An Agent Based Modeling Approach to Predicting the Effect of Anthropogenic Pressures on the Movement Patterns of Mongolian Gazelles

Transcription

1 An Agent Based Modeling Approach to Predicting the Effect of Anthropogenic Pressures on the Movement Patterns of Mongolian Gazelles Presenters: Michael Kleyman, Kyle Orlando, William Weston-Dawkes Other Contributors: C. Gibb, M. Koelbel, R. Natoli, M. Rice, C. Weber Mentor: Professor B.Fagan

2 Presentation Overview Research Question Project Overview Literature Review Methodology Preliminary Results Future Work

3 Research Question How does landscape exploitation and fragmentation affect the movements of highly mobile animals in a highly dynamic landscape?

4 Specific Focus: Mongolian Gazelles in the Eastern Mongolian Steppe Fragmentation Highly Mobile Highly Dynamic [1] Trans-Mongolian Railway [2] Visualization of gazelle movement path T. Mueller et al., 2007 [1] [2]

5 Project Overview To create a behavioral model of Mongolian Gazelle movement within the Eastern Mongolian Steppe that captures gazelle behavior with respect to anthropogenic pressures, shifting food sources, and herd movement. Future Work: To use the trained model to infer how the introduction of additional anthropogenic features may affect gazelles [1] [1] Drive net along the Eastern Mongolian Steppe

6 Motivation Mongolian Megafauna Mongolian Wild Ass: Endangered Przewalski s Horse : Endangered Bactrian Camel : Critically Endangered Altai argali: Vulnerable Saiga: Critically Endangered [1] Mongolian Wild Ass Mongolian Gazelle:.. Statuses from The IUCN Red List of Threatened Species [1] [2] [3] [2] Mongolian Gazelle [3] Saiga

7 Literature Review:The Mongolian Gazelle The Mongolian Gazelle undertakes long-distance nomadic movements to cope with a highly variable landscape and food availability. Restriction of range is a high threat to the survival of nomadic species. Population centers, roads, and railroads may limit their range, cutting the Mongolian Gazelle off from possible food sources. K. Olson et al., 2009 [1] Mongolian gazelle mega-herd [1]

8 Nomadic vs. Migratory Behaviors Migratory: defined as seasonally recurring and predictable movements of individuals throughout a geographic environment, (i.e.: Monarch butterflies having well-defined migration route) Nomadic: defined as less regular movements in response to environmental fluctuations, and typically characterized by between-year variability in the geographic location of reproduction events.

9 The Mongolian Eastern Steppe Fragmentation Caused by anthropogenic factors: o Mining operations o Railroads o Local humans and livestock Landscape Dynamics Irregular weather => Highly variable vegetation T.Y. Ito et al., 2013

10 Methodology Overview Created a computer model of gazelle agents interacting within a continually changing landscape o Software framework: Repast Simphony 2.1 (Java) o Such highly mobile species and dynamic landscapes require a flexible and robust approach. o We model driving factors in gazelle movement o Individual level movement mechanisms are drawn from literature o Population level movement patterns are trained using statistical measures o Validation needs to be an inherent feature of the initial modeling phase in order to ensure the utility of further modeling T. Mueller, W. Fagan, V. Grimm, 2011

11 Screenshot from the model

12 Individual-Based Neural Network Genetic Algorithms (INGs) Neural network generates movement decisions at each step of the model Genetic algorithm optimizes the weights in the connections of the neural network, i.e. genes determine the weights Trains an overall movement pattern that is validated using quantitative statistical distributions drawn from real data. T. Mueller, W. Fagan, V. Grimm, 2011

13 Empirical Data Raw Data: Time-series Mongolian Gazelle movement data ranging from the end of 2007 to mid 2012 NDVI values for all patches of land in Mongolia at same time points Location of population centers and anthropogenic features. E.g., Major cities and towns Calculated Movement Metrics: Frequency distribution of NDVI values of the land gazelles graze on. Distribution of distances gazelles are to the nearest anthropogenic feature. Semivariance vs. Temporal lag

14 Artificial Neural Net Architecture T. Mueller, W. Fagan, V. Grimm, 2011

15 Why No Back Error Propagation? Back error propagation trains a neural network based on each output generated. An ING framework allows for a pattern-based approach to modeling We draw movement mechanisms from established literature on the subject of Mongolian Gazelle movement. The genetic algorithm trains a movement decision based on the overall movement pattern of the model individuals

16 Genetic Algorithm 20%: Replicated into the next generation from agents of the current generation with top 20% fitness 60%: Created using Reproduction algorithm 20%: Have random generated codes

17 Obstacle Distance Distribution Fitness Reproduction Algorithm 1. Select 2 parents using fitness based stochastic universal sampling a. Multi-parameter fitness is determined by using pareto-front dominance NDVI Distribution Fitness Crossover: Independent Assortment a. Eliminates linked gene problem 2. Mutate a. Creeping Mutation

18 NDVI Frequency Graph Encapsulates Mongolian Gazelle behavior with respect to vegetation resources

19 Minimum Distance from Population Centers Encapsulates Mongolian Gazelle behavior with respect to anthropogenic features

20 Conclusions Nomadic movement behavior of a population on a real landscape can be modeled with individual neural network decisions Neural network movement decision making can be trained or optimized with a genetic algorithm to fit movement patterns generated from empirical data. Frequency Distribution Patterns can be used as a fitness measure for agents

21 Possible Improvements Run the model for more generations Add two more fitness metrics: o o MCI:Movement Coordination Index PDI: Population Dispersion Index Use a different Genetic Algorithm which maintains diversity within the population: o o NSGA-II SPEA-II obio/ deb-nsga-ii.pdf Courses/Spring- 2007/CIS830/Handouts/P8. pdf

22 Semivariogram Characterizes Mongolian Gazelle movement patterns over varying temporal scales

23 KS-Statistic for Semivariance Validation Plan - Definition of Trained Training Set (70%) Validation Set(30%) Well Trained Sweet Spot Generations How do we plan to avoid overfitting the training data?

24 Future Work Introduce hypothetical anthropogenic features into the landscape (e.g. a railroad or highway) and assess their effect on the survivability of Mongolian Gazelle populations. Assess ways to mitigate the effects of anthropogenic features 9/dsc00979.jpg

25 Thanks Swarmfest Our mentor: Dr. Bill Fagan Dr. Thomas Mueller Dr. Kirk Olson Gemstone: o Dr. Frank Coale o Dr. Kristan Skendall o Vickie Hill Sesync Repast Development Team Team Path Members

26 Questions?