1.5.1. Life-history traits of the species
Individual life-cycle properties such as lifespan, time to first reproduction, fecundity and generation time control important population-level aspects related to abundance and population structure and subsequently determine the population’s resilience to natural (La Montagne and McCauley, 2001) and anthropogenic (Stark et al. 2004 a; Solomon et al. 2008; Preuss et al.
2009; Thorbek et al. 2010) stressors. In fact, species exhibiting differences in these key life history traits respond differently to equal levels of mortality or inhibition of reproduction (Stark et al. 2004 a).
1.5.2. Density dependence
Important biological processes regulating population dynamics of several invertebrate (daphnids, copepods, springtails; see Preuss et al. 2009; Sibly et al. 2000; Ferguson and Joly, 2002; respectively) and vertebrate (fish, wood mice, birds, see Hazlerigg et al. 2012; Stenseth et al. 2002; Rodenhouse et al. 2003; respectively) species are highly density-dependent. These processes can include the reproductive strategy, feeding behavior, growth and/ or survival of individuals. Density-dependence is very important as it influences the adaptive mechanisms of populations to different stress sources like population resistance to starvation (Preuss et al.
2009), resilience to exploitation (Hazlerigg et al. 2012) but also its sensitivity to chemical stress exposure (Linke-Gamenick et al. 1999; Solomon et al. 2008; Preuss et al. 2010; Hazlerigg et al.
2012). In the latter case, it has been shown that density-dependence can alleviate the severity of the chemical stressor impact (Solomon et al. 2008; Hazlerigg, 2011) or conversely, to increase it
General introduction
5 (Klüttgen and Ratte, 1994; Forbes et al. 2003; Knillmann et al. 2012 a). Accounting for density-dependence is therefore crucial for assessing the real impact of toxicants on population dynamics.
1.5.3. Natural inter-individual variability
Organisms of the same species, even those originating from the same mother and belonging to the same clone or brood (in the case of Daphnia; Boersma, 1997) are not identical (Grimm and Uchmański, 2002; Bolnick et al. 2011; Jager, 2013). This inherent heterogeneity is expressed through different behaviors towards abiotic conditions, resource use, anti-predatory defenses or competitive ability (Bolnick et al. 2011). This, in turn leads to different physiological parameters related to the feeding, growth, development, reproduction or survival of individuals. Natural inter-individual variability also generates different intrinsic sensitivities to chemicals (Naylor et al. 1990; Jager, 2013). In comparison, toxicity tests are conducted under optimal conditions in which all efforts are deployed to reduce this natural variability (Sakwinska, 2004). For instance, in Daphnia reproduction tests, all test individuals should belong to the same clone, originate from the same culture and be the same age (≤ 24 hours, OECD, 211). In these tests, differences among individuals are considered by means of replicates which are often reduced to a minimum for practicability (10 and 20 animals at least for each tested concentration in the reproduction and acute toxicity tests, respectively; OECD, 211;
OECD, 202). Results generate a general response of the species to a certain treatment (Jager, 2013). In comparison, the overall population dynamics and its response to chemical exposure are the result of the sensitivity of each individual within that population (Preuss et al. 2010;
Thorbek et al. 2010).
Chapter 1
6 1.5.4. Multiple stress exposure and biological interactions
In natural systems, abiotic (temperature) and biological factors (e.g. food, predation and competition) and their interactions control population dynamics and very likely influence their resilience to chemical stress exposure (Heugens et al. 2006; Coors et al. 2008; Solomon et al.
2008). These co-occurring factors may act additively, synergistically, or antagonistically and alter the population sensitivity to the chemical. Nonetheless, biological interactions are only marginally considered in ERA, i.e. via mesocosm experiments, which are costly, time consuming and are subsequently exclusively used for higher tier risk assessment (Bednarska et al. 2013). In addition, these experiments can only provide information on the specific types of biological and chemicals interactions (EFSA, 2013), which were taken into account in the experimental design, whereas field situations are characterized by a wide range of possible scenarios (Hommen et al. 2010). Thus, complementary methods are needed in environmental risk assessment to account for multiple stress exposure and the variable environmental conditions in the field.
1.5.5. Understanding the mode of action of the toxicant
In most standard toxicity tests, chemical toxicity is evaluated from the negative effects on reproduction, growth or survival of individuals. The mechanisms that lead to such effects are overlooked. In reality, similar inhibition levels of reproduction or survival would lead to different impacts on populations depending on the individual process that was targeted by the toxicant (Martin, 2013) and which provoked the observed magnitude of effect on reproduction or survival. In other cases, capturing how the toxicant acts on individuals can sometimes be crucial for identifying the relevant population-level endpoint. In fact, according to the current methodology in chemicals’ risk assessment, unacceptable effects occur when a reduction in the
General introduction
7 population abundance is observed (or, if the ecological recovery option is used, when the population abundance cannot recover within a given time frame). In reality, adverse effects of chemicals on populations might not be expressed via a reduction in the population size but other important population endpoints can be altered as well. For instance, in many organisms, not all developmental stages are equally sensitive to toxicant exposure as observed for instance in daphnids exposed to p353-nonylphenol (Preuss et al. 2008; Gergs et al. 2013) or copepods to triphenyltin (Kulkarni et al. 2013). Size distribution is a very important response endpoint that regulates population dynamics and controls their resilience from exposure to natural (La Montagne and McCauley, 2001) and chemical (Stark and Banken, 1999; Gergs et al. 2013) stressors. More importantly, an alteration in the size structure is not necessarily accompanied with a reduction in the population abundance (Gergs et al. 2013). In such cases, size- (stage) dependent toxicity would induce negative drawbacks on the dynamics of the populations, which might not be perceived if we only look at the total abundance (Stark and Banken, 1999; Gergs et al. 2013). Thus, attention should be paid to the mode of action of the toxicant, and on the consequently affected population endpoints.