Open Access
Knowl. Manag. Aquat. Ecosyst.
Number 417, 2016
Article Number 21
Number of page(s) 9
Published online 03 May 2016

© S. Guareschi et al., published by EDP Sciences, 2016

Licence Creative Commons
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1 Introduction

Several studies have stressed the importance of light variation and the influence of day/night conditions on plants and animals by examining both physiological and ecological points of view (e.g., Gerrish et al., 2009; Gaston et al., 2013). For instance for terrestrial insects, their distribution or features can markedly differ from day to night as a survival strategy to avoid insectivorous birds (e.g., Guevara and Avilés, 2013). Unlike limnological research on lacustrine zooplankton distributions, which has long since recognized that species use different habitats in the daytime than they do at nighttime (e.g., Hutchinson, 1967; Dodson, 1990), very little is known about riverine communities. This is especially true for no-target taxa for conservation policies like invertebrates (e.g. Cardoso et al., 2011).

Most of the information about freshwater macroinvertebrate ecology has been collected during daytime sampling activities. Biomonitoring activities on aquatic ecosystems, which are probably the commonest way to obtain constant aquatic macroinvertebrate samples and data, originate from fieldwork done in the daytime. Any nocturnal activity is usually avoided for safety reasons, and also given the logistical constraints on researchers and technicians. Existing studies that are directly related to light and darkness dynamics in streams have generally investigated the relationship between macroinvertebrate drift and the predatory activity of fish (e.g., Roussel et al., 1999; Davey et al., 2011; Conallin et al., 2012). In line with this, numerous macroinvertebrate taxa usually demonstrate a peak in drift activity related to darkness or nighttime conditions (e.g., Hansen and Closs, 2007).

Night is an important part of the diel cycle and several macroinvertebrates have demonstrated some form of nocturnal activity in different aquatic ecosystems (e.g. feeding or avoiding predation, Elliott 2000, 2002; Hampton and Duggan, 2003; Florencio et al., 2011). In a North American stream, for example, it has been observed that several caddisfly larvae (Sericostomatidae) burrow during the day and resurface at night (Bergey and Resh, 1994).

Previous experimental approaches on day/night changes in aquatic macroinvertebrate assemblages have focused mainly on specific species, such as freshwater crustaceans (Johnson and Covich, 2000; Elliot 2005a), Ephemeroptera-Plecoptera-Trichoptera taxa (Elliot, 2000, 2002) or gastropods (Lombardo et al., 2010). Likewise, marine and transitional (e.g., Guest et al., 2003) or lentic and temporary ecosystems (Marklund et al., 2001; Florencio et al., 2011) have been previously investigated, but less is known about riverine ecosystems and the entire stream macroinvertebrate community (see Copp et al., 2005, for an example in England).

This study evaluated and compared structural and functional metrics along the same river stretch, and different environmental variables were measured to: (i) provide a complete and quantitative assessment of day-to-night changes in the riverine macroinvertebrate community; (ii) assess the relevance of different environmental variables on macroinvertebrate compositions in different diel phases.

By addressing the first objective, we can gain an understanding of whether the biological and ecological information obtained during a specific period may be representative of the general condition. By dealing with the second objective, we can investigate if different environmental variables are specifically associated with macroinvertebrate community compositions in the daytime and/or at nighttime.

After considering the nocturnal activities of some aquatic taxa and that some physico-chemical stream variables (e.g. water dissolved oxygen, pH) at night have been previously suggested as being capable of triggering nocturnal movements in lotic ecosystems (Wiley and Kohler, 1980; Brittain and Eikeland, 1988) we hypothesized that some discordances should be detected. We also expected to find main differences in the composition metrics values, such as aggregation. Similar results would demonstrate the consistency of the sampling data in any diel cycle stage.

Changes in macroinvertebrate distribution and diel variation are crucial for gaining a clearer understanding of population dynamics in aquatic ecosystems. Information on the diel activity patterns of macroinvertebrate larvae can enable us to understand the ecological role of particular taxa or groups, and may also prove useful when designing fieldwork studies and strategies. Throughout the article, we used the terms day and daytime to refer to daylight hours, and night and nighttime to refer to periods of darkness, which are in accordance with Helfman (1986).

2 Methods

2.1 Study area

This study was performed in the Lemme stream, a typical small-sized (length 35 km) perennial Apennine stream tributary of the Orba stream (Po Basin, Piemonte Region, NW Italy). This kind of lotic ecosystem is widespread in continental and peninsular Italy as the Apennine Mountains extensively cover the country (Gumiero et al., 2009). Dense woodlands with small scattered urban areas cover the entire catchment, where riparian deciduous vegetation is abundant. The sampling site (44° 3649.71′′N/8° 5038.55′′E, 342 m a.s.l.) is a representative 60-meter long stretch, with a mixture of riffles and runs, no deep pools, but with a moderate and constant slope. Riverbed width is approximately 4.0–5.0 m, and the substrate had the following particle composition: 10% sand, 30% gravel, 50% pebbles and 10% boulders (see Bo et al., 2007 for further details).

As artificial lights, or extended and simulated periods of light and dark conditions, can apparently influence stream communities and associated terrestrial ecosystems (e.g., Fuller et al., 1986; Perkin et al., 2011; Meyer and Sullivan, 2013), a pristine stretch, located far from homes and villages, was considered to avoid any effects of anthropogenic factors on field data.

thumbnail Fig. 1

Example of sampling strategy (Surber distribution) in the studied Lemme stream reach (D1–D35: daytime Surbers, N1–N35; nighttime Surbers). The gray arrow indicates flow direction.

2.2 Biological and environmental data

Macroinvertebrates were collected with a 33 × 33 cm Surber sampler (255-μm mesh) from December 13 to 20 in 2011. On a daily basis, five Surber samples were taken near midday and five near midnight to avoid dates with a full moon in order to maximize variation at natural light levels between night and day. Similar schedules have been considered in other studies (e.g., Elliot, 2002). Fieldwork was conducted at the end of fall, a period during which invertebrate abundance and diversity are generally higher in these ecosystems following natural leaf fall (Bo and Fenoglio, 2011). The Surber sampler is a quantitative approach (data refer to the sampled surface area), and is the most widely used technique in macroinvertebrate research and stream biomonitoring in Italy. Sampling units were selected to obtain a complete representation of all the microhabitat types along the stretch. However, as benthic taxa are not expected to recolonize disturbed sediments as quickly as other aquatic taxa do (e.g. fish), nighttime sampling was done approximately 0.5 m upstream from the corresponding daytime sampling point (following Copp et al., 2005). By the time field activities had ended, 70 Surber units had been obtained along the studied stretch: 35 in the daytime (D) and 35 at nighttime (N) (Figure 1).

After collecting macroinvertebrates, they were preserved in 96% ethanol and identified in the laboratory at the family level using the taxonomic keys proposed by Tachet et al. (2010). The use of higher taxonomic levels (e.g. family) has been widely utilized in previous studies on invertebrates because of the high correlation between family and species richness (Bournaud et al., 1996; Baldi, 2003; Sánchez-Fernandez et al., 2006). Dolédec et al. (2000) evidenced minor differences in some functional traits, including dispersal, between macroinvertebrate taxonomic levels, and concluded that genus, and even the families level, can be suitable for describing many processes in macroinvertebrate communities. This can make measuring taxonomic richness and composition easier, which enables the study of the entire macroinvertebrate community. Moreover, macroinvertebrate family richness is generally considered one of the main biological metrics in aquatic ecosystem bioassessments (e.g. Birk et al., 2012).

The richness (No. of taxa), abundance (No. of individuals), composition, level of aggregation (Morisita Index), invertebrate biomass, biomonitoring results and indicator taxa were evaluated and compared. In order to complement our analysis and to consider functional information, each collected taxon was assigned to a functional feeding group (FFG: scrapers, shredders, gatherers, filterers and predators), as set out by Merritt and Cummins (1996). Each richness group value (No. individuals/group) was compared between D and N. Moreover, to obtain biomonitoring information, a test was run with the macroinvertebrate-based index STAR_ICMi. This is the official multimetric index used in Italy for assessing water course quality status according to European legislation (for further details, see Buffagni et al., 2006 and Buffagni and Erba, 2007). The index values were obtained and investigated for both the D and N periods.

To characterize the study areas and to assess the relevance of different factors, specific site variables (water velocity, depth, substrate size and coarse particulate organic matter (CPOM) amount) were measured at each sampling point (corresponding to each Surber) and were used in the multivariate analysis. Current velocity was measured with an Eijkelkamp 13.14 portable instrument, while the mean substrate diameter was calculated by weighing the mean diameter of all the mineral microhabitat sizes by the previously determined coverage percentage. The proportions of grain size classes on the surface area were visually estimated according to class as sand, gravel, microlithal, mesolithal, macrolithal and megalithal, as considered in the STAR_ICMi protocol adopted in Italy (Buffagni and Erba, 2007).

To quantify CPOM, leaves and other vegetal detritus (diameter > 1 mm) were collected from each Surber sample. In the laboratory, this material was air-dried for 24 h, oven-dried (105 °C) for 24 h and weighed on an electronic balance (accuracy 0.01 g). The collected macroinvertebrate individuals were also oven-dried at 105 °C for 4 h, after which dry weight was recorded to obtain the macroinvertebrate biomass for each sample (accuracy 0.01 g).

2.3 Statistical analysis

To ensure that the results were consistent, the completeness of the entire taxa inventory generated was assessed using a nonparametric estimator (chao2, the “specpool” function of the R package “vegan”), as suggested by Walther and Moore (2005), which has already been used in macroinvertebrate community analyses (e.g., Martínez-Sanz et al., 2010).

To examine the diel differences in different community descriptors (abundance, family richness, Shannon Index, biomonitoring results and functional role richness), t-tests on the transformed data (log or square root) were run. Homoscedasticity and normal data distribution were not constituted (not even with numerous transformations) for variables “No. of scrapers” and “biomass”. In these cases, analyses were performed by nonparametric Wilcoxon tests.

To investigate if the daytime and nighttime communities presented different aggregation levels, the Morisita Index of Intraspecific Aggregation (Morisita, 1959; Krebs, 1999) was resorted to. In order to assess the differences in the aggregation values between D and N, we examined the differences obtained with what would be expected by chance alone. To do this, we compared the difference in the aggregation values obtained in our samples (D value vs. N value) with the difference obtained from 9999 random draws of an equal number of sampling sites taken from the entire pool of sites to obtain a p-value (n = 70, including both daytime and nighttime).

A nonmetric multidimensional scaling (NMDS) analysis was performed to visualize possible dissimilarities or patterns between the macroinvertebrate communities from the different sampling periods (D or N) using the entire dataset (No. sites = 70). Bray-Curtis distance was used as a dissimilarity measure, and stress was employed to test goodness of fit. Linear fittings, using the “envfit” function (vegan package, Oksanen et al., 2013), were performed between the environmental variables and the output of each NMDS ordination (D and N, previously performed) to identify the environmental factors that drove the composition of macroinvertebrate communities at nighttime and in the daytime. The significance of the fitted vectors was assessed by a permutation procedure (9999 permutations). A Permanova analysis (Anderson, 2001) was carried out using Bray-Curtis distance to test whether there was a significant difference between the a priori proposed division (D and N) in terms of macroinvertebrate communities.

The daytime STAR_ICMi value was obtained by considering the median value after 1000 randomizations of 10 Surber data (among 35 daytime Surbers). At the same time, the 2.5 and 97.5 percentiles were calculated to obtain the confidence interval (p.c.i.). The same procedure was followed using the nighttime Surber data to obtain a nighttime median STAR_ICMi value and its p.c.i..

Finally, an indicator value analysis (Dufrêne and Legendre, 1997) was carried out using the indicspecies package (De Cáceres and Legendre, 2009) to select the indicator family for each sampling period (D and N). This analysis evaluates the affinity of each taxon for each a priori-defined group and provides an indicator value (herein called IV). All the analyses were performed with the R 3.0.1 software of the R statistical environment (R Development Core Team, 2013).

3 Results

3.1 Environmental characteristics and macroinvertebrates community

The current velocity, depth, CPOM and mean diameter of the substrate of the studied stretch were 0.13 ± 0.2m·s-1, 18.23 ± 9.6cm, 2.32 ± 2.9g and 9.89 ± 6.9cm, respectively (mean ± SD). No differences were observed between the D and N measurements (p> 0.05, nonparametric Wilcoxon test).

We collected and identified 21 459 organisms of 50 macroinvertebrate higher taxa (49 families, plus Hydrachnidia, No. sites = 70). The most abundant taxon was Chironomidae (Diptera), followed by Taeniopterygidae (Plecoptera), Simuliidae (Diptera), Hydropsychidae (Trichoptera) and Baetidae (Ephemeroptera). Each one had more than 500 individuals in all (Table 1). The nonparametric estimator of species richness (chao2) suggested that at least 90.9% (50 of 55) of the total number of expected taxa were recorded in our pool of sampling units. The results showed that the data sets compiled for the Lemme stream stretch (as a whole) could be considered realistically complete if compared to the values proposed by other authors (e.g., Jiménez-Valverde and Hortal, 2003; Sánchez-Fernández et al., 2008).

Table 1

Taxa list and number of individuals obtained in the Lemme stream (Order and Family, except Hydrachnidia). D = daytime; N = nighttime (35 Surber in both cases).

3.2 Comparing day and night communities

No differences were found between the daytime and nighttime assemblages for any studied community descriptor (t-test results, Table 2), or for the invertebrate biomass or the number of scrapers present (Wilcoxon test p = 0.112 and p = 0.833, respectively). Forty-two taxa were obtained in the daytime and 46 at nighttime. The mean number of taxa in each Surber was strikingly similar under both conditions (mean ± SD = 11 ± 3 in the daytime; 11 ± 4 at nighttime). In both cases Chironomidae (Diptera) and Taeniopterygidae (Plecoptera) were the most abundant taxa. The biomass mean values for each Surber were 0.177g ± 0.08 and 0.142g ± 0.10 for D and N, respectively. According to the intraspecific aggregation values obtained by the Morisita index, the daytime community gave a higher value (1.604) compared with the nighttime community (1.502). However, the difference (0.102) did not significantly differ from those obtained by chance after randomization (permutations test, p> 0.05).

The NMDS ordinations of the macroinvertebrates communities (Figure 2, first two axes displayed) showed no clear clustered patterns, but the daytime and nighttime sites completely overlapped. The final stress value for the three-dimensional ordination was 0.19. According to the ‘envfit’ analysis results, both communities (D and N) presented similar relationships to the environmental variables (Table 3). In each case, velocity was the most important variable to be related with community compositions in terms of explained data variance. However, the r2 value obtained for the daytime community was higher (r2 = 0.58vs. r2 = 0.46, Table 3). This variable was followed by “CPOM values” for the nighttime community and by the “mean substrate diameter” for the daytime community (both with p< 0.01, Table 3).

The Permanova test showed no significant differences (F-value = 0.50, r2 = 0.01, p> 0.05) in the macroinvertebrate assemblage composition between the two pre-defined groups (D and N).

Table 2

Results of the t-test between the daytime and nighttime data. Variables “Scrapers” and “Biomass” are not displayed as they were evaluated with Wilcoxon tests (see the values and the results in the main text). df: degrees of freedom for the t-statistic.

thumbnail Fig. 2

NMDS plot where the a priori identified sites are colored. Black denotes the sites that belong to the daytime sampling (n = 35), while white indicates the sites that belong to the nighttime sampling (n = 35). 3D stress = 0.19

Table 3

Correlations of environmental variables with the NMDS ordinations of macroinvertebrates (D and N separately) and the significance of the correlation based on the envfit function (9999 permutations). The goodness-of-fit statistic is the squared correlation coefficient (r2) (mean_diam = mean riverbed substrate diameter).

The median STAR_ICMi values were 0.960 and 0.985 for the D and the N period, respectively. In both cases, ecological status always varied between the Good and High classes, with a prevalence of the latter and their p.c.i. (0.866–1.059 and 0.855–1.085, respectively) almost completely overlapping. Finally, the indicator value analysis did not identify strong groups of indicator taxa for the two community conditions under study (D and N). Only oligochaetes, which belong to the family Naididae, presented a high (IV = 0.49) and significant indicator value for the nighttime community (p = 0.036). No indicator taxa were obtained for the daytime community, and all the other families displayed low values with no significant relationships found in both cases.

4 Discussion

This study analyzed macroinvertebrate communities from daytime and nighttime samplings. No relevant differences were found when taxonomic, functional and biological information was considered. In this freshwater ecosystem (Northern Apennine stream), the information obtained at the family level (even for biomonitoring purposes) during a period (daytime or nighttime) was representative of the entire diel cycle and macroinvertebrate community.

When focusing on an intermittent small stream in Australia, Dell et al. (2015) recently obtained similar results, and detected no functional differences between the diurnal and nocturnal invertebrate communities in a specific experimental pool bed. Similarly, few or no diel differences in invertebrate densities were found in a British river (Copp et al., 2005).

Our findings contrast with our initial prediction. We suggest three potential reasons for the lack of major differences between the day and night data: (i) the heterotrophic condition of small-order streams (e.g., mid-mountain Apennine streams); (ii) presence of predators under both night and day conditions; (iii) absence of taxa with a specific phototaxis (e.g. positive phototaxis: movement toward light). The influence of these possibilities, either alone or combined, could be responsible for the results we obtained. Regarding the first reason, it is important to stress that allochthonous material (such as riparian leaves and wood) is a crucial energy source in these freshwater ecosystems (Allan and Castillo, 2007), and the source and availability of these components is largely independent of the light patterns that reach the aquatic system. A second reason could be the presence of different predators in the daytime and at nighttime, which could lead to similar predation pressure on macroinvertebrates. The confirmed presence of diurnal fish predators, such as brown trout (Salmo trutta, Linnaeus, 1758) and chub (Squalius cephalus, Linnaeus, 1758), could be balanced by the presence of nocturnal white-clawed crayfish (Austropotamobius pallipes complex), with abundant populations in the study area and in this watercourse (Zaccara et al., 2004; Nardi et al., 2005). Normally this crayfish has been considered an opportunistic omnivorous animal, but the importance of macroinvertebrates in its diet has been well documented in such ecosystems, and even in different life stages (Scalici and Gibertini, 2007). The third and last suggestion could be connected with the indicator values analysis results. In our case, the only nighttime indicator taxa were the oligochaetes of the family Naididae. This finding seems to indicate that taxa with a specific phototaxis were lacking. The wider distribution and greater abundance of these aquatic worms at nighttime has already been, be it indirectly, reported by Elliott (2005b), whose study analyzed gut content and the diet of a predaceous caddisfly. This could be related to the nocturnal habits of oligochaetes as a group. However, additional studies are essential before being able to recommend their use as indicator taxa.

When considering the second aim of this research work, we found very few differences when studying small-scale environmental variables. Velocity was the most important variable to be related with the macroinvertebrate composition in both cases. This variable has already been demonstrated as one of the most important factors of macroinvertebrate community structures in different streams and regions (Boyero and Bailey, 2001; Brooks et al., 2005; Guareschi et al., 2014). The importance of the CPOM resource for stream macroinvertebrate distribution has been previously stressed in similar Apennine ecosystems, particularly in relation to shredders distribution (Fenoglio et al., 2005).

In our fieldwork study, the only difference found in terms of community composition and environmental variables was represented by the “mean riverbed substrate diameter” variable, which was relevant when considering the daytime data. The dependence of community structure on riverbed typology is a matter of constant debate (e.g., Culp et al., 1983; Barnes et al., 2013; Laini et al., 2014), although features like size, constitution or complexity have often been shown to influence macroinvertebrate diversity, structure or preferences in different streams and regions (e.g., Gayraud and Philippe, 2001; Graça et al., 2004; Barnes et al., 2013; Fu et al., 2015).

4.1 Final remarks and future research lines

In this kind of research, it is important to stress that some specific results may be affected by the sampling methods employed. For instance in lentic ecosystems, Florencio et al. (2011) suggested that dip-netting was especially appropriate for sampling macroinvertebrates in different microhabitats, whereas fyke nets were a better option for capturing nocturnal and fast-swimming invertebrates. Similar problems may affect lotic ecosystems, where the use of a Surber sampler alone might not be capable of collecting fast-swimming invertebrates like some Coleoptera (e.g., family Gyrinidae), or skaters like aquatic Hemiptera (e.g., Gerridae). We opted for the Surber sampler for both conditions because obtaining quantitative and comparable data was considered the most important factor and was in line with our study aims.

Nevertheless, more specific findings could probably be obtained by complementary methods to evaluate specific movements (e.g., traps or artificial substrates in different directions, Bruno et al., 2012) and to better comprehend the importance of each small scale variable. Further research is also required on the possibility of intra-stretch changes in macroinvertebrates distribution at nighttime by separately analyzing different habitats (e.g.: surface/bottom, sand/pebbles, sediment/macrophytes). For instance, by focusing on macrophyte patches (Ceratophyllum demersum) in an Argentinian stream, Ferreiro (2014) found a higher abundance value for invertebrates (planktonic and benthonic organisms) at nighttime, and suggested quite an abrupt change once the sun sets.

Our results potentially represent a reference and natural condition in Apennine streams as to day-night changes at the family level, and may provide useful information about sampling strategies. As behavioral adaptations may be species-specific, it would be interesting to compare these results by studying the entire macroinvertebrate community at a higher taxonomic resolution (e.g. species), despite this entailing having to make considerable efforts in terms of resources.

Coupling experimental approaches (laboratory) with fieldwork observations in different geographic areas is recommended. Complementary studies in urban streams and on the effects of light pollution are also advisable (Meyer and Sullivan, 2013), and even by considering different wavelengths (e.g., Barmuta el al., 2001) would be extremely interesting to better understand diel cycles in stream macroinvertebrates and their relationship with artificial pollution.

The authors have declared no conflict of interest.


We thank native speakers Helen Warburton and Marie Palmer for proofreading this article and Andrea Rosati for his useful comments. Dr. Alex Laini was supported by a grant from “Consorzio dell’Oglio” (Italy).


  • Allan J.D. and Castillo M.M., 2007. Stream Ecology: Structure and Function of Running Waters, 2nd ed., Springer, Dordrecht, The Netherlands, 436 p. [Google Scholar]
  • Anderson M.J., 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol., 26, 32–46. [Google Scholar]
  • Baldi A., 2003. Using higher taxa as surrogates of species richness: a study based on 3700 Coleoptera, Diptera and Acari species in Central-Hungarian reserves. Basic Appl. Ecol., 4, 589–593. [CrossRef] [Google Scholar]
  • Barmuta L.A., Mckenny C.E.A. andSwain R., 2001. The responses of a lotic mayfly Nousia sp. (Ephemeroptera: Leptophlebiidae) to moving water and light of different wavelengths. Freshw. Biol., 46, 567–573. [CrossRef] [Google Scholar]
  • Barnes J.B., Vaughan I.P. andOrmerod S.J., 2013. Reappraising the effects of habitat structure on river macroinvertebrates. Freshw. Biol., 58, 2154–2167. [CrossRef] [Google Scholar]
  • Bergey E.A. andResh V. H., 1994. Effects of burrowing by a stream caddisfly on case-associated algae. J. N. Am. Benthol. Soc., 13, 379–390. [CrossRef] [Google Scholar]
  • Birk S., Bonne W., Borja A., Brucet S., Courrat A., Poikane S., Solimini A., van de Bund W., Zampoukas N. andHering D., 2012. Three hundred ways to assess Europe’s surface waters: an almost complete overview of biological methods to implement the Water Framework Directive. Ecol. Indic., 18, 31–41. [CrossRef] [Google Scholar]
  • Bo T. andFenoglio S., 2011. Impacts of a micro-sewage effluent on the biota of a small Apennine creek. J. Freshw. Ecol., 26, 537–545. [Google Scholar]
  • Bo T., Fenoglio S., Malacarne G., Pessino M. andSgariboldi F., 2007. Effect of clogging on stream macroinvertebrates: an experimental approach. Limnologica, 37, 186–192. [CrossRef] [Google Scholar]
  • Bournaud M., Cellot B., Richoux P. andBerrahou A., 1996. Macroinvertebrate community structure and environmental characteristics along a large river: congruity of patterns for identification to species or family. J. N. Am. Benthol. Soc., 15, 232–253. [CrossRef] [Google Scholar]
  • Boyero L. andBailey R.C., 2001. Organization of macroinvertebrate communities at a hierarchy of spatial scales in a tropical stream. Hydrobiologia, 464, 219–225. [CrossRef] [Google Scholar]
  • Brittain J.E. andEikeland T.J., 1988. Invertebrate Drift – A Review. Hydrobiologia, 166, 77–93. [CrossRef] [Google Scholar]
  • Brooks A.J., Haeusler T.I.M., Reinfelds I. andWilliams S., 2005. Hydraulic microhabitats and the distribution of macroinvertebrate assemblages in riffles. Freshw. Biol., 50, 331–334. [CrossRef] [Google Scholar]
  • Bruno M.C., Bottazzi E. andRossetti G., 2012. Downward, upstream or downstream? Assessment of meio-and macrofaunal colonization patterns in a gravel-bed stream using artificial substrates. Ann. Limnol. - Int. J. Lim., 48, 371–381. [CrossRef] [EDP Sciences] [Google Scholar]
  • Buffagni A. and Erba S., 2007. Macroinvertebrati acquatici e Direttiva 2000/60/ EC (WFD). Parte A. Metodo di campionamento per i fiumi guadabili. Resource document. IRSA-CNR. 118 p. Available from:{%}2803{%}29.pdf. (In Italian, accessed 17 February 2015). [Google Scholar]
  • Buffagni A., Erba S., Cazzola M., Murray-Bligh J., Soszka H. andGenoni P., 2006. The Star common metrics approach to the WFD intercalibration process: full application for small, lowland rivers in three European countries. Hydrobiologia, 566, 379–399. [CrossRef] [Google Scholar]
  • Cardoso P., Erwin T.L., Borges P.A. andNew T.R., 2011. The seven impediments in invertebrate conservation and how to overcome them. Biol. Conserv., 144, 2647–2655. [CrossRef] [Google Scholar]
  • Conallin J., Jyde M., Filrup K. andPedersen S., 2012. Diel foraging and shelter use of large juvenile brown trout (Salmo trutta) under food satiation. Knowl. Manag. Aquat. Ecosyst., 404, 05. [CrossRef] [EDP Sciences] [Google Scholar]
  • Copp G.H., Spathari S. andTurmel M., 2005. Consistency of diel behaviour and interactions of stream fishes and invertebrates during summer. River Res. Appl., 21, 75–90. [CrossRef] [Google Scholar]
  • Culp J.M., Walde S.J. andDavies R.W., 1983. Relative importance of substrate particle size and detritus to stream benthic macroinvertebrate microdistribution. Can. J. Fish Aquat. Sci., 40, 1568–1574. [CrossRef] [Google Scholar]
  • Davey A.J., Booker D.J. andKelly D.J., 2011. Diel variation in stream fish habitat suitability criteria: implications for instream flow assessment. Aquat. Conserv. 21, 132–145. [CrossRef] [Google Scholar]
  • De Cáceres M. andLegendre P., 2009. Associations between species and groups of sites: indices and statistical inference. Ecology, 90, 3566–3574. [CrossRef] [PubMed] [Google Scholar]
  • Dell A.I., Zhao L., Brose U., Pearson R.G. andAlford R.A., 2015. Population and community body size structure across a complex environmental gradient. Adv. Ecol. Res., 52, 115–167. [CrossRef] [Google Scholar]
  • Dodson S., 1990. Predicting diel vertical migration of zooplankton. Limnol. Oceanogr., 35, 1195–1200 [CrossRef] [Google Scholar]
  • Dolédec S., Olivier J.M. andStatzner B., 2000. Accurate description of the abundance of taxa and their biological traits in stream invertebrate communities: effects of taxonomic and spatial resolution. Archiv für Hydrobiologie – Fundam. Appl. Limnol., 148, 25–43. [CrossRef] [Google Scholar]
  • Dufrêne M. andLegendre P., 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr., 67, 345–366. [Google Scholar]
  • Elliott J.M., 2000. Contrasting diel activity and feeding patterns of four species of carnivorous stoneflies. Ecol. Entomol., 25, 26–34. [CrossRef] [Google Scholar]
  • Elliott J.M., 2002. A quantitative study of day-night changes in the spatial distribution of insects in a stony stream. J. Anim. Ecol., 71, 112–122. [CrossRef] [Google Scholar]
  • Elliott J.M., 2005a. Day-night changes in the spatial distribution and habitat preferences of freshwater shrimps, Gammarus pulex, in a stony stream. Freshw. Biol., 50, 552–566. [CrossRef] [Google Scholar]
  • Elliott J.M., 2005b. Contrasting diel activity and feeding patterns of four instars of Rhyacophila dorsalis (Trichoptera). Freshw. Biol., 50, 1022–1033. [CrossRef] [Google Scholar]
  • Fenoglio S., Bo T., Agosta P. andMalacarne G., 2005. Temporal and spatial patterns of coarse particulate organic matter and macroinvertebrate distribution in a low-order Apennine stream. J. Freshw. Ecol., 20, 539–547. [CrossRef] [Google Scholar]
  • Ferreiro N., 2014. Evidence on Night Movements of Macroinvertebrates to Macrophytes in a Pampean Stream. Open J. Mod. Hydrol., 4, 95–100. [CrossRef] [Google Scholar]
  • Florencio M., Díaz-Paniagua C., Gomez-Mestre I. andSerrano L., 2011. Sampling macroinvertebrates in a temporary pond: comparing the suitability of two techniques to detect richness, spatial segregation and diel activity. Hydrobiologia, 689, 121–130. [CrossRef] [Google Scholar]
  • Fu L., Jiang Y., Ding J., Liu Q., Peng Q.Z., Kang M.Y. andWang L.Z., 2015. Spatial variation of macroinvertebrate community structure and associated environmental conditions in a subtropical river system of southeastern China. Knowl. Manag. Aquat. Ecosyst., 416, 17. [CrossRef] [EDP Sciences] [Google Scholar]
  • Fuller R.L., Roelofs J.R. andFry T.J., 1986. The importance of algae to stream invertebrates. J. N. Am. Benthol. Soc., 5, 290–296. [CrossRef] [Google Scholar]
  • Gaston K.J., Bennie J., Davies T.W. andHopkins J., 2013. The ecological impacts of nighttime light pollution: a mechanistic appraisal. Biol. Rev., 88, 912–927. [CrossRef] [Google Scholar]
  • Gayraud S. andPhilippe M., 2001. Does subsurface interstitial space influence general features and morphological traits of the benthic macroinvertebrate community in streams? Archiv für Hydrobiologie – Fundam. Appl. Limnol., 151, 667–686. [CrossRef] [Google Scholar]
  • Gerrish G.A., Morin J.G., Rivers T.J. andPatrawala Z., 2009. Darkness as an ecological resource: the role of light in partitioning the nocturnal niche. Oecologia, 160, 525–536. [CrossRef] [PubMed] [Google Scholar]
  • Graça M.A., Pinto P., Cortes R., Coimbra N., Oliveira S., Morais M., Carvalho M.J. andMalo J., 2004. Factors Affecting Macroinvertebrate Richness and Diversity in Portuguese Streams: a Two Scale Analysis. Int. Rev. Hydrobiol., 89, 151–164. [CrossRef] [Google Scholar]
  • Guareschi S., Laini A., Racchetti E., Bo T., Fenoglio S. andBartoli M., 2014. How do hydromorphological constraints and regulated flows govern macroinvertebrate communities along an entire lowland river? Ecohydrology, 7, 366–377. [CrossRef] [Google Scholar]
  • Guest M.A., Connolly R.M. andLoneragan N.R., 2003. Seine nets and beam trawls compared by day and night for sampling fish and crustaceans in shallow seagrass habitat. Fish. Res., 64, 185–196. [CrossRef] [Google Scholar]
  • Guevara J. andAvilés L., 2013. Community-wide body size differences between nocturnal and diurnal insects. Ecology, 94, 537–543. [CrossRef] [PubMed] [Google Scholar]
  • Gumiero B., Maiolini B., Rinaldi M., Surian N., Boz B. and Moroni F., 2009. The Italian Rivers. In: Rivers of Europe. Academic Press, Amsterdam, 467–495. [Google Scholar]
  • Hampton S.E. andDuggan I.C., 2003. Diel habitat shifts of macrofauna in a fishless pond. Mar. Freshwater Res., 54, 797–805. [CrossRef] [Google Scholar]
  • Hansen E.A. andCloss G.P., 2007. Temporal consistency in the long-term spatial distribution of macroinvertebrate drift along a stream reach. Hydrobiologia, 575, 361–371. [CrossRef] [Google Scholar]
  • Helfman G.S., 1986. Fish behaviour by day, night and twilight. In: The behaviour of teleost fishes. Springer, USA, 366–387. [Google Scholar]
  • Hutchinson G.E., 1967. A treatise on limnology, Wiley, New York, Vol. 2, 1115 p. [Google Scholar]
  • Jiménez-Valverde A. andHortal J., 2003. Las curvas de acumulación de especies y la necesidad de evaluar la calidad de los inventarios biológicos. Rev. Ibérica Aracnol., 8, 151–161. [Google Scholar]
  • Johnson S.L. andCovich A.P., 2000. The importance of night-time observations for determining habitat preferences of stream biota. Regul. River., 16, 91–99. [CrossRef] [Google Scholar]
  • Krebs C.J., 1999. Ecological Methodology, 2nd ed., Addison-Wesley Educational Publishers, Menlo Park, 624 p. [Google Scholar]
  • Laini A., Vorti A., Bolpagni R. andViaroli P., 2014. Small-scale variability of benthic macroinvertebrates distribution and its effects on biological monitoring. Ann. Limnol. - Int. J. Lim., 50, 211–216. [CrossRef] [EDP Sciences] [Google Scholar]
  • Lombardo P., Miccoli F.P., Giustini M. andCicolani B., 2010. Diel activity cycles of freshwater gastropods under natural light: patterns and ecological implications. Ann. Limnol. - Int. J. Lim., 46, 29–40. [CrossRef] [EDP Sciences] [Google Scholar]
  • Marklund O., Blindow I. andHargeby A., 2001. Distribution and diel migration of macroinvertebrates within dense submerged vegetation. Freshw. Biol., 46, 913–924. [CrossRef] [Google Scholar]
  • Martínez-Sanz C., García-Criado F., Aláez C.F. andAláez M.F., 2010. Assessment of richness estimation methods on macroinvertebrate communities of mountain ponds in Castilla y León (Spain). Ann. Limnol. - Int. J. Lim., 46, 101–110. [CrossRef] [EDP Sciences] [Google Scholar]
  • Merritt R.W. and Cummins K.W., 1996. An introduction to the aquatic insects of North America, 3rd ed., Kendall/Hunt, Dubuque, IO, 862 p. [Google Scholar]
  • Meyer L.A. andSullivan S.M.P., 2013. Bright lights, big city: influences of ecological light pollution on reciprocal stream-riparian invertebrate fluxes. Ecol. Appl., 23, 1322–1330. [CrossRef] [PubMed] [Google Scholar]
  • Morisita M., 1959. Measuring of the dispersion of individuals and analysis of the distributional patterns. Mem. Fac. Sci. Kyushu Univ. Ser. E 2, 215–235. [Google Scholar]
  • Nardi P.A., Bernini F., Bo T., Bonardi A., Fea G., Ghia D., Negri A., Razzetti E., Rossi S. andSpairani M. 2005. Status of Austropotamobius pallipes complex in the watercourses of the Alessandria province (NW Italy). Bull. Fr. Pêche Piscic., 376-377, 585–598. [CrossRef] [EDP Sciences] [Google Scholar]
  • Oksanen J., Guillaume Blanchet F., Kindt R., Legendre P., Minchin P.R., O’Hara R.B., Simpson G.L., Solymos P., Henry M., Stevens H. and Wagner H., 2013. Vegan: Community Ecology Package. R package version 2.0–8. Available from: [Google Scholar]
  • Perkin E.K., Hölker F., Richardson J.S., Sadler J.P., Wolter C. and Tockner K., 2011. The influence of artificial light on stream and riparian ecosystems: questions, challenges, and perspectives. Ecosphere 2, art. 122. [Google Scholar]
  • R Development Core Team, 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from: (accessed 23 December 2014). [Google Scholar]
  • Roussel J.M., Bardonnet A. andClaude A., 1999. Microhabitats of brown trout when feeding on drift and when resting in a lowland salmonid brook: effects on weighted usable area. Archiv für Hydrobiologie – Fundam. Appl. Limnol., 146, 413–429. [CrossRef] [Google Scholar]
  • Sánchez-Fernández D., Abellán P., Mellado A., Velasco J. andMillán A., 2006. Are water beetles good indicators of biodiversity in Mediterranean aquatic ecosystems? The case of Segura river basin (SE Spain). Biodivers. Conserv., 15, 4507–4520. [CrossRef] [Google Scholar]
  • Sánchez-Fernández D., Lobo J.M., Abellán P., Ribera I. andMillán A., 2008. Bias in freshwater biodiversity sampling: the case of Iberian water beetles. Divers. Distrib., 14, 754–762. [CrossRef] [Google Scholar]
  • Scalici M. andGibertini G., 2007. Feeding habits of the crayfish Austropotamobius pallipes (Decapoda, Astacidae) in a brook in Latium (central Italy). Ital. J. Zool., 74, 157–168. [CrossRef] [Google Scholar]
  • Tachet H., Richoux P., Bournaud M. and Usseglio-Polatera P., 2010. Invertébrés d’eau douce: systématique, biologie, écologie, CNRS Editions, Paris, France, 588 p. [Google Scholar]
  • Walther B.A. andMoore J.L., 2005. The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography, 28, 815–829. [CrossRef] [Google Scholar]
  • Wiley M.J. andKohler S., 1980. Positioning Changes of Mayfly Nymphs Due to Behavioral Regulation of Oxygen Consumption. Can. J. Zool., 58, 618–622. [CrossRef] [Google Scholar]
  • Zaccara S., Stefani F., Galli P., Nardi P. A. andCrosa G. 2004. Taxonomic implications in conservation management of white-clawed crayfish (Austropotamobius pallipes) (Decapoda, Astacidae) in Northern Italy. Biol. Conserv., 120, 1–10. [CrossRef] [Google Scholar]

Cite this article as: S. Guareschi, A. Laini, S. Fenoglio, M. Paveto, T. Bo, 2016. Change does not happen overnight: a case study on stream macroinvertebrates. Knowl. Manag. Aquat. Ecosyst., 417, 21.

All Tables

Table 1

Taxa list and number of individuals obtained in the Lemme stream (Order and Family, except Hydrachnidia). D = daytime; N = nighttime (35 Surber in both cases).

Table 2

Results of the t-test between the daytime and nighttime data. Variables “Scrapers” and “Biomass” are not displayed as they were evaluated with Wilcoxon tests (see the values and the results in the main text). df: degrees of freedom for the t-statistic.

Table 3

Correlations of environmental variables with the NMDS ordinations of macroinvertebrates (D and N separately) and the significance of the correlation based on the envfit function (9999 permutations). The goodness-of-fit statistic is the squared correlation coefficient (r2) (mean_diam = mean riverbed substrate diameter).

All Figures

thumbnail Fig. 1

Example of sampling strategy (Surber distribution) in the studied Lemme stream reach (D1–D35: daytime Surbers, N1–N35; nighttime Surbers). The gray arrow indicates flow direction.

In the text
thumbnail Fig. 2

NMDS plot where the a priori identified sites are colored. Black denotes the sites that belong to the daytime sampling (n = 35), while white indicates the sites that belong to the nighttime sampling (n = 35). 3D stress = 0.19

In the text

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