Fire and tree mortality database (FTM)

Cansler, C. Alina; Hood, Sharon M.; Varner, J. Morgan; Mantgem, Phillip J. van; Agne, Michelle C.; Andrus, Robert A.; Ayres, Matthew P.; Ayres, Bruce D.; Bakker, Jonathan D.; Battaglia, Michael A.; Bentz, Barbara; Breece, Carolyn R.; Brown, James K.; Cluck, Daniel R.; Coleman, Tom W.; Corace, R. Gregory; Covington, W. Wallace; Cram, Douglas S.; Cronan, James B.; Crouse, Joseph E.; Das, Adrian J.; Davis, Ryan S.; Dickinson, Darci M.; Fitzgerald, Stephen Arthur; Fulé, Peter Z.; Ganio, Lisa M.; Grayson, Lindsay; Halpern, Charles B.; Hanula, Jim L.; Harvey, Brian; Hiers, J. Kevin; Huffman, David W.; Keifer, MaryBeth; Keyser, Tara L.; Kobziar, Leda N.; Kolb, Thomas E.; Kolden, Crystal A.; Kopper, Karen E.; Kreitler, Jason; Kreye, Jesse K.; Latimer, Andrew M.; Lerch, Andrew P.; Lombardero, Marı́a J.; McDaniel, Virginia L.; McHugh, Charles W.; McMillin, Joel D.; Moghaddas, Jason J.; O’Brien, Joseph J.; Perrakis, Daniel D. B.; Peterson, David W.; Prichard, Susan J.; Progar, Robert A.; Raffa, Kenneth F.; Reinhardt, Elizabeth D.; Restaino, Joseph C.; Roccaforte, John Paul; Rogers, Brendan M.; Ryan, Kevin C.; Safford, Hugh D.; Santoro, Alyson E.; Shearman, Timothy M.; Shumate, Alice M.; Sieg, Carolyn Hull; Smith, Sheri L.; Smith, Rebecca J.; Stephenson, Nathan L.; Steuver, Mary; Stevens, Jens T.; Stoddard, Michael T.; Thies, Walter G.; Vaillant, Nicole M.; Weiss, Shelby A.; Westlind, Douglas J.; Woolley, Travis J.; Wright, Micah

doi:10.2737/rds-2020-0001

Cited by 3 publications

(6 citation statements)

References 2 publications

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“…The FTM database (Cansler, Hood, Varner, van Mantgem, Agne, Andrus, Ayres, Ayres, Bakker, Battaglia, Bentz, et al, 2020; Cansler, Hood, Varner, van Mantgem, Agne, Andrus, Ayres, Ayres, Bakker, Battaglia, et al, 2020) is publicly available at https://doi.org/10.2737/RDS-2020-0001. R code (Shearman, 2022) is available in Zenodo at https://doi.org/10.5281/zenodo.7061866.…”

Section: Data Availability Statementmentioning

confidence: 99%

“…Understanding the factors important to post‐fire tree mortality requires large amounts of data across multiple fires. We analyzed data from the Fire and Tree Mortality (FTM) Database (Cansler, Hood, Varner, van Mantgem, Agne, Andrus, Ayres, Ayres, Bakker, Battaglia, et al, 2020; Cansler, Hood, Varner, van Mantgem, Agne, Andrus, Ayres, Ayres, Bakker, Battaglia, Bentz, et al, 2020), a collection of 164,293 individual tree records including measures of fire injury, tree diameter, and status (live/dead). The data include 142 species from 409 fires spanning 11 western US states from 1981 to 2016 (Cansler, Hood, Varner, van Mantgem, Agne, Andrus, Ayres, Ayres, Bakker, Battaglia, et al, 2020; Cansler, Hood, Varner, van Mantgem, Agne, Andrus, Ayres, Ayres, Bakker, Battaglia, Bentz, et al, 2020, Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Predictive accuracy of post‐fire conifer death declines over time in models based on crown and bole injury

Shearman

Varner

Hood

et al. 2022

Ecological Applications

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A key uncertainty of empirical models of post‐fire tree mortality is understanding the drivers of elevated post‐fire mortality several years following fire, known as delayed mortality. Delayed mortality can represent a substantial fraction of mortality, particularly for large trees that are a conservation focus in western US coniferous forests. Current post‐fire tree mortality models have undergone limited evaluation of how injury level and time since fire interact to influence model accuracy and predictor variable importance. Less severe injuries potentially serve as an indicator for vulnerability to additional stressors such as bark beetle attack or moisture stress. We used a collection of 164,293 individual tree records to examine post‐fire tree mortality in eight western USA conifers: Abies concolor, Abies grandis, Calocedrus decurrens, Larix occidentalis, Pinus contorta, Pinus lambertiana, Pinus ponderosa, and Pseudotsuga menziesii. We evaluated the importance of fire injury predictors on discriminating between surviving trees versus immediate and delayed post‐fire mortality. We fit balanced random forest models for each species using cumulative tree mortality from 1 to 5‐years post‐fire. We compared these results to multi‐class random forest models using first‐year mortality, 2–5‐year mortality, and survival 5‐years post‐fire as a response variable. Crown volume scorched, diameter at breast height, and relative bark char height, were used as predictor variables. The cumulative mortality models all predicted trees that died within 1‐year of fire with high accuracy but failed to predict 2–5‐year mortality. The multi‐class models were an improvement but had lower accuracy for predicting 2–5‐year mortality. Multi‐class model accuracies ranged from 85% to 95% across all species for predicting 1‐year post‐fire mortality, 42%–71% for predicting 2–5‐year mortality, and 64%–85% for predicting trees that lived past 5‐years. Our study highlights the differences in tree species tolerance to fire injury and suggests that including second‐order predictors such as beetle attack or climatic water stress before and after fire will be critical to improve accuracy and better understand the mechanisms and patterns of fire‐caused tree death. Random forest models have potential for management applications such as post‐fire harvesting and simulating future stand dynamics.

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Section: Data Availability Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive accuracy of post‐fire conifer death declines over time in models based on crown and bole injury

Shearman

Varner

Hood

et al. 2022

Ecological Applications

View full text Add to dashboard Cite

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“…The FTM database is available for download from the USDA Forest Service Research Data Archive 25 . The FTM database includes standardized field observations of fire injury and survival from 164,293 individual trees.…”

Section: Data Recordsmentioning

confidence: 99%

“…Fireinjury variables that were derived from field-measured variables are described in the text. Full descriptions are documented in the metadata in Cansler et al25 . (2020) 7:194 | https://doi.org/10.1038/s41597-020-0522-7…”

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confidence: 99%

The Fire and Tree Mortality Database, for empirical modeling of individual tree mortality after fire

et al. 2020

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Wildland fires have a multitude of ecological effects in forests, woodlands, and savannas across the globe. A major focus of past research has been on tree mortality from fire, as trees provide a vast range of biological services. We assembled a database of individual-tree records from prescribed fires and wildfires in the United States. The Fire and Tree Mortality (FTM) database includes records from 164,293 individual trees with records of fire injury (crown scorch, bole char, etc.), tree diameter, and either mortality or top-kill up to ten years post-fire. Data span 142 species and 62 genera, from 409 fires occurring from 1981-2016. Additional variables such as insect attack are included when available. The FTM database can be used to evaluate individual fire-caused mortality models for pre-fire planning and post-fire decision support, to develop improved models, and to explore general patterns of individual fire-induced tree death. The database can also be used to identify knowledge gaps that could be addressed in future research.

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“…While some of the logistic regression models in FOFEM have been tested for prediction accuracy using external data Ganio and Progar 2017;Grayson et al 2017;Hood and Lutes 2017;Kane et al 2017), there has not been a systematic effort to independently evaluate models across a range of tree taxa in the USA, largely due to lack of existing evaluation data. To address the lack of available testing data, we developed the largest and most comprehensive collection of observations of fire-caused tree mortality in the continental US, the Fire and Tree Mortality (FTM) database, which is described in detail in Cansler et al (2020a) and available in an open-access online archive (Cansler et al 2020b). We used these data to conduct the largest evaluation to date of the post-fire tree mortality models included in FOFEM.…”

Section: Introductionmentioning

confidence: 99%

A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species

et al. 2020

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Background Predictive models of post-fire tree and stem mortality are vital for management planning and understanding fire effects. Post-fire tree and stem mortality have been traditionally modeled as a simple empirical function of tree defenses (e.g., bark thickness) and fire injury (e.g., crown scorch). We used the Fire and Tree Mortality database (FTM)—which includes observations of tree mortality in obligate seeders and stem mortality in basal resprouting species from across the USA—to evaluate the accuracy of post-fire mortality models used in the First Order Fire Effects Model (FOFEM) software system. The basic model in FOFEM, the Ryan and Amman (R-A) model, uses bark thickness and percentage of crown volume scorched to predict post-fire mortality and can be applied to any species for which bark thickness can be calculated (184 species-level coefficients are included in the program). FOFEM (v6.7) also includes 38 species-specific tree mortality models (26 for gymnosperms, 12 for angiosperms), with unique predictors and coefficients. We assessed accuracy of the R-A model for 44 tree species and accuracy of 24 species-specific models for 13 species, using data from 93 438 tree-level observations and 351 fires that occurred from 1981 to 2016. Results For each model, we calculated performance statistics and provided an assessment of the representativeness of the evaluation data. We identified probability thresholds for which the model performed best, and the best thresholds with either ≥80% sensitivity or specificity. Of the 68 models evaluated, 43 had Area Under the Receiver Operating Characteristic Curve (AUC) values ≥0.80, indicating excellent performance, and 14 had AUCs <0.7, indicating poor performance. The R-A model often over-predicted mortality for angiosperms; 5 of 11 angiosperms had AUCs <0.7. For conifers, R-A over-predicted mortality for thin-barked species and for small diameter trees. The species-specific models had significantly higher AUCs than the R-A models for 10 of the 22 models, and five additional species-specific models had more balanced errors than R-A models, even though their AUCs were not significantly different or were significantly lower. Conclusions Approximately 75% of models tested had acceptable, excellent, or outstanding predictive ability. The models that performed poorly were primarily models predicting stem mortality of angiosperms or tree mortality of thin-barked conifers. This suggests that different approaches—such as different model forms, better estimates of bark thickness, and additional predictors—may be warranted for these taxa. Future data collection and research should target the geographical and taxonomic data gaps and poorly performing models identified in this study. Our evaluation of post-fire tree mortality models is the most comprehensive effort to date and allows users to have a clear understanding of the expected accuracy in predicting tree death from fire for 44 species.

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Fire and tree mortality database (FTM)

Cited by 3 publications

References 2 publications

Predictive accuracy of post‐fire conifer death declines over time in models based on crown and bole injury

Predictive accuracy of post‐fire conifer death declines over time in models based on crown and bole injury

The Fire and Tree Mortality Database, for empirical modeling of individual tree mortality after fire

A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species

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