Taxonomic Chauvinism in Pesticide Ecotoxicology
Bibliographic record
Abstract
When taxonomic “chauvinism” (the preference for some species over others) takes hold within the natural sciences, we risk missing the forest for the trees. Sir David Attenborough said, “It is the range of biodiversity that we must care for—the whole thing—rather than just one or two stars.” (British Broadcasting Corporation, 2000). Environmental scientists as a collective should not favor select species without clear cause to protect and understand ecosystems. The reasons why taxonomic bias exists are myriad and some simply inevitable. Pawar (2003) posited that organisms being more difficult to work with than others, which is often a consideration in toxicity testing, might cause bias. Others have proposed that “societal preference” may be a driver. Troudet et al. (2017) observed significant skewing in species selection in the largest repository for biodiversity data, the Global Biodiversity Information Facility. The species studied correlated significantly with organism web pages on the Internet as a whole. Biologists have openly debated the topic, and Wilson et al. (2007) argued that taxonomic bias hinders the assessment of ecological theory and patterns, along with novel discoveries. The questions we have are ecotoxicologists exhibiting the same preference for certain species (and to what degree), and what might this mean for environmental protection? The first part is the easiest to address. To explore the potential for taxonomic preference in ecotoxicology, we identified the species chosen to investigate the toxicity of pesticides and their citation rates (which may indicate “popularity” of species) in articles published in the journal Environmental Toxicology and Chemistry (ET&C) over a 30-year period. We identified 918 papers that conducted toxicity testing with a pesticide (laboratory or field) in ET&C from 1982 to 2020. The taxa studied were identified as vertebrate, bird, mammal, fish, amphibian, reptile, invertebrate, insect, crustacean, worm, mollusk, zooplankton, primary producer, terrestrial plant, macrophyte, and microbiota, along with the type of pesticide (e.g., herbicide, insecticide, and fungicide). Note that a paper may report on multiple species or pesticides. As well, the frequency of articles by taxonomic group cited from 1982 to 2012 was determined. Only articles published up to 2012 (853 papers) were included, to allow 10 years of citations to accrue. Only papers reporting a single species were assessed so that the species could be associated with the citation. The total and number of citations by year as of July 2021 were drawn from Google Scholar. We found that some species and groups received greater representation in the pesticide literature than others (Table 1). Vertebrates, invertebrates, and primary producers comprised approximately 47%, 42%, and 11% of the articles, respectively; and most involved aquatic species (70%) compared to terrestrial (30%). Fish were the most studied group (168) and with insects (160), zooplankton (143), birds (124), and amphibians (118) represented the majority of species (>70%). This gives a glimpse into the relative underrepresentation of the other groups of organisms (i.e., reptiles, macrophytes, terrestrial plants, mollusks, worms, phytoplankton, and crustacea) within ecotoxicology. More articles studied the effect of herbicides on vertebrates (112) and invertebrates (82) than primary producers (70), whereas for insecticide tests most involved vertebrates (328), then invertebrates (313), and then primary producers (28). The mean total number of citations was greater for studies that investigated the effect of pesticides on invertebrates and vertebrates compared to primary producers (Figure 1). The species or genera with the greatest mean citations and mean citation rates were Pimephales promelas, Rana spp., Oncorhynchus spp., and Daphnia spp. It is evident from this brief analysis that there is some taxonomic bias in ecotoxicology, which we feel comes as no real surprise to those in the discipline. Overall, the proportion of organism classes tested for pesticides is similar to that seen in the conservation biology literature. Titley et al. (2017) found that half of the articles on animal biodiversity studied vertebrates and the rest invertebrates, but this is not representative of the distribution of known animal species because 95% are invertebrates. The drivers of such trends within ecotoxicology are likely in part similar to those in the natural sciences as a whole. To wit, a species may be prevalent because of ease of access and culturing, relatively wide distributions, and amenability to straightforward testing in the laboratory. Societal interest can lead to the selection of model species in environmental toxicology that have economic and cultural importance, especially fish, which in turn are preferred explicitly for these reasons as effect measures and assessments endpoints in ecological risk assessment. Both drivers can then further justify the selection of species used in standard test guidelines for toxicity testing developed by regulatory bodies. The larger question of whether this species bias means that ecological risk assessments, regulations, and our shared goal of environmental protection are impacted as a result is much more difficult to parse. Overall, we feel there has been a successful track record of effective risk assessments for chemical contaminants these past few decades, so this may be a case of “if it ain't broke, don't fix it.” And to be clear, we are not advocating for expanding toxicity testing simply to reflect relative species proportions or groups currently not tested, especially vertebrates. Rather, our aim is to get a sense of possible taxonomic skew to ensure conversations about what this might mean, if anything. A trend that did stand out is the relative underinvestigation of primary producers for the effects of herbicides relative to vertebrates and invertebrates. This seems to be a clear case of overtesting certain groups for which the inherent risk is less, potentially to the detriment of environmental protection. We have long known that taxonomic composition can alter significantly the outcomes of pesticide species sensitivity distribution–derived exceedances that are used increasingly in regulations and guideline derivation (Maltby et al., 2005). If our data are skewed to certain groups, our protection thresholds may be erroneous and our ability to predict risk to the ecosystem as a whole undermined. At this stage, our primary recommendation is that ecotoxicologists think closely about why they test the species they do for the compound(s) under investigation. We should be identifying and prioritizing responses to species gaps to reduce uncertainty in individual chemical risk assessments. Data, associated metadata, and calculation tools are available from the corresponding author (prosserr@uoguelph.ca).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".