Integrating land cover structure and functioning to predict biodiversity patterns: a hierarchical modelling framework designed for ecosystem management

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Bastos, R (Author) Other; Monteiro, AT (Author) Other; Carvalho, D (Author) Other; Gomes, C (Author) Other; Travassos, P (Author) Other; Honrado, JP (Author) FCUP; Santos, M (Author) Other; Cabral, JA (Author) Other

Journal

Title: Landscape Ecology

Vol. 31

Pages: 701-710

ISSN: 0921-2973

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00G-VK7

DOI: 10.1007/s10980-015-0302-5

Abstract (EN): Land-use/land-cover (LU/LC) dynamics is one of the main drivers of global environmental change. In the last years, aerial and satellite imagery have been increasingly used to monitor the spatial extent of changes in LU/LC, deriving relevant biophysical parameters (i.e. primary productivity, climate and habitat structure) that have clear implications in determining spatial and temporal patterns of biodiversity, landscape composition and ecosystem services. An innovative hierarchical modelling framework was developed in order to address the influence of nested attributes of LU/LC on community-based ecological indicators. Founded in the principles of the spatially explicit stochastic dynamic methodology (StDM), the proposed methodological advances are supported by the added value of integrating bottom-up interactions between multi-scaled drivers. The dynamics of biophysical multi-attributes of fine-scale subsystem properties are incorporated to inform dynamic patterns at upper hierarchical levels. Since the most relevant trends associated with LU/LC changes are explicitly modelled within the StDM framework, the ecological indicators' response can be predicted under different social-economic scenarios and site-specific management actions. A demonstrative application is described to illustrate the framework methodological steps, supporting the theoretic principles previously presented. We outline the proposed multi-model framework as a promising tool to integrate relevant biophysical information to support ecosystem management and decision-making.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10