Book

An interesting new book but Alison Heppenstall, Andrew Crooks, Linda See and Mike Batty
"Agent-Based Model of Geographical Systems"

as stated on Andrew Crooks' blog 'GIS and agent-based modelling':

[the book] brings together a comprehensive set of papers on the background, theory, technical issues and applications of agent-based modelling (ABM) within geographical systems. This collection of papers (see below) is an invaluable reference point for the experienced agent-based modeller as well those new to the area. Specific geographical issues such as handling scale and space are dealt with as well as practical advice from leading experts about designing and creating ABMs, handling complexity, visualising and validating model outputs. With contributions from many of the world’s leading research institutions (see map below), the latest applied research (micro and macro applications) from around the globe exemplify what can be achieved in geographical context.

This book is relevant to researchers, postgraduate and advanced undergraduate students, and professionals in the areas of quantitative geography, spatial analysis, spatial modelling, social simulation modelling and geographical information sciences.

Book Contents:

Part 1: Computational Modelling: Techniques for Simulating Geographical Systems

1. Perspectives on Agent-Based Models and Geographical Systems.
2. A Generic Framework for Computational Spatial Modelling.
3. A Review of Microsimulation and Hybrid Agent Based Approach.
4. Cellular Automata in Urban Spatial Modelling.
5. Introduction to Agent-Based Modelling.

Part 2: Principles and Concepts of Agent-Based Modelling.

6. Agent-Based Models - Because they're Worth it?
7. Agent-Based Modelling and Complexity.
8. Designing and Building an Agent-Based Model.
9. Modelling Human Behaviour in Agent-Based Models.
10. Calibration and Validation of Agent-Based Models of Land Cover Change.
11. Networks in Agent-Based Social Simulation.

Part 3: Methods, Techniques and Tools for the Design and Construction of Agent-Based Models: 

12. The Integration of Agent-Based Modelling and Geographical Information for Geospatial Simulation.
13. Space in Agent-Based Models.-
14. Large Scale Agent-Based Modelling: A Review and Guidelines for Model Scaling.
15. Uncertainty and Error.-
16. Agent-Based Extensions to a Spatial Microsimulation Model of Demographic Change.
17. Designing, Formulating, and Communicating Agent-Based Models.-
18. Agent Tools Techniques and Methods for Macro and Microscopic Simulation.

Part 4: Fine-Scale, Micro Applications of Agent-Based Models: 

19. Using Agent-Based Models to Simulate Crime.
20. Urban Geosimulation.
21. Applied Pedestrian Modelling.
22. Business Applications and Research Questions using Spatial Agent-Based Models.
23. Using Agent-Based Models for Education Planning. Is the UK Education System Agent Based?
24. Simulating Spatial Health Inequalities.
25. ABM of Residential Mobility, Housing Choice and Regeneration.-
26. Do Land Markets Matter? A Modelling Ontology and Experimental Design to Test the Effects of Land Markets for an Agent-Based Model of Ex-urban Residential Land-Use Change.
27. Exploring Coupled Housing and Land Market Interactions Through an Economic Agent-Based Model (CHALMS).

Part 5: Linking Agent-Based Models to Aggregate Applications Macro:

28. Exploring Urban Dynamics in Latin American Cities using an Agent-Based Simulation Approach.
29. An Agent-Based/Network Approach to Spatial Epidemics.
30. An Agent-Based Modelling Application of Shifting Cultivation.
31. Towards New Metrics for Urban Road Networks. Some Preliminary Evidence from Agent-Based Simulations.
32. A Logistic Based Cellular Automata Model for Continuous Urban Growth Simulation: A Case Study of the Gold Coast City, Australia.
33. Exploring Demographic and Lot Effects in an ABM/LUCC of Agriculture in the Brazilian Amazon.
34. Beyond Zipf: An Agent Based Understanding of City Size Distributions.
35. The Relationship of Dynamic Entropy Maximising and Agent Based Approaches in Urban Modelling.
36. Multi-Agent System Modelling for Urban Systems: The Series of SIMPOP Models.
37. Reflections and Conclusions: Geographical Models to Address Grand Challenges

Looks particularly interesting but I'll be waiting to hear a few more reviews before I take the plunge and buy it on impulse. Very interesting though....

VERY relevant paper from 2001

I just found an extremely relevant paper form 2001 where someone has actually implemented an agent-based model of panther movements to explore the feasibility of re-introducing panthers to a location in Northern Florida.

The paper doesn't explicitly acknowledge its agent-based modelling methodology, but it definitely uses ABMs to model panthers moving around a real landscape. There are many similarities to my work here. The exact methodology does differ but is essence they have panthers looking at neighbouring cells, choosing a location and moving to that location, based on habitat, prey resource, roads and human density, whilst simultaneously interacting with other panthers in the area.

This is THE paper to reference.

And I am one very happy camper! :)

Reference:
Cramer, P.C. & Portier, K.M. (2001) Modelling Florida panther movements in response to human attributes of the landscape and ecological settings. Ecological Modelling 140: 51-80
(available from ScienceDirect, or Mendeley).

Habitat preference and territory development

Getting jaguar agents to mimic real-life individuals and create territories is one of the most important features to try and get right in this type of agent-based model.

Exploring the way jaguars move through landscapes involves understanding how they interact with each other and with the environment. Territory formation and avoidance (or not) of conspecifics are integral to getting these interactions to be as close to mimicking those found in real populations as possible.

So.... some kind of pheromone seems that it should elicit an easy territory response in the agents. 'Marking' each cell with some level of pheromone as the agents move into and out of the cell and then allowing this 'pheromone' to degrade over time should allow territories to organically emerge during the simulation and with a bit of tweaking as to how quickly the pheromones should degrade and how 'strong' they should be to other individuals and to themselves.

The main idea is that individuals should not want to re-trace their steps too often, and that generally individuals should try and avoid each other - either due to possible conflicts between adults and due to resource depletion.

We know a bit about how large and flexible territories are in wild jaguar populations and so we settled on a maximum pheromone level of 100, with a degradation rate of 0.98, so that the pheromone reduced by 0.02% each timestep. This gave realistic territory sizes.

The strength of the pheromone is equal to its level, except for an agents own pheromone which is reduced to 0.15% of its current level; a deterrent to re-entering the cell but not enough to mean that the individual would not want to re-treat to its territory if meeting another individual or unsuitable habitat was the only other option.

The addition of pheromones, and the basic least-cost model idea of the simulation led to the following output, where clearly define territories (individuals in different colours) and habitat preferences can be seen:

Some individuals have been 'pushed' out of the forest area due to the population size. This is intentional. A higher number of individuals creates stress for any single individual. Without some level of stress, individuals will be content on remaining in fairly isolated areas. Some stress is needed in order for individuals to move and seek new areas in which lead a lifestyle of least-cost, so prompting the migration of individuals from one side of the landscape to the other.

Jaguar vs Leopard Identification

I found this and thought it summed things up pretty well for anyone not sure on what the differences are between a jaguar and a leopard.

Jaguars also live in central and south America, whilst Leopards are found in central and southern Africa  and parts of Asia.
Jaguars also tend to be heavier and stockier than their leopard cousins.

p.s. The Leopard scientific name should read Panthera Pardus.