ORCID Profile
0000-0002-4305-1647
Current Organisations
Northern Arizona University
,
Shanghai Astronomical Observatory
,
Leibniz-Institut für Astrophysik Potsdam
,
Michigan State University
,
USDA Forest Service Pacific Northwest Region
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Publisher: Oxford University Press (OUP)
Date: 2019
DOI: 10.1093/MNRAS/STZ565
Publisher: Wiley
Date: 12-04-2022
DOI: 10.1111/BRV.12852
Abstract: Bio ersity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well‐being. Understanding how bio ersity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying bio ersity data. A major challenge is that bio ersity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Bio ersity Observation Network (GEO BON) has developed the Essential Bio ersity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret bio ersity observation data from erse sources. Mapping and analyzing EBVs can help to evaluate how aspects of bio ersity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of bio ersity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within‐species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic bio ersity monitoring with respect to theory, s ling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: ( i ) Genetic Diversity ( ii ) Genetic Differentiation ( iii ) Inbreeding and ( iv ) Effective Population Size ( N e ). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large‐scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for bio ersity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all bio ersity and species' long‐term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic bio ersity monitoring regionally and globally.
Publisher: MDPI AG
Date: 10-12-2020
DOI: 10.3390/RS12244041
Abstract: Finding trees that are resistant to pathogens is key in preparing for current and future disease threats such as the invasive white pine blister rust. In this study, we analyzed the potential of using hyperspectral imaging to find and diagnose the degree of infection of the non-native white pine blister rust in southwestern white pine seedlings from different seed-source families. A support vector machine was able to automatically detect infection with a classification accuracy of 87% (κ = 0.75) over 16 image collection dates. Hyperspectral imaging only missed 4% of infected seedlings that were impacted in terms of vigor according to expert’s assessments. Classification accuracy per family was highly correlated with mortality rate within a family. Moreover, classifying seedlings into a ‘growth vigor’ grouping used to identify the degree of impact of the disease was possible with 79.7% (κ = 0.69) accuracy. We ranked hyperspectral features for their importance in both classification tasks using the following features: 84 vegetation indices, simple ratios, normalized difference indices, and first derivatives. The most informative features were identified using a ‘new search algorithm’ that combines both the p-value of a 2-s le t-test and the Bhattacharyya distance. We ranked the normalized photochemical reflectance index (PRIn) first for infection detection. This index also had the highest classification accuracy (83.6%). Indices such as PRIn use only a small subset of the reflectance bands. This could be used for future developments of less expensive and more data-parsimonious multispectral cameras.
Publisher: Springer International Publishing
Date: 2017
DOI: 10.1007/13836_2017_2
Location: United States of America
No related grants have been discovered for Jeremy Johnson.