Multiagent simulations of hunting wild meat in a village in eastern Cameroon

Informing conservation management about structural versus functional connectivity: a case-study of Cross River gorillas

Connectivity among subpopulations is vital for the persistence of small and fragmented populations. For management interventions to be effective conservation planners have to make the critical distinction between structural connectivity (based on landscape structure) and functional connectivity (which considers both landscape structure and organism-specific behavioral attributes) which can differ considerably within a given context. We assessed spatial and temporal changes in structural and functional connectivity of the Cross River gorilla Gorilla gorilla diehli (CRG) population in a 12,000 km(2) landscape in the Nigeria-Cameroon border region over a 23-year period, comparing two periods: 1987-2000 and 2000-2010. Despite substantial forest connections between occupied areas, genetic evidence shows that only limited dispersal occurs among CRG subpopulations. We used remotely sensed land-cover data and simulated human pressure (using a spatially explicit agent-based model) to assess human impact on connectivity of the CRG population. We calculated cost-weighted distances between areas occupied by gorillas as measures of connectivity (structural based on land-cover only, functional based on both land-cover and simulated human pressure). Whereas structural connectivity decreased by 5% over the 23-year period, functional connectivity decreased by 11%, with both decreasing more during the latter compared to the earlier period. Our results highlight the increasing threat of isolation of CRG subpopulations due to human disturbance, and provide insight into how increasing human influence may lead to functional isolation of wildlife populations despite habitat continuity, a pressing and common issue in tropical Africa often not accounted for when deciding management interventions. In addition to quantifying threats to connectivity, our study provides crucial evidence for management authorities to identify actions that are more likely to be effective for conservation of species in human-dominated landscapes. Our approach can be easily applied to other species, regions, and scales.

Design of an Agent-Based Model to Examine Population-Environment Interactions in Nang Rong District, Thailand

The design of an Agent-Based Model (ABM) is described that integrates Social and Land Use Modules to examine population-environment interactions in a former agricultural frontier in Northeastern Thailand. The ABM is used to assess household income and wealth derived from agricultural production of lowland, rain-fed paddy rice and upland field crops in Nang Rong District as well as remittances returned to the household from family migrants who are engaged in off-farm employment in urban destinations. The ABM is supported by a longitudinal social survey of nearly 10,000 households, a deep satellite image time-series of land use change trajectories, multi-thematic social and ecological data organized within a GIS, and a suite of software modules that integrate data derived from an agricultural cropping system model (DSSAT - Decision Support for Agrotechnology Transfer) and a land suitability model (MAXENT - Maximum Entropy), in addition to multi-dimensional demographic survey data of individuals and households. The primary modules of the ABM are the Initialization Module, Migration Module, Assets Module, Land Suitability Module, Crop Yield Module, Fertilizer Module, and the Land Use Change Decision Module. The architecture of the ABM is described relative to module function and connectivity through uni-directional or bi-directional links. In general, the Social Modules simulate changes in human population and social networks, as well as changes in population migration and household assets, whereas the Land Use Modules simulate changes in land use types, land suitability, and crop yields. We emphasize the description of the Land Use Modules - the algorithms and interactions between the modules are described relative to the project goals of assessing household income and wealth relative to shifts in land use patterns, household demographics, population migration, social networks, and agricultural activities that collectively occur within a marginalized environment that is subjected to a suite of endogenous and exogenous dynamics.

Effect of small-scale heterogeneity of prey and hunter distributions on the sustainability of bushmeat hunting

Bushmeat is the main source of protein and the most important source of income for rural people in the Congo Basin, but intensive hunting of bushmeat species is also a major concern for conservationists. Although spatial heterogeneity in hunting effort and in prey populations at the landscape level plays a key role in the sustainability of hunted populations, the role of small-scale heterogeneity within a village hunting territory in the sustainability of hunting has remained understudied. We built a spatially explicit multiagent model to capture the dynamics of a system in which hunters and preys interact within a village hunting territory. We examined the case of hunting of bay duikers (Cephalophus dorsalis) in the village of Ntsiété, northeastern Gabon. The impact of hunting on prey populations depended on the spatial heterogeneity of hunting and prey distribution at small scales within a hunting area. Within a village territory, the existence of areas hunted throughout the year, areas hunted only during certain seasons, and unhunted areas contributed to the sustainability of the system. Prey abundance and offtake per hunter were particularly sensitive to the frequency and length of hunting sessions and to the number of hunters sharing an area. Some biological parameters of the prey species, such as dispersal rate and territory size, determined their spatial distribution in a hunting area, which in turn influenced the sustainability of hunting. Detailed knowledge of species ecology and behavior, and of hunting practices are crucial to understanding the distribution of potential sinks and sources in space and time. Given the recognized failure of simple biological models to assess maximum sustainable yields, multiagent models provide an innovative path toward new approaches for the assessment of hunting sustainability, provided further research is conducted to increase knowledge of prey species' and hunter behavior.

Computational models of collective behavior

Computational models of human collective behavior offer promise in providing quantitative and empirically verifiable accounts of how individual decisions lead to the emergence of group-level organizations. Agent-based models (ABMs) describe interactions among individual agents and their environment, and provide a process-oriented alternative to descriptive mathematical models. Recent ABMs provide compelling accounts of group pattern formation, contagion and cooperation, and can be used to predict, manipulate and improve upon collective behavior. ABMs overcome an assumption that underlies much of cognitive science--that the individual is the crucial unit of cognition. The alternative advocated here is that individuals participate in collective organizations that they might not understand or even perceive, and that these organizations affect and are affected by individual behavior.

Predicting nearest agent distances in artificial worlds

In a number of multi-agent artificial life studies where agents interact over limited distances, the emergence and/or evolution of a specific behavior may depend critically upon interagent distances. Little theoretical analysis has been done previously concerning how to predict such distances. In this paper, we derive a probabilistic method that, for an agent at an arbitrary location in a two-dimensional cellular world, predicts the expected distance to a nearest other agent. Our method works for many world topologies, and we apply it to determine the expected distance for six commonly used ones. Further, the method is readily adapted to handle special restrictions. Over a wide variety of agent densities we show that the theoretically predicted distances are largely in agreement with the distances measured in computational experiments with randomly placed agents. We then utilize our prediction method to interpret recent observations that an imprecise threshold in the density of agents exists for the evolution of communication. We thus illustrate that, despite its conceptual simplicity, our method can aid the analysis and even the design of complex artificial environments populated by agents that have the potential to interact with one another.