Free Access
Issue
Ann. Limnol. - Int. J. Lim.
Volume 56, 2020
Article Number 3
Number of page(s) 9
DOI https://doi.org/10.1051/limn/2020002
Published online 24 March 2020

© EDP Sciences, 2020

1 Introduction

Demographic expansion and the demand for food cause an increase of agricultural activities all over the world (Hooke et al., 2012). As a consequence, large areas with native vegetation have been deforested (Le Polain de Waroux et al., 2016). In cultivated areas, there are important aquatic ecosystems with innumerable ecosystem services (e.g. habitat provision, water, thermal regulation), which are essential for the maintenance of aquatic and terrestrial biodiversity (Vidal-Abarca and Suarez, 2013).

Many studies report the negative effects of agriculture on small streams. One of the most important effects consists in changes in the water chemical composition, related to nitrogen and phosphorus input, from agricultural soil fertilization (Rhodes et al., 2001; Bu et al., 2014). Nutrients increase primary productivity in rivers and the consumption of oxygen by decomposer microorganisms (Munn et al., 2010). Agricultural impact can decrease the water quality (Bu et al., 2014), leading to the extinction of sensitive aquatic organisms (Selvakumar et al., 2014). Besides, the deforestation of riparian vegetation by agricultural practices increases the temperature of the water and facilitates the input of agricultural chemicals into the streams, affecting the processing of pollutants and the habitat structure, increasing the sedimentation of the margins, and, consequently, causing the elimination of trophic groups of aquatic invertebrates (Hamada et al., 2014). Riparian zones degradation usually leads to a decrease in the diversity and dominance of some benthic invertebrate groups less tolerant to anthropogenic changes (Arnaiz et al., 2011; Selvakumar et al., 2014; Valente-Neto et al., 2015). Notably, the diversity and abundance of sensitive aquatic insects, especially the orders Ephemeroptera, Plecoptera and Trichoptera (EPT), are altered by riparian deforestation in streams (Kasangaki et al., 2008; Ferreira et al., 2017). Insects of the EPT orders are widely used in biomonitoring programs as part of biological indices for water quality assessment (Nessimian et al., 2008; Biasi et al., 2008).

Beta diversity is a component of gamma diversity (Whittaker, 1960) and can be defined as the variation in species composition in a community between different locations or time periods (Anderson et al., 2011). Two mechanisms can manifest biological beta diversity: (i) turnover, which is the substitution of species between communities, or (ii) nestedness, which occurs when the observed community is part of a larger community (Baselga, 2010). Both patterns can be analysed along a spatial, environmental, or temporal gradient (Korhonen et al., 2010; Gianuca et al., 2017). The nestedness pattern can occur due to many factors, among which the environmental conditions stand out (Pinha et al., 2017), this act as filters that cause extinction and selective colonization of species (Wright et al., 1997).

In recent years, an increase in ecological research on beta diversity occurred, especially because of its usefulness in the establishment of conservation areas (Scolar et al., 2016; Bergamin et al., 2017). Approaches to studies with beta diversity are effective in assessing the effect of habitat fragmentation (Hu et al., 2011), among other negative effects caused by anthropisation. In this study, we evaluate if agricultural activities can generate nestedness patterns of EPT assemblages in subtropical streams. The EPT assemblages are sensitive to human disturbances (Theodoropoulos and Iliopoulou-Georgudaki, 2010), and the dissimilarity of the assemblages increases with the degradation of riparian zones. The EPT insects are excellent beta diversity study models because they are distributed in numerous aquatic environments, especially streams, and are very sensitive to disturbances, directly reflecting changes in the structure and composition of the stream insect communities (Ferreira et al., 2017). Still, we believe that the streams assemblages most affected by agriculture will be nested assemblages of the less degraded streams. In addition, we evaluated whether local (i.e. physicochemical variables) and/or regional factors (i.e. land cover) influence beta diversity.

2 Material & methods

2.1 Study area

We collected water samples, aquatic insects, and landscape information in 10 small-order streams (≤3rd order) located in Southern Brazil (Fig. 1). The climate is classified as humid subtropical, with an average precipitation of 1781 mm and one mean annual temperature of 18.7 °C (Biasi et al., 2008). The streams are located in the Atlantic Forest biome, and the vegetation is characterized as a mix between semi-deciduous seasonal forest and mixed ombrophilous forest (Oliveira-Filho et al., 2015). The region is predominantly agricultural being approximately 70% of the landscape has agricultural use. The most developed crops are soy and corn. The few forest remnants are located in areas with greater slope and on the banks of water bodies (Rovani et al., 2019).

thumbnail Fig. 1

Location of the streams studied in southern Brazil.

2.2 Collection of aquatic insects

We collected the insects with a Surber sampler (area: 0.09 m2; mesh: 0.25 mm) in riffle stream with stony substratum (three subsamples) and leaf litter substratum (three subsamples) during November 2016. We chose these two substrate types, as they are predominant in small streams in the study region (Biasi et al., 2008). We obtained a total of 60 sample units (10 streams × 2 types of microhabitat × 3 samples in each). We fixed the samples in the field with 96% ethanol and performed the screening of the organisms under a stereomicroscope. We identified the larvae of EPT up to the genus taxonomic level according to Olifiers et al. (2004), Pes et al. (2005), Mugnai et al. (2010), and Segura et al. (2011). After identification, the organisms were stored in glass pots containing 70% ethanol and deposited in the Aquatic Invertebrate Collection of the Regional Museum of Alto Uruguay (MuRAU) of Universidade Regional Integrada do Alto Uruguai e das Missões, Brazil.

2.3 Limnological variables

In the streams, we measured in situ the variables of water temperature, pH, electrical conductivity, dissolved oxygen, and turbidity using a HORIBA® multiparameter analyser. Also, we collected water samples to determine the concentrations of total dissolved nitrogen (TDN) and dissolved organic carbon (DOC) using a TOC Analyser (SHIMADZU®) and orthophosphates by spectrophotometry. For all measured limnological variables, we performed 3 measurements in each sampled stretch to characterize the stream in multivariate analyzes. All methodologies followed the recommendations described in Standard Methods (APHA, 2012).

2.4 Land cover classification

We quantified the land cover classes in tree vegetation, agriculture, and pasture in the drainage area of each of the 10 streams studied. The classification of land uses followed the method of the supervised classification by maximum likelihood (maxlike). For demarcation of the land uses of riparian zones, we used the Brazilian Environmental Legislation (Brazilian Law #12,651/2012) and the Environmental Conflicts Charter. The calculation of the drainage areas and the classification of land uses were performed through the software MapInfo 8.5 and Idrisi 32. The cartographic base was used in a scale of 1:35,000, composed of articulated leaves and an ETM + sensor image of Landsat 7 satellite from the year 2016, with spatial resolution of 15 m; spectral bands 3, 4, 5; and a panchromatic view.

2.5 Data analysis

Initially, we standardized the environmental variables using “decostand” function (range: 0 to 1) and then a principal component analyzes (PCA) were performed to order the streams according to their limnological characteristics and land cover variables. Due to the high variability in the abundance of larvae in each subsample, we cumulated the quantities of larvae collected in both substrates in each stream. We calculated the rarified richness for each stream based on the lowest abundance found. We evaluated the environmental effects on the abundance and richness of EPT using the first two principal components scores as predictor variables, from the application of linear regressions. We define the first two principal components from a Broken-Stick analysis. The Broken-Stick analysis is recommended as a stopping rule in principal component analysis (Jackson, 1993), provided that the observed eigenvalues are greater than the corresponding random components. Also, we performed a Canonical Correspondence Analysis (CCA) to observe the relationships between environmental variables and the abundance of EPT genus.

To evaluate the existence of the nested pattern, the “nestedtemp” function was used, which calculates the nesting by means of a temperature matrix with presence and absence data set (Rodríguez‐Gironés and Santamaría, 2006). The total of unexpected absences and presences is calculated as the temperature statistic (T), with values ranging from T = 0 (perfectly nested pattern) to T = 100 (non-nesting) (Rodríguez‐Gironés and Santamaría, 2006). To evaluate if the value of nested differs from the expected by chance, we use null models (“oecosimu” function). We used SIM1 that produces a simulated model matrix in which all the arrangements are equally distributed. All the data analyses were performed using ‘vegan’ package (Oksanen et al., 2017) R statistical software (R Core Team, 2018).

3 Results

3.1 Environmental variables

In the streams drainage area, we observed a percentage of vegetation that varies from ∼6% to ∼47% (Tab. 1) and a predominance of agricultural land use (from ∼24% to ∼88%). In streams with a higher percentage of agricultural occupation (S9 and S10), we observed higher values of electrical conductivity and nitrite concentrations (Tab. 1). The stream S6 showed greater influence of organic contamination, distinguishing itself from the other streams. Streams S4 and S5 did not show marked variations compared to the other streams. On the other hand, in streams with a higher percentage of natural vegetation (S1 and S2), we observed neutral pH values (pH ∼7) and high oxygenation (>8 mg L−1) (Tab. 1).

In the PCA made with the environmental variables, the first two principal components (PC1 and PC2) explained 73% of the total variation of the streams environmental data (Fig. 2). The PC1 explained 42% of the data variability and was positively associated with vegetation (r = 0.74, p = 0.01) and pasture (r = 0.84, p < 0.01) land cover, dissolved oxygen (r = 0.78, p < 0.01) and pH (r = 0.81, p < 0.01). The second principal component (PC2) explained 32% and was associated positively with agriculture land cover (r = 0.74, p = 0.1) and negatively with TDN (r = –0.68, p = 0.03), nitrite (r = –0.83, p < 0.01), orthophosphates (r = –0.85, p < 0.01) and electrical conductivity (r = –0.78, p < 0.01). The streams ordination showed a clear pattern defined by land uses in drainage area. The S1 to S3 were associated with the high percentages of riparian vegetation and water oxygenation. On the other hand, S7 to S10 were ordered by the high percentages of agriculture practice in the drainage area. The S6 was isolated from the others, being ordered by high concentrations of nutrients from residues generated by animal breeding (Fig. 3).

Table 1

Limnological and land cover variables of studied streams in southern Brazil. Veg: vegetation; Agri: agriculture; Past: pasture; Temp: temperature; DO: dissolved oxygen; EC: electrical condutivy; DTN: dissolved total nitrogen; DOC: dissolved organic carbon; Turb: turbidity; PO4: orthophosphates; NO2: nitrite.

thumbnail Fig. 2

Principal Components Analysis and Broken-Stick analysis of the physicochemical and land cover variables obtained in the streams studied in southern Brazil. Veg: vegetation; Agri: agriculture; Past: pasture; Temp: temperature; DO: dissolved oxygen; EC: electrical conductivity; DTN: dissolved total nitrogen; DOC: dissolved organic carbon; Turb: turbidity; PO4: orthophosphates; NO2: nitrite.

thumbnail Fig. 3

Linear regressions between the first principal component (PC1) and second principal component (PC2) generated by the environmental variables and land cover of the studied streams with the abundance and rarefied richness of Ephemeroptera, Plecoptera and Trichoptera assemblages.

3.2 Aquatic insects

We collected a total of 2632 individuals distributed in 30 genera, being 15 genera belonging to the order Ephemeroptera, 13 genera to the order Trichoptera, and two genera to the order Plecoptera (Tab. 2). Trichoptera was the most abundant order (57% of the total), followed by Ephemeroptera (41%) and Plecoptera (2%). The most abundant genera were Smicridea (Trichoptera, 30.2%), Caenis (Ephemeroptera, 24.5%) and Itaura (Trichoptera, 20%). On the other hand, the least abundant genera were Marilia (Trichoptera), Nectopsyche (Trichoptera), Askola (Ephemeroptera), and Callibaetis (Ephemeroptera), with only one specimen collected.

The abundance was influenced by the PC2 scores (F 1,8 = 7.1, p = 0.02; Fig. 3), which synthesize the variation generated by the higher percentage of agriculture in the stream adjacent areas. On the other hand, the rarefied richness was influenced by the PC1 scores (F 1,8 = 10.1, p = 0.01; Fig. 3).

We observed a distribution of organisms based on the environmental quality of the streams. The first two axes of CCA explained 57% of the data variation (Fig. 4). The Smicridea, Tricorythopsis, Worwaldia and Callibaetis were associated with agricultural practices and higher concentrations of nitrite, orthophosphates and electrical conductivity. On the other hand, Massartela, Tupiara, Phylloicus and Thraulodes were associated with streams with higher amount of riparian vegetation, greater oxygenation and pH near neutrality. Finally, Perissoplebiodes, Askola, Nectopsyche and Cyrnellus presented smaller quantities and were exclusive of stream S1 (larger amount of riparian vegetation).

Table 2

Abundance of Ephemeroptera, Plecoptera and Trichoptera genera collected in stone and leaf substrates in streams of southern Brazil. The values correspond to the sum of the abundances of the 6 subsamples performed.

thumbnail Fig. 4

Canonical Correspondence Analysis of the physicochemical and land cover variables and Ephemeroptera, Plecoptera and Trichoptera abundance in the streams studied in southern Brazil. Veg: vegetation; Agri: agriculture; Past: pasture; DO: dissolved oxygen; EC: electrical condutivy; DTN: dissolved total nitrogen; PO4: orthophosphates; NO2: nitrite.

3.3 Nestedness of EPT assemblages

We observed that the nestedness of EPT assemblages was observed on more impacted streams (T = 16.7, p = 0.01; Fig. 5). The main environmental factors of nestedness were agricultural uses, riparian vegetation and dissolved oxygen. EPT assemblages of streams with higher percentage of agriculture and/or lower oxygen concentration (S4, S5, S6, S7, S9 and S10) were nested on assemblage of streams with higher percentage of vegetation and/or higher concentrations of dissolved oxygen (S1, S8, S3 and S2). In addition to these environmental variables, higher nutrient concentrations (i.e. nitrite and orthophosphate) led to lower taxonomic richness in S6. In the stream site (S6) we observed low taxa richness (5 genera) and abundance (40 specimens), highlighting the low tolerance of EPT insects in impacted environments.

thumbnail Fig. 5

Incidences matrix from nestedness analysis on Ephemeroptera, Plecoptera and Trichoptera assemlages in studied streams (S1-S10) in southern Brazil. Red blocks (taxa presence), white blocks (taxa absence). The curved line shows isoclines of prefect nestedness.

4 Discussion

In our study the hypothesis was corroborated being the EPT assemblages significantly nested, this pattern was caused by the replacement of native vegetation by agriculture crops. In addition, the input of anthropogenic organic matter (e.g. agricultural waste, nutrients) in some streams has potentiated this negative effect on aquatic insect communities. This result coincides with others where aquatic insect nested patterns were evaluated in headwater streams and environmental gradients (Heino et al., 2010; Bertaso et al., 2015). The nested pattern can be originated by habitat nesting, where unusual habitats are nested; consequently, the species adapted to those habitats will also be nested (Cook, 1995). In addition, habitat fragmentation is also the cause of nestedness, where there is a generation of patches with new environmental characteristics, local extinction of specialized species and the persistence of more generalized species, which will represent the highest part of the species assemblage (Rocha et al., 2008; Gimenez et al., 2015).

Aquatic invertebrates strongly respond to the water quality and physical habitats, especially EPT insects (Merovich and Petty, 2010). Some studies show that anthropogenic activities such as agriculture can modify stream water chemistry, and consequently the EPT assemblage structure (Egler et al., 2012; Copatti et al., 2013; Selvakumar et al., 2014). The EPT are organisms sensitive to anthropogenic disorders, such as high concentrations of nutrients in water, excessive sedimentation and low oxygenation (Copatti et al., 2013; Ongaratto et al., 2018). On larger scales, the replacement of vegetation by agriculture can generate environmental changes that contribute to the reduction of EPT abundance and richness (Ferreira et al., 2017). The positive effect observed between PC1 and EPT richness is explained by the favorable ecological functions of riparian zones to aquatic systems. The sensitivity of these organisms can cause their disappearance from streams with marked changes in water quality. Water oxygenation is a key point for these insects in their immature phases. In addition, they have low tolerance to changes in nutrient concentrations. The regulation of temperature, well oxygenated water, low nutrients and the supply of allochthonous organic matter generate favorable conditions for the establishment of these organisms that are sensitive to environmental disturbances (Kasangaki et al., 2008; Bertaso et al., 2015; Bruno et al., 2014). In contrast, streams with adjacent areas impacted by agricultural land use present increases in the input of chemical fertilizers like total nitrogen and losses in the input of allochthonous organic matter (i.e. organic matter supply by riparian vegetation, especially leaves), also generate habitat simplification (Riseng et al., 2011; Bu et al., 2014). Likewise, riparian deforestation negatively affects the abundance of intolerant aquatic insects (Kasangaki et al., 2008; Nessimian et al., 2008). According to Bertaso et al. (2015) the conversion of native vegetation by agriculture causes greater sediment input into streams, negatively affecting the occurrence of sensitive organisms (e.g. EPT). The decrease in taxonomic richness is the main feature of community nestedness mechanisms (Baselga, 2010). The studied streams are inserted in a predominantly agricultural landscape (Rovani et al., 2019). Thus, the anthropogenic pressure that these small streams suffer is very strong, causing the species decrease (Ferreira et al., 2017).

Still, even the results are not so clear the association of land use in the streams surrounding area causes changes in the water physicochemical conditions and habitat physical characteristics. The intensity of agricultural activity associated with water alterations and the natural vegetation loss led to a probable nestedness of habitat. The streams assemblage in places with poor limnological conditions represents part of the assemblages that are in places with greater limnological conditions (Hylander et al., 2005). In our case, for example, Perissoplebiodes, Askola, Nectopsyche and Cynellus appeared only in one stream. The same was observed for Phylloicus and Anacroneuria that were sampled in streams with better limnological conditions and greater species diversity (Ferreira et al., 2017). Oppositely, they were almost absent in streams with poor limnological conditions. These genera were affected by habitat modifications expressed in riparian forest loss, low water oxygenation, high total nitrogen concentration and low allochthonous organic matter.input. We observed that Smicridea, Tricorythopsis, Worwaldia and Callibaetis showed greater sensitivity to adverse conditions. This sensitivity may be associated to the feeding habits of these organisms. The way these insects obtain food can facilitate the establishment of them in streams with worse environmental conditions. Collectors and scrapers organisms are more common in places without riparian vegetation and high amount of fine organic matter (Ferreira et al., 2017).

Small streams are characterized by their high complexity and dendritic hierarchical structure. This increases the complexity of these environments, resulting in greater species diversity. Thus, stream size can be an important factor in structuring aquatic communities. Heino et al. (2009) observed a relationship between stream size and nested patterns of aquatic insects. According to the authors, this relationship occurs due to the effect of disturbances intensity in these environments and the speed of organism reestablishment. In addition, Milesi and Melo (2014) cited that the position of small streams in the watershed contribute to the establishment of nested patterns, since confluence positioned in lower portions of the watershed support larger numbers of species. These arguments support the hypothesis that the structure of aquatic insect communities depends not only on water quality, but also on the watershed physical characteristics (Merovich and Petty, 2010).

In summary, the perfect nestedness occurs when a poor species assemblage was a subset of a richer species assemblage (Baselga, 2010). In addition, the streams with lowest environmental integrity had EPT assemblages nested in streams of the highest environmental integrity. Thus, we conclude that agricultural activities cause changes in the streams environmental integrity. These environmental changes led to the nestedness of EPT assemblages in the studied streams. Thus, agricultural activities are an important factor for the management of water resources. In conservation actions on aquatic environments, managers should carefully observe the intensity of agricultural activities in the small streams drainage area. We further conclude that the water physicochemical variables are altered by variations in land use. This causes environmental changes affecting organisms that are sensitive to pollution and fragmentation, such as EPT. These anthropogenic changes influence the structure of the EPT assemblages (e.g. reducing abundance and richness), as well as altering its trophic (Ferreira et al., 2017) and taxonomic composition (Ongaratto et al., 2018). Future studies should cover a larger spatial scale to test the decay of similarity of aquatic insects assemblages within a fragmented landscape. Studies about the theory of intermediate disturbance in beta diversity and the effect of forest patches size and the distance between them could clarify the patterns of extinction and colonization of insects of the EPT orders located in fragmented environments related to agriculture practices.

Acknowledgements

We thank the support of Maiane Bury, Mayara Breda, Mariana Menegat, Patricia Lazari and Wanessa Deliberalli to support in the field and laboratoty works. We would like to thank Mauricio Hepp for assistance in producing Figure 1 (map). We thank to Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for scholarship for first author. LUH received financial support of the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, Proc. #421632/2016-0) and grants (#305203/2017-7).

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Cite this article as: Huiñocana JCS, Albertoni EF, Picolotto RC, Milesi SV, Hepp LU. 2020. Nestedness of insect assemblages in agriculture-impacted Atlantic forest streams. Ann. Limnol. - Int. J. Lim. 56: 3

All Tables

Table 1

Limnological and land cover variables of studied streams in southern Brazil. Veg: vegetation; Agri: agriculture; Past: pasture; Temp: temperature; DO: dissolved oxygen; EC: electrical condutivy; DTN: dissolved total nitrogen; DOC: dissolved organic carbon; Turb: turbidity; PO4: orthophosphates; NO2: nitrite.

Table 2

Abundance of Ephemeroptera, Plecoptera and Trichoptera genera collected in stone and leaf substrates in streams of southern Brazil. The values correspond to the sum of the abundances of the 6 subsamples performed.

All Figures

thumbnail Fig. 1

Location of the streams studied in southern Brazil.

In the text
thumbnail Fig. 2

Principal Components Analysis and Broken-Stick analysis of the physicochemical and land cover variables obtained in the streams studied in southern Brazil. Veg: vegetation; Agri: agriculture; Past: pasture; Temp: temperature; DO: dissolved oxygen; EC: electrical conductivity; DTN: dissolved total nitrogen; DOC: dissolved organic carbon; Turb: turbidity; PO4: orthophosphates; NO2: nitrite.

In the text
thumbnail Fig. 3

Linear regressions between the first principal component (PC1) and second principal component (PC2) generated by the environmental variables and land cover of the studied streams with the abundance and rarefied richness of Ephemeroptera, Plecoptera and Trichoptera assemblages.

In the text
thumbnail Fig. 4

Canonical Correspondence Analysis of the physicochemical and land cover variables and Ephemeroptera, Plecoptera and Trichoptera abundance in the streams studied in southern Brazil. Veg: vegetation; Agri: agriculture; Past: pasture; DO: dissolved oxygen; EC: electrical condutivy; DTN: dissolved total nitrogen; PO4: orthophosphates; NO2: nitrite.

In the text
thumbnail Fig. 5

Incidences matrix from nestedness analysis on Ephemeroptera, Plecoptera and Trichoptera assemlages in studied streams (S1-S10) in southern Brazil. Red blocks (taxa presence), white blocks (taxa absence). The curved line shows isoclines of prefect nestedness.

In the text

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