Philippa Kaur

1.
Dolganov, V. N. A new shark from the family Squalidae caught on the Naska Submarine Ridge. Zool. Zh. 63, 1589–1591 (1984).
Google Scholar 
2.
Grace, M. A., Doosey, M. H, Bart, H. L., Naylor, G. J. First record of Mollisquama sp. (Chondrichthyes: Squaliformes: Dalatiidae) from the Gulf of Mexico, with a morphological comparison to the holotype description of Mollisquama parini Dolganov. Zootaxa 3948,587–600 (2015).

3.
Grace, M. A. et al. A new Western North Atlantic Ocean kitefin shark (Squaliformes: Dalatiidae) from the Gulf of Mexico. Zootaxa 4619, 109–120 (2019).
Article  Google Scholar 

4.
Denton, J. S. et al. Cranial morphology in Mollisquama sp. (Squaliformes; Dalatiidae) and patterns of cranial evolution in dalatiid sharks. J. Anat. 233, 15–32 (2018).
PubMed  PubMed Central  Article  Google Scholar 

5.
Munk, O. & Jorgensen, J. M. Putatively luminous tissue in the abdominal pouch of a male dalatiine shark, Euprotomicroides zantedeschia Hulley & Penrith, 1966. Acta Zool. (Stockh) 69, 247–251 (1988).
Article  Google Scholar 

6.
Stehmann, M. & Krefft, G. Results of the research cruises of FRV “Walter Herwig” to South America. LXVIII. Complementary redescription of the dalatiine shark Euprotomicroides zantedeschia Hulley & Penrith, 1966 (Chondrichthyes, Squalidae), based on a second specimen from the western south Atlantic. Arch. Fisch. Wiss. 30, 1–30 (1988).

7.
Stehmann, M. F. W., Van Oijen, M. & Kamminga, P. Re-description of the rare taillight shark Euprotomicroides zantedeschia (Squaliformes, Dalatiidae), based on third and fourth record from off Chile. Cybium 40, 187–197 (2016).
Google Scholar 

8.
Haddock, S. H. D., Moline, M. A. & Case, J. F. Bioluminescence in the sea. Annu. Rev. Mar. Sci. 2, 443–493 (2009).
ADS  Article  Google Scholar 

9.
Widder, E. A. Bioluminescence in the ocean: origins of biological, chemical, and ecological diversity. Science 328, 704–708 (2010).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Pollerspöck, J. & Straube, N. Bibliography Database|Shark-References. www.shark-references.com (2015).

11.
Straube, N., Li, C., Claes, J. M., Corrigan, S. & Naylor, G. J. Molecular phylogeny of Squaliformes and first occurrence of bioluminescence in sharks. BMC Evol. Biol. 15, 162 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

12.
Gruber, D. F. et al. Biofluorescence in catsharks (Scyliorhinidae): fundamental description and relevance for elasmobranch visual ecology. Sci. Rep. 6, 24751 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Bowlby, M. R. & Case, J. F. Ultrastructure and neuronal control of luminous cells in the copepod Gaussia princeps. Biol. Bull. 180, 440–446 (1991).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Robison, B. H., Reisenbichler, K. R., Hunt, J. C. & Haddock, S. H. Light production by the arm tips of the deep-sea cephalopod Vampyroteuthis infernalis. Biol. Bull. 205, 102–109 (2003).
PubMed  Article  PubMed Central  Google Scholar 

15.
Haddock, S. H. D., Dunn, C. W., Pugh, P. R. & Schnitzler, C. E. Bioluminescent and red-fluorescent lures in a deep-sea siphonophore. Science 309, 263 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Claes, J. M., Krönström, J., Holmgren, S. & Mallefet, J. Nitric oxide in the control of luminescence from lantern shark (Etmopterus spinax) photophores. J. Exp. Biol. 17, 3005–3011 (2010).
Article  CAS  Google Scholar 

17.
Denton, E. J., Herring, P. J., Widder, E. A., Latz, M. F. & Case, J. F. The roles of filters in the photophores of oceanic animals and their relation to vision in the oceanic environment. Proc. R. Soc. B 225, 63–97 (1985).
ADS  Google Scholar 

18.
Herring, P. J. Depth distribution of the carotenoid pigments and lipids of some oceanic animals. 2. Decapod crustaceans. J. Mar. Biol. Assoc. UK 53, 539–562 (1973).

19.
Herring, P. J. Bioluminescent signals and the role of reflectors. J. Opt. A Pure Appl. Op. 2, R29 (2000).
ADS  Article  Google Scholar 

20.
Anctil, M. The epithelial luminescent system of Chaetopterus variopedatus. Can. J. Zool. 57, 1290–1310 (1979).
Article  Google Scholar 

21.
Huvard, A. L. Ultrastructure of the light organ and immunocytochemical localization of luciferase in luminescent marine ostracods (Crustacea: Ostracoda: Cypridinidae). J. Morphol. 218, 181–193 (1993).
PubMed  Article  PubMed Central  Google Scholar 

22.
Hubbs, C. L., Iwai, T. & Matsubara, K. External and internal characters, horizontal and vertical distribution, luminescence, and food of the dwarf pelagic shark Euprotomicrurus bispinatus. Bull. Scripps Inst. Ocenogr. 10, 1–81 (1967).
Google Scholar 

23.
Schorno, S. Biogenesis of Hagfish Slime: Timing and Process of Slime Gland Refilling in Hagfishes (Eptatretus stoutii and Myxine glutinosa). (Doctoral dissertation, University of Guelph, USA, 2018).

24.
Clarke, G. L., Conover, R. J., David, C. N. & Nicol, J. A. C. Comparative studies of luminescence in copepods and other pelagic marine animals. J. Mar. Biol. Assoc. UK 42, 541–564 (1962).
Article  Google Scholar 

25.
Dilly, P. N. & Herring, P. J. The light organ and ink sac of Heteroteuthis dispar (Mollusca: Cephalopoda). J. Zool. 186, 47–59 (1978).
Article  Google Scholar 

26.
Bowlby, M. R., Widder, E. A. & Case, J. F. Disparate forms of bioluminescence from the amphipods Cyphocaris faurei, Scina crassicornis and S. borealis. Mar. Biol. 108, 247−253 (1991).

27.
Gosliner, T. M. & Vallès, Y. Shedding light onto the genera (Mollusca: Nudibranchia) Kaloplocamus and Plocamopherus with description of new species belonging to these unique bioluminescent dorids. Veliger 48, 178–205 (2006).
Google Scholar 

28.
Nicol, J. A. C. Histology of the light organs of Pholas dactylus (Lamellibranchia). J. Mar. Biol. Assoc. UK 39, 109–115 (1960).
Article  Google Scholar 

29.
Nicol, J. A. C. Observations on luminescence in pelagic animals. J. Mar. Biol. Assoc. UK 37, 705–752 (1958).
Article  Google Scholar 

30.
Sivan, G. Fish venom: pharmacological features and biological significance. Fish Fish. 10, 159–172 (2009).
Article  Google Scholar 

31.
Ziegman, R. & Alewood, P. Bioactive components in fish venoms. Toxins 7, 1497–1531 (2015).

32.
Borges, M. H. et al. Combined proteomic and functional analysis reveals rich sources of protein diversity in skin mucus and venom from the Scorpaena plumieri fish. J. Proteom. 187, 200–211 (2018).
CAS  Article  Google Scholar 

33.
Gorman, L. M. et al. The venoms of the lesser (Echiichthys vipera) and greater (Trachinus draco) weever fish—a review. Toxicon 6, 100025 (2020).
Article  Google Scholar 

34.
Duchatelet, L., Claes, J. M. & Mallefet, J. Embryonic expression of encephalopsin supports bioluminescence perception in lanternshark photophores. Mar. Biol. 166, 21 (2019).
Article  Google Scholar 

35.
Duchatelet, L., Pinte, N., Tomita, T., Sato, K. & Mallefet, J. Etmopteridae bioluminescence: dorsal pattern specificity and aposematic use. Zool. Lett. 5, 9 (2019).
Article  Google Scholar 

36.
Morin, J. G. Luminaries of the reef: The history of luminescent ostracods and their courtship displays in the Caribbean. J. Crust. Biol. 39, 227–243 (2019).
Article  Google Scholar 

37.
Galloway, T. W. & Welch, P. S. Studies on a phosphorescent bermudian annelid, Odontosyllis enopla Verill. Trans. Am. Microsc. Soc. 30, 13–39 (1911).
Article  Google Scholar 

38.
Markert, R. E., Markert, B. J. & Vertrees, N. J. Lunar periodicity in spawning and luminescence in Odontosyllis enopla. Ecology 42, 414–415 (1961).
Article  Google Scholar 

39.
Gabe, M. Techniques histologiques (Masson et Cie Editeurs, Paris, 1968).
Google Scholar 

40.
Letunic, I. PhyloT. https://phlot.biobyte.de (2015).

41.
Harvey, E. N. Studies on bioluminescence: VI. Light production by a Japanese Pennatulid, Cavernularia haberi. Am. J. Physiol. 42, 349–358 (1917).
CAS  Article  Google Scholar 

42.
Haneda, Y. Luminosity in Rocellaria grandis (Deshayes) (Lamellibranchia). Kagaku Nanyo 2, 36–39 (1939).
Google Scholar 

43.
Herring, P. J. Bioluminescence in decapod crustacea. J. Mar. Biol. Assoc. UK 56, 1029–1047 (1976).
Article  Google Scholar 

44.
Herring, P. J. Studies on bioluminescent marine amphipods. J. Mar. Biol. Assoc. UK 61, 161–176 (1981).
Article  Google Scholar 

45.
Herring, P. J. Bioluminescence in the Crustacea. J. Crust. Biol. 5, 557–573 (1985).
Article  Google Scholar 

46.
Herring, P. J. Systematic distribution of bioluminescence in living organisms. J. Biol. Chem. 1, 147–163 (1987).
CAS  Google Scholar 

47.
Herring, P. J. Copepod luminescence. Hydrobiologia 167, 183–195 (1988).
Article  Google Scholar 

48.
Haddock, S. H. & Case, J. F. Bioluminescence spectra of shallow and deep-sea gelatinous zooplankton: ctenophores, medusae and siphonophores. Mar. Biol. 133, 571–582 (1999).
Article  Google Scholar 

49.
Deheyn, D. D. & Latz, M. I. Internal and secreted bioluminescence of the marine polychaete Odontosyllis phosphorea (Syllidae). Invert. Biol. 128, 31–45 (2009).
Article  Google Scholar 

50.
Thuesen, E. V., Goetz, F. E. & Haddock, S. H. Bioluminescent organs of two deep-sea arrow worms, Eukrohnia fowleri and Caecosagitta macrocephala, with further observations on bioluminescence in chaetognaths. Biol. Bull. 219, 100–111 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Jones, A. & Mallefet, J. Study of the luminescence in the black brittle-star Ophiocomina nigra: toward a new pattern of light emission in ophiuroids. Zoosymposia 7, 139–145 (2012).
Article  Google Scholar 

52.
Gouveneaux, A. Bioluminescence of Tomopteridae species (Annelida): multidisciplinary approach. (Doctoral dissertation, Centre National de la Recherche Scientifique, Université catholique de Louvain, Belgium, 2016).

53.
Paitio, J., Oba, Y. & Meyer-Rochow, V. B. Bioluminescent fishes and their eyes. In Luminescence—An Outlook on the Phenomena and Their Applications. (Thirumalai, J., Ed.) (Intech, London, 2016).

54.
Verdes, A. & Gruber, D. F. Glowing worms: Biological, chemical, and functional diversity of bioluminescent annelids. Integr. Comp. Biol. 57, 18–32 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Poulsen, J. Y. New observations and ontogenetic transformation of photogenic tissues in the tubeshoulder Sagamichthys schnakenbecki (Platytroctidae, Alepocephaliformes). J. Fish Biol. 94, 62–76 (2019).
PubMed  Article  PubMed Central  Google Scholar 

56.
Robison, B. H. Bioluminescence in the benthopelagic holothurian Enypniastes eximia. J. Mar. Biol. Assoc. UK 72, 463–472 (1992).
Article  Google Scholar 

57.
Claes, J. M., Nilsson, D. E., Straube, N., Collin, S. P. & Mallefet, J. Iso-luminance counterillumination drove bioluminescent shark radiation. Sci. Rep. 4, 4328 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

58.
Parsons, G. R., Ingram, G. W. & Havard, R. First record of the goblin shark Mitsukurina owstoni, Jordan (Family Mitsukurinidae) in the Gulf of Mexico. Southeast. Nat. 1, 189–192 (2002).
Article  Google Scholar  More

Video bio-logger hardware
Supplementary Fig. 1a shows an example of the video bio-loggers used during this study. It measures 85 mm length × 35 mm width × 15 mm height. The bio-loggers were attached to either the bird’s back or abdomen by taping them to the bird’s feathers using waterproof tape. When attaching the bio-logger to a bird’s abdomen, a harness made of Teflon ribbon (Bally Ribbon Mills, USA) was also used. When working with streaked shearwaters, the bio-loggers used a 3.7 V 600 mAh battery and weighed approximately 26–27 g. When working with black-tailed gulls, the bio-loggers used a 3.7 V 720 mAh battery and weighed approximately 30 g.
The bio-loggers are controlled by an ATmega328 MCU (32 KB programme memory, 2 KB RAM) and have an integrated video camera (640 × 480, 15 FPS) that can be controlled by the MCU, with the video data streamed directly to its own dedicated storage. Note that digital cameras such as the one used in this bio-logger have a delay of several seconds from powering on to when they can begin recording, which in the case of our bio-logger resulted in a 2- to 3-s delay between when the MCU signals the start of recording and the actual start of recording when attempting to save energy by powering off the camera when not in use (see also Yoshino et al.13 for another example of this camera delay). Our bio-loggers also include several low-cost sensors that are controlled by the MCU (see Supplementary Fig. 1b). Each of these sensors can be used by the MCU as input for AIoA applications (e.g., camera control) and can be archived to long-term storage for analysis upon device retrieval. The bio-loggers had an average battery life of approximately 2 h when continuously recording video and approximately 20 h when recording from all other (i.e., only low-cost) sensors.
Activity recognition method
We achieve AIoA by employing machine learning to conduct activity (behaviour) recognition on board the bio-logging devices. We do this by training an activity recognition model in advance using low-cost sensor data that has been labelled by an ecologist to identify the behaviours that he/she wants to capture. In the case of the black-tailed gulls, we use accelerometer-based features since they can be used to detect the body movements of the animals with only a small (e.g., 1-s) delay between when data collection begins and when behaviours can first be detected. Such features are often used when detecting body movements in human activity recognition studies in order to recognise activities such as running and eating37. For animal-based AI, such body movements can be useful to detect similar types of behaviours, such as flying and foraging38. See Fig. 3 for an example of how such accelerometer-based features can be extracted from raw data and used to train a decision tree classifier model. The features were extracted from 1-s windows of 25 Hz acceleration data, with the raw 3-axis acceleration data converted to net magnitude of acceleration data prior to feature extraction. The activity recognition processes were run once per second on the 1-s windows of data, allowing us to detect target behaviours within about 1 s of their start. See Supplementary Table 1 for descriptions and estimated sizes for all the features used in this study. In addition, Supplementary Fig. 5a shows the acceleration-based features ranked by their Normalised Gini Importance when used to classify behaviours for black-tailed gulls.
Fig. 3: Generating decision trees from acceleration data.

a We start by converting the raw three-axis data (row one) into magnitude of acceleration values (row two) and segmenting the data into 1-s windows. We then extract the ACC features listed in Supplementary Table 1 from each window. Rows three and four show examples of the features extracted, which can be used to differentiate between the behaviours. For example, Crest can be used to identify Flying behaviour, since its values are higher for Flying than for the other two behaviours. b An example decision tree generated from the feature values shown in the lower two rows of (a), with each leaf (grey) node representing a final predicted class for a 1-s segment of data. Supplementary Data 1 provides source data of this figure.

Full size image

The energy-saving microcontroller units (MCUs) in bio-loggers tend to have limited memory and low computing capability, which makes it difficult to run the computationally expensive processes needed for the pretrained machine learning models on board the bio-loggers. Therefore, this study proposes a method for generating space-efficient, i.e., programme memory efficient, decision tree classifier models that can be run on such MCUs. Decision trees are well suited for use on MCUs, since the tree itself can be implemented as a simple hierarchy of conditional logic statements, with most of the space needed for the tree being used by the algorithms needed to extract meaningful features from the sensor data, such as the variance or kurtosis of 1-s windows of data. In addition, since each data segment is classified by following a single path through the tree from the root node to the leaf node that represents that data segment’s estimated class, an added benefit of using a tree structure is that the MCU only needs to extract features as they are encountered in the path taken through the tree, allowing the MCU to run only a subset of the feature extraction processes for each data segment. However, the feature extraction algorithms needed by the tree can be prohibitively large, e.g., kurtosis requires 680 bytes (Supplementary Table 1), when implemented on MCUs that typically have memory capacities measured in kilobytes, e.g., 32 KB, which is already largely occupied by the functions needed to log sensor data to storage.
Standard decision tree algorithms, e.g., scikit-learn’s default algorithm, build decision trees that maximise classification accuracy with no option to weight the features used in the tree based on a secondary factor such as memory usage39. The trees are built starting from the root node, with each node in the tree choosing from among all features the one feature that can best split the training data passed to it into subsets that allow it to differentiate well between the different target classes. A new child node is then created for each of the subsets of training data output from that node, with this process repeating recursively until certain stopping conditions are met, e.g., the subsets generated by a node reach a minimum size. Figure 4b shows an example of a decision tree built using scikit-learn’s default decision tree classifier algorithm using the black-tailed gull data, which results in an estimated memory footprint of 1958 bytes (Supplementary Table 1). Note that since the basic system functions needed to record sensor data to long-term storage already occupy as much as 95% of the bio-logger’s flash memory, incorporating a decision tree with this large of a memory footprint can cause the programme to exceed the bio-logger’s memory capacity (see the bio-logger source code distributed as Supplementary Software for more details).
Fig. 4: Generating space-efficient trees.

a Our process for the weighted random selection of features. We start with a list of features along with their required programme memory sizes in bytes (first panel). Each feature is assigned a weight proportional to the inverse of its size, illustrated using a pie chart where each feature has been assigned a slice proportional to its weight (second panel). We then perform weighted random selection to choose the subset of features that will be used when creating a new node in the tree. In this example, we have randomly placed four dots along the circumference of the circle to simulate the selection of four features (second panel). The resulting subset of features will then be compared when making the next node in the decision tree (third panel). b Example decision tree built using scikit-learn’s default decision tree classifier algorithm using the black-tailed gull data described in “Methods”. Each node is coloured based on its corresponding feature’s estimated size in bytes when implemented on board the bio-logger (scale shown in the colour bar). c Several space-efficient decision trees generated using the proposed method from the same data used to create the tree in (b). d Example space-efficient tree selected from the trees shown in (c) that costs much less than the default tree in (b) while maintaining almost the same accuracy.

Full size image

In this study, we propose a method for generating decision tree classifiers that can fit in bio-loggers with limited programme memory (e.g., 32 KB) that is based on the random forests algorithm40, which is a decision tree algorithm that injects randomness into the trees it generates by restricting the features compared when creating the split at each node in a tree to a randomly selected subset of the features, as opposed to the standard decision tree algorithm that compares all possible features at each node, as was described above. Our method modifies the original random forests algorithm by using weighted random selection when choosing the subset of features to compare when creating each node. Figure 4a illustrates the weighted random selection process used by our method. We start by assigning each feature a weight that is proportional to the inverse of its size. We then use these weights to perform weighted random selection when selecting a group of features to consider each time we create a new node in the tree, with the feature used at that node being the best candidate from amongst these randomly selected features.
Using our method for weighted random selection of nodes described above, we are then able to generate randomised trees that tend to use less costly features. When generating these trees, we can estimate the size of each tree based on the sizes of the features used in the tree and limit the overall size by setting a threshold and discarding trees above that threshold. Figure 4c shows an example batch of trees output by our method where we have set a threshold size of 1000 bytes. We can then select a single tree from amongst these trees that gives our desired balance of cost to accuracy. In this example, we have selected the tree shown in Fig. 4d based on its high estimated accuracy. Comparing this tree to Fig. 4b, we can see that our method generated a tree that is 42% the size of the default tree while maintaining close to the same estimated accuracy. We developed our method based on scikit-learn’s (v.0.20.0) RandomForestClassifier.
In addition, in order to achieve robust activity recognition, our method also has the following features: (i) robustness to sensor positioning, (ii) robustness to noise, and (iii) reduction of sporadic false positives. Note that robustness to noise and positioning are extremely important when deploying machine learning models on bio-loggers, as the models will likely be generated using data collected in previous years, possibly using different hardware and methods of attachment. While there is a potential to improve prediction accuracy by removing some of these variables, e.g., by collecting from the same animal multiple times using the same hardware, moving to more animal-dependent models is generally not practical as care must be taken to minimise the handling of each animal along with the amount of time the animals spend with data loggers attached34. See “Robust activity recognition” for more details.
GPS features
Due to the low resolution of GPS data (e.g., metre-level accuracy), GPS-based features cannot detect body movements with the same precision as acceleration-based features, but are useful when analysing patterns in changes in an animal’s location as it traverses its environment. For animal-based AI, these features can be used to differentiate between different large-scale movement patterns, such as transit versus ARS. In this study, we used GPS-based features to detect ARS by streaked shearwaters. These features were extracted once per minute from 1/60th Hz GPS data using 10-min windows. Supplementary Fig. 5b shows these features ranked by their Normalised Gini Importance when used to classify behaviours for streaked shearwaters. Supplementary Fig. 2 shows an example of two such 10-min windows that correspond to target (ARS) and non-target (transit) behaviour, along with several examples of GPS-based features extracted from those windows. Supplementary Table 1 describes all the GPS features used in this study along with their estimated sizes when implemented on board our bio-logger. Note that the variance and mean cross features were extracted after first rotating the GPS positions around the mean latitude and longitude values at angles of 22.5°, 45.0°, 67.5°, and 90.0° in order to find the orientation that maximised the variance in the longitude values (see Supplementary Table 1, feature Y: rotation). This was done to provide some measure of rotational invariance to these values without the need for a costly procedure such as principal component analysis. The primary and secondary qualifiers for these features refer to whether the feature was computed on the axis with maximised variance vs. the perpendicular axis, respectively.
Robust activity recognition
In this study, we also incorporated two methods for improving the robustness of the recognition processes in the field. First, we addressed how loggers can be attached to animals at different positions and orientations, such as on the back to maximise GPS reception or on the abdomen to improve the camera’s field of view during foraging. For example, during our case study involving black-tailed gulls, the AI models were trained using data collected from loggers mounted on the birds’ backs, but in many cases were used to detect target behaviour on board loggers mounted on the birds’ abdomens. We achieved this robustness to positioning by converting the three-axis accelerometer data to net magnitude of acceleration values, thereby removing the orientation information from the data. To test the robustness of the magnitude data, we evaluated the difference in classification accuracy between raw three-axis acceleration data and magnitude of acceleration data when artificial rotations of the collection device were introduced into the test data. In addition, we also evaluated the effectiveness of augmenting the raw three-axis data with artificially rotated data as an alternative to using the magnitude of acceleration data. These results are shown in Supplementary Fig. 6a. Note that the results for the magnitude of acceleration data are constant across all levels of test data rotation, since the magnitudes are unaffected by the rotations. Based on these results, we can see that extracting features from magnitude of acceleration data allows us to create features that are robust to the rotations of the device that can result from differences in how the device is attached to an animal.
Next, we addressed the varying amount of noise that can be introduced into the sensor data stemming from how loggers are often loosely attached to a bird’s feathers. We achieved this noise robustness by augmenting our training dataset with copies of the dataset that were altered with varying levels of random artificial noise, with this noise added by multiplying all magnitude of acceleration values in each window of data by a random factor. We tested the effect of this augmentation by varying the amount of artificial noise added to our training and testing data and observing how the noise levels affected performance (see Supplementary Fig. 6b). Based on these results, the training data used for fieldwork was augmented using the 0.2 level. Note that at higher levels of simulated noise (test noise greater than 0.15) the training noise settings of 0.25 and 0.3 both appear to outperform the 0.2 setting. However, since these results are based solely on laboratory simulations, we chose to use the more conservative setting of 0.2 in the field.
Reduction of sporadic false positives
When activating the camera to capture target behaviour, it is possible to reduce the number of false positives (i.e., increase our confidence in the classifier’s output) by considering multiple consecutive outputs from the classifier before camera activation. We accomplish this using two methods. In the first, we assume that the classifier can reliably detect the target behaviour throughout its duration, allowing us to increase our confidence in the classifier’s output by requiring multiple consecutive detections of the target behaviour before activating the camera. We employed this method when detecting ARS behaviour for streaked shearwaters using GPS data, since the characteristics of the GPS data that allow for detection of the target behaviour were expected to be consistent throughout its duration, with the number of consecutive detections required set to 2.
In the second method, we assume that the classifier cannot reliably detect the target behaviour throughout its duration, since the actions corresponding to the target behaviour that the classifier can reliably detect occur only sporadically throughout its duration. In this case, we can instead consider which behaviours were detected immediately prior to the target behaviour. When controlling the camera for black-tailed gulls, we assume that detection of the target behaviour (foraging) is more likely to be a true positive after detecting flying behaviour, since the birds typically fly when searching for their prey. Therefore, we required that the logger first detect five consecutive windows of flying behaviour to enter a flying state in which it would activate the camera immediately upon detecting foraging. This flying state would time out after ten consecutive windows of stationary behaviour. Note that in this case, while the intervals of detectable target behaviour may be short and sporadic, the overall duration of the target behaviour is still long enough that we can capture video of the behaviour despite the delay between behaviour detection and camera activation (see “Video bio-logger hardware” for details).
Procedures of experiment of black-tailed gulls
We evaluated the effectiveness of our method by using AIoA-based camera control on board ten bio-loggers that were attached to black-tailed gulls (on either the bird’s abdomen or back) from a colony located on Kabushima Island near Hachinohe City, Japan18, with the AI trained to detect possible foraging behaviour based on acceleration data. The possible foraging events were identified based on dips in the acceleration data. The training data used for the AI was collected at the same colony in 2017 using Axy-Trek bio-loggers (TechnoSmArt, Roma, Italy). These Axy-Trek bio-loggers were mounted on the animals’ backs when collecting data. Along with the AIoA-based bio-loggers, three bio-loggers were deployed using a naive method (periodic sampling), with the cameras controlled by simply activating them once every 15 min. All 13 loggers recorded 1-min duration videos.
Sample size was determined by the time available for deployment and the availability of sensor data loggers. The birds were captured alive at their nests by hand prior to logger deployment and subsequent release. Loggers were fitted externally within 10 min to minimise disturbance. Logger deployment was undertaken by the ecologists participating in this study. Loggers that suffered hardware failures (e.g., due to the failure of the waterproofing material used on some loggers) were excluded.
Ethics statement
All experimental procedures were approved by the Animal Experimental Committee of Nagoya University. Black-tailed gulls: the procedures were approved by the Hachinohe city mayor, and the Aomori Prefectural Government. Streaked shearwaters: the study was conducted with permits from the Ministry of the Environment, Japan.
Statistics and reproducibility
Fisher’s exact tests were done using the exact2x2 package (v. 1.6.3) of R (v. 3.4.3). The GLMM analysis was conducted using the lmerTest package (v. 2.0–36) of R (v. 3.4.3). In regards to reproducibility, no experiments as such were conducted, rather our data are based on tracked movements of individual birds.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

Ethics statement
All procedures for fieldwork in Pacuare Nature Reserve followed approved protocol under Monash University’s School of Biological Sciences Animal Ethics Committee (Protocol No. BSCI/2016/13), the University of Maryland Center for Environmental Sciences’ Institutional Animal Care and Use Committee (IACUC) (Research Protocol No. S-CBL-16–11), and the Costa Rican Ministerio Del Ambiente y Energia, Sistema Nacional de Áreas de Conservación (SINAC), Área de Conservación La Amistad Caribe (ACLAC) (RESOLUCIÓN SINAC-ACLAC-PIME-VS-R-022-2016; RESOLUCIÓN SINAC-ACLAC-PIME-VS-R-025-2016). The study was performed in accordance with the approved guidelines.
Hatchling tracking
To examine in-situ factors of turtle dispersal into the offshore environment, leatherback hatchlings were tagged with coded acoustic transmitters between 20 August and 3 September 2016 in Pacuare Nature Reserve, Limón Province, Costa Rica (Fig. 1). At Pacuare, hatchlings were obtained from hatchery, incubator-reared, and relocated nests. Hatchery nests consisted of eggs collected as they were laid, transported in plastic bags for less than 5 km, and reburied in two separate protected areas. In these protected, monitored enclosures, the eggs were safeguarded (e.g. from predation) and otherwise developed naturally. Relocated eggs were collected from nests laid the night prior, transported in plastic bags a short distance above the high tide line, reburied on the nesting beach and unmonitored thereafter, until such time as hatchlings emerged. Hatchlings from hatchery and relocated nests were collected as they naturally emerged from the buried nests. Incubator-reared eggs were collected as they were laid, transported up to 1 km in vacuum-sealed bags, and raised under 3 treatments: control, low-oxygen, and high-oxygen in accordance with the Williamson38 protocol. Eggs were incubated for the first 5 days of development in: hypoxia (1% O2) for the low-oxygen treatment and hyperoxia (42% O2) for the high-oxygen treatment. As they did not have to expend time and energy exiting a nest, incubator-hatched turtles were left to absorb their yolk for 2 days38. Turtles held post-emergence from their nest (hatchery and relocated hatchlings) or eggs (incubator-reared hatchlings) were kept in moistened, sand-lined incubators at approximately 30 °C to reduce energy expenditure prior to trial release and prevent potential decreases in swimming performance39. To minimise the influence of genetic relatedness, hatchlings were taken from all available nests (n = 9 in total from hatchery, relocated, and incubator nests) at the time of the study, resulting in parentage by nine females. Turtles were weighed and measured prior to trials using a scale and calliper. To prevent overheating on the boat, turtles were transported in a bucket covered by a wet towel with a moistened cloth inside.
Acoustic tracking was conducted using Vemco V5-180 kHz transmitter tags (0.38 g in-water weight; 0.65 g in-air weight) and tethered to the turtle via a line-float-transmitter assembly (6.85 g in-air weight) and Vetbond based on Gearheart et al.24 and Hoover et al.25 (Fig. S1). The monofilament line in the line-float-transmitter assembly was a total length of 2 m; the first float was suspended 1.5 m behind the hatchling, and the second float was an additional 0.5 m. The brightly coloured orange floats (4.4 cm by 1.9 cm) allowed for visual tracking in the water when acoustic signal was insufficient. Tracking began outside the surf zone, approximately 0.4 km from shore, where turtles were taken via a small 6 m, 150 hp motorboat. The release location was the approximate midpoint of the two hatcheries where hatchlings were collected. Between sunrise and sunset, each turtle was followed at a distance of 10–20 m in the boat using a Vemco VR100 acoustic receiver and VH180-D-10M directional hydrophone22. The V5 tag detection range was approximately 200 m. The VR100 receiver stored the detections, and the data were downloaded to reconstruct hatchling movement paths. The mobile acoustic receiver allowed tracking of the turtles’ movements for a longer period and over a broader area than visual tracking alone because turtles were found acoustically when visual contact was lost.
Hatchlings were tracked only during daylight hours over a 3-week period given hatchling and boat availability. Although hatchlings generally emerge during cooler, evening hours of the day in Costa Rica, no effect on the overall innate behaviour of hatchlings was anticipated18,40. The tracking data should still be indicative of the orientation and speed at which hatchlings are likely to swim. Nighttime tracking was logistically infeasible because of the hazards associated with the oceanic entry point. For a track to provide enough data for inclusion in the analysis, a minimum tracking time of 30 min was established. Turtles were tracked individually for approximately 90 min. Track duration was a trade-off between obtaining a large sample of tracks to account for individual variability, while providing robust speed and orientation information. At the end of each track, the turtle was recovered with a small net, the line-float-transmitter attachment was completely removed, and the turtle was released at the recovery location. The Velcro piece easily removed from the carapace, and there were no evident damages, marks, or lesions from this attachment method on the leatherback hatchlings. Handling was kept to a minimum to reduce any unnecessary stress on the turtles.
Surface current trajectories
Two drifters were used to obtain data on local sea surface currents to evaluate the effect of currents on hatchling movements. A Pacific Gyre Microstar drifter was deployed at the beginning of each turtle track (Fig. S2A). The drifter’s surface float was equipped with a GPS unit that used the iridium short burst data service to broadcast location coordinates every 5 min. A flag was attached to the surface float for increased visibility. Sea surface temperature was recorded by the drifter with a Pacific Gyre probe with 0.1 °C accuracy. The position and temperature data of each drifter release were retrieved from the Pacific Gyre website (https://www.pacificgyre.com). One drifter track was removed from analysis because it entered the surf zone and did not represent nearshore surface currents.
A secondary drifter was launched when equipment permitted at the approximate halfway point during tracking of a turtle. This better estimated the immediate currents the hatchling was experiencing and was used to estimate shifts in the nearshore currents as the turtles headed offshore. This second drifter was constructed using a Davis Instruments aluminium radar reflector with 80 cm of parachute cord attached to a 20.3 cm diameter Panther Plast trawl float (Fig. S2B). The centre of the drifter sat 1 m from the water’s surface, similar to the depth of the Microstar drifter. A piece of wood affixed to the top of the float had a Samsung Galaxy Core Prime mobile phone attached in a waterproof bag. A GPS application was started with each drifter release to provide locations at one minute intervals. Foam tubing was zip-tied around the middle of the trawl float to maintain the GPS unit in an upright position. The float had a flag attached for visibility on the water. Positions were stored on the mobile phone and downloaded upon retrieval of the drifter. Both drifters were recovered at the completion of each individual turtle track.
Environmental data
To understand the influence of tidal states and bathymetry on local currents experienced by hatchling leatherbacks, daily tidal currents were obtained at Limón, Costa Rica (10.00° N, 83.03° W; https://tides.mobilegeographics.com). Periods of peak tides (i.e. high and low) were defined as one hour before and after the measured minima or maxima, with ebb and flow tides between those periods of peak tides. Tidal states were categorised as: high, ebb, low, and flow (Fig. S3A). High-resolution, near-shore bathymetry data were obtained at a 0.0011° resolution from the Global Multi-Resolution Topography Synthesis dataset (https://www.gmrt.org). Missing values were filled with data from the General Bathymetric Chart of the Oceans dataset (GEBCO-2014; https://www.gebco.net; 0.0042° resolution). Bathymetric values for each hatchling track were extracted with a ‘bilinear’ interpolation in the R ‘raster’ package41 (Fig. S3B). All analyses were conducted in the R environment42.
Hatchling movement analysis
An acclimation period of five min was applied to each turtle track to provide time for the hatchling to orient and adjust to the water temperature. Intervals greater than five min between recorded hatchling positions were removed to prevent erroneous calculations (0.03% of recorded positions). These time lapses occurred when the boat was actively searching for a hatchling. Despite the combination of surface floats and the directional hydrophone, maintaining visual and acoustic contact with turtles was challenging, even in calm waters. Many hatchlings dove for short periods ( More

1.
IUCN. A Study on Aid to the Environment Sector in Vietnam. (Ministry of Planning and Investment and UNDP, 1999).
2.
Myers, N. et al. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
ADS  CAS  Article  Google Scholar 

3.
Sterling, E. J. & Hurley, M. M. Conserving biodiversity in Vietnam: applying biogeography to conservation research. Proc. Calif. Acad. Sci. 56, Supplement I, No.9, 98–118 (2005).

4.
Thuoc, P. & Long, N. Overview of the coastal fisheries of Vietnam. In Status and Management of Tropical Coastal Fisheries in Asia. (eds. Silvestre, G. & Pauly, D.) 96–106 (ICLARM, 1997).

5.
Ng, P. K. L. & Tan, K. S. The state of marine biodiversity in the South China Sea. Raffles Bull. Zool. (Supplement No. 8), 3–7 (2000).

6.
Burke, L., Selig, E. & Spalding, M. Reefs at Risk in Southeast Asia. (Resource for the Future, 2002).

7.
Benthem, W., van Lavieren, L. P. & Verheugt, W. J. M. Mangrove rehabilitation in the costal Mekong Delta, Vietnam. In An International Perspective on Wetland Rehabilitation. (ed. Streever, W.) 29–36 (Springer, 1999).

8.
McNally, R., McEwin, A. & Holland, T. The Potential for Mangrove Carbon Projects in Vietnam. (Netherlands Development Organization [SNV], 2011).

9.
Veettil, B. K. et al. Mangroves of Vietnam: historical development, current state of research and future threats. Estuar. Coast. Shelf Sci. 218, 212–236 (2019).
ADS  Article  Google Scholar 

10.
Duke, N. C. Mangroves of the Kien Giang Biosphere Reserve Viet Nam. (Deutsche Gesellschaft fur International Zusammenarbeit GmbH, 2012).

11.
Cuong, C. V., Russell, M., Brown, S. & Dart, P. Using shoreline video assessment for coastal planning and restoration in the context of climate change in Kien Giang, Vietnam. Ocean Sci. J. 50, 413–432 (2015).
ADS  Article  Google Scholar 

12.
Nguyen, T. P., Luom, T. T. & Parnell, K. E. Mangrove allocation for coastal protection and livelihood improvement in Kien Giang Province, Vietnam: constraints and recommendations. Land Use Policy 63, 401–407 (2017).
Article  Google Scholar 

13.
Tue, N. T. et al. Food sources of macro-invertebrates in an important mangrove ecosystem of Vietnam determined by dual stable isotope signatures. J. Sea Res. 72, 14–21 (2012).
ADS  Article  Google Scholar 

14.
Thanh, N. V. et al. The Zoobenthos of the Can Gio Mangrove Ecosystem (Publishing House for Science and Technology, 2013).

15.
Zvonareva, S. & Kantor, Y. Checklist of gastropod molluscs in mangroves of Khanh Hoa province, Vietnam. Zootaxa 4162, 401–437 (2016).
Article  Google Scholar 

16.
Thach, N. N. Shells of Vietnam (Conchbooks, 2005).

17.
Thach, N. N. Recently Collected Shells of Vietnam (L’Informatore Piceno, 2007).

18.
Thach, N. N. New Shells of Southeast Asia. Sea Shells & Land Snails (48HrBooks Company, 2017).

19.
Raven, H. & Vermeulen, J. J. Notes on molluscs from NW Borneo and Singapore. 2. A synopsis of the Ellobiidae (Gastropoda, Pulmonata). Vita Malacol. 4, 29–62 (2007).
Google Scholar 

20.
Lutaenko, K. A., Prozorova, L. A., Ngo, X. Q. & Bogatov, V. V. First reliable record of Mytilopsis sallei (Récluz, 1849) (Bivalvia: Dreissenidae) in Vietnam. Korean J. Malacol. 35, 355–360 (2019).
Google Scholar 

21.
Prozorova, L. A. et al. Mangrove mollusk fauna of the Kien Giang Province in the Mekong River delta (South Vietnam). In The 1st International Conference on North East Asia Biodiversity, Vol. 1, 67 (2018a).

22.
Prozorova, L. A. et al. New for the Mekong Delta and Vietnam fauna mollusk families. In The 1st International Conference on North East Asia Biodiversity Vol. 1, 65–66 (2018b).

23.
Prendergast, J. R. et al. Rare species, the coincidence of diversity hotspots and conservation strategies. Nature 365, 335–337 (1993).
ADS  Article  Google Scholar 

24.
Raphael, M. G. & Molina, R. Conservation of Rare or Little-known Species: Biological, Social, and Economic Considerations (Island Press, Washington, D.C., 2013).
Google Scholar 

25.
Mouillot, D. et al. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biol. 11, e1001569 (2013).
CAS  Article  Google Scholar 

26.
Golding, R. E. Molecular phylogenetic analysis of mudflat snails (Gastropoda: Euthyneura: Amphiboloidea) supports an Australasian centre of origin. Mol. Phylogenet. Evol. 63, 72–81 (2012).
Article  Google Scholar 

27.
Thach, N. N. Vietnamese New Mollusks. Seashells-Land Snails-Cephalopods, with 59 New Species. (Thach, N. N., 2016).

28.
Golding, R. E., Ponder, W. F. & Byrne, M. Taxonomy and anatomy of Amphiboloidea (Gastropoda: Heterobranchia: Archaeopulmonata). Zootaxa 1476, 1–50 (2007).
Article  Google Scholar 

29.
Golding, R. E., Byrne, M. & Ponder, W. F. Novel copulatory structures and reproductive functions in Amphiboloidea (Gastropoda, Heterobranchia, Pulmonata). Invertebr. Biol. 127, 168–180 (2008).
Article  Google Scholar 

30.
Davis, G. M. Mollusks as indicators of the effects of herbicides on mangroves in South Vietnam. In The Effects of Herbicides in South Vietnam: Part B, Working papers. (ed. National Academy of Sciences, National Research Council) 1–29 (National Academy of Sciences, National Research Council, 1974).

31.
Academy of Natural Sciences. MAL. Occurrence dataset. GBIF. https://doi.org/10.15468/xp1dhx (2019a).

32.
Academy of Natural Sciences. ANSP Malacology Collection. The Academy of Natural Sciences, Philadelphia. https://clade.ansp.org/malacology/collections/index.html (2019b).

33.
Akiba, M. & Sasaki, T. Mollusca specimens of Ryukyu University Museum (Fujukan). Version 1.1. National Museum of Nature and Science, Japan. GBIF. https://doi.org/10.15468/qgmdhb (2019).

34.
Creuwels, J. Naturalis Biodiversity Center (NL) – Mollusca. Naturalis Biodiversity Center. GBIF. https://doi.org/10.15468/yefvnk (2019).

35.
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
CAS  Article  Google Scholar 

36.
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
Article  Google Scholar 

37.
Lanfear, R. et al. PartitionFinder 2: new methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2017).
CAS  PubMed  Google Scholar 

38.
Tanabe, A. S. Phylogears version 2.2.2012.02.13. 2012. Life is fifthdimension. https://www.fifthdimension.jp/ (2012).

39.
Ronquist, F. & Huelsenbeck, J. P. MRBAYES 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).
CAS  Article  Google Scholar 

40.
Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
CAS  Article  Google Scholar 

41.
Rambaut, A., Drummond, A. J. & Suchard, M. Tracer v1.6. Molecular evolution, phylogenetics and epidemiology. https://tree.bio.ed.ac.uk/software/tracer/ (2013).

42.
Minh, B. Q., Nguyen, M. A. T. & von Haeseler, A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 30, 1188–1195 (2013).
CAS  Article  Google Scholar 

43.
Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).
CAS  Article  Google Scholar 

44.
Kuroda, T. Two families new to the Molluscan fauna of Japan. Venus 1, 10–15 (1928).
Google Scholar 

45.
GADM. GADM database, version 3.4. GDAM maps and data. https://gadm.org/index.html (2018).

46.
QGIS development team. QGIS geographic information system version 2.18, open source geospatial foundation project. QGIS. https://qgis.osgeo.org (2018). More

1.
Araújo, A. et al. Invited review: Paleoparasitology—Perspectives with new techniques. Rev. Inst. Med. Trop. S. P. 40(6), 371–376 (1998).
Article  Google Scholar 
2.
De Baets, K., Dentzien-Dias, P., Harrison, G. W. M., Littlewood, D. T. J. & Parry, L. A. 2020) Identification and macroevolution of parasites (topics in geobiology. In The Evolution and Fossil Record of Parasitism (eds De Baets, K. & Huntley, J.) (Springer, New York, 2020).
Google Scholar 

3.
Dentzien-Dias, P. C. et al. Tapeworm eggs in a 270 million-year-old shark coprolite. PLoS ONE 8(1), e55007. https://doi.org/10.1371/journal.pone.0055007 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

4.
Hugot, J. P. et al. Discovery of a 240 million-year-old nematode parasite egg in a cynodont coprolite sheds light on the early origin of pinworms in vertebrates. Parasite Vector 7(1), 486. https://doi.org/10.1186/s13071-014-0486-6 (2014).
Article  Google Scholar 

5.
Da Silva, P. A. et al. A new ascarid species in cynodont coprolite dated of 240 million years. An. Acad. Bras. Cienc. 86(1), 265–296 (2014).
Article  PubMed  Google Scholar 

6.
Cardia, D. F. F., Bertini, R. J., Camossi, L. G. & Letizio, L. A. The first record of ascaridoidea eggs discovered in crocodyliformes hosts from the upper Cretaceous of Brazil. Rev. Bras. Paleontol. 21(3), 238–244 (2018).
Article  Google Scholar 

7.
Poinar, G. Jr. & Boucot, A. J. Evidence of intestinal parasites of dinosaurs. Parasitology 133(2), 245–249 (2006).
Article  PubMed  Google Scholar 

8.
Beltrame, M. O., Fugassa, M. H., Barberena, R., Udrizar-Sauthier, D. E. & Sardella, N. H. New record of anoplocephalid eggs (Cestoda: Anoplocephalidae) collected from the rodent coprolites from archaeological and paleontological sites of Patagonia, Argentina. Parasitol. Int. 62, 431–434 (2013).
Article  PubMed  Google Scholar 

9.
Beltrame, M. O., Tietze, E., Pérez, A. E., Bellusci, A. & Sardella, N. H. Ancient parasites from endemic deer from “Cueva Parque Diana” archeological site, Patagonia, Argentina. Parasitol. Res. 116(2), 1523–1531 (2017).
Article  PubMed  Google Scholar 

10.
Fugassa, M. H., Petrigh, R. S., Fernández, P. M., Carballido Calatayud, M. & Belleli, C. Fox parasites in pre-Columbian times: Evidence from the past to understand the current helminth assemblages. Acta Trop. 185, 380–384 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

11.
Sianto, L. et al. Helminths in feline coprolites up to 9000 years in the Brazilian Northeast. Parasitol. Int. 63, 851–857 (2014).
Article  PubMed  PubMed Central  Google Scholar 

12.
Barrios-de Pedro, S. Integrative Study of the Coprolites from Las Hoyas (upper Barremian; La Huérguina Formation, Cuenca, Spain). Unpublished PhD thesis. Universidad Autónoma de Madrid (Spain) (2019).

13.
Poyato-Ariza, F. J. & Buscalioni, A. D. Las Hoyas: A Cretaceous Wetland (Dr. Friedrich Pfeil Verlag, München, 2016).
Google Scholar 

14.
Martin, T. et al. A Cretaceous eutricondont and integument evolution in early mammals. Nature 526, 380–384 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
Iniesto, M. et al. A.I. Involvement of microbial mats in early fossilization by decay delay and formation of impressions and replicas of vertebrates and invertebrates. Sci. Rep. 6, 1–12. https://doi.org/10.1038/srep25716 (2016).
CAS  Article  Google Scholar 

16.
Iniesto, M. et al. Plant tissue decay in long-term experiments with microbial mats. Geosci. J. 8(11), 387. https://doi.org/10.3390/geosciences8110387 (2018).
ADS  CAS  Article  Google Scholar 

17.
Poyato-Ariza, F. J., Talbot, M. R., Fregenal-Martínez, M. A., Meléndez, N. & Wenz, S. First isotopic and multidisciplinary evidence for nonmarine coelacanths and pycnodontiform fishes: Palaeoenvironmental implications. Palaeogeogr. Palaeoclimatol. Palaeoecol. 144, 64–84 (1998).
Article  Google Scholar 

18.
Buscalioni, A. D. & Fregenal-Martínez, M. A. A holistic approach to the palaeoecology of Las Hoyas Konservat-Lagerstätte (La Huérguina Formation, Lower Cretaceous, Iberian ranges, Spain). J. Iber. Geol. 36(2), 297–326 (2010).
Article  Google Scholar 

19.
Fregenal-Martínez, M. A., Meléndez, N., Muñoz-García, M. B., Elez, J. & de la Horra, R. The stratigraphic record of the late Jurassic-early Cretaceous rifting in the Alto Tajo-Serranía de Cuenca region (Iberian Ranges, Spain): Genetic and structural evidences for a revision and a new lithostratigraphic proposal. Rev. Soc. Geol. Esp. 30(1), 113–142 (2017).
Google Scholar 

20.
Buscalioni, A. D. et al. The wetlands of Las Hoyas. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 238–253 (Dr. Friedrich Pfeil Verlag, München, 2016).
Google Scholar 

21.
Timm, T., Vinn, O. & Buscalioni, A. D. Soft-bodied annelids (Oligochaeta) from the lower Cretaceous (La Huerguina formation) of the Las Hoyas Konservat-Lagerstätte, Spain. Neues. Jahrb. Geol. P.-A. 280(3), 315–324 (2016).
Article  Google Scholar 

22.
Buatois, L. A., Fregenal-Martínez, M. A. & de Gibert, J. M. Short-term colonization trace-fossil assemblages in a carbonate lacustrine Konservat-Lagerstätte (Las Hoyas fossil site, Lower Cretaceous, Cuenca, centra Spain). Facies 43, 145–156 (2000).
Article  Google Scholar 

23.
de Gibert, J. M., Moratalla, J. J., Mángano, M. G. & Buatois, L. A. Ichnoassemblage (trace fossils). In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 195–201 (Dr. Friedrich Pfeil Verlag, München, 2016).
Google Scholar 

24.
Barrios-de-Pedro, S., Poyato-Ariza, F. J., Moratalla, J. J. & Buscalioni, A. D. Exceptional coprolite association from the early Cretaceous continental Lagerstätte of Las Hoyas, Cuenca, Spain. PLoS ONE 13(5), E0196982. https://doi.org/10.1371/journal.pone.0196982 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

25.
Barrios-de Pedro, S., Chin, K. & Buscalioni, A. D. The late Barremian ecosystem of Las Hoyas sustained by fishes and shrimps as inferred from coprofabrics. Cretac. Res. 110, 104409. https://doi.org/10.1016/j.cretres.2020.104409 (2020).
Article  Google Scholar 

26.
Poyato-Ariza, F. J. & Martín-Abad, H. Osteichthyan fishes. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 114–132 (Dr. Friedrich Pfeil Verlag, München, 2016).
Google Scholar 

27.
de Gibert, J. M. et al. The fish trace fossil Undichnafrom the Cretaceous of Spain. Paleontol. 42, 409–427 (2003).
Article  Google Scholar 

28.
Sprent, J. F. A. Ascaridoid nematodes of amphibians and reptiles: Dujardinascaris. Supplementary review article. J. Helminthol. 51, 251–285 (1977).
Google Scholar 

29.
Foreyt, W. J. Veterinary Parasitology. Reference Manual (Blackwell Publishing Profesional, Iowa, 2001).
Google Scholar 

30.
Sullivan, T. A Color Atlas of Parasitology (University of San Francisco, San Francisco, 2004).
Google Scholar 

31.
Rajesh, N. V., Kalpana Devi, R., Jayathangaraj, M. G., Raman, M. & Sridhar, R. Intestinal parasites in captive mugger crocodiles (Crocodylus palustris) in south India. J. Trop. Med. Parasit. 37(2), 69–73 (2014).
Google Scholar 

32.
King, S. & Scholz, T. Trematodes of the family Opisthorchiidae: A minireview, Korean. J. Parasitol. 39(3), 209–221 (2001).
CAS  Google Scholar 

33.
Olsen, O. W. Animal Parasites: Their Life Cycles and Ecology 3rd edn. (University Park Press, Baltimore, London, 1974).
Google Scholar 

34.
Chen, T. C. General Parasitology 2nd edn. (Academic Press Inc., Florida, 1986).
Google Scholar 

35.
Gegenbaur, C. Gundriss der Vergleichenden Anatomie (Wilhelm Engelmann, Leipzig, 1859).
Google Scholar 

36.
Rudolphi, C. A. Entozoorum Sive Vermium Intesstinalium (Historia Naturalis, Amsterdam, 1808).
Google Scholar 

37.
Yamaguti, S. The Digenetic-Trematodes of Vertebrates Volume I (Parts 1 and 2) (Interscience Publisjers Inc., New York, 1958).
Google Scholar 

38.
Ditrich, O., Giboda, M., Scholz, T. & Beer, S. A. Comparative morphology of eggs of the Haplorchiinae (Trematoda: Heterophyidae) and some other medically important heterophyid and opisthorchiid flukes. Folia. Parasit. 39, 123–132 (1992).
CAS  Google Scholar 

39.
Cobb, N. A. The english word “nema”. J. A. M. A. 98, 75 (1932).
Google Scholar 

40.
Skrjabin, K. I. & Karokhin, V. I. On the rearrangement of nematodes of the order Ascaridata Skrjabin, 1915. Dokl. Akad. Nauk. Soiuza. Sov. Sotsialisticheskikh. Resp. 48(4), 297–299 (1945).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Ubelaker, J. E. & Allison, V. F. Scanning electron microscopy of the eggs of Ascaris lumbricoides, A. suum, Toxocara canis, and T. mystax. J. Parasitol. 61(5), 802–807 (1975).
CAS  Article  PubMed  Google Scholar 

42.
Dujardin, F. Histoire Naturelle des Helminthes ou Vers Intestinaux (Librairie Encyclopedique de Roret, Paris, 1845).
Google Scholar 

43.
Cardoso, A. M. C., de Souza, A. J. S., Menezes, R. C., Pereira, W. L. A. & Tortelly, R. Gastric lesions in free-ranging black caimans (Melanosuchus niger) associated with Brevimulticaecum species. Vet. Pathol. 50(4), 582–584 (2013).
CAS  Article  PubMed  Google Scholar 

44.
Tellez, M. & Nifong, J. Gastric nematode diversity between estuarine and inland freshwater populations of the American alligator (Alligator mississippienses, daudin 1802), and the prediction of intermediate hosts. Int. J. Parasitol.-Par. 3, 227–235 (2014).
Article  Google Scholar 

45.
Villegas, A. & González-Solís, D. Gastrointestinal helminth parasites of the American crocodile (Crocodylus Acutus) in southern Quintana, Roo, Mexico. Herpetol. Conserv. Biol. 4(3), 346–351 (2009).
Google Scholar 

46.
Cardia, D. F. F., Bertini, R. J., Camossi, L. G. & Letizio, L. A. First record of Acanthocephala parasites eggs in coprolites preliminary assigned to Crocodyliformes from the Adamantina Formation (Bauru Group, upper Cretaceous), Sao Paulo, Brazil. An. Acad. Bras. Cienc. 91(2), e20170848. https://doi.org/10.1590/0001-3765201920170848 (2019).
Article  Google Scholar 

47.
Qvarnström, M., Niedźwiedzki, G. & Žigaitė, Ž. Vertebrate coprolites (fossil faeces): An underexplored Konservat-Lagerstätte. Earth Sci. Rev. 162, 44–57 (2016).
ADS  Article  Google Scholar 

48.
Uddin, M. H., Bae, Y. M., Choi, M. H. & Hong, S. T. Production and deformation of Clonorchis sinensis eggs during in vitro maintenance. PLoS ONE 7(12), e52676. https://doi.org/10.1371/journal.pone.0052676 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

49.
Grobbelaar, A., Van As, L. L., Butler, H. J. B. & Van As, J. G. Ecology of Diplostomid (Trematoda: Digenea) infection in freshwater fish in Southern Africa. Afr. Zool. 49(2), 222–232 (2014).
Article  Google Scholar 

50.
Schell, S. C. How to Know the Trematodes (William C. Brown Company Publishers, Iowa, 1970).
Google Scholar 

51.
McConnaughey, M. Life Cycle of Parasites. Reference Module in Biomedical Sciences (Elsevier, Amsterdam, 2014).
Google Scholar 

52.
Tsubokawa, D. et al. Collection methods of trematode eggs using experimental animal models. Parasitol. Int. 65, 584–587 (2016).
Article  PubMed  Google Scholar 

53.
Wolf, D. et al. Diagnosis of gastrointestinal parasites in reptiles: Comparison of two coprological methods. Acta. Vet. Scand. 56(1), 44. https://doi.org/10.1186/s13028-014-0044-4 (2014).
Article  PubMed  PubMed Central  Google Scholar 

54.
Mehlhorn, H. Encyclopedia of Parasitology (Springer, Berlin, 2016).
Google Scholar 

55.
Dai, W. et al. Phylogenomic perspective on the relationships and evolutionary history of the major otocephalan lineages. Sci. Rep. 8, 205. https://doi.org/10.1038/s41598-017-18432-5 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

56.
Kappas, I., Vittas, S., Pantzartzi, C. N., Drosopoulou, E. & Scouras, Z. G. A time-calibrated mitogenome phylogeny of catfish (Teleostei: Siluriformes). PLoS ONE 11(12), E0166988. https://doi.org/10.1371/journal.pone.0166988 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

57.
Anderson, R. C. Nematode Parasites of Vertebrates: Their Development and Transmission 2nd edn. (CABI Publishing, Wallingford, 2000).
Google Scholar 

58.
Valles-Vega, I., Molina-Fernández, D., Benítez, R., Hernández-Trujillo, S. & Adroher, F. J. Early development and life cycle of Contracaecum multipapillatum s.l. from a brown pelican Pelecanus occidentalis in the Gulf of California, Mexico. Dis. Aquat. Organ. 125, 167–178 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

59.
Klimpel, S., Palm, H. W., Rückert, S. & Piatkowski, U. The life cycle of Anisakis simplex in the Norwegian Deep (northern North Sea). Parasitol. Res. 94(1), 1–9 (2004).
Article  PubMed  PubMed Central  Google Scholar 

60.
Cardia, D. F. F., Bertini, R. J., Camossi, L. G. & Letizio, L. A. Two new species of ascaridoid nematodes in Brazilian Crocodylomorpha from the upper Cretaceous. Parasitol. Int. 72, 101947. https://doi.org/10.1016/j.parint.2019.101947 (2019).
Article  PubMed  PubMed Central  Google Scholar 

61.
Buscalioni, A. D. & Chamero, B. Crocodylomorpha. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 162–169 (Dr. Friedrich Pfeil Verlag, München, 2016).
Google Scholar 

62.
Esch, G. W., Barger, M. A. & Fellis, K. J. The transmission of digenetic trematodes: Style, elegance, complexity. Integr. Comp. Biol. 42, 304–312 (2002).
Article  PubMed  Google Scholar 

63.
Delvene, G. & Clive Munt, M. Mollusca. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 57–63 (Dr. Friedrich Pfeil Verlag, München, 2016).
Google Scholar 

64.
Delclós, X. & Soriano, C. Insecta. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 70–88 (Dr. Friedrich Pfeil Verlag, München, 2016).
Google Scholar 

65.
Garassino, A. Decapoda. In Las Hoyas: A Cretaceous Wetland (eds Poyato-Ariza, F. J. & Buscalioni, A. D.) 98–102 (Dr. Friedrich Pfeil Verlag, München, 2016).
Google Scholar 

66.
Heimhofer, U. et al. Deciphering the depositional environment of the laminated Crato fossil beds (early Cretaceous, Araripe Basin, North-eastern Brazil. Sedimentology 57(2), 677–694 (2010).
ADS  CAS  Article  Google Scholar 

67.
de Gibert, J. M., Fregenal-Martínez, M. A., Buatois, L. A. & Mángano, M. G. Trace fossils and their palaeoecological significance in lower Cretaceous lacustrine conservation deposits, El Montsec, Spain. Palaeogeogr. Palaeoclimatol. Palaeoecol. 156, 89–101 (2000).
Article  Google Scholar 

68.
Ferreira, L. F., Reinhard, K. & Araújo, A. Fundamentos da Paleoparasitología 1st edn. (Editora Fiocruz, Rio de Janeiro, 2011).
Google Scholar 

69.
Ritchie, L. S. An ether sedimentation technique for routine stool examination. Bull. U. S. Army. Med. Dep. 8, 326 (1948).
CAS  PubMed  PubMed Central  Google Scholar 

70.
Rasband, W.S. ImageJ. (U.S. National Institutes of Health, Bethesda, 1997–2018). https://imagej.nih.gov/ij/. More

1.
Ramankutty, N. et al. Trends in global agricultural land use: Implications for environmental health and food security. Annu. Rev. Plant Biol. 69, 789–815 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. USA 108, 20260–20264 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Bender, S. F., Wagg, C. & van der Heijden, M. G. A. An underground revolution: Biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31, 440–452 (2016).
PubMed  Article  PubMed Central  Google Scholar 

4.
Schröder, P. et al. Discussion paper: Sustainable increase of crop production through improved technical strategies, breeding and adapted management—A European perspective. Sci. Total Environ. 678, 146–161 (2019).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

5.
Bardgett, R. D., Mommer, L. & De Vries, F. T. Going underground: Root traits as drivers of ecosystem processes. Trends Ecol. Evol. 29, 692–699 (2014).
PubMed  Article  PubMed Central  Google Scholar 

6.
Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Wissuwa, M., Mazzola, M. & Picard, C. Novel approaches in plant breeding for rhizosphere-related traits. Plant Soil 321, 409–430 (2009).
CAS  Article  Google Scholar 

8.
Backer, R. et al. Plant growth-promoting rhizobacteria: Context, mechanisms of action, and roadmap to commercialization of biostimulants for sustainable agriculture. Front. Plant Sci. 871, 1–17 (2018).
Google Scholar 

9.
Bulgarelli, D. et al. Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe 17, 392–403 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Chaparro, J. M., Badri, D. V. & Vivanco, J. M. Rhizosphere microbiome assemblage is affected by plant development. ISME J. 8, 790–803 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl. Acad. Sci. USA 112, E911–E920 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Food and Agriculture Organization of United Nations. World Food and Agriculture Statistical Workbook 2018 https://www.fao.org/3/ca1796en/ca1796en.pdf (2018).

13.
International Potato Centre. Annual Report 2017 https://cipotato.org/annualreport2017/ (2017).

14.
Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, 1–14 (2017).
Article  CAS  Google Scholar 

15.
Lareen, A., Burton, F. & Schäfer, P. Plant root-microbe communication in shaping root microbiomes. Plant Mol. Biol. 90, 575–587 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Adair, K. L. & Douglas, A. E. Making a microbiome: The many determinants of host-associated microbial community composition. Curr. Opin. Microbiol. 35, 23–29 (2017).
PubMed  Article  PubMed Central  Google Scholar 

17.
Donn, S., Kirkegaard, J. A., Perera, G., Richardson, A. E. & Watt, M. Evolution of bacterial communities in the wheat crop rhizosphere. Environ. Microbiol. 17, 610–621 (2015).
PubMed  Article  PubMed Central  Google Scholar 

18.
Grayston, S. J., Wang, S., Campbell, C. D. & Edwards, A. C. Selective influence of plant species on microbial diversity in the rhizosphere. Soil Biol. Biochem. 30, 369–378 (1998).
CAS  Article  Google Scholar 

19.
Esperschütz, J., Gattinger, A., Mäder, P., Schloter, M. & Fließbach, A. Response of soil microbial biomass and community structures to conventional and organic farming systems under identical crop rotations. FEMS Microbiol. Ecol. 61, 26–37 (2007).
PubMed  Article  CAS  PubMed Central  Google Scholar 

20.
Francioli, D. et al. Mineral vs. organic amendments: Microbial community structure, activity and abundance of agriculturally relevant microbes are driven by long-term fertilization strategies. Front. Microbiol. 7, 1–16 (2016).
Article  Google Scholar 

21.
Lupatini, M., Korthals, G. W., de Hollander, M., Janssens, T. K. S. & Kuramae, E. E. Soil microbiome is more heterogeneous in organic than in conventional farming system. Front. Microbiol. 7, 1–13 (2017).
Article  Google Scholar 

22.
Kätterer, T., Börjesson, G. & Kirchmann, H. Changes in organic carbon in topsoil and subsoil and microbial community composition caused by repeated additions of organic amendments and N fertilisation in a long-term field experiment in Sweden. Agric. Ecosyst. Environ. 189, 110–118 (2014).
Article  Google Scholar 

23.
Liu, B., Tu, C., Hu, S., Gumpertz, M. & Ristaino, J. B. Effect of organic, sustainable, and conventional management strategies in grower fields on soil physical, chemical, and biological factors and the incidence of Southern blight. Appl. Soil Ecol. 37, 202–214 (2007).
Article  Google Scholar 

24.
Liu, Y. et al. Direct and indirect influences of 8 year of nitrogen and phosphorus fertilisation on glomeromycota in an alpine meadow ecosystem. New Phytol. 194, 523–535 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Liu, W. et al. Arbuscular mycorrhizal fungi in soil and roots respond differently to phosphorus inputs in an intensively managed calcareous agricultural soil. Sci. Rep. 6, 1–11 (2016).
Article  CAS  Google Scholar 

26.
Beauregard, M. S. et al. Various forms of organic and inorganic P fertilizers did not negatively affect soil- and root-inhabiting AM fungi in a maize–soybean rotation system. Mycorrhiza 23, 143–154 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Wemheuer, B., Thomas, T. & Wemheuer, F. Fungal endophyte communities of three agricultural important grass species differ in their response towards management regimes. Microorganisms 7, 37 (2019).
CAS  Article  Google Scholar 

28.
Hartman, K. et al. Erratum: Correction to: Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming (Microbiome (2018) 6 1 (14)). Microbiome 6, 74 (2018).
PubMed  Article  PubMed Central  Google Scholar 

29.
Estonian Weather Service. Meteorological Yearbook of Estonia 2017 https://www.ilmateenistus.ee/wp-content/uploads/2018/03/aastaraamat_2017.pdf (2018).

30.
De Leon, D. G. et al. Different wheat cultivars exhibit variable responses to inoculation with arbuscular mycorrhizal fungi from organic and conventional farms. PLoS ONE 15, 1–17 (2020).
Google Scholar 

31.
Van Reeuwijk, L. P. Nitrogen in Procedures for soil analysis 6th edn (ed. Van Reeuwijk L. P.) (International Soil Reference and Information Centre, Wageningen, 2002).
Google Scholar 

32.
Nikitin, B. A. Methods for soil humus determination. Agric.Chem. (Agrokhimya) 3, 156–158 (1999) in Russian
Google Scholar 

33.
Egnér, H., Riehm, H. & Domingo, W. R. Untersuchungen über die chemische Bodenanalyse als Grundlage für die Beurteilung des Nährstoffzustandes der Böden. II. Chemische Extraktionsmethoden zur Phosphor- und Kaliumbestimmung 199–215 (The Annals of the Royal Agricultural College of Sweden, 1960) in German

34.
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).
PubMed  Article  CAS  PubMed Central  Google Scholar 

35.
Riit, T. et al. Oomycete-specific ITS primers for identification and metabarcoding. MycoKeys 14, 17–30 (2016).
Article  Google Scholar 

36.
Anslan, S., Bahram, M., Hiiesalu, I. & Tedersoo, L. PipeCraft: Flexible open-source toolkit for bioinformatics analysis of custom high-throughput amplicon sequencing data. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.12692 (2017).
Article  PubMed  Google Scholar 

37.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 2016, 1–22 (2016).
Google Scholar 

38.
Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Abarenkov, K. et al. The UNITE database for molecular identification of fungi—Recent updates and future perspectives. New Phytol 186, 281–285 (2010).
PubMed  Article  PubMed Central  Google Scholar 

40.
Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 4, 914–919 (2013).
Google Scholar 

41.
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 1–9 (2009).
Article  CAS  Google Scholar 

43.
Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
Article  Google Scholar 

44.
Agrios, G. N. In Plant Pathology 5th edn (ed. Agrios, G. N.) (Elsevier Academic Press, Amsterdam, 2005).

45.
Jensen, B., Lübeck, P. S. & Jørgensen, H. J. L. Clonostachys rosea reduces spot blotch in barley by inhibiting prepenetration growth and sporulation of Bipolaris sorokiniana without inducing resistance. Pest Manag. Sci. 72, 2231–2239 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Knudsen, I. M. B., Hockehull, J. & Jensen, D. N. Biocontrol of seedling diseases of barley and wheat caused by Fusarium culmorum and Bipolaris sorokiniana: Effects of selected fungal antagonists on growth and yield components. Plant Pathol 44, 467–477 (1995).
Article  Google Scholar 

47.
Bálint, M. et al. Millions of reads, thousands of taxa: Microbial community structure and associations analyzed via marker genesa. FEMS Microbiol. Rev. 40, 686–700 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

48.
Clarke, K. R. & Gorley, R. N. PRIMERv7: User Manual/Tutorial (PRIMER-E, Plymouth, 2015).
Google Scholar 

49.
Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods 1–214 (PRIMER-E, Plymouth, 2008).
Google Scholar 

50.
Anderson, M. J. & Willis, T. J. Canonical analysis of principal coordinates: A useful method of constrained ordination for ecology. Ecology 84, 511–525 (2003).
Article  Google Scholar 

51.
Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9, 683–693 (2006).
PubMed  Article  PubMed Central  Google Scholar 

52.
McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data. Ecology 82, 290–297 (2001).
Article  Google Scholar 

53.
Broeckling, C. D., Broz, A. K., Bergelson, J., Manter, D. K. & Vivanco, J. M. Root exudates regulate soil fungal community composition and diversity. Appl. Environ. Microbiol. 74, 738–744 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Hu, L. et al. Root exudate metabolites drive plant–soil feedbacks on growth and defense by shaping the rhizosphere microbiota. Nat. Commun. 9, 1–13 (2018).
ADS  Article  CAS  Google Scholar 

55.
Badri, D. V. & Vivanco, J. M. Regulation and function of root exudates. Plant Cell Environ. 32, 666–681 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Emmett, B. D., Youngblut, N. D., Buckley, D. H. & Drinkwater, L. E. Plant phylogeny and life history shape rhizosphere bacterial microbiome of summer annuals in an agricultural field. Front. Microbiol. 8, 1–16 (2017).
Article  Google Scholar 

57.
Hawes, M. C., Gunawardena, U., Miyasaka, S. & Zhao, X. The role of root border cells in plant defense. Trends Plant Sci. 5, 128–133 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Hawes, M. C., Bengough, G., Cassab, G. & Ponce, G. Root caps and rhizosphere. J. Plant Growth Regul. 21, 352–367 (2002).
CAS  Article  Google Scholar 

59.
Koroney, A. S. et al. Root exudate of Solanum tuberosum is enriched in galactose-containing molecules and impacts the growth of pectobacterium atrosepticum. Ann. Bot. 118, 797–808 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Moody, S. F., Clarke, A. E. & Bacic, A. Structural analysis of secreted slime from wheat and cowpea roots. Phytochemistry 27, 2857–2861 (1988).
CAS  Article  Google Scholar 

61.
Wang, Q., Wang, N., Wang, Y., Wang, Q. & Duan, B. Differences in root-associated bacterial communities among fine root branching orders of poplar (Populus × euramericana (Dode) Guinier.). Plant Soil 421, 123–135 (2017).
CAS  Article  Google Scholar 

62.
Tedersoo, L., Mett, M., Ishida, T. A. & Bahram, M. Phylogenetic relationships among host plants explain differences in fungal species richness and community composition in ectomycorrhizal symbiosis. New Phytol. 199, 822–831 (2013).
PubMed  Article  PubMed Central  Google Scholar 

63.
Rich, S. M. & Watt, M. Soil conditions and cereal root system architecture: Review and considerations for linking Darwin and Weaver. J. Exp. Bot. 64, 1193–1208 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Watt, M., Magee, L. J. & McCully, M. E. Types, structure and potential for axial water flow in the deepest roots of field-grown cereals. New Phytol. 178, 135–146 (2008).
PubMed  Article  PubMed Central  Google Scholar 

65.
Watt, M., Schneebeli, K., Dong, P. & Wilson, I. W. The shoot and root growth of Brachypodium and its potential as a model for wheat and other cereal crops. Funct. Plant Biol. 36, 960–969 (2009).
PubMed  Article  PubMed Central  Google Scholar 

66.
Yamaguchi, J. Measurement of root diameter in field-grown crops under a microscope without washing. Soil Sci. Plant Nutr. 48, 625–629 (2002).
Article  Google Scholar 

67.
Yamaguchi, J., Tanaka, A. & Tanaka, A. Quantitative observation on the root system of various crops growing in the field. Soil Sci. Plant Nutr. 36, 483–493 (1990).
Article  Google Scholar 

68.
Detheridge, A. P. et al. The legacy effect of cover crops on soil fungal populations in a cereal rotation. Agric. Ecosyst. Environ. 228, 49–61 (2016).
Article  Google Scholar 

69.
Tedersoo, L. et al. Tree diversity and species identity effects on soil fungi, protists and animals are context dependent. ISME J. 10, 346–362 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
Chen, M. et al. Soil eukaryotic microorganism succession as affected by continuous cropping of peanut—Pathogenic and beneficial fungi were selected. PLoS ONE 7, e40659 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

71.
Song, X., Pan, Y., Li, L., Wu, X. & Wang, Y. Composition and diversity of rhizosphere fungal community in Coptis chinensis Franch. Continuous cropping fields. PLoS ONE 13, 1–14 (2018).
Google Scholar 

72.
Bennett, A. J., Bending, G. D., Chandler, D., Hilton, S. & Mills, P. Meeting the demand for crop production: The challenge of yield decline in crops grown in short rotations. Biol. Rev. 87, 52–71 (2012).
PubMed  Article  PubMed Central  Google Scholar 

73.
Öpik, M., Moora, M., Liira, J. & Zobel, M. Composition of root-colonizing arbuscular mycorrhizal fungal communities in different ecosystems around the globe. J. Ecol. 94, 778–790 (2006).
Article  Google Scholar 

74.
Sýkorová, Z., Wiemken, A. & Redecker, D. Cooccurring Gentiana verna and Gentiana acaulis and their neighboring plants in two Swiss upper montane meadows harbor distinct arbuscular mycorrhizal fungal communities. Appl. Environ. Microbiol. 73, 5426–5434 (2007).
PubMed  Article  CAS  PubMed Central  Google Scholar 

75.
Francioli, D. et al. Plant functional group drives the community structure of saprophytic fungi in a grassland biodiversity experiment. Plant Soil https://doi.org/10.1007/s11104-020-04454-y (2020).
Article  Google Scholar 

76.
Mariotte, P. et al. Plant–soil feedback: Bridging natural and agricultural sciences. Trends Ecol. Evol. 33, 129–142 (2018).
PubMed  Article  PubMed Central  Google Scholar 

77.
Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).
PubMed  Article  PubMed Central  Google Scholar 

78.
Paungfoo-Lonhienne, C. et al. Nitrogen fertilizer dose alters fungal communities in sugarcane soil and rhizosphere. Sci. Rep. 5, 1–6 (2015).
Article  CAS  Google Scholar 

79.
Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).
PubMed  Article  PubMed Central  Google Scholar 

80.
Rousk, J., Brookes, P. C. & Bååth, E. Fungal and bacterial growth responses to N fertilization and pH in the 150-year ‘Park Grass’ UK grassland experiment. FEMS Microbiol. Ecol. 76, 89–99 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Strickland, M. S. & Rousk, J. Considering fungal: Bacterial dominance in soils—Methods, controls, and ecosystem implications. Soil Biol. Biochem. 42, 1385–1395 (2010).
CAS  Article  Google Scholar 

82.
Marschner, P., Kandeler, E. & Marschner, B. Structure and function of the soil microbial community in a long-term fertilizer experiment. Soil Biol. Biochem. 35, 453–461 (2003).
CAS  Article  Google Scholar 

83.
Ai, C. et al. Distinct responses of soil bacterial and fungal communities to changes in fertilization regime and crop rotation. Geoderma 319, 156–166 (2018).
ADS  CAS  Article  Google Scholar 

84.
Giacometti, C. et al. Chemical and microbiological soil quality indicators and their potential to differentiate fertilization regimes in temperate agroecosystems. Appl. Soil Ecol. 64, 32–48 (2013).
Article  Google Scholar 

85.
Liu, M. et al. Organic amendments with reduced chemical fertilizer promote soil microbial development and nutrient availability in a subtropical paddy field: The influence of quantity, type and application time of organic amendments. Appl. Soil. Ecol. 42, 166–175 (2009).
Article  Google Scholar 

86.
Lin, X. et al. Long-term balanced fertilization decreases arbuscular mycorrhizal fungal diversity in an arable soil in north China revealed by 454 pyrosequencing. Environ. Sci. Technol. 46, 5764–5771 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

87.
Mäder, P., Edenhofer, S., Boller, T., Wiemken, A. & Niggli, U. Arbuscular mycorrhizae in a long-term field trial comparing low-input (organic, biological) and high-input (conventional) farming systems in a crop rotation. Biol. Fertil. Soils 31, 150–156 (2000).
Article  Google Scholar 

88.
Song, G. et al. Contrasting effects of long-term fertilization on the community of saprotrophic fungi and arbuscular mycorrhizal fungiin a sandy loam soil. Plant Soil Environ. 61, 127–136 (2015).
CAS  Article  Google Scholar 

89.
Sun, R. et al. Fungal community composition in soils subjected to long-term chemical fertilization is most influenced by the type of organic matter. Environ. Microbiol. 18, 5137–5150 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

90.
Setälä, H. & McLean, M. A. Decomposition rate of organic substrates in relation to the species diversity of soil saprophytic fungi. Oecologia 139, 98–107 (2004).
ADS  PubMed  Article  PubMed Central  Google Scholar 

91.
van Agtmaal, M. et al. Exploring the reservoir of potential fungal plant pathogens in agricultural soil. Appl. Soil Ecol. 121, 152–160 (2017).
Article  Google Scholar 

92.
Chung, Y. R., Hoitink, H. A. H. & Lipps, P. E. Interactions between organic-matter decomposition level and soilborne disease severity. Agric. Ecosyst. Environ. 24, 183–193 (1988).
Article  Google Scholar  More

1.
Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Chang. 9, 323–329 (2019).
ADS  Article  Google Scholar 
2.
Franklin, J. Mapping Species Distributions: Spatial Inference and Prediction (Cambridge University Press, Cambridge, 2010).
Google Scholar 

3.
Zanin, M. & dos Neves, B. S. Current felid (Carnivora: Felidae) distribution, spatial bias, and occurrence predictability: testing the reliability of a global dataset for macroecological studies. Acta Oecol. 101, 103–488 (2019).
Article  Google Scholar 

4.
Lomolino, M. V. & Heaney, L. R. Frontiers of Biogeography: New Directions in the Geography of Nature. (sidalc.net, 2004).

5.
Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 1–8 (2015).
Google Scholar 

6.
Peterson, A. T., Soberon, J. & Sanchez-Cordero, V. Conservatism of ecological niches in evolutionary time. Science 285, 1265–1267 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Raxworthy, C. J. et al. Predicting distributions of known and unknown reptile species in Madagascar. Nature 426, 837–841 (2003).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Hu, J. et al. Niche conservatism in Gynandropaa frogs on the southeastern Qinghai-Tibetan Plateau. Sci. Rep. 6, 32624 (2016).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Morinière, J. et al. Phylogenetic niche conservatism explains an inverse latitudinal diversity gradient in freshwater arthropods. Sci. Rep. 6, 26340 (2016).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

10.
Liu, H. et al. Strong phylogenetic signals and phylogenetic niche conservatism in ecophysiological traits across divergent lineages of Magnoliaceae. Sci. Rep. 5, 12246 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Liu, H., Edwards, E. J., Freckleton, R. P. & Osborne, C. P. Phylogenetic niche conservatism in C4 grasses. Oecologia 170, 835–845 (2012).
ADS  PubMed  Article  PubMed Central  Google Scholar 

12.
Crisp, M. D. et al. Phylogenetic biome conservatism on a global scale. Nature 458, 754–756 (2009).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Lorenzen, E. D. et al. Species-specific responses of Late Quaternary megafauna to climate and humans. Nature 479, 359–364 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Aguirre-Gutiérrez, J., Serna-Chavez, H. M., Villalobos-Arambula, A. R., de la Rosa, J. A. P. & Raes, N. Similar but not equivalent: ecological niche comparison across closely-related Mexican white pines. Div. Dist. 21, 245–257 (2014).
Article  Google Scholar 

15.
Perret, D. L., Leslie, A. B. & Sax, D. F. Naturalized distributions show that climatic disequilibrium is structured by niche size in pines (Pinus L.). Glob. Ecol. Biogeogr. 28, 429–441 (2018).
Google Scholar 

16.
Graham, C. H., Ron, S. R., Santos, J. C., Schneider, C. J. & Moritz, C. Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution 58, 1781–1793 (2004).
PubMed  Article  PubMed Central  Google Scholar 

17.
Kozak, K. H. & Wiens, J. J. Climatic zonation drives latitudinal variation in speciation mechanisms. Proc. Biol. Sci. 274, 2995–3003 (2007).
PubMed  PubMed Central  Google Scholar 

18.
Moussalli, A., Moritz, C., Williams, S. E. & Carnaval, A. C. Variable responses of skinks to a common history of rainforest fluctuation: concordance between phylogeography and palaeo-distribution models. Mol. Ecol. 18, 483–499 (2009).
PubMed  Article  PubMed Central  Google Scholar 

19.
Afonso Silva, A. C. et al. Tropical specialist vs. climate generalist: Diversification and demographic history of sister species of Carlia skinks from northwestern Australia. Mol. Ecol. 26, 4045–4058 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Logan, M. L., Huynh, R. K., Precious, R. A. & Calsbeek, R. G. The impact of climate change measured at relevant spatial scales: new hope for tropical lizards. Glob. Chang. Biol. 19, 3093–3102 (2013).
ADS  PubMed  Article  PubMed Central  Google Scholar 

21.
Moritz, C. et al. Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science 322, 261–264 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Kamilar, J. M. & Muldoon, K. M. The climatic niche diversity of malagasy primates: a phylogenetic perspective. PLoS ONE 5, e11073 (2010).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

23.
Braz, A. G., Lorini, M. L. & Vale, M. M. Climate change is likely to affect the distribution but not parapatry of the Brazilian marmoset monkeys (Callithrix spp.). Div. Dist. 25, 536–550 (2018).
Article  Google Scholar 

24.
Cooper, N., Freckleton, R. P. & Jetz, W. Phylogenetic conservatism of environmental niches in mammals. Proc. Biol. Sci. 278, 2384–2391 (2011).
PubMed  PubMed Central  Google Scholar 

25.
Lyu, Y., Wang, X. & Luo, J. Geographic patterns of insect diversity across China’s nature reserves: the roles of niche conservatism and range overlapping. Ecol. Evol. 10, 3305–3317 (2020).
PubMed  Article  PubMed Central  Google Scholar 

26.
Hiller, A. E. et al. Niche conservatism predominates in adaptive radiation: comparing the diversification of Hawaiian arthropods using ecological niche modelling. Biol. J. Linn. Soc. Lond. 127, 479–492 (2019).
Article  Google Scholar 

27.
Kabir, M. et al. Habitat suitability and movement corridors of grey wolf (Canis lupus) in Northern Pakistan. PLoS ONE 12, e0187027 (2017).
PubMed  Article  CAS  PubMed Central  Google Scholar 

28.
Amano, T. & Sutherland, W. J. Four barriers to the global understanding of biodiversity conservation: wealth, language, geographical location and security. Proc. Biol. Sci. 280, 20122649 (2013).
PubMed  PubMed Central  Google Scholar 

29.
Bellard, C. et al. Vulnerability of biodiversity hotspots to global change. Glob. Ecol. Biogeogr. 23, 1376–1386 (2014).
Article  Google Scholar 

30.
Molotoks, A. et al. Global projections of future cropland expansion to 2050 and direct impacts on biodiversity and carbon storage. Glob. Chang. Biol. 24, 5895–5908 (2018).
PubMed  Article  PubMed Central  Google Scholar 

31.
Mittermeier, R. A., Turner, W. R., Larsen, F. W., Brooks, T. M. & Gascon, C. Global biodiversity conservation: the critical role of hotspots. Biodivers. Hotspots https://doi.org/10.1007/978-3-642-20992-5_1 (2011).
Article  Google Scholar 

32.
Johnson, W. E. et al. The late Miocene radiation of modern Felidae: a genetic assessment. Science 311, 73–77 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Tamma, K., Marathe, A. & Ramakrishnan, U. Past influences present: mammalian species from different biogeographic pools sort environmentally in the Indian subcontinent. Front. Biogeogr. 8 (2016).

34.
Mukherjee, S., Duckworth, J. W., Silva, A., Appel, A. & Kittle, A. Prionailurus rubiginosus. IUCN Red List Threat. Species https://doi.org/10.2305/IUCN.UK.2016-1.RLTS.T18149A50662471.en (2016).
Article  Google Scholar 

35.
Mukherjee, S. et al. Prionailurus viverrinus. IUCN Red List Threat. Species https://doi.org/10.2305/IUCN.UK.2016-2.RLTS.T18150A50662615.en (2016).
Article  Google Scholar 

36.
Ross, J. et al. Prionailurus bengalensis. IUCN Red List Threat. Species https://doi.org/10.2305/IUCN.UK.2015-4.RLTS.T18146A50661611.en (2015).
Article  Google Scholar 

37.
Nowell, K. & Jackson, P. Wild cats: status survey and conservation action plan ((IUCN, Gland, Switzerland, 1996).

38.
Sunquist, M. & Sunquist, F. Wild Cats of the World (University of Chicago Press, Chicago, 2012).
Google Scholar 

39.
Pocock, R. I. The Fauna of British India Including Ceylon and Burma Vol. 1 (Taylor And Francis Ltd, London, 1939).
Google Scholar 

40.
Mukherjee, S. et al. Ecology driving genetic variation: a comparative phylogeography of jungle cat (Felis chaus) and leopard cat (Prionailurus bengalensis) in India. PLoS ONE 5, e13724 (2010).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

41.
Gray, T. N. E. et al. Felis chaus. IUCN Red List Threat. Species https://doi.org/10.2305/IUCN.UK.2016-2.RLTS.T8540A50651463.en (2016).
Article  Google Scholar 

42.
Boitani, L. et al. What spatial data do we need to develop global mammal conservation strategies?. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 2623–2632 (2011).
PubMed  Article  PubMed Central  Google Scholar 

43.
Bartholomé, E. & Belward, A. S. GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26, 1959–1977 (2005).
ADS  Article  Google Scholar 

44.
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: species distribution modeling (2011).

45.
Grassman, L. I. Jr., Tewes, M. E., Silvy, N. J. & Kreetiyutanont, K. Spatial organization and diet of the leopard cat (Prionailurus bengalensis) in north-central Thailand. J. Zool. 266, 45–54 (2005).
Article  Google Scholar 

46.
Thatte, P. et al. Human footprint differentially impacts genetic connectivity of four wide-ranging mammals in a fragmented landscape. Divers. Distrib. 7, 247 (2019).
Google Scholar 

47.
Kalle, R., Ramesh, T., Qureshi, Q. & Sankar, K. Predicting the distribution pattern of small carnivores in response to environmental factors in the Western Ghats. PLoS ONE 8, e79295 (2013).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

48.
Wilting, A. et al. Modelling the species distribution of flat-headed cats (Prionailurus planiceps), an endangered South-East Asian small felid. PLoS ONE 5, e9612 (2010).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

49.
Srivathsa, A., Parameshwaran, R., Sharma, S. & Ullas Karanth, K. Estimating population sizes of leopard cats in the Western Ghats using camera surveys. J. Mammal. 96, 742–750 (2015).
Article  Google Scholar 

50.
Bashir, T., Bhattacharya, T., Poudyal, K., Sathyakumar, S. & Qureshi, Q. Integrating aspects of ecology and predictive modelling: implications for the conservation of the leopard cat (Prionailurus bengalensis) in the Eastern Himalaya. Acta Theriol. 59, 35–47 (2014).
Article  Google Scholar 

51.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Article  Google Scholar 

52.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Article  Google Scholar 

53.
Mayaux, P. et al. Validation of the global land cover 2000 map. IEEE Trans. Geosci. Remote Sens. 44, 1728–1739 (2006).
ADS  Article  Google Scholar 

54.
Hijmans, R. J. raster: geographic data analysis and modelling (2014).

55.
Mukherjee, S., Goyal, S. P., Johnsingh, A. J. T. & Leite, M. R. The importance of rodents in the diet of jungle cat (Felis chaus), caracal (Caracal caracal) and golden jackal (Canis aureus) in Sariska Tiger Reserve, Rajasthan, India. J. Zool. 262, 405–411 (2004).
Article  Google Scholar 

56.
Shehzad, W. et al. Carnivore diet analysis based on next-generation sequencing: application to the leopard cat (Prionailurus bengalensis) in Pakistan. Mol. Ecol. 21, 1951–1965 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

57.
Rajaratnam, R., Sunquist, M., Rajaratnam, L. & Ambu, L. Diet and habitat selection of the leopard cat (Prionailurus bengalensis borneoensis) in an agricultural landscape in Sabah, Malaysian Borneo. J. Trop. Ecol. 23, 209–217 (2007).
Article  Google Scholar 

58.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).
Article  Google Scholar 

59.
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).
PubMed  Article  PubMed Central  Google Scholar 

60.
Anderson, R. P. & Gonzalez, I. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecol. Modell. 222, 2796–2811 (2011).
Article  Google Scholar 

61.
Kramer-Schadt, S. et al. The importance of correcting for sampling bias in MaxEnt species distribution models. Divers. Distrib. 19, 1366–1379 (2013).
Article  Google Scholar 

62.
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Article  Google Scholar 

63.
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2006).
Article  Google Scholar 

64.
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).
Article  Google Scholar 

65.
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many?: How to use pseudo-absences in niche modelling?. Methods Ecol. Evol. 3, 327–338 (2012).
Article  Google Scholar 

66.
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Div. Dist. 17, 43–57 (2011).
Article  Google Scholar 

67.
Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
Article  Google Scholar 

68.
Galante, P. J. et al. The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity. Ecography 41, 726–736 (2017).
Article  Google Scholar 

69.
Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Modell. 133, 225–245 (2000).
Article  Google Scholar 

70.
Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).
PubMed  Article  Google Scholar 

71.
Schoener, T. W. The anolis lizards of Bimini: resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).
Article  Google Scholar 

72.
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).
PubMed  Article  Google Scholar 

73.
Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).
Article  Google Scholar 

74.
Swets, J. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).
ADS  MathSciNet  CAS  PubMed  MATH  Article  PubMed Central  Google Scholar 

75.
Ferro, C. A. T. & Stephenson, D. B. Extremal dependence indices: improved verification measures for deterministic forecasts of rare binary events. Weather Forecast. 26, 699–713 (2011).
ADS  Article  Google Scholar 

76.
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2011).
Article  Google Scholar 

77.
Di Cola, V. et al. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).
Article  Google Scholar 

78.
UNEP-WCMC & IUCN. Protected Planet:The World Database on Protected Areas. Protected Planethttps://www.protectedplanet.net (2018).

79.
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, 2018).
Google Scholar 

80.
Otto-Bliesner, B. L., Marshall, S. J., Overpeck, J. T., Miller, G. H. & Hu, A. Simulating Arctic climate warmth and icefield retreat in the last interglaciation. Science 311, 1751–1753 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Wilting, A. et al. Evolutionary history and conservation significance of the Javan leopard Panthera pardus melas. J. Zool. 299, 239–250 (2016).
Article  Google Scholar 

82.
Cooper, D. M. et al. Predicted pleistocene-holocene range shifts of the tiger (Panthera tigris). Divers. Distrib. 22, 1199–1211 (2016).
Article  Google Scholar 

83.
McSweeney, C. F., Jones, R. G., Lee, R. W. & Rowell, D. P. Selecting CMIP5 GCMs for downscaling over multiple regions. Clim. Dyn. 44, 3237–3260 (2014).
Article  Google Scholar 

84.
Nogués-Bravo, D. Predicting the past distribution of species climatic niches. Glob. Ecol. Biogeogr. 18, 521–531 (2009).
Article  Google Scholar 

85.
Rowan, J. et al. Geographically divergent evolutionary and ecological legacies shape mammal biodiversity in the global tropics and subtropics. Proc. Natl. Acad. Sci. USA 117, 1559–1565 (2020).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

86.
Clavel, J., Julliard, R. & Devictor, V. Worldwide decline of specialist species: toward a global functional homogenization?. Front. Ecol. Environ. 9, 222–228 (2011).
Article  Google Scholar 

87.
Hof, A. R., Jansson, R. & Nilsson, C. Future climate change will favour non-specialist mammals in the (sub)arctics. PLoS ONE 7, e52574 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

88.
Davey, C. M., Chamberlain, D. E., Newson, S. E., Noble, D. G. & Johnston, A. Rise of the generalists: evidence for climate driven homogenization in avian communities. Glob. Ecol. Biogeogr. 21, 568–578 (2011).
Article  Google Scholar 

89.
Pradervand, J.-N., Pellissier, L., Randin, C. F. & Guisan, A. Functional homogenization of bumblebee communities in alpine landscapes under projected climate change. Clim. Change Responses 1, 1 (2014).
Article  Google Scholar 

90.
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Ecology. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

91.
Huey, R. B. et al. Why tropical forest lizards are vulnerable to climate warming. Proc. Biol. Sci. 276, 1939–1948 (2009).
PubMed  PubMed Central  Google Scholar 

92.
Araújo, M. B. et al. Quaternary climate changes explain diversity among reptiles and amphibians. Ecography 31, 8–15 (2008).
Article  Google Scholar 

93.
Fordham, D. A., Saltré, F., Brown, S. C., Mellin, C. & Wigley, T. M. L. Why decadal to century timescale palaeoclimate data are needed to explain present-day patterns of biological diversity and change. Glob. Chang. Biol. 24, 1371–1381 (2018).
ADS  PubMed  Article  PubMed Central  Google Scholar 

94.
Fordham, D. A. et al. PaleoView: a tool for generating continuous climate projections spanning the last 21 000 years at regional and global scales. Ecography 40, 1348–1358 (2017).
Article  Google Scholar 

95.
Andam, K. S., Ferraro, P. J., Pfaff, A., Sanchez-Azofeifa, G. A. & Robalino, J. A. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl. Acad. Sci. USA 105, 16089–16094 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

96.
Pressey, R. L. et al. How well protected are the forests of north-eastern New South Wales? Analyses of forest environments in relation to formal protection measures, land tenure, and vulnerability to clearing. For. Ecol. Manag. 85, 311–333 (1996).
Article  Google Scholar 

97.
Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

98.
Connor, T. et al. Effects of grain size and niche breadth on species distribution modeling. Ecography 41, 1270–1282 (2017).
Article  Google Scholar 

99.
Seo, C., Thorne, J. H., Hannah, L. & Thuiller, W. Scale effects in species distribution models: implications for conservation planning under climate change. Biol. Lett. 5, 39–43 (2009).
PubMed  Article  PubMed Central  Google Scholar 

100.
Latinne, A. et al. Influence of past and future climate changes on the distribution of three Southeast Asian murine rodents. J. Biogeogr. 42, 1714–1726 (2015).
Article  Google Scholar 

101.
Radchuk, V., Kramer-Schadt, S., Fickel, J. & Wilting, A. Distributions of mammals in Southeast Asia: the role of the legacy of climate and species body mass. J. Biogeogr. https://doi.org/10.1111/jbi.13675 (2019).
Article  Google Scholar 

102.
Patel, R. P. et al. Genetic structure and phylogeography of the leopard cat (Prionailurus bengalensis) inferred from mitochondrial genomes. J. Hered. 108, 349–360 (2017).
PubMed  Article  PubMed Central  Google Scholar 

103.
Sreehari, R. & Nameer, P. O. Small carnivores of Parambikulam Tiger Reserve, southern Western Ghats, India. J. Threat. Taxa 8, 9306 (2016).
Article  Google Scholar 

104.
Past Interglacials Working Group of PAGES. Interglacials of the last 800,000 years. Rev. Geophys. 54, 162–219 (2016).
ADS  Article  Google Scholar 

105.
Luo, S.-J. et al. Sympatric Asian felid phylogeography reveals a major Indochinese-Sundaic divergence. Mol. Ecol. 23, 2072–2092 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

106.
Mukherjee, S., Adhya, T., Thatte, P. & Ramakrishnan, U. Survey of the fishing cat prionailurus viverrinus Bennett, 1833 (Carnivora: Felidae) and some aspects impacting its conservation in India. J. Threat. Taxa 04, 3355–3361 (2012).
Article  Google Scholar 

107.
Shekhar Palei, H., Palei, H. S., Das, U. P. & Debata, S. The vulnerable fishing cat Prionailurus viverrinus in Odisha, eastern India: status and conservation implications. Zool. Ecol. 28, 69–74 (2018).
Article  Google Scholar 

108.
Nayak, S., Shah, S. & Borah, J. First record of rusty-spotted cat Prionailurus rubiginosus (Mammalia: Carnivora: Felidae) from Ramgarh-Vishdhari Wildlife Sanctuary in semi-arid landscape of Rajasthan, India. J. Threat. Taxa 9, 9761 (2017).
Article  Google Scholar 

109.
Lamichhane, B. R. et al. Rusty-spotted cat: 12th cat species discovered in Western Terai of Nepal. Cat News 64, 30–32 (2016).
Google Scholar 

110.
Anwar, M. & Vattakavan, J. Rusty spotted cat in Katerniaghat Wildlife Sanctuary, Uttar Pradesh State, India. Cat News 56, 12–13 (2012).
Google Scholar 

111.
Harihar, A., Chanchani, P., Pariwakam, M., Noon, B. R. & Goodrich, J. Defensible inference: questioning global trends in tiger populations. Conserv. Lett. 10, 502–505 (2017).
Article  Google Scholar 

112.
Mantyka-Pringle, C. S. et al. Climate change modifies risk of global biodiversity loss due to land-cover change. Biol. Conserv. 187, 103–111 (2015).
Article  Google Scholar 

113.
Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. Biol. Sci.285 (2018).

114.
Prestele, R. et al. Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison. Glob. Chang. Biol. 22, 3967–3983 (2016).
ADS  PubMed  Article  PubMed Central  Google Scholar  More

1.
Brown, W. L. & Wilson, E. O. Character displacement. Syst. Zool. 5, 49 (1956).
Article  Google Scholar 
2.
Jeffries, M. & Lawton, J. Enemy free space and the structure of ecological communities. Biol. J. Linn. Soc. 23, 269–286 (1984).
Article  Google Scholar 

3.
Reynolds, J. D. Crayfish extinctions and crayfish plague in central Ireland. Biol. Conserv. 45, 279–285 (1988).
Article  Google Scholar 

4.
Stephen, W. P. Solitary bees in North American agriculture: A perspective. In For non-native crops, whence pollinators of the future (eds Strickler, K. & Cane, J. H.) 41–66 (Entomological Society of America, 2003).

5.
Goulson, D. & Hanley, M. E. Distribution and forage use of exotic bumblebees in South Island New Zealand. N. Z. J. Ecol. 28, 225–232 (2004).
Google Scholar 

6.
Morales, C. L. & Aizen, M. A. Invasive mutualisms and the structure of plant–pollinator interactions in the temperate forests of north-west Patagonia. Argentina. J. Ecol. 94, 171–180 (2006).
Article  Google Scholar 

7.
Vergara, C. H. Environmental impact of exotic bees introduced for crop pollination. In Bee Pollination in Agricultural Ecosystems (eds James, R. R. & Pitts-Singer, T. L.) 145–165 (Oxford University Press, Oxford, 2008).
Google Scholar 

8.
Roberts, R. B. The nesting biology, behavior and immature stages of Lithurge chrysurus, an adventitious wood-boring bee in New Jersey (Hymenoptera: Megachilidae). J. Kans. Entomol. Soc. 51, 735–745 (1978).
Google Scholar 

9.
Mangum, W. A. & Brooks, R. W. First records of Megachile (Callomegachile) sculpturalis Smith (Hymenoptera: Megachilidae) in the Continental United States. J. Kans. Entomol. Soc. 70, 140–142 (1997).
Google Scholar 

10.
Russo, L. Positive and negative impacts of non-native bee species around the world. Insects 7, 69 (2016).
PubMed Central  Article  Google Scholar 

11.
Goulson, D. Effects of introduced bees on native ecosystems. Annu. Rev. Ecol. Evol. Syst. 34, 1–26 (2003).
Article  Google Scholar 

12.
Inoue, M. N., Yokoyama, J. & Washitani, I. Displacement of Japanese native bumblebees by the recently introduced Bombus terrestris (L.) (Hymenoptera: Apidae). J. Insect Conserv. 12, 135–146 (2008).
Article  Google Scholar 

13.
Morales, C. L., Arbetman, M. P., Cameron, S. A. & Aizen, M. A. Rapid ecological replacement of a native bumble bee by invasive species. Front. Ecol. Environ. 11, 529–534 (2013).
Article  Google Scholar 

14.
Schmid-Hempel, R. et al. The invasion of southern South America by imported bumblebees and associated parasites. J. Anim. Ecol. 83, 823–837 (2014).
PubMed  Article  PubMed Central  Google Scholar 

15.
Cane, J. H. Exotic non-social bees (Hymenoptera: Apoidea) in North America: Ecological implications. In For non-native crops, whence pollinators of the future (eds Strickler, K. & Cane, J. H.) 113–126 (Entomological Society of America, 2003).

16.
Paini, D. R. Impact of the introduced honey bee (Apis mellifera) (Hymenoptera: Apidae) on native bees: A review. Austral Ecol. 29, 399–407 (2004).
Article  Google Scholar 

17.
Mallinger, R. E., Gaines-Day, H. R. & Gratton, C. Do managed bees have negative effects on wild bees? A systematic review of the literature. PLoS ONE 12, e0189268 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

18.
Ascher, J. S. & Pickering, J. Apoidea species—identification guide—Discover Life. https://www.discoverlife.org/mp/20q?guide=Apoidea_species&flags=HAS: (2020).

19.
Batra, S. Osmia cornifrons and Pithitis smaragdula, two Asian bees introduced into the United States for crop pollination. in Proceedings 4th International Symposium on Pollination. Maryland Agricultural Experimental Station Miscellaneous Publication (1978).

20.
Droege, S. USGS PWRC – Native Bee Inventory and Monitoring Lab (BIML). https://doi.org/10.15468/6AUTVB (2020).

21.
Cane, J. H., Griswold, T. & Parker, F. D. Substrates and materials used for nesting by North American Osmia bees (Hymenoptera: Apiformes: Megachilidae). Ann. Entomol. Soc. Am. 100, 350–358 (2007).
Article  Google Scholar 

22.
Droege, S., Engler, J., Sellers, E. & O’Brien, L. National Protocol Framework for the Inventory and Monitoring of Bees (U.S. Fish and Wildlife Service, Washington, D.C., 2016).
Google Scholar 

23.
LeBuhn, G., Droege, S., Connor, E., Gemmill-Herren, B. & Azzu, N. Protocol to Detect and Monitor Pollinator Communities: Guidance for Practitioners (Food and Agriculture Organization of the United Nations, Rome, 2016).
Google Scholar 

24.
Droege, S. Impact of color and size of bowl trap on numbers of bees captured. J. Insect Conserv. https://doi.org/10.1007/s10841-016-9914-6 (2006).
Article  Google Scholar 

25.
Gonzalez, V. H. et al. Effect of pan trap size on the diversity of sampled bees and abundance of bycatch. J. Insect Conserv. https://doi.org/10.1007/s10841-020-00224-4 (2020).
Article  Google Scholar 

26.
Wilson, J. S. et al. Sampling bee communities using pan traps: Alternative methods increase sample size. J. Insect Conserv. 20, 919–922 (2016).
Article  Google Scholar 

27.
Westphal, C. et al. Measuring bee diversity in different European habitats and biogeographical regions. Ecol. Monogr. 78, 653–671 (2008).
Article  Google Scholar 

28.
Greenleaf, S. S., Williams, N. M., Winfree, R. & Kremen, C. Bee foraging ranges and their relationship to body size. Oecologia 153, 589–596 (2007).
ADS  PubMed  Article  PubMed Central  Google Scholar 

29.
Frey, B. J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).
ADS  MathSciNet  CAS  PubMed  MATH  Article  PubMed Central  Google Scholar 

30.
Shapiro, L. H., Tepedino, V. J. & Minckley, R. L. Bowling for bees: optimal sample number for “bee bowl” sampling transects. J. Insect Conserv. 18, 1105–1113 (2014).
Article  Google Scholar 

31.
Joe, H. & Zhu, R. Generalized poisson distribution: The property of mixture of poisson and comparison with negative binomial distribution. Biom. J. 47, 219–229 (2005).
MathSciNet  PubMed  MATH  Article  PubMed Central  Google Scholar 

32.
Didham, R. K. et al. Interpreting insect declines: Seven challenges and a way forward. Insect Conserv. Divers. 13, 103–114 (2020).
Article  Google Scholar 

33.
Maeta, Y. Comparative studies on the biology of bees of the genus Osmia of Japan, with special reference to their management for pollinations of crops (Hymenoptera: Megachilidae). Bull. Tohoku Nat. Agric. Exp. Stn. 57, 1–221 (1978).
Google Scholar 

34.
Bosch, J. & Kemp, W. P. How to Manage the Blue Orchard Bee: As an Orchard Pollinator (Sustainable Agriculture Network, San José, 2001).
Google Scholar 

35.
Kraemer, M. E., Favi, F. D. & Niedziela, C. E. Nesting and pollen preference of Osmia lignaria lignaria (Hymenoptera: Megachilidae) in Virginia and North Carolina orchards. Environ. Entomol. 43, 932–941 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Tompkins, D. M., White, A. R. & Boots, M. Ecological replacement of native red squirrels by invasive greys driven by disease. Ecol. Lett. 6, 189–196 (2003).
Article  Google Scholar 

37.
Prenter, J., MacNeil, C., Dick, J. T. A. & Dunn, A. M. Roles of parasites in animal invasions. Trends Ecol. Evol. 19, 385–390 (2004).
PubMed  Article  PubMed Central  Google Scholar 

38.
Stephen, W. P., Vandenberg, J. D. & Fichter, B. L. Etiology and epizootiology of chalkbrood in the alfalfa leafcutting bee, Megachile rotundata, with notes on Ascosphaera species. Oregon State Univ. Agric. Exp. Stn. Bull. 653, 1–10 (1981).
Google Scholar 

39.
Hedtke, S. M., Blitzer, E. J., Montgomery, G. A. & Danforth, B. N. Introduction of non-native pollinators can lead to trans-continental movement of bee-associated fungi. PLoS ONE 10, e0130560 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

40.
Klinger, E. G. Virulence Evolution of Fungal Pathogens in Social and Solitary Bees with an Emphasis on Multiple Infections. (Utah State University, Logan 2015).
Google Scholar 

41.
Kamijo, K. A revision of the species of the Monodontomerinae occurring in Japan (Hymenoptera: Chlacidoidea) [Taxonomic Studies on the Torymidae of Japan, 2]. Insecta Matsumurana 26, 89–98 (1963).
Google Scholar 

42.
Kamijo, K. Description of five new species of Eulophinae from Japan and other notes (Hymenoptera: Chalcidoidea). Insecta Matsumurana 28, 69–78 (1965).
Google Scholar 

43.
Grissell, E. Discovery of Monodontomerus osmiae Kamijo (Hymenoptera: Torymidae) in the New World. Proc. Entomol. Soc. Wash. 105, 243–245 (2003).
Google Scholar 

44.
Majka, C. G., Philips, T. K. & Sheffield, C. Ptinus sexpunctatus Panzer (Coleoptera: Anobiidae, Ptininae) newly recorded in North America. Entomol. News 118, 73–76 (2007).
Article  Google Scholar 

45.
Torchin, M. E. & Mitchell, C. E. Parasites, pathogens, and invasions by plants and animals. Front. Ecol. Environ. 2, 183–190 (2004).
Article  Google Scholar 

46.
MacIvor, J. S. & Packer, L. ‘Bee hotels’ as tools for native pollinator conservation: A premature verdict? PLoS ONE 10, e0122126 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

47.
Park, Y. L. et al. Nest-to-nest dispersal of Chaetodactylus krombeini (Acari, Chaetodactylidae) associated with Osmia cornifrons (Hym., Megachilidae). J. Appl. Entomol. 133, 174–180 (2009).
Article  Google Scholar 

48.
Maeta, Y. & Kitamura, T. Studies on the apple pollination by Osmia. II. Characteristics and underlying problems in utilizing Osmia. Kontyu 33, 17–34 (1965).

49.
Kobayashi, M. Problems in the utilisation of Eristalis cerealis as pollinator. Shokubutsu Boeki 26, 473–478 (1972).
Google Scholar 

50.
Biddinger, D. J. et al. Development of the mason bee, Osmia cornifrons, as an alternative pollinator to honey bees and as a targeted delivery system for biocontrol agents in the management of fire blight. Penn Fruit News 90, 35–44 (2009).
Google Scholar 

51.
West, T. P. & McCutcheon, T. W. Evaluating Osmia cornifrons as pollinators of highbush blueberry. Int. J. Fruit Sci. 9, 115–125 (2009).
Article  Google Scholar 

52.
Portman, Z. M., Bruninga-Socolar, B. & Cariveau, D. P. The state of bee monitoring in the United States: a call to refocus away from bowl traps and towards more effective methods. Ann. Entomol. Soc. Am. https://doi.org/10.1093/aesa/saaa010 (2020).
Article  Google Scholar  More