|
Project properties |
|
| Title | A review of biodiversity metrics |
| Group | Plant Ecology and Nature Conservation Group |
| Project type | thesis |
| Credits | 36 |
| Supervisor(s) | Coline Boonman and José van Paassen |
| Examiner(s) | Coline Boonman |
| Contact info | Coline Boonman |
| Begin date | 2025/01/01 |
| End date | 2025/12/31 |
| Description | Researchers go to the field and gather lots of information, repeatedly over space and time. Species names, species abundances, maybe even trait data are collected. But what can be done with this enormous amount of data? Spatial patterns, time series, comparison of treatments, considering species richness, abundance, Shannon index, mean species abundance, community weighted averages, traits, trait clusters, or changes in any of these. Are you interested in the common species, or the rare ones? What spatial scale should you consider (e.g. Paulssen et al., 2024)?
The type of metric that can be calculated depends on the type of data available, in turn determining the type of analyses that can be performed (e.g. Fortier et al., 2024). But what exactly can be done with specific types of data, what metrics can bring forward the patterns you are trying to highlight, what type of analysis fits specific research questions? Using simulations, you can create your own dataset that include trends that are known to you (e.g. Saravia et al., 2024). Simulations are there for a powerful tool to understand the differences in biodiversity metrics and different types of analyses. This thesis will consist of three parts. First, a literature review on the different types of metrics that exist and the analyses that are most common, after which a selection of metrics is made to use in further steps. Perhaps some meetings with researchers from PEN can give you additional insights. Second, simulations to test the selected metrics. Third, real data available at Plant Ecology and Nature Conservation (insect or plants, time series or spatial repetition) to verify and compare with the simulation results. When you are interested in this topic as a BSc thesis, we can modify the setup so that it fits the 12 ECTS requirements. As a short example, data on insect species and abundances was gathered at different locations with varying productivity. Looking at Heteroptera or ‘true bug’ species, it can be seen that some species are more (or only) found in locations with low productivity while others are found everywhere (on average in locations with higher productivity). However, when considering plot level data, calculating Heteroptera species richness, the high number of species associated with low productivity of locations is not as clear. When reversing the story, the high diversity of species associated at low productive locations is only mildly visible when telling the story with a well-known, often-used biodiversity metric (species richness) to describe species composition. References Fortier, R., Kullberg, A., Soria Ahuanari, R., Coombs, L., Ruzo, A. and Feeley, K. (2024). Hotter Temperatures Reduce the Diversity and Alter the Composition of Woody Plants in an Amazonian Forest. Global Change Biology, 30: e17555. https://doi.org/10.1111/gcb.17555 Paulssen, J., Brunet, J., Cousins, S.A.O., Decocq, G., De Frenne, P., De Smedt, P. et al. (2024). Patterns of local plant diversity and community saturation in deciduous forests in Europe. Journal of Vegetation Science, 35, e13318. https://doi.org/10.1111/jvs.13318 Saravia, L. A., Balza, U., & Momo, F. (2024). Why there are more species in several small patches versus few large patches: A multispecies modelling approach. Functional Ecology, 00, 1–11. https://doi.org/10.1111/1365-2435.14695 |
| Used skills | Data analysis, Simulations, Modelling, R |
| Requirements | Experience in R and affinity with large datasets and modelling |