Ecology

Abiotic parametersThe temperature regime experienced by the embryos was purposefully natural and therefore varied between the three air exposure treatments. The subtidal treatment, where embryos were continuously submerged in water, remained around 9.5 °C for the duration of the experiment, while the intertidal treatments experienced dips in temperature during outside air exposure down to 2.5 °C and 0.8 °C, for the low and high intertidal respectively (Fig. 3A). Accumulated thermal units (ATU; days × temperature post collection until hatch) for each air exposure treatment were 79.6, 75.4 and 65.3 for subtidal, low intertidal and high intertidal, respectively. Despite differences in thermal regime, peak hatch was on the same day (March 14, 2021) for all air exposure and CO2 treatments, estimated at 11 dpf.Figure 3Temperature and pH experienced by the herring embryos and larvae throughout the experiment. Hourly measurements of (A) air/water temperature experienced by herring embryos in each of the tidal treatments (subtidal: continuous immersion in 9.5 °C water; low intertidal: 2 × 2 h air exposure; high intertidal: 5 + 9 h air exposure) and (B) pH levels in the tanks for each of the CO2 treatments (greens = control, 400 µatm CO2, yellows = medium, 1600 µatm CO2, reds = high, 3000 µatm CO2); dots are pH levels measured in the individual jars during larval incubation.Full size imageThe pH levels in the tanks were measured hourly and were stable over the course of the embryonic incubation period, with no overlap between treatments, although there was some overlap between individual jars. Control treatment was consistently around a pH of 8, the medium treatment had a pH of 7.4 and the high CO2 treatment had a mean pH of 7.1 (Table 1). After hatch, when the larvae were transferred to the jars, circulation and gas exchange between jars and tank were not as high and CO2 accumulated in the jars over time, leading to pH levels deviating from tank pH levels (Fig. 3B). Although oxygen levels remained high (7–9 mg/L), the pH dropped from a mean 8–7.6 in the control on two occasions, and was brought back up with a partial water exchange from the incubation tank water. The pH in the medium and high CO2 treatments were not as affected (Fig. 3B), however, final water chemistry measurements after completion of the experiment (2 days post water exchange) revealed much higher CO2 levels in all treatments (Table 1: day 15).Table 1 Mean water parameters for each treatment (mean of 3 tanks ± S.D.) at the beginning (day 1, 2021-03-06) and end (day 6, 2021-03-12) of embryonic incubation and mean parameters in the jars (N = 9) at the end of larval incubation (day 15, 2021-03-19); Temperature (T), salinity, pCO2, total CO2 (TCO2) measured at distinct sampling intervals with the BoL; total alkalinity (TA) and pH (on the total scale) calculated with CO2SYS.Full size tableEffect of air exposure and CO2 treatment during embryonic developmentNeither embryonic survival nor growth were significantly affected by treatment in our experiment. Percent daily embryonic mortality was low and not significantly affected by CO2 treatment or air exposure (CO2: p = 0.088, F2 = 2.45; Tide: p = 0.11, F2 = 2.19; CO2*Tide: p = 0.18, F2 = 1.59) . Egg diameter at 6 dpf was also not significantly affected by treatment (CO2: p = 0.38, X2 (2, N = 30) = 1.92; Tide: p = 0.83, X2 (2, N = 30) = 0.33; CO2*Tide: p = 0.08, X2 (2, N = 30) = 8.25). Metabolic rate, as indicated by embryonic heart rate, was significantly affected by air exposure at 6 dpf (p  *; 0.1  >).Full size image More

Pfennig, D. The adaptive significance of an environmentally-cued developmental switch in an anuran tadpole. Oecologia 85, 101–107 (1990).ADS 
PubMed 
Article 

Google Scholar 
Brönmark, C. & Miner, J. G. Predator-induced phenotypical change in body morphology in Crucian carp. Science 258, 1348–1350 (1992).ADS 
PubMed 
Article 

Google Scholar 
Wikelski, M. & Thom, C. Marine iguanas shrink to survive El Niño. Nature 403, 37–38 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Agrawal, A. A. Phenotypic plasticity in the interactions and evolution of species. Science 294, 321–326 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Huchard, E., English, S., Bell, M. B. V., Thavarajah, N. & Clutton-Brock, T. Competitive growth in a cooperative mammal. Nature 533, 532–534 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Travis, J. Evaluating the adaptive role of morphological plasticity. In: Ecological morphology (pp. 99–122) (The University of Chicago Press, Chicago, 1994).Lázaro, J., Dechmann, D. K. N., LaPoint, S., Wikelski, M. & Hertel, M. Profound reversible seasonal changes of individual skull size in a mammal. Curr. Biol. 27, R1106–R1107 (2017).PubMed 
Article 
CAS 

Google Scholar 
Lázaro, J. & Dechmann, D. K. Dehnel’s phenomenon. Ecol. Evol. 31, R463–R465 (2021).
Google Scholar 
Bronstein, J. L. The evolution of facilitation and mutualism. J. Ecol. 97, 1160–1170 (2009).Article 

Google Scholar 
Leigh, J. The evolution of mutualism. J. Environ. Biol. 23, 2507–2528 (2010).
Google Scholar 
Liu, C., Yang, D. R. & Peng, Y. Q. Body size in a pollinating fig wasp and implications for stability in a fig-pollinator mutualism. Entomol. Exper. Appl. 138, 249–255 (2011).Article 

Google Scholar 
Pires, M. M., Guimarães, P. R., Galetti, M. & Jordano, P. Pleistocene megafaunal extinctions and the functional loss of long-distance seed-dispersal services. Ecography 41, 153–163 (2018).Article 

Google Scholar 
Boucher, D., James, S. & Keeler, K. The ecology of mutualism. Annu. Rev. Ecol. Syst. 13, 315–347 (1982).Article 

Google Scholar 
Irwin, R. E. & Brody, A. K. Nectar robbing in Ipomopsis aggregata: effects on pollinator behavior and plant fitness. Oecologia 116, 519–527 (1998).ADS 
PubMed 
Article 

Google Scholar 
Allen, G. The Anemonefishes: their classification and biology (T.F.H. Publications, 1972).
Google Scholar 
Fautin, D.G. & Allen, G.R. Field guide to anemonefishes and their host sea anemones. (Western Australian Museum, Perth, 1992).Ollerton, J., McCollin, D., Fautin, D. G. & Allen, G. R. Finding NEMO: nestedness engendered by mutualistic organization in anemonefish and their hosts. Proc. R. Soc. B Biol. Sci. 274, 591–598 (2006).Article 

Google Scholar 
Fricke, H. & Fricke, S. Monogamy and sex change by aggressive dominance in coral reef fish. Nature 266, 830–832 (1977).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Buston, P. M. Size and growth modification in clownfish. Nature 424, 145–146 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Mariscal, R. N. The nature of the symbiosis between Indo-Pacific anemone fishes and sea anemones. Mar. Biol. 6, 58–65 (1970).Article 

Google Scholar 
Elliott, J. K., Elliott, J. M. & Mariscal, R. N. Host selection, location, and association behaviors of anemonefishes in field settlement experiments. Mar. Biol. 122, 377–389 (1995).Article 

Google Scholar 
Verde, A. E., Cleveland, A. & Lee, R. W. Nutritional exchange in a tropical tripartite symbiosis II: direct evidence for the transfer of nutrients from host anemone and zooxanthellae to anemonefish. Mar. Biol. 162, 2409–2429 (2015).Article 
CAS 

Google Scholar 
Cleveland, A., Verde, E. A. & Lee, R. W. Nutritional exchange in a tropical tripartite symbiosis: direct evidence for the transfer of nutrients from anemonefish to host anemone and zooxanthellae. Mar. Biol. 158, 589–602 (2011).Article 

Google Scholar 
Sale, P. F. Effect of cover on agonistic behavior of a reef fish: a possible spacing mechanism. Ecology 53, 753–758 (1972).Article 

Google Scholar 
Fricke, H. W. & Holzberg, S. Social units and hermaphroditism in a pomacentrid fish. Naturwissenschaften 61, 367–368 (1974).ADS 
Article 

Google Scholar 
Fricke, H. W. Control of different mating systems in a coral reef fish by one environmental factor. Anim. Behav. 28, 561–569 (1980).Article 

Google Scholar 
Mitchell, J. S. & Dill, L. M. Why is group size correlated with the size of the host sea anemone in the false clown anemonefish?. Canad. J. Zool. 83, 372–376 (2005).Article 

Google Scholar 
Chausson, J., Srinivasan, M. & Jones, G. P. Host anemone size as a determinant of social group size and structure in the orange clownfish (Amphiprion percula). PeerJ 6, e5841 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Reed, C., Branconi, R., Majoris, J., Johnson, C. & Buston, P. Competitive growth in a social fish. Biol. Lett. 15, 20180737 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Buston, P. M. Mortality is associated with social rank in the clown anemonefish (Amphiprion percula). Mar. Biol. 143, 811–815 (2003).Article 

Google Scholar 
Branconi, R. et al. Ecological and social constraints combine to promote evolution of non-breeding strategies in clownfish. Comm. Biol. 3, 1–7 (2020).Article 
CAS 

Google Scholar 
Schmiege, P. F., D’Aloia, C. C. & Buston, P. M. Anemonefish personalities influence the strength of mutualistic interactions with host sea anemones. Mar. Biol. 164, 24 (2017).Article 

Google Scholar 
Barbasch, T. A. & Buston, P. M. Plasticity and personality of parental care in the clown anemonefish. Anim. Behav. 136, 65–73 (2018).Article 

Google Scholar 
Abramoff, M. D., Magalhaes, P. J. & Ram, S. J. Image PROcessing with ImageJ. Biophoto. Int. 11, 36–42 (2004).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2020).Goodrich, B., Gabry, J., Ali I. & Brilleman, S. Rstanarm: Bayesian applied regression modeling via Stan. R package version 2.21.1 https://mc-stan.org/rstanarm (2020).Weatherley, A. H. Approaches to understanding fish growth. Trans. Am. Fish. Soc. 119, 662–672 (1990).Article 

Google Scholar 
Gabry, J. Shinystan: interactive visual and numerical diagnostics and posterior analysis for Bayesian models. R package version 2.5.0. https://CRAN.R-project.org/package=shinystan (2018).Gelman, A., Goodrich, B., Gabry, J. & Vehtari, A. R-squared for Bayesian regression models. Am. Stat. 3, 307–309 (2018).MathSciNet 

Google Scholar 
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. Performance: an R package for assessment, comparison and testing of statistical models. J. Open Sour. Softw. 6, 60 (2021).
Google Scholar 
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. & Gelman, A. Visualization in Bayesian workflow. J. R. Stat. Soc. Ser. A Stat. Soc. 182, 389–402 (2019).MathSciNet 
Article 

Google Scholar 
Gabry, J. & Mahr, T. Bayesplot: plotting for bayesian models. R package version 1.8.0. https://mc-stan.org/bayesplot/ (2021).Elliott, J. K. & Mariscal, R. N. Coexistence of nine anemonefish species: differential host and habitat utilization, size and recruitment. Mar. Biol. 138, 23–36 (2001).Article 

Google Scholar 
Buston, P. M. Forcible eviction and prevention of recruitment in the clown anemonefish. Behav. Ecol. 14, 576–582 (2003).Article 

Google Scholar 
Fautin, D. G. & Allen, G. R. Anemone fishes and their host sea anemones: a guide for aquarists and divers. Sea Challengers (1997).Beldade, R., Blandin, A., O’Donnell, R. & Mills, S. C. Cascading effects of thermally-induced anemone bleaching on associated anemonefish hormonal stress response and reproduction. Nat. Commun. 8, 1–9 (2017).ADS 
CAS 
Article 

Google Scholar 
Cortese, D. et al. Physiological and behavioural effects of anemone bleaching on symbiont anemonefish in the wild. Funct. Ecol. 35, 663–674 (2021).Article 

Google Scholar 
Scherbatskoy, E. C. et al. Characterization of a novel picornavirus isolated from moribund aquacultured clownfish. J. Gen. Virol. 101, 735–745 (2020).CAS 
PubMed 
Article 

Google Scholar 
Saenz-Agudelo, P., Jones, G. P., Thorrold, S. R. & Planes, S. Mothers matter: contribution to local replenishment is linked to female size, mate replacement and fecundity in a fish metapopulation. Mar. Biol. 162, 3–14 (2014).Article 

Google Scholar 
Barbasch, T. A. et al. Substantial plasticity of reproduction and parental care in response to local resource availability in a wild clownfish population. Oikos 129, 1844–1855 (2020).Article 

Google Scholar 
Sebens, K. P. The ecology of indeterminate growth in animals. A. Rev. Ecol. Syst. 18, 371–407 (1987).Article 

Google Scholar 
Buston, P. M. & García, M. B. An extraordinary life span estimate for the clown anemonefish Amphiprion percula. J. Fish Biol. 70, 1710–1719 (2007).Article 

Google Scholar 
Chamberlain, S. A., Kilpatrick, J. R. & Holland, J. N. Do extrafloral nectar resources, species abundances, and body sizes contribute to the structure of ant–plant mutualistic networks?. Oecologia 164, 741–750 (2010).ADS 
PubMed 
Article 

Google Scholar 
Marting, P. R., Kallman, N. M., Wcislo, W. T. & Pratt, S. C. Ant-plant sociometry in the Azteca-Cecropia mutualism. Sci. Rep. 8, 1–15 (2018).Article 
CAS 

Google Scholar 
Fordyce, J. A. The evolutionary consequences of ecological interactions mediated through phenotypic plasticity. J. Exp. Biol. 209, 2377–2383 (2006).PubMed 
Article 

Google Scholar 
West-Eberhard, M. J. Developmental plasticity and evolution (Oxford University Press, 2003).Book 

Google Scholar 
West-Eberhard, M. J. Phenotypic accommodation: adaptive innovation due to developmental plasticity. J. Exp. Zool. B Mol. Develop. Evol. 304, 610–618 (2005).Article 

Google Scholar 
Moczek, A. P. et al. The role of developmental plasticity in evolutionary innovation. Proc. R. Soc. B Biol. Sci. 278, 2705–2713 (2011).Article 

Google Scholar  More

Angilletta, M. J., Niewiarowski, P. H. & Navas, C. A. The evolution of thermal physiology in ectotherms. J. Therm. Biol 27, 249–268. https://doi.org/10.1016/S0306-4565(01)00094-8 (2002).Article 

Google Scholar 
Ebersole, J. L., Liss, W. J. & Frissell, C. A. Cold water patches in warm streams: physicochemical characteristics and the influence of shading. JAWRA J. Am. Water Resour. Assoc. 39, 355–368. https://doi.org/10.1111/j.1752-1688.2003.tb04390.x (2003).ADS 
Article 

Google Scholar 
Comte, L. & Grenouillet, G. Do stream fish track climate change? Assessing distribution shifts in recent decades. Ecography 36, 1236–1246. https://doi.org/10.1111/j.1600-0587.2013.00282.x (2013).Article 

Google Scholar 
Kurylyk, B. L., MacQuarrie, K. T. B., Linnansaari, T., Cunjak, R. A. & Curry, R. A. Preserving, augmenting, and creating cold-water thermal refugia in rivers: Concepts derived from research on the Miramichi River, New Brunswick (Canada). Ecohydrology 8, 1095–1108. https://doi.org/10.1002/eco.1566 (2015).Article 

Google Scholar 
Ebersole, J. L., Quiñones, R. M., Clements, S. & Letcher, B. H. Managing climate refugia for freshwater fishes under an expanding human footprint. Front. Ecol. Environ. 18, 271–280. https://doi.org/10.1002/fee.2206 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Caissie, D. The thermal regime of rivers: a review. Freshw. Biol. 51, 1389–1406. https://doi.org/10.1111/j.1365-2427.2006.01597.x (2006).Article 

Google Scholar 
Dick, J. J., Tetzlaff, D. & Soulsby, C. Landscape influence on small-scale water temperature variations in a moorland catchment. Hydrol. Process. 29, 3098–3111. https://doi.org/10.1002/hyp.10423 (2015).ADS 
Article 

Google Scholar 
Fullerton, A. H. et al. Rethinking the longitudinal stream temperature paradigm: region-wide comparison of thermal infrared imagery reveals unexpected complexity of river temperatures. Hydrol. Process. 29, 4719–4737. https://doi.org/10.1002/hyp.10506 (2015).ADS 
Article 

Google Scholar 
Fullerton, A. H. et al. Longitudinal thermal heterogeneity in rivers and refugia for coldwater species: Effects of scale and climate change. Aquatic Sci. 80, 3. https://doi.org/10.1007/s00027-017-0557-9 (2018).Article 

Google Scholar 
Segura, C., Caldwell, P., Sun, G., McNulty, S. & Zhang, Y. A model to predict stream water temperature across the conterminous USA. Hydrol. Process. 29, 2178–2195. https://doi.org/10.1002/hyp.10357 (2015).ADS 
Article 

Google Scholar 
Jonkers, A. R. T. & Sharkey, K. J. The differential warming response of Britain’s rivers (1982–2011). PLOS One 11, e0166247. https://doi.org/10.1371/journal.pone.0166247 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jackson, F. L., Hannah, D. M., Fryer, R. J., Millar, C. P. & Malcolm, I. A. Development of spatial regression models for predicting summer river temperatures from landscape characteristics: Implications for land and fisheries management. Hydrol. Process. 31, 1225–1238. https://doi.org/10.1002/hyp.11087 (2017).ADS 
Article 

Google Scholar 
Maheu, A., Poff, N. L. & St-Hilaire, A. A classification of stream water temperature regimes in the conterminous USA. River Res. Appl. 32, 896–906. https://doi.org/10.1002/rra.2906 (2016).Article 

Google Scholar 
Steel, E. A., Sowder, C. & Peterson, E. E. Spatial and temporal variation of water temperature regimes on the Snoqualmie River network. J. Am. Water Resour. Assoc. 52, 769–787. https://doi.org/10.1111/1752-1688.12423 (2016).Article 

Google Scholar 
Kearney, M. R., Matzelle, A. & Helmuth, B. Biomechanics meets the ecological niche: The importance of temporal data resolution. J. Exp. Biol. 215, 922–933. https://doi.org/10.1242/jeb.059634 (2012).Article 
PubMed 

Google Scholar 
Burgmer, T., Hillebrand, H. & Pfenninger, M. Effects of climate-driven temperature changes on the diversity of freshwater macroinvertebrates. Oecologia 151, 93–103. https://doi.org/10.1007/s00442-006-0542-9 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Isaak, D. J., Young, M. K., Nagel, D. E., Horan, D. L. & Groce, M. C. The cold-water climate shield: Delineating refugia for preserving salmonid fishes through the 21st century. Glob. Change Biol. 21, 2540–2553. https://doi.org/10.1111/gcb.12879 (2015).ADS 
Article 

Google Scholar 
Steel, E. A., Beechie, T. J., Torgersen, C. E. & Fullerton, A. H. Envisioning, quantifying, and managing thermal regimes on river networks. Bioscience 67, 506–522. https://doi.org/10.1093/biosci/bix047 (2017).Article 

Google Scholar 
Budescu, D. V. Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychol. Bull. 114, 542–551. https://doi.org/10.1037/0033-2909.114.3.542 (1993).Article 

Google Scholar 
Singhal, B. B. S. & Gupta, R. P. Applied Hydrogeology of Fractured Rocks. 2 edn, 408 (Springer, 2010).Shimizu, T. Relation between scanty runoff from mountainous watershed and geology, slope and vegetation (in Japanese with English summary). Bull. Forestry Forest Prod. Res. Inst. 310, 109–128 (1980).
Google Scholar 
Iwasaki, K., Nagasaka, Y. & Nagasaka, A. Geological effects on the scaling relationships of groundwater contributions in Forested Watersheds. Water Resour. Res. 57, e2021WR029641. https://doi.org/10.1029/2021WR029641 (2021).ADS 
Article 

Google Scholar 
Ishiyama, N. et al. The role of geology in creating stream climate-change refugia along climate gradients. bioRxiv, 2022.2005.2002.490355, https://doi.org/10.1101/2022.05.02.490355 (2022).Kanno, Y., Vokoun, J. C. & Letcher, B. H. Paired stream-air temperature measurements reveal fine-scale thermal heterogeneity within headwater brook trout stream networks. River Res. Appl. 30, 745–755. https://doi.org/10.1002/rra.2677 (2014).Article 

Google Scholar 
Snyder, C. D., Hitt, N. P. & Young, J. A. Accounting for groundwater in stream fish thermal habitat responses to climate change. Ecol. Appl. 25, 1397–1419. https://doi.org/10.1890/14-1354.1 (2015).Article 
PubMed 

Google Scholar 
Carslaw, D. C. & Ropkins, K. Openair—an R package for air quality data analysis. Environ. Model. Softw. 27–28, 52–61. https://doi.org/10.1016/j.envsoft.2011.09.008 (2012).Article 

Google Scholar 
Pinheiro, J. C. & Bates, D. M. Mixed-Effects Models in S and S-PLUS. (Springer, 2000).Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models. (Cambridge University Press, 2006).Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794. https://doi.org/10.7717/peerj.4794 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Clarke, P. When can group level clustering be ignored? Multilevel models versus single-level models with sparse data. J. Epidemiol. Commun. Health 62, 752. https://doi.org/10.1136/jech.2007.060798 (2008).CAS 
Article 

Google Scholar 
Theall, K. P. et al. Impact of small group size on neighbourhood influences in multilevel models. J. Epidemiol. Commun. Health 65, 688–695. https://doi.org/10.1136/jech.2009.097956 (2011).Article 

Google Scholar 
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x (2013).Article 

Google Scholar 
Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. Royal Soc. Interface 14, 20170213. https://doi.org/10.1098/rsif.2017.0213 (2017).Article 

Google Scholar 
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. performance: An R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139. https://doi.org/10.21105/joss.03139 (2021).ADS 
Article 

Google Scholar 
Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. Multivariate Data Analysis: A Global Perspective. 7 edn, (Prentice Hall, 2009).Azen, R. & Budescu, D. V. The dominance analysis approach for comparing predictors in multiple regression. Psychol. Methods 8, 129–148. https://doi.org/10.1037/1082-989x.8.2.129 (2003).Article 
PubMed 

Google Scholar 
Grömping, U. Estimators of relative importance in linear regression based on variance decomposition. Am. Stat. 61, 139–147. https://doi.org/10.1198/000313007X188252 (2007).MathSciNet 
Article 

Google Scholar 
Luo, W. & Azen, R. Determining predictor importance in hierarchical linear models using dominance analysis. J. Educ. Behav. Stat. 38, 3–31. https://doi.org/10.3102/1076998612458319 (2013).Article 

Google Scholar 
R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. (2020).Erickson, T. R. & Stefan, H. G. Linear air/water temperature correlations for streams during open water periods. J. Hydrol. Eng. 5, 317–321. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:3(317) (2000).Article 

Google Scholar 
Webb, B. W., Clack, P. D. & Walling, D. E. Water–air temperature relationships in a Devon river system and the role of flow. Hydrol. Process. 17, 3069–3084. https://doi.org/10.1002/hyp.1280 (2003).ADS 
Article 

Google Scholar 
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. Circlize implements and enhances circular visualization in R. Bioinformatics.
30, 2811–2812. https://doi.org/10.1093/bioinformatics/btu393 (2014).Sugimoto, S., Nakamura, F. & Ito, A. Heat budget and statistical analysis of the relationship between stream temperature and riparian forest in the Toikanbetsu River Basin, Northern Japan. J. For. Res. 2, 103–107. https://doi.org/10.1007/BF02348477 (1997).Article 

Google Scholar 
Dugdale, S. J., Malcolm, I. A., Kantola, K. & Hannah, D. M. Stream temperature under contrasting riparian forest cover: Understanding thermal dynamics and heat exchange processes. Sci. Total Environ. 610–611, 1375–1389. https://doi.org/10.1016/j.scitotenv.2017.08.198 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Timm, A., Ouellet, V. & Daniels, M. Riparian land cover, water temperature variability, and thermal stress for aquatic species in urban streams. Water 13, 2732. https://doi.org/10.3390/w13192732 (2021).Article 

Google Scholar 
Mitchell, S. A simple model for estimating mean monthly stream temperatures after riparian canopy removal. Environ. Manage. 24, 77–83. https://doi.org/10.1007/s002679900216 (1999).CAS 
Article 
PubMed 

Google Scholar 
Horne, J. P. & Hubbart, J. A. A spatially distributed investigation of stream water temperature in a contemporary mixed-land-use watershed. Water 12, 1756. https://doi.org/10.3390/w12061756 (2020).Article 

Google Scholar 
Graham, C. B., Barnard, H. R., Kavanagh, K. L. & McNamara, J. P. Catchment scale controls the temporal connection of transpiration and diel fluctuations in streamflow. Hydrol. Process. 27, 2541–2556. https://doi.org/10.1002/hyp.9334 (2013).ADS 
Article 

Google Scholar 
Sun, H., Kasahara, T., Otsuki, K., Saito, T. & Onda, Y. Spatio-temporal streamflow generation in a small, steep headwater catchment in Western Japan. Hydrol. Sci. J. 62, 818–829. https://doi.org/10.1080/02626667.2016.1266635 (2017).Article 

Google Scholar 
Sophocleous, M. Interactions between groundwater and surface water: The state of the science. Hydrogeol. J. 10, 52–67. https://doi.org/10.1007/s10040-001-0170-8 (2002).ADS 
CAS 
Article 

Google Scholar 
Arnott, S., Hilton, J. & Webb, B. W. The impact of geological control on flow accretion in lowland permeable catchments. Hydrol. Res. 40, 533–543. https://doi.org/10.2166/nh.2009.017 (2009).Article 

Google Scholar 
Calvache, M. L., Duque, C., Fontalva, J. M. G. & Crespo, F. Processes affecting groundwater temperature patterns in a coastal aquifer. Int. J. Environ. Sci. Technol. 8, 223–236. https://doi.org/10.1007/BF03326211 (2011).Article 

Google Scholar 
Nejadhashemi, A. P., Wardynski, B. J. & Munoz, J. D. Evaluating the impacts of land use changes on hydrologic responses in the agricultural regions of Michigan and Wisconsin. Hydrol. Earth Syst. Sci. 2011, 3421–3468, https://doi.org/10.5194/hessd-8-3421-2011 (2011).Macedo, M. N. et al. Land-use-driven stream warming in southeastern Amazonia. Philos. Trans. R Soc. Lond. B Biol. Sci. 368, 20120153–20120153. https://doi.org/10.1098/rstb.2012.0153 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Carlson, K. M. et al. Influence of watershed-climate interactions on stream temperature, sediment yield, and metabolism along a land use intensity gradient in Indonesian Borneo. J. Geophys. Res. Biogeosci. 119, 1110–1128. https://doi.org/10.1002/2013JG002516 (2014).Article 

Google Scholar 
Wang, Y. I., He, B. I. N. & Takase, K. Effects of temporal resolution on hydrological model parameters and its impact on prediction of river discharge. Hydrol. Sci. J. 54, 886–898. https://doi.org/10.1623/hysj.54.5.886 (2009).Article 

Google Scholar 
Levin, S. A. The problem of pattern and scale in ecology: The Robert H MacArthur award lecture. Ecology 73, 1943–1967. https://doi.org/10.2307/1941447 (1992).Article 

Google Scholar 
García Molinos, J. & Donohue, I. Downscaling the non-stationary effect of climate forcing on local-scale dynamics: The importance of environmental filters. Clim. Change 124, 333–346. https://doi.org/10.1007/s10584-014-1077-4 (2014).ADS 
Article 

Google Scholar 
Newman, E. A., Kennedy, M. C., Falk, D. A. & McKenzie, D. Scaling and complexity in landscape ecology. Front. Ecol. Evolution https://doi.org/10.3389/fevo.2019.00293 (2019).Article 

Google Scholar 
Atkinson, S. E., Woods, R. A. & Sivapalan, M. Climate and landscape controls on water balance model complexity over changing timescales. Water Resour. Res. 38, 50-51–50-17, https://doi.org/10.1029/2002WR001487 (2002).Engel, M. et al. Controls on spatial and temporal variability in streamflow and hydrochemistry in a glacierized catchment. Hydrol. Earth Syst. Sci. 23, 2041–2063. https://doi.org/10.5194/hess-23-2041-2019 (2019).ADS 
Article 

Google Scholar 
Karlsen, R. H. et al. Landscape controls on spatiotemporal discharge variability in a boreal catchment. Water Resour. Res. 52, 6541–6556. https://doi.org/10.1002/2016WR019186 (2016).ADS 
Article 

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42. https://doi.org/10.1038/nature01286 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Weiskopf, S. R. et al. Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Sci. Total Environ. 733, 137782. https://doi.org/10.1016/j.scitotenv.2020.137782 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 3109. https://doi.org/10.1038/s41467-019-10924-4 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kingsford, R. T. Conservation management of rivers and wetlands under climate change—a synthesis. Mar. Freshw. Res. 62, 217–222. https://doi.org/10.1071/MF11029 (2011).CAS 
Article 

Google Scholar  More

Nash, R. D. M., Valencia, A. H. & Geffen, A. J. The origin of Fulton’s condition factor—setting the record straight. Fisheries 31(5), 236–238 (2006).
Google Scholar 
Tarkan, A. S., Gaygusuz, Ö., Acıpınar, H., Gürsoy, Ç. & Özuluğ, M. Length–weight relationships of fishes from the Marmara region (NW-Turkey). J. Appl. Ichthyol. 22(4), 271–273 (2006).Article 

Google Scholar 
Al Nahdi, A., de Leaniz, C. G. & King, A. J. Spatio-temporal variation in length-weight relationships and condition of ribbonfish Trichiurus lepturus (Linnaeus, 1758): Implications for fisheries. PLoS One 11(8), e0161989 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Froese, R., Tsikliras, A. C. & Stergiou, K. I. Editorial note on weight–length relations of fishes. Acta Ichthyol. Piscat. 41(4), 261–263 (2011).Article 

Google Scholar 
Torres, M. A. et al. Length–weight relationships for 22 crustecans and cephalopods from the Gulf of Cadiz (SW Spain). Aquat. Liv. Resour. 30, 12 (2017).Article 

Google Scholar 
Rocha, F., Guerra, A. & Gonzalez, A. F. A review of reproductive strategies in cephalopods. Biol. Rev. 76, 291–304 (2001).CAS 
PubMed 
Article 

Google Scholar 
Laptikhovsky, V. & Salman, A. On reproductive strategies of the epipelagic octopods of the superfamily Argonautoidea (Cephalopoda: Octopoda). Mar. Biol. 142, 321–326 (2003).Article 

Google Scholar 
Forsythe, J. W. & van Heukelem, W. F. Growth. In Cephalopod Life Cycles (ed. Boyle, P. R.) 135–156 (Academic Press, 1987).
Google Scholar 
Jereb, P., et al. (eds) 2015. Cephalopod biology and fisheries in Europe: II. Species Accounts. ICES Cooperative Research Report No. 325, p. 360.Salman, A. Cephalopod research in the eastern Mediterranean (East of 23°E): A review. Boll. Malacol. 45, 47–59 (2009).
Google Scholar 
Salman, A. & İzmirli, C. Ege Üniversitesi Su Ürünleri Fakültesi Müzesi (ESFM)’nin cephalopod envanteri. EgeJFAS 37(4), 357–361. https://doi.org/10.12714/egejfas.37.4.06) (2020) (in Turkish).Article 

Google Scholar 
Önsoy, B. & Salman, A. Reproductive biology of the common cuttlefish Sepia officinalis L. (Sepiida: Cephalpoda) in the Aegean Sea. Turk. J. Vet. Anim. Sci. 29, 613–619 (2005).
Google Scholar 
Salman, A. Reproductive biology of the elegant cuttlefish (Sepia elegans) in the Eastern Mediterranean. Turk. J. Fish. Aquat. Sci. 15(2), 265–272 (2015).Article 

Google Scholar 
Dursun, D., Eronat, E. G. T., Akalın, M. & Salman, M. A. Reproductive biology of pink cuttlefish Sepia orbignyana in the Aegean Sea (eastern Mediterranean). Turk. J. Zool. 37, 576–581 (2013).Article 

Google Scholar 
Salman, A. Reproductive biology of Sepietta oweniana (Pfeffer, 1908) (Sepiolidae: Cephalopoda) in the Aegean Sea. Sci. Mar. 62(4), 379–383 (1998).Article 

Google Scholar 
Salman, A. & Önsoy, B. Reproductive biology of the bobtail squid Rossia macrosoma (Cephalopoda: Sepiolidea) from the eastern Mediterranean. Turk. J. Fish. Aquat. Sci. 10, 81–86 (2010).Article 

Google Scholar 
Salman, A. Fecundity and spawning strategy of shortfin squid Illex coindetii (Oegopsida: Ommastrephidae), in the eastern Mediterranean. Turk. J. Fish. Aquat. Sci. 17, 841–849 (2017).
Google Scholar 
Mangold-Wirz, K. Biologie des céphalopodes benthiques et nectoniques de la Mer Catalane. Vie Millieu suppl. 13, 1–285 (1963).
Google Scholar 
Salman, A. Fecundity, spawning strategy and oocyte development of shortfin squid Alloteuthis media (Myopsida: Loliginidae) in the eastern Mediterranean. Cah. Biol. Mar. 55, 163–171 (2014).
Google Scholar 
Önsoy, B. & Salman, A. Reproduction patterns of the Mediterranean endemic, Eledone moschata (Lamarck, 1798) (Octopoda: Cephalopoda) in the eastern Mediterranean. (In Turkish) 1st National Malacology Congress, 1–3 September 2004, Izmir-Turkey (Bilal Öztürk & Alp Salman, eds). Turk. J. Aquat. Life 2(2), 55–60 (2004).
Google Scholar 
Tesch, F. W. Age and growth. In Methods for Assessment of Fish Production in Fresh Waters (ed. Ricker, W. E.) 99–130 (Blackwell Scientific Publications, 1971).
Google Scholar 
Merella, P., Quetglas, A., Alemany, F. & Carbonell, A. Length–weight relationship of fishes and cephalopods from the Balearic Islands (western Mediterranean). Naga ICLARM Q. 20(3–4), 66–68 (1997).
Google Scholar 
Manfrin Piccinetti, G. & Giovanardi, O. Données sur la biologie de Sepia officinalis L. dans l’Adriatique obtenues lors de expéditions pipeta. FAO Fish. Rep. 290, 135–138 (1984).
Google Scholar 
Bello, G. Length–weight relationship in males and females of Sepia orbignyana and Sepia elegans (Cephalopoda: Sepiidae). Rapp. Comm. Int. Mer. Médit. 31(2), 254 (1988).
Google Scholar 
Ragonese, S. & Jereb, P. Length-weight relationship and growth of the pink and elegant cuttlefish Sepia orbignyana and Sepia elegans in the Sicilian Channel. In Acta of the 1st International Symposium on the Cuttlefish (ed. Boucaud-Camou, E.) 31–47 (SEPIA. Centre de Publications de l’Universite de Caen, 1991).
Google Scholar 
Akyol, O. & Metin, G. An investigation on determination of some morphological characteristics of Cephalopods in Izmir Bay (Aegean Sea). EU J. Fish. Aquat. Sci. 18(3–4), 357–365 (2001).
Google Scholar 
Lefkaditou, E., Verriopoulos, G. & Valavanis, V. VII9. Research on Cephalopod resources in Hellas. In State of Hellenic Fisheries (eds Papaconstantinou, C. et al.) 440–451 (HCMR Publications, 2007).
Google Scholar 
Duysak, Ö., Sendão, J., Borges, T., Türeli, C. & Erdem, Ü. Cephalopod distribution in Iskenderun bay (eastern Mediterranean–Turkey). J. Fish. Sci. 2, 118–125 (2008).
Google Scholar 
Giordano, D. et al. Distribution and biology of Sepietta oweniana (Pfeffer, 1908) (Cephalopoda: Sepiolidae) in the southern Tyrrhenian Sea (central Mediterranean Sea). Cah. Biol. Mar. 50, 1–10 (2009).
Google Scholar 
Andriguetto, J. M. Jr. & Haimovici, M. Effects of fixation and preservation methods on the morphology of a Loliginid squid (Cephalopoda: Myopsida). Am. Malac Bull. 6(2), 213–217 (1988).
Google Scholar 
Sanchez, P. Donnés preliminaires sur la biologie de trois species de cephalopods de la Mer Catalan. Rapp. Comm. Int. Mer. Médit. 30(2), 247 (1986).
Google Scholar 
Belcari, P., Sartor, P., Nannini, N. & De Ranieri, S. Length-weight relationship of Toda- ropsis eblanae (Cephalopoda: Ommastrephidae) of the northern Tyrrhenian Sea in relation to sexual maturation. Biol. Mar. Mediter. 6, 524–528 (1999).
Google Scholar 
Belcari, P. Length–weight relationship in relation to sexual maturation of Illex coindetii (Cephalopoda: Ommastrephidae) in the northern Tyrrhenian Sea (western Mediterranean). Sci. Mar. 60, 379–384 (1996).
Google Scholar 
Petric, M., Ferri, J., Skeljo, F. & Krstulovic Sifner, S. Body and beak measures of Illex coindetii (Cephalopoda: Ommastrephidae) and their relation to growth and maturity. Cah. Biol. Mar. 51, 275–287 (2010).
Google Scholar 
Ceriola, L., Ungaro, N. & Toteda, F. Some information on the biology of Illex coindetii Verany, 1839 (Cephalopoda, Ommastrephidae) in the south-western Adriatic Sea (central Mediterranean). Fish. Res. 82, 41–49 (2006).Article 

Google Scholar 
Arvanitidis, C. et al. A comparison of the fishery biology of three Illex coindetii Verany, 1839 (Cephalopoda: Ommastrephidae) populations from the European Atlantic and Mediterranean Waters. Bull. Mar. Sci. 71, 129–146 (2002).
Google Scholar 
Quetglas, A., Alemany, F., Carbonell, A., Merella, P. & Sanchez, P. Some aspects of the biology of Todarodes sagittatus (Cephalopoda: Ommastrephidae) from the Balearic Sea (western Mediterranean). Sci. Mar. 62, 73–82 (1998).Article 

Google Scholar 
Krstulovic Sifner, S. K. & Vrgoc, N. Population structure, maturation and reproduction of the European squid, Loligo vulgaris, in the central Adriatic Sea. Fish. Res. 69, 239–249 (2004).Article 

Google Scholar 
Moreno, A. et al. Biological variation of Loligo vulgaris (Cephalopoda: Loliginidae) in the eastern Atlantic and Mediterranean. Bull. Mar. Sci. 71(1), 515–534 (2002).
Google Scholar 
Guerra, A. & Manriquez, M. Parametros biometricos de Octopus vulgaris. Invest. Pesq. 44, 177–198 (1980).
Google Scholar 
Quetglas, A., Alemany, F., Carbonell, A., Merella, P. & Sanchez, P. Biology and fishery of Octopus vulgaris Cuvier, 1797, caught by trawlers in Mallorca (Balearic Sea, western Mediterranean). Fish. Res. 36, 237–249 (1998).Article 

Google Scholar 
Sanchez, P., & Obarti, R. 1993. The biology and fishery of Octopus vulgaris caught with clay pots on the Spanish Mediterranean coast. In: Jereb, P., Allcock, A. L., Lefkaditou, E., Piatkowski, U., Hastie, L. C., Pierce, G. J. (Eds.) 2015. Cephalopod biology and fisheries in Europe: II. Species Accounts. ICES Cooperative Research Report No. 325, p 360.Gonzalez, M., Barcala, E., Perez-Gil, J. L., Carrasco, M. N. & Garcia-Martinez, M. C. Fisheries and reproductive biology of Octopus vulgaris (Mollusca: Cephalopoda) in the Gulf of Alicante (Northwestern Mediterranean). Medit. Mar. Sci. 12, 369–389 (2011).Article 

Google Scholar 
Jabeur, C., Nouira, T., Khoufi, W., Mosbahi, D. S. & Ezzedine-Najai, S. Age and growth of Octopus vulgaris Cuvier, 1797 along the east coast of Tunisia. J. Shellf. Res. 31, 119–124 (2012).Article 

Google Scholar 
Quetglas, A., Ordines, F., Gonzalez, M. & Franco, I. Life history of the bathyal octopus Pteroctopus tetracirrhus (Mollusca, Cephalopoda) in the Mediterranean Sea. Deep Sea Res. Part I 56, 1379–1390 (2009).Article 

Google Scholar 
Quetglas, A., Gonzalez, M. & Franco, I. Biology of the upper-slope cephalopod Octopus salutii from the western Mediterranean Sea. Mar. Biol. 146, 1131–1138 (2005).Article 

Google Scholar 
Moriyasu, M. Etude biometrique de la croissance d’E. cirrhosa [LAM. 1798 (Cephalopoda, Octopoda)] du Golfe du Lion. Oceanol. Acta 6, 35–41 (1983).
Google Scholar 
Massi, D. Effetti del congelamento sull’accuratezza delle misure in Eledone cirrhosa (Lamarck, 1798). Biol. Mar. Suppl. al Notiziario S.I.B.M. 1, 379–380 (1993).
Google Scholar 
Agnesi, S., Belluscio, A. & Ardizzone, G. D. Biologia e dinamica di populazione di Eledone cirrhosa (Cephalopoda: Octopoda) nel Tirreno Centrale. Biol. Mar. Mediterr. 5, 336–348 (1998).
Google Scholar 
Giordano, D. et al. Population dynamics and distribution of Eledone cirrhosa (Lamarck, 1798) in the Southern Tyrrhenian Sea (Central Mediterranean). Cah. Biol. Mar. 51, 213–227 (2010).
Google Scholar 
Krstulovic Sifner, S. K. & Vrgoc, N. Reproductive cycle and sexual maturation of the musky octopus Eledone moschata (Cephalopoda: Octopodidae) in the northern and central Adriatic Sea. Sci. Mar. 73, 439–447 (2009).Article 

Google Scholar 
Ikica, Z., Krstulovic Sifner, S. & Joksimovic, A. Some preliminary data on biological aspects of the musky octopus, Eledone moschata (Lamarck, 1798) (Cephalopoda: Octopodidae) in Montenegrin waters. Stud. Mar. 25, 21–36 (2011).
Google Scholar 
Akyol, O., Şen, H. & Kinacigil, H. T. Reproductive biology of Eledone moschata (Cephalopoda: Octopodidae) in the Aegean Sea (Izmir Bay, Turkey). J. Mar. Biol. Assoc. UK 87, 967–970 (2007).Article 

Google Scholar 
Quetglas, A., Gonzalez, M., Carbonell, A. & Sanchez, P. Biology of the deep-sea octopus Bathypolypus sponsalis (Cephalopoda: Octopodidae) from the western Mediterranean Sea. Mar. Biol. 138, 785–792 (2001).Article 

Google Scholar  More

It’s surprisingly hard to catch a sloth. Although they’re slow — very, very slow — if you climb a tree to catch one, it will move along to the next tree. Once you climb the new tree, it will move back again.My team does this regularly, as we conduct the Sloth Backpack Project, a data-logging initiative here in Costa Rica, where many sloths coexist with people. In 2017, I wanted to do more than research, so I started the Sloth Conservation Foundation.In this photograph, I’m fitting a backpack to a brown-throated three-fingered sloth (Bradypus variegatus) that we named Baguette, after a nearby bakery. The backpack will collect data on her location, movement and living patterns.We had found Baguette about 20 minutes earlier, balancing atop construction fencing as she attempted to escape two pit bulls. Baguette wasn’t all that grateful. She’s a feisty old girl. She’s old: she’s missing fingers, and she’s got scars on her face.I adore sloths, but I also envy them. They’re a powerful symbol of the slowness that our society needs more of. They don’t let anything stress them out unless it’s really important — they just get on with life.The backpack project will help us to understand sloth behaviour, so we can better protect them as the urban environment grows. This year, I received a €50,000 (US$52,220) Future For Nature award, which we will use to train a dog to detect sloth faeces. We can use faeces as a proxy for sloth numbers and locations in the region, and ultimately work out the boundaries of the species, how fast populations are declining and which conservation measures work.I’m happy I’ve moved away from academia — I can put all my energy into conservation as opposed to bashing out papers. That’s what I feel ecology should focus on — how we can use what we’re learning to give back to other species. More

Hughes, T. P. et al. Climate change, human impacts, and the resilience of coral reefs. Science 301(5635), 929–933 (2003).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Hoegh-Guldberg, O. E. et al. Coral reefs under rapid climate change and ocean acidification. Science 318(5857), 1737–1742 (2007).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Soares, M. et al. The flourishing and vulnerabilities of zoantharians on Southwestern Atlantic reefs. Mar. Environ. Res. 173(3), 105535 (2021).Ban, N. C. et al. Designing, implementing and managing marine protected areas: Emerging trends and opportunities for coral reef nations. J. Exp. Mar. Biol. Ecol. 408(1–2), 21–31 (2011).Article 

Google Scholar 
Magris, R. A., Pressey, R. L., Mills, M., Vila-Nova, D. A. & Floeter, S. Integrated conservation planning for coral reefs: Designing conservation zones for multiple conservation objectives in spatial prioritisation. Glob. Ecol. Conserv. 11, 53–68 (2017).Article 

Google Scholar 
Vercammen, A. et al. Evaluating the impact of accounting for coral cover in large-scale marine conservation prioritizations. Divers. Distrib. 25(10), 1564–1574 (2019).Article 

Google Scholar 
Giakoumi, S., Grantham, H. S., Kokkoris, G. D. & Possingham, H. P. Designing a network of marine reserves in the Mediterranean Sea with limited socio-economic data. Biol. Conserv. 144(2), 753–763 (2011).Article 

Google Scholar 
Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543(7647), 665–669 (2017).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Magris, R. A. et al. A blueprint for securing Brazil’s marine biodiversity and supporting the achievement of global conservation goals. Divers. Distrib. 27(2), 198–215 (2021).Article 

Google Scholar 
Day, J. C. Zoning—lessons from the Great Barrier Reef marine park. Ocean Coast. Manag. 45(2–3), 139–156 (2002).Article 

Google Scholar 
Agardy, T. Ocean Zoning: Making Marine Management More Effective (Earthscan, 2010).Makino, A., Klein, C. J., Beger, M., Jupiter, S. D. & Possingham, H. P. Incorporating conservation zone effectiveness for protecting biodiversity in marine planning. PLoS ONE 8(11), e78986 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Villa, F., Tunesi, L. & Agardy, T. Zoning marine protected areas through spatial multiple-criteria analysis: The case of the Asinara Island National Marine Reserve of Italy. Conserv. Biol. 16(2), 515–526 (2002).Article 

Google Scholar 
Muhl, E. K., Esteves Dias, A. C. & Armitage, D. Experiences with governance in three marine conservation zoning initiatives: Parameters for assessment and pathways forward. Front. Mar. Sci. 7, 629 (2020).Article 

Google Scholar 
Beger, M. et al. Integrating regional conservation priorities for multiple objectives into national policy. Nat. Commun. 6(1), 1–8 (2015).Article 
CAS 

Google Scholar 
Ban, N. C. et al. A social–ecological approach to conservation planning: Embedding social considerations. Front. Ecol. Environ. 11(4), 194–202 (2013).Article 

Google Scholar 
Teh, L. C., Teh, L. S. & Jumin, R. Combining human preference and biodiversity priorities for marine protected area site selection in Sabah, Malaysia. Biol. Conserv. 167, 396–404 (2013).Article 

Google Scholar 
Sarker, S., Rahman, M. M., Yadav, A. K. & Islam, M. M. Zoning of marine protected areas for biodiversity conservation in Bangladesh through socio-spatial data. Ocean Coast. Manag. 173, 114–122 (2019).Article 

Google Scholar 
Day, J. C., Kenchington, R. A., Tanzer, J. M. & Cameron, D. S. Marine zoning revisited: How decades of zoning the Great Barrier Reef has evolved as an effective spatial planning approach for marine ecosystem-based management. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 9–32 (2019).Article 

Google Scholar 
Claudet, J. et al. Assessing the effects of marine protected area (MPA) on a reef fish assemblage in a northwestern Mediterranean marine reserve: Identifying community-based indicators. Biol. Conserv. 130(3), 349–369 (2006).Article 

Google Scholar 
Emslie, M. J. et al. Expectations and outcomes of reserve network performance following re-zoning of the Great Barrier Reef Marine Park. Curr. Biol. 25(8), 983–992 (2015).CAS 
PubMed 
Article 

Google Scholar 
McClure, E. C. et al. Higher fish biomass inside than outside marine protected areas despite typhoon impacts in a complex reefscape. Biol. Cons. 241, 108354 (2020).Article 

Google Scholar 
Bender, M. G. et al. Local ecological knowledge and scientific data reveal overexploitation by multigear artisanal fisheries in the Southwestern Atlantic. PLoS ONE 9(10), e110332 (2014).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Hamilton, R. J. et al. Hyperstability masks declines in bumphead parrotfish (Bolbometopon muricatum) populations. Coral Reefs 35(3), 751–763 (2016).Article 
ADS 

Google Scholar 
Pereira, P. H. C., Ternes, M. L. F., Nunes, J. A. C. & Giglio, V. J. Overexploitation and behavioral changes of the largest South Atlantic parrotfish (Scarus trispinosus): Evidence from fishers’ knowledge. Biol. Conserv. 254, 108940 (2021).Article 

Google Scholar 
Mumby, P. J. et al. Fishing, trophic cascades, and the process of grazing on coral reefs. Science 311(5757), 98–101 (2006).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Mumby, P. J. & Harborne, A. R. Marine reserves enhance the recovery of corals on Caribbean reefs. PLoS ONE 5(1), e8657 (2010).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Topor, Z. M., Rasher, D. B., Duffy, J. E. & Brandl, S. J. Marine protected areas enhance coral reef functioning by promoting fish biodiversity. Conserv. Lett. 12(4), e12638 (2019).Article 

Google Scholar 
Liu, C., White, M. & Newell, G. Measuring and comparing the accuracy of species distribution models with presence–absence data. Ecography 34(2), 232–243 (2011).CAS 
Article 

Google Scholar 
Miranda, R. J. et al. Integrating long term ecological research (LTER) and marine protected area management: Challenges and solutions. Oecol. Aust. 24(2), 279–300 (2020).Article 

Google Scholar 
ICMBIO. Plano de Manejo da Área de Proteção Ambiental Costa dos Corais. ICMBio/MMA (2021).Jones, K. R. et al. Area requirements to safeguard Earth’s marine species. One Earth 2(2), 188–196 (2020).Article 
ADS 

Google Scholar 
Figueiredo, M. S. & Grelle, C. E. V. Predicting global abundance of a threatened species from its occurrence: Implications for conservation planning. Divers. Distrib. 15(1), 117–121 (2009).Article 

Google Scholar 
Pearce, J. & Ferrier, S. The practical value of modelling relative abundance of species for regional conservation planning: A case study. Biol. Conserv. 98(1), 33–43 (2001).Article 

Google Scholar 
Ferreira, H. M., Magris, R. A., Floeter, S. R. & Ferreira, C. E. Drivers of ecological effectiveness of marine protected areas: A meta-analytic approach from the Southwestern Atlantic Ocean (Brazil). J. Environ. Manag. 301, 113889 (2021).Article 

Google Scholar 
Mills, M. et al. Real-world progress in overcoming the challenges of adaptive spatial planning in marine protected areas. Biol. Conserv. 181, 54–63 (2015).Article 

Google Scholar 
Bennett, N. J. et al. Local support for conservation is associated with perceptions of good governance, social impacts, and ecological effectiveness. Conserv. Lett. 12(4), e12640 (2019).Article 

Google Scholar 
Oldekop, J. A., Holmes, G., Harris, W. E. & Evans, K. L. A global assessment of the social and conservation outcomes of protected areas. Conserv. Biol. 30(1), 133–141 (2016).CAS 
PubMed 
Article 

Google Scholar 
Emslie, M. J. et al. Decades of monitoring have informed the stewardship and ecological understanding of Australia’s Great Barrier Reef. Biol. Conserv. 252, 108854 (2020).Article 

Google Scholar 
Gerhardinger, L. C., Godoy, E. A., Jones, P. J., Sales, G. & Ferreira, B. P. Marine protected dramas: The flaws of the Brazilian national system of marine protected areas. Environ. Manag. 47(4), 630–643 (2011).Article 
ADS 

Google Scholar 
Oliveira, E. A., Martelli, H., Silva, A. C. S. E., Martelli, D. R. B. & Oliveira, M. C. L. Science funding crisis in Brazil and COVID-19: Deleterious impact on scientific output. Anais Acad. Bras. Ciênc. 92, 1–2 (2020).
Floeter, S. R., Halpern, B. S. & Ferreira, C. E. L. Effects of fishing and protection on Brazilian reef fishes. Biol. Conserv. 128(3), 391–402 (2006).Article 

Google Scholar 
Bender, M. G., Floeter, S. R. & Hanazaki, N. Do traditional fishers recognise reef fish species declines? Shifting environmental baselines in E astern B razil. Fish. Manag. Ecol. 20(1), 58–67 (2013).Article 

Google Scholar 
Hoey, A. S. & Bonaldo, R. M. (eds) Biology of Parrotfishes (CRC Press, Boca Raton, 2018).
Google Scholar 
Frédou, T. & Ferreira, B. P. Bathymetric trends of Northeastern Brazilian snappers (Pisces, Lutjanidae): Implications for the reef fishery dynamic. Braz. Arch. Biol. Technol. 48(5), 787–800 (2005).Article 

Google Scholar 
Guerra, A. S. Wolves of the Sea: Managing human-wildlife conflict in an increasingly tense ocean. Mar. Policy 99, 369–373 (2019).Article 

Google Scholar 
Hawkins, J. P. & Roberts, C. M. Effects of fishing on sex-changing Caribbean parrotfishes. Biol. Cons. 115(2), 213–226 (2004).Article 

Google Scholar 
Tuya, F. et al. Effect of fishing pressure on the spatio-temporal variability of the parrotfish, Sparisoma cretense (Pisces: Scaridae), across the Canarian Archipelago (eastern Atlantic). Fish. Res. 7(1), 24–33 (2006).Article 

Google Scholar 
Steneck, R. S., Arnold, S. N. & Mumby, P. J. Experiment mimics fishing on parrotfish: Insights on coral reef recovery and alternative attractors. Mar. Ecol. Prog. Ser. 506, 115–127 (2014).Article 
ADS 

Google Scholar 
Taylor, B. M., Trip, E. D., & Choat, J. H. Dynamic demography: Investigations of life-history variation in the parrotfishes. In Biology of Parrotfishes 69–98 (CRC Press, 2018).Moura, R. L. & Francini-Filho, R. B. Reef and Shore Fishes of the Abrolhos Region, Brazil Vol. 38, 40–55 (RAP Bulletin of Biological Assessment, Washington, 2005).
Google Scholar 
Francini-Filho, R. B., Moura, R. L., Ferreira, C. M. & Coni, E. O. Live coral predation by parrotfishes (Perciformes: Scaridae) in the Abrolhos Bank, eastern Brazil, with comments on the classification of species into functional groups. Neotrop. Ichthyol. 6, 191–200 (2008).Article 

Google Scholar 
Freitas, M. O. et al. Age, growth, reproduction and management of Southwestern Atlantic’s largest and endangered herbivorous reef fish, Scarus trispinosus Valenciennes, 1840. PeerJ 7, e7459 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Pinheiro, H. T. et al. An inverted management strategy for the fishery of endangered marine species. Front. Mar. Sci. 8, 172 (2021).Article 

Google Scholar 
Correia, M. D. Scleractinian corals (Cnidaria: Anthozoa) from reef ecosystems on the Alagoas coast, Brazil. J. Mar. Biol. Assoc. U. K. 91, 659–668 (2011).CAS 
Article 

Google Scholar 
Santos, D. K. F., Rufino, R. D., Luna, J. M., Santos, V. A. & Sarubbo, L. A. Biosurfactants: Multifunctional biomolecules of the 21st century. Int. J. Mol. Sci. 17(3), 401 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
de Oliveira, S. et al. Oil spill in South Atlantic (Brazil): Environmental and governmental disaster. Mar. Policy 115, 103879 (2020).Article 

Google Scholar 
Teixeira, L. M. P. & Creed, J. C. A decade on: An updated assessment of the status of marine non-indigenous species in Brazil. Aquat. Invasions 15(1), 30–43 (2020).Article 

Google Scholar 
Braga, M. D. A. et al. Retirement risks: Invasive coral on old oil platform on the Brazilian equatorial continental shelf. Mar. Pollut. Bull. 165, 112156 (2021).CAS 
PubMed 
Article 

Google Scholar 
Luiz, O. J. et al. Multiple lionfish (Pterois spp.) new occurrences along the Brazilian coast confirm the invasion pathway into the Southwestern Atlantic. Biol. Invasions 23, 3013–3019 (2021).Article 

Google Scholar 
Maida, M., & Ferreira, B. P. Coral reefs of Brazil: An overview. In Proceedings of the 8th International Coral Reef Symposium, Vol. 1, 263–274 (Smithsonian Tropical Research Institute Panamá, 1997).Pereira, P. H. C., Macedo, C. H., Nunes, J. D. A. C., Marangoni, L. F. D. B. & Bianchini, A. Effects of depth on reef fish communities: Insights of a “deep refuge hypothesis” from Southwestern Atlantic reefs. PLoS ONE 13(9), e0203072 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
ICMBIO. Plano de Manejo da Área de Proteção Ambiental Costa dos Corais (ICMBio/MMA, 2013).Hill, J. & Wilkinson, C. E. Methods for Ecological Monitoring of Coral Reefs Vol. 117 (Australian Institute of Marine Science, Townsville, 2004).
Google Scholar 
Dalapicolla, J. Tutorial de modelos de distribuição de espécies: guia prático usando o MaxEnt e o ArcGIS 10. Laboratório de Mastozoologia e Biogeografia. Universidade Federal do Espírito Santo, Vitória. Retrieved, 6 (2016).Phillips, S. J., Dudík, M., & Schapire, R. E. A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-First International Conference on Machine learning, Vol. 83 (2004).Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3–4), 231–259 (2006).Article 

Google Scholar 
Anderson, R. P. & Martınez-Meyer, E. Modeling species’ geographic distributions for preliminary conservation assessments: An implementation with the spiny pocket mice (Heteromys) of Ecuador. Biol. Conserv. 116(2), 167–179 (2004).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40(7), 887–893 (2017).Article 

Google Scholar 
Rodrigues, E. D. C., Rodrigues, F. A., Rocha, R. L. A. & Corrêa, P. L. P. An adaptive maximum entropy approach for modeling of species distribution. Mem. WTA 108–117 (2010).Rodrigues, E. S. D. C., Rodrigues, F. A., Ricardo, L. D. A., Corrêa, P. L. & Giannini, T. C. Evaluation of different aspects of maximum entropy for niche-based modeling. Procedia Environ. Sci. 2, 990–1001 (2010).Article 

Google Scholar 
Hattab, T. et al. The use of a predictive habitat model and a fuzzy logic approach for marine management and planning. PLoS ONE 8(10), e76430 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Galante, P. J. et al. The challenge of modeling niches and distributions for data-poor species: A comprehensive approach to model complexity. Ecography 41(5), 726–736 (2018).Article 

Google Scholar 
Silber, G. K. et al. Projecting marine mammal distribution in a changing climate. Front. Mar. Sci. 4, 413 (2017).Article 

Google Scholar 
Perkins-Taylor, I. E. & Frey, J. K. Predicting the distribution of a rare chipmunk (Neotamias quadrivittatus oscuraensis): Comparing MaxEnt and occupancy models. J. Mammal. 101(4), 1035–1048 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Lee, C. M., Lee, D. S., Kwon, T. S., Athar, M. & Park, Y. S. Predicting the global distribution of Solenopsis geminata (Hymenoptera: Formicidae) under climate change using the MaxEnt model. Insects 12(3), 229 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Possingham, H., Ball, I. & Andelman, S. Mathematical methods for identifying representative reserve networks. In Quantitative methods for conservation biology 291–306 (Springer, New York, 2000).Terrell, G. R. & Scott, D. W. Variable kernel density estimation.  Ann. Stat. 20(3), 1236–1265 (1992).
O’Brien, S. H., Webb, A., Brewer, M. J. & Reid, J. B. Use of kernel density estimation and maximum curvature to set Marine Protected Area boundaries: Identifying a Special Protection Area for wintering red-throated divers in the UK. Biol. Conserv. 156, 15–21 (2012).Article 

Google Scholar 
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).Article 

Google Scholar  More

This section is devoted to show results and to highlight eventual effects of the interplay between the nonlinearity characterizing the system dynamics and the presence of noisy fluctuations for the irradiance variable.Analysis of experimental dataThe need of taking into account noisy fluctuations of such an environmental variable is well demonstrated in Fig. 1. In the first panel (a) the experimental time behaviour of the irradiance is shown. This noisy curve is based on the experimental data (purple points) of the Boussole buoy located in the Gulf of Lion, collected over a period of nine years, precisely from 2004 to 2013. The time series of the experimental data presents quite a few gaps in time due to the malfunction of the buoy. This aspect has been remedied by merging the experimental data with those of the OASIM model validated for the Boussole site61 (yellow points). The latter is a multispectral atmospheric radiative transfer model that is in turn forced by experimental-model data based on ECMWF ERAINTERIM reanalyses which provide, for example, cloud cover data. The radiative model is partly stochastic since it considers the effects stemming from the presence of clouds, averaged along a single day (this explains why the yellow points are slightly less scattered). We see that the OASIM model accurately reproduces the profile which emerges from the experimental data. Further, we stress that the experimental data are only used in this initial analysis. In the biogeochemical simulations the irradiance signal is fully reconstructed starting from a realistic seasonal cycle combined with a range of different random fluctuations, and the information from OASIM is not used. In the second panel (b) the daily (black points) as well as the three-month (red points) running mean of the experimental series are plotted. Figure 1c shows the irradiance noisy fluctuations (INF) which have been obtained by subtracting the three-month running mean curve (3MRM, red curve in Fig. 1b) from the daily running mean one (DRM, black curve in Fig. 1b) and normalizing with respect to the mean of the 3MRM ((overline{3MRM})), namely (INF = (DRM – 3MRM) / overline{3MRM}). We see that a seasonal overall trend with higher oscillations during the winter time can be seen, implying that the characteristics of the noise may change over the year. Moreover, a slight imbalance between positive and negative values of the noisy fluctuations (that is, different values of the maximum fluctuation intensity) is present. The physical reason for the occurrence of such an aspect can be ascribed to the fact that the maximum value of solar irradiance corresponds to that measured during a sunny day. Conversely, the minimum level tends to zero corresponding to a dense darkness. While the former is close to the mean value of the solar irradiance (most of all in summer), the latter is much further away and then a natural asymmetry arises in the random fluctuations. However, it should be noted that, apart from the intense spikes, the asymmetry is not so pronounced, as proved by the mean value (red line in Fig. 1c) which is practically zero, namely (0.4%) of the (overline{3MRM}). Therefore, basing on this last observation, to model the noise affecting the irradiance dynamics, as a first approximation we consider a symmetric Gaussian autocorrelated noise as described in the next subsection.On the basis of such experimental results, we postulate the hypothesis that random fluctuations of light cannot be neglected, most of all in the study of ecological systems where light profoundly determines the system dynamics, governing fundamental processes at the basis of of the food web.Figure 1(a) Experimental data (purple points) of the stochastic solar irradiance collected by the Boussole buoy in a time-window of 9 years (2004-2013); the yellow points are the data generated by the OASIM model used to fill the gaps present in the experimental time-series due to malfunctioning of the buoy. (b) Daily (black points) and three-month (red points) running mean of the light curve in panel (a). (c) Irradiance noisy fluctuations (INF), obtained by subtracting the three-month running mean curve (3MRM) from the daily running mean one (DRM) and normalizing with respect to the mean value of 3MRM ((overline{3MRM})), namely (INF = (DRM – 3MRM) / overline{3MRM}); the red line represents the mean value of such fluctuations. Data already presented and validated in61.Full size imageSolar irradianceThe solar irradiance forcing is derived considering a deterministic seasonal oscillation combined with an Ornstein-Uhlenbeck process. The coefficient of variation (CV) of simulated light forcing, Fig. 2, (CV=sigma / mu) ((mu) and (sigma) being mean value and standard deviation calculated over both time and numerical realizations), is shown for 231 (D-tau) pairs. D and (tau) represent the intensity of a Gaussian noise source and the auto-correlation time of the fluctuations, respectively (see Eqs. (2) and (3)).Each pixel represents the mean value on time of CV calculated with respect to 1000 different stochastic realizations. Figure 2Coefficient of variation ((CV=sigma / mu)) of irradiance resulting from numerical integration of model equations for 231 (D-tau) different scenarios.Full size imageIt is easy to see the agreement between the results obtained from the numerical integration and the theoretical ones derivable from Eq. (5) by putting (text {var}{F_L(0)}=0) and (t gg 1), getting (sigma ^2_L=D / 2tau). In Fig. 2, indeed, the maximum values of (sigma) lie in the upper left part of the plot corresponding to small (high) values of (tau) (D). As it is clear the values of D have been chosen in order to obtain a relative standard deviation ranging from (5%mu) to (60%mu). We underline that, in this case, it is possible to interchangeably consider (sigma) and CV since the dependence of CV on D and (tau) does not differ from that of (sigma) (meaning that the dependence of (sigma) is not altered by dividing by (mu)) (results not shown).Effects on population dynamicsIn this section the noise-induced effects on the population dynamics are examined. The nine planktonic populations present a different qualitative behaviour of the CV, compared to that of the irradiance. In this case, the CV is characterized by a strong non-monotonic dependence on the parameter (tau). This aspect can be appreciated in Fig. 3 where different curves of CV versus the time correlation parameter are shown for different fixed values of D.Figure 3Coefficient of variation ((CV=sigma / mu)) of the nine planktonic populations resulting from numerical integration of model equations plotted versus the considered values of (tau); the different curves are related to different values of the noise intensity D.Full size imageThe existence of a maximum value for CV can be appreciated for each species. Although the qualitative behaviour is the same for all strains, particular attention has to be payed on diatoms and nanoflagellates. All the other species, indeed, present a percent variation of standard deviation between (2%) and (15%). In the case of nanoflagellates, instead, the D-dependent range is (20-90%), while diatoms reach values over the (100%) for the highest values of D. Therefore, these two species, in particular, and the whole system, in general, are extremely sensitive to the auto-correlation time which characterizes the noise.We note that the different curves related to the different selected values of D approach the horizontal axis, tending asymptotically to vanish as (tau) increases. Such a behaviour can be explained by the fact that high values of (tau) give rise to a more correlated dynamics, so that (tau rightarrow infty) implies fully correlated time-behaviours corresponding to the deterministic case. In this instance, then, all the different realizations give the same results, making the standard deviation vanish. The same happens, independently of the value of (tau), for low values of noise intensity for which the corresponding curves approach the same almost vanishing value (see orange, gray and yellow lines). Differently from the previous case, when (tau rightarrow 0) the noise tends to a delta-correlated noise, that is a white noise; for (tau ne 0), instead, the noise spectrum is not flat, being characterized by a Cauchy-Lorentz distribution. The strong nonmonotonicity of CV with respect to (tau), emerging when there are relatively high values of CV, implies a greater variability of the system biomass. Lower values of CV indicate that the system dynamics is less influenced by the presence of noise where very little or no differences with respect to the deterministic case are present. Conversely, high values of CV clearly demonstrate the remarkable signature of the presence of an impacting noise source. It is interesting to note that the noise influence on the ecosystem strongly depends on both (tau) and D, that is, just an intense noise is not enough to generate a greater response of the ecosystem. In particular, experimental data are characterized by a CV approximately equal to 0.361, which corresponds to values of D and (tau) lying on the diagonal strip in Fig. 2 ranging from ((tau ,D)=(0.5,10^4)) to ((tau ,D)=(365,10^7)). Finally we note the presence of a noise suppression effect. High values of D, indeed, can generate slight effects when the correlation time (tau) does not take on suitable values.The results shown here are an extension of the previous work by Benincà et al.56. There, the authors analyse a simpler, less realistic model of two interacting populations, whose dynamics is affected by a randomly fluctuating temperature. In that case, moreover, the deterministic oscillations of the temperature are suppressed, and the system exhibits intrinsic Lotka-Volterra oscillations whose frequency match with the characteristic one(s) of the noise. On the contrary, here, the observed maximum response (see Fig. 3) cannot be interpreted as a synchronization effect, since our model does not present intrinsic Lotka-Volterra-like oscillations and the periodic population variability is only due to the deterministic forcing(s).The nonmonotonic behaviour of the CV can be then interpreted as the signature of the intimate interplay between the ecological system and the noise. This interplay, indeed, has a pivotal role in both determining the dynamics of the populations and defining the characteristics of the ecosystem.In Fig. 3 it can be observed that the value of (tau) for which CV is maximum strongly depends on the noise intensity D. In particular, it is possible to note that the peaks in Fig. 3 move towards higher values of (tau) as the noise intensity increases. Thus, Fig. 3 demonstrates that the maximum-response effect to the random fluctuations is sensitive to the noise intensity D.However, it is important to underline that the response of the system to the noisy signal does not depend on the yearly oscillations induced by the deterministic forcings. Indeed, by considering constant the deterministic part of all external forcings (temperature, irradiance, wind and salinity), the non monotonic behaviour of CV with respect to both (tau) and D is still present, provided that the populations are not extinct (plot not shown). In this scenario indeed, besides dinoflagellates, diatoms and nanoflagellates are practically extinct as well, exhibiting thus a constant vanishing variance. All the other strains, instead, present qualitatively the same nonmonotonicity with only slight differences (shift of the peaks and different mean values of the CV curves), probably due to the extinction of diatoms and nanoflagellates which causes relevant differences in the system dynamics. More specifically, the system’s response seems to depend on both the noise intensity and the correlation time (see Fig. 3).In this scenario (absence of seasonal driving) we have studied the dependence on both parameters D and (tau) of the probability density functions (PDFs) of the non-vanishing populations. In Fig. 4, the PDFs of bacteria (B1), picophytoplankton (P3), microzooplankton (Z5) and etherotrophic nanoflagellates (Z6) are plotted for (tau =0.5) and eight different values of the parameter D.Figure 4Dependence of the probability density functions of non-vanishing populations on the parameter D for (tau =0.5). The curves are normalized within the interval taken into account. For this reason the relative peaks of the curves in the bottom panels have different values compared to those of the top panels. However, the figure aims at showing the existence of the value of the noise intensity for which the system is more sensitive as well as the generation of a stationary out-of-equilibrium state induced by the noise.Full size imageWe see that the mean value and the variance of these populations are strongly affected by the presence of random fluctuations in the irradiance. Specifically, as the noise intensity increases the mean values of picophytoplankton and bacteria concentrations exhibit a shift. In particular, the results indicate that picophytoplankton is disavantaged by the presence of a noisy component in the irradiance, which indeed tends to inhibit its ability to absorbe the solar light, slowing down its growth. As a consequence, since phytoplankton and bacteria compete for the same resources, as the former declines the latter are favoured, with a compensation mechanism which allows their predators (zooplankton populations) to be almost not affected by the noisy behaviour of the irradiance. Further, we note that for intermediate values of the noise intensity ((D = 10^4 – 10^5)) a maximum of the variance occurs (the PDFs are clearly spread on a wider range of values). Such an effect indicates that the noisy behaviour of irradiance strongly influences the whole ecosystem dynamics. Moreover, the nonmonotonic behaviour of the variance (its PDFs become larger and then tighter again as the noise intensity increases) indicates that the noise pushes the ecosystem away from equilibrium, driving it towards a non-equilibrium steady state. Finally, we note that the nonmonotonic behaviour of CV as a function of the noise intensity remains also in the presence of seasonal driving.Figure 5Coefficient of variation ((CV=sigma / mu)) of nine planktonic populations resulting from numerical integration of model equations plotted versus the considered values of D; different curves correspond to different values of the correlation time (tau).Full size imageFigure 5 shows indeed the nonmonotonic response of the ecosystem to the change of D when the deterministic seasonal cycling of the four environmental parameters (temperature, irradiance, wind and salinity) is present. It is easy to observe that also in this instance the major noise-induced effect appears in nanoflagellates and diatoms with a percent standard deviation of 50(%) and 100(%), respectively. The coalescence of different curves (related to different values of (tau)), as D decreases, is due to the fact that for (D rightarrow 0) the impact of the noise is negligible and the evolution of the system practically resembles the deterministic one. On the contrary, for higher values of D remarkable differences arise and clear peaks of CV appear in the considered range of variation.These plots show that, for a fixed value of (tau), there exists a value of the noise intensity for which the planktonic concentrations are maximally spread around their mean values (corresponding to the maximum value of CV and then of the variance). Moreover, such a nonmonotonic behaviour suggests the presence of a resonance, which can be interpreted as the effect of the interplay between the nonlinearity of the system and the environmental random fluctuations.Also in this case, the interplay between the two parameters D and (tau) in determining and characterizing the dynamics of the ecosystem transparently emerges. The value of D corresponding to the maximum value of CV, indeed, basically depends on the specific value of (tau).Finally, we point out that the different dynamic scenarios identified by the D-(tau) couples can be experienced by the system during the year, since the two parameters may seasonally vary depending on the different weather conditions. In other words, a seasonally varying noise (see Fig. 1c) may cause the nine populations explore different regions of the D-(tau) space during the year. Therefore, the results reported in this paper can highlight the detectable yearly variability of a marine ecosystem which does not stem from the deterministic seasonal variation of environmental parameters.Effects on the organic carbonIn this subsection the effects of the irradiance noise on the biogechemistry are analysed. In Fig. 6 the dependence on (tau) of both the CV [panel (a)] and the mean value concentration [panel (b)] of detritus, labile dissolved organic carbon (L-DOC), semi-labile dissolved organic carbon (SL-DOC) and gross primary production (GPP) are shown. All these biogeochemical properties are correlated with carbon cycling. Gross primary production is related to the amount of carbon entering in the ecosystem, and is related to the maximum energy available in the ecosystem progressively dissipated in the trophic web. Gross primary production is directly affected by light fluctuation and its CV shape is very similar to that of the irradiance, Fig. 2. We selected also detritus and DOC because they are important indicators for the carbon cycling dynamics and are related to the cycling of chemicals like heavy metals62. The different curves, related to different values of D, approach the same (vanishing) value for large (tau). As previously discussed for the CV [Fig. 6(a)] of biomass concentrations, this circumstance is due to the fact that, in this case, the system dynamics tends to the deterministic case, characterized by a unique possible realization implying a vanishing standard deviation. For high correlation times thus the system is insensitive to the noise intensity. On the contrary, for small values of (tau), different values of D lead to significant differences of the variance. In particular, detritus, L-DOC and SL-DOC exhibit a clear non-monotonic behaviour whose maximum value depends on the combined values of D-(tau). Only the GPP presents a decreasing monotonic behaviour.The dependence of the mean value concentration on (tau), instead, is qualitatively the same for all the four parameters. Also in this case we can note a diversification with respect to D occurring at small (tau) and a (deterministic) constant value arising for low (high) values of D ((tau)).These results manifest that not only the population dynamics, but also all the biogeochemical processes are profoundly affected by the presence of stochastic environmental variables. The values and the behaviour of the examined quantities are indeed determined by the intimate interplay between the intensity and the time correlation of the noise fluctuations.Figure 6(a) Coefficient of variation ((CV=sigma / mu)) and (b) mean value concentration ((mu)) of detritus, labile dissolved organic carbon (L-DOC), semi-labile dissolved organic carbon (SL-DOC) and gross primary production (GPP) resulting from numerical integration of model equations plotted versus the considered values of (tau); the different curves are related to different values of the correlation time D.Full size image More

Armbrust, E. V. The life of diatoms in the world’s oceans. Nature 459, 185–192 (2009).CAS 
Article 

Google Scholar 
Mann, D. G. The species concept in diatoms. Phycologia 38, 437–495 (1999).Article 

Google Scholar 
Smetacek, V. Diatoms and the ocean carbon cycle. Protist 150, 25–32 (1999).CAS 
Article 

Google Scholar 
Rynearson, T. A. et al. Major contribution of diatom resting spores to vertical flux in the sub-polar North Atlantic. Deep. Res. Part I Oceanogr. Res. Pap. 82, 60–71 (2013).CAS 
Article 

Google Scholar 
Allen, J. T. et al. Diatom carbon export enhanced by silicate upwelling in the northeast Atlantic. Nature 437, 728–732 (2005).CAS 
Article 

Google Scholar 
Boyd, P. W., Strzepek, R., Fu, F. & Hutchins, D. A. Environmental control of open-ocean phytoplankton groups: Now and in the future. Limnol. Oceanogr. 55, 1353–1376 (2010).CAS 
Article 

Google Scholar 
Hátún, H., Somavilla, R., Rey, F., Johnson, C. & Mathis, M. The subpolar gyre regulates silicate concentrations in the North Atlantic. Sci. Rep. 1–9 https://doi.org/10.1038/s41598-017-14837-4 (2017).Bopp, L. Response of diatoms distribution to global warming and potential implications: a global model study. Geophys. Res. Lett. 32, 2–5 (2005).Article 
CAS 

Google Scholar 
Warner, A. J. & Hays, G. C. Sampling by the Continuous Plankton Recorder survey. Prog. Oceanogr. 6611, 237–256 (1994).Article 

Google Scholar 
Edwards, M., Beaugrand, G., Reid, P. C., Rowden, A. A. & Jones, M. B. Ocean climate anomalies and the ecology of the North Sea. Mar. Ecol. Prog. Ser. 239, 1–10 (2002).Article 

Google Scholar 
Allen, S. et al. Interannual stability of phytoplankton community composition in the North-East atlantic. Mar. Ecol. Prog. Ser. 655, 43–57 (2020).Article 

Google Scholar 
Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).CAS 
Article 

Google Scholar 
Wihsgott, J. U. et al. Observations of vertical mixing in autumn and its effect on the autumn phytoplankton bloom. Prog. Oceanogr. 177, 1157–1165 (2019).Article 

Google Scholar 
Kamykowski, D. & Zentara, S. J. Predicting plant nutrient concentrations from temperature and sigma-T in the upper kilometer of the world ocean. Deep. Res. Part A-Oceanogr. Res. Pap. 33, 89–105 (1986).CAS 
Article 

Google Scholar 
Kamykowski, D. & Zentara, S. J. Changes in world ocean nitrate availability through the 20th century. Deep. Res. Part I-Oceanogr. Res. Pap. 52, 1719–1744 (2005).Article 

Google Scholar 
Behrenfeld, M. J. et al. Climate-driven trends in contemporary ocean productivity. Nature 444, 752–755 (2006).CAS 
Article 

Google Scholar 
López-Urrutia, A., San Martin, E., Harris, R. P. & Irigoien, X. Scaling the metabolic balance of the oceans. Proc. Natl. Acad. Sci. USA 103, 8739–8744 (2006).Article 
CAS 

Google Scholar 
Richardson, A. J. & Schoeman, D. S. Climate impact on plankton ecosystems in the Northeast Atlantic. Science 305, 1609–1612 (2004).CAS 
Article 

Google Scholar 
Edwards, M., Johns, D., Leterme, S., Svendsen, E. & Richardson, A. Regional climate change and harmful algal blooms in the northeast Atlantic. Limnol. Oceanogr. 51, 820–829 (2006).Article 

Google Scholar 
Hinder, S. L. et al. Changes in marine dinoflagellate and diatom abundance under climate change. Nat. Clim. Chang. 2, 271–275 (2012).Article 

Google Scholar 
Batten, S. et al. CPR sampling: the technical background, materials and methods, consistency and comparability. Prog. Oceanogr. 58, 193–215 (2003).Article 

Google Scholar 
Reid, P. C. et al. The Continuous Plankton Recorder: concepts and history, from plankton indicator to undulating recorders. Prog. Oceanogr. 58, 117–173 (2003).Article 

Google Scholar 
Edwards, M., Beaugrand, G., Hays, G. C., Koslow, J. A. & Richardson, A. J. Multi-decadal oceanic ecological datasets and their application in marine policy and management. Trends Ecol. Evol. 25, 602–610 (2010).Article 

Google Scholar 
Edwards, M. et al. North Atlantic warming over six decades drives decreases in krill abundance with no associated range shift. Commun. Biol. 4, 1–10 (2021).Article 

Google Scholar 
Richardson, A. J. et al. Using continuous plankton recorder data. Prog. Oceanogr. 68, 27–74 (2006).Article 

Google Scholar 
Hélaouët, P., Beaugrand, G. & Reygondeau, G. Reliability of spatial and temporal patterns of C. finmarchicus inferred from the CPR survey. J. Mar. Syst. 153, 18–24 (2016).Article 

Google Scholar 
Owens, N. J. P. et al. All plankton sampling systems underestimate abundance: response to “Continuous plankton recorder underestimates zooplankton abundance” by J.W. Dippner and M. Krause. J. Mar. Syst. 128, 240–242 (2013).Article 

Google Scholar 
Jonas, T. D., Walne, A., Beaugrand, G., Gregory, L. & Hays, G. C. The volume of water filtered by a Continuous Plankton Recorder sample: the effect of ship speed. J. Plankton Res. 26, 1499–1506 (2004).Article 

Google Scholar 
O’Reilly, C. H., Zanna, L. & Woollings, T. Assessing external and internal sources of Atlantic multidecadal variability using models, proxy data, and early instrumental indices. J. Clim 32, 7727–7745 (2019).Article 

Google Scholar 
Qin, M., Dai, A. & Hua, W. Quantifying contributions of internal variability and external forcing to atlantic multidecadal variability since 1870. Geophys. Res. Lett. 47, 1–11 (2020).
Google Scholar 
Mann, M. E., Steinman, B. A. & Miller, S. K. Absence of internal multidecadal and interdecadal oscillations in climate model simulations. Nat. Commun. 11, 1–9 (2020).Article 
CAS 

Google Scholar 
Enfield, D. B., Mestas-Nuñez, A. M. & Trimble, P. J. The Atlantic Multidecadal Oscillation and its relation to rainfall and river flows in the continental U.S. Geophys. Res. Lett. 28, 2077–2080 (2001).Article 

Google Scholar 
Gray, S. T., Graumlich, L. J., Betancourt, J. L. & Pederson, G. T. A tree-ring based reconstruction of the Atlantic Multidecadal Oscillation since 1567 A.D. Geophys. Res. Lett. 31, 2–5 (2004).Article 

Google Scholar 
Edwards, M., Beaugrand, G., Helaouët, P., Alheit, J. & Coombs, S. Marine ecosystem response to the Atlantic Multidecadal Oscillation. PLoS One 8, e57212 (2013).CAS 
Article 

Google Scholar 
Jones, P. D., New, M., Parker, D. E. & Martin, S. & Rigor, I. G. Surface air temperature and its changes over the past 150 years. Rev. Geophys. 37, 173–199 (1999).Article 

Google Scholar 
Beaugrand, G. et al. Reorganization of North Atlantic marine copepod biodiversity and climate. Science296, 1692–1694 (2002).CAS 
Article 

Google Scholar 
Alvain, S., Moulin, C., Dandonneau, Y. & Bréon, F. M. Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery. Deep Sea Res. Part I Oceanogr. Res. Pap. 52, 1989–2004 (2005).Article 

Google Scholar 
Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996).Article 

Google Scholar 
Huang, B. et al. Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).Article 

Google Scholar 
Lam, N. S. N. Spatial interpolation methods: a review. Am. Cartogr. 10, 129–150 (1983).Article 

Google Scholar 
Beaugrand, G., McQuatters-Gollop, A., Edwards, M. & Goberville, E. Long-term responses of North Atlantic calcifying plankton to climate change. Nat. Clim. Chang. 3, 263–267 (2012).Article 
CAS 

Google Scholar 
Beaugrand, G. et al. Prediction of unprecedented biological shifts in the global ocean. Nat. Clim. Chang. 9, 237–243 (2019).Article 

Google Scholar  More