Whether considering organic or inorganic contaminants, the literature is rich with examples of sympatric species varying (sometimes by orders of magnitude) in contaminant body burdens. Intuitively, it makes sense that such interspecific differences in bioaccumulation are a function of species traits. Organisms accumulate contaminants directly from their ambient media (e.g., water, air, soil, sediments) and from their diets; morphological and physiological traits will be fundamental drivers of these bioaccumulation processes. For example, in the case of metals, biodynamic modeling approaches (Luoma and Rainbow 2005) have demonstrated that species-specific physiological traits related to ionoregulation and digestive processes drive bioaccumulation differences among taxa. Similarly, accumulation of organic contaminants has been shown to be related to organism size and lipid content (Hendriks et al. 2001), of which the latter is discussed in more detail below.
For organic contaminants in particular, body size and related surface area-to-volume relationships can exert a profound influence on the bioconcentration of contaminants. Several investigators have reported that bioconcentration is inversely proportional to the volume or weight of different species (Preuss et al. 2008). Baird and Van den Brink (2007) included dry mass as 1 of 5 characteristics that could together describe approximately 79% of the interspecific variability in sensitivity to several different compounds. Although biomass is easy to measure, it is possible that the surface area-to-volume relationships may be more powerful predictors of bioconcentration rate differences between species. However, surface area-to-volume relationships are significantly more difficult to measure than biomass, particularly for small invertebrates with complex 3-dimensional gill surfaces. In addition to size and/or body mass differences among species, the nature of body surfaces may be radically different among species. Crustaceans, for example, tend to have calcium-rich integuments; by contrast, insects may be soft and membranous or heavily armored with chitin. The means by which different integument types or biological barriers affect the diffusion rates of organic contaminants remain still poorly understood (Boudou et al. 1991), although methods do exist. Further progress through genomic and proteomic approaches will likely be made in this area in the near future.
Respiratory strategies (e.g., having surfaces such as gills) can also be important determinants of bioaccumulation. Indeed, respiratory strategy was also among the 4 key explanatory traits used by Baird and Van den Brink (2007), as described above. Buchwalter et al. (2002) found that water-breathing (dissolved oxygen–breathing) insects tend to be more permeable to water and have faster chlorpyrifos uptake rates than do comparably sized air-breathing species. Across all species in that study, water permeability was strongly correlated with chlorpyrifos uptake rates. In both respiratory strategies, body size still exerted first-order control of overall permeability. However, the use of morphological traits as predictors of physiological processes should be treated with some caution. More research is needed to identify the importance of morphological traits in relation to bioaccumulation and to develop quantitative relationships between traits and their affected processes. For example, the presence or absence of gills may be less informative than the relative permeable surface area of a water-breathing species. Intersegmental membranes in some insect species may serve as gaseous exchange surfaces and can be similarly permeable to external gills. Therefore, other characteristics, such as the degree of sclerotization, may not be good approximates for integument permeability, as some species with membranous integuments can be quite impermeable, despite common perceptions to the contrary (Buchwalter et al. 2002).
In contrast to traits that focus on the interface of the organism and its environment, the internal composition of species can vary considerably as well. Perhaps the best understood trait in relation to the bioaccumulation of organic compounds is lipid content (Hendriks et al. 2005). We make a distinction between the overall quantity of lipid (percentage lipid) and the qualities of various lipid pools at the organism level. Although the initial critical body residue concept did not explicitly consider the role of lipids in toxicity (McCarty and Mackay 1993), Di Toro et al. (2000) later identified the membrane lipid fraction (polar lipids) in the organism as the generic site of toxic action for contaminants eliciting baseline toxicity such as organic compounds, uncouplers, and inhibitors of photosynthesis or ATP synthesis (Escher and Hermens 2002; Hendriks et al. 2005).
Storage lipids, in contrast, may act as a transient sink for hydrophobic organic contaminants in organisms. The amount and composition of storage lipids within a given organism undergo dramatic seasonal fluctuations as compared with polar lipids (Naesje et al. 2006) and depend on food quality and quantity (Goulden and Place 1993), density dependence (Cleuvers et al. 1997), and life stage (Bychek and Gushchina 1999). The composition and distribution of lipids within an organism may modify intrinsic toxicity; therefore, insights into lipid dynamics and the relationship to contaminant partitioning could provide a stronger basis for understanding toxicity of tissue residues and predicting effects. Because hydrophobic organic chemicals preferentially partition into lipids, it has become common practice to normalize bioaccumulation data to the lipid content of the sample (Escher and Hermens 2002). Furthermore, the partitioning of hydrophobic contaminants in tissues follows predictable patterns. For instance, fugacity-based approaches are used increasingly to understand and predict bioaccumulation differences among species. Species' lipid content and body size are the most important traits included in quantitative bioaccumulation models (Arnot and Gobas 2004; Hendriks et al. 2005). Nevertheless, the role and influence of lipid should be interpreted with caution, particularly when comparing different studies, given the uncertainties associated with lipid measurement. For example, a variety of analytical methods using different solvent combinations and ratios generated differences in lipid concentrations for the same sample (Smedes 1999; Manirakiza et al. 2001).
Depending on the contaminant and the species, diet may be a primary exposure pathway and may therefore alter toxicity (Fisher and Hook 2002). For terrestrial vertebrates, diet is assumed to be the major source of exposure for many contaminants. Diet can also be the primary route of exposure for many metals to aquatic invertebrates (Martin et al. 2007), although it is generally accepted that dietary uptake of organic chemicals is less important than uptake from ambient media (Gomes et al. 2004). In those cases in which direct uptake from water by animals appears relatively unimportant (e.g., Se), food web dynamics may drive bioaccumulation differences among species (Stewart et al. 2004; Conley et al. 2009). In this example, both food choice and digestive processes drive bioaccumulation differences among species.
Differences in the dietary assimilation of metals appear to be profoundly different among species and diets. Dietary assimilation efficiencies of Cd in predatory stoneflies were found to range from 85% to 90% (Martin et al. 2007), whereas they could be as low as 5% in zebrafish (Danio rerio Hamilton) fed daphnids (Liu et al. 2002) and as low as 3% in silversides (Menidia sp.) fed copepods (Reinfelder and Fisher 1994). Dietary uptake models for organic contaminants in fish have been reviewed by Barber (2008), where assimilation efficiencies ranged from 40% to 100%. Assimilation efficiencies of chemicals must be distinguished from assimilation efficiencies of food components such as lipids. The extraction of lipids from food during the gut passage in fish, for example, increases the fugacity of organic contaminants in the gut, which in turn leads to further transfer of organic contaminants from the gut into the fish (Gobas et al. 1999). This provides the mechanistic explanation for biomagnification, i.e., an increase of fugacitiy in food chains (Kelly et al. 2004). Assimilation efficiencies of organic contaminants in several taxa have been reviewed by Hendriks (2001), who also established a quantitative relationship to organism body weight. Dietary uptake of toxicants (metals or organics) depends on the food choice, ingestion rate, and assimilation efficiency, as well as on the concentration within the food source, which is in turn triggered by the traits of the food source itself. Despite a growing body of literature associated with the dietary assimilation of metals in aquatic organisms, this remains a relatively difficult trait to measure with precision, because assimilation can vary with diet type, ration, and concentration.
Bioaccumulation of contaminants is a function of both uptake (directly from surrounding media and diet) and loss. Mechanistically, much less is known about the traits that drive loss rate differences among species for a given contaminant, but numerous studies have measured profound differences among species. The rapid elimination of metals by the caddis fly Hydropsyche (Cain et al. 2006) may help explain the observed metal tolerance of this genus. Furthermore, in the case of Cd in aquatic insects, it appears that the elimination capacity of species is not arbitrary, but seems to cluster phylogenetically (Buchwalter et al. 2008). Thus, more comparative studies in different faunal groups may eventually be a means of predicting the ability of a taxon to eliminate toxicants based on phylogenetic considerations. The relationship between elimination rates of organic compounds and body size has been quantified for several taxa (Hendriks et al. 2001; Kooijman et al. 2004).
Another important process that can shift elimination rates of organic compounds is biotransformation (see below). Further traits triggering elimination of a compound include ventilation rate, fecal egestion, growth dilution, and reproduction (maternal transfer) and the influence of these traits on elimination are reviewed and discussed in detail elsewhere (Barron 1990; Mackay and Fraser 2000) but will be addressed briefly in the following section.
Several other traits might be expected to play roles in differential contaminant bioaccumulation but have received significantly less attention. For example, species with high growth rates may be aided in limiting bioaccumulation relative to slower-growing species via the process of growth dilution. Slow growth rates may contribute to the relatively high bioaccumulation tendency of some freshwater mussels, for example, but high water filtration rates may be a more important trait in this regard. The ability to close the operculum in mollusks might be advantageous for avoiding exposure in the short term but is unlikely to be beneficial in chronic exposure scenarios (Cope et al. 2008). Other processes may also result in reduction of contaminant burdens in tissues. For example, the transfer of some contaminants such as Se to eggs is known to be important to fish (Coyle et al. 1993), daphnids (Lam and Wang 2006), and insects (Conley et al. 2009). For these types of contaminants, traits such as fecundity, which are known to vary tremendously among taxa, may play a modifying role. However, this trait is likely to be more important in determining population recovery and resilience (see below). Other contaminants, such as Cd and Mn, may be lost during the molting process, as was shown for a shrimp species (Keteles and Fleeger 2001) and a mayfly (Cid et al. 2010), and molting frequency can also vary widely across species.
Once a toxicant is taken up into an organism, its internal distribution is driven by partitioning, either through passive diffusion or active biological transport, which emphasizes the relevance of body size as a trait. Partitioning depends on the physicochemical characteristics of the toxicant, and for organic compounds partition may be determined by the fugacity capacities of the compartments (tissues, organs) involved (Mackay 2004). Consequently, the traits lipid content, lipid distribution, and lipid composition (e.g., storage lipid, membrane lipids) will affect the partitioning processes of organic chemical stressors and, by doing so, alter the temporary concentration at the target site.
Aside from body size and lipid characteristics, the way in which basic “Bauplan” affects the distribution of contaminants is poorly understood. For example, even the fundamentals of how different types of circulatory systems, presence or absence of barriers separating the site of action from the rest of the organism, or characteristics of specific organs affect the distribution of contaminants are unknown. Once further relevant traits and their relationship to internal distribution are identified, these may be linked to pharmacokinetic models, such as the physiologically-based pharmaco-kinetic (PBPK) and adsorption distribution metabolism excretion (ADME) models (Barber 2008) or dynamic energy budget models (DEB) (Kooijman and Bedaux 1996), which are well developed for some model systems but are rarely applied to nonmodel species because of their high data demand. In contrast, processes of active biological transport may be very suitable to incorporate into these types of models. For example, processes like the sequestration of metals into cell-specific granules, e.g., cuprosomes for Cu (Joosse and Verhoef 1987) or transport of xenobiotics across membranes, which may also be inhibited by other organic compounds (Epel et al. 2008), can influence the distribution of toxicants significantly but yet are difficult to combine with available modeling approaches.
Biotransformation is defined as the enzymatic conversion of a toxicant to a structurally different product with altered chemical and toxicological properties. This process is one of the major confounding factors in the prediction of toxicokinetics and toxicodynamics of organic chemicals. Birds, mammals, fish, and many aquatic invertebrates are able to metabolize a range of organic toxicants extensively (Stegeman and Kloepper-Sams 1987; Boon et al. 1997; Livingstone 1998), although this ability appears to be species-specific (Chambers and Carr 1995). Biotransformation includes direct chemical changes in the structure of the parent compound (phase I reactions) together with the conjugation of the parent compound with hydrophilic groups (phase II reactions) to facilitate excretion. Various enzymes and/or proteins are involved in these biotransformation pathways, e.g., cytochrome P450, mixed-function oxidase, metallothioneins (see also below), and glutathione-S-transferase. The presence and translation rates of all these enzymes reflect biotransformation potential and can differ even between closely related species (Rust et al. 2004). Biotransformation can lead to the formation of compounds that can be either more or less toxic than the parent, depending on the compound or enzyme combination (Perkins and Schlenk 2000). Both metabolites and conjugates have been shown to persist in several invertebrates and fish (Preuss et al. 2008), although their contribution to toxicity has yet to be determined. Biotransformation may dominate toxicokinetics; if biotransformation is faster than efflux, toxicokinetics is no longer a simple partitioning process between 2 phases (Preuss et al. 2008). Hence any trait that determines biotransformation is important for explaining the variability in intrinsic sensitivity between species and could therefore be described in general as biotransformation potential (see below). Biotransformation rates are also related to the substance's structural properties (e.g., structure, functional groups present). For contaminants that can be biotransformed, failure to address biotransformation can lead to either over- or underestimation of toxicological risk depending on the toxicity of the transformation product. Applying a traits-based approach to this topic could be done in a very detailed way, measuring the presence and amount of all possible enzymes within all species. However, this approach would come up with thousands of new traits defined by the type and subtype of an enzyme. Because this approach would likely take an unreasonable amount of time, other approaches will be necessary.
One possible alternative might be to use the biotransformation potential of a species as a trait. For fish it was demonstrated that in vitro tests can be used to determine the biotransformation potential of a species–compound combination (Fitzsimmons et al. 2007; Dyer et al. 2009). Nevertheless, confounding factors for the use biotransformation rate as traits are many, especially because biotransformation rates, just as sensitivity, strongly depend on the species–compound combination and the physicochemical conditions (Fitzsimmons et al. 2007). Before biotransformation can be used within a traits-based approach, further research in this area is needed, as well as data-mining algorithms to fill the gaps in mechanistic knowledge and to consolidate this information.