Dataset Information

Spatio-temporal ecological niche modelling of multigenerational insect migrations.

ABSTRACT: Modelling ecological niches of migratory animals requires incorporating a temporal dimension, in addition to space. Here, we introduce an approach to model multigenerational migratory insects using time-partitioned environmental variables (by months and years) and time- and behaviour-partitioned records (breeding records to model reproductive habitat). We apply this methodology to modelling the Palearctic-African migratory cycle of the Painted Lady butterfly (Vanessa cardui), based on data encompassing 36 years (646 breeding sites from 30 countries). Each breeding record is linked to a particular time (month and year), and the associated values of the bioclimatic variables are used for an ensemble modelling strategy, to finally obtain monthly projections. The results show obligated movements, mostly latitudinal, for the species' successive generations across the overall range, and only scattered locations show high probabilities of reproduction year-round. The southernmost reproductive areas estimated for the Palearctic-African migratory pool reach equatorial latitudes from December to February. We thus propose a potential distribution for the winter 'missing generations' that would expand the V. cardui migration cycle to encompass about 15 000 km in latitude, from northernmost Europe to equatorial Africa. In summer, Europe represents the major temporary resource for V. cardui, while January and February show the lowest overall suitability values, and they are potentially the most vulnerable period for the species to suffer yearly bottlenecks. In summary, we demonstrate the potential of the proposed niche modelling strategy to investigate migratory movements of insects.

SUBMITTER: Menchetti M

PROVIDER: S-EPMC6742982 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Spatio-temporal ecological niche modelling of multigenerational insect migrations.

Menchetti Mattia M Guéguen Maya M Talavera Gerard G

Proceedings. Biological sciences 20190904 1910

Modelling ecological niches of migratory animals requires incorporating a temporal dimension, in addition to space. Here, we introduce an approach to model multigenerational migratory insects using time-partitioned environmental variables (by months and years) and time- and behaviour-partitioned records (breeding records to model reproductive habitat). We apply this methodology to modelling the Palearctic-African migratory cycle of the Painted Lady butterfly (Vanessa cardui), based on data encom ...[more]

PMID: 31480976

Similar Datasets

Project description:BackgroundGliomas are the most common types of brain cancer, well known for their aggressive proliferation and the invasive behavior leading to a high mortality rate. Several mathematical models have been developed for identifying the interactions between glioma cells and tissue microenvironment, which play an important role in the mechanism of the tumor formation and progression.MethodsBuilding and expanding on existing approaches, this paper develops a continuous three-dimensional model of avascular glioma spatio-temporal evolution. The proposed spherical model incorporates the interactions between the populations of four different glioma cell phenotypes (proliferative, hypoxic, hypoglychemic and necrotic) and their tissue microenvironment, in order to investigate how they affect tumor growth and invasion in an isotropic and homogeneous medium. The model includes two key variables involved in the proliferation and invasion processes of cancer cells; i.e. the extracellular matrix and the matrix-degradative enzymes concentrations inside the tumor and its surroundings. Additionally, the proposed model focuses on innovative features, such as the separate and independent impact of two vital nutrients, namely oxygen and glucose, in tumor growth, leading to the formation of cell populations with different metabolic profiles. The model implementation takes under consideration the variations of particular factors, such as the local cell proliferation rate, the variable conversion rates of cells from one category to another and the nutrient-dependent thresholds of conversion. All model variables (cell densities, ingredients concentrations) are continuous and described by reaction-diffusion equations.ResultsSeveral simulations were performed using combinations of growth and invasion rates, for different evolution times. The model results were evaluated by medical experts and validated on experimental glioma models available in the literature, revealing high agreement between simulated and experimental results.ConclusionsBased on the experimental validation, as well as the evaluation by clinical experts, the proposed model may provide an essential tool for the patient-specific simulation of different tumor evolution scenarios and reliable prognosis of glioma spatio-temporal progression.

Project description:BackgroundThe Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely affected by the pandemic. The diffusion of Coronavirus disease 2019 (COVID-19) in Italy has followed a peculiar spatial pattern, however the attention of the scientific community has so far focussed almost exclusively on the prediction of the evolution of the disease over time.MethodsOfficial freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon.ResultsThree subcomponents characterize the fitted model. The first describes the transmission of the illness within provinces; the second accounts for the transmission between nearby provinces; the third is related to the evolution of the disease over time. At the local level, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, the component accounting for the spatial interaction with surrounding areas is prevalent for provinces that are strongly involved by contagions. Moreover, the proposed model provides good forecasts for the number of infections at local level while controlling for delayed reporting.ConclusionsA strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While containment policies may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is needed to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the European Union or the United States, the internal border checks among states have largely been abolished.

Dataset Information

Spatio-temporal ecological niche modelling of multigenerational insect migrations.

Publications

Spatio-temporal ecological niche modelling of multigenerational insect migrations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets