Tropical forests are the most biodiverse ecosystems in the world, where hundreds of species coexist in the same space (Wright, 2002; Barlow et al., 2018). Species coexisting in these ecosystems use the same local set of resources and interact in positive or negative ways (e.g., facilitation, competition); the multidimensional nature of these interactions has significant consequences to ecosystem function due to driving the full community use of resources and their final patterns of important ecosystems attributes such as carbon stock and uptake and biodiversity (Wright, 2002; Hart & Marshall, 2013; Barraclough, 2015; Schmid et al., 2020). Understanding the underlying mechanisms of species interactions in tropical forests has been a central issue in ecology, especially considering its fundamental role in maintaining biodiversity (Chesson, 2000; Wrigth, 2002; Hart et al., 2017).

In general, species that coexist have different ecological requirements or different ecological niches (Wright, 2002; Amarasekare et al., 2004; Barraclough, 2015; Kraft et al., 2015). When their niches overlap, species may face short-term consequences in the local community scale, such as competitive exclusion; and longterm consequences in evolutionary or biome scales, such as niche differentiation and habitat partitioning (Wright, 2002; Barraclough, 2015; Kraft et al., 2015; Chen et al., 2020). In addition, the mechanisms of species coexistence are also influenced by resource availability, which may strongly control the importance of interactions in community assembly and define its most important mechanisms (Holmgren & Scheffer, 2010; Cadotte & Tucker, 2017). For example, in harsh environments process such as facilitation may have relatively greater importance than competition (Wright, 2002; Carrión et al., 2017). In addition, under restrictive conditions, species adopt a series of physiological mechanisms to assist in their survival by increasing their resistance to stressful environmental conditions and enhancing their ability to obtain and use resources efficiently (Cadotte & Tucker, 2017; van der Sande et al., 2017). These mechanisms can be observed in broad ecological strategies, such as resprouting, scleromorphic traits or deciduousness, or in the level of adaptation of internal structures and associated processes (e.g., variations in diameter and length of vessels, stomatal conductance) (Zeppel et al., 2015; Pausas et al., 2016; Jimenez-Rodriguez et al., 2018; He et al., 2019).

Species coexistence also affects forest ecosystem function, stability, resilience and important ecosystem services such as carbon uptake and carbon stocks (Barraclough, 2015; van der Sande et al., 2017; van der Plas, 2019). High species diversity usually promotes high productivity and ecosystem stability because the niches of high numbers of coexisting species tend to be complementary rather than overlapping (Tilman, 1999; van der Sande et al., 2017). Coexisting species with different requirements are able to exploit available resources more efficiently and, consequently, the overall ecosystem achieves higher productivity (Chesson, 2000; van der Sande et al., 2017).

Questions related to the maintenance and management of species coexistence in tropical forests have been addressed with different approaches, such as theoretical and statistical models and empirical data (Chesson, 2000; Wright, 2002; Hart et al., 2017; Chen et al., 2020; Schmid et al., 2020). The emergence of new methodologies of machine learning and statistical analysis may potentially contribute to our understanding of important ecological patterns: coexistence between species, their relation to environment and also in identifying indicators species of the ecosystems. An example is provided by the data-mining technique association rule analysis, or ARA, that is an important market baskets analysis (Silverstein et al., 1998; Rossi et al., 2014). ARA is widely used in online product sales through different algorithms for allowing efficient identification of associations between elements in extensive datasets (Agrawal et al., 1993; Ferrarini & Tomaselli, 2010; Zumel et al., 2019). However, despite its potential, its use in scientific contexts is still scarce and, to our knowledge, even more so in ecological studies. The few examples of use of ARA in ecological contexts are Leote et al. (2020), that used to identify indicator species of arthropods; and Ferrarini & Tomaselli (2010) and Rossi et al., (2014) that used ARA to identify relations between vegetation and environmental attributes in studies at landscape level.

Here, we used ARA to assess patterns of species coexistence using as study case the tropical seasonal forest (deciduous and semideciduous), that may be broadly characterized by enduring high temperatures and the alternation between a rainy and a dry season every year, that imply deciduousness of most of the species under strong water stress (DRYFLOR, 2016). Most studies on tropical species coexistence have mainly focused on rainforests, despite of less the significant contribution of seasonal forests to tropical biodiversity and ecosystem service provision (Sunderland et al., 2015; DRYFLOR, 2016). We thus applied ARA on the data of 44 sites of tropical seasonal forests in order to (1) identify consistent associations between species (i.e., patterns of species coexistence) that may characterize these forest types, (2) evaluate functional and taxonomic similarities between consistently associated species. In addition, we discuss the feasibility of applying ARA into ecological research as a tool to identify patterns of species coexistence, structure, function and diversity of communities.

Association rule analysis (ARA) with the established criteria resulted in 238 (out of 928 rules) significant and frequent pairwise species associations in semideciduous forests, and in 11 (out of 62) significant and frequent pairwise species associations in deciduous forests (Tables S3 and S4 [suppl.]). In the semideciduous forests, the pairwise associations are formed by a group of 33 species in 28 genera and 18 families. In the deciduous forests, the pairwise associations are formed by a group of 8 species in 8 genera and 5 families. In semideciduous forests, maximum support was 0.68 and maximum coverage was 0.68; maximum lift was 1.47 for the association between Dendropanax cuneatus (DC.) Decne. & Planch. (Araliaceae) and Annona dolabripetala Raddi (Annonaceae) (Table S3 [suppl.]; Fig 3 - a). In deciduous forests, maximum support was 0.68 and maximum coverage was 0.73; maximum lift was 1.21 for the association between Ptilochaeta bahiensis Turcz. (Malpighiaceae) and Cenostigma pluviosa (DC.) L.P. Queiroz (Fabaceae) (Table S4[suppl.]; Fig 3 - b). In both semideciduous and deciduous forests, some species pairs were mutually related, with occurrence dependence found in both directions; i.e., the association was consistent when swapping the species in the left and right sides.

Most of the pairs are formed by taxonomically unrelated species, at least as far as the family level (Table S2 [suppl.]). In the semideciduous forests, only 5.8% of all pairwise associations were between confamilial species. In the deciduous forests, Goniorrhachis marginata Taub. and Machaerium acutifolium Vogel. formed the only confamilial species pair (both belong to Fabaceae) (~ 9 % do total; Table S2 [suppl.]). In the rules selected for semideciduous forests, there are associations between species with different functional characteristics (Fig S1 [suppl.]), but most associations occur between species with similar potential size (DBH) and wood density (Fig S1 – a [suppl.]). In deciduous forests the trend seems to be the same, but the low number of rules selected prevents an accurate assessment (Fig S1 – b [suppl.]). In general, association rules tend to be formed by taxonomically unrelated species that are similar but not functionally equal.

FigureFigure 3.Representation of the first 20 association rules between pairs of species according to the lift values for semideciduous forests (a) and of the 11 selected association rules between pairs of species for deciduous forests (b). For deciduous forests, since only 11 rules emerged in this vegetation type, the figure represents all of them. Each circle represents an association rule and the arrows point to its participating species. Circle sizes are directly related to rule support values (the larger the circle, the higher the support), while circle colors are related to the rule lift value (the stronger the color, the greater the lift value). The arrow points the direction of co-occurrence between species, considering the reference species of the association rule. The situation in which two species have two arrows between them, each pointed in one direction, points out that co-occurrence occurs in both directions. In other words, species X associates with Y and Y associates with X.

Our results showed the existence of consistent pairwise associations between species in semideciduous and deciduous forests, which are characteristic of their communities. However, widespread associations of species of broad occurrence are more frequent and are associated with a greater species number in the semideciduous forest, in comparison with the deciduous forests. In addition, we found that these pairs are mainly formed by non-confamilial species (of different botanical families) with similar functional characteristics.

The widely occurring associations between species in these seasonal forests indicate that a set of broad species has their occurrence dependent on each other in communities, and that their associations are characteristic of these vegetation types and important to their structure and functioning. These co-occurrences are formed by species characteristic of these vegetation types, which have a wide occurrence in these forests conditioned by past evolutionary occupation processes (Oliveira-Filho & Fontes, 2000; Pennington et al., 2009; Santos et al., 2012; Moro et al., 2016; Neves et al., 2017). This result points out that there is a great chance of sampling the presented co-occurrences in tree community randomly sampled in these regions.

The greater number of species and coexistence rules observed in the semideciduous forests compared to the deciduous forests is related to the different environmental conditions between the two vegetation types. Because they are subjected to more restrictive ecological conditions such as high temperatures and a long dry season that explain the greater species deciduousness, often species in deciduous forests tend to occur in environments closeto their survival limit (Pennington et al., 2009; Allen et al., 2017). Thus, small variations in environmental conditions may generate variations in ecological filters and thus select a distinct set of species (Santos et al., 2012; Apgaua et al., 2015; Maia et al., 2020; Souza et al., 2020). In fact, these results reflected the most frequent species in our deciduous forest dataset. It is also important to highlight the different sampling intensity between vegetation types, that probably influenced the result due to a lower number of trees sampled, and consequently a lower chance to identify association. But we consider that the great difference (238 x 11 rules) is strong enough to indicate the trend of more association rules in semideciduous forests.

The lower number of coexistences in deciduous forests can also be explained by the lower participation of interactions in community assembly in restrictive environments according to stress-gradient hypothesis (Holmgren & Scheffer 2010; Kraft et al. 2015; Cadotte & Tucker, 2017). In this perspective, associations thus would have a secondary role in the deciduous forest assembly and function and would be linked mainly to facilitation processes (Holmgren & Scheffer, 2010; Hart & Marshall, 2013; Cadotte & Tucker, 2017; Carrión et al. 2017). It is important to emphasize that adopting broader criteria in the measures of association rule analysis (more permissive values) may allow the achievement of a greater number of association rules, but without considering the assumption of high frequency in the communities.

The result found that most of the significant coexistences are composed of non-confamilial and functionally similar species (especially in semideciduous species where the number of relationships is greater) suggests the existence of niche adaptation processes for the occupation of the same environments. Species of different families with similar ecological requirements may thus have undergone niche differentiation processes after the occupation of habitats, as a way of persistence and avoiding competitive exclusion due to niche overlap (Wright, 2002; Barraclough, 2015; Cadotte & Tucker 2017; Chen et al. 2020). In addition, the coexistence between these species may have been consolidated after processes of modification of their ecological patterns, such as decrease of representativity in abundance and/or biomass (Holmgren & Scheffer, 2010; Hart & Marshal, 2013; Kraft et al., 2015; Hart et al., 2017; Chen et al., 2020). Thus, the species coexistence seasonal tropical forest would be associated with changes in ecological requirements and persistence strategies by species, consequently impacting ecosystem functioning (Wright, 2002; Hart & Marshal, 2013; Kraft et al., 2015).

Here we demonstrate the feasibility of applying the novel association rule analysis (ARA) methodology to ecological studies of tree communities in seasonal tropical forests, but that can be extended to other biological groups. As demonstrated here, the methodology is promising to identify coexistence relationships (both positive and negative) in large ecological data sets, which can also be used to identify relationships with environmental drivers synthesized into categorical variables, although in this case other robuster analysis approaches may be more appropriate (Ferrarini & Tomaselli, 2010; Rosssi et al., 2014; Leote et al., 2020). The use of ARA approach can also be used to identify indicators species in ecosystems, being an alternative to traditional approaches in situations of extensive datasets (Leote et al., 2020). In vegetation studies we call attention to the need of sampling intensity and for the also need of inclusion criterium standardization, since species richness and composition are strongly influenced by them. Our results indicate that association rules analysis is an interesting alternative to the traditional analysis of ecological data and that it should be incorporated into future studies, joint to other novel data mining and data science tools (e.g., machine learning, neural networks, natural language processing and artificial intelligence) broadly used in non-scientific contexts (Hahsler, 2006; Zumel et al., 2019). All of these approaches are already in consolidated use with positive results in organizations, so that they can also assist in the resolution of relevant ecological issues, in addition to allowing a revisiting of consolidated patterns, as they offer new perspectives in relation to the data. Its use must also be associated with the incorporation of other objects of ecological studies, in order to contribute to the understanding of biodiversity patterns and ecosystem functioning.