How will climate change affect global biodiversity? [Also see Report by Urban et al. ]

Premise of the Study: Herbarium specimens are increasingly used to study reproductive phenology. Here, we ask whether classifying reproduction into progressively finer‐scale stages improves our understanding of the relationship between climate and reproductive phenology.Methods: We evaluated Acer rubrum herbarium specimens across eastern North America, classifying them into eight reproductive phenophases and four stages of leaf development. We fit models with different reproductive phenology categorization schemes (from detailed to broad) and compared model fits and coefficients describing temperature, elevation, and year effects. We fit similar models to leaf phenology data to compare reproductive to leafing phenology.Results: Finer‐scale reproductive phenophases improved model fits and provided more precise estimates of reproductive phenology. However, models with fewer reproductive phenophases led to similar qualitative conclusions, demonstrating that A. rubrum reproduces earlier in warmer locations, lower elevations, and in recent years, as well as that leafing phenology is less strongly influenced by temperature than is reproductive phenology.Discussion: Our study suggests that detailed information on reproductive phenology provides a fuller understanding of potential climate change effects on flowering, fruiting, and leaf‐out. However, classification schemes with fewer reproductive phenophases provided many similar insights and may be preferable in cases where resources are limited.

Shifts in wildflower phenology in response to climate change are well documented in the scientific literature. The majority of studies have revealed phenological shifts using in-situ observations, some aided by citizen science efforts (e.g., National Phenology Network). Such investigations have been instrumental in quantifying phenological shifts but are challenged by the fact that limited resources often make it difficult to gather observations over large spatial scales and long-time frames. However, recent advances in finer scale satellite imagery may provide new opportunities to detect changes in phenology. These approaches have documented plot level changes in vegetation characteristics and leafing phenology, but it remains unclear whether they can also detect flowering in natural environments. Here, we test whether fine-resolution imagery (<10 m) can detect flowering and whether combining multiple sources of imagery improves the detection process. Examining alpine wildflowers at Mt. Rainier National Park (MORA), we found that high-resolution Random Forest (RF) classification from 3-m resolution PlanetScope (from Planet Labs) imagery was able to delineate the flowering season captured by ground-based phenological surveys with an accuracy of 70% (Cohen’s kappa = 0.25). We then combined PlanetScope data with coarser resolution but higher quality imagery from Sentinel and Landsat satellites (10-m Sentinel and 30-m Landsat), resulting in higher accuracy predictions (accuracy = 77%, Cohen’s kappa = 0.39). The model was also able to identify the timing of peak flowering in a particularly warm year (2015), despite being calibrated on normal climate years. Our results suggest PlanetScope imagery holds utility in global change ecology where temporal frequency is important. Additionally, we suggest that combining imagery may provide a new approach to cross-calibrate sensors to account for radiometric irregularity inherent in fine resolution PlanetScope imagery. The development of this approach for wildflower phenology predictions provides new possibilities to monitor climate change effects on flowering communities at broader spatiotemporal scales. In protected and tourist areas where the flowering season draws large numbers of visitors, such as Mt. Rainier National Park, the modeling framework presented here could be a useful tool to manage and prioritize park resources.

Predicting how climate change will influence forests is challenging. Forest communities are expected to lose cold-adapted trees near their low latitude/elevation range limits, while warm-adapted trees should increase in abundance near their high latitude/elevation limits ( i.e., ‘thermophilization’). However, slow-growing and long-lived trees, paired with climatic sensitivities that differ by species could add complexity to these predictions. To address these possibilities, we use demographic data collected from Mount Rainier National Park to examine: 1) the likelihood of rapid population growth or decline near range limits; 2) differences in climatic sensitivities between tree species at juvenile and adult stages; and 3) whether forest communities have already changed in response to recent warming. Our results suggest focal species are unlikely to shift their ranges rapidly with warming because recruit densities are low and time to reproductive maturity slow near upper range limits, while surviv...

Identifying the factors that influence the climate sensitivity of treeline species is critical to understanding carbon sequestration, forest dynamics, and conservation in high elevation forest/meadow ecotones. Using tree cores from four sub-alpine conifer species collected from three sides of Mt. Rainier, WA, USA, we investigated the influences of species identity and sites with different local climates on radial growth–climate relationships. We created chronologies for each species at each site, determined influential plant-relevant annual and seasonal climatic variables influencing growth, and investigated how the strength of climate sensitivity varied across species and location. Overall, similar climate variables constrained growth on all three sides of the mountain for each of the four study species. Summer warmth positively influenced radial growth, whereas snow, spring warmth, previous summer warmth, and spring humidity negatively influenced growth. We discovered only a few subtle differences in the climate sensitivity of co-occurring species at the same site and between the same species at different sites in pairwise comparisons. A model including species by climate interactions provided the best balance between parsimony and fit, but did not lead to substantially greater predictive power relative to a model without site or species interactions. Our results imply that at treeline in moist temperate regions like Mt. Rainier, the same climatic variables drive annual variation in growth across species and locations, despite species differences in physiology and site differences in mean climates.