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47 results for “macrosystems”
Soil Temperature and Water Content in Macrosystems Biodiversity Project at Harvard Forest 2011-2012
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. Readings of soil temperature and soil moisture were taken with HOBO sensors from November 2011 to November 2012. These sensors were installed at five experimental tree growth plots installed by the Enquist Lab (PI, Brian Enquist) from the University of Arizona as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Soil Chemistry and Moisture in Macrosystems Biodiversity Project at Harvard Forest 2012
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. Soil chemistry (TN, TC, NH4-N, NO3-N, and pH) and moisture measurements were taken from soil cores from an array of 21 1m2 subplots and processed by the University of Oklahoma Institute for Environmental Genomics as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Soil Bacteria and Archaea in Macrosystems Biodiversity Project at Harvard Forest 2012
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This field experiment focused on soil microbes. DNA was extracted and purified from soil cores from an array of 21 1m2 subplots. The V4 region of the 16S rRNA genes for bacteria and archaea were amplified and sequenced using Illumina MiSeq by the University of Oklahoma Institute for Environmental Genomics as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Soil Invertebrate Species in Macrosystems Biodiversity Project at Harvard Forest 2012
Leaf litter invertebrates and soil microbes were sampled in an array of 21 1m2 subplots by the Kaspari Ant Lab at the University of Oklahoma as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Tree Growth in Macrosystems Biodiversity Project at Harvard Forest 2011-2013
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This dataset contains annual growth measurements of trees along a series of transects using the measures of diameter at breast height and/or diameter and ground height at the five Gentry plots set up at Harvard Forest. These plots were set up by the Enquist Lab (PI, Brian Enquist) from the University of Arizona as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
American Residential Macrosystems - Leaf functional traits and raw data in five major metropolitan areas, 2012-2013
"We used leaf functional traits in residential yards and nearby natural areas to assess biotic ecological homogenization in five cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, and Minneapolis-St. Paul, MN."
American Residential Macrosystems - Complete municipal ordinance documents across six U.S. cities, 2017-2019
These data files are the complete city codes, or municipal ordinances (n=156), across the metropolitan regions of Los Angeles, CA; Phoenix, AZ; Miami, FL; Baltimore, MD; Boston, MA; Minneapolis/St. Paul, MN. The documents were gathered for the specific purposes of a content analysis of how cities regulate residential landscapes; however, the documents include regulations on the books for the municipalities sampled for this project.
American Residential Macrosystems - Bird community data within parks and residential yards in six major metropolitan areas in the United States, 2017-2018
"This dataset includes abundance of breeding bird species recorded in residential yards and nearby natural and interstitial areas (i.e.unmanaged vegetation areas in the residential/wildland interface) in six cities across the U.S. Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis-St. Paul, MN, and Phoenix, AZ. Yards were grouped in 4 categories based on fertilizer input frequency, landscaping style and their impact on hydrology: high-input lawns, low-input lawns, wildlife-certified yards and yards with low impact on hydrology (or rain gardens). Bird data was collected via standardized 10-min point counts during the breeding season in 2017 or 2018. "
Macrosystems EDDIE Module 5 version 2: Introduction to Ecological Forecasting (Instructor Materials)
Ecological forecasting is a tool that can be used for understanding and predicting changes in populations, communities, and ecosystems. Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Ecological forecasters develop and update forecasts using the iterative forecasting cycle, in which they make a hypothesis of how an ecological system works; embed their hypothesis in a model; and use the model to make a forecast of future conditions. When observations become available, they can assess the accuracy of their forecast, which indicates if their hypothesis is supported or needs to be updated before the next forecast is generated. In this Macrosystems EDDIE (Environmental Data-Driven Inquiry & Exploration) module, students will apply the iterative forecasting cycle to develop an ecological forecast for a National Ecological Observation Network (NEON) site. Students will use NEON data to build an ecological model that predicts primary productivity. Using their calibrated model, they will learn about the different components of a forecast with uncertainty and compare productivity forecasts among NEON sites. The overarching goal of this module is for students to learn fundamental concepts about ecological forecasting and build a forecast for a NEON site. Students will work with an R Shiny interface to visualize data, build a model, generate a forecast with uncertainty, and then compare the forecast with observations. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This EDI data package contains instructional materials necessary to teach the module. Intructional materials (instructor manual, introductory presentation for the module, and a presentation to introduce students and instructors to R Shiny) are provided in both pdf and editable formats with
Macrosystems EDDIE Module 1: Climate Change Effects on Lake Temperatures
Climate change is modifying the thermal structure of lakes around the globe. Because it is difficult to predict how lakes will respond to the many different aspects of climate change (e.g., altered temperature, precipitation, wind, etc.), many researchers are using models to manipulate climate scenarios and simulate lake responses. Lake simulation models provide a powerful tool for exploring the sensitivity of lake thermal structure characteristics to weather. In this module, students will learn how to set up a lake model (General Lake Model; GLM) and "force" the model with climate scenarios of their own design to test hypotheses about how lakes may change in the future. Once students have mastered running one climate scenario for their lake, they will learn how to use distributed computing tools to scale up and run hundreds of different climate scenarios for their lakes. The overarching goal of this module is for students to explore new modeling and computing tools while learning fundamental concepts about how climate change will affect lakes. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This dataset contains instructional materials and the files necessary to run the complete module. Readers are referred to the GLM science manual (Hipsey et al. 2014) for further details on model configuration.
American Residential Macrosystems - Soil chemistry data within parks and residential yards in six major metropolitan areas in the United States, 2017-2018
"In six major U.S. metropolitan cities (Boston, Baltimore, Los Angeles, Miami, Minneapolis St. Paul, and Phoenix), 1 meter soil cores were collected to evaluate soil microbial carbon and nitrogen cycle processes that are sensitive to land management. Laboratory methods followed those used by Raciti et al. (2011a,b) to measure microbial biomass carbon and nitrogen content, microbial respiration, potential net nitrogen mineralization, potential net nitrifcation, potential denitrifcation, and pools of extractable ammonium and nitrate. "
American Residential Macrosystems - Tree Survey Data for six major metropolitan areas, 2012-2013
"This dataset contains measurements and iTree output (www.itreetools.org) of woody plant species observed in residential yards and nearby natural areas. Data were collected to assess biotic ecological homogenization in six cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis/St. Paul, MN, and Phoenix, AZ."
Macrosystems EDDIE Module 3: Teleconnections
Ecosystems can be influenced by teleconnections, in which meteorological, societal, and/or ecological phenomenon link remote regions via cause and effect relationships. Because it is difficult to predict how ecosystems will respond to drivers from remote regions, many researchers are using models to simulate different teleconnection scenarios and see how ecosystems respond. For example, lake simulation models provide a powerful tool for exploring how lake thermal structure and ice cover respond to climate teleconnections such as the El Nino/Southern Oscillation (ENSO). In this module, students will learn how to set up a lake model and "force" the model with climate scenarios to test hypotheses about how far-away drivers interact with local lake characteristics to affect lake temperatures and ice cover in different lakes. The overarching goal of this module is for students to explore new modeling and computing tools while learning fundamental concepts about how teleconnections affect lake temperatures and ice cover. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This dataset contains instructional materials and the files necessary to run the complete module. Readers are referred to the GLM science manual (Hipsey et al. 2014; 2019) for further details on model configuration.
Macrosystems EDDIE Module 8: Using Ecological Forecasts to Guide Decision-Making (Instructor Materials)
Because of increased variability in populations, communities, and ecosystems due to land use and climate change, there is a pressing need to know the future state of ecological systems across space and time. Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to preemptively prepare for fluctuations in important ecosystem services. However, forecasts must be effectively designed and communicated to those who need them to make decisions in order to realize their potential for protecting natural resources. In this module, students will explore real ecological forecast visualizations, identify ways to represent uncertainty, make management decisions using forecast visualizations, and learn decision support techniques. Lastly, students customize a forecast visualization for a specific stakeholder's decision needs. The overarching goal of this module is for students to understand how forecasts are connected to decision-making of stakeholders, or the managers, policy-makers, and other members of society who use forecasts to inform decision-making. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This EDI data package contains instructional materials and the files necessary to teach the module. Readers are referred to the Zenodo data package (Woelmer et al. 2022; DOI: 10.5281/zenodo.7074674) for the R Shiny application code needed to run the module locally.
Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts (Instructor Materials)
This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Forecast uncertainty is derived from multiple sources, including model parameters and driver data, among others. Knowing the uncertainty associated with a forecast enables forecast users to evaluate the forecast and make more informed decisions. This module will guide students through an exploration of the sources of uncertainty within an ecological forecast, how uncertainty can be quantified, and steps that can be taken to reduce the uncertainty in a forecast that students develop for a lake ecosystem, using data from the National Ecological Observatory Network (NEON). Students will visualize data, build a model, generate a forecast with uncertainty, and then compare the contributions of various sources of forecast uncertainty to total forecast uncertainty. The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module6/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module6) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module6_R) of the module, and both code repositories have been published with DOIs to Zenodo (R Shiny version at https://zenodo.org/doi/10.5281/zenodo.10380759 and RMarkdown versi
Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts (Instructor Materials)
This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. To be useful for management, ecological forecasts need to be both accurate enough for managers to be able to rely on them for decision-making and include a representation of forecast uncertainty, so managers can properly interpret the probability of future events. To improve forecast accuracy, forecasts can be updated with observational data once they become available, a process known as data assimilation. Recent improvements in environmental sensor technology and an increase in the number of sensors deployed in ecosystems have increased the availability of data for assimilation to develop and improve forecasts for natural resource management. In this module, students will explore how assimilating data with different amounts of observation uncertainty and at different temporal frequencies affects forecasts of lake water quality, using data from the U.S. National Ecological Observatory Network (NEON). The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module7/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module7) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module7_R) of the module, and both code r
Macrosystems EDDIE Module 2: Cross-Scale Interactions
Environmental phenomena are often driven by multiple factors that interact across space and over time. In freshwater lakes and reservoirs worldwide, phytoplankton blooms are increasing in frequency and severity due to interactions between local, regional, and continental drivers, including land use (local) and climate change (regional) drivers. Because it is difficult to predict how phytoplankton blooms will respond to the different aspects of land use and climate change simultaneously, many researchers are using lake simulation models, which provide a powerful tool for exploring the sensitivity of ecosystems to multiple factors. In this module, students will learn how to set up a lake simulation model and "force" the model with climate and land use scenarios to test hypotheses about how local and regional drivers interact to promote or suppress phytoplankton blooms in different lakes. The overarching goal of this module is for students to explore new modeling and computing tools while learning fundamental concepts about how non-linear macrosystem-level phenomena (e.g., lake phytoplankton blooms) can occur through cross-scale interactions. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This dataset contains instructional materials and the files necessary to run the complete module. Readers are referred to the GLM science manual (Hipsey et al. 2014) for further details on model configuration.
American Residential Macrosystems - Presence/absence and cultivation status of plant species within residential yards in seven major metropolitan areas, 2012-2013
"We used the presence and absence of plant species in residential yards and nearby natural areas to assess biotic ecological homogenization in seven cities across the U.S. that span major ecological biomes and climatic regions (Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis-St. Paul, MN, Phoenix, AZ. and Salt Lake City, UT). "
American Residential Macrosystems - Presence/absence of plant species within land use groups in residential yards in six major metropolitan areas in the United States, 2017-2018
"This dataset includes presence/absence of plant species recorded in residential yards and nearby natural and interstitial areas (i.e.unmanaged vegetation areas in the residential/wildland interface) in six cities across the U.S. Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis-St. Paul, MN, and Phoenix, AZ. Yards were grouped in 4 categories based on fertilizer input frequency, landscaping style and their impact on hydrology: high-input lawns, low-input lawns, wildlife-certified yards and yards with low impact on hydrology (or rain gardens)."
Macrosystem community change in lake phytoplankton and its implications for diversity and function
<p><span><strong>Aim</strong>: </span><span>We use lake phytoplankton community data to quantify the spatio-temporal and scale-dependent impacts of eutrophication, land-use, and climate change</span><span> </span><span>on species niches and community assembly processes while accounting for species traits and phylogenetic constraints.</span></p> <p><span><strong>Location</strong>: </span><span>Finland</span></p> <p><span><strong>Time period</strong>: </span><span>1977-2017</span></p> <p><span><strong>Major taxa</strong>: </span><span>Phytoplankton</span></p> <p><span><strong>Methods</strong>: </span><span>We use Hierarchical Modelling of Species Communities (HMSC) to</span><span> model meta-community trajectories at 853 lakes over four decades of environmental change, including a hierarchical spatial structure to account for scale-dependent processes. Using a 'region of common profile' approach, we evaluate compositional changes of species communities and trait profiles and investigate their temporal development. </span></p> <p><span><strong>Results</strong>: </span><span>We demonstrate the emergence of novel and widespread community composition clusters in previously more compositional homogeneous communities, with cluster-specific community trait profiles, indicating functional differences. A strong phylogenetic signal of species' responses to the environment implies similar responses among closely related taxa. Community cluster-specific species prevalence point to lower taxonomic dispersion within the current dominant clusters compared to the historically dominant cluster and overall higher prevalence of smaller species sizes within communities. Our findings denote profound spatio-temporal structuring of species co-occurrence patterns and highlight</span><span> </span><span>functional differences of lake phytoplankton communities.</span></p> <p><span><strong>Main conclusions</strong>: </span><span>Diverging community trajectories have led to a nationwide reshuffling of lake phytoplankton communities. At regional and national scales, lakes are not single entities but metacommunity hubs in an interconnected waterscape. The assembly mechanisms of phytoplankton communities are strongly structured by spatio-temporal dynamics, which have led to novel community types but only a minor part of this reshuffling could be linked to temporal environmental change.</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.