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Background: EuroAmerican land-use and its legacies have transformed forest structure and composition across the United States (US). More accurate reconstructions of historical states are critical to understanding the processes governing past, current, and future forest dynamics. Here we present new gridded (8x8km) reconstructions of pre-settlement (1800s) forest composition and structure from the upper Midwestern US (Minnesota, Wisconsin, and most of Michigan), using 19th Century Public Land Survey System (PLSS), with estimates of relative composition, above-ground biomass, stem density, and basal area for 28 tree types. This mapping is more robust than past efforts, using spatially varying correction factors to accommodate sampling design, azimuthal censoring, and biases in tree selection. Changes in Forest Structure: We compare pre-settlement to modern forests using US Forest Service Forest Inventory and Analysis (FIA) data to show the prevalence of lost forests (pre-settlement forests with no current analog), and novel forests (modern forests with no past analogs). Differences between pre-settlement and modern forests are spatially structured owing to differences in land-use impacts and accompanying ecological responses. Modern forests are more homogeneous, and ecotonal gradients are more diffuse today than in the past. Novel forest assemblages represent 28% of all FIA cells, and 28% of pre-settlement forests no longer exist in a modern context. Lost forests include tamarack forests in northeastern Minnesota, hemlock and cedar dominated forests in north-central Wisconsin and along the Upper Peninsula of Michigan, and elm, oak, basswood and ironwood forests along the forest-prairie boundary in south central Minnesota and eastern Wisconsin. Novel FIA forest assemblages are distributed evenly across the region, but novelty shows a strong relationship to spatial distance from remnant forests in the upper Midwest, with novelty predicted at between 20 to 60km from remnants, depending on historical forest type. The spatial relationships between remnant and novel forests, shifts in ecotone structure and the loss of historic forest types point to significant challenges for land managers if landscape restoration is a priority. The spatial signals of novelty and ecological change also point to potential challenges in using modern spatial distributions of species and communities and their relationship to underlying geophysical and climatic attributes in understanding potential responses to changing climate. The signal of human settlement on modern forests is broad, spatially varying and acts to homogenize modern forests relative to their historic counterparts, with significant implications for future management. [ABSTRACT FROM AUTHOR]

We present a gridded 8 km-resolution data product of the estimated composition of tree taxa at the time of Euro-American settlement of the northeastern United States and the statistical methodology used to produce the product from trees recorded by land surveyors. Composition is defined as the proportion of stems larger than approximately 20 cm diameter at breast height for 22 tree taxa, generally at the genus level. The data come from settlement-era public survey records that are transcribed and then aggregated spatially, giving count data. The domain is divided into two regions, eastern (Maine to Ohio) and midwestern (Indiana to Minnesota). Public Land Survey point data in the midwestern region (ca. 0.8-km resolution) are aggregated to a regular 8 km grid, while data in the eastern region, from Town Proprietor Surveys, are aggregated at the township level in irregularly-shaped local administrative units. The product is based on a Bayesian statistical model fit to the count data that estimates composition on the 8 km grid across the entire domain. The statistical model is designed to handle data from both the regular grid and the irregularly-shaped townships and allows us to estimate composition at locations with no data and to smooth over noise caused by limited counts in locations with data. Critically, the model also allows us to quantify uncertainty in our composition estimates, making the product suitable for applications employing data assimilation. We expect this data product to be useful for understanding the state of vegetation in the northeastern United States prior to large-scale Euro-American settlement. In addition to specific regional questions, the data product can also serve as a baseline against which to investigate how forests and ecosystems change after intensive settlement. The data product is being made available at the NIS data portal as version 1.0. [ABSTRACT FROM AUTHOR]

Interdisciplinary collaboration is essential to understand ecological systems at scales critical to human decision making. Current reward structures are problematic for scientists engaged in interdisciplinary research, particu-larly early career researchers, because academic culture tends to value only some research outputs, such as pri-mary-authored publications. Here, we present a framework for the costs and benefits of collaboration, with a focus on early career stages, and show how the implementation of novel measures of success can help defray the costs of collaboration. Success measures at team and individual levels include research outputs other than publications, including educational outcomes, dataset creation, outreach products (eg blogs or social media), and the application of scientific results to policy or management activities. Promotion and adoption of new measures of success will require concerted effort by both collaborators and their institutions. Expanded mea-sures should better reflect and reward the important work of both disciplinary and interdisciplinary teams at all career stages, and help sustain and stimulate a collaborative culture within ecology. [ABSTRACT FROM AUTHOR]