Laura P. Leites

Height-growth response to climatic changes differs among populations of Douglas-fir: a novel analysis of historic data

Projected climate change will affect existing forests, as substantial changes are predicted to occur during their life spans. Species that have ample intraspecific genetic differentiation, such as Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), are expected to display population-specific growth responses to climate change. Using a mixed-effects modeling approach, we describe three-year height (HT) growth response to changes in climate of interior Douglas-fir populations. We incorporate climate information at the population level, yielding a model that is specific to both species and population. We use data from provenance tests from previous studies that comprised 236 populations from Idaho, Montana, and eastern Washington, USA. The most sensitive indicator of climate was the mean temperature of the coldest month. Population maximum HT and HT growth response to changes in climate were dependent on seed source climate. All populations had optimum HT growth when transferred to climates with warmer winters; those originating in sites with the warmest winters were taller across sites and had highest HT growth at transfer distances closest to zero; those from colder climates were shortest and had optimum HT growth when transferred the farthest. Although this differential response damped the height growth differences among populations, cold-climate populations still achieved their maximum growth at lower temperatures than warm-climate populations. The results highlight the relevance of understanding climate change impacts at the population level, particularly in a species with ample genetic variation among populations.

Estimating Canopy Height of Deciduous Forests at a Regional Scale with Leaf-Off, Low Point Density LiDAR

Abstract. As we strive toward a more accurate understanding and quantification of carbon pools in forested ecosystems, the development of regional-scale maps of forest characteristics is essential in order to establish baselines and monitor change. Light Detection and Ranging (LiDAR) is increasingly being used to improve our understanding of forested ecosystems on a broad spatial scale, although obtaining data can be expensive and time consuming. We evaluated the effectiveness of using freely available low point density, leaf-off LiDAR collected for the entire state of Pennsylvania, in the United States, to create an accurate regional-scale dominant/codominant canopy height model for state forests in Pennsylvania. We evaluated several methodologies using an inventory dataset with over 1400 sample plots. The developed canopy height model was accurate to about 10% of the field-measured dominant/codominant tree heights for each plot, although it underestimated the field values. Root mean square error relative to the mean field height ranged between 3.5% and 12.5% across all site and forest variables such as forest community type, age, and height class. Factors that affected the accuracy of the canopy height model included tree density, slope, and percent evergreen cover. Résumé. Tout d’abord, en mettant à l’épreuve une compréhension des réserves de carbones dans l’écosystème forestiers, le développement des échelles de cartes régionales qui englobent les caractéristiques des forêts est crucial afin d’établir une base et surveiller des changements. Le « Light Detection and Ranging » (LiDAR) est utilisé de plus en plus afin d’améliorer notre compréhension par rapport à l’écosystème forestiers sur une large échelle spatiale; cependant cette cueillette de données peut être dispendieux et prendre beaucoup de temps. Nous avons évalués l’efficacité en utilisant une faible densité de points qui se nomme le « leaf-off » LiDAR, afin de créer une échelle régionale dominant/codominant précis du modèle d’hauteur de canopie pour l’état forestiers a Pennsylvania dans les États Unis. De plus, nous avons examinés plusieurs méthodologies en utilisant un terrain inventaires données avec 1400 échantillons. Le développement du modèle d’hauteur de canopie était précis jusqu’à 10% du terrain mesuré, cependant les valeurs de champs étaient sous-estimés. L’erreur de la moyenne quadratique relative à la moyenne du terrain d’hauteur était entre 3,5% et 12,5% à travers tout le site et les variables forestiers, tels que le type forestier communautaire, l’âge et la classe d’hauteur. Les facteurs qui ont affectés la précision du modèle d’hauteur de canopie incluaient la densité d’arbre, la pente et le pourcentage de couverture des feuilles persistantes.

Community assembly responses to warming and increased precipitation in an early successional forest

Experimental climate manipulations provide the opportunity to link predicted changes in climate to the process of community assembly. We studied plant community assembly of a recently harvested forest exposed to three years of experimental 2°C warming and 20% increased precipitation. By the end of the experiment, trees were the only functional group that shifted composition in response to warming and precipitation treatments (p = 0.03), while the composition of the grass, forbs, and shrub/small tree/vine functional groups were unresponsive. Individual species within groups were associated with specific treatments, but did not result in a predictable community composition shift. Temporal dynamics of functional group cover were more sensitive to treatment effects than single, static measures of plant community responses such as biomass. Both static and dynamic plant analyses revealed interactive effects of warming and increased precipitation on cover and biomass of grass and all plants together (grass cover p < 0.01, grass biomass p = 0.02, total cover p < 0.01, total biomass p = 0.05). Short forb cover was negatively affected by increased precipitation throughout our experiment (p = 0.03). Grass, tree, and shrub/small tree/vine functional groups showed independent year effects on cover that can be attributed to successional development of the forest community (all p ≤ 0.01). Random forest modeling indicated that cover of other plant functional groups and static plot-level variables such as plot location and components of soil texture were often the most important predictors of cover for a given functional group, while temperature and moisture availability measures were the least important. Importance of predictors of functional group cover varied greatly among random forest models from different treatments, suggesting that diverse environmental factors constrain functional group cover and may provide resilience of community assembly to climate change.