Philippa Kaur

To assess coral holobiont health, we used the commercially available Urinalysis strips (Accutest URS-14 100 strips Urinalysis Reagent Test Paper; ca. $11–35 USD/ bottle) that are designed to detect disease markers in humans, potentially indicating diabetes, metabolic abnormalities, liver diseases, kidney function, and urinary tract infections. There are ten tests on each strip that provide an initial assessment of health using the following standardized markers: leukocytes, nitrite, urobilinogen, protein, pH, blood, specific gravity, ketone, bilirubin, and glucose. To apply this test to coral extracts, we used ambient and stressed nubbins in a comparative approach to identify trends in the data. We presumed that some of the tests are likely not applicable to corals, whereas others such as ketones and leukocytes, which target conserved animal pathways (see below) could prove useful. Samples from three different coral experiments (two bleaching experiments and one environmental survey) were used to assess the response and utility of the different tests on each strip. The first bleaching experiments was done in 2019 on two Hawaiian species: Montipora capitata (stress resistant) and Pocillopora acuta (stress sensitive); this research has been previously described in detail11,12. Briefly, three M. capitata colonies (different genotypes) were selected, with samples collected from each in triplicate at three timepoints (referred to as T1, T3, and T5) that span a period of prolonged thermal stress (16 days). The second bleaching experiment was done in 2021 and included nubbins from M. capitata, P. acuta, and P. compressa. These corals were maintained for 9 days under heat treatment or ambient conditions, with samples collected at the beginning and end of the experimental period from both conditions (see Methods). Finally, the third experiment was done in June 2021 on wild colonies of M. capitata, P. acuta, and P. compressa (Fig. 1A) from Kāneʻohe Bay, Oʻahu, Hawaiʻi (Fig. 1B) and analyzed to assess the extent of natural variability in the test strip results. It should be noted that the test strip results from each experiment are independent (i.e., analyzed separately to measure relative differences within the test population) due to differences in tissue freezing time, storage length, and handling, that affect the results.Figure 1Analysis of Hawaiian corals. (A) The three targeted species in Kāneʻohe Bay. Images created by D. Bhattacharya. (B) Sites of wild coral collection (marked by the yellow circles) in Kāneʻohe Bay in June 2021. Nubbins from two colonies (n = 3) of M. capitata were collected from six reefs: Reef 8, 9, Reef 13, Reef 27, Reef 41, and Reef 43 and from three sites near the Hawaiʻi Institute of Marine Biology (HIMB) on Coconut Island (Moku o Loʻe). This image was adapted from the Pacific Islands ocean observing system (https://www.pacioos.hawaii.edu/projects/coral/)33.Full size imagePortable device for test strip analysisTo facilitate field testing and allow accurate measurement of test strip results in the R, G, and B channels, a combination of 3D printing technology and computer vision was applied to the problem. A custom opaque phone holder was produced using 3D printing (Ultimaker Inc. [https://ultimaker.com]) to control light source quality and quantity during the procedure and for calculating test strip scores (Fig. 2A). This portable phone holder was designed to allow field personnel to conduct experiments and collect results in a convenient and user-friendly manner. Inside the black opaque phone holder, two LED diodes served as a controllable and consistent light source to eliminate noise introduced by any change in light levels (between or during) the test strip reactions, allowing for more accurate and consistent data collection19. Frames of the videos (taken using the smart phone positioned in the holder) were captured for different reagents according to the recommended reading time given by the user manual. For test strip RGB value measurement (Fig. 2B), an automated machine learning method, shown in Fig. 2C, was used as a replacement for traditional, manual methods. Our computer vision workflow, named TestStripDX, uses YOLOv418, which is one of the most mature, accurate, and popular computer vision models available, to isolate a target feature in an image and provide the metadata associated with the object (in this case, each test along the strip), such as position within the coordinates of a rectangular box. Sample pictures of strips were manually annotated and used for training the TestStripDX pipeline (Supplementary Data 1): i.e., these images were used to train a custom detector to identify the different reagent tests along a strip and thereafter, to provide the relevant RGB values for each test (Fig. 2C). Figure 2D shows the comparison between data collected using the manual (ImageJ) versus automated (TestStripDX) methods for a partial list of samples and reagent strips (data shown in Table S1). The very close association of the regression line (R2 = 0.996) with the artificial diagonal line validates the utility and accuracy of the computer vision method.Figure 2Portable instrument for test strip analysis. (A) The 3D-printed phone holder used to analyze the test strips. (B) Example test strip. (C) Flow chart for the training and application of the machine learning method (TestStripDX) used to analyze test strips. (D) Comparison of test strip colorimetric measurement values produced by TestStripDX and ImageJ, for a set of representative images (Table S1). The enforced diagonal matches very closely to the regression line.Full size imageCoral color score measurementsAs described above, color scores provide a proxy for coral health, and are generated by measuring the RGB channels of the bleached (unhealthy) and brown (healthy: i.e., pigmentation provided by the algal symbionts) areas of coral nubbins. For the CoralDX workflow, we trained a custom detector that can recognize coral nubbins, as well as red, green, and blue colored blocks (standards) which are used to normalize the R, G, and B channels in each image (see Supplementary Data 2). The images taken in this case were from a lab environment (Fig. 3A), but this approach could potentially be used in any location provided a background with a uniform color is used in the image. To achieve this goal, a background panel containing the red, blue, and green color blocks would be placed behind (smaller) coral nubbins to make the measurements. It is clear that for large colonies, this approach may prove challenging to apply but we expect that additional testing and modification to the method will allow us to design a better-suited tool for field use. To achieve our goal of automatically obtaining colorimetric measurements of coral nubbins, we needed to devise a method that provides YOLOv4 with areas that are not limited by the standard rectangular boxes used as input for this model. To accommodate irregular nubbin shapes, an additional step (training and testing steps are shown in Fig. 3B) was added to the automated method (Fig. 2C). Our approach uses computer vision-based edge detection to eliminate most of the background surrounding the edges of the coral nubbins, allowing for accurate quantification of the RGB values of the targeted piece of coral. After edge detection, we obtain a picture with a black background highlighting the coral shape as a “mask” (Fig. 3C; 2nd image from left). We then place the mask onto the original coral image, measuring non-zero R, G, and B values (Fig. 3C; 3rd and 4th images from left). This method is superior to selecting coral areas for manual analysis using tools such as the handsfree selection function in ImageJ, which are generally hard to manipulate, have low fault tolerance, and are time-consuming. The RGB values extracted from the coral nubbins and color blocks were investigated using principal component analysis (PCA) to generate Euclidean distances (color scores) among coral nubbins according to treatment, time point, and colony11,16. In Fig. 3D, the correlation between bleaching scores generated using CoralDX and ImageJ for M. capitata and P. acuta are presented for a representative set of nubbins cultured under ambient or thermal stress conditions in the 2019 bleaching experiment (Tables S2, S3). As is apparent, the regression lines show strong correlations (R2 = 0.968 for M. capitata and R2 = 0.991 for P. acuta), supporting the utility of the automated method. The data in both cases are very closely associated with the artificial diagonal line shown in the images, substantiating the strong positive correlation between the scores from the two approaches. The small differences in score values between the methods is primarily explained by difficulties in cropping the edges of coral nubbins that are heavily bleached; the contrast between the coral nubbin and the white background is lessened, creating discrepancies between the nubbin edges identified by CoralDX and ImageJ. This issue can be addressed by testing different color backgrounds to find the optimal set-up. Another potential contributing factor is the differences in the area selected for the color blocks between manual (ImageJ) versus automated methods, although this might only have a minor effect on the results. The CoralDX workflow is not computationally intensive and easily portable to other platforms, such as personal computers and smart phones, allowing for easy deployment in the field.Figure 3Analysis of coral color scores. (A) Example image of M. capitata coral nubbins used for automated color score analysis, with target areas marked with the black (coral nubbins) and yellow (color standards) boxes. The nubbin on the far right is used to demonstrate the masking procedure. (B) Flow chart for the training and application of the machine learning method (CoralDX) used to analyze coral color scores. (C) Example of image processing for one M. capitata coral nubbin showing (from left to right) the original, masked, segmented, and masked RGB nubbin, with the final (right) image used for color score measurements. (D) Comparison of color score values produced by CoralDX and ImageJ, for a set of representative coral nubbins from the 2019 bleaching experiment (Tables S2, S3). The enforced diagonal matches very closely to the regression line in both analyses.Full size imageTest strip resultsArmed with these new tools, we generated the test strip data for different coral samples, where larger “relative enzymatic activity” (REA, measured using the RGB values) values indicate increased reactivity (i.e., increased levels of products targeted by the test). The Accutest URS-14 100 strips test for ketones (using the sodium nitroprusside reaction) measures acetoacetate and assumes the presence of β-hydroxybutyrate and acetone. The former (β-hydroxybutyrate) acts as a signal to regulate metabolism and maintain energy homeostasis during nutrient deprivation. In this process, β-hydroxybutyrate is converted to acetoacetate. Ketone bodies are transported into tissues and converted into acetyl-CoA by thiolases, which then enters the TCA cycle and is oxidized in the mitochondria for energy. Bleaching in corals which are incapable of obtaining adequate energy stores through heterotrophy results in diminished growth rates, degraded reproductive capacity, amplified susceptibility to disease, and elevated mortality rates for the entire colony20. Although ketosis has not been explored in cnidarians, transcriptomic data generated from the M. capitata samples measured in this study12 demonstrate expression of the KEGG pathway for degradation of ketone bodies (Fig. S1). The combination of time-point and treatment (field, T1-AT, T1-HT, T3-HT, T5-HT) was the most significant factor impacting the ketone REA scores (p-value = 0.010) (see details of the PERMANOVA analysis in the “Methods” section). Given this framework, we find that the M. capitata samples remain steady throughout the bleaching period, except for a decrease in enzymatic activity at T3-HT (Fig. 4A). This result is supported by the transcriptomic data, which shows this pathway to be uniformly expressed at all timepoints, except T3-HT, when acetyl-CoA C-acetyltransferase is up-regulated in comparison to T1-HT (fold change [FC] = 1.52) and down-regulated at T5-HT (FC = − 1.84) (Table S4). A possible explanation for this result is that at T3-HT, when the first evidence of bleaching was present, the photosynthetic rate of the symbiotic algae was elevated due to the thermal stress, resulting in greater energy production and a decrease in the abundance of ketone bodies within the coral, but without sufficient stress to cause significant bleaching. During this time, acetyl-CoA C-acetyltransferase enzymatic activity favored the production of acetoacetyl-CoA. However, as dysbiosis continued and the corals no longer had access to the photosynthetic products provided by the symbionts, they produced more acetoacetate and ketosis was detected by the test strips. This response occurs despite the observation that M. capitata can persist heterotrophically and meet much of its energy needs in the absence of algal symbionts21. Interestingly, the three M. capitata field samples show similar amounts of ketone bodies to lab stressed corals, suggesting the presence of stressors in the natural environment.Figure 4Test strip results from the 2019 bleaching experiment. (A) Ketone test strip results for M. capitata, showing genotype-specific (see legend) differences in response. (B) Leukocytes test strip results for M. capitata, showing genotype-specific (see legend) differences in response. These are standard box plots, with the boxes representing the first (Q1) to third (Q3) quartiles. The lines in the boxes are the median (Q2) values and lines (“whiskers”) extending beyond the boxes are the minimum and maximum values, excluding outliers. (C) PCA of the ketone and leukocytes test strip data for the field, ambient, and T1-HT, T3-HT, and T5-HT timepoints.Full size imageThe leukocyte test measures the activity of leukocyte esterase (presence of white blood cells) and other signs of infection in human subjects. This test putatively assesses the coral innate immunity response, which includes the same phases in response to infection and loss of tissue integrity as other invertebrates: recognition, signaling, and effector response22. Corals contain multiple types of immune cells, such as amoebocytes and fibroblasts23. Amoebocytes are amoeboid cells residing in the mesoglea that remove necrotic tissue, encapsulate foreign particles, and generally display phagocytic activity to aid in organism defense against pathogens, which is the cnidarian principal mechanism of immunity24. Amoebocytes can be melanin-containing, agranular, or granular based cells, depending on the signaling pathway22. M. capitata shows an overall increase in enzymatic activity at T1-HT, signaling a heightened immune response (Fig. 4B). This relatively higher level of enzymatic activity decreases at T3 and T5. The field samples show levels that are comparable to the T3-HT and T5-HT thermal stress acclimated colonies and the T1 ambient (Amb) colonies. Again, a combination of time-point and treatment was the most significant factor impacting the leukocyte REA scores (p-value = 0.001). Additional research needs to be done to understand the cause of this response. Nonetheless, the results are consistent with the widely accepted hypothesis that M. capitata adapts well to bleaching conditions12. PCA of the M. capitata test strip results shows a clear separation of the T1-HT coral data from the T3-HT and T5-HT values, with the ambient and field samples intermixed among the latter two timepoints (Fig. 4C). This result again highlights the initial robust response to stress by M. capitata followed by acclimation to the heat treatment that is reflected in the field samples.A noteworthy aspect of the test strip results is the divergence in response to thermal stress among different colonies. This result has also been found for coral metabolomic data11. As described above, each holobiont integrates a complex set of biotic interactions between the host animal and microbiome, explaining the high variation in ketone and leukocytes test results, often between replicate nubbins from one colony and more frequently, between different colonies (Fig. 4A). Existing data using omics methods demonstrate that the stress response of the coral holobiont varies from colony to colony15. The metabolome is controlled by the coral animal genotype, microbial consortium, and environmental conditions, among other factors, and can fluctuate greatly based on individual metabolite turnover rates and the timing of sampling25,26. Therefore, accounting for natural variation in the stress response phenotype and its importance for effective testing methods is a crucial aspect of our work. Our results demonstrate that broad population level sampling (dozens to 100 s of colonies/genotypes) is likely needed to account for the inherent genetic and metabolic variation present in wild coral populations.We also did a more limited analysis of coral stress responses using the ketone and leukocyte test strips with three species (M. capitata, P. acuta, P. compressa) in a 2021 bleaching experiment in which we sampled multiple coral genotypes at time 0 and after 9 days of heat treatment (3ºC increase from ambient; see Methods). These results are based on analysis of 8–9 different coral genotypes (summarized in Fig. 5). Interpreted in the same way as described above, we see that there is substantial genotype-based variation in the stress response. Nonetheless, consistent with the 2019 data, M. capitata shows evidence of a thermal stress response in the ketone and leukocytes tests (Fig. 5A, B). P. acuta shows a more limited response, whereas P. compressa appears to have fully acclimated to the stress regime with lowered reactivity at the end of the experiment. Species identity was the most significant factor impacting the leukocyte and ketone REA scores (p-value = 0.002 and 0.033, respectively), but a combination of species and treatment (AT vs. HT) was found to be significant for leukocytes (p-value = 0.026). PCA of the M. capitata test results shows separation between the ambient samples and some of the high temperature treated samples along PC1; both the ketone and leukocytes tests contribute to the spread of samples along PC1. This reinforces the conclusion that there is evidence for a thermal stress response in the ketone and leukocytes tests of M. capitata however, the significant genotypic variability (particularly apparent when compared with Fig. 4) obscures the differences between the conditions for some of the samples. PCA of the P. acuta and P. compressa test results mirrors our conclusion that the ketone and leukocytes tests in these species show a limited response to stress, with no separation between the samples from the different conditions.Figure 5Test strip results from the 2021 bleaching experiment for three Hawaiian coral species. (A) Ketone test strip results for M. capitata, P. acuta, and P. compressa showing genotype-specific variation in response under the ambient (Amb) and high temperature (HT) treatments. (B) Leukocytes test strip results for M. capitata, P. acuta, and P. compressa showing genotype-specific variation in response under the ambient and high temperature (HT) treatments. These are standard box plots, with the boxes representing the first (Q1) to third (Q3) quartiles. The lines in the boxes are the median (Q2) values and lines (“whiskers”) extending beyond the boxes are the minimum and maximum values, excluding outliers. (C) PCA of the ketone and leukocytes test strip data for the Amb and HT treatments for the three Hawaiian coral species.Full size imageAnalysis of wild populationsTo assess natural variation, we collected apparently healthy M. capitata, P. acuta, and P. compressa nubbins (3 replicate nubbins per colony) from six reefs in Kāneʻohe Bay and from three sites near the Hawaiʻi Institute of Marine Biology on Coconut Island (Moku o Loʻe) (see Fig. 1) and analyzed these tissue extracts using the ketone and leukocytes tests. This analysis shows wide variation in the results with some interesting exceptions. Species identity was the most significant factor impacting the leukocytes and ketone REA scores (p-value = 0.002 and 0.001, respectively), but for leukocytes, colony identity, regardless of species, was also found to be a significant factor (p-value = 0.040). The M. capitata ketone test results are consistent among different reefs and within the same colony with the exception of some colonies (e.g., Colony 8 from Reef 9 and Colony 16 from Reef 43) that show wide intra-colony variation (Fig. 6A). Most of the ketone data for M. capitata fall between REAs of 10–20. In contrast, P. acuta shows more variation in the wild populations for the ketone test, suggesting that many of these coral colonies live under stressful conditions in the field (Fig. 6B). A similar situation to M. capitata, in terms of REAs, holds for the ketone test of P. compressa colonies that show more limited variation (Fig. 6C). The leukocytes test shows high variation for M. capitata (Fig. 6D), P. acuta (Fig. 6E), and P. compressa (Fig. 6F) colonies. These results again point out the complex nature of genome-environment interaction with respect to metabolic syndromes, both at the colony level and among different regions (replicates) of the same colony. For example, P. compressa Colonies 13–15 from Reef 9 show little to no intra-colony variation for the ketone test, yet another colony from this reef (Colony 16) shows high variation among replicates (Fig. 6C). In contrast, P. compressa Colonies 13–15 are far more variable when using the leukocytes test (Fig. 6F). On the basis of the more predictable lab-based results reported above, we interpret these “noisy” field data as evidence of the immense variation in the stress phenome of wild coral populations. Overall, the field results indicate that a starting set of test strip values, followed by repeated field sampling over time of wild colonies may be needed for accurate stress diagnosis, rather than the one-time measurement approach used here. Clearly, more work is needed with wild colony analysis, particularly under varying degrees of thermal stress and apparent bleaching, to fully realize the potential of the technique we present.Figure 6Test strip results from analysis of the 2021 collection of three Hawaiian coral species from the wild. (A and D) Ketone and leukocytes test strip results, respectively, for M. capitata showing intraindividual variation and among the different reefs (see legend) that were sampled (Fig. 1B). (B and E) Ketone and leukocytes test strip results, respectively, for P. acuta showing intraindividual variation and among the different reefs that were sampled. (C and F) Ketone and leukocytes test strip results, respectively, for P. compressa showing intraindividual variation and among the different reefs (see legend) that were sampled. These are standard box plots, with the boxes representing the first (Q1) to third (Q3) quartiles. The lines in the boxes are the median (Q2) values and lines (“whiskers”) extending beyond the boxes are the minimum and maximum values, excluding outliers.Full size imageAnalysis of transcriptomic dataTo identify pathways that may support the M. capitata leukocytes test strip results, which showed the most response in terms of change in REA at T1-HT (Fig. 4B), we analyzed existing transcriptomic (RNA-seq) data derived from the same coral nubbins. The RNA-seq and metabolomic data from these samples have been previously analyzed11,12. Here we searched for co-expression modules that contain genes that are up-regulated at the start of the thermal stress regime (T1-HT) when compared to the ambient treatment. It is at this timepoint that we find a strong cross-reaction with the leukocytes test, followed by loss of cross-reactivity at T3-HT and T5-HT (back down to T1-Amb levels), putatively indicating acclimation (Fig. 4B). As described above, the wound healing response in corals is complex and the (Urinalysis) leukocytes results need to be interpreted as a syndrome involving multiple pathways of stress and immune response. With these considerations in mind, we identified a module (Module 2; see Williams et al.12) of up-regulated genes that contains several markers associated with the coral stress response (Fig. 7). These include a tumor necrosis factor-activated receptor (TNFR)-Cys domain-containing protein (fold-change [FC] = 1.12) that is a well-known mediator of apoptosis and cell death that is functionally conserved in corals. Some members of the TNF family are associated with bleaching27. The most highly up-regulated gene in this module is C-type lysozyme 2 (FC = 2.42) that provides an anti-microbial function (e.g., digestion of peptidoglycan), and is likely expressed as a result of stress-induced dysbiosis in M. capitata. Other markers of stress that are up-regulated in Module 2 include E3 ubiquitin-protein ligase (FC = 1.03) involved in protein degradation, two protein disulfide-isomerase (FCs = 1.32, 1.09) involved in cellular defense against protein misfolding via chaperone activity28, and a metalloproteinase inhibitor 3 (FC = 1.42) which likely functions as a physiological anti-inflammatory molecule29. These data, although not directly substantiating the leukocytes test strip results, provide evidence that the wound healing and immune response pathways were up-regulated in Module 2 at T1-HT (albeit weakly, due to the stress resilience of M. capitata) as suggested by Fig. 4B.Figure 7Gene co-expression analysis. Module 2 representing significantly up-regulated genes in M. capitata from T1-HT in the 2019 differential gene expression analysis12. This module is enriched in genes involved in the wound healing and immune response. The legend for level of up-regulation is shown. Putative gene annotations are also shown.Full size image More

Graham, N. A. J. & Nash, K. L. The importance of structural complexity in coral reef ecosystems. Coral Reefs https://doi.org/10.1007/s00338-012-0984-y (2013).Drake, J. L. et al. How corals made rocks through the ages. Glob. Change Biol. 26, 31–53 (2020).Zawada, K. J. A., Madin, J. S., Baird, A. H., Bridge, T. C. L. & Dornelas, M. Morphological traits can track coral reef responses to the Anthropocene. Funct. Ecol. 33, 962–975 (2019).Article 

Google Scholar 
Wehrberger, F. & Herler, J. Microhabitat characteristics influence shape and size of coral-associated fishes. Mar. Ecol. Prog. Ser. 500, 203–214 (2014).Article 

Google Scholar 
Munday, P. & Jones, G. The ecological implications of small body size among coral-reef fishes. Oceanogr. Mar. Biol. 36, 373–411 (1998).
Google Scholar 
Pereira, P. H. C. & Munday, P. L. Coral colony size and structure as determinants of habitat use and fitness of coral-dwelling fishes. Mar. Ecol. Prog. Ser. 553, 163–172 (2016).Article 

Google Scholar 
Doszpot, N., McWilliam, M., Pratchett, M., Hoey, A. & Figueira, W. Plasticity in three-dimensional geometry of branching corals along a cross-shelf gradient. Diversity 11, 44 (2019).Article 

Google Scholar 
Ow, Y. X. & Todd, P. A. Light-induced morphological plasticity in the scleractinian coral Goniastrea pectinata and its functional significance. Coral Reefs 29, 797–808 (2010).Article 

Google Scholar 
Soto, D., De Palmas, S., Ho, M. J., Denis, V. & Chen, C. A. Spatial variation in the morphological traits of Pocillopora verrucosa along a depth gradient in Taiwan. PLoS ONE 13, 1–20 (2018).Article 
CAS 

Google Scholar 
Bruno, J. F. & Edmunds, P. J. Clonal variation for phenotypic plasticity in the coral Madracis Mirabilis. Ecology 78, 2177–2190 (1997).Article 

Google Scholar 
Todd, P. A. Morphological plasticity in scleractinian corals. Biol. Rev. 83, 315–337 (2008).Willis, B. L. Phenotypic plasticity versus phenotypic stability in the reef corals Turbinaria mesenterina and Pavona cactus. Proc. Fifth Int. Coral Reef. Symp. 4, 107–112 (1985).
Google Scholar 
Grottoli, A. G. et al. The cumulative impact of annual coral bleaching can turn some coral species winners into losers. Glob. Change Biol. 20, 3823–3833 (2014).Article 

Google Scholar 
Smith, L. W., Barshis, D. & Birkeland, C. Phenotypic plasticity for skeletal growth, density and calcification of Porites lobata in response to habitat type. Coral Reefs 26, 559–567 (2007).Article 

Google Scholar 
Barnes, D. Growth in colonial scleractinians. Bull. Marine Sci. 280–298 (1973).Anthony, K. R. N. & Hoegh-Guldberg, O. Variation in coral photosynthesis, respiration and growth characteristics in contrasting light microhabitats: an analogue to plants in forest gaps and understoreys? Funct. Ecol. 17, 246–259 (2003).Article 

Google Scholar 
Kramer, N., Tamir, R., Eyal, G. & Loya, Y. Coral morphology portrays the spatial distribution and population size-structure along a 5–100 m depth gradient. Front. Mar. Sci. 7, 615 (2020).Article 

Google Scholar 
Dubé, C. E., Mercière, A., Vermeij, M. J. A. & Planes, S. Population structure of the hydrocoral Millepora platyphylla in habitats experiencing different flow regimes in Moorea, French polynesia. PLoS ONE 12, 1–20 (2017).
Google Scholar 
Chappell, J. Coral morphology, diversity and reef growth. Nature 286, 249–252 (1980).Article 

Google Scholar 
Paz-García, D. A. et al. Morphological variation and different branch modularity across contrasting flow conditions in dominant Pocillopora reef-building corals. Oecologia 178, 207–218 (2015).PubMed 
Article 

Google Scholar 
Laverick, J. H., Tamir, R., Eyal, G. & Loya, Y. A generalized light-driven model of community transitions along coral reef depth gradients. Glob. Ecol. Biogeogr. 29, 1554–1564 (2020).Article 

Google Scholar 
Bongaerts, P., Ridgway, T., Sampayo, E. M. & Hoegh-Guldberg, O. Assessing the “deep reef refugia” hypothesis: focus on Caribbean reefs. Coral Reefs 29, 1–19 (2010).Article 

Google Scholar 
Sherman, C. E., Locker, S. D., Webster, J. M. & Weinstein, D. K. In Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) 849–878 (Springer International Publishing, 2019).Kahng, S. E., Copus, J. M. & Wagner, D. Recent advances in the ecology of mesophotic coral ecosystems (MCEs). Curr. Opin. Environ. Sustainability 7, 72–81 (2014).Article 

Google Scholar 
Tamir, R., Eyal, G., Kramer, N., Laverick, J. H. & Loya, Y. Light environment drives the shallow to mesophotic coral community transition. Ecosphere 10, e02839 (2019).Article 

Google Scholar 
Lichtenthaler, H. K., Ač, A., Marek, M. V., Kalina, J. & Urban, O. Differences in pigment composition, photosynthetic rates and chlorophyll fluorescence images of sun and shade leaves of four tree species. Plant Physiol. Biochem. 45, 577–588 (2007).CAS 
PubMed 
Article 

Google Scholar 
Bragg, J. G. & Westoby, M. Leaf size and foraging for light in a sclerophyll woodland. Funct. Ecol. 16, 633–639 (2002).Article 

Google Scholar 
Sæbø, A., Krekling, T. & Appelgren, M. Light quality affects photosynthesis and leaf anatomy of birch plantlets in vitro. Plant Cell, Tissue Organ Cult. 41, 177–185 (1995).Article 

Google Scholar 
Einbinder, S. et al. Changes in morphology and diet of the coral Stylophora pistillata along a depth gradient. Mar. Ecol. Prog. Ser. 381, 167–174 (2009).Article 

Google Scholar 
Mass, T. et al. Photoacclimation of Stylophora pistillata to light extremes: metabolism and calcification. Mar. Ecol. Prog. Ser. 334, 93–102 (2007).CAS 
Article 

Google Scholar 
Kramer, N. et al. Efficient light-harvesting of mesophotic corals is facilitated by coral optical traits. Funct. Ecol. 36, 406–418 (2022).CAS 
Article 

Google Scholar 
Einbinder, S. et al. Novel adaptive photosynthetic characteristics of mesophotic symbiotic microalgae within the reef-building coral, Stylophora pistillata. Front. Mar. Sci. 3, 195 (2016).Article 

Google Scholar 
Martinez, S. et al. Energy sources of the depth-generalist mixotrophic coral Stylophora pistillata. Front. Mar. Sci. 7, 1–16 (2020).CAS 
Article 

Google Scholar 
Anthony, K. R. N., Hoogenboom, M. O. & Connolly, S. R. Adaptive variation in coral geometry and the optimization of internal colony light climates. Funct. Ecol. 19, 17–26 (2005).Article 

Google Scholar 
Kahng, S. E., Watanabe, T. K., Hu, H.-M., Watanabe, T. & Shen, C.-C. Moderate zooxanthellate coral growth rates in the lower photic zone. Coral Reefs https://doi.org/10.1007/s00338-020-01960-4 (2020).Wangpraseurt, D. et al. The in situ light microenvironment of corals. Limnol. Oceanogr. 59, 917–926 (2014).Article 

Google Scholar 
Dustan, P. Depth-dependent photoadaption by zooxanthellae of the reef coral Montastrea annularis. Mar. Biol. 68, 253–264 (1982).CAS 
Article 

Google Scholar 
Dubinsky, Z., Falkowski, P. G., Porter, J. W. & Muscatine, L. Absorption and utilization of radiant energy by light- and shade-adapted colonies of the hermatypic coral Stylophora pistillata. Proc. R. Soc. B: Biol. Sci. 222, 203–214 (1984).CAS 

Google Scholar 
Falkowski, P. G. & Dubinsky, Z. Light-shade adaptation of Stylophora pistillata, a hermatypic coral from the Gulf of Eilat. Nature 289, 172–174 (1981).Article 

Google Scholar 
Kahng, S. E. et al. In Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) 801–828 (Springer International Publishing, 2019).Hoogenboom, M. O., Connolly, S. R. & Anthony, K. R. N. Interactions between morphological and physiological plasticity optimize energy acquisition in corals. Ecology 89, 1144–1154 (2008).PubMed 
Article 

Google Scholar 
House, J. E. et al. Moving to 3D: relationships between coral planar area, surface area and volume. PeerJ 6, e4280 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Zawada, K. J. A., Dornelas, M. & Madin, J. S. Quantifying coral morphology. Coral Reefs 38, 1281–1292 (2019).Article 

Google Scholar 
Malik, A. et al. Molecular and skeletal fingerprints of scleractinian coral biomineralization: From the sea surface to mesophotic depths. Acta Biomaterialia 1–14 https://doi.org/10.1016/j.actbio.2020.01.010 (2020).Todd, P. A., Ladle, R. J., Lewin-Koh, N. J. I. & Chou, L. M. Genotype x environment interactions in transplanted clones of the massive corals Favia speciosa and Diploastrea heliopora. Mari. Ecol. Prog. Ser. https://doi.org/10.3354/meps271167 (2004).Crabbe, M. J. C. & Smith, D. J. Modelling variations in corallite morphology of Galaxea fascicularis coral colonies with depth and light on coastal fringing reefs in the Wakatobi Marine National Park (S.E. Sulawesi, Indonesia). Computational Biol. Chem. 30, 155–159 (2006).CAS 
Article 

Google Scholar 
Studivan, M. S., Milstein, G. & Voss, J. D. Montastraea cavernosa corallite structure demonstrates distinct morphotypes across shallow and mesophotic depth zones in the Gulf of Mexico. PLoS ONE 14, e0203732 (2019).Wangpraseurt, D., Larkum, A. W. D., Ralph, P. J. & Kühl, M. Light gradients and optical microniches in coral tissues. Front. Microbiol. 3, 1–9 (2012).Article 

Google Scholar 
Enríquez, S., Méndez, E. R. & Iglesias-Prieto, R. Multiple scattering on coral skeletons enhances light absorption by symbiotic algae. Limnol. Oceanogr. 50, 1025–1032 (2005).Article 

Google Scholar 
Wangpraseurt, D., Jacques, S. L., Petrie, T. & Kühl, M. Monte Carlo modeling of photon propagation reveals highly scattering coral tissue. Front. Plant Sci. 7, 1–10 (2016).Article 

Google Scholar 
Muko, S., Kawasaki, K., Sakai, K., Takasu, F. & Shigesada, N. Morphological plasticity in the coral Porites sillimaniani and its adaptive significance. Bull. Mar. Sci. 66, 225–239 (2000).
Google Scholar 
Klaus, J. S., Budd, A. F. & Fouke, B. Environmental controls on corallite morphology in the reef coral Montastraea annularis Hot springs microbiology View project Positive Accretion in the Deep: Carbonate budget analysis of Caribbean mesophotic coral reef habitats View project. Bull. Mar. Sci. 80, 233–260 (2007).
Google Scholar 
Enríquez, S., Méndez, E. R., Hoegh-Guldberg, O. & Iglesias-Prieto, R. Key functional role of the optical properties of coral skeletons in coral ecology and evolution. Proc. R. Soc. B: Biol. Sci. 284, (2017).Wangpraseurt, D. et al. Microscale light management and inherent optical properties of intact corals studied with optical coherence tomography. J. R. Soc. Interface 16, 20180567 (2019).Groves, S. H. et al. Growth rates of Porites astreoides and Orbicella franksi in mesophotic habitats surrounding St. Thomas, US Virgin Islands. Coral Reefs 37, 345–354 (2018).Article 

Google Scholar 
Shlesinger, T., Grinblat, M., Rapuano, H., Amit, T. & Loya, Y. Can mesophotic reefs replenish shallow reefs? Reduced coral reproductive performance casts a doubt. Ecology https://doi.org/10.1002/ecy.2098 (2018).Kaniewska, P. et al. Importance of macro- versus microstructure in modulating light levels inside coral colonies. J. Phycol. 47, 846–860 (2011).PubMed 
Article 

Google Scholar 
Swain, T. D. et al. Relating coral skeletal structures at different length scales to growth, light availability to symbiodinium, and thermal bleaching. Front. Mar. Sci. 5, (2018).Bongaerts, P. & Smith, T. B. In Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) 881–895 (Springer International Publishing, 2019).Bongaerts, P., Riginos, C., Brunner, R., Englebert, N. & Smith, S. R. Deep reefs are not universal refuges: reseeding potential varies among coral species. Sci. Adv. 3, 1–40 (2017).Article 

Google Scholar 
Serrano, X. M. et al. Long distance dispersal and vertical gene flow in the Caribbean brooding coral Porites astreoides. Sci. Rep. 6, 21619 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fantazzini, P. et al. Gains and losses of coral skeletal porosity changes with ocean acidification acclimation. Nat. Commun. 6, 7785 (2015).Mollica, N. R. et al. Ocean acidification affects coral growth by reducing skeletal density. Proc. Natl Acad. Sci. USA 115, 1754–1759 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fordyce, A. J., Ainsworth, T. D., Leggat, W. & Katherine, M. Light capture, skeletal morphology, and the biomass of corals’ boring endoliths. mSphere 6, e00060–21 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Muscatine, L. The role of symbiotic algae in carbon and energy flux in coral reefs. Ecosyst. World 25, 75–87 (1990).
Google Scholar 
Houlbrèque, F. & Ferrier-Pagès, C. Heterotrophy in tropical scleractinian corals. Biol. Rev. 84, 1–17 (2009).PubMed 
Article 

Google Scholar 
Tremblay, P., Gori, A., Maguer, J. F., Hoogenboom, M. & Ferrier-Pagès, C. Heterotrophy promotes the re-establishment of photosynthate translocation in a symbiotic coral after heat stress. Sci. Rep. 6, 38112 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lesser, M. P. et al. Photoacclimatization by the coral Montastraea cavernosa in the mesophotic zone: light, food, and genetics. Ecology https://doi.org/10.1890/09-0313.1 (2010).Pinheiro, H. T., Eyal, G., Shepherd, B. & Rocha, L. A. Ecological insights from environmental disturbances in mesophotic coral ecosystems. Ecosphere 10, e02666 (2019).Article 

Google Scholar 
Wangpraseurt, D. et al. In vivo microscale measurements of light and photosynthesis during coral bleaching: evidence for the optical feedback loop? Front. Microbiol. 8, 1–12 (2017).Article 

Google Scholar 
Veron, C., Stafford-Smith, M., Turak, E. & DeVantier, L. Corals of the World (Australian Institute of Marine Science, 2000).Loya, Y. The Red Sea coral Stylophora pistillata is an r strategist. Nature 259, 478–480 (1976).Article 

Google Scholar 
Drake, J. L. et al. Physiological and transcriptomic variability indicative of differences in key functions within a single coral colony. Front. Mar. Sci. 8, 768 (2021).Article 

Google Scholar 
Jacques, S., Li, T. & Prahl, S. mcxyz. c, a 3D Monte Carlo simulation of heterogeneous tissues. omlc.org/software/mc/mcxyz (2013).Tuchin, V. V. Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnostics (SPIE PRESS, 2015).Wang, L., Jacques, S. L. & Zheng, L. MCML—Monte Carlo modeling of light transport in multi-layered tissues. Computer Methods Prog. Biomed. 47, 131–146 (1995).CAS 
Article 

Google Scholar 
Jacques, S. L., Wangpraseurt, D. & Kühl, M. Optical properties of living corals determined with diffuse reflectance. Spectrosc. Front Mar. Sci. 6, 1–9 (2019).Article 

Google Scholar 
Hill, R. et al. Spatial heterogeneity of photosynthesis and the effect of temperature-induced bleaching conditions in three species of corals. Mar. Biol. 144, 633–640 (2004).Article 

Google Scholar 
Ritchie, R. J. & Larkum, A. W. D. Modelling photosynthesis in shallow algal production ponds. Photosynthetica 50, 481–500 (2012).CAS 
Article 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using {lme4}. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Luo, D., Ganesh, S. & Koolaard, J. predictmeans: calculate predicted means for linear models. Preprint at https://CRAN.R-project.org/package=predictmeans (2021).Clarke, K. R. Non‐parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

Google Scholar 
Kramer, N., Guan, J., Chen, S., Wangpraseurt, D. & Loya, Y. Morpho-functional traits of the coral Stylophora pistillata enhance light capture for photosynthesis at mesophotic depths. Dryad, Dataset https://doi.org/10.5061/dryad.7d7wm37w7 (2022). More

The generalist accumulates extracellular nitriteWe first tested whether the generalist accumulates substantial extracellular nitrite under our experimental conditions, and thus creates a niche for the specialist. To accomplish this, we grew the generalist alone in bioreactors with anoxic ACS medium amended with 12 mM nitrate as the growth-limiting substrate and measured the extracellular concentrations of nitrate and nitrite over time. We performed these experiments at pH 6.5 (strong nitrite toxicity) and 7.5 (weak nitrite toxicity).We observed a substantial accumulation of extracellular nitrite regardless of the pH (Fig. 3A, B). When grown at pH 6.5 (strong nitrite toxicity), extracellular nitrite accumulated to a concentration comparable to the initial nitrate concentration (measured maximum extracellular nitrite concentration, 11.8 mM; measured initial nitrate concentration, 12.0 mM) and was subsequently consumed to below the detection limit (Fig. 3A). When grown at pH 7.5 (weak nitrite toxicity), extracellular nitrite again accumulated to a concentration comparable to the initial nitrate concentration (measured maximum extracellular nitrite concentration, 11.7 mM; measured initial nitrate concentration, 12.9 mM) and was subsequently consumed to below the detection limit (Fig. 3B). During growth at pH 6.5, substantial nitrite consumption did not begin until a prolonged period of time after nitrate consumption was complete, resulting in a relatively long duration of nitrite availability (Fig. 3A). During growth at pH 7.5, in contrast, substantial nitrite consumption began immediately after nitrate consumption was complete, resulting in a relatively short duration of nitrite availability (Fig. 3B). The longer duration of nitrite availability at pH 6.5 indicates that the duration of the niche created by the generalist for the specialist depends on pH.Fig. 3: Growth and nitrogen oxide dynamics of the generalist in batch culture.We grew the generalist alone in a bioreactor at A pH 7.5 (weak nitrite toxicity) or B pH 6.5 (strong nitrite toxicity) under anoxic conditions with nitrate as the growth-limiting substrate. Blue squares are measured extracellular nitrate concentrations, yellow triangles are measured extracellular nitrite concentrations, and black circles are measured cell densities. We measured extracellular nitrate and nitrite concentrations with IC and cell densities with FC. C Measured durations of nitrite availability for the generalist growing in batch culture. We grew the generalist alone in 96-well microtiter plates under anoxic conditions with nitrate as the growth-limiting substrate. Open symbols are durations of nitrite availability at pH 6.5 and closed symbols are durations of nitrite availability at pH 7.5. Each symbol is an independent biological replicate.Full size imageTo routinely quantify the duration of nitrite availability, we grew the generalist alone with varying amounts of nitrate as the growth-limiting substrate. We then quantified the length of time from when the growth rate with nitrate was maximum to when the growth rate with nitrite was maximum. This cell density-based proxy measure is valid because the growth of the generalist is directly linked to the consumption of nitrate and nitrite (Fig. 3A, B). The cell density of the generalist was initially linearly correlated with nitrate consumption at both pH 6.5 (strong nitrite toxicity) (two-sided Pearson correlation test; r = −0.96, p = 1.5 × 10–8, n = 15) (Fig. 3A) and 7.5 (weak nitrite toxicity) (two-sided Pearson correlation test; r = −1.00, p = 2.2 × 10–16, n = 30) (Fig. 3B). After nitrate was depleted, the cell density of the generalist became linearly correlated with nitrite consumption at both pH 6.5 (strong nitrite toxicity) (two-sided Pearson correlation test; r = −0.97, p = 3 × 10–4, n = 7) (Fig. 3A) and 7.5 (weak nitrite toxicity) (two-sided Pearson correlation test; r = −0.97, p = 6.8 × 10–10, n = 16) (Fig. 3B). We further validated our cell density-based approach by testing for concordance with our IC-based direct measures of the duration of nitrite availability. We observed a significant positive and linear relationship between the cell density- and IC-based measures (two-sided Pearson correlation test; r = 0.999, p = 0.023, n = 3) (linear regression model; slope = 1.19, intercept = −2.31, r2 = 0.99) (Supplementary Fig. S2), which further validates our cell density-based approach to routinely estimate the duration of nitrite availability.Using our cell density-based approach, we found that the duration of nitrite availability was significantly longer at pH 6.5 (strong nitrite toxicity) than at 7.5 (weak nitrite toxicity) regardless of the initial nitrate concentration (two-sample two-sided t-tests; Holm-adjusted p  0.92, Holm-adjusted p  0.6), and thus followed model predictions (Fig. 4A). However, when the specialist was initially rare (measured initial log rS/Gs of –3.19, –2.65, and –0.88), the relative abundances of the specialist continuously decreased between the third and twelfth transfers (Mann–Kendall trend tests; tau = –0.61 to –0.89, p  0 were dominated by phenotype C (dominant ancestral phenotype with a long time delay between nitrate and nitrite consumption), while generalist isolates from co-cultures with initial rS/Gs  More

Erwin, D. H. & Tweedt, S. Ecological drivers of the Ediacaran-Cambrian diversification of Metazoa. Evol. Ecol. 26, 417–433 (2012).Article 

Google Scholar 
Laflamme, M., Darroch, S. A., Tweedt, S. M., Peterson, K. J. & Erwin, D. H. The end of the Ediacara biota: Extinction, biotic replacement, or Cheshire Cat?. Gondwana Res. 23, 558–573 (2013).ADS 
Article 

Google Scholar 
Mángano, M. G. & Buatois, L. A. Decoupling of body-plan diversification and ecological structuring during the Ediacaran-Cambrian transition: Evolutionary and geobiological feedbacks. Proc. R. Soc. B-Biol. Sci. 281, 20140038 (2014).Article 

Google Scholar 
Mángano, M. G. & Buatois, L. A. The Cambrian revolutions: Trace-fossil record, timing, links and geobiological impact. Earth-Sci. Rev. 173, 96–108 (2017).ADS 
Article 

Google Scholar 
Mángano, M. G. & Buatois, L. A. The rise and early evolution of animals: Where do we stand from a trace-fossil perspective?. Interface Focus 10, 20190103 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Darroch, S. A. et al. Biotic replacement and mass extinction of the Ediacara biota. Proc. R. Soc. B 282, 20151003 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Darroch, S. A., Smith, E. F., Laflamme, M. & Erwin, D. H. Ediacaran extinction and Cambrian explosion. Trends Ecol. Evol. 33, 653–663 (2018).PubMed 
Article 

Google Scholar 
Schiffbauer, J. D. et al. The latest Ediacaran Wormworld fauna: Setting the ecological stage for the Cambrian explosion. GSA Today 26, 4–11 (2016).Article 

Google Scholar 
Zamora, S., Deline, B., Javier Álvaro, J. & Rahman, I. A. The Cambrian Substrate Revolution and the early evolution of attachment in suspension-feeding echinoderms. Earth-Sci. Rev. 171, 478–491 (2017).ADS 
Article 

Google Scholar 
Hantsoo, K. G., Kaufman, A. J., Cui, H., Plummer, R. E. & Narbonne, G. M. Effects of bioturbation on carbon and sulfur cycling across the Ediacaran-Cambrian transition at the GSSP in Newfoundland, Canada. Can. J. Earth Sci. 55, 1240–1252 (2018).ADS 
CAS 
Article 

Google Scholar 
Boyle, R. A., Dahl, T. W., Bjerrum, C. J. & Canfield, D. E. Bioturbation and directionality in Earth’s carbon isotope record across the Neoproterozoic-Cambrian transition. Geobiology 16, 252–278 (2018).CAS 
PubMed 
Article 

Google Scholar 
van de Velde, S., Mills, B. J., Meysman, F. J., Lenton, T. M. & Poulton, S. W. Early Palaeozoic ocean anoxia and global warming driven by the evolution of shallow burrowing. Nat. Commun. 9, 2554 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gougeon, R. C., Mangano, M. G., Buatois, L. A., Narbonne, G. M. & Laing, B. A. Early Cambrian origin of the shelf sediment mixed layer. Nat. Commun. 9, 1909 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Seilacher, A. & Pflüger, F. In Biostabilization of Sediments (eds. Krumbein W. E., Peterson D. M., & Stal L. J.) 97–105 (Bibliotheks und Informationsystem der Carl von Ossietzky Universität, 1994).Seilacher, A. Biomat-related lifestyles in the Precambrian. Palaios 14, 86–93 (1999).ADS 
Article 

Google Scholar 
Buatois, L. A. et al. Quantifying ecospace utilization and ecosystem engineering during the early Phanerozoic: The role of bioturbation and bioerosion. Sci. Adv. 6, eabb0618 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hagadorn, J. W. & Bottjer, D. J. Restriction of a late Neoproterozoic biotope; suspect-microbial structures and trace fossils at the Vendian-Cambrian transition. Palaios 14, 73–85 (1999).ADS 
Article 

Google Scholar 
Buatois, L. A. & Mangano, M. G. Early colonization of the deep sea: Ichnologic evidence of deep-marine benthic ecology from the Early Cambrian of northwest Argentina. Palaios 18, 572–581 (2003).ADS 
Article 

Google Scholar 
Clausen, S., Álvaro, J. J. & Zamora, S. Replacement of benthic communities in two Neoproterozoic-Cambrian subtropical-to-temperate rift basins, High Atlas and Anti-Atlas, Morocco. J. Afr. Earth Sci. 98, 72–93 (2014).ADS 
Article 

Google Scholar 
Mángano, M. G. & Buatois, L. A. In The Trace-Fossil Record of Major Evolutionary Events: Volume 1: Precambrian and Paleozoic (eds. Mángano M. G. & Buatois L. A.) 73–126 (Springer Netherlands, 2016).Bayet-Goll, A., Buatois, L. A., Mangano, M. G. & Daraei, M. The interplay of environmental constraints and bioturbation on matground development along the marine depositional profile during the Ordovician Radiation. Geobiology 20, 33–270 (2022).Article 
CAS 

Google Scholar 
Bayet-Goll, A., Daraei, M., Geyer, G., Bahrami, N. & Bagheri, F. Environmental constraints on the distribution of matground and mixground ecosystems across the Cambrian Series 2–Miaolingian boundary interval in Iran: A case study for the central sector of northern Gondwana. J. Afr. Earth Sci. 176, 104120 (2021).Article 

Google Scholar 
Minter, N. J. et al. In The Trace-Fossil Record of Major Evolutionary Events: Volume 1: Precambrian and Paleozoic (eds. Mángano M. G. & Buatois L. A.) 157–204 (Springer Netherlands, 2016).Minter, N. J. et al. Early bursts of diversification defined the faunal colonization of land. Nat. Ecol. Evol. 1, 0175 (2017).Article 

Google Scholar 
Nemec, W. & Steel, R. J. In Fan Deltas: sedimentology and tectonic settings (eds. Nemec W. & Steel R. J.) 3–13 (Blackie and Son, 1988).Postma, G. An analysis of the variation in delta architecture. Terra Nova 2, 124–130 (1990).ADS 
Article 

Google Scholar 
Prior, D. B. & Bornhold, B. D. Submarine sedimentation on a developing Holocene fan delta. Sedimentology 36, 1053–1076 (1989).ADS 
Article 

Google Scholar 
Piper, D. J. W., Kontopoulos, N., Anagnostou, C., Chronis, G. & Panagos, A. G. Modern fan deltas in the western Gulf of Corinth, Greece. Geo-Mar. Lett. 10, 5–12 (1990).ADS 
Article 

Google Scholar 
Rasmussen, H. Nearshore and alluvial facies in the Sant Llorenç del Munt depositional system: Recognition and development. Sediment. Geol. 138, 71–98 (2000).ADS 
Article 

Google Scholar 
Steel, R., Rasmussen, H., Eide, S., Neuman, B. & Siggerud, E. Anatomy of high-sediment supply, transgressive tracts in the Vilomara composite sequence, Sant LlorencË del Munt, Ebro Basin, NE Spain. Sediment. Geol. 138, 125–142 (2000).ADS 
Article 

Google Scholar 
Zavala, C. et al. Deltas: A new classification expanding Bates’s concepts. J. Palaeogeogr. 10, 23 (2021).ADS 
Article 

Google Scholar 
Ekdale, A. A. & Lewis, D. W. Trace fossils and paleoenvironmental control of ichnofacies in a late Quaternary gravel and loess fan delta complex, New Zealand. Palaeogeogr. Palaeoclimatol. Palaeoecol. 81, 253–279 (1991).Article 

Google Scholar 
Buatois, L. A. & Mángano, M. G. Ichnology: Organism-substrate Interactions in Space and Time (Cambridge University Press, 2011).Book 

Google Scholar 
Hovikoski, J., Uchman, A., Alsen, P. & Ineson, J. Ichnological and sedimentological characteristics of submarine fan-delta deposits in a half-graben, Lower Cretaceous Palnatokes Bjerg Formation, NE Greenland. Ichnos 26, 28–57 (2019).Article 

Google Scholar 
Sendra, J., Reolid, M. & Reolid, J. Palaeoenvironmental interpretation of the Pliocene fan-delta system of the Vera Basin (SE Spain): Fossil assemblages, ichnology and taphonomy. Palaeoworld 29, 769–788 (2020).Article 

Google Scholar 
Kreis, L. K. et al. Lower Paleozoic map series: Saskatchewan. Miscellaneous Report 2004–8 (CD-ROM) (Saskatchewan Industry and Resources, 2004).
Google Scholar 
Marsh, A. & Love, M. In Saskatchewan Ministry of the Economy, Saskatchewan Geological Survey, Saskatchewan Geological Survey Vol. Open File 2014-1, set of 156 maps (2014).Sawatzky, H. B., Agarwal, R. G. & Wilson, W. Helium prospects of southwest Saskatchewan. 26 (Saskatchewan Department of Mineral Resources, 1960).Fyson, W. K. Deadwood and Winnipeg stratigraphy in southwestern Saskatchewan. Report 64, 37 (Saskatchewan Department of Mineral Resources, 1961).Kent, D. M. Paleotectonic controls on sedimentation in northern Williston Basin area, Saskatchewan. AAPG Bull. 67, 1345–1345 (1983).
Google Scholar 
Kent, D. M. & Haidl, F. M. The distribution of Ashern and Winnipegosis strata (Middle Devonian) on the Swift Current Platform, southern Saskatchewan. Summary of Investigations, Miscellaneous Report 93-4, 201–206 (Saskatchewan Geological Survey, Saskatchewan Energy and Mines, 1993).MacEachern, J. A., Zaitlin, B. A. & Pemberton, S. G. A sharp-based sandstone of the Viking Formation, Joffre Field, Alberta, Canada; criteria for recognition of transgressively incised shoreface complexes. J. Sediment. Res. 69, 876–892 (1999).ADS 
Article 

Google Scholar 
Pemberton, S. G., Frey, R. W. & Bromley, R. G. The ichnotaxonomy of Conostichus and other plug-shaped ichnofossils. Can. J. Earth Sci. 25, 866–892 (1988).ADS 
Article 

Google Scholar 
Hall, J. & Whitfield, R. P. Paleontology. In US Geol. Expl. 40th Par. Rept. 4, 197–302 (1877).Walcott, C. D. Cambrian and Lower Ozarkian trilobites. Smithson. Misc. Coll. 75, 53–60 (1924).
Google Scholar 
Meek, F. B. Preliminary paleontological report, consisting of lists and descriptions of fossils, with remarks on the ages of the rocks in which they were found. In U. S. Geol. Surv. Terr. 6th Ann. Rept., 429–518 (1873).Walcott, C. D. Cambrian geology and paleontology of Cambrian trilobites. Smithson. Misc. Coll. 64, 157–258 (1916).
Google Scholar 
Harding, S. C. & Ekdale, A. A. Trace fossils and glauconitic pellets provide insight into Cambrian siliciclastic marine environments. Palaios 33, 256–265 (2018).ADS 
Article 

Google Scholar 
Shillito, A. P. & Davies, N. S. The Tumblagooda Sandstone revisited: Exceptionally abundant trace fossils and geological outcrop provide a window onto Palaeozoic littoral habitats before invertebrate terrestrialization. Geol. Mag. 157, 1939–1970 (2020).ADS 
Article 

Google Scholar 
Shillito, A. P. & Davies, N. S. Archetypally Siluro-Devonian ichnofauna in the Cowie Formation, Scotland: Implications for the myriapod fossil record and Highland Boundary Fault Movement. Proc. Geol. Assoc. 128, 815–828 (2017).Article 

Google Scholar 
Buatois, L. A. et al. The invasion of the land in deep time: integrating Paleozoic records of paleobiology, ichnology, sedimentology, and geomorphology. Integr. Comp. Biol. 0, 1–35. https://doi.org/10.1093/icb/icac059 (2022).Davies, N. S. & Gibling, M. R. Paleozoic vegetation and the Siluro-Devonian rise of fluvial lateral accretion sets. Geology 38, 51–54 (2010).ADS 
Article 

Google Scholar 
McMahon, W. J. & Davies, N. S. Evolution of alluvial mudrock forced by early land plants. Science 359, 1022–1024 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fedo, C. M. & Cooper, J. D. Braided fluvial to marine transition; the basal Lower Cambrian Wood Canyon Formation, southern Marble Mountains, Mojave Desert, California. J. Sediment. Res. 60, 220–234 (1990).
Google Scholar 
Eyre, B. Early Cambrian alluvial fan-deltas in the Georgina Basin, Australia. Aust. J. Earth Sci. 41, 27–36 (1994).ADS 
Article 

Google Scholar 
MacNaughton, R. B., Dalrymple, R. & Narbonne, G. M. Early Cambrian braid-delta deposits, MacKenzie Mountains, north-western Canada. Sedimentology 44, 587–609 (1997).ADS 
Article 

Google Scholar 
Muhlbauer, J. G. & Fedo, C. M. Architecture of a river-dominated, wave- and tide-influenced, pre-vegetation braid delta: Cambrian middle member of the Wood Canyon Formation, southern Marble Mountains, California, U.S.A. J. Sediment. Res. 90, 1011–1036 (2020).ADS 
Article 

Google Scholar 
McMahon, W. J., Davies, N. S. & Went, D. J. Negligible microbial matground influence on pre-vegetation river functioning: Evidence from the Ediacaran-Lower Cambrian Series Rouge, France. Precambrian Res. 292, 13–34 (2017).ADS 
CAS 
Article 

Google Scholar 
Mikuláš, R. Trace fossils from the Paseky Shale (Early Cambrian, Czech Republic). J. Czech Geol. Soc. 40, 37–44 (1995).
Google Scholar 
MacNaughton, R. B. & Narbonne, G. M. Evolution and ecology of Neoproterozoic-Lower Cambrian trace fossils, NW Canada. Palaios 14, 97–115 (1999).ADS 
Article 

Google Scholar 
Buatois, L. A. et al. Colonization of brackish-water systems through time: Evidence from the trace-fossil record. Palaios 20, 321–347 (2005).ADS 
Article 

Google Scholar 
Hofmann, R., Mángano, M. G., Elicki, O. & Shinaq, R. Paleoecologic and biostratigraphic significance of trace fossils from shallow- to marginal-marine environments from the Middle Cambrian (Stage 5) of Jordan. J. Paleontol. 86, 931–955 (2012).Article 

Google Scholar 
Mángano, M. G., Buatois, L. A., Hofmann, R., Elicki, O. & Shinaq, R. Exploring the aftermath of the Cambrian explosion: The evolutionary significance of marginal- to shallow-marine ichnofaunas of Jordan. Palaeogeogr. Palaeoclimatol. Palaeoecol. 374, 1–15 (2013).Article 

Google Scholar 
Mángano, M. G. et al. Were all trilobites fully marine? Trilobite expansion into brackish water during the early Palaeozoic. Proc. R. Soc. Lond. B Biol. Sci. 288, 20202263 (2021).
Google Scholar 
Siggerud, E. I. H. & Steel, R. J. Architecture and trace-fossil characteristics of a 10000–20000 Year, fluvial-to-marine sequence, SE Ebro Basin, Spain. J. Sediment. Res. 69, 365–383 (1999).ADS 
Article 

Google Scholar 
Lockley, M. G., Rindsberg, A. K. & Zeiler, R. M. The paleoenvironmental significance of the nearshore Curvolithus ichnofacies. Palaios 2, 255–262 (1987).ADS 
Article 

Google Scholar 
Folk, R. L. Petrology of Sedimentary Rocks (Hemphill Publishing Company, 1980).
Google Scholar 
Wentworth, C. K. A scale grade and class terms for clastic sediments. J. Geol. 30, 377–392 (1922).ADS 
Article 

Google Scholar 
Pettijohn, F. J., Potter, P. E. & Siever, R. Sand and Sandstone 2nd edn. (Springer, 1987).Book 

Google Scholar 
Ingram, R. L. Terminology for the thickness of stratification and parting units in sedimentary rocks. Geol. Soc. Am. Bull. 65, 937–938 (1954).ADS 
Article 

Google Scholar 
Reineck, H.-E. Sedimentgefüge im Bereich der südliche Nordsee. Abh. Senckb. Naturforsch. Ges. 505, 1–138 (1963).
Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Needham, D. M. et al. The microbiome of a bacterivorous marine choanoflagellate contains a resource-demanding obligate bacterial associate. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01174-0 (2022). More

Study systemWe conducted our research at the Jornada Caves, New Mexico, USA from 8 to 29 June 2018. This remote cave site on private land in the Chihuahuan Desert occupies an elevated volcanic plateau at approximately 1500 m altitude, with the remains of collapsed lava tubes forming a deep canyon with cave and arch features. The site was chosen because of the presence of a population of Swainson’s Hawks (Buteo swainsoni) that predates the population of Mexican Free-tailed Bats (Tadarida brasiliensis) that emerge from the caves en masse daily throughout the summer39. The bats migrate to the site during their breeding season from May to September40, and use the caves as a day roost before flying to their feeding grounds at dusk. The population consists of a maternal colony of approximately 700,000 to 900,000 bats which inhabit two connected caves named North and South. The largest and most reliable emergence was from the South cave, occurring every evening without exception. Emergence from the North cave was less reliable, with no bats emerging at all on some nights during the first week of observations. The numbers of bats were topped up in the second week by new arrivals, and emergence from the North cave was reliable thereafter. Emergence began at a variable time between approximately 18:30 and 20:00 MDT and lasted from 10 to 25 min depending on the number of bats emerging. Sunset was between 20:16 and 20:21 MDT, so the bats usually emerged in broad daylight. During the third week of observations, a substantial second emergence usually occurred at each cave, beginning around 0.5 h after the end of the first emergence, when fewer hawks were present. No ethical issues were identified by the Animal Welfare and Ethical Review Board of the University of Oxford’s Department of Zoology. We attended only as observers, and never entered the caves, so the risk of causing disturbance as the bats emerged was low41.Video observationsWe recorded video of the hawks attacking the bats every evening from 8 to 29 June 2018, except for one evening that had to be missed due to bad weather. We used three pairs of high-definition video cameras (Lumix DMC-FZ1000/2500, Panasonic Corporation, Osaka, Japan) to enable reconstruction of the three-dimensional flight trajectories of the hawks and bats, setting the camera lens to its widest zoom setting. We recorded 25 Hz video at 3840 × 2160 pixels for the first three days and 50 Hz video at 1920 × 1080 pixels for the remainder of the study (Movie S1). This higher frame rate proved necessary to facilitate tracking of the bats’ erratic movements but was traded off against lower spatial resolution. Each camera pair was set in widely spaced stereo configuration to enable three-dimensional reconstruction of the attacks, with a baseline distance of 16 to 27 m. The cameras were mounted on tripods which were adjusted to the same height using an optical level kit (GOL20D/BT160/GR500, Robert Bosch GmbH, Gerlingen, Germany). We used the same optical level kit to measure the baseline distance between the cameras.We set up two camera pairs facing approximately north and south across the South cave for the duration of the study. As the swarm’s overall flight direction was variable and influenced by the wind, we positioned the north- and south-facing camera pairs to allow them to be panned from northeast to northwest and from southeast to southwest, respectively. This enabled us to cover most flight directions, except due east (where the bats rarely flew) and due west (which was subject to glare). We set up a third camera pair to view the emergence that occurred from the North cave from the second week onward. When leaving the North cave, the bats usually flew along the lava tube and beneath a rock arch before climbing out of the canyon. We therefore positioned the cameras close to where the swarm began climbing out above the canyon rim, aiming to capture attacks as the hawks swooped low over the canyon.The hawks consistently appeared within a few minutes of the start of emergence, which enabled us to observe the general direction in which the bat swarm was emerging, and to reorient the cameras to view the swarm before the attacks began. As soon as the bats began emerging, the cameras were turned on and left to record. To begin with, all fieldworkers retreated into make-shift hides, but these were gradually phased out for reasons of practicality. The birds quickly became habituated to our presence, venturing close to the cave even when fieldworkers were present. Each attack began with the hawk approaching the swarm in level flight or stooping in from above. This was followed by fast flight through the stream of bats, with one or more attempts made to grab a bat using a pitch-up, pitch-down, or rolling grab manoeuvre with the legs and talons extended (Movie S1). If the first attack was unsuccessful, then the hawks would usually perform further short-range swoops through the stream until they made a catch. Once a bat was caught, the hawk would drift away from the swarm, to consume its prey on the wing.VideogrammetryWe synchronized the videos using the DLTdv5 video tracking toolbox42 in MATLAB R2020a (MathWorks Inc., Natick, MA). To do so, we matched the complex motions involved in the hawks’ attack manoeuvres visually between videos, and applied the relevant frame offset to synchronize them to the nearest frame. To verify the accuracy of this method, we compared the position of the hawk’s wings between the two videos for the three pairs of frames used for synchronization, and again for the three pairs of frames recorded 50 frames later (Fig. S3). This comparison shows that the frame synchronization remains stable as expected over this 1 s time interval, for the randomly selected flight displayed in Fig. S3. Nevertheless, because the cameras’ shutters were not electronically synchronized, this post hoc procedure can only guarantee synchronization of the frames to within ±0.01 s at the 50 Hz frame rate (see Fig. S3). To assess the sensitivity of our trajectory reconstructions to this remaining synchronization error, we compared the flight trajectories that we had already reconstructed with those that would have been reconstructed had the videos been shifted ±1 frame (Fig. S4). This comparison shows that the displacement of the trajectories resulting from a synchronization error of ±1 frame is small in comparison to their path length, and that their shape remains approximately the same, even for the two stooping flight trajectories plotted in Fig. S4.We used the DLTdv5 toolbox to identify the pixel coordinates of the hawk in both videos within a pair, manually tracking the visual centre of the subject’s body from the point at which it appeared in both cameras up to the point of interception. We used the same method to track the bat that the hawk caught or attempted to catch during the terminal attack sequences that we recorded at close range. The bats were too distant to be tracked individually in recordings of the hawks’ long-range approaches, but the point of actual or attempted capture was nevertheless obvious from the hawks’ flight behaviour. We aimed to reconstruct all attack trajectories that were captured by both cameras within a pair. We were able to reconstruct n = 62 terminal attack trajectories, drawn from n = 50 separate attack flights (i.e. n = 12 of these comprised follow-on attack passes, up to a maximum of four consecutive passes made in cases where the first attack pass was unsuccessful; see Supporting Data and Code for details). We were also able to reconstruct n = 26 long-range approaches. Hence, as the population of hawks peaked at approximately 20 birds, there will have been repeated sampling within individuals in both cases.We calibrated the cameras by matching 15 points across both frames, including background features and points on the hawk, which we selected with the objective of covering as much of the capture volume as possible. The image coordinates of these calibration points were exported from the DLTdv5 toolbox into custom-written software in MATLAB, which solved the camera collinearity equations43 using a nonlinear least squares bundle adjustment implemented using the MATLAB Optimization Toolbox R2020a (see Supporting Data and Code). The bundle adjustment routine identifies jointly optimal estimates of the camera calibration parameters and unknown spatial coordinates of the calibration points, by minimizing the sum of the squared reprojection error of the associated image points. The reprojection error of an image point matched across camera views is defined as the difference between its measured image coordinates and those expected under the camera calibration model given its estimated spatial coordinates. This nonlinear approach enabled us to self-calibrate the cameras using identified features of the environment, whilst also incorporating prior knowledge of the intrinsic and extrinsic camera parameters. This in turn avoided the need to move a known calibration object through the very large imaging volume.We set the calibrated baseline distance between the cameras equal to the measurement that we made of this in the field using the optical level. We fixed the focal length of each camera at 1468.9 pixels for the 1920 × 1080 recordings and at 3918.5 pixels for the 3840 × 2160 recordings. These values were estimated using the Camera Calibrator toolbox in MATLAB, from a set of 20 calibration images of a checkerboard pattern held in front of the camera. Lens distortions were found to be minimal, and we therefore assumed a central perspective projection43 in which we assumed no lens distortion and no principal point offset with respect to the camera sensor. The resulting stereo camera calibration was used to solve for the spatial coordinates of the tracked hawk and bat in MATLAB. This is a least squares solution, in the sense that it minimizes the sum of the squared reprojection error for each image point matched across stereo video frames. We therefore report the root mean square (RMS) reprojection error as a check on the accuracy of the calibrations and reconstructions.For the terminal attack trajectories filmed at close range, the mean RMS reprojection error of the 16 calibrations was 0.73 ± 0.35 pixels, whilst for the reconstructed flight trajectories it was 1.22 ± 1.18 pixels for the hawks and 1.87 ± 2.39 pixels for the bats over all n = 62 flights (mean ± SD). For the long-range approaches filmed at a distance, the RMS reprojection error of the 18 calibrations was 0.53 ± 0.61 pixels, whilst for the reconstructed flight trajectories it was 1.08 ± 1.07 pixels for the hawks over all n = 28 flights (mean ± SD). The sub-pixel reprojection error that we achieved in the calibrations is appropriate to the method. The higher reprojection error of the reconstructions is also to be expected, because whereas the bundle adjustment optimizes the camera calibration parameters jointly with the estimated spatial coordinates of the calibration points, the calibration is held fixed in the reconstructions. In addition, any spatiotemporal error in the matching of points across camera frames will manifest itself as reprojection error in the reconstructions.The foregoing calibration reconstructs the spatial coordinates of the matched image points in a Cartesian coordinate system aligned with the sensor axes of one of the cameras. To aid visualization and interpretation of the flight trajectories, we therefore transformed the spatial coordinates of the hawks and bats into an Earth axis system in which the z axis was vertical. To do so, we filmed and reconstructed the ballistic trajectory of a small rock thrown high into the air through the volume of stereo overlap. We identified the image coordinates of the peak of its parabolic path, together with the image coordinates of two flanking points located ±20 or 25 frames to either side. We took the line dropped from the peak of the parabola perpendicular to the line connecting the two flanking points to define the direction of gravitational acceleration. We then used this to identify the rotation needed to transform the spatial coordinates of the hawks and bats into Earth axes with the z axis as vertical. Finally, we made use of the fact that the two cameras in each pair were fixed at the same height to verify the transformation to Earth axes. For the 16 calibrations used to reconstruct the terminal attack trajectories, the inclination of the baseline between the cameras in Earth axes had a median absolute value of just 1.2˚ (1st, 3rd quartiles: 0.8˚, 2.2˚), providing independent validation of the calibration method that we used.Trajectory analysisAll trajectory analysis was done using custom-written software in MATLAB R2020a (see Supporting Data and Code). We used piecewise cubic Hermite interpolation of the reconstructed trajectories to estimate the spatial coordinates of the hawk or bat for any occasional frames in which this was obscured. We then smoothed the trajectories using quintic spline fitting. For the long-range approaches, we used a spline tolerance designed to remove an RMS spatial position error of 0.5 m, corresponding approximately to the wing length of a hawk. For the terminal attack trajectories, we used a tolerance designed to remove an RMS position error of 0.12 m, corresponding approximately to the wing length of a bat. These values were chosen as representative estimates of the accuracy with which it was possible to match points across frames at long and close range, respectively. Finally, we differentiated and evaluated the splines analytically to estimate the velocity and acceleration of the bird and bat at an up-sampled frequency of 2 kHz. This ensured a suitably small integration step size for the subsequent numerical simulations. On average, the hawks flew faster than the bats (Fig. S5A), so were tracked over longer distances (Fig. S5B), but with considerable overlap in their respective distributions.We simulated the hawk’s attack trajectory in the Earth axes using a guidance law of the form:$${{{{{bf{a}}}}}}(t){{{{{boldsymbol{=}}}}}}N{{{{{boldsymbol{omega }}}}}}(t-tau )times {{{{{bf{v}}}}}}(t){{{{{boldsymbol{-}}}}}}K{{{{{boldsymbol{delta }}}}}}(t-tau )times {{{{{bf{v}}}}}}(t)$$
(1)
where a is the hawk’s commanded centripetal acceleration, v is its velocity, ω is the angular velocity of the line-of-sight r from the hawk to its target, and δ is the deviation angle between r and v, written in vector form with δ mutually perpendicular to r and v. Here, t is time, τ is a fixed time delay, and N and K are guidance constants. With K = 0, Eq. 1 describes proportional navigation (PN), whereas with N = 0, Eq. 1 describes pure proportional pursuit (PP). In the case that K ≠ 0 and N ≠ 0, Eq. 1 describes mixed PN + PP guidance. Dividing through by the hawk’s speed (v=left|{{{{{bf{v}}}}}}right|) converts the commanded centripetal acceleration to the commanded angular velocity. It can therefore be seen that Eq. 1 generalizes, in vector form, the PN + PP guidance law that is written as (dot{gamma }(t)=Ndot{lambda }(t-tau )-Kdelta (t-tau )) in the main text, where the magnitudes of the scalar turn rate, scalar line-of-sight rate, and scalar deviation angle are given respectively as (left|dot{gamma }right vert=left|{{{{{bf{a}}}}}}right|/left|{{{{{bf{v}}}}}}right|), (left|dot{lambda }right vert=left|{{{{{boldsymbol{omega }}}}}}right|), and (left|deltaright vert=left|{{{{{boldsymbol{delta }}}}}}right|).Our simulations make use of the kinematic equations:$${{{{{bf{r}}}}}}={hat{{{{{{bf{x}}}}}}}}_{{{{{{rm{T}}}}}}}-{{{{{bf{x}}}}}}$$
(2)
$${{{{{boldsymbol{omega }}}}}}=frac{{{{{{bf{r}}}}}},times left({hat{{{{{{bf{v}}}}}}}}_{{{{{{rm{T}}}}}}}-{{{{{bf{v}}}}}}right)}{{left|{{{{{bf{r}}}}}}right|}^{{{{{{bf{2}}}}}}}}$$
(3)
$${{{{{boldsymbol{delta }}}}}}=left({{{cos }}}^{-1}frac{{{{{{bf{r}}}}}},cdot, {{{{{bf{v}}}}}}}{left|{{{{{bf{r}}}}}}right|,left|{{{{{bf{v}}}}}}right|}right)left(frac{{{{{{bf{r}}}}}},times {{{{{bf{v}}}}}}}{left|{{{{{bf{r}}}}}},times {{{{{bf{v}}}}}}right|}right)$$
(4)
where x is the simulated position of the hawk, and where ({hat{{{{{{bf{x}}}}}}}}_{{{{{{rm{T}}}}}}}) and ({hat{{{{{{bf{v}}}}}}}}_{{{{{{rm{T}}}}}}}) are the measured position and velocity of the target with respect to the Earth axes. Our simulations are implemented in discrete time by coupling the guidance law (Eq. 1) with the kinematic equations (Eqs. 2–4) using the difference equations:$${{{{{{bf{x}}}}}}}_{n+1}={{{{{{bf{x}}}}}}}_{n}+Delta t,{{{{{{bf{v}}}}}}}_{n}.$$
(5)
$${{{{{{bf{v}}}}}}}_{n+1}={hat{v}}_{n+1},frac{{{{{{{bf{v}}}}}}}_{n}+Delta t,{{{{{{bf{a}}}}}}}_{n}}{left|{{{{{{bf{v}}}}}}}_{n}+Delta t,{{{{{{bf{a}}}}}}}_{n}right|}$$
(6)
where the subscript notation indicates the values of the variables at successive time steps, such that ({t}_{n+1}={t}_{n}+Delta t), and where (hat{v}) is the hawk’s measured groundspeed. The simulations were initiated given the hawk’s measured initial position ({{{{{{bf{x}}}}}}}_{0}={hat{{{{{{bf{x}}}}}}}}_{0}) and velocity ({{{{{{bf{v}}}}}}}_{0}={hat{{{{{{bf{v}}}}}}}}_{0}), and were used to predict the trajectory that it would follow under the guidance law (Eq. 1) parameterized by the guidance constants N and K, and time delay τ. Note that Eq. 6 matches the hawk’s simulated groundspeed (v=left|{{{{{bf{v}}}}}}right|) to its measured groundspeed (hat{v}) at all times, such that the guidance law is only used to command turning. We verified that the step size of our simulations ((Delta t=5times {10}^{-4}) s) was small enough to guarantee the numerical accuracy of the fitted guidance parameters and prediction error to the level of precision at which they are reported in the Results.We defined the prediction error η of each simulation as the mean absolute distance between the measured and simulated flight trajectories:$$eta=frac{1}{k}mathop{sum }limits_{n=1}^{k}left|{{{{{{bf{x}}}}}}}_{n}-{hat{{{{{{bf{x}}}}}}}}_{n}right|$$
(7)
where (hat{{{{{{bf{x}}}}}}}) is the hawk’s simulated position, and k is the number of time steps in the simulation. We fitted the guidance constants K and/or N under the various combinations of guidance law (i.e. PN, PP or PN + PP) and target definition (i.e. measured bat position, final bat position, final hawk position) for delays of 0 ≤ τ ≤ 0.1 s at 0.02 s spacing corresponding to the inter-frame interval. In each case, we used a Nelder–Mead simplex algorithm in MATLAB to find the value of K and/or N that minimised the prediction error η for each flight at the given time delay τ. To ensure that we fitted the same section of flight for all time delays 0 ≤ τ ≤ 0.1 s, we began each simulation from 0.1 s after the first point on the trajectory, and ended the simulation at the time of intercept or near-miss. However, as we found the best-fitting delay to be τ = 0, we subsequently re-fitted the simulations with no delay to begin from the first point on the trajectory and report these simulations in the Results. For the terminal attack trajectories, we took the first point on the trajectory to be the earliest point from which it was possible to track the bat that the hawk caught or attempted to catch, and took the time of intercept or near-miss to be the time at which the measured distance between the hawk and bat was minimal. For the long-range approaches, we tested a range of alternative start points from 1.0 s up to a maximum of 20.0 s before the observed grab manoeuvre, in 0.2 s intervals, to accommodate the fact that the hawk could sometimes be tracked for longer than it appeared to be engaged in directed attack behaviour.Statistical analysisAll statistics were computed using MATLAB R2020a. As the hawks could not be individually identified, we were unable to control for repeated measures from the same individual, and therefore treated each attack trajectory as an independent sample. Because the distributions of the model parameters and errors are skewed (Fig. 2), we report their median, denoted using tilde notation, together with a bias-corrected and accelerated bootstrap 95% confidence interval (CI) computed using 100,000 resamples44. For robustness, we use two-tailed sign tests to compare their distributions between different guidance models and target definitions. We state sample proportions together with a 95% confidence interval (CI) computed using the Clopper–Pearson method. We used a two-tailed Fisher’s exact test to compare the odds of success in attacks on lone bats versus attacks on the swarm. Following our previous observational study18, bats classified as lone bats were judged to be flying >5 body lengths from their nearest neighbours and/or appeared to be flying in a different direction to the coordinated members of the swarm (Table S3).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Garber, P. A. Foraging strategies among living primates. Annu. Rev. Anthropol. 16, 339–364 (1987).Article 

Google Scholar 
Stephens, D. W. & Krebs, J. K. Foraging Theory (Princeton University Press, 1987).Book 

Google Scholar 
Felton, A. M. et al. Nutritional ecology of Ateles chamek in lowland Bolivia: How macronutrient balancing influences food choices. Int. J. Primatol. 30, 675–696 (2009).Article 

Google Scholar 
Marshall, A. J. & Wrangham, R. W. Evolutionary consequences of fallback foods. Int. J. Primatol. 28, 1219–1235 (2007).Article 

Google Scholar 
Rothman, J. M., Raubenheimer, D., Bryer, M. A. H., Takahashi, M. & Gilbert, C. C. Nutritional contributions of insects to primate diets: Implications for primate evolution. J. Hum. Evol. 71, 59–69 (2014).PubMed 
Article 

Google Scholar 
Felton, A. M. et al. Protein content of diets dictates the daily energy intake of a free-ranging primate. Behav. Ecol. 20, 685–690 (2009).Article 

Google Scholar 
Clare, E. L., Symondson, W. O. C. & Fenton, M. B. An inordinate fondness for beetles? Variation in seasonal dietary preferences of night-roosting big brown bats (Eptesicus fuscus). Mol. Ecol. 23, 3633–3647 (2014).PubMed 
Article 

Google Scholar 
Stevenson, P. R., Quinones, M. J. & Ahumada, J. A. Influence of fruit availability on ecological overlap among four neotropical primates at Tinigua National Park, Colombia. Biotropica 32, 533–544 (2000).Article 

Google Scholar 
Chapman, C. Patterns of foraging and range use by three species of neotropical primates. Primates 29, 177–194 (1988).Article 

Google Scholar 
Felton, A. M., Felton, A., Lindenmayer, D. B. & Foley, W. J. Nutritional goals of wild primates. Funct. Ecol. 23, 70–78 (2009).Article 

Google Scholar 
Kay, R. On the use of anatomical features to infer foraging behavior in extinct primates. In Adaptations for Foraging in Nonhuman Primates (eds Rodman, P. & Cant, J.) 21–53 (Columbia University Press, 1984).Chapter 

Google Scholar 
Bravo, S. P. Seed dispersal and ingestion of insect-infested seeds by black howler monkeys in flooded forests of the Parana River, Argentina: Insect-infested seed ingestion and dispersal. Biotropica 40, 471–476 (2008).Article 

Google Scholar 
Deluycker, A. M. Insect prey foraging strategies in Callicebus oenanthe in Northern Peru: Insect foraging in Callicebus oenanthe. Am. J. Primatol. 74, 450–461 (2012).PubMed 
Article 

Google Scholar 
Link, A. Insect-eating by spider monkeys. Neotropical Primates 11, 104–107 (2003).ADS 

Google Scholar 
MacKinnon, K. C. Food choice by juvenile capuchin monkeys (Cebus capucinus) in a tropical dry forest. In New Perspectives in the Study of Mesoamerican Primates (eds Estrada, A. et al.) 349–365 (Kluwer Academic Publishers, 2006). https://doi.org/10.1007/0-387-25872-8_17.Chapter 

Google Scholar 
Fonseca, M. L., Cruz, D. M., Acosta Rojas, D. C., Páez Crespo, J. & Stevenson, P. R. Influence of arthropod and fruit abundance on the dietary composition of highland Colombian woolly monkeys (Lagothrix lagotricha lugens). Folia Primatol. (Basel) 90, 240–257 (2019).Article 

Google Scholar 
Vargas, S. A. et al. Population density and ecological traits of highland woolly monkeys at Cueva de los Guacharos National Park, Colombia. In High Altitude Primates (eds Grow, N. B. et al.) 85–102 (Springer, 2014). https://doi.org/10.1007/978-1-4614-8175-1_5.Chapter 

Google Scholar 
Bryer, M. A. H., Chapman, C. A., Raubenheimer, D., Lambert, J. E. & Rothman, J. M. Macronutrient and energy contributions of insects to the diet of a frugivorous monkey (Cercopithecus ascanius). Int. J. Primatol. 36, 839–854 (2015).Article 

Google Scholar 
Gómez-Posada, C., Rey-Goyeneche, J. & Tenorio, E. A. Ranging responses to fruit and arthropod availability by a tufted capuchin group (Sapajus apella) in the Colombian Amazon. In Movement Ecology of Neotropical Forest Mammals (eds Reyna-Hurtado, R. & Chapman, C. A.) 195–215 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-030-03463-4_12.Chapter 

Google Scholar 
Mallott, E. K., Garber, P. A. & Malhi, R. S. Integrating feeding behavior, ecological data, and DNA barcoding to identify developmental differences in invertebrate foraging strategies in wild white-faced capuchins (Cebus capucinus): Mallott et al. Am. J. Phys. Anthropol. 162, 241–254 (2017).PubMed 
Article 

Google Scholar 
Defler, T. R. & Defler, S. B. Diet of a group of Lagothrix lagothricha lagothricha in southeastern Colombia. Int. J. Primatol. 17, 161–190 (1996).Article 

Google Scholar 
Di Fiore, A. Diet and feeding ecology of woolly monkeys in a western Amazonian rain forest. Int. J. Primatol. 25, 767–801 (2004).Article 

Google Scholar 
Stevenson, P. R., Quinones, M. J. & Ahumada, J. A. Ecological strategies of woolly monkeys (Lagothrix lagotricha) at Tinigua National Park, Colombia. Am. J. Primatol. 32, 123–140 (1994).PubMed 
Article 

Google Scholar 
Izawa, K. Foods and feeding behavior of monkeys in the upper Amazon basin. Primates 16, 295–316 (1975).Article 

Google Scholar 
Peres, C. A. Diet and feeding ecology of gray woolly monkeys (Lagothrix lagotricha cana) in central Amazonia: Comparisons with other atelines. Int. J. Primatol. 15, 333–372 (1994).Article 

Google Scholar 
Stevenson, P. R. Activity and ranging patterns of Colombian woolly monkeys in north-western Amazonia. Primates 47, 239–247 (2006).PubMed 
Article 

Google Scholar 
Milton, K. & Nessimian, J. L. Evidence for insectivory in two primate species (Callicebus torquatus lugens and Lagothrix lagothricha lagothricha) from northwestern Amazonia. Am. J. Primatol. 6, 367–371 (1984).PubMed 
Article 

Google Scholar 
Soini, P. A synecological study of a primate community in the Pacaya-Samiria National Reservee, Peru. Primate Conserv. 7, 63–71 (1986).
Google Scholar 
Pickett, S. B., Bergey, C. M. & Di Fiore, A. A metagenomic study of primate insect diet diversity: A metagenomic study of primate diet. Am. J. Primatol. 74, 622–631 (2012).CAS 
PubMed 
Article 

Google Scholar 
Estupiñan, L. & Muñoz, D. Estudio ecológico comparativo de la artropofauna presente en los receptáculos axilares de dos bromeliáceas epífitas en diferentes bosques andinos. In Estudios ecológicos del páramos y del bosque altoandino Cordillera Oriental de Colombia (eds Mora, L. & Sturm, H.) 679–696 (Academia Colombiana de Ciencias Exactas, Físicas y Naturales, 1995).
Google Scholar 
Solé, R. V. & Montoya, M. Complexity and fragility in ecological networks. Proc. R. Soc. Lond. B Biol. Sci. 268, 2039–2045 (2001).Article 

Google Scholar 
Symondson, W. O. C. Molecular identification of prey in predator diets. Mol. Ecol. 15, 3790–3798 (2002).
Google Scholar 
Gunst, N., Boinski, S. & Fragaszy, D. M. Development of skilled detection and extraction of embedded prey by wild brown capuchin monkeys (Cebus apella apella). J. Comp. Psychol. 124, 194–204 (2010).PubMed 
Article 

Google Scholar 
Panger, M. A. et al. Cross-site differences in foraging behavior of white-faced capuchins (Cebus capucinus). Am. J. Phys. Anthropol. 119, 52–66 (2002).PubMed 
Article 

Google Scholar 
Agostini, I. & Visalberghi, E. Social influences on the acquisition of sex-typical foraging patterns by juveniles in a group of wild tufted capuchin monkeys (Cebus nigritus). Am. J. Primatol. 65, 335–351 (2005).PubMed 
Article 

Google Scholar 
Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).CAS 
Article 

Google Scholar 
Creer, S. et al. The ecologist’s field guide to sequence-based identification of biodiversity. Methods Ecol. Evol. 7, 1008–1018 (2016).Article 

Google Scholar 
Clare, E. L., Fraser, E. E., Braid, H. E., Fenton, M. B. & Hebert, P. D. N. Species on the menu of a generalist predator, the eastern red bat (Lasiurus borealis): Using a molecular approach to detect arthropod prey. Mol. Ecol. 18, 2532–2542 (2009).PubMed 
Article 

Google Scholar 
Thuo, D. et al. Food from faeces: Evaluating the efficacy of scat DNA metabarcoding in dietary analyses. PLoS One 14, e0225805 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Siegenthaler, A., Wangensteen, O. S., Benvenuto, C., Campos, J. & Mariani, S. DNA metabarcoding unveils multiscale trophic variation in a widespread coastal opportunist. Mol. Ecol. 28, 232–249 (2019).CAS 
PubMed 
Article 

Google Scholar 
De Barba, M. et al. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet. Mol. Ecol. Resour. 14, 306–323 (2014).PubMed 
Article 
CAS 

Google Scholar 
Esnaola, A., Arrizabalaga-Escudero, A., González-Esteban, J., Elosegi, A. & Aihartza, J. Determining diet from faeces: Selection of metabarcoding primers for the insectivore Pyrenean desman (Galemys pyrenaicus). PLoS One 13, e0208986 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Mallott, E. K. & Amato, K. R. The microbial reproductive ecology of white-faced capuchins (Cebus capucinus). Am. J. Primatol. 80, e22896 (2018).PubMed 
Article 

Google Scholar 
Wray, A. K. et al. Predator preferences shape the diets of arthropodivorous bats more than quantitative local prey abundance. Mol. Ecol. 30, 855–873 (2021).PubMed 
Article 

Google Scholar 
Quiroga-González, C. et al. Monitoring the variation in the gut microbiota of captive woolly monkeys related to changes in diet during a reintroduction process. Sci. Rep. 11, 6522 (2021).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Prada, C. M. & Stevenson, P. R. Plant composition associated with environmental gradients in tropical montane forests (Cueva de Los Guacharos National Park, Huila, Colombia). Biotropica 48, 568–576 (2016).Article 

Google Scholar 
García-Toro, C., Link, A., Páez Crespo, J. & Stevenson, P. R. Home range and daily traveled distances of highland Colombian woolly monkeys (Lagothrix lagothricha lugens): Comparing spatial data from GPS collars and direct follows. In Movement Ecology of Neotropical Forest Mammals (eds Reyna-Hurtado, R. & Chapman, C. A.) 173–193 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-030-03463-4_3.Chapter 

Google Scholar 
Baulu, J. & Redmond, D. E. Some sampling considerations in the quantitation of monkey behavior under field and captive conditions. Primates 19, 391–399 (1978).Article 

Google Scholar 
Julliot, C. Seed dispersal by red howling monkeys (Alouatta seniculus) in the tropical rain forest of French Guiana. Int. J. Primatol. 17, 239–258 (1996).Article 

Google Scholar 
Hurlbert, S. H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211 (1984).Article 

Google Scholar 
Russo, L., Stehouwer, R., Heberling, J. M. & Shea, K. The composite insect trap: An innovative combination trap for biologically diverse sampling. PLoS One 6, e21079 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ohmart, C. P., Stewart, L. G. & Thomas, J. R. Phytophagous insect communities in the canopies of three Eucalyptus forest types in south-eastern Australia. Austral Ecol. 8, 395–403 (1983).Article 

Google Scholar 
Erwin, T. L. Tropical forests: Their richness in Coleoptera and other arthropod species. Coleopt. Bull. 36, 74–75 (1982).
Google Scholar 
Schowalter, T. D., Webb, J. W. & Crossley, D. A. Communtiy structure and nutrient content of canopy arthropods in clearcut and uncut forest ecosystems. Ecology 62, 1010–1019 (1981).Article 

Google Scholar 
Stevenson, P. R. Phenological patterns of woody vegetation at Tinigua Park, Colombia: Methodological comparisons with emphasis on fruit production. Caldasia 26, 125–150 (2004).
Google Scholar 
Vargas, I. & Stevenson, P. R. Patrones fenológicos en la Estación Biológica Mosiro Itajura-Caparú: Producción de frutos estimada a partir de transectos fenológicos y trampas de frutos. In Estación Biológica Mosiro Itajura-Caparú: Biodiversidad en el territorio Yagojé-Apaporis (eds Alarcón-Nieto, G. & Palacios, E.) 99–104 (Conservación Internacional Colombia, 2009).
Google Scholar 
Bautista, S. Patrones de productividad de frutos y dispersión de semillas en diferentes bosques de Colombia, y su relación con la biomasa de primates (2019).King, R. A., Read, D. S., Traugott, M. & Symondson, W. O. C. Invited Review: Molecular analysis of predation: A review of best practice for DNA-based approaches: Optimizing molecular analysis of predation. Mol. Ecol. 17, 947–963 (2008).CAS 
PubMed 
Article 

Google Scholar 
Mata, V. A. et al. How much is enough? Effects of technical and biological replication on metabarcoding dietary analysis. Mol. Ecol. 28, 165–175 (2019).CAS 
PubMed 
Article 

Google Scholar 
Zeale, M. R. K., Butlin, R. K., Barker, G. L. A., Lees, D. C. & Jones, G. Taxon-specific PCR for DNA barcoding arthropod prey in bat faeces: DNA barcoding. Mol. Ecol. Resour. 11, 236–244 (2011).CAS 
PubMed 
Article 

Google Scholar 
Jusino, M. A. et al. An improved method for utilizing high-throughput amplicon sequencing to determine the diets of insectivorous animals. Mol. Ecol. Resour. 19, 176–190 (2019).CAS 
PubMed 
Article 

Google Scholar 
Aldasoro, M. et al. Gaining ecological insight on dietary allocation among horseshoe bats through molecular primer combination. PLoS One 14, e0220081 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA: For Biodiversity Research and Monitoring (Oxford University Press, 2018).Book 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Ratnasingham & Hebert. bold: The barcode of life data system (http://www.barcodinglife.org). Mol. Ecol. Notes 7, 355–364 (2007).Palmer, J. M., Jusino, M. A., Banik, M. T. & Lindner, D. L. Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. PeerJ 6, e4925 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Phillips, C. A. & McGrew, W. C. Identifying species in chimpanzee (Pan troglodytes) feces: A methodological lost cause?. Int. J. Primatol. 34, 792–807 (2013).Article 

Google Scholar 
Liu, M., Clarke, L. J., Baker, S. C., Jordan, G. J. & Burridge, C. P. A practical guide to DNA metabarcoding for entomological ecologists. Ecol. Entomol. 45, 373–385 (2020).Article 

Google Scholar 
Porter, T. M. & Hajibabaei, M. Over 2.5 million COI sequences in GenBank and growing. PLoS One 13, e0200177 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Valentini, A., Pompanon, F. & Taberlet, P. DNA barcoding for ecologists. Trends Ecol. Evol. 24, 110–117 (2009).PubMed 
Article 

Google Scholar 
Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F. & Taberlet, P. DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biol. Lett. 10, 20140562 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hajibabaei, M. et al. A minimalist barcode can identify a specimen whose DNA is degraded. Mol. Ecol. Notes 6, 959–964 (2006).CAS 
Article 

Google Scholar 
Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 270, 313–321 (2003).CAS 
Article 

Google Scholar 
Piñol, J., Senar, M. A. & Symondson, W. O. C. The choice of universal primers and the characteristics of the species mixture determine when DNA metabarcoding can be quantitative. Mol. Ecol. 28, 407–419 (2019).PubMed 
Article 
CAS 

Google Scholar 
R Studio Team. R Studio: Integrated Development of R (Rstudio, PBC, 2020).Hijmans, R. & van Etten, J. raster: Geographic analysis and modeling with raster data (2012).Wickham, H. ggplot2: Elegant graphics for data analysis (2016).Di Fiore, A. & Rodman, P. S. Time allocation patterns of lowland woolly monkeys (Lagothrix lagotricha poeppigii) in a neotropical Terra Firma Forest. Int. J. Primatol. 22, 449–480 (2001).Article 

Google Scholar 
Dew, J. L. Foraging, food choice, and food processing by sympatric ripe-fruit specialists: Lagothrix lagotricha poeppigii and Ateles belzebuth belzebuth. Int. J. Primatol. 26, 1107–1135 (2005).Article 

Google Scholar 
Deblauwe, I. & Janssens, G. P. J. New insights in insect prey choice by chimpanzees and gorillas in Southeast Cameroon: The role of nutritional value. Am. J. Phys. Anthropol. 135, 42–55 (2008).PubMed 
Article 

Google Scholar 
de Carvalho Jr, O., Ferrari, S. F. & Strier, K. B. Diet of a muriqui group (Brachyteles arachnoides) in continuous primary forest. Primates 45, 201–204 (2004).Article 

Google Scholar 
Talebi, M., Bastos, A. & Lee, P. C. Diet of southern muriquis in continuous Brazilian Atlantic forest. Int. J. Primatol. 26, 1175–1187 (2005).Article 

Google Scholar 
Kowalzik, B. K., Pavelka, M. S. M., Kutz, S. J. & Behie, A. Parasites, primates, and ant-plants: Clues to the life cycle of Controrchis spp. in black howler monkeys (Alouatta pigra) in Southern Belize. J. Wildl. Dis. 46, 1330–1334 (2010).PubMed 
Article 

Google Scholar 
Tebbich, S., Taborsky, M., Fessl, B., Dvorak, M. & Winkler, H. Feeding behavior of four arboreal Darwin’s finches: Adaptations to spatial and seasonal variability. Condor 106, 95–105 (2004).Article 

Google Scholar 
Páez Crespo, J. Comportamiento y caracterización genética de churucos de montaña (Lagothrix lagothricha lugens): Inferencias en la filopatría de machos (Universidad de los Andes, 2016).
Google Scholar 
Blüthgen, N., Verhaagh, M., Goitía, W. & Blüthgen, N. Ant nests in tank bromeliads—An example of non-specific interaction. Insectes Soc. 47, 313–316 (2000).Article 

Google Scholar 
Huxley, C. Symbiosos between ants and epiphytes. Biol. Rev. 55, 321–340 (1980).Article 

Google Scholar 
Brehm, G., Pitkin, L. M., Hilt, N. & Fiedler, K. Montane Andean rain forests are a global diversity hotspot of geometrid moths: Hotspot of geometrid moths. J. Biogeogr. 32, 1621–1627 (2005).Article 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Campuzano, E. F., Ibarra-Núñez, G., Machkour-M’Rabet, S., Morón-Ríos, A. & Jiménez, M. L. Diversity and seasonal variation of ground and understory spiders from a tropical mountain cloud forest. Insect Sci. 27, 826–844 (2020).PubMed 
Article 

Google Scholar 
Miller, J. S. & Thiaucourt, P. Diversity of prominent moths (Lepidoptera: Noctuoidea: Notodontidae) in the cloud forests of northeastern Ecuador, with descriptions of 27 new species. Ann. Entomol. Soc. Am. 104, 1033–1077 (2011).Article 

Google Scholar 
Lambert, J. E. Primate digestion: Interactions among anatomy, physiology, and feeding ecology. Evol. Anthropol. 7, 8–20 (1998).Article 

Google Scholar 
Janiak, M. C. No evidence of copy number variation in acidic mammalian chitinase genes (CHIA) in new world and old world monkeys. Int. J. Primatol. 39, 269–284 (2018).Article 

Google Scholar 
Remis, M. J. & Dierenfeld, E. S. Digesta passage, digestibility and behavior in captive gorillas under two dietary regimens. Int. J. Primatol. 25, 825–845 (2004).Article 

Google Scholar 
Wolda, H. Seasonality of tropical insects. J. Anim. Ecol. 49, 277 (1980).Article 

Google Scholar 
Yanoviak, S. P., Walker, H. & Nadkarni, N. M. Arthropod assemblages in vegetative vs. humic portions of epiphyte mats in a neotropical cloud forest. Pedobiologia 48, 51–58 (2004).Article 

Google Scholar 
Augspurger, C. K. Seedling survival of tropical tree species: Interactions of dispersal distance, light-gaps, and pathogens. Ecology 65, 1705–1712 (1984).Article 

Google Scholar 
Richards, L. A. & Windsor, D. M. Seasonal variation of arthropod abundance in gaps and the understorey of a lowland moist forest in Panama. J. Trop. Ecol. 23, 169–176 (2007).Article 

Google Scholar 
Tercel, M. P. T. G., Symondson, W. O. C. & Cuff, J. P. The problem of omnivory: A synthesis on omnivory and DNA metabarcoding. Mol. Ecol. 30, 2199–2206 (2021).PubMed 
Article 

Google Scholar  More

Zabalza, A., Orcaray, L., Fernandez-Escalada, M., Zulet-Gonzalez, A. & Royuela, M. The pattern of shikimate pathway and phenylpropanoids after inhibition by glyphosate or quinate feeding in pea roots. Pestic Biochem. Physiol. 141, 96–102. https://doi.org/10.1016/j.pestbp.2016.12.005 (2017).CAS 
Article 
PubMed 

Google Scholar 
Amrhein, N., Deus, B., Gehrke, P. & Steinrucken, H. C. The site of the inhibition of the shikimate pathway by glyphosate: II. Interference of glyphosate with chorismate formation in vivo and in vitro. Plant Physiol. 66, 830–834. https://doi.org/10.1104/pp.66.5.830 (1980).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Landrigan, P. J. & Belpoggi, F. The need for independent research on the health effects of glyphosate-based herbicides. Environ. Health 17, 51. https://doi.org/10.1186/s12940-018-0392-z (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Tu, M. & Randall, J. Adjuvants. Tu, M. et al. Weed Control Methods Handbook the Nature Conservancy. 1–24. (TNC, 2003).Brausch, J. M. & Smith, P. N. Toxicity of three polyethoxylated tallowamine surfactant formulations to laboratory and field collected fairy shrimp, Thamnocephalus platyurus. Arch. Environ. Contam. Toxicol. 52, 217–221. https://doi.org/10.1007/s00244-006-0151-y (2007).CAS 
Article 
PubMed 

Google Scholar 
Brausch, J. M., Beall, B. & Smith, P. N. Acute and sub-lethal toxicity of three POEA surfactant formulations to Daphnia magna. Bull. Environ. Contam. Toxicol. 78, 510–514. https://doi.org/10.1007/s00128-007-9091-0 (2007).CAS 
Article 
PubMed 

Google Scholar 
Tsui, M. T. & Chu, L. M. Aquatic toxicity of glyphosate-based formulations: Comparison between different organisms and the effects of environmental factors. Chemosphere 52, 1189–1197. https://doi.org/10.1016/S0045-6535(03)00306-0 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Adam, A., Marzuki, A., Abdul Rahman, H. & Abdul Aziz, M. The oral and intratracheal toxicities of ROUNDUP and its components to rats. Vet. Hum. Toxicol. 39, 147–151 (1997).CAS 
PubMed 

Google Scholar 
Howe, C. M. et al. Toxicity of glyphosate-based pesticides to four North American frog species. Environ. Toxicol. Chem. 23, 1928–1938. https://doi.org/10.1897/03-71 (2004).CAS 
Article 
PubMed 

Google Scholar 
Mesnage, R., Benbrook, C. & Antoniou, M. N. Insight into the confusion over surfactant co-formulants in glyphosate-based herbicides. Food Chem. Toxicol. 128, 137–145. https://doi.org/10.1016/j.fct.2019.03.053 (2019).CAS 
Article 
PubMed 

Google Scholar 
Mesnage, R., Bernay, B. & Seralini, G. E. Ethoxylated adjuvants of glyphosate-based herbicides are active principles of human cell toxicity. Toxicology 313, 122–128. https://doi.org/10.1016/j.tox.2012.09.006 (2013).CAS 
Article 
PubMed 

Google Scholar 
Chlopecka, M., Mendel, M., Dziekan, N. & Karlik, W. The effect of glyphosate-based herbicide Roundup and its co-formulant, POEA, on the motoric activity of rat intestine—In vitro study. Environ. Toxicol. Pharmacol. 49, 156–162. https://doi.org/10.1016/j.etap.2016.12.010 (2017).CAS 
Article 
PubMed 

Google Scholar 
Authority, E. F. S. Request for the evaluation of the toxicological assessment of the co-formulant POE-tallowamine. EFSA J. 13, 4303 (2015).
Google Scholar 
Bolognesi, C. et al. Genotoxic activity of glyphosate and its technical formulation Roundup. J. Agric. Food Chem. 45, 1957–1962 (1997).CAS 
Article 

Google Scholar 
Hao, Y. et al. Roundup((R)) confers cytotoxicity through DNA damage and mitochondria-associated apoptosis induction. Environ. Pollut. 252, 917–923. https://doi.org/10.1016/j.envpol.2019.05.128 (2019).CAS 
Article 
PubMed 

Google Scholar 
Luo, L. et al. In vitro cytotoxicity assessment of roundup (glyphosate) in L-02 hepatocytes. J. Environ. Sci. Health B 52, 410–417. https://doi.org/10.1080/03601234.2017.1293449 (2017).CAS 
Article 
PubMed 

Google Scholar 
Young, F., Ho, D., Glynn, D. & Edwards, V. Endocrine disruption and cytotoxicity of glyphosate and roundup in human JAr cells in vitro. Synthesis 14, 17 (2015).
Google Scholar 
Weinhold, B. Mystery in a bottle: Will the EPA require public disclosure of inert pesticide ingredients?. Environ. Health Perspect. 118, A168-171. https://doi.org/10.1289/ehp.118-a168 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Richmond, M. E. Glyphosate: A review of its global use, environmental impact, and potential health effects on humans and other species. J. Environ. Stud. Sci. 8, 416–434 (2018).Article 

Google Scholar 
Cole, R. D., Anderson, G. L. & Williams, P. L. The nematode Caenorhabditis elegans as a model of organophosphate-induced mammalian neurotoxicity. Toxicol. Appl. Pharmacol. 194, 248–256. https://doi.org/10.1016/j.taap.2003.09.013 (2004).CAS 
Article 
PubMed 

Google Scholar 
Lai, C. H., Chou, C. Y., Ch’ang, L. Y., Liu, C. S. & Lin, W. Identification of novel human genes evolutionarily conserved in Caenorhabditis elegans by comparative proteomics. Genome Res. 10, 703–713. https://doi.org/10.1101/gr.10.5.703 (2000).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Negga, R. et al. Exposure to glyphosate- and/or Mn/Zn-ethylene-bis-dithiocarbamate-containing pesticides leads to degeneration of gamma-aminobutyric acid and dopamine neurons in Caenorhabditis elegans. Neurotox. Res. 21, 281–290. https://doi.org/10.1007/s12640-011-9274-7 (2012).CAS 
Article 
PubMed 

Google Scholar 
Negga, R. et al. Exposure to Mn/Zn ethylene-bis-dithiocarbamate and glyphosate pesticides leads to neurodegeneration in Caenorhabditis elegans. Neurotoxicology 32, 331–341. https://doi.org/10.1016/j.neuro.2011.02.002 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Schuske, K., Beg, A. A. & Jorgensen, E. M. The GABA nervous system in C. elegans. Trends Neurosci. 27, 407–414. https://doi.org/10.1016/j.tins.2004.05.005 (2004).CAS 
Article 
PubMed 

Google Scholar 
McIntire, S. L., Jorgensen, E. & Horvitz, H. R. Genes required for GABA function in Caenorhabditis elegans. Nature 364, 334–337. https://doi.org/10.1038/364334a0 (1993).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Eastman, C., Horvitz, H. R. & Jin, Y. Coordinated transcriptional regulation of the unc-25 glutamic acid decarboxylase and the unc-47 GABA vesicular transporter by the Caenorhabditis elegans UNC-30 homeodomain protein. J. Neurosci. 19, 6225–6234 (1999).CAS 
Article 

Google Scholar 
Bamber, B. A., Beg, A. A., Twyman, R. E. & Jorgensen, E. M. The Caenorhabditis elegans unc-49 locus encodes multiple subunits of a heteromultimeric GABA receptor. J. Neurosci. 19, 5348–5359 (1999).CAS 
Article 

Google Scholar 
Risley, M. G., Kelly, S. P., Jia, K., Grill, B. & Dawson-Scully, K. Modulating behavior in C. elegans using electroshock and antiepileptic drugs. PLoS ONE 11, e0163786. https://doi.org/10.1371/journal.pone.0163786 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pandey, R. et al. Baccoside A suppresses epileptic-like seizure/convulsion in Caenorhabditis elegans. Seizure 19, 439–442. https://doi.org/10.1016/j.seizure.2010.06.005 (2010).Article 
PubMed 

Google Scholar 
Risley, M. G., Kelly, S. P. & Dawson-Scully, K. Electroshock induced seizures in adult C. elegans. Bio-Protoc. 7, 163786 (2017).Article 

Google Scholar 
Risley, M. G., Kelly, S. P., Minnerly, J., Jia, K. & Dawson-Scully, K. egl-4 modulates electroconvulsive seizure duration in C. elegans. Invert. Neurosci. 18, 8. https://doi.org/10.1007/s10158-018-0211-9 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McVey, K. A. et al. Exposure of C. elegans eggs to a glyphosate-containing herbicide leads to abnormal neuronal morphology. Neurotoxicol. Teratol. 55, 23–31. https://doi.org/10.1016/j.ntt.2016.03.002 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Burchfield, S. L. et al. Acute exposure to a glyphosate-containing herbicide formulation inhibits Complex II and increases hydrogen peroxide in the model organism Caenorhabditis elegans. Environ. Toxicol. Pharmacol. 66, 36–42. https://doi.org/10.1016/j.etap.2018.12.019 (2019).CAS 
Article 
PubMed 

Google Scholar 
Weisskopf, M. G., Moisan, F., Tzourio, C., Rathouz, P. J. & Elbaz, A. Pesticide exposure and depression among agricultural workers in France. Am. J. Epidemiol. 178, 1051–1058. https://doi.org/10.1093/aje/kwt089 (2013).Article 
PubMed 

Google Scholar 
Kamel, F. et al. Pesticide exposure and self-reported Parkinson’s disease in the agricultural health study. Am. J. Epidemiol. 165, 364–374. https://doi.org/10.1093/aje/kwk024 (2007).CAS 
Article 
PubMed 

Google Scholar 
Tanner, C. M. Advances in environmental epidemiology. Mov. Disord. 25(Suppl 1), S58-62. https://doi.org/10.1002/mds.22721 (2010).Article 
PubMed 

Google Scholar 
Dick, F. D. Parkinson’s disease and pesticide exposures. Br. Med. Bull. 79–80, 219–231. https://doi.org/10.1093/bmb/ldl018 (2006).CAS 
Article 
PubMed 

Google Scholar 
Brown, T. P., Rumsby, P. C., Capleton, A. C., Rushton, L. & Levy, L. S. Pesticides and Parkinson’s disease–Is there a link?. Environ. Health Perspect. 114, 156–164. https://doi.org/10.1289/ehp.8095 (2006).Article 
PubMed 

Google Scholar 
Firestone, J. A. et al. Pesticides and risk of Parkinson disease: A population-based case-control study. Arch. Neurol. 62, 91–95. https://doi.org/10.1001/archneur.62.1.91 (2005).Article 
PubMed 

Google Scholar 
Martinez, M. A. et al. Neurotransmitter changes in rat brain regions following glyphosate exposure. Environ. Res. 161, 212–219. https://doi.org/10.1016/j.envres.2017.10.051 (2018).CAS 
Article 
PubMed 

Google Scholar 
Kalueff, A. V. & Nutt, D. J. Role of GABA in anxiety and depression. Depress. Anxiety 24, 495–517. https://doi.org/10.1002/da.20262 (2007).CAS 
Article 
PubMed 

Google Scholar 
Mohler, H. The GABA system in anxiety and depression and its therapeutic potential. Neuropharmacology 62, 42–53. https://doi.org/10.1016/j.neuropharm.2011.08.040 (2012).CAS 
Article 
PubMed 

Google Scholar 
Brambilla, P., Perez, J., Barale, F., Schettini, G. & Soares, J. C. GABAergic dysfunction in mood disorders. Mol. Psychiatry 8, 721–737. https://doi.org/10.1038/sj.mp.4001362 (2003) (715).CAS 
Article 
PubMed 

Google Scholar 
Xia, G. et al. Reciprocal control of obesity and anxiety-depressive disorder via a GABA and serotonin neural circuit. Mol. Psychiatry 26, 2837–2853. https://doi.org/10.1038/s41380-021-01053-w (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Martinez, A. & Al-Ahmad, A. J. Effects of glyphosate and aminomethylphosphonic acid on an isogeneic model of the human blood-brain barrier. Toxicol. Lett. 304, 39–49. https://doi.org/10.1016/j.toxlet.2018.12.013 (2019).CAS 
Article 
PubMed 

Google Scholar 
Goetz, T., Arslan, A., Wisden, W. & Wulff, P. GABA(A) receptors: Structure and function in the basal ganglia. Prog. Brain Res. 160, 21–41. https://doi.org/10.1016/S0079-6123(06)60003-4 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Shaw, W. Elevated urinary glyphosate and clostridia metabolites with altered dopamine metabolism in triplets with autistic spectrum disorder or suspected seizure disorder: A case study. Integr. Med. (Encinitas) 16, 50–57 (2017).
Google Scholar 
Gaupp-Berghausen, M., Hofer, M., Rewald, B. & Zaller, J. G. Glyphosate-based herbicides reduce the activity and reproduction of earthworms and lead to increased soil nutrient concentrations. Sci. Rep. 5, 12886. https://doi.org/10.1038/srep12886 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kanabar, M. et al. Roundup negatively impacts the behavior and nerve function of the Madagascar hissing cockroach (Gromphadorhina portentosa). Environ. Sci. Pollut. Res. Int. https://doi.org/10.1007/s11356-021-13021-6 (2021).Article 
PubMed 

Google Scholar 
Loscher, W., Fassbender, C. P. & Nolting, B. The role of technical, biological and pharmacological factors in the laboratory evaluation of anticonvulsant drugs. II. Maximal electroshock seizure models. Epilepsy Res. 8, 79–94. https://doi.org/10.1016/0920-1211(91)90075-q (1991).CAS 
Article 
PubMed 

Google Scholar 
Castel-Branco, M. M., Alves, G. L., Figueiredo, I. V., Falcao, A. C. & Caramona, M. M. The maximal electroshock seizure (MES) model in the preclinical assessment of potential new antiepileptic drugs. Methods Find. Exp. Clin. Pharmacol. 31, 101–106. https://doi.org/10.1358/mf.2009.31.2.1338414 (2009).CAS 
Article 
PubMed 

Google Scholar 
Luszczki, J. J. et al. Anticonvulsant and acute neurotoxic effects of imperatorin, osthole and valproate in the maximal electroshock seizure and chimney tests in mice: A comparative study. Epilepsy Res. 85, 293–299. https://doi.org/10.1016/j.eplepsyres.2009.03.027 (2009).CAS 
Article 
PubMed 

Google Scholar 
Suthakaran, N. et al. O-GlcNAc transferase OGT-1 and the ubiquitin ligase EEL-1 modulate seizure susceptibility in C. elegans. PLoS ONE 16, e0260072. https://doi.org/10.1371/journal.pone.0260072 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hedberg, D. & Wallin, M. Effects of Roundup and glyphosate formulations on intracellular transport, microtubules and actin filaments in Xenopus laevis melanophores. Toxicol. In Vitro 24, 795–802. https://doi.org/10.1016/j.tiv.2009.12.020 (2010).CAS 
Article 
PubMed 

Google Scholar  More