Collembola (Hexapoda) diversity in a Caatinga-Cerrado transition protected área

ABSTRACT

Collembola are major components of soil mesofauna, playing crucial roles in this environment. However, studies on this fauna in the Neotropical Region are mostly focused on taxonomy. The aim of this study was to investigate how vegetation structure influences the diversity and composition of epiedaphic Collembola assemblages across different vegetation types in Serra das Confusões National Park (PNSC), Brazil. Specimens were collected during two samplings, and were sorted, morphotyped, mounted, and identified. We performed environmental characterization, evaluated alpha and beta diversity, used the IndVal index for indicator species analysis, and conducted a Redundancy Analysis to assess the influence of environmental parameters on assemblages. We sampled 29,616 specimens, comprising 40 morphospecies and six nominal species. Our data showed significant differences between the vegetation types, with the highest abundances and richness recorded during the first sampling in the semideciduous forest, due to the dominance of taxa related to open habitats with limited moisture. The main environmental variables influencing diversity patterns were canopy cover, leaf litter height, silt percentage and soil temperature, reinforcing the dominance of species better adapted to aboveground and open habitats. We also present a comprehensive inventory of families and genera for PNSC, resulting in increased knowledge about the group in the region.

KEY WORDS:
Collembola assemblages; community ecology; habitat heterogeneity; Neotropical Region; soil fauna

INTRODUCTION

Soil fauna plays a key role in sustaining vital ecosystem functions such as nutrient cycling, organic matter decomposition, and maintenance of soil structure. These organisms are commonly classified by body size into: microfauna (<0.1 mm; e.g., protozoa, nematodes); mesofauna (0.1-2 mm; e.g., microarthropods, enchytraeids); and macrofauna (>2 mm; e.g., earthworms, termites, millipedes) (Bardgett 2005). Mesofauna, in particular, serve as a crucial link between microbial activity and higher trophic levels, mediating the flow of energy and nutrients within soil ecosystems (Brussaard 1997, Giller et al. 1997, Chamorro-Martínez et al. 2022).

Springtails (Collembola) stand out as a functionally important group of soil mesofauna. Their small size, short life cycle and generalist diet, primarily saprophagous or fungivorous, allow them to inhabit a wide range of environments, reaching high population densities (Deharveng 1996, Hopkin 1997, Christiansen and Bellinger 1998, Cassagne et al. 2003, Martins et al. 2020). Such populations support different food chains comprehending small invertebrate predators, regulate microbial communities, promote organic matter decomposition and improve the overall soil fertility (Bellini et al. 2023a).

Springtails are also excellent bioindicators of soil health due to their sensitivity to environmental changes (Doran and Parkin 1994, Zeppelini et al. 2008). Accordingly, the distribution and diversity of Collembola are closely linked to environmental conditions. For instance, abiotic factors like temperature and moisture affect them due to their low desiccation tolerance, while vegetation affects food availability and habitat structure, influencing Collembola community structure (Hopkin 1997, Ferreira et al. 2013, 2018, Hasegawa et al. 2014, Pompeo et al. 2016). Even so, the diversity, distribution, and ecological roles of Collembola remain poorly understood across tropical regions (Potapov et al. 2020).

Recent surveys have expanded the knowledge on Collem bola taxonomic richness in Brazil through the description of new species (Zeppelini and Bellini 2024, Zeppelini et al. 2025), yet sampling gaps persist in Caatinga and Cerrado biomes despite their high biodiversity and endemism (Abrantes et al. 2010, Scariot et al. 2005). Ecotones between these domains remain particularly understudied (Zeppelini et al. 2025). The Caatinga, an exclusively Brazilian biome, is a seasonally dry tropical forest featuring irregular rainfall, high temperatures, and xerophytic vegetation (Cole 1960, Albuquerque et al. 2012, Santos et al. 2014), while the Cerrado savanna has distinct wet/dry seasons, nutrient-poor soils, and a mosaic of grassland, shrubland, and woodland formations (Lopes and Cox 1977, Ribeiro and Walter 2008). These contrasting conditions likely filter Collembola communities (Lima et al. 2024), favoring drought-adapted (Caatinga) or flood-resistant (Cerrado) species.

One important area within the ecotonal region between Caatinga and Cerrado is the Serra das Confusões National Park (PNSC), located in the state of Piauí, Brazil. Its eastern portion is predominantly covered by Caatinga vegetation, formed by a mosaic of arboreal, shrubby, and mixed formations. As the park territory advances westward, its plant physiognomy gradually becomes more Cerrado-like (Andrade-Lima 1981, 1989, IBAMA 2003, Tabarelli and Silva 2003). Although the PNSC was recognized as a priority area for the conservation of semiarid ecosystems, studies on its soil arthropod fauna remain scarce and focused mainly on taxonomic descriptions (Nunes et al. 2020, 2021, Zeppelini et al. 2025), highlighting the need for broader ecological investigations.

The habitat heterogeneity hypothesis suggests that structurally complex environments support greater species richness by offering a wider range of niches and resources (MacArthur and MacArthur 1961, Tews et al. 2004, Silva and Brandão 2010, Bardgett and Van Der Putten 2014). Heterogeneous landscapes, such as those found in the PNSC, provide structural and microclimatic variation that enhances shelter, protection, and food availability, potentially promoting richer and more abundant soil communities (Morris 2000, Zardo et al. 2010, Spiller et al. 2018). These characteristics reinforce the ecological relevance of transitional zones like the PNSC for understanding biodiversity patterns of tropical Collembola and other soil fauna.

Our study aimed to investigate how vegetation structure influences the diversity and composition of epiedaphic Collembola communities across three vegetation types in the PNSC: semideciduous forest, steppe savanna, and carrasco. Here, habitat heterogeneity is defined as variations in vegetation types, litter cover, and microclimatic conditions across the sampled formations. We hypothesize that these vegetation types support distinct Collembola assemblages, and the semideciduous forest will shelter the highest abundance, richness and diversity due to its greater structural complexity and resource availability.

MATERIAL AND METHODS

Study area

Sampling was conducted in the Serra das Confusões National Park (Fig. 1, Table 1), located in the southern region of the state of Piauí state, Brazil, encompassing an area of 823,854.54 ha (Tabarelli and Silva 2003). The regional climate is classified as semi-arid (BSh) according to the Köppen-Geiger system, with an average annual temperature of approximately 27 °C (Kottek et al. 2006, IBAMA 2003, CPRM 2004, INMET 2025a). The PNSC hosts heterogeneous vegetation, including xerophytic species (such as cacti and bromeliads), perennial vegetation in canyon bottoms, and grasses in drier, more exposed areas. To ensure comprehensive sampling, the predominant vegetation types were selected: semideciduous forest, steppe savanna, and carrasco.

Table 1
List of geographic coordinates taken from GPS for the five sets of pitfall traps in each vegetation type (semideciduous forest, steppe savanna, and carrasco) in the Serra das Confusões National Park (PNSC), Piauí State, Brazil.

Vegetation type	Sets	Coordinates (DMS)
Forest	1	9°13’18.92”S; 43°29’22.59”W
Forest	2	9°13’17.68”S; 43°29’23.68”W
Forest	3	9°13’13.58”S; 43°29’27.57”W
Forest	4	9°13’13.73”S; 43°29’29.16”W
Forest	5	9°13’12.66”S; 43°29’30.69”W
Savanna	1	9°13’12.38”S; 43°29’24.28”W
Savanna	2	9°13’13.79”S; 43°29’23.93”W
Savanna	3	9°13’15.27”S; 43°29’23.03”W
Savanna	4	9°13’15.72”S; 43°29’21.96”W
Savanna	5	9°13’16.27”S; 43°29’20.58”W
Carrasco	1	9°13’29.88”S; 43°27’52.07”W
Carrasco	2	9°13’29.15”S; 43°27’53.01”W
Carrasco	3	9°13’28.36”S; 43°27’54.37”W
Carrasco	4	9°13’28.82”S; 43°27’55.74”W
Carrasco	5	9°13’28.41”S; 43°27’56.96”W

The semideciduous forest, located within the canyons, contains perennial vegetation associated with historically more humid conditions, including bromeliads, bryophytes, pteridophytes, and broad-leaved trees ranging from 20 to 30 meters in height, forming a relatively continuous canopy (Zaher et al. 2002). The steppe savanna is characterized by a predominance of xerophytic species, particularly cacti and bromeliads, typical of the Caatinga biome (Zappi 2008), and occurs on rocky outcrops, shallow soils and sandy deposits over sandstone exposed to intense solar radiation (Zaher et al. 2002). The carrasco consists of dense arboreal vegetation on sandy soils, composed mainly by deciduous or semideciduous species (Araújo 1998). It differs from the typical Caatinga by a higher density of woody vegetation, thin-trunked non-stratified trees, and the near absence of cacti and bromeliads (Andrade-Lima 1978).

Figure 1
Distribution of the sampling sites in Serra das Confusões National Park (PNSC), and its location along the Cerrado-Caatinga transition, Piauí State, Brazil: (A) left side, the semideciduous forest transect at the bottom of a canyon about 50 m deep; right side, the steppe savanna vegetation transect, (B) The carrasco vegetation transect.

Sampling design and taxonomic identification

A representative area was delineated for each vegetation type to assess the influence of habitat complexity on assemblage structure, with a minimum distance of 100 m between sampling areas (Fig. 1). A handheld GPS was used to georeference the sampling sites (Table 1). Two samplings were carried out, one in November 2022 and the other in March 2023. A 200 m transect was established in each area, along which five sets of three pitfall traps were aligned. Each trap consisted of a 400 mL disposable cup filled with 70% ethanol, and remained installed for 48 consecutive hours. After this period, the material was collected and temporarily stored in 70% ethanol before being transported to the laboratory for analysis. Within each set, traps were spaced 1 meter apart to constitute a composite sample, and each set was placed 50 m apart along the transect. The distance between sampling sets (replicates) was selected to ensure sample independence, given that Collembola are small, wingless organisms with limited mobility. This methodology has been successfully applied in studies across different vegetation types in Brazil (Ferreira et al. 2018, Silva et al. 2022, Lima et al. 2024).

After field sampling, specimens were sorted, counted, and morphotyped under a stereomicroscope. Subsequently, glass slides were prepared following a standard protocol, combining Arlé and Mendonça (1982) and Jordana et al. (1997) methods. Taxonomic identification was performed using specialized keys and literature, including Massoud (1967), Jordana et al. (1997), Bretfeld (1999), Christiansen and Bellinger (2000), Potapov (2001), Bellinger et al. (1996-2024), Nunes and Bellini (2018), and Nunes et al. (2019, 2020).

Measurement of environmental variables

A 5×5 m area was delimited around each set of pitfall traps to measure the following environmental parameters: air and soil temperature, soil moisture, relative air humidity, soil pH, leaf litter, canopy cover, and plant richness. A digital thermo-hygrometer Incoterm was used to measure air temperature and humidity. Soil pH was measured with a portable meter Akson AK95, and soil temperature with a thermometer Jprolab inserted at a 5 cm depth, all conducted directly on-site. The leaf litter was measured by inserting a graduated ruler until it reached the soil surface. To estimate the canopy cover at each sampling site, a photograph of the vegetation cover was taken from the centre of each sampling point, at a height of approximately two meters, and it was subsequently evaluated in ImageJ v. 1.54f. This software processes photographs based on contrast differences and quantifies the percentage of bright areas, resulting from sunlight, and of darker areas, which indicate vegetation cover (Rasband 1997-2024). Since we were aiming for the epiedaphic springtails, the soil gravimetric moisture content was determined using wet and dry weight values of samples, collected at a depth of 5 cm and dehydrated at 105 °C for 48 hours in laboratory. Soil samples were sent to Empresa de Pesquisa Agropecuária do Rio Grande do Norte (EMPARN) and to the Centro de Ciências Agrárias da Universidade Federal da Paraíba (UFPB) the for grain size analysis.

The estimation of plant richness was carried out visually, within the delimited areas around pitfall traps sets, by analyzing and comparing the external morphological features of the plants found at each site, such as phyllotaxy, stem structure, leaf characteristics such as type, shape, margin, and venation, as well as flower traits. For the purpose of this study, which does not aim for floristic taxonomic diversity, we focused on distinguishing between different plant morphospecies as a measure of plant richness around each set of pitfall traps, following the same protocols of Lima et al. (2024).

Ecological data and statistical analyses

The Collembola and environmental data were organized into a single matrix and all statistical analyses were performed using R software v. 4.3.3 (R Core Team 2020). The multivariate biotic matrix was standardized using the “Hellinger” method from the “decostand” function, following Legendre and Legendre (2003). The environmental variables were standardized using the “standardize” method with the “decostand” function from the “vegan” package (Oksanen et al. 2020).

To identify which variables showed the greatest differences between vegetation types and samplings, including their interaction, an analysis of environmental characterization was conducted using a Permutational Multivariate Analysis of Variance (PERMANOVA) based on the Euclidean distance, adjusted with Bonferroni correction with 9999 permutations, using the “adonis2” function from the “vegan” package (Oksanen et al. 2020). Then, the results were plotted in a Principal Component Analysis (PCA).

The species accumulation curve was checked using the “specaccum” function from the “vegan” package prior to diversity analyses (Oksanen et al. 2020), comparing the observed and estimated species richness to obtain the collection efficiency based on Jackknife 1 estimator. To estimate species richness (S) we used the “specnumber” function from the same package.

The total abundance and alpha diversity (by species richness) was assessed individually in relation to the spatiotemporal factors (vegetation types and samplings, including their interaction) using Generalized Linear Mixed Models (GLMMs), from the “glmmTMB” package (Brooks et al. 2017). The distribution of the negative binomial type 2 response variable was used for alpha diversity, while for the abundance, the normal distribution was employed. Subsequently, pairwise comparisons that showed significance were tested post hoc using the Tukey test. Mixed models were used due to the lack of independence of the response variable, resulting in the replication in each sampling. According to Zuur et al. (2009), this statistical tool can be used by including a covariate (replicate) in the model to reduce or eliminate the effect of this lack of independence. These univariate analyses were validated a posteriori regarding the distribution of residuals using the “DHArma” package (Hartig and Lohse 2022).

Beta diversity partitioning analyses were conducted using the “betadiver” function in the “vegan” package (Oksanen et al. 2020), through the Whittaker index method. A Permutational Multivariate Analysis of Variance (PERMANOVA) with 9999 permutations was performed using the “adonis” function from the “vegan” package (Oksanen et al. 2020). This analysis was performed to explore differences in species composition between different samplings and vegetation types, using Bray-Curtis distance matrices adjusted with Bonferroni correction, also using the “adonis2” function from the “vegan” package (Oksanen et al. 2020). Beta diversity was calculated using the Whittaker beta diversity method (βw) (Whittaker 1960), with the “mod.bet” function from the “RVAideMemoire” package (Hervé 2023).

The species/morphospecies composition was assessed in relation to spatiotemporal factors using PERMANOVA, and it was adjusted by Bonferroni with 9,999 permutations using the “adonis2” function of the “vegan” package (Oksanen et al. 2020). The biotic data matrix, standardized by the Hellinger method, was calculated using Euclidean distances (Legendre and Legendre 2012), and plotted in a Non-Metric Multidimensional Scaling (NMDS) analysis by vegetation types, samplings, and the interaction of these factors. This procedure was carried out to visualize the association of each species/morphospecies with the sampled spatiotemporal factors.

The analysis of indicator species/morphospecies was based on the Indicator Value index (IndVal), using the “IndVal” function from the “labdsv” package (Roberts 2014). It ranges from 0 to 100%, where zero indicates no relationship, and 100% exclusive occurrence of the species in a particular habitat (Cáceres and Legendre 2009).

A Redundancy Analysis (RDA) was used to study the correlation between the effects of the abiotic and biotic matrices. Among the various abiotic factors, multicollinearity was assessed, and those presenting values above 3 were removed following Zuur et al. (2009). The significance of the model and axes were verified through “anova” and “permutest” functions, respectively, in the “vegan” package (Oksanen et al. 2020), while the correlation between environmental and biotic variables extracted from the RDA was determined using the “envfit” function, in the “vegan” package (Oksanen et al. 2020). The functions “adonis2”, “anova”, “permutest”, and “envfit” were executed with 9,999 permutations.

RESULTS

Environmental characterization

Environmental variables varied across vegetation types and sampling periods (Supplementary Table S1). In the semideciduous forest, we observed higher levels of soil moisture, leaf litter, and silt, whereas in the steppe savanna the soil temperature was notably higher. Conversely, the carrasco exhibited elevated levels of sand, plant richness and pH (F = 10.0488, p = 0.0001, Fig. 2A). In the first sampling we recorded higher levels of litter, sand, and soil moisture, while the second showed higher levels of silt and plant richness (F = 2.8528, p = 0.0167, Fig. 2B). In terms of spatiotemporal interactions between the vegetation types and sampling, we observed similarities with the aforementioned patterns, although these were not statistically significant (F = 1.1717, p = 0.3039, Fig. 2C).

Figure 2
Environmental characterization of each sampled area in this study during samplings I and II through PCA: (A) comparison between semideciduous forest, steppe savanna, and carrasco, (B) comparison between samplings I and II, and (C) interaction of variables with each other. *Indicates statistically significant data (p ≤ 0.05). Descriptive data of the abiotic and biotic factors of each vegetation types are available in Supplementary Table S1.

Collembola abundance, α and β diversity

A total of 29,616 Collembola specimens were collected across both samplings, comprising three orders, 12 families, 29 genera, 40 morphospecies, and six nominal species (Table 2). Sminthurididae and Neanuridae were the most abundant families, followed by Entomobryidae. The most dominant morphospecies was Neotropiella sp. 1, followed by Sphaeridia sp. 1 and Denisiella sp. 1. The richest genus was Denisiella Folsom & Mills, 1938 with four morphospecies, followed by Seira Lubbock, 1870, Lepidocyrtinus Börner, 1903, Brachystomella Ågren, 1903, and Calvatomina Yosii, 1966, with three morphospecies each; and Lepidocyrtus Bourlet, 1839; Trogolaphysa Mills, 1938 and Neotropiella Handschin, 1942, with two morphospecies each, while the remaining genera had only one morphospecies recorded.

Table 2
Abundance of Collembola species/morphospecies sampled in different vegetation types (semideciduous forest, steppe savanna, and carrasco) and samplings (I and II) in the Serra das Confusões National Park (PNSC), Piauí State, Brazil.

Family/species and morphospecies		Vegetation types/Samplings						Samplings/Total abundance
		Forest		Savanna		Carrasco		I	II	Total
		I	II	I	II	I	II
Entomobryidae	Seira sp. 1	5	2	47	407	31	691	83	1,100	1,183
	Seira sp. 2	38	27	9	23	3	73	50	123	173
	Seira sp. 3	45	11	24	6	6	140	75	157	232
	Lepidocyrtus sp. 1	53	202	0	2	6	25	59	229	288
	Lepidocyrtus sp. 2	4	0	9	0	2	0	15	0	15
	Pseudosinella triocellata Nunes & Bellini, 2018	5	5	11	0	0	1	16	6	22
	Lepidosira neotropicalis Nunes & Bellini, 2019	1	31	0	0	0	4	1	35	36
	Lepidocyrtinus sp. 1	17	0	1	0	0	0	18	0	18
	Lepidocyrtinus sp. 2	0	0	0	0	1	0	1	0	1
	Lepidocyrtinus sp. 3	0	1	0	0	0	0	0	1	1
	Trogolaphysa sp. 1	27	72	0	0	0	0	27	72	99
	Trogolaphysa sp. 2	13	6	0	0	0	0	13	6	19
	Cyphoderus sp. 1	0	1	0	0	1	6	1	7	8
	Entomobrya sp. 1	1	0	0	0	0	0	1	0	1
Orchesellidae	Capbrya brasiliensis Nunes, Santos-Costa & Bellini, 2020	0	1	122	49	0	0	122	50	172
Isotomidae	Isotomidae sp. 1	6	11	24	1	289	0	319	12	331
	Isotomidae sp. 2	1	0	0	0	0	0	1	0	1
	Proisotoma sp. 1	73	0	215	0	288	0	576	0	576
	Desoria sp. 1	427	0	15	0	1	0	443	0	443
Sminthuridae	Pararrhopalites palaciosi Zeppelini & Brito, 2014	590	7	355	0	173	0	1,118	7	1,125
	Varelasminthurus sp. 1	244	0	1	1	0	1	245	2	247
	Szeptyckitheca andrzeji Medeiros, Bellini & Weiner, 2023	3	1	0	0	4	0	7	1	8
	Temeritas sp. 1	4	0	19	0	25	0	48	0	48
Sminthurididae	Denisiella sp. 1	2,556	0	1,947	0	0	0	4,503	0	4,503
	Denisiella sp. 2	0	0	0	0	825	0	825	0	825
	Denisiella sp. 3	8	0	11	0	0	0	19	0	19
	Denisiella sp. 4	0	0	0	0	190	0	190	0	190
	Sphaeridia sp. 1*	2,489	0	1,332	0	2,465	0	6,286	0	6,286
	Parasminthurides sp. 1	0	0	0	0	1	0	1	0	1
Collophoridae	Collophora sp. 1	30	0	51	1	462	0	543	1	544
Dicyrtomidae	Calvatomina sp. 1	26	0	162	0	1	0	189	0	189
	Calvatomina sp. 2	93	4	47	0	1	0	141	4	145
	Calvatomina sp. 3	0	0	3	0	0	0	3	0	3
Katiannidae	Sminthurinus sp. 1	1	0	0	1	213	0	214	1	215
	Stenognathellus sp. 1	3	0	0	0	9	0	12	0	12
	Arborianna sp. 1	1	0	0	0	14	0	15	0	15
Bourletiellidae	Stenognathriopes sp. 1	38	0	37	1	85	0	160	1	161
	Prorastriopes sp. 1	2	0	16	7	255	0	273	7	280
	Bourletiellidae sp. 1	0	0	0	0	0	5	0	5	5
Neanuridae	Neotropiella sp. 1	3,709	167	2,499	0	3,243	80	9,451	247	9,698
	Neotropiella sp. 2	0	0	68	0	33	0	101	0	101
	Arlesia sp. 1	4	0	0	0	3	0	7	0	7
Brachystomellidae	Brachystomella sp. 1	111	2	0	0	0	0	111	2	113
	Brachystomella sp. 2	2	0	0	0	0	0	2	0	2
	Brachystomella sp. 3	0	1	0	0	25	1	25	2	27
Hypogastruridae	Ceratophysella rogerarlei Palacios-Vargas, Bellini & Cipola, 2018	1,029	0	189	0	10	0	1,228	0	1,228
Total abundance		11,659	552	7,214	499	8,665	1,027	27,538	2,078	29,616
Species richness		35	18	25	11	30	11	44	24	46

• *All Sphaeridia specimens were grouped into a single morphospecies, Sphaeridia sp. 1, due to the difficulties in separating different species under the stereomicroscope.

During the first sampling, the overall abundance was markedly higher (27,538 individuals), with the semideciduous forest showing the greatest abundance, followed by carrasco and steppe savanna. In the second sampling, overall abundance decreased substantially (2,078 individuals), with carrasco presenting the highest numbers. Sphaeridia Folsom & Mills, 1938 presented at least two morphospecies, detected post-identification. However, due to their minute size, with most individuals with less than 0.5 mm in length, and similar color pattern, we could not confidently separate them in distinct morphospecies under the stereomicroscope. For this reason, they were grouped in Sphaeridia sp. 1.

The species accumulation curve indicated that the sampled morphospecies/species richness was representative of the local assemblies. Jackknife 1 displayed a slightly increasing accumulation curve, suggesting potential non-sampled species in PNSC, but following a similar stabilization pattern compared to the observed richness (Fig. 3).

Figure 3
Species accumulation curve in three vegetation types of Serra das Confusões National Park (PNSC), Piauí State, Brazil, in two samplings.

The abundance of Collembola in the three studied areas was similar, with no significant differences (F = 1.769, p = 0.4129, Fig. 4A). They exhibited a higher abundance during the first sampling compared to the second one (F = 51.992, p < 0.001, Fig. 4B). In terms of the spatiotemporal interaction (vegetation types and sampling), the pattern of a higher Collembola abundance during the first sampling persisted across all the areas, although it was not statistically significant (F = 3.107, p = 0.2115, Fig. 4C).

Figure 4
Comparison of Collembola abundance between vegetation types and samplings: (A) semideciduous forest, steppe savanna, and carrasco, (B) samplings I and II, and (C) interaction between spatiotemporal factors. *Indicates statistical differences (p ≤ 0.05) within compared groups.

The alpha diversity, measured by species richness, was similar between the semideciduous forest, steppe savanna, and carrasco (F = 3.1345, p = 0.2086, Fig. 5A). Higher values were observed during the first sampling compared to the second one, resulting in significant differences between them (F = 20.4410, p < 0.001, Fig. 5B). In the spatiotemporal interaction, this pattern of higher species richness was maintained during the first sampling in the three areas, but without statistical significance (F = 4.1459, p = 0.1258, Fig. 5C).

Figure 5
Comparison of Collembola morphospecies richness (alpha diversity) in the studied areas: (A) semideciduous forest, steppe savanna, and carrasco, (B) samplings I and II, and (C) interaction between spatiotemporal factors. *Indicates statistical differences (p ≤ 0.05) within compared groups.

The beta diversity (βw) showed higher values in the steppe savanna (F = 11.1424, p = 0.0002, Fig. 6A), and during the second sampling (F = 24.7229, p = 0.0001, Fig. 6B). Concerning the interaction of spatiotemporal factors, the highest βw values were observed in steppe savanna during the second sampling, while the lowest were recorded in the semideciduous forest during the first sampling. The remaining factors exhibited intermediate values (F = 4.7274, p = 0.0277, Fig. 6C).

Figure 6
Comparative boxplots of beta diversity between vegetations types and samplings expressed by the Whittaker index: (A) semideciduous forest, steppe savanna, and carrasco, (B) samplings I and II, and (C) interaction between spatiotemporal factors. *Indicates statistical differences (p ≤ 0.05) within compared groups. For three-factor group (vegetation type), different letters indicate statistical differences (p ≤ 0.05) between each compared couple. For comparing two factors (samplings), letters were not used due to the limited number of comparisons.

Assembly composition

The Collembola assembly in the steppe savanna was mainly represented by Seira sp. 1 and Capbrya brasiliensis Nunes, Santos-Costa & Bellini, 2020, while the semideciduous forest and carrasco were similar, both characterized by Brachystomella sp. 3, Isotomidae sp. 1 and Calvatomina sp. 2 (F = 8.7939, p < 0,001, Fig. 7A). The main morphospecies observed during the first sampling were Brachystomella sp. 1, Varelasminturus sp. 1 and Entomobrya sp. 1, while in the second, were Seira sp. 2, Seira sp. 3 and Bourletiellidae sp. 1 (F = 30.6800, p < 0,001, Fig. 7B). In all the vegetation types, during the first sampling, the composition of the assembly was similar, primarily represented by Entomobrya sp. 1, Brachystomella sp. 1 and Lepidocyrtinus sp. 2. During the second sampling we observed a change in the assembly, and in the steppe savanna there was a predominance of Seira sp. 1 and C. brasiliensis, in the semideciduous forest of Szeptyckitheca andrzeji Medeiros, Bellini & Weiner, 2023, and in the carrasco of Bourletiellidae sp. 1 (F = 6.8599, p < 0,001, Fig. 7C).

Figure 7
Morphospecies composition of Collembola assemblage between: (A) vegetation types (semideciduous forest, steppe savanna, and carrasco), (B) samplings (I and II), and (C) the interaction of these factors. *Indicates statistically significant data (p ≤ 0.05). Legends for species/morphospecies are detailed in Table 4.

Indicator species/morphospecies

Seven indicator species/morphospecies of Collembola were identified in the semideciduous forest, one in the steppe savanna, and three in the carrasco (Table 3). During the first sampling 18 indicator species/morphospecies were recorded, whereas only one was recorded during the second sampling (Table 3).

Table 3
Indicator species for each vegetation type (semideciduous forest, steppe savanna, and carrasco) and samplings (I and II). A p-value of ≤ 0.05 indicates statistical significance of the data.

Species/morphospecies	Vegetation type/Sampling	IndVal%	p-Value
Trogolaphysa sp. 1	Forest	0.9000000	0.001
Lepidocyrtus sp. 1	Forest	0.7083333	0.004
Trogolaphysa sp. 2	Forest	0.7000000	0.001
Varelasminthurus sp. 1	Forest	0.4939271	0.027
Calvatomina sp. 2	Forest	0.4682759	0.037
Lepidosira neotropicalis	Forest	0.4444444	0.034
Brachystomella sp. 1	Forest	0.4000000	0.029
Capbrya brasiliensis	Savanna	0.6959302	0.001
Seira sp. 3	Carrasco	0.5663793	0.001
Sminthurinus sp. 1	Carrasco	0.4953488	0.004
Arborianna sp. 1	Carrasco	0.3733333	0.001
Neotropiella sp. 1	I	0.9745308	0.001
Sphaeridia sp. 1*	I	0.9333333	0.001
Collophora sp. 1	I	0.9316176	0.001
Prorastriopes sp. 1	I	0.8000000	0.001
Stenognathriopes sp. 1	I	0.7287785	0.001
Parasminthurides sp. 1	I	0.7287704	0.001
Denisiella sp. 1	I	0.6666667	0.001
Proisotoma sp. 1	I	0.6500000	0.005
Calvatomina sp. 2	I	0.5834483	0.007
Denisiella sp. 3	I	0.6666667	0.001
Temeritas sp. 1	I	0.5333333	0.004
Desoria sp. 1	I	0.5333333	0.001
Ceratophysella rogerarlei	I	0.5333333	0.001
Calvatomina sp. 1	I	0.4666667	0.011
Neotropiella sp. 2	I	0.4000000	0.017
Sminthurinus sp. 1	I	0.3981395	0.032
Stenognathellus sp. 1	I	0.3333333	0.049
Lepidocyrtus sp. 2	I	0.3333333	0.037
Lepidosira neotropicalis	II	0.5185185	0.009

• *All Sphaeridia specimens were grouped into a single morphospecies, Sphaeridia sp. 1.

Influence of environmental parameters on species diversity patterns

Upon relating the abundance of Collembola species to environmental variables in RDA, only the first axis was significant (56.6% of the explained variance; RDA, F = 15.3207, p = 0.0001, Fig. 8, Table 4). Therefore, only the eigenvalues of this axis were considered in the analysis. The abundance of Sphaeridia sp. 1, Pararrhopalites palaciosi Zeppelini & Brito, 2014, Denisiella sp. 2, Neotropiella sp. 1, Ceratophysella rogerarlei Palacios-Vargas, Bellini & Cipola, 2018, Desoria sp. 1, Prorastriopes sp. 1 and Denisiella sp. 1 was positively influenced by the canopy cover, increased leaf litter, and silt percentage in the soil, with a negative relationship with soil temperature. Meanwhile, the abundance of Trogolaphysa sp. 1, Lepidocyrtus sp. 1, Seira sp. 2, Seira sp. 3, C. brasiliensis and Seira sp. 1 were positively influenced by soil temperature, and negatively associated with the canopy cover, increased leaf litter, and silt percentage in the soil.

Figure 8
Results of the redundancy analysis indicating the environmental variables that had the greatest influence in the abundance of Collembola species observed in this study. *Indicates statistical significance of the axis (p ≤ 0.05). Legends for species/morphospecies are detailed in Table 4.

Table 4
Results of the Redundancy Analysis: ordination of the first two axes, with environmental variables and species/morphospecies abundance.

	Eigenvalues	Prop. Exp. (%)	F	p
RDA1	0.1999	55,64	15.320	*0.0001
RDA2	0.0571	15,90	4.379	0.0828
Environmental Variables	RDA1	RDA2	R²	p
Soil temperature	0.03794	-0.99928	0.5642	*0.0003
Leaf litter	-0.96973	0.24416	0.1905	*0.0548
pH	0.99586	-0.09085	0.1080	0.2192
Soil moisture	-0.74817	0.66351	0.1409	0.1251
Sand	-0.94899	0.31532	0.1416	0.1297
Silt	-0.88754	-0.46073	0.2237	*0.0343
Canopy cover	-0.31313	0.94971	0.5858	*0.0001
Plant richness	0.13327	0.99108	0.0894	0.2839
Species/morphospecies	RDA1	RDA2
Pararrhopalites palaciosi (Par_pal)	-0.1888807	0,0079
Denisiella sp. 1 (Den_sp1)	-0.2471705	-0,0041
Denisiella sp. 2 (Den_sp2)	-0.0749929	0,0070
Denisiella sp. 3 (Den_sp3)	-0.0260055	-0,0020
Denisiella sp. 4 (Den_sp4)	-0.0388998	0,0029
Collophora sp. 1 (Col_sp1)	-0.1264308	0,0026
Sphaeridia sp. 1 (Sph_sp1)	-0.5805404	0,0013
Varelasminturus sp. 1 (Var_sp1)	-0.0623142	-0,0011
Calvatomina sp. 2 (Cal_sp2)	-0.0381068	0,0095
Calvatomina sp. 1 (Cal_sp1)	-0.0616277	-0,0032
Sminthurinus sp. 1 (Smi_sp1)	-0.0597158	0,0038
Stenognathellus sp. 1 (Ste_sp1)	-0.0182487	0,0079
Stenognathriopes sp. 1 (Ste_sp1.1)	-0.0636027	0,0068
Temeritas sp. 1 (Tem_sp1)	-0.0378049	0,0015
Calvatomina sp. 3 (Cal_sp3)	-0.0019271	-0,0015
Proisotoma sp. 1 (Pro_sp1)	-0.0613078	0,0012
Parasminthurides sp. 1 (Par_sp1)	-0.0027417	0,0013
Arborianna sp. 1 (Arb_sp1)	-0.0160562	0,0010
Szeptyckitheca andrzeji (Sze_and)	-0.0049466	0,0087
Bourletiellidae sp. 1 (Bou_sp1)	0.0224340	0,0097
Seira sp. 1 (Sei_sp1)	0.7469369	-0,0012
Seira sp. 2 (Sei_sp2)	0.1810511	0,0081
Capbrya brasiliensis (Cap_bra)	0.0584991	-0,0015
Lepidocyrtus sp. 1 (Lep_sp1)	0.1747836	0,0025
Trogolaphysa sp. 1 (Tro_sp1)	0.0143057	0,0087
Trogolaphysa sp. 2 (Tro_sp2)	0.0035631	0,0032
Lepidocyrtus sp. 2 (Lep_sp2)	-0.0181182	-0,0015
Pseudosinella triocellata (Pse_tri)	0.0028659	0,0096
Seira sp. 3 (Sei_sp3)	0.1660109	0,0012
Cyphoderus sp. 1 (Cyp_sp1)	0.0657044	0,0056
Lepidosira neotropicalis (Lep_neo)	0.0533120	0,0058
Lepidocyrtinus sp. 1 (Lep_sp1.1)	-0.0182641	0,0039
Lepidocyrtinus sp. 2 (Lep_sp2.1)	-0.0027417	0,0013
Lepidocyrtinus sp. 3 (Lep_sp3)	0.0015241	0,0054
Entomobrya sp. 1 (Ent_sp1)	-0.0026555	0,0018
Isotomidae sp. 1 (Iso_sp1)	-0.0388469	0,0015
Isotomidae sp. 2 (Iso_sp2)	-0.0026555	0,0018
Prorastriopes sp. 1 (Pro_sp1.1)	-0.1177626	-0,0067
Desoria sp. 1 (Des_sp1)	-0.0839348	-0,0065
Neotropiella sp. 1 (Neo_sp1)	-0.4112998	0,0020
Brachystomella sp. 1 (Bra_sp1)	-0.0436358	0,0017
Neotropiella sp. 2 (Neo_sp2)	-0.0507279	-0,0037
Brachystomella sp. 2 (Bra_sp2)	-0.0015070	0,0010
Arlesia sp. 1 (Arl_sp1)	-0.0117193	0,0047
Ceratophysella rogerarlei (Cer_rog)	-0.1242505	-0,0059
Brachystomella sp. 3 (Bra_sp3)	-0.0003662	0,0010

• * Indicates statistically significant data (p ≤ 0.05)

DISCUSSION

Records of Collembola taxa

All springtail families found in this study had been previously recorded in Brazil, with nearly all genera holding nominal species also documented to the country. The exception is Stenognathellus Stach, 1956 (Katiannidae), which has no recorded species in Brazil (Zeppelini et al. 2025).

Regarding the Entomobryomorpha, Pseudosinella Schäffer, 1897 and Proisotoma Börner, 1901 constitute new genera records to the sampled area (Zeppelini et al. 2025). Contrarily, almost all genera of Symphypleona constitute new records for PNSC, with the sole exception of Temeritas Richards, 1963, recorded in Bellini (2014). Regarding the Poduromorpha, only Neotropiella constitutes a new generic record for PNSC (Bellini 2014, Palacios-Vargas et al. 2018).

From this study alone, we were able to sample 40 morphospecies and six nominal species. Currently, there are 29 nominal species of springtails recorded for the entire Piauí state, with some of these records coming from recent taxonomic descriptions and surveys (Bellini 2014, Palacios-Vargas et al. 2018, Nunes and Bellini 2018, 2019, Nunes et al. 2019, 2020, 2021, Medeiros et al. 2022, 2023, Bellini et al. 2023b, Rodrigues et al. 2024, 2025, Zeppelini et al. 2025). Comparatively, the Collembola richness recorded in other ecological studies in the Caatinga varied between 15-17 species/morphospecies (Ferreira et al. 2013, 2018, Zeppelini et al. 2013). Here, we conducted a more comprehensive sampling, which remarkably improved our knowledge of the group in the studied area as well as in the Cerrado and Caatinga biomes, supporting that intensive collections in the Brazilian semiarid region can reveal a rich and potentially unknown fauna.

Effects of environmental variables on Collembola diversity

Heterogeneous environments promote Collembola diversity by offering a greater variety of microhabitats that provide shelter, humidity, protection from predators, and increased availability of food resources. Environmental variables such as soil moisture, litter density, and plant richness play a fundamental role in this process. Soil moisture, in particular, is a key factor for the maintenance of Collembola populations, given the strong dependence of most species on high humidity levels (Coulson et al. 1996, Daghighi et al. 2017, Chang et al. 2021, Wang et al. 2022). Litter contributes to moisture retention and reduces direct solar radiation, while also enriching the soil with organic matter and promoting microbial activity, especially of fungi and bacteria that form an essential part of Collembola diets (Wardle et al. 2006, Widenfalk et al. 2015, Ferreira et al. 2018, Nascimento et al. 2019, Bomfim et al. 2021, Lima et al. 2024). Plant richness, in turn, increases habitat structural complexity and the variety of trophic resources, supporting greater abundance and richness of springtails (Wardle et al. 2006, Milcu and Manning 2011). The high levels of soil moisture, litter density, and silt content recorded in the semideciduous forest during the first sampling suggest favorable conditions for sheltering a greater species richness, partially supporting our initial hypothesis. In the second sampling, a higher abundance of individuals observed in the carrasco area was associated with a higher plant richness, as expected, as well as higher sand content, and soil pH. Despite the lack of a statistically significant relationship between the overall plant richness and Collembola diversity in our study, the carrasco data supports it may have some level of influence in the Collembola community in the PNSC, as observed in other studies to different Brazilian biomes, including the Caatinga and the Pampa (Ferreira et al. 2018, Silva et al. 2022, Jorge et al. 2023, Lima et al. 2024).

Although Collembola are highly sensitive to high temperatures and low humidity, some species have shown greater tolerance to warmer environments, such as in the steppe savanna. This may explain the increased beta diversity in this area. For instance, C. brasiliensis exhibited good adaptation to elevated soil temperatures in the PNSC. Its widely known distribution, from the semiarid Caatinga through various types of Cerrado to the northern Atlantic Forest, supports such adaptability (Nunes et al. 2020), also characterizing it as a potentially generalist species.

The higher beta diversity recorded during the second sampling, along with a reduced occurrence of species/morphospecies, particularly from the orders Symphypleona and Poduromorpha, was attributed to environmental changes between sampling periods, especially the reduction in canopy cover and litter layer thickness, both of which critical for the survival of Collembola (Gurgel-Gonçalves et al. 2006, Zeppelini et al. 2008, Nascimento et al. 2019). This variation is also very likely related to the higher rainfall recorded during the first sampling period. Although not included directly in our analyses, data from the Brazilian National Institute of Meteorology (INMET 2025b) indicated that in November 2022, the PNSC experienced an atypically high rainfall volume (370.4 mm), substantially greater than the same period in 2020 (1.6 mm, data for 2021 were unavailable). This indicates that the study was conducted during a year with markedly irregular rainfall patterns, as March, typically the rainiest month, recorded only 116.6 mm of rainfall, which was less than the amount recorded in November, usually considered the onset of the rainy season (INMET 2025b). In this sense, although not explicitly captured by the measured environmental parameters aside from soil moisture, our data indicate that seasonality likely played a significant role in the observed differences between samplings.

It is evident that the three investigated phytophysiognomies present distinct environmental conditions. However, some species were shared among them. Aside from the presence of some more generalist species, this pattern is also likely related to the geographical proximity of the areas. Previous studies have shown that such proximity facilitates the formation of ecotone zones and promotes a degree of ecological connectivity, allowing for partial overlap between communities (Kark and Van Rensburg 2006, Brownstein et al. 2015, Shea et al. 2021). This spatial connectivity can reduce community differentiation across areas and partially explain the weaker spatial patterns observed for total abundance and richness, when compared to temporal differences. This is consistent with the similarity observed between nearby areas, such as the semideciduous forest and the steppe savana (Fig. 1).

In several studies, species of Seira have been commonly found in hot, semiarid habitats such as those in the Brazilian Caatinga, and some of them are well adapted to such conditions (Palacios-Vargas and Castaño-Meneses 2003, Bellini and Zeppelini 2009, Ferreira et al. 2013, Lima et al. 2024). In the present study, Seira was the most abundant genus in both the steppe savanna and the second sampling. Its resilience under adverse conditions is associated with adaptations such as a dense covering of macrochaetae, dorsal scales, and body pigmentation, which together provide protection against dehydration and UV radiation (Christiansen and Bellinger 2000, Bellini and Godeiro 2017, Winck et al. 2017). It is also plausible that some species of Seira hold additional, yet unidentified, physiological traits that enhance survival in hot and dry tropical environments. This genus belongs to Entomobryidae, the largest family of Collembola, with approximately 2,300 described species worldwide, primarily comprising epiedaphic and atmobiotic forms which may also be found in arid environments (Bellinger et al. 1996-2024, Bellini and Godeiro 2017). Accordingly, Entomobryidae was the most species-rich family observed in this study.

The most abundant and second most species-rich family sampled in our study was Sminthurididae. Although some studies conducted in the Caatinga highlight Entomobryidae as the most diverse, and at times, also the most abundant family of Collembola (Ferreira et al. 2013, Lima et al. 2024), recent species descriptions suggest a hidden and unexplored taxonomic richness within Sminthurididae in this biome (Medeiros et al. 2022, 2023). Among Symphypleona, Sminthurididae is notable for being the only family with markedly reduced, spherical collophore sacs (Richards 1968, Bretfeld 1999). These sacs are important surfaces for salt and water exchange. However, elongation of these structures can lead to increased water loss under unfavorable conditions (Hopkin 1997), while shorter sacs may enhance survival in environments with limited humidity, such as semiarid regions.

Regarding other relevant sampled taxa, the most abundant genus recorded in our study was Neotropiella, a finding consistent with the observations of Ferreira et al. (2018) in another Caatinga locality. Many Neotropical Poduromorpha, including Neotropiella specimens, are typically highly pigmented, often entirely blue or darkened, a condition also observed to our samples. This intense pigmentation provides increased protection against solar radiation, particularly ultraviolet light (Salmon et al. 2014, Winck et al. 2017), and may represent an adaptation to more exposed edaphic habitats. It is also noteworthy that Neotropiella specimens exhibit notably larger eyes compared to other Poduromorpha springtails, a trait likely associated with more effective exploration of epiedaphic environments (Bellini et al. 2020).

It is important to note that our study was conducted over a relatively short time frame. Factors such as climatic fluctuations and seasonal changes, which were not fully addressed in our analyses, may have influenced the Collembola community in the PNSC, as previously discussed in relation to the atypical rainfall patterns observed between the samplings. Thus, although this study significantly contributes to the knowledge of the group, it provides only a preliminary view of how environmental variables affect their diversity. Long-term studies are needed to validate these findings and deepen our understanding of Collembola ecological dynamics in the PNSC.

Indicator species and contributions to the conservation of PNSC

Preserving natural ecosystems of significant ecological value is the primary objective of PNSC. Collembola are regarded as potential bioindicators of soil conditions across various types of ecosystems and vegetation (IBAMA 2003, Zeppelini et al. 2008, Oliveira-Filho et al. 2016, Santos et al. 2018, Machado et al. 2019). To integrate these factors, this study provides a list of indicator species/morphospecies for each type of vegetation as detailed in Table 3. The extensive territorial area of PNSC, combined with limited financial, human, and infrastructural resources, hinders the implementation of effective public policies for the management, maintenance, and monitoring of its fauna, particularly soil invertebrates. Furthermore, the region’s economic activities, which were previously solely related to the subsistence of the local population, now also cater to the interests of mining companies, further exacerbating the deficit in implementing public policies (ICMBio 2018).

Providing a list of indicator species/morphospecies is essential to start outlining plans to promote the sustainable use of these environments, particularly those that host rare or threatened species. Among the various purposes of indicator species, one can highlight the assessment of environmental conditions and the provision of evidence of habitat degradation, as the expected distribution of these species is often constrained by a more specific requirement for favorable environmental conditions (McGeoch 1998, Lutinski et al. 2014). An example is that, during the second sampling, the single identified indicator species was Lepidosira neotropicalis Nunes & Bellini, 2019, presenting itself as a more robust ecological indicator of potential environmental changes. This species typically exhibits a larger body compared to most Neotropical springtails and is associated with forested areas (Nunes et al. 2019), possibly displaying a preference for high plant diversity and higher silt content in the soil, establishing itself in areas with vegetation that offer such environmental conditions. In this context, given the importance of springtails as potential indicators of soil quality, and considering the limited or non-existent conservation strategies specifically designed for soil invertebrates in Brazil (ICMBio 2018), this analysis offers essential support and some guidance in identifying areas that could improve conservation efforts for Collembola fauna within the PNSC.

ACKNOWLEDGMENTS

We thank Ayrla Silva and Sabrina Ferreira for helping in the samplings, and Paolla de Souza for identifying part of the Entomobryidae.

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ADDITIONAL NOTES

• ZooBank register
  https://zoobank.org/367C45A6-C8B4-4EC9-A47B-5A2F385BBB34
• Data Availability
  Datasets generated or analyzed in this study are available from the corresponding author on reasonable request.
• Funding
  This research was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico grant numbers 309114/2021-7 (BCB) and 442421/2023-0 (AOM and FAMF); and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior grant number 001 (BMSS, MGML, GSM and CDDS).
• How to cite this article
  Sampaio BMS, Lima MGM, Nunes RC, Marques AO, Medeiros GS, Freire FAM, Silva CDD, Winck B, Bellini BC (2025) Collembola (Hexapoda) diversity in a Caatinga-Cerrado transition protected area. Zoologia 42: e25004. https://doi.org/10.1590/S1984-4689.v42.e25004
• Published by
  Sociedade Brasileira de Zoologia at Scientific Electronic Library Online - https://www.scielo.br/zool

Supplementary Material

Table S1. Descriptive data of biotic and abiotic factors of each vegetation type and season. The data are distributed by replicas in each season (sampling sites).

Table S1. Descriptive data of biotic and abiotic factors of each vegetation type and season. The data are distributed by replicas in each season (sampling sites).

Authors: Sampaio BMS, Nunes RC, Marques AO, Bellini BC

Data type: Ecologic data.

Copyright notice: This dataset is made available under the Open Database License (https://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.

Link: https://doi.org/10.1590/S1984-4689.v42.e0004

Edited by

• Editorial responsibility
  Andressa Paladini

Data availability

Datasets generated or analyzed in this study are available from the corresponding author on reasonable request.

Data citations

Zeppelini D, Queiroz GC, Bellini BC, Brito RA, Oliveira JVLC, et al. (2025) Collembola. In: Catálogo Taxonômico da Fauna do Brasil. PNUD, PNUD, http://fauna.jbrj.gov.br/fauna/faunadobrasil/379 [Accessed: 09/05/2025]

Publication Dates

• Publication in this collection
  05 Dec 2025
• Date of issue
  2025

History

• Received
  21 Jan 2025
• Accepted
  07 Aug 2025