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

The Department of Agriculture National Institute of Food and Agriculture awarded Regents of the University of Michigan $741,354 on October 1, 2025, under the Cooperative Forestry Research program (CFDA 10.202).

The award funds research to determine how soil carbon, nitrogen, and phosphorus stocks have changed following forest-to-cropland conversion in the Western Lake Erie Basin, and to estimate soil organic carbon sequestration and nutrient retention potential from reforestation in the region. The recipient will develop integrated stand-level models of forest multifunctionality that account for interdependencies across atmospheric, vegetation, and soil components, with the goal of identifying ecological connections, feedbacks, and thresholds to inform sustainable management and conservation.

Work includes consolidating research data from University of Michigan School for Environment and Sustainability forest properties from networked and offline digital and non-digital sources, integrating geospatial analysis and metadata for legacy datasets, developing and establishing a data pipeline for preparing and publishing environmental data to publicly available research databases in coordination with other University of Michigan research properties such as the Biological Station, and engaging SEAS faculty and local stakeholders to assess property use for research, teaching, and outreach and to prioritize alignment with regional, national, or global data networks.

Performance occurs in Ann Arbor, Michigan. The period of performance runs from October 1, 2025, through September 30, 2027.

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.