Philippa Kaur

Ah-King, M. Flexible mate choice in Encyclopedia of Animal Behavior, 2nd edn Vol. 4 (ed Jae Chun Choe) 421–431 (Academic Press, 2019).Harestad, A. S. & Bunnell, F. L. Home range and body weight—A reevaluation. Ecology 60, 389–402 (1979).Article 

Google Scholar 
O’Neill, R. V., Milne, B. T., Turner, M. G. & Gardner, R. H. Resource utilization scales and landscape pattern. Landsc. Ecol. 2, 63–69 (1988).Article 

Google Scholar 
Tricas, T. C. Determinants of feeding territory size in the corallivorous butterflyfish, Chaetodon multicinctus. Anim. Behav. 37, 830–841. https://doi.org/10.1016/0003-3472(89)90067-5 (1989).Article 

Google Scholar 
Tremblay, I., Thomas, D., Blondel, J., Perret, P. & Lambrechts, M. M. The effect of habitat quality on foraging patterns, provisioning rate and nestling growth in Corsican Blue Tits Parus caeruleus. Ibis 147, 17–24. https://doi.org/10.1111/j.1474-919x.2004.00312.x (2005).Article 

Google Scholar 
Watts, D. P. The influence of male mating tactics on habitat use in Mountain Gorillas (Gorilla gorilla beringei). Primates 35, 35–47. https://doi.org/10.1007/BF02381484 (1994).Article 

Google Scholar 
Lescroël, A. et al. Working less to gain more: when breeding quality relates to foraging efficiency. Ecology 91, 2044–2055. https://doi.org/10.1890/09-0766.1 (2010).Article 
PubMed 

Google Scholar 
Tufto, J., Anderson, R. & Linnell, J. Habitat use and ecological correlates of home range size in a small cervid: the roe deer. J. Anim. Ecol. 65, 715–724. https://doi.org/10.2307/5670 (1996).Article 

Google Scholar 
Morellet, N. et al. Seasonality, weather and climate affect home range size in roe deer across a wide latitudinal gradient within Europe. J. Anim. Ecol. 82, 1326–1339. https://doi.org/10.1111/1365-2656.12105 (2013).Article 
PubMed 

Google Scholar 
Anderson, D. P. et al. Scale-dependent summer resource selection by reintroduced elk in Wisconsin, USA. J. Wildl. Manag. 69, 298–310. https://doi.org/10.2193/0022-541X(2005)069%3c0298:SSRSBR%3e2.0.CO;2 (2005).Article 

Google Scholar 
Olsson, P. M. O. et al. Movement and activity patterns of translocated elk (Cervus elaphus nelsoni) on an active coal mine in Kentucky. Wildl. Biol. Pract. 3, 1–8. https://doi.org/10.2461/wbp.2007.3.1 (2007).Article 

Google Scholar 
Porter, W. P., Sabo, J. L., Tracy, C. R., Reichman, O. J. & Ramankutty, N. Physiology on a landscape scale: plant–animal interactions. Integr. Comp. Biol. 42, 431–453. https://doi.org/10.1093/icb/42.3.431 (2002).Article 
PubMed 

Google Scholar 
Berg, J. E. et al. Mothers’ movements: shifts in calving area selection by partially migratory elk. J. Wildl. Manag. 85, 1476–1489. https://doi.org/10.1002/jwmg.22099 (2021).Article 

Google Scholar 
Lehman, C. P. et al. Elk resource selection at parturition sites, Black Hills, South Dakota. J. Wildl. Manag. 80, 465–478. https://doi.org/10.1002/jwmg.1017 (2016).Article 

Google Scholar 
Johnson, B. K., Kern, J. W., Wisdom, M. J., Findholt, S. L. & Kie, J. G. Resource selection and spatial separation of mule deer and elk during spring. J. Wildl. Manag. 64, 685–697. https://doi.org/10.2307/3802738 (2000).Article 

Google Scholar 
Grace, J. & Easterbee, N. The natural shelter for red deer (Cervus elaphus) in a Scottish glen. J. Appl. Ecol. 16, 37–48. https://doi.org/10.2307/2402726 (1979).Article 

Google Scholar 
Demarchi, M. W. & Bunnell, F. L. Estimating forest canopy effects on summer thermal cover for Cervidae (deer family). Can. J. For. Res. 23, 2419–2426. https://doi.org/10.1139/x93-299 (1993).Article 

Google Scholar 
Parker, K. L. & Gillingham, M. P. Estimates of critical thermal environments for mule deer. J. Range. Manag. 43, 73–81 (1990).Article 

Google Scholar 
Proffitt, K. M. et al. Changes in elk resource selection and distributions associated with a late-season elk hunt. J. Wildl. Manag. 74, 210–218. https://doi.org/10.2193/2008-593 (2010).Article 

Google Scholar 
Webb, S. L., Dzialak, M. R., Harju, S. M., Hayden-Wing, L. D. & Winstead, J. B. Influence of land development on home range use dynamics of female elk. Wildl. Res. 38, 163–167. https://doi.org/10.1071/WR10101 (2011).Article 

Google Scholar 
Rumble, M. A., Benkobi, L. & Gamo, R. S. Elk responses to humans in a densely roaded area. Intermt. J. Sci. 11, 10–24 (2005).
Google Scholar 
McCorquodale, S. M. Sex-specific movements and habitat use by elk in the Cascade Range of Washington. J. Wildl. Manag. 67, 729–741. https://doi.org/10.1890/15-1607.1 (2003).Article 

Google Scholar 
Saïd, S. & Servanty, S. The influence of landscape structure on female roe deer home-range size. Landsc. Ecol. 20, 1003–1012. https://doi.org/10.1007/s10980-005-7518-8 (2005).Article 

Google Scholar 
Seddon, P. J., Armstrong, D. P. & Maloney, R. F. Developing the science of reintroduction biology. Conserv. Biol. 21, 303–312. https://doi.org/10.1111/j.1523-1739.2006.00627.x (2007).Article 
PubMed 

Google Scholar 
Hale, S. L. & Koprowski, J. L. Ecosystem-level effects of keystone species reintroduction: a literature review. Restor. Ecol. 26, 439–445. https://doi.org/10.1111/rec.12684 (2018).Article 

Google Scholar 
Cheyne, S. M. Wildlife reintroduction: considerations of habitat quality at the release site. BMC Ecol. 6, 5. https://doi.org/10.1186/1472-6785-6-5 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
Hegel, T. M., Gates, C. C. & Eslinger, D. The geography of conflict between elk and agricultural values in the Cypress Hills, Canada. J. Eniron. Manag. 90, 222–235. https://doi.org/10.1016/j.jenvman.2007.09.005 (2009).Article 

Google Scholar 
Walter, W. D. et al. Management of damage by elk (Cervus elaphus) in North America: a review. Wildl. Res. 37, 630–646. https://doi.org/10.1071/WR10021 (2010).Article 

Google Scholar 
Jung, T. S. Extralimital movements of reintroduced bison (Bison bison): implications for potential range expansion and human–wildlife conflict. Eur. J. Wildl. Res. 63, 35. https://doi.org/10.1007/s10344-017-1094-5 (2017).Article 

Google Scholar 
Buchholtz, E. K., Stronza, A., Songhurst, A., McCulloch, G. & Fitzgerald, L. A. Using landscape connectivity to predict human-wildlife conflict. Biol. Conserv. 248, 108677. https://doi.org/10.1016/j.biocon.2020.108677 (2020).Article 

Google Scholar 
Hodgson, J. A., Moilanen, A., Wintle, B. A. & Thomas, C. D. Habitat area, quality and connectivity: striking the balance for efficient conservation. J. Appl. Ecol. 48, 148–152. https://doi.org/10.1111/j.1365-2664.2010.01919.x (2011).Article 

Google Scholar 
Murie, O. The Elk of North America (Stackpole Co., 1951).
Google Scholar 
VDWR. Virginia elk management plan 2019–2028 (ed Virginia Department of Wildlife Resources) (Virginia Department of Wildlife Resources, 2019).Lituma, C. M. et al. Terrestrial wildlife in the post-mined Appalachian landscape: status and opportunities in Appalachia’s Coal-Mined Landscapes (eds Carl E. Zipper & Jeff Skousen) 135–166 (Springer, 2021).Lupardus, J. L., Muller, L. I. & Kindall, J. L. Seasonal forage availability and diet for reintroduced elk in the Cumberland Mountains, Tennessee. Southeast. Nat. 10, 53–74. https://doi.org/10.1656/058.010.0105 (2011).Article 

Google Scholar 
Schneider, J. et al. Food habits of reintroduced elk in southeastern Kentucky. Southeast. Nat. 5, 535–546. https://doi.org/10.1656/1528-7092(2006)5[535:Fhorei]2.0.Co;2 (2006).Article 

Google Scholar 
Smith, T. N., Keller, B. J., Chitwood, M. C., Hansen, L. P. & Millspaugh, J. J. Diet composition and selection of recently reintroduced elk in Missouri. Am. Midl. Nat. 180, 143–159. https://doi.org/10.1674/0003-0031-180.1.143 (2018).Article 

Google Scholar 
Franklin, J. A., Zipper, C. E., Burger, J. A., Skousen, J. G. & Jacobs, D. F. Influence of herbaceous ground cover on forest restoration of eastern US coal surface mines. New. For. 43, 905–924. https://doi.org/10.1007/s11056-012-9342-8 (2012).Article 

Google Scholar 
Popp, J. N., Toman, T., Mallory, F. F. & Hamr, J. A century of elk restoration in eastern North America. Restor. Ecol. 22, 723–730. https://doi.org/10.1111/rec.12150 (2014).Article 

Google Scholar 
Cook, J. G., Irwin, L. L., Bryant, L. D., Riggs, R. A. & Thomas, J. W. Relations of forest cover and condition of elk: a test of the thermal cover hypothesis in the summer and winter. Wildl. Monogr. 141, 3–61 (1998).
Google Scholar 
Parker, K. L. & Robbins, C. T. Thermoregulation in mule deer and elk. Can. J. Zool. 62, 1409–1422. https://doi.org/10.1139/z84-202 (1984).Article 

Google Scholar 
Mao, J. S. et al. Habitat selection by elk before and after wolf reintroduction in Yellowstone National Park. J. Wildl. Manag. 69, 1691–1707. https://doi.org/10.2193/0022-541X (2005).Article 

Google Scholar 
Wolff, J. O. & Van Horn, T. Vigilance and foraging patterns of American elk during the rut in habitats with and without predators. Can. J. Zool. 81, 266–271. https://doi.org/10.1139/z03-011 (2003).Article 

Google Scholar 
Beck, J. L. & Peek, J. M. Diet composition, forage selection, and potential for forage competition among elk, deer, and livestock on aspen–sagebrush summer range. Rangel. Ecol. Manag. 58, 135–147. https://doi.org/10.2111/03-13.1 (2005).Article 

Google Scholar 
Ford, W. M., Johnson, A. S. & Hale, P. E. Nutritional quality of deer browse in southern Appalachian clearcuts and mature forests. For. Ecol. Manag. 67, 149–157. https://doi.org/10.1016/0378-1127(94)90013-2 (1994).Article 

Google Scholar 
Sikes, R. S., Gannon, W. L. & The American Care and Use Committee of the American Society of Mammalogists. Guidelines of the American Society of Mammalogists for the use of wild mammals in research. J. Mammal. 92, 235–253. https://doi.org/10.1644/10-mamm-f-355.1 (2011).Percie du Sert, N. et al. The ARRIVE guidelines 2.0: updated guidelines for reporting animal research. PLoS Biol. 18, e3000410. https://doi.org/10.1371/journal.pbio.3000410 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Powell, J. W. Physiographic Regions of the United States. (American Book Company, 1895).Braun, E. L. Forests of the Cumberland Mountains. Ecol. Monogr. 12, 413–447. https://doi.org/10.2307/1943039 (1942).Article 

Google Scholar 
Clark, J. B. The Vascular Flora of Breaks Interstate Park, Pike County, Kentucky, and Dickenson County, Virginia Master of Science thesis, Eastern Kentucky University (2012).Pericak, A. A. et al. Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine. PLoS ONE 13, e0197758. https://doi.org/10.1371/journal.pone.0197758 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Boettner, F. et al. An assessment of the natural assets in the Appalachian Region: forest resources (ed Appalachian Regional Commission Report) 97 (Washington, DC, 2014).NOAA. Summary of monthly normals Grundy, VA 1991 – 2020 data (National Oceanic and Atmospheric Administration (2022).U.S. Geological Survey (USGS) Gap Analysis Project (GAP). GAP/LANDFIRE national terrestrial ecosystems 2011: U.S. Geological Survey data release (2016).Clark, M. The Nature Conservancy Eastern Division & North Atlantic Landscape Conservation Cooperative. Terrestrial habitat, Northeast data (2017).ESRI. ArcGIS desktop version 10.8.1 (Environmental Systems Research Institute, 2020).Ford, W. M. et al. Influence of elevation and forest type on community assemblage and species distribution of shrews in the central and southern Appalachians in Advances in the Biology of the Shrews II Vol. 1(eds. J.F. Merritt, S. Churchfield, R. Hutterer and B.A. Sheftel) 303–315(Special Publication of the International Society of Shrew Biologists, 2006).Kniowski, A. B. & Ford, W. M. Predicting intensity of white-tailed deer herbivory in the Central Appalachian Mountains. J. For. Res. 29, 841–850. https://doi.org/10.1007/s11676-017-0476-6 (2018).Article 

Google Scholar 
Fleming, C. H. & Calabrese, J. M. ctmm: continuous-time movement modeling. R package version 0.6.0 (2021).R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).Fleming, C. H. et al. Estimating where and how animals travel: an optimal framework for path reconstruction from autocorrelated tracking data. Ecology 97, 576–582. https://doi.org/10.1890/15-1607.1 (2016).Article 
PubMed 

Google Scholar 
Hijmans, R. J. raster: geographic data analysis and modeling. R package version 3.4-5 (2020).Becker, R. A., Chambers, J. M. & Wilks, A. R. The New S Language (Wadsworth and Brooks/Cole, 1988).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. B. H. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Inference: A Practical Use of the Information-Theoretic Approach (Springer, 1998).Turner, M. G., Wu, Y., Romme, W. H. & Wallace, L. L. A landscape simulation model of winter foraging by large ungulates. Ecol. Modell. 69, 163–184. https://doi.org/10.1016/0304-3800(93)90026-O (1993).Article 

Google Scholar 
Taper, M. L. & Gogan, P. J. P. The northern Yellowstone elk: density dependence and climatic conditions. J. Wildl. Manag. 66, 106–122. https://doi.org/10.2307/3802877 (2002).Article 

Google Scholar 
Green, R. A. & Bear, G. D. Seasonal cycles and daily activity patterns of Rocky Mountain elk. J. Wildl. Manag. 54, 272–279. https://doi.org/10.2307/3809041 (1990).Article 

Google Scholar 
Craighead, J. J., Craighead, F. C. J., Ruff, R. L. & O’Gara, B. W. Home ranges and activity patterns of nonmigratory elk of the Madison Drainage herd as determined by biotelemetry. Wildl. Monogr. 33, 3–50 (1973).
Google Scholar 
Gittleman, J. L. & Thompson, S. D. Energy allocation in mammalian reproduction. Am. Zool. 28, 863–875. https://doi.org/10.1093/icb/28.3.863 (1988).Article 

Google Scholar 
Beier, P. & McCullough, D. R. Factors influencing white-tailed deer activity patterns and habitat use. Wildl. Monogr. 109, 3–51 (1990).
Google Scholar 
Ciuti, S., Davini, S., Luccarini, S. & Apollonio, M. Variation in home range size of female fallow deer inhabiting a sub-Mediterranean habitat. Rev. Ecol. 58, 381–395 (2003).
Google Scholar 
Vore, J. M. & Schmidt, E. M. Movements of female elk during calving season in northwest Montana. Wildl. Soc. Bull. 29, 720–725 (2001).
Google Scholar 
Wickstrom, M. L., Robbins, C. T., Hanley, T. A., Spalinger, D. E. & Parish, S. M. Food intake and foraging energetics of elk and mule deer. J. Wildl. Manag. 48, 1285–1301. https://doi.org/10.2307/3801789 (1984).Article 

Google Scholar 
Van Soest, P. J. Allometry and ecology of feeding behavior and digestive capacity in herbivores: a review. Zoo. Biol. 15, 455–479 (1996). https://doi.org/10.1002/(SICI)1098-2361(1996)15:53.0.CO;2-AEsmaeili, S. et al. Body size and digestive system shape resource selection by ungulates: a cross-taxa test of the forage maturation hypothesis. Ecol. Lett. 24, 2178–2191. https://doi.org/10.1111/ele.13848 (2021).Article 
PubMed 

Google Scholar 
Demment, M. W. & Van Soest, P. J. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125, 641–672 (1985).Article 

Google Scholar 
Anderson, D. P. et al. Factors influencing female home range sizes in elk (Cervus elaphus) in North American landscapes. Landsc. Ecol. 20, 257–271. https://doi.org/10.1007/s10980-005-0062-8 (2005).Article 

Google Scholar 
Maigret, T. A., Cox, J. J. & Yang, J. Persistent geophysical effects of mining threaten ridgetop biota of Appalachian forests. Front. Ecol. Environ. 17, 85–91. https://doi.org/10.1002/fee.1992 (2019).Article 

Google Scholar 
Beier, P. Sex differences in quality of white-tailed deer diets. J. Mammal. 68, 323–329. https://doi.org/10.2307/1381471 (1987).Article 

Google Scholar 
Parker, K. L., Barboza, P. S. & Gillingham, M. P. Nutrition integrates environmental responses of ungulates. Funct. Ecol. 23, 57–69. https://doi.org/10.1111/j.1365-2435.2008.01528.x (2009).Article 

Google Scholar 
Wichrowski, M. W., Maehr, D. S., Larkin, J. L., Cox, J. J. & Olsson, M. P. O. Activity and movements of reintroduced elk in southeastern Kentucky. Southeast. Nat. 4, 365–374. https://doi.org/10.1656/1528-7092(2005)004[0365:Aamore]2.0.Co;2 (2005).Article 

Google Scholar 
Relyea, R. A., Lawrence, R. K. & Demarais, S. Home range of desert mule deer: testing the body-size and habitat-productivity hypotheses. J. Wildl. Manag. 64, 146–153. https://doi.org/10.2307/3802984 (2000).Article 

Google Scholar  More

Valle-Levinson, A. Contemporary Issues in Estuarine Physics (Cambridge University Press, 2010).Book 

Google Scholar 
Singh, S. Analysis of plankton diversity and density with physico-chemical parameters of open pond in town Deeg (Bhratpur) Rajasthan, India. Int. Res. J. Biol. Sci 4, 61–69 (2015).
Google Scholar 
Roussel, M., Pontier, D., Cohen, J.-M., Lina, B. & Fouchet, D. Quantifying the role of weather on seasonal influenza. BMC Public Health 16, 1–14 (2016).Article 

Google Scholar 
Davies, O., Abowei, J. & Tawari, C. Phytoplankton community of Elechi creek, Niger Delta, Nigeria-a nutrient-polluted tropical creek. Am. J. Appl. Sci. 6, 1143–1152 (2009).Article 
CAS 

Google Scholar 
Choudhury, S. & Panigrahy, R. Seasonal distribution and behavior of nutrients in the Greek and coastal waters of Gopalpur, East coast of India: Mahasagar. Bull. Natl. Inst. Oeanogr 24, 91–88 (1991).
Google Scholar 
Ratheesh, K., Krishnan, A., Das, R. & Vimexen, V. Seasonal phytoplankton succession in Netravathi-Gurupura estuary, Karnataka, India: Study on a three tier hydrographic platform. Estuar. Coast. Shelf Sci. 242, 106830 (2020).Article 

Google Scholar 
Deng, Y., Tang, X., Huang, B. & Ding, L. Effect of temperature and irradiance on the growth and reproduction of the green macroalga, Chaetomorpha valida (Cladophoraceae, Chlorophyta). J. Appl. Phycol. 24, 927–933 (2012).Article 
CAS 

Google Scholar 
Gamier, J., Billen, G. & Coste, M. Seasonal succession of diatoms and Chlorophyceae in the drainage network of the Seine River: Observation and modeling. Limnol. Oceanogr. 40, 750–765 (1995).Article 

Google Scholar 
Meng, F. et al. Phytoplankton alpha diversity indices response the trophic state variation in hydrologically connected aquatic habitats in the Harbin Section of the Songhua River. Sci. Rep. 10, 1–13 (2020).Article 

Google Scholar 
Köhler, J. Growth, production and losses of phytoplankton in the lowland River Spree. I. Population dynamics. J. Plankton Res. 15, 335–349 (1993).Article 

Google Scholar 
Murrell, M. C. & Caffrey, J. M. High cyanobacterial abundance in three northeastern Gulf of Mexico estuaries. Gulf Caribbean Res. 17, 95–106 (2005).Article 

Google Scholar 
Haldar, G., Rahman, M. & Haroon, A. Hilsa, Tenualosa ilisha (Ham.) fishery of the Feni River with reference to the impacts of the flood control structure. J. Zool. 7, 51–56 (1992).
Google Scholar 
Hossain, M. S., Sarker, S., Chowdhury, S. R. & Sharifuzzaman, S. Discovering spawning ground of Hilsa shad (Tenualosa ilisha) in the coastal waters of Bangladesh. Ecol. Model. 282, 59–68 (2014).Article 

Google Scholar 
Bhaumik, U. & Sharma, A. The fishery of Indian Shad (Tenualosa ilisha) in the Bhagirathi-Hooghly river system. Fishing Chimes 31, 21–27 (2011).
Google Scholar 
Mitra, G. & Devsundaram, M. P. On the hilsa of Chilka Lake with note on the Hilsa in Orissa. J. Asiatic Soc. Sci. 20, 33–40 (1954).
Google Scholar 
Abdul, W., Phillips, M. & Beveridge, M. (WorldFish (WF), 2020).Hasan, K. M. M., Wahab, M. A., Ahmed, Z. F. & Mohammed, E. Y. The biophysical assessments of the hilsa fish (Tenualosa ilisha) habitat in the lower Meghna, Bangladesh (International Institute for Environment and Development, 2015).Begum, M. et al. Fatty acid composition of Hilsa (Tenualosa ilisha) fish muscle from different locations in Bangladesh. Thai J. Agric. Sci. 52, 172–179 (2019).
Google Scholar 
Jónasdóttir, S. H. Fatty acid profiles and production in marine phytoplankton. Mar. Drugs 17, 151 (2019).Article 

Google Scholar 
Otero, P., Ruiz-Villarreal, M., Peliz, Á. & Cabanas, J. M. Climatology and reconstruction of runoff time series in northwest Iberia: Influence in the shelf buoyancy budget off Ría de Vigo. Sci. Mar. 74, 247–266 (2010).Article 

Google Scholar 
Grasshoff, K., Kremling, K. & Ehrhardt, M. Methods of Seawater Analysis (Wiley, 2009).
Google Scholar 
Parsons, T., Maita, Y. & Lalli, C. A manual of chemical and biological methods for seawater analysis. Pergamon, Oxford sized algae and natural seston size fractions. Mar. Ecol. Prog. Ser. 199, 43–53 (1984).
Google Scholar 
Scor-Unesco, W. Determination of photosynthetic pigments. Determination of Photosynthetic Pigments in Sea-water, 9–18 (1966).Snow, G., Bate, G. & Adams, J. The effects of a single freshwater release into the Kromme Estuary. 2: Microalgal response. Water SA-Pretoria 26, 301–310 (2000).CAS 

Google Scholar 
Ward, H. B. & Whipple, G. C. Freshwater Biology Vol. 2, 12–48 (Willey, London, 1959).
Google Scholar 
Prescott, G. W. Algae of the western Great Lakes area. (1962).Bellinger, E. G. A Key to Common Algae: Freshwater, Estuarine and Some Coastal Species (Institution of Water and Environmental Management London, 1992).
Google Scholar 
Kimmerer, W. J. & Slaughter, A. M. A new electivity index for diet studies that use count data. Limnol. Oceanogr. Methods 19, 552–565 (2021).Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. R Development Core Team. nlme: Linear and nonlinear mixed effects models, 2012. http://CRAN.R-project.org/package=nlme. R package version, 3.1–103 (2020).Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).Article 

Google Scholar 
Galili, T., O’Callaghan, A., Sidi, J. & Sievert, C. heatmaply: an R package for creating interactive cluster heatmaps for online publishing. Bioinformatics 34, 1600–1602 (2018).Article 
CAS 

Google Scholar 
Wickham, H., Chang, W. & Wickham, M. H. Package ‘ggplot2’. Create Elegant Data Visualisations Using the Grammar of Graphics. Version 2, 1–189 (2016).Peterson, B. G. et al. Package ‘PerformanceAnalytics’. R Team Cooperation (2018).Lewis, R. E. & Uncles, R. J. Factors affecting longitudinal dispersion in estuaries of different scale. Ocean Dyn. 53, 197–207 (2003).Article 

Google Scholar 
Shaha, D., Cho, Y.-K., Seo, G.-H., Kim, C.-S. & Jung, K. Using flushing rate to investigate spring-neap and spatial variations of gravitational circulation and tidal exchanges in an estuary. Hydrol. Earth Syst. Sci. 14, 1465–1476 (2010).Article 

Google Scholar 
Shaha, D. C., Cho, Y.-K., Kim, T.-W. & Valle-Levinson, A. Spatio-temporal variation of flushing time in the Sumjin River Estuary. Terrestr. Atmos. Ocean. Sci. 23, 119 (2012).Article 

Google Scholar 
Shivaprasad, A. et al. Seasonal stratification and property distributions in a tropical estuary (Cochin estuary, west coast, India). Hydrol. Earth Syst. Sci. 17, 187–199 (2013).Article 

Google Scholar 
Haralambidou, K., Sylaios, G. & Tsihrintzis, V. A. Salt-wedge propagation in a Mediterranean micro-tidal river mouth. Estuar. Coast. Shelf Sci. 90, 174–184 (2010).Article 
CAS 

Google Scholar 
Dyer, K. R. Estuaries: A physical introduction (1973).Rahman, M. et al. Impact assessment of twenty-two days fishing ban in the major spawning grounds of Tenualosa ilisha (Hamilton, 1822) on its spawning success in Bangladesh. J. Aquac. Res. Dev. 8, 489 (2017).Article 

Google Scholar 
Alves, A. S. et al. Spatial distribution of subtidal meiobenthos along estuarine gradients in two southern European estuaries (Portugal). J. Mar. Biol. Assoc. U.K. 89, 1529–1540 (2009).Article 
CAS 

Google Scholar 
Teixeira, H., Salas, F., Borja, A., Neto, J. & Marques, J. A benthic perspective in assessing the ecological status of estuaries: The case of the Mondego estuary (Portugal). Ecol. Ind. 8, 404–416 (2008).Article 

Google Scholar 
Garmendia, M. et al. Eutrophication assessment in Basque estuaries: Comparing a North American and a European method. Estuar. Coasts 35, 991–1006 (2012).Article 

Google Scholar 
Istvánovics, V. Eutrophication of Lakes and Reservoirs. Lake Ecosystem Ecology 47–55 (Elsevier, 2010).
Google Scholar 
Dodds, W. K. Eutrophication and trophic state in rivers and streams. Limnol. Oceanogr. 51, 671–680 (2006).Article 
CAS 

Google Scholar 
Bricker, S., Ferreira, J. & Simas, T. An integrated methodology for assessment of estuarine trophic status. Ecol. Model. 169, 39–60 (2003).Article 
CAS 

Google Scholar 
Vega, M., Pardo, R., Barrado, E. & Debán, L. Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Res. 32, 3581–3592 (1998).Article 
CAS 

Google Scholar 
Huang, Y., Yang, C., Wen, C. & Wen, G. S-type dissolved oxygen distribution along water depth in a canyon-shaped and algae blooming water source reservoir: Reasons and control. Int. J. Environ. Res. Public Health 16, 987 (2019).Article 
CAS 

Google Scholar 
Rahman, M. & Cowx, I. Lunar periodicity in growth increment formation in otoliths of hilsa shad (Tenualosa ilisha, Clupeidae) in Bangladesh waters. Fish. Res. 81, 342–344 (2006).Article 

Google Scholar 
Rahman, M. J. Population Biology and Management of hilsa shad (Tenualosa ilisha) in Bangladesh (University of Hull, 2001).Milton, D. A. & Chenery, S. R. Movement patterns of the tropical shad hilsa (Tenualosa ilisha) inferred from transects of 87Sr/86Sr isotope ratios in their otoliths. Can. J. Fish. Aquat. Sci. 60, 1376–1385 (2003).Article 

Google Scholar 
Rahman, S., Sarker, M. R. H. & Mia, M. Y. Spatial and temporal variation of soil and water salinity in the South-Western and South-Central Coastal Region of Bangladesh. Irrig. Drain. 66, 854–871 (2017).Article 

Google Scholar 
Kida, S. & Yamazaki, D. The mechanism of the freshwater outflow through the Ganges–Brahmaputra–Meghna delta. Water Resour. Res. 56, e2019WR026412 (2020).Article 

Google Scholar 
Sarma, V. et al. Intra-annual variability in nutrients in the Godavari estuary, India. Contin. Shelf Res. 30, 2005–2014 (2010).Article 

Google Scholar 
Burford, M. et al. Controls on phytoplankton productivity in a wet–dry tropical estuary. Estuar. Coast. Shelf Sci. 113, 141–151 (2012).Article 
CAS 

Google Scholar 
Vitousek, P. M. et al. Towards an ecological understanding of biological nitrogen fixation. Biogeochemistry 57, 1–45 (2002).Article 

Google Scholar 
Galloway, J. N. & Cowling, E. B. Reactive nitrogen and the world: 200 years of change. Ambio 31, 64–71 (2002).Article 

Google Scholar 
Kennish, M. & De Jonge, V. in Human-Induced Problems (Uses and Abuses) 113–148 (Elsevier Inc., 2012).Alongi, D., Boto, K. & Robertson, A. Nitrogen and phosphorus cycles. Coastal and Estuarine Studies, 251–251 (1993).Wolanski, E., McLusky, D., Laane, R. & Middleburg, J. (Academic Press, 2011).Suthers, I., Rissik, D. & Richardson, A. Plankton: A Guide to Their Ecology and Monitoring for Water Quality (CSIRO Publishing, 2019).Book 

Google Scholar 
Mackay, D. W. & Fleming, G. Correlation of dissolved oxygen levels, fresh-water flows and temperatures in a polluted estuary. Water Res. 3, 121–128 (1969).Article 

Google Scholar 
Lomas, M. W. & Glibert, P. M. Temperature regulation of nitrate uptake: A novel hypothesis about nitrate uptake and reduction in cool-water diatoms. Limnol. Oceanogr. 44, 556–572 (1999).Article 
CAS 

Google Scholar 
Dortch, Q. The interaction between ammonium and nitrate uptake in phytoplankton. Mar. Ecol. Prog. Ser. Oldendorf 61, 183–201 (1990).Article 
CAS 

Google Scholar 
Admiraal, W., Riaux-Gobin, C. & Laane, R. W. Interactions of ammonium, nitrate, and D-and L-amino acids in the nitrogen assimilation of two species of estuarine benthic diatoms. Mar. Ecol. Prog. Ser. 40, 267–273 (1987).Article 
CAS 

Google Scholar 
Rabalais, N., Turner, R., Dortch, Q., Wiseman, W. Jr. & Sen Gupta, B. Nutrient changes in the Mississippi River and system responses on the adjacent continental shelf. Estuaries 19, 386 (1996).Article 
CAS 

Google Scholar 
Gholizadeh, M. H., Melesse, A. M. & Reddi, L. Water quality assessment and apportionment of pollution sources using APCS-MLR and PMF receptor modeling techniques in three major rivers of South Florida. Sci. Total Environ. 566, 1552–1567 (2016).Article 

Google Scholar 
Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007).Article 

Google Scholar 
Teichberg, M. et al. Eutrophication and macroalgal blooms in temperate and tropical coastal waters: Nutrient enrichment experiments with Ulva spp. Glob. Change Biol. 16, 2624–2637 (2010).Article 

Google Scholar 
Valiela, I. & Bowen, J. Nitrogen sources to watersheds and estuaries: Role of land cover mosaics and losses within watersheds. Environ. Pollut. 118, 239–248 (2002).Article 
CAS 

Google Scholar 
Woodland, R. J. et al. Nitrogen loads explain primary productivity in estuaries at the ecosystem scale. Limnol. Oceanogr. 60, 1751–1762 (2015).Article 

Google Scholar 
Howarth, R. et al. Coupled biogeochemical cycles: Eutrophication and hypoxia in temperate estuaries and coastal marine ecosystems. Front. Ecol. Environ. 9, 18–26 (2011).Article 

Google Scholar 
Winder, J. A. & Cheng, D. M. Quantification of Factors Controlling the Development of Anabaena Circinalis Blooms (Urban Water Research Association of Australia, 1995).
Google Scholar 
Descy, J.-P. Phytoplankton composition and dynamics in the River Meuse (Belgium). Arch. Hydrobiol. Supplementband. Monographische Beiträge 78, 225–245 (1987).
Google Scholar 
Robarts, R. D. & Zohary, T. Temperature effects on photosynthetic capacity, respiration, and growth rates of bloom-forming cyanobacteria. NZ J. Mar. Freshw. Res. 21, 391–399 (1987).Article 
CAS 

Google Scholar 
Visser, P. M., Ibelings, B. W., Bormans, M. & Huisman, J. Artificial mixing to control cyanobacterial blooms: A review. Aquat. Ecol. 50, 423–441 (2016).Article 
CAS 

Google Scholar 
Krishnan, A., Das, R. & Vimexen, V. Seasonal phytoplankton succession in Netravathi-Gurupura estuary, Karnataka, India: Study on a three tier hydrographic platform. Estuar. Coast. Shelf Sci. 242, 106830 (2020).Article 

Google Scholar 
Srinivas, L., Seeta, Y. & Reddy, M. Bacillariophyceae as ecological indicators of water quality in Manair Dam, Karimnagar, India. Int. J. Sci. Res. Sci. Tech 4, 468–474 (2018).
Google Scholar 
Mohanty, B. P. et al. Fatty acid profile of Indian shad Tenualosa ilisha oil and its dietary significance. Natl. Acad. Sci. Lett. 35, 263–269 (2012).Article 
CAS 

Google Scholar 
De, D. et al. Nutritional profiling of hilsa (Tenualosa ilisha) of different size groups and sensory evaluation of their adults from different riverine systems. Sci. Rep. 9, 1–11 (2019).Article 
CAS 

Google Scholar 
Hasan, K. M. M., Ahmed, Z. F., Wahab, M. A. & Mohammed, E. Y. Food and Feeding Ecology of hilsa (Tenualosa ilisha) in Bangladesh’s Meghna River Basin. (International Institute for Environment and Development, 2016). More

Marine phytoplankton both follow and actively influence the environment they inhabit. Unpacking the complex ecological and biogeochemical roles of these tiny organisms can help reveal the workings of the Earth system.
Phytoplankton are the workers of an ocean-spanning factory converting sunlight and raw nutrients into organic matter. These little organisms — the foundation of the marine ecosystem — feed into a myriad of biogeochemical cycles, the balance of which help control the distribution of carbon on the Earth surface and ultimately the overall climate state. As papers in this issue of Nature Geoscience show, phytoplankton are far from passive actors in the global web of biogeochemical cycles. The functioning of phytoplankton is not just a matter for biologists, but is also important for geoscientists seeking to understand the Earth system more broadly.Phytoplankton are concentrated where local nutrient and sea surface temperatures are optimal, factors which aren’t always static in time. Prominent temperature fluctuations, from seasonal to daily cycles, are reflected in phytoplankton biomass, with cascading effects on other parts of marine ecosystems, such as economically-important fisheries. In an Article in this issue, Keerthi et al., show that phytoplankton biomass, tracked by satellite measurements of chlorophyll for relatively small ( More

Whether land acts as a carbon sink or source depends largely on two opposite fluxes: carbon uptake through photosynthesis and carbon release through turnover. Turnover occurs through multiple processes, including but not limited to, leaf senescence, tree mortality, and respiration by plants, microbes, and animals. Each of these processes is sensitive to climate, and ecologists and climatologists have been working to figure out how temperature regulates biological activities and to what extent the carbon cycle responds to global warming. Previous theoretical and experimental studies have yielded conflicting relationships between temperature and carbon turnover, with large variations across ecosystems, climate and time-scale1,2,3,4. Writing in Nature Geoscience, Fan et al.5 find that hydrometeorological factors have an important influence on how the turnover time of land carbon responds to changes in temperature. More

Banerjee, A. K. et al. Setting the priorities straight-Species distribution models assist to prioritize conservation targets for the mangroves. Sci. Total Environ. 806, 150937 (2022).Article 
CAS 

Google Scholar 
Duke, N. C. et al. A world without mangroves?. Science 317(5834), 41–42 (2007).Article 
CAS 

Google Scholar 
Friess, D. A. Ecosystem services and disservices of mangrove forests: Insights from historical colonial observations. Forests 7(9), 183 (2016).Article 

Google Scholar 
Hu, W. et al. Mapping the potential of mangrove forest restoration based on species distribution models: A case study in China. Sci. Total Environ. 748, 142321 (2020).Article 
CAS 

Google Scholar 
Blankespoor, B., Dasgupta, S. & Lange, G. M. Mangroves as a protection from storm surges in a changing climate. Ambio 46(4), 478–491 (2017).Article 

Google Scholar 
FAO. TheWorld’s Mangroves 1980–2005. Food and Agriculture Organization of the United Nations, Rome. http://www.fao.org/3/a1427e/a1427e00.htm. (2007).Abd-El Monsef, H., Hassan, M. A. & Shata, S. Using spatial data analysis for delineating existing mangroves stands and siting suitable locations for mangroves plantation. Comput. Electron. Agric. 141, 310–326 (2017).Article 

Google Scholar 
Donato, D. C. et al. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 4, 293–297. https://doi.org/10.1038/ngeo1123 (2011).Article 
CAS 

Google Scholar 
Aheto, D. W. et al. Community-based mangrove forest management: Implications for local livelihoods and coastal resource conservation along the Volta estuary catchment area of Ghana. Ocean Coast. Manag. 127, 43–54 (2016).Article 

Google Scholar 
Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Chang. 26, 152–158. https://doi.org/10.1016/j.gloenvcha.2014.04.002 (2014).Article 

Google Scholar 
Stephanie, S. R. et al. Conservation and restoration of mangroves: Global status, perspectives, and prognosis. Ocean Coast. Manag. 154, 72–82. https://doi.org/10.1016/j.ocecoaman.2018.01.009 (2018).Article 

Google Scholar 
Friess, D. A. et al. Mangroves give cause for conservation optimism, for now. Curr. Biol. 30, R153–R154 (2020).Article 
CAS 

Google Scholar 
Valiela, I., Bowen, J. L. & York, J. K. Mangrove forests: One of the world’s threatened major tropical environments: At least 35% of the area of mangrove forests has been lost in the past two decades, losses that exceed those for tropical rain forests and coral reefs, two other well-known threatened environments. Bioscience 51, 807–815. https://doi.org/10.1641/0006-3568(2001)051[0807:MFOOTW]2.0.CO;2 (2001).Article 

Google Scholar 
Feller, I. C. et al. Biocomplexity in mangrove ecosystems. Ann. Rev. Mar. Sci. 2, 395–417 (2010).Article 
CAS 

Google Scholar 
Polidoro, B. A. et al. The loss of species: Mangrove extinction risk and geographic areas of global concern. PLoS ONE 5, e10095 (2010).Article 

Google Scholar 
IUCN. Global Assessments of Mangrove Losses and Degradation, 2016; https://www.iucn.org/sites/dev/files/content/documents/mangroveloss-brief-4pp-19.10.low_.pdf.Sreelekshmi, S., Nandan, S. B., Kaimal, S. V., Radhakrishnan, C. K. & Suresh, V. R. Mangrove species diversity, stand structure and zonation pattern in relation to environmental factors—a case study at Sundarban delta, east coast of India. Reg. Stud. Mar. Sci. 35, 101111 (2020).
Google Scholar 
Sahana, M. et al. Assessing coastal island vulnerability in the Sundarban Biosphere Reserve, India, using geospatial technology. Environ. Earth Sci. 78(10), 1–22 (2019).Article 

Google Scholar 
FSI. India State of Forest Report. Forest Survey of India, Dehradun (2017).Ellison, A. M., Mukherjee, B. B. & Karim, A. Testing patterns of zonation in mangroves: Scale dependence and environmental correlates in the Sundarbans of Bangladesh. J. Ecol. 88(5), 813–824 (2000).Article 

Google Scholar 
Sahana, M., Rehman, S., Sajjad, H. & Hong, H. Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of Sundarban Biosphere Reserve, India. CATENA 189, 104450 (2020).Article 

Google Scholar 
Sahana, M. & Sajjad, H. Vulnerability to storm surge flood using remote sensing and GIS techniques: A study on Sundarban Biosphere Reserve, India. Rem. Sens. Appl. Soc. Env. 13, 106–120 (2019).
Google Scholar 
Chowdhury, M. Q. et al. Nature and periodicity of growth rings in two Bangladeshi mangrove species. IAWA J. 29(3), 265–276 (2008).Article 

Google Scholar 
Sarker, S. K., Reeve, R., Thompson, J., Paul, N. K. & Matthiopoulos, J. Are we failing to protect threatened mangroves in the Sundarbans world heritage ecosystem?. Sci. Rep. 6(1), 1–12 (2016).Article 

Google Scholar 
Iftekhar, M. S. & Saenger, P. Vegetation dynamics in the Bangladesh Sundarbans mangroves: A review of forest inventories. Wetlands Ecol. Manage. 16(4), 291–312 (2008).Article 

Google Scholar 
Siddiqi, N. A. In Mangrove forestry in Bangladesh, Institute of Forestry and Environmental Sciences. University of Chittagong, Chittagong, Bangladesh 201 (2001).Lewis, R. R. III. Ecological engineering for successful management and restoration of mangrove forests. Ecol. Eng. 24(4), 403–418 (2005).Article 

Google Scholar 
Peterson, T. A., Papeş, M. & Eaton, M. Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent. Ecography 30, 550–560. https://doi.org/10.1111/j.0906-7590.2007.05102.x (2007).Article 

Google Scholar 
Stockwell, D. & Peters, D. The GARP modelling system: problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13, 143–158. https://doi.org/10.1080/136588199241391 (1999).Article 

Google Scholar 
Guisan, A. & Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 8, 993–1009. https://doi.org/10.1111/j.1461-0248.2005.00792.x (2005).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

Google Scholar 
Feng, Z. et al. Dynamics ofmangrove forests in Shenzhen Bay in response to natural and anthropogenic factors from 1988 to 2017. J. Hydrol. 591, 125271. https://doi.org/10.1016/j.jhydrol.2020.125271 (2020).Article 

Google Scholar 
Kaky, E. & Gilbert, F. Using species distribution models to assess the importance of Egypt’s protected areas for the conservation of medicinal plants. J. Arid Environ. 135, 140–146. https://doi.org/10.1016/j.jaridenv.2016.09.001 (2016).Article 

Google Scholar 
Pecchi, M. et al. Species distribution modelling to support forest management A literature review. Ecol. Model. 411, 108817 (2019).Article 

Google Scholar 
Spiers, J. A., Oatham, M. P., Rostant, L. V. & Farrell, A. D. Applying species distribution modelling to improving conservation-based decisions: A gap analysis of Trinidad and Tobago’s endemic vascular plants. Biodivers. Conserv. 27, 2931–2949 (2018).Article 

Google Scholar 
Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159 (2009).Article 

Google Scholar 
Fois, M., Cuena-Lombraña, A., Fenu, G. & Bacchetta, G. Using species distribution models at local scale to guide the search of poorly known species: Review, methodological issues and future directions. Ecol. Model. 385, 124–132 (2018).Article 

Google Scholar 
Gilani, H., Goheer, M. A., Ahmad, H. & Hussain, K. Under predicted climate change: Distribution and ecological niche modelling of six native tree species in Gilgit-Baltistan, Pakistan. Ecol. Indic. 111, 106049 (2020).Article 

Google Scholar 
Ellison, A. M., Felson, A. J. & Friess, D. A. Mangrove rehabilitation and restoration as experimental adaptive management. Front. Mar. Sci. 7, 327. https://doi.org/10.3389/fmars.2020.00327 (2020).Article 

Google Scholar 
Ellison, A. M. Mangrove restoration: Do we know enough?. Restor. Ecol. 8(3), 219–229 (2000).Article 

Google Scholar 
Brown, B., Fadillah, R., Nurdin, Y., Soulsby, I., & Ahmad, R. CASE STUDY: Community Based Ecological Mangrove Rehabilitation (CBEMR) in Indonesia. In From small (12–33 ha) to medium scales (400 ha) with pathways for adoption at larger scales ( > 5000 ha). SAPI EN. S. Surveys and Perspectives Integrating Environment and Society 7.2 (2014).Rodríguez-Rodríguez, J. A., Mancera-Pineda, J. E. & Tavera, H. Mangrove restoration in Colombia: Trends and lessons learned. For. Ecol. Manage. 496, 119414 (2021).Article 

Google Scholar 
Romañach, S. S. et al. Conservation and restoration
of mangroves: Global status, perspectives, and prognosis. Ocean Coast Manag. 154, 72–82. https://doi.org/10.1016/j.ocecoaman.2018.01.009 (2018).Article 

Google Scholar 
Sulochanan, B. et al. Water and sediment quality parameters of the restored mangrove ecosystem of Gurupura River and natural mangrove ecosystem of Shambhavi River in Dakshina Kannada, India. Marine Pollution Bulletin 176, 113450. https://doi.org/10.1016/j.marpolbul.2022.113450 (2022).Lovelock, C. E., Barbier, E. & Duarte, C. M. Tackling the mangrove restoration challenge. PLoS Biol. 20(10), e3001836 (2022).Article 
CAS 

Google Scholar 
Lovelock, C. E. & Brown, B. M. Land tenure considerations are key to successful mangrove restoration. Nature Ecol. Evol. 3(8), 1135–1135 (2019).Article 

Google Scholar 
Su, J., Friess, D. A. & Gasparatos, A. A meta-analysis of the ecological and economic outcomes of mangrove restoration. Nat. Commun. 12(1), 1–13 (2021).Article 

Google Scholar 
Lee, S. Y., Hamilton, S., Barbier, E. B., Primavera, J. & Lewis, R. R. Better restoration policies are needed to conserve mangrove ecosystems. Nature Ecol. Evol. 3(6), 870–872 (2019).Article 

Google Scholar 
Chakraborty, S., Sahoo, S., Majumdar, D., Saha, S. & Roy, S. Future Mangrove suitability assessment of Andaman to strengthen sustainable development. J. Clean. Prod. 234, 597–614 (2019).Article 

Google Scholar 
Charrua, A. B., Bandeira, S. O., Catarino, S., Cabral, P. & Romeiras, M. M. Assessment of the vulnerability of coastal mangrove ecosystems in Mozambique. Ocean Coast. Manag. 189, 105145 (2020).Article 

Google Scholar 
Hu, W. et al. Predicting potential mangrove distributions at the global northern distribution margin using an ecological niche model: Determining conservation and reforestation involvement. For. Ecol. Manage. 478, 118517 (2020).Article 

Google Scholar 
Rodríguez-Medina, K., Yañez-Arenas, C., Peterson, A. T., Euán Ávila, J. & Herrera-Silveira, J. Evaluating the capacity of species distribution modeling to predict the geographic distribution of the mangrove community in Mexico. PLoS ONE 15(8), e0237701 (2020).Article 

Google Scholar 
Wang, Y. et al. Simulating spatial change of mangrove habitat under the impact of coastal land use: Coupling MaxEnt and Dyna-CLUE models. Sci. Total Environ. 788, 147914 (2021).Article 
CAS 

Google Scholar 
Gopal, B. & Chauhan, M. Biodiversity and its conservation in the Sundarban mangrove ecosystem. Aquat. Sci. 68(3), 338–354 (2006).Article 

Google Scholar 
Sahana, M., Rehman, S., Paul, A. K. & Sajjad, H. Assessing socio-economic vulnerability to climate change-induced disasters: Evidence from Sundarban Biosphere Reserve, India. Geol. Ecol. Landsc. 5(1), 40–52 (2021).Article 

Google Scholar 
Giri, C. et al. Mangrove forest distributions and dynamics (1975–2005) of the tsunami-affected region of Asia. J. Biogeogr. 35(3), 519–528 (2008).Article 

Google Scholar 
Giri, C., Pengra, B., Zhu, Z., Singh, A. & Tieszen, L. L. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar. Coast. Shelf Sci. 73(1–2), 91–100 (2007).Article 

Google Scholar 
Islam, S. N. & Gnauck, A. Effects of salinity intrusion in mangrove wetlands ecosystems in the Sundarbans: An alternative approach for sustainable management. Wetlands Monitor. Modell. Manag. 2007, 315 (2007).
Google Scholar 
Hazra, S., Ghosh, T., DasGupta, R. & Sen, G. Sea level and associated changes in the Sundarbans. Sci. Cult. 68(9/12), 309–321 (2002).
Google Scholar 
Purkait, B. Coastal erosion in response to wave dynamics operative in Sagar Island, Sundarban delta, India. Front. Earth Sci. China 3(1), 21–33 (2009).Article 

Google Scholar 
World Bank (2014). Building resilience for sustainable development of the Sundarbans: Strategy report (No. 20116; World Bank Other Operational Studies). The World Bank Group. https://ideas.repec.org/p/wbk/wboper/20116.html.Das, M. A. H. U. A. Impact of commercial coastal fishing on the environment of Sundarbans for sustainable development. Asian Fish. Sci. 22(1), 157–167 (2009).
Google Scholar 
Hoq, M. E. An analysis of fisheries exploitation and management practices in Sundarbans mangrove ecosystem, Bangladesh. Ocean Coast. Manag. 50(5–6), 411–427 (2007).Article 

Google Scholar 
Census of India (2011). Primary census abstract, census of India. The government of India, Registrar General and Census Commissioner of India, Ministry of Home Affairs, New Delhi, India. https://censusindia.gov.in/nada/index.php/catalog/41021Chowdhury, A. & Maiti, S. K. Assessing the ecological health risk in a conserved mangrove ecosystem due to heavy metal pollution: A case study from Sundarbans Biosphere Reserve, India. Hum. Ecol. Risk Assess. Int. J. 22(7), 1519–1541 (2016).Article 
CAS 

Google Scholar 
Hajra, R. et al. Unravelling the association between the impact of natural hazards and household poverty: Evidence from the Indian Sundarban delta. Sustain. Sci. 12(3), 453–464 (2017).Article 

Google Scholar 
Sahana, M. & Sajjad, H. Assessing Influence of Erosion and Accretion on Landscape Diversity in Sundarban Biosphere Reserve, Lower Ganga Basin: A Geospatial Approach. In Quaternary Geomorphology in India, (eds Das, B. et al.) (Springer, Cham, 2019). https://doi.org/10.1007/978-3-319-90427-6_10 (2018).Chaudhuri, A. B., Choudhury, A., Hussain, Z., & Acharya, G. Mangroves of the Sundarbans. Vol. I. India, The IUCN Wetlands Programme 247 (IUCN, 1994).GBIF.org. GBIF Occurrence Download, 2018. https://www.gbif.org/. Avicennia marina: https://doi.org/10.15468/dl.vmlooq and R. mucronata: https://doi.org/10.15468/dl.ewnqnm (accessed March 2019).Mandal, R. N. & Naskar, K. R. Diversity and classification of Indian mangroves: A review. Trop. Ecol. 49(2), 131–146 (2008).
Google Scholar 
Mandal, A. K., & Nandi, N. C. Fauna of Sundarban mangrove ecosystem, west Bengal, India, Vol. 3 (Zoological Survey of India, 1989).Mitra, A. & Pal, S. The Oscillating Mangrove Ecosystem and the Indian Sundarbans (WWF-India-WBSO, 2002).Naskar, K., & Guha Bakshi, D. N. Mangrove Swamps of the Sundarbans (Naya Prokash, 1987).Barik, J. & Chowdhury, S. True mangrove species of Sundarbans delta, West Bengal, eastern India. Check list 10(2), 329–334. https://doi.org/10.15560/10.2.329 (2014).IUCN 2018. The IUCN Red List of Threatened Species. Version 2018. 2018. Electronic database accessible, accessed 15 Nov 2018; http://www.iucnredlist.org.Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003).MATH 

Google Scholar 
Cavanaugh, K. C. et al. Climate-driven regime shifts in a mangrove–salt marsh ecotone over the past 250 years. Proc. Natl. Acad. Sci. 116(43), 21602–21608 (2019).Article 
CAS 

Google Scholar 
Naskar, K. & Mandal, R. Ecology and Biodiversity of Indian Mangroves, Vol. 1 (Daya Books, 1999).Figueiredo, F. O. et al. Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species. J. Biogeogr. 45(1), 190–200 (2018).Article 

Google Scholar 
Booth, T. H., Nix, H. A., Busby, J. R. & Hutchinson, M. F. BIOCLIM: The first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Divers. Distrib. 20(1), 1–9 (2014).Article 

Google Scholar 
Asbridge, E., Lucas, R., Ticehurst, C. & Bunting, P. Mangrove response to environmental change in Australia’s Gulf of Carpentaria. Ecol. Evol. 6(11), 3523–3539 (2016).Article 

Google Scholar 
He, Q. & Silliman, B. R. Climate change, human impacts, and coastal ecosystems in the Anthropocene. Curr. Biol. 29(19), R1021–R1035. https://doi.org/10.1016/j.cub.2019.08.042 (2019).Beaumont, L. J., Hughes, L. & Poulsen, M. Predicting species distributions: Use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol. Model. 186(2), 251–270 (2005).Article 

Google Scholar 
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R (Cambridge University Press, 2017).Book 

Google Scholar 
Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).Article 

Google Scholar 
STR Annual Report. In Conservator of Forest & Field Director, Sundarban Tiger Reserve. Canning, West Bengal, India: Directorate of Forests, Government of West Bengal (2013–2014).Segurado, P. & Araujo, M. B. An evaluation of methods for modelling species distributions. J. biogeogr. 31(10), 1555–1568. https://doi.org/10.1111/j.1365-2699.2004.01076.x (2004).Kadmon, R., Farber, O. & Danin, A. A systematic analysis of factors affecting the performance of climatic envelope models. Ecol. Appl. 13(3), 853–867. https://doi.org/10.1890/1051-0761(2003)013[0853:ASAOFA]2.0.CO;2 (2003).Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. distribut. 14(5), 763–773. https://doi.org/10.1111/j.1472-4642.2008.00482.x (2008).Simard, M. et al. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nat. Geosci. 12(1), 40–45 (2019).Article 
CAS 

Google Scholar 
Hoguane, A. M., Hill, A. E., Simpson, J. H. & Bowers, D. G. Diurnal and tidal variation of temperature and salinity in the Ponta Rasa mangrove swamp, Mozambique. Estuar. Coast. Shelf S. 49(2), 251–264. https://doi.org/10.1006/ecss.1999.0499 (1999).  Article 
CAS 

Google Scholar 
Sanders, C. J. et al. Are global mangrove carbon stocks driven by rainfall? J. Geophys. Res. Biogeosci. 121(10), 2600–2609. https://doi.org/10.1002/2016JG003510 (2016).Srivastava, J., Farooqui, A. & Seth, P. Pollen-vegetation relationship in surface sediments, Coringa mangrove ecosystem, India: palaeoecological applications. Palynology 43(3), 451–466. https://doi.org/10.1080/01916122.2018.1458755 (2019).Nandy, P., Das, S., Ghose, M. & Spooner-Hart, R. Effects of salinity on photosynthesis, leaf anatomy, ion accumulation and photosynthetic nitrogen use efficiency in five Indian mangroves. Wetlands Ecol. Manage. 15(4), 347–357 (2007).Article 
CAS 

Google Scholar 
Washington, W., Kathiresan, K. & Bingham, B. L. Biology of mangroves and mangrove ecosystems. Adv. Mar. Biol. 2001, 40 (2001).
Google Scholar 
Blasco, F., Aizpuru, M. & Gers, C. Depletion of the mangroves of Continental Asia. Wetlands Ecol. Manage. 9(3), 255–266 (2001).Article 

Google Scholar 
Datta, D. & Deb, S. Forest structure and soil properties of mangrove ecosystems under management scenarios: Experiences from the intensely humanized landscape of Indian Sunderbans. Ocean Coast. Manag. 140, 22–33 (2017).Article 

Google Scholar 
Wahid, S. M., Babel, M. S. & Bhuiyan, A. R. Hydrologic monitoring and analysis in the Sundarbans mangrove ecosystem, Bangladesh. J. Hydrol. 332(3–4), 381–395 (2007).Article 

Google Scholar 
Iftekhar, M. S. & Islam, M. R. Degeneration of Bangladesh’s Sundarbans mangroves: A management issue. Int. For. Rev. 6(2), 123–135 (2004).
Google Scholar 
Saenger, P. Mangrove Ecology, Silviculture, and Conservation (Kluwer Academic Publishers, 2002).Book 

Google Scholar 
Feka, Z. N. Sustainable management of mangrove forests in West Africa: A new policy perspective?. Ocean Coast. Manag. 116, 341–352. https://doi.org/10.1016/j.ocecoaman.2015.08.006 (2015).Article 

Google Scholar 
Giri, S. et al. A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique. J. Coast Conserv. 18, 359–367. https://doi.org/10.1007/s11852-014-0322-3 (2014).Article 

Google Scholar 
Moschetto, F. A., Ribeiro, R. B. & De Freitasa, D. M. Urban expansion, regeneration and socioenvironmental vulnerability in a mangrove ecosystem at the southeast coastal of São Paulo, Brazil. Ocean Coast. Manag. 24, 105418. https://doi.org/10.1016/j.ocecoaman.2020.105418 (2020).Article 

Google Scholar 
Tuholskea, C., Tane, Z., López-Carra, D., Roberts, D. & Cassels, S. Thirty years of land use/cover change in the Caribbean: Assessing the relationship between urbanization and mangrove loss in Roatán, Honduras. Appl. Geogr. 88, 84–93. https://doi.org/10.1016/j.apgeog.2017.08.018 (2017).Article 

Google Scholar 
Kantharajan, G. et al. Vegetative structure and species composition of mangroves along the Mumbai coast, Maharashtra, India. Reg. Stud. Mar. Sci. 19, 1–8 (2018).
Google Scholar 
Marcinko, C. L. et al. The development of a framework for the integrated assessment of SDG trade-offs in the Sundarban Biosphere Reserve. Water 13(4), 528 (2021).Article 

Google Scholar 
Sahana, M. et al. Assessing Wetland ecosystem health in Sundarban Biosphere Reserve using pressure-state-response model and geospatial techniques. Remot. Sens. Appl. Soc. Environ. 26, 100754. https://doi.org/10.1016/j.rsase.2022.100754 (2022).Saha, S., & Choudhury, A. Vegetation Analysis of Restored And Natural Mangrove Forest In Sagar Island, Sundarbans, East Coast of India. Indian J. Mar. Sci. 24, 133–136. http://nopr.niscpr.res.in/bitstream/123456789/37297/1/IJMS%2024%283%29%20133-136.pdf (1995).Balke, T. & Friess, D. A. Geomorphic knowledge for mangrove restoration: A pantropical categorization. Earth Surf. Process. Landf. 41, 231–239. https://doi.org/10.1002/esp.3841 (2016).Article 

Google Scholar 
Alongi, D. M. Mangrove forests of timor-leste: Ecology, degradation and vulnerability to climate change. In Mangrove ecosystems of Asia 199–212 (Springer, 2014).Biswas, S. R., Mallik, A. U., Choudhury, J. K. & Nishat, A. A unified framework for the restoration of Southeast Asian mangroves—bridging ecology, society and economics. Wetlands Ecol. Manage. 17(4), 365–383 (2009).Article 

Google Scholar 
Dubey, S. K., Censkowsky, U., Roy, M., Chand, B. K., & Dey, A. Framework for rapid evaluation of a mangrove restoration site: A case study from Indian Sundarban. In Sabkha Ecosystems 363–378 (Springer, 2019).Islam, M. M. & Shamsuddoha, M. Coastal and marine conservation strategy for Bangladesh in the context of achieving blue growth and sustainable development goals (SDGs). Environ. Sci. Pol. 87, 45–54. https://doi.org/10.1016/j.envsci.2018.05.014 (2018).Article 

Google Scholar 
Bosire, J., Celliers, L., Groeneveld, J., Paula, J. & Schleyer, M.H. Regional State of the Coast Report-Western Indian Ocean. UNEP-Nairobi Convention and WIOMSA 546 (2015).Owuor, M. A., Mulwa, R., Otieno, R., Icely, J. & Newton, A. Valuing mangrove biodiversity and ecosystem services: A deliberative choice experiment in Mida Creek, Kenya. Ecosyst. Serv. 40, 101040. https://doi.org/10.1016/j.ecoser.2019.101040 (2019).Article 

Google Scholar 
Barwell, L. et al. (2018). Regional
State of the Coast Report Western Indian Ocean. The United Nations Environment
Programme/Nairobi Convention Secretariat. https://wedocs.unep.org/handle/20.500.11822/9700?show=fullde Jesús Arce-Mojica, T., Nehren, U., Sudmeier-Rieux, K., Miranda, P. J. & Anhuf, D. Nature-based solutions (NbS) for reducing the risk of shallow landslides: where do we stand? Int. J. disaster risk reduct. 41, 101293. https://doi.org/10.1016/j.ijdrr.2019.101293 (2019).Bardhan, M. An empirical study on mangrove restoration in Indian Sundarbans—a community-based environmental approach. In Modern Cartography Series, vol. 10 387–405 (Academic Press, 2021).Kumar, M. C., Bholanath, M. & Debashis, S. Study on utility and revival through community approach in sundarbans mangrove. Int. J. Soc. Sci. https://doi.org/10.5958/2321-5771.2014.00101.X (2014).Article 

Google Scholar 
Chakraborty, S. K., Giri, S., Chakravarty, G. & Bhattacharya, N. Impact of eco-restoration on the biodiversity of Sundarbans Mangrove Ecosystem, India. Water Air Soil Pollut. Focus 9(3), 303–320 (2009).Article 

Google Scholar 
Paulson Institute. Research report on mangrove protection and restoration strategy in China, 2020; https://paulsoninstitute.org.cn/wpcontent/uploads/2020/06/%E4%B8%AD%E5%9B%BD%E7%BA%A2%E6%A0%91%E6%9E%97%E4%BF%9D%E6%8A%A4%E4%B8%8E%E6%81%A2%E5%A4%8D%E6%88%98%E7%95%A5%E7%A0%94%E7%A9%B6%E6%8A%A5%E5%91%8A%E2%80%94%E6%91%98%E8%A6%81%E7%89%88.pdf.Fan, H. Q. & Wang, W. Q. Some thematic issues for mangrove conservation in China. J. Xiamen Univ. Nat. Sci 56, 323–330. https://doi.org/10.6043/j.issn.0438-0479.201612003 (2017).Article 

Google Scholar 
Wang, W., Fu, H., Lee, S. Y., Fan, H. & Wang, M. Can strict protection stop the decline of mangrove ecosystems in China? Fromrapid destruction to rampant degradation. Forests 11, 55. https://doi.org/10.3390/f11010055 (2020).Article 

Google Scholar 
Roy, A. K. D. & Alam, K. Participatory forest management for the sustainable management of the sundarbans mangrove forest. Am. J. Env. Sci. 8(5), 549–555. https://doi.org/10.3844/ajessp.2012.549.555 (2012).Article 

Google Scholar 
Selvam, V. et al. In Toolkit for establishing coastal bioshield. M. S. Swaminathan Research Foundation, Centre for Research on Sustainable Agriculture and Rural Development (2005).Raju, J. S. S. N. Xylocarpus (Meliaceae): A less-known mangrove taxon of the Godavari estuary, India. Curr. Sci. 84(7), 879–881. https://www.currentscience.ac.in/Volumes/84/07/0879.pdf (2003).
Google Scholar 
Siddiqui, A. H. & Khair, A. Infestation status of heart rot disease of pasur (Xylocarpus mekongensis), tree in the sundarbans. Indian For. 138(2), 165–168 (2012).
Google Scholar 
Iqbal, M. & Hossain, M. Tourists’ willingness to pay for restoration of Sundarbans Mangrove forest ecosystems: A contingent valuation modeling study. Env. Dev. Sustain. 2022, 1–22 (2022).
Google Scholar 
Ekka, A. & Pandit, A. Willingness to pay for restoration of natural ecosystem: A study of Sundarban mangroves by contingent valuation approach. Indian J. Agric. Econ. 67, 902 (2012).
Google Scholar 
Datta, D., Chattopadhyay, R. N. & Guha, P. Community based mangrove management: A review on status and sustainability. J. Env. Manag. 107, 84–95. https://doi.org/10.1016/j.jenvman.2012.04.013 (2012).Article 

Google Scholar 
Ghosh, A., Schmidt, S., Fickert, T. & Nusser, M. The Indian Sundarban mangrove forests: History, utilization, conservation strategies and local perception. Diversity 7(2), 149–169. https://doi.org/10.3390/d7020149 (2015).Article 
CAS 

Google Scholar 
Ranjan, R. Optimal mangrove restoration through community engagement on coastal lands facing climatic risks: The case of Sundarbans region in India. Land Use Policy 81, 736–749 (2019).Article 

Google Scholar 
Dutta, M., Roy, S. & Nibirh, S. Joint forest management and forest protection committees: Negotiation systems and the design of incentives—a case study of West Bengal. Electron. J. https://doi.org/10.2139/ssrn.2245965 (2001).Article 

Google Scholar 
McKee, K. L., Rooth, J. E. & Feller, I. C. Mangrove recruitment after forest disturbance is facilitated by herbaceous species in the Caribbean. Ecol. Appl. 17(6), 1678–1693 (2007).Article 

Google Scholar 
Begam, M. et al. Native salt-tolerant grass species for habitat restoration, their acclimation and contribution to improving edaphic conditions: A study from a degraded mangrove in the Indian Sundarbans. Hydrobiologia 803(1), 373–387 (2017).Article 
CAS 

Google Scholar 
Donnelly, M. & Walters, L. Trapping of Rhizophora mangle propagules by coexisting early successional species. Estuaries Coasts 37, 1562–1571 (2014).Article 

Google Scholar 
Ren, H. et al. Sonneratia apetala Buch. Ham in the mangrove ecosystems of China: An invasive species or restoration species?. Ecol. Eng. 35(8), 1243–1248 (2009).Article 

Google Scholar 
Cheong, S.-M. et al. Coastal adaptation with ecological engineering. Nature Clim. Change 3, 787–791. https://doi.org/10.1038/nclimate1854 (2013).Article 

Google Scholar  More

Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).Article 
CAS 
PubMed 

Google Scholar 
Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).Article 
PubMed 

Google Scholar 
Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).Article 
CAS 
PubMed 

Google Scholar 
Schulze, K. et al. An assessment of threats to terrestrial protected areas. Conserv. Lett. 11, e12435 (2018).Article 

Google Scholar 
Bingham, H. C. et al. (eds). Protected Planet Report 2020 (UNEP-WCMC & IUCN, 2021); https://livereport.protectedplanet.net/Buchanan, G. M., Butchart, S. H., Chandler, G. & Gregory, R. D. Assessment of national-level progress towards elements of the Aichi Biodiversity Targets. Ecol. Indic. 116, 106497 (2020).Article 

Google Scholar 
Xu, H. et al. Ensuring effective implementation of the post-2020 global biodiversity targets. Nat. Ecol. Evol. 5, 411–418 (2021).Article 
PubMed 

Google Scholar 
Report of the Open-ended Working Group on the Post-2020 Global Biodiversity Framework on Its Third Meeting (CBD Secretariat, 2022); https://www.cbd.int/conferences/post2020/wg2020-03/documentsRodrigues, A. S. & Cazalis, V. The multifaceted challenge of evaluating protected area effectiveness. Nat. Commun. 11, 5147 (2020).Article 
PubMed Central 
CAS 
PubMed 

Google Scholar 
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23215 (2019).Article 
PubMed Central 
CAS 
PubMed 

Google Scholar 
Starnes, T. et al. The extent and effectiveness of protected areas in the UK. Glob. Ecol. Conserv. 30, e01745 (2021).Article 

Google Scholar 
Kremen, C. et al. Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science 320, 222–226 (2008).Article 
CAS 
PubMed 

Google Scholar 
Cazalis, V. et al. Mismatch between bird species sensitivity and the protection of intact habitats across the Americas. Ecol. Lett. 24, 2394–2405 (2021).Article 
PubMed 

Google Scholar 
Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12, e1001891 (2014).Article 
PubMed Central 
PubMed 

Google Scholar 
Gamero, A. et al. Tracking progress toward EU biodiversity strategy targets: EU policy effects in preserving its common farmland birds. Conserv. Lett. 10, 395–402 (2017).Article 

Google Scholar 
Pellissier, V. et al. Effects of Natura 2000 on nontarget bird and butterfly species based on citizen science data. Conserv. Biol. 34, 666–676 (2020).Article 
CAS 
PubMed 

Google Scholar 
Princé, K., Rouveyrol, P., Pellissier, V., Touroult, J. & Jiguet, F. Long-term effectiveness of Natura 2000 network to protect biodiversity: a hint of optimism for common birds. Biol. Conserv. 253, 108871 (2021).Article 

Google Scholar 
Cunningham, C. A., Thomas, C. D., Morecroft, M. D., Crick, H. Q. P. & Beale, C. M. The effectiveness of the protected area network of Great Britain. Biol. Conserv. 257, 109146 (2021).Article 

Google Scholar 
Duckworth, G. D. & Altwegg, R. Effectiveness of protected areas for bird conservation depends on guild. Divers. Distrib. 24, 1083–1091 (2018).Article 
PubMed Central 
PubMed 

Google Scholar 
Rada, S. et al. Protected areas do not mitigate biodiversity declines: a case study on butterflies. Divers. Distrib. 25, 217–224 (2019).Article 

Google Scholar 
Terraube, J., Van Doninck, J., Helle, P., & Cabeza, M. Assessing the effectiveness of a national protected area network for carnivore conservation. Nat. Commun. 11, 2957 (2020).Article 
PubMed Central 
CAS 
PubMed 

Google Scholar 
Lenoir, J. et al. Species better track the shifting isotherms in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 
PubMed 

Google Scholar 
van Teeffelen, A., Meller, L., van Minnen, J., Vermaat, J. & Cabeza, M. How climate proof is the European Union’s biodiversity policy? Regional Environ. Change 15, 997–1010 (2015).Article 

Google Scholar 
Thomas, C. D. & Gillingham, P. K. The performance of protected areas for biodiversity under climate change. Biol. J. Linn. Soc. Lond. 115, 718–730 (2015).Article 

Google Scholar 
Gillingham, P. K. et al. The effectiveness of protected areas in the conservation of species with changing geographical ranges. Biol. J. Linn. Soc. Lond. 115, 707–717 (2015).Article 

Google Scholar 
Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).Article 

Google Scholar 
Stokstad, E. Species? Climate? Cost? Ambitious goal means trade-offs. Science 371, 555 (2021).Article 
CAS 
PubMed 

Google Scholar 
Brlík, V. et al. Long-term and large-scale multispecies dataset tracking population changes of common European breeding birds. Sci. Data 8, 21 (2021).Article 
PubMed Central 
PubMed 

Google Scholar 
Stanbury, A. et al. The status of bird populations: the fifth Birds of Conservation Concern in the United Kingdom, Channel Islands and Isle of Man and second IUCN Red List assessment of extinction risk for Great Britain. Br. Birds 114, 723–747 (2021).
Google Scholar 
Dudley, N. (ed). Guidelines for Applying Protected Area Management Categories (IUCN, 2008).Deguignet, M. et al. Measuring the extent of overlaps in protected area designations. PLoS ONE 12, e0188681 (2017).Article 
PubMed Central 
PubMed 

Google Scholar 
JNCC. Common Standards Monitoring: Introduction to the Guidance Manual (JNCC Resource Hub, 2004).Hayhow, D. B. et al. State of Nature 2019 (RSPB, 2019).Schleicher, J. et al. Statistical matching for conservation science. Conserv. Biol. 34, 538–549 (2019).Article 
PubMed Central 
PubMed 

Google Scholar 
Waldron, A. et al. Protecting 30% of the Planet for Nature: Costs, Benefits and Economic Implications (Campaign for Nature, 2020); https://helda.helsinki.fi/handle/10138/326470Franks, S. E., Roodbergen, M., Teunissen, W., Carrington Cotton, A. & Pearce‐Higgins, J. W. Evaluating the effectiveness of conservation measures for European grassland‐breeding waders. Ecol. Evol. 8, 10555–10568 (2018).Article 
PubMed Central 
PubMed 

Google Scholar 
Pearce-Higgins, J. W. et al. Site-based adaptation reduces the negative effects of weather upon a southern range margin Welsh black grouse Tetrao tetrix population that is vulnerable to climate change. Clim. Change 153, 253–265 (2019).Article 

Google Scholar 
Jellesmark, S. et al. A counterfactual approach to measure the impact of wet grassland conservation on U.K. breeding bird populations. Conserv. Biol. 35, 1575–1585 (2021).Article 
PubMed 

Google Scholar 
Morrison, C. A. et al. Covariation in population trends and demography reveals targets for conservation action. Proc. Biol. Sci. 288, 20202955 (2021).PubMed Central 
PubMed 

Google Scholar 
Donald, P. F. et al. International conservation policy delivers benefits for birds in Europe. Science 317, 810–813 (2007).Article 
CAS 
PubMed 

Google Scholar 
Martay, B. et al. Monitoring landscape-scale environmental changes with citizen scientists: Twenty years of land use change in Great Britain. J. Nat. Conserv. 44, 33–42 (2018).Article 

Google Scholar 
Sullivan, M. J. P., Newson, S. E. & Pearce‐Higgins, J. W. Changing densities of generalist species underlie apparent homogenization of UK bird communities. Ibis 158, 645–655 (2016).Article 

Google Scholar 
Wauchope, H. S. et al. Evaluating impact using time-series data. Trends Ecol. Evol. 36, 196–205 (2021).Article 
PubMed 

Google Scholar 
Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Change 2, 121–124 (2012).Article 

Google Scholar 
Lehikoinen, P., Santangeli, A., Jaatinen, K., Rajasärkkä, A. & Lehikoinen, A. Protected areas act as a buffer against detrimental effects of climate change—evidence from large‐scale, long‐term abundance data. Glob. Change Biol. 25, 304–313 (2019).Article 

Google Scholar 
Gaüzère, P., Jiguet, F. & Devictor, V. Can protected areas mitigate the impacts of climate change on bird’s species and communities? Diversity Distrib. 22, 625–637 (2016).Article 

Google Scholar 
Neate‐Clegg, M. H. C., Jones, S. E. I., Burdekin, O., Jocque, M. & Şekercioğlu, Ç. H. Elevational changes in the avian community of a Mesoamerican cloud forest park. Biotropica 50, 805–815 (2018).Article 

Google Scholar 
Oliver, T. H. et al. Large extents of intensive land use limit community reorganization during climate warming. Glob. Change Biol. 23, 2272–2283 (2017).Article 

Google Scholar 
Hiley, J. R., Bradbury, R. B., Holling, M. & Thomas, C. D. Protected areas act as establishment centres for species colonizing the UK. Proc. Biol. Sci. 280, 20122310 (2013).PubMed Central 
PubMed 

Google Scholar 
Thomas, C. D. et al. Protected areas facilitate species’ range expansions. Proc. Natl Acad. Sci. USA 109, 14063–14068 (2012).Article 
PubMed Central 
CAS 
PubMed 

Google Scholar 
Grace, M. K. et al. Testing a global standard for quantifying species recovery and assessing conservation impact. Conserv. Biol. 35, 1833–1849 (2021).Article 
PubMed 

Google Scholar 
Gibbons, D. W., Reid, J. B. & Chapman, R. A. The New Atlas of Breeding Birds in Britain & Ireland 1988–1991 (T. & A. D. Poyser, 1993).Balmer, D. E. et al. Bird Atlas 2007–11: the Breeding and Wintering Birds of Britain and Ireland (BTO, 2013).Gillings, S. et al. Breeding and wintering bird distributions in Britain and Ireland from citizen science bird atlases. Glob. Ecol. Biogeogr. 28, 866–874 (2019).Article 

Google Scholar 
Freeman, S. N., Noble, D. G., Newson, S. E. & Baillie, S. R. Modelling population changes using data from different surveys: the Common Birds Census and the Breeding Bird Survey. Bird Study 54, 61–72 (2007).Article 

Google Scholar 
Robinson, R. A., Julliard, R. & Saracco, J. F. Constant effort: studying avian population processes using standardised ringing. Ring. Migr. 24, 199–204 (2009).Article 

Google Scholar 
Cave, V. M., Freeman, S. N., Brooks, S. P., King, R. & Balmer, D. E. in Modeling Demographic Processes in Marked Populations, 949–963 (Springer, 2009).Rowland, C. S. et al. Land Cover Map 2015 (1km Percentage Aggregate Class, GB) (eds Thomson, D. L. et al) (Environmental Information Data Centre, 2017); https://doi.org/10.5285/7115bc48-3ab0-475d-84ae-fd3126c20984Rowland, C. S. et al. Land Cover Map 2015 (1km Percentage Aggregate Class, N. Ireland) (Environmental Information Data Centre, 2017); https://doi.org/10.5285/362feaea-0ccf-4a45-b11f-980c6b89a858ASTER Global Digital Elevation Model V003 (dataset). NASA EOSDIS Land Processes DAAC (NASA/METI/AIST/Japan Space Systems and U.S./Japan ASTER Science Team, 2019); https://doi.org/10.5067/ASTER/ASTGTM.003Schiavina, M., Freire, S. & MacManus, K. GHS-SMOD R2019A – GHS Settlement Layers, Updated and Refined REGIO Model 2014 in Application to GHS-BUILT R2018A and GHS-POP R2019A, Multitemporal (1975-1990-2000-2015) (European Commission Joint Research Centre, 2019); https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218Robinson, R. A. BirdFacts: Profiles of Birds Occurring in Britain & Ireland (BTO, 2005).Gibbons, D. W. et al. Bird species of conservation concern in the United Kingdom, Channel Islands and Isle of Man: revising the Red Data List. RSPB Conserv. Rev. 10, 7–18 (1996).
Google Scholar 
Stone, B. H. et al. Population estimates of birds in Britain and in the United Kingdom. Br. Birds 90, 1–22 (1997).
Google Scholar 
Woodward, I. et al. Population estimates of birds in Great Britain and the United Kingdom. Br. Birds 113, 69–104 (2020).
Google Scholar 
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).Article 
PubMed Central 
PubMed 

Google Scholar 
Bull, J. W., Strange, N., Smith, R. J. & Gordon, A. Reconciling multiple counterfactuals when evaluating biodiversity conservation impact in social‐ecological systems. Conserv. Biol. 35, 510–521 (2020).Article 
PubMed 

Google Scholar 
Jellesmark, S. et al. Assessing the global impact of targeted conservation actions on species abundance. Preprint at bioRxiv https://doi.org/10.1101/2022.01.14.476374 (2022).Wauchope, H. S. et al. Protected areas have a mixed impact on waterbirds but management helps. Nature 605, 103–107 (2022).Article 
CAS 
PubMed 

Google Scholar 
Ho, D. E., Imai, K., King, G. & Stuart, E. A. MatchIt: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42, 1–28 (2011).Article 

Google Scholar 
Wood, S. N. Generalized Additive Models: an Introduction with R 2nd edn (Chapman and Hall/CRC, 2017).Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package v.0.4.4 (2021); https://CRAN.R-project.org/package=DHARMaJetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).Article 
CAS 
PubMed 

Google Scholar 
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar 
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).Article 
CAS 
PubMed 

Google Scholar 
Johnston, A. et al. Species traits explain variation in detectability of UK birds. Bird Study 61, 340–350 (2014).Article 

Google Scholar 
Hill, M. O. Diversity and evenness: a unifying notation and its consequences. Ecology 54, 427–432 (1973).Article 

Google Scholar 
Ho, D. E., Imai, K., King, G. & Stuart, E. A. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Anal. 15, 199–236 (2007).Article 

Google Scholar 
Julliard, R., Clavel, J., Devictor, V., Jiguet, F. & Couvet, D. Spatial segregation of specialists and generalists in bird communities. Ecol. Lett. 9, 1237–1244 (2006).Article 
PubMed 

Google Scholar 
Devictor, V., Julliard, R., Couvet, D. & Jiguet, F. Birds are tracking climate warming, but not fast enough. Proc. Biol. Sci. 275, 2743–2748 (2008).PubMed Central 
PubMed 

Google Scholar  More

Khan, M. et al. Plant species and communities assessment in interaction with edaphic and topographic factors; an ecological study of the mount Eelum District Swat Pakistan. Saudi J. Biol. Sci. 24(4), 778–786 (2017).Article 

Google Scholar 
Ur Rahman, A. et al. Impact of multiple environmental factors on species abundance in various forest layers using an integrative modeling approach. Global Ecol. Conserv. 29, e01712 (2021).Article 

Google Scholar 
Arneth, A., Uncertain future for vegetation cover. Nature 524(7563), 44–45.Goldsmith, F., Description and analysis of vegetation. Methods Plant Ecol. (1976).Rahman, I. U. et al. First insights into the floristic diversity, biological spectra and phenology of Manoor Valley Pakistan. Pak. J. Bot 50(3), 1113–1124 (2018).
Google Scholar 
Khan, S.M., Plant communities and vegetation ecosystem services in the Naran Valley, Western Himalaya, 2012, University of Leicester.Haq, F., Ahmad, H. & Iqbal, Z. Vegetation description and phytoclimatic gradients of subtropical forests of Nandiar Khuwar catchment District Battagram. Pak. J. Bot 47(4), 1399–1405 (2015).
Google Scholar 
Iqbal, M. et al. A novel approach to phytosociological classification of weeds flora of an agro-ecological system through Cluster, two way cluster and indicator species analyses. Ecol. Ind. 84, 590–606 (2018).Article 

Google Scholar 
Shaw, M. R. et al. Grassland responses to global environmental changes suppressed by elevated CO2. Science 298(5600), 1987–1990 (2002).Article 

Google Scholar 
Drenovsky, R.E., Effects of mineral nutrient deficiencies on plant performance in the desert shrubs Chrysothamnus nauseosus ssp. consimilis and Sarcobatus vermiculatus2002: University of California, Davis.Iqbal, M. et al. Vegetation classification of the Margalla Foothills, Islamabad under the influence of edaphic factors and anthropogenic activities using modern ecological tools. Pak. J. Bot 53(5), 1831–1843 (2021).Article 

Google Scholar 
Bai, Y. et al. Landscape-level dynamics of grassland-forest transitions in British Columbia. J. Range Manag. 57(1), 66–75 (2004).Article 

Google Scholar 
Zhao, T. et al. Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm. Remote Sens. Environ. 257, 112321 (2021).Article 

Google Scholar 
Austin, M., Chapter 2: Vegetation and environment: discontinuities and continuities. IN VAN DER MAAREL, E.(Ed.) Végétation ecology. Etats‐Unis, 2005, Blackwell Publishing.Peña-Claros, M. et al. Soil effects on forest structure and diversity in a moist and a dry tropical forest. Biotropica 44(3), 276–283 (2012).Article 

Google Scholar 
Miao, R. et al. Effects of long-term grazing exclusion on plant and soil properties vary with position in dune systems in the Horqin Sandy Land. CATENA 209, 105860 (2022).Article 

Google Scholar 
Abbas, Z. et al. Plant communities and anthropo-natural threats in the Shigar valley,(Central Karakorum) Baltistan-Pakistan. Pak. J. Bot. 52, 987–994 (2020).Article 

Google Scholar 
Anwar, S., et al., Plant diversity and communities pattern with special emphasis on the indicator species of a dry temperate forest: A case study from Liakot area of the Hindu Kush mountains, Pakistan. Trop. Ecol. 1–16 (2022).Mumshad, M. et al. Phyto-ecological studies and distribution pattern of plant species and communities of Dhirkot, Azad Jammu and Kashmir, Pakistan. PLoS ONE 16(10), e0257493 (2021).Article 

Google Scholar 
Baldeck, C. A. et al. Soil resources and topography shape local tree community structure in tropical forests. Proc. R. Soc. B Biol. Sci. 280(1753), 20122532 (2013).Article 

Google Scholar 
Guerra, T. N. F. et al. Influence of edge and topography on the vegetation in an Atlantic Forest remnant in northeastern Brazil. J. For. Res. 18(2), 200–208 (2013).Article 

Google Scholar 
Townsend, A. R., Asner, G. P. & Cleveland, C. C. The biogeochemical heterogeneity of tropical forests. Trends Ecol. Evol. 23(8), 424–431 (2008).Article 

Google Scholar 
Becknell, J. M. & Powers, J. S. Stand age and soils as drivers of plant functional traits and aboveground biomass in secondary tropical dry forest. Can. J. For. Res. 44(6), 604–613 (2014).Article 

Google Scholar 
Geri, F., Rocchini, D. & Chiarucci, A. Landscape metrics and topographical determinants of large-scale forest dynamics in a Mediterranean landscape. Landsc. Urban Plan. 95(1–2), 46–53 (2010).Article 

Google Scholar 
Lomolino, M. V. Elevation gradients of species-density: historical and prospective views. Glob. Ecol. Biogeogr. 10(1), 3–13 (2001).Article 

Google Scholar 
Zhang, K. et al. An integrated flood risk assessment approach based on coupled hydrological-hydraulic modeling and bottom-up hazard vulnerability analysis. Environ. Model. Softw. 148, 105279 (2022).Article 

Google Scholar 
Liu, Y. et al. A hybrid runoff generation modelling framework based on spatial combination of three runoff generation schemes for semi-humid and semi-arid watersheds. J. Hydrol. 590, 125440 (2020).Article 

Google Scholar 
Mir, A. Y. et al. Ethnopharmacology and phenology of high-altitude medicinal plants in Kashmir Northern Himalaya. Ethnobot. Res. Appl. 22, 1–15 (2021).
Google Scholar 
Vetaas, O. R. & Grytnes, J. A. Distribution of vascular plant species richness and endemic richness along the Himalayan elevation gradient in Nepal. Glob. Ecol. Biogeogr. 11(4), 291–301 (2002).Article 

Google Scholar 
Li, W. et al. Fine root biomass and morphology in a temperate forest are influenced more by the nitrogen treatment approach than the rate. Ecol. Ind. 130, 108031 (2021).Article 

Google Scholar 
Su, N. et al. Landscape context determines soil fungal diversity in a fragmented habitat. CATENA 213, 106163 (2022).Article 

Google Scholar 
Yang, Y., et al., Nitrogen fertilization weakens the linkage between soil carbon and microbial diversity: a global meta‐analysis. Global Change Biol. (2022).Ahmad, Z. et al. Weed species composition and distribution pattern in the maize crop under the influence of edaphic factors and farming practices: A case study from Mardan Pakistan. Saudi J. Biol. Sci. 23(6), 741–748 (2016).Article 

Google Scholar 
Rahman, A. U. et al. Ecological assessment of plant communities and associated edaphic and topographic variables in the Peochar Valley of the Hindu Kush mountains. Mt. Res. Dev. 36(3), 332–341 (2016).Article 

Google Scholar 
Ashton, P. S. A contribution of rain forest research to evolutionary theory. Ann. Mo. Bot. Gard. 64(4), 694–705 (1977).Article 

Google Scholar 
Yang, Y. et al. Negative effects of multiple global change factors on soil microbial diversity. Soil Biol. Biochem. 156, 108229 (2021).Article 

Google Scholar 
Pärtel, M. Local plant diversity patterns and evolutionary history at the regional scale. Ecology 83(9), 2361–2366 (2002).Article 

Google Scholar 
Taylor, D.R., Aarssen, L.W., & Loehle, C. On the relationship between r/K selection and environmental carrying capacity: A new habitat templet for plant life history strategies. Oikos 239–250 (1990).Knapp, A. K. et al. Rainfall variability, carbon cycling, and plant species diversity in a mesic grassland. Science 298(5601), 2202–2205 (2002).Article 

Google Scholar 
Zscheischler, J. et al. Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes. J. Geophys. Res. Biogeosci. 121(8), 2186–2198 (2016).Article 

Google Scholar 
Gao, C. et al. Simulation and design of joint distribution of rainfall and tide level in Wuchengxiyu Region China. Urban Clim. 40, 101005 (2021).Article 

Google Scholar 
Wang, S. et al. Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards. J. Hydrol. 603, 126964 (2021).Article 

Google Scholar 
Zhang, K. et al. The sensitivity of North American terrestrial carbon fluxes to spatial and temporal variation in soil moisture: An analysis using radar-derived estimates of root-zone soil moisture. J. Geophys. Res. Biogeosci. 124(11), 3208–3231 (2019).Article 

Google Scholar 
Yang, Y. et al. Increasing contribution of microbial residues to soil organic carbon in grassland restoration chronosequence. Soil Biol. Biochem. 170, 108688 (2022).Article 

Google Scholar 
Li, J. et al. Differential mechanisms drive species loss under artificial shade and fertilization in the Alpine Meadow of the Tibetan Plateau. Front. Plant Sci. 13, 832473–832473 (2022).Article 

Google Scholar 
Fischer, C. et al. How do earthworms, soil texture and plant composition affect infiltration along an experimental plant diversity gradient in grassland?. PLoS ONE 9(6), e98987 (2014).Article 

Google Scholar 
Zhao, T. et al. Soil moisture experiment in the Luan River supporting new satellite mission opportunities. Remote Sens. Environ. 240, 111680 (2020).Article 

Google Scholar 
Marandi, A., Polikarpus, M. & Jõeleht, A. A new approach for describing the relationship between electrical conductivity and major anion concentration in natural waters. Appl. Geochem. 38, 103–109 (2013).Article 

Google Scholar 
Xu, J. et al. Modeling of coupled transfer of water, heat and solute in saline loess considering sodium sulfate crystallization. Cold Reg. Sci. Technol. 189, 103335 (2021).Article 

Google Scholar 
Chen, X. et al. Spatiotemporal characteristics and attribution of dry/wet conditions in the Weihe River Basin within a typical monsoon transition zone of East Asia over the recent 547 years. Environ. Model. Softw. 143, 105116 (2021).Article 

Google Scholar 
Ali, G., Siddique, S. & Suliman, M. Effect of canopy cover on natural regeneration of pinus wallichiana in moist temperate forest of Yakh Tangay, District Shangla Swat Pakistan. FUUAST J. Biol. 8(2), 193–201 (2018).
Google Scholar 
Zhang, K. et al. Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China. Nat. Hazard. 19(1), 93–105 (2019).Article 

Google Scholar 
Khan, W. et al. Vegetation mapping and multivariate approach to indicator species of a forest ecosystem: A case study from the Thandiani sub Forests Division (TsFD) in the Western Himalayas. Ecol. Ind. 71, 336–351 (2016).Article 

Google Scholar 
Iqbal, J. & Ahmed, M. Vegetation description of some pine forests of Shangla district of Khyber Pakhtunkhwa Pakistan: A preliminary study. FUUAST J. Biol. 4(1), 83–88 (2014).
Google Scholar 
Sparrow, B. D. et al. A vegetation and soil survey method for surveillance monitoring of rangeland environments. Front. Ecol. Evol. 8, 157 (2020).Article 

Google Scholar 
Esri, R., ArcGIS desktop: release 10. Environmental Systems Research Institute, CA (2011).Salzer, D., & Willoughby, J. Standardize this! The futility of attempting to apply a standard quadrat size and shape to rare plant monitoring. in Proceedings of the symposium of the North Coast Chapter of the California Native Plant Society: the ecology and management of rare plants of northwestern California. Arcata, CA. Sacramento, CA: The California Native Plant Society (2004).Bano, S. et al. Eco-Floristic studies of native plants of the Beer Hills along the Indus River in the districts Haripur and Abbottabad Pakistan. Saudi J. Biol. Sci. 25(4), 801–810 (2018).Article 

Google Scholar 
Perveen, A. & Qaiser, M. Pollen flora of Pakistan–XXXI Betulaceae. Pak. J. Bot. 31, 243–246 (1999).
Google Scholar 
Raunkiaer, C., The life forms of plants and statistical plant geography; being the collected papers of C. Raunkiaer. The life forms of plants and statistical plant geography; being the collected papers of C. Raunkiaer. (1934).Hussain, S.S., Pakistan manual of plant ecology1984: National Book Foundation.Kamran, S. et al. The role of graveyards in species conservation and beta diversity: A vegetation appraisal of sacred habitats from Bannu Pakistan. J. For. Res. 31(4), 1147–1158 (2020).Article 

Google Scholar 
Manan, F. et al. Environmental determinants of plant associations and evaluation of the conservation status of Parrotiopsis jacquemontiana in Dir, the Hindu Kush Range of Mountains. Trop. Ecol. 61(4), 509–526 (2020).Article 

Google Scholar 
Tfaily, M. M. et al. Sequential extraction protocol for organic matter from soils and sediments using high resolution mass spectrometry. Anal. Chim. Acta 972, 54–61 (2017).Article 

Google Scholar 
Chaney, R., Slonim, S., & Slonim, S. Determination of calcium carbonate content in soils, in Geotechnical properties, behavior, and performance of calcareous soils1982, ASTM International.McCune, B., & Mefford, M. PC-ORD, Multivariate analysis of ecological data, Version 5 for Windows edition. MjM Software Design, Gleneden Beach, Oregon USA (2005).Lepš, J., & Šmilauer, P. Multivariate analysis of ecological data using CANOCO2003: Cambridge university press.Xie, W. et al. A novel hybrid method for landslide susceptibility mapping-based geodetector and machine learning cluster: A case of Xiaojin county, China. ISPRS Int. J. Geo Inf. 10(2), 93 (2021).Article 

Google Scholar 
Li, L., Lei, Y. & Pan, D. Economic and environmental evaluation of coal production in China and policy implications. Nat. Hazards 77(2), 1125–1141 (2015).Article 

Google Scholar 
Team, R.C., R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2013).Anwar, S. et al. Floristic composition and ecological gradient analyses of the Liakot Forests in the Kalam region of District Swat Pakistan. J. For. Res. 30(4), 1407–1416 (2019).Article 

Google Scholar 
Haq, S. M. et al. Exploring and understanding the floristic richness, life-form, leaf-size spectra and phenology of plants in protected forests: A case study of Dachigam National Park in Himalaya Asia. Acta Ecol. Sin. 41(5), 479–490 (2021).Article 

Google Scholar 
Ilyas, M. et al. A Preliminary checklist of the vascular flora of Kabal Valley, Swat Pakistan. Pak. J. Bot 45(2), 605–615 (2013).
Google Scholar 
Amjad, M. S. et al. Floristic composition, biological spectrum and phenological pattern of vegetation in the subtropical forest of Kotli District, AJK Pakistan. Pure Appl. Biol. (PAB) 6(2), 426–447 (2017).
Google Scholar 
Shaheen, H. et al. Species diversity, community structure, and distribution patterns in western Himalayan alpine pastures of Kashmir Pakistan. Mount. Res. Dev. 31(2), 153–159 (2011).Article 

Google Scholar 
Abbas, Z. et al. Ethnobotany of the balti community, tormik valley, karakorum range, baltistan, pakistan. J. Ethnobiol. Ethnomed. 12(1), 1–16 (2016).Article 

Google Scholar 
Ahmed, M. et al. Phytosociology and structure of Himalayan forests from different climatic zones of Pakistan. Pak. J. Bot. 38(2), 361 (2006).MathSciNet 

Google Scholar 
Shehzadi, S. et al. Floristic compositions along an 18-Km long transect in Ayubia National Park District Abbottabad Pakistan. Pak. J. Bot. 41(5), 2115–2127 (2009).
Google Scholar 
Khan, W., et al., Life forms, leaf size spectra and diversity indices of plant species grown in the Thandiani forests, district Abbottabad, Khyber Pakhtunkhwa, Pakistan. Saudi J. Biol. Sci.Kharkwal, G. et al. Phytodiversity and growth form in relation to altitudinal gradient in the Central Himalayan (Kumaun) region of India. Curr. Sci. 1, 873–878 (2005).
Google Scholar 
Bennie, J. et al. Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol. Model. 216(1), 47–59 (2008).Article 

Google Scholar 
Choudhary, K. & Nama, K. S. Phyto-diversity of Mukundara hills national park of Kota district, Rajasthan India. Adv. Appl. Sci. Res. 5(1), 18–23 (2014).
Google Scholar 
Shimwell, D.W., Description and classification of vegetation (1971).Malik, Z.H., Comparative study of vegetation of GungaChotti and Bedori Hills, Distric Bagh, Azad Jammu and Kashmir with special reference to range conditions, 2005, University of Peshawar, Pakistan.Khan, W. et al. Life forms, leaf size spectra, regeneration capacity and diversity of plant species grown in the Thandiani forests, district Abbottabad, Khyber Pakhtunkhwa Pakistan. Saudi J. Biol. Sci. 25(1), 94–100 (2018).Article 

Google Scholar 
Grytnes, J. A. & Vetaas, O. R. Species richness and altitude: A comparison between null models and interpolated plant species richness along the Himalayan altitudinal gradient Nepal. Am. Nat. 159(3), 294–304 (2002).Article 

Google Scholar 
Majid, A., Khan, M. & Calixto, E. Ecological assessment of plant communities along the edaphic and topographic gradients of biha valley, District Swat Pakistan. Appl. Ecol. Environ. Res. 16(5), 5611–5631 (2018).Article 

Google Scholar 
Khan, S.M., et al., Vegetation dynamics in the Western Himalayas, diversity indices and climate change. Sci. Tech. Dev. 31(3), 232–243 (2012).Khan, S. M. et al. Identifying plant species and communities across environmental gradients in the Western Himalayas: Method development and conservation use. Eco. Inform. 14, 99–103 (2013).Article 

Google Scholar 
Shaheen, H. & Shinwari, Z. K. Phyto diversity and endemic richness of Karambar lake vegetation from Chitral Hindukush-Himalayas. Pak. J. Bot 44(1), 17–21 (2012).
Google Scholar 
Wana, D., Plant communities and diversity along altitudinal gradients from Lake Abaya to Chencha Highlands, 2002, MA Thesis, School of Graduate Studies, Addis Ababa University. Addis Ababa.Canfora, L. et al. Is soil microbial diversity affected by soil and groundwater salinity? Evidences from a coastal system in central Italy. Environ. Monit. Assess. 189(7), 1–15 (2017).Article 

Google Scholar 
Liu, S., et al., The distribution characteristics and human health risks of high-fluorine groundwater in coastal plain: A case study in Southern Laizhou Bay, China. Front. Environ. Sci. 568 (2022).Niu, Y. et al. Vegetation distribution along mountain environmental gradient predicts shifts in plant community response to climate change in alpine meadow on the Tibetan Plateau. Sci. Total Environ. 650, 505–514 (2019).Article 

Google Scholar 
Nadal-Romero, E. et al. Effects of slope angle and aspect on plant cover and species richness in a humid Mediterranean badland. Earth Surf. Proc. Land. 39(13), 1705–1716 (2014).Article 

Google Scholar  More

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