Abstract

Life on Earth is characterised by an amazing variety of living forms which are in continuous evolution to better adapt to the surrounding environment and highly connected one to the other. A deep investigation of different living systems has recently been favoured by the huge quantity of data nowadays available. In this thesis the student will tackle the problem of inferring the total biodiversity of an ecosystem when only scattered samples are observed. In particular, she/he will study a novel analytical framework, which, by exploiting the scaling invariance property of the negative binomial distribution, generates accurate and robust predictions. He/she will apply it on ecological systems of interest, comparing the estimates over different sampling times. She/he will investigate how natural and/or human-dictated landscape changes affect the predicted global ecological patterns.

References

• Tovo, Anna, et al. ‘‘Upscaling species richness and abundances in tropical forests.’’ Science advances 3.10 (2017): e1701438.
• Tovo, Anna, et al. ‘‘Inferring macro‐ecological patterns from local presence/absence data.’’ Oikos (2019).