Bridge Building Needed
Large databases of animal space use (eg, GPS), environmental conditions (e.g., GIS) and sophisticated models for statistical and behavioural analysis are now improving our field of research at a rapid pace. However, there still exists an unfortunate level of skepticism among some field ecologists towards some classes of theoretical models. On the other hand, there also exists a similar despair among many theoreticians over lack of consensus over common concepts that seek to link true behaviour to simplistic model representations. A better foundation of animal ecology as a hard science with high predictive power of models could be achieved if both “camps” collaborate better to clarify (a) what regards sound criticism from empiricists of some models’ realism, (b) some unfortunate misconceptions of theoretical terms among some field ecologists, and (c) reasons for a reluctance among many theoreticians to explore more complex kinds of space use, in particular related to spatial memory outside the “mechanistic” modelling approach.
Below, I single out three aspects in particular, which could benefit from better bridge building between empiric data and theory.
The first bridge: Homogeneous vs. heterogeneous environment. Some ecologists (both field ecologists and theoreticians) have criticized some simulated movement conditions for lack of realism by not including a variable habitat. In a recent post I elaborated on this theme (Archive: "Homogeneous or Heterogeneous Environment?". Posted January 30, 2016), and advocated that a homogeneous environment in many contexts represents an ideal initial playground for the sake of disentangling the influence on movement of intrinsic (cognitive) and extrinsic (environmental) origin. In addition to simulation models, homogeneous conditions are for this reason also utilized in biological experiments. I have already posted a nice example showing how lab mice; i.e., real animals, revealed specific statistical-mechanical properties of movement in a totally homogeneous field. Likewise, simplistic simulations in a homogeneous arena may in contribute to explore and illuminate the qualitative difference between a mechanistic (“Markovian”) and a multi-scaled (“Parallel processing”) kind of memory map utilization, which is an intrinsic process property. This fundamental clarification shows the power of modelling as a tool to improve theoretical ecology and produce clear and testable hypotheses. Follow-up simulations – using either the Markovian or the parallel processing framework as an assumption – should then add realism and explore more specific hypotheses by invoking habitat heterogeneity (see my book for details).
The second bridge: Random movement. In this post I described various classes of stochastic movement; for example, Brownian motion, Levy walk and Multi-scaled random walk, with respective corner positions of the Scaling cube. In particular, Brownian motion (classic random walk) and its variant correlated random walk have been applied extensively in theoretical ecology to represent animal movement. However, as all field ecologists know – and all theoreticians should know – animals do not move stochastically. A physicist may describe a particle performing Brownian motion as a stochastic kind of motion, but still acknowledging that the particle’s jagged path is the result of a series of completely deterministic collision events at micro-scales. Each of these motion-influencing events could in principle (albeit not in practice) be described by an extensive deterministic equation. Thus, a stochastic model like Brownian motion represents an immensely effective system simplification by replacing deterministic process details with a stochastic model with high predictive power (consider the successful theory of diffusion). Similarly, the application of stochastic representations of movement in ecological models does not necessarily imply that the modeller consider movement to be non-deterministic at the behavioral (micro-scale) level. Stochastic movement is a complementary process abstraction, allowing for a range of ecological parameter estimates from real data; for example the diffusion rate as a function of various biological and ecological conditions.
The third bridge: Extending the framework of stochastic modelling. All field ecologists working on vertebrates (and many invertebrates) know from first-hand experience that animals generally (a) have the cognitive capacity to utilize a memory map and (b) consequently show indications of relating to their environment over a range of spatial and temporal scales. However, from the theoretical side it is an unfortunate fact that many stochastic models either …
The common denominator for all three bridges is a need for ecologists in general to pay closer attention to the biophysics of animal space use; i.e., acknowledging the complementarity between animal behaviour and statistical-mechanical representations of this behaviour, for the sake of developing novel models with high predictive power.
Below, I single out three aspects in particular, which could benefit from better bridge building between empiric data and theory.
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| Roe deer. Photo: AOG. |
The second bridge: Random movement. In this post I described various classes of stochastic movement; for example, Brownian motion, Levy walk and Multi-scaled random walk, with respective corner positions of the Scaling cube. In particular, Brownian motion (classic random walk) and its variant correlated random walk have been applied extensively in theoretical ecology to represent animal movement. However, as all field ecologists know – and all theoreticians should know – animals do not move stochastically. A physicist may describe a particle performing Brownian motion as a stochastic kind of motion, but still acknowledging that the particle’s jagged path is the result of a series of completely deterministic collision events at micro-scales. Each of these motion-influencing events could in principle (albeit not in practice) be described by an extensive deterministic equation. Thus, a stochastic model like Brownian motion represents an immensely effective system simplification by replacing deterministic process details with a stochastic model with high predictive power (consider the successful theory of diffusion). Similarly, the application of stochastic representations of movement in ecological models does not necessarily imply that the modeller consider movement to be non-deterministic at the behavioral (micro-scale) level. Stochastic movement is a complementary process abstraction, allowing for a range of ecological parameter estimates from real data; for example the diffusion rate as a function of various biological and ecological conditions.
The third bridge: Extending the framework of stochastic modelling. All field ecologists working on vertebrates (and many invertebrates) know from first-hand experience that animals generally (a) have the cognitive capacity to utilize a memory map and (b) consequently show indications of relating to their environment over a range of spatial and temporal scales. However, from the theoretical side it is an unfortunate fact that many stochastic models either …
- disregard spatio-temporal memory effects,
- disregard the biophysical difference between a mechanistic and a non-mechanistic kind of movement (Markovian vs. non-Markovian; see above), or
- show a reluctance to explore theory for a combination of memory and the observed tendency for scale-free space use. Site fidelity and the consequential emergence of a home range – including a potential for self-reinforcing patch utilization – are prime examples where non-Markovian model design should be considered.
The common denominator for all three bridges is a need for ecologists in general to pay closer attention to the biophysics of animal space use; i.e., acknowledging the complementarity between animal behaviour and statistical-mechanical representations of this behaviour, for the sake of developing novel models with high predictive power.