Philippa Kaur

Database utilizedThe database comprises information obtained with permission from the Catalogue of Endangered Languages that is hosted on the Endangered Languages Project platform (https://www.endangeredlanguages.com/). The Endangered Languages Project was first developed and launched by Google, and is currently overseen by First People’s Cultural Council and the Institute for Language Information and Technology at Eastern Michigan University. Information about the languages in this project is provided by the Catalogue, which is produced by the University of Hawai’i at Mānoa and Eastern Michigan University, with funding provided by the U.S. National Science Foundation (Grants #1058096 and #1057725) and the Luce Foundation. The project is supported by a team of global experts comprising its Governance Council and Advisory Committee.In general, the Catalogue aims to present all languages that communities and scholars have pointed out to be at some level of risk as well as languages that have become dormant. In addition to being the largest database of endangered languages globally, the Catalogue is updated periodically based on feedback gathered from language communities and scholars worldwide. The data therefore represents what was most accurately known about the state of each language’s vitality at its point of utilization. At the time of usage, there were 3423 languages represented in the Catalogue that were determined to be at various levels of risk. Assessment of each language’s risk level is carried out using the Language Endangerment Index, which was developed for the Catalogue’s purposes. The Index is used to assess the level of endangerment of any given language based on whether there is intergenerational transmission of the language (whether the language is being passed on to younger generations), its absolute number of speakers, speaker number trends (whether numbers are stable, increasing, or decreasing), and domains of language use (whether the language is used in a wide number of domains or limited ones). The levels of endangerment that the Index generates include ‘safe’, ‘vulnerable’, ‘threatened’, ‘endangered’, ‘severely endangered’, and ‘critically endangered’. Languages for which it remains unclear if the language has gone extinct or whose last fluent speaker is reported to have died in recent times are referred to as ‘dormant’. Given that the focus of the Catalogue is languages that are at some level of threat, safe languages are excluded in general. Where locality information is available, each language is also accompanied with its latitudinal and longitudinal coordinates.Steps taken to prepare the data for network analysisThe data obtained from the Catalogue was further organized and cleaned up for analysis.

1.

Identifier code
Where available, the ISO 639-3 code for each language was utilized as its unique identifier. Otherwise, its LINGUIST List local use code was utilized. These are temporary codes that are not in the current version of the ISO 639-3 Standard for languages. For languages with neither, unique 3-letter codes were constructed.

2.

Endangerment level
Each language’s endangerment level appeared together with a level of certainty score in the same cell in the original data file. Both pieces of information were split into separate columns and only endangerment levels were utilized.
For languages where different data were available in the Catalogue depending on resource utilized, the data was listed in additional columns. The endangerment level data points utilized in these cases were the ones with the most complete and updated information. If there was no data available regarding endangerment level, this information was also reflected.

3.

Coordinates
Where exact coordinates were not available, coordinates were approximated using Google maps based on the location description provided in the Catalogue source (e.g., the Tel Aviv district), attained from other sources such as Glottolog, UNESCO Atlas of the World’s Languages in Danger, or approximated from maps provided in other sources. ‘NA’ was indicated in the field for coordinates if none could be found.
Coordinates found to be inaccurate were rejected, for example in the instance that coordinates provided indicate a different location than the country the language is supposedly found in. The above steps were then taken to populate the coordinates field.
In instances where a language appears in more than one country, these are listed in separate rows as separate entries. Where there are two sets of coordinates for a country, the set that best corresponds with the written description in the Catalogue source, has greater detail, or is more recent is chosen. Where there are more than two sets of coordinates, a middle point is chosen as being representative of the language’s location, by plotting all coordinates on MapCustomizer (www.mapcustomizer.com).

4.

Language family
On the Catalogue, the information regarding language family may be multi-tiered. For example, Laghuu falls under the Lolo-Burmese branch of the Sino-Tibetan family. For this study, the broader family is utilized—in the case of Laghuu the label ‘Sino-Tibetan’ is used.
Mixed languages, pidgins, and creoles have all been categorized as ‘contact languages’.
Language isolates are listed as ‘isolates’.

5.

Region

The Catalogue groups ‘Mexico, Central America, Caribbean’ together under region. Central America and Caribbean are listed as separate regions in this study, with Mexico falling under Central America.Network constructionA spatial network of endangered languages was constructed from the database. Each node represented an endangered language, and edges or links depicted the distance between the locations of the languages as specified in the database. A distance matrix containing the distances between all endangered languages was computed by using functions from the ‘geosphere’ R package. Specifically, Haversine distances were computed for each pair of longitude and latitude points in the dataset. The radius of the earth used in the Haversine distance calculation is 6,378,137 m (for more details see: https://www.rdocumentation.org/packages/geosphere/versions/1.5-14/topics/distHaversine). Haversine distance refers to the shortest distance between two points on a spherical earth, also referred to as the “great-circle-distance”29.Sensitivity analyses of edge thresholdsThe distance matrix is a fully connected network with weighted, undirected links. We set out to capture the strongest or “closest” spatial relationships among the endangered languages, therefore an edge threshold was applied to the distance matrix such that only the edges in the xth lowest percentile were retained in the spatial network. Such an approach allows for the analysis of the most meaningful (i.e., the physically closest) spatial relations in the dataset and how they relate to language endangerment status. The edges were then transformed into unweighted connections to create a simple unweighted, undirected graph for analysis. In order to determine the value of x (i.e., the percentile at which the edge threshold is to be applied), we constructed 10 spatial networks that retained edges with distances below the 1st, 2nd, 3rd… 10th percentile (in increments of 1%) of all distances in the matrix. Additional information of the distances depicted by the edges in each of the 10 networks is provided in Supplementary Information.These 10 networks were then analyzed for their macro- and meso-scale network properties. A summary of macro and meso-scale network measures used in this analysis and their definitions is provided in Table 1, which depicts the 10 networks showing similar patterns in their network structures.Table 1 An overview of macro- and meso-level network measures of spatial networks with different thresholds.Full size tableResultsAs expected, network density and average degree of the networks, which serve as indicators of the number of edges relative to the number of nodes in the network, increased as the edge threshold used to connect nodes became more liberal. The relatively high values of C (i.e., high levels of local clustering among nodes) and low values of ASPL (i.e., relatively short paths despite large size of network) suggested the presence of small world structure30. The community detection analysis using the Louvain method31 indicated strong evidence of community structure in the networks—suggesting the presence of clusters of endangered languages.The point at which the vast majority of nodes was located within the largest connected component of the network occurred at the 5% edge threshold. Because the 5% network was not too fragmented, we report the analyses conducted on the largest connected component of the 5% network in the following subsections. Please see Supplementary Information for additional details behind the rationale for selecting the 5% network for further analyses. The smaller connected components were excluded. Note however that our results are robust across spatial networks of various edge thresholds (due to lack of space, please see Supplementary Information for a complete summary of all reported analyses conducted on all 10 spatial networks).Macro-level analysis: assortative mixing of endangerment statusesMethodTo investigate the macro-level structure of the spatial network of endangered languages, we computed the assortativity coefficient of the spatial network. Specifically, we wanted to know if the endangerment statuses of the languages tended to cluster at the global level of the entire network. If the assortativity coefficient is positive, the languages in the network would tend to be connected to languages of similar levels of endangerment. If the assortativity coefficient is negative, the languages in the network would tend to be connected to languages of dissimilar levels of endangerment.ResultsThere is a significant positive correlation (Spearman’s rank correlation) between the endangerment status of connected pairs of endangered languages in the network, r = 0.20, p  More

Study speciesCane toads (Rhinella marina) are large (to  > 1 kg) bufonids (Fig. 1a). Although native to north-eastern South America, these toads have been translocated to many countries worldwide to control insect pests12. Adult cane toads forage at night for insect prey and retreat to moist shelter-sites per day13. Small body size (and thus, high desiccation rate) restricts young toads to the margins of natal ponds14, but adult toads can survive even in highly arid habitats if they have access to water13,15. Cane toads prefer open habitats for foraging12, and thus can thrive in post-fire landscapes16,17. Cane toads in post-fire landscapes tend to have lower parasite burdens, probably because free-living larvae of their lungworm parasites cannot survive either the fire or the more sun-exposed post-fire landscape18.Figure 1taken from study sites between Casino, Grafton, and surrounds, NSW, by S.W. Kaiser.The cane toad Rhinella marina (a), and unburned, (b) and burned (c) habitats in which toads were collected and radio-tracked. Photographs were Full size imageStudy areaEast of the Great Dividing Range, near-coastal Clarence Dry Sclerophyll Forests of north-eastern New South Wales (NSW) are dominated by Spotted gum (Corymbia variegata) and Pink bloodwood (Corymbia intermedia)19. Fires are common, but typically cover relatively small areas before they are extinguished. In the summer of 2019–2020, however, prolonged drought followed by an unusually hot summer resulted in massive fires across this region, burning almost 100,000 km2 of vegetation9. In the current study, the toads we measured and dissected came from several sites within 75 km of the city of Casino (for site locations, see Fig. 2, Table 1, and18). The impacts of fire on faunal abundance and attributes shift with time since fire; for example, the abundance of a particular species may be reduced by fire (due to mortality from flames) but then increase as individuals from surrounding areas migrate to the recently-burned site to exploit new ecological opportunities provided by that landscape8. We chose to study this system 1-year post-fire, to allow time for such longer-term effects to be manifested.Figure 2Sampling sites relative to fire history. Sample sites are burned (red circles), and unburned (green squares). See Table 1 for key to sites. The legend shows the extent of burn a year prior to our study. Map created in QGIS 3.22.3. Fire history available from https://datasets.seed.nsw.gov.au/dataset/fire-extent-and-severity-mapping-fesm CC BY 4.0.Full size imageTable 1 Sampling sites and sample sizes for dissected and radio-tracked cane toads (Rhinella marina) in New South Wales, Australia.Full size tableSurveys of toad abundanceTo quantify toad abundance in burned and unburned sites, one observer (MJG) walked 100-m transects along roads at night (N = 23 and 8 respectively), recording all toads and native frogs (both adult and juvenile). The smaller number of unburned sites reflects the massive spatial scale of the wildfires, which made it difficult to find unburned areas. The transect sites were not the same as those sampled by “toad-busters” (below). We sampled both burned and unburned sites on each night, to de-confound effects of weather conditions with fire treatment. We scored frogs as well as toads to provide an estimate of overall anuran abundance and activity, and so that we could examine toad abundance relative to frog abundance as well as absolute toad numbers.“Toad-buster” sampleBecause of their ecological impact on native fauna, cane toads are culled by community groups as well as by government authorities12,20. We asked “toad-buster” groups to record whether the sites at which they collected toads had been burned during the 2019–2020 fires, or had remained unburned (Table 1). The toads were humanely euthanized (cooled-then-pithed: see21). The euthanasia method is brief (a few hours in the refrigerator, followed by pithing) and thus should not have affected any of the traits that we measured. For all of these toads, we measured body length (snout-urostyle length = SUL) and mass, and determined sex based on external morphology (skin colour and rugosity, nuptial pads: see22). A subset of toads (chosen to provide relatively equal numbers of males and females, and with equal numbers from burned and unburned sites) was dissected to provide data on mass of internal organs (fat bodies, liver, ovaries), reproductive condition (state of ovarian follicle development) and diet (mass and identity of prey items). To select the subsample of toads for dissection, we took relatively equal numbers of male and female toads from each bag of toads that was provided to us by the “toad-busters”. For logistical reasons, we were unable to dissect all of the toads that had been collected. Overall, we obtained data on morphology, diets and other traits from 481 fully dissected and 1443 partially dissected cane toads.Radio-trackingTo explore habitat use and movement patterns, we radio-tracked 57 toads over the course of two fieldtrips (0900–1800 h from 20 Nov 2021 to 6 Dec 2021 and 25 Jan 2022 to 10 Feb 2022). We selected seven sites (4 burned, 3 unburned) within 28 km of Tabbimoble, NSW (see Table 1 for locations and sample sizes of tracked toads). We hand-captured toads found active at night. These were measured, and their sex determined by external morphology (see above) and behaviour (release calls, given only by males: see23). We then fitted the toads with radio-transmitters (PD-2; Holohil Systems, Ontario, Canada; weighing ≤ 3.8 g) on cotton waist-belts, and released them at the site of capture. Tracked toads were 88.2–160.9 mm SUL (mass 70.1–546.3 g); thus, transmitters weighed  20 mm thick) within the quadrat, and estimated exposure of the toad within its refuge (the percentage of the animal’s body exposed to the naked eye). We then selected a compass bearing at random and walked 20 m in that direction where we rescored all of the above habitat attributes, to quantify habitat features in the broader environment (i.e., not just in microhabitats used by toads). We used those “random” sites to quantify overall habitat attributes of burned and unburned sites. Temperature was recorded by directing a temperature gun (Digitech QM7221) on (or otherwise close-to) toads and at a random point on the ground for random replicates. In total, we gathered radio-tracking data on movements and habitat variables from 57 cane toads, each of which was tracked for 5 days. Recaptured toads were euthanized by cooling-then-pithing.Morphological traitsTo obtain an index of body condition of toads, we regressed ln mass against ln SUL, and used the residual scores from that general linear regression as our estimate of body condition. Negative residual scores show an individual that weighs less-than-expected based on its body length. Likewise, we regressed mass of the fat bodies, liver and stomach against body mass to obtain indices of energy stores and stomach-content volumes relative to body mass. We scored male secondary sexual characteristics using the system of Bowcock et al.22. In their system, three sexually dimorphic traits (nuptial pad size, skin roughness and skin colouration) are scored from 0 to 2, and the scores from those three traits are summed to create a final value (on a 6-point scale) for the degree of elaboration of male-specific secondary sexual characteristics. We scored reproductive condition in adult female toads based on whether or not egg masses were visible during dissection, based on dissected toads from both “toad-buster” and telemetry samples.Statistical methodsData were analysed in R version 4.2.025. We used Linear Mixed Models (LMMs), Generalised Linear Mixed Models (GLMMs) and logistic regressions for our analyses. The R packages ‘tidyverse’26, ‘lmerTest’27, and ‘performance’28 were used.Habitat dataWe compared habitat variables between burned and unburned sites, and attributes of toads in burned versus unburned sites, using GLMMs (with negative binomial distribution) for count data (models were checked for overdispersion29) and LMMs on distance data, using ln-transformations where required to achieve normality. LMMs were used on non-normal percentage data, which were ln- and then logit-transformed (using log[(P + e)/(1 − P + e)], where e is the lowest non-zero number, halved)30. We used toad id, site (sampling location) and sampling trip (2019 versus 2020) as random factors.Anuran transect dataCounts of toads in burned versus unburned areas were compared both directly via GLMMs with a negative binomial distribution and relative to the numbers of frogs sighted along the same transects (binding the columns in R as ‘number of toads, number of amphibians – number of toads’ and using a GLMM with a binomial distribution). We used site as a random factor.Telemetry dataFor telemetry data, we analysed response variables via LMMs, and ln-transformed data where relevant to achieve normality.Dissection dataWe used LMMs for SUL, body mass, body condition and organ mass residuals (e.g., fat body mass relative to body mass). For prey item data, we used a poisson distribution with row number as a random factor, as the negative binomial and beta distribution GLMMs were overdispersed (see31). We used LMM for number of prey items and number of prey groups, with site as a random factor. Where models failed to converge, we reduced or removed the error term(s). Analyses were restricted to toads ≥ 70 mm SUL, because animals below this size were difficult to sex. We also performed nominal logistic regression to explore variation in sex ratio and male secondary sexual traits.Reproductive conditionWe used LMM for male secondary sexual characteristic display, using site as a random factor. For ovary presence, we used a binomial GLMM with a logit link, using site as a random factor. We used a LMM of the residual values from ovary mass relative to body mass (ln-transformed), using site as a random factor.Ethics declarationsAll procedures were performed in accordance with the relevant guidelines and regulations approved by Macquarie University Animal Ethics Committee (ARA Number: 2019/040-2) and in accordance with ARRIVE guidelines. More

The avian plumage color gamut is much more diverse than previously estimated2. We demonstrate that hummingbird barbule structural colors contribute substantially to the total color diversity of living birds, occurring in areas of the avian color space that were sparsely occupied in Stoddard and Prum2, which most notably included saturated blues, greens, and true purples (blue + red). Such regions of the avian color space were suggested to be unoccupied because these colors are challenging to create, rather than because they might function poorly for communication2. Our results support this hypothesis because hummingbird coloration densely occupies these regions of the avian color gamut (Fig. 2d), using plumage patches that generally play particularly important roles in hummingbird communication, such as throat and crown plumage patches (Supplementary Fig. 5)16,17. The greater color diversity uncovered by our study suggests that barbule structural coloration is the most versatile class of all plumage coloration mechanisms and poses the least constraints on the evolvability of plumage color diversity. Barbule structural colors evolve through changes in the size, shape, spacing, and refractive index of barbule melanosome nanostructures, but little is known about how changes in these parameters themselves evolve18.The UV/V + green region of avian color space remains mostly unoccupied (Fig. 2c, d). It is challenging to create colors with separate reflectance peaks within the wavelength sensitivities of non-adjacent color cones because the peaks must be highly saturated to avoid stimulating neighboring cones2. However, this idea does not explain why there are far more true purple (blue + red) than UV/V + green plumage colors. Notably, birds particularly fail to fill the more UV/V regions (those closer to the UV/V vertex) of UV/V + green color space, which might indicate that it is difficult to create spectra with uv/v wavelength peaks higher than those in the m wavelengths.The differences between our methods and those of Stoddard and Prum2 likely contribute in part to the larger gamut size when comparing species data but not overall data. While the number of species included in our study was comparable to that of Stoddard and Prum2 (114 vs 111 species, respectively), we measured almost twice as many plumage patches as they did (+1600 vs. 965 patches). To prevent erroneous distortion to iridescent colors we did not average the three measurements per patch. Both studies measured six standard patches for all species and additional patches if necessary to capture other plumage color variation. The larger number of plumage patches we measured reflects how color diverse hummingbird plumages are. Our methods preserved the natural variation in hue due to iridescence and avoided the distorted flattening caused by averaging highly saturated peaks with slightly different peak hues. Although our methods are biased toward increasing variation, they are necessary to accurately capture the phenomenon of iridescent hummingbird coloration.There are multiple reasons why the hummingbird color gamut is so diverse. The size of the hummingbird color gamut, like the achieved color gamut of any clade, constitutes a combination of the history of selection on color function, the clade’s evolved capacities for color production, the age of the clade, and the number of species. Hummingbirds excel at all these criteria. The 336 species of extant hummingbirds have radiated rapidly over the last 22 million years19. Hummingbird plumage color diversity has evolved through a long history of persistent sexual and social selection on plumage coloration. Hummingbirds have polygynous breeding systems characterized by female only parental care, female mate choice, and often elaborate male courtship displays. Intersexual selection in hummingbirds has contributed to elaborate radiation in brilliant plumage coloration as well as vocalizations and non-vocal feather sounds14,16,20. Hummingbird plumage color evolution rates have even been shown to positively correlate with hummingbird speciation rates14. Furthermore, in some species, brilliant monomorphic plumage ornaments apparently function in aggressive, intra- and interspecific defense of floral resources21 and appear to be associated with socioecological features related to resource competition19. Our finding that crown and throat patches, which flash brilliantly when the head of the bird is oriented toward the observer, are more diverse in coloration than other plumage regions highlights the role of plumage coloration in direct inter-individual communication and social interactions.The mechanistic properties of hummingbird barbule structural color further explain the exceptional diversity of hummingbird plumage coloration. Hummingbird barbule structural coloration is among the most complex plumage coloration mechanisms, comprised of stacks of hollow, air-filled melanosomes, surrounded by a thin superficial, solid keratin cortex as well as sometimes superficial, miniature melanin platelets which lie just beneath this cortex9,10,11,12,13. Complex nanostructures allow for independent tuning of multiple components, and, hence, greater achievable color diversity12,18,22. Barbule structural color permits the production of any peak-reflected wavelength by varying the thickness of melanosome arrays, which can produce a diversity of single-peak spectra-hues, such as the unusual diversity of greens, blues, and blue + greens seen in hummingbirds (Fig. 2b). Hummingbird melanosomes are among the most unusual in birds in being both disc-shaped and air-filled9,10,11,12,13,23. The air in the center of hummingbird melanosomes approaches the maximum possible biological difference in refractive index (air = 1.0, melanin = ~1.7), which results in the efficient production of brilliant colors with the fewest layers of melanosomes, such that resulting spectra are narrow and near saturation13,24. Such spectra can thereby create colors that extend further in color space (Fig. 2a–c).Barbule structural color also allows for the production of plumage spectra with multiple saturated peaks, creating saturated color combinations that are not as commonly produced via other plumage coloration mechanisms. However, researchers have yet to identify exactly how hummingbird multipeak spectra are produced12,13, emphasizing the need for further analyses of the optics of hummingbird feathers. Many hummingbird melanosome arrays are non-ideal– i.e., the products of the thicknesses and refractive indices of the melanin and air cavity layers are not equal25. Non-ideal thin films can create more highly saturated, pure tone colors of the primary peak while also introducing additional, harmonic spectral peaks at shorter wavelengths25, which allows for complex reflectance spectra with multiple bright peaks within the avian visible spectrum. Also, melanosome arrays with a large average layer thickness ( >~300 nm) can create colors with fundamental interference peaks in the infrared and multiple, harmonic peaks in the avian visible range (300–700 nm). The presence of minute, superficial melanin platelets below the cortex in hummingbird barbules is also correlated with secondary, lower wavelength reflectance peaks, but the precise optical mechanism remains to be established12. These different nanostructural elements all contribute to distinctive multipeak reflectance spectra that can stimulate non-adjacent color cone combinations, which Stoddard and Prum2 identified as particularly difficult to accomplish: UV/V-purple (uv/v + s + l wavelengths; Schistes geoffroyi cheek, Fig. 4g); true purple (s + l wavelengths; Atthis ellioti gorget, Fig. 4h); UV/V-green (uv/v + m; Schistes geoffroyi crown, Fig. 4a); and UV/V-red (uv/v + l; Heliangelus viola, Fig. 4b). With multipeak spectra the potential for creating new and different colors is greatly expanded, allowing for a more versatile evolution of novel colors.Unexpectedly, the hummingbird plumage color gamut is larger in volume when modeled with the VS-type (34.2%) than with the UVS-type (29.6%) visual system. This apparently unique result contrasts notably with both Stoddard and Prum’s2 and our revised estimate of the color gamut of all birds combined– VS gamut = 40.5%; UVS gamut = 47.3%. Multiple previous analyses have shown that the UVS cone-type visual system does a more efficient job of discriminating the colors of natural objects because of the broader separation between the peak spectral sensitivities of the uv and s (blue) cone types2,26,27. Because the UVS-type visual system produces an even greater increase in color volume for a diverse plant color data set over the VS-type visual system, Stoddard and Prum2 rejected the hypothesis that the UVS-type visual system had specifically evolved to expand the diversity of avian color stimuli.However, our observations that the hummingbird plumage gamut is substantially greater in volume with the VS-visual system than with the more efficient UVS-visual system strongly suggests another hypothesis: Hummingbird plumage may have specifically evolved to be more diverse within the hummingbird VS-type color visual system via selection for highly saturated plumage colors. Given diversity in hue, the way to achieve greater color gamut volume, i.e., greater plumage color diversity, is through highly chromatic color vectors that extend toward the limits of the color space. The two visual systems map variation in wavelength to different maximum potential chroma—i.e., wavelengths with color vectors that extend toward the edges, faces, and vertices of the tetrahedron6. Color vectors that extend towards the vertices, i.e., plumage that best corresponds to a singular cone type’s peak sensitivity, have the highest maximum potential chroma because vertices are the regions furthest away from the tetrahedron’s center. Thus, hummingbird plumages may have specifically evolved to have maximum chroma within their own VS-visual system via peaks that correspond most closely to the peak sensitivities of the VS- rather than the UVS-visual system. For example, when comparing the UVS and VS plumage color gamuts for hummingbirds, it is notable that hummingbird coloration extends much further into the UV/V regions of color space for the VS-visual system (Supplementary Fig. 2). While in the VS system these color points map toward the v vertex, in the UVS-visual system they map towards the uv-s edge and the uv-s-l face. Such color vectors that contribute to expanded color volume of the VS gamut could have evolved by sexual or social selection for highly saturated plumage colors that are near in hue to the specific sensitivity peaks of hummingbird receptor cone types. Such selection could note preferences within some hummingbird species for hues with maximally possible chroma, not merely for maximal chroma of a given hue.Hummingbirds have tetrachromatic color vision with substantial sensitivity in the near ultraviolet28,29. Recently, Stoddard et al.30 used a series of elegant experiments with hummingbird feeders and LED lights to demonstrate for the first time that hummingbirds can distinguish non-spectral colors distributed throughout the tetrachromatic color space. However, the presence of this remarkably proficient four-color vision in hummingbirds poses an interesting evolutionary conundrum. Recent phylogenetic analyses have established that hummingbirds and swifts are phylogenetically embedded within the nocturnal caprimulgiforms31,32. The most parsimonious hypothesis is that the immediate ancestors of swifts and hummingbirds were extensively nocturnal for approximately 8 million years before they re-evolved diurnal ecology and behavior31. Given that an evolutionary history of nocturnality can lead to the degradation or loss of opsin genes33,34, it should be a high priority to establish what effect that ancestral nocturnality may have had on the molecular physiology and anatomy of the hummingbird color visual system.Our attempt to document the color diversity of an avian family has revealed that current estimates of the total avian color gamut are likely inaccurately low. Similar studies sampling from other color-diverse families, such as sunbirds (Nectariniidae), parrots (Psittacidae), tanagers (Thraupidae), birds of paradise (Paradiseidae), manakins (Pipridae), and starlings (Sturnidae), most of which have already been studied for their plumage coloration35,36,37,38,39, would help us obtain a better estimate of the true avian color gamut. More

DEGs under low-temperature stressThe results from the field experiments indicated that the daily mean values of A, Fvʹ/Fmʹ, ETR and Fv/Fm decreased in the LT group, the PSII function was impaired, and the photosynthetic capacity was weakened. Through the specific analysis of the “Photosynthesis” pathway (pathway ID ko00195) in the LT group, it was found that PSII, the cytochrome b6f. complex (Cyt b6f.), PSI and ATPase exhibited differential gene expressions. Figure 9 shows the structural pattern diagram for photosynthesis. The parts marked by white boxes indicate that the structure has DEGs. The gene expressions of CP43, CP47, D1 protein and Cytb559 of PSII changed. The inner peripheral antenna pigment proteins, CP43 and CP47, of PSII bind to chlorophyll. They accept the excitation energy transferred from the surrounding antenna complex and transfer this energy to the reaction centre complex. Changes in CP43 and CP47 affect the absorption and transmission of light energy. In the PSII reaction centre, light energy is converted into chemical energy. P680 absorbs light and is excited to become P680*, and then transfers electrons to pheophytin (Pheo). At the same time, the PSII oxygen-evolving complex obtains electrons from water molecules, the water molecules are split and releases oxygen and protons. As one of the two core proteins that compose the reaction centre complex, the D1 protein combines with various cofactors that are related to the original charge separation and electron transfer. The D1 protein plays an important role in the process of photosynthetic electron transfer. Studies have found that low temperatures can induce allosteric inactivation of the D1 protein, which results in changes in the structure of thylakoid membranes and hinders electron transfer8. As part of the reaction centre, Cytb559 can adjust the photoinhibition sensitivity of PSII through redox changes so that the PSII reaction centre is protected from damage9. The light energy absorption, energy conversion and electron transfer functions of PSII are impaired, which result in significant decreases in Fv/Fm to levels far below the normal value. The results of Xiangchun Song are similar to those presented in this paper: the PS II reaction centre of A. mongolicus seedlings is irreversibly inactivated or the thylakoid membrane is damaged under subzero low temperature stress, which may produce serious photoinhibition. However, Song believes that the peripheral antenna component of the optical system is more affected than the core complex at low temperatures, which was not observed in the corresponding results in this study10.Figure 9Photosynthesis of A. mongolicus under low-temperature stress. The areas outlined by white boxes indicate the differentially expressed genes in these structures.Full size imageThe gene expressions of Cyt b6, PrtD and Cyt f in Cyt b6f. changed. Cyt b6f. changes not only affect the electron transport function of photosynthesis but also affect ATP synthesis. Pheo transfers the received electrons to plastid quinone (PQ). PQ receives electrons and protons to form plastid hydroquinone (PQH2). Then, the electrons of PQH2 are transferred to plastid cyanin (PC) on PSI through Cyt b6f., and hydrogen protons are released into the cavity of the thylakoid to form a transmembrane proton gradient. The transmembrane proton gradient is the driving force for ATP synthesis.The function of PSI is to transfer electrons from PC to ferredoxin for the reduction of NADP+. Recent studies have found that PSI is more sensitive to light and more prone to selective photoinhibition than PS II under low temperature and weak light conditions11,12. The KEGG analysis results indicated that the LHCI complex, PsaF and PsaE subunits of PSI showed differential gene expressions. The main function of the LHCI light-harvesting pigment protein complex is to capture light energy. PsaF is a low-molecular-weight protein that is distributed in the membrane. Some studies have suggested that the N-terminal amino acid sequence of eukaryotic PsaF is involved in the binding of PSI and PC13. PsaE, PsaD and PsaC together form the docking site of ferredoxin on the PSI receptor side14,15. Ferredoxin and ferredoxin-NADP+ reductase in the photosynthetic electron transport chain are also affected, which results in hindrance of NADPH synthesis. The F-type H+/Na+ transport ATPase subunits also show differential gene expressions, which lead to impaired ATP synthesis. Low temperatures affect the ability to absorb light energy, transfer electrons, convert light energy into electric energy, and synthesize NADPH as well as ATP, which ultimately lead to declines in Fv’/Fm’ and ETR and impair the photosynthesis capacity of A. mongolicus.Compared with the light reaction, low temperatures have a greater impact on the dark reaction. Because the dark reaction process is composed of many complex enzymatic reactions, the enzyme activity is very susceptible to temperature. The KEGG results show that 13 related enzymes were differentially expressed in the “carbon sequestration of photosynthesis” (ko00710). The Rubisco enzyme is a key enzyme that determines the direction and efficiency of photosynthetic carbon metabolism in C3 plants and is sensitive to temperature16. The results also show that the expression levels of 10 differentially expressed genes of Rubisco enzymes all declined. In the Calvin cycle, the gene expressions of only transketolase and glyceraldehyde-3-phosphate dehydrogenase are not sensitive to temperature. In addition, the reduction phase of the dark reaction requires the use of NADPH and ATP that are produced by the light reaction. The inhibition of NADPH and ATP synthesis will inevitably affect the normal progression of the Calvin cycle.Chloroplast respiration is an O2-dependent electron transport pathway in chloroplasts. Chloroplast respiration includes the nonphotochemical reduction of PQ by NAD(P) H and the reoxidation of PQ by terminal oxidase, which can consume excess electrons to protect plants from damage due to photooxidation.Figure 10 shows the partial KEGG enrichment metabolic pathway in the LT group. There were three significant enrichment pathways related to carbohydrate metabolism: fructose and mannose metabolism (ko00051), butanoate metabolism (ko00650) and C5-branched dibasic acid metabolism (ko00660). The metabolism of fructose and mannose includes the ascorbic acid biosynthetic pathway. Ascorbic acid (ASA), also known as vitamin C, can be used as a cofactor of violaxanthin de-epoxidase to participate in the lutein cycle and consume excess light energy and protect plants from harm.Figure 10The regulatory mechanism of A. mongolicus under low-temperature stress. The white ovals represent the enriched metabolic pathways. The blue rectangles represent significantly enriched KEGG metabolic pathways. The pathways are followed by the physiological structures and substances or physiological processes in which the expressions of related genes change.Full size imageLow temperatures damage cell membranes first. Increasing the mass fraction of unsaturated fatty acids in the membrane is beneficial to improve the stability and fluidity of the membrane. Some studies have shown that the degree of unsaturation of fatty acids in adult leaves of A. mongolicus that grow naturally in the field is lower in summer and higher in autumn and winter17. The significantly enriched pathways related to unsaturated fatty acid metabolism were alpha-linolenic acid metabolism (ko00592), linoleic acid metabolism (ko00591) and arachidonic acid metabolism (ko00590). Various proteins, such as linoleate 13S-lipoxygenase and cytochrome P450 family 2 subfamily J (CYP2J), which are involved in the metabolism of linoleic acid, showed differences in their gene expressions. Linoleate 13S-lipoxygenase is a common lipoxygenase in plants that can catalyse the production of precursors of several important compounds, including jasmonic acid. CYP2J is a group of P450 haem thiolate proteins, which are mainly distributed on the endoplasmic reticulum and inner mitochondrial membrane and are involved in the synthesis of sterol hormones, including brassinosteroids. Because light systems are distributed on the thylakoid membrane, damage to this membrane will affect the progress of plant photosynthesis.Plant hormone signal transduction (ko04075) plays an important role in plant resistance to stress. Studies have shown that JAs have physiological functions, such as inducing stomatal closure, inhibiting photosynthesis, promoting respiration and promoting leaf senescence18,19. Treating plants with exogenous methyl jasmonate can induce the transcription of the heat shock protein family, increase the synthesis of antioxidants, reduce lipoxygenase activity and enhance the ability of plants to resist cold damage20.Figure 11 shows the regulatory mechanism of A. mongolicus in the HL group. The MapMan analysis results show that the DEGs of the LHCII complex and those for the assembly and maintenance of PSII are significantly changed. LHCII contains chlorophyll and carotenoids, which can capture and transmit light energy. Chlorophyll is an important photosynthetic pigment that captures light energy and drives electrons to the reaction centre. The chlorophyll molecule in the reaction centre is related to photochemical quenching. The entire chlorophyll biosynthesis process (e.g., L-glutamyl-tRNA → chlorophyll a → chlorophyll b) involves 15 enzymes. The analysis found that 4/5 of the enzymes’ expression genes were changed. Carotenoids include carotene and lutein, and their synthesis is affected by high temperatures. Lutein participates in the lutein cycle, which can dissipate excess light energy and prevent membrane lipids from being peroxidized and thus maintain the stability of the thylakoid membrane structure and protect A. mongolicus. from high temperature stress and strong light stress.Figure 11The regulatory mechanism of A. mongolicus. under high-temperature stress. The white ovals represent enriched metabolic pathways. The red rectangles represent significantly enriched KEGG metabolic pathways. The pathways are followed by the physiological structures and substances or physiological processes in which the expressions of related genes change.Full size imageThe D1 protein in the PSII reaction centre is rapidly degraded under strong light conditions. To maintain the normal physiological needs of plants, the degraded D1 protein will be replaced by the new D1 protein that is produced by the repair mechanism. The reversible inactivation of the PSII reaction centre can protect the photosynthetic system and avoid destruction. This may be the reason for the significant changes in the DEGs that are involved in the assembly and maintenance of PSII.Rubisco is the main site for high-temperature inhibition of the Calvin cycle16. The KEGG analysis found that there were 7 (4↑, 3↓) DEGs of Rubisco. SBPase catalyses the conversion of sedum heptulose-1,7-diphosphate (SBP) into sedum heptulose-7-phosphate (S7P) in the renewal phase. Under low-temperature stress, only transketolase and glyceraldehyde-3-phosphate dehydrogenase remained unchanged in the Calvin cycle. In addition, NDH-mediated cyclic electron transfer may decreased the photooxidation damage that is caused by high-temperature stress by shunting the excess electrons that were generated by the inhibition of CO2 assimilation to the chloroplast respiratory pathway21.In the HT group, the net photosynthetic rates of the leaves showed two peaks on the diurnal change curves, and there was an obvious phenomenon of midday photosynthesis depression. The daily average A values were greater than those of the CK group. These results show that A. mongolicus has a complete photosynthetic structure protection mechanism and can adapt to high-temperature environments. The pathway of significant enrichment related to carbohydrate metabolism in the HT group was the same as that in the LT group. The enrichment degrees of the fructose and mannose metabolic pathways were higher only in the HT group, and C5-branched dibasic acid metabolism and butanoate metabolism were higher in the LT group.Under high temperature and strong light conditions, the balance between production and removal of reactive oxygen species (ROS) in plant cells was broken, and large amounts of reactive oxygen species accumulated in the cells. Active oxygen can cause lipid peroxidation of the biomembrane, enlarge membrane pores, increase the permeability, and affect the spatial structures of enzymes on the membrane, which thus leads to chloroplast destruction. In severe cases, ROS will cause serious injury or even death to plants22. The gene expressions of FabH and acetyl-CoA carboxylase (ACCase) changed during the synthesis of unsaturated fatty acids in the HT group.There are two types of active oxygen scavenging mechanisms in plants. (1) The enzymatic detoxification system: superoxide dismutase (SOD), ascorbate peroxidase (APX), and catalase (CAT). (2) Nonenzymatic antioxidants: ASA, carotenoids, glutathione, mannitol, and flavonoids23.Secondary metabolites result from long-term adaptation of plants to their environments. They can improve the ability of plants to protect themselves, compete for survival, and coordinate the relationship between plants and the environment. The significant enrichment pathways related to the biosynthesis of secondary metabolites in the HT group consisted of phenylpropane biosynthesis (ko00940), flavonoid biosynthesis (ko00941) and isoflavone biosynthesis (ko00943). The phenylpropanoid biosynthesis pathway is one of the three main secondary metabolic pathways in plants. It starts from phenylalanine and generates different phenylpropane metabolites through multistep reactions, such as flavonoids, isoflavones, anthocyanins and lignin24,25. Anthocyanins can protect plants from light damage by quenching free oxygen radicals and reducing the absorption of light energy. Hughes studied 10 species of evergreen broad-leaved trees and found that red leaves containing anthocyanins always maintained higher Fv/Fm levels than green leaves. Fv’/Fm’ is related to nonphotochemical quenching. This means that trees with red leaves rely more on the light-damage defence function of anthocyanins than on the light-damage defence mediated by lutein26.Riboflavin metabolism (ko00740) and biotin metabolism (ko00780) are two significantly enriched cofactors and vitamin metabolic pathways. Riboflavin is the precursor of flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD). As a prosthetic group of flavinases, FAD participates in multiple biochemical processes, such as mitochondrial electron transport, photosynthesis, fatty acid oxidation and folate metabolism, in plants27. Riboflavin can induce antioxidant accumulations in plant cells and can also promote plant growth by affecting the ethylene signalling pathway28. Biotin (e.g., VH or VB7), as an essential cofactor for biotin-dependent carboxylase, plays an important role in the life activities of plants. Common biotin-dependent carboxylase enzymes are pyruvate carboxylase (PC) and ACCase. PC is present in the mitochondria and participates in the replenishment mechanism of the tricarboxylic acid cycle. ACCase plays a pivotal role in the feedback regulation of fatty acid synthesis and is the site of action for the feedback regulation of fatty acid synthesis29.The four pathways related to amino acid metabolism showed differences in the HT group. The enrichment degrees of each pathway were as follows: valine, leucine and isoleucine biosynthesis (ko00290)  > biosynthesis of amino acids (ko01230)  > lysine biosynthesis (ko00300)  > glycine, serine and threonine metabolism (ko00260). The branched chain amino acids, valine, leucine and isoleucine and their derivatives, are beneficial to plant growth and plant responses to stress30. As an essential amino acid, lysine metabolism affects many physiological reactions, such as the tricarboxylic acid cycle, abiotic and biotic stress responses, and starch metabolism31. The glycine, serine and threonine metabolic pathways combined with the GO enrichment results showed that the genes related to glycine catabolism and glycine dehydrogenation/decarboxylase activity changed greatly. It is known that when the activity of mitochondrial glycine decarboxylase increases, both photorespiration and photosynthesis will increase32.In terms of hormones, salicylic acid, cytokinin, and abscisic acid (ABA) can improve plant active oxygen scavenging ability. Salicylic acid can decrease the damage to seedlings due to high temperatures by improving the ability of plants to resist oxidative stress and increasing the contents of osmotic adjustment substances in cells33. Salicylic acid also has the function of delaying the degradation of D1 protein and speeding up the recovery of D1 protein when high temperatures are no longer present34. ABA can improve the heat tolerance of plants by regulating the expressions of heat stress-induced genes at the transcriptional level35.In conclusion, A. mongolicus has weak resistance to low temperatures and good adaptation to high temperatures. At the physiological level, under low-temperature stress, the proportion of Y (NO) increased, the function of PSII was damaged, and photosynthesis was inhibited. A. mongolica maintains normal physiological activities by regulating the circadian rhythm, increasing the synthesis of unsaturated fatty acids and changing the effects of plant hormones. Under high-temperature stress, A. mongolicus maintains normal photosynthesis by adjusting gsw as well as water utilization and by increasing the proportion of Y (NPQ). At the same time, A. mongolicus uses LHCII to consume excess energy, continuously assembles and maintains the normal function of PSII, and changes the types of antioxidants, such as by synthesizing anthocyanins, flavonoids, and isoflavones, to protect itself from injury. In addition, the porphyrin and chlorophyll metabolisms, carotenoid metabolism, plant hormones, amino acid metabolism, unsaturated fatty acid synthesis and other metabolic pathways that are related to the differentially expressed genes changed greatly. More

In their original description of V. rhodanica, Fischer & Riou16 determined that the previously undescribed genus was a Jurassic relative of V. infernalis. This assignment was based on the configuration of the arm crown and armature, fin type, presence of luminous organs, lateral eyes, and the absence of an ink sac. Assuming this assignment is correct, then V. rhodanica is a member of the suborder Vampyromorphina, which includes the family Vampyroteuthidae22,29.Reappraisal of the anatomy shows that V. rhodanica and V. infernalis both have 8 arms and uniserial suckers flanked by cirri. They both possess V. infernalis-like sucker attachments34,36, which are broader at the base and taper up to a radially symmetrical sucker.Both species have distinctly modified arms though the morphology differs in each. V. infernalis, has retractable filaments in the position of arm pair II27,33,34, though there is no evidence of these appendages in V. rhodanica. Instead, the species has elongate dorsal arms (arm pair I) with a unique configuration of suckers and cirri on the distal section.The suckers and cirri of V. rhodanica are more numerous than those of V. infernalis27,37. They are also more closely positioned. Proportionally, the suckers of both species have a consistent ratio to mantle length37, though the diameter of the cirri and infundibulum are greater in V. rhodanica. The V. infernalis-like attachment1,3,34 is present in both species, though in V. rhodanica, the distal part of the neck protrudes into the acetabular cavity. Of note, the sucker stalks on the dorsal arms of V. rhodanica are more elongate than those on the other arms (Figs. 2b,c, and 3a,b). This variation in suckers and their attachments suggests a specialized function between the dorsal and sessile appendages. On the longer dorsal arms, the larger sucker diameter, and more elongate stalks (Figs. 2b and 4) indicate the potential for increased mobility over their extant relatives, and possibly facilitated additional manipulation and prey capture capability.Figure 4Hypothesised reconstruction of V. rhodanica based on the data from this study (A. Lethiers, CR2P). The scale is based on measurements from the holotype (MNHN.B.74247) and the arm crown is completed using dimensions from MNHN.B.74244.Full size imageIn addition to the arm crown specialization, V. rhodanica has a more streamlined shape than V. infernalis, which is caused by a proportionally narrower head. Their muscular body is narrower and more elongate than the gelatinous V. infernalis16,27,37 suggesting a higher energy locomotory style. This is consistent with increased predation relative to the modern form. Observations in this study support many assertions of Fischer & Riou16 about the characters in V. rhodanica, though the presence of luminous organs cannot be confirmed. Rather than luminous organs much larger than those present in the deep-sea, extant V. infernalis, it is possible that these structures represent displaced cartilage prior to fossilization (Supplementary Fig. 6).Two other genera from the La Voulte-sur-Rhône locality, Gramadella and Proteroctopus are, like V. rhodanica, considered to be Incertae sedis Vampyromorpha22. All three share morphological similarities that include an elongated mantle fused with the head, and a longer dorsal arm pair with armature on the distal ends1,16,22,38. Neither the second nor fourth arm pair have been modified. Each has one pair of fins. In Gramadella, the fins are lateral and skirt-like16,38. In V. rhodanica and Proteroctopus these fins are located posteriorly1,16.V. rhodanica shows the greatest length variation between the dorsal and sessile arms (Fig. 4), though proportionally, Gramadella, and Proteroctopus have longer dorsal arms1,31. Fischer & Riou31 and Kruta et al.1 described biserial suckers in their descriptions of Gramadella, and Proteroctopus, respectively. In Proteroctopus, these suckers have a proportionally smaller diameter than the uniserial row in V. rhodanica, and do not exhibit the same tapered pattern.None of these specimens shows evidence of an ink sac, though it is present in contemporaneous genera from the same assemblage (Mastigophora, Rhomboteuthis and Romaniteuthis)8,16. That this character occurs only in some taxa from the same assemblage suggests variation in ecology, possibly associated with the steep, bathymetric relief in the La Voulte-sur-Rhône paleoenvironment11. The mosaic of characters found within the coleoid taxa at La Voulte-sur-Rhône suggests that Mesozoic vampyromorphs co-occurred in different ecological niches during the mid-Jurassic.Today, extant V. infernalis is uniquely adapted to a low-energy, deep-sea mode of life27,28,29,39, though the timing of character acquisition and progression of this ecology is unclear24. It is hypothesised that the vampyromorph Necroteuthis Kretzoi 1942 was already exploiting this niche by the Oligocene29, and that the initial shift to offshore environments was possibly driven by onshore competition24,29. The data obtained here suggests that V. rhodanica, the purportedly oldest-known genus of the Vampyromorphina group, was an active predator following a pelagic mode of life.Indeed, several anatomical details, mainly found in the brachial crown, seem to support this hypothesis. Though we cannot directly compare functionality of the arm crown elements with other Jurassic taxa, we can infer function based on observation in modern forms. In Octopoda, the sister group to Vampyromorpha, suckers are attached to the arm by a cylindrical layer of muscle, encircling oblique musculature40,41, that connects the arm musculature and the lateral margin of the acetabulum34,40,41,42. This facilitates a variety of functions including locomotion, manipulation, and prey retention43. The sucker attaches by flattening the infundibulum against the surface and then the encircling epithelium creates a watertight seal36,40,41,42,43,44,45. Contraction of the radial acetabular muscles provides the pressure differential required to create the suction force43,44,46.The stalked sucker attachments2,34 of decabrachians (Fig. 3d, and Supplementary Fig. 4) are muscular35 and connect the musculature of the arm with the base of the sucker, forming part of the acetabulum33,34. Tension on the sucker stretches this muscular attachment, which pulls locally on the acetabular base. This facilitates a greater pressure differential inside the sucker, allowing the teeth on the sucker ring to maintain the hold47.Extant V. infernalis lack decabrachian-like stalks2,18 and the neck of the attachment joins to the base of the acetabulum (Fig. 3c, and Supplementary Fig. 4), rather than being inserted into it18. The infundibulum is not distinct, and the suckers do not provide strong suction27. Instead, suckers function by secreting mucus to coat detritus—marine snow captured by retractable filaments—which is then moved to the mouth by cirri7,27.A mosaic of these characters is present in V. rhodanica (Fig. 3a,b), therefore, suggesting their potential for increased attachment and hold on prey over extant V. infernalis. These include a larger infundibular diameter, a neck attachment integrated with the acetabular muscles, and the elongated stalks of the dorsal suckers.Additionally, the paired, filamentous cirri observed in extant cirrates48 are present in V. rhodanica (Fig. 4, and Supplementary Fig. 2). In extant forms they are understood to have a sensory function and are used in the detection and capture of prey48. In V. infernalis, they serve to transport the food proximally along the arms to the mouth27. The greater diameters of cirri, and placement along the entire arm in V. rhodanica (Fig. 4), suggests an increased sensory function in these fossil forms.The shape of the arms also contributes to the suction potential49 in coleoids. Functional analysis in Octopoda highlights a positive correlation between distal tapering of the arms and their flexibility. A tapered, flexible arm facilitates more precise adhesion than a cylindrical-shaped one and requires a greater force for sucker detachment49. Suckers detach sequentially, rather than the more simultaneous release observed in models of arms with less taper variation. The tapered diameter of the suckers, like those seen on the sessile arms of V. rhodanica, potentially facilitated this kind of sequential detachment49 allowing them more adherence force and flexibility. Though V. rhodanica has just two suckers on the distal tips of their dorsal arms, the most distal is marginally smaller in diameter than the proximal one. On the dorsal arms, this tapering is observed in conjunction with a well-developed axial nerve cord (Fig. 2b). In extant forms, the nerve cord facilitates complex motor functions42. The combination of these characters in V. rhodanica suggests their arms had increased potential to be actively used in prey capture50 over extant V. infernalis.Though arm crown characters offer insight on the ecology of V. rhodanica, in fossil coleoid phylogenies only a few characters are based on the suckers1, 3. Two studies that have attempted to create a phylogeny using morphological characters that include both fossil and extant taxa return V. rhodanica and V. infernalis as sister taxa1,3. These matrices are, by necessity, heavily influenced by the gladius51 and more than 50% of the characters are based on this feature1,3. Indeed, the authors1 note that the lack of gladius data for some fossil forms, including V. rhodanica, creates an inherent bias in the phylogenetic matrix. Fischer & Riou16 suggested that V. rhodanica and V. infernalis are related on the basis of the observable morphological characters in the family Vampyroteuthidae, though without morphological information on the gladius, a recent systematic synthesis of fossil Octobrachia22 positioned V. rhodanica as Vampyromorpha Incertae sedis.X-ray CT analysis in this study did not allow a reconstruction of the gladius. Nevertheless, it does provide new data on soft tissues, and permits comparisons between extant and fossil taxa. Specifically, we can add distinct states to 4 of the 132 characters in the existing phylogenetic matrix from Sutton et al.3 that was modified and used in Kruta et al.1. These four characters (#89–#92) represent the suckers, and sucker attachments. Detailed examination revealed that the sessile and dorsal arms have the Vampyroteuthis-like attachment. In the dorsal arms, this is more elongated, though it cannot be considered pedunculate like those seen in modern decabrachians. Indeed, the attachment type (plug and base34) is the same, only the length varies. As previously discussed, this variation may have functional implications.When updated with these new data, the matrix from this study returns the same topology seen in Kruta et al.1 that supports the positioning of V. rhodanica and V. infernalis as sister taxa. Further, it strengthens their relationship as they both share a sucker attachment that is not clearly attached to the arm muscles, a state that was previously considered autapomorphic in V. infernalis. However, it is important to note that no additional characters were added for the gladius, which is the cornerstone of coleoid systematics52. Indeed, just 29 of the 132 matrix characters can so far be coded for V. rhodanica, with only 9 of these relating to the 74 states of the gladius.Assuming the phylogenetic work so far is correct, then both species belong to the family Vampyromorphina, and are joined by the Oligocene fossil Necroteuthis hungarica29. While the lack of gladius characters precludes a full phylogenetic understanding of this group, preservation and observation of the soft tissues allow us to infer information regarding palaeobiology.The data obtained in this study demonstrates that the characters observed in V. infernalis, including the sucker attachments and lack of ink sac, were present in Jurassic Vampyromorpha. Comparative anatomy of V. rhodanica and extant V. infernalis revealed that the fossil taxon displayed more morphological variation and were more diversified than previously understood. The assemblage of characters observed in V. rhodanica are consistent with a pelagic predatory lifestyle and corroborate the likelihood of a distinctly different ecological niche. These findings support the hypothesis that a shift towards a deep-sea environment occurred prior to the Oligocene5,29. More

Thiel, M. et al. The Humboldt Current System of northern and central Chile—Oceanographic processes, ecological interactions and socioeconomic feedback. Oceanogr. Mar. Biol. Annu. Rev. 45, 195–344 (2007).
Google Scholar 
FAO. The State of World Fisheries and Aquaculture 2020. Sustainability in action 2020.Castilla, J. C. & Camus, P. A. The Humboldt-El Niño scenario: Coastal benthic resources and anthropogenic influences, with particular reference to the 1982/83 ENSO. S. Afr. J. Mar. Sci. 12, 703–712. https://doi.org/10.2989/02577619209504735 (1992).Article 

Google Scholar 
Alheit, J. & Niquen, M. Regime shifts in the Humboldt Current ecosystem. Prog. Oceanogr. 60, 201–222. https://doi.org/10.1016/j.pocean.2004.02.006 (2004).Article 

Google Scholar 
González, H. E. et al. Carbon fluxes within the epipelagic zone of the Humboldt Current System off Chile: The significance of euphausiids and diatoms as key functional groups for the biological pump. Prog. Oceanogr. 83, 217–227. https://doi.org/10.1016/j.pocean.2009.07.036 (2009).Article 

Google Scholar 
Quiñones, R. A., Levipan, H. A. & Urrutia, H. Spatial and temporal variability of planktonic archaeal abundance in the Humboldt Current System off Chile. Deep Sea Res. Part II 56, 1073–1082. https://doi.org/10.1016/j.dsr2.2008.09.012 (2009).Article 

Google Scholar 
Antezana, T. Euphausia mucronata: A keystone herbivore and prey of the Humboldt Current System. Deep Sea Res. Part II 57, 652–662. https://doi.org/10.1016/j.dsr2.2009.10.014 (2010).Article 

Google Scholar 
Anguita, C., Gelcich, S., Aldana, M. & Pulgar, J. Exploring the influence of upwelling on the total allowed catch and harvests of a benthic gastropod managed under a territorial user rights for fisheries regime along the Chilean coast. Ocean Coast. Manag. 195, 105256. https://doi.org/10.1016/j.ocecoaman.2020.105256 (2020).Article 

Google Scholar 
González, J. E., Yannicelli, B. & Stotz, W. The interplay of natural variability, productivity and management of the benthic ecosystem in the Humboldt Current System: Twenty years of assessment of Concholepas concholepas fishery under a TURF management system. Ocean Coast. Manag. 208, 105628. https://doi.org/10.1016/j.ocecoaman.2021.105628 (2021).Article 

Google Scholar 
Canales, T. M. et al. Endogenous, climate, and fishing influences on the population dynamics of Small Pelagic Fish in the Southern Humboldt Current Ecosystem. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00082 (2020).Article 

Google Scholar 
González, J. E., Ortiz, M. Exploring harvest strategies in a benthic habitat in the Humboldt Current System (Chile): A study case. In Marine Coastal Ecosystems Modelling and Conservation: Latin American Experiences 127–141 (Springer International Publishing, 2021). https://doi.org/10.1007/978-3-030-58211-1_6.Ortiz, M. Pre-image population indices for anchovy and sardine species in the Humboldt Current System off Peru and Chile: Years decaying productivity. Ecol. Ind. 119, 106844. https://doi.org/10.1016/j.ecolind.2020.106844 (2020).Article 

Google Scholar 
Tognelli, M. F., Silva-Garcia, C., Labra, F. A. & Marquet, P. A. Priority areas for the conservation of coastal marine vertebrates in Chile. Biol. Conserv. 126, 420–428. https://doi.org/10.1016/j.biocon.2005.06.021 (2005).Article 

Google Scholar 
Bustamante, C., Vargas-Caro, C. & Bennett, M. B. Not all fish are equal: Functional biodiversity of cartilaginous fishes (Elasmobranchii and Holocephali) in Chile. J. Fish Biol. 85, 1617–1633. https://doi.org/10.1111/jfb.12517 (2014).CAS 
Article 
PubMed 

Google Scholar 
Sarmiento-Devia, R. A., Harrod, C. & Pacheco, A. S. Ecology and Conservation of Sea Turtles in Chile. Chelonian Conserv. Biol. 14, 21–33. https://doi.org/10.2744/ccab-14-01-21-33.1 (2015).Article 

Google Scholar 
Pérez-Álvarez, M. J., Alvarez, E., Aguayo-Lobo, A. & Olavarría, C. Occurrence and distribution of Chilean dolphin (Cephalorhynchus eutropia) in coastal waters of central Chile. N.Z. J. Mar. Freshw. Res. 41, 405–409. https://doi.org/10.1080/00288330709509931 (2007).Article 

Google Scholar 
Pacheco, A. S. et al. Cetacean diversity revealed from whale-watching observations in Northern Peru. Aquat. Mamm. 45, 116–122. https://doi.org/10.1578/AM.45.1.2019.116 (2019).Article 

Google Scholar 
Buchan, S. J., Vásquez, P., Olavarría, C. & Castro, L. R. Prey items of baleen whale species off the coast of Chile from fecal plume analysis. Mar. Mamm. Sci. 37, 1116–1127 (2021).Article 

Google Scholar 
Hucke-Gaete, R. et al. From Chilean Patagonia to Galapagos, Ecuador: Novel insights on blue whale migratory pathways along the Eastern South Pacific. PeerJ 6, e4695. https://doi.org/10.7717/peerj.4695 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Llapapasca, M. A. et al. Modeling the potential habitats of dusky, commons and bottlenose dolphins in the Humboldt Current System off Peru: The influence of non-El Niño vs. El Niño 1997–98 conditions and potential prey availability. Prog. Oceanogr. 168, 169–181. https://doi.org/10.1016/j.pocean.2018.09.003 (2018).Article 

Google Scholar 
Sepúlveda, M. et al. From whaling to whale watching: Identifying fin whale critical foraging habitats off the Chilean coast. Aquat. Conserv. Mar. Freshw. Ecosyst. 28, 821–829. https://doi.org/10.1002/aqc.2899 (2018).Article 

Google Scholar 
Williams, R. et al. Chilean blue whales as a case study to illustrate methods to estimate abundance and evaluate conservation status of rare species. Conserv. Biol. 25, 526–535. https://doi.org/10.1111/j.1523-1739.2011.01656.x (2011).Article 
PubMed 

Google Scholar 
Moore, J. E. & Barlow, J. Bayesian state-space model of fin whale abundance trends from a 1991–2008 time series of line-transect surveys in the California Current. J. Appl. Ecol. 48, 1195–1205. https://doi.org/10.1111/j.1365-2664.2011.02018.x (2011).Article 

Google Scholar 
Campbell, G. S. et al. Inter-annual and seasonal trends in cetacean distribution, density and abundance off southern California. Deep Sea Res. Part II 112, 143–157. https://doi.org/10.1016/j.dsr2.2014.10.008 (2015).Article 

Google Scholar 
Nichol, L. M., Wright, B. M., O’Hara, P. & Ford, J. K. B. Risk of lethal vessel strikes to humpback and fin whales off the west coast of Vancouver Island, Canada. Endanger. Species Res. 32, 373–390. https://doi.org/10.3354/esr00813 (2017).Article 

Google Scholar 
Pennino, M. G. et al. A spatially explicit risk assessment approach: Cetaceans and marine traffic in the Pelagos Sanctuary (Mediterranean Sea). PLoS One 12, e0179686. https://doi.org/10.1371/journal.pone.0179686 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Van Waerebeek, K. & Reyes, J. C. Catch of small cetaceans at Pucusana Port, central Peru, during 1987. Biol. Conserv. 51, 15–22. https://doi.org/10.1016/0006-3207(90)90028-N (1990).Article 

Google Scholar 
Mangel, J. C. et al. Small cetacean captures in Peruvian artisanal fisheries: High despite protective legislation. Biol. Conserv. 143, 136–143. https://doi.org/10.1016/j.biocon.2009.09.017 (2010).Article 

Google Scholar 
Campbell, E., Pasara-Polack, A., Mangel, J. C. & Alfaro-Shigueto, J. Use of small cetaceans as bait in small-scale fisheries in Peru. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.534507 (2020).Article 

Google Scholar 
Reyes, J. C. & Oporto, J. A. Gillnet fisheries and cetaceans in the southeast Pacific. Report of the International Whaling Commission 467–474 (1994).Aguayo-Lobo, A. Los cetáceos y sus perspectivas de conservación. Estudios Oceanológicos 18, 35–43 (1999).
Google Scholar 
Félix, F., Muñoz, M., Falconí, J., Botero, N., Haase, B., et al. Entanglement of humpback whales in artisanal fishing gear in Ecuador. J. Cetacean. Res. Manag. 283–290 (2020).Félix, F. et al. Challenges and opportunities for the conservation of marine mammals in the Southeast Pacific with the entry into force of the U.S. Marine Mammal Protection Act. Reg. Stud. Mar. Sci. 48, 102036. https://doi.org/10.1016/j.rsma.2021.102036 (2021).Article 

Google Scholar 
García-Cegarra, A. M. & Pacheco, A. S. Collision risk areas between fin and humpback whales with large cargo vessels in Mejillones Bay (23°S), northern Chile. Mar. Policy 103, 182–186. https://doi.org/10.1016/j.marpol.2018.12.022 (2019).Article 

Google Scholar 
Santos-Carvallo, M. et al. Impacts of whale-watching on the short-term behavior of Fin Whales (Balaenoptera physalus) in a marine protected area in the southeastern pacific. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.623954 (2021).Article 

Google Scholar 
Villagra, D., García-Cegarra, A., Gallardo, D. I. & Pacheco, A. S. Energetic effects of whale-watching boats on humpback whales on a breeding ground. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.600508 (2021).Article 

Google Scholar 
Buckland, S., Anderson, D., Burnham, K., Laake, J., Borchers, D., Thomas, L. Introduction to Distance Sampling Estimating Abundance of Biological Populations. (Oxford University Press, 2001).Hedley, S. L. & Buckland, S. T. Spatial models for line transect sampling. JABES 9, 181–199. https://doi.org/10.1198/1085711043578 (2004).Article 

Google Scholar 
Williams, R., Hedley, S. L., Hammond, P. S. Modeling distribution and abundance of Antarctic baleen whales using ships of opportunity (2006).DoniolValcroze, T., Berteaux, D., Larouche, P. & Sears, R. Influence of thermal fronts on habitat selection by four rorqual whale species in the Gulf of St. Lawrence. Mar. Ecol. Prog. Ser. 335, 207–216. https://doi.org/10.3354/meps335207 (2007).Article 

Google Scholar 
Scales, K. L. et al. Should I stay or should I go? Modelling year-round habitat suitability and drivers of residency for fin whales in the California Current. Divers. Distrib. 23, 1204–1215. https://doi.org/10.1111/ddi.12611 (2017).Article 

Google Scholar 
Bedriñana-Romano, L. et al. Integrating multiple data sources for assessing blue whale abundance and distribution in Chilean Northern Patagonia. Divers. Distrib. https://doi.org/10.1111/ddi.12739 (2018).Article 

Google Scholar 
Bedriñana-Romano, L. et al. Defining priority areas for blue whale conservation and investigating overlap with vessel traffic in Chilean Patagonia, using a fast-fitting movement model. Sci. Rep. 11, 2709. https://doi.org/10.1038/s41598-021-82220-5 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pirotta, E., Matthiopoulos, J., MacKenzie, M., Scott-Hayward, L. & Rendell, L. Modelling sperm whale habitat preference: A novel approach combining transect and follow data. Mar. Ecol. Prog. Ser. 436, 257–272. https://doi.org/10.3354/meps09236 (2011).Article 

Google Scholar 
Mendelssohn, R. rerddapXtracto: Extracts Environmental Data from “ERDDAP” Web Services. (2020).Lau-Medrano, W. grec: Gradient-Based Recognition of Spatial Patterns in Environmental Data. (2020).Belkin, I. M. & O’Reilly, J. E. An algorithm for oceanic front detection in chlorophyll and SST satellite imagery. J. Mar. Syst. 78, 319–326. https://doi.org/10.1016/j.jmarsys.2008.11.018 (2009).Article 

Google Scholar 
Hijmans, R. J., van Etten, J., Cheng, J., Sumner, M., Mattiuzzi, M., Greenberg, J. A., et al. raster: Geographic Data Analysis and Modeling. (2018).Royle, J. A. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60, 108–115. https://doi.org/10.1111/j.0006-341X.2004.00142.x (2004).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
Chelgren, N. D., Samora, B., Adams, M. J. & McCreary, B. Using spatiotemporal models and distance sampling to map the space use and abundance of newly metamorphosed Western Toads (Anaxyrus boreas). Herpetol. Conserv. Biol. 6, 16 (2011).
Google Scholar 
Hartig, F., Lohse, L. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. (2022).Gelman, A., Meng, X.-L. & Stern, H. Posterior predictive assessment of model fitness via realized discrepancies. Stat. Sin. 6, 733–760. https://doi.org/10.2307/24306036 (1996).MathSciNet 
Article 
MATH 

Google Scholar 
Kery, M. & Royle, J. A. Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS: Volume 1: Prelude and Static Models. (Academic Press, 2015).R DCT. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2015).Plummer, M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. (2003).Fonnesbeck, C. J., Garrison, L. P., Ward-Geiger, L. I. & Baumstark, R. D. Bayesian hierarchichal model for evaluating the risk of vessel strikes on North Atlantic right whales in the SE United States. Endanger. Species Res. 6, 87–94. https://doi.org/10.3354/esr00134 (2008).Article 

Google Scholar 
Vanderlaan, A. S. M., Taggart, C. T., Serdynska, A. R., Kenney, R. D. & Brown, M. W. Reducing the risk of lethal encounters: Vessels and right whales in the Bay of Fundy and on the Scotian Shelf. Endanger. Species Res. 4, 283–297. https://doi.org/10.3354/esr00083 (2008).Article 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x (2008).Article 
PubMed 

Google Scholar 
Hijmans, R. J., Phillips, S., Leathwick, J., Elith, J. & Hijmans, M. R. J. Package ‘dismo’. Circles 9, 1–68 (2017).
Google Scholar 
Daneri, G. et al. Primary production and community respiration in the Humboldt Current System off Chile and associated oceanic areas. Mar. Ecol. Prog. Ser. 197, 41–49. https://doi.org/10.3354/meps197041 (2000).Article 

Google Scholar 
Montecino, V. & Lange, C. B. The Humboldt Current System: Ecosystem components and processes, fisheries, and sediment studies. Prog. Oceanogr. 83, 65–79. https://doi.org/10.1016/j.pocean.2009.07.041 (2009).Article 

Google Scholar 
Escribano, R., Hidalgo, P. & Krautz, C. Zooplankton associated with the oxygen minimum zone system in the northern upwelling region of Chile during March 2000. Deep Sea Res. Part II 56, 1083–1094. https://doi.org/10.1016/j.dsr2.2008.09.009 (2009).Article 

Google Scholar 
Perez-Alvarez, M. et al. Fin whales (Balaenoptera physalus) feeding on Euphausia mucronata in nearshore waters off North-Central Chile. Aquat. Mamm. 32, 109–113. https://doi.org/10.1578/AM.32.1.2006.109 (2006).Article 

Google Scholar 
Riquelme-Bugueño, R. et al. Fatty acid composition in the endemic Humboldt Current krill, Euphausia mucronata (Crustacea, Euphausiacea) in relation to the phytoplankton community and oceanographic variability off Dichato coast in central Chile. Prog. Oceanogr. 188, 102425. https://doi.org/10.1016/j.pocean.2020.102425 (2020).Article 

Google Scholar 
Escribano, R., Marin, V. & Irribarren, C. Distribution of Euphausia mucronata at the upwelling area of Peninsula Mejillones, northern Chile: The influence of the oxygen minimum layer. Sci. Mar. 64, 69–77. https://doi.org/10.3989/scimar.2000.64n169 (2000).Article 

Google Scholar 
Riquelme-Bugueno, R., Escribano, R. & Gomez-Gutierrez, J. Somatic and molt production in Euphausia mucronata off central-southern Chile: The influence of coastal upwelling variability. Mar. Ecol. Prog. Ser. 476, 39–57 (2013).Article 

Google Scholar 
Savoca, M. S. et al. Baleen whale prey consumption based on high-resolution foraging measurements. Nature 599, 85–90. https://doi.org/10.1038/s41586-021-03991-5 (2021).CAS 
Article 
PubMed 

Google Scholar 
Roman, J. & McCarthy, J. J. The whale pump: Marine mammals enhance primary productivity in a coastal basin. PLoS One 5, e13255. https://doi.org/10.1371/journal.pone.0013255 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hucke-Gaete, R. Whales might also be an important component in patagonian fjord ecosystems: Comment to Iriarte et al. Ambio 40, 104–105. https://doi.org/10.1007/s13280-010-0110-8 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Lavery, T. J. et al. Whales sustain fisheries: Blue whales stimulate primary production in the Southern Ocean. Mar. Mamm. Sci. https://doi.org/10.1111/mms.12108 (2014).Article 

Google Scholar 
Roman, J. et al. Whales as marine ecosystem engineers. Front. Ecol. Environ. 12, 377–385. https://doi.org/10.1890/130220 (2014).Article 

Google Scholar 
Hucke-Gaete, R., Osman, L. P., Moreno, C. A., Findlay, K. P. & Ljungblad, D. K. Discovery of a blue whale feeding and nursing ground in southern Chile. Proc. R. Soc. Lond. B 271, S170–S173. https://doi.org/10.1098/rsbl.2003.0132 (2004).Article 

Google Scholar 
Buchan, S. J. & Quiones, R. A. First insights into the oceanographic characteristics of a blue whale feeding ground in northern Patagonia, Chile. Mar. Ecol. Prog. Ser. 554, 183–199. https://doi.org/10.3354/meps11762 (2016).CAS 
Article 

Google Scholar 
Findlay, K., Pitman, R., Tsurui, T., Sakai, K., Ensor, P., Iwakami, H., et al. IWC-southern whale and ecosystem research (IWC/SOWER) blue whale Cruise, Chile. Documento Técnico, IWC 1998 (1998).Branch, T. A. et al. Past and present distribution, densities and movements of blue whales Balaenoptera musculus in the Southern Hemisphere and northern Indian Ocean. Mamm. Rev. 37, 116–175. https://doi.org/10.1111/j.1365-2907.2007.00106.x (2007).Article 

Google Scholar 
Barlow, D. R., Klinck, H., Ponirakis, D., Garvey, C. & Torres, L. G. Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci. Rep. 11, 6915. https://doi.org/10.1038/s41598-021-86403-y (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Galletti-Vernazzani, B., Jackson, J. A., Cabrera, E., Carlson, C. A. Jr. & RLB.,. Estimates of abundance and trend of chilean blue whales off Isla de Chiloé, Chile. PLoS One 12, e0168646. https://doi.org/10.1371/journal.pone.0168646 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Friedlaender, A. S., Goldbogen, J. A., Hazen, E. L., Calambokidis, J. & Southall, B. L. Feeding performance by sympatric blue and fin whales exploiting a common prey resource. Mar. Mamm. Sci. 31, 345–354. https://doi.org/10.1111/mms.12134 (2015).Article 

Google Scholar 
Abrahms, B. et al. Memory and resource tracking drive blue whale migrations. PNAS 116, 5582–5587 (2019).CAS 
Article 

Google Scholar 
Clarke, R., Aguayo, A. & Basulto, S. Whale observation and whale marking off the coast of Chile in 1964. Sci. Rep. Whales Res. Inst. Tokyo 30, 117–178 (1978).
Google Scholar 
Allison, C. IWC individual and summary catch databases Version 5.5 (12 February 2013). Available from the International Whaling Commission 135 (2013).Pastene, L. A., Acevedo, J. & Branch, T. A. Morphometric analysis of Chilean blue whales and implications for their taxonomy. Mar. Mamm. Sci. 36, 116–135. https://doi.org/10.1111/mms.12625 (2020).Article 

Google Scholar 
Rendell, L., Whitehead, H. & Escribano, R. Sperm whale habitat use and foraging success off northern Chile: Evidence of ecological links between coastal and pelagic systems. Mar. Ecol. Prog. Ser. 275, 289–295. https://doi.org/10.3354/meps275289 (2004).Article 

Google Scholar 
Jaquet, N. & Whitehead, H. Scale-dependent correlation of sperm whale distribution with environmental features and productivity in the South Pacific. Mar. Ecol. Prog. Ser. 135, 1–9. https://doi.org/10.3354/meps135001 (1996).Article 

Google Scholar 
O’Hern, J. E., Biggs, D. C. Sperm whale (Physeter macrocephalus) habitat in the Gulf of Mexico: Satellite observed ocean color and altimetry applied to small-scale variability in distribution. Aquat. Mamm. 35 (2009).Koen Alonso, M., Crespo, E. A., García, N. A., Pedraza, S. N. & Coscarella, M. A. Diet of dusky dolphins, Lagenorhynchus obscurus, in waters off Patagonia, Argentina. Fish. Bull. 96, 366–374 (1998).
Google Scholar 
García-Godos, I., Waerebeek, K. V., Reyes, J. C., Alfaro-Shigueto, J. & Arias-Schreiber, M. Prey occurrence in the stomach contents of four small cetacean species in Peru. Latin Am. J. Aquat. Mamm. 6, 171–183. https://doi.org/10.5597/lajam00122 (2007).Article 

Google Scholar 
Dans, S. L., Crespo, E. A., Koen-Alonso, M., Markowitz, T. M., Berón Vera, B., Dahood, A. D. Chapter 3—Dusky dolphin trophic ecology: Their role in the food web. In The Dusky Dolphin (eds. Würsig, B., Würsig, M.) 49–74 (Academic Press, 2010). https://doi.org/10.1016/B978-0-12-373723-6.00003-5.Romero, M. A. et al. Feeding habits of two sympatric dolphin species off North Patagonia, Argentina. Mar. Mamm. Sci. 28, 364–377 (2012).Article 

Google Scholar 
Loizaga de Castro, R. et al. Feeding ecology of dusky dolphins Lagenorhynchus obscurus: Evidence from stable isotopes. J. Mammal. 97, 310–320. https://doi.org/10.1093/jmammal/gyv180 (2016).Article 

Google Scholar 
Cipriano, F. W. Behavior and occurrence patterns, feeding ecology, and life history of dusky dolphins (Lagenorhynchus obscurus) off Kaikoura, New Zealand. (1992).Benoit-Bird, K. J., Würsig, B. & Mfadden, C. J. Dusky dolphin (lagenorhynchus obscurus) foraging in two different habitats: Active acoustic detection of dolphins and their prey. Mar. Mamm. Sci. 20, 215–231. https://doi.org/10.1111/j.1748-7692.2004.tb01152.x (2004).Article 

Google Scholar 
Van Waerebeek, K. Records of dusky dolphins Lagenorhynchus obscurus (Gray, 1828) in the eastern South Pacific. Beaufortia (1992).Selzer, L. A. & Payne, P. M. The distribution of white-sided (Lagenorhynchus acutus) and common dolphins (Delphinus delphis) vs. Environmental features of the continental shelf of the Northeastern United States. Mar. Mamm. Sci. 4, 141–153. https://doi.org/10.1111/j.1748-7692.1988.tb00194.x (1988).Article 

Google Scholar 
Neumann, D. R. Seasonal movements of short-beaked common dolphins (Delphinus delphis) in the north-western Bay of Plenty, New Zealand: Influence of sea surface temperature and El Niño/La Niña. N.Z. J. Mar. Freshw. Res. 35, 371–374. https://doi.org/10.1080/00288330.2001.9517007 (2001).Article 

Google Scholar 
Peters, K. J. et al. Foraging ecology of the common dolphin Delphinus delphis revealed by stable isotope analysis. Mar. Ecol. Prog. Ser. 652, 173–186. https://doi.org/10.3354/meps13482 (2020).CAS 
Article 

Google Scholar 
Brand, D. et al. Common dolphins, common in neritic waters off southern Israel, demonstrate uncommon dietary habits. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 15–21. https://doi.org/10.1002/aqc.3165 (2021).Article 

Google Scholar 
Barlow, J. & Taylor, B. L. Estimates of sperm whale abundance in the Northeastern temperate pacific from a combined acoustic and visual survey. Mar. Mamm. Sci. 21, 429–445. https://doi.org/10.1111/j.1748-7692.2005.tb01242.x (2005).Article 

Google Scholar 
Cañadas, A., Desportes, G. & Borchers, D. Estimation of g (0) and abundance of common dolphins (Delphinus delphis) from the NASS-95 Faroese survey. J. Cetac. Res. Manag. 6, 191–198 (2004).
Google Scholar 
Miller, D. L., Burt, M. L., Rexstad, E. A. & Thomas, L. Spatial models for distance sampling data: Recent developments and future directions. Methods Ecol. Evol. 4, 1001–1010. https://doi.org/10.1111/2041-210X.12105 (2013).Article 

Google Scholar 
Sigourney, D. B. et al. Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: Insights from simulations and an application to fin whales (Balaenoptera physalus). PeerJ 8, e8226. https://doi.org/10.7717/peerj.8226 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Panigada, S. et al. Mediterranean fin whales at risk from fatal ship strikes. Mar. Pollut. Bull. 52, 1287–1298. https://doi.org/10.1016/j.marpolbul.2006.03.014 (2006).CAS 
Article 
PubMed 

Google Scholar 
Ribeiro, S., Viddi, F. A. & Freitas, T. R. Behavioural responses of Chilean dolphins (Cephalorhynchus eutropia) to boats in Yaldad Bay, southern Chile. Aquat. Mamm. 31, 234 (2005).Article 

Google Scholar 
Bearzi, G. et al. Overfishing and the disappearance of short-beaked common dolphins from western Greece. Endanger. Species Res. 5, 1–12. https://doi.org/10.3354/esr00103 (2008).Article 

Google Scholar 
Reeves, R. R., McClellan, K. & Werner, T. B. Marine mammal bycatch in gillnet and other entangling net fisheries, 1990 to 2011. Endanger. Species Res. 20, 71–97. https://doi.org/10.3354/esr00481 (2013).Article 

Google Scholar 
van der Hoop, J. M. et al. Vessel strikes to large whales before and after the 2008 Ship Strike Rule. Conserv. Lett. 8, 24–32. https://doi.org/10.1111/conl.12105 (2015).Article 

Google Scholar 
Erbe, C., Reichmuth, C., Cunningham, K., Lucke, K. & Dooling, R. Communication masking in marine mammals: A review and research strategy. Mar. Pollut. Bull. 103, 15–38. https://doi.org/10.1016/j.marpolbul.2015.12.007 (2016).CAS 
Article 
PubMed 

Google Scholar 
González-But, J. C. & Sepúlveda, M. Captura incidental del delfín común (Delphinus delphis) en la pesquería industrial de cerco, norte de Chile. Rev. Biol. Mar. Oceanogr. 51, 429–433. https://doi.org/10.4067/S0718-19572016000200019 (2016).Article 

Google Scholar 
Alvarado-Rybak, M. et al. Pathological findings in cetaceans sporadically stranded along the Chilean Coast. Front. Mar. Sci. 7, 684. https://doi.org/10.3389/fmars.2020.00684 (2020).Article 

Google Scholar 
Dans, S. L., Koen, A. M., Pedraza, S. & Crespo, E. A. Incidental catch of dolphins in trawling fisheries off Patagonia, Argentina: Can populations persist?. Ecol. Appl. 13, 754–762. https://doi.org/10.1890/1051-0761(2003)013[0754:ICODIT]2.0.CO;2 (2003).Article 

Google Scholar 
Childerhouse S, Baxter A. Human interactions with dusky dolphins: A management perspective, Chapter 12. In The Dusky Dolphin (eds. Würsig, B. & Würsig, M.) 245–275 (Academic Press, 2010). https://doi.org/10.1016/B978-0-12-373723-6.00012-6.Mannocci, L. et al. Assessing the impact of bycatch on dolphin populations: The case of the common dolphin in the Eastern North Atlantic. PLoS One 7, e32615. https://doi.org/10.1371/journal.pone.0032615 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Thompson, F. N., Abraham, E. R. & Berkenbusch, K. Common dolphin (Delphinus delphis) Bycatch in New Zealand commercial trawl fisheries. PLoS One 8, e64438. https://doi.org/10.1371/journal.pone.0064438 (2013).Article 
PubMed 
PubMed Central 

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Life cycle cost modelAn analysis of life cycle costs is an economic analysis of the assessment of the total cost of acquisition, ownership and liquidation of a product. It is applicable during the entire life cycle of the product or a life cycle stage or combination of different stages21 and22.There are five period phases of the vehicle life cycle:Generally, the total costs for the above listed phases are acquisition costs, ownership costs and liquidation costs21 and22. For the LCC model, I recommend to divide the life cycle costs into four categories:$$LCC={C}_{P}+{C}_{M}+{C}_{O}+{C}_{D},$$
(1)
$${LCC}_{s}=frac{LCC}{t},$$
(2)

where LCC—the life cycle cost of vehicles, LCCs—the specific life cycle cost of vehicles, CP—the vehicle purchase cost, CM—the maintenance cost, CO—operating state of vehicle cost, CD—the vehicle disposal cost, t—the time of vehicle operation.The model for evaluating the economic viability of products is based on the general LCC model which is based on acquisition and ownership costs$$LCC={C}_{P}+{C}_{OW},$$
(3)

where CP—purchase cost, COW—ownership costs.Acquisition cost (CP) is represented by the purchase price at the time of acquisition of the assessed passenger vehicle.Ownership cost (COW) is significant during the life cycle of a motor vehicle and varies according to the type of the vehicle. This cost includes the costs of maintenance and operation time can be defined as follows10$${C}_{Ow}={C}_{M}+{C}_{O},$$
(4)

where CM—cost of maintenance, CO—operation cost.The cost of ownership a vehicle (COW) can be defined as follows$${C}_{OW}={C}_{O}+{C}_{MC}+{C}_{MP},$$
(5)

where CO—operation cost, CMC—corrective maintenance cost, CMP—preventive maintenance cost.The cost of ownership (COW) may include the operating and maintenance costs which consist of the corrective maintenance cost (CMC) and the cost of preventive maintenance (CMP) of a motor vehicle.Calculation of operating costsOperating cost CO is determined by the price and amount consumed of conventional or alternative types of fuel. It cover the cost of fuel CF, operating fluids, oils and lubricants COL that are supplied during vehicle operation (not during service inspection), tyres CT, accumulator batteries CAB, vehicle insurance fee and road tax or other mandatory fees CIRT, cost of the motorway tax sticker CMT, mandatory vehicle inspection and emission measurement in special vehicles CETC. The costs are calculated according to$${C}_{O}={C}_{F}+{C}_{OL}+{C}_{T}+{C}_{AB}+{C}_{IRT}+{C}_{MT}+{C}_{ETC}.$$
(6)
Fuel costs (CF) are affected by the average consumption of a given type of propulsion vehicle. Then the comparative fuel costs (CF) can be expressed by the equation$${C}_{F}=frac{{bar{c}}_{aF}}{100}{p}_{F}{t}_{l},$$
(7)

where CF—total fuel costs (EUR), (bar{c})aF—average fuel consumption (l/100 km), pF—fuel price (EUR/l), tl—service life of a passenger vehicle (km).Costs for operating fluids, oils and lubricants (COL) are any costs for operating fluids, oils and lubricants that are replenished during operation and not during service maintenance; it can be expressed by the equation$${C}_{OL}=frac{{bar{c}}_{aOL}}{100}{p}_{OL}{t}_{l},$$
(8)

where (bar{c})aOL—average consumption of oil and lubricant (l/100 km), pOL—price of oil and lubricant (EUR/l).The cost of tyres (CT) can be expressed by the equation$${C}_{T}=frac{{t}_{l}}{{bar{d}}_{aT}}{n}_{T}{p}_{T},$$
(9)

where (bar{d})aT—average life of a passenger vehicle tyre (km), nt—number of tyres on the passenger vehicle (pc), pT—price of one piece of tyre (EUR).Accumulator battery costs (CAB) —can be expressed by the equation$${C}_{AB}=frac{{t}_{l}}{{bar{d}}_{aAB}}{n}_{AB}{p}_{AB},$$
(10)

where (bar{d}_{aB})—average life of one accumulator battery (km), nAB—number of accumulator batteries in the passenger vehicle (pc), pAB—price of an accumulator battery (EUR).Costs arising from laws (CIRT) are the costs of motor vehicle insurance (compulsory liability, accident insurance, or other). Some of them can be omitted in case of the same costs due to the simplification of the model. Otherwise, they can be expressed by the equation$${C}_{IRT}=left({C}_{SI}+{C}_{AI}+{C}_{RT}+{C}_{R}right){t}_{la},$$
(11)
where CS1—price of mandatory annual insurance of a passenger vehicle (EUR), CA1—price of the annual accident insurance of a passenger vehicle (EUR), CRT—price of annual road tax (EUR), CR—price of statutory fee (EUR), tla—operating time of the passenger vehicle until decommissioning (years).The cost of obtaining a motorway sticker (CMT) may be omitted if the same type of passenger vehicle is compared. Otherwise, the cost of a motorway sticker (CMT) can be expressed by the equation$${C}_{MT}={c}_{MT}{t}_{la},$$
(12)

where cMT—price of annual motorway sticker for the passenger vehicle (EUR).The costs of the mandatory vehicle inspection and emission measurement (CETC) include the costs incurred for the measurement of emissions of the drive engine unit (CE) and for the technical inspection of the passenger vehicle (CTC). For the proposed model, the costs of the mandatory technical inspections and emission measurements can be expressed by the equation$${C}_{ETC}=left({C}_{E}+{C}_{TC}right)frac{{y}_{n}}{{t}_{la}},$$
(13)

where CE—costs related to the measurement of passenger vehicle emissions (EUR), CTC—costs of mandatory technical inspection (EUR), yn—number of years of legal validity of emission measurement and technical condition for the given type of the passenger vehicle (years).Calculation of maintenance costThe total costs for vehicle maintenance CM consist of the cost of preventive maintenance CMP and the cost of corrective maintenance CMC10,11$${C}_{M}={C}_{MC}+{C}_{MP}.$$
(14)
Vehicle maintenance costs include the cost of material and the cost of labour$${C}_{M}={(C}_{MCM}+{C}_{MCL}+{C}_{MCF})+left({C}_{MPM}+{C}_{MPL}+{C}_{MPF}right),$$
(15)

where CM—cumulative maintenance costs, CMC—corrective maintenance costs, CMP—preventive maintenance costs, CMCM—costs of material used for corrective maintenance, CMCL—costs of labour force for corrective maintenance, CMCF—costs of workshop equipment used for corrective maintenance, CMPM—costs of material used for preventive maintenance, CMPL—costs of labour force for preventive maintenance, CMPF—costs of workshop equipment used for preventive maintenance.

Preventive maintenance costs (CMP) are costs that include all costs associated with preventive maintenance performed to reduce degradation and mitigate the likelihood of failure. At present, preventive maintenance is performed at predetermined time intervals (according to the manufacturer’s preventive maintenance program) or when a specified number of kilometres are not covered before the next service maintenance, depending on the time. In practice, for passenger cars, it is usually 1 or 2 years, depending on the use of engine oil. This mainly includes the cost of:

material consumed during preventive maintenance,

work spent on preventive maintenance,

workshop equipment, training of preventive maintenance specialists.$${C}_{MP}=frac{{t}_{l}}{MTB{M}_{p}}left({C}_{MPM}+{(bar{c}}_{p}{bar{t}}_{pm})right),$$
(17)

where MTBMp—mean operating time between preventive maintenances (km), CMPM—costs of material used for preventive maintenance (EUR), (bar{c})p—average hourly cost of labour and workshop equipment used for maintenance (EUR/hour), ̅tpm—mean time of labour-intensity per one preventive maintenance (hour).

Design of a model for the analysis of selected life cycle costs of a passenger motor vehicleThe model for performing an analysis of life cycle costs for the purchase of a new motor vehicle is based on the basic Eq. (3), (18). We will not count the costs of improvement (CE) and the costs of the decommissioning phase (CD) for the mentioned model due to the calculations of costs that are unnecessary for the analysis. Then the model can be expressed as follows$$LCC={C}_{P}+{C}_{O}+{C}_{M}.$$
(18)
Then, the following Eqs. (6), (7), (8), (9), (10), (11), (12), (13), (16) and (17) are substituted into the given equation, and the selected costs can be calculated for individual vehicles. The resulting model for calculating the LCC costs has the following form$$LCC={C}_{p}+frac{{bar{c}}_{aF}}{100}{p}_{F}{t}_{l}+frac{{bar{c}}_{aOL}}{100}{p}_{OL}{t}_{l}+frac{{t}_{l}}{{bar{d}}_{aT}}{n}_{T}{p}_{T}+frac{{t}_{l}}{{bar{d}}_{aAB}}{n}_{AB}{p}_{AB}+{C}_{SI}{t}_{la}+{c}_{MT}{t}_{la}+left({C}_{E}+{C}_{TC}right)frac{{y}_{n}}{{t}_{la}}+frac{{t}_{l}}{MTBF}left({bar{c}}_{m}+{(bar{c}}_{p}{bar{t}}_{pc})right)+frac{{t}_{l}}{MTB{M}_{p}}left({C}_{OMPM}+{bar{(c}}_{p}{bar{t}}_{pm})right).$$
(19)
It is presented in a Fig. 6.Figure 6Structure of model input parameters for LCC model calculation.Full size imageIn this way, the cumulative costs for each passenger motor vehicle are calculated. Since the passenger motor vehicles may have a different service life tl which is expressed in kilometres, it is recommended to convert this equation to specific costs which are related to one kilometre of use. The selected LCCS life cycle specific costs can be expressed by the following equation$${LCC}_{S}=frac{LCC}{{t}_{l}}.$$
(20)
LCC model input values and items affecting ownership costs for alternative drivesThe process of the calculation of selected life cycle costs for the propulsion of passenger vehicles and the structure of individual cost items is shown in Fig. 6. These are the input parameters to the LCC model.The total life cycle costs are divided into two main cost groups, which are the ownership and acquisition costs for a given drive type. Fuel costs are determined by the price and the quantity of conventional or alternative fuel consumed. For the calculation of the selected LCCs, the authors of the paper assume that the availability of conventional and alternative fuels is not limited in any way. It is assumed that the availability of fuels is ideal, which is not entirely true in practice. This is dependent on the support for each alternative fuel in each state.In practice, therefore, multiple costs may arise due to the distance to the refuelling station to provide alternative fuels such as E85, CNG, LPG and hydrogen. In addition, there is a distance to the charging station for electric drives.Another item that affects the cost of operation for hybrid passenger vehicles is the percentage of alternative fuel driving, which can have a significant impact on life cycle costs. Values for this item are given as a percentage, which is then converted into the number of kilometres driven on alternative and conventional fuel.One of the important parameters for calculating the life cycle operating costs for the hybrid-electric and electric drive is the setting of a threshold value for the capacity of the electric vehicle battery (EV battery) when the replacement is performed. For the model calculation, a limit value of 70% of the electric vehicle battery capacity at 20 °C was set.Return on investmentReturn on investment (ROI) is a performance measure used to evaluate the efficiency or profitability of an investment or compare the efficiency of a number of different investments. ROI tries to directly measure the amount of return on a particular investment, relative to the investment’s cost. To calculate ROI, the benefit (or return) of an investment is divided by the cost of the investment. The result is expressed as a percentage or a ratio12,23.For our calculation of the return on investment ROI on alternative and conventional passenger car propulsion the following formula is used, which is expressed as a percentage$$ROI=frac{{LCC}_{A}-{LCC}_{C}}{{LCC}_{C}}100,$$
(21)

where LCCA—selected live cycle costs of the alternative passenger car propulsion (EUR), LCCC—selected live cycle costs of the conventional passenger car propulsion (EUR).The return on investment of an alternative vehicle ROIAV purchase expresses after how many kilometres the increased cost of purchasing an alternative fuel vehicle compared to a conventional one is recovered. If the value is negative, the payback will not occur for various reasons. The following equation is used to calculate ROIAV$${ROI}_{AV}=frac{{C}_{{P}_{AV}}-{C}_{{P}_{CV}}}{frac{{C}_{O{W}_{CV}}-{C}_{O{W}_{AV}}}{{t}_{l}}}$$
(22)

where ({C}_{{P}_{AV}})—purchase cost on alternative vehicle (EUR), ({C}_{{P}_{CV}})—purchase cost on conventional vehicle (EUR), ({C}_{O{W}_{CV}})—ownership cost on conventional vehicle (EUR), ({C}_{O{W}_{AV}})—ownership cost on alternative vehicle (EUR), tl—service life of the passenger vehicle (km).Ownership costs on conventional vehicle are expressed by the following equation$${C}_{{OW}_{CV}}={left(frac{{bar{c}}_{aF}}{100}{p}_{F}{t}_{l}+frac{{bar{c}}_{aOL}}{100}{p}_{OL}{t}_{l}+frac{{t}_{l}}{{bar{d}}_{aT}}{n}_{T}{p}_{T}+frac{{t}_{l}}{{bar{d}}_{aAB}}{n}_{AB}{p}_{AB}+{C}_{SI}{t}_{la}+{c}_{MT}{t}_{la}+left({C}_{E}+{C}_{TC}right)frac{{y}_{n}}{{t}_{la}}+frac{{t}_{l}}{MTBF}left({bar{c}}_{m}+{(bar{c}}_{p}{bar{t}}_{pc})right)+frac{{t}_{l}}{MTB{M}_{p}}left({C}_{OMPM}+({bar{c}}_{p}{bar{t}}_{pm})right)right)}_{CV}.$$
(23)
Ownership costs on alternative vehicle are expressed by the following equation$${C}_{{OW}_{AV}}={left(frac{{bar{c}}_{aF}}{100}{p}_{F}{t}_{l}+frac{{bar{c}}_{aOL}}{100}{p}_{OL}{t}_{l}+frac{{t}_{l}}{{bar{d}}_{aT}}{n}_{T}{p}_{T}+frac{{t}_{l}}{{bar{d}}_{aAB}}{n}_{AB}{p}_{AB}+{C}_{SI}{t}_{la}+{c}_{MT}{t}_{la}+left({C}_{E}+{C}_{TC}right)frac{{y}_{n}}{{t}_{la}}+frac{{t}_{l}}{MTBF}left({bar{c}}_{m}+{(bar{c}}_{p}{bar{t}}_{pc})right)+frac{{t}_{l}}{MTB{M}_{p}}left({C}_{OMPM}+({bar{c}}_{p}{bar{t}}_{pm})right)right)}_{AV}.$$
(24)
The rate of return on investment for the purchase of an alternative vehicle depending on the kilometres travelled to is expressed by the following equation$${ROI}_{AV({t}_{o})}={(C}_{{P}_{AV}}-{C}_{{P}_{CV}})-({C}_{O{W}_{CV}left({t}_{o}right)}-{C}_{O{W}_{AV}left({t}_{o}right)}) quad text{when} ;to = (0-tl)$$
(25)

where to—operation of the passenger vehicle (km). More

Birenboim, A. The influence of urban environments on our subjective momentary experiences. Environ. Plan. B-Urban Anal. CIty Sci. 45, 915–932. https://doi.org/10.1177/2399808317690149 (2018).Article 

Google Scholar 
Flores, A., Pickett, S. T. A., Zipperer, W. C., Pouyat, R. V. & Pirani, R. Adopting a modern ecological view of the metropolitan landscape: The case of a greenspace system for the New York City region. Landsc. Urban Plan. 39, 295–308. https://doi.org/10.1016/S0169-2046(97)00084-4 (1998).Article 

Google Scholar 
Weijs-Perrée, M., Dane, G., Berg, P. V. D. & Dorst, M. V. A multi-level path analysis of the relationships between the momentary experience characteristics, satisfaction with urban public spaces, and momentary- and long-term subjective wellbeing. Int. J. Environ. Res. Public Health. https://doi.org/10.3390/ijerph16193621 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Paulin, M. J. et al. Application of the natural capital model to assess changes in ecosystem services from changes in green infrastructure in Amsterdam. Ecosyst. Serv. 43, 101114. https://doi.org/10.1016/j.ecoser.2020.101114 (2020).Article 

Google Scholar 
Derkzen, M. L., van Teeffelen, A. J. A., Verburg, P. H. & Diamond, S. Quantifying urban ecosystem services based on high-resolution data of urban green space: An assessment for Rotterdam, the Netherlands. J. Appl. Ecol. 52, 1020–1032. https://doi.org/10.1111/1365-2664.12469 (2015).Article 

Google Scholar 
Leiva, M. A., Santibanez, D. A., Ibarra, S., Matus, P. & Seguel, R. A five-year study of particulate matter (PM2.5) and cerebrovascular diseases. Environ. Pollut. 181, 1–6. https://doi.org/10.1016/j.envpol.2013.05.057 (2013).CAS 
Article 

Google Scholar 
Venkataramanan, V. et al. Knowledge, attitudes, intentions, and behavior related to green infrastructure for flood management: A systematic literature review. Sci. Total Environ. 720, 137606. https://doi.org/10.1016/j.scitotenv.2020.137606 (2020).CAS 
Article 
PubMed 

Google Scholar 
Wang, G. Z., Han, Q. & De Vries, B. The multi-objective spatial optimization of urban land use based on low-carbon city planning. Ecol. Indic. 125, 107540. https://doi.org/10.1016/j.ecolind.2021.107540 (2021).CAS 
Article 

Google Scholar 
Cameron, R. W. F. et al. The domestic garden—Its contribution to urban green infrastructure. Urban For. Urban Green. 11, 129–137. https://doi.org/10.1016/j.ufug.2012.01.002 (2012).Article 

Google Scholar 
De la Sota, C., Ruffato-Ferreira, V. J., Ruiz-Garcia, L. & Alvarez, S. Urban green infrastructure as a strategy of climate change mitigation. A case study in northern Spain. Urban For. Urban Green. 40, 145–151. https://doi.org/10.1016/j.ufug.2018.09.004 (2019).Article 

Google Scholar 
Pongsakorn, S., Jiang, X. R. & Sullivan, W. C. Green infrastructure, green stormwater infrastructure, and human health a review. Curr. Landscape. Ecol. Rep. 2, 96–110. https://doi.org/10.1007/s40823-017-0028-y (2017).Article 

Google Scholar 
Liu, O. Y. & Russo, A. Assessing the contribution of urban green spaces in green infrastructure strategy planning for urban ecosystem conditions and services (Sust. Cities Soc., 2021). https://doi.org/10.1016/j.scs.2021.102772.Book 

Google Scholar 
McMahon, E. T. Green infrastructure. Plan. Commission. J. (2000).Mell, I. C. Green Infrastructure Concepts, Perceptions and Its Use in Spatial Planning. Doctor of Philosophy Thesis (Planning and Landscape Newcastle University, 2010).
Google Scholar 
Wang, J. X. & Banzhaf, E. Towards a better understanding of green infrastructure: A critical review. Ecol. Indic. 85, 758–772. https://doi.org/10.1016/j.ecolind.2017.09.018 (2018).Article 

Google Scholar 
Young, R., Zanders, J., Lieberknecht, K. & Fassman-Beck, E. A comprehensive typology for mainstreaming urban green infrastructure. J. Hydrol. 519, 2571–2583. https://doi.org/10.1016/j.jhydrol.2014.05.048 (2014).Article 

Google Scholar 
Wang, J. X., Xu, C., Pauleit, S., Kindler, A. & Banzhaf, E. Spatial patterns of urban green infrastructure for equity: A novel exploration. J. Clean Prod. 238, 117858. https://doi.org/10.1016/j.jclepro.2019.117858 (2019).Article 

Google Scholar 
Cook, E. A. Landscape structure indices for assessing urban ecological networks. Landsc. Urban Plan. 58, 269–280 (2002).Article 

Google Scholar 
Vogt, P. & Riitters, K. GuidosToolbox: Universal digital image object analysis. Eur. J. Remote Sens. 50, 352–361. https://doi.org/10.1080/22797254.2017.1330650 (2017).Article 

Google Scholar 
Vogt, P., Riitters, K. H., Estreguil, C., Kozak, J. & Wade, T. G. Mapping spatial patterns with morphological image processing. Landsc. Ecol. 22, 171–177. https://doi.org/10.1007/s10980-006-9013-2 (2007).Article 

Google Scholar 
Kuttner, M., Hainz-Renetzeder, C., Hermann, A. & Wrbka, T. Borders without barriers—Structural functionality and green infrastructure in the Austrian-Hungarian transboundary region of Lake Neusiedl. Ecol. Indic. 31, 59–72. https://doi.org/10.1016/j.ecolind.2012.04.014 (2013).Article 

Google Scholar 
Ma, Q. W., Li, Y. H. & Xu, L. H. Identification of green infrastructure networks based on ecosystem services in a rapidly urbanizing area. J. Clean Prod. 300, 126945. https://doi.org/10.1016/j.jclepro.2021.126945 (2021).Article 

Google Scholar 
Furberg, D., Ban, Y. & Mörtberg, U. Monitoring urban green infrastructure changes and impact on habitat connectivity using high-resolution satellite data. Remote Sens. 12, 3072. https://doi.org/10.3390/rs12183072 (2020).Article 

Google Scholar 
Barbati, A., Corona, P., Salvati, L. & Gasparella, L. Natural forest expansion into suburban countryside: Gained ground for a green infrastructure?. Urban For. Urban Green. 12, 36–43. https://doi.org/10.1016/j.ufug.2012.11 (2013).Article 

Google Scholar 
Fluhrer, T., Chapa, F. & Hack, J. A methodology for assessing the implementation potential for retrofitted and multifunctional urban green infrastructure in public areas of the global south. Sustainability https://doi.org/10.3390/su13010384 (2021).Article 

Google Scholar 
Carroll, C., McRae, B. H. & Brookes, A. Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America. Conserv. Biol. 26, 78–87. https://doi.org/10.1111/j.1523-1739.2011.01753.x (2012).Article 
PubMed 

Google Scholar 
Saura, S. & Torne, J. Conefor Sensinode 2.2: A software package for quantifying the importance of habitat patches for landscape connectivity. Environ. Modell. Softw. 24, 135–139 (2009).Article 

Google Scholar 
Jaworek-Jakubska, J., Filipiak, M., Michalski, A. & Napierała-Filipiak, A. Spatio-temporal changes of urban forests and planning evolution in a highly dynamical urban area: The case study of Wrocław, Poland. Forests 11, 17. https://doi.org/10.3390/f11010017 (2019).Article 

Google Scholar 
Ren, Z. B., He, X. Y., Zheng, H. F. & Wei, H. X. Spatio-temporal patterns of urban forest basal area under China’s rapid urban expansion and greening: Implications for urban green infrastructure management. Forests 9, 272. https://doi.org/10.3390/f9050272 (2018).Article 

Google Scholar 
Elliott, R. M. et al. Identifying linkages between urban green infrastructure and ecosystem services using an expert opinion methodology. Ambio 49, 569–583. https://doi.org/10.1007/s13280-019-01223-9 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
García, A. M., Santé, I., Loureiro, X. & Miranda, D. Green infrastructure spatial planning considering ecosystem services assessment and trade-off analysis. Application at landscape scale in Galicia region (NW Spain). Ecosyst. Serv. 43, 101115. https://doi.org/10.1016/j.ecoser.2020.101115 (2020).Article 

Google Scholar 
Tiwari, A. & Kumar, P. Integrated dispersion-deposition modelling for air pollutant reduction via green infrastructure at an urban scale. Sci. Total Environ. 723, 138078. https://doi.org/10.1016/j.scitotenv.2020.138078 (2020).CAS 
Article 
PubMed 

Google Scholar 
Zhang, Y. Q. et al. Unexpected air quality impacts from implementation of green infrastructure in urban environments: A Kansas City case study. Sci. Total Environ. 744, 140960. https://doi.org/10.1016/j.scitotenv.2020.140960 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Alizadehtazi, B., Gurian, P. L. & Montalto, F. A. Observed variability in soil moisture in engineered urban green infrastructure systems and linkages to ecosystem services. J. Hydrol. 590, 125381. https://doi.org/10.1016/j.jhydrol.2020.125381 (2020).Article 

Google Scholar 
Dennis, M., Cook, P. A., James, P., Wheater, C. P. & Lindley, S. J. Relationships between health outcomes in older populations and urban green infrastructure size, quality and proximity. BMC Public Health https://doi.org/10.1186/s12889-020-08762-x (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Van Oijstaeijen, W., Van Passel, S. & Cools, J. Urban green infrastructure: A review on valuation toolkits from an urban planning perspective. J. Environ. Manag. 267, 110603. https://doi.org/10.1016/j.jenvman.2020.110603 (2020).Article 

Google Scholar 
Majekodunmi, M., Emmanuel, R. & Jafry, T. A spatial exploration of deprivation and green infrastructure ecosystem services within Glasgow city. Urban For. Urban Green. 52, 126698. https://doi.org/10.1016/j.ufug.2020.126698 (2020).Article 

Google Scholar 
Liberalesso, T., Oliveira Cruz, C., Matos Silva, C. & Manso, M. Green infrastructure and public policies: An international review of green roofs and green walls incentives. Land Use Pol. 96, 104693. https://doi.org/10.1016/j.landusepol.2020.104693 (2020).Article 

Google Scholar 
Lin, H. Y., Qian, J., Yan, L. J. & Huang, S. R. Analysis of spatial-temporal pattern and scenario simulation of green infrastructure in Wuyi County based on morphological spatial pattern analysis and CA-Markov model. Acta Agricult. Zhejiangensis. https://doi.org/10.3969/j.issn.1004-1524.2019.07.21 (2019).Article 

Google Scholar 
Mitsova, D., Shuster, W. & Wang, X. H. A cellular automata model of land cover change to integrate urban growth with open space conservation. Landsc. Urban Plan. 99, 141–153. https://doi.org/10.1016/j.landurbplan.2010.10.001 (2011).Article 

Google Scholar 
Dennis, M. et al. Mapping urban green infrastructure: A novel landscape-based approach to incorporating land use and land cover in the mapping of human-dominated systems. Land 7, 17. https://doi.org/10.3390/land7010017 (2018).Article 

Google Scholar 
Hu, Y. J. et al. Urban expansion and farmland loss in Beijing during 1980–2015. Sustainability 10, 3927. https://doi.org/10.3390/su10113927 (2018).Article 

Google Scholar 
Li, W. J., Wang, Y., Xie, S. Y., Sun, R. H. & Cheng, X. Impacts of landscape multifunctionality change on landscape ecological risk in a megacity, China: A case study of Beijing. Ecol. Indic. 117 (2020).Song, W., Pijanowski, B. C. & Tayyebi, A. Urban expansion and its consumption of high-quality farmland in Beijing, China. Ecol. Indic. 54, 60–70. https://doi.org/10.1016/j.ecolind.2015.02.015 (2015).Article 

Google Scholar 
Li, Z. Z., Cheng, X. Q. & Han, H. R. Future impacts of land use change on ecosystem services under different scenarios in the ecological conservation area, Beijing, China. Forests https://doi.org/10.3390/f11050584 (2020).Article 

Google Scholar 
Liu, D. Y. et al. Interoperable scenario simulation of land-use policy for Beijing-Tianjin-Hebei region, China. Land Use Pol. 75, 155–165. https://doi.org/10.1016/j.landusepol.2018.03.040 (2018).Article 

Google Scholar 
Mo, W. B., Wang, Y., Zhang, Y. X. & Zhuang, D. F. Impacts of road network expansion on landscape ecological risk in a megacity, China: A case study of Beijing. Sci. Total Environ. 574, 1000–1011. https://doi.org/10.1016/j.scitotenv.2016.09.048 (2017).CAS 
Article 
PubMed 

Google Scholar 
Melgani, F. & Bruzzone, L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42, 1778–1790. https://doi.org/10.1109/Tgrs.2004.831865 (2004).Article 

Google Scholar 
Zhang, C., Wang, T. J., Atkinson, P. M., Pan, X. & Li, H. P. A novel multi-parameter support vector machine for image classification. Int. J. Remote Sens. 36, 1890–1906. https://doi.org/10.1080/01431161.2015.1029096 (2015).CAS 
Article 

Google Scholar 
Peterson, L. K., Bergen, K. M., Brown, D. G., Vashchuk, L. & Blam, Y. Forested land-cover patterns and trends over changing forest management eras in the Siberian Baikal region. For. Ecol. Manag. 257, 911–922. https://doi.org/10.1016/j.foreco.2008.10.037 (2009).Article 

Google Scholar 
Sang, L. L., Zhang, C., Yang, J. Y., Zhu, D. H. & Yun, W. J. Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Math. Comput. Model. 54, 938–943. https://doi.org/10.1016/j.mcm.2010.11.019 (2011).Article 

Google Scholar 
Liu, D. Y., Zheng, X. Q. & Wang, H. B. Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecol. Model. 417, 108924. https://doi.org/10.1016/j.ecolmodel.2019.108924 (2020).Article 

Google Scholar 
Kazak, J. K. The use of a decision support system for sustainable urbanization and thermal comfort in adaptation to climate change actions-The case of the Wroclaw larger urban zone (Poland). Sustainability https://doi.org/10.3390/su10041083 (2013).Article 

Google Scholar 
Sonnenberg, F. A. & Beck, J. R. Markov-models in medical decision-making—A practical guide. Med. Decis. Mak. 13, 322–338. https://doi.org/10.1177/0272989×9301300409 (1993).CAS 
Article 

Google Scholar 
Nadoushan, M. A., Soffianian, A. & Alebrahim, A. Modeling land use/cover changes by the combination of Markov chain and cellular automata Markov CA-Markov models. Int. J. Environ. Health Res. https://doi.org/10.4103/WKMP-0092.159922 (2015).Article 

Google Scholar 
Mansour, S., Al-Belushi, M. & Al-Awadhi, T. Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Pol. 91, 104414. https://doi.org/10.1016/j.landusepol.2019.104414 (2020).Article 

Google Scholar 
Karimi, H., Jafarnezhad, J., Khaledi, J. & Ahmadi, P. Monitoring and prediction of land use/land cover changes using CA-Markov model: A case study of Ravansar County in Iran. Arab. J. Geosci. https://doi.org/10.1007/s12517-018-3940-5 (2018).Article 

Google Scholar 
Mondal, M. S., Sharma, N. C. P. K. G. & Kappas, M. Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. Egypt. J. Remote Sens. Space Sci. https://doi.org/10.1016/j.ejrs.2016.08.001 (2016).Article 

Google Scholar 
Liu, Q. et al. Multi-scenario simulation of land use change and its eco-environmental effect in Hainan Island based on CA-Markov model. Ecol. Environ. Sci. 30, 1522–1531. https://doi.org/10.16258/j.cnki.1674-5906.2021.07.021 (2021).Article 

Google Scholar 
Pontius, R. G. Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogramm. Eng. Remote Sens. 68, 1041–1049 (2002).
Google Scholar 
Soille, P. & Vogt, P. Morphological segmentation of binary patterns. Pattern Recognit. Lett. 30, 456–459 (2009).Article 

Google Scholar 
Chang, Q., Liu, X. W., Wu, J. S. & He, P. MSPA-based urban green infrastructure planning and management approach for urban sustainability: Case study of Longgang in China. J. Urban Plan. Dev. https://doi.org/10.1061/(asce)up.1943-5444.0000247 (2015).Article 

Google Scholar 
Li, K. M. et al. Spatiotemporal evolution characteristics of urban green infrastructure in central Liaoning urban agglomeration during the past 20 years based on landscape ecology and morphology. Acta Ecol. Sin. https://doi.org/10.5846/stxb202007221918 (2021).Article 

Google Scholar 
Ning, J. et al. Spatiotemporal patterns and characteristics of land-use change in China during 2010–2015. J. Geogr. Sci. 28, 547–562. https://doi.org/10.1007/s11442-018-1490-0 (2018).Article 

Google Scholar 
Sawyer, S. C., Epps, C. W. & Brashares, J. S. Placing linkages among fragmented habitats: Do least-cost models reflect how animals use landscapes?. J. Appl. Ecol. 48, 668–678. https://doi.org/10.1111/j.1365-2664.2011.01970.x (2011).Article 

Google Scholar 
Yin, G. Y., Liu, L. M. & Jiang, X. L. The sustainable arable land use pattern under the tradeoff of agricultural production, economic development, and ecological protection—An analysis of Dongting Lake basin, China. Environ. Sci. Pollut. Res. 24, 25329–25345. https://doi.org/10.1007/s11356-017-0132-x (2017).Article 

Google Scholar  More