More stories

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    Excess plant growth worsens droughts

    1.Stephenson, N. L. Am. Nat. 135, 649–670 (1990).Article 

    Google Scholar 
    2.Mencuccini, M. et al. New Phytol. 222, 1207–1222 (2019).Article 

    Google Scholar 
    3.Ellison, D. et al. Glob. Change Biol. 18, 806–820 (2012).Article 

    Google Scholar 
    4.Jump, A. S. et al. Glob Change Biol. 23, 3742–3757 (2017).Article 

    Google Scholar 
    5.Zhang, Y., Keenan, T. F. & Zhou, S. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01551-8 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Sankaran, M. J. Ecol. 107, 1531–1549 (2019).Article 

    Google Scholar 
    7.Foster, D. et al. Biosciences 53, 77–88 (2003).Article 

    Google Scholar 
    8.Tilman, D. & Wedin, D. Nature 353, 653–655 (1991).Article 

    Google Scholar 
    9.Pfeiffer, M. et al. Biogeosciences 17, 5829–5847 (2020).CAS 
    Article 

    Google Scholar 
    10.Brodribb, T. J. et al. Science 368, 261–266 (2020).CAS 
    Article 

    Google Scholar 
    11.Slette, I. J. et al. Glob. Change Biol. 25, 3193–3200 (2019).Article 

    Google Scholar 
    12.Bernardino, P. N. et al. Remote Sens. 12, 2332 (2020).Article 

    Google Scholar  More

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    A question of the sexes

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    UAV reveals substantial but heterogeneous effects of herbivores on Arctic vegetation

    1.Atkins, J. L. et al. Cascading impacts of large-carnivore extirpation in an African ecosystem. Science 364, 173–177 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Borer, E. T. et al. Herbivores and nutrients control grassland plant diversity via light limitation. Nature 508, 517–520 (2014).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Hempson, G. P., Archibald, S. & Bond, W. J. A continent-wide assessment of the form and intensity of large mammal herbivory in Africa. Science 350, 1056–1061 (2015).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Schmitz, O. J. Herbivory from individuals to ecosystems. Annu. Rev. Ecol. Evol. Syst. 39, 133–152 (2008).Article 

    Google Scholar 
    5.Adler, P., Raff, D. & Lauenroth, W. The effect of grazing on the spatial heterogeneity of vegetation. Oecologia 128, 465–479 (2001).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Olff, H. & Ritchie, M. E. Effects of herbivores on grassland plant diversity. Trends Ecol. Evol. 13, 261–265 (1998).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Weeber, J., Hempson, G. P. & February, E. C. Large herbivore conservation in a changing world: Surface water provision and adaptability allow wildebeest to persist after collapse of long-range movements. Glob. Change Biol. 26, 2841–2853 (2020).ADS 
    Article 

    Google Scholar 
    8.Senft, R. L., Rittenhouse, L. R. & Woodmansee, R. G. Factors influencing patterns of cattle grazing behavior on shortgrass steepe. Rangel. Ecol. Manag. Range Manag. Arch. 38, 82–87 (1985).
    Google Scholar 
    9.McNaughton, S. J., Banyikwa, F. F. & McNaughton, M. M. Promotion of the cycling of diet-enhancing nutrients by African grazers. Science 278, 1798–1800 (1997).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Olofsson, J., De Mazancourt, C. & Crawley, M. J. Spatial heterogeneity and plant species richness at different spatial scales under rabbit grazing. Oecologia 156, 825–834 (2008).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Oksanen, L., Fretwell, S. D., Arruda, J. & Niemela, P. Exploitation ecosystems in gradients of primary productivity. Am. Nat. 118, 240–261 (1981).Article 

    Google Scholar 
    13.Oksanen, T. et al. The impact of thermal seasonality on terrestrial endotherm food web dynamics: A revision of the Exploitation Ecosystem Hypothesis. Ecography 43, 1859–1877 (2020).Article 

    Google Scholar 
    14.Fine, P. V. et al. The growth–defense trade-off and habitat specialization by plants in Amazonian forests. Ecology 87, S150–S162 (2006).PubMed 
    Article 
    PubMed Central 

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

    Google Scholar 
    16.Oliver, T., Roy, D. B., Hill, J. K., Brereton, T. & Thomas, C. D. Heterogeneous landscapes promote population stability. Ecol. Lett. 13, 473–484 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Siewert, M. B. & Olofsson, J. Scale-dependency of Arctic ecosystem properties revealed by UAV. Environ. Res. Lett. 15, 094030 (2020).ADS 
    Article 

    Google Scholar 
    18.Siewert, M. B. High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: A case study in a sub-Arctic peatland environment. Biogeosciences 15, 1663–1682 (2018).ADS 
    Article 

    Google Scholar 
    19.Post, E. et al. Ecological dynamics across the Arctic associated with recent climate change. Science 325, 1355–1358 (2009).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Newton, E. J., Pond, B. A., Brown, G. S., Abraham, K. F. & Schaefer, J. A. Remote sensing reveals long-term effects of caribou on tundra vegetation. Polar Biol. 37, 715–725 (2014).Article 

    Google Scholar 
    21.Eklundh, L., Johansson, T. & Solberg, S. Mapping insect defoliation in Scots pine with MODIS time-series data. Remote Sens. Environ. 113, 1566–1573 (2009).ADS 
    Article 

    Google Scholar 
    22.Ehrich, D. et al. Documenting lemming population change in the Arctic: Can we detect trends?. Ambio https://doi.org/10.1007/s13280-019-01198-7 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Olofsson, J., Tømmervik, H. & Callaghan, T. V. Vole and lemming activity observed from space. Nat. Clim. Change 2, 880–883 (2012).ADS 
    Article 

    Google Scholar 
    24.Hambäck, P. A., Schneider, M. & Oksanen, T. Winter herbivory by voles during a population peak: The relative importance of local factors and landscape pattern. J. Anim. Ecol. 67, 544–553 (1998).Article 

    Google Scholar 
    25.Siewert, M. B. et al. Comparing carbon storage of Siberian tundra and taiga permafrost ecosystems at very high spatial resolution: Ecosystem carbon in taiga and tundra. J. Geophys. Res. Biogeosciences 120, 1973–1994 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    26.Virtanen, T. & Ek, M. The fragmented nature of tundra landscape. Int. J. Appl. Earth Obs. Geoinf. 27(Part A), 4–12 (2014).ADS 
    Article 

    Google Scholar 
    27.Siewert, M. B., Lantuit, H., Richter, A. & Hugelius, G. Permafrost causes unique fine-scale spatial variability across tundra soils. Glob. Biogeochem. Cycles 35, e2020GB006659 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    28.Koh, L. P. & Wich, S. A. Dawn of drone ecology: Low-cost autonomous aerial vehicles for conservation. Trop. Conserv. Sci. 5, 121–132 (2012).Article 

    Google Scholar 
    29.Assmann, J. J., Kerby, J. T., Cunliffe, A. M. & Myers-Smith, I. H. Vegetation monitoring using multispectral sensors—best practices and lessons learned from high latitudes. J. Unmanned Veh. Syst. 7, 54–75 (2018).Article 

    Google Scholar 
    30.Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth. Bioscience 51, 933 (2001).Article 

    Google Scholar 
    31.Barrio, I. C. et al. Herbivory network: An international, collaborative effort to study herbivory in Arctic and alpine ecosystems. Polar Sci. 10, 297–302 (2016).ADS 
    Article 

    Google Scholar 
    32.Siewert, M. B., Hugelius, G., Heim, B. & Faucherre, S. Landscape controls and vertical variability of soil organic carbon storage in permafrost-affected soils of the Lena River Delta. CATENA 147, 725–741 (2016).CAS 
    Article 

    Google Scholar 
    33.Olofsson, J. et al. Long-term experiments reveal strong interactions between lemmings and plants in the fennoscandian highland tundra. Ecosystems 17, 606–615 (2014).Article 

    Google Scholar 
    34.Virtanen, R., Parviainen, J. & Henttonen, H. Winter grazing by the Norwegian lemming (Lemmus lemmus) at Kilpisjärvi (NW Finnish Lapland) during a moderate population peak. Ann. Zool. Fenn. 39, 335–341 (2002).
    Google Scholar 
    35.Johnson, D. R. et al. Exclusion of brown lemmings reduces vascular plant cover and biomass in Arctic coastal tundra: resampling of a 50 $mathplus$ year herbivore exclosure experiment near Barrow, Alaska. Environ. Res. Lett. 6, 045507 (2011).Article 

    Google Scholar 
    36.Petit Bon, M. et al. Interactions between winter and summer herbivory affect spatial and temporal plant nutrient dynamics in tundra grassland communities. Oikos 129, 1229–1242 (2020).CAS 
    Article 

    Google Scholar 
    37.Virtanen, R., Henttonen, H. & Laine, K. Lemming grazing and structure of a snowbed plant community: A long-term experiment at Kilpisjärvi, Finnish Lapland. Oikos 79, 155–166 (1997).Article 

    Google Scholar 
    38.Domine, F. et al. Snow physical properties may be a significant determinant of lemming population dynamics in the high Arctic. Arct. Sci. 4, 813–826 (2018).Article 

    Google Scholar 
    39.Aunapuu, M. et al. Spatial patterns and dynamic responses of arctic food webs corroborate the exploitation ecosystems hypothesis (EEH). Am. Nat. 171, 249–262 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Hoset, K. S., Kyrö, K., Oksanen, T., Oksanen, L. & Olofsson, J. Spatial variation in vegetation damage relative to primary productivity, small rodent abundance and predation. Ecography 37, 894–901 (2014).Article 

    Google Scholar 
    41.Hoset, K. S. et al. Changes in the spatial configuration and strength of trophic control across a productivity gradient during a massive rodent outbreak. Ecosystems 20, 1421–1435 (2017).Article 

    Google Scholar 
    42.Lindén, E., Gough, L. & Olofsson, J. Large and small herbivores have strong effects on tundra vegetation in Scandinavia and Alaska. Ecol. Evol. 11, 12141–12152 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Fulton, E. A., Blanchard, J. L., Melbourne-Thomas, J., Plagányi, É. E. & Tulloch, V. J. D. Where the ecological gaps remain, a Modelers’ perspective. Front. Ecol. Evol. 7, 424 (2019).Article 

    Google Scholar 
    44.Owen-Smith, N., Fryxell, J. M. & Merrill, E. H. Foraging theory upscaled: The behavioural ecology of herbivore movement. Philos. Trans. R. Soc. B Biol. Sci. 365, 2267–2278 (2010).CAS 
    Article 

    Google Scholar 
    45.Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).ADS 
    Article 

    Google Scholar 
    46.Street, L. E., Shaver, G. R., Williams, M. & Van Wijk, M. T. What is the relationship between changes in canopy leaf area and changes in photosynthetic CO2 flux in arctic ecosystems?. J. Ecol. 95, 139–150 (2007).Article 

    Google Scholar 
    47.Morris, D. W., Dupuch, A. & Halliday, W. D. Climate-induced habitat selection predicts future evolutionary strategies of lemmings. Evol. Ecol. Res. 14, 689–705 (2012).
    Google Scholar 
    48.Kausrud, K. L. et al. Linking climate change to lemming cycles. Nature 456, 93–97 (2008).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Cunliffe, A. M., Assmann, J. J., Daskalova, G., Kerby, J. T. & Myers-Smith, I. H. Aboveground biomass corresponds strongly with drone-derived canopy height but weakly with greenness (NDVI) in a shrub tundra landscape. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aba470 (2020).Article 

    Google Scholar 
    50.Myllymäki, A., Paasikallio, A., Pankakoski, E. & Kanervo, V. Removal experiments on small quadrats as a means of rapid assessment of the abundance of small mammals. Ann. Zool. Fenn. 8, 177–185 (1971).
    Google Scholar 
    51.Inglada, J. & Christophe, E. The Orfeo Toolbox remote sensing image processing software. In 2009 IEEE International Geoscience and Remote Sensing Symposium vol. 4 IV–733 (IEEE, 2009).52.Leutner, B., Horning, N., Schwalb-Willmann, J. & Hijmans, R. J. RStoolbox: Tools for remote sensing data analysis. R Package Version 026 7, 1991–2007 (2019).
    Google Scholar 
    53.Conrad, O. et al. System for automated geoscientific analyses (SAGA) v. 2.1.4. Geosci. Model Dev. 8, 1991–2007 (2015).ADS 
    Article 

    Google Scholar 
    54.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
    Article 

    Google Scholar 
    55.Hussain, M., Chen, D., Cheng, A., Wei, H. & Stanley, D. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS J. Photogramm. Remote Sens. 80, 91–106 (2013).ADS 
    Article 

    Google Scholar 
    56.Tewkesbury, A. P., Comber, A. J., Tate, N. J., Lamb, A. & Fisher, P. F. A critical synthesis of remotely sensed optical image change detection techniques. Remote Sens. Environ. 160, 1–14 (2015).ADS 
    Article 

    Google Scholar 
    57.Hijmans, R. J. et al. raster: Geographic Data Analysis and Modeling. (2020).58.Pebesma, E. & Graeler, B. gstat: Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation. (2020).59.Fortin, M.-J. & Dale, M. R. T. Spatial Autocorrelation. In The SAGE Handbook of Spatial Analysis 88–103 (SAGE Publications, Ltd, 2009). https://doi.org/10.4135/9780857020130.n6.60.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).61.QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. (2020). http://qgis.osgeo.org. Accessed 12 Sept 2020 More

  • in

    Tracking forest loss and fragmentation between 1930 and 2020 in Asian elephant (Elephas maximus) range in Nepal

    1.Lambin, E. F. et al. The causes of land-use and land-cover change: Moving beyond the myths. Glob. Environ. Chang. 11, 261–269 (2001).Article 

    Google Scholar 
    2.Lawler, J. J. et al. Projected land-use change impacts on ecosystem services in the United States. Proc. Natl. Acad. Sci. USA 111, 7492–7497 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Bongaarts, J. IPBES, 2019. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services. Population and Development Review vol. 45 (2019).4.Pardini, R. OBSOLETE: Fragmentation and habitat loss. Ref. Modul. Earth Syst. Environ. Sci. 2, 10–11. https://doi.org/10.1016/b978-0-12-409548-9.09824-9 (2018).Article 

    Google Scholar 
    5.Anthony, B. & Wasambo, J. Human-wildlife conflict study report. Human Wildl. Confl. Stud. Rep. 2, 55 (2009).
    Google Scholar 
    6.Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).Article 

    Google Scholar 
    7.Collinge, S. K. Ecological consequences of habitat fragmentation: Implications for landscape architecture and planning. Landsc. Urban Plan. 36, 59–77 (1996).Article 

    Google Scholar 
    8.Pierri-Daunt, A. B. & Tanaka, M. O. Assessing habitat fragmentation on marine epifaunal macroinvertebrate communities: An experimental approach. Landsc. Ecol. 29, 17–28 (2014).Article 

    Google Scholar 
    9.Fahrig, L. et al. Is habitat fragmentation bad for biodiversity?. Biol. Conserv. 230, 179–186 (2019).Article 

    Google Scholar 
    10.Bustamante, R. O., Serey, I. A. & Pickett, S. T. A. Forest fragmentation, plant regeneration and invasion processes across edges in Central Chile. In How Landscapes Change Ecological Studies (Analysis and Synthesis), 162 (eds Bradshaw, G. A. & Marquet, P. A.) 145–160 (Springer, 2003).
    Google Scholar 
    11.Chaplin-Kramer, R. et al. Degradation in carbon stocks near tropical forest edges. Nat. Commun. 6, 1–6 (2015).Article 
    CAS 

    Google Scholar 
    12.Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Symes, W. S., Edwards, D. P., Miettinen, J., Rheindt, F. E. & Carrasco, L. R. Combined impacts of deforestation and wildlife trade on tropical biodiversity are severely underestimated. Nat. Commun. 9, 20 (2018).ADS 
    Article 
    CAS 

    Google Scholar 
    14.Singh, S. et al. Modeling the spatial dynamics of deforestation and fragmentation using multi-layer perceptron neural network and landscape fragmentation tool. Ecol. Eng. 99, 543–551 (2017).Article 

    Google Scholar 
    15.Bustamante, R. O. & Simonetti, J. A. Is Pinus radiata invading the native vegetation in Central Chile? Demographic responses in a fragmented forest. Biol. Invas. 7, 243–249 (2005).Article 

    Google Scholar 
    16.Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. Proc. Natl. Acad. Sci. USA 114, 10678–10683 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Cardillo, M. et al. Evolution: Multiple causes of high extinction risk in large mammal species. Science (80–) 309, 1239–1241 (2005).ADS 
    CAS 
    Article 

    Google Scholar 
    18.Woodroffe, R., Thirgood, S. & Rabinowitz, A. People and Wildlife: Conflict or Coexistence (Cambridge University Press, 2005).Book 

    Google Scholar 
    19.Goswami, V. R. et al. Community-managed forests and wildlife-friendly agriculture play a subsidiary but not substitutive role to protected areas for the endangered Asian elephant. Biol. Conserv. 177, 74–81 (2014).Article 

    Google Scholar 
    20.Wittemyer, G., Elsen, P., Bean, W. T., Burton, A. C. O. & Brashares, J. S. Accelerated human population growth at protected area edges. Science (80–) 321, 123–126 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    21.Shaffer, L. J., Khadka, K. K., Van Den Hoek, J. & Naithani, K. J. Human-elephant conflict: A review of current management strategies and future directions. Front. Ecol. Evol. 6, 58 (2019).Article 

    Google Scholar 
    22.Desai, A. A. & Riddle, H. S. Human–Elephant Conflict in Asia. (2015).23.Thouless, C. R. et al. African elephant status report 2016: An update from the African elephant database. Occasional paper series of the IUCN Species Survival Commission. IUCN Species Survical Commun. 4, 309 (2016).
    Google Scholar 
    24.Leimgruber, P. et al. Fragmentation of Asia’s remaining wildlands: Implications for Asian elephant conservation. Anim. Conserv. 6, 347–359 (2003).Article 

    Google Scholar 
    25.Koirala, R. K., Raubenheimer, D., Aryal, A., Pathak, M. L. & Ji, W. Feeding preferences of the Asian elephant (Elephas maximus) in Nepal. BMC Ecol. 16, 1–9 (2016).Article 

    Google Scholar 
    26.Sukumar, R. A brief review of the status, distribution and biology of wild Asian elephants Elephas maximus. Int. Zoo Yearb. 40, 1–8 (2006).Article 

    Google Scholar 
    27.Baskaran, N. Ranging and Resource Use by Asian elephant in Nilgiri Biosphere Reserve Southern India. (1998).28.Branco, P. S. et al. Determinants of elephant foraging behaviour in a coupled human-natural system: Is brown the new green?. J. Anim. Ecol. 88, 780–792 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Fernando, P. et al. Ranging behavior of the Asian elephant in Sri Lanka. Mamm. Biol. 73, 2–13 (2008).Article 

    Google Scholar 
    30.Naha, D. et al. Landscape predictors of human–leopard conflicts within multi-use areas of the Himalayan region. Sci. Rep. 10, 20 (2020).Article 
    CAS 

    Google Scholar 
    31.DNPWC. The Elephant Conservation Action Plan for Nepal. (2009).32.Ram, A. K. Status distribution and habitat use by Asian elephants in Nepal. (2020).33.ten Velde, P. A Status Report of Nepal’s Wild Elephant Population. (1997).34.Ram, A. K. et al. Patterns and determinants of Elephant attacks on humans in Nepal. Ecol. Evol. 11, 11639–11650. https://doi.org/10.1002/ece3.7796 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Wikramanayake, E. et al. Designing a conservation landscape for tigers in human-dominated environments. Conserv. Biol. 18, 839–844 (2004).Article 

    Google Scholar 
    36.Smith, J. L. D. & Mishra, H. R. Status and distribution of Asian elephants in Central Nepal. Oryx 26, 10–14 (1992).Article 

    Google Scholar 
    37.Shrestha, M. N., Shrestha, K. . & Dhakal, T. R. Hatti byabasthapan yojana tarujma pratibedan (Report on Planning for Elephant Management). Kathmandu: Janchbujh kendra bibhag raj durbar (Department Investigation Center, Nepali Royal Palace (in Nepali version). (HMGN palace investigation centre, Principal Secretariat of His Majesty King, Royal Palace, Kathmandu, Nepal (in Nepali version), 1985).38.Kharel, F. R. The challenge of managing domesticated Asian elephants in Nepal. in Giants on our Hands (Proceedings of the international workshop on the domesticated Asian elehant) 103–103 (FAO Regional Office for Asia and the Pacific of United Nations, Maliwan Mansion Phra Atit Road, Bangkok 10200 Thailand, 2002).39.Gee, E. P. Report on a Survey of Rhinoceros Area of Nepal, prepared for the survival service commission of the International Union for the Conservation of Nature and Natural resources. (1959).40.MoFSC. Strategy and Action Plan 2015–2025 for Terai Arc landscape, Nepal. (2015).41.Subedi, N. et al. Progress Report on Faunal Biodiversity Assessment in Chure Range of Nepal. (President Chure-Terai Madhesh Conservation Development Board and National Trust for Nature Conservation, Khumaltar, Lalitpur, 2021).42.DFRS. State of Nepal’s Forests. Forest Resource Assessment (FRA) Nepal, Department of Forest Research and Survey (DFRS). Kathmandu, Nepal. (Ministry of Forest and Soil Conservation, Nepal, 2015). 978-9937-8896-3-6.43.Reddy, C. S. et al. Assessment and monitoring of deforestation and forest fragmentation in South Asia since the 1930s. Glob. Planet. Change 161, 132–148 (2018).ADS 
    Article 

    Google Scholar 
    44.Reddy, S. C. et al. Quantifying nationwide land cover and historical changes in forests of Nepal (1930–2014): Implications on forest fragmentation. Biodivers. Conserv. 27, 91–107 (2018).Article 

    Google Scholar 
    45.Aulestia, M. J. S. Understanding land use and land cover dynamics in the Chure region of Nepal: Integrating physiographic, socio-economic and policy drivers. (2019).46.Laurie, A. The Ecology and Behaviour of the Greater One-Horned Rhinoceros, a dissertation submitted to the University of Cambridge for the degree of Doctor of Philosophy. Behaviour (1978).47.Rimal, S., Adhikari, H. & Tripathi, S. Habitat suitability and threat analysis of Greater One-horned Rhinoceros Rhinoceros unicornis Linnaeus, 1758 (Mammalia: Perissodactyla: Rhinocerotidae) in Rautahat District, Nepal. J. Threat. Taxa 10, 11999–12007 (2018).Article 

    Google Scholar 
    48.Peh, K. S. H. Invasive species in Southeast Asia: The knowledge so far. Biodivers. Conserv. 19, 1083–1099 (2010).Article 

    Google Scholar 
    49.Lamichhane, B. R. et al. Using interviews and biological sign surveys to infer seasonal use of forested and agricultural portions of a human-dominated landscape by Asian elephants in Nepal. Ethol. Ecol. Evol. 30, 331–347 (2018).Article 

    Google Scholar 
    50.Acharya, K. P., Paudel, P. K., Neupane, P. R. & Köhl, M. Human-wildlife conflicts in Nepal: Patterns of human fatalities and injuries caused by large mammals. PLoS One 11, 1–18 (2016).
    Google Scholar 
    51.Carter, N. H., Shrestha, B. K., Karki, J. B., Pradhan, N. M. B. & Liu, J. Coexistence between wildlife and humans at fine spatial scales. Proc. Natl. Acad. Sci. USA 109, 15360–15365 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Choudhury, A. Human-Elephant conflicts in northeast India. Hum. Dimens. Wildl. 9, 261–270 (2004).Article 

    Google Scholar 
    53.Reddy, C. S., Sreelekshmi, S., Jha, C. S. & Dadhwal, V. K. National assessment of forest fragmentation in India: Landscape indices as measures of the effects of fragmentation and forest cover change. Ecol. Eng. 60, 453–464 (2013).Article 

    Google Scholar 
    54.Puyravaud, J. P. Standardizing the calculation of the annual rate of deforestation. For. Ecol. Manag. 177, 593–596 (2003).Article 

    Google Scholar 
    55.Puyravaud, J. P., Gubbi, S., Poornesha, H. C. & Davidar, P. Deforestation increases frequency of incidents with elephants (Elephas maximus). Trop. Conserv. Sci. 12, 20 (2019).Article 

    Google Scholar 
    56.Puyravaud, J. P., Davidar, P. & Laurance, W. F. Cryptic destruction of India’s native forests. Conserv. Lett. 3, 390–394 (2010).Article 

    Google Scholar 
    57.Sampson, C. et al. Effects of illegal grazing and invasive Lantana camara on Asian elephant habitat use. Biol. Conserv. 220, 50–59 (2018).Article 

    Google Scholar 
    58.Roever, C. L., van Aarde, R. J. & Leggett, K. Functional responses in the habitat selection of a generalist mega-herbivore, the African savannah elephant. Ecography (Cop.) 35, 972–982 (2012).Article 

    Google Scholar 
    59.Liu, P., Wen, H., Lin, L., Liu, J. & Zhang, L. Habitat evaluation for Asian elephants (Elephas maximus) in Lincang: Conservation planning for an extremely small population of elephants in China. Biol. Conserv. 198, 113–121 (2016).Article 

    Google Scholar 
    60.Ripple, W. J. et al. Collapse of the world’s largest herbivores. Sci. Adv. 1, 2 (2015).Article 

    Google Scholar 
    61.Sukumar, R. The Asian Elephant: Ecology and Management Vol 8 254 (Cambridge University Press, 1989).
    Google Scholar 
    62.Desai, A. A. & Baskaran, N. Impact of human activities on the ranging behaviour of elephants in the Nilgiri biosphere Reserve, South India. Bombay Nat. Hist. Soc. 93, 25 (1996).
    Google Scholar 
    63.Smit, I. P. J., Grant, C. C. & Devereux, B. J. Do artificial waterholes influence the way herbivores use the landscape? Herbivore distribution patterns around rivers and artificial surface water sources in a large African savanna park. Biol. Conserv. 136, 85–99 (2007).Article 

    Google Scholar 
    64.Smit, I. P. J., Grant, C. C. & Whyte, I. J. Landscape-scale sexual segregation in the dry season distribution and resource utilization of elephants in Kruger National Park, South Africa: Biodiversity research. Divers. Distrib. 13, 225–236 (2007).Article 

    Google Scholar 
    65.Birkett, P. J., Vanak, A. T., Muggeo, V. M. R., Ferreira, S. M. & Slotow, R. Animal perception of seasonal thresholds: Changes in elephant movement in relation to rainfall patterns. PLoS One 7, 25 (2012).
    Google Scholar 
    66.Wilson, S., Davies, T. E., Hazarika, N. & Zimmermann, A. Understanding spatial and temporal patterns of human-elephant conflict in Assam, India. Oryx https://doi.org/10.1017/S0030605313000513 (2015).Article 

    Google Scholar 
    67.Neupane, D., Kunwar, S., Bohara, A. K., Risch, T. S. & Johnson, R. L. Willingness to pay for mitigating human-elephant conflict by residents of Nepal. J. Nat. Conserv. 36, 65–76 (2017).Article 

    Google Scholar 
    68.Neupane, D., Kwon, Y., Risch, T. S., Williams, A. C. & Johnson, R. L. Habitat use by Asian elephants: Context matters. Glob. Ecol. Conserv. 17, e00570 (2019).Article 

    Google Scholar 
    69.Goswami, V. R., Medhi, K., Nichols, J. D. & Oli, M. K. Mechanistic understanding of human-wildlife conflict through a novel application of dynamic occupancy models. Conserv. Biol. 29, 1100–1110 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    70.Reddy, C. S. et al. Conservation priorities of forest ecosystems: Evaluation of deforestation and degradation hotspots using geospatial techniques. Ecol. Eng. 91, 2 (2016).Article 

    Google Scholar 
    71.Nandy, S., Kushwaha, S. P. S. & Dadhwal, V. K. Forest degradation assessment in the upper catchment of the river Tons using remote sensing and GIS. Ecol. Indic. 11, 509–513 (2011).Article 

    Google Scholar 
    72.Suba, R. B. et al. Rapid expansion of oil palm is leading to human–elephant conflicts in north Kalimantan province of Indonesia. Trop. Conserv. Sci. 10, 25 (2017).Article 

    Google Scholar 
    73.Naha, D., Sathyakumar, S., Dash, S., Chettri, A. & Rawat, G. S. Assessment and prediction of spatial patterns of human-elephant conflicts in changing land cover scenarios of a human-dominated landscape in North Bengal. PLoS One 14, 25 (2019).
    Google Scholar 
    74.Laudari, H. K., Aryal, K. & Maraseni, T. A postmortem of forest policy dynamics of Nepal. Land Use Policy 91, 25 (2020).Article 

    Google Scholar 
    75.Gee, E. P. Report on a brief survey of the wild life resources of Nepal, including the rhinoceros. Oryx 7, 67–76 (1963).Article 

    Google Scholar 
    76.Kanel, K. R. & Acharya, D. P. Re-Inventing Forestry Agencies: Institutional Innovation to Support Community Forestry in Nepal. Re-Inventing Forestry Agencies: Experiences of Institutional Restructuring in Asia and the Pacific vol. 4 (FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS REGIONAL OFFICE FOR ASIA AND THE PACIFIC Bangkok, 2008, 2008).77.Dahal, G. R., Pokharel, B. K., Khanal, D. R. & Pokhrel, P. R. A framework for classifying subsistence production types of Nepal. J. For. Livelih. 15, 15–26 (2017).Article 

    Google Scholar 
    78.Ranjit, Y. History of forest management in Nepal: An analysis of political and economic perspective. Econ. J. Nepal 42, 12–28 (2019).Article 

    Google Scholar 
    79.Adhikari, J. & Dhungana, H. The state and forest resources: An historical analysis of policies affecting forest management in the Nepalese Tarai. Himal. J. Assoc. Nepal Himal. Stud. 29, 43–56 (2010).
    Google Scholar 
    80.Ram, A.K. & Acharya, H. Status distribution and habitat use by Asian elephants in Nepal. In A Compendium of Conservation Bulletien. 155–160 (Department of National Parks and Wildlife Conservation, Nepal, 2020).81.GoN/PCTMCDB. President Chure-Tarai Madhesh Conservation and Management Master Plan. (2017).82.Chaudhary, B. et al. Detailed Final Report Report with Major Findings (Part-I). 1–19 (2018).83.CBS. National Population Census. Central Bureau of Statistics Vol. 08, 2014 (Central Bureau of Statistics Ramshah Path, 2011).
    Google Scholar 
    84.Hamilton, A. C. & Radford, E. A. Identification and Conservation of Impeortant Plant Areas for Medicinal Plants in the Himalaya. Project and Workshop Report (Plantlife International, Salisbury, UK) and Ethnobotanical Society of Nepal (Kathmandu, Nepal, 2007).85.Chaudhary, R. P., Uprety, Y. & Rimal, S. K. Deforestation in Nepal: Causes, consequences, and responses. Biol. Environ. Hazards Risks Disast. 20, 20. https://doi.org/10.1016/B978-0-12-394847-2.00020-6 (2016).Article 

    Google Scholar 
    86.Neupane, D., Johnson, R. L. & Risch, T. S. How do land-use practices affect human–elephant conflict in Nepal?. Wildl. Biol. 17, wlb.00313 (2017).Article 

    Google Scholar 
    87.Acharya, K. P., Paudel, P. K., Jnawali, S. R., Neupane, P. R. & Köhl, M. Can forest fragmentation and configuration work as indicators of human–wildlife conflict? Evidences from human death and injury by wildlife attacks in Nepal. Ecol. Indic. 80, 74–83 (2017).Article 

    Google Scholar 
    88.DNPWC. Elephant Conservation Action Plan of Nepal (2010–2019). 1–30 (2010).89.Wilcove, D. S., McLellan, C. H. & Dobson, A. P. Habitat fragmentation in the temperate zone. In Conservation Biology 237–256 (The Science of Scarcity and Diversity, 1986).
    Google Scholar 
    90.FAO. State of the World’s Forests. Food and Agriculture Organization of The United Nations, Rome (2014).91.Padalia, H. et al. Assessment of historical forest cover loss and fragmentation in Asian elephant ranges in India. Environ. Monit. Assess. 191, 25 (2019).Article 

    Google Scholar 
    92.Sudhakar Reddy, C. et al. Quantification and monitoring of deforestation in India over eight decades (1930–2013). Biodivers. Conserv. 25, 93–116 (2016).Article 

    Google Scholar 
    93.Kaim, D. et al. Broad scale forest cover reconstruction from historical topographic maps. Appl. Geogr. 67, 39–48 (2016).Article 

    Google Scholar 
    94.Kaim, D. et al. Uncertainty in historical land-use reconstructions with topographic maps. Quaest. Geogr. 33, 55–63 (2014).Article 

    Google Scholar 
    95.Gorelick, N. et al. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).ADS 
    Article 

    Google Scholar 
    96.Wang, Y. et al. Mapping tropical disturbed forests using multi-decadal 30 m optical satellite imagery. Remote Sens. Environ. 221, 474–488 (2019).ADS 
    Article 

    Google Scholar 
    97.Huang, H. et al. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens. Environ. 202, 166–176 (2017).ADS 
    Article 

    Google Scholar 
    98.Midekisa, A. et al. Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing. PLoS One 12, 1–15 (2017).
    Google Scholar 
    99.Zurqani, H. A., Post, C. J., Mikhailova, E. A., Schlautman, M. A. & Sharp, J. L. Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf. 69, 175–185 (2018).ADS 
    Article 

    Google Scholar 
    100.Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M. & Rigol-Sanchez, J. P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 67, 93–104 (2012).ADS 
    Article 

    Google Scholar 
    101.ESRI. ArcGIS Desktop105 (ESRI, 2016).
    Google Scholar 
    102.Elkie, P., Rempel, R. & Carr, A. Patch Analyst User’s Manual. Ont. Min. Natur. Resour. Northwest Sci. & Technol. Thunder Bay, Ont. TM-002, 16. (1999).103.Vogt, P. et al. Mapping spatial patterns with morphological image processing. Landsc. Ecol. 22, 171–177 (2007).Article 

    Google Scholar 
    104.Dutta, K., Sudhakar Reddy, C., Sharma, S. & Jha, C. S. Quantification and monitoring of forest cover changes in Agasthyamalai Biosphere Reserve, Western Ghats, India (1920–2012). Curr. Sci. 110, 508–520 (2016).Article 

    Google Scholar 
    105.Shapiro, A. C., Aguilar-Amuchastegui, N., Hostert, P. & Bastin, J. F. Using fragmentation to assess degradation of forest edges in Democratic Republic of Congo. Carbon Balance Manag. 11, 25 (2016).Article 
    CAS 

    Google Scholar  More

  • in

    A state-space approach to understand responses of organisms, populations and communities to multiple environmental drivers

    1.Northrup, J. M., Rivers, J. W., Yang, Z. & Betts, M. G. Synergistic effects of climate and land-use change influence broad-scale avian population declines. Glob. Change Biol. 25, 1561–1575 (2019).Article 

    Google Scholar 
    2.Thackeray, S. J. et al. Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob. Change Biol. 16, 3304–3313 (2011).Article 

    Google Scholar 
    3.González-Ortegón, E., Blasco, J., Vay, L. L. & Giménez, L. A multiple stressor approach to study the toxicity and sub-lethal effects of pharmaceutical compounds on the larval development of a marine invertebrate. J. Hazard. Mater. 263, 233–238 (2013).PubMed 
    Article 
    CAS 

    Google Scholar 
    4.Byrne, M. & Przeslawski, R. Multistressor impacts of warming and acidification of the ocean on marine invertebrates’ life histories. Integr. Comp. Biol. 53, 582–596 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Keeling, R. F., Kärtzinger, A. & Gruber, N. Ocean deoxygenation in a warming world. Annu. Rev. Mar. Sci. 2, 199–229 (2010).Article 

    Google Scholar 
    6.Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).PubMed 
    Article 

    Google Scholar 
    7.Griffen, B., Belgrad, B. A., Cannizzo, Z. J., Knotts, E. R. & Hancock, E. R. Rethinking our approach to multiple stressor studies in marine environments. Mar. Ecol. Prog. Ser. 543, 273–281 (2016).Article 

    Google Scholar 
    8.Gunderson, A., Armstrong, E. & Stillman, J. Multiple stressors in a changing world: the need for an improved perspective on physiological responses to the dynamic marine environment. Annu. Rev. Mar. Sci. 8, 357–378 (2016).Article 

    Google Scholar 
    9.Orr, J. A. et al. Towards a unified study of multiple stressors: divisions and common goals across research disciplines. Proc. R. Soc. B. 287, 20200421 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Piggott, J. J., Townsend, C. R. & Matthaei, C. D. Climate warming and agricultural stressors interact to determine stream macroinvertebrate community dynamics. Glob. Change Biol. 21, 1887–1906 (2015).Article 

    Google Scholar 
    11.Tekin, E. et al. Using a newly introduced framework to measure ecological stressor interactions. Ecol. Lett. 23, 1391–1403 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Côté, I. M., Darling, E. S. & Brown, C. J. Interactions among ecosystem stressors and their importance in conservation. Proc. R. Soc. B: Biol. Sci. 283, 20152592 (2016).Article 

    Google Scholar 
    13.Breitburg, D. L. et al. In Successes, Limitations, and Frontiers in Ecosystem Science (eds. Pace, M. L. & Groffman, P. M.) Ch. 17 (Springer, 1998).14.Sinclair, B. J., Ferguson, L. V., Salehipour-shirazi, G. & MacMillan, H. A. Cross-tolerance and cross-talk in the cold: relating low temperatures to desiccation and immune stress in insects. Integr. Comp. Biol. 53, 545–556 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Vinebrooke, D. et al. Impacts of multiple stressors on biodiversity and ecosystem functioning: the role of species co-tolerance. Oikos 104, 451–457 (2004).Article 

    Google Scholar 
    16.Boyd, P. W. et al. Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change—A review. Glob. Change Biol. 24, 2239–2261 (2018).Article 

    Google Scholar 
    17.De Laender, F. Community- and ecosystem-level effects of multiple environmental change drivers: beyond null model testing. Glob. Change Biol. 24, 5021–5030 (2018).Article 

    Google Scholar 
    18.Borer, E. T. et al. Finding generality in ecology: a model for globally distributed experiments. Meth. Ecol. Evol. 5, 65–73 (2014).Article 

    Google Scholar 
    19.Fraser, L. H. et al. Coordinated distributed experiments: an emerging tool for testing global hypotheses in ecology and environmental science. Front. Ecol. Environ. 11, 147–155 (2013).Article 

    Google Scholar 
    20.Dunham, A. E. & Beaupre, S. J. In Experimental Ecology: Issues and Perspectives (eds Resetarits, W. & Bernardo, J.) Ch. 2 (Oxford Univ. Press, 1998).21.Morin, P. J. In Experimental Ecology: Issues and Perspectives (eds Resetarits, W. & Bernardo, J.) Ch. 3 (Oxford Univ. Press, 1998).22.Moran, E. V., Hartig, F. & Bell, D. M. Intraspecific trait variation across scales: implications for understanding global change responses. Glob. Change Biol. 22, 137–150 (2016).Article 

    Google Scholar 
    23.Violle, C., Reich, P. B., Pacala, S. W., Enquist, B. J. & Kattge, J. The emergence and promise of functional biogeography. Proc. Natl Acad. Sci. USA 111, 13690–13696 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Carter, H. A., Ceballos-Osuna, L., Miller, N. A. & Stillman, J. H. Impact of ocean acidification on metabolism and energetics during early life stages of the intertidal porcelain crab Petrolisthes cinctipes. J. Exp. Biol. 216, 1412–1422 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Appelbaum, S. L., Pan, T. C. F., Hedgecock, D. & Manahan, D. T. Separating the nature and nurture of the allocation of energy in response to global change. Integr. Comp. Biol. 54, 284–295 (2014).Article 

    Google Scholar 
    26.Barner, A. K. et al. Generality in multispecies responses to ocean acidification revealed through multiple hypothesis testing. Glob. Change Biol. 24, 4464–4477 (2018).Article 

    Google Scholar 
    27.Spitzner, F., Giménez, L., Meth, R., Harzsch, S. & Torres, G. Unmasking intraspecific variation in offspring responses to multiple environmental drivers. Mar. Biol. 166, 112 (2019).Article 
    CAS 

    Google Scholar 
    28.Torres, G., Thomas, D. N., Whiteley, N. M., Wilcockson, D. & Giménez, L. Maternal and cohort effects modulate offspring responses to multiple stressors. Proc. R. Soc. B 287, 20200492 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Blanquart, F., Kaltz, O., Nuismer, S. L. & Gandon, S. A practical guide to measuring local adaptation. Ecol. Lett. 16, 1195–1205 (2013).30.Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Coleman, R. et al. A continental scale evaluation of the role of limpet grazing on rocky shores. Oecologia 147, 556–564 (2006).PubMed 
    Article 

    Google Scholar 
    32.Hewitt, J. E., Thrush, S. F., Dayton, P. K. & Bonsdorff, E. The effect of spatial and temporal heterogeneity on the design and analysis of empirical studies of scale‐dependent systems. Am. Nat. 169, 398–408 (2007).PubMed 
    Article 

    Google Scholar 
    33.Levin, S. A. The problem of pattern and scale in ecology. Ecology 73, 1943–1967 (1992).Article 

    Google Scholar 
    34.Wiens, J. A. Spatial scaling in ecology. Funct. Ecol. 3, 385–397 (1989).Article 

    Google Scholar 
    35.Benedetti-Cecchi, L. Variance in ecological consumer-resource interactions. Nature 407, 370–374 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    36.Schäfer, R. B. & Piggott, J. J. Advancing understanding and prediction in multiple stressor research through a mechanistic basis for null models. Glob. Change Biol. 24, 1817–1826 (2018).Article 

    Google Scholar 
    37.Hastie, T, Tibshirani, R. & Friedman, J. The Elements of Statistical Learning (Springer, 2009).38.Garfinkel, A., Shevtsov, J. & Guo, Y. Modelling Life (Springer, 2017).39.Durrant, H. M. S., Clark, G. F., Dworjanyn, S. A., Byrne, M. & Johnston, E. L. Seasonal variation in the effects of ocean warming and acidification on a native bryozoan, Celleporaria nodulosa. Mar. Biol. 160, 1903–1911 (2013).Article 

    Google Scholar 
    40.Jensen, G. C., McDonald, P. S. & David, A. A. East meets west: competitive interactions between green crab Carcinus maenas, and native and introduced shore crab Hemigrapsus spp. Mar. Ecol. Prog. Ser. 225, 251–262 (2002).Article 

    Google Scholar 
    41.Jungblut, S., Beermann, J., Boos, K., Saborowski, R. & Hagen, W. Population development of the invasive crab Hemigrapsus sanguineus (De Haan, 1853) and its potential native competitor Carcinus maenas (Linnaeus, 1758) at Helgoland (North Sea) between 2009 and 2014. Aquat. Inv. 12, 85–96 (2017).Article 

    Google Scholar 
    42.Fischer, E. M. & Schär, C. Consistent geographical patterns of changes in high-impact European heatwaves. Nat. Geosci. 3, 398 (2010).CAS 
    Article 

    Google Scholar 
    43.Christidis, N., Jones, G. S. & Stott, P. A. Dramatically increasing chance of extremely hot summers since the 2003 European heatwave. Nat. Clim. Change 5, 46–50 (2015).Article 

    Google Scholar 
    44.Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Progr. Oceanogr. 141, 227–238 (2016).Article 

    Google Scholar 
    45.Arias-Ortiz, A. et al. A marine heatwave drives massive losses from the world’s largest seagrass carbon stocks. Nat. Clim. Change 8, 338–344 (2018).CAS 
    Article 

    Google Scholar 
    46.Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).Article 

    Google Scholar 
    47.Giraldo-Ospina, A., Kendrick, G. A. & Hovey, R. K. Depth moderates loss of marine foundation species after an extreme marine heatwave: could deep temperate reefs act as a refuge? Proc. R. Soc. B 287, 20200709 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Pandori, L. L. M. & Sorte, C. J. B. The weakest link: sensitivity to climate extremes across life stages of marine invertebrates. Oikos 128, 621–629 (2019).Article 

    Google Scholar 
    49.Tilman, D. Competition and biodiversity in spatially structured habitats. Ecology 75, 2–16 (1994).Article 

    Google Scholar 
    50.Gouvêa, L. P. et al. Interactive effects of marine heatwaves and eutrophication on the ecophysiology of a widespread and ecologically important macroalga. Limnol. Oceanogr. 62, 2056–2075 (2017).Article 
    CAS 

    Google Scholar 
    51.Hayashida, H., Matear, R. J. & Strutton, P. G. Background nutrient concentration determines phytoplankton bloom response to marine heatwaves. Glob. Change Biol. 26, 4800–4811 (2020).Article 

    Google Scholar 
    52.Von Biela, V. R. et al. Extreme reduction in nutritional value of a key forage fish during the Pacific marine heatwave of 2014-2016. Mar. Ecol. Prog. Ser. 613, 171–182 (2019).Article 

    Google Scholar 
    53.Dawirs, R. R., Püschel, C. & Schorn, F. Temperature and growth in Carcinus maenas L. (Decapoda: Portunidae) larvae reared in the laboratory from hatching through metamorphosis. J. Exp. Mar. Biol. Ecol. 100, 47–74 (1986).Article 

    Google Scholar 
    54.Torres, G. & Giménez, L. Temperature modulates compensatory responses to food limitation at metamorphosis in a marine invertebrate. Funct. Ecol. 34, 1564–1576 (2020).Article 

    Google Scholar 
    55.Roman, J. O. E. & Palumbi, S. R. A global invader at home: population structure of the green crab, Carcinus maenas, in Europe. Mol. Ecol. 13, 2891–2898 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Zeng, C., Rotllant, G., Gimenez, L. & Romano, N. In The Natural History of Crustacea: Developmental Biology and Larval Ecology (eds Anger, K., Harzsch, S. & Thiel, M.) Vol. 7, Ch. 7 (Oxford Univ. Press, 2020).57.Nougué, O., Svendsen, N., Jabbour-Zahab, R., Lenormand, T. & Chevin, L.-M. The ontogeny of tolerance curves: habitat quality vs. acclimation in a stressful environment. J. Anim. Ecol. 85, 1625–1635 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Seuront, L., Nicastro, K. R., Zardi, G. I. & Goberville, E. Decreased thermal tolerance under recurrent heat stress conditions explains summer mass mortality of the blue mussel Mytilus edulis. Sci. Rep. 9, 17498 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Enriquez-Urzelai, U. et al. Ontogenetic reduction in thermal tolerance is not alleviated by earlier developmental acclimation in Rana temporaria. Oecologia 189, 385–394 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Epifanio, C. E., Dittel, A. I., Park, S., Schwalm, S. & Fouts, A. Early life history of Hemigrapsus sanguineus, a non-indigenous crab in the Middle Atlantic Bight (USA). Mar. Ecol. Prog. Ser. 170, 231–238 (1998).Article 

    Google Scholar 
    61.Karlsson, R., Obst, M. & Berggren, M. Analysis of potential distribution and impacts for two species of alien crabs in Northern Europe. Biol. Inv. 21, 3109–3119 (2019).Article 

    Google Scholar 
    62.Sulkin, S., Blanco, A., Chan, J. & Bryant, M. Effects of limiting access to prey on development of first zoeal stage of the brachyuran crabs Cancer magister and Hemigrapsus oregonensis. Mar. Biol. 131, 515–521 (1998).Article 

    Google Scholar 
    63.Warton, D. I. & Hui, F. K. C. The arcsine is asinine: the analysis of proportions in ecology. Ecology 92, 3–10 (2011).PubMed 
    Article 

    Google Scholar 
    64.Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).Article 

    Google Scholar 
    65.Zuur, A., Ieno, E. N., Walker, N., Savaliev, A. A. & Smith, G. M. Mixed Effect Models and Extensions in Ecology with R (Springer, 2009).66.R core team. R: a language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/ (2017).67.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. and R Core Team. nlme: linear and nonlinear mixed effects models. R package version 3.1-137. (2018).68.Giménez, L. & Torres, G. Effect of simulated heatwaves on larval performance of two marine invertebrates. PANGAEA https://doi.org/10.1594/PANGAEA.934715 (2021). More

  • in

    Histological findings of sperm storage in green turtle (Chelonia mydas) oviduct

    1.Hamann, M., Jessop, T., Limpus, C. & Whittier, J. Interactions among endocrinology, seasonal reproductive cycles and the nesting biology of the female green sea turtle. Mar. Biol. 140, 823–830 (2002).CAS 
    Article 

    Google Scholar 
    2.Chaloupka, M. et al. Encouraging outlook for recovery of a once severely exploited marine megaherbivore. Glob. Ecol. Biogeogr. 17, 297–304 (2007).Article 

    Google Scholar 
    3.Kitayama, C. et al. Infection by and molecular features of Learedius learedi (Digenea: Schistosomatoidea) in green sea turtles (Chelonia mydas) on the Ogasawara islands, Japan. J Parasitol. 105(4), 533–538 (2019).Article 

    Google Scholar 
    4.Kondo, S., Morimoto, Y., Sato, T. & Suganuma, H. Factors affecting the long-term population dynamics of green turtles (Chelonia mydas) in Ogasawara, Japan: Influence of natural and artificial production of hatchlings and harvest pressure. Chelonian Conserv. Biol. 16, 83–92 (2017).Article 

    Google Scholar 
    5.Hatase, H., Sato, K., Yamaguchi, M., Takahashi, K. & Tsukamoto, K. Individual variation in feeding habitat use by adult female green sea turtles (Chelonia mydas): Are they obligately neritic herbivores? Oecologia 149, 52–64 (2006).ADS 
    Article 

    Google Scholar 
    6.Nishizawa, H. et al. Composition of green turtle feeding aggregations along the Japanese archipelago: Implications for changes in composition with current flow. Mar. Biol. 160(10), 2671–2685 (2013).Article 

    Google Scholar 
    7.Wood, J. R. & Wood, F. E. Reproductive biology of captive green sea turtles (Chelonia mydas). Am. Zool. 20, 499–505 (1980).Article 

    Google Scholar 
    8.Ulrich, G. F. & Parkes, A. S. The green sea turtle (Chelonia mydas): Further observations on breeding in captivity. J. Zool. 185(2), 237–251 (1978).Article 

    Google Scholar 
    9.Gist, D. H. & Congdon, J. D. Oviductal sperm storage as a reproductive tactic of turtles. J. Exp. Zool. 282, 526–534 (1998).CAS 
    Article 

    Google Scholar 
    10.Gist, D. H. & Jones, J. M. Sperm storage within the oviduct of turtles. J. Morphol. 199, 379–384 (1989).Article 

    Google Scholar 
    11.Holt, W. V. Mechanisms of sperm storage in the female reproductive tract: An interspecies comparison. Reprod. Domest. Anim. 46, 68–74 (2011).Article 

    Google Scholar 
    12.Orr, T. J. & Brennan, P. L. R. Sperm storage: Distinguishing selective processes and evaluating criteria. Trends Ecol. Evol. 30, 261–272 (2015).Article 

    Google Scholar 
    13.Blackburn, D. G. Structure, function, and evolution of the oviducts of squamate reptiles, with special reference to viviparity and placentation. J. Exp. Zool. 282, 560–617 (1998).CAS 
    Article 

    Google Scholar 
    14.Matsuzaki, M. & Sasanami, T. Sperm storage in the female reproductive tract: A conserved reproductive strategy for better fertilization success. In Avian Reproduction. Advances in Experimental Medicine and Biology Vol. 1001 (ed. Sasanami, T.) 173–186 (Springer, 2017).
    Google Scholar 
    15.Girling, J. E. The reptilian oviduct: A review of structure and function and directions for future research. J. Exp. Zool. 293, 141–170 (2002).Article 

    Google Scholar 
    16.Almeida-Santos, S. M. & Salomão, M. G. Long-term sperm storage in the female Neotropical Rattlesnake Crotalus durissus terrificus (Viperidae: Crotalinae). Jpn. J. Herpetol. 17, 46–52 (1997).Article 

    Google Scholar 
    17.Sever, D. M. & Hopkins, W. A. Oviductal sperm storage in the ground skink Scincella laterale Holbrook (Reptilia: Scincidae). J. Exp. Biol. 301, 599–611 (2004).
    Google Scholar 
    18.Bakst, M. R. Fate of fluorescent stained sperm following insemination: New light on oviducal sperm transport and storage in the turkey. Biol. Reprod. 50, 987–992 (1994).CAS 
    Article 

    Google Scholar 
    19.Sasanami, T., Matsuzaki, M., Mizushima, S. & Hiyama, G. Sperm storage in the female reproductive tract in birds. J. Reprod. Dev. 59, 334–338 (2013).Article 

    Google Scholar 
    20.Palmer, B. D. & Guillette, L. J. Jr. Histology and functional morphology of the female reproductive tract of the tortoise Gopherus polyphemus. Am. J. Anat. 183, 200–211 (1988).CAS 
    Article 

    Google Scholar 
    21.Xiangkun, H. et al. Seasonal changes of sperm storage and correlative structures in male and female soft-shelled turtles, Trionyx sinensis. Anim. Reprod. Sci. 108, 435–445 (2008).Article 

    Google Scholar 
    22.Seminoff, J. A. The IUCN Red List of Threatened Species 2004: e.T4615A11037468. https://doi.org/10.2305/IUCN.UK.2004.RLTS.T4615A11037468.en (2004)23.Bjorndal, K. A. & Jackson, J. B. C. Roles of sea turtles in marine ecosystems: Reconstructing the past. In The Biology of Sea Turtles Vol. 2 (eds Lutz, P. L. et al.) 259–273 (CRC Press, 2003).
    Google Scholar 
    24.Gist, D. H., Bagwill, A., Lance, V., Sever, D. M. & Elsey, R. M. Sperm storage in the oviduct of the American alligator. J. Exp. Zool. 309, 581–587 (2008).Article 

    Google Scholar 
    25.Gist, D. H. & Fischer, E. N. Fine structure of the sperm storage tubules in the box turtle oviduct. J. Reprod. Fertil. 97, 463–468 (1993).CAS 
    Article 

    Google Scholar 
    26.Chen, S. et al. Sperm storage and spermatozoa interaction with epithelial cells in oviduct of Chinese soft-shelled turtle, Pelodiscus sinensis. Ecol. Evol. 5, 3023–3030 (2015).Article 

    Google Scholar 
    27.Miller, J. D. Reproduction in sea turtles. In The Biology of Sea Turtles (eds Lutz, P. L. & Musick, J. A.) 51–81 (CRC Press, 1997).
    Google Scholar 
    28.Pearse, D. E. & Avise, J. C. Turtle mating systems: Behavior, sperm storage, and genetic paternity. J. Hered. 92, 206–211 (2001).CAS 
    Article 

    Google Scholar 
    29.Pearse, D. E., Janzen, F. J. & Avise, J. C. Genetic markers substantiate long-term storage and utilization of sperm by female painted turtles. Heredity 86, 378–384 (2001).CAS 
    Article 

    Google Scholar 
    30.Sarkar, S., Sarkar, N. & Maiti, B. Oviductal sperm storage structure and their changes during the seasonal (dissociated) reproductive cycle in the soft-shelled turtle Lissemys punctata punctata. J. Exp. Zool. A Comp. Exp. Biol. 295, 83–91 (2003).PubMed 

    Google Scholar 
    31.Bagwill, A., Sever, D. M. & Elsey, R. M. Seasonal variation of the oviduct of the American alligator, Alligator mississippiensis (Reptilia: Crocodylia). J. Morphol. 270, 702–713 (2009).Article 

    Google Scholar 
    32.Han, X. et al. Ultrastructure of anterior uterus of the oviduct and the stored sperm in female soft-shelled turtle, Trionyx sinensis. Anat. Rec. 291, 335–351 (2008).Article 

    Google Scholar 
    33.Nogueira, K. O. P. C., Araújo, V. A., Sartori, S. S. R. & Neves, C. A. Phagocytosis of spermatozoa by epithelial cells in the vagina of the lizard Hemidactylus mabouia (Reptilia, Squamata). Micron 42, 377–380 (2011).Article 

    Google Scholar  More

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    Pharmacological modulation of fish-induced depth selection in D. magna: the role of cholinergic and GABAergic signalling

    Phototactic behaviourThe optimization results of the proposed behavioural setup allowed the phototactic behaviour of the studied D. magna clone and the effects of FK treatment on this behaviour to be monitored and quantified. Furthermore, the effects of the FKs were evident only upon light exposure, were more apparent after a short (5 min) acclimation to light and were tightly regulated by the light intensity. The above-mentioned factors agree with previous studies, which found a marked positive phototactism of clone P132,8528 and that the effects of the FKs become consistent after 5 min of light exposure28. Moreover, it has also been reported that light intensity controls anti-predatory defences in Daphnia29.The effects of the pharmacological treatments were consistent for GABAergic and muscarinic cholinergic compounds across two or three identical non-consecutive experiments performed over more than one year. Consistency of the toxicological results and, in particular, of the behavioural responses should be compulsory in toxicological studies to increase the credibility and robustness of the findings30,31. Agonists of these two neurotransmitter receptors (DZP, PILO) and the antagonist of the GABA receptor (PICRO) affected the induction of the phototactic behavioural changes (i.e., interfered with fish recognition). The receptor agonists DZP and PILO counteracted the negative phototactism evoked by the FKs, whereas PICRO enhanced the effect of the FKs, increasing the negative phototactism. None of the three applied substances when applied alone induced anti-predatory fish phototactic behaviour, indicating that these compounds interfered with the FK sensorial pathway. Alternatively, the muscarinic cholinergic antagonist SCOP interfered with phototaxis itself, almost completely abolishing the positive phototactic behaviour of the studied clone under both control and FK conditions. This indicates that the muscarinic cholinergic signalling pathway could potentially be a major regulator of anti-predatory fish phototactic behaviour. In D. pulex and D. galeata, the formation of neck teeth or helmets in response to predatory kairomones released by invertebrate predators has been related to a series of biological reactions that involve kairomone perception and neuronal signals, which are converted into endocrine signals and subsequently induce changes in the expression of morphogenetic factors32,33. We previously showed that DZP, PILO, PICRO and SCOP were neuroactive in D. magna, affecting sensitization and/or habituation motile responses to repetitive light stimuli34; thus, it is likely that these compounds disrupted neurological signalling pathways related to the phototactism shifts caused by FK perception or to the phototaxis itself.Little is known about how phototaxis is neuronally coded. In D. pulex, both in silico and experimental works have shown that histaminergic neurons may mediate phototactic responses to UV irradiation12. By using histamine immunohistochemistry, the previous authors labelled putative photoreceptors in the compound eye and neuronal projections from these cells to the brain. The D. pulex genome also has a putative Drosophila orthologue of histidine decarboxylase (the rate-limiting biosynthetic enzyme for histamine), as well as two putative histamine-gated chloride channels (hclA and hclB orthologues). Exposure of D. magna to cimetidine, an H2 receptor antagonist known to block both hclA and hclB in D. melanogaster, inhibited the negative phototactic responses of these orthologues to UV irradiation. In another study, it was found that short-day photoperiods induced a significant increase in light-avoidance behaviours relative to controls and increased glutamate signalling, which is a critical pathway in arthropod light-avoidance behaviour35. It has also been reported that a group of serotonergic cells located in the protocerebrum probably control phototactic behaviour16. Notably, the perception of predatory kairomones and neuronal and cellular wiring is largely unknown in Daphnia2. For example, the receptors that detect invertebrate cues from Notonecta in D. longicephala were shown to be located on the first antennae, from which neurites extend into the deutocerebrum of the brain. However, key olfactory neuronal structures, such as olfactory glomeruli in the deutocerebrum, were not found2.Our results obtained for DZP, an agonist of the GABAA receptor, agree with those of Weiss et al.11, who found that co-exposure to FKs and exogenous GABA ameliorated life history changes to FKs in a D. pulex clone, whereas co-exposure with the GABAA antagonist PICRO did not have any effect. The ineffectiveness of PICRO on the modulation of FK effects in D. pulex found by Weiss et al.11 might indicate species differences resulting from different receptor amino acid sequences. For example, GABAA receptor subtypes with a single amino acid replacement make the Drosophila GABAA receptor PICRO-insensitive36. Indeed, in crustaceans, lobster GABAA receptors were also found to be insensitive to PICRO37. There is also the possibility that FK-mediated changes in phototactic behaviour and life history traits may be controlled by different mechanisms6.Reported information on the modulatory effects of cholinergic compounds on anti-predatory defences in Daphnia is limited to invertebrate predatory cues, which, according to previous studies, should be regulated by neurological mechanisms distinct from those of fish2,11. Our results showed that the neurological cholinergic mechanisms that modulate induced defence responses against invertebrate predators or that mimic these responses are also able to do the same for fish predation but in the opposite way. Physostigmine and carbaryl, which are acetylcholinesterase inhibitors that increase acetylcholine receptor activity, enhanced and mimicked, respectively, the morphogenetic effects of invertebrate kairomones in several Daphnia species11,21,23. Conversely, atropine, which is a muscarinic acetylcholine receptor (AChR) inhibitor like SCOP, diminished neck tooth formation in D. pulex11,21. In our study, SCOP alone abolished the positive phototactism of the studied clone, which mimicked the effects of the FKs. Conversely, PILO, which is a muscarine AChR agonist, ameliorates the phototactic responses to FKs.The nicotinic AChR agonists (NICO, IMI) and antagonist (MEC) only marginally affected the phototactic responses to the FKs. This indicates that muscarinic cholinergic signalling but not nicotinic signalling is involved in phototaxis/phototactic behaviour. It is therefore possible that both FK and SCOP treatment, through inhibition of muscarinic cholinesterase receptor activity, diminished the positive phototaxis of the studied clone, and PILO activation of these receptors ameliorated the effects of the FKs. In insects, neurons that connect olfactory inputs to higher-order brain areas that coordinate behavioural responses are thought to be under cholinergic control38.In general, GABA is known to have inhibitory functions. It has been proposed that the continuous activation of the GABAergic neuronal pathway by endogenous GABA without predatory cues prevents life history shifts11, which in our case would be the transition from positive to negative phototaxis. FKs and PICRO relieve inhibition, which can be re-established by the experimental application of GABAA receptor agonists such as DZP or GABA itself. Our results and those of Weiss et al.11 agree with the previous argument.Equi-effective mixtures of the tested agonists and antagonists had similar effects on D. magna responses to FKs as the single mixture compound treatments did, indicating that the joint effects of agonists and antagonists of the GABAergic and cholinergic signalling pathways can act cooperatively and probably independently, modulating the effects of FKs. This is in line with other findings that showed that key ecophysiological responses in Daphnia are regulated by several signalling receptor pathways, which likely ensures more robust control. This is the case for the storage lipid dynamics associated with moulting and reproduction39.The involvement of additional neurotransmitter signalling pathways, such as the serotonergic pathway, can also be taken into consideration despite being less consistent. Agonists of the serotonin receptor (such as serotonin) or treatments that increase serotonin levels (such as fluoxetine) ameliorated the effects of the FKs in only one experiment, but treatments that decreased serotonin, such as PCPA, increased the effects of the FKs in two out of the three experiments. Previously, we reported that serotonin activity in the brains of D. magna increased with algae food levels, and thus, the effects of fluoxetine on the enhancement of brain serotonin levels could only be observed under limited food conditions24. This indicates that the high levels of food used in our experiments probably prevented fluoxetine from increasing the already high serotonin levels in the central nervous system. Interestingly, inducible fish kairomone changes in phototactic behaviour in Daphnia increased with food level40, which is probably related to high levels of serotonin. On the other hand, the effects of PCPA, which decreases serotonin concentrations26, are unlikely to be modulated by food since this drug inhibits tryptophan hydrolase, the serotonin synthesis rate-limiting enzyme in D. magna41. This is apparently the case in our study.Neurophysiological stimulation experiments with dopaminergic/adrenergic agonists and antagonists were inconclusive since in only one out of two experiments the dopaminergic agonist APO diminish negative phototaxis after FK exposure. We also did not find any effects from the glutamatergic agonists and antagonists on phototactism. This could be related to the low stability of dopaminergic compounds in water and the reported small effects of glutaminergic compounds on the Daphnia motile response to light34.Consistent failure of the tested antihistaminergic drugs to modulate phototactism to visible light disagrees with previous findings that discovered that these drugs affected phototactism but at much higher doses12.Metabolomic changesThe study of metabolomic changes across the treatments that modulated FK-mediated phototactic changes or altered phototaxis provided further experimental evidence of the involvement of key neurological signalling metabolic pathways. Caution must be exercised, however, since the studied receptor agonist and antagonist drugs do not change the neurotransmitters or their related metabolites. Nevertheless, little is known about how these drugs may affect the Daphnia neuronal metabolome. The cholinergic neurotransmitter system is one of the most important systems that plays a pivotal role in learning and memory in animal species, including D. magna34,42. Whole-body concentrations of acetylcholine decreased in females exposed to FKs and those exposed to SCOP and increased in those exposed to the agonists PILO and DZP. Thus, it is possible to establish a direct link between the decreased levels of acetylcholine and decreased positive phototactism in the studied clone. The results obtained for the GABAergic and serotonergic signalling pathways were less convincing, as FKs alone did not consistently affect the levels of GABA and serotonin. However, co-exposure to FK and the GABAA receptor agonist DZP increased endogenous GABA levels, which is in line with the results reported by Weiss et al.11, who also found that the addition of exogenous GABA ameliorated FK effects. Interestingly, the summarized results depicted in Fig. 4 showed that serotonin levels dereased upon exposure to SCOP, PICRO and PILO but PILO also increase the levels of the serotonin degradation metabolite 5-HIAA. This may indicate that PILO may affect the turnover rather than the levls of serotionin.Previous findings have reported altered responses to light in D. magna individuals lacking serotonin16. Therefore, it is possible to establish a link between the observed marked negative phototactism of females exposed to SCOP and low levels of serotonin.Dopaminergic- and adrenergic-related metabolites deserve special attention, although there is only evidence that dopamine is involved in the proliferation and structural formation of morphological defences in Daphnia for invertebrate kairomones22. In some invertebrates, adrenergic signalling is considered to be absent, and the analogous functions are performed by octopamine43. In our study, fish kairomones and SCOP decreased the levels of dopamine and octopamine, whereas females co-treated with the agonists DZP and PILO and FKs showed relatively high levels of dopamine. In the insect Drosophila melanogaster, which shares many gene signalling pathways with Daphnia44, individuals deficient in dopamine show reduced positive phototactism45. Unfortunately, it is not possible to know whether the observed changes in DA in the whole bodies of D. magna indicate that DA is less used or used in excess. Figure 4 indicates that FK and SCOP reduced both DA and its intermediary metabolite L-DOPA. SCOP also increased the DA degradation metabolite 3-MT and two norepinephrine metabolites/neurotransmitters (NOEM, EPPY) that ultimately depend on DA. This means that FK decreased DA probably decreasing its intermediary metabolite L-DOPA, whereas SCOP decreased DA to a greater extent decreasing its intermediary L-DOPA but also increasing its turnover rate. Our neurophysiological stimulation experiments with dopaminergic active compounds are also not conclusive. This suggests that further research is needed to study the involvement of dopaminergic signalling in the response to fish. Existing studies on adrenergic signalling in daphnids indicated that β-blockers such as propranolol diminish the heart rate46 and motile responses to light27, which are related to the known role of adrenergic signalling that regulates blood pressure47 and other fight-or-flight responses to stress48. Future research is needed to elucidate the involvement of OCT, EPPY and NORM in the phototactic response of D. magna to FKs.In summary, this study provides consistent results that muscarinic cholinergic and GABAergic receptor agonists and antagonists are able to ameliorate or enhance, respectively, the phototactic response of adult females from the studied D. magna clone to FKs. Furthermore, inhibition of the muscarinic acetylcholine receptor by SCOP induced the phototactic response to fish kairomones. This may indicate that muscarinic cholinergic antagonists changed phototaxis, whereas muscarinic cholinergic agonists and GABAergic agonists and antagonists changed the perception of FKs. Serotonergic agonists and antagonists were also able to diminish and increase FK effects, respectively, but only in half of the trials performed. The fact that we could not observe effects from the remaining neuroactive agents (i.e., dopaminergic, histaminergic, glutamatergic) could simply be because they are not relevant for predator-induced anti-phototaxis. The study of neurotransmitters and their related metabolite changes allowed us to identify acetylcholine and GABA as putative key metabolites associated with the observed phototactic modulatory effects of FK and cholinergic and GABAergic compounds. Increased and decreased levels of dopamine in the whole bodies of D. magna were related to positive and negative phototactic behaviours, respectively, but could not be related to neurophysiological studies with the tested dopaminergic drugs. More

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    Drying up

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