This document summarizes research on modeling and understanding masting in European tree species. It discusses how masting patterns can be reproduced spatially and temporally using context-dependent data to parameterize models. It also examines integrating masting processes mechanistically by relating it to underlying physiological mechanisms like resource accumulation. Challenges include relating seed production to multiple interacting factors and implementing processes not captured in some models. A resource budget model provides the best existing approach, but complete process-based masting models do not exist yet.

1. Understanding and modeling
masting in European tree species
G.Vacchiano, D.Ascoli, A.Hacket Pain
giorgio.vacchiano@unimi.it

2. Mast seeding
2
PRO

3. It all began
on a midsummer night…

4. 4
Valdieri (Italy), August 2003

5. 5
After (year 2006)Before (year 2000)
One year later: beech mast year
What drives beech regeneration?

6. Fire
Severity
Litter Canopy
Cover Depth Cover Vitality
(N. Plot) (%) (mm) (%) (1-4)
Control 66 40 93 3,6
Low 65 36 84 3,4
Medium 50 28 80 2,6
High 7 16 70 1,7
Forest ecosystems after fire+mast:

7. Increasing fire severity
(Ascoli et al. 2015, For Ecol Manage)
Regeneration after fire+mast:

8. http://www.biol.canterbury.ac.nz/masting/global.shtml
Masting trees in Europe
Quercus robur Fagus sylvatica Picea abies
Larix decidua Castanea sativa Pinus nigra

9. The MASTREE database
(Ascoli et al. 2017, Ecology)

10. Seed and fruits
Flowers Pollen
Dendro
The MASTREE database

11. Reil et al. 2015
Peer reviewed
research
Fig 2. Mean beech mast intensity of the previous year, bank vole abundance, and human PUUV
incidence in Germany 2001–2012. Bars = mean beech mast intensity of the previous year, solid line =
bank vole abundance, and broken line = human PUUV incidence…
The MASTREE database

12. Grey literature
AFZ*-Der Wald series
*AFZ: Allgemeine Forst Zeitschrift für Wald und Forstwirtschaft
The MASTREE database

13. Staatsklänge Nagold
Field
surveys
The MASTREE database

14. Pollen monitoring
Online
data
The MASTREE database

15. - Sources: 359 (Burckhardt 1875 – Zwolak et al. 2016)
- Series: 1747 (longest: 117 yrs – Hase 1964, Denmark)
- 18348 observations
- Time span: 1677-2016 beech, 1791-2016 spruce
- Geographical extent: 28 countries
- Type: 67% ordinal , 33% continuous
The MASTREE database

16. Frequency
Masting class
0.39
0.29
0.14 0.09 0.08
1
2
3
4
5
Years seed/m2
1990 250
1991 32
1992 345
1993 12
1994 120
… …
2015 70
1
Ord
…
Continuous vs. ordinal data

17. FR6
1
2
3
4
5
1
Years
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2
1
3
5
1
2
5
3
5 5
1 12
4
4
1 2
4
1
2
3
1
1
4
1
5
3
5
1
4
1
1
2
3
Masting
chronology
Spatial aggregation

18. Is beech masting synchronized across Europe?
Vacchiano et al. 2017, New Phytol. 215(2)

19. Vacchiano et al. 2017, New Phytol. 215(2)
Is beech masting synchronized across Europe?

20. Atlantic
Continental
Mediterranean
Vacchiano et al. 2017, New Phytol. 215(2)
Is beech masting synchronized across Europe?

21. (Pearse et al. 2016 New Phyt)
Why does beech masting synchronize across Europe?

22. -
+
Summer
2 years before
Summer
1 year before
Correlation
Mean
. Significant < 0.05
* Significant < 0.01
Why does beech masting synchronize across Europe?
Vacchiano et al. 2017, New Phytol. 215(2)
Masting chronologies

23. (Pearse et al. 2016 New Phyt)
Why does beech masting synchronize across Europe?

24. Wet summer -2
Resource
accumulation
• C intake by
photosynthesis
• Faster N mineralization
• Higher N, P uptake
Hot (dry) summer -1
Gibberellin induction
• Resources allocated to
flowering
• Gene regulation ??
Year of masting
Pollen coupling (if
limited)
• Higher precipitaIon
favors pollinaIon
• Absence of "canceling"
factors, eg spring frost
• Resources depleted in
year +1
Why does beech masting synchronize across Europe?

25. DE1 DE2 DE9
DEF DK0 NL1
SE2 UKJ
Masting intensity vs. TMX
Are weather cues stable across time?
-
+
Summer
2 years before
Summer
1 year before
Vacchiano et al. 2017, New Phytol. 215(2)

26. Weather vs. Northern Atlantic Oscillation (NAO)
Summer Temperature Precipitation
NAO+
1950-2015: Climate Prediction Centre NAO Index (NOAA)
1900-2015: Hurrell et al. 1995 NAO Index (HURRELL)
1826-2015: Jones et al. 1997 NAO Index (JONES)
What drives beech masting synchrony?
Ascoli et al. 2017, Nature Communications

27. European MAST_index (1850-2015)
[% chronologies in class 4-5 % chronologies in class 1-2]
What drives beech masting synchrony?
Ascoli et al. 2017, Nature Communications

28. Wavelet coherence analysis - MAST_index vs. NAO-winter
year
What drives beech masting synchrony?
Ascoli et al. 2017, Nature Communications

29. Summer yr-2
Winter
Spring yr0
Summer yr-1
Spring NAO (NOAA)
Winter NAO (NOAA)
Summer NAO (NOAA)
LowHighfrewuency
+0.40
***
-0.50
***
+0.37
***
+0.51
***
What drives beech masting synchrony?
Ascoli et al. 2017, Nature Communications

30. 1958 2004
Heatwave summer 2003
not induced by NAO
but by tropical hot air mass
What drives beech masting synchrony?
Ascoli et al. 2017, Nature Communications

31. § Literature review within EU PROFOUND Cost action
§ Stand, landscape, and global scale (DGVM)
§ Does modeling masting make a difference?
§ How can modelers implement masting at different scales?
§ What processes remain poorly understood?
(Vacchiano et al. 2018, Eco Mod)
How to predict and model masting?

32. We propose that masting can be integrated using two
approaches:
1. PATTERNS à reproducing demographic and
ecological patterns (both spatially and temporally)
using context-dependent data
2. PROCESSES à understanding and reproducing
mechanistic processes
(usually more general, but more complex and not
necessarily more accurate)
How to predict and model masting?

33. PATTERNS
à175 papers from systematic Scopus search
àvast amount of information for parameterising masting patterns
àVariety of biomes, species, PFTs
How to predict and model masting?
(Vacchiano et al. 2018, Eco Mod)

34. PATTERNS
Kelly et al. 2013
àSeed production and weather cues
How to predict and model masting?
àVariability, synchrony, (frequency)

35. Some challenges with a pattern based approach… e.g. Masting
response to climate change not well understood
How to predict and model masting?

36. PROCESSES
Patterns emerge
from series of
physiological
processes
How to predict and model masting?
(Vacchiano et al. 2018, Eco Mod)

37. How to predict and model masting?
Strong tradition relating
masting to resources (69%)
à Not just carbon:
nutrients (N) key
resource regulating
masting
à processes related to
resources are linked to
all 3 patterns,
especially variability
(Vacchiano et al. 2018, Eco Mod)

38. Modelling masting processes
Complete process-based models of masting do not exist (yet)
à there is no existing model that could easily be “plugged in”
à Resource budget model the closest (?)
(i) tree accumulates
C & nutrients
(ii) Reproduction only
when reserves >
threshold
(iii) Excess allocated
to flowers
(iv) Successful
pollination
(v) Fruits mature,
depleting reserves
Recommendations
(Isagi 1997)

39. A struggle: relating seed production to multiple and
interacting proximate causes
àonly 40% of algorithms had a multivariate structure
à Also, some processes have not/are impossible to
implement in some models
(e.g. pollination in models that don't have flowers)
à Implementing patterns as a first step to improve model
predictions
à Process-based implementation based on RBM
Recommendations

40. Acknowledgements
Fabio Berzaghi , Manuel Esteban Lucas-Borja, Thomas Caignard, Alessio Collal@, Paola Mairota,
Ciprian Palaghianu, Christopher P.O. Reyer, Tanja G.M. Sanders, Eliane Schermer, Thomas
Wohlgemuth, Marco Turco, Renzo MoJa, Janet Maringer, Marco Conedera, Igor Drobyshev,
Mara Cirolli, Władysław Kantorowicz, Chris@an Zang, Silvio Schueler, Luc Croisé, Pietro Piussi,13
Roberta BerreU, Ciprian Palaghianu , Marjana Westergren,15onathan G. A. Lageard, Anton
Burkart, Regula Gehrig Bichsel, Peter A. Thomas, Burkhard Beudert, Rolf Övergaard
Thank you for your attention.