Uncertainty underlines almost every problem in mathematical ecology, and understanding its implications leads to substantial new mathematical challenges. Issues of uncertainty arise particularly in the structure of models, as reflected by the choice of state variables and model functions, uncertainty in parameters, initial conditions, etc. Uncertainty can greatly affect the determination of the current ecosystem state (e.g., stochastic versus deterministic description) and hence prediction of its dynamics. In ecological models uncertainty can be a real nuisance due to the phenomenon known as model sensitivity: models can be sensitive to the mathematical formulations of the constituent functions. This structural sensitivity can substantially reduce predictability of models. Whereas parameter-based sensitivity methods are now relatively well-developed, the mathematical framework to investigate structural sensitivity, when the entire function is unknown, is in its early stage and this represents a major challenge both in mathematics and ecology. In particular, there is a strong need for reliable mathematical tools to investigate structural sensitivity of biological models directly from data.
In addition, ecosystems are known to sometimes exhibit a sudden (catastrophic) regime shift, which is referred to as the tipping points, and this can be linked to a bifurcation in the model as a response to parameter changes (e.g., due to global climate changes). Development of robust techniques to identify reliable early warning signals of approaching catastrophic transition is a major challenge since the current methods are not always reliable and could result in false alarms, which can be very costly.
One of the goals of the ecosystem management is to estimate the risk of undesirable events. Coping with uncertainty (e.g., by providing the minimal required amount of information about the system) is therefore crucial to enable ecosystem managers to make the right decision in order to guarantee that the risk of undesirable event will not exceed the critical level. Lack of information about underlining processes calls into question the assumption that classical optimal control theory will always be successful. More research is needed to develop the mathematical framework for ecosystem management, in particular looking for an optimal balance between models complexity and their predictive power under a given level of uncertainty.
The main goal of the workshop is to bring together applied mathematicians, theoretical ecologists, empiricists and statisticians in order to address the above raised issues related to ecosystem understanding, modelling, and management to cope with uncertainty
|Monday, October 26, 2015|
|Tuesday, October 27, 2015|
|Wednesday, October 28, 2015|
|Thursday, October 29, 2015|
|Friday, October 30, 2015|
|Abbott, Karenemail@example.com||Biology, Case Western Reserve University|
|Berec, Ludekfirstname.lastname@example.org||Department of Biosystematics and Ecology, Biology Centre CAS, Institute of Entomology|
|Bjornstad, Ottaremail@example.com||Entomology, Pennsylvania State University|
|Blasius, Berndfirstname.lastname@example.org||Institute of Chemistry and Biology of the Marine Environment, Carl von Ossietzky University Oldenburg|
|Bolker, Benemail@example.com||Math & statistics and Biology, McMaster University|
|Cosner, Chrisfirstname.lastname@example.org||Department of Mathematics, University of Miami|
|Cuddington, Kimemail@example.com||Biology, University of Waterloo|
|Cushing, Jimfirstname.lastname@example.org||Mathematics, University of Arizona|
|Dakos, Vasilisemail@example.com||Integrative Ecology, Eustachian Biologica de Donana|
|De Angelis, Donaldfirstname.lastname@example.org||Department of Biology, University of Miami|
|Diekmann, Odo||O.Diekmann@uu.nl||Mathematics, Utrecht University|
|Dunne, Jenniferemail@example.com||n/a, Santa Fe Institute|
|Fagan, Billfirstname.lastname@example.org||Biology, University of Maryland|
|Fussmann, Gregoremail@example.com||Department of Biology, McGill University|
|Hastings, Alanfirstname.lastname@example.org||Department of Environmental Science and Policy, University of California, Davis|
|Holt, Robertemail@example.com||Zoology, University of Florida|
|Johnson, Leahfirstname.lastname@example.org||Integrative Biology, University of South Florida|
|Kooi, B.W. (Bob)||email@example.com|
|Kuehn, Christianfirstname.lastname@example.org||Mathematics, Vienna University of Technology|
|Lewis, Markemail@example.com||Canada Research Chair in Mathematical Biology, University of Alberta|
|Liu, Rongsong||Rongsong.Liu@uwyo.edu||Mathematics, University of Wyoming|
|Lundberg, Perfirstname.lastname@example.org||Biology, Department of Biology, Lund University|
|Morozov, Andrewemail@example.com||Mathematics, University of Leicester|
|Munch, Stevefirstname.lastname@example.org||Ecology and Evolutionary Biology, University of California, Santa Cruz|
|Petrovskii, Sergeiemail@example.com||Mathematics, University of Leicester|
|Poggiale, Jean-Christophefirstname.lastname@example.org||Institut Pytheas (OSU), Aix-Marseille University|
|Rossberg, Axel||Axel@Rossberg.net||Environment and Ecosystems Division, Centre for Environment, Fisheries & Aquaculture Science|
Estimation of extinction thresholds arising from Allee effects is notoriously difficult. Traditionally, a point estimate is substituted for the Allee effect strength in adequately formulated population models. However, since any point estimate entails an underlying uncertainty, accounting for this uncertainty inevitably affects risk of population extinction. I will show that the probability of population extinction decreases sigmoidally with increasing population density, even in the absence of any stochasticity, and predict how adding stochastic noise modifies the effect. Modelling suggests that the impact of uncertainty in the Allee effect strength estimate increases as the Allee effect strength itself increases and decreases as the species recovery potential increases. This is by no means good, since we aim to preferentially and efficiently manage slowly recovering populations prone to strong Allee effects. I will argue, somewhat paradoxically, that the impact of the uncertainty can be mitigated by diversifying Allee effect experiments such that we put more emphasis on larger groups. Uncertainty also surrounds estimates of population density, especially in low-density populations. The joint effect of both these kinds of uncertainty on the probability of population extinction will also be discussed.
Historically, both experimental and theoretical ecologists have sought to emulate the development of early theory in the physical sciences: the ideal that a few simple equations may accurately predict the complex movement of celestial bodies or interactions among molecules in mixing gasses. In the environmental sciences, such simple clockworks have rarely been found, and rather than predictable stable or recurring patterns, erratic patterns abound. The discovery in the late 1970s through mid-1980s of certain ecological models—such as the Ricker or discrete logistic maps—suggesting erratic fluctuations through dynamic chaos caused what cautionaries may characterize as ecology’s period of “rational exuberance” with respect to hoping that a small set of mathematical equations may explain the erratic dynamics of real-world ecological communities. Upon much discussion, the field as a whole grew skeptical of this idea during the late 90s. During the subsequent 3 decades, mathematical theories of the sensitivity and predictability of ecological and epidemiological systems have been much refined. I will discuss a handful of case studies that I believe were pivotal in changing our more recent understanding of 'Uncertainty, Sensitivity and Predictability in Ecology'.
Despite the pretentious title, the talk will just consist of a few loose remarks followed by a brief description of Linear Chain Trickery (i.e., a characterization of kernels for delay equations that allow reduction to ordinary differential equations) mainly in the context of epidemic models.
In this talk, I am going to illustrate current techniques based upon multiple time scale dynamical systems and stochastic analysis that can be used to detect early-warning signs for drastic transitions. In particular, the role of scaling laws will be emphasized and several examples from mathematical biology will be given including theoretical modelling components as well as data analysis.
Many ecological problems require monitoring and sampling of `alien' population, where the information obtained as a result of monitoring is then used for making decision about means of control. In ecological applications, data used for decision making are often sparse due to financial, labour, and other restrictions on the sampling routine. The same sparse data can also be noisy because of the inherent nature of the ecological problem.
One example of a monitoring procedure based on sparse and noisy data is given by a widespread and important problem of pest insect abundance evaluation from the insect density in an agricultural field. An inaccurate estimate of the pest abundance obtained because of uncertainty in data can result in the wrong decision about a control action (e.g. unnecessary application of pesticides). Thus in our talk we discuss how to quantify the effect of data sparseness and noise in the pest insect monitoring problem. It will be argued that noise is a negligible factor in comparison with the uncertainty of evaluation arising as a result of poor sampling.
In a first part, some problems observed with ecosystem models are discussed, focusing on the choice of the formulations of the biological processes involved, with several examples from the literature. Providing explicit relations between individuals properties and population or community dynamics allows to build model formulations on a mechanistic basis. We discuss some examples where this approach can be useful for understanding the community dynamics. The functional response in predator - prey systems is an example of ecological process involving several levels of organization and time scales. Its mathematical formulation should depend on the applications of the model : which spatial scales are considered? Is the environment homogeneous or heterogeneous? These questions should shape the choice of the formulations used in models. Moreover, the data used to develop a model are often acquired in conditions which are different than those of the applications. For instance, some formulations are based on data obtained in laboratory experiments, while the models are used to describe natural environments. Scaling up methods, which provide explicit links between different organization levels or between several temporal/spatial scales, are then useful to build formulations adapted for models used in the natural environment. Several applications to marine systems modelling are then presented.
Structural instability denotes situations where small changes in parameters (or external pressures) can fundamentally change the state of a system, in ecological communities typically through extirpations. I will argue based on models and data that structural instability increases with species richness and that natural communities tend to be packed to the point where invasion of any new species leads to extirpation of one other on average. As a result, ecological communities are inherently structurally unstable; detailed predictions of changes in ecosystem state in response to anthropogentic pressures are often impossible. Facing this challenge, managers have two options: to manage at the level of higher emergent properties, e.g. community size spectra, or to engineer desired ecosystem states and to stabilize them through adaptive management. I will discuss both options for the case of fisheries management.