PRIMARY MODES OF TREE MORTALITY IN SOUTHWESTERN AMAZON FORESTS

Abstract People who have to make decisions about cost-effective management for gypsy moth need help in predicting and evaluating its effects. Field plot data collected during recent outbreaks in Pennsylvania are being used to develop guides for predicting forest stand losses to the pest Presented here are some of the more useful products of that effort to date. Easy-to-measure data for forest characteristics such as species composition and crown condition can be collected and applied in models that estimate potential stand and tree mortality and changes in timber value. North. J. Appl. For. 2:21-23, June 1984.

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Reply to Leuzinger et al.: Drought-induced tree mortality temperature sensitivity requires pressing forward with best available science

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.0909227106 ◽

2009 ◽

Vol 106 (38) ◽

pp. E107-E107 ◽

Cited By ~ 7

Author(s):

H. D. Adams ◽

M. Guardiola-Claramonte ◽

G. A. Barron-Gafford ◽

J. C. Villegas ◽

D. D. Breshears ◽

...

Keyword(s):

Temperature Sensitivity ◽

Tree Mortality ◽

Best Available Science

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

Fire Ecology ◽

10.1186/s42408-020-00082-0 ◽

2020 ◽

Vol 16 (1) ◽

Author(s):

C. Alina Cansler ◽

Sharon M. Hood ◽

Phillip J. van Mantgem ◽

J. Morgan Varner

Keyword(s):

Tree Mortality ◽

Characteristic Curve ◽

Small Diameter ◽

Fire Effects ◽

Tree Level ◽

Clear Understanding ◽

Bark Thickness ◽

Mortality Models ◽

Species Specific ◽

Stem Mortality

Abstract 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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