# Microclimate and forest density drive plant population dynamics under climate change

Pieter Sanczuk, Karen de Pauw, Emiel de Lombaerde, Miska Luoto, Camille Meeussen, Sanne Govaert, Thomas Vanneste, Leen Depauw, Jörg Brunet, Sara Cousins, et al.

► To cite this version:

Pieter Sanczuk, Karen de Pauw, Emiel de Lombaerde, Miska Luoto, Camille Meeussen, et al. Mi-croclimate and forest density drive plant population dynamics under climate change. Nature Climate Change, 2023, 13 (8), pp.840-847. 10.1038/s41558-023-01744-y. hal-04259473

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
1 Microclimate and forest density drive plant population dynamics under climate change

2 Pieter Sanczuk¹*, Karen De Pauw¹, Emiel De Lombaerde¹, Miska Luoto², Camille Meeussen¹,

3 Sanne Govaert¹, Thomas Vanneste¹, Leen Depauw¹, Jörg Brunet³, Sara A.O. Cousins⁴, Cristina

4 Gasperini⁵, Per-Ola Hedwall³, Giovanni Iacopetti⁵, Jonathan Lenoir⁶, Jan Plue⁷, Federico Selvi⁵,

5 Fabien Spicher⁶, Jaime Uria-Diez³, Kris Verheyen¹, Pieter Vangansbeke¹, Pieter De Frenne¹

6

7 ¹Forest & Nature Lab, Department of Environment, Faculty of Bioscience Engineering, Ghent

8 University, Geraardsbergsesteenweg 267, 9090 Melle-Gontrode, Belgium

9

¹²University of Helsinki, Department of Geosciences and Geography, Gustaf Hällströmin katu 2,
10 00014 Helsinki, Finland

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13Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Box
14 190, 234 22 Lomma, Sweden

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14
15Landscapes, Environment and Geomatics, Department of Physical Geography, Stockholm
16 University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden

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17Department of Agriculture, Food, Environment and Forestry, University of Florence, P. le
18 Cascine 28, 50144 Florence, Italy

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19UMR CNRS 7058 "Ecologie et Dynamique des Systèmes Anthropisés" (EDYSAN), Université
de Picardie Jules Verne, 1 Rue des Louvels, 80000 Amiens, France

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23IVL Swedish Environmental Institute, Stockholm, Sweden

21
*Corresponding author: Pieter Sanczuk

22 Email: Pieter.Sanczuk@UGent.be
23 **Abstract**

Macroclimatic changes are impacting ecosystems worldwide. The majority of terrestrial species, however, lives in the shade of trees where impacts of macroclimate change are buffered. Yet, how microclimate buffering can impact future below-canopy biodiversity redistributions at the continental scale is unknown. Here we assess the effects of changes in **microclimate and forest density** on plant population dynamics under macroclimate change. We built 25-m resolution mechanistic demographic distribution models at European extent based on plant demography responses to changes in the environment in a unique cross-continental climate change transplant experiment. We show that changes in **microclimate and light** due to canopy opening amplify macroclimate change impacts on forest biodiversity, while shady forest floors due to dense tree canopies mitigate severe warming impacts. The **microclimate and forest density** thus emerge as powerful tools for forest managers and policy makers to shelter forest biodiversity from climate change.

36

37 **Key words**

Climate change experiment, demography, integral projection model, forest, microclimate, transplant experiment, species distribution, understorey.

40
41 **Main**

42 Numerous species are shifting their distributions towards higher elevations and latitudes due to
43 the warming of the climate system¹⁻³. Predictive models that quantify range shifts under climate
44 change almost exclusively rely on projected free-air macroclimatic conditions. Macroclimate
45 data are estimated from weather stations that record free-air temperatures at 1.5 to 2 m above
46 short grass, thereby failing to describe the relevant microclimatic conditions that are experienced
47 by the majority of terrestrial species on Earth⁴. Moreover, microclimatic conditions are highly
48 variable at fine spatial grains (typically 100 m to much finer) while macroclimate data are
49 generally only available at a coarse resolutions (typically 1 km or coarser)⁵. To date, piling
50 evidence points towards the importance of microclimatic and other fine-grained environmental
51 conditions for species' range shifts under climate change⁶⁻¹². If we are to improve our predictive
52 accuracy on future range dynamics, we cannot ignore this important part of the environment¹³.
53

53
54 In forests, directional changes towards species adapted to warmer temperatures remain somewhat
55 elusive, with slower, absent or even disparate trends frequently observed⁷,⁸. For instance, many
56 forest communities are lagging behind predictions based on macroclimatic temperature
57 increases⁷,¹⁴⁻¹⁶. Next to slow species' demography and dispersal rates¹⁷,¹⁸, these 'climatic lags'
58 are attributed to processes operating at fine spatial grains.

59 Due to their highly complex structure, trees are ecosystem engineers that attenuate variation in
60 below-canopy climatic conditions (i.e. the forest microclimate) and buffer forest species from
61 macroclimatic temperature extremes¹⁹⁻²². Microclimates determine major forest ecological
62 processes such as nutrient cycling²³, evapotranspiration²⁴, tree regeneration²⁵, soil seed bank
63 composition²⁶ and understorey species range dynamics¹⁵,²⁷,²⁸.
The capacity of forests to buffer below-canopy temperature fluctuations is highly heterogenous in space and time, and inherently links to forest structural complexity, forest density and distance to the forest edge: simple-structured forests and forest edges usually have less buffered microclimates¹⁹,²¹ and these conditions typically result in community reordering towards tall, competitive and warm-affinity generalists¹⁶,²⁹⁻³¹. However, how and to what extent fine-grained forest microclimates determine understorey plant responses to climate change across a large geographic extent remains an open question³². The relevance of considering forest microclimates is further enhanced since climate change has now initiated the largest pulse of forest disturbances and canopy opening in at least 170 years in Europe³³,³⁴, which takes away a line of defence of below-canopy biodiversity.

Here we designed a unique continental-scale transplant experiment along the entire latitudinal gradient of the European temperate broadleaved forest biome. Using an integral projection modelling (IPM) framework, we integrated the data to build mechanistic demographic distribution models (DDMs) at 25 m resolution. DDMs allow to predict mechanistically-informed range-wide population dynamics based on the responses of demographic vital rates of a species' life cycle (that is, survival, growth and reproduction) to changes in the environment. In contrast to the widely used species distribution models³⁵, DDMs facilitate the integration of biologically relevant mechanisms at ecologically meaningful spatial scales via experimental research³⁶,³⁷.

Both manipulative experiments (experimentally changed environmental conditions where species live) and transplant experiments (translocation of species towards new environments) are
frequently applied to unravel plant responses to changing environmental conditions²⁹,³⁰,³⁸,³⁹.
Especially when replicated at fine spatial grains (e.g. < 100 m) across several locations covering
a large spatial extent (e.g. > 1,000 km), such experiments are particularly suited to understand of
the effects of environmental drivers in a biogeographical context⁴⁰. We established transplant
experiments in five contrasting biogeographical areas along a c. 1,750 km latitudinal gradient,
and along two crossed microclimate gradients driven by forest structure and distance to the forest
edge (Methods; Fig. 1a,b). In three of the geographic regions, additional experimental heating
(simulating macroclimate warming using infrared heaters) and irradiation (simulating forest
canopy opening using fluorescent tubes) treatments were applied in a full-factorial design
(Methods; Fig 1c, Extended Data Fig. 1). Integrating the demographic data from the transplant
experiment, DDMs were parameterized to project the current and future distributions of common
understorey plant species, taking into account the effects of forest microclimate and forest cover
density.

Methodologically, we focused on a set of twelve common understorey plant species native to
European broadleaved forests, selected along a forest specialist – generalist spectrum inferred
from the Colonization Capacity Index (CCI)⁴¹. The species can thus be expected to respond
differently to macroclimatic and microclimatic gradients. We transplanted 8,064 individuals into
mesocosm communities that were stratified assemblages of four species from contrasting
ecological groups. A total of 25,997 *in situ* demographic measurements across six vital rates
(survival probability, growth rate, flowering probability, number of flowers, number of seeds and
seedling sizes) were regressed against plant size – describing each individual's state⁴² – and the
environment using mixed-effect models (Methods). The vital rate models were integrated into
IPMs to infer the effect of the environment on population dynamics³³. Demography-based distribution maps for each species were generated by projecting the IPMs across the study area (i.e. a broad window around the European temperate broadleaved forest biome) under all combinations of current and future macroclimatic conditions and forest cover density scenarios (following the protocol of ³⁶,³⁷). Projections of the DDMs under future macroclimatic conditions were produced for the period 2070 (i.e. the average macroclimate over the period 2061 – 2080) for the worst-case climate scenario (i.e. representative concentration pathway [RCP] 8.5). Projections of the DDM under forest cover density scenarios were based on changes in the current forest cover density (from minus 50% to plus 50%). In all model predictions, we assumed temperature offsets to stay constant over time.

120

## Plant vital rate responses

Across all species, plant size and the environmental variables predicted a substantial part of the variation in the vital rates (average marginal R² = 0.33; average conditional R² = 0.43; Fig. 2; Supplementary Table 1). Among all vital rates, the macroclimate variables growing-season temperature and precipitation were strong predictors of the individual's survival probability (after model selection, growing-season temperature and precipitation was included in the vital rate models of survival of respectively ten, and nine focal species), and in general suggested lower plant survival in colder and dryer macroclimatic conditions (Fig. 2). Since Darwin's essay 'On the origin of species', abiotic stress brought about by e.g. harsh climatic conditions has been postulated to dominate poleward (in latitude) and upward (in elevation) limits of species ranges⁴⁴. This pattern has already been generally validated⁴⁵⁻⁴⁷, and our results seem to mirror this pattern (but see ⁴⁸⁻⁵⁰). In line with a similar approach in a South African shrub species³⁶, our
results furthermore suggest that distribution patterns in the focal species are predominantly driven by hampered survival rather than reduced growth or fecundity.

Also local environmental conditions typically variable at fine spatial grains have been postulated to drive range dynamics across broad spatial extents⁴⁸,⁵⁰,⁵¹. Biotic interactions, for example, were shown to affect range limits through cumulated stress induced by harsh macroclimatic conditions and competition¹⁰,¹². Likewise, environmental stress induced by unfavourable forest microclimates can possibly alter range dynamics in forest species³², but this remained untested.

Our results indeed hint towards the importance of forest microclimatic conditions (in terms of summer and winter temperature offsets), and forest cover density on multiple vital rates that contribute to population growth. Interestingly, in contrast to the macroclimate variables, forest microclimate variables had strong effects on several vital rates from which the direction of the effect was dependent on the species' specialism to forests. Among all vital rates, this pattern was again the most pronounced for survival probability (after model selection, summer and winter temperature offsets, and forest cover density was included in the vital rate models of survival of eight, nine, and nine focal species, respectively; Fig. 2).

Typical forest specialists had the highest survival probability when winter minima (i.e. more positive winter temperature offset values) and summer maxima (i.e. more negative summer temperature offset values) were buffered below tree canopies (Fig. 2; Supplementary Table 4). In contrast, the survival probability of forest generalist species responded to a lesser extent to buffered temperature extremes but were negatively affected by high forest cover density, possibly because these species lack physiological adaptations to cope with low light levels⁵². For
example, based on our data, we estimated that, on average, an increase of the thermal buffering
during summer from -1°C to -2°C (i.e. what is typically observed along a 100 meter forest edge
to interior transect²¹), resulted in a 37% increase in the survival probability in *Oxalis acetosella*,
a typical forest specialist, but only in an 11% increase in the survival probability in *Urtica
dioica*, a fast-colonizing generalist (Extended Data Fig. 7). Similarly, an increase of the forest
cover density from 50% to 60% resulted in a 13% higher survival probability in *Anemone
nemorosa*, another emblematic forest specialist in temperate Europe, but a 6% lower survival
probability in the generalist *Urtica dioica*. Naturally, forest managers cannot manipulate the
forest cover density (and thus light availability) without altering the subcanopy temperature
offsets as both are linked and jointly determine the specific microclimatic conditions that are
experienced by understorey species. Nevertheless, managers can strive to a specific set of
microclimatic conditions (e.g. shady forest-floor conditions that are buffered from free-air
temperature extremes through maintaining a high canopy cover), hereby deploying
microclimates as a tool to “manipulate” species’ turnover in understorey plant communities.

170

171 **Demographic distribution model predictions**

172 Integrating the effects of the local and regional environmental drivers on all vital rates that
173 contribute to the generative population growth allowed us to project the population growth rate
174 (λ) across Europe (Fig. 3; Extended Data Fig. 12 for geographic uncertainty of λ). We generated
175 mechanistically-informed distribution maps at 25 m resolution for the extent of temperate
176 Europe. As expected from the vital rate models (Fig. 2), the DDMs showed substantial variation
177 in λ, both at the continental-level (area of 28 million km²) and at the landscape-level extent (three
178 areas of 4 km²), indicating the importance of fine-grained microclimatic conditions (i.e. summer
and winter temperature offsets), and forest cover density, next to the coarse-grained macroclimatic conditions (i.e. growing-season temperature and precipitation) in driving forest plant population dynamics. While evidently the variation in $\lambda$ at the landscape-level represents only a subtle part of the variation at the continental extent, these results highlight the fundamental role of forest microclimatic conditions on the local persistence of understorey populations¹⁵.

Considerable changes were predicted in the spatial distribution for all understorey species due to macroclimate change (Fig. 4; Extended Data Figs.10,11). Interestingly, and very relevant in light of the accelerating rates of canopy disturbances beyond historical averages³³,⁵³,⁵⁴, we detected that changes of the forest cover density (and thus altered light regimes at the forest floor) may amplify macroclimate change effects on forest understorey plants. We furthermore found that both the direction (positive or negative) and magnitude of the species’ population responses was dependent on their specialism to forests (Fig. 5, Supplementary Table 4). For example, in *Anemone nemorosa*, a 50% reduction of the forest cover density would **likely** amplify the negative impact of macroclimate change by not less than 195%. In contrast, in the generalists *Alliaria petiolata* and *Urtica dioica*, beneficial effects brought about by canopy opening **could** far exceeded the negative impacts of macroclimate change on population growth (Extended Data Figs. 10,11). Mechanistically, the vital rate models suggest that forest specialist species were negatively impacted by canopy opening due to higher light levels that hamper survival, likely as an inevitable consequence of elevated competitive interactions with more generalist species.

On the contrary, an increase of the forest cover density could mitigate macroclimate change impacts on forest specialist species **by limiting abrupt light stress**. This trend corroborates
202 observational data<sup>55</sup>, and highlights the importance of a continuous cover forest management
203 under climate change. We find, for instance, in *Anemone nemorosa* that an increase of 20%
204 forest cover density mitigates the impacts of macroclimate change by not less than 65%, while an
205 increase of 50% compensate almost the entire (92%) effect (Fig. 5, Extended Data Figs. 10,11).

206 While direct impacts of macroclimate change on canopy disturbances are increasingly apparent<sup>53</sup>
207 with cascading effects on understorey biodiversity<sup>56</sup>, managers should maximally retain dense
208 forest canopies to maintain shady forest-floor conditions to mitigate severe impacts on
209 understorey communities. Nonetheless, to sustainably preserve diverse forest communities,
210 policy makers should first merit key priority to tackle drivers of macroclimate change.

211
212 In all model predictions, we assumed temperature offsets to stay constant over time although
213 forest disturbance reduces offset values<sup>19,21</sup> and warming enhances offset values under
214 maintained forest cover<sup>22</sup>. Thus, our model predictions likely underestimated the effect of
215 changes in the forest cover density on plant population dynamics under macroclimate change,
216 and are thus conservative. High-resolution projected temperature offsets under climate change
217 are indeed not yet available, and are most likely driven by a complex interplay among many
218 environmental variables, including macroclimate and forest cover density, but also by e.g.
219 dynamics in cloud cover<sup>57</sup>. Using constant temperature offset values in all future predictions,
220 however, allowed us to isolate changes in species' demography within the community driven by
221 the independent effect of light<sup>30</sup>.

222
223 **Challenges in demographic distribution modelling**
While the mechanistic nature of DDMs brought advantages over more correlative approaches such as species distribution modelling, it also has methodological limitations that should be considered when interpreting the results. First, DDMs require us to make assumptions on the structure of the vital rate models. While more complex vital models are likely to better describe the natural complexity of the system (e.g. by including higher order polynomials and interactions terms), the robust parameterization of these models typically trades off with the number of observations in the data set. For this reason, we here only considered monotonous relationships of the vital rates to the environment, while a qualitative sensitivity analyses against this approach suggested that some vital rate models are likely to follow a more complex response curve (Extended Data Fig. 7). For instance, the vital rates survival and flowering probability in *Allium ursinum* more likely followed a unimodal response to the winter temperature offset gradient which is not captured by the assumed monotonous response curve in our models. Second, DDMs are data hungry models. Ideally, demographic data should be collected in the critical macro- and microenvironmental conditions within the study area. This is, however, often impossible due to a limitation of time and resources. Given that our work aimed to assess fine-grained variation in λ, we put maximal effort the collect demographic data in the critical microclimatic conditions, while the range of sampled macroclimatic conditions within the study area was still not fully covered (Extended Data Fig. 14). Model extrapolation is inherent to predictive ecology, and the accuracy of the presented DDM predictions (far) beyond the macroclimatic range that was sampled (in our case, e.g. at higher elevations in the Alps) is evidently lower. A well-designed plot network which *a priori* considers the goal of the study, is thus key in the efficient and successful application of DDMs.

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**Conclusions**

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In sum, we here built demographic distribution models based on mechanistic plant responses to both microclimate and macroclimate. We found that fine-grained microclimatic conditions **and forest density**, in addition to the macroclimatic conditions, **play a fundamental role in understorey species' distributions**. Our results suggest that changes in forest-floor conditions, regardless of whether the drivers of canopy opening are natural or anthropogenic, can amplify macroclimate change impacts on forest biodiversity. We propose that sustainable forest management should conserve conditions that favour the survival of forest specialist species through maintaining a high canopy cover to limit both abrupt light stress and temperature increase. The DDMs **suggest** that macroclimate change impacts on forest specialist species (e.g. emblematic species such as *Anemone nemorosa* and *Allium ursinum*) **could** be mitigated by increased forest density. Forest generalists whose ranges often promptly respond to changes in the macroclimate system, **are likely to keep** on taking advantage from the natural variation in microclimate and light availability after disturbance that is inherent to European broadleaved forest dynamics.

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Our integrative methodological framework (R-code for the analyses of the DDMs available as Supplementary Material) is adaptable to many other species and ecosystems to examine the importance of fine-grained environmental conditions in a warming world, and can undoubtedly improve our understanding of species range dynamics under 21st century climate change.
<figure><img src="image_2.png" /></figure>

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**Figure 1** Overview of the study. (a) Experimental locations with heating and irradiation treatments (red), control plots (black) and the Temperate broadleaved and mixed forest biome⁵⁸ (shaded). (b) Experimental sites (yellow) along two microclimate gradients created by forest structure and forest edge distance. (c) One experimental site with heating and irradiation treatments. (d) Nine mesocosm communities per treatment. (e) A mesocosm community containing four individuals of four contrasting species. (f-g) Temperature offset values in dense vs. simple forests and forest cores vs. edges. (h) Forest cover density in dense vs. simple forests. In all boxplots, we present median (horizontal line), 1st and 3rd quantile (lower and upper hinges, 1.5 time the inter-quartile-range (whiskers), and outliers (points).

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<figure><img src="image_3.png" /><figcaption>**Figure 2** Four key vital rates survival probability (survival), growth rate (growth), flowering probability (flowering) and the number of flowers (# flowers) regressed against individual plant height (plant size) and the environmental covariates. Values are (generalized) linear mixed-effect model (LMM) coefficient estimates [± standard error] (after model selection; presented in horizontal way). Species are ranked following the Colonization Capacity Index (CCI). Significant effects (p < .05) are in bold. Non-significant effects (p ≥ .05) are faded. Model coefficient estimates for the vital rates number of seeds and seedling size are presented in Extended Data Fig. 9. See Supplementary Table 1 for model details.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 3** Range model predictions of the current distributions (estimated as the population growth rate, λ) at the continental scale and the landscape scale (three example areas of 2 × 2 km [400 ha] around the centroid of the experimental sites in Italy [IT], Belgium [BE] and South Sweden [S-SW]) based on population growth rates (λ). For three species (*Oxalis acetosella*, *Vinca minor* and *Geranium sylvaticum*), the data did not allow to construct DDMs due to the absence of observations related to fecundity. Maps are in an equal area projection [EPSG:3035].</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 4** Predicted change in future population growth rate (Δλ) under future macroclimate change (RCP 8.5) and 50% forest density decrease at the continental scale and the landscape scale (three areas of 2 × 2 km [400 ha] around the centroid of the experimental sites in Italy [IT], Belgium [BE] and South Sweden [S-SW]). Changes are expressed relative to the range model predictions of the current distributions (Fig. 3). Maps are in an equal area projection [EPSG:3035]. See Extended data Fig. 10 for maps on all environmental change scenarios.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 5** Predicted change in future population growth rate ($\lambda$) for different climate and forest density scenarios (50% decrease and 50% increase). Plotted are continental-scale average % change [± standard deviation (SD)] in $\lambda$ relative to the baseline predictions (Fig. 3) for all combinations of future macroclimate change and increased and decreased forest density. Species are ranked from specialists (left) to generalists (right) following their colonization capacity index (CCI).</figcaption></figure>

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420 Methods

421 1. Study region

422 The study region encompasses all forested area within a broad window around the European
423 temperate broadleaf and mixed forest biome⁵⁸, between 10°W - 20°E and 40°N - 61°N (Fig. 1).

424

425 2. Study species

426 Twelve species were selected based on their common occurrence in the understorey of temperate
427 European forests and their difference in affinity to light and warmth (Anemone nemorosa, Oxalis
428 acetosella, Carex sylvatica, Vinca minor, Poa nemoralis, Allium ursinum, Deschampsia
429 cespitosa, Geranium sylvaticum, Geranium robertianum, Geum urbanum, Alliaria petiolata and
430 Urtica dioica). The species' light preference was inferred from the Colonization Capacity Index
431 (CCI)⁴¹, a continuous gradient from -100 (generalist species) to +100 (typical forest specialist
432 species) representing their association with ancient, structurally complex forests (Supplementary
433 Table 1). The CCI generally coincides with the forest specialist – generalist spectrum⁴¹. For one
434 species (Geranium sylvaticum), the CCI was inferred based on expert knowledge because no
435 value was available. The species' warmth preferences were inferred from their Thermal Niche
436 Optimum (TNO), which were calculated as the mean annual temperature within the species'
437 distribution range⁵⁹. Using the species' temperature and light preferences, the species were
438 categorized in four ecological groups: cold-adapted forest specialists; warm-adapted forest
439 specialists; cold-adapted forest generalists and warm-adapted forest generalists.

440

441 3. Transplant experiment
A large-scale transplant experiment was installed in early Spring 2019 in five regions along a c. 1,750 km long transect spanning the entire European temperature broadleaved forest biome²⁹ (Fig. 1a). In each region, four experimental locations were established with contrasting forest structure (simple vs. dense) and distance to the forest edge (edge vs. core; Fig. 1b), hereby capturing a major part of the natural variation in macroclimate, microclimate and light availability in European broadleaf forests. Dense forest stands typically had a well-developed shrub and tree layer, high basal area and canopy cover. Simple forest stands were characterized by a high canopy openness and the absence of a shrub layer. In each forest stand, experimental plots were installed at c. 2 m from the south-facing forest edge and in the forest core (minimal distance of 50 m from any forest edge). In total, 20 experimental sites (5 regions × 2 forest types × 2 sites per forest type) were established.

In three regions (Italy, Belgium and South Sweden), experimental heating and irradiance treatments were applied in a full-factorial design (Fig. 1c, Extended Data Fig. 1). Hence, in these regions, each site contained four experimental plots: heating, lighting, heating and lighting, and a control. The experimental sites in France and Central Sweden contained only control plots. In total, the transplant experiment consisted of 56 experimental plots. The experimental treatments were applied during the growing season for three consecutive years, from May 2019 (after installation) till 30 September 2019 and from 1 February till 30 September in 2020 and 2021. More detail on the experimental design are provided in Supplementary Methods 1.

All experimental plots contained nine mesocosm communities, each composed of four individuals (replicates) of four different species (one from each of the two ecological groups, see study species) randomly planted in a rectangular grid of 4 × 4 (27 cm × 37 cm; Fig. 1d, e). The
four species were sampled from the set of twelve species in a stratified way that ensured a
combination of one species from each ecological group in each mesocosm, with all species
occurring in equal numbers at the plot level. In total, at the onset of the transplant experiment, it
contained 8,064 individuals (56 experimental plots × 9 mesocosms × 4 species × 4 individuals)
and 672 individuals per species (12 replicates × 56 experimental plots). All biological material
originated from the same source in Belgium or Germany to minimize potential effects of local
adaptation (Supplementary Table 1).

472

473 4. Plant demographic data

474 Demographic data were collected at the individual plant level (Extended Data Fig. 2). Summer-
475 flowering plant species were measured in July 2019, 2020 and 2021. Spring-flowering plant
476 species were measured in April and May 2020 and 2021. For all species, we measured survival,
477 natural plant height (distance between the soil surface and the uppermost photosynthetic
478 tissue⁶⁰), flowering status and the number of flowers. For *Anemone nemorosa* and *Geum
479 urbanum*, we additionally counted the number of seeds (when present) in 2021. For *Alliaria
480 petiolata* and *Allium ursinum* we measured the seedling size (natural height) in 2021 at the plot
481 level (since parent plants were not exactly known). See Extended Data Fig. 3 for details on the
482 annual sampling sizes.

483

484 5. Environmental data

485 We quantified the environment based on large-scale macroclimate conditions and local-scale
486 variation in forest microclimates and forest density. Five variables were selected that capture
487 major non-edaphic environmental axes in European temperate forest systems at both large- and
small-spatial gradients, and that are potential drivers of plant demography: average growing season temperature; cumulative growing season precipitation; winter and summer temperature offset due to canopy cover and distance to the forest edge; and forest density (supplementary Table 2)¹⁹,²⁹. Each variable was assessed at the level of the experimental site (for use in the vital rate models and IPMs), and retrieved as raster grids at the extent of the study region (for use in the DDMs) (see Extended Data Fig. 2 for a flowchart of the data).

5.1. Environment of the experimental sites

The *average growing season temperature* and *cumulative growing season precipitation* were calculated as the average of monthly mean growing season (April till July) temperatures and as the sum of monthly mean growing season cumulative precipitation for the most recent available 30-year period (1970-2000) extracted at 30 arcsec (~1 km) resolution from WorldClim v2.1 [projection EPSG:4326]⁶¹.

Winter and summer temperature offset values were calculated as subcanopy microclimate temperatures minus macroclimate temperatures. The winter offset metric positively relates to the capacity of canopies to buffer free-air thermal minima and increased with forest structure and distance to the forest edge. The summer offset metric negatively relates to the capacity of canopies to buffer free-air thermal maxima and decreases with forest structure and distance to the forest edge¹⁹,²¹. In situ microclimate temperatures were measured at 15 cm above the soil surface using TMS-4 loggers (TOMST, Prague, Czech Republic) installed in the centre of each control plot (n = 20), and covered by a white radiation shield. Local air temperatures were recorded at 15 min intervals from 17 May 2019 till 30 June 2021. Hourly mean microclimate temperatures were calculated to match the temporal resolution of the macroclimate data. Hourly mean macroclimate
temperatures at 2 m height for each control site were extracted from the ERA5-land reanalysis data base⁶² at the spatial resolution of 0.1 × 0.1 degrees (native resolution of 9 km) [EPSG:4326].

Hourly offset values (ΔT °C) were calculated as microclimate minus macroclimate temperatures.

Finally, for each experimental site, summer offsets were calculated as the average offset from June till August. Winter offsets were calculated as the average offset from December till February.

*Forest density* was quantified as the cumulative percentage of tree canopy cover and shrub cover within a circle with radius of 9 m around the centre of each experimental site. The cumulative percentage was standardized to a maximum of 100%.

5.2. Environmental data of the study area

Gridded *average growing season temperature* and *cumulative growing season precipitation* data were calculated as the average of monthly mean growing season temperatures and as the sum of monthly mean growing season cumulative precipitation for the most recent available 30-year period (1970-2000) at 30 arcsec (~1 km) resolution from WorldClim v2.1 [EPSG:4326] and projected to an equal area projection [EPSG:3035].

Gridded *summer temperature offset* data were calculated as the average offset from June till August. Gridded *winter temperature offset* data were calculated as the average offset from December till February. Gridded Monthly temperature offset data were retrieved from Haesen et al., (2021)²⁰ at the resolution of 25 m [EPGS:3035].

Gridded *forest density* data were retrieved from the 2015 Copernicus tree cover density map and represent the average horizontal tree cover density (in a range of 0 - 100 %; https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/status-
534 *maps/2015*) in a grid of 20 × 20 m [EPSG:3035]. Gridded forest density data were resampled to
535 25 m resolution using a bilinear interpolation.

536
537 5.3. Future environmental data
538 The future average growing season temperature and cumulative growing season precipitation
539 for the period 2070 (i.e. average for the period 2061 – 2080) were retrieved from WorldClim
540 v2.1 under phase 5 of the Coupled Model Intercomparison Project (CMIP5) at 30 arcsec
541 resolution for the representative concentration pathway RCP8.5. Gridded future average growing
542 season temperature (mean of monthly maximum and minimum temperatures) and cumulative
543 precipitation data were calculated as averages across four General Circulation Models (GCMs) to
544 consider uncertainty related to each GCM. The GCMs were selected based on minimal
545 interdependency following⁶³ and data availability in WorldClim v2.1. We included MIROC5,
546 INMCM4, ACCESS1-0 and CNRM-CM5.

547 To evaluate the effects of forest management (and hence changes in forest structure) on
548 population dynamics, two additional forest cover density maps with a hypothetical decrease and
549 increase of 50% forest cover density (always with a maximum of 100%) were simulated. A
550 decrease of 50% in forest density in response to management or natural disturbance is a realistic
551 scenario: the average disturbance severity in European disturbed forest patches is 77% (with 0%
552 indicating zero loss and 100% indicating complete replacement)⁵³.

553 For the sake of simplicity, we assumed temperature offset values to stay constant over time
554 although macroclimate warming might enhance offset values²². Our estimates are thus
555 conservative.

556
557 6. Demographic models

558 6.1. Vital rate models

559 Individual-level measurements of six vital rates describing key transitions in the plant's generative life cycle were regressed against experimental site-level environmental data (5.1) and plant size (for implementation in the IPMs) (Fig. S2, S4). The six vital rates included survival probability (n = 12 species), growth rate (n = 12), flowering probability (n = 8), number of reproductive structures (flowers or inflorescences; n = 8), number of seeds per fruit (n = 2) and seedling sizes (n = 5). Vital rates of species with too few or no observations were not modelled and introduced as constant values in the IPMs (Supplementary Tables 1,3).

566
567 The vital rates were analysed with mixed-effect models in the R package *lme4*<sup>64</sup> for each species
568 separately. Prior to modelling, the state variable 'plant size' was checked for normality based on
569 visual inspection and transformed (*ln* or *sqrt*) when appropriate (Supplementary Table 1,
570 Extended Data Fig. 5). To account for the nested experimental design, 'mesocosm ID' (9 levels)
571 was included as random intercept in all models of vital rates that were measured at the level of
572 the individual. As a sensitivity analysis against this approach, we also tested an alternative
573 random effect term accounting for the fully nested study design: 'mesocosm ID' nested within
574 'experimental site' nested within 'forest type' nested within 'region'. This, however, did not
575 changed the general trends in parameter estimates of the fixed effects (Extended Data Fig. 6).
576 We therefore opted to keep the simpler random structure in all models. To account for
577 environmental stochasticity across the study period, 'sampling year' was included as an
578 additional random intercept in all vital rates models that were measured for more than one annual
579 transition. Growth rate and the seedling size were regressed with linear mixed models (LMMs).
Survival and flowering probability (binary distributed) were regressed with generalized linear mixed-effect models (GLMMs) and a binomial error distribution. The number of reproductive structures and the number of seeds were regressed with GLMMs and a Poisson error distribution.

The vital rate models included plant size (when relevant) and all environmental variables as fixed effect terms. Pairwise spearman correlations (r) among the environmental variables were acceptable (median |r| = 0.17; maximum |r| = 0.62)⁶⁵. The seedling size models of *Alliaria petiolata* and *Allium ursinum* and the seed number models did not include the macroclimate predictors to avoid over-extrapolation because these vital rates were only measured in the sites of Belgium and Sweden⁶⁶. The optimal level of model complexity was evaluated based on the lowest Akaike information criterion with small-sample correction (AICc) by comparing all possible nested models with reduced complexity using the R package *MuMln*⁶⁷. For the model selection of the LMMs growth rate and the seedling size, we first set the restricted maximum likelihood (REML) argument to “FALSE”. Once the best model structure was selected, we set REML to TRUE for exact coefficient estimation⁶⁴. Model fit of the selected models was assessed as the percentage of variance explained by the fixed effects (marginal R²; R²_m), and the percentage of variance explained by both fixed and random effects (conditional R²; R²_c) following Nakagawa & Schielzeth (2013)⁶⁸. We only tested linear responses of the vital rates to the environmental gradients and plant size. Nevertheless, we also performed a qualitative sensitivity analyses against this approach by fitting the vital rate models with more flexible Generalized Additive Models (GAMs; details in Supplementary Methods 2; Extended Data Fig. 7).

601
To analyse the relation between the species association with ancient forests (inferred from the CCI) and the responses of the survival probability to changes in the environment, we ran linear models with the parameter coefficients of this vital rate model as response variable, and the species' CCI as predictor. The linear models were ran in a Bayesian framework using the R package *brms*<sup>69</sup> that allows to take the standard errors around the model coefficient estimates into account. We used default chain parameters.

## 6.2. Integral Projection Models (IPMs)

The effects of the environment and experimental treatments on population growth rates ($\lambda$) were investigated by combining the vital rate models (as built in section 6.1) into Integral Projection Models (IPMs<sup>42</sup>). IPMs are analogous to matrix population models that allow the integration of multiple vital rates regressed against a continuous state variable, and are well-suited to gain mechanistic understanding on population dynamics to changes in the environment by including environmental covariates into each vital rate model of the IPM<sup>43</sup>. We built IPMs based on 'plant height' (sqrt- or ln-transformed; see 6.1) as the continuous state variable. For biennial species (*Geranium robertianum* and *Alliaria petiolata*), IPMs included 'age' as an additional state variable to explicitly account for their biennial life cycle<sup>70,71</sup>. We did not construct IPMs (and DDMs) for *Vinca minor*, *Geranium sylvaticum* and *Oxalis acetosella* due to the absence of fecundity data in these species. For these species, responses to the environment were only inferred based on the vital rates survival probability and growth rate.

Because we could not infer vital rates related to vegetative reproduction (clonal growth) due to the lack of empirical data (this would require destructive sampling), the approach presented here
estimates population growth rates that apply on generative population growth in the phase of
population establishment. Nevertheless, we acknowledge that for some forest plants, vegetative
reproduction is an important aspect of the life cycle. In addition, the estimated λ implicitly
accounts for the effects of neighbouring competitors as all individuals were transplanted into
community mesocosms. Details on the IPM structure are described in Supplementary Methods 3.

630

631 6.3. Demographic Distribution Models (DDMs)

632 6.3.1. Demography-based distribution maps (i.e. maps of λ)

633 Demography-based distribution maps (i.e. maps of λ) for each species were produced by
634 projecting the IPMs across the study extent³⁶,³⁷ (Extended Data Fig 2). Both continental- and
635 landscape-scale maps were produced to assess the model’s behaviour along macro- and
636 microclimate gradients. Continental-extent maps were computed by iterating the IPM for each
637 species in 1,000,000 random forested locations sampled within the study area. IPMs in these
638 locations were supplied with environmental covariate values extracted from the 25-m resolution
639 environmental layers. Landscape-extent maps were computed by iterating the IPM in all forested
640 25 × 25 m cells in a squared area of 2 × 2 km (400 ha) around the centroid of the experimental
641 plots in Italy, Belgium and South Sweden. Hence, both continental-extent and landscape-extent
642 maps relied on 25 meter resolution environmental data. Projected maps of λ were computed for
643 the current environmental conditions and all combinations of future macroclimate change and a
644 50% increase or decrease in forest cover density.

645 In all model future scenarios, we assumed temperature offsets to stay constant over time
646 although forest disturbance reduces offset values¹⁹,²¹ and warming enhances offset values under
647 maintained forest cover²². The rationale behind this decision was two-fold: (1) projected future
high-resolution below-canopy temperature offset data at the European scale are simply not yet available to date; (2) using constant temperature offset values under different forest management and climate-change scenarios allowed us to isolate the effect of changes in species' demography within the community driven by the independent effect of light³⁰.

The effects of the experimental heating and irradiation treatments on the intercepts of the vital rates were not included in the future DDM predictions. Hence, we applied a *space-for-time* substitution to make future predictions, i.e. we only used the spatial gradient from the transplant experiment to infer changes over time, not including the temporal change brought on by the experimental treatments. All DDM predictions implicitly included forest edge effects by the vital rate responses to less buffered microclimates and more light availability that are typical for forest edges²¹,⁷².

Geographic uncertainty of population growth rates ($\lambda$) was quantified by bootstrapping (Supplementary Methods 4).

Parallel computation was implemented using the R packages *foreach*⁷³ and *doParallel*⁷⁴. Maps were produced using the R package *tmap*⁷⁵, and are in an equal area projection [EPSG:3035]. In all maps, $\lambda$ values higher than the 97.5% quantile were projected to the 97.5% quantile to avoid extremely high $\lambda$ values in response to marginal environments that obscure the main gradient of the maps.

### 6.3.2 Scenario analyses under variable forest cover density

To quantify the impact of variable forest cover density (e.g. in response to changes in the forest management), we calculated the population growth rate in a subset of 10,000 random locations under all combinations of current and future climatic conditions and an increased and decreased
671 forest cover density of 10%, 20%, 30%, 40%, and 50%. The proportional change (relative to the
672 population growth rate under the baseline climate and forest cover conditions) was calculated
673 and plotted for each species.

674

675 6.3.3 Validation of the distribution maps

676 A general and convenient method to validate DDM predictions is currently not existing. We here
677 developed a method to test whether the DDMs perform better than random based on a
678 quantitative comparison of the average population growth rates in occurrence locations (i.e.
679 locations were the species was observed) versus random background locations. The average
680 population growth rates in occurrence locations are expected to be higher compared to the
681 background locations if a model is better than random. Significant differences between the two
682 groups were tested with one-sample t-tests. Occurrence locations for each species were extracted
683 from the Global Biodiversity Information Facility (GBIF, http://www.gbif.org). To improve the
684 data quality, all occurrence locations were subject to a systematic cleaning protocol
685 (Supplementary Methods 5). Unfortunately, we could not collect enough ‘real’ absence data
686 points for all species, and therefore had to work with background locations as an alternative. At
687 these background locations, the target species could be either absent or unobserved.

688 For each species, population growth rates were calculated for 1,000 forested occurrence locations
689 and 1,000 forested background locations. Because the median coordinate uncertainty of the
690 cleaned occurrence locations after cleaning was still relatively low (333 meter), we calculated the
691 population growth rate as the average population growth rate of all raster cell within an area of
692 ~1 km² (i.e. circular area with a radius of 564 meter) around these locations. Additional details
693 are provided in Supplementary Methods 6.
694 All analyses were performed in R version 4.1.0⁷⁶.
695 **Data availability**

696 Macroclimate data are available through the global climate archive WorldClim
697 (https://www.worldclim.org/data/index.html). Spatial temperature offset data between forest and
698 open field conditions are available at https://doi.org/10.6084/m9 figshare.14618235²⁰. Tree cover
699 density data are available at https://land.copernicus.eu/pan-european/high-resolution-
700 layers/forests/tree-cover-density/status-maps/2015. Georeferenced observation records used for
701 the continental scale validation of the Demographic Distribution Models (DDMs) are available at
702 https://doi.org/10.15468/dl.3nvzc8 All experimental plant demographic data and site-level
703 environmental data will be made available on an online repository such as FigShare
704 (https://figshare.com/) (temporary private link to the data is already available:
705 https://figshare.com/s/35b070818230ce5795d3).

706

707 **Code availability**

708 All scripts to reproduce the methods, analyses and figures will be made available on an online
709 repository such as FigShare (https://figshare.com/) (temporary private link to the code to the
710 code: https://figshare.com/s/35b070818230ce5795d3).

711

712 **Acknowledgements**

713 We thank Kris Ceunen, Hans Rudolf Schaffner, Ulf Johansson, Sarah Vaneenooghe, Jonathan
714 Van Loo, Sam Van Bouchaute, Linnea Glav Lundin, Tom Augustijns and Stef Haesen for
715 fieldwork and technical assistance.

716 Funding: This work was supported by the European Research Council (ERC) (ERC Starting
717 Grant FORMICA 757833, 2018, http://www.formica.ugent.be); the Research Foundation
718 Flanders (FWO) (K.D.P. ASP035-19, S.G. G0H1517N), and the FWO Scientific research
719 network FLEUR (http://www.fleur.ugent.be).

720

721 Author contributions

722 PS, KDP, EDL, ML, KV, PV and PDF conceived the ideas and designed methodology; all
723 authors collected data; PS analysed data in collaboration with EDL, ML, PV and PDF; PS led the
724 writing of the manuscript in collaboration with KDP, ED, PV and PDF. All authors contributed
725 critically to the draft and gave final approval for publication.

726

727 Competing interests

728 None of the authors have a conflict of interest
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