Science of the Total Environment 662 (2019) 816–823 Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv Habitat selection response of the freshwater shrimp Atyaephyra desmarestii experimentally exposed to heterogeneous copper contamination scenarios Victoria C. Vera-Vera a, Francisco Guerrero a,b, Julián Blasco c, Cristiano V.M. Araújo c,⁎ a b c Department of Animal Biology, Plant Biology and Ecology, University of Jaén, 23071 Jaén, Spain Center of Advanced Studies in Earth Sciences, University of Jaén, 23071 Jaén, Spain Department of Ecology and Coastal Management, Institute of Marine Sciences of Andalusia, CSIC, 11510 Puerto Real, Cádiz, Spain H I G H L I G H T S G R A P H I C A L A B S T R A C T • The ability of the freshwater shrimp Atyaephyra desmarestii to avoid Cu was tested. • Gradients and patches of contamination by copper were simulated in the HeMHAS. • In linear contamination gradient, shrimps signiﬁcantly avoided copper. • In heterogeneous contamination scenarios, shrimps' distribution tended to be random. • In meta-ecosystem scenarios, clean areas prevented organisms of a continuous exposure. a r t i c l e i n f o Article history: Received 26 December 2018 Received in revised form 22 January 2019 Accepted 23 January 2019 Available online 24 January 2019 Editor: Damia Barcelo Keywords: Atyaephyra desmarestii Avoidance Habitat connectivity HeMHAS Non-forced exposure Preference a b s t r a c t In contaminated aquatic ecosystems, it is expected that organisms suffer some effects caused by the contaminants. However, for mobile organisms inhabiting heterogeneously contaminated ecosystems, the continuous exposure to contaminants can be avoided by moving to less contaminated habitats. The present study evaluated the habitat selection of the freshwater shrimp Atyaephyra desmarestii experimentally exposed to different copper concentrations to verify whether the heterogeneous contamination distribution and the connectivity between habitats with different copper levels could generate a random population distribution similar to metapopulation. The experiments were performed in the HeMHAS (Heterogeneous Multi-Habitat Assay System), a non-forced multi-compartmented exposure system, in which it is possible to simulate the distribution of contaminants in a linear gradient or as patches of contamination. Copper was used to simulate a linear contamination gradient (26 to 105 μg/L Cu) and two patchy scenarios with three contamination levels [reference zone (R: 26 ± 7 μg/L Cu), mixing zone (M: 61 ± 2 μg/L Cu) and disturbed zone (D: 101 ± 12 μg/L Cu)], with two mixing zones or one central mixing zone in a heterogeneous scenario. In the copper gradient scenario, a clear trend of shrimps (59.6 ± 8.0% of the population) moving to the reference zones and an avoidance of 66.7 ± 11.1% of the most contaminated zone were observed. For the patchy scenarios, a random distribution of organisms (34, 36 and 30% for R, M and D zones, respectively) was observed in the scenario with one mixing zone; on the other hand, a slight preference for the reference zones (44.9 ± 4.8%) was evidenced in the scenario with two mixing zones. As shrimps are able to select less contaminated areas, it is highly important to preserve clean zones in heterogeneously contaminated environments, such as the arrangement in meta-ecosystems, as the less- or ⁎ Corresponding author. E-mail address: [email protected] (C.V.M. Araújo). https://doi.org/10.1016/j.scitotenv.2019.01.304 0048-9697/© 2019 Elsevier B.V. All rights reserved. V.C. Vera-Vera et al. / Science of the Total Environment 662 (2019) 816–823 817 uncontaminated zones might represent less stressful areas to protect populations against continuous contamination exposure. © 2019 Elsevier B.V. All rights reserved. 1. Introduction Ecosystems are complex units that integrate multiple interactions between the environment and organisms. When ecosystems are connected, those interactions drive their dynamics, homeostasis as well as the exchanges between them (Fleeger et al., 2003; Ewers et al., 2007; Fuller et al., 2015) such as the meta-ecosystem concept described by Loreau et al. (2003). Due to this connectivity, the environmental contamination in an ecosystem could affect other undisturbed adjacent ones (Spromberg et al., 1998; Clements and Rohr, 2009; Moe et al., 2013). One of the possible consequences to occur in contaminationaffected ecosystems is the evasion of the organisms avoiding the most disturbed areas (Ǻtland and Barlaup, 1995; Hansen et al., 1999b; Tierney, 2016), which could generate a dynamic similar to the metapopulation and metacommunity arrangement (Grimm et al., 2003). When organisms decide to escape from disturbed areas, the community structure and dynamics of the avoided areas, as well as the adjacent ones are likely to be affected (Exley, 2000; Fleeger et al., 2003; Wells et al., 2004). Although escaping is a solution for the survival of the avoiders (Moe et al., 2013), the effects to ecosystems could lead to the loss of abundance and biodiversity (Spromberg et al., 1998; Lopes et al., 2004). This change in the composition of the community might still exert inﬂuence on the ecological relationships such as competition by space and food and predation pressure in the destination area (Hansen et al., 1999a; Exley, 2000; Fleeger et al., 2003; Wells et al., 2004). Traditionally, the potential impact of contaminants on organisms is determined by ecotoxicity tests (Calow, 1989; Maltby and Calow, 1989) that provide important evidence of the contamination-caused environmental disturbance (Faggio et al., 2016; Capillo et al., 2018), although the experimental conditions are a little reductionist when compared to the environmental complexity (Straalen, 2003; Segner et al., 2014; Khan, 2018). For instance, the exposure of organisms to contaminants in ecotoxicity tests is normally performed in conﬁned environments (forced exposure), in which organisms are mandatorily and continuously in contact with the contaminants. Therefore, no option to escape from the contaminants and to avoid their toxic effects is considered. However, some studies have applied non-forced exposure as an alternative method to assess toxicity (Jones, 1947, 1948; Folmar, 1976; Gunn and Noakes, 1986; Hartwell et al., 1989; Smith and Bailey, 1990; Richardson et al., 2001). In the last decade, a non-forced, free-choice, multi-compartmented exposure system developed by Lopes et al. (2004) and slightly modiﬁed by Rosa et al. (2012) has proved to be a suitable tool to assess contamination-driven spatial avoidance (Araújo et al., 2016a; Vasconcelos et al., 2016; Silva et al., 2017, 2018). The aim of the approach based on multi-compartmented exposure systems is to analyze the displacement of organisms between multiple compartments with different levels of contamination and then to verify how the contaminants drive the spatial distribution of organisms (Lopes et al., 2004; Moreira-Santos et al., 2008; Rosa et al., 2012; Araújo et al., 2016b, 2016c, 2018a, 2018b; Islam et al., 2019). Although this approach does not solve the problems related to the shortcomings of the environmental relevance of ecotoxicity tests, it provides a complementary method to simulate environmental heterogeneity regarding contamination. Evidence of the spatial avoidance response in multicompartmented systems has been observed in laboratory studies with several aquatic organisms such as: copepods (Araújo et al., 2014a), cladocerans (Lopes et al., 2004), mollusks (Araújo et al., 2012), decapods (Araújo et al., 2019), amphibians (Araújo et al., 2014b) and ﬁsh (Moreira-Santos et al., 2008; Silva et al., 2017, 2018) exposed to different contamination sources. The spatial avoidance response is a potential mechanism used by mobile organisms to prevent toxicity (Wells et al., 2004; Tierney et al., 2010; Hellou, 2011), which could be expected in habitats where there is heterogeneity in the contamination distribution and if the connectivity between ecosystems is considered (Kimball and Levin, 1985). Taking into account that generally neither the toxicants nor organisms are uniformly distributed in space (Spromberg et al., 1998), a new non-forced, multicompartmented exposure system (HeMHAS - Heterogeneous MultiHabitat System Assay; Araújo et al., 2018b) has been developed recently to simulate heterogeneously contaminated habitats. The HeMHAS makes it possible to assess the dynamics of the displacement of organisms between habitats and the processes of contamination avoidance in a more complex scenario, as organisms can move freely among different zones (Araújo et al., 2018b). When organisms are exposed in a non-forced, multi-compartmented exposure system, the concept of environmental disturbance/impact goes beyond the toxicity at the individual level and its focus turns towards the spatial distribution and habitat preference, as when organisms displace to less aversive environment no toxicity is necessarily expected to occur (De Lange et al., 2006; Tierney et al., 2010). If we consider that the dispersion of contaminants can also affect the populations' spatial distribution in adjacent undisturbed habitats (Spromberg et al., 1998; Moe et al., 2013), assessing the organisms' habitat preference, and therefore their spatial distribution, could help to understand the dynamics of the displacement of organisms in wider geographic areas (considering connected ecosystems), thus integrating the communities and landscape ecology: the meta-ecosystem framework (Loreau et al., 2003). Although, the organisms' preferential distribution tends to be towards the less contaminated habitat in an environment with a contamination gradient, we hypothesize that, in a heterogeneous (patchy) contamination scenario, the organisms might tend to present a dynamic of dispersion with a random (not preferential) displacement between habitats, where less/uncontaminated habitats might play a crucial role as refuges to prevent toxicity. To test this hypothesis, the shrimp Atyaephyra desmarestii was exposed to copper in the HeMHAS as previous studies have shown that this species is able to detect and avoid contamination (Araújo et al., 2019) and copper in particular (Araújo et al., 2018c). The aim of the present study was, ﬁrstly, to assess the ability of this species to avoid copper and select a less disturbed habitat when exposed to copper in a heterogeneous contamination scenario. Secondly, it was intended to assess whether the presence of undisturbed habitats in a heterogeneous contamination scenario allows organisms to display a very dynamic and random distribution, in which it is not possible to detect a clear avoidance of the most contaminated environments. 2. Materials and methods 2.1. Test organism The freshwater shrimp A. desmarestii (Millet, 1831) was used as the test organism. This species is distributed along Mediterranean and Central European regions (Van den Brink and Van Der Velde, 1986; Moog et al., 1999; Tittizer et al., 2000; Fidalgo and Gerhardt, 2002). Shrimps were caught by a hand net in an uncontaminated area (Araújo et al., 2018c, 2019) from the river Guadalete (36°47′55.05″ N y 5°19′53.04″ W). After sampling, shrimps were transported to the laboratory where they were acclimated for one week before the experiments. Acclimation was performed in Guadalete river water during the ﬁrst two days and afterwards the river water was gradually replaced by old dechlorinated tap water: 25% on day 3, 50% on day 4 and 100% on day 5. Shrimps were 818 V.C. Vera-Vera et al. / Science of the Total Environment 662 (2019) 816–823 maintained in 40 L aquaria, with continuous aeration, at 20 °C and photoperiod of 12/12 (light/dark). No food was provided during acclimation. Only shrimps with 2.5 to 2.6 cm were used in the experiments to avoid differences in the response due to the life stage. Mortality was lower than 5% during acclimation. 2.2. HeMHAS – heterogeneous multi-habitat assays system The HeMHAS (70 × 30 × 7 cm: length, width and height; Araújo et al., 2018b) is a non-forced, multi-compartmented exposure system formed by 18, 6 cm high, circular compartments, 10 cm in diameter and a capacity of 320 mL, which are distributed in a 3 × 6 arrangement (3 compartments on a vertical axis in each one of 6 zones on a longitudinal axis) (Fig. 1). All compartments are connected to all the adjacent compartments. The bottom of the compartments is concave and presents a smooth ascending slope that reaches the connections. The opening and closing of the connections between the compartments is controlled by gates at the connections. A lid for each compartment prevents the organisms from jumping out. The system was constructed in acetal (polyoxymethylene) to prevent adsorption of contaminants. The HeMHAS was patented by the Spanish Patent and Trademark Ofﬁce (#P201731426). A schematic representation of the zones and their distribution composing different contamination scenarios is shown in Fig. 2. For each scenario, control experiments (no copper, only tap water in all the compartments) were performed to prove that the organisms' distribution is random in the absence of contamination. In total, six experiments were performed, which were identiﬁed by letters as indicated in Fig. 2. The experiments were performed in triplicate and lasted 3 h. In the experiments with copper and before introducing the copper concentrations into the compartments, the gates were closed to isolate the compartments from each other and to avoid the mixing of the concentrations. In the control experiments, all the compartments were ﬁlled with the same tap water used in the acclimation of organisms. Afterwards, one shrimp was put in each compartment (load of one organism per 320 mL) and then the gates were placed with the hole positioned down, allowing the organisms to move between compartments. The organisms' position was recorded at each 30 min for the control experiments (no copper) and at each 15 min for the experiments with copper during the 3 h exposure period. Shorter observation periods were used in experiments with copper in order to detect a possible increase in the displacement of organisms stimulated by copper. 2.4. Statistical analysis 2.3. Habitat selection experiments Different experiments with different contamination scenarios were simulated: experiments with a linear copper gradient and two groups of experiments with copper contamination patches using different spatial distributions of copper and different conﬁgurations of connectivity between compartments. In each scenario, three copper levels were used: 0 μg/L (reference zone - R), 50 μg/L (mixing zone - M) and 150 μg/L (disturbed zone - D). This range was chosen because copper concentrations described to trigger avoidance in cladocerans and ﬁsh varies between 15 and 180 μg/L (Moreira-Santos et al., 2008; Silva et al., 2018; Islam et al., 2019; see also the review by Araújo and Blasco, 2019). Due to the mix occurring in the HeMHAS, the concentrations in each zone were determined at the end of the experiments by ICP-MS (Agilent 7900; with a detection limit of 0.0025 μg/L). Each zone is represented by six compartments, which were distributed in groups of two to three contiguous compartments. Zones with the same contamination level were coded with a number, such as R1 and R2, indicating in this case that there are two reference zones; the same was applied to the other zones. The mean copper concentration values ranged as follows (experiments E and F): 26 ± 7 (zone R), 61 ± 2 (zone M) and 101 ± 12 (zone D) μg/L. For experiment D with a copper gradient, the concentrations measured were: 26 ± 4 (R1), 33 ± 1 (R2), 49 ± 1 (M1), 71 ± 2 (M2), 92 ± 3 (D1) and 105 ± 9 μg/L (D2). The distributions (in %) of the shrimps (after arcsine transformation) in the control (using only dechlorinated tap water) and avoidance (with the copper gradient) tests were evaluated by a mixed-design (repeated measures) ANOVA, in which time was considered a repeated measure (within-subjects factor) and compartments were treated as a between-subjects factor. The sphericity of the repeated measures was checked by using Mauchly's test to correct the degrees of freedom if necessary; if sphericity was violated (the variances of the differences are not equal: P b 0.05), the Greenhouse-Geisser correction for degrees of freedom was applied (see Tables with Mauchly's test in Supplementary material). When statistically signiﬁcant differences (P b 0.05) were observed for any factor (time or compartment), the Bonferroni post hoc test was employed to identify differences in the percentage of organisms for time and compartments. When the response did not vary with time (results of the repeated measures ANOVA were not signiﬁcant; P N 0.05), the data of the organisms' distribution were considered as the means from the different observation times (0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 h for the control experiments and 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75 and 3.0 h for the experiments with copper). For experiment D (with a copper gradient), the quantiﬁcation of the number of organisms that avoided (avoiders) copper was calculated per zone (concentrations) as described by Moreira-Santos et al. (2008): Avoiders ¼ NE −N O Fig. 1. Diagram of the HeMHAS. The lid and the gate used, respectively, to cover the compartments and to open or close the connections between compartments are represented on the right. The displacement of the organisms is possible when the gate is placed with the hole positioned down (Araújo et al., 2018b). where NE is the number of organisms expected in a given zone and NO represents the number of organisms observed in a given zone. For the zones with the highest copper concentration, NE was the number of organisms introduced at the beginning (6 shrimps: 1 for each compartment) of the experiment (it is supposed that no organism is expected to move from another zone towards this one). For the other zones, NE was the number of organisms inserted at the beginning of the test plus the organisms introduced in the adjacent zone with a higher copper concentration (it is assumed that organisms move towards a less contaminated zone). As each zone contained six shrimps (from six compartments), the NE values (from the most to the least contaminated zones:) were 6, 12 and 18 for the zones D, M and R, respectively. For a given zone, NO was determined considering the organisms inhabiting the zone studied and the organisms present in the highest copper concentration. For example, for the most contaminated zone, NO was the number of organisms observed in that zone (considering the six compartments, as there is no a most contaminated zone); for V.C. Vera-Vera et al. / Science of the Total Environment 662 (2019) 816–823 819 Fig. 2. Levels of copper, the copper contamination scenarios and their respective control experiments used to assess the habitat selection response of Atyaephyra desmarestii. Experiments A, B and C represent the control experiments (on the left), while the experiments with copper are indicated by the letters D, E and F (on the right). Experiment D represents a linear copper contamination gradient and experiments E and F represent heterogeneous copper contamination patches, differing regarding the connections between compartments and the disposition of the mixing zones. the adjacent zone (M), NO represents the organisms observed in that zone plus the organisms present in the most contaminated zone; for the less contaminated zone, NO is deﬁned by the organisms observed in that zone and in the zones with higher copper levels (i.e., sum of all the organisms in the system). The following equation was used to calculate the percentage of avoidance for each zone (concentration): Avoidance ð%Þ ¼ Avoiders 100: NE The AC50 (concentration that triggers an avoidance of 50% in the population) value and its respective conﬁdence interval (C.I. – 95%) of copper to shrimps were calculated by using PritProbit 1.63 (Sakuma, 1998) from the mean avoidance percentage (mean from the exposure times of 1, 2 and 3 h) observed for each copper concentration. 3. Results 3.1. Control experiments with no copper: the organisms' distribution 3.1.1. Experiment A The distribution of the organisms among the three zones (after the Greenhouse correction as sphericity was not assumed; Table S1a – Supplementary material) did not vary with time (F = 0.009; P = 0.998) nor for the interaction time ∗ compartment (F = 1.537; P = 0.231; Table S1b – Supplementary material). Regarding the percentage of organisms in each zone (34.8 ± 7.7%, 25.0 ± 5.6% and 40.1 ± 4.4% for the zones R1, R2 and R3, respectively – Fig. 3), no statistically signiﬁcant difference was observed (F = 4.998; P = 0.053; Table S1c – Supplementary material). Therefore, this control experiment was considered acceptable because there was no a trend to avoid the extremities (this behavior could be critical for the copper gradient experiments). 3.1.2. Experiment B The distribution of the organisms among the three zones throughout the time (sphericity was assumed: Table S2a – Supplementary material) did not vary with time (F = 0.080; P = 0.995) but did for the interaction time ∗ compartment (F = 2.615; P = 0.020; Table S2b – Supplementary material). Regarding the percentage of organisms (25.6 ± 5.3%, 35.5 ± 3.5% and 38.9 ± 1.9% for the zones R1, R2 and R3, respectively – Fig. 3), ANOVA detected a statistically different distribution of the organisms (F = 9.603; P = 0.013; Table S2c – Supplementary material), although this difference was not detected by the Bonferronni test (Table S2d - Supplementary material). The control was considered acceptable because there was no a trend to avoid the extremities (this behavior could be critical for the copper gradient experiments). 820 V.C. Vera-Vera et al. / Science of the Total Environment 662 (2019) 816–823 Fig. 3. Mean percentages (n = 6 exposure times; and standard deviation) of the shrimps Atyaephyra desmarestii found in different zones (R1, R2 and R3) in the experiments A, B and C (control experiments with culture water) during 3 h exposure. Similar letters indicate no statistically signiﬁcant difference by the Bonferroni test (signiﬁcance: P b 0.05). 3.1.3. Experiment C The distribution of the organisms among the three zones (after the Greenhouse correction as sphericity was not assumed; Table S3a – Supplementary material) did not vary with time (F = 0.049; P = 0.966) nor for the interaction time ∗ compartment (F = 1.185; P = 0.364) (Table S3b – Supplementary material). Regarding the percentage of organisms (34.9 ± 6.3%, 33.6 ± 3.9% 31.5 ± 3.3% for the zones R1, R2 and R3, respectively – Fig. 3), the organisms' distribution was not statistically different (F = 0.177; P = 0.842; Table S3c – Material supplementary). This control was also considered acceptable as no trend to avoid the extremes was evidenced. Fig. 4), the results showed no statistically signiﬁcant difference (F = 0.286; P = 0.761; Table S5c – Supplementary material). 3.2.3. Experiment F No mortality was recorded during this experiment. The distribution of the organisms among the three zones (considering the Greenhouse correction as sphericity was not assumed; Table S6a – Supplementary material) did not vary with time (F = 0.029; P = 0.997) nor for the interaction time ∗ compartment (F = 1.351; P = 0.276) (Table S6b – Supplementary material). However, statistically signiﬁcant differences (F = 28.402; P b 0.001; Table S6c – Supplementary material) were observed for the distribution of organisms (44.9 ± 4.8%, 33.2 ± 5.1% and 21.9 ± 0.5% for the zones R, M and D, respectively – Fig. 4). 3.2. Experiments with copper 4. Discussion 3.2.1. Experiment D No mortality was recorded during this experiment. The distribution of the organisms among the three zones (after the Greenhouse correction as sphericity was not assumed; Table S4a – Supplementary material) did not vary with time (F = 0.084; P = 0.975) nor for the interaction time ∗ compartment (F = 1.502; P = 0.226) (Table S4b – Supplementary material). The percentage of organisms (59.6 ± 8.0%, 28.9 ± 3.5% and 11.6 ± 4.5% for the zones R, M and D, respectively – Fig. 4) was statistically different (F = 53.414; P b 0.0001; Table S4c – Material supplementary) between the three zones. The mean avoidance of the population [AC50 = 70.1 (55.3–96.6) μg/L] was calculated based on the avoidance percentage and considering the copper concentrations in each compartment (not by zones) (Fig. 5). 3.2.2. Experiment E No mortality was recorded during this experiment. The distribution of the organisms among the three zones, after the Greenhouse correction (sphericity was not assumed; Table S5a – Supplementary material), did not vary with time (F = 0.029; P = 0.996) nor for the interaction time ∗ compartment (F = 1.374; P = 0.269) (Table S5b – Supplementary material). Regarding the percentage of organisms (34.1 ± 8.9%, 35.6 ± 7.9% and 30.2 ± 10.2% for the zones R, M and D, respectively – The present study simulated a linear copper gradient and two heterogeneous environments with patches contaminated by copper in the HeMHAS to check if the freshwater shrimp A. desmarestii was able to detect and avoid contamination. The initial results of the control experiments (experiments A, B and C) indicated that the HeMHAS and the laboratory conditions did not affect the habitat selection by shrimps as the organisms were randomly distributed in the absence of contamination. A previous study using the HeMHAS as the test system, in which the ﬁsh Danio rerio was exposed to a copper gradient, also showed the suitability of the HeMHAS as an exposure system, as it caused no interference in the displacement of the organisms (Araújo et al., 2018b). Checking this randomness is crucial for the interpretation of the avoidance response in multi-compartmented systems and ensuring that no confounding factor is interfering in the selection of zones by the organisms (Lopes et al., 2004; Moreira-Santos et al., 2008; Silva et al., 2018). When the organisms were exposed to the copper gradient (experiment D), a clear avoidance was observed such as previously described by other authors using different test systems and organisms (Hartwell et al., 1989; Svecevičius, 1999; Richardson et al., 2001; Lefcort et al., 2004; see also review by Araújo et al., 2016a). Using other multicompartmented linear systems, avoidance to copper has been also evidenced for different species, such: as the cladocera Daphnia longispina Fig. 4. Mean percentages (n = 12 exposure times; and standard deviation) of the shrimps Atyaephyra desmarestii found in different zones (R, M and D) in the experiments D, E and F (avoidance experiments with copper) during 3 h exposure. Different letters indicate statistically signiﬁcant differences by the Bonferroni test (signiﬁcance: P b 0.05). V.C. Vera-Vera et al. / Science of the Total Environment 662 (2019) 816–823 Fig. 5. Mean percentage (from exposure periods of 1, 2 and 3 h) of avoidance by the shrimp Atyaephyra desmarestii exposed to a linear copper gradient. (Lopes et al., 2004), the marine shrimp Litopenaeus vannamei (Araújo et al., 2016c), tadpoles of the amphibians Leptodactylus latrans, Lithobates catesbeianus and Pelophylax perezi (Araújo et al., 2014b), the freshwater ﬁsh D. rerio (Moreira-Santos et al., 2008; Islam et al., 2019) and Poecilia reticulata (Silva et al., 2018) and the marine ﬁsh Rachycentron canadum (Araújo et al., 2016c). The sensitivity of A. desmarestii to avoid copper (AC50 around 70 μg/L) is comparable to the avoidance response to copper observed by other authors that reported AC50 for cladocerans and ﬁsh varying from around 15 to 180 μg/L (Moreira-Santos et al., 2008; Silva et al., 2018; Islam et al., 2019; see also the review by Araújo and Blasco, 2019). The avoidance response to copper in the HeMHAS has only been described for D. rerio until now, which responded in a similar way to A. desmarestii, with an AC50 of 60 μg/L (Araújo et al., 2018b). In any experiments, neither erratic movements, grouping behavior nor signals of lethargy were observed in the shrimps. Behavioral changes as torpidity or moribundity due to exposure to copper can occur when high concentrations (from 200 μg/L) are used such as have been described for tadpoles (Araújo et al., 2014b). The use of the HeMHAS has made it possible to simulate a more heterogeneous scenario of contamination as concentrations could be arranged in patches, such as in experiments E and F. If, on the one hand, it was expected that under a marked linear gradient the shrimps would move to less contaminated zones, on the other hand, in a patchy contamination scenario a random distribution was expected due to the higher possibility of ﬁnding less contaminated zones, which would lead to a higher displacement. This hypothesis was supported by the data from experiment E (no clear preference for the less contaminated zones was observed), but not for experiment F (less contaminated zones were preferred). Although the shrimps tended to avoid the contamination by copper, the randomness of the behavior regarding habitat preference observed in experiment E highlights the importance of clean zones to the dispersion dynamics of the populations. Due to an almost continuous observation (12 observations during the 3 h period), the preference behavior cannot be attributed to chance. Since ecotoxicology is not limited to assessing toxic effects, but also tries to understand and explain changes in the dynamic of populations driven by contamination (Jepson and Sherratt, 1996; Spromberg et al., 1998; Exley, 2000; Chapman, 2002), identifying potential effects on the dispersion pattern of organisms might be a ﬁrst step to prevent alterations in the community, such as the partial or total disappearance of a population due to its ﬂeeing towards other areas (Lopes et al., 2004; Scott and Sloman, 2004; Rosa et al., 2012; Moe et al., 2013). In fact, to improve the ecological aspect of ecotoxicological studies, the concepts of colonization and recolonization of recovering habitats (Van den Brink, 2008; Araújo et al., 2018d; Islam et al., 2019), habitat fragmentation due to a chemical barrier (Fuller et al., 2015; Araújo et al., 2018e, 2019) and metapopulation 821 (assessing the quality of connected habitats, the dynamics and persistence of populations – Grimm et al., 2003) a should be more frequently used. The HeMHAS represents, therefore, a novel approach to studying the risk linked to environmental contamination by which it is possible to assess avoidance and preference responses in a patchy and more complex contamination scenario. The simulation of the heterogeneity of habitat makes it possible to assess how contamination might condition the dispersion of the organisms and their habitat selection processes. Although the traditional ecotoxicity tests are not able to simulate spatial heterogeneity due to the forced exposure scenario (Chapman, 2002; Roberts et al., 2008), the non-forced, multi-compartmented systems, such as the HeMHAS, help to increase the ecological relevance of environmental risk assessment studies by providing a complementary tool to ecotoxicology (Araújo et al., 2018b). This relevance is applicable for mobile organisms and scenarios in which an environmental heterogeneity exists. The environmental heterogeneity occurs in time and space very frequently (Palmer et al., 2010), therefore, ecotoxicological studies should integrate this factor not only by performing seasonal sampling (temporal heterogeneity) in different areas (spatial heterogeneity), but also by considering the habitat connectivity and the possible displacement of organisms between habitats (Spromberg et al., 1998; Fleeger et al., 2003). The reduction or loss of connectivity between habitats due to contamination might produce important changes in the ecosystems regarding: the biological interactions (Branco et al., 2012; Hurd et al., 2016), reduction in the resilience and resistance of the ecosystems (Schmitt-Jansen et al., 2008; Clements and Rohr, 2009) and in the genetic diversity (Roark et al., 2005; Ribeiro and Lopes, 2013), restrictions of habitable areas (Saunders and Sprague, 1967), alterations in the migratory routes (Gray, 1990; Smith and Bailey, 1990) and increases in the vulnerability of the community (Atchison et al., 1987; Moe et al., 2013). Due to the heterogeneity of the habitat quality, patchy environments have been considered of great importance to the maintenance of populations as they favor the displacement dynamics of organisms (Wu et al., 1993; Fuller et al., 2015). In this sense, the mixing zones of the present study (named zone M - moderately contaminated) might have played an important role as zones tolerable by organisms and might have served, just like the clean zones, to minimize the deleterious effects of the contaminants, favoring the random dispersion of organisms (Sherratt and Jepson, 1993; Maurer and Holt, 1996). Zones functioning like this might play an important role for the interactions and ﬂows between ecosystems in a meta-ecosystem arrangement (Loreau et al., 2003). Habitat heterogeneity, regarding contamination, should be considered with special interest in ecotoxicology because the stability and recovery of some ecosystems depend to some extent (not exclusively) on the environmental heterogeneity (Palmer et al., 2010). Finally, it is important to highlight that the habitat heterogeneity does not imply a reduced risk of contamination as organisms tend to displace randomly between areas, instead, the present study shows the importance of preserving clean areas to guarantee the presence of at least some refuges where organisms can avoid the toxic effects of contamination. Acknowledgements V. Vera-Vera thanks The Postgraduate Iberoamerican University Association (AUIP) for the Master's fellowship. C.V.M. Araújo is grateful to the Spanish Ministry of Science, Innovation and Universities for a Juan de la Cierva contract (IJCI-2014-19318). The authors thank David Roque and Antonio Moreno their technical assistance in the ﬁeldwork and Jon Nesbit for his revision of the English text. This study was partially funded by the Explora project (#CGL2017-92160-EXP). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.01.304. 822 V.C. 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