Ecological strategies and functional structure of bryophytes across environmental and spatial gradients in the Caatinga, Brazil

Ecological strategies and functional structure of bryophytes across environmental and spatial gradients in the Caatinga, Brazil | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 September 2025 V1 Latest version Share on Ecological strategies and functional structure of bryophytes across environmental and spatial gradients in the Caatinga, Brazil Authors : Jhonyd Marmo 0000-0002-5067-5255 and Mércia Silva 0000-0002-1391-9038 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175751676.66355961/v1 259 views 162 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract We assessed how environmental and spatial gradients influence the ecological strategies and functional structure of bryophyte assemblages across the Caatinga biome, a seasonally dry tropical forest in Brazil. From 1994–2024, we compiled data on 439 bryophyte taxa (mosses, liverworts, and hornworts) from 52 localities, incorporating 11 functional traits and seven variables representing climatic, topographic, and spatial gradients. Functional structure was quantified using Functional Redundancy (FRed), Functional Over-Redundancy (FORed), and Functional Vulnerability (FVuln), and a functional space was constructed via Principal Coordinates Analysis (PCoA) and tested against null models to assess environmental filtering. Relationships among environmental variables, functional indices, and traits were evaluated using Generalized Linear Models (GLMs), RLQ analysis, and the Fourth-Corner method. We identified 133 functional entities. Taxonomic richness was positively correlated with FRed and FORed but negatively with FVuln. The observed functional space was smaller than expected under null models, indicating ecological constraints. RLQ analysis revealed strong associations among matrices, with latitude, precipitation of the wettest quarter, and temperature seasonality as primary drivers. Traits such as costa, papillae, and monoicous systems were associated with higher latitudes, gametophyte curling with wettest-quarter temperature, and vulnerable life forms with aridity. Longitude significantly predicted FRed, FORed, and FVuln. Together, these results indicate that the functional diversity of Caatinga bryophytes is shaped by environmental and spatial filters, driving divergent ecological strategies: xeric bryophytes display adaptive plasticity for water optimization and solar radiation protection, whereas humid-environment species invest in water storage and retention mechanisms. The high FVuln (≈44% single-species entities) highlights susceptibility and sampling gaps, underscoring the need to conserve humid enclaves as critical refugia for maintaining functional diversity and ecosystem resilience under climate change. Keywords: Functional diversity; Seasonally Dry Tropical Forest; Environmental filters; Functional traits; RLQ. 1. Introduction Understanding how species’ morphological and reproductive strategies influence their interactions with the environment is fundamental for determining the structure and functioning of ecological communities (Lancaster et al ., 2017). This approach is especially relevant in Seasonally Dry Tropical Forests (SDTFs), as it allows for the inference of response patterns to environmental gradients, the identification of mechanisms that sustain biodiversity (Vilà‐Cabrera et al ., 2015), and the comparison of these processes across ecosystems, thus contributing to climate change predictions and the development of strategic conservation actions (Nock et al ., 2016). Predominantly found in the Neotropical region, SDTFs extend from northwestern Mexico to northern Argentina, occurring in isolated fragments with varying levels of conservation (Linares-Palomin et al ., 2011). In Brazil, the Caatinga is the main representative of SDTFs, characterized by pronounced seasonality and prolonged dry periods, which pose significant challenges to plant communities by affecting ecological processes and water balance dynamics (Mutti et al ., 2019). Although historically underestimated in terms of biodiversity, the Caatinga harbors a rich and unique flora, encompassing vegetation types that vary in their aspects according to a gradient that ranges from xeric to more humid conditions (Moro et al ., 2024) However, the Caatinga has been increasingly affected by severe anthropogenic pressures, including deforestation, intensive land use, and the spread of invasive species, as well as by the impacts of global warming, which collectively heighten the vulnerability of its ecosystems (Teixeira et al ., 2021) In this context, bryophytes (hornworts, liverworts, and mosses) play an ecologically significant yet paradoxical role. Although sensitive to fluctuations in temperature, moisture, and light, these plants exhibit remarkable resilience in dry environments. Their poikilohydric nature enables them to withstand complete desiccation during droughts and rapidly resume metabolic activity with the onset of rainfall (Oliver et al ., 2005; Proctor et al ., 2007). In dry forests, bryophytes also form part of biological soil crusts that perform essential hydrological functions, such as moisture retention and soil water infiltration (Szyja et al ., 2023). High bryophyte diversity is noticeable in the Caatinga, particularly in rocky areas and specific microclimates (Marmo and Silva, 2025). Within the framework of functional ecology applied to bryophytes in the Caatinga, recent studies suggest that functional diversity may not be directly associated with regional environmental variables, such as mean temperature or precipitation, but rather with local-specific factors such as microhabitat characteristics (Silva et al ., 2018). For instance, traits such as life form and light tolerance are influenced by the morphology and physiology of phorophytes, including bark roughness, deciduousness, and water retention capacity (Souza et al ., 2021). These factors can act as environmental filters that shape the functional composition of bryophyte assemblages in seasonally dry environments. However, there is evidence that while environmental factors strongly influence bryophyte assemblages at local scales (Silva et al ., 2014; Marmo and Silva, 2025), stochastic processes such as dispersal limitation become more relevant at regional scales (Silva et al ., 2014; Monteiro et al ., 2023). Despite recent advances, significant knowledge gaps remain regarding how environmental and spatial factors directly influence the morphological traits and ecological strategies of bryophytes in the Caatinga. Addressing these gaps is crucial given the environmental heterogeneity of the Caatinga and increasing threats to its biodiversity. Therefore, this study aims to assess the relationships between the functional and taxonomic structure of bryophyte assemblages and environmental and spatial parameters across the Caatinga at a regional scale. We hypothesize that xeric environments impose stronger environmental filters, resulting in a more constrained functional space characterized by traits associated with water conservation and protection against solar radiation (e.g., life form), as well as have lower species richness than more humid environments, which are expected to present more permissive filters, supporting a wider range of ecological strategies, an expanded functional space, and higher species richness. 2. Materials and methods 2.1. Study region The Caatinga spans the Northeast and the far north of Minas Gerais State in Brazil, covering an area of 912,529 km² (Silva and Barbosa, 2017). The region is characterized by irregular rainfall distribution with long dry periods interrupted by rainfalls lasting over just a few months, defining a semiarid climate. Most precipitation is concentrated in only 3 to 5 months of the year, exhibiting high temporal variability (Silva et al ., 2019). The vegetation is predominantly xerophytic, composed mainly of woody, deciduous, and semi-deciduous species adapted to prolonged water stress (Marques et al ., 2020). In addition, there are subhumid and humid enclaves known as ‘brejos de altitude’, humid forest areas found between 500 and 1100 meters in elevation, surrounded by typical Caatinga vegetation (Andrade-Lima, 2014). 2.2. Database and data curation For this study, three datasets were compiled: 1) Taxonomic dataset: We selected peer-reviewed studies conducted in the Caatinga that provided species lists, resulting in 52 inventoried localities (see Supplementary Material) (Fig. 1). All three divisions of bryophytes were considered: hornworts (Anthocerotophyta), mosses (Bryophyta), and liverworts (Marchantiophyta). Species names were updated using the ’U.Taxonstand’ package in the R environment (version 4.4.3), based on The Bryophyte Nomenclator (https://www.bryonames.org/). Species with doubtful records, uncertain nomenclature, or for which key morphological and reproductive traits used in this study (Table 1) could not be completed were excluded (see Supplementary Material). For studies that did not report geographic coordinates, we searched the Reflora Virtual Herbarium (https://reflora.jbrj.gov.br/reflora/herbarioVirtual/) and SpeciesLink (https://specieslink.net/search/) to locate specimens with coordinates and locality descriptions consistent with those mentioned in the original studies. When such information was available, coordinates were assigned to the respective sites. In cases where multiple coordinates were provided for the same locality, the average of the reported values was used. 2) Functional trait dataset: The selected morphological and reproductive traits represent characteristics related to protection from solar radiation, water storage capacity, and reproductive strategies (Table 1). Trait data were compiled from taxonomic databases such as Flora and Funga of Brazil (https://floradobrasil.jbrj.gov.br/) and World Flora Online (http://www.worldfloraonline.org/), as well as from specialized literature containing taxonomic descriptions of the species. 3) Environmental and spatial dataset: The environmental dataset included 19 bioclimatic variables and elevation obtained from WorldClim 2.1 (Fick and Hijmans, 2017), as well as the aridity index retrieved from the Global Aridity Index 3 (Zomer et al ., 2022), all at a spatial resolution of 30 arc-seconds. The spatial variables used were the latitude and longitude of the sampled localities. Environmental variables were extracted using QGIS software version 3.42.2 via the Point Sampling Tool plugin. After dataset compilation, the Caatinga shapefile (https://terrabrasilis.dpi.inpe.br/downloads/) was subdivided into 0.25° × 0.25° grid cells to spatially standardize localities. The 52 sampled localities were distributed across 36 distinct grid cells. In cases where multiple localities were found within the same grid cell, we calculated the average of the environmental variables and geographic coordinates to generate a single representative value per cell for each variable. The grid cell size was defined to strike a balance between locality aggregation and the preservation of environmental gradient and heterogeneity; larger cells could lead to excessive aggregation and a substantial loss of spatial and environmental variability. To select the most informative variables and reduce dimensionality, we performed a Spearman correlation analysis (using the base R cor function), considering variables as correlated when the coefficient was ≥ 0.7. The initial set of variables was selected based on the characteristics of the study area and further refined following Marmo & Silva (2025). Subsequently, a Principal Component Analysis (PCA) was conducted using the ’FactoMineR::PCA’ function. The explained variance of each principal component was evaluated, and the first three components were retained as representative of total variability (’factoextra::get_eig’). Variables with absolute loadings > 0.7 on at least one of the three main axes were considered significant (’factoextra::get_pca_var’). The final selected variables were: Elevation (EL), Aridity Index (AI), Latitude (LAT), Longitude (LON), Precipitation of the Wettest Quarter (BIO16), Mean Temperature of the Wettest Quarter (BIO8), and Temperature Seasonality (BIO4). 2.3. Data Analysis 2.3.1 Alpha taxonomic and functional diversity Alpha taxonomic diversity (tα) was defined as the total number of species recorded at each locality, while alpha functional diversity (fα) was evaluated based on indices derived from functional entities: Functional Entity Richness (FEr), Functional Redundancy (FRed), Functional Over-Redundancy (FORed), and Functional Vulnerability (FVuln). These indices were calculated using the ’mFD::alpha.fd.fe’ function, after assembling species into functional entities (’mFD::sp.to.fe’). Functional entities group species sharing identical combinations of morphological and reproductive trait values and are characterized as follows: 1) FEr: total number of functional entities per assemblage; 2) FRed: average number of species per functional entity; 3) FORed: degree to which the species are concentrated in few entities (values near 0 indicate even distribution; near 1 indicate high concentration); 4) FVuln: proportion of entities containing only one species (high values indicate greater functional risk; low values indicate higher functional resilience) [Magneville et al ., 2022] The relationship between tα and FRed, FORed, and FVuln was assessed using Spearman’s rank correlation (’cor.test’), following verification of data normality using the Shapiro-Wilk test (’shapiro.test’). Both functions are part of base R. 2.3.2 Funtional space analysis Two types of functional spaces were constructed: 1) Global functional space of species: A Gower distance matrix—suitable for combining continuous and categorical variables—was generated using ’vegan::vegdist’ and used as input for a Principal Coordinates Analysis (PCoA) via ’vegan::cmdscale’. The scores of the first two principal axes defined the global functional space of species (’funspace::funspace’), which was subsequently partitioned by bryophyte phylum. The relationship between original trait values and PCoA axes was evaluated using ‘vegan::envfit’ with 999 permutations. The robustness and significance of the global functional space were tested with ‘funspace::funspaceNull’ using two null models: (i) a multivariate normal distribution, simulating ecological constraints and producing an ellipsoidal space centered on the most common traits, and (ii) a uniform distribution, assuming all trait combinations are equally probable, generating an approximately rectangular space (Carmona et al ., 2024). 2) Global functional space of climatic classes: Based on the aridity index, localities were classified into three climatic categories: semiarid (0.2–0.5), dry subhumid (0.5–0.65), and humid (>0.65) [Zomer et al ., 2022]. A global functional space was then built to evaluate how assemblage-level functional traits are distributed across these climatic contexts. Initially, the Community-Weighted Mean (CWM) of each trait was calculated (’FD::functcomp’). A Euclidean distance matrix (’dist’, base R) was computed from these CWMs and subjected to a PCoA, following the same rationale used for the species-level space. The resulting functional space was partitioned into three categories—semiarid, dry subhumid, and humid. Assemblages with fewer than five species were excluded from this analysis to avoid outliers that may arise from CWM calculations based on small species pools. 2.3.3 RLQ, Fourth-Corner, and Generalized Linear Models (GLMs) To assess the relationship between FRed, FORed, and FVuln and the environmental and spatial variables, we applied Generalized Linear Models (GLMs) using ’glmmTMB::glmmTMB’. For FRed, we used the Gamma distribution, appropriate for continuous and strictly positive values. FORed and FVuln, which include values of 0 and 1, were transformed to allow modeling with the Beta distribution, suitable for proportions in the open interval (0,1) (Smithson and Verkuilen, 2006). Model selection was performed using ’MuMIn::dredge’, considering models with ΔAIC < 2. Variable importance was assessed using ’MuMIn::sw’, with variables considered relevant if the importance ≥ 0.6. In models showing convergence issues or poorly fitted residuals—indicating insufficient explanatory power—additional variables were sequentially included in decreasing order of relative importance until model fit and diagnostics were satisfactory. Model assumptions were verified using simulated residuals (’DHARMa::simulateResiduals’). From these residuals, we conducted the testUniformity (uniformity of residual distribution), testDispersion (over- or underdispersion), and testOutliers (presence of outliers) tests. To explore trait responses to environmental and spatial gradients, we performed an RLQ analysis to link three matrices: environmental and spatial variables (R), assemblage composition (L), and species traits (Q) (’ade4::rlq’). The significance of the resulting relationships between traits and environmental/spatial variables was then assessed using the Fourth-Corner method (’ade4::fourthcorner’) [Dray and Legendre, 2008]. 3. Results 3.1. Alpha taxonomic and functional diversity A total of 450 accepted bryophyte taxa were compiled; however, eleven species (10 mosses and 1 liverwort) were excluded from the analyses due to missing trait data (see Supplementary Material). The final dataset comprised 439 taxa (428 species, 4 subspecies, and 9 varieties) across 62 families. Anthocerotophyta was represented by only three species (Anthocerotaceae – 1 spp.; Notothyladaceae – 2 spp.). Bryophyta was the most diverse, with 236 taxa (228 spp., 2 subsp., 6 var.) across 36 families, notably Fissidentaceae (37 spp.), Leucobryaceae (29 spp.), and Pottiaceae (20 spp.). Marchantiophyta accounted for 202 taxa (197 spp., 2 subsp., 3 var.) in 24 families, with Lejeuneaceae (80 spp.), Frullaniaceae (17 spp.), Plagiochilaceae, and Lepidoziaceae (17 spp. each) being the most representative. The locality with the highest tα was Serra do Orobó (110 spp.), followed by Chapada do Ibiapaba National Park (79 spp.) and Chapada do Araripe (74 spp.). A total of 133 functional entities were identified. FEr ranged from 1 to 56; FRed from 1 to 1.96; FORed from 0 to 0.31; and FVuln from 0.56 to 1. Taxonomic richness (tα) was positively correlated with FRed (ρ = 0.877; p < 0.001) and FORed (ρ = 0.896; p < 0.001), and negatively correlated with FVuln (ρ = –0.763; p < 0.001). 3.2. Functional space The global functional space of species exhibited a FRic of 0.47 and FDiv of 0.68. Anthocerotophyta showed the lowest FRic (0.05) and FDiv (0.34); Bryophyta had FRic = 0.39 and FDiv = 0.69; and Marchantiophyta had FRic = 0.33 and FDiv = 0.71 (Fig. 2). All traits were positively correlated with environmental and spatial variables (see Supplementary Material). The normal null model estimated a mean functional space area of 0.772, whereas the observed area was 0.471 (p = 0.001; SES = –8). The uniform null model estimated an expected area of 0.653, also higher than the observed value (p = 0.001; SES = –27.22). The analysis of global climatic classes revealed marked differences in functional diversity. The global FRic was 15.98 and FDiv was 0.53. The semiarid class showed FRic = 13.55 and FDiv = 0.47; the dry subhumid class exhibited FRic = 15.53 and FDiv = 0.61; and the humid class had FRic = 9.32 and FDiv = 0.41 (Fig. 3). All traits were significant in the functional space (see Supplementary Material). The normal null model estimated a mean expected area of 16.86, close to the observed area (15.98; p = 0.636; SES = –0.49), indicating no evidence of non-random structure. The uniform model estimated 17.65, with an observed area of 15.979 (p = 0.059; SES = –1.9), also lacking strong evidence to reject randomness. 3.3. RLQ, Fourth-Corner, and GLMs The RLQ analysis revealed a strong association between the environmental, spatial, functional, and taxonomic matrices (inertia = 0.485; p < 0.0001 for both models). The traits that contributed most were vulnerable life form (LF.V = 3.31), lobule (L = 0.48), and tolerant life form (LF.T = 0.45). Among the environmental and spatial variables, the most important were latitude (LAT = 0.34), precipitation of the wettest quarter (BIO16 = 0.31), and temperature seasonality (BIO4 = 0.29) (Fig. 4). The Fourth-Corner analysis also identified significant associations between traits and environmental/spatial variables (Table 2) (see full results in the Supplementary Material). In the GLMs, the initial model for FRed (AIC = 18.5) did not detect significant predictors. AICc selection indicated LON as the only relevant variable (AICc = 11.4, estimate = –0.037, p 0.05; dispersion = 1.053, p > 0.05; outliers = 1). For FORed, the initial model (AIC = –62.7) was also non-significant, but the most parsimonious model included LON (AICc = –67.7, estimate = –0.193, p 0.05; dispersion = 0.774, p > 0.05; outliers = 1). The initial model for FVuln (AIC = –41.9) found no significant variables. AICc selection identified LON as the key predictor (AICc = –48.2, estimate = 0.185, p 0.05; dispersion = 0.873, p > 0.05; outliers = 1). However, residual plots indicated a ”Combined adjusted quantile test significant”, suggesting there was some structure not captured by the model. A second model for FVuln, including LON and BIO4 (AIC = –47.0), resolved this issue: LON remained significant (estimate = 0.176, p 0.05), and the model diagnostics confirmed a good fit (KS: D = 0.134, p > 0.05; dispersion = 0.875, p > 0.05; outliers = 1) (see Supplementary Material). 4. Discussion The compilation of 450 taxa from published floristic surveys confirms the high taxonomic diversity of bryophytes in the Caatinga. This figure is close to the 491 taxa reported by Marmo & Silva (2025) for the Caatinga, though their study included specimens deposited in herbaria, including some not linked to scientific publications, broadening the taxonomic representation. The richest grid cells were those encompassing mountain ranges and plateaus, which are characterized by high elevations and humidity, conditions that favor the presence of bryophytes (Bôas-Bastos et al ., 2017; Batista et al ., 2018). According to Pharo and Zartman (2007), the richness and composition of bryophyte assemblages in fragmented landscapes are influenced by environmental (local) and spatial (regional) factors. Silva et al . (2018) discusses the influence of these factors on morphological and reproductive traits, which reflects the different ecological strategies of each species. The conditions in elevated and humid environments are ideal for bryophyte development, allowing a broader functional space and the coexistence of species with diverse trait combinations (Spasojevic et al ., 2014). However, FRed or FORed do not always indicate lower FVuln. In the Caatinga, of the 133 functional entities identified in this study, 59 (≈44%) were occupied by only one species. For example, in Serra do Orobó (Bôas-Bastos et al ., 2017) — the richest locality with 110 species — 56 FEs were recorded, 36 of which (64%) were single-species entities, indicating high FVuln even in species-rich areas. Indeed, in the Caatinga, a large proportion of bryophytes (about 45%) are recorded only once (singletons) or twice (doubletons), reinforcing their vulnerability, as many may represent unique functional entities (Marmo and Silva, 2025). This pattern reflects sampling gaps in the biome, exemplifying the Hutchinsonian shortfall: a lack of knowledge about the ecological niches and functional traits of species (Hortal et al ., 2015). Mosses showed the highest FRic, likely due to their morphological diversity, reflected in varied leaf cell structures, gametophyte size and color, growth forms, physiological plasticity (Schofield, 1985) and sexual systems (Maciel-Silva and Pôrto, 2024). This morphological diversity enables homoiochlorophyllous desiccation-tolerant (HDT) species, such as Syntrichia ruralis (Hedw.) F. Weber & D. Mohr, to persist in heterogeneous semiarid grasslands (Hamerlynck et al ., 2002). Liverworts exhibited the highest FDiv, including both thallose and leafy representatives with contrasting strategies. Hornworts exhibited the lowest FRic and FDiv, likely due to their fewer species and narrower trait diversity. Additionally, they are restricted to more humid environments (Oliveira and Bastos, 2009). In xeric environments, stronger environmental filters favor adaptations like costae (Glime, 2021), papillae (Dilks and Proctor, 1979), and gametophyte curling (Proctor et al ., 2007) along with reduced dependence on water for sexual reproduction, often associated with monoicous systems (Glime, 2021). Fabronia ciliaris var. polycarpa (Hook.) W.R. Buck, for instance, develops sporophytes in the dry season and gametangia in the rainy season (Nunes et al ., 2015), while Campylopus lamellatus Mont. shows physiological adaptations such as chlorophyll a variation between the dry and rainy seasons (Silva et al ., 2020). In this study, xeric-adapted traits were positively correlated with increasing latitude and higher temperatures during the wettest quarter (BIO8), suggesting stronger environmental filtering to the north of the São Francisco River. This region is drier and less climatically stable compared to the more humid southern Caatinga near Chapada Diamantina (Marmo and Silva, 2025). Bryophytes may show contrasting responses to latitudinal variation. In Chile, bryophyte richness increases southward, especially in the Magellanic subantarctic region. This pattern is linked to their tolerance to desiccation and to cold weather as well as to the role of dominant winds in dispersing spores and asexual structures across long distances (Rozzi et al ., 2008). On the other hand, in Europe, moss diversity shows an opposite pattern—richness increases northward, where cooler climates are more suitable for poikilohydric plants. According to the authors, southern Europe’s warm and dry conditions are less favorable for mosses, explaining the lower species richness in these regions (Mateo et al ., 2016) Along the longitudinal gradient (west-east), we observed lower FRed and FORed and higher FVuln. This indicates that despite regular rainfall modulated by the Intertropical Convergence Zone (ITCZ) (Pagotto et al ., 2015), locations closer to the ocean at higher longitudes do not consistently provide more favorable environments for bryophytes. In contrast, elevated and humid areas like Chapada Diamantina, situated approximately 320 km from the coast, had significant functional richness. This particular region boasts the highest bryophyte richness in the Caatinga (Marmo and Silva, 2025), with its mountain ranges and moderate aridity playing a key role in promoting both local endemism and functional diversity (Silva and Souza, 2018). Thus, in the Caatinga, the climatic variation between xeric and humid areas results in distinct ecological strategies for bryophytes. These differences have, in fact, been observed across various environments. Species from humid areas invest in water retention traits [e.g., Philonotis cernua (Wilson) D.G. Griffin & W.R. Buck, Hygroamblystegium varium (Hedw.) Mönk.], even near water sources. In contrast, species like Campylopus filifolius (Hornsch.) Spruce from variable environments exhibit high sensitivity to water loss and adapt quickly to moisture fluctuations (Ribeiro et al ., 2022). Xeric bryophytes tend to display high adaptive plasticity to optimize water use under stress, whereas humid-environment species invest in water storage and retention mechanisms. Nevertheless, it is important to consider that under-sampled regions within the Caatinga (Marmo and Silva, 2025) may still reveal high taxonomic and functional diversity, which could reshape the spatial patterns identified here. 5. Conclusion Our findings highlight the influence of latitude and longitude on the functional diversity of bryophytes in the Caatinga. 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Version 3 of the global aridity index and potential evapotranspiration database. – Scientific Data 9: 409, https://doi.org/10.1038/s41597-022-01493-1. Figures Fig. 1. Map of the 52 inventoried localities (white circles) in the Caatinga, Brazil. The grid cells are 0.25° × 0.25°. The Aridity Index follows the classification of Zomer et al . (2022): semi-arid (0.2–0.5), dry sub-humid (0.5–0.65), and humid (>0.65). Fig. 2. Functional space of Caatinga bryophytes: a) global functional space; b) functional space of hornworts (Anthocerotophyta); c) functional space of mosses (Bryophyta); d) functional space of liverworts (Marchantiophyta). Blue arrows represent functional traits and their length is proportional to their contribution to the model. Fig. 3. Functional space of assemblages by climatic class: a) Semiarid functional space; b) Dry subhumid functional space; c) Humid functional space; d) Global functional space of climatic classes. Red arrows represent functional traits, with arrow length proportional to their contribution to the model; dashed lines indicate functional space density boundaries, and black dots represent the localities. Fig. 4. (A) RLQ biplot illustrating the distribution of sampling sites in relation to environmental and spatial variables and functional traits. (B) Relative contribution of functional traits to the structuring of the RLQ axes. (C) Relative contribution of environmental and spatial variables to the RLQ axes. The morphological and reproductive traits considered were: AC (Air chambers), AR (Asexual reproduction), CO (Presence of costa), DP (Dark pigmentation), GC (Gametophyte curling), HP (Hair point), L (Presence of lobule), LF_I (Intermediate life form), LF_T (Tolerant life form), LF_V (Vulnerable life form), PA (Presence of papillae), SR_D (Dioicous sexual system), SR_M (Monoicous sexual system), SR_P (Polyoicous sexual system), and WC (Water storage cells). The environmental and spatial variables included: AI (Aridity Index), BIO16 (Precipitation of the Wettest Quarter), BIO4 (Temperature Seasonality), BIO8 (Mean Temperature of the Wettest Quarter), EL (Elevation), LAT (Latitude), and LON (Longitude). Tables Tab. 1. Morphological and reproductive traits of bryophytes in the Caatinga compiled from the literature. Phyllum/Morphological and reproductive traits Associated functions References Hornworts, mosses and liverworts Assexual reproduction (AR) — binary variable Survival in xeric environments in the absence of water Frey & Kürschner (2011) Sexual reproduction system: monoicous (SR_M), dioicous (SR_D) and polyoicous (SR_P) — categorical variable Variation in water requirements for sexual reproduction Glime (2021) Life form: intermediate (LF_I), tolerant (LF_T), and vulnerable (LF_V) — categorical variable Response to desiccation levels and protection from solar radiation (LF_I = mat, weft, fan; LF_T = tuft, cushion, complex thalloid; LF_V = pendent, dendroid, simple thalloid) Adapted from Mägdefrau (1982) Hornworts and tallose liverworts Air chambers (AC) — binary variable Facilitation of gas exchange in photosynthesis, transpiration, and respiration Apostolakos, Galatis & Mitrakos (1982) Mosses Water storage cells (WC) (e.g., hyaline cells, alar cells, leucocysts, cancellinae) —- binary variable Water storage over time Brezeanu, Cogălniceanu, & Mihai (2009) Hair points (HP) — binary variable Protection of photosynthetic cells and reduction of water loss Pan et al . (2016) Mosses and liverworts Gametophyte curling (GC) — binary variable The curling of leaves/gametophyte (convolute, crisped, or twisted) protects internal tissues Adapted from Proctor et a l. (2007) Papillae (PA) — binary variable Osmotic regulation and rapid water transport within cells Dilks & Proctor (1979) Dark pigmentation (DP) — binary variable Protection of chlorophyll from direct light exposure Xie & Lou (2009) Costa (CO) — binary variable Facilitation of water absorption and transport Glime (2021) Leafy liverworts Lobule (L) — binary variable External water storage Renner (2015) Tab. 2. Significant relationships (p ≤ 0.05) resulting from the Fourth-Corner analysis between environmental/spatial variables and functional traits. Pairwise (Variables / Traits) Obs Std.Obs Pvalue.adj LAT / CO 0.154181 3.158025 0.01 * LAT / PA 0.090031 2.635121 0.03 * LAT / SR_D -0.16068 -3.0161 0.01 * LAT / SR_M 0.162296 2.99524 0.02 * LAT / DP 0.126913 2.627974 0.05 BIO8 / GC 0.153916 3.216107 0.01* AI / LF_V 0.165815 3.269921 0.01* Note: The morphological and reproductive traits considered were: CO (Presence of costa), DP (Dark pigmentation), GC (Gametophyte curling), LF_V (Vulnerable life form), PA (Presence of papillae), SR_D (Dioicous sexual system), SR_M (Monoicous sexual system), Environmental and spatial variables: AI (Aridity Index), BIO8 (Mean Temperature of the Wettest Quarter), LAT (Latitude), Information & Authors Information Version history V1 Version 1 10 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords biology biome deserts and xeric shrublands ecology ecosystem species richness Authors Affiliations Jhonyd Marmo 0000-0002-5067-5255 Universidade Federal de Pernambuco Centro de Biociencias View all articles by this author Mércia Silva 0000-0002-1391-9038 [email protected] Universidade Federal de Pernambuco Centro de Biociencias View all articles by this author Metrics & Citations Metrics Article Usage 259 views 162 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jhonyd Marmo, Mércia Silva. Ecological strategies and functional structure of bryophytes across environmental and spatial gradients in the Caatinga, Brazil. Authorea . 10 September 2025. DOI: https://doi.org/10.22541/au.175751676.66355961/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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