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Mehmet Yavuzatmaca Department of Biology, Faculty of Arts and Science, Bolu Abant İzzet Baysal University, Gölköy 14280, Bolu, Turkey

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Abuzer Çelekli Department of Biology, Faculty of Arts and Science, Gaziantep University, 27310 Gaziantep, Turkey

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Muzaffer Dügel Department of Biology, Faculty of Arts and Science, Bolu Abant İzzet Baysal University, Gölköy 14280, Bolu, Turkey

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Ömer Lekesiz Department of Biology, Faculty of Arts and Science, Osmaniye Korkut Ata University, 80000 Osmaniye, Turkey

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Abstract

The potential of ostracod species as indicators of water quality and the influence of key ecological parameters on their distribution were evaluated by collecting samples from 39 streams during the spring, summer, and autumn seasons. Thirty-seven ostracod taxa (23 living, 14 subfossil) were identified, with Hemicypris anomala (Klie, 1938) being newly reported for Turkey. The summer season exhibited the highest diversity, indicated by the highest Shannon index value of 2.119, while the spring season showed the lowest diversity with a Shannon index value of 1.673. The variability in species composition was greater in the autumn than in other seasons. Among the nine factors affecting species distribution, the first three were found to be total nitrogen, magnesium, and elevation. Ilyocypris decipiens Masi, 1905 and Stenocypris intermedia Klie, 1932 indicated medium water quality in relation to the electrical conductivity, dissolved oxygen, total nitrogen and phosphorus. Stenocypris intermedia depicted waters of medium quality for biological oxygen demand and poor quality for nitrate. Ilyocypris monstrifica (Norman, 1862) signaled waters of good quality for total nitrogen and medium quality for dissolved oxygen. Potamocypris fallax Fox, 1967 and Potamocypris similis G.W. Müller, 1912 characterized good and medium water quality regarding dissolved oxygen and total phosphorus, respectively. Cypridopsis vidua (O.F. Müller, 1776) was identified as a positive-pollution indicator related to ammonium. The results suggest that ostracods have great potential as indicators of water quality, but more detailed studies on water quality parameters and ostracods are needed.

Abstract

The potential of ostracod species as indicators of water quality and the influence of key ecological parameters on their distribution were evaluated by collecting samples from 39 streams during the spring, summer, and autumn seasons. Thirty-seven ostracod taxa (23 living, 14 subfossil) were identified, with Hemicypris anomala (Klie, 1938) being newly reported for Turkey. The summer season exhibited the highest diversity, indicated by the highest Shannon index value of 2.119, while the spring season showed the lowest diversity with a Shannon index value of 1.673. The variability in species composition was greater in the autumn than in other seasons. Among the nine factors affecting species distribution, the first three were found to be total nitrogen, magnesium, and elevation. Ilyocypris decipiens Masi, 1905 and Stenocypris intermedia Klie, 1932 indicated medium water quality in relation to the electrical conductivity, dissolved oxygen, total nitrogen and phosphorus. Stenocypris intermedia depicted waters of medium quality for biological oxygen demand and poor quality for nitrate. Ilyocypris monstrifica (Norman, 1862) signaled waters of good quality for total nitrogen and medium quality for dissolved oxygen. Potamocypris fallax Fox, 1967 and Potamocypris similis G.W. Müller, 1912 characterized good and medium water quality regarding dissolved oxygen and total phosphorus, respectively. Cypridopsis vidua (O.F. Müller, 1776) was identified as a positive-pollution indicator related to ammonium. The results suggest that ostracods have great potential as indicators of water quality, but more detailed studies on water quality parameters and ostracods are needed.

Introduction

The quality of water plays a crucial role in sustaining human life, impacting various aspects such as domestic use, agriculture, fisheries, aquatic recreation, and more. Additionally, it influences the health of ecosystems, including the conservation of biodiversity. Water quality parameters are divided into categories of physical, chemical, and biological properties, affecting the natural ecological systems and water used by humans (Wetzel 2001, Boyd 2020). These parameters undergo natural changes due to climatic, hydrologic, geologic, and biologic factors and unnaturally through human activities, such as discharge of industrial or agricultural activities into natural systems, forestry, and urbanization. The impact of human activities on freshwater resources throughout the world is an undeniable fact. Therefore, wisely and optimally using freshwater resources is an important human responsibility, as stated by Wetzel (2001), without forgetting that all living organisms have equal rights to use water.

Changes in water quality can extensively affect biodiversity patterns (Petsch et al. 2021). Consequently, a decline in benthic invertebrate diversity and density (Death & Winterbourn 1995, Külköylüoğlu 2004), differentiation of plant and animal species composition in aquatic systems (Boyd 2020), and even the disappearance of some species from the system can be observed after such changes. Biomonitoring, a cost-efficient way to use living organisms (e.g., indicator species) to assess the impact of pollutants on ecosystems and gather information about habitat quality, has been emphasized (Hopkin 1988). The European Water Framework Directive requires the use of bioindicators such as phytoplankton, benthic algae (diatoms), benthic invertebrates (especially macroinvertebrates) and fish to evaluate the status of surface water bodies (EU Water Framework Directive 2000). Although ostracods, as a meiobenthic group, are the second largest group of Crustacea in non-marine environments (Balian et al. 2010) with 2,330 subjective species (Meisch et al. 2019), they have not been widely used in the EU and have not been proposed by the EU WFD as bioindicators. This situation prompts the question, “Why?”.

Ostracods are small (0.3 – 8 mm in length) bivalve aquatic crustaceans, inhabit various marine and non-marine environments, including aquatic, semi-aquatic, and terrestrial habitats. In non-marine habitats, ostracods can be encountered in/on sediments and in waters when swimming among macrophytes (no true planktonic non-marine ostracod species exist) (Martens & Horne 2016). Their low-magnesium calcite carapaces undergo nine molting stages to reach maturity, with the chemical properties of ambient waters influencing the carapace composition at each stage (De Deckker et al. 1988, Meisch 2000). This calcified carapace allows ostracods to easily fossilize in sediments, with their history dating back to the Early Ordovician (ca. 485 million years old) (Williams et al. 2008). Therefore, the calcified valves of ostracods have been used by paleontologists to determine past environmental conditions (De Deckker et al. 1988). Ostracod distribution in aquatic bodies is related to the physico-chemical variables (e.g., DO, EC, salinity, etc.) of the waters (Külköylüoğlu 2013) and their ability to reproduce both sexually and asexually (Meisch 2000), coupled with passive dispersion and the parthenogenetic reproduction, allows ostracods to be widely distributed in aquatic bodies. These characteristics make ostracods unique organisms because one female is enough to establish a population (Martens et al. 2008), their drought-resistant eggs can remain active for up to a century (Martens 1994) and they can be passively distributed among connected or unconnected water bodies by wind (for eggs), animals, plants, and humans (Proctor et al. 1967, Brochet et al. 2010, Battauz et al. 2017). Moreover, ostracods exhibit quick responses to environmental changes due to their short reproductive periods (Delorme 2001) and developmental stages (Cohen & Morin 1990). Therefore, ostracods could be an ideal bioindicator but, as mentioned above, they have not been suggested as bioindicators for surface waters by the EU Water Framework Directive (2000). This does not imply that ostracods cannot be used as bioindicators. A review of the literature reveals studies using ostracods as indicators to estimate present and past environmental conditions and for different purposes. For example, Cyprideis torosa (Jones, 1850) is used as an indicator of water salinity, with nodes on the carapace developing in habitats with <0.05 ppt salinities but not above this level (Keyser 2005). Padmanabha and Belagali (2008) recorded a negative relationship between water quality index and species diversity of ostracods but a positive relationship with population density in the lakes of Mysore.

Ostracods have been employed in a wide range of studies, including paleoenvironmental (paleoclimatic, paleosalinity, and paleooceanographic) reconstruction, ecotoxicity monitoring (e.g., herbicides, pesticides, oil spills or heavy metal pollution), and biostratigraphic indicator studies (De Deckker & Forester 1988, Ruiz et al. 2013, Parameswari et al. 2020). Although macroinvertebrates (e.g., Ephemeroptera, Trichoptera, and Plecoptera) have commonly been used to detect the water quality in freshwaters, their fossils are rarely used. At this point, ostracods offer great potential and advantages in detecting the quality of freshwater bodies compared to macroinvertebrates due to their well-preserved fossils and widespread abundance and distribution (Boomer & Attwood 2007). In literature (e.g., Bromley & Por 1975, Curry 1999, Mezquita et al. 2001, Pieri et al. 2012, Yavuzatmaca 2020), most studies have used ostracods as bioindicators, but more often qualitatively or semi-quantitatively, as stated by Boomer and Attwood (2007). This may be accepted as an answer to the question “Why?” Therefore, studies dealing with the quantitative indicator potential of ostracods using modern statistical analyses, including quantitative population analyses like indicator species analysis (De Cáceres & Legendre 2009), and perhaps even development of an index for ostracods, are needed. In the present study, we aim to i) determine the ecological variables that effect the distribution of ostracod species composition, ii) estimate the optimum levels of individual ostracod species for different environmental variables, and iii) calculate the indicator values of individual ostracod species for water quality class boundaries provided by Turkish surface water quality regulation standards (TSWQR 2016).

Material and methods

Study area

The Ceyhan River (formerly Leucosyrus) Basin, which extends into the interior of Central Anatolia from the Iskenderun Bay, is located between 36° 55′ and 38° 72′ north latitudes and 35° 45′ and 37° 81′ east longitudes in the Eastern Mediterranean Region of Turkey. Among the provinces that make up the Ceyhan River basin boundary (Fig. 1), Kahramanmaraş, with 989 industrial facilities, has the most possibilities to affect the water quality of the Ceyhan basin, followed by Osmaniye with 235 and Adana with 176 industrial facilities. The Ceyhan Basin generally presents a mountainous landscape comprising paleozoic, mesozoic and tertiary bedrocks, with landforms ranging from sea level at Iskenderun Bay in the south to 3,000 m a.s.l. at the Southeastern Taurus Mountains in the north. The basin features steep mountainous terrain with karstic carbonate formations and extensive alluvial plains. The annual averages of total precipitation, air temperature, evaporation, sunshine, and wind speed in the basin were reported as follows: 727.3 mm, 14.8 °C, 1,421.3 mm, 2,680.2 h, and 1.9 m s−1, respectively, in the basin (CHKYP 2019, BİDEP 2020, Akbulut et al. 2022).

Fig. 1.
Fig. 1.

Map of Ceyhan River Basin and the locations of sampled streams

Citation: Animal Taxonomy and Ecology 70, 1; 10.1556/1777.2024.09741

Sampling

Ostracod specimens were collected from surface sediments (up to 2–3 cm) of 39 streams (S1–S39) in the Ceyhan River Basin using a standard sized hand net (200 μm mesh size) (Fig. 1). ArcGIS & ArcMAP software were used to construct the map of the Ceyhan River Basin. Additionally, a 30 m Tandem-X Digital Elevation Model (DEM) was used for the image, and twenty elevational classes (from sea level to maximum) were created for the map (Fig. 1). Each of the studied streams was sampled three times in April (Spring) and October (Autumn) of 2021 and in August (Summer) of 2022. Collected sediment samples were kept in 250 ml plastic bottles and fixed with 70% ethanol in situ.

A global positioning system (GPS) was used to record the elevation and coordinates (geographical data) of each sampled stream before ostracod sampling (Fig. 1 and Table S1 in Supplementary Materials). Then, using a YSI Professional Plus multi-probe device, various water parameters were measured without mixing waters to eliminate the possible results of “Pseudoreplication” (Hurlbert 1984). The measured parameters included dissolved oxygen concentration (DO, mg L−1), water temperature (Tw, °C), electrical conductivity (EC, μS cm−1), pH, salinity (ppt), total dissolved solids (TDS, mg L−1) and oxidation and reduction potential (ORP, mV). Following these measurements, 500 mL polyethylene bottles were used to collect water samples from each station for chemical analyses, including the determination of the biological oxygen demand (BOD5, mg L−1), and the collected water samples were kept at +4 °C during the field survey and transportation to the laboratory.

Laboratory analyses

The Hach LT 200 Thermoreactor, Hach Cuvette tests and Hach Lange DR 5000 spectrophotometer were used to determine the values of the anions: sulfate (SO42−, mg L−1), total nitrogen (TN, mg L−1), total phosphorus (TP, μg L−1), nitrate (NO31−, mg L−1), nitrite (NO21−, mg L−1), fluorine (F1−, mg L−1) and chloride (CI1−, mg L−1); cations: ammonium (NH41+, mg L−1), iron (Fe2+, mg L−1), calcium (Ca2+, mg L−1), magnesium (Mg2+, mg L−1), potassium (K1+, mg L−1) and sodium (Na1+, mg L−1) and total organic carbon (TOC, mg L−1). The estimation of biological oxygen demand (BOD5, mg L−1) was conducted using a Hach BOD Trak 2 device (Hach 2005, 2010). For each station, water samples were placed in 420 mL opaque dark bottles and then incubated at 20 °C. After 5 days, BOD5 values were recorded. Total hardness (mmol CaCO3/L) was calculated using the standard procedure provided in APHA (2012).

Sediment samples were initially washed under pressurized tap water and then put into 250 ml plastic bottles, fixed with 70% ethanol. Subsequently, ostracod specimens and valves were sorted from the sediments using dissecting needles and Pasteur pipettes under an Olympus ACH 1X stereo microscope. The separated specimens were placed in glass vials containing 70% ethanol for further studies. Adult individuals with complete soft body parts and carapaces were dissected in lactophenol solution for taxonomic identification under a light microscope (Olympus BX-51). The identification of specimens to taxonomic levels (species or genus) was conducted following Meisch (2000) and Karanovic (2012). Additionally, the taxonomic nomenclature of species was cross-checked based on Meisch et al. (2019).

Statistical analyses

In all statistical analyses, adult specimens with complete soft body parts and carapaces were used. Furthermore, all species (occurring at least once) were included in each analysis unless otherwise stated. The species diversity of samples in different seasons (spring, summer, and autumn) was estimated by calculating the Shannon index (Shannon & Weaver 1949). This analysis was performed with Species Diversity and Richness 4 software (Seaby & Henderson 2006). A test of the homogeneity of multivariate dispersion, or Permutational Analysis of Multivariate Dispersions (PERMDISP), was carried out to examine the variation in species composition, beta diversity, among seasons based on distances between sample units and the centroid of the group using the Bray-Curtis dissimilarity measure (Anderson 2006). The significance of the model was tested by a permutation analysis (999 iterations), and the result of this analysis was visualized with the aid of an unconstrained Principal Coordinate Analysis (PCoA). If homogeneity was not satisfied (or if PERMDISP results were significant), a permutest (999 iterations) was used to determine which seasons had significantly different dispersion. A nonparametric Multivariate Permutational Variance Analysis (PERMANOVA, Anderson 2001) was used to evaluate the changes in the species composition over the seasons in the sampled streams. A pairwise PERMANOVA was used to assess significant differences amongst seasons, with a total of 999 permutations to evaluate significance. Detrended Correspondence Analysis was used to test the suitability of data to analyze the data with a unimodal technique (Table S2 in Supplementary Materials), such as Canonical Correspondence Analysis (CCA) (Lepš & Šmilauer 2003). Accordingly, relationships between environmental variables and ostracod species that occurred two or more times were explored by CCA using CANOCO 4.5 software along with the Monte Carlo permutation test (999 iterations). The percentage of explained variation in ostracod species composition by regional (elevation) and local (dissolved oxygen, pH, water temperature, electrical conductivity, sulfate, total nitrogen, total phosphorus, nitrate, ammonium, calcium, magnesium, and biological oxygen demand) factors was elucidated by Variation partitioning (VP, Borcard et al. 1992) analysis using the adjusted R2 (Peres-Neto et al. 2006). The importance of model and fractions explained by regional and local factors were evaluated with the aid of 999 random permutations. Indicator possibilities of ostracod species occurring at least two times for different classes of water quality were found by Indicator species analysis (De Cáceres & Legendre 2009). To perform indicator species analysis, sampled sites with adult living ostracods were separated into different water quality classes (I–IV, see Table 1) according to the Turkish surface water quality regulation standards (TSWQR 2016). The optimum level of the ostracod species occurring at least two times for different environmental variables was determined by weighted averaging regression in C2 software (Juggins 2003). PERMDISP, PERMANOVA and VP analyses were performed in R Statistical Software (v4.1.3, R Core Team 2022) using the R package Vegan 2.6–4 (Oksanen et al. 2022) but utilizing the R package indicspecies (De Cáceres & Legendre 2009) for indicator species analysis.

Table 1.

Class boundaries of some water quality parameters provided by Turkish surface water quality regulation standards (TSWQR 2016)

Water quality parameters ↓Water quality classes
I (very good)II (good)III (medium)IV (poor)
pH6–96–96–96–9
Electrical conductivity (µS cm−1)<4001,0003,000>3,000
Dissolved oxygen (mg L−1)>863<3
Biological oxygen demand (mg L−1)<4820>20
Ammonium (mg L−1)<0.212>2
Nitrate (mg L−1)<31020>20
Total nitrogen (mg L−1)<3.511.525>25
Total phosphorus (mg L−1)<0.080.20.8>0.8
Fluorine (μg L−1)≤1,0001,5002,000>2,000

Results

A total of 37 ostracod taxa (23 living and 14 sub-fossil) were reported from the 39 streams sampled in three different seasons in the present study (Table 2). Among living species, Ilyocypris bradyi (28 times), Psychrodromus olivaceus (21 times), Potamocypris fallax (18 times), Neglecandona angulata (17 times), Prionocypris zenkeri (11 times) and Ilyocypris inermis (10 times) were encountered more than 10 times during sampling periods, while six species were found only once (Candonopsis scourfieldi, Darwinula stevensoni, Hemicypris anomala, Herpetocypris reptans, H. intermedia and Kovalevskiella bulgarica) (see Table S1 in Supplementary Materials). Bisexual populations of I. bradyi (1♂) and I. inermis (1♂, 27♀) were found in S15a and S25c, respectively. Among sampling seasons, summer had the lowest abundance (255 individuals) but the highest Shannon index value (H' = 2.119) compared with the spring (H' = 1.673, abund. = 361) and autumn (H' = 1.773, abund. = 464, Table 2). Of the living species, I. inermis, I. bradyi, N. angulata, P. fallax, P. zenkeri, and P. olivaceus were common in all three sampling seasons. Minimum and maximum values of ecological variables for the living species are provided in Table S3 in Supplementary Materials.

Table 2.

Number of adult individuals (only number, e.g., 3), juvenile (j), carapace (ca) and valves (v) of 37 ostracod taxa found in three different sampling seasons (spring, summer and autumn), and Shannon index values of each season

TaxaCodeSpring (April, 2021)Summer (August, 2022)Autumn (October, 2021)
Candona sp.Csp1j, 2ca, 2v8j, 1ca, 12v2j, 1ca, 3v
Candonopsis scourfieldiCs4
Cypria sp.Cyp1v
Cypridopsis viduaCv3, 1ca, 1v
Cypridopsis sp.Cyrp3ca, 5v2ca
Darwinula stevensoniDs1
Eucypris sp.Esp1 damaged, 3j, 3v
Fabaeformiscandona sp.Fsp1v
Hemicypris anomalaHa6, 3j, 2ca, 1v
Herpetocypris brevicaudataHeb2, 1j
Herpetocypris intermediaHin2
Herpetocypris reptansHer2, 6j, 8v
Herpetocypris sp.Hesp1v4v
Heterocypris incongruensHi12, 3ca, 2v
Heterocypris salinaHs4ca, 2v17, 34ca, 14v7, 1j
Heterocypris sp.Hsp6v5v1ca, 5v
Ilyocypris bradyiIb102, 43ca, 35v35, 2j, 29ca, 106v92, 10j, 13ca, 34v
Ilyocypris decipiensId16, 3ca, 7v10, 1j, 4ca, 5v
Ilyocypris inermisIi29, 17v54, 5ca, 43v8, 3v
Ilyocypris monstrificaIm1, 1ca, 1v12, 2j, 17ca, 15v
Ilyocypris sp.Isp1ca, 10v7v1ca, 10v
Kovalevskiella bulgaricaKb1,1v
Limnocythere inopinataLi3
Limnocythere sp.Lisp1ca, 1v
Neglecandona angulataNa92, 5j, 12ca, 44v42, 30j, 22ca, 50v8, 1j, 6ca, 12v
Neglecandona neglectaNn11,3v2v
Potamocypris fallaxPf30, 24ca, 20v34, 25ca, 111v192, 4j, 23ca, 63v
Potamocypris similisPs4, 6ca, 3v
Potamocypris variegataPv11
Potamocypris sp.Pop1ca, 6v12ca, 27v8v
Prionocypris zenkeriPz22, 6ca, 22v7, 7j, 4ca, 16v7, 7j, 4ca, 24v
Pseudocandona sp.Psep2ca, 2v4ca, 2v
Psychrodromus olivaceusPo83, 16j, 4ca, 20v39, 4j, 4ca, 24v98, 22j, 2ca, 22v
Psychrodromus sp.Psp1j, 1ca, 4v1j, 2ca, 17v5v
Sarscypridopsis sp.Ssp1v
Schellencandona sp.Scsp1v
Stenocypris intermediaSi1, 1j, 1v6, 3j, 6v
TotalAdults361255464
Juveniles265263
Carapaces9615977
Valves193460234
Shannon IndexH'1.6732.1191.773
Variance H'0.0010.0020.003
Exp H'5.3288.3235.89
All Sample Index2.035
Jackknife Std Error0.089

The results of PERMDISP showed significant differences (F = 3.42, P = 0.04) in the variability of species composition among seasons. The autumn season indicated significantly higher species composition variability with an average distance of 0.662 to the centroid, compared to the spring and summer seasons (0.617 average distance to the centroid in spring and 0.616 in summer), whereas spring and summer showed nonsignificant differences in the variability of species composition (Fig. 2). Pairwise PERMANOVA indicated nonsignificant changes in the species composition between the dual season comparisons.

Fig. 2.
Fig. 2.

Principal Coordinate Analysis (PCoA) constructed from ostracod communities in 39 streams sampled in different seasons (spring, summer, and autumn). Black circles, green pluses and red triangles represent spring, summer, and autumn samples, respectively

Citation: Animal Taxonomy and Ecology 70, 1; 10.1556/1777.2024.09741

The longest gradient length (8.483) of DCA is larger than 4 (Table S2 in Supplementary Materials), suggesting the application of CCA. The first two axes of CCA elucidated a moderately low relationship (44.2%) between species and environmental variables, but with a high cumulative percentage variance of species data equaling 20 (Table S2 in Supplementary Materials). Of the environmental variables, total nitrogen (TN, λ = 0.64, P = 0.002, F = 6.92), magnesium (Mg2+, λ = 0.40, P = 0.002, F = 4.56), elevation (Elev, λ = 0.29, P = 0.006, F = 3.54), sulfate (SO42−, λ = 0.28, P = 0.002, F = 3.40), pH (λ = 0.25, P = 0.008, F = 3.25), dissolved oxygen (DO, λ = 0.23, P = 0.006, F = 2.96), nitrate (NO31−, λ = 0.23, P = 0.010, F = 3.22), electrical conductivity (EC, λ = 0.17, P = 0.012, F = 2.40) and water temperature (Tw, λ = 0.17, P = 0.020 F = 2.33) displayed significantly important effects on the distribution of ostracod species, but ammonium (NH41+, λ = 0.14, P = 0.136, F = 1.95), total phosphorus (TP, λ = 0.09, P = 0.238, F = 1.28), calcium (Ca2+, λ = 0.08, P = 0.306, F = 1.18) and biological oxygen demand (BOD5, λ = 0.03, P = 0.794, F = 0.54) did not. The distribution of ostracod species on the ordination plot of CCA is given in Fig. 3. Among the species, Ilyocypris monstrifica showed a very close association with EC. Neglecandona angulata indicated a relatively close relationship with Elev, while Ilyocypris decipiens and Stenocypris intermedia located on the opposite side of Elev but on the same site of TN, NO31−, Ca2+ and BOD5 (Fig. 3). Results of variation partitioning indicated that regional (elevation) and local (DO, pH, Tw, EC, SO42−, TN, TP, NO31−, NH41+, Ca2+, Mg2+ and BOD5) factors used in the CCA analysis explain about 19.40% of the variation in the species composition. Only regional factor delineated 5% of the explained variation, while local factors explained 13.97%. All the explained fractions of variation were found to be statistically important (P < 0.001).

Fig. 3.
Fig. 3.

CCA ordination plots: A) species and environmental variables (species - filled up triangles; environmental variables - vector lines). Dashed lines indicate non-significant impacts of environmental variables on the species distribution. Codes of species and abbreviations of environmental variables are given in Table 2 and Material and Methods, respectively. B) samples (illustrated as circles and coded as S12a where “S12” is the site and lowercase letters “a”, “b” and “c” signify the spring, autumn, and summer seasons, respectively)

Citation: Animal Taxonomy and Ecology 70, 1; 10.1556/1777.2024.09741

According to the indicator species analysis, I. decipiens and Stenocypris intermedia were found to be the statistically important indicator species of Class III water quality based on the EC, DO, TN, and TP gradients. Similarly, Stenocypris intermedia was also statistically indicated the conditions with Class III (P < 0.05) and IV (P < 0.001) water qualities based on the BOD5 and NO31− gradients, respectively. I. monstrifica significantly (P < 0.05) depicted Classes II and III waters for TN and DO gradients, respectively. The species in the same genus, P. fallax importantly (P < 0.05) pointed to Classes II water quality for DO gradient, while P. similis (P < 0.05) reflected Class III water quality for TP gradient (Table 3).

Table 3.

Statistically important indicator species of water quality classes (WQC) based on electrical conductivity (EC), dissolved oxygen (DO), biological oxygen demand (BOD5), ammonium (NH41+), nitrate (NO31−), total nitrogen (TN) and total phosphorus (TP) gradients defined by TSWQR (2016). The significance levels are as follows ***0.001, **0.01 and *0.05

WQCII (good)III (medium)IV (poor)
ECIlyocypris decipiens*, Stenocypris intermedia*
DOPotamocypris fallax*Ilyocypris decipiens *, Ilyocypris monstrifica*, Stenocypris intermedia *
BOD5Stenocypris intermedia *
NH41+Cypridopsis vidua*
NO31-Ilyocypris decipiens ***, Stenocypris intermedia ***
TNIlyocypris decipiens *, Ilyocypris monstrifica*Ilyocypris decipiens *, Stenocypris intermedia *
TPIlyocypris decipiens ***, Potamocypris similis*, Stenocypris intermedia **

In line with the analysis using weighted averaging regression, Stenocypris intermedia exhibited the highest optimum levels for TN (12.74 mg L−1), TP (0.32 mg L−1), NO31− (167.51 mg L−1), BOD5 (7.43 mg L−1) and EC (956 μS cm−1) but one of the lowest levels for DO (4.22 mg L−1) amongst species. Prionocypris zenkeri and I. decipiens had the highest optimum levels for pH (8.69) and water temperature (26.04 °C), respectively. I. monstrifica was among the species with the lowest DO (4.24 mg L−1) but highest TN (6.25 mg L−1) optimum levels. P. similis, along with I. decipiens, showed elevated TP optimum level (0.25 mg L−1 and 0.31 mg L−1, respectively) after S. intermedia. P. fallax displayed a moderate optimum level for DO (6.89 mg L−1) among the ostracod species in the present study (for more details, see Table 4).

Table 4.

Optimum levels of 17 species for elevation (Elev), water temperature (Tw), electrical conductivity (EC), pH, dissolved oxygen concentration (DO), biological oxygen demand (BOD5), sulfate (SO42−), total nitrogen (TN), total phosphorus (TP), nitrate (NO31−), ammonium (NH41+), calcium (Ca2+) and magnesium (Mg2+). Only species found on at least two occasions (Count) are included. N2 represents the Hill's coefficient value, indicating the measure of effective number of occurrences

SpeciesCountMaxN2ElevTwECpHDOBOD5SO42−TNTPNO31−NH41+Ca2+Mg2+
Ilyocypris bradyi28529.18745.2012.83294.928.148.293.6814.031.520.072.570.0558.5910.52
Neglecandona angulata17427.701,196.3511.60252.328.238.553.228.641.410.041.920.0554.209.62
Prionocypris zenkeri1197.361,175.1113.55294.128.698.663.1521.501.230.072.210.1155.5912.74
Ilyocypris inermis10275.321,153.0716.47306.468.227.013.169.481.760.111.080.0448.0419.65
Psychrodromus olivaceus21804.701,135.8712.04229.378.157.553.3016.370.990.070.860.0546.3314.39
Potamocypris fallax181203.891,157.8611.02269.468.246.893.946.331.360.092.230.0555.109.30
Heterocypris salina5123.03769.0021.91577.688.176.013.6360.983.670.1614.840.3565.0736.66
Cypridopsis vidua313.00349.6717.67525.678.214.665.1340.563.540.216.661.2654.5638.97
Heterocypris incongruens313.00968.6711.80250.078.269.412.577.991.910.082.920.0353.9710.31
Ilyocypris monstrifica372.2589.0022.22617.398.164.244.5938.306.250.2021.830.0571.6937.28
Ilyocypris decipiens3162.1960.0826.04722.858.023.764.1351.217.750.3168.090.0776.6325.74
Herpetocypris brevicaudata212.001,033.5022.10484.008.045.632.5252.701.360.081.500.0566.1418.82
Neglecandona neglecta212.001,020.0011.05231.158.1910.202.209.471.380.072.580.0149.669.64
Potamocypris variegata212.001,123.0024.30558.858.066.012.4264.361.840.101.860.0567.6423.61
Stenocypris intermedia351.8137.8620.21956.008.004.227.4394.2812.740.32167.510.0598.7539.05
Limnocythere inopinata221.80846.6713.20342.078.146.403.7729.710.990.121.400.0563.3418.27
Potamocypris similis231.601,035.2522.03405.188.634.842.0221.081.960.250.790.0439.5031.34

Discussion

Hemicypris anomala was reported for the first time from Turkey, making it the third species of the genus Hemicypris in the country, following H. congenera and H. inversa (Külköylüoğlu et al. 2015). K. bulgarica was initially found in Rezve stream (Thrace) for the Turkish freshwater Ostracoda fauna (Özuluğ & Yaltalıer 2008). The distribution of K. bulgarica extended from the northwest to the southeast in Turkey with the present study.

The significant effects of electrical conductivity (EC) and water temperature on the species composition of ostracods in the present study support the findings of over half of the 42 studies compiled by Çapraz et al. (2022). In the same source, pH was also being reported as an important factor in 13 out of these 42 studies and pH was one of the effective variables herein. These findings reinforce the previous statements about the importance of ionic composition, particularly EC, in streams for ostracods due to osmoregulation (Smith et al. 2003). Additionally, the importance of pH on the calcification process of ostracods has been highlighted (Higuti et al. 2010), and water temperature has been recognized as a vital factor for the growth and development of ostracods (Aguilar-Alberola & Mesquita-Joanes 2014). The importance of Ca2+ and Mg2+ in waters for ostracods to calcify their carapaces is well-established (De Deckker et al. 1988). The surveyed region has karstic carbonate rock formations or limestone rocks composed of calcite (CaCO3) and dolomite (MgCO3), with a prevalence of calcite in its natural composition (Boyd 2020). In the light of this information, the concentration of Mg2+ would be expected to be a limiting factor rather than Ca2+ in the present study, our findings corroborate this. Sulfate, a form of sulfur in freshwater, serves as a nutrient for plants and animals and is used as a reactant and component of industrial products and fuels. In industrialized areas like Kahramanmaraş, Osmaniye and Adana, high sulfate concentrations in rainfall attributed to the combustion of fossil fuels, releasing sulfur dioxide into the atmosphere (Boyd 2020). Given the industrial possibilities in these areas, the presence of sulfate can be understood among the effective environmental variables shaping the distribution of ostracods. Similarly, Szlauer-Łukaszewska and Pešić (2020) emphasized the significant effects of nitrates and conductivity, as well as phosphate, which was not effective here, on the ostracod assemblage in the Krąpiel valley. Recently, Cours et al. (2021) pinpointed the importance of ammonium and total phosphorus concentrations for the ostracod community structure in northern Belgium. These studies underline the relationships between ostracods and water quality variables (e.g., nitrate, nitrite, ammonia, ammonium, phosphate, sulfate, etc.) commonly used to classify the trophic status of water bodies. All these indications highlight the significant potential of ostracods as bioindicators. Consequently, more studies are needed to firmly establish ostracods as reliable bioindicators for surface water qualities.

The present study documented 16 individuals of I. decipiens, captured during three separate occurrence frequencies. Regarding the EC gradient, the observed range (562–1,099 μS cm−1, Table S3 in Supplementary Materials) and optimum value (722.85 μS cm−1, Table 4) in the present study align with the established range (301–3,170 μS cm−1 (Iglikowska & Namiotko 2012, Yavuzatmaca 2020) and calculated optimum values (350–804 μS cm−1 (Külköylüoğlu et al. 2019, Külköylüoğlu et al. 2023) from the literature suggest that I. decipiens may has the potential to characterize the waters spanning from good to medium water qualities. Results from indicator value analysis, along with the range of TN of streams where the species is present (1.66–15.3 mg L−1 (Table S3 in Supplementary Materials) and its optimum value (7.75 mg L−1 (Table 4), propose that I. decipiens mostly associates with good water quality concerning the TN gradient. However, this range of TN is above the critical value (>1.5 mg L−1) recommended for classifying eutrophic water bodies (Boyd 2020). Despite this, the presence of the species as an indicator of poor water quality implies its ability to tolerate eutrophic waters. Limited information about the preferences of species is available, but Iglikowska and Namiotko (2012) collected species in waters with 1 mg L−1 NO31−, while a range from 11.33 to 224.85 mg L−1 was found in the present study. This indicates that species can tolerate nitrate concentration exceeding the permissible maximum value (50 mg L−1) for drinking water by TS266 (2005). In line with these findings, the species significantly pointed out the poor water quality in relation to NO31− gradient. The range of DO for species in the present study (Table S3 in Supplementary Materials) was fitting to the range (3.23–7.7 mg L−1 (Roca & Baltanás 1993, Iglikowska & Namiotko 2012)) provided in the literature. Parallel to this narrow range, we estimated a low DO optimum value (3.76 mg L−1 (Table 4) for species, but higher optimum values of 7.90 mg L−1 and 9.20 mg L−1 were published by Külköylüoğlu et al. (2021) and (2023), respectively. This suggests that species may prefer low oxygen values even if it tolerates a wide range. The presence of I. decipiens in waters with a range of 0.01–0.25 mg L−1 PO43− (Iglikowska & Namiotko 2012 Yavuzatmaca 2022) was announced previously. In addition to these findings, the results in the present study (Tables 3, 4 and S3) mentioned that the species prefers moderately high TP concentrations (0.01–0.334). This is noteworthy, considering the infrequent occurrence of TP concentrations exceeding 0.5 mg L−1 in water bodies (Boyd 2020).

Ilyocypris monstrifica was encountered three times with a total of 13 individuals. A range from 1.77 to 14.5 mg L−1 (Yavuzatmaca 2020, Akdemir & Külköylüoğlu 2021) comprising the water qualities from poor to very good concerning DO gradient for species was available in the literature. The minimum and maximum values of DO for species found in the present study (Table S3 in Supplementary Materials) fall within this documented range. Similar to the present study (Fig. 3), Szlauer-Łukaszewska (2014) underlined the strongly negative correlation of the species with DO but a positive correlation with nitrogen. It seems that species exhibits a similar ecological behavior to I. decipiens. Despite being found in a wide range of DO levels, I. monstrifica significantly indicates a moderate water quality characterized by relatively low oxygen concentrations (Table 3). Although the TN range (5.87–6.33 mg L−1) of I. monstrifica is narrower than the I. decipiens, both species signified good water quality based on TN gradient (Table 3). Due to the limited ecological information available for both species in the literature, further comments or comparisons cannot be made.

Potamocypris fallax occurred 18 times, with an abundance totaling 256 individuals (Table 2). The species was collected in a wide range of DO concentrations, ranging from 1.7 to 10.6 (Yavuzatmaca et al. 2015, the present study). Yavuzatmaca (2020) emphasized the presence of P. fallax in a group of species indicating the sites with a 10.77 mg L−1 mean DO value. All these findings recommend that the species prefers well-oxygenated aquatic environments. Indeed, P. fallax denoted the good water quality associated with the DO gradient in the present study (Table 3). P. similis showed two occurrence frequencies, including four individuals (Table 2). Most recently, Yavuzatmaca (2022) found the species in a water body with a TP concentration of 0.01 mg L−1. We collected species in waters with a range of 0.201–0.415 mg L−1 TP (Table S3 in Supplementary Materials). It seems that the species chooses aquatic bodies TP concentrations below the 0.5 mg L−1, which is a critical value since an exceeding value displays the eutrophication or wastewater (Boyd 2020). Although founding of species as the indicator of medium water quality (Table 3), the aforementioned information suggests that the presence of species in an aquatic body may signal a change in water quality from good to medium in terms of TP gradient.

Cypridopsis vidua is a well-known cosmopolitan species (Külköylüoğlu 2013). Mezquita et al. (2001) collected the species in waters with a range of 0–0.053 meq L−1 (0–0.956 mg L−1) ammonium values in the Iberian Mediterranean brooks. Gifre et al. (2002) underlined the occurrence of C. vidua in a group of ostracods characterizing the freshwater springs with submerged macrophytes and a high production/respiration ratio and collected it from water bodies with a range of 0–78.92 µM (0–9.0861 mg L−1) NH41+ concentration. Szlauer-Łukaszewska et al. (2021) found a 62% correlation between C. vidua and NH41+ in springs in the River Krąpiel valley (NW Poland). We collected only 3 individuals from three different streams sampled in the autumn (Table S1 in Supplementary Materials) with a range of 0.05–3.69 mg L−1 NH41+ concentrations (Table S3 in Supplementary Materials). This wide range and the optimum value (1.26 mg L−1, Table 4) also exceed the critical value (0.5 mg L−1) of ammonium defined by TS266 (2005) for drinking water. All these, along with the indication of C. vidua to poor water quality (Table 3), suggest that C. vidua is a positive-pollution indicator (Rygg 1985) related to ammonium. This is because high concentrations of ammonium in water bodies cause eutrophication (Boyd 2020).

Stenocypris intermedia was reported in the range of 174–1,099 μS cm−1 EC (Yavuzatmaca 2022, this study). The optimum value of the species (956 μS cm−1) for EC corresponds to the good water, but the species significantly pointed to the medium water quality in terms of EC (Table 3). This shows that the species may tolerate a wide range of EC, and caution should be exercised when using species as an indicator of EC. Minimum and maximum values of DO concentration for the species is ranged from 0.6 to 8.6 mg L−1 (Rasouli & Aygen 2017, Yavuzatmaca et al. 2017) in the literature. The species was found to be an indicator of medium water quality for DO and BOD5 values (Table 3). The interval of BOD5 (1.8–9.30 mg L−1) herein corresponds to the BOD5 range of natural waters as 1–10 mg L−1 (Boyd 2020). On the other hand, the indicator value of species implied that species may tolerate the organic pollution coming from aquaculture effluents (BOD5 = 10–30 mg L−1) (Boyd 2020). Stenocypris intermedia was found in a wide interval (23.5–224.8 mg L−1) of NO31−. This range is extremely critical and dangerous when compared with the recommended (25 mg L−1) and permissible maximum (50 mg L−1) nitrate levels for drinking water outlined in TS266 (2005) in the sense of water quality. The TN (6.25–15.3 mg L−1) and TP (0.251–0.334 mg L−1) intervals where species present correspond to the values of these variables used to indicate the eutrophic water bodies (Boyd 2020). The species was also found to characterize poor for NO31− gradient and medium water qualities for both of TN and TP gradients in the present study. All these suggest that the presence of S. intermedia may indicate poor or bad water quality.

In all, more studies involving ostracods and water quality parameters are needed to use these organisms, found in almost every aquatic environment, more effectively to forecast present as well as past environmental conditions.

Acknowledgements

We thank to Mrs. Mary Theresa Dorothy Williams (North Carolina State University) for her help with English. This study was supported by The Scientific and Technological Research Council of Turkey (Project No: 119Y494).

Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1556/1777.2024.09741.

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  • Szlauer-Łukaszewska A, Pešić V (2020) Habitat factors differentiating the occurrence of Ostracoda (Crustacea) in the floodplain of a small lowland River Kra piel (N-W Poland). Knowledge and Management of Aquatic Ecosystems (421): 23. https://doi.org/10.1051/kmae/2020012

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  • Szlauer-Łukaszewska A, Pešić V, Zawal A (2021) Environmental factors shaping assemblages of ostracods (Crustacea: Ostracoda) in springs situated in the River Krąpiel valley (NW Poland). Knowledge and Management of Aquatic Ecosystems (422): 14. https://doi.org/10.1051/kmae/2021010

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  • Yavuzatmaca M (2022) Determination of environmental variables groups affecting the occurrence of non-marine ostracods (Crustacea) in the Eastern Mediterranean region of Turkey. Biologia 77: 31853202. https://doi.org/10.1007/s11756-022-01208-2

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  • Yavuzatmaca M, Külköylüoğlu O, Yılmaz O (2015) Distributional patterns of non-marine ostracoda (Crustacea) in Adiyaman province (Turkey). Annales de Limnologie - International Journal of Limnology 51(2): 101113. https://doi.org/10.1051/limn/2015005

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  • Proctor VW, Malone CR, DeVlaming VL (1967) Dispersal of aquatic organisms: Viability of disseminules recovered from the intestinal tract of captive killdeer. Ecology 48(4): 672676. https://doi.org/10.2307/1936517

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  • R Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

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    • Search Google Scholar
    • Export Citation
  • Roca JR, Baltanás A (1993) Ecology and distribution of ostracoda in Pyrenean springs. Journal of Crustacean Biology 13(1): 165174. https://doi.org/10.2307/1549131

    • Search Google Scholar
    • Export Citation
  • Ruiz F, Abad M, Bodergat AM, Carbonel P, Rodríguez-Lázaro J, González-Regalado ML, et al. (2013) Freshwater ostracods as environmental tracers. International Journal of Environmental Science and Technology 10(5): 11151128. https://doi.org/10.1007/s13762-013-0249-5

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    • Export Citation
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    • Export Citation
  • Seaby R, Henderson P (2006) Species diversity and richness version 4. Pisces Conservation Ltd., Lymington, Hampshire, 132 pp.

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    • Search Google Scholar
    • Export Citation
  • Szlauer-Łukaszewska A (2014) The dynamics of seasonal ostracod density in groyne fields of the Oder River (Poland). Journal of Limnology 73(2): 96109. https://doi.org/10.4081/jlimnol.2014.865

    • Search Google Scholar
    • Export Citation
  • Szlauer-Łukaszewska A, Pešić V (2020) Habitat factors differentiating the occurrence of Ostracoda (Crustacea) in the floodplain of a small lowland River Kra piel (N-W Poland). Knowledge and Management of Aquatic Ecosystems (421): 23. https://doi.org/10.1051/kmae/2020012

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    • Export Citation
  • Szlauer-Łukaszewska A, Pešić V, Zawal A (2021) Environmental factors shaping assemblages of ostracods (Crustacea: Ostracoda) in springs situated in the River Krąpiel valley (NW Poland). Knowledge and Management of Aquatic Ecosystems (422): 14. https://doi.org/10.1051/kmae/2021010

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    • Search Google Scholar
    • Export Citation
  • Wetzel R (2001) Limnology: lake and river ecosystems. Academic Press, Elsevier Science, USA, 1006 pp.

  • Williams M, Siveter DJ, Salas MJ, Vannier J, Popov LE, Ghobadi Pour M (2008) The earliest ostracods: the geological evidence. Senckenbergiana lethaea 88(1): 1121. https://doi.org/10.1007/BF03043974

    • Search Google Scholar
    • Export Citation
  • Yavuzatmaca M (2020) Species assemblages of Ostracoda (Crustacea) from west-site of Turkey: Their indicator potential for lotic and lentic habitats. Biologia 75(12): 23012314. https://doi.org/10.2478/s11756-020-00494-y

    • Search Google Scholar
    • Export Citation
  • Yavuzatmaca M (2022) Determination of environmental variables groups affecting the occurrence of non-marine ostracods (Crustacea) in the Eastern Mediterranean region of Turkey. Biologia 77: 31853202. https://doi.org/10.1007/s11756-022-01208-2

    • Search Google Scholar
    • Export Citation
  • Yavuzatmaca M, Külköylüoğlu O, Yılmaz O (2015) Distributional patterns of non-marine ostracoda (Crustacea) in Adiyaman province (Turkey). Annales de Limnologie - International Journal of Limnology 51(2): 101113. https://doi.org/10.1051/limn/2015005

    • Search Google Scholar
    • Export Citation
  • Yavuzatmaca M, Külköylüoğlu O, Yılmaz O, Akdemir D (2017) On the relationship of ostracod species (Crustacea) to shallow water ion and sediment phosphate concentration across different elevational range (Sinop, Turkey). Turkish Journal of Fisheries and Aquatic Sciences 17(6): 13331346. https://doi.org/10.4194/1303-2712-v17_6_40

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    • Export Citation
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The author instruction is available in PDF.
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Barna PÁLL-GERGELY, PhD; Attila HETTYEY, PhD
Plant Protection Institute, HUN-REN Centre for Agricultural Research
Address: 1022 Budapest, Herman Ottó út 15.
E-mail: pallgergely2@gmail.com; hettyey.attila@atk.hun-ren.hu

Animal Taxonomy and Ecology
Language English
Size B5
Year of
Foundation
1955
Volumes
per Year
1
Issues
per Year
4
Founder Magyar Tudományos Akadémia
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher

Chief Executive Officer, Akadémiai Kiadó

ISSN

3004-300X (Print)

ISSN

3004-3018 (Online)

Cover photo:  Miklós Laczi: Nászruhás mocsári béka (Rana arvalis)

 

 

Co-Editor(s)-in-Chief:

Barna PÁLL-GERGELY, PhD - taxonomy

(Plant Protection Institute, HUN-REN Centre for Agricultural Research, Budapest, Hungary)

Attila HETTYEY, PhD - ecology

(Plant Protection Institute, HUN-REN Centre for Agricultural Research, Budapest, Hungary)

 

Associate Editors

  • Gergely HORVÁTH (Department of Systematic Zoology and Ecology, Eötvös Loránd University, Budapest, Hungary)
  • Zoltán IMREI (Plant Protection Institute, HUN-REN Centre for Agricultural Research, Budapest, Hungary)
  • Péter KÓBOR (Plant Protection Institute, HUN-REN Centre for Agricultural Research, Budapest, Hungary)
  • Petr KOČÁREK (Department of Biology and Ecology, Faculty of Science, University of Ostrava, Czechia)
  • Zoltán KORSÓS (Department of Ecology, University of Veterinary Medicine, Budapest, Hungary)
  • Robin KUNDRATA (Department of Zoology, Faculty of Science, Palacky University in Olomouc, Czechia)
  • Zoltán LÁSZLÓ (Hungarian Department of Biology and Ecology, Faculty of Biology and Geology, Babeş-Bolyai University, Cluj-Napoca, Romania)
  • György MAKRANCZY (Natural History Museum, Budapest, Hungary)
  • Daniel Fernández MARCHÁN (Universidad Complutense de Madrid, Faculty of Biological Sciences, Madrid, Spain)
  • Gergely SZÖVÉNYI (Department of Systematic Zoology and Ecology, Eötvös Loránd University, Budapest, Hungary)
  • Tamás SZŰTS (Department of Ecology, University of Veterinary Medicine Budapest, Budapest, Hungary)

External advisers

  • Zoltán BARTA (Department of Evolutionary Zoology and Human Biology, University of Debrecen, Debrecen, Hungary)
  • András BÁLDI (Centre for Ecological Research, Vácrátót, Hungary)
  • Péter BATÁRY (Centre for Ecological Research, Vácrátót, Hungary)
  • Csaba CSUZDI (Department of Zoology, Eszterházy Károly Catholic University, Eger, Hungary)
  • András DEMETER (European Commission, Directorate-General for the Environment, Brussels, Belgium)
  • Sergey ERMILOV (Tyumen State University, Tyumen, Russia)
  • László GALLÉ (Department of Ecology, University of Szeged, Szeged, Hungary)
  • Mark E. HAUBER (Department of Psychology, Hunter College, New York, USA)
  • Gábor HERCZEG (Department of Systematic Zoology and Ecology, Eötvös Loránd University, Budapest, Hungary)
  • Erzsébet HORNUNG (Department of Ecology, Szent István University, Budapest, Hungary)
  • Ladislav JEDLIČKA (Department of Zoology, Comenius University, Bratislava, Slovakia)
  • András LIKER (Department of Limnology, University of Pannonia, Veszprém, Hungary)
  • Gábor LÖVEI (Department of Agroecology, Aarhus University, Denmark)
  • Tibor MAGURA (Department of Ecology, University of Debrecen, Debrecen, Hungary)
  • József MAJER (Department of Hydrobiology, University of Pécs, Pécs, Hungary)
  • Wayne N. MATHIS (Department of Entomology, Smithsonian Institution, Washington, USA)
  • István MATSKÁSI (Hungarian Natural History Museum, Budapest, Hungary)
  • Csaba MOSKÁT (Animal Ecology Research Group, Hungarian Academy of Sciences and Hungarian Natural History Museum, Budapest, Hungary)
  • Maxim NABOZHENKO (Caspian Institute of Biological Resources, Dagestan Scientific Centre, Russian Academy of Sciences, Makhachkala, Russia)
  • Roy A. NORTON (State University of New York, Syracuse, USA)
  • Tatsuo OSHIDA (Laboratory of Wildlife Biology, Obihiro University of Agriculture and Veterinary Medicine, Hokkaido, Japan)
  • Tomas PAVLÍČEK (Institute of Evolution, Haifa, Israel)
  • Dávid RÉDEI (National Chung Hsing University, Taichung, Taiwan)
  • Rudolf ROZKOŠNÝ (Department of Zoology and Ecology, Masaryk University, Brno, Czech Republic)
  • Lajos RÓZSA (Institute of Evolution, Centre for Ecological Research, Budapest, Hungary)
  • Ferenc SAMU (Plant Protection Institute, Centre for Agricultural Research, Budapest, Hungary)
  • Mark A. SARVARY (Investigative Biology Teaching Laboratories, Cornell University, Ithaca, New York, USA)
  • Spyros SFENTHOURAKIS (Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus)
  • Emma SHERLOCK (The National History Museum, London, UK)
  • Péter SÓLYMOS (Department of Biological Sciences, University of Alberta, Edmonton, Canada)
  • Zoltán VARGA (Department of Evolutionary Zoology and Human Biology, University of Debrecen, Debrecen, Hungary)
  • Zsolt VÉGVÁRI (Institute of Aquatic Ecology, Centre for Ecological Research, Budapest, Hungary)
  • Judit VÖRÖS (Department of Zoology, Hungarian Natural History Museum, Budapest, Hungary)

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