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Ethnobotanical surveys were conducted to locate culturally important, regionally scarce, and disappearing medicinal plants via a novel participatory methodology which involves healer-expert knowledge in interactive spatial modeling to prioritize conservation efforts and thus facilitate health promotion via medicinal plant resource sustained availability. These surveys, conducted in the Maya Mountains, Belize, generate ethnobotanical, ecological, and geospatial data on species which are used by Q'eqchi' Maya healers in practice. Several of these mountainous species are regionally scarce and the healers are expressing difficulties in finding them for use in promotion of community health and wellness. Based on healers' input, zones of highest probability for locating regionally scarce, disappearing, and culturally important plants in their ecosystem niches can be facilitated by interactive modeling. In the present study, this is begun by choosing three representative species to train an interactive predictive model. Model accuracy was then assessed statistically by testing for independence between predicted occurrence and actual occurrence of medicinal plants. A high level of accuracy was achieved using a small set of exemplar data. This work demonstrates the potential of combining ethnobotany and botanical spatial information with indigenous ecosystems concepts and Q'eqchi' Maya healing knowledge via predictive modeling. Through this approach, we may identify regions where species are located and accordingly promote for prioritization and application of in situ and ex situ conservation strategies to protect them. This represents a significant step toward facilitating sustained culturally relative health promotion as well as overall enhanced ecological integrity to the region and the earth.

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Mission planning is one of the core steps to effectively exploit the capabilities of multi-level cooperative control of multiple semi-autonomous entities, such as Unmanned Aerospace Vehicles (UAVs). In this paper, we describe a methodology of Team Dynamics and Tactics (TDT) for mission planning in a military operation. This method for mission planning is implemented in a TDT module for an interconnected system called Strategies for Human-Automaton Resource Entity Deployment (SHARED). The main purpose of TDT is to develop and provide an effective target selection algorithm and an optimal salvo size algorithm to destroy the opposing force combat capabilities. Furthermore, the TDT mission plan will find an optimal assignment of decoys and avoid collateral damage. The proposed mission-planning scheme supplies the optimum degree of force for campaign objectives by using a linear integer programming with fuzzy objective function to allocate the best UAVs and weapons against each target and a parameter Nash game with proportional feedback control to determine optimum salvo size for each UAV. The effectiveness of the proposed scheme is illustrated by a Suppression of Enemy Air Defenses (SEAD) example, and is demonstrated in a simulation environment based on the Boeing C4ISim Open Experimentation .Platform (Boeing OEP).