Philippa Kaur

Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).Article 

Google Scholar 
McDonald-Madden, E. et al. ‘True’ conservation progress. Science 323, 43–44 (2009).Article 
CAS 
PubMed 

Google Scholar 
Corson, C. et al. Everyone’s Solution? Defining and redefining protected areas at the Convention on Biological Diversity. Conservation and Society 190–202. https://www.jstor.org/stable/26393154?seq=1#metadata_info_tab_contents (Accessed 1st March 2022) (2014).Caro, T. & Berger, J. Can behavioural ecologists help establish protected areas?. Philos. Trans. R. Soc. B 374, 20180062 (2019).Article 

Google Scholar 
Barnes, M. D. et al. Wildlife population trends in protected areas predicted by national socio-economic metrics and body size. Nat. Commun. 7, 12747 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Watson, J. E. M. et al. Bolder science needed now for protected areas. Conserv. Biol. 30, 243–248 (2016).Article 
PubMed 

Google Scholar 
Joppa, L. N. & Pfaff, A. High and far: Biases in the location of protected areas. PLoS One 4, e8273 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Runge, C. A., Martin, T. G., Possingham, H. P., Willis, S. G. & Fuller, R. A. Conserving mobile species. Front. Ecol. Environ. 12, 395–402 (2014).Article 

Google Scholar 
Thirgood, S. et al. Can parks protect migratory ungulates? The case of the Serengeti wildebeest. Anim. Conserv. 7, 113–120 (2004).Article 

Google Scholar 
Craigie, I. D. et al. Large mammal population declines in Africa’s protected areas. Biol. Conserv. 143, 2221–2228 (2010).Article 

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

Google Scholar 
Hansen, A. J. & DeFries, R. Ecological mechanisms linking protected areas to surrounding lands. Ecol. Appl. 17, 974–988 (2007).Article 
PubMed 

Google Scholar 
Beresford, A. E. et al. Poor overlap between the distribution of Protected Areas and globally threatened birds in Africa. Anim. Conserv. 14, 99–107 (2011).Article 

Google Scholar 
Haynes, G. Elephants (and extinct relatives) as earth-movers and ecosystem engineers. Geomorphology 157–158, 99–107 (2012).Article 
ADS 

Google Scholar 
Terborgh, J., Davenport, L. C., Ong, L. & Campos-Arceiz, A. Foraging impacts of Asian megafauna on tropical rain forest structure and biodiversity. Biotropica 50, 84–89 (2018).Article 

Google Scholar 
Galanti, V., Preatoni, D., Martinoli, A., Wauters, L. A. & Tosi, G. Space and habitat use of the African elephant in the Tarangire-Manyara ecosystem, Tanzania: Implications for conservation. Mamm. Biol. 71, 99–114 (2006).Article 

Google Scholar 
Williams, C. et al. Elephas maximus. The IUCN Red List of Threatened Species. e.T7140A45818198. https://doi.org/10.2305/IUCN.UK.2020-3.RLTS.T7140A45818198.en. Accessed on 15 February 2022. (2020)Stokke, S. & Du Toit, J. T. Sexual segregation in habitat use by elephants in Chobe National Park, Botswana. Afr. J. Ecol. 40, 360–371 (2002).Article 

Google Scholar 
Chowdhury, S. et al. Protected areas in South Asia: Status and prospects. Sci. Total Environ. 811, 152316 (2022).Article 
ADS 
CAS 
PubMed 

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

Google Scholar 
Fernando, C., Weston, M. A., Corea, R., Pahirana, K. & Rendall, A. R. Asian elephant movements between natural and human-dominated landscapes mirror patterns of crop damage in Sri Lanka. Oryx https://doi.org/10.1017/S0030605321000971 (2022).Article 

Google Scholar 
Santini, L., Saura, S. & Rondinini, C. Connectivity of the global network of protected areas. Divers. Distrib. 22, 199–211 (2016).Article 

Google Scholar 
Brennan, A. et al. Functional connectivity of the world’s protected areas. Science 376, 1101–1104 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Kumar, M. A. & Singh, M. Behavior of Asian elephant (Elephas maximus) in a land-use mosaic: Implications for human-elephant coexistence in the Anamalai Hills, India. Wildl. Biol. Pract. 6, 69–80 (2010).
Google Scholar 
Rathnayake, C. W. M., Jones, S., Soto-Berelov, M. & Wallace, L. Human–elephant conflict and land cover change in Sri Lanka. Appl. Geogr. 143, 102685 (2022).Article 

Google Scholar 
Chan, A. N. et al. Landscape characteristics influence ranging behavior of Asian elephants at the human-wildlands interface in Myanmar. Mov. Ecol. 10, 1–15 (2022).Article 

Google Scholar 
Magioli, M. et al. Land-use changes lead to functional loss of terrestrial mammals in a Neotropical rainforest. Perspect. Ecol. 19, 161–170 (2021).
Google Scholar 
Fernando, P. et al. The future of Asian elephant conservation: Setting sights beyond protected area boundaries. in Conservation Biology in Asia 252–260 (2006).Kumar, M. A., Vijayakrishnan, S. & Singh, M. Whose habitat is it anyway? Role of natural and anthropogenic habitats in conservation of charismatic species. Trop. Conserv. Sci. 11, 194008291878845 (2018).Article 

Google Scholar 
Sirua, H. Nature above people: Rolston and “fortress” conservation in the South. Ethics Environ. 11, 71–96 (2006).Article 

Google Scholar 
Keerthipriya, P. et al. Musth and its effects on male–male and male–female associations in Asian elephants. J. Mammal. 101, 259–270 (2020).Article 

Google Scholar 
Eisenberg, J. F., Mckay, G. M. & Jainudeen, M. R. Reproductive behavior of the Asiatic elephant (Elephas maximus maximus). Behaviour 38, 193–225 (1971).Article 
CAS 
PubMed 

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

Google Scholar 
Hollister-Smith, J. A., Alberts, S. C. & Rasmussen, L. E. L. Do male African elephants, Loxodonta africana, signal musth via urine dribbling?. Anim. Behav. 76, 1829–1841 (2008).Article 

Google Scholar 
LaDue, C. A., Vandercone, R. P. G., Kiso, W. K. & Freeman, E. W. Behavioral characterization of musth in Asian elephants (Elephas maximus): Defining progressive stages of male sexual behavior in in-situ and ex-situ populations. Appl. Anim. Behav. Sci. 251, 105639 (2022).Article 

Google Scholar 
LaDue, C. A., Goodwin, T. E. & Schulte, B. A. Concentration-dependent chemosensory responses towards pheromones are influenced by receiver attributes in Asian elephants. Ethology 124, 387–399 (2018).Article 

Google Scholar 
Goldenberg, S. Z., de Silva, S., Rasmussen, H. B., Douglas-Hamilton, I. & Wittemyer, G. Controlling for behavioural state reveals social dynamics among male African elephants, Loxodonta africana. Anim. Behav. 95, 111e119 (2014).Article 

Google Scholar 
Chave, E. et al. Variation in metabolic factors and gonadal, pituitary, thyroid, and adrenal hormones in association with musth in African and Asian elephant bulls. Gen. Comp. Endocrinol. 276, 1–13 (2019).Article 
CAS 
PubMed 

Google Scholar 
Glaeser, S. S. et al. Characterization of longitudinal testosterone, ocrtisol, and musth in male Asian Elephants (Elephas maximus), effects of aging, and adrenal responses to social changes and health events. Animals 12, 1332 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
de Silva, S., Ranjeewa, A. D. G. & Kryazhimskiy, S. The dynamics of social networks among female Asian elephants. BMC Ecol. 11, 17 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Nandini, S., Keerthipriya, P. & Vidya, T. N. C. Group size differences may mask underlying similarities in social structure: A comparison of female elephant societies. Behav. Ecol. 29, 145–159 (2018).Article 

Google Scholar 
de Silva, S. & Wittemyer, G. A comparison of social organization in Asian elephants and African Savannah elephants. Int. J. Primatol. 33, 1125–1141 (2012).Article 

Google Scholar 
Nandini, S., Keerthipriya, P. & Vidya, T. N. C. Seasonal variation in female Asian elephant social structure in Nagarahole-Bandipur, southern India. Anim. Behav. 134, 135–145 (2017).Article 

Google Scholar 
de Silva, S., Schmid, V. & Wittemyer, G. Fission-fusion processes weaken dominance networks among female Asian elephants in a productive habitat. Behav. Ecol. 28, 243–252 (2017).Article 

Google Scholar 
de Silva, S., Ranjeewa, A. D. G. & Weerakoon, D. Demography of Asian elephants (Elephas maximus) at Uda Walawe National Park, Sri Lanka based on identified individuals. Biol. Conserv. 144, 1742–1752 (2011).Article 

Google Scholar 
Ginsberg, J. R. & Young, T. P. Measuring association between individuals or groups in behavioural studies. Anim. Behav. 44, 377–379 (1992).Article 

Google Scholar 
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal, Complex Syst. 5, 1–9 (2014).
Google Scholar 
Liechti, J. I. & Bonhoeffer, S. A time resolved clustering method revealing longterm structures and their short-term internal dynamics. arXiv:1912.04261 (2020).Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Wikramanayake, E. D. et al. An ecology-based method for defining priorities for large mammal conservation: The tiger as case study. Conserv. Biol. 12, 865–878 (2008).Article 

Google Scholar 
Chundawat, R. S., Sharma, K., Gogate, N., Malik, P. K. & Vanak, A. T. Size matters: Scale mismatch between space use patterns of tigers and protected area size in a tropical dry forest. Biol. Conserv. 197, 146–153 (2016).Article 

Google Scholar 
Tucker, M. A. et al. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Karanth, K. K. & DeFries, R. Nature-based tourism in Indian protected areas: New challenges for park management. Conserv. Lett. 4, 137–149 (2011).Article 

Google Scholar 
Brown, J. L. et al. Comparative endocrinology of testicular, adrenal and thyroid function in captive Asian and African elephant bulls. Gen. Comp. Endocrinol. 151, 153–162 (2007).Article 
CAS 
PubMed 

Google Scholar 
Slotow, R. et al. Older bull elephants control young males. Nature 408, 425–426 (2000).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Poole, J. H., Lee, P. C., Njiraini, N. & & Moss, C. J. Longevity, competition, and musth: A long-term perspective on male reproductive strategies. in The Amboseli Elephants: A Long‐Term Perspective on a Long‐Lived Mammal 272–286 (2011).Poole, J. H. Announcing intent: The aggressive state of musth in African elephants. Anim. Behav. 37, 153–155 (1989).Article 

Google Scholar 
Poole, J. H. Mate guarding, reproductive success and female choice in African elephants. Anim. Behav. 37, 842–849 (1989).Article 

Google Scholar 
Poole, J. H. Rutting behavior in elephants: The phenomenon of musth in African elephants. Anim. Behav. 102, 283–316 (1987).
Google Scholar 
Foley, A. M. et al. Reproductive effort and success of males in scramble-competition polygyny: Evidence for trade-offs between foraging and mate search. J. Anim. Ecol. 87, 1600–1614 (2018).Article 
PubMed 

Google Scholar 
Fernando, P., Leimgruber, P., Prasad, T. & Pastorini, J. Problem-elephant translocation: Translocating the problem and the elephant?. PLoS One 7, e50917 (2012).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Archie, E. A., Morrison, T. A., Foley, C. A. H., Moss, C. J. & Alberts, S. C. Dominance rank relationships among wild female African elephants, Loxodonta africana. Anim. Behav. 71, 117–127 (2006).Article 

Google Scholar 
Wittemyer, G. & Getz, W. M. Hierarchical dominance structure and social organization in African elephants, Loxodonta africana. Anim. Behav. 73, 671–681 (2007).Article 

Google Scholar 
Wittemyer, G., Getz, W. M., Vollrath, F. & Douglas-Hamilton, I. Social dominance, seasonal movements, and spatial segregation in African elephants: A contribution to conservation behavior. Behav. Ecol. Sociobiol. 61, 1919–1931 (2007).Article 

Google Scholar 
Gunaryadi, D., Sugiyo, & Hedges, S. Community-based human-elephant conflict mitigation: The value of an evidence-based approach in promoting the uptake of effective methods. PLoS One 12, e0173742 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Wilson, G. et al. Between a rock and a hard place: Rugged terrain features and human disturbance affect behaviour and habitat use of Sumatran elephants in Aceh, Sumatra, Indonesia. Biodivers. Conserv. 30, 597–618 (2021).Article 

Google Scholar 
de Silva, S. et al. Demographic variables for wild Asian elephants using longitudinal observations. PLoS One 8, e82788 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
LaDue, C. A., Eranda, I., Jayasinghe, C. & Vandercone, R. P. G. Mortality patterns of Asian elephants in a region of human–elephant conflict. J. Wildl. Manag. 85, 794–802 (2021).Article 

Google Scholar 
Ram, A. K. et al. Tracking forest loss and fragmentation between 1930 and 2020 in Asian elephant (Elephas maximus) range in Nepal. Sci. Rep. 11, 1–13 (2021).Article 
ADS 

Google Scholar 
Neupane, D., Kwon, Y., Risch, T. S. & Johnson, R. L. Changes in habitat suitability over a two decade period before and after Asian elephant recolonization. Glob. Ecol. Conserv. 22, e01023 (2020).Article 

Google Scholar 
de Silva, S. & Leimgruber, P. Demographic tipping points as early indicators of vulnerability for slow-breeding megafaunal populations. Front. Ecol. Evol. 7, 171 (2019).Article 

Google Scholar 
Rodrigo, M. Farmers move to occupy a critical elephant corridor in Sri Lanka. Mongabay (2021).de la Torre, J. A. et al. There will be conflict—Agricultural landscapes are prime, rather than marginal, habitats for Asian elephants. Anim. Conserv. 24, 720–732 (2021).Article 

Google Scholar 
Rood, E., Ganie, A. A. & Nijman, V. Using presence-only modelling to predict Asian elephant habitat use in a tropical forest landscape: Implications for conservation. Divers. Distrib. 16, 975–984 (2010).Article 

Google Scholar 
Evans, L. J., Goossens, B., Davies, A. B., Reynolds, G. & Asner, G. P. Natural and anthropogenic drivers of Bornean elephant movement strategies. Glob. Ecol. Conserv. 22, e00906 (2020).Article 

Google Scholar 
Morales-Hidalgo, D., Oswalt, S. N. & Somanathan, E. Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. For. Ecol. Manag. 352, 68–77 (2015).Article 

Google Scholar 
Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl. Acad. Sci. U.S.A. 118, 1–8 (2021).Article 

Google Scholar 
Goswami, V. R., Vasudev, D. & Oli, M. K. The importance of conflict-induced mortality for conservation planning in areas of human–elephant co-occurrence. Biol. Conserv. 176, 191–198 (2014).Article 

Google Scholar 
de Silva, S. & Leimgruber, P. Demographic tipping points as early indicators of vulnerability for slow-breeding megafaunal populations. Front. Ecol. Evol. 7, 1–13 (2019).Article 
CAS 

Google Scholar 
Hettiarachchi, K. ‘Gathering’ shuns ‘brimming’ Minneriya. The Sunday Times (2021).Srinivasaiah, N., Kumar, V., Vaidyanathan, S., Sukumar, R. & Sinha, A. All-male groups in Asian elephants: A novel, adaptive social strategy in increasingly anthropogenic landscapes of southern India. Sci. Rep. 9, 1–11 (2019).Article 
ADS 
CAS 

Google Scholar 
de Silva, E. M. K. et al. Feasibility of using convolutional neural networks for individual-identification of wild Asian elephants. Mamm. Biol. https://doi.org/10.1007/S42991-021-00206-2 (2022).Article 

Google Scholar 
de Silva, S. The Elephant Attribute Recording System (EARS): A tool for individual-based research on Asian elephants. Gajah 40, 46 (2014).
Google Scholar 
Jainudeen, M. R., Katongole, C. B. & Short, R. V. Plasma testosterone levels in relation to musth and sexual activity in the male Asiatic elephant, Elephas maximus. J. Reprod. Fertil. 29, 99–103 (1972).Article 
CAS 
PubMed 

Google Scholar 
Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).Article 

Google Scholar 
Whitehead, H. Analyzing Animal Societies (University of Chicago Press, 2008).Book 

Google Scholar 
Bates, D., Mächler, M., Bolker, B., & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Dekker, D., Krackhardt, D. & Snijders, T. A. B. Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika 72, 563–581 (2007).Article 
MathSciNet 
PubMed 
PubMed Central 
MATH 

Google Scholar  More

Last year, female researchers received Aus$95 million less than male researchers in investigator grants from the Australian National Health and Medical Research Council.Credit: Lisa Maree Williams/Getty

Australian research agency introduces ‘Game-changing’ gender quotasIn an attempt to achieve gender equity, Australia’s leading health and medical research funding organization plans to award half of its research grants for its largest funding programme to women and non-binary applicants, starting next year.The National Health and Medical Research Council (NHMRC) announced the move last month. It will apply to researchers at the mid-career and senior level applying for the agency’s investigator grants, which fund research and salaries. Grants will also be fixed at Aus$400,000 (US$252,000) per year for five years. Many countries struggle to achieve gender equity in research funding, and the NHMRC will be one of the first agencies to introduce gender quotas at this scale, say researchers.“It’s game-changing,” says Anna-Maria Arabia, chief executive of the Australian Academy of Science in Canberra. The plan “directly removes a barrier that’s historically led to attrition in the research workforce and has led to the significant under-representation of women at senior levels”, she says.In 2021, 254 investigator grants were awarded, worth Aus$400 million in total. But when two researchers in Melbourne reviewed the data, they found that men had received 23% more of the grants, worth an extra Aus$95 million, than had women. There was an outcry from researchers. This year, the agency conducted its own review of investigator-grant outcomes from the past three years and found that the biggest gap was among the most senior researchers. A subsequent discussion paper and consultations with researchers informed the latest decision.The NHMRC has been working for a decade to address gender inequity in its grant funding. For example, in 2017, it introduced ‘structural priority funding’, which reserves extra money — around 8% of the overall grant budget — for high-quality ‘near-miss’ research applications led by women.But this did not address the gender imbalance among the most established researchers. In 2021, only 20% of the applicants in this group were women.The council will be looking to see whether awarding equal numbers of grants by gender leads to an increase in the number of senior women applying for leadership grants.No-fishing zone boosts tuna catch ratesLarge no-fishing areas can drive the recovery of commercially valuable fish species, a study suggests. Researchers examined ten years’ worth of fisheries data from the vicinity of Papahānaumokuākea Marine National Monument, a 1.5-million-square-kilometre protected area off the northwestern Hawaiian islands.They found that after the area expanded in 2016, catch rates — the number of fish caught for every 1,000 hooks deployed — went up (S. Medoff et al. Science 378, 313–316; 2022). The increases were greater the closer the boats were to the no-fishing zone. At up to 100 nautical miles, the catch rate for yellowfin tuna (Thunnus albacares) increased by 54%, and that for bigeye tuna (Thunnus obesus) by 12%. The size of the protected area probably played a part in the positive effects, as did the fact that it runs from west to east, allowing tropical fish to move in their preferred temperature range without leaving the zone.

SOURCE: S. Medoff et al. More

RESEARCH BRIEFINGS
02 November 2022

The world’s largest tropical peatland complex is in the central Congo Basin. A drying of the climate between 5,000 and 2,000 years ago triggered decomposition of peat in the Congo Basin and emission of carbon into the atmosphere. The tipping point at which drought results in carbon release might accelerate future climate change if regional droughts become more common. More

Yang, X., Quam, M. B., Zhang, T. & Sang, S. Global burden for dengue and the evolving pattern in the past 30 years. J. Travel Med. 28, taab146 (2021).Simmons, C. P., Farrar, J. J., van Vinh Chau, N. & Wills, B. Dengue. N. Engl. J. Med. 366, 1423–1432 (2012).Article 
CAS 
PubMed 

Google Scholar 
Brady, O. J. et al. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl. Trop. Dis. 6, e1760 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Morin, C. W., Comrie, A. C. & Ernst, K. Climate and dengue transmission: Evidence and implications. Environ. Health Perspect. 121, 1264–1272 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Brady, O. J. et al. Global temperature constraints on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission. Parasites Vectors 7, 338 (2014).Betanzos-Reyes, Á. F. et al. Association of dengue fever with Aedes spp. abundance and climatological effects. Salud Pública de México 60, 12 (2017).WHO. Dengue and severe dengue. (2022).Gubler, D. J. Dengue and dengue hemorrhagic fever. Clin. Microbiol. Rev. 11, 480–496 (1998).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Delatte, H. et al. Aedes albopictus, vecteur des virus du chikungunya et de la dengue à La Réunion : biologie et contrôle. Parasite 15, 3–13 (2008).Article 
CAS 
PubMed 

Google Scholar 
Kles, V., Michault, A., Rodhain, F., Mevel, F. & Chastel, C. A serological survey regarding Flaviviridae infections on the island of Reunion (1971–1989). Bull. Soc. Pathol. Exot. 1990(87), 71–76 (1994).
Google Scholar 
Pierre, V. et al. Epidémie de dengue 1 à la Réunion en 2004. Journal de Veille Sanitaire (2005).Vincent, M. et al. From the threat to the large outbreak: dengue on Reunion Island, 2015 to 2018. Eurosurveillance 24, (2019).Cellule Santé Publique France en Région, ARS. Situation de la dengue à La Réunion au 15 décembre 2020. https://www.lareunion.ars.sante.fr/avec-le-retour-de-lete-agissons-des-maintenant-contre-la-dengue (2020).Agence Régionale de Santé. Communiqué de presse: dengue à La Réunion. Situation au 28 juillet 2021. https://www.lareunion.ars.sante.fr/system/files/2021-07/2021-07-28-Dengue-Situation à La Réunion_0.pdf (2021).Hafsia, S. et al. Overview of dengue outbreaks in the southwestern Indian Ocean and analysis of factors involved in the shift toward endemicity in Reunion Island: A systematic review. PLoS Negl. Trop. Dis. 16, e0010547 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Reiter, P., Fontenille, D. & Paupy, C. Aedes albopictus as an epidemic vector of chikungunya virus: Another emerging problem?. Lancet. Infect. Dis 6, 463–464 (2006).Article 
PubMed 

Google Scholar 
Njenga, M. K. et al. Tracking epidemic Chikungunya virus into the Indian Ocean from East Africa. J. Gen. Virol. 89, 2754–2760 (2008).Article 
CAS 

Google Scholar 
Soumahoro, M.-K. et al. The Chikungunya epidemic on La Réunion Island in 2005–2006: a cost-of-illness study. PLoS Negl. Trop. Dis. 5, e1197 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Larrieu, S., Balleydier, E., Renault, P., Baville, M. & Filleul, L. [Epidemiological surveillance du chikungunya on Reunion Island from 2005 to 2011]. Médecine tropicale : Revue du Corps de Santé colonial 72 Spec No, 38–42 (2012).Soghigian, J. et al. Genetic evidence for the origin of Aedes aegypti, the yellow fever mosquito, in the southwestern Indian Ocean. Mol. Ecol. 29, 3593–3606 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kotsakiozi, P. et al. Population structure of a vector of human diseases: Aedes aegypti in its ancestral range Africa. Ecol. Evol. 8, 7835–7848 (2018).Article 
PubMed 

Google Scholar 
MacGregor, M. E. Aedes (Stegomyia) mascarensis, MacGregor: A new Mosquito from Mauritius. Bull. Entomol. Res. 14, 409–412 (1924).Article 

Google Scholar 
Salvan, M. & Mouchet, J. Aedes albopictus et Aedes aegypti à l’Ile de La Réunion. Ann. Soc. Belg. Med. Trop. 74, 323–326 (1994).CAS 
PubMed 

Google Scholar 
Bagny, L., Delatte, H., Quilici, S. & Fontenille, D. Progressive Decrease in Aedes aegypti distribution in Reunion Island since the 1900s. J. Med. Entomol. 46, 1541–1545 (2009).Article 
PubMed 

Google Scholar 
Le Vassal, J. J. paludisme à l’Ile de La Réunion. Per Gli Stud Della Maria 8, 18–27 (1907).
Google Scholar 
Delatte, H. et al. Geographic distribution and developmental sites of Aedes albopictus (Diptera: Culicidae) during a chikungunya epidemic event. Vector-Borne Zoon. Dis. 8, 25–34 (2008).Article 
CAS 

Google Scholar 
Hamon, J. Etudes biologique et systématique des Culicinae de l’Ile de La Réunion. Mem. Inst. Scient. Madagascar 4, 521–541 (1953).
Google Scholar 
Bouyer, J. & Lefrançois, T. Boosting the sterile insect technique to control mosquitoes. Trends Parasitol. 30, 271–273 (2014).Article 
PubMed 

Google Scholar 
Haramboure, M. et al. Modelling the control of Aedes albopictus mosquitoes based on sterile males release techniques in a tropical environment. Ecol. Model. 424, 109002 (2020).Article 

Google Scholar 
Bouyer, J., Yamada, H., Pereira, R., Bourtzis, K. & Vreysen, M. J. B. Phased conditional approach for mosquito management using sterile insect technique. Trends Parasitol. 36, 325–336 (2020).Article 
PubMed 

Google Scholar 
Bouyer, J. & Vreysen, M. J. B. Yes, irradiated sterile male mosquitoes can be sexually competitive!. Trends Parasitol. 36, 877–880 (2020).Article 
CAS 
PubMed 

Google Scholar 
Organization, W. H. Guidelines for laboratory and field testing of mosquito larvicides. WHO/CDS/WHOPES/GCDPP/2005.13 (2005).World Health Organization and Special Programme for Research and Training in Tropical Diseases and World Health Organization. Department of Control of Neglected Tropical Diseases and World Health Organization. Epidemic and Pandemic Alert. Dengue: Guidelines for diagnosis, treatment, prevention and control. (World Health Organization, 2009).Yap, H. H. Preliminary report on the color preference for oviposition by Aedes albopictus (Skuse) in the field. Southeast Asian J. Trop. Med. Public Health 6, 1–2 (1975).
Google Scholar 
Yap, H. H., Lee, C. Y., Chong, N. L., Foo, A. E. S. & Lim, M. P. Oviposition site preference of Aedes albopictus in the laboratory. J. Am. Mosquito Control Assoc. Mosquito News 11, 128–132 (1995).CAS 
PubMed 

Google Scholar 
Marin, G., Mahiba, B., Arivoli, S. & Tennyson, S. Does colour of ovitrap influence the ovipositional preference of Aedes aegypti Linnaeus 1762 (Diptera: Culicidae). Int. J. Mosq. Res 7, 11–15 (2020).CAS 

Google Scholar 
Claudel, I. et al. To bait or not to bait? Optimizing the use of adult mosquito traps for monitoring arbovirus vector populations in La Réunion Island. (2022). https://doi.org/10.21203/rs.3.rs-1798972/v1.Cleveland, W. S. Visualizing data. (Hobart press, 1993).Lamigueiro, Ó. P. Displaying time series, spatial, and space-time data with R. (Chapman; Hall/CRC, 2018).Yoshioka, M. et al. Diet and density dependent competition affect larval performance and oviposition site selection in the mosquito species Aedes albopictus (Diptera: Culicidae). Parasites Vectors 5, (2012).Wong, J., Stoddard, S. T., Astete, H., Morrison, A. C. & Scott, T. W. Oviposition site selection by the dengue vector Aedes aegypti and its implications for dengue control. PLoS Negl. Trop. Dis. 5, e1015 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Hawley, W. A. The biology of aedes albopictus. J. Am. Mosquito Control Assoc. Suppl 1, 1–39 (1988).Delatte, H., Gimonneau, G., Triboire, A. & Fontenille, D. Influence of temperature on immature development, survival, longevity, fecundity, and gonotrophic cycles of Aedes albopictus, vector of Chikungunya and dengue in the Indian Ocean. J. Med. Entomol. 46, 33–41 (2009).Article 
CAS 
PubMed 

Google Scholar 
Daugherty, M. P., Alto, B. W. & Juliano, S. A. Invertebrate carcasses as a resource for competing Aedes albopictus and Aedes aegypti (Diptera: Culicidae). J. Med. Entomol. 37, 364–372 (2000).Article 
CAS 
PubMed 

Google Scholar 
Papaj, D. R. & Rausher, M. D. Individual variation in host location by phytophagous insects. Herbivorous Insects: Host seeking behavior and mechanisms 77–127 (1983).Valladares, G. & Lawton, J. H. Host-plant selection in the holly leaf-miner: Does mother know best?. J. Anim. Ecol. 60, 227 (1991).Article 

Google Scholar 
Ellis, A. M. Incorporating density dependence into the oviposition preference-offspring performance hypothesis. J. Anim. Ecol. 77, 247–256 (2008).Article 
PubMed 

Google Scholar 
Juliano, S. A., OMeara, G. F., Morrill, J. R. & Cutwa, M. M. Desiccation and thermal tolerance of eggs and the coexistence of competing mosquitoes. Oecologia 130, 458–469 (2002).Costanzo, K. S., Kesavaraju, B. & Juliano, S. A. Condition-specific competion in container mosquitoes: The role of non-competing life-history stages. Ecology 86, 3289–3295 (2005).Article 
PubMed 

Google Scholar 
Sanchez, M. & Probst, J.-M. Distribution and conservation status of the Manapany day gecko, Phelsuma inexpectata MERTENS, 1966, an endemic threatened reptile from Réunion Island (Squamata: Gekkonidae). Cahiers scientifiques de l’océan Indien occidental 2, (2011).Braks, M. A. H., Honório, N. A., Lounibos, L. P., De-Oliveira, R. L. & Juliano, S. A. Interspecific competition between two invasive species of container mosquitoes, Aedes aegypti and Aedes albopictus (Diptera: Culicidae), in Brazil. Ann. Entomol. Soc. Am. 97, 130–139 (2004).Article 

Google Scholar 
Moore, C. G. & Fisher, B. R. Competition in mosquitoes.1 Density and species ratio effects on growth, mortality, fecundity, and production of growth retardant2. Ann. Entomol. Soc. Am. 62, 1325–1331 (1969).Madeira, N. G., Macharelli, C. A. & Carvalho, L. R. Variation of the Oviposition Preferences of Aedes aegypti in Function of Substratum and Humidity. Mem. Inst. Oswaldo Cruz 97, 415–420 (2002).Article 
CAS 
PubMed 

Google Scholar 
Bellini, R. et al. Use of the sterile insect technique against Aedes albopictus in Italy: first results of a pilot trial. in Area-wide control of insect pests 505–515 (Springer, 2007).Boussès, P., Dehecq, J. S., Brengues, C. & Fontenille, D. Inventaire actualisé des moustiques (Diptera : Culicidae) de l’île de La Réunion, océan Indien. Bulletin de la Société de pathologie exotique 106, 113–125 (2013).Article 
PubMed 

Google Scholar 
Zeileis, A., Kleiber, C. & Jackman, S. Regression models for count data in R. J. Stat. Softw. 27, (2008).Sileshi, G. Selecting the right statistical model for analysis of insect count data by using information theoretic measures. Bull. Entomol. Res. 96, 479–488 (2006).CAS 
PubMed 

Google Scholar 
Guthery, F. S., Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: A practical information-theoretic approach. J. Wildl. Manag. 67, 655 (2003).Article 

Google Scholar 
Hurvich, C. M. & Tsai, C.-L. Model selection for extended quasi-likelihood models in small samples. Biometrics 51, 1077–1084 (1995).Article 
CAS 
PubMed 
MATH 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach. 496 (Springer-Verlag, 2002).Manly, B. F. J. Randomization, bootstrap and Monte Carlo methods in biology. 399 (CRC Press / Chapman & Hall, 2006). https://doi.org/10.1201/9781315273075.Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 65–70 (1979).R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2022).Lesnoff, M. & Lancelot, R. aods3: analysis of overdispersed data using S3 methods. (2018).Barton, K. MuMIn: Multi-Model Inference. (2022).Xie, Y., Dervieux, C. & Riederer, E. R Markdown Cookbook. (Chapman; Hall/CRC, 2020). https://doi.org/10.1201/9781003097471. More

Birds and housing62 males and 8 females of house sparrows were caught with mist nets in September and October 2019 in several sites in Kraków, Poland. Before releasing them to the outdoor aviary located on the campus of the Jagiellonian University, Kraków, Poland, each bird was weighed and banded with a metal band. The aviary measured 3.5 m in width, 10.0 m in length, 2.5 m in height, and was outfitted with trees, bushes, perches, wooden shelters, a water source, and food dishes. Initially, birds were maintained with water and a mixture of seeds: wheat, barley, millet, and sunflower seeds, provided ad libitum. Additionally, they had access to sand with shells and sepia.Experimental designAfter a few weeks of acclimation to captivity, the aviary was divided into two separate parts (3.5 × 5 m): aviary no. 1 (A1) and aviary no. 2 (A2). At the same time male individuals were assigned to two crossed experimental treatments, ensuring that in each aviary birds originated from all sampled populations. The experiment comprised of two different treatments conducted simultaneously—one designed to simulate a deficiency in an environmental factor influencing colouration (the quality of available food), the other—to introduce physiological stress and facilitate trade-offs in the allocation of resources limited by the first treatment (an immune response induced by a bacteria-derived compound, S1).The dietary manipulation was achieved by feeding one group of birds with a low-quality protein food (diet reduced in exogenous amino acids, namely phenylalanine and tyrosine content, which are precursors essential for melanin synthesis; PT-reduced diet), and the other one with a wholesome diet (control diet). At the same time, two levels of immune challenge were achieved within each dietary group, by injecting half of the birds with either lipopolysaccharide (LPS) from the cell wall of Escherichia coli, or a 0.9% saline vehicle (as a control). Four females were placed in each group of males to alleviate interspecific conflicts occurring in all-male sparrow flocks, but they did not take part in the experiments. After three weeks of experiment, birds housed in A1 were moved to A2, whereas birds from A2 were moved to A1.Immune challengeBefore receiving injections, birds were first weighed and then transferred from the outdoor aviary to the laboratory. 31 house sparrows (from both dietary groups) were injected intraperitoneally with 0.026 mg LPS (serotype O55:B5, Sigma-Aldrich) diluted in 0.1 mL of 0.9% saline vehicle, so that each bird received a dose of ca. 1 mg/kg body mass, which had previously been shown to induce sickness behaviour in another passerine, the white-crowned sparrow, Zonotrichia leucophrys55. 31 control males were injected with the same volume (0.1 mL) of 0.9% saline vehicle. All individuals were injected twice throughout the experiment with an interval of three weeks between the injections. Birds were always injected at the same time in the morning and early afternoon (between 9:00 am and 12:30 pm).Diet manipulationDuring the six weeks of the experiment (S1), birds received synthetic diet ad libitum, which constituted of a mixture of protein (WPC80, free amino acids and whey protein isolate BiPRO GMP 9000 (Agropur Inc., Appleton, USA)), fats, carbohydrates, and fiber30. The ingredients were thoroughly mixed to produce small pellets (6 mm in diameter) that the sparrows consumed readily. The experimental diet had phenylalanine and tyrosine at 42% (N = 32) of their level in the control diet (N = 30)30. The food pellets were prepared by ZooLab (zoolab.pl/en/home, Sędziszów, Poland). Each bird was weighed before and after the experiment to monitor potential effects of diet on body mass of each animal. Following the experiment, during the next three consecutive days, the amount of food consumed by passerines within every 24 h (starting from 10 am each day to 10 am next day) was noted for both compartments of the aviary. Because of different numbers of individuals per aviary, an overall weight of food consumed in A1 and A2 was calculated per individual, respectively.Feathers samplingMoult of the black bib feathers was stimulated at the end of the moulting period occurring in natural conditions in early November. At day 1 of the dietary/immunological experiment (S1) a small area of the bib (around 25 mm2) was plucked from each male sparrow held in A1. At day 2 the same procedure was performed on individuals from A2. The time difference is orders of magnitude smaller than the timescale of feather growth and hence it would not affect the results in any way.Because the feather growth rate may differ during melanogenesis, with consequences for final colouration (if feathers grow at a faster rate, pigments may be deposited over a larger surface and therefore result in less intense colouration56, we measured the rate of feather development during the course of the experiment. After three weeks of the experiment, three feathers from the upper, central, and lower region of the previously plucked bib were plucked once again. The mass of the collected feathers was determined to the nearest 0.01 mg (XP26 Micro Balance, Mettler-Toledo, Greinfensee, Switzerland). The experiment was completed after six weeks after fully regrown and developed feathers from the bib and PC2 were sampled the second time (S1). Three feathers from the central part of previously plucked bib region were collected to perform transmission electron microscopy (TEM) imaging, whereas the feathers obtained from the rest of the regrown bib area were subjected to electron paramagnetic resonance (EPR) spectroscopy and feather microstructure analyses (greater spatial density of melanized barbs or barbules may affect colouration17.Feathers measurementsReflectance measurementsAn USB4000 spectrophotometer (range 300–700 nm) with the PX-2 Pulsed Xenon Lamp (Ocean Optics, Dunedin, FL, USA) and a bifurcated probe with 7 × 400 μm optical fibres, equipped with a permanently attached 3 mm long black collar, was used to quantify the brightness of the bib feathers collected at the end of the experiment. The measurements were taken with 90 ms integration time and the probe held at 90° to a feather’s surface. Calibration measurements of a Spectralon white standard (Ocean Optics. Largo, FL, USA) were taken every 15 min during measurements. The order in which the samples were measured was randomized in terms of belonging to the experimental group. From each sample (N = 62), seven feathers were chosen and stacked in one pile on a piece of black paper. Ten reflectance measurements were taken on each pile, avoiding distal, brighter parts of the feathers. The obtained spectra were averaged and smoothed in the package ‘pavo’57. Brightness was calculated as a sum of the reflectance values over all wavelengths of a spectrum, and its lower values were interpreted as those indicative of a more melanin-rich feathers (i.e., absorbing more light).Feather developmentEach feather (3 per individual; N = 62 individuals) was laid on a white card and covered by a microscope slide to flatten the naturally curved feathers. Digital photographs were taken using camera (Canon EOS 7D) and imported to ImageJ v1.52a Software (National Institutes of Health, USA). The lengths of fully developed and undeveloped (still in sheath) parts of each feather were measured. To estimate the degree of a feather’s development, the length of the developed part of the vane was divided by its total length (quill with rachis plus the developed vane, Fig. 4A).Figure 4House sparrow feathers sampled from bib after three weeks of the experiment. Feathers during development (A), a TEM cross-sections of feather sampled from bib after the experiment (B).Full size imageFeather densityBarb density measurements were performed on the sampled regrown black bib feathers (N = 2–3 for each individual; N = 62 individuals), but because of their sparser structure we calculated the number of non-down (i.e., rigid) barbs on both sides of the vane, and divided this number by two (to obtain an average single-sided number of barbs) and then by the length of the rachis.Melanosome density (TEM)Feathers sampled from the bib of male sparrows (N = 62) were fixed for transmission electron microscopy (TEM) analysis in a mixture of 0.25 M sodium hydroxide and 0.1% Tween for 20 to 30 min on a bench-top shaker. Next, the feathers were treated with formic acid and ethanol in the ratio of 2:3 for 2.5 h and dehydrated twice for 20 min in 100% ethanol. Samples were embedded in a mixture of the PolyBed 812 resin (20 ml), DDSA (9 ml), NMA (12 ml) and DMP-30 (0.82 ml). Resin infiltration was gradual from 15% resin content in ethanol through 50%, 70% to 100% without alcohol. Each step lasted for 24 h. Then, the feathers were placed in silicone embedding moulds (Agar Scientific) and transferred to an oven. The polymerization proceeded at the temperature of 60 °C for 16 h. The epoxy resin blocks were then trimmed to get rid of excess resin. The surface of each block was prepared by its trimming, starting from the end of the feather, to approximately 5 mm using a glass knife. Next, ultrathin sections (70 nm) were cut with a diamond knife (DIATOME A. G., Berno, Switzerland) on a microtome (UC7, Leica, Wetzlar, Germany) and collected on single slot grids coated with a formvar film. The sections were then contrasted in uranyl acetate and lead citrate for 3 min. They were viewed and photographed with a transmission electron microscope (TEM) JEOL 2100HT (Jeol Ltd, Tokyo, Japan) for the purpose of investigating the number and density of the embedded pigment granules. For each individual three photographs of the cross-sections from a similar feather region were selected. Melanosome density was measured as the number of melanin granules observed in the barb cross-section divided by its area. Images were analysed using Adobe Photoshop (cross-sections area) and ImageJ (number of melanosomes, Fig. 4B).Melanin content: electron paramagnetic resonance (EPR) spectroscopyQuality and quantity of melanin pigments58 in individual feather samples obtained from the bib of house sparrows (N = 57) were characterized using a Varian E3 spectrometer (Varian, Sunnyvale, LA, USA) equipped with a rectangular resonance (TE 102) cavity. Five milligrams of feathers per individual were placed inside the Wilmad finger quartz dewar WG-816-Q (Rototec-Spintec GmbH, Griesheim, Germany). Prior to inserting the vessel into the resonance cavity of the EPR spectrometer, feathers were pressed down the quartz finger to a height of approximately 0.5 cm to ensure comparable volumes of each sample. Measurements were performed at room temperature, at X-band (9.26–9.27 GHz frequency), using the following parameters: magnetic field range 3240–3340 Gs, microwave power 1 mW, modulation frequency 100 kHz, modulation amplitude and time constant—5 Gs and 0.3 s for quantitative analysis, 1 Gs and 0.1 s for qualitative analysis. An EPR signal was recorded as its first derivative, averaged from three consecutive scans, lasting 160 s each (giving a total of 480 s of scan time per EPR spectrum). Then, the following parameters were measured: peak-to-peak amplitude, area under the microwave absorption curve (the integral intensity of the recorded signal) and linewidth of the EPR absorption curve (ΔH;59).Statistical analysesStatistical analysis was performed in R (version 4.0.2,60) using a two-way ANOVA test, with bird’s diet (control vs. PT-reduced) and applied immune challenges (LPS vs. saline-injections) as the independent variables. The following parameters were used as the dependent variables: feathers reflectance (brightness), feather growth rate, feather density (number of barbs per mm), and melanisation level (expressed as the EPR spectrum amplitude measured in arbitrary units [a.u.]). The density of melanosomes was analysed by fitting a linear mixed-effects model. In this model, melanosome density was used as the dependent variable, with diet, immunological challenge, and slice ID as independent variables, and individual ID as a random-effect term. Additionally, to assess the reliability of measurements, the intraclass correlation coefficient (i.e., technical repeatability) was calculated. The models’ residuals were checked for normality and homoscedasticity. Mean food consumption per individual was analysed by the Friedman test. Body mass before and after the experiment was analysed by fitting a linear mixed-effect model. Body mass was used as the dependent variable, whereas diet, immunological challenge, and time as the independent variables, and individual ID as a random-effect term. The model included the following interaction terms: time × diet, time × injection, and diet × injection, and was reduced by removing the non-significant interactions. Results are reported with appropriate statistical tests and estimates (accompanied by standard errors) signifying relevant factor contrasts (relative to the reference group, which in all analyses was diet: control; injection: LPS, body mass: before experiment).
Ethical noteAll applicable national and institutional guidelines for the care and use of animals were followed. The research was performed under permit no. 25/2019 (with a supplementary permit no. 78/2020) from the 2nd Local Institutional Animal Care and Use Committee in Kraków. More

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Predicting the risk of extinction from climate change requires an understanding of the interactions between species. An analysis of how changes in rainfall affect competition between plant species offers a way of tackling this challenge. More

All the results were reported relative to the control, unless specifically stated to the contrary or for clarity.Growth of cucumber plants in response to different biofertilizersSoilThere was no significant difference in cucumber growth before microbial fertilizer was applied. However, some microbial fertilizers significantly increased cucumber height and stem diameter when they were applied within 4 weeks from when the seedlings were planted (Fig. 1a,b,e,f). In the second week, SHZ and SMF increased plant height by 11.2 and 9.5%, respectively. In the third week, S267, SBS, SBH, SM and SHZ increased plant height by 12.0, 13.8, 15.0, 20.5 and 26.9%, respectively (Fig. 1a). In the fourth and fifth weeks, some treatments significantly increased cucumber height. In the second and third weeks, S267 significantly increased stem diameter by 21.2 and 16.8% (Fig. 1b).Figure 1Effect of different biofertilizer treatments on the growth of cucumber seedlings produced in soil or substrate in a greenhouse. S267 = Trichoderma Strain 267 added to soil; SBH = Bacillus subtilis and T. harzianum biofertilizers added to soil; SBS = B. subtilis biofertilizer added to the soil; SM = Compound biofertilizer added to soil; SHZ = T. harzianum biofertilizer added to soil; SCK = Untreated soil. US267 = T.267 biofertilizer added to substrate; USBH = B. subtilis and T. harzianum biofertilizers added to substrate; USBS = B. subtilis biofertilizer added to substrate; USM = Compound biofertilizer added to substrate; USHZ = T. harzianum biofertilizer added to substrate; USCK = Untreated substrate.Full size imageOver the subsequent 5 weeks, some microbial fertilizer treatments decreased cucumber height and stem diameter (Fig. 1g,h).SubstrateThere were no significant differences in cucumber growth before microbial fertilizer microbial fertilizer was applied (Fig. 1c,d,g,h). However, within 4 weeks of applying the microbial fertilizer, each biofertilizer treatment applied significantly increased cucumber height (Fig. 1c). US267 and USHZ significantly increased cucumber height by 39.8–75.4% and 56.1–86.1%, respectively. US267, USM and USHZ significantly increased the stem diameter by 76.8–108.9%, 71.1–97.6% and 80.4–122.4%, respectively (Fig. 1d).Over the subsequent 5 weeks, US267, USM and USHZ treatments continued to significantly increase cucumber height and stem diameter (Fig. 1g,h).Changes in the taxonomic composition of soil-borne fungal pathogensSoilBiofertilizers application significantly reduced the taxonomic composition of soil-borne fungal pathogens at different times during the cucumber growth period (Tables 1 and 2). Fusarium spp. were significantly reduced (T, 63.8% reduction, P  More

Bobbink, R. et al. Ecol. Appl. 20, 30–59 (2010).Article 
PubMed 

Google Scholar 
Olff, H. & Ritchie, M. E. Trends Ecol. Evol. 13, 261–265 (1998).Article 
PubMed 

Google Scholar 
DeMalach, N., Zaady, E. & Kadmon, R. Ecol. Lett. 20, 60–69 (2017).Article 
PubMed 

Google Scholar 
Borer, E. T. et al. Nature 508, 517–520 (2014).Article 
PubMed 

Google Scholar 
Harpole, W. S. et al. Nature 537, 93–96 (2016).Article 
PubMed 

Google Scholar 
Eskelinen, A., Harpole, W. S., Jessen, M.-T., Virtanen, R. & Hautier, Y. Nature https://doi.org/10.1038/s41586-022-05383-9 (2022).Article 

Google Scholar 
Koerner, S. E. et al. Nature Ecol. Evol. 2, 1925–1932 (2018).Article 
PubMed 

Google Scholar 
Chesson, P. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
Coley, P. D., Bryant, J. P. & Chapin, F. S. Science 230, 895–899 (1985).Article 
PubMed 

Google Scholar 
Hautier, Y., Niklaus, P. A. & Hector, A. Science 324, 636–638 (2009).Article 
PubMed 

Google Scholar 
Allan, E. & Crawley, M. J. Ecol. Lett. 14, 1246–1253 (2011).Article 
PubMed 

Google Scholar  More