Ecosystem mapping for the implementation of the European Biodiversity Strategy at the national level: The case of Italy

Blasi, C.; Capotorti, Giulia; Ortí, Marta María Alós; Anzellotti, I.; Attorre, Fabio; Azzella, Mattia M.; Carli, Emanuela; Copiz, R.; Garfì, Vittorio; Manes, Fausto; Marando, Federica; Marchetti, Marco; Mollo, Barbara; Zavattero, Laura

doi:10.1016/j.envsci.2017.09.002

Cited by 45 publications

(24 citation statements)

References 65 publications

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“…This level of uncertainty shows that classification identified dominant classes, in other words each point was allocated to an ecological class with an absolute minimum probability of at least 0.56. In addition, the ecological classification also shows that areas (the 6 sampling sites) with putatively the same land cover are still ecologically different when considering the full set of environmental variables (temperature, precipitation, elevation, evapo-transpiration and vegetation), and that geographical proximity is a dominant factor in ecological clustering (see also (74,75)) ( Figure 4). It is therefore not surprising that sites cluster much more strongly within country than within ecotype; e.g.…”

Section: Discussionmentioning

confidence: 97%

Improved spatial ecological sampling using open data and standardization: an example from malaria mosquito surveillance

Sedda

Lucas

Djogbénou

et al. 2018

Preprint

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Vector-borne disease control relies on efficient vector surveillance, mostly carried out using traps whose number and locations are often determined by expert opinion rather than a rigorous quantitative sampling design. In this work we first propose a framework for ecological sampling design which in its preliminary stages can take into account environmental conditions obtained from open data (i.e. remote sensing and meteorological stations). These environmental data are used to delimit the area into ecologically homogenous strata. By employing a model-based sampling design, the traps are deployed among the strata using a mixture of random and grid locations which allows balancing predictions and fitting accuracies. Sample sizes and the effect of ecological strata on sample sizes are estimated from previous sampling campaigns. Notably, we found that a configuration of 30 locations with 4 households each (120 traps) will have a similar accuracy in the estimates of mosquito abundance as 300 random samples. In addition, we show that random sampling independently from ecological strata, produces biased estimates of the mosquito abundance. Finally, we propose standardizing reporting of sampling designs to allow transparency and repetition / re-use in subsequent sampling campaigns. KeywordsMosquito sampling, stratification, lattice sampling design, model-based geostatistics, Sub-Saharan Africa. variance and Temp mean. Medium EVI mean, elevation and Precipitation. Red 20 Forest. Medium ET mean amplitude and variance, EVI mean, variance and amplitude, Temp mean and variance, Elevation and Precipitation; High Temp amplitude. Dark Green 25 Mixture of Cultivated land, Shrubland and Wetland. Low ET mean, EVI variance and Temp mean. Medium Elevation and Precipitation. Orange 35 Mixture of Shrubland and Grassland. Low Temp amplitude and Precipitation; Medium ET mean and variance, EVI mean, variance and amplitude, Temp mean and variance; High ET amplitude. Light green 45 Mixture of Shrubland, Grassland and Wetland. Low EVI amplitude, Temp variance and Precipitation; Medium ET mean and amplitude, EVI mean and variance, Temp mean and variance; High Temp mean and ET variance. Pink 55 Mixture of Urban, Forest, Wetland and Grassland. Low Temp mean; Medium ET mean, EVI mean and variance, and Precipitation; High Elevation. Yellow 60 Wetland. Low ET mean, EVI mean and variance; Medium Temp mean and Elevation; High Precipitation. Navy 65 Mixture of Urban, Tundra, Wetland, Water bodies and Grassland. Low ET variance and amplitude; Medium Elevation; High ET mean, EVI mean, variance and amplitude, Temp amplitude and Precipitation. Purple 75 Mixture of Water bodies and Urban. Low ET mean, EVI mean and amplitude, Temp variance and Precipitation; Medium ET amplitude and EVI variance; High ET variance and Temp mean. Sky 85 Mixture of Grassland, Water bodies and Urban. Low ET variance and amplitude, and Elevation; Medium Temp mean; High ET mean, EVI mean, variance and amplitude, Temp variance and Precipitation. Brown 95 Mixture of Wetland, Wate...

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Section: Discussionmentioning

confidence: 97%

Improved spatial ecological sampling using open data and standardization: an example from malaria mosquito surveillance

Sedda

Lucas

Djogbénou

et al. 2018

Preprint

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“…Targeted coordination per sector and national GI-specific policies would help achieve the multifunctional nature of GI to a greater extent. Further study is needed of whether sectoral policy coordination would be best achieved by focusing on the five sectors that already seem to include principles for GI (water management; agriculture, forestry and fisheries; climate change adaptation and mitigation; environmental protection; rural development) [1,2,17,[38][39][40] or by focusing on the policy sectors that seem to lack such principles (finance, energy, health, social services) [5].…”

Section: Discussionmentioning

confidence: 99%

“…Several policy themes complement GI. In the academic literature, three sectors or policy themes provide the greatest synergies with GI in Europe; 'biodiversity' [2,17], 'rural development and agriculture' [38][39][40] and 'urban development and green areas' [5,8,9,13,41,42]. In their policy analysis of 14 European countries, Davies and Lafortezza [1] report that four synergetic policy themes were prominent in national policies: social cohesion, green economy, biodiversity and health.…”

Section: Background and Theoretical Frameworkmentioning

confidence: 99%

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Implementing Green Infrastructure in Spatial Planning in Europe

Slätmo

Nilsson

Turunen

2019

Land

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Interest in green infrastructure (GI) has grown in research, policy and planning in recent decades. The central idea behind GI is the understanding of the physical non-built-up environment as an infrastructure capable of delivering a wide variety of benefits to society, including the ability to preserve biodiversity; to provide food, feed, fuel and fibre; to adapt to and mitigate climate change and to contribute to enhanced human health and quality of life. The European Union (EU) has had a GI strategy since 2013, and member states are involved in several strategic and applied GI initiatives and projects. The aim of this study is to explore if and how the European strategy has been implemented. The study adds to the body of knowledge of current GI policies and measures in Europe via an online survey and insights into previous research. The survey reveals that GI is integrated into one or more policy sectors in all 32 countries covered. In 11 of the 32 countries, GI-specific policies are already in place or are being drawn up at a national level. In general, the respondents see the responsibility for GI policy and strategy as a matter of national governments and the implementation as a matter of local governments. They also see the LIFE+ and Horizon 2020 project funds, the European Regional Development Fund (ERDF) and the European Agricultural Fund for Rural Development (EAFRD), as the most important EU funding sources for the implementation of the GI strategy. The study also identifies availability of georeferenced information, zoning and biotope area factor as three of the spatial planning tools used to implement GI.

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“…Land use/land cover types were classified by combining the thematic information of the Lazio Region land use map at 1:10,000 scale [65], which extends the EU Corine Land-Cover legend up to the IV/V hierarchical level, with the thematic information of the vegetation cover Map of the Province of Rome at 1:25,000 scale [51]. Such an integration allowed the identification of the different ecosystem types occurring in the study area [66].…”

Section: Step C-prioritisation Of Gi Restoration and Conservation Actmentioning

confidence: 99%

Local Scale Prioritisation of Green Infrastructure for Enhancing Biodiversity in Peri-Urban Agroecosystems: A Multi-Step Process Applied in the Metropolitan City of Rome (Italy)

2019

Self Cite

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Urban–rural interfaces represent complex systems that require complex solutions for sustainable development and resilience against pollution, habitat fragmentation, biodiversity loss and impaired flux of ecosystem services (ES). Green infrastructure (GI) is increasingly recognised as an effective tool for addressing such a complexity, but needs priority setting to maximise benefits and minimise drawbacks of implementation. Therefore, a prioritisation approach focused on biodiversity and ES in peri-urban areas is required. In the present work, a systematic and hierarchical framework is proposed for setting priority GI objectives, location and actions aimed at enhancing local biodiversity, ES flux and farming sustainability in urban peripheries. By means of a case study in the Metropolitan City of Rome, the framework allowed identification of the main demand for ES and biodiversity; the most suitable location for GI implementation; and the best cost-effective actions. The GI implementation showed an improvement in terms of wooded hedgerow density, an increase regarding the ecological connectivity of riparian ecosystems, and an increment of agroecosystems designated to enhance the ecological network and wildlife support. Finally, the prioritisation framework contributes to fostering environmental benefits while complying with regulations and management practices from the regional to the farm/field decision level.

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Ecosystem mapping for the implementation of the European Biodiversity Strategy at the national level: The case of Italy

Cited by 45 publications

References 65 publications

Improved spatial ecological sampling using open data and standardization: an example from malaria mosquito surveillance

Improved spatial ecological sampling using open data and standardization: an example from malaria mosquito surveillance

Implementing Green Infrastructure in Spatial Planning in Europe

Local Scale Prioritisation of Green Infrastructure for Enhancing Biodiversity in Peri-Urban Agroecosystems: A Multi-Step Process Applied in the Metropolitan City of Rome (Italy)

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