Layout 1 [Journal of Entomological and Acarological Research 2013; 45:e14] [page 73] Response of chironomid species (Diptera, Chironomidae) to water temperature: effects on species distribution in specific habitats L. Marziali,1 B. Rossaro2 1CNR-IRSA Water Research Institute, U.O.S. Brugherio, Brugherio (MB); 2Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, Milan, Italy Abstract The response of 443 chironomid species to water temperature was analyzed, with the aim of defining their thermal optimum, tolerance limits and thermal habitat. The database included 4442 samples main- ly from Italian river catchments collected from the 1950s up to date. Thermal preferences were calculated separately for larval and pupal specimens and for different habitats: high altitude and lowland lakes in the Alpine ecoregion; lowland lakes in the Mediterranean ecore- gion; heavily modified water bodies; kryal, krenal, rhithral and potamal in running waters. Optimum response was calculated as mean water temperature, weighted by species abundances; tolerance as weighted standard deviation; skewness and kurtosis as 3rd and 4th moment sta- tistics. The responses were fitted to normal uni- or plurimodal Gaussian models. Cold stenothermal species showed: i) unimodal response, ii) tolerance for a narrow temperature range, iii) optima closed to their minimum temperature values, iv) leptokurtic response. Thermophilous species showed: i) optima at different temperature val- ues, ii) wider tolerance, iii) optima near their maximum temperature values, iv) platikurtic response, often fitting a plurimodal model. As expected, lower optima values and narrower tolerance were obtained for kryal and krenal, than for rhithral, potamal and lakes. Thermal response curves were produced for each species and were discussed according to species distribution (i.e. altitudinal range in running water and water depth in lakes), voltinism and phylogeny. Thermal optimum and tolerance limits and the definition of the thermal habi- tat of species can help predicting the impact of global warming on freshwater ecosystems. Introduction Global warming is affecting freshwater macroinvertebrate commu- nities with alteration of species distribution and phenology. In partic- ular, increased water temperature will induce a change in distribution of species, which will react following their thermal optimum along an altitudinal and/or latitudinal gradient (Hughes, 2000; Nyman et al., 2005; Bonada et al., 2007; Sheldon, 2012). According to species adaptations, each habitat will show different sensibility: in Southern Europe, kryal, krenal, high altitude lakes and ponds are supposed to be sensitive habitats, being characterized by stenotopic taxa directly influenced by water temperature (Boggero et al., 2006; Rossaro et al., 2006a; Tixier et al., 2009; Jacobsen et al., 2012; Lencioni et al., 2012). A lot of species won’t probably survive global warming, since spatial isolation may give little opportunity to migrate elsewhere. On the contrary, the response of habitats at lower altitude is poorly understood, as species thermal optimum and tolerance are less known and other factors generally contribute in structuring biotic communi- ties (Jacobsen et al., 1997). Moreover, some studies showed that local adaptations may induce different thermal sensibility of single species at different sites and habitats. In particular, acclimation temperature during lifetime was proved to affect tolerance of populations (Dallas & Rivers-Moore, 2012). Besides, microevolutionary dynamics at local scale may separate the response of populations, and consequently their fitness (Hogg et al., 1998; Van Doorsalaen et al., 2009). Therefore it is necessary to determine the extent to which thermal response of species varies among habitats, to determine which communities are more menaced by global warming. Studies on aquatic organisms based on lethal or sub-lethal end- points (e.g. death, ability to escape unfavourable conditions, growth, reproduction, etc.) were carried out in experimental mesocosms or lab tests to derive thermal performance curves that relate species response to water temperature (Hester & Doyle, 2011; Dallas & Rivers- Moore, 2012), with definition of critical thermal maxima or minima. This approach may be successful to detect biological or physiological processes mostly affected by altered temperature. Nonetheless thermal history, acclimation, rate of temperature change, test duration, life stage have been shown to affect results. Moreover, the difficulty of taxa identification may hinder test application at species level, and many Correspondence: Laura Marziali, CNR-IRSA Water Research Institute, U.O.S. Brugherio, Via del Mulino 19, 20861 Brugherio (MB), Italy. Tel.: +39.039.21694207 - Fax: +39.039.2004692. E-mail: marziali@irsa.cnr.it Key words: Chironomidae, thermal tolerance, ecological traits, global warming. Acknowledgements: data stored in the CHIRDB were collected within sam- pling surveys supported by different grants. Details about the projects are in the publications of the first Author quoted in the reference paragraph. Received for publication: 4 April 2013. Revision received: 29 April 2013. Accepted for publication: 30 May 2013. ©Copyright L. Marziali and B. Rossaro, 2013 Licensee PAGEPress, Italy Journal of Entomological and Acarological Research 2013; 45:e14 doi:10.4081/jear.2013.e14 This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (by-nc 3.0) which permits any noncom- mercial use, distribution, and reproduction in any medium, provided the orig- inal author(s) and source are credited. Journal of Entomological and Acarological Research 2012; volume 44:eJournal of Entomological and Acarological Research 2013; volume 45:e14 Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 73 No n- co mm er cia l response, ii) tolerance for a narrow temperature range, iii) optima No n- co mm er cia l response, ii) tolerance for a narrow temperature range, iii) optima closed to their minimum temperature values, iv) leptokurtic response. No n- co mm er cia l closed to their minimum temperature values, iv) leptokurtic response. Thermophilous species showed: i) optima at different temperature val- No n- co mm er cia l Thermophilous species showed: i) optima at different temperature val- ues, ii) wider tolerance, iii) optima near their maximum temperature No n- co mm er cia l ues, ii) wider tolerance, iii) optima near their maximum temperature values, iv) platikurtic response, often fitting a plurimodal model. As No n- co mm er cia l values, iv) platikurtic response, often fitting a plurimodal model. As expected, lower optima values and narrower tolerance were obtained No n- co mm er cia l expected, lower optima values and narrower tolerance were obtained for kryal and krenal, than for rhithral, potamal and lakes. Thermal No n- co mm er cia l for kryal and krenal, than for rhithral, potamal and lakes. Thermal ular, increased water temperature will induce a change in distribution No n- co mm er cia l ular, increased water temperature will induce a change in distributionof species, which will react following their thermal optimum along an No n- co mm er cia l of species, which will react following their thermal optimum along analtitudinal and/or latitudinal gradient (Hughes, 2000; Nyman No n- co mm er cia l altitudinal and/or latitudinal gradient (Hughes, 2000; Nyman 2005; Bonada No n- co mm er cia l 2005; Bonada No n- co mm er cia l No n- co mm er cia l Correspondence: Laura Marziali, CNR-IRSA Water Research Institute, U.O.S. No n- co mm er cia l Correspondence: Laura Marziali, CNR-IRSA Water Research Institute, U.O.S. Brugherio, Via del Mulino 19, 20861 Brugherio (MB), Italy. No n- co mm er cia l Brugherio, Via del Mulino 19, 20861 Brugherio (MB), Italy. Tel.: +39.039.21694207 - Fax: +39.039.2004692. No n- co mm er cia l Tel.: +39.039.21694207 - Fax: +39.039.2004692. Key words: Chironomidae, thermal tolerance, ecological traits, global warming.No n- co mm er cia l Key words: Chironomidae, thermal tolerance, ecological traits, global warming. us e Global warming is affecting freshwater macroinvertebrate commu- us e Global warming is affecting freshwater macroinvertebrate commu- nities with alteration of species distribution and phenology. In partic-us e nities with alteration of species distribution and phenology. In partic- ular, increased water temperature will induce a change in distributionus e ular, increased water temperature will induce a change in distribution on ly tat of species can help predicting the impact of global warming on on ly tat of species can help predicting the impact of global warming on on ly [page 74] [Journal of Entomological and Acarological Research 2013; 45:e14] studies considered genera, families or even orders (Dallas & Rivers- Moore, 2012). More realism could be achieved determining the temperature range that organisms experience in the field (Rossaro, 1991a, 1991b, 1991c). Data from different ecological surveys in freshwater ecosystems could be gained and specimens collected can be identified at species level. In this way a large amount of data for each species can be gathered. This approach could be successful to determine species thermal preferences and tolerance limits (i.e. temperature beyond which organisms avoid) in different habitats, seasons and life stages. In fact, empirical data may allow going beyond local adaptations of taxa and drawbacks of manipulation tests. This approach was recently adopted at European scale (AQEM project) (Hering et al., 2004) for many macroinvertebrate groups collecting published data to derive species’ ecological prefer- ences (Schmidt-Kloiber & Hering, 2012). Nonetheless species respons- es have been expressed as qualitative rather than quantitative fea- tures, because most publications do not provide raw data. Therefore much work is still needed to better quantify the response to natural and anthropogenic factors, as a valuable tool for biomonitoring. For what concerns water temperature, among macroinvertebrate taxa, insects were shown to be mainly responsive to this pressure (Bonada et al., 2007; Čiamporová-Zat’ovičová et al., 2010; Dallas & Rivers-Moore, 2012). In particular, chironomids are a suitable indicator group, being characterized by a large number of species with a wide range of respons- es to environmental factors (Lindegaard et al., 1995). Fossil remains of these dipterans in lake sediments have been used as proxy to reconstruct shifts in air and water temperature, since many species were shown to respond rapidly to climatic fluctuations (Larocque et al., 2001; Lotter et al., 2012). Moreover, they have been used as indicators of oxygen con- centration (Rossaro et al., 2007b) and trophic levels in lakes (Sæther 1979, Rossaro et al., 2011) and as indicators of organic (Raunio et al., 2007) and toxic (Cortelezzi et al., 2011) pollution in rivers. Nonetheless many studies showed that water temperature is one of the main factors determining taxa assemblages and species distribution (Rossaro, 1991a, 1991b, 1991c; Brooks & Birks, 2000; Medeiros & Quinlan, 2011). Lack of information could be possibly filled by biogeographic studies considering ecological equivalents in different regions (Jacobsen et al., 1997, 2012; Hamerlik & Brodersen 2010; Hamerlik et al., 2011), but species names are often not corresponding in different areas, since at large spatial scale biogeographic gradients may be present (Catalan et al., 2009) or, at smaller scale, taxonomic determination by different experts often affects data comparability (Kernan et al., 2009; Heiri et al., 2011). Therefore at present only data at regional scale can be likely compared. The present research aims at quantitatively determine the thermal response of chironomid species in different freshwater habitats in Southern Europe, following the empirical approach. At this purpose, chironomid samples collected in many surveys mostly from Italy but also from other Alpine and Mediterranean countries are considered. Species response to altitude, source distance in rivers and water depth in lakes is also determined. Different life stages are analyzed. Materials and methods To investigate the thermal response of chironomid species the CHIRDB database (Rossaro et al., 2006b) was used. This database con- tains records about chironomid samples collected in freshwater ecosys- tems mainly in Italy, but also in Algeria, Austria, France, Switzerland and Germany from the 1950s up to date (Table 1). Other data were derived from published papers (Table 1). A map of the sampling sites is shown in Figure 1. Sampling sites were grouped into different habitats: – kryal=glacial streams above the tree line (Rossaro et al., 2006b); note that this definition of kryal is more extended than the one given by Milner & Petts (1994) and water temperature can be much higher than 2°C – krenal=springs (Vannote et al., 1980) – rhithral=mountain reach of rivers below the tree line (Vannote et al., 1980) – potamal=lowland reach of rivers (Vannote et al., 1980) – Alpine lowland lakes=natural lakes within the Alpine ecoregion (with latitude >44° 00’) with altitude below 800 m a.s.l. (Tartari et al., 2006) – Alpine high altitude lakes=natural lakes within the Alpine ecore- gion (with latitude >44° 00’) with altitude above 800 m a.s.l. (Tartari et al., 2006) – Mediterranean lakes=natural lowland lakes within the Mediterranean ecoregion (with latitude <44° 00’), with altitude below 800 m a.s.l. (Tartari et al., 2006) – heavily modified water bodies=reservoirs and artificial lakes – brackish ponds=ponds with high salinity (water conductivity >2500 µS cm–1 at 20°C) (Tartari et al., 2006) Sampling sites are summarized in Table 2. Samples are grouped into river catchments and the number of samples collected in each habitat is reported. The same site was generally sampled covering all seasons. Chironomid samples were collected using different tools, according to the habitat: i) pond net collections of larvae from small water bodies (krenal, kryal, high altitude Alpine lakes) (Rossaro et al., 2006b); ii) surber net collections of larvae in stony bottom streams (rhithral) (Rossaro, 1991b, 1991c, 1992, 1993; Marziali et al., 2010a, 2010b); iii) Ekman, Petersen, Ponar dredge samples of larvae from natural lowland lakes and heavily modified water bodies, brackish ponds and from large rivers (potamal) (Rossaro, 1988; Battegazzore et al., 1992; Rossaro et al., 2006a, 2011); iv) drift samples of pupal exuviae using a Brundin net (lakes, kryal, krenal, rhithral, potamal) (Rossaro, 1991b, 1991c); v) adult captures collected with hand nets, emergence traps or Malaise traps (Rossaro, 1987); imagines were used for confirming species identifications, but were not considered for data analysis. For each sampling site latitude, longitude, altitude (m a.s.l.), dis- tance from source (km) in running waters and sampling depth (m) in lakes were recorded in the field or were derived using geographic infor- mation system-based cartographic data (http://www.sinanet.isprambi- ente.it). Water temperature (°C) was measured with a field multiprobe during the samplings. Chironomid samples were slide mounted and identified to species using specialized keys (Wiederholm 1980, 1983, 1986; Ferrarese & Rossaro, 1981; Ferrarese, 1983; Rossaro, 1982; Nocentini, 1985; Langton, 1991) and comparing different life stages (e.g. larval exuviae with pupae; pupal exuviae with imagines). In the present work, the abundances of 309 species as larvae (18,886 records) and 325 species as pupal exuviae (7619 records) from 4442 samples were considered. Chironomid species nomenclature and systematics follow Sæther (1977), Rossaro (1991c), Sæther (2000), Cranston et al. (2012). Data analysis Data were stored in a Microsoft Access database (CHIRDB) (Rossaro et al., 2006b). Data on larval samples were expressed as specimens per square meter when collected with Surber (rhithral) and dredge sam- ples (lowland lakes, heavily modified water bodies, potamal, brackish ponds); and as number of specimens for unit of effort (about 15 min sampling) when collected with pond nets (high altitude lakes, kryal, krenal). Data on pupal exuviae samples collected with a Brundin net in all habitats were expressed as number of specimens per unit of effort (about 15 min sampling). Records of species abundances matching water temperature meas- ures were selected using MS-Access queries and were imported into Article Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 74 No n- co mm er cia l many studies showed that water temperature is one of the main factors No n- co mm er cia l many studies showed that water temperature is one of the main factors determining taxa assemblages and species distribution (Rossaro, 1991a, No n- co mm er cia l determining taxa assemblages and species distribution (Rossaro, 1991a, 1991b, 1991c; Brooks & Birks, 2000; Medeiros & Quinlan, 2011). Lack of No n- co mm er cia l 1991b, 1991c; Brooks & Birks, 2000; Medeiros & Quinlan, 2011). Lack of information could be possibly filled by biogeographic studies considering No n- co mm er cia l information could be possibly filled by biogeographic studies considering et al. No n- co mm er cia l et al., 1997, 2012; No n- co mm er cia l , 1997, 2012; , 2011), but species names No n- co mm er cia l , 2011), but species names are often not corresponding in different areas, since at large spatial scale No n- co mm er cia l are often not corresponding in different areas, since at large spatial scale biogeographic gradients may be present (Catalan No n- co mm er cia l biogeographic gradients may be present (Catalan et al. No n- co mm er cia l et al., 2009) or, at No n- co mm er cia l , 2009) or, at smaller scale, taxonomic determination by different experts often affects No n- co mm er cia l smaller scale, taxonomic determination by different experts often affects , 2009; Heiri No n- co mm er cia l , 2009; Heiri et al. No n- co mm er cia l et al., 2011). Therefore at No n- co mm er cia l , 2011). Therefore at present only data at regional scale can be likely compared. No n- co mm er cia l present only data at regional scale can be likely compared. The present research aims at quantitatively determine the thermal No n- co mm er cia l The present research aims at quantitatively determine the thermal response of chironomid species in different freshwater habitats inNo n- co mm er cia l response of chironomid species in different freshwater habitats in Southern Europe, following the empirical approach. At this purpose,No n- co mm er cia l Southern Europe, following the empirical approach. At this purpose, Ekman, Petersen, Ponar dredge samples of larvae from natural lowland No n- co mm er cia l Ekman, Petersen, Ponar dredge samples of larvae from natural lowlandlakes and heavily modified water bodies, brackish ponds and from large No n- co mm er cia l lakes and heavily modified water bodies, brackish ponds and from largerivers (potamal) (Rossaro, 1988; Battegazzore No n- co mm er cia l rivers (potamal) (Rossaro, 1988; Battegazzore al. No n- co mm er cia l al., 2006a, 2011); iv) drift samples of pupal exuviae using a Brundin net No n- co mm er cia l , 2006a, 2011); iv) drift samples of pupal exuviae using a Brundin net (lakes, kryal, krenal, rhithral, potamal) (Rossaro, 1991b, 1991c); v) No n- co mm er cia l (lakes, kryal, krenal, rhithral, potamal) (Rossaro, 1991b, 1991c); v) adult captures collected with hand nets, emergence traps or Malaise No n- co mm er cia l adult captures collected with hand nets, emergence traps or Malaise traps (Rossaro, 1987); imagines were used for confirming species No n- co mm er cia l traps (Rossaro, 1987); imagines were used for confirming species us e the habitat: i) pond net collections of larvae from small water bodies us e the habitat: i) pond net collections of larvae from small water bodies (krenal, kryal, high altitude Alpine lakes) (Rossaro us e (krenal, kryal, high altitude Alpine lakes) (Rossaro surber net collections of larvae in stony bottom streams (rhithral) us e surber net collections of larvae in stony bottom streams (rhithral) (Rossaro, 1991b, 1991c, 1992, 1993; Marziali us e (Rossaro, 1991b, 1991c, 1992, 1993; Marziali Ekman, Petersen, Ponar dredge samples of larvae from natural lowlandus e Ekman, Petersen, Ponar dredge samples of larvae from natural lowland lakes and heavily modified water bodies, brackish ponds and from largeus e lakes and heavily modified water bodies, brackish ponds and from large on ly Sampling sites are summarized in Table 2. Samples are grouped into on ly Sampling sites are summarized in Table 2. Samples are grouped into river catchments and the number of samples collected in each habitat on lyriver catchments and the number of samples collected in each habitatThe same site was generally sampled covering all seasons. on lyThe same site was generally sampled covering all seasons. Chironomid samples were collected using different tools, according toon ly Chironomid samples were collected using different tools, according to the habitat: i) pond net collections of larvae from small water bodieson ly the habitat: i) pond net collections of larvae from small water bodies (krenal, kryal, high altitude Alpine lakes) (Rossaro on ly (krenal, kryal, high altitude Alpine lakes) (Rossaro Matlab environment for statistical analyses. The moment statistics, used for describing probability distributions, were then calculated. The expected value of a random variable (the mean) is derived by the first moment, the variance by the second moment, the skewness (i.e. the asymmetry of the probability distribution) by the third moment, the kurtosis (i.e. the peakedness of the probability distribution) by the fourth moment (Khurshid, 2007). The water temperature range experienced by each species was divided into 20 equally-ranged classes and the frequency of the species in each of the 20 classes was calculated. A thermal response curve was then pro- duced for each species relating species abundance to water temperature. The formulae used to calculate the first (weighted average), second (weighted standard deviation), third (skewness=g1) and fourth (kur- tosis=g2) central moments can be found in Sokal & Rohlf (1981). [Journal of Entomological and Acarological Research 2013; 45:e14] [page 75] Article Table 1. Data stored in the CHIRDB database are derived from different surveys here summarized. Country Region River catchment Sampling years References Italy Aosta Valley Dora Baltea river 1995-98 Rossaro et al., 2006b; unpublished data Trentino-Alto Adige Sarca, Adige and Noce rivers 1990, 1996-98, 2005 Boggero et al., 2006; Lencioni et al., 2007 Lakes Lases, Lamar, Caldonazzo 1996, 2000, 2004-07 Lencioni et al., 2006 and Tenno (Brenta river) Lombardy Oglio and Mincio rivers 1978-83, 2006 Rossaro, 1991c Lambro and Olona rivers 1977-78, 1986-87, 2003 Unpublished data Brembo and Serio rivers 1980-81, 2003 Po river 1977-93 Rossaro 1987, 1988; Battegazzorre et al., 1992 Adda river 1977, 1988-89, 2001-07 Unpublished data Ticino river 1979, 1985, 2001-04, 2009-10 Berra et al., 2004 Lake Garda 1970-71, 1982, 2004, 2007, 2011 Rossaro et al., 2006a, 2011; Bonomi, 1974 Lakes Viverone and Avigliana 2005-06 Rossaro et al., 2006a, 2011 Lake Varese 1987, 1994-97, 2002-05 Rossaro et al., 2006a, 2011 Lake Monate 1977, 2004-05 Rossaro et al., 2006a, 2011; Nocentini, 1979 Lake Como 1980-84, 2004-05, 2007 Unpublished data Lakes Comabbio, Alserio, Pusiano and Annone 1967, 1977, 2004-07 Rossaro et al., 2006a, 2011 Lake Iseo 1967, 2003-04 Unpublished data Piedmont Lake Mergozzo 1963-64, 1971-72, 1975, 1994, 2010 Rossaro et al., 2006a, 2011; Nocentini, 1979 Lake Maggiore 1953-54, 1960-61, 1966-67, 1985-88, Rossaro et al., 2006a, 2011; Nocentini, 1963 1995-96, 2004, 2007, 2009-10 Ticino river 1985-87, 1991-94, 2000, 2007 Boggero et al., 2006; Unpublished data Dora Baltea river 2005 Boggero et al., 2006 Agogna river 1976-77, 1981-82 Rossaro, 1991c Toce river 1991-94, 2000 Unpublished data Sesia river 1987 Unpublished data Lake Lugano 2004-04 Unpublished data Po and Tanaro rivers 1989-90 Unpublished data Lake Orta 1976 Unpublished data Emilia Romagna Po and Trebbia river 1977-83 Rossaro 1987, 1988; Battegazzore et al., 1992 Taro river 2001-03 Marziali et al., 2010b Liguria Danè river 1998-99 Unpublished data Toscana Magra river 2001 Unpublished data Marche Potenza river 1986 Rossaro, 1988 Abruzzo Tordino, Vomano and Aterno rivers 1978, 1986-92, 1995, 2010 Unpublished data Lazio Tevere and Nera rivers 1989-90 Unpublished data Trasimeno river 2003 Unpublished data Lakes Bolsena, Bracciano and Vico 1970-73 Rossaro et al., 2006a, 2007a Umbria Tevere river 1977-03 Campania Sele river 2000-01 Marziali et al., 2010a Puglia Ofanto river 1990 Unpublished data Sardinia Cedrino and Rio Mannu rivers 1978, 1986 Unpublished data Lazio, Abruzzo, Heavily modified water bodies 1976-77, 1934-85, 1989, 1991 Unpublished data Basilicata, (Fibreno, Brasimone, Scontrone, Puglia, Sicily Pertusillo, Occhito, Dirillo) Switzerland Ticino river 2005 Boggero et al., 2006 France Garonna river 2004 Unpublished data Germany Donau river 2006 Free et al., 2009 Austria Donau river 2006 Free et al., 2009 Algeria Algerian wadi 2007 Zerguine et al., 2009; Chaib et al., 2011 Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 75 No n- co mm er cia l Lakes Comabbio, Alserio, Pusiano and Annone 1967, 1977, 2004-07 No n- co mm er cia l Lakes Comabbio, Alserio, Pusiano and Annone 1967, 1977, 2004-07 No n- co mm er cia l 1967, 2003-04 No n- co mm er cia l 1967, 2003-04 Lake Mergozzo 1963-64, 1971-72, 1975, 1994, 2010 Rossaro No n- co mm er cia l Lake Mergozzo 1963-64, 1971-72, 1975, 1994, 2010 Rossaro Lake Maggiore 1953-54, 1960-61, 1966-67, 1985-88, Rossaro No n- co mm er cia l Lake Maggiore 1953-54, 1960-61, 1966-67, 1985-88, Rossaro 1995-96, 2004, 2007, 2009-10 No n- co mm er cia l 1995-96, 2004, 2007, 2009-10 1985-87, 1991-94, 2000, 2007 Boggero No n- co mm er cia l 1985-87, 1991-94, 2000, 2007 Boggero Agogna river No n- co mm er cia l Agogna river Toce river No n- co mm er cia l Toce river No n- co mm er cia l Sesia river No n- co mm er cia l Sesia river Lake Lugano No n- co mm er cia l Lake Lugano Po and Tanaro rivers No n- co mm er cia l Po and Tanaro rivers No n- co mm er cia l Lake Orta No n- co mm er cia l Lake Orta Po and Trebbia river No n- co mm er cia l Po and Trebbia river No n- co mm er cia l Taro river No n- co mm er cia l Taro river No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l u se 1970-71, 1982, 2004, 2007, 2011 Rossaro us e 1970-71, 1982, 2004, 2007, 2011 Rossaro 1987, 1994-97, 2002-05 us e 1987, 1994-97, 2002-051977, 2004-05 us e 1977, 2004-05 1980-84, 2004-05, 2007us e 1980-84, 2004-05, 2007 Lakes Comabbio, Alserio, Pusiano and Annone 1967, 1977, 2004-07us e Lakes Comabbio, Alserio, Pusiano and Annone 1967, 1977, 2004-07 on lyRossaro 1987, 1988; Battegazzorre on lyRossaro 1987, 1988; Battegazzorre 1970-71, 1982, 2004, 2007, 2011 Rossaro on ly 1970-71, 1982, 2004, 2007, 2011 Rossaro [page 76] [Journal of Entomological and Acarological Research 2013; 45:e14] Article Figure 1. Map of the sampling sites. Table 2. River catchments with mean latitude and longitude, and number of samples collected in each habitat. River catchment lat long kn kr rh pt AL al ME hm br Garonna (France) 44°00’00’’ 02°00’00’’ 0 0 10 0 0 0 0 0 0 Donau (Germany) 47°41’19’’ 11°26’16’’ 0 0 0 0 50 0 0 0 0 Donau (Austria) 47°47’17’’ 13°20’17’’ 0 0 0 0 41 0 0 0 0 Dora Baltea 45°37’24’’ 07°35’14’’ 7 44 29 1 0 47 0 0 0 Sesia 45°38’00’’ 07°55’00’’ 0 0 0 0 1 0 0 0 0 Orta 45°49’00’’ 08°24’00’’ 0 0 0 0 1 0 0 0 0 Agogna 45°36’02’’ 08°28’03’’ 17 0 107 0 0 0 0 0 0 Ticino (CH) 46°24’33’’ 08°36’25’’ 0 0 4 0 0 14 0 0 0 Ticino (NO) 45°37’00’’ 08°38’00’’ 0 0 0 9 0 0 0 0 0 Ticino (MI) 45°22’33’’ 09°24’28’’ 37 0 0 35 0 0 0 0 0 Toce 46°15’35’’ 08°16’27’’ 0 0 11 0 19 0 0 0 0 Maggiore (CH) 46°26’09’’ 08°48’11’’ 0 0 0 0 18 0 0 0 0 Maggiore (VB) 45°48’21’’ 08°34’16’’ 0 0 0 0 303 0 0 0 0 Maggiore (VA) 45°51’12’’ 08°40’10’’ 0 0 0 0 78 0 0 0 0 Mergozzo 45°57’21’’ 08°27’36’’ 0 0 0 0 162 0 0 0 0 Varese 45°50’96’’ 08°43’73’’ 0 0 1 0 119 0 0 0 0 Lugano 46°28’06’’ 09°38’12’’ 0 0 3 0 14 0 0 0 0 Olona 45°30’11’’ 09°20’52’’ 0 0 0 43 0 0 0 0 0 Lambro 45°48’37’’ 09°16’60’’ 0 0 0 1 163 0 0 0 0 Adda (SO) 46°19’02’’ 09°43’01’’ 1 24 0 0 0 0 0 0 0 To be continued on next page Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 76 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l Table 2. River catchments with mean latitude and longitude, and number of samples collected in each habitat. No n- co mm er cia l Table 2. River catchments with mean latitude and longitude, and number of samples collected in each habitat. No n- co mm er cia l River catchment lat long kn kr rh pt AL al ME hm br No n- co mm er cia l River catchment lat long kn kr rh pt AL al ME hm br Garonna (France) 44°00’00’’ 02°00’00’’ No n- co mm er cia l Garonna (France) 44°00’00’’ 02°00’00’’ 0 0 No n- co mm er cia l 0 0 No n- co mm er cia l No n- co mm er cia l Donau (Germany) 47°41’19’’ 11°26’16’’ No n- co mm er cia l Donau (Germany) 47°41’19’’ 11°26’16’’ 0 0 0 0 No n- co mm er cia l 0 0 0 0 Donau (Austria) 47°47’17’’ 13°20’17’’ No n- co mm er cia l Donau (Austria) 47°47’17’’ 13°20’17’’ No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l Dora Baltea 45°37’24’’ 07°35’14’’ 7 44 29No n- co mm er cia l Dora Baltea 45°37’24’’ 07°35’14’’ 7 44 29 45°38’00’’ 07°55’00’’No n- co mm er cia l 45°38’00’’ 07°55’00’’ us e o nly The first central moment has the meaning of optimum response value, the second moment can be interpreted as a measure of tolerance (Ter Braak & Prentice, 1988). A positive value of g1 means a response curve skewed to the right, i.e. the optimum value is closer to the mini- mum response value. A negative value of g1 means a response curve skewed to the left, i.e. optimum water temperature is closer to the max- imum response value. A positive value of g2 is a measure of the peaked- ness of a curve. A curve with a high g2 (>3) is called leptokurtic and it has a defined peak, i.e. the species has a defined optimum tempera- ture. A negative value of g2 means a platykurtic response or flat response, i.e. the species is present over a wide range of water temper- ature values. In general, a negative value of g2 suggests a bi- or pluri- modal Gaussian distribution (Khurshid, 2007). Moment calculations were performed converting in Matlab® envi- [Journal of Entomological and Acarological Research 2013; 45:e14] [page 77] Article Table 2. Continued from previous page. River catchment lat long kn kr rh pt AL al ME hm br Adda (LC) 45°48’16’’ 09°23’27’’ 0 0 3 0 21 0 0 0 0 Adda (MI) 45°37’00’’ 09°29’97’’ 0 0 0 17 0 0 0 0 0 Adda (LO) 45°16’02’’ 09°37’00’’ 0 0 1 19 4 0 0 0 0 Adda (CR) 45°28’00’’ 09°31’00’’ 0 0 0 18 0 0 0 0 0 Adda (BG) 46°07’00’’ 09°53’00’’ 13 0 0 1 0 0 0 0 0 Sarca 46°08’02’’ 10°37’32’’ 87 206 115 0 0 15 0 0 0 Noce 46°17’00’’ 10°40’00’’ 0 3 0 0 1 0 0 0 0 Adige (BZ) 46°02’41’’ 11°15’33’’ 0 0 0 0 4 0 0 0 0 Adige (TN) 46°20’25’’ 10°29’21’’ 0 0 1 0 114 38 0 0 0 Brenta 46°01’34’’ 11°19’39’’ 0 0 0 0 78 0 0 0 0 Como 45°40’01’’ 09°17’02’’ 0 0 0 0 107 0 0 0 0 Brembo 45°42’46’’ 09°38’39’’ 1 0 56 0 0 0 0 1 0 Serio 45°30’10’’ 09°44’12’’ 1 0 36 0 0 0 0 0 0 Iseo 45°40’24’’ 09°35’38’’ 0 0 0 0 28 0 0 0 0 Oglio 45°35’17’’ 09°45’14’’ 2 4 25 2 51 0 0 0 0 Mincio (MN) 45°33’32’’ 10°39’45’’ 0 0 0 0 6 0 0 0 0 Garda(VR) 45°41’00’’ 10°41’01’’ 0 0 0 0 353 0 0 0 0 Po (MI and PV) 45°41’05’’ 09°16’02’’ 0 0 216 103 46 0 0 0 0 Po (PC) 45°07’00’’ 10°25’06’’ 0 0 0 427 0 0 0 0 0 Po (FE) 44°10’00’’ 12°00’00’’ 0 0 0 1 0 0 0 0 0 Tanaro 44°21’00’’ 08°11’04’’ 0 0 85 27 0 0 0 0 0 Danè 44°16’00’’ 08°25’00’’ 0 0 95 0 0 0 0 0 0 Trebbia 44°29’16’’ 09°21’18’’ 4 0 11 0 5 0 0 0 0 Taro 44°35’30’’ 09°33’21’’ 2 0 31 28 0 0 0 0 0 Magra 44°22’00’’ 09°53’00’’ 0 0 1 0 0 0 0 0 0 Reno (Brasimone) 44°08’00’’ 11°08’00’’ 0 0 0 0 0 0 0 1 0 Potenza 43°19’00’’ 13°24’00’’ 0 0 10 10 0 0 0 0 0 Tevere (PG) 43°18’00’’ 12°18’00’’ 0 0 0 3 0 0 0 0 0 Trasimeno 43°10’00’’ 12°00’00’’ 0 0 0 0 0 0 2 0 0 Bolsena 42°35’00’’ 11°55’00’’ 0 0 0 0 0 0 102 0 0 Bracciano 42°07’00’’ 12°14’00’’ 0 0 0 0 0 0 59 0 0 Vico 42°18’00’’ 12°10’00’’ 0 0 0 0 0 0 40 0 0 Tordino-Vomano 42°36’00’’ 13°38’00’’ 0 0 2 3 0 0 0 1 0 Nera 42°25’00’’ 13°05’00’’ 0 0 2 0 0 0 0 0 0 Aterno-Pescara 42°26’00’’ 13°22’00’’ 12 0 4 0 0 0 0 1 0 Sangro (Scontrone) 41°34’00’’ 13°38’00’’ 1 0 2 1 2 0 0 4 0 Fortore (Occhito) 41°35’00’’ 14°57’00’’ 0 0 0 0 0 0 0 14 0 Liri (Fibreno) 41°38’00’’ 13°22’00’’ 0 0 0 0 0 0 1 0 0 Ofanto 40°52’00’’ 15°05’00’’ 0 0 1 0 0 0 0 0 0 Cedrino 40°35’00’’ 09°42’00’’ 1 0 0 0 0 0 0 0 0 Sele 40°33’00’’ 15°19’00’’ 0 0 33 0 0 0 0 0 0 Agri (Pertusillo) 40°16’00’’ 15°56’00’’ 0 0 0 0 0 0 0 103 0 rio Mannu 39°18’00’’ 09°08’00’’ 0 0 2 0 0 0 0 0 3 Dirillo 37°08’00’’ 14°45’00’’ 0 0 0 0 0 0 0 4 0 Kebir (Algeria) 36°46’38’’ 08°19’31’’ 0 0 90 0 0 0 0 0 0 lat, latitude; long, longitude; kn, krenal; kr, kryal; rh, rhithral; pt, potamal; AL, Alpine ecoregion lowland lakes; al, Alpine ecoregion high altitude lakes; ME, Mediterranean ecoregion lakes; hm, heavily modified water bodies; br, brackish ponds. Abbreviations in brackets are Italian provinces. Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 77 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 31 28 No n- co mm er cia l 31 280 0 1 0 0 0 0 0 0 No n- co mm er cia l 0 0 1 0 0 0 0 0 0 No n- co mm er cia l No n- co mm er cia l 0 0 0 0 0 0 0 1 0 No n- co mm er cia l 0 0 0 0 0 0 0 1 0 10 10 No n- co mm er cia l 10 10 No n- co mm er cia l No n- co mm er cia l 0 0 0 3 0 0 0 0 0 No n- co mm er cia l 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 2 0 0 No n- co mm er cia l 0 0 0 0 0 0 2 0 0 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 0 0 0 0 0 0 No n- co mm er cia l 0 0 0 0 0 0 0 0 0 0 0 0 No n- co mm er cia l 0 0 0 0 0 0 No n- co mm er cia l No n- co mm er cia l 0 0 0 0 0 0 No n- co mm er cia l 0 0 0 0 0 0 0 0 2 3 0 0 0 1 0 No n- co mm er cia l 0 0 2 3 0 0 0 1 0 No n- co mm er cia l No n- co mm er cia l 0 0 2 0 0 0 0 0 0 No n- co mm er cia l 0 0 2 0 0 0 0 0 0 Aterno-Pescara 42°26’00’’ 13°22’00’’ 12 No n- co mm er cia l Aterno-Pescara 42°26’00’’ 13°22’00’’ 12 No n- co mm er cia l No n- co mm er cia l Sangro (Scontrone) 41°34’00’’ 13°38’00’’ No n- co mm er cia l Sangro (Scontrone) 41°34’00’’ 13°38’00’’ 1 0 2 1 2 0 0 4 0 No n- co mm er cia l 1 0 2 1 2 0 0 4 0 Fortore (Occhito) 41°35’00’’ 14°57’00’’ No n- co mm er cia l Fortore (Occhito) 41°35’00’’ 14°57’00’’ No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l Liri (Fibreno) 41°38’00’’ 13°22’00’’ No n- co mm er cia l Liri (Fibreno) 41°38’00’’ 13°22’00’’ 40°52’00’’ 15°05’00’’No n- co mm er cia l 40°52’00’’ 15°05’00’’No n- co mm er cia l Cedrino 40°35’00’’ 09°42’00’’No n- co mm er cia l Cedrino 40°35’00’’ 09°42’00’’ us e us e us e 0 0 0 0 0 us e 0 0 0 0 0 us e us e 0 0 0 0 0 0 us e 0 0 0 0 0 0 0 5 0 0 0 0us e 0 5 0 0 0 0us e us e o nlyon ly on ly 0 0 0 0 6 0 0 0 0 on ly 0 0 0 0 6 0 0 0 0 0 0 0 0 on ly0 0 0 0 on ly on ly0 0 0 0 on ly0 0 0 0 0 0 0 0 0on ly 0 0 0 0 0on ly on ly 0 0 0 1 0 0 0 0 0on ly 0 0 0 1 0 0 0 0 0 0 0 0 0 0 on ly 0 0 0 0 0 [page 78] [Journal of Entomological and Acarological Research 2013; 45:e14] ronment, version R2012a, some FORTRAN programs, program 9 (Davies, 1971) and program STATFD (Rohlf, 1987). The central moment calculation formulae were used also to analyze the response of species to altitude, water depth (for lacustrine species) and distance from source (for lotic species). Regression between species optima for water temperature and standard devia- tion, g1 or g2 was also calculated to relate species optimum and toler- ance characters. To represent graphically species response to water temperature the Curve-Fitting Matlab® toolbox was used, fitting species abundances against water temperature values; the toolbox allows to fit many dif- ferent models, in particular the one-, two- or n-term Gaussian library model: y = a1 * e – ((x–m1)/s1) 2+... an * e – ((x–mn)/sn) 2 where 1 and n are the peaks to be fitted, a1 and an are the amplitude, m1 and mn the centroid (location), s1 and sn are coefficients related to the peak width. Separate models were tested for each species collected as larvae and pupal exuviae in the different habitats. The fitted curves given in Figures 2-11 are the ones giving the best fit (i.e. the lowest mean square error). Models with more than three terms (see formula) were not considered to avoid overfitting. Regression curves between optima for water temperature (as dependent variable) and optima for altitude, water depth, distance from source (as independent variables) were calculated. Results Of all available data, 281 samples were from kryal, 186 from krenal, 987 from rhithral, 749 from potamal, 1903 from lakes in the Alpine ecoregion (i.e. 114 from high altitude lakes and 1789 from lowland lakes), 204 from natural lakes in the Mediterranean ecoregion, 129 from heavily modified water bodies, 3 from brackish ponds (Table 2). A total of 443 chironomid species were present in the sampling sites. Water temperature Thermal response was first calculated considering all data on larvae (i.e. joining all habitats) to generally characterize each species’ prefer- ences for water temperature. Results for the 55 species present in ≥100 records are given in Table 3. For each species the number of samples used to calculate the weighted mean, standard deviation, skewness and kurtosis are reported. In general, species with preference for low temper- ature had a lower standard deviation than species with optima in warm waters. For this reason the former can be defined as cold stenothermal, the latter as warm eurithermal. In fact, the r2 value obtained regressing optimum water temperature of each species with its standard deviation was significant [r2=0.48, 53 degree of freedom (df), P<0.01]. The regression between optimum for water temperature (m°C) and skewness (g1) (Table 3) gave an inverse relation (r2=0.34, 53 df, P<0.01). As well, optimum for water temperature (m°C) and kurtosis (g2) were inversely related (r2=0.22, 53 df, P<0.01). These relations sug- gest that cold stenothermal species generally show a response curve skewed to the right, with optimum value closed to minimum values, and leptokurtic (i.e. unimodal trend); whereas thermophilous species gener- ally show a curve skewed to the left, with optimum value closed to maxi- mum values, and platykurtic (i.e. bi- or plurimodal trend). Thermal response was then calculated for each separate habitat to better characterize each species’ preferences (i.e. using data on larvae collected with the same sampling method) (Appendix). The thermal response of some species is represented in Figures 2-9. For example, thermal curves for Conchapelopia pallidula are shown in Figure 2. Optimum response calculated from 615 records (all habitats pooled, Figure 2A) was 13.54°C, with a standard deviation of 5.93°C, a small positive skewness of 0.34 and a negative kurtosis of �1.03 (Table 3). The negative kurtosis suggested a trimodal response with three peaks at 8.13°C (main peak), 11.39°C and 22.42°C (secondary peaks). Peaks were at 4.93°C (main peak), 7.45°C and 20.77°C considering only samples from Alpine lowland lakes (Figure 2B). Optimum for rhithral samples was 13.9 °C (unimodal response) (Figure 2C, Appendix), while potamal samples gave a trimodal response with peaks at 11.5 °C, 18.64 °C and 23.89 °C (Figure 2D). Article Figure 2. Response of Conchapelopia pallidula larvae (number of individuals m–2) to water temperature (°C) in all habitats (A), Alpine ecoregion lowland lakes (B), rhithral (C) and potamal (D). Figure 3. Thermal response of Diamesini larvae. Response of Diamesa bertrami (number of individuals m–2) to water tempera- ture (°C) in all habitats (A), kyral (B), krenal (C) and rhithral (D). Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 78 No n- co mm er cia l pooled, Figure 2A) was 13.54°C, with a standard deviation of 5.93°C, a No n- co mm er cia l pooled, Figure 2A) was 13.54°C, with a standard deviation of 5.93°C, a No n- co mm er cia l 114 from high altitude lakes and 1789 from lowland No n- co mm er cia l 114 from high altitude lakes and 1789 from lowland lakes), 204 from natural lakes in the Mediterranean ecoregion, 129 No n- co mm er cia l lakes), 204 from natural lakes in the Mediterranean ecoregion, 129 from heavily modified water bodies, 3 from brackish ponds (Table 2). A No n- co mm er cia l from heavily modified water bodies, 3 from brackish ponds (Table 2). A total of 443 chironomid species were present in the sampling sites. No n- co mm er cia l total of 443 chironomid species were present in the sampling sites. small positive skewness of 0.34 and a negative kurtosis of �1.03 (Table 3). No n- co mm er cia l small positive skewness of 0.34 and a negative kurtosis of �1.03 (Table 3).The negative kurtosis suggested a trimodal response with three peaks at No n- co mm er cia l The negative kurtosis suggested a trimodal response with three peaks at 8.13°C (main peak), 11.39°C and 22.42°C (secondary peaks). Peaks were No n- co mm er cia l 8.13°C (main peak), 11.39°C and 22.42°C (secondary peaks). Peaks were at 4.93°C (main peak), 7.45°C and 20.77°C considering only samples No n- co mm er cia l at 4.93°C (main peak), 7.45°C and 20.77°C considering only samples from Alpine lowland lakes (Figure 2B). Optimum for rhithral samples No n- co mm er cia l from Alpine lowland lakes (Figure 2B). Optimum for rhithral samples was 13.9 °C (unimodal response) (Figure 2C, Appendix), while potamal No n- co mm er cia l was 13.9 °C (unimodal response) (Figure 2C, Appendix), while potamal No n- co mm er cia l u se collected with the same sampling method) (Appendix). us e collected with the same sampling method) (Appendix). The thermal response of some species is represented in Figures 2-9. us e The thermal response of some species is represented in Figures 2-9.For example, thermal curves for us e For example, thermal curves for Figure 2. Optimum response calculated from 615 records (all habitats us e Figure 2. Optimum response calculated from 615 records (all habitats pooled, Figure 2A) was 13.54°C, with a standard deviation of 5.93°C, aus e pooled, Figure 2A) was 13.54°C, with a standard deviation of 5.93°C, a small positive skewness of 0.34 and a negative kurtosis of �1.03 (Table 3).us e small positive skewness of 0.34 and a negative kurtosis of �1.03 (Table 3). on ly unimodal trend); whereas thermophilous species gener- on ly unimodal trend); whereas thermophilous species gener- ally show a curve skewed to the left, with optimum value closed to maxi- on ly ally show a curve skewed to the left, with optimum value closed to maxi- i.e. on lyi.e. bi- or plurimodal trend). on lybi- or plurimodal trend).Thermal response was then calculated for each separate habitat to on lyThermal response was then calculated for each separate habitat to better characterize each species’ preferences (on ly better characterize each species’ preferences ( collected with the same sampling method) (Appendix).on ly collected with the same sampling method) (Appendix). The thermal response of some species is represented in Figures 2-9.on ly The thermal response of some species is represented in Figures 2-9. [Journal of Entomological and Acarological Research 2013; 45:e14] [page 79] Article Table 3. Thermal response (°C) of species (larvae) in all habitats: number of samples, weighted mean, standard deviation, skewness and kurtosis of species abundance vs water temperature values. Only the species with ≥100 records in the dataset are reported. Species are in phylogenetic order. Species n m (°C) SD (°C) g1 g2 Procladius choreus 1018 13.39 5.7 0.63 −0.65 Macropelopia nebulosa 127 10.5 5.17 0.47 −0.63 Zavrelimyia barbatipes 128 11.58 4.11 −0.21 0.45 Conchapelopia pallidula 615 13.54 5.93 0.34 −1.03 Rheopelopia ornata 111 14.77 4.3 −0.19 −1.15 Pseudodiamesa branickii 115 5.63 3.08 0.8 0.71 Diamesa steinboecki 106 1.98 1.45 1.06 1.19 Diamesa latitarsis 134 3.43 1.97 0.85 0.27 Diamesa bertrami 200 2.68 1.96 1.16 0.79 Diamesa tonsa 186 7.19 4.66 0.61 −0.18 Diamesa zernyi 215 3.72 2.51 1.62 8.22 Prodiamesa olivacea 246 9.48 4.33 1.79 3.56 Brillia bifida 202 11.38 4.76 0.19 −0.69 Tvetenia calvescens 537 11.08 5.81 0.06 −1.24 Eukiefferiella brevicalcar 133 4.51 1.94 1.66 6.37 Eukiefferiella claripennis 215 14.7 4.41 −0.49 −0.3 Eukiefferiella minor 176 6.8 3.78 0.72 0.41 Psectrocladius (Psectrocladius) oxyura 283 12.17 6.22 0.43 −1.04 Rheocricotopus effusus 124 13.15 5.83 −0.16 −0.49 Rheocricotopus fuscipes 245 16.97 7.97 0.06 −1.49 Synorthocladius semivirens 128 13.38 4.42 −0.16 −0.78 Orthocladius (Euorthocladius) rivicola 366 9.85 4.7 0.52 −0.01 Orthocladius frigidus 261 6.17 3.72 1.25 1.4 Orthocladius oblidens 138 9.18 5.5 1.16 0.21 Orthocladius rhyacobius 212 12.14 4.02 −0.15 −0.24 Orthocladius rubicundus 111 12.45 3.19 0.55 0.91 Paratrichocladius rufiventris 253 17.33 6.32 0.17 −0.82 Cricotopus annulator 161 14.24 4.79 0.09 0.16 Cricotopus bicinctus 276 14.63 5.08 −0.23 −1.04 Cricotopus (Isocladius) sylvestris 183 11.19 5.08 0.82 −0.09 Parametriocnemus stylatus 218 11.14 4.97 0.36 −0.83 Parakiefferiella bathophila 117 5.89 3.69 3.66 12.52 Thienemanniella partita 107 7.73 4.08 0.93 0.3 Corynoneura scutellata 259 11.07 4.06 −0.5 −0.35 Tanytarsus gregarius 421 11.11 6.8 0.72 −1.07 Cladotanytarsus atridorsum 268 14.59 5.11 0.63 −1.05 Paratanytarsus lauterborni 101 10.53 3.01 3.1 9.11 Micropsectra atrofasciata 490 13.79 5.33 0.52 0.88 Micropsectra pallidula 125 6.3 3.58 1.1 0.44 Pagastiella orophila 115 8.12 4.63 1.43 0.75 Pseudochironomus prasinatus 209 13.95 6.56 0.02 −1.37 Paratendipes albimanus 351 12.22 4.43 1.35 0.65 Microtendipes pedellus 394 12.29 2.73 0.6 1.06 Polypedilum convictum 138 15.44 4.07 −0.61 0.44 Polypedilum laetum 112 16.65 5.52 −0.14 −0.38 Polypedilum nubeculosum 566 12.08 4.09 1.26 1.58 Endochironomus tendens 106 12.51 3.91 0.8 0.08 Dicrotendipes nervosus 276 10.08 5.24 0.86 0 Glyptotendipes pallens 154 13.88 7.65 0.08 −1.25 Chironomus anthracinus 525 13.54 6.35 0.5 −1.44 Chironomus plumosus 571 11.19 6.1 0.67 −0.59 Chironomus riparius 333 15.28 4.65 0.32 1.44 Cladopelma viridulum 294 13.63 5.98 0.51 −0.7 Cryptochironomus defectus 473 13.86 5.67 0.43 −0.74 Demicryptochironomus vulneratus 143 12.96 7.28 0.44 −1.36 n, number of samples; m, weighted mean; SD, standard deviation; g1, skewness; g2, kurtosis. Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 79 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 12.14 No n- co mm er cia l 12.14 No n- co mm er cia l No n- co mm er cia l 12.45 No n- co mm er cia l 12.45 17.33 No n- co mm er cia l 17.33 No n- co mm er cia l No n- co mm er cia l 14.24 No n- co mm er cia l 14.24 14.63 No n- co mm er cia l 14.63 No n- co mm er cia l No n- co mm er cia l 218 No n- co mm er cia l 218 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 117 No n- co mm er cia l 117 107 No n- co mm er cia l 107 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 259 No n- co mm er cia l 259 421 No n- co mm er cia l 421 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l u se us e us e 7.97 us e 7.974.42 us e 4.42 us e us e 4.7us e 4.7 3.72us e 3.72 on ly on ly on ly 1.66 on ly 1.66 on ly on ly−0.49 on ly−0.490.72 on ly0.72 on ly on ly on ly [page 80] [Journal of Entomological and Acarological Research 2013; 45:e14] Many cold stenothermal species such as Diamesa zernyi and Pseudokiefferiella parva showed only one maximum, with a high g2, i.e. leptokurtic response (Table 3, Appendix). Species with low temperature optimum (cold stenothermal) showed a response curve skewed to the right (g1>0). Diamesa bertrami showed a moderately platykurtic response (g2=0.79), with a trimodal curve consid- ering all habitats (Figure 3A), a bimodal curve with main peak at 2.76°C in kryal samples (with a second peak at 0.93°C) (Figure 3B), a unimodal response in krenal with peak at 3.90°C (Figure 3C), a trimodal response in rhithral with peaks at 3.67°C, 6.79°C and 8.52°C (Figure 3D). Species with optimum at high temperatures (thermophilous species) showed a response curve skewed to the left (g1<0). For exam- ple, Cricotopus (Isocladius) sylvestris in potamal (Figure 4C, Appendix) showed optimum at 17.80°C and g1=2.13; Paratanytarsus mediterra- neus in potamal (Figure 5D; Appendix) had optimum at 19.42°C and a g1=1.59. Tanytarsus brundini in rhithral with optimum at 14.37°C and a negative g1 (g1=0.29) is an example of a curve moderately skewed to the left (Figure 5B; Appendix). Some exceptions were shown: Paratrichocladius rufiventris (Figure 4A) had optimum temperature value of 17.33°C and a response curve skewed to the right (g1>0, i.e. g1=0.17) (Table 3). A negative value of g2 was an index of a bi- or plurimodal response; Tanytarsus gregarius in Alpine ecoregion lakes with a negative g2 (g2=1.09; Appendix) had a bimodal response with two peaks at 5.68°C and 20.66°C (Figure 5C); the very different optima suggest the presence of two populations, the former inhabiting high depth habitats (down to 350 m depth) charac- terized by low temperatures. Similarly, it was possible to compare the response of Polypedilum nubeculosum larvae in different habitats (Figure 8). A plurimodal response was evident, with different peaks in different habitats. The response of the larval and pupal stages was compared in different habitats (Figures 6-7, Table 4). For example, larvae of Micropsectra atro- fasciata in rhithral showed peaks at 6.63°C, 11.83°C and 17.84°C (Figure 6C), while pupal exuviae at 8.91°C, 12.65°C and 15.92°C (Figure 7C); in potamal larvae had peaks at 6.26°C, 9.43°C and 17.95°C (Figure 6D), while pupal exuviae at 9.40°C, 13.53°C and 18.39°C (Figure 7D). The response of species belonging to the same genus was also ana- lyzed (Figures 7 and 9). Chironomus anthracinus showed a bimodal response in Alpine lowland lakes (Figure 9A). Chironomus plumosus had a trimodal response in Alpine lowland lakes, and the main peak was at the lowest temperature (Figure 9B); a similar response was observed in Mediterranean lakes (Figure 9C). Chironomus riparius showed a unimodal response in the rhithral habitat (optimum at 15 °C) (Figure 9D, Appendix). Altitude The response to altitude for the most frequently captured species is reported in Table 5. All data on larvae were used (i.e. all habitats). The regression between optima for altitude and for water temperature was calculated selecting 78 species present in at least 66 samples, for which both altitude and water temperature values were available. This selection gave the highest r2. Regression coefficient was negative (r2=0.60, 76 df, P<0.01, Figure 10). At high altitudes, Zavrelimyia barbatipes, Corynoneura scutellata, Paratanytarsus austriacus showed an optimum water temperature higher than predicted by altitude, whereas D. bertra- mi, Paratrichocladius skirwithensis, Orthocladius (Eudactylocladius) fuscimanus had temperature optima lower than expected by altitude; at Article Figure 4. Thermal response of Orthocladiini larvae. Response of Paratrichocladius rufiventris (number of individuals m–2) to water temperature (°C) in all habitats (A) and rhithral (B); response of Cricotopus (Isocladius) sylvestris in potamal (C); response of Corynoneura scutellata in Alpine ecoregion high altitude lakes (D). Table 4. Thermal response (°C) of Micropsectra atrofasciata (Chironominae) in specific habitats at different life stages: num- ber of samples, weighted mean, standard deviation, skewness and kurtosis of species abundance vs water temperature values. Life stage Habitat n m (°C) SD (°C) g1 g2 Larvae Rhythral 363 14.20 6.17 0.42 −0.33 Pupal exuviae Rhythral 89 13.24 4.11 0.45 0.50 Larvae Potamal 37 13.50 5.48 −0.03 −1.02 Pupal exuviae Potamal 79 14.86 5.87 −0.06 −1.09 Larvae Alpine lakes 48 14.05 4.62 0.67 2.47 Pupal exuviae Alpine lakes 56 16.31 7.54 0.58 −1.38 n, number of samples; m, weighted mean; SD, standard deviation; g1, skewness; g2, kurtosis; Alpine lakes, Alpine ecoregion lowland lakes. Figure 5. Thermal response of Tanytarsini larvae. Response of Tanytarsus brundini (number of individuals m–2) to water temper- ature (°C) in all habitats (A) and rhithral (B); response of Tanytarsus gregarius in Alpine ecoregion lowland lakes (C); response of Paratanytarsus mediterraneus in potamal (D). Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 80 No n- co mm er cia l in rhithral showed peaks at 6.63°C, 11.83°C and 17.84°C (Figure No n- co mm er cia l in rhithral showed peaks at 6.63°C, 11.83°C and 17.84°C (Figure 6C), while pupal exuviae at 8.91°C, 12.65°C and 15.92°C (Figure 7C); in No n- co mm er cia l 6C), while pupal exuviae at 8.91°C, 12.65°C and 15.92°C (Figure 7C); in potamal larvae had peaks at 6.26°C, 9.43°C and 17.95°C (Figure 6D), No n- co mm er cia l potamal larvae had peaks at 6.26°C, 9.43°C and 17.95°C (Figure 6D), while pupal exuviae at 9.40°C, 13.53°C and 18.39°C (Figure 7D). No n- co mm er cia l while pupal exuviae at 9.40°C, 13.53°C and 18.39°C (Figure 7D). The response of species belonging to the same genus was also ana- No n- co mm er cia l The response of species belonging to the same genus was also ana- showed a bimodal No n- co mm er cia l showed a bimodal No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l Life stage Habitat n m (°C) SD (°C) g1 g2 No n- co mm er cia l Life stage Habitat n m (°C) SD (°C) g1 g2Larvae Rhythral 363 14.20 6.17 0.42 −0.33 No n- co mm er cia l Larvae Rhythral 363 14.20 6.17 0.42 −0.33 No n- co mm er cia l No n- co mm er cia l Pupal exuviae Rhythral 89 13.24 4.11 0.45 0.50 No n- co mm er cia l Pupal exuviae Rhythral 89 13.24 4.11 0.45 0.50 Larvae Potamal 37 13.50 5.48 −0.03 −1.02 No n- co mm er cia l Larvae Potamal 37 13.50 5.48 −0.03 −1.02 No n- co mm er cia l No n- co mm er cia l Pupal exuviae Potamal 79 14.86 5.87 −0.06 −1.09 No n- co mm er cia l Pupal exuviae Potamal 79 14.86 5.87 −0.06 −1.09 us e Table 4. Thermal response (°C) of us e Table 4. Thermal response (°C) of (Chironominae) in specific habitats at different life stages: num- us e (Chironominae) in specific habitats at different life stages: num-ber of samples, weighted mean, standard deviation, skewness and us e ber of samples, weighted mean, standard deviation, skewness andkurtosis of species abundance vs water temperature values. us e kurtosis of species abundance vs water temperature values. us e us e Life stage Habitat n m (°C) SD (°C) g1 g2us e Life stage Habitat n m (°C) SD (°C) g1 g2 on ly had temperature optima lower than expected by altitude; at on ly had temperature optima lower than expected by altitude; at Table 4. Thermal response (°C) of on ly Table 4. Thermal response (°C) of (Chironominae) in specific habitats at different life stages: num- on ly (Chironominae) in specific habitats at different life stages: num- [Journal of Entomological and Acarological Research 2013; 45:e14] [page 81] Article Table 5. Response of species (larvae) to altitude (m a.s.l.) in all habitats: number of samples, weighted mean, standard deviation, skew- ness and kurtosis of species abundance vs site altitude values. Only the species with ≥100 records in the dataset are reported. Species are in phylogenetic order. Species n m (m a.s.l.) SD (m a.s.l.) g1 g2 Tanypus punctipennis 118 237 207 2.78 16.96 Procladius choreus 1530 437 303 2.42 7.53 Macropelopia nebulosa 274 1278 524 −0.95 −0.67 Ablabesmyia monilis 143 662 513 1.97 3.43 Zavrelimyia barbatipes 243 1961 540 −2.09 3.16 Conchapelopia pallidula 1005 363 285 3.14 14.63 Rheopelopia ornata 137 177 160 2.22 8.04 Pseudodiamesa branickii 262 1913 611 −1.09 0.11 Diamesa steinboecki 119 2559 221 −2.42 8.87 Diamesa latitarsis 171 2213 572 −1.60 2.59 Diamesa bertrami 277 1933 653 −0.86 0.04 Diamesa tonsa 409 897 654 1.27 0.75 Diamesa zernyi 353 2145 564 −1.14 1.04 Pseudokiefferiella parva 119 2348 475 −1.52 2.49 Prodiamesa olivacea 393 300 421 3.56 12.80 Brillia longifurca 100 458 264 0.87 0.95 Brillia bifida 413 434 298 1.76 6.13 Cardiocladius fuscus 148 677 750 1.60 0.77 Tvetenia calvescens 840 1281 945 0.14 −1.81 Eukiefferiella brevicalcar 162 2013 461 −1.55 2.01 Eukiefferiella claripennis 353 651 691 2.00 2.23 Eukiefferiella minor 324 1489 772 −0.39 −1.52 Psectrocladius (Psectrocladius) oxyura 334 272 373 4.56 20.39 Rheocricotopus chalybeatus 116 342 168 1.50 5.34 Rheocricotopus effusus 205 866 743 1.17 −0.33 Rheocricotopus fuscipes 515 361 242 3.10 17.76 Synorthocladius semivirens 212 451 280 4.10 22.43 Orthocladius (Eudactylocladius) fuscimanus 124 1825 709 −1.25 −0.09 Orthocladius (Euorthocladius) rivicola 618 1052 902 0.66 −1.40 Orthocladius excavatus 141 335 152 1.96 15.17 Orthocladius frigidus 463 1767 743 −0.90 −0.49 Orthocladius oblidens 179 305 188 1.73 2.60 Orthocladius rhyacobius 312 422 228 0.79 1.82 Orthocladius rubicundus 204 409 214 1.19 6.43 Paratrichocladius rufiventris 456 737 610 0.81 −1.18 Paratrichocladius skirwithensis 210 1849 538 −1.57 1.75 Cricotopus annulator 245 412 335 3.81 17.08 Cricotopus bicinctus 422 189 198 1.31 5.93 Cricotopus fuscus 169 1067 624 0.17 −1.18 Cricotopus tremulus 126 968 725 0.75 −0.27 Cricotopus triannulatus 220 220 231 2.56 8.14 Cricotopus (Isocladius) sylvestris 276 322 593 2.89 6.69 Metriocnemus hygropetricus 180 937 685 0.88 −0.59 Chaetocladius laminatus 142 1628 913 −0.44 −1.62 Paratrissocladius excerptus 114 434 242 −0.07 −0.01 Heterotrissocladius marcidus 174 1936 595 −1.45 1.02 Parametriocnemus stylatus 349 1137 878 0.51 −1.19 Parakiefferiella bathophila 165 226 138 4.06 28.87 Thienemanniella partita 173 1141 904 0.19 −1.69 Corynoneura scutellata 395 2130 447 −3.37 11.70 Stempellina bausei 115 426 209 0.00 −1.67 Tanytarsus gregarius 652 561 577 1.21 −0.31 Cladotanytarsus atridorsum 342 406 136 1.92 17.06 Paratanytarsus austriacus 135 2087 311 −2.58 8.72 To be continued on next page Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 81 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 866 No n- co mm er cia l 866 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 361 No n- co mm er cia l 361 451 No n- co mm er cia l 451 No n- co mm er cia l No n- co mm er cia l 1825 No n- co mm er cia l 1825 No n- co mm er cia l No n- co mm er cia l 463 No n- co mm er cia l 463 No n- co mm er cia l No n- co mm er cia l 179 No n- co mm er cia l 179 312 No n- co mm er cia l 312 No n- co mm er cia l No n- co mm er cia l 204 No n- co mm er cia l 204 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l u se 945 us e 945 us e us e 461 us e 461691 us e 691 us e us e 772us e 772 on ly on ly on ly −1.52 on ly −1.52 3.56 on ly3.56 on ly on ly0.87 on ly0.87 1.76 on ly 1.76 on ly on ly [page 82] [Journal of Entomological and Acarological Research 2013; 45:e14] Article Table 5. Continued from previous page. Species n m (m a.s.l.) SD (m a.s.l.) g1 g2 Paratanytarsus lauterborni 125 410 549 1.76 1.19 Micropsectra atrofasciata 890 425 361 3.06 10.30 Micropsectra contracta 386 402 114 8.52 93.32 Micropsectra notescens 108 527 313 0.31 1.08 Micropsectra pallidula 166 2184 293 −1.55 3.46 Pagastiella orophila 127 575 245 −0.77 −0.90 Pseudochironomus prasinatus 256 396 202 0.30 −1.74 Paratendipes albimanus 464 308 172 2.83 17.00 Microtendipes pedellus 510 204 213 3.86 18.41 Polypedilum convictum 145 347 167 −0.32 −1.23 Polypedilum laetum 199 340 294 3.06 15.31 Polypedilum cultellatum 100 142 153 1.68 2.34 Polypedilum nubeculosum 812 228 143 5.21 61.77 Phaenopsectra flavipes 149 399 429 2.03 3.05 Endochironomus tendens 140 148 198 6.14 57.78 Stictochironomus pictulus 101 460 443 2.21 2.90 Dicrotendipes nervosus 373 270 104 1.28 1.94 Glyptotendipes pallens 237 241 67 1.56 18.49 Chironomus anthracinus 751 482 356 1.79 3.65 Chironomus plumosus 762 283 132 2.04 7.37 Chironomus riparius 521 229 199 0.93 −0.24 Cladopelma viridulum 390 238 133 6.26 70.75 Parachironomus arcuatus 113 195 98 2.73 16.60 Paracladopelma camptolabis 107 631 546 1.21 0.57 Paracladopelma nigritulum 188 388 55 10.07 221.97 Cryptochironomus defectus 606 305 156 0.93 0.25 Demicryptochironomus vulneratus 163 226 88 3.18 12.09 n, number of samples; m, weighted mean; SD, standard deviation; g1, skewness; g2, kurtosis. Figure 6. Thermal response of Polypedilum nubeculosum larvae (number of individuals m–2) to water temperature (°C) in Alpine ecoregion lowland lakes (A), Mediterranean ecoregion lakes (B), rhithral (C) and potamal (D). Figure 7. Thermal response of Micropsectra spp. larvae. Response of M. pallidula (number of individuals m–2) to water temperature (°C) in krenal (A); response of M. atrofasciata in Alpine ecoregion lowland lakes (B), rhithral (C) and potamal (D). Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 82 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 226 No n- co mm er cia l 226 No n- co mm er cia l u se us e us e us e us e 133 us e 133 us e 98us e 98us e us e 546us e 546 55 us e 55 on ly on ly on ly 2.21 on ly 2.21 1.28 on ly1.28 on ly on ly1.56 on ly1.56 1.79on ly 1.79on ly on ly 2.04on ly 2.04 lower altitudes, the higher temperature optima were observed for P. mediterraneus, P. rufiventris and Tanypus punctipennis and the lower for Orthocladius oblidens, Pagastiella orophila, Parakiefferiella bathophila, Prodiamesa olivacea, Diamesa tonsa. Depth Response of lacustrine species (i.e. larvae in Alpine ecoregion lowland lakes) to depth is summarized in Table 6. Only few species showed opti- mum at >40 m depth (Micropsectra contracta, Paracladopelma nigritulum), others had maxima at lower depth (e.g. at 20-25 m, Procladius choreus, Prodiamesa olivacea). Response curves of some species are shown in Figure 11. C. plumosus, C. anthracinus, Demicryptochironomus vulneratus and T. gregarius showed a wide range of depth tolerance (Table 6). Source distance The optimum values for source distance were calculated for species (i.e. larvae in running water habitats) for which at least 100 samples were available (Table 7). A relation between optimum for water temper- ature and for source distance was calculated for the 75 species present in ≥81 samples. The relation is shown in Figure 12, with r2=0.33 (73 df, P<0.01) fitting a linear model. As expected, cold stenothermal species had optimum near the stream source (e.g. Diamesa species) while eurithermal ones (Endochironomus tendens, C. riparius, Glyptotendipes pallens, C. I. sylvestris, Cricotopus triannulatus, Cricotopus bicinctus) showed optimum at high distance from source. Discussion Notwithstanding the approximation of joining data collected with different sampling methods in different habitats, some generalizations could be argued by the analysis of the dataset on larvae collections. Thermophilous species often showed platikurtic responses, fitting plu- rimodal Gaussian models, with: i) optima closed to their maximum temperature values, ii) wide tolerance, iii) negative skewness and neg- ative kurtosis (Rossaro, 1991a, 1991c). On the contrary, species restricted to few habitats, such as kryal (e.g. Diamesa steinboecki, Diamesa latitarsis) or krenal (e.g. Chaetocladius laminatus, Micropsectra pallidula), showed low optima for water temperature (cold stenothermal) and low tolerance (stenoecious). These species often showed: i) optima closed to their minimum temperature values, ii) tolerance for a narrow temperature range, iii) positive skewness and positive kurtosis (Rossaro, 1991c). Even if a bimodal response can be fitted, the two maxima are generally rather closed to each other (Figure 3). These species could be thus more sensitive to an increas- ing trend of temperature (Hester & Doyle, 2011). For a better approximation of species preferences and tolerance, optima for water temperature were calculated for each species in dif- ferent habitats, thus considering data collected with the same sampling strategy (Appendix). As expected, lower values were obtained for kryal and krenal, and higher values for rhithral, potamal and lakes. Most taxa showed different responses according to the habitat. When data are [Journal of Entomological and Acarological Research 2013; 45:e14] [page 83] Article Table 6. Response of lacustrine species (larvae) to water depth (m depth) in Alpine ecoregion lowland lakes: number of samples, weight- ed mean, standard deviation, skewness and kurtosis of species abundance vs sampling depth values Only the species with ≥100 records in the dataset are reported. Species are in phylogenetic order. Species n m (m depth) SD (m depth) g1 g2 Procladius choreus 1046 21.02 23.02 3.17 23.24 Conchapelopia pallidula 232 4.87 3.34 4.05 31.03 Prodiamesa olivacea 179 21.70 17.71 1.08 1.21 Psectrocladius (Psectrocladius) oxyura 255 4.97 2.39 1.67 4.75 Orthocladius oblidens 110 4.99 2.14 1.97 20.31 Parakiefferiella bathophila 113 5.86 3.27 2.22 5.64 Tanytarsus gregarius 459 10.15 32.08 4.62 23.67 Cladotanytarsus atridorsum 253 3.62 2.38 2.65 17.66 Micropsectra contracta 359 84.91 56.80 1.33 1.62 Pagastiella orophila 116 7.10 2.77 2.69 13.03 Pseudochironomus prasinatus 212 4.26 3.04 6.85 140.29 Paratendipes albimanus 295 4.44 8.81 7.69 152.02 Microtendipes pedellus 228 6.06 4.29 1.54 3.00 Polypedilum nubeculosum 377 3.27 7.90 8.12 126.29 Dicrotendipes nervosus 232 5.70 3.88 2.05 4.46 Chironomus anthracinus 529 13.39 18.62 8.41 113.15 Chironomus plumosus 480 9.73 49.52 7.03 51.04 Cladopelma viridulum 270 8.34 16.38 10.19 163.69 Paracladopelma nigritulum 171 73.31 41.44 0.88 −0.64 Cryptochironomus defectus 423 6.51 4.75 3.70 52.20 Demicryptochironomus vulneratus 144 4.06 24.24 11.46 138.48 n, number of samples; m, weighted mean; SD, standard deviation; g1, skewness; g2, kurtosis. Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 83 No n- co mm er cia l Table 6. Response of lacustrine species (larvae) to water depth (m depth) in Alpine ecoregion lowland lakes: number of samples, weight- No n- co mm er cia l Table 6. Response of lacustrine species (larvae) to water depth (m depth) in Alpine ecoregion lowland lakes: number of samples, weight- ed mean, standard deviation, skewness and kurtosis of species abundance No n- co mm er cia l ed mean, standard deviation, skewness and kurtosis of species abundance in the dataset are reported. Species are in phylogenetic order. No n- co mm er cia l in the dataset are reported. Species are in phylogenetic order. No n- co mm er cia l n m (m depth) SD (m depth) g1 No n- co mm er cia l n m (m depth) SD (m depth) g1 1046 No n- co mm er cia l 1046 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 232 No n- co mm er cia l 232 No n- co mm er cia l No n- co mm er cia l Psectrocladius (Psectrocladius) oxyura No n- co mm er cia l Psectrocladius (Psectrocladius) oxyura No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l u se strategy (Appendix). As expected, lower values were obtained for kryal us e strategy (Appendix). As expected, lower values were obtained for kryal and krenal, and higher values for rhithral, potamal and lakes. Most taxa us e and krenal, and higher values for rhithral, potamal and lakes. Most taxashowed different responses according to the habitat. When data are us e showed different responses according to the habitat. When data areo nly (Figure 3). These species could be thus more sensitive to an increas- on ly (Figure 3). These species could be thus more sensitive to an increas- ing trend of temperature (Hester & Doyle, 2011). on lying trend of temperature (Hester & Doyle, 2011).For a better approximation of species preferences and tolerance, on lyFor a better approximation of species preferences and tolerance,optima for water temperature were calculated for each species in dif- on lyoptima for water temperature were calculated for each species in dif- ferent habitats, thus considering data collected with the same samplingon ly ferent habitats, thus considering data collected with the same sampling strategy (Appendix). As expected, lower values were obtained for kryalon ly strategy (Appendix). As expected, lower values were obtained for kryal and krenal, and higher values for rhithral, potamal and lakes. Most taxa on ly and krenal, and higher values for rhithral, potamal and lakes. Most taxa [page 84] [Journal of Entomological and Acarological Research 2013; 45:e14] available for the same species in different habitats, as for Orthocladius (Euorthocladius) rivicola, optimum values are lower in krenal (2.83°C) and kryal (5.23°C) than in rhithral (11.98°C), potamal or lakes. Other species (e.g. M. atrofasciata) did not show significant differences between optima values in different habitats, but the response curves were very different (Figures 7-8). These species are euryecious and eurythermal with more than one generation per year with different water temperature optimum for the different populations developing during the year. Among stenothermal taxa, some species at lower altitude habitats (rhithral, potamal) showed restricted tolerance to temperature, being potentially good indicators of climate change. For example, Microtendipes pedellus showed optimum for warm temperature (12.29°C), but a narrow range of tolerance (SD=2.73°C). For these taxa, the increasing temperature trend may induce a migra- tion toward higher elevations, changing in some years the response curve to altitude (Nyman et al., 2005; Bonada et al., 2007) and increasing species diversity at high elevation sites (Čiamporová-Zat’ovičová et al., 2010; Jacobsen et al., 2012). Alternatively, species may adapt to higher temperature, showing altered thermal curves in some years (Hogg et al., 1998; Van Doorsalaen et al., 2009). In the case of cold stenothermal or stenotopic species, a probable loss is expected (Jacobsen et al., 2012), as was observed in some localities in the Apennines for some species, such as Diamesa insignipes (Rossaro et al., 2006b). Even if species response to altitude is surely influenced by water temperature, high elevations also imply different habitats and different ecological conditions. Therefore species distribution could be con- strained by other factors. For example, the CHIDB data showed that some species colonizing high altitude lakes such as Zavrelimyia spp., Heterotrissoclaius marcidus, C. scutellata and P. austriacus are more warm stenothermal than predicted by altitude, while species living in kryal, krenal or rhithral habitats such as Diamesa spp., Pseudodiamesa branickii and P. parva (Rossaro, 2006b) are more cold stenothermal than expected. Likewise, at lower altitude species living in the profundal zone of lakes, such as P. olivacea, P. bathophila, Micropsectra radialis and C. plumosus as well as species living in lowland springs such as Brillia bifida, Chaetocladius perennis or in the interstitial habitats as Hydrobaenus distylus are cold stenothermal. For what concerns lacustrine species, distribution could be affected by water depth beside water temperature (Rossaro et al., 2006a; Luoto, 2012). Only few species showed an optimum depth below 20 m (e.g. M. contracta, P. nigritulum). Their distribution plotted against depth showed that they have more than one maximum, often with the main peak at lower depth than the other peaks (Figure 11). Results suggest that possibly depth does not influence species distribution directly, but indirectly through temperature, dissolved oxygen or competition. Different thermal optimum values were derived for different life stages (i.e. larvae vs pupal exuviae), probably due to species phenolo- gy. In particular, pupation in chironomids has a short duration, lasting at most 72 h (Langton, 1995). Therefore pupal exuviae are found in specific seasons and times. On the contrary, larval stage has a long duration, lasting most lifetime. According to species voltinism, more than one generation per year was often observed. This occurs both in lacustrine and in lotic species. This could explain bimodal or trimodal responses of species. Lindegaard & Mortensen (1988) observed that chironomids generally do not have more than four generations per year, but some species (e.g. C. riparius) have surely more than four generation per year in Southern Europe areas. Thus, a plurimodal response could also be expected, but more data are needed to fit plurimodal models with a higher number of parameters. Likewise, plurimodal response could be due to spatial distribution of species, which may show preferences for more than one specific habi- tat; local adaptations of single populations may as well be responsible for plurimodal trends of some species (Dallas & Rivers-Moore, 2012). In fact, such curves were mostly achieved for eurythermal and eurye- cious species. Sometimes curves with two peaks might suggest the presence of more than one species instead of more than one popula- tion. This is the case of taxa belonging to genera rich in species, which are not easily separated at the larval stage, such as Diamesa [e.g. D. latitarsis/steinboecki (juvenilia), Appendix] and Tanytarsus spp. Article Figure 8. Thermal response of Micropsectra atrofasciata pupal exuviae (number of individuals m–2) to water temperature (°C) in all habitats (A), Alpine ecoregion lowland lakes (B), rhithral (C) and potamal (D). Figure 9. Thermal response of Chironomus spp. larvae. Response of C. anthracinus (number of individuals m–2) to water temperature (°C) in Alpine ecoregion lowland lakes (A); response of C. plumosus in Alpine ecoregion lowland lakes (B), and Mediterranean ecoregion lakes (C); response of C. riparius in rhithral (D). Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 84 No n- co mm er cia l tat; local adaptations of single populations may as well be responsible No n- co mm er cia l tat; local adaptations of single populations may as well be responsiblefor plurimodal trends of some species (Dallas & Rivers-Moore, 2012). No n- co mm er cia l for plurimodal trends of some species (Dallas & Rivers-Moore, 2012). No n- co mm er cia l (Rossaro, 2006b) are more cold stenothermal No n- co mm er cia l (Rossaro, 2006b) are more cold stenothermal Likewise, at lower altitude species living in the profundal zone of No n- co mm er cia l Likewise, at lower altitude species living in the profundal zone of Micropsectra radialis No n- co mm er cia l Micropsectra radialis and No n- co mm er cia l and C. No n- co mm er cia l C. In fact, such curves were mostly achieved for eurythermal and eurye- No n- co mm er cia l In fact, such curves were mostly achieved for eurythermal and eurye- cious species. Sometimes curves with two peaks might suggest the No n- co mm er cia l cious species. Sometimes curves with two peaks might suggest the presence of more than one species instead of more than one popula- No n- co mm er cia l presence of more than one species instead of more than one popula- tion. This is the case of taxa belonging to genera rich in species, which No n- co mm er cia l tion. This is the case of taxa belonging to genera rich in species, which No n- co mm er cia l u se expected, but more data are needed to fit plurimodal models with a us e expected, but more data are needed to fit plurimodal models with a higher number of parameters. us e higher number of parameters. Likewise, plurimodal response could be due to spatial distribution of us e Likewise, plurimodal response could be due to spatial distribution of species, which may show preferences for more than one specific habi-us e species, which may show preferences for more than one specific habi- tat; local adaptations of single populations may as well be responsibleus e tat; local adaptations of single populations may as well be responsible on ly This could explain bimodal or trimodal responses of species. on ly This could explain bimodal or trimodal responses of species. Lindegaard & Mortensen (1988) observed that chironomids generally on lyLindegaard & Mortensen (1988) observed that chironomids generallydo not have more than four generations per year, but some species ( on lydo not have more than four generations per year, but some species () have surely more than four generation per year in on ly) have surely more than four generation per year in Southern Europe areas. Thus, a plurimodal response could also beon ly Southern Europe areas. Thus, a plurimodal response could also be expected, but more data are needed to fit plurimodal models with aon ly expected, but more data are needed to fit plurimodal models with a [Journal of Entomological and Acarological Research 2013; 45:e14] [page 85] Article Table 7. Response of lotic species (larvae) to distance from source in all riverine habitats: number of samples, weighted mean, standard deviation, skewness and kurtosis of species abundance vs distance from source values. Only the species with ≥100 records in the dataset are reported. Species are in phylogenetic order. Species n m (km) SD (km) g1 g2 Procladius choreus 497 84.56 83.31 1.27 0.06 Zavrelimyia barbatipes 118 3.86 20.05 9.52 123.78 Conchapelopia pallidula 663 81.40 134.03 3.35 10.99 Pseudodiamesa branickii 173 15.96 33.89 2.17 4.00 Diamesa steinboecki 108 0.69 7.32 15.03 226.29 Diamesa latitarsis 123 4.26 13.38 5.16 29.23 Diamesa bertrami 205 2.22 16.28 12.63 218.61 Diamesa tonsa 324 12.20 61.51 23.06 817.69 Diamesa zernyi 229 1.90 10.74 12.79 238.84 Prodiamesa olivacea 207 128.57 96.06 0.14 −1.76 Brillia bifida 302 19.64 31.95 3.35 19.85 Cardiocladius fuscus 115 18.68 79.56 13.42 331.58 Tvetenia calvescens 588 20.91 39.27 4.24 24.07 Eukiefferiella brevicalcar 131 0.81 11.87 20.57 475.86 Eukiefferiella claripennis 243 19.03 32.48 6.56 50.10 Eukiefferiella minor 216 8.79 19.15 4.85 46.65 Psectrocladius (Psectrocladius) oxyura 162 60.00 16.03 0.20 52.13 Rheocricotopus effusus 138 28.92 30.16 0.67 −0.12 Rheocricotopus fuscipes 391 48.28 98.83 5.40 30.02 Synorthocladius semivirens 163 22.23 40.18 3.74 24.97 Orthocladius (Euorthocladius) rivicola 457 28.15 66.53 6.22 42.93 Orthocladius excavatus 109 31.70 87.00 7.89 133.38 Orthocladius frigidus 322 6.39 52.28 33.79 1454.39 Orthocladius oblidens 121 55.14 26.38 0.47 7.27 Orthocladius rhyacobius 215 35.31 89.49 5.16 85.39 Orthocladius rubicundus 106 57.75 43.71 0.40 −0.22 Paratrichocladius rufiventris 317 3.76 35.13 29.05 1517.03 Paratrichocladius skirwithensis 134 14.01 23.29 2.15 3.32 Cricotopus annulator 176 34.10 68.19 7.05 60.01 Cricotopus bicinctus 241 128.77 120.05 1.29 2.98 Cricotopus triannulatus 197 131.92 128.18 1.72 5.47 Cricotopus (Isocladius) sylvestris 150 139.59 119.98 0.02 −0.86 Metriocnemus hygropetricus 132 31.94 50.11 4.62 42.91 Chaetocladius laminatus 117 13.86 30.01 5.65 45.96 Parametriocnemus stylatus 241 16.18 27.90 4.51 33.89 Parakiefferiella bathophila 101 63.12 1.86 −5.20 2484.04 Thienemanniella partita 133 12.60 53.86 9.37 101.81 Corynoneura scutellata 233 12.58 61.42 6.02 39.72 Tanytarsus gregarius 238 67.50 30.09 10.27 181.68 Cladotanytarsus atridorsum 104 57.74 17.82 7.56 111.14 Micropsectra atrofasciata 529 35.41 66.56 8.86 269.38 Micropsectra pallidula 120 1.54 2.29 2.80 15.69 Pseudochironomus prasinatus 119 54.93 5.42 −1.21 11.16 Paratendipes albimanus 130 32.79 28.69 5.33 95.49 Microtendipes pedellus 235 53.65 38.30 2.77 11.10 Polypedilum laetum 164 59.77 73.36 3.91 23.25 Polypedilum nubeculosum 434 90.31 86.70 2.38 9.45 Dicrotendipes nervosus 188 72.25 78.47 6.66 46.00 Glyptotendipes pallens 138 152.20 113.95 0.91 2.80 Chironomus anthracinus 273 57.22 19.98 −1.93 2.80 Chironomus plumosus 282 26.89 44.06 9.88 134.42 Chironomus riparius 227 213.81 69.27 −1.73 2.69 Cladopelma viridulum 131 50.98 23.96 −1.54 0.53 Cryptochironomus defectus 236 84.54 67.67 2.65 9.59 Demicryptochironomus vulneratus 134 53.81 16.62 −2.59 5.90 n, number of samples; m, weighted mean; SD, standard deviation; g1, skewness; g2, kurtosis. Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 85 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 35.31 No n- co mm er cia l 35.31 No n- co mm er cia l No n- co mm er cia l 57.75 No n- co mm er cia l 57.75 3.76 No n- co mm er cia l 3.76 No n- co mm er cia l No n- co mm er cia l 14.01 No n- co mm er cia l 14.01 34.10 No n- co mm er cia l 34.10 No n- co mm er cia l No n- co mm er cia l 197 No n- co mm er cia l 197 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 150 No n- co mm er cia l 150 132 No n- co mm er cia l 132 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l 117 No n- co mm er cia l 117 241 No n- co mm er cia l 241 No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l No n- co mm er cia l u se us e us e 40.18 us e 40.1866.53 us e 66.53 us e us e 87.00us e 87.00 52.28us e 52.28 on ly on ly on ly 6.56 on ly 6.56 on ly on ly4.85 on ly4.850.20 on ly0.20 on ly on ly on ly [page 86] [Journal of Entomological and Acarological Research 2013; 45:e14] Conclusions Chironomids are considered generalist, opportunistic, r-strategy organisms and their distribution is driven by environmental variables, such as water temperature (Rempel & Harrison, 1987), substrate com- position (Rae, 1985), current velocity (Caspers, 1983) and other vari- ables such as competition, parasitism, predation and other biological constraints (Tokeshi, 1995; Vodopich & Cowell, 1984). Water tempera- ture has been often recognized as the factor that accounts for the largest percentage of variation in community composition (Heiri et al., 2011). Beyond direct effects caused by increased water temperature, such as distribution, phenology and adaptation, also indirect effects are expected, such as different balance of inter- and intra-specific relation, i.e. competition, predation and parasitism (Tixier et al., 2009). These latter aspects still need to be investigated. Some chironomid species showed unimodal response to water tem- perature (Larocque et al., 2001), but bimodal and trimodal responses were also frequently found. The present data emphasized that standard deviation generally increased with optimum temperature, meaning that eurythermal species are often warm-water adapted, while cold- water dwellers are mostly stenothermal. Nonetheless some warm stenothermal species were also found, being possibly good indicators of water temperature in lowland habitats (e.g. M. pedellus). Aquatic insect ecology can be interpreted by an evolutionary perspec- tive. Entire orders of aquatic insects probably evolved in cool habitats. Thus, groups inhabiting warmer waters are considered later descen- dants of cool-adapted ancestral lines (Ward & Stanford, 1982; Ward, 1992). It is supposed that plesiomorphic species are cold stenothermal while apomorphic species are warm stenothermal or eurythermal. The chironomid ancestral habitat is supposed to be cool head-waters (Brundin, 1966; Cranston & Oliver, 1987; Cranston et al., 2012) and ecology and biogeography of Diamesinae gives support to this state- ment (Serra-Tosio, 1973; Rossaro, 1995). A phylogenetic trend from plesiomorphic cold-stenothermal species to apomorphic warm adapted species was then hypothesized (Rossaro, 1991c), since a general trend toward increasing adaptation to warm habitats was observed from cold stenothermal Diamesini to warm eurythermal Chironomini (Rossaro et al., 2007b). This was confirmed only in part, likely because: i) ecologi- cal data on species are incomplete, ii) the evolutionary tree of chirono- mids is not completely known (Cranston et al., 2012), iii) the relation between thermal response and the position of a taxon in the phyloge- netic tree may be observed at different taxonomic hierarchy, i.e. at the level of populations within the same species, of species within the same genus or of genus within the same tribe. In this paper emphasis is given to water temperature, with the aim of quantifying the responses of single species in different habitats and to describe the detailed pattern of response. The authors acknowledge that results may be biased, being a different number of data available for each species, with a different spatial and temporal resolution in different sites, and thus optimum values must be interpreted with caution. Nevertheless it must be considered the difficulty of selecting a balanced database for a large number of species, some of which rare, living in specialized habi- tats, others common and widespread, living in different habitats. The data considered in the present paper are still fragmentary and will be revised in the future, as soon as new information will become available. At pres- ent, a comparison of quantitative results with other published papers is Article Figure 10. Correlation between species optima for water tem- perature (°C) vs optima for altitude (m a.s.l.). Figure 11. Response of Prodiamesa olivacea (A), Micropsectra contracta (B), Paracladopelma nigritulum (C), Chironomus anthracinus (D) larvae (number of individuals m–2) to water depth (m) in Alpine ecoregion lowland lakes. Jear_2013_2:Hrev_master 16/09/13 13.56 Pagina 86 No n- co mm er cia l ture has been often recognized as the factor that accounts for the No n- co mm er cia l ture has been often recognized as the factor that accounts for the et al. No n- co mm er cia l et al., No n- co mm er cia l , 2011). Beyond direct effects caused by increased water temperature, No n- co mm er cia l 2011). Beyond direct effects caused by increased water temperature, such as distribution, phenology and adaptation, also indirect effects are No n- co mm er cia l such as distribution, phenology and adaptation, also indirect effects are expected, such as different balance of inter- and intra-specific relation, No n- co mm er cia l expected, such as different balance of inter- and intra-specific relation, et al. No n- co mm er cia l et al., 2009). These No n- co mm er cia l , 2009). These Some chironomid species showed unimodal response to water tem- No n- co mm er cia l Some chironomid species showed unimodal response to water tem- , 2001), but bimodal and trimodal responses No n- co mm er cia l , 2001), but bimodal and trimodal responses were also frequently found. The present data emphasized that standard No n- co mm er cia l were also frequently found. The present data emphasized that standard deviation generally increased with optimum temperature, meaning No n- co mm er cia l deviation generally increased with optimum temperature, meaning that eurythermal species are often warm-water adapted, while cold- No n- co mm er cia l that eurythermal species are often warm-water adapted, while cold- water dwellers are mostly stenothermal. Nonetheless some warmNo n- co mm er cia l water dwellers are mostly stenothermal. Nonetheless some warm stenothermal species were also found, being possibly good indicators ofNo n- co mm er cia l stenothermal species were also found, being possibly good indicators ofNo n- co mm er cia l u se on ly on ly on ly recommended. For example, a comparison could be achieved with esti- mated tolerance and optima for lacustrine species used as climate proxy in palaeolimnological studies (Larocque et al., 2001; Larocque-Tobler et al., 2012), even if available data are mainly from Northern areas. Otherwise, a comparison could be carried out with sensitivity derived from specific studies on existing chironomid communities (Tixier et al., 2009; Čiamporová-Zat’ovičová et al., 2010; Hamerlik & Jacobsen, 2012). Knowledge on thermal tolerance of species is important for a long- term management and monitoring of aquatic ecosystems exposed to the effects of climate change. In fact, thermal curves can help antici- pate impacts of climate change to various species by quantifying their thermal habitat (Hester & Doyle, 2011). Species response under differ- ent global change scenarios can thus be predicted (Bonada et al., 2007; Sauer et al., 2011). To this purpose, more understanding into species adaptations by acclimation and genetics is also needed (Hogg et al., 1998; Van Doorsalaen et al., 2009). References BATTEGAZZORE M., PETERSEN R.C., MORETTI G.P., ROSSARO B., 1992 - An evaluation of the environmental quality of the river Po using benthic macroinvertebrates. - Arch. Hydrobiol. 125: 175-206. BERRA E., FORCELLA M., GIACCHINI R., MARZIALI L., ROSSARO B., PARENTI P., 2004 - Evaluation of enzyme biomarkers in freshwater macroinvertebrates of Taro and Ticino river, Italy. - Int. J. Lim. 40: 169-180. 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In fact, thermal curves can help antici- No n- co mm er cia l the effects of climate change. In fact, thermal curves can help antici- pate impacts of climate change to various species by quantifying their No n- co mm er cia l pate impacts of climate change to various species by quantifying their thermal habitat (Hester & Doyle, 2011). Species response under differ- No n- co mm er cia l thermal habitat (Hester & Doyle, 2011). Species response under differ- ent global change scenarios can thus be predicted (Bonada No n- co mm er cia l ent global change scenarios can thus be predicted (Bonada et al. No n- co mm er cia l et al., 2007; No n- co mm er cia l , 2007; , 2011). To this purpose, more understanding into species No n- co mm er cia l , 2011). To this purpose, more understanding into species adaptations by acclimation and genetics is also needed (Hogg No n- co mm er cia l adaptations by acclimation and genetics is also needed (Hogg No n- co mm er cia l BATTEGAZZORE M., PETERSEN R.C., MORETTI G.P., ROSSARO B.,No n- co mm er cia l BATTEGAZZORE M., PETERSEN R.C., MORETTI G.P., ROSSARO B., 1992 - An evaluation of the environmental quality of the river PoNo n- co mm er cia l 1992 - An evaluation of the environmental quality of the river Po CASPERS N., 1983 - Chironomiden-Emergenz zweier Lunzer Bäche, No n- co mm er cia l CASPERS N., 1983 - Chironomiden-Emergenz zweier Lunzer Bäche,1972. - Arch. Hydrobiol. Suppl. 65: 484-549. No n- co mm er cia l 1972. - Arch. Hydrobiol. Suppl. 65: 484-549. 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