Philippa Kaur

1.Palumbi, S. R. Humans as the world’s greatest evolutionary force. Science 293, 1786–1790 (2001).ADS 
PubMed 
Article 
CAS 

Google Scholar 
2.Hendry, A. P., Gotanda, K. M. & Svensson, E. I. Human Influences on Evolution, and the Ecological and Societal Consequences (The Royal Society, 2017).Book 

Google Scholar 
3.Otto, S. P. Adaptation, speciation and extinction in the Anthropocene. Proc. R. Soc. B 285, 20182047 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Dynesius, M. & Nilsson, C. Fragmentation and flow regulation of river systems in the northern third of the world. Science 266, 753–762 (1994).ADS 
PubMed 
Article 
CAS 

Google Scholar 
5.Gibson, L., Wilman, E. N. & Laurance, W. F. How green is ‘green’energy?. Trends Ecol. Evol. 32, 922–935 (2017).PubMed 
Article 

Google Scholar 
6.Calles, O. & Greenberg, L. Connectivity is a two-way street—the need for a holistic approach to fish passage problems in regulated rivers. River Res. Appl. 25, 1268–1286 (2009).Article 

Google Scholar 
7.Poff, N. L. et al. The natural flow regime. Bioscience 47, 769–784 (1997).Article 

Google Scholar 
8.Haraldstad, T. et al. Anthropogenic and natural size-related selection act in concert during brown trout (Salmo trutta) smolt river descent. Hydrobiologia, 1–14 (2020).9.Limburg, K. E. & Waldman, J. R. Dramatic declines in North Atlantic diadromous fishes. Bioscience 59, 955–965 (2009).Article 

Google Scholar 
10.Belletti, B. et al. More than one million barriers fragment Europe’s rivers. Nature 588, 436–441 (2020).ADS 
PubMed 
Article 
CAS 

Google Scholar 
11.Klemetsen, A. et al. Atlantic salmon Salmo salar L., brown trout Salmo trutta L. and Arctic charr Salvelinus alpinus (L.): a review of aspects of their life histories. Ecol. Freshw. Fish 12, 1–59 (2003).Article 

Google Scholar 
12.Thorstad, E. B., Økland, F., Aarestrup, K. & Heggberget, T. G. Factors affecting the within-river spawning migration of Atlantic salmon, with emphasis on human impacts. Rev. Fish Biol. Fish. 18, 345–371 (2008).Article 

Google Scholar 
13.Parrish, D. L., Behnke, R. J., Gephard, S. R., McCormick, S. D. & Reeves, G. H. Why aren’t there more Atlantic salmon (Salmo salar)?. Can. J. Fish. Aquat. Sci. 55, 281–287 (1998).Article 

Google Scholar 
14.Larinier, M. Fish passage experience at small-scale hydro-electric power plants in France. Hydrobiologia 609, 97–108 (2008).Article 

Google Scholar 
15.Coutant, C. C. & Whitney, R. R. Fish behavior in relation to passage through hydropower turbines: a review. Trans. Am. Fish. Soc. 129, 351–380 (2000).Article 

Google Scholar 
16.Montèn, E. Fish and Turbines: Fish Injuries During Passage Through Power Station Turbines (Nordsteds Tryckeri, 1985).
Google Scholar 
17.Pracheil, B. M., DeRolph, C. R., Schramm, M. P. & Bevelhimer, M. S. A fish-eye view of riverine hydropower systems: the current understanding of the biological response to turbine passage. Rev. Fish Biol. Fisheries 26, 153–167 (2016).Article 

Google Scholar 
18.Calles, O., Rivinoja, P. & Greenberg, L. A Historical perspective on downstream passage at hydroelectric plants in swedish rivers. In: Ecohydraulics. Wiley (2013).19.Silva, A. T. et al. The future of fish passage science, engineering, and practice. Fish Fish. 19, 340–362 (2017).Article 

Google Scholar 
20.Noonan, M. J., Grant, J. W. A. & Jackson, C. D. A quantitative assessment of fish passage efficiency. Fish Fish. 13, 450–464 (2012).Article 

Google Scholar 
21.Scruton, D. A., McKinley, R. S., Kouwen, N., Eddy, W. & Booth, R. K. Improvement and optimization of fish guidance efficiency (FGE) at a behavioural fish protection system for downstream migrating Atlantic salmon (Salmo salar) smolts. River Res. Appl. 19, 605–617 (2003).Article 

Google Scholar 
22.Mallen-Cooper, M. & Brand, D. A. Non-salmonids in a salmonid fishway: what do 50 years of data tell us about past and future fish passage?. Fish. Manage. Ecol. 14, 319–332 (2007).Article 

Google Scholar 
23.Bunt, C., Castro-Santos, T. & Haro, A. Performance of fish passage structures at upstream barriers to migration. River Res. Appl. 28, 457–478 (2012).Article 

Google Scholar 
24.Haugen, T. O., Aass, P., Stenseth, N. C. & Vøllestad, L. A. Changes in selection and evolutionary responses in migratory brown trout following the construction of a fish ladder. Evol. Appl. 1, 319–335 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Mallen-Cooper, M. & Stuart, I. G. Optimising Denil fishways for passage of small and large fishes. Fish. Manage. Ecol. 14, 61–71 (2007).Article 

Google Scholar 
26.Maynard, G. A., Kinnison, M. & Zydlewski, J. D. Size selection from fishways and potential evolutionary responses in a threatened Atlantic salmon population. River Res. Appl. 33, 1004–1015 (2017).Article 

Google Scholar 
27.Lothian, A. J. et al. Are we designing fishways for diversity? Potential selection on alternative phenotypes resulting from differential passage in brown trout. J Environ Manag 262, 110317 (2020).Article 

Google Scholar 
28.Haraldstad, T., Haugen, T. O., Kroglund, F., Olsen, E. M. & Höglund, E. Migratory passage structures at hydropower plants as potential physiological and behavioural selective agents. R. Soc. Open Sci. 6, 190 (2019).Article 

Google Scholar 
29.Conrad, J. L., Weinersmith, K. L., Brodin, T., Saltz, J. B. & Sih, A. Behavioural syndromes in fishes: a review with implications for ecology and fisheries management. J. Fish Biol. 78, 395–435 (2011).PubMed 
Article 
CAS 

Google Scholar 
30.Dochtermann, N. A., Schwab, T. & Sih, A. The contribution of additive genetic variation to personality variation: heritability of personality. Proc. R. Soc. B: Biol. Sci. 282, 20142201 (2015).Article 

Google Scholar 
31.Réale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B: Biol. Sci. 365, 4051–4063 (2010).Article 

Google Scholar 
32.Haraldstad, T., Höglund, E., Kroglund, F., Haugen, T. O. & Forseth, T. Common mechanisms for guidance efficiency of descending Atlantic salmon smolts in small and large hydroelectric power plants. River Res. Appl. 34, 1179–1185 (2018).Article 

Google Scholar 
33.Larsen, M. H., Thorn, A. N., Skov, C. & Aarestrup, K. Effects of passive integrated transponder tags on survival and growth of juvenile Atlantic salmon Salmo salar. Anim. Biotelem. 1, 19 (2013).Article 

Google Scholar 
34.Vollset, K. W. et al. Systematic review and meta-analysis of PIT tagging effects on mortality and growth of juvenile salmonids. Rev. Fish Biol. Fish, 1–16 (2020).35.Adriaenssens, B. & Johnsson, J. I. Natural selection, plasticity and the emergence of a behavioural syndrome in the wild. Ecol. Lett. 16, 47–55 (2013).PubMed 
Article 

Google Scholar 
36.Dingemanse, N. J. et al. Behavioural syndromes differ predictably between 12 populations of three-spined stickleback. J. Anim. Ecol. 76, 1128–1138 (2007).PubMed 
Article 

Google Scholar 
37.Larsen, M. H. et al. Effects of emergence time and early social rearing environment on behaviour of Atlantic salmon: consequences for juvenile fitness and smolt migration. PLoS ONE 10, e0119127 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Castanheira, M. F., Herrera, M., Costas, B., Conceição, L. E. & Martins, C. I. Can we predict personality in fish? Searching for consistency over time and across contexts. PLoS ONE 8, e62037 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Huntingford, F. et al. Coping strategies in a strongly schooling fish, the common carp Cyprinus carpio. J. Fish Biol. 76, 1576–1591 (2010).PubMed 
Article 
CAS 

Google Scholar 
40.Brown, C., Jones, F. & Braithwaite, V. Correlation between boldness and body mass in natural populations of the poeciliid Brachyrhaphis episcopi. J. Fish Biol. 71, 1590–1601 (2007).Article 

Google Scholar 
41.R Development Core Team. R: A language and environment for statistical computing.). R Foundation for Statistical Computing (2016).42.Akaike, H. A. new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
43.Anderson, D. R. Model-Based Interference in the Life Sciences: A Primer on Evidence (Springer, 2008).MATH 
Book 

Google Scholar 
44.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020).45.Brunham, A. & Anderson D, R. Model selection and multimodel inference: A practical information-theoretic approach. 2nd edn (Springer-Verlag, New York 2002).46.Fjeldstad, H. P., Alfredsen, K. & Boissy, T. Optimising Atlantic salmon smolt survival by use of hydropower simulation modelling in a regulated river. Fish. Manage. Ecol. 21, 22–31 (2014).Article 

Google Scholar 
47.Calles, O. et al. Anordning för upp- och nedströmspassage av fisk vid vattenanläggningar (2013).48.Larinier, M., Travade, F. The development and evaluation of downstream bypasses for juvenile salmonids at small hydroelectric plants in France. Innov. Fish Passage Technol. 25–42 (1999).49.Turnpenny, A. W. H., O`Keeffe, N. Screening for intake and Outfalls: a best practice guide (2005).50.Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).PubMed 
Article 

Google Scholar 
51.Taylor, M. K. & Cooke, S. J. Repeatability of movement behaviour in a wild salmonid revealed by telemetry. J. Fish Biol. 84, 1240–1246 (2014).PubMed 
Article 
CAS 

Google Scholar 
52.Odling-Smee, L. & Braithwaite, V. A. The role of learning in fish orientation. Fish Fish. 4, 235–246 (2003).Article 

Google Scholar 
53.Lucon-Xiccato, T., Montalbano, G. & Bertolucci, C. Personality traits covary with individual differences in inhibitory abilities in 2 species of fish. Curr. Zool. 66, 187–195 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Endler, J. A. Natural Selection in the Wild (Princeton University Press, 1986).
Google Scholar 
55.Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: a meta-analysis. Anim. Behav. 77, 771–783 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: an ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 
Article 

Google Scholar 
57.Wuerz, Y. & Krüger, O. Personality over ontogeny in zebra finches: long-term repeatable traits but unstable behavioural syndromes. Front. Zool. 12, S9 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Wolf, M. & Weissing, F. J. Animal personalities: consequences for ecology and evolution. Trends Ecol. Evol. 27, 452–461 (2012).PubMed 
Article 

Google Scholar 
59.Cordero-Rivera, A. Behavioral diversity (ethodiversity): a neglected level in the study of biodiversity. Front. Ecol. Evol. 5, 7 (2017).ADS 
Article 

Google Scholar 
60.Biro, P. A. & Post, J. R. Rapid depletion of genotypes with fast growth and bold personality traits from harvested fish populations. Proc. Natl. Acad. Sci. 105, 2919–2922 (2008).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Uusi-Heikkilä, S., Wolter, C., Klefoth, T. & Arlinghaus, R. A behavioral perspective on fishing-induced evolution. Trends Ecol. Evol. 23, 419–421 (2008).PubMed 
Article 

Google Scholar 
62.Cooke, S. J., Suski, C. D., Ostrand, K. G., Wahl, D. H. & Philipp, D. P. Physiological and behavioral consequences of long-term artificial hselection for vulnerability to recreational angling in a teleost fish. Physiol. Biochem. Zool. 80, 480–490 (2007).PubMed 
Article 

Google Scholar  More

Host and mutual symbionts decline at different rates following consecutive cyclones and bleachingBefore and after disturbances, we surveyed Acropora corals known to host Gobiodon coral gobies along line (30 m) and cross (two 4-m by 1-m belt) transects. In February 2014, prior to cyclones and bleaching events, most of these Acropora corals were inhabited by Gobiodon coral gobies. Gobies were not found in corals under 7-cm average diameter, therefore we only sampled bigger corals. The vast majority of transects (95%) had Acropora corals. On average there were 3.24 ± 0.25 (mean ± standard error) Acropora coral species per transect (Fig. 2a) and a total of 17 species were observed among all 2014 transects. Average coral diameter was 25.4 ± 1.0 cm (Fig. 2b), with some corals reaching over 100 cm. Only 4.1 ± 1.4% of corals lacked any goby inhabitants (Fig. 2c). On average there were 3.37 ± 0.26 species of gobies per transect (Fig. 2d) and a total of 13 species among all 2014 transects. In each occupied coral there were 2.20 ± 0.14 gobies (Fig. 2e), with a maximum of 11 individuals of the same species.Figure 2Effects of consecutive climate disturbances on coral and goby populations. Changes in Acropora (a) richness (n = 279), and (b) average diameter (n = 244), (c) percent goby occupancy (n = 244) and Gobiodon (d) richness (n = 279), and (e) group size (n = 230) per transect (n = sample size per variable) before and after each cyclone (black cyclone symbols) and after two consecutive heatwaves/bleaching events (white coral symbols) around Lizard Island, Great Barrier Reef, Australia. Error bars are standard error. Fish and coral symbols above each graph illustrate the change in means for each variable among sampling events from post-hoc tests. Figures were illustrated in R (v3.5.2)33 and Microsoft Office PowerPoint 2016.Full size imageIn January–February 2015, 9 months after Cyclone Ita (category 4) struck from the north (Supplementary Fig. 1), follow-up surveys revealed no changes to coral richness (p = 0.986, see Supplementary Table 1 for all statistical outputs) relative to February 2014, but corals were 19% smaller (p  More

1.Mitchard, E. T. A., Saatchi, S. S., Gerard, F. F., Lewis, S. L. & Meir, P. Measuring woody encroachment along a forest-savanna boundary in Central Africa. Earth Interact. 13, 1–29 (2009).Article 

Google Scholar 
2.Murphy, B. P. & Bowman, D. M. J. S. What controls the distribution of tropical forest and savanna?. Ecol. Lett. 15, 748–758 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Staver, A. C., Archibald, S. & Levin, S. Tree cover in sub-Saharan Africa: Rainfall and fire constrain forest and savanna as alternative stable states. Ecology 92, 1063–1072 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Bowman, D. M. J. S., Murphy, B. P. & Banfai, D. S. Has global environmental change caused monsoon rainforests to expand in the Australian monsoon tropics?. Landsc. Ecol. 25, 1247–1260 (2010).Article 

Google Scholar 
5.Favier, C., Namur, C. D. & Dubois, M. F. Forest progression modes in littoral Congo, central atlantic Africa. J. Biogeogr. 31, 1445–1461 (2004).Article 

Google Scholar 
6.Puyravaud, J. P., Dufour, C. & Aravajy, S. Rain forest expansion mediated by successional processes in vegetation thickets in the Western Ghats of India. J. Biogeogr. 30, 1067–1080 (2003).Article 

Google Scholar 
7.Tng, D. Y. P. et al. Humid tropical rain forest has expanded into eucalypt forest and savanna over the last 50 years. Ecol. Evol. 2, 34–45 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Mariano, V., Rebolo, I. F. & Christianini, A. V. Fire sensitive species dominate seed rain after fire supression: implications for plant community diversity and woody encroachment in the Cerrado. Biotropica 51, 5–9 (2019).Article 

Google Scholar 
9.Ferreira, A. V., Bruna, E. M. & Vasconcelos, H. L. Seed predators limit plant recruitment in neotropical savannas. Oikos 120, 1013–1022 (2010).Article 

Google Scholar 
10.Azihou, A. F., Glèlè Kakaï, R. & Sinsin, B. Do isolated gallery-forest trees facilitate recruitment of forest seedlings and saplings in savannna?. Acta Oecol. 53, 11–18 (2013).ADS 
Article 

Google Scholar 
11.Duarte, L. D. S., Dos-Santos, M. M. G., Hartz, S. M. & Pillar, V. D. Role of nurse plants in Araucaria Forest expansion over grassland in south Brazil. Austral Ecol. 31, 520–528 (2006).Article 

Google Scholar 
12.Hoffmann, W. A. The Effects of Fire and Cover on Seedling Establishment in a Neotropical Savanna. J. Ecol. 84, 383–393 (1996).Article 

Google Scholar 
13.Hoffmann, W. A., Orthen, B. & Franco, A. C. Constraints to seedling success of savanna and forest trees across the savanna-forest boundary. Oecologia 140, 252–260 (2004).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Lawes, M. J., Murphy, B. P., Midgley, J. J. & Russell-Smith, J. Are the eucalypt and non-eucalypt components of Australian tropical savannas independent?. Oecologia 166, 229–239 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Russell-Smith, J., Stanton, P. J., Whitehead, P. J. & Edwards, A. Rain forest invasion of eucalypt-dominated woodland savanna, iron range, north-eastern Australia: I. Successional processes. J. Biogeogr. 31, 1293–1303 (2004).Article 

Google Scholar 
16.Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. Inclusion of facilitation into ecological theory. Trends Ecol. Evol. 18, 119–125 (2003).Article 

Google Scholar 
17.Callaway, R. M. Positive interactions among plants. Bot. Rev. 61, 306–349 (1995).Article 

Google Scholar 
18.Slocum, M. G. & Horvitz, C. C. Seed arrival under different genera of trees in a neotropical pasture. Plant Ecol. 149, 51–62 (2000).Article 

Google Scholar 
19.Schlawin, J. R. & Zahawi, R. A. ‘Nucleating’ succession in recovering neotropical wet forests: the legacy of remnant trees. J. Veg. Sci. 19, 485–492 (2008).Article 

Google Scholar 
20.Slocum, M. G. How tree species differ as recruitment foci in a tropical pasture. Ecology 82, 2547–2559 (2001).Article 

Google Scholar 
21.Fujita, T. Ficus natalensis facilitates the establishment of a montane rain-forest tree in south-east African tropical woodlands. J. Trop. Ecol. 30, 303–310 (2014).Article 

Google Scholar 
22.de Dantas, V. L. et al. Plant dispersal strategies and the colonization of araucaria forest patches in a grassland-forest mosaic. J. Veg. Sci. 18, 847–858 (2007).Article 

Google Scholar 
23.Campbell, B., Frost, P. & Byron, N. Miombo woodlands and their use: overview and key issues. In The Miombo in transition: woodlands and welfare in Africa (ed. Campbell, B.) 1–5 (Center for International Forestry Research, 1996).
Google Scholar 
24.White, F., Dowsett-Lemaire, F. & Chapman, S. Evergreen Forest Flora of Malawi (Royal Botanic Gardens, 2001).
Google Scholar 
25.Chapman, J. D. PART II Description of the forest. In The evergreen forests of Malawi (eds. Chapman, J. D. & White, F.) 113–180 (Commonwealth Forestry Institute, 1970).
Google Scholar 
26.Hoffmann, W. A. et al. Ecological thresholds at the savanna-forest boundary: how plant traits, resources and fire govern the distribution of tropical biomes. Ecol. Lett. 15, 759–768 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Frazer, G. W., Canham, C. D., & Lertzman, K. P. Gap Light Analyzer (GLA) 2.0: Imaging software to extract canopy structure and gap light transmission indices from true-colour fisheye photographs. http://remmain.rem.sfu.ca/downloads/Forestry/GLAV2UsersManual.pdf (1999).28.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
29.Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).MathSciNet 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
30.Trauernicht, C., Murphy, B. P., Portner, T. E. & Bowman, D. M. J. S. Tree cover-fire interactions promote the persistence of a fire-sensitive conifer in a highly flammable savanna. J. Ecol. 100, 958–968 (2012).Article 

Google Scholar 
31.Castro, J., Zamora, R., Hódar, J. A. & Gómez, J. M. Seedling establishment of a boreal tree species (Pinus sylvestris) at its southernmost distribution limit: consequences of being in a marginal Mediterranean habitat. J. Ecol. 92, 266–277 (2004).Article 

Google Scholar 
32.Gómez-Aparicio, L., Gómez, J. M., Zamora, R. & Boettinger, J. L. Canopy vs. soil effects of shrubs facilitating tree seedlings in Mediterranean montane ecosystems. J. Veg. Sci. 16, 191–198 (2005).Article 

Google Scholar 
33.Smit, C., Den Ouden, J. & Díaz, M. Facilitation of Quercus ilex recruitment by shrubs in Mediterranean open woodlands. J. Veg. Sci. 19, 193–200 (2008).Article 

Google Scholar 
34.Rao, S. J., Iason, G. R., Hulbert, I. A. R., Elston, D. A. & Racey, P. A. The effect of sapling density, heather height and season on browsing by mountain hares on birch. J. Appl. Ecol. 40, 626–638 (2003).Article 

Google Scholar 
35.de Dantas, V. L., Hirota, M., Oliveira, R. S. & Pausas, J. G. Disturbance maintains alternative biome states. Ecol. Lett. 19, 12–19 (2016).Article 

Google Scholar 
36.Terborgh, J. et al. Megafaunal influences on tree recruitment in African equatorial forests. Ecography 39, 180–186 (2016).Article 

Google Scholar 
37.Ripple, R. et al. Bushmeat hunting and extinction risk to the world’s mammals. R. Soc. Open Sci. 3, 160498. https://doi.org/10.1098/rsos.160498 (2016).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Hegerl, C., Burgess, N., Nielsen, M., Martin, E., Ciolli, M., & Rovero, F. Using camera trap data to assess the impact of bushmeat hunting on forest mammals in Tanzania. Oryx 51(1), 87–97 (2017).Article 

Google Scholar 
39.Bowman, D. M. J. S. & Panton, W. J. Factors that control monsoon-rainforest seedling establishment and growth in North Australian Eucalyptus Savanna. J. Ecol. 81, 297–304 (1993).Article 

Google Scholar 
40.Ruggiero, C. P. G., Batalha, M. A., Pivello, V. R. & Meirelles, S. T. Soil-vegetation relationships in cerrado (Brazilian savanna) and semideciduous forest, Southeastern Brazil. Plant Ecol. 160, 1–16 (2002).Article 

Google Scholar 
41.Viani, R. A. G., Rodrigues, R. R., Dawson, T. E. & Oliveira, R. S. Savanna soil fertility limits growth but not survival of tropical forest tree seedlings. Plant Soil 349, 341–353 (2011).CAS 
Article 

Google Scholar 
42.Chen, J. et al. Soil nutrient availability determines the facilitative effects of cushion plants on other plant species at high elevations in the south-eastern Himalayas. Plant Ecolog. Divers. 8, 199–210 (2015).Article 

Google Scholar 
43.Zahawi, R. A., Holl, K. D., Cole, R. J. & Reid, J. L. Testing applied nucleation as a strategy to facilitate tropical forest recovery. J. Appl. Ecol. 50(2013), 88–96 (2013).Article 

Google Scholar 
44.Clark, C. J., Poulsen, J. R., Connor, E. F. & Parker, V. T. Fruiting trees as dispersal foci in a semi-deciduous tropical forest. Oecologia 139, 66–75 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Carlo, T. A. & Aukema, J. E. Female-directed dispersal and facilitation between a tropical mistletoe and a dioecious host. Ecology 86, 3245–3251 (2005).Article 

Google Scholar 
46.Bond, W. J., Woodward, F. I. & Midgley, G. F. The global distribution of ecosystems in a world without fire. New Phytol. 165, 525–538 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

Laris also notes that people in West Africa overwhelmingly set early dry season (EDS) fires. This is true for Burkina Faso, Senegal, Benin, Togo, Ghana, which all have an early burning pattern (See Table 1). However, this is not the case for Nigeria, Sierra Leone and Guinea-Bissau, which have most emissions in the late dry season (LDS) (see Table 1). Also, if we sum the total EDS and LDS emissions for West African Countries, then 45% of emissions occur in the EDS and 55% in the late dry season (see Table 1). The total West African contribution is around 8% of the total African savanna emissions—a relatively small contributor.We haven’t suggested that the early burning practise would work for all of West Africa, but the evidence suggests that it would work for Nigeria, Sierra Leone and Guinea-Bissau (see Table 1). We agree, many of the West African countries have significant EDS burning patterns like Burkina Faso, Senegal, Benin, Togo and Ghana and would not benefit from the approach. However, for those countries with significant EDS burning that still have significant LDS emissions as well, such as Mali and Côte d’Ivoire, there may be some opportunity for further emissions reductions through improved fire management practices as presented in our paper3.Laris1 also points out that the same EDS regime proposed is one that was developed by indigenous people and that it has been applied by Africans for centuries. The same is true for Australia, but colonial occupation altered that, as it has in some areas of Africa. A new incentive in the form of carbon payments for early burning in Australia has empowered local indigenous people to reconnect to their traditional lands and fulfil their cultural obligations and a diversity burning practices14. More

1.Chesson, P. General theory of competitive coexistence in spatially-varying environments. Theor. Popul. Biol. 58, 211–237 (2000).2.Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton Univ. Press, 2001).3.Ellner, S. P., Snyder, R. E., Adler, P. B. & Hooker, G. An expanded modern coexistence theory for empirical applications. Ecol. Lett. 22, 3–18 (2019).4.Rosenzweig, M. L. Paradox of enrichment: destabilization of exploitation ecosystems in ecological time. Science 171, 385–387 (1971).5.Costantino, R. F., Cushing, J. M., Dennis, B. & Desharnais, R. A. Experimentally induced transitions in the dynamic behaviour of insect populations. Nature 375, 227–230 (1995).6.Fussmann, G. F., Ellner, S. P., Shertzer, K. W. & Hairston, N. G. Jr. Crossing the Hopf bifurcation in a live predator-prey system. Science 290, 1358–1360 (2000).7.Dalziel, B. D. et al. Persistent chaos of measles epidemics in the prevaccination United States caused by a small change in seasonal transmission patterns. PLoS Comput. Biol. 12, e1004655 (2016).8.Darwin, C. On the Origin of Species by Means of Natural Selection, or The Preservation of Favoured Races in the Struggle for Life (John Murray, 1859).9.Gause, G. F. Experimental analysis of Vito Volterra’s mathematical theory of the struggle for existence. Science 79, 16–17 (1934).10.Hutchinson, G. E. The paradox of the plankton. Am. Nat. 95, 137–145 (1961).Article 

Google Scholar 
11.Chesson, P. Multispecies competition in variable environments. Theor. Popul. Biol. 45, 227–276 (1994).Article 

Google Scholar 
12.McCann, K., Hastings, A. & Huxel, G. R. Weak trophic interactions and the balance of nature. Nature 395, 794–798 (1998).13.Rooney, N., McCann, K., Gellner, G. & Moore, J. C. Structural asymmetry and the stability of diverse food webs. Nature 442, 265–269 (2006).14.Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).15.May, R. M. Host-parasitoid systems in patchy environments: a phenomenological model. J. Anim. Ecol. 47, 833–844 (1978).Article 

Google Scholar 
16.Briggs, C. J. & Hoopes, M. F. Stabilizing effects in spatial parasitoid–host and predator–prey models: a review. Theor. Popul. Biol. 65, 299–315 (2004).17.Vicsek, T. & Zafeiris, A. Collective motion. Phys. Rep. 517, 71–140 (2012).Article 

Google Scholar 
18.Berdahl, A., Torney, C. J., Ioannou, C. C., Faria, J. J. & Couzin, I. D. Emergent sensing of complex environments by mobile animal groups. Science 339, 574–576 (2013).19.Nagy, M., Akos, Z., Biro, D. & Vicsek, T. Hierarchical group dynamics in pigeon flocks. Nature 464, 890–893 (2010).20.Dalziel, B. D., Corre, M. L., Côté, S. D. & Ellner, S. P. Detecting collective behaviour in animal relocation data, with application to migrating caribou. Methods Ecol. Evol. 7, 30–41 (2015).Article 

Google Scholar 
21.Torney, C. J. et al. Inferring the rules of social interaction in migrating caribou. Phil. Trans. R. Soc. B 373, 20170385 (2018).22.Fryxell, J. M., Mosser, A., Sinclair, A. R. E. & Packer, C. Group formation stabilizes predator–prey dynamics. Nature 449, 1041–1043 (2007).23.Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I. & Shochet, O. Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75, 1226 (1995).CAS 
Article 

Google Scholar 
24.Buhl, J. et al. From disorder to order in marching locusts. Science 312, 1402–1406 (2006).25.King, A. J., Fehlmann, G., Biro, D., Ward, A. J. & Fürtbauer, I. Re-wilding collective behaviour: an ecological perspective. Trends Ecol. Evol. 33, 347–357 (2018).26.Sumpter, D. J. T. Collective Animal Behavior (Princeton Univ. Press, 2010).27.Guttal, V. & Couzin, I. D. Social interactions, information use, and the evolution of collective migration. Proc. Natl Acad. Sci. USA 107, 16172–16177 (2010).CAS 
Article 

Google Scholar 
28.Barbier, M. & Watson, J. R. The spatial dynamics of predators and the benefits and costs of sharing information. PLoS Comput. Biol. 12, e1005147 (2016).29.Lotka, A. J. Analytical note on certain rhythmic relations in organic systems. Proc. Natl Acad. Sci. USA 6, 410–415 (1920).CAS 
Article 

Google Scholar 
30.Rosenzweig, M. L. & MacArthur, R. H. Graphical representation and stability conditions of predator-prey interactions. Am. Nat. 97, 209–223 (1963).Article 

Google Scholar 
31.Couzin, I. D., Krause, J., James, R., Ruxton, G. D. & Franks, N. R. Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218, 1–11 (2002).32.Couzin, I. D., Krause, J., Franks, N. R. & Levin, S. A. Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005).33.MacArthur, R. H. Population ecology of some warblers of northeastern coniferous forests. Ecology 39, 599–619 (1958).Article 

Google Scholar 
34.Dalziel, B. D., Thomann, E., Medlock, J. & De Leenheer, P. Global analysis of a predator-prey model with variable predator search rate. J. Math. Biol. 81, 159–183 (2020).35.Lukas, D. & Clutton-Brock, T. Social complexity and kinship in animal societies. Ecol. Lett. 21, 1129–1134 (2018).36.Purves, D. W., Lichstein, J. W., Strigul, N. & Pacala, S. W. Predicting and understanding forest dynamics using a simple tractable model. Proc. Natl Acad. Sci. USA 105, 17018–17022 (2008).CAS 
Article 

Google Scholar 
37.Dalziel, B. D. et al. Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities. Science 362, 75–79 (2018).38.Monk, C. T. et al. How ecology shapes exploitation: a framework to predict the behavioural response of human and animal foragers along exploration-exploitation trade-offs. Ecol. Lett. 21, 779–793 (2018).39.Hutchins, D. A. & Fu, F. Microorganisms and ocean global change. Nat. Microbiol. 2, 17058 (2017).40.Zakem, E. J. et al. Ecological control of nitrite in the upper ocean. Nat. Commun. 9, 1206 (2018).41.Axtell, R. L. Zipf distribution of U.S. firm sizes. Science 293, 1818–1820 (2001).42.Turchin, P. et al. Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization. Proc. Natl Acad. Sci. USA 115, E144–E151 (2018).CAS 
Article 

Google Scholar 
43.Press, W. H. Numerical Recipes in C (Cambridge Univ. Press, 1986). More

1.Lewis, S. L. & Maslin, M. A. Defining the anthropocene. Nature 519, 171–180 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Pinto-Ledezma, J. N. & Rivero Mamani, M. L. Temporal patterns of deforestation and fragmentation in lowland Bolivia: Implications for climate change. Clim. Change 127, 43–54 (2014).ADS 
Article 

Google Scholar 
4.Allen, J. M., Folk, R. A., Soltis, P. S., Soltis, D. E. & Guralnick, R. P. Biodiversity synthesis across the green branches of the tree of life. Nat. Plants 5, 11–13 (2019).PubMed 
Article 

Google Scholar 
5.Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, IPBES. Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Accessed 15 Feb 2021. https://zenodo.org/record/3553579. https://doi.org/10.5281/ZENODO.3553579 (2019). 6.Cavender-Bares, J., Balvanera, P., King, E. & Polasky, S. Ecosystem service trade-offs across global contexts and scales. Ecol. Soc. 20, art22 (2015).Article 

Google Scholar 
7.Chaplin-Kramer, R. et al. Global modeling of nature’s contributions to people. Science 366, 255–258 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
8.Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Article 
CAS 
PubMed 

Google Scholar 
9.Watson, J. E. M. et al. Set a global target for ecosystems. Nature 578, 360–362 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. 3, 539–551 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Mateo, R. G., Mokany, K. & Guisan, A. Biodiversity models: What if unsaturation is the rule?. Trends Ecol. Evol. 32, 556–566 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
12.Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
13.Ferrier, S. & Guisan, A. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43, 393–404 (2006).Article 

Google Scholar 
14.Guisan, A. & Rahbek, C. SESAM—A new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages: Predicting spatio-temporal patterns of species assemblages. J. Biogeogr. 38, 1433–1444 (2011).Article 

Google Scholar 
15.Cavender-Bares, J., Schweiger, A. K., Pinto-Ledezma, J. N. & Meireles, J. E. Applying remote sensing to biodiversity science. in Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J. et al.) 13–42 (Springer, 2020). https://doi.org/10.1007/978-3-030-33157-3_2.Chapter 

Google Scholar 
16.Fawcett, D. et al. Advancing retrievals of surface reflectance and vegetation indices over forest ecosystems by combining imaging spectroscopy, digital object models, and 3D canopy modelling. Remote Sens. Environ. 204, 583–595 (2018).ADS 
Article 

Google Scholar 
17.Randin, C. F. et al. Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sens. Environ. 239, 111626 (2020).ADS 
Article 

Google Scholar 
18.Turner, W. Sensing biodiversity. Science 346, 301–302 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.D’Amen, M., Pradervand, J.-N. & Guisan, A. Predicting richness and composition in mountain insect communities at high resolution: A new test of the SESAM framework: Community-level models of insects. Glob. Ecol. Biogeogr. 24, 1443–1453 (2015).Article 

Google Scholar 
20.Pottier, J. et al. The accuracy of plant assemblage prediction from species distribution models varies along environmental gradients: Climate and species assembly predictions. Glob. Ecol. Biogeogr. 22, 52–63 (2013).Article 

Google Scholar 
21.Zurell, D. et al. Testing species assemblage predictions from stacked and joint species distribution models. J. Biogeogr. 47, 101–113 (2020).Article 

Google Scholar 
22.D’Amen, M. et al. Improving spatial predictions of taxonomic, functional and phylogenetic diversity. J. Ecol. 106, 76–86 (2018).Article 

Google Scholar 
23.Dobrowski, S. Z. et al. Modeling plant ranges over 75 years of climate change in California, USA: Temporal transferability and species traits. Ecol. Monogr. 81, 241–257 (2011).Article 

Google Scholar 
24.Soria-Auza, R. W. et al. Impact of the quality of climate models for modelling species occurrences in countries with poor climatic documentation: A case study from Bolivia. Ecol. Model. 221, 1221–1229 (2010).Article 

Google Scholar 
25.Rocchini, D. et al. Satellite remote sensing to monitor species diversity: Potential and pitfalls. Remote Sens. Ecol. Conserv. 2, 25–36 (2016).Article 

Google Scholar 
26.Schulte to Bühne, H. & Pettorelli, N. Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science. Methods Ecol. Evol. 9, 849–865 (2018).Article 

Google Scholar 
27.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Hobi, M. L. et al. A comparison of Dynamic Habitat Indices derived from different MODIS products as predictors of avian species richness. Remote Sens. Environ. 195, 142–152 (2017).ADS 
Article 

Google Scholar 
29.Radeloff, V. C. et al. The Dynamic Habitat Indices (DHIs) from MODIS and global biodiversity. Remote Sens. Environ. 222, 204–214 (2019).ADS 
Article 

Google Scholar 
30.Pinto-Ledezma, J. N. & Cavender-Bares, J. Using remote sensing for modeling and monitoring species distributions. In Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J., Gamon, J. A. & Townsend, P. A.) 199–223 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-33157-3_9.31.Fernández, N., Ferrier, S., Navarro, L. M. & Pereira, H. M. Essential biodiversity variables: Integrating in-situ observations and remote sensing through modeling. In Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J., Gamon, J. A. & Townsend, P. A.) 485–501 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-33157-3_18.32.Skidmore, A. K. et al. Priority list of biodiversity metrics to observe from space. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01451-x (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
33.Saatchi, S., Buermann, W., ter Steege, H., Mori, S. & Smith, T. B. Modeling distribution of Amazonian tree species and diversity using remote sensing measurements. Remote Sens. Environ. 112, 2000–2017 (2008).ADS 
Article 

Google Scholar 
34.He, K. S. et al. Will remote sensing shape the next generation of species distribution models?. Remote Sens. Ecol. Conserv. 1, 4–18 (2015).Article 

Google Scholar 
35.Cord, A. F., Meentemeyer, R. K., Leitão, P. J. & Václavík, T. Modelling species distributions with remote sensing data: Bridging disciplinary perspectives. J. Biogeogr. 40, 2226–2227 (2013).Article 

Google Scholar 
36.Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants 2, 16024 (2016).PubMed 
Article 

Google Scholar 
37.Scherrer, D., D’Amen, M., Fernandes, R. F., Mateo, R. G. & Guisan, A. How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer. Methods Ecol. Evol. 9, 2155–2166 (2018).Article 

Google Scholar 
38.Cavender-Bares, J. Diversification, adaptation, and community assembly of the American oaks (Quercus), a model clade for integrating ecology and evolution. New Phytol. 221, 669–692 (2019).PubMed 
Article 

Google Scholar 
39.Cavender-Bares, J., Ackerly, D. D., Baum, D. A. & Bazzaz, F. A. Phylogenetic overdispersion in Floridian oak communities. Am. Nat. 163, 823–843 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Cavender-Bares, J. et al. The role of diversification in community assembly of the oaks (Quercus L.) across the continental U.S. Am. J. Bot. 105, 565–586 (2018).PubMed 
Article 

Google Scholar 
41.Colwell, R. K. & Rangel, T. F. Hutchinson’s duality: The once and future niche. Proc. Natl. Acad. Sci. 106, 19651–19658 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Townsend Peterson, A. et al. Ecological Niches and Geographic Distributions. (Princeton University Press, 2011). Book 

Google Scholar 
43.Cavender-Bares, J., Fontes, G. C. & Pinto-Ledezma, J. Open questions in understanding the adaptive significance of plant functional trait variation within a single lineage. New Phytol. https://doi.org/10.1111/nph.16652 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
44.Cavender-Bares, J., Kitajima, K. & Bazzaz, F. A. Multiple trait associations in relation to habitat differentiation among 17 Floridian oak species. Ecol. Monogr. 74, 635–662 (2004).Article 

Google Scholar 
45.Menges, E. S. & Hawkes, C. V. Interactive effects of fire and microhabitat on plants of Florida scrub. Ecol. Appl. 8, 935–946 (1998).Article 

Google Scholar 
46.Calabrese, J. M., Certain, G., Kraan, C. & Dormann, C. F. Stacking species distribution models and adjusting bias by linking them to macroecological models: Stacking species distribution models. Glob. Ecol. Biogeogr. 23, 99–112 (2014).Article 

Google Scholar 
47.Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl. Acad. Sci. 104, 13384–13389 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Pinto-Ledezma, J. N., Jahn, A. E., Cueto, V. R., Diniz-Filho, J. A. F. & Villalobos, F. Drivers of phylogenetic assemblage structure of the Furnariides, a widespread clade of lowland neotropical birds. Am. Nat. 193, E41–E56 (2019).PubMed 
Article 

Google Scholar 
49.Gamon, J. A. et al. Consideration of scale in remote sensing of biodiversity. In Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J., Gamon, J. A. & Townsend, P. A.) 425–447 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-33157-3_16.50.Pollock, L. J. et al. Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods Ecol. Evol. 5, 397–406 (2014).Article 

Google Scholar 
51.Ovaskainen, O. et al. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20, 561–576 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Ovaskainen, O. Joint Species Distribution Modelling: with Applications in R (Cambridge University Press, 2020).Book 

Google Scholar 
53.Poggiato, G. et al. On the interpretations of joint modeling in community ecology. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.01.002 (2021).Article 
PubMed 

Google Scholar 
54.Wilkinson, D. P., Golding, N., Guillera-Arroita, G., Tingley, R. & McCarthy, M. A. Defining and evaluating predictions of joint species distribution models. Methods Ecol. Evol. 12, 394–404 (2021).Article 

Google Scholar 
55.Bystrova, D. et al. Clustering species with residual covariance matrix in joint species distribution models. Front. Ecol. Evol. 9, 601384 (2021).Article 

Google Scholar 
56.Mateo, R. G. et al. Hierarchical species distribution models in support of vegetation conservation at the landscape scale. J. Veg. Sci. 30, 386–396 (2019).ADS 
Article 

Google Scholar 
57.Petitpierre, B. et al. Will climate change increase the risk of plant invasions into mountains?. Ecol. Appl. 26, 530–544 (2016).PubMed 
Article 

Google Scholar 
58.Cavender-Bares, J. et al. Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales. Am. J. Bot. 104, 966–969 (2017).PubMed 
Article 

Google Scholar 
59.Schweiger, A. K. et al. Spectral Niches Reveal Taxonomic Identity and Complementarity in Plant Communities. (2020) https://doi.org/10.1101/2020.04.24.060483. 60.Cavender-Bares, J. et al. Associations of leaf spectra with genetic and phylogenetic variation in oaks: Prospects for remote detection of biodiversity. Remote Sens. 8, 221 (2016).ADS 
Article 

Google Scholar 
61.Meireles, J. E. et al. Leaf reflectance spectra capture the evolutionary history of seed plants. New Phytol. 228, 485–493 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
62.Williams, L. J. et al. Remote spectral detection of biodiversity effects on forest biomass. Nat. Ecol. Evol. 5, 46–54 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
63.Alonso, K. et al. Data products, quality and validation of the DLR earth sensing imaging spectrometer (DESIS). Sensors 19, 4471 (2019).ADS 
CAS 
PubMed Central 
Article 

Google Scholar 
64.Stavros, E. N. et al. ISS observations offer insights into plant function. Nat. Ecol. Evol. 1, 0194 (2017).Article 

Google Scholar 
65.Féret, J.-B. & Asner, G. P. Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecol. Appl. 24, 1289–1296 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Cavender-Bares, J. et al. BII-Implementation: The causes and consequences of plant biodiversity across scales in a rapidly changing world. Res. Ideas Outcomes 7, e63850 (2021).Article 

Google Scholar 
67.Hipp, A. L. et al. Sympatric parallel diversification of major oak clades in the Americas and the origins of Mexican species diversity. New Phytol. 217, 439–452 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Cavender-Bares, J. et al. Phylogeny and biogeography of the American live oaks (Quercus subsection Virentes): A genomic and population genetics approach. Mol. Ecol. 24, 3668–3687 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
69.Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).Article 

Google Scholar 
70.Lobo, J. M., Jiménez-Valverde, A. & Hortal, J. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33, 103–114 (2010).Article 

Google Scholar 
71.Barnett, D. T. et al. The plant diversity sampling design for The National Ecological Observatory Network. Ecosphere 10, e02603 (2019).
Google Scholar 
72.Deblauwe, V. et al. Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics: Remotely sensed climate data for tropical species distribution models. Glob. Ecol. Biogeogr. 25, 443–454 (2016).Article 

Google Scholar 
73.Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling. R package version 1.3. https://CRAN.R-project.org/package=dismo (2020).74.Myneni, R. B. et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231 (2002).ADS 
Article 

Google Scholar 
75.Gower, S. T., Kucharik, C. J. & Norman, J. M. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sens. Environ. 70, 29–51 (1999).ADS 
Article 

Google Scholar 
76.Reich, P. B. Key canopy traits drive forest productivity. Proc. R. Soc. B Biol. Sci. 279, 2128–2134 (2012).Article 

Google Scholar 
77.Xiao, Z. et al. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Trans. Geosci. Remote Sens. 52, 209–223 (2014).ADS 
Article 

Google Scholar 
78.Soberón, J. Grinnellian and Eltonian niches and geographic distributions of species. Ecol. Lett. 10, 1115–1123 (2007).PubMed 
Article 

Google Scholar 
79.Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).ADS 
Article 

Google Scholar 
80.Dubuis, A. et al. Predicting spatial patterns of plant species richness: A comparison of direct macroecological and species stacking modelling approaches: Predicting plant species richness. Divers. Distrib. 17, 1122–1131 (2011).Article 

Google Scholar 
81.Schoener, T. W. Anolis lizards of Bimini: Resource partition in a complex fauna. Ecology 49, 704–726 (1968).Article 

Google Scholar 
82.Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).Article 

Google Scholar 
83.Cooper, J. C. & Soberón, J. Creating individual accessible area hypotheses improves stacked species distribution model performance. Glob. Ecol. Biogeogr. 27, 156–165 (2018).Article 

Google Scholar 
84.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many? How to use pseudo-absences in niche modelling?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
85.Carlson, C. J. et al. The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock and wildlife. Nat. Microbiol. 4, 1337–1343 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Chipman, H. A., George, E. I. & McCulloch, R. E. BART: Bayesian additive regression trees. Ann. Appl. Stat. 4, 266–298 (2010).MathSciNet 
MATH 
Article 

Google Scholar 
87.Yen, J. D. L., Thomson, J. R., Vesk, P. A. & Mac Nally, R. To what are woodland birds responding? Inference on relative importance of in-site habitat variables using several ensemble habitat modelling techniques. Ecography 34, 946–954 (2011).Article 

Google Scholar 
88.Carlson, C. J. embarcadero: Species distribution modelling with Bayesian additive regression trees in r. Methods Ecol. Evol. 11, 850–858 (2020).Article 

Google Scholar 
89.Dorie, V. dbarts: Discrete Bayesian Additive Regression Trees Sampler. (2020).90.Hastie, T. & Tibshirani, R. Bayesian backfitting. Stat. Sci. 15(3), 196–223 (2000). MathSciNet 
MATH 
Article 

Google Scholar 
91.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS): Assessing the accuracy of distribution models. J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
92.Di Cola, V. et al. ecospat: An R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).Article 

Google Scholar 
93.Hipp, A. L. et al. Genomic landscape of the global oak phylogeny. New Phytol. 226, 1198–1212 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).Article 

Google Scholar 
95.Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 
Article 

Google Scholar 
96.Kruschke, J. Doing Bayesian Data Analysis, 2nd Ed. (2014).97.Mills, J. A. & Parent, O. Bayesian MCMC estimation. In Handbook of Regional Science (eds Fischer, M. M. & Nijkamp, P.) 1571–1595 (Springer, Berlin, 2014). https://doi.org/10.1007/978-3-642-23430-9_89.Chapter 

Google Scholar 
98.Carpenter, B. et al. Stan : A probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).Article 

Google Scholar 
99.Goodrich, B., Gabry, J., Ali, I. & Brilleman, S. rstanarm: Bayesian applied regression modeling via Stan. (2020).100.Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
101.Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).ADS 
Article 

Google Scholar  More

1.Palme, R. Monitoring stress hormone metabolites as a useful, non-invasive tool for welfare assessment in farm animals. Anim. Welf. 21, 331–337 (2012).CAS 
Article 

Google Scholar 
2.Jankord, R. & Herman, J. P. Limbic regulation of hypothalamo-pituitary-adrenocortical function during acute and chronic stress. Ann. N. Y. Acad. Sci. 1148, 64–73 (2008).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Romero, L. M. Physiological stress in ecology: Lessons from biomedical research. Trends Ecol. Evol. 19, 249–255 (2004).PubMed 
Article 

Google Scholar 
4.Haase, C. G., Long, A. K. & Gillooly, J. F. Energetics of stress: Linking plasma cortisol levels to metabolic rate in mammals. Biol. Lett. 12, 20150867 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
5.Selye, H. A syndrome produced by diverse nocuous agents. Nature 1936, 32 (1936).ADS 
Article 

Google Scholar 
6.Fink, G. Stress Science: Neuroendocrinology (Academic Press, Elsevier Science, 2010).
Google Scholar 
7.Hing, S., Narayan, E. J., Thompson, R. C. A. & Godfrey, S. S. The relationship between physiological stress and wildlife disease: Consequences for health and conservation. Wildl. Res. 43, 51 (2016).Article 

Google Scholar 
8.Tsigos, C. & Chrousos, G. P. Hypothalamic-pituitary-adrenal axis, neuroendocrine factors and stress. J. Psychosom. Res. 53, 865–871 (2002).PubMed 
Article 

Google Scholar 
9.Tryphonopoulos, P. D., Letourneau, N. & Azar, R. Approaches to salivary cortisol collection and analysis in infants. Biol. Res. Nurs. 16, 398–408 (2014).PubMed 
Article 

Google Scholar 
10.Palme, R. Non-invasive measurement of glucocorticoids: Advances and problems. Physiol. Behav. 199, 229–243 (2019).CAS 
PubMed 
Article 

Google Scholar 
11.Russell, E., Koren, G., Rieder, M. & Van Uum, S. Hair cortisol as a biological marker of chronic stress: Current status, future directions and unanswered questions. Psychoneuroendocrinology 37, 589–601 (2012).CAS 
PubMed 
Article 

Google Scholar 
12.McEwen, B. S. Central effects of stress hormones in health and disease: Understanding the protective and damaging effects of stress and stress mediators. Eur. J. Pharmacol. 583, 174–185 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.O’Connor, E. A. et al. The impact of chronic environmental stressors on growing pigs, Sus scrofa (Part 1): Stress physiology, production and play behaviour. Animal 4, 1899–1909 (2010).PubMed 
Article 

Google Scholar 
14.Kadarmideen, H. N. & Janss, L. L. G. Population and systems genetics analyses of cortisol in pigs divergently selected for stress. Physiol. Genomics 29, 57–65 (2007).CAS 
PubMed 
Article 

Google Scholar 
15.Romano, M. C. et al. Stress in wildlife species: Noninvasive monitoring of glucocorticoids. NeuroImmunoModulation 17, 209–212 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Sales, L. P. et al. Niche conservatism and the invasive potential of the wild boar. J. Anim. Ecol. 86, 1214–1223 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
17.Briedermann, L. Schwarzwild (Franckh-Kosmos Verlags-GmnH & Co. KG, 2009).
Google Scholar 
18.Keuling, O. et al. Eurasian wild boar Sus scrofa (Linnaeus, 1758). In Ecology, Conservation and Management of Wild Pigs and Peccaries (eds Melletti, M. & Meijaard, E.) 202–233 (Cambridge University Press, 2018).
Google Scholar 
19.Niedersächsisches Ministerium für Ernährung, Landwirtschaft und Verbraucherschutz. Aktuelle Jagdzeiten in Niedersachsen (konsolidierte Fassung) Stand: 25. Januar 2021 inkl. Verordnung zur Durchführung des Nieders. Jagdgesetzes (DVO-NJagdG) vom 23. Mai 2008 (Nds. GVBl. S. 194), zuletzt geändert durch Verordnung vom 18. Januar 2021 (Nds. GVBl. S. 24). (2021). https://www.ml.niedersachsen.de/download/163729/Aktuelle_Jagdzeiten_in_Niedersachsen_Stand_25.01.2021_nicht_vollstaendig_barrierefrei_.pdf. Accessed 01 June 2021.20.Casas-Díaz, E. et al. Hematologic and biochemical reference intervals for Wild Boar (Sus scrofa) captured by cage trap. Vet. Clin. Pathol. 44, 215–222 (2015).PubMed 
Article 

Google Scholar 
21.Gentsch, R. P., Kjellander, P. & Röken, B. O. Cortisol response of wild ungulates to trauma situations: Hunting is not necessarily the worst stressor. Eur. J. Wildl. Res. 64, 11 (2018).Article 

Google Scholar 
22.Adcock, S. J. J., Martin, G. M. & Walsh, C. J. The stress response and exploratory behaviour in Yucatan minipigs (Sus scrofa): Relations to sex and social rank. Physiol. Behav. 152, 194–202 (2015).CAS 
PubMed 
Article 

Google Scholar 
23.Bratton, S. P. The effect of the European wild boar (Sus scrofa) on gray beech forest in the great smokey mountains. Ecology 56, 1356–1366 (1975).Article 

Google Scholar 
24.Singer, F. J., Swank, W. T. & Clebsh, E. E. C. The effects of wild pig rooting in a deciduous forest. J. Wildl. Manage. 48, 464–473 (1984).CAS 
Article 

Google Scholar 
25.Wlazelko, M. & Labudzki, L. Über Nahrungskomponenten und trophische Stellung des Schwarzwildes im Forschungsgebiet Zielonka. Z. Jagdwiss. 38, 81–87 (1992).
Google Scholar 
26.Killian, G., Miller, L., Rhyan, J. & Doten, H. Immunocontraception of Florida feral swine with a single-dose GnRH vaccine. Am. J. Reprod. Immunol. 55, 378–384 (2006).CAS 
PubMed 
Article 

Google Scholar 
27.Gortázar, C., Ferroglio, E., Höfle, U., Frölich, K. & Vicente, J. Diseases shared between wildlife and livestock: A European perspective. Eur. J. Wildl. Res. 53, 241–256 (2007).Article 

Google Scholar 
28.Gräber, R., Strauß, E. & Johanshon, S. Wild und Jagd—Landesjagdbericht 2017/2018 (Niedersächsisches Ministerium für Ernährung, Landwirtschaft und Verbraucherschutz, Hannover, 2018).29.Wölfel, H. Bewegungsjagden (Leopold Stocker Verlag, 2003).30.Eisenbarth, E. & Ophoven, E. Bewegungsjagd auf Schalenwild (Franckh-Kosmos Verlags-GmbH & Co., 2002).
Google Scholar 
31.Böhm, E. Drückjagd auf Sauen (Neumann-Neudamm, 2004).
Google Scholar 
32.Bradshaw, E. L. & Bateson, P. Welfare implications of culling red deer (Cervus elaphus). Anim. Welf. 9, 3–24 (2000).
Google Scholar 
33.Sheriff, M. J., Dantzer, B., Delehanty, B., Palme, R. & Boonstra, R. Measuring stress in wildlife: Techniques for quantifying glucocorticoids. Oecologia 166, 869–887 (2011).ADS 
PubMed 
Article 

Google Scholar 
34.Hellhammer, D. H., Wüst, S. & Kudielka, B. M. Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinology 34, 163–171 (2009).CAS 
PubMed 
Article 

Google Scholar 
35.Palme, R., Rettenbacher, S., Touma, C., El-Bahr, S. M. & Möstl, E. Stress hormones in mammals and birds: Comparative aspects regarding metabolism, excretion, and noninvasive measurement in fecal samples. Ann. N. Y. Acad. Sci. 1040, 162–171 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
36.Kanitz, E., Otten, W., Tuchscherer, M. & Manteuffel, G. Effects of prenatal stress on corticosteroid receptors and monoamine concentrations in limbic areas of suckling piglets (Sus scrofa) at different ages. J. Vet. Med. Ser. A 50, 132–139 (2003).CAS 
Article 

Google Scholar 
37.Campbell, E. A. et al. Plasma corticotropin-releasing hormone concentrations during pregnancy and parturition. J. Clin. Endocrinol. Metab. 64, 1054–1059 (1987).CAS 
PubMed 
Article 

Google Scholar 
38.Seth, S., Lewis, A. J. & Galbally, M. Perinatal maternal depression and cortisol function in pregnancy and the postpartum period: A systematic literature review. BMC Pregn. Childbirth 16, 124 (2016).Article 
CAS 

Google Scholar 
39.Gethöffer, F. Reproduktionsparameter und Saisonalität der Fortpflanzung des Wildschweins (Sus scrofa) in drei Untersuchungsgebieten Deutschlands (University of Veterinary Medicine Hannover, 2005).
Google Scholar 
40.Frauendorf, M., Gethöffer, F., Siebert, U. & Keuling, O. The influence of environmental and physiological factors on the litter size of wild boar (Sus scrofa) in an agriculture dominated area in Germany. Sci. Total Environ. 541, 877–882 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
41.Gethöffer, F., Sodeikat, G. & Pohlmeyer, K. Reproductive parameters of wild boar (Sus scrofa) in three different parts of Germany. Eur. J. Wildl. Res. 53, 287–297 (2007).Article 

Google Scholar 
42.DWD. Deutscher Wetterdienst—Wetter und Klima—Klimadaten (2019). https://www.dwd.de. Accessed 01 Oct 2019.43.Keuling, O., Stier, N. & Roth, M. Annual and seasonal space use of different age classes of female wild boar Sus scrofa L.. Eur. J. Wildl. Res. 54, 403–412 (2008).Article 

Google Scholar 
44.Malmsten, A., Jansson, G., Lundeheim, N. & Dalin, A.-M. The reproductive pattern and potential of free ranging female wild boars (Sus scrofa) in Sweden. Acta Vet. Scand. 59, 52 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
45.R Core Team. R: A Language and Environment for Statistical Computing Version R3.5.2.  R Foundation for Statistical Computing, Vienna, Austria.  http://www.R-project.org/ (2018).46.Dunn, O. J. Multiple comparisons using rank sums. Technometrics 6, 241–252 (1964).Article 

Google Scholar 
47.Ogle, D. H., Wheeler, P. & Dinno, A. FSA: Fisheries Stock Analysis. R Package Version 0.8.25. https://github.com/droglenc/FSA (2019).48.Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R Package Version 0.2.3. http://www.sthda.com/english/rpkgs/ggpubr (2019).49.Palme, R. Measuring fecal steroids: Guidelines for practical application. Ann. N. Y. Acad. Sci. 1046, 75–80 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
50.Cockrem, J. F. Individual variation in glucocorticoid stress responses in animals. Gen. Comp. Endocrinol. 181, 45–58 (2013).CAS 
PubMed 
Article 

Google Scholar 
51.Mormède, P. et al. Exploration of the hypothalamic-pituitary-adrenal function as a tool to evaluate animal welfare. Physiol. Behav. 92, 317–339 (2007).PubMed 
Article 
CAS 

Google Scholar 
52.Goymann, W. Noninvasive monitoring of hormones in bird droppings: Physiological validation, sampling, extraction, sex differences, and the influence of diet on hormone metabolite levels. Ann. N. Y. Acad. Sci. 1046, 35–53 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
53.Guilliams, T. G. & Edwards, L. Chronic stress and the HPA axis: Clinical assessment and therapeutic considerations. Stand. 9, 1–12 (2010).
Google Scholar 
54.Merta, D., Mocala, P., Pomykacz, M. & Frackowiak, W. Autumn-winter diet and fat reserves of wild boars (Sus scrofa) inhabiting forest and forest-farmland environment in south-western Poland. Folia Zool. 63, 95–102 (2014).Article 

Google Scholar 
55.Poteaux, C. et al. Socio-genetic structure and mating system of a wild boar population. J. Zool. 278, 116–125 (2009).Article 

Google Scholar 
56.Kaminski, G., Brandt, S., Baubet, E. & Baudoin, C. Life-history patterns in female wild boars (Sus scrofa): Mother–daughter postweaning associations. Can. J. Zool. 83, 474–480 (2005).Article 

Google Scholar 
57.Krause, J. & Ruxton, G. D. Living in Groups. Oxford Series in Ecology and Evolution (Oxford University Press, 2002).
Google Scholar 
58.Kudielka, B. M. & Kirschbaum, C. Sex differences in HPA axis responses to stress: A review. Biol. Psychol. 69, 113–132 (2005).PubMed 
Article 

Google Scholar 
59.Balhara, Y. S., Verma, R. & Gupta, C. Gender differences in stress response: Role of developmental and biological determinants. Ind. Psychiatry J. 20, 4 (2012).Article 

Google Scholar 
60.Sutherland, M. A., Rodriguez-Zas, S. L., Ellis, M. & Salak-Johnson, J. L. Breed and age affect baseline immune traits, cortisol, and performance in growing pigs. J. Anim. Sci. 83, 2087–2095 (2005).CAS 
PubMed 
Article 

Google Scholar 
61.Foury, A. et al. Stress hormones, carcass composition and meat quality in Large White × Duroc pigs. Meat Sci. 69, 703–707 (2005).CAS 
PubMed 
Article 

Google Scholar 
62.Ruis, M. A. W. et al. The circadian rhythm of salivary cortisol in growing pigs: Effects of age, gender, and stress. Physiol. Behav. 62, 623–630 (1997).CAS 
PubMed 
Article 

Google Scholar  More

Intra- and interspecific differences: comparison with former studies on Pagellus species and other fish speciesTo understand the relationship between function, shape, and the environment, it is essential to include the morphological variability of otoliths, considering biological and environmental variability leads to otolith shape heterogeneity through morpho-functional adaptation to different habitats. Several authors have highlighted changes in otolith shape between species and, in many cases, among populations of the same species (e.g., herrings, salmonids, and lutjanids). The intra-specific variability of otolith morphology and shape are the basis of stock separation and assessment and is related, especially in sagittae, with environmental (e.g., water temperature, salinity, and depth) and biological factors (e.g., sex, ontogeny, and genetic variability)20.The analysis of the three Pagellus species revealed that otolith morphology and morphometry did not follow those described in a previous study1 conducted in the western Mediterranean Sea and the Atlantic Ocean in term of rectangularity, circularity, sagitta aspect ratio and sagitta length to total fish length ratio. Although the images provided in our study closely resembled those from research in other geographical areas, the morphometric measures (obtained according to the procedures and methods described in the previous literature1,20,23,32) exhibited several differences. Considering the scale of our study compared to previous studies, it is difficult to provide an entirely valid comparison; the differences in sagitta morphology and morphometry could have been triggered by biotic and abiotic parameters (e.g., temperature, salinity, genotype, habitat type, differences in food quality and quantity)13,33,34,35. Such environmental and genetic factors may be primary drivers of otolith morphometry and morphology among fishes in different habitats. Therefore, detected shape differences are at the basis of fish stock differentiation36.Our results indicate that the min–max circularity and rectangularity of P. bogaraveo from the Southern Tyrrhenian Sea differ from those calculated in a previous study1 in the western Mediterranean Sea, and the north and central-eastern Atlantic ocean. Moreover, the increase in circularity in larger specimens, confirms a greater tendency toward circular than elliptical otolith shape in southern Tyrrhenian Sea species compared to those in other Mediterranean and Atlantic areas.Despite statistical differences and correlations in this study supported the hypothesis that some changes in sagitta morphology are related to fish size differences, several aspects and studies should be performed to better understand this relation. The negative correlation between the ratio of sulcus acusticus surface to the entire sagitta, rostral morphology, and the increase in specimen’s size was related to the expansion in the length and surface of the entire sagitta and rostral area in larger specimens. These features, with no statistical relevant increment in sulcus acusticus surface and increased rostrum length, could be correlated with more pronounced peripheral sagitta growth in this species. Sagitta, in fact, after fish pelagic phase, might increases its surface in the rostral area and the margins. Since the present study did not take into account ontogenetic stages and specimens age, it is hard to relate this result with sagitta and sulcus acusticus growth. But reading this increase by an ecological point of view, it could be related to the lifecycle of the species. During the juvenile stage, in the early stage of pelagic life, the species inhabits shallow water. Adults inhabit deep-water environments, migrating down the continental slope to a depth of 800 m after the juvenile stage. These changes in habitat might be the cause of morphological variations in the sagittae, highlighting the relationship between sagitta features and environmental and biological factors.The P. acarne specimens demonstrated the highest number of morphometrical parameters that did not follow those of the same species described in a previous study (i.e., circularity, rectangularity, sagitta length to total fish length ratio, and sagitta aspect ratio)1. These morphometrical changes are reflected in otolith shape. The otoliths from specimens in our study were largely circular, with highly irregular margins and a rostrum that varied in length and width through the left and right sagitta, as indicated by the significant differences in rostrum aspect ratio values.The morphometrical results in P. erythrinus revealed differences in circularity, rectangularity, sagitta length to total fish length ratio and sagitta aspect ratio compared to a previous study1 in the western Mediterranean Sea and north-central eastern Atlantic ocean.The P. erythrinus specimens were characterized by a pentagonal otoliths shape, and increased circularity compared to the same species from other areas. The results also indicated small differences between the left and right sagitta. This small differences were previously described in other Mediterranean sub-areas, for example, otolith width values in P. erythrinus specimens collected in the Gulf of Tunisia37,38.As said above for P. bogaraveo, it is hard to relate the differences between juveniles and adults with fish growth due to the absence in present paper of ontogenetic and age analysis. The higher width than length, demonstrated by min–max width values in Tables 1 and 2, in sagittae of adults P. erythrinus specimens could be correlated with an exponential increment in fish size compared to the sagittal length. Further analyses on ontogenetic development of this species are required to better define the sagitta growth related to fish growth.The increase in sulcus acusticus surface exhibited in the adult specimens could be correlated with feeding habits; during its adult life, this species is a benthic feeder and inhabits deeper environments than juveniles28,29.Although meaningful lateral dimorphism of the sagittae was detected only in flatfish, statistical analysis revealed several small differences between the left and right sagitta in P. erythrinus and P. acarne, as previously described in other round fish species, such as Chelon ramada (Risso, 1827)40, Diplodus annularis (Linnaeus, 1758)41, Diplodus puntazzo (Walbaum, 1792)42, Clupea harengus (Linnaeus, 1758)43, and Scomberomorus niphonius (Cuvier, 1832)44.Our study confirmed slight differences between width values in left and right sagitta previously described in P. erythrinus and extend the differences to other parameters, such as circularity and rectangularity (Tables 1, 2). Concerning P. acarne, however, marginal differences between the left and right sagittae were observed for the first time.This slight differences are supported by the literature concerning genetic and environmental stressors41. Since the functional morphology of otoliths is not completely understood, it is difficult to find a direct link between these small differences and the ecology of the species. However, several eco-functional factors, such as feeding behavior, deserve attention as fundamental for a better understanding of the relationship between otolith features and species habitat. For example, P. erythrinus largely preys on strictly benthic organisms, such as polychaetes, brachyuran crabs, and benthic crustaceans. Most of these species frequently escape predators by hiding under the sandy substrate. Other Sparidae (Lythognathus mormyrus, Linnaeus, 1758) feed on benthic fauna, engulfing sediment and filtering it in the buccal cavity, demonstrated by the high percentage of detritus and benthic remains (e.g., scales, urchin spines, and benthic foraminifers) in the gut and stomach contents29. To engulf sediment, P. erythrinus performs a particular movement with the head and body, laterally shifting and pushing forward, to dig the bottom sand and reach prey. This kind of behavior, common in all benthopelagic species with the same feeding habits, could influence the sagitta growth and morphology, triggering small differences between the left and right sagitta. Further studies on this and other species with this behavior (e.g., L. mormyrus) are necessary to confirm this hypothesis.Concerning inter-specific differences in sagitta morphology among the three species, it is difficult to read the results obtained in this study eco-morphologically since an insufficient understanding of the functional morphology and physiology of otoliths prohibits a direct relationship, valid for all the species, between eco-functional features and otolith morphology. Nevertheless, as expected, the shape analysis (Fig. 1) revealed clear differences between the three congeneric species. Considering several ecological, functional, and biological features in each species, the results have demonstrated a sagitta morphology that could be in accordance with the ecology and lifestyle of these three congeneric seabreams.Relationship between otolith morphology and ecology/lifestyleThe sagittae of P. acarne exhibited a shape resembling those in other pelagic species, with a long rostrum and the entire sagitta elongated and narrower than those in other two seabream species. The species that show the most pelagic habits, with largely planktivorous feeding at a small size, adapt also to benthopelagic feeding activity in adult life. The statistically relevant similarity found in P. bogaraveo could be proof of the ecomorphological adaptation of sagittae to pelagic and demersal environments. This hypothesis may be confirmed by marked differences in shape compared to those in P. erythrinus, which is the most benthic among the three species.Pagellus erythrinus was the species with the shortest rostrum. It also has the most benthic habits, largely preying on epibenthic and infaunal species. Moreover, its ecology and life cycle differ among the three species under study since they are strictly related to the benthic environment. This lifestyle could be in accordance with the differences observed in the shape analysis results. The sagitta contours appeared more circular and wider than those in the other two species. The PCA and LDA also confirmed the most difference in shape among the three species.The species with the most marked antirostrum and sagitta shape was P. bogaraveo, which is a cross between the other two congeneric species. Pagellus bogaraveo is a demersal species, which inhabits the deep biocenosis and feeds in both benthic and mesopelagic environments. Furthermore, the ecology of this species could support the sagitta shape described in our study27,30.Otolith morphology and morphometry in congeneric Pagellus species described in this study has followed the relationship between sagittal parameters, habitat, and depth described in previous literature15. According to several authors, the percentage of species with large otoliths increases with depth, except for abyssal depth. The specimens of P. bogaraveo analyzed in this paper (especially adult individuals) had larger otoliths than the other two Pagellus species due to their demersal habits (they inhabit the continental slope to a depth of 800 m). A larger sagitta is essential in demersal environments to compensate for light reduction by providing improved acoustic communication, sound perception15,45, and a sense of equilibrium46.Sulcus shapeConsidering the sulcus acusticus, in the otolith atlas for the western Mediterranean Sea and Atlantic ocean1, studies describing and comparing otoliths10 and the diversity and variability of otoliths in teleost fishes9, the sulcus in P. bogaraveo, P. acarne and P. erythrinus was described as heterosulcoid, with an ostium shorter than the cauda and a long, narrowed rostrum, especially in adult P. bogaraveo and P. acarne individuals. Heterosulcoid otoliths were also observed in south Tyrrhenian Sea Pagellus individuals, with marked differences between juvenile and adult specimens. In a demersal species, such as P. bogaraveo, juveniles live in shallow, coastal water. Once adults, they inhabit deeper water (to a depth of 800 m). Changes in the crystalline and morphological structure of sulcus acusticus between juveniles and adults reflect this species’ need to adapt to deeper environments with less light.The results indicate that in P. bogaraveo, the sulcus acusticus does not differ in surface between juvenile and adult specimens. This feature could be correlated with earlier sulcus acusticus development in this species, compared to P. erythrinus and P. acarne, emphasizing the role of the sulcus acusticus in this demersal species48,49. This might also confirm the strict correlation between biological and environmental factors and sagitta morphology in studied seabreams species.Another morphological feature of the sulcus acusticus, which might support the ecology of the species, is the deep ostium and cauda. In adult specimens of P. bogaraveo and P. acarne, the sulcus structure deeply penetrated in the sagitta carbonate structure. Conversely, in adult P. erythrinus specimens, the sulcus did not penetrate as deeply as in the other two seabream species. This sulcal feature could correspond with the ecology and feeding behavior of P. erythrinus, which specializes in benthic strategies, including small differences between left and right sagitta and the absence of the notch and antirostrum in sagittae.Although the deeper sulcus acusticus in P. bogaraveo and P. acarne might be linked to depth distribution, as in P. bogaraveo, it may also correspond with high mobility related to feeding behavior, as in both P. bogaraveo and P. acarne. The different depths of sulcus acusticus can change the thickness of the otolithic membrane, by varying the relative motion of otoliths with the macula sacculi2. As previously demonstrated50, the different thicknesses of the otolithic membrane induce differences in mechanical resistance between the otolith and sensory epithelium.The differences in sulcus acusticus and otolith ratio between P. bogaraveo specimens and the other congeneric species, demonstrated by the results, might be also correlated to the differences in habitat, feeding habits, and soundscape.Despite the lack of information concerning the physiological ear response related to variations in macula or sulcus size, the sensory hair cells in macula sacculi are likely to be affected by changes in sulcus depth, shape, 3D structure (planar vs. curved), and surface.The significant difference in relative sulcus area may be due to typical alteration in this parameter concerning differences in the mobility patterns, food, feeding behavior, and spatial niche.Higher relative sulcus area ratios have been observed in the deepest species or those with high mobility49. In our study, the morphometry results concerning the sulcus did not follow those in the previous literature, displaying higher values in P. erythrinus and P. acarne compared to P. bogaraveo, although the latter inhabits a deeper environment than the other congeneric species.This higher relative sulcus acusticus surface and the larger, curved sulcus acusticus of P. acarne and P. erythrinus could be correlated with higher mobility in these species (especially P. acarne). In P. erythrinus, however, these features might be related to its benthic lifestyle.As demonstrated by the PCA and LDA of sulcus acusticus parameters, P. erythrinus and P. acarne, which share similar depths and habitats, revealed marked similarities, whereas P. bogaraveo, which lives in the deepest strata of the water column, displayed the most different sulcus acusticus. However, PCA and LDA indicated that the otolith shape in the entire P. erythrinus sagitta was significantly different compared to those in P. acarne and P. bogaraveo.These features could provide a reading key for sagitta and sulcus acusticus eco-morphology in the life cycle and environmental adaptation of fish.The connection between the otoliths and the macula sacculi is fundamental for transducing environmental acoustic signals and for the relative motion of fish (balance). The sulcus acusticus is the area of the otoliths in which this connection occurs.Features of the textureFurthermore, the external textural organization23 changes between juveniles and adults or when environmental changes occur. The differences in the external textural organization found in juveniles and adults support those reported in the literature concerning other species39. Figures 3b, c and 5a–h, demonstrate that our study supported this prediction. However, P. bogaraveo and P. erythrinus juveniles, compared with other species (such as gurnards)23 displayed a more uniform, mineralized, external textural organization.Figure 5SEM images of the crystalline structure of P. bogaraveo, juveniles (a, b) and adults (c, d), and P. erythrinus, juveniles (e, f) and adults (g, h) sagittae.Full size imageAccording to previous literature8, improved hearing capabilities in a species are closely related to a higher value of relative sulcus area ratio. Habitat features, such as depth, feeding strategies, mobility, trophic distribution, and ontogeny, could also influence this ratio.Hence, it may be concluded that morphological differences in sulcus acusticus shape and surface among species are important for comprehending the ecomorphological and eco-functional role of sagitta 2,48.Comparing the intra-specific differences indicated by our results with those in the literature, discussing other populations, we cannot determine whether site-differences observed in sagitta shape are related to genetic evolution and/or adaptative response to environment. To make this distinction it would require a specific experiment in which offspring from different populations are raised in a controlled environment.Furthermore, the knowledge about physiology and functional morphology is insufficient to provide a clear correlation between inter-specific differences among the three congeneric Pagellus species and their ecological and functional features. However, differences in sagitta morphology and morphometry among these three Pagellus species may be related to differences in lifestyle, ecology, and biology since they follow the ecomorphological features of sagittae and species ecology described in the literature. More