Widlife management

Wildlife management

Human impacts on the environment are having an important effect on biodiversity. Climate change, population growth, urban expansion, food production, and changing land use are altering ecosystems in profound ways. Biodiversity is critical for Earth’s ecosystems and for human life. Thanks to the increasing availability of data coming from different sources (e.g., remote sensing, data logging, citizen science, fisheries) is time to move from description to forecasting. Advances in data analysis and machine learning allow this transition to be effective and informative for focused conservation strategies.

Santoro M, Iaccarino D, Bellisario B. 2020. Host biological factors
and geographic locality influence predictors of parasite communities in sympatric sparid fishes off the southern Italian coast. Scientific Reports. 10:13283

Machine learning helps identifying important predictors of parasite communities in sympatric species

Key question: How do the ecological relationships between hosts and parasites can be mediated by interaction of host’s biological factors, physiological condition, diet and the environmental components?
How to: We used boosted regression tree models to study the parasite communities of two sympatric sparid fishes, the salema Sarpa salpa and the white seabream Diplodus sargus, to investigate the role of specific host’s traits in two contiguous coastal areas along the southern-western Tyrrhenian coast of Italy characterized by different degree of deterioration.
Take home message: Our findings suggest that the parasite community of salema and white seabream responded differently to specific biological factors, highlighting how the environmental conditions under which they live may exert a strong influence on the parasite communities of each host fish.

Santoro M, Bellisario B, Crocetta F, Degli Uberti B, Palomba M. 2020. A molecular and ecological study of Grillotia (Cestoda: Trypanorhyncha) larval infection in small to mid-sized benthonic sharks in the Gulf of Naples, Mediterranean Sea. Ecology and Evolution. 2021;00:1–12.

Machine learning helps identifying the factors that determine the occurrence and the level of infection of Grillotia in benthonic sharks

Key question: Trypanorhyncha cestodes comprise a wide range of heteroxenous parasites infecting elasmobranchs as definitive hosts. Limited data exist on the larval infection of these cestodes and the role of intermediate and paratenic hosts in the life cycle of these parasites. We investigated the factors that determine the occurrence and the level of infection of Grillotia plerocerci in the skeletal muscles of benthonic sharks.
How to: Boosted regression trees were used to model the relationship between the abundance of infection with both morphological and physiological predictors in each host.
Take home message: Present results suggest that the two genotypes could be involved in different consumer-resource systems and confirm most of the examined shark species as transport hosts of Grillotia species for unknown larger top predators.

Address

Department of Ecological and Biological Sciences (DEB)

Tuscia University of Viterbo
Largo dell'Università Snc 01100 Viterbo (Italy)

Contacts

bruno.bellisario@unitus.it

‌

HTML Generator