Animal Space Use

The Lévy flight foraging (LFF) hypothesis describes a toggling between classic Lévy flight/walk (LW) and classic Brownian motion (BM) as a function of the individual’s current resource field properties (its “environment”). Both states of motion are statistical by nature – and explicitly defined as such. The LFF describes movement as random walk in two disparate modes; scale-free LW versus scale-specific BM. However, the LFF premise of animals moving like drunken LW/BM walkers logically does not make sense unless the animal in question does not possess a capacity for spatial memory utilization or because the environment is so volatile that returning to a previous location has no fitness value with respect to optimal foraging. Under these premises of value-less spatial map utlilization the LFF hypothesis should be expected to make sense, otherwise one should expect to find better compliance with other movement-related models and hypotheses. The theoretical model developments surroundin

Over the last years we have seen a range of interesting and important books appearing in the field of animal movement and space use. In this post I mention four of them. Unfortunately, only two of these books (disregarding my own contribution) presents any reference to animals’ capacity for spatial reorientation beyond the individual’s current perceptual field. However, all that is offered in these two titles – covering hundreds of pages with deep theory – is a couple of sentences or paragraphs. Why such a slow implementation in mainstream models with respect to this key aspect of behavioural ecology? Why such stubbornness to bridge theory to empirical knowledge by including spatial memory as an important factor that influences how animals use their habitat? First, I was happy to discover that Turchin’s classic book “Quantitative analysis of animal movement” (Turchin 1998) has now appeared in new print, dated 2015. However, like the original version you will search in vain for any

Scale-free distribution of displacement lengths is often found in animal data, both vertebrates and invertebrates. In marine species this pattern has often been interpreted in the context of the Lévy flight foraging hypothesis (LFF), where optimal search is predicting a scale-free power law compliant movement when prey patches are scarce and unpredictably distributed and a more classic and scale-specific Brownian motion-like motion when such patches are encountered (Viswanathan et al . 1999). In a study on the jellyfish Rhizostoma octopus such an apparent toggling between two foraging modes were found, but critical questions were also raised by the authors (Hays et al . 2012). Here I come the authors “to the rescue” by suggesting that an alternative model – the Multi-scaled Random Walk (MRW) – could be included when testing statistical classes of foraging behaviour. I cite from their Discussion: In some periods (when integrated vertical movement was low), vertical excursions were

In this post I present a guideline that summarizes how a memory-based model with an increasing pile of empirical verification covering many species – the Multi-scaled Random Walk model (MRW) – may be applied in ecological research. The methods are in part based on published papers and in part based on some of the novel methods which you find scattered throughout this blog. In the following, let us assume for a given data set that we have verified MRW compliance (using the standard memory-less models or alternative memory-implementing models as null hypotheses) by performing the various tests that have already been proposed in my papers, blog posts and book. Typically, a standard procedure should be to verify (a) site fidelity; i.e., presence of a home range, (b) scale-free space use by studying the step length distribution from high frequency sampling, and (c) the fractal dimension D≈1 of the spatial scatter of relocations in the resolution range between the dilution effect (very sm

There is no doubt among field ecologists that animals from a broad range of taxa and over wide range of ecological conditions utilize their environment in a spatial memory-influenced manner. Spatial map utilization have now been verified also well beyond vertebrates, like dragonflies and some solitary wasps. To me at least it is thus a mystery why theoretical models that are void of influence from a memory map; for example ARS, Lévy walk and CTRW (see Part I, II), are still dominating ecological research with mostly no critical questions asked about their feasibility. It is a fact that the memory-less mainstream models all have a premise that the data should not be influenced by map-dependent site fidelity. In other words, applying ARS, Lévy walk and CTRW models as stochastic representation of space use also implies accepting that the animal’s path is self-crossing by chance only, and not influenced by targeted returns. Such returns can be expected to seriously disrupt results on – f

In Part I three models for temporally constrained space use were summarized. Here in Part II I put them more explicitly into the context of ecology with focus on some key assumptions for the respective models. Area restricted search (ARS), Lévy walk (LW) and Continuous time random walk (CTRW) are statistical representations of disparate classes of temporally constrained space use without explicit consideration of spatial memory effects. Hence, below I reflect on a fourth model, Multi-scaled Random Walk (MRW), where site fidelity gets a different definition relative to its spatially memory-less counterparts. Picture: A cattle egret Bubulcus ibis is foraging within a wide perimeter surrounding its breeding site. Spatial memory is utilized not only to be able to return to the nest but also to revisit favored foraging locations during a bout, based on a memory map of past experience. Photo AOG. First, ARS is typically formulated as a composite random walk-like behaviour in statistic

Temporally constrained space use is a key property of animal movement. With respect to vertebrates three main statistical representations are particularly popular among modelers, based on disparate theoretical foundations. Which one should one use for analysis of a particular data set? As always in ecological research, one needs some simple protocol to distinguish between alternative model assumptions. Animals paths are neither straight lines nor a dense dot of positions from juggling back and forth at the same spot. Typically we see a complicated combination of these two extreme patterns; some quite straightforward moves occasionally abrupted by more jagged movement. In order to infer behavioural and ecological results from space use one needs to study the data in the context of a realistic theoretical framework. Outside the realm of temporal site fidelity; e.g., a drifting home range (Doncaster and Macdonald 1991), ecological textbooks typically explain the mixture of straight a

Examples of positive feedback loops in population dynamics abound. Even if the majority of models are focusing on negative feedback, like the logistic growth function, non-equilibrium “boom and bust” kind of model designs have also been developed. In this post I elaborate on the particular kind of positive feedback loop that emerges from cross-scale dual-direction flow of individuals that is based on the parallel processing conjecture. The image to the right illustrates – in simplistic terms – a spatially extended population model of standard kind (e.g., a coupled map lattice design) where each virtually demarcated local population j at spatial resolution i and at a given point in time t contains N ij individuals. No borders for local migration are assumed; i.e ., the environment is open both internally and externally towards neighbour sites.Typically, these individuals are set to be subject to a locally negative feedback loop in accordance to principles of density dependent regul

The standard theories on animal space use rest on some shaky behavioural assumptions, as elaborated on in my papers, in my book and here in my blog. One of these assumptions regards the assumed lack of influence of positive feedback, in particular the self-reinforcing effect that emerge when individuals are moving around with a cognitive capacity for both temporal and spatial memory utilization. The common ecological methods to study individual habitat use; like the utilization distribution (a kernel density distribution with isopleth demarcations), use/availability analysis, and so on, explicitly build on statistical theory that not only disregards such positive feedback, but in fact requires that this emergent property is not influencing the system under scrutiny. Unfortunately, most memory-enhanced numerical models to simulate space use are rigged to comply with negative rather than positive feedback effects. For example, the model animal successively stores its local experience w

In empirical data GPS fixes are never exact positions. A “fuzziness field” will always be introduced due to uncertain geolocation. When analyzing a set of fixes in the context of multi-scaled space use, are the parameter estimates sensitive to this kind of statistical error? Simultaneously, I also explore the effect on constraining the potential home range by disallowing sallies to the outermost range of available area. To explore the triangulation error effect on space use analysis I have simulated Multi-scaled random walk in a homogeneous environment with N=10,000 fixes (of which the first 1,000 fixes were discarded) under two scenaria; a “sharp” location (no uncertainty polygons), and strong fuzziness. The latter introduced a random displacement to each x-y coordinate with a standard deviation (SD) of magnitude approximately equal to the system condition’s Characteristic scale of space use (CSSU). Displacements to the outermost parts of the given arena was disallowed, to study how