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How Well Do the Spring Indices Predict Phenological Activity Across Plant Species?

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How Well Do the Spring Indices Predict Phenological Activity Across Plant Species? How well do the spring indices predict phenological activity across plant species? Item Type Article Authors Gerst, Katharine L; Crimmins, Theresa M; Posthumus, Erin E; Rosemartin, Alyssa H; Schwartz, Mark D Citation Gerst, K.L., Crimmins, T.M., Posthumus, E.E. et al. How well do the spring indices predict phenological activity across plant species?. Int J Biometeorol (2020). https://doi.org/10.1007/ s00484-020-01879-z DOI 10.1007/s00484-020-01879-z Publisher SPRINGER Journal INTERNATIONAL JOURNAL OF BIOMETEOROLOGY Rights © ISB 2020. Download date 02/10/2021 14:10:42 Item License http://rightsstatements.org/vocab/InC/1.0/ Version Final accepted manuscript Link to Item http://hdl.handle.net/10150/638368 1 How well do the Spring Indices predict phenological activity across plant species? 2 3 Katharine L. Gerst1,2,*, Theresa M. Crimmins1,2, Erin E. Posthumus1,2, Alyssa H. 4 Rosemartin1,2,and Mark D. Schwartz3 5 6 1USA National Phenology Network, National Coordinating Office, Tucson, AZ, USA 7 2School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA 8 3Department of Geography, University of Wisconsin-Milwaukee, Milwaukee, WI, USA 9 10 *email for correspondence: [email protected] 11 12 Katharine Gerst ORCID ID: https://orcid.org/0000-0002-6154-906X 13 Theresa Crimmins ORCID ID: https://orcid.org/0000-0001-9592-625X 14 Alyssa Rosemartin ORCID ID: https://orcid.org/0000-0002-8934-6539 15 Erin Posthumus ORCID ID: https://orcid.org/0000-0003-3855-2380 16 Mark Schwartz ORCID ID: https://orcid.org/0000-0002-6739-0416 17 18 19 Abstract 20 The Spring Indices, models that represent the onset of spring-season biological activity, were 21 developed using a long-term observational record from the mid-to-late 20th century of three 22 species of lilacs and honeysuckles contributed by volunteer observers across the nation. The 23 USA National Phenology Network (USA-NPN) produces and freely delivers maps of Spring 24 Index onset dates at fine spatial scale for the United States. These maps are used widely in 25 natural resource planning and management applications. The extent to which the models 26 represent activity in a broad suite of plant species is not well documented. In this study, we used 27 a rich record of observational plant phenology data (37,819 onset records) collected in recent 28 years (1981-2017) to evaluate how well gridded maps of the Spring Index models predict leaf 29 and flowering onset dates in a) 19 species of ecologically important, broadly distributed 30 deciduous trees and shrubs, and b) the lilac and honeysuckle species used to construct the 31 models. The extent to which the Spring Indices predicted vegetative and reproductive 32 phenology varied by species and with latitude, with stronger relationships revealed for shrubs 33 than trees and with the Bloom Index compared to the Leaf Index, and reduced concordance 34 between the Indices at higher latitudes. These results allow us to use the Indices as indicators 35 of when to expect activity across widely distributed species and can serve as a yardstick to 36 assess how future changes in the timing of spring will impact a broad array of trees and shrubs 37 across the United States. 38 39 40 41 42 Keywords: Spring Indices, plant phenology, citizen science, phenological model, deciduous 43 trees 44 45 46 Introduction 47 48 Phenology is a valuable metric for understanding how species and ecosystems respond to 49 climate change and environmental variability. Long-term datasets from around the globe have 50 shown that warmer spring temperatures result in earlier leaf out and flowering in many species 51 (Visser and Both 2005, Parmesan 2007, Cook et al. 2012). Such shifts have consequences for 52 ecosystem functioning and species interactions (Polgar et al. 2014, Wang et al. 2016, Renner 53 and Zohner 2018). The datasets used to document these patterns are often limited in spatial 54 extent and taxonomic representation and do not necessarily reflect how other species in their 55 communities respond (e.g. Heberling et al. 2019). Thus, while site-specific long-term datasets 56 have provided insight into how phenology is shifting among species, we are limited in extending 57 patterns and predictions of future change across spatial scales and ecosystems. 58 59 One way to extend our understanding of spatial and temporal phenological trends and patterns 60 is to use models. The use of models enables an estimate of the proxy variable to be calculated 61 at any location and point in time with available phenological driver data. The Spring Indices, 62 which indicate the onset of spring-season biological activity based on early-season 63 temperatures (Schwartz 2006, Schwartz et al. 2013) are such models. Continental-scale maps 64 of the Spring Indices have been generated using gridded daily temperature data products and 65 have been used to evaluate how the timing of spring in U.S. National Parks and U.S. Fish and 66 Wildlife Refuges has changed over the past 100 years (Monahan et al. 2016, Waller et al. 67 2018), and patterns of how the timing of spring has changed and is expected to advance with 68 climate change (Schwartz et al. 2006, Ault et al. 2013, Peterson and Abatzaglou 2014, Ault et 69 al. 2015, Labe et al. 2016). 70 71 The Spring Indices, derived from a long-term dataset of lilacs and honeysuckles, have been 72 applied widely. The Indices have been used to understand the impact of climate change on 73 seasonal patterns in the US Global Change Information System 74 (https://data.globalchange.gov/), the National Climate Assessment (USGCRP 2018), and the 75 EPA’s Climate Indicators Report (US Environmental Protection Agency 2016). However, how 76 well the Spring Indices represent – or can be used to predict – the activity of other plant species 77 has not been thoroughly evaluated. A small number of studies have investigated agreement 78 between the Spring Index models and phenological activity in crop species (Hu et al. 2005, 79 Wolfe et al. 2005), fruit trees (Schwartz et al. 1997, Ault et al. 2013) and native trees (Liang and 80 Schwartz 2014). A more comprehensive evaluation is needed, as the Indices are being widely 81 adopted in resource management and communication about the timing of spring (e.g. Monahan 82 et al. 2016, Waller et al. 2018). A clearer understanding of the performance of these models in 83 estimating the timing of leaf and bloom activity in other plant species can directly inform the 84 timing of management activities such as restoration actions, invasive species detection or 85 treatment, and planning for tourist visitation such as for wildflower viewing (Biederman 2014, 86 Enquist et al. 2014, Wallace et al. 2016). Further, these predictions could provide advance 87 warning of allergen outbreaks, support risk assessment of expected frosts, and improve 88 planning for tourism activities such as leaf peeping (Sakurai et al. 2011). 89 90 In this study, we investigate how well the Spring Indices predict leaf and bloom activity in other 91 plant species. Because spring season activity in many plant species in temperate environments 92 are cued by accumulated temperature (Schaber and Badeck 2003, Linkosalo et al. 2008, Basler 93 2016, Melaas et al. 2016), we hypothesize that the Spring Indices – which are primarily a 94 function of accumulated springtime warmth -- predict the onset of green-up and flowering in a 95 broad range of deciduous trees and shrubs across temperate regions of the United States. 96 The lilac and honeysuckle species on which the Spring Indices are based are some of the 97 earliest to put on leaves and flowers in the spring season; accordingly, we anticipate that the 98 Spring Indices will be more highly correlated with species that are active earlier in the season, 99 and that the Bloom Index will show greater performance for predicting the timing of activity in 100 species that are active later in the season, such as deciduous trees that leaf out after shrubs. 101 We predict a stronger correspondence at more southern latitudes where plants may have little 102 or no chilling requirement for spring activity (Liang and Schwartz 2014). 103 104 Gridded maps of indices such as the Spring Indices are subject to error resulting from the 105 methods used to generate the input datasets (Bishop and Beier 2013). Several factors – 106 including elevation, coastal effects, slope and aspect, riparian zones, land use, and land cover – 107 complicate temperature estimates across space (Daly 2006, Daly et al. 2008). Further, the 108 errors in gridded temperature products vary across time and space (Pielke et al. 2002, Daly 109 2006, Beier et al. 2012, Fernandez et al. 2013), and such errors impact ecological studies that 110 use these data (Baker et al. 2016). In this study, we also evaluate the level of agreement 111 between the timing of leaf out and bloom in the individual species predicted by a set of gridded 112 Spring Indices maps (lilacs and honeysuckles) and in-situ observations from across the country. 113 This evaluation provides greater insight into how well the modeled layers represent plant activity 114 on the ground and the suitability of these gridded layers for comparisons such as those 115 undertaken in this study. We investigate whether agreement between predictions and reports of 116 lilac and honeysuckle phenology varies by latitude as well as whether agreement varies across 117 years. 118 119 Materials and methods 120 121 Spring Indices 122 123 The Spring Index models are a suite of two models that represent the mean of 3 individual 124 species models to predict the “start of spring” – either timing of first leaf or first bloom -- at a 125 particular location (Schwartz 1997, Schwartz et al.
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