Random Returns Are Not Random
The statistical-mechanical universality class Multi-scaled random walk (MRW) is a relatively recent addition to the menagerie of various types of random walk (RW). It seeks to capture some key aspects of animal space use, in particular the combined effects of spatio-temporal memory and scale-free movement. In this post I put focus on the statistical property of return events to previously visited positions (memory-dependent site fidelity). How can such events feasibly and realistically be treated as random, knowing that the animal by targeted returns will tend to revisit more profitable patches with a higher probability than other locations? In particular, one specific aspect of the answer, revealed here in detail for the first time, will probably surprise you. Complexity turned into simplicity!
What regards the the transition from deterministic behaviour to a RW process in general terms, search my blog for "Markov", or you may for example look into Gautestad (2013). To place MRW in the context of other universality classes of RW, I refer to the Blog post "The Scaling Cube" (December 25, 2015).
MRW have some key properties that distinguishes it from other classes of RW; in particular
- the movement is influenced by spatio-temporal memory, which allows it to revisit previous locations outside the animal's perceptual field,
- the animal is assumed to utilize its environment in a scale-free manner (the Parallel processing conjecture),
- the memory is "infinite", meaning that return probability is by default not expected to deteriorate over time*, and
- the energy and risk costs related to travel distance when targeting a previous location is expected to be negligible.
- The initial discovery of new locations is a function of exploratory moves.
- The most profitable locations; i.e., sites with the highest return value, will consequently appear at random locations along an animal's path.
- Hence, in a statistical-mechanical sense it is feasible to expect targeted return events to specific locations to appear randomly.
- Each location along a historic path according to standard MRW has an a priori uniform return probability. Despite this, over time more profitable sites will receive more returns. A local superposition of return events will feed a positive feedback process - a self-reinforcing tendency for site fidelity for these sites.
This property is explicable in statistical-mechanical terms: by coarse-graining the spatial scale of a given path to the coarser level of an intra-home range region, the principle of a mean field approximation should be expected to apply. Even if the local process is non-homogeneous with respect to movement rules; i.e., non-proportional revisit-probability at the fine-grained scale of the given MRW path, the over-all pattern will "average-out" to become indistinguishable from a proportional return probability at this coarsened resolution of the region***. Thus, it makes sense to disregard spatially fine-grained non-proportional return preference from the perspective of CSSU, which regards a coarser-grained spatial attribute of space use relative to the fine-grained path itself.
Only if returns are disproportionately targeting the given region itself on expense of returns to other parts of the home range; i.e., by disregarding finer-grained intra-regional degree of disproportionate return intensity at the finer-grained path scale, the CSSU at the given regional scale will be affected. However, this is the key aspect of CSSU itself, which will vary in magnitude as a function of behavioural intensity of local habitat selection as it is observed at the chosen spatial scale and time window.
The statistical-mechanical concept of CSSU is consequently a central ecological expression of animal space use, given compliance with the MRW basics (first-level tests). The first follow-up approach is to calculate the CSSU magnitude at the over-all scale of the observed home range, averaging away local heterogeneity (second-level tests). Next, one could drill into analyses of local and/or temporal subsets of the data and study how CSSU turns out in these finer-masked situations (third level tests).
The MRW model has many interesting biophysical aspects, some already revealed and others still to be discovered, described and subsequently utilized in ecological research.
*) From behavioural-ecological arguments on should expect the return probability to specific sites to decline as a function of increased uncertainty of site profitability; e.g., due to increased environmental variability and unpredictability over space and time. However, under a broad range of conditions long term (long memory) site fidelity has been shown to rule animal behaviour. See for example Marchand et al. (2017):
"The assumption that toads returning to a previous refuge choose one at random may seem unrealistic. Yet it fits the data better than two alternative models we tested, where the probability of return and/or the choice of refuge were distance-dependent." Marchand et al. 2017, p 68.
I commented on this result in the blog post "Fowler’s Toads: the MRW Model Gains Additional Support" (July 21, 2017).
***) The important mean field concept for modelling animal space use is explained and illustrated in my book. You can also look into a general Wikipedia description: https://en.wikipedia.org/wiki/Mean-field_theory
Gautestad, A. O. 2013. Lévy meets Poisson: a statistical artifact may lead to erroneous re-categorization of Lévy walk as Brownian motion. The American Naturalist 181:440-450.
Gautestad, A. O. and A. Mysterud. 2013. The Lévy flight foraging hypothesis: forgetting about memory may lead to false verification of Brownian motion. Movement Ecology 1:1-18.
Marchand, P, M. M. Boenke and D. M. Green. 2017. A stochastic movement model reproduces patterns of site fidelity and long-distance dispersal in a population of Fowler’s toads (Anaxyrus fowleri). Ecological Modelling 360:63–69.