Self-thinning of biodiverse plant communities follows the Intermediate Disturbance Hypothesis

doi:10.21203/rs.3.rs-5124339/v1

Biomass-density relations have been extensively studied for monospecific plant stands in forestry systems, leading to the self-thinning law, Interspecific Boundary Line and efficiency of space occupation. Later, came experiments with mixed-species stands testing the effects of biodiversity on productivity. Here, we test biomass-density relations in plant communities to better understand their dynamics and application as an ecological indicator. The biodiverse stands were subject to self-thinning and a local boundary line. A feedback loop was observed where biodiversity promoted efficient space occupations but, as these approached their maximum, fierce competition for space eliminated the weaker species. In the background, water availability mediated the strength of this interaction. Our results fit the ‘intermediate disturbance hypothesis’ and can unify seemingly contradicting past evidence and theories. The efficiency of space occupation used as an ecological indicator further helped understand the interaction between an invasive weed and the autochthonous community, as well as the benefit brought-about to a specific species assembly developing from within a mat of dead lawn. The latter denies Savory’s holistic management theory stating that the desertification of grasslands in Africa and elsewhere resulted from the accumulation of dead plant biomass occupying space otherwise available for the growth of new plants.

Earth and environmental sciences/Ecology/Community ecology

Earth and environmental sciences/Ecology/Grassland ecology

Earth and environmental sciences/Ecology/Forestry

Biological sciences/Plant sciences/Plant ecology

Biomass-density relations

biodiversity

plant community

competition

self-thinning

1.1 The fundamentals of biomass-density relations

Plants in an even-aged monospecific stand undergoing active growth will increase their biomass, and subsequent competitive stress induces mortality of the weaker individuals. Their elimination releases resources (space, light and nutrients) facilitating the further growth of survivors. This dynamic, which became known as self-thinning, reflects the efficiency of space occupation, with more efficient stands exhibiting higher biomass under similar densities (i.e., numbers of individuals per unit area). Initial studies established a relationship between density (D) and mean plant mass (w) given by w = kD^− 3/2 or equivalently log₁₀w = log₁₀k − 1.5 log₁₀D (Yoda et al., 1963; Cousens & Hutchings, 1983; Lonsdale & Watkinson, 1983; Hutchings, 1983). Here, w refers to above-ground biomass and k is an allometric constant. With time, this relationship evolved into an equivalent derived from stand biomass per unit area (B) and density: B = kD^− 1/2 or equivalently log₁₀B = log₁₀k − 0.5log₁₀D (Westoby, 1981). This new relation solved two problems: (i) auto-correlation, as the former w-D relation required the number of individuals (D) to estimate the quantities on both sides of the equation (w = B/D), and (ii) mean biomass increasing without actual growth but just because smaller individuals died (Westoby, 1981; Weller, 1987). Substantial debate followed as to what the self-thinning law is and whether such law really existed at all (Osawa & Sugita, 1989; Lonsdale, 1990; Weller, 1991). Henceforth, biomass–density relationships were categorized into three different aspects (Fig. 1):

The intraspecific dynamic self-thinning line is the straight line that is approached, then followed by the time trajectory of a crowded monospecific stand as it grows (Weller, 1987, 1989, 1991; Osawa & Sugita, 1989; Lonsdale, 1990). Stands show different thinning lines depending on the carrying capacity set by the current environmental conditions. Flatter slopes associated with lower intercepts reflect smaller carrying capacities and higher intraspecific competition.

The species (or higher ranked taxon) boundary line, i.e., the upper boundary of possible biomass–density combinations for a given species or taxon from the plant kingdom (Weller, 1991). This line is fit to the most extreme biomass-density combinations observed in stands of the same group, providing information about its maximum capacity to pack biomass above-ground.

The interspecific biomass–density relationship - IBDR (Scrosati, 2000) - and its static upper boundary characterizing the maximum biomass–density limit for all the species which make up the plant kingdom. Weller (1989) analysed plant data setting the boundary at log₁₀B = 3.91 − 0.33 log ₁₀D. However, this boundary was estimated from Ordinary Least Squares (OLS) and thus, although applied to the most extreme stands, it still determined their central tendency. The re-analysis by Scrosati (2000) set the plant boundary at log₁₀B = 4.87 − 0.33 log₁₀D. More recently, Creed et al. (2019) termed this boundary the Interspecific Boundary Line (IBL).

More recently, new IBLs were determined specific for seagrasses (Vieira et al. 2018c, 2022b) and for algae (Creed et al., 2019), each estimated from its own meta-analysis comprising thousands of observations from many tens of studies on tens of species. Their finding demonstrates that the fundamentals grounding the biomass-density relations and theory hold for any photoautotroph in any environment on Earth. Nevertheless, the fact that terrestrial plants, seagrasses and algae, each have their own IBL, raises the question about their causes.

Concomitantly to the determination of new IBLs specific for algae and seagrasses, the estimated perpendicular distances of the monitored stands to their respective IBLs (Fig. 1b), representing the free space left available and thus, their efficiencies of space occupation, were demonstrated as biological indicators of their health, among other aspects of their biology and ecology (Vieira et al. 2018c, 2019, 2022b, 2024; Creed et al., 2019).

1.2 Dynamics of biodiverse plant communities

Biomass-density relations, namely self-thinning and IBL, generally report on even-aged monospecific stands. Earlier monitoring of biomass-density relations in biodiverse stands were manipulative experiments of mixed-species stands comprising of two annual plants (Black, 1960; Harper & McNaughton, 1962; White and Harper, 1970; Bazzaz & Harper, 1976), which can hardly be considered as true tests on the effects of biodiversity. Furthermore, all these studies focused on self-thinning and none addressed the IBL issue. Nonetheless, a few insights were provided. Self-thinning was not significantly different between monocultures and mixed-species stands. Mimicking the monospecific stands, in mixed-species stands the dominant species prevailed, contributing with biomass growth to the self-thinning process, whereas the subordinate species contributed with mortality (Black, 1960; White & Harper, 1970; Bazzaz & Harper, 1976). However, this depends on the ratio between the strengths of interspecific and intraspecific competition (Harper, 1967), and thus, on the niche differentiation necessary for the co-existence of species (Hutchinson, 1957).

Henceforth, the relation between biodiversity and productivity, or some measure of plant or stand size, became a matter of utmost importance in ecology and forestry. Monitoring of wetlands, grasslands, shrublands and forests yield results of all sorts: positive and negative correlations, and unimodal relations, depending on geographical location, spatial resolution, experimental set-up, management intensity, climate, ecosystem type and taxonomic group, among others. Simpler statistical inference, meta-analysis and more complex modelling, all were used to obtain these results and search for models and theories that could unify them (e.g. Gillman & Wright, 2006; Harrison & Grace, 2007; Cardinale et al., 2011; Zhang et al., 2012; Grace et al., 2016; Humbert et al., 2016; Liang et al., 2016; Minden et al., 2016; Chen et al., 2018; Fichtner et al., 2018; Jucker et al., 2014, 2020; Urgoiti et al., 2023). However, consensus is yet to be achieved.

Biomass-density studies with large perennial trees face the constraint of being virtually impossible to measure the stands’ biomass. It is also challenging to infer it from tree morphometrics due to the winged and flared stems, hollows, loose bark, and limited ladder access (Sheil et al., 2017). Therefore, in studies on self-thinning in tree stands, biomass is replaced by some other metric of tree size (e.g. quadratic mean diameter or basal area) and their distributions, which hampers the comparison with the studies and theories focusing on the biomass-density relations. Nevertheless, there is accumulating evidence that biodiversity, and functional diversity in particular, promotes productivity of forests and forestry systems with canopies occupying space more efficiently (Morin et al., 2011; Zhang et al., 2012; Pretzsch, 2014; Sapijanskas et al., 2014; Jucker et al., 2015; Williams et al., 2017; Fichtner et al., 2018; Kunz et al., 2019; Urgoiti et al., 2023). Even in monospecific tree stands, structural diversity and uneven size distributions led to more efficient space occupations (Pretzsch et al., 2024).

The objective of our study was to test the fundamentals of biomass-density relations with reference to biodiverse plant communities, hoping that this approach may reveal an effective way to advance knowledge on this fundamental matter of ecology. Hence, the task #1 in this study was to determine whether self-thinning, an IBL and the efficiency of space occupation as an ecological indicator, hold for biodiverse plant communities. For that, three plots with annual plant species were monitored without human interference except for the sampling. The plots under comparison were (E1) east-side plot during winter-spring 2021, bare, sandy clay soil, three transects, 50 quadrats totalling 9.1m² sampled, (W1) west-side plot during winter-spring 2021, dry dead lawn over lawn-prepared soil, four transects, 49 quadrats totalling 10.7m² sampled, and (W2) west-side plot during winter-spring 2022, bare lawn-prepared soil, three transects, 33 quadrats totalling 9m² sampled. Overall, 10 transects totalling 131 quadrats were monitored, with 17089 individual plants from 46 taxon, and with species richness varying from 2 to 15 at an average of 4.27 species per quadrat (α diversity) and 12 to 38 at an average of 24 species per plot (β diversity).

Biodiversity in plant stands is not static. It evolves from colonization of bare substrate by pioneer species (e.g. grasslands) to climax communities (e.g. woods and forests) in a process called ‘ecological succession’ (Odum, 1969; Connell & Slatyer, 1977). Usually, earlier species create the conditions for the emergence of other species through ‘facilitation’ by protecting from excess sun and UV, preserving moisture, promoting the establishment of a community of microbes, fungus and small animals, and enabling the development of a functional ecosystem with complete biogeochemical cycles. Yet, not all species promote ecological succession. Some species inhibit competitors leading to a halt in the succession (Connell & Slatyer, 1977). Invasive species often have this effect in the communities that they encroach (Gusev, 2017; Jean Baptiste et al., 2019; Gallego-Tévar et al., 2020). Carpobrotus edulis and Oxalis pes-caprae are typical cases of inhibitor invaders in southern-European plant communities (Papini et al., 2017; Campoy et al., 2018; Mugnai, 2022; Global Invasive Species Database, 2024). In our study, task #2 assessed whether the effect of the invasive Oxalis pes-caprae on the autochthonous plant community was related with its efficiency of space occupation.

Grazing, by controlling the standing stock of plant communities (Liu et al., 2020; Wei et al., 2022), also affects their efficiency of space occupation. Furthermore, intense grazing prevents the ecological succession from developing towards climax stages (Gibson & Brown, 1992; Kemp & King, 2001; Milchunas & Vandever, 2014). Hence, herbivory is a fundamental determinant of landscape ecology. One of the most famous examples is the chain of interactions among large carnivores, large herbivores, plants and geomorphology taking place in Yellowstone natural park (Scheiffer, 2018; Farquhar, 2023). Recently, the question has been raised about the effects of standing stocks of ungrazed dead plant biomass. In the controversial ‘holistic management’ proposal by Savory (2013a,b), the desertification of grasslands in Africa and elsewhere were attributed to the accumulation of dead plant biomass, occupying space otherwise available, and thus preventing the growth of new plants. Such accumulation was allegedly due to the lack of grazing, consequence of the severe reduction of herds of large herbivores. The proposed solution was getting livestock replacing wild herds in their ecosystem function. This proposal, focusing on carbon sequestration to mitigate climate change, received plenty of criticism concerning anecdotal evidence and lack of scientific soundness (Briske et al., 2013; Nordborg & Röös, 2016). Here, we bypass the carbon sequestration feud, and the task #3 in this work is to assess the effect of the accumulation of dry dead grass on the development and efficiency of space occupation by plant communities. For that, one of the plots we monitored, including 4 transects and totalling 49 quadrats, was covered by a thick mat of dry dead lawn.

The large interannual variation in the autumn-to-spring precipitation in Portugal was evident from the contrast between 2021 and 2022, with 2021 having higher precipitation as compared to 2022 (Supplementary Fig.1). The thick mat of dry dead lawn had an average lawn shoot density of 667 shoot∙m² and an average lawn biomass of 354 gDW∙m². Considering all plots, a total of 17089 individuals from 46 taxa were counted and identified during the monitoring experiment. The taxa distributions among different plots were 12 taxa in E1, 22 taxa in W1 and 38 taxa in W2 (Table 1). These correspond to the β diversity, whereas the α diversity was given by the species per quadrat used in the posterior analysis.

Table 1 – Taxa observed during the monitoring and their identifier code number.

id. code	Taxa	E1	W1	W2
1	Echium plantagineum	x
2	Orobanche sp.	x		x
3	Erodium moschatum	x	x
4	Bromus sp.	x
5	Glebionis coronaria (ex Chrysantenum sp.)	x		x
6	Malva hispanica	x		x
7	Galactites tomentosus	x	x	x
8	Convulvulus sp. or Aristolochia sp.	x	x	x
9	Grass (unidentified)	x	x	x
10	Panicum repens	x		x
11	Oxalis pes-caprae	x	x	x
12	Vulpia sp.			x
13	Asparagaceae family			x
14	Stenotaphrum secundatum (Floratan)			x
15	Crepis sp.			x
16	Picris echioides			x
17	Fumaria sp.			x
18	Urtica sp.			x
19	Stellaria media			x
20	Forb (unidentified)			x
21	Polycarpon tetraphyllum			x
22	Silene sp.			x
23	Trifolium sp.			x
24	Papaver rhoeas			x
25	Sherardia arvensis			x
26	Anagallis arvensis			x
27	Arisarum simorrhinum			x
28	Solanum sp.			x
29	Calamintha nepeta			x
30	Lathyrus sp.		x	x
31	Medicago sp.		x	x
32	Conyza sp.		x	x
33	Hypochaeris glabra		x	x
34	Euphorbia sp.		x	x
35	Senecio vulgaris		x	x
36	Sonchus oleraceus	x	x	x
37	Aetheorhiza bulbosa		x	x
38	Allium sp.		x	x
39	Geranium molle		x	x
40	Andryala integrifolia		x	x
41	Gladiolus sp.		x	x
42	Urospermum picroides		x
43	unknown species (a)		x
44	unknown species (b)		x
45	Sisymbrium officinale		x
46	Centaurea sp.		x

2.1 Self-thinning and a local boundary line

Similarly to monospecific plant stands in forestry and agricultural systems, the natural biodiverse plant communities were also subject to self-thinning (Fig.2). At the beginning of the monitoring in early January, the pioneer plant communities in all plots showed high recruitment densities and low overall biomass. As time passed, the plant communities evolved to lower densities of individuals and higher stand biomass. This was the typical self-thinning trajectory that takes place during the active growth season. However, during April/May the plant communities tended to lose the larger individuals, resulting in decreased biomass. This was due to mortality of the winter-blooming individuals or taxa. From May onwards, most of the individuals and species desiccated and died.

The biodiverse communities of annual, short-lived plants that we monitored soon reached an upper boundary for the biomass-density relation (Fig.2), demonstrating that the limitations to space occupation previously found for monospecific plant stands apply as well to biodiverse plant stands. The quantile regression determined this local boundary line to be given by log₁₀B=4.76-0.63log₁₀D, with θ=0.566 and cosine(θ)=0.844; which places much lower than the plant IBL determined by Scrosati (2000) (Fig. 3). This local boundary line should not be considered an IBL specific of annual species since the mixed-species stands in the manipulative experiments by Black (1960), White & Harper (1970) and Bazzaz & Harper (1976) with annual species often surpassed it (Fig.3b). Furthermore, the data Weller (1989) and Scrosati (2000) used to determine the plant IBL also included annual, small-sized, short-lived species, besides perennial big trees. We question whether this local boundary represents a time-independent boundary to local communities subject to local conditions, mechanistically comparable with the universal IBL by Weller (1987) and Scrosati (2000). Alternatively, this boundary may represent the minimum self-thinning possible under the local conditions i.e., the maximum biomass increment with the minimum mortality, given by the steepest regression slope and highest intercept, and representing the community’s biomass-density timeseries under the least competition (White & Harper, 1970; Weller, 1987, 1989, 1991, Osawa & Sugita, 1989; Lonsdale & Watkinson, 1983; Lonsdale, 1990).

For unknown reasons a local boundary line was found placing substantially below the ultimate plant IBL determined by Weller (1989) and Scrosati (2000). Previously, an IBL for algae was found placing significantly above the plant IBL (Creed et al., 2019), and another IBL for seagrasses was found placing significantly below the plant IBL (Vieira et al., 2018c, 2022b). The inability to determine why any of these boundary lines place above or below the original plant IBL shows that much remains to be known about the factor(s) driving the space occupation efficiency by stands of terrestrial plants, seagrasses and seaweeds. Nevertheless, these factors affect biodiverse plant stands in the same manner that they affect mono-specific plant stands, resulting in similar dynamics, as are the case of self-thinning and a boundary line.

Competition for light has been suggested as the ultimate factor driving self-thinning and a boundary line (White & Harper, 1970; Duarte & Kalff, 1987; Morin et al., 2011; Jucker et al., 2014; Fichtner et al., 2018; Searle et al., 2022). Accordingly, Westoby (1977) proposes that self-thinning relates better with leaf area than with above-ground biomass. Our monitoring, taking place in Portugal, started in early January (peak winter), when daylength is 10h, and ended in late May, when daylength is 15h. Still, the boundary line was common to all observations within this timeframe. Therefore, we question whether, in the present case, the factor time required for self-tinning to act added to factor light. Time has been reported as a fundamental factor mediating the interactions between self-thinning and other community properties. For example, biodiversity is expected to take long to affect the productivity of plant communities (Jucker et al., 2020), whereas a meta-analysis on grazing exclusion experiments in alpine grasslands (always longer than 1 year) determined that the experiments with longer duration led to lower biomass, productivity and biodiversity (Liu et al., 2020). In the proceeding sections we show how biodiversity interacts with self-thinning and the efficiency of space occupation, and how other factors in the background, such as time and precipitation, mediate the strength of this interaction.

2.2 Effect of biodiversity (task #1)

Biodiversity influenced how close a plant community approached the local boundary line, corresponding to how efficiently it could occupy the available space. The application of indexes for species richness or evenness, in all cases showed that the maximum space occupation efficiency was attained at intermediate α diversity values (Fig.4a-d). Too few species and/or very uneven species distributions, occupied space less efficiently. This is in accordance with previous results showing that increasing biodiversity, functional diversity and structural diversity lead to forest canopies occupying space more efficiently (Pretzsch, 2014; Sapijanskas et al., 2014; Jucker et al., 2015; Williams et al., 2017; Kunz et al., 2019; Urgoiti et al., 2023; Pretzsch et al., 2024). However, there is a limitation to this effect as our study also showed that too many species and/or species distributions too even, also occupied space less efficiently. The answer to this dynamic came from a shift in paradigm. So far, biodiversity has been seen as the predictor and the efficiency of space occupation (as well as density, biomass and self-thinning) as the response. We tested the other way around, with biodiversity as a function of the efficiency of space occupation (Fig.4e,f). The α diversity attained its maximum at intermediate efficiencies of space occupation (d_plant), and decreased significantly thereafter as the efficiencies of space occupation improved towards their maximum i.e., as d_plant approached zero. These results suggest a feedback loop where each of biodiversity and efficiency of space occupation influences the other iteratively over time. In this feedback loop biodiversity promotes more efficient space occupations. However, as time passes, more species emerge and:

as space occupation approaches its maximum, fierce competition eliminates the species that are less competitive (Fig.4 path pA).
if/when the new cohorts develop fast, they may approach the boundary line without giving time and space for the emergence of additional species, thus never letting biodiversity increase substantially (Fig.4 path pB).
the observation of the W2 community positioned substantially apart from the E1 and W1 communities in this dynamic (Fig.4a,e) indicates the presence of yet another factor acting on the background. This factor was the reduced precipitation during the winter-spring of 2022, which disabled the W2 communities to attain larger stand biomass (Fig.2). Consequently, the resulting less efficient space occupations allowed for higher biodiversity (Fig.4 path pC).

We present the results above in a manner more connected with the standard self-thinning and biomass-density graphics and theory, as well as with the general ecological theory. Maximum biodiversity was achieved at intermediate distances from the local boundary line, corresponding to intermediate space occupation efficiencies (Fig.5). Too far away from the IBL corresponded either to early colonization or to situations where the environmental conditions where too adverse for most species. On the other hand, too close to the IBL the unavailability of free space led to fierce competition eliminating the weaker individuals and species. This fits the famous ‘intermediate disturbance hypothesis’ by Connell (1978), according to which biodiversity reaches its maximum at intermediate levels of environmental disturbance and stress: where the abiotic disturbance frequency and stress are high, few species can thrive and only the fast-maturating are favoured. On the opposite extreme, where the environment is stable, and disturbance and stress are low, the biotic factors dominate the communities’ dynamics, with competitive exclusion also decreasing biodiversity (Connell, 1978; Wilson, 1990; Osman, 2015). Water availability and drought are included among the environmental stressors of terrestrial plant communities (Wilson, 1990). The wetter winter-spring to which plots E1 and W1 were subjected to allowed them to self-thin to higher stand biomass and space occupation efficiencies than plot W2, with this plot being subjected to a relatively drier winter-spring (Fig.5). Consequently, the α diversities were higher in W2 than in E1 and W1, and the β diversities were 38 taxa in W2, 22 taxa in W1 and 12 taxa in E1. Again, this fits the ‘intermediate disturbance hypothesis’ with W2 corresponding to the intermediate water stress promoting the highest biodiversity, and E1 and W1 corresponding to the low water stress where competitive exclusion eliminating the weaker species decreases the biodiversity. For the E1 and W1 plots pertaining to low environmental stress, in areas where the new annual cohorts started with already high densities and biomass - and thus, closer to the local IBL with less free space still available – self-thinning followed path pB. This path never crossed conditions for larger biodiversity. The constant unavailability of free space never allowed for the rise of biodiversity. On the other hand, in areas where the new annual cohorts started with lower densities and biomass - and thus, further away from the local IBL with more free space still available – self-thinning followed path pA. This path crossed conditions for larger biodiversity. Extending the availability of free space for longer allowed for the emergence of more species. Only when this sell-thinning path pA approached the local IBL, competition became fiercer and the weaker individuals and species were eliminated. For the W2 plot pertaining to intermediate environmental stress, the new annual cohorts started with lower densities and biomass. Self-thinning followed path pC, never closely approaching the local IBL, and thus with more free space available for the emergence of new species.

The dynamics identified and presented in this work can unify seemingly contradicting evidence and hypotheses about how biodiversity relates to stand density and productivity. The past lack of consensus and contradicting evidence may have resulted from:

(i) lack of knowledge about this dynamic that we now unveiled,

(ii) productivity achieving its maximum at roughly half (depending on the model) the environmental carrying capacity (from fundamental population dynamics), which, for plant stands, is far from the most efficient space occupations that leads to competitive exclusion.

2.3 Effect of the invasive Oxalis pes-caprae (task #2)

The emergence and replacement of Oxalis pes-caprae by autochthonous species followed well-established models of ecological succession (see Odum, 1969; Connell & Slatyer, 1977). There were two pulses of biodiversity (or cohorts of new species) (Fig.6a). The first pulse, peaking roughly around February, was in strong direct competition with Oxalis pes-caprae. During both winters (of 2021 and 2022) the invasive Oxalis pes-caprae always surged earlier than the autochthonous species. This was the core of its competitive advantage, as it occupied most of the available space, leaving little space for its late competitors (Fig.6b). This is similar to the compaction of the canopies of plants with modular construction, which became known as the Phalanx clonal growth form (Sintes et al., 2005, 2006; Brun et al., 2006a,b, 2007; Vieira et al., 2018c). In summary, during the winter bloom, Oxalis pes-caprae became the dominant competitor constraining the emergence of most other species by taking the space available ahead of the others. During January/February most of the space had already been taken (Figs 2 and 6), and in the quadrats where Oxalis had not been so efficient occupying space, the autochthonous species took it. With increasing daylength and temperatures, Oxalis pes-caprae adults were the first declining, thus opening space for other species to thrive. This second pulse, from late March onwards (Fig.6a), barely competed with the Oxalis pes-caprae decaying adults (Fig.6b). On the contrary, in the beginning of this second pulse, the new recruits benefitted from the presence of the decaying Oxalis pes-caprae adults providing protection from direct sun light and foraging birds, and reducing water losses by evaporation from soils. This was obvious during the field sampling and its signal is perceptible in Figure 6 from late March to mid-April. In this particular time-frame, higher species richness and lower d_{autochthonous} were often associated with more Oxalis pes-caprae (i.e., d_Oxalis was closer to zero). Similar situations of larger adults protecting their own, as well as the community developing below them, from stress from desiccation and predation has been reported for algae (Menge, 1978; Scrosati & DeWreede, 1998; Vieira et al., 2018a,b, 2022a) seagrasses (Heck & Orth, 1980; Blundon & Kennedy, 1982; Unsworth et al., 2012; Gagnon et al., 2020; Ruiz-Montoya et al., 2021; Galván & Puente, 2023) and terrestrial plants (Gunnarsson et al., 2009; Šipoš & Kindlmann, 2013; Faralli et al., 2022). With time passing, the Oxalis pes-caprae was eradicated and the second pulse of biodiversity bursted. In summary, the decaying large adults of Oxalis pes-caprae created a microclimate more favourable for the understory recruits of autochthonous species, thus facilitating the emergence of the spring bloom. From late May onwards almost everything dried and died.

2.4 Effects of a thick mat of dead lawn (task #3)

The presence of a thick mat of dead lawn (in plot W1) did not impede the growth of new individuals and the development of two new colonization events, one during winter and another one during spring. In the comparison among the three plots, to avoid bias from eventual uneven sampling, the 15 smallest d_plant, representing the 15 best space occupation efficiencies, were selected from each plot (Fig.7). A permutation test applied to this sub-set of 45 observations showed that the differences among plots were significant (p<0.0001). Post-hoc test showed that all plots differed from each other i.e., E1≠W1 (p=0.0021) W1≠W2 (p=0.0253) and E1≠W2 (p<0.0001). Thus, the developing plant community constrained by the thick mat of dead lawn during 2021 (W1) was able to grow and attain space occupation efficiencies better than those attained by the new plant community in the same plot during 2022, unconstrained by the dead lawn mat (W2). On the other hand, the community in W1 during 2021 was more biodiverse than the community in E1 during 2021, with the major difference being the presence/absence of the thick mat of dead lawn. Hence, a major effect of the thick mat of dead lawn was facilitating the colonization by new recruits, presumably by providing shelter from herbivores and creating a more favourable understory microclimate. This was similar to the effect of decaying large adults of Oxalis pes-caprae shown in the previous section 3.2. These findings refute the claim by Savory (2013a,b) that the desertification of grasslands results from the accumulation of dead plant biomass, occupying space otherwise available, and thus preventing the growth of new plants. On the contrary, the presence of dead plant biomass may act as a facilitator of the early ecological succession by creating an understory microclimate more favourable for the new recruits (our results; Odum, 1969; Connell & Slatyer, 1977). In fact, plant canopies buffer the microclimate creating more stable and favourable environments for live to thrive. This effect is evident when protecting from extreme heat or cold, and from desiccation (De Frenne, et al. 2019; Huang et al, 2024).

Besides quantitatively, the composition of the plant community was qualitatively affected by the environmental conditions. To compare the effects of the plots in the community composition, the densities (D) of each taxon were normalized: D_i,norm = (D_i-µ_D)/σ_D, where subscript i refers to the i^th quadrat, µ is mean and σ is standard deviation. Then, their plot mean normalized densities (µW1, µE1 and µW2) were estimated and compared pair-wise (Fig.8). The names of the taxa and their identifiers are provided in Table 1. Taxa 42 to 46 were associated with plot W1, hence benefitting from the presence of the thick mat of dead loan. The dead lawn presumably created an understory micro-climate and protected from herbivory. Taxa 1 to 5 were associated to plot E1, hence benefitting from the absence of the thick mat of dead lawn and the wet winter. These taxa were competitively superior to the others in full sun and water availability. Most of the taxa 6 to 41 were associated to plot W2, hence benefitting from the absence of the thick mat of dead lawn and the dry winter. Either these taxa are competitively superior under drier conditions or, following the ‘intermediate disturbance hypothesis’ (Connell, 1978; Wilson, 1990; Osman, 2015), are generally competitively inferior but benefitted from the milder competition resulting from less efficient space occupations by the taxa that usually dominate when water abounds.

The fundamentals of biomass-density relations stipulated for forestry mono-specific plant stands apply as well to natural biodiverse plant stands. The biodiverse plant communities that we monitored were also subject to self-thinning and a local boundary line. Biodiversity and space occupation efficiency interacted in a feedback loop: biodiversity promoted more efficient space occupations but, as these approached their maximum, fierce competition eliminated the weaker species. Hence, the best space occupation efficiencies were attained at intermediate biodiversity levels and the highest biodiversity was attained at intermediate space occupation efficiencies. In the background, water availability mediated the strength of this interaction. These observations fit the ‘intermediate disturbance hypothesis’ and can unify seemingly contradicting past evidence and theories.

During the winter bloom, the invasive Oxalis pes-caprae got its competitive advantage from surging ahead of the other species, occupying almost all space available beforehand. However, later during warmer weather, the presence of its decaying adult fronds benefitted many spring-blooming species by providing shelter and a more favourable understory micro-climate.

The same facilitation process benefitted the plant community developing from within a thick mat of dead lawn. These results deny Savory in his holistic management theory stating that the desertification of grasslands in Africa and elsewhere resulted from the accumulation of dead plant biomass occupying space otherwise available for the growth of new plants.

In conclusion, the efficiency of space occupation is a useful ecological indicator applicable to biodiverse plant stands, whose application may nevertheless require factoring-out the own effect of biodiversity.

4.1. Experimental design

The experiment was performed in Charneca de Caparica, south of Lisbon, Portugal, during the January to May period (winter-spring) in 2021 and 2022. The climate at this location is Mediterranean, characterized by wet and mild winters with landscapes full of green and flowers, giving place to hot and dry summers, with landscapes of yellow/brown dead and dry grasslands. Portugal experiences a large interannual variation in the autumn-to-spring precipitation. Meteorological data was obtained from the two nearest weather stations run by the Portuguese institute for the sea and atmosphere (IPMA). These stations are located in Lisbon and Setúbal, respectively to the north and to the south of our experiment location.

The vegetation consisted of a mixture of C3 annual species emerging after the first rains in autumn and senescing in late spring. Sampling took place in two adjacent plots, each plot covering approximately 80 m². The plot towards the east-side was bare, containing a sandy clay soil. Towards the west-side, the plot had previously been a lawn of Floratan, a cultivar of St. Augustine’s grass (Stenotaphrum secundatum), with the soil enriched with topsoil several years prior to our data collection. The property abandonment resulted in the plot being covered by a thick mat of dry dead lawn that was removed during the summer of 2021. Therefore, several months prior to the 2022 sampling the plot on the west-side became also bare. The plot on the east-side was not sampled in 2022 as it was no longer available. Thus, the treatments under comparison were:

(E1) East-side plot 2021 – bare, sandy clay soil and high precipitation. Three transects (A, B and C), 50 quadrats totalling 9.1m² sampled.

(W1) West-side plot 2021 – dry dead lawn over lawn-prepared soil and high precipitation. Four transects (A, B, C and D), 49 quadrats totalling 10.7m² sampled.

(W2) West-side plot 2022 – bare lawn-prepared soil and low precipitation. Three transects (A, B and C), 33 quadrats totalling 9m² sampled.

Three or four transects were performed within each treatment. From each transect, quadrats were periodically sampled starting early January and ending late May. Initially, quadrats were 0.4×0.4m = 0.16m². However, as time passed and plants grew, the larger sizes of some species demanded increasing the size of the quadrats. This was done case-by-case, with the larger quadrat being 0.7×0.85m = 0.6m². Within each quadrat, each individual was removed, identified to the lowest possible taxonomic level, its below-ground biomass was cut-off and the remaining above-ground biomass put in the respective taxon bag. All plant material was oven-dried for 72h at 60°C and weighted in a precision scale.

4.2. Additional Data

For comparison, additional biomass-density data was obtained from manipulative experiments with mixed-species stands of annual plants by Black (1960), White & Harper (1970), and Bazzaz & Harper (1976). Several of the genera used in these manipulative experiments, namely Medicago, Trifolium and Papaver, were also present in our natural stands. Black (1960) cultivated Trifolium pratense and Medicago sativa in pure stands and in mixtures sown at eight densities, from 50 to 12500 plants∙m^− 2. Here, we used the data from proportion treatments of 6250:250, 6250:50, 50:6250 and 250:6250. Plants were harvested at 7 and 9.5 weeks after sowing. White & Harper (1970) cultivated Brassica napus and Raphanus sativus sown at four proportion treatments of 3:0, 2:1, 1:2 and 0:3 and three soil fertility levels. Plants were harvested at 6.5, 13 and 17 weeks after sowing. Bazzaz & Harper (1976) cultivated Sinapis alba and Lepidium sativum sown at 1:1 proportion, and low and high soil fertility treatments, and plants were harvested at 4.5, 6, 8 and 10 weeks after sowing.

4.3. Biomass-density relations

The quadrats from the three treatments were compared in the log₁₀D-log₁₀B plot. The existence of self-thinning trajectories as well as of an IBL specific to our data were determined. The IBL was estimated using quantile regression to the 99% quantile (Zhang et al., 2005; Vieira et al., 2018c, 2022b; Creed et al., 2019). The space occupation efficiency (d_plant) for each quadrat was estimated from its perpendicular distance to the IBL (Fig. 1b). The smaller this distance (i.e., the closer d_plant was to zero), the higher the space occupation efficiency, with less free space left available to occupy. This approach has been successfully applied to algae (d_alga), where it identified the effects of taxonomy, functional form and clonal growth (Creed et al., 2019), as well as to seagrasses (d_grass), where it identified the effects of light, temperature, nutrients, season, species, location and organic matter in the sediment (Vieira et al., 2018c, 2019, 2022b, 2024). For convenience, in this study we estimated d_plant relative to the locally determined IBL, specific to local environmental conditions, instead of relative to the universal plant IBL determined by Scrosati (2000).

4.4. Data Analysis

Quantile Regression aims at any given quantile of the bivariate distribution (Cade & Noon, 2003; Zhang et al., 2005; Geraci, 2019). The coefficients yielding the i^th quantile line are found by optimization/search algorithms minimizing the quantile loss function. Quantile Regressions can be fit to linear (Cade & Noon, 2003; Zhang et al., 2005; Vieira et al., 2018c, 2019, 2022b; Creed et al., 2019) and non-linear forms (Geraci, 2019; Vieira et al., 2022b, 2024). Furthermore, these can be merged in Quantile Regression Splines (Koenker et al., 1994; Jhong & Koo, 2019; Kitahara et al., 2020; Leontidou et al., 2023; Yao et al., 2023). Linear Quantile Regressions have been used to find the Interspecific Boundary Lines (IBL) of plants (Zhang et al., 2005), algae (Creed et al., 2019), and seagrasses (Vieira et al., 2018c, 2022b). In these cases, the upper boundary has been estimated by the upper-most quantiles (e.g., the 99.5% quantile). Here, a local IBL was tested using the 99% quantile. Then, the d_plant dependency on several biodiversity metrics was estimated from 2nd and 3rd order polynomial Quantile Regressions, as well as Quantile Regression Splines merging 2nd and 3rd order polynomial and exponential functions. Notice that the d_plant tends to zero as the quadrat approaches the IBL (Fig. 1b). Therefore, when the objective was to find the boundary to the minimum d_plant, the 0.5% and the 1% quantiles were used. Later, 2nd and 3rd order polynomial Quantile Regressions were used to estimate the biodiversity dependency on d_plant. Here, the 99% quantile was used. Furthermore, the biodiversity metrics were log-transformed for regression fitting.

The treatments E1, W1 and W2 of the biodiverse plant communities were subject to a Permutation Test with d_plant as the response variable. Permutation Tests are the non-parametric, Randomization methods (or Monte Carlo methods) version of the traditional ANOVA (Manly, 1991). To compare among the different treatments in a fair manner (i.e., to avoid bias of different sorts and sources), the 15 smaller d_plant were selected from each treatment.

Acknowledgements

Vasco Vieira wishes to acknowledge the three researchers that most influenced him to follow the research on biomass-density relations, namely Joel Creed, Ricardo Scrosati and Rui Santos.

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DataFinal.xlsx
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Supplementary Figure 1 – Air temperature and precipitation influencing the monitorization experiment, averaged from weather stations in Lisbon and Setúbal. Monitorizations of plant communities took place from January to June 2021 (2021) and from January to June 2022 (2022)

Self-thinning of biodiverse plant communities follows the Intermediate Disturbance Hypothesis

Status:

Version 1

Abstract

Figures

1. Introduction

1.1 The fundamentals of biomass-density relations

1.2 Dynamics of biodiverse plant communities

2. Results and Discussion

3. Conclusions

4. Material and Methods

4.1. Experimental design

4.2. Additional Data

4.3. Biomass-density relations

4.4. Data Analysis

Declarations

Acknowledgements

References

Additional Declarations

Supplementary Files

Status:

Version 1