As the climate emergency escalates, the role of forests in carbon sequestration is paramount. This paper proposes a framework that integrates local capacities, multi-source remote sensing data, and meta-learning to enhance forest carbon assessment methodologies in data-scarce regions. By integrating multi-source optical and radar remote sensing data alongside community forest inventories, we applied a meta-modelling approach using stacked generalization ensemble to estimate forest above-ground carbon (AGC). We also conducted a Kruskal–Wallis test to determine significant differences in AGC among different tree species. The Kruskal–Wallis test (p = 1.37 × 10⁻¹³) and Dunn post-hoc analysis revealed significant differences in carbon stock potential among tree species, with Afzelia quanzensis ($\tilde{x}$= 12 kg/ha, P-holm-adj. = 0.05) and the locally known species M’buta ($\tilde{x}$ = 6 kg/ha, P-holm-adj. = 5.45 × 10⁻⁹) exhibiting a significantly higher median AGC. Our results further showed that combining optical and radar remote sensing data substantially improved prediction accuracy compared to single-source remote sensing data. To improve forest carbon assessment, we employed stacked generalization, combining multiple machine learning algorithms to leverage their complementary strengths and address individual limitations. This ensemble approach yielded more robust estimates than conventional methods. Notably, a stacking ensemble of support vector machines and random forest achieved the highest accuracy (R² = 0.84, RMSE = 1.36), followed by an ensemble of all base learners (R² = 0.83, RMSE = 1.39). Additionally, our results demonstrate that factors such as the diversity of base learners and the sensitivity of meta-leaners to optimization can influence stacking performance.

We employ a dendrochronological approach to examine spatial variation in radial tree growth-climate relationships and responses to global climate oscillations across the geographic ranges of Picea glauca and Picea engelmannii. Tree-ring chronologies were constructed for 77 sites and analyzed using correlation coefficients for monthly climatic variables. In general, for Picea glauca, stands in the western portion of its range had significant negative correlations with temperature during fall, winter, and spring; only summer temperature was positively correlated with tree growth. In eastern North America, Picea glauca growth was negatively correlated with winter and spring temperature. Across the north-south range of Picea engelmannii, growth of northern populations was positively correlated with temperature in most seasons except spring, which had negative correlations, and response to winter precipitation differed between northern and southern populations. However, some monthly values for both species correlations were altered due to decadal oscillation phase, especially for the Pacific Decadal Oscillation.

Climate change may have spatially variable impacts on growth of trees in topographically diverse environments, making generalizing across broad spatial and temporal extents inappropriate. Therefore, topography must be considered when analyzing growth response to climate. We address these topo-climatic relationships in the Canadian Rocky Mountains, focusing on lodgepole pine (Pinus contorta Douglas ex Louden) and interior spruce (Picea glauca (Moench) Voss × Picea engelmannii hybrid Parry) growth response to climate, Palmer drought severity index (PDSI), aspect, and slope angle. Climate variables correlate with older lodgepole pine growth on south- and west-facing slopes, including previous August temperature, winter and spring precipitation, and previous late-summer and current spring PDSI, but younger lodgepole pine were generally less sensitive to climate. Climate variables correlate with interior spruce growth on all slope aspects, with winter temperature and PDSI important for young and old individuals. Numerous monthly growth–climate correlations are not temporally stable, with shifts over the past century, and response differs by slope aspect and angle. Both species are likely to be negatively affected by moisture stress in the future in some, but not all, topographic environments. Results suggest species-specific and site-specific spatiotemporally diverse climate–growth responses, indicating that climate change is likely to have spatially variable impacts on radial growth response in mountainous environments.

Using modeled geographic range and importance projections of 133 tree species in the eastern United States we compare regional scale patterns of forest species diversity, similarity, evenness and community composition under present conditions with two future climate change scenarios. Overall, there is an increase in tree species richness on a pixel-by-pixel basis under future conditions, with a general shift to higher diversity in the northern portion of the United States. Percent similarity in species composition between current forest composition and future species composition under the low scenario averages 53.1%, and is lower for the high climate change scenario (45%), with the lowest similarity occurring in northern forests and the northern United States forest-prairie ecotone. In the future, species evenness increases over a large portion of the study area compared with current conditions. Forest community composition is stable in the southeastern United States, lower Great Lakes region and western extent of the eastern forests. Novel species assemblages develop in northern Wisconsin and Michigan, the northeastern United States, the southern forest-prairie ecotone and the southern coastal plain. Our study highlights geographic regions likely to experience substantial shifts in species composition and forest structure that should be monitored for early signs of forest change.

Ongoing climate change is likely to put increased selection pressures on the phenology of plants, yet for many species their abilities to respond to environmental cues are unknown. The present research focuses on using herbarium specimens to examine how 14 native plant species in Alberta have adjusted or adapted to changes in temperature and precipitation over the past century. We specifically investigate the impact of flowering-time responses and determine (1) whether herbaria collections contain sufficient evidence of these phenological responses to climate in plant species in Alberta, and (2) whether the responses are dependent on the typical moisture regime of their habitat. We compared plants from mesic and xeric habitats in terms of their phenological responses to air temperature and precipitation. In this study, the taxonomic relationships between the species were considered by selecting 14 species representing seven different angiosperm orders (one pair of species for each order). By collating data on the peak flowering date over the past century using preserved specimens, we found that on average, species from xeric habitats are more responsive to temperature, but not precipitation. This tendency might be explained by the thermal properties of mesic habitats, a finding that may lead to ways to predict the degree to which environmental cues will govern flowering.