Anemonefish have finer color discrimination in the ultraviolet

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   eLife

   Jun 22, 2023

   Author Response

   Reviewer #1 (Public Review):

   This paper studies color vision in anemonefish. The central conclusion of the paper is that anemonefish use signals from their UV cones to discriminate colors that would not otherwise be distinguishable; this differs from other fish in which UV cones extend the range of wavelengths of sensitivity but do not add a dimension to color vision. The work fits into a rich history of studies investigating how color vision fits into an animal's ecological niche. My primary concerns regard the microspectrophotometry data from single cones and some aspects of the presentation of the behavioral data.

   Microspectrophotometry

   The spectral properties of the cone types are a key issue for interpreting the results. These were measured using MSP, and fits are shown in Figure 2. The raw data shown in Fig. …

   Author Response

   Reviewer #1 (Public Review):

   This paper studies color vision in anemonefish. The central conclusion of the paper is that anemonefish use signals from their UV cones to discriminate colors that would not otherwise be distinguishable; this differs from other fish in which UV cones extend the range of wavelengths of sensitivity but do not add a dimension to color vision. The work fits into a rich history of studies investigating how color vision fits into an animal's ecological niche. My primary concerns regard the microspectrophotometry data from single cones and some aspects of the presentation of the behavioral data.

   Microspectrophotometry

   The spectral properties of the cone types are a key issue for interpreting the results. These were measured using MSP, and fits are shown in Figure 2. The raw data shown in Fig. S1 appears more complicated than indicated in the main text. The templates miss the measurements across broad wavelength bands in each cone type. Particularly concerning is the high UV absorbance across cone types and the long-wavelength absorbance in the UV cone. It is not clear how this picture supports the relatively simple description of cone types and spectral sensitivities given in the main text and which forms the basis of the modeling.

   Microspectrophotometry is an inherently noise-prone measurement technique, particularly for very small photoreceptor outer segments such as that of single cones, which are also difficult to detect as intact, isolated (nonoverlapping) cells. As such, the absorbance curve fitting and derived lambda max (λmax) values should be treated as estimates. The accuracy of these estimates is adequate for this type of study, and visual modelling results have been shown to be robust against small errors (±10 nm λmax) in photoreceptor sensitivity for multiple species [see Lind, O. & Kelber, A. (2009). Vis Res. 49(15), 1939-1947; and Bitton, PP. et al. (2017). PLOS ONE, 12: e0169810]. We consider it highly unlikely that small shifts in cone λmax from measurement error would make a meaningful difference to the colour discrimination thresholds.

   It should be noted that the raw data shown in the original Supplementary Figure 1, included all scans overlain with an average absorbance curve for presentation purposes; however, the actual lambda max values for different cone types were measured and then averaged among individual scans fitted with photopigment absorbance curve templates. For clarity and transparency, we have now provided three multipaned plots (see Figure 1 – figure supplements 1-3) showing the individual pre- and post-bleach scans of absorbance spectra, fitted absorbance curve templates, and R2 values from the best visual pigment template fit.

   It is worth noting that most of the cone absorbance spectra found in our study closely resemble those in λmax and quality to those measured in another anemonefish species (Amphiprion akindynos) [see Supplementary Figure 1 in Stieb S. et al. (2019). Sci Rep. 9, 16459]. These cone λmax values can also be reconciled with previous estimates on opsin λmax based on amino acid sequences and cone opsin expression in the A. ocellaris retina characterised in Mitchell LJ et al. (2021). GBE, 13: evab184.

   Evidence that the unusual long-wavelength absorbance detected in a couple of the single cone (pre-bleach) measurements were not of visual pigment in origin comes from post-bleach scans, which showed their persistence (i.e., did not show a photobleaching response) and were likely instead contaminants (e.g., blood, RPE pigment). UV absorbance in some of the double cone measurements (above that expected of the prebleached beta peak from chromophore spectral absorption) can be attributed to either noise from scans as is quite typical of MSP and/or partial (accidental) bleaching from stray light sources. Although utmost care was taken to minimise contamination and unintended bleaching sometimes it is unavoidable.

   We refer the Reviewer to multiple published studies for further examples of typical MSP measurements that share similar levels of noise to ours e.g., see Figure 1 in Knott B. et al. (2013). JEB, 216:4454-4461; Figure 3 in Schott, RK et al. (2015). PNAS, 113(2): 356-361; Figure 2 in Dalton BE et al. (2014). Proc R Soc B. 281; Figure 5 in Tosetto, JE et al. (2021). Brain Behav Evol. 96: 103-123.

   Presentation

   The results are not presented in a straightforward way - at least for this reviewer. What is missing for me is a clear link between the psychometric curves in Figure 3A and the discrimination thresholds indicated in Figure 3B and Figure 4. Figure 3A is only discussed in the text on line 289 - after Figure 4 has been introduced and discussed. It would have been very helpful for me if the psychometric curves were first introduced and described, then the relation to Figure 3B was clearly indicated (perhaps with a single psychometric curve as an example). Similarly for Figure 4 the relationship between specific psychometric curves and the threshold plotted would be quite helpful. Currently it takes a careful reading to understand why being below the dashed line in Figure 4 is important.

   We have made the following changes, including the introduction of the psychometric curves earlier in the results (lines 236-249) and moved the psychometric function comparison before the mention of Figure 4. Additionally, to make the association between the plotted colour loci and psychometric curves clearer, we have added a smaller psychometric curve plot adjacent to the colour space (in Figure 3B) using red as an example which has an averaged psychometric curve overlying the individual fish curves. The figure caption (lines 250-274) explains that the plotted colour loci and given thresholds are mean values calculated from the individual fish behavioural data.

   We have also added a brief reminder that the theoretical limit of colour discrimination is predicted by the RNL model as 1∆S, where in our task fish should be just able to distinguish targets from grey distractors (see lines 222-224). To clarify, the plotted values in Figure 4B are both the individual fish thresholds (points) and average threshold (black bar) per colour set. The individual threshold values are taken at a correct choice probability of 50% from fitted psychometric curves of fish behavioural performance (shown in Figure 3A).

   RNL model

   The data is fit and interpreted in the context of the receptor noise limited model. The paragraph in the discussion about complementary color pairs suggests that this model is incorrect (text around line 332). Consideration of how the results depend on the RNL model is important, especially given the interpretation here.

   The inability of the RNL model to account for the observed asymmetry between color discrimination thresholds implies that they cannot be solely attributed to photoreceptor noise. We can therefore infer from the asymmetry that thresholds are set by a higher-level process, whether that involves post-receptor processes within the inner retina or in the brain remains to be investigated. As explained in lines 396-397 one possibility is that activation of the UV receptor suppresses noise in the visual pathway or enhances the saliency of colors for anemonefish. The high sensitivity to violet-green, which was found in all six of the fish tested, is consistent with the heightened saliency of this color (lines 397-399).

   Figure 3B

   This is the key figure in the paper. But several issues make seeing the data in this figure difficult. First, the important part of the figure is buried near the origin and hard to see. Can you show a surface that connects the thresholds in the different chromatic directions, or otherwise highlight the regions of discriminable and not discriminable colors?

   See previous comment. In short, we have taken the advice of the Reviewer and added highlighted areas around the regions of discriminable colors in Figure 3B to help visually separate them from the non-discriminable regions of colors (from grey). Additionally, we have added an inset showing an enlarged image of the area surrounding the centre of colour space.

   Reviewer #2 (Public Review):

   Mitchell and colleagues examined the contribution of a UV-sensitive cone photoreceptor to chromatic detection in Amphiprion ocellaris, a type of anemonefish. First, they used biophysical measurements to characterize the response properties of the retinal receptors, which come in four spectrally-distinct subtypes: UV, M1, M2, and L. They then used these spectral sensitivities to construct a 4-dimensional (tetrahedral) color space in which stimuli with known spectral power distributions can be represented according to the responses they elicit in the four cone types. A novel five-LED display was used to test the fish's ability to detect "chromatic" modulations in this color space against a background of random-intensity, "achromatic" distractors that produce roughly equal relative responses in the four cone types. A subset of stimuli, defined by their high positive UV contrast, were more readily detected than other colors that contained less UV information. A well-established model was used to link calculated receptor responses to behavioral thresholds. This framework also enabled statistical comparisons between models with varying number of cone types contributing to discrimination performance, allowing inferences to be drawn about the dimensionality of color vision in anemonefish.

   The authors make a compelling case for how UV light in the anemonefish habitat is likely an important ecological source of information for guiding their behavior. The authors are to be commended for developing an elegant behavioral paradigm to assess visual performance and for incorporating a novel display device especially suited to addressing hypotheses about the role of UV light in color perception. While the data are suggestive of behavioral tetrachromacy in anemonefish, there are some aspects of the study that warrant additional consideration:

   1. One challenge faced by many biological imaging systems is longitudinal chromatic aberration (LCA) - that is, the focal power of the system depends on wavelength. In general, focal power increases with decreasing wavelength, such that shorter wavelengths tend to focus in front of longer wavelengths. In the human eye, at least, this focal power changes nonlinearly with wavelength, with the steepest changes occurring in the shorter part of the visible spectrum (Atchison & Smith, 2005). In the fish eye, where the visible spectrum extends to even shorter wavelengths, it seems plausible that a considerable amount of LCA may exist, which could in turn cause UV-enriched stimuli to be more salient (relative to the distractor pixels) due to differences in perceived focus rather than due solely to differences in their respective spectral compositions. Such a mechanism has been proposed by Stubbs & Stubbs (2016) as a means for supporting "color vision" in monochromatic cephalopods (but see Gagnon et al. 2016). It would be worth discussing what is known about the dispersive properties of the crystalline lens in A. ocellaris (or similar species), and whether optical factors could produce sufficient cues in the retinal image that might explain aspects of the behavioral data presented in the current study.

   This is an interesting point, and we appreciate the reviewer’s thoughtful comment regarding this topic especially as LCA increases exponentially in the UV. Although we certainly cannot disprove such a mechanism in the present study, we are highly sceptical that LCA could be used by reef fish and is involved in the heightened saliency of UV stimuli. Previous work has found that LCA is mostly corrected for in the teleost retina of both marine and freshwater species by graded, multifocal lenses that focus different wavelengths at the same depth as their maximally sensitive cone photoreceptors [e.g., for evidence in African cichlids see Kröger, R. H. H. et al. (1999). J Comp Physiol. A, 184, 361-369; Malkki, P. E. & Kröger, R. H. H. (2005). J Opt. A, 7, 691-700; and for various reef fishes see Karpestam, B. et al. (2007). J Exp Biol., 210, 16: 2923-2931]. In essence, LCA is corrected in the eyes of many teleosts by accurately tuning longitudinal spherical aberration through having a graded density lens. We draw particular attention to the latter reference which comparatively examined the optical properties of reef fish lenses, including diurnal, planktivorous damselfishes (from the same family as anemonefishes, Pomacentridae). They found that not only were the lenses of these species highly UV-transmissive (as we show in anemonefish), but all were multifocal and capable of focusing both visible (non-UV) and UV wavelengths. Considering the coastal cephalopod species examined thus far, all of them contain only one type of visual pigment which is packed in their long photoreceptor (150-450µm long outer segment) across an entire retina (Chung and Marshall 2016, Proceeding B). Theoretically, given these long photoreceptors, the LCA and the resulting differentials of focal length onto different patches of photoreceptors or different depth of the outer segment might provide cues for colour discrimination even though no behavioural evidence exists to prove this hypothesis yet. Unlike the cephalopod case, the four specific spectral cones arranged in a mosaic pattern along with their very short outer segments (5-10µm) in the anemonefish retina likely makes the LCA less effective in this retinal design.

   We have added a short paragraph (Lines 400-412) discussing the possibility of an optical mechanism contributing to heightened UV saliency with a particular focus on LCA and our thoughts on why we consider it an unlikely mechanism in anemonefish.

   1. The authors provide a quantitative description of anemonefish visual performance within the context of a well-developed receptor-based framework. However, it was less clear to me what inferences (if any) can be drawn from these data about the post-receptoral mechanisms that support tetrachromatic color vision in these organisms. Would specific cone-opponent processes account for instances where behavioral data diverged from predictions generated with the "receptor noise limited" model described in the text? The general reader may benefit from more discussion centered on what is known (or unknown) about the organization of cone-opponent processing in anemonefish and related species.

   In short, we do not know the specific opponent interactions of anemonefish cones. The RNL model assumes all possible opponent interactions in its calculations. From our results, very little can be said about the post-receptor mechanisms involved in their putative tetrachromatic vision. We would like to avoid overreaching beyond what our data can show. A future directions section has now been added to the discussion (lines 467-497), which briefly mentions the known UV opponency in larval zebrafish and that future investigation in anemonefish should attempt to disentangle the specific opponent (chromatic) and non-opponent (achromatic) circuits in the anemonefish retina.

   Reviewer #3 (Public Review):

   The comments below focus mainly on ways that the data and analysis as currently present do not to this reviewer compel the conclusions the authors wish to draw. It is possible that further analysis and/or clarification in the presentation would more persuasively bolster the authors' position. It also seems possible that a presentation with more limited conclusions but clarity on exactly what has been demonstrated and where additional future work is needed would make a strong contribution to the literature.

   • Fig 3A. It might be worth emphasizing a bit more explicitly that the x-axis (delta S) is the result of a model fit to the data being shown, since this then means that if RNL model fit the data perfectly, all of the thresholds would fall at deltaS = 1. They don't, so I would like to see some evaluation from the authors' experience with this model as to whether they think the deviations (looks like the delta S range is ~0.4 to ~1.6 in Figure 4B) represent important deviations of the data from the model, the non-significant ANOVA notwithstanding. For example, Figure 4B suggests that the sign of the fit deviations is driven by the sign of the UV contrast and that this is systematic, something that would not be picked up by the ANOVA. Quite a bit is made of the deviations below, but that the model doesn't fully account for the data should be brought out here I think. As the authors note elsewhere, deviations of the data from the RNL model indicate that factors other than receptor noise are at play, and reminding the reader of this here at the first point it becomes clear would be helpful.

   We have now stated more explicitly in the figure caption for Figure 3A, that the delta S values presented were calculated by fitting fish behavioral data to the RNL model. To test the overall effect that the sign of the UV contrast had on the discrimination threshold, we have now included ‘contrast’ (positive or negative) as another fixed effect in the linear mixed effects model. We have now included details of this test in the results which shows the systematic effect (lines 338-340). Additionally, as suggested we now briefly introduce in the results the idea that factors other than receptor noise are causing the observed deviations in data from the RNL model.

   • Line 217 ff, Figure 4, Supplemental Figure 4). If I'm understanding what the ANOVA is telling us, it is that the deviations of the data across color directions and fish (I think these are the two factors based on line 649) is that the predictions deviate significantly from the data, relative to the inter-fish variability), for the trichromatic models but not the tetrachromatic model. If that's not correct, please interpret this comment to mean that more explanation of the logic of the test would be helpful.

   The interpretation of the ANOVA by the Reviewer is mostly correct. We had the variables color set and Fish ID, with threshold delta S as the dependent variable. This showed that deviations from the predicted threshold were significant relative to the inter-fish variability for the trichromatic models. Missing details describing the ANOVA have now been added to the methods (lines 789-798).

   Assuming that the above is right about the nature of the test, then I don't think the fact that the tetrachromatic model has an additional parameter (noise level for the added receptor type) is being taken into account in the model comparison. That is, the trichromatic models are all subsets of the tetrachromatic model, and must necessarily fit the data worse. What we want to know is whether the tetrachromatic model is fitting better because its extra parameter is allowing it to account for measurement noise (overfitting), or whether it is really doing a better job accounting for systematic features of the data. This comparison requires some method of taking the different number of parameters into account, and I don't think the ANOVA is doing that work. If the models being compared were nested linear models, than an F-ratio test could be deployed, but even this doesn't seem like what is being done. And the RNL model is not linear in its parameters, so I don't think that would be the right model comparison test in any case.

   Typical model comparison approaches would include a likelihood ratio test, AIC/BIC sorts of comparisons, or a cross-validation approach.

   If the authors feel their current method does persuasively handle the model comparison, how it does so needs to be brought out more carefully in the manuscript, since one of the central conclusions of the work hinges at least in part on the appropriateness of such a statistical comparison.

   Our visual model comparisons were aimed at assessing whether a trichromatic or tetrachromatic model best fit the colour discrimination data. The trichromatic and tetrachromatic models assume two and three opponency pathways, respectively. If the fish were not tetrachromatic, and instead trichromatic, then we would expect that the RNL model should better fit the data with two opponency mechanisms (rather than three). Our reason for making this assessment, is because of the possibility that not all the cones could be contributing to colour vision and could be used exclusively for achromatic tasks (e.g., luminance vision or motion detection). However, according to our finding that the data best fit the tetrachromatic model (i.e., how the behavioural discrimination thresholds more closely fitted the theoretical prediction of 1∆S), it is likely that anemonefish used all four cones for colour vision.

   We have also now repeated our analysis using unweighed delta S values which are calculated using general n-dimensional models of colour vision (using the PAVO2 package). These models essentially follow the same initial steps followed by the RNL model (and many others) but omit the receptor noise correction stage. After comparing (using ANOVA, see lines 303-311) the predicted thresholds with the data in this non-RNL space, it was found that again the tetrachromatic model predictions did not deviate significantly from the data relative to individual fish performance; however, we also found that the trichromatic model without M2 cone input no longer differed from the predicted values. In this case, it seems that the extra noise parameter did contribute to the difference in fit. Whether this is a biologically meaningful comparison (as all photoreceptors contain noise) is an open question. We have added a short statement explicitly framing our interpretation of anemonefish having a 3-D colour space to being in accordance with the closeness of RNL model predictions (lines 370-371, 506-508).

   • Also on the general point on conclusions drawn from the model fits, it seems important to note that rejecting a trichromatic version of the RNL model is not the same as rejecting all trichromatic models. For example, a trichromatic model that postulates limiting noise added after a set of opponent transformations will make predictions that are not nested within those of RNL trichromatic models. This point seems particularly important given the systematic failures of even the tetrachromatic version of the RNL model.

   This is a good point. We have limited our conclusions to specifically address trichromatic models generated within the framework of the RNL model by adding in the conclusion section that fish psychophysical thresholds were best explained by the RNL model when all four cone types contributed to colour vision (see lines 370-371, 506-508). In this same sentence, we have also added in parentheses that “suggesting (but not proving) tetrachromacy” (line 508). We have also edited the abstract to state that our results were “…best described by a tetrachromatic model using all four cone types…”, rather than stating we have shown tetrachromacy (lines 36-37).

   • More generally, attempts to decide whether some human observers exhibit tetrachromacy have taught us how hard this is to do. Two issues, beyond the above, are the following. 1) If the properties of a trichromatic visual system vary across the retina, then by imaging stimuli on different parts of the visual field an observer can in principle make tetrachromatic discriminations even though visual system is locally trichromatic at each retinal location. 2) When trying to show that there is no direction in a tetrachromatic receptor space to which the observer is blind, a lot of color directions need to be sampled. Here, 9 directions are studied. Is that enough? How would we know? The following paper may be of interest in this regard: Horiguchi, Hiroshi, Jonathan Winawer, Robert F. Dougherty, and Brian A. Wandell. "Human trichromacy revisited." Proceedings of the National Academy of Sciences 110, no. 3 (2013): E260-E269. Although I'm not suggesting that the authors conduct additional experiments to try to address these points, I do think they need to be discussed. We agree with the reviewer, that colour discriminability achieved by tetrachromatic vision could in theory be achieved by the combined effect of localised, distinct forms of trichromacy. Evidence in other fishes suggests that such multiple forms of trichromacy across the retina likely exist in many species. However, the behavioural effects of this retinal setup remain to be studied likely due to its extremely difficult nature. We have added a new section titled “future directions” (Lines 474-489), in which we discuss the possibility that distinct forms of trichromacy in the anemonefish retina could in theory achieve colour discrimination on par with tetrachromatic vision. We also give suggestions on how this could be investigated.

   Although we tried to include as many colour directions as practically possible in our experiment, we have certainly not provided an exhaustive range that completely encompasses anemonefish colour space. Whether 9 colour directions are adequate to assess the dimensionality of their color vision is difficult to say. As addressed in the previous comment, we now acknowledge this limitation by refining our conclusion, saying that our results do not prove tetrachromacy.

   • Line 277 ff. After reading through the paper several times, I remain unsure about what the authors regard as their compelling evidence that the UV cone has a higher sensitivity or makes an omnibus higher contribution to sensitivity than other cones (as stated in various forms in the title, Lines 37-41, 56-57, 125, 313, 352 and perhaps elsewhere).

   At first, I thought they key point was that the receptor noise inferred via the RNL model as slightly lower (0.11) for the UV cone than for the double cones (0.14). And this is the argument made explicitly at line 326 of the discussion. But if this is the argument, what needs to be shown is that the data reject a tetrachromatic version of the RNL model where the noise value of all the cones is locked to be the same (or something similar), with the analysis taking into account the fewer parametric degrees of freedom where the noise parameters are so constrained. That is, a careful model comparison analysis would be needed. Such an analysis is not presented that I see, and I need more convincing that the difference between 0.11 and 0.14 is a real effect driven by the data. Also, I am not sanguine that the parameters of a model that in some systematic ways fails to fit the data should be taken as characterizing properties of the receptors themselves (as sometimes seems to be stated as the conclusion we should draw).

   We have performed various modelling scenarios where receptor noise was adjusted for each channel; however, the UV channel was consistently found to be more sensitive than the other channels. In (the original) Supplementary Figure 6 (now Figure 4 – figure supplements 1 and 2), we show predicted dS values calculated using receptor noise levels in the exact manner that the Reviewer suggests by ranging from 0.05 to 0.15, and most importantly, included scenarios where receptor noise was held equal across cone types and others where it was varied between single cones and double cones. None of the models adjusted the data so that sensitivity was equal across all four channels, which means that by an unknown mechanism, the UV channel is more sensitive, but this is unrelated to noise levels. Our best-fit receptor noise values of 0.11 (for single cones) and 0.14 (for double cones) are estimate values and should be treated as such till actual receptor noise measurements are made.

   Then, I thought maybe the argument is not that the noise levels differ, but rather that the failures of the model are in the direction of thresholds being under predicted for discriminations that involve UV cone signals. That's what seems to be being argued here at lines 277 ff, and then again at lines 328 ff of the discussion. But then the argument as I read it more detail in both places switches from being about the UV cones per se to being about postive versus negative UV contrast. That's fine, but it's distinct from an argument that favors omnibus enhanced UV sensitivity, since both the UV increments and decrements are conveyed by the UV cone; it's an argument for differential sensitivity for increments versus decrements in UV mediated discriminations. The authors get to this on lines 334 of the discussion, but if the point is an increment/decrement asymmetry the title and many of the terser earlier assertions should be reworked to be consistent with what is shown.

   To clarify our argument, we found that the colour discrimination thresholds were systematically lower than predicted by the RNL model for colours which elicited higher UV cone stimulation relative to other cone types. These colours we refer to as UV positive based on the sign direction of their contrast against grey distractors produced by higher UV/V LED channel (i.e., in a positive direction). Whereas colours with UV negative chromatic contrast had lower UV cone stimulation relative to the other cone types. Therefore, our interpretation of the importance of UV cone signals for colour discrimination are congruent with the results. In the discussion, we suggest a possibility that activation of the UV receptor suppresses noise downstream in the visual pathway or enhances the saliency of colours (see lines 397-398). This activation of the UV receptor would, of course, be at its highest for colours with positive UV chromatic contrast.

   Note that we have added to the discussion the possibility that colour preferences or a difference in attentiveness might have contributed to differences in discrimination thresholds (see discussion lines 412-413, 427-428, 433-435, 456-466, and 469-473). However, we consider it a less likely explanation due to a couple of reasons, including 1) a lack of difference in responsiveness across colour sets in their timing to peck the target, and 2) any non-learnt bias would have likely been overridden or at least weakened by training prior to the experiment where colours were rewarded equally (see lines 462-466).

   We have edited the results (lines 334-352) to make our point clearer and by changing the subtitle to be more explicit: “Lower discrimination thresholds induced by positive UV contrast”. The subsection begins by explaining the different types of UV chromatic contrast by elevation angle and, finally, how this division among colour sets was a major determinant of colour discrimination thresholds.

   Perhaps the argument with respect to model deviations and UV contrast independent of sign could be elaborated to show more systematically that the way the covariation with the contrasts of the other cone stimulations in the stimulus set goes, the data do favor deviations from the RNL in the direction of enhanced sensitivity to UV cone signals, but if this is the intent I think the authors need to think more about how to present the data in a manner that makes it more compelling than currently, and walk the reader carefully through the argument.

   We have added to the results the linear mixed-effects model output with ‘contrast’ (positive/negative) added as a fixed effect. This analysis shows that the sign direction of UV contrast was a strong predictor of threshold (see address to previous comments and lines 399-401, 790-799).

   • On this point, if the authors decide to stick with the enhanced UV sensitivity argument in the revision, a bit more care about what is meant by "the UV cone has a comparatively high sensitivity (line 313 and throughout)" needs more unpacking. If it is that these cones have lower inferred noise (in the context of a model that doesn't account for at least some aspects of the data), is this because of properties of the UV cones, or the way that post-receptoral processing handles the signals from these cones mimicking a cone effect in the model. And if it is thought that it is because of properties of the cones, some discussion of what those properties might be would be helpful. As I understand the RNL model, relative numbers of cones of each type are taken into account, so it isn't that. But could it be something as simple as higher photopigment density or larger entrance aperture (thus more quantum catches and higher SNR)?

   It is unknown what aspect of the cone morphology or physiology sets the activation or inactivation threshold. Electrophysiological data collected from the UV cones of other fish species e.g., in goldfish and zebrafish [see Hawryshyn & Beauchamp (1985). 25, Vis Res.; and Yoshimatsu et al. (2020). 107, Neuron.] show that they have exceptionally high sensitivity. What has not been shown is that having a UV cone can improve colour discrimination.

   Previous quantitative cone opsin gene expression analysis showed that the single cone opsins (SWS1 and SWS2B) are expressed at lower levels than all double cone opsin genes. This difference in expression combined with the smaller size of single cone outer segments than the double cones make it unlikely that a larger photoreceptor size, higher volume or packing density of visual pigment is responsible. Contrary to our findings, these aspects of the different cone types (if they had an effect) would instead predict that double cones have a higher SNR, and non-UV colours would be more discriminable. We have now added these details to the discussion (see lines 391-397).

   • Line 288 ff. The fact that the slopes of the psychometric functions differed across color directions is, I think, a failure of the RNL model to describe this aspect of the data, and tells us that a simple summary of what happens for thresholds at delta S = 1 does not generalize across color directions for other performance levels. Since one of the directions where the slope is shallower is the UV direction, this fact would seem to place serious limits on the claim that discrimination in the UV direction is enhanced relative to other directions, but it goes by here without comment along those lines. Some comment here, both about implications for fit of RNL model and about implications for generalizations about efficacy of UV receptor mediated discrimination and UV increment/decrement asymmetries, seems important.

   The variation in the psychometric functions is difficult to interpret and cannot be explained by the RNL model. What the RNL model predicts is delta S based on low level factors (namely receptor noise). In the discussion, we completely agree with the notion that the asymmetry in thresholds from predicted values, and the variation in psychometric slopes cannot be explained by the RNL model, e.g., this is heavily implied by “colour discrimination thresholds cannot be directly attributed to noise in the early stages of the visual pathway…” (lines 388-390). To clarify the inability of the RNL model to account for this aspect of the data, we have included a statement (see line 390).

   It is a good point that this could be an indication of heterogeneity in colour space. Heterogeneity in discrimination thresholds across animal colour space (both surrounding the threshold area and for more saturated regions) has been explored in detail using trichromatic triggerfish by Green N. F. et al. (2022). JEB, 7(225):jeb243533. We have added this idea to the discussion (see lines 490-498). For UV, it seems that two of the five fish (#34 and 20) had noticeably shallower curves than the others tested for UV (fish #19, 33, 36). Both also varied more in their ability to distinguish targets, as shown by their wider confidence intervals. One of these two fish (#34) was retested for UV at the end of the experiment, and in the secondary assessment had a steeper psychometric curve more in line with the other fish in the experiment (see Figure 3 – figure supplement 1 and added lines 247-250). Based on this discrepancy in performance between assessments, it is also possible that individual learning effects had a role in impacting the shape of the psychometric curve. Note, this had minimal effect on colour discrimination thresholds and any differences were in the direction of change observed across colour sets in the experiment (i.e., lower dS for UV positive directions).

   • Line 357 ff. Up until this point, all of the discussion of differences in threshold across stimulus sets has been in terms of sensitivity. Here the authors (correctly) raise the possibility that a difference in "preference" across stimulus sets could drive the difference in thresholds as measured. Although the discussion is interesting and germaine, it does to some extent further undercut the security of conclusions about differential sensitivity across color directions relative to the RNL model predictions, and that should be brought out for the reader here. The authors might also discuss about how a future experiment might differentiate between a preference explanation and a sensitivity explanation of threshold differences.

   We have now added a paragraph (see lines 469-473) discussing that future work should test for color preferences and suggest how this could be done using a similar foraging task. We also include our thoughts immediately prior on why it is unlikely that a colour preference was a major contribution towards the results. In short, we consider it unlikely as fish showed no evidence of reduced latency for pecking at targets across the colour sets and because the training regime prior to the experiment equally rewarded fish for all colours and would likely have overridden a strong preference (at least in this specific foraging context).

   • RNL model. The paper cites a lot of earlier work that used the RNL model, but I think many readers will not be familiar with it. A bit more descriptive prose would be helpful, and particularly noting that in the full dimensional receptor space, if the limiting noise at the photoreceptors is Gaussian, then the isothreshold contour will be a hyper-ellipsoid with its axes aligned with the receptor directions.

   There is now added explanation of the RNL model (see lines 141-151), particularly on its assumptions that it only receives chromatic input and that discrimination is limited by noise arising in the photoreceptors and not by any specific opponent mechanisms. We also added the mention of the expected hyper-ellipsoid shape of isothreshold contours if receptor noise is Gaussian. Note, while we appreciate the importance of the reader to understand the basic functionality of the model, we wanted to avoid overloading the introduction with details on the RNL model which is not the focus of the paper. The RNL model is well-established in the field of visual ecology and animal vision research for well over a decade and has been thoroughly dissected by previous methodological reviews. We refer to one of these more recent reviews by Olsson et al. (2018) Behav Ecol. 29(2):273-282, and direct the reader to the methods section for further details on the RNL model.

   • Use of cone isolating stimuli? For showing that all four cone classes contribute to what the authors call color discrimination, a more direct approach would seem to be to use stimuli that target stimulation of only one class of cone at a time. This might require a modified design in which the distractors and target were shown against a uniform background and approximately matched in their estimated effect on a putative achromatic mechanism. Did the authors consider this approach, and more generally could they discuss what they see as its advantages and disadvantages for future work.

   The Reviewer is correct in that a targeted approach of isolated cone stimulation would be the optimal approach to demonstrating tetrachromatic colour vision. However, the extreme spectral overlap in the absorption curves of anemonefish cones, particularly in the mid-wavelength region makes this problematic in using the current LED display. We added to the discussion ways that this could be studied in the future (see lines 474-489). This might be possible (but still challenging) using a monochromator, but such technology severely limits the diversity of stimuli which can be created and usually restricts experiments to a simple paired choice design (or grey card experiment). The traditional paired choice experiment requires animals to be trained to distinguish a specific colour, while the Ishihara-like task trains animals to distinguish targets using an odd-one-out approach. This latter approach is highly efficient, as it does not require retraining when testing a new colour (i.e., fish learnt the task not a specific colour). Here, we wanted to assess colour discrimination in multiple directions to compare performance, and the flexible LED display combined with a generalisable task was important.

   The above assumes that anemonefish do not use multiple trichromatic systems. In which case, the use of standard experimental stimuli (e.g., a monochromator, an LED display) would be unsuitable as they illuminate the whole retina. To definitively test the range of opponent interactions, it would be necessary to make electrophysiological measurements targeting the transmitting neurons using a retinal multielectrode array (MEA) approach or by in-vivo calcium imaging (lines 484-486).

   We understand that our results are not a direct test of the dimensionality of anemonefish colour vision and should not be interpreted as such, as we do not have direct evidence of tetrachromacy. To recognize this limitation of our data, we have drawn back some of our conclusive statements that claimed to have demonstrated tetrachromacy.

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2. 

   eLife

   Jun 17, 2023

   eLife assessment

   This paper studies color vision in anemonefish. The central conclusion of the paper is that anemonefish use signals from their UV cones to discriminate colors that would not otherwise be distinguishable; this differs from other fish in which UV cones extend the range of wavelengths of sensitivity but do not add a dimension to color vision. The work fits into a rich history of studies investigating how color vision fits into an animal's ecological niche. At the same time, the manuscript needs to more clearly establish and convey the degree to which each conclusion is supported by the data and where the limits of certainty lie.

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3. 

   eLife

   Jun 17, 2023

   Reviewer #1 (Public Review):

   This paper studies color vision in anemonefish. The central conclusion of the paper is that anemonefish use signals from their UV cones to discriminate colors that would not otherwise be distinguishable; this differs from other fish in which UV cones extend the range of wavelengths of sensitivity but do not add a dimension to color vision. The work fits into a rich history of studies investigating how color vision fits into an animal's ecological niche. My primary concerns regard the microspectrophotometry data from single cones and some aspects of the presentation of the behavioral data.

   Microspectrophotometry
   The spectral properties of the cone types are a key issue for interpreting the results. These were measured using MSP, and fits are shown in Figure 2. The raw data shown in Fig. S1 appears more …

   Reviewer #1 (Public Review):

   This paper studies color vision in anemonefish. The central conclusion of the paper is that anemonefish use signals from their UV cones to discriminate colors that would not otherwise be distinguishable; this differs from other fish in which UV cones extend the range of wavelengths of sensitivity but do not add a dimension to color vision. The work fits into a rich history of studies investigating how color vision fits into an animal's ecological niche. My primary concerns regard the microspectrophotometry data from single cones and some aspects of the presentation of the behavioral data.

   Microspectrophotometry
   The spectral properties of the cone types are a key issue for interpreting the results. These were measured using MSP, and fits are shown in Figure 2. The raw data shown in Fig. S1 appears more complicated than indicated in the main text. The templates miss the measurements across broad wavelength bands in each cone type. Particularly concerning is the high UV absorbance across cone types and the long-wavelength absorbance in the UV cone. It is not clear how this picture supports the relatively simple description of cone types and spectral sensitivities given in the main text and which forms the basis of the modeling.

   Presentation
   The results are not presented in a straightforward way - at least for this reviewer. What is missing for me is a clear link between the psychometric curves in Figure 3A and the discrimination thresholds indicated in Figure 3B and Figure 4. Figure 3A is only discussed in the text on line 289 - after Figure 4 has been introduced and discussed. It would have been very helpful for me if the psychometric curves were first introduced and described, then the relation to Figure 3B was clearly indicated (perhaps with a single psychometric curve as an example). Similarly for Figure 4 the relationship between specific psychometric curves and the threshold plotted would be quite helpful. Currently it takes a careful reading to understand why being below the dashed line in Figure 4 is important.

   RNL model
   The data is fit and interpreted in the context of the receptor noise limited model. The paragraph in the discussion about complementary color pairs suggests that this model is incorrect (text around line 332). Consideration of how the results depend on the RNL model is important, especially given the interpretation here.

   Figure 3B
   This is the key figure in the paper. But several issues make seeing the data in this figure difficult. First, the important part of the figure is buried near the origin and hard to see. Can you show a surface that connects the thresholds in the different chromatic directions, or otherwise highlight the regions of discriminable and not discriminable colors?

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4. 

   eLife

   Jun 17, 2023

   Reviewer #2 (Public Review):

   Mitchell and colleagues examined the contribution of a UV-sensitive cone photoreceptor to chromatic detection in Amphiprion ocellaris, a type of anemonefish. First, they used biophysical measurements to characterize the response properties of the retinal receptors, which come in four spectrally-distinct subtypes: UV, M1, M2, and L. They then used these spectral sensitivities to construct a 4-dimensional (tetrahedral) color space in which stimuli with known spectral power distributions can be represented according to the responses they elicit in the four cone types. A novel five-LED display was used to test the fish's ability to detect "chromatic" modulations in this color space against a background of random-intensity, "achromatic" distractors that produce roughly equal relative responses in the four cone …

   Reviewer #2 (Public Review):

   Mitchell and colleagues examined the contribution of a UV-sensitive cone photoreceptor to chromatic detection in Amphiprion ocellaris, a type of anemonefish. First, they used biophysical measurements to characterize the response properties of the retinal receptors, which come in four spectrally-distinct subtypes: UV, M1, M2, and L. They then used these spectral sensitivities to construct a 4-dimensional (tetrahedral) color space in which stimuli with known spectral power distributions can be represented according to the responses they elicit in the four cone types. A novel five-LED display was used to test the fish's ability to detect "chromatic" modulations in this color space against a background of random-intensity, "achromatic" distractors that produce roughly equal relative responses in the four cone types. A subset of stimuli, defined by their high positive UV contrast, were more readily detected than other colors that contained less UV information. A well-established model was used to link calculated receptor responses to behavioral thresholds. This framework also enabled statistical comparisons between models with varying number of cone types contributing to discrimination performance, allowing inferences to be drawn about the dimensionality of color vision in anemonefish.

   The authors make a compelling case for how UV light in the anemonefish habitat is likely an important ecological source of information for guiding their behavior. The authors are to be commended for developing an elegant behavioral paradigm to assess visual performance and for incorporating a novel display device especially suited to addressing hypotheses about the role of UV light in color perception. While the data are suggestive of behavioral tetrachromacy in anemonefish, there are some aspects of the study that warrant additional consideration:

   1. One challenge faced by many biological imaging systems is longitudinal chromatic aberration (LCA) - that is, the focal power of the system depends on wavelength. In general, focal power increases with decreasing wavelength, such that shorter wavelengths tend to focus in front of longer wavelengths. In the human eye, at least, this focal power changes nonlinearly with wavelength, with the steepest changes occurring in the shorter part of the visible spectrum (Atchison & Smith, 2005). In the fish eye, where the visible spectrum extends to even shorter wavelengths, it seems plausible that a considerable amount of LCA may exist, which could in turn cause UV-enriched stimuli to be more salient (relative to the distractor pixels) due to differences in perceived focus rather than due solely to differences in their respective spectral compositions. Such a mechanism has been proposed by Stubbs & Stubbs (2016) as a means for supporting "color vision" in monochromatic cephalopods (but see Gagnon et al. 2016). It would be worth discussing what is known about the dispersive properties of the crystalline lens in A. ocellaris (or similar species), and whether optical factors could produce sufficient cues in the retinal image that might explain aspects of the behavioral data presented in the current study.

   2. The authors provide a quantitative description of anemonefish visual performance within the context of a well-developed receptor-based framework. However, it was less clear to me what inferences (if any) can be drawn from these data about the post-receptoral mechanisms that support tetrachromatic color vision in these organisms. Would specific cone-opponent processes account for instances where behavioral data diverged from predictions generated with the "receptor noise limited" model described in the text? The general reader may benefit from more discussion centered on what is known (or unknown) about the organization of cone-opponent processing in anemonefish and related species.

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5. 

   eLife

   Jun 17, 2023

   Reviewer #3 (Public Review):

   The comments below focus mainly on ways that the data and analysis as currently present do not to this reviewer compel the conclusions the authors wish to draw. It is possible that further analysis and/or clarification in the presentation would more persuasively bolster the authors' position. It also seems possible that a presentation with more limited conclusions but clarity on exactly what has been demonstrated and where additional future work is needed would make a strong contribution to the literature.

   * Fig 3A. It might be worth emphasizing a bit more explicitly that the x-axis (delta S) is the result of a model fit to the data being shown, since this then means that if RNL model fit the data perfectly, all of the thresholds would fall at deltaS = 1. They don't, so I would like to see some evaluation …

   Reviewer #3 (Public Review):

   The comments below focus mainly on ways that the data and analysis as currently present do not to this reviewer compel the conclusions the authors wish to draw. It is possible that further analysis and/or clarification in the presentation would more persuasively bolster the authors' position. It also seems possible that a presentation with more limited conclusions but clarity on exactly what has been demonstrated and where additional future work is needed would make a strong contribution to the literature.

   * Fig 3A. It might be worth emphasizing a bit more explicitly that the x-axis (delta S) is the result of a model fit to the data being shown, since this then means that if RNL model fit the data perfectly, all of the thresholds would fall at deltaS = 1. They don't, so I would like to see some evaluation from the authors' experience with this model as to whether they think the deviations (looks like the delta S range is ~0.4 to ~1.6 in Figure 4B) represent important deviations of the data from the model, the non-significant ANOVA notwithstanding. For example, Figure 4B suggests that the sign of the fit deviations is driven by the sign of the UV contrast and that this is systematic, something that would not be picked up by the ANOVA. Quite a bit is made of the deviations below, but that the model doesn't fully account for the data should be brought out here I think. As the authors note elsewhere, deviations of the data from the RNL model indicate that factors other than receptor noise are at play, and reminding the reader of this here at the first point it becomes clear would be helpful.

   * Line 217 ff, Figure 4, Supplemental Figure 4). If I'm understanding what the ANOVA is telling us, it is that the deviations of the data across color directions and fish (I think these are the two factors based on line 649) is that the predictions deviate significantly from the data, relative to the inter-fish variability), for the trichromatic models but not the tetrachromatic model. If that's not correct, please interpret this comment to mean that more explanation of the logic of the test would be helpful.

   Assuming that the above is right about the nature of the test, then I don't think the fact that the tetrachromatic model has an additional parameter (noise level for the added receptor type) is being taken into account in the model comparison. That is, the trichromatic models are all subsets of the tetrachromatic model, and must necessarily fit the data worse. What we want to know is whether the tetrachromatic model is fitting better because its extra parameter is allowing it to account for measurement noise (overfitting), or whether it is really doing a better job accounting for systematic features of the data. This comparison requires some method of taking the different number of parameters into account, and I don't think the ANOVA is doing that work. If the models being compared were nested linear models, than an F-ratio test could be deployed, but even this doesn't seem like what is being done. And the RNL model is not linear in its parameters, so I don't think that would be the right model comparison test in any case.

   Typical model comparison approaches would include a likelihood ratio test, AIC/BIC sorts of comparisons, or a cross-validation approach.

   If the authors feel their current method does persuasively handle the model comparison, how it does so needs to be brought out more carefully in the manuscript, since one of the central conclusions of the work hinges at least in part on the appropriateness of such a statistical comparison.

   * Also on the general point on conclusions drawn from the model fits, it seems important to note that rejecting a trichromatic version of the RNL model is not the same as rejecting all trichromatic models. For example, a trichromatic model that postulates limiting noise added after a set of opponent transformations will make predictions that are not nested within those of RNL trichromatic models. This point seems particularly important given the systematic failures of even the tetrachromatic version of the RNL model.

   * More generally, attempts to decide whether some human observers exhibit tetrachromacy have taught us how hard this is to do. Two issues, beyond the above, are the following. 1) If the properties of a trichromatic visual system vary across the retina, then by imaging stimuli on different parts of the visual field an observer can in principle make tetrachromatic discriminations even though visual system is locally trichromatic at each retinal location. 2) When trying to show that there is no direction in a tetrachromatic receptor space to which the observer is blind, a lot of color directions need to be sampled. Here, 9 directions are studied. Is that enough? How would we know? The following paper may be of interest in this regard: Horiguchi, Hiroshi, Jonathan Winawer, Robert F. Dougherty, and Brian A. Wandell. "Human trichromacy revisited." Proceedings of the National Academy of Sciences 110, no. 3 (2013): E260-E269. Although I'm not suggesting that the authors conduct additional experiments to try to address these points, I do think they need to be discussed.

   * Line 277 ff. After reading through the paper several times, I remain unsure about what the authors regard as their compelling evidence that the UV cone has a higher sensitivity or makes an omnibus higher contribution to sensitivity than other cones (as stated in various forms in the title, Lines 37-41, 56-57, 125, 313, 352 and perhaps elsewhere).

   At first, I thought they key point was that the receptor noise inferred via the RNL model as slightly lower (0.11) for the UV cone than for the double cones (0.14). And this is the argument made explicitly at line 326 of the discussion. But if this is the argument, what needs to be shown is that the data reject a tetrachromatic version of the RNL model where the noise value of all the cones is locked to be the same (or something similar), with the analysis taking into account the fewer parametric degrees of freedom where the noise parameters are so constrained. That is, a careful model comparison analysis would be needed. Such an analysis is not presented that I see, and I need more convincing that the difference between 0.11 and 0.14 is a real effect driven by the data. Also, I am not sanguine that the parameters of a model that in some systematic ways fails to fit the data should be taken as characterizing properties of the receptors themselves (as sometimes seems to be stated as the conclusion we should draw).

   Then, I thought maybe the argument is not that the noise levels differ, but rather that the failures of the model are in the direction of thresholds being under predicted for discriminations that involve UV cone signals. That's what seems to be being argued here at lines 277 ff, and then again at lines 328 ff of the discussion. But then the argument as I read it more detail in both places switches from being about the UV cones per se to being about postive versus negative UV contrast. That's fine, but it's distinct from an argument that favors omnibus enhanced UV sensitivity, since both the UV increments and decrements are conveyed by the UV cone; it's an argument for differential sensitivity for increments versus decrements in UV mediated discriminations. The authors get to this on lines 334 of the discussion, but if the point is an increment/decrement asymmetry the title and many of the terser earlier assertions should be reworked to be consistent with what is shown.

   Perhaps the argument with respect to model deviations and UV contrast independent of sign could be elaborated to show more systematically that the way the covariation with the contrasts of the other cone stimulations in the stimulus set goes, the data do favor deviations from the RNL in the direction of enhanced sensitivity to UV cone signals, but if this is the intent I think the authors need to think more about how to present the data in a manner that makes it more compelling than currently, and walk the reader carefully through the argument.

   * On this point, if the authors decide to stick with the enhanced UV sensitivity argument in the revision, a bit more care about what is meant by "the UV cone has a comparatively high sensitivity (line 313 and throughout)" needs more unpacking. If it is that these cones have lower inferred noise (in the context of a model that doesn't account for at least some aspects of the data), is this because of properties of the UV cones, or the way that post-receptoral processing handles the signals from these cones mimicking a cone effect in the model. And if it is thought that it is because of properties of the cones, some discussion of what those properties might be would be helpful. As I understand the RNL model, relative numbers of cones of each type are taken into account, so it isn't that. But could it be something as simple as higher photopigment density or larger entrance aperture (thus more quantum catches and higher SNR)?

   * Line 288 ff. The fact that the slopes of the psychometric functions differed across color directions is, I think, a failure of the RNL model to describe this aspect of the data, and tells us that a simple summary of what happens for thresholds at delta S = 1 does not generalize across color directions for other performance levels. Since one of the directions where the slope is shallower is the UV direction, this fact would seem to place serious limits on the claim that discrimination in the UV direction is enhanced relative to other directions, but it goes by here without comment along those lines. Some comment here, both about implications for fit of RNL model and about implications for generalizations about efficacy of UV receptor mediated discrimination and UV increment/decrement asymmetries, seems important.

   * Line 357 ff. Up until this point, all of the discussion of differences in threshold across stimulus sets has been in terms of sensitivity. Here the authors (correctly) raise the possibility that a difference in "preference" across stimulus sets could drive the difference in thresholds as measured. Although the discussion is interesting and germaine, it does to some extent further undercut the security of conclusions about differential sensitivity across color directions relative to the RNL model predictions, and that should be brought out for the reader here. The authors might also discuss about how a future experiment might differentiate between a preference explanation and a sensitivity explanation of threshold differences.

   * RNL model. The paper cites a lot of earlier work that used the RNL model, but I think many readers will not be familiar with it. A bit more descriptive prose would be helpful, and particularly noting that in the full dimensional receptor space, if the limiting noise at the photoreceptors is Gaussian, then the isothreshold contour will be a hyper-ellipsoid with its axes aligned with the receptor directions.

   * Use of cone isolating stimuli? For showing that all four cone classes contribute to what the authors call color discrimination, a more direct approach would seem to be to use stimuli that target stimulation of only one class of cone at a time. This might require a modified design in which the distractors and target were shown against a uniform background and approximately matched in their estimated effect on a putative achromatic mechanism. Did the authors consider this approach, and more generally could they discuss what they see as its advantages and disadvantages for future work.

   Read the original source
6. 

   Version published to 10.1101/2022.12.01.518784v1 on bioRxiv

   Dec 2, 2022