Early onset of spring increases the phenological mismatch between plants and pollinators
TL;DR: The mechanism of phenological mismatch and its ecological impact on plant-pollinator interactions based on long-term monitoring demonstrates the mechanism of mismatch can decrease seed production and may affect the population dynamics of spring ephemerals.
Abstract: Climate warming accelerates the timing of flowering and insect pollinator emergence, especially in spring. If these phenological shifts progress independently between species, features of plant-pollinator mutualisms may be modified. However, evidence of phenological mismatch in pollination systems is limited. We investigated the phenologies of a spring ephemeral, Corydalis ambigua, and its pollinators (bumble bees), and seed-set success over 10-14 years in three populations. Although both flowering onset and first detection of overwintered queen bees in the C. ambigua populations were closely related to snowmelt time and/or spring temperature, flowering tended to be ahead of first pollinator detection when spring came early, resulting in lower seed production owing to low pollination service. Relationships between flowering onset time, phenological mismatch, and seed-set success strongly suggest that phenological mismatch is a major limiting factor for reproduction of spring ephemerals. This report demonstrates the mechanism of phenological mismatch and its ecological impact on plant-pollinator interactions based on long-term monitoring. Frequent occurrence of mismatch can decrease seed production and may affect the population dynamics of spring ephemerals.
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TL;DR: It is suggested that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process-based phenology modeling, and on the scaling of phenology from species to landscape-level.
Abstract: Plant phenology, the annually recurring sequence of plant developmental stages, is important for plant functioning and ecosystem services and their biophysical and biogeochemical feedbacks to the climate system. Plant phenology depends on temperature, and the current rapid climate change has revived interest in understanding and modeling the responses of plant phenology to the warming trend and the consequences thereof for ecosystems. Here, we review recent progresses in plant phenology and its interactions with climate change. Focusing on the start (leaf unfolding) and end (leaf coloring) of plant growing seasons, we show that the recent rapid expansion in ground- and remote sensing- based phenology data acquisition has been highly beneficial and has supported major advances in plant phenology research. Studies using multiple data sources and methods generally agree on the trends of advanced leaf unfolding and delayed leaf coloring due to climate change, yet these trends appear to have decelerated or even reversed in recent years. Our understanding of the mechanisms underlying the plant phenology responses to climate warming is still limited. The interactions between multiple drivers complicate the modeling and prediction of plant phenology changes. Furthermore, changes in plant phenology have important implications for ecosystem carbon cycles and ecosystem feedbacks to climate, yet the quantification of such impacts remains challenging. We suggest that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process-based phenology modeling, and on the scaling of phenology from species to landscape-level.
750 citations
Cites background from "Early onset of spring increases the..."
...…phe‐ nological responses to warming between plants and animals may result in bird species not breeding at the time of maximal food supply (Matthysen, Adriaensen, & Dhondt, 2011; Merila, Kruuk, & Sheldon, 2001) or cause the reduction in seed production for some tree species (Kudo & Ida, 2013)....
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TL;DR: Some of the existing understanding of autumn phenology is synthesized and five areas ripe for future climate change research are identified, to address common pitfalls in autumnal research and to support the conservation and management of vulnerable ecosystems and taxa.
Abstract: Autumn remains a relatively neglected season in climate change research in temperate and arctic ecosystems. This neglect occurs despite the importance of autumn events, including leaf senescence, fruit ripening, bird and insect migration, and induction of hibernation and diapause. Changes in autumn phenology alter the reproductive capacity of individuals, exacerbate invasions, allow pathogen amplification and higher disease-transmission rates, reshuffle natural enemy‐prey dynamics, shift the ecological dynamics among interacting species, and affect the net productivity of ecosystems. We synthesize some of our existing understanding of autumn phenology and identify five areas ripe for future climate change research. We provide recommendations to address common pitfalls in autumnal research as well as to support the conservation and management of vulnerable ecosystems and taxa.
362 citations
TL;DR: This work reviews whether this continuously ongoing phenomenon, also known as trophic asynchrony, is becoming more common under ongoing rapid climate change, and investigates limited evidence of phenological mismatch in mutualistic interactions.
Abstract: Phenological mismatch results when interacting species change the timing of regularly repeated phases in their life cycles at different rates. We review whether this continuously ongoing phenomenon...
314 citations
TL;DR: In this paper, the authors present the first explicit appraisal of how phenology can make a key contribution to contemporary conservation biology, focusing on shifts in plant phenology induced by global change, their impacts on species diversity and plant-animal interactions in the tropics, and how conservation efforts could be enhanced in relation to plant resource organization.
242 citations
TL;DR: It is emphasized how an appreciation of the evolutionary processes shaping insect life histories is necessary to forecast changes in insect phenology and their demographic consequences.
Abstract: Insect phenologies are changing in response to climate warming. Shifts toward earlier seasonal activity are widespread; however, responses of insect phenology to warming are often more complex. Many species have prolonged their activity periods; others have shown delays. Furthermore, because of interspecific differences in temperature sensitivity, warming can increase or decrease synchronization between insects and their food plants and natural enemies. Here, I review recent findings in three areas — shifts in phenology, changes in voltinism, and altered species interactions — and highlight counterintuitive responses to warming caused by the particularities of insect life cycles. Throughout, I emphasize how an appreciation of the evolutionary processes shaping insect life histories is necessary to forecast changes in insect phenology and their demographic consequences.
217 citations
Cites background from "Early onset of spring increases the..."
...) pollinators seem to reduce reproductive output by the plant in early springs [55]....
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References
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Journal Article•
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
272,030 citations
"Early onset of spring increases the..." refers background in this paper
...Although long-term studies on phenological interactions between plants and pollinators are very limited, there is a perspective that the phenological mismatch in the pollination process may be uncommon in natural ecosystems (e.g., Rafferty and Ives 2011)....
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TL;DR: This is the rst book on generalized linear models written by authors not mostly associated with the biological sciences, and it is thoroughly enjoyable to read.
Abstract: This is the rst book on generalized linear models written by authors not mostly associated with the biological sciences. Subtitled “With Applications in Engineering and the Sciences,” this book’s authors all specialize primarily in engineering statistics. The rst author has produced several recent editions of Walpole, Myers, and Myers (1998), the last reported by Ziegel (1999). The second author has had several editions of Montgomery and Runger (1999), recently reported by Ziegel (2002). All of the authors are renowned experts in modeling. The rst two authors collaborated on a seminal volume in applied modeling (Myers and Montgomery 2002), which had its recent revised edition reported by Ziegel (2002). The last two authors collaborated on the most recent edition of a book on regression analysis (Montgomery, Peck, and Vining (2001), reported by Gray (2002), and the rst author has had multiple editions of his own regression analysis book (Myers 1990), the latest of which was reported by Ziegel (1991). A comparable book with similar objectives and a more speci c focus on logistic regression, Hosmer and Lemeshow (2000), reported by Conklin (2002), presumed a background in regression analysis and began with generalized linear models. The Preface here (p. xi) indicates an identical requirement but nonetheless begins with 100 pages of material on linear and nonlinear regression. Most of this will probably be a review for the readers of the book. Chapter 2, “Linear Regression Model,” begins with 50 pages of familiar material on estimation, inference, and diagnostic checking for multiple regression. The approach is very traditional, including the use of formal hypothesis tests. In industrial settings, use of p values as part of a risk-weighted decision is generally more appropriate. The pedagologic approach includes formulas and demonstrations for computations, although computing by Minitab is eventually illustrated. Less-familiar material on maximum likelihood estimation, scaled residuals, and weighted least squares provides more speci c background for subsequent estimation methods for generalized linear models. This review is not meant to be disparaging. The authors have packed a wealth of useful nuggets for any practitioner in this chapter. It is thoroughly enjoyable to read. Chapter 3, “Nonlinear Regression Models,” is arguably less of a review, because regression analysis courses often give short shrift to nonlinear models. The chapter begins with a great example on the pitfalls of linearizing a nonlinear model for parameter estimation. It continues with the effective balancing of explicit statements concerning the theoretical basis for computations versus the application and demonstration of their use. The details of maximum likelihood estimation are again provided, and weighted and generalized regression estimation are discussed. Chapter 4 is titled “Logistic and Poisson Regression Models.” Logistic regression provides the basic model for generalized linear models. The prior development for weighted regression is used to motivate maximum likelihood estimation for the parameters in the logistic model. The algebraic details are provided. As in the development for linear models, some of the details are pushed into an appendix. In addition to connecting to the foregoing material on regression on several occasions, the authors link their development forward to their following chapter on the entire family of generalized linear models. They discuss score functions, the variance-covariance matrix, Wald inference, likelihood inference, deviance, and overdispersion. Careful explanations are given for the values provided in standard computer software, here PROC LOGISTIC in SAS. The value in having the book begin with familiar regression concepts is clearly realized when the analogies are drawn between overdispersion and nonhomogenous variance, or analysis of deviance and analysis of variance. The authors rely on the similarity of Poisson regression methods to logistic regression methods and mostly present illustrations for Poisson regression. These use PROC GENMOD in SAS. The book does not give any of the SAS code that produces the results. Two of the examples illustrate designed experiments and modeling. They include discussion of subset selection and adjustment for overdispersion. The mathematic level of the presentation is elevated in Chapter 5, “The Family of Generalized Linear Models.” First, the authors unify the two preceding chapters under the exponential distribution. The material on the formal structure for generalized linear models (GLMs), likelihood equations, quasilikelihood, the gamma distribution family, and power functions as links is some of the most advanced material in the book. Most of the computational details are relegated to appendixes. A discussion of residuals returns one to a more practical perspective, and two long examples on gamma distribution applications provide excellent guidance on how to put this material into practice. One example is a contrast to the use of linear regression with a log transformation of the response, and the other is a comparison to the use of a different link function in the previous chapter. Chapter 6 considers generalized estimating equations (GEEs) for longitudinal and analogous studies. The rst half of the chapter presents the methodology, and the second half demonstrates its application through ve different examples. The basis for the general situation is rst established using the case with a normal distribution for the response and an identity link. The importance of the correlation structure is explained, the iterative estimation procedure is shown, and estimation for the scale parameters and the standard errors of the coef cients is discussed. The procedures are then generalized for the exponential family of distributions and quasi-likelihood estimation. Two of the examples are standard repeated-measures illustrations from biostatistical applications, but the last three illustrations are all interesting reworkings of industrial applications. The GEE computations in PROC GENMOD are applied to account for correlations that occur with multiple measurements on the subjects or restrictions to randomizations. The examples show that accounting for correlation structure can result in different conclusions. Chapter 7, “Further Advances and Applications in GLM,” discusses several additional topics. These are experimental designs for GLMs, asymptotic results, analysis of screening experiments, data transformation, modeling for both a process mean and variance, and generalized additive models. The material on experimental designs is more discursive than prescriptive and as a result is also somewhat theoretical. Similar comments apply for the discussion on the quality of the asymptotic results, which wallows a little too much in reports on various simulation studies. The examples on screening and data transformations experiments are again reworkings of analyses of familiar industrial examples and another obvious motivation for the enthusiasm that the authors have developed for using the GLM toolkit. One can hope that subsequent editions will similarly contain new examples that will have caused the authors to expand the material on generalized additive models and other topics in this chapter. Designating myself to review a book that I know I will love to read is one of the rewards of being editor. I read both of the editions of McCullagh and Nelder (1989), which was reviewed by Schuenemeyer (1992). That book was not fun to read. The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities.
10,520 citations
"Early onset of spring increases the..." refers methods in this paper
...Overdispersion was corrected by overdispersion parameter with deviance according to McCullagh and Nelder (1989)....
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TL;DR: A consistent temperature-related shift is revealed in species ranging from molluscs to mammals and from grasses to trees, suggesting that a significant impact of global warming is already discernible in animal and plant populations.
Abstract: Over the past 100 years, the global average temperature has increased by approximately 0.6 °C and is projected to continue to rise at a rapid rate1. Although species have responded to climatic changes throughout their evolutionary history2, a primary concern for wild species and their ecosystems is this rapid rate of change3. We gathered information on species and global warming from 143 studies for our meta-analyses. These analyses reveal a consistent temperature-related shift, or ‘fingerprint’, in species ranging from molluscs to mammals and from grasses to trees. Indeed, more than 80% of the species that show changes are shifting in the direction expected on the basis of known physiological constraints of species. Consequently, the balance of evidence from these studies strongly suggests that a significant impact of global warming is already discernible in animal and plant populations. The synergism of rapid temperature rise and other stresses, in particular habitat destruction, could easily disrupt the connectedness among species and lead to a reformulation of species communities, reflecting differential changes in species, and to numerous extirpations and possibly extinctions.
4,532 citations
"Early onset of spring increases the..." refers background in this paper
...Phenological shifts caused by climate change have been reported from many natural ecosystems during recent decades especially in high-latitude and highaltitude regions and in spring (Menzel and Fabian 1999, Fitter and Fitter 2002, Root et al. 2003, Gordo and Sanz 2005)....
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TL;DR: A scaled Wald statistic is presented, together with an F approximation to its sampling distribution, that is shown to perform well in a range of small sample settings and has the advantage that it reproduces both the statistics and F distributions in those settings where the latter is exact.
Abstract: Restricted maximum likelihood (REML) is now well established as a method for estimating the parameters of the general Gaussian linear model with a structured covariance matrix, in particular for mixed linear models. Conventionally, estimates of precision and inference for fixed effects are based on their asymptotic distribution, which is known to be inadequate for some small-sample problems. In this paper, we present a scaled Wald statistic, together with an F approximation to its sampling distribution, that is shown to perform well in a range of small sample settings. The statistic uses an adjusted estimator of the covariance matrix that has reduced small sample bias. This approach has the advantage that it reproduces both the statistics and F distributions in those settings where the latter is exact, namely for Hotelling T2 type statistics and for analysis of variance F-ratios. The performance of the modified statistics is assessed through simulation studies of four different REML analyses and the methods are illustrated using three examples.
3,862 citations
Book•
01 Jan 1992
1,741 citations
"Early onset of spring increases the..." refers background in this paper
...…relationships of dependent variables to specific categorical independent variables with least-squares means, which represented average responses as though the measured effects of other independent variables considered in an analysis (e.g., covariates) were held constant (Milliken and Johnson 1984)....
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