Short titel:
CS toolboxLecturers:
• Lionel Hertzog, Thünen Institut for Biodiversity, Bundesallee 68, 38116 Braunschweig, DE, lionel.hertzog@thuenen.de• Diana Bowler, German Centre for Integrative Diversity (iDiv), Deutscher Platz 5e, 04103 Leipzig, DE, diana.bowler@idiv.de
• Swantje Löbel, Insitute for Geoecology, Department Landscape Ecology and Environmental Systems Analysis, TU Braunschweig, Langer Kamp 19c, 38106 Braunschweig, s.loebel@tu-braunschweig.de
Duration:
8 hoursDate/Time:
Sunday 29.08.2021, Time 9:30 – 17:30Costs:
free participationMaximum number of participants:
20Course content:
The majority of biodiversity monitoring data is from Citizen Science (CS) programs. CS provide a diverse range of opportunities to study biodiversity change at large spatial scales and over long time-scales. Data from CS programs are also increasingly available with cultural shift to open data and science.In this workshop we will consider the challenges of different types of CS data ranging from structured, semi-structured and opportunistic data. We will especially focus on accounting for biases (such as spatial) and heterogeneity in observational and sampling processes.
We will mostly focus in bayesian techniques, which are most adaptable to complex scenario. Techniques for different types of CS data will be covered such as:
- Structured dataset, monitoring data collected following a protocol with fixed sampling location and effort
- Semi-structured, all monitoring observations during a certain period recorded, observer choose sampling location and record effort (i.e. eBird complete checklists)
- Opportunistic, observer report only some observations, sampling effort varies across space and time, and among recorders (i.e. GBIF)
Data integration across these different types of dataset will also be covered. Workshop participants will be asked, ahead of the workshop, to provide their main interests so that models covered during the workshop will address participants needs.
We will use combination of stats tools: base R, JAGS, Stan and INLA with practical examples based on open data.
The course is targeted towards ecologists that:
• have experience using R
• have basic knowledge of Generalized Linear Mixed effect Models
• are working (or planning to work) with CS data