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Design of optimal ecosystem monitoring networks: hotspot detection and biodiversity patterns

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An Erratum to this article was published on 06 May 2017

Abstract

Effective monitoring of ecosystems is crucial for assessing and possibly anticipating shifts, quantifying ecosystem services, and optimal decision making based on shifts and services. The selection of monitoring sites is typically suboptimal following local stakeholder or research interests that do not allow to capture ecosystem patterns and dynamics as a whole. The design of optimal monitoring network is crucial for the accurate determination of biodiversity patterns of ecosystems. A novel model for the design of optimal monitoring networks for biodiversity based on the concept of the value of information (VoI) is proposed. The VoI is assigned to species richness that is the economically and ecologically valuable metric. As a case study the trinational frontier ecosystem among Brazil, Peru, and Bolivia is considered for the model. A multiresolution texture-based model estimates species richness and turnover on satellite imagery calibrated on different sets of information coming from forest plot data organized in network topologies. The optimal monitoring network is the network that minimizes the integrated VoI defined as the variation of the VoI in the 28 years considered. This is equivalent to minimize the sum of the species turnover of the ecosystem. The small world network is identified as the optimal and most resilient monitoring network whose nodes are the hotspots of species richness. The hotspots are identified as the sites whose VoI is the highest for the whole period considered. Hence, the hotspots are the most valuable communities for inferring biodiversity patterns and the most ecologically/economically valuable according to the richness—resilience hypothesis. Most hotspots are honored by the small world network that can be thought as the ”backbone” ecological network of the ecosystem. The small world monitoring network has an accuracy ~50 % higher than other network topologies in predicting biodiversity patterns. This network has the highest VoI at any time step and scale considered; thus, it guarantees to track changes of ecosystems in space and time. The network that results from the optimal trade-off between data value with their uncertainty and relevance, has deep implications for understanding ecosystem function and for management decisions. The model allows to include preferences for ecosystem communities by using differential weights on the VoI of these communities, and economic constraints that limit the extension of the network. Because of the optimal integration of environmental, social, and economical factors the model allows a sustainable monitoring and planning of biodiversity for the future.

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Acknowledgments

The authors acknowledge the funding from NSF CNH: Global Sensitivity and Uncertainty Analysis in the Evaluation of Social-Ecological Resilience: Theoretical Debates Over Infrastructure Impacts on Livelihoods and Forest Change (Award number 1114924). M.C. acknowledges funding form the Minnesota Discovery, Research and InnoVation Economy (MnDRIVE) initiative at the University of Minnesota. The Multiresolution Texture Analysis Model has been submitted for patenting as ''Detecting Species Diversity by Image Texture Analysis'' (Convertino M., and Mangoubi R.) with the support of the Charles Stark Draper Laboratory, Inc., Cambridge (MA), and the approval of the University of Florida Research Foundation.

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Authors and Affiliations

  1. HumNat Lab, School of Public Health, Environmental Health Sciences Division and Public Health Informatics Program, University of Minnesota, Minneapolis, MN, USA

    Matteo Convertino

  2. Institute on the Environment, University of Minnesota, Minneapolis, MN, USA

    Matteo Convertino

  3. Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, USA

    Matteo Convertino

  4. Department of Agricultural and Biological Engineering, and Florida Climate Institute, University of Florida, Gainesville, FL, USA

    Rafael Muñoz-Carpena & Gregory A. Kiker

  5. Department of Sociology, Criminology, and Law, University of Florida, Gainesville, FL, USA

    Stephen G. Perz

  6. Academic Health Center, University of Minnesota, Mayo Memorial Building, Suite 1232 12th fl., MMC807, 420 Delaware St. SE, Minneapolis, MN, 55455, USA

    Matteo Convertino

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An erratum to this article is available at http://dx.doi.org/10.1007/s00477-017-1422-z.

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Convertino, M., Muñoz-Carpena, R., Kiker, G.A. et al. Design of optimal ecosystem monitoring networks: hotspot detection and biodiversity patterns. Stoch Environ Res Risk Assess 29, 1085–1101 (2015). https://doi.org/10.1007/s00477-014-0999-8

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