Biology subjects

Gonzalez, E. J.

Publications and source records attributed to Gonzalez, E. J..

2 recordsLinked to original sources

bioRxiv2023

Integrated integral population models

O_LIData integration allows obtaining better descriptions and forecasting of a populations behaviour by incorporating data at both the individual and population levels. Structured population models include the matrix population models (MPMs), which structure a population through a discrete state variable, and the integral population models (IPMs), which use a continuous variable. Two decades ago, the integrated version of MPMs appeared, but their corresponding version for IPMs is still missing. C_LIO_LIHere, we propose the integrated integral population model (IIPM). This model takes up the ideas behind existing models used to describe and forecast the dynamics of continuously structured populations: IPMs, which use individual data, and inverse IPMs, which use population data. Particularly, we emphasise the construction and fitting of the IIPM under a Bayesian framework and use the Soay sheep database to compare the population dynamics generated by the IIPM and these existing models. C_LIO_LIThe IIPM constructed with the Soay sheep data had a good performance both at the individual (vital rates) and population (size and structure) levels, because, as they are constrained to fit both sets of data, they produce a balanced population dynamics. In turn, the IPM produced the best individual estimates and the worst population estimates, whilst the inverse IPM produced the worst individual estimates and the best population estimates. C_LIO_LIThe objective of a structured population model should be to correctly describe population patterns. IPMs, by not using population data, fail in this objective. An IIPM solves this problem. C_LI

ecology↗

bioRxiv2023

Capturing temporal heterogeneity of communities: a temporal β-diversity based on Hill numbers and time series analysis

Beta-diversity is a term used to refer to the heterogeneity in the composition of species through space or time. Despite a consensus on the advantages of measuring {beta}-diversity using data on species abundances through Hill numbers, we still lack a measure of temporal {beta}-diversity based on this framework. In this paper, we present the mathematical basis for a temporal {beta}-diversity measure, based on both signal processing and Hill numbers theory through the partition of temporal -diversity. The proposed measure was tested in four hypothetical simulated communities with species varying in temporal concurrence and abundance and two empirical data sets. The values of each simulation reflected community heterogeneity and changes in abundance over time. In terms of -diversity, q-values are closely related to total richness (S) and show a negative exponential pattern when they increase. For -diversity, q-value profiles were more variable than -diversity, and different decaying patterns in -diversity can be observed among simulations. Temporal {beta}-diversity shows different patterns, which are principally related to the rate of change between - and -diversity. Our framework provides a direct and objective approach for comparing the heterogeneity of temporal community patterns; this measure can be interpreted as the effective number of completely different unique communities over the sampling period indicating either a larger variety of community structures or higher species heterogeneity through time. This method can be applied to any ecological community that has been monitored over time.

ecology↗