Making the Science of Animal Space Use Less Soft

A provocative headline is a double-edged sword. Why do I indicate that one of the most rapidly developing fields of animal ecology should still be regarded as a soft science? When it comes to individual space use rest assured that I’m thrilled by the substantial leaps forward in some parts of the theory of animal whereabouts. On the other hand, I also have critical comments. In my view there is still too strong disconnection between some general properties of movement-related animal behaviour and theoretical representations of this behaviour in models.

Patch and resource sharing – butterfly Aglais io and bumblebee. Photo: AOG.

Both in my book and in previous blog posts I have repeatedly pointed out the unfortunate fact that contemporary models in the field often referred to as “movement ecology” have matured into two quite distinct premise foundations. On one hand  we see a broadened recognition of scale-free movement as a quite general property and on the other hand also a broadened recognition that spatial memory is influencing movement under common ecological conditions. The former insight is often referred to a “Lévy walk-like movement”, while the latter focus regards strong and recent developments of the classic theory related to home range behaviour, philopatric dispersal, and similar memory map-dependent properties. Unfortunately, these two directions of theoretical development – scaling and memory aspects – still tend to be progressing quite independently of each-other. Lévy walk is inherently void of spatial memory influence, and site fidelity models are generally building on a scale-specific framework with spatial memory influence as  a model extension of this framework. Hence, a scientific field consisting of disparate theories when it comes to the other camp’s main system premises has to fail. Thus, I dare to call animal movement theory unnecessarily soft – until a unification is reached.

To summarize, from a bird’s view of the current state I dare to categorize the science of animal movement as soft science due to lack of coherence between on one hand modelling the sliding scale from scale-specific to scale-free space use, and on the other hand modelling the variable strength of directed returns to familiar patches – on the basis of a common theoretical framework.

When I during the early 90’s started locking my focus on these two aspects of animal behaviour the interplay between multi-scaled space use and the simultaneous expression of site fidelity, both fields of research were in respective camps surrounded by confusion and controversies.  Conferences, workshops and books on animal space use were typically surrounded by a myriad of ideas and concepts in the emergent field of landscape ecology. Thus, as far as I recall the scene back then, nobody else seemed to be tempted to make the science of animal space use even messier by studying model coherence between scaling and spatial memory. The general attitude was that “scale-free movement” – if it was at all recognized – might be interesting as a concept, but the theory had a far way to go before it could reach general acceptance with consequences for statistical and dynamical modelling and ecological analysis.On the other hand, “the Lévy camp” typically disregarded the site fidelity aspect of movement all together, except for occasional and brief reference to the concept as a challenge for the future.

Progress in respective camps have obviously been strongly propelled by large databases of animal movement that has been collected from modern GPS technology. Thus, theory and empirical data have been brought closer together. Wildlife ecology, computer modelling and advanced statistical analysis is thriving together in many strong research groups. Still, I’m waiting for bolder steps towards stronger unification between scaling and memory.

Wildlife ecologists have much to look forward to from such a leap towards better model realism. Consider all the interesting aspects that could be more easily studied and tested when the respective hypotheses are based on a more coherent theory of animal space use. Respective quantification of parameters connected to scaling and memory will then be founded on models with stronger predictive power. It is tempting lo start listing a long string of examples of potential theory applications at this point, but first things first. Conferences and workshops on the scaling/memory issue lay in the cards!

Statistical Independence in Space Use Data?

Already from the early days of statistical analysis of home range data one particular property has received particular attention, the degree of serial autocorrelation of the individual’s positions during a period of sampling. In short, if a data series is autocorrelated (due to high frequency sampling of fixes), one is advised to use methods involving path analysis. If data is non-autocorrelated, one should use methods involving a study of locally varying fix density (like classic use-availability analysis). In the latter case, verification of non-significant serial autocorrelation is assumed to imply that each fix can be treated as an independent representation of visits to this particular part of the home range. However, this assumption is in fact deeply erroneous, and it continues to hamper progress in statistical analysis of animal space use.

The willow warbler Phylloscopus trochilus toggles between two continents during a year, and between various preferred intra-home range patches at a within-season temporal scale. In fact, the home range itself is an emergent property from non-random crossing of its movement path. Photo: AOG.

The Schoener’s ratio (t2/r2) (Shoener 1981) or other methods can easily be applied to test for serial independency in space use data. The ‘time to independence’ (TTI) is estimated by subsampling sets of observations and thus increasing the time intervals between spatial fixes, and calculating t2/r2. The null hypothesis of independence is rejected; i.e., auto-correlation is supported, if the observed t2/r2 is outside the defined critical values around the expected value of 2. However, does a ratio with t2/r2 ∼ 2 satisfy a series of independent events? It does not!

The Schoener’s ratio and other methods in wildlife ecology rest on an assumption that data represents a pattern that is generated from a Markov-compliant process (this property has been repeatedly explained and criticized both in my book, my papers, and in this blog; for example in “The Beautiful Anatomy of a Home Range” and in “A Statistical-Mechanical Perspective on Site Fidelity – Part I“). An animal that tends to revisit previous patches by directed returns, i.e., utilizing a memory map, will violate the basic statistical assumption of TTI-methods. In short, such revisits lead to self-reinforcing use of some patches on expense of other patches with a priori similar qualities. Strangely, this confounding effect does not seem to frighten ecologists from applying TTI as a criterion for statistical independence. My wild guess is that…

  • One third of ecologists disregard the memory effect on fix dispersion data because they are not aware of the issue in relation to the TTI assumption.
  • One third disregard memory effect because they believe it doesn’t matter much for their subsequent data analysis
  • One third disregard memory because everybody else seems to do so.

The latter category reflects sloppy science. Hopefully you are not there. If you are in the first category, you are currently seeking personal updating. Welcome.

The second category regards sound scientific skepticism. However, my impression is that many ecologists after scrutinizing papers on this issue may agree on the memory effect on space use data, but they are still reluctant to step away from common methods – even when it comes to concept-testing the alternative methods that are now emerging. Moving out of one’s personal comfort zone is a mental struggle with uncertain reward. Thus, status quo prevails.


Schoener, T.W. 1981 An empirically based estimate of home range. Theoretical Population Biology, 20, 281325.