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    Human attachment site preferences of ticks parasitizing in New York

    The attachment site of ticks has been studied in the context of both animal and human tick preference. In Oklahoma, a study of horses indicated that A. americanum preferentially bites the inguinal area, while I. scapularis and D. albipictus, the moose-tick, primarily bite the chest and axillary region, with D. albipictus often being found on the back18. A survey of dogs and cats across the US identified a similar distribution of ticks on dogs, with the attachment being most common on the abdomen, axillary and inguinal regions. However, this was species-specific with D. variabilis preferring the head and neck specifically19. Cats were more successfully parasitized by I. scapularis which preferred the head and A. americanum, which preferred the tail and perianal region19. This is similar to a study of tick distribution on wild black bears (Ursus americanus) in Pennsylvania, indicating that the primary tick present was I. scapularis and that the greatest numbers were found in association with the ears and muzzle20. In these cases, the ability for ticks to attach to specific areas is most likely a result of the grooming habits and abilities of the animals in question.Studies of anatomical region preference in humans also reported tick bite-site specificity associated with particular tick species. For example, in Korea, H. longicornis was determined to prefer abdomen and lower extremities (33%) and the abdomen/inguinal area (26.4%)21, which is a behavior similar to that of A. americanum observed here. Although H. longicornis is present in New York1, insufficient numbers were detected to draw definitive conclusions about its biting preference here. Additionally, a study in England (I. ricinus) reported that tick bites were most common in the legs (50%) of adult humans, but in the head and necks of children (43%)22, a differentiation that our survey does not at this time include. A similar phenomenon was observed in Russia, where tick bites were most common on the head and neck of all individuals (39.2%), but were much more common in children (84.9%)23. This study determined that the bite-site of single tick bites that resulted in infection with the Tick-Borne Encephalitis virus (TBEV) were associated with lethal outcomes if the bites were located on the head, neck, arms or axilla, while less lethality was associated with bites to the lower limbs and groin. This is most directly analogous to the transmission of DTV by I. scapularis, suggesting that bite site may have a similar relationship to disease outcomes in the related North American pathogen/vector pair.Under normal circumstances, ticks exist in sylvatic cycles with specific host preferences based on the tick species and life stage, with spillover to humans occasionally occurring for species with generalist feeding habits. Therefore, the feeding behaviors of ticks are variable, and this influences the ways that the ticks interact with humans.Ixodes scapularis is less specific in host-site preferenceThe primary life stages of I. scapularis that bite humans include nymphs and adult females, although males may also be found on humans. The body segment preference of I. scapularis is less specific than for D. variabilis, which prefers the head, and A. americanum, which prefers the thighs and pelvic region. Ixodes scapularis is primarily found on the central trunk, including the groin/pelvic region, the abdomen, the thoracic region, and the head/neck. This varies between the life stages, with more adults found in the thoracic/abdominal region of the body and nymphs being more commonly found on the arms and legs. This is partly due to the substantial size difference between adult and nymph/larval I. scapularis, with larvae being almost imperceptible and nymphs having a total body length of two to four millimeters. This results in nymphs/larvae being much more difficult to see, allowing them to more readily attach to the most visible portions of the human body while adults are restricted mostly to areas covered by clothing and hair.The presence of ticks on the head and neck indicates that I. scapularis tends to climb, although not with the preference for hair observed with D. variabilis. They appear to spend substantial time moving on the host, a period where they can be removed easily without having had a chance to potentially transmit pathogens by biting. On deer, this corresponds to a preference to move toward the neck and ears where the ticks are more difficult to dislodge24,25. On humans, it results in wide distribution across the whole body with less location specificity than other ticks.In addition to body region and life stage identification, I. scapularis ticks were also screened for several pathogens to determine if infection status influences host site preference. Anaplasma phagocytophilum, B. microti, and other pathogens (DTV and B. miyamotoi) did not influence the body segment the ticks chose to feed. However, in ticks infected with B. burgdorferi, a statistically significant change in the distribution of tick bites marked by an increased report of tick bites in the midsection and a decreased tick bites in the arms, legs, and head. While this may suggest a change in tick behavior/fitness in response to infection, it may also relate to the differences in infection rates of adult and nymph/larval ticks. Larvae, having never fed, are not infected with B. burgdorferi, and the rate of infection in nymphs is lower than that of adults1. Nymphs are less likely to be infected and are more likely to attach to the arms and legs, which is a potential source of the observed difference in infection rates. However, it remains unclear why this is not observed for the other pathogens that follow the same trend of increased infection rate in adult versus nymph/larval ticks.Bacterial and protozoal agents transmitted by I. scapularis take several hours for an infectious dose to be transmitted26,27,28. Therefore, prompt detection and removal of ticks is important for preventing tick-borne disease. Furthermore, understanding where the ticks attach allows them to be more easily detected, and also assists in preparing protective clothing for individuals entering tick-endemic areas. Additionally, knowing the biting location of I. scapularis could aid in detecting potential erythema migrans, a skin condition that occurs at the point of B. burgdorferi infected tick exposure in about 80% of cases29, which is highly diagnostic for both Lyme disease and STARI, which is transmitted by A. americanum.
    Amblyomma americanum prefers the thighs and groin of subjectsAmblyomma americanum, the lone star tick, is present throughout the southern portion of New York and is particularly dominant on Long Island1. This species is relatively large, fast, and aggressive, feeding on various animals, including deer, medium-sized animals, and birds30. As a generalist feeder, both adult and nymph/larval A. americanum often bite humans in endemic areas. This experiment identified six larvae, 107 nymphs, and 48 adult A. americanum from human sources. The dominance of nymph submissions is likely due to the large size of the tick, making nymphs and adults easier to spot in more visible areas.In terms of body segment location, all life stages of A. americanum were most often found in the thigh/groin/pelvic region. Considering that most humans encounter ticks while walking through vegetation, the ticks most likely first adhere to the legs and move upward before biting. In this case, the ticks bite rapidly instead of ascending in large numbers to the torso or head. This area is also almost invariably covered in relatively tight-fitting clothing. The closeness of the fabric may also assist in inducing the ticks to feed by slowing their ascent and creating contact to induce biting.While it does not transmit the same range of pathogens as I. scapularis, A. americanum is still a medically significant species. This species can transmit Ehrlichia chaffeensis and E. ewingii31,32, which are at present rare in New York, but are likely to increase as more A. americanum becomes established. Amblyomma americanum is also associated with Southern Tick-Borne Rash Associated Illness (STARI)11, a disease of unknown etiology that has previously been observed in New York33 and with galactose-alpha-1,3-galactose (alpha-gal) allergy, a reaction to the tick’s saliva that can result in a long term, potentially serious allergic sensitivity to the consumption of red meat. While the attachment time required to transmit or induce these pathogens is still unclear, prompt detection and removal of the tick is still recommended. Knowing the approach of the tick and where it is likely to be found improves this process.Additionally, it is unclear if the results observed for A. americanum also apply to the related A. maculatum, the vector of Rickettsia parkeri, a cause of spotted fever. These ticks have been observed in the southernmost portions of New York with a high infection rate with R. parkeri34. Since early R. parkeri infection may result in a visible eschar, understanding where the eschar is most likely located can be critical for rapid diagnosis before the onset of severe disease symptoms. Considering the similarities in behavior between the two Amblyomma species, it may have similar preferences to A. americanum. Other escharotic diseases, such as F. tularensis, may also be present and linked to a tick with a highly dissimilar segment preference. The location of the escar itself, therefore, may be at least partially diagnostic for specific pathogens. However, at present, the sample size within this community engaged passive surveillance program is too small to assess its biting behavior in detail.
    Dermacentor variabilis exhibits preference for the human headIn this study, D. variabilis was almost exclusively encountered in its adult life stage. This indicates that while the adult ticks are generalist feeders that may bite humans, the nymph and larval stages are not and have much greater host specificity, either feeding exclusively on a specific type of animal or being restricted to the vicinity of animal burrows. The exact identity of the preferred larval and nymphal host of D. variabilis in New York could not be determined from these data, but is presumed to be one or several rodent species, lagomorph, or mesocarnivore with broad distribution across the eastern United States.Additionally, D. variabilis was unique among the three species of ticks studied here. It had a strong bias toward the head and neck of human hosts, as opposed to a higher preference for the midsection and pelvis/groin with I. scapularis and especially A. americanum. This is clear evidence of climbing behavior, tending upward, but is also indicative of a strong preference for dense hair. In contrast to I. scapularis and A. americanum, D. variabilis in its adult stage is less likely to feed on deer35,36, with a preference for canids36, hence its colloquial name as the “American dog tick”. Hair provides the ticks with the same benefits as feeding on canids. It protects them from being immediately detected and removed, obscuring them until they can feed extensively. This can be of potential medical consequence in the case of tick paralysis, a condition of flaccid paralysis associated with the bite of Dermacentor spp. ticks30. In such cases, prompt removal of the tick is critical for treatment. Therefore, understanding its most likely location can be useful for removal of the tick before the onset of the condition, diagnostically to confirm the presence of the tick, or during treatment to ensure its removal. Considering that the tick will most likely be adult, it should be relatively obvious with careful observation.Limitations of this studyThe data described in this manuscript derived from a set of ticks submitted by general public, with site location from a questionnaire completed upon tick submission. While speciation and pathogen testing were performed under laboratory conditions, the public completed the initial survey and is therefore subject to a level of inherent error and ambiguity. In the context of this study, this mainly concerns whether the body location submitted concerns an attachment or a tick that is still crawling over the potential host in preparation for biting. The term “attachment” may be colloquially interpreted as to contain both categories, or a person can potentially be mistaken about the state of the tick. While ticks filled with blood have fed, the situation is more indeterminate for short-duration attachments where the ticks have not yet begun to engorge. This may introduce some level of error from ticks found on a body segment that were not, at the time of collection, attached. However, the data are overall still useful for predicting the most likely location where ticks of specific species can be found on a person. Studies with test subjects and ticks under controlled conditions may assist in elucidating this matter further. Additionally, this data set was compiled without regard to gender and age group. This data was not collected with this version of the questionnaire; therefore, the tick attachment cannot be stratified by any demographic parameters of tick submitters. More

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    Meiotic transmission patterns of additional genomic elements in Brachionus asplanchnoidis, a rotifer with intraspecific genome size variation

    Many eukaryotes display intraspecific genome size (GS) variation due to varying amounts of non-coding DNA1,2,3,4,5. Such GS variation can be mediated by additional genomic elements, which are physically represented either by extra (B-)chromosomes or by large heterozygous insertions into the regular chromosomes. On a DNA sequence level, non-coding DNA can be classified as highly repetitive, e.g. interspersedly repeated transposable elements or tandemly repeated satellite DNA, or as the result of previous duplications of the genome followed by pseudogenization6. The long-term gain and loss of such non-coding DNA sequences is thought to be governed by largely neutral evolutionary processes, and their excessive accumulation in some genomes can be explained by genetic drift7,8, even though selection might also sometimes play a role9,10.Non-coding DNA can affect organisms in different ways. A large number of studies document correlations between genome size and organismic traits such as cell size11,12, body size13,14, or developmental rates15, sometimes even at the within-population level13. Under some circumstances, differential amounts of non-coding DNA might even affect fitness16. Furthermore, DNA can have coding-independent effects that operate at lower levels, such as intragenomic selection. For example, (additional) genomic elements might increase their own fitness by increasing their transmission rates to offspring by meiotic drive, sometimes at the expense of their host’s fitness17,18,19. Meiotic drive in this classical sense occurs during the chromosome segregation during the meiotic divisions, even though later stages during gametogenesis can also be affected20. Recognizing and disentangling such effects is important for a better understanding of the evolution of eukaryotic genomes, in particular, the evolutionary causes of the large intraspecific genome size variation.Here we study meiotic transmission patterns of additional genomic elements in the monogonont rotifer Brachionus aplanchnoidis. Individuals of this species can differ by up to almost two-fold in genome size, which is mediated by several Megabase-sized independently segregating genomic elements (ISEs) consisting mainly of tandemly repeated satellite DNA21. The genomic data are consistent with a mixture of both B-chromosomes and large insertions to normal chromosomes21,22. Individual rotifers and their clonal offspring can be characterized by the number and size of their ISEs and their composition stays constant through hundreds of asexual (mitotic) generations22. Occasionally, monogonont rotifers engage in sexual reproduction (Fig. 1), producing sexual females, whose oocytes undergo classical meiosis with two polar bodies formed23. Unfertilized haploid eggs develop mitotically into males, and sperm production does not involve any meiotic maturation divisions24. By analyzing the genome size distributions of haploid males produced by different mother clones, it has been shown that ISEs segregate in a manner suggesting that they do not pair with each other, nor with any other part of the genome22. For instance, a clone containing three ISEs will produce males (and gametes) that might contain either zero, one, two, or three ISEs, corresponding to four different GS classes of the males in this clone. The frequencies of these different GS classes roughly approximated those expected by random segregation. However, previous studies in B. asplanchnoidis did not resolve different steps during meiotic transmission, so they were not designed to detect meiotic drive or subsequent changes in meiotic transmission, and they also did not test whether there were subtle deviations from completely independent segregation.Figure 1Schematics of rotifer life cycle. Monogonont rotifers are cyclical parthenogens, capable of both ameiotic parthenogenesis and sexual reproduction. The production of sexual females is triggered by quorum sensing chemicals, released by the animals themselves at high population density. In contrast to parthenogenetic females, sexual females produce oocytes by meiosis, and give rise to either haploid males or diploid resting eggs, depending on whether they get fertilized by a male24.Full size imageIn the present study, we test for meiotic transmission biases of ISEs. If meiotic transmission would be completely unbiased, the frequencies of haploid oocytes, or males, with different numbers of ISEs should be identical to those expected by random segregation. For example, a mother with two ISEs should produce males with zero, one, or two ISEs (hence, three male GS classes), which have relative frequencies of 0.25, 0.5, and 0.25, respectively. However, if ISEs avoid segregating into polar bodies due to meiotic drive17,20,25, one would expect to see an increase in the relative frequency of male GS classes with two ISEs, compared to those with no ISE . By contrast, if ISEs are preferentially sequestered into polar bodies due to meiotic drag 7,26, the GS class with two ISEs should be underrepresented. Our experimental approach for detecting meiotic transmission biases relies on measuring (by flow-cytometry) the observed relative frequencies of each male GS class and comparing these to their relative frequencies expected under unbiased transmission (Fig. 2). To allow for clear comparisons, the main output variable in these analyses is the observed/expected ratio (O/E-ratio), i.e., the observed frequency divided by the expected relative frequency for each GS class. If there were no transmission biases, O/E-ratios across all GS classes should equal one. In contrast, O/E-ratios larger than one indicate overrepresentation of a certain GS class, and if O/E ratios increase or decrease with genome size, this indicates drive or drag at a meiotic or postmeiotic stage (Fig. 2d,h).Figure 2Principle of inferring meiotic transmission patterns from the genome size distributions of haploid rotifer males. The first four panels (a–d) show a rotifer clone with one ISE (i.e., two corresponding male GS classes). The last four panels (e–h) show a clone with four ISEs (i.e., five corresponding male GS classes). a, e Example of flow cytometry data. b, f Conceptual model of ISE meiotic segregation. c, g Theoretically predicted GS distributions of males (relative to the female GS) under meiotic drive, meiotic drag, or in the absence of meiotic drive. d, h Theoretically predicted O/E ratios (observed vs. expected frequencies of different male GS classes) under drive, drag, or on absence of drive. O/E values of  > 1 indicate over-representation of a GS class (relative to the frequency expected from unbiased transmission).Full size imageWe implemented these ideas in a mathematical model that contains the two parameters, transmission bias and cosegregation bias. Values for transmission bias may range from − 1 to 1 in our model. For instance, a value of 0.1 denotes a 10% increase in probability that an ISE segregates towards the egg pole (this is equivalent to a transmission rate of 0.55 for this ISE, i.e. mild meiotic drive). Concerning the second parameter, cosegregation bias, a positive value means that pairs of ISEs have an increased probability of being sequestered towards the same pole (irrespective of whether this is the egg pole or polar body pole), while a negative bias favors migration towards opposite poles. Please note that a cosegregation bias value of − 1 (i.e., 100% probability that ISEs migrate towards opposite poles) resembles the default segregation pattern of regular chromosomes. By estimating the transmission bias and cosegregation bias parameter for each rotifer clone, we tried to infer and compare general meiotic transmission patterns across clones, even if they contained different numbers and types of ISEs.Transmission biases may not only arise during meiosis, as described above but also during later stages of male embryonic development. For instance, they might be caused by differences in the survival of embryos, or due to differences in the fitness of hatched males containing different numbers of ISEs. To address these potential sources of variation, we compared the transmission biases in relatively young, synchronized male eggs, older eggs accumulating in growing cultures, and hatched males. Finally, to address the question of whether a high number of ISEs affects male embryonic survival in general, we estimated and compared hatching rates of (haploid) male eggs and (diploid) female eggs in 19 rotifer clones of different genome sizes (which is highly correlated with the number and size of ISEs in the genome22).Our results suggested that the ISEs in B. asplanchnoidis exhibit diverse meiotic segregation patterns: In some rotifer clones, transmission bias was positive, while the ISEs of other clones showed negative transmission bias (indicative of drag). Furthermore, we obtained evidence for a negative cosegregation bias in some clones, i.e., pairs of ISEs showed an increased probability to segregate towards opposite poles. Overall, these transmission patterns seemed to be determined early in the haploid life cycle, probably at or shortly after meiosis, since early and late stages of male embryonic development showed very similar GS distributions. Finally, we found that very large genome size (i.e., a large numbers of ISEs) was associated with reduced male embryonic survival. More

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    Habitat selection by free-roaming domestic dogs in rabies endemic countries in rural and urban settings

    Study sites and study designThe study was performed in the frame of a dog ecology research project, with details on the study locations published elsewhere15,42,43. For the current study, five study sites located in Indonesia and Guatemala were included. Site selection was carried out by each country’s research team, taking into consideration rural and urban settings, as well as differing expected number of dogs present at each location. The Indonesian study sites were semi-urban Habi and rural Pogon, in the Sikka regency, at the eastern area of Flores Island (Supplementary Fig. 6). In Guatemala, the study sites were Poptún (urban setting), Sabaneta and La Romana (both rural settings), located in the Guatemalan department of Péten, in the northern part of the country (Supplementary Fig. 7). Data were collected during May to June 2018 in Guatemala and from July to September 2018 in Indonesia.In each location, a 1 km2 area was predefined using Google Earth within which the study took place. The 1 km2 area was chosen because of the research goals of another part of the project, investigating the contact network of the dogs15. Within these areas, the teams visited all dog-owning households. In each household, the study was presented to an adult of the family, who was then asked if they owned a dog and if they were willing to participate in the study. After the dog owner’s oral or written consent was granted, a questionnaire was answered, and the dogs collared. The handling of the dogs was performed by a trained veterinarian or a trained veterinary paramedic of the team.The questionnaire data was collected through interviews with the dog owners. Multiple dogs per household could be included as multiple entries in the questionnaire. The detailed questionnaire contains information on the household location, dog demographics (age, sex, reproductive status) and management (dog’s purpose, origin, confinement, vaccination status, feeding and human-mediated transportation within and outside the pre-determined area).All dogs of a household fulfilling the inclusion criteria were equipped with a geo-referenced contact sensor (GCS) developed by Bonsai Systems (https://www.bonsai-systems.com), containing a GPS module and an Ultra-High-Frequency (UHF) sensor for contact data recording43,44. GCS devices report a 5-m maximum accuracy, a run-time of up to 10 years, can store up to 4 million data points and carry a lithium-polymer-battery (LiPo). For this study, only GPS data were analysed. The GCS were set to record each dog’s geographical position at one-minute intervals. Dogs remained collared for 3 to 5 days with the duration of the data collection being limited by the device’s battery capacity, as batteries were not re-charged or changed during the study. Throughout the time of recording, date, hour, GPS coordinates and signal quality (HDOP) raw data were collected by the GPS module and amassed into the workable databases.Exclusion criteria were dogs of less than four months of age (since they were not big enough to carry a collar), sick dogs and pregnant bitches (to avoid any risk of stress-induced miscarriages). Reasons for non-participation of eligible dogs included dog owner’s absence, dog’s absence, inability to catch the dog, and refusal of participation by the dog owner. In addition, dogs foreseen for slaughtering within the following four days were excluded in Indonesia to ensure data collection for at least four to five days. All dogs included in this study were constantly free roaming or at least part-time (day only, night only and for some hours a day). Human and/or animal ethical approval were obtained depending on the country-specific regulations. All the procedures were carried out in accordance with relevant guidelines. Ethical clearance was granted in Guatemala by the UVG’s International Animal Care and Use Committee [Protocol No. I-2018(3)] and the Community Development Councils of the two rural sites, which included Maya Q’eqchi’ communities45. In Indonesia, the study was approved by the Animal Ethics Commission of the Faculty of Veterinary Medicine, Nusa Cendana University (Protocol KEH/FKH/NPEH/2019/009). In addition, dogs that participated in the study were vaccinated against rabies and/or dewormed to acknowledge the owners for their participation in the study.Data cleaningData were stored in an application developed by Bonsai Systems compatible with Apple operating system (iOS iPhone Operating Systems), downloaded as individual csv file for each unit, and further analysed in R (version 3.6.1)46.The GPS data were cleaned based on three automatised criteria. First, the speed was calculated between any two consecutive GPS fixes, and fixes with speed of  > 20 km/h were excluded, given the implausibility of a dog running at such speed over a one-minute timespan47. It is noteworthy that car travel causes speeds over 20 km/h. However, as we were interested in analysing the dog’s behaviour outside of car transports, removing these fixes was in line with our objectives. Second, the Horizontal Dilution of Precision (HDOP), which is a measure of accuracy48 and automatically recorded by the devices for each GPS fix, was used to exclude fixes with low precision. According to Lewis et al.49, GPS fixes with HDOP higher than five were excluded, which deleted 1.3% of data in Habi, 2.2% in Pogon, 3.3% in Poptún, 1.8% in La Romana and 2.1% in Sabaneta. Third, the angles built by three consecutive fixes were calculated for each dog. When studying animals’ trajectories as their measure of movement, acute inner angles are often connected to error GPS fixes50. The fixes having the 2.5% smallest angles were excluded, to target those fixes with highest risks of being errors, while balancing against the loss of GPS fixes due to the cleaning process. With the exclusion of the smallest angles, 2.6% of data were deleted in Habi, 3% in Pogon, 2.9% in Poptún, 2.6% in La Romana and 2.7% in Sabaneta. After the automatised cleaning was concluded, 18 obvious error GPS fixes (unachievable or inexplicable locations by dogs) still prevailed in the Habi dataset and were manually removed.Habitat resource identification and calculation of terrain slopeTo analyse habitat selection of the collared FRDD, resources were delimited by a 100% Minimum Convex Polygon (MCP) including all cleaned GPS fixes per study site, using QGIS51 (Fig. 1).Figure 1GPS fixes plotted over a Google satellite imagery layer with its respective outlined computed Minimum Convex Polygon (MCP) delimitating the habitat available for the study population in: (a) Habi; (b) Pogon; (c) Poptún; (d) La Romana and (e) Sabaneta. Source QGIS (version 3.4 Madeira, http://qgis.org), map data: Google Satellite.Full size imageResources were defined by taking into consideration the following criteria: resources are (i) likely to impact upon movement patterns of dogs, (ii) identifiable by landscape satellite topography, and (iii) chosen considering information on relevant gathering places for FRDD observed by the field teams. Three resources were disclosed in all study sites: buildings, roads and vegetation coverage. All habitat relevant resources were manually identified within the available area (MCP) in QGIS using satellite imagery. All building-like structures were identified using vector polygons and summed under the layer “buildings”. Roads were identified and manually traced using vector lines in all sites, except in Poptún where the roads were automatically traced using an OpenStreetMap road layer of the area (https://www.openstreetmap.org/export). A buffer vector polygon was generated to encompass the full potential width of the roads, with a 5 m width in Habi and Poptún (semi-urban and urban site) and a 2 m width in Pogon, La Romana and Sabaneta (rural sites). In Habi, a “beach” layer was defined by generating a five-meter buffer from the shoreline in both directions using a vector polygon. The layer “sea” was defined as the vector polygon resulting from the difference between the MCP sea outer limit and the beach buffer polygon. Vegetation coverage was distinct between study sites with sparse vegetation and bushes present in all sites except Pogon, and dense forest-like vegetation present in La Romana and Pogon. These two types of vegetation were defined as “low” and “high vegetation”, respectively. In Habi and La Romana, “low” and “high vegetation”, respectively, were manually identified using vector polygons and summarised under the respective layers. Finally, open field in Habi, high vegetation in Pogon and low vegetation in Poptún, La Romana and Sabaneta were the last vector layers to be established since they represented the difference between all other polygon vector layers and the MCP total area. After all resource vector polygons had been created, an encompassing vector layer was generated by merging all resource polygon vectors for final resource classification (Fig. 2). As part of the resource classification in Habi, the airport terminal and runaway as well as waterways enclosed in the MCP area were identified but excluded from the analysis.Figure 2(a) Habi, (b) Pogon, (c) Poptún, (d) La Romana and (e) Sabaneta Habitat classification vector layers. The different habitat resources, identifiable by colour, were merged to create the comprehensive Habitat classification vector. In the Indonesian sites (a, b) and Guatemalan sites (c–e) buildings are coloured red, vegetation low in Habi, Poptún, La Romana and Sabaneta is coloured light green, vegetation high in Pogon and La Romana dark green, roads black, beach yellow, sea dark blue, airport grey, waterways light blue and open field light orange. The airport area (gray) and waterways (light blue) in Habi were not classified as separate habitat layers and were excluded from further analysis. Source QGIS (version 3.4 Madeira, http://qgis.org), map data: Google Satellite.Full size imageAfter the construction of the habitat resource layers, all GPS fixes were assigned to the respective resource they were located, using the QGIS join attributes by location algorithm. Fixes located exactly on the MCP border in Indonesia were not classified automatically and had to be manually classified to the respective resource.In non-flat topographies (all locations expect Habi) we tested the hypothesis of whether the steepness would influence the dogs’ movement patterns. The degrees of slope were calculated using a 30-m raster-cell resolution (STRM 1-Arc Second Global, downloaded from the United States Geological Survey (USGS) Earth Explorer, https://earthexplorer.usgs.gov/). The slope was assigned by the QGIS join attributes by location algorithm to each GPS fix.Statistical analysisTo quantify habitat selection in each study site, we compared resources used by the dogs with the resources available, according to Freitas et al.52. Adapting the methodology applied by O’Neill et al.18, the observed number of GPS fixes for each dog was used to generate an equivalent number of locations that were randomly distributed within the MCP area using the Random points in layer bound vector tool from QGIS. For example, if dog “D300” had 100 recorded GPS fixes, 100 random points were generated within the MCP of the respective study site and assigned to “D300”. Random points were then assigned to the respective resources and slope of that location, as previously done with the observed GPS fixes. Using this approach, the habitat resources used by each dog could be compared to the available resources in the respective study site, using a regression model.Observation independence is a fundamental presupposition of any regression model. However, the spatial nature of the point-referenced data permits perception of spatial dependence. In our dataset, spatial autocorrelation was proven for all study sites using the Moran’s I test. Therefore, we applied a spatial regression model, which takes into consideration spatial autocorrelation while exploring the effects of the study variables. A mixed effects logistic regression model accounting for spatial autocorrelation was created to quantify the effect of variables on used (i.e. observed GPS fix) versus available (i.e. randomly generated GPS fixes) resources, using the fitme function in the spaMM package in R53,54. The model’s binary outcome variable was defined as either observed (1) or random (0) GPS fix, i.e. the dog being present or absent from a position. The explanatory variable was the resource classification with “buildings”, “roads”, “low vegetation”, “beach”, “sea” and “open field” as levels in Habi; “buildings”, “roads” and “high vegetation” in Pogon; “buildings”, “roads”, “low vegetation” in Poptún and Sabaneta; and “buildings”, “roads”, and “high” and “low vegetation” in La Romana. Different habitat resources were used interchangeably as reference level. In all study sites except Habi, the slope was included as an additional explanatory variable. As observations were not evenly distributed in time, with less observations recorded towards the end of the study, a variable ”hour” was added as an additional continuous fixed effect.Each observed GPS fix was assigned to the hour of its record, with the earliest timestamp registered in each study site being assigned the hour zero. The randomly generated points were randomly assigned to an hour within the determined time continuum of the observed GPS fixes. As our focus was investigating habitat selection at a population-level, we assumed there was no within-dog autocorrelation (space/time) and each dog was independent and exhibited no group behaviour38. Still, to partially account for spatial autocorrelation of each dog’s household, the random effects included in models were defined as each dog’s household geographical location recorded during fieldwork by a GPS device. The restricted maximum likelihood (REML) through Laplace approximations, which can be applied to models with non-Gaussian random effects55, and the Matérn correlation function were used to fit the spatial models with the Matérn family dispersion parameter ν, indicator of strength of decay in the spatial effect, was set at 0.554. More

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    Comparison of the effects of litter decomposition process on soil erosion under simulated rainfall

    Study area descriptionYangtze River Basin is situated in central China (Fig. 1). Its geographical coordinates are between 30° 48′ 30″–31° 02′ 30″ N and 112° 48′ 45″–113° 03′ 45″ E. Taizishan is located in the transition zone between the north and south of China, with an altitude of 403–467.4 m. It belongs to the subtropical monsoon humid climate zone and has obvious karst landforms. The farm area is 7576 hectares, the forest coverage rate is 82.0%, and the vegetation is mainly Masson pine, fir, and various broad-leaved tree species. Increased forest coverage reduces sediment production30. The soil is mainly viscous yellow–brown soil and loess parent material. Rain is concentrated in summer, with an average annual rainfall of 1094.6 mm and an average annual temperature of 16.4 °C. Rainfall-related flood risk increased in the Yangtze River Delta in recent years31.The study was based in a Pinus massoniana forest in the Taizishan forest farm of Hubei Province. The Pinus massoniana (Masson pine) is a common species distributed in Central China.Figure 1Geographic location of the study area. Maps were generated using ArcGIS 10.8 for Desktop (http://www.esri.com/software/arcgis/arcgis-for-desktop).Full size imageExperiment designWe chose the Pinus massoniana forest with 47a in the study area as the research object. In the typical Pinus massoniana forest, the separate layers of litter (semi-decomposed and non-decomposed layers) were collected from several 1 m × 1 m quadrat and placed in grid bags. The litter of the semi-decomposed layer have no complete outline, and the color was brown. As the litter leaves of the completely decomposed layer are powdery and are combined with the soil layer, this layer is difficult to collect. Before testing, it was necessary to clean the soil off the pine needles and then allow the litter to dry naturally. The characteristics of the semi-decomposed and non-decomposed litter layers are shown in Table 1. The soil samples need to be dried and screened by 10 mm. When filling the soil trough, every 0.1 m of soil thickness was one layer, for a total of four layers (0.4 m). The characteristics by soil particle sizes are different (Fig. 2). The soil samples were dried naturally, crushed, and then sieved. The soil trough (2 m long, 0.5 m wide and 0.5 m deep) was filled to have a bulk density of 1.53 g·m−3. In this process, an appropriate amount of water was sprinkled on the surface of each soil layer to achieve a soil moisture content consistent with the surrounding, undisturbed, or natural, state. The simulation experiment was conducted in the Jiufeng rainfall laboratory at Beijing Forestry University, China. We used a rainfall simulation system (QYJY-503T, Qingyuan Measurement Technology, Xi’an, China) used a rotary downward spray nozzle. The system is able to simulate a wide range of rainfall intensities (10 to 300 mm h−1) using various water pressure and nozzle sizes controlled by a computer system.Table 1 Characteristics of the non-decomposed and semi-decomposed layers of Pinus massoniana litter.Full size tableFigure 2Soil particle composition of study area soil layers.Full size imageAccording to the results of the field forest investigation, the litter was covered with the experimental treatments shown in Table 2. The treatments mass coverage of non-decomposed litter layer was named as follows: N1 denoted litter mass coverage 0 g·m−2, N2 was ‘the non-decomposed litter mass coverage 100 g·m−2’, N3 was ‘the non-decomposed litter mass coverage 200 g·m−2’, and N4 was ‘the non-decomposed litter mass coverage 400 g·m−2’, N5 was ‘the semi-decomposed litter mass coverage 100 g·m−2’, N6 was ‘the non-decomposed litter mass coverage 100 g·m−2 and the semi-decomposed litter mass coverage 100 g·m−2’, N7 was ‘the non-decomposed litter mass coverage 200 g·m−2 and the semi-decomposed litter mass coverage 100 g·m−2’. N2, N3 and N4 were the undissolved state of litter layer, and N4 (non-decomposed state, ND), N7 (initial stage of litter decomposition, ID), N6 (middle stage of litter decomposition, MD) and N5 (final stage of litter decomposition, FD) respectively represent different stages of litter decomposition.Table 2 The experimental design of this study.Full size tableAccording to the rainfall in the Taizishan area of Hubei Province, erosive rainfall and extreme rainstorms were selected as the research conditions. Summer rainfall events occur mainly in the summer in this area, and a rainfall intensity of 60 mm·h−1 was the most common erosive rainfall intensity. Under extreme weather conditions, the rainfall intensity can reach up to 120 mm·h−1. Our experiments were conducted with 60 and 120 mm·h−1 rain intensities with a rainfall that lasted 1 h. According to the field investigation data of forest land, this area is a low mountain and hilly area with a slope mostly between 5° and 10°. Therefore, 5° and 10° were selected for the slope treatments in this study. The combination of slope and rainfall intensity was named as follows: T1 denoted ‘Slope 5° and rainfall intensity 60 mm·h−1’, T2 was ‘Slope 10° and rainfall intensity 60 mm·h−1’, T3 was ‘Slope 5° and rainfall intensity 120 mm·h−1’, and T4 was ‘Slope 10° and rainfall intensity 120 mm·h−1’. With two rainfall intensities, two slopes, seven litter coverage gradient and two repetitions combined, this study had a total of 56 rainfall events.Experimental procedureBefore the test, the soil samples were wetted for 10 h and then drained for 2 h to eliminate the effect of the initial soil moisture on the soil detachment measurement. When the simulated rainfall started, all the runoff and sediment produced from plot were collected every 5 min in the first 10 min, and then collected once every 10 min during the subsequent 50 min. At the same time, runoff velocity, depth and temperature were measured and vernier calliper (accuracy 0.02 mm) respectively.The overland flow velocity was measured using dying method (KMnO4 solution)32. After judging the flow pattern, we confirmed the correction coefficient K value (in laminar flow state, K = 0.67; transition flow state, K = 0.70; turbulent flow state, K = 0.8). The average velocity of overland flow was obtained by multiplying the correction coefficient K and the instantaneous velocity. Runoff depth was measured using vernier calliper (accuracy 0.02 mm). Runoff temperature was measured using thermometer. When the rainfall experiment finished, the collected runoff samples were measured volumetric cylinder and then settled for at least 12 h. The clear water was decanted, and the samples were put into an oven to dry for 24 h under 105 °C. The sediment sample was dried and weighed with an electronic scale.Calculation of hydrodynamic parametersOverland flow has the characteristics of a thin water layer, large fluctuations of the underlying surface, and unstable flow velocity. At present, most scholars use open-channel flow theory to study overland flow33,34. In open-channel flow theory, the Reynold’s number (Re), Froude constant (Fr), flow index (m), resistance coefficient (f), and soil separation rate (({D}_{r})) are the basic parameters of overland flow dynamics, through Reynold’s number (Re), Froude constant (Fr), flow index (m) can distinguish flow patterns. Re is calculated as:$$Re=Rcdot V/nu ,$$where Re is the Reynolds number of the water flow, which is dimensionless, and can be used to judge the flow state of overland flow. When Re ≤ 500, the flow pattern is laminar; when 500   5000, the flow pattern is turbulent. R is the hydraulic radius (m), which is generally replaced by flow depth as measured by a vernier calliper (accuracy 0.02 mm). (V) is the average velocity (m·s−1); (nu) is the kinematic viscosity coefficient (m2·s−1), and the calculation formula is (nu) = 0.01775·10−4·(1 + 0.0337 t + 0.00021 t2), where t is the test overland flow temperature35.Fr is the Froude constant, which is the ratio of the inertial force to gravity and can be used to distinguish overland flow as rapid flow, slow flow, or critical flow. When Fr  1, the fluid is rapid flow.Fr is calculated as:$$Fr=V/sqrt{gcdot R},$$where (Fr) is the Froude constant of the water flow, which is dimensionless; (V) is the average velocity (m·s−1); g is the acceleration of gravity and has a constant value of 9.8 m·s−2; R is a hydraulic radius (m), and is generally replaced by flow depth as measured by a vernier calliper (accuracy 0.02 mm).Regression fitting is made for runoff depth (h) and single width flow (Q). The runoff depth equation for slope is as follows:$$h=k{q}^{m},$$where q is the single width flow (L·m−1·s−1); h is the depth of water on the slope (m); and m is the flow index, which reflects the turbulent characteristics of the flow state. The larger m is, the more energy the flow consumes in the work of resistance. The comprehensive index (k) reflects the characteristics of the underlying surface and the water viscosity of the slope flow. The larger k is, the stronger the surface material of the slope works on the flow.The resistance of overland flow reflects the inhibition effect of different underlying surface conditions on the velocity of overland flow. The Darcy–Weisbach formula is widely used in research because of its two advantages: applicability and dimensionlessness under laminar and turbulent flow conditions36,37.The resistance coefficient (f) is calculated as follows:$$f=8cdot gcdot Rcdot J/{V}^{2},$$where the resistance coefficient f has no dimension; g is the acceleration of gravity and is always 9.8 m·s−2; R is a hydraulic radius (m), generally replaced by flow depth measured by a vernier calliper (accuracy 0.02 mm); (V) is the average velocity (m·s−1); and J is the hydraulic gradient, which can be converted by the gradient in a uniform flow state and is generally replaced by the sine value of the gradient.Shear stress ((tau)) is the main driving force that affects the stripping of soil particles from the surface soil38. Shear stress is calculated as:$$tau =rcdot gcdot Rcdot J,$$where (tau) is the shear force of runoff (Pa); and r is the density of water and sediment concentration flow (kg·m−3). This study used a muddy water mass and volume ratio in the unseparated state to calculate the density of water and sediment concentration flow.Flow power (W) is the runoff power per unit area of water and refers to the power consumed by the weight of water acting on the riverbed surface to transport runoff and sediment. W is calculated as:$$W=tau cdot V,$$where W is the flow power (N·m−1·s−1); and (tau) is the shear force of runoff (Pa).Soil separation rate (({D}_{r})) refers to the quality of soil in which soil particles are separated from the soil per unit time. The calculation formula is as follows:$${D}_{r}={W}_{d}-{W}_{w}/tcdot A,$$where ({D}_{r}) is the rate of soil separation (kg·m−2·s−1); ({W}_{w}) is the dry weight of soil before the test; ({W}_{d}) is the dry weight of soil after the test, measured by the drying method (kg); t is the scouring time (s); and A is the surface area of the soil sample (m2). More

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    Tree species composition mapping with dimension reduction and post-classification using very high-resolution hyperspectral imaging

    Vo, Q. T., Oppelt, N., Leinenkugel, P. & Kuenzer, C. Remote sensing in mapping mangrove ecosystems: An object-based approach. Remote Sens. 5, 183–201. https://doi.org/10.3390/rs5010183 (2013).Article 

    Google Scholar 
    Kertész, Á. & Křeček, J. Landscape degradation in the world and in Hungary. Hung. Geogr. Bull. 68, 201–221. https://doi.org/10.15201/hungeobull.68.3.1 (2019).Article 

    Google Scholar 
    Vorster, A. G., Evangelista, P. H., Stovall, A. E. L. & Ex, S. Variability and uncertainty in forest biomass estimates from the tree to landscape scale: The role of allometric equations. Carbon Balance Manag. 15, 8. https://doi.org/10.1186/s13021-020-00143-6 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Blackman, A. Evaluating forest conservation policies in developing countries using remote sensing data: An introduction and practical guide. For. Policy Econ. 34, 1–16. https://doi.org/10.1016/j.forpol.2013.04.006 (2013).Article 

    Google Scholar 
    Wilfong, B. N., Gorchov, D. L. & Henry, M. C. Detecting an invasive shrub in deciduous forest understories using remote sensing. Weed Sci. 57, 512–520. https://doi.org/10.1614/WS-09-012.1 (2009).Article 
    CAS 

    Google Scholar 
    Dyderski, M. K. & Pawlik, Ł. Spatial distribution of tree species in mountain national parks depends on geomorphology and climate. For. Ecol. Manag. 474, 118366. https://doi.org/10.1016/j.foreco.2020.118366 (2020).Article 

    Google Scholar 
    Milosevic, D., Dunjić, J. & Stojanović, V. Investigating micrometeorological differences between saline steppe, forest-steppe and forest environments in northern Serbia during a clear and sunny autumn day. Geogr. Pannonica 24(3), 176–186. https://doi.org/10.5937/gp24-25885 (2020).Article 

    Google Scholar 
    Modzelewska, A., Fassnacht, F. E. & Stereńczak, K. Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data. Int. J. Appl. Earth Obs. Geoinf. 84, 101960. https://doi.org/10.1016/j.jag.2019.101960 (2020).Article 

    Google Scholar 
    Wulder, M. Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Prog. Phys. Geogr. Earth Environ. 22, 449–476. https://doi.org/10.1177/030913339802200402 (1998).Article 

    Google Scholar 
    Tang, L., Shao, G. & Dai, L. Roles of digital technology in China’s sustainable forestry development. Int. J. Sustain. Dev. World Ecol. 16, 94–101. https://doi.org/10.1080/13504500902794000 (2009).Article 

    Google Scholar 
    Richter, R., Reu, B., Wirth, C., Doktor, D. & Vohland, M. The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area. Int. J. Appl. Earth Obs. Geoinform. 52, 464–474. https://doi.org/10.1016/j.jag.2016.07.018 (2016).Article 

    Google Scholar 
    Thenkabail, P., Gumma, M., Teluguntla, P. & Ahmed, M. I. Hyperspectral remote sensing of vegetation and agricultural crops. Photogramm. Eng. Remote Sens. 80, 695–723 (2014).
    Google Scholar 
    Fassnacht, F. E. et al. Review of studies on tree species classification from remotely sensed data. Remote Sens. Environ. 186, 64–87. https://doi.org/10.1016/j.rse.2016.08.013 (2016).Article 

    Google Scholar 
    Vangi, E. et al. The new hyperspectral satellite PRISMA: Imagery for forest types discrimination. Sensors 21, 1182. https://doi.org/10.3390/s21041182 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Burai, P., Beko, L., Lenart, C., Tomor, T. & Kovacs, Z. Individual tree species classification using airborne hyperspectral imagery and lidar data. In 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) 1–4. https://doi.org/10.1109/WHISPERS.2019.8921016 (2019).Kumar, B., Dikshit, O., Gupta, A. & Singh, M. K. Feature extraction for hyperspectral image classification: A review. Int. J. Remote Sens. 41, 6248–6287. https://doi.org/10.1080/01431161.2020.1736732 (2020).Article 

    Google Scholar 
    Li, X., Li, Z., Qiu, H., Hou, G. & Fan, P. An overview of hyperspectral image feature extraction, classification methods and the methods based on small samples. Appl. Spectrosc. Rev. https://doi.org/10.1080/05704928.2021.1999252 (2021).Article 

    Google Scholar 
    Wang, J. & Chang, C.-I. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Trans. Geosci. Remote Sens. 44, 1586–1600. https://doi.org/10.1109/TGRS.2005.863297 (2006).Article 

    Google Scholar 
    Hamada, Y., Stow, D. A., Coulter, L. L., Jafolla, J. C. & Hendricks, L. W. Detecting Tamarisk species (Tamarix spp.) in riparian habitats of Southern California using high spatial resolution hyperspectral imagery. Remote Sens. Environ. 109, 237–248. https://doi.org/10.1016/j.rse.2007.01.003 (2007).Article 

    Google Scholar 
    Ibarrola-Ulzurrun, E., Marcello, J. & Gonzalo-Martin, C. Assessment of component selection strategies in hyperspectral imagery. Entropy 19, 666. https://doi.org/10.3390/e19120666 (2017).Article 
    MathSciNet 

    Google Scholar 
    Dabiri, Z. & Lang, S. Comparison of independent component analysis, principal component analysis, and minimum noise fraction transformation for tree species classification using APEX hyperspectral imagery. ISPRS Int. J. Geo-Inf. 7, 488. https://doi.org/10.3390/ijgi7120488 (2018).Article 

    Google Scholar 
    Priyadarshini, K. N., Sivashankari, V., Shekhar, S. & Balasubramani, K. Comparison and evaluation of dimensionality reduction techniques for hyperspectral data analysis. Proceedings 24, 6. https://doi.org/10.3390/IECG2019-06209 (2019).Article 

    Google Scholar 
    Arslan, O., Akyürek, Ö., Kaya, Ş & Şeker, D. Z. Dimension reduction methods applied to coastline extraction on hyperspectral imagery. Geocarto Int. 35, 376–390. https://doi.org/10.1080/10106049.2018.1520920 (2020).Article 

    Google Scholar 
    Kadavi, P. R., Lee, W.-J. & Lee, C.-W. Analysis of the pyroclastic flow deposits of mount sinabung and Merapi using landsat imagery and the artificial neural networks approach. Appl. Sci. 7, 935. https://doi.org/10.3390/app7090935 (2017).Article 

    Google Scholar 
    Schlosser, A. D. et al. Building extraction using orthophotos and dense point cloud derived from visual band aerial imagery based on machine learning and segmentation. Remote Sens. 12, 2397. https://doi.org/10.3390/rs12152397 (2020).Article 

    Google Scholar 
    Latifi, H., Fassnacht, F. & Koch, B. Forest structure modeling with combined airborne hyperspectral and LiDAR data. Remote Sens. Environ. 121, 10–25. https://doi.org/10.1016/j.rse.2012.01.015 (2012).Article 

    Google Scholar 
    Clark, M. L., Roberts, D. A. & Clark, D. B. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sens. Environ. 96, 375–398. https://doi.org/10.1016/j.rse.2005.03.009 (2005).Article 

    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/ICIECS.2009.5363456 (2004).Article 

    Google Scholar 
    Belgiu, M. & Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011 (2016).Article 

    Google Scholar 
    Manandhar, R., Odeh, I. O. A. & Ancev, T. Improving the accuracy of land use and land cover classification of landsat data using post-classification enhancement. Remote Sens. 1, 330–344. https://doi.org/10.3390/rs1030330 (2009).Article 

    Google Scholar 
    Thakkar, A. K., Desai, V. R., Patel, A. & Potdar, M. B. Post-classification corrections in improving the classification of Land Use/Land Cover of arid region using RS and GIS: The case of Arjuni watershed, Gujarat, India. Egypt. J. Remote Sens. Space Sci. 20, 79–89. https://doi.org/10.1016/j.ejrs.2016.11.006 (2017).Article 

    Google Scholar 
    El-Hattab, M. M. Applying post classification change detection technique to monitor an Egyptian coastal zone (Abu Qir Bay), Egypt. J. Remote Sens. Space Sci. 19, 23–36. https://doi.org/10.1016/j.ejrs.2016.02.002 (2016).Article 

    Google Scholar 
    Bhosale, N., Manza, R., Kale, K., Scholar, R. & Professor, A. Analysis of effect of gaussian, salt and pepper noise removal from noisy remote sensing images. Pceedings of teh Second International Conference on ERCICA 386–390. http://rameshmanza.in/Publication/Narayan_Bhosle/Analysis%20of%20Effect%20of%20Gaussian.pdf (2014).Schöll, K., Kiss, A., Dinka, M. & Berczik, Á. Flood-pulse effects on zooplankton assemblages in a river-floodplain system (Gemenc Floodplain of the Danube, Hungary). Int. Rev. Hydrobiol. 97, 41–54. https://doi.org/10.1002/iroh.201111427 (2012).Article 

    Google Scholar 
    Ágoston-Szabó, E., Schöll, K., Kiss, A. & Dinka, M. The effects of tree species richness and composition on leaf litter decomposition in a Danube oxbow lake (Gemenc, Hungary). Fundam. Appl. Limnol. https://doi.org/10.1127/fal/2017/0675 (2017).Article 

    Google Scholar 
    Guti, G. Water bodies in the Gemenc floodplain of the Danube, Hungary: (A theoretical basis for their typology). Opusc Zool. 33, 49–60 (2001).
    Google Scholar 
    Berczik, Á. & Dinka, M. Bibliography of hydrobiological research on the Gemenc and Béda: Karapancsa floodplains of the River Danube (1498–1436 rkm) including the publications of the Danube Research Institute of the Hungarian Academy of Sciences between 1968 and 2017. Opusc. Zool. 49, 191–197. https://doi.org/10.18348/opzool.2018.2.191 (2018).Article 

    Google Scholar 
    Ceulemans, R., McDonald, A. J. S. & Pereira, J. S. A comparison among eucalypt, poplar and willow characteristics with particular reference to a coppice, growth-modelling approach. Biomass Bioenergy 11, 215–231. https://doi.org/10.1016/0961-9534(96)00035-9 (1996).Article 

    Google Scholar 
    Haneca, K., Katarina, Č & Beeckman, H. Oaks, tree-rings and wooden cultural heritage: A review of the main characteristics and applications of oak dendrochronology in Europe. J. Archaeol. Sci. 36, 1–11. https://doi.org/10.1016/j.jas.2008.07.005 (2009).Article 

    Google Scholar 
    Jones, T. G., Coops, N. C. & Sharma, T. Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada. Remote Sens. Environ. 114, 2841–2852. https://doi.org/10.1016/j.rse.2010.07.002 (2010).Article 

    Google Scholar 
    Sothe, C. et al. Tree species classification in a highly diverse subtropical forest integrating UAV-based photogrammetric point cloud and hyperspectral data. Remote Sens. 11, 1338. https://doi.org/10.3390/rs11111338 (2019).Article 

    Google Scholar 
    Nambiar, E. K. S. & Sands, R. Competition for water and nutrients in forests. Can. J. For. Res. 23, 1955–1968. https://doi.org/10.1139/x93-247 (1993).Article 

    Google Scholar 
    Mayoral, C., Calama, R., Sánchez-González, M. & Pardos, M. Modelling the influence of light, water and temperature on photosynthesis in young trees of mixed Mediterranean forests. New For. 46, 485–506. https://doi.org/10.1007/s11056-015-9471-y (2015).Article 

    Google Scholar 
    Stojanović, D. B., Levanič, T., Matović, B. & Orlović, S. Growth decrease and mortality of oak floodplain forests as a response to change of water regime and climate. Eur. J. For. Res. 134, 555–567. https://doi.org/10.1007/s10342-015-0871-5 (2015).Article 

    Google Scholar 
    Dyderski, M. K. & Jagodziński, A. M. Impact of invasive tree species on natural regeneration species composition, diversity, and density. Forests 11, 456. https://doi.org/10.3390/f11040456 (2020).Article 

    Google Scholar 
    Jia, S., Ji, Z., Qian, Y. & Shen, L. Unsupervised band selection for hyperspectral imagery classification without manual band removal. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5, 531–543. https://doi.org/10.1109/JSTARS.2012.2187434 (2012).Article 

    Google Scholar 
    Karpouzli, E. & Malthus, T. The empirical line method for the atmospheric correction of IKONOS imagery. Int. J. Remote Sens. 24, 1143–1150. https://doi.org/10.1080/0143116021000026779 (2003).Article 

    Google Scholar 
    Richards, J. A. Remote Sensing Digital Image Analysis (Springer, 2013). https://doi.org/10.1007/978-3-642-30062-2.Book 

    Google Scholar 
    Sharifi Hashjin, S. & Khazai, S. A new method to detect targets in hyperspectral images based on principal component analysis. Geocarto Int. 37, 2679–2697. https://doi.org/10.1080/10106049.2020.1831625 (2022).Article 

    Google Scholar 
    Kaiser, H. F. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187–200 (1958).Article 
    MATH 

    Google Scholar 
    Shah, C. A., Arora, M. K. & Varshney, P. K. Unsupervised classification of hyperspectral data: An ICA mixture model based approach. Int. J. Remote Sens. 25, 481–487. https://doi.org/10.1080/01431160310001618040 (2004).Article 

    Google Scholar 
    Tharwat, A. Independent component analysis: An introduction. Appl. Comput. Inform. 17, 222–249. https://doi.org/10.1016/S1364-6613(00)01813-1 (2020).Article 

    Google Scholar 
    Villa, A., Chanussot, J., Jutten, C., Benediktsson, J. A. & Moussaoui, S. On the use of ICA for hyperspectral image analysis. In 2009 IEEE International Geoscience and Remote Sensing Symposium vol. 4 IV-97-IV–100. https://doi.org/10.1109/IGARSS.2009.5417363 (2009).Hyvärinen, A. & Oja, E. Independent component analysis: Algorithms and applications. Neural Netw. 13, 411–430. https://doi.org/10.1016/s0893-6080(00)00026-5 (2000).Article 
    PubMed 

    Google Scholar 
    Otukei, J. R. & Blaschke, T. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int. J. Appl. Earth Obs. Geoinf. 12, S27–S31. https://doi.org/10.1016/j.jag.2009.11.002 (2010).Article 

    Google Scholar 
    Murty, M. N. & Raghava, R. Kernel-based SVM. In Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks (eds Murty, M. N. & Raghava, R.) 57–67 (Springer, 2016). https://doi.org/10.1007/978-3-319-41063-0_5.Chapter 
    MATH 

    Google Scholar 
    Seidl, D., Ružiak, I., Koštialová Jančíková, Z. & Koštial, P. Sensitivity analysis: A tool for tailoring environmentally friendly materials. Expert Syst. Appl. 208, 118039. https://doi.org/10.1016/j.eswa.2022.118039 (2022).Article 

    Google Scholar 
    Zhao, D., Pang, Y., Liu, L. & Li, Z. Individual tree classification using airborne LiDAR and hyperspectral data in a natural mixed forest of Northeast China. Forests 11, 303. https://doi.org/10.3390/f11030303 (2020).Article 

    Google Scholar 
    Aksoy, S. & Akcay, H. G. Multi-resolution segmentation and shape analysis for remote sensing image classification. In Proceedings of 2nd International Conference on Recent Advances in Space Technologies, 2005. RAST 2005. 599–604 (2005). https://doi.org/10.1109/RAST.2005.1512638.Dalponte, M., Ørka, H. O., Ene, L. T., Gobakken, T. & Næsset, E. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sens. Environ. 140, 306–317. https://doi.org/10.1016/j.rse.2013.09.006 (2014).Article 

    Google Scholar 
    Amini, S., Homayouni, S., Safari, A. & Darvishsefat, A. A. Object-based classification of hyperspectral data using Random Forest algorithm. Geo-Spat. Inf. Sci. 21, 127–138. https://doi.org/10.1080/10095020.2017.1399674 (2018).Article 

    Google Scholar 
    Congalton, R. G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37, 35–46. https://doi.org/10.1016/0034-4257(91)90048-B (1991).Article 

    Google Scholar 
    Foody, G. M. Status of land cover classification accuracy assessment. Remote Sens. Environ. 80, 185–201. https://doi.org/10.1016/S0034-4257(01)00295-4 (2002).Article 

    Google Scholar 
    Tharwat, A. Classification assessment methods. Appl. Comput. Inform. 17, 168–192. https://doi.org/10.1016/j.aci.2018.08.003 (2020).Article 

    Google Scholar 
    Field, F. Discovering Statistics Using IBM SPSS Statistics. SAGE Publications Ltd https://uk.sagepub.com/en-gb/eur/discovering-statistics-using-ibm-spss-statistics/book257672 (2022).R Core Team. R: A language and environment for statistical computing. https://www.gbif.org/tool/81287/r-a-language-and-environment-for-statistical-computing (2022).Galucci, M. Generalized Mixed Models module. R package version 2.0.5. https://gamlj.github.io/gzlmmixed.html More

  • in

    The impact of the first United Kingdom COVID-19 lockdown on environmental air pollution, digital display device use and ocular surface disease symptomatology amongst shielding patients

    Knight, H. et al. Impacts of the COVID-19 Pandemic and Self-Isolation on Students and Staff in Higher Education: A Qualitative Study. Int. J. Environ. Res. Public Health 18, 10675 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Higham, J. E., Ramírez, C. A., Green, M. A. & Morse, A. P. UK COVID-19 lockdown: 100 days of air pollution reduction? Air Quality. Atmosphere & Health https://doi.org/10.1007/s11869-020-00937-0 (2020).Article 

    Google Scholar 
    Office, P. M. s. Slides and datasets to accompany coronavirus press conference. (2020).Organization, W. H. WHO global air quality guidelines: particulate matter (PM2. 5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide: executive summary. (2021).Singh, A. et al. Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford UK. Environ. Pollut. 293, 118584. https://doi.org/10.1016/j.envpol.2021.118584 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Shi, Z. et al. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances 7, eabd6696, doi:doi:https://doi.org/10.1126/sciadv.abd6696 (2021).Lee, J. D., Drysdale, W. S., Finch, D. P., Wilde, S. E. & Palmer, P. I. UK surface NO2 levels dropped by 42% during the COVID-19 lockdown: impact on surface O3. Atmos. Chem. Phys. 20, 15743–15759. https://doi.org/10.5194/acp-20-15743-2020 (2020).Article 
    CAS 

    Google Scholar 
    Shi, Z. et al. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances 7, eabd6696, doi:https://doi.org/10.1126/sciadv.abd6696 (2021).Ropkins, K. & Tate, J. E. Early observations on the impact of the COVID-19 lockdown on air quality trends across the UK. Sci. Total Environ. 754, 142374. https://doi.org/10.1016/j.scitotenv.2020.142374 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Nwanaji-Enwerem, J. C., Allen, J. G. & Beamer, P. I. Another invisible enemy indoors: COVID-19, human health, the home, and United States indoor air policy. J Expo Sci Environ Epidemiol 30, 773–775. https://doi.org/10.1038/s41370-020-0247-x (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rasha, A., Karan Jetly, J. & Shqran, S. Indoor Air Quality Monitoring Systems: A Comprehensive Review of Different IAQM Systems. International Journal of Knowledge-Based Organizations (IJKBO) 11, 1–14, doi:https://doi.org/10.4018/ijkbo.2021070101 (2021).World Health Organization. Regional Office for, E. WHO guidelines for indoor air quality: selected pollutants. xxv, 454 p. (World Health Organization. Regional Office for Europe, 2010).Stafoggia, M. et al. Long-term exposure to ambient air pollution and incidence of cerebrovascular events: Results from 11 European cohorts within the ESCAPE project. Environ. Health Perspect 122, 919–925. https://doi.org/10.1289/ehp.1307301 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brook, R. D. et al. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American heart association. Circulation 121, 2331–2378. https://doi.org/10.1161/CIR.0b013e3181dbece1 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Raaschou-Nielsen, O. et al. Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European study of cohorts for air pollution effects (ESCAPE). Lancet Oncol. 14, 813–822. https://doi.org/10.1016/s1470-2045(13)70279-1 (2013).Article 
    PubMed 

    Google Scholar 
    Guan, W. J., Zheng, X. Y., Chung, K. F. & Zhong, N. S. Impact of air pollution on the burden of chronic respiratory diseases in China: Time for urgent action. Lancet 388, 1939–1951. https://doi.org/10.1016/s0140-6736(16)31597-5 (2016).Article 
    PubMed 

    Google Scholar 
    Atkinson, R. W. et al. Acute effects of particulate air pollution on respiratory admissions: Results from APHEA 2 project. Air pollution and health: A European approach. Am. J. Respir. Crit. Care Med. 164, 1860–1866. https://doi.org/10.1164/ajrccm.164.10.2010138 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Stapleton, F. et al. TFOS DEWS II epidemiology report. Ocular Surf. 15, 334–365. https://doi.org/10.1016/j.jtos.2017.05.003 (2017).Article 

    Google Scholar 
    Starr, C. E. et al. Dry eye disease flares: A rapid evidence assessment. Ocul. Surf. 22, 51–59. https://doi.org/10.1016/j.jtos.2021.07.001 (2021).Article 
    PubMed 

    Google Scholar 
    Torricelli, A. A. et al. Correlation between signs and symptoms of ocular surface dysfunction and tear osmolarity with ambient levels of air pollution in a large metropolitan area. Cornea 32, e11-15. https://doi.org/10.1097/ICO.0b013e31825e845d (2013).Article 
    PubMed 

    Google Scholar 
    Hwang, S. H. et al. Potential importance of ozone in the association between outdoor air pollution and dry eye disease in South Korea. JAMA Ophthalmol. 134, 503–510. https://doi.org/10.1001/jamaophthalmol.2016.0139 (2016).Article 
    PubMed 

    Google Scholar 
    Wiwatanadate, P. Acute air pollution-related symptoms among residents in Chiang Mai Thailand. J. Environ. Health 76, 76–84 (2014).CAS 
    PubMed 

    Google Scholar 
    Alves, M., Novaes, P., Morraye Mde, A., Reinach, P. S. & Rocha, E. M. Is dry eye an environmental disease? Arq. Bras. Oftalmol. 77, 193–200 https://doi.org/10.5935/0004-2749.20140050 (2014).Bourcier, T. et al. Effects of air pollution and climatic conditions on the frequency of ophthalmological emergency examinations. Br. J. Ophthalmol. 87, 809–811. https://doi.org/10.1136/bjo.87.7.809 (2003).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hao, R. et al. Impact of air pollution on the ocular surface and tear cytokine levels: A multicenter prospective cohort study. Front. Med. (Lausanne) 9, 909330. https://doi.org/10.3389/fmed.2022.909330 (2022).Article 
    PubMed 

    Google Scholar 
    Vehof, J., Snieder, H., Jansonius, N. & Hammond, C. J. Prevalence and risk factors of dry eye in 79,866 participants of the population-based lifelines cohort study in the Netherlands. Ocul. Surf. 19, 83–93. https://doi.org/10.1016/j.jtos.2020.04.005 (2021).Article 
    PubMed 

    Google Scholar 
    Wolffsohn, J. S. et al. Demographic and lifestyle risk factors of dry eye disease subtypes: A cross-sectional study. Ocul. Surf. 21, 58–63. https://doi.org/10.1016/j.jtos.2021.05.001 (2021).Article 
    PubMed 

    Google Scholar 
    Núñez-Álvarez, C. & Osborne, N. N. Enhancement of corneal epithelium cell survival, proliferation and migration by red light: Relevance to corneal wound healing. Exp. Eye Res. 180, 231–241. https://doi.org/10.1016/j.exer.2019.01.003 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Marek, V. et al. Blue light phototoxicity toward human corneal and conjunctival epithelial cells in basal and hyperosmolar conditions. Free Radic. Biol. Med. 126, 27–40. https://doi.org/10.1016/j.freeradbiomed.2018.07.012 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Talens-Estarelles, C., García-Marqués, J. V., Cerviño, A. & García-Lázaro, S. Determining the best management strategy for preventing short-term effects of digital display use on dry eyes. Eye Contact Lens 48, 416–423. https://doi.org/10.1097/icl.0000000000000921 (2022).Article 
    PubMed 

    Google Scholar 
    GOV.UK. COVID-19: guidance on protecting people defined on medical grounds as extremely vulnerable, (2020).Joy, M. et al. Reorganisation of primary care for older adults during COVID-19: A cross-sectional database study in the UK. Br. J. Gen. Pract. 70, e540–e547. https://doi.org/10.3399/bjgp20X710933 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schiffman, R. M., Christianson, M. D., Jacobsen, G., Hirsch, J. D. & Reis, B. L. Reliability and validity of the ocular surface disease index. Arch. Ophthalmol. 118, 615–621. https://doi.org/10.1001/archopht.118.5.615 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Amparo, F. & Dana, R. Web-based longitudinal remote assessment of dry eye symptoms. Ocul. Surf. 16, 249–253. https://doi.org/10.1016/j.jtos.2018.01.002 (2018).Article 
    PubMed 

    Google Scholar 
    Inomata, T. et al. Characteristics and risk factors associated with diagnosed and undiagnosed symptomatic dry eye using a smartphone application. JAMA Ophthalmol. 138, 58–68. https://doi.org/10.1001/jamaophthalmol.2019.4815 (2020).Article 
    PubMed 

    Google Scholar 
    Toth, M. & Jokić-Begić, N. Psychological contribution to understanding the nature of dry eye disease: A cross-sectional study of anxiety sensitivity and dry eyes. Health Psychol. Behav. Med. 8, 202–219. https://doi.org/10.1080/21642850.2020.1770093 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mehra, D. & Galor, A. Digital screen use and dry eye: A review. Asia-Pacific J. Ophthalmol. 9, 491–497. https://doi.org/10.1097/apo.0000000000000328 (2020).Article 

    Google Scholar 
    Galor, A., Kumar, N., Feuer, W. & Lee, D. J. Environmental factors affect the risk of dry eye syndrome in a United States veteran population. Ophthalmology 121, 972–973. https://doi.org/10.1016/j.ophtha.2013.11.036 (2014).Article 
    PubMed 

    Google Scholar 
    Courtin, R. et al. Prevalence of dry eye disease in visual display terminal workers: A systematic review and meta-analysis. BMJ Open 6, e009675. https://doi.org/10.1136/bmjopen-2015-009675 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Torricelli, A. A. et al. Effects of ambient levels of traffic-derived air pollution on the ocular surface: Analysis of symptoms, conjunctival goblet cell count and mucin 5AC gene expression. Environ. Res. 131, 59–63. https://doi.org/10.1016/j.envres.2014.02.014 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gupta, S. K., Gupta, V., Joshi, S. & Tandon, R. Subclinically dry eyes in urban Delhi: An impact of air pollution?. Ophthalmologica 216, 368–371. https://doi.org/10.1159/000066183 (2002).Article 
    CAS 
    PubMed 

    Google Scholar 
    Berg, E. J. et al. Climatic and environmental correlates of dry eye disease severity: A report from the dry eye assessment and management (DREAM) study. Trans. Vision Sci. Technol. 9, 25–25. https://doi.org/10.1167/tvst.9.5.25 (2020).Article 

    Google Scholar 
    Lang, S.-J., Abel, G. A., Mant, J. & Mullis, R. Impact of socioeconomic deprivation on screening for cardiovascular disease risk in a primary prevention population: A cross-sectional study. BMJ Open 6, e009984. https://doi.org/10.1136/bmjopen-2015-009984 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Denniston, A. K. et al. United Kingdom diabetic retinopathy electronic medical record (UK DR EMR) users group: Report 4, real-world data on the impact of deprivation on the presentation of diabetic eye disease at hospital services. Br. J. Ophthalmol. 103, 837–843. https://doi.org/10.1136/bjophthalmol-2018-312568 (2019).Article 
    PubMed 

    Google Scholar 
    Nessim, M., Denniston, A. K., Nolan, W., Holder, R. & Shah, P. Research into Glaucoma and Ethnicity (ReGAE) 8: Is there a relationship between social deprivation and acute primary angle closure?. Br. J. Ophthalmol. 94, 1304–1306. https://doi.org/10.1136/bjo.2009.160721 (2010).Article 
    PubMed 

    Google Scholar 
    Sharma, H. E. et al. The role of social deprivation in severe neovascular age-related macular degeneration. Br. J. Ophthalmol. 98, 1625–1628. https://doi.org/10.1136/bjophthalmol-2014-304959 (2014).Article 
    PubMed 

    Google Scholar 
    Bo, M., Salizzoni, P., Clerico, M. & Buccolieri, R. Assessment of indoor-outdoor particulate matter air pollution: A review. Atmosphere 8, 136 (2017).Article 

    Google Scholar 
    Strøm-Tejsen, P., Zukowska, D., Fang, L., Space, D. R. & Wyon, D. P. Advantages for passengers and cabin crew of operating a gas-phase adsorption air purifier in 11-h simulated flights. Indoor Air 18, 172–181. https://doi.org/10.1111/j.1600-0668.2007.00511.x (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Mandell, J. T., Idarraga, M., Kumar, N. & Galor, A. Impact of air pollution and weather on dry eye. J. Clin. Med. https://doi.org/10.3390/jcm9113740 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Navarro, D. Learning Statistics with R. (Daniel Joseph Navarro, 2015). More

  • in

    Multi-proxy dentition analyses reveal niche partitioning between sympatric herbivorous dinosaurs

    Versluys, J. Die Kaubewegungen von Trachodon. Palaontol. Z. 4, 80–87 (1922).
    Google Scholar 
    Kripp, D. Die Kaubewegung und Lebensweise von Edmontosaurus spec. auf Grund der mechanischkonstruktiven analyse. Palaeobiologica 5, 409–422 (1933).
    Google Scholar 
    Ostrom, J. H. Cranial morphology of the hadrosaurian dinosaurs of North America. Bull. Am. Mus. Nat. Hist. 122, 39–186 (1961).
    Google Scholar 
    Ostrom, J. H. A functional analysis of jaw mechanics in the dinosaur. Triceratops. Postilla. 88, 1–35 (1964).MathSciNet 

    Google Scholar 
    Galton, P. M. The cheeks of ornithischian dinosaurs. Lethaia 6, 67–89. https://doi.org/10.1111/j.1502-3931.1973.tb00873.x (1973).Article 

    Google Scholar 
    Galton, P. M. Herbivorous adaptations of Late Triassic and Early Jurassic dinosaurs. In The Beginning of the Age of Dinosaurs (ed. Padian, K.) 203–221 (Cambridge University Press, 1986).
    Google Scholar 
    Weishampel, D. B. Hadrosaurid jaw mechanics. Acta Palaeontol. Pol. 28, 271–280 (1983).
    Google Scholar 
    Weishampel, D. B. The evolution of jaw mechanisms in ornithopod dinosaurs. Adv. Anat. Embryol. Cell. Biol. 87, 1–2 (1984).Article 
    CAS 
    PubMed 

    Google Scholar 
    Weishampel, D. B. Interactions between Mesozoic plants and vertebrates: fructifications and seed predation. Neues Jahrb. Geol. Paläontol. Abh. 167, 224–250 (1984).
    Google Scholar 
    Weishampel, D. B. & Norman, D. B. Vertebrate herbivory in the Mesozoic: Jaws, plants, and evolutionary metrics. In Paleobiology of the Dinosaurs Special Papers 238 (ed. Farlow, J. O.) 87–100 (Geological Society of America, 1989).Chapter 

    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Feeding mechanisms in some small herbivorous dinosaurs: processes and patterns. In Biomechanics and Evolution (eds Rayner, J. M. V. & Wooton, R. J.) 161–181 (Cambridge University Press, 1991).
    Google Scholar 
    Sereno, P., Zijin, Z. & Lin, T. A new psittacosaur from Inner Mongolia and the parrot-like structure and function of the psittacosaur skull. Proc. Roy. Soc. B. 277, 199–209. https://doi.org/10.1098/rspb.2009.0691 (2010).Article 

    Google Scholar 
    Barrett, P. M. Paleobiology of herbivorous dinosaurs. Annu. Rev. Earth Planet. Sci. 42(1), 207–230. https://doi.org/10.1146/annurev-earth-042711-105515 (2014).Article 
    CAS 

    Google Scholar 
    Erickson, G. M. et al. Wear biomechanics in the slicing dentition of the giant horned dinosaur Triceratops. Sci. Adv. 1(5), e1500055. https://doi.org/10.1126/sciadv.1500055 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nabavizadeh, A. Hadrosauroid jaw mechanics and the functionalsignificance of the predentary bone. In The hadrosaurs: Proceedings of the International Hadrosaur Symposium (eds Evans, D. & Eberth, D.) 467–482 (Indiana University Press, 2014).
    Google Scholar 
    Nabavizadeh, A. Evolutionary trends in the jaw adductor mechanics of ornithischian dinosaurs. Anat. Rec. 299(3), 271–294. https://doi.org/10.1002/ar.23306 (2016).Article 

    Google Scholar 
    Nabavizadeh, A. new reconstruction of cranial musculature in ceratopsian dinosaurs: Implications for jaw mechanics and ‘cheek’anatomy. FASEB J. 30, lb27–lb27. https://doi.org/10.1096/fasebj.30.1_supplement.lb27 (2016).Article 

    Google Scholar 
    Nabavizadeh, A. new reconstruction of cranial musculature in ornithischian dinosaurs: Implications for feeding mechanismsand buccal anatomy. Anat. Rec. 303, 347–362. https://doi.org/10.1002/ar.23988 (2020).Article 

    Google Scholar 
    Varriale, F. J. Dental microwear reveals mammal-like chewing in the neoceratopsian dinosaur Leptoceratops gracilis. PeerJ 4, e2132. https://doi.org/10.7717/peerj.2132 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Melstrom, K. M., Chiappe, L. M. & Smith, N. D. Exceptionally simple, rapidly replaced teeth in sauropod dinosaurs demonstrate a novel evolutionary strategy for herbivory in Late Jurassic ecosystems. BMC Evol. Biol. 21(1), 1–12. https://doi.org/10.1186/s12862-021-01932-4 (2021).Article 

    Google Scholar 
    Norman, D. B. On the cranial morphology and evolution of ornithopod dinosaurs. Proc. Zool. Soc. Lond. 52, 521–547 (1984).
    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Ornithopod feeding mechanisms: Their bearing on the evolution of herbivory. Am. Nat. 126, 151–164. https://doi.org/10.1086/284406 (1985).Article 

    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Vegetarian dinosaurs chew it differently-living mammals can chew plants for more effectively than reptiles. Yet some dinosaurs were surprisingly adept chewers. This unexpected ability may have been crucial in their evolution. New Sci. 114(1559), 42–45 (1987).
    Google Scholar 
    Rybczynski, N., Tirabasso, A., Bloskie, P., Cuthbertson, R. & Holliday, C. A three-dimensional animation model of Edmontosaurus (Hadrosauridae) for testing chewing hypotheses. Palaeontol. Electron. 11(2), 9A (2008).
    Google Scholar 
    Williams, V. S., Barrett, P. M. & Purnell, M. A. Quantitative analysis of dental microwear in hadrosaurid dinosaurs, and the implications for hypotheses of jaw mechanics and feeding. PNAS 106(27), 11194–11199. https://doi.org/10.1073/pnas.0812631106 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cuthbertson, R. S., Tirabasso, A., Rybczynski, N. & Holmes, R. B. Kinetic limitations of intracranial joints in Brachylophosaurus canadensis and Edmontosaurus regalis (Dinosauria: Hadrosauridae), and their implications for the chewing mechanics of hadrosaurids. Anat. Rec. 295, 968–979. https://doi.org/10.1002/ar.22458 (2012).Article 

    Google Scholar 
    Erickson, G. M. & Zelenitsky, D. K. Osteohistology and occlusal morphology of Hypacrosaurus stebengeri teeth throughout ontogeny with comments on wear-induced form and function. In Hadrosaurs (eds Eberth, D. A. & Evans, D. C.) 422–432 (Indiana University Press, 2014).
    Google Scholar 
    Barrett, P. M. Tooth wear and possible jaw action of Scelidosaurus harrisonii Owen and a review of feeding mechanisms in other thyreophoran dinosaurs. In The Armored Dinosaurs (ed. Carpenter, K.) 25–52 (Indiana University Press, 2001).
    Google Scholar 
    Rybczynski, N. & Vickaryous, M. K. Evidence of complex jaw movement in the Late Cretaceous ankylosaurid Euoplocephalus tutus (Dinosauria: Thyreophora). In The Armored Dinosaurs (ed. Carpenter, K.) 299–317 (Indiana University Press, 2001).
    Google Scholar 
    Mallon, J. C. & Anderson, J. S. The functional and palaeoecological implications of tooth morphology and wear for the megaherbivorous dinosaurs from the Dinosaur Park Formation (Upper Campanian) of Alberta, Canada. PLoS ONE 9(6), e98605. https://doi.org/10.1371/journal.pone.0098605 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mallon, J. C. & Anderson, J. S. Implications of beak morphology for the evolutionary paleoecology of the megaherbivorous dinosaurs from the Dinosaur Park Formation (upper Campanian) of Alberta, Canada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 394, 29–41. https://doi.org/10.1016/j.palaeo.2013.11.014 (2014).Article 

    Google Scholar 
    Ősi, A., Barrett, P. M., Földes, T. & Tokai, R. Wear pattern, dental function, and jaw mechanism in the Late Cretaceous ankylosaur Hungarosaurus. Anat. Rec. 297(7), 1165–1180. https://doi.org/10.1002/ar.22910 (2014).Article 

    Google Scholar 
    Ősi, A., Prondvai, E., Mallon, J. & Bodor, E. R. Diversity and convergences in the evolution of feeding adaptations in ankylosaurs (Dinosauria: Ornithischia). Hist. Biol. 29(4), 539–570. https://doi.org/10.1080/08912963.2016.1208194 (2017).Article 

    Google Scholar 
    Hill, R. V., D’Emic, M. D., Bever, G. S. & Norell, M. A. A complex hyobranchial apparatus in a Cretaceous dinosaur and the antiquity of avian paraglossalia. Zool. J. Linn. Soc. 175(4), 892–909. https://doi.org/10.1111/zoj.12293 (2015).Article 

    Google Scholar 
    Lautenschlager, S., Brassey, C. A., Button, D. J. & Barrett, P. M. Decoupled form and function in disparate herbivorous dinosaur clades. Sci. Rep. 6(1), 1–10. https://doi.org/10.1038/srep26495 (2016).Article 
    CAS 

    Google Scholar 
    Skutschas, P. P. et al. Wear patterns and dental functioning in an Early Cretaceous stegosaur from Yakutia, Eastern Russia. PLoS ONE 16(3), e0248163. https://doi.org/10.1371/journal.pone.0248163 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Strickson, E., Prieto-Márquez, A., Benton, M. J. & Stubbs, T. L. Dynamics of dental evolution in ornithopod dinosaurs. Sci. Rep. 6, 28904. https://doi.org/10.1038/srep28904 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Virág, A. & Ősi, A. Morphometry, microstructure, and wear pattern of neornithischian dinosaur teeth from the Upper Cretaceous Iharkút locality (Hungary). Anat. Rec. 300(8), 1439–1463. https://doi.org/10.1002/ar.23592 (2017).Article 

    Google Scholar 
    Mallon, J. C. & Anderson, J. S. Skull ecomorphology of megaherbivorous dinosaurs from the Dinosaur Park Formation (Upper Campanian) of Alberta, Canada. PLoS ONE 8(7), e67182. https://doi.org/10.1371/journal.pone.0067182 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Botfalvai, G., Ősi, A. & Mindszenty, A. Taphonomic and paleoecologic investigations of the Late Cretaceous (Santonian) Iharkút vertebrate assemblage (Bakony Mts, northwestern Hungary). Palaeogeogr. Palaeoclimatol. Palaeoecol. 417, 379–405. https://doi.org/10.1016/j.palaeo.2014.09.032 (2015).Article 

    Google Scholar 
    Botfalvai, G., Haas, J., Bodor, E. R., Mindszenty, A. & Ősi, A. Facies architecture and palaeoenvironmental implications of the upper Cretaceous (Santonian) Csehbánya formation at the Iharkút vertebrate locality (Bakony Mountains, Northwestern Hungary). Palaeogeogr. Palaeoclimatol. Palaeoecol. 441, 659–678. https://doi.org/10.1016/j.palaeo.2015.10.018 (2016).Article 

    Google Scholar 
    Ősi, A. et al. The Late Cretaceous continental vertebrate fauna from Iharkút, western Hungary: A review. In Bernissart Dinosaurs and Early Cretaceous Terrestrial Ecosystems (ed. Godefroit, P.) 532–569 (Indiana University Press, 2012).
    Google Scholar 
    Wells, N. A. Making thin sections. In Paleotechniques (eds Feldmann, R. M. et al.) 120–129 (University of Tennessee, 1989).
    Google Scholar 
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675. https://doi.org/10.1038/nmeth.2089 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evans, A. R. Surfer Manipulator. http://evomorph.org/surfermanipulator (2011).Evans, A. R., Wilson, G. P., Fortelius, M. & Jernvall, J. High-level similarity of dentitions in carnivorans and rodents. Nature 445, 78–81. https://doi.org/10.1038/nature05433 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wilson, G. P. et al. Adaptive radiation of multituberculate mammals before the extinction of dinosaurs. Nature 483, 457–460. https://doi.org/10.1038/nature10880 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ungar, P. S. Dental microwear of European Miocene catarrhines: Evidence for diets and tooth use. J. Hum. Evol. 31, 355–366. https://doi.org/10.1006/jhev.1996.0065 (1996).Article 

    Google Scholar 
    Ungar, P. S. A semiautomated image analysis procedure for the quantification of dental microwear II. Scanning. 17, 57–59. https://doi.org/10.1002/sca.4950170108 (1995).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ungar, P. S., Brown, C. A., Bergstrom, T. S. & Walker, A. Quantification of dental microwear by tandem scanning confocal microscopy and scale-sSensitive fractal analyses. Scanning 25, 185–193. https://doi.org/10.1002/sca.4950250405 (2003).Article 
    PubMed 

    Google Scholar 
    Ungar, P. S., Merceron, G. & Scott, R. S. Dental microwear texture analysis of Varswater bovids and Early Pliocene paleoenvironments of langebaanweg, Western Cape Province, South Africa. J. Mammal. Evol. 14, 163–181. https://doi.org/10.1007/s10914-007-9050-x (2007).Article 

    Google Scholar 
    Scott, J. R. Dental microwear texture analysis of extant African Bovidae. Mammalia 76, 157–217. https://doi.org/10.1515/mammalia-2011-0083 (2012).Article 

    Google Scholar 
    Merceron, G., Hofman-Kaminska, E. & Kowalczyk, R. 3D dental microwear texture analysis of feeding habits of sympatric ruminants in the Białowieza Primeval Forest, Poland. For. Ecol. Manag. 328, 262–269. https://doi.org/10.1016/j.foreco.2014.05.041 (2014).Article 

    Google Scholar 
    Caporale, S. S. & Ungar, P. S. Rodent incisor microwear as a proxy for ecological reconstruction. Palaeogeog. Palaeocl. Palaeoecol. 446, 225–233. https://doi.org/10.1016/j.palaeo.2016.01.013 (2016).Article 

    Google Scholar 
    R Core Team. R. A language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/ (2021).Erickson, G. M. Incremental lines of von Ebner in dinosaurs and the assessment of tooth replacement rates using growth line counts. PNAS 93(25), 14623–14627. https://doi.org/10.1073/pnas.93.25.14623 (1996).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Godefroit, P. et al. Extreme tooth enlargement in a new Late Cretaceous rhabdodontid dinosaur from Southern France. Sci. Rep. 7(1), 1–9. https://doi.org/10.1038/s41598-017-13160-2 (2017).Article 
    CAS 

    Google Scholar 
    Edmund, G. Tooth replacement phenomena in the lower vertebrates. Life. Sci. Contrib. R. Ont. Mus. 52, 1–190 (1960).
    Google Scholar 
    D’Emic, M. D., Whitlock, J. A., Smith, K. M., Fisher, D. C. & Wilson, J. A. Evolution of high tooth replacement rates in sauropod dinosaurs. PLoS ONE 8(7), e69235. https://doi.org/10.1371/journal.pone.0069235 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ősi, A., Prondvai, E., Butler, R. & Weishampel, D. B. Phylogeny, histology and inferred body size evolution in a new rhabdodontid dinosaur from the Late Cretaceous of Hungary. PLoS ONE 7(9), e44318. https://doi.org/10.1371/journal.pone.0044318 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Weishampel, D. B., Jianu, C. M., Csiki, Z. & Norman, D. B. Osteology and phylogeny of Zalmoxes (ng), an unusual euornithopod dinosaur from the latest Cretaceous of Romania. J. Syst. Palaeontol. 1(2), 65–123. https://doi.org/10.1017/S1477201903001032 (2003).Article 

    Google Scholar 
    Melstrom, K. M. The relationship between diet and tooth complexity in living dentigerous saurians. J. Morphol. 278, 500–522 (2017).Article 
    PubMed 

    Google Scholar 
    LeBlanc, A. R. H., Reisz, R. R., Evans, D. C. & Bailleul, A. M. Ontogeny reveals function and evolution of the hadrosaurid dinosaur dental battery. BMC Evol. Biol. 16(1), 1–13. https://doi.org/10.1186/s12862-016-0721-1 (2016).Article 

    Google Scholar 
    Erickson, G. M. et al. Complex dental structure and wear biomechanics in hadrosaurid dinosaurs. Science 338(6103), 98–101. https://doi.org/10.1126/science.1224495 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Iguanodontidae and related Ornithopoda. In The Dinosauria (eds Weishampel, D. B. et al.) 510–533 (University of California Press, 1990).
    Google Scholar 
    Hulke, J. W. An attempt at a complete osteology of Hypsilophodon foxii, a British Wealden dinosaur. Philos. Trans. R. Soc. Lond. 172, 1035–1062. https://doi.org/10.1098/rstl.1882.0025 (1882).Article 

    Google Scholar 
    Sternberg, C. H. Thescelosaurus edmontonensis, n. sp., and classification of the Hypsilophodontidae. J. Paleontol. 14, 481–494 (1940).
    Google Scholar 
    Galton, P. M. The ornithischian dinosaur Hypsilophodon from the Wealden of the Isle of Wight. Bull. Br. Mus. Nat. Hist. 25(1), 1–152 (1974).
    Google Scholar 
    Norman, D. B. On the anatomy of Iguanodon atherfieldensis (Ornithischia: Ornithopoda). Bull. Inst. Roy. Sci. Nat. Belgique 56, 281–372 (1986).
    Google Scholar 
    Norman, D. B. & Barrett, P. M. Ornithischian dinosaurs from the lower Cretaceous (Berriasian) of England. Spec. Pap. Palaeontol. 68, 161–190 (2002).
    Google Scholar 
    Kosch, J. C. & Zanno, L. E. Sampling impacts the assessment of tooth growth and replacement rates in archosaurs: Implications for paleontological studies. PeerJ 8, e9918. https://doi.org/10.7717/peerj.9918 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Janis, C. M. & Fortelius, M. On the means whereby mammals achieve increased functional durability of their dentitions with special reference to limiting factors. Biol. Rev. 63, 197–230. https://doi.org/10.1111/j.1469-185X.1988.tb00630.x (1988).Article 
    CAS 
    PubMed 

    Google Scholar 
    You, H., Ji, Q. & Li, D. Lanzhousaurus magnidens gen. et sp. nov. from Gansu Province, China: The largest-toothed herbivorous dinosaur in the world. Geol. Bull. Chi 24(9), 785–794 (2005).
    Google Scholar 
    Suarez, C. A., You, H. L., Suarez, M. B., Li, D. Q. & Trieschmann, J. B. Stable isotopes reveal rapid enamel elongation (amelogenesis) rates for the Early Cretaceous iguanodontian dinosaur Lanzhousaurus magnidens. Sci. Rep. 7, 15319. https://doi.org/10.1038/s41598-017-15653-6 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Upchurch, P. & Barrett, P. M. The evolution of sauropod feeding mechanisms. In Evolution of Herbivory in Terrestrial Vertebrates: Perspectives from the Fossil Record (ed. Sues, H. D.) 79–122 (Cambridge University Press, 2000).Chapter 

    Google Scholar 
    Sereno, P. C. & Wilson, J. A. Structure and evolution of a sauropod tooth battery in Curry. In The Sauropods: Evolution and Paleobiology (eds Rogers, K. A. & Wilson, J. A.) 157–177 (University of California Press, 2005).
    Google Scholar 
    Brown, B. & Schlaikjer, E. M. The structure and relationships of Protoceratops. Ann. N. Y. Acad. Sci. 40(3), 133–265. https://doi.org/10.1111/j.1749-6632.1940.tb57047.x (1940).Article 

    Google Scholar 
    Solounias, N., Teaford, M. & Walker, A. Interpreting the diet of extinct ruminants-the case of a non-browsing giraffid. Paleobiology 14, 287–300. https://doi.org/10.1017/S009483730001201X (1988).Article 

    Google Scholar 
    Walker, A. & Teaford, M. Inferences from quantitative analysis of dental microwear. Folia Primatol. 53, 177–189. https://doi.org/10.1159/000156415 (1989).Article 
    CAS 

    Google Scholar 
    Ungar, P. S. Mammalian dental function and wear: A review. Biosurf. Biotribol. 1(1), 25–41. https://doi.org/10.1016/j.bsbt.2014.12.001 (2015).Article 
    MathSciNet 

    Google Scholar 
    Janis, C. M. An estimation of tooth volume and hypsodonty indices in ungulate mammals, and the correlation of these factors with dietary preferences. Mém. Mus. Natl. Hist. Nat. Sér. C Géol. 53, 367–387 (1988).
    Google Scholar 
    Lucas, P. W. et al. The role of dust, grit and phytoliths in tooth wear. Ann. Zool. Fenn. 51(1–2), 143–152. https://doi.org/10.5735/086.051.0215 (2014).Article 

    Google Scholar 
    Winkler, D. E. et al. Shape, size, and quantity of ingested external abrasives influence dental microwear texture formation in guinea pigs. Proc. Nat. Acad. Sci. 117, 22264–22273. https://doi.org/10.1073/pnas.2008149117 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kaiser, T. M. et al. Nano-indentation of native phytoliths and dental tissues: Implications for herbivore-plant combat and dental wear proxies. Evol. Syst. 2, 55–63. https://doi.org/10.3897/evolsyst.2.22678 (2018).Article 

    Google Scholar 
    Winkler, D. E. et al. Forage silica and water content control dental surface texture in guinea pigs and provide implications for dietary reconstruction. Proc. Nat. Acad. Sci. 116, 1325–1330. https://doi.org/10.1073/pnas.1814081116 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ősi, A. & Makádi, L. New remains of Hungarosaurus tormai (Ankylosauria, Dinosauria) from the Upper Cretaceous of Hungary: Skeletal reconstruction and body mass estimation. Palaontol. Z. 83(2), 227–245. https://doi.org/10.1007/s12542-009-0017-5 (2009).Article 

    Google Scholar 
    Winkler, D. E., Schulz-Kornas, E., Kaiser, T. M. & Tütken, T. Dental microwear texture reflects dietary tendencies in extant Lepidosauria despite their limited use of oral food processing. Proc. R. Soc. B 286, 20190544. https://doi.org/10.1098/rspb.2019.0544 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bestwick, J., Unwin, D. M., Butler, R. J. & Purnell, M. A. Dietary diversity and evolution of the earliest flying vertebrates revealed by dental microwear texture analysis. Nat. Commun. 11, 1–9. https://doi.org/10.1038/s41467-020-19022-2 (2020).Article 
    CAS 

    Google Scholar 
    Sakaki, H. et al. Non-occlusal dental microwear texture analysis of a titanosauriform sauropod dinosaur from the Upper Cretaceous (Turonian) Tamagawa Formation, northeastern Japan. Cret. Res. 136, 105218. https://doi.org/10.1016/j.cretres.2022.105218 (2022).Article 

    Google Scholar 
    Fiorillo, A. R. Dental microwear on the teeth of Camarasaurus and Diplodocus; implications for sauropod paleoecology. In Fifth Symposium on Mesozoic Terrestrial Ecosystems and Biota (eds Kielan-Jaworowska, Z. et al.) 23–24 (Paleontologisk Museum, 1991).
    Google Scholar 
    Mallon, J. C., Cuthbertson, R. S. & Tirabasso, A. Hadrosaurid jaw mechanics as revealed by cranial joint limitations and dental microwear analysis. In Hadrosaur Symposium Abstract Volume (eds Braman, D. R. et al.) 87–90 (Royal Tyrrell Museum of Palaeontology, 2011).
    Google Scholar 
    Fiorillo, A. R. Dental microwear patterns of the sauropod dinosaurs Camarasaurus and Diplodocus: Evidence for resource partitioning in the Late Jurassic of North America. Hist. Biol. 13, 1–16. https://doi.org/10.1080/08912969809386568 (1998).Article 

    Google Scholar 
    Sereno, P. C. et al. Structural extremes in a Cretaceous dinosaur. PLoS ONE 2(11), e1230. https://doi.org/10.1371/journal.pone.0001230 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Whitlock, J. A. Inferences of diplodocoid (Sauropoda: Dinosauria) feeding behavior from snout shape and microwear analyses. PLoS ONE 6(4), e18304. https://doi.org/10.1371/journal.pone.0018304 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fiorillo, A. R. Microwear patterns on the teeth of northern high latitude hadrosaurs with comments on microwear patterns in hadrosaurs as a function of latitude and seasonal ecological constraints. Palaeontol. Electron. 14(3), 20A (2011).
    Google Scholar 
    Bell, P. R., Snively, E. & Shychoski, L. A comparison of the jaw mechanics in hadrosaurid and ceratopsid dinosaurs using finite element analysis. Anat. Rec. 292(9), 1338–1351. https://doi.org/10.1002/ar.20978 (2009).Article 

    Google Scholar 
    Chin, K. & Gill, B. D. Dinosaurs, dung beetles, and conifers: Participants in a Cretaceous food web. Palaios 11, 280–285. https://doi.org/10.2307/3515235 (1996).Article 

    Google Scholar 
    Brown, C. M. et al. Dietary palaeoecology of an early Cretaceous armoured dinosaur (Ornithischia; Nodosauridae) based on floral analysis of stomach contents. Roy. Soc. Open Sci. 7(6), 200305. https://doi.org/10.1098/rsos.200305 (2020).Article 
    CAS 

    Google Scholar 
    Crane, P. C., Friis, E. M. & Pedersen, K. R. The origin and early diversification of angiosperms. Nature 374, 27–33 (1995).Article 
    CAS 

    Google Scholar 
    Friis, E. M., Crane, P. R. & Pedersen, K. R. Early Flowers and Angiosperm Evolution 1–596 (Cambridge University Press, 2011). https://doi.org/10.1017/CBO9780511980206.Book 

    Google Scholar 
    Benson, R. B., Hunt, G., Carrano, M. T. & Campione, N. Cope’s rule and the adaptive landscape of dinosaur body size evolution. Palaeontology 61, 13–48. https://doi.org/10.1111/pala.12329 (2018).Article 

    Google Scholar 
    Hummel, J. et al. In vitro digestibility of fern and gymnosperm foliage: Implications for sauropod feeing ecology and diet selection. Proc. Royal Soc. B 275, 1015–1021. https://doi.org/10.1098/rspb.2007.1728 (2008).Article 

    Google Scholar 
    Gee, C. T. Dietary options for the sauropod dinosaurs from an integrated botanical and paleobotanical perspective. In Biology of the Sauropod Dinosaurs: Understanding the Life of Giants (eds Klein, K. et al.) 34–56 (Indiana University Press, 2011).
    Google Scholar 
    Peters, R. H. The Ecological Implications of Body Size 1–329 (Cambridge University Press, 1983).Book 

    Google Scholar 
    Jarman, P. J. The social organisation of antelope in relation to their ecology. Behaviour 48, 215–267 (1974).Article 

    Google Scholar  More

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    Alternative stable ecological states observed after a biological invasion

    Study systemOur focal ecosystem is in Selvíria, state of Mato Grosso do Sul, Brazil ((hbox {20}^{circ }) (22′) (41.86”) S, (hbox {51}^{circ }) (24′) (58.90”) W), on a property owned by the São Paulo State University (UNESP). The location covers 350 ha of pasture composed of liverseed grass (Urochloa decumbens). The native vegetation was removed, pasture areas were implemented, and livestock was introduced in the 1970s, maintaining this configuration during the following 50 years. The climate of this area is categorized as equatorial savanna, with dry periods concentrated mostly during the winter, from April to August. During our sampling period (from November 23th, 1989, to November 19th, 2015), no vermifuges and insecticides that could affect negatively the community of dung beetles associated with cow pads were used1.The native dung beetle community at this site is composed of dwellers and tunnelers. Dwellers comprise the Aphodiinae subfamily, whereas all the tunnelers belong to the Scarabaeinae subfamily31. In total, there were eight species classified as dwellers (Ataenius crenulatus, A. picinus and Atanius aequalis-platensis grouped as one species, Blackburneus furcatus, Genieridium bidens, Labarrus pseudolividus, Nialaphodius nigrita and Trichillum externepunctatum) and ten native tunnelers (Ateuchus nr. puncticollis, A. vividus, Canthidium nr. pinotoides, Dichotomius bos, D. semiaeneus, D. sexdentatus, Ontherus appendiculatus, O. dentatus, O. sulcator). These species were chosen for our study because, as the invasive tunneler D. gazella (also from the Scarabaeinae subfamily), they all co-occur in pasture and exploit the same resource (cow pad)32. The initial establishment of D. gazella caused the loss of most of the native tunnelers from the community, with the invader becoming the overwhelming representative of the functional group, and an initial decrease of abundance for dwellers. Differently from native tunnelers, however, dwellers were able to recover their number a few years after invasion (Fig. 1a, Fig. S1).As reported in1, the abundance of dung beetles was significantly affected by both local minimum temperature and relative humidity. The influence of these two factors is expected, as they determine egg and larval survival and development of dung beetles. For example, because dung beetles are poikilotherms, environmental temperature is key to their development and fecundity33. One of the main dweller species, Labarrus pseudolividus, is widely found in locations with temperature averages ranging between (hbox {12},^{circ }hbox {C}) and (hbox {18},^{circ }hbox {C})34, making it tolerant to colder local temperatures. On the other hand, for D. gazella the lower developmental threshold is (hbox {15.5},^{circ }hbox {C}) (individuals cannot survive below this temperature), and the optimum temperature for population growth is (hbox {28},^{circ }hbox {C})35. For both groups, physiological growth and reproduction rates are maintained even when outside temperatures are close to the lower developmental threshold; dwellers, for example, live inside the dung pile, where temperature is higher and less variable than outside36,37. However, while tunnelers oviposit deep in the soil to protect the eggs, warmer and drier conditions reduce dweller egg viability on dung piles since they are exposed38. Low humidity conditions lead to drier dung and can cause egg and insect dessication. In addition, dwellers from our focal system have Palearctic evolutionary origins39; D. gazella’s natural distribution ranges from central to southern Africa40, presenting high physiological plasticity that allows it to tolerate high temperatures and low relative humidity better than other tunneler species41.Functional-group data collection and community structure characterizationDung beetles were collected once a week in a black-light flight intercept trap42, which guarantees the collection of coprophagic beetles. During all collection periods, climate variables were also collected from a meteorological station located within 2 km of our collecting site. See1 for the complete description of the collection process and database. For our purposes, we retained the species, number of individuals per species, and climate variables for each week sampled (Supplementary Information, SI, Figs. S1–S2).We focused first on the weekly abundance data, which we needed to process in order to avoid spurious results in our analyses stemming from the measurement protocol. Specifically, we filtered out seasonal low values associated with sampling in the coldest periods, when few beetles are captured because the reduced activity in all functional groups restricts their spatio-temporal distribution43. Including such samples would not be representative of the community and could bias the analysis since we are investigating community composition (i.e. proportions, very sensitive to low sampling). Thus, we considered only samples with a total number of beetles (that is, summing up all groups together) higher than the value of the median of all data, a conservative threshold that retains observations that allow for as much representation of the community as possible. As will become evident in the Results section and Supplementary Information, less conservative choices for the threshold did not alter our main conclusions.Following Mesquita -Filho et al.1, we categorized all sampled species into either dwellers or tunnelers. D. gazella is a tunneler and, as explained above, the native tunneler species experienced massive declines in abundance after its establishment, leaving D. gazella as almost the single representative in the tunneler functional group during the period of observation1. Thus, given the sharp contrast in community composition, we also separated the data into before and after invasion using to that end the 200th week, when D. gazella was first observed at the study site (September 11th, 1993, starting date for what we will call “after invasion”, our focal period henceforth).To describe community functional composition (i.e. system state) through time, we derived a normalized functional group ratio. First, because the abundance of each functional group spanned up to four orders of magnitude, we performed a logarithmic transformation of the number of captured insects from each group i, (log _{10}(N_{i}+K)), following  Yamamura44. Here, we chose (K=1), but the value of K did not alter our results qualitatively. In addition, the original data showed random mismatches in the phenology of each group, which gave the wrong impression of extreme short-term shifts in functional group dominance within the community. To avoid such artifacts, we used nonparametric local regression (LOESS)45 to smooth the dynamics of each group46. For this smoothing, we employed the loess function in the R software 3.6.147 with a smooth parameter equal to 0.25, but other moderate values (or an optimal value calculated with Bayesian inference by the R function optimal_span) did not alter our conclusions. Finally, we extracted back from the smoothed curve the number of beetles within each functional group to calculate the fraction (f_{dwell}) that measures the relative abundance of dwellers:$$begin{aligned} f_{dwell} = frac{N_D}{N_D+N_T} end{aligned}$$
    (1)
    where (N_D) corresponds to the number of dwellers per week and (N_T) corresponds to the number of native tunnelers (for the period before invasion), or only the number of D. gazella observed per week (after invasion), using their corresponding smoothed curves. Including also native tunnelers after invasion did not alter our conclusions.Climate driverWe devised a single climatic driver variable that merges the weekly measurement of temperature and relative humidity over the years, abiotic factors key to the survival and reproduction of both groups (see above). We first converted minimum temperatures and relative humidity to normalized climate variables using a min-max normalization (a feature scaling that uses the total range of temperatures or relative humidity, respectively, as normalization factor):$$begin{aligned} T = frac{T_{week} – T_{min}}{T_{max}-T_{min}};;,~ ~ ~ ~ ~ ~ RH = frac{RH_{week} – RH_{min}}{RH_{max}-RH_{min}};;, end{aligned}$$
    (2)
    where T corresponds to the normalized temperature, (T_{week}) is the weekly temperature, and (T_{max}) and (T_{min}) are the absolute maximum and minimum temperatures observed during the whole sampling period, respectively. We used a similar notation for relative humidity, RH. Based on the information above regarding beetle response to climate, the merged climate factor c was defined as the relationship:$$begin{aligned} c = frac{T}{RH};;, end{aligned}$$
    (3)
    for (RHne 0). That is, higher temperatures and/or drier conditions (expected to favor D. gazella) lead to higher values for c. On the other hand, lower temperatures and/or more humid conditions (expected to favor dwellers) imply lower values for c. Intermediate values of c can represent either moderate or extreme values for both T and RH.Identifying ecological states and quantifying resilienceWith our (f_{dwell}) data as an index of community composition (i.e. system state), we calculated kernel density functions to interpolate a continuous probability distribution of the relative fraction of dwellers in the community, (p_{n}(f_{dwell})) (function density, R software 3.1.647) for a given range of climatic driver c values. We grouped the (f_{dwell}) data using ranges for c of size 0.4, to ensure a significant amount of weekly samples that allowed for the reconstruction of these probability distributions (see Table S1, first column). Note that bins with extreme values showed few data points (see first and last rows in Table S1), and thus were rejected to prevent misleading results due to reduced sampling. Also note that, for the density function, we used the default Gaussian kernel with a smoothing bandwidth adjusted to be (50%) larger than the default value (“adjust” argument set to 1.5). This conservative choice aims to reduce the effect of the different sampling across c bins and to ensure that differences among distributions across c values are not the result of spurious sampling noise.Further, we transformed the kernel density function:$$begin{aligned} V(f_{dwell}) = -ln (p_{n}(f_{dwell})) end{aligned}$$
    (4)
    This (V(f_{dwell})) function, called potential (e.g.48), shows by design well-defined minima for the most frequently observed values of (f_{dwell}) (i.e. configurations most frequently observed for the community, which conform the modes of the probability distribution) in a given group of data. At these points, the potential exhibits a change of trend from decreasing to increasing, and therefore its derivative shows a change of sign. Eq. (4), thus, provides a simple criterion to identify possible system states, which is a reason why potentials have been used extensively across disciplines49,50,51. Nonetheless, because the position of extrema is invariant under the transformation, using probability distributions instead would not alter our conclusions.Representing the potential obtained from all the (f_{dwell}) system states associated with a same range of climatic driver c values allowed us to identify stable community configurations associated with a specific climate. The comparison of the potentials obtained for different c ranges enabled the description of how the community changed in response to climatic variation. The location of the minima revealed which states were stable for a given value of the climatic driver; the presence of two minima, then, flagged the existence of bistability (i.e. two different community compositions possible for the same c value).These minima are materialized as wells in the potential’s landscape, which provides an easy way to understand the concept of stability: the dynamics of the system for the given value of the driver will eventually “fall” into a well (either a state dominated by dwellers or a state dominated by tunnelers), with the shape of the well (e.g. its depth) determining how difficult it is for the system to “escape” that state. Therefore, the area inside a well provides quantification of the tendency of a system to stay in that specific state, i.e. the resilience of the associated ecological state or how strong a perturbation has to be to move the system from such an ecological state to another2,3,50,51,52,53. Thus, in addition to number and location of wells, measuring their associated area allowed us to further characterize the resilience of the community. To this end, we first set a visualization window common to all potentials. Specifically, we plotted the potentials within a range for the vertical variable (the potential, V) given by ([-1.5,1.5]); the horizontal variable (fraction of dwellers, (f_{dwell})) is by definition bounded between 0 and 1. For potentials that showed one single well, the area of the well was measured as the area above the potential curve within this visualization window. For potentials that showed two wells (bistability), we measured the value of the potential at the local maximum separating the two wells, and established that value as the upper (horizontal) line closing the area of each well. To ensure all cases were comparable and eliminate any arbitrariness of the choices above, we expressed resilience as a relative area; in other words, we further normalized the well area by the total area across wells for that potential, which means that any single-well case will show a resilience (or relative area) of 1, and the resilience of the two wells when there is bistability adds up to 1.Figure 1Left: Community composition by functional group for all weeks of observation1. Green represents dwellers, blue represents tunnelers, and orange represents the invader D. gazella. Right: Sketch of responses of the community composition to the climatic driver (i.e. phase diagram) expected from the physiological and behavioral characteristics of the functional groups in the community as described in text: linear (red), or non-linear but monotonic without (blue) or with (brown) hysteresis.Full size imageIdentifying ecological transitionsMeasuring a state variable, (f_{dwell}), and a driver, c (order and control parameter, respectively, in the jargon of regime shift theory), allowed us to study how their observed behavior over time materializes in a driver-state relationship (the so-called phase diagram) defining the possible shifts in dominance (i.e. regime shifts) that the community may undergo as climate changes12. The non-monotonic temporal behavior of the components of the order parameter (i.e. dwellers and tunneler availability) and the components of the control parameter (i.e. temperature and relative humidity) makes it difficult to predict the shape of the phase diagram, and therefore whether we can expect alternative stable states in the focal example. For such cases, the dominance of the dung beetle community could (1) shift in a linear fashion toward the functional group favored by climatic conditions; (2) shift between functional groups in non-linear threshold response to climatic conditions without hysteresis; or (3) shift between functional groups in non-linear threshold response to climatic conditions with hysteresis –and thus showing bistability (see Fig. 1b, or12). Other possibilities, e.g. a non-linear shift between functional groups where one group is favored at intermediate climatic conditions12 are discarded as the invader is better suited for warmer and drier conditions. To evaluate which of these possibilities occurred, we represented (f_{dwell}) as a function of c, as well as the location of the minima shown by the potentials above. In addition to the emerging shape of this relationship, this plot can reveal the presence of alternative stable states if two or more different points occur for the same value of the control parameter, c. More