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Abstract Species with different life histories and communities that vary in their seasonal constraints tend to shift their phenology (seasonal timing) differentially in response to climate warming.We investigate how these variable phenological shifts aggregate to influence phenological overlap within communities. Phenological advancements of later season species and extended durations of early season species may increase phenological overlap, with implications for species' interactions such as resource competition.We leverage extensive historic (1958–1960) and recent (2006–2015) weekly survey data for communities of grasshoppers along a montane elevation gradient to assess the impact of climate on shifts in the phenology and abundance distributions of species. We then examine how these responses are influenced by the seasonal timing of species and elevation, and how in aggregate they influence degrees of phenological overlap within communities.In warmer years, abundance distributions shift earlier in the season and become broader. Total abundance responds variably among species and we do not detect a significant response across species. Shifts in abundance distributions are not strongly shaped by species' seasonal timing or sites of variable elevations. The area of phenological overlap increases in warmer years due to shifts in the relative seasonal timing of compared species. Species that overwinter as nymphs increasingly overlap with later season species that advance their phenology. The days of phenological overlap also increase in warm years but the response varies across sites of variable elevation. Our phenological overlap metric based on comparing single events—the dates of peak abundance—does not shift significantly with warming.Phenological shifts are more complex than shifts in single dates such as first occurrence. As abundance distributions shift earlier and become broader in warm years, phenological overlap increases. Our analysis suggests that overall grasshopper abundance is relatively robust to climate and associated phenological shifts but we find that increased overlap can decrease abundance, potentially by strengthening species interactions such as resource competition.
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The accuracy of phenology estimators for use with sparsely sampled presence‐only observations
Abstract Phenology is one of the most immediate responses to global climate change, but data limitations have made examining phenology patterns across greater taxonomic, spatial and temporal scales challenging. One significant opportunity is leveraging rapidly increasing data resources from digitized museum specimens and community science platforms, but this assumes reliable statistical methods are available to estimate phenology using presence‐only data. Estimating the onset or offset of key events is especially difficult with incidental data, as lower data densities occur towards the tails of an abundance distribution.The Weibull distribution has been recognized as an appropriate distribution to estimate phenology based on presence‐only data, but Weibull‐informed estimators are only available for onset and offset. We describe the mathematical framework for a new Weibull‐parameterized estimator of phenology appropriate for any percentile of a distribution and make it available in anrpackage,phenesse. We use simulations and empirical data on open flower timing and first arrival of monarch butterflies to quantify the accuracy of our estimator and other commonly used phenological estimators for 10 phenological metrics: onset, mean and offset dates, as well as the 1st, 5th, 10th, 50th, 90th, 95th and 99th percentile dates. Root mean squared errors and mean bias of the phenological estimators were calculated for different patterns of abundance and observation processes.Results show a general pattern of decay in performance of estimates when moving from mean estimates towards the tails of the seasonal abundance curve, suggesting that onset and offset continue to be the most difficult phenometrics to estimate. However, with simple phenologies and enough observations, our newly developed estimator can provide useful onset and offset estimates. This is especially true for the start of the season, when incidental observations may be more common.Our simulation demonstrates the potential of generating accurate phenological estimates from presence‐only data and guides the best use of estimators. The estimator that we developed, phenesse, is the least biased and has the lowest estimation error for onset estimates under most simulated and empirical conditions examined, improving the robustness of these estimates for phenological research.
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- PAR ID:
- 10456801
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 11
- Issue:
- 10
- ISSN:
- 2041-210X
- Format(s):
- Medium: X Size: p. 1273-1285
- Size(s):
- p. 1273-1285
- Sponsoring Org:
- National Science Foundation
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