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Lifespan of salt marshes in New York are calculated using conceptual marsh units defined by Defne and Ganju (2018) and Welk and others (2019, 2020a, 2020b, 2020c). The lifespan calculation is based on estimated sediment supply and sea-level rise (SLR) predictions after Ganju and others (2020). Sea level predictions are local estimates which correspond to the 0.3, 0.5, and 1.0 meter increase in Global Mean Sea Level (GMSL) scenarios by 2100 from Sweet and others (2022). The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem...
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These model objects are the outputs of three Boosted Regression Tree models (for three different time periods) to explore the role of climate change and variability in driving ecological change and transformation. Response variables were the proportion of sites in each ecoregion with peak rates of change at 100-year time steps. Predictor variables included temperature anomaly, temperature trend, temperature variability, precipitation anomaly, precipitation trend, precipitation variability and ecoregion, also at 100-yr time steps. Models focused on the most distant time periods (0-21000 BP and 7500 - 21000 BP) show that rapid vegetation change was initiated across these landscapes once a 2 ℃ temperature increase...
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This file provides a table of all the of Species of Greatest Conservation Need listed in the North Central states' (MT, WY, CO, ND, SD, NE, and KS) State Wildlife Action Plans as of summer 2020. Species are organized by the number of states which listed them as Species of Greatest Conservation Need, and then by scientific name. Federal status is also provided for each species. This table is adapted from an unpublished species list compiled by the North Central Climate Adaptation Science Center.
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These model objects are the outputs of two Bayesian hierarchical models (one for the Middle Rockies and one for the Southern Rockies) to explore the role of landscape characteristics in climate-driven ecological change and transformation. We used the rate of change for each site at 100-yr time steps as the response variable, and included elevation, CHILI, aspect, slope, and TPI as fixed effects in the models, run separately for each ecoregion. We included a random intercept of site to quantify the magnitude of site-level variation in rate-of-change that may be unaccounted for by our covariates.
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The East Pisco Basin is one of several forearc basins situated on the coastal plain of Peru between the Andean Cordillera and Peru-Chile Trench. During the Cenozoic, successive marine transgressions across the East Pisco Basin deposited sequences of Paleogene and Neogene age. Biochronologic studies suggest that a hiatus of approximately 12 million years (~32-20 Ma) separates the youngest Paleogene deposits from the oldest Neogene deposits. A newly recognized lower Miocene sequence, provisionally named the Tunga Formation, shortens that hiatus. The following database provides location and description of samples from the East Pisco Basin, checklists of microfossil assemblages, and taxonomic notes for those assemblages.
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A workshop was conducted to gain insight into climate change impacts and climate-informed management actions of relevance to a habitat management plan in the North Central region. A pre-workshop and post-workshop survey were conducted.
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This database integrates a list of vegetation transformations that occurred across the Southern and Middle Rockies since 21,000 years ago, the age of occurrence, the type of vegetation switch that occurred, whether the rates of vegetation change peaked at that time, and when applicable, the duration of peak rates of vegetation change.
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We developed a screening system to identify introduced plant species that are likely to increase wildfire risk, using the Hawaiian Islands to test the system and illustrate how the system can be applied to inform management decisions. Expert-based fire risk scores derived from field experiences with 49 invasive species in Hawai′i were used to train a machine learning model that predicts expert fire risk scores from among 21 plant traits obtained from literature and databases. The model revealed that just four variables can identify species categorized as higher fire risk by experts with 90% accuracy, while low risk species were identified with 79% accuracy. We then used the predictive model to screen 365 naturalized...
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We routinely encounter uncertainty when we make decisions – from picking a new morning coffee to choosing where to live. Even decisions that are supported by science contain some level of remaining uncertainty. In the context of conservation and wildlife management, the potential for uncertainty to influence decisions is perhaps most obvious when we think about predicting how actions (or non-actions) will have lasting impacts into the future. Our abilities to precisely predict future climatic and ecological conditions and determine the exact consequences of our actions are, and will remain, limited. Conservation practitioners and land and wildlife managers must navigate these challenges to make science-informed...
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This study set out to answer the question: “What data and modeling frameworks are needed to provide scientists reliable, climate-informed, water temperature estimates for freshwater ecosystems that can assist watershed management decision making?” To accomplish this, the study gathered existing stream temperature data, identified data gaps, deployed stream temperature monitoring devices, and developed and tested a stream temperature model that could be regionalized across the Northeast domain. We partnered with another funded project team, led by Jana Stewart at WI USGS to collect data from over 10,000 locations across the climate science center domain. This collection effort aided in identifying data gaps where...
map background search result map search result map Bringing People, Data, and Models Together – Addressing Impacts of Climate Change on Stream Temperature Turning Uncertainty into Useful Information for Conservation Decisions Pre and Post Participatory Climate Change Workshop Survey for the North Central Region from 2018-2020 Climate drivers of rapid ecological change at the landscape scale over the last 21,000 years in the Middle and Southern Rockies, U.S.A. The role of landscape characteristics in climate-driven rapid ecological change over the last 21,000 years in the Middle and Southern Rockies, U.S.A. Type and speed of vegetation transformations over the past 21,000 years in the Middle and Southern Rockies, U.S.A. Species of Greatest Conservation Need in the North Central Region Fire Risk Scores from Predictive Model Based on Flammability and Fire Ecology of Non-Native Hawaiian Plants from 2020-2021 Lifespan of marsh units in New York salt marshes Microfossil Samples from the East Pisco Basin, southern Peru Lifespan of marsh units in New York salt marshes Climate drivers of rapid ecological change at the landscape scale over the last 21,000 years in the Middle and Southern Rockies, U.S.A. The role of landscape characteristics in climate-driven rapid ecological change over the last 21,000 years in the Middle and Southern Rockies, U.S.A. Type and speed of vegetation transformations over the past 21,000 years in the Middle and Southern Rockies, U.S.A. Fire Risk Scores from Predictive Model Based on Flammability and Fire Ecology of Non-Native Hawaiian Plants from 2020-2021 Microfossil Samples from the East Pisco Basin, southern Peru Species of Greatest Conservation Need in the North Central Region Pre and Post Participatory Climate Change Workshop Survey for the North Central Region from 2018-2020 Bringing People, Data, and Models Together – Addressing Impacts of Climate Change on Stream Temperature Turning Uncertainty into Useful Information for Conservation Decisions