To understand populations’ space use one needs to understand the individual’s space use. To understand the individuals’ space use one needs to acknowledge the profound influence of spatio-temporal memory capacity combined with multi-scale landscape utilization, which continues to be empirically verified at a high pace in a surprisingly wide range of taxa. Complex space use has wide-ranging consequences for the traditional way of thinking when it comes to formulate these processes in models. In a nutshell, the old and hard-dying belief in the balance of nature needs a serious re-formulation, since complexity implies “strange” fluctuations of abundance over space, time and scale. A fresh perspective is needed with respect to inter-species interactions (community ecology) and environmental challenges from habitat destruction, fragmentation and chemical attacks. We need to address the challenge by rethinking also the very basic level of how we perceive an ecosystem’s constituents: how we assume individuals, populations and communities to relate to their surroundings in terms of statistical mechanics.
Stuart L. Pimm summarizes the Grand Ecological Challenge well in his book The Balance of Nature? (1991). Here he illustrates the need to rethink old perceptions linked to the implicit balancing principle of carrying capacity*, and he stresses the importance of understanding limits to how far population properties like resilience and resistance may be stretched before cascading effects appear. In particular, he advocates the need to extend the perspective from short-series local-scale population dynamics to long-term and broad scale community dynamics. In this regard, his book is as timely today as it was 27 years ago. However, in my view the challenge goes even deeper than the need to extending spatio-temporal scales and the web of species interactions.
My own approach towards the Grand Ecological Challenge started with similar thoughts and concerns as raised by Pimm**. However, as I gradually drifted from being a field ecologist towards actually attempting to model parsimonious population systems I found the theoretical toolbox to be void of key instruments to build realistic dynamics. In fact, the current methods were in many respects even seriously misleading, due to what I considered some key dissonant model assumptions.
In my book (Gautestad 2015), and here in my subsequent blog, I have summarized how – for example – individual-based modelling generally rests on a very unrealistic perception of site fidelity (March 23, 2017: “Why W. H. Burt is Now Hampering Progress in Modern Home Range Analysis“). I have also found it necessary to start from scratch when attempting to build what I consider a more realistic framework for population dynamics (November 8, 2017: “MRW and Ecology – Part IV: Metapopulations?“), for the time being culminating with my recent series of post on “Simulating Populations” (part I-X).
I guess the main take-home message from the present post is:
- Without a realistic understanding; i.e., modelling power, of individual dispersion over space, time and scale it will be futile to build a theoretical framework with deep explanatory and predictive value with respect to population dynamics and population ecology. In other words, some basic aspects of system complexity at the “particle level” needs to be resolved.
- Since we in this respect typically are considering either the accumulation of space use locations during a time interval (e.g., a series of GPS fixes) or a population’s dispersion over space and how it changes over time, we need a proper formulation of the statistical mechanics of these processes.In other words, when simplifying extremely complicated systems into a manageable set of smaller set of variables, parameters and key interactions, we have to invoke the hidden layer.
- With a realistic set of basic assumptions in this respect, the modelling framework will in due course be ready to be applied on issues related to the Grand Ecological Challenge – as so excellently summarized by Pimm in 1991. In other words, before we can have any hope of a detailed prediction of a local or regional faith of a given species or community of species under a given set of circumstances, we need to build models that are void of the classical system assumptions that have cemented the belief in the so-called balance of nature.
*) The need to rethink the concept of carrying capacity and accompanying “balance” (density dependent regulation) should be obvious from the simulations of the Zoomer model. Here a concept of carrying capacity (called CC) is introduced at a local scale only, where – logically – the crunch from overcrowding is felt by the individuals. By coarse-graining to a larger pixel than this finest system resolution we get a mosaic of local population densities where each pixel contains a heterogeneous collection of intra-pixel (local) CC-levels. If “standard” population dynamic principles applies, the population change when averaging the responses over a large number of pixels with similar density should be the same whether one considers the density at the coarser pixel or the average density of the embedded finer-grained sub-pixels. This mathematical simplification follows from the mean field principle. In other words, the sum equals the parts. On the other hand, if the principle of multi-scaled dynamics applies, two pixels at the coarser scale containing a similar average population density may respond differently during the next time increment due to inter-scale influence. At any given resolution the dynamics is as a function not only of the intra-pixel heterogeneity within the two pixels but also of their respective neighbourhood densities; i.e., the condition at an even coarser scale. The latter is obviously not compliant with the mean field principle, and thus requires a novel kind of population dynamical modelling.
**) In the early days I was particularly inspired by Strong et al. (1984), O’Neill et al. (1986) and L. R. Taylor; for example, Taylor (1986).
Gautestad, A. O. 2015, Animal Space Use: Memory Effects, Scaling Complexity, and Biophysical Model Coherence Indianapolis, Dog Ear Publishing.
O’Neill, R. V., D. L. DeAngelis, J. B. Wade, and T. F. H. Allen. 1986. A Hierarchical Concept of Ecosystems. Monographs in Population Biology. Princeton, Princeton University Press.
Pimm, S. L. 1991, The balance of nature? Ecological issues in the conservation of species and communities. Chicago, The University of Chicago Press.
Strong, D.E., Simberloff, D., Abele, L.G. & Thistle, A.B. (eds). 1984. Ecological Communities: Conceptual Issues and the Evidence. Princeton,Princeton University Press.
Taylor, L. R. 1986. Synoptic dynamics, migration and the Rothamsted insect survey. J. Anim. Ecol. 55:1-38.