Phytoplankton plays an important role in aquatic ecosystems as it accounts for most of global primary production and affects the biogeochemical processes, trophic dynamics and biodiversity architecture. LifeWatch Italy, one of the ERIC's national nodes, designed the Phytoplankton Virtual Research Environment (Phyto VRE) to support researches on phytoplankton guilds and their relative structure, organization and ecological function. The Phyto VRE services, in fact, enable researchers to analyse and process phytoplankton data at a level of resolution from individual cells to whole guilds. In particular, the Atlas of Phytoplankton and the Atlas of Shapes provide harmonised data on taxonomy and morphological and demographic traits.


Learning Objectives

It is expected that, with this tutorial, participants will be better positioned to:

1. Illustrate how to access and navigate the two Atlas of the Phyto VRE;

2. Discuss the main characteristics of a phytoplankton species;

3. Examine the possible shapes of a phytoplankton species;

4. Present the computational models for surface area and volume of phytoplankton species.


Contents and Structure

Training Blocks:

1. Introduction to Phytoplankton Species and Shapes;

2. Atlas of Phytoplankton;

3. Atlas of Shapes;

4. Presentation of Computational Model service;

5. Overview of LifeWatch Italy Phytoplankton VRE services.


Target Audience

The resource is aimed at marine, transitional and freshwater scientists and students involved in phytoplankton identification, classification, morphological traits association and measurement.

This is a study-lab training in which topics are presented by short texts, practical sessions are introduced and explained and assignments are completed by students. It is made of seven units: three theoretical units, each followed by a practical unit. In order to exemplify the use of the proposed methods and models, a final unit is devoted to a case study on benthic macroinvertebrates in the Po River delta, providing the data, the R code and some interpretative comments. Practical units are based on the use of the R software, with purpose-specific libraries and functions. Quizzes are given following every unit, together with practical assignments to be addressed with the R software.


After this introductory unit, the first practical unit will be devoted to briefly introduce the R software. Biodiversity partitioning will be the subject of the next two units, where methodology and software for γα and β diversity profiling will be described and applied to a sample data set. The theory behind mixed effects modeling will be sketched and applied to investigate the variation of biodiversity measures. The last practical unit exemplifies the use of the R implementation of mixed effects modeling routines with data from ecological surveys. Finally, we will summarize and exemplify the proposed methods with the complete analysis of a case study.

This tutorial is a practial guide for Species Distribution Modelling (SDM). The tutorial will use the "dismo" package of "R" and will show the guidelines on how to model species distribution starting from occorency data (got from internet, or users ones) and a set of bioclimatic variables. 
After a brief introduction of SDM, with some examples of biological application, this tutorial will show a guide (split in 5 main sections or modules) that leads the user step by step how to obtain a distribution map. Afterwards there will be some suggestions on how to deal with common issues and statistical errors that may affect the analysis and to evaluate properly the output. 

The Incidence Function Model describes presence/absence of a species in the patches of a highly fragmented landscape at discrete time intervals (years) as the result of colonization and extinction processes. The IFM ignores local dynamics sin ce they are faster than metapopulation dynamics in producing changes in the size of local populations (Hanski, 1994).
In the IFM, the process of occupancy of patch i is described by a first-order Markov chain with two states, {O, i} (empty and occupied, respectively). The extinction probability of a population in a patch is constant in time and is assumed to decrease with increasing patch area, and the colonization probability is assumed to be a sigmoidal function increasing with connectivity. The IFM is the best known spatially explicit metapopulation model in literature.
This model has been applied to conservation problems and to area-wide pest-management.
First, a short introduction to discrete time, finite space, homogeneous Markov chain will be provided, aiming at understanding the basic mathematics of the IFM. Then, the IFM model will be discussed by deeply considering (a) the role of the parameters and how they affect metapopulation dynamics; (b) variations to the basic model (rescue effect, time-dependent colonization probabilities). Finally, we will move on to the use of the free software R to deal with simulation and parameter estimation.

       A tutorial to guide users on the use of LifeWatch ERIC Metadata