# Positive associations among rare species and their persistence in ecological assemblages

Joaquín Calatayud, Enrique Andivia, Adrián Escudero, Carlos J. Melián, Rubén Bernardo-Madrid, Markus Stoffel, Cristina Aponte, Nagore G. Medina, Rafael Molina-Venegas, Xavier Arnan, et al.

► To cite this version:

Joaquín Calatayud, Enrique Andivia, Adrián Escudero, Carlos J. Melián, Rubén Bernardo-Madrid, et al. Positive associations among rare species and their persistence in ecological assemblages. Nature Ecology & Evolution, 2020, 4 (1), pp.40-45. 10.1038/s41559-019-1053-5. hal-04523089

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1 Positive associations among rare species and their persistence in ecological assemblages

2 assemblages

3 Joaquín Calatayud¹,²*, Enrique Andivia³,⁴, Adrián Escudero⁵, Carlos J. Melián⁶, Rubén

4 Bernardo-Madrid⁷, Markus Stoffel⁸,⁹,¹⁰, Cristina Aponte¹¹, Nagore G. Medina¹²,¹³, Rafael

5 Molina-Venegas¹⁴, Xavier Arnan¹⁵, Martin Rosvall¹, Magnus Neuman¹, Jorge Ari

6 Noriega², Fernanda Alves-Martins², Isabel Draper¹³, Arantzazu Luzuriaga⁵, Juan

7 Antonio Ballesteros-Cánovas⁸,¹⁰, César Morales-Molino¹⁶,¹⁷, Pablo Ferrandis¹⁸, Asier

8 Herrero³,¹⁹, Luciano Pataro¹³, Leandro Juen²⁰, Alex Cea²¹, Jaime Madrigal-González¹⁰.

9 ¹ Integrated Science Lab, Department of Physics, Umeå University,
10 901 87 Umeå, Sweden.

11 ²Departamento de Biogeografía y Cambio Global, Museo Nacional de

12 Ciencias Naturales (MNCN-CSIC), C/ José Gutiérrez Abascal 2, 28006,

13 Madrid, Spain.

14 ³Departamento de Ciencias de la Vida, Edificio de Ciencias,

15 Universidad de Alcalá, ctra. Madrid-Barcelona km 33.6, 28805 Alcalá

16 de Henares, Madrid, Spain.

17 ⁴Departamento de Biodiversidad, Ecología y Evolución, Universidad

18 Complutense de Madrid, C/ José Antonio Novais 12, 28040, Madrid,

19 Spain.

20 ⁵Departamento de Biología, Geología, Física y Química inorgánica.

21 ESCET., Universidad Rey Juan Carlos, C/ Tulipán s/n, Móstoles, C.P.

22 28933, Madrid, Spain.

23 ⁶Department of Fish Ecology and Evolution, Eawag, Seestrasse 79,

24 6047 Kastanienbaum, Switzerland.

25 ⁷Departamento de Biología de la Conservación, Estación Biológica de

26 Doñana CSIC, C/ Americo Vespucio, 26, 41092, Isla de la Cartuja,

27 Sevilla, Spain.

28 ⁸Dendrolab, Department of Earth Sciences, University of Geneva, rue

29 des Maraîchers 13, CH-1205 Geneva, Switzerland.

30 ⁹Department F.-A. Forel for Environmental and Aquatic Sciences,

31 University of Geneva, Boulevard Carl-Vogt 66, 1205, Geneva,

32 Switzerland.

33 ¹⁰Climate Change Impacts and Risks in the Anthropocene (C-CIA),

34 Institute for Environmental Sciences (ISE), University of Geneva, 66

35 Boulevard Carl Vogt, 1205, Geneva, Switzerland.

36 ¹¹Plant Science, The University of Melbourne, Burnley, 500 Yarra

37 Boulevard, Richmond, Victoria, 3121, Australia.

38 ¹²Department of Botany, Faculty of Sciences, University of South

39 Bohemia, České Budějovice, Czech Republic.

40 ¹³Departamento de Biología (Botánica), Facultad de Ciencias,
41 Universidad Autónoma de Madrid, calle Darwin 2, Madrid, Spain.

42 ¹⁴Plant Ecology, Institute of Plant Sciences (IPS), University of Bern,

43 Altenbergrain 21, 3013 Bern, Switzerland.

44 ¹⁵CREAF (Centre de Recerca Ecològica i Aplicacions Forestals) Campus

45 de Bellaterra (UAB), Edifici C, 08193, Cerdanyola del Vallès Catalunya,

46 Spain.

47 ¹⁶UMR CNRS 5805 EPOC - OASU, Université de Bordeaux Site de

48 Talence - Bâtiment B18N Allée Geoffroy Saint-Hilaire, CS 50023,

49 33615 Pessac Cedex, France.

50 ¹⁷Institute of Plant Sciences and Oeschger Centre for Climate Change

51 Research, University of Bern, Altenbergrain 21, 3013, Bern,

52 Switzerland.

53 ¹⁸Higher Technical School of Agricultural and Forestry Engineering and

54 Botanical Institute, University of Castilla-La Mancha, 02071 Albacete,

55 Spain.

56 ¹⁹Department of Plant Biology and Ecology, Pharmacy Faculty,

57 University of Basque Country, 01006 Vitoria-Gasteiz, Basque Country.

58 ²⁰Laboratótio do Ecologia e Zoologia de Invertebrados, Instituto de

59 Ciências Biológicas, Universidade Federal do Pará, Rua Augusto

60 Correia, 1, Barrio Guama, CEP 66075-110, Belém, Pará, Brazil.

61 ²¹Departamento de Biología, Facultad de Ciencias, Universidad de La

62 Serena, Avenida Raúl Bitrán Nachary, 1305, Casilla 554, La Serena,

63 Chile.

64 * Correspondence to: j.calatayud.ortega@gmail.com

65

66 According to the competitive exclusion principle, species with low competitive

67 abilities should be excluded by more efficient competitors, and yet they generally

68 remain as rare species. Here, we describe the positive and negative spatial

69 association networks of 326 disparate assemblages, showing a general organization

70 pattern that simultaneously supports the primacy of competition and the

71 persistence of rare species. *Abundant* species monopolize negative associations in

72 about 90% of the assemblages. Contrarily, rare species are mostly involved in

73 positive associations, forming small network modules. Simulations *suggest* that

74 positive interactions among rare species and microhabitat preferences are the most

75 likely mechanisms underpinning this pattern and rare species persistence. The

3

4
76 consistent results across taxa and geography suggest a general explanation for the
77 maintenance of biodiversity in competitive environments.

78 Rare species, in terms of low abundance, are the main component of
79 the diversity of ecological assemblages¹. However, despite decades of
80 intense investigation, general mechanisms behind the persistence of
81 these species remain unclear. In theory, the widely assumed effects
82 of competition between pairs of species should preclude the
83 persistence of weak competitors and the high diversity observed in
84 natural assemblages²,³. Explanations for this diversity paradox include
85 the differential roles of niche partition, intraspecific competition,
86 facilitation, indirect and neutral interactions⁴⁻⁹, among others. Yet,
87 there is no consensus to explain rare species persistence across taxa
88 and environmental conditions so far.

89 The spatial arrangement of individuals plays a crucial role for
90 unveiling the mechanisms underpinning species assembly and
91 coexistence¹⁰⁻²¹. Because individuals within assemblages are not
92 homogeneously distributed, their spatial organization may both
93 reflect important assembly processes¹⁰,¹¹ and induce species
94 coexistence *per se*¹². For example, the patchy distribution of a
95 dominant species might prevent the monopolization of resources and
96 allow the existence of its subordinate species¹²,¹³. Hence, considering
97 spatial aspects of coexistence appears to be an important step in
98 elucidating assembly mechanisms¹². The spatial sorting of species can
99 be the outcome of divergent habitat preferences, dispersal abilities,
100 and biotic interactions, although the role of interactions is thought to
prevail under rather homogeneous environmental conditions, and especially at very fine spatial scales<sup>11,14,15</sup>. The organization of species within assemblages can be translated into association networks of species that are spatially aggregated (positive networks) or segregated (negative networks). Association networks of disparate biological assemblages can provide valuable empirical evidence of the main forces driving the assembly of species<sup>16-20</sup>, helping to reveal general mechanisms underlying species coexistence.

Here we describe a general pattern of positive and negative species associations that is consistent with the competitive exclusion paradigm but, at the same time, can explain the persistence of rare species in natural assemblages. We base our results on a dataset of 326 assemblages that meet the following criteria: (i) each assemblage comprises taxa from only one trophic guild, thereby excluding the possibility that species associations result from direct predation or parasitism; (ii) each assemblage shows reduced spatial extent and low environmental heterogeneity, to increase the likelihood that species associations are mainly due to biotic interactions; (iii) the abundance of at least ten species is recorded in a minimum of ten samples, to improve statistical power (Appendix S1); (iv) the dataset represents a wide variety of biomes (e.g., tropical forests, deserts, temperate steppes and polar climates), thus avoiding biome-specific results; and (v) it encompasses a diversity of taxa (such as bryophytes, vascular plants, and insects among others), to ensure the generalization of our results across taxonomic and
functional groups. We generated positive and negative association networks for each assemblage by comparing the observed spatial association patterns among species to a null model<sup>21</sup>. Species pairs that significantly deviate from random expectations receive positive or negative links in their respective association networks (Fig. 1).

We first analysed whether the structure of positive and negative association networks can reflect predictions from the competitive exclusion principle. Given that competition is heavily emphasized in the literature<sup>2</sup>, one would expect species to be more segregated than aggregated in natural systems. If so, negative networks should be more densely connected (i.e., more links per species) than their positive counterparts. In accordance, negative networks were significantly more connected than their positive pairs in a notable 93.2% of all assemblages (t = 17.006, P < 0.001, Fig. 2a). Differences in connectivity remained similar after accounting for differences in network size (t = -16.815, P < 0.001, for 78.8% of the assemblages) or when calculating differences in the average number of links (i.e. average species degree; t = -14.689, P < 0.001, for 69.0% of the assemblages). Furthermore, if abundance is considered to be an expression of the species' competitive abilities<sup>22</sup>, the number of segregations should be monopolized by the most abundant species. Accordingly, results indicated a strong positive correlation between abundance and species degree in negative networks (mean Spearman's ρ = 0.65, SD = 0.23), but a weak or even negative correlation in positive networks (mean ρ = 0.02, SD = 0.38), with
differences between networks being statistically significant (t= - 23.881, P < 0.001, Fig. 2b). Moreover, we found evidence showing that a particular species is more often involved in negative associations when it becomes abundant (Appendix S2). Both the greater density of links and the relationship between species degree and abundance in negative networks support current knowledge about the prevailing role of competitive interactions in sustaining the dominance of abundant species.

Yet, if the competitive exclusion principle is supported across several assemblages, how can rare species persist? To search for potential mechanisms answering this question we looked at the role played by rare species in association networks. Curiously, we found that rare species are mostly involved in positive associations in 91.7% of the assemblages studied, where positive networks showed a higher incidence of less abundant species than their negative pairs (t= 22.425, P < 0.001, Fig. 2c). Such spatial aggregations, however, do not occur among every rare species in the assemblage. In fact, we found that 91.1% of positive networks were more modular than their negative counterparts (t= 39.676, P < 0.001, Fig. 2d). This result remained similar after accounting for network size and connectivity (t= 11.306, P < 0.001, for 67.3% of assemblages, Methods). Moreover, while 60.7% of positive networks were significantly modular, only 13.8% of negative ones showed this pattern (Methods). Taken together, these findings show that rare species tend to generate modular networks of positive spatial associations.
The patterns of negative and positive associations networks remain largely invariant regardless of different probability thresholds to detect significant associations, the use of quantitative links and assumptions of disparate null models (Appendix S1). This robust and conspicuous spatial organization *point to suggest* that the underlying mechanisms can also be responsible for the persistence of rare species. On the one hand, dissimilar habitat preferences between dominant species and groups of weak competitors¹⁴ may generate this pattern, enhancing also rare species persistence. Indeed, numerical simulations (Methods and Appendix S3) show that this possibility increased the probabilities of reproducing realized association network patterns, regardless of different interaction networks reflecting hypothesized assembly mechanisms (Fig. 3a and Appendix S3). This, however, mainly occurs when habitat preferences are strong, a situation that should arise under marked environmental gradients most likely far from the reality of the fine-scaled assemblages studied here (Methods and Table S1). Complementarily, positive interactions *within* groups of rare species may also contribute and/or generate these modular positive networks. This may moreover increase the persistence of weak competitors since, just as in harsh abiotic environments²³, the biotic harshness produced by superior competitors could be counterbalanced by positive interactions among rare species. Accordingly, simulations show that the inclusion of positive interactions within groups of weak competitors increases the chance of species persistence by 58.2% compared to assemblages
ruled by competition alone (Appendix S3). Our simulations also reveal that this hypothesis most likely reproduces the observed patterns in association networks compared to other stabilizing mechanisms such as neutral colonization-extinction dynamics⁹, intransitive competition²⁴,²⁵, differential density-dependent effects²⁶,²⁷ or facilitation by nurse species⁷ (Figs. 3b, S1 and S2; and Appendix S3). Interestingly, the combination of habitats and positive interactions yields the highest probability of reproducing the observed network patterns (Fig. 3a and Fig. S2). This further suggests that, even under strong differences in habitat preferences, stabilizing forces, such as facilitation or complementarity, *would be necessary to* enhance the coexistence of groups of rare species in reduced microhabitats¹⁵,¹⁷.
Besides habitat selection, it seems that modular positive interactions among rare species can contribute to the pattern we found and the persistence of these species, which agrees with recent experimental evidence²⁸.

Overall, our results show that ecological assemblages are consistently organized in positive and negative association networks across the main biological groups (i.e., animals and plants) and geography (Fig. 4 and Table S2). This ubiquity sheds light on the long-standing diversity paradox as the potential mechanisms leading to this organizational pattern can also enhance the persistence of rare species. Modular positive interactions among weak competitors emerged as a plausible mechanism even when assessed in conjunction with different microhabitat preferences. Questions remain
about the relative contribution and feedbacks of these positive
interactions and microhabitats. Nevertheless, the generality of the
findings presented here bring us closer to understanding the
assemblage of the vast biodiversity on Earth.

## 230 Materials and Methods

231 *Data acquisition*

232 Assemblage data were collected from published studies in peer-reviewed journals
233 and our own surveys (Appendix S4 and Table S1). Each assemblage consists of at least
234 ten samples where the abundance of at least ten species of the same trophic guild was
235 recorded. In order to minimize the effects of environmental heterogeneity and
236 dispersion on spatial patterns, we only included datasets that showed (i) low
237 environmental variability across samples (excluding surveys where any kind of
238 environmental gradient was reported or no clear information about it was provided), (ii)
239 a very reduced spatial extent (median = 0.1 ha; ranging from 0.01 to 25.6 ha), (iii) a
240 very small grain size to increase the probability of physical and/or chemical contact
241 among all species in the samples (median = 100 m²; ranging from 0.002 to 400 m²,
242 respectively), and (iv) standardization among samples to avoid sampling biases (e.g.
243 effects of area). Following these criteria, we gathered a total of 385 datasets distributed
244 worldwide and representing a wide taxonomic spectrum, including bryophytes (n= 71),
245 tracheophytes (n=279), anthozoans (n=7), and insects (n=28). Abundance was estimated
246 as the number of individuals per sample in most of the assemblages, but a small number
247 of assemblages included abundance data estimated as the percentage cover of the
248 sampled surface (especially in bryophytes and plants). Since some null models only
249 accept integer data (see below), we rounded percentages when necessary. Finally, we
250 only used those assemblages where both positive and negative networks showed at least
251 two links (n=326).

252

253 Generation of association networks

254 For each assemblage, we calculated similarity in abundance distribution across
255 samples for each species pair i and j using the Schoener's index³¹,

$$
256 \qquad S(i, j) = 1 - \frac{\sum_{k=1}^{N} |p_{ik} - p_{jk}|}{2}
$$

257 where *N* is the number of samples and *p*<sub>*ik*</sub> is the proportion of the total abundance of
258 species *i* present in sample *k* (*p*<sub>*ik*</sub>=*x*<sub>*ik*</sub>/*∑*<sub>*k*=1</sub><sup>*N*</sup> *x*<sub>*ik*</sub>). We compared observed similarities to 999
259 null values obtained through randomization of species abundances using a fixed-fixed
260 algorithm (i.e., row and column totals are kept constant). For each observed similarity
261 value, two one-tailed p-values were calculated as the proportion of null values (plus the
262 observation) that were higher than or equal to and lower than or equal to the observed
263 value for positive and negative associations, respectively. We considered an aggregation
264 or segregation significant in those cases where associated p-values in any of the two
265 tests were lower than or equal to 0.05. Alternative probability thresholds and null
266 models provided quantitatively and qualitatively similar results (Appendix S1; Figs. S3
267 and S4). Significantly aggregated and segregated species pairs were used to generate
268 unweighted links in the positive and negative association networks of each assemblage,
269 respectively. It is important to note that the frequency of spurious associations (i.e.,
270 Type I errors) may be thought to be relatively high in species rich assemblages due to
271 multiple comparisons (but see ref³²). However, species pairwise similarities were
272 compared against null values generated using a fixed-fixed assemblage-wise null model
273 (i.e., a strict null model making null hypotheses among comparisons to be different but
274 intrinsically interdependent). This partially alleviates the detection of false positives
while preventing the use of powerful false discovery rate methods³³. Nevertheless, we
used the same nominal error (i.e., α = 5%) to detect both positive and negative
associations, making the rate of false discoveries equal in both types of networks, and
allowing unbiased comparisons of their structures. Indeed, results remained largely
constant when using different nominal errors (Appendix S1 and Fig. S3).

280

281 Network structure comparison

To explore whether positive and negative association networks reflected
competitive processes we compared their connectivity and their relationships between
abundance (calculated as the sum of the abundances across samples) and species degree
(i.e., species' number of links) for each pair of network types. Connectivity is defined as
the number of realised links relative to the number of potential links. This measure of
connectivity may be negatively correlated with network size. Hence, we also used
residual connectivity obtained from the residuals of a linear regression between the
number of observed and potential links, both log-transformed³⁴. On the other hand, the
relationship between abundance and species degree was assessed using the Spearman's
ρ correlation coefficient. Finally, to search for differences between network types we
used a paired Student's t-test, where the alternative hypothesis was that negative
networks present higher means than their positive pairs.

To determine if rare species have a larger participation in positive association
networks, we compared the average relative abundance, weighted by the number of
links of each species in the network, between the species involved in positive and
negative networks. We also explored if positive networks were more modular than their
negative pairs calculating modularity with the index proposed by Newman³⁵ (Q) along
with the optimisation algorithm of Louvain³⁵. The algorithm was run 100 times, and we
selected the partition that showed the highest modularity value. Since modularity can be related to network size and connectivity, we compared observed and null modularity values from random networks generated using a null model that maintains the number of links and nodes, as well as the degree sequence (implemented in the RandNetGen software³⁷). Then, we computed relative modularity values as $Q_r = -2(P - 0.5)$, where P represents the proportion of null cases showing modularity higher than or equal to the observation. A paired Student's t-test was used to explore the differences between network types in all cases.

Finally, we explored whether the probability of finding the above-explained differences of positive and negative networks was related to the number of samples per assemblage (as indicative of sampling effort), an approximation of null model degrees of freedom (Appendix S1), latitude, longitude, taxonomic group (i.e., animals or plants) and species richness. To do so, we firstly generated four binomial dependent variables, based on whether i) the negative networks of each assemblage were more densely connected than their positive pairs; ii) the negative networks present higher positive abundance-degree relationships; iii) the positive networks tend to be composed of less abundant species; and iv) the positive networks were more modular. Then, we fitted logistic models with a logit link function.

## Numerical simulations

We ran simulations to explore whether different interaction matrices and/or habitat preferences can generate the patterns observed in association networks. We designed a simulation model composed of 20 samples and ten species, whose individuals were randomly distributed at the outset. Individuals reproduce, colonise a randomly chosen sample or die, with probabilities dependent on the density of individuals and the sample
carrying capacity (K = 100). We subsequently incorporated the effects of both
competition and positive interactions by modifying these probabilities depending on the
species identities of co-occurring individuals (Appendix S3). That is, individuals of
dominant species reduce the probability of reproduction and colonisation, while
increasing mortality probability, of co-occurring individuals of subordinate species.
Benefactor individuals have the opposite effects on beneficiary individuals'
probabilities (Fig. S5a and b).

We further incorporated the effects of dissimilarities in habitat preferences by
setting four habitats preferred by different groups of species (Fig. S5c). Specifically, the
probabilities of reproduction, survival (i.e., one minus the mortality probability) and
colonisation in non-preferred habitats were multiplied by a habitat-tolerance coefficient,
β, ranging between 0 (null tolerance) and 1 (total tolerance; Appendix S3). Hence, when
β = 0, individuals are highly specialist and only allowed to reproduce, survive or
colonize in preferred habitat, whereas β = 1 corresponds to a neutral habitat scenario.

We ran simulations following six hypotheses explaining species assembly and
coexistence (Fig. 3 and Appendix S3). i) A neutral interaction model, where all species
were ecologically equivalent⁹. ii) A hierarchical competition model with one strong
competitor. iii) An intraspecific density-dependent model, where superior competitors
suffer more from intraspecific competition⁵. iv) A model incorporating intransitive
competition²⁵, where the superior competitor is outcompeted by three species, which, in
turn, outcompete all species except specific pairs (i.e., theoretically promoting the
generation of empirical association patterns; see Appendix S3). v) A nurse model⁷ with
four superior competitors, three of which facilitate different pairs of subordinate
species. vi) A model reflecting positive interactions within three groups of three rare
species. Fourteen additional matrices with different settings to these six general models
350 were also explored (see Appendix S3; and Figs. S1 and S6).

351 Simulations were run using a wide range of combinations (n=216) where
352 demographic rates (i.e., reproduction, mortality and dispersal) had different relative
353 importance (Appendix 3.4). In addition, we also used five values of the habitat-
354 tolerance parameter (β). For each interaction matrix and parameter combination, we ran
355 25 replicates of 5,000 iterations each. We quantified the probability of simulated
356 association networks showing empirical patterns (i.e., differences between positive and
357 negative networks in connectivity, abundance-degree relationship, abundance and
358 modularity), as well as the probability of persistence of all species (i.e., non-extinction),
359 as the proportion of all our replicates showing these patterns. Finally, these probabilities
360 were averaged across the parameter space defined by demographic rates where the
361 Competition model, under neutral habitat preferences, fulfilled expectations from the
362 competitive exclusion principle (i.e., weak competitors went extinct; P (non-extinction)
363 = 0; see Appendix S3 and Fig. S2).

364

365 **Acknowledgments.** We are very grateful to Joaquín Hortal and Stefano Allesina for
366 their critical comments on an early version of the manuscript. The simulations were
367 performed on resources provided by the Swedish National Infrastructure for Computing
368 (SNIC) at HPC2N. J.C. is supported by the Carl Tryggers Foundation for Scientific
369 Research (CTS 16:384). E.A. is supported by a postdoctoral grant founded by the
370 Universidad Complutense de Madrid. C.J.M is supported by the Swiss National Science
371 Foundation (SNSF-31003A-144162). R.B.M. is supported by the Spanish Ministry of
372 Science and Innovation Predoctoral Fellowship BES-2013-065753. M.S., J.A.B.C. and
373 J.M.G acknowledge support from the University of Geneva. X.A. was supported by a
374 Ramón y Cajal research contract by the Spanish Ministry of Economy and
375 Competitiveness (RYC-2015-18448). M.R. is supported by the Swedish Research
376 Council grant 2016-00796. JAN was supported by a Colombian COLCIENCIAS PhD

377 scholarship. F.A.M. is grateful to CAPES for a PhD scholarship (120147/2016-01).

378 A.L., P.F. and J.M.G. were funded by AGORA Project (ref. CGL2016-77417-P;

379 MINECO, Spain). C.M.M. was supported by an IdEx Bordeaux Postdoctoral

380 Fellowship (VECLIMED project). A.H. was supported by the University of Alcalá Own

381 Research Programme's 2018 Postdoctoral Grant and Basque Country Government

382 funding support to FisioClimaCO2 (IT1022-16) research group. L.J. received

383 productivity grants from CNPq (process: 307597/2016-4).

384 **Author contributions.** J.C. and J.M.G. conceived the ideas; J.C. and J.M.G. designed

385 the analyses with contributions from E.A., A.E., C.J.M. and R.B.M.; J.C., E.A., R.B.M.,

386 M.S., C.A., X.A., N.G.M., J.A.N., F.A.M., I.D., A.L., J.A.B.C., C.M.M., P.F., A.H.,

387 L.P., L.J., A.C., and J.M.G. collected the data; J.C. analyzed the data with assistance

388 from C.J.M., M.R. and M.N.; J.C., E.A. and J.M.G. lead the writing in close

389 collaboration with A.E., C.J.M., R.B.M., M.S., C.A., and R.M.V.; all authors

390 contributed to the development and writing of the paper.

391 **Code availability.** We will make all R scripts used in this study freely available

392 upon acceptance of the manuscript or if required by the referees during revision.

393 **Data availability.** We will make the dataset used in this study freely available

394 upon acceptance of the manuscript or if required by the referees during revision.

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a

<figure><img src="image_2.png" /></figure>

b

<figure><img src="image_3.png" /></figure>

491

Fig. 1. Approaching assembly mechanisms through the lens of positive and negative association networks. a, Species segregations and aggregations can inform on the main mechanisms underlying ecological assemblages. These spatial patterns are measured between species pairs using the similarity in the spatial distribution of their individuals. Observed similarities are compared with those obtained by a null model to distinguish actual associations from those generated by chance. Species pairs whose individuals are more aggregated in samples than expected by chance receive a positive link in association networks (blue nodes). Species pairs whose individuals are more segregated than randomly expected receive a negative link in association networks (red nodes). b, Positive (blue) and negative (red) networks of a tropical rainforest tree
assemblage (see "Barra_Paraguacu" in Table S1). The size of the
nodes is proportional to the species' abundances at the assemblage
level. Networks were plotted using the Fruchterman-Reingold force-
directed layout algorithm²⁹.
a

<figure><img src="image_4.png" /></figure>

b

<figure><img src="image_5.png" /></figure>

c

<figure><img src="image_6.png" /></figure>

d

<figure><img src="image_7.png" /></figure>

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**Fig. 2. The contrasting patterns of positive and negative association networks.** **a,** The higher connectivity of negative networks indicates that species segregation dominates over species aggregation. **b,** These segregations are monopolized mostly by dominant species, as shown by the strong relationship between abundance and species degree (i.e., number of links of a species) in negative networks. **c,** In contrast, less abundant species are more prone to generate positive associations, although, **d,** these associations only occur among specific groups of rare species, as indicated by the higher modularity of positive networks. Main histograms show the differences in network features between pairs of positive and negative networks. Insets show the raw values for both
522 types of networks.
a

<figure><img src="image_8.png" /></figure>

b

<figure><img src="image_9.png" /></figure>

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Fig. 3. Positive interactions among weak competitors alone or together with habitat preferences reproduce realized association patterns. **a**, Dissimilarities in habitat preferences between dominants and groups of rare species may generate the empirical patterns of association networks, regardless of different assembly mechanisms. However, this only occurs when habitat specialization is strong. Moreover, the combination of habitats and positive interactions among weak competitors (*Positive rare*) yields the highest probabilities. The y-axis represents the average probabilities of finding the four empirical patterns, and the x-axis
534 depicts a gradient of habitat specialisation (see Methods and
535 Appendix S3). **b**, All theoretical models explaining species coexistence
536 increase the chance of species persistence (Non-extinction) relative to
537 simulated assemblages only driven by hierarchical competition.
538 However, positive interactions among groups of rare species is the
539 most likely model to generate simulated assemblages showing the
540 same association networks as empirical assemblages (Connectivity,
541 Fig. 2a; Abundance-degree, Fig. 2b; Abundance, Fig. 2c; and
542 Modularity, Fig. 2d). The y-axis represents the probability of
543 simulated association networks showing empirical differences
544 between positive and negative networks across different
545 combinations of reproduction, mortality and dispersal rates where
546 interactions are expressed (see Methods and Appendix S3). Error bars
547 depict confidence intervals at α = 0.05. P: positive networks. N:
548 negative networks.

549
<figure><img src="image_10.png" /><figcaption>**Fig. 4.** Organization of association networks remains invariant across the globe and regardless of taxa. Circles and triangles represent plant and animal assemblages, respectively. Green colour depicts assemblages where positive networks were both composed of less abundant species and more modular than negative counterparts, whereas red colour shows assemblages where these patterns were not found. Map colours represent the Earth climatic zones proposed by Bailey³⁰.</figcaption></figure>
# Positive associations among rare species and their persistence in ecological assemblages

Joaquín Calatayud, Enrique Andivia, Adrián Escudero, Carlos J. Melián, Rubén Bernardo-Madrid, Markus Stoffel, Cristina Aponte, Nagore G. Medina, Rafael Molina-Venegas, Xavier

Arnan, Martin Rosvall, Magnus Neuman, Jorge Ari Noriega, Fernanda Alves-Martins, Isabel

Draper, Arantzazu Luzuriaga, Juan Antonio Ballesteros-Cánovas, César Morales-Molino, Pablo

Ferrandis, Asier Herrero, Luciano Pataro, Leandro Juen, Alex Cea, Jaime Madrigal-González

Correspondence to: j.calatayud.ortega@gmail.com

## Content

<table><tr><td>Appendix S1. Sensitivity analyses</td><td>2</td></tr><tr><td>S1.1. Sensitivity analysis to probability thresholding and weighted network analysis</td><td>2</td></tr><tr><td>S1.2. Sensitivity analysis to null model performance</td><td>6</td></tr><tr><td>S1.3. Effects of null model space</td><td>8</td></tr><tr><td>Appendix S2. Association patterns across assemblages</td><td>10</td></tr><tr><td>Appendix S3. Extended numerical simulations</td><td>11</td></tr><tr><td>S3.1 Model</td><td>11</td></tr><tr><td>S3.2 Biotic interactions scenarios</td><td>15</td></tr><tr><td>S3.3 Habitat preference scenarios</td><td>17</td></tr><tr><td>S3.4 Simulation settings</td><td>21</td></tr><tr><td>S3.5 Analytical Protocol</td><td>22</td></tr><tr><td>S3.6 Results</td><td>24</td></tr><tr><td>Appendix S3. Abundance surveys and data preparation</td><td>28</td></tr><tr><td>S4.1 Abundance surveys in single trophic level assemblages</td><td>28</td></tr><tr><td>S4.2 Data preparation</td><td>31</td></tr><tr><td>Figures</td><td>33</td></tr><tr><td>Tables</td><td>42</td></tr><tr><td>References</td><td>54</td></tr></table>
# Appendix S1. Sensitivity analyses

## S1.1. Sensitivity analysis to probability thresholding and weighted network analysis.

The establishment of positive and negative links in association networks relies on the comparison of observed similarities between pairs of species against those generated using a null model. Here, spatial associations that depart from null expectations more than a given probability threshold are classified as either positive or negative links. We used a 0.05 probability threshold for both negative and positive links. Although this constitutes a commonly used nominal error, the question remains as to whether the observed patterns of negative and positive networks are robust to different thresholds. Additionally, during the probability thresholding we only used information on link presence-absence yet link weights may provide richer information. Here, we first tested whether the observed patterns in association networks are robust to different probability thresholds. Then, we also explored whether the structure of weighted association networks is similar to the one of non-weighted networks.

To explore the robustness of association network patterns to different probability thresholds, we established positive or negative links based on 20 different nominal errors ranging from 0.010 to 0.499. Notice that we used a 0.499 probability, instead of 0.500, since positive and negative links were established based on the same null distribution and an association with a 0.500 probability could show the same probability of being negative or positive. Then, for each assemblage and network type, we computed connectivity, abundance-degree relationship, mean species abundance weighted by the number of links of the species in each network, and modularity as described in Methods. Finally, for each probability threshold we calculated the proportions of assemblages where: i) negative networks were more densely connected than
positive ones; ii) negative networks showed a stronger degree-abundance relationship than
positive pairs; iii) negative networks showed higher mean abundance, weighted by the number of
links, than positive counterparts; and iv) positive networks were more modular than negative
ones.

The generation and analysis of quantitative association networks entail some
methodological challenges. Regarding link weights, it may be possible to assign weights based
on a standard effect sizes, SES<sub>ij</sub>=(x<sub>obs</sub>−x̄<sub>null</sub>)/sd (x<sub>null</sub>), where x<sub>obs</sub> represents the observed
pairwise similarity and x<sub>null</sub> the null similarity values. This approach classifies positive and
negative links while conferring a weight, but it has some important drawbacks, being among the
most important the assumption that the null values follow a normal distribution, and the
dependence of this measure on the number of null model realisations (see, for instance ref¹). To
avoid this issue, we calculated a non-standard effect size as, ES<sub>ij</sub>=−2(P<sub>ij</sub>−0.5), where P<sub>ij</sub>
represents the probability for the association between species i and j of being higher than and
equal to, P<sub>ij</sub> (x<sub>obs</sub>≥x<sub>null</sub>), or lower than and equal to null model expectations, P<sub>ij</sub> (x<sub>obs</sub>≤x<sub>null</sub>),
respectively for positive and negative links. This is a non-parametric measure of magnitude
ranging between -1 and 1. However, its estimation has a known limitation since the probability
used for detecting positive associations is not complementary to the probability used for
detecting negative ones (i.e. P<sub>ij</sub> (x<sub>obs</sub>≥x<sub>null</sub>)≠1−P<sub>ij</sub> (x<sub>obs</sub>≤x<sub>null</sub>)). This implies that for each pair of
species we have two non-complementary probabilities that in an extreme case can be equal (i.e.
if all null values are equal to the observation, both positive and negative acceptance probabilities
are equal to 1). We overcame this limitation by computing the ES only for those associations
(either positive or negative) showing probabilities of acceptance lower than 0.5 and assigning a
zero otherwise,
$$ES_{ij} = \begin{cases} -2(P_{ij} - 0.5), & \text{if } P_{ij} < 0.5 \\ 0, & \text{otherwise} \end{cases}$$

This measure ranges between 0 and ≈1, and was calculated independently for positive and negative links. We used the 0.5 threshold since it ensures that each species pair has a maximum of one ES value higher than zero (i.e. if $P_{ij}(x_{obs} \ge x_{null}) < 0.5$, then $P_{ij}(x_{obs} \le x_{null}) > 0.5$ always, and vice versa). The corrected ES provides two important pieces of information since it provides an estimate of the effect size and the sign of the spatial aggregation, either positive (if $ES_{positive} > 0$ and $ES_{negative} = 0$) or negative (if $ES_{positive} = 0$ and $ES_{negative} > 0$). Notice also that it should be possible that an association cannot be classified either as positive or negative (when $ES_{positive} = 0$ and $ES_{negative} = 0$). This, however, would only occur when a reduced null model space results in more than half of the null values being equal to the observation, which, as shown below (see Appendix S1.3), is very unlikely in our dataset.

We measured different properties of weighted positive and negative association networks using analogous indices to non-weighted networks. To explore whether negative associations were more prevalent than positive ones, we first developed a quantitative measure of connectivity for our particular networks. Non-weighted network link density (or connectivity) is measured as the number of observed links divided by the number of potential ones,

$$C = \frac{N}{0.5 \times n \times (n-1)}, \text{ where } N \text{ denotes the number of observed links and } n \text{ the number of nodes.}$$

Analogously, weighted network density (herein, weighted connectivity) can be calculated as the sum of link weights relative to the maximum expected. In our case, the maximum weight expected for a single link is equal to 0.998. Here notice that probabilities are calculated as the proportion of 999 null values plus the observed value ($n=1000$) being equal or different to the
observed value. Thus, when all null values are different from the observed one we have,
$P_{ij}=1/1000$ and then, $ES_{ij}=-2(\frac{1}{1000}-0.5)=0.998$. The potential maximum weighted connectivity can be therefore measured as 0.998 times the number of potential links,
$C_{w_{max}}=0.998 \times 0.5 \times n \times (n-1)$; where n represents the number of nodes. Accordingly, we measured weighted connectivity as,

$$C_w = \frac{\sum_{i=1}^{L} l_i}{0.998 \times 0.5 \times n \times (n-1)}$$

where L represents the total number of links and $l_i$ the weight of link i. This index ranges from 0 (when there is no links) to 1 (for a fully connected graph with all links weights equal to 0.998).

We also studied the role of dominant and rare species in generating positive or negative associations. To do so, we explored the relationship between species abundance and the average weight of positive and negative links jointly. That is, we first multiplied negative links by -1, and then calculated the average link weight for each species, including both positive and negative links. When a given species has more and/or stronger negative links this index is negative, and vice versa. Then, we used the Spearman's rank correlation coefficient to measure the relationship between abundance and this index. Negative relationships between this mean link weight and species abundance indicate that abundant species have more and/or stronger negative links, whereas rare species have more and/or stronger positive links. Finally, to explore whether positive weighted networks were more modular than negative pairs, we used the Louvain algorithm² to optimize the quantitative version of the modularity index proposed by Newman³.
Results from the sensibility analysis revealed that the configuration of positive and negative association networks was, in general, highly robust to the probability threshold used for defining significant associations (Fig. S3). The only exception was for the differences in connectivity between positive and negative networks. For this property, we found a decrease in the proportion of assemblages where negative networks were more connected than positive pairs as the probability threshold increases (Fig. S3). In any case, the main result holds even with less robust thresholds since the proportion of assemblages where negative networks were more densely connected was always higher than 0.60.

The above results also agree with the analysis of weighted association networks. That is, negative networks were more densely connected that positive pairs in 0.61 of the assemblages ($t = -14.244, P < 0.001$), and the mean link weight was negatively correlated with abundance in 0.95 of assemblages (average Spearman's $p = -0.528, sd = 0.285$). Similarly, 0.92 assemblages showed positive networks being more modular than negative pairs ($t = 31.169, P < 0.001$). Overall, these results showed that our findings are robust to different probability thresholds and the use of quantitative information.

## S1.2. Sensitivity analysis to null model performance

Association networks inference relies on the accurate detection of significant spatial association patterns among species pairs (see Methods). The significance of pairwise associations was assessed by using a fixed-fixed null model (hereafter, null model I), which randomizes species abundances while *keeps* constant the marginal totals of rows and columns (see “r2dtable” algorithm in ref⁴). Thus, this model assumes that: (i) species populations and
carry capacities of samples are constant, and (ii) species can occur across all samples. This null model has been recommended due to its low type I and II error rates⁵. Moreover, its assumptions fit rather well with the reduced spatial scale and the putatively homogeneous conditions of the studied assemblages. Nevertheless, we explored whether alternative null models might yield different results. To do so, we used two other alternative null models (hereafter, null models II and III) to generate the association networks. The null model II is similar to null model I, but it also maintains all zero entries in the species per sample matrix (see “quasiswap count” algorithm in ref⁴). Thus, the null model II includes the assumption of the existence of forbidden occurrences, more typical of systems showing some degree of environmental heterogeneity. This further assumption should reduce the number of significant positive and negative associations.

On the other hand, the null model III maintains the frequency of occurrence and abundance of species, as well as the species richness of samples (see “independent swap” in ref⁵). This null model assumes that species are divided in subpopulations distributed across samples and that each subpopulation has specific growth restrictions. Additionally, by constraining species frequency of occurrence and sample richness this null model assumes restrictions in species ranges more typical from systems with marked dispersal or environmental barriers. Although these assumptions are rather far from the reality of our assemblages (since we only used assemblages where samples were close enough to allow dispersion among them and with apparently low environmental variability, see Methods), we used this null model to show that our results are robust to assumptions associated with disparate null models. In all cases we used 999 null replicates and calculated significance as explained in Methods.

Once association networks were generated based on each null model approach, we assessed differences in structure between positive and negative networks. In particular, we studied
differences in: (i) connectivity, (ii) the relationship between abundance and species degree, (iii) species abundances, and (iv) modularity (see Methods). Overall, our results remained qualitatively and quantitatively similar irrespective of the null model approach, thus corroborating the robustness of the patterns observed in association networks (Table S3 and Fig. S4).

### S1.3. Effects of null model space

The null model space —or “degrees of freedom”— defines the degree of differences among null model realizations. This critically determines the capacity to detect significant associations and, in turn, the association network patterns. For instance, if the null model space is small enough, observed and null values can be equal, precluding the detection of significant positive or negative associations. The null model space is only controlled by the constraints assumed and the related matrix characteristics. However, determining a large enough null model space *a priori* is a complicated task, even more when considering the different constraints of the null models we used. In an attempt to handle this, we selected assemblage surveys with at least ten species and ten sites, which constitute a proxy to ensure a large enough null model space in general.

However, given the particular constraints of each null model, using matrix dimension as a proxy may not work in all situations. For instance, null models II and III keep non-zero elements (i.e. matrix fill), and this should then also contribute to the space of these null models. We, therefore, explored whether our results are affected by the null model space.

We estimated the null model space for each assemblage and null model as the average (and minimum) dissimilarity among null model realizations. To do so, we firstly calculated the proportion of non-overlapping individuals between each pair of realizations (e.g. realizations A and B) as,
$$Ov = \frac{\sum_{i=j=1}^{N} \zeta r_{ij}^{A} - r_{ij}^{B} \lor \zeta}{2T} \zeta$$

where $r_{ij}^A$ represents the abundance of species i in site j of the realization A; N the number of entries in the matrix of species per sites and, T the total number of individuals. This dissimilarity measure is the matrix level equivalent of 1 minus the Schoener's index previously used and ranges between 0 (when both realizations are identical) and 1 (when there is no overlap between realizations). We computed this index for each pair of the 999 realizations of each null model and assemblage. Then, we measure the extent of the null model space using both the mean and minimum dissimilarity among all possible pairs. When the null model space is large we should find a large proportion of non-overlapping individuals between realizations. Finally, we explored the relationship between null model space and matrix dimension, as well as its effects on the probability of obtaining the patterns of positive and negative association networks.

On average across all assemblages the mean proportion of non-overlapping individuals was 0.34 ± 0.02; 0.58 ± 0.01; and 0.58 ± 0.01; respectively for null models I, II and III (mean and confidence interval at α = 0.05). Hence, even in the cases of the null model producing fewer individual switches, 34% of individuals were switched on average across different null model realisations. Similarly, the minimum proportion of non-overlapping individuals was 0.27 ± 0.02; 0.42 ± 0.01; and 0.38 ± 0.02; respectively for each null model. Hence, on average, at least 27% of individuals were switched. Moreover, we found that no null model realizations were equal, regardless of assemblage or null model (i.e. the minimum dissimilarity was always higher than zero). These results show that in all cases the null model space was large enough as to not generate two identical null matrices after 999 realizations, which strongly suggests that our
results are not affected by restrictions in the degrees of freedom of the null models. Indeed, we found non-significant effects of the minimum dissimilarity in the regression models explaining the probabilities of finding positive and negative network differences (P > 0.01 in all cases, see Table S2). Notice that we used the minimum dissimilarity in these models, instead of the mean dissimilarity, since it represents the most restrictive measure of null model space and it was highly correlated with the mean dissimilarity (r = 0.99). Finally, we found a strong correlation between the minimum dissimilarity and matrix size (r = 0.75, 0.76 and 0.77; respectively for null models I, II and III), which validates the use of matrix size as a proxy for null model space size.

## Appendix S2. Association patterns across assemblages

Whether a particular species is more often involved in negative associations when common or vice versa is an interesting question.

We explored this idea using those species that were present in at least 5 assemblages (n=384).

We first standardised the abundance of species i in each assemblage as follows:

$$ \text{Std. } A_i = \frac{\log (A_i \cdot \hat{i}) - \mu_{\log(A)}}{\sigma_{\log(A)}}, \hat{i} $$

where $\mu_{\log(A)}$ is the mean log-transformed abundance of the species in a given assemblage and $\sigma_{\log(A)}$ the standard deviation. Then, we calculated the balance of positive, $N_i^{pos}$, and negative associations, $N_i^{-\hat{i}}$, normalized by the number of species in the assemblage, N, also for species i in a given assemblage,

$$ B_i = \frac{N_i^{-\hat{i}} - N_i^{pos}}{N} \cdot \hat{i} $$
This balance is negative if a given species has more positive than negative associations. Finally, we used the Spearman’s correlation coefficient to explore the relationship between both variables. For most species (64.1 %) we obtained a positive relationship between both variables (mean Spearman’s ρ = 0.162 ± 0.041 at α = 0.05), indicating that, in general, species tend to be involved in more negative associations when common. Generalisations of this result should be taken with caution since the species occurring in five or more assemblages only represent less than 5% of the total number of species used in this study and were exclusively plants.

# Appendix S3. Extended numerical simulations

## S3.1 Model

We ran simulations accounting for biotic and habitat factors to explore whether proposed mechanisms enhancing rare species persistence do indeed promote species coexistence while leading to the observed structural patterns of positive and negative association networks. Here, we explain the general features of the model and the following sections contain the biotic scenarios (S2.2), the habitat ones (S2.3), the simulation settings (S2.4), the analytical protocol (S2.5) and results and discussion (S2.6).

We designed a simulation model composed of 20 samples and ten different species – similar to the mean sampling size of the empirical assemblages, whose individuals were randomly distributed at the outset. Individuals reproduce, release propagules to colonize a randomly chosen sample (i.e. dispersion), or die⁷. These processes occur with given probabilities, affecting reproduction ($α_R$), mortality ($α_M$) and dispersal rates ($α_D$). Each sampling site had a constant carrying capacity, which reflects the amount of resources available at the sample, and resources proportionally decrease as the number of individuals increases. As resource decreases in the
sample the probability of reproduction ($P_R$) and successful colonization ($P_c$) decrease, whereas the probability of mortality ($P_d$) increases. Hence, in a neutral scenario –where individuals of all species are ecologically equivalent⁸, the probabilities of reproduction, $P_R$, and mortality, $P_d$, of an individual $i$ in a given sample will be determined by both, intrinsic factors and resource availability in the sample, such that

$$P_R(i) = \alpha_R \left( 1 - \frac{\sum_{j=1}^{N_j} N_j}{K} \right) \qquad (\text{Eq. 1}),$$

$$P_D(i) = \alpha_M \frac{\sum_{j=1}^{N_j} N_j}{K} \qquad (\text{Eq. 2}),$$

where $N_j$ is the number of individuals of species $j$ and $K$ represents the carrying capacity.

Similarly, the probability of an individual $i$ to colonise a given sample will be

$$P_C(i) = \frac{\alpha_D}{N_s} \left( 1 - \frac{\sum_{j=1}^{N_j} N_j}{K} \right) \qquad (\text{Eq. 3}),$$

where $N_s$ represents the number of samples. Note that when the number of individuals reaches the carrying capacity ($\sum_{j=1}^{N_j} N_j = K$) the probabilities of reproduction and colonization are equal to 0, while the probability of mortality is equal to the intrinsic mortality ($P_D = \alpha_M$).

To incorporate the effects of different competitive abilities we assumed that, when co-occurring, individuals of dominant species acquire resources more efficiently than individuals of
less competitive species, decreasing their probabilities of reproduction and colonization and increasing the probability of mortality. Analogously, the effect of positive interactions can be incorporated assuming that benefactor individuals will increase the resource uptake efficiency of beneficiary ones. This, however, seems rather difficult to occur within assemblages of species belonging to the same trophic level, since some degree of resource interference can be always expected. We therefore took a conservative step and simulated complementarity based positive interactions⁹. That is, rather than simulating increases in resources uptake, we consider a positive interaction by setting the negative impact of competition under the expectations of a neutral interaction. By doing so “beneficiary” individuals will have increased probabilities of reproduction and colonization while reduced probability of mortality as compared to a neutral interaction (Fig. S5a). We modify equations 1, 2 and 3 to include the strength of competition and complementarity as follows,

$$P_R(i) = \alpha_R \left( 1 - \frac{\sum_{j=1} N_j \theta_i^j}{K} \right) \qquad (\text{Eq. 4}),$$

$$P_D(i) = \alpha_M \frac{\sum_{j=1} N_j \theta_i^j}{K} \qquad (\text{Eq. 5}),$$

$$P_C(i) = \frac{\alpha_D}{N_s} \left( 1 - \frac{\sum_{j=1} N_j \theta_i^j}{K} \right) \qquad (\text{Eq. 6}),$$
where $\theta_i^j$ represents the effects of species $j$ on species $i$. Notice that $\theta_i^j > 1$ comparatively reduce $P_R$ and $P_C$ while increasing $P_D$, representing dominance of species $j$ on species $i$. On the other hand, $\theta_i^j < 1$ produce the opposite effect (i.e., a relative increase in $P_R$ and $P_C$ while decreasing $P_D$), meaning that species $j$ complements species $i$. These equations also accommodate intraspecific interaction effects (i.e., when $i=j$), which allow us to explore intraspecific density-dependent processes (see below).

At the assemblage level, the interaction effects can be described by the competition-complementarity assemblage matrix, where the columns represent the effects of other species to a given species, the rows depict the effects of a given species to other species and the diagonal represent the intraspecific effects (Fig. S5b). Hence, the column marginal totals represent the total effect of all species to a given species. Large differences in these values between species would induce system-wide extinctions of weaker competitors. On the other hand, marginal total of rows represents the total effect exerted by a given species to the others, which will be related to system-wide species abundances at steady state (Fig. S5b). This simulation scheme allows exploring the patterns of spatial association networks generated by interaction matrices reflecting different assembly mechanisms. Notice, nevertheless, that the inference of the interaction effects in the competition-complementarity assemblage matrix (i.e. $\theta_i^j$ values) is challenging because the large number of parameters and the lack of empirical knowledge. To overcome this issue, we established constraints to the row and column marginal totals. Arbitrary $\theta_i^j$ values will not produce aberrant results as long as marginal totals fulfil these conditions. Specifically, the system-wide persistence of all species should rely on very similar column marginal totals between species, regardless of specific $\theta_i^j$ values. In the same way, row marginal totals can be
constrained to guarantee the generation of species abundance distributions like empirical ones
(i.e. few abundant and many rare species).

S3.2 Biotic interactions scenarios

We studied 20 interaction matrices summarizing the proposed mechanisms on species assemblages and rare species persistence. Six of them are presented in the main text and here are identified by an asterisk. A neutral interaction matrix (**Neutrality matrix**) was generated considering that all individuals were ecologically equivalent⁸ with all competition-complementarity coefficients equal to 1 (Fig. S6). To strictly test that the empirical patterns of association networks cannot be generated by only negative interactions we generated 13 interaction matrices with only negative interactions but reflecting different stabilizing hypotheses. We first generated three matrices assuming hierarchical (or transitive) competition with one superior competitor (**Competition 1 matrix***); three superior competitors (**Competition 2 matrix**) and four superior competitors (**Competition 3 matrix**). Intransitive competition has been proposed as the underlying mechanism explaining species coexistence¹⁰,¹¹,¹². This hypothesis assumes that intransitive competition networks stabilize species abundances. Because of intransitive competition can take any form⁹, we studied seven different interaction matrices. The first two ones represent a case where competitive effects were reciprocal between superior competitors and there were two superior competitors (**Intransitive 1 matrix**, see Fig. S6); and three superior competitors (**Intransitive 2 matrix**). In both cases, superior competitors outcompeted different species in the community theoretically enhancing the achievement of associations among rare species due to the same responses to dominant ones. The next two matrices had the same structure than the former ones, but in this case weak competitors also
exerted a negative effect on stronger ones (*Intransitive 3 and 4 matrices*, see Fig. S6). Finally, we generated three intransitive matrices with perfect intransitive competition¹². In the first one, we assumed that a single superior competitor dominates over all the other species but one, which in turn, dominates over the (former) superior competitor (*Pure intransitive 1 matrix*, see Fig. S6).

In the second intransitive matrix, we assumed the longest intransitive loop possible, where all species outcompete one species but are outcompeted by another one (*Pure intransitive 2 matrix*, see Fig. S6). In the last intransitive matrix explored, we strictly test if intransitive competition is not enough to generate the association network patterns we found in this study.

For this, we generated an interaction matrix forcing the appearance of these patterns. This matrix was characterized by one superior competitor outcompeting the rest of the species but three species, which, in turn, outcompete all other species but different pairs of weak competitors (*Pure intransitive 3 matrix*\*, see Fig. S6). This should result in the appearance of a dominant species with a larger number of negative associations while positive association networks should tend to be modular. The final three matrices with only negative interactions reflect a stabilizing hypothesis based on asymmetrical density dependent effects¹³,¹⁴. This hypothesis assumes that rare species are not poor competitors, but they experience a high negative impact of intraspecific competition¹⁴. The interaction matrices for this hypothesis assumed the existence of a larger number of superior competitors that also show high negative effect exerted by conspecifics (see Fig. S6, *Density dependence 1*\*, *2 and 3 matrices*). Notice that we used a large number of superior competitors since these species represent rare species and the number of rare species is, in general, large in empirical assemblages. Moreover, this scenario should produce virtually same results than assuming that other species in the community were interfering in resources-based interactions (e.g. when strong competitor suffer a higher predation pressure).
Facilitation from nurse species has also been hypothesized as a mechanism enhancing rare species persistence¹⁵. As in the case of intransitive competition, the interaction matrix representing this mechanism can take many forms. Thus, to test if facilitation from nurse species is not enough to generate the observed association patterns, we set three interaction matrices forcing the appearance of them. In the first one, we assumed that there are three superior competitors negatively affecting all species but two different species that are positively affected (*Nurse 1 matrix*, see Fig. S6). These three species can therefore be considered as nurse species facilitating different pairs of species. The second scenario was similar but also including a superior competitor that negatively affects the rest of the species (*Nurse 2 matrix*\*). In the last one, we assumed there are two superior competitors facilitating different sets of the community (*Nurse 3 matrix*). Perhaps one of the most parsimonious hypotheses explaining species coexistence would assume that competitive exclusion does not exist. Hence, we constructed an interaction matrix where this is the case (i.e., the positive matrix with all $\theta_i^j$ values lower < 1, Fig. S6). Modular positive interactions were assumed to explore if this mechanism would be enough to generate the observed empirical patterns (*Positive matrix*). Generalized complementarity among rare species may also stabilize assemblages. Although this hypothesis should be not very plausible in real assemblages, we also generate an interaction matrix reflecting this possibility to explore if it can generate observed patterns in association networks (*Positive negative matrix*). Finally, we generated a matrix reflecting modular positive interactions among poor competitors. This matrix was characterized by the presence of a dominant species and three groups of weak competitors interacting positively between them (*Positive rare*\*, see Fig. S6).
### S3.3 Habitat preference scenarios

Besides biotic interactions, differences in microhabitat preferences may also lead to spatial aggregations and segregations¹⁶ and, in turn, to the observed patterns of association networks. Indeed, if groups of rare species would persist in diverse microhabitats where dominant species performed worse, we could also expect negative networks to be centralized around dominant species and modular positive networks composed of rare species. To account for this, we strictly selected assemblages at fine scale and with apparently reduce environmental heterogeneity (Appendix S3) – where biotic interactions should prevail, partially solving this issue. However, unappreciated heterogeneity might exist in some assemblages, generating microhabitat conditions and reducing the role of biotic interactions. Moreover, even in the absence of abiotic heterogeneity, some microhabitats can be generated by the presence and interactions with other species in the community (e.g. soil bacteria for plants or predators for insects). These biotic habitats are more difficult to control for and might have similar effects as abiotic ones on the distribution of species’ abundances.

The existence of microhabitats might question the link between spatial association patterns and biotic interactions within species of the same trophic guild. However, this would only apply when considering the theoretical extremes of a gradient between the effects of habitat specificity and competition/facilitation alike interactions. In real world, both processes more likely interact to determine the coexistence of species and the spatial distribution of their individuals, with their relative importance depending on the intensity of both the habitat specificity and competitive/facilitative interactions. This interaction between biotic and habitat factors hampers the feasibility of a priori predictions on the consequences of microhabitats on the structure of association networks, especially when occurring in concert with different competition/facilitation
interaction matrices. For instance, assuming a perfect hierarchical competition matrix, the existence of different microhabitats only preferred by groups of weak competitors should not be enough to produce the patterns observed in association networks. Indeed, within each microhabitat a weak competitor would turn into the strongest one, excluding remaining species that also prefer this microhabitat and preventing the existence of modular positive association networks. Here we have thus conducted numerical simulations to explore whether, and in combination with which interaction matrix, the existence of microhabitats and different habitat preferences can generate the observed association network patterns.

We distributed a number of habitats within the simulated assemblages and determined preferred and non-preferred habitat for each of the species. Following the rationale of the general model in section S2.1, we assumed that the probabilities of reproduction, mortality and successful colonization in preferred habitats is only determined by species interactions according to equations 4, 5 and 6; respectively. To simulate habitat specificity, we first assumed that the probabilities of reproduction and colonization decrease in non-preferred habitat according to a parameter (β) representing the habitat tolerance. To incorporate the effects of habitat specificity and biotic interactions on the probabilities of reproduction and colonization we modified equations 4 and 6 as follows,

$$P_{R_{NP}}(i) = \beta P_{R}(i) = \beta \alpha_{R} \left( 1 - \frac{\sum_{j=1} N_{j} \theta_{i}^{j}}{K} \right) \qquad (\text{Eq. 7}),$$

$$P_{C_{NP}}(i) = \beta P_{C}(i) = \frac{\beta \alpha_{D}}{N_{s}} \left( 1 - \frac{\sum_{j=1} N_{j} \theta_{i}^{j}}{K} \right) \qquad (\text{Eq. 8}).$$
The tolerance parameter (β) ranged between 0 (i.e., when individuals are completely excluded in non-preferred habitat), and 1 (i.e., when habitat specificity does not exist and individuals' performance will be only driven by interactions). Thus, when β=1, $P_{RNP}$ and $P_{CNP}$ are equal to $P_R$ and $P_c$, respectively.

We assumed that habitat specificity influences the survival probability $P_{SNP}$ according to the β parameter, thus modifying equation 5 as follows,

$$P_{S_{NP}}(i)=\beta(1-P_{D}(i))=\beta\left(1-\alpha_{M} \frac{\sum_{j=1}^{N_{j}} \theta_{i}^{j}}{K}\right) \quad (\text{Eq. 9}).$$

By doing so when β=1 (i.e. there is no habitat specificity), the probability of survival will have equal consequences to the probability of mortality in preferred habitats.

There exists a plethora of combinations to distribute habitats and species preference for them. Moreover, the scenarios grow exponentially when trying to combine habitat specificity and interaction matrices. In an attempt to reduce this wide space of combinations while strictly testing our hypothesis, we distributed habitat and habitat preferences in a way that theoretically should generate the association network patterns of surveyed assemblages. That is, assuming neutral interactions, the *a priori* most likely habitat preference scenario leading to the observed patterns would be characterized by a large habitat preferred by few dominant species and many microhabitats preferred by different groups of rare species. We therefore set four habitats (Fig. S5c) across the 20 samples: one occupying 11 samples and only preferred by a single dominant species; and the remaining three habitats distributed in three samples each and preferred by three weak competitors each. We chose this configuration since it is directly comparable and compatible to the interaction matrices presented in the main text (i.e. producing one abundant
species and three groups of three rare species, see Fig. S6). In that way and for instance, when combining the Competition 1 matrix, we set the single dominant species to be alone in its preferred and largest habitat, so that it cannot exclude weak competitors. In other words, combining this habitat scenario and these interaction matrices allow us to place the single superior competitor in the largest habitat and the remaining groups of weak competitors in their corresponding small habitats.

### S3.4 Simulation settings

We first studied the patterns generated by the 20 interaction matrices when there is no habitat specificity. Similarly to the interaction effects, the parametrization of the intrinsic factors controlling reproduction ($\alpha_R$), mortality ($\alpha_M$) and dispersion ($\alpha_D$) based on empirical values is challenging¹⁷ and even more when considering the wide taxonomic spectrum covered in this study. To circumvent this fact, we explored a wide parameter space by using all possible combinations for these parameters based on six different values ranging from 0 to 1 (i.e. 0; 0.2; 0.4; 0.6; 0.8 and 1). Thus, for each interaction matrix we generated 216 ($6\alpha_R \times 6\alpha_M \times 6\alpha_D$) possible combinations. In addition, to account for the intrinsic stochasticity of these models we ran 25 replicates for each parameter combination and interaction matrix, making a total of 108k simulations (25 replicates x 216 parameter combinations x 20 interaction matrices).

We then explored the effects of combing habitat preferences and biotic interactions. We set a gradient of habitat specificity using five values for the $\beta$ parameter evenly distributed between 0 and 1. Hence, habitat specificity ranges from complete exclusion in non-preferred habitats ($\beta = 0$, i.e. the probabilities of reproduction, colonization and survival are equal to zero) to not exits ($\beta = 1$; i.e. these probabilities are equal in preferred and non-preferred habitats). For the sake of
simplicity and interpretability (see above) we only combined the habitat scenario with the six
interaction matrices presented in the main text (i.e. the Neutrality, Competition 1, Pure
intransitive 1, Density dependence 1, Nurse 2 and Positive rare matrices, according to Fig. S6).
For each interaction matrix and β value we replicated the simulations explained above, making a
total of approx. 160K simulations (25 replicates x 216 parameter combinations x 6 interaction
matrices x 5 habitat specificity values).

Each simulation was run during 5000 iterations since comparisons with 1000 and 3000
iterations showed that, on average, a steady state was reached (see Fig. S1). Initial conditions in
all cases consisted in a carrying capacity of 100 individuals per sample. Only twenty individuals
were allowed to disperse in each run. Species abundances were equal for all species and were set
so that the sum of all individuals was equal to 5% of the system-wide carrying capacity (i.e. 10
individuals per species). Then, individuals were randomly placed among samples.

S3.5 Analytical Protocol

We explored the structure of the positive and negative association networks resulting from
the scenarios following the same protocol as for the surveyed assemblages. That is, at the end of
each simulation we measured species pairwise associations using the Schoener's index¹⁸. Then,
we explored which pairs of species significantly deviated from a fixed-fixed null model (at α =
0.05; see Methods). By doing so, we detected significant positive and negative species
associations and derived the corresponding association networks. Once the association networks
were generated, we explored i) whether negative networks were more densely connected than
their positive pairs; ii) whether there was a stronger positive relationship between species degree
and abundance in negative networks than positive ones; iii) whether positive networks were
composed of less abundant species and iv) if positive networks were more modular than positive ones. Moreover, to evaluate which interaction matrices promoted species coexistence, v) we also examined if all species survived in the system at the end of each simulation. We used the Louvain algorithm² to optimize the modularity index proposed by Newman³ and selected the partition with the highest modularity value across 100 runs.

Finally, we identified which of the simulated interaction matrices alone or in conjunction with habitat preference more likely reproduce the observed differences in positive and negative association networks found on the surveyed assemblages. To do so, we firstly calculated the proportion of simulated assemblages, with the same interaction matrix and $\alpha_R, \alpha_M, \alpha_D$ and $\beta$ values (n=25), showing the four above mentioned patterns (i.e. the proportion where: (i) negative networks were more densely connected, (ii) the abundance-degree relationship was stronger and positive in negative networks, (iii) positive networks tend to be composed of less abundant species and (iv) positive networks were more modular). Additionally, we also calculated the proportion of assemblages where all species survived at the end of the simulations. These proportions indicated the probability of finding empirical differences between positive and negative networks, as well as the probability of non-extinctions in the system. We, then, computed the average of these probabilities for each interaction matrix and investigated whether these proportions varied across the parameter space defined by $\alpha_R, \alpha_M, \alpha_D$ and $\beta$ for each interaction matrix. Here notice that in the main text we present average probabilities over a parameter space where demographic rates ($\alpha_R, \alpha_M, \alpha_D$) allows the expression of interactions. This space was defined using the Competition 1 model under neutral habitat preferences ($\beta$ =1) as baseline and selecting those combinations of rates where species went extinct (i.e. $P_{\text{Competition 1}}$ (non-extinction) = 0). By doing so, we focus on results of different demographic rates that fulfil
theoretical and empirical expectations when only hierarchical competition is operating (i.e. weak
competitors go extinct). Results obtained using all possible combinations of rates were similar
and are discussed below (Appendix S2.6.1, see also Fig. S2).

We conducted all analyses in the R software¹⁹, using different functions of the “igraph”²⁰
and “vegan”² packages. R scripts for the simulation protocol are available upon request.

S3.6 Results

We found qualitatively and quantitatively similar results using different numbers of
iterations (n= 1000, 3000, and 5000; Fig. S1). Therefore, we will only refer to the results derived
from simulations using 5000 iterations. Regarding the simulations where habitat preferences
were neutral, the interaction matrix including a superior competitor and modular positive
interactions among weak competitor species (*Positive rare matrix*) showed the highest averaged
probabilities of finding the four empirical patterns (Fig. 3b and Fig. S1). Thus, in the absence of
different habitat preferences, positive interactions among weak competitors are more likely to
yield similar positive and negative association networks to those found in the empirical
assemblages. Notice that the matrices only including modular positive interactions (*Positive
matrix*) also produced to some extent high probabilities of finding the empirical networks
patterns, especially for the differences in connectivity and modularity between negative and
positive association networks. While obtaining modular positive networks agrees with a priori
expectations for this matrix, higher connectivity in negative ones might be counterintuitive. This
can, nonetheless, be explained attending to the indirect effects produced by the modular structure
of complementarity based positive interactions. That is, in our simulations two co-occurring
individuals complementing each other will obtain available resources faster, thus reducing the
performance of other “non-complemented” individuals and generating an indirect negative
interaction. Given the number of species within each module, the number of these indirect
negative interactions is larger than the number of direct positives ones, leading, therefore, to a
higher connectivity in negative association networks.

Regarding species persistence, the simulations conducted with the *Positive rare* interaction
matrix resulted in higher probabilities of survival of all species in the system, compared to the
competition matrices (Fig. 3b; Fig. S1 and S2a). This supports the idea that the persistence of
rare species can rely on the establishment of positive interactions among them, which would
mitigate the negative impact of superior competitors. Survival probabilities were, nonetheless,
also high for the rest of interaction matrices (Fig. 3b; Fig. S1 and S2a), which shows that these
hypotheses might theoretically enhance species coexistence. However, the network structures
obtained following these hypotheses were far from those empirically observed. Therefore,
although neutrality, intraspecific density-dependent processes, intransitive competition and
facilitation from nurse species might allow rare species persistence, our results suggest that these
mechanisms are unlike to drive the empirical patterns of species assemblages.

It is worth mentioning that the averaged probabilities of finding the structural pattern
observed on surveyed association network were lower when all possible combinations of
demographic parameters were used (i.e. reproduction, mortality and dispersal rates; compare Fig.
3b with Fig. S2a). In fact, these values were never higher than 0.5. On the contrary, survival
probabilities were considerably higher, even in the case of the competition matrices. These
results show that different combinations of the demographic parameters can mask the effects of
biotic interactions. The interplay between these factors has been previously proposed as a
mechanism allowing species coexistence by buffering the negative effects of competition<sup>21,22</sup>. For
instance, low reproduction rates ($\alpha_R \approx 0$) will lead to under-saturated samples with increased resource availability and hence allowing species coexistence. On the other hand, relatively high dispersal abilities will allow weak competitors to more easily escape from samples dominated by strong competitors. Our results confirm that this interplay can certainly promote species coexistence in the absence of other mechanisms. Yet, they also show that differences in these demographic factors are not enough to generate the observed features of association networks (i.e. despite the *Competition 1* model yield a considerably high probability of non-extinction, the empirical patterns are not likely reproduced, Fig. S2a). Interestingly, the increment in non-extinction probabilities resulting from the *Positive rare matrix* when compared to the *Competition 1* matrix, was highly related to the average probability of finding the network features across the parameter space (R=0.77, P < 0.001). In other words, for those combinations of demographic parameters ($\alpha_R, \alpha_S$, and $\alpha_E$) where the *Positive rare matrix* showed higher species survival probabilities than the *Competition 1* matrix, we also found a high probability of obtaining the observed structural patterns of surveyed association networks. Thus, when interactions become important (e.g. when non-extinction probability equals zero in the *Competition 1* matrix), positive interactions among weak competitors yields simulated association networks similar to empirical ones with higher probability (compare Fig. 3b with Fig. S2a).

Taken together, these results confirm that the structure of empirical association networks can reflect mechanisms allowing rare species persistence and that these mechanisms are most likely based on positive interactions among weak competitors, at least in the absence of habitat heterogeneity.
Habitat preference simulations revealed an increase in the probabilities of finding empirical association networks patterns for all interaction matrices. As expected, these probabilities increased as habitat specificity become more intense (Fig. 3b). These results suggest that the existence of diverse microhabitats may act as “refugia” for rare species, allowing their persistence and producing the empirical patterns, independently of interaction matrices. Yet, this uniquely occurs when habitat specificity is notably marked (β < 0.5; meaning that reproduction, survival and colonization is reduced at least to the half in non-preferred habitat), a situation that should only occur when microhabitat heterogeneity is very sharp and patent, which most surely is not case in our assemblages. Moreover, the combination of habitat preferences and the positive interaction among rare species yield the highest probabilities to find empirical patterns of association networks, regardless of habitat specificity levels (Fig. 3a and Fig. S2b). This shows that the effects of habitat preferences are enhanced by positive association among rare species (or *vice versa*). Hence, our results suggest that, even when habitat preferences are acting, modular positive interactions among rare species would still more likely promote the empirical patterns of association networks. Explanations for this are based on the stabilizing effects of complementarity or facilitation among rare species in preferred microhabitats. That is, in our simulations the persistence of different rare species into small microhabitats rely on large enough and evenly distributed (sub)populations that are able to buffer the effects of ecological drift²³. In other words, even assuming that rare species interact neutrally, the reduced dimension of microhabitats would promote the “fixation” of one species per microhabitat due to stochastic processes in small population sizes²³.

Interestingly, the *Nurse* and the *Density dependence* matrices also produced empirical patterns at high probability, especially when habitat specificity was intense (Fig. 3a and Fig.
S2b). This can be explained by the interactions that are expressed in each microhabitat. That is, in the case of the *Nurse* matrix there are two positive and one neutral interactions between the species preferring same small habitats. In the case of the *Density dependent* matrix there were three species suffering more from intraspecific competition in each small habitat, which according to our model framework represents a kind of complementarity (i.e. an individual will perform better when co-occurring with an individual from other species than with an individual of its own species). Hence, in both cases some stabilizing forces are acting to enhance rare species coexistence in microhabitats. This further support the idea that, even when habitat specificity is intense, cooperation among rare species would more likely generate the empirical patterns of association networks while promoting the persistence of rare species.

## Appendix S3. Abundance surveys and data preparation

We compared the structure of negative and positive association networks using a comprehensive database of 427 assemblages worldwide distributed and representing a wide array of taxa including bryophytes, tracheophytes, vertebrates and arthropods. More information about these assemblages, including geographic coordinates, extent and gain, and the reference of the primary study in which they were included can be found in Table S1. We established a criterion of minimum number of samples and species on the assemblage datasets to be included in the study. Thus, all assemblage datasets consist in at least ten species of the same guild and a minimum of ten samples where the abundance of each species was recorded. The raw data used in this study is provided upon request.
S4.1 Abundance surveys in single trophic level assemblages

In this section, we provide information on sampling methodology for those assemblage
datasets (n=87) that have not been extracted from published studies.

Tree species were sampled in two montane temperate mixed forests of Switzerland, Central Europe (see “alps_tree” and “jura_tree” in Table S1 for geographic coordinates). In each site, all the tree individuals (saplings included) were identified and quantified in ten non-overlapping plots of 100 m² with similar ecological conditions (i.e. assemblage samples). Each plot was at least 10 m apart from the closest one.

Abundance (i.e. number of aboveground individuals) of herbaceous plant species was sampled in squared sampling plots of 50 cm x 50 cm within a 30 m x 20 m stand located in sand dunes in central Iberian Peninsula (Cuéllar, Spain; see “erodium” in Table S1). The stand was selected in an open area without influence of trees or taller shrubs species. A total of 75 sampling plots were randomly distributed maintaining a minimum distance of 1 m between adjacent plots. Sampling was conducted during the period of maximum productivity in June 2016.

Angiosperm trees in Brazil were sampled in five different geographic locations in the state of Bahia ("Barra_Paraguacu", "Bonito_Bahia", "Itubera_Bahia", "Maracas_Bahia", "Semidecidous_Bahia" in Table S1). In each locality, several plots (ranging from 13 to 49) were established representing at least 10% of each forest area (i.e. assemblage samples). Due to differences in tree density between the different forest types, sampling plots had different sizes (100 m² for tropical rainforest and 400 m² for Brazilian savanna). Plots were at least 50 m apart from the closest one. Species abundance data included all the trees with at least 15 cm of perimeter at breast height in tropical rainforest sites and 10 cm in Brazilian savanna.
Herbaceous species in central and southern Spain were sampled in 71 sites on soils derived from gypsum outcrops on south-oriented gentle slopes. At each site, a 30 x 30 m square plot was established within homogeneous vegetated areas. The composition and structure of perennial vascular plants was assessed using four 30 m long linear transects parallel to the slope and 8 m apart in each plot. Species-specific cover was estimated visually in 12 quadrats (1.5 m x 1.5 m) placed in each plot (i.e. assemblage samples), with three quadrats per transect line (see "herbs_ym" from 1 to 36 and "herbs_yal" from 1 to 35 in Table S1).

Dung beetles in central Spain were sampled in a grassland located in Guadarrama Mountains at an altitude of 1500 m a.s.l. during three consecutive days (from April 28th to 30th in 2015, see "guada_dung_d_1", "guada_dung_d_2", "guada_dung_d_3" in Table S1). Each day, 10 traps baited with cattle dung were distributed around a circumference of approximately 50 m of radius in each plot (with traps being at least 30 m apart from each other). Baits were introduced into a piece of nylon stocking to avoid the stagnancy of beetles along different sampling events. All traps were checked every 30 min from dawn to dusk (approx. from 7:00 am to 8:00 pm), and all individuals we collected and subsequently identified in the laboratory. The individuals from the 10 traps were pooled together obtaining an estimation of the species abundances each 30 min. For this dataset we explored temporary patterns of species pairwise aggregation and segregation.

Dung beetles were also sampled in mountain forests in the north-western slope of Sierra Nevada de Santa Marta, Colombia (see "colom_dung_480", "colom_dung_875", "colom_dung_1280" in Table S1). Sampling was conducted in three different locations between December 1998 and January 1999. At each location, three altitudes at intervals of 400 m were selected (i.e. 480, 875 and 1280 m). Dung beetles were trapped using a linear transect of 10
pitfall traps without any preservative solution and with aerial bait. Traps were separated at least
by 30 m, baited with 25 ml of fresh human dung and collected each hour during a period of 24
hours. The individuals from the 10 traps of each plot were pooled together obtaining an
estimation of the species abundances each hour. For this dataset we also explored temporary
patterns of species pairwise aggregation and segregation.

Abundance of ephemeral plant species was estimated in two localities in the semiarid
ecosystem of the Fray Jorge National Park (Coquimbo Region, Chile; see “anuals_fray_1” and
“anuals_fray_2” in Table S1). In each locality, 10 sampling quadrats were randomly distributed
in a 50 m x 50 m area where species abundance was estimated as relative cover. Sampling was
conducted during the period of maximum productivity in July 2010. These data are part of a
more extensive experiment including artificial shading treatments and shrub influence. The
sampling plots included in this study were all distributed in open habitats outside the influence of
taller shrubs of trees.

S4.2 Data preparation

In this section, we provide information about data preparation for those assemblage datasets
where it was necessary to fulfil the criteria on reduce spatial extent and low environmental
heterogeneity.

Some ant assemblage datasets were obtained from Arnan & Blüthgen²⁴. This dataset
consisted in a total of 24 plot surveys (distributed in three different habitat types within four
locations and during two seasons) in Barcelona, north-east Spain. Each plot contained 60 bait
stations separated each other at least 5 m, where the identity and abundance of each species was
recorded (see ref²⁴ for details). We considered each plot survey as an independent assemblage
and selected only ten of them following our criteria of at least ten different species at each assemblage (see “PHFIG_Spring”, “PHGRF_Spring”, “PHGRF_Summer”, “PHVAC_Spring”, “PHVAC_Summer”, “SHRFIG_Spring”, “SHRFIG_Summer”, “SHRGRF_Spring”, “SHRVAC_Spring” and “SHRVAC_Summer” in Table S1).
<figure><img src="image_11.png" /><figcaption>Fig. S1.</figcaption></figure>

**Fig. S1.** Positive interactions among weak competitors yields the highest probability of reproducing empirical network patterns over 20 different models. Mean probability (y-axis) of finding simulated assemblages that show empirical network patterns under neutral habitats for each interaction matrix (x-axis) and for simulations with (a) 1000, (b) 3000 and (c) 5000 iterations (see also Fig. S6 and Appendix S2.6 for a detailed explanation of these results).
<figure><img src="image_12.png" /><figcaption>Fig. S2.</figcaption></figure>

**Fig. S2.** Positive interactions among rare species is the model that best reproduces the empirical patterns for a broad range of demographic parameters. The effects of interactions are masked at some combinations of demographic parameters (i.e., reproduction, mortality and colonization) in simulated assemblages. **a**, This produces, for instance, that the *Competition* model do not always end up with the single dominant species (i.e., the non-extinction probability in this matrix is not always equal
to zero, as it would be expected based on the competitive exclusion
principle; see also Appendix S2.6.1). Still, even when using all the possible
combinations of demographic rates, positive interaction among rare species
is the best model for reproducing the empirical patterns. **b**, This also holds
for different levels of habitat specialization. **a**, The y-axis represents the
probability of simulated association networks showing empirical differences
between positive and negative networks across the entire parameter space
defined by reproduction, mortality and dispersal rates (Appendix S2). **b**, The
y-axis represents the average probabilities of finding the four empirical
patterns across the completed parameter space, whereas the x-axis depicts
a gradient of habitat specialization (Appendix S2). Error bars depict
confidence intervals at α = 0.05. P: positive networks. N: negative networks.
See also Fig. 3.
<figure><img src="image_13.png" /><figcaption>Fig. S3.</figcaption></figure>

**Fig. S3.** The proportion of assemblages showing differences between positive and negative networks is insensitive to the probability threshold used to detected significant associations. The dotted line depicts the probability threshold used in the main text (P = 0.05, see Appendix S1.1).
<figure><img src="image_14.png" /><figcaption>Fig. S4.</figcaption></figure>

**Fig. S4.** Network patterns remain largely invariant when using different null models to detect significant associations. Results of (a) a fixed-fixed null model; (b) a fixed-fixed null model that also maintains matrix fill and; (c) a null model that keeps constant the species abundance and frequency of occurrence, as well as, sample species richness (null models I, II and III respectively in Appendix S1.2).
<figure><img src="image_15.png" /><figcaption>Fig. S5.</figcaption></figure>

**Fig. S5.** a, Reproduction, mortality and colonization probabilities from an individual of species *i* vary according to the interaction effects exerted by an individual of species *j* ($\theta_i^j$). Interaction coefficients lower than 1 lead to a decrease of mortality probabilities while reproduction and colonisation increase compared to a neutral interaction (where $\theta_i^j=\theta_i^j$). On the contrary, when interaction coefficients are higher than 1 ($\theta_i^j>1$), the probability of mortality increases, and the probabilities of reproduction and colonisation are lower than in a neutral scenario. In the x-right extreme, when the
interaction coefficient is equal to the carrying capacity ($\theta_i^j=K$), the probabilities of reproduction and colonization are equal to zero while the probability of mortality is equal to 1. **b**, An example of the interaction matrices used in the simulations. Values in column *j* represent the effects of other species on the focal species. Values in row *i* represent the effects of the focal species on the other species. The marginal total of column $j(\sum_i x_{ij})$ depicts the combined effects of all species on the focal species, whereas the marginal total of row *i* $(\sum_j x_{ij})$ represents the overall competitive ability of the focal species. **c**, Matrix representation of the habitat preferences. Each color presents one habitat and the species that prefer it.
<figure><img src="image_16.png" /><figcaption>Fig. S6.</figcaption></figure>

**Fig. S6.** Interaction matrices reflecting different hypotheses about species coexistence and rare species survival. Cell numbers represent
the interaction effects (θ). Red colour indicates competitive interactions (i.e., θ>1), and green colour depicts positive ones (i.e., θ<1). * denotes the matrices that were presented in the main text and combined with the habitat preference scenario (see Appendix S2.2).
Tables

<table><caption>Table S1.</caption><thead><tr><th>ID</th><th>Latitud e</th><th>Longitu de</th><th>Taxon or T. level</th><th>Nº sampl es</th><th>Nº speci es</th><th>Grain (m²)</th><th>Extent (ha)</th><th>Referenc e</th></tr></thead><tbody><tr><td>allacher</td><td>48.70</td><td>11.50</td><td>Trees</td><td>10</td><td>21</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>allpahua</td><td>-3.95</td><td>-73.42</td><td>Trees</td><td>10</td><td>276</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>alps_tree</td><td>46.33</td><td>6.87</td><td>Trees</td><td>10</td><td>17</td><td>100.00 0</td><td>2.000</td><td>Appendix S3</td></tr><tr><td>alterdoc</td><td>-2.50</td><td>-54.97</td><td>Trees</td><td>10</td><td>34</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>altodemi</td><td>10.92</td><td>-73.83</td><td>Trees</td><td>10</td><td>87</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>altosapa</td><td>7.17</td><td>-75.90</td><td>Trees</td><td>10</td><td>64</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>amotape</td><td>-4.15</td><td>-80.62</td><td>Trees</td><td>10</td><td>54</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>anchicay</td><td>3.75</td><td>-76.83</td><td>Trees</td><td>10</td><td>143</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>ankarif</td><td>-16.32</td><td>46.82</td><td>Trees</td><td>10</td><td>96</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>antado</td><td>7.25</td><td>-75.92</td><td>Trees</td><td>10</td><td>162</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>ants_Darwin_A</td><td>-12.70</td><td>130.98</td><td>Ants</td><td>100</td><td>46</td><td>0.002</td><td>0.032</td><td>26</td></tr><tr><td>ants_Darwin_B</td><td>-12.70</td><td>130.98</td><td>Ants</td><td>100</td><td>38</td><td>0.002</td><td>0.032</td><td>26</td></tr><tr><td>ants_Darwin_C</td><td>-12.70</td><td>130.98</td><td>Ants</td><td>100</td><td>40</td><td>0.002</td><td>0.032</td><td>26</td></tr><tr><td>ants_Darwin_D</td><td>-12.70</td><td>130.98</td><td>Ants</td><td>100</td><td>37</td><td>0.002</td><td>0.032</td><td>26</td></tr><tr><td>anuals_fray_1</td><td>-30.63</td><td>-71.67</td><td>Herbs</td><td>10</td><td>18</td><td>0.090</td><td>0.250</td><td>Appendix S3</td></tr><tr><td>anuals_fray_2</td><td>-30.63</td><td>-71.67</td><td>Herbs</td><td>10</td><td>16</td><td>0.090</td><td>0.250</td><td>Appendix S3</td></tr><tr><td>araracua</td><td>-0.42</td><td>-72.33</td><td>Trees</td><td>11</td><td>265</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>avalanch</td><td>11.30</td><td>76.58</td><td>Trees</td><td>10</td><td>69</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>bablersp</td><td>38.53</td><td>-90.67</td><td>Trees</td><td>10</td><td>23</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>baitete</td><td>-5.17</td><td>145.80</td><td>Trees</td><td>10</td><td>147</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>bakosar</td><td>1.75</td><td>110.42</td><td>Trees</td><td>10</td><td>137</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>bankamp</td><td>40.50</td><td>-81.30</td><td>Trees</td><td>10</td><td>23</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>banyong</td><td>5.00</td><td>9.17</td><td>Trees</td><td>10</td><td>197</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>base_encina_a</td><td>31.06</td><td>-8.73</td><td>Bryophytes</td><td>14</td><td>11</td><td>0.040</td><td>0.023</td><td>27</td></tr><tr><td>base_encina_b</td><td>32.55</td><td>-4.92</td><td>Bryophytes</td><td>18</td><td>11</td><td>0.040</td><td>0.020</td><td>27</td></tr></tbody></table>
<table><thead><tr><th>ID</th><th>Latitud e</th><th>Longitu de</th><th>Taxon or T. level</th><th>Nº sampl es</th><th>Nº speci es</th><th>Grain (m²)</th><th>Extent (ha)</th><th>Referenc e</th></tr></thead><tbody><tr><td>belem</td><td>-1.50</td><td>-47.98</td><td>Trees</td><td>10</td><td>129</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>benito</td><td>15.33</td><td>-92.25</td><td>Trees</td><td>10</td><td>30</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>berbicer</td><td>5.50</td><td>-58.80</td><td>Trees</td><td>10</td><td>101</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>beza1</td><td>-23.63</td><td>44.57</td><td>Trees</td><td>10</td><td>31</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>beza2</td><td>-23.63</td><td>44.57</td><td>Trees</td><td>10</td><td>40</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>bilsa</td><td>0.62</td><td>-79.85</td><td>Trees</td><td>10</td><td>121</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>blohmr</td><td>8.57</td><td>-67.58</td><td>Trees</td><td>10</td><td>65</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>boraceia</td><td>-23.38</td><td>-46.00</td><td>Trees</td><td>10</td><td>124</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>bosquede</td><td>19.50</td><td>-96.95</td><td>Trees</td><td>10</td><td>42</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>brio_fo_100</td><td>42.94</td><td>-6.51</td><td>Bryophyt es</td><td>20</td><td>24</td><td>0.040</td><td>8.019</td><td>28</td></tr><tr><td>brio_fo_102</td><td>42.04</td><td>-3.61</td><td>Bryophyt es</td><td>19</td><td>21</td><td>0.040</td><td>6.024</td><td>28</td></tr><tr><td>brio_fo_103</td><td>42.58</td><td>-3.95</td><td>Bryophyt es</td><td>19</td><td>13</td><td>0.040</td><td>6.776</td><td>28</td></tr><tr><td>brio_fo_105</td><td>40.95</td><td>-4.38</td><td>Bryophyt es</td><td>16</td><td>10</td><td>0.040</td><td>19.878</td><td>28</td></tr><tr><td>brio_fo_106</td><td>40.27</td><td>-2.83</td><td>Bryophyt es</td><td>20</td><td>20</td><td>0.040</td><td>3.786</td><td>28</td></tr><tr><td>brio_fo_109</td><td>41.00</td><td>-2.71</td><td>Bryophyt es</td><td>20</td><td>23</td><td>0.040</td><td>8.258</td><td>28</td></tr><tr><td>brio_fo_112</td><td>40.82</td><td>-3.35</td><td>Bryophyt es</td><td>20</td><td>12</td><td>0.040</td><td>3.982</td><td>28</td></tr><tr><td>brio_fo_113</td><td>41.88</td><td>-4.10</td><td>Bryophyt es</td><td>20</td><td>14</td><td>0.040</td><td>2.859</td><td>28</td></tr><tr><td>brio_fo_114</td><td>41.74</td><td>-2.63</td><td>Bryophyt es</td><td>19</td><td>15</td><td>0.040</td><td>11.933</td><td>28</td></tr><tr><td>brio_fo_115</td><td>40.34</td><td>-4.15</td><td>Bryophyt es</td><td>20</td><td>20</td><td>0.040</td><td>11.933</td><td>28</td></tr><tr><td>brio_fo_116</td><td>41.74</td><td>-5.12</td><td>Bryophyt es</td><td>14</td><td>13</td><td>0.040</td><td>11.143</td><td>28</td></tr><tr><td>brio_fo_118</td><td>39.96</td><td>-6.66</td><td>Bryophyt es</td><td>19</td><td>11</td><td>0.040</td><td>10.030</td><td>28</td></tr><tr><td>brio_fo_12</td><td>42.27</td><td>-6.07</td><td>Bryophyt es</td><td>20</td><td>16</td><td>0.040</td><td>22.490</td><td>28</td></tr><tr><td>brio_fo_14</td><td>42.87</td><td>-5.63</td><td>Bryophyt es</td><td>19</td><td>17</td><td>0.040</td><td>13.409</td><td>28</td></tr><tr><td>brio_fo_18</td><td>42.77</td><td>-4.13</td><td>Bryophyt es</td><td>20</td><td>11</td><td>0.040</td><td>6.519</td><td>28</td></tr><tr><td>brio_fo_2</td><td>42.51</td><td>-5.32</td><td>Bryophyt es</td><td>13</td><td>12</td><td>0.040</td><td>18.831</td><td>28</td></tr></tbody></table>
<table><thead><tr><td>ID</td><td>Latitud e</td><td>Longitu de</td><td>Taxon or T. level</td><td>Nº sampl es</td><td>Nº speci es</td><td>Grain (m²)</td><td>Extent (ha)</td><td>Referenc e</td></tr></thead><tbody><tr><td>brio_fo_20</td><td>39.87</td><td>-5.31</td><td>Bryophyt es</td><td>19</td><td>16</td><td>0.040</td><td>9.750</td><td>28</td></tr><tr><td>brio_fo_22</td><td>40.30</td><td>-3.37</td><td>Bryophyt es</td><td>20</td><td>14</td><td>0.040</td><td>2.147</td><td>28</td></tr><tr><td>brio_fo_24</td><td>40.38</td><td>-3.44</td><td>Bryophyt es</td><td>20</td><td>17</td><td>0.040</td><td>7.489</td><td>28</td></tr><tr><td>brio_fo_26</td><td>39.36</td><td>-5.71</td><td>Bryophyt es</td><td>17</td><td>12</td><td>0.040</td><td>9.171</td><td>28</td></tr><tr><td>brio_fo_28</td><td>39.39</td><td>-6.43</td><td>Bryophyt es</td><td>20</td><td>14</td><td>0.040</td><td>6.477</td><td>28</td></tr><tr><td>brio_fo_29</td><td>39.57</td><td>-7.00</td><td>Bryophyt es</td><td>20</td><td>20</td><td>0.040</td><td>7.649</td><td>28</td></tr><tr><td>brio_fo_3</td><td>41.45</td><td>-5.81</td><td>Bryophyt es</td><td>20</td><td>11</td><td>0.040</td><td>6.392</td><td>28</td></tr><tr><td>brio_fo_31</td><td>40.42</td><td>-3.87</td><td>Bryophyt es</td><td>19</td><td>23</td><td>0.040</td><td>7.624</td><td>28</td></tr><tr><td>brio_fo_32</td><td>40.24</td><td>-3.92</td><td>Bryophyt es</td><td>20</td><td>18</td><td>0.040</td><td>9.980</td><td>28</td></tr><tr><td>brio_fo_33</td><td>40.29</td><td>-3.25</td><td>Bryophyt es</td><td>20</td><td>22</td><td>0.040</td><td>8.043</td><td>28</td></tr><tr><td>brio_fo_34</td><td>40.34</td><td>-4.26</td><td>Bryophyt es</td><td>20</td><td>16</td><td>0.040</td><td>2.443</td><td>28</td></tr><tr><td>brio_fo_36</td><td>40.74</td><td>-3.85</td><td>Bryophyt es</td><td>20</td><td>25</td><td>0.040</td><td>8.285</td><td>28</td></tr><tr><td>brio_fo_38</td><td>39.78</td><td>-4.55</td><td>Bryophyt es</td><td>20</td><td>21</td><td>0.040</td><td>3.677</td><td>28</td></tr><tr><td>brio_fo_4</td><td>41.14</td><td>-6.73</td><td>Bryophyt es</td><td>20</td><td>25</td><td>0.040</td><td>22.673</td><td>28</td></tr><tr><td>brio_fo_41</td><td>39.68</td><td>-4.63</td><td>Bryophyt es</td><td>20</td><td>21</td><td>0.040</td><td>4.760</td><td>28</td></tr><tr><td>brio_fo_42</td><td>39.68</td><td>-4.91</td><td>Bryophyt es</td><td>19</td><td>25</td><td>0.040</td><td>9.084</td><td>28</td></tr><tr><td>brio_fo_43</td><td>40.05</td><td>-5.13</td><td>Bryophyt es</td><td>20</td><td>17</td><td>0.040</td><td>6.280</td><td>28</td></tr><tr><td>brio_fo_44</td><td>40.12</td><td>-4.64</td><td>Bryophyt es</td><td>20</td><td>15</td><td>0.040</td><td>5.628</td><td>28</td></tr><tr><td>brio_fo_45</td><td>40.52</td><td>-5.72</td><td>Bryophyt es</td><td>20</td><td>20</td><td>0.040</td><td>9.216</td><td>28</td></tr><tr><td>brio_fo_46</td><td>40.75</td><td>-5.53</td><td>Bryophyt es</td><td>18</td><td>14</td><td>0.040</td><td>3.501</td><td>28</td></tr><tr><td>brio_fo_47</td><td>40.62</td><td>-6.60</td><td>Bryophyt es</td><td>20</td><td>16</td><td>0.040</td><td>22.646</td><td>28</td></tr><tr><td>brio_fo_48</td><td>41.26</td><td>-6.21</td><td>Bryophyt es</td><td>20</td><td>11</td><td>0.040</td><td>4.331</td><td>28</td></tr><tr><td>brio_fo_49</td><td>40.95</td><td>-5.98</td><td>Bryophyt es</td><td>20</td><td>12</td><td>0.040</td><td>6.988</td><td>28</td></tr><tr><td>brio_fo_51</td><td>40.74</td><td>-4.55</td><td>Bryophyt es</td><td>20</td><td>19</td><td>0.040</td><td>5.306</td><td>28</td></tr><tr><td>brio_fo_52</td><td>41.41</td><td>-3.21</td><td>Bryophyt es</td><td>19</td><td>14</td><td>0.040</td><td>4.954</td><td>28</td></tr><tr><td>brio_fo_54</td><td>41.59</td><td>-6.17</td><td>Bryophyt es</td><td>18</td><td>22</td><td>0.040</td><td>10.807</td><td>28</td></tr></tbody></table>
<table><thead><tr><th>ID</th><th>Latitud e</th><th>Longitu de</th><th>Taxon or T. level</th><th>Nº sampl es</th><th>Nº speci es</th><th>Grain (m²)</th><th>Extent (ha)</th><th>Referenc e</th></tr></thead><tbody><tr><td>brio_fo_55</td><td>41.80</td><td>-6.21</td><td>Bryophyt es</td><td>19</td><td>15</td><td>0.040</td><td>9.765</td><td>28</td></tr><tr><td>brio_fo_56</td><td>42.04</td><td>-6.10</td><td>Bryophyt es</td><td>19</td><td>11</td><td>0.040</td><td>15.029</td><td>28</td></tr><tr><td>brio_fo_59</td><td>41.84</td><td>-4.82</td><td>Bryophyt es</td><td>18</td><td>12</td><td>0.040</td><td>24.079</td><td>28</td></tr><tr><td>brio_fo_61</td><td>40.30</td><td>-2.25</td><td>Bryophyt es</td><td>20</td><td>13</td><td>0.040</td><td>7.630</td><td>28</td></tr><tr><td>brio_fo_62</td><td>40.57</td><td>-4.15</td><td>Bryophyt es</td><td>20</td><td>18</td><td>0.040</td><td>2.282</td><td>28</td></tr><tr><td>brio_fo_63</td><td>40.95</td><td>-3.76</td><td>Bryophyt es</td><td>20</td><td>15</td><td>0.040</td><td>5.273</td><td>28</td></tr><tr><td>brio_fo_64</td><td>40.97</td><td>-3.80</td><td>Bryophyt es</td><td>20</td><td>10</td><td>0.040</td><td>6.097</td><td>28</td></tr><tr><td>brio_fo_65</td><td>40.94</td><td>-3.77</td><td>Bryophyt es</td><td>20</td><td>10</td><td>0.040</td><td>12.718</td><td>28</td></tr><tr><td>brio_fo_66</td><td>41.03</td><td>-3.94</td><td>Bryophyt es</td><td>20</td><td>15</td><td>0.040</td><td>4.783</td><td>28</td></tr><tr><td>brio_fo_67</td><td>40.91</td><td>-3.99</td><td>Bryophyt es</td><td>20</td><td>11</td><td>0.040</td><td>5.877</td><td>28</td></tr><tr><td>brio_fo_68</td><td>40.81</td><td>-3.78</td><td>Bryophyt es</td><td>19</td><td>13</td><td>0.040</td><td>11.717</td><td>28</td></tr><tr><td>brio_fo_69</td><td>40.81</td><td>-3.61</td><td>Bryophyt es</td><td>18</td><td>12</td><td>0.040</td><td>7.089</td><td>28</td></tr><tr><td>brio_fo_7</td><td>41.13</td><td>-5.47</td><td>Bryophyt es</td><td>20</td><td>19</td><td>0.040</td><td>2.168</td><td>28</td></tr><tr><td>brio_fo_70</td><td>41.17</td><td>-3.88</td><td>Bryophyt es</td><td>20</td><td>18</td><td>0.040</td><td>3.960</td><td>28</td></tr><tr><td>brio_fo_72</td><td>40.42</td><td>-3.25</td><td>Bryophyt es</td><td>20</td><td>12</td><td>0.040</td><td>7.378</td><td>28</td></tr><tr><td>brio_fo_75</td><td>40.70</td><td>-2.41</td><td>Bryophyt es</td><td>18</td><td>12</td><td>0.040</td><td>5.524</td><td>28</td></tr><tr><td>brio_fo_80</td><td>40.34</td><td>-5.18</td><td>Bryophyt es</td><td>20</td><td>14</td><td>0.040</td><td>13.068</td><td>28</td></tr><tr><td>brio_fo_86</td><td>42.97</td><td>-4.90</td><td>Bryophyt es</td><td>20</td><td>11</td><td>0.040</td><td>7.116</td><td>28</td></tr><tr><td>brio_fo_87</td><td>42.79</td><td>-4.71</td><td>Bryophyt es</td><td>20</td><td>10</td><td>0.040</td><td>1.038</td><td>28</td></tr><tr><td>brio_fo_93</td><td>40.28</td><td>-5.84</td><td>Bryophyt es</td><td>20</td><td>14</td><td>0.040</td><td>4.958</td><td>28</td></tr><tr><td>brio_fo_94</td><td>42.16</td><td>-7.60</td><td>Bryophyt es</td><td>18</td><td>25</td><td>0.040</td><td>2.035</td><td>28</td></tr><tr><td>brio_fo_96</td><td>42.64</td><td>-7.14</td><td>Bryophyt es</td><td>20</td><td>26</td><td>0.040</td><td>0.951</td><td>28</td></tr><tr><td>brisefer</td><td>-20.38</td><td>57.43</td><td>Trees</td><td>10</td><td>76</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>burling</td><td>38.95</td><td>-77.17</td><td>Trees</td><td>10</td><td>23</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cabezade</td><td>-10.33</td><td>-75.30</td><td>Trees</td><td>10</td><td>153</td><td>100.00 0</td><td>0.100</td><td>25</td></tr></tbody></table>
<table><thead><tr><td>ID</td><td>Latitud e</td><td>Longitu de</td><td>Taxon or T. level</td><td>Nº sampl es</td><td>Nº speci es</td><td>Grain (m²)</td><td>Extent (ha)</td><td>Referenc e</td></tr></thead><tbody><tr><td>calima</td><td>3.92</td><td>-77.00</td><td>Trees</td><td>10</td><td>263</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>camorin</td><td>-22.93</td><td>-43.37</td><td>Trees</td><td>10</td><td>157</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>campano</td><td>11.13</td><td>-74.20</td><td>Trees</td><td>10</td><td>106</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>candamo</td><td>-13.50</td><td>-69.83</td><td>Trees</td><td>10</td><td>236</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>capeira</td><td>-2.00</td><td>-79.97</td><td>Trees</td><td>10</td><td>60</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>carajas</td><td>-5.50</td><td>-51.00</td><td>Trees</td><td>10</td><td>175</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>carara</td><td>9.77</td><td>-84.53</td><td>Trees</td><td>10</td><td>153</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>carlosbo</td><td>-24.25</td><td>-46.93</td><td>Trees</td><td>10</td><td>149</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>carpanta</td><td>4.58</td><td>-73.67</td><td>Trees</td><td>10</td><td>47</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cary</td><td>46.83</td><td>-73.75</td><td>Trees</td><td>10</td><td>20</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cedarblu</td><td>39.77</td><td>-86.15</td><td>Trees</td><td>10</td><td>25</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cedral</td><td>4.75</td><td>-75.55</td><td>Trees</td><td>10</td><td>141</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cega_climb</td><td>41.36</td><td>-4.26</td><td>Trees</td><td>40</td><td>33</td><td>50.000</td><td>2.000</td><td>29</td></tr><tr><td>centinel</td><td>-0.58</td><td>-79.33</td><td>Trees</td><td>10</td><td>140</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>ceroneb1</td><td>0.83</td><td>-66.18</td><td>Trees</td><td>10</td><td>95</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>ceroneb2</td><td>0.83</td><td>-66.18</td><td>Trees</td><td>10</td><td>81</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cerroayp</td><td>-4.58</td><td>-79.53</td><td>Trees</td><td>10</td><td>56</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cerroelp</td><td>13.00</td><td>-85.92</td><td>Trees</td><td>10</td><td>68</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cerroesp</td><td>10.47</td><td>-72.83</td><td>Trees</td><td>10</td><td>77</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cerroolu</td><td>12.30</td><td>-85.40</td><td>Trees</td><td>10</td><td>96</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>chamela1</td><td>19.50</td><td>-105.50</td><td>Trees</td><td>10</td><td>89</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>chamela2</td><td>19.50</td><td>-105.50</td><td>Trees</td><td>10</td><td>87</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>chamela3</td><td>19.50</td><td>-105.50</td><td>Trees</td><td>10</td><td>102</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>chaquima</td><td>-14.57</td><td>-68.47</td><td>Trees</td><td>10</td><td>81</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>chiba</td><td>35.15</td><td>140.15</td><td>Trees</td><td>10</td><td>47</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>chirinos</td><td>-5.42</td><td>-78.88</td><td>Trees</td><td>10</td><td>101</td><td>100.00 0</td><td>0.100</td><td>25</td></tr></tbody></table>
<table><thead><tr><td>ID</td><td>Latitud e</td><td>Longitu de</td><td>Taxon or T. level</td><td>Nº sampl es</td><td>Nº speci es</td><td>Grain (m²)</td><td>Extent (ha)</td><td>Referenc e</td></tr></thead><tbody><tr><td>choros_op</td><td>-29.26</td><td>-71.41</td><td>Herbs</td><td>50</td><td>14</td><td>2.250</td><td>0.450</td><td>30</td></tr><tr><td>choros_un</td><td>-29.26</td><td>-71.41</td><td>Herbs</td><td>50</td><td>15</td><td>2.250</td><td>0.450</td><td>30</td></tr><tr><td>cochacas</td><td>-11.85</td><td>-71.32</td><td>Trees</td><td>10</td><td>169</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>colom_dung_12 80</td><td>10.87</td><td>-73.72</td><td>Beetles</td><td>18</td><td>13</td><td>1 hour</td><td>24 hours</td><td>Appendix S3</td></tr><tr><td>colom_dung_48 0</td><td>10.87</td><td>-73.72</td><td>Beetles</td><td>19</td><td>24</td><td>1 hour</td><td>24 hours</td><td>Appendix S3</td></tr><tr><td>colom_dung_87 5</td><td>10.87</td><td>-73.72</td><td>Beetles</td><td>16</td><td>12</td><td>1 hour</td><td>24 hours</td><td>Appendix S3</td></tr><tr><td>colorado</td><td>9.97</td><td>-75.17</td><td>Trees</td><td>10</td><td>121</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>colosoi</td><td>9.50</td><td>-75.80</td><td>Trees</td><td>10</td><td>111</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>constanc</td><td>-4.25</td><td>-72.75</td><td>Trees</td><td>10</td><td>233</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cuangos</td><td>-3.48</td><td>-78.23</td><td>Trees</td><td>10</td><td>170</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cueva</td><td>11.80</td><td>-73.47</td><td>Trees</td><td>11</td><td>92</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cuevas</td><td>6.67</td><td>-76.50</td><td>Trees</td><td>10</td><td>126</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cuivre</td><td>39.20</td><td>-91.00</td><td>Trees</td><td>10</td><td>29</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>curundu</td><td>8.98</td><td>-79.55</td><td>Trees</td><td>10</td><td>85</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>curuyuqr</td><td>-18.75</td><td>-62.30</td><td>Trees</td><td>10</td><td>51</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cutervo</td><td>-6.17</td><td>-78.67</td><td>Trees</td><td>10</td><td>120</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cuyas</td><td>-4.53</td><td>-79.73</td><td>Trees</td><td>10</td><td>54</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>cuzcoama</td><td>-12.58</td><td>-69.15</td><td>Trees</td><td>10</td><td>168</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>davies</td><td>-17.80</td><td>145.57</td><td>Trees</td><td>10</td><td>116</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>ducke</td><td>-3.00</td><td>-59.97</td><td>Trees</td><td>10</td><td>238</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>encanto</td><td>-14.63</td><td>-60.70</td><td>Trees</td><td>10</td><td>111</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>erodium</td><td>41.37</td><td>-4.25</td><td>Herbs</td><td>73</td><td>19</td><td>0.250</td><td>0.060</td><td>Appendix S3</td></tr><tr><td>esmerald</td><td>0.90</td><td>-79.62</td><td>Trees</td><td>10</td><td>101</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>farall</td><td>3.50</td><td>-76.58</td><td>Trees</td><td>10</td><td>133</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>fincam</td><td>2.27</td><td>-76.20</td><td>Trees</td><td>10</td><td>108</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>galeraz</td><td>10.80</td><td>-75.25</td><td>Trees</td><td>10</td><td>52</td><td>100.00 0</td><td>0.100</td><td>25</td></tr></tbody></table>
<table><thead><tr><th>ID</th><th>Latitud e</th><th>Longitu de</th><th>Taxon or T. level</th><th>Nº sampl es</th><th>Nº speci es</th><th>Grain (m²)</th><th>Extent (ha)</th><th>Referenc e</th></tr></thead><tbody><tr><td>genting</td><td>3.97</td><td>101.63</td><td>Trees</td><td>10</td><td>194</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>guada_dung_d_1</td><td>40.75</td><td>-4.02</td><td>Beetles</td><td>16</td><td>16</td><td>0.5 hours</td><td>8 hours</td><td>Appendix S3</td></tr><tr><td>guada_dung_d_2</td><td>40.75</td><td>-4.02</td><td>Beetles</td><td>21</td><td>18</td><td>0.5 hours</td><td>11 hours</td><td>Appendix S3</td></tr><tr><td>guada_dung_d_3</td><td>40.75</td><td>-4.02</td><td>Beetles</td><td>19</td><td>18</td><td>0.5 hours</td><td>10 hours</td><td>Appendix S3</td></tr><tr><td>herbs_yal_1</td><td>38.48</td><td>-0.85</td><td>Shrubs</td><td>12</td><td>18</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_10</td><td>38.48</td><td>-0.85</td><td>Shrubs</td><td>12</td><td>24</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_11</td><td>38.18</td><td>-1.24</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_12</td><td>38.18</td><td>-1.24</td><td>Shrubs</td><td>12</td><td>10</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_13</td><td>38.18</td><td>-1.24</td><td>Shrubs</td><td>12</td><td>13</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_14</td><td>38.18</td><td>-1.24</td><td>Shrubs</td><td>12</td><td>11</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_15</td><td>38.18</td><td>-1.25</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_16</td><td>38.48</td><td>-0.84</td><td>Shrubs</td><td>12</td><td>24</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_17</td><td>37.86</td><td>-1.87</td><td>Shrubs</td><td>12</td><td>10</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_18</td><td>37.86</td><td>-1.87</td><td>Shrubs</td><td>12</td><td>10</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_19</td><td>37.86</td><td>-1.87</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_2</td><td>38.49</td><td>-1.62</td><td>Shrubs</td><td>12</td><td>15</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_20</td><td>38.47</td><td>-0.83</td><td>Shrubs</td><td>12</td><td>14</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_21</td><td>37.86</td><td>-1.87</td><td>Shrubs</td><td>12</td><td>10</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_22</td><td>37.13</td><td>-2.05</td><td>Shrubs</td><td>12</td><td>17</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_23</td><td>37.13</td><td>-2.05</td><td>Shrubs</td><td>12</td><td>15</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_24</td><td>37.13</td><td>-2.06</td><td>Shrubs</td><td>12</td><td>16</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_25</td><td>37.13</td><td>-2.06</td><td>Shrubs</td><td>12</td><td>11</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_26</td><td>37.13</td><td>-2.06</td><td>Shrubs</td><td>12</td><td>13</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_27</td><td>37.09</td><td>-2.09</td><td>Shrubs</td><td>12</td><td>17</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_28</td><td>37.09</td><td>-2.09</td><td>Shrubs</td><td>12</td><td>17</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_29</td><td>37.09</td><td>-2.09</td><td>Shrubs</td><td>12</td><td>17</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr></tbody></table>
<table><thead><tr><th>ID</th><th>Latitud e</th><th>Longitu de</th><th>Taxon or T. level</th><th>Nº sampl es</th><th>Nº speci es</th><th>Grain (m²)</th><th>Extent (ha)</th><th>Referenc e</th></tr></thead><tbody><tr><td>herbs_yal_3</td><td>38.49</td><td>-1.62</td><td>Shrubs</td><td>12</td><td>13</td><td>2.250</td><td>0.090</td><td>S3<br>Appendix S3</td></tr><tr><td>herbs_yal_30</td><td>38.47</td><td>-0.83</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_31</td><td>37.09</td><td>-2.09</td><td>Shrubs</td><td>12</td><td>14</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_32</td><td>38.47</td><td>-0.83</td><td>Shrubs</td><td>12</td><td>13</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_33</td><td>38.47</td><td>-0.83</td><td>Shrubs</td><td>12</td><td>11</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_34</td><td>38.45</td><td>-0.78</td><td>Shrubs</td><td>12</td><td>18</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_35</td><td>38.49</td><td>-1.62</td><td>Shrubs</td><td>12</td><td>15</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_4</td><td>38.49</td><td>-1.62</td><td>Shrubs</td><td>12</td><td>14</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_5</td><td>38.44</td><td>-0.78</td><td>Shrubs</td><td>12</td><td>22</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_6</td><td>38.44</td><td>-0.78</td><td>Shrubs</td><td>12</td><td>17</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_7</td><td>38.25</td><td>-0.91</td><td>Shrubs</td><td>12</td><td>21</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_8</td><td>38.26</td><td>-0.91</td><td>Shrubs</td><td>12</td><td>16</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_yal_9</td><td>38.18</td><td>-1.24</td><td>Shrubs</td><td>12</td><td>11</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_1</td><td>40.14</td><td>-3.62</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_10</td><td>40.22</td><td>-3.58</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_11</td><td>40.19</td><td>-3.59</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_12</td><td>40.21</td><td>-3.59</td><td>Shrubs</td><td>12</td><td>15</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_13</td><td>40.21</td><td>-3.59</td><td>Shrubs</td><td>12</td><td>13</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_14</td><td>39.82</td><td>-3.43</td><td>Shrubs</td><td>12</td><td>16</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_15</td><td>39.80</td><td>-3.43</td><td>Shrubs</td><td>12</td><td>10</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_16</td><td>39.85</td><td>-3.33</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_17</td><td>40.05</td><td>-3.05</td><td>Shrubs</td><td>12</td><td>14</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_18</td><td>40.06</td><td>-3.05</td><td>Shrubs</td><td>12</td><td>17</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_19</td><td>40.06</td><td>-3.06</td><td>Shrubs</td><td>12</td><td>18</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_2</td><td>40.15</td><td>-3.43</td><td>Shrubs</td><td>12</td><td>13</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr></tbody></table>
<table><thead><tr><td>ID</td><td>Latitud e</td><td>Longitu de</td><td>Taxon or T. level</td><td>Nº sampl es</td><td>Nº speci es</td><td>Grain (m²)</td><td>Extent (ha)</td><td>Referenc e</td></tr></thead><tbody><tr><td>herbs_ym_20</td><td>40.07</td><td>-3.07</td><td>Shrubs</td><td>12</td><td>13</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_21</td><td>40.08</td><td>-3.07</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_22</td><td>40.08</td><td>-3.05</td><td>Shrubs</td><td>12</td><td>10</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_23</td><td>40.09</td><td>-3.02</td><td>Shrubs</td><td>12</td><td>13</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_24</td><td>40.13</td><td>-2.99</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_25</td><td>40.13</td><td>-2.99</td><td>Shrubs</td><td>12</td><td>10</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_26</td><td>40.02</td><td>-3.13</td><td>Shrubs</td><td>12</td><td>10</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_27</td><td>40.01</td><td>-3.38</td><td>Shrubs</td><td>12</td><td>15</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_28</td><td>39.85</td><td>-3.63</td><td>Shrubs</td><td>12</td><td>11</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_29</td><td>39.87</td><td>-3.63</td><td>Shrubs</td><td>12</td><td>13</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_3</td><td>40.14</td><td>-3.62</td><td>Shrubs</td><td>12</td><td>15</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_30</td><td>40.24</td><td>-3.08</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_31</td><td>40.25</td><td>-3.05</td><td>Shrubs</td><td>12</td><td>16</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_32</td><td>40.20</td><td>-3.14</td><td>Shrubs</td><td>12</td><td>14</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_33</td><td>40.02</td><td>-3.11</td><td>Shrubs</td><td>12</td><td>14</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_34</td><td>40.17</td><td>-3.08</td><td>Shrubs</td><td>12</td><td>14</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_35</td><td>40.33</td><td>-2.93</td><td>Shrubs</td><td>12</td><td>15</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_36</td><td>40.37</td><td>-2.98</td><td>Shrubs</td><td>12</td><td>14</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_4</td><td>40.13</td><td>-3.62</td><td>Shrubs</td><td>12</td><td>15</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_5</td><td>40.12</td><td>-3.62</td><td>Shrubs</td><td>12</td><td>12</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_6</td><td>40.15</td><td>-3.43</td><td>Shrubs</td><td>12</td><td>15</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_7</td><td>40.18</td><td>-3.42</td><td>Shrubs</td><td>12</td><td>20</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_8</td><td>40.27</td><td>-3.14</td><td>Shrubs</td><td>12</td><td>15</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>herbs_ym_9</td><td>40.24</td><td>-3.06</td><td>Shrubs</td><td>12</td><td>15</td><td>2.250</td><td>0.090</td><td>Appendix S3</td></tr><tr><td>heustobm</td><td>39.55</td><td>-84.72</td><td>Trees</td><td>10</td><td>20</td><td>100.00</td><td>0.100</td><td>25</td></tr><tr><td>heustomf</td><td>39.55</td><td>-84.72</td><td>Trees</td><td>10</td><td>24</td><td>100.00</td><td>0.100</td><td>25</td></tr></tbody></table>
<table><thead><tr><th>ID</th><th>Latitud e</th><th>Longitu de</th><th>Taxon or T. level</th><th>Nº sampl es</th><th>Nº speci es</th><th>Grain (m²)</th><th>Extent (ha)</th><th>Referenc e</th></tr></thead><tbody><tr><td>huamani</td><td>-0.67</td><td>-77.67</td><td>Trees</td><td>10</td><td>162</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>humboldt</td><td>-8.83</td><td>-75.00</td><td>Trees</td><td>10</td><td>158</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>incahuar</td><td>-15.92</td><td>-67.58</td><td>Trees</td><td>10</td><td>160</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>indiana</td><td>-3.52</td><td>-73.70</td><td>Trees</td><td>10</td><td>223</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>indianca</td><td>40.50</td><td>-95.72</td><td>Trees</td><td>10</td><td>25</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>jatunsac</td><td>-1.70</td><td>-77.60</td><td>Trees</td><td>10</td><td>244</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>jauneche</td><td>-1.10</td><td>-79.63</td><td>Trees</td><td>10</td><td>96</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>jejuimi</td><td>-24.13</td><td>-55.53</td><td>Trees</td><td>10</td><td>87</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>jenarohe</td><td>-4.92</td><td>-73.75</td><td>Trees</td><td>10</td><td>245</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>jonesmil</td><td>40.80</td><td>-79.35</td><td>Trees</td><td>10</td><td>15</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>jura_tree</td><td>47.48</td><td>7.77</td><td>Trees</td><td>10</td><td>17</td><td>100.00 0</td><td>8.000</td><td>Appendix S3</td></tr><tr><td>kanealle</td><td>41.67</td><td>-78.80</td><td>Trees</td><td>10</td><td>13</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>kennedy</td><td>11.80</td><td>-74.20</td><td>Trees</td><td>10</td><td>67</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>kenting</td><td>21.92</td><td>120.83</td><td>Trees</td><td>10</td><td>69</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>khaoyai</td><td>14.33</td><td>101.83</td><td>Trees</td><td>10</td><td>145</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>kitlope1</td><td>53.70</td><td>-127.83</td><td>Trees</td><td>10</td><td>10</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>korup</td><td>5.73</td><td>8.70</td><td>Trees</td><td>10</td><td>133</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>lagenoa</td><td>-11.80</td><td>-75.42</td><td>Trees</td><td>10</td><td>112</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>laplanad</td><td>1.13</td><td>-77.97</td><td>Trees</td><td>10</td><td>130</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>laraya</td><td>8.33</td><td>-74.92</td><td>Trees</td><td>10</td><td>160</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>laselva</td><td>10.43</td><td>-84.20</td><td>Trees</td><td>10</td><td>136</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>lasjoyas</td><td>19.58</td><td>-104.13</td><td>Trees</td><td>10</td><td>39</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>laurelri</td><td>39.95</td><td>-79.37</td><td>Trees</td><td>10</td><td>14</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>linhares</td><td>-19.30</td><td>-40.70</td><td>Trees</td><td>10</td><td>220</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>luquillo</td><td>18.18</td><td>-65.83</td><td>Trees</td><td>10</td><td>42</td><td>100.00 0</td><td>0.100</td><td>25</td></tr></tbody></table>
<table><thead><tr><td>ID</td><td>Latitud e</td><td>Longitu de</td><td>Taxon or T. level</td><td>Nº sampl es</td><td>Nº speci es</td><td>Grain (m²)</td><td>Extent (ha)</td><td>Referenc e</td></tr></thead><tbody><tr><td>madden</td><td>9.10</td><td>-79.60</td><td>Trees</td><td>10</td><td>131</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>madidi</td><td>-13.58</td><td>-68.77</td><td>Trees</td><td>10</td><td>198</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>madidiri</td><td>-13.58</td><td>-68.77</td><td>Trees</td><td>10</td><td>176</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>magsasay</td><td>10.40</td><td>-84.50</td><td>Trees</td><td>10</td><td>148</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>makokou1</td><td>0.57</td><td>12.87</td><td>Trees</td><td>10</td><td>138</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>makokou2</td><td>0.57</td><td>12.87</td><td>Trees</td><td>10</td><td>118</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>manaus</td><td>-3.13</td><td>-60.20</td><td>Trees</td><td>10</td><td>102</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>maquipuc</td><td>0.12</td><td>-78.62</td><td>Trees</td><td>10</td><td>137</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>mariquit</td><td>5.25</td><td>-74.83</td><td>Trees</td><td>10</td><td>94</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>martin</td><td>-39.50</td><td>-73.17</td><td>Trees</td><td>10</td><td>19</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>miazi</td><td>-4.30</td><td>-78.67</td><td>Trees</td><td>10</td><td>186</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>mirador</td><td>-40.23</td><td>-73.30</td><td>Trees</td><td>10</td><td>16</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>mishnfl</td><td>-3.78</td><td>-73.50</td><td>Trees</td><td>10</td><td>266</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>mishws</td><td>-3.78</td><td>-73.50</td><td>Trees</td><td>10</td><td>195</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>mogote</td><td>18.42</td><td>-66.25</td><td>Trees</td><td>10</td><td>47</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>montgome</td><td>43.17</td><td>-77.62</td><td>Trees</td><td>10</td><td>18</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>motozint</td><td>15.33</td><td>-92.20</td><td>Trees</td><td>10</td><td>16</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>mtcam</td><td>4.80</td><td>9.00</td><td>Trees</td><td>10</td><td>124</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>mtsthila</td><td>45.62</td><td>-73.58</td><td>Trees</td><td>10</td><td>12</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>mudumal1</td><td>11.60</td><td>76.53</td><td>Trees</td><td>10</td><td>15</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>mudumal2</td><td>11.60</td><td>76.53</td><td>Trees</td><td>10</td><td>19</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>murri</td><td>6.58</td><td>-76.83</td><td>Trees</td><td>10</td><td>179</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>nadugani</td><td>11.45</td><td>76.38</td><td>Trees</td><td>10</td><td>106</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>nanjensh</td><td>22.00</td><td>120.83</td><td>Trees</td><td>10</td><td>85</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>ndakani</td><td>2.37</td><td>16.15</td><td>Trees</td><td>10</td><td>96</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>ndakanni</td><td>2.37</td><td>16.32</td><td>Trees</td><td>10</td><td>122</td><td>100.00 0</td><td>0.100</td><td>25</td></tr></tbody></table>
<table><thead><tr><th>ID</th><th>Latitud e</th><th>Longitu de</th><th>Taxon or T. level</th><th>Nº sampl es</th><th>Nº speci es</th><th>Grain (m²)</th><th>Extent (ha)</th><th>Referenc e</th></tr></thead><tbody><tr><td>newcaldo</td><td>-22.17</td><td>166.83</td><td>Trees</td><td>11</td><td>172</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>nosymang</td><td>-15.50</td><td>49.77</td><td>Trees</td><td>10</td><td>210</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>nuevomun</td><td>-10.65</td><td>-66.77</td><td>Trees</td><td>10</td><td>149</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>nwbranch</td><td>39.30</td><td>-77.30</td><td>Trees</td><td>10</td><td>20</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>omofor</td><td>7.00</td><td>5.00</td><td>Trees</td><td>10</td><td>72</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>osasiren</td><td>8.50</td><td>-83.92</td><td>Trees</td><td>10</td><td>144</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>palanan</td><td>17.13</td><td>122.52</td><td>Trees</td><td>10</td><td>144</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>parqueer</td><td>-24.58</td><td>-64.70</td><td>Trees</td><td>10</td><td>41</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>pasoh30</td><td>3.00</td><td>102.33</td><td>Trees</td><td>10</td><td>210</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>pasoh40</td><td>3.00</td><td>102.33</td><td>Trees</td><td>10</td><td>221</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>perinet</td><td>-18.92</td><td>48.42</td><td>Trees</td><td>10</td><td>214</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>perromue</td><td>-1.60</td><td>-80.70</td><td>Trees</td><td>10</td><td>69</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>persever</td><td>-14.63</td><td>-62.62</td><td>Trees</td><td>10</td><td>116</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>PHFIG_Spring</td><td>41.70</td><td>2.27</td><td>Ants</td><td>60</td><td>10</td><td>0.003</td><td>0.113</td><td>24</td></tr><tr><td>PHGRF_Spring</td><td>41.27</td><td>1.92</td><td>Ants</td><td>53</td><td>14</td><td>0.003</td><td>0.113</td><td>24</td></tr><tr><td>PHGRF_Summer</td><td>41.27</td><td>1.92</td><td>Ants</td><td>56</td><td>15</td><td>0.003</td><td>0.113</td><td>24</td></tr><tr><td>PHVAC_Spring</td><td>41.58</td><td>1.93</td><td>Ants</td><td>57</td><td>10</td><td>0.003</td><td>0.113</td><td>24</td></tr><tr><td>PHVAC_Summer</td><td>41.58</td><td>1.93</td><td>Ants</td><td>58</td><td>11</td><td>0.003</td><td>0.113</td><td>24</td></tr><tr><td>pipeline</td><td>9.17</td><td>-79.75</td><td>Trees</td><td>10</td><td>161</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>Barra_Paraguac u</td><td>-12.63</td><td>-38.80</td><td>Trees</td><td>49</td><td>60</td><td>100.00 0</td><td>0.490</td><td>Appendix S3</td></tr><tr><td>Bonito_Bahia</td><td>-11.99</td><td>-41.10</td><td>Trees</td><td>13</td><td>28</td><td>400.00 0</td><td>0.520</td><td>Appendix S3</td></tr><tr><td>Itubera_Bahia</td><td>-13.72</td><td>-39.20</td><td>Trees</td><td>29</td><td>155</td><td>100.00 0</td><td>0.290</td><td>Appendix S3</td></tr><tr><td>Maracas_Bahia</td><td>-13.47</td><td>-40.44</td><td>Trees</td><td>42</td><td>35</td><td>400.00 0</td><td>1.680</td><td>Appendix S3</td></tr><tr><td>Semidecidous_B ahia</td><td>-11.91</td><td>-41.25</td><td>Trees</td><td>31</td><td>64</td><td>100.00 0</td><td>0.310</td><td>Appendix S3</td></tr><tr><td>potomac</td><td>38.80</td><td>-76.57</td><td>Trees</td><td>11</td><td>25</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>providen</td><td>13.35</td><td>-81.37</td><td>Trees</td><td>10</td><td>65</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>pugu</td><td>-6.83</td><td>39.80</td><td>Trees</td><td>10</td><td>88</td><td>100.00 0</td><td>0.100</td><td>25</td></tr></tbody></table>
<table><thead><tr><td>ID</td><td>Latitud e</td><td>Longitu de</td><td>Taxon or T. level</td><td>Nº sampl es</td><td>Nº speci es</td><td>Grain (m²)</td><td>Extent (ha)</td><td>Referenc e</td></tr></thead><tbody><tr><td>puyehue</td><td>-40.72</td><td>-72.30</td><td>Trees</td><td>10</td><td>16</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>quiapaca</td><td>-18.33</td><td>-59.50</td><td>Trees</td><td>10</td><td>83</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>quinceoc</td><td>19.73</td><td>-104.25</td><td>Trees</td><td>10</td><td>47</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>ranchoqu</td><td>8.70</td><td>-83.55</td><td>Trees</td><td>10</td><td>124</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>riachuel</td><td>-27.00</td><td>-58.00</td><td>Trees</td><td>10</td><td>47</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>rioheath</td><td>-12.83</td><td>-68.83</td><td>Trees</td><td>10</td><td>135</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>riomanso</td><td>7.50</td><td>-76.80</td><td>Trees</td><td>11</td><td>220</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>rionegro</td><td>-9.83</td><td>-65.67</td><td>Trees</td><td>10</td><td>169</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>riopal1</td><td>-0.57</td><td>-79.33</td><td>Trees</td><td>10</td><td>119</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>riopal2</td><td>-0.57</td><td>-79.33</td><td>Trees</td><td>10</td><td>122</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>riotavar</td><td>-13.35</td><td>-69.67</td><td>Trees</td><td>10</td><td>200</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>roundslo</td><td>17.33</td><td>-77.42</td><td>Trees</td><td>10</td><td>45</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>roundtop</td><td>17.33</td><td>-77.42</td><td>Trees</td><td>10</td><td>57</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>ruissalo</td><td>60.53</td><td>22.47</td><td>Trees</td><td>10</td><td>10</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>sabanaru</td><td>10.50</td><td>-72.92</td><td>Trees</td><td>10</td><td>52</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>sacram</td><td>-16.30</td><td>-67.80</td><td>Trees</td><td>10</td><td>100</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>sakaera2</td><td>14.50</td><td>102.00</td><td>Trees</td><td>10</td><td>37</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>sakaerat</td><td>14.50</td><td>102.00</td><td>Trees</td><td>10</td><td>81</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>salta</td><td>-24.67</td><td>-64.50</td><td>Trees</td><td>10</td><td>25</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>sanfelas</td><td>29.68</td><td>-82.43</td><td>Trees</td><td>10</td><td>19</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>sansebas</td><td>-1.60</td><td>-80.70</td><td>Trees</td><td>10</td><td>96</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>santacru</td><td>-17.77</td><td>-63.70</td><td>Trees</td><td>10</td><td>63</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>santotom</td><td>4.92</td><td>-74.83</td><td>Trees</td><td>10</td><td>76</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>saul</td><td>3.63</td><td>-53.20</td><td>Trees</td><td>10</td><td>149</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>semengoh</td><td>1.83</td><td>110.80</td><td>Trees</td><td>10</td><td>224</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>shiringa</td><td>-10.33</td><td>-75.17</td><td>Trees</td><td>10</td><td>191</td><td>100.00 0</td><td>0.100</td><td>25</td></tr></tbody></table>
<table><thead><tr><th>ID</th><th>Latitud e</th><th>Longitu de</th><th>Taxon or T. level</th><th>Nº sampl es</th><th>Nº speci es</th><th>Grain (m²)</th><th>Extent (ha)</th><th>Referenc e</th></tr></thead><tbody><tr><td>SHRFIG_Spring</td><td>41.70</td><td>2.27</td><td>Ants</td><td>58</td><td>16</td><td>0.003</td><td>0.113</td><td>24</td></tr><tr><td>SHRFIG_Summe r</td><td>41.70</td><td>2.27</td><td>Ants</td><td>56</td><td>13</td><td>0.003</td><td>0.113</td><td>24</td></tr><tr><td>SHRGRF_Spring</td><td>41.27</td><td>1.92</td><td>Ants</td><td>51</td><td>11</td><td>0.003</td><td>0.113</td><td>24</td></tr><tr><td>SHRVAC_Spring</td><td>41.58</td><td>1.93</td><td>Ants</td><td>54</td><td>11</td><td>0.003</td><td>0.113</td><td>24</td></tr><tr><td>SHRVAC_Summ er</td><td>41.58</td><td>1.93</td><td>Ants</td><td>53</td><td>10</td><td>0.003</td><td>0.113</td><td>24</td></tr><tr><td>Siemianice1</td><td>54.50</td><td>17.05</td><td>Beetles</td><td>52</td><td>27</td><td>300 cm3</td><td>1.000</td><td>5</td></tr><tr><td>Siemianice2</td><td>54.50</td><td>17.05</td><td>Beetles</td><td>49</td><td>26</td><td>300 cm3</td><td>1.000</td><td>5</td></tr><tr><td>Siemianice3</td><td>54.50</td><td>17.05</td><td>Beetles</td><td>27</td><td>26</td><td>300 cm3</td><td>1.000</td><td>5</td></tr><tr><td>Siemianice4</td><td>54.50</td><td>17.05</td><td>Beetles</td><td>27</td><td>26</td><td>300 cm3</td><td>1.000</td><td>5</td></tr><tr><td>Siemianice5</td><td>54.50</td><td>17.05</td><td>Beetles</td><td>63</td><td>27</td><td>300 cm3</td><td>1.000</td><td>5</td></tr><tr><td>Siemianice6</td><td>54.50</td><td>17.05</td><td>Beetles</td><td>55</td><td>26</td><td>300 cm3</td><td>1.000</td><td>5</td></tr><tr><td>Siemianice7</td><td>54.50</td><td>17.05</td><td>Beetles</td><td>33</td><td>27</td><td>300 cm3</td><td>1.000</td><td>5</td></tr><tr><td>Siemianice8</td><td>54.50</td><td>17.05</td><td>Beetles</td><td>47</td><td>26</td><td>300 cm3</td><td>1.000</td><td>5</td></tr><tr><td>sierraro</td><td>22.83</td><td>-83.00</td><td>Trees</td><td>11</td><td>47</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>sietecue</td><td>4.58</td><td>-73.67</td><td>Trees</td><td>10</td><td>77</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>suderhac</td><td>54.60</td><td>9.28</td><td>Trees</td><td>10</td><td>15</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>tahuampa</td><td>-3.78</td><td>-73.50</td><td>Trees</td><td>10</td><td>169</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>tamblat2</td><td>-12.78</td><td>-69.28</td><td>Trees</td><td>10</td><td>158</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>tambo</td><td>-12.78</td><td>-69.28</td><td>Trees</td><td>10</td><td>156</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>tamboall</td><td>-12.83</td><td>-69.28</td><td>Trees</td><td>10</td><td>187</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>tambupl</td><td>-12.82</td><td>-69.72</td><td>Trees</td><td>10</td><td>131</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>tarapoto</td><td>-6.58</td><td>-76.42</td><td>Trees</td><td>10</td><td>102</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>tayrona</td><td>11.33</td><td>-74.30</td><td>Trees</td><td>10</td><td>68</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>tidroute</td><td>41.70</td><td>-79.40</td><td>Trees</td><td>10</td><td>20</td><td>100.00 0</td><td>0.100</td><td>25</td></tr><tr><td>tikus_1</td><td>-5.86</td><td>106.58</td><td>Corals</td><td>10</td><td>54</td><td>30.000</td><td>0.040</td><td>31</td></tr><tr><td>tikus_2</td><td>-5.86</td><td>106.58</td><td>Corals</td><td>10</td><td>30</td><td>30.000</td><td>0.040</td><td>31</td></tr><tr><td>tikus_3</td><td>-5.86</td><td>106.58</td><td>Corals</td><td>10</td><td>29</td><td>30.000</td><td>0.040</td><td>31</td></tr></tbody></table>
<table><caption>**Table S1.** Geographic coordinates (Latitude and Longitude), taxon or trophic level, number of samples, number of species and grain (m²) and extent (ha)</caption><thead><tr><th>ID</th><th>Latitude</th><th>Longitude</th><th>Taxon or T. level</th><th>Nº samples</th><th>Nº species</th><th>Grain (m²)</th><th>Extent (ha)</th><th>Reference</th></tr></thead><tbody><tr><td>tikus_4</td><td>-5.86</td><td>106.58</td><td>Corals</td><td>10</td><td>35</td><td>30.000</td><td>0.040</td><td>31</td></tr><tr><td>tikus_5</td><td>-5.86</td><td>106.58</td><td>Corals</td><td>10</td><td>26</td><td>30.000</td><td>0.040</td><td>31</td></tr><tr><td>tikus_6</td><td>-5.86</td><td>106.58</td><td>Corals</td><td>10</td><td>23</td><td>30.000</td><td>0.040</td><td>31</td></tr><tr><td>tikus_7</td><td>-5.86</td><td>106.58</td><td>Corals</td><td>10</td><td>25</td><td>30.000</td><td>0.040</td><td>31</td></tr><tr><td>trunk_alcornoque</td><td>35.27</td><td>-6.07</td><td>Bryophytes</td><td>13</td><td>14</td><td>0.040</td><td>0.013</td><td>32</td></tr><tr><td>trunk_encina_a</td><td>32.51</td><td>-6.03</td><td>Bryophytes</td><td>12</td><td>11</td><td>0.040</td><td>0.031</td><td>27</td></tr><tr><td>trunk_encina_b</td><td>33.74</td><td>-4.29</td><td>Bryophytes</td><td>12</td><td>11</td><td>0.040</td><td>0.031</td><td>27</td></tr><tr><td>trunk_loro</td><td>34.96</td><td>-4.67</td><td>Bryophytes</td><td>15</td><td>17</td><td>0.040</td><td>0.013</td><td>32</td></tr><tr><td>tutunend</td><td>5.77</td><td>-76.58</td><td>Trees</td><td>10</td><td>274</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>tuxtlas</td><td>18.58</td><td>-95.13</td><td>Trees</td><td>10</td><td>102</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>tysongla</td><td>38.50</td><td>-90.52</td><td>Trees</td><td>10</td><td>26</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>tysonwoo</td><td>38.50</td><td>-90.52</td><td>Trees</td><td>10</td><td>23</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>uchire</td><td>10.15</td><td>-65.43</td><td>Trees</td><td>10</td><td>67</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>ucumari</td><td>4.00</td><td>-75.50</td><td>Trees</td><td>10</td><td>110</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>ufhortic</td><td>29.67</td><td>-82.33</td><td>Trees</td><td>10</td><td>32</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>uppsala</td><td>59.85</td><td>17.63</td><td>Trees</td><td>10</td><td>13</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>valleyvi</td><td>38.25</td><td>-90.62</td><td>Trees</td><td>10</td><td>23</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>varirata</td><td>-9.50</td><td>147.50</td><td>Trees</td><td>10</td><td>210</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>vencer</td><td>-5.75</td><td>-77.67</td><td>Trees</td><td>10</td><td>164</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>yanaigua</td><td>-19.70</td><td>-62.10</td><td>Trees</td><td>10</td><td>32</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>yanam1</td><td>-3.43</td><td>-72.85</td><td>Trees</td><td>10</td><td>216</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>yanam2</td><td>-3.43</td><td>-72.85</td><td>Trees</td><td>10</td><td>228</td><td>100.000</td><td>0.100</td><td>25</td></tr><tr><td>yanamtah</td><td>-3.47</td><td>-72.83</td><td>Trees</td><td>10</td><td>164</td><td>100.000</td><td>0.100</td><td>25</td></tr></tbody></table>
for the assemblage datasets (ID) analyzed in the study. Notice that some
coordinates were approximated from locations described in original sources.
<table><caption>Table S2.</caption><thead><tr><th>Dependent</th><th>Explanatory</th><th>Coeffs.</th><th>z-value</th><th>p-value</th><th>Dev. expl.</th></tr></thead><tbody><tr><td rowspan="6">Connectivity (P&lt;N)</td><td>Plant vs Animal</td><td>-0.773</td><td>-0.599</td><td>0.549</td><td rowspan="6">0.06</td></tr><tr><td>Latitude</td><td>-0.749</td><td>-1.581</td><td>0.114</td></tr><tr><td>Longitude</td><td>0.365</td><td>0.918</td><td>0.359</td></tr><tr><td>Species richness</td><td>0.328</td><td>0.5</td><td>0.617</td></tr><tr><td>Sampling effort</td><td>0.029</td><td>0.083</td><td>0.934</td></tr><tr><td>Null model d.f.</td><td>-0.646</td><td>-0.922</td><td>0.357</td></tr><tr><td rowspan="6">Abundance-degree (P&lt;N)</td><td>Plant vs Animal</td><td>-19.518</td><td>-0.001</td><td>0.999</td><td rowspan="6">0.24</td></tr><tr><td>Latitude</td><td>-0.681</td><td>-1.105</td><td>0.269</td></tr><tr><td>Longitude</td><td>-0.491</td><td>-0.715</td><td>0.475</td></tr><tr><td>Species richness</td><td>2.448</td><td>1.893</td><td>0.058</td></tr><tr><td>Sampling effort</td><td>183.519</td><td>0.01</td><td>0.992</td></tr><tr><td>Null model d.f.</td><td>-2.541</td><td>-1.544</td><td>0.123</td></tr><tr><td rowspan="6">Abundance (P&lt;N)</td><td>Plant vs Animal</td><td>-14.376</td><td>-0.009</td><td>0.993</td><td rowspan="6">0.36</td></tr><tr><td>Latitude</td><td>-0.063</td><td>-0.062</td><td>0.95</td></tr><tr><td>Longitude</td><td>-0.391</td><td>-0.413</td><td>0.679</td></tr><tr><td>Species richness</td><td>10.984</td><td>1.404</td><td>0.16</td></tr><tr><td>Sampling effort</td><td>6.581</td><td>1.168</td><td>0.243</td></tr><tr><td>Null model d.f.</td><td>1.902</td><td>1.083</td><td>0.279</td></tr><tr><td rowspan="6">Modularity (P&gt;N)</td><td>Plant vs Animal</td><td>1.659</td><td>1.579</td><td>0.114</td><td rowspan="6">0.37</td></tr><tr><td>Latitude</td><td>0.324</td><td>0.429</td><td>0.668</td></tr><tr><td>Longitude</td><td>0.541</td><td>0.827</td><td>0.408</td></tr><tr><td>Species richness</td><td>15.408</td><td>3.175</td><td>0.015</td></tr><tr><td>Sampling effort</td><td>0.195</td><td>0.481</td><td>0.631</td></tr><tr><td>Null model d.f.</td><td>0.755</td><td>0.684</td><td>0.494</td></tr></tbody></table>

Table S2. The differences between positive and negative network properties were in general unaffected by sampling effort (estimated as the number of samples), null model degrees of freedom (see Appendix S1.3), species richness, latitude, longitude or taxa.

Generalized linear model summary statistics including explained deviance (Dev. expl.) for each model. Connectivity (P< N): Probability of negative networks to be more densely connected than their positive pairs.

Abundance-degree (P< N): Probability of dominant species to monopolizing negative links but not positive ones (i.e., a stronger positive abundance-degree relationship in negative networks). Abundance (P< N): probability of positive networks tending to be composed of less abundant species.
Modularity (P>N): probability of positive networks being more modular than their negative pairs.
<table><caption>Table S3.</caption><thead><tr><th rowspan="2">Feature</th><th rowspan="2">H<sub>1</sub></th><th colspan="3">Null model I (n=326)</th><th colspan="3">Null model II (n=305)</th><th colspan="3">Null model III (n=252)</th></tr><tr><th>p-value</th><th>t-value</th><th>Prop. cases</th><th>p-value</th><th>t-value</th><th>Prop. cases</th><th>p-value</th><th>t-value</th><th>Prop. cases</th></tr></thead><tbody><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.00</td><td></td><td></td></tr><tr><td>Connectivity</td><td>P &lt; N</td><td>17.006</td><td>&lt;0.001</td><td>0.932</td><td>18.686</td><td>&lt;0.001</td><td>0.905</td><td>11.155</td><td>1</td><td>0.865</td></tr><tr><td>Degree vs abundance</td><td>P &lt; N</td><td>23.881</td><td>&lt;0.001</td><td>0.859</td><td>10.938</td><td>&lt;0.001</td><td>0.702</td><td>7.479</td><td>1</td><td>0.512</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.00</td><td></td><td></td></tr><tr><td>Abundance</td><td>P &lt; N</td><td>22.425</td><td>&lt;0.001</td><td>0.971</td><td>16.538</td><td>&lt;0.001</td><td>0.869</td><td>10.306</td><td>1</td><td>0.778</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>&lt;0.00</td><td></td><td></td></tr><tr><td>Modularity</td><td>P &gt; N</td><td>39.676</td><td>&lt;0.001</td><td>0.911</td><td>32.343</td><td>&lt;0.001</td><td>0.951</td><td>14.272</td><td>1</td><td>0.849</td></tr></tbody></table>

Table S3. Summary of results from different null model approaches.

Results are based on the comparison of (i) connectivity, (ii) abundance-degree relationship, (iii) species abundance distribution and (iv) modularity between positive association networks (P) and their negative pairs (N). $H_1$: Alternative hypothesis in paired Student's tests. Prop. Cases: the proportion of assemblages where the alternative hypothesis was observed. The number of assemblages analysed (i.e. those showing at least two links in both types of networks) is also provided for each null model. P: Positive association networks. N: Negative association networks.
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