscn As a Platform For Communication Modeling And Datamentioning

confidence: 99%

“…Arguably, this can only be achieved by increasing interoperability within ISCN and among partner networks, organizations, and members. Interoperability is broadly defined as the ability of a system to work with or use the parts of another system (Chen, Doumeingts, & Vernadat, 2008;Vargas et al, 2017).…”

Section: Interoperability Of Iscnmentioning

confidence: 99%