Ecology

1.Global Assessment Report on Biodiversity and Ecosystem Services (IPBES, 2019).2.Bastin, J. F. et al. The global tree restoration potential. Science 366, 76–79 (2019).
Google Scholar 
3.Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Zhang, J., Fu, B., Stafford-smith, M., Wang, S. & Zhao, W. Improve forest restoration initiatives to meet Sustainable Development Goal 15. Nat. Ecol. Evol. 5, 10–13 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Holl, K. D. & Brancalion, P. H. S. Tree planting is not a simple solution. Science 368, 580–581 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
6.Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Messier, C. et al. For the sake of resilience and multifunctionality, let’s diversify planted forests! Conserv. Lett. https://doi.org/10.1111/conl.12829 (2021).8.Baeten, L. et al. Identifying the tree species compositions that maximize ecosystem functioning in European forests. J. Appl. Ecol. 56, 733–744 (2019).
Google Scholar 
9.Schuldt, A. et al. Biodiversity across trophic levels drives multifunctionality in highly diverse forests. Nat. Commun. 9, 2989 (2018).PubMed 
PubMed Central 

Google Scholar 
10.Eisenhauer, N. et al. A multitrophic perspective on biodiversity–ecosystem functioning research. Adv. Ecol. Res. 61, 1–54 (2019).PubMed 
PubMed Central 

Google Scholar 
11.Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Huang, Y. et al. Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science 362, 80–83 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Liu, X. et al. Tree species richness increases ecosystem carbon storage in subtropical forests. Proc. R. Soc. B 285, 20181240 (2018).PubMed 
PubMed Central 

Google Scholar 
14.Tobner, C. M. et al. Functional identity is the main driver of diversity effects in young tree communities. Ecol. Lett. 19, 638–647 (2016).PubMed 
PubMed Central 

Google Scholar 
15.Van de Peer, T., Verheyen, K., Ponette, Q., Setiawan, N. N. & Muys, B. Overyielding in young tree plantations is driven by local complementarity and selection effects related to shade tolerance. J. Ecol. 106, 1096–1105 (2018).
Google Scholar 
16.Staples, T. L., Dwyer, J. M., England, J. R. & Mayfield, M. M. Productivity does not correlate with species and functional diversity in Australian reforestation plantings across a wide climate gradient. Glob. Ecol. Biogeogr. 28, 1417–1429 (2019).
Google Scholar 
17.Cheesman, A. W., Preece, N. D., van Oosterzee, P., Erskine, P. D. & Cernusak, L. A. The role of topography and plant functional traits in determining tropical reforestation success. J. Appl. Ecol. 55, 1029–1039 (2018).CAS 

Google Scholar 
18.Ma, L. et al. Species identity and composition effects on community productivity in a subtropical forest. Basic Appl. Ecol. 55, 87–97 (2021).
Google Scholar 
19.Gross, N. et al. Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol. 1, 0132 (2017)..20.Violle, C. et al. The return of the variance: intraspecific variability in community ecology. Trends Ecol. Evol. 27, 244–252 (2012).PubMed 
PubMed Central 

Google Scholar 
21.Diaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Diaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl Acad. Sci. USA 104, 20684–20689 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Bruelheide, H. et al. Global trait— environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018).PubMed 
PubMed Central 

Google Scholar 
24.van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).PubMed 
PubMed Central 

Google Scholar 
25.Grime, J. P. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).
Google Scholar 
26.Chiang, J. M. et al. Functional composition drives ecosystem function through multiple mechanisms in a broadleaved subtropical forest. Oecologia 182, 829–840 (2016).PubMed 
PubMed Central 

Google Scholar 
27.Roscher, C. et al. Using plant functional traits to explain diversity–productivity relationships. PLoS ONE 7, e36760 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Barry, K. E. et al. The future of complementarity: disentangling causes from consequences. Trends Ecol. Evol. 34, 167–180 (2019).PubMed 
PubMed Central 

Google Scholar 
29.Tilman, D., Lehman, C. L. & Thomson, K. T. Plant diversity and ecosystem productivity: theoretical considerations. Proc. Natl Acad. Sci. USA 94, 1857–1861 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Turnbull, L., Isbell, F., Purves, D. W., Loreau, M. & Hector, A. Understanding the value of plant diversity for ecosystem functioning through niche theory. Proc. R. Soc. B 283, 20160536 (2016).PubMed 
PubMed Central 

Google Scholar 
31.Salisbury, C. L. & Potvin, C. Does tree species composition affect productivity in a tropical planted forest? Biotropica 47, 559–568 (2015).
Google Scholar 
32.Bruelheide, H. et al. Designing forest biodiversity experiments: general considerations illustrated by a new large experiment in subtropical China. Methods Ecol. Evol. 5, 74–89 (2014).
Google Scholar 
33.Chen, Y. et al. Directed species loss reduces community productivity in a subtropical forest biodiversity experiment. Nat. Ecol. Evol. 4, 550–559 (2020).PubMed 
PubMed Central 

Google Scholar 
34.Laliberte, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 
PubMed Central 

Google Scholar 
35.Allan, E. et al. A comparison of the strength of biodiversity effects across multiple functions. Oecologia 173, 223–237 (2013).PubMed 
PubMed Central 

Google Scholar 
36.Luo, S. et al. Community-wide trait means and variations affect biomass in a biodiversity experiment with tree seedlings. Oikos 129, 799–810 (2020).
Google Scholar 
37.Lu, H., Mohren, G. M. J., den Ouden, J., Goudiaby, V. & Sterck, F. J. Overyielding of temperate mixed forests occurs in evergreen–deciduous but not in deciduous–deciduous species mixtures over time in the Netherlands. For. Ecol. Manag. 376, 321–332 (2016).
Google Scholar 
38.Toïgo, M. et al. Difference in shade tolerance drives the mixture effect on oak productivity. J. Ecol. 106, 1073–1082 (2018).
Google Scholar 
39.Forrester, D. I., Bauhus, J., Cowie, A. L. & Vanclay, J. K. Mixed-species plantations of Eucalyptus with nitrogen-fixing trees: a review. For. Ecol. Manag. 233, 211–230 (2006).
Google Scholar 
40.Montagnini, F. & Piotto, D. in Silviculture in the Tropics (eds Günter. S. et al.) 501–511 (Springer, 2011).41.Trogisch, S. et al. The significance of tree–tree interactions for forest ecosystem functioning. Basic Appl. Ecol. 55, 33–52 (2021).
Google Scholar 
42.Cardinale, B. J. et al. Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc. Natl Acad. Sci. USA 104, 18123–18128 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Guerrero-Ramírez, N. R. et al. Diversity-dependent temporal divergence of ecosystem functioning in experimental ecosystems. Nat. Ecol. Evol. 1, 1639–1642 (2017).PubMed 
PubMed Central 

Google Scholar 
44.Kunz, M. et al. Neighbour species richness and local structural variability modulate aboveground allocation patterns and crown morphology of individual trees. Ecol. Lett. 22, 2130–2140 (2019).PubMed 
PubMed Central 

Google Scholar 
45.Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Martínez-Garza, C., Bongers, F. & Poorter, L. Are functional traits good predictors of species performance in restoration plantings in tropical abandoned pastures? For. Ecol. Manag. 303, 35–45 (2013).
Google Scholar 
47.Mayoral, C., van Breugel, M., Cerezo, A. & Hall, J. S. Survival and growth of five Neotropical timber species in monocultures and mixtures. For. Ecol. Manag. 403, 1–11 (2017).
Google Scholar 
48.Poorter, L. & Bongers, F. Leaf traits are good predictors of plant performance across 53 rain forest species. Ecology 87, 1733–1743 (2006).PubMed 
PubMed Central 

Google Scholar 
49.Ammer, C. Diversity and forest productivity in a changing climate. New Phytol. 221, 50–66 (2019).PubMed 
PubMed Central 

Google Scholar 
50.Brancalion, P. H. S. & Holl, K. D. Guidance for successful tree planting initiatives. J. Appl. Ecol. 57, 2349–2361 (2020).
Google Scholar 
51.Ruiz-Jaen, M. & Potvin, C. Can we predict carbon stocks in tropical ecosystems from tree diversity? Comparing species and functional diversity in a plantation and a natural forest. New Phytol. 189, 978–987 (2011).PubMed 
PubMed Central 

Google Scholar 
52.Grossman, J. J., Cavender-Bares, J., Hobbie, S. E., Reich, P. B. & Montgomery, R. A. Species richness and traits predict overyielding in stem growth in an early-successional tree diversity experiment. Ecology 98, 2601–2614 (2017).PubMed 
PubMed Central 

Google Scholar 
53.Kambach, S. et al. How do trees respond to species mixing in experimental compared to observational studies? Ecol. Evol. 9, 11254–11265 (2019).PubMed 
PubMed Central 

Google Scholar 
54.Finegan, B. et al. Does functional trait diversity predict above-ground biomass and productivity of tropical forests? Testing three alternative hypotheses. J. Ecol. 103, 191–201 (2015).
Google Scholar 
55.Piston, N. et al. Multidimensional ecological analyses demonstrate how interactions between functional traits shape fitness and life history strategies. J. Ecol. 107, 2317–2328 (2019).
Google Scholar 
56.McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol. 178, 719–739 (2008).PubMed 
PubMed Central 

Google Scholar 
57.O’Brien, M. J. et al. A synthesis of tree functional traits related to drought-induced mortality in forests across climatic zones. J. Appl. Ecol. 54, 1669–1686 (2017).
Google Scholar 
58.Freschet, G. T. et al. Root traits as drivers of plant and ecosystem functioning: current understanding, pitfalls and future research needs. New Phytol. https://doi.org/10.1111/nph.17072 (2020).59.Jucker, T. et al. Good things take time—diversity effects on tree growth shift from negative to positive during stand development in boreal forests. J. Ecol. 108, 2198–2211 (2020).
Google Scholar 
60.McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).PubMed 
PubMed Central 

Google Scholar 
61.Laughlin, D. C. The intrinsic dimensionality of plant traits and its relevance to community assembly. J. Ecol. 102, 186–193 (2014).
Google Scholar 
62.Fiedler, S., Perring, M. P. & Tietjen, B. Integrating trait-based empirical and modeling research to improve ecological restoration. Ecol. Evol. 8, 6369–6380 (2018).PubMed 
PubMed Central 

Google Scholar 
63.Zhang, Y., Chen, H. Y. H. & Reich, P. B. Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis. J. Ecol. 100, 742–749 (2012).
Google Scholar 
64.Schnabel, F. et al. Drivers of productivity and its temporal stability in a tropical tree diversity experiment. Glob. Change Biol. 25, 4257–4272 (2019).
Google Scholar 
65.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).66.Krober, W., Zhang, S., Ehmig, M. & Bruelheide, H. Linking xylem hydraulic conductivity and vulnerability to the leaf economics spectrum—a cross-species study of 39 evergreen and deciduous broadleaved subtropical tree species. PLoS ONE 9, e109211 (2014).67.Eichenberg, D., Purschke, O., Ristok, C., Wessjohann, L. & Bruelheide, H. Trade-offs between physical and chemical carbon-based leaf defence: of intraspecific variation and trait evolution. J. Ecol. 103, 1667–1679 (2015).CAS 

Google Scholar 
68.Krober, W., Heklau, H. & Bruelheide, H. Leaf morphology of 40 evergreen and deciduous broadleaved subtropical tree species and relationships to functional ecophysiological traits. Plant Biol. 17, 373–383 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Sokal, R. R. & Rohlf, F. J. Biometry (W.H. Freeman and Company, 1995).70.Schmid, B., Baruffol, M., Wang, Z. & Niklaus, P. A. A guide to analyzing biodiversity experiments. J. Plant Ecol. 10, 91–110 (2017).
Google Scholar  More

One of the striking features of the Anthropocene is a rapid degradation of natural ecosystems1,2, and an alarming decline of many species, which ultimately may lead to extinctions3,4,5. Whereas conserving ecosystem functions is increasingly recognised as a vital need for humans6,7,8, the interspecific interactions underpinning these functions are poorly understood9,10. However, conserving such interactions can be particularly important when taxa providing high-value ecosystem services are involved10,11.Ground beetles (Coleoptera: Carabidae) have been long known for their benefits in agroecosystems12,13. They play an important role in suppressing pests14, but several carabid species also consume seeds of herbaceous plants, making them a valuable asset for weed control as well15.Fungi are also of vital significance in most of the world’s terrestrial ecosystems16. Mycorrhizal fungi improve nutrient uptake by a large range of plant species through intimate and specialised associations17, other fungi play a crucial role in decomposition18, and yet others are pathogens of both crops and pests in agroecosystems19. Fungal parasitism is one of the crucial agents of evolution20.Fungi and carabids often co-occur, and they can potentially interact in many ways. The soil environment carabids often inhabit is a reservoir of fungal propagules where the beetles can feed on spores, hyphae or fruiting bodies21. They may also be responsible for dispersal of spores of certain fungi22. Several parasitic or entomopathogenic fungi are in an obligatory relationship with their beetle hosts23, therefore, the population decline of a ground beetle species could potentially lead to overlooked extinction cascades24. However, our knowledge of the fungal-carabid interactions is still limited concerning the frequency of these interactions and on how their exact nature affect the parties involved. Indeed, we do not even have a catalogue of the carabid-fungi interactions, and they have not yet been organized into a comprehensive database. Such a database would be of particular importance from an integrated pest management point of view because both fungi and carabids can deliver ecosystem services, but how their interactions, and potential synergies or antagonisms, influence the delivery of these services is poorly understood.In order to have a detailed overview of the interactions between Carabidae and the fungal kingdom, we collated a database containing previously reported associations between these taxa. Carabid and fungal species involved in the interaction, the type of the interaction (e. g. parasitic, pathogenic, mutualistic, or trophic interactions), the location (country) the interaction was reported from, and the publication source combined with detailed notes to each questionable entry comprised one record. Publications available in printed formats only were either digitized and data were extracted using semi-automatic text-mining processes, or they were manually screened. We aimed at possible completeness, using a wide range of databases and search engines and several languages to cover most of the published literature.Both ground beetle and fungal names were validated and their higher taxonomical classifications were also extracted. When it was possible, historical localities were converted to their current country names. The full bibliographical details were also stored in the database.The database covers a time-period from 1793 to 2020, spans over all geographic sub-regions defined by the United Nations (“UNSD — Methodology”, unstats.un.org. Retrieved 2020–10–11) with recorded associations from 129 countries. Our effort yielded 3,378 unique associations in 5,564 records between 1,776 carabid and 676 fungal species. Although rapidly developing molecular methods have largely facilitated the mapping of complex interaction networks in ecological studies25,26,27, due to the historic nature of our dataset, most of the records rely on traditional taxonomical identification. Yet, 16 records were based purely on metabarcoding studies; comments linked to these associations clearly identify them.Whilst we found relatively few pathogenic interactions, a great diversity between ectoparasitic Laboulbeniales fungi and carabids was revealed (Fig. 1). Soft bodied, cave-dwelling members of the Trechinae subfamily were particularly prone to these parasitic infections. Little information was available on mutualistic relationships but the presence of Yarrowia yeast reported from the gut of several carabid species28 is probably beneficial for both parties. The data show two distinct peaks in publications registering new associations, in the early 19th century and in the late 20th century (Fig. 2a) but the steady increase in the cumulative number of associations (Fig. 2b) suggests that further research is required to fully resolve this association network. Although we believe that most of the data published so far were collected, data submission will remain open to researchers wishing to contribute.Fig. 1The number of unique associations between Carabidae subfamilies and fungal classes. Side bar plots show the number of species in each subfamily/class recorded in our dataset.Full size imageFig. 2The number of recorded unique associations over time. Changes in the number of new records (a) and in the cumulative number (b) per year. Dark green lines indicate smoothed trends.Full size image More

1.Christie, K. S., Gilbert, S. L., Brown, C. L., Hatfield, M. & Hanson, L. Unmanned aircraft systems in wildlife research: Current and future applications of a transformative technology. Front. Ecol. Environ. 14, 241–251 (2016).Article 

Google Scholar 
2.Anderson, K. & Gaston, K. J. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ. 11, 138–146 (2013).Article 

Google Scholar 
3.Chabot, D. & Bird, D. M. Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?. J. Unmanned Veh. Syst. 3, 137–155 (2015).Article 

Google Scholar 
4.Sasse, D. B. Job-related mortality of wildlife workers in the United States, 1937–2000. Wildl. Soc. Bull. 4, 1015–1020 (2003).
Google Scholar 
5.Wiegmann, D. A. & Taneja, N. Analysis of injuries among pilots involved in fatal general aviation airplane accidents. Accid. Anal. Prev. 35, 571–577 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Vas, E., Lescroël, A., Duriez, O., Boguszewski, G. & Grémillet, D. Approaching birds with drones: first experiments and ethical guidelines. Biol. Lett. 11, 20140754 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Hodgson, J. C., Baylis, S. M., Mott, R., Herrod, A. & Clarke, R. H. Precision wildlife monitoring using unmanned aerial vehicles. Sci. Rep. 6, 22574. https://doi.org/10.1038/srep22574 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Egan, C. C., Blackwell, B. F., Fernández-Juricic, E. & Klug, P. E. Testing a key assumption of using drones as frightening devices: Do birds perceive drones as risky?. The Condor 122, 1–15. https://doi.org/10.1093/condor/duaa014 (2020).Article 

Google Scholar 
9.Hahn, N. et al. Unmanned aerial vehicles mitigate human–elephant conflict on the borders of Tanzanian Parks: A case study. Oryx 51, 513–516 (2017).Article 

Google Scholar 
10.FAA. Protocol for the Conduct and Review of Wildlife Hazard Site Visits, Wildlife Hazard Assessments, and Wildlife Hazard Management Plan. (2018).11.Dolbeer, R. A., Begier, M. J., Miller, P. R., Weller, J. R. & Anderson, A. L. Wildlife strikes to civil aircraft in the United States 1990–2019. 124 (Federal Aviation Administration, Washington, D.C., USA, 2021).12.Bivings, A. in Bird Strike Committee Europe. 481–487.13.Wandrie, L. J., Klug, P. E. & Clark, M. E. Evaluation of two unmanned aircraft systems as tools for protecting crops from blackbird damage. Crop Prot. 117, 15–19 (2019).Article 

Google Scholar 
14.Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).Article 

Google Scholar 
15.Cooper, W. E., Samia, D. S. & Blumstein, D. T. Chapter five-FEAR, spontaneity, and artifact in economic escape theory: A review and prospectus. Adv. Study Behav. 47, 147–179 (2015).Article 

Google Scholar 
16.Lima, S. L., Blackwell, B. F., DeVault, T. L. & Fernandez-Juricic, E. Animal reactions to oncoming vehicles: A conceptual review. Biol. Rev. Camb. Philos. Soc. 90, 60–76. https://doi.org/10.1111/brv.12093 (2015).Article 
PubMed 

Google Scholar 
17.Bernhardt, G. E., Blackwell, B. F., DeVault, T. L. & Kutschbach-Brohl, L. Fatal injuries to birds from collisions with aircraft reveal anti-predator behaviours. Ibis https://doi.org/10.1111/j.1474-919X.2010.01043.x (2010).Article 

Google Scholar 
18.McEvoy, J. F., Hall, G. P. & McDonald, P. G. Evaluation of unmanned aerial vehicle shape, flight path and camera type for waterfowl surveys: Disturbance effects and species recognition. PeerJ 4, e1831 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Mulero-Pázmány, M. et al. Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review. PLoS ONE 12, e0178448 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Tinbergen, N. Social releasers and the experimental method required for their study. Wilson Bull., 6–51 (1948).21.Kirk, D. A. & Mossman, M. J. in Bird of the World (ed Cornell Lab of Ornithology) (Poole, A.F.,Gill, F.B., Ithaca, NY, USA, 2020).22.FAA. Wildlife Strike Database, wildlife.faa.gov (2020).23.DeVault, T. L. et al. Estimating interspecific economic risk of bird strikes with aircraft. Wildl. Soc. Bull. 42, 94–101 (2018).Article 

Google Scholar 
24.DeVault, T. L., Blackwell, B. F., Seamans, T. W. & Belant, J. L. Identification of off airport interspecific avian hazards to aircraft. J. Wildl. Manag. 80, 746–752 (2016).Article 

Google Scholar 
25.Kluever, B. M., Pfeiffer, M. B., Barras, S. C., Dunlap, B. G. & Humberg, L. A. Black vulture conflict and management in the United States: Damage trends, management overview, and research needs. Hum. Wildl. Interact. 14, 8 (2020).
Google Scholar 
26.Walters, J. R. Anti-predatory behavior of lapwings: field evidence of discriminative abilities. Wilson Bull., 49–70 (1990).27.Septon, G. Peregrine falcon strikes turkey vulture. Passenger Pigeon 53, 192 (1991).
Google Scholar 
28.Coleman, J. S. & Fraser, J. D. Predation on black and Turkey vultures. Wilson Bull. 98, 600–601 (1986).
Google Scholar 
29.Rush, G. P., Clarke, L. E., Stone, M. & Wood, M. J. Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony-nesting seabirds. Ecol. Evol. 8, 12322–12334 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Bennitt, E., Bartlam-Brooks, H. L. A., Hubel, T. Y. & Wilson, A. M. Terrestrial mammalian wildlife responses to unmanned aerial systems approaches. Sci. Rep. 9, 2142. https://doi.org/10.1038/s41598-019-38610-x (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Weston, M. A., O’Brien, C., Kostoglou, K. N. & Symonds, M. R. Escape responses of terrestrial and aquatic birds to drones: Towards a code of practice to minimize disturbance. J. Appl. Ecol. 57, 777–785 (2020).Article 

Google Scholar 
32.Belant, J. L., Seamans, T. W., Gabrey, S. W. & Dolbeer, R. A. Abundance of gulls and other birds at landfills in northern Ohio. Am. Midl. Nat. 134, 30–40 (1995).Article 

Google Scholar 
33.Barnas, A. F. et al. A standardized protocol for reporting methods when using drones for wildlife research. J. Unmanned Veh. Syst. 8, 89–98 (2020).Article 

Google Scholar 
34.DeVault, T. L., Blackwell, B. F., Seamans, T. W., Lima, S. L. & Fernández-Juricic, E. Effects of vehicle speed on flight initiation by turkey vultures: implications for bird-vehicle collisions. PLoS ONE 9, e87944 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
35.Doppler, M. S., Blackwell, B. F., DeVault, T. L. & Fernández-Juricic, E. Cowbird responses to aircraft with lights tuned to their eyes: Implications for bird–aircraft collisions. The Condor 117, 165–177 (2015).Article 

Google Scholar 
36.Blackwell, B. F., Fernandez-Juricic, E., Seamans, T. W. & Dolan, T. Avian visual system configuration and behavioural response to object approach. Anim. Behav. 77, 673–684 (2009).Article 

Google Scholar 
37.DeVault, T. L., Reinhart, B. D., Brisbin, I. L., Rhodes, O. E. & Bechard. Flight Behavior of Black and Turkey Vultures: Implications for reducing bird–aircraft collisions. J. Wildl. Manag. 69, 601–608. https://doi.org/10.2193/0022-541X(2005)069[0601:FBOBAT]2.0.CO;2 (2005).38.Runyan, A. M. & Blumstein, D. T. Do individual differences influence flight initiation distance?. J. Wildl. Manag. 68, 1124–1129 (2004).Article 

Google Scholar 
39.Rebolo-Ifrán, N., Grilli, M. G. & Lambertucci, S. A. Drones as a threat to wildlife: YouTube complements science in providing evidence about their effect. Environ. Conserv. 46, 205–210 (2019).Article 

Google Scholar 
40.Fernández-Juricic, E., Deisher, M., Stark, A. C. & Randolet, J. Predator detection is limited in microhabitats with high light intensity: An experiment with Brown-headed Cowbirds. Ethology 118, 341–350 (2012).Article 

Google Scholar 
41.Koch, D. D. Glare and contrast sensitivity testing in cataract patients. J. Cataract Refract. Surg. 15, 158–164 (1989).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Vorobyev, M. & Osorio, D. Receptor noise as a determinant of colour thresholds. Proc. Royal Soc. B. 265, 351–358 (1998).CAS 
Article 

Google Scholar 
43.Ödeen, A. & Håstad, O. The phylogenetic distribution of ultraviolet sensitivity in birds. BMC Evol. Biol. 13, 36 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Hill, G. E., Hill, G. E., McGraw, K. J. & Kevin, J. Bird coloration: mechanisms and measurements. Vol. 1 (Harvard University Press, 2006).45.Maia, R., Eliason, C. M., Bitton, P. P., Doucet, S. M. & Shawkey, M. D. pavo: Asn R package for the analysis, visualization and organization of spectral data. Methods Ecol. Evol. 4, 906–913 (2013).
Google Scholar 
46.Lakens, D. Sample Size Justification. (2021).47.Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. 82, 591–605 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Hurlbert, S. H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211. https://doi.org/10.2307/1942661 (1984).Article 

Google Scholar 
49.Garamszegi, L. Z. A simple statistical guide for the analysis of behaviour when data are constrained due to practical or ethical reasons. Anim. Behav. 120, 223–234 (2016).Article 

Google Scholar 
50.Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach. (Springer Science & Business Media, 2002).51.Nauman, L. E. Spatial distribution in a turkey vulture roost, The Ohio State University, (1965).52.Bertram, B. C. Living in groups: predators and prey. Behavioural ecology: an evolutionary approach, 221–248 (1978).53.Blackwell, B. F. et al. Social information affects Canada goose alert and escape responses to vehicle approach: Implications for animal–vehicle collisions. PeerJ 7, e8164. https://doi.org/10.7717/peerj.8164 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Blackwell, B. F., Seamans, T. W., Fernández-Juricic, E., Devault, T. L. & Outward, R. J. Avian responses to aircraft in an airport environment. J. Wildl. Manag. 83, 893–901 (2019).Article 

Google Scholar 
55.Beauchamp, G. Social predation: how group living benefits predators and prey. (Elsevier, 2013).56.Fox, J., Friendly, M. & Weisberg, S. Hypothesis tests for multivariate linear models using the car package. The R Journal 5, 39–52 (2013).Article 

Google Scholar 
57.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:1406.5823(2014).58.DeVault, T. L., Blackwell, B. F., Seamans, T. W., Lima, S. L. & Fernandez-Juricic, E. Speed kills: Ineffective avian escape responses to oncoming vehicles. Proc. R. Soc. B. 282, 20142188. https://doi.org/10.1098/rspb.2014.2188 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
59.DeVault, T. L. et al. Can experience reduce collisions between birds and vehicles?. J. Zool. 301, 17–22. https://doi.org/10.1111/jzo.12385 (2016).Article 

Google Scholar 
60.Rhoades, E. & Blumstein, D. T. Predicted fitness consequences of threat-sensitive hiding behavior. Behav. Ecol. 18, 937–943 (2007).Article 

Google Scholar 
61.Cooper Jr, W. E. Factors affecting risk and cost of escape by the broad-headed skink (Eumeces laticeps): predator speed, directness of approach, and female presence. Herpetologica, 464–474 (1997).62.Cooper, W. E. Jr., Hawlena, D. & Pérez-Mellado, V. Interactive effect of starting distance and approach speed on escape behavior challenges theory. Behav. Ecol. 20, 542–546 (2009).Article 

Google Scholar 
63.Fernández-Juricic, E., Jimenez, M. D. & Lucas, E. Alert distance as an alternative measure of bird tolerance to human disturbance: Implications for park design. Environ. Conserv. 28, 263–269. https://doi.org/10.1017/S0376892901000273 (2001).Article 

Google Scholar 
64.Dill, L. M. The escape response of the zebra danio (Brachydanio rerio) I. The stimulus for escape. Anim. Behav. 22, 711–722 (1974).Article 

Google Scholar 
65.Sun, H. & Frost, B. J. Computation of different optical variables of looming objects in pigeon nucleus rotundus neurons. Nat. Neurosci. 1, 296–303 (1998).CAS 
PubMed 
Article 

Google Scholar 
66.Pfeiffer, M. B., Iglay, R. B., Seamans, T. W., Blackwell, B. F. & DeVault, T. L. Deciphering interactions between white-tailed deer and approaching vehicles. Transp. Res. D Transp. Environ. 79, 102251. https://doi.org/10.1016/j.trd.2020.102251 (2020).Article 

Google Scholar 
67.Collins, S. A., Giffin, G. J. & Strong, W. T. Using flight initiation distance to evaluate responses of colonial-nesting Great Egrets to the approach of an unmanned aerial vehicle. J. Field. Ornithol. 90, 382–390 (2019).Article 

Google Scholar 
68.Kane, S. A., Fulton, A. H. & Rosenthal, L. J. When hawks attack: Animal-borne video studies of goshawk pursuit and prey-evasion strategies. J. Exp. Biol. 218, 212–222 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
69.Frid, A. & Dill, L. Human-caused disturbance stimuli as a form of predation risk. Conserv. Ecol. 6 (2002).70.Lambertucci, S. A., Shepard, E. L. & Wilson, R. P. Human-wildlife conflicts in a crowded airspace. Science 348, 502–504 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Ballejo, F., Plaza, P., Speziale, K. L., Lambertucci, A. P. & Lambertucci, S. A. Plastic ingestion and dispersion by vultures may produce plastic islands in natural areas. Sci. Total Environ. 755, 142421. https://doi.org/10.1016/j.scitotenv.2020.142421 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
72.Conover, M. R. Resolving human-wildlife conflicts: the science of wildlife damage management. (CRC press, 2001).73.Pfeiffer, M. B., Blackwell, B. F. & DeVault, T. L. Collective effect of landfills and landscape composition on bird–aircraft collisions. Hum.–Wildl. Interact. 14, 43–54 (2020).74.Dolbeer, R. A. Aerodrome bird hazard prevention: case study at John F. Kennedy International Airport. (1999).75.Blackwell, B. F. et al. Exploiting avian vision with aircraft lighting to reduce bird strikes. J. Appl. Ecol. 49, 758–766 (2012).Article 

Google Scholar 
76.Goller, B., Blackwell, B. F., DeVault, T. L., Baumhardt, P. E. & Fernández-Juricic, E. Assessing bird avoidance of high-contrast lights using a choice test approach: Implications for reducing human-induced avian mortality. PeerJ 6, e5404 (2018).PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Nekola, J. C. & White, P. S. The distance decay of similarity in biogeography and ecology. J. Biogeogr. 26, 867–878 (1999).Article 

Google Scholar 
2.Soininen, J., McDonald, R. & Hillebrand, H. The distance decay of similarity in ecological communities. Ecography 30, 3–12 (2007).Article 

Google Scholar 
3.Whittaker, R. H. Communities and Ecosystems (MacMillan Publishing, 1975).
Google Scholar 
4.Pulliam, H. R. On the relationship between niche and distribution. Ecol. Lett. 3, 349–361 (2000).Article 

Google Scholar 
5.Pulliam, H. Sources, sinks, and population regulation. Am. Nat. 132, 652–661 (1988).Article 

Google Scholar 
6.Hanski, I. & Gilpin, M. Metapopulation dynamics: Brief history and conceptual domain. Biol. J. Linn. Soc. 42, 3–16 (1991).Article 

Google Scholar 
7.MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, 2001).Book 

Google Scholar 
8.Tuomisto, H. & Ruokolainen, K. Analyzing or explaining beta diversity? Understanding the targets of different methods of analysis. Ecology 87, 2697–2708 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Astorga, A. et al. Distance decay of similarity in freshwater communities: Do macro- and microorganisms follow the same rules?: Decay of similarity in freshwater communities. Glob. Ecol. Biogeogr. 21, 365–375 (2012).Article 

Google Scholar 
10.Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).Article 

Google Scholar 
11.Nekola, J. C. & Brown, J. H. The wealth of species: Ecological communities, complex systems and the legacy of Frank Preston. Ecol. Lett. 10, 188–196 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Hubbell, S. The Unified Neutral Theory of Biodiversity and Biogeography (MPB-32) (Princeton University Press, 2001).
Google Scholar 
13.Fodelianakis, S., Valenzuela-Cuevas, A., Barozzi, A. & Daffonchio, D. Direct quantification of ecological drift at the population level in synthetic bacterial communities. ISME J. https://doi.org/10.1038/s41396-020-00754-4 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Gravel, D., Canham, C. D., Beaudet, M. & Messier, C. Reconciling niche and neutrality: The continuum hypothesis: Reconciling niche and neutrality. Ecol. Lett. 9, 399–409 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Legendre, P., Borcard, D. & Peres-Neto, P. R. Analyzing beta diversity: Partitioning the spatial variation of community composition data. Ecol. Monogr. 75, 435–450 (2005).Article 

Google Scholar 
16.Wilson, K. A., Cabeza, M. & Klein, C. J. Fundamental concepts of spatial conservation prioritization. In Spatial Conservation Prioritization: Quantitative Methods & Computational Tools (eds Moilanen, A. et al.) 16–27 (Oxford University Press, 2009).
Google Scholar 
17.Morlon, H. et al. A general framework for the distance-decay of similarity in ecological communities. Ecol. Lett. 11, 904–917 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Tuomisto, H. Dispersal, environment, and floristic variation of western Amazonian forests. Science 299, 241–244 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Gómez-Rodríguez, C. & Baselga, A. Variation among European beetle taxa in patterns of distance decay of similarity suggests a major role of dispersal processes. Ecography 41, 1825–1834 (2018).Article 

Google Scholar 
20.Stella, J. C., Rodríguez-González, P. M., Dufour, S. & Bendix, J. Riparian vegetation research in Mediterranean-climate regions: Common patterns, ecological processes, and considerations for management. Hydrobiologia 719(1), 291–315 (2013).Article 

Google Scholar 
21.Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).Article 

Google Scholar 
22.Rouquette, J. R. et al. Species turnover and geographic distance in an urban river network. Divers. Distrib. 19, 1429–1439 (2013).Article 

Google Scholar 
23.Kuglerová, L., Jansson, R., Sponseller, R. A., Laudon, H. & Malm-Renöfält, B. Local and regional processes determine plant species richness in a river-network metacommunity. Ecology 96, 381–391 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Zhang, Z., Gao, J. & Cai, Y. The effects of environmental factors and geographic distance on species turnover in an agriculturally dominated river network. Environ. Monit. Assess. 191, 201 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Jost, L., Chao, A. & Chazdon, R. Compositional similarity and beta diversity. In Biological Diversity: Frontiers in Measurement and Assessment (eds Magurran, A. & McGill, B.) 66–84 (Oxford University Press, 2011).
Google Scholar 
26.Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth. Bioscience 51, 933 (2001).Article 

Google Scholar 
27.Miranda, P., Coelho, F., Tomé, A. & Valente, M. Climate Change in Portugal. Scenarios, Impacts and Adaptation Measures—SIAM Project (Gradiva, 2002).
Google Scholar 
28.CIS-WFD. River and lakes—Typology, reference conditions and classification systems, Common Implementation Strategy for the Water Framework Directive (2000/60/EC), Guidance document no 10. 94 (2003).29.INAG. Manual para a avaliação biológica da qualidade da água em sistemas fluviais segundo a DQA—Protocolo de amostragem e análise para os macrófitos (2008).30.Agência Portuguesa do Ambiente. Plano de Gestão da Região Hidrográfica do Tejo, Relatório técnico, Versão Extensa Parte 2—Caracterização e Diagnóstico da Região Hidrográfica. (2012).31.Oksanen, J. et al. vegan: Community Ecology Package—Version 2.7-7. https://CRAN.R-project.org/package=vegan (2021).32.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).33.Peterson, E. E., Theobald, D. M. & Ver Hoef, J. M. Geostatistical modelling on stream networks: Developing valid covariance matrices based on hydrologic distance and stream flow. Freshw. Biol. 52, 267–279 (2007).Article 

Google Scholar 
34.Csardi, G. & Nepusz, T. The Igraph software package for complex network research. InterJournal Complex Syst. 1695, 1–9 (2005).
Google Scholar 
35.Lu, B., Sun, H., Harris, P., Xu, M. & Charlton, M. Shp2graph: Tools to convert a spatial network into an Igraph graph in R. ISPRS Int. J. Geo-Inf. 7, 293 (2018).Article 

Google Scholar 
36.Vogt, J. & Foisneau, S. CCM River and Catchment Database—Version 2.0 Analysis Tools. (2007).37.Monteiro-Henriques, T. et al. Bioclimatological mapping tackling uncertainty propagation: Application to mainland Portugal. Int. J. Climatol. 36, 400–411 (2016).Article 

Google Scholar 
38.Ward, J. V. & Stanford, J. A. The serial discontinuity concept: Extending the model to floodplain rivers. Regul. Rivers Res. Manag. 10, 159–168 (1995).Article 

Google Scholar 
39.Dias, F. S., Betancourt, M., Rodríguez-González, P. M. & Borda-de-Água, L. A Bayesian Approach for Analysing Pairwise Comparisons: A Case Study Using Species Composition Similarity (2021) https://doi.org/10.32942/osf.io/sn5jr.40.Stan Development Team. Stan Functions Reference Version 2.25. (2020).41.McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall/CRC, 2020).Book 

Google Scholar 
42.Rodríguez-González, P. M., Ferreira, M. T., Albuquerque, A., Santo, D. E. & Rego, P. R. Spatial variation of wetland woods in the latitudinal transition to arid regions: A multiscale approach. J. Biogeogr. 35, 1498–1511 (2008).Article 

Google Scholar 
43.Stan Development Team. RStan: the R interface to Stan Version 2.21. https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started (2020).44.Betancourt, M. Hierarchical Modeling (2020).45.Muneepeerakul, R., Weitz, J. S., Levin, S. A., Rinaldo, A. & Rodriguez-Iturbe, I. A neutral metapopulation model of biodiversity in river networks. J. Theor. Biol. 245, 351–363 (2007).ADS 
MathSciNet 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
46.Thompson, R. & Townsend, C. A truce with neutral theory: Local deterministic factors, species traits and dispersal limitation together determine patterns of diversity in stream invertebrates: Neutral theory and local determinism. J. Anim. Ecol. 75, 476–484 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Steinitz, O., Heller, J., Tsoar, A., Rotem, D. & Kadmon, R. Environment, dispersal and patterns of species similarity. J. Biogeogr. 33, 1044–1054 (2006).Article 

Google Scholar 
48.Nilsson, C., Brown, R. L., Jansson, R. & Merritt, D. M. The role of hydrochory in structuring riparian and wetland vegetation. Biol. Rev. Camb. Philos. Soc. 85, 837–858 (2010).PubMed 
PubMed Central 

Google Scholar 
49.Gelmi-Candusso, T. A. et al. Estimating seed dispersal distance: A comparison of methods using animal movement and plant genetic data on two primate-dispersed Neotropical plant species. Ecol. Evol. 9, 8965–8977 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Rodríguez-González, P. M. et al. A spatial stream-network approach assists in managing the remnant genetic diversity of riparian forests. Sci. Rep. 9, 6741 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Ward, J. V., Tockner, K., Arscott, D. B. & Claret, C. Riverine landscape diversity. Freshw. Biol. 47, 517–539 (2002).Article 

Google Scholar 
52.Fraaije, R. G. A. et al. Spatial patterns of water-dispersed seed deposition along stream riparian gradients. PLoS ONE 12, e0185247 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Bendix, J. Flood disturbance and the distribution of riparian species diversity. Geogr. Rev. 87, 468–483 (1997).Article 

Google Scholar 
54.Kuglerová, L., Dynesius, M., Laudon, H. & Jansson, R. Relationships between plant assemblages and water flow across a boreal forest landscape: A comparison of liverworts, mosses, and vascular plants. Ecosystems 19, 170–184 (2016).Article 
CAS 

Google Scholar 
55.Wubs, E. R. J. et al. Going against the flow: A case for upstream dispersal and detection of uncommon dispersal events. Freshw. Biol. 61, 580–595 (2016).CAS 
Article 

Google Scholar 
56.Carrera, M., Gyakum, J. & Lin, C. Observational study of wind channeling within the St. Lawrence river valley. J. Appl. Meteorol. Climatol. 48, 2341–2361 (2009).ADS 
Article 

Google Scholar 
57.Kuparinen, A., Katul, G., Nathan, R. & Schurr, F. M. Increases in air temperature can promote wind-driven dispersal and spread of plants. Proc. R. Soc. B Biol. Sci. 276, 3081–3087 (2009).Article 

Google Scholar 
58.Soomers, H. et al. Wind and water dispersal of wetland plants across fragmented landscapes. Ecosystems 16, 434–451 (2013).Article 

Google Scholar 
59.Jones, K. N. Analysis of pollinator foraging: Tests for non-random behaviour. Funct. Ecol. 11, 255–259 (1997).Article 

Google Scholar 
60.Ferreira, M. T. & Aguiar, F. Riparian and aquatic vegetation in Mediterranean-type streams (western Iberia). Limnetica 25, 411–424 (2005).
Google Scholar 
61.Petts, G. E. & Amoros, C. Fluvial hydrosystems: a management perspective. In The Fluvial Hydrosystems (eds Petts, G. E. & Amoros, C.) 263–278 (Springer Netherlands, 1996) https://doi.org/10.1007/978-94-009-1491-9_12.Chapter 

Google Scholar 
62.Benda, L. et al. The network dynamics hypothesis: How channel networks structure riverine habitats. Bioscience 54, 413–427 (2004).Article 

Google Scholar 
63.QGIS Development Team. QGIS Geographic Information System-Version 3.20.3. (2021). More

1.Castillo, L. E., de la Cruz, E. & Ruepert, C. Ecotoxicology and pesticides in tropical aquatic ecosystems of Central America. Environ. Toxicol. Chem. 16, 41–51. https://doi.org/10.1002/etc.5620160104 (1997).Article 
CAS 

Google Scholar 
2.Moreno-González, R. & León, V. Presence and distribution of current-use pesticides in surface marine sediments from a Mediterranean coastal lagoon (SE Spain). Environ. Sci. Pollut. Res. 24, 8033–8048. https://doi.org/10.1007/s11356-017-8456-0 (2017).Article 
CAS 

Google Scholar 
3.Hernández-Romero, A. H., Tovilla-Hernández, C., Malo, E. A. & Bello-Mendoza, R. Water quality and presence of pesticides in a tropical coastal wetland in southern Mexico. Mar. Pollut. Bull. 48, 1130–1141. https://doi.org/10.1016/j.marpolbul.2004.01.003 (2004).Article 
PubMed 
CAS 

Google Scholar 
4.Wurl, O. & Obbard, J. P. Organochlorine pesticides, polychlorinated biphenyls and polybrominated diphenyl ethers in Singapore’s coastal marine sediments. Chemosphere 58, 925–933. https://doi.org/10.1016/j.chemosphere.2004.09.054 (2005).ADS 
Article 
PubMed 
CAS 

Google Scholar 
5.Carvalho, F. P. et al. Organic contaminants in the marine environment of Manila Bay, Philippines. Arch. Environ. Contam. Toxicol. 57, 348–358. https://doi.org/10.1007/s00244-008-9271-x (2009).Article 
PubMed 
CAS 

Google Scholar 
6.Australian Government and Queensland Government. Reef 2050 Water Quality Improvement Plan, Monitoring Program. (Australian and Queensland Governments, 2018). https://www.reefplan.qld.gov.au/tracking-progress/paddock-to-reef/modelling-and-monitoring.7.O’Brien, D. et al. Spatial and temporal variability in pesticide exposure downstream of a heavily irrigated cropping area: Application of different monitoring techniques. J. Agric. Food Chem. 64, 3975–3989. https://doi.org/10.1021/acs.jafc.5b04710 (2016).Article 
PubMed 
CAS 

Google Scholar 
8.Warne, M. St. J., Smith, R. & Turner, R. Analysis of pesticide mixtures discharged to the lagoon of the Great Barrier Reef, Australia. Environ. Pollut. 265, 114088. https://doi.org/10.1016/j.envpol.2020.114088 (2020).Article 
PubMed 
CAS 

Google Scholar 
9.Shaw, M. et al. Monitoring pesticides in the Great Barrier Reef. Mar. Pollut. Bull. 60, 113–122. https://doi.org/10.1016/j.marpolbul.2009.08.026 (2010).Article 
PubMed 
CAS 

Google Scholar 
10.Kennedy, K. et al. The influence of a season of extreme wet weather events on exposure of the World Heritage Area Great Barrier Reef to pesticides. Mar. Pollut. Bull. 64, 1495–1507. https://doi.org/10.1016/j.marpolbul.2012.05.014 (2012).Article 
PubMed 
CAS 

Google Scholar 
11.Mercurio, P. et al. Degradation of herbicides in the tropical marine environment: Influence of light and sediment. PLoS ONE 11, e0165890. https://doi.org/10.1371/journal.pone.0165890 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
12.Gallen, C. et al. Marine Monitoring Program: Annual report for inshore pesticide monitoring 2017–18. Report for the Great Barrier Reef Marine Park Authority. http://elibrary.gbrmpa.gov.au/jspui/handle/11017/3489. (2019).13.Smith, R. et al. Large-scale pesticide monitoring across Great Barrier Reef catchments–paddock to reef integrated monitoring, modelling and reporting program. Mar. Pollut. Bull. 65, 117–127. https://doi.org/10.1016/j.marpolbul.2011.08.010 (2012).Article 
PubMed 
CAS 

Google Scholar 
14.Oettmeier, W. Herbicide resistance and supersensitivity in photosystem II. Cell. Mol. Life Sci. 55, 1255–1277. https://doi.org/10.1007/s000180050370 (1999).Article 
PubMed 
CAS 

Google Scholar 
15.Davis, A., Lewis, S., Brodie, J. & Benson, A. The potential benefits of herbicide regulation: A cautionary note for the Great Barrier Reef catchment area. Sci. Total Environ. 490, 81–92. https://doi.org/10.1016/j.scitotenv.2014.04.005 (2014).ADS 
Article 
PubMed 
CAS 

Google Scholar 
16.King, J., Alexander, F. & Brodie, J. Regulation of pesticides in Australia: The Great Barrier Reef as a case study for evaluating effectiveness. Agr. Ecosyst. Environ. 180, 54–67. https://doi.org/10.1016/j.agee.2012.07.001 (2013).Article 

Google Scholar 
17.Devlin, M. et al. Advancing our Understanding of the Source, Management, Transport and Impacts of Pesticides on the Great Barrier Reef 2011–2015. Report for the Queensland Department of Environment and Heritage Protection. (Tropical Water & Aquatic Ecosytem Research (TropWATER) Publication, James Cook University, 2015). https://www.qld.gov.au/environment/assets/documents/agriculture/sustainable-farming/reef/rp104c-pesticide-report.pdf/.18.Flores, F., Collier, C. J., Mercurio, P. & Negri, A. P. Phytotoxicity of four photosystem II herbicides to tropical seagrasses. PLoS ONE 8, e75798. https://doi.org/10.1371/journal.pone.0075798 (2013).ADS 
Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
19.Haynes, D. et al. The occurrence and impact of herbicides in the Great Barrier Reef, Australia. Reef Res. 10, 3–5 (2000).
Google Scholar 
20.Negri, A. P., Flores, F., Röthig, T. & Uthicke, S. Herbicides increase the vulnerability of corals to rising sea surface temperature. Limnol. Oceanogr. 56, 471–485. https://doi.org/10.4319/lo.2011.56.2.0471 (2011).ADS 
Article 
CAS 

Google Scholar 
21.Marques, J. A., Flores, F., Bianchini, A., Uthicke, S. & Negri, A. P. Acclimation history modulates effect size of calcareous algae (Halimeda opuntia) to herbicide exposure under future climate scenarios. Sci. Total Environ. 736, 140308. https://doi.org/10.1016/j.scitotenv.2020.140308 (2020).Article 
CAS 

Google Scholar 
22.van Dam, J. W., Negri, A. P., Mueller, J. F. & Uthicke, S. Symbiont-specific responses in foraminifera to the herbicide diuron. Mar. Pollut. Bull. 65, 373–383. https://doi.org/10.1016/j.marpolbul.2011.08.008 (2012).Article 
PubMed 
CAS 

Google Scholar 
23.Thomas, M. C., Flores, F., Kaserzon, S., Fisher, R. & Negri, A. P. Toxicity of ten herbicides to the tropical marine microalgae Rhodomonas salina. Sci. Rep. 10, 1–16. https://doi.org/10.1038/s41598-020-64116-y (2020).Article 
CAS 

Google Scholar 
24.Magnusson, M., Heimann, K. & Negri, A. P. Comparative effects of herbicides on photosynthesis and growth of tropical estuarine microalgae. Mar. Pollut. Bull. 56, 1545–1552. https://doi.org/10.1016/j.marpolbul.2008.05.023 (2008).Article 
PubMed 
CAS 

Google Scholar 
25.Muscatine, L. The role of symbiotic algae in carbon and energy flux in reef corals. Coral Reefs 25, 1–29 (1990).
Google Scholar 
26.Oettmeier, W. Herbicides of photosystems II. In Structure, Function and Molecular Biology (ed. Barber, J.) 349–408 (Elsevier, 1992).
Google Scholar 
27.Jones, R. J., Muller, J., Haynes, D. & Schreiber, U. Effects of herbicides diuron and atrazine on corals of the Great Barrier Reef, Australia. Mar. Ecol. Prog. Ser. 251, 153–167. https://doi.org/10.3354/meps251153 (2003).ADS 
Article 
CAS 

Google Scholar 
28.Jones, R. J. & Kerswell, A. P. Phytotoxicity of Photosystem II (PSII) herbicides to coral. Mar. Ecol. Prog. Ser. 261, 149–159. https://doi.org/10.3354/meps261149 (2003).ADS 
Article 
CAS 

Google Scholar 
29.Cantin, N. E., Negri, A. P. & Willis, B. L. Photoinhibition from chronic herbicide exposure reduces reproductive output of reef-building corals. Mar. Ecol. Prog. Ser. 344, 81–93. https://doi.org/10.3354/meps07059 (2007).ADS 
Article 
CAS 

Google Scholar 
30.Negri, A. et al. Effects of the herbicide diuron on the early life history stages of coral. Mar. Pollut. Bull. 51, 370–383. https://doi.org/10.1016/j.marpolbul.2004.10.053 (2005).Article 
PubMed 
CAS 

Google Scholar 
31.Decelle, J. et al. Worldwide occurrence and activity of the reef-building coral symbiont Symbiodinium in the open ocean. Curr. Biol. 28, 3625–3633. https://doi.org/10.1016/j.cub.2018.09.024 (2018).Article 
PubMed 
CAS 

Google Scholar 
32.Baker, A. C. Reef corals bleach to survive change. Nature 411, 765–766. https://doi.org/10.1038/35081151 (2001).ADS 
Article 
PubMed 
CAS 

Google Scholar 
33.Muller-Parker, G., D’elia, C. F. & Cook, C. B. Coral Reefs in the Anthropocene 99–116 (Springer, 2015). https://pdfs.semanticscholar.org/191e119/119ba111eab744a4054c4068f4057a4003bb4058bd4001b9628.pdf.34.Chakravarti, L. J., Negri, A. P. & Oppen, M. J. Thermal and herbicide tolerances of chromerid algae and their ability to form a symbiosis with corals. Front. Microbiol. 10, 173. https://doi.org/10.3389/fmicb.2019.00173 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
35.van Dam, J., Uthicke, S., Beltran, V., Mueller, J. & Negri, A. Combined thermal and herbicide stress in functionally diverse coral symbionts. Environ. Pollut. 204, 271–279. https://doi.org/10.1016/j.envpol.2015.05.013 (2015).Article 
PubMed 
CAS 

Google Scholar 
36.Mercurio, P. et al. Contribution of transformation products towards the total herbicide toxicity to tropical marine organisms. Sci. Rep. 8, 1–12. https://doi.org/10.1038/s41598-018-23153-4 (2018).Article 
CAS 

Google Scholar 
37.Magnusson, M., Heimann, K., Ridd, M. & Negri, A. P. Pesticide contamination and phytotoxicity of sediment interstitial water to tropical benthic microalgae. Water Res. 47, 5211–5221. https://doi.org/10.1016/j.watres.2013.06.003 (2013).Article 
PubMed 
CAS 

Google Scholar 
38.Thomas, M. C., Flores, F., Kaserzon, S., Reeks, T. & Negri, A. P. Toxicity of the herbicides diuron, propazine, tebuthiuron, and haloxyfop to the diatom Chaetoceros muelleri. Sci. Rep. 10, 19592. https://doi.org/10.1038/s41598-020-76363-0 (2020).ADS 
Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
39.Warne, M. St. J., King, O. & Smith, R. Ecotoxicity thresholds for ametryn, diuron, hexazinone and simazine in fresh and marine waters. Environ. Sci. Pollut. Res. 25, 3151–3169. https://doi.org/10.1007/s11356-017-1097-5 (2018).Article 
CAS 

Google Scholar 
40.Traas, T. P. et al. In Species Sensitivity Distributions in Ecotoxicology (eds Posthuma, L. et al.) 315–344 (CRC Press, 2002).
Google Scholar 
41.ANZG. Australian and New Zealand Guidelines for Fresh and Marine Water Quality. 1–103 (Australian and New Zealand Governments and Australian State and Territory Governments, 2018). http://waterquality.gov.au/anz-guidelines.42.King, O., Smith, R., Mann, R. & Warne, M. St. J. Proposed Aquatic Ecosystem Protection Guideline Values for Pesticides Commonly Used in the Great Barrier Reef catchment Area: Part 2— Bromacil, Chlorothalonil, Fipronil, Fluometuron, Fluroxypyr, Haloxyfop, MCPA, Pendimethalin, Prometryn, Propazine, Propiconazole, Terbutryn, Triclopyr and Terbuthylazine. (Department of Environment and Science, 2017). https://www.publications.qld.gov.au/dataset/proposed-guideline-values-27-pesticides-used-in-the-gbr-catchment.43.King, O., Smith, R., Mann, R. & Warne, M. St. J. Proposed Aquatic Ecosystem Protection Guideline Values for Pesticides Commonly Used in the Great Barrier Reef Catchment Area: Part 1–2, 4-D, Ametryn, Diuron, Glyphosate, Hexazinone, Imazapic, Imidacloprid, Isoxaflutole, Metolachlor, Metribuzin, Metsulfuron-methyl, Simazine and Tebuthiuron 296 (Department of Environment and Science, 2017). https://www.publications.qld.gov.au/dataset/proposed-guideline-values-27-pesticides-used-in-the-gbr-catchment.44.Marie, D., Rigaut-Jalabert, F. & Vaulot, D. An improved protocol for flow cytometry analysis of phytoplankton cultures and natural samples. Cytom. Part A 85, 962–968. https://doi.org/10.1002/cyto.a.22517 (2014).Article 
CAS 

Google Scholar 
45.Warne, M. St. J. et al. Revised Method for Deriving Australian and New Zealand Water Quality Guideline Values for Toxicants: Update of 2015 Version. Prepared for the Revision of the Australian and New Zealand Guidelines for Fresh and Marine Water Quality 48 (Australian and New Zealand Governments and Australian State and Territory Governments, 2018). https://www.waterquality.gov.au/sites/default/files/documents/warne-wqg-derivation2018.pdf.46.Vinyard, D. J., Ananyev, G. M. & Charles Dismukes, G. Photosystem II: The reaction center of oxygenic photosynthesis. Annu. Rev. Biochem. 82, 577–606. https://doi.org/10.1146/annurev-biochem-070511-100425 (2013).Article 
PubMed 
CAS 

Google Scholar 
47.Haworth, P. & Steinback, K. E. Interaction of herbicides and quinone with the qb-protein of the diuron-resistant Chlamydomonas reinhardtii mutant Dr2. Plant Physiol. 83, 1027–1031. https://doi.org/10.1104/pp.83.4.1027 (1987).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
48.USEPA. ECOTOX User Guide: ECOTOXicology Database System. Version 5.0. (United States Environmental Protection Agency, 2019) http://cfpub.epa.gov/ecotox/.49.Magnusson, M. Effects of Priority Herbicides and Their Breakdown Products on Tropical, ESTUARINE Microalgae of the Great Barrier Reef Lagoon. PhD thesis, James Cook University (2009).50.MacBean, C. The Pesticide Manual: A World Compendium (British Crop Protection Council, 2012).
Google Scholar 
51.Haq, S., Bachvaroff, T. R. & Place, A. R. Characterization of acetyl-CoA carboxylases in the basal dinoflagellate Amphidinium carterae. Mar. Drugs 15, 149. https://doi.org/10.3390/md15060149 (2017).Article 
PubMed Central 
CAS 

Google Scholar 
52.Tang, C. Y., Huang, Z. & Allen, H. C. Interfacial water structure and effects of Mg2+ and Ca2+ binding to the COOH headgroup of a palmitic acid monolayer studied by sum frequency spectroscopy. J. Phys. Chem. B 115, 34–40. https://doi.org/10.1021/jp1062447 (2011).Article 
PubMed 
CAS 

Google Scholar 
53.Brzozowska, A., Duits, M. H. & Mugele, F. Stability of stearic acid monolayers on artificial sea water. Colloid Surf. A 407, 38–48 (2012).Article 
CAS 

Google Scholar 
54.McCourt, J. & Duggleby, R. Acetohydroxyacid synthase and its role in the biosynthetic pathway for branched-chain amino acids. Amino Acids 31, 173–210. https://doi.org/10.1007/s00726-005-0297-3 (2006).Article 
PubMed 
CAS 

Google Scholar 
55.Genty, B., Briantais, J.-M. & Baker, N. R. The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. BBA 990, 87–92. https://doi.org/10.1016/S0304-4165(89)80016-9 (1989).Article 
CAS 

Google Scholar 
56.Jeong, H. J. et al. Heterotrophic feeding as a newly identified survival strategy of the dinoflagellate Symbiodinium. Proc. Natl. Acad. Sci. U.S.A. 109, 12604–12609. https://doi.org/10.1073/pnas.1204302109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Ralph, P., Smith, R., Macinnis-Ng, C. & Seery, C. Use of fluorescence-based ecotoxicological bioassays in monitoring toxicants and pollution in aquatic systems. Toxicol. Environ. Chem. 89, 589–607. https://doi.org/10.1080/02772240701561593 (2007).Article 
CAS 

Google Scholar 
58.OECD. Test No. 201: Freshwater Alga and Cyanobacteria, Growth Inhibition Test, OECD Guidelines for the Testing of Chemicals, Section 2 (OECD Publishing, 2011).
Google Scholar 
59.Kamei, M., Takayama, K., Ishibashi, H. & Takeuchi, I. Effects of ecologically relevant concentrations of Irgarol 1051 in tropical to subtropical coastal seawater on hermatypic coral Acropora tenuis and its symbiotic dinoflagellates. Mar. Poll. Bull. 150, 110734. https://doi.org/10.1016/j.marpolbul.2019.110734 (2020).Article 
CAS 

Google Scholar 
60.McKenzie, M. R., Templeman, M. A. & Kingsford, M. J. Detecting effects of herbicide runoff: The use of Cassiopea maremetens as a biomonitor to hexazinone. Aquat. Toxicol. 221, 105442. https://doi.org/10.1016/j.aquatox.2020.105442 (2020).Article 
PubMed 
CAS 

Google Scholar 
61.Howe, P. L., Reichelt-Brushett, A. J., Clark, M. W. & Seery, C. R. Toxicity estimates for diuron and atrazine for the tropical marine cnidarian Exaiptasia pallida and in-hospite Symbiodinium spp. using PAM chlorophyll-a fluorometry. J. Photochem. Photobiol. B 171, 125–132. https://doi.org/10.1016/j.jphotobiol.2017.05.006 (2017).Article 
PubMed 
CAS 

Google Scholar 
62.Takahashi, S., Whitney, S. M. & Badger, M. R. Different thermal sensitivity of the repair of photodamaged photosynthetic machinery in cultured Symbiodinium species. Proc. Natl. Acad. Sci. U.S.A. 106, 3237–3242. https://doi.org/10.1073/pnas.0808363106 (2009).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
63.Jones, R. The ecotoxicological effects of Photosystem II herbicides on corals. Mar. Pollut. Bull. 51, 495–506. https://doi.org/10.1016/j.marpolbul.2005.06.027 (2005).Article 
PubMed 
CAS 

Google Scholar 
64.Rowen, D. J., Templeman, M. A. & Kingsford, M. J. Herbicide effects on the growth and photosynthetic efficiency of Cassiopea maremetens. Chemosphere 182, 143–148. https://doi.org/10.1016/j.chemosphere.2017.05.001 (2017).ADS 
Article 
PubMed 
CAS 

Google Scholar 
65.Cantin, N. E., van Oppen, M. J., Willis, B. L., Mieog, J. C. & Negri, A. P. Juvenile corals can acquire more carbon from high-performance algal symbionts. Coral Reefs 28, 405. https://doi.org/10.1007/s00338-009-0478-8 (2009).ADS 
Article 

Google Scholar 
66.Fitt, W. & Trench, R. The relation of diel patterns of cell division to diel patterns of motility in the symbiotic dinoflagellate Symbiodinium microadria ticum Freudenthal in culture. New Phytol. 94, 421–432 (1983).Article 

Google Scholar 
67.Randall, C. J. et al. Sexual production of corals for reef restoration in the Anthropocene. Mar. Ecol. Prog. Ser. 635, 203–232. https://doi.org/10.3354/meps13206 (2020).ADS 
Article 

Google Scholar 
68.Baird, A. H., Bhagooli, R., Ralph, P. J. & Takahashi, S. Coral bleaching: The role of the host. Trends Ecol. Evol. 24, 16–20. https://doi.org/10.1016/j.tree.2008.09.005 (2009).Article 
PubMed 

Google Scholar 
69.Flores, F., Kaserzon, S., Elisei, G., Ricardo, G. & Negri, A. P. Toxicity thresholds of three insecticides and two fungicides to larvae of the coral Acropora tenuis. PeerJ 8, e9615. https://doi.org/10.7717/peerj.9615 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
70.LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580. https://doi.org/10.1016/j.cub.2018.07.008 (2018).Article 
PubMed 
CAS 

Google Scholar 
71.Trenfield, M. A. et al. Aluminium, gallium, and molybdenum toxicity to the tropical marine microalga Isochrysis galbana. Environ. Toxicol. Chem. 34, 1833–1840. https://doi.org/10.1002/etc.2996 (2015).Article 
PubMed 
CAS 

Google Scholar 
72.Hennige, S., Suggett, D., Warner, M., McDougall, K. & Smith, D. Photobiology of Symbiodinium revisited: Bio-physical and bio-optical signatures. Coral Reefs 28, 179–195. https://doi.org/10.1007/s00338-008-0444-x (2009).ADS 
Article 

Google Scholar 
73.Klueter, A., Trapani, J., Archer, F. I., McIlroy, S. E. & Coffroth, M. A. Comparative growth rates of cultured marine dinoflagellates in the genus Symbiodinium and the effects of temperature and light. PLoS ONE 12, e0187707. https://doi.org/10.1371/journal.pone.0187707 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
74.Rogers, J. E. & Davis, R. H. Application of a new micro-culturing technique to assess the effects of temperature and salinity on specific growth rates of six Symbiodinium isolates. Bull. Mar. Sci. 79, 113–126 (2006).
Google Scholar 
75.Sakami, T. Effects of temperature, irradiance, salinity and inorganic nitrogen concentration on coral zooxanthellae in culture. Fish. Res. 66, 1006–1013. https://doi.org/10.1046/j.1444-2906.2000.00162.x (2000).Article 
CAS 

Google Scholar 
76.Schreiber, U., Müller, J. F., Haugg, A. & Gademann, R. New type of dual-channel PAM chlorophyll fluorometer for highly sensitive water toxicity biotests. Photosynth. Res. 74, 317–330. https://doi.org/10.1023/A:1021276003145 (2002).Article 
PubMed 
CAS 

Google Scholar 
77.Karim, W., Nakaema, S. & Hidaka, M. Temperature effects on the growth rates and photosynthetic activities of Symbiodinium cells. J. Mar. Sci. Eng. 3, 368–381. https://doi.org/10.3390/jmse3020368 (2015).Article 

Google Scholar 
78.Mercurio, P., Mueller, J. F., Eaglesham, G., Flores, F. & Negri, A. P. Herbicide persistence in seawater simulation experiments. PLoS ONE 10, e0136391. https://doi.org/10.1371/journal.pone.0136391 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
79.Mercurio, P. Herbicide Persistence and Toxicity in the Tropical Marine Environment. PhD thesis, The University of Queensland (2016).80.Fisher, R., Ricardo, G. & Fox, D. jags NEC: A Bayesian No Effect Concentration (NEC) Package. https://github.com/AIMS/NEC-estimation. (2019).81.Fox, D. R. A Bayesian approach for determining the no effect concentration and hazardous concentration in ecotoxicology. Ecotoxicol. Environ. Saf. 73, 123–131. https://doi.org/10.1016/j.ecoenv.2009.09.012 (2010).Article 
PubMed 
CAS 

Google Scholar  More

Design of species assemblages with contrasting species and functional diversitiesWe designed eight assemblages of native and perennial plants differing in terms of species richness (three levels), functional diversity of the traits involved in plant–arthropod interactions (two levels) and species identity (two sets of species). We combined these first two factors to define four categories of plant assemblages for further study:

Low functional diversity and medium species richness (14 species), LFMS;

High functional diversity and low species richness (9 species), HFLS;

High functional diversity and medium species richness (14 species), HFMS;

High functional diversity and high species richness (29 species), HFHS.

For each of these four categories, we designed two assemblages with different species identities, as described in the Supplementary information, resulting in eight plant assemblages in total. Functional characterization was based on a rough classification of plant species into functional groups (Supplementary Table S1), according to the mains traits involved in plant–species interactions easily accessible from databases: (1) flower resources, i.e. floral and extrafloral nectar or pollen, (2) accessibility of the resource, depending on flower shape, (3) availability of the resource, i.e. the flowering period and (4) flowering height.We generated the seed mixtures from commercial seeds, using ecotypes of local origin wherever possible (northern part of the Parisian basin, France). All applicable international, national, and institutional guidelines relevant for the use of plants were followed.Experimental designThe experiment was conducted between 2013 and 2017 in a 6.5-ha field at Grignon, France (N 48.837, E 1.956), on a deep loamy clay soil, in which soil depth decreased along a gradient from north to south. The field was divided in three blocks running from north to south to take this soil heterogeneity into account.Each assemblage was sown on a 6 × 44 m2 strip, with three replicates (Supplementary Fig. S2), with each assemblage represented once per block. A control treatment, sown with the same crop species as the rest of the field, was also included in the experimental design, resulting in nine experimental treatments in total. From the autumn of 2013 to the 2017 harvest, a winter barley–maize–faba bean–oilseed rape rotation was grown in the field. Crops were managed without insecticide treatment, but with a mean of 0.75 fungicide and 1.25 herbicide treatments per year. The observations were made in faba bean in 2016 and in oilseed rape in 2017.Botanical assessments and functional characterization of the plant communitiesBotanical assessments were conducted in April and June, in 2016 and 2017. In each treatment, the vegetation was assessed in 3 × 15 m2 plots at a position representative of the whole strip, generally in the center of the strip, to prevent edge effects. The percentage of the ground covered by each sown or spontaneously growing plant species was estimated by eye, by the same observer in each case. We noted the phenological development stage of each species in each treatment on an 11-point scale, to ensure an accurate assessment of flowering phenology. In the control plots (sown with the crop species only), we took into account the resources provided by weed species.The functional characterization of plant communities was based on the plant traits assumed to be involved in plant–parasitoid interactions6 (Supplementary Table S3). These traits were related to (1) the provision of trophic resources (presence of floral and extrafloral nectar, qualitative estimation of floral nectar), (2) the temporal availability of the resource (date of flowering onset and duration of flowering), (3) flower attractiveness (flower or inflorescence diameter, color, UV reflectance pattern), (4) nectar accessibility (flower opening diameter, corolla height, nectar depth and nectar tube diameter) and (5) the provision of physical habitats (leaf distribution, vegetative and flowering height). We measured most of these traits, particularly all those relating to flower morphology, phenology and nectar provision (see more detailed methods in the Supplementary information). Only a few were retrieved from previous publications and online databases: flower color and UV reflectance pattern, leaf distribution, vegetative and flower height.These traits were used (1) to determine the accessibility of nectar to each parasitoid (see below) and (2) to calculate the functional diversity of the plant assemblages. We calculated functional dispersion as the abundance-weighted mean distance of individual species from the centroid of all species in the trait space50 and Rao quadratic entropy51. Since these two parameters were highly correlated (Supplementary information), we considered only functional dispersion a measurement of functional diversity. The traits associated with the provision, availability and accessibility of nectar resources were measured for all the dicotyledonous species sown and for all spontaneous species occurring in the plant communities and flowering during parasitoid activity. Overall, considering the traits we measured and those retrieved from databases, the trait matrix was complete for more than 95% of the species, accounting for 99.6% of total plant cover.Assessment of the levels of parasitism on five herbivorous pests of faba bean and oilseed rapeIn the adjacent crop, 5 and 20 m from the wildflower strip, we measured the level of parasitism in one herbivorous pest of faba bean (2016) and four herbivorous pests of oilseed rape (2017). We chose a distance close to the strip (5 m) to prevent confounding effects with the other adjacent strips, knowing that their effect is the strongest in the first few meters from the strip52. A further distance was also chosen (20 m) to determine whether the strips promoted biological control at field level, while taking into account the spatial constraint of the distance between strips (50 m between opposing strips).All the protocols are detailed in the Supplementary information. Parasitism was assessed in Bruchus rufimanus larvae after the visual examination of faba bean seeds after harvest. For oilseed rape, we collected and reared Ceutorhynchus pallidactylus and Psylliodes chrysocephala larvae until the adult stage or parasitoid emergence. In Brassicogethes aeneus larvae, parasitism was assessed by observing the eggs of Tersilochus heterocerus in the host larvae in oilseed rape flowers. Finally, after oilseed rape harvest, we retrieved cocoons of Dasineura brassicae from the soil, which we dissected, recording the number of cocoons occupied by parasitoids.Measurement of parasitoid traitsWe carried out morphological measurements on parasitoids (Supplementary Table S4), to determine their degree of access to the nectar provided by plants, as a function of the size of their mouthparts and head, which limit corolla penetration, using an approach analogous to that of van Rijn and Wäckers16. Parasitoid individuals, preserved in 70% ethanol, were obtained (1) from our rearing experiments (for Bruchus rufimanus, Psylliodes chrysocephala and Ceutorhynchus pallidactylus), (2) from the dissection of cocoons for Dasineura brassicae or (3) by field sampling in the flower strips with a sweep net in April 2017 to collect Tersilochus heterocerus, parasitoids of Brassicogethes aeneus identified with53. For each parasitoid species or morphospecies, we measured, on at least 10 individuals, proboscis length, proboscis width (at mid-length)54 and the maximum dorsal head width, including the eyes. Observations were carried out under a binocular microscope (Leica M80, 60 ×) linked to a video camera (Moticam 10, Motic), and measurements were made with ImageJ v1.50i digital image analysis software (National Institute of Health, Bethesda, http://imagej.nih.gov/ij).Nectar resources for parasitoidsWe estimated the amount of nectar provided by the plants by summing, for each flower strip corresponding to a treatment, the percent cover of plants providing available and accessible nectar, as assessed in vegetation surveys. Separate estimates were obtained for each parasitoid species or morphospecies.Plant species producing floral or extrafloral nectar were first selected on the basis of the observations detailed in the botanical assessment section. Nectar was considered to be available when it was produced during the period of parasitoid activity (Supplementary Table S4), by selecting species at the flowering stage or producing extrafloral nectar based on the phenological observations carried out during the botanical assessments. Nectar accessibility depended on morphological matching between plants and insects. Extrafloral nectar, which is not enclosed in a perianth, but produced on bracts or stipules, was considered to be accessible. We determined the accessibility of floral nectar with a mechanistic trait-based approach (Supplementary Information), by adapting the geometric model proposed by van Rijn and Wäckers16. A decision tree was built (Fig. 2) to take into account the three constraints limiting nectar accessibility: (1) ability of the insect to penetrate the flower, which is dependent on head size and flower opening, (2) ability to reach the nectar, which depends on proboscis length, nectar depth and corolla height, and (3) proboscis width and nectar tube diameter in the presence of nectar.Statistical analysesWe investigated the effects of the different plant assemblages on the rates of parasitism for the five herbivorous species, at 5 and 20 m from the flower strip, considered separately as individual response variables. We first tested the effect of each assemblage (nine treatments as factors) on parasitism rates. We used generalized linear mixed models in the lme package55, with a binomial error distribution. The models included plot (n = 9 flower strips × 3 replicates = 27), strip (1–3) or block (1–3) as a random effect. All models were run three times with each random effect variable, and the model giving the lowest AIC was retained. Strips consistently yielded the lowest AIC. This factor was therefore introduced as a random effect variable for all statistical analyses. The significance of the fixed effects was evaluated by type II analyses of deviance with Wald chi-squared tests from the Anova function from the car package56. If a significant effect (p value  More

1.Sih, A., Crowley, P., McPeek, M., Petranka, J. & Strohmeier, K. Predation, competition, and prey communities: A review of field experiments. Annu. Rev. Ecol. Syst. 16, 269–311 (1985).Article 

Google Scholar 
2.Werner, E. E. & Peacor, S. D. A review of trait-mediated indirect interactions in ecological communities. Ecology 84, 1083–1100 (2003).Article 

Google Scholar 
3.Abrams, P. A. The evolution of predator–prey interactions: theory and evidence. Annu. Rev. Ecol. Syst. 31, 79–105 (2000).Article 

Google Scholar 
4.Lima, S. L. & Bednekoff, P. A. Temporal variation in danger drives antipredator behavior: The predation risk allocation hypothesis. Am. Nat. 153, 649–659 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Peacor, S. D. & Werner, E. E. Nonconsumptive effects of predators and trait-mediated indirect effects. Encycl. Life Sci. https://doi.org/10.1002/9780470015902.a0021216 (2008).Article 

Google Scholar 
6.Schmitz, O. J., Krivan, V. & Ovadia, O. Trophic cascades: The primacy of trait-mediated indirect interactions. Ecol. Lett. 7, 153–163 (2004).Article 

Google Scholar 
7.Mittelbach, G. G. Fish foraging and habitat choice: a theoretical perspective. In Handbook of Fish Biology and Fisheries, Volume 1 Fish Biology (eds Hart, P. J. B. & Reynolds, J. D.) 251–266 (Blackwell, 2002).Chapter 

Google Scholar 
8.Mittelbach, G. G. & McGill, B. J. Community Ecology (Oxford University Press, 2019) https://doi.org/10.1017/CBO9781107415324.004.Book 

Google Scholar 
9.Lima, S. L. Nonlethal effects in the ecology of predator-prey interactions. Bioscience 48, 25–34 (1998).Article 

Google Scholar 
10.Jeschke, J. M., Laforsch, C. & Tollrian, R. Animal prey defenses. In Encyclopedia of Ecology 189–194 (2008).11.Harvell, C. D. The ecology and evolution of inducible defenses. Q. Rev. Biol. 65, 323–340 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Peckarsky, B. L. et al. Revisiting the classics: Considering nonconsumptive effects in textbook examples of predator prey interactions. Ecology 89, 2416–2425 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Goricki, Š et al. Environmental DNA in subterranean biology: Range extension and taxonomic implications for Proteus. Sci. Rep. 7, 91–93 (2017).Article 
CAS 

Google Scholar 
14.Sket, B. Distribution of Proteus (Amphibia: Urodela: Proteidae) and its possible explanation. J. Biogeogr. 24, 263–280 (1997).Article 

Google Scholar 
15.Jugovic, J., Prevorčnik, S., Aljančič, G. & Sketa, B. The atyid shrimp (Crustacea: Decapoda: Atyidae) rostrum: Phylogeny versus adaptation, taxonomy versus trophic ecology. J. Nat. Hist. 44, 2509–2533 (2010).Article 

Google Scholar 
16.Aljančič, M. Prehrana močerila. Proteus 23, 224–225 (1961).
Google Scholar 
17.Parzefall, J., Durand, J. P. & Sket, B. Prouteus anguinus Laurenti, 1768—Grottenolm. In Handbuch der Reptilien und Amphibien Europas (ed. Böhme, W.) 59–76 (Aula-Verlag, 1999).
Google Scholar 
18.Trontelj, P., Blejec, A. & Fišer, C. Ecomorphological convergence of cave communities. Evolution 66, 3852–3865 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Karaman, S. Podrod Orniphargus u Jugoslaviji I. & II. in O nekim amfipodima—izopodima Balkana i o njihovoj sistematici 119–159 (Srpska akademija nauka-Posebna izdanja CLXIII, 1950).20.Fišer, C., Trontelj, P. & Sket, B. Phylogenetic analysis of the Niphargus orcinus species-aggregate (Crustacea: Amphipoda: Niphargidae) with description of new taxa. J. Nat. Hist. 40, 2265–2315 (2006).Article 

Google Scholar 
21.Bollache, L. Ï., Kaldonski, N., Troussard, J. P., Lagrue, C. & Rigaud, T. Spines and behaviour as defences against fish predators in an invasive freshwater amphipod. Anim. Behav. 72, 627–633 (2006).Article 

Google Scholar 
22.Copilaş-Ciocianu, D., Borza, P. & Petrusek, A. Extensive variation in the morphological anti-predator defense mechanism of Gammarus roeselii Gervais, 1835 (Crustacea:Amphipoda). Freshw. Sci. 39, 47–55 (2020).Article 

Google Scholar 
23.Veech, J. A. A probabilistic model for analysing species co-occurrence. Glob. Ecol. Biogeogr. 22, 252–260 (2013).Article 

Google Scholar 
24.Borko, Š, Trontelj, P., Seehausen, O., Moškrič, A. & Fišer, C. A subterranean adaptive radiation of amphipods in Europe. Nat. Commun. 12, 1–12 (2021).Article 
CAS 

Google Scholar 
25.SubBioDB. Subterranean Fauna Database. Research group for speleobiology, Biotechnical faculty, University of Ljubljana. https://db.subbio.net/ (2021).26.Culver, D. C., Fong, D. W. & Jernigan, R. W. Species interactions in cave stream communities: Experimental results and microdistribution effects. Am. Midl. Nat. 126, 364 (1991).Article 

Google Scholar 
27.Lavoie, K. H., Helf, K. L. & Poulson, T. L. The biology and ecology of North American cave crickets. J. Cave Karst Stud. 69, 114–134 (2007).
Google Scholar 
28.Ercoli, F. et al. Differing trophic niches of three French stygobionts and their implications for conservation of endemic stygofauna. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 2193–2203 (2019).Article 

Google Scholar 
29.Pacioglu, O. et al. Ecophysiological and life-history adaptations of Gammarus balcanicus (Schäferna, 1922) in a sinking-cave stream from Western Carpathians (Romania). Zoology 139, 125754 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Parimuchová, A., Dušátková, L. P., Kováč, Ľ & Macháčková, T. The food web in a subterranean ecosystem is driven by intraguild predation. Sci. Rep. https://doi.org/10.1038/s41598-021-84521-1 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Premate, E. et al. Cave amphipods reveal co-variation between morphology and trophic niche in a low-productivity environment. Freshw. Biol. 66, 1876–1888 (2021).Article 

Google Scholar 
32.Sacco, M. et al. Elucidating stygofaunal trophic web interactions via isotopic ecology. PLoS ONE 14, 1–25 (2019).MathSciNet 
Article 
CAS 

Google Scholar 
33.Pohlman, J. W., Iliffe, T. M. & Cifuentes, L. A. A stable isotope study of organic cycling and the ecology of an anchialine cave ecosystem. Mar. Ecol. Prog. Ser. 155, 17–27 (1997).ADS 
CAS 
Article 

Google Scholar 
34.Graening, G. O. & Brown, A. V. Ecosystem dynamics and pollution effects in an Ozark cave stream. J. Am. Water Resour. Assoc. 39, 1497–1507 (2003).ADS 
CAS 
Article 

Google Scholar 
35.Manenti, R., Melotto, A., Guillaume, O., Ficetola, G. F. & Lunghi, E. Switching from mesopredator to apex predator: How do responses vary in amphibians adapted to cave living?. Behav. Ecol. Sociobiol. 74, 1–13 (2020).Article 

Google Scholar 
36.Uiblein, F. & Juberthie, C. Predation in caves: the effects of prey immobility and darkness on the foraging behaviour of two salamanders, Euproctus asper and Proteus anguinus. Behav. Process. 28, 33–40 (1992).CAS 
Article 

Google Scholar 
37.Prevorčnik, S., Verovnik, R., Zagmajster, M. & Sket, B. Biogeography and phylogenetic relations within the Dinaric subgenus Monolistra (Microlistra) (Crustacea: Isopoda: Sphaeromatidae), with a description of two new species. Zool. J. Linn. Soc. 159, 1–21 (2010).Article 

Google Scholar 
38.Mammola, S. Finding answers in the dark: Caves as models in ecology fifty years after Poulson and White. Ecography 42, 1331–1351 (2019).Article 

Google Scholar 
39.Culver, D. C. & Pipan, T. The Biology of Caves and Other Subterranean Habitats (Oxford University Press, 2009).
Google Scholar 
40.Kellner, K. F. & Swihart, R. K. Accounting for imperfect detection in ecology: A quantitative review. PLoS ONE 9, e111436 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
41.Mackenzie, D. I., Bailey, L. L. & Nichols, J. D. Investigating species co-occurrence patterns when species are detected imperfectly. J. Anim. Ecol. 73, 546–555 (2004).Article 

Google Scholar 
42.Vörös, J., Márton, O., Schmidt, B. R., Tünde Gál, J. & Jelić, D. Surveying Europe’s only cave-dwelling chordate species (Proteus anguinus) using environmental DNA. PLoS ONE 12, e0170945 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Niemiller, M. L. et al. Evaluation of eDNA for groundwater invertebrate detection and monitoring: A case study with endangered Stygobromus (Amphipoda: Crangonyctidae). Conserv. Genet. Resour. 10, 247–257 (2018).Article 

Google Scholar 
44.Yonezawa, S., Nakano, T., Nakahama, N., Tomikawa, K. & Isagi, Y. Environmental DNA reveals cryptic diversity within the subterranean amphipod genus Pseudocrangonyx Akatsuka & Komai, 1922 (Amphipoda: Crangonyctoidea: Pseudocrangonyctidae) from Central Japan. J. Crustac. Biol. 40, 479–483 (2020).Article 

Google Scholar 
45.Arntzen, J. W. et al. Proteus anguinus. IUCN Red List Threat. Species (2009).46.Communities, T. C. of E. Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. Official J. Eur. Communities 35, 8–51 (1992).
Google Scholar 
47.Vörös, J., Ursenbacher, S. & Jelić, D. Population genetic analyses using 10 new polymorphic microsatellite loci confirms genetic subdivision within the olm, Proteus anguinus. J. Hered. 110, 211–218 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Gorički, Š & Trontelj, P. Structure and evolution of the mitochondrial control region and flanking sequences in the European cave salamander Proteus anguinus. Gene 378, 31–41 (2006).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
49.Gravel, D., Albouy, C. & Thuiller, W. The meaning of functional trait composition of food webs for ecosystem functioning. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150268 (2016).Article 

Google Scholar 
50.Schmitz, O. Predator and prey functional traits: Understanding the adaptive machinery driving predator-prey interactions. F1000Research 6, 1767 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
51.R Development Core Team. A language and environment for statistical computing. (2020).52.R Studio Team. RStudio: Integrated Development for R. (2020).53.Wickham, H. & Bryan, J. readxl: Read Excel Files. R package version 1.3.1. (2019).54.Dragulescu, A. A. & Arendt, C. xlsx: Read, Write, Format Excel 2007 and Excel 97/2000/XP/2003 Files. R package version 0.6.1. (2018).55.Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version 0.8.3. (2019).56.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, 2016).57.Kong, D. Ipaper: Collection of personal practical R functions. (2021).58.Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439–446 (2018).Article 

Google Scholar 
59.Hijmas, R. J. raster: Geographic Data Analysis and Modeling. (2020).60.Baddeley, A., Rubak, E. & Turner, R. Spatial Point Patterns: Methodology and Applications with R (Chapman and Hall/CRC Press, 2015).MATH 
Book 

Google Scholar 
61.Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. R package version 0.5.0. (2020).62.Griffith, D. M., Veech, J. A. & Marsh, C. J. Cooccur: Probabilistic species co-occurrence analysis in R. J. Stat. Softw. 69, 1–17 (2016).Article 

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

Google Scholar 
64.Meade, A. & Pagel, M. Bayes Traits V3. (2017).65.Griffin, R. H. btw: Run BayesTraitsV3 from R. (2018). More

1.Norris, K. S. & Dohl, T. P. The Structure and Functions of Cetacean Schools (1979).2.Frantzis, A. & Herzing, D. L. Mixed-species associations of striped dolphins (Stenella coeruleoalba), short-beaked common dolphins (Delphinus delphis), and Risso’s dolphins (Grampus griseus) in the Gulf of Corinth (Greece, Mediterranean Sea).” Aquatic Mammals 28.2 (2002): 188–197.3.Crossman, C., Barrett-Lennard, L. & Taylor, E. Population structure and intergeneric hybridization in harbour porpoises Phocoena phocoena in British Columbia, Canada. Endang. Species. Res. 26, 1–12 (2014).Article 

Google Scholar 
4.Espada, R., Olaya-Ponzone, L., Haasova, L., Martín, E. & García-Gómez, J. C. Hybridization in the wild between Tursiops truncatus (Montagu 1821) and Delphinus delphis (Linnaeus 1758). PLoS ONE 14, e0215020 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Herzing, D. L., Moewe, K. & Brunnick, B. J. Interspecies interactions between Atlantic spotted dolphins, Stenella frontalis and bottlenose dolphins, Tursiops truncatus, on Great Bahama Bank Bahamas. Aquat. Mamm. 29, 335–341 (2003).Article 

Google Scholar 
6.Herzing, D. L. Vocalizations and associated underwater behavior of free-ranging Atlantic spotted dolphins, Stenella frontalis and bottlenose dolphins, Tursiops truncatus. Aquat. Mamm. 22, 61–80 (1996).
Google Scholar 
7.Herzing, D. L. & Johnson, C. M. Interspecific interactions between Atlantic spotted dolphins (Stenella frontalis) and bottlenose dolphins (Tursiops truncatus) in the Bahamas 1985–1995. Aquat. Mamm. 23, 85–99 (1997).
Google Scholar 
8.Orr, J. R. & Harwood, L. A. Possible aggressive behavior between a narwhal (Monodon monoceros) and a beluga (Delphinapterus leucas). Mar. Mamm. Sci. 14, 182–185 (1998).Article 

Google Scholar 
9.Puig-Lozano, R. et al. Retrospective study of traumatic intra-interspecific interactions in stranded cetaceans, Canary Islands. Front. Vet. Sci. 7, 107 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Shane, S. Relationship between pilot whales and Risso’s dolphins at Santa Catalina Island, California, USA. Mar. Ecol. Prog. Ser. 123, 5–11 (1995).ADS 
Article 

Google Scholar 
11.Haelters, J. & Everaarts, E. Two cases of physical interaction between white-beaked dolphins (Lagenorhynchus albirostris) and juvenile harbour porpoises (Phocoena phocoena) in the southern North Sea. Aquat. Mamm. 37, 198 (2011).Article 

Google Scholar 
12.Jepson, P. D. & Baker, J. R. Bottlenosed dolphins (Tursiops truncatus) as a possible cause of acute traumatic injuries in porpoises (Phocoena phocoena). Vet. Rec. 143, 614–615 (1998).CAS 
PubMed 
Article 

Google Scholar 
13.Patterson, I. A. P., Reid, R. J., Wilson, B., Grellier, K. & Ross, H. M. Evidence for infanticide in bottlenose dolphins: An explanation for violent interactions with harbour porpoises?. Proc. R. Soc. Lond. Ser. B Biol. Sci. 265, 1167–1170 (1998).CAS 
Article 

Google Scholar 
14.Ross, H. M. & Wilson, B. Violent interactions between bottlenose dolphins and harbour porpoises. Proc. R. Soc. Lond. Ser. B Biol. Sci. 263, 283–286 (1996).ADS 
Article 

Google Scholar 
15.Wilson, B., Reid, R. J., Grellier, K., Thompson, P. M. & Hammond, P. S. Considering the temporal when managing the spatial: A population range expansion impacts protected areas-based management for bottlenose dolphins. Anim. Conserv. 7, 331–338 (2004).Article 

Google Scholar 
16.Alonso, J. M., López, A., González, A. F. & Santos, M. B. Evidence of violent interactions between bottlenose dolphin (Tursiops truncatus) and other cetacean species in NW Spain. In Proceedings of the 14th Annual Conference of The European Cetacean Society (2000).17.López, A. & Rodriguez, A. Agresion de arroas (Tursiops truncatus) a toniña (Phocoena phocoena). Eubalaena 6, 23–27 (1995).
Google Scholar 
18.Methion, S. & Díaz López, B. Spatial segregation and interspecific killing of common dolphins (Delphinus delphis) by bottlenose dolphins (Tursiops truncatus). Acta Ethol. 24, 95–106 (2021).Article 

Google Scholar 
19.Parsons, K. M., Durban, J. W. & Claridge, D. E. Male-male aggression renders bottlenose dolphin (Tursiops truncatus) unconscious. Aquat. Mamm. 29, 360–362 (2003).Article 

Google Scholar 
20.Robinson, K. P. Agonistic intraspecific behavior in free-ranging bottlenose dolphins: Calf-directed aggression and infanticidal tendencies by adult males. Mar. Mamm. Sci. 30, 381–388 (2014).Article 

Google Scholar 
21.Scott, E. M., Mann, J., Watson-Capps, J. J., Sargeant, B. L., & Connor, R. C. Aggression in bottlenose
dolphins: evidence for sexual coercion, male-male competition, and female tolerance through analysis of tooth-rake
marks and behaviour. Behaviour 21–44 (2005).22.Díaz López, B., López, A., Methion, S. & Covelo, P. Infanticide attacks and associated epimeletic behaviour in free-ranging common bottlenose dolphins (Tursiops truncatus). J. Mar. Biol. Assoc. 98, 1159–1167 (2018).Article 

Google Scholar 
23.Cotter, M. P., Maldini, D. & Jefferson, T. A. “Porpicide” in California: Killing of harbor porpoises (Phocoena phocoena) by coastal bottlenose dolphins (Tursiops truncatus). Mar. Mamm. Sci. 28, E1–E15 (2012).Article 

Google Scholar 
24.Forney, K. A. Environmental models of cetacean abundance: Reducing uncertainty in population trends. Conserv. Biol. 14, 1271–1286 (2000).Article 

Google Scholar 
25.Gowans, S., Würsig, B. & Karczmarski, L. The social structure and strategies of delphinids: predictions based on an ecological framework. In Advances in Marine Biology Vol. 53, 195–294 (Elsevier, 2007).26.Miller, E. H. Territorial behavior. In Encyclopedia of marine mammals 1156–1166 (Academic Press, 2009).27.Díaz López, B. Bottlenose dolphins and aquaculture: Interaction and site fidelity on the north-eastern coast of Sardinia (Italy). Mar. Biol. 159, 2161–2172 (2012).Article 

Google Scholar 
28.Bearzi, G., Piwetz, S. & Reeves, R. R. Odontocete adaptations to human impact and vice versa. In Ethology and Behavioral Ecology of Odontocetes (ed. Würsig, B.) 211–235 (Springer International Publishing, 2019) https://doi.org/10.1007/978-3-030-16663-2_10.Chapter 

Google Scholar 
29.Bonizzoni, S. et al. Fish farming and its appeal to common bottlenose dolphins: Modelling habitat use in a Mediterranean embayment: Fish farming appeal to bottlenose dolphins. Aquatic Conserv. Mar. Freshw. Ecosyst. 24, 696–711 (2014).Article 

Google Scholar 
30.Díaz López, B. Bottlenose dolphin (Tursiops truncatus) predation on a marine fin fish farm: Some underwater observations. Aquat. Mamm. 32, 305–310 (2006).Article 

Google Scholar 
31.Díaz López, B., Marini, L. & Polo, F. The impact of a fish farm on a bottlenose dolphin population in the Mediterranean Sea. Thalassas 21, 65–70 (2005).
Google Scholar 
32.Piroddi, C., Bearzi, G. & Christensen, V. Marine open cage aquaculture in the eastern Mediterranean Sea: A new trophic resource for bottlenose dolphins. Mar. Ecol. Prog. Ser. 440, 255–266 (2011).ADS 
Article 

Google Scholar 
33.Díaz López, B. The bottlenose dolphin Tursiops truncatus foraging around a fish farm: Effects of prey abundance on dolphins’ behavior. Curr. Zool. 55, 243–248 (2009).Article 

Google Scholar 
34.Castellote, M., Brotons, J. M., Chicote, C., Gazo, M. & Cerdà, M. Long-term acoustic monitoring of bottlenose dolphins, Tursiops truncatus, in marine protected areas in the Spanish Mediterranean Sea. Ocean Coast. Manag. 113, 54–66 (2015).Article 

Google Scholar 
35.Aznar, F. et al. Long-term changes (1990–2012) in the diet of striped dolphins Stenella coeruleoalba from the western Mediterranean. Mar. Ecol. Prog. Ser. 568, 231–247 (2017).ADS 
Article 

Google Scholar 
36.Calzada, N., Aguilar, A., Grau, E. & Lockyer, C. Patterns of growth and physical maturity in the western Mediterranean striped dolphin, Stenella coeruleoalba (Cetacea: Odontoceti). Can. J. Zool. 75, 632–637 (1997).Article 

Google Scholar 
37.Meissner, A. M., MacLeod, C. D., Richard, P., Ridoux, V. & Pierce, G. Feeding ecology of striped dolphins, Stenella coeruleoalba, in the north-western Mediterranean Sea based on stable isotope analyses. J. Mar. Biol. Assoc. 92, 1677–1687 (2012).CAS 
Article 

Google Scholar 
38.Chen, I., Watson, A. & Chou, L.-S. Insights from life history traits of Risso’s dolphins (Grampus griseus) in Taiwanese waters: Shorter body length characterizes northwest Pacific population. Mar. Mamm. Sci. 27, E43–E64 (2011).Article 

Google Scholar 
39.Barnett, J. et al. Postmortem evidence of interactions of bottlenose dolphins (Tursiops truncatus) with other dolphin species in south-west England. Vet. Rec. 165, 441–444 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Townsend, F. I. & Staggs, L. Atlas of Skin Diseases of Small Cetaceans (Todd Speakman, 2020).
Google Scholar 
41.Jefferson, T. A., Stacey, P. J. & Baird, R. W. A review of Killer Whale interactions with other marine mammals: Predation to co-existence. Mamm. Rev. 21, 151–180 (1991).Article 

Google Scholar 
42.Weller, D. W. et al. Observations of an interaction between sperm whales and short-finned pilot whales in the Gulf of Mexico. Mar. Mamm. Sci. 12, 588–594 (1996).ADS 
Article 

Google Scholar 
43.Baird, R. W. An interaction between Pacific white-sided dolphins and a neonatal harbor porpoise. Mammalia 62, 129–133 (1998).
Google Scholar 
44.Wedekin, L. L., Daura-Jorge, F. G. & Simoes-Lopes, P. C. An aggressive interaction between bottlenose dolphins (Tursiops truncatus) and estuarine dolphins (Sotalia guianensis) in southern Brazil. Aquat. Mamm. 30, 391–397 (2004).Article 

Google Scholar 
45.Campbell-Malone, R. et al. Gross and histologic evidence of sharp and blunt trauma in north Atlantic right whales (Eubalaena glacialis) killed by vessels. J. Zoo Wildl. Med. 39, 37–55 (2008).PubMed 
Article 

Google Scholar 
46.Moore, M. et al. Criteria and case definitions for serious injury and death of pinnipeds and cetaceans caused by anthropogenic trauma. Dis. Aquat. Org. 103, 229–264 (2013).CAS 
Article 

Google Scholar 
47.Read, A. & Murray, K. Gross Evidence of Human-Induced Mortality in Small Cetaceans (2000).48.Gozalbes, P. et al. Cetáceos y tortugas marinas en la Comunitat Valenciana. 20 años de seguimiento (2010).49.Gómez de Segura, A., Hammond, P. S. & Raga, J. A. Influence of environmental factors on small cetacean distribution in the Spanish Mediterranean. J. Mar. Biol. Assoc. 88, 1185–1192 (2008).Article 

Google Scholar 
50.Cañadas, A., Sagarminaga, R., De Stephanis, R., Urquiola, E. & Hammond, P. S. Habitat preference modelling as a conservation tool: Proposals for marine protected areas for cetaceans in southern Spanish waters. Aquat. Conserv. Mar. Freshw. Ecosyst. 15, 495–521 (2005).Article 

Google Scholar 
51.Gannier, A. Diel variations of the striped dolphin distribution off the French Riviera (Northwestern Mediterranean Sea). Aquat. Mamm. 25, 123–134 (1999).
Google Scholar 
52.Blanco, C., Aznar, J. & Raga, J. A. Cephalopods in the diet of the striped dolphin Stenella coeruleoalba from the western Mediterranean during an epizootic in 1990. J. Zool. 237, 151–158 (1995).Article 

Google Scholar 
53.Archer II, F. I. Striped dolphin: Stenella coeruleoalba. In Encyclopedia of Marine Mammals 1127–1129 (Academic Press, 2009).54.Fraija-Fernández, N. et al. Long term boat-based surveys in the Central Spanish Mediterranean (2003–2013): Cetacean diversity and distribution. In Proceeding of the 29th Conference of the European Cetacean Society (2015).55.Blanco, C., Salomón, O. & Raga, J. A. Diet of the bottlenose dolphin (Tursiops truncatus) in the western Mediterranean Sea. J. Mar. Biol. Assoc. 81, 1053–1058 (2001).Article 

Google Scholar 
56.Praca, E. & Gannier, A. Ecological niches of three teuthophageous odontocetes in the northwestern Mediterranean Sea. Ocean Sci. 4, 49–59 (2008).ADS 
Article 

Google Scholar 
57.Bearzi, G., Fortuna, C. M. & Reeves, R. R. Ecology and conservation of common bottlenose dolphins Tursiops truncatus in the Mediterranean Sea. Mamm. Rev. 39, 92–123 (2009).Article 

Google Scholar 
58.Epperly, S. P. et al. Beach strandings as an indicator of at-sea mortality of sea turtles. Bull. Mar. Sci. 59(2), 289–297 (1996).
Google Scholar 
59.Peltier, H. et al. The significance of stranding data as indicators of cetacean populations at sea: Modelling the drift of cetacean carcasses. Ecol. Ind. 18, 278–290 (2012).Article 

Google Scholar 
60.Martínez-Cedeira, J. A. et al. How many strand? Offshore marking and coastal recapture of cetacean carcasses. In Abstract Book—25th Conference of the European Cetacean Society 332 (2011).61.Gulland, F. M., Dierauf, L. A. & Whitman, K. L. CRC Handbook of Marine Mammal medicine (CRC Press, 2018).
Google Scholar 
62.Isidoro-Ayza, M. et al. Brucella ceti infection in dolphins from the Western Mediterranean sea. BMC Vet. Res. 10, 206 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
63.Rubio-Guerri, C. et al. Unusual striped dolphin mass mortality episode related to cetacean morbillivirus in the Spanish Mediterranean sea. BMC Vet. Res. 9, 106 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
64.Kuiken, T. & Hartmann, M. G. Proceedings of the First ECS Workshop on Cetacean Pathology: Dissection Techniques and Tissue Sampling. Vol. 17 (1991).65.Geraci, J. R. & Lounsbury, V. J. Marine Mammals Ashore: A Field guide for Strandings (National Aquarium in Baltimore, 2005).
Google Scholar 
66.Pugliares, K. R. et al. Marine Mammal Necropsy: An Introductory Guide for Stranding Responders and Field Biologists (Woods Hole Oceanographic Institution, 2007) https://doi.org/10.1575/1912/1823.Book 

Google Scholar 
67.Long, D. J. & Jones, R. E. White shark predation and scavenging on cetaceans in the eastern North Pacific Ocean. In Great White Sharks: The Biology of Carcharodon carcharias 293–307 (1996).68.Rubio-Guerri, C. et al. Simultaneous diagnosis of Cetacean morbillivirus infection in dolphins stranded in the Spanish Mediterranean sea in 2011 using a novel Universal Probe Library (UPL) RT-PCR assay. Vet. Microbiol. 165, 109–114 (2013).PubMed 
Article 

Google Scholar 
69.Van Devanter, D. R. et al. Detection and analysis of diverse herpesviral species by consensus primer PCR. J. Clin. Microbiol. 34, 1666–1671 (1996).CAS 
Article 

Google Scholar 
70.Alton, G. G., Jones, L. M., Angus, R. D. & Verger, J. M. Techniques for the Brucellosis Laboratory (Institut National de la Recherche Agronomique (INRA), 1988).
Google Scholar  More