Next Article in Journal
Equivalences of Riemann Integral Based on p-Norm
Previous Article in Journal
A Heuristic Method for Certifying Isolated Zeros of Polynomial Systems
Previous Article in Special Issue
Ecological Diversity: Measuring the Unmeasurable
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Progress in Mathematical Ecology

by
Sergei Petrovskii
Department of Mathematics, University of Leicester, Leicester LE1 7RH, UK
Mathematics 2018, 6(9), 167; https://doi.org/10.3390/math6090167
Submission received: 11 September 2018 / Accepted: 11 September 2018 / Published: 13 September 2018
(This article belongs to the Special Issue Progress in Mathematical Ecology)
Mathematical modelling plays a special role in ecology. Although traditional ecology is a largely empirical science, replicated experiments are not often possible because of the high complexity of ecological interactions and the impossibility to reproduce the weather conditions. Moreover, large-scale field experiments (where the consequences are usually not fully known) can be damaging for the ecological communities and costly or even dangerous for humans. Mathematical modelling provides an efficient supplement and sometimes even a substitute to an empirical study; it creates a virtual laboratory where different hypotheses can be tested safely, and at relatively low cost.
Application of mathematics to problems arising in ecology and population dynamics—to which we broadly refer as mathematical ecology—has a long and glorious history. It was mutually beneficial for both of the disciplines involved and there are many examples of that. Prey–predator population cycles are one of the core concepts of contemporary ecological theory, but the route of this idea goes back to the works of Vito Volterra. Food web analysis is a fruitful and fast developing area of ecology, but its progress would hardly be the same without the insights and tools from the mathematical theory of networks. Ecological research on the spatial spread of traits and species was the starting point for the mathematical theory of diffusion-reaction waves, and Alan Turing’s study on morphogenesis led to the development of the theory of self-organized pattern formation.
New times have brought new challenges and stimulated further developments in mathematical ecology. This volume of Mathematics presents a carefully selected collection of papers that, taken together, provide a rather comprehensive account of the state of the art in this science. A broad range of problems is addressed. The first two papers [1,2] consider some new aspects of the classical problem of pattern formation. The dynamics of infectious diseases is another old problem where many aspects are still poorly understood; this is addressed [3,4]. Comparisons between different patterns of animal movement are made, [5] and the effect of dispersal and migration on the population dynamics in fragmented habitats are considered [6,7]. Biological invasion of an alien population of “ecosystem engineers” is considered [8], where it is shown how nonlinear feedbacks may lead to a self-organized regime shift resulting in the collapse of the invading population. The structural complexity of ecosystems is revisited in a review paper [9], where the meaning and value of various biodiversity indices is reassessed. The final paper [10] provides a new mathematical insight into a classical problem of genealogy.
We hope that, along with, obviously, shedding new light on several important problems, this volume also brings new questions and hence will stimulate future advances in mathematical ecology.

References

  1. Köhnke, M.C.; Malchow, H. Impact of Parameter Variability and Environmental Noise on the Klausmeier Model of Vegetation Pattern Formation. Mathematics 2017, 5, 69. [Google Scholar] [CrossRef]
  2. Banerjee, M.; Mukherjee, N.; Volpert, V. Prey-Predator Model with a Nonlocal Bistable Dynamics of Prey. Mathematics 2018, 6, 41. [Google Scholar] [CrossRef]
  3. Barbara, F.; La Morgia, V.; Parodi, V.; Toscano, G.; Venturino, E. Analysis of the Incidence of Poxvirus on the Dynamics between Red and Grey Squirrels. Mathematics 2018, 6, 113. [Google Scholar] [CrossRef]
  4. Fatehi, F.; Kyrychko, Y.N.; Blyuss, K.B. Effects of Viral and Cytokine Delays on Dynamics of Autoimmunity. Mathematics 2018, 6, 66. [Google Scholar] [CrossRef]
  5. Ahmed, D.A.; Petrovskii, S.V.; Tilles, P.F.C. The “Lévy or Diffusion” Controversy: How Important Is the Movement Pattern in the Context of Trapping? Mathematics 2018, 6, 77. [Google Scholar] [CrossRef]
  6. Alharbi, W.; Petrovskii, S.V. Critical Domain Problem for the Reaction—Telegraph Equation Model of Population Dynamics. Mathematics 2018, 6, 59. [Google Scholar] [CrossRef]
  7. Pal, N.; Samanta, S.; Martcheva, M.; Chattopadhyay, J. Role of Bi-Directional Migration in Two Similar Types of Ecosystems. Mathematics 2018, 6, 36. [Google Scholar] [CrossRef]
  8. Fontanari, J.F. The Collapse of Ecosystem Engineer Populations. Mathematics 2018, 6, 9. [Google Scholar] [CrossRef]
  9. Daly, A.J.; Baetens, J.M.; De Baets, B. Ecological Diversity: Measuring the Unmeasurable. Mathematics 2018, 6, 119. [Google Scholar] [CrossRef]
  10. Slade, P.F. Linearization of the Kingman Coalescent. Mathematics 2018, 6, 82. [Google Scholar] [CrossRef]

Share and Cite

MDPI and ACS Style

Petrovskii, S. Progress in Mathematical Ecology. Mathematics 2018, 6, 167. https://doi.org/10.3390/math6090167

AMA Style

Petrovskii S. Progress in Mathematical Ecology. Mathematics. 2018; 6(9):167. https://doi.org/10.3390/math6090167

Chicago/Turabian Style

Petrovskii, Sergei. 2018. "Progress in Mathematical Ecology" Mathematics 6, no. 9: 167. https://doi.org/10.3390/math6090167

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop