Applying cumulative effects to strategically advance large-scale ecosystem restoration.
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ABSTRACT:
SUBMITTER: Diefenderfer HL
PROVIDER: S-EPMC8597595 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveThis paper offers insight into the processes that have shaped the Eskind Biomedical Library's (EBL's) strategic direction and its alignment to the institution's transformative vision.SettingThe academic biomedical library has a notable track record for developing and pioneering roles for information professionals focused on a sophisticated level of information provision that draws from and fuels practice evolutions.StrategyThe medical center's overall transformative vision informs the creation of a fully aligned library strategic plan designed to effectively contribute to the execution of key organizational goals. Annual goals reflect organizational priorities and contain quantifiable and measurable deliverables. Two strategic themes, facilitating genetic literacy and preserving community history, are described in detail to illustrate the concept of goal setting.ConclusionThe strategic planning model reflects EBL's adaptation to the ever-changing needs of its organization. The paper provides a characterization of a workable model that can be replicated by other institutions.
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Project description:To fully understand ecosystem functioning under global change, we need to be able to measure the stability of ecosystem functioning at multiple spatial scales. Although a number of stability components have been established at small spatial scales, there has been little progress in scaling these measures up to the landscape. Remote sensing data holds huge potential for studying processes at landscape scales but requires quantitative measures that are comparable from experimental field data to satellite remote sensing. Here we present a methodology to extract four components of ecosystem functioning stability from satellite-derived time series of Enhanced Vegetation Index (EVI) data. The four stability components are as follows: variability, resistance, recovery time and recovery rate in ecosystem functioning. We apply our method to the island of Ireland to demonstrate the use of remotely sensed data to identify large disturbance events in productivity. Our method uses stability measures that have been established at the field-plot scale to quantify the stability of ecosystem functioning. This makes our method consistent with previous small-scale stability research, whilst dealing with the unique challenges of using remotely sensed data including noise. We encourage the use of remotely-sensed data in assessing the stability of ecosystems at a scale that is relevant to conservation and management practices.
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Project description:Radiotherapy is integral to the care of a majority of patients with cancer. Despite differences in tumor responses to radiation (radioresponse), dose prescriptions are not currently tailored to individual patients. Recent large-scale cancer cell line databases hold the promise of unravelling the complex molecular arrangements underlying cellular response to radiation, which is critical for novel predictive biomarker discovery. Here, we present RadioGx, a computational platform for integrative analyses of radioresponse using radiogenomic databases. We fit the dose-response data within RadioGx to the linear-quadratic model. The imputed survival across a range of dose levels (AUC) was a robust radioresponse indicator that correlated with biological processes known to underpin the cellular response to radiation. Using AUC as a metric for further investigations, we found that radiation sensitivity was significantly associated with disruptive mutations in genes related to nonhomologous end joining. Next, by simulating the effects of different oxygen levels, we identified putative genes that may influence radioresponse specifically under hypoxic conditions. Furthermore, using transcriptomic data, we found evidence for tissue-specific determinants of radioresponse, suggesting that tumor type could influence the validity of putative predictive biomarkers of radioresponse. Finally, integrating radioresponse with drug response data, we found that drug classes impacting the cytoskeleton, DNA replication, and mitosis display similar therapeutic effects to ionizing radiation on cancer cell lines. In summary, RadioGx provides a unique computational toolbox for hypothesis generation to advance preclinical research for radiation oncology and precision medicine. SIGNIFICANCE: The RadioGx computational platform enables integrative analyses of cellular response to radiation with drug responses and genome-wide molecular data. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/79/24/6227/F1.large.jpg.See related commentary by Spratt and Speers, p. 6076.
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Project description:Monitoring indicator species is a pragmatic approach to natural resource assessments, especially when the link between the indicator species and ecosystem state is well justified. However, conducting ecosystem assessments over representative spatial scales that are insensitive to local heterogeneity is challenging. We examine the link between polychlorinated biphenyl (PCB) contamination and population density of an aquatic habitat specialist over a large spatial scale using non-invasive genetic spatial capture-recapture. Using American mink (Neovison vison), a predatory mammal and an indicator of aquatic ecosystems, we compared estimates of density in two major river systems, one with extremely high levels of PCB contamination (Hudson River), and a hydrologically independent river with lower PCB levels (Mohawk River). Our work supports the hypothesis that mink densities are substantially (1.64-1.67 times) lower in the contaminated river system. We demonstrate the value of coupling the indicator species concept with well-conceived and spatially representative monitoring protocols. PCBs have demonstrable detrimental effects on aquatic ecosystems, including mink, and these effects are likely to be profound and long-lasting, manifesting as population-level impacts. Through integrating non-invasive data collection, genetic analysis, and spatial capture-recapture methods, we present a monitoring framework for generating robust density estimates across large spatial scales.
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Project description:Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.
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Project description:Habitat and water quality restoration projects are commonly used to enhance coastal resources or mitigate negative impacts of water quality stressors. Significant resources have been expended for restoration projects, yet much less attention has focused on evaluating broad regional outcomes beyond site-specific assessments. This study presents an empirical framework to evaluate multiple datasets in the Tampa Bay area (Florida, USA) to identify 1) the types of restoration projects that have produced the greatest improvements in water quality, and 2) over which time frames different projects may produce water quality benefits. Information on the location and date of completion of 887 restoration projects from 1971 to 2017 were spatially and temporally matched with water quality records at each of 45 long-term monitoring stations in Tampa Bay. The underlying assumption was that the developed framework could identify differences in water quality changes between types of restoration projects based on aggregate estimates of chlorophyll-a concentrations before and after the completion of one to many projects. Water infrastructure projects to control point source nutrient loading into the Bay were associated with the highest likelihood of chlorophyll-a reduction, particularly for projects occurring prior to 1995. Habitat restoration projects were also associated with reductions in chlorophyll-a, although the likelihood of reductions from the cumulative effects of these projects were less than those from infrastructure improvements alone. The framework is sufficiently flexible for application to different spatiotemporal contexts and could be used to develop reasonable expectations for implementation of future water quality restoration activities throughout the Gulf of Mexico.
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Project description:Higher tree density, more fuels, and a warmer, drier climate have caused an increase in the frequency, size, and severity of wildfires in western U.S. forests. There is an urgent need to restore forests across the western United States. To address this need, the U.S. Forest Service began the Four Forest Restoration Initiative (4FRI) to restore four national forests in Arizona. The objective of this study was to evaluate how restoration of ~400,000 ha under the 4FRI program and projected climate change would influence carbon dynamics and wildfire severity from 2010 to 2099. Specifically, we estimated forest carbon fluxes, carbon pools and wildfire severity under a moderate and fast 4FRI implementation schedule and compared those to status quo and no-harvest scenarios using the LANDIS-II simulation model and climate change projections. We found that the fast-4FRI scenario showed early decreases in ecosystem carbon due to initial thinning/prescribed fire treatments, but total ecosystem carbon increased by 9-18% over no harvest by the end of the simulation. This increased carbon storage by 6.3-12.7 million metric tons, depending on the climate model, equating to removal of carbon emissions from 55,000 to 110,000 passenger vehicles per year until the end of the century. Nearly half of the additional carbon was stored in more stable soil pools. However, climate models with the largest predicted temperature increases showed declines by late century in ecosystem carbon despite restoration. Our study uses data from a real-world, large-scale restoration project and indicates that restoration is likely to stabilize carbon and the benefits are greater when the pace of restoration is faster.
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Project description:Ethiopia aims to restore 15 million ha degraded forests and woodlands, but effects on the potentially contrasting goals of long-term carbon storage, biodiversity and sustainable livelihoods are unknown. To quantify the effects of grazing exclusion on vegetation and fire behaviour, we established six 30 × 30 m fenced exclosures with grazed controls, in a mesic wooded savanna. Experimental burns were done after 1.5 years. Tree seedlings were few but more common inside fences. Field layer cover and biomass increased inside fences, and grass species increased in numbers and cover. Fire intensity was higher inside fences, killing shrubs and saplings but not mature trees. Interviews confirmed that overgrazing has resulted in "cool fires", causing shrub encroachment. High-intensity fires occurred in the 1980s after a zoonotic disease killed most livestock. Short-term increase in carbon storage through fire and grazing exclusion may lead to loss of pasture, and in the long-term increased wildfire risk.
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Project description:Conserving different spatial and temporal dimensions of biological diversity is considered necessary for maintaining ecosystem functions under predicted global change scenarios. Recent work has shifted the focus from spatially local (α-diversity) to macroecological scales (β- and γ-diversity), emphasizing links between macroecological biodiversity and ecosystem functions (MB-EF relationships). However, before the outcomes of MB-EF analyses can be useful to real-world decisions, empirical modeling needs to be developed for natural ecosystems, incorporating a broader range of data inputs, environmental change scenarios, underlying mechanisms, and predictions. We outline the key conceptual and technical challenges currently faced in developing such models and in testing and calibrating the relationships assumed in these models using data from real ecosystems. These challenges are explored in relation to two potential MB-EF mechanisms: "macroecological complementarity" and "spatiotemporal compensation." Several regions have been sufficiently well studied over space and time to robustly test these mechanisms by combining cutting-edge spatiotemporal methods with remotely sensed data, including plant community data sets in Australia, Europe, and North America. Assessing empirical MB-EF relationships at broad spatiotemporal scales will be crucial in ensuring these macroecological processes can be adequately considered in the management of biodiversity and ecosystem functions under global change.
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