Ecology

Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Merilä, J. & Hendry, A. P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol. Appl. 7, 1–14 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Angert, A. L., LaDeau, S. L. & Ostfeld, R. S. Climate change and species interactions: ways forward. Ann. N. Y. Acad. Sci. 1297, 1–7 (2013).ADS 
PubMed 
Article 

Google Scholar 
Yang, L. H. & Rudolf, V. H. W. Phenology, ontogeny and the effects of climate change on the timing of species interactions. Ecol. Lett. 13, 1–10 (2010).CAS 
PubMed 
Article 

Google Scholar 
Kersting, D. K. et al. Experimental evidence of the synergistic effects of warming and invasive algae on a temperate reef-builder coral. Sci. Rep. 5, 18635 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou, Y. et al. Warming reshaped the microbial hierarchical interactions. Glob. Chang. Biol. 27, 6331–6347 (2021).CAS 
PubMed 
Article 

Google Scholar 
Grainger, T. N., Rego, A. I. & Gilbert, B. Temperature-dependent species interactions shape priority effects and the persistence of unequal competitors. Am. Nat. 191, 197–209 (2018).PubMed 
Article 

Google Scholar 
Ørsted, M., Schou, M. F. & Kristensen, T. N. Biotic and abiotic factors investigated in two Drosophila species: evidence of both negative and positive effects of interactions on performance. Sci. Rep. 7, 40132 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Sniegula, S., Golab, M. J. & Johansson, F. Size-mediated priority and temperature effects on intra-cohort competition and cannibalism in a damselfly. J. Anim. Ecol. 88, 637–648 (2019).PubMed 
Article 

Google Scholar 
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Parmesan, C. Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Chang. Biol. 13, 1860–1872 (2007).ADS 
Article 

Google Scholar 
Carter, S. K. & Rudolf, V. H. W. Shifts in phenological mean and synchrony interact to shape competitive outcomes. Ecology 100, e02826 (2019).PubMed 
Article 

Google Scholar 
Rudolf, V. H. W. Nonlinear effects of phenological shifts link interannual variation to species interactions. J. Anim. Ecol. 87, 1395–1406 (2018).PubMed 
Article 

Google Scholar 
Rasmussen, N. L., Allen, B. G. V. & Rudolf, V. H. W. Linking phenological shifts to species interactions through size-mediated priority effects. J. Anim. Ecol. 83, 1206–1215 (2014).PubMed 
Article 

Google Scholar 
Bailey, L. D. & Pol, M. van de. Tackling extremes: challenges for ecological and evolutionary research on extreme climatic events. J. Anim. Ecol. 85, 85–96 (2016).Walker, R., Wilder, S. M. & González, A. L. Temperature dependency of predation: increased killing rates and prey mass consumption by predators with warming. Ecol. Evol. 10, 9696–9706 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Schulte, P. M. The effects of temperature on aerobic metabolism: towards a mechanistic understanding of the responses of ectotherms to a changing environment. J. Exp. Biol. 218, 1856–1866 (2015).PubMed 
Article 

Google Scholar 
Anholt, B. R. Cannibalism and early instar survival in a larval damselfly. Oecologia 99, 60–65 (1994).ADS 
PubMed 
Article 

Google Scholar 
Johansson, F. & Crowley, P. H. Larval cannibalism and population dynamics of dragonflies. in Aquatic insects: challenges to populations (eds. Lancaster, J. & Briers, R. A.) 36–54 (CABI, 2008). doi:https://doi.org/10.1079/9781845933968.0036.Takashina, N. & Fiksen, Ø. Optimal reproductive phenology under size-dependent cannibalism. Ecol. Evol. 10, 4241–4250 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Crumrine, P. W. Body size, temperature, and seasonal differences in size structure influence the occurrence of cannibalism in larvae of the migratory dragonfly, Anax junius. Aquat. Ecol. 44, 761–770 (2010).Article 

Google Scholar 
Op de Beeck, L., Verheyen, J. & Stoks, R. Competition magnifies the impact of a pesticide in a warming world by reducing heat tolerance and increasing autotomy. Environ. Pollut. 233, 226–234 (2018).Enriquez-Urzelai, U., Nicieza, A. G., Montori, A., Llorente, G. A. & Urrutia, M. B. Physiology and acclimation potential are tuned with phenology in larvae of a prolonged breeder amphibian. Oikos 2022, e08566 (2022).Article 

Google Scholar 
Knight, C. M., Parris, M. J. & Gutzke, W. H. N. Influence of priority effects and pond location on invaded larval amphibian communities. Biol. Invasions 11, 1033–1044 (2009).Article 

Google Scholar 
Raczyński, M., Stoks, R., Johansson, F., Bartoń, K. & Sniegula, S. Phenological shifts in a warming world affect physiology and life history in a damselfly. Insects 13, 622 (2022).PubMed 
PubMed Central 
Article 

Google Scholar 
Murillo-Rincón, A. P., Kolter, N. A., Laurila, A. & Orizaola, G. Intraspecific priority effects modify compensatory responses to changes in hatching phenology in an amphibian. J. Anim. Ecol. 86, 128–135 (2017).PubMed 
Article 

Google Scholar 
Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).Article 

Google Scholar 
Jermacz, Ł. et al. Continuity of chronic predation risk determines changes in prey physiology. Sci. Rep. 10, 6972 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Raczyński, M., Stoks, R., Johansson, F. & Sniegula, S. Size-mediated priority effects are trait-dependent and consistent across latitudes in a damselfly. Oikos 130, 1535–1547 (2021).Article 

Google Scholar 
Peacor, S. D. & Werner, E. E. Predator effects on an assemblage of consumers through induced changes in consumer foraging behavior. Ecology 81, 1998–2010 (2000).Article 

Google Scholar 
Stoks, R., Block, M. D., Meutter, F. V. D. & Johansson, F. Predation cost of rapid growth: behavioural coupling and physiological decoupling. J. Anim. Ecol. 74, 708–715 (2005).Article 

Google Scholar 
Hermann, S. L. & Landis, D. A. Scaling up our understanding of non-consumptive effects in insect systems. Curr. Opin. Insect. Sci. 20, 54–60 (2017).PubMed 
Article 

Google Scholar 
Sniegula, S., Nsanzimana, J. d’Amour & Johansson, F. Predation risk affects egg mortality and carry over effects in the larval stages in damselflies. Freshw. Biol. 64, 778–786 (2019).Preisser, E. L. & Orrock, J. L. The allometry of fear: interspecific relationships between body size and response to predation risk. Ecosphere 3, art77 (2012).Gehr, B. et al. Evidence for nonconsumptive effects from a large predator in an ungulate prey?. Behav. Ecol. 29, 724–735 (2018).Article 

Google Scholar 
Jiménez-Cortés, J. G., Serrano-Meneses, M. A. & Córdoba-Aguilar, A. The effects of food shortage during larval development on adult body size, body mass, physiology and developmental time in a tropical damselfly. J. Insect Physiol. 58, 318–326 (2012).PubMed 
Article 

Google Scholar 
Weissburg, M., Smee, D. L., Ferner, M. C., Schmitz, A. E. O. J. & Bronstein, E. J. L. The sensory ecology of nonconsumptive predator effects. Am. Nat. 184, 141–157 (2014).PubMed 
Article 

Google Scholar 
Zhang, D.-W., Xiao, Z.-J., Zeng, B.-P., Li, K. & Tang, Y.-L. Insect behavior and physiological adaptation mechanisms under starvation stress. Front. Physiol. 10, 163 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Arnett, H. A. & Kinnison, M. T. Predator-induced phenotypic plasticity of shape and behavior: parallel and unique patterns across sexes and species. Curr. Zool. 63, 369–378 (2017).PubMed 

Google Scholar 
Bell, A. M., Dingemanse, N. J., Hankison, S. J., Langenhof, M. B. W. & Rollins, K. Early exposure to nonlethal predation risk by size-selective predators increases somatic growth and decreases size at adulthood in threespined sticklebacks. J. Evol. Biol. 24, 943–953 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
De Block, M. & Stoks, R. Compensatory growth and oxidative stress in a damselfly. Proc. Royal Soc. B 275, 781–785 (2008).Article 

Google Scholar 
Lee, W.-S., Monaghan, P. & Metcalfe, N. B. The trade-off between growth rate and locomotor performance varies with perceived time until breeding. J. Exp. Biol. 213, 3289–3298 (2010).PubMed 
Article 

Google Scholar 
Catalán, A. M. et al. Community-wide consequences of nonconsumptive predator effects on a foundation species. J. Anim. Ecol. 90, 1307–1316 (2021).PubMed 
Article 

Google Scholar 
Preisser, E. L., Bolnick, D. I. & Benard, M. F. Scared to death? The effects of intimidation and consumption in predator-prey interactions. Ecology 86, 501–509 (2005).Article 

Google Scholar 
Gjoni, V., Basset, A. & Glazier, D. S. Temperature and predator cues interactively affect ontogenetic metabolic scaling of aquatic amphipods. Biol. Lett. 16, 20200267 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Miller, L. P., Matassa, C. M. & Trussell, G. C. Climate change enhances the negative effects of predation risk on an intermediate consumer. Glob. Chang. Biol. 20, 3834–3844 (2014).ADS 
PubMed 
Article 

Google Scholar 
Beckerman, A. P., Rodgers, G. M. & Dennis, S. R. The reaction norm of size and age at maturity under multiple predator risk. J. Anim. Ecol. 79, 1069–1076 (2010).PubMed 
Article 

Google Scholar 
Lancaster, L. T., Morrison, G. & Fitt, R. N. Life history trade-offs, the intensity of competition, and coexistence in novel and evolving communities under climate change. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 372, 20160046 (2017).Sniegula, S., Janssens, L. & Stoks, R. Integrating multiple stressors across life stages and latitudes: combined and delayed effects of an egg heat wave and larval pesticide exposure in a damselfly. Aquat. Toxicol. 186, 113–122 (2017).CAS 
PubMed 
Article 

Google Scholar 
Stoks, R., Block, M. D., Slos, S., Doorslaer, W. V. & Rolff, J. Time constraints mediate predator-induced plasticity in immune function, condition, and life history. Ecology 87, 809–815 (2006).PubMed 
Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Pintanel, P., Tejedo, M., Salinas-Ivanenko, S., Jervis, P. & Merino-Viteri, A. Predators like it hot: thermal mismatch in a predator-prey system across an elevational tropical gradient. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13516 (2021).Article 
PubMed 

Google Scholar 
Stoks, R., Swillen, I. & Block, M. D. Behaviour and physiology shape the growth accelerations associated with predation risk, high temperatures and southern latitudes in Ischnura damselfly larvae. J. Anim. Ecol. 81, 1034–1040 (2012).PubMed 
Article 

Google Scholar 
Wang, Y.-J., Sentis, A., Tüzün, N. & Stoks, R. Thermal evolution ameliorates the long-term plastic effects of warming, temperature fluctuations and heat waves on predator–prey interaction strength. Funct. Ecol. 35, 1538–1549 (2021).Article 

Google Scholar 
Sniegula, S., Golab, M. J. & Johansson, F. Cannibalism and activity rate in larval damselflies increase along a latitudinal gradient as a consequence of time constraints. BMC Evol. Biol. 17, 167 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Gyssels, F. & Stoks, R. Behavioral responses to fish kairomones and autotomy in a damselfly. J. Ethol. 24, 79–83 (2006).Article 

Google Scholar 
McPeek, M. A., Grace, M. & Richardson, J. M. L. Physiological and behavioral responses to predators shape the growth/predation risk trade-off in damselflies. Ecology 82, 1535–1545 (2001).Article 

Google Scholar 
Beermann, J., Boos, K., Gutow, L., Boersma, M. & Peralta, A. C. Combined effects of predator cues and competition define habitat choice and food consumption of amphipod mesograzers. Oecologia 186, 645–654 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schoener, T. W. Theory of feeding strategies. Annu. Rev. Ecol. Evol. Syst. 2, 369–404 (1971).Article 

Google Scholar 
Dijkstra, K., Schröter, A. & Lewington, R. Field Guide to the Dragonflies of Britain and Europe. Second edition. (Bloomsbury Publishing, 2020).Corbet, P. S., Suhling, F. & Soendgerath, D. Voltinism of Odonata: a review. Int. J. Odonatol. 9, 1–44 (2006).Article 

Google Scholar 
Zwick, P. & Corbet, P. S. Dragonflies: behaviour and ecology of Odonata. (Comstock Publishing Associates, 1999).Fontana-Bria, L., Selfa, J., Tur, C. & Frago, E. Early exposure to predation risk carries over metamorphosis in two distantly related freshwater insects. Ecol. Entomol. 42, 255–262 (2017).Article 

Google Scholar 
Sniegula, S., Raczyński, M., Golab, M. J. & Johansson, F. Effects of predator cues carry over from egg and larval stage to adult life-history traits in a damselfly. Freshw. Sci. 39, 804–811 (2020).Article 

Google Scholar 
Chivers, D. P., Wisenden, B. D. & Smith, R. J. F. Damselfly larvae learn to recognize predators from chemical cues in the predator’s diet. Anim. Behav. 52, 315–320 (1996).Article 

Google Scholar 
Mikolajczuk, P. Stwierdzenie wylotu drugiej generacji tężnicy małej Ischnura pumilio (Charpentier, 1825) i tężnicy wytwornej Ischnura elegans (Vander Linden, 1820) (Odonata: Coenagrionidae) w Polsce środkowo-wschodniej. Odonatrix 1, (2014).De Block, M., Pauwels, K., Van Den Broeck, M., De Meester, L. & Stoks, R. Local genetic adaptation generates latitude-specific effects of warming on predator-prey interactions. Glob. Chang. Biol. 19, 689–696 (2013).ADS 
PubMed 
Article 

Google Scholar 
IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2021).Buskirk, J. V., Krügel, A., Kunz, J., Miss, F. & Stamm, A. The rate of degradation of chemical cues indicating predation risk: an experiment and review. Ethology 120, 942–949 (2014).Article 

Google Scholar 
Hagler, J. R. & Jackson, C. G. Methods for marking insects: current techniques and future prospects. Annu. Rev. Entomol. 46, 511–543 (2001).CAS 
PubMed 
Article 

Google Scholar 
Crumrine, P. W. Size structure and substitutability in an odonate intraguild predation system. Oecologia 145, 132–139 (2005).ADS 
PubMed 
Article 

Google Scholar 
Strobbe, F. & Stoks, R. Life history reaction norms to time constraints in a damselfly: differential effects on size and mass. Biol. J. Linn. Soc. 83, 187–196 (2004).Article 

Google Scholar 
De Block, M., McPeek, M. A. & Stoks, R. Stronger compensatory growth in a permanent-pond Lestes damselfly relative to temporary-pond Lestes. Oikos 117, 245–254 (2008).Article 

Google Scholar 
Marsh, J. B. & Weinstein, D. B. Simple charring method for determination of lipids. J. Lipid Res. 7, 574–576 (1966).CAS 
PubMed 
Article 

Google Scholar 
Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).CAS 
PubMed 
Article 

Google Scholar 
Stoks, R., Block, M. D. & McPeek, M. A. Physiological costs of compensatory growth in a damselfly. Ecology 87, 1566–1574 (2006).PubMed 
Article 

Google Scholar 
R Development Core Team. R: The R Project for Statistical Computing. Vienna, Austria https://www.r-project.org/ (2019).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Cyrus, A. Z., Swiggs, J., Santidrian Tomillo, P., Paladino, F. V. & Peters, W. S. Cannibalism causes size-dependent intraspecific predation pressure but does not trigger autotomy in the intertidal gastropod Agaronia propatula. J. Molluscan Stud. 81, 388–396 (2015).Jara, F. G. Trophic ontogenetic shifts of the dragonfly Rhionaeschna variegata: the role of larvae as predators and prey in Andean wetland communities. Ann. Limnol. 50, 173–184 (2014).Article 

Google Scholar 
Fréchette, M. & Lefaivre, D. On self-thinning in animals. Oikos 73, 425–428 (1995).Article 

Google Scholar 
Johansson, F., Stoks, R., Rowe, L. & De Block, M. Life history plasticity in a damselfly: effects of combined time and biotic constraints. Ecology 82, 1857–1869 (2001).Article 

Google Scholar 
Mikolajewski, D. J., Conrad, A. & Joop, G. Behaviour and body size: plasticity and genotypic diversity in larval Ischnura elegans as a response to predators (Odonata: Coenagrionidae). Int. J. Odonatol. 18, 31–44 (2015).Article 

Google Scholar 
Antoł, A. & Sniegula, S. Damselfly eggs alter their development rate in the presence of an invasive alien cue but not a native predator cue. Ecol. Evol. 11, 9361–9369 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Hassall, C. & Thompson, D. J. The effects of environmental warming on Odonata: a review. Int. J. Odonatol. 11, 131–153 (2008).Article 

Google Scholar 
Debecker, S. & Stoks, R. Pace of life syndrome under warming and pollution: integrating life history, behavior, and physiology across latitudes. Ecol. Monogr. 89, e01332 (2019).Article 

Google Scholar 
Anderson, T. L. & Semlitsch, R. D. Top predators and habitat complexity alter an intraguild predation module in pond communities. J. Anim. Ecol. 85, 548–558 (2016).PubMed 
Article 

Google Scholar 
Norling, U. Growth, winter preparations and timing of emergence in temperate zone odonata: control by a succession of larval response patterns. Int. J. Odonatol. 24, 1–36 (2021).Article 

Google Scholar 
Abrams, P. A., Leimar, O., Nylin, S. & Wiklund, C. The effect of flexible growth rates on optimal sizes and development times in a seasonal environment. Am. Nat. 147, 381–395 (1996).Article 

Google Scholar 
Arendt, J. D. Adaptive intrinsic growth rates: an integration across taxa. Q. Rev. Biol. 72, 149–177 (1997).Article 

Google Scholar 
Bobrek, R. Odonate phenology recorded in a Central European location in an extremely warm season. Biologia 76, 2957–2964 (2021).Article 

Google Scholar 
Dmitriew, C. M. The evolution of growth trajectories: what limits growth rate?. Biol. Rev. 86, 97–116 (2011).PubMed 
Article 

Google Scholar 
Śniegula, S., Johansson, F. & Nilsson-Örtman, V. Differentiation in developmental rate across geographic regions: a photoperiod driven latitude compensating mechanism?. Oikos 121, 1073–1082 (2012).Article 

Google Scholar 
Angell, C. S. et al. Development time mediates the effect of larval diet on ageing and mating success of male antler flies in the wild. Proc. R. Soc. B 287, 20201876 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Johansson, F., Watts, P. C., Sniegula, S. & Berger, D. Natural selection mediated by seasonal time constraints increases the alignment between evolvability and developmental plasticity. Evolution 75, 464–475 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Nilsson-Örtman, V. & Rowe, L. The evolution of developmental thresholds and reaction norms for age and size at maturity. PNAS 118, (2021).Rohner, P. T. & Moczek, A. P. Evolutionary and plastic variation in larval growth and digestion reveal the complex underpinnings of size and age at maturation in dung beetles. Ecol. Evol. 11, 15098–15110 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Rolff, J., Fellowes, M & Holloway, G. Insect Evolutionary Ecology: Proceedings of the Royal Entomological Society’s 22nd Symposium. (CABI Oxford University Press, 2006).Beukeboom, L. W. Size matters in insects: an introduction. Entomol. Exp. Appl. 166, 2–3 (2018).Article 

Google Scholar 
Honěk, A. Intraspecific variation in body size and fecundity in insects: a general relationship. Oikos 66, 483–492 (1993).Article 

Google Scholar 
Lee, W.-S., Monaghan, P. & Metcalfe, N. B. Experimental demonstration of the growth rate–lifespan trade-off. Proc. R. Soc. B 280, 20122370 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Burraco, P., Díaz-Paniagua, C. & Gomez-Mestre, I. Different effects of accelerated development and enhanced growth on oxidative stress and telomere shortening in amphibian larvae. Sci. Rep. 7, 7494 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dańko, M. J., Dańko, A., Golab, M. J., Stoks, R. & Sniegula, S. Latitudinal and age-specific patterns of larval mortality in the damselfly Lestes sponsa: Senescence before maturity?. Exp. Gerontol. 95, 107–115 (2017).PubMed 
Article 

Google Scholar 
Kong, J. D., Hoffmann, A. A. & Kearney, M. R. Linking thermal adaptation and life-history theory explains latitudinal patterns of voltinism. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180547 (2019).Śniegula, S., Gołąb, M. J. & Johansson, F. Time constraint effects on phenology and life history synchrony in a damselfly along a latitudinal gradient. Oikos 125, 414–423 (2016).Article 

Google Scholar 
Popova, O. N. & Haritonov, AYu. Disclosure of biotopical groups in the population of the dragonfly Coenagrion armatum (Charpentier, 1840). Contemp. Probl. Ecol. 7, 175–181 (2014).Article 

Google Scholar 
Mikolajewski, D. J., De Block, M. & Stoks, R. The interplay of adult and larval time constraints shapes species differences in larval life history. Ecology 96, 1128–1138 (2015).PubMed 
Article 

Google Scholar 
Wolf, J. B. & Wade, M. J. What are maternal effects (and what are they not)? Philos. Trans. R Soc. Lond. B Biol. Sci. 364, 1107–1115 (2009).Zehnder, C. B., Parris, M. A. & Hunter, M. D. Effects of maternal age and environment on offspring vital rates in the Oleander Aphid (Hemiptera: Aphididae). Environ. Entomol. 36, 910–917 (2007).PubMed 
Article 

Google Scholar 
Hernández, C. M., van Daalen, S. F., Caswell, H., Neubert, M. G. & Gribble, K. E. A demographic and evolutionary analysis of maternal effect senescence. PNAS 117, 16431–16437 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shama, L. N. S., Campero-Paz, M., Wegner, K. M., De Block, M. & Stoks, R. Latitudinal and voltinism compensation shape thermal reaction norms for growth rate. Mol. Ecol. 20, 2929–2941 (2011).PubMed 
Article 

Google Scholar 
Sniegula, S., Golab, M. J., Drobniak, S. M. & Johansson, F. Seasonal time constraints reduce genetic variation in life-history traits along a latitudinal gradient. J. Anim. Ecol. 85, 187–198 (2016).PubMed 
Article 

Google Scholar 
De Block, M. & Stoks, R. Adaptive sex-specific life history plasticity to temperature and photoperiod in a damselfly. J. Evol. Biol. 16, 986–995 (2003).PubMed 
Article 

Google Scholar 
Verberk, W. C. E. P. et al. Shrinking body sizes in response to warming: explanations for the temperature–size rule with special emphasis on the role of oxygen. Biol. Rev. 96, 247–268 (2021).PubMed 
Article 

Google Scholar 
Sheriff, M. J., Peacor, S. D., Hawlena, D. & Thaker, M. Non-consumptive predator effects on prey population size: a dearth of evidence. J. Anim. Ecol. 89, 1302–1316 (2020).PubMed 
Article 

Google Scholar 
Wirsing, A. J., Heithaus, M. R., Brown, J. S., Kotler, B. P. & Schmitz, O. J. The context dependence of non-consumptive predator effects. Ecol. Lett 24, 113–129 (2021).PubMed 
Article 

Google Scholar 
McCauley, S. J., Rowe, L. & Fortin, M.-J. The deadly effects of ‘nonlethal’ predators. Ecology 92, 2043–2048 (2011).PubMed 
Article 

Google Scholar 
Palacios, M. del M. & McCormick, M. I. Positive indirect effects of top-predators on the behaviour and survival of juvenile fishes. Oikos 130, 219–230 (2021).Thaler, J. S., McArt, S. H. & Kaplan, I. Compensatory mechanisms for ameliorating the fundamental trade-off between predator avoidance and foraging. PNAS 109, 12075–12080 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Janssens, L., Van Dievel, M. & Stoks, R. Warming reinforces nonconsumptive predator effects on prey growth, physiology, and body stoichiometry. Ecology 96, 3270–3280 (2015).PubMed 
Article 

Google Scholar 
Hawlena, D. & Schmitz, O. J. Physiological stress as a fundamental mechanism linking predation to ecosystem functioning. Am. Nat. 176, 537–556 (2010).PubMed 
Article 

Google Scholar 
Nation, J. L. Insect Physiology and Biochemistry. (CRC Press, 2011). doi:https://doi.org/10.1201/9781420061789.Rudolf, V. H. W. & Singh, M. Disentangling climate change effects on species interactions: effects of temperature, phenological shifts, and body size. Oecologia 173, 1043–1052 (2013).ADS 
PubMed 
Article 

Google Scholar 
Pfennig, D. W. Effect of predator-prey phylogenetic similarity on the fitness consequences of predation: a trade-off between nutrition and disease?. Am. Nat. 155, 335–345 (2000).PubMed 
Article 

Google Scholar 
Lee, K. P., Simpson, S. J. & Wilson, K. Dietary protein-quality influences melanization and immune function in an insect. Funct. Ecol. 22, 1052–1061 (2008).Article 

Google Scholar 
Wu, Q., Patočka, J. & Kuča, K. Insect Antimicrobial Peptides, a Mini Review. Toxins (Basel) 10, 461 (2018).Bullard, B. et al. The molecular elasticity of the insect flight muscle proteins projectin and kettin. PNAS 103, 4451–4456 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mamat-Noorhidayah, Yazawa, K., Numata, K. & Norma-Rashid, Y. Morphological and mechanical properties of flexible resilin joints on damselfly wings (Rhinocypha spp.). PLoS One 13, e0193147 (2018).Muthukrishnan, S., Merzendorfer, H., Arakane, Y. & Kramer, K. J. 7 – Chitin Metabolism in Insects. in Insect Molecular Biology and Biochemistry (ed. Gilbert, L. I.) 193–235 (Academic Press, 2012). doi:https://doi.org/10.1016/B978-0-12-384747-8.10007-8.Van Dievel, M., Stoks, R. & Janssens, L. Beneficial effects of a heat wave: higher growth and immune components driven by a higher food intake. J. Exp. Biol. 220, 3908–3915 (2017).PubMed 

Google Scholar  More

Defining alpine habitat and mountain regionsWe defined alpine habitat as the area above climatic treeline, including the nival belt, where temperature, wind, drought, snow, or nightly frost limit vegetation growth to shrubs, krummholz, or fragmented tree patches less than 3 m in height3,23,24. Realized treeline can be markedly lower than the climatic treeline due to the absence of continuous forest at lower elevations, or human activities such as logging, burning, and livestock grazing25. While anthropogenically influenced treeline produces habitat reminiscent of alpine meadows, these habitats are not climatically representative of alpine ecosystems and thus they were not included when assembling this dataset. Climatic treeline elevation varies globally based on latitude, topography, aspect, and proximity to the coast (i.e., oceanic influence)11. Therefore, we defined alpine habitat separately for each mountain region based on local climate and published accounts of alpine vegetation. While alpine habitats usually occur above at least 1,500 m elevation globally, at high latitudes ( >55°N or 41°S) this elevation can be as low as ~400 m26 (Fig. 2).Fig. 2The median (triangular points) and range (error bars) of treeline elevation for each of the main mountain regions covered in the dataset (Fig. 1). The mountain regions are arranged from north to south (left to right) and the grey dashed line represents the relative position of the equator. Treeline elevation was derived from different sources depending on the region (see ‘Data sources’ in the dataset). The abbreviation ‘NA’, such as in ‘Northwestern NA’, refers to North America.Full size imageThe alpine habitats we identified broadly align with the ‘lower alpine’, ‘upper alpine’, and ‘nival’ belts mapped by Korner et al.9 and made available by the Global Mountain Biodiversity Assessment project (http://www.mountainbiodiversity.org/explore)27,28. However, certain areas, such as the Sierras de Córdoba, Argentina or the Isthmian Páramo on volcanoes in Central America were classified as ‘upper montane’ by Korner et al.9 based on thermal belts alone. For the purposes of this dataset, we considered these regions alpine habitat based on published measurements of treeline and distinct alpine plant communities facilitated by a mixture of temperature, precipitation, nightly frost, and wind constraints. For example, the Drakensberg range in South Africa was identified as ‘upper montane’ only, but botanical studies have characterized the region as Themeda-Festuca grassland from 1,900–2,800 m and alpine heathlands above 2,800 m13, representing extensive habitat above treeline. As a result, our definition of alpine habitat expands on the thermal belts mapped by Korner et al.9. In this way, the avian communities we identified retain species lineages that are confined to cooler high elevation habitats, representing remnants of more extensive alpine ecosystems from the last glaciation event.We grouped mountain ranges into 12 global regions and 38 subregions based on similar climates and alpine vegetation stemming from shared geographic position (Tables 2, 3; Fig. 1). The ‘Islands’ category represents very limited alpine habitat on four disparate islands that do not easily fit within any other major region, but nevertheless occur in subtropical or tropical realms: Hawaii, Sumatra, Borneo, and the Canary Islands. Alpine breeding birds and life-history traits were identified for each individual region so that future analyses can either include or remove mountain ranges depending on their definition of alpine habitat. This approach also promotes comparisons of avian communities at a finer scale across the full diversity of alpine habitats.Table 2 Description of the major regions and specific mountain ranges in the Americas that are included in the dataset.Full size tableTable 3 Description of the major regions and specific mountain ranges from Eurasia, Africa, and Oceania, plus the miscellaneous mountain ‘Islands’ region.Full size tableAlpine breeding bird speciesFor each region described in Tables 2 and 3, we assembled a list of alpine breeding species from published literature, environmental assessment reports, regional monitoring schemes, bird atlases, and expert knowledge following the most recent International Ornithology Committee taxonomy, version 12.129. An alpine breeding bird is any species that nests above treeline, regardless of how frequently, such that all or a certain proportion of a species is dependent on alpine habitat during the breeding season. Due to certain data-deficiencies underlying existing species range estimates above treeline, using knowledge from regional experts was the most accurate method to assemble a global list of alpine breeding birds for most mountain regions. See the Technical Validation section for specifics on how we validated the use of expert knowledge when assembling species and their traits for the Global Alpine Breeding Bird list.Species traitsWe included species traits that fall under three general topics: 1) alpine breeding propensity, 2) ecological traits, and 3) conservation value. Alpine breeding propensity includes breeding habitat specialization and alpine breeding status, ecological traits include migration behaviour and nest traits, while conservation value encompasses mountain endemism and conservation status. Together, these variables broadly reflect alpine habitat use during the breeding season globally, as well as provide the basis for evaluating the conservation potential and risks for alpine bird communities. We recorded general trait specifications for each species using available resources such as Birds of the World30, the IUCN Red List31, and AVONET21. We then solicited region-specific traits from regional experts and the same review process was conducted for these traits as for alpine breeding evidence (see Technical Validation). All traits were specific to alpine breeding birds whenever possible. The global distribution of each species trait can be visualized in Fig. 3.Fig. 3The global distribution for each trait included in the dataset, including (a–c) alpine breeding propensity, (d–f) ecological traits, and (g–i) traits relevant to conservation status and data uncertainty. In all cases except panel c the y-axis is the proportion of all 1,310 alpine breeding species identified in the dataset. Panel c depicts the elevational breeding distribution expected from the different combinations of breeding specialization and alpine breeding status to visualize the probability of breeding above treeline. In Panel e, ‘BP’ refers to brood parasite. See Table 4 or the metadata for full descriptions of each trait.Full size imageSpecialization for breeding in alpine habitats (hereafter ‘breeding specialization’) and the propensity to breed in alpine habitats (hereafter ‘breeding status’) form a tiered estimate of alpine breeding behaviour. First, we classified each species into one of three breeding specialization categories to differentiate among species that predominantly breed above treeline (alpine specialists), breed both above and below treeline (elevational generalists) or are limited to high latitude tundra habitats (tundra specialists). The latter includes alpine-Arctic or alpine-Antarctic transition zones, where species nest in higher, drier tundra (approximately >400 m elevation) but may also breed in wet tundra at lower or coastal elevations. In this way, we clearly identified species that breed in alpine tundra habitat, but where tundra is the primary driver of breeding presence, not necessarily selection for high elevation. Under breeding status, we quantified the likelihood of breeding above treeline relative to below treeline as common, uncommon, or rare. Alpine specialists are always common alpine breeders (regardless of their density and distribution), but generalists or tundra specialists can be common, uncommon, or rare breeders in alpine habitats depending on whether they are found breeding consistently above treeline, more often breeding below treeline, or only incidentally breeding in the alpine, respectively. Together, these variables identify a species’ relative probability of breeding along the elevational gradient and with respect to the treeline (Fig. 3).We used two nest traits to identify the general breeding niche of each species: nest type and nest site. Nest type included three primary category levels (open cup, cavity, domed nest), while nest site was subdivided into seven levels (ground, bank, shrub, tree, rock, cliff, and glacier). Brood parasite species, which will use a range of nest types and sites depending on the host species, were placed in an additional ‘brood parasite’ category for each nest trait. A species with an open cup or domed nest is limited to placing the nest on the ground, in vegetation (e.g., a shrub or stunted tree), or on a cliff, while cavity nesters may be in a bank (i.e., burrow/tunnel), in a rock (e.g., crevice), or in a tree (e.g., natural or excavated cavity). If nest traits were undescribed for a certain species, we inferred nest traits from the most closely related species in similar high elevation habitats (see Data uncertainty).Species were assigned to three migration categories based on their predominant behaviour: resident, short-distance, and long-distance migrants. Resident species remain near their breeding habitat year-round, allowing for occasional, short-term movements in response to extreme weather events. Short-distance migrants conduct seasonal altitudinal migrations, short latitudinal migrations, or nomadic movements where the species remains within the general breeding region (e.g., within the temperate zone). Long-distance migrants travel extensive distances to winter in an entirely different region than their breeding habitat (e.g., temperate breeders to tropical habitats). A general threshold of 3,000 km was used to distinguish between short- and long-distance migrants because it approximates the distance traveled from the Himalayas to the southern coast of India, Northern Europe to the Mediterranean coast, or Alaska to California. In other words, this distance represents a relatively consistent reference across global regions. While there are finer-scale migration designations that could be made, such as partial or altitudinal migration, we lack detailed movement data for most species and regions. Although a global list of potential altitudinal migrants exists that can be incorporated with this alpine breeding bird dataset if desired32, altitudinal migration often co-occurs with short-distance latitudinal movements and there are considerable differences in migration behaviour among subspecies, populations, and even individuals33. We therefore chose to use established migration categories that align with other global trait databases. In fact, our migration designations were largely congruent with those in AVONET21, with the primary difference being between resident and short-distance migrants. We identified ~200 short-distance migrants that were considered sedentary (resident) under the AVONET classification. This difference is to be expected given that we defined migration behaviour for alpine breeding populations compared to global trait values for all populations. For many species, alpine breeding birds will depart higher elevations during winter to avoid severe weather conditions, even though low elevation populations of the same species may be predominantly resident34. Therefore, the three broad categories chosen here are intended to balance available information with sufficient accuracy to provide data useful for large-scale life-history and biogeographic analyses of alpine breeding birds.Mountain endemism refers to a species whose breeding range is restricted by physical, environmental, or biological barriers to a general mountain region and the surrounding low elevation habitat. For example, a species breeding only on the Tibetan Plateau was classified as an endemic species, but a species that breeds across the Tibetan Plateau, the Himalayas, and the Altai Mountains was classified as non-endemic. When possible, we also classified endemism for defined subspecies. Species endemism is a more conservative metric, while subspecies endemism attempts to estimate additional cryptic endemism given that species differentiation is not well-defined for many high elevation birds. For example, the Caucasus Mountains support several distinct subspecies isolated from their primary distributions, including the Great rosefinch (Carpodacus rubicilla rubicilla), Dunnock (Prunella modularis obscura), and Güldenstädt’s redstart (Phoenicurus erythrogastrus erythrogastrus).Finally, conservation status refers to the IUCN Red List designations, version 2022-131. In addition to the traditional IUCN categories (e.g., Least Concern, Near Threatened, Vulnerable, etc.), we also included a Not Assessed (NA) category that generally occurred when a species was recently split. See Table 4 for complete definitions of all traits.Table 4 Definitions of species traits included in the Global Alpine Breeding Bird dataset.Full size tableData uncertaintyGlobally, there is significant variation in accessibility to alpine habitats and funding for alpine research. As a result, uncertainty in alpine breeding status may differ among regions and species. For example, in New Guinea, mist-net surveys and point counts across elevation have identified species that frequently use alpine habitat, but a dearth of breeding biology studies means that there are few nest records above treeline. It is thus necessary to codify this level of uncertainty for each species.To this effect, we included a variable termed ‘Data reliability’, which is a four-level categorical variable from 0 to 3 that is based on the number of reported nests that have been found and described for each species. We used the presence of nest descriptions to evaluate uncertainty because active nests are the must fundamental form of evidence for breeding above treeline, and therefore it is reasonable that a species with less existing knowledge about nest traits or nesting behaviours will have considerably more uncertainty around its designation as an alpine breeding species. For this variable, 0 indicates that nest traits are undescribed for a given species, 1 means less than five nests have been described, 2 indicates more than five nests have been described, but all from a single population, and therefore there is limited understanding of geographic variation, while 3 occurs when nests have been described from multiple populations or regions. If nest traits were undescribed for a species (data reliability = 0), then nest type and site were inferred from the most closely related species with available data, and whenever possible, a congener was selected that also breeds at high elevations or in alpine habitats. While the nest traits of most species have been sufficiently described, there is a significant proportion of alpine breeding birds with less available data (27.0%; Fig. 3i). The relative number of described nests was derived from Birds of the World30. We recognize that these data may not reflect true knowledge of nest traits given that not all species accounts have been recently updated. However, it does represent a consistent data source that allowed us to approximate data reliability sufficiently for our purposes.In combination, data reliability and alpine breeding status fully characterize alpine breeding uncertainty. For example, a species considered a rare alpine breeder with a data reliability of 3, means that there is strong evidence for breeding above treeline, but only incidentally under very specific circumstances. However, a rare alpine breeder with a data reliability of zero (i.e., nest undescribed), means that the likelihood of breeding above treeline may be probable based on behavioural observations, but further confirmation is required. When using this dataset for analyses, one must decide whether to use a conservative approach or consider all potential alpine breeding species with the appropriate caveats (see Usage Notes). More

Kobayashi, H. & Kohshima, S. Unique morphology of the human eye. Nature 387(6635), 767–768 (1997).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kobayashi, H. & Kohshima, S. Unique morphology of the human eye and its adaptive meaning: Comparative studies on external morphology of the primate eye. J. Hum. Evol. 40(5), 419–435 (2001).CAS 
PubMed 
Article 

Google Scholar 
Mayhew, J. A. & Gómez, J. C. Gorillas with white sclera: A naturally occurring variation in a morphological trait linked to social cognitive functions. Am. J. Primatol. 77, 869–877 (2015).PubMed 
Article 

Google Scholar 
Perea-García, J. O. Quantifying ocular morphologies in extant primates for reliable interspecific comparisons. J. Lang. Evol. 1(2), 151–158 (2016).Article 

Google Scholar 
Perea-García, J. O., Kret, M. E., Monteiro, A. & Hobaiter, C. Scleral pigmentation leads to conspicuous, not cryptic, eye morphology in chimpanzees. Proc. Natl. Acad. Sci. 116(39), 19248–19250 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Caspar, K., Biggemann, M., Geissmann, T. & Begall, S. Ocular pigmentation in humans, great apes, and gibbons is not suggestive of communicative functions. Sci. Rep. 11, 1–14 (2021).Article 

Google Scholar 
Mearing, A. S. & Koops, K. Quantifying gaze conspicuousness: Are humans distinct from chimpanzees and bonobos?. J. Hum. Evol. 157, 103043. https://doi.org/10.1016/J.JHEVOL.2021.103043 (2021).Article 
PubMed 

Google Scholar 
Perea-García, J. O., Danel, D. P. & Monteiro, A. Diversity in primate external eye morphology: Previously undescribed traits and their potential adaptive value. Symmetry 13, 1270 (2021).ADS 
Article 

Google Scholar 
Banks, M. S., Sprague, W. W., Schmoll, J., Parnell, J. A. & Love, G. D. Why do animal eyes have pupils of different shapes?. Sci. Adv. 1(7), e1500391 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Corfield, J. R. et al. Anatomical specializations for nocturnality in a critically endangered parrot, the kakapo (Strigops habroptilus). PLoS ONE 6(8), e22945 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lisney, T. J. et al. Ecomorphology of eye shape and retinal topography in waterfowl (Aves: Anseriformes: Anatidae) with different foraging modes. J. Comp. Physiol. A. 199(5), 385–402 (2013).Article 

Google Scholar 
Lisney, T. J., Iwaniuk, A. N., Bandet, M. V. & Wylie, D. R. Eye shape and retinal topography in owls (Aves: Strigiformes). Brain Behav. Evol. 79(4), 218–236 (2012).PubMed 
Article 

Google Scholar 
Duke-Elder, S. S. The eye in evolution. In System of Ophthalmology (ed. Duke-Elder, S. S.) 453 (Henry Kimpton, 1985).
Google Scholar 
-Miller, D., & Sanghvi, S. (1990). Contrast sensitivity and glare testing in corneal disease. In Glare and Contrast Sensitivity for Clinicians (pp. 45–52). Springer.De Broff, B. M. & Pahk, P. J. The ability of periorbitally applied antiglare products to improve contrast sensitivity in conditions of sunlight exposure. Arch. Ophthalmol. 121(7), 997–1001 (2003).Article 

Google Scholar 
Caspar, K. R., Mader, L., Pallasdies, F., Lindenmeier, M. & Begall, S. Captive gibbons (Hylobatidae) use different referential cues in an object-choice task: Insights into lesser ape cognition and manual laterality. PeerJ 6, e5348 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kaplan, G. & Rogers, L. J. Patterns of gazing in orangutans (Pongo pygmaeus). Int. J. Primatol. 23(3), 501–526 (2002).Article 

Google Scholar 
Kamilar, J. M. & Bradley, B. J. Interspecific variation in primate coat colour supports Gloger’s rule. J. Biogeogr. 38(12), 2270–2277 (2011).Article 

Google Scholar 
Santana, S. E., Lynch Alfaro, J. & Alfaro, M. E. Adaptive evolution of facial colour patterns in Neotropical primates. Proc. R. Soc. B Biol. Sci. 279(1736), 2204–2211 (2012).Article 

Google Scholar 
Santana, S. E., Alfaro, J. L., Noonan, A. & Alfaro, M. E. Adaptive response to sociality and ecology drives the diversification of facial colour patterns in catarrhines. Nat. Commun. 4(1), 1–7 (2013).Article 

Google Scholar 
Delhey, K. A review of Gloger’s rule, an ecogeographical rule of colour: Definitions, interpretations and evidence. Biol. Rev. 94(4), 1294–1316 (2019).PubMed 

Google Scholar 
Zhang, P. & Watanabe, K. Preliminary study on eye colour in Japanese macaques (Macaca fuscata) in their natural habitat. Primates 48(2), 122–129 (2007).PubMed 
Article 

Google Scholar 
Bradley, B. J., Pedersen, A. & Mundy, N. I. Brief communication: blue eyes in lemurs and humans: Same phenotype, different genetic mechanism. Am. J. Phys. Anthropol. 139(2), 269–273 (2009).PubMed 
Article 

Google Scholar 
Meyer, W. K., Zhang, S., Hayakawa, S., Imai, H. & Przeworski, M. The convergent evolution of blue iris pigmentation in primates took distinct molecular paths. Am. J. Phys. Anthropol. 151(3), 398–407 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Negro, J. J., Blázquez, M. C. & Galván, I. Intraspecific eye color variability in birds and mammals: A recent evolutionary event exclusive to humans and domestic animals. Front. Zool. 14(1), 1–6 (2017).Article 

Google Scholar 
van den Berg, T. J. T. P., IJspeert, J. K. & De Waard, P. W. T. Dependence of intraocular straylight on pigmentation and light transmission through the ocular wall. Vis. Res. 31(7–8), 1361–1367 (1991).PubMed 
Article 

Google Scholar 
Mure, L. S. Intrinsically photosensitive retinal ganglion cells of the human retina. Front. Neurol. 12, 636330 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Wald, L. (2018). Basics in solar radiation at earth surface. ffhal-01676634ff.Workman, L. Blue eyes keep away the winter blues: Is blue eye pigmentation an evolved feature to provide resilience to seasonal affective disorder. OA J. Behav. Sci. Psychol. 1(1), 180002 (2018).MathSciNet 

Google Scholar 
Smith, A. R. Color gamut transform pairs. ACM Siggraph Comput. Graph. 12(3), 12–19 (1978).CAS 
Article 

Google Scholar 
Kamilar, J. M. & Cooper, N. Phylogenetic signal in primate behaviour, ecology and life history. Philos. Trans. R. Soc. B: Biol. Sci. 368(1618), 20120341 (2013).Article 

Google Scholar 
Leutenegger, W. & Kelly, J. T. Relationship of sexual dimorphism in canine size and body size to social, behavioral, and ecological correlates in anthropoid primates. Primates 18(1), 117–136. https://doi.org/10.1007/bf02382954 (1977).Article 

Google Scholar 
Gómez, J. C. (1996). Ostensive behavior in great apes: The role of eye contact. Reaching into thought: The minds of the great apes, 131–151.Dovidio, J. F., & Ellyson, S. L. (1985). Pattern of visual dominance behavior in humans. In Power, Dominance, and Nonverbal Behavior (pp. 129–149). Springer.Nakatsukasa, M. Locomotor differentiation and different skeletal morphologies in mangabeys (Lophocebus and Cercocebus). Folia Primatol. 66(1–4), 15–24 (1996).CAS 
Article 

Google Scholar 
Smith, R. J. & Jungers, W. L. Body mass in comparative primatology. J. Hum. Evol. 32(6), 523–559 (1997).CAS 
PubMed 
Article 

Google Scholar 
Fioletov, V., Kerr, J. B. & Fergusson, A. The UV index: Definition, distribution and factors affecting it. Can. J. Public Health 101(4), I5–I9 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Jablonski, N. G. & Chaplin, G. Human skin pigmentation as an adaptation to UV radiation. Proc. Natl. Acad. Sci. 107(Supplement 2), 8962–8968 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Do, M. T. H. & Yau, K. W. Intrinsically photosensitive retinal ganglion cells. Physiol. Rev. (2010).Pickard, G. E. & Sollars, P. J. Intrinsically photosensitive retinal ganglion cells. Rev. Physiol. Bioch. Pharmacol. 162, 59–90 (2012).Goel, N., Terman, M. & Terman, J. S. Depressive symptomatology differentiates subgroups of patients with seasonal affective disorder. Depress. Anxiety 15(1), 34–41 (2002).PubMed 
Article 

Google Scholar 
Münch, M. et al. Blue-enriched morning light as a countermeasure to light at the wrong time: Effects on cognition, sleepiness, sleep, and circadian phase. Neuropsychobiology 74(4), 207–218 (2016).PubMed 
Article 

Google Scholar 
Davidson, G. L., Thornton, A. & Clayton, N. S. Evolution of iris colour in relation to cavity nesting and parental care in passerine birds. Biol. Let. 13(1), 20160783 (2017).Article 

Google Scholar 
Volpato, G. L., Luchiari, A. C., Duarte, C. R. A., Barreto, R. E. & Ramanzini, G. C. Eye color as an indicator of social rank in the fish Nile tilapia. Braz. J. Med. Biol. Res. 36, 1659–1663 (2003).CAS 
PubMed 
Article 

Google Scholar 
Fosbury, R. A. & Jeffery, G. Reindeer eyes seasonally adapt to ozone-blue Arctic twilight by tuning a photonic tapetum lucidum. Proc. R. Soc. B 289(1977), 20221002 (2022).PubMed 
PubMed Central 
Article 

Google Scholar 
Allen, W. L., Stevens, M. & Higham, J. P. Character displacement of Cercopithecini primate visual signals. Nat. Commun. 5(1), 1–10 (2014).Article 

Google Scholar 
Frost, P. European hair and eye color: A case of frequency-dependent sexual selection?. Evol. Hum. Behav. 27(2), 85–103 (2006).Article 

Google Scholar 
Hart, D. (2000). Primates as prey: Ecological, morphological and behavioral relationships between primate species and their predators.Liebal, K., Waller, B. M., Slocombe, K. E. & Burrows, A. M. Primate communication: a multimodal approach. (Cambridge University Press, 2014).
Google Scholar 
Whitham, W., Schapiro, S. J., Troscianko, J. & Yorzinski, J. L. Chimpanzee (Pan troglodytes) gaze is conspicuous at ecologically-relevant distances. Sci. Rep. 12(1), 1–7 (2022).Article 

Google Scholar 
Kano, F., Kawaguchi, Y. & Hanling, Y. Experimental evidence that uniformly white sclera enhances the visibility of eye-gaze direction in humans and chimpanzees. Elife 11, e74086 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Emery, N. J. The eyes have it: The neuroethology, function and evolution of social gaze. Neurosci. Biobehav. Rev. 24, 581–604 (2000).CAS 
PubMed 
Article 

Google Scholar 
-Bourjade, M. (2016). Social attention. Int. Encycl. Primatol. 1–2.Petersen, R. M., Dubuc, C. & Higham, J. P. Facial displays of dominance in non-human primates. In The facial displays of leaders (pp. 123–143) (Palgrave Macmillan, Cham, 2018).Laitly, A., Callaghan, C. T., Delhey, K. & Cornwell, W. K. Is color data from citizen science photographs reliable for biodiversity research?. Ecol. Evol. 11(9), 4071–4083 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Chan, I. Z., Stevens, M. & Todd, P. A. PAT-GEOM: A software package for the analysis of animal patterns. Methods Ecol. Evol. 10(4), 591–600 (2019).Article 

Google Scholar 
Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125(1), 1–15 (1985).Article 

Google Scholar 
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3(2), 217–223 (2012).Article 

Google Scholar 
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Orme, D. et al. The caper package: Comparative analysis of phylogenetics and evolution in R. R Pack. Vers. 5(2), 1–36 (2013).
Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
-Williamson, E. A., Maisels, F. G., Groves, C. P., Fruth, B. I., Humle, T., & Morton, F. B. (2013). Handbook of the Mammals of the World Volume 3: Primates. More

Barnes, D. K. A., Galgani, F., Thompson, R. C. & Barlaz, M. Accumulation and fragmentation of plastic debris in global environments. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 1526 (2009).Article 

Google Scholar 
Cole, M., Lindeque, P., Halsband, C. & Galloway, T. S. Microplastics as contaminants in the marine environment: A review. Mar. Pollut. Bull. 62, 2588–2597 (2011).CAS 
PubMed 
Article 

Google Scholar 
Waller, C. L. et al. Microplastics in the Antarctic marine system: An emerging area of research. Sci. Total Environ. 598, 220–227 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fang, C. et al. Microplastic contamination in benthic organisms from the Arctic and sub-Arctic regions. Chemosphere 209, 298–306 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Suaria, G. et al. Floating macro- and microplastics around the Southern Ocean: Results from the Antarctic Circumnavigation Expedition. Environ. Int. 136, 105494 (2020).PubMed 
Article 

Google Scholar 
Stark, J.S., Raymond, T., Deppeler, S.L. & Morrison, A.K. Antarctic Seas in World Seas: An Environmental Evaluation (ed. Sheppard, C.) 44 (Academic Press 2019).Mishra, A. K., Singh, J. & Mishra, P. P. Microplastics in Polar Regions: An early warning to the world’s pristine ecosystem. Sci. Total Environ. 784, 147149 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bargagli, R. Environmental contamination in Antarctic ecosystems. Sci. Total Environ. 400, 212–226 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gregory, M. R., Kirk, R. M. & Mabin, M. C. G. Pelagic tar, oil, plastics and other litter in surface waters of the New Zealand sector of the Southern Ocean, and on Ross Dependancy shores. N. Z. Antarct. Rec. 6, 12–26 (1984).
Google Scholar 
Van Franeker, J. A. & Bell, P. J. Plastic Ingestion by Petrels Breeding in Antarctica. Mar. Poll. Bull. 19(12), 672–674 (1988).Article 

Google Scholar 
Harper, P. C. & Fowler, J. A. Plastics pellets in New Zeland storm-killed prions (Pachyptila spp) 1958–1977. Notornis 34, 65–70 (1987).
Google Scholar 
Kelly, A. et al. Microplastic contamination in east Antarctic sea ice. Mar. Poll. Bull. 154, 111130 (2020).CAS 
Article 

Google Scholar 
Gigault, J. et al. Current opinion: What is a nanoplastic?. Environ. Pollut. 235, 1030–1034 (2018).CAS 
PubMed 
Article 

Google Scholar 
Dawson, A. et al. Turning microplastics into nanoplastics through digestive fragmentation by Antarctic krill. Nat. Commun. 9, 1001 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bergami, E. et al. Plastics everywhere: First evidence of polystyrene fragments inside the common Antarctic collembolan Cryptopygus antarcticus. Biol. Lett. 16, 20200093 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sfriso, A. A. et al. Microplastic accumulation in benthic invertebrates in Terra Nova Bay (Ross Sea, Antarctica). Environ. Int. 137, 105587 (2020).CAS 
PubMed 
Article 

Google Scholar 
Jones-Williams, K. et al. Close encounters—microplastic availability to pelagic amphipods in sub-Antarctic and Antarctic surface waters. Environ. Int. 140, 105792 (2020).CAS 
PubMed 
Article 

Google Scholar 
Bessa, F. et al. Microplastics in gentoo penguins from the Antarctic region. Sci Rep 9, 14191 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Le Guen, C. et al. Microplastic study reveals the presence of natural and synthetic fibres in the diet of King Penguins (Aptenodytes patagonicus) foraging from South Georgia. Environ. Int. 134, 105303 (2020).PubMed 
Article 

Google Scholar 
Fragão, J. et al. Microplastics and other anthropogenic particles in Antarctica: Using penguins as biological samplers. Sci. Total Environ. 20, 788 (2021).
Google Scholar 
International Maritime Organization (IMO), Resolution A. 1087 (28): Guidelines for the Designation of Special Areas under MARPOL, in Assembly, 28th Session, Agenda Item 12, (2013).Waller, C. L. & Hughes, K. A. Plastics in the Southern Ocean. Antarct. 30, 269 (2018).Article 

Google Scholar 
Aves, A. R. First evidence of microplastics in Antarctic snow et al. First evidence of microplastics in Antarctic snow. Cryosphere 16, 2127–2145 (2022).ADS 
Article 

Google Scholar 
Vacchi, M., La Mesa, M. & Castelli, A. Diet of two coastal nototheniid fish from Terra Nova Bay, Ross Sea. Antarct. 6, 61–65 (1994).Article 

Google Scholar 
Froese, R., & Pauly D. (eds) FishBase. World Wide Web electronic publication—FishBase (September, 2022).La Mesa, M., Dalù, E. M. & Vacchi, M. Trophic ecology of the emerald notothen Trematomus bernacchii (Pisces, Nototheniidae) from Terra Nova Bay, Ross Sea, Antarctica. Polar Biol. 27, 721–728 (2004).Article 

Google Scholar 
Lamesa, M., Eastman, J. T. & Vacchi, M. The role of notothenioid fish in the food web of the Ross Sea shelf waters: A review. Polar Biol. 27, 321–338. https://doi.org/10.1007/s00300-004-0599-z (2004).Article 

Google Scholar 
Soggia, F., Ianni, C., Magi, E. & Frache, R. Antarctic environmental Specimen Bank in Environmental Contamination in Antarctica, a Challenge to Analytical Chemistry (ed. Caroli, S., Cescon, P., Walton, B.T.) 305–325 (Elsevier, 2001).Anger, P. M. et al. Raman microspectroscopy as a tool for microplastic particle analysis. TrAC Trends Analyt. Chem. 109, 214–226 (2018).CAS 
Article 

Google Scholar 
Savoca, S. et al. Microplastics occurrence in the Tyrrhenian waters and in the gastrointestinal tract of two congener species of seabreams. Environ. Toxicol. Pharmacol. 67, 35–41 (2019).CAS 
PubMed 
Article 

Google Scholar 
Capillo, G. et al. Quali-quantitative analysis of plastics and synthetic microfibers found in demersal species from Southern Tyrrhenian Sea (Central Mediterranean). Mar. Poll. Bull. 150, 110596 (2020).CAS 
Article 

Google Scholar 
Bottari, T. et al. Plastics occurrence in the gastrointestinal tract of Zeus faber and Lepidopus caudatus from the Tyrrhenian Sea. Mar. Poll. Bull. 146, 408–416 (2019).CAS 
Article 

Google Scholar 
Filgueiras, A. V., Preciado, I., Cartón, A. & Gago, J. Microplastic ingestion by pelagic and benthic fish and diet composition: A case study in the NW Iberian shelf. Mar. Poll. Bull. 160, 111623 (2020).CAS 
Article 

Google Scholar 
Mancuso, M. et al. Investigating the effects of microplastic ingestion in Scyliorhinus canicula from the South of Sicily. Sci. Total Environ. 850, 157875 (2022).ADS 
Article 

Google Scholar 
Savoca, S. et al. Ingestion of plastic and non-plastic microfibers by farmed gilthead sea bream (Sparus aurata) and common carp (Cyprinus carpio) at different life stages. Sci. Total Environ. 782, 146851 (2021).ADS 
CAS 
Article 

Google Scholar 
Rodrìguez-Romeu, O. et al. Are anthropogenic fibres a real problem for red mullets (Mullus barbatus) from the NW Mediterranean?. Sci. Total Environ. 733, 139336 (2020).ADS 
PubMed 
Article 

Google Scholar 
Bansode, M. A., Eastman, J. T. & Aronson, R. B. Feeding biomechanics of five demersal Antarctic fishes. Polar Biol. 37, 1835–1848. https://doi.org/10.1007/s00300-014-1565-z (2014).Article 

Google Scholar 
Munari, C. et al. Microplastics in the sediments of Terra Nova Bay (Ross Sea, Antarctica). Mar. Poll. Bull. 122, 161–165 (2017).CAS 
Article 

Google Scholar 
Cincinelli, A. et al. Microplastic in the surface waters of the Ross Sea (Antarctica): Occurrence, distribution and characterization by FTIR. Chemosphere 175, 391–400 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Eriksson, C. & Burton, H. Origins and biological accumulation of small plastic particles in fur seals from Macquarie Island. Ambio 32, 380–384 (2003).PubMed 
Article 

Google Scholar 
Carr, S. A. Sources and dispersive modes of micro-fibers in the environment. Integr. Environ. Assess. Manag 13(3), 466–469 (2017).CAS 
PubMed 
Article 

Google Scholar 
Gavigan, J. et al. Synthetic microfiber emissions to land rival those to waterbodies and are growing. PLoS ONE 15(9), e0237839 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Manshoven, E. et al. Microplastic pollution from textile consumption in Europe. Eionet Report – ETC/CE 2022/1 (2022).Remy, F. et al. When microplastic is not plastic: The ingestion of artificial cellulose fibers by macrofauna living in seagrass macrophytodetritus. Environ. Sci. Technol. 49, 11158–11166 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Savoca, S. et al. Detection of anthropogenic cellulose microfibers in Boops boops from the northern coasts of Sicily (Central Mediterranean). Sci. Total Environ. 691, 455–465 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Raina, M.A., Gloy, Y.S. & Gries, T. Weaving technologies for manufacturing denim in Denim. Woodhead Publishing Series in Textiles (ed. Paul, R.) 159–187 (2015).Lots, F. A. E. et al. A Large-Scale Investigation of Microplastic Contamination: Abundance and Characteristics of Microplastics in European Beach Sediment. Mar. Pollut. Bull. 123, 219–226 (2017).CAS 
PubMed 
Article 

Google Scholar 
Athey, S. N. et al. The Widespread Environmental Footprint of Indigo Denim Microfibers from Blue Jeans. Environ. Sci. Technol. Lett. 7, 840–847 (2020).CAS 
Article 

Google Scholar 
Lellis, B. et al. Effects of textile dyes on health and the environment and bioremediation potential of living organisms. Biotech. Res. Inn. 3, 275–290 (2019).Article 

Google Scholar 
Sandhya, S. Biodegradation of azodyes under anaerobic condition: Role of azoreductase Biodegradation of azo dyes. The handbook of environmental chemistry (ed. Erkurt ,H.A.) 9, 39–57 (Springer, 2010).Oehlmann, J.R. et al. A critical analysis of the biological impacts of plasticizers on wildlife. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364 (1526), 2047e2062 (2009).Aquino, J. M. et al. Electrochemical degradation of a real textile wastewater using β-PbO2 and DSA® anodes. Chem. Eng. J. 251, 138–145 (2014).CAS 
Article 

Google Scholar 
Newman, M. C. Fundamentals of Ecotoxicology: The Science of Pollution (CRC Press, 2015).
Google Scholar 
Khatri, J., Nidheesh, P. V., Singh, T. A. & Kumar, M. S. Advanced oxidation processes based on zero-valent aluminium for treating textile wastewater. Chem. Eng. J. 348, 67–73 (2018).CAS 
Article 

Google Scholar 
Athey, S. N. & Erdle, L. M. Are we underestimating anthropogenic microfiber pollution? A critical review of occurrence, methods, and reporting. Environ. Tox. Chem. 41, 822–837 (2022).CAS 
Article 

Google Scholar 
Stone, C., Windsor, F. M., Munday, M. & Durance, I. Natural or synthetic – how global trends in textile usage threaten freshwater environments. Sci. Total Environ. 718, 134689 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wright, S. L. & Kelly, F. J. Plastic and human health: A micro issue?. Environ. Sci. Technol. 51, 6634–6647 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ziajahromi, S., Neale, P. A. & Leusch, F. D. Wastewater treatment plant effluent as a source of microplastics: Review of the fate, chemical interactions and potential risks to aquatic organisms. Water Sci. Technol. 74(10), 2253–2269 (2016).CAS 
PubMed 
Article 

Google Scholar 
Aronson, R. B., Thatje, S., McClintock, J. B. & Hughes, K. A. Anthropogenic impacts on marine ecosystems in Antarctica. Ann. N. Y. Acad. Sci. 1223, 82–1072011 (2011).ADS 
PubMed 
Article 

Google Scholar 
Hynes, N. R. J. et al. Modern enabling techniques and adsorbents based dye removal with sustainability concerns in textile industrial sector – A comprehensive review. J. Clean. Prod. 272, 122636 (2020).CAS 
Article 

Google Scholar 
Savoca, S. et al. Plastics occurrence in juveniles of Engraulis encrasicolus and Sardina pilchardus in the Southern Tyrrhenian Sea. Sci Total Environ. 718, 137457 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Galgani, F., Hanke, G., Werner, S. D. V. L. & De Vrees, L. Marine litter within the European marine strategy framework directive. Ices J. Mar. Sci. 70, 1055–1064 (2013).Article 

Google Scholar 
Bottari, T. et al. Microplastics in the bogue, Boops boops: A snapshot of the past from the southern Tyrrhenian Sea. J. Hazardous Mat. 424(15), 127669 (2022).CAS 
Article 

Google Scholar 
Pedà, C. et al. Coupling gastro-intestinal tract analysis with an airborne contamination control method to estimate litter ingestion in demersal elasmobranchs. Front. Environ. Sci. 8, 119 (2020).Article 

Google Scholar  More

The interaction between the two factors (substrates and CRF rates) was statistically significant by the F-test (p  More

Deforestation trajectories and economic driversCambodia has undergone significant forest loss in recent decades—with 2.6 million hectares of forest cover loss occurring since 2001, equating to 29.5% of forest cover7 and 1.45 billion tonnes of CO2 emissions8. The deforestation rates have increased by 76% in the last decade (2011–2021) compared to the previous (2001–2010; Fig. 1b)7. We find forest loss has occurred within three distinct Phases demonstrated by changepoint analysis: (1) Phase 1: steady rise from 2000 to 2009 (average = 0.82%/year), (2) Phase 2: peak years from 2010 to 2013 (average = 2.3%/year), (3) Phase 3: moderate phase from 2014 to 2021 (average = 1.6%/year). Whilst the annual rate of deforestation has declined since the Phase 2, Cambodia currently has the highest country-level annual rate of forest loss globally7, illustrating the relentless deforestation spreading across the landscape. Critically, much of this forest loss and degradation is occurring in mature primary forests (Fig. 1b), which hold significant carbon and are home to rich biodiversity and keystone species17,18,19.
This deforestation in Cambodia has been attributed to the widespread development of Economic Land Concessions (ELCs), the expansion of numerous agricultural frontiers and relentless illegal logging20,21,22. These drivers have been abetted by the establishment of an extensive national road network (Fig. 1a)20—developed to promote economic growth and urban–rural connectivity23. The majority (88.4%) of these roads have been funded by foreign governments (the People’s Republic of China: 38.5%, Japan: 37.9%, and Republic of Korea: 12.0%)18—all of whom have established land concessions within Cambodia’s borders24 through the allocation of state land into private land for long-term industrial plantations22,25. The expansion of ELCs across Cambodia (average addition of 105,000 ha/year of ELC land since 1998) has been directly attributed to up to 40% of the country’s deforestation21, with further indirect impacts due to encroachment into rural community lands (indigenous areas, community forests, subsistence agricultural fields). This results in landlessness, poverty, and land conflicts, forcing communities to migrate in search of arable land, further contributing to the growing degradation and destruction of forests22,26,27,28,29.Strategic government interventionProtected areas expanded across Cambodia in 1993 following a royal decree26; the legal details of which were delineated in the 2008 Protected Areas Law, introducing protected categories, wildlife corridors and strict laws prohibiting development9. While over 80 protected areas currently exist covering 35% of Cambodian land10, they are still under substantial threat30. In further efforts to curb deforestation, the Royal Government of Cambodia ordered the suspension of new ELCs and revocation of a subset of existing ELCs in 2012 (Order 01BB)31. This resulted in a reduction of ELCs from a peak of ~ 2.1 million ha in 2012 to ~ 1.6 million ha from 2014 onward (Fig. 1b), with a significant positive correlation between the quantity of land classified as ELCs and the country-level deforestation rate (R = 0.87, p  More

Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS 
CAS 
PubMed 

Google Scholar 
van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2020).ADS 
PubMed 

Google Scholar 
Wagner, D. L. Insect declines in the anthropocene. Annu. Rev. Entomol. 65, 457–480 (2020).CAS 
PubMed 

Google Scholar 
Wilson, E. O. The little things that run the world (the importance and conservation of invertebrates). Conserv. Biol. 1, 344–346 (1987).
Google Scholar 
Isaacs, R., Tuell, J., Fiedler, A., Gardiner, M. & Landis, D. Maximizing arthropod-mediated ecosystem services in agricultural landscapes: The role of native plants. Front. Ecol. Environ. 7, 196–203 (2009).
Google Scholar 
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).PubMed 
PubMed Central 

Google Scholar 
Raven, P. H. & Wagner, D. L. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. Proc. Natl. Acad. Sci. U.S.A. 118, e2002548117 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Neves, B. & Pires, I. M. The Mediterranean diet and the increasing demand of the olive oil sector: Shifts and environmental consequences. Region. 5, 101–112 (2018).
Google Scholar 
Silveira, A. et al. The sustainability of agricultural intensification in the early 21st century: Insights from the olive oil production in Alentejo (Southern Portugal). In Changing Societies: Legacies and Challenges. The Diverse Worlds of Sustainability, 247–275 (2018).Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Salomone, R. & Ioppolo, G. Environmental impacts of olive oil production: A Life Cycle Assessment case study in the province of Messina (Sicily). J. Clean. Prod. 28, 88–100 (2012).
Google Scholar 
Rallo, L. et al. High-density olive plantations. Hortic. Rev. Am. Soc. Hortic. Sci. 41, 303–383 (2013).
Google Scholar 
Santos, S. A. P., Pereira, J. A., Torres, L. M. & Nogueira, A. J. A. Evaluation of the effects, on canopy arthropods, of two agricultural management systems to control pests in olive groves from north-east of Portugal. Chemosphere 67, 131–139 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Gkisakis, V., Volakakis, N., Kollaros, D., Bàrberi, P. & Kabourakis, E. M. Soil arthropod community in the olive agroecosystem: Determined by environment and farming practices in different management systems and agroecological zones. Agric. Ecosyst. Environ. 218, 178–189 (2016).
Google Scholar 
Beaufoy, G. EU Policies for Olive Farming. Unsustainable on all counts (WWF and Birdlife International, Brussels, 2001).
Google Scholar 
EFNCP. The environmental impact of olive oil production in the EU: Practical options for improving the environmental impact. European Forum on Nature Conservation and Pastoralism & Asociación para el Análisis y Reforma de la Política Agro-rural, Brussels. https://ec.europa.eu/environment/agriculture/pdf/oliveoil.pdf (2000).Vanwalleghem, T. Quantifying the effect of historical soil management on soil erosion rates in Mediterranean olive orchards. Agric. Ecosyst. Environ. 142, 341–351 (2011).
Google Scholar 
Simões, M. P., Belo, A. F., Pinto-Cruz, C. & Pinheiro, A. C. Natural vegetation management to conserve biodiversity and soil water in olive orchards. Span. J. Agric. Res. 12, 633–643 (2014).
Google Scholar 
Milgroom, J., Soriano, M. A., Garrido, J. M., Gómez, J. A. & Fereres, E. The influence of a shift from conventional to organic olive farming on soil management and erosion risk in southern Spain. Renew. Agric. Food Syst. 22, 1–10 (2007).
Google Scholar 
Lodolini, E. M. & Neri, D. Organic olive farming. African J. Agric. Res. 8, 6426–6434 (2013).
Google Scholar 
Rallo, L. Iberian olive growing in a time of change. Chron. Horticult. 49, 27–30 (2010).
Google Scholar 
Diez, C. M. et al. Cultivar and tree density as key factors in the long-term performance of super high-density olive orchards. Front. Plant Sci. 7, 1–13 (2016).
Google Scholar 
Allen, H. D., Randall, R. E., Amable, G. S. & Devereux, B. J. The impact of changing olive cultivation practices on the ground flora of olive groves in the Messara and Psiloritis regions, Crete, Greece. L. Degrad. Dev. 17, 249–327 (2006).
Google Scholar 
Herrera, J. M., Costa, P., Medinas, D., Marques, J. T. & Mira, A. Community composition and activity of insectivorous bats in Mediterranean olive farms. Anim. Conserv. 18, 557–566 (2015).
Google Scholar 
Costa, A. et al. Structural simplification compromises the potential of common insectivorous bats to provide biocontrol services against the major olive pest Prays oleae. Agric. Ecosyst. Environ. 287, 106708 (2020).
Google Scholar 
Morgado, R. et al. A Mediterranean silent spring? The effects of olive farming intensification on breeding bird communities. Agric. Ecosyst. Environ. 288, 106694 (2020).
Google Scholar 
Ruano, F. et al. Use of arthropods for the evaluation of the olive-orchard management regimes. Agric. For. Entomol. 6, 111–120 (2004).
Google Scholar 
Jerez-Valle, C., García, P. A., Campos, M. & Pascual, F. A simple bioindication method to discriminate olive orchard management types using the soil arthropod fauna. Appl. Soil Ecol. 76, 42–51 (2014).
Google Scholar 
Carpio, A. J., Castro, J. & Tortosa, F. S. Arthropod biodiversity in olive groves under two soil management systems: Presence versus absence of herbaceous cover crop. Agric. For. Entomol. 21, 58–68 (2018).
Google Scholar 
Rey, P. J. et al. Landscape-moderated biodiversity effects of ground herb cover in olive groves: Implications for regional biodiversity conservation. Agric. Ecosyst. Environ. 277, 61–73 (2019).
Google Scholar 
Mccomb, W. C. & Noble, R. E. Invertebrate use of natural tree cavities and vertebrate nest boxes. Am. Midl. Nat. 107, 163–172 (1982).
Google Scholar 
Bovyn, R. A., Lordon, M. C., Grecco, A. E., Leeper, A. C. & LaMontagne, J. M. Tree cavity availability in urban cemeteries and city parks. J. Urban Ecol. 5, 1–9 (2019).
Google Scholar 
Ribera, I., Dolédec, S., Downie, I. & Foster, G. Effect of land disturbance and stress on species traits of ground beetle assemblages. Ecology 82, 1112–1129 (2001).
Google Scholar 
Barbaro, L. & van Halder, I. Linking bird, carabid beetle and butterfly life-history traits to habitat fragmentation in mosaic landscapes. Ecography 32, 321–333 (2009).
Google Scholar 
Steffan-Dewenter, I. & Tscharntke, T. Butterfly community structure in fragmented habitats. Ecol. Lett. 3, 449–456 (2000).
Google Scholar 
Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).ADS 
PubMed 

Google Scholar 
Medinas, D. et al. Road effects on bat activity depend on surrounding habitat type. Sci. Total Environ. 660, 340–347 (2019).ADS 
CAS 
PubMed 

Google Scholar 
INE. Estatísticas Agrícolas – 2018. Lisboa. Instituto Nacional de Estatística. https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_publicacoes&PUBLICACOESpub_boui=358629204&PUBLICACOESmodo=2 (2019).Rodríguez-Cohard, J. C., Sánchez-Martínez, J. D. & Garrido-Almonacid, A. Strategic responses of the European olive-growing territories to the challenge of globalization. Eur. Plan. Stud. 28, 2261–2283 (2020).
Google Scholar 
Reis, P. O olival em Portugal. Dinâmicas, tecnologias e relação com o desenvolvimento rural. Instituto Nacional de Investigação Agrária e Veterinária. http://www.iniav.pt/fotos/editor2/caderno_olivalemportugal.pdf (2014).Yi, Z., Jinchao, F., Dayuan, X., Weiguo, S. & Axmacher, J. C. A comparison of terrestrial arthropod sampling methods. J. Resour. Ecol. 3, 174–182 (2012).
Google Scholar 
Leather, S. R. Insect Sampling in Forest Ecosystems (Wiley-Blackwell, New Jersey, 2008).
Google Scholar 
Paredes, D., Cayuela, L. & Campos, M. Synergistic effects of ground cover and adjacent vegetation on natural enemies of olive insect pests. Agric. Ecosyst. Environ. 173, 72–80 (2013).
Google Scholar 
Porcel, M., Cotes, B., Castro, J. & Campos, M. The effect of resident vegetation cover on abundance and diversity of green lacewings (Neuroptera: Chrysopidae) on olive trees. J. Pest Sci. 90, 195–206 (2017).
Google Scholar 

Álvarez, H. A. et al. Semi-natural habitat complexity affects abundance and movement of natural enemies in organic olive orchards. Agric. Ecosyst. Environ. 285, 106618 (2019).
Google Scholar 
Paredes, D., Cayuela, L., Gurr, G. M. & Campos, M. Is ground cover vegetation an effective biological control enhancement strategy against olive pests?. PLoS ONE 10, e0117265 (2015).PubMed 
PubMed Central 

Google Scholar 
Gkisakis, V. D. et al. Olive canopy arthropods under organic, integrated, and conventional management. The effect of farming practices, climate and landscape. Agroecol. Sustain. Food Syst. 42, 843–858 (2018).
Google Scholar 
Sanz-Cortés, F. et al. Phenological growth stages of olive trees (Olea europaea). Ann. Appl. Biol. 140, 151–157 (2002).
Google Scholar 
Rodríguez, E., González, B. & Campos, M. Natural enemies associated with cereal cover crops in olive groves. Bullet. Insectol. 65, 43–49 (2012).
Google Scholar 
Morente, M., Campos, M. & Ruano, F. Evaluation of two different methods to measure the effects of the management regime on the olive-canopy arthropod community. Agric. Ecosyst. Environ. 259, 111–118 (2018).
Google Scholar 
Cardenas, M., Pascual, F., Campos, M. & Pekar, S. The spider assemblage of olive groves under three management systems. Environ. Entomol. 44, 509–518 (2015).PubMed 

Google Scholar 
Hegazi, E. M. et al. Seasonality in the occurrence of two lepidopterous olive pests in Egypt. Insect Sci. 18, 565–574 (2011).
Google Scholar 
Markó, V., Keresztes, B., Fountain, M. T. & Cross, J. V. Prey availability, pesticides and the abundance of orchard spider communities. Biol. Control 48, 115–124 (2009).
Google Scholar 
Picchi, M. S., Marchi, S., Albertini, A. & Petacchi, R. Organic management of olive orchards increases the predation rate of overwintering pupae of Bactrocera oleae (Diptera: Tephritidae). Biol. Control 108, 9–15 (2017).
Google Scholar 
Caruso, T. & Migliorini, M. Micro-arthropod communities under human disturbance: Is taxonomic aggregation a valuable tool for detecting multivariate change? Evidence from Mediterranean soil oribatid coenoses. Acta Oecol. 30, 46–53 (2006).ADS 

Google Scholar 
Schipper, A. M., Lotterman, K., Geertsma, M., Leuven, R. S. E. W. & Hendriks, A. J. Using datasets of different taxonomic detail to assess the influence of floodplain characteristics on terrestrial arthropod assemblages. Biodivers. Conserv. 19, 2087–2110 (2010).
Google Scholar 
Timms, L. L., Bowden, J. J., Summerville, K. S. & Buddle, C. M. Does species-level resolution matter? Taxonomic sufficiency in terrestrial arthropod biodiversity studies. Insect Conserv. Divers. 6, 453–462 (2013).
Google Scholar 
Unwin, D. M. A Key to the Families of British Beetles (Field Studies Council, 1984).Goulet, H. & Huber, J. Hymenoptera of the World: An identification Guide to Families. (Agriculture Canada publication, 1993).Johnson, N. F. & Triplehorn, C. A. Borror and DeLong’s Introduction to the Study of Insects 7th edn. (Thomson Brooks/Cole, Belmont, 2005).
Google Scholar 
Fletcher, M. J., and updates. Identification keys and checklists for the leafhoppers, planthoppers and their relatives occurring in Australia and neighbouring areas (Hemiptera: Auchenorrhyncha). https://idtools.dpi.nsw.gov.au/keys/auch/index.html (2009).Mata, L. & Goula, M. Clave de familias de Heterópteros de la Península Ibérica (Insecta, Hemiptera, Heteroptera). Versión 1. Publicaciones del Centre de Recursos de Biodiversitat Animal, Universitat de Barcelona. http://www.ub.edu/crba/publicacions/Clau%20heteropters/Volum4_Clave_de_Familias_de_Heteropteros_de_la_P.Iberica.pdf (2011).Oosterbroek, P. The European families of the Diptera. Identification, diagnosis, biology. (Royal Dutch Society for Natural History (KNNV) Publishing, Utrecht, 2015).World Spider Catalog. Version 19. Natural History Museum Bern. http://wsc.nmbe.ch (2018).Campos, M. Lacewing in Andalusian olive orchards. In Lacewing in the Crop Environment (eds McEwen, P. et al.) 492–497 (Cambridge University Press, Cambridge, 2001).
Google Scholar 
Wilson, E. O. & Hölldobler, B. The rise of the ants: A phylogenetic and ecological explanation. Proc. Natl. Acad. Sci. U. S. A. 102, 7411–7414 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Martínez-Núñez, C. et al. Ant community potential for pest control in olive groves: Management and landscape effects. Agric. Ecosyst. Environ 305, 107185 (2021).
Google Scholar 
Bianchi, F. J. J. A., Booij, C. J. H. & Tscharntke, T. Sustainable pest regulation in agricultural landscapes: A review on landscape composition, biodiversity and natural pest control. Proc. R. Soc. B. 273, 1715–1727 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Holland, J. M. et al. Semi-natural habitats support biological control, pollination and soil conservation in Europe. A review. Agron. Sustain. Dev. 37, 31 (2017).
Google Scholar 

Paredes, D. et al. Landscape simplification increases Bactrocera oleae abundance in olive groves: Adult population dynamics in different land uses. J. Pest Sci. https://doi.org/10.1007/s10340-022-01489-1 (2022).Article 

Google Scholar 
Thies, C., Roschewitz, I. & Tscharntke, T. The landscape context of cereal aphid–parasitoid interactions. Proc. R. Soc. B. 285, 203–210 (2005).
Google Scholar 
Pinto-Correia, T., Ribeiro, N. & Sá-Sousa, P. Introducing the montado, the cork and holm oak agroforestry system of Southern Portugal. Agrofor. Syst. 82, 99–104 (2011).
Google Scholar 
Morgado, R. et al. Drivers of irrigated olive grove expansion in Mediterranean landscapes and associated biodiversity impacts. Landsc. Urban Plan. 225, 104429 (2022).
Google Scholar 
Direção-Geral do Território. Carta de Uso e Ocupação do Solo de Portugal Continental para 2015 (COS2015). http://www.dgterritorio.pt/dados_abertos/cos/ (2015).Roswell, M., Dushoff, J. & Winfree, R. A conceptual guide to measuring species diversity. Oikos 130, 321–338 (2021).
Google Scholar 
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar 
Penado, A. et al. From pastures to forests: Changes in Mediterranean wild bee communities after rural land abandonment. Insect Conserv. Divers. 15, 325–336 (2022).
Google Scholar 
Ovaskainen, O. & Abrego, N. Joint Species Distribution Modelling. With Applications in R. (Cambridge University Press, 2020).Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
Google Scholar 
Macgregor-Fors, I. & Payton, M. E. Contrasting diversity values: Statistical inferences based on overlapping confidence intervals. PLoS ONE 8, e56794 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tikhonov, G. et al. Joint species distribution modelling with the r-package Hmsc. Methods Ecol. Evol. 11, 442–447 (2019).
Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2021).
Google Scholar 
Wickramasinghe, L. P., Harris, S. H., Jones, G. & Jennings, N. V. Abundance and species richness of nocturnal insects on organic and conventional farms: Effects of agricultural intensification on bat foraging. Conserv. Biol. 8, 1283–1292 (2004).
Google Scholar 
Galloway, A. D., Seymour, C. L., Gaigher, R. & Pryke, J. S. Organic farming promotes arthropod predators, but this depends on neighbouring patches of natural vegetation. Agric. Ecosyst. Environ. 310, 107295 (2021).
Google Scholar 
Hevia, V., Ortega, J., Azcárate, F. M., López, C. A. & González, J. A. Exploring the effect of soil management intensity on taxonomic and functional diversity of ants in Mediterranean olive groves. Agric. For. Entomol. 21, 109–118 (2019).
Google Scholar 
Vitanović, E. et al. Arthropod communities within the olive canopy as bioindicators of different management systems. Span. J. Agric. Res. 16, e0301 (2018).
Google Scholar 
Vasconcelos, S. et al. Long-term consequences of agricultural policy decisions: How are forests planted under EEC regulation 2080/92 affecting biodiversity 20 years later?. Biol. Conserv. 236, 393–403 (2019).
Google Scholar 
Tscharntke, T. et al. When natural habitat fails to enhance biological pest control—five hypotheses. Biol. Conserv. 204, 449–458 (2016).
Google Scholar 
Ortega, M., Pascual, S. & Rescia, A. J. Spatial structure of olive groves and scrublands affects Bactrocera oleae abundance: A multi-scale analysis. Basic Appl. Ecol. 17, 696–705 (2016).
Google Scholar 
Martínez-Núñez, C. et al. Direct and indirect effects of agricultural practices, landscape complexity and climate on insectivorous birds, pest abundance and damage in olive groves. Agric. Ecosyst. Environ. 304, 107145 (2020).
Google Scholar 
Paredes, D., Karp, D. S., Chaplin-Kramer, R., Benítez, E. & Campos, M. Natural habitat increases natural pest control in olive groves: Economic implications. J. Pest Sci. 92, 1111–1121 (2019).
Google Scholar 
Attwood, S. J., Maron, M., House, P. N. & Zammit, C. Do arthropod assemblages display globally consistent responses to intensified agricultural land use and management?. Glob. Ecol. Biogeogr. 17, 585–599 (2008).
Google Scholar 
Miranda, M. A., Miquel, M., Terrassa, J., Melis, N. & Monerris, M. Parasitism of Bactrocera oleae (Diptera; Tephritidae) by Psyttalia concolor (Hymenoptera; Braconidae) in the Balearic Islands (Spain). J. Appl. Entomol. 132, 798–805 (2008).
Google Scholar 
Álvarez, H. A., Morente, M., Campos, M. & Ruano, F. L. madurez de las cubiertas vegetales aumenta la presencia de enemigos naturales y la resiliencia de la red trófica de la copa del olivo. Ecosistemas 28, 92–106 (2019).
Google Scholar 
Rusch, A., Valantin-Morison, M., Sarthou, J. P. & Roger-Estrade, J. Biological control of insect pests in agroecosystems. Effects of crop management, farming systems, and seminatural habitats at the landscape scale: A review. Adv. Agron. 109, 219–259 (2010).
Google Scholar 
Greenop, A., Cook, S. M., Wilby, A., Pywell, R. F. & Woodcock, B. A. Invertebrate community structure predicts natural pest control resilience to insecticide exposure. J. Appl. Ecol. 57, 2441–2453 (2020).CAS 

Google Scholar 
Porcel, M., Ruano, F., Cotes, B., Peña, A. & Campos, M. Agricultural management systems affect the green lacewing community (Neuroptera: Chrysopidae) in olive orchards in southern Spain. Environ. Entomol. 42, 97–106 (2013).CAS 
PubMed 

Google Scholar 
Stamou, G. P. Arthropods of Mediterranean-Type Ecosystems (Springer, 2012).Santos, J. L. et al. A farming systems approach to linking agricultural policies with biodiversity and ecosystem services. Front. Ecol. Environ. 19, 168–175 (2021).
Google Scholar 
Ribeiro, P. F. et al. An applied farming systems approach to infer conservation-relevant agricultural practices for agri-environment policy design. Land Use Policy 58, 165–172 (2016).
Google Scholar 
Herrera, J. M. et al. A food web approach reveals the vulnerability of biocontrol services by birds and bats to landscape modification at regional scale. Sci. Rep. 11, 1–10 (2021).
Google Scholar 
Solomou, A. D. & Sfougaris, A. I. Bird community characteristics as indicators of sustainable management in olive grove ecosystems of Central Greece. J. Nat. Hist. 49, 301–325 (2015).
Google Scholar 
Piñeiro, V. et al. A scoping review on incentives for adoption of sustainable agricultural practices and their outcomes. Nat Sustain. 3, 809–820 (2020).
Google Scholar  More

Sievers, M. et al. Trends Ecol. Evol. 34, 807–817 (2019).Article 

Google Scholar 
Barbier, E. B. et al. Ecol. Monogr. 81, 169–193 (2011).Article 

Google Scholar 
zu Ermgassen, P. S. E. et al. Estuar. Coast. Shelf Sci. 248, 107159 (2021).Article 

Google Scholar 
Spalding, M. & Parrett, C. L. Mar. Policy 110, 103540 (2019).Article 

Google Scholar 
Dahdouh-Guebas, F. et al. Estuar. Coast. Shelf Sci. 248, 106942 (2021).Article 

Google Scholar 
Dahdouh-Guebas, F. et al. Front. Mar. Sci. 7, 603651 (2020).Article 

Google Scholar 
Friess, D. A. & McKee, K. L. in Dynamic Sedimentary Environments of Mangrove Coasts (eds Sidik, F. & Friess, D.A.) Ch. 7 (Elsevier, 2021).Lee, S. Y. et al. Glob. Ecol. Biogeogr. 23, 726–743 (2014).Article 

Google Scholar 
Goldberg, L., Lagomasino, D., Thomas, N. & Fatoyinbo, T. Glob. Change Biol. 26, 5844–5855 (2020).Article 

Google Scholar 
Cannicci, S. et al. Proc. Natl Acad. Sci. USA 118, e2016913118 (2021).CAS 
Article 

Google Scholar 
Bouillon, S., Koedam, N., Raman, A. & Dehairs, F. Oecologia 130, 441–448 (2002).CAS 
Article 

Google Scholar 
Adame, M. F. et al. Glob. Chang. Biol. 27, 2856–2866 (2021).CAS 
Article 

Google Scholar 
Pittman, S. et al. Mar. Ecol. Prog. Ser. 663, 1–29 (2021).Article 

Google Scholar 
Nagelkerken, I., Sheaves, M. T., Baker, R. & Connolly, R. M. Fish Fish. 16, 362–371 (2015).Article 

Google Scholar 
Huxham, M., Whitlock, D., Githaiga, M. & Dencer-Brown, A. Curr. For. Rep. 4, 101–110 (2018).
Google Scholar 
Bryan-Brown, D. N. et al. Sci. Rep. 10, 7117 (2020).CAS 
Article 

Google Scholar 
Curnick, D. J. et al. Science 363, 239–239 (2019).Article 

Google Scholar 
Dahdouh-Guebas, F. & Cannicci, S. Front. Mar. Sci. 8, 799543 (2021).Article 

Google Scholar 
Bruelheide, H. et al. Nat. Ecol. Evol. 2, 1906–1917 (2018).Article 

Google Scholar 
Harvey, B. P., Marshall, K. E., Harley, C. D. G. & Russell, B. D. Trends Ecol. Evol. 37, 20–29 (2021).Article 

Google Scholar 
Rahman, M. M. et al. Nat. Commun. 12, 3875 (2021).CAS 
Article 

Google Scholar 
Yando, E. S. et al. Biol. Conserv. 263, 109355 (2021).Article 

Google Scholar 
Krauss, K. W. & Osland, M. J. Ann. Bot. 125, 213–234 (2020).PubMed 

Google Scholar 
Asbridge, E. F. et al. Estuar. Coast. Shelf Sci. 228, 106353 (2019).Article 

Google Scholar 
Sippo, J. Z., Lovelock, C. E., Santos, I. R., Sanders, C. J. & Maher, D. T. Estuar. Coast. Shelf Sci. 215, 241–249 (2018).Article 

Google Scholar 
Erftemeijer, P. L. A. & Hamerlynck, O. J. Coast. Res. 42, 228–235 (2005).
Google Scholar 
Abhik, S. et al. Sci. Rep. 11, 20411 (2021).CAS 
Article 

Google Scholar 
Osland, M. J., Day, R. H. & Michot, T. C. Divers. Distrib. 26, 1366–1382 (2020).Article 

Google Scholar 
Dahdouh-Guebas, F. et al. Curr. Biol. 15, 579–586 (2005).CAS 
Article 

Google Scholar 
Turschwell, M. P. et al. Biol. Conserv. 247, 108637 (2020).Article 

Google Scholar 
Saintilan, N. et al. Science 368, 1118–1121 (2020).CAS 
Article 

Google Scholar 
Xie, D. et al. Environ. Res. Lett. 15, 114033 (2020).Article 

Google Scholar 
Ewel, K. C., Twilley, R. R. & Ong, J. E. Glob. Ecol. Biogeogr. Lett. 7, 83–94 (1998).Article 

Google Scholar 
Dahdouh-Guebas, F. in Vers une Nouvelle Synthèse Ecologique: de L’écologie Scientifique au Développement Durable. (ed. Meerts, P.) 182–193 (Centre Paul Duvigneaud de Documentation Ecologique, 2013).Gallup, L., Sonnenfeld, D. A. & Dahdouh-Guebas, F. Ocean Coast. Manage. 185, 105001 (2020).Article 

Google Scholar 
Rist, S. & Dahdouh-Guebas, F. Environ. Dev. Sustain. 8, 467–493 (2006).Article 

Google Scholar 
Foell, J., Harrison, E. & Stirrat, R. L. Participatory Approaches to Natural Resource Management: The Case of Coastal Zone Management in the Puttalam District, Sri Lanka. Project R6977 (School of African and Asian Studies, University of Sussex, 2000).Beymer-Farris, B. A. & Bassett, T. J. Glob. Environ. Change 22, 332–341 (2012).Article 

Google Scholar 
Lovelock, C. E. & Brown, B. M. Nat. Ecol. Evol. 3, 1135 (2019).Article 

Google Scholar 
Dahdouh-Guebas, F. et al. J. Ethnobiol. Ethnomed. 2, 24 (2006).CAS 
Article 

Google Scholar  More