Conceptual and practical issues limit the utility of statistical estimators of phenological events

Abstract Widespread shifts in phenological events in response to climate change have inspired phenological monitoring programs and new methods for analyzing sparse phenological data. For example, the Weibull distribution is increasingly used to estimate the dates of hard‐to‐observe phenological even...

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Autores principales:	Amy M. Iler, Parris T. Humphrey, Jane E. Ogilvie, Paul J. CaraDonna
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	climate change flowering phenology habitat heterogeneity phenology sampling Weibull distribution Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/fcbd5b2c24b04f39b0f7f193c2c0a2ac
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spelling	oai:doaj.org-article:fcbd5b2c24b04f39b0f7f193c2c0a2ac2021-11-29T07:06:42ZConceptual and practical issues limit the utility of statistical estimators of phenological events2150-892510.1002/ecs2.3828https://doaj.org/article/fcbd5b2c24b04f39b0f7f193c2c0a2ac2021-11-01T00:00:00Zhttps://doi.org/10.1002/ecs2.3828https://doaj.org/toc/2150-8925Abstract Widespread shifts in phenological events in response to climate change have inspired phenological monitoring programs and new methods for analyzing sparse phenological data. For example, the Weibull distribution is increasingly used to estimate the dates of hard‐to‐observe phenological events, such as first and last flowering dates, in sparsely or unsystematically sampled data sets. In contrast, recent application of the Weibull estimator to an intensely and systematically sampled flowering phenology data set unexpectedly found different results than a previous analysis of the observed dates; at issue is whether different aspects of phenological curves shift uniformly or disparately. We used this case study to (1) raise conceptual and technical issues around when and how to infer phenological events using Weibull (or other) estimates, and (2) re‐analyze the data set in question with these considerations in mind. Our re‐analysis using the Weibull estimator shows that first, peak, and last flowering dates shift disparately through time, supporting the original analysis of the observed dates. We show that off‐the‐shelf usage of statistical estimators to generalize about an unsampled population may be inappropriate without considering how well sampled the focal study population is and how biological features such as habitat heterogeneity influence the natural scope of the unsampled population.Amy M. IlerParris T. HumphreyJane E. OgilviePaul J. CaraDonnaWileyarticleclimate changeflowering phenologyhabitat heterogeneityphenologysamplingWeibull distributionEcologyQH540-549.5ENEcosphere, Vol 12, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	climate change flowering phenology habitat heterogeneity phenology sampling Weibull distribution Ecology QH540-549.5
spellingShingle	climate change flowering phenology habitat heterogeneity phenology sampling Weibull distribution Ecology QH540-549.5 Amy M. Iler Parris T. Humphrey Jane E. Ogilvie Paul J. CaraDonna Conceptual and practical issues limit the utility of statistical estimators of phenological events
description	Abstract Widespread shifts in phenological events in response to climate change have inspired phenological monitoring programs and new methods for analyzing sparse phenological data. For example, the Weibull distribution is increasingly used to estimate the dates of hard‐to‐observe phenological events, such as first and last flowering dates, in sparsely or unsystematically sampled data sets. In contrast, recent application of the Weibull estimator to an intensely and systematically sampled flowering phenology data set unexpectedly found different results than a previous analysis of the observed dates; at issue is whether different aspects of phenological curves shift uniformly or disparately. We used this case study to (1) raise conceptual and technical issues around when and how to infer phenological events using Weibull (or other) estimates, and (2) re‐analyze the data set in question with these considerations in mind. Our re‐analysis using the Weibull estimator shows that first, peak, and last flowering dates shift disparately through time, supporting the original analysis of the observed dates. We show that off‐the‐shelf usage of statistical estimators to generalize about an unsampled population may be inappropriate without considering how well sampled the focal study population is and how biological features such as habitat heterogeneity influence the natural scope of the unsampled population.
format	article
author	Amy M. Iler Parris T. Humphrey Jane E. Ogilvie Paul J. CaraDonna
author_facet	Amy M. Iler Parris T. Humphrey Jane E. Ogilvie Paul J. CaraDonna
author_sort	Amy M. Iler
title	Conceptual and practical issues limit the utility of statistical estimators of phenological events
title_short	Conceptual and practical issues limit the utility of statistical estimators of phenological events
title_full	Conceptual and practical issues limit the utility of statistical estimators of phenological events
title_fullStr	Conceptual and practical issues limit the utility of statistical estimators of phenological events
title_full_unstemmed	Conceptual and practical issues limit the utility of statistical estimators of phenological events
title_sort	conceptual and practical issues limit the utility of statistical estimators of phenological events
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/fcbd5b2c24b04f39b0f7f193c2c0a2ac
work_keys_str_mv	AT amymiler conceptualandpracticalissueslimittheutilityofstatisticalestimatorsofphenologicalevents AT parristhumphrey conceptualandpracticalissueslimittheutilityofstatisticalestimatorsofphenologicalevents AT janeeogilvie conceptualandpracticalissueslimittheutilityofstatisticalestimatorsofphenologicalevents AT pauljcaradonna conceptualandpracticalissueslimittheutilityofstatisticalestimatorsofphenologicalevents
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Conceptual and practical issues limit the utility of statistical estimators of phenological events

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