1 Running title: Seed banks following forest succession
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4 Plue, Jan
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*; Verheyen, Kris²; Van Calster, Hans³; Marage, Damien
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; Thompson, Ken
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;
5 Kalamees, Rein 6 ; Jankowska-Blaszczuk, Malgorzata 7 ; Bossuyt, Beatrijs 8 & Hermy, Martin 1 *
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1 Division Forest, Nature and Landscape Research, KULeuven, Celestijnenlaan 200E, Leuven, Belgium
2 Laboratory of Forestry, Ghent University, Geraardsbergsesteenweg 267, Melle-Gontrode, Belgium;
Kris.Verheyen@UGent.be
3 Institute for Forest and Nature Research, Kliniekstraat 25, 1070 Brussels, Belgium; Hans.vancalster@inbo.be
4 AgroParisTech, ENGREF Nancy, UMR 1092, F-54000 Nancy, France; Marage.damien@akeone.net
5 Department Animal and Plant Sciences, The University of Sheffield, Western Bank, Sheffield S10 2TN, UK;
Ken.thompson@sheffield.ac.uk
6 Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, 40 Lai, 51005 Tartu,
Estonia; Rein@ut.ee
7 Botany Department, The Jan Kochanowski University of Humanities and Sciences, 15 Świetokrzyska Street,
Kielce, Poland; mjanko@ujk.kielce.pl
8 Terrestrial Ecology Unit, Ghent University, K. L. Ledeganckstraat 35, Ghent, Belgium;
Beatrijs.bossuyt@UGent.be
20 * Corresponding author(s): Jan Plue & Martin Hermy
21 Division Forest, Nature & Landscape Research
22 Celestijnenlaan 200E; 3001 Leuven
23 Tel.: +3216329757; Fax.: +3216329760
24 E-mail: Jan.plue@gmail.com
; Martin.hermy@ees.kuleuven.be
25 Word Count (Text & References): 6084
26 Keywords: Ancient forest; Former land use; Land use Intensity; Post-agricultural succession;
27 Post-clearcut succession; Recent forest
28 Nomenclature: Lambinon et al. (1998); Gleason & Cronquist (1991)

29 Abstract (242 words; Max 250)
30 Question (i) How does former land use and land use intensity affect the seed bank
31 development during post-agricultural succession? (ii) How does time since the last clearcut
32 change the seed bank composition during post-clearcut succession?
33 Methods One dataset was compiled per succession type using the following selection
34 criteria: (i) a dataset included a successional series, (ii) plots were located in mesotrophic forest
35 plant communities and (iii) vegetation data were available. The post-agricultural succession
36 dataset comprised 76 recent forest plots (8 studies); the post-clearcut succession dataset
37 comprised of 218 ancient forest plots (3 studies). Each dataset was analysed separately using
38 either Linear Mixed Models or Generalized Linear Models, controlling for both environmental
39 heterogeneity and variation between study locations.
40 Results In the post-agricultural succession dataset, land use and time significantly
41 affected nearly all the studied seed bank characteristics. Seed banks on former arable land
42 recovered poorly even after 150 year of restored forest cover, whereas moderate land use
43 intensities (grasslands, heathlands) yielded faster recovering seed banks. Time was a
44 significant determinant of all but two soil seed bank characteristics during post-clearcut
45 succession. Seed banks in managed ancient forest differed strongly in their characteristics
46 opposed to primary forest seed banks.
47 Conclusions Forest seed banks bear the marks of a former land use and/or forest management
48 and continue to do so for at least 150 years. Nevertheless, time since the last major disturbance,
49 being either former land use or clearcutting, remains a significant determinant of the seed bank.
50
51

52 Introduction
53 Seed bank dynamics during secondary forest succession have been of interest because of the
54 role persistent seed banks fulfil in successional vegetation dynamics. Especially in the earliest
55 stages of secondary forest succession, the persistent seed bank will be a dominant driver in
56 community assembly (e.g. Marks & Mohler 1985), next to the extant vegetation and seed input
57 via (long-distance) dispersal. Despite a declining input from the seed bank (increasing
58 vegetation-seed bank dissimilarity (Dölle & Schmidt 2009)), the seed bank remains important
59 for the herbaceous community, long after the forest canopy closes. Indeed, seeds may
60 germinate from the persistent seed bank and their seedlings may subsequently be recruited
61 either in forests with recurrent short-interval disturbances (e.g. coppiced forests; Brown &
62 Oosterhuis 1981), in forest canopy gaps (Mladenoff 1990; Naaf & Wulf 2007) or in small gaps
63 in the herbaceous canopy (Rydgren et al. 2004; Jankowska-Blaszczuk & Grubb 2006; Hautala
64 et al. 2008). Particularly the latter phenomenon increasingly adds to the view of seed banks as a
65 functional part of non-successional communities (Kalamees & Zobel 2002), including
66 temperate deciduous forests. Unfortunately, many seed bank studies remain a snapshot at a
67 single moment in time, with little thought on how seed bank composition or characteristics
68 arose and/or are changing (Fenner & Thompson 2005). Hence, a thorough understanding of
69 temporal seed bank changes and their determinants over the course of secondary forest
70 succession is required, as it will provide insight into the role seed banks could play in the
71 dynamics of understorey populations and communities.
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73 The initial form of the seed bank at the start of secondary forest succession, in terms of
74 composition and characteristics, will depend on the type/ intensity of the former land use

75 (Thompson & Grime 1979), rendering it a key determinant of the persistent seed bank during
76 succession (Bossuyt & Hermy 2001). Indeed, the seed bank generally reflects the severity and
77 predictability of the disturbance regimes associated with specific land use types. Small-seeded,
78 high-density and diverse seed banks in arable land with regular ploughing (e.g. Roberts &
79 Vankat 1991) and low-density, species poor seed banks in forests with long-rotational timber
80 harvesting (e.g. Bossuyt et al. 2002; Van Calster et al. 2008a; Plue et al. in press) present both
81 extremes of the seed bank spectrum. Furthermore, there is a strong association between seed
82 persistence and habitat type, triggered by the typical spatial and temporal patterns of the
83 disturbance regime (Thompson et al. 1998). Consequently, one might hypothesize that
84 successional forest seed banks may longer bear the legacies of former land use as the land use
85 intensity increases (Plue et al. 2009), analogous to the patterns observed in the herbaceous
86 understorey forest vegetation (Dupouey et al. 2002; Dambrine et al. 2007). Yet, the majority of
87 seed bank studies have so far indicated that the former land use, being either pasture, grassland
88 or heathland, no longer conveyed a significant effect on recent forest seed banks, once forest
89 stands reached beyond the age of 50 years (Bossuyt & Hermy 2001). Hence, time since
90 disturbance is perceived as a second major determinant of forest soil seed banks, both in post-
91 agricultural, recent forests (Roberts & Vankat 1991; Bossuyt et al. 2002) and in post-clearcut,
92 ancient forests (Plue et al. in press). Indeed, seed predation, seed senescence, failed
93 germination and a changing input from the herb layer during succession are but a few of the
94 seed bank and vegetation processes which will affect and change the seed bank composition
95 through time.
96

97 However, despite some generalizations on successional seed bank dynamics, most established
98 patterns and processes concerning seed banks of temperate deciduous forest are far from
99 definite. Therefore, we aimed to establish common underlying patterns in seed bank dynamics
100 in the course of secondary succession, through a plot-based quantitative review of both post-
101 agricultural and post-clearcut forest succession in NW-Europe and N-America. The two main
102 goals of the review encompassed 1) unravelling both the effects of former land use intensity
103 and time since former land use during post-agricultural succession in recent forests and 2)
104 understanding the effect of time since disturbance during post-clearcut succession in ancient
105 forests.
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107 Material & Methods
108 Data collection & description
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Datasets
Studies containing suitable datasets were located in ISI Web of Knowledge
©
via queries
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112 using the keywords “recent forest”, “ancient forest”, “soil seed bank”, “land use history” and
“succession”, in various combinations. The number of studies was narrowed down further by
113 carefully checking the manuscript, as each dataset had to meet a number of criteria. The final
114 selection of datasets (for characteristics, see Table 1) was based on the following criteria: (i)
115 each study included a secondary successional series from either old-fields or clearcuts in
116 ancient forest, (ii) studies were located in temperate deciduous forest with mesotrophic plant
117 communities as rich alluvial forests and (coniferous) forests on poor sandy soils were
118 excluded, and (iii) vegetation data were preferably available. Raw seed bank and vegetation
119 data were extracted from the manuscript, or were retrieved through contacting the

120 corresponding author(s). Seed bank data were retrieved from the individual studies in seed
121 numbers per species per forest plot. All studies used germination trials to estimate seed bank
122 characteristics and composition, thus working with a subset of viable, germinated seeds.
123 Detailed information on sampling and germination protocols can be found in Appendix A.1.
124 Vegetation and seed bank data were transformed to presence-absence data to overcome
125 differences in sampling intensity (plot size) and to be able to perform the Raup & Crick
126 dissimilarity analysis. The dataset of Bossuyt et al. (2006) lacked vegetation data and was not
127 incorporated in the analyses requiring vegetation data.
128 This resulted in a post-agricultural succession dataset containing 76 recent forest plots
129 from eight individual studies and a post-clearcut succession dataset containing 218 plots from
130 four individual studies, of wich one plot was omitted as it contained no species.
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133
Seed bank characteristics
Seed bank characteristics were calculated per plot: species richness, Shannon-Wiener
134 diversity index, the presence-absence based share of ancient forest species to the seed bank
135 ( sensu Hermy et al. 1999 (Europe); only for the European data sets), the CSR seed bank
136 signature (only for the European data sets; Hunt et al. 2004) and the Raup & Crick vegetation-
137 seed bank dissimilarity (Vellend et al. 2007). The probabilistic Raup & Crick dissimilarity
138 measure was chosen as it is unbiased by the large α-diversity differences between the seed
139 bank and the herbaceous understorey forest vegetation (Vellend et al. 2007). Seed density from
140 European studies was calculated without Juncus effusus (Bossuyt et al. 2002) because of the
141 vast abundance of this species, possibly confounding results. Additionally, two seed bank
142 related traits were compiled for all recorded seed bank species: mean seed weight [mg] and life

143 span [annual, biennial or perennial]. Seed weight was analyzed because lighter (smaller) seeds
144 (e.g. seed banking species from arable land) may persist longer, keeping overall seed weight
145 equally lower as forest succession progresses. Life span was analyzed as arable lands almost
146 exclusively contain annual and biennial seed banking species, whereas grassland seed banks
147 yield more perennial species, both persistently altering seed banks during forest succession.
148 Traits from European species were gathered from the LEDA traitbase (Kleyer et al. 2008), and,
149 if necessary, supplemented with data from Grime et al. (2007) or the Ecological Flora of the
150 British Isles ( http://www.ecoflora.co.uk/ ). American species traits were collected from (i) the
151 Seed Information Database at the Kew Royal Botanical Garden (seed weight;
152 http://data.kew.org/sid/ ), (ii) USDA plant database (life span), (iii) Gleason & Cronquist (1991)
153 (life span) and (iv) online American flora’s (life span; http://efloras.org
). Plant synonyms were
154 identified using the IPNI database ( http://www.ipni.org/ ) to recover the correct plant traits.
155 Using the presence-absence seed bank composition, either the mean value (seed weight) or the
156 relative contribution to the plot seed bank of a trait value (annuals; biennials) were calculated.
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159
Data analysis
As detailed environmental data were lacking from most studies, we used soil acidity
160 (pH H
2
O) as a proxy for the overall abiotic soil conditions. Soil acidity was incorporated into
161 both analyses as a fixed factor, with levels corresponding to the various soil buffer ranges (as
162 defined by Ulrich 1983). Each level contained a sufficient large number of plots to allow
163 meaningful statistical inference (four levels in post-agricultural succession dataset; three levels
164 in post-clearcut succession dataset). This procedure enabled to account for environmental
165 variation in both datasets. The two datasets were analyzed separately to establish a posteriori

166 whether or not both post-agricultural and post-clearcut forest succession in soil seed banks
167 would be similar, abiding by the same processes and driving factors.
168
169 Post-agricultural succession dataset
170 To test whether the various seed bank characteristics changed significantly with land
171 use intensity and time since former land, a Linear Mixed Model was applied. Land use
172 intensity [high: arable land; moderate: grassland, pastures, (wooded) meadows, heathland
173 (these four land use types were in casu only moderate in their disturbance intensity) and a
174 reference state: managed ancient forest] and time since former land use (0-50 yrs; 51-100 yrs;
175 101-150 yrs; 250 yrs) were incorporated into the mixed model as one integrated fixed factor
176 (Land use × Time). This fixed factor counted seven levels. The first six factor levels were
177 defined by the interaction term between land use intensity and time since former land use,
178 adding managed ancient forest as the seventh factor level. This procedure was applied because
179 the post-agricultural dataset lacked young stands in managed ancient forests. This would have
180 strongly confounded the mixed model, should we have opted to apply a full-factorial model
181 with fixed factors land use intensity and time since disturbance. For similar reasons, the fixed
182 factor pH class (four levels: pH H
2
O 3.8-4.2, 4.2-5.0, 5.0-6.2, >6.2), was only incorporated as a
183 main effect, as not all levels were present at each level of the land use-time factor, preventing
184 correct modeling of their interaction term. Location was incorporated into the mixed model as
185 a random factor to eliminate variation between studies from the model. Non-significant model
186 terms were progressively omitted, omitting the most insignificant term first and subsequently
187 re-running the reduced model at each step. We are aware that this simplified mixed model

188 structure may have eliminated significant effects. Therefore, its results will be discussed with
189 caution.
190
191 Post-clearcut succession dataset
192 Seed bank characteristics were analysed using Generalized Linear Models (GzLMs)
193 with fixed factors time since disturbance (0-50 yrs; 51-100 yrs; 101-150 yrs; 151-250 yrs), pH
194 (three levels: pH H
2
O 3.8-4.2, 4.2-5.0, >5.0) and location. Location was included as fixed
195 factor into the GzLMs and not as a random factor in a Linear Mixed Model (cf. post-
196 agricultural succession dataset), as three locations are too few to accurately estimate a random
197 intercept in a Linear Mixed Model. The interaction term between the fixed factors time since
198 disturbance and pH was included in the model. The interaction terms including location were
199 omitted as not all locations contained all levels of time since disturbance or pH. Moreover, we
200 were only interested if there was an overall effect of study location on seed bank
201 characteristics. Non-significant model terms were progressively omitted from the GzLMs,
202 which were re-run at each occasion after first omitting the most insignificant model term. The
203 plots from undisturbed primary forest (>500 yrs; Jankowska-Blaszczuk et al. 1997) were not
204 included in the GzLMs, as these plots only occurred over a narrow soil acidity range,
205 confounding the results of the GzLMs (Gotelli & Ellison 2004).
206 The random/fixed effect of location in both analyses will not be presented or discussed
207 here as we were only interested in excluding this variation from both datasets. However, the
208 main effect of the random/fixed factor location is presented in Appendix A.2 to allow readers
209 to assess the amount of variation represented by the factor in the various models.
210

211 Results
212 Post-agricultural succession: the effects of former land use and time
213
214
Eight species (1 – 39 species) was the median number of species recovered from the seed bank. Seed density per plot (without Juncus effusus ) ranged from 10 to 26750 seeds/m
2
,
215 with a median plot seed density of 3261 seeds/m
2
. Recent forest seed banks contained a median
216 of 15% ancient forest species (0 – 50%) and 25% of annuals and biennials (0 – 75%). The
217 median seed weight of the seed bank species was 0.72mg (0.08 – 2.39mg). The vegetation was
218 relatively similar to the seed bank (only 43% median dissimilarity; 0 – 99%).
219 All Linear Mixed Models successfully modeled the ten seed bank variables (Table 2),
220
221 excluding the between-study variation. Apart from the ruderal seed bank signature, the integrated Land use × Time factor had a significant effect on all remaining seed bank
222 characteristics. The explanatory power was only moderate for the modeling of the Shannon-
223 Wiener diversity index and the percentage of ancient forest species. Particularly in the latter
224 case, pH appeared of great importance (Table 2). Soil pH had a significant effect on six seed
225 bank characteristics (Table 2). Additionally, pH was the only significant parameter in the
226 ruderal seed bank signature model. The boxplots illustrating the main effect of soil pH on the
227 seed bank characteristics can be found in Appendix A.3.
228 Seed bank species richness declined with time, mainly due to a high species richness in
229 youngest forest stands (0-50 yrs) on former arable land (Fig. 1a). Seed bank species richness on
230 former arable land was consistently higher compared to the species richness of ancient forest
231 seed banks, irrespective of stand age. Recent forest seed banks of grasslands contained similar
232 amounts of species as did ancient forest seed banks. Only the youngest stands’ seed banks on
233 former arable land had the highest species diversity compared to ancient forest seed banks,

234 when correcting for seed numbers (Shannon-Wiener diversity index; Fig. 1b). Seed banks in
235 recent forests on (mostly) former grasslands contained similar seed numbers as seed banks of
236 ancient forests. Seed banks of recent forests on former arable land still contained more seeds
237 than ancient forest, independent of stand age (Fig. 1c). The competitive seed bank signature
238 was consistently lower in recent forests with an intermediate level of former land use intensity
239 compared to ancient forests (Fig. 1d). Recent forests’ seed banks on former arable land had a
240 share of competitive species which appeared to decrease with age, but this share was similar to
241 ancient forest from a stand age over 100 years (Fig. 1d). The stress-tolerant seed bank signature
242 was significantly lower and higher on resp. former arable land and former grasslands/pastures
243 compared to the stress-tolerant seed bank signature of ancient forests. The ruderal seed bank
244 signature remained relatively constant over land use class and time (Fig. 1f). Contrary to our
245 expectations, a high percentage of ancient forest species was present in the seed bank of former
246 arable land. Average seed weight increased significantly with time in all recent forests, though
247 it appeared to recover faster on former grasslands/pastures (50 years; Fig. 1h). Recent forest
248 stands’ seed banks were more dissimilar to the vegetation than seed banks of ancient forest.
249 This discrepancy became of similar magnitude once recent forest stands had reached 100 years
250 of age (Fig. 1i). Seed banks of (mostly) former grasslands had a vegetation-seed bank
251 dissimilarity which appeared to behave rather erratically (Fig. 1i). The percentage of annuals
252 and biennials was high in ancient forests, whereas in recent forests, it appeared consistently
253 higher on former arable lands, and persistently lower on former grasslands (Fig. 1j.),
254 irrespective of stands age.
255
256 Post-clearcut succession: the effect of time

257 The plot seed bank yielded a median of nine species, with plot species numbers ranging
258 from one to 27 species. Seed density per plot (without Juncus effusus ) ranged from 0 to 26308
259 seeds/m
2
, with a median plot seed density of 3872 seeds/m
2
. The plot seed bank had a median
260 contribution of ancient forest species of 17% (0 – 50%) and of annuals and biennials of 20% (0
261
– 67%). The median seed weight was high: 0.81mg (0.02 – 4.82mg). The seed bank was highly
262 dissimilar to the standing vegetation (91% median dissimilarity; 0 – 94%).
263 Time since the last clearcut significantly and directly affected eight out of ten seed bank
264 characteristics. Soil pH contributed significantly in explaining variation in seven out of ten
265 seed bank characteristics. On the one hand, the percentage of ancient forest species and the
266 Raup & Crick dissimilarity solely depended upon time since the last clearcut. On the other
267 hand, the ruderal seed bank signature solely depended upon soil acidity. Average seed weigth
268 remained constant throughout the dataset, in function of both time since the last clearcut and
269 soil pH.
270 Species richness increased with increasing habitat productivity, but decreased
271 consistently with age across the environmental gradient, as did seed density for the most part
272 (Fig. 2a, c). The Shannon-Wiener index was significantly higher on richer soil conditions,
273 while it simultaneously showed a significant decrease over time, notably in the oldest stand age
274 category (151-250 yrs) (Fig. 2b). The share of competitive species in the seed bank declined
275 during post-clearcut forest succession, yet the consistent decline in competitive character with
276 increasing stand age was eliminated in the most fertile environments (Fig. 2d). The share of
277 ruderal species in the seed bank increased with soil pH (Fig. 2f). The stress-tolerant seed bank
278 character increased as the forest environment became less productive, except for the 0-50 year
279 old stands with a constant stress-tolerant character. Stress-tolerance increased with age in the

280 most acid conditions, while this pattern reversed in the fertile environments (Fig. 2e). The
281 share of annual and biennial species differed significantly between age classes, being lowest in
282 the oldest stands (151-250 yrs). The relation to soil pH was quite erratic (Fig. 2j). The seed
283 bank – vegetation dissimilarity and percentage of ancient forest species increased with time,
284 notably rising once forest stands mature beyond 50 years of age (Fig. 2g, i).
285
286 Discussion
287
288
Time over land use or land use over time?
Former land use does indeed leave a persistent mark on the forest seed bank (Bossuyt &
289 Hermy 2001; Table 2; Fig. 1) and does so dependent on the former land use intensity (Bossuyt
290 & Hermy 2001; Plue et al. 2009; Fig. 1) and stand age (Bossuyt & Hermy 2001; Fig. 1).
291 However, our findings only correspond in part to those of Bossuyt & Hermy (2001). One of
292
293 their main conclusions relates to the stand age threshold of 50 years. Bossuyt & Hermy (2001)
– supported by the findings of e.g. Hill & Stevens (1981), Warr et al. (1994) and Dougall &
294 Dodd (1997) – deem the impact of former land use on the forest seed bank should start to
295 diminish. Yet, can we confirm this?
296 On the one hand, the review of Bossuyt & Hermy (2001) did not incorporate former
297 arable lands – it contained only former grasslands and heathlands – with highly persistent,
298 high-density and diverse seed banks (Roberts & Vankat 1991). These specific characteristics
299 (e.g. increased seed density, species richness) and even particular species (e.g. Verbascum
300 thapsus ) have recently been shown to persist in 1600 years-old ancient forests with regular
301 disturbance regimes (Plue et al. 2009). Indeed, typical seed bank characteristics of former
302 arable lands, i.e. increased species richness, increased seed density and a larger share of

303 annuals and biennials, remained altered compared to ancient forest seed banks after 150 years
304 (Fig. 1a, c, h, j). Given the high overall persistence in annuals and biennials (Thompson et al.
305 1998), this need not be surprising (Plue et al. 2008; Plue et al. 2009). Other characteristics (e.g.
306 Shannon-Wiener diversity index, CSR-signature, seed weight, vegetation-seed bank
307 dissimilarity) did differ significantly compared to ancient forests, but significantly recovered
308 within 50-150 years (Fig. 1b, d, e, f, h, i). These seed bank features may possibly be more
309 responsive to processes such as e.g. density-dependent and/or preferential seed predation and
310 seed senescence, which may trigger a faster decline in species richness opposed to seed density
311 (Fig. 1a, c). Hence, species diversity may recover within 50 years time. The CSR-signature,
312 seed weight and vegetation-seed bank dissimilarity may additionally reflect the increased seed
313 input from the forest vegetation (Bossuyt et al. 2002). Indeed, the seed input from typical
314 herbaceous forest understorey species (Fig. 1g), which are less competitive (Fig. 1d), more
315 stress-tolerant (Fig. 1e; Hermy et al. 1999) and bear heavier seeds (Fig. 1h; Verheyen et al.
316 2003), may equally induce a swifter recovery of these characteristics, lowering the vegetation-
317 seed bank dissimilarity (Fig. 1i).
318 On the other hand, forest plots labeled as ‘medium’ disturbed, containing in casu
319 mostly former grasslands, did not differ markedly in species richness, species diversity and
320 seed density from ancient forest seed banks, irrespective of stand age (Fig. 1a, b, c). In this
321 respect, our results largely parallel the findings of Bossuyt & Hermy (2001). However, beyond
322 the species level, the competitive and stress-tolerant seed bank signature were respectively
323 significantly lower and higher independent of stand age, opposed to ancient forests (Fig. 1d, e).
324 Yet, no obvious explanation was at hand. Indeed, annuals and biennials were present in low

325 amounts (Fig. 1j), while seeds appeared similar in weight (except for low seed weight in 50
326 year old recent forest stands), compared to ancient forest seed banks (Fig. 1h).
327 Nevertheless, all the former seems to suggest that, despite of the partial recovery of the
328 seed bank with time since former land use, the seed bank still yields typical characteristics of
329 the former land use. Our results confirm that the influence of the former land use diminished
330 beyond the threshold of 50 years put forward by Bossuyt & Hermy (2001) (see e.g. Granström
331 1988). However, several seed bank characteristics have not yet returned to their state in
332 managed ancient forest, not even after 150 years of forest cover. Moreover, there is little reason
333 to assume that the seed bank may ever recover (Plue et al. 2008; Plue et al. 2009), if frequent
334 natural or man-made disturbances would allow for seed bank replenishment (Van Calster et al.
335 2008a).
336
337
338
Will managed ancient forests ever resemble primary forests?
Duffy & Meier (1992) questioned whether the forest herb layer would ever recover
339 after clear-cutting of the forest canopy. The results on the successional seed bank dynamics in
340 ancient forests compared to primary forest seed banks, put forward a similar hypothesis.
341 Let us assume that all ancient forests have never known another land use than managed
342 forest, which is rather likely for the studies in this review (Jankowska-Blaszczuk & Grubb
343 1997; Van Calster et al. 2008a; Plue et al. in press). Hence, natural steady-state canopy
344 dynamics (e.g. Emborg et al. 2000) have been replaced by modern silvicultural systems,
345 encompassing tree harvest cycles of 100-150 years. Hence, small-scale patchy canopy gap
346 dynamics (~ shifting-mosaic) which maintain structurally diverse forest stands in which all
347 developmental stages are present, become replaced by infrequent stand-scale disturbances

348 (clearcuts in this case) yielding even-aged forest stands. Indeed, during this 100-150 year
349 period between large stand-scale disturbances, the forest is in a relatively stable aggradational
350 phase (Bormann & Likens 1979; Emborg et al. 2000), characterized by few natural gap-
351 disturbances and prolonged low-light levels. At most, foresters return every 5-15 years to thin
352 stands, creating temporary increased light levels. This severe change in canopy cover dynamics
353 induces the seed bank of light-demanding early-successional species to deplete as the absence
354 of light for extended periods of time inhibits seed bank replenishment. This is reflected by
355 declining seed bank species richness and seed density with time, independent of the abiotic
356 conditions (Fig. 2a, c). Furthermore, the seed bank depletion during successional series occurs
357 predictably through elimination of increasingly persistent seed banking species (Van Calster et
358 al. 2008a; Plue et al. in press). Seed senescence on the one hand and the inability to replenish
359 the seed bank on the other, thus appear the principal drivers of post-clearcut successional seed
360 bank dynamics (Van Calster et al. 2008a; Plue et al. in press). Additionally, few forest herb
361 layer species invest in a persistent seed bank (Bossuyt & Hermy 2001), yet their contribution
362 does grow with time (cf. increasing stress-tolerance with age, rise in percentage ancient forest
363 species (Fig. 2d, g); Bossuyt et al. 2002; Plue et al. in press).
364 Nevertheless, comparing these findings to the primary forest seed bank of Bialowieza
365 forest (Poland) (>500 yrs; Fig. 2), reveals a big contrast with the successional ancient forest
366 series very similar to the findings of another unique study in a tract of remnant primary
367 deciduous forest in Quebec (Canada; Leckie et al. 2000). Though only in the highest soil pH
368 category (Fig. 2), plots in the natural forest of Bialowieza contained considerably more species
369 than the oldest managed ancient forest stands (Fig. 2a) (Jankowska-Blaszczuk & Grubb 1997;
370 Leckie et al. 2000). Adding up the considerably larger seed weight (Fig. 2g) and the larger

371 share of ancient forest species (ca. 30% on average; Fig. 2e), likely responsible for the
372 increased species richness, the ecological seed bank profile - i.e. the collection of all seed bank
373 characteristics - in a primary forest seems to be quite different from the observed seed bank
374 profile in managed ancient forest (Leckie et al. 2000). Could the lack of steady-state canopy
375 conditions be responsible for this observation? Possibly, as the unpredictable temporal and
376 spatial canopy gaps characteristic of steady-state canopy conditions, seem most adequately
377 colonized by early-successional species via a persistent seed bank. Hence, early-successional
378 light-demanding species (>500 yrs; Fig. 2j) occupy their own narrow niche in natural
379 temperate forests. Yet, the large share of ancient forest species in these primary forests (ca.
380 30%; Fig. 2.e), also recorded ànd thought to be quite exceptional by Leckie et al. (2000), does
381 not fit common theories which deem ancient forest species do not need, and therefore do not
382 form a significant persistent seed bank (Hermy & Verheyen 2007). However, the shifting-
383 mosaic pattern of the forest canopy locally allows more light to filter through, triggering
384 enhanced population fitness and growth (Valverde & Silvertown 1998; Van Calster et al.
385 2008b). As a result, typical forest species may produce larger amounts of more viable seeds,
386 being either more likely to persist or more likely to be retrieved via sampling. In any case, this
387 hypothesis, supported by the increased ancient forest species seed bank share in a forested
388 series (Bossuyt et al. 2002; Plue et al. in press), seems to suggest that ancient forest species do
389 form a small functioning seed bank (Leckie et al. 2000; Jankowska-Blaszczuk & Grubb 1997)
390 as a buffer against temporary adverse environmental conditions or stochastic extinction events.
391 However, given the 100-150 year rotation cycles, with succession being set back each time, it
392 remains doubtful whether seed banks may once return to a state comparable to primary forest
393 (Duffy & Meier 1992).

394
395 Conclusions
396 Forest seed banks can indeed be considered a ‘memory’ of human interference in
397 temperate deciduous forests (Bakker et al. 1996), as our findings suggest that forest seed banks
398 bear the scars of a former land use and/or forest management for at least 150 years, mostly
399 irrespective of environmental conditions. Hence, our results imply that the influence of a
400 former land use does not necessarily diminish as forest stands mature beyond 50 years
401 (Bossuyt & Hermy 2001).
402 Inherent to succession, time – a substitute for processes such as seed senescence, seed
403 predation, secondary seed dispersal, failed germination and/or changing input from the herb
404 layer – is another important determinant of soil seed banks in both post-agricultural and post-
405 clearcut forest successions. Yet, temporal effects diverge. Indeed, despite the same series of
406 detrimental processes experienced by the soil seed bank in recent and ancient forests, time
407 induces a partial recovery of some seed bank characteristics in recent forests, yet it inhibits
408 ancient forest seed banks to return towards their initial state in primary forests.
409 Hence, this study adds to the already substantial body of literature, illustrating the
410 persistent impact man has on the natural environment, and encourages an increasing awareness
411 among researchers to incorporate land use history in ecological studies, including seed bank
412 studies.
413
414 Acknowledgements

415 J.P. wishes to thank Carol Baskin for help with collecting American seed traits & Dr. Thomas
416 Ludemann for sending his paper. Two anonymous referees are acknowledged for their
417 constructive critiques which significantly improved the manuscript.
418
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558

559 Tables
560 Table 1 Main characteristics of the 13 collected datasets.
Location Former land use Time since disturbance n Reference
Old Field succession
Europe
Vlaams Brabant, Belgium
Viroin, Belgium
Southern Black Forest, Germany
Durham County, United Kingdom
Southern Alps, France
Laelatu, western Estonia
USA
South-western Ohio, USA
North Carolina, USA
Forest succession
†
Arable fields
Grassland
Pasture/Grassland
Arable fields
Arable fields/Pasture/Heathland
Wooded meadow
Arable fields
Arable fields
Time since former land use
55/97/116/>250
41/133/>250
150/>250
50/80/100/>150
50/75/115/>250
20/>100
10/50/90/>250°
33/58/85/112/>250°
Stand age
8/8/8/12
2/2/1
6/7
1/1/1/1
2/3/3/8
10/10
1/1/1/1
2/2/2/2/2
Bossuyt et al. (2002)
Bossuyt et al. (2006)
Ludemann (1994)
Donelan & Thompson (1980)
Marage et al. (2006)
Kalamees & Zobel (1998)
Roberts & Vankat (1991)
Oosting & Humphreys (1940)
Europe
Montargis, central France Forest 20/40/70/100/120 8/16/6/12/6 Van Calster et al. (2008)
Hasbruch forest, northern Germany
Hasbruch forest, northern Germany
Hasbruch forest, northern Germany
Bialowieza forest, eastern Poland
561
Forest
Forest
Forest
Forest
40/80/120/>250
40/80/120/>250
120/>250
80/>250*
12/12/12/12
12/12/12/12
12/12
25/25
Plue et al. (in press)
Plue et al. (in press)
Plue et al. (unpublished data)
Jankowska-Blaszczuk et al. (1997)
>100/150/250 Ancient forest (stand age is approximate);
†
All stands are ancient forest; * Primary forest; ° Old-growth forest
562
563
Background information on sampling and germination protocols can be found in Appendix A.1

572
573
574
575
576
577
578
564 Table 2 Main effects of the fixed factors in the Linear Mixed Models applied on the seed
565 bank characteristics of the post-agricultural succesion dataset.
566
567
568
569
570 n AIC ∆AIC Land use x Time df F pH df F
Species richness
Shannon-Wiener Diversity Index
Seed density [#/m²] ²
Competitive signature 4
Stresstolerant signature ² ,4
Ruderal signature ² ,4
% Ancient forest species ² ,4
Raup & Crick dissimilarity [%]
Weight [mg] ³
(Bi)Annuals [%]
110 620.42
108
108
95
95
95
196.64
340.08
127.89
143.84
96.67
95 678.57
101 214.07
110 126.64
110 79.64
-
20.63
-
6.16
17.51
17.81
-
21.43
-
18.26
6
6
6
6
6
-
6
6
6
6
8.55***
2.29*
9.86***
8.37***
5.66***
-
3.01*
3.89**
3.53**
16.48***
3
-
3
3
-
3
3
-
3
-
7.26***
3.98**
17.69***
*** p ≤0.001; ** 0.001< p ≤0.01; * 0.01< p ≤0.05; AIC Akaike’s Information Criterion, a measure of the goodness
-
16.13***
4.60**
-
-
6.4**
- of fit for the final estimated model; ∆AIC observed change in the goodness of fit through model simplification compared to the full model; ² Square root transformed; ³ Ln(X + 1) transformed; 4 only on the European data; An extended table including the random effect of Location can be found in Appendix A.2.
571
579
580
581
582

583 Table 3 Main effects of the fixed factors Time and pH in the Generalized Linear Models
584 applied on the seed bank characteristics of the post-clearcut succession dataset.
585
586
587
588
589 n AIC ∆AIC Time df F pH df F
Species richness
Shannon-Wiener Diversity Index
Seed density [#/m²]
Competitive signature
Stresstolerant signature²
Ruderal signature
% Ancient forest species
193 950.07 32.45 3 54.82*** 2 48.33***
192 238.77 2.77
193 600.32 5.11
191
191
309.75
452.76
7.35
-
191 271.87 8.98
3
3
3
3
-
192 1499.39 13.52 3
18.91***
37.68***
9.31*
11.12*
-
7.98*
2
2
14.38**
22.53***
2 18.90***
2 27.02***
2 59.88***
- -
Raup & Crick dissimilarity [%]
Weight [mm]
168 587.07 4.69
191 32.17 11.96
3
-
9.93*
-
-
-
-
-
(Bi)Annuals [%] 189 175.97 6.44 3 14.76** 2 29.35***
*** p ≤0.001; ** 0.001< p ≤0.01; * 0.01< p ≤0.05; AIC Akaike’s Information Criterion, a measure of the goodness of fit for the final estimated model; ∆AIC observed change in the goodness of fit through model simplification compared to the full model; ² Only variable with significant Time × pH interaction term (df 6, F 32.43, p ≤0.001);
An extended table including the main effect of Location can be found in Appendix A.2.
590