Practical solutions for bottlenecks in ecosystem services mapping

Palomo, Ignacio; Willemen, L.; Drakou, Evangelia G.; Burkhard, Benjamin; Crossman, Neville D.; Bellamy, Chloe; Burkhard, Kremena; Campagne, C. Sylvie; Dangol, Anuja; Franke, Jonas; Kulczyk, Sylwia; Clec’h, Solen Le; Malak, Dania Abdul; Muñoz, Lorena; Naruševičius, Vytautas; Ottoy, Sam; Roelens, Jennifer; Sing, Louise; Thomas, Amy; Meerbeek, Koenraad Van; Verweij, P.J.F.M.

doi:10.3897/oneeco.3.e20713

Cited by 23 publications

(13 citation statements)

References 54 publications

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“…Second, models to map ecosystem services are notoriously difficult to validate and calibrate with primary data representative of the study area, especially across large regions (Palomo et al, 2018), such as in our case. Third, more explicit consideration of the role of landscape configuration in driving ecosystem service supply would increase the biophysical realism of our approach (Chaplin-Kramer et al, 2015).…”

Section: Discussionmentioning

confidence: 96%

Widespread and major losses in multiple ecosystem services as a result of agricultural expansion in the Argentine Chaco

Barral

Villarino

Levers

et al. 2020

Journal of Applied Ecology

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1. Where agriculture expands into tropical and subtropical forests, social-ecological impacts are typically strong. However, where and how frontier development impacts on ecosystem functioning and services is often unclear, including which services trade-off against agricultural production. This constitutes a major barrier towards planning for more sustainable outcomes in deforestation frontiers. 2. Here we assessed spatiotemporal change in multiple ecosystem services in the Argentine Chaco, a global deforestation hotspot. We modelled and mapped five ecosystem functions (i.e. carbon storage in biomass, carbon storage in soil, erosion control, excess rainfall retention by vegetation and soil fertility) which together provide three ecosystem services (i.e. agricultural suitability, climate regulation and flood regulation) for 1985, 2000 and 2013. We then employed this information to identify and map: (a) main trade-offs between ecosystem services and agricultural production, and (b) bundles of changes in ecosystem services through the use of Self-Organizing Maps. 3. Our results highlight that land-use changes since 1985 have led to widespread and drastic declines in ecosystem functions and services across the Argentine Chaco. Mean losses of ecosystem services ranged between 6% and 10% for flood regulation, climate regulation and agricultural suitability. The largest losses occurred in the Dry Chaco subregion between 2000 and 2013. 4. We find two main types of trade-offs between regulating ecosystem services and agricultural production. Increases in crop and pasture production occurred along with large and moderate losses, respectively, in flood regulation and climate regulation over 20% of the region. 5. Our mapping of bundles identified five common patterns of change in ecosystem services, delineating areas of stable or degrading ecosystem service supply. This provides a powerful template for adaptive spatial planning. 6. Synthesis and applications. Using the Argentinean Chaco as an example, we demonstrate how combining fine-scale land-use maps with biophysical models provides deep insights into the spatiotemporal patterns of changes in ecosystem services, and their trade-offs with agricultural production. The periodic updating of maps of trade-offs and bundles of change in ecosystem services provides key inputs for

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Section: Discussionmentioning

confidence: 96%

Widespread and major losses in multiple ecosystem services as a result of agricultural expansion in the Argentine Chaco

Barral

Villarino

Levers

et al. 2020

Journal of Applied Ecology

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“…Moreover, any semi-natural grassland that is located out of a Natura 2000 site is not eligible for agri-environmental support, even if it is actively managed. (Eigenbrod et al, 2010;Palomo et al, 2018) have identified the lack of data as one of the main obstacles in the process. Thus, the use of proxy data or surrogates becomes relevant in national scale ES assessments, especially for regulating and maintenance services (Maes et al, 2012).…”

Section: Es Hotspots Mapmentioning

confidence: 99%

Country-scale mapping of ecosystem services provided by semi-natural grasslands

Villoslada

Ward

Bunce

et al. 2019

Science of The Total Environment

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Estonia and potentially other regions facing similar challenges. MATERIALS AND METHODS Study areaEstonia is located in the Baltic region between Latvia, Russia and Finland, in the border between the Boreal and Nemoral zones (Metzger et al., 2005). Despite its relatively small size (45228 km 2 ) Estonia exhibits a high geological, morphological, and climatic diversity (Arold, 2005). Within Estonia, there are ten semi-natural grassland habitats, based on the Annex I Habitats Directive classification (Council Directive, 1992). Semi-natural grasslands in Estonia have exceptionally high levels of biodiversity, in particular wooded meadows (76 species/m 2 ; Kukk 2004, 1997), alvars (63 species/m 2 ; Partel et al., 1999), floodplain meadows (50 species/m 2 ; Truus and Puusild, 2009), and coastal meadows (34 species/m 2 ; Burnside et al., 2007), among the most species rich habitats in Northern Europe (Benstead et al., 1999). Semi-natural grasslands are the result of long term, low-intensity management practices, in the form of grazing and mowing (Paal, 1998). In Estonia, some of these semi-natural habitats have been managed for centuries (Helm et al., 2005), leading to iconic landscapes such as the Laelatu wooded meadow (Sammul et al., 2003). The area of seminatural grasslands in Estonia has decreased since the late 1950s (Kana et al., 2008).

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“…The output of such studies may also represent a contribution in the framework of ecosystem services mapping, which is becoming mainstream in many sustainability assessments, but its impact on real world decision-making is still limited [56].…”

Section: Discussionmentioning

confidence: 99%

Efficient Estimation of Biomass from Residual Agroforestry

Bascietto

Sperandio

Bajocco

2020

IJGI

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Cost-effective sampling methods for the estimation of variables of interest that are time-consuming are a major concern. Ranked set sampling (RSS) is a sampling method that assumes that a set of sampling units drawn from the population can be ranked by other means without the actual measurement of the variable of interest. We used data on vegetation dynamics from satellite remote sensing as a means in which to rapidly rank sampling units across various land covers and to estimate their residual agroforestry biomass contribution for a small cogeneration facility located in the center of a study area in central Italy. A remote sensing map used as an auxiliary variable in RSS enabled us to cut down the photo-interpretation of the residual biomass present in sampling units from 745 to 139, increase the relative precision of the estimate over common simple random sampling, and avoid individual subjective bias being introduced. The photo-interpretation of the sampling units resulted in a 1.12 Mg ha−1 year−1 mean annual density of residual biomass supply, although unevenly distributed among land cover classes; this led to an estimate of a yearly supply of 132 Gg over the whole 2276 km2 wide study area. Further applications of this study might include the spatial quantification of biomass supply-related ecosystem services.

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Practical solutions for bottlenecks in ecosystem services mapping

Cited by 23 publications

References 54 publications

Widespread and major losses in multiple ecosystem services as a result of agricultural expansion in the Argentine Chaco

Widespread and major losses in multiple ecosystem services as a result of agricultural expansion in the Argentine Chaco

Country-scale mapping of ecosystem services provided by semi-natural grasslands

Efficient Estimation of Biomass from Residual Agroforestry

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