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Running Head Anatomy of a Phenological Mismatch 1 Title The bioRxiv preprint doi: https://doi.org/10.1101/2020.12.22.423968; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. 1 Running Head Anatomy of a phenological mismatch 2 Title The anatomy of a phenological mismatch: interacting consumer demand and resource 3 characteristics determine the consequences of mismatching 4 Authors Luke R. Wilde1, Josiah E. Simmons2, Rose J. Swift3, Nathan R. Senner1 5 Affiliations 6 1 Department of Biological Sciences, University of South Carolina, 715 Sumter St., Columbia, 7 SC 29208 8 2 Division of Biological Sciences, University of Montana, Missoula, Montana 9 3 U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th Street SE 10 Jamestown, ND 58401 11 12 13 14 15 16 17 18 19 20 21 22 23 These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data. bioRxiv preprint doi: https://doi.org/10.1101/2020.12.22.423968; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. 24 Abstract 25 Climate change has caused shifts in seasonally recurring biological events and the temporal 26 decoupling of consumer-resource pairs – i.e., phenological mismatching. Despite the 27 hypothetical risk mismatching poses to consumers, they do not invariably lead to individual- or 28 population-level effects. This may stem from how mismatches are typically defined, e.g., an 29 individual or population is ‘matched’ or ‘mismatched’ based on the degree of asynchrony with a 30 resource pulse. However, because both resource availability and consumer demands change over 31 time, this categorical definition can obscure within- or among-individual fitness effects. We 32 therefore developed models to identify the effects of resource characteristics on individual- and 33 population-level processes and determine how the strength of these effects change throughout a 34 consumer’s life. We then measured the effects of resource characteristics on the growth, daily 35 survival, and fledging rates of Hudsonian godwit (Limosa haemastica) chicks hatched near 36 Beluga River, Alaska. At the individual-level, chick growth and survival improved following 37 periods of higher invertebrate abundance but were increasingly dependent on the availability of 38 larger prey as chicks aged. At the population level, seasonal fledging rates were best explained 39 by a model including age-structured consumer demand. Our study suggests that modelling the 40 effects of mismatching as a disrupted interaction between consumers and their resources 41 provides a biological mechanism for how mismatching occurs and clarifies when it matters to 42 individuals and populations. Given the variable responses to mismatching across consumer 43 populations, such tools for predicting how populations may respond under future climatic 44 conditions will be invaluable. 45 46 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.22.423968; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. 47 Keywords 48 mismatch; climate change; Bayesian hierarchical model; ontogeny; resource availability bioRxiv preprint doi: https://doi.org/10.1101/2020.12.22.423968; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. 49 Introduction 50 Shifts in the timing of recurring biological events (i.e., phenology) are among the best 51 documented effects of climate change (Parmesan & Yohe, 2003). Higher spring temperatures 52 have led to earlier peaks in seasonal resources (Thackeray et al., 2016), but slower rates of 53 phenological advance at upper trophic levels mean that future climate conditions will likely lead 54 to a greater decoupling of consumer-resource pairs – i.e., ‘mismatching’ – and heightened 55 extinction risk for consumer populations (Both & Visser, 2001; Both et al., 2009). However, 56 despite the theoretical risks imposed by climate-induced mismatching, mismatches do not 57 invariably lead to reduced individual fitness (Dunn et al., 2011; Corkery et al., 2019) or negative 58 demographic effects for populations (Visser et al., 2012; Reed et al., 2013; Keogan et al., 2020). 59 Recent studies have proposed improved methodologies for studying mismatches (Visser & 60 Gienapp, 2019; Kharouba & Wolkovich, 2020), but overcoming the empirical-theoretical 61 disconnect in phenological studies may first require an improved mechanistic framework to help 62 elucidate how mismatching occurs (Takimoto & Sato, 2020). 63 The match-mismatch hypothesis presents mismatching as the disrupted interaction 64 between consumer demands and resource availability (Cushing, 1990). Most empirical studies 65 categorize individuals or populations as ‘matched’ or ‘mismatched’ depending on the synchrony 66 between the timing of a single life-history event and resource availability (Cushing, 1974; Visser 67 et al., 1998). Contrary to this categorization, however, both resource availability and consumer 68 demands vary over time, and being ‘matched’ does not guarantee that consumers have sufficient 69 food (Saalfeld et al., 2019; Keogan et al., 2020). Rather, changes to continuous resource 70 characteristics like quantity (i.e., biomass) and quality (i.e., per-capita size) directly affect 71 consumer fitness, but the effects of these factors are rarely measured in studies of mismatching. bioRxiv preprint doi: https://doi.org/10.1101/2020.12.22.423968; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. 72 Moreover, energetic demand changes throughout an individual’s life (Yang & Rudolf, 2010), 73 meaning that an individual’s sensitivity to resource availability is not constant (Dunn et al., 74 2011). Viewing mismatching simply as asynchrony in time, instead of as the disrupted 75 interaction between consumer demand and resource availability, can obscure the cumulative 76 effects of mismatching and mask population-level consequences (Yang & Rudolf, 2010; Kerby 77 et al., 2012). Although many conceptual models have been proposed to address this potential 78 issue, a more robust methodology to model mismatching in relation to the interaction of 79 consumer demands and resources is still lacking (Chmura et al., 2019; Visser & Gienapp, 2019). 80 Incorporating both age-structured consumer demand and multiple facets of resource 81 availability into mismatch models likely requires a re-examination of our statistical concept of 82 mismatching (Visser & Both, 2005; Kellermann & van Riper, 2015). Phenologies are generally 83 modelled as frequency curves on a temporal axis (Fig. 1; Cushing, 1974; Visser et al., 1998), 84 whereby match is estimated as the difference in peak dates (i.e., date models) or proportion of 85 overlapping area (i.e., overlap models). Both date and overlap models have been criticized in the 86 literature, however (Lindén, 2018; Ramakers et al., 2020). Furthermore, while date and overlap 87 models agree if consumer and resource curves are symmetrical (Fig. 1a,b), date models can be 88 biased when phenologies are skewed or multimodal, or in cases of low resource availability (Fig. 89 1c,d,e). Because overlap models account for the full interaction of consumer demand and 90 resource availability posed in the match-mismatch hypothesis (Kerby et al., 2012), they may be 91 better able to capture the mechanism of mismatching. Even so, overlap models have received 92 mixed support in empirical tests (Ramakers et al., 2020). 93 94 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.22.423968; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. 95 96 Figure 1. Peak dates (vertical lines) and frequency curves (phenologies) of consumers (solid) and 97 resources (dashed). Difference in peak dates and peak overlap (shaded area; percent area under 98 the curve) models are approximately equivalent when both the consumer (solid) and resource 99 (dashed) curves are symmetrical (a, b). In this case, mismatching is a function of temporal 100 displacement. However, date and overlap model estimates differ when either curve is skewed (c), 101 the consumer phenology is multimodal (d), or the curves are aligned but have low overlapping 102 area due to reduced resource abundance (e). bioRxiv preprint doi: https://doi.org/10.1101/2020.12.22.423968; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. This article is a US Government work.
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