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.

A simple explanation for declining temperature sensitivity with warming.

Recently, multiple studies have reported declining phenological sensitivities (∆ days per ℃) with higher temperatures. Such observations have been used to suggest climate change is reshaping biological processes, with major implications for forecasts of future change. Here, we show that these results may simply be the outcome of using linear models to estimate nonlinear temperature responses, specifically for events that occur after a cumulative thermal threshold is met-a common model for many biological events. Corrections for the nonlinearity of temperature responses consistently remove the apparent decline. Our results show that rising temperatures combined with linear estimates based on calendar time produce the observations of declining sensitivity-without any shift in the underlying biology. Current methods may thus undermine efforts to identify when and how warming will reshape biological processes.

Spatial and temporal shifts in photoperiod with climate change.

Climate change causes both temporal (e.g. advancing spring phenology) and geographic (e.g. range expansion poleward) species shifts, which affect the photoperiod experienced at critical developmental stages ('experienced photoperiod'). As photoperiod is a common trigger of seasonal biological responses - affecting woody plant spring phenology in 87% of reviewed studies that manipulated photoperiod - shifts in experienced photoperiod may have important implications for future plant distributions and fitness. However, photoperiod has not been a focus of climate change forecasting to date, especially for early-season ('spring') events, often assumed to be driven by temperature. Synthesizing published studies, we find that impacts on experienced photoperiod from temporal shifts could be orders of magnitude larger than from spatial shifts (1.6 h of change for expected temporal vs 1 min for latitudinal shifts). Incorporating these effects into forecasts is possible by leveraging existing experimental data; we show that results from growth chamber experiments on woody plants often have data relevant for climate change impacts, and suggest that shifts in experienced photoperiod may increasingly constrain responses to additional warming. Further, combining modeling approaches and empirical work on when, where and how much photoperiod affects phenology could rapidly advance our understanding and predictions of future spatio-temporal shifts from climate change.

Prioritizing conservation actions in urbanizing landscapes.

Urbanization-driven landscape changes are harmful to many species. Negative effects can be mitigated through habitat preservation and restoration, but it is often difficult to prioritize these conservation actions. This is due, in part, to the scarcity of species response data, which limit the predictive accuracy of modeling to estimate critical thresholds for biological decline and recovery. To address these challenges, we quantify effort required for restoration, in combination with a clear conservation objective and associated metric (e.g., habitat for focal organisms). We develop and apply this framework to coho salmon (Oncorhynchus kisutch), a highly migratory and culturally iconic species in western North America that is particularly sensitive to urbanization. We examine how uncertainty in biological parameters may alter locations prioritized for conservation action and compare this to the effect of shifting to a different conservation metric (e.g., a different focal salmon species). Our approach prioritized suburban areas (those with intermediate urbanization effects) for preservation and restoration action to benefit coho. We found that prioritization was most sensitive to the selected metric, rather than the level of uncertainty or critical threshold values. Our analyses highlight the importance of identifying metrics that are well-aligned with intended outcomes.

How do climate change experiments alter plot-scale climate?

To understand and forecast biological responses to climate change, scientists frequently use field experiments that alter temperature and precipitation. Climate manipulations can manifest in complex ways, however, challenging interpretations of biological responses. We reviewed publications to compile a database of daily plot-scale climate data from 15 active-warming experiments. We find that the common practices of analysing treatments as mean or categorical changes (e.g. warmed vs. unwarmed) masks important variation in treatment effects over space and time. Our synthesis showed that measured mean warming, in plots with the same target warming within a study, differed by up to 1.6  ∘ C (63% of target), on average, across six studies with blocked designs. Variation was high across sites and designs: for example, plots differed by 1.1  ∘ C (47% of target) on average, for infrared studies with feedback control (n = 3) vs. by 2.2  ∘ C (80% of target) on average for infrared with constant wattage designs (n = 2). Warming treatments produce non-temperature effects as well, such as soil drying. The combination of these direct and indirect effects is complex and can have important biological consequences. With a case study of plant phenology across five experiments in our database, we show how accounting for drier soils with warming tripled the estimated sensitivity of budburst to temperature. We provide recommendations for future analyses, experimental design, and data sharing to improve our mechanistic understanding from climate change experiments, and thus their utility to accurately forecast species' responses.

Phenological sequences: how early-season events define those that follow.

PREMISE OF THE STUDY: Plant phenology is a critical trait, as the timings of phenophases such as budburst, leafout, flowering, and fruiting, are important to plant fitness. Despite much study about when individual phenophases occur and how they may shift with climate change, little is known about how multiple phenophases relate to one another across an entire growing season. We test the extent to which early phenological stages constrain later ones, throughout a growing season, across 25 angiosperm tree species. METHODS: We observed phenology (budburst, leafout, flowering, fruiting, and senescence) of 118 individual trees across 25 species, from April through December 2015. KEY RESULTS: We found that early phenological events weakly constrain most later events, with the strongest constraints seen between consecutive stages. In contrast, interphase duration was a much stronger predictor of phenology, especially for reproductive events, suggesting that the development time of flowers and fruits may constrain the phenology of these events. CONCLUSIONS: Much of the variation in later phenological events can be explained by the timing of earlier events and by interphase durations. This highlights that a shift in one phenophase may often have cascading effects on later phases. Accurate forecasts of climate change impacts should therefore include multiple phenophases within and across years.

Climate determines upper, but not lower, altitudinal range limits of Pacific Northwest conifers.

Does climate determine species' ranges? Rapid rates of anthropogenic warming make this classic ecological question especially relevant. We ask whether climate controls range limits by quantifying relationships between climatic variables (precipitation, temperature) and tree growth across the altitudinal ranges of six Pacific Northwestern conifers on Mt. Rainier, Washington, USA. Results for three species (Abies amabilis, Callitropsis nootkatensis, Tsuga mertensiana) whose upper limits occur at treeline (> 1600 m) imply climatic controls on upper range limits, with low growth in cold and high snowpack years. Annual growth was synchronized among individuals at upper limits for these high-elevation species, further suggesting that stand-level effects such as climate constrain growth more strongly than local processes. By contrast, at lower limits climatic effects on growth were weak for these high-elevation species. Growth-climate relationships for three low-elevation species (Pseudotsuga menziesii, Thuja plicata, Tsuga heterophylla) were not consistent with expectations of climatic controls on upper limits, which are located within closed-canopy forest (< 1200 m). Annual growth of these species was poorly synchronized among individuals. Our results suggest that climate controls altitudinal range limits at treeline, while local drivers (perhaps biotic interactions) influence growth in closed-canopy forests. Climate-change-induced range shifts in closed-canopy forests will therefore be difficult to predict accurately.