“Estimating abundance and phenology from transect count data with GLMs”

Estimating population abundance is central to population ecology. With increasing concern over declining insect populations, estimating trends in abundance has become even more urgent. At the same time, there is an emerging interest in quantifying phenological patterns, in part because phenological shifts are one of the most conspicuous signs of climate change. Existing techniques to fit activity curves (and thus both abundance and phenology) to repeated transect counts of insects (a common form of data for these taxa) frequently fail for sparse data, and often require advanced knowledge of statistical computing. These limitations prevent us from understanding both population trends and phenological shifts, especially in the at‐risk species for which this understanding is most vital. Here we present a method to fit repeated transect count data with Gaussian curves using linear models and show how robust abundance and phenological metrics can be obtained using standard regression tools. We then apply this method to nine years of Baltimore checkerspot data using generalized linear models (GLMs). This case study illustrates the ability of our method to fit even years with only a few non‐zero survey counts, and identifies a significant negative relationship between population size and growing degree days (GDD) each year. We believe our new method provides a key tool to unlock previously‐unusable data sets, and may provide a useful middle ground between ad hoc metrics of abundance and phenology, and custom‐coded mechanistic models.

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“…Data and code will be available on Dryad: <http://dx.doi. org/10.5061/dryad.rn8pk0p9h> (Edwards and Crone 2021).…”

Section: Author Contributionsmentioning

confidence: 99%

Estimating abundance and phenology from transect count data with GLMs

Edwards

Crone

2021

Oikos

Self Cite

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“…For univoltine species, we fit phenometrics for species–cell–year combinations that had at least five observations, four distinct days of collecting and three distinct collectors. Three days with observations have been demonstrated to produce useful phenoestimates of unimodal species using survey data (Edwards & Crone, 2021). However, multimodal species may have longer durations and more complex seasonal abundances, making their phenometrics harder to estimate (Belitz et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Phenological research based on natural history collections: Practical guidelines and a lepidopteran case study

et al. 2022

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Natural history collections (NHCs) have been indispensable to understanding longer‐term trends of the timing of seasonal events. Massive‐scale digitization of specimens promises to further enable phenological research, especially the ability to move towards a deeper understanding of drivers of change and how trait–environment interactions shape phenological sensitivity. Despite the promise of NHCs to answer fundamental phenology questions, the use of these data resources presents unique and often overlooked challenges requiring specialized workflow steps, such as assembling multisource data, accounting for date imprecision and making decisions about trade‐offs between data density and spatial resolution. We provide a set of key best practice recommendations and showcase these via a case study that utilizes NHC data to test hypotheses about spatiotemporal trends in adult Lepidoptera (i.e. butterflies and moths) flight timing across North America. Our case study is a worked example of these best practices, helping practitioners recognize and overcome potential pitfalls at each step, from data acquisition and cleaning, to delineating spatial units and proper estimation of phenological metrics and associated uncertainty, to building appropriate models. We confirm and extend the critical importance of voltinism and diapause strategy, but less‐so daily activity patterns, for predicting Lepidoptera phenology spatiotemporal trends. Our case study also showcases the unique power of NHC data to test existing hypotheses and generate new insights about temporal phenological trends. Specifically, migratory species and species that enter diapause as adults are advancing the start of flight periods in more recent years, even after accounting for climate context. These results highlight the physiological and adaptive differences between species with different overwintering strategies. We close by noting the value of partnerships between data scientists, museum experts and ecological modellers to fully harness the power of digital data resources to address pressing global change challenges. These partnerships can extend approaches for integrating multiple data types to fully unlock our understanding of the tempo, mode, drivers and outcomes of phenological changes at greater spatial, temporal and taxonomic scales. Read the free Plain Language Summary for this article on the Journal blog.

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“…We set minimum thresholds separately for univoltine (5 observations, 4 distinct days of collecting, and 3 distinct collectors) and multivoltine (10 total observations, 8 distinct collecting days, and 3 unique collectors) species. These differences reflect the challenges with longer (multivoltine) versus shorter (univoltine) flight periods, based on simulations from and empirical work (Edwards & Crone, 2021).…”

Section: Methodsmentioning

confidence: 99%

Weather anomalies more important than climate means in driving insect phenology

Guralnick

CAMPBELL

Belitz³

2022

Preprint

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Studies of long-term trends in phenology often rely on climatic averages, overlooking climate variability. Here we test the hypothesis that unusual weather conditions are particularly critical in driving adult insect phenology. First, we generated phenological estimates for Lepidoptera (moths and butterflies) across the Eastern USA, and over a 70 year period, using natural history collections data. Next, we assembled a set of key predictors, including the number of unusually warm and cold days prior to, and during, adult flight period. We then used phylogenetically informed linear mixed effects models to evaluate effects of unusual weather events, climate context, species traits, and their interactions on the flight onset, offset and duration. We found increasing numbers of both warm and cold days was the strongest climate driver in our models, dramatically increasing flight duration. This strong effect is driven by differential onset and termination dynamics. For flight onset, impact of unusual climate conditions is dependent on climatic context, but for flight cessation, more unusually cold days always lead to later termination. These results show that understanding phenological responses under global change must account for unusual or anomalous weather events, especially given these events are predicted to increase in frequency and severity.

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“Estimating abundance and phenology from transect count data with GLMs”

Cited by 8 publications

References 86 publications

Estimating abundance and phenology from transect count data with GLMs

Estimating abundance and phenology from transect count data with GLMs

Phenological research based on natural history collections: Practical guidelines and a lepidopteran case study

Weather anomalies more important than climate means in driving insect phenology

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