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1. Spatial patterns of tree and shrub biomass in a deciduous forest using leaf-off and leaf-on lidar

   https://doi.org/10.1139/cjfr-2018-0033  

   Brubaker, Kristen M.; Johnson, Quincey K.; Kaye, Margot W. (September 2018, Canadian Journal of Forest Research)

   Understanding patterns of aboveground carbon storage across forest types is increasingly important as managers adapt to threats of global change. We combined field measures of aboveground biomass with lidar to model fine-scale biomass in deciduous forests located in two watersheds; one watershed was underlain by sandstone and the other by shale. We measured tree and shrub biomass across three topographic positions for both watersheds and analyzed biomass using mixed models. The watershed underlain by shale had 60% more aboveground biomass than the sandstone watershed. Although spatial patterns of biomass were different across watersheds, both had higher (between about 40% and 55%) biomass values at the toe-slope position than at the ridge-top position. To model fine-scale spatial patterns of biomass, we tested the effectiveness of leaf-on and leaf-off lidar combined with topographic metrics to develop a spatially explicit random forest model of tree and shrub biomass across both watersheds. Leaf-on variables were more important for modeling shrub biomass, while leaf-off variables were more effective at modeling tree biomass. Our model of tree and shrub biomass reflects the distribution of biomass across both watersheds at a fine scale and highlights the potential of abiotic factors such as topography and bedrock to affect carbon storage. 

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2. Ecological forecasting of tree growth: Regional fusion of tree‐ring and forest inventory data to quantify drivers and characterize uncertainty

   https://doi.org/10.1111/gcb.16038  

   Heilman, Kelly A.; Dietze, Michael C.; Arizpe, Alexis A.; Aragon, Jacob; Gray, Andrew; Shaw, John D.; Finley, Andrew O.; Klesse, Stefan; DeRose, R. Justin; Evans, Margaret E. K. (January 2022, Global Change Biology)

   Abstract Robust ecological forecasting of tree growth under future climate conditions is critical to anticipate future forest carbon storage and flux. Here, we apply three ingredients of ecological forecasting that are key to improving forecast skill: data fusion, confronting model predictions with new data, and partitioning forecast uncertainty. Specifically, we present the first fusion of tree‐ring and forest inventory data within a Bayesian state‐space model at a multi‐site, regional scale, focusing onPinus ponderosavar.brachypterain the southwestern US. Leveraging the complementarity of these two data sources, we parsed the ecological complexity of tree growth into the effects of climate, tree size, stand density, site quality, and their interactions, and quantified uncertainties associated with these effects. New measurements of trees, an ongoing process in forest inventories, were used to confront forecasts of tree diameter with observations, and evaluate alternative tree growth models. We forecasted tree diameter and increment in response to an ensemble of climate change projections, and separated forecast uncertainty into four different causes: initial conditions, parameters, climate drivers, and process error. We found a strong negative effect of fall–spring maximum temperature, and a positive effect of water‐year precipitation on tree growth. Furthermore, tree vulnerability to climate stress increases with greater competition, with tree size, and at poor sites. Under future climate scenarios, we forecast increment declines of 22%–117%, while the combined effect of climate and size‐related trends results in a 56%–91% decline. Partitioning of forecast uncertainty showed that diameter forecast uncertainty is primarily caused by parameter and initial conditions uncertainty, but increment forecast uncertainty is mostly caused by process error and climate driver uncertainty. This fusion of tree‐ring and forest inventory data lays the foundation for robust ecological forecasting of aboveground biomass and carbon accounting at tree, plot, and regional scales, including iterative improvement of model skill. 

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3. From town to national park: Understanding the long-term effects of hunting and logging on tree communities in Central Africa

   https://doi.org/https://doi.org/10.1016/j.foreco.2021.119571  

   Maicher, Vincent; Clark, Connie J.; Harris, David J.; Medjibe, Vincent P.; Poulsen, John R. (January 2021, Forest ecology and management)

   null (Ed.)

   Anthropogenic disturbances are changing the structure and composition of tropical forests worldwide. Multiple disturbances often occur simultaneously in forests: for example, hunting and logging are within-forest disturbances that impact vast areas of seemingly intact rainforests. Despite recent work on the individual effects of these disturbances, our understanding of how they interact to influence tree communities is still limited. In northern Republic of Congo, we explored the effects of hunting and logging on tree communities. Over an 8-year period, we monitored 12,552 tree stems (≥ 10 cm diameter-at-breast height) spread over 30 1-ha plots along a gradient of human disturbance to compare the tree diversity between hunted and logged forest, once-logged forest, and protected forest free of both disturbances. Tree density, species richness, and community composition were affected by both hunting and logging. Forest close to human settlements was richer, more heterogenous, and more dynamic in species composition across censuses. In hunted and logged forest, fast-growing secondary species with low shade tolerance replaced old growth species. Comparatively, the once-logged forest had the greatest stem density and intermediate species richness with an increased density of shade-bearing species over time. Both tree species spatial turnover and tree recruitment were greatly affected by proximity to human settlements. A shift towards abiotically dispersed trees and increasing seed predation by rodents near villages can partly explain the differences in tree recruitment across the forest types. The combination of hunting and logging seems to have a greater impact on tree communities than either single disturbance, especially with nearness to villages. 

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4. Important Insect and Disease Threats to United States Tree Species and Geographic Patterns of Their Potential Impacts

   https://doi.org/10.3390/f10040304  

   Potter, Kevin; Escanferla, Maria; Jetton, Robert; Man, Gary (April 2019, Forests)

   Diseases and insects, particularly those that are non-native and invasive, arguably pose the most destructive threat to North American forests. Currently, both exotic and native insects and diseases are producing extensive ecological damage and economic impacts. As part of an effort to identify United States tree species and forests most vulnerable to these epidemics, we compiled a list of the most serious insect and disease threats for 419 native tree species and assigned a severity rating for each of the 1378 combinations between mature tree hosts and 339 distinct insect and disease agents. We then joined this list with data from a spatially unbiased and nationally consistent forest inventory to assess the potential ecological impacts of insect and disease infestations. Specifically, potential host species mortality for each host/agent combination was used to weight species importance values on approximately 132,000 Forest Inventory and Analysis (FIA) plots across the conterminous 48 United States. When summed on each plot, these weighted importance values represent an estimate of the proportion of the plot’s existing importance value at risk of being lost. These plot estimates were then used to identify statistically significant geographic hotspots and coldspots and of potential forest impacts associated with insects and diseases in total, and for different agent types. In general, the potential impacts of insects and diseases were greater in the West, where there are both fewer agents and less diverse forests. The impact of non-native invasive agents, however, was potentially greater in the East. Indeed, the impacts of current exotic pests could be greatly magnified across much of the Eastern United States if these agents are able to reach the entirety of their hosts’ ranges. Both the list of agent/host severities and the spatially explicit results can inform species-level vulnerability assessments and broad-scale forest sustainability reporting efforts, and should provide valuable information for decision-makers who need to determine which tree species and locations to target for monitoring efforts and pro-active management activities. 

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5. Tree Species Traits Determine the Success of LiDAR-Based Crown Mapping in a Mixed Temperate Forest

   https://doi.org/10.3390/rs12020309  

   Hastings, Jack H.; Ollinger, Scott V.; Ouimette, Andrew P.; Sanders-DeMott, Rebecca; Palace, Michael W.; Ducey, Mark J.; Sullivan, Franklin B.; Basler, David; Orwig, David A. (January 2020, Remote Sensing)

   The ability to automatically delineate individual tree crowns using remote sensing data opens the possibility to collect detailed tree information over large geographic regions. While individual tree crown delineation (ITCD) methods have proven successful in conifer-dominated forests using Light Detection and Ranging (LiDAR) data, it remains unclear how well these methods can be applied in deciduous broadleaf-dominated forests. We applied five automated LiDAR-based ITCD methods across fifteen plots ranging from conifer- to broadleaf-dominated forest stands at Harvard Forest in Petersham, MA, USA, and assessed accuracy against manual delineation of crowns from unmanned aerial vehicle (UAV) imagery. We then identified tree- and plot-level factors influencing the success of automated delineation techniques. There was relatively little difference in accuracy between automated crown delineation methods (51–59% aggregated plot accuracy) and, despite parameter tuning, none of the methods produced high accuracy across all plots (27—90% range in plot-level accuracy). The accuracy of all methods was significantly higher with increased plot conifer fraction, and individual conifer trees were identified with higher accuracy (mean 64%) than broadleaf trees (42%) across methods. Further, while tree-level factors (e.g., diameter at breast height, height and crown area) strongly influenced the success of crown delineations, the influence of plot-level factors varied. The most important plot-level factor was species evenness, a metric of relative species abundance that is related to both conifer fraction and the degree to which trees can fill canopy space. As species evenness decreased (e.g., high conifer fraction and less efficient filling of canopy space), the probability of successful delineation increased. Overall, our work suggests that the tested LiDAR-based ITCD methods perform equally well in a mixed temperate forest, but that delineation success is driven by forest characteristics like functional group, tree size, diversity, and crown architecture. While LiDAR-based ITCD methods are well suited for stands with distinct canopy structure, we suggest that future work explore the integration of phenology and spectral characteristics with existing LiDAR as an approach to improve crown delineation in broadleaf-dominated stands. 

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