The purpose of the award is to conduct a program of state forestry research and to develop a trained pool of forest scientists capable of conducting needed forestry research

We will 1) determine how soil carbon (C), nitrogen (N), and phosphorus (P) stocks have changed since the transition from forest to cropland in the western lake erie basin (WLEB). 2) estimate the magnitude of soil organic C sequestration and N and P retention that could be expected with reforestation in a portion of the WLEB. 3) develop integrated models of forest multifunctionality at the stand-level that incorporate processes, products, drivers and, most importantly, codependencies across the atmospheric, vegetation, and soil components of a forest with the goal of identifying connections, feedbacks, and thresholds that advance integrated science and that inform sustainable management and conservation. The integrated analysis we propose will increase accuracy and precision of estimates of forest function by accounting for the interdependencies of water usage, biomass production, and decomposition, i.e., the integrated multifunctionality of systems. We will 1) identify and consolidate research data from the university of michigan (UM) school for environment and sustainability (SEAS) forest properties from networked and off-line digital, and non-digital sources, and screen for valuable historical datasets. Integrate geospatial analysis and metadata for legacy data from each property. 2) develop and establish a data pipeline for preparing and publishing environmental data from UM SEAS properties to publicly available research databases, in coordination with approaches taken by other UM research properties such as UM biological station. 3) engage SEAS faculty and local stakeholders to understand a) how faculty use properties for research, teaching and outreach B) data networks to add value in research on UM SEAS properties C) prioritize regional, national or global data networks and granting opportunities suitable for UM SEAS properties. We aim to comprehensively understand and forecast the dynamics of michigan's forest ecosystems under changing environmental and anthropogenic pressures, with potential implications for broader forested regions in the great lakes and the US. We propose three targeted objectives. 1) empirical assessment of historical changes: to delineate past and ongoing shifts in forest ecosystems, we will conduct in-depth analyses from ground-based forest inventories with HIG H-fidelity remote sensing data. 2) mechanistic projections of future forest dynamics: we will parameterize process-based models to encapsulate the fine scale ecological processes that drive tree growth and forest structure, which in turn will enable US to examine the forecasted trajectories of forest carbon dynamics in the face of diverse climate and management scenarios. 3) stakeholder engagement through innovative tools: to democratize access to scientific findings for stakeholders, we will develop an intuitive geospatial visualization platform and augment IT with an innovative AI-driven conversational interface, leveraging large language models. We will assess the diet of gray wolves in the upper peninsula of michigan in the context of human activity and disturbance. We will use our findings to help inform adaptative management of boreal forest ecosystems to protect their long-term health and resilience. We aim to 1) collect scat and tissue samples from wolves and prey species. 2) establish an isotope library of predator and prey species in the upper peninsula. 3) analyze how temporal and spatial variation in wolf diets are influenced by anthropogenic activities. Beneficiaries of the project include farmers, forest/land managers, policymakers, conservation/environmental organizations. Conservationists, and local decision makers, researchers and academics, industries dependent on forest resources, and the general public.