This is an evaluation of Kubo et al (2022).
Confidence (from 0 - 5): 5
Quality scale rating
“On a ‘scale of journals’, what ‘quality of journal’ should this be published in?: Note: 0= lowest/none, 5= highest/best”
Confidence (from 0 - 5): 5
See HERE for a more detailed breakdown of the evaluators’ ratings and predictions.
Kubo et al assessed the possible impacts of wildlife trade bans on non-target species using an online auction dataset spanning 10-years. The authors demonstrated spillover effects in the form of increased trade volume involving closely related species. The spillover effects differed between the three broad groups studied, leading the authors to posit that spillover effects may differ as a function of demand for the banned species, as well as the availability of legal alternatives in the market. Overall, I thought that this is an interesting and topical paper that provides important support for anecdotes of unintended negative outcomes from trade bans. I was also intrigued by the authors’ application of synthetic difference-in-differences (SDID) which seemed a potentially powerful method for assessing the broad effects of policy decisions.
Despite my general appreciation of this work, I feel that the evidence supporting the authors’ overarching conclusion was not presented with sufficient clarity. This is because the modelling approach is fairly advanced, yet the details provided were too scant.
The most important component of this study, in my opinion, lies in the authors’ selection of “spillover” and “control” species, as I expect this to be highly influential on SDID outcomes. For “spillover” species, I recommend the authors better justify their selection by explaining, from a buyer’s point-of-view, why these would be realistic alternatives. The authors provide strong justification for giant water bugs (i.e., same market name), but not for salamanders and freshwater fish. “Spillover” species for the latter two were close-relatives, which could be a reasonable choice if the authors cite evidence to establish the logic that underlies a potential buyer’s decision to choose phylogenetically close alternatives in the event of a ban. As these are likely to be kept as pets, perhaps other species traits (e.g., appearance, size) that may not necessarily be linked to phylogeny may be more important? To clarify, I do not believe that the authors’ approach is wrong. I do, however, suggest the authors better explain their selection process.
Relatedly, “control” units were defined as “trades in the same categories as banned species, excluding potential spillover species” (Page 12, Paragraph 2). This is too vague for readers to follow and potentially replicate. I could not deduce what the term “categories” refer to. The identities of top control units were detailed in Fig S2 and Fig S4, but the texts were in Japanese (Fig S2) or too small to read (S4). From what I could tell, some of the control units were congeners of the banned salamander species and selected “spillovers”. I therefore wondered about how phylogenetic relatedness of “spillover” species were ranked and how the authors decided that spillover effects would not also affect the trade of “control” species.
With my admittedly limited understanding of SDID, I am also wondering if the issues regarding “spillover” and “control” species selection could have been averted if the authors use an unrelated group of animals (e.g., turtles) to parameterise their synthetic controls, assuming this group was not subject to similar bans. This may also help overcome the potential issue of any spillover effects in the currently selected “control” units which could obfuscate the estimation of DiD values. If the appeal of SDID was the allowance for differences in trend between intervention and control groups before the ban, do control units need to be close relatives of the spillover species?
I appreciate the novelty of applying SDID, but I am concerned that there is insufficient context to ease comprehension if this work were to be submitted to journals with a broader readership. I think the description of Eq. 1 as a method to solving the “minimisation problem” epitomises my concern. I could be in the minority, but “minimisation” is not a term I encounter frequently in my reading. Therefore, I did not initially understand why there was a “minimisation problem” that had to be solved, much less understand how to solve it. I suggest the authors provide a brief explanation about what SDID (or even DiD) achieves in simpler terms (e.g., assess the effects of interventions by comparing observed outcomes against predicted outcomes representing non-intervention).
I liked the figures presented in this paper. In particular, I appreciate the clean aesthetics of the plots presented here. However, figures depicting outcomes of SDID in the main text and the supplementary section can be difficult to decipher without additional details about the application of SDID (or even of DID and SC). Without prior knowledge, the captions and text do not provide sufficient information about what the readers should look out for in the plots on the left side of Figures 3, S3, and S5. For instance, the caption mentions “arrows” indicating estimated effects, but the arrows are difficult to see on the plot. Moreover, I recommend the authors include additional information about the vertical lines representing ban enforcement, as well as the significance of trend lines representing post-ban averages and the SDID synthetic control, respectively. This will make it easier to understand what the “estimates” in plots on the right of Figures 3, S3, and S5 signify. Relatedly, the captions specify that plots on the right of these figures represent “estimates concerning trade volumes of each taxon”. In my understanding, these should instead refer to the estimated spillover effects of the ban? If my interpretation is correct, the labelling of a 0 value for estimates (i.e., vertical broken line) as “Trade (n)” is quite confusing. I recommend the use of more precise descriptions in the plot and captions.
I appreciate the concise nature of the paper. The authors did a good job of providing key information but I believe that there is some room for improvement. First, some context about the volume or relative importance of online auctions as a platform for trading in animals could help readers better understand the significance and applicability of findings to the wider wildlife trade. Second, the authors provide additional information about the relevant policies in the methods section, but this information may be better placed in earlier parts of the text to avert confusion about focal species selection. Third, I believe that the argumentation leading to the authors’ conceptualisation of spillover effects (Fig. 5) can be further developed. The authors argue that spillover effects may be diluted when more alternatives are available in the market, but they do not explain what “alternatives” mean in the context of the wildlife (e.g., pet) trade. The text (page 6) assumes that animals in the “freshwater taxon” were potential alternatives to the golden venus chub, while animals within the “salamander taxon” were potential alternatives to the Tokyo salamander. These assumptions imply that potential buyers are unlikely to consider taxonomically distant animals as alternatives to banned species, yet I am unaware of supporting studies/papers. I recommend the authors provide additional justification for this assumption, preferably by citing relevant literature.
Finally, there were several instances of imprecise or unclear writing. I list these below, along with some suggestions for the authors’ consideration:
1) Page 2: “It activated the underground market” suggests that underground markets only came into existence when CITES regulations came into effect. Perhaps consider revising to “These regulations coincided with a growing underground market”.
2) Page 2: “Even a few empirical studies have focused on introducing trade ban policies on banned species” is a confusing sentence. Consider revising to “A small number of empirical studies focus on quantifying the effects of trade bans, but the focus was on species that were the targets of the ban”.
3) Page 7: Two sentences about exotic species trade and native species policies in developed countries were quite confusing to read. I recommend editing the sentences to “An increase in exotic species trade can increase overexploitation risk in source countries and lead to population declines unless appropriate management is implemented. Developing source countries may struggle to cope with the additional management needs as they often struggle to implement robust natural resource governance”.
4) Page 9: “evidence regarding cross-country spillovers” seems to be a very serious issue but no citations were provided to help readers learn more about it. I recommend citing the relevant sources.
5) Page 9: “We suggest the development of a database comprising banned and non-banned species” is a vague statement that may cover all known species. I recommend the authors be more specific, perhaps by narrowing the statement down to species known to be in the trade.
In conclusion, I believe that this is a very promising study with an important, policy-relevant message. However, the paper needs to be revised for clarity. In particular, additional details about the study’s modelling approach will help improve reader comprehension and strengthen the authors’ argument about the significance of spillover effects from trade bans.
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