Prioritizing the conservation needs of United States tree species: Evaluating vulnerability to forest insect and disease threats

Potter, Kevin M.; Escanferla, Maria Eugenia; Jetton, Robert M.; Man, Gary; Crane, Barbara S.

doi:10.1016/j.gecco.2019.e00622

Cited by 36 publications

(4 citation statements)

References 67 publications

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“…Although the surveys are intensive, not all trees in the plot are measured because the four subplots do not cover the total area of each surveyed plot (https://www.fia.fs.fed.us/library/databasedocumentation/current/ver70/FIADB%20User%20Guide%20P2_7-0_ntc.final.pdf). Data of this type is commonly used in community ecology, community phylogenetics and biogeography (e.g., Cavender-Bares et al, 2018;Potter et al, 2019).…”

Section: 3species and Community Composition Validation Datamentioning

confidence: 99%

Predicting species distributions and community composition using remote sensing products

Pinto‐Ledezma

Cavender‐Bares

2020

Preprint

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AbstractAccurate predictions of species composition and diversity are critical to the development of conservation actions and management strategies. In this paper using oak assemblages distributed across the conterminous United States as study model, we assessed the performance of stacked species distribution models (S-SDMs) and remote sensing products in building the next-generation of biodiversity models. This study represents the first attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found three main results. First, environmental predictors derived entirely from remote sensing products represent adequate covariates for biodiversity modeling. Second, applying constraints to assemblage predictions, such as imposing the probability ranking rule, results in more accurate species diversity predictions. Third, independent of the stacking procedure (bS-SDM versus cS-SDM), biodiversity models do not recover the observed species composition with high spatial resolution, i.e., correct species identities at the scale of individual plots. However, they do return reasonable predictions at macroecological scales (1 km). Our results provide insights for the prediction of assemblage diversity and composition at different spatial scales. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models to accurately predict and monitor ecological assemblages through time and space.

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Section: 3species and Community Composition Validation Datamentioning

confidence: 99%

Predicting species distributions and community composition using remote sensing products

Pinto‐Ledezma

Cavender‐Bares

2020

Preprint

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“…In ecology, large-scale spatial information on tree species improves the understanding of the ecology of tree species [4,5]. In the environmental field, tree species information facilitates the estimation of wildlife habitat [6] and forest insect abundance [7,8].…”

Section: Introduction and Review Approach 1significance Of Tree Speci...mentioning

confidence: 99%

A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-Based Methods

Zhong,

Dai,

Fang

et al. 2024

Forests

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Timely and accurate information on tree species is of great importance for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. A review of the remote sensing data and deep learning tree species classification methods is lacking in its analysis of unimodal and multimodal remote sensing data and classification methods in this field. To address this gap, we search for major trends in remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss some limitations of tree species classification.

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“…Two of these pathogens and pests, chestnut blight (Cryphonectria parasitica) and emerald ash borer (Agrilus planipennis, EAB) have had a profound impact on public awareness: chestnut blight because this disease caused the ecological extinction of the iconic American chestnut (Castanea dentata) and EAB because of the widespread, rapid and continuing loss of ash trees from streets, parks, and forests (14). The severe impact of the loss of chestnut, pales in comparison to the economic and ecological damage already inflicted by EAB, the most destructive and economically devastating invasive insect pest of forest trees in North American history (15). EAB, a beetle native to Asia, was discovered in Michigan, United States and in Ontario, Canada in 2002 (16).…”

Section: Introductionmentioning

confidence: 99%

Profiles of secoiridoids and alkaloids in tissue of susceptible and resistant green ash progeny reveal patterns of induced responses to emerald ash borer inFraxinus pennsylvanica

Stanley

Carey

Mason

et al. 2022

Preprint

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The emerald ash borer (Agrilus planipennis, EAB) invasion in North America threatens most North American Fraxinus species, including green ash (F. pennsylvanica), the mostly widely distributed species. A small number of green ash (lingering ash, 0.1-1%) survive years of heavy EAB attack and kill more EAB larvae when challenged in greenhouse studies than susceptible controls. We combined untargeted metabolomics with intensive phenotyping of segregating F1 progeny from susceptible or lingering ash parents to detect chemotypes associated with defensive responses to EAB. We examined three contrasting groups: low larval kill (0-25% of larvae killed), high larval kill (55-95% of larvae killed) and uninfested. Contrasting the chemotypes of these groups revealed evidence of an induced response to EAB. Infested trees deployed significantly higher levels of select secoiridoids than uninfested trees. Within the infested group, the low larval kill (LLK) individuals deployed significantly higher levels of select secoiridoids than the high larval kill (HLK) individuals. The HLK individuals deployed significantly higher concentrations of three metabolites annotated as aromatic alkaloids compared to the LLK and uninfested individuals. We propose a two-part model for the North American Fraxinus response to EAB wherein every individual has the capacity to detect and respond to EAB, but only certain trees mount an effective defense, killing enough EAB larvae to prevent or minimize lethal damage to the vascular system. Integration of intensive phenotyping of structured populations with metabolomics reveals the multi-faceted nature of the defenses deployed in naive host populations against invasive species.

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Prioritizing the conservation needs of United States tree species: Evaluating vulnerability to forest insect and disease threats

Cited by 36 publications

References 67 publications

Predicting species distributions and community composition using remote sensing products

Predicting species distributions and community composition using remote sensing products

A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-Based Methods

Profiles of secoiridoids and alkaloids in tissue of susceptible and resistant green ash progeny reveal patterns of induced responses to emerald ash borer inFraxinus pennsylvanica

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