Assessment of restoration ecological and societal outcomes

Task 1.1: Harmonize and explore the restoration datasets (UCB, WEnR)

The COSAR dataset features about 200 restoration datasets that include a variety of restoration projects. Task 1.1 will (i) compile all the ecological datasets into a single spatialized and as much as possible harmonised database and (ii) prepare the datasets for subsequent analyses (e.g. depending on type of BA/CI/BACI data) by grouping them according to their properties (e.g. type of restoration projects, taxonomic groups) and uncovering potential gradients underlying the restoration data (e.g. gradients of land use, river sizes). Task 1.1 will gather social media information a posteriori, i.e. from the location of the restoration sites, their surroundings, and monitoring periods from different social media platforms (e.g. Flickr, Twitter, Instagram, cell site data). 

Task 1.2: Define relevant references and metrics of ecological restoration outcomes (INRAE, WEnR, EAWAG)

To evaluate outcomes at restored sites, we need reference points (e.g. WFD-based target, expected outcome, before-disturbance status, historical status, etc.) against which restored conditions can be compared. Hereby, reference points depend on the indicators used and will be sensitive for effect size. Thus, benefiting from stakeholders reflections (WP6), we will define the needed reference(s) for ecological outcomes assessment. Then, we will be able to analyse the ecological indicators and effect sizes. To do so, we will both (i) use the original indicators used in the respective projects and (ii) calculate, on the original datasets, generic metrics of: (i) community-level recovery, e.g. taxonomic and functional richness, equitability, alpha/beta/gamma diversity, turnover and nestedness, and (ii) ecosystem-level recovery, e.g. composition of feeding guilds, distribution of ecosystem engineers, rare species, native vs. invasive species, dispersal-limited species, stability indices. For each dataset, we will extract effect sizes of restoration (e.g. based on null modelling procedures) and compare them across datasets. A final set of relevant metrics of ecological outcomes will be proposed for further analyses.

Task 1.3: Define relevant references andmetrics of societal restoration outcomes (UCB, EAWAG) 

Similarly to Task 1.2, reference points will be set for societal outcomes assessment based on discussions with stakeholders. We will identify the relevant indicators of societal outcomes to be derived from social media data, i.e. expected to be particularly relevant to cultural, provisional, regulating, and supporting services, and to be used in further analyses.

Task 1.4: Prepare ecological data and assess restoration ecological outcomes (WEnR, EAWAG, INRAE, UCB) 

Task 1.4 will prepare the ecological monitoring datasets with respect to the list of metrics and references identified in Task 1.2. For the respective metrics, species information, such as trait data, species status (e.g. rare species, IUCN status), guilds, and other ecological information based on trait databases (e.g. freshwater information platform, DISPERSE) and IUCN Red List will be added to the common list of species. We will calculate all ecological metrics, explore their characteristics (e.g. distribution, spatial structure) and compare their before-after restoration dynamics.

Task 1.5: Prepare social media data and assess restoration societal outcomes (UCB)

We will run different analyses on the social media data from restored sites (gathered in Task 1.1): (i) Frequency analyses of cell site data and social media postings allow identifying and quantifying the stream use patterns over time with regard to the implementation of the restoration measures, as first derivations for use conflicts and trade-offs between habitat development / biodiversity and ecosystem services, (ii) Content analysis of the photos based on artificial intelligent image recognition algorithms, already developed by UCB, reveal the different ecosystem services and associated values (Kaiser et al., under review; Song et al., 2020); (iii) Semantic analysis of text associated with the photos (user-generated tags) reveals information about individual meanings and feelings that users assign to the photos. We will prepare the social media data with respect to the list of societal indicators identified in Task 1.3 (instrumental values of nature, relational values, clusters of specific activities). Then, we will calculate and explore the before-after restoration dynamics of societal indicators, e.g. change in the user groups before- vs. after restoration according to specific activities detected in the photos.