Download the Book of Abstracts
Preliminary Schedule
13 October
09h00m

Registration

09h30m

Opening Session (room 6.01)

09h45m

Plenary Session A (room 6.01)
Speaker: Delfim Torres
Chairman: Eduardo Liz

10h45m

Coffee Break

11h15m
11h40m
12h05m

Session 1 (room 6.01)
Speaker: Filipe Martins
Speaker: José Joaquim Oliveira
Speaker: Ana Carvalho
Chairman: Pedro Lima

12h30m

Lunch

14h00m

Plenary Session B (room 6.01)
Speaker: Luís BordadeÁgua
Chairman: Delfim Torres

15h00m
15h25m
15h50m

Session 2 (room 6.01)
Speaker: Pedro Lima
Speaker: José Rodrigues
Speaker: Daniel Figueiredo
Chairman: Filipe Martins

16h15m

Coffee Break

16h45m
17h10m
17h35m
18h00m

Session 3 (room 6.01)
Speaker: Ana Matos
Speaker: Carla Henriques
Speaker: Cristina Canavarro
Speaker: Schehrazad Selmane
Chairman: Paulo Rebelo


Session 4 (room 6.02)
Speaker: Sandra Vaz
Speaker: Amira Asta
Speaker: Fernando Carapau
Speaker: Rosário Laureano
Chairman: Melike Aydogan


20h00m

Conference Dinner

14 October
09h00m

Plenary Session C (room 6.01)
Speaker: Eduardo Liz
Chairman: Carlota Rebelo

10h00m

Coffee Break

10h30m

Plenary Session D (room 6.01)
Speaker: Carlota Rebelo
Chairman: Gonçalo Marques

11h30m
11h55m
12h20m

Session 5 (room 6.01)
Speaker: Cristiana Silva
Speaker: Ana Paião
Speaker: Paulo Rebelo
Chairman: José Joaquim Oliveira

12h45m

Lunch

14h15m

Plenary Session E (room 6.01)
Speaker: Gonçalo Marques
Chairman: Luís BordadeÁgua

15h15m
15h40m
16h05m

Session 6 (room 6.01)
Speaker: Melike Aydogan
Speaker: André Ribeiro
Speaker: Python Paul
Chairman: Sandra Vaz


Session 7 (room 6.02)
Speaker: José Martins
Speaker: João Gonçalves
Speaker: Subhas Khajanchi
Chairman: Fernando Carapau


Plenary Session A
Stability and Optimal Control of Delayed Epidemiological Models
Delfim F. M. Torres
We consider some mathematical models that are given by a system of ordinary
differential equations. Optimal control strategies are proposed
to minimize the number of infectious and/or latent individuals,
as well as the cost of interventions. Delays are introduced
in the models, representing, e.g., the time delay on the diagnosis
and commencement of treatment of individuals, incubation
and/or pharmacological delays. The stability of the
disease free and endemic equilibriums is investigated for any time delay.
Corresponding optimal control problems, with time delays in both state
and control variables, are studied. Some open questions are formulated.
The talk is based on several works done with Cristiana J. Silva
and collaborators: see [1, 2, 3].
Research partially supported by project TOCCATA,
reference PTDC/EEIAUT/2933/2014, funded by Project
3599  Promover a Produção Científica e Desenvolvimento
Tecnológico e a Constituição de Redes Temáticas (3599PPCDT)
and FEDER funds through COMPETE 2020, Programa Operacional
Competitividade e Internacionalização (POCI), and by national
funds through Fundação para a Ciência e a Tecnologia (FCT)
and CIDMA, within project UID/MAT/04106/2013.
[1] D. Rocha, C. J. Silva and D. F. M. Torres,
Stability and optimal control of a delayed HIV model, Math. Methods Appl. Sci., in press.
DOI:10.1002/mma.4207
[2] C. J. Silva, H. Maurer and D. F. M. Torres,
Optimal control of a tuberculosis model with state and control Delays,
Math. Biosci. Eng. 14 (2017), no. 1, 321337.
[3] C. J. Silva and D. F. M. Torres,
A TBHIV/AIDS coinfection model and optimal control treatment,
Discrete Contin. Dyn. Syst. 35 (2015), no. 9, 46394663.
Plenary Session B
The Scaling Of Species Diversity
Luís BordadeÁgua
Species abundance distributions are central to the description of the diversity of a
community and have played a major role in the development of theories of biodiversity and
biogeography [1]. However, most work on species abundance distributions has focused on one
single scale, typically a spatial scale. Instead, here we look at the evolution of species
abundance distributions as a function of area and describe its scaling properties. A practical consequence of being able to describe how species abundance distributions evolve as a function of area is to predict how they look at larger scales, which we do by looking at the scaling properties of its moments. The reasoning is the following: if we know how the moments behave as a function of area then we can extrapolate the moments, then, if we know the moments of a distribution, we can reconstruct its probability density function. There are two venues to reconstruct the probability density function. One is if we consider one specific distribution and know how its parameters relate to the moments. The other is non parametric, and it is based on results from probability theory, which tells us that the moments are the coefficients of the Maclaurin expansion of the characteristic function [2]. The latter approach, however, is not practical in real situations and we use here a method based on discrete orthonormal Tchebichef moments [3]. To exemplify this procedure we use data on tree and shrub species from a 50ha plot of tropical rain forest in Barro Colorado Island, Panama [4]. First, we assess the application of the method within the 50 ha plot and, then, we predict the species abundance distribution for larger areas up to 500ha. We predict that this approach will be of major importance in conservation biology studies because it allows extrapolation of the relative species abundance distribution to larger areas and not only of the number of species [5].
This talk is based on a joint work with Henrique M. Pereira, Stephen P. Hubbell and Paulo A.
V. Borges.
[1] Hubbell, S.P., The unified neutral theory of biodiversity and biogeography, Princeton
University Press, Princeton NJ USA, 2001.
[2] Feller, W. An introduction to probability theory and its applications, Wiley, London, 1971.
[3] Mukundan, R., Ong, S. H. and Lee, P.A., Image analysis by Tchebichef moments, IEEE Trans.
Image Proc., Vol. 10 (2001), pp. 13571364.
[4] Condit, R., Tropical forest census plots: methods and results from Barro Colorado Island,
Panama and a comparison with other plots. Springer and R. G. Landes Company, Georgetown
TX USA, 1998.
[5] BordadeÁgua, L., Borges, P.A.V., Hubbell, S.P. and Pereira, H.P., Spatial scaling of species abundance distributions, Ecography, Vol. 35, (2012), pp. 549556.
Plenary Session C
Population Responses To Harvesting In A DiscreteTime Seasonal Model
Eduardo Liz
Population dynamics of many species are influenced by seasonality, and seasonal interactions have the potential to modify important factors such as population abundance and population stability [1]. We consider a discrete semelparous population model with an annual cycle divided into a breeding and a nonbreeding season, and introduce harvesting into the model following [2]. We report some interesting phenomena such as conditional and nonsmooth hydra effects [3], coexistence of two nontrivial attractors, and hysteresis. Our results highlight the importance of several often underestimated issues that are crucial for management, such as census timing and intervention time.
[1] I. I. Ratikainen et al., When density dependence is not instantaneous: theoretical developments and management implications, Ecol. Lett., Vol. 11 (2008), pp. 184198.
[2] N. Jonzen and P. Lundberg, Temporally structured density dependence and population man
agement, Ann. Zool. Fennici, Vol. 36 (1999), pp. 3944.
[3] P. A. Abrams, When does greater mortality increase population size? The long story and
diverse mechanisms underlying the hydra effect, Ecol. Lett., Vol. 12 (2009), pp. 462474.
Plenary Session D
Recent Results on Epidemiological Models and on PreyPredator Models
Carlota Rebelo
Mathematical analysis is a useful tool to give insights in very
different mathematical biology problems.
In this talk we will present two examples of this fact.
First of all we consider a simple epidemiological model with heterogeneity and discuss the relation between variance in the susceptibility of the individuals and prevalence of infection.
Then we consider predatorprey models. Using the notion of basic reproduction
number R_0, given by Nicolas Bacaer in the case of periodic models we prove uniform persistence when R_0 > 1. We will give some examples such as models including competition among predators, preymesopredatorsuperpredatormodels and LeslieGower systems.
This talk is based in joint works with N. Bacaer, M. Garrione, M.G.M.Gomes and A. Margheri.
[1] A. Margheri, C.Rebelo and M.G.M. Gomes, On the correlation between variance in individual
susceptibilities and infection prevalence in populations, Journal of Math. Biol., 71, (2015)
16431661.
[2] M. Garrione and C. Rebelo, Persistence in seasonally varying predatorprey systems via the basic reproduction number, Nonlinear Analysis: Real World Applications, 30, (2016) 7398.
[3] C. Rebelo, A. Margheri and N. Bacaer, Persistence in seasonally forced epidemiological models, J. Math. Biol., 64, (2012) 933949.
Plenary Session E
Modelling Organisms With Dynamic Energy Budgets
Gonçalo Marques
One of the basic requirements of quantitative research is the creation and use of mathematical models, both in the design of experiments and in the analysis of their results. Dynamic energy budget (DEB) theory [1] is a framework where the full life cycle of individual organisms can be modelled and its energetics can be quantified. All the key processes are included, such as feeding, digestion, storage, maintenance, growth, development, reproduction, product formation, respiration and aging. The theory amounts to a set of simple processbased rules for the uptake and use of substrates (food, nutrients, light) by individuals. It has farreaching implications for population dynamics and metabolic organization.
In this framework the individual can effectively be modelled in terms of a dynamical system and is defined by a set of parameters. One of the crucial first steps when using DEB is to estimate the parameters for the species of interest. We will start by presenting the standard estimation procedure and the resulting Addmypet collection with more than 400 species [2]. In parallel we will show the case of a model for a parasitic wasp [3].
Finally we will discuss the challenges and the latest developments implemented to make the estimation/optimization process more userfriendly.
This talk is based in a joint work with A. L. Llandres, J.Casas, D. Lika, S. Augustine, L. Pecquerie, S.A.L.M. Kooijman and T. Domingos.
[1] S.A.L.M. Kooijman, Dynamic Energy Budget Theory for Metabolic Organization, Cambridge University Press, Cambridge, 2010.
[2] Addmypet collection, curated by D. Lika, G.M. Marques S. Augustine, L. Pecquerie, S.A.L.M. Kooijman, http://www.bio.vu.nl/thb/deb/deblab/add_my_pet, 2016
[3] A. L. Llandres, G. M. Marques, J. Maino, S.A.L.M. Kooijman, M. R. Kearney, J. Casas, A Dynamical Energy Budget for the whole lifecycle of holometabolous insects. Ecological Monographs 85, (2014), 353–371