Microclimate and dry years interfere with landscape structure effects on intraspecific trait variation

doi:10.21203/rs.3.rs-5311721/v1

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Microclimate and dry years interfere with landscape structure effects on intraspecific trait variation

https://doi.org/10.21203/rs.3.rs-5311721/v1

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Context

Predicting how changes in weather patterns and land use jointly impact populations is a pressing task in ecology. Microclimate may play a key role in species’ local persistence by modulating regional weather effects. We lack sufficient empirical evidence to understand the relative effects of landscape structure and habitat conditions on intraspecific trait variation.

Objectives

Using a spatially and temporally replicated demographic dataset, we tested the relative effect of landscape structure (area and connectivity of remnant habitat fragments), microclimate (heat load), and fluctuation in weather conditions (study year) on intraspecific plant trait variation, and we investigated whether the local heat load modulated the weather effects on the studied traits.

Methods

We performed repeated measurements of stem height, leaf area, number of stems, main inflorescence length and number of primary side inflorescences of 569 permanently marked individuals of the grassland specialist Salvia nemorosa L. We sampled 13 populations encompassing microhabitats exposed to different heat load levels, over three consecutive years.

Results

Mature individuals had fewer stems in isolated, and taller stems in small habitat fragments. High levels of heat load and dry years affected negatively all measured traits, and the negative effects of exposure to high heat load were generally exacerbated in dry years.

Conclusions

Exposure to strong environmental stressors could ultimately obscure the real effect of human impact on plant populations. Landscape planning for conservation of dry grassland species should ensure not only improved habitat connectivity but also high-quality habitats with heterogeneous microclimates able to buffer weather extremes.

climate change

functional biogeography

geographic isolation

habitat area

habitat connectivity

Great Hungarian Plain

heat load

microclimate

Salvia nemorosa

Changes in land use have severely rearranged the structure of natural landscapes, causing habitat fragmentation and loss of habitat amount and connectivity (Plieninger et al., 2016). With accelerated climate change, ecologists are pressed to understand how populations affected by the modified landscape structure cope with altered weather patterns (Maclean & Early, 2023; Plieninger et al., 2016). Microclimate may modulate the regional weather effects, thus playing a key role in the persistence of plant populations in remnant habitat patches. The combination of ecological pressures acting at different spatial and temporal scales may enhance plant trait variation and amplify or alleviate the risk of local extinction (McKeon et al., 2023). However, accurate forecasts of population persistence are impeded by lack of empirical evidence for relative effects of landscape structure, macro- and microclimate on intraspecific trait variation (Griffen & Drake, 2008; Opedal et al., 2015).

The modified landscape structure may affect plant populations in predictable ways (Fahrig, 2017; Hanski & Ovaskainen, 2003; Ibáñez et al., 2014; Zambrano et al., 2019). For example, habitat fragmentation can negatively impact species’ reproductive success and dispersal by reducing the diversity of pollinators and seed dispersers, which exerts differential pressures on plants with different life histories (Girão et al., 2007; Lopes et al., 2009, May et al., 2019). Trait shifts attributed to habitat fragmentation are often due to isolation effects, because isolation (connectivity loss) can be an aspect of habitat fragmentation, and fragmentation effects are higher in the most isolated and smallest habitat fragments (Fahrig, 2003, 2017; Haddad et al., 2015; Hanski & Ovaskainen, 2003; Ottaviani et al., 2020). Negative effects specifically attributed to loss of connectivity include hindered pollen and seed dispersal that may cause genetic deterioration and drift (Deák et al., 2018; Ibáñez et al., 2014; Tewksbury et al., 2002; Uroy et al., 2019), and consequently lowered survival chances of species that invest in pollen and seed dispersal compared to species that invest more in other fitness-related traits (Jacquemyn et al., 2012, Deák et al. 2021). Local populations may respond to isolation via plastic or adaptive trait shifts, e.g. an increased pappus width enabled increased seed dispersal distance (Dener et al., 2021), selection for increased autogamy improved female reproductive fitness in plants with pollination limitation (Xiao et al., 2016), seed numbers lowered due to reduced chances of dispersal (Rossetto & Kooyman, 2005). Thus, geographic isolation triggers spatial variation in traits between populations globally, yet its strength may vary across biological systems (Csergő et al., 2024).

The reduction of the suitable habitat area for certain species is another common attribute of fragmented landscapes, and it can negatively affect a large array of plant traits related to dispersal, establishment, and persistence (Haddad et al., 2015, Zambrano et al., 2019). Due to small population size and consequently lowered genetic diversity and increased inbreeding, small habitat area may decrease reproduction and increase local extinction rates (Zambrano et al., 2019). A low amount of area available for colonisation along with modified interspecific relationships (e.g., increased competition and herbivore pressure) may also cause failure to establish and grow, e.g., lower seed number and quality, shorter and fewer stems (Lowe et al., 2005; Zambrano et al., 2019). Thus, small habitat area affects negatively the persistence of species with small stature and lacking autogamy, and of species with poor dispersal ability and random dispersal patterns (Astegiano et al., 2015,). On the other hand, species with life history traits enhancing local persistence such as high wood density, may be more successful in small habitat fragments (Herrero-Jáuregui et al., 2022; Kimberley et al., 2014, Haddad et al. 2015). As with isolation, local populations may respond to constraints from small habitat area and associated processes with plastic or adaptive trait shifts. Increased competition in small habitat fragments could cause trait shifts towards more acquisitive strategies, e.g. fewer and taller stems and larger leaf areas, and limited reproduction (Ibáñez et al., 2014). Several evolutionary trait shifts were related to increased post-dispersal competitive advantages and likelihood of establishment in small habitats: increased resprouting ability, seed weight and seed size (Burns, 2016; Kavanagh & Burns, 2014). Thus, small habitat area also exerts differential pressures on species with different life histories, underlying spatial variation in trait diversity (Fahrig, 2020; Herceg-Szórádi et al., 2023, Ibáñez et al., 2014).

In addition to spatial constraints due to reconfigured habitats, plant populations in remnant habitat fragments face limitations due to the warming climate and restricted availability of suitable microhabitats for establishment or growth (McKeon et al., 2023; Riba et al., 2002). Plant traits related to light and water use are the most sensitive to higher air temperature, light availability and drought stress specific to remnant habitat fragments (Magnago et al., 2014). Rising temperatures and associated drought are therefore expected to trigger exacerbated responses of these traits in fragmented habitats. Expected effects include increased leaf mass per area (LMA) and decreased specific leaf area (SLA) and plant height for traits involved in persistence, and decreased flower or inflorescence number, but increased inflorescence size for traits involved in establishment (Descamps et al., 2021; Niinemets, 2001; Wright et al., 2004). However, the amount of heat that individuals receive through solar radiation can vary in areas with a heterogenous topography. For example, southern and southwestern slope aspects in the Northern hemisphere receive solar radiation for longer periods and with greater intensity compared to northern and northeastern slope aspects, creating warmer and drier environment (He et al., 2017). As a result, microclimate, known to directly influence the ecophysiology of individuals, can strongly modulate the effect of macroclimate conditions on plant traits, and ultimately, the availability of suitable microclimates will drive the overall performance of local populations (De Frenne et al., 2013; Deák et al., 2024; Fekete et al., 2023; Kemppinen et al., 2024). For example, higher variation in leaf size and increased leaf mass per area was reported on plant individuals growing on south-facing slopes compared to north-facing slopes, suggesting a diversity in plant water use and leaf balance energy depending on exposure to local conditions (Ackerly et al., 2002; Li et al., 2021).

Furthermore, despite major advances in functional landscape ecology, trait-based models of habitat fragmentation syndromes are often limited to snapshot data records. These approaches lack the ability to correctly predict future chances of population persistence in fragmented landscapes under climate change, for which repeated measurements over several years, in populations subject to different macro- and microclimate conditions are necessary (Buckley & Puy, 2022; Christiansen et al., 2024; Holt et al., 2022). Repeated measurements were key to our understanding of effects of water availability on hydraulic and leaf traits (Anderegg, 2015), or environmental variation effects on fruit morphology (Sato et al., 2000; Yamada et al., 1993). However, due to inherent data collection difficulties, studies of intraspecific trait variation in fragmented landscapes replicated both spatially and temporally are not common (Nicolè et al., 2011; Valdés et al., 2014).

We addressed this knowledge gap, and we examined the relative effect of habitat area and connectivity, local heat load and annual weather fluctuations on intraspecific trait variation in fragmented Salvia nemorosa L. populations over three consecutive years. We examined whether vegetative traits involved in the process of plant persistence (number of stems, stem height, leaf area) and generative traits involved in the process of plant establishment (main inflorescence length and number of side inflorescences) show effects normally expected due to limiting factors, or, on the contrary, they show plastic or adaptive shifts expected in response to those limitations (Table 1). We expected that i) in isolated and small habitat fragments, plants potentially negatively impacted by genetic deterioration or modified biotic interactions would have limited growth and reproduction e.g., a lower number of stems and shorter stems. However, we hypothesized that plants might have responded to these limitations to avoid the negative consequences of impeded persistence and establishment. Thus, in isolation, plants could have developed taller stems and longer main inflorescences better visible to pollinators, or more side inflorescences to extend the chances of successful pollination; likewise, in small habitats, plants could have developed taller stems and larger leaf area as a response to the likely increased interspecific competition. We further expected that ii) in microhabitats exposed to excessive levels of heat load both the vegetative and generative traits will be constrained compared to habitats with more benign microclimate. However, we expected that the main inflorescence length may have increased under high levels of heat load, to improve the chances of early season reproduction. Finally, we expected that iii) trait responses to elevated heat load would be exacerbated in years with hot and dry weather.

Table 1

Table showing the measured *Salvia nemorosa* traits, the plant process involved, and expected trait responses to constraints by geographic isolation, small area and heat stress. Negative sign indicates effects normally expected due to limiting factors, and positive sign indicates possible trait shifts in response to those limitations.
Measured trait	Plant process	Isolation (Genetic deterioration and drift due to limited pollen and seed dispersal)	Small area (Genetic deterioration due to small population size, modified biotic interactions)	Heat load (microclimate)	Heat and water stress (hot and dry years)
Number of stems	Persistence	-	- + Increased bud bank (number of stems) to increase the likelihood of individual persistence	-	-
Stem height	Persistence	- + Increased visibility for pollinators	- + Improved light acquisition in response to competition	-	-
Leaf area	Persistence	-	- + Faster resource acquisition in response to competition	-	-
Main inflorescence length	Establishment (early season)	- + Increased visibility for pollinators	-	- + Boosted early season reproduction	- + Boosted early season reproduction
Number of side inflorescences	Establishment (late season)	- + Bet-hedging, extended chances of pollination	- + Bet-hedging, extended chances of establishment	-	-

Study sites

We chose kurgans as a model system. Kurgans are ancient burial mounds built in large numbers during the Late Copper, Bronze and Iron Ages throughout the steppe biome in Eurasia (Deák et al., 2020). Primarily due to the intensive agricultural practices, approximately 57% of pristine European steppe grasslands in Chernozem soils have been destroyed or abandoned (Török et al., 2016). Due to their morphology and cultural significance, many kurgans escaped cultivation, representing one of the last refuges for populations of steppe and dry grasslands species in intensively used landscapes (Deák et al., 2020). Being separated from each other and other grasslands by croplands, farmlands and roads, they represent a good example of habitats with severed connectivity, which may be challenging to overcome for species with modest dispersal abilities such as S. nemorosa (Novák & Konvička, 2006). Previous studies found kurgans to favour species with large seed mass, tall growth and autonomous reproduction (Deák et al., 2021a), and to have lower plant density of specialist species compared to large steppe enclaves (Dembicz et al., 2016).

The study was located in the Great Hungarian Plain in Eastern Hungary, Europe (47° 19' 32"N − 21° 6' 58"E, Fig. 1a,b). The climate is continental with mean annual temperatures of 10–11 ºC, average precipitations of 500–600 mm and a typical elevation of 90–120 m.a.s.l. (National Meteorological Service of Hungary, 2022). In general, soils are highly fertile chernozems, making them ideal for agriculture (Deák et al., 2021a). We worked on 13 sites (11 kurgans and two reference flat grasslands) where S. nemorosa was present, spanning across 135 km (E - W) and 79 km (N - S) (Fig. 1b). Kurgans were selected to represent different habitat area (basal area ranged between 528–4,321 m²) and connectivity (Hanski connectivity index ranged between 0–338.4). S. nemorosa grew in remnant grassland patches dominated by Festuca rupicola, and other typical grasses were Agropyron cristatus, Stipa capillata, Bromus inermis. Due to lack of management, on kurgans patches of weeds (Carduus spp., Cirsium spp., Rumex spp.), native woody species (Crataegus monogyna, Prunus spinosa), and invasive woody species (Gleditsia triacanthos, Robinia pseudoacacia, Lycium barbarum) were typically present. The reference grasslands were usually mown in June-July.

Study species

Salvia nemorosa L. or Wood sage (Lamiaceae) is a zoogamous, short-lived hemicryptophyte, without active seed dispersal mechanisms and without obvious clonality, typically growing 20–50 cm tall (PADAPT, 2024). It has simple leaves with opposite leaf arrangement and can grow a large number of stems, most of which are usually generative in mature individuals. The inflorescence is a pseudo-spike composed of cymose inflorescences arranged in a verticillaster. The main inflorescence generally develops side inflorescence pairs that can further produce secondary and tertiary side inflorescences, thus extending the flowering time. The species is disturbance tolerant, thermophile, halophobe, heliophile and xero-tolerant, and it is characteristic of the Salvio-Festuceum rupicolae loess grassland association (Borhidi, 1995). It is a European species with a Pontic – Mediterranean - Central European range, where it is normally exposed to severe fluctuations in rainfall distribution and high temperatures (PADAPT, 2024). Due to the severe decline and degradation of dry grasslands, S. nemorosa has often found refuge in small habitat islands such as kurgans and roadside verges over large parts of its distribution area (Deák et al., 2018; Moysiyenko & Sudnik-Wójcikowska, 2010; Valkó et al., 2018).

Data collection

Data collection followed the protocol of Plantpopnet, a spatially distributed model system for population ecology (Buckley et al., 2019). Data was collected at peak flowering of S. nemorosa in June-July, over three consecutive years between 2021–2023. Eight sites were censused each year throughout the length of the study, further four sites were censused only twice as new sites were added to the study between 2022–2023, while one site was only censused once in 2021 due to subsequent census difficulties. We established up to four permanent transects of varying lengths (4–10 m), depending on the area of the habitat patch, where the number of individuals was representative of the habitat (Fig. 1c). On kurgans, S. nemorosa individuals were often limited to only one side of the kurgan (typically on South-Southwestern slopes or on the top), but on three kurgans a larger number of individuals allowed setting up transects on two contrasting slopes. Along each transect, we laid down permanent contiguous plots of 0.5 x 0.5 m, within which we permanently marked all S. nemorosa individuals with a numbered linoleum tag, including new individuals each consecutive year, and if there were no individuals, the plot was marked as empty (Fig. 1d). We recorded site (GPS coordinates, date of visit, management, habitat type), and transect level (cardinal aspect and slope) information. For each plant, we recorded in-situ three vegetative traits: height of the tallest stem, number of stems, length and width of two biggest leaves forming a leaf pair, and two reproductive traits: inflorescence length and number of primary side inflorescence pairs. To calculate mean leaf area, we approximated the measured leaf shapes to a triangle, and we multiplied the leaf length and width, which we averaged across the two measured leaves.

Data selection

In this study, we narrowed our analyses only to mature plants, to capture maximum attainable growth responses that can be related to the reproductive effort of same individuals. We defined mature individuals as plants that had a flowering probability (i.e., the likelihood that a plant develops inflorescence) higher than 50%. For this, we modelled the flowering state (yes or no) as a function of stem height, by fitting a Generalized Linear Mixed-effects Model (GLMM) with binomial error distribution in the lme4 package in R (Bates et al., 2015). Fixed effects were the tallest stem height in interaction with study year, while study site, transect, plot and plant were nested random effects. We tested the model assumptions using the DHARMa package (Hartig, 2022). We then used the generic function predict to obtain the flowering probability values for each individual, and individuals predicted a flowering probability lower than 50% were removed from further analyses. The model structure and results are presented in Supporting material (Figure S1,Table S5).

To ensure data quality, we removed imperfect observations e.g., dry plants or inflorescences, extreme values due to unusual growing circumstances, etc. After this step, the final number of plants involved in the models was 569 mature individuals, ranging between 13–49 (2021), 12–67 (2022) and 19–71 (2023) plants per site and per year.

Landscape structure and microclimate

We used the connectivity index by Hanski et al., (2000) to quantify the level of habitat isolation within a 300 m buffer around the focal habitat patch (a lower value indicating higher isolation):

$$\:{CI}_{i}={\sum\:}_{ij=1}^{n}\text{e}\text{x}\text{p}(-{\propto\:d}_{ij}){A}_{j}^{\beta\:}$$

where A_j is the area of a neighbouring grassland within the 300 m buffer area, d_ij is the distance from the focal patch to the neighbouring grassland, α is a species-specific parameter related to the species’ dispersal ability, and β a parameter describing the scaling of immigration, both set to 0.5 based on the assumption of a relatively low dispersal of 200 m for S. nemorosa. The area of the neighbouring grassland was calculated from digitised present day (2014–2016) habitat maps (source: Unified National Projection System, 1:10,000 topographic map of Hungary (Institute and Museum of Military History, Budapest), Satellite images (Google Maps) and field surveys, as in Deák et al., 2021b).

We calculated the habitat area as the basal area of the kurgan or the area of the grassland patch using the same method as described above (Deák et al., 2021b). To ease calculations due to the disproportionately large area of one of the reference grasslands compared to the area of the kurgans, we unified the Hanski connectivity and habitat area values for the two reference grasslands to the value of the grassland with the smaller area (232,061 m²).

To characterise the microclimate of the different transects we used the heat load index, a direct measure of incident solar radiation on a land surface (Buttrick et al., 2015) that combines aspect (converted into degrees, slope, and latitude values (McCune & Keon, 2002), and which we calculated with a function by (Zelený & Lin, (2019) in R, using the first equation:

$$\:Heat\:load=\:{e}^{(-1.467+1.582*cos\left(L\right)*cos\left(S\right)-1.500*cos\left(A\right)*sin\left(S\right)*sin\left(L\right)-0.262*sin\left(L\right)*sin\left(S\right)+0.607*sin\left(A\right)*sin\left(S\right))}$$

where L is the site latitude, A is the aspect where the S. nemorosa populations where found, and S is the slope angle of the transect.

Data analysis

We fitted Generalized Mixed Effects models (GLMM) using the glmmTMB package in R (Brooks et al., 2017; R Core Team, 2021) to test the effect of isolation, habitat area, heat load and study year on five measured traits of S. nemorosa (tallest stem height, number of stems, mean leaf area, main inflorescence length, and number of primary side inflorescence pairs). The vegetative trait models had the Hanski index, habitat area, heat load and study year specified as fixed effects, while in the reproductive trait models the date of visit was an additional fixed effect to cover the variance due to the continuous growth of the open inflorescence. Study site, transect, plot, and plant were nested random effects in all models, to avoid pseudo replication due to repeated measurements. We fitted models of vegetative traits and inflorescence length with a Gaussian error distribution, while the number of primary side inflorescences model was fitted with a truncated Poisson error distribution with log link. Due to dealing with only two reference grasslands which had very large area compared to the kurgans, we fitted all models on two separate datasets: a) kurgans-only, b) kurgans and reference grasslands, referred to hereafter as “all sites”. This way we kept the structure of the models relatively simple and avoided model singularity due to the low number of reference grasslands. In the main text we highlight the significant relationships obtained using both datasets, and we present the model structure and results for the kurgans-only dataset in Table 2, and for the “all sites” dataset in Table S2.

Table 2

Details of linear mixed-effects models (LMMs) testing the effect of landscape structure, microclimate and study year on five *Salvia nemorosa* traits using data collected on 11 kurgans. The first two columns show the response variable and the model structure, and the explanatory variables respectively. The next columns show the coefficient means (β) and standard errors SE (β), the p value determined by anova tests, the conditional (R²c) and marginal (R²m) R squared values of the model and the p values corresponding to the Kolmogorov-Smirnov test (K.S.) of model fit. Significant effects are shown in bold letters.
Response variable and model structure	Explanatory variables	Estimate (β)	SE (β)	p	R²	p (K.S. test)
Stem height glmmTMB(log(stem_hei_noinf_allplants) ~ hload_sc + Hanski_sc + log_habitat_area_sc + c_year + (1 \| site_id/trans_id/plot_id/plant_id), data = stemhe_noNA2, REML = TRUE, control = glmmTMBControl(optCtrl = list(iter.max = 1e3,eval.max = 1e3)), family = gaussian(link= "identity"))	(Intercept)	5.952	0.039	< 0.001	R²c = 0.751 R²m = 0.578	0.165
	Heat load	-0.175	0.027	< 0.001
	Hanski index	0.030	0.044	0.508
	Kurgan area	< 0.001	0.043	0.994
	year 2022	-0.364	0.019	< 0.001
	year 2023	0.093	0.018	< 0.001
Mean leaf area glmmTMB(sqrt(mean_leafarea) ~ hload_sc + Hanski_sc + log_habitat_area_sc + c_year + (1 \| site_id/trans_id/plot_id/plant_id), data = leafarea_noNA2, REML = TRUE, control = glmmTMBControl(optCtrl = list(iter.max = 1e3,eval.max = 1e3)), family = gaussian(link= "identity"))	(Intercept)	7.646	0.059	< 0.001	R²c = 0.459 R²m = 0.216	0.128
	Heat load	-0.165	0.045	0.002
	Hanski index	0.096	0.068	0.169
	Kurgan area	0.001	0.058	0.996
	year 2022	-0.260	0.044	< 0.001
	year 2023	0.207	0.041	< 0.001
Number of stems glmmTMB(log(num_stems) ~ hload_sc + Hanski_sc + log_habitat_area_sc + c_year + (1 \| site_id/trans_id/plot_id/plant_id), data = numstems_noNA2, REML = TRUE, control = glmmTMBControl(optCtrl = list(iter.max = 1e3,eval.max = 1e3)), family = gaussian(link= "identity"))	(Intercept)	2.171	0.045	< 0.001	R²c = 0.851 R²m = 0.093	0.218
	Heat load	-0.176	0.043	< 0.001
	Hanski index	0.196	0.048	< 0.001
	Kurgan area	0.008	0.041	0.883
	year 2022	-0.190	0.029	< 0.001
	year 2023	-0.395	0.029	< 0.001
Inflorescence length glmmTMB(inf_len ~ hload_sc + Hanski_sc + habitat_area_sc + date_number_sc + c_year + (1 \| site_id/trans_id/plot_id/plant_id), data = alldatjoin4a_large, REML = TRUE, control = glmmTMBControl(optCtrl = list(iter.max = 1e3,eval.max = 1e3)), family = gaussian(link= "identity"))	(Intercept)	132.526	7.691	< 0.001	R²c = 0.459 R²m = 0.214	0.252
	Heat load	-1.830	4.567	0.783
	Hanski index	-3.713	7.663	0.496
	Kurgan area	-1.864	6.945	0.746
	Date of visit	-15.158	6.229	0.207
	year 2022	-3.124	2.668	< 0.001
	year 2023	-62.959	5.519	< 0.001
Number of primary side inflorescence pairs glmmTMB(Prim_side_inf_pairs ~ hload_sc + Hanski_sc + log_habitat_area_sc + date_number_sc + c_year + (1 \| site_id/trans_id/plot_id/plant_id), data = alldatjoin5a_large, REML = TRUE, control = glmmTMBControl(optCtrl = list(iter.max = 1e3,eval.max = 1e3)), ziformula = ~ 1, family = truncated_compois(link = "log"))	(Intercept)	-0.037	0.221	< 0.001	R²c = 0.366 R²m = 0.147	0.965
	Heat load	-0.273	0.129	0.017
	Hanski index	0.145	0.169	0.302
	Kurgan area	-0.005	0.159	0.974
	Date of visit	0.309	0.098	< 0.001
	year 2022	-0.030	0.176	0.009
	year 2023	0.478	0.196	0.009

Because habitat area and connectivity were correlated (Figure S2), we refitted the models with either habitat area or Hanski index excluded from both datasets (Table S3, Table S4). This approach allowed us to identify uncertain area or connectivity effects due to the presence of both terms in the main models.

Finally, to investigate whether microclimate effects were modulated by annual weather fluctuations, we refitted the models to test the effect of the interaction between heat load and study year on the measured traits. Because of increased model complexity causing non-convergence warnings, we omitted the Hanski index, habitat area and date of visit from this set of models, while keeping the rest of the model structure unchanged (except for the number of primary side inflorescences model which this time was fitted with a Poisson error distribution with log link). For this analysis we used the kurgans-only dataset, due to uniform habitat conditions producing more robust models (Table 3).

Table 3

Details of linear mixed-effects models (LMMs) testing the interaction between heat load (microclimate) and study year (weather proxy) on five *Salvia nemorosa* traits using data collected on 11 kurgans. The first two columns show the response variable and the model structure, and the explanatory variables respectively. The next columns show the coefficient means (β) and standard errors SE (β), the p value determined by anova tests, the conditional (R²c) and marginal (R²m) R squared values of the model and the p values corresponding to the Kolmogorov-Smirnov test (K.S.) of model fit. Significant effects are shown in bold letters.
Response variable and model structure	Explanatory variables	Estimate (β)	SE (β)	p	R2	p (K.S. test)
Stem height glmmTMB(log(stem_hei_noinf_allplants) ~ hload_sc * c_year + (1 \| site_id/trans_id/plot_id/plant_id), data = stemhe_noNA2, REML = TRUE, control = glmmTMBControl(optCtrl = list(iter.max = 1e3,eval.max = 1e3)), family = gaussian(link= "identity"))	(Intercept)	5.926	0.041	< 0.001	R²c = 0.765 R²m = 0.575	0.221
	Heat load	-0.224	0.031	< 0.001
	year 2022	-0.341	0.020	< 0.001
	year 2023	0.113	0.019	< 0.001
	Heat load:year 2022	0.049	0.018	< 0.001
	Heat load:year 2023	0.075	0.019	< 0.001
Mean leaf area glmmTMB(sqrt(mean_leafarea) ~ hload_sc * c_year + (1 \| site_id/trans_id/plot_id/plant_id), data = leafarea_noNA2, REML = TRUE, control = glmmTMBControl(optCtrl = list(iter.max = 1e3,eval.max = 1e3)), family = gaussian(link= "identity"))	(Intercept)	47.443	1.593	< 0.001	R²c = 0.500 R²m = 0.211	0.156
	Heat load	-2.939	1.429	< 0.001
	year 2022	-5.799	0.993	< 0.001
	year 2023	4.118	0.945	< 0.001
	Heat load:year 2022	-1.963	0.928	< 0.001
	Heat load:year 2023	0.040	0.954	< 0.001
Number of stems glmmTMB(num_stems ~ hload_sc * c_year + (1 \| site_id/trans_id/plot_id/plant_id), data = numstems_noNA2, REML = TRUE, control = glmmTMBControl(optCtrl = list(iter.max = 1e3,eval.max = 1e3)), family = poisson(link = "log"))^^	(Intercept)	2.105	0.071	< 0.001	R²c = 0.854 R²m = 0.066	0.081
	Heat load	-0.241	0.068	0.0118
	year 2022	-0.158	0.030	< 0.001
	year 2023	-0.380	0.030	< 0.001
	Heat load:year 2022	0.031	0.028	< 0.001
	Heat load:year 2023	0.261	0.031	< 0.001
Inflorescence length glmmTMB(inf_len ~ hload_sc * c_year + (1 \| site_id/trans_id/plot_id/plant_id), data = alldatjoin4a_large, REML = FALSE, control = glmmTMBControl(optCtrl = list(iter.max = 1e3,eval.max = 1e3)), family = gaussian(link= "identity"))^^	(Intercept)	129.660	7.185	< 0.001	R²c = 0.478 R²m = 0.234	0.077
	Heat load	6.210	5.802	0.021
	year 2022	-61.188	5.545	< 0.001
	year 2023	-10.906	5.204	< 0.001
	Heat load:year 2022	-16.508	5.454	< 0.001
	Heat load:year 2023	1.803	5.621	< 0.001
Number of primary side inflorescence pairs glmmTMB(Prim_side_inf_pairs ~ hload_sc * c_year + (1 \| site_id/trans_id/plot_id/plant_id), data = alldatjoin5a_large, REML = TRUE, control = glmmTMBControl(optCtrl = list(iter.max = 1e3,eval.max = 1e3)), ziformula = ~ 1, family = poisson(link = log))	(Intercept)	0.181	0.175	0.302	R²c = 0.533 R²m = 0.309	0.314
	Heat load	-0.447	0.147	0.003
	year 2022	-0.984	0.163	< 0.001
	year 2023	-0.392	0.134	< 0.001
	Heat load:year 2022	-0.191	0.135	< 0.001
	Heat load:year 2023	0.436	0.135	< 0.001

We tested if residuals followed the expected distribution using the Kolmogorov-Smirnov test, and the diagnostics of overdispersion and zero inflation using the DHARMa package in R (Hartig, 2022). We explored the model goodness-of-fit by calculating Nakagawa’s conditional and marginal R² values, using the performance::r2 command in the performance package in R (Lüdecke et al., 2021). We examined whether the effect of explanatory variables was significant by comparing the full models encompassing all variables with reduced models from which the variable of interest was removed, using ANOVA tests. To make the effect size of variables comparable within each set of models, all continuous predictor variables were centred on 0 and scaled to have unit variance. In models fitted with Gaussian error distribution, the response variables were log- or square root transformed if the transformation improved the normality of residuals.

The descriptive statistics for the analysed dataset are presented in Table S1.

The Hanski connectivity index was moderately positively, significantly correlated with number of stems, but the relationship was detectable only in the more homogeneous dataset containing exclusively kurgans (Fig. 2a, Table 2). In models refitted without habitat area, the Hanski connectivity index was still positively, significantly correlated with the number of stems, indicating unequivocal effect of isolation (kurgans-only dataset, Table S3).

Habitat area had a moderately negative, significant effect on the number of stems and a negative, marginally significant effect on the stem height, but the effects were detectable only in the dataset including all sites and were due to comparatively lower number of stems and shorter stems in the two large reference grasslands (Fig. 2b, Table S2). However, in models refitted without the Hanski index, the correlation between habitat area and the number of stems was not maintained, indicating that isolation contributed to some extent to this relationship. Instead, the negative, marginally significant effect of habitat area on the stem height became significant, indicating that the effect of area on the stem height was initially blurred by associated isolation effects (all sites dataset, Fig. 2c, Table S4).

There was a strong negative, significant effect of heat load on all measured traits except for the inflorescence length in models using the kurgans-only dataset (Table 2), and qualitatively similar results in models using all sites, except for the inflorescence length model where the heat load effect was significant (Table S2).

Study year had significant effect on all measured traits in models using both the kurgans-only and the “all sites” datasets. Compared to the study year 2021, the dry 2022 year had a strong negative, significant effect on all traits, with a subsequent recovery during 2023 except for the number of stems, which lowered even further (Table 2, Table S2). The interaction between the local heat load and the study year was significant in models on each trait, with the effect of heat load turning either negative or stronger in the dry year 2022 compared to 2021 or 2023 (Fig. 3, Table 3).

There was no relationship between heat load and Hanski index and heat load and habitat area, thus habitats with different degree of isolation and area were positioned randomly along the heat load gradient (Fig. 4).

Using a spatially and temporally replicated dataset in combination with a local heat load gradient we showed that the strong heat load gradient modulated by temporal fluctuations in weather conditions were dominant drivers of trait variation in S. nemorosa, and for some traits these effects were superimposed on constraints due to land use changes. We propose that effects of microhabitat exposure to excessive heat load amplified by dry years may obscure the real effect of altered landscape structure on intraspecific trait variation.

Effect of habitat area and isolation

We found intraspecific trait variation associated with constraints from isolation and small area of habitat patches in our system. On isolated kurgans, S. nemorosa individuals had lower number of stems compared to kurgans benefiting from higher connectivity, although the models explained a low proportion of variation in this trait. The poorer performance of S. nemorosa in isolation was in line with lower seed germination rates of the species in the same habitats, indicative of genetic deterioration due to severed connectivity (Szász, 2022). This is an important limitation with potential negative consequences for the future dynamic of isolated S. nemorosa populations, because the number of stems determines major vital rates in plants in general, and in this species (Zayani, 2024), and constraints on plant size could be early warning signals of population declines typical of remnant habitat fragments (Deák et al., 2021b). Individuals had significantly taller stems on kurgans compared to large reference grasslands, suggesting that processes associated with small area affect the studied species on all kurgans. The result does not clearly support the expectation of impeded growth due to genetic deterioration in small habitat fragments (Lowe et al., 2005; Zambrano et al., 2019), instead it confirms responses to increased levels of competition in small habitat patches, which may cause shift towards more acquisitive strategies in plants (Botta-Dukát, 2021). Moderate competitors such as S. nemorosa (Kelemen et al., 2013) may be expected to respond readily to increased levels of competition by growing taller stems, and community level studies confirm shifts towards taller species in the system (Deák et al., 2024). We further expected that hemicryptophytes such as S. nemorosa could further respond to increased competition by increased investment in self-maintenance favouring local persistence by e.g. increased bud bank on the rhizome or shift to large sizes (Biddick et al., 2019; Zambrano et al., 2019), however in S. nemorosa this is a less likely cause of the increased number of stems on kurgans. Because both area effects were solely driven by the two reference grasslands having individuals with significantly fewer and shorter stems, the management history of the sites could be at least partially the trigger of the responses. Lack of disturbance, typical of abandoned grasslands, enables forbs to grow a larger number of stems and reach higher local abundance, while frequent mowing, a regular management practice in extensive grasslands, may reset the life cycle of individuals at critical life stages, reducing the biomass (Csergő et al., 2013; Dee & Baum, 2019). As most kurgans are unmanaged, this may on the one hand increase levels of competition, but on the other hand leave S. nemorosa individuals time to reach sizes closer to their biological schedules.

Effects of microhabitat heat load

We found strong negative responses in all traits to increased heat load. This result aligns with abundant evidence of heat stress having negative impacts on plant growth and reproduction (Descamps et al. 2021; Tene et al., 2023; Willi & Buskirk, 2022), and mirrors the interspecific patterns of trait distributions in the same system (Deák et al., 2024; Deák et al., 2021a). The trait distribution in S. nemorosa followed expectations from the “species interactions – abiotic stress” hypothesis, which provides a theoretical framework to explain limits to species distribution along a low stress (high competition) – high stress (low competition) gradient (Kirk et al., 2021; Louthan et al., 2015). S. nemorosa individuals occurred between relatively lower heat load conditions (northerly, “cooler” side of kurgans) and high heat load conditions (southerly, “hotter” side of kurgans). This gradient causes whole community level shifts from strong competitor grasses and taller forbs to stress-tolerant plants or plants with shorter life cycles on kurgans (Deák et al., 2024). Along this gradient, as a balanced CSR strategist species (Kelemen et al. 2013), S. nemorosa shifted from competitive avoidance and fast resource acquisition responses under low heat load conditions (tall stems, large leaf area, several side inflorescences) (Botta-Dukát, 2021), towards stress tolerance and self-maintenance responses under high heat load conditions (shorter stems, small leaf area, few side inflorescences). Each type of response may have cascading effects on the persistence strategies of the species, for example a higher number of side inflorescences under more competitive conditions could enable a bet-hedging strategy extending the chances of pollination and seed maturation and dispersal, while a lower number of side inflorescences and a shorter inflorescence under more stressful conditions could enable a faster life cycle with fast seed development and maturation. Our result thus underscores the importance of local processes favouring or jeopardizing the persistence of individuals in remnant population fragments (Dupré & Ehrlén, 2002; Griffen and Drake 2008, Ibánez et al. 2014, Marini et al., 2012; Xu et al., 2017). We further advance that trait shifts expected as a result of the modified landscape structure may have been blurred by the strong responses to the heat stress gradient typical of kurgans (Deák et al., 2024), because populations subject to different degrees of isolation and habitat area were positioned randomly along the heat load gradient.

Effects of temporal weather fluctuations

Our approach provided a unique opportunity to study how trait responses to the strong heat load gradient may be modulated by annual weather conditions, and we found that effects of the heat load on traits were strongly dependent on the study year. Across the study interval, the year 2022 was the hottest and driest in the studied region. For example, in June, the month of peak flowering of S. nemorosa, average temperatures exceeded the 1991–2020 average temperatures with 2–3°C, and total June precipitation was only 30–40% of normal decadal values. By comparison, the June of year 2021 was 1–2°C warmer, and 20–30% drier than the previous decade, while the June of year 2023 was 0-0.5°C cooler and 100–120% wetter than the previous decade (Hungarian Meteorological Service, https://www.met.hu/). Weather conditions are known to synchronise fluctuations of spatially separated populations (Moran, 1953; Salguero-Gómez & Gamelon, 2021), which was the case of the extremely dry year 2022 that exacerbated the negative heat load effects in all traits. In small populations (or sub-populations) occupying the stressful end of the gradient, such years may have catastrophic consequences (Grear & Burns, 2007; Salguero-Gómez & Gamelon, 2021). In the light of predicted increasing frequency of dry years, the long-term fate of these sub(populations) may become uncertain (Orbán et al., 2023; Spinoni et al., 2018). However, more favourable years (lower temperatures and higher precipitation periods) de-synchronized the response of different traits to the heat stress gradient, with some of the traits remaining unresponsive the heat load gradient. Interactive effects of habitat quality and climate on plant traits are likely frequent yet largely underexplored (Nicolè et al., 2011), and the amount of noise induced to the system challenge approaches of snapshot trait surveys for describing land use change effects on plant populations. Consequently, long-term observations across environmental stress gradients are necessary to capture the relative contribution of natural and human stressors to the spatial patterns of plant traits in structured landscapes (Buckley & Puy, 2022; Compagnoni et al., 2024).

Our results suggest that intraspecific trait variation along local heat stress gradients may interfere with plant responses to landscape reconfiguration, potentially concealing important human footprint effects on plant populations. In particular, the role of dry years in altering the effects of local exposure to heat is critical in populations confined to remnant habitat fragments. Predictive ecological models of intraspecific trait variation in human-altered landscapes can be useful tools in the conservation of dry grassland species. However, landscape management tools should consider not only improved habitat connectivity but also habitats with heterogeneous microclimates capable of buffering negative effects of weather extremes.

Author Contribution

A.M.C conceptualized the study, with input from B.D and O.V. B.D. and O.V. provided critical help to initialize the fieldwork by providing S. nemorosa occurrence and abundance datasets and provided logistical support for the fieldwork throughout the study. B.D. calculated the Hanski connectivity index and habitat area. A.M.C., S.O., V.Sz. and K.V.N collected the data. S.O.J. analysed the data and wrote the first version of the manuscript with substantial input from A.M.C. K.V.N. provided critical input to data cleaning and analysis. All authors contributed substantial intellectual input at different stages of the manuscript.

Acknowledgement

We thank very much Nour Zayani Elhouda, Odongo Hilda Meso, Ferenc Galyas and Mónika Sulyok for help with data collection and fieldwork assistance. We are indebted to Csergő Opel Alkatrész Centrum and Eli Vendégház Püspökladány for assistance with travel and accommodation. We thank the anonymous reviewers for taking the time to read our manuscript.

Data Availability

Data is available on request from the authors.

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Microclimate and dry years interfere with landscape structure effects on intraspecific trait variation

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Abstract

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INTRODUCTION

METHODS

Study sites

Study species

Data collection

Data selection

Landscape structure and microclimate

Data analysis

RESULTS

DISCUSSION

Effect of habitat area and isolation

Effects of microhabitat heat load

Effects of temporal weather fluctuations

CONCLUSIONS

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

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