Use of demand for and spatial flow of ecosystem services to identify priority areas

Verhagen, Willem; Kukkala, Aija S.; Moilanen, Atte; Teeffelen, A.J.A. van; Verburg, Peter H.

doi:10.1111/cobi.12872

Cited by 91 publications

(59 citation statements)

References 32 publications

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“…recreation and air quality regulation), than others (e.g. carbon sequestration) (Casado-Arzuaga et al, 2013;Vejre, Jensen, and Thorsen, 2010;Verhagen et al, 2016a). Here, the relations between vegetation and ES are little studied, although ES have received much attention in an urban context (e.g.…”

Section: Introductionmentioning

confidence: 99%

Testing the applicability of ecosystem services mapping methods for peri-urban contexts: A case study for Paris

Roussel

Schulp

Verburg

et al. 2017

Ecological Indicators

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Through their semi-natural and agricultural areas, peri-urban regions are pivotal in providing ecosystem services (ES) to city dwellers. To quantify the ES provided by these areas, it is possible to use ES mapping methods. Many ES mapping methods rely on land cover maps, but most maps are coarse compared to the peri-urban scale. Nevertheless, readily-available land use data and methods are often used to map ES at such scales, without contextualisation. As a result, such methods may not be able to capture the diversity that is present in the periurban vegetation, which could have consequences for their accuracy and furthermore for urban planning policies. To increase our understanding of the applicability of ES mapping methods in peri-urban regions, we assessed to what degree sites with similar plant composition in the green belt of Paris, France, were also projected to have similar ES bundles. We considered two commonly used ES model types: proxy-based models (here: look-up tables) and phenomenological models. We used 252 sites for which botanical survey data were available and applied the ES models to seven ES relevant in the peri-urban context. A cluster analysis was used to group sites, hence facilitating analyse of the spatial congruence between types of vegetation and bundles of ES. Clustering sites based on plant composition revealed six distinct clusters. Clustering sites based on ES bundles as estimated by phenomenological models and proxy-based models, resulted in four and two clusters, respectively. The proxy-based clustering only highlighted broad-leaved forests as an important ES supply source. The phenomenological model

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Section: Introductionmentioning

confidence: 99%

Testing the applicability of ecosystem services mapping methods for peri-urban contexts: A case study for Paris

Roussel

Schulp

Verburg

et al. 2017

Ecological Indicators

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“…Our methods contribute to conservation triage efforts in biodiversity hotspots around the world and can be developed to stakeholders' preferences by weighting variables based on their priorities or by incorporating other variables that are locally pertinent to conservation goals. For instance, variables that reflect important ecosystem services for local communities (Verhagen et al 2017) and key socioeconomic factors (Di Minin et al 2017) could be used in conjunction with similar variables presented here in order to further develop our rankings. Their advantage is that they do not require considerable computational power or high-resolution spatial analyses, which are often inaccessible to developing countries, and they can be adapted to the quantity and quality of data available.…”

Section: Resultsmentioning

confidence: 99%

Methods for prioritizing protected areas using individual and aggregate rankings

et al. 2020

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Despite their legal protection status, protected areas (PAs) can benefit from priority ranks when ongoing threats to their biodiversity and habitats outpace the financial resources available for their conservation. It is essential to develop methods to prioritize PAs that are not computationally demanding in order to suit stakeholders in developing countries where technical and financial resources are limited. We used expert knowledge-derived biodiversity measures to generate individual and aggregate priority ranks of 98 mostly terrestrial PAs on Madagascar. The five variables used were state of knowledge (SoK), forest loss, forest loss acceleration, PA size and relative species diversity, estimated by using standardized residuals from negative binomial models of SoK regressed onto species diversity. We compared our aggregate ranks generated using unweighted averages and principal component analysis (PCA) applied to each individual variable with those generated via Markov chain (MC) and PageRank algorithms. SoK significantly affected the measure of species diversity and highlighted areas where more research effort was needed. The unweighted-and PCA-derived ranks were strongly correlated, as were the MC and PageRank ranks. However, the former two were weakly correlated with the latter two. We recommend using these methods simultaneously in order to provide decision-makers with the flexibility to prioritize those PAs in need of additional research and conservation efforts.

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“…However, ignoring processes such as ES demands tends to produce maps of priority areas in remote zones where benefits to society are relatively small (Verhagen et al 2016). It can also generate maps that give a distorted perception of the scale and extent of a service flow (Drakou et al 2017), or can limit the sustainability applications regarding the supply and use of ES (Burkhard et al 2012;Quintas-Soriano et al 2014).…”

Section: Bottleneck 7 Over-simplificationmentioning

confidence: 99%

Practical solutions for bottlenecks in ecosystem services mapping

Palomo¹,

Willemen²,

Drakou³

et al. 2018

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Use of demand for and spatial flow of ecosystem services to identify priority areas

Cited by 91 publications

References 32 publications

Testing the applicability of ecosystem services mapping methods for peri-urban contexts: A case study for Paris

Testing the applicability of ecosystem services mapping methods for peri-urban contexts: A case study for Paris

Methods for prioritizing protected areas using individual and aggregate rankings

Practical solutions for bottlenecks in ecosystem services mapping

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