Philippa Kaur

1.Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change 26, 152–158 (2014).Article 

Google Scholar 
2.Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1997).ADS 
CAS 
Article 

Google Scholar 
3.Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Lindeboom, H. The coastal zone: An ecosystem under pressure. In Oceans 2020: Science Trends and the Challenge of Sustainability (ed. Field, J. G.) 49–84 (Island Press, 2002).
Google Scholar 
5.Airoldi, L., Balata, D. & Beck, M. W. The Gray Zone: Relationships between habitat loss and marine diversity and their applications in conservation. J. Exp. Mar. Biol. Ecol. 366, 8–15 (2008).Article 

Google Scholar 
6.Islam, S. & Tanaka, M. Impacts of pollution on coastal and marine ecosystems including coastal and marine fisheries and approach for management: A review and synthesis. Mar. Pollut. Bull. 48, 624–649 (2004).CAS 
Article 

Google Scholar 
7.Vikas, M. & Dwarakish, G. S. Coastal pollution: A review. Aquat. Procedia 4, 381–388 (2015).Article 

Google Scholar 
8.Blaber, S. J. M. et al. Effects of fishing on the structure and functioning of estuarine and nearshore ecosystems. ICES J. Mar. Sci. 57, 590–602 (2000).Article 

Google Scholar 
9.Hussein, C. et al. Assessing the impact of artisanal and recreational fishing and protection on a white seabream (Diplodus sargus sargus) population in the north-western Mediterranean Sea using a simulation model. Part 1: Parameterization and simulations. Fish. Res. 108, 163–173 (2011).Article 

Google Scholar 
10.Hawkins, A. D. & Popper, A. N. A sound approach to assessing the impact of underwater noise on marine fishes and invertebrates. ICES J. Mar. Sci. 74, 635–651 (2017).Article 

Google Scholar 
11.Beck, M. W. et al. The identification, conservation, and management of estuarine and marine nurseries for fish and invertebrates. Bioscience 51, 633–641 (2001).Article 

Google Scholar 
12.Carr, M. H. Habitat selection and recruitment of an assemblage of temperate zone reef fishes. J. Exp. Mar. Biol. Ecol. 146, 113–137 (1991).Article 

Google Scholar 
13.Sheaves, M., Baker, R. & Johnston, R. Marine nurseries and effective juvenile habitats: an alternative view. Mar. Ecol. Prog. Ser. 318, 303–306 (2006).ADS 
Article 

Google Scholar 
14.Leis, J. M. Are larvae of demersal fishes plankton or nekton?. Adv. Mar. Biol. https://doi.org/10.1016/S0065-2881(06)51002-8 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Raventos, N. & Macpherson, E. Planktonic larval duration and settlement marks on the otoliths of Mediterranean littoral fishes. Mar. Biol. 138, 1115–1120 (2001).Article 

Google Scholar 
16.Di Franco, A. et al. Dispersal of larval and juvenile seabream: Implications for Mediterranean marine protected areas. Biol. Conserv. 192, 361–368 (2015).Article 

Google Scholar 
17.Di Franco, A. et al. Assessing dispersal patterns of fish propagules from an effective Mediterranean marine protected area. PLoS ONE 7, e52108 (2012).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Di Franco, A. & Guidetti, P. Patterns of variability in early-life traits of fishes depend on spatial scale of analysis. Biol. Lett. 7, 454–456 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Dahlgren, C. P. & Eggleston, D. B. Ecological processes underlying ontogenetic habitat shifts in a coral reef fish. Ecology 81, 2227–2240 (2000).Article 

Google Scholar 
20.Macpherson, E. Ontogenetic shifts in habitat use and aggregation in juvenile sparid fishes. J. Exp. Mar. Biol. Ecol. 220, 127–150 (1998).Article 

Google Scholar 
21.Dahlgren, C. P. et al. Marine nurseries and effective juvenile habitats: Concepts and applications. Mar. Ecol. Prog. Ser. 312, 291–295 (2006).ADS 
Article 

Google Scholar 
22.Jennings, S. & Blanchard, J. L. Fish abundance with no fishing: predictions based on macroecological theory. J. Anim. Ecol. 73, 632–642 (2004).Article 

Google Scholar 
23.Jones, G. P. The importance of recruitment to the dynamics of a coral reef fish population. Ecology 71, 1691–1698 (1990).Article 

Google Scholar 
24.Sheaves, M., Baker, R., Nagelkerken, I. & Connolly, R. M. True value of estuarine and coastal nurseries for fish: Incorporating complexity and dynamics. Estuaries Coasts 38, 401–414 (2015).Article 

Google Scholar 
25.Harmelin-Vivien, M. L., Harmelin, J. G. & Leboulleux, V. Microhabitat requirements for settlement of juvenile Sparid fishes on Mediterranean rocky shores. Hydrobiologia 301, 309–320 (1995).Article 

Google Scholar 
26.Garcia-Rubies, A. & Macpherson, E. Substrate use and temporal pattern of recruitment in juvenile fishes of the Mediterranean littoral. Mar. Biol. 124, 35–42 (1995).Article 

Google Scholar 
27.Vigliola, L. Contrôle et régulation du recrutement des Sparidés (Poissons, Téléostéens) en Méditerranée : importance des processus pré- et post-installation benthique. Thèse Doct Sci Univ Aix-Marseille II Marseille. (1998).28.Cheminée, A. Ecological Functions, Transformations and Management of Infralittoral Rocky Habitats from the North-Western Mediterranean: The Case of Fish (Teleostei) Nursery Habitats (University of Nice, 2012).
Google Scholar 
29.Macpherson, E. & Zika, U. Temporal and spatial variability of settlement success and recruitment level in three blennoid fishes in the northwestern Mediterranean. Mar. Ecol. Prog. Ser. 182, 269–282 (1999).ADS 
Article 

Google Scholar 
30.Heck, K., Hays, G. & Orth, R. Critical evaluation of the nursery role hypothesis for seagrass meadows. Mar. Ecol. Prog. Ser. 253, 123–136 (2003).ADS 
Article 

Google Scholar 
31.Félix-Hackradt, F. C., Hackradt, C. W., Treviño-Otón, J., Pérez-Ruzafa, A. & García-Charton, J. A. Temporal patterns of settlement, recruitment and post-settlement losses in a rocky reef fish assemblage in the South-Western Mediterranean Sea. Mar. Biol. 160, 2337–2352 (2013).Article 

Google Scholar 
32.Cuadros, A. Settlement and Post-Settlement Processes of Mediterranean Littoral Fishes: Influence of Seascape Attributes and Environmental Conditions at Different Spatial Scales (Universidad de las Islas Baleares, 2015).
Google Scholar 
33.Bussotti, S. & Guidetti, P. Timing and habitat preferences for settlement of juvenile fishes in the marine protected area of torre guaceto (south-eastern Italy, Adriatic Sea). Ital. J. Zool. 78, 243–254 (2011).Article 

Google Scholar 
34.Bariche, M., Letourneur, Y. & Harmelin-Vivien, M. Temporal fluctuations and settlement patterns of native and lessepsian herbivorous fishes on the lebanese coast (Eastern Mediterranean). Environ. Biol. Fishes 70, 81–90 (2004).Article 

Google Scholar 
35.Mosconi, P. & Chauvet, C. Growth spatio-temporal variability of juveniles of sea-bream (Sparus aurata) between lagoonal and sea areas in the south of Lion’s Gulf. Vie Milieu Paris 40, 305–311 (1990).
Google Scholar 
36.Verdiell-Cubedo, D., Oliva-Paterna, F. J., Ruiz-Navarro, A. & Torralva, M. Assessing the nursery role for marine fish species in a hypersaline coastal lagoon (Mar Menor, Mediterranean Sea). Mar. Biol. Res. 9, 739–748 (2013).Article 

Google Scholar 
37.Letourneur, Y., Darnaude, A., Salen-Picard, C. & Harmelin-vivien, M. Spatial and temporal variations of fish assemblages in a shallow Mediterranean soft-bottom area (Gulf of Fos, France). Oceanol. Acta 24, 273–285 (2001).Article 

Google Scholar 
38.Le Pape, O. et al. Sources of organic matter for flatfish juveniles in coastal and estuarine nursery grounds: A meta-analysis for the common sole (Solea solea) in contrasted systems of Western Europe. J. Sea Res. 75, 85–95 (2013).Article 

Google Scholar 
39.Guidetti, P. & Bussotti, S. Recruitment of Diplodus annularis and Spondyliosoma cantharus (Sparidae) in shallow seagrass beds along the Italian coasts (Mediterranean Sea). Mar. Life 7, 47–52 (1997).
Google Scholar 
40.Guidetti, P. & Bussotti, S. Fish fauna of a mixed meadow composed by the seagrasses Cymodocea nodosa and Zostera noltii in the Western Mediterranean. Oceanol. Acta 23, 759–770 (2000).Article 

Google Scholar 
41.Guidetti, P. & Bussotti, S. Effects of seagrass canopy removal on fish in shallow Mediterranean seagrass (Cymodocea nodosa and Zostera noltii) meadows: a local-scale approach. Mar. Biol. 140, 445–453 (2002).Article 

Google Scholar 
42.Cuadros, A. et al. The three-dimensional structure of Cymodocea nodosa meadows shapes juvenile fish assemblages (Fornells Bay, Minorca Island). Reg. Stud. Mar. Sci. (2017).43.Francour, P. & Le Direac’h, L. Recrutement de l’ichtyofaune dans l’herbier superficiel à Posidonia oceanica de la réserve naturelle de Scandola (Corse, Méditerranée nord-occidentale): données préliminaires. Trav. Sci. Parc. Nat. Régional Corse 46, 71–91 (1994).
Google Scholar 
44.Francour, P. & Le Direac’h, L. Analyse spatiale du recrutement des poissons de l’herbier à Posidonia oceanica dans la réserve naturelle de Scandola (Corse, Méditerranée nord-occidentale). Contrat Parc Naturel Régional de la Corse & GIS Posidonie. LEML Publ Nice 1–23 (2001).45.Francour, P. & Le Direach, L. Le recrutement des poissons dans les herbiers à Posidonia oceanica : quels sont les facteurs influents ? in XXXIX AFL Congress 67–78 (1995).46.Le Direac’h, L. & Francour, P. Recrutement de Diplodus annularis (Sparidae) dans les herbiers de posidonie de la Réserve Naturelle de Scandola (Corse). Trav. Sci. Parc. Nat. Rég. Corse 57, 42–75 (1998).
Google Scholar 
47.Guidetti, P. Differences among fish assemblages associated with Nearshore Posidonia oceanica Seagrass Beds, Rocky–algal Reefs and unvegetated sand habitats in the Adriatic Sea. Estuar. Coast. Shelf Sci. 50, 515–529 (2000).ADS 
Article 

Google Scholar 
48.Félix-Hackradt, F. C., Hackradt, C. W., Treviño-Otón, J., Pérez-Ruzafa, Á. & García-Charton, J. A. Habitat use and ontogenetic shifts of fish life stages at rocky reefs in South-western Mediterranean Sea. J. Sea Res. 88, 67–77 (2014).ADS 
Article 

Google Scholar 
49.Félix-Hackradt, F. C. et al. Environmental determinants on fish post-larval distribution in coastal areas of south-western Mediterranean Sea. Estuar. Coast. Shelf Sci. 129, 59–72 (2013).ADS 
Article 

Google Scholar 
50.Cheminée, A. et al. Nursery value of Cystoseira forests for Mediterranean rocky reef fishes. J. Exp. Mar. Biol. Ecol. 442, 70–79 (2013).Article 

Google Scholar 
51.Cheminée, A. et al. Juvenile fish assemblages in temperate rocky reefs are shaped by the presence of macro-algae canopy and its three-dimensional structure. Sci. Rep. 7, 14638 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
52.Cuadros, A. et al. Juvenile fish in Cystoseira forests: Influence of habitat complexity and depth on fish behaviour and assemblage composition. Mediterr. Mar. Sci. 20, 380–392 (2019).Article 

Google Scholar 
53.Hinz, H., Reñones, O., Gouraguine, A., Johnson, A. F. & Moranta, J. Fish nursery value of algae habitats in temperate coastal reefs. PeerJ 7, e6797 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Thiriet, P. D. et al. Abundance and diversity of Crypto- and Necto-Benthic coastal fish are higher in marine forests than in structurally less complex macroalgal assemblages. PLoS ONE 11, e0164121 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
55.Thiriet, P. Comparaison de la Structure des Peuplements de Poissons et des Processus Écologiques Sous-Jacents, Entre les Forêts de Cystoseires et des Habitats Structurellement Moins Complexes, dans l’Infralittoral Rocheux de Méditerranée Nord-Occidentale (University of Nice, 2014).
Google Scholar 
56.Cheminée, A. et al. Shallow rocky nursery habitat for fish: Spatial variability of juvenile fishes among this poorly protected essential habitat. Mar. Pollut. Bull. 119, 245–254 (2017).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
57.Mercader, M. et al. Spatial distribution of juvenile fish along an artificialized seascape, insights from common coastal species in the Northwestern Mediterranean Sea. Mar. Environ. Res. 137, 60–72 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Tournois, J. et al. Lagoon nurseries make a major contribution to adult populations of a highly prized coastal fish. Limnol. Oceanogr. 62, 1219–1233 (2017).ADS 
Article 

Google Scholar 
59.Cuadros, A. et al. Settlement and post-settlement survival rates of the white seabream (Diplodus sargus) in the western Mediterranean Sea. PLoS ONE 13, e0190278 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
60.Cheminée, A., Francour, P. & Harmelin-Vivien, M. Assessment of Diplodus spp. (Sparidae) nursery grounds along the rocky shore of Marseilles (France, NW Mediterranean). Sci. Mar. 75, 181–188 (2011).Article 

Google Scholar 
61.Pastor, J., Koeck, B., Astruch, P. & Lenfant, P. Coastal man-made habitats: Potential nurseries for an exploited fish species, Diplodus sargus (Linnaeus, 1758). Fish. Res. 148, 74–80 (2013).Article 

Google Scholar 
62.Vigliola, L. et al. Spatial and temporal patterns of settlement among sparid fishes of the genus Diplodus in the northwestern Mediterranean. Mar. Ecol.-Prog. Ser. 168, 45–56 (1998).ADS 
Article 

Google Scholar 
63.Vigliola, L. & Harmelin-Vivien, M. Post-settlement ontogeny in three Mediterranean reef fish species of the Genus Diplodus. Bull. Mar. Sci. 68, 271–286 (2001).
Google Scholar 
64.Cuadros, A. et al. Seascape attributes, at different spatial scales, determine settlement and post-settlement of juvenile fish. Estuar. Coast. Shelf Sci. 185, 120–129 (2017).ADS 
Article 

Google Scholar 
65.Morat, F. et al. Diet of the Mediterranean european shag, Phalacrocorax aristotelis desmarestii, in a northwestern mediterranean area: a competitor for local fisheries?. Sci. Rep. Port. Cros. Natl. Park 28, 113–132 (2014).
Google Scholar 
66.Morat, F. et al. Offshore–onshore linkages in the larval life history of sole in the Gulf of Lions (NW-Mediterranean). Estuar. Coast. Shelf Sci. 149, 194–202 (2014).ADS 
CAS 
Article 

Google Scholar 
67.La Mesa, G., Louisy, P. & Vacchi, M. Assessment of microhabitat preferences in juvenile dusky grouper (Epinephelus marginatus) by visual sampling. Mar. Biol. 140, 175–185 (2002).Article 

Google Scholar 
68.Vacchi, M., La Mesa, G., Finoia, M. G., Guidetti, P. & Bussotti, S. Protection measures and juveniles of dusky grouper, Epinephelus marginatus (Lowe, 1834) (Pisces, Serranidae), in the Marine Reserve of Ustica Island (Italy, Mediterranean Sea). Mar. Life 9, 63–70 (1999).
Google Scholar 
69.Bodilis, P., Ganteaume, A. & Francour, P. Presence of 1 year-old dusky groupers along the French Mediterranean coast. J. Fish Biol. 62, 242–246 (2003).Article 

Google Scholar 
70.Bodilis, P., Ganteaume, A. & Francour, P. Recruitment of the dusky grouper (Epinephelus marginatus) in the north-western Mediterranean Sea. Cybium 27, 123–129 (2003).
Google Scholar 
71.Mercader, M. et al. Observation of juvenile dusky groupers (Epinephelus marginatus) in artificial habitats of North-Western Mediterranean harbors. Mar. Biodivers. 47, 371–372 (2016).Article 

Google Scholar 
72.Raventos, N. & Macpherson, E. Environmental influences on temporal patterns of settlement in two littoral labrid fishes in the Mediterranean Sea. Estuar. Coast. Shelf Sci. 63, 479–487 (2005).ADS 
Article 

Google Scholar 
73.Raventos, N. & Macpherson, E. Effect of pelagic larval growth and size-at-hatching on post-settlement survivorship in two temperate labrid fish of the genus Symphodus. Mar. Ecol. Prog. Ser. 285, 205–211 (2005).ADS 
Article 

Google Scholar 
74.Macpherson, E. & Raventos, N. Settlement patterns and post-settlement survival in two Mediterranean littoral fishes: influences of early-life traits and environmental variables. Mar. Biol. 148, 167–177 (2005).Article 

Google Scholar 
75.Raventos, N. Effects of wave action on nesting activity in the littoral five-spotted wrasse, Symphodus roissali,(Labridae), in the northwestern Mediterranean Sea. Sci. Mar. 68, 257–264 (2004).Article 

Google Scholar 
76.Schunter, C. et al. A novel integrative approach elucidates fine-scale dispersal patchiness in marine populations. Sci. Rep. 9, 10796 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Biagi, F., Gambaccini, S. & Zazzetta, M. Settlement and recruitment in fishes: The role of coastal areas. Ital. J. Zool. 65, 269–274 (1998).Article 

Google Scholar 
78.Franco, A. et al. Use of shallow water habitats by fish assemblages in a Mediterranean coastal lagoon. Estuar. Coast. Shelf Sci. 66, 67–83 (2006).ADS 
Article 

Google Scholar 
79.Harmelin-Vivien, M. L. et al. Évaluation visuelle des peuplements et populations de Poissons: Méthodes et problèmes. Rev. Ecol. Terre Vie 40, 467–539 (1985).
Google Scholar 
80.Faillettaz, R. et al. Spatio-temporal patterns of larval fish settlement in the northwestern Mediterranean Sea. Mar. Ecol. Prog. Ser. 650, 153–173 (2020).ADS 
Article 

Google Scholar 
81.Le Direach, L. et al. Programme NUhAGE : Nurseries, habitats, génie écologique, Rapport final. Contrat GIS Posidonie: MIO: P2A développement/Agence de l’Eau Rhône-Méditerranée-Corse-Conseil Général du Var. 1–146 (2015).82.Orellana, S., Hernández, M. & Sansón, M. Diversity of Cystoseira sensu lato (Fucales, Phaeophyceae) in the eastern Atlantic and Mediterranean based on morphological and DNA evidence, including Carpodesmia gen. emend. and Treptacantha gen. emend. Eur. J. Phycol. 54, 447–465 (2019).CAS 
Article 

Google Scholar 
83.Ballesteros, E. Els vegetals i la zonació litoral: espècies, comunitats i factors que influeixen en la seva distribució. (1992).84.Medrano, A. et al. Ecological traits, genetic diversity and regional distribution of the macroalga Treptacantha elegans along the Catalan coast (NW Mediterranean Sea). Sci. Rep. 10, 19219 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Clarke, K. R., Gorley, R. N., Somerfield, P. J. & Warwick, R. M. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation. (Primer-E Ltd, 2001).86.Clarke, K. R. & Gorley, R. N. Primer v6: User Manual/Tutorial-Primer-E Ltd. (2006).87.Anderson, M., Gorley, R. & Clarke, K. PERMANOVA+ for PRIMER: guide to software and statistical methods. (Primer-e, 2008).88.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2017).89.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).MATH 
Book 

Google Scholar 
90.August, P. V. The role of habitat complexity and heterogeneity in structuring tropical mammal communities. Ecology 64, 1495–1507 (1983).Article 

Google Scholar 
91.Wedding, L. M., Lepczyk, C. A., Pittman, S. J., Friedlander, A. M. & Jorgensen, S. Quantifying seascape structure: Extending terrestrial spatial pattern metrics to the marine realm. Mar. Ecol. Prog. Ser. 427, 219–223 (2011).ADS 
Article 

Google Scholar 
92.Thiriet, P., Cheminée, A., Mangialajo, L. & Francour, P. How 3D complexity of macrophyte-formed habitats affect the processes structuring fish assemblages within coastal temperate seascapes? in Underwater Seascapes (eds. Musard, O. et al.) 185–199 (Springer, 2014).93.Cheminée, A., Merigot, B., Vanderklift, M. A. & Francour, P. Does habitat complexity influence fish recruitment?. Mediterr. Mar. Sci. 17, 39–46 (2016).Article 

Google Scholar 
94.Mercader, M. et al. Is artificial habitat diversity a key to restoring nurseries for juvenile coastal fish? Ex situ experiments on habitat selection and survival of juvenile seabreams. Restor. Ecol. 27, 1155–1165 (2019).Article 

Google Scholar 
95.Winemiller, K. O. & Leslie, M. A. Fish assemblages across a complex, tropical freshwater/marine ecotone. Environ. Biol. Fishes 34, 29–50 (1992).Article 

Google Scholar 
96.Nagelkerken, I. et al. Importance of mangroves, seagrass beds and the shallow coral reef as a nursery for important coral reef fishes, using a visual census technique. Estuar. Coast. Shelf Sci. 51, 31–44 (2000).ADS 
Article 

Google Scholar 
97.Adams, A. J. et al. Nursery function of tropical back-reef systems. Mar. Ecol. Prog. Ser. 318, 287–301 (2006).ADS 
Article 

Google Scholar 
98.Vigliola, L., Harmelin-Vivien, M. & Meekan, M. G. Comparison of techniques of back-calculation of growth and settlement marks from the otoliths of three species of Diplodus from the Mediterranean Sea. Can. J. Fish. Aquat. Sci. 57, 1291–1299 (2000).Article 

Google Scholar 
99.Ventura, D., Lasinio, G. J. & Ardizzone, G. Temporal partitioning of microhabitat use among four juvenile fish species of the genus Diplodus (Pisces: Perciformes, Sparidae). Mar. Ecol. 36, 1013–1032 (2015).ADS 
Article 

Google Scholar 
100.Thibaut, T., Blanfune, A., Boudouresque, C. F. & Verlaque, M. Decline and local extinction of Fucales in French Riviera: the harbinger of future extinctions?. Mediterr. Mar. Sci. 16, 206–224 (2015).Article 

Google Scholar 
101.Thibaut, T., Pinedo, S., Torras, X. & Ballesteros, E. Long-term decline of the populations of Fucales (Cystoseira spp. and Sargassum spp.) in the Alberes coast (France, North-western Mediterranean). Mar. Pollut. Bull. 50, 1472–1489 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
102.Thibaut, T. et al. Unexpected temporal stability of cystoseira and sargassum forests in port-cros, one of the Oldest Mediterranean Marine National Parks. Cryptogam. Algol. 37, 61–90 (2016).Article 

Google Scholar 
103.Blanfuné, A., Boudouresque, C. F., Verlaque, M. & Thibaut, T. The fate of Cystoseira crinita, a forest-forming Fucale (Phaeophyceae, Stramenopiles), in France (North Western Mediterranean Sea). Estuar. Coast. Shelf Sci. 181, 196–208 (2016).ADS 
Article 

Google Scholar 
104.Sales, M., Cebrian, E., Tomas, F. & Ballesteros, E. Pollution impacts and recovery potential in three species of the genus Cystoseira (Fucales, Heterokontophyta). Estuar. Coast. Shelf Sci. 92, 347–357 (2011).ADS 
CAS 
Article 

Google Scholar 
105.Sala, E., Boudouresque, C. F. & Harmelin-Vivien, M. Fishing, trophic cascades, and the structure of algal assemblages: Evaluation of an old but untested paradigm. Oikos 82, 425–439 (1998).Article 

Google Scholar 
106.Sala, E., Kizilkaya, Z., Yildirim, D. & Ballesteros, E. Alien marine fishes deplete algal biomass in the Eastern Mediterranean. PLoS ONE 6, e17356 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
107.Planes, S. et al. Spatio-temporal variability in growth of juvenile sparid fishes from the Mediterranean littoral zone. J. Mar. Biol. Assoc. UK 79, 137–143 (1999).Article 

Google Scholar 
108.Macpherson, E. et al. Mortality of juvenile fishes of the genus Diplodus in protected and unprotected areas in the western Mediterranean Sea. Mar. Ecol. Prog. Ser. 160, 135–147 (1997).ADS 
Article 

Google Scholar 
109.Pankhurst, N. W. & Munday, P. L. Effects of climate change on fish reproduction and early life history stages. Mar. Freshw. Res. 62, 1015–1026 (2011).CAS 
Article 

Google Scholar 
110.Hidalgo, M. et al. Accounting for ocean connectivity and hydroclimate in fish recruitment fluctuations within transboundary metapopulations. Ecol. Appl. 29, e01913 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
111.Nagelkerken, I., Sheaves, M., Baker, R. & Connolly, R. M. The seascape nursery: A novel spatial approach to identify and manage nurseries for coastal marine fauna. Fish Fish. 16, 362–371 (2015).Article 

Google Scholar 
112.Colloca, F. et al. The seascape of demersal fish nursery areas in the North Mediterranean Sea, a first step towards the implementation of spatial planning for trawl fisheries. PLoS ONE 10, e0119590 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
113.Cheminée, A., Feunteun, E., Clerici, S., Bertrand, C. & Francour, P. Management of infralittoral habitats: towards a seascape scale approach. in Underwater Seascapes: From geographical to ecological perspectives (eds. Musard, O., Francour, P. & Feunteun, E.) 240 (Springer, 2014).114.Grober-Dunsmore, R., Pittman, S. J., Caldow, C., Kendall, M. S. & Frazer, T. K. A landscape ecology approach for the study of ecological connectivity across tropical marine seascapes. Ecol. Connect. Trop. Coast. Ecosyst. 1, 493–530 (2009).
Google Scholar 
115.Meinesz, A., Lefevre, J. R. & Astier, J. M. Impact of coastal development on the infralittoral zone along the southeastern Mediterranean shore of continental France. Mar. Pollut. Bull. 23, 343–347 (1991).Article 

Google Scholar 
116.Boudouresque, C. F. et al. The Management of Mediterranean Coastal Habitats: A Plea for a Socio-ecosystem-Based Approach. in Evolution of Marine Coastal Ecosystems under the Pressure of Global Changes (eds. Ceccaldi, H.-J. et al.) 297–320 (Springer, 2020).117.Seitz, R. D., Wennhage, H., Bergström, U., Lipcius, R. N. & Ysebaert, T. Ecological value of coastal habitats for commercially and ecologically important species. ICES J. Mar. Sci. 71, 648–665 (2014).Article 

Google Scholar 
118.Boudouresque, C. F. et al. Protection and conservation of Posidonia oceanica meadows. RAMOGE and RAC. (SPA publisher, 2012).119.Sartoretto, S. et al. An integrated method to evaluate and monitor the conservation state of coralligenous habitats: The INDEX-COR approach. Mar. Pollut. Bull. 120, 222–231 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
120.Meinesz, A. & Blanfuné, A. 1983–2013: Development of marine protected areas along the French Mediterranean coasts and perspectives for achievement of the Aichi target. Mar. Policy 54, 10–16 (2015).Article 

Google Scholar  More

1.PlasticsEurope. Plastics the—Facts 2019: An Analysis of European Plastics Production, Demand and Waste Data (PlasticsEurope, 2019).
Google Scholar 
2.Eriksen, M. et al. Plastic pollution in the world’s oceans: More than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea. PLoS ONE 9, e111913. https://doi.org/10.1371/journal.pone.0111913 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Borrelle, S. B. et al. Predicted growth in plastic waste exceeds efforts to mitigate plastic pollution. Science 369, 1515–1518. https://doi.org/10.1126/science.aba3656 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
4.Bergmann, M., Tekman, M. B. & Gutow, L. Sea change for plastic pollution. Nature 544, 297. https://doi.org/10.1038/544297a (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
5.Imhof, H. K. et al. Spatial and temporal variation of macro-, meso- and microplastic abundance on a remote coral island of the Maldives, Indian Ocean. Mar. Pollut. Bull. 116, 340–347. https://doi.org/10.1016/j.marpolbul.2017.01.010 (2017).CAS 
Article 
PubMed 

Google Scholar 
6.Obbard, R. W. Microplastics in polar regions: The role of long range transport. Curr. Opin. Environ. Sci. Health 1, 24–29. https://doi.org/10.1016/j.coesh.2017.10.004 (2018).Article 

Google Scholar 
7.Wessel, C. C., Lockridge, G. R., Battiste, D. & Cebrian, J. Abundance and characteristics of microplastics in beach sediments: Insights into microplastic accumulation in northern Gulf of Mexico estuaries. Mar. Pollut. Bull. 109, 178–183. https://doi.org/10.1016/j.marpolbul.2016.06.002 (2016).CAS 
Article 
PubMed 

Google Scholar 
8.Woodall, L. C. et al. The deep sea is a major sink for microplastic debris. Royal Soc. Open Sci. https://doi.org/10.1098/rsos.140317 (2014).Article 

Google Scholar 
9.Barnes, D. K. A., Galgani, F., Thompson, R. C. & Barlaz, M. Accumulation and fragmentation of plastic debris in global environments. Philos. Trans. Royal Soc. Lond. Ser. B, Biol. Sci. 364, 1985–1998. https://doi.org/10.1098/rstb.2008.0205 (2009).CAS 
Article 

Google Scholar 
10.Arthur, C., Baker, J. E. & Bamford, H. A. Proceedings of the International Research Workshop on the Occurrence, Effects, and Fate of Microplastic Marine Debris, September 9–11, 2008 (University of Washington Tacoma, 2009).
Google Scholar 
11.Hartmann, N. B. et al. Are we speaking the same language? Recommendations for a definition and categorization framework for plastic Debris. Environ. Sci. Technol. 53, 1039–1047. https://doi.org/10.1021/acs.est.8b05297 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
12.Lusher, A. in Marine Anthropogenic Litter (eds Bergmann, M., Gutow, L. & Klages, M.) 245–307 (Springer International Publishing, 2015).13.Cole, M., Lindeque, P., Halsband, C. & Galloway, T. S. Microplastics as contaminants in the marine environment: A review. Mar. Pollut. Bull. 62, 2588–2597. https://doi.org/10.1016/j.marpolbul.2011.09.025 (2011).CAS 
Article 
PubMed 

Google Scholar 
14.Wright, S. L., Thompson, R. C. & Galloway, T. S. The physical impacts of microplastics on marine organisms: A review. Environ. Pollut. 178, 483–492. https://doi.org/10.1016/j.envpol.2013.02.031 (2013).CAS 
Article 
PubMed 

Google Scholar 
15.de Sá, L. C., Oliveira, M., Ribeiro, F., Rocha, T. L. & Futter, M. N. Studies of the effects of microplastics on aquatic organisms: What do we know and where should we focus our efforts in the future?. Sci. Total Environ. 645, 1029–1039. https://doi.org/10.1016/j.scitotenv.2018.07.207 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
16.Bråte, I. L. N. et al. Mytilus spp. as sentinels for monitoring microplastic pollution in Norwegian coastal waters: A qualitative and quantitative study. Environ. Pollut. 243, 383–393. https://doi.org/10.1016/j.envpol.2018.08.077 (2018).CAS 
Article 
PubMed 

Google Scholar 
17.Lusher, A. L., Tirelli, V., O’Connor, I. & Officer, R. Microplastics in Arctic polar waters: The first reported values of particles in surface and sub-surface samples. Sci. Rep. 5, 14947. https://doi.org/10.1038/srep14947 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
18.Cózar, A. et al. The Arctic Ocean as a dead end for floating plastics in the North Atlantic branch of the thermohaline circulation. Sci. Adv. 3, e1600582. https://doi.org/10.1126/sciadv.1600582 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Kanhai, L. D. K. et al. Microplastics in sub-surface waters of the Arctic Central Basin. Mar. Pollut. Bull. 130, 8–18. https://doi.org/10.1016/j.marpolbul.2018.03.011 (2018).CAS 
Article 
PubMed 

Google Scholar 
20.Tekman, M. B. et al. Tying up loose ends of microplastic pollution in the Arctic: Distribution from the sea surface through the water column to deep-sea sediments at the HAUSGARTEN observatory. Environ. Sci. Technol. 54, 4079–4090. https://doi.org/10.1021/acs.est.9b06981 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
21.Obbard, R. W. et al. Global warming releases microplastic legacy frozen in Arctic Sea ice. Earth’s Future 2, 315–320. https://doi.org/10.1002/2014EF000240 (2014).ADS 
Article 

Google Scholar 
22.Peeken, I. et al. Arctic sea ice is an important temporal sink and means of transport for microplastic. Nat. Commun. 9, 1505. https://doi.org/10.1038/s41467-018-03825-5 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Kanhai, L. D. K., Gardfeldt, K., Krumpen, T., Thompson, R. C. & O’Connor, I. Microplastics in sea ice and seawater beneath ice floes from the Arctic Ocean. Sci. Rep. 10, 5004. https://doi.org/10.1038/s41598-020-61948-6 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Bergmann, M. et al. White and wonderful? Microplastics prevail in snow from the Alps to the Arctic. J. Sci. Adv. 5, eaax1157. https://doi.org/10.1126/sciadv.aax1157 (2019).ADS 
CAS 
Article 

Google Scholar 
25.Amélineau, F. et al. Microplastic pollution in the Greenland Sea: Background levels and selective contamination of planktivorous diving seabirds. Environ. Pollut. 219, 1131–1139. https://doi.org/10.1016/j.envpol.2016.09.017 (2016).CAS 
Article 
PubMed 

Google Scholar 
26.Kühn, S. et al. Plastic ingestion by juvenile polar cod (Boreogadus saida) in the Arctic Ocean. Polar Biol. 41, 1269–1278. https://doi.org/10.1007/s00300-018-2283-8 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Fang, C. et al. Microplastic contamination in benthic organisms from the Arctic and sub-Arctic regions. Chemosphere 209, 298–306. https://doi.org/10.1016/j.chemosphere.2018.06.101 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
28.Bråte, I. L. N. et al. Microplastics in Marine Bivalves from the Nordic Environment Vol. 504 (Nordic Council of Ministers, 2020).Book 

Google Scholar 
29.Misund, O. A. et al. Norwegian fisheries in the Svalbard zone since 1980. Regulations, profitability and warming waters affect landings. Polar Sci. 10, 312–322. https://doi.org/10.1016/j.polar.2016.02.001 (2016).ADS 
Article 

Google Scholar 
30.Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. Oikos 69, 373–386. https://doi.org/10.2307/3545850 (1994).Article 

Google Scholar 
31.Foster, M. S. Rhodoliths: Between rocks and soft places. J. Phycol. 37, 659–667 (2001).Article 

Google Scholar 
32.Fredericq, S. et al. The critical importance of rhodoliths in the life cycle completion of both macro- and microalgae, and as holobionts for the establishment and maintenance of marine biodiversity. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00502 (2019).Article 

Google Scholar 
33.Krayesky-Self, S. et al. Eukaryotic life inhabits rhodolith-forming coralline algae (Hapalidiales, Rhodophyta), remarkable marine benthic microhabitats. Sci. Rep. 7, 45850. https://doi.org/10.1038/srep45850 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Kamenos, N. A., Moore, P. G., Hall-Spencer, J. & Donnan, D. Maerl: Its value as a habitat for commercial species. Shellfish News 18, 8–9 (2004).
Google Scholar 
35.Kamenos, N. A., Moore, P. G. & Hall-Spencer, J. M. Nursery-area function of maerl grounds for juvenile queen scallops Aequipecten opercularis and other invertebrates. Mar. Ecol. Prog. Ser. 274, 183–189. https://doi.org/10.3354/meps274183 (2004).ADS 
Article 

Google Scholar 
36.Gagnon, P., Matheson, K. & Stapleton, M. Variation in rhodolith morphology and biogenic potential of newly discovered rhodolith beds in Newfoundland and Labrador (Canada). Bot. Mar. 55, 85–99 (2012).Article 

Google Scholar 
37.Teichert, S. et al. Rhodolith beds (Corallinales, Rhodophyta) and their physical and biological environment at 80°31’N in Nordkappbukta (Nordaustlandet, Svalbard Archipelago, Norway). Phycologia 51, 371–390 (2012).Article 

Google Scholar 
38.Teichert, S. et al. Arctic rhodolith beds and their environmental controls. Facies 60, 15–37. https://doi.org/10.1007/s10347-013-0372-2 (2014).Article 

Google Scholar 
39.Teichert, S. Hollow rhodoliths increase Svalbard’s shelf biodiversity. Sci. Rep. 4, 6972. https://doi.org/10.1038/srep06972 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.Denisenko, S. G., Denisenko, N. V., Lehtonen, K. K., Andersin, A. B. & Laine, A. O. Macrozoobenthos of the Pechora Sea (SE Barents Sea): Community structure and spatial distribution in relation to environmental conditions. Mar. Ecol. Prog. Ser. 258, 109–123 (2003).ADS 
Article 

Google Scholar 
41.Rees, H. L. & Dare, P. J. Sources of Mortality and Associated Life-Cycle Traits of Selected Benthic Species: A Review Vol. 33, 36 (CEFAS Directorate of Fisheries Research, 1993).
Google Scholar 
42.Sejr, M. K. et al. Growth and production of Hiatella arctica (Bivalvia) in a high-Arctic fjord (Young Sound, Northeast Greenland). Mar. Ecol. Prog. Ser. 244, 163–169. https://doi.org/10.3354/meps244163 (2002).ADS 
Article 

Google Scholar 
43.Witman, J. D. & Sebens, K. P. Regional variation in fish predation intensity: A historical perspective in the Gulf of Maine. Oecologia 90, 305–315. https://doi.org/10.1007/bf00317686 (1992).ADS 
Article 
PubMed 

Google Scholar 
44.Kamenos, N. A., Moore, P. G. & Hall-Spencer, J. M. Small-scale distribution of juvenile gadoids in shallow inshore waters; what role does maerl play?. ICES J. Mar. Sci. 61, 422–429 (2004).Article 

Google Scholar 
45.Teichert, S., Voigt, N. & Wisshak, M. Do skeletal Mg/Ca ratios of Arctic rhodoliths reflect atmospheric CO2 concentrations?. Polar Biol. 43, 2059–2069. https://doi.org/10.1007/s00300-020-02767-3 (2020).Article 

Google Scholar 
46.Ragazzola, F. et al. Phenotypic plasticity of coralline algae in a High CO2 world. Ecol. Evol. 3, 3436–3446. https://doi.org/10.1002/ece3.723 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
47.Teichert, S. & Freiwald, A. Polar coralline algal CaCO3-production rates correspond to intensity and duration of the solar radiation. Biogeosciences 11, 833–842. https://doi.org/10.5194/bg-11-833-2014 (2014).ADS 
Article 

Google Scholar 
48.Büdenbender, J., Riebesell, U. & Form, A. Calcification of the Arctic coralline red algae Lithothamnion glaciale in response to elevated CO2. Mar. Ecol. Prog. Ser. 441, 79–87 (2011).ADS 
Article 

Google Scholar 
49.Wisshak, M. et al. Habitat Characteristics and Carbonate Cycling of Macrophyte-Supported Polar Carbonate Factories (Svalbard)—Cruise No. MSM55—June 11–June 29, 2016—Reykjavik (Iceland)—Longyearbyen (Norway) 58 (Bremen, 2017).50.Löder, M. G. J. et al. Enzymatic purification of microplastics in environmental samples. Environ. Sci. Technol. 51, 14283–14292. https://doi.org/10.1021/acs.est.7b03055 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
51.Hufnagl, B. et al. A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers. Anal. Methods 11, 2277–2285. https://doi.org/10.1039/C9AY00252A (2019).CAS 
Article 

Google Scholar 
52.Yanfang, L., Hua, Z. & Cheng, T. A review of possible pathways of marine microplastics transport in the ocean. Anthr. Coasts 3, 6–13. https://doi.org/10.1139/anc-2018-0030 (2020).Article 

Google Scholar 
53.Erni-Cassola, G., Zadjelovic, V., Gibson, M. I. & Christie-Oleza, J. A. Distribution of plastic polymer types in the marine environment; A meta-analysis. J. Hazard. Mater. 369, 691–698. https://doi.org/10.1016/j.jhazmat.2019.02.067 (2019).CAS 
Article 
PubMed 

Google Scholar 
54.Choy, C. A. et al. The vertical distribution and biological transport of marine microplastics across the epipelagic and mesopelagic water column. Sci. Rep. 9, 7843. https://doi.org/10.1038/s41598-019-44117-2 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Kooi, M. et al. The effect of particle properties on the depth profile of buoyant plastics in the ocean. Sci. Rep. 6, 33882. https://doi.org/10.1038/srep33882 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Vinay Kumar, B. N., Löschel, L. A., Imhof, H. K., Löder, M. G. J. & Laforsch, C. Analysis of microplastics of a broad size range in commercially important mussels by combining FTIR and Raman spectroscopy approaches. Environ. Pollut. https://doi.org/10.1016/j.envpol.2020.116147 (2020).Article 
PubMed 

Google Scholar 
57.Löder, M. G. J. & Gerdts, G. in Marine Anthropogenic Litter (eds Bergmann, M., Gutow, L. & Klages, M.) 201–227 (Springer International Publishing, 2015).58.Wisshak, M. et al. Epibenthos dynamics and environmental fluctuations in two contrasting Polar carbonate factories (Mosselbukta and Bjørnøy-Banken, Svalbard). Front. Mar. Sci. 6, 667. https://doi.org/10.3389/fmars.2019.00667 (2019).Article 

Google Scholar 
59.Frias, J. P. G. L., Lyashevska, O., Joyce, H., Pagter, E. & Nash, R. Floating microplastics in a coastal embayment: A multifaceted issue. Mar. Pollut. Bull. 158, 111361. https://doi.org/10.1016/j.marpolbul.2020.111361 (2020).CAS 
Article 
PubMed 

Google Scholar 
60.Rochman, C. M. et al. Anthropogenic debris in seafood: Plastic debris and fibers from textiles in fish and bivalves sold for human consumption. Sci. Rep. 5, 14340. https://doi.org/10.1038/srep14340 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
61.Digka, N., Tsangaris, C., Torre, M., Anastasopoulou, A. & Zeri, C. Microplastics in mussels and fish from the Northern Ionian Sea. Mar. Pollut. Bull. 135, 30–40. https://doi.org/10.1016/j.marpolbul.2018.06.063 (2018).CAS 
Article 
PubMed 

Google Scholar 
62.Santana, M. F. M., Ascer, L. G., Custódio, M. R., Moreira, F. T. & Turra, A. Microplastic contamination in natural mussel beds from a Brazilian urbanized coastal region: Rapid evaluation through bioassessment. Mar. Pollut. Bull. 106, 183–189. https://doi.org/10.1016/j.marpolbul.2016.02.074 (2016).CAS 
Article 
PubMed 

Google Scholar 
63.Gomiero, A., Strafella, P., Øysæd, K. B. & Fabi, G. First occurrence and composition assessment of microplastics in native mussels collected from coastal and offshore areas of the northern and central Adriatic Sea. Environ. Sci. Pollut. Res. Int. 26, 24407–24416. https://doi.org/10.1007/s11356-019-05693-y (2019).CAS 
Article 
PubMed 

Google Scholar 
64.Mathalon, A. & Hill, P. Microplastic fibers in the intertidal ecosystem surrounding Halifax Harbor, Nova Scotia. Mar. Pollut. Bull. 81, 69–79. https://doi.org/10.1016/j.marpolbul.2014.02.018 (2014).CAS 
Article 
PubMed 

Google Scholar 
65.Van Cauwenberghe, L., Claessens, M., Vandegehuchte, M. B. & Janssen, C. R. Microplastics are taken up by mussels (Mytilus edulis) and lugworms (Arenicola marina) living in natural habitats. Environ. Pollut. 199, 10–17. https://doi.org/10.1016/j.envpol.2015.01.008 (2015).CAS 
Article 
PubMed 

Google Scholar 
66.Li, J. et al. Using mussel as a global bioindicator of coastal microplastic pollution. Environ. Pollut. 244, 522–533. https://doi.org/10.1016/j.envpol.2018.10.032 (2019).CAS 
Article 
PubMed 

Google Scholar 
67.Woodall, L. C. et al. Using a forensic science approach to minimize environmental contamination and to identify microfibres in marine sediments. Mar. Pollut. Bull. 95, 40–46. https://doi.org/10.1016/j.marpolbul.2015.04.044 (2015).CAS 
Article 
PubMed 

Google Scholar 
68.Kowalski, N., Reichardt, A. M. & Waniek, J. J. Sinking rates of microplastics and potential implications of their alteration by physical, biological, and chemical factors. Mar. Pollut. Bull. 109, 310–319. https://doi.org/10.1016/j.marpolbul.2016.05.064 (2016).CAS 
Article 
PubMed 

Google Scholar 
69.Kooi, M., Nes, E. H. V., Scheffer, M. & Koelmans, A. A. Ups and downs in the ocean: Effects of biofouling on vertical transport of microplastics. Environ. Sci. Technol. 51, 7963–7971. https://doi.org/10.1021/acs.est.6b04702 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
70.Barrows, A. P. W., Cathey, S. E. & Petersen, C. W. Marine environment microfiber contamination: Global patterns and the diversity of microparticle origins. Environ. Pollut. 237, 275–284. https://doi.org/10.1016/j.envpol.2018.02.062 (2018).CAS 
Article 
PubMed 

Google Scholar 
71.Halsband, C. & Herzke, D. Plastic litter in the European Arctic: What do we know?. Emerg. Contam. 5, 308–318. https://doi.org/10.1016/j.emcon.2019.11.001 (2019).Article 

Google Scholar 
72.Bergmann, M., Lutz, B., Tekman, M. B. & Gutow, L. Citizen scientists reveal: Marine litter pollutes Arctic beaches and affects wild life. Mar. Pollut. Bull. 125, 535–540. https://doi.org/10.1016/j.marpolbul.2017.09.055 (2017).CAS 
Article 
PubMed 

Google Scholar 
73.von Moos, N., Burkhardt-Holm, P. & Köhler, A. Uptake and effects of microplastics on cells and tissue of the blue mussel Mytilus edulis L. after an experimental exposure. Environ. Sci. Technol. 46, 11327–11335. https://doi.org/10.1021/es302332w (2012).ADS 
CAS 
Article 

Google Scholar 
74.Kolandhasamy, P. et al. Adherence of microplastics to soft tissue of mussels: A novel way to uptake microplastics beyond ingestion. Sci. Total Environ. 610–611, 635–640. https://doi.org/10.1016/j.scitotenv.2017.08.053 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
75.Löder, M. G. J., Kuczera, M., Mintenig, S., Lorenz, C. & Gerdts, G. Focal plane array detector-based micro-Fourier-transform infrared imaging for the analysis of microplastics in environmental samples. J. Environ. Chem. 12, 563–581. https://doi.org/10.1071/EN14205 (2015).CAS 
Article 

Google Scholar 
76.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
77.R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
78.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn, 498 (Springer, 2002).Book 

Google Scholar 
79.Vegan: Community Ecology Package (2020). More

Freshwater fish are important contributors to human livelihoods, food and nutrition, recreation, ecosystem services, and biological diversity. Yet, they inhabit some of the most threatened ecosystems globally1, face higher declines relative to marine and terrestrial species2, and are disproportionally understudied3,4. Inland fisheries are subjected to a suite of anthropogenic stressors across aquatic-terrestrial landscapes5, including flow alterations, dams, invasive species, sedimentation, drought, and pollution6,7,8. Evaluating stressors and their impacts on global inland fisheries is essential for effective management, monitoring, and conservation6, but unlike marine fisheries, there is no standardized method to assess inland fisheries9.Data inputs for a fisheries threat assessment typically include baseline information, such as species-specific landings or in situ population data (volume and composition), size (population and landings), and biomass. In addition, multi-stressor interactions (e.g., synergistic, additive) across complex habitats often warrant cross-ecosystem and cross-sector evaluations at multiple scales10,11. However, in the case of inland fisheries, these data inputs are severely deficient and often disparate in many regions12,13, which challenges the development of a global assessment. Thus, evaluating stressors and their impacts on inland fisheries necessitates the use of additional data sources (e.g., expert knowledge) beyond those typically derived directly from fish or fish habitats12,14. Local and subject-matter expertise can provide contextualized insights where spatial data are limited or unattainable (e.g., emerging threats15) and where empirical evidence is incomplete (e.g., multi-stressor interactions).Expert elicitation (i.e., expert opinion synthesis, where opinion is the preliminary state of knowledge of an individual) is increasingly used to inform ecological evaluations and guide water infrastructure, development, food security, and conservation decision-making and assessments, especially in data-poor scenarios14,16. While spatial data can be integrated as a suite of individual stressors (i.e., input variables) within ranking systems for the development of vulnerability or habitat assessments for conservation purposes14,17, the utilization of spatial variables is limited by the method for determining relative impacts (i.e., value judgment)18. Cumulate impact scores and systematic weighted ranking of threats are often based on geographically biased, small sized, or non-representative subsets of experts’ opinions (e.g., global weight determination from eight experts5). Thus, data collection for this study was motivated by the development of a global assessment of threats to major inland fisheries, and the overarching need for tractable freshwater indicators. The data generated contribute essential relative influence scores for the assessment and provide a timely snapshot of inland fisheries as perceived by fisheries professionals. Threat composition and influence have broader potential applications to inform vulnerability and adaptation components of freshwater conservation and management targets (e.g., United Nations (UN) Sustainable Development Goals, UN International Decade “Water for Sustainable Development,” Convention on Biological Diversity, Ramsar Convention on Wetlands).This paper introduces a dataset that can help address a knowledge gap in understanding natural and human influences on inland fisheries with local, contextualized fishery evaluations. Derived from an electronic survey, data comprise perceptions from fisheries professionals (n = 536) on the relative influence and spatial associations of fishery threats, recent successes, and adaptive capacity measures within the respondent’s fishery of expertise.In the context of the survey, we use the term “threat” as a proximate human activity or process (“direct threat”) causing degradation or impairment (“stress”; e.g., reduced population size, fragmented riparian habitat) to ecological targets (e.g., species, communities, ecosystems; in this case, fishery)19. We considered only the threats most proximate and direct to the target (fishery) and excluded stresses (i.e., symptoms, degraded key attributes) and contributing factors (i.e., root causes, underlying factors). For example, we considered pollutants (direct threat) rather than the pollution source (contributing factor) or the resulting contaminated water (stress, effect). We addressed the ambiguity of the term ‘fishery’20 by allowing respondents to indicate a geographic location (specific point) within their fishery area. This allows for spatial attribution with an inclusive use of ‘fishery’ as it pertains to threats (e.g., threats to a fish population of fishery-targeted species, catch characteristics, or the habitat in which the fishery operates).We structured survey questions about the occurrence and relative influence of threats to the production and health of inland fisheries using 29 specified individual threats derived from well-studied pressures to inland fisheries in addition to pressures emerging as threats to fisheries (e.g., climate change, plastics15). We categorized individual threats into five well-established categories: habitat degradation, pollution, overexploitation, species invasion, and climate change1,7 for organizational context in the survey. We also designed survey questions specifically to understand the social adaptive capacity of fishers using five major community-level domains: fisher access to assets (e.g., financial, technological, service), fisher and institutional flexibility to adapt to changing conditions (e.g., livelihood alternatives, adaptive management), social capital and organization to enable cooperation and collective action (e.g., co-management), learning and problem-solving for responding to threats, and fishers’ sense of agency to influence and shape actions and outcomes21.This dataset can be useful as an overview assessment, on which future assessments may expand for specific temporal or spatial interests. Some data in this dataset (e.g., microplastics, invasive species disturbances) are otherwise unattainable at relevant scales from geospatial information and therefore provide novel information. Potential uses include demographic influences on threat perceptions, spatial distribution of adaptive capacity measures paired with climate change or other threats, external factors driving multi-stressor interactions, and paired geospatial and expert-derived threat analysis. These data can provide insights on fisheries as a coupled human-natural system and inform regional and global freshwater assessments. More

1.Keenan, R. J. et al. Forest ecology and management dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manag. 352, 9–20 (2015).Article 

Google Scholar 
2.Siitonen, J. Forest management, coarse woody debris and saproxylic organisms: Fennoscandian boreal forests as an example. Ecol. Bull. 49, 11–41 (2001).
Google Scholar 
3.Stokland, J. N., Siitonen, J. & Jonsson, B. G. Biodiversity in Dead Wood (Cambridge University Press, 2012).Book 

Google Scholar 
4.Nordén, J., Penttilä, R., Siitonen, J., Tomppo, E. & Ovaskainen, O. Specialist species of wood-inhabiting fungi struggle while generalists thrive in fragmented boreal forests. J. Ecol. 101, 701–712 (2013).Article 

Google Scholar 
5.Tikkanen, O.-P., Martikainen, P., Hyvärinen, E., Junninen, K. & Kouki, J. Red-listed boreal forest species of Finland: Associations with forst structure, tree species, and decaying wood. Ann. Zool. Fennici 43, 373–383 (2006).
Google Scholar 
6.Sippola, A.-L., Lehesvirta, T. & Renvall, P. Effect of selective logging on coarse woody debris and diversity of wood-decaying polypores in eastern Finland. Ecol. Bull. 49, 243–254 (2001).
Google Scholar 
7.Axelsson, A. L., Östlund, L. & Hellberg, E. Changes in mixed deciduous forests of boreal Sweden 1866–1999 based on interpretation of historical records. Landsc. Ecol. 17, 403–418 (2002).Article 

Google Scholar 
8.Eriksson, S., Skånes, H., Hammer, M. & Lönn, M. Current distribution of older and deciduous forests as legacies from historical use patterns in a Swedish boreal landscape (1725–2007). For. Ecol. Manag. 260, 1095–1103 (2010).Article 

Google Scholar 
9.Wallenius, T. H., Lilja, S. & Kuuluvainen, T. Fire history and tree species composition in managed Picea abies stands in southern Finland: Implications for restoration. For. Ecol. Manag. 250, 89–95 (2007).Article 

Google Scholar 
10.Stokland, J. N. Host-tree associattions. In Biodiversity in Dead Wood (eds Stokland, J. N. et al.) 82–109 (Cambridge University Press, 2012).Chapter 

Google Scholar 
11.Kouki, J., Arnold, K. & Martikainen, P. Long-term persistence of aspen – A key host for many threatened species—Is endangered in old-growth conservation areas in Finland. J. Nat. Conserv. 12, 41–52 (2004).Article 

Google Scholar 
12.Komonen, A., Tuominen, L., Purhonen, J. & Halme, P. Landscape structure influences browsing on a keystone tree species in conservation areas. For. Ecol. Manag. 457, 117724 (2020).Article 

Google Scholar 
13.Purhonen, J. et al. Morphological traits predict host-tree specialization in wood-inhabiting fungal communities. Fungal Ecol. 46, 100863 (2020).Article 

Google Scholar 
14.Dowding, P. Nutrient uptake and allocation during substrate exploitation by fungi. In The Fungal Community. Its Organization and Role in the Ecosystems (eds Wicklow, D. T. & Carroll, G. C.) 612–636 (Marcel Dekker Inc, 1981).
Google Scholar 
15.Boddy, L., Frankland, J. & van West, P. Ecology of Saprotrophic Basidiomycetes (Elsevier Ltd, 2008).
Google Scholar 
16.Kahl, T. et al. Wood decay rates of 13 temperate tree species in relation to wood properties, enzyme activities and organismic diversities. For. Ecol. Manag. 391, 86–95 (2017).Article 

Google Scholar 
17.Abrego, N. & Salcedo, I. Variety of woody debris as the factor influencing wood-inhabiting fungal richness and assemblages: Is it a question of quantity or quality?. For. Ecol. Manag. 291, 377–385 (2013).Article 

Google Scholar 
18.Lindblad, I. Wood-inhabiting fungi on fallen logs of Norway spruce: Relations to forest management and substrate quality. Nord. J. Bot. 18, 243–255 (1998).Article 

Google Scholar 
19.Tomao, A., Antonio Bonet, J., Castaño, C. & de-Miguel, S. How does forest management affect fungal diversity and community composition? Current knowledge and future perspectives for the conservation of forest fungi. For. Ecol. Manag. 457, 1176 (2020).Article 

Google Scholar 
20.Bader, P., Jansson, S. & Jonsson, B. G. Wood-inhabiting fungi and substratum decline in selectively logged boreal spruce forests. Biol. Conserv. 72, 355–362 (1995).Article 

Google Scholar 
21.Heilmann-Clausen, J. & Christensen, M. Does size matter?. For. Ecol. Manag. 201, 105–117 (2004).Article 

Google Scholar 
22.Nordén, B., Götmark, F., Tönnberg, M. & Ryberg, M. Dead wood in semi-natural temperate broadleaved woodland: Contribution of coarse and fine dead wood, attached dead wood and stumps. For. Ecol. Manag. 194, 235–248 (2004).Article 

Google Scholar 
23.Ottosson, E. et al. Diverse ecological roles within fungal communities in decomposing logs of Picea abies. FEMS Microbiol. Ecol. 91, 1–13 (2015).Article 
CAS 

Google Scholar 
24.Juutilainen, K., Mönkkönen, M., Kotiranta, H. & Halme, P. The effects of forest management on wood-inhabiting fungi occupying dead wood of different diameter fractions. For. Ecol. Manag. 313, 283–291 (2014).Article 

Google Scholar 
25.Jönsson, M., Ruete, A., Kellner, O., Gunnarsson, U. & Snäll, T. Will forest conservation areas protect functionally important diversity of fungi and lichens over time?. Biodivers. Conserv. https://doi.org/10.1007/s10531-015-1035-0 (2016).Article 

Google Scholar 
26.Abrego, N., Norberg, A. & Ovaskainen, O. Measuring and predicting the influence of traits on the assembly processes of wood-inhabiting fungi. J. Ecol. https://doi.org/10.1111/1365-2745.12722 (2017).Article 

Google Scholar 
27.Bässler, C. et al. Functional response of lignicolous fungal guilds to bark beetle deforestation. Ecol. Indic. 65, 149–160 (2016).Article 

Google Scholar 
28.Bässler, C., Heilmann-Clausen, J., Karasch, P., Brandl, R. & Halbwachs, H. Ectomycorrhizal fungi have larger fruit bodies than saprotrophic fungi. Fungal Ecol. 17, 205–212 (2015).Article 

Google Scholar 
29.Sherwood, M. A. Convergent evolution in discomycetes from bark and wood. Bot. J. Linn. Soc. 82, 15–34 (1981).Article 

Google Scholar 
30.Unterseher, M., Otto, P. & Morawetz, W. Species richness and substrate specificity of lignicolous fungi in the canopy of a temperate, mixed deciduous forest. Mycol. Prog. 4, 117–132 (2005).Article 

Google Scholar 
31.Dawson, S. K. & Jönsson, M. Just how big is intraspecific trait variation in basidiomycete wood fungal fruit bodies?. Fungal Ecol. 46, 100865 (2020).Article 

Google Scholar 
32.Dawson, S. K. et al. Handbook for the measurement of macrofungal functional traits: A start with basidiomycete wood fungi. Funct. Ecol. 33, 372–387 (2019).Article 

Google Scholar 
33.Zanne, A. E. et al. Fungal functional ecology: Bringing a trait-based approach to plant-associated fungi. Biol. Rev. 95, 409–433 (2020).PubMed 
Article 

Google Scholar 
34.Nordén, B., Ryberg, M., Götmark, F. & Olausson, B. Relative importance of coarse and fine woody debris for the diversity of wood-inhabiting fungi in temperate broadleaf forests. Biol. Conserv. 117, 1–10 (2004).Article 

Google Scholar 
35.Stokland, J. N. & Larsson, K. Forest ecology and management legacies from natural forest dynamics : Different effects of forest management on wood-inhabiting fungi in pine and spruce forests. For. Ecol. Manag. 261, 1707–1721 (2011).Article 

Google Scholar 
36.Cajander, A. K. Forest types and their significance. Acta For. Fenn. 56, 1–69 (1949).
Google Scholar 
37.Ahti, T., Hämet-Ahti, L. & Jalas, J. Vegetation zones and their sections in northwestern Europe. Ann. Bot. Fenn. 5, 169–211 (1968).
Google Scholar 
38.Renaud, V., Innes, J. L., Dobbertin, M. & Rebetez, M. Comparison between open-site and below-canopy climatic conditions in Switzerland for different types of forests over 10 years (1998–2007). Theor. Appl. Climatol. 105, 119–127 (2011).ADS 
Article 

Google Scholar 
39.Renvall, P. Community structure and dynamics of wood-rotting Basidiomycetes on decomposing conifer trunks in northern Finland. Karstenia 35, 1–51 (1995).Article 

Google Scholar 
40.Abrego, N., Halme, P., Purhonen, J. & Ovaskainen, O. Fruit body based inventories in wood-inhabiting fungi: Should we replicate in space or time?. Fungal Ecol. 20, 225–232 (2016).Article 

Google Scholar 
41.Halme, P. & Kotiaho, J. S. The importance of timing and number of surveys in fungal biodiversity research. Biodivers. Conserv. 21, 205–219 (2012).Article 

Google Scholar 
42.Purhonen, J., Huhtinen, S., Kotiranta, H. & Kotiaho, J. S. Detailed information on fruiting phenology provides new insights on wood-inhabiting fungal detection. Fungal Ecol. 27, 175–177 (2017).Article 

Google Scholar 
43.Royal Botanic Gardens Kew, Landcare Research-NZ & Chinese Academy of Science. Index Fungorum. www.indexfungorum.org 01.03.2017 (2017).44.Barton, K. MuMIn: Multi-Model Inference. R Package Version 1.43.6. https://CRAN.R-project.org/package=MuMIn 15.11.2020 (2019).45.R Core Team. R: A Language and Environment for Statistical Computing. Available at: https://www.r-project.org/ (2017).46.Magnusson, A. et al. glmmTMB: Generalized Linear Mixed Models Using Template Model Builder. https://cran.r-project.org/web/packages/glmmTMB/glmmTMB.pdf 30.08.2018 (2018).47.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.4-4. https://cran.r-project.org/web/packages/vegan/index.html 30.12.2017 (2017).48.Abrego, N., Bässler, C., Christensen, M. & Heilmann-Clausen, J. Implications of reserve size and forest connectivity for the conservation of wood-inhabiting fungi in Europe. Biol. Conserv. 191, 469–477 (2015).Article 

Google Scholar 
49.Halme, P. et al. The effects of habitat degradation on metacommunity structure of wood-inhabiting fungi in European beech forests. Biol. Conserv. 168, 24–30 (2013).Article 

Google Scholar 
50.Edman, M., Kruys, N. & Jonsson, B. G. Local dispersal sources strongly affect colonization patterns of wood-decaying fungi on spruce logs. Ecol. Appl. 14, 893–901 (2004).Article 

Google Scholar 
51.Komonen, A. & Müller, J. Dispersal ecology of deadwood organisms and connectivity conservation. Conserv. Biol. 32, 535–545 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Abrego, N. & Salcedo, I. How does fungal diversity change based on woody debris type? A case study in Northern Spain. Ekologija 57, 109–119 (2011).Article 

Google Scholar 
53.Juutilainen, K., Halme, P., Kotiranta, H. & Mönkkönen, M. Size matters in studies of dead wood and wood-inhabiting fungi. Fungal Ecol. 4, 342–349 (2011).Article 

Google Scholar 
54.Heilmann-Clausen, J. & Christensen, M. Wood-inhabiting macrofungi in Danish beech-forests ? conflicting diversity patterns and their implications in a conservation perspective. Biol. Conserv. 122, 633–642 (2005).Article 

Google Scholar 
55.Moore, D., Gange, A. C., Gange, E. G. & Boddy, L. Fruit bodies: Their production and develpoment in relation to environment. In Ecology of Saprotrophic Basidiomycetes (eds Boddy, L. et al.) (Elsevier, 2008).
Google Scholar 
56.Junninen, K., Similä, M., Kouki, J. & Kotiranta, H. Assemblages of wood-inhabiting fungi along the gradients of succession and naturalness in boreal pine-dominated forests in Fennoscandia. Ecography (Cop.) 29, 75–83 (2006).Article 

Google Scholar 
57.Agren, J. & Zackrisson, O. Age and size structure of Pinus sylvestris populations on mires in Central and Northern Sweden. J. Ecol. 78, 1049–1062 (1990).Article 

Google Scholar 
58.Niemelä, T., Wallenius, T. & Kotiranta, H. The kelo tree, a vanishing substrate of specified wood-inhabiting fungi. Polish Bot. J. 47, 91–101 (2002).
Google Scholar 
59.Venugopal, P., Julkunen-Tiitto, R., Junninen, K. & Kouki, J. Phenolic compounds in Scots pine heartwood: Are kelo trees a unique woody substrate?. Can. J. For. Res. 46, 225–233 (2016).CAS 
Article 

Google Scholar 
60.Jonsson, B. G. et al. Dead wood availability in managed Swedish forests – Policy outcomes and implications for biodiversity. For. Ecol. Manag. 376, 174–182 (2016).Article 

Google Scholar 
61.Runnel, K. & Lõhmus, A. Deadwood-rich managed forests provide insights into the old-forest association of wood-inhabiting fungi. Fungal Ecol. 27, 155–167 (2017).Article 

Google Scholar 
62.Junninen, K. & Komonen, A. Conservation ecology of boreal polypores: A review. Biol. Conserv. 144, 11–20 (2011).Article 

Google Scholar 
63.Krah, F. S. et al. Independent effects of host and environment on the diversity of wood-inhabiting fungi. J. Ecol. 106, 1428–1442. https://doi.org/10.1111/1365-2745.12939 (2018).Article 

Google Scholar 
64.Hoppe, B. et al. Linking molecular deadwood-inhabiting fungal diversity and community dynamics to ecosystem functions and processes in Central European forests. Fungal Divers. 77, 367–379 (2016).Article 

Google Scholar 
65.Kubartová, A., Ottosson, E., Dahlberg, A. & Stenlid, J. Patterns of fungal communities among and within decaying logs, revealed by 454 sequencing. Mol. Ecol. 21, 4514–4532 (2012).PubMed 
Article 

Google Scholar 
66.Kazartsev, I., Shorohova, E., Kapitsa, E. & Kushnevskaya, H. Decaying Picea abies log bark hosts diverse fungal communities. Fungal Ecol. 33, 1–12 (2018).Article 

Google Scholar 
67.von Bonsdorff, T. et al. New national and regional biological records for Finland 8. Contributions to agaricoid, gastroid and ascomycetoid taxa of fungi 5. Memo. Soc. pro Fauna Flora Fenn. 92, 120–128 (2016).
Google Scholar 
68.von Bonsdorff, T. et al. New national and regional biological records for Finland 5. Contributions to agaricoid and ascomycetoid taxa of fungi 4. Memo. Soc. pro Fauna Flora Fenn. 91, 56–66 (2015).
Google Scholar 
69.Frøslev, T. G. et al. Man against machine: Do fungal fruitbodies and eDNA give similar biodiversity assessments across broad environmental gradients?. Biol. Conserv. 233, 201–212 (2019).Article 

Google Scholar 
70.Esri. ArcMap, version 10.5.1. http://desktop.arcgis.com/en/arcmap/ 04.09.2017 (2017). Available at: http://desktop.arcgis.com/en/arcmap/. More

From SSN to PFCsWe analyzed the 1,914,171 proteins from 885 MAGs from marine plankton, recovered from 12 geographically bound assemblies of metagenomic sets corresponding to a total of 93 Tara Oceans samples from the 0.2 to 3 µm and 0.2 to 1.6 µm size fractions21. A flowchart of our bioinformatic pipeline is available in Supplementary Fig. 1. 39.6% of the MAGs’ proteins (757,457) were involved in our SSN, i.e., they had at least one similarity relationship with another protein that satisfied the chosen threshold of 80% similarity and 80% coverage (see “Methods”). In total, 51.1% of the network proteins could be annotated to 4922 unique molecular function IDs in the KEGG database37, associated with 327 distinct metabolic pathways (a full list of these pathways is available in Supplementary Data 1). In total, 85.2% of the network proteins were annotated to 17,009 eggNOG functional descriptions38,39.The SSN involved 233,756 connected components (CCs), i.e., groups of nodes (here proteins) connected together by at least one path and disconnected from the rest of the network. According to KEGG and eggNOG databases, 15.3% and 48.5% of the CCs remained without any functional annotation (i.e., all sequences from the CC were unmatched in the databases, or had a match but were not yet linked to any biological function, Table 1), and 14.8% were functionally unannotated for both databases. We ranked the functional homogeneity of CCs involving at least one functional annotation from 0 (all annotations in the CC are different) to 1 (all annotations in the CC are the same) and found mean homogeneity scores of 0.99 over 1 for KEGG annotations and 0.94 over 1 for eggNOG ones (see “Methods” for score calculation details). Only 88 (0.04%) CCs had a homogeneity score below 0.5 in both annotation databases, all with sizes below five proteins. 177 CCs (0.07%) had a score below 0.8 in both databases, all under 12 proteins in size. These CCs were kept in the analysis while tagged as poorly homogenous. We thereafter considered each CC as a PFC, numbered from #1 to #233,756.Table 1 Metrics computed on the 233,756 protein functional clusters (PFC) from the sequence similarity network of MAGs proteins.Full size tableTo check for the influence of taxonomic relationships between the MAGs on our PFCs, we computed different metrics based on MAGs taxonomic annotations provided by Delmont et al.21. (Table 1). This taxonomic annotation based on 43 single-copy core genes allowed to annotate 100% of the MAGs at the domain level, and 95% of the MAGs at the phylum level, the remaining 5% corresponding to Bacteria MAGs of unidentified phyla21. Only 1330 PFCs (0.6%) mixed proteins from the Archaea and Bacteria domains. PFCs were very homogeneous at the phylum level, then the homogeneity decreased at lower taxonomic rank, meaning that PFCs studied here were generally not specific from a single class, order, family, genus, or MAG (Table 1). In total, 7834 PFCs (3.4%) were only composed of proteins with no functional annotation in KEGG and eggNOG databases, and no taxonomic annotation under the phylum level. Their sizes ranged from 2 to 30 proteins (mean of 2.62). Their 20,552 proteins came from Euryarchaeota MAGs (12,458; 60.6%), Bacteria MAGs of unidentified phylum (2742; 13.3%), Candidatus Marinimicrobia MAGs (2451; 11.9%), Proteobacteria MAGs (1528; 7.4%), Acidobacteria MAGs (1031; 5%), Verrucomicrobia MAGs (103; 0.5%), Planctomycetes MAGs (89; 0.4%), Bacteroidetes MAGs (79; 0.4%), Chloroflexi MAGs (59; 0.3%) and Candidate Phyla Radiation MAGs (12; 0.05%). We hereafter considered these functionally and taxonomically unknown PFCs as “dark” PFCs40,41. Their nucleotidic sequences are available in separate supplementary files (see “Data availability”). The abundance of dark PFCs was significantly different from the abundance of other PFCs in 85 samples over 93 (two-sided Wilcoxon rank-sum test, p-value  More

1.Mendo, T., Semmens, J. M., Lyle, J. M., Tracey, S. R. & Moltschaniwskyj, N. Reproductive strategies and energy sources fueling reproductive growth in a protracted spawner. Mar. Biol. 163, 2 (2016).Article 

Google Scholar 
2.Stephens, P. A., Boyd, I. L., McNamara, J. M. & Houston, A. I. Capital breeding and income breeding: their meaning, measurement, and worth. Ecology 90, 2057–2067 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Bonnet, X., Bradshaw, D. & Shine, R. Capital versus income breeding: An ectothermic perspective. Oikos 83, 333–342 (1998).Article 

Google Scholar 
4.Johnson, R. A. Capital and income breeding and the evolution of colony founding strategies in ants. Insectes Soc. 53, 316–322 (2006).Article 

Google Scholar 
5.Wheatley, K. E., Bradshaw, C. J., Harcourt, R. G. & Hindell, M. A. Feast or famine: Evidence for mixed capital–income breeding strategies in Weddell seals. Oecologia 155, 11–20 (2008).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Tammaru, T. & Haukioja, E. Capital breeders and income breeders among Lepidoptera: Consequences to population dynamics. Oikos 77, 561–564 (1996).Article 

Google Scholar 
7.McHuron, E. A., Costa, D. P., Schwarz, L. & Mangel, M. State-dependent behavioural theory for assessing the fitness consequences of anthropogenic disturbance on capital and income breeders. Methods Ecol. Evol. 8, 552–560 (2017).Article 

Google Scholar 
8.Williams, C. T. et al. Seasonal reproductive tactics: Annual timing and the capital-to-income breeder continuum. Philos. Trans. R. Soc. B 372, 20160250 (2017).Article 

Google Scholar 
9.Kerby, J. & Post, E. Capital and income breeding traits differentiate trophic match–mismatch dynamics in large herbivores. Philos. Trans. R. Soc. B 368, 20120484 (2013).Article 

Google Scholar 
10.Zeng, Y., McLay, C. & Yeo, D. C. Capital or income breeding crabs: Who are the better invaders?. Crustaceana 87, 1648–1656 (2014).Article 

Google Scholar 
11.Griffen, B. D. The timing of energy allocation to reproduction in an important group of marine consumers. PLoS ONE 13, e0199043 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Boudreau, S. A. & Worm, B. Ecological role of large benthic decapods in marine ecosystems: A review. Mar. Ecol. Prog. Ser. 469, 195–213 (2012).ADS 
Article 

Google Scholar 
13.Holland, D. S. & Kasperski, S. The impact of access restrictions on fishery income diversification of US West Coast fishermen. Coast. Manag. 44, 452–463 (2016).Article 

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

Google Scholar 
15.Anilkumar, G. Reproductive physiology of female crustaceans. Ph.D. thesis, University of Calicut, India (1980).16.Lovrich, G. A., Romero, M. C., Tapella, F. & Thatje, S. Distribution, reproductive and energetic conditions of decapod crustaceans along the Scotia Arc (Southern Ocean). Sci. Mar. 69, 183–193 (2005).Article 

Google Scholar 
17.Sainmont, J., Andersen, K. H., Varpe, Ø. & Visser, A. W. Capital versus income breeding in a seasonal environment. Am. Nat. 184, 466–476 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Griffen, B. D. Metabolic costs of capital energy storage in a small-bodied ectotherm. Ecol. Evol. 7, 2423–2431 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Varpe, Ø., Jørgensen, C., Tarling, G. A. & Fiksen, Ø. The adaptive value of energy storage and capital breeding in seasonal environments. Oikos 118, 363–370 (2009).Article 

Google Scholar 
20.Varpe, Ø., Jørgensen, C., Tarling, G. A. & Fiksen, Ø. Early is better: Seasonal egg fitness and timing of reproduction in a zooplankton life-history model. Oikos 116, 331–1342 (2007).Article 

Google Scholar 
21.Warner, G. F. The life history of the mangrove tree crab, Aratus pisoni. J. Zool. 153, 321–335 (1967).Article 

Google Scholar 
22.Díaz, H. & Conde, J. E. Population dynamics and life history of the mangrove crab Aratus pisonii (Brachyura, Grapsidae) in a marine environment. Bull. Mar. Sci. 45, 148–163 (1989).
Google Scholar 
23.de Arruda Leme, M. H. & Negreiros-Fransozo, M. L. Reproductive patterns of Aratus pisonii (Decapoda: Grapsidae) from an estuarine area of São Paulo northern coast, Brazil. Rev. Biol. Trop. 46, 673–678 (1998).
Google Scholar 
24.Cannizzo, Z. J., Lang, S. Q., Benitez-Nelson, B. & Griffen, B. D. An artificial habitat increases the reproductive fitness of a range-shifting species within a newly colonized ecosystem. Sci. Rep. 10, 554 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Riley, M. E., Vogel, M. & Griffen, B. D. Fitness-associated consequences of an omnivorous diet for the mangrove tree crab Aratus pisonii. Aquat. Biol. 20, 35–43 (2014).Article 

Google Scholar 
26.López, B. & Conde, J. E. Dietary variation in the crab Aratus pisonii (H. Milne Edwards, 1837)(Decapoda, Sesarmidae) in a mangrove gradient in northwestern Venezuela. Crustaceana 86, 1051–1069 (2013).Article 

Google Scholar 
27.Erickson, A. A., Feller, I. C., Paul, V. J., Kwiatkowski, L. M. & Lee, W. Selection of an omnivorous diet by the mangrove tree crab Aratus pisonii in laboratory experiments. J. Sea Res. 59, 59–69 (2008).ADS 
Article 

Google Scholar 
28.Beever, J. W., Simberloff, D. & King, L. L. Herbivory and predation by the mangrove tree crab Aratus pisonii. Oecologia 43, 317–328 (1979).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Riley, M. E., Johnston, C. A., Feller, I. C. & Griffen, B. D. Range expansion of Aratus pisonii (mangrove tree crab) into novel vegetative habitats. Southeast. Nat. 13, N43–N48 (2014).Article 

Google Scholar 
30.Cannizzo, Z. J. & Griffen, B. D. Changes in spatial behaviour patterns by mangrove tree crabs following climate-induced range shift into novel habitat. Anim. Behav. 121, 79–86 (2016).Article 

Google Scholar 
31.Riley, M. E. & Griffen, B. D. Habitat-specific differences alter traditional biogeographic patterns of life history in a climate-change induced range expansion. PLoS ONE 12, e0176263 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Cannizzo, Z. J. & Griffen, B. D. An artificial habitat facilitates a climate-mediated range expansion into a suboptimal novel ecosystem. PLoS ONE 14, e0207416 (2019).Article 
CAS 

Google Scholar 
33.Bernardo, J. & Agosta, S. J. Evolutionary implications of hierarchical impacts of nonlethal injury on reproduction, including maternal effects. Biol. J. Linn. Soc. 86, 309–331 (2005).Article 

Google Scholar 
34.Griffen, B. D., Cannizzo, Z. J., Carver, J. & Meidell, M. Reproductive and energetic costs of injury in the mangrove tree crab. Mar. Ecol. Prog. Ser. 640, 127–137 (2020).ADS 
Article 

Google Scholar 
35.De Arruda Leme, M. H., De Sousa Soares, V. & Pinheiro, M. A. A. Population dynamics of the mangrove tree crab Aratus pisonii (Brachyura: Sesarmidae) in the estuarine complex of Cananéia-Iguape, São Paulo, Brazil. Pan-Am. J. Aquat. Sci. 9, 259–266 (2014).
Google Scholar 
36.Skov, M. W. et al. Marching to a different drummer: Crabs synchronize reproduction to a 14-month lunar-tidal cycle. Ecology 86, 1164–1171 (2005).Article 

Google Scholar 
37.Schmidt, A. J., Bemvenuti, C. E. & Diele, K. Effects of geophysical cycles on the rhythm of mass mate searching of a harvested mangrove crab. Anim. Behav. 84, 333–340 (2012).Article 

Google Scholar 
38.Dronkers, J. J. Tidal Computations in Rivers and Coastal Waters (Wiley, 1964).
Google Scholar 
39.Varpe, Ø. Life history adaptations to seasonality. Integr. Comp. Biol. 57, 943–960 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
40.Conde, J. E. et al. Population and life history features of the crab Aratus pisonii (Decapoda: Grapsidae) in a subtropical estuary. Interciencia 25, 151–158 (2000).
Google Scholar 
41.Elner, R. W. & Beninger, P. G. Multiple reproductive strategies in snow crab, Chionoecetes opilio: Physiological pathways and behavioral plasticity. J. Exp. Mar. Biol. Ecol. 193, 93–112 (1995).Article 

Google Scholar 
42.Tepolt, C. K. & Somero, G. N. Master of all trades: Thermal acclimation and adaptation of cardiac function in a broadly distributed marine invasive species, the European green crab, Carcinus maenas. J. Exp. Biol. 217, 1129–1138 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Ruiz, G., Fofonoff, P., Steves, B. & Dahlstrom, A. Marine crustacean invasions in North America: a synthesis of historical records and documented impacts. In In the Wrong Place-Alien Marine Crustaceans: Distribution, Biology and Impacts 215–250. (Springer, 2011).44.Griffen, B. D. & Mosblack, H. Predicting diet and consumption rate differences between and within species using gut ecomorphology. J. Anim. Ecol. 80, 854–863 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Cannizzo, Z. J., Dixon, S. R. & Griffen, B. D. An anthropogenic habitat within a suboptimal colonized ecosystem provides improved conditions for a range-shifting species. Ecol. Evol. 8, 1521–1533 (2018).PubMed 
PubMed Central 
Article 

Google Scholar  More

Here, we analyzed the burrowing mechanisms of beetle larvae. Beetle larvae were placed on the soil surface to make sure they could burrow into the soil (Fig. 1a). In order to observe the burrowing behavior, a two-dimensional (2D) observation tank (130 × 210 ×  ~ 20 mm) was constructed (Fig. 1b); we succeeded in observing the dynamics of the larvae under a 2D soil condition (Fig. 1c, Supplementary Movie 1). The larvae burrowed by rotating themselves (Fig. 1d, Supplementary Movie 1). Rotation was observed regardless of sex. All observed individuals proceeded towards the bottom and stopped when rotating at the bottom layer (Fig. 1c).Figure 1Burrowing dynamics of scarab beetle (Trypoxylus dichotomus) larva. (a) Burrowing images. After beetle is put on the soil, they can burrow in a short time ( More

Assumptions for the space-for-time substitutionThe main methodological concept in this study is the notion of a space-for-time substitution. Such approach has previously been used in various studies to estimate the effect of land cover change on temperature20,22 or on the surface energy balance22,72. The overarching assumption behind the method is that the difference in properties of neighbouring patches of land can serve as a surrogate for changes in time. While this main assumption largely holds for land surface properties, such as skin temperature, it requires a more detailed articulation into several underlying assumptions in order to apply the approach to atmospheric properties such as cloud cover. This is because atmospheric properties are prone to lateral movements, partially decoupling them from the land cover directly below them, and thus adding considerable complexity to the analysis.The first underlying assumption is that the method will be mostly sensitive to low-level convective clouds generated in the boundary layer (i.e. cumulus clouds). These are typically formed under stable conditions of high pressure and low wind, and are thus expected to have a higher spatial correlation with the underlying landscape elements. Other types of low-level clouds, such as stratus clouds, are typically much more uniformly spread across the landscape, which would result in no difference in CFrC when comparing two distinct and neighbouring vegetation classes. For medium- or high-level clouds, their position will be determined mostly by the state of the atmosphere rather than by the land surface, resulting in a very low correlation with vegetation spatial patterns. The space-for-time substitution approach would thus similarly result in white noise.The second assumption is that the boundary layer cumulus clouds will see very limited lateral advection between the moment of their formation and the satellite observation. Cumulus clouds over land show a very stable climatology where the peak formation is largely confined to the early afternoon (around 14:00), timing which remains very stable across space and season43. Therefore this assumption should largely hold if the observations are made at this time.The third assumption is that if we consider topographically flat terrain that is away from a coastline, general weather conditions are essentially the same at a local scale (i.e. a region of radius circa 25 km around a given point). Within such an area, we then assume that variations in low cloud cover are mostly determined by local differences in surface properties, themselves determined by the type and condition of the present land cover.Preparation of input datasetsThis study requires gridded geospatial datasets for two variables: cloud fractional cover and land fractional cover. Both datasets used here have been prepared in the frame of the European Space Agency’s (ESA) Climate Change Initiative (CCI)73.The Cloud CCI55 provides a series of cloud properties derived from distinct satellite Earth observation platforms in a harmonized way. Here we use their cloud fractional cover variable (henceforth CFrC), which describes the fraction of a 0.05° × 0.05° pixel covered by clouds based on observations made at a finer spatial resolution at the given time of the satellite overpass. We chose to use Cloud CCI dataset based on the MODIS instrument on-board of the Aqua platform for two reasons. First, the timing of overpass of the Aqua platform (circa 13:30 local time at the Equator) coincides very well with the timing of peak of cumulus cloud formation43, thus greatly limiting the extent of possible cloud advection between the moment of cloud formation and observation. Second, native spatial resolution of the MODIS instrument is superior to the alternative (AVHRR), and should result in a better sensitivity to the presence of small cumulus clouds. More specifically, out of the 5 spectral bands of the MODIS instrument used by the Cloud CCI to characterize cloud properties (bands 1, 2, 20, 31 and 32), two of them (bands 1 and 2) have a native spatial resolution of 250 m. While these are aggregated to 1 km (the spatial resolution of the other MODIS bands) prior to their ingestion in the cloud retrieval algorithm, their finer native granularity and quality should prove to be an asset for small cumulus cloud detection. The CCI MODIS-AQUA CFrC data is available for the period 2004–2014. The values are first averaged from daily to monthly scale, and then a single monthly value is calculated for every pixel over the period 2004–2014. The results are 12 layers each representing the multi-annual average CFrC for a given month.The second type of data needed for the analysis is the fraction of the 0.05° × 0.05° pixels that are covered by distinct vegetation types (essentially trees and grasses) and by other land cover classes (urban areas, bare soil, etc.). These are derived from the Land Cover CCI54, a set of consistent annual maps describing, with a spatial resolution of 300 m, how the terrestrial surface is covered based on The United Nations Land Cover Classification Scheme74. This information is aggregated both spatially and thematically using a specifically designed framework75 to produce maps of general land fractional cover with a spatial resolution of 0.05° to match that of the cloud fractional cover data. The procedure is very similar to that done in a previous study22. For the context of this study, which has a focus on afforestation, the interest lies on transitions among three main vegetated classes, namely: deciduous forest, evergreen forests and herbaceous vegetation. Herbaceous vegetation is composed of both grasses and crops, irrespective of management practice such as irrigation. While irrigation has a clear biophysical effect of its own60, we deemed the land cover product was not consistent enough for this specific class. For reasons that are explained in the respective methodological section below, the full compositional description of the landscape is necessary (i.e. beyond the classes of interest), and therefore land cover fractions of the following classes are also generated: shrublands, savannas, wetlands, water, bare or sparsely vegetated, snow or ice, and urban.Retrieving potential cloud fractional cover changeUnder the above-mentioned assumptions, we apply a space-for-time substitution algorithm developed in a previous study22 to the cloud fractional cover and land fractional cover datasets. We summarize the main aspects of the methodology, along with the few necessary adaptations, but the reader requiring more detail is redirected to the original papers22,76. The approach consists in applying an un-mixing operation over a spatially moving window containing n pixels. Over each window we apply a linear regression based on a matrix X containing the explanatory variables, in which each column of X represents the fractional cover of a given land cover type for each of the n pixels. The response variable is a vector y containing the n values of CFrC for the n pixels, while the vector β represents the regression coefficients:$${bf{y}}={bf{X}}beta$$
(1)
This is equivalent to solving the following system of equations:$$left{begin{array}{ll}{y}_{1}=&{beta }_{1}{x}_{11}+{beta }_{2}{x}_{12}+…+{beta }_{m}{x}_{1m}\ {y}_{2}=&{beta }_{1}{x}_{21}+{beta }_{2}{x}_{22}+…+{beta }_{m}{x}_{2m}\ vdots &\ {y}_{n}=&{beta }_{1}{x}_{n1}+{beta }_{2}{x}_{n2}+…+{beta }_{m}{x}_{nm}end{array}right.$$
(2)
in which the digits of the subscript of x, e.g. xij, represent the land cover fraction j in pixel i, for the n pixels in the moving window and the m classes that are considered. Once identified, we can use the β coefficients to predict the local y value corresponding to a given composition x, including that composed of a single land cover j by setting xj = 1 and all other x values to zero. However, applying a regression directly on X carries a risk due to the compositional nature of the data (i.e. the sum of each row adds up to one), as the analysis of any given subset of compositional components can lead to very different patterns, results and conclusions77. To avoid this, we reduce the dimensionality of X through singular value decomposition (SVD) after removing the mean of each column:$$({bf{X}}-{bf{M}})={bf{U}}{bf{D}}{{bf{V}}}^{t}$$
(3)
where M is the appropriate matrix of column means, U and V are the matrices containing, respectively, the left-hand and right-hand singular vectors, and D is a diagonal matrix containing the singular values representing the standard deviations of the ensuing dimensions. The squared values of D represent the variance explained by each dimension, and can thus serve to define z, a reduced subset of dimensions that conserves 100% of the original matrix’s variation. The corresponding z right-hand singular vectors, Vz, can then be used to find the appropriately transformed predictor matrix of reduced dimension Z as follows:$${bf{Z}}=({bf{X}}-{bf{M}}){{bf{V}}}_{z}$$
(4)
which can now be regressed onto the CFrC y:$$y={bf{Z}}{beta }_{z}+varepsilon$$
(5)
where Z has been augmented with a leading column of 1s to accommodate an intercept term in the regression. We then use the standard method to obtain an estimate of βz:$${beta }_{z}={left({{bf{Z}}}^{t}{bf{Z}}right)}^{-1}{{bf{Z}}}^{t}y$$
(6)
However, because of the matrix transformation from X to Z, the regression coefficients βz do not provide direct information on the relationship between land fractional cover and cloud fractional cover (as in a normal regression). To identify the z values associated with a particular vegetation or land cover type (within the local analysis defined by the moving window), we define a ‘dummy pixel’ whose composition contains only a single class, with all other classes in its composition set to zero. This pixel’s composition is then transformed, and its y value predicted. This is the y associated with that vegetation type. To generalize this for all compositional components of interest, we define a matrix P with as many rows as these compositional components that we wish to predict. P is centred on the same column means as above (M, specific to each local analysis), and then multiplied by the correct number of transposed right-hand singular vectors (Vz, again, specific to each local analysis).$${{bf{Z}}}_{{rm{p}}}=({bf{P}}-{bf{M}}){{bf{V}}}_{z}$$
(7)
Predicted yp values for each vegetation or land cover type (identified by predicting the appropriately transformed ‘dummy pixels’) are then calculated as:$${y}_{{rm{p}}}={{bf{Z}}}_{{rm{p}}}{beta }_{z}$$
(8)
The expected change in variable y associated with a transition from vegetation type A (e.g. herbaceous vegetation) to vegetation type B (e.g. deciduous forest) at the centre of the local window is then the difference between the yp predicted for each ‘pure’ vegetation type:$${{Delta }}{y}_{{rm{A}}to {rm{B}}}={y}_{rm{B}}-{y}_{rm{A}}$$
(9)
The uncertainty in the estimation of ΔyA→B can be expressed as a standard deviation using the following expression:$${sigma }_{{rm{A}}to {rm{B}}}=sqrt{{sigma }_{rm{A}}^{2}+{sigma }_{rm{B}}^{2}-2{sigma }_{rm{AB}}}$$
(10)
where ({sigma }_{rm{A}}^{2}) and ({sigma }_{rm{B}}^{2}) are the variances in the estimates of yA and yB, and σAB is their covariance. These variances and covariances are in turn obtained from the covariance matrix, defined from the regression as:$${mathbf{Sigma }}={{bf{Z}}}_{{rm{p}}}{rm{Var}}[beta ]{{bf{Z}}}_{{rm{p}}}^{t}$$
(11)
The diagonal terms in Σ are the variances of individual predictions of (individual) classes. The off-diagonal parts of Σ hold the covariances between these predictions. As a reminder, the uncertainty σA→B calculated in this way is related to the methodological uncertainty and does not include the uncertainty in the input variables of land cover or cloud fractional cover.In the default set-up for this study, we concentrate on two transitions: herbaceous vegetation to deciduous forest and herbaceous vegetation to evergreen forest. These are calculated using a spatial window of 7 × 7 pixels, each pixel being of 0.05°, resulting in a squared spatial window of circa 35 km in size. To ensure there are enough values to do the un-mixing over each window, we established that there must be a minimum of 60% of valid values in each window, and that at least 40% must have distinct compositions. The operation is applied to all 12 monthly layers of CFrC, resulting in 12 maps of Δy with a 0.05° spatial resolution for each of the two vegetation cover transitions.Post-processingA series of post-processing steps are required to ensure the results of the Δy maps can be used to evaluate the effect of land on cloud cover. The first step is to mask all pixels in which there is insufficient co-occurrence of the two vegetation classes involved in the transition. This co-occurrence is quantified by an index of vegetation co-occurrence76, Ic, calculated from the land fractional cover layers using the same spatial moving window of 7 × 7 pixels as used before. This index is calculated pairwise, i.e. for 2 vegetation classes of interest A and B, using two vectors pA and pB, describing the presence of these two vegetation classes in each of the i pixels in the moving window. It also requires the definition of another i point evenly distributed along a hypothetical line B = 1 − A in the two-dimensional space describing the presences of vegetation class A and vegetation class B. These points, whose position in the 2-D space are labelled qA and qB, represent an ideal situation of maximum co-occurrence that serves as a reference to establish the index. The formal definition of the index is thus:$${I}_{{rm{c}}}=1-frac{{sum }_{i}min {sqrt{{left({q}_{A}-{p}_{A}right)}^{2}+{left({q}_{B}-{p}_{B}right)}^{2}}}}{{sum }_{i}sqrt{{q}_{A}^{2}+{q}_{B}^{2}}}$$
(12)
The minimum operator in the numerator selects the smallest distance that a given point p can have to any of the q points. The sum relates to the sum of this distance for all i points in the spatial moving window. The denominator characterizes the maximum distance that the point p can encounter. Ic will range from 0 to 1 corresponding to a gradient of ‘no presence of either class’ to ‘full and evenly balanced presence of both classes’. As in76, we retain only pixels with Ic ≥ 0.5 where we consider that there is sufficient information at local scale concerning both vegetation types to derive meaningful information about the target land cover transition.The second step is to remove the potential orographical effects, which can be especially problematic given that forests are more likely to be located over mountainous areas due to human action56. Here we mask the areas where considerable topographical variation occurs within the 7 × 7 pixel moving window of interest using the same implementation described in76. This involves using 3 different indicators, v1, v2 and v3, calculated over the moving window based on μh and σh, which are, respectively, the mean and the standard deviation of elevation over each grid cell of the input cloud dataset. These are defined as follows:$${v}_{1}=frac{1}{n}mathop{sum }limits_{i=1}^{n}{sigma }_{h,i}$$
(13)
$${v}_{2}=| {mu }_{h}-frac{1}{n}mathop{sum }limits_{i=1}^{n}{mu }_{h,i}|$$
(14)
$${v}_{3}=| {sigma }_{h}-{v}_{1}|$$
(15)
For an interpretation of these metrics, readers are invited to read76. These three indicators are combined together in a single layer depicting all pixels satisfying all of the following conditions: v1  More