Bibliographic record
Abstract
With global temperatures rising and extreme weather events happening on both ends of the weather spectrum it is important to collate the known information of seedling temperature thresholds. This is specifically looking at the temperature thresholds, either maximum or minimum, of Pinaceae seedlings with data mainly collected from the Pinus genus as well as some data from both Abies and Pincea seedlings. Used the search engine Google Scholar in order to find the relevant research papers. Specifically looked for and used papers that included exact temperatures of when photosystem II (PSII) was at T50 for both maximum and minimum temperatures. In the spreadsheet you will find the species name used in the paper and the accepted species name. Latitude and longitude is included for each data point with the altitude also noted for the papers that included it. The life stage of when the experiment was carried out on the plants was also noted, it would either be as a seedling or the seed. The thermal tolerance was noted and the tolerance measure was either the lower T50 (LT50), maximum T50 (MT50), or the maximum T95 (MT95). References were also included for each species named. Search terms: - Heat tolerance pinus seedlings - Maximum thermal tolerance thresholds in pinus - Heat tolerance picea seedling - Maximum thermal tolerance thresholds in picea - Heat tolerance abies seedlings - Maximum thermal tolerance thresholds in abies References: Bigras, F.J. (2000). Selection of white spruce families in the context of climate change: heat tolerance. Tree Physiology, 20(18), pp.1227–1234. doi:https://doi.org/10.1093/treephys/20.18.1227. Boydak, M. and Caliskan, S. (2016). Effects of heat shock on seed germination of Turkish red pine (Pinus brutia). Bosque (Valdivia), 37(2), pp.327–333. doi:https://doi.org/10.4067/s0717-92002016000200011. Chang, H., An, J., Roh, Y. and Son, Y. (2020). Experimental warming and drought treatments reduce physiological activities and increase mortality of Pinus koraiensis seedlings. Plant Ecology, 221(7), pp.515–527. doi:https://doi.org/10.1007/s11258-020-01030-3. Climent, J., Costa e Silva, F., Chambel, M.R., Pardos, M. and Almeida, M.H. (2009). Freezing injury in primary and secondary needles of Mediterranean pine species of contrasting ecological niches. Annals of Forest Science, [online] 66(4), pp.407–407. doi:https://doi.org/10.1051/forest/2009016. Kunert, N., Hajek, P., Hietz, P., Morris, H., Rosner, S. and Tholen, D. (2021). Summer temperatures reach the thermal tolerance threshold of photosynthetic decline in temperate conifers. Plant Biology. doi:https://doi.org/10.1111/plb.13349. Man, R., Lu, P. and Dang, Q.-L. (2020). Cold tolerance of black spruce, white spruce, jack pine, and lodgepole pine seedlings at different stages of spring dehardening. New Forests, 52(2), pp.317–328. doi:https://doi.org/10.1007/s11056-020-09796-0. Marias, D.E., Meinzer, F.C., Woodruff, D.R. and McCulloh, K.A. (2016). Thermotolerance and heat stress responses of Douglas-fir and ponderosa pine seedling populations from contrasting climates. Tree Physiology. doi:https://doi.org/10.1093/treephys/tpw117. Méthy, M., Gillon, D. and Houssard, C. (1997). Temperature-induced changes of photosystem II activity in Quercus ilex and Pinus halepensis. Canadian Journal of Forest Research, 27(1), pp.31–38. doi:https://doi.org/10.1139/x96-127. Norgaard Nielsen, C.C. and Rasmussen, H.N. (2009). Frost hardening and dehardening in Abies procera and other conifers under differing temperature regimes and warm-spell treatments. Forestry, 82(1), pp.43–59. doi:https://doi.org/10.1093/forestry/cpn048. Ordoñez-Salanueva, C.A., Orozco-Segovia, A., Mattana, E., Castillo-Lorenzo, E., Davila-Aranda, P., Pritchard, H.W., Ulian, T. and Flores-Ortiz, C.M. (2021). Thermal niche for germination and early seedling establishment at the leading edge of two pine species, under a changing climate. Environmental and Experimental Botany, 181, p.104288. doi:https://doi.org/10.1016/j.envexpbot.2020.104288. Ouyang, F., Sun, M., Cui, X., Mulualem Tigabu, Zhang, H., Deng, J., Wang, J., Wei, Y. and He, R. (2023). Picea pungens exhibits greatest tolerance to short-time thermal stress compared to Picea abies, and Picea omorika. New Forests. doi:https://doi.org/10.1007/s11056-023-10002-0. Rehschuh, R. and Ruehr, N.K. (2021). Diverging responses of water and carbon relations during and after heat and hot drought stress in Pinus sylvestris. Tree Physiology, 42(8), pp.1532–1548. doi:https://doi.org/10.1093/treephys/tpab141. Sales, E., Cañizares, E., Pereira, C., Pérez-Oliver, M.A., Nebauer, S.G., Pavlović, I., Novák, O., Segura, J. and Arrillaga, I. (2022). Changing Temperature Conditions during Somatic Embryo Maturation Result in Pinus pinaster Plants with Altered Response to Heat Stress. International Journal of Molecular Sciences, 23(3), p.1318. doi:https://doi.org/10.3390/ijms23031318.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.500 | 0.260 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".