Integrating experiments to predict interactive cue effects on spring phenology with warming

Climate change has advanced plant phenology globally 4-6 d °C^-1 on average. Such shifts are some of the most reported and predictable biological impacts of rising temperatures. Yet as climate change has marched on, phenological shifts have appeared muted over recent decades - failing to match simple predictions of an advancing spring with continued warming. The main hypothesis for these changing trends is that interactions between spring phenological cues - long-documented in laboratory environments - are playing a greater role in natural environments due to climate change. Here, we argue that accurately linking shifts observed in long-term data to underlying phenological cues is slowed by biases in observational studies and limited integration of insights from laboratory studies. We synthesize seven decades of laboratory experiments to quantify how phenological cue-space has been studied and how treatments compare with shifts caused by climate change. Most studies focus on one cue, limiting our ability to make accurate predictions, but some well-studied forest species offer opportunities to advance forecasting. We outline how greater integration of controlled-environment studies with long-term data could drive a new generation of laboratory experiments, built on physiological insights, that would transform our fundamental understanding of phenology and improve predictions.

Identifying "Useful" Fitness Models: Balancing the Benefits of Added Complexity with Realistic Data Requirements in Models of Individual Plant Fitness

AbstractDirect species interactions are commonly included in individual fitness models used for coexistence and local diversity modeling. Though widely considered important for such models, direct interactions alone are often insufficient for accurately predicting fitness, coexistence, or diversity outcomes. Incorporating higher-order interactions (HOIs) can lead to more accurate individual fitness models but also adds many model terms, which can quickly result in model overfitting. We explore approaches for balancing the trade-off between tractability and model accuracy that occurs when HOIs are added to individual fitness models. To do this, we compare models parameterized with data from annual plant communities in Australia and Spain, varying in the extent of information included about the focal and neighbor species. The best-performing models for both data sets were those that grouped neighbors based on origin status and life form, a grouping approach that reduced the number of model parameters substantially while retaining important ecological information about direct interactions and HOIs. Results suggest that the specific identity of focal or neighbor species is not necessary for building well-performing fitness models that include HOIs. In fact, grouping neighbors by even basic functional information seems sufficient to maximize model accuracy, an important outcome for the practical use of HOI-inclusive fitness models.

Increased variance in temperature and lag effects alter phenological responses to rapid warming in a subarctic plant community

Summer temperature on the Cape Churchill Peninsula (Manitoba, Canada) has increased rapidly over the past 75 years, and flowering phenology of the plant community is advanced in years with warmer temperatures (higher cumulative growing degree days). Despite this, there has been no overall shift in flowering phenology over this period. However, climate change has also resulted in increased interannual variation in temperature; if relationships between phenology and temperature are not linear, an increase in temperature variance may interact with an increase in the mean to alter how community phenology changes over time. In our system, the relationship between phenology and temperature was log-linear, resulting in a steeper slope at the cold end of the temperature spectrum than at the warm end. Because below-average temperatures had a greater impact on phenology than above-average temperatures, the long-term advance in phenology was reduced. In addition, flowering phenology in a given year was delayed if summer temperatures were high the previous year or 2 years earlier (lag effects), further reducing the expected advance over time. Phenology of early-flowering plants was negatively affected only by temperatures in the previous year, and that of late-flowering plants primarily by temperatures 2 years earlier. Subarctic plants develop leaf primordia one or more years prior to flowering (preformation); these results suggest that temperature affects the development of flower primordia during this preformation period. Together, increased variance in temperature and lag effects interacted with a changing mean to reduce the expected phenological advance by 94%, a magnitude large enough to account for our inability to detect a significant advance over time. We conclude that changes in temperature variability and lag effects can alter trends in plant responses to a warming climate and that predictions for changes in plant phenology under future warming scenarios should incorporate such effects.