INTRODUCTION:The U.S. Army Corps of Engineers (USACE) serves as the Nation's environmental engineer. Within this capacity, the USACE designs, plans, oversees, and manages multiple civil works and military construction efforts. The advancement of sustainable design and development practices that beneficially integrate engineering and ecology is a goal of USACE projects (https://ewn.el.erdc.dren.mil/About.html). Once completed, USACE projects are often in service for many decades; therefore, USACE planners must consider long-term changes in the environmental setting of each project (Cann 2010). Wildlife distributions change over time in response to the gain and loss of habitat, as well as other natural processes. Regulatory actions, such as the designation of critical habitat under the Endangered Species Act, can allow for habitat to be preserved promoting source populations that can expand their distribution if conditions are favorable. The listing or de-listing of Threatened and Endangered Species can have direct impacts on USACE projects coming to fruition after years of planning. In an effort to predict potential impacts to USACE restoration sites from species range shifts either into or out of an area, this technical note provides a methodological framework and promotes a model design that has the capacity to inform future decision making. PURPOSE:This effort will develop a working model that can serve as a tool to predict range shifts of threatened, endangered and at-risk species (TER-S), as environmental conditions are altered by climate change (CC). This tool will assist the USACE with future planning and preparation for restoration projects that incorporate management for TER-S already present within the North Atlantic Division (NAD). Changes in climate have an impact on a wide variety of components within natural environments. Temperature and precipitation changes impact vegetation phenology that may disrupt ecosystems in a way that changes TER-S habitats. With the wide breadth of potential impacts from CC, earlier efforts have focused on developing tools for specific circumstances and/or impacts. However, as each of these factors change independently to impact other components, a more comprehensive methodology is needed to conduct a robust assessment of the impacts of CC across a variety of situations and locations.Models that display where TER-S are currently located, and to what extent these range shifts will occur, will be of great importance towards future project planning and resource management. Britzke et al. (2014) outlines existing research products for physical upland climate drivers (e.g., precipitation, temperature, land classification) that are suitable for delineating biome shift vulnerability. For example, some TER-S are strongly associated with specific vegetation communities and forecasting vegetation dynamics can be causally linked to TER-S ranges. Typically, these approaches used species distribution and regression models to statistically 2 understand changes to spatial ranges given a ...

This article examines important characteristics missing from state-of-the-art representations of geographic processes with regard to concurrency. We investigate: what geographic process concurrency is and what opportunities for improved computational models exist by explicitly representing geographic process concurrency in a way that is mutually understandable by the human and the machine. Unapt representations of geographic process concurrency can lead to non-deterministic geographic dynamic modeling outcomes, excessive cognitive burdens when reasoning about how concurrent processes interact, and even inconsistent results. We use a geographic dynamic modeling example to reveal the existing research gap, and to demonstrate that graphical languages can be used AbstractWe present a novel framework called geoexpression for representing geographic process concurrency and discuss its implications for geographic dynamic modeling. Unapt representations of geographic process concurrency can lead to non-deterministic geographic dynamic modeling outcomes, excessive cognitive burdens when reasoning about how concurrent processes interact, and even inconsistent results. After demonstrating the importance of geographic process concurrency, we examine how the characteristics of geographic process concurrency are missing from other state-of-the-art geographic process representations. The geoexpression framework adopts Petri networks to allow researchers to understand how to relate geographic process concurrency to observed modeling patterns. Of interest are the real-world geographic patterns formed by concurrent geographic processes.

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