Scaling up functional traits for ecosystem services with remote sensing: concepts and methods

Ecology and Evolution - Tập 6 Số 13 - Trang 4359-4371 - 2016
Oscar J. Abelleira Martínez1,2,3, Alexander K. Fremier3,4, Sven Günter1,5, Zayra Ramos Bendaña1,6, Lee A. Vierling6, Sara M. Galbraith1,7, Nilsa A. Bosque‐Pérez7, Jenny Ordóñez8,8
1Centro Agronómico Tropical de Investigación y Enseñanza, Turrialba, Costa Rica
2Departamento de Ciencias Agroambientales Universidad de Puerto Rico Mayagüez Puerto Rico
3Department of Fish and Wildlife Sciences, University of Idaho, Moscow, Idaho
4School of the Environment, Washington State University, Pullman, Washington
5Thünen Institute of International Forestry and Forest Economics, Hamburg, Germany
6Department of Natural Resources and Society, University of Idaho, Moscow, Idaho
7Department of Plant, Soil, and Entomological Sciences, University of Idaho, Moscow, Idaho
8World Agroforestry Centre Latin America Regional Office Turrialba Costa Rica

Tóm tắt

AbstractEcosystem service‐based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot‐level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community‐weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.

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Ecology and Evolution

Tập 6 Số 13

4359-4371

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