This study utilized the North American PhenoCam network to evaluate phenological characteristics and their relationships with geographic and climatic factors across deciduous broadleaf (n = 39) and evergreen needleleaf (n = 13) forests over the past decade. Using high temporal resolution near-surface imagery, key phenological indicators including the start, end, and length of growing season were derived and analyzed using linear regression and structural equation modeling. The results revealed substantial spatial variation; the evergreen needleleaf sites exhibited earlier starts to the growing season (112 vs. 130 Julian date), later ends to the growing season (286 vs. 264 Julian date), and longer lengths for the growing season (172 vs. 131 days) compared with the deciduous broadleaf sites. Latitude was significantly related to the start of the growing season and the length of the growing season at the deciduous broadleaf sites (R2 = 0.28–0.41, p < 0.01), while these relationships were weaker at the evergreen needleleaf sites, and elevation had mixed effects. The mean annual temperature strongly influenced the phenology for both forest types (R2 = 0.18–0.76, p < 0.01), whereas longitude, distance to the coast, and precipitation had negligible effects. Temporal trends in the phenological indicators were sporadic across both the deciduous broadleaf and evergreen needleleaf sites. Structural equation modeling revealed distinct causal pathways for each forest type, highlighting complex interactions among the geographical and climatic variables. At the deciduous broadleaf sites, geographical factors (latitude, elevation, and distance to the nearest coast) predominated the mean annual temperature, which in turn significantly affected phenological development (χ2 = 2.171, p = 0.975). At the evergreen needleleaf sites, geographical variables had more complex effects on the climatic factors, start of the growing season, and end of the growing season, with the end of the growing season emerging as the primary determinant of growing season length (χ2 = 0.486, p = 0.784). The PhenoCam network provides valuable fine-scale phenological dynamics, offering great insights for forest management, biodiversity conservation, and understanding carbon cycling under climate change.
Chang et al. (Wed,) studied this question.