Mapping multiple ecosystem services indicators: Toward an objective-oriented approach

被引:41
|
作者
Le Clec'h, Solen [1 ]
Oszwald, Johan [1 ]
Decaens, Thibaud [2 ]
Desjardins, Thierry [3 ]
Dufour, Simon [1 ]
Grimaldi, Michel [3 ]
Jegou, Nicolas [4 ]
Lavelle, Patrick [5 ]
机构
[1] Univ Europeenne Bretagne Rennes 2, Lab LETG Rennes, COSTEL UMR CNRS 6554, Rennes, France
[2] Univ Montpellier 3, Univ Montpellier, Ctr Ecol Fonct & Evolut, EPHE,SupAgro Montpellier,INRA,IRD,UMR CNRS 5175, 1919 Route Mende, F-34293 Montpellier 5, France
[3] Ctr IRD France Nord, UMR iEES Paris 242, IRD, Paris, France
[4] Univ Europeenne Bretagne Rennes 2, Dept Math Appl Sci Sociales, Rennes, France
[5] IRD, UMR iEES Paris 242, CIAT, Medellin, Colombia
关键词
Ecosystem services associations; Ecosystem services indicators; Trade-offs; Hotspots; PCA; Scoring; Land-cover; Brazilian Amazon; LAND-USE; TRADE-OFFS; LANDSCAPE; CONSERVATION; SCALES; BIODIVERSITY; ASSESSMENTS; MANAGEMENT; PRIORITIES; SUPPORT;
D O I
10.1016/j.ecolind.2016.05.021
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Quantifying and mapping ecosystem services (ES), their indicators and their relationships is of crucial importance for environmental management. In this article, we analyze the spatial distribution of multiple-ES indicators at three locations on the pioneer fronts of the Brazilian Amazon. We identify trade-offs and synergies between six ES indicators for soil, vegetation and biodiversity characterization. We also propose spatial representations of multiple-ES indicators (vegetation carbon stocks, rates of water infiltration into soil, soil chemical quality, soil carbon stocks, biodiversity and richness in Sphingidae). Finally, we discuss three different methods to map them depending on the goals of the maps, arguing that maps lean on objective-oriented approaches. The study is based on remote sensing and sampling data from 135 sampling points. We created multiple-ES indicators maps based on Principal Component Analysis (PCA), a score of ES richness, and discrimination of land cover units. PCA is an appropriate tool for showing high correlations between indicators, nevertheless has notable limitations for visual communication. The scoring method may help mapping ES hotspots, however it fails to consider relationships among them. The land-cover-based method has the advantage of being simple and easy to interpret, still it may not consider important indicators not related to land-cover changes. We discuss the interests and limitations of these different ways to map multiple-ES indicators, regarding the final goals of the maps. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:508 / 521
页数:14
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