More stories

  • in

    Habitat partitioning, co-occurrence patterns, and mixed-species group formation in sympatric delphinids

    Pianka, E. R. Niche overlap and diffuse competition. Proc. Natl. Acad. Sci. 71, 2141–2145 (1974).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

    Google Scholar 
    Tokeshi, M. Species Coexistence: Ecological and Evolutionary Perspectives. (Wiley-Blackwell, 2009).Grinnell, J. Geography and evolution. Ecology 5, 225–229 (1924).Article 

    Google Scholar 
    Roughgarden, J. Resource partitioning among competing species—A coevolutionary approach. Theor. Popul. Biol. 9, 388–424 (1976).Article 
    MathSciNet 
    CAS 
    PubMed 
    MATH 

    Google Scholar 
    Syme, J., Kiszka, J. J. & Parra, G. J. Dynamics of cetacean mixed-species groups: A review and conceptual framework for assessing their functional significance. Front. Mar. Sci. 8, 1–19 (2021).Article 

    Google Scholar 
    Stensland, E., Angerbjörn, A. & Berggren, P. Mixed species groups in mammals. Mamm. Rev. 33, 205–223 (2003).Article 

    Google Scholar 
    Cords, M. & Würsig, B. A Mix of Species: Associations of Heterospecifics Among Primates and Dolphins. in Primates and Cetaceans: Field Research and Conservation of Complex Mammalian Societies (eds. Yamagiwa, J. & Karczmarski, L.) 409–431 (Springer, 2014). doi:https://doi.org/10.1007/978-4-431-54523-1_21.Goodale, E., Beauchamp, G. & Ruxton, G. D. Mixed-Species Groups of Animals: Behavior, Community Structure, and Conservation. (Academic Press, 2017).Krause, J. & Ruxton, G. D. Living in Groups. Oxford Series in Ecology and Evolution (Oxford University Press, 2002).Heymann, E. W. & Buchanan-Smith, H. M. The behavioural ecology of mixed-species troops of callitrichine primates. Biol. Rev. 75, 169–190 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sridhar, H. & Guttal, V. Friendship across species borders: factors that facilitate and constrain heterospecific sociality. Philos. Trans. R. Soc. B Biol. Sci. 373, 1–9 (2018).Greenberg, R. Birds of many feathers: The formation and structure of mixed-species flocks of forest birds. in On the Move: How and Why Animals Travel in groups (eds. Boinski, S. & Gerber, P. A.) 521–558 (University of Chicago Press, 2000).Waser, P. M. ‘Chance’ and mixed-species associations. Behav. Ecol. Sociobiol. 15, 197–202 (1984).Article 

    Google Scholar 
    Whitesides, G. H. Interspecific associations of Diana monkeys, Cercopithecus diana, in Sierra Leone, West Africa: biological significance or chance?. Anim. Behav. 37, 760–776 (1989).Article 

    Google Scholar 
    Waser, P. M. Primate polyspecific associations: Do they occur by chance?. Anim. Behav. 30, 1–8 (1982).Article 

    Google Scholar 
    Alexander, R. D. The evolution of social behavior. Annu. Rev. Ecol. Syst. 5, 325–383 (1974).Article 

    Google Scholar 
    Kasozi, H. & Montgomery, R. A. Variability in the estimation of ungulate group sizes complicates ecological inference. Ecol. Evol. 10, 6881–6889 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Syme, J., Kiszka, J. J. & Parra, G. J. How to define a dolphin ‘group’? Need for consistency and justification based on objective criteria. Ecol. Evol. 12, 1–18 (2022).Article 

    Google Scholar 
    Hutchinson, J. M. C. & Waser, P. M. Use, misuse and extensions of ‘ideal gas’ models of animal encounter. Biol. Rev. 82, 335–359 (2007).Article 
    PubMed 

    Google Scholar 
    Gotelli, N. J. Null model analysis of species co-occurrence patterns. Ecology 81, 2606–2621 (2000).Article 

    Google Scholar 
    Astaras, C., Krause, S., Mattner, L., Rehse, C. & Waltert, M. Associations between the drill (Mandrillus leucophaeus) and sympatric monkeys in Korup National Park. Cameroon. Am. J. Primatol. 73, 127–134 (2011).Article 
    PubMed 

    Google Scholar 
    Mammides, C., Chen, J., Goodale, U. M., Kotagama, S. W. & Goodale, E. Measurement of species associations in mixed-species bird flocks across environmental and human disturbance gradients. Ecosphere 9, 1–14 (2018).Article 

    Google Scholar 
    Ovaskainen, O., Abrego, N., Halme, P. & Dunson, D. Using latent variable models to identify large networks of species-to-species associations at different spatial scales. Methods Ecol. Evol. 7, 549–555 (2016).Article 

    Google Scholar 
    Pollock, L. J. et al. Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods Ecol. Evol. 5, 397–406 (2014).Article 

    Google Scholar 
    Warton, D. I. et al. So Many variables: Joint modeling in community ecology. Trends Ecol. Evol. 30, 766–779 (2015).Article 
    PubMed 

    Google Scholar 
    Ovaskainen, O. et al. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20, 561–576 (2017).Article 
    PubMed 

    Google Scholar 
    Ovaskainen, O. & Abrego, N. Joint Species Distribution Modelling. (Cambridge University Press, 2020). https://doi.org/10.1017/9781108591720.Blanchet, F. G., Cazelles, K. & Gravel, D. Co-occurrence is not evidence of ecological interactions. Ecol. Lett. 23, 1050–1063 (2020).Article 
    PubMed 

    Google Scholar 
    Haak, C. R., Hui, F. K., Cowles, G. W. & Danylchuk, A. J. Positive interspecific associations consistent with social information use shape juvenile fish assemblages. Ecology 101, 1–16 (2020).Article 

    Google Scholar 
    Bastianelli, G., Wintle, B. A., Martin, E. H., Seoane, J. & Laiolo, P. Species partitioning in a temperate mountain chain: Segregation by habitat vs. interspecific competition. Ecol. Evol. 7, 2685–2696 (2017).Aspin, T. & House, A. Alpha and beta diversity and species co-occurrence patterns in headwaters supporting rare intermittent-stream specialists. Freshw. Biol. n/a, (2022).Astarloa, A. et al. Identifying main interactions in marine predator-prey networks of the Bay of Biscay. ICES J. Mar. Sci. 76, 2247–2259 (2019).Article 

    Google Scholar 
    Parra, G. J. Resource partitioning in sympatric delphinids: space use and habitat preferences of Australian snubfin and Indo-Pacific humpback dolphins. J. Anim. Ecol. 75, 862–874 (2006).Article 
    PubMed 

    Google Scholar 
    Parra, G. J., Wojtkowiak, Z., Peters, K. J. & Cagnazzi, D. Isotopic niche overlap between sympatric Australian snubfin and humpback dolphins. Ecol. Evol. 12, 1–11 (2022).Article 

    Google Scholar 
    Kiszka, J. J. et al. Ecological niche segregation within a community of sympatric dolphins around a tropical island. Mar. Ecol. Prog. Ser. 433, 273–288 (2011).Article 
    ADS 

    Google Scholar 
    Bearzi, M. Dolphin sympatric ecology. Mar. Biol. Res. 1, 165–175 (2005).Article 

    Google Scholar 
    Zaeschmar, J. R. et al. Occurrence of false killer whales (Pseudorca crassidens) and their association with common bottlenose dolphins (Tursiops truncatus) off northeastern New Zealand. Mar. Mammal Sci. 30, 594–608 (2014).Article 

    Google Scholar 
    Elliser, C. R. & Herzing, D. L. Long-term interspecies association patterns of Atlantic bottlenose dolphins, Tursiops truncatus, and Atlantic spotted dolphins, Stenella frontalis, in the Bahamas. Mar. Mammal Sci. 32, 38–56 (2016).Article 

    Google Scholar 
    Kiszka, J. J., Perrin, W. F., Pusineri, C. & Ridoux, V. What drives island-associated tropical dolphins to form mixed-species associations in the southwest Indian Ocean?. J. Mammal. 92, 1105–1111 (2011).Article 

    Google Scholar 
    Brown, A. M., Bejder, L., Cagnazzi, D., Parra, G. J. & Allen, S. J. The north west cape, Western Australia: A potential hotspot for Indo-Pacific humpback dolphins Sousa chinensis?. Pacific Conserv. Biol. 18, 240–246 (2012).Article 

    Google Scholar 
    Allen, S. J., Cagnazzi, D., Hodgson, A. J., Loneragan, N. R. & Bejder, L. Tropical inshore dolphins of north-western Australia: Unknown populations in a rapidly changing region. Pacific Conserv. Biol. 18, 56–63 (2012).Article 

    Google Scholar 
    Palmer, C., Parra, G. J., Rogers, T. & Woinarski, J. Collation and review of sightings and distribution of three coastal dolphin species in waters of the Northern Territory. Australia. Pacific Conserv. Biol. 20, 116–125 (2014).Article 

    Google Scholar 
    Corkeron, P. J. Aspects of the Behavioral Ecology of Inshore Dolphins Tursiops truncatus and Sousa chinensis in Moreton Bay, Australia. in The Bottlenose Dolphin (eds. Leatherwood, S. & Reeves, R.) 285–293 (Elsevier, 1990). https://doi.org/10.1016/B978-0-12-440280-5.50018-4.Haughey, R. et al. Distribution and habitat preferences of Indo-Pacific Bottlenose Dolphins (Tursiops aduncus) inhabiting coastal waters with mixed levels of protection. Front. Mar. Sci. 8, 1–20 (2021).Article 

    Google Scholar 
    Hanf, D., Hodgson, A. J., Kobryn, H., Bejder, L. & Smith, J. N. Dolphin distribution and habitat suitability in North Western Australia: Applications and Implications of a Broad-Scale, Non-targeted Dataset. Front. Mar. Sci. 8, 1–18 (2022).Article 

    Google Scholar 
    Hunt, T. N., Allen, S. J., Bejder, L. & Parra, G. J. Identifying priority habitat for conservation and management of Australian humpback dolphins within a marine protected area. Sci. Rep. 10, 1–14 (2020).Article 

    Google Scholar 
    Hunt, T. N. Demography, habitat use and social structure of Australian humpback dolphins (Sousa sahulensis) around the North West Cape, Western Australia: Implications for conservation and management. PhD Thesis, Flinders University, Adelaide, Australia. (Flinders University, 2018).Cassata, L. & Collins, L. B. Coral reef communities, habitats, and substrates in and near sanctuary zones of Ningaloo marine park. J. Coast. Res. 241, 139–151 (2008).Article 

    Google Scholar 
    CALM MPRA. Management plan for the Ningaloo Marine Park and Muiron Islands Marine Management Area 2005–2015. (2005).Hunt, T. N. et al. Demographic characteristics of Australian humpback dolphins reveal important habitat toward the southwestern limit of their range. Endanger. Species Res. 32, 71–88 (2017).Article 

    Google Scholar 
    Mann, J. Behavioral sampling methods for cetaceans: A review and critique. Mar. Mammal Sci. 15, 102–122 (1999).Article 

    Google Scholar 
    Python Software Foundation. Python Language Reference, version 3.8.0. at https://www.python.org/ (2016).QGIS Development Team. QGIS Geographic Information System, version 3.8.3 Zanzibar. at http://qgis.osgeo.org (2019).Zanardo, N., Parra, G., Passadore, C. & Möller, L. Ensemble modelling of southern Australian bottlenose dolphin Tursiops sp. distribution reveals important habitats and their potential ecological function. Mar. Ecol. Prog. Ser. 569, 253–266 (2017).Hanberry, B. B. Finer grain size increases effects of error and changes influence of environmental predictors on species distribution models. Ecol. Inform. 15, 8–13 (2013).Article 

    Google Scholar 
    Gottschalk, T. K., Aue, B., Hotes, S. & Ekschmitt, K. Influence of grain size on species–habitat models. Ecol. Modell. 222, 3403–3412 (2011).Article 

    Google Scholar 
    Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

    Google Scholar 
    Passadore, C., Möller, L. M., Diaz-Aguirre, F. & Parra, G. J. Modelling dolphin distribution to inform future spatial conservation decisions in a marine protected area. Sci. Rep. 8, 1–14 (2018).Article 
    CAS 

    Google Scholar 
    Parra, G. J., Schick, R. & Corkeron, P. J. Spatial distribution and environmental correlates of Australian snubfin and Indo-Pacific humpback dolphins. Ecography (Cop.) 29, 396–406 (2006).Article 

    Google Scholar 
    Conrad, O. et al. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci. Model Dev. 8, 1991–2007 (2015).R Core Team. R version 3.6.1. at https://www.r-project.org/ (2019).RStudio Team. RStudio: Integrated Develpment for R. at http://rstudio.com/ (2019).Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).Article 

    Google Scholar 
    Tikhonov, G. et al. Joint species distribution modelling with the r-package Hmsc. Methods Ecol. Evol. 11, 442–447 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).Article 
    MATH 

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

    Google Scholar 
    Tjur, T. Coefficients of determination in logistic regression models—A new proposal: The coefficient of discrimination. Am. Stat. 63, 366–372 (2009).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Syme, J. The behavioural ecology of mixed-species groups of delphinids. PhD Thesis, Flinders University, Adelaide, Australia. (Flinders University, 2023).Wang, J. Y. Bottlenose Dolphin, Tursiops aduncus, Indo-Pacific Bottlenose Dolphin. in Encyclopedia of Marine Mammals (eds. Würsig, B., Thewissen, J. G. M. & Kovacs, K. M.) 125–130 (Elsevier, 2018). https://doi.org/10.1016/B978-0-12-804327-1.00073-X.Parra, G. J. & Jefferson, T. A. Humpback Dolphins. in Encyclopedia of Marine Mammals (eds. Würsig, B., Thewissen, J. G. M. & Kovacs, K. M.) 483–489 (Elsevier, 2018). https://doi.org/10.1016/B978-0-12-804327-1.00153-9.Dröge, E., Creel, S., Becker, M. S. & M’soka, J. Spatial and temporal avoidance of risk within a large carnivore guild. Ecol. Evol. 7, 189–199 (2017).Article 
    PubMed 

    Google Scholar 
    Browning, N. E., Cockcroft, V. G. & Worthy, G. A. J. Resource partitioning among South African delphinids. J. Exp. Mar. Bio. Ecol. 457, 15–21 (2014).Article 

    Google Scholar 
    Kiszka, J. J., Méndez-Fernandez, P., Heithaus, M. R. & Ridoux, V. The foraging ecology of coastal bottlenose dolphins based on stable isotope mixing models and behavioural sampling. Mar. Biol. 161, 953–961 (2014).Article 
    CAS 

    Google Scholar 
    Saayman, G. S. & Tayler, C. K. The socioecology of humpback dolphins (Sousa sp.). in Behavior of Marine Animals Current Perspectives in Research Volume 3: Cetaceans (eds. Winn, H. E. & Olla, B. L.) 165–226 (Springer, 1979).Gowans, S. & Whitehead, H. Distribution and habitat partitioning by small odontocetes in the Gully, a submarine canyon on the Scotian Shelf. Can. J. Zool. 73, 1599–1608 (1995).Article 

    Google Scholar 
    Clua, E. Mixed-species feeding aggregation of dolphins, large tunas and seabirds in the Azores. Aquat. Living Resour. 14, 11–18 (2001).Article 

    Google Scholar 
    Quérouil, S. et al. Why do dolphins form mixed-species associations in the azores?. Ethology 114, 1183–1194 (2008).Article 

    Google Scholar 
    Heithaus, M. R. & Dill, L. M. Food availability and tiger shark predation risk influence bottlenose dolphin habitat use. Ecology 83, 480–491 (2002).Article 

    Google Scholar  More

  • in

    Sand fly population dynamics in areas of American cutaneous leishmaniasis, Municipality of Paraty, Rio de Janeiro, Brazil

    Owing to drastic changes in the environment caused by human interference, wild mammals that are reservoirs of Leishmania have invaded residential areas where species of sand flies with eclectic feeding habits are found, and established a transmission cycle that eventually reaches humans23,24,25. In the study area, it was observed that the largest frequency of specimens over the years was captured in the residential environment, which are represented by residential and peridomicile areas. The lowest frequency was captured in the borders of the forest.The municipality of Paraty, located on the southern coast in the state of Rio de Janeiro, where the study was conducted, has many preserved areas of the Atlantic Forest and its climate is wet with no dry season13, which was confirmed during the three years of the present study, where the relative air humidity stayed high every month. The highest average rainfalls occur in summer and fall (autumn). The average temperature during the hottest months of the year was between approximately 25 °C and 26 °C, with a maximum of 31 °C, and in the coldest months, the temperature averaged between 20 and 21 °C, with a minimum of 16 °C, exhibiting an ideal environment for the activity of sand flies throughout the year.Barretto26 noted that atmospheric conditions, such as relative humidity, rainfall, and temperature directly influence the activity of these sand fly species. Migonemyia migonei and Ny. whitmani had lower activity at temperatures below 15 °C, Pi. fischeri below 10 °C, and Ny. intermedia at temperatures below 9.5 °C. The author also reported that heavy rains prevent sand flies from leaving their shelters; however, this can increase their density within residences, especially for species located next to residential areas. Light rain will not impede their activity, but in these conditions, they are not as frequently observed as they usually are. However, during rain periods, especially in the hot and humid summer period, the density of sand flies increases considerably.In the present study, four key vector species of Leishmania braziliensis Vianna, 1911, the etiologic agent of tegumentary leishmaniasis, were captured throughout the year. The most frequent was Ny. intermedia, followed by Pi. fischeri, Mg. migonei, and Ny. whitmani. Carvalho et al.27, in the State of Pernambuco, northeast region of Brazil, reported having found Mg. migonei infected with Leishmania infantum Nicolle, 1908, the etiologic agent of visceral leishmaniasis.According to Forattini28, there are sand fly species that are essentially resistant to climate changes throughout the seasons. Several are found, albeit in lower densities, during the cooler, dry months, while others disappear during this period. However, other factors also influence the incidence of sand flies in the same location, even under the same temperature and humidity conditions. Thus, to study the seasonality of sand fly species, it is important to perform systematized captures, for a period exceeding two years, to minimize the effects of these additional factors, for example, atypical years with a longer period of drought or humidity, more or less high temperatures, months with higher than expected rainfall or control measures applied by the municipality.In studies carried out in the Northeast region of Brazil, in a study carried out in the municipality of Codó, in the State of Maranhão, an inversely proportional correlation of the captured sandflies was observed in relation to relative air humidity, a direct correlation in relation to temperature and precipitation, a correlation directly proportional29. In the municipality of Sobral, State of Ceará, in the first year of the study, observed a negative correlation with temperature and a high positive correlation with humidity and precipitation, however, in the following year, there was no correlation between the density of captured sandflies and climatic variables30. The same occurred in this study, in the municipality of Paraty, in relation to relative air humidity and precipitation, but in relation to temperature, a strong positive correlation was obtained.In the studied area Ny. intermedia occurred in greater numbers in every month of the year, except in June and July, when it was less frequent than Pi. fischeri. The same pattern was observed for these two species, i.e., a gradual increase in abundance beginning in August, peak abundance in summer (January), followed by a decrease until winter (July). Brito et al.31, when researching the northern coast of the state of São Paulo, municipality of São Sebastião, noted the opposite, that Ny. intermedia had the highest abundance peaks during the driest and coldest period of the year, i.e., from May to August. However, the authors also emphasized the presence of this species throughout the year, mainly in the residential environment, and they stressed the importance of seasonal analyses for periods longer than a year.In the São Francisco River region, in the state of Minas Gerais, on the banks of the Rio Velhas, Saraiva et al.32, in a study over a two-year period, observed a different pattern. In the first year of study, after the rainy season from February to May, with high humidity and high temperature, Ny. intermedia was captured in greater numbers than during other months of the year. In the second year, peaks occurred in October, March, and June, with the highest peak in March, when there was elevated rainfall, high humidity, and high temperatures.In the state of Rio de Janeiro, in Serra dos Órgãos National Park, Aguiar and Soucasaux33 analyzed the monthly frequency in human bait and observed that Ny. fischeri was captured in every month except November. In the hot and humid period, from December to February, there was a gradual increase in the average abundances of this species, and then a slight decrease began in March and continued into April. During the cold and dry period of May and June, abundances started to increase, then decreased in July, and peaked in August. During August, Pi. fischeri was the dominant species of wildlife, and in September, abundances began to decline again.Mayo et al.34, studying the southeastern region of the state of São Paulo, observed that there was a seasonal trend in the abundance for species Mg. migonei, Ny. whitmani, Ny. intermedia, and Pi. fischeri, with abundance peaks recorded during the cooler, drier season (April to September) and low abundances during the warmer, wetter season (October to March). The authors revealed that the occurrence of intense fires in the study area in October, which caused severe environmental change, possibly interfered with the population dynamics of the species. In the present study, the opposite trend of seasonality was shown for the four key species, Ny. intermedia, Pi. fischeri, Mg. migonei, and Ny. whitmani, then what was observed by the above authors, the highest abundances occurred during the hottest period, increasing gradually until a maximum peak in January, and lowest abundances were seen during the coldest period, in July for the first three species, and in June for Ny. whitmani.In the neighboring municipality of this study in Angra dos Reis, in the Ilha Grande, Carvalho et al.35 reinforced the epidemiological importance of Ny. intermedia in the State of Rio de Janeiro and highlighted the role of Mg. migonei in the transmission of cutaneous leishmaniasis with its high rate of infection natural by Leishmania. Still in the same region, along the southern coast of the State of Rio de Janeiro, Aguiar et al.8 conducted systematic catches for two years, with the aim being to analyze the monthly frequency of sand flies in residential and forest environments. The authors discovered results like what occurred in this study in Paraty, that the four most important species caught, Ny. intermedia, Pi. fischeri, Mg. migonei, and Ny. whitmani, had higher average numbers during the hot and humid period of the year, i.e., between October and January, with a maximum peak in December for Ny. intermedia and Pi. fischeri, and January for Mg. migonei. The prevalence of Ny. intermedia was evident in every month, both inside the residence and around the residential area. In the colder and drier season, from May to August, there was a balance with Pi. fischeri, but from August, inside the residence, and from September, around the residence, the frequency increased until it reached its peak in December. There was a gradual increase in the frequency of this species in the warmer and wetter period (between October and January), with average temperatures ranging from 26 to 29 °C and relative air humidity between 84 and 87%.Condino et al.36, when studying the southwestern region of the state of São Paulo, observed that Ny. intermedia and Ny. whitmani had the highest frequencies during the months of May, September, and December with temperatures ranging from 21 to 25.7 °C and rainfall between 66.7 and 195.1 mm. In June, the lowest frequency of sand flies was observed, which then increased until a maximum peak in September. Temperature data and rainfall index were not correlated with the density of specimens, especially as the study was carried out over only one year. In this study, the opposite was observed for Ny. intermedia and Ny. whitmani in the month of May, one of the months with the lowest density.In the city of Petrópolis, state of Rio de Janeiro, Souza et al.24 observed a prevalence of Ny. intermedia and Ny. whitmani, with the latter species prevailing around the residence. Migonemyia migonei and Pi. fischeri were also present but to a lesser extent. In the forest, Ny. whitmani was more abundant, followed by Pi. fischeri, while Ny. intermedia was found at lower abundances. However, Ny. intermedia and Pi. fischeri were present during every month of the year. The authors also found a significant correlation between the number of sand flies and environmental changes such as temperature, relative humidity, and rainfall. The same was observed, in this study, in the forest with Ny. intermedia, however, in this environment the number of Pi. fischeri specimens was higher than that of Ny. whitmani.In the north of Espírito Santo, Virgens et al.37 observed that Ny. intermedia was present in almost every month of the study period, with peaks in the warmer and wetter months. The authors highlighted that the low numbers of this species were recorded during and after high rainfall periods, suggesting that heavy rain is unfavorable for the development of immature forms, as breeding sites in altered habitats suffered a greater impact because of extreme weather conditions.In a study carried out by Guimarães et al.38 to observe the competence of Mg. Migonei to Leishmania infantum, concluded that this species is highly susceptible to the development of this parasite and that in addition to its anthropophilia and abundance in areas with an active focus of visceral leishmaniasis, it can act as a vector of this disease in Latin America.In the studied area, Ny. intermedia, one of the main vectors of the etiological agent of tegumentary leishmaniasis in the region2, was present in significant numbers in the home environment throughout all months of the year. The species Pi. fischeri was present over the months in expressive numbers in all types and locations of capture, that is, both in the environment altered by human activity and in the natural environment where leishmaniasis occurs in its natural enzootic cycle. Migonemyia migonei, present throughout the year in the peridomestic environment, showed its association with the dog, where it was prevalent throughout the year in the kennel, being an important vector of the etiological agent of tegumentary leishmaniasis, as well as being suspected in areas of visceral leishmaniasis transmission, where the main vector of this disease is not found. And Ny. whitmani present in the peridomicile, mainly in the hottest months of the year, in addition to the forest and forest margins, it was observed that in this study region the species is emerging through a selective process of adaptation in environments that were negatively affected by the increase of human activity. Thus, despite observing a period of greater frequency of sand flies in the hottest months of the year, a period with high rainfall, the high relative humidity is observed throughout the year, as well as the presence of species of epidemiological importance Ny. intermedia, Pi. fischeri, Mg. migonei and Ny. whitmani, who are involved in the propagation of the etiological agent of tegumentary leishmaniasis to humans and animals, causing greater contact between the region’s inhabitants with these dipterans and thus, a greater risk of contracting the disease. More

  • in

    Impacts of recent climate change on crop yield can depend on local conditions in climatically diverse regions of Norway

    Rahaman, A. et al. The increasing hunger concern and current need in the development of sustainable food security in the developing countries. Trends Food Sci. Technol. 113, 423–429. https://doi.org/10.1016/j.tifs.2021.04.048 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Porter, J. R. et al. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 485–533 (Cambridge University Press, 2014).
    Google Scholar 
    Yan, H. et al. Crop traits enabling yield gains under more frequent extreme climatic events. Sci. Total Environ. 808, 152170. https://doi.org/10.1016/j.scitotenv.2021.152170 (2022).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Lobell, D. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change. 3, 497–501. https://doi.org/10.1038/nclimate1832 (2013).Article 
    ADS 

    Google Scholar 
    Vermeulen, S. J. et al. Addressing uncertainty in adaptation planning for agriculture. Proc. Natl. Acad. Sci. 110, 8357–8362. https://doi.org/10.1073/pnas.1219441110 (2013).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    FAO. Climate Change and Food Security: Risks and Responses (FAO, 2015).
    Google Scholar 
    Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989. https://doi.org/10.1038/ncomms6989 (2015).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Ding, Z. et al. Modeling the combined impacts of deficit irrigation, rising temperature and compost application on wheat yield and water productivity. Agric. Water Manag. 244, 106626. https://doi.org/10.1016/j.agwat.2020.106626 (2021).Article 

    Google Scholar 
    Malhi, G. S., Kaur, M. & Kaushik, P. Impact of climate change on agriculture and its mitigation strategies: A review. Sustainability 13, 1318 (2021).Article 
    CAS 

    Google Scholar 
    Persson, T. & Kværnø, S. Impact of projected mid-21st century climate and soil extrapolation on simulated spring wheat grain yield in Southeastern Norway. J. Agric. Sci. 155, 361–377. https://doi.org/10.1017/S0021859616000241 (2017).Article 

    Google Scholar 
    Zhu, X. & Troy, T. J. Agriculturally relevant climate extremes and their trends in the world’s major growing regions. Earth’s Future 6, 656–672. https://doi.org/10.1002/2017EF000687 (2018).Article 
    ADS 

    Google Scholar 
    Fischer, T. et al. Increase in irrigated wheat yield in north-west Mexico from 1960 to 2019: Unravelling the negative relationship to minimum temperature. Field Crops Res. 275, 108331. https://doi.org/10.1016/j.fcr.2021.108331 (2022).Article 
    ADS 

    Google Scholar 
    Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620. https://doi.org/10.1126/science.1204531 (2011).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Harkness, C. et al. Adverse weather conditions for UK wheat production under climate change. Agric. For. Meteorol. 282, 107862. https://doi.org/10.1016/j.agrformet.2019.107862 (2020).Article 
    ADS 
    PubMed 

    Google Scholar 
    Seehusen, T. & Uhlen, A. K. Analyses of yield gaps for the production of wheat and barley in Norway, potential to increase yields on existing farmland. Norwegian Institute for Bioeconomics, Report 5/166/2019 (2020).Hakala, K. et al. Sensitivity of barley varieties to weather in Finland. J. Agric. Sci. 150, 145–160. https://doi.org/10.1017/S0021859611000694 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Peltonen-Sainio, P., Jauhiainen, L., Hakala, K. & Ojanen, H. Climate change and prolongation of growing season, changes in regional potential for field crop production in Finland. Agric. Food Sci. 18, 171–190. https://doi.org/10.2137/145960609790059479 (2009).Article 

    Google Scholar 
    Fleisher, D. H. et al. A potato model intercomparison across varying climates and productivity levels. Glob. Change Biol. 23, 1258–1281. https://doi.org/10.1111/gcb.13411 (2017).Article 
    ADS 

    Google Scholar 
    Moen, A. National Atlas of Norway: Vegetation (Hønefoss, 1999).
    Google Scholar 
    Bakkestuen, V., Erikstad, L. & Halvorsen, R. Step-less models for regional environmental variation in Norway. J. Biogeogr. 35, 1906–1922 (2008).Article 

    Google Scholar 
    Statistics-Norway. 2020. https://www.ssb.no/jord-skog-jakt-og-fiskeri/statistikker/stjord (Accessed 10 November 2020).Hanssen-Bauer, I. et al. Climate in Norway 2100 – a knowledge base for climate adaptation. Norwegian Centre for Climate Sciences, Report 1/2017 49 (2017).Blandford, D., Gaasland, I., Vårdal, E. & McIntosh, C. Greenhouse gas emissions, land use, and food supply under the paris climate agreement—Policy choice in Norway. Appl. Econ. Perspect. Policy 41, 249–264. https://doi.org/10.1093/aepp/ppy011 (2019).Article 

    Google Scholar 
    Rötter, R. P. et al. What would happen to barley production in Finland if global warming exceeded 4 °C? A model-based assessment. Eur. J. Agron. 35, 205–214. https://doi.org/10.1016/j.eja.2011.06.003 (2011).Article 

    Google Scholar 
    Ozturk, I., Sharif, B., Baby, S., Jabloun, M. & Olesen, J. E. The long-term effect of climate change on productivity of winter wheat in Denmark, scenario analysis using three crop models. J. Agric. Sci. 155, 733–750. https://doi.org/10.1017/S0021859616001040 (2017).Article 
    CAS 

    Google Scholar 
    An, H. & Carew, R. Effect of climate change and use of improved varieties on barley and canola yield in Manitoba. Can. J. Plant Sci. 95, 127–139. https://doi.org/10.1139/CJPS-2014-221 (2014).Article 

    Google Scholar 
    Zhou, Z., Plauborg, F., Kristensen, K. & Andersen, M. Dry matter production, radiation interception and radiation use efficiency of potato in response to temperature and nitrogen application regimes. Agric. For. Meteorol. 232, 595–605. https://doi.org/10.1016/j.agrformet.2016.10.017 (2017).Article 
    ADS 

    Google Scholar 
    Jensen, K. J. S. et al. Yield and development of winter wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.) in field experiments with variable weather and drainage conditions. Eur. J. Agron. 122, 126075. https://doi.org/10.1016/j.eja.2020.126075 (2021).Article 
    CAS 

    Google Scholar 
    Lobell, D. B., Cahill, K. N. & Field, C. B. Historical effects of temperature and precipitation on California crop yields. Clim. Change 81, 187–203. https://doi.org/10.1007/s10584-006-9141-3 (2007).Article 
    ADS 

    Google Scholar 
    Skjelvag, A. O. Climatic conditions for crop production in Nordic countries. Agric. Food Sci. Finland 7(2), 149–160 (1998).Article 

    Google Scholar 
    Norsk-Klimaservicesenter. https://seklima.met.no/ (2020).Erikstad, L. & Bakkestuen, V. Calculating cumulative effects in GIS using a stepless multivariate model. MethodsX 8, 101407. https://doi.org/10.1016/j.mex.2021.101407 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Aune-Lundberg, L. & Strand, G.-H. The content and accuracy of the CORINE land cover dataset for Norway. Int. J. Appl. Earth Observ. Geoinform. 96, 102266. https://doi.org/10.1016/j.jag.2020.102266 (2021).Article 

    Google Scholar 
    QGIS Geographic Information System (QGIS Association, 2020).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
    Google Scholar 
    Lobell, D. B. & Field, C. B. Global scale climate–crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2, 014002. https://doi.org/10.1088/1748-9326/2/1/014002 (2007).Article 
    ADS 

    Google Scholar 
    Shumway, R. H. & Stoffer, D. S. Time Series Analysis and its Applications Vol. 560 (Springer, 2016).MATH 

    Google Scholar 
    Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R. J. 9, 378–400 (2017).Article 

    Google Scholar 
    Lüdecke, D., Ben Shachar, M., Patil, I., Waggoner, P. & Makowski, D. Performance: An R Package for Assessment, Comparison and Testing of Statistical Models (2021).Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.3.3.0 (2020).Friedman, J. H., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(22), 2010. https://doi.org/10.18637/jss.v033.i01 (2010).Article 

    Google Scholar 
    Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B-Methodol. 58, 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x (1996).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Hastie, T., Tibshirani, R. & Friendman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2009).Book 
    MATH 

    Google Scholar 
    Meinshausen, N. & Bühlmann, P. Stability selection. J. Roy. Stat. Soc. B 72, 417–473. https://doi.org/10.2307/40802220 (2010).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Efron, B. & Stein, C. The jackknife estimate of variance. Ann. Stat. 9, 586–596. https://doi.org/10.1214/aos/1176345462 (1981).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Milborrow, S. plotmo: Plot a Model’s Residuals, Response, and Partial Dependence Plots. R package version 3.5.7 (2020).Liu, H. Xu, X. & Li, J.J. HDCI: High Dimensional Confidence Interval Based on Lasso and Bootstrap. R package version 1.0–2 (2017).. Seehusen, T. & Uhlen, A. K. Analyses of yield gaps for the production of wheat and barley in Norway, potential to increase yields on existing farmland. Norwegian Institute for Bioeconomics, Report 5/166/2019. http://hdl.handle.net/11250/2637490 (2019).Stabbetorp, H. Dyrkingsomfang og avling i kornproduksjonen. Norsk institutt for bioøkonomi, Report 4 (1) (2017).Ebrahimi, E. et al. Assessing the impact of climate change on crop management in winter wheat—A case study for Eastern Austria. J. Agric. Sci. 154, 1153–1170. https://doi.org/10.1017/S0021859616000083 (2016).Article 

    Google Scholar 
    Kristensen, K., Schelde, K. & Olesen, J. Winter wheat yield response to climate variability in Denmark. J. Agric. Sci. 148, 1–15. https://doi.org/10.1017/S0021859610000675 (2010).Article 

    Google Scholar 
    Thaler, S., Eitzinger, J., Trnka, M. & Dubrovsky, M. Impacts of climate change and alternative adaptation options on winter wheat yield and water productivity in a dry climate in Central Europe. J. Agric. Sci. 150, 537–555. https://doi.org/10.1017/S0021859612000093 (2012).Article 
    CAS 

    Google Scholar 
    Ortiz, R. et al. Climate change, can wheat beat the heat?. Agr. Ecosyst. Environ. 126, 46–58. https://doi.org/10.1016/j.agee.2008.01.019 (2008).Article 

    Google Scholar 
    Semenov, M., Stratonovitch, P., Alghabari, F. & Gooding, M. Adapting wheat in Europe for climate change. J. Cereal Sci. 59, 245–256. https://doi.org/10.1016/j.jcs.2014.01.006 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Roberts, M. J., Braun, N. O., Sinclair, T. R., Lobell, D. B. & Schlenker, W. Comparing and combining process-based crop models and statistical models with some implications for climate change. Environ. Res. Lett. 12, 095010. https://doi.org/10.1088/1748-9326/aa7f33 (2017).Article 
    ADS 

    Google Scholar 
    Zhu, X., Troy, T. & Devineni, N. Stochastically modeling the projected impacts of climate change on rainfed and irrigated US crop yields. Environ. Res. Lett. 14, 074021. https://doi.org/10.1088/1748-9326/ab25a1 (2019).Article 
    ADS 

    Google Scholar 
    Lobell, D. & Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett. 12, 015001. https://doi.org/10.1088/1748-9326/aa518a (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Flø, S. et al. Rom for bruk av Norsk korn. Felleskjøpet, Report 49 (2017).Lillemo, M., Reitan, L. & Bjornstad, A. Increasing impact of plant breeding on barley yields in central Norway from 1946 to 2008. Plant Breeding 129, 484–490. https://doi.org/10.1111/j.1439-0523.2009.01710.x (2010).Article 

    Google Scholar 
    Wonneberger, R., Ficke, A. & Lillemo, M. Mapping of quantitative trait loci associated with resistance to net form net blotch (Pyrenophora teres f. teres) in a doubled haploid Norwegian barley population. PLoS One 12, e0175773. https://doi.org/10.1371/journal.pone.0175773 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moore, F. C. & Lobell, D. B. The fingerprint of climate trends on European crop yields. Proc. Natl. Acad. Sci. 112, 2670–2675. https://doi.org/10.1073/pnas.1409606112 (2015).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Martin, P. et al. Recent warming across the North Atlantic region may be contributing to an expansion in barley cultivation. Clim. Change 145, 351–365. https://doi.org/10.1007/s10584-017-2093-y (2017).Article 
    ADS 

    Google Scholar 
    Martin, P., Wishart, J., Dalmannsdottir, S., Halland, H. & Thomsen, a. M. Recent warming and the thermal requirement of barley in coastal Norway. Norwegian Institute for Bioeconomics, Report T2.4.3 ii (2018).Cattivelli, L., Ceccarelli, S., Romagosa, I. & Stanca, M. Abiotic stresses in Barley: Problems and solutions. In Barley: Production, Improvement, and Uses Vol. 4 (ed. Ullrich, S.) 282–306 (Blackwell UP, 2011).
    Google Scholar 
    Hura, T. Wheat and barley acclimatization to abiotic and biotic stress. Int. J. Mol. Sci. 21, 7423. https://doi.org/10.3390/ijms21197423 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kolberg, D., Persson, T., Mangerud, K. & Riley, H. Impact of projected climate change on workability, attainable yield, profitability and farm mechanization in Norwegian spring cereals. Soil Till. Res. 185, 122–138. https://doi.org/10.1016/j.still.2018.09.002 (2019).Article 

    Google Scholar 
    Olesen, J. E. et al. Impacts and adaptation of European crop production systems to climate change. Eur. J. Agron. 34, 96–112. https://doi.org/10.1016/j.eja.2010.11.003 (2011).Article 

    Google Scholar 
    Gammans, M., Mérel, P. & Ortiz-Bobea, A. Negative impacts of climate change on cereal yields: Statistical evidence from France. Environ. Res. Lett. 12, 054007. https://doi.org/10.1088/1748-9326/aa6b0c (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Ahmed, I., Harrison, M., Meinke, H. & Zhou, M. Barley phenology: physiological and molecular mechanisms for heading date and modelling of genotype-environment- management interactions. Plant Growth InTech 8, 175–202. https://doi.org/10.5772/64827 (2016).Article 
    CAS 

    Google Scholar 
    Hossain, A., da Silva, J. A. T., Lozovskaya, M. V. & Zvolinsky, V. P. High temperature combined with drought affect rainfed spring wheat and barley in South-Eastern Russia. Saudi J. Biol. Sci. 19, 473–487. https://doi.org/10.1016/j.sjbs.2012.07.005 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Møllerhagen, P. Norsk potetproduksjon 2011. Bioforsk, Report 7(1) (2012).Hermansen, A., Lu, D. & Forbes, G. Potato production in China and Norway, similarities, differences and future challenges. Potato Res. 55, 197–203. https://doi.org/10.1007/s11540-012-9224-7 (2012).Article 

    Google Scholar 
    Hermansen, A., Nærstad, R., Le, V. & Nordskog, B. In Proceedings of the Eleventh EuroBlight Workshop (The Norwegian Institute for Agricultural and Environmental Research, Hamar, 2018).Raymundo, R. et al. Climate change impact on global potato production. Eur. J. Agron. 100, 87–98. https://doi.org/10.1016/j.eja.2017.11.008 (2018).Article 

    Google Scholar 
    Rabia, A., Yacout, D., Shahin, S., Mohamed, A. & Abdelaty, E. Towards sustainable production of potato under climate change conditions. Curr. J. Appl. Sci. Technol. 18, 200–207. https://doi.org/10.14456/cast.2018.15 (2018).Article 

    Google Scholar 
    Haverkort, A. J., Franke, A. C., Engelbrecht, F. A. & Steyn, J. M. Climate change and potato production in contrasting South African agro-ecosystems. Potato Res. 56, 67–84. https://doi.org/10.1007/s11540-013-9230-4 (2013).Article 

    Google Scholar 
    Martinelli, F. et al. Advanced methods of plant disease detection A review. Agron. Sustain. Dev. 35, 1–25. https://doi.org/10.1007/s13593-014-0246-1 (2015).Article 

    Google Scholar 
    Borus, D. Impacts of Climate Change on the Potato (Solanum Tuberosum L.) Productivity in Tasmania, Australia and Kenya (University of Tasmania, 2017).
    Google Scholar 
    Fageria, N., Baligar, V. & Jones, C. Growth and Mineral Nutrition of Field Crops Vol. 5, 586 (CRC Press, 2010).Book 

    Google Scholar 
    Fleisher, D. H. et al. Effects of elevated CO2 and cyclic drought on potato under varying radiation regimes. Agric. For. Meteorol. 171, 270–280. https://doi.org/10.1016/j.agrformet.2012.12.011 (2013).Article 
    ADS 

    Google Scholar 
    Haverkort, A. J. & Struik, P. C. Yield levels of potato crops: Recent achievements and future prospects. Field Crop Res. 182, 76–85. https://doi.org/10.1016/j.fcr.2015.06.002 (2015).Article 

    Google Scholar 
    Van Oort, P. A. J., Timmermans, B. G. H., Meinke, H. & Van Ittersum, M. K. Key weather extremes affecting potato production in the Netherlands. Eur. J. Agron. 37, 11–22. https://doi.org/10.1016/j.eja.2011.09.002 (2012).Article 

    Google Scholar 
    Najafi, E., Devineni, N., Khanbilvardi, R. & Kogan, F. Understanding the changes in global crop yields through changes in climate and technology. Earth’s Future 6, 410–427. https://doi.org/10.1002/2017EF000690 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Pulatov, B., Anna Maria, J. N., Karin, H. & Maj-Lena, L. Modeling climate change impact on potato crop phenology, and risk of frost damage and heat stress in northern Europe. Agric. For. Meteorol. 214, 281–292. https://doi.org/10.1016/j.agrformet.2015.08.266 (2015).Article 
    ADS 

    Google Scholar  More

  • in

    Comparison between strip sampling and laser ablation methods to infer seasonal movements from intra-tooth strontium isotopes profiles in migratory caribou

    Britton, K. Isotope analysis for mobility and climate studies. In Archaeological Science: An Introduction (eds Britton, K. & Richards, M.) 99–124 (Cambridge University Press, Cambridge, 2020). https://doi.org/10.1017/9781139013826.005.Chapter 

    Google Scholar 
    Evans, J. A., Tatham, S., Chenery, S. R. & Chenery, C. A. Anglo-Saxon animal husbandry techniques revealed though isotope and chemical variations in cattle teeth. Appl. Geochem. 22, 1994–2005 (2007).Article 
    ADS 
    CAS 

    Google Scholar 
    Laffoon, J. E., Plomp, E., Davies, G. R., Hoogland, M. L. P. & Hofman, C. L. The movement and exchange of dogs in the prehistoric caribbean: An isotopic investigation. Int. J. Osteoarchaeol. 25, 454–465 (2015).Article 

    Google Scholar 
    Balasse, M., Ambrose, S. H., Smith, A. B. & Price, T. D. The seasonal mobility model for prehistoric herders in the south-western Cape of South Africa assessed by isotopic analysis of sheep tooth enamel. J. Archaeol. Sci. 29, 917–932 (2002).Article 

    Google Scholar 
    Bentley, R. A. & Knipper, C. Transhumance at the early Neolithic settlement at Vaihingen (Germany). Antiquity 79, 1–3 (2005).
    Google Scholar 
    Hoppe, K. A., Koch, P. L., Carlson, R. W. & Webb, S. D. Tracking mammoths and mastodons: Reconstruction of migratory behavior using strontium isotope ratios. Geology 27, 439–442 (1999).Article 
    ADS 
    CAS 

    Google Scholar 
    Wooller, M. J. et al. Lifetime mobility of an Arctic woolly mammoth. Science 373, 806–808 (2021).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Britton, K. et al. Strontium isotope evidence for migration in late Pleistocene Rangifer: Implications for Neanderthal hunting strategies at the Middle Palaeolithic site of Jonzac, France. J. Hum. Evol. 61, 176–185 (2011).Article 
    PubMed 

    Google Scholar 
    Gigleux, C., Grimes, V., Tütken, T., Knecht, R. & Britton, K. Reconstructing caribou seasonal biogeography in Little Ice Age (late Holocene) Western Alaska using intra-tooth strontium and oxygen isotope analysis. J. Archaeol. Sci. Rep. 23, 1043–1054 (2019).
    Google Scholar 
    Price, T. D., Meiggs, D., Weber, M.-J. & Pike-Tay, A. The migration of Late Pleistocene reindeer: Isotopic evidence from northern Europe. Archaeol. Anthropol. Sci. 9, 371–394 (2017).Article 

    Google Scholar 
    Britton, K. et al. Multi-isotope zooarchaeological investigations at Abri du Maras: The paleoecological and paleoenvironmental context of Neanderthal subsistence strategies in the Rhône Valley during MIS 3. J. Hum. Evol. 174, 103292 (2023).Article 
    PubMed 

    Google Scholar 
    Bentley, R. A. Strontium isotopes from the earth to the archaeological skeleton: A review. J. Archaeol. Method Theory 13, 135–187 (2006).Article 

    Google Scholar 
    Crowley, B. E., Miller, J. H. & Bataille, C. P. Strontium isotopes (87Sr/86Sr) in terrestrial ecological and palaeoecological research: Empirical efforts and recent advances in continental-scale models. Biol. Rev. 92, 43–59 (2017).Article 
    PubMed 

    Google Scholar 
    Bataille, C. P., Crowley, B. E., Wooller, M. J. & Bowen, G. J. Advances in global bioavailable strontium isoscapes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 555, 109849 (2020).Article 

    Google Scholar 
    Guiserix, D. et al. Simultaneous analysis of stable and radiogenic strontium isotopes in reference materials, plants and modern tooth enamel. Chem. Geol. 606, 121000 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Weber, M. et al. Strontium uptake and intra-population 87Sr/86Sr variability of bones and teeth—controlled feeding experiments with rodents (Rattus norvegicus, Cavia porcellus). Front Ecol. Evol. 8, 569940 (2020).Article 

    Google Scholar 
    Johnson, L., Montgomery, J., Evans, J. & Hamilton, E. Contribution of strontium to the human diet from querns and millstones: An experiment in digestive strontium isotope uptake. Archaeometry 61, 1366–1381 (2019).Article 
    CAS 

    Google Scholar 
    Dalle, S. et al. Strontium isotopes and concentrations in cremated bones suggest an increased salt consumption in Gallo-Roman diet. Sci. Rep. 12, 9280 (2022).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Britton, K. et al. Sampling plants and malacofauna in 87Sr/86Sr bioavailability studies: Implications for isoscape mapping and reconstructing of past mobility patterns. Front. Ecol. Evol. 8, 579473 (2020).Article 

    Google Scholar 
    Snoeck, C. et al. Towards a biologically available strontium isotope baseline for Ireland. Sci. Total Environ. 712, 136248 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Evans, J. A., Montgomery, J., Wildman, G. & Boulton, N. Spatial variations in biosphere 87Sr/86Sr in Britain. J. Geol. Soc. Lond. 167, 1–4 (2010).Article 
    CAS 

    Google Scholar 
    Kohn, M. J. & Cerling, T. E. Stable isotope compositions of biological apatite. In Phosphates: Geochemical, Geobiological and Materials Importance Vol. 48 (eds Kohn, M. et al.) 455–488 (De Gruyter Mouton, 2002).Chapter 

    Google Scholar 
    Britton, K., Grimes, V., Dau, J. & Richards, M. P. Reconstructing faunal migrations using intra-tooth sampling and strontium and oxygen isotope analyses: A case study of modern caribou (Rangifer tarandus granti ). J. Archaeol. Sci. 36, 1163–1172 (2009).Article 

    Google Scholar 
    Passey, B. H. & Cerling, T. E. Tooth enamel mineralization in ungulates: Implications for recovering a primary isotopic time-series. Geochim. Cosmochim. Acta 66, 3225–3234 (2002).Article 
    ADS 
    CAS 

    Google Scholar 
    Green, D. R. et al. Synchrotron imaging and Markov Chain Monte Carlo reveal tooth mineralization patterns. PLoS ONE 12, e0186391 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boethius, A., Ahlstrom, T., Kielman-Schmitt, M., Kjallquist, M. & Larsson, L. Assessing laser ablation multi-collector inductively coupled plasma mass spectrometry as a tool to study archaeological and modern human mobility through strontium isotope analyses of tooth enamel. Archaeol. Anthropol. Sci. 14, 97 (2022).Article 

    Google Scholar 
    Czére, O. et al. The bodies in the ‘Bog’: A multi-isotope investigation of individual life-histories at an unusual 6th/7th AD century group burial from a roman latrine at Cramond, Scotland. Archaeol. Anthropol. Sci. 14, 67 (2022).Article 

    Google Scholar 
    Deniel, C. & Pin, C. Single-stage method for the simultaneous isolation of lead and strontium from silicate samples for isotopic measurements. Anal. Chim. Acta 426, 95–103 (2001).Article 
    CAS 

    Google Scholar 
    Pellegrini, M. et al. Faunal migration in late-glacial central Italy: Implications for human resource exploitation. Rapid. Commun. Mass Sp. 22, 1714–1726 (2008).Article 
    CAS 

    Google Scholar 
    Evans, J. A. et al. Biosphere Isotope Domains GB (V1): Interactive website. British Geological Survey Interactive Resource. https://mapapps.bgs.ac.uk/biosphereisotopedomains/index.html?_ga=2.164355263.1833482666.1666628466-655647728.1666628466 (2018) https://doi.org/10.5285/3b141dce-76fc-4c54-96fa-c232e98010ea.Holt, E., Evans, J. A. & Madgwick, R. Strontium (87Sr/86Sr) mapping: A critical review of methods and approaches. Earth Sci. Rev. 216, 103593 (2021).Article 
    CAS 

    Google Scholar 
    Willmes, M. et al. Improvement of laser ablation in situ micro-analysis to identify diagenetic alteration and measure strontium isotope ratios in fossil human teeth. J. Archaeol. Sci. 70, 102–116 (2016).Article 
    CAS 

    Google Scholar 
    Vroon, P. Z., van der Wagt, B., Koornneef, J. M. & Davies, G. R. Problems in obtaining precise and accurate Sr isotope analysis from geological materials using laser ablation MC-ICPMS. Anal. Bioanal. Chem. 390, 465–476 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Copeland, S. R. et al. Strontium isotope ratios (87Sr/86Sr) of tooth enamel: A comparison of solution and laser ablation multicollector inductively coupled plasma mass spectrometry methods. Rapid. Commun. Mass Sp 22, 3187–3194 (2008).Article 
    CAS 

    Google Scholar 
    Montgomery, J., Evans, J. A. & Horstwood, M. S. A. Evidence for long-term averaging of strontium in bovine enamel using TIMS and LA-MC-ICP-MS strontium isotope intra-molar profiles. Environ. Archaeol. 15, 32–42 (2010).Article 

    Google Scholar 
    Lazzerini, N. et al. Monthly mobility inferred from isoscapes and laser ablation strontium isotope ratios in caprine tooth enamel. Sci Rep 11, 2277 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lugli, F. et al. Tracing the mobility of a Late Epigravettian (~ 13 ka) male infant from Grotte di Pradis (Northeastern Italian Prealps) at high-temporal resolution. Sci. Rep. 12, 8104 (2022).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dahl, S. G. et al. Incorporation and distribution of strontium in bone. Bone 28, 446–453 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Nava, A. et al. Early life of Neanderthals. PNAS 117, 28719–28726 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Festa-Bianchet, M., Ray, J. C., Boutin, S., Cote, S. & Gunn, A. Conservation of caribou (Rangifer tarandus) in Canada: An uncertain future. Can. J. Zool. 89, 419–434 (2011).Article 

    Google Scholar 
    Vors, L. S. & Boyce, M. S. Global declines of caribou and reindeer. Glob. Chang Biol. 15, 2626–2633 (2009).Article 
    ADS 

    Google Scholar 
    Bjørklund, I. Domestication, reindeer husbandry and the development of Sámi pastoralism. Acta Boreal. 30, 174–189 (2013).Article 

    Google Scholar 
    Britton, K. Prey species movements and migrations in ecocultural landscapes: reconstructing late Pleistocene herbivore seasonal spatial behaviours. In Multi-Species Archaeology (ed. Pilaar-Birch, S.) 347–367 (Routledge, 2018).Chapter 

    Google Scholar 
    Le Corre, M., Dussault, C. & Côté, S. D. Where to spend the winter? The role of intraspecific competition and climate in determining the selection of wintering areas by migratory caribou. Oikos 129, 512–525 (2020).Article 

    Google Scholar 
    Baltensperger, A. P. & Joly, K. Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate. Mov. Ecol. 7, 18 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cameron, M. D., Joly, K., Breed, G. A., Mulder, C. P. H. & Kielland, K. Pronounced fidelity and selection for average conditions of calving area suggestive of spatial memory in a highly migratory ungulate. Front Ecol. Evol. 8, 409 (2020).Article 

    Google Scholar 
    Dau, J. Units 21D, 22A, 22B, 22C, 22D, 22E, 23, 24 and 26A: Western Arctic Herd. Caribou survey-inventory management report, July 1 2004–June 30 2006. In Brown, P. Juneau (Ed.), Federal Aid in Wildlife Restoration. (2007).Britton, K. Multi-isotope Analysis and the Reconstruction of Prey Species Palaeomigrations and Palaeoecology (Durham University, 2010).
    Google Scholar 
    Brown, W. A. B. & Chapman, N. G. Age assessment of fallow deer (Dama dama): From a scoring scheme based on radiographs of developing permanent molariform teeth. J. Zool. 224, 367–379 (1991).Article 

    Google Scholar 
    Drucker, D., Bocherens, H., Pike-Tay, A. & Mariotti, A. Traçage isotopique de changements alimentaires saisonniers dans le collagène de dentine: Étude préliminaire sur des caribous actuels. Comptes Rendus de l’Academie de Sci. Ser. IIa: Sci. de la Terre et des Planet. 333, 303–309 (2001).ADS 

    Google Scholar 
    Fox-Dobbs, K., Leonard, J. A. & Koch, P. L. Pleistocene megafauna from eastern Beringia: Paleoecological and paleoenvironmental interpretations of stable carbon and nitrogen isotope and radiocarbon records. Palaeogeogr. Palaeoclimatol. Palaeoecol. 261, 30–46 (2008).Article 

    Google Scholar 
    Pederzani, S. & Britton, K. Oxygen isotopes in bioarchaeology: Principles and applications, challenges and opportunities. Earth Sci. Rev. 188, 77–107 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Ma, C., vander Zanden, H. B., Wunder, M. B. & Bowen, G. J. assignR: An R package for isotope-based geographic assignment. Methods Ecol. Evol. 11, 996–1001 (2020).Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).Alaska Center for Conservation Science. Range for the Western Arctic Caribou Herd. https://accscatalog.uaa.alaska.edu/dataset/ranges-arctic-alaska-caribou-herds (2019).Berg, M., Loonen, M. J. J. E. & Çakırlar, C. Judging a reindeer by its teeth: A user-friendly tooth wear and eruption pattern recording scheme to estimate age-at-death in reindeer (Rangifer tarandus). Int. J. Osteoarchaeol. 31, 417–428 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Passey, B. H. et al. Inverse methods for estimating primary input signals from time-averaged isotope profiles. Geochim. Cosmochim. Acta 69, 4101–4116 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Zazzo, A., Balasse, M. & Patterson, W. P. High-resolution δ13C intratooth profiles in bovine enamel: Implications for mineralization pattern and isotopic attenuation. Geochim. Cosmochim. Acta 69, 3631–3642 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Blumenthal, S. A. et al. Stable isotope time-series in mammalian teeth: In situ δ18O from the innermost enamel layer. Geochim. Cosmochim. Acta 124, 223–236 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Zazzo, A. et al. A refined sampling strategy for intra-tooth stable isotope analysis of mammalian enamel. Geochim. Cosmochim. Acta 84, 1–13 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Trayler, R. B. & Kohn, M. J. Tooth enamel maturation reequilibrates oxygen isotope compositions and supports simple sampling methods. Geochim. Cosmochim. Acta 198, 32–47 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Taillon, J., Festa-Bianchet, M. & Côté, S. D. Shifting targets in the tundra: Protection of migratory caribou calving grounds must account for spatial changes over time. Biol. Conserv. 147, 163–173 (2012).Article 

    Google Scholar 
    Joly, K., Gurarie, E., Hansen, D. A. & Cameron, M. D. Seasonal patterns of spatial fidelity and temporal consistency in the distribution and movements of a migratory ungulate. Ecol. Evol. 11, 8183–8200 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Le Corre, M., Dussault, C. & Côté, S. D. Weather conditions and variation in timing of spring and fall migrations of migratory caribou. J. Mammal. 98, 260–271 (2017).
    Google Scholar 
    Reimers, E. Rangifer population ecology: A Scandinavian perspective. Rangifer 17, 105 (1997).Article 

    Google Scholar 
    Bendrey, R., Vella, D., Zazzo, A., Balasse, M. & Lepetz, S. Exponentially decreasing tooth growth rate in horse teeth: Implications for isotopic analyses. Archaeometry 57, 1104–1124 (2015).Article 
    CAS 

    Google Scholar 
    Zazzo, A. et al. The isotope record of short- and long-term dietary changes in sheep tooth enamel: Implications for quantitative reconstruction of paleodiets. Geochim. Cosmochim. Acta 74, 3571–3586 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Aubert, M. et al. In situ oxygen isotope micro-analysis of faunal material and human teeth using a SHRIMP II: A new tool for palaeo-ecology and archaeology. J. Archaeol. Sci. 39, 3184–3194 (2012).Article 
    CAS 

    Google Scholar 
    Keeley, A. T. H., Beier, P. & Gagnon, J. W. Estimating landscape resistance from habitat suitability: Effects of data source and nonlinearities. Landsc. Ecol. 31, 2151–2162 (2016).Article 

    Google Scholar 
    Beikman, H. M. Geologic Map of Alaska (U.S. Geological Survey, 1980).
    Google Scholar 
    Couturier, S., Côté, S. D., Huot, J. & Otto, R. D. Body-condition dynamics in a northern ungulate gaining fat in winter. Can. J. Zool. 87, 367–378 (2009).Article 
    CAS 

    Google Scholar 
    Johnson, C. M. & Fridrich, C. J. Non-monotonic chemical and O, Sr, Nd, and Pb isotope zonations and heterogeneity in the mafic- to silicic-composition magma chamber of the Grizzly Peak Tuff, Colorado. Contrib. Mineral. Petr. 105, 677–690 (1990).Article 
    ADS 
    CAS 

    Google Scholar 
    Fisher, C. M. et al. Sm–Nd isotope systematics by laser ablation-multicollector-inductively coupled plasma mass spectrometry: Methods and potential natural and synthetic reference materials. Chem. Geol. 284, 1–20 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Zhang, W. et al. Improved in situ Sr isotopic analysis by a 257 nm femtosecond laser in combination with the addition of nitrogen for geological minerals. Chem. Geol. 479, 10–21 (2018).Article 
    ADS 
    CAS 

    Google Scholar  More

  • in

    Microbiomes of a disease-resistant genotype of Acropora cervicornis are resistant to acute, but not chronic, nutrient enrichment

    Acropora Biological Review Team. Atlantic Acropora Status Review: Report to National Marine Fisheries Service (Acropora Biological Review Team, 2005).
    Google Scholar 
    Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean Corals. Science 301, 958–960 (2003).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Jackson, E. J., Donovan, M., Cramer, K. & Lam, V. Status and Trends of Caribbean Coral Reefs: 1970–2012 306 (International Union for the Conservation of Nature, 2012).
    Google Scholar 
    Schopmeyer, S. A. et al. Regional restoration benchmarks for Acropora cervicornis. Coral Reefs 36, 1047–1057 (2017).ADS 

    Google Scholar 
    Lirman, D. et al. Propagation of the threatened staghorn coral Acropora cervicornis: Methods to minimize the impacts of fragment collection and maximize production. Coral Reefs 29, 729–735 (2010).ADS 

    Google Scholar 
    Mercado-Molina, A. E., Ruiz-Diaz, C. P. & Sabat, A. M. Demographics and dynamics of two restored populations of the threatened reef-building coral Acropora cervicornis. J. Nat. Conserv. 24, 17–23 (2015).
    Google Scholar 
    Young, C., Schopmeyer, S. & Lirman, D. A review of reef restoration and coral propagation using the threatened genus Acropora in the Caribbean and Western Atlantic. Bull. Mar. Sci. 88, 1075–1098 (2012).
    Google Scholar 
    Carne, L., Kaufman, L. & Scavo, K. Measuring success for Caribbean acroporid restoration: key results from ten years of work in southern Belize. In Proc. 13th International Coral Reef Symposium, Honolulu (Abstract No. 27909) (2016).Ware, M. et al. Survivorship and growth in staghorn coral (Acropora cervicornis) outplanting projects in the Florida Keys National Marine Sanctuary. PLoS ONE 15, e0231817 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shaver, E. C. et al. A roadmap to integrating resilience into the practice of coral reef restoration. Glob. Change Biol. 28, 4751–4764 (2022).CAS 

    Google Scholar 
    DeFilippo, L. B. et al. Assessing the potential for demographic restoration and assisted evolution to build climate resilience in coral reefs. Ecol. Appl. 32, e2650 (2022).PubMed 
    PubMed Central 

    Google Scholar 
    Lapointe, B. E., Brewton, R. A., Herren, L. W., Porter, J. W. & Hu, C. Nitrogen enrichment, altered stoichiometry, and coral reef decline at Looe Key, Florida Keys, USA: A 3-decade study. Mar. Biol. 166, 108 (2019).
    Google Scholar 
    Montenero, K. A. Florida Keys Integrated Ecosystem Assessment Ecosystem Status Report. https://doi.org/10.25923/F7CE-ST38.Palacio-Castro, A. M., Dennison, C. E., Rosales, S. M. & Baker, A. C. Variation in susceptibility among three Caribbean coral species and their algal symbionts indicates the threatened staghorn coral, Acropora cervicornis, is particularly susceptible to elevated nutrients and heat stress. Coral Reefs 40, 1601–1613 (2021).
    Google Scholar 
    Vega Thurber, R. L. et al. Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching. Glob. Change Biol. 20, 544–554 (2014).ADS 

    Google Scholar 
    Zaneveld, J. R. et al. Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales. Nat. Commun. 7, 11833 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bruno, J. F. et al. Thermal stress and coral cover as drivers of coral disease outbreaks. PLoS Biol. 5, e124 (2007).PubMed 
    PubMed Central 

    Google Scholar 
    Wiedenmann, J. et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 160–164 (2012).ADS 

    Google Scholar 
    Rädecker, N., Pogoreutz, C., Voolstra, C. R., Wiedenmann, J. & Wild, C. Nitrogen cycling in corals: The key to understanding holobiont functioning? Trends Microbiol. 23, 490–497 (2015).PubMed 

    Google Scholar 
    Shantz, A. A. & Burkepile, D. E. Context-dependent effects of nutrient loading on the coral–algal mutualism. Ecology 95, 1995–2005 (2014).PubMed 

    Google Scholar 
    Burkepile, D. E. et al. Nitrogen identity drives differential impacts of nutrients on coral bleaching and mortality. Ecosystems 23, 798–811 (2020).CAS 

    Google Scholar 
    Fabricius, K. E. Effects of terrestrial runoff on the ecology of corals and coral reefs: Review and synthesis. Mar. Pollut. Bull. 50, 125–146 (2005).CAS 
    PubMed 

    Google Scholar 
    Ferrier-Pagès, C., Gattuso, J.-P., Dallot, S. & Jaubert, J. Effect of nutrient enrichment on growth and photosynthesis of the zooxanthellate coral Stylophora pistillata. Coral Reefs 19, 103–113 (2000).
    Google Scholar 
    Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome: Underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 317–340 (2016).CAS 
    PubMed 

    Google Scholar 
    Krediet, C. J., Ritchie, K. B., Paul, V. J. & Teplitski, M. Coral-associated micro-organisms and their roles in promoting coral health and thwarting diseases. Proc. R. Soc. B Biol. Sci. 280, 20122328 (2013).
    Google Scholar 
    Mao-Jones, J., Ritchie, K. B., Jones, L. E. & Ellner, S. P. How microbial community composition regulates coral disease development. PLoS Biol. 8, e1000345 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Zilber-Rosenberg, I. & Rosenberg, E. Role of microorganisms in the evolution of animals and plants: The hologenome theory of evolution. FEMS Microbiol. Rev. 32, 723–735 (2008).CAS 
    PubMed 

    Google Scholar 
    West, A. G. et al. The microbiome in threatened species conservation. Biol. Conserv. 229, 85–98 (2019).
    Google Scholar 
    Ritchie, K. Regulation of microbial populations by coral surface mucus and mucus-associated bacteria. Mar. Ecol. Prog. Ser. 322, 1–14 (2006).ADS 
    CAS 

    Google Scholar 
    Rohwer, F., Seguritan, V., Azam, F. & Knowlton, N. Diversity and distribution of coral-associated bacteria. Mar. Ecol. Prog. Ser. 243, 1–10 (2002).ADS 

    Google Scholar 
    Klinges, G., Maher, R. L., Thurber, R. L. V. & Muller, E. M. Parasitic ‘Candidatus aquarickettsia rohweri’ is a marker of disease susceptibility in Acropora cervicornis but is lost during thermal stress. Environ. Microbiol. 22, 5341–5355 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Williams, S. D. et al. Geographically driven differences in microbiomes of Acropora cervicornis originating from different regions of Florida’s Coral Reef. PeerJ 10, e13574 (2022).PubMed 
    PubMed Central 

    Google Scholar 
    Klinges, J. G., Patel, S. H., Duke, W. C., Muller, E. M. & Vega Thurber, R. L. Phosphate enrichment induces increased dominance of the parasite Aquarickettsia in the coral Acropora cervicornis. FEMS Microbiol. Ecol. 98, 013 (2022).
    Google Scholar 
    Rosales, S. M. et al. Microbiome differences in disease-resistant vs susceptible Acropora corals subjected to disease challenge assays. Sci. Rep. 9, 18279 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gignoux-Wolfsohn, S., Precht, W., Peters, E., Gintert, B. & Kaufman, L. Ecology, histopathology, and microbial ecology of a white-band disease outbreak in the threatened staghorn coral Acropora cervicornis. Dis. Aquat. Org. 137, 217–237 (2020).
    Google Scholar 
    Miller, N., Maneval, P., Manfrino, C., Frazer, T. K. & Meyer, J. L. Spatial distribution of microbial communities among colonies and genotypes in nursery-reared Acropora cervicornis. PeerJ 8, e9635 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Aguirre, E. G., Million, W. C., Bartels, E., Krediet, C. J. & Kenkel, C. D. Host-specific epibiomes of distinct Acropora cervicornis genotypes persist after field transplantation. Coral Reefs. https://doi.org/10.1007/s00338-022-02218-x (2022).Article 

    Google Scholar 
    Shaver, E. C. et al. Effects of predation and nutrient enrichment on the success and microbiome of a foundational coral. Ecology 98, 830–839 (2017).PubMed 

    Google Scholar 
    Muller, E. M., Bartels, E. & Baums, I. B. Bleaching causes loss of disease resistance within the threatened coral species Acropora cervicornis. eLife 7, e35066 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Miller, M. W. et al. Genotypic variation in disease susceptibility among cultured stocks of Elkhorn and Staghorn corals. PeerJ 7, e6751 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Sunagawa, S., Woodley, C. M. & Medina, M. Threatened corals provide underexplored microbial habitats. PLoS ONE 5, e9554 (2010).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pantos, O. et al. The bacterial ecology of a plague-like disease affecting the Caribbean coral Montastrea annularis. Environ. Microbiol. 5, 370–382 (2003).CAS 
    PubMed 

    Google Scholar 
    Sheu, S.-Y., Liu, L.-P., Tang, S.-L. & Chen, W.-M. Thalassotalea euphylliae sp. nov., isolated from the torch coral Euphyllia glabrescens. Int. J. Syst. Evol. Microbiol. 66, 5039–5045 (2016).CAS 
    PubMed 

    Google Scholar 
    Nakagawa, T., Iino, T., Suzuki, K.-I. & Harayama, S. Ferrimonas futtsuensis sp. nov. and Ferrimonas kyonanensis sp. nov., selenate-reducing bacteria belonging to the Gammaproteobacteria isolated from Tokyo Bay. Int. J. Syst. Evol. Microbiol. 56, 2639–2645 (2006).CAS 
    PubMed 

    Google Scholar 
    Maher, R. L. et al. Coral microbiomes demonstrate flexibility and resilience through a reduction in community diversity following a thermal stress event. Front. Ecol. Evol. 8, 1 (2020).ADS 

    Google Scholar 
    Bourne, D., Iida, Y., Uthicke, S. & Smith-Keune, C. Changes in coral-associated microbial communities during a bleaching event. ISME J. 2, 350–363 (2008).CAS 
    PubMed 

    Google Scholar 
    Ziegler, M. et al. Coral bacterial community structure responds to environmental change in a host-specific manner. Nat. Commun. 10, 3092 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McDevitt-Irwin, J. M. et al. Responses of coral-associated bacterial communities to local and global stressors. Front. Mar. Sci. 4, 262 (2017).
    Google Scholar 
    Klinges, J. G. et al. Phylogenetic, genomic, and biogeographic characterization of a novel and ubiquitous marine invertebrate-associated Rickettsiales parasite, Candidatus aquarickettsia rohweri, gen. nov., sp. nov. ISME J. 13, 2938–2953 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Muscatine, L., Falkowski, P. G., Dubinsky, Z., Cook, P. A. & McCloskey, L. R. The effect of external nutrient resources on the population dynamics of zooxanthellae in a reef coral. Proc. R. Soc. Lond. B 236, 311–324 (1989).ADS 

    Google Scholar 
    Waite, D. W. et al. Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. Nov.). Front. Microbiol. 8, 682 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Waite, D. W. et al. Addendum: Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. Nov.). Front. Microbiol. 9, 772 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Rosales, S. M. et al. Bacterial metabolic potential and micro-eukaryotes enriched in stony coral tissue loss disease lesions. Front. Mar. Sci. 8, 776859 (2022).
    Google Scholar 
    Ricci, F. et al. Beneath the surface: Community assembly and functions of the coral skeleton microbiome. Microbiome 7, 159 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Yang, S.-H. et al. Metagenomic, phylogenetic, and functional characterization of predominant endolithic green sulfur bacteria in the coral Isopora palifera. Microbiome 7, 3 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Cai, L. et al. Metagenomic analysis reveals a green sulfur bacterium as a potential coral symbiont. Sci. Rep. 7, 9320 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Allgeier, J. E., Burkepile, D. E. & Layman, C. A. Animal pee in the sea: Consumer-mediated nutrient dynamics in the world’s changing oceans. Glob. Change Biol. 23, 2166–2178 (2017).ADS 

    Google Scholar 
    Hughes, D. J. et al. Coral reef survival under accelerating ocean deoxygenation. Nat. Clim. Change 10, 296–307 (2020).ADS 
    CAS 

    Google Scholar 
    Miura, N. et al. Ruegeria sp. strains isolated from the reef-building coral Galaxea fascicularis inhibit growth of the temperature-dependent pathogen Vibrio coralliilyticus. Mar. Biotechnol. 21, 1–8 (2019).CAS 

    Google Scholar 
    Bruno, J. F., Petes, L. E., Harvell, C. D. & Hettinger, A. Nutrient enrichment can increase the severity of coral diseases. Ecol. Lett. 6, 1056–1061 (2003).
    Google Scholar 
    Ezzat, L. et al. Thermal stress interacts with surgeonfish feces to increase coral susceptibility to dysbiosis and reduce tissue regeneration. Front. Microbiol. 12, 620458 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Gajigan, A. P., Diaz, L. A. & Conaco, C. Resilience of the prokaryotic microbial community of Acropora digitifera to elevated temperature. Microbiol. Open 6, e00478 (2017).
    Google Scholar 
    MacKnight, N. J. et al. Microbial dysbiosis reflects disease resistance in diverse coral species. Commun. Biol. 4, 679 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Palacio-Castro, A. M., Rosales, S. M., Dennison, C. E. & Baker, A. C. Microbiome signatures in Acropora cervicornis are associated with genotypic resistance to elevated nutrients and heat stress. Coral Reefs 41, 1389–1403 (2022).
    Google Scholar 
    Vollmer, S. V. & Kline, D. I. Natural disease resistance in threatened staghorn corals. PLoS ONE 3, e3718 (2008).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Parkinson, J. E. et al. Extensive transcriptional variation poses a challenge to thermal stress biomarker development for endangered corals. Mol. Ecol. 27, 1103–1119 (2018).CAS 
    PubMed 

    Google Scholar 
    Siebeck, U. E., Logan, D. & Marshall, N. J. CoralWatch—A flexible coral bleaching monitoring tool for you and your group. In Proc. 11th Int. Coral Reef Symp. Ft Lauderdale, Florida, 7–11 July, Vol. 1392, 5 (2008).Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
    PubMed 

    Google Scholar 
    Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).
    Google Scholar 
    Messyasz, A., Maher, R. L., Meiling, S. S. & Thurber, R. V. Nutrient enrichment predominantly affects low diversity microbiomes in a marine trophic symbiosis between algal farming fish and corals. Microorganisms 9, 1873 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Magurran, A. E. Ecological Diversity and Its Measurement (Princeton University Press, 1988).
    Google Scholar 
    Lahti, L. & Shetty, S. Microbiome R Package.Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
    Google Scholar 
    Oksanen, J. et al. vegan: Community Ecology Package (2019).Martinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using adonis. R Package Version 0.0.1 (2017).Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).MathSciNet 
    PubMed 
    MATH 

    Google Scholar 
    Kaul, A., Mandal, S., Davidov, O. & Peddada, S. D. Analysis of microbiome data in the presence of excess zeros. Front. Microbiol. 8, 2114 (2017).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Strong effects of food quality on host life history do not scale to impact parasitoid efficacy or life history

    Wajnberg, É. et al. (eds) Behavioral Ecology of Insect Parasitoids: From Theoretical Approaches to Field Applications 1st edn. (Blackwell Publishing Ltd, 2008).
    Google Scholar 
    Godfray, H. C. J. Parasitoids: Behavioral and Evolutionary Ecology (Princeton University Press, 1994).Book 

    Google Scholar 
    Morris, R. J., Lewis, O. T. & Godfray, H. C. J. Apparent competition and insect community structure: Towards a spatial perspective. Annales Zoologica Fennici 42, 1–14 (2005).
    Google Scholar 
    Forbes, A. A., Bagley, R. K., Beer, M. A., Hippee, A. C. & Widmayer, H. A. Quantifying the unquantifiable: Why Hymenoptera, not Coleoptera, is the most speciose animal order. BMC Ecol. 18, 1–11 (2018).Article 

    Google Scholar 
    Hassell, M. P. & Waage, J. K. Host–parasitoid population interactions. Annu. Rev. Entomol. 29, 89–114 (1984).Article 

    Google Scholar 
    Lafferty, K. D. et al. Parasites in food webs: The ultimate missing links. Ecol. Lett. 11, 533–546 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Van Veen, F. J. F., Van Holland, P. D. & Godfray, H. C. J. Stable coexistence in insect communities due to density- and trait-mediated indirect effects. Ecology 86, 3182–3189 (2005).Article 

    Google Scholar 
    Schmidt, M. H. et al. Relative importance of predators and parasitoids for cereal aphid control. Proc. R. Soc. Lond. Series B Biol. Sci. 270, 1905–1909 (2003).Article 

    Google Scholar 
    Mills, N. J. & Wajnberg, É. Optimal foraging behavior and efficient biological control methods. In Behavioral Ecology of Insect Parasitoids: From Theoretical Approaches to Field Applications 1st edn (eds Wajnberg, É. et al.) 1–30 (Blackwell Publishing, 2008).
    Google Scholar 
    Vinson, S. B. Host suitability for insect parasitoids. Annu. Rev. Entomol. 25, 397–419 (1980).Article 

    Google Scholar 
    Benrey, B. & Denno, R. F. The slow-growth-high-mortality hypothesis: A test using the cabbage butterfly. Ecology 78, 987–999 (1997).
    Google Scholar 
    Chau, A. & Mackauer, M. Host-instar selection in the aphid parasitoid Monoctonus paulensis (Hymenoptera: Braconidae, Aphidiinae): Assessing costs and benefits. Can. Entomol. 133, 549–564 (2001).Article 

    Google Scholar 
    Strand, M. R. & Obrycki, J. J. Host specificity of insect parasitoids and predators. Bioscience 46, 422–429 (1996).Article 

    Google Scholar 
    Vinson, S. B. Host selection by insect parasitoids. Annu. Rev. Entomol. 21, 109–133 (1976).Article 

    Google Scholar 
    Wang, X. G. & Messing, R. H. Fitness consequences of body-size-dependent host species selection in a generalist ectoparasitoid. Behav. Ecol. Sociobiol. 56, 513–522 (2004).Article 

    Google Scholar 
    Liu, Z., Xu, B., Li, L. & Sun, J. Host-size mediated trade-off in a parasitoid Sclerodermus harmandi. PLoS ONE 6, e23260 (2011).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, X. Y., Yang, Z. Q., Wu, H. & Gould, J. R. Effects of host size on the sex ratio, clutch size, and size of adult Spathius agrili, an ectoparasitoid of emerald ash borer. Biol. Control 44, 7–12 (2008).Article 

    Google Scholar 
    Hardy, I. C. W., Griffiths, N. T. & Godfray, H. C. J. Clutch size in a parasitoid wasp: A manipulation experiment. J. Anim. Ecol. 61, 121–129 (1992).Article 

    Google Scholar 
    Scriber, J. M. & Slansky, F. The nutritional ecology of immature insects. Annu. Rev. Entomol. 26, 183–211 (1981).Article 

    Google Scholar 
    Moreau, J., Benrey, B. & Thiery, D. Assessing larval food quality for phytophagous insects: Are the facts as simple as they appear?. Funct. Ecol. 20, 592–600 (2006).Article 

    Google Scholar 
    Davidowitz, G., D’Amico, L. J. & Nijhout, H. F. The effects of environmental variation on a mechanism that controls insect body size. Evolut. Ecol. Res. 6, 49–62 (2004).
    Google Scholar 
    Williams, I. S. Slow-growth, high-mortality-a general hypothesis, or is it?. Ecol. Entomol. 24, 490–495 (1999).Article 

    Google Scholar 
    Chen, K. & Chen, Y. Slow-growth high-mortality: A meta-analysis for insects. Insect Sci. 25, 337–351 (2018).Article 
    PubMed 

    Google Scholar 
    Waldbauer, G. P. The consumption and utilization of food by insects. Adv. Insect Physiol. 5, 229–288 (1968).Article 

    Google Scholar 
    Hochuli, D. F. Insect herbivory and ontogeny: How do growth and development influence feeding behaviour, morphology and host use?. Austral. Ecol. 26, 563–570 (2001).Article 

    Google Scholar 
    Holmes, L. A., Nelson, W. A. & Lougheed, S. C. Food quality effects on instar-specific life histories of a holometabolous insect. Ecol. Evol. 10, 626–637 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kagata, H. & Ohgushi, T. Bottom-up trophic cascades and material transfer in terrestrial food webs. Ecol. Res. 21, 26–34 (2006).Article 

    Google Scholar 
    Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Vidal, M. C. & Murphy, S. M. Bottom-up vs top-down effects on terrestrial insect herbivores: A meta-analysis. Ecol. Lett. 21, 138–150 (2018).Article 
    PubMed 

    Google Scholar 
    Harvey, J. A. Factors affecting the evolution of development strategies in parasitoid wasps: The importance of functional constraints and incorporating complexity. Entomol. Exp. Appl. 117, 1–13 (2005).Article 

    Google Scholar 
    Charnov, E. L., Los-den Hartogh, R. L., Jones, W. T. & van den Assem, J. Sex ratio evolution in a variable environment. Nature 289, 27–33 (1981).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Larson, A. O. The bean weevil and the southern Cowpea weevil in California. United States Department of Agriculture. Technical Bulletin No. 593, Washington, D. C. (1938).Askew, R. R. & Shaw, M. R. Parasitoid communities: their size, structure and development in Insect Parasitoids: 13th Symposium of Royal Entomological Society of London (eds. Waage, J.K. & Greathead, D.J. 225–264 (1986).Holmes, L. A., Nelson, W. A., Dyck, M. & Lougheed, S. C. Enhancing the usefulness of artificial seeds in seed beetle model systems research. Methods Ecol. Evol. 11, 1701–1706 (2020).Article 

    Google Scholar 
    Ellers, J., Van Alphen, J. J. M. & Sevenster, J. G. A field study of size-fitness relationships in the parasitoid Asobara tabida. J. Anim. Ecol. 67, 318–324 (1998).Article 

    Google Scholar 
    Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. 99, 673–686 (2004).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall/CRC, 2017).Book 
    MATH 

    Google Scholar 
    Wood, S. N. Thin-plate regression splines. J. Roy. Stat. Soc. B 65, 95–114 (2003).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2020). Accessed 3 April 2020.Burnham, K. P. & Anderson, D. R. Model Selection and Inference: A Practical Information-Theoretical Approach 2nd edn. (Springer-Verlag, 2002).MATH 

    Google Scholar 
    Wood, S. N., Pya, N. & Saefken, B. Smoothing parameter and model selection for general smooth models (with discussion). J. Am. Stat. Assoc. 111, 1548–1575 (2016).Article 
    CAS 

    Google Scholar 
    Bolker, B., & R Development Core Team Tools for general maximum likelihood estimation. Version 1.0.20. (2017). Accessed 4 April 2020.Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometical. J. 50, 346–363 (2008).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Rose, N. L., Yang, H., Turner, S. D. & Simpson, G. L. An assessment of the mechanisms for the transfer of lead and mercury from atmospherically contaminated organic soils to lake sediments with particular reference to Scotland, UK. Geochim. Cosmochim. Acta 82, 113–135 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Holmes, L. A., Nelson, W. A. & Lougheed, S. C. Data from: Food quality effects on instar-specific life histories of a holometabolous insect. Dryad Digital Repository. https://doi.org/10.5061/dryad.d7wm37px7 (2020).Therneau, T. A Package for Survival Analysis in R. R package version 3.2-13. https://CRAN.R-project.org/package=survival. (2021). Accessed 3 April 2020.Efron, B. The Jackknife, the Bootstrap, and Other Resampling Plans (Society for Industrial and Applied Mathematics, 1982).Book 
    MATH 

    Google Scholar 
    Awmack, C. S. & Leather, S. R. Host plant quality and fecundity in herbivorous insects. Annu. Rev. Entomol. 47, 817–844 (2002).Article 
    CAS 
    PubMed 

    Google Scholar 
    Clancy, K. M. & Price, P. W. Rapid herbivore growth enhances enemy attack: Sublethal plant defenses remain a paradox. Ecology 68, 733–737 (1987).Article 

    Google Scholar 
    Loader, C. & Damman, H. Nitrogen content of food plants and vulnerability of Pieris rapae to natural enemies. Ecology 72, 1586–1590 (1991).Article 

    Google Scholar 
    Uesugi, A. The slow-growth high-mortality hypothesis: Direct experimental support in a leafmining fly. Ecol. Entomol. 40, 221–228 (2015).Article 

    Google Scholar 
    Feeny, P. Plant apparency and chemical defense. in Biochemical Interaction Between Plants and Insects. 1–40 (Springer, 1976).Teder, T. & Tammaru, T. Cascading effects of variation in plant vigor on the relative performance of insect herbivores and their parasitoids. Ecol. Entomol. 27, 94–104 (2002).Article 

    Google Scholar 
    Kagata, H., Nakamura, M. & Ohgushi, T. Bottom-up cascade in a tri-trophic system: Different impacts of host-plant regeneration on performance of a willow leaf beetle and its natural enemy. Ecol. Entomol. 30, 58–62 (2005).Article 

    Google Scholar 
    Vet, L. E. M., Lewis, W. J. & Cardé, R. T. Parasitoid foraging and learning. In Chemical Ecology of Insects 2 (eds Cardé, R. T. & Bell, W. J.) 65–101 (Springer, 1995).Chapter 

    Google Scholar 
    Ishii, Y. & Shimada, M. Learning predator promotes coexistence of prey species in host-parasitoid systems. Proc. Natl. Acad. Sci. 109, 5116–5120 (2012).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ode, P. J. & Hardy, I. C. Parasitoid sex ratios and biological control. Behavioral ecology of insect parasitoids. In Behavioral Ecology of Insect Parasitoids: From Theoretical Approaches to field applications (eds Wajnberg, E. et al.) 253–291 (Wiley, 2008).Chapter 

    Google Scholar 
    Xiaoyi, W. & Zhongqi, Y. Behavioral mechanisms of parasitic wasps for searching concealed insect hosts. Acta Ecol. Sin. 28, 1257–1269 (2008).Article 

    Google Scholar 
    Otten, H., Wäckers, F., Battini, M. & Dorn, S. Efficiency of vibrational sounding in the parasitoid Pimpla turionellae is affected by female size. Anim. Behav. 61, 671–677 (2001).Article 

    Google Scholar 
    Kaplan, I., Carrillo, J., Garvey, M. & Ode, P. J. Indirect plant-parasitoid interactions mediated by changes in herbivore physiology. Curr. Opin. Insect Sci. 14, 112–119 (2016).Article 
    PubMed 

    Google Scholar 
    Ode, P. J. Plant toxins and parasitoid trophic ecology. Curr. Opin. Insect Sci. 32, 118–123 (2019).Article 
    PubMed 

    Google Scholar 
    Barbosa, P., Gross, P. & Kemper, J. Influence of plant allelochemicals on the tobacco hornworm and its parasitoid, Cotesia congregate. Ecology 72, 1567–1575 (1991).Article 
    CAS 

    Google Scholar 
    Barbosa, P. Natural enemies and herbivore–plant interactions: Influence of plant allelochemicals and host specificity. In Novel Aspects of Insect–Plant Interactions (eds Barbosa, P. & Letourneau, L. D. K.) 201–230 (Wiley, 1988).
    Google Scholar 
    Ode, P. J. Plant chemistry and natural enemy fitness: Effects on herbivore and natural enemy interactions. Annu. Rev. Entomol. 51, 163–185 (2006).Article 
    CAS 
    PubMed 

    Google Scholar  More

  • in

    Mapping the terraces on the Loess Plateau based on a deep learning-based model at 1.89 m resolution

    Terraces are a land type that is defined by its shape. They have a distinct morphological structure and edge features that distinguish them from other land types. In this study, we define terraces as agricultural land with strip or wavy sections built on slopes greater than 2° along the contour direction. Figure 1 depicts Google Maps satellite images of terraces in the Loess Plateau region. Terraces can be distinguished from other features in remote sensing images based on their colour, morphology, texture, and structure. Terraces can be distinguished from construction land, water, glaciers, and deserts by their colours. Figure 1b–d shows terraces that are primarily green and yellow. Furthermore, terraces are generally distributed along the contour direction, and can therefore be identified based on their morphology. Terraced field ridges curve downward and resemble strips in Fig. 1b,d or circles or ovals in Fig. 1c rather than a neat grid-like distribution. These features differ in morphology from the flat land shown in Fig. 1h. Based on texture and structure, the field area of terraces can be identified based on their strong edge features, as shown in Fig. 1b–d. The edges of terraces have dark stripes caused by oblique illumination received from the sun, and the field ridge of terraces often intercepts part of the sunlight due to their height. Sloping cultivated land, as shown in Fig. 1g, has no evident terraced wall. The outline of sloping cultivated land in the high-resolution image is curved, with no prominent edge features. These findings are critical differences distinguishing terraces and sloping land in high-resolution images.Fig. 1The spatial location of the Loess Plateau and images of various types of cultivated land. (a) The spatial location of the Loess Plateau and Spatial distribution of various cultivated land types images, (b) wide strip-mounted terraces in Longxi, (c) circular wide terraces in central Yulin, (d) high resolution image of Zhuanglang County in July 2019, (e) Zhuanglang County in February 2020, (f) narrow terraces in Shangbao, Chongyi, Jiangxi Province, (g) sloping cropland in Zhenjiang Town, Laibin, Guangxi, and (h) horizontal cropland in the North China Plain.Full size imageDeep learning-based terrace extraction modelThe DLTEM is a terrace extraction model that uses deep learning algorithms and other supplementary information. Initially, a preliminary terrace distribution map was obtained using a deep learning algorithm. It was then combined with the spectral and digital elevation model (DEM) elevation information to fine-tune the results. The final spatial distribution of the terraces was produced by manual correction (Fig. 2). Traditional land classification models or methods typically superimpose spectral, elevation, and morphological texture information from remote sensing images together for training, such as random forest, which is easily ignored in training since morphological texture information accounts for a relatively small amount of the total information. This leads to significant errors while identifying land classes with textural characteristics. In contrast, the DLTEM focuses on morphological texture information from remote sensing images and classifies it into land classes, followed by auxiliary correction through additional information. Thus, this method is more suitable to extract terraces enriched with texture structure information.Fig. 2Flow chart of the deep learning-based terrace extraction model.Full size imageThe UNet++ network is a classic deep learning algorithm that is uniquely unrivaled in extracting colour, morphology, texture, and structure features from images and applying them for classification. In comparison with other Convolutional Neural Network (CNN) classification models (e.g., Fully Convolutional Networks (FCN)), it has high classification accuracy, fast computation speed, strong robustness, and provides variable importance metrics. Therefore, in this study, the UNet++ network was adopted as the network framework for deep learning; the primary data source used was high-resolution satellite imagery from 2019. DEM (SRTM v4.1) data were used to obtain the elevation information and GlobeLand30 data were used to obtain the spectral information. The results were corrected to construct the final map of the distribution of terraces in the Loess Plateau.Study areaThe Loess Plateau, one of China’s four major plateaus, is located in northern central China (34°–40° N and 103°–114° E) (Fig. 1). It is covered by a thick loess layer that ranges in thickness from 50 to 80 m, and is the world’s largest loess deposition area, covering 648,700 km2. The altitude of the Loess Plateau ranges from 800 to 3,000 m, its average annual temperature is 6–14 °C, and its average annual precipitation is 200–700 mm. Since ancient times, the Loess Plateau has been used for agriculture because of its fine grains, fluffy soil texture, and rich soluble mineral nutrients, all of which are conducive to crop cultivation. However, long-term unsustainable land use caused the degradation of the vegetation cover in the Loess Plateau. Moreover, the land is degrading due to considerable nutrient loss caused by long-term water erosion in conjunction with natural conditions, such as arid climate, loose soil, concentrated and heavy rainfall. The fragmented ground in the region has made it susceptible to soil erosion. It has also become the primary source of Yellow River sediment as a result of the massive flow of eroded sediment into the Yellow River, posing a serious threat to the economic and social development of the lower Yellow River basin.Terracing is one of the main measures used to enhance crop yield and conserve soil and water in the region. Since the 1980s, the Chinese government has implemented many large-scale slope-to-terrace projects in the Loess Plateau. Especially in recent years, the outline of the comprehensive management plan for the Loess Plateau area (2010–2030) has been promulgated with a planned area of 2.608 million hectares for slope to terrace conversion, making it the core area of slope to terrace conversion projects in the country.Data preparationAlthough high-resolution satellite images can be an important data source for the spatial distribution of terraces on the Loess Plateau, they are not ideal for terraces classification. On the one hand, a higher resolution image requires more storage space. On the other hand, it reduces the efficiency, prolongs the interpretation time, and increases the noise in the image, affecting the interpretation accuracy. Most of the terraces on the Loess Plateau are wider than 7 m (Fig. 1b–d). These are wide terraces in comparison with the narrow terraces of southern China (Fig. 1f), which are less than 2 m wide. Furthermore, it is also easy to mistake the fish-scale pits constructed for soil and water conservation for terraces because of their similarity in form. However, as the width of their field surface is less than 1.5 m, remote sensing images with a 2 m resolution can effectively prevent the false extraction of such features. Based on the actual situation of this study area, we chose a high-resolution image with a spatial resolution of 1.89 m from Google Maps 16 level as the data source. The colour, texture, and morphological features of terraces in the images show seasonal variations. In autumn and winter, the weather is dry, and the vegetation is less shaded in the Loess Plateau. During this time, even the edge features become more visible and easier to identify. As a result, we selected images from October 2018 to February 2019 whenever possible (Fig. 1c,d).Deep learning network selectionLand classification is the extraction of land types from remote sensing images using image segmentation techniques. As the key technology of image segmentation, the Fully Convolutional Network (FCN) classifies images at the pixel level. FCN follows the network structure pattern of encoding and decoding, which adopts AlexNet as the encoder of the network and then employs transposed convolution to up-sample the feature map output from the final convolutional layer of the encoder to the resolution of the input image to achieve pixel-level image segmentation. However, due to the large error in image pixel boundary localization, Ronneberger et al.29 improved the FCN structure in 2015 by expanding the capacity of the network decoder by adding a contracting path to the encoding and decoding modules to achieve more accurate pixel boundary localisation29. The U-Net network is commonly used in medical image processing because it requires a small number of training samples and is effective in classifying objects with a fixed structure and limited semantic information. This network is comparable to natural image semantic segmentation such as Deeplab v3+, which has a smaller number of model parameters and the same effect.Since the texture and morphological features of terraces and human organs have certain similarities, they are primarily manifested by simple semantic information contained within the terrace images themselves. Thus, high-level semantic information and low-level features of such images become more important. However, high-resolution images are more complicated and variable than medical image patterns, and errors in terrace extraction edge identification using the U-Net network, such as boundary segmentation of terraces and flatlands, still occur. To fully utilize the semantic information of the network, we adopted a nested U-Net architecture, namely the UNet++ network proposed by Zhou et al.28. The network integrates long-connected and short-connected architectures to capture features at different levels by adding a shallower U-Net structure and integrates them via feature superposition to make the scale difference of feature maps smaller when fused to enhance the correct rate of image segmentation edges. However, because the U-Net++ network increases the number of model parameters, this study adopted the sparse matrix approach to accelerate model training and decrease the number of parameters.Data pre-processingData pre-processing is a prerequisite for UNet++ network training, that is, valid input according to the standard format annotation before training can be performed. Since the UNet++ network proposed by Zhou et al.28. is primarily used for medical images, which have characteristics such as fixed image structure, no spatial information, and less pattern variation, labelling medical images is comparatively easier using this method. In contrast, high-resolution remote sensing images have a large number of rasters, many pattern changes, irregular image structure, and spatial information. Therefore, determining how to better annotate high-resolution remote sensing images and reduce the annotation workload becomes critical. First, we vectorized the training sample area and generated the terrace vector dataset using ArcGIS with a high-resolution remote sensing image as the primitive map. Second, we converted the terrace vector dataset into raster data. The information of the raster had to be identical to that of the primitive map, including the size of the raster, its processing range, and its coordinate system. The output was converted to TIFF format to complete the image annotation. Since the raster size input to UNet++ network training is a fixed size, it is much smaller than the original image. To simplify the process of inputting the original image and its annotation information, we added an image import module to DLTEM, which was a sliding window of 400*400, and read the image automatically by setting the corresponding judgement conditions. Finally, the entire high-resolution image was processed automatically into the model in accordance with the established rules for training.The goal of the data enhancement was to improve the universality and robustness of the UNet++ network training results. As mentioned above, the high-resolution images taken simultaneously often included clouds or other anomalies in some areas, as the images were stitched together using multiple sources of data fusion. This can easily form evident stitching traces (Fig. 1c,d) due to the different shooting times and image quality of various data sources, i.e., brightness, saturation, and colour contrast of the images. Thus, the model trained on the original image data has strong limitations, and in many scenes, there are notable matrix-type misclassification regions due to image differences, making extraction work challenging. Therefore, in this study, we first adjusted the brightness, grayscale, and contrast of the training data after input to enhance its colour feature recognition ability. We then altered the scaling of the image, and rotated and transformed the training image from 0° to 360° to enhance morphological feature recognition and the accuracy of the training network in terrace extraction.Parameter settingThe network parameter setting is the most critical hyperparameter for UNet++ network training. They are mainly divided into input image size, batch size, learning rate, number of iterations, objective function, gradient descent strategy, momentum, decay rate, and activation function. Among them, we set the image size to 400*400 pixels based on the actual situation of the terraced area, where the UNet++ network has four scaling times, and the image size must be a multiple of 16. The batch size primarily affects the convergence of the model. If the batch limit is set to one, the model is easily affected by the random perturbation phenomenon and cannot converge to find the optimal solution. Since the batch size is determined by the size of the video memory, the value of the batch is limited by equipment constraints. The model in this study used a 2080Ti video card with 11 GB of video memory, and the batch was set to 8. The learning rate, gradient descent strategy, and objective function play a role in whether the network can find the best classification model better and faster. The learning rate was set to 0.001 for the first 500 generations, with the goal of achieving fast convergence to the target region. The learning rate was then set to 0.0001 for 500–1,000 generations, and the model was fine-tuned by choosing a smaller learning rate to find the model with the highest classification accuracy. Adam was chosen for the gradient descent strategy. The momentum and adaptive learning rate were used to increase the convergence rate. The cross-entropy classification loss function was chosen as the objective function to improve the differentiation between terraced and non-terraced areas. Momentum, decay rate, and activation function were all adopted from the previous default settings of the UNet++ network.Data correctionIn this study, we primarily used high-resolution images from Google Earth as the data source to extract the distribution of terraces on the Loess Plateau. Because this image source only contains a large amount of texture structure information and no vegetation information, it is easy to misjudge and misclassify features with the same morphological structure and edge features, such as permanent snow and ice, water bodies, bare land, and artificial surfaces. Vegetation information was generally processed based on waveband data from multispectral/hyperspectral images. It requires topographic correction, atmospheric correction, radiometric calibration, de-clouding, and other operational processes, which are extremely sophisticated30.GlobeLand30 is a 30 m spatial resolution global surface coverage dataset developed by the National Geomatics Center of China. The most recent GlobeLand30 dataset (v2020) has been updated with data sources from 2017 to the present. Its extensive data sources enable effective reduction of the impacts of cloud cover, with an overall accuracy of 85.72%. The classification accuracy of permanent snow and ice, water bodies, bare land, and artificial surfaces of this dataset is as high as 75.79%, 84.70%, 81.76%, and 86.70%, respectively. Since the update time of v2020 data is similar to that of high-resolution images, it can be used as correction data for vegetation information31.Since the training image data are two-dimensional planar data with no elevation or slope information (Fig. 1g), certain flat fields with visible field bumps are easily misclassified as terraces. The Space Shuttle Radar Topography Mission (SRTM v4.1) DEM has a spatial resolution of 30 m and ranges from 60° N to 56° S, completely covering the Loess Plateau32,33. In this study, these data were treated as terrain correction data. The amendment standard corrects the areas that have been extracted as terraces below 2° to non-terraced areas according to the requirements of the Ministry of Natural Resources of China.The spatial resolution of our extracted terraces is 1.89 m, whereas the spatial resolution of GlobeLand30 and DEM as correction data sources is 30 m, which is difficult to meet the requirements of data processing. Hence, we up-sample the two correction data sources, and then used multi-source data fusion. First, we extracted and up-sampled the terraced areas of glaciers, rivers, and deserts from GlobeLand30 to a spatial resolution of 1.89 m. Secondly, we up-sampled the DEM to 1.89 m using spatial interpolation for its raster centre as the true value of the region and performed a slope calculation for the up-sampled DEM. Further, the spatial distribution maps of glaciers, rivers, deserts, and slope maps of the Loess Plateau with the same resolution as the spatial distribution maps of terraces were available. Finally, we superimposed these images, used the terrace range in the TDMLP as a mask, and assessed the pixels in the mask area one by one. If a pixel belonged to permanent snow and ice, a water body, bare land, or an artificial surface, or had a slope less than 2°, it was modified to the background value. Otherwise, the original value was retained.We made artificial corrections to the data based on the extracted results for the arid areas of the Loess Plateau as well as for the flatter basins, given that these areas do not feature terraces.Training and validation dataFor supervised classification, the selection of sample areas and sample features is crucial. The focus and core of any land classification work is representative and effective training sample selection. To obtain a better sample area selection, we considered the selection of sample areas from three perspectives, i.e., colour texture features, topographic features, and spatial distance of the training samples. First, the terraces in this study are in agricultural land, including cultivated land, woodland, grassland, and other types of land; thus, different types of land will present different texture details. At the same time, high-resolution images from Google Earth are mosaicked. Because of the different acquisition times, the same region and land type will have visible colour differences and stitching traces, which is more common in the Loess Plateau region. Therefore, these factors should be considered in the selection of training samples as much as possible to improve the generability of the model and the correct rate of its extraction. Second, the state of the terraces varies according to topographic features. Among them, gradient, direction, altitude, and climate are the most significant factors. Terraces can be categorised as shallow-slope or steep-slope terraces. Based on slope aspect, altitude, and climate characteristics, they can also be categorised as either easy to identify or hard to identify. Thus, the sample should be inclusive of these types of terraces. According to the first law of geography, terraces in different spatial locations have different morphologies. Therefore, the spatial location of the samples should also be at a certain distance.In summary, we selected one county in each region based on the geomorphic zoning characteristics of the Loess Plateau. In addition, we added one more in the area where the density of terraces may be higher. Finally, we selected the whole area of seven counties (Fig. 3) as the training sample area distribution, covering 2.18% of the overall Loess Plateau area. The colour morphological features, topographic features, spatial location, and imaging quality of terrace images in these regions are highly representative. This method was unique from other classification methods. Most of the traditional methods are based on the single-pixel information of feature layers such as random forests, which tend to ignore the neighbouring information around the point, and thus are subject to misclassification and under classification for land types with outstanding texture information. In our study, we adopted the visual interpretation of the whole domain, which can cover the neighbourhood information of each pixel point more comprehensively. To ensure the uniformity and correctness of visual interpretation, the terraces in the training area were visually interpreted by seven interpreters after uniform professional training. For the disputed and uncertain areas, the seven interpreters carried out interactive interpretation and scoring according to the interpretation results. Finally, two other interpretation experts made the final review and corrections. The interpretation results of the training area were re-examined and revised based on the results of the later interpretations.Fig. 3Distribution of training sample areas and validation sites in terraces on the Loess Plateau.Full size imageTo better assess and compare the validity and correctness of the terraced agricultural area datasets on the Loess Plateau in quantitatively, the validation dataset was divided into two parts: a per-pixel point-based validation set and a field validation dataset of terraces with location information. The extracted datasets were comprehensively evaluated in terms of both pixel scale and field validation.We constructed a single-pixel validation point that evaluates the TDMLP. We applied the Icosahedral Snyder Equal Area Discrete Global Grid created by ArcGIS. Based on this strategy, the study area was partitioned into 972 regions (Fig. 3). To better validate the terrace classification results (excluding non-terrace classes), we placed more validation points within the grid where the terrace distribution is more concentrated. First, we calculated the proportion of terraces in each hexagonal grid to the total area of the hexagonal grid. Second, we separated the terraces into four levels according to the proportion of terraces to the whole grid area as 0–20%, 20–50%, 50–80%, and 80–100% and the number of validation points was 10, 20, 40, and 50, respectively.Since the proportion of the extracted terraced area to the total area was only 14%, direct random point deployment would have led to fewer terraced validation sets and thus would have affected the final data evaluation. Therefore, in the deployment strategy, we ensured that the validation points distributed in the extracted terraces in each grid account for at least one-fifth of the total number of validation points, but for the grid with a smaller proportion of terraces or even 0, this practice was meaningless. Hence, we stipulated that in the grid with a proportion of terraces ≤1%, direct random scattering was to be performed. The final scattered verification points in the terraced and non-terraced areas were 5,194 and 6,226, respectively, with a ratio close to 1:1 for easy verification. The spatial distribution is shown in Fig. 3.We validated the spatial distribution map of terraces on the Loess Plateau from 14 April 2021 to 1 May 2021 and constructed a field validation dataset of terraces with location information. Considering the longitudinal, latitudinal, and vertical heterogeneities of the Loess Plateau, the verification route was divided into two sections, north to south and east to west, to more comprehensively cover all regions of the Loess Plateau. The verification route started at Hohhot in the northeast of the Loess Plateau. It passed through the Datong Basin, followed the Yellow River to the south and the Weihe Plain, and then travelled westward through Mount Liupan to the westernmost part of the Loess Plateau. The route was through 54 counties/districts in 16 cities and six provinces on the Loess Plateau, with a total distance of 3,680 km, covering 15.8% of the counties on the Loess Plateau (total of 341 counties). We also surveyed and sampled the verification points approximately every 5 km along the route and collected data from a total of 815 sample points, covering various types of terraces on the Loess Plateau. The results are shown in Fig. 3. More

  • in

    Pablo Escobar’s ‘cocaine hippos’ spark conservation row

    A hippo swims in Colombia’s Magdalena River, near where Pablo Escobar’s compound was located.Credit: Fernando Vergara/AP/Shutterstock

    Colombian environment minister Susana Muhamad has triggered fear among researchers that she will protect, rather than reduce, a growing population of invasive hippos that threaten the country’s natural ecosystems and biodiversity. Although she did not directly mention the hippos — a contentious issue in Colombia — Muhamad said during a speech in late January that her ministry would create policies that prioritize animal well-being, including the creation of a new division of animal protection.
    Landmark Colombian bird study repeated to right colonial-era wrongs
    The hippos escaped from drug-cartel leader Pablo Escobar’s estate after he died in 1993. Left alone, the male and three females that Escobar had illegally imported from a US zoo established themselves in Colombia’s Magdalena River and some small lakes nearby — part of the country’s main watershed. After years of breeding, the ‘cocaine hippos’ have multiplied to about 150 individuals, scientists estimate.Given that the hippos (Hippopotamus amphibius) — considered the largest invasive animal in the world — have no natural predators in Colombia and have been mating at a steady rate, their population could reach 1,500 in 16 years, according to a modelling study published in 20211. “I do not understand what the government is waiting for to act,” says Nataly Castelblanco Martínez, a Colombian conservation biologist at the Autonomous University of Quintana Roo in Chetumal, Mexico, and co-author of the study. “If we don’t do anything, 20 years from now the problem will have no solution.”Researchers have called for a strict management plan that would eventually reduce the wild population to zero, through a combination of culling some animals and capturing others, then relocating them to facilities such as zoos. But the subject of what to do with the hippos has polarized the country, with some enamoured by the animals’ charisma and value as a tourist attraction and others concerned about the threat they pose to the environment and local fishing communities.‘A bit surreal’Several studies and observations suggest how destructive it could be to allow the Colombian hippo population to explode. A 2019 paper2, for example, showed that, compared with lakes without hippos, those where the animals have taken up residence contain more nutrients and organic matter that favour the growth of cyanobacteria — aquatic microbes associated with toxic algal blooms. These blooms can reduce water quality and cause mass fish deaths, affecting local fishing communities.

    A sign near Doradal, Colombia, warns passersby of the danger of invasive hippos.Credit: Juancho Torres/Anadolu Agency via Getty

    Other scientists have predicted that the hippos could displace endangered species that are native to the Magdalena River, such as the Antillean manatee (Trichechus manatus manatus), by outcompeting them for food and space. They caution that traffic accidents and attacks on people caused by the hippos will become more common. And they warn that wildlife traffickers are already taking advantage of the situation by illegally selling baby hippos — a trend that could intensify.“It’s a bit surreal,” says Jorge Moreno Bernal, a vertebrate palaeontologist at the University of the North in Barranquilla, Colombia. “This is just a taste of what may come.”When Colombian authorities first recognized the speed at which the hippo population was growing, during the 2000s, they acted to reduce their numbers. But in 2009, when photos appeared online after soldiers gunned down Pepe, Escobar’s fugitive male hippo, the outcry from animal-rights activists and others plunged the environment ministry into an “institutional paralysis”, says Sebastián Restrepo Calle, an ecologist at Javeriana University in Bogotá.Researchers say that the hippos don’t belong in Colombia — they are native to sub-Saharan Africa. Simulations run by Castelblanco Martínez and her colleagues suggest that to reduce the population to zero by 2033, about 30 hippos would need to be removed from the wild population per year1. No other course of action, including sterilization or castration, would eradicate them, according to the modelling of various management scenarios, says Castelblanco Martínez.The cost of inactionThe worry now is that, instead of basing decisions on evidence and expertise in conservation, the government is listening to popular opinion, says Restrepo Calle. Neither Muhamad nor representatives of the environment ministry replied to Nature’s requests for comment.
    Ancient stone tools suggest early humans dined on hippo
    “Why prioritize one species over our own ecosystems?” — especially a species that isn’t native, asks Alejandra Echeverri, a Colombian conservation scientist at Stanford University in California. Along with her colleagues, Echeverri published a study last month showing that Colombia has few policies governing invasive species compared with its overall number of biodiversity policies3.Animals-rights advocates, meanwhile, argue that they aren’t ignoring environmental concerns. Luis Domingo Gómez Maldonado, an animal-rights activist and specialist in animal law at Saint Thomas University in Bogotá, says “It’s not about saving the hippos on a whim,” but rather about solving the issue while also giving the hippos justice. “My indisputable position is: let’s save as many individuals as possible, let’s do it ethically.”Researchers, too, say they have the animals’ best interests at heart. “Even if [advocates] don’t see it, we care about the hippos,” Castelblanco Martínez says. “The more time that passes, the more hippos will either have to be culled, castrated or captured.”The question is whether environmental authorities will act swiftly to draft and enforce a management plan that is both ethical and effective. Should they sit on the issue for too long, Castelblanco Martínez warns, rural communities that are most affected by the hippos might take matters into their own hands.If the government doesn’t cull them, she says, people will use shotguns to do it. More