Project Funding: 2018 - 2021

NRA: 2016 NASA: Group on Earth Observations Work Programme   

Funded by NASA

Abstract:
Biodiversity dynamics underpin the regulation and provisioning of many ES, but ES models used commonly in decision-support tools do not adequately represent the relationships between different levels biodiversity (genes, species, ecosystems) and ES. Most ES tools use land-use/land cover (LULC) as the sole input representing ecosystem contribution to ES, and therefore neglect the potential impacts of within-habitat variation or changes in diversity. The Essential Biodiversity Variable (EBV) framework establishes metrics for spatially-explicit representation of biodiversity change over time and addresses these multiple dimensions of biodiversity. In theory, EBVs can feed into models for ecosystem services to represent the benefits of biodiversity to people. However, few analyses demonstrate the extent to which EBVs measured by Earth Observations (EO) represent biodiversity dynamics across multiple taxa, and no tools currently translate EBVs into ES and/or their contribution to human well-being. We propose a two-stage project to fill these gaps: the first stage links EO to the EBVs most relevant to ES, and the second stage links EBVs to ES and their benefits to people. We will develop and test models that use EO to calculate ecosystem-level EBVs (E-EBVs) and predict biodiversity patterns at ecosystem and species level (S-EBVs). These EBVs will then serve as inputs to new models that predict three focal ES that exemplify three categories of services: regulating (water regulation), supporting of provisioning (pollination for crop production), and cultural (wildlife for intrinsic value, and wildlife-based tourism). We will test these new EBV-driven ES models against those driven purely by LULC using independent data on ES production. These models will demonstrate several pathways for using EOs to link biodiversity to ES. For water regulation, E-EBVs calculated solely from EO may be used instead of LULC in process-based models that include other inputs (e.g., precipitation, soil depth and bulk density, etc.) to produce ES supply (seasonal water availability). For pollination and tourism, S-EBVs can be derived from EO with additional predictor variables, ideally from globally-available data (e.g., microclimate, soil type, aspect, etc.). These spatially-explicit S-EBVs can then be used individually or in combination to serve as proxies for ES providers, like pollinators or wildlife, that are essentially the origin of supply for these ES. In both cases these models of ES supply, when combined with models or data for ES demand (e.g., infrastructure, access, location of beneficiaries, etc.), will produce spatially-explicit estimates of ES benefits to people. We plan to analyze and compare EO-EBVs derived from multiple EO mission data sources in Costa Rica, including Landsat, MODIS, Hyperion, and GLAS, as well as airborne data from the 2005 LVIS mission and 2005 CARTA mission. To link EO to E-EBVs to S-EBVs, we will develop predictive models based on species observations collected in Costa Rica, augmented with additional field collection. To link EBVs to ES, we will use a variety of techniques suited to the specific service, including sophisticated hydrologic modeling (water regulation), field exclusion experiments (pollination), and regression analysis with social media data (tourism). For each of these linkages we will: 1) collect or process data for model development, 2) develop model, 3) generate nationwide maps for Costa Rica, 4) test model against independent data sets, and given testing results, 5) produce generalized software tools. We expect this project to fill key gaps in our knowledge of how EO can predict patterns of biodiversity at different levels, and in our understanding of the relationship between biodiversity and ecosystem functions and services. This is an important step forward for the fields of EO and ES, and for support of decisions on conservation of biodiversity and sustainable development.

Publications:

Echeverri, A., Smith, J. R., MacArthur-Waltz, D., Lauck, K. S., Anderson, C. B., Monge Vargas, R., Alvarado Quesada, I., Wood, S. A., Chaplin-Kramer, R., Daily, G. C. 2022. Biodiversity and infrastructure interact to drive tourism to and within Costa Rica. Proceedings of the National Academy of Sciences. 119(11). DOI: 10.1073/pnas.2107662119

Echeverri, A., Smith, J. R., MacArthur-Waltz, D., Lauck, K. S., Anderson, C. B., Monge Vargas, R., Alvarado Quesada, I., Wood, S. A., Chaplin-Kramer, R., Daily, G. C. 2022. Biodiversity and infrastructure interact to drive tourism to and within Costa Rica. Proceedings of the National Academy of Sciences. 119(11). DOI: 10.1073/pnas.2107662119

Langhans, K. E., Schmitt, R. J., Chaplin-Kramer, R., Anderson, C. B., Vargas Bolanos, C., Vargas Cabezas, F., Dirzo, R., Goldstein, J. A., Horangic, T., Miller Granados, C., Powell, T. M., Smith, J. R., Alvarado Quesada, I., Umana Quesada, A., Monge Vargas, R., Wolny, S., Daily, G. C. 2022. Modeling multiple ecosystem services and beneficiaries of riparian reforestation in Costa Rica. Ecosystem Services. 57, 101470. DOI: 10.1016/j.ecoser.2022.101470

More details may be found in the following project profile(s):

• NASA Biological Diversity & Ecological Conservation (BDEC) Project Profile