Ecology

Water samples were filtered twice (see Methods), first through a large filter (0.45 µm, LF or “Large Filter”) and then the filtrate was passed through a small filter (0.05 µm, UF or “Ultrafine Fraction”). Using whole genome shotgun metagenomics from four water samples (LF92 and UF92 from the 92 m depth, LF99 and UF99 from the 99 m depth) as well as one sediment sample, we provide the first comprehensive whole genome shotgun metagenomics investigation of this section of the lake and highlight both the taxonomic composition and potential metabolic strategies for survival, as well as identify areas for deeper investigation.Cell counts and dissolved nutrientsIn order to determine the habitability of the anoxic basin, the cell counts were measured in the oxycline (75 m depth) and the anoxic region (92 and 99 m depth), where oxygen content is  More

Odonnell, C. Population dynamics and survivorship in bats. In Ecology and Behavioral Methods for the Study of Bats (eds Kunz, T. H. & Parsons, S.) 158–176 (The Johns University Press, 2009).
Google Scholar 
Lebreton, J.-D., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).Article 

Google Scholar 
Gibbons, J. W. & Andrews, K. M. PIT tagging: Simple technology at its best. Bioscience 54, 447–454 (2004).Article 

Google Scholar 
Ellison, L. E. et al. A comparison of conventional capture versus PIT reader techniques for estimating survival and capture probabilities of big brown bats (Eptesicus fuscus). Acta Chiropterologica 9, 149–160 (2007).Article 

Google Scholar 
van Harten, E. et al. High detectability with low impact: Optimizing large PIT tracking systems for cave-dwelling bats. Ecol. Evol. 9, 10916–10928 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Schorr, R. A., Ellison, L. E. & Lukacs, P. M. Estimating sample size for landscape-scale mark-recapture studies of North American migratory tree bats. Acta Chiropterologica 16, 231–239 (2014).Article 

Google Scholar 
Baker, G. B. et al. The effect of forearm bands on insectivorous bats (Microchiroptera) in Australia. Wildl. Res. 28, 229–237 (2001).Article 

Google Scholar 
O’Shea, T. J., Ellison, L. E. & Stanley, T. R. Survival estimation in bats: Historical overview, critical appraisal, and suggestions for new approaches. In Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters (ed. Thompson, W. L.) 297–336 (Island Press, 2004).
Google Scholar 
O’Shea, T. J. et al. Recruitment in a Colorado population of big brown bats: Breeding probabilities, litter size, and first-year survival. J. Mammal. 91, 418–428 (2010).Article 

Google Scholar 
O’Shea, T. J., Ellison, L. E. & Stanley, T. R. Adult survival and population growth rate in Colorado big brown bats (Eptesicus fuscus). J. Mammal. 92, 433–443 (2011).Article 

Google Scholar 
Schorr, R. A. & Siemers, J. L. Population dynamics of little brown bats (Myotis lucifugus) at summer roosts: Apparent survival, fidelity, abundance, and the influence of winter conditions. Ecol. Evol. 11, 7427–7438 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Donnell, C. F. J., Edmonds, H. & Hoare, J. M. Survival of PIT-tagged lesser short-tailed bats (Mystacina tuberculata) through a pest control operation using the toxin pindone in bait stations. N. Z. J. Ecol. 35, 291–295 (2011).
Google Scholar 
Edmonds, H., Pryde, M. & O’Donnell, C. Survival of PIT-tagged lesser short-tailed bats (Mystacina tuberculata) through an aerial 1080 pest control operation. N. Z. J. Ecol. 41, 186–192 (2017).
Google Scholar 
Reusch, C. et al. Differences in seasonal survival suggest species-specific reactions to climate change in two sympatric bat species. Ecol. Evol. 9, 7957–7965 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
IUCN. The IUCN red list of threatened species. Version 2020-2. http://www.iucnredlist.org (2020).Lentini, P. E., Bird, T. J., Griffiths, S. R., Godinho, L. N. & Wintle, B. A. A global synthesis of survival estimates for microbats. Biol. Lett. 11, 20150371 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Culina, A., Linton, D. M. & Macdonald, D. W. Age, sex, and climate factors show different effects on survival of three different bat species in a woodland bat community. Glob. Ecol. Conserv. 12, 263–271 (2017).Article 

Google Scholar 
Frick, W. F., Reynolds, D. S. & Kunz, T. H. Influence of climate and reproductive timing on demography of little brown myotis Myotis lucifugus. J. Anim. Ecol. 79, 128–136 (2010).PubMed 
Article 

Google Scholar 
Schorcht, W., Bontadina, F. & Schaub, M. Variation of adult survival drives population dynamics in a migrating forest bat. J. Anim. Ecol. 78, 1182–1190 (2009).PubMed 
Article 

Google Scholar 
Sendor, T. & Simon, M. Population dynamics of the pipistrelle bat: Effects of sex, age and winter weather on seasonal survival. J. Anim. Ecol. 72, 308–320 (2003).Article 

Google Scholar 
Sripathi, K., Raghuram, H., Rajasekar, R., Karuppudurai, T. & Abraham, S. G. Population size and survival in the indian false vampire bat Megaderma lyra. Acta Chiropterologica 6, 145–154 (2004).Article 

Google Scholar 
Papadatou, E., Butlin, R. K., Pradel, R. & Altringham, J. D. Sex-specific roost movements and population dynamics of the vulnerable long-fingered bat, Myotis capaccinii. Biol. Conserv. 142, 280–289 (2009).Article 

Google Scholar 
López-Roig, M. & Serra-Cobo, J. Impact of human disturbance, density, and environmental conditions on the survival probabilities of pipistrelle bat (Pipistrellus pipistrellus). Popul. Ecol. 56, 471–480 (2014).Article 

Google Scholar 
Wilkinson, G. S. & Adams, D. M. Recurrent evolution of extreme longevity in bats. Biol. Lett. 15, 20180860 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
DELWP. National Recovery Plan for the Southern Bent-wing Bat Miniopterus orianae bassanii (2020).Lumsden, L. & Gray, P. Longevity record for a southern bent-wing bat Miniopterus schreibersii bassanii. Australas. Bat Soc. Newsl. 16, 43–44 (2001).
Google Scholar 
Holz, P. H. et al. Virus survey in populations of two subspecies of bent-winged bats (Miniopterus orianae bassanii and oceanensis) in south-eastern Australia reveals a high prevalence of diverse herpesviruses. PLoS ONE 13, e0197625 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Holz, P. H., Lumsden, L. F., Marenda, M. S., Browning, G. F. & Hufschmid, J. Two subspecies of bent-winged bats (Miniopterus orianae bassanii and oceanensis) in southern Australia have diverse fungal skin flora but not Pseudogymnoascus destructans. PLoS ONE 13, e0204282 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Holz, P. H., Lumsden, L. F. & Hufschmid, J. Ectoparasites are unlikely to be a primary cause of population declines of bent-winged bats in south-eastern Australia. Int. J. Parasitol. Parasites Wildl. 7, 423–428 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Holz, P. H., Lumsden, L. F., Legione, A. R. & Hufschmid, J. Polychromophilus melanipherus and haemoplasma infections not associated with clinical signs in southern bent-winged bats (Miniopterus orianae bassanii) and eastern bent-winged bats (Miniopterus orianae oceanensis). Int. J. Parasitol. Parasites Wildl. 8, 10–18 (2019).PubMed 
Article 

Google Scholar 
Holz, P. H., Clark, P., McLelland, D. J., Lumsden, L. F. & Hufschmid, J. Haematology of southern bent-winged bats (Miniopterus orianae bassanii) from the Naracoorte Caves National Park, South Australia. Comp. Clin. Pathol. 29, 231–237 (2020).CAS 
Article 

Google Scholar 
Dwyer, P. D. The population pattern of Miniopterus schreibersii (Chiroptera) in north-eastern New South Wales. Aust. J. Zool. 14, 1073–1137 (1966).Article 

Google Scholar 
Dwyer, P. D. Mortality factors of the bent-winged bat. Vic. Nat. 83, 31–36 (1966).
Google Scholar 
Dwyer, P. D. Seasonal changes in activity and weight of Miniopterus schreibersii blepotis (Chiroptera) in north-eastern NSW. Aust. J. Zool. 12, 52–69 (1964).Article 

Google Scholar 
Bureau of Meteorology. Drought archive. http://www.bom.gov.au/climate/drought/archive.shtml (2019).Dwyer, P. D. Population ranges of Miniopterus schreibersii (Chiroptera) in south-eastern Australia. Aust. J. Zool. 17, 665–686 (1969).Article 

Google Scholar 
Fleischer, T., Gampe, J., Scheuerlein, A. & Kerth, G. Rare catastrophic events drive population dynamics in a bat species with negligible senescence. Sci. Rep. 7, 7370 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas, D. W. Hibernating bats are sensitive to nontactile human disturbance. J. Mammal. 76, 940–946 (1995).Article 

Google Scholar 
Reeder, D. M. et al. Frequent arousal from hibernation linked to severity of infection and mortality in bats with white-nose syndrome. PLoS ONE 7, e38920 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Turbill, C., Bieber, C. & Ruf, T. Hibernation is associated with increased survival and the evolution of slow life histories among mammals. Proc. R. Soc. B Biol. Sci. 278, 3355–3363 (2011).Article 

Google Scholar 
van Harten, E. Population Dynamics of the Critically Endangered, Southern Bent-Winged Bat Miniopterus orianae bassanii (La Trobe University, 2020).
Google Scholar 
PIRSA. History of the south east drainage system – summary. https://www.pir.sa.gov.au/aghistory/natural_resources/water_resources_ag_dev/history_of_the_south_east_drainage_system_-_summary/history_of_the_south_east_drainage_system_-_summary#_ftnref2 (2017).Harding, C., Herpich, D. & Cranswick, R. H. Examining temporal and spatial changes in surface water hydrology of groundwater dependent ecosystems using WOfS (Water Observations from Space): Southern Border Groundwaters Agreement area, South East South Australia. (2018).Holz, P. H., Lumsden, L. F., Reardon, T., Gray, P. & Hufschmid, J. Does size matter? Morphometrics of southern bent-winged bats (Miniopterus orianae bassanii) and eastern bent-winged bats (Miniopterus orianae oceanensis). Aust. Zool. AZ https://doi.org/10.7882/AZ.2019.019 (2020).Article 

Google Scholar 
Rashid, M. M. & Beecham, S. Characterization of meteorological droughts across South Australia. Meteorol. Appl. 26, 556–568 (2019).Article 

Google Scholar 
Culina, A., Linton, D. M., Pradel, R., Bouwhuis, S. & Macdonald, D. W. Live fast, don’t die young: Survival–reproduction trade-offs in long-lived income breeders. J. Anim. Ecol. 88, 746–756 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Kunz, T. H., Whitaker, J. O. & Wadanoli, M. D. Dietary energetics of the insectivorous Mexican free-tailed bat (Tadarida brasiliensis) during pregnancy and lactation. Oecologia 101, 407–415 (1995).CAS 
PubMed 
Article 

Google Scholar 
Adams, R. A. & Hayes, M. A. Water availability and successful lactation by bats as related to climate change in arid regions of western North America. J. Anim. Ecol. 77, 1115–1121 (2008).PubMed 
Article 

Google Scholar 
Henry, M., Thomas, D. W., Vaudry, R. & Carrier, M. Foraging distances and home range of pregnant and lactating little brown bats (Myotis lucifugus). J. Mammal. 83, 767–774 (2002).Article 

Google Scholar 
Lučan, R. & Radil, J. Variability of foraging and roosting activities in adult females of Daubenton’s bat (Myotis daubentonii) in different seasons. Biologia (Bratisl.) 65 (2010).Amorim, F., Jorge, I., Beja, P. & Rebelo, H. Following the water? Landscape-scale temporal changes in bat spatial distribution in relation to Mediterranean summer drought. Ecol. Evol. 8, 5801–5814 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Donnell, C. F. J. Timing of breeding, productivity and survival of long-tailed bats Chalinolobus tuberculatus (Chiroptera: Vespertilionidae) in cold-temperate rainforest in New Zealand. J. Zool. 257, 311–323 (2002).Article 

Google Scholar 
Holz, P. H., Stent, A., Lumsden, L. F. & Hufschmid, J. Trauma found to be a significant cause of death in a pathological investigation of bent-winged bats (Miniopterus orianae). J. Zoo Wildl. Med. 50, 966–971 (2020).PubMed 
Article 

Google Scholar 
Hughes, P. M., Rayner, J. M. V. & Jonesg, G. Ontogeny of ‘true’ flight and other aspects of growth in the bat Pipistrellus pipistrellus. J. Zool. 236, 291–318 (1995).Article 

Google Scholar 
Wund, M. A. Learning and the development of habitat-specific bat echolocation. Anim. Behav. 70, 441–450 (2005).Article 

Google Scholar 
McGuire, L. P. et al. Common condition indices are no more effective than body mass for estimating fat stores in insectivorous bats. J. Mammal. 99, 1065–1071 (2018).Article 

Google Scholar 
Mispagel, C. et al. DDT and metabolites residues in the southern bent-wing bat (Miniopterus schreibersii bassanii) of south-eastern Australia. Chemosphere 55, 997–1003 (2004).CAS 
PubMed 
Article 

Google Scholar 
Allinson, G. et al. Organochlorine and trace metal residues in adult southern bent-wing bat (Miniopterus schreibersii bassanii) in southeastern Australia. Chemosphere 64, 1464–1471 (2006).CAS 
PubMed 
Article 

Google Scholar 
Kolkert, H., Andrew, R., Smith, R., Rader, R. & Reid, N. Insectivorous bats selectively source moths and eat mostly pest insects on dryland and irrigated cotton farms. Ecol. Evol. https://doi.org/10.1002/ece3.5901 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Sherwin, H. A., Montgomery, W. I. & Lundy, M. G. The impact and implications of climate change for bats. Mammal Rev. 43, 171–182 (2013).Article 

Google Scholar 
O’Shea, T. J., Cryan, P. M., Hayman, D. T. S., Plowright, R. K. & Streicker, D. G. Multiple mortality events in bats: A global review. Mammal Rev. 46, 175–190 (2016).Article 

Google Scholar 
Mundinger, C., Scheuerlein, A. & Kerth, G. Long-term study shows that increasing body size in response to warmer summers is associated with a higher mortality risk in a long-lived bat species. Proc. R. Soc. B Biol. Sci. 288, 20210508 (2021).Article 

Google Scholar 
Adams, R. A. & Hayes, M. A. Assemblage-level analysis of sex-ratios in Coloradan bats in relation to climate variables: A model for future expectations. Glob. Ecol. Conserv. 14, e00379 (2018).Article 

Google Scholar 
Crichton, E. G., Seamark, R. F. & Krutzsch, P. H. The status of the corpus luteum during pregnancy in Miniopterus schreibersii (Chiroptera: Vespertilionidae) with emphasis on its role in developmental delay. Cell Tissue Res. 258, 183–201 (1989).CAS 
PubMed 
Article 

Google Scholar 
Olsen, I. C. The analysis of continuous mark-recapture data (Norwegian University of Science and Technology, 2006).
Google Scholar 
Barbour, A. B., Ponciano, J. M. & Lorenzen, K. Apparent survival estimation from continuous mark-recapture/resighting data. Methods Ecol. Evol. 4, 846–853 (2013).Article 

Google Scholar 
van Harten, E. et al. Recovery of southern bent-winged bats (Miniopterus orianae bassanii) after PIT-tagging and the use of surgical adhesive. Aust. Mammal. 42, 216–219 (2020).Article 

Google Scholar 
McDonald, T. L., Amstrup, S. C. & Manly, B. F. Tag loss can bias Jolly-Seber capture-recapture estimates. Wildl. Soc. Bull. 31, 814–822 (2003).
Google Scholar 
van Harten, E. et al. Low rates of PIT-tag loss in an insectivorous bat species. J. Wildl. Manag. 85, 1739–1743 (2021).Article 

Google Scholar 
Lebl, K. & Ruf, T. An easy way to reduce PIT-tag loss in rodents. Ecol. Res. 25, 251–253 (2010).Article 

Google Scholar 
Rigby, E. L., Aegerter, J., Brash, M. & Altringham, J. D. Impact of PIT tagging on recapture rates, body condition and reproductive success of wild Daubenton’s bats (Myotis daubentonii). Vet. Rec. 170, 101 (2012).CAS 
PubMed 
Article 

Google Scholar 
Locatelli, A. G., Ciuti, S., Presetnik, P., Toffoli, R. & Teeling, E. Long-term monitoring of the effects of weather and marking techniques on body condition in the Kuhl’s pipistrelle bat, Pipistrellus kuhlii. Acta Chiropterologica 21, 87–102 (2019).Article 

Google Scholar 
Paniw, M. et al. The myriad of complex demographic responses of terrestrial mammals to climate change and gaps of knowledge: A global analysis. J. Anim. Ecol. 90, 1398–1407 (2021).PubMed 
Article 

Google Scholar 
Frick, W. F., Kingston, T. & Flanders, J. A review of the major threats and challenges to global bat conservation. Ann. N. Y. Acad. Sci. 1469, 5–25 (2020).PubMed 
Article 

Google Scholar 
Brunet-Rossinni, A. K. & Wilkinson, G. S. Methods for age estimation and the study of senescence in bats. In Ecological and Behavioral Methods for the Study of Bats (eds Kunz, T. H. & Parsons, S.) 315–325 (Johns Hopkins University Press, 2009).
Google Scholar 
Churchill, S. Australian Bats (Allen and Unwin, 2008).
Google Scholar 
Laake, J. L. RMark: An R interface for analysis of capture-recapture data with MARK. 25 (2013).Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference (Springer, 2002). https://doi.org/10.1007/b97636.Book 
MATH 

Google Scholar 
Caswell, H. Matrix population models. In Encyclopedia of Environmetrics (eds El-Shaarawi, A. H. & Piegorsch, W. W.) (Wiley, Berlin, 2006). https://doi.org/10.1002/9780470057339.vam006m.Chapter 

Google Scholar 
Dwyer, P. D. The breeding biology of Miniopterus schreibersii blepotis (Termminck) (Chiroptera) in north-eastern NSW. Aust. J. Zool. 11, 219–240 (1963).Article 

Google Scholar 
Richardson, E. G. The biology and evolution of the reproductive cycle of Miniopterus schreibersii and M. australis (Chiroptera: Vespertilionidae). J. Zool. 183, 353–375 (1977).Article 

Google Scholar  More

Richardson, D.M., & Rundel, P.W. Ecology and biogeography of Pinus: An introduction. in Ecology and Biogeography of Pinus (Richardson, D.M. Ed.). 3–40. (Cambridge Press, 1998).Keeley, J. E. Ecology and evolution of pine life histories. Ann. For. Sci. 69, 445–453 (2012).Article 

Google Scholar 
Agee, J.K. Fire and pine ecosystems. in Ecology and Biogeography of Pinus (Richardson, D.M. Ed.). 193–217. (Cambridge Press, 1998).Keeley, J.E., & Zedler, P.H. Evolution of life histories in Pinus. in Ecology and Biogeography of Pinus (Richardson, D.M. Ed.). 219–251. (Cambridge Press, 1998).Pausas, J. G., Bradstock, R., Keith, D. A. & Keeley, J. E. Plant functional traits in relation to fire in crown-fire ecosystems. Ecology 85, 1085–1100 (2004).Article 

Google Scholar 
Hare, R. C. Contribution of bark to fire resistance of southern trees. J. For. 63, 248–251 (1965).
Google Scholar 
Jackson, J. F., Adams, D. C. & Jackson, U. B. Allometry of constitutive defense: A model and a comparative test with tree bark and fire regime. Am. Nat. 153, 614–632 (1999).PubMed 
Article 

Google Scholar 
Stephens, S. L. & Libby, W. J. Anthropogenic fire and bark thickness in coastal and island pine populations from Alta and Baja California. J. Biogeogr. 33, 648–652 (2006).Article 

Google Scholar 
Chapman, H. H. Is the longleaf type a climax?. Ecology 13, 328–334 (1932).Article 

Google Scholar 
Pile, L. S., Wang, G. G., Knapp, B. O., Liu, G. & Yu, D. Comparing morphology and physiology of southeastern US Pinus seedlings: Implications for adaptation to surface fire regimes. Ann. For. Sci. 74, 68 (2017).Article 

Google Scholar 
Rodríguez-Trejo, D. A. & Fulé, P. Z. Fire ecology of Mexican pines and a fire management proposal. Int. J. Wildl. Fire 12, 23–37 (2003).Article 

Google Scholar 
Pausas, J. G. Bark thickness and fire regime. Funct. Ecol. 29, 315–327 (2015).Article 

Google Scholar 
Little, S. & Mergen, F. External and internal changes associated with basal-crook formation in pitch and shortleaf pines. For. Sci. 12, 268–275 (1966).
Google Scholar 
Kolström, T. & Kellomäki, S. Tree survival in wildfires. Silva Fenn. 27, 277–281 (1993).Article 

Google Scholar 
Schwilk, D. W. & Ackerly, D. D. Flammability and serotiny as strategies: Correlated evolution in pines. Oikos 94, 326–236 (2001).Article 

Google Scholar 
Reyes, O. & Casal, M. Effect of high temperatures on cone opening and on the release and viability of Pinus pinaster and P. radiata seeds in NW Spain. Ann. For. Sci. 59, 327–334 (2002).Article 

Google Scholar 
Pausas, J. G. & Keeley, J. E. Epicormic resprouting in fire-prone ecosystems. Trends Plant Sci. 22, 1008–1015 (2017).CAS 
PubMed 
Article 

Google Scholar 
Fonda, R. W., Bellanger, L. A. & Burley, L. L. Burning characteristics of western conifer needles. Northwest Sci. 72, 1–9 (1998).
Google Scholar 
Fonda, R. W. Burning characteristics of needles from eight pine species. For. Sci. 47, 390–396 (2001).
Google Scholar 
Anderson, H. E. Forest fuel ignitability. Fire Tech. 6, 312–319 (1970).CAS 
Article 

Google Scholar 
Martin, R.E., et al. Assessing the flammability of domestic and wildland vegetation. in Proceedings of the 12th Conference Fire and Forest Meteorology. Jekyll Island. 130–137. (1993)Varner, J. M., Kane, J. M., Kreye, J. K. & Engber, E. The flammability of forest and wildland litter: A synthesis. Curr. For. Rep. 1, 91–99 (2015).
Google Scholar 
Fernandes, P. M. & Cruz, M. G. Plant flammability experiments offer limited insight into vegetation–fire dynamics interactions. New Phytol. 194, 606–609 (2012).PubMed 
Article 

Google Scholar 
Wenk, E. S., Wang, G. G. & Walker, J. L. Within-stand variation in understorey vegetation affects fire behaviour in longleaf pine xeric sandhills. Int. J. Wildl. Fire 20, 866–875 (2012).Article 

Google Scholar 
Whelan, A. W., Bigelow, S. W. & O’Brien, J. J. Overstory longleaf pines and hardwoods create diverse patterns of energy release and fire effects during prescribed fire. Front. For. Glob. Change. 4, 25 (2021).Article 

Google Scholar 
Mutch, R. W. Wildland fires and ecosystems—A hypothesis. Ecology 51, 1046–1051 (1970).Article 

Google Scholar 
Troumbis, A. S. & Trabaud, L. Some questions about flammability in fire ecology. Acta Oecol. 10, 167–175 (1989).
Google Scholar 
Midgley, J. J. Flammability is not selected for, it emerges. Aust. J. Bot. 61, 102–106 (2013).Article 

Google Scholar 
Snyder, J. R. The role of fire: Mutch ado about nothing?. Oikos 43, 404–405 (1984).Article 

Google Scholar 
Bond, W. J. & Midgley, J. J. Kill thy neighbour: An individualistic argument for theevolution of flammability. Oikos 73, 79–85 (1995).Article 

Google Scholar 
Gagnon, P. R. et al. Does pyrogenicity protect burning plants?. Ecology 91, 3481–3486 (2010).PubMed 
Article 

Google Scholar 
Vines, R. G. Heat transfer through bark, and the resistance of trees to fire. Aust. J. Bot. 16, 499–514 (1968).Article 

Google Scholar 
Harmon, M. E. Survival of trees after low-intensity surface fires in Great Smoky Mountains National Park. Ecology 65, 796–802 (1984).Article 

Google Scholar 
Schwilk, D. W., Gaetani, M. S. & Poulos, H. M. Oak bark allometry and fire survival strategies in the Chihuahuan Desert Sky Islands, Texas, USA. PLoS ONE 8, e79285 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Stevens, J., Kling, M., Schwilk, D., Varner, J. M. & Kane, J. M. Biogeography of fire regimes in western US conifer forests: a trait-based approach. Glob. Ecol. Biogeogr. 29, 944–955 (2020).Article 

Google Scholar 
Rosell, J. A. Bark thickness across the angiosperms: More than just fire. New Phytol. 211, 90–102 (2016).CAS 
PubMed 
Article 

Google Scholar 
Kane, J. M., Varner, J. M. & Hiers, J. K. The burning characteristics of southeastern oaks: discriminating fire facilitators from fire impeders. For. Ecol. Manag. 256, 2039–2045 (2008).Article 

Google Scholar 
Engber, E. A. & Varner, J. M. Patterns of flammability of the California oaks: The role of leaf traits. Can. J. For. Res. 42, 1965–1975 (2012).Article 

Google Scholar 
Guyette, R. P., Stambaugh, M. C., Dey, D. C. & Muzika, R. Predicting fire frequency with chemistry and climate. Ecosystems 15, 322–335 (2012).Article 

Google Scholar 
Stambaugh, M.C., Varner, J.M., & Jackson, S.T. Biogeography: An interweave of climate, fire, and humans. in Ecological Restoration and Management of Longleaf Pine Forests (Kirkman, K., Jack, S. B. Eds.). 17–38. (CRC Press, 2017).Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).Article 

Google Scholar 
Schwilk, D. W. & Caprio, A. C. Scaling from leaf traits to fire behavior: community composition predicts fire severity in a temperate forest. J. Ecol. 99, 970–980 (2011).Article 

Google Scholar 
Ormeño, E. et al. The relationship between terpenes and flammability of leaf litter. For. Ecol. Manag. 257, 471–482 (2009).Article 

Google Scholar 
Mirov, N. T. The terpenes (in relation to the biology of genus Pinus). Ann. Rev. Biochem. 17, 521–540 (1948).CAS 
PubMed 
Article 

Google Scholar 
Mitić, Z. S. et al. Needle terpenes as chemotaxonomic markers in Pinus: Subsections Pinus and Pinaster. Chem. Biodivers. 14, e1600453 (2017).Article 

Google Scholar 
Baradat, P. & Yazdani, R. Genetic expression for monoterpenes in clones of Pinus sylvestris grown on different sites. Scand. J. For. Res. 3, 25–36 (1987).Article 

Google Scholar 
Hanover, J. W. Applications of terpene analysis in forest genetics. New For. 6, 159–178 (1992).Article 

Google Scholar 
He, T., Pausas, J. G., Belcher, C. M., Schwilk, D. W. & Lamont, B. B. Fire-adapted traits of Pinus arose in the fiery Cretaceous. New Phytol. 194, 751–759 (2012).PubMed 
Article 

Google Scholar 
Saladin, B. et al. Fossils matter: Improved estimates of divergence times in Pinus reveal older diversification. Evol. Biol. 17, 95 (2017).
Google Scholar 
Kreye, J. K. et al. Effects of solar heating on the moisture dynamics of forest floor litter in humid environments: Composition, structure, and position matter. Can. J. For. Res. 48, 1331–1342 (2018).Article 

Google Scholar 
Ganteaume, A., Jappiot, M., Curt, T., Lampin, C. & Borgniet, L. Flammability of litter sampled according to two different methods: Comparison of results in laboratory experiments. Int. J. Wildl. Fire 23, 1061–1075 (2014).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019). https://www.R-project.org/.Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).Article 

Google Scholar 
Orme, D., et al. Caper: Comparative Analyses of Phylogenetics and Evolution in R. Version 1.0.1. https://CRAN.R-project.org/package=caper. (2018).Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).CAS 
PubMed 
Article 

Google Scholar 
Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: A test and review of evidence. Am. Nat. 160, 712–726 (2002).CAS 
PubMed 
Article 

Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. R Package Version 1.43.6. https://CRAN.R-project.org/package=MuMIn. (2019).Little, E.L. Atlas of United States Trees. Vol. 1. Conifers and Important Hardwoods. 1–320. (Miscellaneous Publication 1146, USDA, Forest Service, 1971).Prasad, A.M. & Iverson, L.R. Little’s Range and FIA Importance Value Database for 135 Eastern US Tree Species. http://www.fs.fed.us/ne/delaware/4153/global/littlefia/index.html. (Northeastern Research Station, USDA Forest Service). More

Influence of the PLHP on water depth distributionThe hydrodynamic process of Poyang Lake with and without the PLHP is simulated by M1 and M2, respectively. By comparing and analyzing the simulation results, we obtain the changes of the water depth, i.e., the water depth in M2 minus the water depth in M1, in Poyang Lake. Figure 5 shows the mean monthly water depth differences during September and October in three typical years. The water depth in Poyang Lake has increased obviously in most cases after the operation of the project. Combining the water level processes in Fig. 4 and the water depth differences in Fig. 5, the area of the changes in the water depth is seen to be mainly controlled by the Higher Water Levels (HWL) between M1 and M2. While the magnitude of the changes is mainly controlled by the Differences in Water Levels (DWL) between M1 and M2.Figure 5Water depth variation during September and October in the typical years. The figure was generated by Tecplot2020 (https://www.tecplot.com/).Full size imageAs shown in the water level variations processes in Fig. 4a, in the low-water-level year (2006), where the natural inflow is relatively small, the water level in M2 is much higher than that in M1 and reaches the peak value around 10th of October. The DWL also reaches maximum in this time and then gradually decreases. As a consequence, the water depth increases the most in October 2006. As shown in Fig. 5d, in most areas of Poyang Lake, the Xiuhe River and the Ganjiang River, the water depth is increased by more than 1 m. Especially in the main channel from the PLHP to Tangyin and Wucheng, the increase can exceed 4 m, as shown in the red parts in Fig. 5d. While over the surrounding flooded land, the increase ranges from 1 to 3 m. The increase in the water depth during September was essentially the same as that in October, but the area and magnitude of the changes are slightly reduced. The maximum increase is about 3.1 m, which is mainly concentrated in the main channel on the north of Songmen Mountain.In the medium-water-level year (2018), the water level in M1 is constantly lower than that in M2 during September and October, which can be seen in Fig. 4b. Both the HWL and DWL first increase and then decrease, reaching their maximum values in early September and late September, respectively. Furthermore, the mean monthly HWL and DWL in September are slightly greater than those in October. As a consequence, the area and magnitude of the changes in Fig. 5b are larger than those in Fig. 5e. In September 2018, almost the entire main lake region becomes influenced by the PLHP and the increase in water depth ranges from 0 to 3.4 m. While in October 2018, the increase in water depth ranges from 0 to 3.2 m, and the significant increase is mainly concentrated in the main channel on the north of Songmen Mountain.In the high-water-level year (2010), as shown in Fig. 4c, the HWL rises and the DWL decreases as compared to those in other years. During the first 35 days, the hydrodynamic conditions in M2 are exactly the same as that in M1, so the PLHP has no effect on the water depth in the lake region in September (Fig. 5c). However, after the 6th of October, the water level in M2 begins to be higher than that in M1 under the regulation of the PLHP. Although the DWL is small during this time, the HWL is relatively high and thus results in large areas of water depth increase, as shown in Fig. 5f. The maximum increase in water depth is approximately 1.1 m.In these two months, the northeastern parts of the two national nature reserves are observably influenced., The area of the changes is greatest in September 2018, there has been a marked increase in water depth in most areas of the reserves, with the maximum increase reaching about 2.8 m. While in other periods during September and October, with the exception of September 2010, the water depth is increased significantly in about 1/3 ~ 1/2 area of the reserves, with the maximum increase reaching about 2.6 m. In the meantime, the water depth in the southwestern part of these two reserves remains essentially the same as before the operation of the PLHP, with an increase of less than 0.25 m as shown in the grey parts in Fig. 5.Influence of the PLHP on habitat suitability of Vallisneria natansAccording to the relationship between the habitat suitability of Vallisneria natans and the water depth as mentioned in Fig. 3, the water depth results can be translated into the habitat suitability of Vallisneria natans, as shown in Fig. 6. The first and third rows are the distributions of the habitat suitability during September and October, respectively, in the three typical years before the operation of the PLHP, while the second and fourth rows are distributions of the habitat suitability after the operation of the PLHP. The grey parts in Fig. 6 indicate that the habitat suitability is 0, implying that the area is dry or the water depth is greater than 4 m.Figure 6Distributions of the habitat suitability of Vallisneria natans in typical years without (the first and third rows) and with (the second and fourth rows) the PLHP. The figure was generated by Tecplot2020 (https://www.tecplot.com/).Full size imageAs shown in Fig. 6, the suitable area for the growth of Vallisneria natans is mainly concentrated over the flooded land. As the water depth in the main channel is generally more than 4 m, so the habitat suitability is usually 0 there.Before the operation of the PLHP (M1), the water level in 2010 is higher than that those in 2006 and 2018. Therefore, there are more areas covered by water in 2010, and the habitat suitability in Fig. 6c is greater than those in Fig. 6a,b. Similarly, the habitat suitability in Fig. 6i is greater than those in Fig. 6g,h. Because the lake bed is lower in northeastern part than that in the southwestern part, the water depth generally decreases from northeast to southwest. During September and October in 2006 and 2018, large areas of bed in the northeastern part are covered by water, and the water depth is less than 4 m, which is suitable for the growth of Vallisneria natans. While large areas in the southwestern part of the lake are dry, and thus the habitat suitability is 0, as shown in the grey parts in the southwestern part of the main lake region in Fig. 6a,b,g,h. However, because the water level is relatively high during September and October in 2010, there are almost no dry areas in the lake region. As a result, with the exception of the main channel, where the water depth is greater than 4 m, most of the areas in the lake region are suitable for the growth of Vallisneria natans. The most suitable areas for Vallisneria natans vary between these two months, as the water depth in September 2010 is greater than that in October 2010. As shown in Fig. 6c, the red parts are mainly concentrated in the southwestern part of the lake, because there are large areas of flooded region with water depths ranging from 1 to 2 m, which is ideal for the growth of Vallisneria natans. On the contrary, the water depth in the northeastern flood land is usually between 2-4 m. In Fig. 6i, however, the red parts are mainly concentrated in the northeastern part of the lake, because the water depth there is usually between 1 and 2 m, and the water depth in the southwestern flood land is now usually less than 1 m.After the operation of the PLHP (M2), with the rise of the water level in the lake region, the suitable area for the growth of Vallisneria natans is increased greatly, and the variation is proportional to the DWL during the same period. The increase is most obvious in the low-water-level year (2006), followed by the medium water level year (2018), and becomes insignificant in the high-water-level year (2010). Such a trend is consistent with the previously mentioned variation in the water depth.Since the DWL is relatively small during September and October in 2010, the habitat suitability in this period is changed little between M1 and M2 (Fig. 6). The distribution of the habitat suitability is completely the same between Fig. 6c,f, while the difference in the distributions of habitat suitability between Fig. 6i,l is subtle. As the water level in M1 is relatively low during September and October in 2006 and 2018, the suitable area for the growth of Vallisneria natans in M2 is expanded from northeast to southwest under the influence of the PLHP. In addition, the habitat suitability is increased greatly in Wucheng National Nature Reserve and Nanji National Nature Reserve. Before the operation of the PLHP, there are large areas of dry land in the two reserves, and thus the habitat suitability in these dry areas is 0. After the operation of the PLHP, according the previous research, more than 1/3 of area in the two reserves sees apparent increases in water depth. The water depth is increased to 1 ~ 2 m and thus the habitat suitability is increased to 1.0 in the northeastern part of the reserves. In the southwestern part of the reserves, although the increase of water depth is not significant, most of the lake bed has changed its status from being dry to being wet and the habitat suitability is correspondingly increased from 0 to 0.1 ~ 0.2, as shown in Figs. 6d,e,j,k. This means that the two nature reserves will be more suitable for the growth of Vallisneria natans after the operation of the PLHP, and there it will be easier for Siberian Crane to find food here.Changes in habitat area of Vallisneria natansOn the basis of formula (6), we calculate the habitat area from 1st of September to 31st of October in three typical years, as shown in Fig. 7 and Table 3. In 2006, the habitat area is increased greatly, and the impact of the PLHP on habitat area is most evident in October. Compared with M1, the mean monthly habitat area in M2 is increased by 190.92%. Especially around the 10th of October, the increase can reach about 867.79 km2, accounting for about 1/4 of the total area of the lake. Compared with 2006, the increase of habitat area in 2018 is smaller. The mean monthly habitat area in M2 is increased by 57.07% in September and by 145.27% in October. The largest change occurs in late September, with an increase of about 841.43 km2, which is basically the same as in 2006. While in 2010, compared with M1, the habitat area in M2 is completely the same in September and the average increase in October is only 18.07%, indicating that when the water level is relatively high the operation of the PLHP will make little change to the habitat area.Figure 7Variations of habitat areas of Vallisneria natans in M1 (without the PLHP) and M2 (with the PLHP) and the resulting differences (grey columns).Full size imageTable 3 Mean monthly habitat areas of Vallisneria natans and changes between M1 and M2.Full size tableAfter the operation of the PLHP, the habitat area can reach more than 1000 km2 during the most time of September and October in three typical years, accounting for about 1/3 of the total area of the lake region. In other words, the latest official regulatory scheme of the PLHP is beneficial for the growth of Vallisneria natans in September and October. It means that, whether it is a low-water-level year, medium-water-level year or high-water-level year, before Siberian Crane fly to Poyang Lake for winter, Vallisneria natans will occupy large areas of the flooded land under the regulation of the PLHP. It will ensure that Siberian Crane can consume abundant tubers of Vallisneria natans as food in winter, allowing them to better survive and reproduce in the wetlands of Poyang Lake.In this research, the habitat suitability model of Vallisneria natans in Poyang Lake is established based on the previous research of Chen et al.16. This model only considers the effect of water depth, which mainly influences the growth of aquatic plant by changing the degree of light attenuation34. In fact, temperature and flow velocity can also influence the growth of Vallisneria natans according the relevant studies. However, temperature only plays a decisive role in the germination period38 and is rarely considered as an influencing factor during the growth period of Vallisneria natans. While high flow velocity may adversely affect the growth of Vallisneria natans during the seedling period, adult Vallisneria plants have an extensive root–rhizome system and long ribbon-like leaves to prevent them from being torn apart in rapid water39. Vallisneria natans often begin to sprout in March, reaching the tillering stage in June or July in Poyang Lake region. As a consequence, the temperature and flow velocity will have little effect on the growth of Vallisneria natans during the study period in this research (September and October). Therefore, the water depth can be regarded as the main factor affecting the suitability of Vallisneria natans during the mature period.There have been several scholars who have studied the effect of water depth on the growth of Vallisneria natans in other areas. Xiao et al.40 reported that Vallisneria natans grow rapidly with depths of 110–160 cm, while the growth is severely retarded with a depth of 250 cm. Cao et al.34 carried out experiments in turbid water and reported that the water depth of about 130 cm is most suitable for the growth of Vallisneria natans, while higher water depth will be less favorable for the growth. The inconsistency between these findings and the habitat suitability curve proposed by Chen et al.16 may be explained by the climate differences in different regions, as well as the different degrees of turbidity in water. According to the above analysis, the habitat suitability model of Vallisneria natans in this paper is established based on the habitat suitability curve proposed by Chen et al. (2020) as it represents to the growth characteristics of Vallisneria natans in Poyang Lake region.According to the above analysis, the latest regulatory scheme of the PLHP will effectively increase the water level in the lake region and expand the habitat area of Vallisneria natans, especially in low-water-level years. This finding is different from some of the previous research. Zhu et al.15 used the average water depth during the growing period of Vallisneria natans (from March to October) to reflect the availability of this food resource for Siberian Crane. They found that the PLHP had few influences on Vallisneria natans. This was because the PLHP remain completely open from April to August and it takes effect only in March, September and October during the growing period of Vallisneria natans. Therefore, the average water depth during March to October differs little whether with or without the PLHP, which would certainly underestimate the impact of the PLHP. The present study focuses on September and October, and uses the mean monthly water depth to reflect the habitat suitability of Vallisneria natans. In this period, the natural water level is relatively low in M1, and the operation of the PLHP increases the water level and inundates large areas of otherwise dry land. As a result, the habitat areas of Vallisneria natans observe an increase. More

The world’s population is rapidly urbanizing, particularly along coastlines, where population density is now three times higher than the global average1,2. According to the National Oceanic and Atmospheric Administration (NOAA), almost 40% of the United States (U.S.) population resides in coastal zones with population density being over five times greater in coastal shoreline counties than the national average. As a result, human encroachment on coastal ecosystems is significantly modifying natural landscapes and reducing intact coastal habitat. Along densely populated coastlines, residential development often involves unsustainable land-use planning and armoring of shorelines, where natural habitats such as saltmarshes, mangroves, seagrasses and oyster reefs, are replaced with artificial structures, including vertical bulkheads, seawalls, boat ramps, and other gray infrastructures3. In areas with dense residential development between 50–90% of shorelines can be armored, whereby, on average, 14% of all U.S. shorelines have been modified from their natural conditions and replaced with artificial structures3. This transition represents an extensive loss of natural coastal habitats and the critical ecosystem services they provide.As more ecologically harmful infrastructure is developed to meet the demands of human population growth, urbanization concurrently alters ecosystem services and functions by negatively impacting biodiversity, ecological conditions and environmental quality, specifically through a decrease in native habitat, increased water pollution, and creation of impervious surfaces4. Urbanization may also lead to less resilient and adaptable coastal communities against natural hazards and climate change threats, such as sea level rise and hurricanes. This is because in urban areas, ecosystem functioning is reduced and associated services are lost, resulting in increasing risk of shoreline erosion, saltwater intrusion, storm surges, and coastal flooding2,5.These human-environment interactions in coastal ecosystems can lead to, and at the same time be derived by, decisions that will shape the future structure, function, and sustainability of coastal ecosystems6. These social decisions (e.g., large-scale policies or individual level choices) can have long-lasting consequences for both the environment and society, especially as coastal development increases. Decisions that modify and change the biophysical nature of the environment (e.g., waterfront residents’ decision to use artificial structures for storm protection and shoreline stabilization) impact its ecological functionality7. At the same time, these alterations may change the degree of connectivity that individual humans have to their environments, which might extend to broader societies’ ecological knowledge8,9.Few studies provide evidence that the removal and lack of natural environments in urbanized environments reduces individuals’ environmental connectedness and ecological knowledge, and subsequently lowers pro-environmental behaviors10,11,12. This is of critical importance since a general lack of environmental connectedness, and in particular, a lack of ecological knowledge is a phenomenon often used to explain the non-appreciation of, and deleterious behaviors toward, the natural environment, even though many studies theorize these relationships opposed to empirically test them (e.g., see refs. 13,14 and the discussion in ref. 15 about “nature-deficit disorder”). Furthermore, if there exists a general lack of ecological knowledge, social decisions at the individual level that reflect these limited perceptions (e.g., utilitarian land-use decisions12 or waterfront homeowners’ preference to install a bulkhead) can often cascade to larger societal impacts through domino-effects, where individual decisions trigger similar, reactive decisions by neighbors leading to broader societal patterns16. For example, Gittman et al.17 found that one of the stronger predictors of an individual decision to have an armored shoreline was presence of armoring on a neighboring parcel. When considered across a community scale, such societal patterns can alter natural coastal habitats significantly.In this current study, we investigate the relationship between residents’ knowledge, or mental models, of human-environment interactions, their self-reported pro-environmental behavior, and how these perceptions and behaviors are associated with urbanization. A mental model is the cognitive internal representation of a system in the external world that articulates causal relationships among system components (i.e., abstract concepts)18,19. Mental models that represent causal knowledge can be graphically obtained through cognitive mapping techniques in the form of directed graphs, which are networks in which nodes represent concepts (i.e., system components) and graph edges (arrows) represent the causal relationships between the concepts20. We combine methods from social science, data science, and network science to conduct an analysis using mental models of coastal residents along an urbanization gradient to better understand the interconnections among urbanization, people’s knowledge of human-environment interactions, and their pro-environmental behavior.We surveyed residents across eight coastal states in the northeast U.S., including Maine, New Hampshire, Massachusetts, Rhode Island, Connecticut, New York, New Jersey, and Delaware. We used a fuzzy cognitive mapping (FCM) approach21 to elicit mental models of coastal ecosystems with a focus on environmental connectedness, ecosystem health, human wellbeing, climate, and sustainable coasts (see Methods). Here, we propose a concept, Urbanized Knowledge Syndrome (UKS), which represents recurring patterns in urban dwellers’ mental models about natural ecosystems – their internal understanding of how humans and environment interact. Here, syndrome should not be interpreted as a set of medical signs and symptoms which are associated with a particular disease or disorder. These recurring patterns include (1) diminished systems thinking (e.g., complexity of mental models decreases as degree of urban development increases) and (2) the erosion of cognitive diversity (i.e., diversity of mental models among residents decreases as degree of urban development increases). These patterns demonstrate a type of thinking that is simplified to some extent or otherwise limited or focused on fewer social-ecological relationships than exist in reality.Systems thinking – a holistic view that considers factors and interactions and how they result in a possible outcome – is an important skillset that helps people better understand complex systems and adapt to changes22. Individuals with higher degrees of systems thinking are more likely to consider interdependencies, identify leverage points to intervene within the system and produce desired outcomes23, better anticipate system function and emergence of patterns of behavior24, and avoid unintended consequences18. As such, systems thinking may help coastal residents develop mental models that enable more nuanced reasoning about diverse causal pathways between humans and natural coastal ecosystems25,26,27,28, which may lead to behaviors that are driven by more predominant cognition of complex feedbacks, trade-offs, and reciprocal interdependencies between humans and nature. In contrast, bounded systems thinking (or linear thinking) may lead humans to develop limited cognition of their surrounding world, reduce their ability to accurately and adequately perceive the complexity of the environment they inhabit and interact with22, and thus may give rise to counterproductive behaviors and decisions27,29. For example, a simple causal relationship might be that seawall construction increases coastal protection as a form of structural defense to control shoreline erosion; whereas a more complex relationship might be that seawalls lead to alterations in hydrodynamic processes, which reduces erosion locally and accelerates coastal erosion downstream30, and at the same time, shoreline armoring can also lead to losses of natural coastal habitats and their critical ecosystem functions3.While cities are beneficial to human development, working as engines of socioeconomic change, cultural transformation, and technological innovation, their psychological influences on people and how these influences drive urban residents’ perceptions and behavior must be noted. Firstly, the salience of ecosystem services is limited for inhabitants of more urbanized areas, as compared to rural areas. Exposure to nature provides multiple opportunities for cognitive development which increases the potential for stewardship of the environment and for a stronger recognition of ecosystem functions13. Urban residents, however, are more routinely exposed to built environment and gray infrastructure, such as armored shorelines and artificial structures along coastlines, as opposed to natural environment, and thus their local experience of, and connection to, ecosystem services can be limited31.Secondly, urbanization generally comes with complex technology and commerce, allowing individuals to meet their needs quickly and through many choices with less appreciation of, and first hand experience with, provisioning ecosystem services (e.g., food comes from many grocery stores not a farm or garden; fish comes over a counter not across a dock or the end of a spear; and potable water comes from a pipeline not a spring or well). This may cause the development of a wider gap in human perceptions of benefits received from natural ecosystems32, fostering the emergence of societies that are increasingly disconnected and seemingly independent from ecosystem services31.Finally, residents of urbanized areas may be exposed to a set of social norms, information, and perspectives that encourage anthropocentric values and thinking including human exemptionalism (“the tendency to see human systems as exempt from the constraints of natural environment”33) and human exceptionalism (“the tendency to see humans as biologically unique and discontinuous with the rest of the animal world”34), therefore limiting their understanding of the importance and substantiality of reciprocal interdependencies between humans and natural environment13,34. These urbanization aspects may spark what we call ‘limits to systems thinking’ in the social-ecological realm.Therefore, we hypothesize (H1) that in more urbanized areas, mental models are predominantly characterized by linear thinking of coastal ecosystems, as opposed to systems thinking, where components are connected mostly by simple causal patterns. This class of mental models is associated with limited cognition of synergies and trade-offs, emergence of global patterns from local relationships, reciprocal interdependencies, and feedback loops between humans and natural ecosystems, which may lead to a gap in residents’ perception of nonlinear complex structures. To test our hypothesis, we analyze the structure of causal relationships using the network structure and graph-theoretic metrics of cognitive maps (i.e., graphical representations of mental models). We use cluster analysis to identify predominant classes of mental models about coastal ecosystems. Distinct clusters of mental models represent archetypal cognitions that individuals develop to perceive human-environment interdependencies13,27. We then use network analyses to measure the complexity of causal structures in cognitive maps and determine the overall degree of systems thinking in each cluster (see Methods). Finally, we investigate the association between urbanization and the degree of systems thinking across those clusters.The second important feature that helps systems adapt to changes is diversity, ranging from ecosystems35 to economic systems36. There is also evidence that these same relationships between diversity and adaptability hold true for cultural knowledge systems, governance systems, and among diverse communities and social institutions that function more effectively as resilient collectives28,37,38.In contrast, as cultural homogenization theories explain, survival in cities depends on fitting in and adopting practices that are considered socially normal by the dominant culture39. Although cities are magnets for people from all corners of the world with seemingly more diverse composition of race and ethnicity compared to rural areas40, assimilation of diverse values, beliefs, cultural knowledge, and social norms into a universal, governing culture—sometimes referred to as “cultural colonialism” or “cultural normalization” – is a major component of urban societies41. This cultural normalization among urban dwellers is exacerbated by dominant exposure to the universal language and education system, greater access to the Internet, social media and news outlets, and market-driven policies and global standardizations for laws and finance41.In addition, an important characteristic of urbanization is the centralization of the population into cities, “where neighborhoods in different regions have similar patterns of roads, residential lots, commercial areas, and aquatic features”42. Such physical and environmental homogenization across urban areas, which is visually evident, is influenced by monocentric land-management and policies, economic pressures for land development and use, engineering necessities, codes and standards, and preferences for particular aesthetics and recreations. Prior studies have shown that this homogenization extends to ecological structure, meaning that across urbanized areas, similar built environment and landscape structures can lead to homogenized ecological characteristics, function, and the range of ecosystem services they can supply42,43.Here, we argue that homogenization in cultural, physical, and ecological systems also extends to residents’ perceptions and understanding of human-environment interactions. We, therefore, hypothesize (H2) that increased urbanization is associated with more homogenized mental models of coastal ecosystems. To test our second hypothesis, we measure the structural dissimilarity of individuals’ mental models (i.e., cognitive maps) using some of the widely used methods for comparing graphs44. We measure the mean of pairwise cognitive distances (i.e., a quantitative metric that represents the mean of graph dissimilarity between any two individual cognitive maps) and compare this metric across clusters of mental models, and thus, explore the correspondence between urbanization and mental model homogenization (i.e., testing the hypothesis that urbanization is associated with more similar mental models in terms of causal structures represented in cognitive maps) (see Methods). More

Farmers at a Great Green Wall site in Niger. Researchers have found that the project is not always benefiting the most vulnerable people.Credit: Boureima Hama/AFP/Getty

It’s now 15 years since the African Union gave its blessing to Africa’s Great Green Wall, one of the world’s most ambitious ecological-restoration schemes. The project is intended to combat desertification across the width of Africa, and spans some 8,000 kilometres, from Senegal to Djibouti. Its ambition is staggering: it aims to restore 100 million hectares of degraded land by 2030, capturing 250 million tonnes of carbon dioxide and creating 10 million jobs in the process. But it continues to struggle.An assessment two years ago by independent experts commissioned by the United Nations stated that somewhere between 4% and 20% of the restoration target had been achieved (go.nature.com/39zqgkr). That figure has not changed, according to the latest edition of Global Land Outlook (go.nature.com/3kdjtw5) from the UN Convention to Combat Desertification (UNCCD), out last week. Equally concerning is the fact that funding for the project continues to lag. Africa’s governments and international donors need to find around US$30 billion to reach the 100-million-hectare target. So far, $19 billion has been raised.A pandemic — and now a cost-of-living crisis — has placed demands on all governments, and that means countries might be expected to reduce their green-wall commitments. But the project continues to be weighed down by other difficulties, including the complex system through which it is funded and governed, as well as how its success is measured. These problems can and must be fixed, otherwise it will struggle to achieve its goals.One potential solution — improved metrics — comes from an analysis published last year by Matthew Turner at the University of Wisconsin–Madison and his colleagues (M. D. Turner et al. Land Use Policy 111, 105750; 2021). The researchers explored limitations in the Great Green Wall project metrics by assessing the impact of World Bank funding from 2006 to 2020. As their work indicates, definitions of success depend on which measure is used.In Niger, for example, green-wall projects could be said to be succeeding if measured by the area of eroded soil that has been recovered or by the number of trees that have been planted. But the authors report that these gains were not necessarily benefiting the most vulnerable people. In places, women were being excluded from employment in green-wall projects, and in some cases, local administrations looked to privatize restored land that might instead have been owned by everyone in a community.Broader problems with metrics are highlighted in the UN’s latest land-degradation report. This estimates that nearly half of the land that has been pledged for restoration worldwide will be planted predominantly with fast-growing trees and plants. This will provide only a fraction of the ecosystem services produced by forests that are allowed to naturally regenerate, including significantly less carbon storage, groundwater recharge and wildlife habitat.The Great Green Wall project also needs more predictable funding and more transparent governance. The project was conceived by Africa’s leaders for the benefit of the continent’s people, on the basis of warnings from scientists about the risks of desertification and land degradation. The original idea was not brought to Africa by international donors, as is often the case in international science-based development projects. But it still relies on donor financing, and lots of it — and that brings other problems, among them coordination challenges.The project is the responsibility of an organization set up by the African Union called the Pan African Agency of the Great Green Wall, based in Nouakchott, Mauritania. But some donors, such as the European Union and the World Bank, are not providing most of their Great Green Wall funding through this agency. Instead, they often deal directly with individual governments, because this gives them more control over how their money is spent. It is unfair to expect the Pan African Agency to coordinate a raft of donors doing one-on-one deals with individual countries. Bypassing the Pan African Agency also creates a problem for transparency, because it makes it harder for the African Union to determine precisely who is funding what.In January 2021, at an international biodiversity summit hosted by France, Emmanuel Macron, the French president, announced that the Great Green Wall would receive an extra $14 billion in funding for 5 years. He also said that a new body, called the Great Green Wall Accelerator, based in Bonn, Germany, would be responsible for pulling together funding pledges and tracking progress against targets. This is well-intentioned, but the accelerator needs to coordinate its work with the Pan African Agency. It is not yet clear how this will happen.A potentially more transformative solution was proposed two years ago by a group of UN-appointed experts. They recommended that a single trust fund be set up that all donors could contribute to and through which they could decide funding priorities together. Regrettably, this has not happened, and observers say it is not likely to happen in the current climate.This month, the international community will come together in Abidjan, Côte d’Ivoire, for the 15th conference of the parties to the UNCCD. The green wall’s funders and participating countries will all be there. If a single trust fund is off the table, they must work together to find a better way to coordinate their green-wall project activities. It is also essential that they study the findings of Turner and colleagues’ review. Along with a focus on existing metrics, the Great Green Wall needs evaluation criteria that take better account of the needs of all people in participating countries, particularly the most vulnerable. More

Kollas, C., Körner, C. & Randin, C. F. Spring frost and growing season length co-control the cold range limits of broad-leaved trees. J. Biogeogr. 41, 773–783 (2014).Article 

Google Scholar 
Lenz, A., Hoch, G., Körner, C. & Vitasse, Y. Convergence of leaf-out towards minimum risk of freezing damage in temperate trees. Funct. Ecol. 30, 1480–1490 (2016).Article 

Google Scholar 
Körner, C. et al. Where, why and how? Explaining the low-temperature range limits of temperate tree species. J. Ecol. 104, 1076–1088 (2016).Article 
CAS 

Google Scholar 
Körner, C. Plant adaptation to cold climates. F1000Research 5, 2769 (2016).Article 

Google Scholar 
Vitra, A., Lenz, A. & Vitasse, Y. Frost hardening and dehardening potential intemperate trees from winter to budburst. New Phytol. 216, 113–123 (2017).PubMed 
Article 

Google Scholar 
Dy, G. & Payette, S. Frost hollows of the boreal forest as extreme environments for black spruce tree growth. Can. J. For. Res. 37, 492–504 (2007).Article 

Google Scholar 
Marquis, B., Bergeron, Y., Simard, M. & Tremblay, F. Growing-season frost is a better predictor of tree growth than mean annual temperature in boreal mixedwood forest plantations. Glob. Change Biol. 26, 6537–6554 (2020).ADS 
Article 

Google Scholar 
Hufkens, K. et al. Ecological impacts of a widespread frost event following early spring leaf-out. Glob. Change Biol. 18, 2365–2377 (2012).ADS 
Article 

Google Scholar 
Guo, X., Khare, S., Silvestro, R. & Rossi, S. Minimum spring temperatures at the provenance origin drive leaf phenology in sugar maple populations. Tree Physiol. 40, 1639–1647 (2020).CAS 
PubMed 
Article 

Google Scholar 
Marquis, B., Bergeron, Y., Simard, M. & Tremblay, F. Probability of spring frosts, not growing degree-days, drives onset of spruce bud burst in plantations at the boreal-temperate forest ecotone. Front. Plant Sci. 11, 1031 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Mahony, C. R., Cannon, A. J., Wang, T. & Aitken, S. N. A closer look at novel climates: New methods and insights at continental to landscape scales. Glob. Change Biol. 23, 3934–3955 (2017).ADS 
Article 

Google Scholar 
Roman-Palacios, C. & Wiens, J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl. Acad. Sci. U.S.A. 117, 4211–4217 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chuine, I. A unified model for budburst of trees. J. Theor. Biol. 207, 337–347 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Horvath, P. et al. Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models. Biogeosciences 18, 95–112 (2021).ADS 
CAS 
Article 

Google Scholar 
Wullschleger, S. D. et al. Plant functional types in Earth system models: Past experiences and future directions for application of dynamic vegetation models in high-latitude ecosystems. Ann. Bot. Lond. 114, 1–16 (2014).CAS 
Article 

Google Scholar 
Deser, C. et al. Insights from earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020).ADS 
Article 

Google Scholar 
Machete, R. L. & Smith, L. A. Demonstrating the value of larger ensembles in forecasting physical systems. Tellus A Dyn. Meteorol. Oceanogr. 68, 28393 (2016).Article 

Google Scholar 
Deser, C., Phillips, A., Bourdette, V. & Teng, H. Uncertainty in climate change projections: The role of internal variability. Clim. Dyn. 38, 527–546 (2012).Article 

Google Scholar 
Leduc, M. et al. The ClimEx project: A 50-member ensemble of climate change projections at 12-km resolution over Europe and Northeastern North America with the Canadian regional climate model (CRM5). J. Appl. Meteor. Climatol. 58, 663–693 (2019).ADS 
Article 

Google Scholar 
Innocenti, S., Mailhot, A., Leduc, M., Cannon, A. J. & Frigon, A. Projected changes in the probability distributions, seasonality, and spatiotemporal scaling of daily and subdaily extreme precipitation simulated by a 50-member ensemble over Northeastern North America. J. Geophys. Res. Atmos. 124, 19 (2019).Article 

Google Scholar 
Kay, J. E. et al. The community earth system model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteor. Soc. 96, 1333–1349 (2015).ADS 
Article 

Google Scholar 
Thompson, D. W. J., Barnes, E. A., Deser, C., Foust, W. E. & Phillips, A. S. Quantifying the role of internal climate variability in future climate trends. J. Clim. 28, 6443–6456 (2015).ADS 
Article 

Google Scholar 
Kumar, D. & Ganguly, A. R. Intercomparison of model response and internal variability across climate model ensembles. Clim. Dyn. 51, 207–219 (2018).Article 

Google Scholar 
Gu, L. et al. The contribution of internal climate variability to climate change impacts on droughts. Sci. Total Environ. 684, 229–246 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Mittermeier, M., Braun, M., Hofstätter, M., Wang, Y. & Ludwig, R. Detecting climate change effects on Vb cyclones in a 50-member single-model ensemble using machine learning. Geophys. Res. Lett. 46, 14653–14661 (2019).ADS 
Article 

Google Scholar 
Fyfe, J. C. et al. Large near-term projected snowpack loss over the western United States. Nat. Commun. 8, 14996 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zohner, C. M. et al. Late-spring frost risk between 1959 and 2017 decreased in North America but increased in Europe and Asia. Proc. Natl. Acad. Sci. U.S.A. 117, 12192–12200 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ma, Q., Huang, J.-G., Hänninen, H. & Berninger, F. Divergent trends in the risk of spring frost damage to trees in Europe with recent warming. Glob. Change Biol. 25, 351–360 (2018).ADS 
Article 

Google Scholar 
Cannell, M. G. R. & Smith, R. I. Climatic warming, spring budburst and forest damage on trees. J. Appl. Ecol. 23, 177–191 (1986).Article 

Google Scholar 
Morin, X. & Chuine, I. Will tree species experience increased frost damage due to climate change because of changes in leaf phenology? Can. J. For. Res. 44, 1555–1565 (2014).Article 

Google Scholar 
Fu, Y. et al. Progress in plant phenology modeling under global climate change. Sci. China Earth Sci. 63, 1237 (2020).ADS 
Article 

Google Scholar 
Gill, A. L. et al. Changes in autumn senescence in northern hemisphere deciduous trees: A meta-analysis of autumn phenology studies. Ann Bot. 116, 875–888 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Perrin, M., Rossi, S. & Isabel, S. N. Synchronisms between bud and cambium phenology in black spruce: Early-flushing provenances exhibit early xylem formation. Tree Physiol. 37, 593–603 (2017).PubMed 
Article 

Google Scholar 
Guo, X. et al. Common-garden experiment reveals clinical trends of bud phenology in black spruce populations from a latitudinal gradient in the boreal forest. J. Ecol. https://doi.org/10.1111/1365-2745.13582 (2021).Article 

Google Scholar 
Usmani, A. et al. Ecotypic differentiation of black spruce populations: Temperature triggers bud burst but not bud set. Trees 34, 1313–1321 (2020).Article 

Google Scholar 
Buttò, V., Rozenberg, P., Deslauriers, A., Rossi, S. & Morin, H. Environmental and developmental factors driving xylem anatomy and micro-density in black spruce. New Phytol. 230, 957. https://doi.org/10.1111/nph.17223 (2021).CAS 
Article 
PubMed 

Google Scholar 
Keenan, T. F. & Richardson, A. D. The timing of autumn senescence is affected by the timing of spring phenology: Implications for predictive models. Glob. Change Biol. 21, 2634–2641 (2015).ADS 
Article 

Google Scholar 
Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Silvestro, R. et al. From phenology to forest management: Ecotypes selection can avoid early or late frosts, but not both. For. Ecol. Manage. 436, 21–26 (2019).Article 

Google Scholar 
D’Orangeville, L. et al. Beneficial effects of climate warming on boreal tree growth may be transitory. Nat. Commun. 9, 3213 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Menezes-Silva, P. E. et al. Different ways to die in a changing world: Consequences of climate change for tree species performance and survival through an ecophysiological perspective. Ecol. Evol. 9, 11979–11999 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Schwarz, J. A., Saha, S. & Bauhus, J. Potential of forest thinning to mitigate drought stress: A meta-analysis. For. Ecol. Manage. 380, 261–273 (2016).Article 

Google Scholar 
Marquis, B., Bergeron, Y., Simard, M. & Tremblay, F. Disentangling the effect of topography and microtopography on near-ground growing-season frosts at the boreal-temperate forest ecotone (Québec, Canada). New For. https://doi.org/10.1007/s11056-021-09840-7 (2021).Article 

Google Scholar 
Marquis, B. growth stagnation of planted spruce in boreal mixedwoods: Importance of landscape, microsite, and growing-season frosts. For. Ecol. Manage. 479, 118533 (2021).Article 

Google Scholar 
Pedlar, J. et al. The implementation of assisted migration in Canadian forests. For. Chron. 87, 766–777 (2011).Article 

Google Scholar 
Marris, E. Planting the forest for the future. Nature 459, 906–908 (2009).CAS 
PubMed 
Article 

Google Scholar 
Klisz, M. et al. Limitations at the limit? Diminishing of genetic effects in Norway spruce provenance trials. Front. Plant Sci. 10, 306 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Pedlar, J. H., McKenney, D. W. & Lu, P. Critical seed transfer distances for selected tree species in eastern North America. J. Ecol. https://doi.org/10.1111/1365-2745.13605 (2021).Article 

Google Scholar 
Prudhomme, G. O. et al. Ecophysiology and growth of white spruce seedlings from various seed sources along a climatic gradient support the need for assisted migration. Front. Plant Sci. 8, 2214 (2017).Article 

Google Scholar 
Girardin, M. P. et al. Annual aboveground carbon uptake enhancements from assisted gene flow in boreal black spruce forests are not long-lasting. Nat. Commun. 12, 1169 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zohner, C. M., Mo, L., Sebald, V. & Renner, S. S. Leaf-out in northern ecotypes of wide-ranging trees requires less spring warming, enhancing the risk of spring frost damage at cold range limits. Glob. Ecol. Biogeogr. 29, 1065–1072 (2020).Article 

Google Scholar 
Saucier, J.-P., Baldwin, K., Krestov, P. & Jorgenson, T. Boreal forests. In Routledge Handbook of Forest Ecology (eds Peh, K.S.-H. et al.) 7–29 (Routledge, 2015).
Google Scholar 
Hutchinson, M. F. et al. Development and testing of Canada-wide interpolated spatial models of daily minimum-maximum temperature and precipitation for 1961–2003. J. Appl. Meteorol. Climatol. 48, 725–741 (2009).ADS 
Article 

Google Scholar 
Gennaretti, F., Sangelantoni, L. & Grenier, P. Toward daily climate scenarios for Canadian Arctic coastal zones with more realistic temperature-precipitation interdependence. J. Geophys. Res. Atmos. 120, 862–877 (2015).Article 

Google Scholar 
Mpelasoka, F. S. & Chiew, F. H. S. Influence of rainfall scenario construction methods on runoff projections. J. Hydrometeorol. 10, 1168–1183 (2009).ADS 
Article 

Google Scholar 
Giorgi, F. Thirty years of regional climate modeling: Where are we and where are we going next? J. Geophys. Res. 124, 5696–5723 (2019).Article 

Google Scholar 
Laughlin, G. P. & Kalma, J. D. Frost hazard assessment from local weather and terrain data. Agric. For. Meteorol. 40, 1–16 (1987).ADS 
Article 

Google Scholar 
Sørland, S. L., Schar, C., Luthi, D. & Kjellstrom, E. Bias patterns and climate change signals in GCM-RCM model chains. Environ. Res. Lett. 13, 074017 (2018).ADS 
Article 

Google Scholar 
von Trentini, F., Aalbers, E. E., Fischer, E. M. & Ludwig, R. Comparing interannual variability in three regional single-model initial-condition large ensembles (smiles) over Europe. Earth Syst. Dyn. 11, 1013–1031 (2020).ADS 
Article 

Google Scholar 
Hänninen, H. & Pelkonen, H. Effects of temperature on dormancy release in Norway spruce and Scots pine seedlings. Silva Fenn. 22, 5357 (1988).
Google Scholar 
Lechowicz, M. J. Why do temperate deciduous trees leaf out at different times? Adaptation and ecology of forest communities. Am. Nat. 124, 821–842 (1984).Article 

Google Scholar 
Olson, M. S. et al. The adaptive potential of Populus balsamifera L. to phenology requirements in a warmer global climate. Mol. Ecol. 22, 1214–1230 (2013).CAS 
PubMed 
Article 

Google Scholar 
Hawkins, C. D. B. & Dhar, A. Spring bud phenology of 18 Betula papyrifera populations in British Columbia. Scand. J. For. Res. 27, 507–519 (2012).Article 

Google Scholar 
Bronson, D. R., Gower, S. T., Tanner, M. & Van Herk, I. Effect of ecosystem warming on boreal black spruce bud burst and shoot growth. Glob. Change Biol. 15, 1534–1543 (2009).ADS 
Article 

Google Scholar 
Basler, D. Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe. Agric. For. Meteorol. 217, 10–21 (2016).ADS 
Article 

Google Scholar 
Man, R., Lu, P. & Dang, Q.-L. Insufficient chilling effects vary among boreal tree species and chilling duration. Front. Plant Sci. https://doi.org/10.3389/fpls.2017.01354 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). http://www.R-project.org. More

Dordevic, B. & Dasic, T. Water storage reservoirs and their role in the development, utilization and protection of catchment. SPATIUM Int. Rev. 24, 9–15 (2011).
Google Scholar 
European Community. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Official J. Eur. Union. 5, L327 (2000).

Google Scholar 
Riley, W. D. et al. Small water bodies in Great Britain and Ireland: Ecosystem function, human-generated degradation, and options for restorative action. Sci. Total Environ. 645, 1598–1616. https://doi.org/10.1016/j.scitotenv.2018.07.243 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kujawa, K., Arczyńska-Chudy, E., Janku, K. & Mana, M. Effect of buffer zone structure on diversity of aquatic vegetation in farmland water bodies. Pol. J. Ecol. 68(4), 263–282. https://doi.org/10.3161/15052249PJE2020.68.4.001 (2021).Article 

Google Scholar 
Akasaka, M., Takamura, N., Mitsuhashi, H. & Kadono, Y. Effects of land use on aquatic macrophyte diversity and water quality of ponds. Fresh Biol. 55, 909–922 (2010).CAS 
Article 

Google Scholar 
Pukacz, A., Pełechaty, M., Pełechata, A. & Siepak, M. The differential cover of submerged vegetation vs habitat conditions in the lakes of the Lubuskie Region. Limnol. Rev. 7(2), 95–100 (2007).
Google Scholar 
Scheffer, M., Hosper, S. H., Meijer, M. L., Moss, B. & Jeppesen, E. Alternative equilibria in shallow lakes. Trends Ecol. Evol. 8, 275–279 (1993).CAS 
Article 

Google Scholar 
Scheffer, M. & Jeppesen, E. Alternative stable states. Ecol. Stud. 131, 397–405 (1998).Article 

Google Scholar 
Beck, M. W., Tomcko, C., Valley, R. D. & Staples, D. F. Analysis of macrophyte indicator variation as a function of sampling, temporal and stressor effects. Ecol. Indic. 46, 322–335 (2014).Article 

Google Scholar 
Celewicz-Gołdyn, S. & Kuczyńska-Kippen, N. Ecological value of macrophyte cover in creating habitat for microalgae (diatoms) and zooplankton (rotifers and crustaceans) in small field and forest water bodies. PLoS ONE 12(5), e0177317. https://doi.org/10.1371/journal.pone.0177317 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wilk-Woźniak, E. et al. Effects of the environs of waterbodies on aquatic plants in oxbow lakes (habitat 3150). Ecol. Ind. 98, 736–742. https://doi.org/10.1016/j.ecolind.2018.11.025 (2019).CAS 
Article 

Google Scholar 
Bedla, D. & Misztal, A. Changeability of chemistry of small water reservoirs with diversified use structure of the adjoining areas. Annu. Set Environ. Protect. 16, 421–439 (2014) (ISSSN 1506-218X (in Polish)).
Google Scholar 
Mozgawa, J. Photointerpretation analysis of landscape structure in lake watersheds of Suwałki Landscape. Park. Ekol. Pol. 41, 53–74 (1993).
Google Scholar 
Mioduszewski, W. Small water reservoirs on agricultural areas. Wieś Jutra Zakład Zasobów Wodnych Instytut Melioracji i Użytków Zielonych Falenty. 10(123), 32–34 (2008) (in Polish).
Google Scholar 
Gołdyn, B., Kowalczewska-Madura, K. & Celewicz-Gołdyn, S. Drought and deluge: Influence of environmental factors on water quality of kettle holes in two subsequent years with different precipitation. Limnologica 54, 14–22. https://doi.org/10.1016/j.limno.2015.07.002 (2015).CAS 
Article 

Google Scholar 
Bylak, A. et al. Small stream catchment in a developing city context: The importance of land cover changes on the ecological status of streams and the possibilities for providing ecosystem services. Sci. Total Environ. 815, 151974. https://doi.org/10.1016/j.scitotenv.2021.151974 (2022).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Goyal, V. C. et al. Ecological health and water quality of village ponds in the subtropics limiting their use for water supply and groundwater recharge. J. Environ. Manage. 277, 111450. https://doi.org/10.1016/j.jenvman.2020.111450 (2021).CAS 
Article 
PubMed 

Google Scholar 
Kuczyńska-Kippen, N., Spoljar, M., Mleczek, M. & Zhang, Ch. Elodeides, but not helophytes, increase community diversity and reduce trophic state: Case study with rotifer indices in field ponds. Ecol. Ind. 128, 107829. https://doi.org/10.1016/j.ecolind.2021.107829 (2021).Article 

Google Scholar 
Novikmec, M. et al. Ponds and their catchments: size relationships and influence of land use across multiple spatial scales. Hydrobiologia 774, 155–166. https://doi.org/10.1007/s10750-015-2514-8 (2016).Article 

Google Scholar 
Dudzińska, A., Szpakowska, B. & Pajchrowska, M. Influence of land development on the ecological status of small water bodies. Oceanol. Hydrobiol. Stud. 49(4), 345–353. https://doi.org/10.1515/ohs-2020-0030 (2020).Article 

Google Scholar 
Szpakowska, B. Occurrence and Role of Organic Compounds Dissolved in Surface and Ground Waters of Agricultural Landscape (Wydawnictwo Uniwersytetu Mikołaja Kopernika, 1999) (in Polish).
Google Scholar 
Wysocka-Czubaszek, A. & Banaszuk, P. Migration of nitrogen compounds and the riparian zones in the Upper Narew Valley. Acta Agroph. 2(1), 349–354 (2003) (in Polish).
Google Scholar 
Szpakowska, B., Świerk, D., Pajchrowska, M. & Gołdyn, R. Verifying the usefulness of macrophytes as an indicator of the status of small waterbodies. Sci. Total Environ. 798, 149279. https://doi.org/10.1016/j.scitotenv.2021.149279 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hermanowicz, W., Dojlido, J., Dożańska, W., Koziorowski, B. & Zerbe, J. Physico-Chemical Examinations of Water and Sludge (Arkady, 1999) (in Polish).
Google Scholar 
Aynur, M., Liming, N. & Fang, Y. Spatial evaluation of environmental suitability for human settlement of Kashgar, Northwest China. Fresenius Environ. Bull. 27(9), 5899–5907 (2018).
Google Scholar 
Pham, L., Brabyn, L. & Ashrof, S. Combining Quick Bird, LiDAR and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach. Int. J. Appl. Earth Obs. Geoinf. 50, 187–197. https://doi.org/10.1016/j.jag.2016.03.015 (2016).ADS 
Article 

Google Scholar 
Lepš, J. & Šmilauer, P. Multivariate Analysis of Ecological Data Using CANOCO (Cambridge Univeristy Press, 2003) (ISBN 9780521891080).Book 

Google Scholar 
Mohamed, Z. A. Macrophytes-cyanobacteria allelopathic interactions and their implications for water resources management—A review. Limnologica 63, 122–132. https://doi.org/10.1016/j.limno.2017.02.006 (2017).CAS 
Article 

Google Scholar 
Koc, J. Problems of small reservoirs protection on rural areas. In Problemy ochrony i użytkowania obszarów wiejskich o dużych walorach przyrodniczych (eds Radwan, S. & Lorkiewicz, Z.) 151–156 (UMCS, 2000).
Google Scholar 
Bell, V. A. et al. Long term simulations of macronutrients (C, N and P) in UK freshwaters. Sci. Total Environ. 776, 145813. https://doi.org/10.1016/j.scitotenv.2021.145813 (2021).ADS 
CAS 
Article 

Google Scholar 
Winton, R. S. et al. Anthropogenic influences on Zambian water quality: Hydropower and land-use change. Environ. Sci. Process. Impacts. 23, 981–994. https://doi.org/10.1039/D1EM00006C (2021).CAS 
Article 
PubMed 

Google Scholar 
Wang, Y. et al. Effect and risk assessment of animal manure pollution on Huaihe River Basin, China. Chin. Geogr. Sci. 31, 751–764. https://doi.org/10.1007/s11769-021-1222-8 (2021).Article 

Google Scholar 
Lawniczak-Malińska, A., Ptak, M., Celewicz, S. & Choiński, A. Impact of lake morphology and shallowing on the rate of overgrowth in hard-water eutrophic lakes. Water 10(12), 1827. https://doi.org/10.3390/w10121827 (2018).CAS 
Article 

Google Scholar 
Bosiacka, B., Pacewicz, K. & Pieńkowski, P. Spatial analysis of plant species distribution among small water bodies in an agricultural landscape. Acta Agrobot. 61(2), 93–101. https://doi.org/10.5586/aa.2008.037 (2008).Article 

Google Scholar 
Joniak, T., Kuczyńska-Kippen, N. & Gąbka, M. Effect of agricultural landscape characteristics on the hydrobiota structure in small water bodies. Hydrobiologia 793, 121–133. https://doi.org/10.1007/s10750-016-2913-5 (2017).CAS 
Article 

Google Scholar 
Barling, R. D. & Moore, I. D. Role of buffer strips in management of waterway pollution: A review. Environ. Manage. 18, 543–558. https://doi.org/10.1007/BF02400858 (1994).ADS 
Article 

Google Scholar 
Blanco-Cangui, H., & Lal, R. Buffer strips. In Principles of Soil Conservation and Management (eds Blanco, H. & Lal, R.) 223–257 (Springer, Netherlands, 2008).
Google Scholar 
Borin, M., Passoni, M., Thiene, M. & Tempesta, T. Multiple functions of buffer strips in farming areas. Eur. J. Agron. 32, 103–111. https://doi.org/10.1016/j.eja.2009.05.003 (2010).Article 

Google Scholar 
Ghobrial, M. G., Nassr, H. S. & Kamil, A. W. Bioactivity effect of two macrophyte extracts on growth performance of two bloom-forming cyanophytes. Egypt. J. Aquat. Res. 41(1), 69–81. https://doi.org/10.1016/j.ejar.2015.01.001 (2015).Article 

Google Scholar 
Lacas, J. G., Voltz, M., Gouy, V., Carluer, N. & Gril, J. J. Using grassed strips to limit pesticide transfer to surface water: A review. Agron. Sustain. Dev. 25, 253–266 (2005).CAS 
Article 

Google Scholar 
Świerk, D. & Szpakowska, B. An ecosystem valuation method for small water bodies. Ecol. Chem. Eng. S. 20(2), 397–418. https://doi.org/10.2478/eces-2013-0029 (2013).Article 

Google Scholar 
Marszałek, M., Kowalski, Z. & Makara, A. The possibility of contamination of water-soil environment as a results of the use of pig slurry. Ecol. Chem. Eng. S. 26(2), 313–330. https://doi.org/10.1515/eces-219-0022 (2019).Article 

Google Scholar 
Micek, G., Górecki, J. & Neo, H. Relations: Company and its local milieu in the context of foreign direct investment in Polish pig industry. In Człowiek i Środowisko (eds Górka, Z. & Zborowski, A.) 297–308 (UJ Kraków, 2009).
Google Scholar 
OECD. Agriculture Trade and The Environment: The Pig Sector (OECD, 2003).
Google Scholar 
Barałkiewicz, D. et al. Storm water contamination and its effect on the quality of urban surface waters. Environ. Monit. Assess. 186(10), 6789–6803. https://doi.org/10.1007/s10661-014-3889-0 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
So Fijanic, A., Hulley, M. & Loock, D. Stormwater quality assessment and management for the town of jasper in Alberta, Canada. Water Qual. Res. J. Can. 56(1), 45–56. https://doi.org/10.2166/wqrj.2021.012 (2021).CAS 
Article 

Google Scholar 
Zubala, T. Assessment of the variability of rainwater quality and the functioning of retention reservoirs in the urban area. Rocznik Ochrona Środowiska 22(2), 840–858 (2020).
Google Scholar 
Czerniawski, R., Sługocki, L., Krepski, T., Wilczak, A. & Pietrzak, K. Spatial changes in invertebrate structures as a factor of strong human activity in the bed and catchment area of a small urban stream. Water 12(3), 913. https://doi.org/10.3390/w12030913 (2020).CAS 
Article 

Google Scholar 
Gołdyn, R. et al. Influence of stormwater runoff on macroinvertebrates in a small urban river and a reservoir. Sci. Total Environ. 625, 743–751. https://doi.org/10.1016/j.scitotenv.2017.12.324 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cao, Y., Zhi, Y., Jeppesen, E. & Li, W. Species-specific responses of submerged macrophytes to simulated extreme precipitation: A mesocosm study. Water 11(6), 1160. https://doi.org/10.3390/w11061160 (2019).CAS 
Article 

Google Scholar 
Hilt, S. et al. Restoration of submerged vegetation in shallow eutrophic lakes—A guideline and state of the art in Germany. Limnologica 36, 155–171 (2006).CAS 
Article 

Google Scholar 
Ryszkowski, L. & Kędziora, A. Modification of water flows and nitrogen fluxes by shelterbelts. Ecol. Eng. 29(4), 388–400. https://doi.org/10.1016/j.ecoleng.2006.09.023 (2007).Article 

Google Scholar  More