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Abstract The number and diversity of phenological studies has increased rapidly in recent years. Innovative experiments, field studies, citizen science projects, and analyses of newly available historical data are contributing insights that advance our understanding of ecological and evolutionary responses to the environment, particularly climate change. However, many phenological data sets have peculiarities that are not immediately obvious and can lead to mistakes in analyses and interpretation of results. This paper aims to help researchers, especially those new to the field of phenology, understand challenges and practices that are crucial for effective studies. For example, researchers may fail to account for sampling biases in phenological data, struggle to choose or design a volunteer data collection strategy that adequately fits their project’s needs, or combine data sets in inappropriate ways. We describe ten best practices for designing studies of plant and animal phenology, evaluating data quality, and analyzing data. Practices include accounting for common biases in data, using effective citizen or community science methods, and employing appropriate data when investigating phenological mismatches. We present these best practices to help researchers entering the field take full advantage of the wealth of available data and approaches to advance our understanding of phenology and its implications for ecology. 

Abstract Background and Aims Fruiting remains under-represented in long-term phenology records, relative to leaf and flower phenology. Herbarium specimens and historical field notes can fill this gap, but selecting and synthesizing these records for modern-day comparison requires an understanding of whether different historical data sources contain similar information, and whether similar, but not equivalent, fruiting metrics are comparable with one another. Methods For 67 fleshy-fruited plant species, we compared observations of fruiting phenology made by Henry David Thoreau in Concord, Massachusetts (1850s), with phenology data gathered from herbarium specimens collected across New England (mid-1800s to 2000s). To identify whether fruiting times and the order of fruiting among species are similar between datasets, we compared dates of first, peak and last observed fruiting (recorded by Thoreau), and earliest, mean and latest specimen (collected from herbarium records), as well as fruiting durations. Key Results On average, earliest herbarium specimen dates were earlier than first fruiting dates observed by Thoreau; mean specimen dates were similar to Thoreau’s peak fruiting dates; latest specimen dates were later than Thoreau’s last fruiting dates; and durations of fruiting captured by herbarium specimens were longer than durations of fruiting observed by Thoreau. All metrics of fruiting phenology except duration were significantly, positively correlated within (r: 0.69–0.88) and between (r: 0.59–0.85) datasets. Conclusions Strong correlations in fruiting phenology between Thoreau’s observations and data from herbaria suggest that field and herbarium methods capture similar broad-scale phenological information, including relative fruiting times among plant species in New England. Differences in the timing of first, last and duration of fruiting suggest that historical datasets collected with different methods, scales and metrics may not be comparable when exact timing is important. Researchers should strongly consider matching methodology when selecting historical records of fruiting phenology for present-day comparisons. 

Plant phenology regulates the carbon cycle and land‐atmosphere coupling. Currently, climate models often disagree with observations on the seasonal cycle of vegetation growth, partially due to how spring onset is measured and simulated. Here we use both thermal and leaf area index (LAI) based indicators to characterize spring onset in CMIP6 models. Although the historical timing varies considerably across models, most agree that spring has advanced in recent decades and will continue to arrive earlier with future warming. Across the Northern Hemisphere for the periods 1950–2014, 1981–2014, and 2015–2099 in the historical and SSP5‐8.5 simulations, thermal‐based indicators estimate spring advances of −0.7 ± 0.2, −1.4 ± 0.4, and −2.4 ± 0.7 days/decade, while LAI‐based indicators estimate −0.4 ± 0.3, −0.1 ± 0.3, and −1±1.1 days/decade. Thereby, LAI‐based indicators exhibit weaker trends toward earlier onset, leading to uncertainties from different indices being as large or larger than model uncertainty. Reconciling these discrepancies is critical for understanding future changes in spring onset.

 

Botanical gardens make unique contributions to climate change research, conservation, and public engagement. They host unique resources, including diverse collections of plant species growing in natural conditions, historical records, and expert staff, and attract large numbers of visitors and volunteers. Networks of botanical gardens spanning biomes and continents can expand the value of these resources. Over the past decade, research at botanical gardens has advanced our understanding of climate change impacts on plant phenology, physiology, anatomy, and conservation. For example, researchers have utilized botanical garden networks to assess anatomical and functional traits associated with phenological responses to climate change. New methods have enhanced the pace and impact of this research, including phylogenetic and comparative methods, and online databases of herbarium specimens and photographs that allow studies to expand geographically, temporally, and taxonomically in scope. Botanical gardens have grown their community and citizen science programs, informing the public about climate change and monitoring plants more intensively than is possible with garden staff alone. Despite these advances, botanical gardens are still underutilized in climate change research. To address this, we review recent progress and describe promising future directions for research and public engagement at botanical gardens.

 

Functional traits mediate species' responses to, and roles within, their environment and are constrained by evolutionary history. While we have a strong understanding of trait evolution for macro‐taxa such as birds and mammals, our understanding of invertebrates is comparatively limited. Here, we address this gap in North American beetles with a sample of ground beetles (Carabidae), leveraging a large‐scale collection and digitization effort by the National Ecological Observatory Network (NEON). For 154 ground beetle species, we measured seven morphological traits, which we placed into a recently developed effect–response framework that characterizes traits by how they predict species' effects on their ecosystems or responses to environmental stressors. We then used cytochrome oxidase 1 sequences from the same specimens to generate a phylogeny and tested the evolutionary tempo and mode of the traits. We found strong phylogenetic signal in, and correlations among, ground beetle morphological traits. These results indicate that, for these species, beetle body shape trait evolution is constrained, and phylogenetic inertia is a stronger driver of beetle traits than (recent) environmental responses. Strong correlations among effect and response traits suggest that future environmental drivers are likely to affect both ecological composition and functioning in these beetles.

 

Ecological forecasting provides a powerful set of methods for predicting short‐ and long‐term change in living systems. Forecasts are now widely produced, enabling proactive management for many applied ecological problems. However, despite numerous calls for an increased emphasis on prediction in ecology, the potential for forecasting to accelerate ecological theory development remains underrealized.

Here, we provide a conceptual framework describing how ecological forecasts can energize and advance ecological theory. We emphasize the many opportunities for future progress in this area through increased forecast development, comparison and synthesis.

Our framework describes how a forecasting approach can shed new light on existing ecological theories while also allowing researchers to address novel questions. Through rigorous and repeated testing of hypotheses, forecasting can help to refine theories and understand their generality across systems. Meanwhile, synthesizing across forecasts allows for the development of novel theory about the relative predictability of ecological variables across forecast horizons and scales.

We envision a future where forecasting is integrated as part of the toolset used in fundamental ecology. By outlining the relevance of forecasting methods to ecological theory, we aim to decrease barriers to entry and broaden the community of researchers using forecasting for fundamental ecological insight.