Phenology: data, research, synthesis Organizers: Geoff Henebry, South Dakota State University

1. Introduction & aims Phenology data across the Network Analysis of Konza Prairie Phenology Data Networking, Collaboration, Directions & Opportunities Phenology: data, research, synthesis Organizers: Geoff Henebry, South Dakota State University Doug Goodin, Kansas State University

2. Question: What is Spring?— Growth in everything— Flesh and fleece, fur and feather, Grass and greenworld all together From The May Magnificat By Gerard Manley Hopkins

3. Introduction 1 Phenology can be broadly construed as the study of the pace and timing of biotic responses to seasonal forcings. Diverse data on various aspects of phenology exist across the LTER and ILTER networks. Only a few of these datasets have been collected explicitly as phenological data.

4. Introduction 2 However, there is phenological information latent in many more LTER datasets waiting to be revealed! Phenological phenomena of interest range from the organism and population to the landscape and beyond. With the advent of a National Phenology Network, the LTER network can bring some significant baseline information to the table to advance our understanding of phenological dynamics across a changing planet. After this intro talk, we will discuss on how to marshal the considerable human and data resources of the LTER/ILTER networks to bring phenological research to a point ready for cross-site synthesis.

5. Occurrences of “phenology” in LTER Data Catalog 11 sites with some kind of available “phenological data” site#topics AND 1moth phenology ARC 5SAR backscatter; phenologies of sedges, evergreen, & deciduous plants HBR 1general vegetation phenology HFR 1phenology of woody species; soil warming & phenology JRN 16mesquite phenology study; perennial plant phenology on NPP sites KBS 1baseline spatial variability study KNZ 3plant phenology; bird phenology LUQ 2phenologies of Tabonuco woody species; fern sporophyte growth NWT 6individual plant phenology in nodal plots; N & P fertilization; warming SEV 2plant phenology transect study; core site phenology study SGS 4long term phenology study; phenologies of grasses, forbs, shrubs That’s all?

6. Occurrences of “phenology” on www.lternet.edu • AND: phenology at reference sites • HFR: schoolyard LTER & phenological observations • NTL: “ice phenology” & 1993 regional project • xsite: Climate Initiative Report 2003 • ILTER: Korea

7. Occurrences of “phenology” at prior ASMs • 1993 ASM: 3 abstracts mentioning “phenolo*” • ARC: SAR backscattering & phenology • NWT: effects of N & P fertilization on phenology • VCR: phenology of the rhizosphere • 2003 ASM: 8 abstracts mentioning “phenolo*” • ARC: cultural education • CCR: schoolyard program • KBS: soybean aphids • KBS: western corn rootworm • LUQ: riparian zones in montane forests • LUQ: tropical flowering and fruiting • SGS: cheatgrass abundance • ILTER/Hungary: effects on cover estimation

8. AVHRR AMSR-E Land Surface Phenology: the seasonal progression of the interactions between the vegetated land surface and the lower layers of the atmosphere. Important for understanding weather & climate, water cycle, carbon cycle, and human dimensions of global change. AVHRR+ NCEP MODIS

9. Grasslands phenology emerges from the interactivity of multiple influences as filtered through the specifics of spatial relationships, genetic heritage, and the process of observation. Graphic from Henebry 2003

10. Leopold and Jones (1947) identified 3 desirable qualities in items used for phenological survey: sharpness, visibility, and recurrence. • Sharpness is the relative distinctiveness in the item that reduces variation between observers. • Visibility is the apparentness of the item to the observer. • Recurrence relates to low interannual variation in the phenological item. Leopold, A., and E. Jones. 1947. A phenological record for Sauk and Dane Counties, Wisconsin, 1935-1945. Ecological Monographs 17:83-122.

11. Phenological studies of the grasses that compose the prairie matrix must face low recurrence, poor visibility, and blunted sharpness. • In contrast, focus on the forbs and woody plants that dwell within the prairie yields phenological items that display sharpness, visibility, and recurrence. • Not surprisingly, the scant literature on grasslands phenology tends to focus on showy forbs embedded within grass matrices, rather than the grasses (Henebry 2003). Henebry, G.M. 2003. Grasslands of the North American Great Plains. In: Phenology: An Integrative Environmental Science (M.D. Schwartz, editor). Kluwer, New York. Chapter 3.3, pp. 157-174.

12. Konza Prairie phenology dataset: History • Initiated in June of 1981 by Dr. Lloyd Hulbert. • Data are available through October 1987. • Aim was to determine the seasonal patterns of growth and development for 29 selected grass, forb, and woody plant species characteristic of a range of prairie habitats. • Intended sampling frequency was approximately weekly throughout the growing season (April – November).

13. Konza Prairie phenology dataset: Measurement Scales Four broad phenophases were assessed: (1) initiation of growth; (2) flowering (anthesis); (3) fruits fully developed and ripe; and (4) leaves more than 90% dry. Indicators evaluated at three categories of prevalence: (a) less than 5%; (b) 5-20%; (c) greater than 20%

14. Konza Prairie phenology dataset: Burning and Topoedaphic Factors • Plants were surveyed in a variety of locations across KPBS. • Analysis here focuses only on two ungrazed watersheds with extreme contrasts in burning treatment: • 001d: annually burned in late spring • 020b: long term unburned • Two contrasting soil types: • Florence: thinner, drier soils found in the uplands • Tully: deeper, moister soils found in the lowlands

15. Konza Prairie phenology dataset: Questions What is the hierarchy of forcings and constraints on the phenological patterns of grass species? Interactions are too complex to explore adequately with the relatively short length of the Konza phenological data. However, we can explore three questions here: Q1. For each species, does soil type influence the duration of the developmental interval (DDI) between initiation of growth and the onset of anthesis? Q2. For each species, does burning affect the DDI? Q3. Are there discernible, significant differences in DDI among these species?

16. Konza Prairie phenology dataset: Methods • Seven grasses were selected for analysis, six C4 and one C3 species: • big bluestem,Andropogon gerardii Vitman; • little bluestem, Schizachyrium scoparium (Michx.) Nash; • sideoats grama, Bouteloua curtipendula (Michx.) Torr.; • switchgrass, Panicum virgatum L.; • indiangrass, Sorghastrum nutans (L.) Nash; • meadow dropseed, Sporobolus asper var. asper (Michx.) Kunth. • Scribner’s panicum, Dichanthelium oligosanthes var. scribnerianum (Nash) Gould • Duration of the Developmental Interval (DDI) between initiation of growth (prevalence >20%) and anthesis (1st observations of flowering) measured in accumulated growing degree-day base 0 oC (GDD0). • Data from 1983 through 1987 were analyzed in SAS (Proc GLM) using least-square means and the Tukey-Kramer adjustment for multiple comparisons.

17. Konza Prairie phenology dataset: Results 1 • Observed growing degree-days when initiation of growth is widespread (>20%). • No significant differences among burning treatment x soil type pairs. • Timing of surveys likely fails to catch initiation of Scribner’s panicum (DIOL), a C3 grass. Error bars display two standard errors.

18. Konza Prairie phenology dataset: Results 2 • Calculated growing degree-days between widespread initiation of growth and the onset of anthesis. • Timing of the surveys likely leads to underestimate of developmental interval for Scribner’s panicum (DIOL). Error bars display two standard errors.

19. Konza Prairie phenology dataset: Results 3 Q1. For each species, does soil type influence the duration of the developmental interval (DDI) between initiation of growth and the onset of anthesis? Answer to Q1: Soil type has no significant effect on the DDI for any grass species other than big bluestem (p=0.053). Q2. For each species, does burning affect the duration of the developmental phase? Answers to Q2: Burning has no significant effect on DDI for any grass species other than big bluestem (p=0.018). No significant soil x burning interaction effects on DDI, except for a significant difference (p=0.024) for big bluestem between annually burned upland Florence sites and long term unburned lowland Tully sites.

20. Konza Prairie phenology dataset: Results 4 • Matrix of p-values for pairwise comparisons of developmental intervals. • Significant differences (p-values<0.05) are displayed in bold.

21. Konza Prairie phenology dataset: Results 5 Q3. Are there discernible, significant differences in DDI among the species? Answers to Q3: Evident significant differences in the DDI among grass species. Three distinct groups emerge from the analysis: (1)    earlier developing C4 species—big bluestem, switchgrass, sideoats grama—95%CI = 1200-2050 oC GDD0; (2)    later developing C4 species—indiangrass, little bluestem, tall dropseed—95%CI = 2150-3100 oC GDD0; and (3)    sole C3 species—Scribner’s panicum—95%CI = 165-700 oC GDD0. 

22. Konza Prairie phenology dataset: Discussion 1 • Unique status of Andropogon gerardii (big bluestem) at KPBS. • It is more responsive to the differing environmental conditions that arise from late spring burning, topographic position, and their interaction. • Big bluestem in the lowland Tully sites of long term unburned prairie took nearly twice as long in thermal time to commence flowering as in upland Florence sites on annually burned prairie: 1228 vs. 2146 oC GDD0.

23. Konza Prairie phenology dataset: Discussion 2 • Distinct differences in GDD0 interval among species to identify 3 groups. • Is there some functional significance of the faster developing C4 group? Switchgrass and sideoats grama—the other two grasses in the faster developing C4 group—do not share big bluestem’s responsiveness to local conditions. • Yet, there is evidence of significant ecotypic variation expressed by both sideoats grama (Olmstead 1944; 1945) & switchgrass (McMillan 1956; 1957; 1959). • Statistical power is relatively low due to small sample size. Olmsted, C.E. 1944. Botanical Gazette 106:46-74. Olmsted, C.E. 1945. Botanical Gazette 106:382-401. McMillan, C. 1956. Ecology 37:330-340. McMillan, C. 1957. American Journal of Botany 44:144-153. McMillan, C. 1959. Ecological Monographs 29:285-308.

24. Lessons from the Konza Phenology Dataset • Irregular sampling in space and time. • Lack of observer training. • Short time series  low power in the face of continental climate. • No geospatial data collection. • Need for patience: Patterns emerge only through many seasons of observation. • Testing for relevant differences may not be immediately apparent. • Climate context is important – but what is appropriate baseline?

25. Next steps toward gathering phenological patterns in extant LTER/ILTER data • Identify potential data sets • Identify ancillary data sets • Identify contextual & related regional studies • Masters’ theses & Ph.D. dissertations • literature from nature centers, refugia, parks, etc. • proceedings of state academies of science • proceedings of local and regional conferences • Planning grant/Follow-up funds? • NCEAS working group?