Almost universally, forest inventory and monitoring databases are incomplete, ranging from missing data for only a few records and a few variables, common for small land areas, to missing data for many observations and many variables, common for large land areas. For a wide variety of applications, nearest neighbor (NN) imputation methods have been developed to fill in observations of variables that are missing on some records (Y-variables), using related variables that are available for all records (X-variables). This review attempts to summarize the advantages and weaknesses of NN imputation methods and to give an overview of the NN approaches that have most commonly been used. It also discusses some of the challenges of NN imputation methods. The inclusion of NN imputation methods into standard software packages and the use of consistent notation may improve further development of NN imputation methods. Using X-variables from different data sources provides promising results, but raises the issue of spatial and temporal registration errors. Quantitative measures of the contribution of individual X-variables to the accuracy of imputing the Y-variables are needed. In addition, further research is warranted to verify statistical properties, modify methods to improve statistical properties, and provide variance estimators.

Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods. The models were developed and fit to data collected by the Forest Inventory and Analysis program of the US Forest Service in Washington, Oregon, and California. For predicting cavity tree and snag abundance per stand, all three NB regression models performed better in terms of mean square prediction error than the NN imputation methods. The most similar neighbor imputation, however, outperformed the NB regression models in predicting overall cavity tree and snag abundance. Résumé : Les données sur l'abondance des arbres creux et des chicots sont extrêmement variables et contiennent plusieurs observations nulles. Nous prédisons l'abondance des arbres creux et des chicots à partir de variables facilement disponibles dans les cartes du couvert forestier ou parmi les données obtenues par télédétection en utilisant des modèles de régression binomiale négative (BN), BN à excès de zéros (ZINB) et BN tronquée à zéro (ZANB), ainsi que des méthodes d'imputation par le plus proche voisin. Les modèles sont élaborés et ajustés aux données collectées par le programme d'analyse et d'inventaire forestiers du U.S. Forest Service dans les É tats de Washington, de l'Oregon et de la Californie. Les trois modèles de régression BN offraient une meilleure performance en terme d'erreur quadratique moyenne de prédiction que les méthodes d'imputation par le plus proche voisin pour prédire l'abondance des arbres creux et des chicots par peuplement. Cependant, l'imputation par le voisin le plus semblable donnait de meilleurs résultats que les modèles de régression BN pour prédire l'abondance globale des arbres creux et des chicots. [Traduit par la Rédaction]

Changing climates are altering the structural and functional components of forest ecosystems at an unprecedented rate. Simultaneously, we are seeing a diversification of public expectations on the broader sustainable use of forest resources beyond timber production. As a result, the science and art of silviculture needs to adapt to these changing realities. In this piece, we argue that silviculturists are gradually shifting from the application of empirically derived silvicultural scenarios to new sets of approaches, methods and practices, a process that calls for broadening our conception of silviculture as a scientific discipline. We propose a holistic view of silviculture revolving around three key themes: observe, anticipate and adapt. In observe, we present how recent advances in remote sensing now enable silviculturists to observe forest structural, compositional and functional attributes in near-real-time, which in turn facilitates the deployment of efficient, targeted silvicultural measures in practice that are adapted to rapidly changing constraints. In anticipate, we highlight the importance of developing state-of-the-art models designed to take into account the effects of changing environmental conditions on forest growth and dynamics. In adapt, we discuss the need to provide spatially explicit guidance for the implementation of adaptive silvicultural actions that are efficient, cost-effective and socially acceptable. We conclude by presenting key steps towards the development of new tools and practical knowledge that will ensure meeting societal demands in rapidly changing environmental conditions. We classify these actions into three main categories: re-examining existing silvicultural trials to identify key stand attributes associated with the resistance and resilience of forests to multiple stressors, developing technological workflows and infrastructures to allow for continuous forest inventory updating frameworks, and implementing bold, innovative silvicultural trials in consultation with the relevant communities where a range of adaptive silvicultural strategies are tested. In this holistic perspective, silviculture can be defined as the science of observing forest condition and anticipating its development to apply tending and regeneration treatments adapted to a multiplicity of desired outcomes in rapidly changing realities.

We examined the dynamics of aboveground forest woody carbon pools — live trees, standing dead trees, and down wood — during the first 6 years following wildfire across a wide range of conditions, which are characteristic of California forest fires. From repeated measurements of the same plots, we estimated change in woody carbon pools as a function of crown fire severity as indicated by a post-fire index, years since fire, pre-fire woody carbon, forest type group (hardwood vs. softwood), elevation, and climate attributes. Our analysis relied on 130 U.S. national forest inventory plots measured before and 1 year after fire, with one additional remeasurement within 6 years after fire. There was no evidence of net change in total wood carbon, defined for this study as the wood in standing trees larger than 12.7 cm diameter at breast height and down wood larger than 7.6 cm in diameter, over the post-fire period in any of the three severity classes. Stands that burned at low severity exhibited considerable shifts from live to standing dead and down wood pools. In stands that burned at moderate severity, live wood decreased significantly whereas no net change was detected in standing dead or down wood. High severity fire burning resulted in movement from standing dead to down wood pools. Our results suggest that the carbon trajectories for stand-replacing fires may not be appropriate for the majority of California’s forest area that burned at low to moderate severities.

Forest wildfires consume fuel and are followed by post-fire fuel accumulation. This study examines post-fire surface fuel dynamics over 9 years across a wide range of conditions characteristic of California fires in dry conifer and hardwood forests. We estimated post-fire surface fuel loadings (Mgha−1) from 191 repeatedly measured United States national inventory plots in dry conifer and hardwood stands of 49 California forest wildfires and identified differences across fire severity classes – low, moderate and high. No significant change in duff load was detected within the first 9 years post-fire across all forest types and fire severities. Litter, 1-h and 10-h fuels exhibited a quadratic trend over time in dry conifer stands, peaking ~6 years after fire, whereas hardwood stands displayed a constant rate of increase in those fuel types. For 100- and 1000-h fuels, the annual rate of change was constant for dry conifer and hardwood stands with differing rates of change across fire severity classes. This study was based on an extensive, spatially balanced sample across burned dry conifer and hardwood forests of California. Therefore, the estimated patterns of fuel accumulation are generally applicable to wildfires within this population.

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Understanding climate as a driver of low- to moderate-severity fires in the Montane Cordillera Ecozone of Canada is a priority given predicted and observed increases in frequency and severity of large fires due to climate change. We characterised historical fire-climate associations using 14 crossdated fire-scar records and tree-ring proxy reconstructions of summer drought and annual precipitation from the region. We compared fire-climate associations among years when fires burned in multiple study areas. From 1746 to 1945, there were 32 years with moderate fire synchrony in which four to six study areas recorded fire. During four high fire synchrony years, 7 to 10 study areas recorded fire. Below-average annual precipitation and summer drought synchronised fires, whereas infrequent years of high fire synchrony were preceded by a wet summer. After 1945, decreased fire occurrence and synchrony reflects fire exclusion, suppression and climatic variation. Global climate change manifests as blocking high-pressure ridges that superimpose on longer fire-seasons and increased droughts. Combined, they make dry forests increasingly susceptible to synchronous fires, which are difficult to suppress as observed during the record-breaking 2017, 2018 and 2021 fire seasons in British Columbia.

Dwarf mistletoes ( Arceuthobium species) influence many processes within forested ecosystems, but few studies have examined their distribution in relation to climate. An analysis of 1549 forested plots within a 14.5 million ha region of southeast Alaska provided strong indications that climate currently limits hemlock dwarf mistletoe ( Arceuthobium tsugense (Rosendahl) G.N. Jones) to a subset of the range of its primary tree host, western hemlock ( Tsuga heterophylla (Raf.) Sarg.), with infection varying from a high of 20% of trees at sea level to only 5% by 200 m elevation. Three types of modeling approaches (logistic, most similar neighbors, and random forests) were tested for the ability to simultaneously predict abundance and distribution of host and pathogen as a function of climate variables. Current distribution was explained well by logistic models using growing degree-days, indirect and direct solar radiation, rainfall, snowfall, slope, and minimum temperatures, although accuracy for predicting A. tsugense presence at a particular location was only 38%. For future climate scenarios (A1B, A2, and B1), projected increases for A. tsugense habitat over a century ranged from a low of 374% to a high of 757%, with differences between modeling approaches contributing more to uncertainty than differences between climate scenarios.